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Tekrarlanan Ölçümlerde Varyans-Kovaryans Unsurlarının Tahmin Edilmesinde Farklı Yaklaşımların Performanslarının Karşılaştırılması

Year 2018, Volume: 5 Issue: 3, 310 - 316, 26.07.2018

Serhat Arslan Mehmet Kazım Kara

https://doi.org/10.30910/turkjans.448372

Abstract

Hayvan ıslahında
tekrarlanan gözlem değerleri giderek önem kazanmakta olan bir konudur. Bu
çalışmada tekrarlanan gözlem değerlerini içeren veri setlerinin analiz ve
parametre tahminleri için kullanılan yöntem ve metodlar karşılaştırılmıştır. Bu
modellere alternatif olarak Ali-Schaeffer eğri fonksiyonunun uyumuyla
oluşturulan kovaryans fonksiyon yaklaşımlı şansa bağlı regresyon modelinin
kullanımı araştırılmıştır. Bu amaçla süt sığırları için tutulmuş olan
kayıtlardan sağlanan bir veri tabanı esas alınarak simulasyonla elde edilmiş
bir veri seti üzerinde çalışılmıştır.Zamana bağlı değişimin geçerli olduğu
denetim günü verimleri için uyumu yapılan modellerden uyum büyükten küçüğe
sırasıyla KF+RRM (kovaryans fonksiyonu yaklaşımlı şansa bağlı regresyon
modeli), DRRM (doğrudan şansa bağlı regresyon modeli), TM (tekrarlanabilen
model), ORM (oto-regresif model) ve HM (hayvan model)’de olmuştur. Hatalar
arası oto-korelasyon yapısı en iyi KF-RRM ve ORM modellerinde
açıklanabilmiştir. Tahmin edilen parametreler, varynslar için karşılaştırılmış
ve en hassas parametre tahminleri KF-RRM sonuçlarından elde edilmiş bunu DRRM
izlemiştir.

Keywords

Şansa bağlı regresyon DFREML tekrarlanan ölçümler ortogonal polinom oto-korelasyon.

