This document discusses the connection between uterine health and genetic variations in Holstein cows, focusing on genomic selection for improved fertility. It highlights objectives such as identifying genetic markers for fertility and analyzing factors affecting the risk of uterine diseases and pregnancy loss. Conclusions indicate that while genetic variation for metritis exists and selection is possible, progress may be slow due to low heritability.

1. Uterine Health and Potential Connection
with Genetic Variation
Klibs N. A. Galvão, DVM, MPVM, PhD, Dipl. ACT
College of Veterinary Medicine
University of Florida
galvaok@ufl.edu

2. • Genomic selection for fertility-USDA project
• Effects of disease on fertility
• Preliminary work on genomics (Courtesy of F.
Peñagaricano)
• Conclusion
Outline

3. Pinedo PJ, Santos JE, Galvao K, Seabury C, Rosa JM,
Bicalho RC, Gilbert RO, Schuenemann G, Chebel R,
Thatcher W, Rodriguez-Zas S, Fetrow J
Award # 2012-02115
NIFA AFRI Translational Genomics for Improved Fertility of
Animals
Genomic Selection for Improved Fertility of Dairy
Cows with Emphasis on Cyclicity and Pregnancy

4. 1. To identify genetic markers (SNPs) of fertility in
Holstein cows
2. To obtain genomic-estimated breeding values for
selection for improved fertility
Specific Objectives
USDA Project

5. 1. Is it a cow that does not develop uterine
diseases or disorders?
2. Is it a cow that starts cycling early?
3. Is it a cow that shows strong estrus?
4. Is it a cow that gets pregnant the first try?
5. Is it a cow that does not lose the pregnancy
after it gets pregnant?
What’s a fertile cow?
USDA Project

6. Our Approach
• 12,000 cows
• 2,400 cows/state
• 3 - 6 farms/state
• Cool / hot
season

7. Our Approach
• Uterine health
• Resumption of postpartum ovulation
• Pregnancy per artificial insemination (AI)
• Maintenance of pregnancy
• Other health disorders
Phenotypes:

8. Metritis
≤ 21 DIM
C. Endometritis
≥ 21 DIM
Uterine Diseases

9. 0 5 10 15 20 25 30
Pneumonia
DA
Lameness
Mastitis
S. Ketosis
Calv. Prob.
C. Endom.
Metritis
Summer Cool

10. Variable CR,% P
Healthy 42.2 < 0.01
Metritis 33.2 < 0.01
C. Endometritis 30.0 < 0.01
Calv. Prob. 34.1 < 0.01
S. Ketosis 32.3 < 0.01
Mastitis 34.6 0.09
Lameness 31.8 < 0.01
DA 24.0 < 0.01
Pneumonia 32.4 < 0.01
USDA Project
Factors Affecting the Risk of Pregnancy

11. Variable PL,% P
Healthy 8.5
Metritis 12.7 <0.05
C. Endometritis 13.1 <0.05
Calv. Prob. 11.0 0.36
S. Ketosis 12.8 <0.05
Mastitis 7.8 0.09
Lameness 10.4 0.88
DA 16.2 0.22
Pneumonia 14.9 0.19
USDA Project
Factors Affecting the Risk of Pregnancy Loss

12. USDA Project
• 11,412 cows
Effect of Metritis on Culling

13. Perez et al., unpublished
~5kg milk/d
Effect of Metritis on Milk Yield

14. Variable Level n Metritis 2 (%) OR P value
Metritis-1 Yes 299 20.4 1.6 0.02
No 2028 10.4
Induced-2 Yes 242 18.6 1.5 0.05
No 2085 10.8
Dystocia-2 Yes 664 16.7 2.2 <0.001
No 1649 9.5
Twin-2
Yes 95 43.2 5.3
<0.001
No 2223 10.2
Stillbirth-2 Yes 78 30.8 2.0 0.03
No 2240 10.9
RFM-2 Yes 152 52.6 12.7 <0.001
No 2175 8.8
Clinical Ketosis-2 Yes 444 28.2 3.4 <0.001
No 1883 7.8
Vieira-Neto et a., 2015; JDS Abstr.
Repeatability of Metritis

15. • IL-8 receptor SNP showed no association with metritis or CE
(Galvao et al., 2011).
• SNPs and indel mutations in TLR genes did not show major
effects on metritis or CE (Pinedo et al., 2013).
• Heritability of metritis (7-10%) is low (Hossein-Zadeh and
Ardalan, 2011).
What About Genetics?

