Two distinct classes of QTL determine rust resistance in sorghum

Abstract

BackgroundAgriculture is facing enormous challenges to feed a growing population in the face of rapidly evolving pests and pathogens. The rusts, in particular, are a major pathogen of cereal crops with the potential to cause large reductions in yield. Improving stable disease resistance is an on-going major and challenging focus for many plant breeding programs, due to the rapidly evolving nature of the pathogen. Sorghum is a major summer cereal crop that is also a host for a rust pathogen Puccinia purpurea, which occurs in almost all sorghum growing areas of the world, causing direct and indirect yield losses in sorghum worldwide, however knowledge about its genetic control is still limited. In order to further investigate this issue, QTL and association mapping methods were implemented to study rust resistance in three bi-parental populations and an association mapping set of elite breeding lines in different environments.ResultsIn total, 64 significant or highly significant QTL and 21 suggestive rust resistance QTL were identified representing 55 unique genomic regions. Comparisons across populations within the current study and with rust QTL identified previously in both sorghum and maize revealed a high degree of correspondence in QTL location. Negative phenotypic correlations were observed between rust, maturity and height, indicating a trend for both early maturing and shorter genotypes to be more susceptible to rust.ConclusionsThe significant amount of QTL co-location across traits, in addition to the consistency in the direction of QTL allele effects, has provided evidence to support pleiotropic QTL action across rust, height, maturity and stay-green, supporting the role of carbon stress in susceptibility to rust. Classical rust resistance QTL regions that did not co-locate with height, maturity or stay-green QTL were found to be significantly enriched for the defence-related NBS-encoding gene family, in contrast to the lack of defence-related gene enrichment in multi-trait effect rust resistance QTL. The distinction of disease resistance QTL hot-spots, enriched with defence-related gene families from QTL which impact on development and partitioning, provides plant breeders with knowledge which will allow for fast-tracking varieties with both durable pathogen resistance and appropriate adaptive traits.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-014-0366-4) contains supplementary material, which is available to authorized users.

Genomic Scans across Three Eucalypts Suggest that Adaptation to Aridity is a Genome-Wide Phenomenon

“…2013), drought tolerance in chick peas (Varshney et al. 2014) or disease resistance in sorghum (Wang et al. 2014), but frequently there are numerous such hot-spots distributed across a genome.…”

“…For example, disease resistance may involve few genes of large effect or multiple genes of small effect that cluster in particular regions of the genome across a number of chromosomes (Wang et al. 2001, 2014; Chu et al. 2004).…”

A Genomics Resource for Genetics, Physiology, and Breeding of West African Sorghum

“…LG (Lin et al , 1995) QDTFL9.15 9:50.4-57.9 (Feltus et al , 2006) QDTFL9.33 9:56.1-59.1 (Zhang et al , 2015) S10_9523248 QDTFL10.10 10:7.6-9.7 (Wang et al , 2014) S10_51083132 QDTFL10.26 10:42.1-51.9 (Sangma, 2013) Plant height QTLs S5_61867719 QHGHT5.11 5:62.3-62.9 (Bouchet et al , 2017)…”

Impact of variable water limited environments on grain sorghum yield and lodging in Australia

“…The sorghum pre-breeding project aims to improve the competitiveness of Australian sorghum production by 1) developing and distributing germplasm with improved yield, resistance to stresses and improved quality; 2) providing germplasm and scientific support for other Australian sorghum research; 3) contributing to the coordination and integration of Australian sorghum research. Germplasm development involves the identification, assessment, and utilisation of traits (Henzell & Hare, 1996) through technologies, such as markers (Mace et al, 2008), molecular-assisted selection (MAS) (Jordan et al, 2003), genomic selection (GS) (Hunt et al, 2018), quantitative trait loci (QTL) mapping (Tao et al, 1998Mace et al, 2012;Wang et al, 2014a), and genome-wide association studies (GWAS) (Mace et al, 2013a).…”

“…In recent years, due to the development of molecular markers, the complex genetic control of flowering time in sorghum has been further investigated. To date, through QTL or association mapping, over 360 QTL have been detected in 38 studies, with an average number of 9 QTL per study (Lin et al, 1995;Crasta et al, 1999;Hart et al, 2001;Kebede et al, 2001;Chantereau et al, 2001;Parh, 2005;Feltus et al, 2006;Brown et al, 2006;Ritter et al, 2008;Srinivas et al, 2009;Shiringani et al, 2010;Murphy et al, 2011;El Mannai et al, 2011;Felderhoff et al, 2012;Upadhyaya et al, 2012a, Kong et al, 2013Mace et al, 2013a;Nagaraja Reddy et al, 2013;Phuong et al, 2013;Sakhi et al, 2013;Sangma, 2013;Higgins et al, 2014;Mantilla Perez et al, 2014;Wang et al, 2014a;Mocoeur et al, 2015;Zhang et al, 2015;Burks et al, 2015;Burrell et al, 2015;Gelli et al, 2016;Sukumaran et al, 2016;Zhao et al, 2016;Cuevas et al, 2016;Bai et al, 2017;Bouchet et al, 2017;Boyles et al, 2017). However, most of these studies were conducted on bi-parental populations from crosses between parental lines selected for their diverse phenotypes or diversity panels.…”