Identification and Verification of QTL Associated with Frost Tolerance Using Linkage Mapping and GWAS in Winter Faba Bean

Abstract

Frost stress is one of the abiotic stresses that causes a significant reduction in winter faba bean yield in Europe. The main objective of this work is to genetically improve frost tolerance in winter faba bean by identifying and validating QTL associated with frost tolerance to be used in marker-assisted selection (MAS). Two different genetic backgrounds were used: a biparental population (BPP) consisting of 101 inbred lines, and 189 genotypes from single seed descent (SSD) from the Gottingen Winter bean Population (GWBP). All experiments were conducted in a frost growth chamber under controlled conditions. Both populations were genotyped using the same set of 189 SNP markers. Visual scoring for frost stress symptoms was used to define frost tolerance in both populations. In addition, leaf fatty acid composition (FAC) and proline content were analyzed in BPP as physiological traits. QTL mapping (for BPP) and genome wide association studies (for GWBP) were performed to detect QTL associated with frost tolerance. High genetic variation between genotypes, and repeatability estimates, were found for all traits. QTL mapping and GWAS identified new putative QTL associated with promising frost tolerance and related traits. A set of 54 SNP markers common in both genetic backgrounds showed a high genetic diversity with polymorphic information content (PIC) ranging from 0.31 to 0.37 and gene diversity ranging from 0.39 to 0.50. This indicates that these markers may be polymorphic for many faba bean populations. Five SNP markers showed a significant marker-trait association with frost tolerance and related traits in both populations. Moreover, synteny analysis between Medicago truncatula (a model legume) and faba bean genomes was performed to identify candidate genes for these markers. Collinearity was evaluated between the faba bean genetic map constructed in this study and the faba bean consensus map, resulting in identifying possible genomic regions in faba bean which may control frost tolerance genes. The two genetic backgrounds were useful in detecting new variation for improving frost tolerance in winter faba bean. Of the five validated SNP markers, one (VF_Mt3g086600) was found to be associated with frost tolerance and FAC in both populations. This marker was also associated with winter hardiness and high yield in earlier studies. This marker is located in a gene of unknown function.

QTL mapping and GWAS reveal candidate genes controlling capsaicinoid content in Capsicum

“…The combination of GWAS and QTL analyses can compensate for the limitations of each approach, enabling the identification of loci controlling agronomically important quantitative traits. Such combined approaches have been successfully used to identify candidate genes controlling flowering time, panicle architecture, leaf architecture, frost resistance and seed-related traits in Arabidopsis thaliana, rice (Oryza sativa), maize, winter faba bean (Vicia faba) and soya bean, respectively (Brachi et al, 2010;Crowell et al, 2016;Sallam et al, 2016;Sonah et al, 2015;Tian et al, 2011).…”

Quantitative Trait Loci for Cold Tolerance in Chickpea

“…), and faba bean (Vicia faba L.) (Kahraman et al, 2004;Lejeune-Hénaut et al, 2008;Avia et al, 2013;Klein et al, 2014;Sallam et al, 2016). ), and faba bean (Vicia faba L.) (Kahraman et al, 2004;Lejeune-Hénaut et al, 2008;Avia et al, 2013;Klein et al, 2014;Sallam et al, 2016).…”

“…Several quantitative trait locus (QTL) studies for cold stress have been conducted in several related legumes, including pea, lentil, barrel medic (Medicago truncatula Gaertn. ), and faba bean (Vicia faba L.) (Kahraman et al, 2004;Lejeune-Hénaut et al, 2008;Avia et al, 2013;Klein et al, 2014;Sallam et al, 2016). However, no QTL studies have been published to date regarding cold tolerance in chickpea.…”

Validation of Grain Yield QTLs from Soft Winter Wheat Using a CIMMYT Spring Wheat Panel

“…I t is necessary to validate quantitative trait loci (QTLs) across different genetic backgrounds for more efficient implementation of marker‐assisted selection (MAS) (Sallam et al, 2016; Su et al, 2016; Dao et al, 2017). This is particularly true of QTLs for grain yield (GY), as it is a quantitative trait influenced by many loci with mostly small effects, making the identification and validation of significant marker‐trait associations a challenge.…”