SAES-422 Multistate Research Activity Accomplishments Report

Sections

Status: Approved

Basic Information

Participants

De Vries, Albert, devries@ufl.edu, University of Florida Ma, Li, lima@umd.edu, University of Maryland Lopez-Villalobos, Nicolas, N.Lopez-Villalobos@massey.ac.nz, Massey University, New Zealand Taxis, Tasia, taxistas@msu.edu, Michigan State University Heins, Bradley, hein0106@umn.edu, University of Minnesota Huson, Heather J, hjh3@cornell.edu, Cornell University Dechow, Chad, cdd1@psu.edu, Pennsylvania State University Cockrum, Rebecca R, rcockrum@vt.edu, Virginia Tech Cole, John, john.cole@ars.usda.gov, USDA-ARS Peñagaricano, Francisco, fpenagaricano@ufl.edu, University of Florida Weigel, Kent, kweigel@wisc.edu, University of Wisconsin Hansen, Les, hanse009@umn.edu, University of Minnesota Godfrey, Bob, rgodfre@uvi.edu, University of the Virgin Islands, Administrative Advisor Students: Yvette Steyn

AGENDA

Thursday, November 12 –

20 minutes: Welcome, introductions

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

10 minutes: Discussion – 2021 Annual Meeting.

10 minutes: Election of new SCC84 secretary.

10 minutes: Other business

Business Meeting

Discussions

  • Bob Godfrey, New Administrative Advisor
  • Lakshmi Matukumalli – Primary NIFA contact
  • Frank Siewerdt – Secondary NIFA contact
  • Review NIMSS project website
  • Report by Debora Hamernik from NIFA
  • Brad, Chad, Albert, Heather and Nicolas for committee for prepare for USDA NIFA conference session
  • Discussion on next years meeting in Michigan
  • The next meeting will be held Michigan in 2021

Submitted by Brad Heins, 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

  • Comparison of pure Holstein with Montbéliarde and Viking Red crossbreds (Leslie Hansen and Bradley Heins, University of Minnesota)
  • Comparison of pure Holstein with Jersey, Montbéliarde, Normande and Viking Red crossbreds (Bradley Heins, University of Minnesota)
  • Evaluation of crossbreeding in organic dairy farms (Bradley Heins, University of Minnesota; Chad Dechow, Pennsylvania State University)
  • Genomic predictions for crossbred dairy cattle (Paul VanRaden, USDA-ARS; Bradley Heins, University of Minnesota; Chad Dechow, Pennsylvania State University)
  • Genetic studies have been performed for digital cushion thickness comparing variation within and across the Holstein and Jersey breeds and between sexes (Heather Huson, Cornell, Brad Heins, University of Minnesota)
  • Genetic studies have been performed for mastitis related traits including udder and teat morphology and somatic cell counts across lactation (Heather Huson, Cornell)
  • A comparison of Jersey cattle from Jersey Island (Heather Huson, Cornell)
  • Breeds and within cows for colostrum production (Rebecca Cockrum, Virginia Tech)

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

  • Genetic analysis of feed efficiency (Kent Weigel and
  • Francisco Penagaricano, University of Wisconsin; Paul VanRaden, USDA)
  • Identification of the genetic mechanisms underlying mastitis resistance/susceptibility in dairy cattle and relationship to mammary microbiota (Heather Huson, Cornell University)
  • Reducing dairy cattle lameness with improved genetic understanding and selection for digital cushion thickness (Heather Huson, Cornell University)
  • Effects of dietary components on gene expression and rumen microbiome in preweaned dairy heifers (Rebecca Cockrum, Virginia Tech)
  • Predicting sperm production of young dairy bulls using collection history and management factors (Kent Weigel and Francisco Penagaricano , University of Wisconsin)
  • Prediction of feed intake and residual feed intake in mid-lactation dairy cows using descriptive, performance, behavioral, and blood metabolite data (Francisco Penagaricano, University of Wisconsin).
  • Elucidating the genetic basis and mechanism of complex diseases and traits (Li Ma, University of Maryland)
  • Genomic prediction for milk-production and feed-efficiency traits within North American dairy herds (Kent Weigel, University of Wisconsin)

