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This article is part of the supplement: Genetic Analysis Workshop 15: Gene Expression Analysis and Approaches to Detecting Multiple Functional Loci

Open Access Proceedings

Incorporating quantitative variables into linkage analysis using affected sib pairs

Yen-Feng Chiu1*, Jeng-Min Chiou2, Yi-Shin Chen3, Hui-Yi Kao1 and Fang-Chi Hsu4

Author affiliations

1 Division of Biostatistics and Bioinformatics, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli 350 Taiwan, Republic of China

2 Institute of Statistical Science, Academia Sinica, 128 Academia Road, Taipei 115, Taiwan, Republic of China

3 Department of Nursing, Yuanpei University, 306 Yuanpei Street, Hsinchu 30015, Taiwan, Republic of China

4 Department of Biostatistical Sciences, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157, USA

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Citation and License

BMC Proceedings 2007, 1(Suppl 1):S98  doi:

Published: 18 December 2007

Abstract

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 × 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.