Quantifying missing heritability at known GWAS loci.
Quantifying missing heritability at known GWAS loci.
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DOI：
10.1371/journal.pgen.1003993
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发表时间：
2013
期刊：
影响因子：
4.5
通讯作者：
Price AL
中科院分区：
文献类型：
--
作者：
Gusev A;Bhatia G;Zaitlen N;Vilhjalmsson BJ;Diogo D;Stahl EA;Gregersen PK;Worthington J;Klareskog L;Raychaudhuri S;Plenge RM;Pasaniuc B;Price AL
最近的工作表明，复杂性状的许多缺失遗传力可以通过对所有基因型别SNPs所解释的遗传力的估计来解决。然而，目前尚不清楚有多少遗传力是由于标记不当或已知GWA基因座上额外的因果变异而丢失的。在这里，我们使用方差分量来量化来自WTCCC1和WTCCC2的九种疾病中已知GWAS基因座上所有SNP所解释的遗传力。在考虑了期望值之后，我们观察了已知GWA基因座上的所有SNPs，以解释平均而言比GWAs相关的SNPs更多的遗传性()。对于一些疾病，这种增加是个别显著的：多发性硬化症(MS)()和克罗恩病(CD)()；所有对自身免疫性疾病的分析都排除了研究充分的MHC区域。此外，我们还发现，来自其他相关性状的Gwas基因座也解释了显著的遗传力。所有自身免疫性疾病基因座的结合解释了比已知的MS SNPs()更多的MS遗传性和比已知的CD SNPs()更多的CD遗传性，所有分析的自身免疫性疾病都有类似的增加。我们还观察到，在免疫芯片分型的类风湿性关节炎(RA)样本分析中，与已知的RA SNP(包括在该队列中发现的那些)相比，来自Gwas基因座()的所有SNP的遗传率更高，来自所有自身免疫性疾病基因座()的遗传率更高。我们的方法调整了SNPs之间的LD，这可能会偏离SNPs的遗传力标准估计，即使所有的因果变量都被分型了。通过比较调整后的估计，我们假设因果变异在全基因组范围内的分布丰富了低频等位基因，但已知GWAS基因座的因果变异偏向于常见的等位基因。这些发现对精细图谱研究设计和我们对复杂疾病架构的理解具有重要的指导意义。可遗传疾病有一种未知的潜在“基因架构”，它定义了致病突变的效应大小的分布。了解这种遗传结构是设计疾病图谱研究的重要第一步，关于这种分布的性质已经发展了许多理论。在这里，我们评估了一个假设，即额外的可遗传变异存在于先前已知的相关基因座，但不能用单一的最相关的标记来完全解释。我们发展了基于方差分量分析的方法来量化这种“局部”遗传力，证明了标准策略可能会由于相邻标记之间的相关性而被错误地夸大或缩小，并提出了稳健的调整。在对9种常见疾病的分析中，我们发现局部遗传力平均显著增加，这与平均基因座上的多个常见原因变异相一致。有趣的是，对于自身免疫性疾病，我们在与特定疾病无关但与其他自身免疫性疾病相关的基因座上也观察到了显著的局部遗传性，这意味着一种高度相关的潜在疾病架构。这些发现对未来研究的设计和我们对常见疾病的普遍理解具有重要的意义。
Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn's Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture. Heritable diseases have an unknown underlying “genetic architecture” that defines the distribution of effect-sizes for disease-causing mutations. Understanding this genetic architecture is an important first step in designing disease-mapping studies, and many theories have been developed on the nature of this distribution. Here, we evaluate the hypothesis that additional heritable variation lies at previously known associated loci but is not fully explained by the single most associated marker. We develop methods based on variance-components analysis to quantify this type of “local” heritability, demonstrating that standard strategies can be falsely inflated or deflated due to correlation between neighboring markers and propose a robust adjustment. In analysis of nine common diseases we find a significant average increase of local heritability, consistent with multiple common causal variants at an average locus. Intriguingly, for autoimmune diseases we also observe significant local heritability in loci not associated with the specific disease but with other autoimmune diseases, implying a highly correlated underlying disease architecture. These findings have important implications to the design of future studies and our general understanding of common disease.
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