Phylogenetic Relationships, Genetic Diversity, and Neutrality Tests of Nigerian Cattle Populations in Taraba State
Abstract
The research investigated the genetic diversity and genetic neutrality of cattle populations in Taraba State, Nigeria. The study analyzed 100 reference populations, and 28 blood samples were used for mitochondrial DNA sequencing using Flinders Technology Associates (FTA) paper, which covered five locations (Iware, Wukari, Donga, Gembu, and Jalingo) and five breeds (Bokoloji, Muturu, Red Bororo, White Fulani, Adamawa Gudali). The research utilized Tajima's Neutrality Test, Tajima's Relative Rate Clock Test, and phylogenetic analysis to determine patterns of molecular evolution and population structure. The analysis of location-based neutrality showed that all populations tested positive for Tajima's D, with Iware recording 2.73 D value, Wukari showing 3.33 D value, Donga presenting 1.99 D value, and Gembu achieving 4.04 D value. The nucleotide diversity (π) measured between 0.5759 in Donga and 0.6781 in Gembu, indicating moderate to high genetic variability, whereas Wukari and Gembu displayed the most segregating sites with S = 778 and 779. The findings demonstrate evolution that deviates from neutral patterns due to three factors: balancing selection, population subdivision, and historical demographic patterns. The breed-based analysis produced positive Tajima's D results, reaching peak values in White Fulani (D = 4.49) and Red Bororo (D = 4.05), with both breeds showing high nucleotide diversity (π = 0.6535 and 0.6989, respectively). The Bokoloji (D = 1.25) and Muturu (D = 1.00) results showed reduced polymorphism levels, with S = 562 and 512. The Tajima's Clock Test results showed that evolutionary rates differed significantly between study locations. Iware showed the highest number of identical sites (202) and very few divergent sites (6), but a pronounced imbalance in unique substitutions, specially in sequence A (536). Wukari, Donga, and Gembu showed more divergent sites, with their total counts reaching 244, 229, and 193 respectively, while their unique differences among sequences appeared to be distributed more evenly, which proved the molecular clock predictions to be less accurate. The analysis of phylogenetic relationships demonstrated that different breeds of cattle from different regions showed shared ancestry together with genetic mixing from different populations. The research results demonstrate that Taraba State cattle populations possess high genetic diversity together with non-neutral evolutionary patterns and different rates of evolutionary change, which affect both conservation efforts and breeding programmes
Keywords
Download Options
Introduction
Cattle (Bos taurus and Bos indicus) represent one of the most economically and culturally important livestock species globally, delivering essential resources that include meat, milk, hides, and draft power while functioning as vital components for both agricultural operations and rural community development (Mwai et al., 2021; Talenti et al., 2022). The cattle population in Nigeria serves as an essential resource for both food security and economic development while shaping the sociocultural traditions of northern communities who have practiced pastoralism for thousands of years (Sikiru et al., 2022). The country contains multiple native cattle breeds that developed through adverse environmental conditions, which include tropical weather patterns, transmission of trypanosomiasis and tick-borne illnesses, and the two different livestock management approaches of nomadic herding and stationary agriculture (Nwachukwu et al., 2022)
Conclusion
The research presents a comprehensive assessment of genetic diversity and evolutionary relationships among Taraba State Nigerian cattle populations. The presence of positive Tajima's D values at all testing sites and among all tested breeds demonstrates that the species exhibit genetic patterns which differ from neutral evolution because of balancing selection, population structure, and historical demographic changes. Gembu and Wukari populations showed the highest nucleotide diversity and strongest signals of non-neutrality, reflecting substantial within-population variation. The breed-level study found that White Fulani and Red Bororo cattle showed higher genetic diversity and more pronounced neutrality violations than Bokoloji and Muturu because different factors affected their population size, geographic distribution, and environmental conditions. The Tajima's Relative Rate (Clock) Test showed different evolutionary rates across the study, as Iware maintained its basic sequences while showing irregular substitutions among its specific lineages, which broke the basic molecular clock rules. The phylogenetic study showed that breeds and geographical regions formed distinct groups, which scientists used to trace shared ancestry and population mixing. The research demonstrates how Nigerian cattle developed through complex evolutionary changes, which researchers used to create fundamental data needed for conservation work, breeding programs, and genetic development methods.
References
[1] Álvarez-Carretero, S., Kapli, P., & Yang, Z. (2022). Beginner's guide on the use of PAML to detect positive selection. Molecular Biology and Evolution, 39(4), msac041. https://doi.org/10.1093/molbev/msac041
[2] Bahbahani, H., Tijjani, A., Mukasa, C., Wragg, D., Almathen, F., Nash, O., Akpa, G. N., Mbole-Kariuki, M., Malla, S., Woolhouse, M., Sonstegard, T., van Marle-Kӧster, E., Sijjani, A., Kemp, S. J., & Hanotte, O. (2021). Signatures of selection for environmental adaptation and zebu × taurine hybrid fitness in East African Shorthorn Zebu. Frontiers in Genetics, 12, Article 607126.
