Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes
Open accessAbstract
We introduce cross-trait penalized regression (CTPR), a powerful and practical approach for multi-trait polygenic risk prediction in large cohorts. Specifically, we propose a novel cross-trait penalty function with the Lasso and the minimax concave penalty (MCP) to incorporate the shared genetic effects across multiple traits for large-sample GWAS data. Our approach extracts information from the secondary traits that is beneficial for predicting the primary trait based on individual-level genotypes and/or summary statistics. Our novel implementation of a parallel computing algorithm makes it feasible to apply our method to biobank-scale GWAS data. We illustrate our method using large-scale GWAS data (~1M SNPs) from the UK Biobank ( N = 456,837). We show that our multi-trait method outperforms the recently proposed multi-trait analysis of GWAS (MTAG) for predictive performance. The prediction accuracy for height by the aid of BMI improves from R 2 = 35.8% (MTAG) to 42.5% (MCP + CTPR) or 42.8% (Lasso + CTPR) with UK Biobank data.
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The elastic net algorithm was used because penalization improves prediction accuracy and maintains clinical interpretability compared with less transparent machine learning algorithms.
“We used the elastic net algorithm, given substantial evidence that penalization improves prediction accuracy,25–27 alongside its well-established accuracy and robustness28,29 and ability to maintain clinical interpretability compared with less transparent machine learning algorithms (eg, random forests, neural networks).”
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- Authors
- Chung, W., Chen, J., Turman, C., Lindstrom, S., Zhu, Z., Loh, P.-R., Kraft, P., & Liang, L.
- Affiliations
- Harvard University (US)
- Mayo Clinic (US)
- Broad Institute (US)
- Corresponding author
- Liang, Liming
- Venue
- Nature Communications
- Published
- 2019-02-04
- Volume
- 10
- Issue
- 1
- Pages
- 569-569
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6361917
- OpenAlex
- W2914485454
- Cited by
- 60
- References
- 49
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 9 · 2023: 8 · 2022: 15 · 2021: 9 · 2020: 2 · 2019: 7
- FWCI
- 4.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (16549, 5r01ca049449-18, 5p01dk070756-05, R01 CA49449, UM1-CA-176726, P01DK070756)
- Medical Research Council (MC_PC_17228)
- National Heart, Lung, and Blood Institute (R01HL035464, R01HL116854, R01HL034594, R01HL088521)
- National Human Genome Research Institute (U01HG004728)
- National Eye Institute (R01EY015473, P30EY014104, R01EY022305)
- National Cancer Institute (R03CA165131, R01CA131332, P01CA087969, P01CA055075, UM1CA186107, R01CA050385, R01CA067262)
- National Institute on Deafness and Other Communication Disorders (R03DC013373)
- National Institute of General Medical Sciences (R01GM105857)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- polygenic risk prediction, multi-trait GWAS, LASSO, minimax concave penalty, shared genetic effects, parallel computing, height prediction, body mass index, UK Biobank, biobank-scale data
- MeSH terms
- Algorithms, Genotype, Humans, Models, Genetic, Phenotype, Quantitative Trait Loci, Genome-Wide Association Study
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