Voorlopige richtlijnen voor het beheer van blauwgraslandreservaten

RIN analyse graslandbehee

Exploratory QTL analyses of some pepper physiological traits in two environments

behind phenotypic differences and led to selection of genotypes having favourable traits. Continuous monitoring of environmental conditions has also become an accessible option. Rather than single trait evaluation, we would prefer smarter approaches capable of evaluating multiple, often correlated and time dependent traits simultaneously as a function of genes (QTLs) and environmental inputs, where we would The use of molecular breeding techniques has increased insight into the genetics like to include intermediate genomic information as well. In this paper, an exploratory QTL analysis over two environments was undertaken using available genetic and phenotypic data from segregating recombinant inbred lines (RIL) of pepper (Capsicum annuum). We focused on vegetative traits, e.g. stem length, speed of stem development, number of internodes etc. We seek to improve the estimation of allelic values of these traits under the two environments and determine possible QTL x E interaction. Almost identical QTLs are detected for each trait under the two environments but with varying LOD scores. No clear evidence was found for presence of QTL by environment interactions, despite differences in phenotypes and in magnitude of QTLs expression. Within the EU project SPICY (Voorrips et al., 2010 this issue), a larger number of environments will be studied and more advanced statistical analysis tools will be considered. The correlation between the traits will also be modelled. The identification of markers for the important QTL (Nicolaï et al., 2010 this issue) will improve the speed and accuracy of genomic prediction of these complex phenotype

Crop growth models for the -omics era: the EU-SPICY project

The prediction of phenotypic responses from genetic and environmental information is an area of active research in genetics, physiology and statistics. Rapidly increasing amounts of phenotypic information become available as a consequence of high throughput phenotyping techniques, while more and cheaper genotypic data follow from the development of new genotyping platforms. , A wide array of -omics data can be generated linking genotype and phenotype. Continuous monitoring of environmental conditions has become an accessible option. This wealth of data requires a drastic rethinking of the traditional quantitative genetic approach to modeling phenotypic variation in terms of genetic and environmental differences. Where in the past a single phenotypic trait was partitioned in a genetic and environmental component by analysis of variance techniques, nowadays we desire to model multiple, interrelated and often time dependent, phenotypic traits as a function of genes (QTLs) and environmental inputs, while we would like to include transcription information as well. The EU project 'Smart tools for Prediction and Improvement of Crop Yield' (KBBE-2008-211347), or SPICY, aims at the development of genotype-to-phenotype models that fully integrate genetic, genomic, physiological and environmental information to achieve accurate phenotypic predictions across a wide variety of genetic and environmental configurations. Pepper (Capsicum annuum) is chosen as the model crop, because of the availability of genetically characterized populations and of generic models for continuous crop growth and greenhouse production. In the presentation the objectives and structure of SPICY as well as its philosophy will be discussed

Fauna en terreinkenmerken van bos; een studie naar de relatie tussen terreinkenmerken en de geschiktheid van bos als habitat voor een aantal diersoorten

In dit rapport wordt een methode beschreven waarmee de geschiktheid van het bos als habitat voor verschillende diersoorten kan worden bepaald op basis van de terreinkenmerken van het bos. De methode is gebaseerd op HSI-modellen. Er zijn 10 terreinkenmerken gebruikt om de habitatgeschiktheid te bepalen. Naast het onderdeel dat de habitatgeschiktheid van bos aangeeft, is er een onderdeel toegevoegd dat de bosbeheerder informatie geeft over de soorten die in het bos voor kunnen voorkomen op basis van de ligging en grootte van het bos. Voor zes diergroepen is de relatie tussen de terreinkenmerken en de habitatgeschiktheid weergegeven

Genomic Prediction with 12.5 Million SNPs for 5503 Holstein Friesian Bulls

This study reports the first preliminary results of genomic prediction with whole-genome sequence data (12,590,056 SNPs) for 5503 bulls with accurate phenotypes. Two methods were compared: genome-enabled best linear unbiased prediction (GBLUP) and a Bayesian approach (BSSVS). Results were compared with results using BovineHD genotypes (631,428 SNPs). Results were reported for somatic cell score, interval between first and last insemination, and protein yield. For all traits, and both methods genomic prediction with sequence data showed similar results compared to BovineHD and GBLUP showed similar results compared to BSSVS. However, it remains to be seen if reliability of BSSVS with sequence data will improve after more sampling cycles have been finished

Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle

Background The use of whole-genome sequence data can lead to higher accuracy in genome-wide association studies and genomic predictions. However, to benefit from whole-genome sequence data, a large dataset of sequenced individuals is needed. Imputation from SNP panels, such as the Illumina BovineSNP50 BeadChip and Illumina BovineHD BeadChip, to whole-genome sequence data is an attractive and less expensive approach to obtain whole-genome sequence genotypes for a large number of individuals than sequencing all individuals. Our objective was to investigate accuracy of imputation from lower density SNP panels to whole-genome sequence data in a typical dataset for cattle. Methods Whole-genome sequence data of chromosome 1 (1737 471 SNPs) for 114 Holstein Friesian bulls were used. Beagle software was used for imputation from the BovineSNP50 (3132 SNPs) and BovineHD (40 492 SNPs) beadchips. Accuracy was calculated as the correlation between observed and imputed genotypes and assessed by five-fold cross-validation. Three scenarios S40, S60 and S80 with respectively 40%, 60%, and 80% of the individuals as reference individuals were investigated. Results Mean accuracies of imputation per SNP from the BovineHD panel to sequence data and from the BovineSNP50 panel to sequence data for scenarios S40 and S80 ranged from 0.77 to 0.83 and from 0.37 to 0.46, respectively. Stepwise imputation from the BovineSNP50 to BovineHD panel and then to sequence data for scenario S40 improved accuracy per SNP to 0.65 but it varied considerably between SNPs. Conclusions Accuracy of imputation to whole-genome sequence data was generally high for imputation from the BovineHD beadchip, but was low from the BovineSNP50 beadchip. Stepwise imputation from the BovineSNP50 to the BovineHD beadchip and then to sequence data substantially improved accuracy of imputation. SNPs with a low minor allele frequency were more difficult to impute correctly and the reliability of imputation varied more. Linkage disequilibrium between an imputed SNP and the SNP on the lower density panel, minor allele frequency of the imputed SNP and size of the reference group affected imputation reliability

Genotype imputation accuracy in Holstein Friesian cattle in case of whole-genome sequence data

The use of whole-genome sequence data can lead to more accurate genomic predictions in animal and plants. Despite the fact that costs of sequencing are falling, sequencing a high number of individuals is still far too expensive. A promising approach is to sequence the genomes of a core set of individuals and impute the missing genotypes for the remaining individuals that are genotyped with currently available marker arrays. Relevant questions are how many animals do we need to sequence and what SNP arrays can we impute from for accurate imputation? Sequence data of 124 Holstein Friesian bulls from different countries were provided by the 1000 bull genomes project consortium (www.1000bullgenomes.com). Two chromosomes with distinct sizes (1 and 29) were selected for this study. The Beagle software was used for imputation and accuracy was assessed via cross validation. The 124 bulls were randomly divided into five sets: four sets were merged into a reference set (n_ref=100), and the remaining set in turn as the validation set. For the validation individuals all markers were set to missing, except for markers that occur on two commonly used arrays that include 777k and 54k SNP across the genome. In a second scenario the same was done, except half of the reference individuals were randomly removed (n_ref=50). Accuracy of imputation was calculated by the correlation between true and imputed genotypes per locus. The results will be presented and the impact of the size of the reference set and the marker density will be discussed