Probabilistic natural mapping of gene-level tests for genome-wide association studies

2017 ◽  
Vol 19 (4) ◽  
pp. 545-553 ◽  
Author(s):  
Feng Bao ◽  
Yue Deng ◽  
Mulong Du ◽  
Zhiquan Ren ◽  
Qingzhao Zhang ◽  
...  
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Natural Mapping ◽  
Genome Wide ◽  
Gene Level

scholarly journals Nonlinear post-selection inference for genome-wide association studies

2020 ◽  
Author(s):  
Lotfi Slim ◽  
Clément Chatelain ◽  
Chloé-Agathe Azencott
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Association Testing ◽  
Testing Tools ◽  
Disease Mechanisms ◽  
Novel Approach ◽  
Genome Wide ◽  
Gene Level ◽  
The Uk

AbstractAssociation testing in genome-wide association studies (GWAS) is often performed at either the SNP level or the gene level. The two levels can bring different insights into disease mechanisms. In the present work, we provide a novel approach based on nonlinear post-selection inference to bridge the gap between them. Our approach selects, within a gene, the SNPs or LD blocks most associated with the phenotype, before testing their combined effect. Both the selection and the association testing are conducted nonlinearly. We apply our tool to the study of BMI and its variation in the UK BioBank. In this study, our approach outperformed other gene-level association testing tools, with the unique benefit of pinpointing the causal SNPs.

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