Browsing by Subject "Genomweite Assoziationsstudie"

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Dissection of the genetic architecture of stalk mechanical strength and in vivo haploid induction in maize
(2016) Hu, Haixiao; Melchinger, Albrecht E.
Stalk lodging causes yield losses in maize cultivation ranging from 5 to 20% annually worldwide and stalk mechanical strength is widely accepted as an indirect indicator for its measurement. QTL mapping can reveal the genetic basis of stalk strength and provide information about markers suitable for marker-assisted selection (MAS). Constantly increasing market demands urge maize geneticists and breeders not only to enhance the field performance of new hybrids, but also to improve the breeding process. During the last decade, advances in the double haploid (DH) technology based on in vivo haploid induction (HI) shifted the breeding paradigm and greatly accelerated the breeding process in maize. Further spread of DH technology urgently demands a simple but efficient way for developing new inducers, which could be achieved by introducing the mandatory QTL/gene(s) of HI to advanced breeding lines. Therefore, the main goal of my thesis was to dissect the genetic architecture of stalk strength and detect the mandatory genomic region(s) of HI using genome-wide molecular markers. Several methods have been developed and applied in the literature to evaluate stalk mechanical strength, among which the rind penetrometer resistance (RPR) is a simple, rapid and non-destructive measurement during data collection, whereas stalk bending strength (SBS) is more closely associated with stalk lodging in the field. According to common knowledge in the mechanics of materials, SBS is reflected by the maximum load exerted to breaking (Fmax), the breaking moment (Mmax) and the critical stress (σmax). Thus, to have a complete understanding of the genetic architecture of stalk strength in maize, RPR and SBS (measured by Fmax, Mmax and σmax) were used to characterize stalk strength in our study. Utilizing a segregating population with 216 recombinant inbred lines, our analysis showed that stalk strength traits, RPR and SBS, have high heritability, ranging from 0.75 to 0.91. Nine QTL and one epistatic interaction between QTL were detected for RPR. Two, three and two QTL were detected for Fmax, Mmax and σmax, respectively. All QTL showed minor effects and only one QTL on chromosome 10 had overlapping support intervals between RPR and SBS. Co-locations of QTL and high positive correlations between stalk strength traits and other stalk traits suggested presence of pleiotropism and a complex genetic architecture of stalk strength. Owing to lack of major QTL, MAS solely based on molecular markers was found to be less effective than classical phenotypic selection for stalk strength. However, for SBS we observed considerably higher proportions of genetic variance explained by a genomic selection approach than obtained in QTL mapping with cross validation. Therefore, genomic selection might be a promising tool to improve the efficiency of breeding for stalk strength. All QTL mapping studies conducted hitherto for unraveling the genetic architecture of HI rate detected a major QTL, termed qhir1, in bin 1.04. Dong et al. (2013) further narrowed down this QTL to a 243 kb region. Considering the complex genetic architecture of HI and genetic background noise possibly affecting fine mapping of qhir1, we attempted to validate these results with an alternative approach before embarking on map-based gene isolation. Utilizing 51 maize haploid inducers and 1,482 non-inducers collected worldwide, we were able to investigate the genetic diversity between inducers and non-inducers and detect genomic regions mandatory for HI. The genetic diversity analyses indicated that the inducer group was clearly separated from other germplasm groups and had high familial relatedness. Analyzing our data by a case-control association approach failed because the segregation of HI was heavily confounded with population structure. Moreover, selective sweep approaches commonly used in the literature that are designed for capturing selective sweeps in a long-term evolutionary context failed due to high familial relatedness among inducers. To solve this problem, we developed a novel genome scan approach to detect fixed segments among inducers. With this approach, we detected a segment, termed qhir12, 4.0 Mb in length, within the support interval of the qhir1. This segment was the longest genomic segment detected by our novel approach and was entirely absent in all non-inducers analyzed. However, qhir12 has no overlap with the fine mapping region of Dong et al. (2013), termed qhir11. This indicates that the genomic region harboring the mandatory gene of HI should be confirmed by further experiments to corroborate its existence and identify its location in the maize genome.
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.