Browsing by Person "Liu, Zengting"

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Mendelian randomisation to uncover causal associations between conformation, metabolism, and production as potential exposure to reproduction in German Holstein dairy cattle
(2025) Schwarz, Leopold; Heise, Johannes; Liu, Zengting; Bennewitz, Jörn; Thaller, Georg; Tetens, Jens
Background: Reproduction is vital to welfare, health, and economics in animal husbandry and breeding. Health and reproduction are increasingly being considered because of the observed genetic correlations between reproduction, health, conformation, and performance traits in dairy cattle. Understanding the detailed genetic architecture underlying these traits would represent a major step in comprehending their interplay. Identifying known, putative or novel associations in genomics could improve animal health, welfare, and performance while allowing further adjustments in animal breeding. Results: We conducted genome-wide association studies for 25 different traits belonging to four different complexes, namely reproduction (n = 13), conformation (n = 6), production (n = 3), and metabolism (n = 3), using a cohort of over 235,000 dairy cows. As a result, we identified genome-wide significant signals for all the studied traits. The obtained summary statistics collected served as the input for a Mendelian randomisation approach (GSMR) to infer causal associations between putative exposure and reproduction traits. The study considered conformation, production, and metabolism as exposure and reproduction as outcome. A range of 139 to 252 genome-wide significant SNPs per combination were identified as instrumental variables (IVs). Out of 156 trait combinations, 135 demonstrated statistically significant effects, thereby enabling the identification of the responsible IVs. Combinations of traits related to metabolism (38 out of 39), conformation (68 out of 78), or production (29 out of 39) were found to have significant effects on reproduction. These relationships were partially non-linear. Moreover, a separate variance component estimation supported these findings, strongly correlating with the GSMR results and offering suggestions for improvement. Downstream analyses of selected representative traits per complex resulted in identifying and investigating potential physiological mechanisms. Notably, we identified both trait-specific SNPs and genes that appeared to influence specific traits per complex, as well as more general SNPs that were common between exposure and outcome traits. Conclusions: Our study confirms the known genetic associations between reproduction traits and the three complexes tested. It provides new insights into causality, indicating a non-linear relationship between conformation and reproduction. In addition, the downstream analyses have identified several clustered genes that may mediate this association.
Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traits
(2025) Križanac, Ana-Marija; Reimer, Christian; Heise, Johannes; Liu, Zengting; Pryce, Jennie E.; Bennewitz, Jörn; Thaller, Georg; Falker-Gieske, Clemens; Tetens, Jens
Background: Milk production traits are complex and influenced by many genetic and environmental factors. Although extensive research has been performed for these traits, with many associations unveiled thus far, due to their crucial economic importance, complex genetic architecture, and the fact that causal variants in cattle are still scarce, there is a need for a better understanding of their genetic background. In this study, we aimed to identify new candidate loci associated with milk production traits in German Holstein cattle, the most important dairy breed in Germany and worldwide. For that purpose, 180,217 cattle were imputed to the sequence level and large-scale genome-wide association study (GWAS) followed by fine-mapping and evolutionary and functional annotation were carried out to identify and prioritize new association signals. Results: Using the imputed sequence data of a large cattle dataset, we identified 50,876 significant variants, confirming many known and identifying previously unreported candidate variants for milk (MY), fat (FY), and protein yield (PY). Genome-wide significant signals were fine-mapped with the Bayesian approach that determines the credible variant sets and generates the probability of causality for each signal. The variants with the highest probabilities of being causal were further classified using external information about the function and evolution, making the prioritization for subsequent validation experiments easier. The top potential causal variants determined with fine-mapping explained a large percentage of genetic variance compared to random ones; 178 variants explained 11.5%, 104 explained 7.7%, and 68 variants explained 3.9% of the variance for MY, FY, and PY, respectively, demonstrating the potential for causality. Conclusions: Our findings proved the power of large samples and sequence-based GWAS in detecting new association signals. In order to fully exploit the power of GWAS, one should aim at very large samples combined with whole-genome sequence data. These can also come with both computational and time burdens, as presented in our study. Although milk production traits in cattle are comprehensively investigated, the genetic background of these traits is still not fully understood, with the potential for many new associations to be revealed, as shown. With constantly growing sample sizes, we expect more insights into the genetic architecture of milk production traits in the future.