Initial Estimates of Variance Components and Genetic Parameters for Reproductive Traits in Large White Sows in Kenya. | IJLTEMAS
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Initial Estimates of Variance Components and Genetic Parameters for Reproductive Traits in Large White Sows in Kenya.

Authors

Chrilukovian B. Wasike

Livestock Efficiency Enhancement Group (LEEG), Department of Animal and Fisheries Sciences, Maseno University, P.O. Box Private Bag 40105, Maseno, Kenya (KE)

Milka A. Kadenyi

Livestock Efficiency Enhancement Group (LEEG), Department of Animal and Fisheries Sciences, Maseno University, P.O. Box Private Bag 40105, Maseno, Kenya (KE)

Portas O. Olwande

Livestock Efficiency Enhancement Group (LEEG), Department of Animal and Fisheries Sciences, Maseno University, P.O. Box Private Bag 40105, Maseno, Kenya (KE)

Sophie Miyumo

Department of Animal Breeding and Husbandry in the Tropics and Sub-tropics, University of Hohenheim, Garbenstr. 17, 70599 Stuttgart-Germany (KE)

Article Information

DOI: 10.51583/IJLTEMAS.2025.1411000104

Subject Category: Animal breeding and genetics

Volume/Issue: 14/11 | Page No: 1088-1097

Publication Timeline

Submitted: 2025-12-23

Published: 2025-12-23

Abstract

This study aimed at estimating variance components and genetic parameters for reproductive traits of Large White sows in Kenya, in order to facilitate genetic improvement of reproductive efficiency in sows through selective breeding. 1145 records comprising 1129 records of litter size at birth (LSB), 1101 records of number of piglets born alive (NPBA), 1114 records of litter size at weaning (LSW) and 681 records of inter-farrowing interval (IFI) were obtained from 4 farms in western Kenya. After editing, a total of 1138 records of at least 2 traits were available for analysis. Genetic variance components were 0.10±0.03 for LSB, 1.33±1.80 for NPBA, 0.01±0.21for LSW and 23.28±22.71 for IFI.  Phenotypic variance components were 7.63±0.33 for LSB, 6.67±0.29 for NPBA, 6.03±0.26 for LSW and 589.40±25.48 for IFI. Heritability estimates for reproductive traits were generally low. The estimates were 0.014±0.040 for LSB, 0.011±0.039 for NPBA, (0.001±0.035 for LSW and 0.039±0.038 for IFI. The standard errors for estimates were high. This is because, reproductive traits are strongly influenced by environmental factors. High environmental variability relative to genetic variability can make it difficult to accurately estimate heritability. The genetic correlation coefficients were 0.227.for LSB and NPBA, -0.555 for LSB and LSW, 0.865 for LSB and IFI, -0.924 for NPBA and LSW, -0.283 for NPBA and IFI as well as -0.079 for LSW and IFI. Phenotypic correlation coefficients were 0.810±0.011 for LSB and NPBA, 0.655±0.018 for LSB and LSW, 0.019±0.031 for LSB and IFI, 0.821±0.010 for NPBA and LSW, 0.004±0.031 for NPBA and IFI as well as 0.034±0.031 for LSW and IFI, The study concluded that, the low genetic variance compared to phenotypic variance across traits indicates a strong influence of environmental factors on reproductive traits, limiting the genetic contribution to variability. The very low heritability values with high standard errors highlight the limited potential for genetic improvement through selection and emphasize the use of other sources of information like progeny and ancestors. The genetic correlations with both positive and negative relationships, suggest the need for careful, balanced selection to avoid unfavorable genetic trade-offs between traits. Strong positive phenotypic correlations between traits like LSB and NPBA  suggest shared environmental influences, underscoring the importance of improved management for overall reproductive performance.

