Genome-Wide Association Study Based on Mega-Analysis to Detect Genomic Regions Associated with Prolificacy in Sheep

Document Type : Research Articles

Author

• Hossein Mohammadi

Department of Animal Science, Faculty of Agriculture, Arak University, Arak, Iran

10.22067/ijasr.2023.83596.1164

Abstract

Introduction: Genetic architecture of sheep reproduction is increasingly gaining scientific interest due to the major impact on sheep production systems. The number of lambs per lambing is one of the most important reproductive traits in sheep. Many studies have reported that genetic mechanisms play an important role in the variation of litter size in sheep. Reproductive traits normally show low heritability and therefore response to conventional selection methods is not satisfactory for these traits. Considering the genetic information of the genetic variants underlying reproduction variability could efficiently increase the selection efficacy. Genome-wide association studies (GWAS) have been used to identify associations between genotypes and phenotypes as well as candidate genes for reproductive economically important traits. Statistical power in GWAS is mostly affected by sample size. The low sample size is hence a main obstacle in GWAS. Combining multiple data sets of different studies for joint (mega) GWAS provides an opportunity to increase the sample size required for GWAS. This study was performed to identify genomic regions affecting litter size in different sheep breeds using the mega-analysis of GWAS.
Materials and Methods: Multi-population joint GWAS was performed using genotypic and phenotypic data of three sheep breeds including native Zandi and two breed retrieved from the database. Quality control was performed using the Plink software. The markers or individuals were removed from the further study based on the following criteria: (1) unknown chromosomal or physical location, call rate <0.90, missing genotype frequency >0.05, minor allele frequency (MAF) < 0.05, and a Pvalue for Hardy–Weinberg equilibrium test less than 10^-6. Before analysis, imputation of missing genotypes for combined data set was implemented by LD-kNNi method. Mega-analysis was performed using a mixed linear model in TASSEL software considering kinship and population structure (top five components of principal component analysis (PCA)) as confounding effects. The quantile–quantile (Q–Q) plot was visualized by plotting the distribution of obtained vs. expected log10 (P-value). The association results along the genome and the significant SNPs were visualized in the Manhattan plot. To account for multiple test problem and identify the genome-wide significance level, Bonferroni test was used based on the number of independent SNPs obtained from pairwise linkage disequilibrium analysis. After GWAS analysis, the 500 bp sequence upstream and downstream of the significant SNP was explored to identify the adjacent candidate genes using ARS-UI_Ramb_v2.0 (Genome Data Viewer).
Results and Discussion: In the present study, we implemented a mega GWAS using three different sheep breed data to identify the genetic mechanisms responsible for litter size in sheep. After quality control, 671 animals and 45167 SNP markers were kept for further analysis. The results of the mega-analysis identified nine marker on chromosome on chromosomes 1 (two SNP), 2, 3 (two SNP), 10, 13 (two SNP), and 22. The quantile–quantile plot that features the total distribution of the observed P-values (−log10 P-values) of quality passed SNPs vs. the expected values, showed the effective control for confounding effects. Many of the significant SNPs identified in this study were located in or very adjacent to known genes (DLG1, CLSTN2, INHBE, TCFL5, and RBP4) that have been already reported for their contribution to fertility and pregnancy success. It has been reported that the RBP4 gene is expressed during the period of fast elongation of the pig blastocyst which is a crucial period for the survival of the embryos. Also, it has been suggested that CLSTN2 has the main contribution in uterine and conceptus physiology during the establishment of pregnancy and therefore can be considered as a candidate gene for litter size. INHBE has an essential function during ovulation and pregnancy through extracellular matrix (ECM) components degradation and therefore enabling cell migration and angiogenesis.
Conclusion: Comparison of the results of this study with previous reports showed that the mega-analysis of GWAS, compared to the meta-analysis already reported for GWAS results, had comparable power in identifying genomic regions influencing litter size in sheep but identified fewer genomic regions than individual GWAS for each breed. No previously reported major genes controlling litter size in sheep were identified using our mega GWAS. The results of our research are suggested for further investigations in identifying causal genetic variants or genomic regions underlying the litter size variation in sheep and can be used to understand the genetic mechanism controlling this trait.

