batchTest: Batch Effects of Genotyping
In GWASTools: Tools for Genome Wide Association Studies
Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
Description
batchChisqTest calculates Chi-square values for batches from
2-by-2 tables of SNPs, comparing each batch with the other batches.
batchFisherTest calculates Fisher's exact test values.
Usage
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batchChisqTest(genoData, batchVar, snp.include = NULL,
chrom.include = 1:22, sex.include = c("M", "F"),
scan.exclude = NULL, return.by.snp = FALSE,
correct = TRUE, verbose = TRUE)
batchFisherTest(genoData, batchVar, snp.include = NULL,
chrom.include = 1:22, sex.include = c("M", "F"),
scan.exclude = NULL, return.by.snp = FALSE,
conf.int = FALSE, verbose = TRUE)
Arguments
genoData
GenotypeData object
batchVar
A character string indicating which annotation
variable should be used as the batch.
snp.include
A vector containing the IDs of SNPs to include.
chrom.include
Integer vector with codes for chromosomes to
include. Ignored if snp.include is not NULL. Default is 1:22 (autosomes). Use 23, 24, 25, 26, 27 for X, XY, Y, M, Unmapped
respectively
sex.include
Character vector with sex to include. Default is
c("M", "F"). If sex chromosomes are present in chrom.include,
only one sex is allowed.
scan.exclude
A vector containing the IDs of scans to
be excluded.
return.by.snp
Logical value to indicate whether snp-by-batch
matrices should be returned.
conf.int
Logical value to indicate if a confidence interval should be computed.
correct
Logical value to specify whether to apply the Yates continuity correction.
verbose
Logical value specifying whether to show progress information.
Details
Because of potential batch effects due to sample processing and genotype calling, batches are an important experimental design factor.
batchChisqTest calculates the Chi square values from 2-by-2
table for each SNP, comparing each batch with the other batches.
batchFisherTest calculates Fisher's Exact Test from 2-by-2
table for each SNP, comparing each batch with the other batches.
For each SNP and each batch, batch effect is evaluated by a 2-by-2 table:
# of A alleles, and # of B alleles in the batch, versus
# of A alleles, and # of B alleles in the other batches.
Monomorphic SNPs are set to NA for all batches.
The default behavior is to combine allele frequencies from males and
females and return results for autosomes only. If results for sex
chromosomes (X or Y) are desired, use chrom.include with values
23 and/or 25 and sex.include="M" or "F".
If there are only two batches, the calculation is only performed once
and the values for each batch will be identical.
Value
batchChisqTest returns a list with the following elements:
mean.chisq
a vector of mean chi-squared values for each batch.
lambda
a vector of genomic inflation factor computed as median(chisq)
/ 0.456 for each batch.
chisq
a matrix of chi-squared values with SNPs as rows and
batches as columns. Only returned if return.by.snp=TRUE.
batchFisherTest returns a list with the following elements:
mean.or
a vector of mean odds-ratio values for each
batch. mean.or is computed as 1/mean(pmin(or, 1/or)) since the odds ratio is >1 when the batch has a higher allele frequency than the other batches and <1 for the reverse.
lambda
a vector of genomic inflation factor computed as
median(-2*log(pval) / 1.39 for each batch.
Each of the following is a matrix with SNPs as rows and batches as
columns, and is only returned if return.by.snp=TRUE:
pval
P value
oddsratio
Odds ratio
confint.low
Low value of the confidence interval for the odds
ratio. Only returned if conf.int=TRUE.
confint.high
High value of the confidence interval for the odds
ratio. Only returned if conf.int=TRUE.
batchChisqTest and batchFisherTest both also return the
following if return.by.snp=TRUE:
allele.counts
matrix with total number of A and B alleles over
all batches.
min.exp.freq
matrix of minimum expected allele frequency with SNPs as rows and
batches as columns.
