Incorporating quantitative variables into linkage analysis using affected sib pairs.
Rheumatoid arthritis is a complex disease in which environmental factors interact with genetic factors that influence susceptibility. Incorporating information about related quantitative traits or environmental factors into linkage mapping could therefore greatly improve the efficiency and precision of identifying the disease locus. Using a multipoint linkage approach that allows the incorporation of quantitative variables into multipoint linkage mapping based on affected sib pairs, we incorporated data on anti-cyclic citrullinated peptide antibodies, immunoglobulin M rheumatoid factor and age at onset into genome-wide linkage scans. The strongest evidence of linkage was observed on chromosome 6p with a p-value of 3.8 x 10(-15) for the genetic effect. The trait locus is estimated at approximately 45.51-45.82 cM, with standard errors of the estimates range from 0.82 to 1.26 cM, depending on whether and which quantitative variable is incorporated. The standard error of the estimate of trait locus decreased about 28% to 35% after incorporating the additional information from the quantitative variables. This mapping technique helps to narrow down the regions of interest when searching for a susceptibility locus and to elucidate underlying disease mechanisms.
Chiu
Yen-Feng
YF
Division of Biostatistics and Bioinformatics, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli 350 Taiwan, Republic of China. yfchiu@nhri.org.tw
Chiou
Jeng-Min
JM
Chen
Yi-Shin
YS
Kao
Hui-Yi
HY
Hsu
Fang-Chi
FC