Brief Summary of Minutes of Annual Meeting

Brief Summary of Minutes of Annual Meeting AGENDA

November 20 –

10 minutes: Welcome, introductions

15 minutes: Update from USDA-NIFA and administrative advisor.

Business Meeting

Report by Frank Siewerdt from NIFA

Albert Devries elected chair for the 2022 meeting

The next meeting will be held in Michigan in 2022

Discussion on next year’s meeting in Michigan

Election of new SCC84 secretary will happen at the beginning of the next meeting (2022)

Station Reports

Submitted by Christian Maltecca, Chair and acting secretary

Accomplishments

Objective 1: Recommend breeding strategies for optimal use of breed resources, maintenance and(or) exploitation of within-breed (additive and non-additive) genetic variation

• Analyzing data on genome variation in crossbred cattle from three different types of crossbreeding programs (rotational crossbreeding, creation of a composite breed, and F1 generation hybrid with backcrossing to ancestry breed). (Cornell)
• Comparison of crossbreds of Holstein, Jersey, Montbéliarde, Normande and Viking Red to Holsteins for feed efficiency fed two different diets. (UofM, PennState)
• Determine milk, fat, protein production and SCS, fertility, and survival for beta casein A2 genotypes of organic Holstein cows. (UofM, PennState)
• Fatty acid profiles of Holstein, Grazecross and ProCROSS cows in an organic and conventional dairy herd (UofM)
• Estimation and Management of Inbreeding and Inbreeding Depression in North American Cattle under Genomic Selection (NCSU)
• Current state of inbreeding, genetic diversity, and selective histories in 5 breeds of U.S. Dairy Cattle (NCSU)

Objective 2: Capture phenotypic data for novel and economically important traits to elucidate their genetic regulation and potential for genomic selection

• Signatures of selection for thermal stress (Purdue)
• Milking temperament in Holstein cattle (Purdue)
• Livestock behavioral genomics (Purdue)
• Purdue Animal Sciences Research Data Ecosystem (Purdue)
• Functional longevity in Holstein based on RRM (Purdue)
• Calf health (including disease transmission ability in pens) (Purdue)
• Lactation persistency and extended lactations (Purdue)
• Analysis of milk microbiota using 16SrRNA sequence data – This project will assess correlations between milk microbiota and milk components and production or health parameters. It will also establish normal variation of milk microbiota within cow across lactation and across cows. (Cornell)
• Inheritance of DNA methylation patterns and influence of breeding strategies and reproductive technologies – A new project is being developed to explore this concept. It will include correlation of performance traits with DNA methylation and inheritance patterns. (Cornell)
• Circulating metabolites indicate differences in high and low residual feed intake Holstein dairy cows. (UW)
• Predicting daily milk yield for primiparous cows using data of within herd relatives to capture genotype-by-environment interactions. (UW).
• Effect of diet energy level and genomic residual feed intake on bred Holstein dairy heifer growth and feed efficiency. (UW).
• Gene mapping, gene-set analysis, and genomic prediction of postpartum blood calcium in Holstein cows. (UW)
• Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables. (UW)
• Comparison of single-breed and multi-breed training populations for infrared predictions of novel phenotypes in Holstein cows (UW)
• Evaluation of bull fertility in Italian Brown Swiss dairy cattle using cow field data. (UW)
• Assessment of the relationship between postpartum health and mid-lactation performance, behavior, and feed efficiency in Holstein dairy cows. (UW)
• Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data. (UW)
• Integrating genomic and infrared spectral data improves the prediction of milk protein
• composition in dairy cattle. (UW)
• Assessing feed efficiency in early and mid-lactation and its associations with performance and health in Holstein (UW)
• Response to ad libitum milk allowance of Holstein and crossbred dairy and dairy-beef calves in an automated feeding system (UofM)
• Optimization of multi-period planning problems (UFL)

Update dynamic programming model 

• Extend with more traits, including multiple service sires
• Separate current from future lactations 
• Calculate insemination values 

Improve prediction of fertility with data from precision dairy farming sensors and machine learning methods 

• Embrio Transfer and fertility evaluations (USDA AGIL)
• Flexible Testing and Milk-Only Records (USDA AGIL)

Objective 3: Collaborate with the National Animal Germplasm Program (NAGP) Dairy Committee, Animal Genomics and Improvement Laboratory (AGIL), and the Council on Dairy Cattle Breeding (CDCB) to improve genetic variation of dairy and optimize economic merit indices.

