Genome-Wide Association Mapping for Heat Stress Responsive Traits in Field Pea

Abstract

Environmental stress hampers pea productivity. To understand the genetic basis of heat resistance, a genome-wide association study (GWAS) was conducted on six stress responsive traits of physiological and agronomic importance in pea, with an objective to identify the genetic loci associated with these traits. One hundred and thirty-five genetically diverse pea accessions from major pea growing areas of the world were phenotyped in field trials across five environments, under generally ambient (control) and heat stress conditions. Statistical analysis of phenotype indicated significant effects of genotype (G), environment (E), and G × E interaction for all traits. A total of 16,877 known high-quality SNPs were used for association analysis to determine marker-trait associations (MTA). We identified 32 MTAs that were consistent in at least three environments for association with the traits of stress resistance: six for chlorophyll concentration measured by a soil plant analysis development meter; two each for photochemical reflectance index and canopy temperature; seven for reproductive stem length; six for internode length; and nine for pod number. Forty-eight candidate genes were identified within 15 kb distance of these markers. The identified markers and candidate genes have potential for marker-assisted selection towards the development of heat resistant pea cultivars.

Identification and Characterization of Contrasting Genotypes/Cultivars for Developing Heat Tolerance in Agricultural Crops: Current Status and Prospects

“…Advent of improved sequencing technologies that allow faster sequencing of genomes at lower costs led to generation of profuse SNP markers that enabled genome-wide association studies (GWAS) for elucidating novel genomic regions controlling heat stress tolerance. GWAS for identifying heat stress tolerance genomic regions have been conducted in rice (Lafarge et al, 2017), maize (Yuan et al, 2019), wheat (Maulana et al, 2018), barley (Cantalapiedra et al, 2017), pea (Tafesse et al, 2020), chickpea (Thudi et al, 2014;Jha et al, 2018;Varshney et al, 2019), and in Brassica (Rahaman et al, 2018).…”

Analysis and Identification of QTL for Resistance to Sclerotinia sclerotiorum in Pea (Pisum sativum L.)

“…The only reported QTL mapping study for resistance to white mold in pea was conducted on F 2− derived F 3 family lines from a cross between Lifter and PI240515 (Tashtemirov, 2012). The recent availability of full genome sequence of the field pea (Kreplak et al, 2019) has contributed to a surge in identification of trait-associated markers and candidate genes for a number of agronomic, seed morphology and seed quality traits (Gali et al, 2019;Dissanayaka et al, 2020), and frost (Beji et al, 2020) and heat tolerance (Tafesse et al, 2020). Similarly, we expect that these genomic resources will also contribute to fine mapping of disease resistance QTLs including those associated with white mold resistance and a better understanding of the underlying genetics.…”

Application of Genomics to Understand Salt Tolerance in Lentil

“…Therefore, an alternative approach is to understand the genetic basis of salt responsive traits and develop salt-tolerant cultivars. Many other grain legumes and higher plants have attempted this approach for abiotic and biotic characteristics, including soil salinity [1,20,21]. Recently, GWAS has been used as a powerful tool to dissect the genetic basis of many phenotypic traits using genetically diverse populations [22,23].…”

“…Recently, GWAS has been used as a powerful tool to dissect the genetic basis of many phenotypic traits using genetically diverse populations [22,23]. In general, this approach statistically confirms the association strength between a genotype and phenotype and provides information on molecular markers, alleles and candidate genes that contribute to specific traits [21,23]. Currently, it has widely been applied in many plants, including rice (Oryza sativa L.) [22,24,25], cotton (Gossypium hirsutum L.) [26,27], soybean (Glycine max L.) [28] and maize (Zea mays L.) [29], for identifying precise chromosomal locations to define many potential candidate genes for salt stress.…”