Clustering Onion Population SSR Data from Baldwin et al 2012 Using AdeGenet
Samantha Baldwin and John McCallum
Tuesday, July 15, 2014
Analysis of Population Structure in 24 Populations using genomic SSR data
Development of robust genomic simple sequence repeat markers for estimation of genetic diversity within and among bulb onion (Allium cepa L.) populations
See http://link.springer.com/article/10.1007%2Fs11032-012-9727-6
library(adegenet)## Loading required package: ade4
## ==========================
## adegenet 1.4-2 is loaded
## ==========================
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## - to start, type '?adegenet'
## - to browse adegenet website, type 'adegenetWeb()'
## - to post questions/comments: adegenet-forum@lists.r-forge.r-project.orgGFHS<-read.genepop('GFHSApril2011no145.gen')##
## Converting data from a Genepop .gen file to a genind object...
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##
## File description: Title line:"Onion diversity2011"
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## ...done.summary(GFHS)##
## # Total number of genotypes: 276
##
## # Population sample sizes:
## P12 W202A P34 Nasik Red 3000163
## 12 12 12 12 12
## 3000181 3000187 3000161 3000172 3000152
## 12 12 12 12 12
## 3000153 3000156 3000192 3000148 3000112
## 12 12 12 12 12
## 3000143 VioletdeGalmi Faridpuri self 3000154 3000149
## 12 12 12 12 12
## 3000159 3000164 3000110
## 12 12 12
##
## # Number of alleles per locus:
## L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15 L16 L17 L18
## 20 8 5 8 4 6 4 17 12 13 4 4 6 6 7 5 6 4
## L19 L20
## 14 5
##
## # Number of alleles per population:
## 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## 54 33 48 43 59 65 64 47 46 70 57 49 49 45 56 45 52 41 54 53 60 58 47
##
## # Percentage of missing data:
## [1] 8.75
##
## # Observed heterozygosity:
## L01 L02 L03 L04 L05 L06 L07 L08 L09
## 0.48018 0.28980 0.21484 0.19912 0.29545 0.32482 0.22015 0.40226 0.56855
## L10 L11 L12 L13 L14 L15 L16 L17 L18
## 0.48855 0.19024 0.18889 0.19763 0.24336 0.35688 0.26872 0.02335 0.26296
## L19 L20
## 0.31349 0.15074
##
## # Expected heterozygosity:
## L01 L02 L03 L04 L05 L06 L07 L08 L09 L10
## 0.8405 0.5757 0.3343 0.3432 0.5149 0.4760 0.3339 0.7210 0.7805 0.6966
## L11 L12 L13 L14 L15 L16 L17 L18 L19 L20
## 0.2219 0.2313 0.2785 0.5161 0.5106 0.3700 0.6411 0.3759 0.8039 0.2614Perform heterogeneity test
summaryGFHS<-summary(GFHS) ##
## # Total number of genotypes: 276
##
## # Population sample sizes:
## P12 W202A P34 Nasik Red 3000163
## 12 12 12 12 12
## 3000181 3000187 3000161 3000172 3000152
## 12 12 12 12 12
## 3000153 3000156 3000192 3000148 3000112
## 12 12 12 12 12
## 3000143 VioletdeGalmi Faridpuri self 3000154 3000149
## 12 12 12 12 12
## 3000159 3000164 3000110
## 12 12 12
##
## # Number of alleles per locus:
## L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15 L16 L17 L18
## 20 8 5 8 4 6 4 17 12 13 4 4 6 6 7 5 6 4
## L19 L20
## 14 5
##
## # Number of alleles per population:
## 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## 54 33 48 43 59 65 64 47 46 70 57 49 49 45 56 45 52 41 54 53 60 58 47
##
## # Percentage of missing data:
## [1] 8.75
##
## # Observed heterozygosity:
## L01 L02 L03 L04 L05 L06 L07 L08 L09
## 0.48018 0.28980 0.21484 0.19912 0.29545 0.32482 0.22015 0.40226 0.56855
## L10 L11 L12 L13 L14 L15 L16 L17 L18
## 0.48855 0.19024 0.18889 0.19763 0.24336 0.35688 0.26872 0.02335 0.26296
## L19 L20
## 0.31349 0.15074
##
## # Expected heterozygosity:
## L01 L02 L03 L04 L05 L06 L07 L08 L09 L10
## 0.8405 0.5757 0.3343 0.3432 0.5149 0.4760 0.3339 0.7210 0.7805 0.6966
## L11 L12 L13 L14 L15 L16 L17 L18 L19 L20
## 0.2219 0.2313 0.2785 0.5161 0.5106 0.3700 0.6411 0.3759 0.8039 0.2614bartlett.test(list(summaryGFHS$Hexp,summaryGFHS$Hobs))##
## Bartlett test of homogeneity of variances
##
## data: list(summaryGFHS$Hexp, summaryGFHS$Hobs)
## Bartlett's K-squared = 3.611, df = 1, p-value = 0.0574Perform T -test of Obs Vs Expected Heterozygosity
t.test(summaryGFHS$Hexp,summaryGFHS$Hobs,pair=T,var.equal=T,alter="greater")##
## Paired t-test
##
## data: summaryGFHS$Hexp and summaryGFHS$Hobs
## t = 6.2, df = 19, p-value = 2.947e-06
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## 0.1495 Inf
## sample estimates:
## mean of the differences
## 0.2074Get F statistics
fstat(GFHS)## Loading required package: hierfstat## Warning: package 'hierfstat' was built under R version 3.1.1## Loading required package: gtools
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## Attaching package: 'hierfstat'
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## The following object is masked from 'package:adegenet':
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## read.fstat## pop Ind
## Total 0.2648 0.4299
## pop 0.0000 0.2245High within population variation as expected
Run G-statistic Tests of Population Structuring
gtest <- gstat.randtest(GFHS,nsim=99)
gtest## Monte-Carlo test
## Call: gstat.randtest(x = GFHS, nsim = 99)
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## Observation: 2490
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## Based on 99 replicates
## Simulated p-value: 0.01
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 8.712 1797.718 6307.578Cluster With find.clusters
Supervised clustering showed ~ 80 PC asymptote and ~ 12 clusters
grp.BIC <- find.clusters.genind(GFHS,n.pca = 80, n.clust = 12,choose=FALSE)
dapc1 <- dapc.genind(GFHS, grp.BIC$grp,n.pca=80,n.da=4)
scatter(dapc1)