Heritability can be defined as the proportion of variation within a trait that can be attributed to inherited genetic factors (Falconer and Mackay, 1996). A heritability estimate ranges between 0 and 1 where an estimate of < 0.10 may be considered low and a heritability of > 0.30 might be considered high. Genetic correlations range from -1 to 1 with zero indicating no genetic association between two traits and a genetic correlation close to -1 or 1 meaning two traits are very similar.

Disease resistance

Contributing factors to wean-to-finish survival are numerous and reported in reviews by Gebhardt et al. (2020a) and Gebhardt et al. (2020b). Regarding infectious contributors to wean-to-finish survival, Lundeheim (1979) showed heritability estimates for pneumonia ranged from 0.04 to 0.14 and reported a heritability estimate for pleuritis of 0.13. The same study reported genetic correlation estimates between health disorders with growth rate, backfat and muscling were low (rg = -0.16 to 0.15).

Similarly, Kadowaki et al. (2012) showed a heritability estimate for mycoplasma pneumonia score of 0.07. The same authors reported genetic correlation estimates between health disorders with growth rate and backfat were low (rg = -0.13 to 0.04). Lundeheim (1988) reported heritability estimates for chronic pneumonia and chronic pleuritis of 0.23 and 0.08, respectively. In contrast, Henryon et al. (2003) found a heritability estimate for respiratory lesions of < 0.01. Taken together, these results suggest genetic selection to reduce the incidence of respiratory disorders in the finishing period may be possible.

Selecting against pigs that require antibiotics is perhaps another strategy to enhance wean-to-finish survival. Gorssen et al. (2021) estimated heritability for a range of phenotypes derived from finishing pen treatment records. The authors reported heritability estimates for all antibiotic treatments ranged from 0.18 to 0.44 and treatments specifically against respiratory diseases varied from 0.01 to 0.15. In agreement, Henryon et al. (2001) found heritability estimates for treatments of respiratory disease and diarrhea of 0.12 and 0.16, respectively. Under a natural disease challenge, Putz et al. (2019) reported heritability estimates for treatments per pig ranged from 0.13 to 0.29.

In contrast, in a high health herd, Guy et al. (2018) showed heritability estimates for pig treatments ranged from 0.04 to 0.06. The same study reported common litter effects for pig treatments varied from 0.09 to 0.18. Again, this demonstrates the relative importance of the litter a pig was reared in on its subsequent wean-to-finish health. Collectively, these findings establish the treatment of disease in the wean-to-finish phase to be a heritable trait that may be improved through genetic selection.

The studies by Putz et al. (2019) and Gorssen et al. (2021) correlated individual treatment records with wean-to-finish survival and finishing survival, respectively. Putz et al. (2019) reported a genetic correlation estimate between treatments per pig and wean-to-finish mortality of 0.93 indicating they were very similar traits. In agreement, Gorssen et al. (2021) found a genetic correlation estimate of 0.60 between used antibiotic dose with finishing mortality. Jointly, these results suggest genetic selection for phenotypes computed from treatment records could indirectly improve wean-to-finish survival or add accuracy to genetic selection for wean-to-finish survival. Hence these studies outline the importance of maintaining digitized pig treatment records, perhaps even more so within a genetic commercial test herd.

Immunological traits are perhaps another strategy to enhance disease resistance and improve wean-to-finish survival. A multitude of immunological traits have been measured and reported to be heritable (Edfors-Lilja et al., 1994; Henryon et al., 2006; Chen et al., 2020; Roth et al., 2022). Roth et al. (2022) studied 22 immune traits within an environment with low disease pressure. The authors reported heritability estimates for immune traits ranged from 0.02 to 0.61 and 0.01 to 0.66 for Landrace and Large White Breeds, respectively.

In contrast, Chen et al. (2020) evaluated 11 immune traits within a disease challenged environment. Yet the authors showed heritability estimates for immune traits ranged from 0.13 to 0.54. Again, common litter effects reported by Roth et al. (2022) and Chen et al. (2020) were at times sizeable (0.02 to 0.56 and 0.00 to 0.56, respectively). Chen et al. (2020) further reported greater IgM traits were genetically associated with lower wean-to-finish mortality (rg = -0.03 to -0.32). Taken together, these results suggest a variety of immunological traits would respond to genetic selection. Perhaps greater genetic associations between immune traits and wean-to-finish survival are needed before geneticists would consider their routine collection.

Incorporating commercial level data

Typically nucleus level animals are maintained and measured in very high health farms. Yet commercial level animals may be reared in environments with greater disease pressure and higher stocking densities. Hence genetic progress at the genetic nucleus level may not fully transmit to the commercial level. In other words, genetic nucleus improvements in growth rate, backfat, etc. may not be fully realized by commercial producers.

Evidence for this comes from Zumbach et al. (2006) who reported estimated genetic correlations between nucleus and commercial levels for growth rate and backfat ranged from 0.53 to 0.80 and 0.83 to 0.89, respectively. Similarly, Peškovičová et al. (2002) found genetic correlation estimates between different environments for growth rate and backfat were 0.48 to 0.57 and 0.75, respectively. Genetic correlation estimates that are less than 1.00 indicate traits are different. In other words, growth rate can be viewed as a different trait in the nucleus and the commercial level when the genetic correlation between the different levels of the genetic pyramid is less than 1.00.

To account for disease pressures and other environmental differences between the nucleus and commercial levels, geneticists leverage commercial test herds. Commercial test herds commonly house commercial maternal line females and mate them to the youngest, highest indexing terminal sires. The offspring are then evaluated for growth rate, carcass characteristics, etc. and then the commercial test herd data feeds back into genetic evaluations. The benefit of the commercial test herd data is that it adds accuracy to traits of economic importance. Hence incorporating commercial level data helps ensure genetic progress at the nucleus level will transmit to the commercial level.

References

Chen, Y., L. E. Tibbs-Cortes, C. Ashley, A. M. Putz, K. S. Lim, M. K. Dyck, F. Fortin, G. S. Plastow, J. C. M. Dekkers, J. C. S. Harding, and PigGen Canada. 2020. The genetic basis of natural antibody titers of young healthy pigs and relationships with disease resilience. BMC genomics, 21:1-17.

Edfors-Lilja, I., E. Wattrang, U. Magnusson, and C. Fossum. 1994. Genetic variation in parameters reflecting immune competence of swine. Vet. Immunol. Immunopathol. 40:1-16.

Falconer, D. S., and T. F. C. Mackay. 1996. Introduction to Quantitative Genetics, Ed 4. Longmans Green, Harlow, Essex, UK.

Gebhardt, J. T., M. D. Tokach, S. S. Dritz, J. M. DeRouchey, J. C. Woodworth, R. D. Goodband, and S. C. Henry. 2020a. Postweaning mortality in commercial swine production. I: review of non-infectious contributing factors. Trans. Anim. Sci. 4:462-484.

Gebhardt, J. T., M. D. Tokach, S. S. Dritz, J. M. DeRouchey, J. C. Woodworth, R. D. Goodband, and S. C. Henry. 2020b. Postweaning mortality in commercial swine production II: review of infectious contributing factors. Trans. Anim. Sci. 4:485-506.

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