TY - JOUR
T1 - mBAT-combo
T2 - A more powerful test to detect gene-trait associations from GWAS data
AU - Li, Ang
AU - Liu, Shouye
AU - Bakshi, Andrew
AU - Jiang, Longda
AU - Chen, Wenhan
AU - Zheng, Zhili
AU - Sullivan, Patrick F.
AU - Visscher, Peter M.
AU - Wray, Naomi R.
AU - Yang, Jian
AU - Zeng, Jian
N1 - Funding Information:
This research was supported by the Australian National Health and Medical Research Council (1177268, 1113400, 1173790), Australian Research Council (FL180100072), and the Westlake Education Foundation. Additional support via US NIMH R01 MH121545. A full list of acknowledgments to the data sets used in this study can be found in the supplemental note. J.Y. J.Z. and N.R.W. conceived and supervised the study. A.L. J.Z. J.Y. and N.R.W. developed the methods and designed the experiment. A.L. conducted all analyses and developed the software tool with the assistance or guidance from A.B. L.J. W.C. Z.Z. P.F.S. P.M.V. N.R.W. J.Y. and J.Z. S.L. implemented GCTA version with assistance of A.L. A.L. J.Z. N.R.W. and J.Y. wrote the manuscript with the participation of all authors. All the authors approved the final version of the manuscript. The authors declare no competing interests.
Funding Information:
This research was supported by the Australian National Health and Medical Research Council ( 1177268 , 1113400 , 1173790 ), Australian Research Council (FL180100072), and the Westlake Education Foundation . Additional support via US NIMH R01 MH121545 . A full list of acknowledgments to the data sets used in this study can be found in the supplemental note .
Publisher Copyright:
© 2022 American Society of Human Genetics
PY - 2023/1/5
Y1 - 2023/1/5
N2 - 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.
AB - 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.
KW - gene-based test
KW - GWAS
KW - linkage disequilibrium
KW - masking effect
KW - multi-SNP association
KW - set-based test
UR - https://www.scopus.com/pages/publications/85145327653
U2 - 10.1016/j.ajhg.2022.12.006
DO - 10.1016/j.ajhg.2022.12.006
M3 - Article
C2 - 36608683
AN - SCOPUS:85145327653
SN - 0002-9297
VL - 110
SP - 30
EP - 43
JO - American Journal of Human Genetics
JF - American Journal of Human Genetics
IS - 1
ER -