Leveraging population admixture to characterize the heritability of complex traits

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. InglesWilliam 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

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


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 3). We show that h 2 3 = 2F STC (1 ')h 2, where F STC measures frequency differences between populations at causal loci and 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.

Original languageEnglish (US)
Pages (from-to)1356-1362
Number of pages7
JournalNature Genetics
Issue number12
StatePublished - Dec 11 2014

ASJC Scopus subject areas

  • Genetics


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