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Genomic prediction and association analyses of energy corrected milk yield in dairy cows

Year 2021, Volume: 68 Issue: 4, 383 - 388, 27.09.2021

Burak Karacaören

https://doi.org/10.33988/auvfd.775597

Abstract

Energy balance plays a critical role in the maintenance of metabolism for producing milk yield (MY) in dairy cows. In recent years, there has been increasing interest in genetic and genomic analyses of MY. In contrast to MY there is much less information about genomic evaluation of energy corrected milk yield (ECMY). The purpose of this paper is to detect associated single nucleotide polymorphisms (SNPs) with ECMY and genomic prediction (GP) of ECMY using different genomic models with special reference to underlying genetic architecture of ECMY. In this study we used published data of 773 Holstein cows with phenotypic observations for ECMY and dairy farm information with 62410 SNPs. One interesting finding is that some short chromosomes as such chromosomes 5 (included 28446 SNP) and 29 (included 12776 SNP) had higher effects sizes compared with the rest of the genome. A possible explanation for these results may be related with the existence of major genes at the chromosome 5. The GP results showed that ECYM and residuals of ECYM, had the accuracies from a 10-fold cross validations as 0.6422 and 0.3529 respectively. It was found that ECMY could be used for GP due to moderate accuracies. Taken together, dairy farm effects suggest an impact for accuracies of GP.

Keywords

Energy corrected milk yield genome wide association analyses genomic selection milk yield

Supporting Institution

TÜBİTAK

Project Number

118O108

Thanks

This work was supported by the computational resources obtained from Scientific and Technological Research Council of Turkey (TÜBİTAK), Grant No: 118O108.

