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 tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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