Dataset Information

ASAFE: ancestry-specific allele frequency estimation.

ABSTRACT:

Unlabelled

In a genome-wide association study (GWAS) of an admixed population, such as Hispanic Americans, ancestry-specific allele frequencies can inform the design of a replication GWAS. We derive an EM algorithm to estimate ancestry-specific allele frequencies for a bi-allelic marker given genotypes and local ancestries on a 3-way admixed population, when the phase of each admixed individual's genotype relative to the pair of local ancestries is unknown. We call our algorithm Ancestry Specific Allele Frequency Estimation (ASAFE). We demonstrate that ASAFE has low error on simulated data.

Availability and implementation

The R source code for ASAFE is available for download at https://github.com/BiostatQian/ASAFE CONTACT: qszhang@uw.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Zhang QS

PROVIDER: S-EPMC4937201 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Publications

ASAFE: ancestry-specific allele frequency estimation.

Zhang Qian S QS Browning Brian L BL Browning Sharon R SR

Bioinformatics (Oxford, England) 20160503 14

<h4>Unlabelled</h4>In a genome-wide association study (GWAS) of an admixed population, such as Hispanic Americans, ancestry-specific allele frequencies can inform the design of a replication GWAS. We derive an EM algorithm to estimate ancestry-specific allele frequencies for a bi-allelic marker given genotypes and local ancestries on a 3-way admixed population, when the phase of each admixed individual's genotype relative to the pair of local ancestries is unknown. We call our algorithm Ancestry ...[more]

PMID: 27153656

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Project description:Allele frequency estimates in admixed populations, such as Hispanics and Latinos, rely on the sample's specific admixture composition and thus may differ between two seemingly similar populations. However, ancestry-specific allele frequencies, i.e., pertaining to the ancestral populations of an admixed group, may be particularly useful for prioritizing genetic variants for genetic discovery and personalized genomic health. We developed a method, ancestry-specific allele frequency estimation in admixed populations (AFA), to estimate the frequencies of biallelic variants in admixed populations with an unlimited number of ancestries. AFA uses maximum-likelihood estimation by modeling the conditional probability of having an allele given proportions of genetic ancestries. It can be applied using either local ancestry interval proportions encompassing the variant (local-ancestry-specific allele frequency estimations in admixed populations [LAFAs]) or global proportions of genetic ancestries (global-ancestry-specific allele frequency estimations in admixed populations [GAFAs]), which are easier to compute and are more widely available. Simulations and comparisons to existing software demonstrated the high accuracy of the method. We implemented AFA on high-quality imputed data of ∼9,000 Hispanics and Latinos from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), an understudied, admixed population with three predominant continental ancestries: Amerindian, European, and African. Comparison of the European and African estimated frequencies to the respective gnomAD frequencies demonstrated high correlations (Pearson R2 = 0.97-0.99). We provide a genome-wide dataset of the estimated ancestry-specific allele frequencies for available variants with allele frequency between 5% and 95% in at least one of the three ancestral populations. Association analysis of Amerindian-enriched variants with cardiometabolic traits identified five loci associated with lipid traits in Hispanics and Latinos, demonstrating the utility of ancestry-specific allele frequencies in admixed populations.

Project description:MotivationUnique modeling and computational challenges arise in locating the geographic origin of individuals based on their genetic backgrounds. Single-nucleotide polymorphisms (SNPs) vary widely in informativeness, allele frequencies change non-linearly with geography and reliable localization requires evidence to be integrated across a multitude of SNPs. These problems become even more acute for individuals of mixed ancestry. It is hardly surprising that matching genetic models to computational constraints has limited the development of methods for estimating geographic origins. We attack these related problems by borrowing ideas from image processing and optimization theory. Our proposed model divides the region of interest into pixels and operates SNP by SNP. We estimate allele frequencies across the landscape by maximizing a product of binomial likelihoods penalized by nearest neighbor interactions. Penalization smooths allele frequency estimates and promotes estimation at pixels with no data. Maximization is accomplished by a minorize-maximize (MM) algorithm. Once allele frequency surfaces are available, one can apply Bayes' rule to compute the posterior probability that each pixel is the pixel of origin of a given person. Placement of admixed individuals on the landscape is more complicated and requires estimation of the fractional contribution of each pixel to a person's genome. This estimation problem also succumbs to a penalized MM algorithm.ResultsWe applied the model to the Population Reference Sample (POPRES) data. The model gives better localization for both unmixed and admixed individuals than existing methods despite using just a small fraction of the available SNPs. Computing times are comparable with the best competing software.Availability and implementationSoftware will be freely available as the OriGen package in R.Contactranolaj@uw.edu or klange@ucla.eduSupplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

ASAFE: ancestry-specific allele frequency estimation.

Unlabelled

Availability and implementation

Supplementary information

Publications

ASAFE: ancestry-specific allele frequency estimation.

Similar Datasets

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