Overview
Admixed individuals offer unique opportunities to address limited transferability in polygenic scores (PGS), given the substantial trans-ancestry genetic correlation in many complex traits. However, they are rarely considered in PGS training, given the challenges in representing ancestry-matched linkage-disequilibrium reference panels for admixed individuals. We developed inclusive PGS (iPGS), a PGS training strategy to capture ancestry-shared genetic effects by finding the exact solution for penalized regression on individual-level data, thus naturally applicable to admixed individuals.
We host the results from the following studies. You may learn more about our iPGS browser here.
Browse iPGS models from our publication
Here, we present iPGS models for 60 hematological and anthropometric traits in UK Biobank described in the following study:
Power of inclusion: Enhancing polygenic prediction with admixed individuals. Tanigawa and Kellis. Am J Hum Genet (2023)
Hypometric genetics: Improved power in genetic discovery by incorporating quality control flags. Tanigawa and Kellis. Am J Hum Genet (2024).
News
- 2024/10/22: The inclusive polygenic score resources is selected as the winners of the 2024 MIT Prize for Open Data!
- 2024/10/22: Our hypometric genetics (hMG) paper is published at the American Journal of Human Genetics. We introduce "hypometric genetics," studying the genetic basis of binarized traits representing the presence of below-the-limit-of-quantification (BLQ) quality control indicators to improve power in genetic discovery. We update the iPGS browser to host the iPGS models generated as a part of the new study.
- 2024/9/19: Yosuke presented the iPGS paper at the Southern California Symposium on Polygenic Risk Scores. The 10 min presentation video is available at the conference website. The slides are available here.
- 2024/4/29-5/1: Yosuke presented the iPGS paper at the STATGEN2024 conference and STATGEN2024 conference.
- 2023/11/10: Yosuke presented the iPGS paper at Western Bioinformatics Seminar, Ontario, Canada. The slides are available here.
- 2023/11/07: The American Society of Human Genetics posted author interview for our iPGS paper: Inside AJHG: A Chat with Yosuke Tanigawa.
- 2023/11/04: Yosuke presents iPGS at the American Society of Human Genetics annual meeting 2023.
- 2023/10/26: MIT News feature our study: Making genetic prediction models more inclusive.
- 2023/10/26: Our iPGS paper is published at the American Journal of Human Genetics. We release the supplementary data at figshare and publicly launch the iPGS browser.
References
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Y. Tanigawa and M. Kellis. Power of inclusion: Enhancing polygenic prediction with admixed individuals. Am J Hum Genet. (2023).
- We introduce our inclusive PGS approach in this publication.
- Supplementary Data Files at figshare (doi: 10.6084/m9.figshare.22905368)
- MIT News article: Making genetic prediction models more inclusive
- Author interview: Inside AJHG: A Chat with Yosuke Tanigawa
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J. Qian, Y. Tanigawa, W. Du, M. Aguirre, C. Chang, R. Tibshirani, M. A. Rivas, T. Hastie. A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank. PLoS Genet. 16, e1009141 (2020).
- In iPGS, we fit penalized multivariate regression on individual-level data using the BASIL algorithm implemented in the R snpnet package. This publication describes the BASIL algorithm and the R snpnet package.
