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Nat Genet DOI:10.1038/s41588-018-0108-x

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

Publication TypeJournal Article
Year of Publication2018
AuthorsEvans, LM, Tahmasbi, R, Vrieze, SI, Abecasis, GR, Das, S, Gazal, S, Bjelland, DW, de Candia, TR, Goddard, ME, Neale, BM, Yang, J, Visscher, PM, Keller, MC
Corporate AuthorsHaplotype Reference Consortium
JournalNat Genet
Date Published2018 05
KeywordsGene Frequency, Genome, Genome-Wide Association Study, Genotype, Humans, Linkage Disequilibrium, Models, Genetic, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait, Heritable

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.


Alternate JournalNat Genet
PubMed ID29700474
PubMed Central IDPMC5934350
Grant ListR01 DA037904 / DA / NIDA NIH HHS / United States
R01 HG008983 / HG / NHGRI NIH HHS / United States
R01 MH100141 / MH / NIMH NIH HHS / United States