The document discusses improving dairy cattle fertility through translational genomics by identifying and utilizing genetic markers associated with fertility traits. It outlines a research approach focusing on the development of novel SNP markers and their effects on daughter pregnancy rates and embryo development, alongside efforts to disseminate this information to producers. Expected outcomes include enhanced genomic tools for predicting reproduction and increased reliability in breeding values for reproductive traits.

1. Improving Dairy Cattle Fertility
Using Translational Genomics
AFRI 2013-68004-20365
T. Spencer, H. Neibergs, J. Dalton,
M. Chahine, D. Moore, P. Hansen, J. Cole,
and A. De Vries

2. Relationship of Milk Production
and Daughter Pregnancy Rate (DPR)
20
24
28
32
10,000
15,000
20,000
25,000
30,000
1950 1960 1970 1980 1990 2000 2010
DaughterPregnancyRate(%)
MilkProduction(lbs)
Year
Milk Production Rate (lbs) Daughter Pregnancy Rate (%)
(Adapted from Spencer and Hansen) USDA NIFA AFRI 2013-68004-20365

3. Genomic Selection
 Marker-assisted selection (using the whole genome)
 Increased feasibility due to sequencing of bovine genome
and new methods to efficiently genotype animals
 Marker discovery requires carefully phenotyped populations
 Can be based on single nucleotide polymorphisms (SNPs)
and copy number variation (CNV) Illumina 777K
BovineHD Beadchip
How do we increase fertility?
USDA NIFA AFRI 2013-68004-20365

4. Single Nucleotide Polymorphism (SNP)
USDA NIFA AFRI 2013-68004-20365

5.  Long-term goal: Increase fertility of dairy cattle
 Research approach:
• Develop novel genetic markers of fertility in heifers and lactating cows
• Determine effects of specific SNPs on daughter pregnancy rate (DPR) and
embryo development
 Extension approach:
• Disseminate, demonstrate, evaluate and document the impact of using genetic
selection tools to increase fertility on herd management and profitability
Integrated Research-Extension Project

6.  Approach: Using records, Holstein heifers and primiparous cows
will be fertility classified based on pregnancy outcome to AI.
• Heifers: normal reproductive tract, no record of diseases, and
display standing estrus before AI.
• Cows: normal reproductive tract, uncomplicated pregnancy, no
record of diseases before or after AI, and display standing
estrus before AI.
 Fertility phenotypes:
• Highly fertile (pregnant on first AI)
• Subfertile (pregnant after 4th AI)
• Infertile (never pregnant to AI and culled)
Objective 1: Develop Novel Markers Of Fertility
USDA NIFA AFRI 2013-68004-20365

7. Genome-wide Association Study (GWAS)
of Fertility in Holstein Heifers
 Fertility phenotyped by artificial insemination (AI) breeding record
analysis
• 468 High Fertile (pregnant upon first AI)
• 188 Infertile (never pregnant with no obvious physiological problems)
 Animals were genotyped using the Illumina BovineHD 777K BeadChip
Chromosome
-log10(p-value)
(Moraes et al., 2015)
Strong association
Moderate association
USDA NIFA AFRI 2013-68004-20365

8. Objective 2: Determine Effects of SNPs on DPR
and Embryo Development
 Identify (and genotype) bulls with
high or low DPR
 Identify SNPs in genes known to be
involved in reproduction that are
related to DPR
USDA NIFA AFRI 2013-68004-20365(Photos courtesy of Select Sires, Inc.)

9. SNPs in Genes Associated with DPR
 40 SNPs were identified that were related to DPR
 29 of the 40 SNPs were not significantly related to
production traits
• Suggests selection for fertility without negative selection
for milk yield is possible
 Implications – SNPs can be used to improve genetic selection
and to better understand physiological basis for fertility.
(Adapted from Hansen; Cochran et al., 2013) USDA NIFA AFRI 2013-68004-20365

10.  Objective 3: Evaluate the efficiency and profitability of
increasing fertility in dairy cattle using genetic selection tools.
(Computer modeling; Web-based decision support tool).
 Objective 4: Transfer science-based information to dairy
producers, managers, and allied industry personnel, with
strategies to improve fertility using novel genomic information
and tools.
Objectives 3 and 4

11. 2016 DAIRY CATTLE
GENOMICS WORKSHOPS
 Jerome, ID
 Sunnyside, WA
 Stephenville, TX
 Okeechobee, FL
 Tulare, CA
USDA NIFA AFRI 2013-68004-20365

12. Expected Outcomes of the Grant
 Better genomic tools for predicting reproduction
 Increased reliability of estimates of breeding values
for reproductive traits
 More rapid progress in improving dairy cow fertility
20
24
28
32
10,000
15,000
20,000
25,000
30,000
1950 1960 1970 1980 1990 2000 2010
DaughterPregnancyRate(%)
MilkProduction(lbs)
Year
Milk Production Rate (lbs) Daughter Pregnancy Rate (%)
(Adapted from Hansen) USDA NIFA AFRI 2013-68004-20365

13. THANK YOU. GRACÍAS. OBRIGADO.
 Darin Mann, M/M Feedlot, ID
 Fred Muller, Ag Health Laboratories, WA
 Levi Gassaway, Cow Palace Dairy, WA
 Kelly Reed, DeRuyter Brothers Dairy, WA
 Jason Sheehan, J&K Dairy, WA
 Dan DeRuyter, DeRuyter & Son Dairy, WA
 Kevin Gavin & João Moraes, WSU
USDA NIFA AFRI 2013-68004-20365