QTL mapping and genome-wide prediction of heat tolerance in multiple connected populations of temperate maize

Abstract

Climate change will lead to increasing heat stress in the temperate regions of the world. The objectives of this study were the following: (I) to assess the phenotypic and genotypic diversity of traits related to heat tolerance of maize seedlings and dissect their genetic architecture by quantitative trait locus (QTL) mapping, (II) to compare the prediction ability of genome-wide prediction models using various numbers of KASP (Kompetitive Allele Specific PCR genotyping) single nucleotide polymorphisms (SNPs) and RAD (restriction site-associated DNA sequencing) SNPs, and (III) to examine the prediction ability of intra-, inter-, and mixed-pool calibrations. For the heat susceptibility index of five of the nine studied traits, we identified a total of six QTL, each explaining individually between 7 and 9% of the phenotypic variance. The prediction abilities observed for the genome-wide prediction models were high, especially for the within-population calibrations, and thus, the use of such approaches to select for heat tolerance at seedling stage is recommended. Furthermore, we have shown that for the traits examined in our study, populations created from inter-pool crosses are suitable training sets to predict populations derived from intra-pool crosses.

Intercontinental trials reveal stable QTL for Northern corn leaf blight resistance in Europe and in Brazil

“…We tested different training set sizes and observed a linear increase of the prediction accuracy as we increased the number of individuals within the training set (data not shown). This result is in accordance with other studies (Riedelsheimer et al 2013 ; Han et al 2016 ; Van Inghelandt et al 2019 ). For this reason, we compared at a fixed training set of 60 individuals the prediction accuracies of materials with different genetic relationships between the training and prediction sets (Fig.…”

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

“…Classical genetics was earlier used to identify the genetic bases of heat tolerance in various field and vegetable crops (Patel and Hall, 1988;Marfo and Hall, 1992;Gupta et al, 2015;Jha et al, 2019), this approach, however, could not completely explain the genetic nature of heat stress tolerance because of its multigenic nature (Upadhyaya et al, 2011). Subsequent advances in molecular marker technology has allowed identification and precise mapping of genes/QTLs governing heat stress tolerance several crops such as rice (Gui-lian et al, 2009;Lei et al, 2013;Wei et al, 2013;Li M. et al, 2018), maize (Inghelandt et al, 2019), wheat (Mason et al, 2010;Pinto et al, 2010;Paliwal et al, 2012;Lopes-Caitar et al, 2013;Sharma et al, 2017), chickpea (Paul et al, 2018), cowpea (Pottorff et al, 2014), Brassica (Branham et al, 2017) and tomato (Wen et al, 2019). Marker assisted selection can be used to transfer heat tolerant QTLs/genomic region to the elite but heat stress sensitive genotypes if genetic maps with sufficient marker density are available (see Jha et al, 2014).…”

“…Large scale DNA-based marker development during the last decade led to mapping of QTLs linked to heat tolerance in various crops (Jha et al, 2014;Janni et al, 2020). Advances in sequencing technologies especially, next generation sequencing (NGS), genotyping by sequencing (GBS), and other high throughput genotyping platforms have facilitated narrowing down of the heat tolerance QTL regions for analysis of candidate genes (Xu et al, 2017;Kilasi et al, 2018;Inghelandt et al, 2019;Tadesse et al, 2019). Given the huge number of novel SNPs developed recently and GWAS in large set of global crop germplasm, it became possible to identify novel haplotypes/genomic regions controlling heat tolerance (Paul et al, 2018;Varshney et al, 2019;Khan et al, 2020;Weckwerth et al, 2020) and allowed for the assessment of genetic diversity at nucleotide-scale.…”

“…High throughput phenotyping coupled with advanced imaging devices, unmanned vehicles and machine learning, deep learning approaches and molecular genetics tools can further enhance the accuracy of selection of genomic regions associated with heat tolerance. The developments in marker and sequencing technologies are expected to allow genome wide marker profiling facilitating genomic selection for heat tolerance (Tricker et al, 2018;Inghelandt et al, 2019) and thus, rapid breeding for the development of varieties with novel genetic combinations. Similarly, advances in proteomics, transcriptomics and metabolomics will further unravel the complexity of heat stress tolerance in crops by identifying missing links in the current information.…”

Genome-Wide Development and Validation of Cost-Effective KASP Marker Assays for Genetic Dissection of Heat Stress Tolerance in Maize

“…In terms of yield, it is one of the most productive grain crops. However, its production is negatively impacted by high temperature, which is likely to become a major stress in the future because of climate change [ 2 , 3 ]. Exposure to temperatures above 35 °C for a prolonged period is unfavorable for the growth and vigor of most maize germplasm in general.…”