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

  • Reconstitution and modernization of lost Holstein male lineages using samples from a gene bank. (Chad Dechow, Pennsylvania State University)
  • Genetic dissection of dairy bull fertility: from fine mapping to genomic prediction (Francisco Peñagaricano, University of Florida)
  • Early first calving (EFC) as a new trait for selection. (Paul VanRaden, USDA-ARS)
  • Revision of Net Merit $ to include feed intake, early first calving, heifer livability, increased body weight maintenance cost, and reduced value of productive life due to faster genetic trend (Paul VanRaden, USDA-ARS)

 

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

  • To review historic and current genetic diversity in the populations with particular interest in inbreeding levels and signatures of selection (Li Ma, University of Maryland)
  • Developing statistical approaches and computing tools to boost the power of current and next-generation sequencing-based genetic studies (Li Ma, University of Maryland)
  • Admixture and population structure analysis to examine the effect of ancestry and breeding effects (Heather Huson, Cornell; Bradley Heins, University of Minnesota)

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

  • The crossbred cattle project is a collaboration among 3 multistate groups (NY, MN, and PA) as well as a commercial AI company in New Zealand.
  • Lactation curves of Crossbred and Holstein Diary cattle (MN and NZ)
  • Mastitis and lameness projects (NY and MN)

 

 

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)
  • Undergraduate research opportunities (NY, MN, VT, WI, PA)
  • Teaching collaboration with Neogen-Geneseek (NY)

Impacts

  1. The major impacts of this SCC84 group are be summarized as follows: (i) development and implementation of national genetic evaluations for new traits, (ii) development of crossbreeding rotations for dairy farm profitability and feed efficiency, (iii) better understanding of the genetic control of health traits for calves and cows, (iv) improvement in genomic evaluation for future traits, and (v) training of graduate student in dairy cattle genetics. Collaborated 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

