ArticlePLoS computational biology2017
Leveraging functional annotations in genetic risk prediction for human complex diseases.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
117 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets.Nature communications · 2021Pooled it
- Incorporating European GWAS findings improve polygenic risk prediction accuracy of breast cancer among East Asians.Genetic epidemiology · 2021Pooled it
- Polygenic Scores for ADHD: A Meta-Analysis.Research on child and adolescent psychopathology · 2021Pooled it
- Transcriptome-wide association analysis of Alzheimer's disease: construction and clinical validation of transcriptomic risk scores.European archives of psychiatry and clinical neuroscience · 2026Article
- Constructing Genetic Risk Scores: Robust Bayesian Approach through Projected Summary Statistics and Flexible Shrinkage.Journal of the American Statistical Association · 2026Article
- Integrating Genetic, Environmental, Cognitive, and Temperament Data for ADHD Prediction in Explainable Deep Learning Models.medRxiv : the preprint server for health sciences · 2026Article
- Improving genomic prediction accuracy of complex traits by integrating massive types of functional annotation information.Nature communications · 2026Article
- Integrating network annotation from multiple correlated traits to improve polygenic risk scores based on GWAS summary statistics.Research square · 2026Article
- The polygenic, omnigenic and stratagenic models of complex disease risk.Nature genetics · 2026Review
- Improving performance of polygenic risk scores for hypertension across two ancestry groups.PloS one · 2026Article
- Article
- Genetic atlas of plasma metabolome across 40 human common diseases: mapping causal metabolites to disease risk.Genome medicine · 2025Article
- Improving performance of polygenic risk scores for hypertension across two ancestry groups.medRxiv : the preprint server for health sciences · 2025Article
- Statistical construction of calibrated prediction intervals for polygenic score-based phenotype prediction.Nature genetics · 2025Article
- The Role of Polygenic Risk Score in the General Population: Current Status and Future Prospects.Journal of atherosclerosis and thrombosis · 2025Review
- Recent Statistical Innovations in Human Genetics.Annals of human genetics · 2025Review
- Pathway-Specific Polygenic Scores for Predicting Clinical Lithium Treatment Response in Patients With Bipolar Disorder.Biological psychiatry global open science · 2025Article
- PGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohorts.Genome medicine · 2025Article
- Improving polygenic prediction from whole-genome sequencing data by leveraging predicted epigenomic features.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Incorporating multiple functional annotations to improve polygenic risk prediction accuracy.Cell genomics · 2025Article
57 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Genetic risk prediction is an important goal in human genetics research and precision medicine. Accurate prediction models will have great impacts on both disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome wide association studies (GWAS), genetic risk prediction accuracy remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes in the presence of linkage disequilibrium. In this paper, we introduce AnnoPred, a principled framework that leverages diverse types of genomic and epigenomic functional annotations in genetic risk prediction for complex diseases. AnnoPred is trained using GWAS summary statistics in a Bayesian framework in which we explicitly model various functional annotations and allow for linkage disequilibrium estimated from reference genotype data. Compared with state-of-the-art risk prediction methods, AnnoPred achieves consistently improved prediction accuracy in both extensive simulations and real data.
Indexed as
Identifiers
What OpenQuestion holds
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.