Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-10

AUTHORS

Jian Yang, Andrew Bakshi, Zhihong Zhu, Gibran Hemani, Anna A E Vinkhuyzen, Sang Hong Lee, Matthew R Robinson, John R B Perry, Ilja M Nolte, Jana V van Vliet-Ostaptchouk, Harold Snieder, The LifeLines Cohort Study, Tonu Esko, Lili Milani, Reedik Mägi, Andres Metspalu, Anders Hamsten, Patrik K E Magnusson, Nancy L Pedersen, Erik Ingelsson, Nicole Soranzo, Matthew C Keller, Naomi R Wray, Michael E Goddard, Peter M Visscher

ABSTRACT

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 ∼97% and ∼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 ∼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. More... »

PAGES

1114-1120

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/ng.3390

    DOI

    http://dx.doi.org/10.1038/ng.3390

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1001620489

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/26323059


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