ArticleHuman molecular genetics2015
Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models.
Article in Human molecular genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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
20 citing papers in PubMed, 26 citations in OpenAlex.
- Genetic Susceptibility, Dietary Protein Intake, and Changes of Blood Pressure: The POUNDS Lost Trial.Hypertension (Dallas, Tex. : 1979) · 2019Trial
- Ensemble learning for integrative prediction of genetic values with genomic variants.BMC bioinformatics · 2024Article
- Multibreed genomic prediction using summary statistics and a breed-origin-of-alleles approach.Heredity · 2023Article
- Translational Bioinformatics for Human Reproductive Biology Research: Examples, Opportunities and Challenges for a Future Reproductive Medicine.International journal of molecular sciences · 2022Review
- A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2021Article
- GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study.Scientific reports · 2021Article
- A genome-wide association study identifying the SNPs predictive of rapid joint destruction in patients with rheumatoid arthritis.Biomedical reports · 2021Article
- Differences in clinical and genetic characteristics between early- and late-onset narcolepsy in a Han Chinese cohort.Neural regeneration research · 2020Article
- Investigation of prediction accuracy and the impact of sample size, ancestry, and tissue in transcriptome-wide association studies.Genetic epidemiology · 2020Article
- Article
- Genome-wide association study of the seed transmission rate of soybean mosaic virus and associated traits using two diverse population panels.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Article
- Pitfalls and Remedies for Cross Validation with Multi-trait Genomic Prediction Methods.G3 (Bethesda, Md.) · 2019Article
- Integration of genetic and clinical information to improve imputation of data missing from electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Prediction of treatment response in rheumatoid arthritis patients using genome-wide SNP data.Genetic epidemiology · 2018Article
- An update on the genetics of hyperuricaemia and gout.Nature reviews. Rheumatology · 2018Review
- Know Me! Unraveling the Riddle of Calcific Aortic Valve Disease by Bioinformatics.The Tohoku journal of experimental medicine · 2017Article
- Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models.Nature communications · 2017Article
- Cross-Validation Without Doing Cross-Validation in Genome-Enabled Prediction.G3 (Bethesda, Md.) · 2016Article
- Breeding for resistance to gastrointestinal nematodes - the potential in low-input/output small ruminant production systems.Veterinary parasitology · 2016Review
- A comparison of genomic profiles of complex diseases under different models.BMC medical genomics · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 3 institutions in 1 country.
Funding
Abstract
We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge.
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.