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Gene x gene and gene x environment interactions for complex disorders.

Culverhouse R, Hinrichs AL, Jin CH, Suarez BK - BMC Proc (2007)

Bottom Line: We did not mistakenly identify any factors not in the generating model.We failed to identify two genetic loci modifying the risk of RA.After breaking the blind, we examined the true modeling factors in the first 50 data replicates and found that we would not have identified the additional factors as important even had we combined all the data from the first 50 replicates in a single data set.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Medicine, Washington University, 660 South Euclid, GMS-Box 8005, St, Louis, Missouri 63110, USA. rculverh@wustl.edu

ABSTRACT
The restricted partition method (RPM) provides a way to detect qualitative factors (e.g. genotypes, environmental exposures) associated with variation in quantitative or binary phenotypes, even if the contribution is predominantly an interaction displaying little or no signal in univariate analyses. The RPM provides a model (possibly non-linear) of the relationship between the predictor covariates and the phenotype as well as measures of statistical and clinical significance for the model.Blind to the generating model, we used the RPM to screen a data set consisting 1500 unrelated cases and 2000 unrelated controls from Replicate 1 of the Genetic Analysis Workshop 15 Problem 3 data for genetic and environmental factors contributing to rheumatoid arthritis (RA) risk. Both univariate and pair-wise analyses were performed using sex, smoking, parental DRB1 HLA microsatellite alleles, and 9187 single-nucleotide polymorphisms genotypes from across the genome. With this approach we correctly identified three genetic loci contributing directly to RA risk, and one quantitative trait locus for the endophenotype IgM level. We did not mistakenly identify any factors not in the generating model. All the factors we found were detectable with univariate RPM analyses. We failed to identify two genetic loci modifying the risk of RA. After breaking the blind, we examined the true modeling factors in the first 50 data replicates and found that we would not have identified the additional factors as important even had we combined all the data from the first 50 replicates in a single data set.

No MeSH data available.


Related in: MedlinePlus

Model for smoking vs sex. Models approximatelyadditive (R2 = 0.078). Mean = proportion of affected in each genotype group; N = total number of subjects in each genotype group.
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Figure 2: Model for smoking vs sex. Models approximatelyadditive (R2 = 0.078). Mean = proportion of affected in each genotype group; N = total number of subjects in each genotype group.

Mentions: A similar additive effect is found between sex and smoking (Figure 2). In this case, all four of the cells are found to be distinctly different from the others by the RPM. By themselves, sex and smoking account for approximately 5.3% and 2.6% of the trait variation, respectively. Jointly, they account for 7.8%.


Gene x gene and gene x environment interactions for complex disorders.

Culverhouse R, Hinrichs AL, Jin CH, Suarez BK - BMC Proc (2007)

Model for smoking vs sex. Models approximatelyadditive (R2 = 0.078). Mean = proportion of affected in each genotype group; N = total number of subjects in each genotype group.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2367466&req=5

Figure 2: Model for smoking vs sex. Models approximatelyadditive (R2 = 0.078). Mean = proportion of affected in each genotype group; N = total number of subjects in each genotype group.
Mentions: A similar additive effect is found between sex and smoking (Figure 2). In this case, all four of the cells are found to be distinctly different from the others by the RPM. By themselves, sex and smoking account for approximately 5.3% and 2.6% of the trait variation, respectively. Jointly, they account for 7.8%.

Bottom Line: We did not mistakenly identify any factors not in the generating model.We failed to identify two genetic loci modifying the risk of RA.After breaking the blind, we examined the true modeling factors in the first 50 data replicates and found that we would not have identified the additional factors as important even had we combined all the data from the first 50 replicates in a single data set.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Medicine, Washington University, 660 South Euclid, GMS-Box 8005, St, Louis, Missouri 63110, USA. rculverh@wustl.edu

ABSTRACT
The restricted partition method (RPM) provides a way to detect qualitative factors (e.g. genotypes, environmental exposures) associated with variation in quantitative or binary phenotypes, even if the contribution is predominantly an interaction displaying little or no signal in univariate analyses. The RPM provides a model (possibly non-linear) of the relationship between the predictor covariates and the phenotype as well as measures of statistical and clinical significance for the model.Blind to the generating model, we used the RPM to screen a data set consisting 1500 unrelated cases and 2000 unrelated controls from Replicate 1 of the Genetic Analysis Workshop 15 Problem 3 data for genetic and environmental factors contributing to rheumatoid arthritis (RA) risk. Both univariate and pair-wise analyses were performed using sex, smoking, parental DRB1 HLA microsatellite alleles, and 9187 single-nucleotide polymorphisms genotypes from across the genome. With this approach we correctly identified three genetic loci contributing directly to RA risk, and one quantitative trait locus for the endophenotype IgM level. We did not mistakenly identify any factors not in the generating model. All the factors we found were detectable with univariate RPM analyses. We failed to identify two genetic loci modifying the risk of RA. After breaking the blind, we examined the true modeling factors in the first 50 data replicates and found that we would not have identified the additional factors as important even had we combined all the data from the first 50 replicates in a single data set.

No MeSH data available.


Related in: MedlinePlus