Updates on genomic selection

Day 1: Fundamentals of genomic selection

Data Science for Plant Breeding Lab

Piracicaba, September 1^st

Objectives

To provide the basis for understanding the fundamentals of genomic selection

To show how genomic selection can be applied in plant breeding routine

To understand the possible impacts of genomic selection into the genetic progress using the breeder’s equation

To run single-environment genomics selection models

Building the idea

Plant breeding

Covarrubias-Pazaran et al. (2022)

Plant breeding

Traditionally, selection is based on the phenotype, but…

\[ P = G + E + G \times E \]

Genetic markers

What if we could select based on the \(G\) part?
Molecular markers

RAU's IAS study circle

Different genes \(\to\) different proteins \(\to\) different phenotypes

Genetic markers

For a marker to be considered “genetic”, it must be polymorphic, easily identifiable and inheritable

WUR

Genetic markers

International Society of Genetic Genealogy

Codominants
Biallelics
Usually neutral
Frequent throughout the genome
Easily identifiable
Inheritable

Genetic markers

Next generation sequencing

Hess et al. (2020)

Decreased DNA sequencing costs
High throughput (data generation, genome coverage)

Next generation sequencing

Genotyping-by-sequencing (GBS) (Elshire et al., 2011)

Peterson et al. (2014)

Genomics era

About 13 years for the releasing of the Human Genome’s first version

The Human Genome project

Genomics era

Varshney et al. (2021)

Genomics era

National Human Genome Research Institute

From a few dozens to many thousands (even millions) of markers
How can we leverage this information in plant breeding?

Marker-assisted selection

Plant breeding

\[ P = G + E + G \times E \]

We can select based on \(G\)!

QTL mapping

Nature Education

Which markers are related to the trait?
Are these markers next or within the causal gene(s)?
Can I base my selection directly on them?
Inference problem

QTL mapping

Guilherme Pereira

QTL mapping

QTL mapping

QTL mapping

Single-marker analysis: linear regression

\[ y_i = \mu + \beta_1x_i + \varepsilon_i \]

Marker-assisted selection

Selection based on the identified QTL

Jang et al. (2024)

Marker-assisted selection

Marker in linkage disequilibrium with the causal genes
- Indirect selection
Higher selection intensity
Less time required for obtaining improved germplasm
Selection for traits that are hard to measure
Assistance in plant breeding routine procedures (backcrossing, gene pyramiding, …)

Marker-assisted selection

Kushanov et al. (2021)

Marker-assisted backcrossing

Gaia Lugano

Marker-assisted selection?

Jeremykemp

Marker-assisted selection?

“Obviously, the phenotype of interest needs to be unequivocally associated with the genomic region (i.e. the trait ↔︎ QTL correlation), determination of which is the focus of QTL mapping algorithms. However, even if a QTL explains 100% of the phenotypic variance for a trait of interest in a gene pool, the QTL still needs to be unequivocally associated with the marker used to impose selection for it (i.e. the QTL ↔︎ marker correlation). If either of these correlations (trait ↔︎ QTL or QTL ↔︎ marker) is poor, the trait ↔︎ marker association will not reliably improve the phenotype of interest, and so breeders may not only lose confidence in the value of a particular locus, but potentially in marker-assisted selection itself as an effective method.” — Cobb et al. (2019)

Genomic selection

Inheritance of complex traits

Guilherme Pereira

Genomic selection

Most economically important traits are influenced by many genes
It is hard (impossible) to identify them all
Next-Generation Sequencing: whole genome coverage
Genomic approach: use all information available

The main differences between QTL mapping, genome-wide association studies and genomic selection are in the objectives (inference or prediction) and in the statistical methods used to model the genetic effects

Genomic selection

Miedaner et al. (2020)

Genomic selection

Why does it work?

Identity-by-descent (IBD) and Identity-by-state (IBS)

Xu (2022)

Why does it work?

Numerator relationship matrix: Expected IBD

Genomic relationship matrix: IBS

Why does it work?

Linkage between marker and QTL

Aldona Griskeviciene/StrongMind

How does it work?

\[ \bar{y}_i = \mu + \sum_{m=1}^p x_{im} \beta_m + \varepsilon_i \]

\(x_{ij} \to\) allele dosage of marker \(j\) in genotype \(i\)
\(\beta_j \to\) effect of marker \(j\)

If genotypes share marker alleles, and these markers are in linkage disequilibrium with important QTL, then one can predict the performance of unseen genotypes using marker information.

How does it work?

Wallace et al. (2018)

How does it work?

\[ \bar{y}_i = \mu + x_{i1}\beta_1 + x_{i2}\beta_2 + \dots + x_{ip}\beta_p + \varepsilon_i \]

In the multiple regression setting…

Each marker would have a fixed value referred to its contribution to the phenotype, depending on how linked are the marker and the QTL

How does it work?

set.seed(55)
dat = data.frame(
  geno = paste0("G", 1:5),
  y = rnorm(5, mean = 10, sd = 2)
)
dat = cbind(dat, matrix(
  rbinom(35, size = 2, prob = .5),
  ncol = 7,
  nrow = 5,
  dimnames = list(NULL, paste0("M", 1:7))
))
dat

  geno         y M1 M2 M3 M4 M5 M6 M7
1   G1 10.240278  2  2  1  1  1  1  0
2   G2  6.375246  0  1  2  1  1  1  2
3   G3 10.303166  1  2  0  2  2  2  2
4   G4  7.761558  1  1  1  1  0  2  2
5   G5 10.003816  1  1  0  1  2  1  2

How does it work?

mod = lm(y ~ M1 + M2 + M3, data = dat)
summary(mod)


Call:
lm(formula = y ~ M1 + M2 + M3, data = dat)

Residuals:
      1       2       3       4       5 
 0.1897  0.1897 -0.1897 -0.5691  0.3794 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)   7.9045     1.2584   6.282    0.101
M1            0.8514     0.7347   1.159    0.453
M2            0.8685     0.9098   0.955    0.515
M3           -1.2937     0.5019  -2.578    0.236

Residual standard error: 0.7588 on 1 degrees of freedom
Multiple R-squared:  0.9545,    Adjusted R-squared:  0.8179 
F-statistic: 6.988 on 3 and 1 DF,  p-value: 0.2696

How does it work?

mod = lm(y ~ M1 + M2 + M3 + M4, data = dat)
summary(mod)


Call:
lm(formula = y ~ M1 + M2 + M3 + M4, data = dat)

Residuals:
ALL 5 residuals are 0: no residual degrees of freedom!

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  10.5604        NaN     NaN      NaN
M1           -0.8559        NaN     NaN      NaN
M2            3.3347        NaN     NaN      NaN
M3           -2.2423        NaN     NaN      NaN
M4           -3.0353        NaN     NaN      NaN

Residual standard error: NaN on 0 degrees of freedom
Multiple R-squared:      1, Adjusted R-squared:    NaN 
F-statistic:   NaN on 4 and 0 DF,  p-value: NA

How does it work?

mod = lm(y ~ M1 + M2 + M3 + M4 + M5 + M6 + M7, data = dat)
summary(mod)


Call:
lm(formula = y ~ M1 + M2 + M3 + M4 + M5 + M6 + M7, data = dat)

Residuals:
ALL 5 residuals are 0: no residual degrees of freedom!

Coefficients: (3 not defined because of singularities)
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  10.5604        NaN     NaN      NaN
M1           -0.8559        NaN     NaN      NaN
M2            3.3347        NaN     NaN      NaN
M3           -2.2423        NaN     NaN      NaN
M4           -3.0353        NaN     NaN      NaN
M5                NA         NA      NA       NA
M6                NA         NA      NA       NA
M7                NA         NA      NA       NA

Residual standard error: NaN on 0 degrees of freedom
Multiple R-squared:      1, Adjusted R-squared:    NaN 
F-statistic:   NaN on 4 and 0 DF,  p-value: NA

Curse of dimensionality

James et al. (2021)

Solution: Use shrinkage estimators! (we will see some of them later today)

Statistical methods

Alemu et al. (2024)

Population types

Training population: used to estimate the model’s parameters
- Both phenotyped and genotyped
Testing population: used to evaluate the model’s predictive ability
- Both phenotyped and genotyped
Breeding population: where the model will be applied
- Only genotyped

Population types

Baptista et al. (2026)

Model evaluation

k-fold cross-validation

Jia (2017)

Model evaluation

True validation: independent or semi-independent data obtained from a different trial with related genotypes

Gezan et al. (2017)

Model evaluation

Predictive ability (PA): \(\operatorname{Cor}\left(\bar{y},\hat{y}\right)\)

Prediction accuracy (ACC): \(\frac{\operatorname{Cor}\left(\bar{y},\hat{y}\right)}{\sqrt{h^2}}\)

Salvador Gezan

Root of mean squared prediction error: \(\sqrt{\frac{1}{V}\sum^n_{i=1}(\bar{y}_i - \hat{y}_i)^2}\)

Model evaluation

Zhou et al. (2017)

Instant method: correlation within each fold
Hold method: correlation after predicting all folds
- More appropriate when sample size is small

GS in plant breeding

Early selection of unphenotyped individuals

Werner et al. (2023)

GS in plant breeding

Cross selection

Akdemir & Sánchez (2016)

GS and the breeder’s equation

\[ \color{green} {\uparrow R} = \frac{\color{green}{\uparrow \bar{i}} \, h_p \sigma_A}{L C} \]

Marulanda et al. (2016)

GS and the breeder’s equation

\[ \color{green} {\uparrow R} = \frac{\bar{i} \color{green}{\uparrow h_p} \, \sigma_A}{L C} \]

Fernández-González et al. (2023)

GS and the breeder’s equation

\[ \color{red} {\Downarrow R} = \frac{\bar{i}h_p \color{red}{\Downarrow \sigma_A}}{L C} \]

Gaynor et al. (2017)

GS and the breeder’s equation

\[ \color{red} {\downarrow R} = \frac{\bar{i}h_p \color{red}{\downarrow \sigma_A}}{L C} \]

Niehoff et al. (2024)

GS and the breeder’s equation

\[ \color{green} {\Uparrow R} = \frac{\bar{i}h_p \sigma_A}{\color{red}{\downarrow L} \color{red}{\downarrow{C}}} \]

Voss-Fels et al. (2018)

GS and the breeder’s equation

\[ \color{green} {\Uparrow R} = \frac{\bar{i}h_p \sigma_A}{\color{red}{\downarrow L} \color{red}{\downarrow{C}}} \]

El-Dien et al. (2024)

Concluding remarks

Genomic selection has high potential to increase plant breeding efficiency
- Increase selection intensity, decrease costs and generation interval
Inserting genomic selection into a breeding pipeline is not an easy task
- Which stage?
- How to build a proper training population?
- How to perform the selection and control the inbreeding?
- How to deal with phenotypic and genomic data?

Practical session

The dataset

G2F

Phenotypic data

library(tidyverse)

dat = readRDS("../Data/G2F.RDS")
str(dat)

'data.frame':   85949 obs. of  7 variables:
 $ gen      : chr  "B37/C103" "B37/H95" "B73/MO17" "B73/PHG35" ...
 $ year     : int  2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 ...
 $ loc      : chr  "GAH1" "GAH1" "GAH1" "GAH1" ...
 $ env      : chr  "GAH1_2014" "GAH1_2014" "GAH1_2014" "GAH1_2014" ...
 $ blue     : num  4.69 5.98 6.29 9.73 8.05 ...
 $ std.error: num  0.828 0.828 0.828 0.828 0.828 ...
 $ wt       : num  1.91 1.91 1.91 1.91 1.91 ...

Phenotypic data

Show code

ggplot(data = as.data.frame(table(dat$gen, dat$env)),
       aes(x = Var2, y = Var1, fill = as.factor(Freq))) + 
  geom_tile(aes(alpha = as.factor(Freq))) + 
  theme_minimal() + 
  theme(axis.text = element_blank(), legend.title = element_blank(), 
        legend.position = 'top') +
  scale_fill_manual(values = c('#b2bc63','#10342d'), labels = c("Abscent", "Present")) +
  scale_alpha_manual(values = c(.6, 1), labels = c("Abscent", "Present")) + 
  labs(x = "Environment (Year-Location combination)", y = "Hybrid")

Phenotypic data

dat = dat[which(dat$env == "WIH3_2023"),]
str(dat)

'data.frame':   413 obs. of  7 variables:
 $ gen      : chr  "2369/LH123HT" "B14A/OH43" "B37/H95" "B73/MO17" ...
 $ year     : int  2023 2023 2023 2023 2023 2023 2023 2023 2023 2023 ...
 $ loc      : chr  "WIH3" "WIH3" "WIH3" "WIH3" ...
 $ env      : chr  "WIH3_2023" "WIH3_2023" "WIH3_2023" "WIH3_2023" ...
 $ blue     : num  11.3 11.9 13.9 16.1 16.6 ...
 $ std.error: num  0.707 0.707 0.707 0.707 0.707 ...
 $ wt       : num  2 2 2 2 2 ...

Genomic data

Package vcfR (Knaus & Grünwald, 2016)

library(vcfR)
vcf = read.vcfR(file = "../Data/markers.vcf")

Scanning file to determine attributes.
File attributes:
  meta lines: 10
  header_line: 11
  variant count: 2425
  column count: 5908

Meta line 10 read in.
All meta lines processed.
gt matrix initialized.
Character matrix gt created.
  Character matrix gt rows: 2425
  Character matrix gt cols: 5908
  skip: 0
  nrows: 2425
  row_num: 0

Processed variant 1000
Processed variant 2000
Processed variant: 2425
All variants processed

Genomic data

geno = extract.gt(x = vcf)
geno = geno[-grep(',', vcf@fix[,'ALT']),] # Remove multi-allelic mrks
vcf@fix = vcf@fix[-grep(',',vcf@fix[,'ALT']),]
geno = gsub('/', "", geno)
geno[1:5,1:5]

           01CSI6/LH287 01DIB2/LH287 01DIB2/PHP02 2369/DK3IIH6 2369/LH123HT
S1_1007742 "01"         "11"         "11"         "01"         "01"        
S1_1020677 "00"         "00"         "01"         "00"         "01"        
S1_2018002 "00"         "00"         "01"         "01"         "00"        
S1_2101934 "00"         "00"         NA           "00"         "01"        
S1_2275970 "00"         "00"         "00"         "00"         "01"

Genomic data

# Binding reference and alternative allele to marker matrix
geno = cbind(geno, vcf@fix[,c('REF', 'ALT')]) 
geno = apply(geno, 1, function(x){
  y = gsub('0', x['REF'], x)
  z = gsub('1', y['ALT'], y)
  z
})
geno[1:5,1:5]

             S1_1007742 S1_1020677 S1_2018002 S1_2101934 S1_2275970
01CSI6/LH287 "TC"       "GG"       "TT"       "TT"       "AA"      
01DIB2/LH287 "CC"       "GG"       "TT"       "TT"       "AA"      
01DIB2/PHP02 "CC"       "GA"       "TC"       NA         "AA"      
2369/DK3IIH6 "TC"       "GG"       "TC"       "TT"       "AA"      
2369/LH123HT "TC"       "GA"       "TT"       "TC"       "AC"

Genomic data

geno = geno[-which(rownames(geno) %in% c('REF', 'ALT')), ]
geno[grep('CA', geno)] = 'AC'
geno[grep('GA', geno)] = 'AG'
geno[grep('TA', geno)] = 'AT'
geno[grep('GC', geno)] = 'CG'
geno[grep('TC', geno)] = 'CT'
geno[grep('TG', geno)] = 'GT'
geno[1:5,1:5]

             S1_1007742 S1_1020677 S1_2018002 S1_2101934 S1_2275970
01CSI6/LH287 "CT"       "GG"       "TT"       "TT"       "AA"      
01DIB2/LH287 "CC"       "GG"       "TT"       "TT"       "AA"      
01DIB2/PHP02 "CC"       "AG"       "CT"       NA         "AA"      
2369/DK3IIH6 "CT"       "GG"       "CT"       "TT"       "AA"      
2369/LH123HT "CT"       "AG"       "TT"       "CT"       "AC"

Genomic data

Filtering step

Freqsnps = apply(geno, 2, table)
Freqsnps = do.call(rbind, lapply(Freqsnps, data.frame)) |> 
  rownames_to_column("mrk") |> mutate(mrk = gsub('\\..*', '', mrk))
head(Freqsnps)

         mrk Var1 Freq
1 S1_1007742   CC 2968
2 S1_1007742   CT 2566
3 S1_1007742   TT  346
4 S1_1020677   AA  564
5 S1_1020677   AG 2619
6 S1_1020677   GG 2638

Genomic data

Filtering step

Freqall = apply(geno, 2, function(x){
  aux = as.data.frame(table(do.call(c, strsplit(x = x, split = ''))))
  return(aux)
})
Freqall = do.call(rbind, Freqall) |> 
  rownames_to_column("mrk") |> mutate(mrk = gsub('\\..*', '', mrk))
head(Freqall)

         mrk Var1 Freq
1 S1_1007742    C 8502
2 S1_1007742    T 3258
3 S1_1020677    A 3747
4 S1_1020677    G 7895
5 S1_2018002    C 3166
6 S1_2018002    T 8584

Freqmis = apply(geno, 2, function(x) mean(is.na(x)))
head(Freqmis)

 S1_1007742  S1_1020677  S1_2018002  S1_2101934  S1_2275970  S1_2800964 
0.003220885 0.013222580 0.004068486 0.331073063 0.014578742 0.005763689

Genomic data

Removing markers with \(\geq\) 5% of missing data

geno = geno[,-which(Freqmis >= 0.05)]  
vcf@fix = vcf@fix[-which(Freqmis >= 0.05),]  
dim(geno)

[1] 5899 2146

any(is.na(geno))

[1] TRUE

Impute missing data with the mode

for (i in colnames(geno)) {
  if(!any(is.na(geno[,i]))) next else{
    geno[which(is.na(geno[,i])),i] = as.character(
      Freqsnps[which(Freqsnps$mrk == i), ][which.max(Freqsnps[which(Freqsnps$mrk == i), ][,'Freq']), "Var1"]
      )
  }
  rm(i)
}
any(is.na(geno))

[1] FALSE

Genomic data

Removing markers with MAF \(\leq\) 5%

Freqall$Fper = Freqall$Freq / (nrow(geno) * 2)
geno = geno[,-which(Freqall$Fper <= 0.05)]
dim(geno)

[1] 5899 2105

Genomic data

Re-codification according to the dosage of the reference allele

geno.code = matrix(
  nrow = nrow(geno),
  ncol = ncol(geno),
  dimnames = list(rownames(geno), colnames(geno))
)
for (i in colnames(geno)) {
  x = geno[, i]
  geno.code[, i] = nchar(gsub(paste0("[^", vcf@fix[which(vcf@fix[, "ID"] == i),
                                                   'REF'], "]"), "", x))
}
geno.code[1:5,1:5]

             S1_1007742 S1_1020677 S1_2018002 S1_2275970 S1_2800964
01CSI6/LH287          1          2          2          2          2
01DIB2/LH287          0          2          2          2          1
01DIB2/PHP02          0          1          1          2          0
2369/DK3IIH6          1          2          1          2          1
2369/LH123HT          1          1          2          1          2

# saveRDS(geno.code, file = "Data/mrk_filtered.RDS")

Ridge Regression

\[ \boldsymbol y = \boldsymbol 1 \mu + \boldsymbol W \boldsymbol u + \boldsymbol \varepsilon \]

\(\boldsymbol W \to\) matrix of markers recoded to numeric scale
\(\boldsymbol u \to\) vector of marker effects

RR-BLUP assumes that all markers effects are normally distributed and have identical variance:

\[ \boldsymbol u \sim \mathcal N \left(\boldsymbol 0, \sigma^2_u \boldsymbol I \right) \]

Ridge Regression

\[ \boldsymbol u \sim \mathcal N \left(\boldsymbol 0, \sigma^2_u \boldsymbol I \right) \]

Ridge Regression

Solution via mixed model equation

\[ \begin{bmatrix} \boldsymbol{1}^\prime \boldsymbol 1 & \boldsymbol 1^\prime \boldsymbol W \\ \boldsymbol W^\prime \boldsymbol 1 & \left(\boldsymbol W^\prime \boldsymbol W + \lambda \boldsymbol{I} \right) \end{bmatrix} \begin{bmatrix} \boldsymbol \mu \\ \boldsymbol u \end{bmatrix} = \begin{bmatrix} \boldsymbol 1^\prime \boldsymbol y \\ \boldsymbol W^\prime \boldsymbol y \end{bmatrix} \]

Shrinkage factor: Shrinks the marker effects towards zero

\[ \lambda = \frac{\sigma^2_\varepsilon}{\sigma^2_u} \]

Ridge Regression

Assure compatibility between phenotypic and genomic data

all(dat$gen %in% rownames(geno.code))

[1] FALSE

which(!dat$gen %in% rownames(geno.code))

[1] 338 370

dat = dat[which(dat$gen %in% rownames(geno.code)),]
all(dat$gen %in% rownames(geno.code))

[1] TRUE

Ridge Regression

Assure compatibility between genomic and phenotypic data

all(rownames(geno.code) %in% dat$gen)

[1] FALSE

which(!rownames(geno.code) %in% dat$gen)

