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Lynch C, Schenkel FS, van Staaveren N, Miglior F, Kelton D, Baes CF. Investigating the potential for genetic selection of dairy calf disease traits using management data. J Dairy Sci 2024; 107:1022-1034. [PMID: 37730178 DOI: 10.3168/jds.2023-23780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 08/30/2023] [Indexed: 09/22/2023]
Abstract
Genetic selection could be a tool to help improve the health and welfare of calves; however, to date, there is limited research on the genetics of calfhood diseases. This study aimed to understand the current impact of calf diseases, by investigating incidence rates, estimating genetic parameters, and providing industry recommendations to improve calf disease recording practices on farms. Available calf disease data composed of 69,695 Holstein calf disease records for respiratory problems (RESP) and diarrhea (DIAR), from 62,361 calves collected on 1,617 Canadian dairy herds from 2006 to 2021. Single- and multiple-trait analysis using both a threshold and linear animal model for each trait were evaluated. Furthermore, each trait was analyzed using 2 scenarios with respect to minimum disease incidence threshold criterion (herd-year incidence of at least 1% and 5%) to highlight the effect of different filtering thresholds on selection potential. Observed scale heritability estimates for RESP and DIAR ranged from 0.02 to 0.07 across analyses, while estimated genetic correlations between the traits ranged from 0.50 to 0.62. Sires were compared based on their estimated breeding value and their diseased daughter incidence rates. On average, calves born to the bottom 10% of sires were 1.8 times more likely to develop RESP and 1.9 times to develop DIAR compared with daughters born to the top 10% of sires. Results from the current study are promising for the inclusion of both DIAR and RESP in Canadian genetic evaluations. However, for effective genetic evaluation, standardized approaches on data collection and industry outreach to highlight the importance of collecting and uploading this information to herd management software is required. In particular, it is important that the herd management software is accessible to the national milk recording system to allow for use in national genetic evaluation.
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Affiliation(s)
- C Lynch
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - F S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - N van Staaveren
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - F Miglior
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada; Lactanet Canada, Guelph, ON, N1K 1E5, Canada
| | - D Kelton
- Department of Population Medicine, University of Guelph, Ontario, N1G 2W1, Canada
| | - C F Baes
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada; Institute of Genetics, Department of Clinical Research and Veterinary Public Health, University of Bern, Bern, 3001, Switzerland.
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Schneider H, Segelke D, Tetens J, Thaller G, Bennewitz J. A genomic assessment of the correlation between milk production traits and claw and udder health traits in Holstein dairy cattle. J Dairy Sci 2023; 106:1190-1205. [PMID: 36460501 DOI: 10.3168/jds.2022-22312] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 09/01/2022] [Indexed: 11/30/2022]
Abstract
Claw diseases and mastitis represent the most important disease traits in dairy cattle with increasing incidences and a frequently mentioned connection to milk yield. Yet, many studies aimed to detect the genetic background of both trait complexes via fine-mapping of quantitative trait loci. However, little is known about genomic regions that simultaneously affect milk production and disease traits. For this purpose, several tools to detect local genetic correlations have been developed. In this study, we attempted a detailed analysis of milk production and disease traits as well as their interrelationship using a sample of 34,497 50K genotyped German Holstein cows with milk production and claw and udder disease traits records. We performed a pedigree-based quantitative genetic analysis to estimate heritabilities and genetic correlations. Additionally, we generated GWAS summary statistics, paying special attention to genomic inflation, and used these data to identify shared genomic regions, which affect various trait combinations. The heritability on the liability scale of the disease traits was low, between 0.02 for laminitis and 0.19 for interdigital hyperplasia. The heritabilities for milk production traits were higher (between 0.27 for milk energy yield and 0.48 for fat-protein ratio). Global genetic correlations indicate the shared genetic effect between milk production and disease traits on a whole genome level. Most of these estimates were not significantly different from zero, only mastitis showed a positive one to milk (0.18) and milk energy yield (0.13), as well as a negative one to fat-protein ratio (-0.07). The genomic analysis revealed significant SNPs for milk production traits that were enriched on Bos taurus autosome 5, 6, and 14. For digital dermatitis, we found significant hits, predominantly on Bos taurus autosome 5, 10, 22, and 23, whereas we did not find significantly trait-associated SNPs for the other disease traits. Our results confirm the known genetic background of disease and milk production traits. We further detected 13 regions that harbor strong concordant effects on a trait combination of milk production and disease traits. This detailed investigation of genetic correlations reveals additional knowledge about the localization of regions with shared genetic effects on these trait complexes, which in turn enables a better understanding of the underlying biological pathways and putatively the utilization for a more precise design of breeding schemes.
