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Estimating heritability and its enrichment in tissue-specific gene sets in admixed populations
Josep Maria Mercader,
23andMe Research Team,
SIGMA Type 2 Diabetes Consortium,
Benjamin M Neale,
Jose C Florez,
Alkes L. Price,
Hilary K Finucane,
Posted 20 Dec 2018
bioRxiv DOI: 10.1101/503144
Posted 20 Dec 2018
The increasing size and diversity of genome-wide association studies provide an exciting opportunity to study how the genetics of complex traits vary between diverse populations. Here, we introduce covariate-adjusted LD score regression (cov-LDSC), a method to accurately estimate genetic heritability (hg2) and its enrichment in both homogenous and admixed populations with summary statistics and in-sample LD estimates. In-sample LD can be estimated from a subset of the GWAS samples, allowing our method to be applied efficiently to very large cohorts. In simulations, we show that unadjusted LDSC underestimates hg2 by 10%-60% in admixed populations; in contrast, cov-LDSC is robust to all simulation parameters. We apply cov-LDSC to genotyping data from approximately 170,000 Latino, 47,000 African American and 135,000 European individuals. We estimate hg2and detect heritability enrichment in three quantitative and five dichotomous phenotypes respectively, making this, to our knowledge, the most comprehensive heritability-based analysis of admixed individuals. Our results show that most traits have high concordance of hg2 and consistent tissue-specific heritability enrichment between different ethnic groups. For example, estimates of hg2 for BMI are 0.22 ± 0.01, 0.23 ± 0.03 and 0.22 ± 0.01 in Latino, African American and European populations respectively. However, for age at menarche, we observe population-specific heritability differences with estimates of hg2. We observe consistent patterns of tissue-specific heritability enrichment across populations, for example in the limbic system for BMI, the per-standardized-annotation effect size τ* are 0.16 ± 0.04, 0.28 ± 0.11 and 0.18 ± 0.03 in Latino, African American and European populations respectively. Our results demonstrate that our approach is a powerful way to analyze genetic data for complex traits from underrepresented populations.
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