Genome-wide association study for non-normally distributed traits: A case study for stalk lodging in maize

The abundance of new genomic information available has increased the ability of computational tools to study the genetic basis of agricultural traits, notably with the application of the Genome-Wide Association Study (GWAS). A limitation of GWAS is that the assumptions underlying the linear model typically used to conduct the analysis are often violated in nature, and in such cases, the linear model is inappropriate to use. Alternatively, the mixed logistic regression model is well-suited for a genome-wide association study of binomially distributed agronomic traits because it can include fixed and random effects that account for spurious associations. However, the computational burden associated with fitting this model renders it inefficient to use at every genetic marker that are analyzed in the genome-wide association study.Therefore, the purpose of this work was to assess the ability of simpler statistical models to identify promising subsets of genome-wide markers to apply to the mixed logistic regression model. We tested this approach on stalk lodging, a binomially distributed trait measured on a maize (Zea mays L.) diversity panel. This analysis culminated in the mixed logistic regression model identifying genomic regions coinciding with signals associated with closely related quantitative traits. Using genomic data from the same panel, we conducted a simulation study to determine which parameters of the binomial distribution most likely contribute to the detection of quantitative trait nucleotides. The results suggest that the discovery of such signals is maximized when the probability of a successful Bernoulli trial is 0.5. Based on our findings, we present an analytical framework that involves phenotyping binomially distributed traits so that the possibility of identifying associated markers is maximized and then prioritizes subsets of genome-wide markers for fitting the mixed logistic regression model; such prioritization should make it practical to use the mixed logistic regression model to test for marker-trait associations on an average computer

An assessment of the performance of the logistic mixed model for analyzing binary traits in maize and sorghum diversity panels.

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