ANDRADE, S. C. S.

BOSCHIERO, C.

BURT, D. W.

COUTINHO, L. L.

GASPARIN, G.

GHEYAS, A. A.

GODOY, T. F.

LEDUR, M. C.

MONTENEGRO, H.

MOREIRA, G. C. M.

PADUAN, M.

Portuguese

Repository Open Access to Scientific Information from Embrapa

 Proceedings, 10th World Congress of Genetics Applied to Livestock Production  SNP Discovery in a QTL Region Associated With Breast Muscle Deposition on Chicken Chromosome 2 C. Boschiero1*, T.F. Godoy1*, G.C.M. Moreira1, A.A. Gheyas2, G. Gasparin1, M. Paduan1,  S.C.S. Andrade1, H. Montenegro1, D.W. Burt2, M.C. Ledur3 and L.L. Coutinho1 1University of Sao Paulo (ESALQ) - Brazil, 2The Roslin Institute, University of Edinburgh, UK,  3EMBRAPA Swine and Poultry National Research Center – Brazil. *Equal contributors.   ABSTRACT: Eighteen chickens of two experimental lines (broiler and layer) from EMBRAPA were sequenced with Illumina platform. SNPs were detected in a QTL region on chicken chromosome 2 (between microsatellite markers MCW0185 and MCW0264) which was previously associat-ed with breast muscle deposition. Initially, 722,832 SNPs were identified for the 18 chickens together, and 77% of SNPs were retained after quality filtration. Annotation was performed for the unique variants (85,765 SNPs), and from exonic regions 357 SNPs were classified as synonymous, 153 as non-synonymous and 3 as stopgain. A total of 37 genes were analyzed, and three of them were related to muscle development (DTNA, RB1CC1 and C-MOS). We also detected 15 amino-acid changing SNPs which were predicted to be non-tolerated and those were harbored by eight genes: MEP1B, PRKDC, NSMAF, TRAPPC8, SDR16C5, CHD7, ST18 and RB1CC1. Those loss-of-function mutations, and the exonic variants present in genes related to muscle deposition can be considered candidate variants for further studies in chickens.  Keywords: breast muscle; chicken; SNP   Introduction  The breast muscle	  is	  the	  most valuable part of the chicken carcass. To understand the molecular basis of this complex trait, analysis of Quantitative Trait Loci (QTL) has often been used as the initial step, but these QTL regions can be mapped over a large region consisting of millions of bases and containing many genes (Ahsan et al. (2013)).   Recent advances in whole genome sequencing al-low detection of thousands of single nucleotide polymor-phisms (SNPs), allowing characterization of the genetic variants to identify potentially functional mutations.  According to the QTL database (AnimalQTLdb 2014) 119 QTLs were associated with breast muscle. In a specific region between markers MCW0185 and MCW0264 on chromosome 2 (GGA2), Baron et al. (2010) mapped a QTL for breast percentage in a Brazilian F2 Chicken Re-source Population. QTLs for the same trait were also mapped in other chicken populations on GGA2. Nadaf et al. (2009) mapped one QTL associated with breast weight in the same region (MCW0185-MCW0264). Other studies mapped QTLs for the same trait in nearby regions (Nassar et al. (2012); Tercic et al. (2009); Ikeobi et al. (2004)).  The aim of this study was to identify SNPs from next generation sequencing data on a QTL region on chick-en chromosome 2 (105,848,755-112,648,761 bp), which was previously associated with breast muscle (Baron et al. (2010)) and to suggest candidate SNPs in genes associated with muscle deposition.   Materials and Methods  Experimental population. We sequenced 18 chickens from two parental experimental lines developed by EMBRAPA Swine and Poultry National Research Cen-ter, nine from the broiler line (called TT) and nine from the layer line (called CC). The sequenced chickens were the ones used in reciprocal crosses to generate the EMBRAPA F2 Chicken Resource Population for QTL mapping studies. More details have previously been described by Rosário et al. (2009).  QTL region. The target region selected for this study on GGA2 was based on the previous study of Baron et al. (2010), who mapped in the EMBRAPA F2 Chicken Resource Population, one 5% genome-wide significant QTL (F-ratio=15.3) associated with the percentage of breast by line-cross analysis. This QTL was mapped between mi-crosatellite markers MCW0185 and MCW0264 and ex-plained 5.14% of the phenotypic variance. The target region between MCW0185 and MCW0264 markers was defined according to the last chicken reference genome provided by NCBI (105,848,755-112,648,761 bp, Gallus_gallus-4.0), and it contains 66 genes (Biomart, 2013).  Sequencing. Genomic DNA from blood samples of 18 chickens was extracted by Proteinase K (Promega). The library preparation was performed according to Nex-tera™ protocol DNA Sample Preparation kit (Illumina). The sequencing was performed with the Illumina platform (HiSeq1000) using TruSeq SBS kit V3 cBot-HS with 200 cycles (Illumina). The initial sequencing coverage was 18X per chicken and read length was 2 x 100 nucleotides.  