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Application of bivariate mixed counting process models to genetic analysis of rheumatoid arthritis severity.

Sutradhar R, Pinnaduwage D, Bull SB - BMC Proc (2007)

Bottom Line: We sought to i) identify putative genetic determinants of the severity of rheumatoid arthritis in the NARAC (North American Rheumatoid Arthritis Consortium) data, ii) assess whether known candidate genes for disease status are also associated with disease severity in those affected, and iii) determine whether heterogeneity among the severity phenotypes can be explained by genetic and/or host factors.These questions are addressed by developing bivariate mixed-counting process models for numbers of tender and swollen joints to evaluate genetic association of candidate polymorphisms, such as DRB1, and selected single-nucleotide polymorphisms in known candidate genes/regions for rheumatoid arthritis, including PTPN22, and those in the regions identified by a genome-wide linkage scan of disease severity using the dense Illumina single-nucleotide polymorphism panel.Moreover, we found a gain in efficiency when using a bivariate compared to a univariate counting process model.

View Article: PubMed Central - HTML - PubMed

Affiliation: Samuel Lunenfeld Research Institute of Mount Sinai Hospital, 60 Murray Street, Box #18, Lebovic Building, 5th Floor, Prosserman Centre, Toronto, Ontario M5T 3L9, Canada. rinku.sutradhar@ices.on.ca

ABSTRACT
We sought to i) identify putative genetic determinants of the severity of rheumatoid arthritis in the NARAC (North American Rheumatoid Arthritis Consortium) data, ii) assess whether known candidate genes for disease status are also associated with disease severity in those affected, and iii) determine whether heterogeneity among the severity phenotypes can be explained by genetic and/or host factors. These questions are addressed by developing bivariate mixed-counting process models for numbers of tender and swollen joints to evaluate genetic association of candidate polymorphisms, such as DRB1, and selected single-nucleotide polymorphisms in known candidate genes/regions for rheumatoid arthritis, including PTPN22, and those in the regions identified by a genome-wide linkage scan of disease severity using the dense Illumina single-nucleotide polymorphism panel. The counting process framework provides a flexible approach to account for the duration of rheumatoid arthritis, an attractive feature when modeling severity of a disease. Moreover, we found a gain in efficiency when using a bivariate compared to a univariate counting process model.

No MeSH data available.


Related in: MedlinePlus

Likelihood ratio test p-values under univariate and bivariate models (with sex, smoking history, and DRB1) for SNPs on chromosome 1 and 6.
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Figure 3: Likelihood ratio test p-values under univariate and bivariate models (with sex, smoking history, and DRB1) for SNPs on chromosome 1 and 6.

Mentions: Note that, along with sex and smoking history, DRB1 was included in the model since it is significant based on the results of the association analysis above, and also because it has been previously reported to show strong linkage [1] and association with RA. Figure 3 consists of plots of the p-values for each SNP. The dashed line indicates a region-wide Bonferroni significance criterion, computed as the ratio of a selected significant p-value (0.05) over the number of tests performed.


Application of bivariate mixed counting process models to genetic analysis of rheumatoid arthritis severity.

Sutradhar R, Pinnaduwage D, Bull SB - BMC Proc (2007)

Likelihood ratio test p-values under univariate and bivariate models (with sex, smoking history, and DRB1) for SNPs on chromosome 1 and 6.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2367476&req=5

Figure 3: Likelihood ratio test p-values under univariate and bivariate models (with sex, smoking history, and DRB1) for SNPs on chromosome 1 and 6.
Mentions: Note that, along with sex and smoking history, DRB1 was included in the model since it is significant based on the results of the association analysis above, and also because it has been previously reported to show strong linkage [1] and association with RA. Figure 3 consists of plots of the p-values for each SNP. The dashed line indicates a region-wide Bonferroni significance criterion, computed as the ratio of a selected significant p-value (0.05) over the number of tests performed.

Bottom Line: We sought to i) identify putative genetic determinants of the severity of rheumatoid arthritis in the NARAC (North American Rheumatoid Arthritis Consortium) data, ii) assess whether known candidate genes for disease status are also associated with disease severity in those affected, and iii) determine whether heterogeneity among the severity phenotypes can be explained by genetic and/or host factors.These questions are addressed by developing bivariate mixed-counting process models for numbers of tender and swollen joints to evaluate genetic association of candidate polymorphisms, such as DRB1, and selected single-nucleotide polymorphisms in known candidate genes/regions for rheumatoid arthritis, including PTPN22, and those in the regions identified by a genome-wide linkage scan of disease severity using the dense Illumina single-nucleotide polymorphism panel.Moreover, we found a gain in efficiency when using a bivariate compared to a univariate counting process model.

View Article: PubMed Central - HTML - PubMed

Affiliation: Samuel Lunenfeld Research Institute of Mount Sinai Hospital, 60 Murray Street, Box #18, Lebovic Building, 5th Floor, Prosserman Centre, Toronto, Ontario M5T 3L9, Canada. rinku.sutradhar@ices.on.ca

ABSTRACT
We sought to i) identify putative genetic determinants of the severity of rheumatoid arthritis in the NARAC (North American Rheumatoid Arthritis Consortium) data, ii) assess whether known candidate genes for disease status are also associated with disease severity in those affected, and iii) determine whether heterogeneity among the severity phenotypes can be explained by genetic and/or host factors. These questions are addressed by developing bivariate mixed-counting process models for numbers of tender and swollen joints to evaluate genetic association of candidate polymorphisms, such as DRB1, and selected single-nucleotide polymorphisms in known candidate genes/regions for rheumatoid arthritis, including PTPN22, and those in the regions identified by a genome-wide linkage scan of disease severity using the dense Illumina single-nucleotide polymorphism panel. The counting process framework provides a flexible approach to account for the duration of rheumatoid arthritis, an attractive feature when modeling severity of a disease. Moreover, we found a gain in efficiency when using a bivariate compared to a univariate counting process model.

No MeSH data available.


Related in: MedlinePlus