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A polygenic score method boosted by non-additive models

Author

Listed:
  • Rikifumi Ohta

    (The University of Tokyo)

  • Yosuke Tanigawa

    (Massachusetts Institute of Technology
    Broad Institute of MIT and Harvard)

  • Yuta Suzuki

    (The University of Tokyo)

  • Manolis Kellis

    (Massachusetts Institute of Technology
    Broad Institute of MIT and Harvard)

  • Shinichi Morishita

    (The University of Tokyo)

Abstract

Dominance heritability in complex traits has received increasing recognition. However, most polygenic score (PGS) approaches do not incorporate non-additive effects. Here, we present GenoBoost, a flexible PGS modeling framework capable of considering both additive and non-additive effects, specifically focusing on genetic dominance. Building on statistical boosting theory, we derive provably optimal GenoBoost scores and provide its efficient implementation for analyzing large-scale cohorts. We benchmark it against seven commonly used PGS methods and demonstrate its competitive predictive performance. GenoBoost is ranked the best for four traits and second-best for three traits among twelve tested disease outcomes in UK Biobank. We reveal that GenoBoost improves prediction for autoimmune diseases by incorporating non-additive effects localized in the MHC locus and, more broadly, works best in less polygenic traits. We further demonstrate that GenoBoost can infer the mode of genetic inheritance without requiring prior knowledge. For example, GenoBoost finds non-zero genetic dominance effects for 602 of 900 selected genetic variants, resulting in 2.5% improvements in predicting psoriasis cases. Lastly, we show that GenoBoost can prioritize genetic loci with genetic dominance not previously reported in the GWAS catalog. Our results highlight the increased accuracy and biological insights from incorporating non-additive effects in PGS models.

Suggested Citation

  • Rikifumi Ohta & Yosuke Tanigawa & Yuta Suzuki & Manolis Kellis & Shinichi Morishita, 2024. "A polygenic score method boosted by non-additive models," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48654-x
    DOI: 10.1038/s41467-024-48654-x
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    1. Tian Ge & Chia-Yen Chen & Yang Ni & Yen-Chen Anne Feng & Jordan W. Smoller, 2019. "Polygenic prediction via Bayesian regression and continuous shrinkage priors," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    2. Lam C. Tsoi & Philip E. Stuart & Chao Tian & Johann E. Gudjonsson & Sayantan Das & Matthew Zawistowski & Eva Ellinghaus & Jonathan N. Barker & Vinod Chandran & Nick Dand & Kristina Callis Duffin & Cha, 2017. "Large scale meta-analysis characterizes genetic architecture for common psoriasis associated variants," Nature Communications, Nature, vol. 8(1), pages 1-8, August.
    3. Clare Bycroft & Colin Freeman & Desislava Petkova & Gavin Band & Lloyd T. Elliott & Kevin Sharp & Allan Motyer & Damjan Vukcevic & Olivier Delaneau & Jared O’Connell & Adrian Cortes & Samantha Welsh &, 2018. "The UK Biobank resource with deep phenotyping and genomic data," Nature, Nature, vol. 562(7726), pages 203-209, October.
    4. Junyang Qian & Yosuke Tanigawa & Wenfei Du & Matthew Aguirre & Chris Chang & Robert Tibshirani & Manuel A Rivas & Trevor Hastie, 2020. "A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank," PLOS Genetics, Public Library of Science, vol. 16(10), pages 1-30, October.
    5. Fredrick R. Schumacher & Stephanie L. Schmit & Shuo Jiao & Christopher K. Edlund & Hansong Wang & Ben Zhang & Li Hsu & Shu-Chen Huang & Christopher P. Fischer & John F. Harju & Gregory E. Idos & Flavi, 2015. "Genome-wide association study of colorectal cancer identifies six new susceptibility loci," Nature Communications, Nature, vol. 6(1), pages 1-7, November.
    6. Luke R. Lloyd-Jones & Jian Zeng & Julia Sidorenko & Loïc Yengo & Gerhard Moser & Kathryn E. Kemper & Huanwei Wang & Zhili Zheng & Reedik Magi & Tõnu Esko & Andres Metspalu & Naomi R. Wray & Michael E., 2019. "Improved polygenic prediction by Bayesian multiple regression on summary statistics," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    7. Fredrick R. Schumacher & Stephanie L. Schmit & Shuo Jiao & Christopher K. Edlund & Hansong Wang & Ben Zhang & Li Hsu & Shu-Chen Huang & Christopher P. Fischer & John F. Harju & Gregory E. Idos & Flavi, 2015. "Correction: Corrigendum: Genome-wide association study of colorectal cancer identifies six new susceptibility loci," Nature Communications, Nature, vol. 6(1), pages 1-1, December.
    8. John G. Cragg & Russell S. Uhler, 1970. "The Demand for Automobiles," Canadian Journal of Economics, Canadian Economics Association, vol. 3(3), pages 386-406, August.
    9. Yosuke Tanigawa & Junyang Qian & Guhan Venkataraman & Johanne Marie Justesen & Ruilin Li & Robert Tibshirani & Trevor Hastie & Manuel A Rivas, 2022. "Significant sparse polygenic risk scores across 813 traits in UK Biobank," PLOS Genetics, Public Library of Science, vol. 18(3), pages 1-21, March.
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