ArticleAmerican journal of human genetics2014
Effective genetic-risk prediction using mixed models.
Article in American journal of human genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Exploratory Association of 5' and 3' UTRs Variants inInternational journal of molecular sciences · 2026Article
- MIXED MODELING APPROACH FOR CHARACTERIZING THE GENETIC EFFECTS IN A LONGITUDINAL PHENOTYPE.The annals of applied statistics · 2025Article
- Article
- Article
- Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS.Plants (Basel, Switzerland) · 2022Review
- Polygenic Risk Score in African populations: progress and challenges.F1000Research · 2022Review
- Polygenic risk scores: the future of cancer risk prediction, screening, and precision prevention.Medical review (2021) · 2021Review
- GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study.Scientific reports · 2021Article
- A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information.Journal of the American Statistical Association · 2021Article
- Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.American journal of human genetics · 2020Article
- SummaryAUC: a tool for evaluating the performance of polygenic risk prediction models in validation datasets with only summary level statistics.Bioinformatics (Oxford, England) · 2019Article
- Predicting Polygenic Risk of Psychiatric Disorders.Biological psychiatry · 2019Review
- Estimating variance components in population scale family trees.PLoS genetics · 2019Article
- Progress in Polygenic Composite Scores in Alzheimer's and Other Complex Diseases.Trends in genetics : TIG · 2019Review
- Reverse GWAS: Using genetics to identify and model phenotypic subtypes.PLoS genetics · 2019Article
- Complex Trait Prediction from Genome Data: Contrasting EBV in Livestock to PRS in Humans: Genomic Prediction.Genetics · 2019Review
- Population structure in genetic studies: Confounding factors and mixed models.PLoS genetics · 2018Review
- Transformation of Summary Statistics from Linear Mixed Model Association on All-or-None Traits to Odds Ratio.Genetics · 2018Article
- Multiethnic polygenic risk scores improve risk prediction in diverse populations.Genetic epidemiology · 2017Article
- Risk Prediction Modeling on Family-Based Sequencing Data Using a Random Field Method.Genetics · 2017Article
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Authors and funding
2 authors.
Funding
Abstract
For predicting genetic risk, we propose a statistical approach that is specifically adapted to dealing with the challenges imposed by disease phenotypes and case-control sampling. Our approach (termed Genetic Risk Scores Inference [GeRSI]), combines the power of fixed-effects models (which estimate and aggregate the effects of single SNPs) and random-effects models (which rely primarily on whole-genome similarities between individuals) within the framework of the widely used liability-threshold model. We demonstrate in extensive simulation that GeRSI produces predictions that are consistently superior to current state-of-the-art approaches. When applying GeRSI to seven phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) study, we confirm that the use of random effects is most beneficial for diseases that are known to be highly polygenic: hypertension (HT) and bipolar disorder (BD). For HT, there are no significant associations in the WTCCC data. The fixed-effects model yields an area under the ROC curve (AUC) of 54%, whereas GeRSI improves it to 59%. For BD, using GeRSI improves the AUC from 55% to 62%. For individuals ranked at the top 10% of BD risk predictions, using GeRSI substantially increases the BD relative risk from 1.4 to 2.5.
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Registered trials
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.