Author(s)
Xiuwen Zheng, Stephanie Gogarten
See Also
GenotypeData, chisq.test,
fisher.test
Examples
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library(GWASdata)
file <- system.file("extdata", "illumina_geno.gds", package="GWASdata")
gds <- GdsGenotypeReader(file)
data(illuminaScanADF)
genoData <- GenotypeData(gds, scanAnnot=illuminaScanADF)
# autosomes only, sexes combined (default)
res.chisq <- batchChisqTest(genoData, batchVar="plate")
res.chisq$mean.chisq
res.chisq$lambda
# X chromosome for females
res.chisq <- batchChisqTest(genoData, batchVar="status",
chrom.include=23, sex.include="F", return.by.snp=TRUE)
head(res.chisq$chisq)
# Fisher exact test of "status" on X chromosome for females
res.fisher <- batchFisherTest(genoData, batchVar="status",
chrom.include=23, sex.include="F", return.by.snp=TRUE)
qqPlot(res.fisher$pval)
close(genoData)
Example output
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Mon Dec 7 06:36:55 2020 Load genotype dataset ...
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Mon Dec 7 06:36:55 2020 3/77 WG0052809-AMP2
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Mon Dec 7 06:36:55 2020 9/77 WG0052816-AMP2
Mon Dec 7 06:36:55 2020 10/77 WG0052817-AMP2
Mon Dec 7 06:36:55 2020 11/77 WG0052818-AMP2
Mon Dec 7 06:36:55 2020 12/77 WG0053486-AMP2
Mon Dec 7 06:36:55 2020 13/77 WG0053487-AMP2
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Mon Dec 7 06:36:55 2020 16/77 WG0053490-AMP2
Mon Dec 7 06:36:55 2020 17/77 WG0053491-AMP2
Mon Dec 7 06:36:55 2020 18/77 WG0053492-AMP2
Mon Dec 7 06:36:55 2020 19/77 WG0053493-AMP2
Mon Dec 7 06:36:55 2020 20/77 WG0053511-AMP2
Mon Dec 7 06:36:55 2020 21/77 WG0053512-AMP2
Mon Dec 7 06:36:55 2020 22/77 WG0053513-AMP2
Mon Dec 7 06:36:55 2020 23/77 WG0053514-AMP2
Mon Dec 7 06:36:55 2020 24/77 WG0054868-AMP2
Mon Dec 7 06:36:55 2020 25/77 WG0054869-AMP2
Mon Dec 7 06:36:55 2020 26/77 WG0054870-AMP2
Mon Dec 7 06:36:55 2020 27/77 WG0054871-AMP2
Mon Dec 7 06:36:55 2020 28/77 WG0058561-AMP2
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Mon Dec 7 06:36:55 2020 30/77 WG0058563-AMP2
Mon Dec 7 06:36:55 2020 31/77 WG0058564-AMP2
Mon Dec 7 06:36:55 2020 32/77 WG0058565-AMP2
Mon Dec 7 06:36:55 2020 33/77 WG0058566-AMP2
Mon Dec 7 06:36:55 2020 34/77 WG0058567-AMP2
Mon Dec 7 06:36:55 2020 35/77 WG0060468-AMP2
Mon Dec 7 06:36:55 2020 36/77 WG0060469-AMP2
Mon Dec 7 06:36:55 2020 37/77 WG0060470-AMP2
Mon Dec 7 06:36:55 2020 38/77 WG0060472-AMP2
Mon Dec 7 06:36:55 2020 39/77 WG0060473-AMP2
Mon Dec 7 06:36:55 2020 40/77 WG0060474-AMP2
Mon Dec 7 06:36:55 2020 41/77 WG0060475-AMP2
Mon Dec 7 06:36:55 2020 42/77 WG0060476-AMP2
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Mon Dec 7 06:36:55 2020 46/77 WG0061251-AMP2