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Affiliation(s)
- Helen Schneider
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany.
| | - Dierck Segelke
- Vereinigte Informationssysteme Tierhaltung w.V. (VIT), 27283 Verden, Germany
| | - Jens Tetens
- Department of Animal Sciences, University of Göttingen, 37077 Göttingen, Germany
| | - Georg Thaller
- Institute of Animal Breeding and Husbandry, Christian-Albrechts University of Kiel, 24098 Kiel, Germany
| | - Jörn Bennewitz
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany
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Posey JE, O'Donnell-Luria AH, Chong JX, Harel T, Jhangiani SN, Coban Akdemir ZH, Buyske S, Pehlivan D, Carvalho CMB, Baxter S, Sobreira N, Liu P, Wu N, Rosenfeld JA, Kumar S, Avramopoulos D, White JJ, Doheny KF, Witmer PD, Boehm C, Sutton VR, Muzny DM, Boerwinkle E, Günel M, Nickerson DA, Mane S, MacArthur DG, Gibbs RA, Hamosh A, Lifton RP, Matise TC, Rehm HL, Gerstein M, Bamshad MJ, Valle D, Lupski JR. Insights into genetics, human biology and disease gleaned from family based genomic studies. Genet Med 2019; 21:798-812. [PMID: 30655598 PMCID: PMC6691975 DOI: 10.1038/s41436-018-0408-7] [Citation(s) in RCA: 115] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 12/05/2018] [Indexed: 12/16/2022] Open
Abstract
Identifying genes and variants contributing to rare disease phenotypes and Mendelian conditions informs biology and medicine, yet potential phenotypic consequences for variation of >75% of the ~20,000 annotated genes in the human genome are lacking. Technical advances to assess rare variation genome-wide, particularly exome sequencing (ES), enabled establishment in the United States of the National Institutes of Health (NIH)-supported Centers for Mendelian Genomics (CMGs) and have facilitated collaborative studies resulting in novel "disease gene" discoveries. Pedigree-based genomic studies and rare variant analyses in families with suspected Mendelian conditions have led to the elucidation of hundreds of novel disease genes and highlighted the impact of de novo mutational events, somatic variation underlying nononcologic traits, incompletely penetrant alleles, phenotypes with high locus heterogeneity, and multilocus pathogenic variation. Herein, we highlight CMG collaborative discoveries that have contributed to understanding both rare and common diseases and discuss opportunities for future discovery in single-locus Mendelian disorder genomics. Phenotypic annotation of all human genes; development of bioinformatic tools and analytic methods; exploration of non-Mendelian modes of inheritance including reduced penetrance, multilocus variation, and oligogenic inheritance; construction of allelic series at a locus; enhanced data sharing worldwide; and integration with clinical genomics are explored. Realizing the full contribution of rare disease research to functional annotation of the human genome, and further illuminating human biology and health, will lay the foundation for the Precision Medicine Initiative.
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Affiliation(s)
- Jennifer E Posey
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
| | - Anne H O'Donnell-Luria
- Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Boston Children's Hospital, Boston, MA, USA
| | - Jessica X Chong
- Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Tamar Harel
- Department of Genetic and Metabolic Diseases, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Shalini N Jhangiani
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Zeynep H Coban Akdemir
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Steven Buyske
- Department of Genetics, Rutgers University, Piscataway, NJ, USA
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
| | - Davut Pehlivan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Claudia M B Carvalho
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Samantha Baxter
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nara Sobreira
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Pengfei Liu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Baylor Genetics Laboratory, Houston, TX, USA
| | - Nan Wu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Jill A Rosenfeld
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Sushant Kumar
- Computational Biology and Bioinformatics Program, Yale University Medical School, New Haven, CT, USA
| | - Dimitri Avramopoulos
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Janson J White
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Kimberly F Doheny
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
- Center for Inherited Disease Research, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - P Dane Witmer
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
- Center for Inherited Disease Research, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Corinne Boehm
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - V Reid Sutton
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Donna M Muzny
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Eric Boerwinkle
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Human Genetics Center, University of Texas Health Science Center, Houston, TX, USA
| | - Murat Günel
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | | | - Shrikant Mane
- Yale Center for Genome Analysis, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Daniel G MacArthur
- Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Richard A Gibbs
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Ada Hamosh
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Richard P Lifton
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
- Laboratory of Human Genetics and Genomics, The Rockefeller University, New York, NY, USA
| | - Tara C Matise
- Department of Genetics, Rutgers University, Piscataway, NJ, USA
| | - Heidi L Rehm
- Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mark Gerstein
- Computational Biology and Bioinformatics Program, Yale University Medical School, New Haven, CT, USA
| | - Michael J Bamshad
- Department of Pediatrics, University of Washington, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - David Valle
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - James R Lupski
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
- Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA.
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