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Leveraging population admixture to characterize the heritability of complex traits.

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)?). We show that h(2)? = 2FSTC?(1 - ?)h(2), where FSTC 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).

SUBMITTER: Zaitlen N

PROVIDER: S-EPMC4244251 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Leveraging population admixture to characterize the heritability of complex traits.

Zaitlen Noah N Pasaniuc Bogdan B Sankararaman Sriram S Bhatia Gaurav G Zhang Jianqi J Gusev Alexander A Young Taylor T Tandon Arti A Pollack Samuela S Vilhjálmsson Bjarni J BJ Assimes Themistocles L TL Berndt Sonja I SI Blot William J WJ Chanock Stephen S Franceschini Nora N Goodman Phyllis G PG He Jing J Hennis Anselm J M AJ Hsing Ann A Ingles Sue A SA Isaacs William W Kittles Rick A RA Klein Eric A EA Lange Leslie A LA Nemesure Barbara B Patterson Nick N Reich David D Rybicki Benjamin A BA Stanford Janet L JL Stevens Victoria L VL Strom Sara S SS Whitsel Eric A EA Witte John S JS Xu Jianfeng J Haiman Christopher C Wilson James G JG Kooperberg Charles C Stram Daniel D Reiner Alex P AP Tang Hua H Price Alkes L AL

Nature genetics 20141110 12

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), ...[more]

PMID: 25383972

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Project description:BackgroundGenome-wide association studies can have limited power to identify QTL, partly due to the stringent correction for multiple testing and low linkage-disequilibrium between SNPs and QTL. Regional Heritability Mapping (RHM) has been advanced as an alternative approach to capture underlying genetic effects. In this study, RHM was used to identify loci underlying variation in the 16(th) QTLMAS workshop simulated traits.MethodsThe method was implemented by fitting a mixed model where a genomic region and the overall genetic background were added as random effects. Heritabilities for the genetic regional effects were estimated, and the presence of a QTL in the region was tested using a likelihood ratio test (LRT). Several region sizes were considered (100, 50 and 20 adjacent SNPs). Bonferroni correction was used to calculate the LRT thresholds for genome-wide (p < 0.05) and suggestive (i.e., one false positive per genome scan) significance.ResultsGenomic heritabilities (0.31, 0.32 and 0.48, respectively) and genetic correlations (0.80, -0.42 and 0.19, between trait-pairs 1&2, 1&3 and 2&3) were similar to the simulated ones. RHM identified 7 QTL (4 at genome-wide and 3 at suggestive level) for Trait1; 4 (2 genome-wide and 2 suggestive) for Trait2; and 7 (6 genome-wide and 1 suggestive) for Trait3. Only one of the identified suggestive QTL was a false-positive. The position of these QTL tended to coincide with the position where the largest QTL (or several of them) were simulated. Several signals were detected for the simulated QTL with smaller effect. A combined analysis including all significant regions showed that they explain more than half of the total genetic variance of the traits. However, this might be overestimated, due to Beavis effect. All QTL affecting traits 1&2 and 2&3 had positive correlations, following the trend of the overall correlation of both trait-pairs. All but one QTL affecting traits 1&3 were negatively correlated, in agreement with the simulated situation. Moreover, RHM identified extra loci that were not found by association and linkage analysis, highlighting the improved power of this approach.ConclusionsRHM identified the largest QTL among the simulated ones, with some signals for the ones with small effect. Moreover, RHM performed better than association and linkage analysis, in terms of both power and resolution.

Dataset Information

Leveraging population admixture to characterize the heritability of complex traits.

Publications

Leveraging population admixture to characterize the heritability of complex traits.

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