Leveraging population admixture to characterize the heritability of complex traits View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-12

AUTHORS

Noah Zaitlen, Bogdan Pasaniuc, Sriram Sankararaman, Gaurav Bhatia, Jianqi Zhang, Alexander Gusev, Taylor Young, Arti Tandon, Samuela Pollack, Bjarni J Vilhjálmsson, Themistocles L Assimes, Sonja I Berndt, William J Blot, Stephen Chanock, Nora Franceschini, Phyllis G Goodman, Jing He, Anselm J M Hennis, Ann Hsing, Sue A Ingles, William Isaacs, Rick A Kittles, Eric A Klein, Leslie A Lange, Barbara Nemesure, Nick Patterson, David Reich, Benjamin A Rybicki, Janet L Stanford, Victoria L Stevens, Sara S Strom, Eric A Whitsel, John S Witte, Jianfeng Xu, Christopher Haiman, James G Wilson, Charles Kooperberg, Daniel Stram, Alex P Reiner, Hua Tang, Alkes L Price

ABSTRACT

Despite recent progress on estimating the heritability explained by genotyped SNPs (h(2)g), a large gap between h(2)g and estimates of total narrow-sense heritability (h(2)) remains. Explanations for this gap include rare variants or upward bias in family-based estimates of h(2) due to shared environment or epistasis. We estimate h(2) from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (h(2)γ). We show that h(2)γ = 2FSTCθ(1 - θ)h(2), where FSTC measures frequency differences between populations at causal loci and θ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We applied this approach to the analysis of 13 phenotypes in 21,497 African-American individuals from 3 cohorts. For height and body mass index (BMI), we obtained h(2) estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of h(2)g in these and other data but smaller than family-based estimates of h(2). More... »

PAGES

1356-1362

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/ng.3139

    DOI

    http://dx.doi.org/10.1038/ng.3139

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1033662959

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/25383972


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