Keywords

• Association analysis
• Genomic region
• Genetic Marker
• Improving fertility
• Mega-Analysis

Main Subjects

• Genetics & breeding

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©2023 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source

References

1. Abdoli, R., Mirhoseini, S. Z., Ghavi Hossein-Zadeh, N., Zamani, P., & Gondro C. (2018). Genome-wide association study to identify genomic regions affecting prolificacy in Lori-Bakhtiari sheep. Animal Genetics, 49(5), 488-491. Doi: 10.1111/age.12700.
2. Abdoli, R., Mirhoseini, S. Z., Ghavi Hossein-Zadeh, N., Zamani, P., Moradi, M. H., Ferdosi, M. H., Gondro, C., (2019). Genome-wide association study of first lambing age and lambing interval in sheep. Small Ruminant Research, 178, 43–45. https://doi.org/10.1016/j.smallrumres.2019.07.014
3. Begum, F., Ghosh, D., Tseng, G. C., & Feingold, E. (2012). Comprehensive literature review and statistical considerations for GWAS meta-analysis. Nucleic Acids Research, 40, 3777–3784. doi: 10.1093/nar/gkr1255.
4. Bohlouli, M., Mohammadi, H., & Alijani, S. (2013). Genetic evaluation and genetic trend of growth traits of Zandi sheep in semi-arid Iran using random regression models. Small Ruminant Research, 114, 195–201. https://doi.org/10.1016/j.smallrumres.2013.07.005
5. Bouwman, A. C., Daetwyler, H. D., Chamberlain, A. J., Ponce, C. Sargolzaei, M., Schenkel, F. S., Sahana, G., Govignon-Gion, A., Boitard, S., Dolezal, M., Pausch, H., Brøndum, R. F., Bowman, P. J., Thomsen, B., Guldbrandtsen, B., Lund, M. S., & Hayes, B. J. (2018). Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals. Nature Genetics, 50, 362–367. doi: 10.1038/s41588-018-0056-5.
6. Bradbury, P. J., Zhang, Z., Kroon, D. E., Casstevens, T. M., Ramdoss, Y., & Buckler, E. S. (2007). TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics, 23(19), 2633-2635. doi: 10.1093/bioinformatics/btm308.
7. El-Halawany, N., Zhou, X., Al-Tohamy, A. F., El-Sayd, Y. A., Shawky, A. E., Michal, J. J., & Jiang, Z. (2016). Genome-wide screening of candidate genes for improving fertility in Egyptian native Rahmani sheep. Animal Genetics, (4), 513. doi: 10.1111/age.12437.
8. Esmaeili-Fard, S. M., Gholizadeh, M., Hafezian, S. H., & Abdollahi-Arpanahi, R. (2021). Genes and pathways affecting sheep productivity traits: Genetic parameters, genome-wide association mapping, and pathway enrichment analysis. Frontiers in Genetics, 1, 1351. doi: 10.3389/fgene.2021.710613.
9. Fortes, M. R. S., Reverter, A., Kelly, M., McCulloch, R., & Lehnert, S. A. (2013). Genome-wide association study for inhibin, luteinizing hormone, insulin-like growth factor 1, testicular size and semen traits in bovine species. Andrology, 1, 644–650. doi: 10.1111/j.2047-2927.2013.00101.x.
10. Fritsche, L. G., Igl, W., Bailey, J. N., Grassmann, F., Sengupta, S., Bragg‐Gresham, J. L., & Heid, I. M. (2016). A large genome‐wide association study of age‐related macular degeneration highlights contributions of rare and common variants. Nature Genetics, 48(2), 134-143. doi: 10.1038/ng.3448.
11. Gebreyesus, G., Buitenhuis, A. J., Poulsen, N. A., Visker, M. H. P. W., Zhang, Q., van Valenberg, H. J. F., & Bovenhuis H. (2019). Combining multi-population datasets for joint genome-wide association and meta-analyses: The case of bovine milk fat composition traits. Journal of Dairy Science, 102(12), 11124-11141. doi: 10.3168/jds.2019-16676.
12. Gholizadeh, M., & Esmaeili-Fard, S. M. (2022). Multi-population joint genome-wide association study to detect genomic regions associated with litter size in sheep. Animal Production Research, 11(3), 15-26. (In Persian) doi:10.22124/AR.2022.21763.1688
13. Gorski, M., Günther, F., Winkler, T. W., Weber, B., & Heid, I. M. (2019). On the differences between mega- and meta-imputation and analysis exemplified on the genetics of age-related macular degeneration. Genetic Epidemiology, 43(5), 559-576. doi: 10.1002/gepi.22204.
14. Ghiasi, H., & Abdollahi-Arpanahi, R. (2021). The candidate genes and pathways affecting litter size in sheep. Small Ruminant Research, 205, 106546. https://doi.org/10.1016/j.smallrumres.2021.106546
