Title: Analysis of genotype-by-environment interaction in wheat using a structural equation model and chromosome substitution lines Authors
|Dhungana, P - UNIV. OF NEBRASKA|
|Eskridge, K - UNIV. OF NEBRASKA|
|Baenziger, P - UNIV. OF NEBRASKA|
|Gill, K - WASHINGTON STATE UNIV.|
|Dweikat, I - UNIV. OF NEBRASKA|
Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: September 20, 2006
Publication Date: March 1, 2007
Citation: Dhungana, P., Eskridge, K.M., Baenziger, P.S., Campbell, B.T., Gill, K.S., Dweikat, I. 2007. Analysis of genotype-by-environment interaction in wheat using a structural equation model and chromosome substitution lines. Crop Science 47:477-484. Interpretive Summary: Understanding how genetic and environmental factors influence complex traits in crop production systems is a challenging problem in the plant sciences. In wheat production systems, grain yield is one of the most complex and economically important traits. Grain yield is known to be controlled by a combination of genetic, physiological, and environmental factors and is also known to result from the product of three yield component traits: 1) the number of spikes per square meter, 2) the number of kernels per spike, and 3) kernel weight. The complex relationship among genetic and environmental factors for yield component traits and grain yield causes individual wheat cultivars to display inconsistent yields across different growing environments. These complex relationships also change during different stages of plant development. The overall biological system involved among these complex relationships is not well understood. In the present study, we focus on the inconsistent grain yield expression known to be influenced by the expression of genes located on chromosome 3A of wheat. The effect of specific genes controlling the number of spikes per square meter and grain yield were shown to be greatest in environments with higher temperatures during reproductive growth. At the same time, grain yield expression was also lowered due to the indirect effects of genes for thousand kernel weight and the number of kernels per spike. The additional insight provided by this approach will facilitate our basic understanding of the biology connected with complex traits in other plants and organisms. An enhanced understanding of complex traits will facilitate the scientific community’s ability to make future improvements in these traits in a range of organisms.
Technical Abstract: Understanding how genetic and environmental factors influence complex traits, such as wheat grain yield, is a challenging problem in the plant sciences. Wheat grain yield is dependent on a number of supporting traits influenced by relationships between many different genes and a multitude of environmental conditions that affect the traits at different stages during plant development. These differential relationships are defined as genotype-by-environment interaction (GEI), which is the result of complex interrelationships among physiological, molecular, and environmental variables and requires a sensible model that incorporates direct and indirect effects of these complex interrelationships. In the present study, we have demonstrated a robust method to study GEI and complex traits (such as grain yield) that leads to a biologically meaningful conclusion. Using chromosome substitution lines, recombinant inbred chromosome lines, and structural equation modeling, we explained 74% of the total GEI variation for grain yield resulting from the differential expression of grain yield and yield component genes on chromosome 3A. The large wheat grain yield GEI associated with genes on chromosome 3A was explained by the direct effect of spikes per square meter GEI and the indirect effects of thousand kernel weight GEI and kernels per spike GEI. In addition, direct and indirect effects of several molecular marker-by-environmental covariate interactions illustrated the effect of QTL expression sensitivity on the total grain yield GEI. Specifically, the expression of a QTL linked to molecular marker XBarc67 was shown to affect spikes per square meter GEI and ultimately grain yield GEI by displaying a greater effect in environments with warmer temperatures during reproductive growth; a logical conclusion considering the positive allele comes from cv. Wichita which was developed in warmer Kansas environments. Clearly, this study shows that GEI associated with a complex trait such as grain yield is best studied by focusing on the multitude of genetic and environmental factors impacting grain yield and yield component traits. Given the additional insight provided by our approach, using substitution lines in conjunction with structural equation modeling is a reasonable approach for studying GEI in other plants and organisms.