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Leveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies

Author

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  • Shuang Song
  • Wei Jiang
  • Lin Hou
  • Hongyu Zhao

Abstract

Genetic risk prediction is an important problem in human genetics, and accurate prediction can facilitate disease prevention and treatment. Calculating polygenic risk score (PRS) has become widely used due to its simplicity and effectiveness, where only summary statistics from genome-wide association studies are needed in the standard method. Recently, several methods have been proposed to improve standard PRS by utilizing external information, such as linkage disequilibrium and functional annotations. In this paper, we introduce EB-PRS, a novel method that leverages information for effect sizes across all the markers to improve prediction accuracy. Compared to most existing genetic risk prediction methods, our method does not need to tune parameters nor external information. Real data applications on six diseases, including asthma, breast cancer, celiac disease, Crohn’s disease, Parkinson’s disease and type 2 diabetes show that EB-PRS achieved 307.1%, 42.8%, 25.5%, 3.1%, 74.3% and 49.6% relative improvements in terms of predictive r2 over standard PRS method with optimally tuned parameters. Besides, compared to LDpred that makes use of LD information, EB-PRS also achieved 37.9%, 33.6%, 8.6%, 36.2%, 40.6% and 10.8% relative improvements. We note that our method is not the first method leveraging effect size distributions. Here we first justify our method by presenting theoretical optimal property over existing methods in this class of methods, and substantiate our theoretical result with extensive simulation results. The R-package EBPRS that implements our method is available on CRAN.Author summary: Genetic risk prediction is of considerable importance in human genetics. Calculating polygenic risk score (PRS) is the most commonly used approach due to its simplicity as well as the computational efficiency. Many PRS calculation methods have been proposed for accurate prediction by borrowing information from external panels or datasets. Here, instead of external information, we propose a novel PRS method leveraging internal information, namely the effect size distributions, to improve prediction accuracy. Neither parameter tuning nor external information is needed for our method. Extensive simulations and real data applications demonstrate that our method has substantial improvement over existing methods in terms of prediction accuracy.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1007565
    DOI: 10.1371/journal.pcbi.1007565
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    References listed on IDEAS

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    1. Yiming Hu & Qiongshi Lu & Wei Liu & Yuhua Zhang & Mo Li & Hongyu Zhao, 2017. "Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction," PLOS Genetics, Public Library of Science, vol. 13(6), pages 1-22, June.
    2. Yiming Hu & Qiongshi Lu & Ryan Powles & Xinwei Yao & Can Yang & Fang Fang & Xinran Xu & Hongyu Zhao, 2017. "Leveraging functional annotations in genetic risk prediction for human complex diseases," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-16, June.
    3. 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.
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    1. Minta Thomas & Yu-Ru Su & Elisabeth A. Rosenthal & Lori C. Sakoda & Stephanie L. Schmit & Maria N. Timofeeva & Zhishan Chen & Ceres Fernandez-Rozadilla & Philip J. Law & Neil Murphy & Robert Carreras-, 2023. "Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Sara Kress & Akinori Hara & Claudia Wigmann & Takehiro Sato & Keita Suzuki & Kim-Oanh Pham & Qi Zhao & Ashtyn Areal & Atsushi Tajima & Holger Schwender & Hiroyuki Nakamura & Tamara Schikowski, 2022. "The Role of Polygenic Susceptibility on Air Pollution-Associated Asthma between German and Japanese Elderly Women," IJERPH, MDPI, vol. 19(16), pages 1-11, August.
    3. Wei Jiang & Ling Chen & Matthew J. Girgenti & Hongyu Zhao, 2024. "Tuning parameters for polygenic risk score methods using GWAS summary statistics from training data," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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