scholarly journals A Robust and Powerful Set-Valued Approach to Rare Variant Association Analyses of Secondary Traits in Case-Control Sequencing Studies

Genetics ◽  
2016 ◽  
Vol 205 (3) ◽  
pp. 1049-1062 ◽  
Author(s):  
Guolian Kang ◽  
Wenjian Bi ◽  
Hang Zhang ◽  
Stanley Pounds ◽  
Cheng Cheng ◽  
...  
2016 ◽  
Vol 41 (2) ◽  
pp. 98-107 ◽  
Author(s):  
Yu Jiang ◽  
Yunqi Ji ◽  
Alexander B. Sibley ◽  
Yi-Ju Li ◽  
Andrew S. Allen

PLoS Genetics ◽  
2017 ◽  
Vol 13 (12) ◽  
pp. e1007142 ◽  
Author(s):  
Jhih-Rong Lin ◽  
Quanwei Zhang ◽  
Ying Cai ◽  
Bernice E. Morrow ◽  
Zhengdong D. Zhang

2016 ◽  
pp. bbw083 ◽  
Author(s):  
Xuefeng Wang ◽  
Zhenyu Zhang ◽  
Nathan Morris ◽  
Tianxi Cai ◽  
Seunggeun Lee ◽  
...  

2021 ◽  
Author(s):  
Sean J. Jurgens ◽  
James P. Pirruccello ◽  
Seung Hoan Choi ◽  
Valerie N. Morrill ◽  
Mark D. Chaffin ◽  
...  

With the emergence of large-scale sequencing data, methods for improving power in rare variant analyses (RVAT) are needed. Here, we show that adjusting for common variant polygenic scores improves the yield in gene-based RVAT across 65 quantitative traits in the UK Biobank (up to 20% increase at α=2.6x10-6), without a marked increase in false-positive rates or genomic inflation. Our results illustrate how adjusting for common variant effects can aid in rare variant association discovery.


2019 ◽  
Author(s):  
Claudia R. Solis-Lemus ◽  
S. Taylor Fischer ◽  
Andrei Todor ◽  
Cuining Liu ◽  
Elizabeth J. Leslie ◽  
...  

AbstractStandard methods for case-control association studies of rare variation often treat disease outcome as a dichotomous phenotype. However, both theoretical and experimental studies have demonstrated that subjects with a family history of disease can be enriched for risk variation relative to subjects without such history. Assuming family history information is available, this observation motivates the idea of replacing the standard dichotomous outcome variable used in case-control studies with a more informative ordinal outcome variable that distinguishes controls (0), sporadic cases (1), and cases with a family history (2), with the expectation that we should observe increasing number of risk variants with increasing category of the ordinal variable. To leverage this expectation, we propose a novel rare-variant association test that incorporates family history information based on our previous GAMuT framework (Broadaway et al., 2016) for rare-variant association testing of multivariate phenotypes. We use simulated data to show that, when family history information is available, our new method outperforms standard rare-variant association methods like burden and SKAT tests that ignore family history. We further illustrate our method using a rare-variant study of cleft lip and palate.


Sign in / Sign up

Export Citation Format

Share Document