Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traits
Abstract 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...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | deu |
| Published: |
BMC
2025-02-01
|
| Series: | Genetics Selection Evolution |
| Online Access: | https://doi.org/10.1186/s12711-025-00951-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823863595775033344 |
|---|---|
| author | Ana-Marija Križanac Christian Reimer Johannes Heise Zengting Liu Jennie E. Pryce Jörn Bennewitz Georg Thaller Clemens Falker-Gieske Jens Tetens |
| author_facet | Ana-Marija Križanac Christian Reimer Johannes Heise Zengting Liu Jennie E. Pryce Jörn Bennewitz Georg Thaller Clemens Falker-Gieske Jens Tetens |
| author_sort | Ana-Marija Križanac |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-4d950d2b0ecf470f8214888608a8a6bb |
| institution | Kabale University |
| issn | 1297-9686 |
| language | deu |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Genetics Selection Evolution |
| spelling | doaj-art-4d950d2b0ecf470f8214888608a8a6bb2025-02-09T12:04:32ZdeuBMCGenetics Selection Evolution1297-96862025-02-0157112010.1186/s12711-025-00951-9Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traitsAna-Marija Križanac0Christian Reimer1Johannes Heise2Zengting Liu3Jennie E. Pryce4Jörn Bennewitz5Georg Thaller6Clemens Falker-Gieske7Jens Tetens8Department of Animal Sciences, University of GoettingenCenter for Integrated Breeding Research, Department of Animal Sciences, University of GoettingenVereinigte Informationssysteme Tierhaltung w.V. (VIT)Vereinigte Informationssysteme Tierhaltung w.V. (VIT)Agriculture Victoria Research, AgriBio, Centre for AgriBioscienceInstitute of Animal Science, University of HohenheimInstitute of Animal Breeding and Husbandry, Christian-Albrechts-UniversityDepartment of Animal Sciences, University of GoettingenDepartment of Animal Sciences, University of GoettingenAbstract 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.https://doi.org/10.1186/s12711-025-00951-9 |
| spellingShingle | Ana-Marija Križanac Christian Reimer Johannes Heise Zengting Liu Jennie E. Pryce Jörn Bennewitz Georg Thaller Clemens Falker-Gieske Jens Tetens Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traits Genetics Selection Evolution |
| title | Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traits |
| title_full | Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traits |
| title_fullStr | Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traits |
| title_full_unstemmed | Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traits |
| title_short | Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traits |
| title_sort | sequence based gwas in 180 000 german holstein cattle reveals new candidate variants for milk production traits |
| url | https://doi.org/10.1186/s12711-025-00951-9 |
| work_keys_str_mv | AT anamarijakrizanac sequencebasedgwasin180000germanholsteincattlerevealsnewcandidatevariantsformilkproductiontraits AT christianreimer sequencebasedgwasin180000germanholsteincattlerevealsnewcandidatevariantsformilkproductiontraits AT johannesheise sequencebasedgwasin180000germanholsteincattlerevealsnewcandidatevariantsformilkproductiontraits AT zengtingliu sequencebasedgwasin180000germanholsteincattlerevealsnewcandidatevariantsformilkproductiontraits AT jennieepryce sequencebasedgwasin180000germanholsteincattlerevealsnewcandidatevariantsformilkproductiontraits AT jornbennewitz sequencebasedgwasin180000germanholsteincattlerevealsnewcandidatevariantsformilkproductiontraits AT georgthaller sequencebasedgwasin180000germanholsteincattlerevealsnewcandidatevariantsformilkproductiontraits AT clemensfalkergieske sequencebasedgwasin180000germanholsteincattlerevealsnewcandidatevariantsformilkproductiontraits AT jenstetens sequencebasedgwasin180000germanholsteincattlerevealsnewcandidatevariantsformilkproductiontraits |