For decades dairy production systems have faced the challenge of attaining adequate fertility levels. Insufficient reproductive performance will result on reductions on the proportion of cows at their peak production period, increments in insemination costs, and delayed genetic progress. Moreover, impaired fertility is one of the most frequent reasons for culling and increased days open are associated with a greater risk of death or culling in the subsequent lactation.
An historical trend for declining dairy fertility has likely resulted from high prevalence of anovulation, reduced fertilization, and embryonic survival. Contributing factors to this condition include changes in cow physiology associated with greater milk production, challenges for optimal nutritional management, housing, increased herd size, reduced estrus expression, and current genetic makeup. In addition, the level of inbreeding has increased in the Holstein population, with present average values greater than 5%.
An uneventful and timely calving is a desired trait for improved fertility of dairy cows.
Relevant to this problem, starting in the sixties, breeding programs selecting for milk production have been very successful and current trends indicate increments on milk yield per cow of 1 to 2% per year. However, some unfavorable genetic correlations between production and other traits may exist, resulting in undesirable side effects, such as a higher risk for behavioral, physiological, and immunological problems.
However, although these negative associations between production and fertility traits are probable, it is possible to select for improved milk yield and fitness traits, including fertility. This fact is evidenced by increments in reproductive performance occurring in Holsteins after the implementation of genetic evaluations for daughter pregnancy rate (DPR) in 2003. The trend for DPR indicate a partial recovery in dairy fertility, despite no apparent slowing down in the rate of increase of milk production per cow.
Genetic Selection and Improved Fertility: It’s Not That Simple
Fertility traits are multi-factorial in nature, which makes it difficult to determine the degree of involvement of genetics on reproductive outcomes. It has been established that reproductive traits are largely influenced by the environment and may be affected by multiple genes with small individual effects. Consequently, genetic progress for fertility, by way of conventional breeding strategies is hindered by low heritability, which represents the proportion of visible variation attributable to genetic differences among animals.
From the biological perspective, genetic variation affecting fertility may be directly involved in the physiology of reproductive processes. However, genetics may also determine, to some extent, the behavior of other related traits that have an impact on fertility. Among others, these comprise factors such as the ability to maintain adequate body condition and feed intake during the transition period, the potential for adequate immune responses resulting in adequate health, and the capacity to retain early pregnancy.
Some significant obstacles can be anticipated when the logistics of selection for fertility are planned. High costs of reliable data collection, the long time period required for validation, and biased phenotypes, such as non-return rates and DPR, are a few of these challenges. Adding to these limitations, the influence of factors unrelated to fertility, such as breeding policy and voluntary waiting period, is a constant difficulty for precise reproductive estimations.
A New Hope: Genomic Tools are Here to Stay
Although small heritabilities for reproductive performance traits have been reported, when more objective measures of fertility were evaluated (interval to first ovulation, anovulation, and pregnancy loss), heritabilities were moderate to high (0.15 to 0.40). For reproductive disorders, such as metritis and retained placenta, heritability estimates were close to 0.20. Notably, genetic variation is manifest when DPR is considered, as daughters of the highest and lowest sires for DPR differ by 29 days open per lactation.
With the arrival of low cost genotyping, which is the ability to read the DNA, the use of marker analysis (single nucleotide polymorphisms; SNP) in the evaluation of dairy cattle genetics has become a reality. The use of genomic analyses allows for estimation of breeding values at birth, which reduces the costs of proving bulls and increases the genetic gain because of shorter generation intervals. In addition, genotyping platforms commercially available from several companies have become widely used in research, as well as at the farm level, where genotyping of females is gaining momentum.
As with genetic evaluations, genomic selection has extended to multiple traits of economic interest, including more specific health problems. In the US, indirect health predictions are available from the Council on Dairy Cattle Breeding and recent data indicate that these traits result in genetic improvement for resistance to adverse health events. Producer-recorded health events have been successfully used to identify genetic differences between individuals regarding susceptibility to common health disorders including retained placenta, metritis, displaced abomasum, ketosis, lameness, and mastitis.
Matching Fertility and Genomics?
Specific reproductive traits that are currently evaluated by genomic analyses in the US include daughter pregnancy rate, sire calving ease, daughter calving ease, sire stillbirth rate, daughter stillbirth rate, heifer conception rate, and cow conception rate.
New research exploring genomic variation related to novel fertility traits is in progress. As a result, multiple genomic regions associated with variation in cattle reproductive traits have been mapped. More recently, genome-wide association studies (GWAS) performed with thousands of SNP markers have facilitated the resolution of associated regions and the discovery of candidate genes.
Interestingly, genomic markers have been identified for many reproductive traits including ovulation rate, pregnancy rate, DPR, non-return rate, and estrus intensity. Genetic variation has also been identified for gestation length, dystocia and stillbirth, and postpartum fertility. In addition, genomic analyses have offered the capability for locating lethal recessive genes affecting fertility outcomes. As an example, five recessive defects on fertility were recently identified by examining haplotypes that had a high population frequency but were never homozygous. These lethal effects may result in conception, gestation, and stillbirth losses.
