Han S.,University of Hohenheim |
Utz H.F.,University of Hohenheim |
Liu W.,China Agricultural University |
Schrag T.A.,University of Hohenheim |
And 7 more authors.
Theoretical and Applied Genetics | Year: 2016
Key message: QTL analysis forFusariumresistance traits with multiple connected families detected more QTL than single-family analysis. Prediction accuracy was tightly associated with the kinship of the validation and training set. Abstract: QTL mapping has recently shifted from analysis of single families to multiple, connected families and several biometric models have been suggested. Using a high-density consensus map with 2472 marker loci, we performed QTL mapping with five connected bi-parental families with 639 doubled-haploid (DH) lines in maize for ear rot resistance and analyzed traits DON, Gibberella ear rot severity (GER), and days to silking (DS). Five biometric models differing in the assumption about the number and effects of alleles at QTL were compared. Model 2 to 5 performing joint analyses across all families and using linkage and/or linkage disequilibrium (LD) information identified all and even further QTL than Model 1 (single-family analyses) and generally explained a higher proportion pG of the genotypic variance for all three traits. QTL for DON and GER were mostly family specific, but several QTL for DS occurred in multiple families. Many QTL displayed large additive effects and most alleles increasing resistance originated from a resistant parent. Interactions between detected QTL and genetic background (family) occurred rarely and were comparatively small. Detailed analysis of three fully connected families yielded higher pG values for Model 3 or 4 than for Model 2 and 5, irrespective of the size NTS of the training set (TS). In conclusion, Model 3 and 4 can be recommended for QTL-based prediction with larger families. Including a sufficiently large number of full sibs in the TS helped to increase QTL-based prediction accuracy (rVS) for various scenarios differing in the composition of the TS. © 2015, Springer-Verlag Berlin Heidelberg.
Gao H.,Jiangsu Academy of Agricultural Sciences |
Gao H.,Nanjing Agricultural University |
Zhu F.,Jiangsu Academy of Agricultural Sciences |
Zhu F.,Nanjing Agricultural University |
And 6 more authors.
Theoretical and Applied Genetics | Year: 2012
Powdery mildew (PM), caused by Blumeria graminis f. sp. tritici (Bgt), has become a serious disease and caused severe yield losses in the wheat production worldwide. Resistance gene(s) in wheat cultivars can be quickly overcome by newly evolved pathogen races when these genes are employed for long time or in a large area. It is urgent to search for new sources of resistance to be used in wheat breeding. Tabasco is a German resistant cultivar and a new source of resistance gene(s) to PM. An F2 population was developed from a cross between Tabasco and a Chinese susceptible cultivar Ningnuo 1. Infection types in 472 F2 plants and 436 F2-3 families were evaluated by inoculating plants with isolate Bgt19. Results showed that a single dominant gene, designed Pm46, controlled powdery mildew resistance in Tabasco. This gene was located to the short arm of chromosome 5D (5DS) and flanked by simple sequence repeat markers Xgwm205 and Xcfd81 at 18. 9 cM apart. Because another resistance gene Pm2 was also located on 5DS, 15 Bgt isolates were used to inoculate Tabasco and Ulka/8*Cc (Pm2 carrier). The results showed that Tabasco was highly resistant to all of the 15 isolates tested, while Ulka/8*Cc was susceptible to 4 of the isolates, suggesting that Tabasco may carry resistant gene(s) different from Pm2 gene in Ulka/8*Cc. To test the allelism between Pm46 and Pm2, an F2 population between Tabasco and Ulka/8*Cc was developed. Isolate Bgt2, avirulent to both parents, was used to evaluate the F2 population and two susceptible plants were identified from 536 progenies with F2 plants. This result indicated that Pm46 is not allelic to Pm2. Therefore, Pm46 is a new gene for PM resistance identified in this study. © 2012 Springer-Verlag.
Frerichmann S.L.M.,University of Kiel |
Kirchhoff M.,University of Kiel |
Kirchhoff M.,Nordsaat Saatzucht GmbH |
Muller A.E.,University of Kiel |
And 4 more authors.
BMC Plant Biology | Year: 2013
Background: Sugar beet (Beta vulgaris ssp. vulgaris L.) is an important crop for sugar and biomass production in temperate climate regions. Currently sugar beets are sown in spring and harvested in autumn. Autumn-sown sugar beets that are grown for a full year have been regarded as a cropping system to increase the productivity of sugar beet cultivation. However, for the development of these " winter beets" sufficient winter hardiness and a system for bolting control is needed. Both require a thorough understanding of the underlying genetics and its natural variation.Results: We screened a diversity panel of 268 B. vulgaris accessions for three flowering time genes via EcoTILLING. This panel had been tested in the field for bolting behaviour and winter hardiness. EcoTILLING identified 20 silent SNPs and one non-synonymous SNP within the genes BTC1, BvFL1 and BvFT1, resulting in 55 haplotypes. Further, we detected associations of nucleotide polymorphisms in BvFL1 with bolting before winter as well as winter hardiness.Conclusions: These data provide the first genetic indication for the function of the FLC homolog BvFL1 in beet. Further, it demonstrates for the first time that EcoTILLING is a powerful method for exploring genetic diversity and allele mining in B. vulgaris. © 2013 Frerichmann et al.; licensee BioMed Central Ltd.
Lantos C.,Cereal Research Non Profit Ltd. |
Weyen J.,Saaten Union Biotec GmbH |
Orsini J.M.,Saaten Union Biotec GmbH |
Gnad H.,Saaten Union Biotec GmbH |
And 7 more authors.
Plant Breeding | Year: 2013
The efficiency of our anther culture protocol was tested with high- and low-responding genotypes, 'Svilena' and 'Berengar', and 93 F1 winter wheat crosses in 2010 and 2011. Based on data for these genotypes, the effect of genotype influenced the number of embryo-like structures, regenerated plantlets and green plantlets, while the number of albino plantlets was affected by genotype, year and environmental factors. Although genotype also influenced the production of green plantlets from breeding crosses, with green plantlets per 100 anthers ranging from 0.04 to 28.67, the average regeneration rate over all crosses was 5.3 green plantlets/100 anthers, which resulted in a total of 11 416 well-rooted green plantlets. The survival rate of green plantlets following acclimatization was 97.21% in 2010 and 96.34% in 2011. In this study, the phenomenon of albinism and genotype dependency did not hinder the production of more than five thousand green plantlets each year. In our experiments, anther culture proved to be an efficient method in winter wheat breeding programmes with lower costs than alternative technologies. © 2013 Blackwell Verlag GmbH.
Piepho H.-P.,University of Hohenheim |
Muller B.U.,Strube Research GmbH and Co. KG |
Jansen C.,Strube Research GmbH and Co. KG
Communications in Biometry and Crop Science | Year: 2014
Many complex agronomic traits are computed as the product of component traits. For the complex trait to be assessed in a field plot, each of the component traits needs to be measured in the same plot. When data on one or several component traits are missing, the complex trait cannot be computed. If the analysis is to be performed on data for the complex trait, plots with missing data on at least one of the component traits are discarded, even though data may be available on some of the component traits. This paper considers a multivariate mixed model approach that allows making use of all available data. The key idea is to employ a logarithmic transformation of the data in order to convert a product into a sum of the component traits. The approach is illustrated usinga series of sunflower breeding trials. It is demonstrated that the multivariate approach allows making use of all available information in the case of missing data, including plots that may have data only on one of the component traits. © 2014 CBCS.