SNP detection and annotation. Quality trimming of the reads was performed selecting reads with quality ≥24 and minimum length of 65 bp using the SeqyClean tool (v. 1.3.12). The alignment of the reads of each chicken was performed against the latest reference chicken genome (Gallus_gallus-4.0, NCBI) with the Bowtie2 software (v. 2.1.0, Langmead and Salzberg (2012)).  SNP detection were performed in the target region on GGA2 (105,848,755-112,648,761 bp) with the SAMtools package (v. 0.1.19, Li et al. (2009)) using the mpileup option with mapping and base qualities ≥20. After the initial variant calling, a stringent filtration was per-formed to minimize false positive SNPs. The filtration was based on four criteria: quality score (≥30), minimum cover-age (≥5), maximum coverage (≤mean coverage plus 3 standard deviation), and variant supported by both strands.   The functional annotation of unique SNPs (from 18 chickens) was conducted by ANNOVAR (v. 2013Aug23, Wang et al. (2010)) with default parameters using the gene annotation database from ENSEMBL release 71. We used DAVID Gene Functional Classification tool (v. 6.7, Huang et al. (2009)) with default parameters to identify genes involved in metabolic pathways. Among the identified genes, we selected those which were related to muscle development and studied their variants.  The functional effects of the exonic SNPs that caused changes in amino acids (non-synonymous and stop-gain/loss) were predicted with the VEP tool (Variant Effect Predictor, Mclaren et al. (2010)). VEP software predicts SIFT (Sorting Intolerant from Tolerant) scores, which indi-cates whether the SNP is tolerated or non-tolerated (≤0.05) (Ng and Henikoff (2003)).  Results and Discussion  Sequencing and alignment. A total of 2,785,354,494 short reads were obtained, and after the qual-ity trimming ~76.5% were retained. The retained reads were aligned against the chicken genome reference (Gal-lus_gallus-4.0). The depth of sequencing coverage for the 18 chicken ranged from 5.4X to 15.6X with an average of 10.6X for the target region studied.   SNPs detection and filtration. In the target region on GGA2 (MCW0185-MCW0264), we initially identified 722,832 SNPs for the 18 chickens. In total, we detected 341,816 SNPs from the broiler line and 381,016 SNPs from the layer line (Table 1). After filtration, 77% of the SNPs (n=558,767) were retained. In Table 1 we can see the num-ber of filtered SNPs for broiler and layer lines, and also the unique number (no duplicate SNP between the lines). More SNPs were identified in the broiler than in the layer line. The average density of variants in the target QTL region for the 18 chickens was of 4.6 SNPs/kb, and these results are consistent with the global average rate found by Wong et al. (2004) of 5 SNPs/kb. We also identified the number of ho-mozygous (presence of non-reference alleles only) and het-erozygous variants in each of the 18 chickens. Except for three individuals, in all other cases from both lines, we de-tected more homozygous than heterozygous SNPs.  Table 1. Number of SNPs initially identified and after filtering for layer (LY) and broiler (BR) lines in target QTL region.  Initially detected after filtering Lines SNPs unique SNPs SNPs unique SNPs LY 341,816 62,212 271,070 56,152 BR 381,016 81,514 287,697 73,666 LY and BR 722,832 94,674 558,767 85,765 Average 40,157 5,259 31,043 4,764   Functional annotation and genes related to muscle deposition. Annotation was performed for the 85,765 unique SNPs (Table 1) against the genes from EN-SEMBL by ANNOVAR software. The SNPs were classi-fied in genomic regions, such as: intergenic (~63%), intron-ic (~33%), exonic (~0.6%), and other regions (splicing, UTR, etc). Only 0.42% of the SNPs were classified as syn-onymous, and 0.18% as non-synonymous (Table 2).   Table 2. Annotation of filtered unique SNPs for 18 chickens. Variants Total of SNPs SNPs %  All  85,765 100 Intergenic 54,282 63.29 Intronic 28,687 33.45 Exonic 513 0.60 Splicing 1 0.001 ncRNA 16 0.02 UTR5 78 0.09 UTR3 544 0.63 Upstream 880 1.03 Downstream 761 0.89 Exonic   Synonymous 357 0.42 Nonsynonymous 153 0.18 Stopgain 3 0.003 The percentage was calculated based on the total annotated SNPs.   Mutations in coding regions were further analyzed using the VEP tool to predict the SIFT scores which indi-cates if those are functionally tolerated or not. Not tolerated SNPs are important because they are predicted to alter the protein biological function.   