Genetic variants and underlying mechanism influencing variance heterogeneity in maize

Authors: LI, HUI - Huazhong Agricultural University; WANG, MIN - Huazhong Agricultural University; LI, WEIJUN - Huazhong Agricultural University; HE, LINLIN - Huazhong Agricultural University; ZHOU, YUANYUAN - Huazhong Agricultural University; ZHU, JIANTANG - Huazhong Agricultural University; RONGHUI, CHE - Huazhong Agricultural University; Warburton, Marilyn; YANG, XIAOHONG - Huazhong Agricultural University; YAN, JIANBING - Huazhong Agricultural University

Submitted to: Plant Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/20/2020
Publication Date: 4/28/2020
Citation: Li, H., Wang, M., Li, W., He, L., Zhou, Y., Zhu, J., Ronghui, C., Warburton, M.L., Yang, X., Yan, J. 2020. Genetic variants and underlying mechanism influencing variance heterogeneity in maize. Plant Journal. 103(3):1089–1102. https://doi.org/10.1111/tpj.14786.
DOI: https://doi.org/10.1111/tpj.14786

Interpretive Summary: Plant breeding has always tried to select the plants with the best traits. Traditionally, this has been done by selecting the plants that looked the best, but with some traits, plants could look good because of factors in the environment in which they were grown; this good performance was not passed on to their offspring. Recently, geneticists have been able to identify some of the genes that cause good expression of the trait and select them in the lab; while these are passed on to the offspring, not all the genetic information needed for best expression of the trait have ever been identified. This is partly because sometimes, the combination of two genes gives a synergistic boost to the trait, and having only one of the two genes gives less than half as good performance. This extra boost is often hidden by traditional genetics experiments, and this paper shows an analysis method to uncover these synergies and exploit them. This should improve plant breeding for any trait of interest, and will therefore be a very important new plant breeding tool.

Technical Abstract: Traditional genetic studies focus on identifying genetic variants associated with the mean value of a quantitative trait. Because genetic variants also influence phenotypic variation via heterogeneity, we performed a variance-heterogeneity genome wide association study (vGWAS) to examine contribution of variance heterogeneity to oil-related quantitative traits. We identified 79 unique vSNPs from the sequences of 77 candidate variance heterogeneity genes of 21 oil-related traits using Levene’ test (P < 1.0 × 10-5). About one-third of the candidate genes encode enzymes working in lipid metabolic pathways, and most of which define clear expression variance QTLs (evQTL). The vSNPs specifically associated with the genetic variance heterogeneity of oil concentration, 40% can be explained by additional linked mean-effects genetic variants. Furthermore, we demonstrated that gene x gene interactions play important roles in the formation of fatty acid compositional variance heterogeneity. The interaction pattern was validated for one gene pair (GRMZM2G035341 and GRMZM2G152328) using Yeast two-hybrid (Y2H), Bimolecular fluorescent complimentary (BiFC) and Co-immunoprecipitation (Co-IP) analysis. Our findings have implications for uncovering the genetic basis of hidden additive genetic effects, epistatic interaction effects, and indicated opportunities to stabilize efficient high-oil maize breeding and selection.