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Leveraging population admixture to characterize the heritability of complex traits.

Noah ZaitlenBogdan PasaniucSriram SankararamanGaurav BhatiaJianqi ZhangAlexander GusevTaylor YoungArti TandonSamuela PollackBjarni J VilhjálmssonThemistocles L AssimesSonja I BerndtWilliam J BlotStephen ChanockNora FranceschiniPhyllis G GoodmanJing HeAnselm J M HennisAnn HsingSue A InglesWilliam IsaacsRick A KittlesEric A KleinLeslie A LangeBarbara NemesureNick PattersonDavid ReichBenjamin A RybickiJanet L StanfordVictoria L StevensSara S StromEric A WhitselJohn S WitteJianfeng XuChristopher HaimanJames G WilsonCharles KooperbergDaniel O StramAlex P ReinerHua TangAlkes L Price
Published in: Nature genetics (2014)
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).
Keyphrases
  • genome wide
  • body mass index
  • african american
  • dna methylation
  • copy number
  • genome wide association
  • genome wide association study
  • gene expression
  • electronic health record