Keywords

Pigs, fertility traits, heritability

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References

1. Ajayi, B., & Akinokun, J. (2013). Evaluation of some litter traits and heritability estimates of Nigerian Indigenous pigs. International Journal of Applied Agriculture and Apiculture Research, 9(1–2), 113–119. [Google Scholar] [Crossref]

2. Alves, K., Schenkel, F. S., Brito, L. F., & Robinson, A. (2018). Estimation of direct and maternal genetic parameters for individual birth weight, weaning weight, and probe weight in Yorkshire and Landrace pigs. Journal of Animal Science, 96(7), 2567–2578. [Google Scholar] [Crossref]

3. Blanford, J. I., Blanford, S., Crane, R. G., Mann, M. E., Paaijmans, K. P., Schreiber, K. V., & Thomas, M. B. (2013). Implications of temperature variation for malaria parasite development across Africa. Scientific Reports, 3(1), 1–11. [Google Scholar] [Crossref]

4. Briggs, D., & Walters, S. M. (2016). Plant variation and evolution. Cambridge University Press. [Google Scholar] [Crossref]

5. Chaudhary, R. (2017). GENETIC EVALUATION FOR TRAITS OF ECONOMIC IMPORTANCE USING PEDIGREE INFORMATION AND SNP MARKERS IN CROSSBRED PIGS. [Google Scholar] [Crossref]

6. de Oliveira, L. F., Lopes, P. S., Dias, L. C. C. M., e Silva, L. M. D., Silva, H. T., Guimarães, S. E. F., Marques, D. B. D., da Silva, D. A., & Veroneze, R. (2023). Estimation of genetic parameters and inbreeding depression in Piau pig breed. Tropical Animal Health and Production, 55(1), 14. [Google Scholar] [Crossref]

7. Dieguez, D. G. (2020). Genomic selection accounting for non-additive genetic effects in pig and corn crossbreeding schemes. [Google Scholar] [Crossref]

8. Do, D. N., & Haja, N. K. (2018). Genetic factors affecting feed efficiency, feeding behaviour and related traits in pigs University of Denmark, Denmark. In Achieving sustainable production of pig meat Volume 2 (pp. 97–118). Burleigh Dodds Science Publishing. [Google Scholar] [Crossref]

9. Do, D. N., Strathe, A. B., Ostersen, T., Pant, S. D., & Kadarmideen, H. N. (2014). Genome-wide association and pathway analysis of feed efficiency in pigs reveal candidate genes and pathways for residual feed intake. Frontiers in Genetics, 5, 307. [Google Scholar] [Crossref]

10. Dobrzański, J., Mulder, H. A., Knol, E. F., Szwaczkowski, T., & Sell‐Kubiak, E. (2020). Estimation of litter size variability phenotypes in Large White sows. Journal of Animal Breeding and Genetics, 137(6), 559–570. [Google Scholar] [Crossref]

11. Duenk, P., Bijma, P., Wientjes, Y. C., & Calus, M. P. (2020). Predicting the purebred-crossbred genetic correlation from genetic variances within, and covariance between parental lines. 130–130. [Google Scholar] [Crossref]

12. Fangmann, A. M. (2018). Genomic and conventional evaluations for fertility traits in pigs. [Google Scholar] [Crossref]

13. Freyer, G. (2018). Maximum number of total born piglets in a parity and individual ranges in litter size expressed as specific characteristics of sows. Journal of Animal Science and Technology, 60, 1–7. [Google Scholar] [Crossref]

14. Hermesch, S., Luxford, B., & Graser, H.-U. (2001). Genetic parameters for piglet mortality, within litter variation of birth weight, litter size and litter birth weight. 14, 211–214. [Google Scholar] [Crossref]

15. Herrero-Medrano, J., Mathur, P., Napel, J. ten, Rashidi, H., Alexandri, P., Knol, E., & Mulder, H. (2015). Estimation of genetic parameters and breeding values across challenged environments to select for robust pigs. Journal of Animal Science, 93(4), 1494–1502. [Google Scholar] [Crossref]

