RNA-seq based SNP discovery in liver transcriptome of Polish Landrace pigs
DOI:
https://doi.org/10.12775/TRVS.2019.005Keywords
Single nucleotide variations, SNPs, genetic markers, RNA-seq, illumina, paired-end read, GATK, SAMtools, mapping, bioinformatics, NGS, transcriptome, liver, Landrace, pig, Omega-6 and omega-3 polyunsaturated fatty acidsAbstract
Background: RNA-seq technology is most commonly used in quantitative measurement of gene expression levels and identification of non-annotated transcripts. It is also used for the coding SNPs (cSNPs) discoveries in an efficient and cost-effective way. The aim of this study was to identify the putative genetic cSNPs variants in liver transcriptome of Polish Landrace pigs fed with high and low (normal) omega-6 and omega-3 polyunsaturated fatty acids (PUFAs) diets.
Methods: RNA-seq based NGS experiment was performed on Polish Landrace pigs fed with high and low PUFAs diets. Total RNA were isolated from liver tissues of low PUFAs (n=6) and high PUFAs dietary group (n=6) of Polish Landrace pigs. The RNA-seq libraries preparations were performed by mRNA enrichment, mRNA fragmentation, second strand cDNA synthesis, adaptor ligation, size selection and PCR amplification using the illumina TruSeq RNA Sample Prep Kit v2 (Illumina, San Diego CA, USA), followed by NGS sequencing on MiSeq illumina platform. The quality control (QC) of raw RNA-seq data of liver transcriptome was performed using the Trimmomatic and FastQC tools. The paired-end mapping of the liver transcriptome RNA-seq data (n=12) was performed on the reference genome Sus scrofa v.10.2, followed by cSNPs discovery using GATK and SAMtools bioinformatics SNPs caller tools.
Results: Two pooled paired-end libraries of 151bp liver transcriptome of Polish Landrace pigs were generated from MiSeq instrument and subsequent Fastq RNA-seq data were submitted to NCBI SRA database (https://www.ncbi.nlm.nih.gov/sra). Our study identified 25.3 million paired-end reads: representing 13,509,248 paired-end reads of high PUFAs dietary group and 11,815,696 paired-end reads of low PUFAs dietary group of Polish Landrace pigs liver transcriptome. The SNP discovery results revealed identification of 25909 homozygous and 23290 heterozygous cSNPs in the liver transcriptome of both dietary groups of Polish Landrace pigs. With regards to same or alternative SNPs alleles encoding amino acids regions, a total of 27141 synonymous cSNP and 5989 non-synonymous cSNPs were identified in liver transcriptome representing high PUFAs dietary group. However, a total of 15128 synonymous cSNPs and 3900 non-synonymous cSNPs were identified in liver transcriptome representing low PUFAs dietary groups of Polish Landrace pigs. The identification of single nucleotide variations (SNVs) representing substitutions of all four possibilities (A,T,G,C) were ranged 2872 to 6868 SNVs (high PUFAs) and 2574 to 3654 SNVs (low PUFAs) in the homozygous cSNPs and 2452 to 2678 SNVs (high PUFAs) and 2094 to 2230 SNVs (low PUFAs) in the heterozygous cSNPs of liver transcriptomes of Polish Landrace pigs, respectively.
Conclusions: Study concluded that identification of cSNPs dataset representing the liver transcriptome of Polish Landrace pigs fed with a control diet (low) and pigs fed with a PUFAs diet (high) may be helpful to develop a new set of genetic markers for trait-associated studies (viz., growth and metabolic traits) specific to Polish Landrace pig breed. Such cSNP markers eventually can be utilized in the genetic improvement of the pig production traits using the genome-wide association studies (GWAS) and to finally implement on marker assisted selection (MAS) and genomics selection (GS) program in active breeding population of Polish Landrace pigs in Poland.
References
Zwane AA, Schnabel RD, Hoff J, Choudhury A, Makgahlela ML, Maiwashe A, Van Marle-Koster E, Taylor JF. Genome-Wide SNP Discovery in Indigenous Cattle Breeds of South Africa. Front Genet. 2019;10: 273.
Wang W, Gan J, Fang D, Tang H, Wang H, Yi J, Fu M. Genome-wide SNP discovery and evaluation of genetic diversity among six Chinese indigenous cattle breeds in Sichuan. PLoS One. 2018;13:e0201534.
Pareek CS, Błaszczyk P, Dziuba P, Czarnik U, Fraser L, Sobiech P, Pierzchała M, Feng Y, Kadarmideen HN, Kumar D. Single nucleotide polymorphism discovery in bovine liver using RNA-seq technology. PLoS One. 2017;12:e0172687.
Pareek CS, Smoczyński R, Kadarmideen HN, Dziuba P, Błaszczyk P, Sikora M, Walendzik P, Grzybowski T, Pierzchała M, Horbańczuk J, Szostak A, Ogluszka M, Zwierzchowski L, Czarnik U, Fraser L, Sobiech P, Wąsowicz K, Gelfand B, Feng Y, Kumar D. Single Nucleotide Polymorphism Discovery in Bovine Pituitary Gland Using RNA-Seq Technology. PLoS One. 2016;11:e0161370.
Doran AG, Creevey CJ. Snpdat: easy and rapid annotation of results from de novo snp discovery projects for model and non-model organisms. BMC Bioinformatics. 2013;14:45.
DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, Del Angel G, Rivas MA, Hanna M, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43:491–498.
Stothard P, Choi JW, Basu U, Sumner-Thomson JM, Meng Y, Liao X, Moore SS. Whole genome resequencing of black Angus and Holstein cattle for SNP and CNV discovery. BMC Genomics. 2011;12:559.
