مطالعه ارتباط ژنومی چند جمعیتی مشترک برای شناسایی مکان های ژنومی موثر بر چندقلوزایی در گوسفند

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار، گروه علوم دامی، دانشگاه کشاورزی و منابع طبیعی ساری

2 دانش‌آموخته دکتری، گروه علوم دامی، دانشگاه کشاورزی و منابع طبیعی ساری

چکیده

چندقلوزایی یکی از مهمترین صفات اقتصادی در گوسفند با تنوع داخل و بین نژادی است. این مطالعه به منظور شناسایی مکان­های ژنومی موثر بر چندقلوزایی در گوسفند با رویکرد مگاآنالیز مطالعه ارتباط ژنومی و با استفاده از داده­های ژنوتیپی و فنوتیپی شش نژاد گوسفند از پایگاه داده انجام شد. کنترل کیفیت با استفاده از نرم ‌افزار Plink و ایمپیوتیشن با روش LD-kNNi انجام شد. مگاآنالیز با استفاده از مدل خطی مختلط در نرم افزار TASSEL  با در نظر گرفتن خویشاوندی و ساختار جمعیت انجام شد. پس از پایان کنترل کیفیت، تعداد 305 حیوان و 351615 نشانگر SNP با متوسط MAF  برابر با 33/0 برای ادامه تجزیه مورد استفاده قرار گرفتند. نتایج مگاآنالیز، یک SNP را روی کروموزوم 21 در سطح ژنوم و 10 SNP در سطح کروموزومی روی کروموزوم های 1، 2، 3، 14، 17 و 22 شناسایی کرد که به­طور معنی­داری با چندقلوزایی در گوسفند در ارتباط بودند. ژن­های OPCML، GULP1، RBP4، MMP2 و LPCAT2 شناسایی شده در این تحقیق، نقش موثری در باروری و موفقیت آبستنی دارند. نتایج این تحقیق می‌تواند در درک ساز و کار ژنتیکی کنترل­کننده چندقلوزایی در گوسفند مورد استفاده قرار گیرد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Multi-population joint genome-wide association study to detect genomic regions associated with litter size in sheep

نویسندگان [English]

  • M. Gholizadeh 1
  • S. M. Esmaeili-Fard 2
1 Associate Professor, Department of Animal Science and Fisheries, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
2 Department of Animal Science, Faculty of Animal Science and Fisheries, Agricultural Sciences & Natural Resources University of Sari, Mazandaran, Iran
چکیده [English]

Introduction: Reproduction is one of the most important economic traits in sheep with within and between-breeds variation. Reproductive traits normally show low to medium 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 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 sheep using the mega-analysis of GWAS.
Materials and methods: Multi-population joint GWAS was performed using genotypic and phenotypic data of six sheep breeds 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 <0.95, missing genotype frequency >0.05, minor allele frequency (MAF) < 0.05, and a P-value 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 and chromosome-wide significance level, Bonferroni test was used based on the number of independent SNPs obtained from pairwise linkage disequilibrium analysis. After GWAS analysis, the 300 bp sequence upstream and downstream of the significant SNP was explored to identify the adjacent candidate genes using Ovis aries_v4.0 (UCSC).
Results and discussion: In the present study, we implemented a mega GWAS using six different sheep breed data to identify the genetic mechanisms responsible for litter size in sheep. After quality control, 305 animals and 351,615 SNP markers with a mean MAF of 0.33 were kept for further analysis. The results of the mega-analysis identified one marker on chromosome 21 at the genome-wide level and 10 markers at the chromosome-wide level on chromosomes 1, 2, 3, 14, 17, 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 (OPCML, GULP1, RBP4, MMP2, and LPCAT2) that have been already reported for their contribution to fertility and pregnancy success. It has been reported that OPCML is more consistently expressed in cells lining the uterus, oviduct, and rete ovarii. OPCML has been reported as a tumor suppressor protein that is frequently inactivated in epithelial ovarian cancer. 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 RBP4 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. MMP2 has an essential function during ovulation and pregnancy through extracellular matrix (ECM) components degradation and therefore enabling cell migration and angiogenesis.
Conclusions: 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.

