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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

RPM model for the DR alleles inherited from bothparents (R2 = 0.56). Mean = proportion of affected in each genotype group;N = total number of subjects in each genotype group.
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Figure 1: RPM model for the DR alleles inherited from bothparents (R2 = 0.56). Mean = proportion of affected in each genotype group;N = total number of subjects in each genotype group.

Mentions: Combining the DR alleles inherited from the mother and the father results in the two-factor model with the highest R2. Individually, these factors each account for approximately 37% of the trait variation (see Table 1). When analyzed jointly, this jumps to 56%. The model chosen by the RPM is illustrated in Figure 1. The values in the grid indicate the number of subjects in each of the categories, "Mean" gives the proportion of subjects in each group who are affected, and "N" gives the total number of individuals in each group in the final model. The model is symmetric about the main diagonal, indicating that the effect of inheriting a risk allele from either parent is approximately the same. The diagonally banded pattern indicates that the effects of these two factors are approximately additive. This suggests that a single-locus genotypic analysis would provide approximately the same information as keeping the parental alleles as two separate factors. For some later analyses, we followed this approach.


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

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

RPM model for the DR alleles inherited from bothparents (R2 = 0.56). 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 1: RPM model for the DR alleles inherited from bothparents (R2 = 0.56). Mean = proportion of affected in each genotype group;N = total number of subjects in each genotype group.
Mentions: Combining the DR alleles inherited from the mother and the father results in the two-factor model with the highest R2. Individually, these factors each account for approximately 37% of the trait variation (see Table 1). When analyzed jointly, this jumps to 56%. The model chosen by the RPM is illustrated in Figure 1. The values in the grid indicate the number of subjects in each of the categories, "Mean" gives the proportion of subjects in each group who are affected, and "N" gives the total number of individuals in each group in the final model. The model is symmetric about the main diagonal, indicating that the effect of inheriting a risk allele from either parent is approximately the same. The diagonally banded pattern indicates that the effects of these two factors are approximately additive. This suggests that a single-locus genotypic analysis would provide approximately the same information as keeping the parental alleles as two separate factors. For some later analyses, we followed this approach.

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