Abstract
The analysis of genome-wide genetic association studies generally starts with univariate statistical tests of each single-nucleotide polymorphism. The standard approach is the Cochran-Armitage trend test or its logistic regression equivalent although this approach can lose considerable power if the underlying genetic model is not additive. An alternative is the MAX test, which is robust against the three basic modes of inheritance. Here, the asymptotic distribution of the MAX test is derived using the generalized linear model together with the Delta method and multiple contrasts. The approach is applicable to binary, quantitative, and survival traits. It may be used for unrelated individuals, family-based studies, and matched pairs. The approach provides point and interval effect estimates and allows selecting the most plausible genetic model using the minimum P-value. R code is provided. A Monte-Carlo simulation study shows that the asymptotic MAX test framework meets type I error levels well, has good power, and good model selection properties for minor allele frequencies ≥0.3. Pearson’s χ2-test is superior for lower minor allele frequencies with low frequencies for the rare homozygous genotype. In these cases, the model selection procedure should be used with caution. The use of the MAX test is illustrated by reanalyzing findings from seven genome-wide association studies including case–control, matched pairs, and quantitative trait data.
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Acknowledgements
We acknowledge funding from the Deutsche Forschungsgemeinschaft (KO 2250/4-1, HO 1687/9-1), the European Union (BiomarCare, grant number: HEALTH-2011-278913), and the German Ministry of Education and Research (CARDomics, grant numbers: 01KU0908A and 01KU0908B). This work made use of data and samples generated by the 1958 Birth Cohort (NCDS). Access to these resources was enabled via the 58READIE Project funded by Wellcome Trust and Medical Research Council (grant numbers WT095219MA and G1001799). A full list of the financial, institutional, and personal contributions to the development of the 1958 Birth Cohort Biomedical resource is available at http://www2.le.ac.uk/projects/birthcohort. Genotyping was undertaken as part of the Wellcome Trust Case Control Consortium under Wellcome Trust award 076113, and a full list of the investigators who contributed to the generation of the data is available at http://www.wtccc.org.uk. We gratefully acknowledge the successful cooperation with the Bernhard Nocht Institute of Tropical Medicine, Hamburg, Germany (director: Rolf D. Horstmann), and the School of Medical Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana (former dean: Tsiri Agbenyega) on the Ghanaian malaria study. This study makes use of data generated by MalariaGEN Genomic Epidemiology Network.28 A full list of the investigators who contributed to the generation of the data is available from www.MalariaGEN.net. Funding for the MalariaGEN project was provided by the Foundation for the National Institutes of Health, the Wellcome Trust, and the Grand Challenges in Global Health Initiative.
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Loley, C., König, I., Hothorn, L. et al. A unifying framework for robust association testing, estimation, and genetic model selection using the generalized linear model. Eur J Hum Genet 21, 1442–1448 (2013). https://doi.org/10.1038/ejhg.2013.62
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DOI: https://doi.org/10.1038/ejhg.2013.62