References

  • Aulchenko YS, De Koning DJ, Haley C (2007): Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis. Genetics, 177, 577-585.
  • Bennewitz J, Reinsch N, Guiard V, et al (2004): Multiple quantitative trait loci mapping with cofactors and application of alternative variants of the false discovery rate in an enlarged granddaughter design. Genetics, 168, 1019-1027.
  • Buttchereit N, Stamer E, Junge W, et al (2011): Genetic relationships among daily energy balance, feed intake, body condition score, and fat to protein ratio of milk in dairy cows. J Dairy Sci, 94, 1586-1591.
  • Clancey E, Kiser JN, Moraes JG, et al (2019): Genome‐wide association analysis and gene set enrichment analysis with SNP data identify genes associated with 305‐day milk yield in Holstein dairy cows. Anim Genet, 50, 254-258.
  • Gebreyesus G, Buitenhuis AJ, Poulsen NA, et al (2019): Multi-population GWAS and enrichment analyses reveal novel genomic regions and promising candidate genes underlying bovine milk fatty acid composition. BMC Genom, 20, 178.
  • Habier D, Fernando RL, Garrick DJ (2013): Genomic BLUP decoded: a look into the black box of genomic prediction. Genetics, 194, 597-607.
  • Han B, Yuan Y, Li Y, et al (2019): Single nucleotide polymorphisms of NUCB2 and their genetic associations with milk production traits in dairy cows. Genes, 10, 449.
  • Iung LHS, Petrini J, Ramírez-Díaz J, et al (2019): Genome-wide association study for milk production traits in a Brazilian Holstein population. J Dairy Sci, 102, 5305-5314.
  • Jiang J, Ma L, Prakapenka D, et al (2019): A Large-Scale genome-wide association study in U.S. holstein cattle. Front Genet, 14, 412.
  • Krattenmacher N, Thaller G, Tetens J (2019): Analysis of the genetic architecture of energy balance and its major determinants dry matter intake and energy-corrected milk yield in primiparous Holstein cows. J Dairy Sci, 102, 3241-3253.
  • Li B, Fikse WF, Løvendahl P, et al (2018): Genetic heterogeneity of feed intake, energy-corrected milk, and body weight across lactation in primiparous Holstein, Nordic Red, and Jersey cows. J Dairy Sci, 101, 10011-10021.
  • Liu JJ, Liang AX, Campanile G, et al (2018): Genome-wide association studies to identify quantitative trait loci affecting milk production traits in water buffalo. J Dairy Sci, 101, 433-444.
  • Lopdell TJ, Tiplady K, Couldrey C, et al (2019): Multiple QTL underlie milk phenotypes at the CSF2RB locus. Genet Sel Evol, 51, 1.
  • McClure MC, Morsci NS, Schnabel RD, et al (2010): A genome scan for quantitative trait loci influencing carcass, post‐natal growth and reproductive traits in commercial Angus cattle. Anim Genet, 41, 597-607.
  • Moser G, Lee SH, Hayes BJ, et al (2015): Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS Genet, 11, e1004969.
  • Ostersen T, Christensen OF, Henryon M, et al (2011): Deregressed EBV as the response variable yield more reliable genomic predictions than traditional EBV in pure-bred pigs. Genet Sel Evol, 43, 38.
  • R Development Core Team (2013): A language and environmental for statistical computing. R Foundation for Statistical Computing; Vienna, Austria.
  • Saatchi M, Schnabel RD, Taylor JF, et al (2014): Large-effect pleiotropic or closely linked QTL segregate within and across ten US cattle breeds. BMC Genom, 15, 442.
  • Schultz NE, Weigel KA (2019): Inclusion of herdmate data improves genomic prediction for milk-production and feed-efficiency traits within North American dairy herds. J Dairy Sci, 102, 11081-11091.
  • Song H, Li L, Zhang Q, et al (2018): Accuracy and bias of genomic prediction with different de-regression methods. Animal, 12, 1111-1117.
  • Svishcheva GR, Axenovich TI, Belonogova NM, et al (2012): Rapid variance components-based method for whole-genome association analysis. Nat Genet, 44, 1166-1170.
  • Tam V, Patel N, Turcotte M, et al (2019): Benefits and limitations of genome-wide association studies. Nat Rev Genet, 20, 467-484.
  • Tiezzi F, de Los Campos G, Gaddis KP, et al (2017): Genotype by environment (climate) interaction improves genomic prediction for production traits in US Holstein cattle. J Dairy Sci, 100, 2042-2056.
  • Veerkamp RF, Coffey MP, Berry DP, et al (2012): Genome-wide associations for feed utilisation complex in primiparous Holstein-Friesian dairy cows from experimental research herds in four European countries. Animal, 6, 1738-49.
  • Wang D, Ning C, Liu JF, et al (2019): Short communication: Replication of genome-wide association studies for milk production traits in Chinese Holstein by an efficient rotated linear mixed model. J Dairy Sci, 102, 2378-2383.
  • Yao C, De Los Campos G, VandeHaar M, et al (2017): Use of genotype × environment interaction model to accommodate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle. J Dairy Sci, 100, 2007-2016.
  • Zhou X, Carbonetto P, Stephens M (2013): Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet, 9, e1003264.

Süt sığırlarında enerjice düzeltilmiş süt veriminin genomik tahmin ve ilişki analizleri

Year 2021, Volume: 68 Issue: 4, 383 - 388, 27.09.2021

Burak Karacaören

https://doi.org/10.33988/auvfd.775597

Abstract

Süt sığırlarında, süt verimi (SV) için enerji dengesi ile metabolizmanın korunması önemlidir. SV için genetik ve genomik analizlerine olan ilgi son yıllarda önem kazanmıştır. Enerjice düzeltilmiş süt verimi (EDSV) konusunda ise SV'den farklı olarak daha az araştırma bulunmaktadır. Bu çalışmanın amacı EDSV'ye sebep olabilecek tek nükleotid polimorfizmlerini (TNP) belirlemek ve bunlar üzerinden farklı genomik modeller kullanarak genomik tahminler (GT) yapmaktır. Bu çalışmada daha önceden yayınlanmış bir veri seti kullanılarak, 773 Holstayn ineğe ait EDSV gözlemleri ile 62410 TNP ve çiftlik bilgileri incelenmiştir. 5. kromozom gibi kısa bir kromozomda (28446 TNP) ve 29. kromozomda (12776 TNP) GT için genomun diğer bölgelerine göre daha yüksek etki büyüklükleri belirlenmiştir. Bu durum 5. kromozomda yer alan major bir gen ile açıklanabilir. GT sonuçları EDSV ve EDSV kalıntıları ile elde edilmiş ve 10 katlı çapraz sorgulama ile 0,6422 ve 0,3529 doğruluk oranları bulunmuştur. Bu da ECYM'nin GT modellerinde orta doğrulukta kullanılabileceğini göstermiştir. Bu çalışmada; çiftlik etkilerinin GT doğruluklarında bir etkiye sahip olduğu gösterilmiştir. 

