$\require{mediawiki-texvc}$
  • 검색어에 아래의 연산자를 사용하시면 더 정확한 검색결과를 얻을 수 있습니다.
  • 검색연산자
검색연산자 기능 검색시 예
() 우선순위가 가장 높은 연산자 예1) (나노 (기계 | machine))
공백 두 개의 검색어(식)을 모두 포함하고 있는 문서 검색 예1) (나노 기계)
예2) 나노 장영실
| 두 개의 검색어(식) 중 하나 이상 포함하고 있는 문서 검색 예1) (줄기세포 | 면역)
예2) 줄기세포 | 장영실
! NOT 이후에 있는 검색어가 포함된 문서는 제외 예1) (황금 !백금)
예2) !image
* 검색어의 *란에 0개 이상의 임의의 문자가 포함된 문서 검색 예) semi*
"" 따옴표 내의 구문과 완전히 일치하는 문서만 검색 예) "Transform and Quantization"
쳇봇 이모티콘
안녕하세요!
ScienceON 챗봇입니다.
궁금한 것은 저에게 물어봐주세요.

논문 상세정보

Abstract

Main objectives of this study were to investigate accuracy, bias and power of linear and threshold model segregation analysis methods for detection of major genes in categorical traits in farm animals. Maximum Likelihood Linear Model (MLLM), Bayesian Linear Model (BALM) and Bayesian Threshold Model (BATM) were applied to simulated data on normal, categorical and binary scales as well as to disease data in pigs. Simulated data on the underlying normally distributed liability (NDL) were used to create categorical and binary data. MLLM method was applied to data on all scales (Normal, categorical and binary) and BATM method was developed and applied only to binary data. The MLLM analyses underestimated parameters for binary as well as categorical traits compared to normal traits; with the bias being very severe for binary traits. The accuracy of major gene and polygene parameter estimates was also very low for binary data compared with those for categorical data; the later gave results similar to normal data. When disease incidence (on binary scale) is close to 50%, segregation analysis has more accuracy and lesser bias, compared to diseases with rare incidences. NDL data were always better than categorical data. Under the MLLM method, the test statistics for categorical and binary data were consistently unusually very high (while the opposite is expected due to loss of information in categorical data), indicating high false discovery rates of major genes if linear models are applied to categorical traits. With Bayesian segregation analysis, 95% highest probability density regions of major gene variances were checked if they included the value of zero (boundary parameter); by nature of this difference between likelihood and Bayesian approaches, the Bayesian methods are likely to be more reliable for categorical data. The BATM segregation analysis of binary data also showed a significant advantage over MLLM in terms of higher accuracy. Based on the results, threshold models are recommended when the trait distributions are discontinuous. Further, segregation analysis could be used in an initial scan of the data for evidence of major genes before embarking on molecular genome mapping.

참고문헌 (42)