References

  • Ali, T.E., Schaeffer, L.R., 1997. Accounting for covariances among test day milk yields in dairy cows. Can. J. Anim. Sci., 630-637.
  • Albuquerque, L.G., Keown, J.F., Van Vleck, L.D., 1998. Variances of direct genetic effects, maternal genetic effects and cytoplasmic inheritance effects for milk yield, fat yield and fat percentage. J. Dairy Sci., 81: 544-549.
  • Arslan, S. 2001.Tekrarlanan Ölçümlerde Random Regresyon Yöntemi ile VaryansKovaryans Unsurlarının Tahmini ve Hayvan Islahında Kullanım Olanakları. Basılmamış Doktora Tezi, YYÜ, Fen Bilimleri Enstitüsü.
  • Brotherstone, S., White, I., Meyer, K., 2000. Genetic modeling of daily milk yield using orthogonal polynomials and parametric curves. Anim. Sci., 70: 407-415.
  • Carvalheira, J.G., V., Blake, R.W., Pollak, E.J., Quass, R.L., Duran-Castro, C.V., 1998. Application of an autoregressive process to estimate genetic parameters and breeding values for daily milk yield in a tropical herds of Lucerna cattle and in United States Holstein Herds. J. Dairy Sci., 81: 2738-2751.
  • Ducrocq, V.P., Besbes, B., 1993. Solution of multiple trait animal models with missing data on some traits. J. Anim. Breed. Genet., 110: 81-92.
  • Everett, R.W., 2000. Evaluating genetics and management using your DHI records. Interbull Bulletin, 27: 18-24.
  • Gengler, N., Tijani, A., Wiggans, G.R., Van Tassel, C.P., Philpot, J.C., 1999. Estimation of Co(Variances of test day yields for first lactation Holsteins in the United States, J. Dairy Sci., 82: 1: 225.e1-225.e14.
  • Gibbons, R.D., Bock, R.D., 1987. Trend in correlated proportions. Psyhometrica, 52: 113-124.
  • Gibbons, R.D., Hedeker, D., 1997. Application of random regression models in clinical study. J. Cons. Clinic., Phyc., 72(1): 154-161.
  • Henderson, C.R., Jr., 1982. Analysis of covariance in the mixed model: Higher level, nonhomogeneus, and random regression. Biometrics, Abstract 38: 623.
  • Hermesch, S., Luxford, B.G., Graser, H.U., 2000a., Genetic parameters for lean meat yield, meat quality, reproduction and feed efficiency traits for Australian pigs, 1. Description of traits and heritability estimates. Livest. Prod. Sci. 65: 261-270.
  • Hermesch, S., Luxford, B.G., Graser, H.U., 2000b., Genetic parameters for lean meat yield, meat quality, reproduction and feed efficiency traits for Australian pigs, 2. Genetic relationships between production, carcase and meat quality traits. Livest. Prod. Sci. 65: 249-259.
  • Hermesch, S., Luxford, B.G., Graser, H.U., 2000c., Genetic parameters for lean meat yield, meat quality, reproduction and feed efficiency traits for Australian pigs, 3. Genetic parameters for reproduction traits and genetic correlations with production, carcase and meat quality traits. Livest. Prod. Sci. 65: 239-248.
  • Jamrozik, J., Schaeffer, L.R., Dekkers, J.C.M., 1997. Genetic evaluation of dairy cattle using test day yields an random regression models. J. Dairy Sci., 80: 1217-1225.
  • Jenkins, T.G., Ferrel, C.L., 1984. A note on lactation curves of crossbreed cows. Anim. Prod. 39: 479-488.
  • Kirkpatrick, M., Heckman, N., 1989. A quantitative genetic model growth shape, reaction norms, and other infinite dimentional chracters. J. Math. Biol., 27: 429-450.
  • Kirkpatrick, M., Lofsvold., Bulmer., 1990. Analysis of the inheritance, selection and evoluation of growth trajectories. Genetics, 124: 979-993.
  • Meyer, K., 1998. DXMRR-A program to estimate covariance functions for longitudinal data by REML. Proc. 6th. World Congress on Genet. Appl. Livest. Prod. Armidale, 11-16 Jan., 27: 465-466.
  • Meyer, K., 1998. Estimating covariance functions for longitudinal data using a regression model. Genet. Sel. Evol., 30: 221-224.
  • Meyer, K., 2000. Random regression to model phenotypic variation in montly weights of Australian beef cows. Livest. Prod. Sci., 65: 19-38.
  • Meyer, K., 2001. Estimates of direct and maternal covariance functions for growth of Australian beef calves from birth to weaning. Genet. Sel. Evol. 33(5): 487-514.
  • Mantysaari, E.A., 1999. Derivation of multiple trait reduced rank random regression model for the first lactation test day records of milk, protein and fat. 50th Annual Meating of EAAP, Zurich, Agust. 22-26, 1999.
  • Misztal, I., Lawlor, T.J., Fernando, R.L., 1997. Dominance model with method R for stature of Holstains, J. Dairy Sci., 80:975-978.
  • Newman, S., McEwan, J., Swan A., Brash L., Hermesch S., 1998. A genetic parameter estimate world wide web site. J. Anim. Sci. 76: Suppl. 1. J. Dairy Sci. 81: Suppl 1: 61.
  • SAS, 1998. SAS/STAT Software, Hangen and Enhanced, SAS, Inst. Inc. Cri, NCI.
  • Schaeffer, L.R., Dekkers, J.C.M., 1994. Random regression models for test-day production in dairy cattle. Proc. 5th World Congr. Genet. Appl. Livest. Prod., Guelph, 18: 44-53.
  • Simianer, H., 1986. A general approach to the use of multiple traits with repeated measurements in estimation of breeding values. Livest. Prod. Sci., 15: 315-324.
  • Strabel, T., Mistal, I., 1999. Genetic parameters for first and second lactation milk yields of Polish black and white cattle with random regression test day models. J. Dairy Sci., 82: 2805-2810.
  • Tijani, A., Wiggans, G.R., Van Tassel, C.P., Philpot, J.C., Gengler, N., 1999. Use of (co)variance functions to describe (co)variances for test day yield. J. Dairy Sci., 82(1): 226e1-226e14.
  • Van der Werf, J.H.J., Schaeffer, L., 1997. Random regression in animal breeding. CGIL, Guelph, June, 25-28.
  • Van der Werf, J.H.J., Goddard, M.E., 1998. Transformation of random regression models to reduced rank. Proc. 6th. World Congr. On Genet. Appl. Livest. Prod., Armidale, 11-16 Jun., 25: 597-600.
  • Wilmink, J.B.M., 1987. Adjustment of test day milk, fat, and protein yields for age, season and stage of lactation. Livest. Prod. Sci., 16: 335.