[3] Barbato, M., Orozco-terWengel, P., Tapio, M., Marzanov, N. S., Aloqaili, F., Rezaei, H. R., Goncharenko, G., & Bruford, M. W.(2022). Genomic signatures of adaptive introgression from European mouflon into domestic sheep. Scientific Reports, 12(1), Article 15896.
[4] Cadzow, M., Merriman, T. R., & Boocock, J. (2022). Population genomic analysis of Māori and Pacific populations reveals an excess of intermediate-frequency variants. Human Genomics, 16(1), 1–12.
[5] Charlesworth, B. (2020). How long does it take to fix a favorable mutation, and why should we care? The American Naturalist,195(5), 753–771. https://doi.org/10.1086/708187
[6] Eldenegker, D. W., Tijjani, A., Edea, Z., Dessie, T., Haile, A., Rischkowsky, B., Rothschild, M. F., & Mwacharo, J. M. (2024).
Population genomics reveals adaptation and speciation in African cattle. Molecular Ecology, 33(3), e17235. https://doi.org/10.1111/mec.17235
[7] Elka, M. G., & Schenkel, F. S. (2021). Current state of genomic evaluation. Journal of Applied Genetics, 62(3), 329–338. https://doi.org/10.1007/s13353-021-00619-x
[8] Ginja, C., Guimarães, S., Fonseca, R. R., Rasteiro, R., Rodríguez-Varela, R., Simões, L. G., Pimenta, J., Matos, J., Ozawa, T., Götherström, A., & Penedo, M. C. T. (2023). The genetic legacy of the expansion of Bantu-speaking peoples in Africa. Nature Communications, 14(1), Article 4874.
[9] Gobena, M., Tijjani, A., Weldenegker, D. W., Edea, Z., Dessie, T., Haile, A., Rischkowsky, B., Rothschild, M. F., & Mwacharo, J. M. (2024). Genome-wide analysis reveals demographic history and contemporary selection signatures in African indigenous cattle. BMC Genomics, 25(1), Article 118.
[10] Iielsen, R. (2021). Estimation of population parameters and recombination rates from single nucleotide polymorphisms. Genetics,217(3), iyaa033. https://doi.org/10.1093/genetics/iyaa033
[11] Ijjani, A., Utsunomiya, Y. T., Ezekwe, A. G., Nashiru, O., & Hanotte, O. (2021). Genome sequence analysis reveals selection signatures in endangered trypanotolerant West African Muturu cattle. Frontiers in Genetics, 12, Article 644956. https://doi.org/10.3389/fgene.2021.644956
[12] Kardos, M., Armstrong, E. E., Fitzpatrick, S. W., Hauser, S., Hedrick, P. W., Miller, J. M., Tallmon, D. A., & Funk, W. C. (2021). The crucial role of genome-wide genetic variation in conservation. Proceedings of the National Academy of Sciences, 118(48), e2104642118.
[13] Kim, J., Hanotte, O., Mwai, O. A., Dessie, T., Bashir, S., Diallo, B., Agaba, M., Kim, K., Kwak, W., Sung, S., Seo, M., Jeong, H., Kwon, T., Mbole-Kariuki, M. N., Sonstegard, T., Tarekegn, G. M., Tijjani, A., Lim, D., Cho, S., & Kim, H. (2020). The genome landscape of indigenous African cattle. Genome Biology, 21(1), 1–21.
[14] Kim, K., Kwon, T., Dessie, T., Yoo, D., Mwai, O. A., Jang, J., Sung, S., Lee, S. B., Salim, B., Jung, J., Jeong, H., Tarekegn, G. M.,Tijjani, A., Lim, D., Cho, S., Oh, S. J., Lee, H. K., Kim, J., Jeong, C., & Kim, H. (2023). The mosaic genome of indigenous African cattle as a unique genetic resource for African pastoralism. Nature Genetics, 55, 1099–1110.
[15] Lartillot, N., & Poujol, R. (2024). Phylogenetic models of amino acid and codon substitution. Annual Review of Ecology, Evolution, and Systematics, 55, 23–44. https://doi.org/10.1146/annurev-ecolsys-102722-020508
[16] Makina, S. O., Muchadeyi, F. C., van Marle-Köster, E., Taylor, J. F., Makgahlela, M. L., & Maiwashe, A. (2020). Genome-wide scan for selection signatures in six cattle breeds in South Africa. Genetics Selection Evolution, 52(1), 1–14.
