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Re-evaluation of SNP heritability in complex human traits

SNP heritability, the proportion of phenotypic variance explained by SNPs, has been reported for many hundreds of traits. Its estimation requires strong prior assumptions about the distribution of heritability across the genome, but the assumptions in current use have not been thoroughly tested. By...

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Autores principales: Speed, Doug, Cai, Na, Johnson, Michael R., Nejentsev, Sergey, Balding, David J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5493198/
https://www.ncbi.nlm.nih.gov/pubmed/28530675
http://dx.doi.org/10.1038/ng.3865
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author Speed, Doug
Cai, Na
Johnson, Michael R.
Nejentsev, Sergey
Balding, David J
author_facet Speed, Doug
Cai, Na
Johnson, Michael R.
Nejentsev, Sergey
Balding, David J
author_sort Speed, Doug
collection PubMed
description SNP heritability, the proportion of phenotypic variance explained by SNPs, has been reported for many hundreds of traits. Its estimation requires strong prior assumptions about the distribution of heritability across the genome, but the assumptions in current use have not been thoroughly tested. By analyzing imputed data for a large number of human traits, we empirically derive a model that more accurately describes how heritability varies with minor allele frequency, linkage disequilibrium and genotype certainty. Across 19 traits, our improved model leads to estimates of common SNP heritability on average 43% (standard deviation 3) higher than those obtained from the widely-used software GCTA, and 25% (standard deviation 2) higher than those from the recently-proposed extension GCTA-LDMS. Previously, DNaseI hypersensitivity sites were reported to explain 79% of SNP heritability; using our improved heritability model their estimated contribution is only 24%.
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spelling pubmed-54931982017-11-22 Re-evaluation of SNP heritability in complex human traits Speed, Doug Cai, Na Johnson, Michael R. Nejentsev, Sergey Balding, David J Nat Genet Article SNP heritability, the proportion of phenotypic variance explained by SNPs, has been reported for many hundreds of traits. Its estimation requires strong prior assumptions about the distribution of heritability across the genome, but the assumptions in current use have not been thoroughly tested. By analyzing imputed data for a large number of human traits, we empirically derive a model that more accurately describes how heritability varies with minor allele frequency, linkage disequilibrium and genotype certainty. Across 19 traits, our improved model leads to estimates of common SNP heritability on average 43% (standard deviation 3) higher than those obtained from the widely-used software GCTA, and 25% (standard deviation 2) higher than those from the recently-proposed extension GCTA-LDMS. Previously, DNaseI hypersensitivity sites were reported to explain 79% of SNP heritability; using our improved heritability model their estimated contribution is only 24%. 2017-05-22 2017-07 /pmc/articles/PMC5493198/ /pubmed/28530675 http://dx.doi.org/10.1038/ng.3865 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Speed, Doug
Cai, Na
Johnson, Michael R.
Nejentsev, Sergey
Balding, David J
Re-evaluation of SNP heritability in complex human traits
title Re-evaluation of SNP heritability in complex human traits
title_full Re-evaluation of SNP heritability in complex human traits
title_fullStr Re-evaluation of SNP heritability in complex human traits
title_full_unstemmed Re-evaluation of SNP heritability in complex human traits
title_short Re-evaluation of SNP heritability in complex human traits
title_sort re-evaluation of snp heritability in complex human traits
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5493198/
https://www.ncbi.nlm.nih.gov/pubmed/28530675
http://dx.doi.org/10.1038/ng.3865
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