A study on the minimum number of loci required for genetic evaluation using a finite locus model

<p<Abstract</p< <p<For a finite locus model, Markov chain Monte Carlo (MCMC) methods can be used to estimate the conditional mean of genotypic values given phenotypes, which is also known as the best predictor (BP). When computationally feasible, this type of genetic prediction pro...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Fernando Rohan L [verfasserIn] Totir Liviu R [verfasserIn] Dekkers Jack CM [verfasserIn] Fernández Soledad A [verfasserIn]

Format:	E-Artikel
Sprache:	Deutsch ; Englisch ; Französisch

Erschienen:	2004

Schlagwörter:	number of loci finite locus models Markov chain Monte Carlo

Übergeordnetes Werk:	In: Genetics Selection Evolution - BMC, 2009, 36(2004), 4, Seite 395-414
Übergeordnetes Werk:	volume:36 ; year:2004 ; number:4 ; pages:395-414

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1186/1297-9686-36-4-395

Katalog-ID:	DOAJ058782648

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allfieldsSound	10.1186/1297-9686-36-4-395 doi (DE-627)DOAJ058782648 (DE-599)DOAJ779a2cebb5884107b63841b298acd6d7 DE-627 ger DE-627 rakwb ger eng fre SF1-1100 QH426-470 Fernando Rohan L verfasserin aut A study on the minimum number of loci required for genetic evaluation using a finite locus model 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<For a finite locus model, Markov chain Monte Carlo (MCMC) methods can be used to estimate the conditional mean of genotypic values given phenotypes, which is also known as the best predictor (BP). When computationally feasible, this type of genetic prediction provides an elegant solution to the problem of genetic evaluation under non-additive inheritance, especially for crossbred data. Successful application of MCMC methods for genetic evaluation using finite locus models depends, among other factors, on the number of loci assumed in the model. The effect of the assumed number of loci on evaluations obtained by BP was investigated using data simulated with about 100 loci. For several small pedigrees, genetic evaluations obtained by best linear prediction (BLP) were compared to genetic evaluations obtained by BP. For BLP evaluation, used here as the standard of comparison, only the first and second moments of the joint distribution of the genotypic and phenotypic values must be known. These moments were calculated from the gene frequencies and genotypic effects used in the simulation model. BP evaluation requires the complete distribution to be known. For each model used for BP evaluation, the gene frequencies and genotypic effects, which completely specify the required distribution, were derived such that the genotypic mean, the additive variance, and the dominance variance were the same as in the simulation model. For lowly heritable traits, evaluations obtained by BP under models with up to three loci closely matched the evaluations obtained by BLP for both purebred and crossbred data. For highly heritable traits, models with up to six loci were needed to match the evaluations obtained by BLP.</p< number of loci finite locus models Markov chain Monte Carlo Animal culture Genetics Totir Liviu R verfasserin aut Dekkers Jack CM verfasserin aut Fernández Soledad A verfasserin aut In Genetics Selection Evolution BMC, 2009 36(2004), 4, Seite 395-414 (DE-627)312849052 (DE-600)2012369-3 12979686 nnns volume:36 year:2004 number:4 pages:395-414 https://doi.org/10.1186/1297-9686-36-4-395 kostenfrei https://doaj.org/article/779a2cebb5884107b63841b298acd6d7 kostenfrei http://www.gsejournal.org/content/36/4/395 kostenfrei https://doaj.org/toc/0999-193X Journal toc kostenfrei https://doaj.org/toc/1297-9686 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 2004 4 395-414
language	German English French
source	In Genetics Selection Evolution 36(2004), 4, Seite 395-414 volume:36 year:2004 number:4 pages:395-414
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callnumber-first	S - Agriculture
author	Fernando Rohan L
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topic_title	SF1-1100 QH426-470 A study on the minimum number of loci required for genetic evaluation using a finite locus model number of loci finite locus models Markov chain Monte Carlo
topic	misc SF1-1100 misc QH426-470 misc number of loci misc finite locus models misc Markov chain Monte Carlo misc Animal culture misc Genetics
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title	A study on the minimum number of loci required for genetic evaluation using a finite locus model
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title_full	A study on the minimum number of loci required for genetic evaluation using a finite locus model
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title_sort	study on the minimum number of loci required for genetic evaluation using a finite locus model
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title_auth	A study on the minimum number of loci required for genetic evaluation using a finite locus model
