Browsing by Person "Schmid, Markus"

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Improving the accuracy of multi-breed prediction in admixed populations by accounting for the breed origin of haplotype segments
(2022) Schmid, Markus; Stock, Joana; Bennewitz, Jörn; Wellmann, Robin
Numerically small breeds have often been upgraded with mainstream breeds. This historic introgression predisposes the breeds for joint genomic evaluations with mainstream breeds. The linkage disequilibrium structure differs between breeds. The marker effects of a haplotype segment may, therefore, depend on the breed from which the haplotype segment originates. An appropriate method for genomic evaluation would account for this dependency. This study proposes a method for the computation of genomic breeding values for small admixed breeds that incorporate phenotypic and genomic information from large introgressed breeds by considering the breed origin of alleles (BOA) in the evaluation. The proposed BOA model classifies haplotype segments according to their origins and assumes different but correlated SNP effects for the different origins. The BOA model was compared in a simulation study to conventional within-breed genomic best linear unbiased prediction (GBLUP) and conventional multi-breed GBLUP models. The BOA model outperformed within-breed GBLUP as well as multi-breed GBLUP in most cases.
Optimization strategies to adapt sheep breeding programs to pasture-based production environments: A simulation study
(2023) Martin, Rebecca; Pook, Torsten; Bennewitz, Jörn; Schmid, Markus
Strong differences between the selection (indoor fattening) and production environment (pasture fattening) are expected to reduce genetic gain due to possible genotype-by-environment interactions (G × E). To investigate how to adapt a sheep breeding program to a pasture-based production environment, different scenarios were simulated for the German Merino sheep population using the R package Modular Breeding Program Simulator (MoBPS). All relevant selection steps and a multivariate pedigree-based BLUP breeding value estimation were included. The reference scenario included progeny testing at stations to evaluate the fattening performance and carcass traits. It was compared to alternative scenarios varying in the progeny testing scheme for fattening traits (station and/or field). The total merit index (TMI) set pasture-based lamb fattening as a breeding goal, i.e., field fattening traits were weighted. Regarding the TMI, the scenario with progeny testing both in the field and on station led to a significant increase in genetic gain compared with the reference scenario. Regarding fattening traits, genetic gain was significantly increased in the alternative scenarios in which field progeny testing was performed. In the presence of G × E, the study showed that the selection environment should match the production environment (pasture) to avoid losses in genetic gain. As most breeding goals also contain traits not recordable in field testing, the combination of both field and station testing is required to maximize genetic gain.
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.