Genome-Wide Association Mapping of Starch Pasting Properties in Maize Using Single-Locus and Multi-Locus Models

Abstract

Maize starch plays a critical role in food processing and industrial application. The pasting properties, the most important starch characteristics, have enormous influence on fabrication property, flavor characteristics, storage, cooking, and baking. Understanding the genetic basis of starch pasting properties will be beneficial for manipulation of starch properties for a given purpose. Genome-wide association studies (GWAS) are becoming a powerful tool for dissecting the complex traits. Here, we carried out GWAS for seven pasting properties of maize starch with a panel of 230 inbred lines and 145,232 SNPs using one single-locus method, genome-wide efficient mixed model association (GEMMA), and three multi-locus methods, FASTmrEMMA, FarmCPU, and LASSO. We totally identified 60 quantitative trait nucleotides (QTNs) for starch pasting properties with these four GWAS methods. FASTmrEMMA detected the most QTNs (29), followed by FarmCPU (19) and LASSO (12), GEMMA detected the least QTNs (7). Of these QTNs, seven QTNs were identified by more than one method simultaneously. We further investigated locations of these significantly associated QTNs for possible candidate genes. These candidate genes and significant QTNs provide the guidance for further understanding of molecular mechanisms of starch pasting properties. We also compared the statistical powers and Type I errors of the four GWAS methods using Monte Carlo simulations. The results suggest that the multi-locus method is more powerful than the single-locus method and a combination of these multi-locus methods could help improve the detection power of GWAS.

Genome-wide association study for yield and yield related traits under reproductive stage drought in a diverse indica-aus rice panel

“…In our study, the correlation among the two seasons (DS and WS) is of lower magnitude which warrants the variable PV for traits across seasons. Our results draw similar interpretations with recent studies that conclude the effectiveness of multi-locus methods, especially Farm-CPU over singlelocus methods (like MLM) for association analysis of traits with either high or low heritability by adequately controlling false positives and negatives, indicated by sharp deviations observed for p-value distribution in qq plots (Xu et al 2018;Kaler and Purcell 2019).…”

Genome-wide association study for yield and yield related traits under reproductive stage drought in a diverse indica-aus rice panel

“…Our results draw similar interpretations with recent studies that conclude the effectiveness of multilocus methods, especially Farm-CPU over single-locus methods (like MLM) for association analysis of traits with either high or low heritability by adequately controlling false positives and negatives, indicated by sharp deviations observed for p-value distribution in qq plots(Xu et al 2018;Kaler and Purcell 2019).The significant and positive correlation among the grain yield and yield related traits except DTF and the colocation of MTAs associated with these traits indicates the contribution of grain yield related traits in contributing to yield improvement under drought stress. Most of the important economic traits such as grain yield, grain quality, biotic and abiotic stresses in different crop species are polygenic in nature.…”

Genome-Wide Association Study Reveals Novel Marker-Trait Associations (MTAs) Governing the Localization of Fe and Zn in the Rice Grain

“…Multi-locus models like FASTmrEMMAa (Zhang et al, 2018), LASSO (Xu et al, 2017), BLASSO (Tamba et al, 2017), FarmCPU, pLARmEB (Zhang et al, 2018), and pKWmEB (Ren et al, 2018) are being used to overcome the limitation above. A few recent studies on plant height and flowering time (Wallace et al, 2016), ear traits (Zhu et al, 2018), and starch pasting properties (Xu et al, 2018) in maize, yield-related features in wheat (Ward et al, 2019), stem rot resistance in soybean (Wei et al, 2017), agronomic traits in foxtail millet (Jaiswal et al, 2019), and panicle architecture in sorghum (Zhou et al, 2019), have demonstrated the power of the FarmCPU model that uses both fixed effect and random effect models iteratively to effectively control the false discovery. In the present study, a comparison of Q-Q plots obtained through different models revealed FarmCPU as a bestfit model with improved power of test statistics.…”