» Articles » PMID: 19477991

Inference of Locus-specific Ancestry in Closely Related Populations

Overview
Journal Bioinformatics
Specialty Biology
Date 2009 May 30
PMID 19477991
Citations 89
Authors
Affiliations
Soon will be listed here.
Abstract

Unlabelled: A characterization of the genetic variation of recently admixed populations may reveal historical population events, and is useful for the detection of single nucleotide polymorphisms (SNPs) associated with diseases through association studies and admixture mapping. Inference of locus-specific ancestry is key to our understanding of the genetic variation of such populations. While a number of methods for the inference of locus-specific ancestry are accurate when the ancestral populations are quite distant (e.g. African-Americans), current methods incur a large error rate when inferring the locus-specific ancestry in admixed populations where the ancestral populations are closely related (e.g. Americans of European descent).

Results: In this work, we extend previous methods for the inference of locus-specific ancestry by the incorporation of a refined model of recombination events. We present an efficient dynamic programming algorithm to infer the locus-specific ancestries in this model, resulting in a method that attains improved accuracies; the improvement is most significant when the ancestral populations are closely related. An evaluation on a wide range of scenarios, including admixtures of the 52 population groups from the Human Genome Diversity Project demonstrates that locus-specific ancestry can indeed be accurately inferred in these admixtures using our method. Finally, we demonstrate that imputation methods can be improved by the incorporation of locus-specific ancestry, when applied to admixed populations.

Availability: The implementation of the WINPOP model is available as part of the LAMP package at http://lamp.icsi.berkeley.edu/lamp.

Citing Articles

AncestryGrapher toolkit: Python command-line pipelines to visualize global- and local- ancestry inferences from the RFMIX version 2 software.

Lisi A, Campbell M Bioinformatics. 2024; 40(11).

PMID: 39412440 PMC: 11534077. DOI: 10.1093/bioinformatics/btae616.


Global and Local Ancestry and its Importance: A Review.

Goli R, Chishi K, Ganguly I, Singh S, Dixit S, Rathi P Curr Genomics. 2024; 25(4):237-260.

PMID: 39156729 PMC: 11327809. DOI: 10.2174/0113892029298909240426094055.


Breed of origin analysis in genome-wide association studies: enhancing SNP-based insights into production traits in a commercial Brangus population.

Zayas G, Rodriguez E, Hernandez A, Rezende F, Mateescu R BMC Genomics. 2024; 25(1):654.

PMID: 38956457 PMC: 11218112. DOI: 10.1186/s12864-024-10465-1.


Genome Analysis Using Whole-Exome Sequencing of Non-Syndromic Cleft Lip and/or Palate from Malagasy Trios Identifies Variants Associated with Cilium-Related Pathways and Asian Genetic Ancestry.

Manojlovic Z, Auslander A, Jin Y, Schmidt R, Xu Y, Chang S Genes (Basel). 2023; 14(3).

PMID: 36980938 PMC: 10048728. DOI: 10.3390/genes14030665.


Local ancestry prediction with .

Moshkov N, Smetanin A, Tatarinova T PeerJ. 2022; 9:e12502.

PMID: 35003914 PMC: 8679960. DOI: 10.7717/peerj.12502.


References
1.
Sankararaman S, Sridhar S, Kimmel G, Halperin E . Estimating local ancestry in admixed populations. Am J Hum Genet. 2008; 82(2):290-303. PMC: 2664993. DOI: 10.1016/j.ajhg.2007.09.022. View

2.
Pei Y, Li J, Zhang L, Papasian C, Deng H . Analyses and comparison of accuracy of different genotype imputation methods. PLoS One. 2008; 3(10):e3551. PMC: 2569208. DOI: 10.1371/journal.pone.0003551. View

3.
Li J, Absher D, Tang H, Southwick A, Casto A, Ramachandran S . Worldwide human relationships inferred from genome-wide patterns of variation. Science. 2008; 319(5866):1100-4. DOI: 10.1126/science.1153717. View

4.
Kimmel G, Shamir R . GERBIL: Genotype resolution and block identification using likelihood. Proc Natl Acad Sci U S A. 2004; 102(1):158-62. PMC: 544046. DOI: 10.1073/pnas.0404730102. View

5.
Hoggart C, Shriver M, Kittles R, Clayton D, McKeigue P . Design and analysis of admixture mapping studies. Am J Hum Genet. 2004; 74(5):965-78. PMC: 1181989. DOI: 10.1086/420855. View