  1. Bodin, L., M. San Cristobal-Gaudy, F. Lecerf, P. Mulsant, B. Bibe, D. Lajous, J. P. Belloc, F. Eychenne, Y. Amigues and J. M. Elsen. 2002. Segregation of major gene influencing ovulation in progeny of Lacaune meat sheep. Genet. Sel. Evol. 34:447-464. 
  2. Geman, S. and D. Geman. 1984. Stochastic relaxation, Gibbs distributions and Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6:721-741. 
  3. Janss, L. L. G., R. Thompson and J. A. M. Van Arendonk. 1995. Application of Gibbs sampling for inference in a mixed major gene-polygenic inheritance model in animal populations. Theor. Appl. Genet. 91:1137-1147. 
  4. Janss, L. L. G. 2004. MaGGic 4.1.: A package of subroutines for genetic analyses with Gibbs sampling. 
  5. Kadarmideen, H. N., R. Rekaya, D. Gianola. 2001. Genetic parameters for clinical mastitis in Holstein-Friesians: a Bayesian analysis. Anim. Sci. 73:229-240. 
  6. Knott, S. A., C. S. Haley and R. Thompson. 1991. Methods of segregation analysis for animal breeding data. a comparison of power. Heredity 68:299-311. 
  7. Miayke, T., T. Dogo, K. Moriya and Y. Sasaki. 1999. Bayesian analysis for existence of segregation of major genes affecting carcass traits in Japanese Black cattle population. J. Anim. Breed. Genet. 116:207-215. 
  8. Yan, X. M., J. Ren, H. S. Ai, N. S. Ding, J. Gao, Y. M. Guo, C. Y. Chen, J. W. Ma, Q. L. Shu and L. S. Huang. 2004. Genetic Variations Analysis and Characterization of the Fifth Intron of Porcine NRAMP1 Gene. Asian-Aust. J. Anim. Sci. 17:1183-1187. 
  9. Gelfand, A. E. and A. F. M. Smith. 1990. Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc. 85:398-409. 
  10. Janss, L. L. G., J. A. M. Van Arendonk and E. W. Brascamp. 1997. Bayesian statistical analyses for presence of single major genes affecting meat quality traits in crossed pig population. Genetics 145:395-408. 
  11. Piper, L. R. and B. M. Bindon. 1982. The Booroola Merino and the performance of medium non-peppin crosses at Armidale. In: The Booroola Merino, (Ed. L. R. Piper and B. M. Bindon), CSIRO, Melbourne, 9-20. 
  12. Le Roy, P., J. Naveau, J. M. Elsen and P. Sellier. 1990. Evidence for a new major gene influencing meat quality in pigs. Genet. Res. 55:33-40. 
  13. Ilahi, H. and H. N. Kadarmideen. 2004. Bayesian Segregation Analysis of Milk Flow in Swiss Dairy Cattle using Gibbs Sampling. Genet. Sel. Evol. 36:563-576. 
  14. Kadarmideen, H. N. and J. C. M. Dekkers. 2001. Generalized marker regression and interval QTL mapping methods for binary traits in half-sib family designs. J. Anim. Breed. Genet. 118:297-309. 
  15. Box, G. E. P. and G. C. Tiao. 1973. Bayesian Inference in Statistical Analysis. Reading-Mass. Addison-Wesley. 
  16. Ilahi, H., E. Manfredi, P. Chastin, F. Monod, J. M. Elsen and P. Le Roy. 2000. Genetic variability in milking speed of dairy goats. Genet. Res. 75:315-319. 
  17. Miayke, T., Y. Sasaki, G. Dolf and C. Gaillard. 2002. Application of constraints on parameters in segregation analysis for binary traits using Gibbs sampling. In: (Ed. I. Hoeschele).In Proc, 7th World Congr. Genet. Appl. Livest. Prod. Montpellier, France. 32:609-612. 
  18. Hagger, C., L. L. G. Janss, H. N. Kadarmideen and G. Stranzinger. 2004. Bayesian Inference on Major Loci in Related Multi Generation Selection Lines of Laying Hens. Poult. Sci. 83:1932-1939. 
  19. Kadarmideen, H. N., R. Thompson and G. Simm. 2000a. Linear and Threshold Model Genetic Parameters for Disease, Fertility and Milk Production in dairy cattle. Anim. Sci. 71:411-419. 
  20. Le Roy, P., J. M. Elsen and S. Knott. 1989. Comparison of four statistical methods for detection of a major gene in a progeny test design. Genet. Sel. Evol. 21:341-357. 
  21. Numerical Algorithms Group. 1990. The NAG Fortran Library Manual, NAG Ltd. Oxford. 
  22. Rebai, A. 1997. Comparison of methods for regression interval mapping in QTL analysis with non-normal traits. Genet. Res. 69:69-74. 