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Mon Dec 7 06:36:55 2020 48/77 WG0061253-AMP2
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Mon Dec 7 06:36:55 2020 51/77 WG0061256-AMP2
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Mon Dec 7 06:36:55 2020 53/77 WG0061258-AMP2
Mon Dec 7 06:36:55 2020 54/77 WG0061291-AMP2
Mon Dec 7 06:36:55 2020 55/77 WG0061293-AMP2
Mon Dec 7 06:36:55 2020 56/77 WG0061294-AMP2
Mon Dec 7 06:36:55 2020 57/77 WG0061533-AMP2
Mon Dec 7 06:36:55 2020 58/77 WG0061534-AMP2
Mon Dec 7 06:36:55 2020 59/77 WG0061536-AMP2
Mon Dec 7 06:36:55 2020 60/77 WG0061537-AMP2
Mon Dec 7 06:36:55 2020 61/77 WG0061538-AMP2
Mon Dec 7 06:36:55 2020 62/77 WG0061539-AMP2
Mon Dec 7 06:36:55 2020 63/77 WG0061540-AMP2
Mon Dec 7 06:36:55 2020 64/77 WG0063164-AMP2
Mon Dec 7 06:36:55 2020 65/77 WG0063165-AMP2
Mon Dec 7 06:36:55 2020 66/77 WG0063166-AMP2
Mon Dec 7 06:36:55 2020 67/77 WG0063167-AMP2
Mon Dec 7 06:36:55 2020 68/77 WG0065237-AMP2
Mon Dec 7 06:36:55 2020 69/77 WG0065238-AMP2
Mon Dec 7 06:36:55 2020 70/77 WG0065239-AMP2
Mon Dec 7 06:36:55 2020 71/77 WG0065240-AMP2
Mon Dec 7 06:36:55 2020 72/77 WG0065241-AMP2
Mon Dec 7 06:36:55 2020 73/77 WG0065242-AMP2
Mon Dec 7 06:36:55 2020 74/77 WG0065243-AMP2
Mon Dec 7 06:36:55 2020 75/77 WG0065244-AMP2
Mon Dec 7 06:36:55 2020 76/77 WG0065245-AMP2
Mon Dec 7 06:36:55 2020 77/77 WG0065246-AMP2
WG0052807-AMP2 WG0052808-AMP2 WG0052809-AMP2 WG0052810-AMP2 WG0052811-AMP2
0.1870673 0.1738736 0.2075799 0.2151794 0.2751302
WG0052813-AMP2 WG0052814-AMP2 WG0052815-AMP2 WG0052816-AMP2 WG0052817-AMP2
0.2396656 0.2098169 0.2563460 0.2820289 0.2921853
WG0052818-AMP2 WG0053486-AMP2 WG0053487-AMP2 WG0053488-AMP2 WG0053489-AMP2
0.2629742 0.1629365 0.1897105 0.1924877 0.1655379
WG0053490-AMP2 WG0053491-AMP2 WG0053492-AMP2 WG0053493-AMP2 WG0053511-AMP2
0.1713587 0.1704014 0.2154593 0.1683599 0.1722597
WG0053512-AMP2 WG0053513-AMP2 WG0053514-AMP2 WG0054868-AMP2 WG0054869-AMP2
0.1712416 0.2155127 0.1685254 0.2272651 0.3502184
WG0054870-AMP2 WG0054871-AMP2 WG0058561-AMP2 WG0058562-AMP2 WG0058563-AMP2
0.3067215 0.2653476 0.1736053 0.2070897 0.2138571
WG0058564-AMP2 WG0058565-AMP2 WG0058566-AMP2 WG0058567-AMP2 WG0060468-AMP2
0.1976060 0.1668862 0.1882501 0.2032808 0.2600943
WG0060469-AMP2 WG0060470-AMP2 WG0060472-AMP2 WG0060473-AMP2 WG0060474-AMP2
0.2570877 0.2709914 0.1545591 0.2069080 0.1580206
WG0060475-AMP2 WG0060476-AMP2 WG0060477-AMP2 WG0060478-AMP2 WG0060479-AMP2
0.1627799 0.1699246 0.1971154 0.2566097 0.2263927
WG0061251-AMP2 WG0061252-AMP2 WG0061253-AMP2 WG0061254-AMP2 WG0061255-AMP2
0.1691294 0.1964273 0.2554832 0.2278570 0.2752351