Comparison of the Performance of Different Approaches in repeated Measurements for Estimation of (Co)variance Components

Year 2018, Volume: 5 Issue: 3, 310 - 316, 26.07.2018

Serhat Arslan Mehmet Kazım Kara

https://doi.org/10.30910/turkjans.448372

Abstract

In animal breeding, repeated measurements are important. In
this study, we compared the methods and models which are used in the analysis
of data sets which contain the repeated measurements and the estimation of
parameters. Morover, as an alternative method, random regression procedure
which used the approach of covariance functions and was formed by compatibility
of Ali-Schaeffer curve function was investigated. A data set was generated by
simulation from retrospective records of dairy cattle. Fitting of the tested
models for test-day yields in time were ranked from the best to the worst were
CF-RRM (covariance function random regression model), DRRM (direct RRM), RM
(repeatability model), ARM (auto-regresive model) and AM (animal model)
respectively. It was determinded that the best models which explain the
auto-correlation structure among the experimental errors were CF-RRM and ARM.
Predicted parameters were compared for variances and the most sensitive
estimation of parameters were obtained by CF-RRM. It was followed by DRRM.

Keywords

Random regression DFREML repeated measurements orthogonal polinomial auto-correlation.

References

  • Ali, T.E., Schaeffer, L.R., 1997. Accounting for covariances among test day milk yields in dairy cows. Can. J. Anim. Sci., 630-637.
  • Albuquerque, L.G., Keown, J.F., Van Vleck, L.D., 1998. Variances of direct genetic effects, maternal genetic effects and cytoplasmic inheritance effects for milk yield, fat yield and fat percentage. J. Dairy Sci., 81: 544-549.
  • Arslan, S. 2001.Tekrarlanan Ölçümlerde Random Regresyon Yöntemi ile VaryansKovaryans Unsurlarının Tahmini ve Hayvan Islahında Kullanım Olanakları. Basılmamış Doktora Tezi, YYÜ, Fen Bilimleri Enstitüsü.
  • Brotherstone, S., White, I., Meyer, K., 2000. Genetic modeling of daily milk yield using orthogonal polynomials and parametric curves. Anim. Sci., 70: 407-415.
  • Carvalheira, J.G., V., Blake, R.W., Pollak, E.J., Quass, R.L., Duran-Castro, C.V., 1998. Application of an autoregressive process to estimate genetic parameters and breeding values for daily milk yield in a tropical herds of Lucerna cattle and in United States Holstein Herds. J. Dairy Sci., 81: 2738-2751.
  • Ducrocq, V.P., Besbes, B., 1993. Solution of multiple trait animal models with missing data on some traits. J. Anim. Breed. Genet., 110: 81-92.
  • Everett, R.W., 2000. Evaluating genetics and management using your DHI records. Interbull Bulletin, 27: 18-24.
  • Gengler, N., Tijani, A., Wiggans, G.R., Van Tassel, C.P., Philpot, J.C., 1999. Estimation of Co(Variances of test day yields for first lactation Holsteins in the United States, J. Dairy Sci., 82: 1: 225.e1-225.e14.
  • Gibbons, R.D., Bock, R.D., 1987. Trend in correlated proportions. Psyhometrica, 52: 113-124.
  • Gibbons, R.D., Hedeker, D., 1997. Application of random regression models in clinical study. J. Cons. Clinic., Phyc., 72(1): 154-161.
  • Henderson, C.R., Jr., 1982. Analysis of covariance in the mixed model: Higher level, nonhomogeneus, and random regression. Biometrics, Abstract 38: 623.
  • Hermesch, S., Luxford, B.G., Graser, H.U., 2000a., Genetic parameters for lean meat yield, meat quality, reproduction and feed efficiency traits for Australian pigs, 1. Description of traits and heritability estimates. Livest. Prod. Sci. 65: 261-270.
  • Hermesch, S., Luxford, B.G., Graser, H.U., 2000b., Genetic parameters for lean meat yield, meat quality, reproduction and feed efficiency traits for Australian pigs, 2. Genetic relationships between production, carcase and meat quality traits. Livest. Prod. Sci. 65: 249-259.
