Multi-environment multi-QTL association mapping identifies disease resistance QTL in barley germplasm from Latin America

Abstract

Multi-environment multi-QTL mixed models were used in a GWAS context to identify QTL for disease resistance. The use of mega-environments aided the interpretation of environment-specific and general QTL. Diseases represent a major constraint for barley (Hordeum vulgare L.) production in Latin America. Spot blotch (caused by Cochliobolus sativus), stripe rust (caused by Puccinia striiformis f.sp. hordei) and leaf rust (caused by Puccinia hordei) are three of the most important diseases that affect the crop in the region. Since fungicide application is not an economically or environmentally sound solution, the development of durably resistant varieties is a priority for breeding programs. Therefore, new resistance sources are needed. The objective of this work was to detect genomic regions associated with field level plant resistance to spot blotch, stripe rust, and leaf rust in Latin American germplasm. Disease severities measured in multi-environment trials across the Americas and 1,096 SNPs in a population of 360 genotypes were used to identify genomic regions associated with disease resistance. Optimized experimental design and spatial modeling were used in each trial to estimate genotypic means. Genome-Wide Association Mapping (GWAS) in each environment was used to detect Quantitative Trait Loci (QTL). All significant environment-specific QTL were subsequently included in a multi-environment-multi-QTL (MEMQ) model. Geographical origin and inflorescence type were the main determinants of population structure. Spot blotch severity was low to intermediate while leaf and stripe rust severity was high in all environments. Mega-environments were defined by locations for spot blotch and leaf rust. Significant marker-trait associations for spot blotch (9 QTL), leaf (6 QTL) and stripe rust (7 QTL) and both global and environment-specific QTL were detected that will be useful for future breeding efforts.

Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

“…Genotype ´ environment interaction has been exploited in genomic studies to map quantitative trait loci in balanced populations (Piepho, 2000;Malosetti et al, 2004;van Eeuwijk et al, 2005;Boer et al, 2007;Mathews et al, 2008) and genome-wide association studies (Gutiérrez et al, 2015) using mixed models. Additionally, GS studies have included GEI information by performing overall predictions across environments (Heffner et al, 2011;Resende et al, 2011), within environments or groups of environments (Burgueño et al, 2012;Ly et al, 2013;Dawson et al, 2013;Heslot et al, 2014), or using marker-by-environment predictions (Jarquín et al, 2014;Lopez-Cruz et al, 2015).…”

Mega‐Environmental Design: Using Genotype × Environment Interaction to Optimize Resources for Cultivar Testing

“…Different approaches have been proposed to study GEI such as linear-bilinear models (Finlay and Wilkinson, 1963;Gauch, 1992;Yan et al, 2001;Crossa and Cornelius, 2002), mixed models (Piepho, 1998;Burgueño et al, 2008;Cullis et al, 2010), and crop growth models (Chapman, 2008;Technow et al, 2015;Malosetti et al, 2016). These methods have been extensively used for different breeding strategies including traditional phenotypic selection and testing (Yan et al, 2007;González-Barrios et al, 2017), quantitative trait loci mapping (Piepho, 2000;Malosetti et al, 2004;Mathews et al, 2008;Quero et al, 2014), genome-wide association mapping (van Eeuwijk et al, 2010;Locatelli et al, 2013;Gutiérrez et al, 2015;Racedo et al, 2016;Monteverde et al, 2018), and genomic selection (Burgueño et al, 2012;Lopez-Cruz et al, 2015;Lado et al, 2016). Environments can then be grouped in sets that produce a similar ranking of the genotypes (mega-environments [MEs]; Braun et al, 1996).…”

Unraveling the molecules hidden in the gray shadows of quantitative disease resistance to pathogens

“…However, both approaches seem to be complementary in the sense that their ultimate goal is the detection of the genes underlying the quantitative complex trait for further cloning. Several examples of the use of the association mapping approach in QDR studies for the more limiting diseases can be found in recent literature (Benson et al, 2015;Gutiérrez et al, 2015;Arruda et al, 2016;Iquira et al, 2015;Olukolu et al, 2016;Turuspekov et al, 2016). However, despite the broad use of this approach, no genes detected by AM for plant QDR have been cloned so far.…”