Dataset Information

LEI: A Novel Allele Frequency-Based Feature Selection Method for Multi-ancestry Admixed Populations.

ABSTRACT: Next-generation sequencing technologies now make it possible to sequence and genotype hundreds of thousands of genetic markers across the human genome. Selection of informative markers for the comprehensive characterization of individual genomic makeup using a high dimensional genomics dataset has become a common practice in evolutionary biology and human genetics. Although several feature selection approaches exist to determine the ancestry proportion in two-way admixed populations including African Americans, there are limited statistical tools developed for the feature selection approaches in three-way admixed populations (including Latino populations). Herein, we present a new likelihood-based feature selection method called Lancaster Estimator of Independence (LEI) that utilizes allele frequency information to prioritize the most informative features useful to determine ancestry proportion from multiple ancestral populations in admixed individuals. The ability of LEI to leverage summary-level statistics from allele frequency data, thereby avoiding the many restrictions (and big data issues) that can accompany access to individual-level genotype data, is appealing to minimize the computation and time-consuming ancestry inference in an admixed population. We compared our allele-frequency based approach with genotype-based approach in estimating admixed proportions in three-way admixed population scenarios. Our results showed ancestry estimates using the top-ranked features from LEI were comparable with the estimates using features from genotype-based methods in three-way admixed population. We provide an easy-to-use R code to assist researchers in using the LEI tool to develop allele frequency-based informative features to conduct admixture mapping studies from mixed samples of multiple ancestry origin.

SUBMITTER: Wathen MJ

PROVIDER: S-EPMC6668412 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Publications

LEI: A Novel Allele Frequency-Based Feature Selection Method for Multi-ancestry Admixed Populations.

Wathen Michael J MJ Gautam Yadu Y Ghandikota Sudhir S Rao Marepalli B MB Mersha Tesfaye B TB

Scientific reports 20190731 1

Next-generation sequencing technologies now make it possible to sequence and genotype hundreds of thousands of genetic markers across the human genome. Selection of informative markers for the comprehensive characterization of individual genomic makeup using a high dimensional genomics dataset has become a common practice in evolutionary biology and human genetics. Although several feature selection approaches exist to determine the ancestry proportion in two-way admixed populations including Af ...[more]

PMID: 31366927

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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:BackgroundAdmixed populations arise when two or more previously isolated populations interbreed. A powerful approach to addressing the genetic complexity in admixed populations is to infer ancestry. Ancestry inference including the proportion of an individual's genome coming from each population and its ancestral origin along the chromosome of an admixed population requires the use of ancestry informative markers (AIMs) from reference ancestral populations. AIMs exhibit substantial differences in allele frequency between ancestral populations. Given the huge amount of human genetic variation data available from diverse populations, a computationally feasible and cost-effective approach is becoming increasingly important to extract or filter AIMs with the maximum information content for ancestry inference, admixture mapping, forensic applications, and detecting genomic regions that have been under recent selection.ResultsTo address this gap, we present MI-MAAP, an easy-to-use web-based bioinformatics tool designed to prioritize informative markers for multi-ancestry admixed populations by utilizing feature selection methods and multiple genomics resources including 1000 Genomes Project and Human Genome Diversity Project. Specifically, this tool implements a novel allele frequency-based feature selection algorithm, Lancaster Estimator of Independence (LEI), as well as other genotype-based methods such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Random Forest (RF). We demonstrated that MI-MAAP is a useful tool in prioritizing informative markers and accurately classifying ancestral populations. LEI is an efficient feature selection strategy to retrieve ancestry informative variants with different allele frequency/selection pressure among (or between) ancestries without requiring computationally expensive individual-level genotype data.ConclusionsMI-MAAP has a user-friendly interface which provides researchers an easy and fast way to filter and identify AIMs. MI-MAAP can be accessed at https://research.cchmc.org/mershalab/MI-MAAP/login/.

Dataset Information

LEI: A Novel Allele Frequency-Based Feature Selection Method for Multi-ancestry Admixed Populations.

Publications

LEI: A Novel Allele Frequency-Based Feature Selection Method for Multi-ancestry Admixed Populations.

Similar Datasets

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