Background: We propose a gene-level association test that accounts for individual relatedness and population structures in pedigree data in the framework of linear mixed models (LMMs). Our method data-adaptively combines the results across a class of score-based tests, only requiring fitting a single null model (under the null hypothesis) for the whole genome, thereby being computationally efficient. Results: We applied our approach to test for association with the high-density lipoprotein (HDL) ratio of post- and pretreatments in GAW20 data. Using the LMM similar to that used by Aslibekyan et al. (PLos One, 7:48663, 2012), our method identified 2 nearly significant genes (APOA5 and ZNF259) near rs964184, whereas neither the other gene-level tests nor the standard test on each individual single-nucleotide polymorphism (SNP) detected any significant gene in a genome-wide scan. Conclusions: Gene-level association testing can be a complementary approach to the SNP-level association testing and our method is adaptive and efficient compared to several other existing gene-level association tests.
Bibliographical noteFunding Information:
We thank the reviewers for many helpful and constructive comments and the organizers of Genetic Analysis Workshop 20. This research was supported by the Minnesota Supercomputing Institute..
Publication of this article was supported by NIH R01 GM031575. This research was funded by NIH grants R21AG057038, R01HL116720, R01GM113250, and R01HL105397. CW was funded by the University of Minnesota Doctoral Dissertation Fellowship.
- Linear mixed models
- Score test