Linkage Analysis for Rheumatoid Arthritis Using Affected Sibling Pairs
Author Information
Author(s): Chiu Yen-Feng, Chiou Jeng-Min, Chen Yi-Shin, Kao Hui-Yi, Hsu Fang-Chi
Primary Institution: National Health Research Institutes
Hypothesis
Incorporating quantitative traits into linkage mapping can improve the identification of disease loci for rheumatoid arthritis.
Conclusion
Incorporating quantitative variables related to rheumatoid arthritis significantly improved the efficiency of identifying disease susceptibility loci.
Supporting Evidence
- The strongest evidence of linkage was observed on chromosome 6p with a p-value of 3.8 × 10-15.
- The standard error of the estimate of trait locus decreased about 28% to 35% after incorporating additional information from quantitative variables.
- Using a total of 375 microsatellite markers, the study analyzed data from 1096 affected sib pairs.
Takeaway
This study shows that using extra information about certain traits can help scientists find the genes that make people more likely to get rheumatoid arthritis.
Methodology
The study used a multipoint linkage approach with affected sib pairs and incorporated data on anti-CCP antibodies, IgM RF, and age at onset into genome-wide linkage scans.
Limitations
The analysis was limited by missing genotype information for some affected sib pairs and the reliance on specific biomarkers.
Participant Demographics
The study included 8017 individuals from 757 multiplex families, with about 90.6% being Caucasian.
Statistical Information
P-Value
3.80 × 10-15
Confidence Interval
[43.39, 48.34]
Statistical Significance
p<0.05
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