| Title: | mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data |
| Journal: | American Journal of Human Genetics |
| Published: | 1 Jan 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36608683/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2022.12.006 |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9892780 |
| Citations: | 15 (15 in last 2 years) as of 8 Aug 2024 |
Publication 10898
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Abstract
Gene-based association tests aggregate multiple SNP-trait associations into sets defined by gene boundaries and are widely used in post-GWAS analysis. A common approach for gene-based tests is to combine SNPs associations by computing the sum of χ2 statistics. However, this strategy ignores the directions of SNP effects, which could result in a loss of power for SNPs with masking effects, e.g., when the product of two SNP effects and the linkage disequilibrium (LD) correlation is negative. Here, we introduce "mBAT-combo," a set-based test that is better powered than other methods to detect multi-SNP associations in the context of masking effects. We validate the method through simulations and applications to real data. We find that of 35 blood and urine biomarker traits in the UK Biobank, 34 traits show evidence for masking effects in a total of 4,273 gene-trait pairs, indicating that masking effects is common in complex traits. We further validate the improved power of our method in height, body mass index, and schizophrenia with different GWAS sample sizes and show that on average 95.7% of the genes detected only by mBAT-combo with smaller sample sizes can be identified by the single-SNP approach with a 1.7-fold increase in sample sizes. Eleven genes significant only in mBAT-combo for schizophrenia are confirmed by functionally informed fine-mapping or Mendelian randomization integrating gene expression data. The framework of mBAT-combo can be applied to any set of SNPs to refine trait-association signals hidden in genomic regions with complex LD structures.</p>
11 Authors
- Ang Li
- Shouye Liu
- Andrew Bakshi
- Longda Jiang
- Wenhan Chen
- Zhili Zheng
- Patrick F Sullivan
- Peter M Visscher
- Naomi R Wray
- Jian Yang
- Jian Zeng
1 Application
| Application ID | Title |
|---|---|
| 12505 | Dissecting the genetic basis of relationships between early-life and later-life events |
Enabling scientific discoveries that improve human health