Research in Progress (RiP)

Archive of previous RiP events:

2025-12-11: 

Recording: https://drive.google.com/file/d/1VOMEzSXPRTYIgVZxzIekviJ6---48T17/view?usp=drive_link

Today at 3:00 PM, we welcome Dr. Davorka Gulisija from UNM Dept. of Biology to speak about genetic variant detection. See you there!

Abstract:

Towards higher resolution into the genetic basis of human complex trait variation

I will present two recent collaborative studies addressing how to improve the detection of variants underlying complex human phenotypic variation. First, I will show that many positively pleiotropic small-effect variants can be detected more effectively through GWAS on integrated phenotypes, using traits linked to obesity. Science has struggled to provide effective solutions to obesity partly because it arises from excess energy intake and expenditure shaped by a complex interplay of numerous traits—dietary intake, physical activity, body mass and composition, and metabolic profiles. Yet obesity-related traits are typically analyzed in isolation. I will present a comprehensive approach that treats energy balance as a compound phenotype spanning multiple trait categories, using UK Biobank participants. This study reveals previously unrecognized patterns of variation in energy balance and identifies new genes and functional pathways, while also illustrating a generalizable statistical framework for integrating diverse complex traits to analyze otherwise elusive compound phenotypes. Second, I will present arguments for rethinking study recruitment strategies. Most complex traits are shaped by thousands of small-effect variants, while environmental and lifestyle factors inflate non-genetic variance and obscure genetic signals. Standard genetic studies often focus on high-risk cohorts to enrich for target phenotypes, but here I will argue that such cohorts are more heavily influenced by environmental rather than genetic effects—further reducing statistical power. In contrast, examining lower-risk groups, such as younger individuals, may reduce residual variance and enhance our ability to detect causal genetic effects. I will illustrate this principle on nine complex traits, again using UK Biobank participants.

-The RiP Team