16. • Phenotype: 28k health data records of 14k Holstein cows across lactations
binary trait (0 = no case, 1 = at least one case of metritis)
• Genotype: 8k animals (60k SNP across the genome)
• Pedigree: 28k animals (5-gen pedigree from Council on Dairy Cow Breeding)
Summary statistics of health data records
Events Number of Records %
Sick (1) Healthy (0)
Metritis 2,721 25,662 9.6
Courtesy of F. Peñagaricano
GWAS

17. 𝐇−𝟏
= 𝐀−𝟏
+
𝟎 𝟎
𝟎 𝐆−𝟏
− 𝐀 𝟐𝟐
−𝟏
𝐀−𝟏
𝐇−𝟏
• In single-step genomic best linear unbiased prediction:
G matrix based on 8k animals
A matrix based on 28k animals (5 generation pedigree)
Results: genetic variance explained by 2.0 Mb window of adjacent SNPs
Courtesy of F. Peñagaricano
GWAS

18. h2 = 0.085
RASSF2
RPS6KA2, CCR6
GSDMC
FADD, CCND1
Courtesy of F. Peñagaricano
Metritis GWAS

19. • GWAS produced a list of putative candidate regions
and genes
– Inflammation: CCR6 C-C Motif Chemokine Receptor 6. CRC
and Crohn’s Dz.
– Cell cycle/proliferation/death: RPS6KA2, GSDMC, FADD,
CCND1, RASSF2. Mostly oncogenes.
Metritis SNP Summary

20. Factor % n P ARR2
95% CI3
Calcium
Normocalcemia 2.5 (1/38) Referent
Sub-hypocalcemia4
44.4 (32/72) < 0.05 11.46 (1.57 - 83.60)
Parity
Multiparous 25.7 (19/74) Referent
Primiparous 38.9 (14/36) 0.24 1.32 (0.82 - 2.11)
Risk group 5
Low risk 14.5 (8/55) Referent
High risk 45.4 (25/55) 0.08 1.79 (0.92 - 3.47)
Incidence of Puerperal Metritis1
1 Puerperal Metritis = presence of watery, fetid discharge within the first 12 d postpartum with fever (T ≥ 39.5°C).
2 ARR = adjusted risk ratio.
3 CI = confidence interval.
4 Sub-hypocalcemia = serum Ca concentration ≤ 8.59 mg/dL in at least 1 d within the first 3 DIM.
5 Low risk = normal calving; High risk = dystocia, twin, stillbirth, retained fetal membranes.
Martinez et al., 2012; JDS
Hypocalcemia as Risk Factor for Metritis

21. Whole Genome Scan
GWAS
CYP27A1
CYP2J2
GC
SNAI2
PIM1
Courtesy of F. Peñagaricano

22. Gene mapping and gene-set analysis for
milk fever in Holstein dairy cattle
Vitamin D Metabolism
dietskin
Vitamin D3
(inactive)
1,25-
Dihydroxyvitamin D3
Calcitriol
(active hormone)
hydroxylation
(liver)
(kidneys)
(plasma)
Vitamin D binding protein
VDRDBP
1,25D3
1,25D3
gene
expression
Vitamin D increases Ca2+ in the blood
• Ca2+ absorption in intestines
• Ca2+ reabsorption by kidneys
• bone resorption
bones/intestines/kidneys
CYP27A1
CYP2J2
GC
SNAI2
PIM1
Courtesy of F. Peñagaricano

23. • Uterine diseases are highly prevalent and deleterious to fertility.
• Genetic variation for metritis exists
• Selection for improved health is possible, although progress will
be slow because of low heritability
• GWAS produced a list of putative candidate regions and genes
– Inflammation: CCR6
– Cell cycle/proliferation/death: RPS6KA2, GSDMC, FADD, CCND1,
RASSF2
• Several genomic regions associated with milk fever
• Major candidate genes closely related to Vitamin D metabolism
• Gene-set analysis revealed mechanisms and biological
processes associated with milk fever such as calcium
homeostasis and immune response
Conclusion