Unraveling the Influence of Heritable Genetic Variation on Nicotine Use and Complex Human Traits

Smoking, a leading cause of preventable deaths worldwide and with both genetic and environmental influences, has been associated with numerous health risks, prompting the need for a deeper understanding of its genetic liability. Developing a deeper comprehension of the genetic factors influencing smoking behaviors may enhance our understanding of molecular pathways, networks, and their associations with other traits, aiming to identify potential treatment targets. Furthermore, the multifaceted nature and diverse genetic and environmental influences of smoking traits makes them exemplary phenotypes for understanding the genetics of complex traits. This dissertation encompasses three novel studies aimed at investigating the genetic influences on nicotine use and other complex traits. Chapter 2 used genome-wide association study summary statistics to examine whether human orthologs of genes identified in mice exposed to nicotine during development influence smoking behaviors in humans. This approach provided insight into the potential shared genetic architecture across species. Chapter 3 explored the use of expression-based gene-single nucleotide polymorphism (SNP) annotations to enhance the identification of relevant tissues associated with complex traits, including smoking behaviors. By leveraging estimated expression effects, this study aimed to uncover tissue-specific mechanisms underlying these traits. In Chapter 4, a transcriptome-wide association study was conducted to identify and characterize relevant SNPs, genes, and tissues associated with nicotine use and other complex traits. This study included diverse cohorts of varying ancestries and proposed methods to leverage multiple unrelated samples for enhanced statistical power. Collectively, the presented findings emphasize the polygenic nature of complex traits, highlight the need for larger and more ancestrally diverse sample sizes, and expand our toolkit for investigating genetic architecture, offering new avenues for exploring the genetics of human complex traits.</p

Transcriptome-wide gene-gene interaction associations elucidate pathways and functional enrichment of complex traits.

It remains unknown to what extent gene-gene interactions contribute to complex traits. Here, we introduce a new approach using predicted gene expression to perform exhaustive transcriptome-wide interaction studies (TWISs) for multiple traits across all pairs of genes expressed in several tissue types. Using imputed transcriptomes, we simultaneously reduce the computational challenge and improve interpretability and statistical power. We discover (in the UK Biobank) and replicate (in independent cohorts) several interaction associations, and find several hub genes with numerous interactions. We also demonstrate that TWIS can identify novel associated genes because genes with many or strong interactions have smaller single-locus model effect sizes. Finally, we develop a method to test gene set enrichment of TWIS associations (E-TWIS), finding numerous pathways and networks enriched in interaction associations. Epistasis is may be widespread, and our procedure represents a tractable framework for beginning to explore gene interactions and identify novel genomic targets