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Authors: Trang T. Le ; Hoyt Gong ; Patryk Orzechowski ; Elisabetta Manduchi and Jason H. Moore

Affiliation: Department of Biostatistics, Epidemiology and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, U.S.A.

Keyword(s): Precision Medicine, Machine Learning, Risk Scores, Genetics.

Abstract: Polygenic Risk Scores (PRS) are aggregation of genetic risk factors of specific diseases and have been successfully used to identify groups of individuals who are more susceptible to those diseases. While several studies have focused on identifying the correct genetic variants to include in PRS, most existing statistical models focus on the marginal effect of the variants on the phenotypic outcome but do not account for the effect of gene-gene interactions. Here, we propose a novel calculation of the risk score that expands beyond marginal effect of individual variants on the outcome. The Multilocus Risk Score (MRS) method effectively selects alternative genotype encodings and captures epistatic gene-gene interactions by utilizing an efficient implementation of the model-based Multifactor Dimensionality Reduction technique. On a diverse collection of simulated datasets, MRS outperforms the standard PRS in the majority of the cases, especially when at least two-way interactions betwee n the variants are present. Our findings suggest that models incorporating epistatic interactions are necessary and will yield more accurate and effective risk profiling. (More)

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Paper citation in several formats:
Le, T.; Gong, H.; Orzechowski, P.; Manduchi, E. and Moore, J. (2020). Expanding Polygenic Risk Scores to Include Automatic Genotype Encodings and Gene-gene Interactions. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - BIOINFORMATICS; ISBN 978-989-758-398-8; ISSN 2184-4305, SciTePress, pages 79-84. DOI: 10.5220/0008869700790084

@conference{bioinformatics20,
author={Trang T. Le. and Hoyt Gong. and Patryk Orzechowski. and Elisabetta Manduchi. and Jason H. Moore.},
title={Expanding Polygenic Risk Scores to Include Automatic Genotype Encodings and Gene-gene Interactions},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - BIOINFORMATICS},
year={2020},
pages={79-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008869700790084},
isbn={978-989-758-398-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - BIOINFORMATICS
TI - Expanding Polygenic Risk Scores to Include Automatic Genotype Encodings and Gene-gene Interactions
SN - 978-989-758-398-8
IS - 2184-4305
AU - Le, T.
AU - Gong, H.
AU - Orzechowski, P.
AU - Manduchi, E.
AU - Moore, J.
PY - 2020
SP - 79
EP - 84
DO - 10.5220/0008869700790084
PB - SciTePress

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