ReviewBioMed research international2015
The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics.
Review in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
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- Haplotype-based autoencoders can reduce the dataset dimension and estimate haplotype block effects in different crop species.BMC bioinformatics · 2025Article
- Integrating gene expression data via weighted multiple kernel ridge regression improved accuracy of genomic prediction.Genetics, selection, evolution : GSE · 2025Article
- Multivariate whole brain neurodegenerative-cognitive-clinical severity mapping in the Alzheimer's disease continuum using explainable AI.medRxiv : the preprint server for health sciences · 2025Article
- Intervention of machine learning in bladder cancer research using multi-omics datasets: systematic review on biomarker identification.Discover oncology · 2025Review
- Machine Learning Enabled Multidimensional Data Utilization Through Multi-Resonance Architecture: A Pathway to Enhanced Accuracy in Biosensing.ACS omega · 2025Article
- An explainable AI approach for mapping multivariate regional brain age and clinical severity patterns in Alzheimer's disease.Biology methods & protocols · 2025Article
- Multivariate Sequential Analytics for Cardiovascular Disease Event Prediction.Methods of information in medicine · 2022Article
- Medication adherence prediction through temporal modelling in cardiovascular disease management.BMC medical informatics and decision making · 2022Article
- Comparison of artificial intelligence algorithms and their ranking for the prediction of genetic merit in sheep.Scientific reports · 2022Article
- Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma.BMC bioinformatics · 2022Article
- Prediction of risk-associated genes and high-risk liver cancer patients from their mutation profile: benchmarking of mutation calling techniques.Biology methods & protocols · 2022Article
- Evaluation and Prediction on the Effect of Ionic Properties of Solvent Extraction Performance of Oily Sludge Using Machine Learning.Molecules (Basel, Switzerland) · 2021Article
- Prediction of plant-level tomato biomass and yield using machine learning with unmanned aerial vehicle imagery.Plant methods · 2021Article
- Limited haplotype diversity underlies polygenic trait architecture across 70 years of wheat breeding.Genome biology · 2021Article
- Item response theory as a feature selection and interpretation tool in the context of machine learning.Medical & biological engineering & computing · 2021Article
- Reinventing quantitative genetics for plant breeding: something old, something new, something borrowed, something BLUE.Heredity · 2020Review
- CUX2, BRAP and ALDH2 are associated with metabolic traits in people with excessive alcohol consumption.Scientific reports · 2020Article
- Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk.American journal of human genetics · 2020Article
Corrections and comments
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2 authors.
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Abstract
In recent years, there has been a considerable amount of research on the use of regularization methods for inference and prediction in quantitative genetics. Such research mostly focuses on selection of markers and shrinkage of their effects. In this review paper, the use of ridge regression for prediction in quantitative genetics using single-nucleotide polymorphism data is discussed. In particular, we consider (i) the theoretical foundations of ridge regression, (ii) its link to commonly used methods in animal breeding, (iii) the computational feasibility, and (iv) the scope for constructing prediction models with nonlinear effects (e.g., dominance and epistasis). Based on a simulation study we gauge the current and future potential of ridge regression for prediction of human traits using genome-wide SNP data. We conclude that, for outcomes with a relatively simple genetic architecture, given current sample sizes in most cohorts (i.e., N < 10,000) the predictive accuracy of ridge regression is slightly higher than the classical genome-wide association study approach of repeated simple regression (i.e., one regression per SNP). However, both capture only a small proportion of the heritability. Nevertheless, we find evidence that for large-scale initiatives, such as biobanks, sample sizes can be achieved where ridge regression compared to the classical approach improves predictive accuracy substantially.
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