   [1]    1    2    3    4    6    7    8    9   10   11   12   13   14   15
  [15]   16   17   18   19   20   21   22   23   24   25   26   27   28   29
  [29]   30   31   32   33   34   35   36   37   38   39   40   41   42   43
  [43]   44   45   46   47   48   49   50   51   52   53   54   55   56   57
  [57]   58   59   60   61   62   64   65   67   68   69   70   71   72   73
  [71]   75   76   77   78   80   81   82   83   84   85   87   88   89   90
  [85]   91   92   93   94   95   96   97   98   99  100  101  102  103  104
  [99]  105  106  107  108  109  110  111  112  113  114  115  116  117  118
 [113]  119  120  121  122  123  124  125  126  127  128  129  130  131  132
 [127]  133  134  135  136  137  138  139  140  141  142  143  144  145  146
 [141]  147  148  149  150  151  152  153  154  155  157  158  159  160  161
 [155]  162  164  165  166  168  169  171  172  173  174  175  177  178  179
 [169]  181  182  183  184  185  186  187  188  189  190  191  192  193  194
 [183]  195  196  197  198  199  200  201  202  203  204  205  206  207  208
 [197]  209  210  211  212  213  214  215  216  217  218  219  220  221  222
 [211]  223  224  225  226  227  228  229  230  231  232  233  234  235  236
 [225]  237  238  239  240  241  242  243  244  245  246  247  248  249  250
 [239]  251  252  253  254  255  256  257  258  259  260  261  262  263  264
 [253]  265  266  267  268  269  270  271  272  273  274  275  276  277  278
 [267]  279  280  281  282  283  284  285  286  287  288  289  290  291  292
 [281]  293  294  295  296  297  298  299  301  302  303  304  305  306  307
 [295]  308  309  310  311  312  313  314  315  316  317  318  319  320  321
 [309]  322  323  324  325  326  327  328  329  330  331  332  333  334  335
 [323]  336  337  338  339  340  341  342  343  345  346  347  348  349  350
 [337]  351  352  353  354  355  356  357  358  359  360  361  362  363  364
 [351]  365  366  367  368  369  370  371  372  373  374  375  376  377  378
 [365]  379  380  381  382  383  384  385  386  387  388  389  390  391  392
 [379]  393  394  395  396  397  399  400  401  402  403  405  406  407  408
 [393]  409  410  411  412  413  414  415  416  417  418  419  420  421  422
 [407]  425  429  430  433  436  438  440  471  482  488  490  493  496  499
 [421]  500  503  504  505  507  510  512  521  523  525  527  530  541  544
 [435]  551  553  556  557  560  561  562  569  570  574  575  579  583  585
 [449]  586  597  599  600  602  603  604  605  609  612  614  616  618  625
 [463]  629  647  668  678  680  684  691  694  695  702  703  706  712  716
 [477]  721  723  725  727  733  744  749  751  756  757  758  763  765  774
 [491]  777  778  779  780  781  782  783  787  788  789  790  791  792  793
 [505]  794  795  796  797  798  799  800  801  802  803  804  805  806  807
 [519]  808  809  810  811  812  813  814  815  816  817  818  819  820  821
 [533]  822  823  824  825  826  827  828  829  830  831  832  833  834  835
 [547]  836  837  838  839  840  841  842  843  844  845  846  847  848  849
 [561]  850  851  852  853  854  855  856  857  858  859  860  861  862  863
 [575]  864  865  866  867  868  869  870  871  872  873  874  875  876  877
 [589]  878  879  880  881  882  883  884  885  886  887  888  889  890  891
 [603]  892  893  894  895  896  897  898  899  900  901  902  903  904  905
 [617]  906  907  908  909  910  911  912  913  914  915  916  917  918  919
 [631]  920  921  922  923  924  925  926  927  928  929  930  931  932  933
 [645]  934  935  936  937  938  939  940  941  942  943  944  945  946  947
 [659]  948  949  950  951  952  953  954  955  956  957  958  959  960  961
 [673]  962  963  964  965  966  967  968  969  970  971  972  973  974  975
 [687]  976  977  978  979  980  981  982  983  984  985  986  987  988  989
 [701]  990  991  992  993  994  995  996  997  998  999 1000 1001 1002 1003
 [715] 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017
 [729] 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031
 [743] 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045
 [757] 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059
 [771] 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073
 [785] 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087
 [799] 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101
 [813] 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115
 [827] 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129
 [841] 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143
 [855] 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157
 [869] 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171
 [883] 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185
 [897] 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199
 [911] 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213
 [925] 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227
 [939] 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241
 [953] 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255
 [967] 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269
 [981] 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283
 [995] 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297
[1009] 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311
[1023] 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325
[1037] 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339
[1051] 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353
[1065] 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367
[1079] 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381
[1093] 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395
[1107] 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409
[1121] 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423
[1135] 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437
[1149] 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451
[1163] 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465
[1177] 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479
[1191] 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493
[1205] 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507
[1219] 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521
[1233] 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535
[1247] 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549
[1261] 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563
[1275] 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577
[1289] 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591
[1303] 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605
[1317] 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619
[1331] 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633
[1345] 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1648
[1359] 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662
[1373] 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676
[1387] 1678 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692
[1401] 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1706 1707
[1415] 1708 1709 1710 1711 1712 1714 1715 1717 1718 1719 1720 1721 1722 1723
[1429] 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1735 1736 1737 1738
[1443] 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752
[1457] 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766
[1471] 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780
[1485] 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794
[1499] 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
[1513] 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822
[1527] 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836
[1541] 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850
[1555] 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864
[1569] 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878
[1583] 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892
[1597] 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906
[1611] 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
[1625] 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934
[1639] 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948
[1653] 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962
[1667] 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976
[1681] 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990
[1695] 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
[1709] 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
[1723] 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032
[1737] 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046
[1751] 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060
[1765] 2061 2062 2063 2064 2065 2066 2067 2068 2070 2071 2072 2073 2074 2075
[1779] 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089
[1793] 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103
[1807] 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117
[1821] 2118 2119 2120 2121 2122 2123 2124 2125 2126 2129 2130 2131 2135 2137
[1835] 2138 2139 2140 2141 2142 2143 2144 2146 2147 2148 2149 2150 2152 2153
[1849] 2154 2155 2156 2157 2159 2160 2161 2163 2164 2165 2166 2167 2168 2169
[1863] 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183
[1877] 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198
[1891] 2199 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213
[1905] 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227
[1919] 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241
[1933] 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255
[1947] 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269
[1961] 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283
[1975] 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297
[1989] 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311
[2003] 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325
[2017] 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339
[2031] 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353
[2045] 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367
[2059] 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381
[2073] 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395
[2087] 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409
[2101] 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423
[2115] 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437
[2129] 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451
[2143] 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465
[2157] 2466 2467 2468 2469 2470 2471 2472 2473 2474 2476 2477 2478 2479 2480
[2171] 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494
[2185] 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508
[2199] 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522
[2213] 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536
[2227] 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550
[2241] 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564
[2255] 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578
[2269] 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592
[2283] 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606
[2297] 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2621
[2311] 2622 2623 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635
[2325] 2636 2637 2639 2640 2641 2642 2644 2646 2647 2650 2655 2656 2657 2659
[2339] 2661 2662 2663 2664 2665 2666 2668 2669 2670 2671 2672 2673 2674 2675
[2353] 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689
[2367] 2690 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704
[2381] 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718
[2395] 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732
[2409] 2733 2734 2735 2736 2737 2738 2739 2741 2742 2743 2744 2745 2746 2747
[2423] 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761
[2437] 2762 2763 2764 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776
[2451] 2777 2778 2779 2780 2781 2782 2783 2785 2786 2787 2788 2789 2790 2791
[2465] 2793 2794 2795 2796 2797 2798 2799 2800 2801 2803 2804 2805 2806 2807
[2479] 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2821 2822 2823
[2493] 2825 2826 2827 2828 2829 2830 2831 2832 2834 2835 2836 2837 2838 2839
[2507] 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853
[2521] 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867
[2535] 2868 2869 2870 2871 2872 2873 2875 2876 2877 2878 2879 2881 2882 2883
[2549] 2884 2885 2887 2888 2889 2890 2892 2894 2895 2896 2897 2898 2899 2900
[2563] 2901 2902 2903 2904 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915
[2577] 2916 2917 2919 2920 2921 2922 2923 2925 2926 2927 2928 2929 2930 2931
[2591] 2932 2933 2934 2935 2937 2938 2939 2940 2942 2943 2944 2945 2946 2947
[2605] 2948 2949 2950 2951 2952 2954 2955 2956 2957 2958 2960 2961 2962 2963
[2619] 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977
[2633] 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2991 2992
[2647] 2993 2994 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007
[2661] 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021
[2675] 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035
[2689] 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049
[2703] 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063
[2717] 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077
[2731] 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091
[2745] 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101 3102 3103 3104 3105
[2759] 3106 3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119
[2773] 3120 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133
[2787] 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147
[2801] 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160 3161
[2815] 3162 3163 3164 3165 3166 3167 3168 3169 3170 3171 3172 3173 3174 3175
[2829] 3176 3177 3178 3179 3180 3181 3182 3183 3184 3185 3186 3187 3188 3189
[2843] 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203
[2857] 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217
[2871] 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231
[2885] 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244 3245
[2899] 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259
[2913] 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273
[2927] 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287
[2941] 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3301 3302
[2955] 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3316 3317
[2969] 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331
[2983] 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341 3342 3343 3344 3345
[2997] 3346 3347 3348 3349 3350 3351 3352 3353 3354 3355 3356 3358 3359 3360
[3011] 3361 3362 3363 3364 3365 3366 3367 3368 3369 3370 3371 3372 3373 3374
[3025] 3375 3376 3377 3378 3379 3380 3381 3382 3383 3384 3385 3386 3387 3388
[3039] 3389 3390 3391 3392 3393 3394 3395 3396 3397 3398 3399 3400 3401 3402
[3053] 3403 3404 3405 3406 3407 3408 3409 3410 3411 3412 3413 3414 3415 3416
[3067] 3417 3418 3419 3420 3421 3422 3423 3424 3425 3426 3427 3428 3429 3430
[3081] 3431 3432 3433 3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444
[3095] 3445 3446 3447 3448 3449 3450 3451 3452 3453 3454 3455 3456 3457 3458
[3109] 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468 3470 3471 3472 3473
[3123] 3474 3475 3476 3477 3478 3479 3480 3481 3482 3483 3484 3485 3486 3487
[3137] 3488 3489 3490 3491 3492 3493 3494 3496 3497 3498 3499 3500 3501 3502
[3151] 3503 3504 3505 3506 3507 3508 3509 3510 3511 3512 3513 3514 3516 3517
[3165] 3518 3520 3521 3522 3523 3524 3525 3527 3528 3529 3530 3531 3532 3533
[3179] 3534 3535 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545 3546 3547
[3193] 3548 3549 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559 3560 3561
[3207] 3562 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573 3574 3576
[3221] 3577 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 3596
[3235] 3597 3598 3599 3601 3602 3604 3605 3606 3607 3608 3609 3610 3611 3612
[3249] 3613 3614 3615 3616 3617 3618 3620 3621 3622 3623 3624 3625 3626 3627
[3263] 3628 3629 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640 3641
[3277] 3642 3643 3644 3645 3646 3647 3648 3649 3650 3651 3652 3653 3654 3655
[3291] 3656 3657 3658 3659 3660 3661 3662 3663 3664 3665 3666 3667 3668 3669
[3305] 3670 3671 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683
[3319] 3684 3685 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697
[3333] 3698 3699 3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711
[3347] 3712 3713 3714 3715 3716 3717 3718 3719 3720 3721 3722 3723 3724 3725
[3361] 3726 3727 3728 3729 3730 3731 3732 3733 3734 3735 3736 3737 3738 3739
[3375] 3740 3741 3742 3743 3744 3745 3746 3747 3748 3749 3750 3751 3752 3753
[3389] 3754 3755 3756 3757 3758 3759 3760 3761 3763 3764 3765 3766 3767 3768
[3403] 3769 3770 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782
[3417] 3783 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796
[3431] 3797 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 3810 3811
[3445] 3812 3813 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825
[3459] 3826 3827 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839
[3473] 3840 3841 3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853
[3487] 3854 3855 3856 3857 3858 3859 3860 3861 3862 3863 3864 3865 3866 3867
[3501] 3868 3869 3870 3871 3872 3873 3875 3876 3877 3878 3879 3880 3881 3882
[3515] 3883 3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 3896
[3529] 3897 3898 3899 3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910
[3543] 3911 3912 3913 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924
[3557] 3925 3926 3927 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 3938
[3571] 3939 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952
[3585] 3953 3954 3955 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 3966
[3599] 3967 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 3980
[3613] 3981 3982 3983 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 3994
[3627] 3995 3996 3997 3998 3999 4000 4001 4002 4003 4004 4005 4006 4007 4008
[3641] 4009 4010 4011 4012 4013 4014 4015 4016 4017 4018 4019 4020 4021 4022
[3655] 4023 4024 4025 4026 4027 4028 4029 4030 4031 4032 4033 4034 4035 4036
[3669] 4037 4038 4039 4040 4041 4042 4043 4044 4045 4046 4047 4048 4049 4050
[3683] 4051 4052 4053 4054 4055 4056 4057 4058 4059 4060 4061 4062 4063 4064
[3697] 4065 4066 4067 4068 4069 4070 4071 4072 4073 4074 4075 4076 4077 4078
[3711] 4079 4080 4081 4082 4083 4084 4085 4086 4087 4088 4089 4090 4091 4092
[3725] 4093 4094 4095 4096 4097 4098 4099 4100 4101 4102 4103 4104 4105 4106
[3739] 4107 4108 4109 4110 4111 4112 4113 4114 4115 4116 4117 4118 4119 4120
[3753] 4121 4122 4123 4124 4125 4126 4127 4128 4129 4130 4131 4132 4133 4134
[3767] 4135 4136 4137 4138 4139 4140 4141 4142 4143 4145 4146 4147 4148 4149
[3781] 4150 4151 4152 4154 4155 4157 4158 4160 4161 4162 4163 4164 4166 4168
[3795] 4170 4172 4174 4175 4177 4180 4182 4183 4186 4188 4191 4193 4194 4196
[3809] 4198 4199 4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 4210 4211
[3823] 4212 4213 4214 4215 4216 4217 4218 4219 4220 4221 4222 4223 4224 4225
[3837] 4226 4227 4228 4229 4230 4231 4232 4233 4234 4235 4236 4237 4238 4239
[3851] 4240 4241 4242 4243 4244 4245 4246 4247 4248 4249 4250 4251 4252 4253
[3865] 4254 4255 4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 4266 4267
[3879] 4268 4269 4270 4271 4272 4273 4274 4275 4276 4277 4278 4279 4280 4281
[3893] 4282 4283 4284 4285 4286 4287 4288 4289 4290 4291 4292 4293 4294 4295
[3907] 4296 4297 4298 4299 4300 4301 4302 4303 4304 4305 4306 4307 4308 4309
[3921] 4310 4311 4312 4313 4314 4315 4316 4317 4318 4319 4320 4321 4322 4323
[3935] 4324 4325 4326 4327 4328 4329 4330 4331 4332 4333 4334 4335 4336 4337
[3949] 4338 4339 4340 4341 4342 4343 4344 4345 4346 4347 4348 4349 4350 4351
[3963] 4352 4353 4354 4355 4356 4357 4358 4359 4360 4361 4362 4363 4364 4365
[3977] 4366 4367 4368 4369 4370 4371 4372 4373 4374 4375 4376 4377 4378 4379
[3991] 4380 4381 4382 4383 4384 4385 4386 4387 4388 4389 4390 4391 4392 4393
[4005] 4394 4395 4396 4397 4398 4399 4400 4401 4402 4403 4404 4405 4406 4407
[4019] 4408 4409 4410 4411 4412 4413 4414 4415 4416 4417 4418 4419 4420 4421
[4033] 4422 4423 4424 4425 4426 4427 4428 4429 4430 4431 4432 4433 4434 4435
[4047] 4436 4437 4438 4439 4440 4441 4442 4443 4444 4445 4446 4447 4448 4449
[4061] 4450 4451 4452 4453 4454 4455 4456 4457 4458 4459 4460 4461 4462 4463
[4075] 4464 4465 4466 4467 4468 4469 4470 4471 4472 4473 4474 4475 4476 4477
[4089] 4478 4479 4480 4481 4482 4483 4484 4485 4486 4487 4488 4489 4490 4491
[4103] 4492 4493 4494 4495 4496 4497 4498 4499 4500 4501 4502 4503 4504 4505
[4117] 4506 4507 4508 4509 4510 4511 4512 4513 4514 4515 4516 4517 4518 4519
[4131] 4520 4521 4522 4523 4524 4525 4526 4527 4528 4529 4530 4531 4532 4533
[4145] 4534 4535 4536 4537 4538 4539 4540 4541 4542 4543 4544 4545 4546 4547
[4159] 4548 4549 4550 4551 4552 4553 4554 4555 4556 4557 4558 4559 4560 4561
[4173] 4562 4563 4564 4565 4566 4567 4568 4569 4570 4571 4572 4573 4574 4575
[4187] 4576 4577 4578 4579 4580 4581 4582 4583 4584 4585 4586 4587 4588 4589
[4201] 4590 4591 4592 4593 4594 4595 4596 4597 4598 4599 4600 4601 4602 4603
[4215] 4604 4605 4606 4607 4608 4609 4610 4611 4612 4613 4614 4615 4616 4617
[4229] 4618 4619 4620 4621 4622 4623 4624 4625 4626 4627 4628 4629 4630 4631
[4243] 4632 4633 4634 4635 4636 4637 4638 4639 4640 4641 4642 4643 4644 4645
[4257] 4646 4647 4648 4649 4650 4651 4652 4653 4654 4655 4656 4657 4658 4659
[4271] 4660 4661 4662 4663 4664 4665 4666 4667 4668 4669 4670 4671 4672 4673
[4285] 4674 4675 4676 4677 4678 4679 4680 4681 4682 4683 4684 4685 4686 4687
[4299] 4688 4689 4690 4691 4692 4693 4694 4695 4696 4697 4698 4699 4700 4701
[4313] 4702 4703 4704 4705 4706 4707 4708 4709 4710 4711 4712 4713 4714 4715
[4327] 4716 4717 4718 4719 4720 4721 4722 4723 4724 4725 4726 4727 4728 4729
[4341] 4730 4731 4732 4733 4734 4735 4736 4737 4738 4739 4740 4741 4742 4743
[4355] 4744 4745 4746 4747 4748 4749 4750 4751 4752 4753 4754 4755 4756 4757
[4369] 4758 4759 4760 4761 4762 4763 4764 4765 4766 4767 4768 4769 4770 4771
[4383] 4772 4773 4774 4775 4776 4777 4778 4779 4780 4781 4782 4783 4784 4785
[4397] 4786 4787 4788 4789 4790 4791 4792 4793 4794 4795 4796 4797 4798 4799
[4411] 4800 4801 4802 4803 4804 4805 4806 4807 4808 4809 4810 4811 4812 4813
[4425] 4814 4815 4816 4817 4818 4819 4820 4821 4822 4823 4824 4825 4826 4827
[4439] 4828 4829 4830 4831 4832 4833 4834 4835 4836 4837 4838 4839 4840 4841
[4453] 4842 4843 4844 4845 4846 4847 4848 4849 4850 4851 4852 4853 4854 4855
[4467] 4856 4857 4858 4859 4860 4861 4862 4863 4864 4865 4866 4867 4868 4869
[4481] 4870 4871 4872 4873 4874 4875 4876 4877 4878 4879 4880 4881 4882 4883
[4495] 4884 4885 4886 4887 4888 4889 4890 4891 4892 4893 4894 4895 4896 4897
[4509] 4898 4899 4900 4901 4902 4903 4904 4905 4906 4907 4908 4909 4910 4911
[4523] 4912 4913 4914 4915 4916 4917 4918 4919 4920 4921 4922 4923 4924 4925
[4537] 4926 4927 4928 4929 4930 4931 4932 4933 4934 4935 4936 4937 4938 4939
[4551] 4940 4941 4942 4943 4944 4945 4946 4947 4948 4949 4950 4951 4952 4953
[4565] 4954 4955 4956 4957 4958 4959 4960 4961 4962 4963 4964 4965 4966 4967
[4579] 4968 4969 4970 4971 4972 4973 4974 4975 4976 4977 4978 4979 4980 4981
[4593] 4982 4983 4984 4985 4986 4987 4988 4989 4990 4991 4992 4993 4994 4995
[4607] 4996 4997 4998 4999 5000 5001 5002 5003 5004 5005 5006 5007 5008 5009
[4621] 5010 5011 5012 5013 5014 5015 5016 5017 5018 5019 5020 5021 5022 5023
[4635] 5024 5025 5026 5027 5028 5029 5030 5031 5032 5033 5034 5035 5036 5037
[4649] 5038 5039 5040 5041 5042 5043 5044 5045 5046 5047 5048 5049 5050 5051
[4663] 5052 5053 5054 5055 5056 5057 5058 5059 5060 5061 5062 5063 5064 5065
[4677] 5066 5067 5068 5069 5070 5071 5072 5073 5074 5075 5076 5077 5078 5079
[4691] 5080 5081 5082 5083 5084 5085 5086 5087 5088 5089 5090 5091 5092 5093
[4705] 5094 5095 5096 5097 5098 5099 5100 5101 5102 5103 5104 5105 5106 5107
[4719] 5108 5109 5110 5111 5112 5113 5114 5115 5116 5117 5118 5119 5120 5121
[4733] 5122 5123 5124 5125 5126 5127 5128 5129 5130 5131 5132 5133 5134 5135
[4747] 5136 5137 5138 5139 5140 5141 5142 5143 5144 5145 5146 5147 5148 5149
[4761] 5150 5151 5152 5153 5154 5155 5156 5157 5158 5159 5160 5161 5162 5163
[4775] 5164 5165 5166 5167 5168 5169 5170 5171 5172 5173 5174 5175 5176 5177
[4789] 5178 5179 5180 5181 5182 5183 5184 5185 5186 5187 5188 5189 5190 5191
[4803] 5192 5193 5194 5195 5196 5197 5198 5199 5200 5201 5202 5203 5204 5205
[4817] 5206 5207 5208 5209 5210 5211 5212 5213 5214 5215 5216 5217 5218 5219
[4831] 5220 5221 5222 5223 5224 5225 5226 5227 5228 5229 5230 5231 5232 5233
[4845] 5234 5235 5236 5237 5238 5239 5240 5241 5242 5243 5244 5245 5246 5247
[4859] 5248 5249 5250 5251 5252 5253 5254 5255 5256 5257 5258 5259 5260 5261
[4873] 5262 5263 5264 5265 5266 5267 5268 5269 5270 5271 5272 5273 5274 5275
[4887] 5276 5277 5278 5279 5280 5281 5282 5283 5284 5285 5286 5287 5288 5289
[4901] 5290 5291 5292 5293 5294 5295 5296 5297 5298 5299 5300 5301 5302 5303
[4915] 5304 5305 5306 5307 5308 5309 5310 5311 5312 5313 5314 5315 5316 5317
[4929] 5318 5319 5320 5321 5322 5323 5324 5325 5326 5327 5328 5329 5330 5331
[4943] 5332 5333 5334 5335 5336 5337 5338 5339 5340 5341 5342 5343 5344 5345
[4957] 5346 5347 5348 5349 5350 5351 5352 5353 5354 5355 5356 5357 5358 5359
[4971] 5360 5361 5362 5363 5364 5365 5366 5367 5368 5369 5370 5371 5372 5373
[4985] 5374 5375 5376 5377 5378 5379 5380 5381 5382 5383 5384 5385 5386 5387
[4999] 5388 5389 5390 5391 5392 5393 5394 5395 5396 5397 5398 5399 5400 5401
[5013] 5402 5403 5404 5405 5406 5407 5408 5409 5410 5411 5412 5413 5414 5415
[5027] 5416 5417 5418 5419 5420 5421 5422 5423 5424 5425 5426 5427 5428 5429
[5041] 5431 5432 5433 5434 5435 5436 5437 5439 5440 5442 5443 5444 5445 5446
[5055] 5448 5449 5450 5452 5453 5454 5455 5456 5457 5458 5459 5460 5461 5462
[5069] 5463 5464 5465 5466 5467 5468 5469 5470 5471 5478 5481 5482 5484 5485
[5083] 5488 5493 5496 5497 5498 5499 5500 5501 5502 5503 5504 5505 5506 5507
[5097] 5508 5509 5510 5511 5512 5513 5514 5515 5516 5517 5518 5519 5520 5521
[5111] 5522 5523 5524 5525 5526 5527 5528 5529 5530 5531 5532 5533 5534 5535
[5125] 5536 5537 5538 5539 5540 5541 5542 5543 5544 5545 5546 5547 5548 5549
[5139] 5550 5551 5552 5553 5554 5555 5556 5557 5558 5559 5560 5561 5562 5563
[5153] 5564 5565 5566 5567 5568 5569 5570 5571 5572 5573 5574 5575 5576 5577
[5167] 5578 5579 5580 5581 5582 5583 5584 5585 5586 5587 5588 5589 5590 5591
[5181] 5592 5593 5594 5595 5596 5597 5598 5599 5600 5601 5602 5603 5604 5605
[5195] 5606 5607 5608 5609 5610 5611 5612 5613 5614 5615 5616 5617 5618 5619
[5209] 5620 5621 5622 5623 5624 5625 5626 5627 5628 5629 5630 5631 5632 5633
[5223] 5634 5635 5636 5637 5638 5639 5640 5641 5642 5643 5644 5645 5646 5647
[5237] 5648 5649 5650 5651 5652 5653 5654 5655 5656 5657 5658 5659 5660 5661
[5251] 5662 5663 5664 5665 5666 5667 5668 5669 5670 5671 5672 5673 5674 5675
[5265] 5676 5677 5678 5679 5680 5681 5682 5683 5684 5685 5686 5687 5688 5689
[5279] 5690 5691 5692 5693 5694 5695 5696 5697 5698 5699 5700 5701 5702 5703
[5293] 5704 5705 5706 5707 5708 5709 5710 5711 5712 5713 5714 5715 5716 5717
[5307] 5718 5719 5720 5721 5722 5723 5724 5725 5726 5727 5728 5729 5730 5731
[5321] 5732 5733 5734 5735 5736 5737 5738 5739 5740 5741 5742 5743 5744 5745
[5335] 5746 5747 5748 5749 5750 5751 5752 5753 5754 5755 5756 5757 5758 5759
[5349] 5760 5761 5762 5763 5764 5765 5766 5767 5768 5769 5770 5771 5772 5773
[5363] 5774 5775 5776 5777 5778 5779 5780 5781 5782 5783 5784 5785 5786 5787
[5377] 5788 5789 5790 5791 5792 5793 5794 5795 5796 5797 5798 5799 5800 5801
[5391] 5802 5803 5804 5805 5806 5807 5808 5809 5810 5811 5812 5813 5814 5815
[5405] 5816 5817 5818 5819 5820 5821 5822 5823 5824 5825 5826 5827 5828 5829
[5419] 5830 5831 5832 5833 5834 5835 5836 5837 5838 5839 5840 5841 5842 5843
[5433] 5844 5845 5846 5847 5848 5849 5850 5851 5852 5853 5854 5855 5856 5857
[5447] 5858 5859 5860 5861 5862 5863 5864 5865 5866 5867 5868 5869 5870 5871
[5461] 5872 5873 5874 5875 5876 5877 5878 5879 5880 5881 5882 5883 5884 5885
[5475] 5886 5887 5888 5889 5890 5891 5892 5893 5894 5895 5896 5897 5898 5899