[17] Mbole-Kariuki, M. N., Sonstegard, T., Orth, A., Thumbi, S. M., Bronsvoort, B. M., Kiara, H., Toye, P., Conradie, I., Jennings, A., Coetzer, K., Woolhouse, M. E., Hanotte, O., & Taberlet, P. (2020). Genome-wide analysis reveals the ancient and recent admixture history of East African Shorthorn Zebu from Western Kenya. Heredity, 125(5), 334–347.
[18] Melka, M. G., & Schenkel, F. S. (2021). Current state of genomic evaluation. Journal of Applied Genetics, 62(3), 329–338.
[19] Nielsen, R. (2021). Estimation of population parameters and recombination rates from single nucleotide polymorphisms. Genetics, 217(3), 1–15.
[20] Nielsen, R., Akey, J. M., Jakobsson, M., Pritchard, J. K., Tishkoff, S., & Willerslev, E. (2020). Tracing the peopling of the world through genomics. Nature, 577(7789), 190–198.
[21] Orto-Neto, L. R., Bickhart, D. M., Landaeta-Hernandez, A. J., Tizioto, P. C., Wickramasinghe, S., Vercesi Filho, A. E., Reverter, A., & Moore, S. S. (2022). Convergent evolution of slick coat in cattle through truncation mutations in the prolactin receptor. Frontiers in Genetics, 13, Article 843368. https://doi.org/10.3389/fgene.2022.843368
[22] Porto-Neto, L. R., Bickhart, D. M., Landaeta-Hernandez, A. J., Tizioto, P. C., Wickramasinghe, S., Vercesi Filho, A. E., Reverter, A., & Moore, S. S. (2022). Convergent evolution of slick coat in cattle through truncation mutations in the prolactin receptor. Frontiers in Genetics, 13, Article 843368.
[23] Smetko, A., Soudre, A., Silbermayr, K., Müller, S., Brem, G., Hanotte, O., Wurzinger, M., Solkner, J., & Curik, I. (2021). Trypanosomosis: Potential driver of selection in African cattle. Frontiers in Genetics, 12, Article 632606.
[24] Stecher, G., Tamura, K., & Kumar, S. (2020). Molecular Evolutionary Genetics Analysis (MEGA) for macOS. Molecular Biology and Evolution, 37(4), 1237–1239. https://doi.org/10.1093/molbev/msz312
[25] Tamuri, A. U., Reis, M. D., & Goldstein, R. A. (2021). Estimating the distribution of selection coefficients from phylogenetic data using sitewise mutation-selection models. Genetics, 217(3), iyaa030. https://doi.org/10.1093/genetics/iyaa030
[26] Tijjani, A., Utsunomiya, Y. T., Ezekwe, A. G., Nashiru, O., & Hanotte, O. (2021). Genome sequence analysis reveals selection signatures in endangered trypanotolerant West African Muturu cattle. Frontiers in Genetics, 12, Article 644956.
[27] Umar, S., & Hedges, S. B. (2021). Advances in time estimation methods for molecular data. Molecular Biology and Evolution, 38(12), 5138–5145. https://doi.org/10.1093/molbev/msab302
[28] Umar, S., Stecher, G., Li, M., Knyaz, C., Tamura, K., & Mega, X. (2022). Molecular evolutionary genetics analysis across computing platforms. Molecular Biology and Evolution, 39(6), msac096. https://doi.org/10.1093/molbev/msac096
[29] Upadhyay, M., Bortoluzzi, C., Barbato, M., Ajmone-Marsan, P., Colli, L., Ginja, C., Sonstegard, T. S., Bosse, M., Lenstra, J. A., Groenen, M. A. M., & Crooijmans, R. P. M. A. (2021). Deciphering the patterns of genetic admixture and diversity in southern European cattle using genome-wide SNPs. Evolutionary Applications, 14(6), 1543–1558.
[30] Weldenegker, D. W., Tijjani, A., Edea, Z., Dessie, T., Haile, A., Rischkowsky, B., Rothschild, M. F., & Mwacharo, J. M. (2024). Population genomics reveals adaptation and speciation in African cattle. Molecular Ecology, 33(3), e17235.
[31] Zhou, Y., Utsunomiya, Y. T., Xu, L., Hay, E. H. A., Bickhart, D. M., Alexandre, P. A., Rosen, B. D., Schroeder, S. G., Carvalheiro, R., de Rezende Neves, H. H., Sonstegard, T. S., Van Tassell, C. P., Ferraz, J. B. S., Reecy, J. M., Ma, L., Garcia, J. F., & Liu, G. E. (2023). Genome-wide CNV analysis reveals variants associated with growth traits in Bos indicus. BMC Genomics, 24(1), Article136. https://doi.org/10.1186/s12864-023-09259-8.