Journals / Kafkas Üniversitesi Veteriner Fakültesi Dergisi / 2018 / Cilt: 24 - Sayı: 1

Investigating the Bias Resulted from Ignoring Bulmer Effect on the Genetic and Economic Output in Progeny Test and Genomic Selection Program

Bulmer Etkisinin Gözardı Edilmesinin Projeni Testinde Genetik ve Ekonomik Sonuçları ve Genomik Seleksiyon Programındaki Olumsuz Etkisinin Araştırılması

Pages
137–144
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results of this study, ignoring Bulmer effect in the investigations concerning comparison between progeny test and genomic selection seems to be unreasonable.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to investigate the degree of bias resulted from ignoring Bulmer effect during the estimation of genetic and economic progress in progeny test and genomic selection programs.
📊
Key Findings & Data:To this end, a deterministic approach based on gene flow method in a time horizon of 70 years was used.
💡
Conclusion & Impact:According to the results of this study, ignoring Bulmer effect in the investigations concerning comparison between progeny test and genomic selection seems to be unreasonable.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

This study aims to investigate the degree of bias resulted from ignoring Bulmer effect during the estimation of genetic and economic progress in progeny test and genomic selection programs. To this end, a deterministic approach based on gene flow method in a time horizon of 70 years was used. In this study, milk production was considered as the selection goal under a four-path selection strategy. In the progeny test, asymptotic genetic variance of sires and dams decreased by 67.59% and 64.97%, respectively. Also, in genomic selection program, asymptotic genetic variance in sires and dams decreased by 68.56% and 63.06%, respectively. The maximum reduction in genetic variance occurred in the first three generations. In the progeny test program, the bias of genetic progress per generation due to ignoring Bulmer effect was four times higher than genomic selection program, but this difference decreased significantly in the results of single round and continuous selection after 20 generations. Bulmer effect resulted in 51.64% and 44.62% reduction in the economic efficiency of progeny test and genomic selection, respectively. According to the results of this study, ignoring Bulmer effect in the investigations concerning comparison between progeny test and genomic selection seems to be unreasonable. Long-term selection has more severe effect on genetic and economic aspects of progeny test in comparison to genomic selection program via decreasing genetic variance.

Özet

Bu çalışmada Bulmer etkisinin göz ardı edilmesinin progeni testinde genetik ve ekonomik sonuçlarının tahmini ile genomik seleksiyon programındaki olumsuz etki derecesinin araştırılması amaçlanmıştır. Bu amaçla, 70 yıla yayılan gen akış metodu temelli belirleyici bir yaklaşım kullanıldı. Çalışmada, 4 yollu seleksiyon stratejisi altında seleksiyon hedefi olarak süt üretimi kullanıldı. Progeni testinde, baba ve annelerin asimtotik genetik varyansı sırasıyla %67.59 ve %64.97 azaltıldı. Genomik seleksiyon programında, baba ve annelerin asimtotik genetik varyansı da sırasıyla %68.56 ve %63.06 azaltıldı. Genetik varyansda maksimum azalma ilk üç nesilde gerçekleşti. Progeni test programında, Bulmer etkisini göz ardı etme sonucu, her nesildeki genetik ilerleme etkisi genomik seleksiyon programından dört kat daha fazlaydı. Ancak bu fark tek tur ve 20 nesil sonrasında devamlı seleksiyon sonuçlarında anlamlı derecede azaldı. Bulmer etkisi, progeni testinin ekonomik verimliliğinde ve genomik seleksiyonda sırasıyla %51.64 ve %44.62 azalmaya neden oldu. Bu çalışmanın sonuçları progeni testi ile genomik seleksiyonun karşılaştırıldığı incelemelerde Bulmer etkisinin göz ardı edilmesinin kabul edilebilir olmadığını göstermiştir. Uzun süreli seleksiyonun genetik varyansı azaltmak suretiyle genetik seleksiyon programı ile karşılaştırıldığında progeni testinin genetik ve ekonomik yönleri üzerine daha ciddi etkileri bulunmaktadır.