16. Hess, A. S. (2016). Genetic and biological factors influencing host response to porcine reproductive and respiratory syndrome virus in growing pigs. [Google Scholar] [Crossref]

17. Homma, C., Hirose, K., Ito, T., Kamikawa, M., Toma, S., Nikaido, S., Satoh, M., & Uemoto, Y. (2021). Estimation of genetic parameter for feed efficiency and resilience traits in three pig breeds. Animal, 15(11), 100384. [Google Scholar] [Crossref]

18. Houaga, I., Mrode, R., Opoola, O., Chagunda, M. G., Mwai, O. A., Rege, J. E., Olori, V. E., Nash, O., Banga, C. B., & Okeno, T. O. (2023). Livestock phenomics and genetic evaluation approaches in Africa: Current state and future perspectives. Frontiers in Genetics, 14, 1115973. [Google Scholar] [Crossref]

19. Kavlak, A. T., & Uimari, P. (2019). Estimation of heritability of feeding behaviour traits and their correlation with production traits in Finnish Yorkshire pigs. Journal of Animal Breeding and Genetics, 136(6), 484–494. [Google Scholar] [Crossref]

20. Knap, P. W. (2022). Pig breeding for increased sustainability. In Animal Breeding and Genetics (pp. 139–179). Springer. [Google Scholar] [Crossref]

21. Knol, E. F., van der Spek, D., & Zak, L. J. (2022). Genetic aspects of piglet survival and related traits: A review. Journal of Animal Science, 100(6), skac190. [Google Scholar] [Crossref]

22. Lee, J.-H., Song, K.-D., Lee, H.-K., Cho, K.-H., Park, H.-C., & Park, K.-D. (2015). Genetic parameters of reproductive and meat quality traits in Korean Berkshire pigs. Asian-Australasian Journal of Animal Sciences, 28(10), 1388. [Google Scholar] [Crossref]

23. Lopez, B. I., Kim, T. H., Makumbe, M. T., Song, C. W., & Seo, K. S. (2017). Variance components estimation for farrowing traits of three purebred pigs in Korea. Asian-Australasian Journal of Animal Sciences, 30(9), 1239. [Google Scholar] [Crossref]

24. Makhanya, L. G. (2018). Phenotypic and reproductive characterisation of kolbroek pigs. [Google Scholar] [Crossref]

25. Mbuthia, J. M., Rewe, T. O., & Kahi, A. K. (2015). Analysis of pig breeding management and trait preferences in smallholder production systems in Kenya. Animal Genetic Resources/Resources Génétiques Animales/Recursos Genéticos Animales, 56, 111–117. [Google Scholar] [Crossref]

26. Meuwissen, T., Hayes, B., & Goddard, M. (2016). Genomic selection: A paradigm shift in animal breeding. Animal Frontiers, 6(1), 6–14. [Google Scholar] [Crossref]

27. Mrode, R., & Pocrnic, I. (2023). Linear Models for the Prediction of the Genetic Merit of Animals. CABI. [Google Scholar] [Crossref]

28. Mugalavai, E. M., Kipkorir, E. C., Raes, D., & Rao, M. S. (2008). Analysis of rainfall onset, cessation and length of growing season for western Kenya. Agricultural and Forest Meteorology, 148(6–7), 1123–1135. [Google Scholar] [Crossref]

29. Mujibi, F. D., Okoth, E., Cheruiyot, E. K., Onzere, C., Bishop, R. P., Fèvre, E. M., Thomas, L., Masembe, C., Plastow, G., & Rothschild, M. (2018). Genetic diversity, breed composition and admixture of Kenyan domestic pigs. PLoS One, 13(1), e0190080. [Google Scholar] [Crossref]

30. Muthui, J. N. (2019). Influence of nutrition and value chain governance on enterprise performance in smallholder pig production in Kenya. [Google Scholar] [Crossref]