Publications

  1. Carvalho MR, C Aboujaoude, F Peñagaricano, JEP Santos, TJ DeVries, BW McBride, and ES Ribeiro (2020) Associations between maternal characteristics and health, survival, and performance of dairy heifers from birth through to first lactation. Journal of Dairy Science 103:823-839.
  2. Cole, J.B., Eaglen, A.E., Maltecca, C., Mulder, H.A., and Pryce, J.E. The future of phenomics in dairy cattle breeding. Anim. Front. 10(2):37–44. 2020.
  3. Cousillas-Boam, G., W.J. Weber, A. Benjamin, S. Kahl, B.J. Heins, T. Elsasser, D. Kerr, and B.A. Crooker. 2020. Effect of Holstein genotype on innate immune and metabolic responses of heifers to lipopolysaccharide (LPS) administration. Domestic Animal Endocrinology 70:106374. https://doi.org/10.1016/j.domaniend.2019.07.002
  4. Dechow, C.D., W.S. Liu, L.W. Specht, and H. Blackburn. 2020. Reconstitution and modernization of lost Holstein male lineages using samples from a gene bank. Journal of Dairy Science 103:4510–4516. doi:10.3168/jds.2019-17753.
  5. Dechow, C.D., K.S. Sondericker, A.A. Enab, and L.C. Hardie. 2020b. Genetic, farm, and lactation effects on behavior and performance of US Holsteins in automated milking systems. Journal of Dairy Science 103:11503–11514. doi:10.3168/jds.2020-18786.
  6.  Freebern, E., D.J.A. Santos, L. Fang, J. Jiang, K.L. Parker Gaddis, G.E. Liu, P.M. VanRaden, C. Maltecca, J.B. Cole, and L. Ma. 2020a. GWAS and fine-mapping of livability and six disease traits in Holstein cattle. BMC Genomics 21:41. doi:10.1186/s12864-020-6461-z.
  7.  Freebern, E., D.J.A. Santos, L. Fang, J. Jiang, K.L. Parker Gaddis, G.E. Liu, P.M. VanRaden, C. Maltecca, J.B. Cole, and L. Ma. 2020b. GWAS and fine-mapping of livability and six disease traits in Holstein cattle. BMC Genomics 21:41. doi:10.1186/s12864-020-6461-z.
  8.  Gross N, F Peñagaricano, and H Khatib (2020) Integration of whole-genome DNA methylation data with RNA sequencing data to identify markers for bull fertility. Animal Genetics 51: 502-510.
  9.  Handcock, R.C., N. Lopez-Villalobos, L.R. McNaughton, P.J. Back, G.R. Edwards, and R.E. Hickson. 2020. Body weight of dairy heifers is positively associated with reproduction and stayability. Journal of Dairy Science 103:4466–4474. doi:10.3168/jds.2019-17545.
  10.  Hazel, A., B. Heins, and L. Hansen. 2020. Fertility and 305-day production of Viking Red-, Montbéliarde-, and Holstein-sired crossbred cows compared with Holstein cows during their first 3 lactations in Minnesota dairy herds. J. Dairy Sci. 103:8683–8697. https://doi.org/10.3168/jds.2020-18196
  11. Hazel, A., B. Heins, and L. Hansen. 2020. Health treatment cost, stillbirth, survival, and conformation of Viking Red-, Montbéliarde-, and Holstein-sired crossbred cows compared with pure Holstein cows during their first 3 lactations. J. Dairy Sci. 103:10917–10939. https://doi.org/10.3168/jds.2020-18604
  12.  Hurst, T.S., N. Lopez-Villalobos, and J.P. Boerman. 2021. Predictive equations for early-life indicators of future body weight in Holstein dairy heifers. Journal of Dairy Science 104:736–749. doi:10.3168/jds.2020-18560
  13.  Huson, H.J.*, Sonstegard, T.S., Godfrey, J., Hambrook, D., Wolfe, C., Wiggans, G., Blackburn, H., Van Tassell, C.P.*, (2020) A genetic investigation of Island Jersey Cattle, the foundation of the Jersey breed. Frontiers in Genetics, 17 April 2020 https://doi.org/10.3389/fgene.2020.00366
  14.  Leal Yepes, F.A.†, Nydam, D.V., Mann, S., Caixeta, L., McArt, J.A.A., Overton, T.R., Wakshlag, J.J., Huson, H.J.*, 2019 Longitudinal phenotypes improve genotype association for hyperketonemia in dairy cattle. Animals Dec 1, 2019, 9(12), 1059,  https://doi.org/10.3390/ani9121059
  15. Li, B., VanRaden, P.M., Guduk, E., O'Connell, J.R., Null, D.J., Connor, E.E., VandeHaar, M.J., Tempelman, R.J., Weigel, K.A., and Cole, J.B. Genomic prediction of residual feed intake in US Holstein dairy cattle. J. Dairy Sci. 103(3):2477–2486. 2020.  
  16. Lima FS, FT Silvestre, F Peñagaricano, and WW Thatcher (2020) Early genomic prediction of daughter pregnancy rate is associated with improved reproductive performance in Holstein dairy cows. Journal of Dairy Science 103:3312-3324.
  17.  Lacey, E.K., K.J. Harvatine, and C.D. Dechow. 2020. Short communication: Diet digestibility measured from fecal samples and associations with phenotypic and genetic merit for milk yield and composition. Journal of Dairy Science 103:5270–5274. doi:10.3168/jds.2019-17450.
  18.  Lopez-Villalobos, N., R.J. Spelman, J. Melis, S.R. Davis, S.D. Berry, K. Lehnert, N.W. Sneddon, S.E. Holroyd, A.K. MacGibbon, and R.G. Snell. 2020. Genetic correlations of milk fatty acid contents predicted from milk mid-infrared spectra in New Zealand dairy cattle. Journal of Dairy Science 103:7238–7248. doi:10.3168/jds.2019-17971.
