Further mapping of quantitative trait loci for female sterility in wheat (Triticum aestivum L.).
ABSTRACT Epistasis underlying fertility plays an important role in crop breeding. Although a new female sterile mutant in wheat, XND126, has been identified and a major quantitative trait locus (QTL), taf1, for the female sterility has been mapped, the genetic architecture of the female sterility needs to be further addressed. To identify the interaction involving the gene(s) controlling the female sterility, an investigation was carried out for the seed setting ratio in an F2 population derived from the cross between XND126 and Gaocheng 8901. Among 1250 simple sequence repeat (SSR) primer pairs in the whole genome, a total of 21 markers, obtained by recessive class approach, along with other ten tightly linked markers on reference maps in wheat, were used to survey 243 F2 individuals. As a result, 28 markers were mapped into five genetic linkage groups. The performance for female sterility for each F2 individual was evaluated simultaneously at the Urumqi and Huai'an experimental stations in 2006-2007. The two phenotypic datasets along with marker information were jointly analysed in the detection of QTL using penalized maximum likelihood approach. A total of six QTLs, including two main-effect QTLs, three epistatic QTLs and one environmental interaction and accounting for 0.67-24.55% of the total phenotypic variance, were identified. All estimated effects accounted for 53.26% of the total phenotypic variation. The taf1 detected in previous study was also located on the same marker interval on chromosome 2DS. These results enrich our understanding of the genetic basis of the female sterility.
- [Show abstract] [Hide abstract]
ABSTRACT: Several biologically significant parameters that are related to rice tillering are closely associated with rice grain yield. Although identification of the genes that control rice tillering and therefore influence crop yield would be valuable for rice production management and genetic improvement, these genes remain largely unidentified. In this study, we carried out functional mapping of quantitative trait loci (QTLs) for rice tillering in 129 doubled haploid lines, which were derived from a cross between IR64 and Azucena. We measured the average number of tillers in each plot at seven developmental stages and fit the growth trajectory of rice tillering with the Wang-Lan-Ding mathematical model. Four biologically meaningful parameters in this model--the potential maximum for tiller number (K), the optimum tiller time (t(0)), and the increased rate (r), or the reduced rate (c) at the time of deviation from t(0)--were our defined variables for multi-marker joint analysis under the framework of penalized maximum likelihood, as well as composite interval mapping. We detected a total of 27 QTLs that accounted for 2.49-8.54% of the total phenotypic variance. Nine common QTLs across multi-marker joint analysis and composite interval mapping showed high stability, while one QTL was environment-specific and three were epistatic. We also identified several genomic segments that are associated with multiple traits. Our results describe the genetic basis of rice tiller development, enable further marker-assisted selection in rice cultivar development, and provide useful information for rice production management.MGG Molecular & General Genetics 10/2010; 284(4):263-71. · 2.58 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Bayesian methods are a popular choice for genomic prediction of genotypic values. The methodology is well established for traits with approximately Gaussian phenotypic distribution. However, numerous important traits are of dichotomous nature and the phenotypic counts observed follow a Binomial distribution. The standard Gaussian generalized linear models (GLM) are not statistically valid for this type of data. Therefore, we implemented Binomial GLM with logit link function for the BayesB and Bayesian GBLUP genomic prediction methods. We compared these models with their standard Gaussian counterparts using two experimental data sets from plant breeding, one on female fertility in wheat and one on haploid induction in maize, as well as a simulated data set. With the aid of the simulated data referring to a bi-parental population of doubled haploid lines, we further investigated the influence of training set size (N), number of independent Bernoulli trials for trait evaluation (n ( i )) and genetic architecture of the trait on genomic prediction accuracies and abilities in general and on the relative performance of our models. For BayesB, we in addition implemented finite mixture Binomial GLM to account for overdispersion. We found that prediction accuracies increased with increasing N and n ( i ). For the simulated and experimental data sets, we found Binomial GLM to be superior to Gaussian models for small n ( i ), but that for large n ( i ) Gaussian models might be used as ad hoc approximations. We further show with simulated and real data sets that accounting for overdispersion in Binomial data can markedly increase the prediction accuracy.Theoretical and Applied Genetics 02/2013; · 3.66 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Gossypium hirsutum is a high yield cotton species that exhibits only moderate performance in fiber qualities. A promising but challenging approach to improving its phenotypes is interspecific introgression, the transfer of valuable traits or genes from the germplasm of another species such as G. barbadense, an important cultivated extra long staple cotton species. One set of chromosome segment introgression lines (CSILs) was developed, where TM-1, the genetic standard in G. hirsutum, was used as the recipient parent and the long staple cotton G. barbadense Hai7124 was used as the donor parent by molecular marker-assisted selection (MAS) in BC(5)S(1–4) and BC(4)S(1–3) generations. After four rounds of MAS, the CSIL population was comprised of 174 lines containing 298 introgressed segments, of which 86 (49.4%) lines had single introgressed segments. The total introgressed segment length covered 2,948.7 cM with an average length of 16.7 cM and represented 83.3% of tetraploid cotton genome. The CSILs were highly varied in major fiber qualities. By integrated analysis of data collected in four environments, a total of 43 additive quantitative trait loci (QTL) and six epistatic QTL associated with fiber qualities were detected by QTL IciMapping 3.0 and multi-QTL joint analysis. Six stable QTL were detected in various environments. The CSILs developed and the analyses presented here will enhance the understanding of the genetics of fiber qualities in long staple G. barbadense and facilitate further molecular breeding to improve fiber quality in Upland cotton.Theoretical and Applied Genetics 02/2012; 124(8):1415-28. · 3.66 Impact Factor