biostudies-literatureZhang JNIH HHS2https://www.ebi.ac.uk/biostudies/studies/S-EPMC9811798biostudies-literatureUnknown24(1)<h4>Background</h4>Gene-based association tests provide a useful alternative and complement to the usual single marker association tests, especially in genome-wide association studies (GWAS). The way of weighting for variants in a gene plays an important role in boosting the power of a gene-based association test. Appropriate weights can boost statistical power, especially when detecting genetic variants with weak effects on a trait. One major limitation of existing gene-based association tests lies in using weights that are predetermined biologically or empirically. This limitation often attenuates the power of a test. On another hand, effect sizes or directions of causal genetic variants in real data are usually unknown, driving a need for a flexible yet robust methodology of gene based BMC bioinformaticsA gene based combination test using GWAS summary data.PMC9811798R01EY027315Liu JGao XRZhang JLiang XGonzales SWang XfalseA gene based combination test using GWAS summary data.<h4>Background</h4>Gene-based association tests provide a useful alternative and complement to the usual single marker association tests, especially in genome-wide association studies (GWAS). The way of weighting for variants in a gene plays an important role in boosting the power of a gene-based association test. Appropriate weights can boost statistical power, especially when detecting genetic variants with weak effects on a trait. One major limitation of existing gene-based association tests lies in using weights that are predetermined biologically or empirically. This limitation often attenuates the power of a test. On another hand, effect sizes or directions of causal genetic variants in real data are usually unknown, driving a need for a flexible yet robust methodology of gene based 2023-01-01T00:00:00Z2023 Jan2025-04-22T04:31:42.209Z2025-02-19T03:30:35.965ZS-EPMC98117983659704710.1186/s12859-022-05114-x