AWclust: point-and-click software for non-parametric population structure analysis

Gao, Xiaoyi; Starmer, Joshua

doi:10.1186/1471-2105-9-77

Cited by 78 publications

(85 citation statements)

References 35 publications

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“…Although we aimed at indica breeding lines, 24 lines were later confirmed to be japonica and removed before data analysis. A few lines were found to be outliers using a model-based population structure analysis implemented in STRUCTURE (Pritchard et al, 2000), and multi-dimensional scaling and cluster analysis implemented in the R packages AWclust (Gao and Starmer, 2008) based on 50 SSR markers evenly distributed among all chromosomes and removed as well (results not shown). Finally, 303 lines had GY data in all testing environments were used in multi-site analysis.…”

Section: Discussionmentioning

confidence: 99%

Genotype-by-environment interaction is important for grain yield in irrigated lowland rice

Liang

Ren

Liu

et al. 2015

Field Crops Research

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Section: Discussionmentioning

confidence: 99%

Genotype-by-environment interaction is important for grain yield in irrigated lowland rice

Liang

Ren

Liu

et al. 2015

Field Crops Research

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“…PCA is a classical non-parametric linear reduction technique used to reveal population structure by arranging all principal components (PCs) according to the explained variance without resorting to a model (Menozzi et al, 1978;Price et al, 2006;Gao and Starmer, 2008). Here, we applied PCA on a genetic relationship matrix (n × n) with pairwise identities by state (IBS) between all individuals (N = 151) as provided by PLINK 1.9 (Chang et al, 2015).…”

Section: Population Structure Analysesmentioning

confidence: 99%

Using Whole-Genome Sequence Information to Foster Conservation Efforts for the European Dark Honey Bee, Apis mellifera mellifera

et al. 2016

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Pollination is a key ecosystem service for agricultural systems and Western honey bees, Apis mellifera, are the most important managed pollinators. Major losses of managed honey bee colonies reinforced the need to take advantage of locally adapted subspecies and ecotypes to buffer populations against various stressors. However, introductions of non-native honey bees from distant lineages are likely to undermine respective conservation efforts unless reliable and cost effective tools can be used to identify hybridization. The purpose of this study is to characterize current population structure and genetic diversity, and to assess the degree of admixture between native and introduced honey bees. Moreover, we aim to select a reduced number of genetic markers to improve conservation management strategies. We take advantage of recent developments in next-generation sequencing and network-based clustering to investigate conservation efforts for the native European Dark honey bee, A. m. mellifera, which is threatened by introgression in most of its range. We collected whole-genome sequence information from haploid drones of A. m. mellifera, A. m. carnica, and Buckfast sampled throughout Switzerland (N = 81), as well as from four Swiss A. m. mellifera conservation areas (N = 39) and from one conservatory in the French Alps (N = 31). Population structure analyses based upon 3.375 M genome-wide SNPs discerned samples by subspecies and geographic origin (Switzerland or France). Ancestry inference indicated admixed individuals in all of the protected areas, calling for improved management efforts. After testing different subsets of ancestry informative SNPs using three different selection strategies (F ST , PCA-based or at random), as few as 50 SNPs are found to be sufficient to differentiate native from introduced honey bees. Therefore, our data suggests that a low-density SNP panel can be a precise and cost-effective tool to support conservation management efforts for managed pollinators.

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“…Genetic clustering was analyzed using the AWclust program. 4 Population-based association between SNPs and phenotypes was analyzed using PLINK software. 11 Gender and genetic clusters defined by the AWclust program were recoded into binary dummy variables and used as covariates in populationbased association analyses.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, we have used the genetic information to construct genetic clusters resembling families, allowing to reduce higher dimensional data (genotypes of each individual) into lower dimensions (cluster groups) based on allele sharing distance relationships. 4 …”

Section: Introductionmentioning

confidence: 99%

Association study by genetic clustering detects multiple inflammatory response loci in non-inbred mice

et al. 2011

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We tested the possibility to map loci affecting the acute inflammatory response (AIR) in an (AIRmax Â AIRmin) F2 intercross mouse population derived from non-inbred parents, by association analysis in the absence of pedigree information. Using 1064 autosomal single nucleotide polymorphisms (SNPs), we clustered the intercross population into 12 groups of genetically related individuals. Association analysis adjusted for genetic clusters allowed to identify two loci, inflammatory response modulator 1 (Irm1) on chromosome 7 previously detected by genetic linkage analysis in the F2 mice, and a new locus on chromosome 5 (Irm2), linked to the number of infiltrating cells in subcutaneous inflammatory exudates (Irm1: P ¼ 6.3 Â 10 À7 ; Irm2: P ¼ 8.2 Â 10 À5) and interleukin 1 beta (IL-1b) production (Irm1: P ¼ 1.9 Â 10 À16 ; Irm2: P ¼ 1.1 Â 10 À6 ). Use of a polygenic model based on additive effects of the rare alleles of 15 or 18 SNPs associated at suggestive genome-wide statistical threshold (Po3.4 Â 10 À3 ) with the number of infiltrating cells or IL-1b production, respectively, allowed prediction of the inflammatory response of progenitor AIR mice. Our findings suggest the usefulness of association analysis in combination with genetic clustering to map loci affecting complex phenotypes in non-inbred animal species.

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AWclust: point-and-click software for non-parametric population structure analysis

Cited by 78 publications

References 35 publications

Genotype-by-environment interaction is important for grain yield in irrigated lowland rice

Genotype-by-environment interaction is important for grain yield in irrigated lowland rice

Using Whole-Genome Sequence Information to Foster Conservation Efforts for the European Dark Honey Bee, Apis mellifera mellifera

Association study by genetic clustering detects multiple inflammatory response loci in non-inbred mice

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