geno.code = geno.code[which(rownames(geno.code) %in% dat$gen),]
all(rownames(geno.code) %in% dat$gen)

[1] TRUE

Don’t forget to check the order of both datasets

all(rownames(geno.code) == dat$gen)

[1] TRUE

Ridge Regression

Package rrBLUP (Endelman, 2011)

library(rrBLUP)
geno_rr = geno.code - 1  # Recoding the marker matrix
geno_rr[1:5,1:5]

             S1_1007742 S1_1020677 S1_2018002 S1_2275970 S1_2800964
2369/LH123HT          0          0          1          0          1
B14A/OH43             1          1          1          1          1
B37/H95              -1          0          0          0          0
B73/MO17              0          1          1          1          0
B73/PHN82             0          0          0          1          0

mod = mixed.solve(y = dat$blue, Z = geno_rr)

Ridge Regression

blup_rr = mod$u

Show code

data.frame(blup_rr) |>  
  rownames_to_column(var = "Marker") |>  
  rename(BLUP = 'blup_rr') |>  
  ggplot(aes(x = Marker, y = BLUP))+
  geom_point(alpha = .6, colour = "#10342d", size = 2)+
  theme_minimal() +
  theme(axis.text.x = element_blank(),
        axis.ticks.x=element_blank(), 
        text = element_text(size = 18))

Ridge Regression

\[ \hat{\boldsymbol g} = \boldsymbol W \hat{\boldsymbol u} \]

GEBV = geno_rr %*% blup_rr

Show code

as.data.frame(GEBV) |> rownames_to_column(var = "gen") |> 
  rename(GEBV = V1) |> 
  ggplot(aes(x = gen, y = GEBV))+
  geom_point(alpha = .75, colour = "#10342d", size = 2) + 
  theme_minimal() +
  theme(axis.text.x = element_blank(), axis.ticks.x=element_blank(),
        text = element_text(size = 18))+
  labs(x = "Hybrid", y = "GEBV")

Ridge Regression

Model parameters

\[ \hat{\sigma}^2_g = \sigma^2_u \times t \]

Vg = mod$Vu * ncol(geno_rr); Vg

[1] 12.80676

Ve = mod$Ve; Ve

[1] 4.854941

mu = mod$beta; mu

[1] 11.81567

geno_her = Vg/(Vg + Ve); geno_her

[1] 0.7251148

cor(dat$blue, GEBV, use = "complete.obs")

         [,1]
[1,] 0.769064

BayesB

\[ \boldsymbol y = \boldsymbol 1 \mu + \boldsymbol W \boldsymbol u + \boldsymbol \varepsilon \]

\(\boldsymbol W \to\) matrix of markers recoded to numeric scale
\(\boldsymbol u \to\) vector of marker effects

BayesB assumes that some markers are unimportant for the trait variation, hence have no effect. Furthermore, it relaxes the assumption that all markers have the same variance:

\[ u_i \sim \mathcal N \left( 0, \sigma^2_{u_i} \right) \]

BayesB

Prior for the variances: scaled inverse \(\chi^2 \left[\sigma^2_{u_i} \sim \chi^{-2}\left(\nu\right)\right]\)

Prior for the importance parameter (say, \(I\)): \(I \sim \operatorname{Bernoulli}(\Pi)\)

\[ \hat{g}_i = I_1 \times w_{1i} \times \hat u_1 + I_2 \times w_{2i} \times \hat u_2 + \dots + I_t \times w_{ti} \times \hat u_t \]

\(I_j \to 1 \mbox{ or } 0\)

BayesB

Package BGLR (Pérez & de los Campos, 2014)

library(BGLR)
X_centred = scale(geno.code, center = TRUE, scale = FALSE)
ETA = list(list(X = X_centred, model = "BayesB")) ## This will determine the model
BayesB = BGLR(y = dat$blue, ETA = ETA, nIter = 80000, burnIn = 20000, 
              thin = 10, verbose = FALSE, saveAt = "Saves/dat/complete_")

In Bayesian models, you should mind the number of iterations, the burn-in phase and the thinning. The more, the better, but it will take longer for your model to run.

Prof. Jose Storopoli

BayesB

Convergence check through model diagnostics: Package coda (Plummer et al., 2006)

library(coda)
varE = read.table("../Saves/dat/complete_varE.dat")[,1]
varE_chain = mcmc(varE[2001:length(varE)]) # burnIn/thin + 1
plot(varE_chain)

BayesB

Effective sample size: The number of independent samples from posterior distributions. The higher, the better. If low, increase nIter or thin

effectiveSize(varE_chain)

   var1 
981.084

Geweke convergence test: evaluates if the MCMC chain reached a stationary state. It should be between -1.96 and 1.96. If not, increase burnIn

geweke.diag(varE_chain)


Fraction in 1st window = 0.1
Fraction in 2nd window = 0.5 

 var1 
1.407

BayesB

GEBV_bb = data.frame(Hybrid = dat$gen, GEBV = BayesB$yHat)
cor(GEBV, GEBV_bb$GEBV)

          [,1]
[1,] 0.9984064

Show code

ggplot(data = GEBV_bb, aes(x = Hybrid, y = GEBV))+
  geom_point(alpha = .75, colour = "#10342d", size = 2) + 
  theme_minimal() +
  theme(axis.text.x = element_blank(), axis.ticks.x=element_blank(),
        text = element_text(size = 18))+
  labs(x = "Hybrid", y = "GEBV")

