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Accurate phenotyping: Reconciling approaches through Bayesian model averaging.

Abstract Genetic research into complex diseases is frequently hindered by a lack of clear biomarkers for phenotype ascertainment. Phenotypes for such diseases are often identified on the basis of clinically defined criteria; however such criteria may not be suitable for understanding the genetic composition of the diseases. Various statistical approaches have been proposed for phenotype definition; however our previous studies have shown that differences in phenotypes estimated using different approaches have substantial impact on subsequent analyses. Instead of obtaining results based upon a single model, we propose a new method, using Bayesian model averaging to overcome problems associated with phenotype definition. Although Bayesian model averaging has been used in other fields of research, this is the first study that uses Bayesian model averaging to reconcile phenotypes obtained using multiple models. We illustrate the new method by applying it to simulated genetic and phenotypic data for Kofendred personality disorder-an imaginary disease with several sub-types. Two separate statistical methods were used to identify clusters of individuals with distinct phenotypes: latent class analysis and grade of membership. Bayesian model averaging was then used to combine the two clusterings for the purpose of subsequent linkage analyses. We found that causative genetic loci for the disease produced higher LOD scores using model averaging than under either individual model separately. We attribute this improvement to consolidation of the cores of phenotype clusters identified using each individual method.
PMID
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Authors

Mayor MeshTerms

Genetic Loci

Genetic Predisposition to Disease

Models, Genetic

Keywords
Journal Title plos one
Publication Year Start




PMID- 28423058
OWN - NLM
STAT- MEDLINE
DA  - 20170419
DCOM- 20170425
LR  - 20170425
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 12
IP  - 4
DP  - 2017
TI  - Accurate phenotyping: Reconciling approaches through Bayesian model averaging.
PG  - e0176136
LID - 10.1371/journal.pone.0176136 [doi]
AB  - Genetic research into complex diseases is frequently hindered by a lack of clear 
      biomarkers for phenotype ascertainment. Phenotypes for such diseases are often
      identified on the basis of clinically defined criteria; however such criteria may
      not be suitable for understanding the genetic composition of the diseases.
      Various statistical approaches have been proposed for phenotype definition;
      however our previous studies have shown that differences in phenotypes estimated 
      using different approaches have substantial impact on subsequent analyses.
      Instead of obtaining results based upon a single model, we propose a new method, 
      using Bayesian model averaging to overcome problems associated with phenotype
      definition. Although Bayesian model averaging has been used in other fields of
      research, this is the first study that uses Bayesian model averaging to reconcile
      phenotypes obtained using multiple models. We illustrate the new method by
      applying it to simulated genetic and phenotypic data for Kofendred personality
      disorder-an imaginary disease with several sub-types. Two separate statistical
      methods were used to identify clusters of individuals with distinct phenotypes:
      latent class analysis and grade of membership. Bayesian model averaging was then 
      used to combine the two clusterings for the purpose of subsequent linkage
      analyses. We found that causative genetic loci for the disease produced higher
      LOD scores using model averaging than under either individual model separately.
      We attribute this improvement to consolidation of the cores of phenotype clusters
      identified using each individual method.
FAU - Chen, Carla Chia-Ming
AU  - Chen CC
AD  - Australian Institute of Marine Science, Cape Cleveland QLD, Australia.
AD  - ARC Centre of Excellence for Mathematical & Statistical Frontiers, Queensland
      University of Technology, Brisbane QLD, Australia.
FAU - Keith, Jonathan Macgregor
AU  - Keith JM
AUID- ORCID: http://orcid.org/0000-0002-9675-3976
AD  - School of Mathematical Sciences, Monash University, Clayton VIC, Australia.
FAU - Mengersen, Kerrie Lee
AU  - Mengersen KL
AD  - ARC Centre of Excellence for Mathematical & Statistical Frontiers, Queensland
      University of Technology, Brisbane QLD, Australia.
LA  - eng
PT  - Journal Article
DEP - 20170419
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - Bayes Theorem
MH  - Chromosome Mapping
MH  - Genetic Linkage
MH  - *Genetic Loci
MH  - *Genetic Predisposition to Disease
MH  - Humans
MH  - Microsatellite Repeats
MH  - *Models, Genetic
MH  - Passive-Aggressive Personality Disorder/classification/diagnosis/*genetics
MH  - Phenotype
EDAT- 2017/04/20 06:00
MHDA- 2017/04/26 06:00
CRDT- 2017/04/20 06:00
PHST- 2016/09/01 [received]
PHST- 2017/04/05 [accepted]
AID - 10.1371/journal.pone.0176136 [doi]
AID - PONE-D-16-34872 [pii]
PST - epublish
SO  - PLoS One. 2017 Apr 19;12(4):e0176136. doi: 10.1371/journal.pone.0176136.
      eCollection 2017.

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