Keywords

Enerjice düzeltilmiş süt verimi genom tabanlı ilişki analizi genomik seleksiyon süt verimi

Project Number

118O108

References

  • Aulchenko YS, De Koning DJ, Haley C (2007): Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis. Genetics, 177, 577-585.
  • Bennewitz J, Reinsch N, Guiard V, et al (2004): Multiple quantitative trait loci mapping with cofactors and application of alternative variants of the false discovery rate in an enlarged granddaughter design. Genetics, 168, 1019-1027.
  • Buttchereit N, Stamer E, Junge W, et al (2011): Genetic relationships among daily energy balance, feed intake, body condition score, and fat to protein ratio of milk in dairy cows. J Dairy Sci, 94, 1586-1591.
  • Clancey E, Kiser JN, Moraes JG, et al (2019): Genome‐wide association analysis and gene set enrichment analysis with SNP data identify genes associated with 305‐day milk yield in Holstein dairy cows. Anim Genet, 50, 254-258.
  • Gebreyesus G, Buitenhuis AJ, Poulsen NA, et al (2019): Multi-population GWAS and enrichment analyses reveal novel genomic regions and promising candidate genes underlying bovine milk fatty acid composition. BMC Genom, 20, 178.
  • Habier D, Fernando RL, Garrick DJ (2013): Genomic BLUP decoded: a look into the black box of genomic prediction. Genetics, 194, 597-607.
  • Han B, Yuan Y, Li Y, et al (2019): Single nucleotide polymorphisms of NUCB2 and their genetic associations with milk production traits in dairy cows. Genes, 10, 449.
  • Iung LHS, Petrini J, Ramírez-Díaz J, et al (2019): Genome-wide association study for milk production traits in a Brazilian Holstein population. J Dairy Sci, 102, 5305-5314.
  • Jiang J, Ma L, Prakapenka D, et al (2019): A Large-Scale genome-wide association study in U.S. holstein cattle. Front Genet, 14, 412.
  • Krattenmacher N, Thaller G, Tetens J (2019): Analysis of the genetic architecture of energy balance and its major determinants dry matter intake and energy-corrected milk yield in primiparous Holstein cows. J Dairy Sci, 102, 3241-3253.
  • Li B, Fikse WF, Løvendahl P, et al (2018): Genetic heterogeneity of feed intake, energy-corrected milk, and body weight across lactation in primiparous Holstein, Nordic Red, and Jersey cows. J Dairy Sci, 101, 10011-10021.
  • Liu JJ, Liang AX, Campanile G, et al (2018): Genome-wide association studies to identify quantitative trait loci affecting milk production traits in water buffalo. J Dairy Sci, 101, 433-444.
  • Lopdell TJ, Tiplady K, Couldrey C, et al (2019): Multiple QTL underlie milk phenotypes at the CSF2RB locus. Genet Sel Evol, 51, 1.
  • McClure MC, Morsci NS, Schnabel RD, et al (2010): A genome scan for quantitative trait loci influencing carcass, post‐natal growth and reproductive traits in commercial Angus cattle. Anim Genet, 41, 597-607.
  • Moser G, Lee SH, Hayes BJ, et al (2015): Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS Genet, 11, e1004969.
  • Ostersen T, Christensen OF, Henryon M, et al (2011): Deregressed EBV as the response variable yield more reliable genomic predictions than traditional EBV in pure-bred pigs. Genet Sel Evol, 43, 38.
  • R Development Core Team (2013): A language and environmental for statistical computing. R Foundation for Statistical Computing; Vienna, Austria.
  • Saatchi M, Schnabel RD, Taylor JF, et al (2014): Large-effect pleiotropic or closely linked QTL segregate within and across ten US cattle breeds. BMC Genom, 15, 442.
  • Schultz NE, Weigel KA (2019): Inclusion of herdmate data improves genomic prediction for milk-production and feed-efficiency traits within North American dairy herds. J Dairy Sci, 102, 11081-11091.
  • Song H, Li L, Zhang Q, et al (2018): Accuracy and bias of genomic prediction with different de-regression methods. Animal, 12, 1111-1117.
  • Svishcheva GR, Axenovich TI, Belonogova NM, et al (2012): Rapid variance components-based method for whole-genome association analysis. Nat Genet, 44, 1166-1170.
  • Tam V, Patel N, Turcotte M, et al (2019): Benefits and limitations of genome-wide association studies. Nat Rev Genet, 20, 467-484.
  • Tiezzi F, de Los Campos G, Gaddis KP, et al (2017): Genotype by environment (climate) interaction improves genomic prediction for production traits in US Holstein cattle. J Dairy Sci, 100, 2042-2056.
  • Veerkamp RF, Coffey MP, Berry DP, et al (2012): Genome-wide associations for feed utilisation complex in primiparous Holstein-Friesian dairy cows from experimental research herds in four European countries. Animal, 6, 1738-49.
  • Wang D, Ning C, Liu JF, et al (2019): Short communication: Replication of genome-wide association studies for milk production traits in Chinese Holstein by an efficient rotated linear mixed model. J Dairy Sci, 102, 2378-2383.
  • Yao C, De Los Campos G, VandeHaar M, et al (2017): Use of genotype × environment interaction model to accommodate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle. J Dairy Sci, 100, 2007-2016.
  • Zhou X, Carbonetto P, Stephens M (2013): Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet, 9, e1003264.
There are 27 citations in total.

