Details

Autor(en) / Beteiligte

• Jakobsdottir, Johanna
• Conley, Yvette P
• Weeks, Daniel E
• Ferrell, Robert E
• Gorin, Michael B
• Weedon, Michael Nicholas

Titel

C2 and CFB genes in age-related maculopathy and joint action with CFH and LOC387715 genes

Ist Teil von

• PloS one, 2008-05, Vol.3 (5), p.e2199-e2199

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2008

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Age-related maculopathy (ARM) is a common cause of visual impairment in the elderly populations of industrialized countries and significantly affects the quality of life of those suffering from the disease. Variants within two genes, the complement factor H (CFH) and the poorly characterized LOC387715 (ARMS2), are widely recognized as ARM risk factors. CFH is important in regulation of the alternative complement pathway suggesting this pathway is involved in ARM pathogenesis. Two other complement pathway genes, the closely linked complement component receptor (C2) and complement factor B (CFB), were recently shown to harbor variants associated with ARM. We investigated two SNPs in C2 and two in CFB in independent case-control and family cohorts of white subjects and found rs547154, an intronic SNP in C2, to be significantly associated with ARM in both our case-control (P-value 0.00007) and family data (P-value 0.00001). Logistic regression analysis suggested that accounting for the effect at this locus significantly (P-value 0.002) improves the fit of a genetic risk model of CFH and LOC387715 effects only. Modeling with the generalized multifactor dimensionality reduction method showed that adding C2 to the two-factor model of CFH and LOC387715 increases the sensitivity (from 63% to 73%). However, the balanced accuracy increases only from 71% to 72%, and the specificity decreases from 80% to 72%. C2/CFB significantly influences AMD susceptibility and although accounting for effects at this locus does not dramatically increase the overall accuracy of the genetic risk model, the improvement over the CFH-LOC387715 model is statistically significant.