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.
News
- 2023/11/10: Yosuke presents iPGS 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 manuscript 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.
