Epistatic interactions of major effect drought QTLs with genetic background loci determine grain yield of rice under drought stress

Abstract

Epistatic interactions of QTLs with the genetic background and QTL-QTL interaction plays an important role in the phenotypic performance of introgression lines developed through genomic-assisted breeding (GAB). In this context, NIL pairs developed with various drought QTL ( qDTY ) combinations in the genetic background of IR64, TDK1- Sub1 and Savitri backgrounds were utilized to study the interactions. Multi-season phenotyping of NIL pairs harboring similar qDTY combinations provided contrasting performance for grain yield under drought (RS) (classified as high and low yielding NILs) but nearly similar performance under non-stress(NS) conditions. Genome wide genotyping data revealed a total of 16, 5 and 6 digenic interactions were detected under RS conditions in low yielding NILs of IR64, TDK1- Sub1 and Savitri respectively while no significant interaction was found in high yielding NILs under RS and NS conditions in any of the genetic backgrounds used in this study. It is evident from this study that existence of epistatic interactions between QTLs with genetic background, QTL-QTL interaction and interactions among background markers loci itself on different chromosomes influences the expression of a complex trait such as grain yield under drought. The generated information will be useful in all the GAB program of across the crops for precise breeding.

Genome-Wide Association Study for Yield and Yield Related Traits under Reproductive Stage Drought in a Diverse indica-aus Rice Panel

“…of genes for different traits as well as of genes with the environment and genetic backgrounds (Xue et al 2009;Wang et al 2012;Kumar et al 2014;Yadav et al 2019). To tackle the restrictive applicability of breeding for complex traits, studies conducted have exploited germplasm for desirable variability (Dixit et al 2014;Mondal et al 2016;Kumar et al 2018) and applied precise selection in experiments under different environments and stress intensity levels to emulate farmers' field conditions.…”

More and more of less and less: Is genomics‐based breeding of dry direct‐seeded rice (DDSR) varieties the need of hour?

“…The QTL pyramiding approach has potential to improve understanding of interactions among QTLs and also helpful in developing new strategies to enhance the efficiency of genomic-breeding programmes. The performance of NILs possessing various QTL combinations (qDTY 1.1 , qDTY 2.1 , qDTY 2.2 , qDTY 3.1 , qDTY 3.2 , qDTY 6.1 , qDTY 12.1 ) determining yield under drought in different genetic backgrounds (IR64, TDK1-Sub1, Savitri, Samba Mahsuri) was reported to be strongly affected by interactions with genetic background loci ( interactions prevailing between introgressed drought QTLs/QTL combinations with genetic backgrounds can be positive as well as negative, and dissection of such interactions at molecular level has provided an important clue while selecting the best drought NILs without any negative interactions for multilocation testing and release (Yadav et al, 2019). Thus, promising NILs in the background of popular varieties with positive interactions and performing well under direct-seeded systems can be useful in future genomic breeding to develop climate-resilient DDSR varieties.…”

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

“…However, complex quantitative traits like grain yield under drought, resistance to other existing and emerging abiotic and biotic stresses are a challenge as they are characterized by interactions of several large and small effect genes for a single trait; of genes for different traits as well as of genes with the environment and genetic backgrounds (Xue at al. 2009;Wang et al 2012;Kumar et al 2014;Yadav et al 2019). To tackle the restrictive applicability of breeding for complex traits, studies conducted have exploited germplasm for desirable variability (Dixit et al 2014;Mondal et al 2016;Kumar et al 2018) and applied precise selection in experiments under different environments and stress intensity levels to emulate farmers' eld conditions.…”