• Evaluation of Jersey breed and NAGP repository sample collection of Jersey breed – a collaborative project with the USDA’s – NAGP and AGIL departments, American Jersey Association, and Cornell University is comparing the NAGP sample collection representing Jersey cattle and conducting a general overview of the breed’s inbreeding, admixture, and adaptation. This project uses genotypes obtained from the CDCB. (Cornell)
• National single-step evaluation of Holsteins and all dairy breeds (UGA)
• Multibreed ssGBLUP evaluations (UGA)
• Flexible Testing and Milk-Only Records (USDA AGIL)
• International Bull Rankings (USDA AGIL)
• Prediction Factors for Yield Traits (USDA AGIL)
• Develop Hoof Health Data Pipeline (USDA AGIL, UofM)
• Genetic evaluations of stillbirth for five US dairy breeds: a data-resource feasibility study (UW)

Objective 4: Develop variant discovery strategies to incorporate functional –omics data into breeding schemes for economically important traits

• Genomic analysis of visceral fat accumulation in Holstein cows. (UW)
• Effect of natural pre-luteolytic prostaglandin F2α pulses on the bovine luteal transcriptome during spontaneous luteal regression (UW)
• Genes and pathways associated with pregnancy loss in dairy cattle (UW)
• Targeted sequencing revealscandidate causal variants for dairy bull subfertility (UW)
• Histological and transcriptomic analysis of adipose and muscle of dairy calves supplemented with 5-hydroxytryptophan. (UW)
• My research design for multiple projects incorporate intensive phenotypic characterization of traits directly on the animals being genotyped and used in the genomic association studies. My team has conducted multiple longitudinal field trials to evaluate phenotypic variation over time and better understand biological mechanisms. (Cornell)
• Crossbred dairy cattle- A new crossbred cattle evaluation is ongoing and uses admixture and population structure analysis to examine the effect of ancestry and breeding effects on crossbred cattle performance and future selection. (Cornell)
• Genomic Partitioning of inbreeding by age and functional annotation in U.S. Dairy Cattle (NCSU)
• Two new software tools for large-scale genetic analyses (NCSU)
  • SSGP: million-scale mixed-model associations
  • MPH: partitioning heritability into many components in related individuals

Objective 5: Carry out interdisciplinary collaborations to improve dairy cow and calf health  through increased partnerships with researchers, industry, and stakeholders 

Objective 6: Create a pipeline of diverse graduate students in the fields of quantitative and functional genetics and bioinformatics via outreach and educational opportunities

Graduate research opportunities (NY, MN, VT, WI, PA, MI, NCSU)