WG0061256-AMP2 WG0061257-AMP2 WG0061258-AMP2 WG0061291-AMP2 WG0061293-AMP2
0.3112525 0.2397782 0.2101280 0.2748663 0.3344871
WG0061294-AMP2 WG0061533-AMP2 WG0061534-AMP2 WG0061536-AMP2 WG0061537-AMP2
0.3437120 0.1626261 0.1898040 0.1661546 0.1552296
WG0061538-AMP2 WG0061539-AMP2 WG0061540-AMP2 WG0063164-AMP2 WG0063165-AMP2
0.2070136 0.1572482 0.1629521 0.1975130 0.1674576
WG0063166-AMP2 WG0063167-AMP2 WG0065237-AMP2 WG0065238-AMP2 WG0065239-AMP2
0.1876182 0.2013255 0.3098652 0.2663549 0.2564204
WG0065240-AMP2 WG0065241-AMP2 WG0065242-AMP2 WG0065243-AMP2 WG0065244-AMP2
0.2825724 0.2923346 0.2629825 0.2600943 0.2576963
WG0065245-AMP2 WG0065246-AMP2
0.2697447 0.2872269
WG0052807-AMP2 WG0052808-AMP2 WG0052809-AMP2 WG0052810-AMP2 WG0052811-AMP2
0 0 0 0 0
WG0052813-AMP2 WG0052814-AMP2 WG0052815-AMP2 WG0052816-AMP2 WG0052817-AMP2
0 0 0 0 0
WG0052818-AMP2 WG0053486-AMP2 WG0053487-AMP2 WG0053488-AMP2 WG0053489-AMP2
0 0 0 0 0
WG0053490-AMP2 WG0053491-AMP2 WG0053492-AMP2 WG0053493-AMP2 WG0053511-AMP2
0 0 0 0 0
WG0053512-AMP2 WG0053513-AMP2 WG0053514-AMP2 WG0054868-AMP2 WG0054869-AMP2
0 0 0 0 0
WG0054870-AMP2 WG0054871-AMP2 WG0058561-AMP2 WG0058562-AMP2 WG0058563-AMP2
0 0 0 0 0
WG0058564-AMP2 WG0058565-AMP2 WG0058566-AMP2 WG0058567-AMP2 WG0060468-AMP2
0 0 0 0 0
WG0060469-AMP2 WG0060470-AMP2 WG0060472-AMP2 WG0060473-AMP2 WG0060474-AMP2
0 0 0 0 0
WG0060475-AMP2 WG0060476-AMP2 WG0060477-AMP2 WG0060478-AMP2 WG0060479-AMP2
0 0 0 0 0
WG0061251-AMP2 WG0061252-AMP2 WG0061253-AMP2 WG0061254-AMP2 WG0061255-AMP2
0 0 0 0 0
WG0061256-AMP2 WG0061257-AMP2 WG0061258-AMP2 WG0061291-AMP2 WG0061293-AMP2
0 0 0 0 0
WG0061294-AMP2 WG0061533-AMP2 WG0061534-AMP2 WG0061536-AMP2 WG0061537-AMP2
0 0 0 0 0
WG0061538-AMP2 WG0061539-AMP2 WG0061540-AMP2 WG0063164-AMP2 WG0063165-AMP2
0 0 0 0 0
WG0063166-AMP2 WG0063167-AMP2 WG0065237-AMP2 WG0065238-AMP2 WG0065239-AMP2
0 0 0 0 0
WG0065240-AMP2 WG0065241-AMP2 WG0065242-AMP2 WG0065243-AMP2 WG0065244-AMP2
0 0 0 0 0
WG0065245-AMP2 WG0065246-AMP2
0 0
Mon Dec 7 06:36:55 2020 Load genotype dataset ...
Mon Dec 7 06:36:55 2020 1/2 0
0 1
1029644 1.8777647 1.8777647
1029716 0.8607194 0.8607194
1029753 0.0000000 0.0000000
1029754 0.7582069 0.7582069
1029822 0.0000000 0.0000000
1029830 0.8960438 0.8960438
Mon Dec 7 06:36:55 2020 Load genotype dataset ...
Mon Dec 7 06:36:56 2020 1/2 0
GWASTools documentation built on Nov. 8, 2020, 7:49 p.m.
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Description Usage Arguments Details Value Author(s) See Also Examples
Description
batchChisqTest calculates Chi-square values for batches from
2-by-2 tables of SNPs, comparing each batch with the other batches.
batchFisherTest calculates Fisher's exact test values.
