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A genome-wide ordered-subset linkage analysis for rheumatoid arthritis
Desh Deep Mandhyan1, Xana Kim-Howard, Matthew Gaines
1Genetic Epidemiology Unit, Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 Northeast 13th Street, Oklahoma City, Oklahoma 73104, USA. Mandhyand@omrf.ouhsc.edu
BMC Proceedings
|May 10, 2008
Summary
Ordered-subset analysis (OSA) improves rheumatoid arthritis (RA) linkage detection by reducing genetic heterogeneity. This technique identified significant linkage signals on chromosomes 2, 4, 9, 18, and 19, aiding in understanding RA
Area of Science:
- Genetics
- Rheumatology
- Statistical Genetics
Background:
- Rheumatoid arthritis (RA) is a prevalent, heterogeneous autoimmune disease with unknown causes.
- Genetic linkage analysis for RA is challenging due to significant heterogeneity.
- Ordered-subset analysis (OSA) is a statistical method designed to address heterogeneity and enhance linkage detection.
Purpose of the Study:
- To apply ordered-subset analysis (OSA) to identify genomic regions linked to rheumatoid arthritis (RA).
- To investigate the utility of 'age of onset' and 'anti-CCP level' as covariates in OSA for RA.
- To improve statistical power and reduce heterogeneity in RA genetic studies.
Main Methods:
- Genome-wide linkage analysis using the ordered-subset analysis (OSA) technique.
- Utilized data from the North American Rheumatoid Arthritis Consortium (NAIAC) study (GAW15).
- Incorporated 'age of onset' and 'anti-CCP level' (anti-cyclic citrullinated peptide) as covariates with 809 Illumina SNP markers in 5713 individuals from 606 Caucasian RA families.
Main Results:
- Significant increases in nonparametric linkage (NPL) scores were observed for 'age of onset' on chromosomes 4 (p=0.000003) and 9 (p=0.002).
- Significant increases in NPL scores were observed for 'anti-CCP level' on chromosomes 2 (p=0.0001), 18 (p=0.00007), and 19 (p=0.0003).
- OSA demonstrated improved evidence for linkage at identified loci.
Conclusions:
- Ordered-subset analysis (OSA) effectively reduces heterogeneity and increases statistical power for detecting linkage in rheumatoid arthritis (RA).
- Covariates 'age of onset' and 'anti-CCP level' are valuable for refining linkage signals in RA.
- OSA is a powerful tool for identifying informative datasets and advancing genetic studies in complex diseases like RA.
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