Desire, Suzanne

Kranis, Andreas

Matika, Oswald

Mcintosh, Kirsten

Rae, A.

Tarsani, Eirini

Watson, Kellie

English

Aim of this study was to discover the genetic variants for average daily gain (ADG),body weight (BW) and primary feather length (PRF) traits in Pekin ducks. Using 1215 ducks and 45 k autosomal SNPs, multivariate and univariate genome-wide association analyses were performed. A total number of 50 markers were identified as common between all analyses and these SNPs were co-localized on chromosome 4. Current findings confirm previously reported findings for growth in ducks while proposing novel genetic loci associated with feather development and growth in duck

Edinburgh Research Explorer

     Edinburgh Research Explorer                                      Multivariate GWAS revealed a critical region on chromosome 4for developmental and feather growth traits in ducksCitation for published version:Tarsani, E, Desire, S, Matika, O, Mcintosh, K, Rae, A, Kranis, A & Watson, K 2022, 'Multivariate GWASrevealed a critical region on chromosome 4 for developmental and feather growth traits in ducks', WorldCongress on Genetics Applied Livestock Production, Rotterdam, Netherlands, 3/07/22 - 8/07/22. <https://www.wageningenacademic.com/pb-assets/wagen/WCGALP2022/71_001.pdf>Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Peer reviewed versionGeneral rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 24. Sep. 2022Multivariate GWAS revealed a critical region on chromosome 4 for developmental and feather growth traits in ducks  E.Tarsani1*, S.Desire1, O.Matika1, K.McIntosh1,2, A.Rae2, A.Kranis1, K.A.Watson1  1 The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, EH25 9RG, Midlothian, UK;2 Cherry Valley Farms Ltd., Grimsby Road., DN37 7DP, Laceby, UK;*etarsani@exseed.ed.ac.uk  Abstract Aim of this study was to discover the genetic variants for average daily gain (ADG), body weight (BW) and primary feather length (PRF) traits in Pekin ducks. Using 1215 ducks and 45 k autosomal SNPs, multivariate and univariate genome-wide association analyses were performed. A total number of 50 markers were identified as common between all analyses and these SNPs were co-localized on chromosome 4. Current findings confirm previously reported findings for growth in ducks while proposing novel genetic loci associated with feather development and growth in ducks.  Introduction Pekin duck has become the predominant breed used for meat, egg and feather production in the global duck industry. Current literature on duck genome-wide association studies (GWAS) is limited to univariate models conducted on quantitative traits such as growth and carcass traits (Deng et al. 2019, Zhou et al. 2018, Zhu et al. 2019), feeding behaviour (Zhu et al. 2019) and egg production (Liu et al. 2021) with  no multivariate GWAS reported so far in ducks. The objectives of the present study were to:  i) identify the genetic variants with pleiotropic effects on growth and primary feather length in ducks and ii) investigate the most plausible pleiotropic genes for the examined traits.   Materials & Methods Data and quality control Genotypic and phenotypic records were provided by Cherry Valley Farm (CVF). The available data consisted of 1215 Pekin ducks (n= 298 males and n=917 females) with phenotypic records on average daily gain (ADG) at 27 to 39 days of age, body weight (BW) at 39 days and primary feather length (PRF) at 39 days of age. Animals were genotyped using a 60K Affymetrix SNP array. After quality control (samples: call rate: 0.95, markers: call rate: 0.95, MAF: 0.05 and HWE Fisher exact test p-value:10-6) a total number of 1215 samples and 45,080 autosomal SNPs were retained for further analyses.   Univariate and multivariate association analyses Data were initially analyzed using linear mixed models fitting effects for sex and hatch as fixed effects and animal as random effects using ASReml (Gilmour et al.2015) software. Univariate association analyses using GEMMA (Zhou and Stephens 2012) software were conducted fitting the same models as those used in the variance component analyses. The traits analysed were ADG, BW and PRF.  The multivariate analysis, fitting a multivariate linear mixed model three traits (ADG, BW and PRF), subsequently performed in GEMMA software using same fixed effects as used before. For all analyses, SNPs with FDR p-value < 0.05 were considered as genome-wide significant. Estimation of the genomic heritability was further implemented via the realized marker information. Finally, the genetic correlation (rg) was estimated between bivariates i.e., ADG and BW, ADG and PRF, BW and PRF.  Positional candidate genes We used the SNPs that reached the significance threshold (FDR p-value) identified across all analyses to search for positional candidate genes. The search was restricted to SNPs within 50 kb downstream and upstream flanking regions of each significant marker for positional candidate genes using the NCBI database and the ZJU1.0 assembly. Due to the absence of a duck QTL database, we used the orthologues of chicken genes to search the documented role of the genes.  Results Estimation of genetic parameters The heritability estimates for additive effects were 0.28±0.04, 0.47±0.04 and 0.17±0.03 respectively for ADG, BW and PRF. The estimates of genetic correlation (rg) between the ADG and BW was high (0.77) with negative and moderate estimates (-0.21) between ADG and PRF, and low and negative estimates (-0.09) between BW and PRF.  