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Mohler V.,Institute for Crop Production and Plant Breeding | Schmolke M.,TU Munich | Paladey E.,Max Rubner Institute | Seling S.,Max Rubner Institute | Hartl L.,Institute for Crop Production and Plant Breeding
Journal of Cereal Science | Year: 2012

Grain hardness, a major determinant influencing end-use quality of common wheat, is mainly controlled by Puroindoline a-D1 (Pina-D1) and Puroindoline b-D1 (Pinb-D1) genes. Recently, additional puroindoline genes, designated Puroindoline b-2 (Pinb-2), were described. This study examined frequencies of Pin-D1 alleles and Pinb-2 variants in 94 West European wheat genotypes and assessed their association with 13 quality traits considering population and family structure. The survey was completed by analyzing the Grain softness protein-1 gene. Results indicated sequence variation only for Pinb-D1 and Pinb-B2 genes. Pinb-D1b was the predominant hard allele. Pinb-B2v3-1 was the most common Pinb-2 variant, followed by a newly discovered variant Pinb-B2v3-5. Association mapping carried out in the whole sample population showed that Pinb-D1 alleles were associated with 11 quality traits, whereas Pinb-B2 variants were only associated with semolina extraction. Considering only the panel of hard wheat genotypes, variation for flour ash content, sedimentation value, gluten index and loaf volume was found to be associated with Pinb-D1 mutations suggesting that different Pinb-D1 mutations might have particular effects on quality traits. Our study indicated that Pinb-D1d was associated with inferior sedimentation value, gluten index and loaf volume, for which reason this mutation should be disregarded in breeding for quality wheat. © 2012 Elsevier Ltd.

Lehermeier C.,TU Munich | Wimmer V.,TU Munich | Albrecht T.,Institute for Crop Production and Plant Breeding | Albrecht T.,TU Munich | And 5 more authors.
Statistical Applications in Genetics and Molecular Biology | Year: 2013

Different statistical models have been proposed for maximizing prediction accuracy in genomebased prediction of breeding values in plant and animal breeding. However, little is known about the sensitivity of these models with respect to prior and hyperparameter specification, because comparisons of prediction performance are mainly based on a single set of hyperparameters. In this study, we focused on Bayesian prediction methods using a standard linear regression model with marker covariates coding additive effects at a large number of marker loci. By comparing different hyperparameter settings, we investigated the sensitivity of four methods frequently used in genome-based prediction (Bayesian Ridge, Bayesian Lasso, BayesA and BayesB) to specification of the prior distribution of marker effects. We used datasets simulated according to a typical maize breeding program differing in the number of markers and the number of simulated quantitative trait loci affecting the trait. Furthermore, we used an experimental maize dataset, comprising 698 doubled haploid lines, each genotyped with 56110 single nucleotide polymorphism markers and phenotyped as testcrosses for the two quantitative traits grain dry matter yield and grain dry matter content. The predictive ability of the different models was assessed by five-fold cross-validation. The extent of Bayesian learning was quantified by calculation of the Hellinger distance between the prior and posterior densities of marker effects. Our results indicate that similar predictive abilities can be achieved with all methods, but with BayesA and BayesB hyperparameter settings had a stronger effect on prediction performance than with the other two methods. Prediction performance of BayesA and BayesB suffered substantially from a nonoptimal choice of hyperparameters. © 2013 Walter de Gruyter GmbH, Berlin/Boston.

Wimmer V.,TU Munich | Lehermeier C.,TU Munich | Albrecht T.,TU Munich | Albrecht T.,Institute for Crop Production and Plant Breeding | And 3 more authors.
Genetics | Year: 2013

In genome-based prediction there is considerable uncertainty about the statistical model and method required to maximize prediction accuracy. For traits influenced by a small number of quantitative trait loci (QTL), predictions are expected to benefit from methods performing variable selection [e.g., BayesB or the least absolute shrinkage and selection operator (LASSO)] compared to methods distributing effects across the genome [ridge regression best linear unbiased prediction (RR-BLUP)]. We investigate the assumptions underlying successful variable selection by combining computer simulations with large-scale experimental data sets from rice (Oryza sativa L.), wheat (Triticum aestivum L.), and Arabidopsis thaliana (L.). We demonstrate that variable selection can be successful when the number of phenotyped individuals is much larger than the number of causal mutations contributing to the trait. We show that the sample size required for efficient variable selection increases dramatically with decreasing trait heritabilities and increasing extent of linkage disequilibrium (LD). We contrast and discuss contradictory results from simulation and experimental studies with respect to superiority of variable selection methods over RR-BLUP. Our results demonstrate that due to long-range LD, medium heritabilities, and small sample sizes, superiority of variable selection methods cannot be expected in plant breeding populations even for traits like FRIGIDA gene expression in Arabidopsis and flowering time in rice, assumed to be influenced by a few major QTL. We extend our conclusions to the analysis of whole-genome sequence data and infer upper bounds for the number of causal mutations which can be identified by LASSO. Our results have major impact on the choice of statistical method needed to make credible inferences about genetic architecture and prediction accuracy of complex traits. © 2013 by the Genetics Society of America.

Weihrauch F.,Institute for Crop Production and Plant Breeding | Baumgartner A.,Institute for Crop Production and Plant Breeding | Felsl M.,Institute for Crop Production and Plant Breeding | Kneidl J.,Institute for Crop Production and Plant Breeding | Lutz A.,Institute for Crop Production and Plant Breeding
Acta Horticulturae | Year: 2013

A cornerstone of integrated pest management in hops is the breeding of cultivars that are tolerant or resistant to Phorodon humuli infestation. During 2010, 2011 and 2012 we have developed a bioassay to assess the aphid susceptibility of different hop genotypes. We chose for these tests the susceptible cultivars 'Hallertauer Magnum' (HM) and 'Herkules' (HS), the aphid-tolerant or-resistant cultivars 'Spalter Select' (SE) and 'Boadicea' (BO), and two apparently resistant genotypes from our germplasm-the male accession "3W" and the breeding line "2005/034/022". Rooted cuttings of these six genotypes were produced immediately after winter dormancy and planted in 2.5 L preserving jars in 12 replications, respectively. Each jar was equipped with one cutting and one aphid larva with the same day of birth, closed with a sheet of gauze and then stored in a climate chamber for 22-28 days. All enclosures of a series were opened synchronously and the aphids in each jar were counted. Altogether 11 test series were conducted. HS was the genotype found to have the highest and a rather even infestation level, and was chosen as standard cultivar (100%) to assess relative infestation levels of the other genotypes. BO revealed by far the lowest infestation levels, emphasizing its very good aphid resistance. "3W" also showed a very good resistance level and yielded even results. The proven aphid tolerance of SE was reflected in oscillating infestation levels of lower than 50% of HS. The breeding line "2005/034/022" displayed very heterogeneous infestations between the single series and its susceptibility level is unclear. HM was similarly susceptible as HS, but the results from single series were more uneven. It is recommended to conduct according bioassays as early as possible during the vegetation period, and to avoid trials later than mid-June. As a result of our trials, we come to the conclusion that the intended development of a simple laboratory bioassay was successful and with it now it will be possible to quickly separate new genotypes into the three classes 'resistant', 'tolerant' and 'susceptible' to aphid infestation.

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