Genome-wide association analysis for body weight identifies candidate genes related to development and metabolism in rainbow trout (Oncorhynchus mykiss)
Growth is one of the most important traits from both a physiological and economic perspective in aquaculture species. Thus, identifying the genomic regions and genes underpinning genetic variation for this trait is of particular interest in several fish species, including rainbow trout. In this work, we perform a genome-wide association study (GWAS) to identify the genomic regions associated with body weight at tagging (BWT) and at 18 months (BW18M) using a dense SNP panel (57 k) and 4596 genotyped rainbow trout from 105 full-sib families belonging to a Chilean breeding population. Analysis was performed by means of single-step GBLUP approach. Genetic variance explained by 20 adjacent SNP windows across the whole genome is reported. To further explore candidate genes, we focused on windows that explained the highest proportion of genetic variance in the top 10 chromosomes for each trait. The main window from the top 10 chromosomes was explored by BLAST using the first and last SNP position of each window to determine the target nucleotide sequence. As expected, the percentage of genetic variance explained by windows was relatively low, due to the polygenic nature of body weight. The most important genomic region for BWT and BW18M were located on chromosomes 15 and 24 and they explained 2.14% and 3.02% of the genetic variance for each trait, respectively. Candidate genes including several growth factors, genes involved in development of skeletal muscle and bone tissue and nutrient metabolism were identified within the associated regions for both traits BWT and BW18M. These results indicate that body weight is polygenic in nature in rainbow trout, with the most important loci explaining as much as 3% of the genetic variance for the trait. The genes identified here represent good candidates for further functional validation to uncover biological mechanisms underlying variation for growth in rainbow trout.
KeywordsCandidate genes Growth GWAS SNP Muscle and bone development Nutrient metabolism
We thank Aguas Claras SA. For providing the fish used in this study.
This study was funded by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP processes numbers 2017/00123-6) and FONDECYT REGULAR N° 1171720. JMY is supported by Nucleo Milenio INVASAL funded by Chile’s government program, Iniciativa Cientifica Milenio from Ministerio de Economia, Fomento y Turismo.
Compliance with ethical standards
Conflict of interest
Rafael Vilhena Reis Neto declares that he has no conflict of interest. Grazyella Massako Yoshida declares that he has no conflict of interest. Jean Paul Lhorente was hired by Aquainnovo S.A. during the course of the study. José Manuel Yáñez declares that he has no conflict of interest.
All applicable institutional guidelines for the care and use of animals were followed. All the experimental and sampling procedures were approved by the Comité de Bioética Animal from the Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile (Certificate N 17,041-VET-UCH).
- Aljada A, Ghanim H, Mohanty P, Kapur N, Dandona P (2002) Insulin inhibits the pro-inflammatory transcription factor early growth response gene-1 (Egr)-1 expression in mononuclear cells (MNC) and reduces plasma tissue factor (TF) and plasminogen activator inhibitor-1 (PAI-1) concentrations. J Clin Endocrinol Metab 87:1419–1422CrossRefGoogle Scholar
- Barría A, Christensen KA, Yoshida GM, Correa K, Jedlicki A, Lhorente JP, Davidson WS, Yáñez JM (2018) Genomic predictions and genome-wide association study of resistance against Piscirickettsia salmonis in coho salmon (Oncorhynchus kisutch) using ddRAD sequencing. G3: Genes, Genomes, Genetics 8:1183–1194Google Scholar
- Chains ML (1982) Cloning and Characterization of cDNA Sequences Corresponding to. New York 257:11078–11086Google Scholar
- Chile U (2016) http://www.subpesca.cl/institucional/602/w3-channel
- Correa K, Lhorente JP, López ME, Bassini L, Naswa S, Deeb N, Genova A, Maass A, Davidson WS, Yáñez JM (2015) Genome-wide association analysis reveals loci associated with resistance against Piscirickettsia salmonis in two Atlantic salmon (Salmo salar L.) chromosomes. BMC Genom 16:854CrossRefGoogle Scholar
- Crawford DPostScript Enhanced guide. AriadneMseUiucEdu 0–1Google Scholar
- Flores-Mara R, Rodríguez FH, Bangera R, Lhorente JP, Neira R, Newman S, Yáñez JM (2017) Resistance against infectious pancreatic necrosis exhibits significant genetic variation and is not genetically correlated with harvest weight in rainbow trout (Oncorhynchus mykiss). Aquaculture 479:155–160CrossRefGoogle Scholar
