From phenotype to genotype: a Bayesian solution

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From phenotype to genotype : a Bayesian solution. / Denwood, Matthew; Mather, A E; Haydon, D T; Matthews, L; Mellor, D J; Reid, S W J.

I: Proceedings. Biological sciences / The Royal Society, Bind 278, Nr. 1710, 07.05.2011, s. 1434-40.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Denwood, M, Mather, AE, Haydon, DT, Matthews, L, Mellor, DJ & Reid, SWJ 2011, 'From phenotype to genotype: a Bayesian solution', Proceedings. Biological sciences / The Royal Society, bind 278, nr. 1710, s. 1434-40. https://doi.org/10.1098/rspb.2010.1719

APA

Denwood, M., Mather, A. E., Haydon, D. T., Matthews, L., Mellor, D. J., & Reid, S. W. J. (2011). From phenotype to genotype: a Bayesian solution. Proceedings. Biological sciences / The Royal Society, 278(1710), 1434-40. https://doi.org/10.1098/rspb.2010.1719

Vancouver

Denwood M, Mather AE, Haydon DT, Matthews L, Mellor DJ, Reid SWJ. From phenotype to genotype: a Bayesian solution. Proceedings. Biological sciences / The Royal Society. 2011 maj 7;278(1710):1434-40. https://doi.org/10.1098/rspb.2010.1719

Author

Denwood, Matthew ; Mather, A E ; Haydon, D T ; Matthews, L ; Mellor, D J ; Reid, S W J. / From phenotype to genotype : a Bayesian solution. I: Proceedings. Biological sciences / The Royal Society. 2011 ; Bind 278, Nr. 1710. s. 1434-40.

Bibtex

@article{3d58e002f938449fbd5b2889596c45dd,
title = "From phenotype to genotype: a Bayesian solution",
abstract = "The study of biological systems commonly depends on inferring the state of a 'hidden' variable, such as an underlying genotype, from that of an 'observed' variable, such as an expressed phenotype. However, this cannot be achieved using traditional quantitative methods when more than one genetic mechanism exists for a single observable phenotype. Using a novel latent class Bayesian model, it is possible to infer the prevalence of different genetic elements in a population given a sample of phenotypes. As an exemplar, data comprising phenotypic resistance to six antimicrobials obtained from passive surveillance of Salmonella Typhimurium DT104 are analysed to infer the prevalence of individual resistance genes, as well as the prevalence of a genomic island known as SGI1 and its variants. Three competing models are fitted to the data and distinguished between using posterior predictive p-values to assess their ability to predict the observed number of unique phenotypes. The results suggest that several SGI1 variants circulate in a few fixed forms through the population from which our data were derived. The methods presented could be applied to other types of phenotypic data, and represent a useful and generic mechanism of inferring the genetic population structure of organisms.",
keywords = "Anti-Bacterial Agents, Bayes Theorem, Drug Resistance, Multiple, Bacterial, Genes, Bacterial, Genetic Heterogeneity, Genetics, Population, Genomic Islands, Genotype, Humans, Markov Chains, Models, Biological, Monte Carlo Method, Phenotype, Salmonella Infections, Salmonella typhimurium",
author = "Matthew Denwood and Mather, {A E} and Haydon, {D T} and L Matthews and Mellor, {D J} and Reid, {S W J}",
year = "2011",
month = "5",
day = "7",
doi = "10.1098/rspb.2010.1719",
language = "English",
volume = "278",
pages = "1434--40",
journal = "Proceedings of the Royal Society B: Biological Sciences",
issn = "0962-8452",
publisher = "The Royal Society Publishing",
number = "1710",

}

RIS

TY - JOUR

T1 - From phenotype to genotype

T2 - a Bayesian solution

AU - Denwood, Matthew

AU - Mather, A E

AU - Haydon, D T

AU - Matthews, L

AU - Mellor, D J

AU - Reid, S W J

PY - 2011/5/7

Y1 - 2011/5/7

N2 - The study of biological systems commonly depends on inferring the state of a 'hidden' variable, such as an underlying genotype, from that of an 'observed' variable, such as an expressed phenotype. However, this cannot be achieved using traditional quantitative methods when more than one genetic mechanism exists for a single observable phenotype. Using a novel latent class Bayesian model, it is possible to infer the prevalence of different genetic elements in a population given a sample of phenotypes. As an exemplar, data comprising phenotypic resistance to six antimicrobials obtained from passive surveillance of Salmonella Typhimurium DT104 are analysed to infer the prevalence of individual resistance genes, as well as the prevalence of a genomic island known as SGI1 and its variants. Three competing models are fitted to the data and distinguished between using posterior predictive p-values to assess their ability to predict the observed number of unique phenotypes. The results suggest that several SGI1 variants circulate in a few fixed forms through the population from which our data were derived. The methods presented could be applied to other types of phenotypic data, and represent a useful and generic mechanism of inferring the genetic population structure of organisms.

AB - The study of biological systems commonly depends on inferring the state of a 'hidden' variable, such as an underlying genotype, from that of an 'observed' variable, such as an expressed phenotype. However, this cannot be achieved using traditional quantitative methods when more than one genetic mechanism exists for a single observable phenotype. Using a novel latent class Bayesian model, it is possible to infer the prevalence of different genetic elements in a population given a sample of phenotypes. As an exemplar, data comprising phenotypic resistance to six antimicrobials obtained from passive surveillance of Salmonella Typhimurium DT104 are analysed to infer the prevalence of individual resistance genes, as well as the prevalence of a genomic island known as SGI1 and its variants. Three competing models are fitted to the data and distinguished between using posterior predictive p-values to assess their ability to predict the observed number of unique phenotypes. The results suggest that several SGI1 variants circulate in a few fixed forms through the population from which our data were derived. The methods presented could be applied to other types of phenotypic data, and represent a useful and generic mechanism of inferring the genetic population structure of organisms.

KW - Anti-Bacterial Agents

KW - Bayes Theorem

KW - Drug Resistance, Multiple, Bacterial

KW - Genes, Bacterial

KW - Genetic Heterogeneity

KW - Genetics, Population

KW - Genomic Islands

KW - Genotype

KW - Humans

KW - Markov Chains

KW - Models, Biological

KW - Monte Carlo Method

KW - Phenotype

KW - Salmonella Infections

KW - Salmonella typhimurium

U2 - 10.1098/rspb.2010.1719

DO - 10.1098/rspb.2010.1719

M3 - Journal article

C2 - 20980306

VL - 278

SP - 1434

EP - 1440

JO - Proceedings of the Royal Society B: Biological Sciences

JF - Proceedings of the Royal Society B: Biological Sciences

SN - 0962-8452

IS - 1710

ER -

ID: 137015385