Three out of 37 genes on the target region with ex-onic variants (non-synonymous and stopgain SNPs) were related to muscle development (DTNA, RB1CC1 and C-MOS), and in all of them nine non-synonymous SNPs were identified. The DTNA gene is a member of the dystrophin family and is involved in the maintenance of homeostasis and neuromuscular junction (Kapsa et al. (2003)). Four tolerated non-synonymous SNPs were identified in this gene, and among them, three were present only in the broil-er line, which has higher muscle deposition. The RB1CC1 gene is involved in muscle development because it is asso-ciated with cell proliferation and growth (Nishimura et al. (2011)). Three non-synonymous SNPs were detected in this gene. SNP rs312375447 (tolerated) was detected in both lines. SNP c.3318C>G (novel and tolerated) was present only in the layer line, and SNP rs314633726 (non-tolerated) was identified in homozygosity in only one chicken of the layer line. The C-MOS gene generates transcripts highly expressed in reproductive tissues (Yew et al. (1993)). Two non-synonymous tolerated SNPs were identified in this gene. A novel SNP (c.904G>A) and SNP rs80770298 were detected only in the broiler line.  Prediction of SIFT scores on 153 non-synonymous and three stopgain SNPs present in the genes of the target region were also performed. SIFT prediction was possible for 145 SNPs, and 15 were classified as not tolerated. Be-sides the non-tolerated SNP identified in RB1CC1 gene, the other 14 non-tolerated SNPs were identified in seven differ-ent genes involved in various important functions (MEP1B, PRKDC, NSMAF, TRAPPC8, SDR16C5, CHD7 and ST18).   Among the non-tolerated SNPs, seven were identi-fied in both lines, five were detected exclusively in the broiler line, and two were present exclusively in the layer line. Although these seven genes were not directly related to muscle development, non-tolerated variants in these genes need to be better investigated. Muscle development is a polygenic trait, and multiple functional mutations from different genes might explain the phenotypic variance.  Conclusion  A large number of SNPs were detected in a QTL region associated with breast muscle in chickens, many of which are novel. By annotating the variants and predicting their functional effects, we suggest a list of candidate func-tional mutations for future studies. Also, SNPs were identi-fied in genes related to muscle deposition (DTNA, RB1CC1 and C-MOS), including non-tolerated and novel SNPs. The-se candidate variants should be further investigated, used in association studies, and validated in other chicken popula-tions.             Acknowledgements  T.F. Godoy, G.C. Moreira, S.C.S. Andrade and H. Montenegro received fellowships from São Paulo Research Foundation – FAPESP (2012/15294-7, 2012/14965-5, 2012/21784-7 and 2012/02906-4). C. Boschiero received a fellowship from the program Science Without Borders - National Council for Scientific and Technological Devel-opment (CNPq, grant 370620/2013-5). L.L. Coutinho is recipient of productivity fellowship from CNPq. We are indebted to M. Brandão for access to the computational resources of the Thunder Cluster.   Literature Cited  Ahsan, M., Li, X., Lundberg, A. E. et al. (2013). Front. Genet. 4:1-8. AnimalQTLdb. http://www.animalgenome.org/cgi-bin/QTLdb/index  Accessed on February 2014. Baron, E. E., Moura, A. S. A. M. T., Ledur, M. C. et al. (2010). Anim. Genet. 42:117-124. Biomart.  http://www.central.biomart.org/  Accessed on November 2013. Huang D.W., Sherman B.T., Lempicki R.A. (2009). Nat. Protoc. 4: 44-57.  Ikeobi, C. O. N., Woolliams, J. A., Morrice, D. R. et al. (2004). Livest. Prod. Sci.87: 143-151.  Kapsa, R., Kornberg A. J., Byrne, E. (2003) Lancet Neurol. 2: 299-310.  Langmead, B., Salzberg, S. L. (2012). Nat. Methods 9: 357-359.  Li, H., Handsaker, B., Wysoker, A. et al. (2009). Bioinformatics 25:2078–9. Mclaren, W., Pritchard, B., Rios, D. et al. (2010). Bioinformatics 16: 2069–2070. Nadaf, J., Pitel, F., Gilbert, H. et al. (2009). Physiol. Genomics 38: 241–249. Nassar, M.K., Goraga, Z.S., Brockmann, G.A. (2012). Anim. Genet. 43: 739-745. Ng, P.C., Henikoff, S. (2003). Nucleic Acids Res. 13:3812–3814. Nishimura, I., Chano, T., Kita, H. et al. (2011). Journal Biol. Chem. 286:43925-43932. Rosário, M. F., Ledur, M. C., Moura, A. S. A. M. T. et al. (2009). Sci. Agri. 2:150–158.  Tercic, D., Holcman, A., Dovc, P. (2009). Anim. Genet. 40:743-748. Wang, G.K., Li M., Hakonarson, H. (2010). Nucleic Acids Re. 38:164. Wong, G.K.S., Liu, B., Wang, J. et al. (2004). Nature 432:717-722.  Yew, N., Strobel, M., Vande Woude, G.F. (1993). Curr. Opin. Genet. Dev. 3:19-25.  

SNP discovery in a QTL region associated with breast muscle deposition on chicken chromosome 2.

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SNP discovery in a QTL region associated with breast muscle deposition on chicken chromosome 2.

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