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GENOME-WIDE ASSOCIATION STUDY FOR MILK PRODUCTION IN IRANIAN BUFFALO
Mahdi Mokhbera,*
a: Department of animal science, Faculty of Agriculture, Urmia University, Urmia, Iran
*Corresponding author: [email protected] (Mahdi Mokhber)
ABSTRACT
A genome wide association study (GWAS) was conducted to identify regions of the genome associated with milk production trait in Iranian water buffaloes. In order to do this study, 303 water buffalo from Azeri (N=207), Khuzestani (N=96) breeds were genotyped with Axiom® Buffalo Genotyping 90K Array. Average of monthly recording of one lactation period were used as phenotypic data for each individual. After adjustments of records for fixed effects, phenotypes were regressed on each single nucleotide polymorphisms (SNP) using a linear regression model by GenABEL package in R. In total, 10 SNPs were identified on BTAs 4, 5, 7, 13, 14, 15 and X as probably associated regions with milk production. These selected SNPs with 400 Kbp neighboring genomic regions were surveyed to find probable candidate genes by annotation. In total, 11 genes identified from the annotation of selected SNPs on the cattle genome (UMD3.1 Bos Taurus). Among the candidate genes identified in these regions, the most important gene that associated with milk production was SCOS2 gene on chromosome BTA5. Further research with additional individual and records is essential to detect associated regions with milk production and to suggestion for commercial application to the genetic improvement.
Keywords: Genome wide association study, single nucleotide polymorphisms, Azeri and Khuzestani buffalo breeds.
INTRODUCTION
Identifying the genes that contributing in milk production and functional traits is considered in dairy cattle since their high economic values (Raven et al., 2014). Genome-wide association studies (GWAS) have led to finding of high number of associated genes with milk production traits in dairy cattle (Grisart et al., 2002). Therefore, kind of studies are required to obtaining genetic progress in production traits. In recent years, research in genomics area has been increased with development of high-density SNP arrays in many livestock species (Cole et al., 2009). Recently, availability of special commercial SNP chip array for water buffalo, genomic base studies is possible for water buffalo (Iamartino et al., 2013). To date, several studies were used GWAS for some economically important traits in domestic animals including cattle (Hayes et al., 2009; Pryce et al., 2010; Alama et al., 2011; Lu et al., 2013), poultry (Yuan et al., 2015; Gu et al., 2011 ), pig (Bergfelder-Drüing et al., 2015; Le et al., 2017) and water buffalo by Bovine 50K and 777K SNP Beadchips (Wu et al., 2013; Borquis et al., 2014) and specific buffalo 90K SNP array (Iamartino et al., 2013; Camargo et al., 2015; El-Halawany et al., 2017). The aim of the current study was to perform a GWAS for milk production trait in
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Iranian buffalo using with Axiom® Buffalo Genotyping 90K Array, to detect genomic regions associated with milk production trait
MATERIALS & METHODS
A set of 303 water buffalo from Azeri (N=207), Khuzestani (N=96) breeds were genotyped with Axiom® Buffalo Genotyping Array which contains 89,988 single nucleotide polymorphism (SNP) markers. Average of monthly recording of one lactation period was used as phenotypic data for each individual. After adjustments of records for fixed effects, phenotypes were regressed on each single nucleotide polymorphisms (SNP) using a linear regression model by GenABEL package in R. Genotype quality control was accomplished using Plink software (Purcell et al., 2007). Genotypes were controlled for Individual call rate, SNP call rate and minor allele frequency (MAF). Then, Individual and SNPs with more than 5% missing data and MAF less than 10% were excluded. After quality control (QC) 303 Individual with a total of 65,185 SNPs remained for future analysis. Before to carrying out the GWAS, genetic homogeneity and population sub-structure of the dataset was tested by Multi Dimension Scaling (MDS) using GenABEL package in R (The R project website, http://www.r-project.org/). Also, Phenotypes were preadjusted using SAS GLM procedure of SAS version 9.4 (SAS 2014, SAS Institute Inc., Cary, NC, USA). Then significant factors and principal component variants were fitted as fixed effects in the GWAS model. Detecting genomic regions that associated with milk production was performed by GenABEL package in R (The R project website, http://www.r-project.org/). The final model was:
LactRecijk= μ+ Farmi+ Parj+ PC1+PC2+PC3+Polygenick+ eijk
where LactRecijk is phenotypic value of each individual; µ is the overall population mean for milk production; Farmi is the fixed effect of the ith farm or herd; Parj is the fixed effect of the jth parturition in recording time; PC1-3 are principal components; Polygenick is the effect of the lth genotype; and eijk is the residual random effect.
Associated genomic regions were used to extract genes from the corresponding areas in UMD3.1 Bos Taurus Genome Assembly using Biomart (www.ensembl.org/biomart/martview). Finally, DAVID (Huang et al., 2009) Enrichment Map Cytoscape plug-in (Merico et al., 2010) were used to perform a gene ontology analysis and construct networks, respectively
RESULTS AND DISCUSSION
The MDS results was revealed stratification between and within each breed, Therefore, structured association analysis was preferred for association model with identified fixed effected that have significant effect on milk production trait. A total of 10 SNPs were detected on BTAs 4(53,514 - 53,914 & 56,047 - 56,447 Kb), 5 (23,396 – 23,796 Kb), 7 (98,075 – 98,475 & 95,760 – 96,160 Kb), 13 (32,530 – 32,930 Kb), 14 (15,856 – 16,256 & 54,200 -54,600 Kb), 15 (25,227 – 25,627 Kb) and X (48,548 – 48,948 Kb) that could be associated with milk production trait.
These SNPs with 400Kbp neighboring genomic regions were surveyed to find probably candidate genes by annotation. In total, 11 gene including C7orf60, MRPL42- SOCS2- CRADD
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, CAST, MRC1, SLC39A12, CSMD3, RBM7, REXO2 and NXPE2 identified from the annotation of selected SNPs on the cattle genome (UMD3.1 Bos Taurus).
Among the candidate genes identified in these regions, the most important gene that associated with milk production was SCOS2 gene on chromosome BTA5. The SOCS family is contain of 8 members that require for the attenuation of cytokine signals in mammary epithelial cells, and acts to limit proliferation through a negative feedback mechanism (Jegalian and Wu, 2002). The SOCS1 and SOCS2 genes are required for the attenuation of the prolactin receptor signaling pathway during pregnancy and lactogenesis (Wei et al., 2013). Arun et al. (2015) suggests that the JAK-STAT-SOCS genes have a significant effect on milk and milk component performance.
Finally, gene ontology analysis was performed using DAVID to detecting biological network among provided genes and the Enrichment Map Cytoscape plug-in to construct networks.
However, there no significant biological pathway was found in gene ontology analysis.
CONCLUSIONS (FACULTATIVE)
Present study is the opening GWAS in Iranian water buffalo. Despite of small sample size, some of detected SNPs were associated with milk production. The most important gene associated with milk production was SCOS2 gene on chromosome BTA5.
Further research with additional individual and records is essential to detect associated regions with milk production to suggestion for commercial application in genomic selection for genetic improvement.
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