ArticlePLoS computational biology2020
Penalized regression and model selection methods for polygenic scores on summary statistics.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure.Briefings in bioinformatics · 2022Pooled it
- Constructing Genetic Risk Scores: Robust Bayesian Approach through Projected Summary Statistics and Flexible Shrinkage.Journal of the American Statistical Association · 2026Article
- Multivariate proteome-wide association study to identify causal proteins for Alzheimer disease.American journal of human genetics · 2025Article
- Polygenic scores and their applications in kidney disease.Nature reviews. Nephrology · 2025Review
- A review of methods and software for polygenic risk score analysis.PeerJ. Computer science · 2025Article
- Gut Microbiome Wellness Index 2 enhances health status prediction from gut microbiome taxonomic profiles.Nature communications · 2024Article
- Inferring causal direction between two traits using RAmerican journal of human genetics · 2024Article
- ON BLOCKWISE AND REFERENCE PANEL-BASED ESTIMATORS FOR GENETIC DATA PREDICTION IN HIGH DIMENSIONS.Annals of statistics · 2024Article
- Article
- Subset scanning for multi-trait analysis using GWAS summary statistics.Bioinformatics (Oxford, England) · 2024Article
- Inferring disease architecture and predictive ability with LDpred2-auto.American journal of human genetics · 2023Article
- Pruning and thresholding approach for methylation risk scores in multi-ancestry populations.Epigenetics · 2023Article
- Inference of causal metabolite networks in the presence of invalid instrumental variables with GWAS summary data.Genetic epidemiology · 2023Article
- Inferring a directed acyclic graph of phenotypes from GWAS summary statistics.bioRxiv : the preprint server for biology · 2023Article
- Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles.bioRxiv : the preprint server for biology · 2023Article
- Improving genetic risk prediction across diverse population by disentangling ancestry representations.Communications biology · 2023Article
- Development and testing of a polygenic risk score for breast cancer aggressiveness.NPJ precision oncology · 2023Article
- Sparse prediction informed by genetic annotations using the logit normal prior for Bayesian regression tree ensembles.Genetic epidemiology · 2023Article
- Clinical utility of polygenic risk scores for coronary artery disease.Nature reviews. Cardiology · 2022 · on this mapReview
- InTACT: An adaptive and powerful framework for joint-tissue transcriptome-wide association studies.Genetic epidemiology · 2021Article
Corrections and comments
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2 authors at 1 institution in 1 country.
Funding
Abstract
Polygenic scores quantify the genetic risk associated with a given phenotype and are widely used to predict the risk of complex diseases. There has been recent interest in developing methods to construct polygenic risk scores using summary statistic data. We propose a method to construct polygenic risk scores via penalized regression using summary statistic data and publicly available reference data. Our method bears similarity to existing method LassoSum, extending their framework to the Truncated Lasso Penalty (TLP) and the elastic net. We show via simulation and real data application that the TLP improves predictive accuracy as compared to the LASSO while imposing additional sparsity where appropriate. To facilitate model selection in the absence of validation data, we propose methods for estimating model fitting criteria AIC and BIC. These methods approximate the AIC and BIC in the case where we have a polygenic risk score estimated on summary statistic data and no validation data. Additionally, we propose the so-called quasi-correlation metric, which quantifies the predictive accuracy of a polygenic risk score applied to out-of-sample data for which we have only summary statistic information. In total, these methods facilitate estimation and model selection of polygenic risk scores on summary statistic data, and the application of these polygenic risk scores to out-of-sample data for which we have only summary statistic information. We demonstrate the utility of these methods by applying them to GWA studies of lipids, height, and lung cancer.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.