  23. Kadarmideen, H. N., L. L. G. Janss and J. C. M. Dekkers. 2000b. Power of quantitative trait locus mapping for polygenic binary traits using generalized and regression interval mapping. Genet. Res. 76:305-317. 
  24. Gianola, D. and J. L. Folley. 1983. Sire evaluation for ordered categorical trait with a threshold models. Genet. Sel. Evol. 17:359-368. 
  25. Le Roy, P. and J. M. Elsen. 1991. First statistical approaches of major gene detection with special reference to discrete traits. In. (Ed. J. M. Elsen, L. Bodin and J. Thimonier), 2nd International Workshop on Major Genes for Reproduction in Sheep. Toulouse, France, INRA Ed. Paris. 47:431-440. 
  26. Yi, N. and S. Xu. 2000. Bayesian mapping of quantitative trait loci for complex binary traits. Genetics 155:1391-1403. 
  27. Thaller, G., L. Dempfle and I. Hoeschele. 1996. Maximum likelihood analysis of rare binary traits under different modes of inheritance. Genetics 143:1819-1829. 
  28. Kim, J. W., S. I. Park and J. S. Yeo. 2003. Linkage Mapping and QTL on Chromosome 6 in Hanwoo (Korean cattle). Asian-Aust. J. Anim. Sci. 16:1402-1405. 
  29. Falconer, D. S. and T. F. C. Mackay. 1996. Introduction to quantitative genetics. 4th edn, Longman, Harlow, London. 
  30. Kadarmideen, H. N. and L. L. G. Janss. 2003. Liability interval mapping of quantitative trait loci for complex polygenic binary diseases under gene by environmental interactions. Proceedings of the XIX International Congress of Genetics, Melbourne, Australia. Section: Animal breeding and Cloning. 6.C.0967: p. 134. 
  31. Ricordeau, G., J. Bouillon, P. Le Roy and J. M. Elsen. 1990. D$\'{e}$terminisme g$\'{e}$n$\'{e}$tique du d$\'{e}$bit de traite au cours de la traite des ch$\`{e}$vres. INRA. Prod. Anim. 3:121-126. 
  32. Robertson, A. L. and I. M. Lerner. 1949. The heritability of all-or -none traits: viability of poultry. Genetics 34:395-411. 
  33. Wright, S. 1934. An analysis of variability in number of digits in an inbred strain of guinea pigs. Genetics 19:506-536. 
  34. Guo, S., W. and E. A. Thompson. 1994. Monte Carlo estimation of mixed models for large complex pedigrees. Biometrics 50:417-432. 
  35. Kadarmideen, H. N., D. Schwörer, H. Ilahi, M. Malek and A. Hofer. 2004. Genetics of osteochondral disease and its relationship with meat quality and quantity, growth and feed conversion traits in pigs. J. Anim. Sci. 82:3118-3127. 
  36. Xu, S. and W. R. Atchley. 1996. Mapping quantitative trait loci for complex binary diseases using line crosses. Genetics 143:1417-1424. 
  37. Gianola, D.1982. Theory and analysis of threshold characters. J. Anim. Sci. 56:1079-1096. 
  38. Ilahi, H. 1999. Variabilite genetique du debit de traite chez les caprins laitiers. Ph.D Thesis, INRA-ENSA de Rennes. 
  39. Elsen, J. M. and P. Le Roy. 1990. Detection of major genes and determination of genotypes application to discrete variables. In Proc, 4th World Congress. Genet. Appl. Livest. Prod. Edinburgh, 23-27 July, 15:37-49. 
  40. Hanset, R. and C. Michaux. 1985. On the genetic determinism of muscular hypertrophy in the Belgian White and Blue cattle breed: I. Experimental data. Genet. Sel. Evol. 15:201-224. 
  41. Hill, W. G. and S. A. Knott. 1990. Identification of genes with large effects. In: Advances in Statistical Methods for Genetic Improvement of Livestock. Gianola, D., Hammond, K. Springer Verlag, New York. p. 477. 
  42. Lee, D. H. 2002. Estimation of Genetic Parameters for Calving Ease by Heifers and Cows Using Multi-trait Threshold Animal Models with Bayesian Approach. Asian-Aust. J. Anim. Sci. 15:1085-1090. 

이 논문을 인용한 문헌 (0)

  1. 이 논문을 인용한 문헌 없음

원문보기

원문 PDF 다운로드

  • ScienceON :
  • KCI :

원문 URL 링크

원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다. (원문복사서비스 안내 바로 가기)

상세조회 0건 원문조회 0건

DOI 인용 스타일