ArticlePLoS genetics2019
Fast and flexible linear mixed models for genome-wide genetics.
Article in PLoS genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- Genome-wide association study of seedling leaf rust resistance in European winter wheat cultivars.Journal of applied genetics · 2025Article
- GWAS-based population genetic analysis identifies bZIP29 as a heterotic gene in maize.Plant communications · 2025Article
- Natural genetic variation quantitatively regulates heart rate and dimension.Nature communications · 2025Article
- Rapid and accurate multi-phenotype imputation for millions of individuals.Nature communications · 2025Article
- Matrix sketching framework for linear mixed models in association studies.Genome research · 2024Article
- Evidence that variation in root anatomy contributes to local adaptation in Mexican native maize.Evolutionary applications · 2024Article
- Genome-wide association analysis reveal candidate genes and haplotypes related to root weight in cucumber (Frontiers in plant science · 2024Article
- BulkLMM: Real-time genome scans for multiple quantitative traits using linear mixed models.bioRxiv : the preprint server for biology · 2023Article
- Tree-based QTL mapping with expected local genetic relatedness matrices.American journal of human genetics · 2023Article
- Automated machine learning for genome wide association studies.Bioinformatics (Oxford, England) · 2023Article
- Tree-based QTL mapping with expected local genetic relatedness matrices.bioRxiv : the preprint server for biology · 2023Article
- An Efficient Bayesian Method for Estimating the Degree of the Skewness of X Chromosome Inactivation Based on the Mixture of General Pedigrees and Unrelated Females.Biomolecules · 2023Article
- Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS.Plants (Basel, Switzerland) · 2022Review
- Article
- An adaptive teosinteProceedings of the National Academy of Sciences of the United States of America · 2022Article
- Average semivariance directly yields accurate estimates of the genomic variance in complex trait analyses.G3 (Bethesda, Md.) · 2022Article
- Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci.G3 (Bethesda, Md.) · 2022Article
- Analysis of genotype-by-environment interactions in a maize mapping population.G3 (Bethesda, Md.) · 2022Article
- Navigating the pitfalls of applying machine learning in genomics.Nature reviews. Genetics · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Linear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced power or biased inference. We describe Grid-LMM (https://github.com/deruncie/GridLMM), an extendable algorithm for repeatedly fitting complex linear models that account for multiple sources of heterogeneity, such as additive and non-additive genetic variance, spatial heterogeneity, and genotype-environment interactions. Grid-LMM can compute approximate (yet highly accurate) frequentist test statistics or Bayesian posterior summaries at a genome-wide scale in a fraction of the time compared to existing general-purpose methods. We apply Grid-LMM to two types of quantitative genetic analyses. The first is focused on accounting for spatial variability and non-additive genetic variance while scanning for QTL; and the second aims to identify gene expression traits affected by non-additive genetic variation. In both cases, modeling multiple sources of heterogeneity leads to new discoveries.
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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.