Usage
1 2 3 4 5 6 7 8 9 | batchChisqTest(genoData, batchVar, snp.include = NULL,
chrom.include = 1:22, sex.include = c("M", "F"),
scan.exclude = NULL, return.by.snp = FALSE,
correct = TRUE, verbose = TRUE)
batchFisherTest(genoData, batchVar, snp.include = NULL,
chrom.include = 1:22, sex.include = c("M", "F"),
scan.exclude = NULL, return.by.snp = FALSE,
conf.int = FALSE, verbose = TRUE)
|
Arguments
genoData |
|
batchVar |
A character string indicating which annotation variable should be used as the batch. |
snp.include |
A vector containing the IDs of SNPs to include. |
chrom.include |
Integer vector with codes for chromosomes to
include. Ignored if |
sex.include |
Character vector with sex to include. Default is
c("M", "F"). If sex chromosomes are present in |
scan.exclude |
A vector containing the IDs of scans to be excluded. |
return.by.snp |
Logical value to indicate whether snp-by-batch matrices should be returned. |
conf.int |
Logical value to indicate if a confidence interval should be computed. |
correct |
Logical value to specify whether to apply the Yates continuity correction. |
verbose |
Logical value specifying whether to show progress information. |
Details
Because of potential batch effects due to sample processing and genotype calling, batches are an important experimental design factor.
batchChisqTest calculates the Chi square values from 2-by-2
table for each SNP, comparing each batch with the other batches.
batchFisherTest calculates Fisher's Exact Test from 2-by-2
table for each SNP, comparing each batch with the other batches.
For each SNP and each batch, batch effect is evaluated by a 2-by-2 table:
# of A alleles, and # of B alleles in the batch, versus
# of A alleles, and # of B alleles in the other batches.
Monomorphic SNPs are set to NA for all batches.
The default behavior is to combine allele frequencies from males and
females and return results for autosomes only. If results for sex
chromosomes (X or Y) are desired, use chrom.include with values
23 and/or 25 and sex.include="M" or "F".
If there are only two batches, the calculation is only performed once and the values for each batch will be identical.
Value
batchChisqTest returns a list with the following elements:
mean.chisq |
a vector of mean chi-squared values for each batch. |
lambda |
a vector of genomic inflation factor computed as |
chisq |
a matrix of chi-squared values with SNPs as rows and
batches as columns. Only returned if |
batchFisherTest returns a list with the following elements:
mean.or |
a vector of mean odds-ratio values for each
batch. |
lambda |
a vector of genomic inflation factor computed as
|
Each of the following is a matrix with SNPs as rows and batches as
columns, and is only returned if return.by.snp=TRUE:
pval |
P value |
oddsratio |
Odds ratio |
confint.low |
Low value of the confidence interval for the odds
ratio. Only returned if |
confint.high |
High value of the confidence interval for the odds
ratio. Only returned if |
batchChisqTest and batchFisherTest both also return the
following if return.by.snp=TRUE:
allele.counts |
matrix with total number of A and B alleles over all batches. |
min.exp.freq |
matrix of minimum expected allele frequency with SNPs as rows and batches as columns. |
Author(s)
Xiuwen Zheng, Stephanie Gogarten
See Also
GenotypeData, chisq.test,
fisher.test
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | library(GWASdata)
file <- system.file("extdata", "illumina_geno.gds", package="GWASdata")
gds <- GdsGenotypeReader(file)
data(illuminaScanADF)
genoData <- GenotypeData(gds, scanAnnot=illuminaScanADF)
# autosomes only, sexes combined (default)
res.chisq <- batchChisqTest(genoData, batchVar="plate")
res.chisq$mean.chisq
res.chisq$lambda
# X chromosome for females
res.chisq <- batchChisqTest(genoData, batchVar="status",
chrom.include=23, sex.include="F", return.by.snp=TRUE)
head(res.chisq$chisq)
# Fisher exact test of "status" on X chromosome for females
res.fisher <- batchFisherTest(genoData, batchVar="status",
chrom.include=23, sex.include="F", return.by.snp=TRUE)
qqPlot(res.fisher$pval)
close(genoData)
|
Example output
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Mon Dec 7 06:36:55 2020 Load genotype dataset ...
