PMID- 16918914
OWN - NLM
STAT- MEDLINE
DCOM- 20060928
LR  - 20071114
IS  - 0006-341X (Print)
IS  - 0006-341X (Linking)
VI  - 62
IP  - 2
DP  - 2006 Jun
TI  - Stochastic search gene suggestion: a Bayesian hierarchical model for gene 
      mapping.
PG  - 495-503
AB  - Mapping the genes for a complex disease, such as diabetes or rheumatoid arthritis 
      (RA), involves finding multiple genetic loci that may contribute to the onset of 
      the disease. Pairwise testing of the loci leads to the problem of multiple 
      testing. Looking at haplotypes, or linear sets of loci, avoids multiple tests but 
      results in a contingency table with sparse counts, especially when using marker 
      loci with multiple alleles. We propose a hierarchical Bayesian model for 
      case-parent triad data that uses a conditional logistic regression likelihood to 
      model the probability of transmission to a diseased child. We define hierarchical 
      prior distributions on the allele main effects to model the genetic dependencies 
      present in the human leukocyte antigen (HLA) region of chromosome 6. First, we 
      add a hierarchical level for model selection that accounts for both locus and 
      allele selection. This allows us to cast the problem of identifying genetic loci 
      relevant to the disease into a problem of Bayesian variable selection. Second, we 
      attempt to include linkage disequilibrium as a covariance structure in the prior 
      for model coefficients. We evaluate the performance of the procedure with some 
      simulated examples and then apply our procedure to identifying genetic markers in 
      the HLA region that influence risk for RA. Our software is available on the 
      website http://www.epigenetic.org/Linkage/ssgs-public/.
FAU - Swartz, Michael D
AU  - Swartz MD
AD  - Department of Statistics, Texas A&M University, 3143 TAMU, College Station, Texas 
      77843, USA. mswartz@stat.tamu.edu
FAU - Kimmel, Marek
AU  - Kimmel M
FAU - Mueller, Peter
AU  - Mueller P
FAU - Amos, Christopher I
AU  - Amos CI
LA  - eng
GR  - 5 R25 CA 090301-04/CA/NCI NIH HHS/United States
GR  - N01 AR02249/AR/NIAMS NIH HHS/United States
GR  - R01AR44422/AR/NIAMS NIH HHS/United States
GR  - R01ES09912/ES/NIEHS NIH HHS/United States
GR  - R01HG02275/HG/NHGRI NIH HHS/United States
GR  - R25 CA57730/CA/NCI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PL  - England
TA  - Biometrics
JT  - Biometrics
JID - 0370625
RN  - 0 (HLA Antigens)
SB  - IM
MH  - Alleles
MH  - *Bayes Theorem
MH  - Biometry
MH  - Chromosome Mapping/*statistics & numerical data
MH  - Chromosomes, Human, Pair 6/genetics
MH  - Data Interpretation, Statistical
MH  - HLA Antigens/genetics
MH  - Humans
MH  - Markov Chains
MH  - *Models, Genetic
MH  - Models, Statistical
MH  - Monte Carlo Method
MH  - Software
MH  - Stochastic Processes
EDAT- 2006/08/22 09:00
MHDA- 2006/09/29 09:00
CRDT- 2006/08/22 09:00
PHST- 2006/08/22 09:00 [pubmed]
PHST- 2006/09/29 09:00 [medline]
PHST- 2006/08/22 09:00 [entrez]
AID - BIOM451 [pii]
AID - 10.1111/j.1541-0420.2005.00451.x [doi]
PST - ppublish
SO  - Biometrics. 2006 Jun;62(2):495-503. doi: 10.1111/j.1541-0420.2005.00451.x.