Project description:The multispecies coalescent has provided important progress for evolutionary inferences, including increasing the statistical rigor and objectivity of comparisons among competing species delimitation models. However, Bayesian species delimitation methods typically require brute force integration over gene trees via Markov chain Monte Carlo (MCMC), which introduces a large computation burden and precludes their application to genomic-scale data. Here we combine a recently introduced dynamic programming algorithm for estimating species trees that bypasses MCMC integration over gene trees with sophisticated methods for estimating marginal likelihoods, needed for Bayesian model selection, to provide a rigorous and computationally tractable technique for genome-wide species delimitation. We provide a critical yet simple correction that brings the likelihoods of different species trees, and more importantly their corresponding marginal likelihoods, to the same common denominator, which enables direct and accurate comparisons of competing species delimitation models using Bayes factors. We test this approach, which we call Bayes factor delimitation (*with genomic data; BFD*), using common species delimitation scenarios with computer simulations. Varying the numbers of loci and the number of samples suggest that the approach can distinguish the true model even with few loci and limited samples per species. Misspecification of the prior for population size ? has little impact on support for the true model. We apply the approach to West African forest geckos (Hemidactylus fasciatus complex) using genome-wide SNP data. This new Bayesian method for species delimitation builds on a growing trend for objective species delimitation methods with explicit model assumptions that are easily tested. [Bayes factor; model testing; phylogeography; RADseq; simulation; speciation.].

| S-EPMC4072903 | biostudies-literature

Genome-wide SNP data of Chinese indigenous horse breed (additional horse samples)

Project description:Genome-wide SNP data of Chinese indigenous horse breed (additional horse samples)

| PRJNA527287 | ENA

Genome-wide SNP data of Chinese indigenous horse breed (Jinjinag horse samples)

Project description:Genome-wide SNP data of Chinese indigenous horse breed (Jinjinag horse samples)

| PRJNA527294 | ENA

Affymetrix SNP array data for 31 Tibetan samples

Project description:This data was used to identify regions of the genome that have undergone positive selection in a high-altitude Tibetan population. Affymetrix SNP arrays were used to genotype DNA extracted from blood samples. This data was used to perform genome-wide scans of positive selection in a native high-altitude Tibetan population.

2010-07-22 | E-GEOD-21661 | biostudies-arrayexpress

NPC genome-wide human SNP array data

Project description:nasopharyngeal carcinoma genome-wide human SNP array data for 4083 NPC cases and 4811 controls

| EGAD00010002296 | EGA

Clustering by genetic ancestry using genome-wide SNP data.

Project description:BackgroundPopulation stratification can cause spurious associations in a genome-wide association study (GWAS), and occurs when differences in allele frequencies of single nucleotide polymorphisms (SNPs) are due to ancestral differences between cases and controls rather than the trait of interest. Principal components analysis (PCA) is the established approach to detect population substructure using genome-wide data and to adjust the genetic association for stratification by including the top principal components in the analysis. An alternative solution is genetic matching of cases and controls that requires, however, well defined population strata for appropriate selection of cases and controls.ResultsWe developed a novel algorithm to cluster individuals into groups with similar ancestral backgrounds based on the principal components computed by PCA. We demonstrate the effectiveness of our algorithm in real and simulated data, and show that matching cases and controls using the clusters assigned by the algorithm substantially reduces population stratification bias. Through simulation we show that the power of our method is higher than adjustment for PCs in certain situations.ConclusionsIn addition to reducing population stratification bias and improving power, matching creates a clean dataset free of population stratification which can then be used to build prediction models without including variables to adjust for ancestry. The cluster assignments also allow for the estimation of genetic heterogeneity by examining cluster specific effects.

| S-EPMC3018397 | biostudies-literature

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