Mon Dec 7 06:36:55 2020 1/77 WG0052807-AMP2
Mon Dec 7 06:36:55 2020 2/77 WG0052808-AMP2
Mon Dec 7 06:36:55 2020 3/77 WG0052809-AMP2
Mon Dec 7 06:36:55 2020 4/77 WG0052810-AMP2
Mon Dec 7 06:36:55 2020 5/77 WG0052811-AMP2
Mon Dec 7 06:36:55 2020 6/77 WG0052813-AMP2
Mon Dec 7 06:36:55 2020 7/77 WG0052814-AMP2
Mon Dec 7 06:36:55 2020 8/77 WG0052815-AMP2
Mon Dec 7 06:36:55 2020 9/77 WG0052816-AMP2
Mon Dec 7 06:36:55 2020 10/77 WG0052817-AMP2
Mon Dec 7 06:36:55 2020 11/77 WG0052818-AMP2
Mon Dec 7 06:36:55 2020 12/77 WG0053486-AMP2
Mon Dec 7 06:36:55 2020 13/77 WG0053487-AMP2
Mon Dec 7 06:36:55 2020 14/77 WG0053488-AMP2
Mon Dec 7 06:36:55 2020 15/77 WG0053489-AMP2
Mon Dec 7 06:36:55 2020 16/77 WG0053490-AMP2
Mon Dec 7 06:36:55 2020 17/77 WG0053491-AMP2
Mon Dec 7 06:36:55 2020 18/77 WG0053492-AMP2
Mon Dec 7 06:36:55 2020 19/77 WG0053493-AMP2
Mon Dec 7 06:36:55 2020 20/77 WG0053511-AMP2
Mon Dec 7 06:36:55 2020 21/77 WG0053512-AMP2
Mon Dec 7 06:36:55 2020 22/77 WG0053513-AMP2
Mon Dec 7 06:36:55 2020 23/77 WG0053514-AMP2
Mon Dec 7 06:36:55 2020 24/77 WG0054868-AMP2
Mon Dec 7 06:36:55 2020 25/77 WG0054869-AMP2
Mon Dec 7 06:36:55 2020 26/77 WG0054870-AMP2
Mon Dec 7 06:36:55 2020 27/77 WG0054871-AMP2
Mon Dec 7 06:36:55 2020 28/77 WG0058561-AMP2
Mon Dec 7 06:36:55 2020 29/77 WG0058562-AMP2
Mon Dec 7 06:36:55 2020 30/77 WG0058563-AMP2
Mon Dec 7 06:36:55 2020 31/77 WG0058564-AMP2
Mon Dec 7 06:36:55 2020 32/77 WG0058565-AMP2
Mon Dec 7 06:36:55 2020 33/77 WG0058566-AMP2
Mon Dec 7 06:36:55 2020 34/77 WG0058567-AMP2
Mon Dec 7 06:36:55 2020 35/77 WG0060468-AMP2
Mon Dec 7 06:36:55 2020 36/77 WG0060469-AMP2
Mon Dec 7 06:36:55 2020 37/77 WG0060470-AMP2
Mon Dec 7 06:36:55 2020 38/77 WG0060472-AMP2
Mon Dec 7 06:36:55 2020 39/77 WG0060473-AMP2
Mon Dec 7 06:36:55 2020 40/77 WG0060474-AMP2
Mon Dec 7 06:36:55 2020 41/77 WG0060475-AMP2
Mon Dec 7 06:36:55 2020 42/77 WG0060476-AMP2
Mon Dec 7 06:36:55 2020 43/77 WG0060477-AMP2
Mon Dec 7 06:36:55 2020 44/77 WG0060478-AMP2
Mon Dec 7 06:36:55 2020 45/77 WG0060479-AMP2
Mon Dec 7 06:36:55 2020 46/77 WG0061251-AMP2
Mon Dec 7 06:36:55 2020 47/77 WG0061252-AMP2
Mon Dec 7 06:36:55 2020 48/77 WG0061253-AMP2
Mon Dec 7 06:36:55 2020 49/77 WG0061254-AMP2
Mon Dec 7 06:36:55 2020 50/77 WG0061255-AMP2
Mon Dec 7 06:36:55 2020 51/77 WG0061256-AMP2
Mon Dec 7 06:36:55 2020 52/77 WG0061257-AMP2
Mon Dec 7 06:36:55 2020 53/77 WG0061258-AMP2
