当前位置: X-MOL 学术Genet. Epidemiol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Meta-MultiSKAT: Multiple phenotype meta-analysis for region-based association test.
Genetic Epidemiology ( IF 1.7 ) Pub Date : 2019-08-21 , DOI: 10.1002/gepi.22248
Diptavo Dutta 1, 2 , Sarah A Gagliano Taliun 1, 2 , Joshua S Weinstock 1, 2 , Matthew Zawistowski 1, 2 , Carlo Sidore 3 , Lars G Fritsche 1, 2 , Francesco Cucca 3, 4 , David Schlessinger 5 , Gonçalo R Abecasis 1, 2 , Chad M Brummett 6, 7 , Seunggeun Lee 1, 2
Affiliation  

The power of genetic association analyses can be increased by jointly meta-analyzing multiple correlated phenotypes. Here, we develop a meta-analysis framework, Meta-MultiSKAT, that uses summary statistics to test for association between multiple continuous phenotypes and variants in a region of interest. Our approach models the heterogeneity of effects between studies through a kernel matrix and performs a variance component test for association. Using a genotype kernel, our approach can test for rare-variants and the combined effects of both common and rare-variants. To achieve robust power, within Meta-MultiSKAT, we developed fast and accurate omnibus tests combining different models of genetic effects, functional genomic annotations, multiple correlated phenotypes, and heterogeneity across studies. In addition, Meta-MultiSKAT accommodates situations where studies do not share exactly the same set of phenotypes or have differing correlation patterns among the phenotypes. Simulation studies confirm that Meta-MultiSKAT can maintain the type-I error rate at the exome-wide level of 2.5 × 10-6 . Further simulations under different models of association show that Meta-MultiSKAT can improve the power of detection from 23% to 38% on average over single phenotype-based meta-analysis approaches. We demonstrate the utility and improved power of Meta-MultiSKAT in the meta-analyses of four white blood cell subtype traits from the Michigan Genomics Initiative (MGI) and SardiNIA studies.

中文翻译:

Meta-MultiSKAT:用于基于区域的关联测试的多表型荟萃分析。

可以通过共同荟萃分析多个相关表型来增强遗传关联分析的能力。在这里,我们开发了一个元分析框架Meta-MultiSKAT,该框架使用摘要统计数据来测试感兴趣区域中多个连续表型和变异之间的关联。我们的方法通过核矩阵对研究之间效果的异质性进行建模,并执行关联的方差成分检验。使用基因型内核,我们的方法可以测试稀有变异以及常见和稀有变异的组合效应。为了获得强大的功能,我们在Meta-MultiSKAT中开发了快速准确的综合测试,结合了遗传效应,功能基因组注释,多种相关表型和研究之间的异质性的不同模型。此外,Meta-MultiSKAT适用于以下情况:研究不完全共享相同的表型集或表型之间的相关模式不同。仿真研究证实,Meta-MultiSKAT可以将I型错误率维持在2.5×10-6的全基因组水平。在不同关联模型下的进一步仿真表明,与基于单个表型的荟萃分析方法相比,Meta-MultiSKAT可以将检测能力平均提高23%至38%。我们在密歇根州基因组计划(MGI)和SardiNIA研究的四个白细胞亚型性状的荟萃分析中证明了Meta-MultiSKAT的效用和增强的功能。仿真研究证实,Meta-MultiSKAT可以将I型错误率维持在2.5×10-6的全基因组水平。在不同关联模型下的进一步仿真表明,与基于单个表型的荟萃分析方法相比,Meta-MultiSKAT可以将检测能力平均提高23%至38%。我们在密歇根州基因组计划(MGI)和SardiNIA研究的四个白细胞亚型性状的荟萃分析中证明了Meta-MultiSKAT的效用和增强的功能。仿真研究证实,Meta-MultiSKAT可以将I型错误率维持在2.5×10-6的全基因组水平。在不同关联模型下的进一步模拟表明,与基于单个表型的荟萃分析方法相比,Meta-MultiSKAT可以将检测能力平均提高23%至38%。我们在密歇根州基因组计划(MGI)和SardiNIA研究的四个白细胞亚型性状的荟萃分析中证明了Meta-MultiSKAT的效用和增强的功能。
更新日期:2019-11-01
down
wechat
bug