Open AccessAn assessment of the performance of the logistic mixed model for analyzing binary traits in maize and sorghum diversity panels
Author(s) -
Esperanza Shenstone,
Julian Cooper,
Brian R. Rice,
Martin Bohn,
Tiffany M. Jamann,
Alexander E. Lipka
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0207752
Subject(s) - genome wide association study , mixed model , spurious relationship , trait , diversity (politics) , generalized linear mixed model , genetic diversity , statistics , random effects model , biology , computer science , genetics , mathematics , meta analysis , population , medicine , environmental health , sociology , gene , genotype , single nucleotide polymorphism , anthropology , programming language
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.