ArticleNature communications2015
A random forest approach to capture genetic effects in the presence of population structure.
Article in Nature communications, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis 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
54 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning for genetic prediction of psychiatric disorders: a systematic review.Molecular psychiatry · 2021Pooled it
- Machine Learning on a Genome-wide Association Study to Predict Late Genitourinary Toxicity After Prostate Radiation Therapy.International journal of radiation oncology, biology, physics · 2018Trial
- AlphaGenome Enhances Personal Gene Expression Prediction but Retains Key Limitations.bioRxiv : the preprint server for biology · 2026Article
- Immune cells and rheumatic heart disease: A 2-sample and Bayesian weighted Mendelian randomization study.Medicine · 2026Article
- Genetic structure analysis of a Northwest Chinese population using a self-developed DIP system and machine learning for forensic ancestry inference.BMC genomics · 2026Article
- Personalized gene expression prediction in the era of deep learning: a review.Briefings in bioinformatics · 2026Review
- Application of artificial intelligence in life science: Historical review and future perspectives.Fundamental research · 2026Review
- Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.Molecular psychiatry · 2025Review
- AlphaGenome Enhances Personal Gene Expression Prediction but Retains Key Limitations.Research square · 2025Article
- Deciphering the evolutionary imprints of Camellia oleifera Abel.: delineating its distinct phylogeographic structure and demographic history through microsatellite and plastid fragment.BMC plant biology · 2025Article
- Applying traditional and machine learning-based GWAS approaches for marker-trait identification in wheat.Frontiers in plant science · 2025Article
- Artificial intelligence in plant science: from image-based phenotyping to yield and trait prediction.Frontiers in plant science · 2025Review
- The Use of AI for Phenotype-Genotype Mapping.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Modeling Chickpea Productivity with Artificial Image Objects and Convolutional Neural Network.Plants (Basel, Switzerland) · 2024Article
- Patterns of human and bots behaviour on Twitter conversations about sustainability.Scientific reports · 2024Article
- Introducing GWAStic: a user-friendly, cross-platform solution for genome-wide association studies and genomic prediction.Bioinformatics advances · 2024Article
- Phenomic Selection for Hybrid Rapeseed Breeding.Plant phenomics (Washington, D.C.) · 2024Article
- Gut-resident microorganisms and their genes are associated with cognition and neuroanatomy in children.Science advances · 2023Article
- Epistasis regulates genetic control of cardiac hypertrophy.Research square · 2023Article
- Comparative analysis of endophyte diversity ofPlant biotechnology (Tokyo, Japan) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate mapping of causal variants in genome-wide association studies requires the consideration of both, confounding factors (for example, population structure) and nonlinear interactions between individual genetic variants. Here, we propose a method termed 'mixed random forest' that simultaneously accounts for population structure and captures nonlinear genetic effects. We test the model in simulation experiments and show that the mixed random forest approach improves detection power compared with established approaches. In an application to data from an outbred mouse population, we find that mixed random forest identifies associations that are more consistent with prior knowledge than competing methods. Further, our approach allows predicting phenotypes from genotypes with greater accuracy than any of the other methods that we tested. Our results show that approaches that simultaneously account for both, confounding due to population structure and epistatic interactions, are important to fully explain the heritable component of complex quantitative traits.
Indexed as
Identifiers
26109276What 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.