نوع مقاله : مقاله پژوهشی
نویسنده
دانشیار، دانشکده کشاورزی و منابع طبیعی دانشگاه تربت حیدریه ، ایران
چکیده
به‌منظور تعیین تداخل اثرات غلبه در برآورد پارامترهای ژنتیکی، از دو مدل افزایشی و افزایشی - غلبه برای برآو رد پارامترهای ژنتیکی صفات مختلف کیفیت 631 لاشه گاو گوشتی نر دورگ استفاده شد. داده ها با استفاده از نرم‌افزار‌های Plink (نسخه 1/9) و GVCBLUP (نسخه 3/9) تجزیه شدند. نتایج نشان داد که بیشتر صفات کیفی لاشه وراثت‌پذیری بالایی داشتند، اما دو صفت ماهیچه چشمی دنده اولتراسوند و وزن لاشه گرم دارای وراثت‌پذیری پایینی (به‌ترتیب 0/15 و 0/11) بودند. واریانس غلبه برای صفات وزن لاشه گرم، ماهیچه چشمی دنده اولتراسوند، چربی پشت اولتراسوند و ماهیچه چشمی دنده (به‌ترتیب 0/13، 0/440، 0/89 و0/33) بالا بود ولی برای سایر صفات (تولید گوشت لخم، درجه ماربلینگ، چربی پشت، ماهیچه چشمی دنده اولتراسوند و درجه لاشه) اثر غلبه مشاهده نشد یا بسیار جزئی برآورد گردید. هنگامی که واریانس غلبه صفات پایین بود، تاثیری بر برآورد GBLUP نداشت. برآورد وراثت‌پذیری صفات مورد بررسی به مقدار کم تحت‌تاثیر افزودن اثر غلبه در مدل قرار گرفت. مهم‌ترین نواحی ژنومی موثر بر صفات کیفی لاشه مربوط یه ژن-های LAP3، THBS4 و PCDH9 بود. پیشنهاد می گردد، برای درک بهتر از ساختار ژنتیکی صفات و برنامه ریزی بهتر اصلاح نژادی، اثرات غلبه در مدل برآورد پارامترهای ژنتیکی اضافه شود.
کلیدواژه‌ها
عنوان مقاله [English]
Estimation of dominance variance and its effects on the evaluation of the genetic parameters for carcass quality traits
نویسنده [English]
- masoud alipanah
چکیده [English]
In order to determine the interference of dominant effects on the estimation of genetic parameters, two models including additive and additive-dominance were used for estimation of genetic parameters of carcass traits in 631 hybrid beef bulls. Data analysis was conducted using Plink (V. 1.9) and GVCBLUP (V. 3.9) softwares. Results of this study showed that most carcass quality traits have high heritability,
but two traits namely hot carcass weight and ultrasound ribeye area had low heritability (0.15 and 0.11). Dominance variances have high contribution to the total variation of hot carcass weight, ultrasound ribeye area, ultrasound backfat thickness and ribeye area (0.13, 0.44, 0.89 and 0.33 respectively). However, dominant effect for other traits (lean meat yield, marbling score, backfat thickness, ultrasound ribeye area and grade of carcass) was not observed or was in very low amount. When dominance variance is low, its effect on GBLUP estimates is negligible. The estimates of heritability did not change significantly by the adding dominance effect into the model. The most important genomic regions that affect the carcass quality traits were belong to LAP3, THBS4 and PCDH9 genes. It is suggested that for the better understanding of the genetic structure of traits and better breeding plan, the dominance effects should be added into the model for genetic
parameter estimation.
کلیدواژه‌ها [English]
- Beef cattle
- Dominance
- Genomic selection
- Heritability
- Meat quality
2. Akanno, EC, Chen L, Abo-Ismail MK, Crowley JJ, Wang Z, Li C, Li C, Basarab JA, MacNeil MD and Plastow GS (2018) Genomewide association scan for heterotic quantitative trait loci in multi-breed and crossbred beef cattle. Genetics Selection Evolution 50(1): 48.
3. An B, Xia J, Chang T, Wang X, Xu L, Zhang L, Gao X, Chen Y, Li J and Gao H (2019) Genome‐wide association study reveals candidate genes associated with body measurement traits in Chinese Wagyu beef cattle. Animal genetics 50(4): 386-390.