BayesB

Model parameters

BayesB$ETA[[1]]$b  # mrk effects

   S1_1007742    S1_1020677    S1_2018002    S1_2275970    S1_2800964 
-2.983448e-03 -5.217345e-03  8.273018e-03  1.093059e-02  3.036825e-03 
   S1_2811950    S1_2888631    S1_3023078    S1_3027593    S1_3113549 
-7.406890e-03  2.947550e-02 -7.906656e-03 -1.864531e-03  4.856219e-03 
   S1_3165324    S1_3224170    S1_3370337    S1_3371362    S1_4082864 
 2.835513e-02 -3.998707e-03  5.914926e-03 -3.119971e-03 -1.418168e-03 
   S1_4083443    S1_4083493    S1_4091412    S1_4786541    S1_4860666 
-2.777275e-03  5.877046e-03  1.597297e-02 -9.538885e-05  1.746898e-02 
   S1_5050985    S1_5051275    S1_5840281    S1_6247673    S1_6726074 
-2.410505e-02 -6.920051e-04  1.253146e-02 -1.041218e-02 -8.192517e-02 
   S1_7019860    S1_8308877    S1_8424731    S1_8614659    S1_8614992 
-4.501163e-03 -1.785514e-02 -2.155534e-02  1.813923e-03  5.008761e-03 
   S1_9389027    S1_9818592   S1_10680837   S1_10913935   S1_12066883 
 3.921309e-03  1.800192e-02 -6.467378e-03 -1.149617e-03  5.414347e-03 
  S1_12071750   S1_13721542   S1_17043397   S1_17084072   S1_19199917 
 2.769135e-02  5.591229e-03 -7.647364e-03  4.103152e-02  1.033825e-02 
  S1_19533759   S1_19817258   S1_19954785   S1_21625862   S1_22816035 
 2.375400e-02 -1.696553e-03  1.812888e-02 -2.674079e-02  1.686418e-02 
  S1_22826147   S1_24749523   S1_24898795   S1_25897362   S1_25904060 
 1.099861e-02  8.797849e-03 -1.636572e-02 -7.754911e-03 -6.422788e-03 
  S1_25937934   S1_26638329   S1_26652199   S1_27721183   S1_27978807 
-1.189497e-02  1.519240e-02  6.451551e-02  4.099261e-02 -3.390501e-03 
  S1_28862889   S1_28901698   S1_28901862   S1_29134257   S1_29231650 
 6.867378e-03 -3.739333e-03 -8.710601e-03  2.712839e-02  1.156916e-02 
  S1_29231833   S1_29238866   S1_36920546   S1_38158762   S1_38176608 
 1.047231e-02  1.173008e-02 -8.241451e-04  2.304783e-03 -7.070043e-03 
  S1_39740428   S1_39838119   S1_42912973   S1_42953805   S1_42966940 
-7.462093e-03 -9.743105e-03  1.016437e-02 -4.835953e-03  7.438759e-04 
  S1_42979644   S1_44793022   S1_45597675   S1_49461817   S1_49469580 
 2.036901e-02 -1.281153e-02 -1.747903e-02 -1.102264e-02 -1.154280e-02 
  S1_49580955   S1_49594425   S1_49649087   S1_49651854   S1_49651956 
-9.631796e-03 -2.075824e-02 -3.293958e-02 -4.808439e-02 -3.211019e-02 
  S1_50588263   S1_50607304   S1_50923878   S1_51051541   S1_52103257 
-7.849333e-03 -1.370826e-02  3.350072e-03 -1.792915e-02 -1.898699e-02 
  S1_52638852   S1_53696912   S1_54271779   S1_54626740   S1_54626750 
 1.233247e-03 -7.632772e-03  3.652932e-03 -4.947358e-03  9.719530e-04 
  S1_54817368   S1_55004474   S1_55146349   S1_55541386   S1_61260951 
-3.679444e-03 -9.820595e-03 -3.391673e-03  2.872637e-03 -1.418612e-02 
  S1_61754639   S1_61799908   S1_63022674   S1_63293951   S1_64145901 
-8.333385e-03  1.517772e-02  3.892120e-02 -1.461486e-02 -3.778814e-02 
  S1_65380919   S1_67232203   S1_67655530   S1_67930774   S1_67930852 
 1.333072e-02 -2.999783e-02 -1.788206e-02 -5.376768e-03  2.402122e-03 
  S1_68588019   S1_69884958   S1_70237562   S1_70237837   S1_70787556 
-1.472336e-02  5.939902e-03  1.360809e-03 -6.231092e-03 -4.918195e-03 
  S1_71083982   S1_72143952   S1_74028862   S1_76061725   S1_79764069 
 8.696396e-02 -1.697589e-02  2.647874e-02 -8.609129e-03 -3.052596e-02 
  S1_79974668   S1_81902385   S1_82376170   S1_82824904   S1_83001157 
 4.844187e-03  7.827462e-03 -2.488023e-02 -1.230117e-02  3.968225e-03 
  S1_84350697   S1_85241654   S1_85242735   S1_90306830   S1_93713464 
-1.480584e-02  2.870255e-03 -9.819560e-03 -1.027971e-01  1.399851e-02 
  S1_98537890   S1_99764648  S1_104266693  S1_154626318  S1_160143536 
-4.840323e-03  2.613854e-03 -3.100658e-02  2.380391e-02 -1.078735e-02 
 S1_168376973  S1_171255509  S1_174893075  S1_175592912  S1_177307583 
-2.581027e-02 -4.857722e-02  4.670545e-03 -2.907351e-02 -4.747964e-03 
 S1_177307602  S1_177392527  S1_177392973  S1_179367636  S1_184392265 
-4.676629e-03  1.249421e-02 -9.401553e-03  1.121193e-02 -6.499064e-03 
 S1_186695297  S1_188811188  S1_188811510  S1_192214923  S1_193302766 
-1.628443e-03  1.102979e-02  8.579479e-03  1.317551e-02  1.740789e-03 
 S1_193403593  S1_194747821  S1_194883376  S1_194883734  S1_195755225 
 8.242451e-04 -5.735182e-03  1.254423e-02  3.415813e-02  8.699521e-03 
 S1_195765736  S1_197658534  S1_199031309  S1_200871315  S1_203690794 
-2.206098e-02  1.185698e-02  2.793341e-04 -7.543128e-03 -7.691437e-03 
 S1_205867610  S1_206556648  S1_210194624  S1_213603082  S1_216085070 
 1.543754e-03  2.094957e-03 -3.127887e-03 -4.228681e-02  2.507195e-02 
 S1_216441690  S1_217340021  S1_218784296  S1_221188116  S1_222074998 
 2.049820e-02 -1.277928e-03 -1.234276e-02  3.105878e-03  8.626687e-03 
 S1_222145931  S1_224504536  S1_224955996  S1_225214186  S1_226580848 
 7.098400e-03 -5.308398e-03  2.197742e-02  1.742009e-02  5.415879e-03 
 S1_227210945  S1_227214041  S1_227836197  S1_228067077  S1_228777111 
-1.440201e-02 -1.526596e-03  1.691751e-02  1.082080e-02 -2.571708e-03 
 S1_229362381  S1_229614348  S1_229624647  S1_231144006  S1_231670600 
 1.358363e-02  1.971710e-02  3.727367e-03  8.931982e-03  3.119278e-02 
 S1_234071369  S1_237980181  S1_239778934  S1_239779108  S1_240319705 
-3.869383e-03  5.876390e-03  5.316005e-03  7.987442e-03 -3.125016e-02 
 S1_245927550  S1_246327000  S1_246528913  S1_246930727  S1_250252471 
-1.212939e-02  1.524867e-02 -4.028503e-02 -2.260454e-02 -2.668080e-02 
 S1_250313669  S1_251426821  S1_252128005  S1_252189312  S1_254268145 
-3.995301e-03  3.248045e-03  1.549467e-02 -1.224892e-02 -1.189809e-02 
 S1_254314212  S1_254730123  S1_254731990  S1_254732450  S1_254738006 
-1.029875e-02 -6.093520e-03  1.655141e-02  1.323570e-02  1.552336e-01 
 S1_254980069  S1_255339302  S1_255352181  S1_257312468  S1_258510992 
 4.237648e-03 -3.068760e-03  2.580453e-03  9.915634e-03 -1.633881e-02 
 S1_259134135  S1_259401874  S1_260544980  S1_261456944  S1_261563363 
 1.851978e-02 -2.876906e-03 -5.314541e-03  6.370435e-03  2.511361e-03 
 S1_261565696  S1_262832685  S1_262984673  S1_263089262  S1_263089768 
 7.472729e-03  6.405608e-03 -2.346688e-03 -3.238767e-03 -1.117508e-02 
 S1_263869417  S1_264032977  S1_264033204  S1_268826064  S1_268833938 
-3.203707e-02  4.914988e-03 -3.989018e-03 -9.964808e-04  2.026811e-02 
 S1_269114894  S1_273735827  S1_274475634  S1_275604002  S1_275722303 
-1.029516e-02  4.164699e-03  1.526176e-02 -3.371744e-02  5.627491e-03 
 S1_275837252  S1_275887092  S1_277707178  S1_278756927  S1_278757478 
-1.431882e-02 -1.844475e-02 -2.665002e-02  6.326795e-03 -3.485954e-02 
 S1_278982963  S1_281052067  S1_281681637  S1_281860320  S1_281860733 
-2.051955e-03  5.396978e-03  1.467631e-02 -1.212718e-02  8.354879e-03 
 S1_281904515  S1_282025244  S1_282332826  S1_282429723  S1_282430143 
-4.647703e-03  5.315642e-03  7.153173e-02  6.607136e-03  4.978581e-03 
 S1_282641564  S1_282866022  S1_282961510  S1_283696325  S1_284567398 
-4.914280e-03 -1.491049e-03  9.657978e-03 -7.435228e-03  1.488656e-03 
 S1_284775241  S1_284776735  S1_284896531  S1_285109719  S1_285256763 
 5.231039e-04  5.716598e-02 -2.659957e-02 -1.626695e-03  1.842745e-02 
 S1_287013302  S1_287112427  S1_287158098  S1_287767988  S1_287896811 
 4.442635e-03  3.742598e-03  8.717710e-03 -4.384164e-03  2.583383e-03 
 S1_287897109  S1_287955406  S1_287995733  S1_288426011  S1_288426392 
 6.802339e-03 -2.112089e-03 -4.424567e-03 -3.771138e-02 -3.132740e-02 
 S1_289282560  S1_289484026  S1_289558741  S1_289559927  S1_289561097 
-7.529655e-03 -8.126505e-02 -5.275206e-02  1.329118e-02 -1.960271e-02 
 S1_290780255  S1_291863254  S1_291904261  S1_291927645  S1_291936844 
-1.038364e-02  1.133276e-02 -1.614464e-02 -5.737333e-03 -5.816933e-03 
 S1_292131439  S1_292188211  S1_292189290  S1_292559475  S1_292607286 
-4.257248e-03  2.026052e-02  1.159795e-02 -1.330186e-02 -4.126250e-03 
 S1_293322158  S1_293666348  S1_295606882  S1_295655169  S1_295655425 
 7.392959e-03  2.267267e-03  7.629339e-03 -1.174345e-02 -1.192161e-02 
 S1_295841168  S1_295842024  S1_295847759  S1_296263882  S1_297155444 
-1.413246e-02  2.248739e-03 -1.704300e-03  2.098070e-02  1.743555e-04 
 S1_297299124  S1_297821279  S1_297835206  S1_298732606  S1_299366946 
 1.359728e-02  6.698471e-03 -2.694405e-02 -8.041955e-03 -1.128287e-02 
 S1_300685135  S1_300695527  S1_300813218  S1_301040186  S1_301110783 
 1.816024e-02  2.225816e-02  1.394782e-02  1.352362e-03  2.346797e-03 
 S1_301664490  S1_301963810  S1_301963849  S1_302074583  S1_302268166 
 6.340871e-03  1.907749e-02 -5.017648e-03 -1.766652e-03 -1.184776e-02 
 S1_302268806  S1_302390178  S1_302404864  S1_302444783  S1_302949773 
-1.047031e-02 -1.377668e-02 -1.378127e-02 -3.402971e-04  1.355066e-03 
 S1_303116466  S1_303133452  S1_303135603  S1_303600537  S1_303814543 
-1.381199e-02  1.236750e-02 -1.685927e-02  1.126090e-03 -4.151292e-02 
 S1_303825999  S1_303832934  S1_303847256  S1_304921234  S1_304923212 
-8.795742e-03  2.275724e-02 -1.012136e-02  1.370953e-02 -3.098178e-02 
 S1_305453976  S1_305526698  S1_306039559  S1_306043136  S1_306125534 
-1.827631e-02 -5.146253e-02  3.155106e-03  1.006760e-02 -4.843958e-04 
 S1_306272682  S1_306412211  S1_307484303    S2_1031301    S2_1186838 
-1.247144e-02 -1.819815e-02 -1.491922e-02 -1.187980e-02 -7.031243e-03 
   S2_1703653    S2_1817244    S2_1817603    S2_1817934    S2_1836809 
-8.322844e-03 -2.260091e-03  9.301031e-03 -1.458768e-02  8.858407e-03 
   S2_2353527    S2_2386652    S2_2532588    S2_2627244    S2_2627262 
-2.982286e-03  1.792975e-02  2.038999e-03 -8.458300e-03 -1.244551e-02 
   S2_2627324    S2_2631480    S2_2926780    S2_3287706    S2_3382610 
-1.250816e-02  1.363455e-02  1.872041e-02  1.689323e-02 -1.861984e-03 
   S2_3845096    S2_4413966    S2_4542405    S2_4588881    S2_4589048 
-2.315795e-02  4.822010e-02 -1.077044e-02  1.034593e-02  2.999709e-03 
   S2_5301652    S2_5456337    S2_6240783    S2_6242936    S2_6256948 
 1.180877e-02 -5.813029e-03 -4.299339e-04  1.013167e-02  1.308642e-02 
   S2_6265132    S2_7036759    S2_7378367    S2_7378516    S2_7529362 
-1.088186e-02 -5.896673e-03 -3.528152e-03  1.549457e-02  5.989630e-03 
   S2_7563816    S2_7873874    S2_8726771    S2_8737432    S2_9227661 
-4.603809e-03  1.411383e-02 -5.479681e-04  3.281466e-04  2.720620e-03 
   S2_9327647    S2_9434197    S2_9442286   S2_10412192   S2_10418334 
-3.028895e-02 -7.822790e-04 -3.015485e-02 -9.705530e-03  1.225036e-02 
  S2_10455798   S2_10607019   S2_10607249   S2_10777567   S2_11041568 
-4.250921e-03 -7.752416e-04 -4.260933e-03 -2.313298e-02 -1.965193e-04 
  S2_11580949   S2_11877524   S2_12448967   S2_12512623   S2_12539331 
-5.553628e-03  6.542734e-03 -7.840794e-03 -3.044722e-02  2.627035e-02 
  S2_12857644   S2_12865397   S2_12865421   S2_13838433   S2_13838469 
 3.581216e-03  1.652190e-02  2.084885e-02 -5.718540e-03 -1.176186e-02 
  S2_13852666   S2_14262838   S2_14263620   S2_14334805   S2_15000603 
 1.201098e-02  2.984145e-02  3.853177e-02  4.095240e-02  5.604068e-03 
  S2_15153785   S2_15168001   S2_15463005   S2_16203011   S2_16521276 
-3.458369e-03 -4.597088e-02  1.041922e-03 -2.381339e-01 -3.872971e-02 
  S2_16864137   S2_16915464   S2_16970778   S2_16989115   S2_16989298 
 7.085978e-03 -3.054932e-02 -4.528385e-02  6.095919e-03 -7.598575e-03 
  S2_18901095   S2_19041249   S2_19420024   S2_19420945   S2_20573368 
-2.222017e-02 -5.998311e-04  2.303892e-03 -4.585165e-03  1.225037e-02 
  S2_21862521   S2_21863424   S2_23121387   S2_23345860   S2_24208693 
-5.202680e-03 -7.844866e-03  7.468311e-03 -5.384574e-03 -1.153260e-02 
  S2_24394755   S2_24396833   S2_24840757   S2_24985120   S2_25700144 
-3.222598e-03  2.156214e-02  2.934428e-02 -1.209733e-02 -3.821315e-03 
  S2_27956236   S2_28997989   S2_31910227   S2_33151923   S2_34908344 
 9.230201e-03  7.403742e-03 -7.789655e-03  1.349371e-02 -6.162476e-03 
  S2_36016162   S2_38098248   S2_39673401   S2_41252709   S2_41252814 
 1.483262e-03 -6.093379e-03 -8.028116e-03 -4.068332e-03  9.471742e-04 
  S2_41698654   S2_41789172   S2_42149885   S2_44236866   S2_44764987 
-3.261245e-02  7.160308e-03  7.533947e-03  3.612950e-03 -1.109289e-02 
  S2_44995641   S2_46237998   S2_48798382   S2_48798392   S2_60778442 
 1.255792e-02 -1.413085e-03  4.064258e-03  5.636160e-03  1.293918e-02 
  S2_62113765   S2_64708270   S2_67297640   S2_72016500   S2_73631324 
 4.974104e-03 -1.566210e-02 -1.185145e-02  6.245823e-03 -5.195902e-03 
  S2_74218926   S2_80878902   S2_82880191   S2_88775207   S2_93026118 
-1.929233e-02 -2.906929e-02 -9.036177e-03  7.166176e-03  4.478512e-03 
 S2_104739030  S2_114366475  S2_115955959  S2_117129501  S2_117153121 
 1.121983e-02 -1.610099e-02  2.257850e-03 -1.328753e-02 -3.477059e-03 
 S2_117944462  S2_124790414  S2_132258688  S2_132258727  S2_132660876 
 6.456713e-03 -5.127148e-03  9.178887e-04 -1.372569e-02 -2.809959e-02 
 S2_136136229  S2_138368017  S2_140636955  S2_140637074  S2_144711138 
-5.688757e-03 -1.997763e-03 -1.396310e-02 -1.069677e-02 -6.694875e-03 
 S2_148340343  S2_149286050  S2_149286155  S2_150333739  S2_150333856 
-9.756794e-03  4.384006e-03  8.052252e-03 -8.727607e-03 -1.818302e-02 
 S2_150358319  S2_150740548  S2_151392770  S2_151627004  S2_151666689 
 7.839029e-03  5.910505e-03 -1.051460e-03 -1.321110e-02 -2.266613e-02 
 S2_154372022  S2_155999724  S2_157711561  S2_158507139  S2_161226600 
 3.473397e-02 -1.219588e-02  6.343747e-03 -1.227760e-02  6.685451e-03 
 S2_161539433  S2_162066636  S2_162271385  S2_162875198  S2_162911496 
-6.629699e-03 -1.259285e-03 -5.714806e-03 -3.272977e-06 -7.249398e-03 
 S2_167022852  S2_169534289  S2_173429896  S2_173444037  S2_175277302 
-2.570565e-02  1.181372e-02 -4.217104e-03  1.236560e-02 -6.133634e-03 
 S2_178994407  S2_179127811  S2_181424418  S2_181739483  S2_181739622 
-7.197134e-03 -2.841502e-02  3.528859e-03  2.456767e-03  6.982668e-03 
 S2_183214851  S2_184952096  S2_187290798  S2_187653788  S2_188429757 
 1.241043e-03 -2.084366e-03  1.132325e-02  1.683400e-02 -5.217788e-03 
 S2_189040174  S2_189040821  S2_189167046  S2_189169925  S2_189195850 
 3.571991e-03 -5.877730e-03 -4.851067e-03  4.323945e-03 -7.172382e-03 
 S2_189818418  S2_189823400  S2_189882931  S2_190430367  S2_190713004 
 1.880523e-02  4.925486e-03 -2.589144e-02  8.359351e-03  1.854113e-02 
 S2_191468717  S2_192530227  S2_192850486  S2_192974313  S2_193250585 
-3.495625e-03 -3.772779e-02  2.161138e-02  2.034461e-02  2.221276e-03 
 S2_193261776  S2_193792543  S2_193792893  S2_193989230  S2_193991522 
 5.017811e-03 -1.475731e-02  8.350903e-03 -1.274759e-02 -9.264597e-03 
 S2_194547427  S2_194547942  S2_195406855  S2_195408641  S2_196878904 
-1.892449e-02 -4.145197e-02  1.011783e-02 -8.790522e-03  1.950832e-03 
 S2_197247863  S2_197758561  S2_197761141  S2_198556024  S2_199239268 
 5.028621e-03  2.802594e-03  1.457194e-03  6.142065e-03 -4.228843e-02 
 S2_199610100  S2_199704028  S2_199704388  S2_201011054  S2_201094706 
-8.152873e-03 -1.300596e-02 -2.544812e-02 -7.239767e-03 -1.084636e-02 
 S2_201094903  S2_201513815  S2_202173051  S2_202955424  S2_204383795 
-1.479637e-02  1.409193e-02  1.131549e-02 -1.158054e-02 -6.218779e-03 
 S2_205022921  S2_205022931  S2_205202588  S2_205275866  S2_205276152 
-1.746796e-02 -9.053742e-03  1.366422e-02 -1.328140e-02  1.392785e-02 
 S2_205556555  S2_205777757  S2_206300482  S2_208410219  S2_208945675 
 1.862860e-02  9.089078e-04 -1.148309e-03  2.914884e-03 -3.903948e-03 
 S2_208952549  S2_208958619  S2_209182717  S2_209289593  S2_209561904 
-8.282681e-03 -5.965830e-03 -3.982484e-03 -5.653203e-03 -4.054946e-02 
 S2_209877341  S2_210221330  S2_210745995  S2_211713899  S2_212609997 
-1.775893e-02  3.483300e-03 -5.999684e-03  1.476998e-02  1.900064e-03 
 S2_212614021  S2_212883158  S2_213741185  S2_214011697  S2_214128791 
-4.727637e-03 -2.833272e-04  2.335404e-02 -6.559032e-04  5.876194e-03 
 S2_214213682  S2_214779597  S2_215426572  S2_215945846  S2_216030260 
 1.172656e-02 -3.428953e-03  7.583819e-03 -2.298610e-03 -1.851635e-02 
 S2_216743479  S2_216746125  S2_216746460  S2_217264737  S2_218076984 
 2.511108e-02 -4.521357e-04 -4.885891e-03  1.044029e-03 -3.139389e-03 
 S2_218077581  S2_218077607  S2_219318723  S2_219587333  S2_219942280 
-3.536575e-03 -2.982672e-03  3.758397e-03 -2.225554e-02 -9.752033e-03 
 S2_220581154  S2_220815115  S2_220863128  S2_220944251  S2_221273186 
-3.254062e-03 -8.548472e-03 -6.982247e-04 -7.527314e-03 -1.239592e-03 
 S2_221287133  S2_221293612  S2_221735220  S2_221757449  S2_221828300 
 3.967786e-04  3.229619e-03 -1.091233e-02 -2.438217e-02 -2.115433e-02 
 S2_222214859  S2_222281423  S2_222315237  S2_222352200  S2_222357904 
-4.060495e-02 -1.139152e-02 -1.538405e-02 -1.868356e-03  6.302311e-03 
 S2_222453733  S2_223422873  S2_223803009  S2_223853736  S2_225504421 
-1.976142e-05  1.660342e-02 -1.668362e-02 -1.587674e-02  8.391922e-03 
 S2_225583999  S2_225584015  S2_225986204  S2_226671459  S2_227288798 
 1.065796e-02  8.184502e-03  2.621375e-04  1.021635e-02 -2.545307e-03 
 S2_229264398  S2_229349944  S2_229538482  S2_229831056  S2_229851299 
 4.142355e-03 -3.291355e-03 -1.096323e-03 -9.786513e-03  5.156493e-03 
 S2_231108120  S2_231258079  S2_231435378  S2_231538085  S2_231794636 
 9.692910e-03 -1.567305e-02 -1.196190e-02  1.228348e-02  1.019915e-02 
 S2_232799291  S2_234480138  S2_234490837  S2_234510183  S2_234525669 
 1.288801e-02  9.778136e-03  3.978352e-03 -4.661200e-03  2.557486e-02 
 S2_234528459  S2_235755898  S2_235943712  S2_237260678  S2_238592722 
 8.603525e-04 -1.563196e-02  8.181666e-03 -1.178718e-02  3.733690e-03 
 S2_238830975  S2_240227739  S2_240249668    S3_1400719    S3_1464877 
-3.513790e-03 -8.490296e-03 -1.214598e-02  1.444133e-02  7.474724e-03 
   S3_1662561    S3_2028219    S3_2239671    S3_2757687    S3_2763048 
 3.386471e-02  1.008800e-02 -4.262270e-03  6.103055e-03 -4.990924e-03 
   S3_2856966    S3_3362245    S3_3362837    S3_3364555    S3_3506120 
 7.523169e-04  1.074459e-03 -1.939918e-02 -2.182129e-02  1.036621e-02 
   S3_3578856    S3_3613106    S3_3613172    S3_3911990    S3_3912752 
-1.396639e-02 -1.328809e-02  4.990845e-04  1.826187e-03 -1.111817e-02 
   S3_4260356    S3_4778638    S3_5615248    S3_5838013    S3_6251167 
-9.030824e-03  7.756901e-03  2.664947e-02  7.610134e-04  2.954081e-03 
   S3_6257096    S3_6470722    S3_6471653    S3_7328976    S3_8566472 
-1.241531e-02 -1.710469e-02  3.246282e-02 -1.335117e-03  7.333599e-03 
   S3_8769130    S3_8968031    S3_9234089    S3_9544369   S3_10076285 
-3.819242e-02 -3.215191e-03  1.655508e-03  2.532077e-03  1.445584e-02 
  S3_10265327   S3_10673156   S3_10954078   S3_10958525   S3_11535801 
-2.063429e-02 -7.205982e-03  1.584860e-02  4.104862e-02  5.117150e-03 
  S3_11540829   S3_11619271   S3_12144719   S3_12145701   S3_12145749 
 2.187162e-03  7.530832e-04 -4.956525e-03  6.048652e-03  2.229684e-03 
  S3_13434231   S3_13723427   S3_14323479   S3_14333218   S3_14801890 
-7.874362e-03  1.149312e-03 -5.628876e-03 -4.261056e-02 -5.028775e-02 
  S3_15210888   S3_17630503   S3_18198302   S3_19025992   S3_19293143 
-5.523570e-03  1.006033e-02  1.029710e-02 -1.266583e-02  4.310657e-03 
  S3_19832283   S3_26051996   S3_32651258   S3_35993809   S3_38037412 
 9.344148e-03 -1.378078e-03 -1.382355e-02  1.119624e-02  9.533552e-04 
  S3_44362768   S3_54376491   S3_54376775   S3_55537184   S3_56529376 
 4.852745e-03 -6.737190e-03 -2.754181e-02  1.270873e-02  1.137352e-02 
  S3_56815476   S3_56999533   S3_56999784   S3_64877981   S3_67441338 
-1.373869e-03 -1.135207e-02  2.692660e-03  3.251181e-03 -2.725279e-02 
  S3_67863836   S3_98255576  S3_102477733  S3_104264609  S3_109881648 
-3.387825e-03  8.290517e-03  1.363571e-02 -7.803004e-03 -7.270111e-04 
 S3_122985736  S3_125219740  S3_125220748  S3_130552961  S3_130552982 
 1.300998e-02  1.456793e-02  1.903373e-02 -4.632523e-03 -2.358604e-03 
 S3_134035633  S3_134442990  S3_135487037  S3_137533609  S3_139336936 
 5.258690e-03 -5.496338e-03  3.404916e-02  2.096623e-02  3.263528e-03 
