Abstract
Genome scan meta-analysis (GSMA) can prove very useful in detecting genetic effects too small to be detected in an individual linkage study and can also lead to more consistent results. In this paper, we propose a new kernel-based estimation procedure for GSMA. Instead of estimating identity by descent between markers, as performed in interval mapping approaches, we estimated directly the nonparametric linkage score between markers using a kernel procedure. The GSMA is then extended to take into account the kernel estimate of the nonparametric linkage score and its variance at a given chromosomal position. The method is applied to the rheumatoid arthritis genome scan data (Genetic Analysis Workshop 15 Problem 2).
Highlights
Rheumatoid arthritis (RA) is a chronic inflammatory disease that primarily affects the synovial tissues of multiple joints in the body
Genome scan meta-analysis (GSMA) that combines the results from several linkage studies can have greater statistical power to detect small genetic effects and can lead to more consistent results
2 performs identically to Method 1, and this was expected because the kernel estimation is only used to smooth the information content and nonparametric linkage (NPL) score functions
Summary
Rheumatoid arthritis (RA) is a chronic inflammatory disease that primarily affects the synovial tissues of multiple joints in the body. Several genome scans for RA studies have been performed to identify susceptibility loci, but most of the results have not been replicated [1] These inconsistencies could arise from the small sample size, low statistical power, and clinical or genetic heterogeneity of these studies. Genome scan meta-analysis (GSMA) that combines the results from several linkage studies can have greater statistical power to detect small genetic effects and can lead to more consistent results. Because of differences in marker maps across studies, they decided to align the marker maps after performing some interval mapping and combining nonparametric linkage (NPL) scores obtained from GeneHunter for markers in a pre-specified interval Their method requires the estimation of identity-by-descent (IBD) sharing probabilities through the interval between two markers [4,5], which can be somewhat inaccurate and imprecise.
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