Browsing by Subject "Genauigkeit"

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Biometrical approaches for analysing gene bank evaluation data on barley (Hordeum spec.)
(2007) Hartung, Karin; Piepho, Hans-Peter
This thesis explored methods to statistically analyse phenotypic data of gene banks. Traits of the barley data (Hordeum spp.) of the gene bank of the IPK-Gatersleben were evaluated. The data of years 1948-2002 were available. Within this period the ordinal scale changed from a 0-5 to a 1-9 scale after 1993. At most gene banks reproduction of accessions is currently done without any experimental design. With data of a single year only rarely do accessions have replications and there are only few replications of a single check for winter and summer barley. The data of 2002 were analysed separately for winter and summer barley using geostatistical methods. For the traits analysed four types of variogram model (linear, spherical, exponential and Gaussian) were fitted to the empirical variogram using non-linear regression. The spatial parameters obtained by non-linear regression for every variogram model then were implemented in a mixed model analysis and the four model fits compared using Akaike's Information Criterion (AIC). The approach to estimate the genetical parameter by Kriging can not be recommended. The first points of the empirical variogram should be explained well by the fitted theoretical variogram, as these represent most of the pairwise distances between plots and are most crucial for neighbour adjustments. The most common well-fitting geostatistical models were the spherical and the exponential model. A nugget effect was needed for nearly all traits. The small number of check plots for the available data made it difficult to accurately dissect the genetical effect from environmental effects. The threshold model allows for joint analysis of multi-year data from different rating scales, assuming a common latent scale for the different rating systems. The analysis suggests that a mixed model analysis which treats ordinal scores as metric data will yield meaningful results, but that the gain in efficiency is higher when using a threshold model. The threshold model may also be used when there is a metric scale underlying the observed ratings. The Laplace approximation as a numerical method to integrate the log-likelihood for random effects worked well, but it is recommended to increase the number of quadrature points until the change in parameter estimates becomes negligible. Three rating methods (1%, 5%, 9-point rating) were assessed by persons untrained (A) and experienced (B) in rating. Every person had to rate several pictograms of diseased leaves. The highest accuracy was found with Group B using the 1%-scale and with Group A using the 5%-scale. With a percentage scale Group A tended to use values that are multiples of 5%. For the time needed per leaf assessment the Group B was fastest when using the 5% rating scale. From a statistical point of view both percent ratings performed better than the ordinal rating scale and the possible error made by the rater is calculable and usually smaller than with ratings by rougher methods. So directly rating percentages whenever possible leads to smaller overall estimation errors, and with proper training accuracy and precision can be further improved. For gene banks augmented designs as proposed by Federer and by Lin et al. offer themselves, so an overview is given. The augmented designs proposed by Federer have the advantage of an unbiased error estimate. But the random allocation of checks is a problem. The augmented design by Lin et al. always places checks in the centre plot of every whole plot. But none of the methods is based on an explicit statistical model, so there is no well-founded decision criterion to select between them. Spatial analysis can be used to find an optimal field layout for an augmented design, i.e. a layout that yields small least significant differences. The average variance of a difference and the average squared LSD were used to compare competing designs, using a theoretical approach based on variations of two anisotropic models and different rotations of anisotropy axes towards field reference axes. Based on theoretical calculations, up to five checks per block are recommended. The nearly isotropic combinations led to designs with large quadratic blocks. With strongly anisotropic combinations the optimal design depends on degree of anisotropy and rotation of anisotropy axes: without rotation small elongated blocks are preferred; the closer the rotation is to 45° the more squarish blocks and the more checks are appropriate. The results presented in this thesis may be summarised as follows: Cultivation for regeneration of accessions should be based on a meaningful and statistically analysable experimental field design. The design needs to include checks and a random sample of accessions from the gene pool held at the gene bank. It is advisable to utilise metric or percentage rating scales. It can be expected that using a threshold model increases the quality of multivariate analysis and association mapping studies based on phenotypic gene bank data.
Using genome-wide association studies to map genes for complex traits in porcine F2 crosses
(2018) Schmid, Markus; Bennewitz, Jörn
In the era of genomics, genome-wide association studies (GWASs) have become the method of choice for gene mapping. This is still of great interest to infer the genetic architecture of quantitative traits and to improve genomic selection in animal breeding. Formerly, linkage analyses were conducted in order to map genes. Therefore, many F2 cross populations were generated by crossing genetically divergent lineages in order to create informative experimental populations. However, a small number of markers and the limited meiotic divisions led to imprecise mapping results. The main objective of the present study was to investigate the use of existing porcine F2 cross data, extended towards single nucleotide polymorphism (SNP) chip genotype information, for quantitative trait loci (QTL) mapping in the genomic era. A special focus was on mapping genes that also segregate within the Piétrain breed since this is an important sire line and genomic selection is applied in this breed. Chapter 1 is a review article of statistical models and experimental populations applied in GWASs. This chapter gives an overview of methods to conduct GWASs using single-marker models and multi-marker models. Further, approaches taking non-additive genetic effects or genotype-by-environment interactions into account are described. Finally, post-GWAS analysis possibilities and GWAS mapping populations are discussed. In chapter 2, the power and precision of GWASs in different F2 populations and a segregating population was investigated using simulated whole-genome sequence data. Further, the effect of pooling data was determined. GWASs were conducted for simulated traits with a heritability of 0.5 in F2 populations derived from closely and distantly related simulated founder breeds, their pooled datasets, and a sample of the common maternal founder breed. The study showed that the mapping power was high (low) in F2 crosses derived from distantly (closely) related founder breeds and highest when several F2 datasets were pooled. By contrast, a low precision was observed in the cross with distantly related founder breeds and the pooling of data led to a precision that was between the two crosses. For genes that also segregated within the common founder breed, the precision was generally elevated and, at equal sample size, the power to map QTL was even higher in F2 crosses derived from closely related founder breeds compared with the founder breed itself. Within and across linkage disequilibrium (LD) structures of such F2 populations were examined in chapter 3 by separately and jointly (pooled dataset) analyzing four F2 datasets generated from different founder breeds. All individuals were genotyped with a 62k SNP chip. The LD decay was faster in crosses derived from closely related founder breeds compared with crosses from phylogenetically distantly related founder populations and fastest when the data of all crosses were pooled. The pooled dataset was also used to map QTL for the economically important traits dressing out and conductivity applying single-marker and Bayesian multi-marker regressions. For these traits, several genome-wide significant association signals were mapped. To infer the suitability of F2 data to map genes in a segregating breeding population, GWAS results of a pooled F2 cross were validated in two samples of the German Piétrain population (chapter 4). All individuals were genotyped using standard 62k SNP chips. The pooled cross contained the data of two F2 crosses, both had Piétrain as one founder breed, and consisted of 595 individuals. Initially, GWASs were conducted in the pooled F2 cross for the production traits dressing yield, carcass length, daily gain and drip loss. Subsequently, QTL core regions around significant trait associated peaks were defined. Finally, SNPs within these core regions were tested for association in the two samples of the current Piétrain population (771 progeny tested boars and 210 sows) in order to validate them in this breed. In total, 15 QTL were mapped and 8 (5) of them were validated in the boar (sow) validation dataset. This approach takes advantage of the high mapping power in F2 data to detect QTL that may not be found in the segregating Piétrain population. The findings showed that many of the QTL mapped in F2 crosses derived from Piétrain still segregate in this breed, and thus, these F2 datasets provide a promising database to map QTL in the Piétrain breed. The thesis ends with a general discussion.