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Improving genetic prediction by leveraging genetic correlations among human diseases and traits

Author

Listed:
  • Robert M. Maier

    (University of Queensland
    Broad Institute
    Massachusetts General Hospital and Harvard Medical School)

  • Zhihong Zhu

    (University of Queensland)

  • Sang Hong Lee

    (University of Queensland
    University of South Australia)

  • Maciej Trzaskowski

    (University of Queensland)

  • Douglas M. Ruderfer

    (Vanderbilt University Medical Center)

  • Eli A. Stahl

    (Icahn School of Medicine at Mount Sinai)

  • Stephan Ripke

    (Broad Institute
    Massachusetts General Hospital and Harvard Medical School
    Charité, Campus Mitte)

  • Naomi R. Wray

    (University of Queensland
    University of Queensland)

  • Jian Yang

    (University of Queensland
    University of Queensland)

  • Peter M. Visscher

    (University of Queensland
    University of Queensland)

  • Matthew R. Robinson

    (University of Queensland
    University of Lausanne
    Swiss Institute of Bioinformatics)

Abstract

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait.

Suggested Citation

  • Robert M. Maier & Zhihong Zhu & Sang Hong Lee & Maciej Trzaskowski & Douglas M. Ruderfer & Eli A. Stahl & Stephan Ripke & Naomi R. Wray & Jian Yang & Peter M. Visscher & Matthew R. Robinson, 2018. "Improving genetic prediction by leveraging genetic correlations among human diseases and traits," Nature Communications, Nature, vol. 9(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-017-02769-6
    DOI: 10.1038/s41467-017-02769-6
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    Cited by:

    1. Clara Albiñana & Zhihong Zhu & Andrew J. Schork & Andrés Ingason & Hugues Aschard & Isabell Brikell & Cynthia M. Bulik & Liselotte V. Petersen & Esben Agerbo & Jakob Grove & Merete Nordentoft & David , 2023. "Multi-PGS enhances polygenic prediction by combining 937 polygenic scores," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Bingxin Zhao & Fei Zou & Hongtu Zhu, 2023. "Cross‐trait prediction accuracy of summary statistics in genome‐wide association studies," Biometrics, The International Biometric Society, vol. 79(2), pages 841-853, June.
    3. Nuzulul Kurniansyah & Matthew O. Goodman & Tanika N. Kelly & Tali Elfassy & Kerri L. Wiggins & Joshua C. Bis & Xiuqing Guo & Walter Palmas & Kent D. Taylor & Henry J. Lin & Jeffrey Haessler & Yan Gao , 2022. "A multi-ethnic polygenic risk score is associated with hypertension prevalence and progression throughout adulthood," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    4. George B. Busby & Scott Kulm & Alessandro Bolli & Jen Kintzle & Paolo Di Domenico & Giordano Bottà, 2023. "Ancestry-specific polygenic risk scores are risk enhancers for clinical cardiovascular disease assessments," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Carla Márquez-Luna & Steven Gazal & Po-Ru Loh & Samuel S. Kim & Nicholas Furlotte & Adam Auton & Alkes L. Price, 2021. "Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    6. Shuang Song & Wei Jiang & Lin Hou & Hongyu Zhao, 2020. "Leveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies," PLOS Computational Biology, Public Library of Science, vol. 16(2), pages 1-18, February.

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