Posts Tagged ‘Molecular markers’

Molecular characterization of diverse CIMMYT maize inbred lines from eastern and southern Africa using single nucleotide polymorphic markers

Posted by Carelia Juarez on , in Journal Articles

Published in BMC Genomics  13 : 113, 2012

Kassa Semagn, Cosmos Magorokosho, Bindiganavile S. Vivek, Dan Makumbi, Yoseph Beyene, Stephen Mugo, B. M. Prasanna and Marilyn L. Warburton

Knowledge of germplasm diversity and relationships among elite breeding materials   is fundamentally important in crop improvement. We genotyped 450 maize inbred lines   developed and/or widely used by CIMMYT breeding programs in both Kenya and Zimbabwe   using 1065 SNP markers to (i) investigate population structure and patterns of relationship   of the germplasm for better exploitation in breeding programs; (ii) assess the usefulness   of SNPs for identifying heterotic groups commonly used by CIMMYT breeding programs;   and (iii) identify a subset of highly informative SNP markers for routine and low   cost genotyping of CIMMYT germplasm in the region.

QTL mapping for milling, gluten quality, and flour pasting properties in a recombinant inbred line population derived from a Chinese soft × hard wheat cross

Posted by on , in Journal Articles

Published in Crop and Pature Science 60: 587-597

QTL mapping for milling, gluten quality, and flour pasting properties in a recombinant inbred line population derived from a Chinese soft × hard wheat cross

Yelun Zhang, Yunpeng Wu, Yonggui Xiao, Jun Yan, Yong Zhang, Yan Zhang, Chuanxi Ma, Xianchun Xia and Zhonghu He

Improvement of processing quality is important for various wheat-based end products. A recombinant inbred line (RIL) population derived from a cross between the Chinese wheat cvv. PH82-2 (hard) and Neixiang 188 (soft) was sown at 3 locations across two seasons to map quantitative trait loci (QTLs) for milling, gluten quality, flour pasting properties, and Chinese white salted noodle (CWSN) qualities. One hundred and eighty-eight microsatellite loci, one rye secalin marker Sec1, one STS marker YP7A, one CAPs marker for the Pinb-D1b allele, and four glutenin subunit markers were used to genotype the population and construct a linkage map for subsequent QTL analysis. In total, 53 QTLs for 16 quality parameters were mainly mapped to glutenin loci Glu-A3 (Glu-A3a:Glu-A3d), Glu-B1 (Bx7+By9:Bx14+By15), and Glu-D1 (Bx2+By12:Bx5+By10), and the grain hardness (Pinb-D1a:Pinb-D1b) locus. The-high-molecular weight glutenin subunits (HMW-GS) 5+10 at the Glu-D1 locus showed large effects on mixograph peak time (MPT), mixograph 8 min width (MTxW), and weakening slope (WS), accounting for 43.1%, 24.2%, and 39.7% of the phenotypic variance, respectively. In contrast, the 1RS (1B.1R translocation) showed large negative effects on MTxW, explaining 42.2% of the phenotypic variance. Two important QTLs were detected for Rapid Viscosity Analyzer (RVA) parameters: one for RVA final viscosity (RFV) near the 1RS and the other for RVA setback (RSb) associated with the Glu-B1 locus, explaining 21.6% and 12.3% of the phenotypic variance, respectively. Two QTLs for noodle adhesiveness were identified: one occurred on chromosome 1A and the other was associated with Glu-B1. Two QTLs for noodle springiness, one associated with the 1RS showing a negative effect on noodle quality and the other mapped to the Ha locus on chromosome 5DS, accounted for 9.4% and 8.1% of the phenotypic variance, respectively. In addition, the Ha locus also showed large effects on flower protein content (FPC), mixograph peak width (MPW), and RVA parameters, especially RVA pasting temperature (RPT), explaining 71.5% of the phenotypic variance.

Predicting quantitative Traits With Regression Models for dense molecular markers and pedigree

Posted by on , in Journal Articles

Published in Genetics 182: 375-385, 2009

Predicting quantitative Traits With Regression Models for dense molecular markers and pedigree

Gustavo de los Campos, Hugo Naya, Daniel Gianola, José Crossa, Andrés Legarra, Eduardo Manfredi, Kent Weigel and José Miguel Cotes

The availability of genomewide dense markers brings opportunities and challenges to breeding programs. An important question concerns the ways in which dense markers and pedigrees, together with phenotypic records, should be used to arrive at predictions of genetic values for complex traits. If a large number of markers are included in a regression model, marker-specific shrinkage of regression coefficients may be needed. For this reason, the Bayesian least absolute shrinkage and selection operator (LASSO) (BL) appears to be an interesting approach for fitting marker effects in a regression model. This article adapts the BL to arrive at a regression model where markers, pedigrees, and covariates other than markers are considered jointly. Connections between BL and other marker-based regression models are discussed, and the sensitivity of BL with respect to the choice of prior distributions assigned to key parameters is evaluated using simulation. The proposed model was fitted to two data sets from wheat and mouse populations, and evaluated using cross-validation methods. Results indicate that inclusion of markers in the regression further improved the predictive ability of models. An R program that implements the proposed model is freely available.

Distribution of the photoperiod insensitive Ppd-D1a allele in Chinese wheat cultivars

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Published in Euphytica 165(3):445-452

Distribution of the photoperiod insensitive Ppd-D1a allele in Chinese wheat cultivars

F. P. Yang, X. K. Zhang, X. C. Xia, D. A. Laurie, W. X. Yang and Z. H. He

Photoperiod response is of great importance for optimal adaptation of bread wheat cultivars to specific environments, and variation is commonly associated with allelic differences at the Ppd-D1 locus on chromosome 2D. A total of 926 Chinese wheat landraces and improved cultivars collected from nine wheat growing zones were tested for their genotypes at the Ppd-D1 locus using allele-specific markers. The average frequency of the photoperiod-insensitive Ppd-D1a allele was 66.0%, with the frequencies of 38.6 and 90.6% in landraces and improved cultivars, respectively. However, the Ppd-D1a allele was present in all improved cultivars released after 1970 except for spring wheats in high latitude northwestern China, and winter wheats in Gansu and Xinjiang. The presence of the Ppd-D1a allele in landraces and improved cultivars increased gradually from north to south, illustrating the relationship between photoperiod response and environment. Ppd-D1a in Chinese wheats is derived from three sources, Japanese landrace Akagomughi and Chinese landraces Mazhamai and Youzimai. The current information is important for understanding the broad adaptation of improved Chinese wheat cultivars.