mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data

Gene-based association tests aggregate multiple SNP-trait associations into sets defined by gene boundaries and are widely used inpost-GWAS analysis. A common approach for gene-based tests is to combine SNPs associations by computing the sum ofc2statistics.However, this strategy ignores the directions of SNP effects, which could result in a loss of power for SNPs with maskingeffects, 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 maskingeffects. We validate the method through simulations and applications to real data. We find that of 35 blood and urine biomarker traits inthe UK Biobank, 34 traits show evidence for masking effects in a total of 4,273 gene-trait pairs, indicating that masking effects is commonin complex traits. We further validate the improved power of our method in height, body mass index, and schizophrenia with differentGWAS sample sizes and show that on average 95.7% of the genes detected only by mBAT-combo with smaller sample sizes can be iden-tified by the single-SNP approach with a 1.7-fold increase in sample sizes. Eleven genes significant only in mBAT-combo for schizo-phrenia are confirmed by functionally informed fine-mapping or Mendelian randomization integrating gene expression data. Theframework of mBAT-combo can be applied to any set of SNPs to refine trait-association signals hidden in genomic regions with complex LD structures