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Behavior Genetics

, Volume 48, Issue 1, pp 55–66 | Cite as

Adaptive SNP-Set Association Testing in Generalized Linear Mixed Models with Application to Family Studies

  • Jun Young Park
  • Chong Wu
  • Saonli Basu
  • Matt McGue
  • Wei Pan
Original Research
  • 401 Downloads

Abstract

In genome-wide association studies (GWAS), it has been increasingly recognized that, as a complementary approach to standard single SNP analyses, it may be beneficial to analyze a group of functionally related SNPs together. Among the existent population-based SNP-set association tests, two adaptive tests, the aSPU test and the aSPUpath test, offer a powerful and general approach at the gene- and pathway-levels by data-adaptively combining the results across multiple SNPs (and genes) such that high statistical power can be maintained across a wide range of scenarios. We extend the aSPU and the aSPUpath test to familial data under the framework of the generalized linear mixed models (GLMMs), which can take account of both subject relatedness and possible population structure. As in population-based GWAS, the proposed aSPU and aSPUpath tests require only fitting a single and common GLMM (under the null hypothesis) for all the SNPs, thus are computationally efficient and feasible for large GWAS data. We illustrate our approaches in identifying genes and pathways associated with alcohol dependence in the Minnesota Twin Family Study. The aSPU test detected a gene associated with the trait, in contrast to none by the standard single SNP analysis. Our aSPU test also controlled Type I errors satisfactorily in a small simulation study. We provide R code to conduct the aSPU and aSPUpath tests for familial and other correlated data.

Keywords

Alcohol dependence aSPU GEE GLMM GWAS Score test 

Notes

Acknowledgements

The authors are grateful to two reviewers and an editor for many helpful comments.

Funding

This research was supported by National Institutes of Health Grants R37DA05147, R01AA09357, R01AA11886, R01DA13240, U01DA024417, R01MH066140, R01GM113250, R01HL105397, R01HL116720, and by the Minnesota Supercomputing Institute.

Compliance with ethical standards

Conflicts of interest

Jun Young Park, Chong Wu, Saonli Basu, Matt McGue, and Wei Pan declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Jun Young Park
    • 1
  • Chong Wu
    • 1
  • Saonli Basu
    • 1
  • Matt McGue
    • 2
  • Wei Pan
    • 1
  1. 1.Division of BiostatisticsUniversity of MinnesotaMinneapolisUSA
  2. 2.Department of PsychologyUniversity of MinnesotaMinneapolisUSA

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