ArticlePloS one2018
An assessment of the performance of the logistic mixed model for analyzing binary traits in maize and sorghum diversity panels.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops.BMC bioinformatics · 2023Article
- Vetinformatics from functional genomics to drug discovery: Insights into decoding complex molecular mechanisms of livestock systems in veterinary science.Frontiers in veterinary science · 2022Review
- Suitability of GWAS as a Tool to Discover SNPs Associated with Tick Resistance in Cattle: A Review.Pathogens (Basel, Switzerland) · 2021Review
- Genetic diversity for starch quality and alkali spreading value in sorghum.The plant genome · 2021Article
- Genome Resequencing, Improvement of Variant Calling, and Population Genomic Analyses Provide Insights into the Seedlessness in the GenusG3 (Bethesda, Md.) · 2020Article
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Authors and funding
6 authors.
Funding
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Abstract
The logistic mixed model (LMM) is well-suited for the genome-wide association study (GWAS) of binary agronomic traits because it can include fixed and random effects that account for spurious associations. The recent implementation of a computationally efficient model fitting and testing approach now makes it practical to use the LMM to search for markers associated with such binary traits on a genome-wide scale. Therefore, the purpose of this work was to assess the applicability of the LMM for GWAS in crop diversity panels. We dichotomized three publicly available quantitative traits in a maize diversity panel and two quantitative traits in a sorghum diversity panel, and them performed a GWAS using both the LMM and the unified mixed linear model (MLM) on these dichotomized traits. Our results suggest that the LMM is capable of identifying statistically significant marker-trait associations in the same genomic regions highlighted in previous studies, and this ability is consistent across both diversity panels. We also show how subpopulation structure in the maize diversity panel can underscore the LMM's superior control for spurious associations compared to the unified MLM. These results suggest that the LMM is a viable model to use for the GWAS of binary traits in crop diversity panels and we therefore encourage its broader implementation in the agronomic research community.
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