کلیدواژه‌ها [English]

  • Prolificacy
  • Sheep
  • Genome-wide association study
  • Mega-analysis
  • Marker
Abdoli R., Mirhoseini S. Z., Ghavi Hossein-Zadeh N., Zamani P. and Gondro C. 2018. Genome-wide association study to identify genomic regions affecting prolificacy in Lori-Bakhtiari sheep. Animal Genetics, 49(5): 488-491.
Abdoli R., Mirhosseini S.  Z., Ghavi Hossein-Zadeh N., Zamani P., Ferdosi M. H. and Gondro C. 2019. Genome-wide association study of four composite reproductive traits in Iranian fat-tailed sheep. Reproduction, Fertility and Development, 31(6): 1127-1133.
Abdollahi-Arpanahi R., Carvalho M. R., Ribeiro E. S. and Peñagaricano F. 2019. Association of lipid-related genes implicated in conceptus elongation with female fertility traits in dairy cattle. Journal of Dairy Science, 102(11): 10020-10029.
Begum F., Ghosh D., Tseng G. C. and Feingold E. 2012. Comprehensive literature review and statistical considerations for GWAS meta-analysis. Nucleic Acids Research, 40: 3777–3784.
Behforouz A., Dastgheib S. A., Abbasi H., Karimi-Zarchi M., Javaheri A., Hadadan A. and Neamatzadeh H. 2021. Association of MMP-2, MMP-3, and MMP-9 polymorphisms with susceptibility to recurrent pregnancy loss. Fetal and Pediatric Pathology, 40(5): 378-386.
Bernal Rubio Y. L., Gualdron Duart J. L. E., Bates R. O., Ernst C. W., Nonneman D., ohrer G. A., King D. A., Shackelford S. D., Wheeler T. L., Cantet R. J. and Steibel J. P. 2015. Implementing meta-analysis from genome-wide association studies for pork quality traits. Journal of Animal Science, 93: 5607–5617.
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., Servin B., Garrick D. J., Reecy J., Vilkki J., Bagnato A., Wang M., Hoff J. L., Schnabel R. D., Taylor J. F., Vinkhuyzen A. A. E., Panitz F., Bendixen C., Holm L. E., Gredler B., Hozé C., Boussaha M., Sanchez M. P., Rocha D., Capitan A., Tribout T., Barbat A., Croiseau P., Drögemüller C., Jagannathan V., Vander Jagt C., Crowley J. J., Bieber A., Purfield D. C., Berry D. P., Emmerling R., Götz K. U., Frischknecht M., Russ I., Sölkner J., Van Tassell C. P., Fries R., Stothard P., Veerkamp R. F., Boichard D., Goddard M. E. and 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.
Bradbury P. J., Zhang Z., Kroon D. E., Casstevens T. M., Ramdoss Y. and Buckler E. S. 2007. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics, 23(19): 2633-2635.
Cohen M., Meisser A. and Bischof P. 2006. Metalloproteinases and human placental invasiveness. Placenta, 27(8): 783-793.
Daetwyler H. D., Villanueva B. and Woolliams J. A. 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach.  PLoS One, 3(10): e3395.
Deady L. D., Shen W., Mosure S. A., Spradling A. C. and  Sun J. 2015. Matrix metalloproteinase 2 is required for ovulation and corpus luteum formation in Drosophila. PLoS Genetics, 11(2): e1004989.
Esmaeili fard S. M., Hafezian S., Gholizadeh M., Abdolahi Arpanahi R. 2019. Gene set enrichment analysis using genome-wide association study to identify genes and biological pathways associated with twinning in Baluchi sheep. Animal Production Research, 8(2): 63-80. (In Persian).
Esmaeili-Fard S. M., Gholizadeh M., Hafezian S. H. and Abdollahi-Arpanahi R. 2021. Genes and pathways affecting sheep productivity traits: Genetic parameters, genome-wide association mapping, and pathway enrichment analysis. Frontiers in Genetics, 1351.
Fleming J. S., McQuillan H. J., Millier M. J. and Sellar G. C. 2009. Expression of ovarian tumour suppressor OPCML in the female CD-1 mouse reproductive tract. Reproduction, 137(4): 721.
Fritsche L. G., Igl W., Bailey J. N., Grassmann F., Sengupta S., Bragg‐Gresham J. L. and 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. 
Gebreyesus G., Buitenhuis A. J., Poulsen N. A., Visker M. H. P. W., Zhang Q., van Valenberg H. J. F. and 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.
Gholizadeh M. and Esmaeili-Fard S. M. 2022. Meta-analysis of genome-wide association studies for litter size in sheep. Theriogenology, 1: 180:103-112.
Gorski M., Günther F., Winkler T. W., Weber B. and 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.