4. Barendse W, Harrison BE, Hawken RJ, Ferguson DM, Thompson JM, Thomas MB et al. (2007) Epistasis betweein calpain 1 and its inhibitor calpastatin within breeds of cattle. Genetics 176: 2601-10.
5. Bolormaa S, Pryce JE, Zhang Y, Reverter A, Barendse W, Hayes BJ and Goddard ME (2015) Non-additive genetic variation in growth, carcass and fertility traits of beef cattle. Genetics Selection Evolution 47(26).
6. Carvalho ME, Baldi FS, Alexandre PA, Santana MHDA, Ventura RV, Bueno RS, Bonin, MDN, Rezende FMD, Coutinho LL, Eler JP and Ferraz JBS. (2019). Genomic regions and genes associated with carcass quality in Nelore cattle. Genetics and Molecular Research 18(1): 1-15.
7. Chen D, Li W, Du M, Wu M and Cao B. (2015). Sequencing and Characterization of Divergent Marbling Levels in the Beef Cattle (Longissimus dorsi Muscle) Transcriptome. Asian-Australasian journal of animal sciences 28(2): 158–165. doi:10.5713/ajas.14.0394
8. Colborg O and Haley CS (2004) Epistatsis: too often neglected in complex trait studies? Nature Reviews Genetics 5(8): 618-25.
9. Heidaritabar M, Wolc M, Arango J, Zeng J, Settar P, Fulton JE, O’Sull ivan NP, Bastiaansen JWM, Fernando RL, Garrick DJ and Dekkers JCM (2016) Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers. Journal of. Animal Breeding and Gentetics 133: 334-346.
10. Hill WG, Goddar ME, Visscher PM (2008) Data and theory point mainly additive genetic variance for complex traits. PLoS Genetics, 4(2): e1000008. doi:10.1371/journal.pgen.1000008.
12. Lu D, Akanno EC, Crowley JJ, Schenkel F, Li H, De Pauw M, Moore SS, Wang Z, Li C, Stothard P, Plastow G, Miller SP, Basarab JA (2016) Accuracy of genomic predictions for feed efficiency traits of beef cattle using 50K and imputed HD genotypes. Journal of Animal Science 94: 1342-53.
13. Mahdavi M, Dashab GR, Valeh MV, Rokouei M and Sargolzaei M (2018) Genomic evaluation and variance component estimation of additive and dominance effects using single nucleotide polymorphism markers in heterogeneous stock mice. Czech Journal of Animal Science 63(12): 492-506.
14. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics 81(3): 559-575.
15. Roberts A. (2018) Genome-wide association study for carcass traits in a composite beef cattle breed. Livestock Science 213: 35-43.
16. Su G, Christensen OF, Ostersen T, Henryon M, Lund MS (2012) Estimating additive and nonadditive genetic variance and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PLoS ONE, 7(9): e45293. Doi: 10.1371/journal.pone.0045293.
17. Twomey A J, Berry DP, Evans RD, Doherty ML, Graham DA AND Purfield DC (2019) Genome-wide association study of endoparasite phenotypes using imputed wholegenome sequence data in dairy and beef cattle. Genetics Selection Evolution 51(1): 15.
18. Wang DY, Wang S AND Hu G (2014) Mixed model for genomic prediction and variance component estimation of additive and dominance effects using SNP markers. PLoS ONE 9(1): e87666.doi. 10.1371/journal.pone. 0087666.
19. Wei W, Hemani G and Haley CS (2014) Detecting epistasis in human complex traits. Genetics 15: 722-733.
20. Xia J, Fan H, Chang T, Xu L, Zhang W, Song Y, ... and Li J. (2017) Searching for new loci and candidate genes for economically important traits through gene-based association analysis of Simmental cattle. Scientific reports 7: 42048. doi : 10.1038/srep42048/
21. Zhang W, Dai X, Wang Q, Xu S and Zhao P (2016) PEPIS: A pipline for estimating epistatic effects in quantitative trait locus mapping and genom-wide association studies. PLOS Computational Bioglogy 12(5): doi:10.1371/journal.pcbi.1004925.
22. Zhang W, Xu L, Gao H, Wu Y, Gao X, Zhang L, ... and Chen Y (2018) Detection of candidate genes for growth and carcass traits using genome-wide association strategy in Chinese Simmental beef cattle. Animal Production Science 58(2): 224-233.
)