 S3_143115991  S3_143118496  S3_143318686  S3_149117323  S3_150637571 
 1.930312e-02  7.322775e-03  1.383099e-02 -1.153093e-02  1.718057e-02 
 S3_150915949  S3_150917243  S3_151448709  S3_152233321  S3_152992674 
-7.693165e-03 -9.281393e-03 -4.799114e-03  6.630519e-03 -2.440978e-04 
 S3_153463524  S3_155399439  S3_155403039  S3_156001676  S3_156073129 
-2.758829e-02  6.541858e-04  2.824944e-04 -7.181990e-03 -1.075129e-02 
 S3_156226908  S3_156227261  S3_156228206  S3_156270994  S3_156277883 
-5.124404e-03 -1.194290e-02  4.858256e-04 -5.414847e-03  5.268907e-04 
 S3_156277910  S3_156763112  S3_156830645  S3_157123498  S3_158076754 
-3.317401e-03 -2.798420e-02 -7.103819e-03 -7.156169e-03  4.049301e-03 
 S3_158144387  S3_158250388  S3_159093153  S3_159200750  S3_159280382 
 3.163100e-03 -5.368564e-03  1.062767e-02 -1.037993e-02  2.365458e-03 
 S3_159658716  S3_159820975  S3_160747686  S3_161085854  S3_161093843 
 1.867372e-02 -2.426072e-02  3.011746e-02  4.989029e-03 -3.364437e-03 
 S3_162004498  S3_162591165  S3_163752888  S3_163926792  S3_164144412 
 3.151823e-04  1.896566e-02 -5.975743e-04 -5.183768e-04  8.238695e-03 
 S3_169098185  S3_169116636  S3_170297266  S3_170391129  S3_171047975 
 4.806534e-03 -1.375080e-02 -1.766543e-02  1.167896e-02 -8.064752e-03 
 S3_171049180  S3_171118467  S3_171380890  S3_171487117  S3_171698307 
-3.277992e-02  4.607636e-02  1.097879e-02 -2.376462e-02 -6.774454e-03 
 S3_172631601  S3_173080450  S3_173668335  S3_174251218  S3_175410413 
-7.531328e-03  1.713241e-02 -1.173317e-02 -3.473330e-02 -6.995355e-03 
 S3_175410430  S3_176077311  S3_177126855  S3_177561829  S3_177563221 
-1.291537e-02 -1.327149e-02 -1.339739e-03  1.282876e-02  3.280254e-03 
 S3_177690776  S3_177693438  S3_178168134  S3_178168227  S3_178405366 
 3.687954e-02 -5.086562e-03 -6.818902e-03  1.274817e-03 -4.636083e-02 
 S3_179993119  S3_180300684  S3_180462077  S3_180463918  S3_181084639 
 1.509649e-02  5.070146e-03  3.530849e-03 -7.784373e-03 -5.664967e-03 
 S3_181089329  S3_181403147  S3_181403809  S3_183585883  S3_184112463 
-1.749292e-02  2.389418e-02 -1.502713e-02  1.275658e-02  1.068371e-02 
 S3_184696223  S3_185047472  S3_185689615  S3_186112529  S3_187195001 
 5.012041e-03 -2.340315e-02 -8.658312e-03 -1.061298e-02 -1.741893e-02 
 S3_188497071  S3_188500699  S3_188500730  S3_188667827  S3_189196859 
-8.274856e-03  4.327463e-03 -3.689941e-04 -7.808146e-03  6.585222e-02 
 S3_192519364  S3_194915220  S3_196393427  S3_199144100  S3_201034518 
-6.514558e-03  9.442480e-03 -3.531360e-03  1.635353e-02 -5.921986e-02 
 S3_201140023  S3_202899898  S3_204443484  S3_206340812  S3_207306147 
-8.097729e-04  8.340370e-04 -1.117588e-03  1.544535e-03  1.071969e-02 
 S3_208260996  S3_209594607  S3_209609982  S3_209610170  S3_209669953 
 8.194202e-03  6.588070e-03 -4.436573e-03  4.594870e-03 -8.483801e-03 
 S3_209803612  S3_210833153  S3_210859046  S3_212068020  S3_212432960 
 3.335557e-03  1.344876e-03 -6.941692e-03 -6.500655e-03 -1.127767e-02 
 S3_213324844  S3_213618693  S3_214045535  S3_214188329  S3_214605122 
-1.750524e-02  3.522285e-03  1.520770e-02  3.166999e-03 -9.570302e-04 
 S3_214725503  S3_215087143  S3_215302622  S3_215863528  S3_215949122 
 7.563005e-03  2.425489e-03  1.550751e-03  8.540449e-03  2.493981e-02 
 S3_217122021  S3_218883017  S3_219791737  S3_220553933  S3_221113183 
-4.086858e-02  9.207065e-03 -1.413818e-02  4.772999e-04 -3.389068e-03 
 S3_221172195  S3_221485615  S3_221603754  S3_221603868  S3_221892178 
-4.657677e-03 -2.020353e-03 -2.062382e-02 -1.805904e-02 -7.385262e-03 
 S3_222666213  S3_222666340  S3_222668770  S3_222761948  S3_222882360 
-1.208710e-03 -4.708545e-03 -8.161731e-03  4.647995e-03 -9.667392e-04 
 S3_223081534  S3_223158468  S3_223221766    S4_1582526    S4_3684946 
-2.974337e-02  1.097307e-02  2.067521e-02  1.933583e-03  1.369614e-02 
   S4_4117851    S4_4421555    S4_4429338    S4_4868446    S4_5038043 
-9.173472e-03 -7.627771e-03 -3.553430e-03  8.866119e-03 -8.170945e-03 
   S4_6215946    S4_6216277    S4_6256393    S4_6374168    S4_6382493 
-2.254142e-02 -4.613921e-03 -1.281501e-04  6.030524e-03 -6.113453e-03 
   S4_6383721    S4_6388262    S4_6397331    S4_7632233    S4_7634160 
 6.547412e-03 -4.007718e-03 -1.375901e-02  2.157102e-02 -2.641386e-02 
   S4_7635295    S4_7967173    S4_8027896    S4_8316239    S4_8454252 
 7.459641e-03  3.772997e-02  1.297831e-02 -8.345737e-03 -1.235447e-02 
  S4_10696101   S4_10786694   S4_10904652   S4_11760644   S4_12766998 
-1.512765e-03 -2.180619e-03  4.000648e-03  1.340698e-02  2.250337e-02 
  S4_13010338   S4_13315712   S4_15099638   S4_15666621   S4_15673073 
-3.661236e-02  7.422659e-03 -7.128392e-03  9.898562e-03  1.129233e-02 
  S4_15975070   S4_16064424   S4_16194115   S4_16268360   S4_16652461 
-4.772237e-02  9.672078e-02  7.590120e-05 -5.048128e-03  1.652810e-03 
  S4_16940584   S4_16944639   S4_18076248   S4_18845218   S4_19113113 
 1.443688e-02  2.546088e-02  2.934645e-02  3.519369e-03  7.739384e-03 
  S4_19533309   S4_19534877   S4_19535516   S4_19535656   S4_19556309 
 1.205222e-02 -8.920429e-03 -1.127469e-02 -2.603209e-03 -7.548841e-03 
  S4_19684514   S4_20268200   S4_20270571   S4_20497213   S4_20598297 
 1.337859e-02  5.236453e-03  1.349259e-02  6.749047e-03 -6.780708e-03 
  S4_21107980   S4_21650368   S4_21903148   S4_21908896   S4_23290605 
 3.876132e-02  3.890089e-02  2.448286e-03  1.110547e-02  1.471924e-02 
  S4_23361153   S4_23371382   S4_24237240   S4_24592534   S4_26899943 
-5.527135e-02  1.760649e-02 -5.738268e-03 -5.347863e-02 -2.356230e-02 
  S4_26905846   S4_27987442   S4_28628470   S4_28770521   S4_28810964 
-1.785938e-02 -3.707222e-02 -5.080781e-02  1.154747e-02 -2.691920e-02 
  S4_30916257   S4_31060579   S4_32789730   S4_33024458   S4_33668810 
 5.845780e-03 -1.316187e-02  1.013048e-02 -4.272095e-03 -6.573369e-03 
  S4_33993933   S4_34193017   S4_34194561   S4_35081018   S4_35081158 
 6.649609e-02  2.140192e-02  2.791750e-02 -2.771574e-02 -3.034278e-02 
  S4_35662011   S4_36249700   S4_37347025   S4_37547893   S4_37548473 
-7.774270e-03 -5.263235e-02  1.219460e-02 -3.101896e-02 -1.053923e-02 
  S4_38327201   S4_38330727   S4_38760190   S4_42420846   S4_42461443 
-2.534644e-02 -6.439514e-03  1.615718e-02  1.152078e-02  1.802753e-02 
  S4_43857521   S4_59942187   S4_65069576   S4_68476055   S4_68509682 
-3.317689e-03 -5.605912e-03 -3.586751e-02 -3.781352e-04 -2.456464e-03 
  S4_73620368   S4_73992822   S4_78663165   S4_81325355   S4_81555504 
 1.671703e-02  6.197517e-03  9.689286e-03  6.388829e-03 -2.214352e-02 
  S4_83555330  S4_123692246  S4_127783045  S4_129510370  S4_130234780 
 6.250321e-03  2.373524e-02  1.365944e-02  4.955055e-03  1.449295e-02 
 S4_130979624  S4_131389825  S4_131570243  S4_132644449  S4_132796082 
-1.633639e-02  1.107565e-02  5.384525e-03  9.126562e-03  2.994836e-03 
 S4_133572067  S4_134019236  S4_138959026  S4_147394691  S4_151119203 
-1.417252e-02 -1.999324e-02  4.162193e-03 -4.106033e-02 -2.616479e-02 
 S4_151260095  S4_153603804  S4_154508054  S4_157787690  S4_158893534 
-3.359103e-04  2.789443e-03 -1.404879e-02  8.351348e-03 -3.503025e-04 
 S4_159136751  S4_159826794  S4_162868828  S4_163363238  S4_166721935 
 6.963494e-03  1.908742e-03  2.146151e-02 -1.979115e-02 -2.731703e-02 
 S4_168846256  S4_168922442  S4_168972787  S4_169358752  S4_170716007 
 1.827129e-02  2.348283e-02 -1.245751e-02  2.310163e-02  1.607144e-02 
 S4_171087001  S4_172016374  S4_172278923  S4_172480572  S4_173206081 
-1.582318e-03 -8.716365e-03  1.252285e-02  2.522746e-02  7.224590e-02 
 S4_173647564  S4_173812038  S4_174235497  S4_174661607  S4_174894581 
 1.511277e-02  2.118381e-02 -3.692542e-03  5.909313e-02 -6.275716e-03 
 S4_175791704  S4_176115520  S4_176599341  S4_176927461  S4_177214147 
 7.027956e-03  1.144114e-03  7.622072e-02 -1.154991e-02  1.218306e-03 
 S4_177233386  S4_177233411  S4_177342963  S4_177343094  S4_177902767 
 6.400295e-03  2.812180e-02  9.452055e-03 -1.572332e-02 -1.330142e-02 
 S4_178069434  S4_178384209  S4_178453956  S4_179370281  S4_179674671 
-3.322811e-02 -2.217870e-02  3.711109e-02  7.329767e-03  2.908303e-02 
 S4_179674809  S4_180001616  S4_180637167  S4_180797642  S4_181091704 
 1.484874e-02 -1.825701e-02 -1.392831e-02 -1.757007e-02 -3.878011e-03 
 S4_182099943  S4_182462132  S4_183484419  S4_183484517  S4_183587157 
-4.625322e-02  4.189231e-02  4.808430e-02  3.457290e-02  6.040723e-03 
 S4_183806901  S4_184081735  S4_184594373  S4_184739545  S4_185719827 
-1.521064e-02 -2.124002e-03  1.437764e-03  1.366170e-02  5.915099e-03 
 S4_186630216  S4_187126058  S4_187162013  S4_188075715  S4_191133446 
 3.974663e-04 -3.005858e-02 -5.606903e-04 -1.730040e-03 -1.936058e-02 
 S4_191603783  S4_192740039  S4_196533266  S4_196533277  S4_196697423 
 2.380672e-02 -4.070380e-02 -1.066289e-02 -9.229409e-03 -7.546939e-03 
 S4_201649912  S4_202964997  S4_203233691  S4_211134934  S4_223755225 
-3.659348e-03 -1.935595e-02  2.616276e-02 -3.291412e-02  9.180945e-03 
 S4_228315634  S4_237052759  S4_238909618  S4_239877963  S4_241516810 
-2.157628e-02 -9.636839e-03 -2.360626e-02  8.124132e-03  1.319918e-02 
 S4_241704963  S4_241832875     S5_392571     S5_846139     S5_846889 
 5.091475e-02 -2.428518e-02  1.045821e-02  5.125438e-03  3.468332e-03 
    S5_960877     S5_973097    S5_1064405    S5_1121641    S5_1223077 
-1.096826e-02 -1.451990e-03  2.750251e-03  4.017293e-03 -2.034380e-02 
   S5_1227922    S5_1403230    S5_1902106    S5_1986447    S5_1986558 
-5.250448e-03 -1.973349e-02  4.509916e-03  2.815553e-04  4.083220e-04 
   S5_1993743    S5_2037046    S5_2037272    S5_2161838    S5_2253809 
-2.723369e-02 -1.230177e-02  2.534661e-02 -5.118787e-03  2.312444e-02 
   S5_2411621    S5_2577845    S5_2791798    S5_2791837    S5_2858836 
 4.389654e-02  1.283883e-04  7.558128e-03  2.755999e-03  9.358673e-03 
   S5_2858950    S5_2859808    S5_3657340    S5_4062418    S5_4108173 
-8.211764e-03 -4.857870e-03 -3.749393e-03  1.864432e-02 -1.884732e-02 
   S5_4229956    S5_4884511    S5_5059451    S5_5557260    S5_5598442 
 9.298661e-03  9.985923e-03 -7.292732e-03  3.138523e-02  9.649515e-03 
   S5_6368813    S5_6825026    S5_7117510    S5_7338807    S5_7701005 
-6.926491e-03 -2.513076e-02  3.716942e-02 -6.154066e-03  9.849385e-04 
   S5_7717382    S5_8109491    S5_8249273    S5_8942275    S5_8975313 
 4.526504e-03 -4.982634e-03  5.248933e-03  2.211588e-04 -1.817179e-02 
   S5_9900858   S5_10519995   S5_10772137   S5_11998173   S5_12127492 
 7.528449e-03  8.173896e-03  5.780350e-03  2.103848e-02  6.391094e-04 
  S5_12815252   S5_12971603   S5_13682681   S5_13702177   S5_14248069 
 6.784065e-03 -1.924174e-03 -5.544140e-03 -1.211050e-02  7.204834e-03 
  S5_14562872   S5_15433436   S5_15441260   S5_15844564   S5_15897858 
 1.470965e-02 -4.251333e-04  4.858602e-03 -1.199459e-02  7.840760e-03 
  S5_15902604   S5_19069144   S5_19069731   S5_19212307   S5_19375485 
-7.555080e-03  9.758190e-04 -1.036660e-03  2.027606e-03 -1.105397e-02 
  S5_20386586   S5_20614224   S5_20841749   S5_21746992   S5_21801726 
 4.607363e-03 -1.230102e-02 -8.922058e-03 -5.566010e-02 -2.725963e-02 
  S5_22426333   S5_24821792   S5_24840547   S5_25947924   S5_27014949 
-6.010388e-02 -1.032833e-02  6.366471e-03  1.103833e-02 -1.518840e-02 
  S5_28912179   S5_30442792   S5_32068951   S5_32090793   S5_32090944 
-1.314733e-02 -4.795423e-03  9.287055e-03  9.670371e-03  4.087103e-03 
  S5_32349939   S5_33073331   S5_33843588   S5_34733643   S5_35406000 
-5.300398e-03  2.443860e-02  3.719598e-03  1.216556e-02 -1.866554e-02 
  S5_38654197   S5_39117682   S5_44150436   S5_44151397   S5_44367258 
 5.091786e-03  8.258411e-03 -3.143226e-02 -3.458204e-02  9.352165e-03 
  S5_51564787   S5_58642117   S5_59732882   S5_59781924   S5_60413372 
-3.404600e-02  2.257167e-02 -2.023486e-04 -2.058215e-02 -2.911251e-02 
  S5_62982504   S5_64000675   S5_68824990   S5_69155559   S5_70657343 
 7.736617e-05 -8.926896e-03 -3.002991e-02 -2.046450e-02 -1.585313e-02 
  S5_71282599   S5_72789465   S5_75244556   S5_76181153   S5_76690334 
-9.188306e-04  5.945016e-03  1.215018e-02  1.564257e-02  1.162428e-02 
  S5_76714903   S5_76809814   S5_76955635   S5_76956064   S5_78825105 
-1.915143e-02 -4.763501e-03 -9.058881e-03 -7.859714e-03 -2.310094e-02 
  S5_80470390   S5_83740384   S5_83797523   S5_88321071   S5_88323212 
-1.036153e-03  2.462113e-03  8.728387e-03 -4.074006e-02 -7.337612e-02 
  S5_88891444   S5_90103757   S5_91076876   S5_94466678   S5_95340368 
 1.314789e-02 -1.488956e-02  1.321245e-02  5.438936e-03 -6.520518e-03 
 S5_113637702  S5_134357391  S5_137215938  S5_137216759  S5_142853398 
 2.833445e-02  5.579439e-03  5.837679e-03  4.573922e-03 -1.817714e-02 
 S5_145203522  S5_146713841  S5_146715057  S5_147071985  S5_147473771 
 1.432642e-02 -2.550177e-02 -6.398908e-02 -1.151232e-02  6.441978e-03 
 S5_147887551  S5_153353332  S5_153353657  S5_154883835  S5_160755567 
 1.528342e-02 -7.223924e-03 -1.871225e-02 -1.160854e-02 -3.382697e-03 
 S5_162523269  S5_162523281  S5_163648244  S5_163943266  S5_166675806 
-1.520488e-02 -1.087830e-02  2.250771e-02 -2.111117e-02  1.544945e-02 
 S5_167631942  S5_167774618  S5_167948506  S5_169309847  S5_171099522 
 1.967971e-02  9.716574e-03  1.215501e-02  1.944702e-02 -1.425181e-02 
 S5_171099708  S5_174583061  S5_174744000  S5_174873345  S5_176356092 
 1.003436e-03 -4.805001e-03 -3.719304e-02 -6.045482e-03  3.618558e-02 
 S5_176844776  S5_177183610  S5_177768562  S5_177935888  S5_178745983 
-1.028567e-02  5.439630e-03  5.368231e-03  2.140039e-02 -2.422185e-03 
 S5_178912346  S5_179642041  S5_179660715  S5_179922594  S5_179923101 
 5.825894e-03  4.754220e-02  1.310448e-03  2.543841e-02  4.936347e-02 
 S5_180189155  S5_180302078  S5_181964400  S5_184371294  S5_185612028 
-1.453021e-02  5.790050e-04  7.626662e-03  1.388118e-03  7.413642e-03 
 S5_185659742  S5_186306420  S5_187238296  S5_187240493  S5_187698419 
-7.762224e-03  1.712373e-03 -1.619220e-05  2.382721e-04 -4.883937e-03 
 S5_187875195  S5_188157823  S5_188213251  S5_189353511  S5_189363603 
-6.914345e-03  7.700219e-03  4.322398e-03 -1.630177e-02 -1.644767e-02 
 S5_192686533  S5_192792986  S5_193315723  S5_194079700  S5_194827709 
 8.333784e-03 -2.403618e-02  2.267058e-02 -6.905564e-03  1.266358e-02 
 S5_195066772  S5_195079729  S5_195080529  S5_195296424  S5_196493942 
 1.350614e-02 -2.190264e-02 -2.041347e-02  8.139322e-02  1.030065e-02 
 S5_196495308  S5_196686417  S5_196688275  S5_196688753  S5_197833779 
 1.172883e-02 -4.518104e-03 -4.138749e-03 -2.249650e-02 -2.130672e-02 
 S5_198023231  S5_198353824  S5_198357291  S5_198497394  S5_198576650 
-8.629781e-03  1.012368e-02 -6.741693e-03 -9.786128e-03 -8.226499e-03 
 S5_199103162  S5_199120264  S5_203416587  S5_203425942  S5_204040867 
 4.991673e-03 -7.143938e-03 -8.313064e-03  2.673880e-03  3.532770e-03 
 S5_204040909  S5_204409553  S5_204693690  S5_205869673  S5_205950398 
-8.592406e-04 -6.054757e-03 -8.684450e-03 -9.836959e-03 -3.132805e-03 
 S5_206240787  S5_206652970  S5_206654542  S5_207186816  S5_207333354 
 3.055281e-03  1.698468e-02  1.236955e-02  3.493542e-02 -2.733447e-04 
 S5_208492316  S5_209484843  S5_209506777  S5_209751223  S5_210099034 
-4.354661e-03  3.416721e-02  5.906598e-03  1.673425e-03 -5.651215e-03 
 S5_210261934  S5_210871718  S5_211204931  S5_211205797  S5_211242216 
 1.053930e-02 -6.235971e-03 -8.670215e-03 -5.398981e-04 -1.320002e-02 
 S5_211285153  S5_211334892  S5_211698429  S5_211843513  S5_211962336 
 9.903305e-03 -7.570040e-03  1.020844e-02  8.975642e-03 -6.366130e-03 
 S5_212055655  S5_212070678  S5_212136026  S5_212255952  S5_212478679 
 7.669591e-03  3.705160e-03  3.270526e-02 -7.477079e-03 -3.455980e-02 
 S5_213003414  S5_213100773  S5_213137320  S5_213160675  S5_213166480 
-3.600438e-03 -1.039654e-02 -1.495843e-02  3.472141e-03 -6.469074e-03 
 S5_213380060  S5_213565655  S5_213571586  S5_213596888  S5_213601432 
 6.950544e-03  3.199133e-03 -7.045239e-03 -3.777332e-03 -1.908395e-02 
 S5_214150762  S5_214254801  S5_214295509  S5_215073009  S5_215367055 
-1.126123e-02  1.590611e-02 -1.048698e-02  4.523095e-03 -2.384168e-02 
 S5_215367218  S5_216252803  S5_216397829  S5_216639488  S5_216679342 
 3.103519e-05  2.602819e-02 -3.357173e-02  4.413878e-03 -1.855662e-02 
 S5_216685919  S5_216902344  S5_217277409  S5_218069890  S5_218071486 
 8.601381e-04 -2.087978e-02  3.701413e-03  3.778460e-02  3.681195e-02 
 S5_218467166  S5_219302032  S5_219699961  S5_220362315  S5_220616742 
 1.406477e-03 -6.025640e-03 -4.259045e-03  5.287320e-02 -1.153900e-02 
 S5_220625279  S5_220826505  S5_220992100  S5_221194880  S5_221195461 
 3.138749e-02  5.259237e-03  4.700550e-03  1.946612e-03  2.301438e-02 
 S5_221698085  S5_221957658  S5_222581144  S5_222820716  S5_223260186 
-1.059825e-02  1.047058e-02 -9.355032e-03  2.008610e-02  1.889625e-02 
 S5_223260805  S5_223366133  S5_223426991  S5_223430784  S5_223430984 
-1.602425e-02  6.685930e-03  1.159824e-02 -1.032804e-02 -5.344283e-03 
 S5_223436095  S5_223645549  S5_223756239  S5_223875842  S5_223901174 
-2.796893e-02 -8.373821e-03  3.224021e-03 -5.757077e-04  4.855360e-04 
 S5_223904377  S5_223909423  S5_224226095  S5_224229262  S5_224633761 
-2.668754e-02 -4.249623e-03 -3.105731e-02  1.296464e-02 -1.463045e-02 
 S5_225053113  S5_226029760    S6_7500192    S6_9172373   S6_10091180 
 2.865368e-02 -2.064606e-02  8.258680e-03  2.101991e-02  9.325852e-03 
  S6_10091291   S6_10170336   S6_10178401   S6_10427934   S6_12017089 
 9.409282e-03  7.763483e-03  1.938737e-02  7.378741e-03  2.953701e-02 
  S6_14800910   S6_25976852   S6_30546465   S6_44127384   S6_51081241 
 2.691066e-03 -3.425610e-03  3.176812e-03  7.143228e-03  2.266282e-02 
  S6_53667694   S6_67037943   S6_67043112   S6_68175871   S6_71847299 
 1.392186e-02 -1.802170e-02 -2.222169e-03 -7.232891e-03  9.009101e-03 
  S6_78216005   S6_82541405   S6_92919825   S6_94929182   S6_95582532 
-3.555730e-02 -1.338209e-02  2.288818e-02 -8.053980e-03 -1.628189e-03 
  S6_96074538   S6_96712764   S6_98298987   S6_98395946   S6_98408157 
 2.354781e-03 -4.419466e-03  3.053826e-02 -1.755272e-03  1.323727e-02 
  S6_98417550  S6_100808400  S6_102701095  S6_102701334  S6_102804778 
 1.653341e-02 -1.378066e-02 -9.050923e-03  1.360117e-02  1.276223e-02 
 S6_104866390  S6_104866578  S6_105780483  S6_105829977  S6_123007536 
 3.004759e-03  1.420171e-02  2.880136e-02  1.305527e-02 -6.968864e-03 
 S6_123298007  S6_123492422  S6_124083395  S6_124872356  S6_127695370 
-4.922901e-03 -1.363324e-04  1.269113e-03  6.994012e-03 -1.210500e-02 
 S6_128029740  S6_128681003  S6_129038174  S6_129595629  S6_131992891 
-5.594121e-03 -1.056242e-02  2.311187e-03 -3.307759e-02 -7.843407e-03 
 S6_132737943  S6_133175017  S6_134125062  S6_135417855  S6_138176820 
-2.398688e-02  9.182962e-02  1.728105e-02  1.961845e-02  1.127292e-02 
 S6_140908265  S6_144100101  S6_144106181  S6_144217241  S6_146437625 
-1.368701e-02 -1.909968e-02 -3.158292e-02  4.559996e-02 -1.255806e-02 
 S6_146489271  S6_148015145  S6_149306116  S6_149716126  S6_152466118 
-2.055908e-03  2.970883e-02  1.146509e-03 -1.542725e-02 -1.431249e-02 
 S6_152993174  S6_153077139  S6_154271835  S6_155722319  S6_156209684 
-6.408362e-03 -6.903695e-04  3.917924e-03  3.918562e-03  1.041517e-02 
 S6_156449605  S6_157086241  S6_157086536  S6_157897148  S6_157903398 
-1.870789e-02 -1.310727e-02  6.021467e-04 -9.697494e-03 -9.402894e-03 
 S6_159085805  S6_159685403  S6_159803498  S6_159819701  S6_159909388 
-5.221733e-04  1.212297e-02  2.136411e-03  5.059098e-03 -1.888858e-02 
 S6_160972138  S6_161843360  S6_161844021  S6_161930736  S6_162413998 
-5.976606e-03  3.494001e-03 -6.761178e-04  6.687268e-03  2.469965e-03 
 S6_163479000  S6_163479145  S6_165082843  S6_165516068  S6_166226097 
-3.016577e-02 -3.720194e-02  1.979891e-02 -2.715264e-04  1.361079e-02 
 S6_166231942  S6_166923929  S6_167093376  S6_167488744  S6_167992991 
 1.204169e-02 -4.621102e-04  4.556586e-04  3.521604e-02  8.751029e-03 
 S6_169940741  S6_170389932  S6_170593838  S6_171072688  S6_171076874 
