Ranking the importance of genetic factors by variable‐selection confidence sets

Abstract

The widespread use of generalized linear models in case-control genetic studies has helped identify many disease-associated risk factors typically defined as DNA variants, or single nucleotide polymorphisms (SNPs). Up to now, most literature has focused on selecting a unique best subset of SNPs based on some statistical perspective. When the noise is large compared to signal, however, multiple biological paths are often found to be supported by a given dataset. We address the ambiguity related to SNP selection by constructing a list of models C called variable selection confidence set (VSCS) C which contains the collection of all well-supported SNP combinations at a user-specified confidence level. The VSCS extends the familiar notion of confidence intervals in the variable selection setting and provides the practitioner with new tools aiding the variable selection activity beyond trusting a single model. Based on the VSCS, we consider natural graphical and numerical statistics measuring the inclusion importance of a SNP based on its frequency in the most parsimonious VSCS models. This work is motivated by available case-control genetic data on age-related macular degeneration, a widespread disease and leading cause of vision loss.

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ORCID for Chao Zheng: orcid.org/0000-0001-7943-6349

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