Microsatellite markers linked to sterility mosaic disease resistance in pigeonpea (Cajanus cajan L. Millsp.)

Author(s):  
Prakash G. Patil ◽  
Byregowda M. ◽  
Bhuvaneshwara R. Patil ◽  
Alok Das ◽  
Mary Reena G.A. ◽  
...  
Author(s):  
Shourabh Joshi T. Revathi ◽  
G. Umadevi C.V. Sameer Kumar ◽  
G. Anuradha

2014 ◽  
Vol 30 (2) ◽  
pp. 188-194 ◽  
Author(s):  
Abhijit Daspute ◽  
B. Fakrudin ◽  
Shivarudrappa B. Bhairappanavar ◽  
S.P. Kavil ◽  
Y.D. Narayana ◽  
...  

Genome ◽  
2002 ◽  
Vol 45 (3) ◽  
pp. 592-599 ◽  
Author(s):  
M A Gore ◽  
A J Hayes ◽  
S C Jeong ◽  
Y G Yue ◽  
G R Buss ◽  
...  

Soybean mosaic virus (SMV) and peanut mottle virus (PMV) are two potyviruses that cause yield losses and reduce seed quality in infested soybean (Glycine max (L.) Merr.) fields throughout the world. Rsv1 and Rpv1 are genes that provide soybean with resistance to SMV and PMV, respectively. Isolating and characterizing Rsv1 and Rpv1 are instrumental in providing insight into the molecular mechanism of potyvirus recognition in soybean. A population of 1056 F2 individuals from a cross between SMV- and PMV-resistant line PI 96983 (Rsv1 and Rpv1) and the susceptible cultivar 'Lee 68' (rsv1 and rpv1) was used in this study. Disease reaction and molecular-marker data were collected to determine the linkage relationship between Rsv1, Rpv1, and markers that target candidate disease-resistance genes. F2 lines showing a recombination between two of three Rsv1-flanking microsatellite markers were selected for fine mapping. Over 20 RFLP, RAPD, and microsatellite markers were used to map 38 loci at high-resolution to a 6.8-cM region around Rsv1 and Rpv1. This study demonstrates that Rsv1 and Rpv1 are tightly linked at a distance of 1.1 cM. In addition, resistance-gene candidate sequences were mapped to positions flanking and cosegregating with these resistance loci. Based on comparisons of genetic markers and disease reactions, it appears likely that several tightly linked genes are conditioning a resistance response to SMV. We discuss the specifics of these findings and investigate the utility of two disease resistance related probes for the screening of SMV or PMV resistance in soybean.Key words: NBS, multigene family, and disease resistance.


2019 ◽  
Vol 10 (2) ◽  
pp. 727
Author(s):  
L.M. Tharageshwari ◽  
A. Thanga Hemavathy ◽  
P. Jayamani ◽  
L. Karthiba
Keyword(s):  

2004 ◽  
Vol 54 (4) ◽  
pp. 319-325 ◽  
Author(s):  
Yoshihiro Okada ◽  
Ryouichi Kanatani ◽  
Syouichi Arai ◽  
Kazutoshi Ito

2013 ◽  
Vol 14 (11) ◽  
pp. 22499-22528 ◽  
Author(s):  
Gous Miah ◽  
Mohd Rafii ◽  
Mohd Ismail ◽  
Adam Puteh ◽  
Harun Rahim ◽  
...  

Author(s):  
E. Okogbenin ◽  
I. Moreno ◽  
J. Tomkins ◽  
C. M. Fauquet ◽  
G. Mkamilo ◽  
...  

2021 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Kaur ◽  
Mamta Pathak ◽  
Deepak Singla ◽  
Abhishek Sharma ◽  
Parveen Chhuneja ◽  
...  

Yellow mosaic disease (YMD) in bitter gourd (Momordica charantia) is a devastating disease that seriously affects its yield. Although there is currently no effective method to control the disease, breeding of resistant varieties is the most effective and economic option. Moreover, quantitative trait locus (QTL) associated with resistance to YMD has not yet been reported. With the objective of mapping YMD resistance in bitter gourd, the susceptible parent “Punjab-14” and the resistant parent “PAUBG-6” were crossed to obtain F4 mapping population comprising 101 individuals. In the present study, the genotyping by sequencing (GBS) approach was used to develop the genetic linkage map. The map contained 3,144 single nucleotide polymorphism (SNP) markers, consisted of 15 linkage groups, and it spanned 2415.2 cM with an average marker distance of 0.7 cM. By adopting the artificial and field inoculation techniques, F4:5 individuals were phenotyped for disease resistance in Nethouse (2019), Rainy (2019), and Spring season (2020). The QTL analysis using the genetic map and phenotyping data identified three QTLs qYMD.pau_3.1, qYMD.pau_4.1, and qYMD.pau_5.1 on chromosome 3, 4, and 5 respectively. Among these, qYMD.pau_3.1, qYMD.pau_4.1 QTLs were identified during the rainy season, explaining the 13.5 and 21.6% phenotypic variance respectively, whereas, during the spring season, qYMD.pau_4.1 and qYMD.pau_5.1 QTLs were observed with 17.5 and 22.1% phenotypic variance respectively. Only one QTL qYMD.pau_5.1 was identified for disease resistance under nethouse conditions with 15.6% phenotypic variance. To our knowledge, this is the first report on the identification of QTLs associated with YMD resistance in bitter gourd using SNP markers. The information generated in this study is very useful in the future for fine-mapping and marker-assisted selection for disease resistance.


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