Skip to main content
ARS Home » Southeast Area » Mississippi State, Mississippi » Crop Science Research Laboratory » Genetics and Sustainable Agriculture Research » Research » Publications at this Location » Publication #278251
Title: Detecting epistatic effects associated with cotton traits by a modified MDR approach

Author
item WU, JIXIANG - South Dakota State University
item Jenkins, Johnie
item McCarty, Jack
item GLOVER, KARL - South Dakota State University

Submitted to: Euphytica
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/12/2012
Publication Date: 9/1/2012
Citation: Wu, J., Jenkins, J.N., McCarty Jr., J.C., Glover, K. 2012. Detecting epistatic effects associated with cotton traits by a modified MDR approach. Euphytica. 187:289-301.

Interpretive Summary: The genetic expression of a trait is usually associated with many genes including their interactions with each other and interactions with the environment. Genetic mapping studies focus primarily on using additive models for data analysis due to the complexity of interaction effects. Thus, there exists a need to identify favorable interaction effects for important plant traits. Several multifactor dimensionality reduction methods (MDR) are being used to identify high-order gene-gene interactions. These methods are generally used with human genetic studies. To analyze plant traits, a mixed model based MDR approach was developed. This approach was used to analyze a cotton data set that included eight agronomic and fiber traits and 20 DNA markers. The results revealed high order genetic interaction effects among these markers were contributing to most of the traits studied. This modified MDR approach can be used with other marker data sets and mapping studies to detect genetic interaction effects.

Technical Abstract: Genetic expression of a trait is complicated and it is usually associated with many genes including their interactions (epistasis) and genotype-by-environment (GE) interactions. Genetic mapping currently focuses primarily on additive models or marginal genetic effects due to the complexity of epistatic effects. Thus, there exists a need to appropriately identify favorable epistatic effects for important biological traits. Several multifactor dimensionality reduction (MDR) based methods are important resources to identify high-order gene-gene interactions. These methods are mainly focused on human genetic studies. Many traits in plant systems are not only quantitatively inherited but also are often measured in repeated field plots under multiple environments. In this study, we proposed a mixed model based MDR approach which is suitable for inclusion of various fixed and random effects. This approach was used to analyze a cotton data set that included eight agronomic and fiber traits and 20 DNA markers. The results revealed high order epistatic effects among these markers were contributing to most of these traits by using the modified MDR approach proposed in this study.