Incorporating Functional Annotations for Fine-Mapping Causal Variants in a Bayesian Framework Using Summary Statistics

被引:35
作者
Chen, Wenan [1 ]
McDonnell, Shannon K. [1 ]
Thibodeau, Stephen N. [2 ]
Tillmans, Lori S. [2 ]
Schaid, Daniel J. [1 ]
机构
[1] Mayo Clin, Dept Hlth Sci Res, Div Biostat, Rochester, MN 55905 USA
[2] Mayo Clin, Dept Lab Med & Pathol, Rochester, MN 55905 USA
基金
美国国家卫生研究院;
关键词
Bayesian fine mapping; annotations; summary statistics; causal variants; GENOME-WIDE ASSOCIATION; PROSTATE-CANCER; VARIABLE SELECTION; LINEAR PREDICTORS; ANDROGEN RECEPTOR; REGRESSION; GENE; REGULARIZATION; IMPUTATION; ENHANCERS;
D O I
10.1534/genetics.116.188953
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Functional annotations have been shown to improve both the discovery power and fine-mapping accuracy in genome-wide association studies. However, the optimal strategy to incorporate the large number of existing annotations is still not clear. In this study, we propose a Bayesian framework to incorporate functional annotations in a systematic manner. We compute the maximum a posteriori solution and use cross validation to find the optimal penalty parameters. By extending our previous fine-mapping method CAVIARBF into this framework, we require only summary statistics as input. We also derived an exact calculation of Bayes factors using summary statistics for quantitative traits, which is necessary when a large proportion of trait variance is explained by the variants of interest, such as in fine mapping expression quantitative trait loci (eQTL). We compared the proposed method with PAINTOR using different strategies to combine annotations. Simulation results show that the proposed method achieves the best accuracy in identifying causal variants among the different strategies and methods compared. We also find that for annotations with moderate effects from a large annotation pool, screening annotations individually and then combining the top annotations can produce overly optimistic results. We applied these methods on two real data sets: a meta-analysis result of lipid traits and a cis-eQTL study of normal prostate tissues. For the eQTL data, incorporating annotations significantly increased the number of potential causal variants with high probabilities.
引用
收藏
页码:933 / +
页数:42
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