Gusev Alexander, Lee S Hong, Trynka Gosia, Finucane Hilary, Vilhjálmsson Bjarni J, Xu Han, Zang Chongzhi, Ripke Stephan, Bulik-Sullivan Brendan, Stahl Eli, Kähler Anna K, Hultman Christina M, Purcell Shaun M, McCarroll Steven A, Daly Mark, Pasaniuc Bogdan, Sullivan Patrick F, Neale Benjamin M, Wray Naomi R, Raychaudhuri Soumya, Price Alkes L
Harvard School of Public Health, Boston, MA 02115, USA.
The University of Queensland, Queensland Brain Institute, Brisbane, QLD 4072, Australia.
Am J Hum Genet. 2014 Nov 6;95(5):535-52. doi: 10.1016/j.ajhg.2014.10.004.
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.
调控变异和编码变异已知在复杂疾病的全基因组关联研究(GWAS)所识别的关联中富集,但它们对性状遗传力的贡献目前尚不清楚。我们将方差成分法应用于11种常见疾病的推算基因型数据,以划分由基因分型单核苷酸多态性(SNP)解释的遗传力(hg(2))在不同功能类别中的情况(同时考虑连锁不平衡导致的共享方差)。广泛的模拟表明,与目前从GWAS汇总统计数据得出的估计值相反,方差成分法在广泛的复杂疾病结构下能准确划分遗传力。在这11种疾病中,来自217种细胞类型的DNaseI超敏位点(DHS)涵盖了16%的推算SNP(以及24%的基因分型SNP),但平均解释了推算SNP中hg(2)的79%(标准误 = 8%)(富集5.1倍;p = 3.7 × 10(-17))以及基因分型SNP中hg(2)的38%(标准误 = 4%)(富集1.6倍,p = 1.0 × 10(-4))。在增强子DHS和细胞类型特异性DHS处观察到进一步的富集。相比之下,占基因组1%的编码变异尽管富集程度最高,但解释的hg(2)不到10%。我们重复了这些发现,但在基于GWAS和外显子芯片进行基因分型的独立精神分裂症队列中,未发现罕见编码变异有显著贡献。我们的结果突出了分析遗传力成分以揭示常见疾病功能结构的价值。