Undergraduate research opportunities (NY, MN,

Publications

1. Landaeta-Hernández*, A.J., Zambrano-Nava, S., Verde, O., Pinto-Santini, L., Montero-Urdaneta, M., Hernández-Fonseca, J.P., Fuenmayor-Morales, C., Sonstegard, T.S., Huson, H.J.,
2. Olson, T.A., (2021) Heat stress response in slick vs normal-haired Criollo Limonero heifers. Tropical Animal Health and Production 53, 445. https://doi.org/10.1007/s11250-021-02856-3
3. Miles, A.M. †, Posbergh, C.J. †, Huson, H.J., (2021) Direct Phenotyping and Principal Component Analysis of Type Traits Implicate Novel QTL in Bovine Mastitis through Genome-Wide Association. Animals April 11(4), 1147. https://doi.org/10.3390/ani11041147
4. Basiel, B.L., L.C. Hardie, B.J. Heins+, C.D. Dechow. 2021. Genetic parameters and genomic regions associated with horn fly resistance in organic Holstein cattle. J. Dairy Sci. 104:12724-12740. https://doi.org/10.3168/jds.2021-20366
5. Haagen, I.W., L.C. Hardie, B.J. Heins, and C.D. Dechow. 2021. Genetic parameters of passivetransfer of immunity for US organic Holstein calves. J. Dairy Sci. 104:2018–2026.https://doi.org/10.3168/jds.2020-19080
6. Haagen, I.W. L.C. Hardie, B.J. Heins, C.D. Dechow. 2021. Genetic parameters of calf

morbidity and stayability for US organic Holstein calves J. Dairy Sci. 104: 11770-11778.

https://doi.org/10.3168/jds.2021-20432

7. Hardie, L., B. Heins, and C. Dechow. 2021. Genetic parameters for stayability of Holsteins in US organic herds. J. Dairy Sci. Volume 104, Issue 4, 4507 – 4515 https://doi.org/10.3168/jds.2020-19399
8. Hazel, A.R., B.J. Heins, and L.B. Hansen. 2021. Herd life, lifetime production, and profitability of Viking Red-sired and Montbéliarde-sired crossbred cows compared with their Holstein herdmates. J. Dairy Sci. Volume 104, Issue 3, 3261 – 3277 https://doi.org/10.3168/jds.2020-19137
9. WM Brown, MJ Martin, C Siberski, JE Koltes, F Peñagaricano, KA Weigel, and HM White (2021) Predicting dry matter intake in mid-lactation Holstein cows using point-in-time data streams available on dairy farms. Journal of Dairy Science (in review).
10. MJ Martin, RS Pralle, IR Bernstein, MJ VandeHaar, KA Weigel, Z Zhou, and HM White (2021)
11. Circulating metabolites indicate differences in high and low residual feed intake Holstein dairy cows. Metabolites (in review).
12. F Zhang, KA Weigel, and VE Cabrera (2021) Predicting daily milk yield for primiparous cows using data of within herd relatives to capture genotype-by-environment interactions. Journal of Dairy Science (in review).
13. K Williams, KA Weigel, W Coblentz, NM Esser, H Schlesser, PC Hoffman, R Ogden, H Su, and MSAkins (2021) Effect of diet energy level and genomic residual feed intake on bred Holstein dairy heifer growth and feed efficiency. Journal of Dairy Science (in press).

