Dataset Information

Local Ancestry Inference in Large Pedigrees.

ABSTRACT: Local ancestry, defined as the genetic ancestry at a genomic location of an admixed individual, is widely used as a genetic marker in genetic association and evolutionary genetics studies. Many methods have been developed to infer the local ancestries in a set of unrelated individuals, a few of them have been extended to small nuclear families, but none can be applied to large (e.g. three-generation) pedigrees. In this study, we developed a method, FamANC, that can improve the accuracy of local ancestry inference in large pedigrees by: (1) using an existing algorithm to infer local ancestries for all individuals in a family, assuming (contrary to fact) they are unrelated, and (2) improving its accuracy by correcting inference errors using pedigree structure. Applied on African-American pedigrees from the Cleveland Family Study, FamANC was able to correct all identified Mendelian errors and most of double crossovers.

SUBMITTER: Wang H

PROVIDER: S-EPMC6957497 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

Local Ancestry Inference in Large Pedigrees.

Wang Heming H Sofer Tamar T Zhang Xiang X Elston Robert C RC Redline Susan S Zhu Xiaofeng X

Scientific reports 20200113 1

Local ancestry, defined as the genetic ancestry at a genomic location of an admixed individual, is widely used as a genetic marker in genetic association and evolutionary genetics studies. Many methods have been developed to infer the local ancestries in a set of unrelated individuals, a few of them have been extended to small nuclear families, but none can be applied to large (e.g. three-generation) pedigrees. In this study, we developed a method, FamANC, that can improve the accuracy of local ...[more]

PMID: 31932708

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Project description:MotivationLocal ancestry inference (LAI) is the high resolution prediction of ancestry labels along a DNA sequence. LAI is important in the study of human history and migrations, and it is beginning to play a role in precision medicine applications including ancestry-adjusted genome-wide association studies (GWASs) and polygenic risk scores (PRSs). Existing LAI models do not generalize well between species, chromosomes or even ancestry groups, requiring re-training for each different setting. Furthermore, such methods can lack interpretability, which is an important element in each of these applications.ResultsWe present SALAI-Net, a portable statistical LAI method that can be applied on any set of species and ancestries (species-agnostic), requiring only haplotype data and no other biological parameters. Inspired by identity by descent methods, SALAI-Net estimates population labels for each segment of DNA by performing a reference matching approach, which leads to an interpretable and fast technique. We benchmark our models on whole-genome data of humans and we test these models' ability to generalize to dog breeds when trained on human data. SALAI-Net outperforms previous methods in terms of balanced accuracy, while generalizing between different settings, species and datasets. Moreover, it is up to two orders of magnitude faster and uses considerably less RAM memory than competing methods.Availability and implementationWe provide an open source implementation and links to publicly available data at github.com/AI-sandbox/SALAI-Net. Data is publicly available as follows: https://www.internationalgenome.org (1000 Genomes), https://www.simonsfoundation.org/simons-genome-diversity-project (Simons Genome Diversity Project), https://www.sanger.ac.uk/resources/downloads/human/hapmap3.html (HapMap), ftp://ngs.sanger.ac.uk/production/hgdp/hgdp_wgs.20190516 (Human Genome Diversity Project) and https://www.ncbi.nlm.nih.gov/bioproject/PRJNA448733 (Canid genomes).Supplementary informationSupplementary data are available from Bioinformatics online.

Dataset Information

Local Ancestry Inference in Large Pedigrees.

Publications

Local Ancestry Inference in Large Pedigrees.

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