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Genomic regions exhibiting positive selection identified from dense genotype data.

ABSTRACT: The allele frequency spectrum of polymorphisms in DNA sequences can be used to test for signatures of natural selection that depart from the expected frequency spectrum under the neutral theory. We observed a significant (P = 0.001) correlation between the Tajima's D test statistic in full resequencing data and Tajima's D in a dense, genome-wide data set of genotyped polymorphisms for a set of 179 genes. Based on this, we used a sliding window analysis of Tajima's D across the human genome to identify regions putatively subject to strong, recent, selective sweeps. This survey identified seven Contiguous Regions of Tajima's D Reduction (CRTRs) in an African-descent population (AD), 23 in a European-descent population (ED), and 29 in a Chinese-descent population (XD). Only four CRTRs overlapped between populations: three between ED and XD and one between AD and ED. Full resequencing of eight genes within six CRTRs demonstrated frequency spectra inconsistent with neutral expectations for at least one gene within each CRTR. Identification of the functional polymorphism (and/or haplotype) responsible for the selective sweeps within each CRTR may provide interesting insights into the strongest selective pressures experienced by the human genome over recent evolutionary history.

SUBMITTER: Carlson CS

PROVIDER: S-EPMC1310643 | biostudies-literature | 2005 Nov

REPOSITORIES: biostudies-literature

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Genomic regions exhibiting positive selection identified from dense genotype data.

Carlson Christopher S CS Thomas Daryl J DJ Eberle Michael A MA Swanson Johanna E JE Livingston Robert J RJ Rieder Mark J MJ Nickerson Deborah A DA

Genome research 20051101 11

The allele frequency spectrum of polymorphisms in DNA sequences can be used to test for signatures of natural selection that depart from the expected frequency spectrum under the neutral theory. We observed a significant (P = 0.001) correlation between the Tajima's D test statistic in full resequencing data and Tajima's D in a dense, genome-wide data set of genotyped polymorphisms for a set of 179 genes. Based on this, we used a sliding window analysis of Tajima's D across the human genome to id ...[more]

PMID: 16251465

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Project description:BackgroundGenomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex diseases and traits. However, methods to infer ROA in dense genotype data have not evolved in step with advances in genome technology that now enable us to rapidly create large high-resolution genotype datasets, limiting our ability to investigate their constituent ROA patterns.MethodsWe report a weighted likelihood approach for inferring ROA in dense genotype data that accounts for autocorrelation among genotyped positions and the possibilities of unobserved mutation and recombination events, and variability in the confidence of individual genotype calls in whole genome sequence (WGS) data.ResultsForward-time genetic simulations under two demographic scenarios that reflect situations where inbreeding and its effect on fitness are of interest suggest this approach is better powered than existing state-of-the-art methods to infer ROA at marker densities consistent with WGS and popular microarray genotyping platforms used in human and non-human studies. Moreover, we present evidence that suggests this approach is able to distinguish ROA arising via consanguinity from ROA arising via endogamy. Using subsets of The 1000 Genomes Project Phase 3 data we show that, relative to WGS, intermediate and long ROA are captured robustly with popular microarray platforms, while detection of short ROA is more variable and improves with marker density. Worldwide ROA patterns inferred from WGS data are found to accord well with those previously reported on the basis of microarray genotype data. Finally, we highlight the potential of this approach to detect genomic regions enriched for autozygosity signals in one group relative to another based upon comparisons of per-individual autozygosity likelihoods instead of inferred ROA frequencies.ConclusionsThis weighted likelihood ROA inference approach can assist population- and disease-geneticists working with a wide variety of data types and species to explore ROA patterns and to identify genomic regions with differential ROA signals among groups, thereby advancing our understanding of evolutionary history and the role of recessive variation in phenotypic variation and disease.

Project description:Declaring "replication" from results of genome wide association (GWA) studies is straightforward when major gene effects provide genome-wide significance for association of the same allele of the same SNP in each of multiple independent samples. However, such unambiguous replication is unlikely when phenotypes display polygenic genetic architecture, allelic heterogeneity, locus heterogeneity and when different samples display linkage disequilibria with different fine structures. We seek chromosomal regions that are tagged by clustered SNPs that display nominally-significant association in each of several independent samples. This approach provides one "nontemplate" approach to identifying overall replication of groups of GWA results in the face of difficult genetic architectures. We apply this strategy to 1 M SNP GWA results for dependence on: a) alcohol (including many individuals with dependence on other addictive substances) and b) at least one illegal substance (including many individuals dependent on alcohol). This approach provides high confidence in rejecting the null hypothesis that chance alone accounts for the extent to which clustered, nominally-significant SNPs from samples of the same racial/ethnic background identify the same sets of chromosomal regions. It identifies several genes that are also reported in other independent alcohol-dependence GWA datasets. There is more modest confidence in: a) identification of individual chromosomal regions and genes that are not also identified by data from other independent samples, b) the more modest overlap between results from samples of different racial/ethnic backgrounds and c) the extent to which any gene not identified herein is excluded, since the power of each of these individual samples is modest. Nevertheless, the strong overlap identified among the samples with similar racial/ethnic backgrounds supports contributions to individual differences in vulnerability to addictions that come from newer allelic variants that are common in subsets of current humans.

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Genomic regions exhibiting positive selection identified from dense genotype data.

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Genomic regions exhibiting positive selection identified from dense genotype data.

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