🔖

Cite This Article

MOJTAHEDIN, A. 1., SEIFDAVATI, J., BOUSTAN, A. 1., AZIZYAN, ?. S. 2., SEYEDSHARIFI, R. (2018). Investigating the Bias Resulted from Ignoring Bulmer Effect on the Genetic and Economic Output in Progeny Test and Genomic Selection Program. Kafkas Üniversitesi Veteriner Fakültesi Dergisi, Cilt: 24 - Sayı: 1, 137–144. https://ajindex.com/articles/272247

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (6)

  1. Van Raden PM, Olson KM, Wiggans GR, Cole JB, Tooker ME: Genomic inbreeding and relationships among Holsteins, Jerseys, and Brown Swiss. J Dairy Sci, 94 (11): 5673-5682, 2011. DOI: 10.3168/jds.2011-4500
  2. Charlier CW, Coppieters W, Rollin F, Desmecht D, Agerholm JS, Cambisano N, Carta E, Dardano S, Dive M, Fasquelle C, Frennet JC, Hanset R, Hubin X, Jorgensen C, Karim L, Kent M, Harvey K, Pearce BR, Simon P, Tama, N, Nie H, Vandeputte S, Lien S, Longeri M, Fredholm M, Harvey R: Highly effective SNP-based association mapping and management of recessive defects in livestock. Nat Genet, 40 (4): 449-, 2008. DOI: 10.1038/ng.96 Dekkers JCM, Gibson JP, Bijma P, Van Arendonk AM: Economic aspects of applied breeding program. Department of Animal Science, Iowa State Univ., Ames, IA, 50014, 126, 2004. Bulmer MG: The effect of selection on genetic variability. Am Nat, (943): 201-211, 1971. DOI: 10.1086/282718 Ponsart CAHD, Le Bourhis AH, Knijn BS, Fritz CC, Guyader-Joly DT, Otter BS, Lacaze EF, Charreaux FL, Schibler CD, Dupassieux G, Mullaart B: Reproductive technologies and genomic selection in dairy cattle. Reprod Fertil Dev, 26 (1): 12-21, 2014. DOI: 10.1071/RD13295 Everett RW: Impact of Genetic Manipulation. J Dairy Sci, 67 (11): 2812-, 1984. DOI: 10.3168/jds.S0022-0302(84)81637-9
  3. Thomasen JR, Egger-Danner C, Willam A, Guldbrandtsen B, Lund MS Sİrensen AC: Genomic selection strategies in a small dairy cattle population evaluated for genetic gain and profit. J Dairy Sci, 97 (1): 458-, 2014. DOI: 10.3168/jds.2013-6599
  4. Schöpke K, Swalve HH: Opportunities and challenges for small populations of dairy cattle in the era of genomics. Animal, 10 (6): 1050-, 2016. DOI: 10.1017/S1751731116000410 Joezy Shekalgorabi S, Shadparvar AA, VaezTorshizi R, Shahrebabak M, Jorjani H: Genetic analysis of a conventional progeny testing. Proceeding of 9th WCGALP, August 1-6,
  5. Leipzig, Germany, 2010.
  6. Gaddis KLP, Cole JB, Clay JS, Maltecca C: Genomic selection for producer-recorded health event data in US dairy cattle. J Dairy Sci, 97 (5): 3190-3199, 2014. DOI: 10.3168/jds.2013-7543 Schaeffer LR: Strategy for applying genome wide selection in dairy cattle. J Anim Breed Genet, 123 (4): 218-223, 2006. DOI: 10.1111/j.1439-0388. 00595.x Hill WG: Prediction and evaluation of response to selection with overlapping generations. Anim Prod, 18 (2): 117-139, 1974. DOI: 10.1017/ S0003356100017372 Demuth H, Beale M: Neural Network Toolbox User's Guide. Matlab User's Guide, Natick, MA. The Math Works