Determining the Molecular Chain of Causality Through Which Genetic Variants Affect Physiology

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Project Summary A central goal of biomedical research is to decipher the genetic basis of complex traits. Though genome-wide association studies (GWAS) have successfully detected thousands of variants that are associated with complex cardiovascular, autoimmune, and neurological diseases, the molecular mechanisms are only known for a very limited subset of these risk-associated variants. A mechanistic understanding of risk-associated variants is difficult to ascertain because a vast majority of variants identified by GWAS are located in regulatory regions, which suggests that gene expression variation contributes a substantial portion of the genetic risk for complex human diseases. However, statistical power to map gene expression variation to genetic variants is often limited by the small sample sizes used in such mapping studies. Our ability to characterize the molecular chain of causality that links genetic variants to complex physiological traits is further limited because evidence is mounting that regulatory variants often manifest their disease-associated effects in specific cell and tissue types. Because of these limitations, I will leverage genetic diversity in the nematode Caenorhabditis elegans to achieve the statistical power necessary to precisely quantify the cell- and tissue-specific effects that genetic variants have on gene expression and physiology. We have recently developed a technique to identify genetic variants that affect cell- and tissue- specific gene expression (expression quantitative trait loci or eQTL) in experimental C. elegans crosses. This approach takes advantage of the short life cycle of C. elegans, the ability to easily generate hundreds of thousands of recombinant individuals, and well-established methods to prepare cells for single-cell RNA sequencing to associate single-cell transcript abundance with genetic variation segregating in experimental crosses. By combining this single-cell eQTL mapping approach with experimental evolution, I have identified several genomic regions that affect organismal fitness and tissue-specific gene expression variation of tens to hundreds of genes. In Aim 1, I will extend the scope of these initial experiments to survey the effects of a wide-range of C. elegans natural genetic variation on organismal fitness and cell- and tissue- specific gene expression. In Aim 2, I will use the vast molecular and genetic toolkit available in C. elegans to determine if the same underlying variants affect both tissue-specific gene expression and fitness. The completion of these aims will 1) characterize the cell- and tissue-specific phenotypic effects of hundreds of thousands of genetic variants; 2) determine if tissue-specific expression differences can affect organismal physiology; and 3) provide a mechanistic understanding of how genetic variation mediates its effect on organismal physiology. Together, these insights will facilitate the interpretation of how regulatory variation affects human health.