Meta-QTL analysis of tan spot resistance in wheat

Liu, Yuan; Salsman, Evan; Wang, Runhao; Galagedara, Nelomie; Zhang, Qijun; Fiedler, Jason D.; Liu, Zhaohui; Xu, Steven S.; Faris, Justin D.; Li, Xuehui

doi:10.1007/s00122-020-03604-1

Cited by 65 publications

(66 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2012) in RIL populations, Kollers et al. (2014) in European winter wheat varieties, and Liu et al., 2020 from three hexaploid wheat mapping population LP573, SK, and TN. Previously, PCR based markers Xfcp620 and Xfcp623 were extensively used to detect tsn 1 gene ( Lu et al., 2006 ; Faris et al., 2010 ).…”

Section: Discussionmentioning

confidence: 99%

“…However, identification of non-race-specific resistance QTL clearly indicates that the inverse gene-for-gene model does not fully explain all interactions that occur in the tan spot pathosystem ( Faris and Friesen, 2005 ; Faris et al., 2012 ). Resistance to tan spot is qualitatively or quantitatively inherited ( Faris and Friesen, 2005 ; Chu et al., 2008 ; Singh et al., 2009 ; Chu et al., 2010 ; Liu et al., 2015 ; Singh et al., 2019 ; Hu et al., 2019 ; Liu et al., 2020 ), as single dominant gene tsn 1 on chromosome 5BL ( Faris et al., 2010 ) confers host sensitivity to Ptr ToxA. The host Ptr ToxC sensitivity gene, Tsc1 was mapped to the short arm of chromosome 1A ( Effertz et al., 2002 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Association Mapping of Seedling Resistance to Tan Spot (Pyrenophora tritici-repentis Race 1) in CIMMYT and South Asian Wheat Germplasm

Phuke¹,

Juliana

et al. 2020

Front. Plant Sci.

View full text Add to dashboard Cite

Tan spot caused by Pyrenophora tritici-repentis ( Ptr ) is an important disease of wheat in many wheat producing areas of the world. A genome wide association study (GWAS) was conducted using 11,401 SNP markers of the Illumina Infinium 15K Bead Chip with whole genome coverage to identify genomic regions associated with resistance to tan spot in a diverse panel of 184 wheat genotypes originating from South Asia and CIMMYT. The GWAS panel was phenotyped for seedling resistance to tan spot with Ptr race 1 in two greenhouse experiments. Besides CIMMYT germplasm, several lines from South Asia (India, Bangladesh and Nepal) showed good degree of resistance to tan spot. Association mapping was conducted separately for individual experiments and for pooled data using mixed linear model (MLM) and Fixed and random model Circulating Probability Unification (FarmCPU) model; no significant MTAs were recorded through the MLM model, whereas FarmCPU model reported nine significant MTAs located on chromosomes 1B, 2A, 2B, 3B, 4A, 5A, 5B, 6A, and 7D. The long arms of chromosomes 5A and 5B were consistent across both environments, in which the Vrn-A1 locus was found in identified region of chromosome 5A, and MTA at IACX9261 on 5BL appears to represent the resistance gene tsn 1 . MTAs observed on chromosomes 1B, 2A, 2B, 3B, 4A, 6A, and 7D have not been reported previously and are likely novel.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Association Mapping of Seedling Resistance to Tan Spot (Pyrenophora tritici-repentis Race 1) in CIMMYT and South Asian Wheat Germplasm

Phuke¹,

Juliana

et al. 2020

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…MQTLs showing resistance against more than one pathotypes may also be important for achieving broad spectrum resistance. Such important MQTLs showing resistance against multiple pathogen races were also identi ed in a recent study on MQTL analysis reported for tan spot resistance in wheat (Liu et al 2020).…”

Section: Discussionmentioning

confidence: 58%

“…CGs identi ed during the present study also deserve to be discussed. In wheat, CGs underlying the MQTLs were also identi ed for several traits including drought tolerance (Kumar et al 2020), tan spot resistance (Liu et al 2020) and fusarium head blight resistance (Venske et al 2019). However, the strategy used by us was novel and not used in any of these earlier reports.…”

