2015 journal article

Identification of additive, dominant, and epistatic variation conferred by key genes in cellulose biosynthesis pathway in Populus tomentosa

DNA RESEARCH, 22(1), 53–67.

co-author countries: China 🇨🇳 Sweden 🇸🇪 United States of America 🇺🇸
author keywords: Chinese white poplar (Populus tomentosa); epistasis; pathway-based multiple gene association; transcript profiling; validation population
MeSH headings : Cellulose / biosynthesis; Cellulose / genetics; Epistasis, Genetic / physiology; Gene Expression Regulation, Plant / physiology; Genes, Plant / physiology; Genetic Linkage / physiology; Genetic Variation; Populus / genetics; Populus / metabolism; Quantitative Trait Loci / physiology
Source: Web Of Science
Added: August 6, 2018

Economically important traits in many species generally show polygenic, quantitative inheritance. The components of genetic variation (additive, dominant and epistatic effects) of these traits conferred by multiple genes in shared biological pathways remain to be defined. Here, we investigated 11 full-length genes in cellulose biosynthesis, on 10 growth and wood-property traits, within a population of 460 unrelated Populus tomentosa individuals, via multi-gene association. To validate positive associations, we conducted single-marker analysis in a linkage population of 1,200 individuals. We identified 118, 121, and 43 associations (P< 0.01) corresponding to additive, dominant, and epistatic effects, respectively, with low to moderate proportions of phenotypic variance (R2). Epistatic interaction models uncovered a combination of three non-synonymous sites from three unique genes, representing a significant epistasis for diameter at breast height and stem volume. Single-marker analysis validated 61 associations (false discovery rate, Q ≤ 0.10), representing 38 SNPs from nine genes, and its average effect (R2 = 3.8%) nearly 2-fold higher than that identified with multi-gene association, suggesting that multi-gene association can capture smaller individual variants. Moreover, a structural gene–gene network based on tissue-specific transcript abundances provides a better understanding of the multi-gene pathway affecting tree growth and lignocellulose biosynthesis. Our study highlights the importance of pathway-based multiple gene associations to uncover the nature of genetic variance for quantitative traits and may drive novel progress in molecular breeding.