Mon Dec 7 06:36:55 2020 54/77 WG0061291-AMP2
Mon Dec 7 06:36:55 2020 55/77 WG0061293-AMP2
Mon Dec 7 06:36:55 2020 56/77 WG0061294-AMP2
Mon Dec 7 06:36:55 2020 57/77 WG0061533-AMP2
Mon Dec 7 06:36:55 2020 58/77 WG0061534-AMP2
Mon Dec 7 06:36:55 2020 59/77 WG0061536-AMP2
Mon Dec 7 06:36:55 2020 60/77 WG0061537-AMP2
Mon Dec 7 06:36:55 2020 61/77 WG0061538-AMP2
Mon Dec 7 06:36:55 2020 62/77 WG0061539-AMP2
Mon Dec 7 06:36:55 2020 63/77 WG0061540-AMP2
Mon Dec 7 06:36:55 2020 64/77 WG0063164-AMP2
Mon Dec 7 06:36:55 2020 65/77 WG0063165-AMP2
Mon Dec 7 06:36:55 2020 66/77 WG0063166-AMP2
Mon Dec 7 06:36:55 2020 67/77 WG0063167-AMP2
Mon Dec 7 06:36:55 2020 68/77 WG0065237-AMP2
Mon Dec 7 06:36:55 2020 69/77 WG0065238-AMP2
Mon Dec 7 06:36:55 2020 70/77 WG0065239-AMP2
Mon Dec 7 06:36:55 2020 71/77 WG0065240-AMP2
Mon Dec 7 06:36:55 2020 72/77 WG0065241-AMP2
Mon Dec 7 06:36:55 2020 73/77 WG0065242-AMP2
Mon Dec 7 06:36:55 2020 74/77 WG0065243-AMP2
Mon Dec 7 06:36:55 2020 75/77 WG0065244-AMP2
Mon Dec 7 06:36:55 2020 76/77 WG0065245-AMP2
Mon Dec 7 06:36:55 2020 77/77 WG0065246-AMP2
WG0052807-AMP2 WG0052808-AMP2 WG0052809-AMP2 WG0052810-AMP2 WG0052811-AMP2
0.1870673 0.1738736 0.2075799 0.2151794 0.2751302
WG0052813-AMP2 WG0052814-AMP2 WG0052815-AMP2 WG0052816-AMP2 WG0052817-AMP2
0.2396656 0.2098169 0.2563460 0.2820289 0.2921853
WG0052818-AMP2 WG0053486-AMP2 WG0053487-AMP2 WG0053488-AMP2 WG0053489-AMP2
0.2629742 0.1629365 0.1897105 0.1924877 0.1655379
WG0053490-AMP2 WG0053491-AMP2 WG0053492-AMP2 WG0053493-AMP2 WG0053511-AMP2
0.1713587 0.1704014 0.2154593 0.1683599 0.1722597
WG0053512-AMP2 WG0053513-AMP2 WG0053514-AMP2 WG0054868-AMP2 WG0054869-AMP2
0.1712416 0.2155127 0.1685254 0.2272651 0.3502184
WG0054870-AMP2 WG0054871-AMP2 WG0058561-AMP2 WG0058562-AMP2 WG0058563-AMP2
0.3067215 0.2653476 0.1736053 0.2070897 0.2138571
WG0058564-AMP2 WG0058565-AMP2 WG0058566-AMP2 WG0058567-AMP2 WG0060468-AMP2
0.1976060 0.1668862 0.1882501 0.2032808 0.2600943
WG0060469-AMP2 WG0060470-AMP2 WG0060472-AMP2 WG0060473-AMP2 WG0060474-AMP2
0.2570877 0.2709914 0.1545591 0.2069080 0.1580206
WG0060475-AMP2 WG0060476-AMP2 WG0060477-AMP2 WG0060478-AMP2 WG0060479-AMP2
0.1627799 0.1699246 0.1971154 0.2566097 0.2263927
WG0061251-AMP2 WG0061252-AMP2 WG0061253-AMP2 WG0061254-AMP2 WG0061255-AMP2
0.1691294 0.1964273 0.2554832 0.2278570 0.2752351
