In recent years, there has been a deluge of data from genome-wide functional assays. Rapid expansion of computational techniques for mapping genetic correlates of intermediate molecular traits, such as gene expression, has offered opportunities to explore molecular mechanisms that underlie disease susceptibility. Here we will develop mechanistically justified statistical methods that enable allele-specific assessment of regulatory activity in individual genomes. We hypothesize that incorporating the dosage modifying effect of regulatory alleles into genetic association analyses will enhance the resolution of the current genotype-phenotype maps, and allow for a more refined mapping of the underlying biological mechanisms. Our proposal is organized into the following aims:
Aim1 : To derive biologically interpretable effect sizes for regulatory variants and to enable allele-specific prediction of gene and isoform dosage, we will develop haplotype-aware models of genetic regulatory variation and apply them to large-scale reference transcriptome datasets.
Aim2 : To enhance the resolution of the current genotype-phenotype maps, we will develop genetic association methods that incorporate expected allele dosages for genes and transcript isoforms and apply them to large biobank and GWAS data.
Aim3 : To improve generalizability of genetic association signals to understudied populations, we will develop genetic association methods that incorporate population-specific regulatory architecture of genes and transcript isoforms.
/Public Health Relevance We will develop novel computational methods that model gene regulatory activity in individual genomes, with important translational implications for genetic association studies of human disease.