Comparison of genome-wide significant SNPs found by multivariate and univariate analyses Figure 1 shows the profiles of the SNP p values (expressed as − log10 values) across the three GWAS. Specifically, a total number of 112 genome-wide significant SNPs (FDR p-value < 0.05) were detected by ADG univariate analysis and these SNPs were dispersed across nine autosomes (1-4, 10, 14, 15, 19 and 28). For BW univariate analysis, a total number of 138 SNPs across seven autosomes (1, 4, 15, 16, 20, 24 and 28) were found to reach genome-wide significance (FDR p-value < 0.05). For PRF univariate analysis, a total number of 62 genome-wide significant SNPs (FDR p-value) were detected and these markers were located on two autosomes (4 and 18). The multivariate analysis resulted in 119 genome-wide significant (FDR p-value < 0.05) SNPs across 10 autosomes (1-4, 11, 14, 15, 18, 19 and 28).   Figure 1. Manhattan plots showing the − log10(p values) of SNPs across the 29 autosomes for average daily gain (ADG) (a), body weight (BW) (b), primary feather length (PRF) and all traits, respectively. Plots were constructed using the gaston R package.  Comparison of SNP signals across the four association analyses resulted in fifty common significant markers on chromosome 4. Note that the 50 significant SNPs residing on chromosome 4 were co-localized in a region spanning 3,498,371 bp (57,648,845 - 61,147,216 bp) with high LD (r2) levels (r2:0.40-1). From the 50 SNPs, n=28 SNPs were located within 8 genes (PPARGC1A, KCNIP4, PACRGL, SLIT2, LDB2, FBXL5, CC2D2A and CPEB2) while the total number of positional candidate genes was 23 (DHX15, PPARGC1A, ADGRA3, KCNIP4, PACRGL, SLIT2, LCORL, NCAPG, FAM184B, MED28, LAP3, CLRN2, QDPR, LDB2, TAPT1, PROM1, FGFBP2, FGFBP1, BST1, FBXL5, CC2D2A, C1QTNF7 and CPEB2).  Discussion Multivariate genotype-phenotype methods are known to increase the statistical power to detect marker-trait associations in the case of shared genetic basis between traits. The present study exploited a multivariate GWAS to identify candidate pleiotropic genetic loci for the ADG, BW and PRF traits in ducks. Our results identified two genes (FGFBP1 and FGFBP2) which are fibroblast growth factors that have been reported to play critical role in embryogenesis and positive regulation of hair and feather development (Mandler and Neubüser 2004). Apart from these candidate pleiotropic genes, our study also supports findings by previous studies for growth traits in ducks. An interesting example is the PPARGC1A gene reported to play a role in promoting fatty acid oxidation of breast muscle in ducks (Fan et al. 2020). The other gene, LDB2  has previously been reported as a candidate gene for body size and carcass traits in ducks (Deng et al. 2019). The KCNIP4 gene, a member of potassium channel-interacting proteins has extensive physiological functions and insulin secretion was identified as an important QTL region associated with growth traits in chickens (Cha et al. 2021). SLIT2 has also been reported to be strongly associated with back yield, feet weight, fat and muscle weight traits in chickens (Cha et al. 2021). Several previous GWAS in chickens reported that chromosome 4 is a critical region influencing growth traits (Xu et al. 2013). We identified large QTL on chromosome 4 that has markers significantly associated with growth in ducks. Current findings by both univariate and multivariate GWA analyses could dissect the duck genetic architecture and have potential use in breeding programmes.  References Deng MT, Zhu F, Yang YZ, Yang FX, Hao JP, et al. (2019). BMC Genomics. 20: 1–13. https://doi.org/10.1186/s12864-018-5379-1. Zhu F, Cheng SR, Yang YZ, Hao JP, Yang FX, et al. (2019). Front Genet. 10: 702. https://doi.org/10.3389/fgene.2019.00702. Zhou Z, Li M, Cheng H, Fan W, Yuan Z, et al. (2018). Nat Commun 2018 91. 9: 1–10. https://doi.org/10.1038/s41467-018-04868-4. Liu H, Zhou Z, Hu J, Guo Z, Xu Y, et al. (2021). Anim Genet. 52: 536–541. https://doi.org/10.1111/age.13063. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, et al. (2007). Am J Hum Genet. 81: 559–75. https://doi.org/10.1086/519795. Gilmour, A. R., Gogel, B. J., Cullis, B. R., Welham, S. J. and Thompson, R. (2015). Structural Specification, VSN International Ltd, Hemel Hempstead,HP1 1ES, UK www.vsni.co.uk Zhou X, Stephens M. (2012). Nat Genet. 44: 821–824. https://doi.org/10.1038/ng.2310. Mandler M, Neubüser A. (2004). Development. 131: 3333–3343. https://doi.org/10.1242/dev.01203. Fan W, Liu W, Liu H, Meng Q, Xu Y, et al. (2020). BMC Genomics. 21: 1–15. https://doi.org/10.1186/s12864-020-6482-7. Deng MT, Zhu F, Yang YZ, Yang FX, Hao JP, et al. (2019). BMC Genomics. 2019;20. https://doi.org/10.1186/s12864-018-5379-1. Cha J, Choo H, Srikanth K, Lee SH, Son JW, et al. (2021). Genes (Basel). 12: 1170. https://doi.org/10.3390/genes12081170. Xu Z, Nie Q, Zhang X. (2013). Curr Genomics. 14: 137–146. https://doi.org/10.2174/1389202911314020006.  

Multivariate GWAS revealed a critical region on chromosome 4 for developmental and feather growth traits in ducks

https://www.research.ed.ac.uk/files/290960775/71_001.pdf

Multivariate GWAS revealed a critical region on chromosome 4 for developmental and feather growth traits in ducks

Abstract

Similar works

Full text

Available Versions

Edinburgh Research Explorer