Yang Jingjing, Fritsche Lars G, Zhou Xiang, Abecasis Gonçalo
Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7491 Trondheim, Norway.
Am J Hum Genet. 2017 Sep 7;101(3):404-416. doi: 10.1016/j.ajhg.2017.08.002. Epub 2017 Aug 24.
Genome-wide association studies (GWASs) have identified many complex loci. However, most loci reside in noncoding regions and have unknown biological functions. Integrative analysis that incorporates known functional information into GWASs can help elucidate the underlying biological mechanisms and prioritize important functional variants. Hence, we develop a flexible Bayesian variable selection model with efficient computational techniques for such integrative analysis. Different from previous approaches, our method models the effect-size distribution and probability of causality for variants with different annotations and jointly models genome-wide variants to account for linkage disequilibrium (LD), thus prioritizing associations based on the quantification of the annotations and allowing for multiple associated variants per locus. Our method dramatically improves both computational speed and posterior sampling convergence by taking advantage of the block-wise LD structures in human genomes. In simulations, our method accurately quantifies the functional enrichment and performs more powerfully for prioritizing the true associations than alternative methods, where the power gain is especially apparent when multiple associated variants in LD reside in the same locus. We applied our method to an in-depth GWAS of age-related macular degeneration with 33,976 individuals and 9,857,286 variants. We find the strongest enrichment for causality among non-synonymous variants (54× more likely to be causal, 1.4× larger effect sizes) and variants in transcription, repressed Polycomb, and enhancer regions, as well as identify five additional candidate loci beyond the 32 known AMD risk loci. In conclusion, our method is shown to efficiently integrate functional information in GWASs, helping identify functional associated-variants and underlying biology.
全基因组关联研究（GWAS）已经确定了许多复杂位点。然而，大多数位点位于非编码区域，其生物学功能未知。将已知功能信息纳入GWAS的综合分析有助于阐明潜在的生物学机制，并对重要的功能变异进行优先级排序。因此，我们开发了一种灵活的贝叶斯变量选择模型，并采用高效的计算技术进行这种综合分析。与以前的方法不同，我们的方法对具有不同注释的变异的效应大小分布和因果概率进行建模，并对全基因组变异进行联合建模以考虑连锁不平衡（LD），从而基于注释的量化对关联进行优先级排序，并允许每个位点有多个相关变异。我们的方法通过利用人类基因组中的分块LD结构，显著提高了计算速度和后验采样收敛性。在模拟中，我们的方法能够准确量化功能富集，并且在对真实关联进行优先级排序方面比其他方法表现更强大，当LD中的多个相关变异位于同一位点时，这种能力提升尤为明显。我们将我们的方法应用于一项针对33976名个体和9857286个变异的年龄相关性黄斑变性的深入GWAS。我们发现非同义变异（因果关系可能性高54倍，效应大小大1.4倍）以及转录、抑制性多梳和增强子区域中的变异的因果关系富集最强，并在32个已知的AMD风险位点之外还识别出另外五个候选位点。总之，我们的方法被证明能够有效地在GWAS中整合功能信息，有助于识别功能相关变异和潜在生物学机制。