- Ghosh M, Cho HW, Park JW, Choi JY, Chung YH, Sharma N, Singh AK, Kim NE, Mongre RK, Huynh D, Jiao ZJ, Do KT, Lee HK, Song KD, Cho BW, Jeong D (2016) Comparative transcriptomic analyses by RNA-seq to elucidate differentially expressed genes in the muscle of korean thoroughbred horses. Appl Biochem Biotechnol 180:588–608CrossRefGoogle Scholar
- Gutierrez AP, Yáñez JM, Fukui S, Fukui S, Swift B, Davidson WS (2015) Genome-Wide Association Study (GWAS) for Growth Rate and Age at Sexual Maturation in Atlantic Salmon (Salmo salar). PLoS One 10:1371Google Scholar
- FAO (2016) FAO fisheries & aquaculture—home. In: State World Fish. AquacGoogle Scholar
- Houston RD, Taggart JB, Cézard T, Bekaert M, Lowe NR, Downing A, Talbot R, Bishop SC, Archibald AL, Bron JE, Penman DJ, Davassi A, Brew F, Tinch AE, Gharbi K, Hamilto A (2014) Development and validation of a high density SNP genotyping array for Atlantic salmon (Salmo salar). BMC Genom 15:90CrossRefGoogle Scholar
- Kim H, Ingermann AR, Tsubaki J, Twigg SM, Walker GE, Oh Y (2004) Insulin-like growth factor-binding protein 3 induces caspase-dependent apoptosis through a death receptor-mediated pathway in MCF-7 human breast cancer cells insulin-like growth factor-binding protein 3 induces caspase-dependent human breast cancer cells. Cancer Res 64(6):2229–2237CrossRefGoogle Scholar
- Lim JH, Jono H, Komatsu K, Woo CH, Lee J, Miyata M, Matsuno T, Xu X, Huang Y, Zhang W, Park SH, Kim YI, Choi YD, Shen H, Heo KS, Xu H, Bourne P, Koga T, Xu H, Yan C, Wang B, Chen LF, Feng XH, Li JD (2012) CYLD negatively regulates transforming growth factor-β-signalling via deubiquitinating Akt. Nat Commun 3:712–771CrossRefGoogle Scholar
- Macqueen DJ, Primmer CR, Houston RD, Nowak BF, Bernatchez L, Bergseth S, Davidson WS, Gallardo-Escárate C, Goldammer T, Guiguen Y, Iturra P, Kijas JW, Koop BF, Lien S, Maass A, Martin SAM, McGinnity P, Montecino M, Naish KA, Nichols KM, Ólafsson K, Omholt SW, Palti Y, Plastow GS, Rexroad CE, Rise ML, Ritchie RJ, Sandve SR, Schulte PM, Tello A, Vidal R, Vik JO, Wargelius A, Yáñez JM (2017) Functional annotation of all salmonid genomes (FAASG): an international initiative supporting future salmonid research, conservation and aquaculture. BMC Genom 18:484CrossRefGoogle Scholar
- McKee KK, Tan CP, Palyha OC, Liu J, Feighner SD, Hreniuk DL, Smith RG, Howard AD, Van der Ploeg LH (1997) Cloning and characterization of two human G protein-coupled receptor genes (GPR38 and GPR39) related to the growth hormone secretagogue and neurotensin receptors. Genomics 46:426–434CrossRefGoogle Scholar
- Mischak H, Goodnight JA, Kolch W, Martiny-Baron G, Schaechtlee C, Kazanietzll MG, Blumbergll PM, Pierce JH, Mushinski J F (1993) Overexpression of protein kinase C-delta and -epsilon in NIH 3T3 cells induces opposite effects on growth, morphology, anchorage dependence, and tumorigenicity. J Biol Chem 268:6090–6096PubMedGoogle Scholar
- Sampath TK, Maliakal JC, Hauschka PV, Jones WK, Sasak H, Tucker RF, White KH, Coughlin JE, Tucker MM, Pang RH (1992) Recombinant human osteogenic protein-1 (hOP-1) induces new bone formation in vivo with a specific activity comparable with natural bovine osteogenic protein and stimulates osteoblast proliferation and differentiation in vitro. J Biol Chem 267:20352–20362PubMedGoogle Scholar
- SERNAPESCA (2016) Informe Sectorial de Pesca y Acuicultura—Año (online) Google Scholar
- Turner SD (2014) qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. https://doi.org/10.1101/005165
- Vallejo RL, Liu S, Gao G, Fragomeni BO, Hernandez AG, Leeds TD, Parsons JE, Martin KE, Evenhuis JP, Welch TJ, Wiens GD, Palti Y (2017) Similar genetic architecture with shared and unique quantitative trait loci for bacterial cold water disease resistance in two rainbow trout breeding populations. Front Genet 8:156CrossRefGoogle Scholar
- Wang H, Misztal I, Aguilar I, Legarra A, Fernando RL, Vitezica Z, Okimoto R, Wing T, Hawken R, Muir WM (2014) Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens. Front Genet 20:5:134Google Scholar
- Wang L, Wan ZY, Bai B, Huang SQ, Chua E, Lee M, Pang HY, Wen YF, Liu P, Liu F, Sun F, Lin G, Ye BQ, Yue GH (2015) Construction of a high-density linkage map and fine mapping of QTL for growth in Asian seabass. Sci Rep 5:1–10Google Scholar
- Yáñez JM, Naswa S, López ME, Bassini L, Correa K, Gilbey J, Bernatchez L, Norris A, Neira R, Lhorente JP, Schnable PS, Newman S, Mileham A, Deeb N, Di Genova A, Maass A (2016) Genomewide single nucleotide polymorphism discovery in Atlantic salmon (Salmo salar): validation in wild and farmed American and European populations. Mol Ecol Resour 16:1002–1011CrossRefGoogle Scholar
- Yoshida GM, Carvalheiro R, Rodríguez FH, Lhorente JP, Yáñez JM (2018b) Single-step genomic evaluation improves accuracy of breeding value predictions for resistance to infectious pancreatic necrosis virus in rainbow trout. Genomics. https://doi.org/10.1016/j.ygeno.2018.01.008 CrossRefPubMedGoogle Scholar