Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits

Evans, Luke M., Tahmasbi, Rasool, Vrieze, Scott I., Abecasis, Gonçalo R., Das, Sayantan, Gazal, Steven, Bjelland, Douglas W., de Candia, Teresa R., Haplotype Reference Consortium, Goddard, Michael E., Neale, Benjamin M., Yang, Jian, Visscher, Peter M. and Keller, Matthew C. (2018) Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits. Nature genetics, 50 5: 737-745. doi:10.1038/s41588-018-0108-x


Author Evans, Luke M.
Tahmasbi, Rasool
Vrieze, Scott I.
Abecasis, Gonçalo R.
Das, Sayantan
Gazal, Steven
Bjelland, Douglas W.
de Candia, Teresa R.
Haplotype Reference Consortium
Goddard, Michael E.
Neale, Benjamin M.
Yang, Jian
Visscher, Peter M.
Keller, Matthew C.
Title Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits
Journal name Nature genetics   Check publisher's open access policy
ISSN 1546-1718
1061-4036
Publication date 2018-04-26
Year available 2018
Sub-type Article (original research)
DOI 10.1038/s41588-018-0108-x
Open Access Status PMC
Volume 50
Issue 5
Start page 737
End page 745
Total pages 9
Place of publication New York, NY, United States
Publisher Nature Publishing Group
Language eng
Abstract Multiple methods have been developed to estimate narrow-sense heritability, h, using single nucleotide polymorphisms (SNPs) in unrelated individuals. However, a comprehensive evaluation of these methods has not yet been performed, leading to confusion and discrepancy in the literature. We present the most thorough and realistic comparison of these methods to date. We used thousands of real whole-genome sequences to simulate phenotypes under varying genetic architectures and confounding variables, and we used array, imputed, or whole genome sequence SNPs to obtain 'SNP-heritability' estimates. We show that SNP-heritability can be highly sensitive to assumptions about the frequencies, effect sizes, and levels of linkage disequilibrium of underlying causal variants, but that methods that bin SNPs according to minor allele frequency and linkage disequilibrium are less sensitive to these assumptions across a wide range of genetic architectures and possible confounding factors. These findings provide guidance for best practices and proper interpretation of published estimates.
Keyword Genome-wide association studies
Software
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Institute for Molecular Bioscience - Publications
 
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Created: Wed, 02 May 2018, 10:05:13 EST