Sini Nagpal

Step Accumulation Patterns and Risk for Cardiovascular Events and Mortality Among Suboptimally Active Adults | Annals of Internal Medicine Background: Although physical activity recommendations increasingly consider daily step counts, it remains unclear whether step accumulation patterns—short versus sustained longer bouts—affect associa...

Longer walks linked with less mortality and cardiovascular risk compared with short bouts of physical activity
@annalsofim.bsky.social
www.acpjournals.org/doi/10.7326/...

Dual exposure-by-polygenic score interactions highlight disparities across social groups in the proportion needed to benefit The transferability of polygenic scores across population groups is a major concern with respect to the equitable clinical implementation of genomic medicine. Since genetic associations are identified...

Heading to Boston for the American Society of Human Genetics #ASHG25 @geneticssociety.bsky.social! Excited to present our work on how pervasive exposure-by-polygenic score interactions can inform more effective clinical and behavioral interventions. #B1070W

www.medrxiv.org/content/10.1...

Polygenic and developmental profiles of autism differ by age at diagnosis - Nature A study of several longitudinal birth cohorts and cross-sectional cohorts finds only moderate overlap in genetic variants between autism that is diagnosed earlier and that diagnosed later, so they may represent aetiologically different conditions.

Pleased to have contributed to this paper out at @nature.com today from @vw1234.bsky.social and Yira (Xinhe) Zhang showing that the common variant contribution to autism varies substantially by age of diagnosis www.nature.com/articles/s41.... Critical for understanding heterogeneity in autism.

This is figure 1, which shows the trajectory analyses in three of the four birth cohorts.

The age that autism is diagnosed may partly reflect underlying biological and developmental differences among individuals with autism, according to a study in Nature. go.nature.com/4gQ5gSV 🧬 🧪

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]

Dual exposure-by-polygenic score interactions highlight d... The transferability of polygenic scores across population...

Excited to share our latest manuscript on "Dual exposure-by-polygenic score interactions highlight disparities across social groups in the proportion needed to benefit" with Greg Gibson (@genomestake),
https://www.medrxiv.org/content/10.1101/2024.07.29.24311065v1 [1/9]

Lots of interesting discussions on polygenic risk scores and their implementation @HarvardPqg - Diversity in Genetics and Genomics.
#polygenicriskscores #prs #environment #diversity #biobanks #precisionmedicine

This year has been special - my doctoral graduation ceremony with my advisor and mentor, Greg Gibson (@genomestake ) and my family 🙂

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/

Honored to be selected as the Postdoctoral Semifinalist for 2022 Charles J. Epstein Awards for Excellence in Human Genetics Research for #ASHG22! Plus, our abstract has been selected as the Reviewer's Choice abstract 😀 #ASHG @GeneticsSociety...

@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

For binary traits, we compared the observed prevalence with the expected prevalence under null; Expected prevalence computed using liability threshold model assuming environmental effects to be additive (no PGSxE).
Past tobacco smoking & coronary artery disease risk

C. Walk pace vs obesity risk: As the PGS increase, genetic effects appear to be larger in the poor environment (slow walk pace), implying genetic variance is greater at the extremes, leading to higher impact on disease risk - decanalization.

Using 10 complex traits and 151 environmental exposures, we compare the prevalence-risk curves wrt exposures to ask the questions 1.For which trait-exposure combination does PGSxE exist? If it exists, can we develop a modeling framework to assess decanalization vs canalization?

Very happy and excited to share the final study of my PhD out in @MolBioEvol. All results from this study can be explored through our Rshiny: https://canalization-gibsonlab.shinyapps.io/rshiny/
https://academic.oup.com/mbe/advance-article-abstract/doi/10.1093/molbev/msac053/6547257

Reading @kph3k's wonderfully written "The Genetic Lottery", while beginning the journey to write my PhD thesis🙂
Any tips and suggestions as I embark on this journey are welcome!