ArticleGenome research2016
Multikernel linear mixed models for complex phenotype prediction.
Article in Genome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multi-omics revolution to promote plant breeding efficiency.Frontiers in plant science · 2022Pooled it
- Sugarcane Breeding in the Genomic Era: Integrative Strategies and Emerging Technologies.Plants (Basel, Switzerland) · 2026Review
- Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework.Journal of animal science and biotechnology · 2024Article
- Tree-based QTL mapping with expected local genetic relatedness matrices.American journal of human genetics · 2023Article
- Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data.Bioinformatics (Oxford, England) · 2023Article
- Fast kernel-based association testing of non-linear genetic effects for biobank-scale data.Nature communications · 2023Article
- Tree-based QTL mapping with expected local genetic relatedness matrices.bioRxiv : the preprint server for biology · 2023Article
- Bayesian linear mixed model with multiple random effects for family-based genetic studies.Frontiers in genetics · 2023Article
- A penalized linear mixed model with generalized method of moments for prediction analysis on high-dimensional multi-omics data.Briefings in bioinformatics · 2022Article
- Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data.PLoS computational biology · 2022Article
- Multi-scale inference of genetic trait architecture using biologically annotated neural networks.PLoS genetics · 2021Article
- A Bayesian linear mixed model for prediction of complex traits.Bioinformatics (Oxford, England) · 2021Article
- Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets.BMC bioinformatics · 2021Article
- Multikernel linear mixed model with adaptive lasso for complex phenotype prediction.Statistics in medicine · 2020Article
- A U-statistics for integrative analysis of multilayer omics data.Bioinformatics (Oxford, England) · 2020Article
- Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data.Bioinformatics (Oxford, England) · 2020Article
- Accurate Prediction of a Quantitative Trait Using the Genes Controlling the Trait for Gene-Based Breeding in Cotton.Frontiers in plant science · 2020Article
- VARIABLE PRIORITIZATION IN NONLINEAR BLACK BOX METHODS: A GENETIC ASSOCIATION CASE STUDYThe annals of applied statistics · 2019Article
- Jackknife Model Averaging Prediction Methods for Complex Phenotypes with Gene Expression Levels by Integrating External Pathway Information.Computational and mathematical methods in medicine · 2019Article
- Factors Affecting the Accuracy of Genomic Selection for Agricultural Economic Traits in Maize, Cattle, and Pig Populations.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Linear mixed models (LMMs) and their extensions have recently become the method of choice in phenotype prediction for complex traits. However, LMM use to date has typically been limited by assuming simple genetic architectures. Here, we present multikernel linear mixed model (MKLMM), a predictive modeling framework that extends the standard LMM using multiple-kernel machine learning approaches. MKLMM can model genetic interactions and is particularly suitable for modeling complex local interactions between nearby variants. We additionally present MKLMM-Adapt, which automatically infers interaction types across multiple genomic regions. In an analysis of eight case-control data sets from the Wellcome Trust Case Control Consortium and more than a hundred mouse phenotypes, MKLMM-Adapt consistently outperforms competing methods in phenotype prediction. MKLMM is as computationally efficient as standard LMMs and does not require storage of genotypes, thus achieving state-of-the-art predictive power without compromising computational feasibility or genomic privacy.
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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.