WG0061256-AMP2 WG0061257-AMP2 WG0061258-AMP2 WG0061291-AMP2 WG0061293-AMP2
0.3112525 0.2397782 0.2101280 0.2748663 0.3344871
WG0061294-AMP2 WG0061533-AMP2 WG0061534-AMP2 WG0061536-AMP2 WG0061537-AMP2
0.3437120 0.1626261 0.1898040 0.1661546 0.1552296
WG0061538-AMP2 WG0061539-AMP2 WG0061540-AMP2 WG0063164-AMP2 WG0063165-AMP2
0.2070136 0.1572482 0.1629521 0.1975130 0.1674576
WG0063166-AMP2 WG0063167-AMP2 WG0065237-AMP2 WG0065238-AMP2 WG0065239-AMP2
0.1876182 0.2013255 0.3098652 0.2663549 0.2564204
WG0065240-AMP2 WG0065241-AMP2 WG0065242-AMP2 WG0065243-AMP2 WG0065244-AMP2
0.2825724 0.2923346 0.2629825 0.2600943 0.2576963
WG0065245-AMP2 WG0065246-AMP2
0.2697447 0.2872269
WG0052807-AMP2 WG0052808-AMP2 WG0052809-AMP2 WG0052810-AMP2 WG0052811-AMP2
0 0 0 0 0
WG0052813-AMP2 WG0052814-AMP2 WG0052815-AMP2 WG0052816-AMP2 WG0052817-AMP2
0 0 0 0 0
WG0052818-AMP2 WG0053486-AMP2 WG0053487-AMP2 WG0053488-AMP2 WG0053489-AMP2
0 0 0 0 0
WG0053490-AMP2 WG0053491-AMP2 WG0053492-AMP2 WG0053493-AMP2 WG0053511-AMP2
0 0 0 0 0
WG0053512-AMP2 WG0053513-AMP2 WG0053514-AMP2 WG0054868-AMP2 WG0054869-AMP2
0 0 0 0 0
WG0054870-AMP2 WG0054871-AMP2 WG0058561-AMP2 WG0058562-AMP2 WG0058563-AMP2
0 0 0 0 0
WG0058564-AMP2 WG0058565-AMP2 WG0058566-AMP2 WG0058567-AMP2 WG0060468-AMP2
0 0 0 0 0
WG0060469-AMP2 WG0060470-AMP2 WG0060472-AMP2 WG0060473-AMP2 WG0060474-AMP2
0 0 0 0 0
WG0060475-AMP2 WG0060476-AMP2 WG0060477-AMP2 WG0060478-AMP2 WG0060479-AMP2
0 0 0 0 0
WG0061251-AMP2 WG0061252-AMP2 WG0061253-AMP2 WG0061254-AMP2 WG0061255-AMP2
0 0 0 0 0
WG0061256-AMP2 WG0061257-AMP2 WG0061258-AMP2 WG0061291-AMP2 WG0061293-AMP2
0 0 0 0 0
WG0061294-AMP2 WG0061533-AMP2 WG0061534-AMP2 WG0061536-AMP2 WG0061537-AMP2
0 0 0 0 0
WG0061538-AMP2 WG0061539-AMP2 WG0061540-AMP2 WG0063164-AMP2 WG0063165-AMP2
0 0 0 0 0
WG0063166-AMP2 WG0063167-AMP2 WG0065237-AMP2 WG0065238-AMP2 WG0065239-AMP2
0 0 0 0 0
WG0065240-AMP2 WG0065241-AMP2 WG0065242-AMP2 WG0065243-AMP2 WG0065244-AMP2
0 0 0 0 0
WG0065245-AMP2 WG0065246-AMP2
0 0
Mon Dec 7 06:36:55 2020 Load genotype dataset ...
Mon Dec 7 06:36:55 2020 1/2 0
0 1
1029644 1.8777647 1.8777647
1029716 0.8607194 0.8607194
1029753 0.0000000 0.0000000
1029754 0.7582069 0.7582069
1029822 0.0000000 0.0000000
1029830 0.8960438 0.8960438
Mon Dec 7 06:36:55 2020 Load genotype dataset ...
Mon Dec 7 06:36:56 2020 1/2 0
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