ArticlePLoS computational biology2022
Efficient ReML inference in variance component mixed models using a Min-Max algorithm.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- metaGE: Investigating genotype x environment interactions through GWAS meta-analysis.PLoS genetics · 2025Pooled it
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- A versatile multi-components mixed model for bacterial-Genome Wide association studies.Nature communications · 2026Article
- Predicted protein 3D structures provide essential insights into the genetic architecture underlying phenotypic diversity in maize.Genome research · 2026Article
- Powerful one-dimensional scan to detect heterotic quantitative trait loci.Nature communications · 2025Article
- Genomic prediction of agronomic traits in perennial ryegrass (Lolium perenne L.) and genotype x environment interactions at the limit of the species distribution.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Genomic prediction-aided incorporation of genetic resources into elite breeding: lessons from a collaborative multiparental design in flint maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Multi-environment GWAS uncovers markers associated to biotic stress response and genotype-by-environment interactions in stone fruit trees.Horticulture research · 2025Article
- Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions.The plant genome · 2025Article
- Genome-wide association for agro-morphological traits in a triploid banana population with large chromosome rearrangements.Horticulture research · 2025Article
- Genome-wide association analysis of flowering date in a collection of cultivated olive tree.Horticulture research · 2025Article
- A computationally efficient algorithm to leverage average information REML for (co)variance component estimation in the genomic era.Genetics, selection, evolution : GSE · 2024Article
- Unveiling the predominance of Saccharum spontaneum alleles for resistance to orange rust in sugarcane using genome-wide association.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Assessing the potential of genetic resource introduction into elite germplasm: a collaborative multiparental population for flint maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Identifying QTLs involved in hybrid performance and heterotic group complementarity: new GWAS models applied to factorial and admixed diallel maize hybrid panels.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Holistic view of the seascape dynamics and environment impact on macro-scale genetic connectivity of marine plankton populations.BMC ecology and evolution · 2023Article
- Article
- Disentangling group specific QTL allele effects from genetic background epistasis using admixed individuals in GWAS: An application to maize flowering.PLoS genetics · 2020Article
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3 authors.
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Abstract
Since their introduction in the 50's, variance component mixed models have been widely used in many application fields. In this context, ReML estimation is by far the most popular procedure to infer the variance components of the model. Although many implementations of the ReML procedure are readily available, there is still need for computational improvements due to the ever-increasing size of the datasets to be handled, and to the complexity of the models to be adjusted. In this paper, we present a Min-Max (MM) algorithm for ReML inference and combine it with several speed-up procedures. The ReML MM algorithm we present is compared to 5 state-of-the-art publicly available algorithms used in statistical genetics. The computational performance of the different algorithms are evaluated on several datasets representing different plant breeding experimental designs. The MM algorithm ranks among the top 2 methods in almost all settings and is more versatile than many of its competitors. The MM algorithm is a promising alternative to the classical AI-ReML algorithm in the context of variance component mixed models. It is available in the MM4LMM R-package.
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