Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-28T00:53:53.576Z Has data issue: false hasContentIssue false
  • English
  • Français

Use of single-step genome-wide association studies for prospecting genomic regions related to milk production and milk quality of buffalo

Published online by Cambridge University Press:  13 November 2018

Camila da Costa Barros ,
Daniel Jordan de Abreu Santos ,
Rusbel Raul Aspilcueta-Borquis ,
Gregório Miguel Ferreira de Camargo ,
Francisco Ribeiro de Araújo Neto  and
Humberto Tonhati
Camila da Costa Barros*
Affiliation:
São Paulo State University (FCAV/UNESP), Jaboticabal, São Paulo, Brazil
Daniel Jordan de Abreu Santos
Affiliation:
São Paulo State University (FCAV/UNESP), Jaboticabal, São Paulo, Brazil
Rusbel Raul Aspilcueta-Borquis
Affiliation:
Universidade Federal da Grande Dourados, Dourados, Mato Grosso do Sul, Brazil
Gregório Miguel Ferreira de Camargo
Affiliation:
Universidade Federal da Bahia, Salvador, Bahia, Brazil
Francisco Ribeiro de Araújo Neto
Affiliation:
Instituto Federal Goiano, Rio Verde, Goias, Brazil
Humberto Tonhati
Affiliation:
São Paulo State University (FCAV/UNESP), Jaboticabal, São Paulo, Brazil
*
*For correspondence; e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The aim of this research communication was to identify chromosome regions and genes that could be related to milk yield (MY), milk fat (%F) and protein percentage (%P) in Brazilian buffalo cows using information from genotyped and non-genotyped animals. We used the 90 K Axiom® Buffalo Genotyping array. A repeatability model was used. An iterative process was performed to calculate the weights of markers as a function of the squared effects of Single Nucleotide Polymorphism (SNP) and allele frequencies. The 10 SNPs with the largest effects for MY, %F and %P were studied and they explained 7·48, 9·94 and 6·56% of the genetic variance, respectively. These regions harbor genes with biological functions that could be related to the traits analyzed. The identification of such regions and genes will contribute to a better understanding of their influence on milk production and milk quality traits of buffaloes.

Keywords

BuffaloesGWASsingle-stepSNP
Type
Research Article
Information
Journal of Dairy Research , Volume 85 , Issue 4 , November 2018 , pp. 402 - 406
Copyright
Copyright © Hannah Dairy Research Foundation 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aguilar, I, Misztal, I, Johnson, D, Legarra, A, Tsuruta, S & Lawlor, T 2010 Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score1. Journal of Dairy Science 93 743–752Google Scholar
De Camargo, GMF, Aspilcueta-Borquis, RR, Fortes, MRS, Porto-Neto, R, Cardoso, DF, Santos, DJA, Lehnert, SA, Reverter, A, Moore, SS & Tonhati, H 2015 Prospecting major genes in dairy buffaloes. BMC Genomics 16 1–14Google Scholar
El-Halawany, N, Abdel-Shafy, H, Shawky, AA, Abdel-Latif, MA, Al-Tohamy, AFM & El-Moneim, OMA 2017 Genome-wide association study or milk production in Egyptian buffalo. Livestock Science 198 10–16Google Scholar
Iamartino, D, Williams, JL, Sonstegard, T, Reecy, J, Van Tassell, C, Nicollazi, EL, Biffani, S, Biscarini, F, Schroeder, S, de Oliveira, DAA, Colleta, A, Garcia, JF, Ali, A, Ramunno, A, Pasquariello, R, Drummond, MG, Bastianetto, E, Fritz, E & Knoltes, J 2013 The buffalo genome and the application of genomics in animal management and improvement. Buffalo Bulletin 32 151–158Google Scholar
Kumar, S, Rao, N & Ge, R 2012 Emerging roles of ADAMTSS in angiogenesis and cancer. Cancers 4 1252–1299Google Scholar
Liu, JJ, Liang, AX, Camanille, G, Plastow, G, Zhang, C, Wang, Z, Salzano, A, Gasparrini, B, Cassandro, M & Yang, LG 2018 Genome-wide association studies to identify quantitative trait loci affecting milk production traits in water buffalo. Journal of Dairy Science 101 433–444Google Scholar
Misztal, I, Aguilar, I, Legarra, A & Vitezica, Z 2002 BLUPF90 family of programs University of Georgia. http://nce.ads.uga.edu/~ignacy/numpub/blupf90/. Accessed November 30, 2016Google Scholar
Utsunomiya, YT, Carmo, AS, Neves, HHR, Carvalheiro, R, Matos, MC, Zavarez, LB, Ito, PKRK, O'Brien, AMP, Solkner, J, Porto-Neto, LR, Schenkel, FS, McEwan, J, Cole, JB, da Silva, MVGB, Van Tassell, CP, Sonstegard, TS & Garcia, JF 2013 Genome-wide mapping of loci explaining variance in scrotal circumference in Nelore Cattle. PLoS ONE 9 e88561Google Scholar
VanRaden, P. M. 2008 Efficient methods to compute genomic predictions. Journal of Dairy Science 91 4414–4423Google Scholar
Wang, H, Misztal, I, Aguilar, I, Legarra, A & Muir, WM 2012 Genome-wide association mapping including phenotypes from relatives without genotypes. Genetics Research 94 73–83Google 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. Frontiers in Genetics 5 1–10Google Scholar
Zhang, Z, Liu, J, Ding, X, Bijma, P, de Koning, DJ & Zhang, Q 2010 Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix. PLoS ONE 5 1–8Google Scholar
Zielke, LG, Bortfeldt, RH, Tetens, J & Brockmann, GA 2011 BDNF contributes to the genetic variance of milk fat yield in German Holstein cattle. Frontiers in Genetics 2 1–8Google Scholar
Zimin, AV, Delcher, AL, Florea, L, Kelley, DR, Schatz, MC, Puiu, D, Hanrahan, F, Pertea, G, Van Tassell, CP, Sonstegard, TS, Marçais, G, Roberts, M, Subramanian, P, Yorke, JA, Salzberg, SL 2009 A whole-genome assembly of the domestic cow, Bos taurus. Genome Biology 10 R42Google Scholar
Supplementary material: PDF

da Costa Barros et al. supplementary material

da Costa Barros et al. supplementary material 1

Download da Costa Barros et al. supplementary material(PDF)
PDF 562.8 KB