Mapping QTLs for physiological and biochemical traits related to grain yield under control and terminal heat stress conditions in bread wheat (Triticum aestivum L.)

Abstract

In order to detect genomic regions with different effects for some of the physiological and biochemical traits of wheat, four experiments were conducted at Research Farm of Agricultural and Natural Resources Research Center of Zabol in 2015-2016 and 2016-2017 growing seasons. The experiments were carried out using four alpha lattice designs with two replications under non-stress and terminal heat stress conditions. Plant materials used in this study included 167 recombinant inbred lines and their parents ('SeriM82' and 'Babax'). Six traits including grain yield (GY), proline content (PRO), water soluble carbohydrates (WSC), maximum efficiency of photosystem II (Fv/Fm), cytoplasmic membrane stability (CMS) and chlorophyll content (CHL) were evaluated. Genetic linkage map consisted of 211 AFLP marker, 120 SSR marker and 144 DArT markers with 1864 cm length and 4.4 cm mean distance. QTL analysis was carried out using a mixedmodel-based composite interval mapping (MCIM) method. By the combined analysis of normal phenotypic values, 27 additive QTLs and five pairs of epistatic effects were identified for studied traits, among which two additive and one epistatic QTL showed significant QTL 9 environment interactions. By the combined analysis of stress phenotypic values, a total of 26 QTLs with additive effects and 5 epistatic QTLs were detected, among which one additive and one epistatic QTL showed QTL 9 environment interactions. Six QTLs with major effects (QGY-2B, QGY-2D, QPro-5B, QWSC-4A, QFv/Fm-6A and QCMS-4B), which were common between two conditions could be useful for marker-assisted selection (MAS) in order to develop heat tolerant and high-performance wheat varieties.

Mapping QTLs of flag leaf morphological and physiological traits related to aluminum tolerance in wheat (Triticum aestivum L.)

“…Among the diverse heavy metals-stress and waterstress tolerance traits targeted in crop breeding, CMS is regarded as a physiological parameter which is mostly affected (Singh et al 2016). QTLs for CMS have been previously reported on LGs 1D, 4B, 5A, 6B, and 7A for drought tolerance across different wheat populations under field conditions (Elshafei et al 2013;Talukder et al 2014;Sohrabi et al 2018), which were in accordance with our results. Variations in WSC content are largely genetically determined.…”

“…Intensities of expression of quantitative traits are affected by the epistatic and QTL 9 environment interactions effects which are important genetic components (Li et al 2014). Epistasis and AE studies have been conducted in wheat for CT and GYLD (Tahmasebi et al 2016), FLMrelated traits (Yang et al 2016), CMS, and WSC (Sohrabi et al 2018). In most of the previous studies carried out on QTL mapping in bread wheat, the associated interactions (AA, AE, and AAE) were not totally investigated (e.g.…”

Mapping QTLs for physiological traits associated with grain yield under timely and delayed sown conditions in wheat (Triticum aestivum L.)

“…The common QTLs in uenced GY, WSC, CMS, PRO, and Fv/Fm in both environments and were found on chromosomes 2B, 2D, 4A, 4B, 5B, and 6A. However, numerous QTLs with signi cant impacts (such as QChl-4A) were found for different traits in just one of the two environmental conditions 27 (Hassan et al, 2018). Under heat and drought stress, Pinto et al 2010 found multiple environmentsensitive QTLs for grain yield and yield-related characteristics in this group 13 .…”

“…Similarly, the SPAD QTLs were dispersed on 2D and 7D, explained 4.3%-17.3% (PVE) of the total variation in chlorophyll content 29 (Tahmasebi et al, 2016). Heat tolerance in eld crops, such as bread wheat, is linked to a number of physiological, biochemical, and morphological characteristics 27 (Hassan et al, 2018). As a result, we examined a variety of physiological and developmental variables to learn more about the components that in uence heat responsiveness to grain weight and the basis of the QTL-controlled tolerance mechanisms.…”

Вариабельность Генома Отечественных Сортов Картофеля: Данные Aflp-Анализа

“…Об эффективности метода говорит частота его использования. AFLP активно и успешно применяется для оценки межсортовой вариабельности у многих сельскохозяйственных культур, в том числе пшеницы (6), ячменя (7), гороха (8,9), перца (10,11).…”