Journal
NATURE GENETICS
Volume 47, Issue 10, Pages 1114-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/ng.3390
Keywords
-
Categories
Funding
- Australian National Health and Medical Research Council [1052684, 1078037, 1050218]
- Australian Research Council [130102666]
- US National Institutes of Health [R01MH100141]
- Sylvia and Charles Viertel Charitable Foundation
- University of Queensland Foundation
- MRC [MC_UU_12015/2, MC_U106179472] Funding Source: UKRI
- Medical Research Council [MC_U106179472, MC_UU_12015/2, MC_PC_15018] Funding Source: researchfish
Ask authors/readers for more resources
We propose a method (GREML-LDMS) to estimate heritability for human complex traits in unrelated individuals using whole-genome sequencing data. We demonstrate using simulations based on whole-genome sequencing data that similar to 97% and similar to 68% of variation at common and rare variants, respectively, can be captured by imputation. Using the GREML-LDMS method, we estimate from 44,126 unrelated individuals that all similar to 17 million imputed variants explain 56% (standard error (s.e.) = 2.3%) of variance for height and 27% (s.e. = 2.5%) of variance for body mass index (BMI), and we find evidence that height- and BMI-associated variants have been under natural selection. Considering the imperfect tagging of imputation and potential overestimation of heritability from previous family-based studies, heritability is likely to be 60-70% for height and 30-40% for BMI. Therefore, the missing heritability is small for both traits. For further discovery of genes associated with complex traits, a study design with SNP arrays followed by imputation is more cost-effective than whole-genome sequencing at current prices.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available