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Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases

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Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. / Schizophrenia Working Group of the Psychiatric Genomics Consortium.

In: American Journal of Human Genetics, Vol. 95, No. 5, 06.11.2014, p. 535-552.

Research output: Contribution to journalJournal articlepeer-review

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Schizophrenia Working Group of the Psychiatric Genomics Consortium 2014, 'Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases', American Journal of Human Genetics, vol. 95, no. 5, pp. 535-552. https://doi.org/10.1016/j.ajhg.2014.10.004

APA

Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014). Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. American Journal of Human Genetics, 95(5), 535-552. https://doi.org/10.1016/j.ajhg.2014.10.004

Vancouver

Schizophrenia Working Group of the Psychiatric Genomics Consortium. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. American Journal of Human Genetics. 2014 Nov 6;95(5):535-552. https://doi.org/10.1016/j.ajhg.2014.10.004

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Schizophrenia Working Group of the Psychiatric Genomics Consortium. / Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. In: American Journal of Human Genetics. 2014 ; Vol. 95, No. 5. pp. 535-552.

Bibtex

@article{a11a538a05c24d2785c543db72d7e5ad,
title = "Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases",
abstract = "Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (hg(2)) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of hg(2) from imputed SNPs (5.1× enrichment; p = 3.7 × 10(-17)) and 38% (SE = 4%) of hg(2) from genotyped SNPs (1.6× enrichment, p = 1.0 × 10(-4)). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained <10% of hg(2) despite having the highest enrichment. We replicated these findings but found no significant contribution from rare coding variants in independent schizophrenia cohorts genotyped on GWAS and exome chips. Our results highlight the value of analyzing components of heritability to unravel the functional architecture of common disease.",
keywords = "Computer Simulation, Genetic Diseases, Inborn, Genetic Variation, Genome-Wide Association Study, Humans, Inheritance Patterns, Models, Genetic, Open Reading Frames, Regulatory Elements, Transcriptional",
author = "Alexander Gusev and Lee, {S. Hong} and Gosia Trynka and Hilary Finucane and Vilhj{\'a}lmsson, {Bjarni J.} and Han Xu and Chongzhi Zang and Stephan Ripke and Brendan Bulik-Sullivan and Eli Stahl and K{\"a}hler, {Anna K.} and Hultman, {Christina M.} and Purcell, {Shaun M.} and McCarroll, {Steven A.} and Mark Daly and Bogdan Pasaniuc and Sullivan, {Patrick F.} and Neale, {Benjamin M.} and Wray, {Naomi R.} and Soumya Raychaudhuri and Price, {Alkes L.} and Jo Knight and {Schizophrenia Working Group of the Psychiatric Genomics Consortium}",
note = "Copyright {\textcopyright} 2014 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.",
year = "2014",
month = nov,
day = "6",
doi = "10.1016/j.ajhg.2014.10.004",
language = "English",
volume = "95",
pages = "535--552",
journal = "American Journal of Human Genetics",
issn = "0002-9297",
publisher = "Cell Press",
number = "5",

}

RIS

TY - JOUR

T1 - Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases

AU - Gusev, Alexander

AU - Lee, S. Hong

AU - Trynka, Gosia

AU - Finucane, Hilary

AU - Vilhjálmsson, Bjarni J.

AU - Xu, Han

AU - Zang, Chongzhi

AU - Ripke, Stephan

AU - Bulik-Sullivan, Brendan

AU - Stahl, Eli

AU - Kähler, Anna K.

AU - Hultman, Christina M.

AU - Purcell, Shaun M.

AU - McCarroll, Steven A.

AU - Daly, Mark

AU - Pasaniuc, Bogdan

AU - Sullivan, Patrick F.

AU - Neale, Benjamin M.

AU - Wray, Naomi R.

AU - Raychaudhuri, Soumya

AU - Price, Alkes L.

AU - Knight, Jo

AU - Schizophrenia Working Group of the Psychiatric Genomics Consortium

N1 - Copyright © 2014 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

PY - 2014/11/6

Y1 - 2014/11/6

N2 - Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (hg(2)) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of hg(2) from imputed SNPs (5.1× enrichment; p = 3.7 × 10(-17)) and 38% (SE = 4%) of hg(2) from genotyped SNPs (1.6× enrichment, p = 1.0 × 10(-4)). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained <10% of hg(2) despite having the highest enrichment. We replicated these findings but found no significant contribution from rare coding variants in independent schizophrenia cohorts genotyped on GWAS and exome chips. Our results highlight the value of analyzing components of heritability to unravel the functional architecture of common disease.

AB - Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (hg(2)) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of hg(2) from imputed SNPs (5.1× enrichment; p = 3.7 × 10(-17)) and 38% (SE = 4%) of hg(2) from genotyped SNPs (1.6× enrichment, p = 1.0 × 10(-4)). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained <10% of hg(2) despite having the highest enrichment. We replicated these findings but found no significant contribution from rare coding variants in independent schizophrenia cohorts genotyped on GWAS and exome chips. Our results highlight the value of analyzing components of heritability to unravel the functional architecture of common disease.

KW - Computer Simulation

KW - Genetic Diseases, Inborn

KW - Genetic Variation

KW - Genome-Wide Association Study

KW - Humans

KW - Inheritance Patterns

KW - Models, Genetic

KW - Open Reading Frames

KW - Regulatory Elements, Transcriptional

U2 - 10.1016/j.ajhg.2014.10.004

DO - 10.1016/j.ajhg.2014.10.004

M3 - Journal article

C2 - 25439723

VL - 95

SP - 535

EP - 552

JO - American Journal of Human Genetics

JF - American Journal of Human Genetics

SN - 0002-9297

IS - 5

ER -