Detecting disease association signals with multiple genetic variants and covariates

Statistical Methods in Medical Research
K F Cheng, J Y Lee

Abstract

Due to the improvements in the efficiency of resequencing technologies, discoveries and analyses of rare variants in sequencing-based association studies at the gene level, or even exome-wide are becoming increasingly feasible. Powerful association tests have been suggested in literature for testing whether a group of variants in a gene region is associated with a particular disease of interest. Their performance depends on the correct assumption of regression model and conditions such as the size of the case and control sample, numbers of causal and noncausal variants (rare or common), variant frequency, effect size and directionality, rate of missing genotype, etc. Most of these model-based tests require genotype data to be complete at each variant. Our previous results showed that in the case of no covariate, the power of these tests might be greatly influenced, when there were missing genotypes and only simple imputation was used. In this paper, we demonstrate by simulations that in the presence of covariates, the type I errors of these approaches might be inflated, even when genotype missing rate was very small. We present an association test based on testing zero proportion of causal variants in the gene region, and show ...Continue Reading

References

Oct 28, 2005·Genome Research·Stephen F SchaffnerDavid Altshuler
Oct 31, 2008·Genes and Immunity·J P HaflerJ A Todd
Feb 14, 2009·PLoS Genetics·Bo Eskerod Madsen, Sharon R Browning
May 18, 2010·American Journal of Human Genetics·Alkes L PriceShamil R Sunyaev
Jul 9, 2011·American Journal of Human Genetics·Michael C WuXihong Lin
Jul 20, 2011·Genetic Epidemiology·Saonli Basu, Wei Pan
Sep 3, 2011·American Journal of Human Genetics·Dan-Yu Lin, Zheng-Zheng Tang

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Citations

Aug 15, 2018·Statistical Methods in Medical Research·J Y LeeK F Cheng

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Methods Mentioned

BETA
genotyping
PCA

Software Mentioned

SKAT
REM

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