Section: Discussionmentioning

confidence: 99%

Meta-QTL Analysis for Stripe Rust Resistance in Wheat

Jan

Saripalli

Kumar

et al. 2021

Preprint

View full text Add to dashboard Cite

Stripe rust caused by Puccinia striiformis f. sp. tritici Eriks. & E. Henn (Pst) is one of the most prevalent wheat diseases causing upto 70% yield losses worldwide. The present study was conducted in wheat for the first time to identify important meta-QTL (MQTL) regions for their use in developing stripe rust resistant wheat cultivars and to understand the genetic architecture of stripe rust resistance in wheat. For this purpose, a dense consensus map consisting of 76,753 markers was constructed and 353 QTLs from earlier studies were projected on this consensus map. As many as 61 MQTLs were identified using 184 (out of 353) original QTLs. Ten important genomic regions including six breeders’ MQTLs (PVE >20%) and four MQTL hotspots were selected to be used by wheat breeders. As many as 409 important candidate genes (CGs) were also identified, which either encoded known R proteins (265) or showed differential expression (144) due to stripe rust infection. These included genes encoding the following proteins: NBS-LRR, WRKY domains, ankyrin repeat domains, sugar transporters, etc. Overall, the present study provided robust MQTLs and underlying CGs which may be potential targets for molecular breeding for development of stripe rust resistant wheat cultivars or may be the target for future molecular studies to understand the mechanism of stripe rust resistance.

show abstract

“…Of these three homeologous genes, Glu‐D1 has the largest effect, whereas Glu‐A1 has the smallest effect on dough and baking quality (Lawrence, MacRitchie, & Wrigley, 1988; Uthayakumaran et al., 2002). Marker–trait associations in bread wheat and, to a lesser extent, in durum wheat have been summarized through meta‐analyses of QTL mapping results for earliness (Hanocq, Laperche, Jaminon, Lainé, & Le Gouis, 2007), disease resistance (Liu, Hall, Griffey, & McKendry, 2009; Löffler, Schön, & Miedaner, 2009; Soriano & Royo, 2015; Liu et al., 2020), height (Griffiths et al., 2012), drought and heat stress tolerance (Acuña‐Galindo, Mason, Subramanian, & Hays, 2015), grain traits (Tyagi, Mir, Balyan, & Gupta, 2015), and shoot and root traits (Iannucci et al., 2017; Soriano & Alvaro, 2019). Although clusters of QTL may indicate the importance of specific genomic regions, they are incomplete indicators of subgenome contributions to variation because they do not account for loci with effects too small to be statistically significant.…”

Section: Introductionmentioning

confidence: 99%

Subgenome contributions to quantitative genetic variation in bread wheat and durum wheat populations

Bernardo¹

2021

Crop Science

View full text Add to dashboard Cite

Bread wheat (Triticum aestivum L.) has the A, B, and D genomes, whereas durum wheat (Triticum turgidum L. ssp. durum) has the A and B genomes. This study aimed to assess the usefulness of the square of predictive ability for subgenome i [r MP(i) 2 ] for estimating subgenome contributions to trait variation and to determine which subgenomes in bread wheat and durum wheat populations had the largest and smallest contributions for different traits. Phenotypic and marker data were obtained for four populations used in published studies to map quantitative trait loci (QTL). Subgenome r MP(i) was calculated by analyzing only the markers within each subgenome. Clear differences were found in subgenome contributions for several but not all traits in the populations studied. In the Louise × Penawawa bread wheat population, the r MP(i) 2 values indicated that the B genome had the largest contribution to variation for milling and baking quality traits, and these results were consistent with prior results showing QTL for such traits on chromosome 3B. For yield, no significant differences in subgenome contributions were found in any of the populations. The D genome, despite its lower polymorphism, had the largest r MP(i) 2 for heading date in the 'Seri' × 'Babax' bread wheat population. In a bread wheat association mapping panel, the r MP(i) 2 values were problematic in that their sum exceeded 1.0 and the subgenome r MP(i) values were not significantly different from the wholegenome r MP. This result was probably caused by linkage disequilibrium between markers found in different subgenomes in the association mapping panel. Abbreviations: h 2 , heritability; JK-Omrabi, Jennah Khetifa/Cham1//Triticum dicoccoides 600545/2 × Omrabi5; N M , number of markers; QTL, quantitative trait locus (or loci); r MP , predictive ability; r MP(i) 2 , square of predictive ability for the ith subgenome; RR-BLUP, ridge regression-best linear unbiased prediction; SNP, single nucleotide polymorphism; SRC water, solvent retention capacity for water.

show abstract

Meta-QTL analysis of tan spot resistance in wheat

Cited by 65 publications

References 62 publications

Association Mapping of Seedling Resistance to Tan Spot (Pyrenophora tritici-repentis Race 1) in CIMMYT and South Asian Wheat Germplasm

Association Mapping of Seedling Resistance to Tan Spot (Pyrenophora tritici-repentis Race 1) in CIMMYT and South Asian Wheat Germplasm

Meta-QTL Analysis for Stripe Rust Resistance in Wheat

Subgenome contributions to quantitative genetic variation in bread wheat and durum wheat populations

Contact Info

Product

Resources

About