  19.  Makanjuola, B.O., C. Maltecca, F. Miglior, F.S. Schenkel, and C.F. Baes. 2020a. Effect of recent and ancient inbreeding on production and fertility traits in Canadian Holsteins. BMC Genomics 21:605. doi:10.1186/s12864-020-07031-w.
  20.  Makanjuola, B.O., F. Miglior, E.A. Abdalla, C. Maltecca, F.S. Schenkel, and C.F. Baes. 2020b. Effect of genomic selection on rate of inbreeding and coancestry and effective population size of Holstein and Jersey cattle populations. Journal of Dairy Science 103:5183–5199. doi:10.3168/jds.2019-18013.
  21.  Maltecca, C., F. Tiezzi, J.B. Cole, and C. Baes. 2020. Symposium review: Exploiting homozygosity in the era of genomics—Selection, inbreeding, and mating programs. Journal of Dairy Science 103:5302–5313. doi:10.3168/jds.2019-17846.
  22.  McWhorter, T.M., Hutchison, J.L., Norman, H.D., Cole, J.B., Fok, G.C., Lourenco, D.A.L., and VanRaden, P.M. Investigating conception rate for beef service sires bred to dairy cows and heifers. J. Dairy Sci. 103(11):10374–10382. 2020.  
  23. Miglior, F., C. Baes, D. Lourenco, F. Penagaricano, and B. Heins. 2020. Introduction: ADSA and Interbull Joint Breeding and Genetics Symposia. J. Dairy Sci. 103:5275–5277. https://doi.org/10.3168/jds.2020-18666
  24.  Miles, A.M. and Huson, H.J., (2020) Graduate student literature review: Understanding the genetics underlying mastitis. Journal of Dairy Science 6 November. https://doi.org/10.3168/jds.2020-18297
  25.  Miles, A.M. and Huson, H.J., (2020) Time- and population dependent genetic patterns underlie bovine milk somatic cell count. Journal of Dairy Science 103(9): 1-13, 1 July.  https://doi.org/10.3168/jds.2020-18322
  26.  Nani JP and F Peñagaricano (2020) Whole-genome homozygosity mapping reveals candidate regions affecting bull fertility in US Holstein cattle. BMC Genomics 21:338.
  27. Nani, J.P., Bacheller, L.R., Cole, J.B., and VanRaden, P.M. Discovering ancestors and connecting relatives in large genomic databases. J. Dairy Sci. 103(2):1729–1734. 2020.
  28. HA Pacheco, FM Rezende, and F Peñagaricano (2020) Gene mapping and genomic prediction of bull fertility using sex chromosome markers. Journal of Dairy Science 103:3304-3311.
  29. Parker Gaddis, K.L., VanRaden, P.M., Cole, J.B., Norman, H.D., Nicolazzi, E., and Dürr, J.W. Symposium review: Development, implementation, and perspectives of health evaluations in the United States J. Dairy Sci. 103(6):5354–5365. 2020.
  30. Pralle RS, NE Schultz, HM White, and KA Weigel (2020) Hyperketonemia GWAS and parity dependent SNP associations in Holstein dairy cows intensively sampled for blood β-hydroxybutyrate concentration. Physiological Genomics 52(8):347-357.
  31. Quick AE, TL Ollivett, BW Kirkpatrick, and KA Weigel (2020) Genomic analysis of bovine respiratory disease and lung consolidation in pre-weaned Holstein calves using clinical scoring and lung ultrasound. Journal of Dairy Science 103:1632-1641.
  32. Rezende FM, M Haile-Mariam, JE Pryce, and F Peñagaricano (2020) Across-country genomic prediction of bull fertility in Jersey dairy cattle. Journal of Dairy Science 103:11618-11627.
  33. Sigdel A, L Liu, R Abdollahi-Arpanahi, I Aguilar, and F Peñagaricano (2020) Genetic dissection of reproductive performance of dairy cows under heat stress. Animal Genetics 51:511–520.
  34. Stambuk, C., E. Staiger, B. Heins, and H. Huson. 2020. Exploring physiological and genetic variation of digital cushion thickness in Holstein and Jersey cows and bulls. J. Dairy Sci. 103:9177–9194. https://doi.org/10.3168/jds.2020-18290
  35. Stambuk, C.R., E.A. Staiger, A. Nazari-Ghadikolaei, B.J. Heins, and H.J. Huson. 2020. Phenotypic characterization and genome-wide association studies of digital cushion thickness in Holstein cows. J. Dairy Sci. 103:3289–3303. https://doi.org/10.3168/jds.2019-17409
  36. Uddin EM, O Santana, KA Weigel, and MA Wattiaux (2020) Enteric methane, lactation performance, digestibility, and metabolism of nitrogen and energy of Holsteins and Jerseys fed 2 levels of forage fiber from alfalfa silage or corn silage. Journal of Dairy Science 103:6087-6099.
  37. VanRaden, P.M., Tooker, M.E., Chud, T.C.S., Norman, H.D., Megonigal, Jr., J.H., Haagen, I.W., and Wiggans, G.R. Genomic predictions for crossbred dairy cattle. J. Dairy Sci. 103(2):1620–1631. 2020.
  38. VanRaden, P.M. Symposium review: How to implement genomic selection. J. Dairy Sci. 103(6):5291–5301. 2020. 
  39. Whitt, C.E.†, Tauer, L., Huson, H.J., (2019) Bull efficiency using dairy genetic traits. PLOS ONE Nov 11, 2019; 14:11 e0223436. https://doi.org/10.1371/journal.pone.0223436
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