15. Hernández-Montiel, W., Martínez-Núñez, M. A., Ramón-Ugalde, J. P., Román-Ponce, S. I., Calderón-Chagoya, R., & Zamora-Bustillos, R. (2020). Genome-wide association study reveals candidate genes for litter size traits in Pelibuey sheep. Animals (Basel), 10(3), 434. doi: 10.3390/ani10030434.
16. Helms, C. (1990). Salting out procedure for human DNA extraction. retrieved april 20, 2010, from http://humgen.wustl.edu/hdk_lab_manual/dna/dna2.html
17. Hong, E. J., Park, S. H., Choi, K. C., Leung, P. C. K., & Jeung, E. B. (2006). Identification of estrogen-regulated genes by microarray analysis of the uterus of immature rats exposed to endocrine disrupting chemicals. Reproduction Biological Endocrinology, 4, 1–12. doi: 10.1186/1477-7827-4-49.
18. Johnston, S. E., McEwan, J. C., Pickering, N. K., Kijas, J. W., Beraldi, D., Pilkington, J. G., Pemberton, J. M., & Slate, J. (2011). Genome-wide association mapping identifies the genetic basis of discrete and quantitative variation in sexual weaponry in a wild sheep population. Molecular Ecology, 20, 2555–2566. doi: 10.1111/j.1365-294X.2011.05076.x.
19. Lehnert, S., & Reverter, T. (2013). Genome-wide association study of tropical composite bulls for reproduction traits. CSIRO Animal, Food and Health Sciences, Published by Meat & Livestock Australia Limited.
20. Marjanovic, J., & Calus, M. P. L. (2020). Factors affecting accuracy of estimated effective number of chromosome segments for numerically small breeds. Journal of Animal Breeding and Genetics, 138, 151-160. doi: 10.1111/jbg.12512.
21. Messer, L. A., Wang, L., Yelich, J., Pomp, D., Geisert, R. D., & Rothschild, M. F. (1996). Linkage mapping of the retinol binding protein 4 (RBP4) gene to porcine chromosome 14. Mammalian Genome, 7, 396-410. doi: 10.1007/s003359900117.
22. Money, D., Gardner, K., Migicovsky, Z., Schwaninger, H., Zhong, G. Y., & Myles, S. (2015). LinkImpute: fast and accurate genotype imputation for nonmodel organisms. G3: Genes, Genomes, Genetics, 5(11), 2383-2390. doi: 10.1534/g3.115.021667.
23. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., & Sham P. C. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, 81(3), 559-575. doi: 10.1086/519795.
24. Ramos, Z., Garrick, D. J., Blair, H. T., Vera, B., Ciappesoni, G., & Kenyon, P. R. (2023). Genomic Regions associated with wool, growth and reproduction traits in Uruguayan Merino sheep. Genes, 14, 167. doi: 10.3390/genes14010167.
25. Tsartsianidou, V., Pavlidis, A., Tosiou, E., Arsenos, G., Banos, G., & Triantafyllidis A. (2023). Novel genomic markers and genes related to reproduction in prolific Chios dairy sheep: A genome-wide association study. Animal, (3), 100723. doi: 10.1016/j.animal.2023.100723.
26. Tenghe, A. M. M., Bouwman, A. C., Berglund, B., Strandberg, E., de Koning, D. J., & Veerkamp, R. F. (2016). Genome wide association study for endocrine fertility traits using single nucleotide polymorphism arrays and sequence variants in dairy cattle. Journal of Dairy Science, 99(7), 5470-5485. doi: 10.3168/jds.2015-10533.
27. Wang, S., Dvorkin, D., & Da, Y. (2012). SNPEVG: A graphical tool for GWAS graphing with mouse clicks. BMC Bioinformatics, 13, 319. doi: 10.1186/1471-2105-13-319.
28. Xu, S. S., Gao, L., Xie, X. L., Ren, Y. L., Shen, Z. Q., Wang, F., & Li, M. H. (2018). Genome-wide association analyses highlight the potential for different genetic mechanisms for litter size among sheep breeds. Frontiers in Genetics, 9, 118. doi: 10.3389/fgene.2018.00118.
29. Ye, H., Li, X., Zheng, T., Hu, C., Pan, Z., Huang, J., Li, J., Li, W., & Zheng, Y. (2017). The hippo signaling pathway regulates ovarian function via the proliferation of ovarian germline stem cells. Cell Physiological Biochemistry, 41, 1051–1062. doi: 10.1159/000464113
30. Zhang, Z., Sui, Z., Zhang, J., Li, Q., Zhang, Y., Wang, C., Li, X., & Xing, F. (2022). Identification of signatures of selection for litter size and pubertal initiation in two sheep populations. Animals, 12, 2520. doi: 10.3390/ani12192520.