  We propose a resampling-based fast variable selection technique for detecting
relevant single nucleotide polymorphisms (SNP) in a multi-marker mixed effect
model. Due to computational complexity, current practice primarily involves
testing the effect of one SNP at a time, commonly termed as `single SNP
association analysis'. Joint modeling of genetic variants within a gene or
pathway may have better power to detect associated genetic variants, especially
the ones with weak effects. In this paper, we propose a computationally
efficient model selection approach -- based on the e-values framework -- for
single SNP detection in families while utilizing information on multiple SNPs
simultaneously. To overcome computational bottleneck of traditional model
selection methods, our method trains one single model, and utilizes a fast and
scalable bootstrap procedure. We illustrate through numerical studies that our
proposed method is more effective in detecting SNPs associated with a trait
than either single-marker analysis using family data or model selection methods
that ignore the familial dependency structure. Further, we perform gene-level
analysis in Minnesota Center for Twin and Family Research (MCTFR) dataset using
our method to detect several SNPs using this that have been implicated to be
associated with alcohol consumption.
