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

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.

Genetic diversity for starch quality and alkali spreading value in sorghum

“…The sorghum diversity panel (SDP) included nearly 800 sorghum conversion lines and represents the genetic diversity of the crop (Stephens et al, 1967;Rosenow et al, 1997aRosenow et al, , 1997b. The SDP is larger than the commonly used Sorghum Association Panel (SAP) (Adeyanju et al, 2015;Boyles et al, T A B L E 2 GWAS results from MLM with significant SNPs and candidate genes for West Lafayette 2013 (WL13) (10 most significant SNPs and S10_60808604, S04_67849488) and 2017 (WL17) at FDR 0.05 2017; Casa et al, 2008;Cuevas et al, 2017;Mace et al, 2013;Morris et al, 2013;Shenstone et al, 2018;Sukumaran et al, 2012). The race and regional classification from our principal component analysis confirmed previous reports on the different sorghum races and geographic origins.…”

“…Over 800 sorghum conversion lines (SC lines) are currently available (Hayes et al., 2015; Kimber et al., 2013). The collection of SC lines was developed to represent the genetic diversity of the crop and provides the basis for sorghum diversity panels such as the Sorghum Association Panel (SAP) (Adeyanju, Little, Yu, & Tesso, 2015; Boyles et al., 2017; Casa et al., 2008; Cuevas, Rosa‐Valentin, Hayes, Rooney, & Hoffmann, 2017; Mace et al., 2013; Morris et al., 2013; Shenstone et al., 2018; Sukumaran et al., 2012) and the Sorghum Diversity Panel (SDP) (Hayes et al., 2015).…”

Genome Resequencing, Improvement of Variant Calling, and Population Genomic Analyses Provide Insights into the Seedlessness in the GenusVitis

“…Seeded and seedless traits were encoded as binary traits of 1 (control) and 2 (case), respectively. Case-control logistic mixed model association test was performed using GENESIS (Gogarten et al 2019) with default logistic mixed model association parameters assessed by Shenstone et al (2018). The Manhattan plots of XP-CLR scores and logistic association p-values were constructed using qqman (Turner 2018) in R package.…”

Heterosis, inbreeding depression and genetic divergence in maize

“…As an alternative, a logistic mixed regression model has been proposed as a methodology that accounts for both population structure and cryptic relatedness when the residuals do not follow a normal distribution and/or when the residual variance varies according to a covariate (i.e. heteroscedasticity) (Chen 2019;Chen et al 2019;Chen et al 2016;Shenstone et al 2018;Wang et al 2016). The logistic mixed regression model can be specified as:…”

“…Gauss-Hermite Quadrature, Adaptive Gauss-Hermite Quadrature, or Laplace approximation, to find the marginal likelihood function (Christensen 2006;Cox and Snell 1981;Kim et al 2013;Kleinbaum 2010;Wang et al 2011;Whittemore and Halpern 2003). The logistic mixed regression model, using one of the numerical or approximated methods, can be implemented in SAS software (SAS Institute, Cary, NC), by using the "PROC GLIMMIX" or "PROC NLMIXED" procedures (Kim et al 2013;Milet and Perdry 2020;Shenstone et al 2018),…”