As indicated previously, some physiological measures of fertility, such as resumption of ovarian cyclicity, have moderate heritabilities. What is interesting is that cows resuming estrous cyclicity soon after calving are more likely to show estrus and to become pregnant in a timely manner. Therefore, decomposing aggregate reproductive phenotypes into their detailed components could result into an effective tool for selection. For example, calving interval could be decomposed into several reproductive components such as the postpartum interval to commencement of estrus cyclicity, expression of estrus, conception, maintenance of pregnancy, and gestation length.
Presently, a major goal for advancing in genomic selection for fertility is the collection of high numbers of accurate fertility phenotypes associated with the corresponding genotypes, coupled to large scale evaluations of the association between direct measures of fertility. These fertility measures include uterine health, resumption of postpartum ovulation, detection of estrus, pregnancy per A.I., and maintenance of pregnancy. Collecting accurate data represents another challenge and potential strategies may include using DHI resources and data recorded within on-farm herd management software programs.
Finally, selection for traits with low heritabilities could be integrated into new reproductive technologies, such as in vitro fertilization (IVF) and embryo transplant, that allow for higher rates of genetic improvement by increasing the reproduction of superior females. It is reported this strategy could increase genetic gain by 10 to 20% compared with traditional breeding schemes.
Our Research Effort
Our team of researchers from multiple United States institutions was awarded a 5-year grant to explore genomic variation associated with reproductive traits in dairy cattle (Genomic Selection for Improved Fertility of Dairy Cows with Emphases on Cyclicity and Pregnancy; Grant no. 2013-68004-20361 from the USDA NIFA). The overall objective was to develop a fertility database with genotypes and phenotypes based on objective and direct measures of fertility in Holstein cows. The subsequent goal was to identify SNPs and haplotypes significantly associated with fertility traits by use of genome-wide analyses and to consider this information to obtain genomic-estimated breeding values that can be applied in selection of dairy cattle for improved fertility.
Consequently, our approach was to test a significant number of cows (approximately 12,000 individuals from 7 states in the USA) that were enrolled at calving and monitored weekly on farm until pregnancy confirmation. The evaluations included uterine health, metabolic status during transition, resumption of postpartum ovulation, estrus, pregnancy per AI, and pregnancy loss, under different management practices and environments.
Our initial analyses indicated that overall, 71% of the population resumed ovarian cyclicity by 50 DIM. Conception rates at first and second A.I. were 32.8% and 33.7%, respectively. Pregnancy loss between 32 and 60 days after A.I. were 10% and 8.7% for first and second A.I, respectively. Overall, 19.7% and 4% of the population was sold or died before 305 DIM.
Using this population, a reproductive index (RI) calculating the predicted probability of pregnancy at first A.I. was developed. The RI considered logistic regression models that included cow-level variables that were thought to have a genetic component (diseases, anovulation, BCS, milk yield, etc.). Interestingly, when the index from this population of cows was categorized as low, medium, and high, there was a consistent agreement between categories of the predicted RI and the measures of fertility collected from dairy cows.
By means of the developed RI, our population of cows was ranked as highly-fertile pregnant (850 cows) and a lowly-fertile non-pregnant (1,750 cows) for subsequent DNA analysis. At this point, preliminary genome-wide analyses with our high- and low-fertility subpopulations are confirming that there is potential for genomic selection in the traits of interest. We are evaluating genomic variation for dichotomous variables (uterine disease, anovulation, detection of estrus, pregnancy per AI, pregnancy loss) and for a continuous variable (predicted probability of pregnancy based on the RI, services per conception) to maximize opportunities for prediction accuracy.
Our initial analyses have estimated the heritability and the marker effects for lameness, metritis, mastitis, resumption of cyclicity, pregnancy after first A.I., and the RI. Significant markers have been associated with genes in chromosomal regions previously reported as contributing to variation in fertility and health traits in dairy cattle. In addition, causal associations among multiple traits, including retained fetal membranes, metritis, clinical endometritis, resumption of cyclicity by 50 days in milk, pregnancy after first A.I., and lameness early in lactation were investigated. Finally, selection models using significant markers are going through checking and further validation. This large scale evaluation will eventually be combined with current selection traits to further refine genomic selection of cattle by dairy producers.
Fertility is a key component of modern dairy production systems. However, a trend for declining dairy fertility has been evident in diverse production systems. Although fertility traits are strongly influenced by the environment, there is evidence for genotypic variation providing opportunity for selection, as suggested by a partial recovery in dairy fertility since the incorporation of daughter pregnancy rate into bull genetic evaluations. There are current efforts placed in collection of high numbers of accurate fertility phenotypes associated with the corresponding genotypes, coupled with large scale evaluations of the association between direct measures of fertility (uterine health, resumption of postpartum ovulation, detection of estrus, pregnancy per A.I., and maintenance of pregnancy) and genomic variation on dairy cows under different management practices and environments. As the cost of genotyping is decreasing, the number of animals subject to genomic evaluations is expected to continue increasing. If adequate markers and causal variants for fertility traits are identified, molecular breeding value could be estimated for each trait enabling selection to proceed population-wide.