Thanks to @arbelharpak, Alison Motsinger-Reif and @raghav_gt for their valuable feedback and comments.

These findings emphasize how individuals experiencing adverse exposures stand to preferentially benefit from interventions that may reduce risk, and highlight the need for more comprehensive sampling across socioeconomic groups in the performance of GWAS. [9/9]

Finally considering the utility of PGSxE, we introduced the notion of proportion needed to benefit (PNB) as the cumulative number needed to treat across PGS thresholds in high vs low-risk exposures & show that it is typically halved between 70th–80th PGS percentile. [8/9]

The predominant mechanism for PGS×E interactions is shown to be amplification of genetic effects in the presence of adverse exposures such as low polyunsaturated fatty acids, mediators of obesity, and social determinants of ill health. [7/9]

Predictive accuracy is significantly improved in the high-risk (adverse) exposures and by including interaction terms with effects as large as those documented for low transferability of PGS across ancestries. [6/9]

While the issue of PGS portability across ancestries is a major focus, these results highlight the need to identify exposures/SDOH where PGSs impose a larger impact on disease and could be more informative in terms of their clinical utility to ameliorate health disparities. [5/9]

Across all disease-exposures, we find evidence of pervasive PGSxE interactions influencing common disease risk. Eg. for incident CAD, key exposures exhibiting multiple interactions are: sex, weekly beer intake, smoking and omega-6 fatty acids. [4/9]

For example, for coronary artery disease (CAD): Low levels of omega-6 fatty acids and past tobacco smoking interact with PGS-CAD to exacerbate incident CAD risk. [3/9]

The impact of PGS on the disease is highly context-specific. We quantify polygenic score-by-exposure (PGSxE) interactions for seven common diseases and pairs of 75 exposures. [2/9]

Very excited to have presented my Reviewer's Choice poster at #ASHG22 on predicted TRS supporting the evidence of canalization of polygenic risk for common diseases and traits in the UK Biobank - PB1592

https://cig.gatech.edu/sini-nagpal-selected-as-postdoctoral-semifinalist/

@uk_biobank #canalization #polygenicrisk #PGSxE #complextraits #commondiseases

This was a very exciting and special project for me. Thanks to the wonderful team, my advisor @genomestake for his mentorship and giving me the opportunity to work on this project and @raghav_gt for helping with the statistical modeling.

Lifestyle related exposures show decanalization for BMI but canalization for WHR, reflecting different evolutionary pressures on the architectures of weight-related traits. Could be explained by recent human behaviors driving BMI vs stabilizing selection for metabolism for WHR.

For continuous traits: Decanalization for BMI wrt Townsend deprivation index

All exposures vs college attainment

(De)canalization is defined based on the observed vs expected deviations at the extremes (Delta) and the departure from null expectation above or below a certain threshold.
All exposures vs CAD