31. Okayo, J., Odera, P., & Oluchiri, S. (2015). Socio-economic characteristics of the community that determine ability to uptake precautionary measures to mitigate flood disaster in Kano Plains, Kisumu County, Kenya. Geoenvironmental Disasters, 2. https://doi.org/10.1186/s40677-015-0034-5 [Google Scholar] [Crossref]

32. Poulsen, B. G., Ask, B., Nielsen, H. M., Ostersen, T., & Christensen, O. F. (2020). Prediction of genetic merit for growth rate in pigs using animal models with indirect genetic effects and genomic information. Genetics Selection Evolution, 52(1), 58. [Google Scholar] [Crossref]

33. Rauw, W. M., & Gomez-Raya, L. (2015). Genotype by environment interaction and breeding for robustness in livestock. Frontiers in Genetics, 6, 310. [Google Scholar] [Crossref]

34. Reproto, R. O. (2020). Genetic selection and advances in swine breeding: A review of its impact on sow’s reproductive traits. Int. J. Res, 7, 41–52. [Google Scholar] [Crossref]

35. Rochus, C., Groenen, M., Bink, M., Huisman, A., Lopes, M., Knol, E., Ducro, B., Bijma, P., & Mulder, H. (2020). Estimating mutation rate and characterising de novo mutations in pigs. 84–84. [Google Scholar] [Crossref]

36. Rydhmer, L. (2000). Genetics of sow reproduction, including puberty, oestrus, pregnancy, farrowing and lactation. Livestock Production Science, 66(1), 1–12. [Google Scholar] [Crossref]

37. Samorè, A. B., & Fontanesi, L. (2016). Genomic selection in pigs: State of the art and perspectives. Italian Journal of Animal Science, 15(2), 211–232. [Google Scholar] [Crossref]

38. Sell-Kubiak, E., Duijvesteijn, N., Lopes, M., Janss, L., Knol, E., Bijma, P., & Mulder, H. (2015). Genome-wide association study reveals novel loci for litter size and its variability in a Large White pig population. BMC Genomics, 16, 1–13. [Google Scholar] [Crossref]

39. Sell-Kubiak, E., Wang, S., Knol, E., & Mulder, H. (2015a). Genetic analysis of within-litter variation in piglets’ birth weight using genomic or pedigree relationship matrices. Journal of Animal Science, 93(4), 1471–1480. [Google Scholar] [Crossref]

40. Sell-Kubiak, E., Wang, S., Knol, E., & Mulder, H. (2015b). Genetic analysis of within-litter variation in piglets’ birth weight using genomic or pedigree relationship matrices. Journal of Animal Science, 93(4), 1471–1480. [Google Scholar] [Crossref]

41. Serão, N., Matika, O., Kemp, R., Harding, J., Bishop, S., Plastow, G., & Dekkers, J. (2014). Genetic analysis of reproductive traits and antibody response in a PRRS outbreak herd. Journal of Animal Science, 92(7), 2905–2921. [Google Scholar] [Crossref]

42. Thekkoot, D., Kemp, R., Rothschild, M., Plastow, G., & Dekkers, J. (2016). Estimation of genetic parameters for traits associated with reproduction, lactation, and efficiency in sows. Journal of Animal Science, 94(11), 4516–4529. [Google Scholar] [Crossref]

43. Ume, S., Onwujiariri, E., & Nnadozie, A. (2020). Pig farmers’ socioeconomic characteristics as determinant to pig production and profitability in the tropics. International Journal of Research and Review, 7(4), 394–405. [Google Scholar] [Crossref]

44. Walsh, B., & Lynch, M. (2018). Evolution and selection of quantitative traits. Oxford University Press. [Google Scholar] [Crossref]

45. Yu, G., Wang, C., & Wang, Y. (2022). Genetic parameter analysis of reproductive traits in Large White pigs. Animal Bioscience, 35(11), 1649. [Google Scholar] [Crossref]

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