-2.427471e-03 -9.618858e-03  1.286185e-02 -4.524375e-03  1.553671e-02 
 S6_171107203  S6_171107240  S6_171170326  S6_171331272  S6_171701195 
-1.232962e-03 -2.178814e-02  3.249192e-02 -2.333064e-02 -1.327912e-02 
 S6_171825055  S6_171825719  S6_172111295  S6_172528568  S6_172699949 
 5.835966e-03  1.325837e-02 -1.092728e-02 -1.016431e-02 -1.871237e-02 
 S6_173144508  S6_173220894  S6_173395982  S6_173844654  S6_173850038 
 1.053440e-04  2.430309e-04 -2.465029e-02  2.975817e-03  1.443001e-02 
 S6_174251053  S6_174425232  S6_174460388  S6_174463143  S6_174474123 
 1.862996e-02 -5.607846e-03 -1.110802e-03 -2.774384e-03 -5.604184e-03 
 S6_174546993  S6_174553511  S6_174554225  S6_174948850  S6_175017858 
-1.871613e-02 -1.477875e-02 -1.378746e-02 -1.034695e-02 -4.129945e-03 
 S6_175155013  S6_175614573  S6_176650245  S6_177006450  S6_177006463 
-3.392948e-03 -8.957373e-03  4.150858e-03 -5.166018e-03 -7.900239e-03 
 S6_177104175  S6_177541212  S6_178280629  S6_178417443  S6_179563522 
-1.001199e-02 -3.067141e-02 -3.238608e-02 -6.150445e-03  4.377086e-03 
 S6_179646907  S6_179653649  S6_179990687  S6_180646401     S7_844671 
 7.727842e-03 -9.677993e-04 -1.040899e-02 -2.576002e-03 -2.051029e-02 
   S7_1056516    S7_1226511    S7_1399948    S7_1623486    S7_1882039 
 2.165691e-03  2.695432e-03 -2.095128e-02 -1.001412e-02  1.504543e-02 
   S7_2222059    S7_3606609    S7_3781862    S7_4034878    S7_4034926 
 3.383894e-02  6.238397e-03  1.165590e-02 -1.046956e-03  2.665492e-02 
   S7_4131827    S7_4132053    S7_4185867    S7_4185885    S7_4185900 
 1.648880e-02  1.987332e-02 -1.162475e-02 -6.346645e-03 -3.257006e-03 
   S7_4224614    S7_4704421    S7_4992595    S7_4993741    S7_5294149 
-2.200560e-04  1.214585e-02 -1.321117e-03  3.032677e-02 -4.581728e-03 
   S7_5576737    S7_5777795    S7_6008227    S7_7731242    S7_7742880 
-6.891199e-05 -1.108618e-03  1.684462e-02 -1.441847e-02 -4.778609e-04 
   S7_8226814    S7_8236858    S7_8359766    S7_9076132    S7_9373969 
-2.064126e-02 -2.090213e-02  2.289042e-03 -3.229227e-03 -9.017884e-03 
   S7_9378165   S7_10079492   S7_10674992   S7_10675006   S7_10810550 
-5.002544e-03 -4.448568e-02  3.181325e-03  4.383946e-03 -4.835775e-03 
  S7_11059965   S7_12026797   S7_13501881   S7_13581391   S7_13945703 
 1.087607e-02  2.836845e-02 -3.079662e-03 -3.213789e-03  1.789627e-03 
  S7_13961624   S7_13961812   S7_13961898   S7_14207155   S7_15392216 
-1.291437e-02 -1.862520e-02 -3.596089e-02 -3.730669e-03 -3.304834e-03 
  S7_18011608   S7_22883047   S7_27927295   S7_28946638   S7_31369870 
-1.172808e-02 -6.473813e-03 -1.290304e-02 -5.838441e-03 -1.380476e-03 
  S7_34053809   S7_34422171   S7_41473602   S7_42251847   S7_44311585 
 1.559672e-03 -8.673584e-03  2.224150e-03  8.171058e-03 -1.381422e-02 
  S7_45606995   S7_47593869   S7_52721525   S7_91010583   S7_95720534 
 3.906052e-03 -1.436422e-02 -5.466833e-04 -1.129984e-02  2.701838e-02 
  S7_96947428  S7_103794469  S7_107046839  S7_109732796  S7_110287645 
-2.435750e-03 -4.020503e-03 -1.447121e-02  2.966280e-03  1.033120e-02 
 S7_112726284  S7_114101603  S7_114102321  S7_114288977  S7_115531617 
 8.396582e-03  6.953543e-03 -4.921300e-04  4.302594e-03 -4.200849e-03 
 S7_116276698  S7_116653725  S7_116780969  S7_117519628  S7_122387013 
-3.448896e-03  1.301401e-03 -7.722811e-03 -1.021974e-02 -7.709539e-04 
 S7_122393459  S7_122707824  S7_124279387  S7_125176636  S7_126118922 
-3.234186e-03  3.191931e-03 -5.021779e-03 -2.104978e-02  6.942259e-03 
 S7_126935343  S7_127045257  S7_128986731  S7_129214069  S7_129807336 
-1.147723e-04  2.533731e-03 -1.158296e-02  4.346083e-03 -1.603700e-02 
 S7_130131479  S7_130175589  S7_130368700  S7_130547743  S7_131668413 
 1.215456e-02  3.040144e-03 -6.542629e-03 -1.803113e-03 -9.082269e-03 
 S7_131772840  S7_132461307  S7_133186810  S7_133186871  S7_133464110 
-3.647988e-03  1.209233e-02 -9.408431e-03 -7.343856e-03 -1.010698e-03 
 S7_133465902  S7_135301936  S7_136214505  S7_136452694  S7_136531397 
-9.724535e-03 -8.180516e-04  1.077092e-02  5.990530e-05  4.183263e-03 
 S7_137510639  S7_139440325  S7_139491179  S7_139496582  S7_139527440 
 2.807503e-03  2.682134e-03 -7.242514e-03  1.094944e-02  2.473815e-02 
 S7_140578194  S7_141747386  S7_141756965  S7_141756990  S7_143069328 
 4.360428e-03  4.800917e-03  2.635155e-04 -1.255836e-02 -3.806346e-03 
 S7_143098054  S7_143327982  S7_143685357  S7_144667243  S7_144877105 
-2.311259e-02 -5.035778e-03  1.075918e-02  5.470908e-03  2.233885e-02 
 S7_145115606  S7_146989468  S7_147788972  S7_148846698  S7_149970872 
-1.848621e-02 -1.188575e-03 -1.419036e-02 -8.002756e-03 -7.160596e-03 
 S7_150341368  S7_150739423  S7_150896426  S7_151122054  S7_151323488 
 7.271377e-03 -1.192644e-02 -1.248936e-02 -1.334959e-02  2.749943e-02 
 S7_152980877  S7_153473280  S7_153571933  S7_161191018  S7_161352998 
-1.931668e-02 -2.613150e-03 -1.964605e-02 -4.173423e-04 -1.090168e-02 
 S7_163227439  S7_164101621  S7_165193358  S7_166294479  S7_166649983 
 4.259358e-03 -3.021033e-03  2.589315e-02  1.629241e-03 -4.307512e-03 
 S7_167438352  S7_170910247  S7_171137187  S7_171218528  S7_171494406 
-1.330102e-02 -9.721939e-03 -4.463950e-03  2.445652e-02  6.068283e-03 
 S7_173083438  S7_173291589  S7_174125838  S7_174487321  S7_174487353 
-2.467625e-02 -4.784480e-03 -4.818234e-03 -1.184756e-02 -2.847859e-03 
 S7_174487367  S7_174506595  S7_174659681  S7_175273833  S7_175482129 
-6.496069e-03 -4.561133e-04 -8.426398e-03  2.187002e-03  7.576819e-02 
 S7_176074888  S7_176076452  S7_176208045  S7_176582006  S7_176908652 
 1.595351e-02  2.412982e-02  3.305973e-03 -4.453769e-02 -1.293303e-03 
 S7_177297045  S7_177589777  S7_177592139  S7_178057883  S7_178343753 
 3.122672e-02 -5.379531e-03 -3.774427e-03  9.698245e-04  1.236793e-03 
 S7_178474803  S7_178518524  S7_178518565  S7_178518778  S7_178522030 
 1.663438e-03 -6.605821e-03 -1.269791e-02 -3.013881e-02 -5.506421e-03 
 S7_178606174  S7_179111058  S7_179111826  S7_179765379  S7_182932513 
 7.072405e-02  1.122663e-02 -2.954873e-02 -1.054884e-02 -1.282170e-02 
 S7_183230255  S7_183251802  S7_183325818  S7_183536615  S7_183788586 
-3.348748e-02 -2.029900e-02  2.832991e-03 -7.183259e-03 -1.101392e-03 
 S7_184554841     S8_612496    S8_1829299    S8_2420621    S8_2623912 
-9.176156e-03  1.213023e-02  1.615555e-02  1.011465e-02  8.002217e-03 
   S8_3642557    S8_3642603    S8_3973029    S8_5223551    S8_7117363 
 1.549158e-02  4.102895e-03  1.971262e-03 -1.834229e-02 -3.239832e-02 
   S8_7117426    S8_7259227    S8_7346951    S8_9215338    S8_9278330 
-4.371490e-02 -1.252377e-02  2.041817e-02  1.294496e-02  9.054842e-03 
  S8_10397644   S8_10712954   S8_11691508   S8_12721256   S8_13771167 
-2.839611e-02  5.884337e-03 -6.263325e-03 -1.872361e-02 -1.270384e-02 
  S8_13999416   S8_14203871   S8_14486971   S8_17315368   S8_18826784 
-1.617356e-02  6.057786e-04 -3.374123e-03  1.063501e-02  9.980950e-03 
  S8_18836435   S8_20695697   S8_22984536   S8_23263047   S8_23439188 
 4.839438e-03  3.062892e-03  3.140765e-03 -1.691365e-03  9.285798e-03 
  S8_23439421   S8_23440142   S8_24407454   S8_24594223   S8_25772299 
 9.572696e-03 -3.723282e-02  2.162209e-02  1.105073e-02  1.035957e-02 
  S8_25776901   S8_25779871   S8_26031524   S8_27837421   S8_28410514 
 2.826855e-02  1.254211e-02 -2.049661e-03  2.486647e-03  1.211538e-02 
  S8_28933320   S8_29366651   S8_29367161   S8_30416206   S8_34786661 
 2.544535e-02  6.988433e-03  6.220046e-03 -4.849963e-03 -3.071907e-03 
  S8_37491523   S8_39607936   S8_41779834   S8_45705606   S8_53180719 
-2.196632e-03  2.784669e-03 -1.836207e-02  6.489966e-03 -2.097678e-03 
  S8_65091959   S8_67365157   S8_67366292   S8_68605842   S8_72283105 
 1.770791e-02 -1.205430e-02 -6.014423e-03  7.319386e-03  1.728583e-02 
  S8_73298586   S8_73725823   S8_74479931   S8_75118540   S8_75124138 
 6.799485e-03 -1.245944e-02  1.160014e-03  5.788997e-03 -3.708426e-02 
  S8_75124250   S8_77543206   S8_78074116   S8_78074363   S8_78087508 
 5.909786e-03 -3.908943e-03  3.677883e-03 -3.627099e-03  9.011914e-03 
  S8_80222882   S8_80579757   S8_80737875   S8_80748080   S8_81892711 
-3.219606e-04  5.154863e-03 -4.656694e-04  4.682890e-03  3.353109e-03 
  S8_82048199   S8_82048896   S8_82162950   S8_86486748   S8_90891762 
 3.785078e-03  7.953602e-03  9.281940e-03 -1.263700e-02  4.919151e-03 
  S8_91070032   S8_92211180   S8_92211196   S8_92273238   S8_92845779 
 1.351725e-02  1.854229e-03  1.039124e-02  2.291447e-02 -1.141913e-02 
  S8_93206352   S8_93206908   S8_93207205   S8_97314946   S8_97365190 
 1.950429e-02  1.623126e-02  8.448409e-03  6.530988e-03  1.981005e-02 
  S8_97459232  S8_103243140  S8_104269101  S8_105803797  S8_106614488 
-5.877875e-03  5.438295e-03  7.718499e-03  2.741346e-03 -6.602949e-03 
 S8_106910226  S8_106912903  S8_108472915  S8_109133633  S8_109392545 
-8.799725e-03 -2.889797e-02  6.881830e-03  4.585098e-03  1.080058e-02 
 S8_109392879  S8_111322877  S8_112613045  S8_113061666  S8_113222492 
-1.625203e-03 -1.867652e-02  1.005111e-02  7.901452e-03  6.447624e-03 
 S8_115324388  S8_118673313  S8_119861994  S8_119990281  S8_120893120 
 7.708061e-03  1.316891e-03 -3.772030e-02 -1.548686e-02 -1.062585e-03 
 S8_121951790  S8_123526088  S8_124908154  S8_126178224  S8_126980958 
-1.345605e-02  1.127829e-02 -4.522491e-04 -3.927682e-03 -7.206646e-03 
 S8_126981184  S8_129126249  S8_129126331  S8_131343001  S8_132992799 
-4.222808e-02  1.943726e-03  3.009113e-03 -1.302880e-02  2.608387e-03 
 S8_136802438  S8_136809284  S8_136906155  S8_137591652  S8_138038762 
-2.207327e-02 -8.608719e-03 -4.486625e-03 -2.065821e-03 -4.304577e-03 
 S8_138671667  S8_139359278  S8_139640865  S8_139641067  S8_139641078 
 2.138461e-02  1.596152e-03  1.267668e-02 -6.598052e-03 -9.523667e-03 
 S8_139735441  S8_140024615  S8_140716187  S8_141669288  S8_142038195 
-3.078500e-03 -6.103465e-03 -1.709632e-02  1.173140e-02 -1.463752e-02 
 S8_142173684  S8_143840137  S8_143932091  S8_144503660  S8_145104273 
-1.074321e-02  1.643989e-02 -7.015347e-03 -1.852650e-02 -6.150803e-03 
 S8_145619184  S8_145638724  S8_145638857  S8_145638907  S8_145639197 
-7.266887e-03  2.859813e-03  1.727965e-02  5.197779e-03 -8.859517e-03 
 S8_145706344  S8_146913681  S8_146915872  S8_147061014  S8_147237160 
 2.757817e-02 -6.264179e-03 -8.553652e-04  2.897404e-02  5.219803e-03 
 S8_147237232  S8_148123116  S8_148123368  S8_148209478  S8_148213738 
 6.837346e-04  2.968330e-02  2.447266e-02 -2.170643e-03  4.735490e-03 
 S8_149334177  S8_151332101  S8_151559281  S8_152930973  S8_152931377 
-9.427072e-03  2.127476e-02  3.228051e-02  3.649000e-02  2.727950e-02 
 S8_153849223  S8_153951065  S8_154256530  S8_155257598  S8_155284422 
-1.822833e-03  1.724517e-02  5.480719e-02 -1.307923e-02  1.891913e-02 
 S8_156077718  S8_157011747  S8_157706115  S8_157816200  S8_157883748 
 1.809751e-02  8.479918e-03  2.062839e-02  3.071945e-03  1.742321e-02 
 S8_158696822  S8_158756134  S8_159984291  S8_159986949  S8_162352976 
 2.997635e-02  3.350001e-02 -4.821044e-03  2.675182e-02  1.645307e-02 
 S8_162612468  S8_162637212  S8_162896145  S8_163012266  S8_163056389 
 2.685942e-03 -1.085455e-02 -1.892766e-02  3.093472e-02 -5.630303e-02 
 S8_163496457  S8_165213412  S8_165221478  S8_165287774  S8_165287815 
 1.885524e-02 -3.317273e-03  4.018321e-02  1.188126e-02 -6.487177e-03 
 S8_165591470  S8_166042246  S8_166202785  S8_167202272  S8_167202317 
 1.561823e-02  1.030621e-02  4.846883e-02 -7.915963e-03 -7.590856e-03 
 S8_168540873  S8_170687375  S8_170842557  S8_171742962  S8_172705488 
 2.317754e-02 -1.898838e-02 -2.816487e-02 -6.496094e-03  9.201590e-03 
 S8_172892623  S8_175564833  S8_175733988  S8_175848932  S8_176114327 
 6.809995e-03 -4.166058e-05 -3.452513e-02  1.814710e-02 -2.940769e-03 
 S8_176230266  S8_176769987  S8_176786513  S8_177101160  S8_177304312 
 5.690301e-03 -5.902216e-03 -2.972595e-02 -2.618772e-02 -4.933317e-03 
 S8_177304499  S8_177315603  S8_178074080  S8_178090822  S8_178286494 
-1.111122e-02  1.960428e-02 -1.161824e-04 -1.906727e-03  9.337230e-03 
 S8_178294802  S8_178483072  S8_178554472  S8_178663730  S8_178664475 
 8.747925e-03  4.416033e-03 -9.946053e-04  1.102590e-03  8.910506e-03 
 S8_178915043  S8_178961641  S8_179029297  S8_179267039  S8_179438577 
-2.212601e-03 -6.374520e-05 -8.060664e-03  1.175835e-02 -6.368486e-03 
 S8_179439574  S8_179548317  S8_179612437  S8_179619835  S8_179957322 
-1.202298e-02  5.823439e-03  2.242189e-03  4.913435e-03 -3.035374e-03 
 S8_179993351  S8_180046179  S8_180189011  S8_180782117  S8_180784833 
-1.827420e-02  1.352237e-02 -1.107002e-02  1.517479e-02  3.387696e-02 
 S8_181111566  S8_181124334  S8_181296649  S8_181689217  S8_181689247 
 2.220767e-02 -2.929287e-03  4.798949e-03 -4.588581e-03 -2.496322e-03 
   S9_3959370    S9_4993690    S9_4995279    S9_8971249    S9_9954525 
 1.433295e-03 -1.038059e-02  1.460773e-02  3.336068e-02  3.750087e-03 
  S9_10304284   S9_12406880   S9_13656461   S9_13943778   S9_13973036 
-1.143513e-02  3.263018e-02 -4.219256e-03  2.992537e-02  2.070227e-02 
  S9_17661474   S9_18541531   S9_18659976   S9_19431348   S9_19992784 
 3.247897e-03 -1.412750e-02 -6.863135e-03  1.681610e-02 -2.638630e-02 
  S9_20057612   S9_20566593   S9_20712634   S9_22312637   S9_22460773 
-2.047508e-02  2.838495e-02  5.195843e-03 -1.180670e-02 -1.407034e-02 
  S9_22683033   S9_22835170   S9_23908973   S9_24533589   S9_25419062 
 2.839692e-02 -4.683292e-03 -8.015166e-03  1.594343e-02  1.436057e-02 
  S9_25652799   S9_26412649   S9_27837811   S9_28606394   S9_29045740 
 2.274125e-02 -5.489402e-03  1.987932e-02 -7.379894e-03  4.220091e-02 
  S9_30376560   S9_30380175   S9_30508507   S9_30509819   S9_30603556 
 4.722769e-03  4.044529e-03  1.698992e-02  4.028250e-04 -2.764643e-03 
  S9_30606494   S9_33192523   S9_34238597   S9_34634719   S9_40569788 
-6.915091e-04 -2.806374e-03  1.612684e-02 -2.781578e-02  2.638682e-03 
  S9_42906699   S9_44261434   S9_45193959   S9_45273886   S9_45350516 
 1.970104e-02  1.829004e-01  1.588782e-02  1.960543e-02 -2.665955e-02 
  S9_49342596   S9_51560322   S9_52535302   S9_52817392   S9_68785274 
-9.330245e-03  1.448713e-04  1.839091e-02 -5.317839e-03  9.482744e-03 
  S9_69024153   S9_70377286   S9_70877809   S9_70878145   S9_73101689 
-4.099905e-02 -2.394094e-02 -3.078929e-02 -4.115584e-02  5.819096e-03 
  S9_82307557   S9_82881269   S9_82881561   S9_89588752   S9_92794731 
-3.317249e-03  1.511813e-02  3.968730e-03 -3.526378e-03 -1.885612e-02 
  S9_93159100   S9_93918483   S9_93980807   S9_96320344   S9_96749966 
-6.453200e-03  2.787596e-03 -6.512654e-03  4.563708e-03  1.010731e-02 
  S9_98196670   S9_99580960  S9_100102144  S9_100102169  S9_101047446 
 1.306873e-02 -2.562653e-02 -2.276117e-02 -1.457429e-02 -6.190215e-03 
 S9_101550215  S9_106076196  S9_108321858  S9_108578090  S9_108578128 
 3.033312e-03  2.442826e-02  3.317470e-03  1.888318e-02  1.371546e-03 
 S9_111891095  S9_115675404  S9_116220024  S9_117454544  S9_117520388 
-1.013866e-03  5.791838e-03 -9.596235e-05  2.164455e-02  6.998103e-04 
 S9_122125436  S9_124773786  S9_126372054  S9_126372093  S9_126374267 
 2.631413e-02  1.299339e-02  7.256486e-03 -2.834467e-03 -2.100319e-02 
 S9_127527339  S9_127789201  S9_128523910  S9_129481422  S9_129572300 
 3.427234e-02 -1.874073e-02 -8.043105e-03  1.291880e-02  1.170000e-02 
 S9_129572647  S9_129874362  S9_131724992  S9_133208756  S9_133319015 
-4.507882e-03  1.873059e-03 -6.576421e-03  1.284279e-02  1.213104e-02 
 S9_133937404  S9_134231118  S9_136741591  S9_136741910  S9_138760923 
-7.796028e-03  2.313551e-03  6.686938e-03 -8.048457e-02  5.344463e-03 
 S9_139489281  S9_139489431  S9_139874801  S9_139876736  S9_140350405 
-7.595989e-03 -1.812126e-02  1.839667e-04 -4.265355e-03 -3.557273e-03 
 S9_142328045  S9_142703131  S9_142999534  S9_143235225  S9_143822305 
-2.924329e-03 -3.252595e-04 -7.251583e-03  1.562984e-02  6.011610e-04 
 S9_144225494  S9_144690039  S9_144834531  S9_144838478  S9_144842802 
-4.302684e-02 -4.373217e-03 -9.694858e-03  4.860246e-03 -4.562340e-02 
 S9_144842900  S9_145277076  S9_145300797  S9_145300931  S9_145628125 
-3.791915e-02 -1.364289e-02 -9.228504e-03 -9.873622e-03 -3.290126e-03 
 S9_145782578  S9_145818801  S9_145936254  S9_145938864  S9_146460911 
 8.260393e-03  1.768614e-02 -9.506964e-03  7.054465e-03 -3.324066e-02 
 S9_146582623  S9_146910687  S9_146910765  S9_146916548  S9_147969421 
-5.442788e-04 -6.217783e-04 -2.344660e-03  1.029790e-02 -3.182374e-03 
 S9_149709836  S9_150739616  S9_151108069  S9_151588111  S9_151732067 
-3.121715e-04 -1.194959e-03  8.883061e-03 -1.681593e-03 -8.046402e-03 
 S9_152064276  S9_152100502  S9_152231751  S9_153224074  S9_153224303 
-1.928065e-02 -9.587526e-03 -4.402484e-03 -7.311452e-03 -4.951435e-03 
 S9_153295416  S9_153529263  S9_153545049  S9_153995457  S9_154334776 
 5.440548e-03  8.660695e-03 -1.444757e-03  2.215988e-03  4.982312e-03 
 S9_155492839  S9_155493451  S9_155615428  S9_155890855  S9_155919344 
 3.028767e-02  2.172354e-02 -3.349671e-02 -9.812601e-03 -2.803506e-02 
   S10_134011    S10_576837    S10_579988    S10_666803   S10_1978209 
-3.575851e-02  1.178510e-02 -1.011086e-02 -6.088109e-03 -1.622866e-02 
  S10_1988995   S10_4226386   S10_4445207   S10_4606827   S10_7912828 
-2.866794e-02  4.579892e-03 -1.432344e-02  9.210773e-03 -1.426141e-02 
  S10_9206258  S10_11678939  S10_11726830  S10_13556481  S10_14895246 
-3.184315e-02 -7.311738e-03  3.438070e-03  5.158249e-03  9.867324e-04 
 S10_18758942  S10_22620592  S10_26017555  S10_28099984  S10_28131693 
-3.078943e-03 -1.745140e-02  7.839023e-03  2.129638e-03 -3.150633e-02 
 S10_31496633  S10_34329818  S10_37516811  S10_40701952  S10_57753560 
-1.067878e-02 -1.020455e-02  3.375225e-02  1.694408e-02  8.215006e-03 
 S10_61534784  S10_67411912  S10_77741095  S10_78957561  S10_78959506 
-4.225724e-02  3.795591e-02 -1.357945e-02  4.736792e-03  2.232446e-02 
 S10_78959606  S10_78960438  S10_78966389  S10_80715492  S10_81354226 
-1.094149e-03  1.349339e-02  1.563745e-02  1.968674e-02  1.072963e-02 
 S10_82019011  S10_83314593  S10_85196318  S10_85755281  S10_88793097 
-1.725188e-02  7.183808e-03 -6.188330e-03  2.974361e-02  1.120455e-02 
 S10_90419375  S10_97106903 S10_102234728 S10_109673528 S10_110777178 
 7.328808e-03 -2.371086e-02  1.569796e-03 -1.060411e-02 -6.053698e-03 
S10_110777396 S10_111491694 S10_112583610 S10_114996351 S10_116328788 
 1.022528e-03 -1.388769e-02  5.763021e-03 -1.975392e-02 -7.487010e-03 
S10_116631409 S10_120177964 S10_124062077 S10_126767342 S10_127721617 
-9.783306e-03  1.048189e-02  2.255397e-02 -7.537205e-03  1.312034e-02 
S10_128804415 S10_131791443 S10_132069282 S10_134595037 S10_134798579 
-1.672405e-02 -3.335322e-03  3.640544e-02  1.199539e-02  1.106853e-02 
S10_135868847 S10_136340251 S10_136476190 S10_136482943 S10_136957021 
-1.236199e-02 -1.665341e-02  1.375729e-02 -3.625634e-03 -1.567911e-02 
S10_137422024 S10_137623845 S10_138915965 S10_138916149 S10_140320829 
 3.905943e-03  2.781684e-02  6.299316e-03  1.022836e-02 -4.031159e-03 
S10_140929929 S10_141762354 S10_142018900 S10_143185867 S10_143185873 
-3.799256e-03 -1.458553e-02 -1.570091e-02  4.285115e-03  3.315123e-03 
S10_144224453 S10_145112713 S10_145588552 S10_145607925 S10_145871698 
 3.114504e-02  3.289173e-02  2.902908e-03 -9.508860e-03  1.191938e-02 
S10_145875510 S10_146904768 S10_146916853 S10_146928552 S10_147173783 
 2.231806e-02 -1.370364e-02  6.662321e-03 -4.285970e-03 -4.657851e-03 
S10_147282479 S10_147282490 S10_147352179 S10_148272189 S10_148510710 
-2.288208e-02 -1.010163e-03  3.708822e-03  2.948365e-03  7.782429e-03 
S10_148511076 S10_149259041 S10_149298931 S10_149394364 S10_149474479 
-1.286108e-02 -1.863267e-03 -5.510381e-04  3.778665e-03 -1.699825e-02 
S10_149508570 S10_149647241 S10_149717807 S10_149851103 S10_150351135 
-1.529520e-03 -1.230710e-03 -3.689050e-02 -1.578210e-02  3.405908e-02 
S10_150646162 S10_150711963 S10_150733250 S10_151045975 S10_151157757 
-1.691038e-02  3.181739e-02  1.136500e-02  2.805817e-03 -8.366586e-03

