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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

LOD score results from genome-wide linkage analysis.
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Figure 1: LOD score results from genome-wide linkage analysis.

Mentions: Directing our attention to chromosome 6, which has been previously reported to show a significant region of linkage (harboring the HLA-DRB1 locus) for RA, we found two regions of modest signal for each trait (see Fig. 1). One region detected for both count variables spanned 26 to 33 Mb. A second region for the tender count variable spanned 114 to 117 Mb, and a differing secondary region for the swollen count variable spanned 155 to 158 Mb. Several other regions of interest were also detected on other chromosomes (Fig. 1). Adjusting this multipoint analysis for linkage disequilibrium using MERLIN produced minor changes (Fig. 1).


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

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

LOD score results from genome-wide linkage analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: LOD score results from genome-wide linkage analysis.
Mentions: Directing our attention to chromosome 6, which has been previously reported to show a significant region of linkage (harboring the HLA-DRB1 locus) for RA, we found two regions of modest signal for each trait (see Fig. 1). One region detected for both count variables spanned 26 to 33 Mb. A second region for the tender count variable spanned 114 to 117 Mb, and a differing secondary region for the swollen count variable spanned 155 to 158 Mb. Several other regions of interest were also detected on other chromosomes (Fig. 1). Adjusting this multipoint analysis for linkage disequilibrium using MERLIN produced minor changes (Fig. 1).

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