Zhang Q, Zhao S, Chen H, Zhang L, Zhang L, Li F, Wang X. SNP discovery and haplotype analysis in the bovine PRKAA2 gene. Mol Biol Rep. 2011;38:1551-6.
Cánovas A, Rincon G, Islas-Trejo A, Wickramasinghe S, Medrano JF. SNP discovery in the bovine milk transcriptome using RNA-Seq technology. Mamm Genome. 2010;21:592-598.
Brookes AJ. The essence of SNPs. Gene. 1999;234:177–186.
Trick M, Long Y, Meng J, Bancroft I. Single nucleotide polymorphism (SNP) discovery in the polyploidy Brassica napus using Solexa transcriptome sequencing. Plant Biotechnol J. 2009;7:334–346.
Jehan T, Lakhanpaul S. Single nucleotide polymorphism (SNP) – methods and applications in plant genetics: a review. Indian J Biotechnol. 2006;5:435–459.
Hiremath PJ, Kumar A, Penmetsa RV, Farmer A, Schlueter JA, Chamarthi SK, Whaley AM, Carrasquilla-Garcia N, Gaur PM, Upadhyaya HD, et al. Large-scale development of cost-effective SNP marker assays for diversity assessment and genetic mapping in chickpea and comparative mapping in legumes. Plant Biotechnol J. 2012;10:1–17.
Garrido-Cardenas JA, Mesa-Valle C, Manzano-Agugliaro F. Trends in plant research using molecular markers. Planta. 2018;247:543–557.
Seeb JE, Carvalho G, Hauser L, Naish K, Roberts S, Seeb LW. Single-nucleotide polymorphism (SNP) discovery and applications of SNP genotyping in nonmodel organisms. Mol Ecol Resour. 2011;11:1–8.
Suh Y, Vijg J. SNP discovery in associating genetic variation with human disease phenotypes. Mutat Res. 2005;573:41-53.
Stouffer K, Nahorski M, Moreno P, Sarveswaran N, Menon D, Lee M, Geoffrey Woods C. Functional SNP allele discovery (fSNPd): an approach to find highly penetrant, environmental-triggered genotypes underlying complex human phenotypes. BMC Genomics. 2017;18:944.
Kumar S, Banks TW, Cloutier S. SNP discovery through next-generation sequencing and its applications. Int J Plant Genomics. 2012;2012:1–15.
Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM, Blaxter ML. Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet. 2011;12:499–510.
Pareek CS, Smoczynski R, Pierzchala M, Czarnik U, Tretyn A. From genotype to phenotype in bovine functional genomics. Brief Funct Genomics. 2011;10:165-171.
Quinn EM, Cormican P, Kenny EM, Hill M, Anney R, Gill M, Corvin AP, Morris DW. Development of strategies for SNP detection in RNA-Seq data: application to lymphoblastoid cell lines and evaluation using 1000 genomes data. PLoS One. 2013;8: e58815.
Ogłuszka M, Szostak A, Te Pas MFW, Poławska E, Urbański P, Blicharski T, Pareek CS, Juszczuk-Kubiak E, Dunkelberger JR, Horbańczuk JO, Pierzchała M. A porcine gluteus medius muscle genome-wide transcriptome analysis: dietary effects of omega-6 and omega-3 fatty acids on biological mechanisms. Genes Nutr. 2017;12: 4.
Huber LA, Hooda S, Fisher-Heffernan RE, Karrow NA, de Lange CFM. Effect of reducing the ratio of omega-6-to-omega-3 fatty acids in diets of low protein quality on nursery pig growth performance and immune response. J Anim Sci. 2018;96: 4348-4359.
Pierzchała M, Ogłuszka M, Goluch D, Poławska E, Blicharski T, Roszczyk A, Nawrocka A, Urbański P, Stepanow K, Ciepłoch A, Sachajko M, Szczepanek J, Juszczuk-Kubiak E, Szczepański A, Buszewska-Forajta M, Pareek CS. RNA-seq based SNP discovery in gluteus medius muscle of Polish Landrace pigs. Trans Res Vet Sci. 2019;2: 51-65.
D'Alessandro E, Giosa D, Sapienza I, Giuffrè L, Cigliano RA, Romeo O, Zumbo A. Whole genome SNPs discovery in Nero Siciliano pig. Genet Mol Biol. 2019;pii: S1415-47572019005021102.
Chomczynski P., Sacchi N. Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal. Biochem. 1987;162:156–159.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30: 2114-2120.
Wingett SW, Andrews S. FastQ Screen: A tool for multi-genome mapping and quality control. Version 2. F1000Res. 2018;7 :1338.
Pareek CS, Sachajko M, Szczepański A, Buszewska-Forajta M, Zarczynska K, Sobiech P, Juszczuk-Kubiak E, Shahzad Q, Lu YQ, Ogłuszka M, Poławska E, PierzchałaM. Quality control assessment of the RNA-Seq data generated from liver and pituitary transcriptome of Hereford bulls using StrandNGS software. Trans Res vet Sci. 2019;2: 9-22.
do Valle ÍF, Giampieri E, Simonetti G, Padella A, Manfrini M, Ferrari A, Papayannidis C, Zironi I, Garonzi M, Bernardi S, Delledonne M, Martinelli G, Remondini D, Castellani G. Optimized pipeline of MuTect and GATK tools to improve the detection of somatic single nucleotide polymorphisms in whole-exome sequencing data. BMC Bioinformatics. 2016;17: 341.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25: 2078–2079.
Downloads
Published
How to Cite
Issue
Section
License
Title, logo and layout of TR in VS are reserved trademarks of TR in VR.
Stats
Number of views and downloads: 897
Number of citations: 0