Gudjonsson A., Gudmundsdottir V., Axelsson G. T., Gudmundsson E. F., Jonsson B. G., Launer L. J., Lamb J. R., Jennings L. L., Aspelund T., Emilsson V. and Gudnason V. 2022. A genome-wide association study of serum proteins reveals shared loci with common diseases. Nature Communications, 13(1): 1-13.
Hayes B. and Goddard M. 2010. Genome-wide association and genomic selection in animal breeding. Genome, 53(11): 876-883.
Hendrix N. D., Wu R., Kuick R., Schwartz D. R., Fearon E. R. and Cho K. R. 2006. Fibroblast growth factor 9 has oncogenic activity and is a downstream target of Wnt signaling in ovarian endometrioid adenocarcinomas. Cancer Research, 66: 1354-1362.
Lin D. Y. and Zeng D. 2010. Meta-analysis of genome-wide association studies: no efficiency gain in using individual participant data. Genetic Epidemiology, 34: 60–66.
Ma C. I. J., Martin C., Ma Z., Hafiane A., Dai M., Lebrun J. J. and Kiss R. S. 2012. Engulfment protein GULP is regulator of transforming growth factor-β response in ovarian cells. Journal of Biological Chemistry, 287(24): 20636-20651.
Marjanovic J. and 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.
Massague J. 1998. TGF-β signal transduction. Annual Review of Biochemistry, 67: 753-791.
Messer L. A., Wang L., Yelich J., Pomp D., Geisert R. D. and Rothschild M. F. 1996. Linkage mapping of the retinol-binding protein 4 (RBP4) gene to porcine chromosome 14. Mammalian Genome, 7: 396-410.
Meuwissen T. H. E., Hayes B. J. and Goddard M. E. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 57(4): 1819–1829.
Money D., Gardner K., Migicovsky Z., Schwaninger H., Zhong G. Y. and Myles S. 2015. LinkImpute: fast and accurate genotype imputation for nonmodel organisms. G3: Genes, Genomes, Genetics, 5(11): 2383-2390.
Pasandideh M., Rahimi-Mianji G., Gholizadeh M. and Fontanesi L. 2017. Detection of genomic regions affecting reproductive traits in Baluchi sheep using high density markers. Animal Production Research, 6(3): 29-41. (In Persian).
Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M. A., Bender D. and Sham P. C. 2007. PLINK: a tool set for whole-enome association and population-based linkage analyses. The American Journal of Human Genetics, 81(3): 559-575.
Schwartz D. R., Wu R., Kardia S. L., Levin A. M., Huang C. C., Shedden K. A., Kuick R., Misek D. E., Hanash S. M., Taylor J. M., Reed H., Hendrix N., Zhai Y., Fearon E. R. and Cho K. R. 2003. Novel candidate targets of β-catenin/T-cell factor signaling identified by gene expression profiling of ovarian endometrioid adenocarcinomas. Cancer Research, 63: 2913-2922.
Sellar G. C., Watt K. P., Rabiasz G. J., Stronach E. A., Li L., Miller E. P., Massie C. E., Miller J., Contreras-Moreira B., Scott D., Brown I., Williams A. R., Bates P. A., Smyth J. F. and Gabra H. 2003. OPCML at 11q25 is epigenetically inactivated and has tumor-suppressor function in epithelial ovarian cancer. Nature Genetics, 34: 337-343.
Sung Y. J., Schwander K., Arnett D. K., Kardia S. L., Rankinen T., Bouchard C., Boerwinkle E., Hunt S. C. and Rao D. C. 2014. An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions. Genetc Epidemiology, 38: 369–378.
Taghizade K., Gholizadeh M., Moradi M. and Rahimi Mianji G. 2020. Investigation of copy number variation in Baluchi sheep genome using comparative analysis of PennCNV and QuantiSNP algorithms. Animal Production Research, 9(1), 29-44. (In Persian).
Tenghe A. M. M., Bouwman A. C., Berglund B., Strandberg E., de Koning D. J.  and 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.
Tsou J. A., Galler J. S., Siegmund K. D., Laird P. W., Turla S., Cozen W., Hagen J. A., Koss M. N. and Laird-Offringa I. A. 2007. Identification of a panel of sensitive and specific DNA methylation markers for lung adenocarcinoma. Molecular Cancer, 6: 70.
VanRaden P. M., van Tassell C. P., Wiggans G. R., Sonstegard T. S., Schnabel R. D., Taylor J. F. and Schenkel F. S. 2009. Invited review: Reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science, 92(1): 16–24.
Xu S. S., Gao L., Xie X. L., Ren Y. L., Shen Z. Q., Wang F. and 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.
Yelich J. V., Pomp D. and Geisert R. D. 1997. Detection of transcripts for retinoic acid receptors, retinol-binding protein, and transforming growth factors during rapid trophoblastic elongation in the porcine conceptus. Biology of Reproduction, 57: 286-294.