BayesB$ETA[[1]]$d # Probability of inclusion: Frequency that the SNP had effect different than 0

   [1] 0.3532 0.3628 0.3640 0.3592 0.3516 0.3616 0.3760 0.3442 0.3468 0.3492
  [11] 0.3726 0.3488 0.3522 0.3618 0.3454 0.3508 0.3596 0.3680 0.3774 0.3508
  [21] 0.3862 0.3424 0.3616 0.3496 0.4858 0.3478 0.3642 0.3652 0.3414 0.3410
  [31] 0.3496 0.3592 0.3586 0.3536 0.3456 0.3962 0.3362 0.3494 0.3996 0.3544
  [41] 0.3616 0.3504 0.3836 0.3896 0.3606 0.3552 0.3478 0.3628 0.3506 0.3468
  [51] 0.3690 0.3638 0.4576 0.4090 0.3600 0.3416 0.3490 0.3502 0.3860 0.3552
  [61] 0.3590 0.3576 0.3372 0.3414 0.3508 0.3534 0.3456 0.3676 0.3458 0.3332
  [71] 0.3598 0.3602 0.3626 0.3570 0.3578 0.3578 0.3750 0.3894 0.4248 0.3892
  [81] 0.3598 0.3698 0.3518 0.3720 0.3618 0.3658 0.3588 0.3510 0.3550 0.3488
  [91] 0.3490 0.3490 0.3576 0.3542 0.3662 0.3606 0.3622 0.4130 0.3700 0.4082
 [101] 0.3712 0.3896 0.3660 0.3692 0.3404 0.3602 0.3516 0.3466 0.3474 0.3594
 [111] 0.4800 0.3746 0.3694 0.3546 0.3946 0.3628 0.3492 0.3750 0.3610 0.3416
 [121] 0.3702 0.3482 0.3642 0.5016 0.3766 0.3480 0.3448 0.3898 0.3836 0.3506
 [131] 0.3800 0.4242 0.3550 0.3750 0.3526 0.3340 0.3546 0.3572 0.3788 0.3550
 [141] 0.3464 0.3532 0.3606 0.3616 0.3394 0.3532 0.3468 0.3542 0.3958 0.3542
 [151] 0.3750 0.3512 0.3458 0.3524 0.3600 0.3450 0.3506 0.3404 0.4070 0.3752
 [161] 0.3544 0.3474 0.3650 0.3616 0.3496 0.3486 0.3708 0.3596 0.3570 0.3482
 [171] 0.3612 0.3594 0.3584 0.3384 0.3400 0.3644 0.3604 0.3398 0.3550 0.3898
 [181] 0.3478 0.3596 0.3400 0.3522 0.3844 0.3564 0.3666 0.4122 0.3662 0.3728
 [191] 0.3454 0.3522 0.3456 0.3734 0.3564 0.3644 0.3496 0.3698 0.3680 0.5114
 [201] 0.3518 0.3588 0.3530 0.3386 0.3564 0.3678 0.3550 0.3506 0.3568 0.3438
 [211] 0.3286 0.3502 0.3538 0.3444 0.3430 0.3802 0.3392 0.3392 0.3540 0.3780
 [221] 0.3506 0.3462 0.3508 0.4024 0.3516 0.3574 0.3706 0.3670 0.3460 0.3842
 [231] 0.3568 0.3666 0.3618 0.3576 0.3474 0.3636 0.3534 0.4604 0.3382 0.3456
 [241] 0.3528 0.3430 0.3584 0.3492 0.3478 0.3502 0.4292 0.3920 0.3364 0.3604
 [251] 0.3524 0.3476 0.3428 0.3368 0.3600 0.3496 0.3624 0.3362 0.4072 0.3850
 [261] 0.3598 0.4796 0.4326 0.3562 0.3724 0.3502 0.3534 0.3514 0.3514 0.3534
 [271] 0.3534 0.3682 0.3498 0.3536 0.3506 0.3604 0.3432 0.3458 0.3634 0.3492
 [281] 0.3600 0.3464 0.3282 0.3678 0.3428 0.3640 0.3612 0.3852 0.3512 0.3410
 [291] 0.3688 0.3586 0.3624 0.3426 0.3542 0.3572 0.3682 0.3500 0.3510 0.3570
 [301] 0.3388 0.3488 0.3624 0.3480 0.3516 0.3654 0.3556 0.3608 0.3558 0.4060
 [311] 0.3524 0.3640 0.3652 0.3502 0.3794 0.3544 0.4360 0.3526 0.3582 0.3414
 [321] 0.3578 0.3582 0.3546 0.3490 0.3534 0.3408 0.3488 0.3484 0.3540 0.3492
 [331] 0.3608 0.3724 0.3480 0.3460 0.3518 0.3426 0.3632 0.3602 0.3634 0.3526
 [341] 0.3826 0.4166 0.3572 0.3680 0.3484 0.3484 0.3380 0.3348 0.3446 0.3690
 [351] 0.3418 0.3526 0.3518 0.3542 0.3660 0.3406 0.3622 0.3450 0.3522 0.3462
 [361] 0.3766 0.3492 0.3750 0.3622 0.3512 0.3594 0.3484 0.3538 0.3758 0.3532
 [371] 0.3518 0.3434 0.3588 0.3862 0.3906 0.3488 0.3542 0.3726 0.3454 0.3666
 [381] 0.3692 0.3790 0.4074 0.4132 0.3344 0.3382 0.4288 0.3490 0.5990 0.3950
 [391] 0.3542 0.3808 0.4216 0.3472 0.3450 0.3612 0.3368 0.3278 0.3618 0.3574
 [401] 0.3492 0.3478 0.3444 0.3664 0.3352 0.3456 0.3828 0.3818 0.3528 0.3572
 [411] 0.3462 0.3598 0.3388 0.3496 0.3488 0.3414 0.3480 0.3522 0.3658 0.3452
 [421] 0.3960 0.3458 0.3620 0.3620 0.3504 0.3524 0.3420 0.3408 0.3496 0.3550
 [431] 0.3650 0.3654 0.3736 0.3564 0.3520 0.3686 0.3866 0.3666 0.3516 0.3406
 [441] 0.3622 0.3598 0.3482 0.3504 0.3398 0.3546 0.3588 0.3576 0.3562 0.3806
 [451] 0.3374 0.3488 0.3650 0.3470 0.3564 0.3534 0.3418 0.3494 0.3570 0.3710
 [461] 0.3566 0.3536 0.3504 0.3648 0.3668 0.3932 0.3422 0.3538 0.3628 0.3472
 [471] 0.3468 0.3410 0.3590 0.3620 0.3370 0.3760 0.3440 0.3562 0.3638 0.3546
 [481] 0.3572 0.3840 0.3706 0.3400 0.3514 0.3630 0.3644 0.3528 0.3596 0.3430
 [491] 0.3418 0.3508 0.3582 0.3376 0.3418 0.3578 0.3384 0.3780 0.3392 0.3586
 [501] 0.3446 0.4032 0.3834 0.3856 0.3632 0.3468 0.3518 0.3444 0.3654 0.3362
 [511] 0.3666 0.4112 0.3568 0.3660 0.3418 0.3480 0.3526 0.3448 0.3430 0.4098
 [521] 0.3488 0.3558 0.3872 0.3500 0.3442 0.3686 0.3506 0.3552 0.3578 0.3428
 [531] 0.3710 0.3470 0.3486 0.3488 0.3640 0.3576 0.3418 0.3328 0.3454 0.3392
 [541] 0.3706 0.3464 0.3420 0.3566 0.4078 0.3692 0.3384 0.3476 0.3658 0.3628
 [551] 0.3502 0.3552 0.3790 0.3528 0.3594 0.3724 0.3606 0.3482 0.3548 0.3676
 [561] 0.3674 0.3416 0.3576 0.3420 0.3502 0.3468 0.3312 0.3350 0.3840 0.3510
 [571] 0.3562 0.3452 0.3494 0.3540 0.3522 0.3534 0.3486 0.3548 0.3722 0.3696
 [581] 0.3930 0.3602 0.3524 0.3506 0.3676 0.3350 0.3694 0.3558 0.3616 0.3614
 [591] 0.3622 0.3388 0.3412 0.3504 0.3428 0.3522 0.3524 0.3534 0.3414 0.3508
 [601] 0.3442 0.3562 0.3600 0.3478 0.3588 0.3434 0.3500 0.3476 0.3364 0.3752
 [611] 0.3470 0.3474 0.3532 0.3668 0.3444 0.3574 0.3428 0.3560 0.3638 0.3474
 [621] 0.3934 0.3554 0.3510 0.3560 0.3492 0.3530 0.3320 0.3668 0.3854 0.3522
 [631] 0.3670 0.3602 0.3554 0.3560 0.3426 0.3618 0.3346 0.3788 0.3416 0.3394
 [641] 0.3548 0.3554 0.3922 0.3470 0.3462 0.4100 0.3488 0.3370 0.3616 0.3532
 [651] 0.3546 0.3470 0.3620 0.4062 0.3542 0.3458 0.3404 0.3516 0.3544 0.3518
 [661] 0.3498 0.3434 0.3456 0.4096 0.4282 0.3614 0.3572 0.3440 0.3602 0.3574
 [671] 0.3528 0.3510 0.3514 0.3554 0.3618 0.3444 0.3634 0.3732 0.3668 0.3508
 [681] 0.3526 0.3400 0.3680 0.3538 0.3762 0.3464 0.3528 0.3442 0.3574 0.3382
 [691] 0.3566 0.3680 0.3710 0.3436 0.3364 0.3412 0.3502 0.3964 0.3602 0.3466
 [701] 0.3686 0.3446 0.3550 0.3428 0.3604 0.3480 0.3592 0.3566 0.3542 0.3412
 [711] 0.3830 0.3492 0.3534 0.3438 0.3398 0.3468 0.3460 0.3468 0.3398 0.3404
 [721] 0.3478 0.3854 0.3484 0.3572 0.3340 0.3522 0.3678 0.3650 0.3478 0.3440
 [731] 0.3688 0.3726 0.3802 0.3360 0.3398 0.3508 0.3624 0.3514 0.3474 0.3494
 [741] 0.3494 0.3648 0.3712 0.3614 0.3520 0.3818 0.4146 0.3634 0.3716 0.3498
 [751] 0.3558 0.3700 0.3556 0.3972 0.3388 0.3502 0.3620 0.3510 0.3738 0.3542
 [761] 0.3922 0.3434 0.3524 0.3580 0.4212 0.3606 0.3548 0.3436 0.3534 0.3528
 [771] 0.3514 0.3758 0.3586 0.3542 0.3570 0.3610 0.3790 0.3580 0.3564 0.3698
 [781] 0.3524 0.3522 0.3322 0.3542 0.4572 0.3474 0.3652 0.3608 0.3584 0.4490
 [791] 0.3304 0.3604 0.3456 0.3458 0.3372 0.3520 0.3570 0.3588 0.3508 0.3472
 [801] 0.3320 0.3396 0.3584 0.3484 0.3414 0.3598 0.3454 0.3528 0.3564 0.3382
 [811] 0.3450 0.3548 0.3502 0.3570 0.3892 0.4086 0.3424 0.3434 0.3488 0.3362
 [821] 0.3466 0.3408 0.3648 0.3678 0.3430 0.3424 0.3464 0.3550 0.3486 0.3636
 [831] 0.3942 0.3572 0.3702 0.3482 0.3594 0.3404 0.3618 0.3440 0.3600 0.3384
 [841] 0.3792 0.3550 0.3498 0.3630 0.3510 0.3744 0.3464 0.3602 0.3810 0.3818
 [851] 0.3372 0.3984 0.3626 0.3482 0.3524 0.3578 0.3406 0.3298 0.3626 0.3828
 [861] 0.3988 0.3480 0.3560 0.3590 0.3684 0.4298 0.4566 0.3568 0.3580 0.3434
 [871] 0.3548 0.3772 0.3904 0.3454 0.3456 0.3616 0.3364 0.3448 0.3488 0.3490
 [881] 0.3606 0.3392 0.3602 0.3586 0.3490 0.4054 0.4096 0.3516 0.3504 0.3582
 [891] 0.4320 0.3620 0.3616 0.4388 0.3792 0.3730 0.4110 0.4296 0.3460 0.3730
 [901] 0.3432 0.3622 0.3614 0.3398 0.3440 0.4564 0.3812 0.3918 0.3668 0.3872
 [911] 0.3634 0.4366 0.3458 0.4022 0.3552 0.3724 0.3544 0.3712 0.3638 0.3712
 [921] 0.3524 0.3518 0.3950 0.3604 0.3622 0.3620 0.3562 0.3448 0.3610 0.3700
 [931] 0.3522 0.3650 0.3534 0.3462 0.3502 0.3686 0.3532 0.3556 0.3638 0.3500
 [941] 0.3506 0.3652 0.3418 0.4142 0.3802 0.3492 0.3512 0.3604 0.3584 0.3432
 [951] 0.3618 0.3488 0.3746 0.3644 0.3752 0.3612 0.3710 0.3532 0.3582 0.3586
 [961] 0.3412 0.3574 0.3486 0.3762 0.4730 0.3584 0.3762 0.3378 0.4360 0.3444
 [971] 0.3394 0.3330 0.4682 0.3472 0.3522 0.3702 0.3794 0.3524 0.3648 0.3476
 [981] 0.3832 0.3742 0.3966 0.3522 0.3914 0.3478 0.3582 0.3434 0.3610 0.3378
 [991] 0.4200 0.4114 0.4148 0.4010 0.3546 0.3582 0.3432 0.3410 0.3608 0.3504
[1001] 0.3514 0.3864 0.3630 0.3584 0.3700 0.3740 0.4116 0.3586 0.3620 0.3466
[1011] 0.3432 0.3614 0.3948 0.3964 0.3474 0.3624 0.3542 0.3830 0.3618 0.3612
[1021] 0.4406 0.3766 0.3462 0.3486 0.3446 0.3640 0.3526 0.3408 0.3456 0.3616
[1031] 0.3470 0.3630 0.3566 0.3512 0.3624 0.3880 0.3568 0.3756 0.3552 0.3748
[1041] 0.4112 0.3544 0.3572 0.3444 0.3550 0.3446 0.3534 0.3500 0.3590 0.3576
[1051] 0.3446 0.3546 0.3552 0.3896 0.3562 0.3598 0.3724 0.3980 0.3394 0.3480
[1061] 0.3688 0.3468 0.3472 0.3502 0.3614 0.3496 0.3508 0.3600 0.3706 0.3480
[1071] 0.3560 0.3462 0.3522 0.3596 0.3440 0.3574 0.3472 0.3384 0.3616 0.3422
[1081] 0.3620 0.3486 0.3634 0.3514 0.3558 0.3502 0.3488 0.3558 0.4346 0.3766
[1091] 0.4428 0.3574 0.3638 0.3484 0.3562 0.3592 0.3500 0.3506 0.3568 0.3400
[1101] 0.3500 0.3762 0.3486 0.3750 0.3608 0.3524 0.3510 0.3900 0.3964 0.3614
[1111] 0.4016 0.3744 0.3682 0.3788 0.3822 0.3538 0.3528 0.3954 0.3756 0.3664
[1121] 0.3462 0.3488 0.3502 0.3572 0.3678 0.3742 0.3566 0.3472 0.3558 0.3692
[1131] 0.3490 0.3506 0.3488 0.4078 0.4630 0.3592 0.3448 0.3472 0.3606 0.3476
[1141] 0.3930 0.3412 0.3606 0.3622 0.3646 0.3604 0.3746 0.4436 0.3544 0.3522
[1151] 0.3520 0.3560 0.3692 0.3536 0.3650 0.3668 0.3536 0.3644 0.3700 0.3598
[1161] 0.3534 0.3548 0.3552 0.3772 0.3572 0.3502 0.3494 0.3960 0.3454 0.4002
[1171] 0.3674 0.3492 0.3502 0.3794 0.3412 0.3498 0.4316 0.3464 0.3726 0.4180
[1181] 0.3588 0.3440 0.3536 0.3492 0.3448 0.3540 0.3530 0.3508 0.3540 0.3538
[1191] 0.3458 0.3444 0.3436 0.3632 0.3804 0.3570 0.3708 0.3692 0.3542 0.3336
[1201] 0.3636 0.3626 0.3740 0.4808 0.3518 0.3624 0.3476 0.3394 0.3712 0.3586
[1211] 0.3546 0.3524 0.3596 0.3586 0.3414 0.3458 0.3582 0.3492 0.3544 0.3518
[1221] 0.3472 0.3468 0.3472 0.3398 0.3566 0.3472 0.3674 0.3598 0.3868 0.3444
[1231] 0.3412 0.4008 0.3432 0.3416 0.3434 0.3582 0.3670 0.3542 0.3514 0.3556
[1241] 0.3512 0.3508 0.3572 0.3524 0.3608 0.3506 0.3480 0.3964 0.3480 0.3920
[1251] 0.3542 0.3510 0.3556 0.3484 0.3516 0.3508 0.3420 0.3484 0.3566 0.3692
[1261] 0.3584 0.3646 0.3612 0.3522 0.3684 0.3488 0.3640 0.3874 0.3456 0.3670
[1271] 0.3454 0.3600 0.3400 0.3894 0.3966 0.3430 0.3446 0.3410 0.4154 0.3508
[1281] 0.3936 0.3650 0.3450 0.3554 0.3680 0.3578 0.3602 0.3372 0.3662 0.3590
[1291] 0.3606 0.3568 0.3532 0.3522 0.3422 0.3642 0.3466 0.3526 0.3522 0.3464
[1301] 0.3740 0.3572 0.3868 0.3502 0.3656 0.3826 0.3590 0.3522 0.3588 0.3444
[1311] 0.3368 0.3596 0.3676 0.3416 0.3862 0.3448 0.3422 0.3592 0.3580 0.3624
[1321] 0.3600 0.3588 0.3484 0.3436 0.3396 0.3998 0.3446 0.3752 0.3368 0.3410
[1331] 0.3416 0.3556 0.3996 0.3424 0.3468 0.3694 0.3616 0.3430 0.3560 0.3512
[1341] 0.3650 0.3764 0.3864 0.3526 0.3434 0.3472 0.3600 0.3586 0.3570 0.3498
[1351] 0.3708 0.3576 0.3566 0.4076 0.3470 0.3796 0.5114 0.3612 0.3736 0.3576
[1361] 0.3530 0.3636 0.3894 0.4154 0.3614 0.3424 0.3808 0.3456 0.3620 0.3612
[1371] 0.3624 0.3520 0.3394 0.3634 0.3464 0.3502 0.3610 0.3376 0.3496 0.3456
[1381] 0.3330 0.3536 0.3430 0.3448 0.3616 0.3550 0.3582 0.3436 0.3462 0.3386
[1391] 0.3912 0.3932 0.3676 0.3466 0.3598 0.3422 0.3556 0.3658 0.3884 0.3584
[1401] 0.3574 0.3620 0.3506 0.3430 0.3658 0.3530 0.3564 0.3934 0.3736 0.3688
[1411] 0.3444 0.3564 0.3436 0.3672 0.3678 0.3530 0.3430 0.3624 0.3516 0.3602
[1421] 0.3706 0.3428 0.3620 0.3548 0.3482 0.3700 0.3554 0.3658 0.3554 0.3374
[1431] 0.3490 0.3562 0.3412 0.3404 0.3458 0.3598 0.3866 0.3874 0.3574 0.3474
[1441] 0.3460 0.3450 0.3596 0.3480 0.3564 0.3462 0.3528 0.3682 0.3464 0.3560
[1451] 0.4100 0.3528 0.3556 0.3606 0.3802 0.3518 0.3650 0.3508 0.3534 0.3568
[1461] 0.3364 0.3508 0.3536 0.4040 0.3482 0.3500 0.3504 0.3660 0.3574 0.3454
[1471] 0.3710 0.3626 0.3482 0.3532 0.3548 0.3426 0.4124 0.3460 0.3586 0.3512
[1481] 0.3564 0.3796 0.3596 0.3558 0.3404 0.3536 0.3576 0.3948 0.3428 0.3408
[1491] 0.3604 0.3488 0.3468 0.3464 0.3492 0.3516 0.3544 0.3774 0.3550 0.3534
[1501] 0.3478 0.3662 0.3552 0.3546 0.3784 0.3474 0.3614 0.3590 0.3506 0.3516
[1511] 0.3556 0.3604 0.3526 0.3540 0.3660 0.3498 0.3492 0.3542 0.3426 0.3466
[1521] 0.3470 0.3568 0.3536 0.3682 0.3482 0.3522 0.3630 0.3560 0.3436 0.3666
[1531] 0.3516 0.3412 0.3548 0.3424 0.3560 0.3554 0.3558 0.3568 0.3580 0.3478
[1541] 0.3424 0.3244 0.3592 0.3362 0.3470 0.3626 0.3420 0.3444 0.3678 0.3766
[1551] 0.3564 0.3638 0.3380 0.3612 0.3510 0.3704 0.3656 0.3578 0.3490 0.3658
[1561] 0.3738 0.3452 0.3460 0.3490 0.3602 0.3498 0.3586 0.3566 0.3610 0.3760
[1571] 0.3704 0.3486 0.3608 0.3504 0.3462 0.3472 0.3492 0.3776 0.3636 0.3432
[1581] 0.3454 0.3532 0.3562 0.3642 0.3502 0.3712 0.3656 0.3550 0.3602 0.3616
[1591] 0.3620 0.3452 0.3444 0.3678 0.4446 0.3600 0.3846 0.3410 0.4194 0.3388
[1601] 0.3882 0.3292 0.3466 0.3396 0.3558 0.3544 0.3592 0.3552 0.3816 0.3480
[1611] 0.4610 0.3524 0.3722 0.3602 0.3502 0.3864 0.3820 0.3434 0.3440 0.3466
[1621] 0.3470 0.3756 0.3676 0.3570 0.3486 0.3548 0.3560 0.3546 0.3716 0.3964
[1631] 0.4154 0.3586 0.3642 0.3570 0.3478 0.3790 0.3410 0.3524 0.3800 0.3530
[1641] 0.3614 0.3460 0.3540 0.3566 0.3524 0.3396 0.3628 0.3452 0.3454 0.3610
[1651] 0.3630 0.3934 0.3784 0.3402 0.3532 0.3892 0.3494 0.3368 0.3418 0.3464
[1661] 0.3888 0.3682 0.3528 0.3574 0.3512 0.3486 0.3496 0.3652 0.3546 0.3504
[1671] 0.3568 0.3598 0.3544 0.3436 0.3716 0.3618 0.3444 0.3308 0.3468 0.3918
[1681] 0.3592 0.3764 0.3524 0.3490 0.3482 0.3546 0.3544 0.3506 0.3528 0.3522
[1691] 0.3696 0.3718 0.3424 0.3680 0.3476 0.3580 0.3472 0.3534 0.3782 0.3584
[1701] 0.3680 0.3624 0.3542 0.3518 0.3648 0.3554 0.3436 0.3480 0.3460 0.3538
[1711] 0.3604 0.3856 0.3512 0.3422 0.3486 0.3562 0.3580 0.3478 0.3650 0.3460
[1721] 0.3488 0.3464 0.3958 0.3552 0.3588 0.3570 0.3538 0.3546 0.3508 0.3532
[1731] 0.4048 0.3604 0.3532 0.3672 0.3566 0.3602 0.3560 0.3560 0.3554 0.3628
[1741] 0.3700 0.3590 0.3612 0.3462 0.3510 0.3526 0.3506 0.3542 0.3628 0.3636
[1751] 0.3606 0.3550 0.3546 0.3688 0.3524 0.3528 0.3466 0.3648 0.3304 0.3492
[1761] 0.3864 0.3474 0.3390 0.3806 0.3526 0.3420 0.3858 0.3918 0.3484 0.3460
[1771] 0.3486 0.3718 0.3958 0.4104 0.3724 0.3462 0.3560 0.4236 0.3552 0.3674
[1781] 0.3678 0.3516 0.3566 0.3542 0.3524 0.3836 0.3930 0.3626 0.3728 0.3706
[1791] 0.3582 0.3486 0.3668 0.4100 0.4364 0.3624 0.3472 0.4060 0.3542 0.3530
[1801] 0.3548 0.3522 0.4254 0.3694 0.3576 0.3708 0.3694 0.3962 0.3650 0.3556
[1811] 0.3620 0.3500 0.3790 0.3606 0.3412 0.3408 0.3562 0.3880 0.3672 0.3568
[1821] 0.3610 0.3724 0.3534 0.3552 0.3438 0.3464 0.3394 0.3638 0.3500 0.3630
[1831] 0.3478 0.3436 0.3580 0.3582 0.3560 0.3520 0.3402 0.3500 0.3560 0.3408
[1841] 0.3562 0.3508 0.3366 0.3638 0.4008 0.3742 0.3370 0.3534 0.3462 0.3412
[1851] 0.3488 0.3360 0.3542 0.4020 0.3568 0.3454 0.3970 0.3556 0.3758 0.3600
[1861] 0.3496 0.3564 0.3582 0.3544 0.3682 0.3592 0.3770 0.3448 0.3584 0.3668
[1871] 0.3872 0.3506 0.3510 0.3638 0.3624 0.3652 0.3536 0.3618 0.3576 0.4084
[1881] 0.3440 0.3528 0.3622 0.3452 0.3512 0.3352 0.3568 0.3632 0.3898 0.3454
[1891] 0.3646 0.6012 0.3596 0.3858 0.3808 0.3604 0.3428 0.3580 0.3450 0.3492
[1901] 0.4098 0.3776 0.4014 0.3940 0.3514 0.3540 0.3460 0.3434 0.3644 0.3674
[1911] 0.3476 0.3624 0.3422 0.3590 0.3624 0.3632 0.3840 0.3648 0.3614 0.3622
[1921] 0.3556 0.3822 0.3450 0.3746 0.3598 0.3402 0.3468 0.3510 0.3566 0.3510
[1931] 0.3776 0.3734 0.3444 0.3568 0.3778 0.3962 0.3660 0.3588 0.3672 0.3508
[1941] 0.3358 0.3536 0.3560 0.3468 0.3366 0.3580 0.3480 0.3376 0.4738 0.3372
[1951] 0.3578 0.3780 0.3558 0.3530 0.3400 0.3462 0.3544 0.3480 0.3478 0.3534
[1961] 0.4094 0.3514 0.3462 0.3358 0.4192 0.4030 0.3590 0.3606 0.3550 0.3442
[1971] 0.3452 0.3616 0.3470 0.3498 0.3834 0.3582 0.3702 0.3480 0.3590 0.3380
[1981] 0.3402 0.3546 0.3566 0.3372 0.3608 0.3656 0.3560 0.3362 0.3350 0.3334
[1991] 0.3488 0.3680 0.3454 0.3382 0.3404 0.3784 0.3626 0.3918 0.3664 0.3902
[2001] 0.3888 0.3550 0.3538 0.3498 0.3584 0.3838 0.3496 0.3580 0.3538 0.3538
[2011] 0.3802 0.3506 0.3572 0.3404 0.3452 0.3584 0.3686 0.3488 0.3646 0.3896
[2021] 0.3554 0.3562 0.3818 0.3736 0.3568 0.4076 0.4002 0.3612 0.3360 0.3860
[2031] 0.3418 0.3598 0.3654 0.3716 0.3648 0.3676 0.3678 0.3542 0.3780 0.3682
[2041] 0.3504 0.3658 0.3530 0.3542 0.3484 0.3536 0.3670 0.3506 0.3654 0.3402
[2051] 0.3576 0.3648 0.3678 0.3432 0.3700 0.3632 0.3458 0.3918 0.3492 0.3532
[2061] 0.3554 0.3622 0.3542 0.3468 0.3662 0.3598 0.3862 0.3472 0.3582 0.3494
[2071] 0.3440 0.3546 0.3498 0.3472 0.3428 0.3884 0.3850 0.3732 0.3458 0.3400
[2081] 0.3728 0.3694 0.3468 0.3494 0.3614 0.3746 0.3486 0.3378 0.3666 0.3642
[2091] 0.3606 0.3536 0.3534 0.3436 0.3702 0.3472 0.3450 0.4062 0.3642 0.3908
[2101] 0.3444 0.3776 0.3582 0.3456 0.3530

