ArticleBioinformatics (Oxford, England)2023
SLEMM: million-scale genomic predictions with window-based SNP weighting.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Pangenome-based structural variant imputation enables large-scale genotype-phenotype studies in dairy cattle.Nature communications · 2026Article
- Incorporating GO/KEGG Functional Annotations Improves the Accuracy and Stability of Genomic Prediction Across Diverse Beef Cattle Populations.Animals : an open access journal from MDPI · 2026Article
- GWKBR: a novel method integrating machine learning and Bayesian inference framework to improve genomic prediction accuracy.Briefings in bioinformatics · 2026Article
- Leveraging Fst and Genetic Distance to Optimize Reference Sets for Enhanced Cross-Population Genomic Prediction.Animals : an open access journal from MDPI · 2026Article
- Comparison of GBLUP and WGBLUP in genomic selection for beef cattle under different LD patterns and mixed multi-population scenarios: a simulation study.BMC genomics · 2025Article
- Comparative evaluation of SNP-weighted, Bayesian, and machine learning models for genomic prediction in Holstein cattle.BMC genomics · 2025Article
- Fine-mapping methods for complex traits: essential adaptations for samples of related individuals.Briefings in bioinformatics · 2025Article
- Assessing the Impact of Different Mixing Strategies on Genomic Prediction Accuracy for Beef Cattle Breeding Values in Multi-Breed Genomic Prediction.Animals : an open access journal from MDPI · 2025Article
- Enhancing Genomic Prediction Accuracy with a Single-Step Genomic Best Linear Unbiased Prediction Model Integrating Genome-Wide Association Study Results.Animals : an open access journal from MDPI · 2025Article
- Weighted GBLUP in Simulated Beef Cattle Populations: Impact of Reference Population, Marker Density, and Heritability.Animals : an open access journal from MDPI · 2025Article
- Mutual information-based best linear unbiased prediction for enhanced genomic prediction accuracy.Journal of animal science · 2025Article
- Benchmarking of feed-forward neural network models for genomic prediction of quantitative traits in pigs.Frontiers in genetics · 2025Article
- Capturing resilience from phenotypic deviations: a case study using feed consumption and whole genome data in pigs.BMC genomics · 2024Article
- MPH: fast REML for large-scale genome partitioning of quantitative genetic variation.Bioinformatics (Oxford, England) · 2024Article
- Genome-wide association analysis of heifer livability and early first calving in Holstein cattle.BMC genomics · 2023Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
motivationThe amount of genomic data is increasing exponentially. Using many genotyped and phenotyped individuals for genomic prediction is appealing yet challenging.
resultsWe present SLEMM (short for Stochastic-Lanczos-Expedited Mixed Models), a new software tool, to address the computational challenge. SLEMM builds on an efficient implementation of the stochastic Lanczos algorithm for REML in a framework of mixed models. We further implement SNP weighting in SLEMM to improve its predictions. Extensive analyses on seven public datasets, covering 19 polygenic traits in three plant and three livestock species, showed that SLEMM with SNP weighting had overall the best predictive ability among a variety of genomic prediction methods including GCTA's empirical BLUP, BayesR, KAML, and LDAK's BOLT and BayesR models. We also compared the methods using nine dairy traits of ∼300k genotyped cows. All had overall similar prediction accuracies, except that KAML failed to process the data. Additional simulation analyses on up to 3 million individuals and 1 million SNPs showed that SLEMM was advantageous over counterparts as for computational performance. Overall, SLEMM can do million-scale genomic predictions with an accuracy comparable to BayesR. AVAILABILITY AND IMPLEMENTATION: The software is available at https://github.com/jiang18/slemm.
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