  • Hermesch, S., Luxford, B.G., Graser, H.U., 2000c., Genetic parameters for lean meat yield, meat quality, reproduction and feed efficiency traits for Australian pigs, 3. Genetic parameters for reproduction traits and genetic correlations with production, carcase and meat quality traits. Livest. Prod. Sci. 65: 239-248.
  • Jamrozik, J., Schaeffer, L.R., Dekkers, J.C.M., 1997. Genetic evaluation of dairy cattle using test day yields an random regression models. J. Dairy Sci., 80: 1217-1225.
  • Jenkins, T.G., Ferrel, C.L., 1984. A note on lactation curves of crossbreed cows. Anim. Prod. 39: 479-488.
  • Kirkpatrick, M., Heckman, N., 1989. A quantitative genetic model growth shape, reaction norms, and other infinite dimentional chracters. J. Math. Biol., 27: 429-450.
  • Kirkpatrick, M., Lofsvold., Bulmer., 1990. Analysis of the inheritance, selection and evoluation of growth trajectories. Genetics, 124: 979-993.
  • Meyer, K., 1998. DXMRR-A program to estimate covariance functions for longitudinal data by REML. Proc. 6th. World Congress on Genet. Appl. Livest. Prod. Armidale, 11-16 Jan., 27: 465-466.
  • Meyer, K., 1998. Estimating covariance functions for longitudinal data using a regression model. Genet. Sel. Evol., 30: 221-224.
  • Meyer, K., 2000. Random regression to model phenotypic variation in montly weights of Australian beef cows. Livest. Prod. Sci., 65: 19-38.
  • Meyer, K., 2001. Estimates of direct and maternal covariance functions for growth of Australian beef calves from birth to weaning. Genet. Sel. Evol. 33(5): 487-514.
  • Mantysaari, E.A., 1999. Derivation of multiple trait reduced rank random regression model for the first lactation test day records of milk, protein and fat. 50th Annual Meating of EAAP, Zurich, Agust. 22-26, 1999.
  • Misztal, I., Lawlor, T.J., Fernando, R.L., 1997. Dominance model with method R for stature of Holstains, J. Dairy Sci., 80:975-978.
  • Newman, S., McEwan, J., Swan A., Brash L., Hermesch S., 1998. A genetic parameter estimate world wide web site. J. Anim. Sci. 76: Suppl. 1. J. Dairy Sci. 81: Suppl 1: 61.
  • SAS, 1998. SAS/STAT Software, Hangen and Enhanced, SAS, Inst. Inc. Cri, NCI.
  • Schaeffer, L.R., Dekkers, J.C.M., 1994. Random regression models for test-day production in dairy cattle. Proc. 5th World Congr. Genet. Appl. Livest. Prod., Guelph, 18: 44-53.
  • Simianer, H., 1986. A general approach to the use of multiple traits with repeated measurements in estimation of breeding values. Livest. Prod. Sci., 15: 315-324.
  • Strabel, T., Mistal, I., 1999. Genetic parameters for first and second lactation milk yields of Polish black and white cattle with random regression test day models. J. Dairy Sci., 82: 2805-2810.
  • Tijani, A., Wiggans, G.R., Van Tassel, C.P., Philpot, J.C., Gengler, N., 1999. Use of (co)variance functions to describe (co)variances for test day yield. J. Dairy Sci., 82(1): 226e1-226e14.
  • Van der Werf, J.H.J., Schaeffer, L., 1997. Random regression in animal breeding. CGIL, Guelph, June, 25-28.
  • Van der Werf, J.H.J., Goddard, M.E., 1998. Transformation of random regression models to reduced rank. Proc. 6th. World Congr. On Genet. Appl. Livest. Prod., Armidale, 11-16 Jun., 25: 597-600.
  • Wilmink, J.B.M., 1987. Adjustment of test day milk, fat, and protein yields for age, season and stage of lactation. Livest. Prod. Sci., 16: 335.
There are 33 citations in total.

Details

Primary Language Turkish
Journal Section Research Articles
Authors

Serhat Arslan

Mehmet Kazım Kara This is me

Publication Date July 26, 2018
Submission Date May 24, 2018
Published in Issue Year 2018 Volume: 5 Issue: 3

Cite

APA Arslan, S., & Kara, M. K. (2018). Tekrarlanan Ölçümlerde Varyans-Kovaryans Unsurlarının Tahmin Edilmesinde Farklı Yaklaşımların Performanslarının Karşılaştırılması. Türk Tarım Ve Doğa Bilimleri Dergisi, 5(3), 310-316. https://doi.org/10.30910/turkjans.448372
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