| Title: | Improved analyses of GWAS summary statistics by reducing data heterogeneity and errors |
| Journal: | Nature Communications |
| Published: | 8 Dec 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34880243/ |
| DOI: | https://doi.org/10.1038/s41467-021-27438-7 |
| URL: | https://www.nature.com/articles/s41467-021-27438-7.pdf |
| Citations: | 42 (27 in last 2 years) as of 8 Aug 2024 |
Publication 10865
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Abstract
Summary statistics from genome-wide association studies (GWAS) have facilitated the development of various summary data-based methods, which typically require a reference sample for linkage disequilibrium (LD) estimation. Analyses using these methods may be biased by errors in GWAS summary data or LD reference or heterogeneity between GWAS and LD reference. Here we propose a quality control method, DENTIST, that leverages LD among genetic variants to detect and eliminate errors in GWAS or LD reference and heterogeneity between the two. Through simulations, we demonstrate that DENTIST substantially reduces false-positive rate in detecting secondary signals in the summary-data-based conditional and joint association analysis, especially for imputed rare variants (false-positive rate reduced from >28% to <2% in the presence of heterogeneity between GWAS and LD reference). We further show that DENTIST can improve other summary-data-based analyses such as fine-mapping analysis.</p>
7 Authors
- Wenhan Chen
- Yang Wu
- Zhili Zheng
- Ting Qi
- Peter M. Visscher
- Zhihong Zhu
- Jian Yang
1 Application
| Application ID | Title |
|---|---|
| 12505 | Dissecting the genetic basis of relationships between early-life and later-life events |
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