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Incorporating family history of disease improves polygenic risk scores in diverse populations

Margaux L.A. Hujoel, Po-Ru Loh, Benjamin M. Neale, Alkes L. Price
doi: https://doi.org/10.1101/2021.04.15.439975
Margaux L.A. Hujoel
1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
2Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School
3Program in Medical and Population Genetics, Broad Institute of MIT and Harvard
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  • For correspondence: mhujoel@broadinstitute.org aprice@hsph.harvard.edu
Po-Ru Loh
2Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School
3Program in Medical and Population Genetics, Broad Institute of MIT and Harvard
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Benjamin M. Neale
3Program in Medical and Population Genetics, Broad Institute of MIT and Harvard
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Alkes L. Price
1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
3Program in Medical and Population Genetics, Broad Institute of MIT and Harvard
4Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
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  • For correspondence: mhujoel@broadinstitute.org aprice@hsph.harvard.edu
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Abstract

Polygenic risk scores derived from genotype data (PRS) and family history of disease (FH) both provide valuable information for predicting disease risk, enhancing prospects for clinical utility. PRS perform poorly when applied to diverse populations, but FH does not suffer this limitation. Here, we explore methods for combining both types of information (PRS-FH). We analyzed 10 complex diseases from the UK Biobank for which family history (parental and sibling history) was available for most target samples. PRS were trained using all British individuals (N=409K), and target samples consisted of unrelated non-British Europeans (N=42K), South Asians (N=7K), or Africans (N=7K). We evaluated PRS, FH, and PRS-FH using liability-scale R2, focusing on three well-powered diseases (type 2 diabetes, hypertension, depression) with R2 > 0.05 for PRS and/or FH in each target population. Averaging across these three diseases, PRS attained average prediction R2 of 5.8%, 4.0%, and 0.53% in non-British Europeans, South Asians, and Africans, confirming poor cross-population transferability. In contrast, PRS-FH attained average prediction R2 of 13%, 12%, and 10%, respectively, representing a large improvement in Europeans and an extremely large improvement in Africans; for each disease and each target population, the improvement was highly statistically significant. PRS-FH methods based on a logistic model and a liability threshold model performed similarly when covariates were not included in predictions (consistent with simulations), but the logistic model outperformed the liability threshold model when covariates were included. In conclusion, including family history greatly improves the accuracy of polygenic risk scores, particularly in diverse populations.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://data.broadinstitute.org/alkesgroup/UKBB/PRSFH/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 15, 2021.
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Incorporating family history of disease improves polygenic risk scores in diverse populations
Margaux L.A. Hujoel, Po-Ru Loh, Benjamin M. Neale, Alkes L. Price
bioRxiv 2021.04.15.439975; doi: https://doi.org/10.1101/2021.04.15.439975
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Incorporating family history of disease improves polygenic risk scores in diverse populations
Margaux L.A. Hujoel, Po-Ru Loh, Benjamin M. Neale, Alkes L. Price
bioRxiv 2021.04.15.439975; doi: https://doi.org/10.1101/2021.04.15.439975

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