2014
DOI: 10.3389/fgene.2014.00293
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A bioinformatics workflow for detecting signatures of selection in genomic data

Abstract: The detection of “signatures of selection” is now possible on a genome-wide scale in many plant and animal species, and can be performed in a population-specific manner due to the wealth of per-population genome-wide genotype data that is available. With genomic regions that exhibit evidence of having been under selection shown to also be enriched for genes associated with biologically important traits, detection of evidence of selective pressure is emerging as an additional approach for identifying novel gene… Show more

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Cited by 62 publications
(51 citation statements)
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“…These ‘unrelated’ individuals were then haplotyped using SHAPEIT 4953 and were annotated with ancestral allele information using the selectionTools pipeline 63 . Haplotype bifurcation diagrams and EHH plots were drawn using the rehh R package 64 .…”
Section: Methodsmentioning
confidence: 99%
“…These ‘unrelated’ individuals were then haplotyped using SHAPEIT 4953 and were annotated with ancestral allele information using the selectionTools pipeline 63 . Haplotype bifurcation diagrams and EHH plots were drawn using the rehh R package 64 .…”
Section: Methodsmentioning
confidence: 99%
“…A region containing each gene, plus 100 kb upstream and 100 kb downstream, was retrieved in VCF format from the 1000 Genomes Project database, using tabix. Patterns of polymorphism at each gene and its surrounding region were analyzed for each population and each region (as defined by the 1000 Genomes Project), using the selectionTools pipeline and custom Perl scripts (74). Several statistical tests were evaluated: frequencybased methods (Tajima's D, Fay and Wu's H); linkage disequilibrium-based methods (Rsb, iHs); and population differentiation-based methods (F ST ).…”
Section: Methodsmentioning
confidence: 99%
“…In this study we combined association analyses between the Gly482Ser genotype and traits either directly (BMI) or indirectly (gout and T2D, which are correlated with BMI) to identify a potential functional role of Gly482Ser. To identify genomic evidence we used a recently-developed analytical pipeline to test selection by a combination of site frequency spectra based statistics, (Tajima’s D , Fay’s and Wu’s H ) as well as haplotype-length based measures that examine selection within populations (iHS) or between populations (XP-EHH, [10]). We also estimated population differentiation ( F ST ), which has also been used as an indicator of selection [25], as well as departure from expected HW equilibrium.…”
Section: Discussionmentioning
confidence: 99%
“…For populations where genome-wide genotypic data were available (i.e., the 1000 Genomes Project and Axiom/Omni-genotyped populations, Table 1), the following statistics were calculated: F ST between sample sets, Tajima’s D , Fay and Wu’s H , and integrated haplotype score (iHS, [18]) for individual populations, and cross population haplotype homozygosity (XP-EHH, [19]) to estimate selection between populations. To calculate these statistics we used a customized analytical pipeline [10]. For these analyses we assumed that the 482Ser allele was the derived allele, based on low frequencies of this allele in African populations [5].…”
Section: Methodsmentioning
confidence: 99%
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