abstract	<p<Abstract</p< <p<For a finite locus model, Markov chain Monte Carlo (MCMC) methods can be used to estimate the conditional mean of genotypic values given phenotypes, which is also known as the best predictor (BP). When computationally feasible, this type of genetic prediction provides an elegant solution to the problem of genetic evaluation under non-additive inheritance, especially for crossbred data. Successful application of MCMC methods for genetic evaluation using finite locus models depends, among other factors, on the number of loci assumed in the model. The effect of the assumed number of loci on evaluations obtained by BP was investigated using data simulated with about 100 loci. For several small pedigrees, genetic evaluations obtained by best linear prediction (BLP) were compared to genetic evaluations obtained by BP. For BLP evaluation, used here as the standard of comparison, only the first and second moments of the joint distribution of the genotypic and phenotypic values must be known. These moments were calculated from the gene frequencies and genotypic effects used in the simulation model. BP evaluation requires the complete distribution to be known. For each model used for BP evaluation, the gene frequencies and genotypic effects, which completely specify the required distribution, were derived such that the genotypic mean, the additive variance, and the dominance variance were the same as in the simulation model. For lowly heritable traits, evaluations obtained by BP under models with up to three loci closely matched the evaluations obtained by BLP for both purebred and crossbred data. For highly heritable traits, models with up to six loci were needed to match the evaluations obtained by BLP.</p<
abstractGer	<p<Abstract</p< <p<For a finite locus model, Markov chain Monte Carlo (MCMC) methods can be used to estimate the conditional mean of genotypic values given phenotypes, which is also known as the best predictor (BP). When computationally feasible, this type of genetic prediction provides an elegant solution to the problem of genetic evaluation under non-additive inheritance, especially for crossbred data. Successful application of MCMC methods for genetic evaluation using finite locus models depends, among other factors, on the number of loci assumed in the model. The effect of the assumed number of loci on evaluations obtained by BP was investigated using data simulated with about 100 loci. For several small pedigrees, genetic evaluations obtained by best linear prediction (BLP) were compared to genetic evaluations obtained by BP. For BLP evaluation, used here as the standard of comparison, only the first and second moments of the joint distribution of the genotypic and phenotypic values must be known. These moments were calculated from the gene frequencies and genotypic effects used in the simulation model. BP evaluation requires the complete distribution to be known. For each model used for BP evaluation, the gene frequencies and genotypic effects, which completely specify the required distribution, were derived such that the genotypic mean, the additive variance, and the dominance variance were the same as in the simulation model. For lowly heritable traits, evaluations obtained by BP under models with up to three loci closely matched the evaluations obtained by BLP for both purebred and crossbred data. For highly heritable traits, models with up to six loci were needed to match the evaluations obtained by BLP.</p<
abstract_unstemmed	<p<Abstract</p< <p<For a finite locus model, Markov chain Monte Carlo (MCMC) methods can be used to estimate the conditional mean of genotypic values given phenotypes, which is also known as the best predictor (BP). When computationally feasible, this type of genetic prediction provides an elegant solution to the problem of genetic evaluation under non-additive inheritance, especially for crossbred data. Successful application of MCMC methods for genetic evaluation using finite locus models depends, among other factors, on the number of loci assumed in the model. The effect of the assumed number of loci on evaluations obtained by BP was investigated using data simulated with about 100 loci. For several small pedigrees, genetic evaluations obtained by best linear prediction (BLP) were compared to genetic evaluations obtained by BP. For BLP evaluation, used here as the standard of comparison, only the first and second moments of the joint distribution of the genotypic and phenotypic values must be known. These moments were calculated from the gene frequencies and genotypic effects used in the simulation model. BP evaluation requires the complete distribution to be known. For each model used for BP evaluation, the gene frequencies and genotypic effects, which completely specify the required distribution, were derived such that the genotypic mean, the additive variance, and the dominance variance were the same as in the simulation model. For lowly heritable traits, evaluations obtained by BP under models with up to three loci closely matched the evaluations obtained by BLP for both purebred and crossbred data. For highly heritable traits, models with up to six loci were needed to match the evaluations obtained by BLP.</p<
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title_short	A study on the minimum number of loci required for genetic evaluation using a finite locus model
url	https://doi.org/10.1186/1297-9686-36-4-395 https://doaj.org/article/779a2cebb5884107b63841b298acd6d7 http://www.gsejournal.org/content/36/4/395 https://doaj.org/toc/0999-193X https://doaj.org/toc/1297-9686
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A study on the minimum number of loci required for genetic evaluation using a finite locus model

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