Single Step Genomics: Methods and Implementation in German Cattle Populations
Doctoral thesis
Date of Examination:2025-03-26
Date of issue:2026-01-08
Advisor:Prof. Dr. Jens Tetens
Referee:Prof. Dr. Jens Tetens
Referee:Prof. Dr. George Thaller
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Abstract
English
Genomics and genomic technologies have significantly advanced the development of many plant and livestock populations. However, their impact on beef cattle populations, particularly in Germany, has been limited. This thesis focuses on the implementation and development of single-step genomic methodologies for German beef cattle breeds. Chapter 2 presents the paper titled “Single-step SNPBLUP Evaluation in Six German Beef Cattle Breeds,” published in the Journal of Animal Breeding and Genetics. This chapter details the first implementation of the single-step Single Nucleotide Polymorphism Best Linear Unbiased Prediction (ssSNPBLUP) genomic evaluation for six German beef cattle breeds. The primary aim is to demonstrate the advantages of integrating pedigree and genomic information into a single genomic evaluation framework, which enables simultaneous evaluation of all animals, regardless of the availability of genomic information. Single step approaches improve the accuracy and efficiency of genetic evaluations. Since the initial implementation for 6 beef cattle breeds, the method has been extended to include Hereford and Salers breeds, further expanding its relevance and utility in Germany's national genetic evaluation system. Building on this, Chapter 3 investigates the integration of international breeding values into national genomic evaluations. The manuscript, “Impact of Deregressed Foreign Breeding Values on the Single-Step Genomic Evaluation of German Beef Cattle Populations,” published in the Journal “Genetics Selection Evolution” explores methods to incorporate Interbeef evaluations, which provide internationally estimated breeding values (EBVs) into the national single step genomic evaluation of four beef cattle breeds. By blending international and national EBVs, this chapter addresses challenges in blending information derived from overlapping data sources while preserving valuable genetic information for increased reliability of breeding values. In this study, scalar and matrix deregression methods for direct and maternal genetic effects were developed and validated using forward validation, with the trait 200-day weight as a case study. Chapter 4 focuses on identifying genomic loci influencing phenotypes and their genetic architecture. Traditional single marker regression (Genome Wide Association Studies (GWAS)) methods, while effective for simple traits, face limitations with complex traits due to factors such as linkage disequilibrium, polygenicity, and incomplete genotype-phenotype records. To address these challenges, this chapter introduces a novel approach based on the ssSNPBLUP framework for estimating SNP effect reliability and defining significance thresholds for SNP markers in a genomic evaluation. Using datasets from German beef and dairy cattle, the method evaluates the influence of reference population size on SNP significance and compares results with conventional GWAS. This work enhances the applicability of ssSNPBLUP for both small and large datasets, bridging gaps in our understanding SNP effects in genomic evaluations. Finally, Chapter 5 examines the genetic diversity of German and Irish beef cattle populations under genomic selection. While genomic selection accelerates genetic gains, it also poses risks of increased inbreeding and reduced genetic diversity, which can hinder long-term sustainability. This chapter evaluates the genomic diversity of six breeds—Angus, Blonde d’Aquitane, Charolais, Simmental, Limousin, and Uckermärker—providing baseline metrics for monitoring diversity and informing strategies to balance genetic improvement with inbreeding management. Through the development and application of single-step genomic methodologies, this thesis contributes to improving the accuracy, efficiency, and sustainability of genetic evaluations in German beef cattle populations.
Keywords: Genomics; Single-step Genomics; Cattle genetics; Quantitative genetics
