genotype sampler; Markov chain Monte Carlo method; Metropolis-Hastings algorithm
Abstract :
[en] Many genetic problems can be solved by Monte Carlo method. This often requires sampling genotype configurations over pedigree. Current available samplers are inefficient for large animal pedigrees. A new sampler suitable for large complex pedigrees has been developed and evaluated. The sampler uses simple and iterative peeling algorithms alternately. The sampler was compared to two other samplers on hypothetical pedigree of 79 individuals and recessive disease. The behaviour of the sampler was evaluated in four experimental designs on real bovine pedigree of 907 903 animals. The application of the sampler was also exemplified in identical by descent study.
Disciplines :
Animal production & animal husbandry Genetics & genetic processes
Author, co-author :
Szydlowski, M.
Gengler, Nicolas ; Université de Liège - ULiège > Gembloux Agro-Bio Tech > Gembloux Agro-Bio Tech
Language :
English
Title :
Sampling genotype configurations in a large complex pedigree
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
van Arendonk J.A.M., Smith C., Kennedy B.W. (1989) Method to estimate genotype probabilities at individual loci in farm livestock. Theor. Appl. Genet., 78, 735 740.
Cannings C., Thompson E.A., Skolnick M.H. (1978) Probability functions on complex pedigrees. Adv. Appl. Prob., 10, 26 61.
Du F.-X., Hoeschele I. (2000) A note on algorithms for genotype and allele elimination in complex pedigrees with incomplete genotype data. Genetics, 156, 2051 2062.
Fernandez S.A., Fernando R.L., Guldbrandtsen B., Totir L.R., Carrquiry A.L. (2001) Sampling genotypes in large pedigrees with loops. Genet. Sel. Evol., 33, 337 367.
Fernandez S.A., Fernando R.L., Guldbrandtsen B., Stricker C., Schelling M., Carriquiry A.L. (2002) Irreducibility and efficiency of ESIP to sample marker genotypes in large pedigrees with loops. Genet. Sel. Evol., 34, 537 555.
Fernando R.L., Totir L.R., Pita F.V.C., Stricker C., Abraham K.J. (2006) Algorithms to compute allele state and origin probabilities for QTL mapping. Proceedings of the Eighth World Congress on Genetics and Applied to Livestock Production, Belo Horizonte, MG, Brazil, 13-18 August 2006, CD-ROM, communication 20-11.
Gengler N., Mayeres P., Szydlowski M. (2007) A simple method to approximate gene content in large pedigree populations: application to the myostatin gene in dual-purpose Belgian Blue cattle. Animal, 1, 21 28.
Heath S.C. (1997) Markov chain Monte Carlo segregation and linkage analysis for oligogenic models. Am. J. Hum. Genet., 61, 748 760.
Heath S.C. (1998) Generating consistent genotypic configurations for multi-allelic loci and large complex pedigrees. Hum. Hered., 48, 1 11.
Kong A. (1991) Analysis of pedigree data using methods combining peeling and Gibbs sampling. In : Computer Science and Statistics: Proceedings of the 23rd Symposium on the Interface EM Keramidas SM Kaufman, pp. 379 385 Interface Foundation of North America, Fairfax Station, VA.
Lange K., Goradia T.M. (1987) An algorithm for automatic genotype elimination. Am. J. Hum. Genet., 40, 250 256.
Lauritzen S.L., Spiegelhalter D.J. (1978) Local computations with probabilities on graphical structures and their applications to expert systems. J. R. Stat. Soc. Ser. B, 50, 157 224.
O'Connell J.R., Weeks D.E. (1995) The VITESSE algorithm for rapid exact multilocus linkage analysis via genotype set-recoding and fuzzy inheritance. Nat. Genet., 11, 402 408.
Szydlowski M. (2006) Markov Chain Monte Carlo sampling on multilocus genotypes. J. Anim. Feed Sci., 15, 685 694.
Thomas A., Gutin A., Abkevich V., Bansal A. (2000) Multilocus linkage analysis by blocked Gibbs sampling. Stat. Comput., 10, 259 269.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.