Details

Primary Language English
Subjects Veterinary Surgery
Journal Section Research Article
Authors

Burak Karacaören 0000-0003-2981-6540

Project Number 118O108
Publication Date September 27, 2021
Published in Issue Year 2021Volume: 68 Issue: 4

Cite

APA Karacaören, B. (2021). Genomic prediction and association analyses of energy corrected milk yield in dairy cows. Ankara Üniversitesi Veteriner Fakültesi Dergisi, 68(4), 383-388. https://doi.org/10.33988/auvfd.775597
AMA Karacaören B. Genomic prediction and association analyses of energy corrected milk yield in dairy cows. Ankara Univ Vet Fak Derg. September 2021;68(4):383-388. doi:10.33988/auvfd.775597
Chicago Karacaören, Burak. “Genomic Prediction and Association Analyses of Energy Corrected Milk Yield in Dairy Cows”. Ankara Üniversitesi Veteriner Fakültesi Dergisi 68, no. 4 (September 2021): 383-88. https://doi.org/10.33988/auvfd.775597.
EndNote Karacaören B (September 1, 2021) Genomic prediction and association analyses of energy corrected milk yield in dairy cows. Ankara Üniversitesi Veteriner Fakültesi Dergisi 68 4 383–388.
IEEE B. Karacaören, “Genomic prediction and association analyses of energy corrected milk yield in dairy cows”, Ankara Univ Vet Fak Derg, vol. 68, no. 4, pp. 383–388, 2021, doi: 10.33988/auvfd.775597.
ISNAD Karacaören, Burak. “Genomic Prediction and Association Analyses of Energy Corrected Milk Yield in Dairy Cows”. Ankara Üniversitesi Veteriner Fakültesi Dergisi 68/4 (September 2021), 383-388. https://doi.org/10.33988/auvfd.775597.
JAMA Karacaören B. Genomic prediction and association analyses of energy corrected milk yield in dairy cows. Ankara Univ Vet Fak Derg. 2021;68:383–388.
MLA Karacaören, Burak. “Genomic Prediction and Association Analyses of Energy Corrected Milk Yield in Dairy Cows”. Ankara Üniversitesi Veteriner Fakültesi Dergisi, vol. 68, no. 4, 2021, pp. 383-8, doi:10.33988/auvfd.775597.
Vancouver Karacaören B. Genomic prediction and association analyses of energy corrected milk yield in dairy cows. Ankara Univ Vet Fak Derg. 2021;68(4):383-8.
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