The Annals of Applied Statistics

Generalized genetic association study with samples of related individuals

Zeny Feng, William W. L. Wong, Xin Gao, and Flavio Schenkel

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Genetic association study is an essential step to discover genetic factors that are associated with a complex trait of interest. In this paper we present a novel generalized quasi-likelihood score (GQLS) test that is suitable for a study with either a quantitative trait or a binary trait. We use a logistic regression model to link the phenotypic value of the trait to the distribution of allelic frequencies. In our model, the allele frequencies are treated as a response and the trait is treated as a covariate that allows us to leave the distribution of the trait values unspecified. Simulation studies indicate that our method is generally more powerful in comparison with the family-based association test (FBAT) and controls the type I error at the desired levels. We apply our method to analyze data on Holstein cattle for an estimated breeding value phenotype, and to analyze data from the Collaborative Study of the Genetics of Alcoholism for alcohol dependence. The results show a good portion of significant SNPs and regions consistent with previous reports in the literature, and also reveal new significant SNPs and regions that are associated with the complex trait of interest.

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Ann. Appl. Stat. Volume 5, Number 3 (2011), 2109-2130.

First available in Project Euclid: 13 October 2011

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Zentralblatt MATH identifier

Genetic association test kinship-inbreeding coefficient logistic regression quasi-likelihood


Feng, Zeny; Wong, William W. L.; Gao, Xin; Schenkel, Flavio. Generalized genetic association study with samples of related individuals. Ann. Appl. Stat. 5 (2011), no. 3, 2109--2130. doi:10.1214/11-AOAS465.

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Supplemental materials

  • Supplementary material: Mathematical justifications and additional results. The supplementary materials of the paper are organized as follows. Appendix A provides the theoretical justification of the variance–covariance matrix Σ_0. Appendix B derives the explicit form of the W_G statistic for a biallelic marker in a single pedigree study design. Appendix C derives the expression of the W_G statistic for a multi-allelic marker in a single pedigree study design. In Appendix D additional results of simulation studies and the results of COGA data analysis are summarized in tables.