Abstract
We propose statistical methods to detect novel genetic variants using only genome-wide association studies (GWAS) summary data without access to raw genotype and phenotype data. With more and more summary data being posted for public access in the post GWAS era, the proposed methods are practically very useful to identify additional interesting genetic variants and shed lights on the underlying disease mechanism. We illustrate the utility of our proposed methods with application to GWAS meta-analysis results of fasting glucose from the international MAGIC consortium. We found several novel genome-wide significant loci that are worth further study.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 76-79 |
| Number of pages | 4 |
| Journal | Computational Biology and Chemistry |
| Volume | 74 |
| DOIs | |
| State | Published - Jun 2018 |
Bibliographical note
Funding Information:This research was supported in part by NIH grant GM083345 and CA134848 . We want to thank the editor and reviewers for their constructive comments that have greatly improved the presentation of the paper. We are grateful to the University of Minnesota Supercomputing Institute for assistance with the computations. Data on glycemic traits have been contributed by MAGIC investigators and have been downloaded from http://www.magicinvestigators.org . We want to thank Dr. James Pankow for pointing out the MAGIC data source to us.
Publisher Copyright:
© 2018 Elsevier Ltd
Keywords
- GWAS
- SNP-set association test
- Summary statistics