BayesB$varE # Residual variance

[1] 4.774841

var(BayesB$yHat)  # Genetic variance

[1] 1.939678

BayesB

Show code

df_bayesB = data.frame(
  Marker = colnames(X_centred),
  Effect = BayesB$ETA[[1]]$b, 
  PPI = BayesB$ETA[[1]]$d
)

ggplot(df_bayesB, aes(x = 1:nrow(df_bayesB), y = PPI)) +
  geom_point(aes(color = PPI > 0.5), alpha = 0.75, size = 1.7) +
  scale_color_manual(values = c("grey", "#10342d")) +
  labs(x = "Markers", y = "Posterior probability of inclusion") +
  theme_minimal() + 
  theme(legend.position = 'top', text = element_text(size = 18))

Cross-validation

Define cross-validation parameters

nfolds = 5
nrept = 5 
seed = 8  

sets = list()
i = 1
repeat{
  set.seed(987 * i)
  sets[[i]] = sample(rep(1:nfolds, length.out = nrow(dat)))
  i = i + 1
  if(i > nrept) break
}
cvdata = lapply(sets, function(x){
  cvdata = dat
  cvdata$set = x
  return(cvdata)
})
for (i in 1:length(cvdata)) cvdata[[i]]$rept = i

Cross-validation

Run RRBLUP in cross-validation

cv_rr = lapply(cvdata, function(x){
  res.list = list()
  for (i in unique(x$set)) {
    
    yNA = x$blue
    yNA[x$set == i] = NA
    
    mod_cv = mixed.solve(y = yNA, Z = geno_rr)
    
    pred = c(mod_cv$beta) + (geno_rr %*% mod_cv$u)
    
    res.list[[i]] = cbind(
      x[x$set == i,],
      yhat = pred[x$set == i,]
    )
  }
  res.list
})
cv_rr = lapply(cv_rr, function(x) do.call(rbind, x))

Cross-validation

Run BayesB in cross-validation

cv_bb = lapply(cvdata, function(x){
  res.list = list()
  for (i in unique(x$set)) {
    
    yNA = x$blue
    yNA[x$set == i] = NA
    
    mod_cv = BGLR(y = yNA, ETA = ETA, nIter = 8000, burnIn = 1000, 
                  thin = 10, verbose = FALSE, saveAt = "modCV")
    unlink(list.files(pattern = "modCV"))
    
    res.list[[i]] = data.frame(gen = x$gen, yhat = mod_cv$yHat) |> 
      left_join(x, by = "gen") |> filter(set == i)
  }
  res.list
})
cv_bb = lapply(cv_bb, function(x) do.call(rbind, x))

Cross-validation

Model evaluation

pa_rr = lapply(cv_rr, function(x) cor(x$blue, x$yhat, use = 'complete.obs'))
mspe_rr = lapply(cv_rr, function(x) sqrt(mean((x$blue - x$yhat)^2, na.rm = T)))

pa_bb = lapply(cv_bb, function(x) cor(x$blue, x$yhat, use = 'complete.obs'))
mspe_bb = lapply(cv_bb, function(x) sqrt(mean((x$blue - x$yhat)^2, na.rm = T)))

Cross-validation

Model evaluation

Show code

aux = data.frame(
  model = rep(c("RRBLUP", "BayesB"), each = nfolds*2),
  value = c(do.call(c, pa_rr), do.call(c, mspe_rr),
            do.call(c, pa_bb), do.call(c, mspe_bb)),
  param = c(rep(c("PA", "RMSPE"), each = 5), rep(c("PA", "RMSPE"), each = 5))
)

ggplot(data = aux, aes(x = model, y = value)) + 
  facet_wrap(~param, scales = "free_y") + 
  geom_boxplot(fill = "#b2bc63") + 
  theme_bw() +
  theme(text = element_text(size = 18))+
  labs(x = "Model") +
  theme(axis.title.y = element_blank())

References

Akdemir, D., & Sánchez, J. I. (2016). Efficient breeding by genomic mating. Frontiers in Genetics, 7, 210. https://doi.org/10.3389/fgene.2016.00210

Alemu, A., Åstrand, J., Montesinos-López, O. A., Sánchez, J. I. y., Fernández-Gónzalez, J., Tadesse, W., Vetukuri, R. R., Carlsson, A. S., Ceplitis, A., Crossa, J., Ortiz, R., & Chawade, A. (2024). Genomic selection in plant breeding: Key factors shaping two decades of progress. Molecular Plant, 17(4), 552–578. https://doi.org/10.1016/j.molp.2024.03.007

Bernardo, R. (1994). Prediction of maize single-cross performance using RFLPs and information from related hybrids. Crop Science, 34(1), cropsci1994.0011183X003400010003x. https://doi.org/10.2135/cropsci1994.0011183X003400010003x

Campos, G. de los, Hickey, J. M., Pong-Wong, R., Daetwyler, H. D., & Calus, M. P. L. (2013). Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics, 193(2), 327–345. https://doi.org/10.1534/genetics.112.143313

Cobb, J. N., Biswas, P. S., & Platten, J. D. (2019). Back to the future: revisiting MAS as a tool for modern plant breeding. Theoretical and Applied Genetics, 132(3), 647–667. https://doi.org/10.1007/s00122-018-3266-4

Covarrubias-Pazaran, G., Gebeyehu, Z., Gemenet, D., Werner, C., Labroo, M., Sirak, S., Coaldrake, P., Rabbi, I., Kayondo, S. I., Parkes, E., Kanju, E., Mbanjo, E. G. N., Agbona, A., Kulakow, P., Quinn, M., & Debaene, J. (2022). Breeding schemes: what are they, how to formalize them, and how to improve them? Frontiers in Plant Science, 12. https://doi.org/10.3389/fpls.2021.791859

El-Dien, O. G., Shalev, T. J., Yuen, M. M. S., Van der Merwe, L., Kirst, M., Yanchuk, A. D., Ritland, C., Russell, J. H., & Bohlmann, J. (2024). Genomic selection in western redcedar: from proof of concept to operational application. New Phytologist, 244(2), 588–602. https://doi.org/10.1111/nph.20022

Elshire, R. J., Glaubitz, J. C., Sun, Q., Poland, J. A., Kawamoto, K., Buckler, E. S., & Mitchell, S. E. (2011). A robust, simple Genotyping-by-Sequencing (GBS) approach for high diversity species. PLOS ONE, 6(5), e19379. https://doi.org/10.1371/journal.pone.0019379

Endelman, J. B. (2011). Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome, 4, 250–255.

Fernández-González, J., Akdemir, D., & Isidro y Sánchez, J. (2023). A comparison of methods for training population optimization in genomic selection. Theoretical and Applied Genetics, 136(3), 30. https://doi.org/10.1007/s00122-023-04265-6

Gaynor, R. C., Gorjanc, G., Bentley, A. R., Ober, E. S., Howell, P., Jackson, R., Mackay, I. J., & Hickey, J. M. (2017). A Two-Part Strategy for Using Genomic Selection to Develop Inbred Lines. Crop Science, 57(5), 2372–2386. https://doi.org/10.2135/cropsci2016.09.0742

Gezan, S. A., Osorio, L. F., Verma, S., & Whitaker, V. M. (2017). An experimental validation of genomic selection in octoploid strawberry. Horticulture Research, 4, 16070. https://doi.org/10.1038/hortres.2016.70

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning (Vol. 103). Springer New York. https://doi.org/10.1007/978-1-4614-7138-7

Knaus, B. J., & Grünwald, N. J. (2016). VcfR: an R package to manipulate and visualize VCF format data. BioRxiv. https://dx.doi.org/10.1101/041277

Kushanov, F. N., Turaev, O. S., Ernazarova, D. K., Gapparov, B. M., Oripova, B. B., Kudratova, M. K., Rafieva, F. U., Khalikov, K. K., Erjigitov, D. S., Khidirov, M. T., Kholova, M. D., Khusenov, N. N., Amanboyeva, R. S., Saha, S., Yu, J. Z., & Abdurakhmonov, I. Y. (2021). Genetic Diversity, QTL Mapping, and Marker-Assisted Selection Technology in Cotton (Gossypium spp.). Frontiers in Plant Science, 12. https://doi.org/10.3389/fpls.2021.779386

Lima, D. C., Washburn, J. D., Varela, J. I., Chen, Q., Gage, J. L., Romay, M. C., Holland, J., Ertl, D., Lopez-Cruz, M., Aguate, F. M., Campos, G. de los, Kaeppler, S., Beissinger, T., Bohn, M., Buckler, E., Edwards, J., Flint-Garcia, S., Gore, M. A., Hirsch, C. N., … Leon, N. de. (2023). Genomes to Fields 2022 Maize genotype by Environment Prediction Competition. BMC Research Notes, 16(1), 148. https://doi.org/10.1186/s13104-023-06421-z

Marulanda, J. J., Mi, X., Melchinger, A. E., Xu, J.-L., Würschum, T., & Longin, C. F. H. (2016). Optimum breeding strategies using genomic selection for hybrid breeding in wheat, maize, rye, barley, rice and triticale. Theoretical and Applied Genetics, 129(10), 1901–1913. https://doi.org/10.1007/s00122-016-2748-5

Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819–1829. https://doi.org/10.1093/genetics/157.4.1819

Niehoff, T. A. M., Napel, J. ten, Bijma, P., Pook, T., Wientjes, Y. C. J., Hegedűs, B., & Calus, M. P. L. (2024). Improving selection decisions with mating information by accounting for Mendelian sampling variances looking two generations ahead. Genetics Selection Evolution, 56(1), 41. https://doi.org/10.1186/s12711-024-00899-2

Pérez, P., & de los Campos, G. (2014). Genome-wide regression and prediction with the BGLR statistical package. Genetics, 198(2), 483–495. https://doi.org/10.1534/genetics.114.164442

Plummer, M., Best, N., Cowles, K., & Vines, K. (2006). CODA: Convergence Diagnosis and Output Analysis for MCMC. R News, 6(1), 7–11. https://journal.r-project.org/archive/

Voss-Fels, K. P., Cooper, M., & Hayes, B. J. (2019). Accelerating crop genetic gains with genomic selection. Theoretical and Applied Genetics, 132(3), 669–686. https://doi.org/10.1007/s00122-018-3270-8

Werner, C. R., Gaynor, R. C., Sargent, D. J., Lillo, A., Gorjanc, G., & Hickey, J. M. (2023). Genomic selection strategies for clonally propagated crops. Theoretical and Applied Genetics, 136(4), 74. https://doi.org/10.1007/s00122-023-04300-6

Zhou, Y., Vales, M. I., Wang, A., & Zhang, Z. (2017). Systematic bias of correlation coefficient may explain negative accuracy of genomic prediction. Briefings in Bioinformatics, 18(5), 744–753. https://doi.org/10.1093/bib/bbw064