Statistical Methods For Genetic Epidemiology

National Institute Of Environmental Health Sciences

Investigators

Linked publications, trials & patents

Abstract

Despite some advances due to the extensive GWAS studies that have been done to associate genetic variants with diseases, known variants have only explained a small proportion of the heritability of most heritable complex health conditions. The birth defect, oral cleft, is a good example. The recurrence risk is known to be about 30-40 fold in a subsequent sibling, but known variants explain little of that recurrence. Given the protective redundancy designed into many biologic pathways, it may be that certain particular SNPs must be jointly present before this defect is expressed. Given that combination, an environmental exposure might also be important. The availability of our algorithm for simulating genomic data with realistic linkage structure provides us with a simulation laboratory for development of new methods for assessing both GxGxG interactions and GxGxE interactions. The method is now able to identify multiple epistatic pathways where the simultaneous presence of certain variant SNPs confers risk for the outcome. We have gone back to the clefting data and mined for epistasis there, separately in the Asian data and the European data. Using our (publicly available) simulation algorithm to create toy data sets, we have refined our machine-learning approach to stochastically search for multi-SNP effects. That paper represents an important advance over existing methods because it could handle a much larger set of candidate SNPs and also test specifically for epistasis in a fundamentally model-free way. In the paper published this year we extended the method to enable inclusion of maternal SNPs as part of the etiology. We have also extended the work to allow the genetic pathways to interact with environmental exposures, and have applied the new methods to oral clefting in relation to maternal smoking during pregnancy. That paper is now under review. The approach can also be used to disentangle possibly distinct epistatic effects in relation to more specific disease subcategories, e.g. the molecular subtypes of breast cancer, or to severity of the outcome, e.g. extreme prematurity versus less extreme prematurity. Future plans include extending the approach to enable the study of quantitative outcomes like blood pressure.