14. L Cavani, MB Poindexter, CD Nelson, JEP Santos, and F Peñagaricano (2021) Gene mapping, gene-set analysis, and genomic prediction of postpartum blood calcium in Holstein cows. Journal of Dairy Science (in press)
15. MJ Martin, JRR Dorea, MR Borchers, RL Wallace, SJ Bertics, SK DeNise, KA Weigel, and HM White (2021) Comparison of methods to predict feed intake and residual feed intake using behaviora and metabolite data in addition to classical performance variables. Journal of Dairy Science 104:8765-8782.
16. LFM Mota, S Pegolo, T Baba, G Morota, F Peñagaricano, G Bittante, and A Cecchinato (2021) Comparison of single-breed and multi-breed training populations for infrared predictions of novel phenotypes in Holstein cows. Animals 11: 1993.
17. HA Pacheco, M Battagin, A Rossoni, A Cecchinato, and F Peñagaricano (2021) Evaluation of bull fertility in Italian Brown Swiss dairy cattle using cow field data. Journal of Dairy Science 104:10896-10904.
18. MJ Martin, KA Weigel, and HM White (2021). Assessment of the relationship between
19. postpartum health and mid-lactation performance, behavior, and feed efficiency in Holstein dairy cows. Animals 11, 1385.
20. LFM Mota, S Pegolo, T Baba, F Peñagaricano, G Morota, G Bittante, and A Cecchinato (2021) Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data. Journal of Dairy Science 104: 8107-8121.
21. T Baba, S Pegolo, LFM Mota, F Peñagaricano, G Bittante, A Cecchinato, and G Morota (2021) Integrating genomic and infrared spectral data improves the prediction of milk protein composition in dairy cattle. Genetics Selection Evolution 53: 29.
22. M Nehme Marinho, R Zimpel, F Peñagaricano, and JEP Santos (2021) Assessing feed efficiency in early and mid-lactation and its associations with performance and health in Holstein. Journal of Dairy Science 104: 5493-5507.
23. A Sigdel, XL Wu, K Parker Gaddis, HD Norman, JA Carrillo, J Burchard, F Peñagaricano and J Dürr (2021) Genetic evaluations of stillbirth for five US dairy breeds: a data-resource feasibility study. Frontiers in Genetics (in review).
24. LC Novo, L Cavani, P Pinedo, P Melendez, and F Peñagaricano (2021) Genomic analysis of visceral fat accumulation in Holstein cows. Frontiers in Genetics (in review).
25. MA Mezera, W Li, L Liu, R Meidan, F Peñagaricano, and MC Wiltbank (2021) Effect of natural pre-luteolytic prostaglandin F2α pulses on the bovine luteal transcriptome during spontaneous luteal regression. Biology of Reproduction 105: 1016-1029.
26. A Sigdel, RS Bisinotto, and F Peñagaricano (2021) Genes and pathways associated with pregnancy loss in dairy cattle. Scientific Reports 11: 13329.
27. R Abdollahi-Arpanahi, HA Pacheco, and F Peñagaricano (2021) Targeted sequencing reveals candidate causal variants for dairy bull subfertility. Animal Genetics 52: 509-513.
28. SL Field, MG Marrero, L Liu, F Peñagaricano, and J Laporta (2021) Histological and transcriptomic analysis of adipose and muscle of dairy calves supplemented with 5-hydroxytryptophan. Scientific Reports 11: 9665.
29. LF Brito, N Bedere, F Douhard, HR Oliveira, M Arnal, F Peñagaricano, AP Schinckel, CF Baes, and  F Miglior (2021) Genetic selection of high-yielding dairy cattle towards sustainable farming systems in a rapidly changing world. Animal (in press) 
30. AE Quick, J Meronek, K Amburn, K Rozeboom, and KA Weigel (2021) Predicting sperm  production of young dairy bulls using collection history and management factors. Journal of  Dairy Science 104:5817-5826. 
31. B Shen, E Freebern, J Jiang, C Maltecca, JB Cole, GE Liu, L Ma Effect of Temperature and Maternal Age on Recombination Rate in Cattle Frontiers in Genetics, 1307
32. MC Fabbri, C Dadousis, F Tiezzi, C Maltecca, E Lozada-Soto, S Biffani.
33. Genetic diversity and population history of eight Italian beef cattle breeds using measures of autozygosity PloS one 16 (10), e0248087
34. BO Makanjuola, C Maltecca, F Miglior, G Marras, EA Abdalla, ... Identification of unique ROH regions with unfavorable effects on production and fertility traits in Canadian Holsteins Genetics selection evolution 53 (1), 1-11
35. LF Brito, N Bedere, F Douhard, HR Oliveira, M Arnal, F Peñagaricano, AP Schinckel, CF Baes, and  F Miglior (2021) Genetic selection of high-yielding dairy cattle towards sustainable farming systems in a rapidly changing world. Animal (in press) 
36. AE Quick, J Meronek, K Amburn, K Rozeboom, and KA Weigel (2021) Predicting sperm  production of young dairy bulls using collection history and management factors. Journal of  Dairy Science 104:5817-5826.