Fertility is a complex, polygenic trait that is concerned with an animal’s quality of being fertile or its ability to conceive offspring. Unfortunately, it is difficult to measure which results in low heritability estimates. Despite low heritability, it is one of the most economically important traits. One of the more effective ways of combating low heritability to improving fertility is through genomic selection. Genomics is a branch of genetics that is concerned with the structure, function, and mapping of all the genetic information (DNA) of an organism.
Genomic selection refers to a form of marker-assisted selection whereby genetic markers, spanning over the whole genome, are linked to all quantitative trait loci (QTL). Genetic markers are sequences of DNA that have been definitively located on the chromosome and QTLs are regions on a chromosome that have been associated with specific quantitative traits. The appeal to genomic selection lies in its ability to identify potential relationships between genetic markers and economically important traits. This makes genomic selection effective for economically important traits with low heritability, such as fertility.
Some of the commonly recorded fertility traits are age at first calving, calving interval, days-to-calving, gestation length, and scrotal size. Scrotal size tends to be of moderate heritability (0.3-0.45); however, most other male fertility traits are in the same low heritability boat (0.05-0.29), as most female fertility traits (see Table 1). The dilemma with this is that the rate at which genetic improvement occurs is higher with more heritable traits (> 0.3). Reason being the environment has a significant influence on traits with low heritability. Climate, disease, soil pH and fertility, nutrition, and other management practices can all impact fertility performance either positively or negatively depending on whether the environmental conditions are such that either high or low fertility are favoured. This can be limiting to any objectives for genetic improvement of fertility traits by selection.
The formula for genetic progress is shown below (Eqn. 1). Genomics can positively influence some of these factors.
For genomic selection to be effective it is important that sufficient phenotypic records are combined with sufficient genotypes in order to establish the relationships between phenotypes and genotypes. This enables the prediction of a genetically enhanced breeding value (GEBV) for animals without recorded phenotypes (e.g., young animals), with a relatively high degree of accuracy (> 0.75) (BREEDPLAN, 2022). The accuracy of prediction can be influenced by marker density. The higher the density of a SNP array (e.g., 50K chip vs 150k chip), the higher the accuracy of prediction (Xu et al., 2020). By predicting the performance of young animals without phenotypes, the generation interval for genetic progress can be shortened and there is greater opportunity for increased selection intensity (Sivalingam et al., 2022). The more selective a breeder is (i.e., the higher the selection intensity) in choosing the parents for the next generation, the higher the rate of genetic progress.
| Table 1. Heritability values for various male and female fertility traits from Brahman and Topical Composite cattle breeds (Adapted from Olasage et al., 2021). | |||
| Trait | Trait description | Brahman | Tropical composite |
| Female traits | |||
| AFC | Age at first calving | 0.15 | 0.06 |
| AGECL | Age at detection of first corpus luteum | 0.56 | 0.46 |
| PPAI | Postpartum anoestrus interval | 0.42 | 0.27 |
| DC1 | Days-to-calving 1st breeding opportunity | 0.07 | 0.04 |
| IGF1c | Cows’ blood concentration of IGF1 at 18 months | 0.46 | 0.42 |
| Male traits | |||
| MAS | Sperm mass activity at 24 months | 0.15 | 0.06 |
| MOT | Sperm mass motility at 24 months | 0.09 | 0.09 |
| PNS | Percentage normal sperm at 24 months | 0.35 | 0.31 |
| SC24 | Scrotal circumference at 24 months | 0.62 | 0.62 |
| IGF1b | Bulls’ blood concentration of IGF1 at 6 months | 0.43 | 0.48 |
Several studies on dairy and beef cattle fertility have reported advances for the trait through genomic technologies. Cai et al. (2019) sought out candidate genes related to dairy cow fertility. The candidate genes they identified that were associated with cow fertility were DIRC, SEMA5B, ADCY5, MYLK, KALRN, UMPS, ITGB5, HEG1. Fontanillas et al. (2022) sought out candidate genes related to beef cow fertility using whole genome sequence data. They reported associates between the following genes and fertility traits: ARHGEF28, RNF150, EPHA6, CNTNAP4, and PLAG1. Lastly, Forutan et al. (2022) sought to define the calving to first heat interval (CFHI) trait based on quality controls that influenced heritability estimates. They reported positive outcomes from their study and were able to estimate a heritability of 6% for the CFHI trait from genomic information obtained from SNP chip arrays.
Further, the US dairy industry decided to increase the amount of focus applied to fertility traits in genomic evaluations. Calving ease, daughter pregnancy rate, stillbirth, and cow and heifer conception rates, were included in the genomic evaluations. Since emphasis for genomic evaluation and selection was placed on these traits, desired genetic progress was observed in the US Holstein population. The positive responses to genomic selection support the efficacy thereof for lowly heritable traits (see Figure 1) (Ma et al., 2019).
In conclusion, there is understandable apprehension towards genomic selection. However, there is supported evidence for the efficacy and success thereof. Even with hard-to-measure, lowly heritable traits, such as fertility traits, advances in genetic progress have been enabled with genomic selection.
Figure 1. Graphs depicting genetic trends on dairy cow fertility over time. A = cow and sire heifer conception rate; B = cow and sire cow conception rate; C = cow and sire calving ease; D = cow and sire stillbirth (Ma et al., 2019).
BREEDPLAN, 2022. Understanding EBV Accuracy. https://breedplan.une.edu.au/general/understanding-ebv-accuracy/#:~:text=EBV%20Accuracy%20Confidence%20Ranges&text=Statistically%2C%20there%20is%20a%2067,standard%20errors%20of%20its%20EBV.
Cai, Z., Guldbrandtsen, B., Lund, M.S. and Sahana, G., 2019. Prioritizing candidate genes for fertility in dairy cows using gene-based analysis, functional annotation and differential gene expression. BMC genomics, 20, pp.1-9.
Fontanillas, E., Prieur, V., Michenet, A., Auvray, G. and Mante, J., 2022, December. Genomic selection of postpartum anoestrus recorded with accelerometer collars to improve beef cattle fertility. In Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP) Technical and species orientated innovations in animal breeding, and contribution of genetics to solving societal challenges (pp. 655-658). Wageningen Academic Publishers.
Forutan, M., Engle, B., Goddard, M.E. and Hayes, B.J., 2022, December. A conditional multi-trait sequence GWAS of heifer fertility in tropically adapted beef cattle. In Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP) Technical and species orientated innovations in animal breeding, and contribution of genetics to solving societal challenges (pp. 1106-1109). Wageningen Academic Publishers.
Ma, L., Cole, J.B., Da, Y.A.N.G. and VanRaden, P.M., 2019. Symposium review: Genetics, genome-wide association study, and genetic improvement of dairy fertility traits. Journal of dairy science, 102(4), pp.3735-3743.
Olasege, B.S., Tahir, M.S., Gouveia, G.C., Kour, J., Porto-Neto, L.R., Hayes, B.J. and Fortes, M.R., 2021. Genetic parameter estimates for male and female fertility traits using genomic data to improve fertility in Australian beef cattle. Animal Production Science.
Sivalingam, J., Vineeth, M.R., Kumar, A., Elango, K. and Ganguly, I., 2022. Genomic Selection for Fertility in Bovines. In Frontier Technologies in Bovine Reproduction (pp. 309-328). Singapore: Springer Nature Singapore.
Xu, Y., Liu, X., Fu, J., Wang, H., Wang, J., Huang, C., Prasanna, B.M., Olsen, M.S., Wang, G. and Zhang, A., 2020. Enhancing genetic gain through genomic selection: from livestock to plants. Plant C