Entropy-supported marker selection and Mantel statistics for haplotype sharing analysis.

Haplotype sharing analysis is a well-established option for the investigation of the etiology of complex diseases. The statistical power of haplotype association methods depends strongly on how the information of unobserved haplotypes can be captured by multilocus genotypes. In this study we combine an entropy-based marker selection algorithm (EMS), with a haplotype sharing-based Mantel statistics into a new algorithm. Genetic markers are iteratively selected by their multilocus linkage disequilibrium (LD), which is assessed by a normalized entropy difference. The initial marker set is gradually enlarged to increase the available information on the amount of sharing around a potential susceptibility marker. Markers are rejected from joint phasing if they do not increase the multilocus LD. In simulated candidate gene studies, the Mantel statistics combined with the new EMS performs as well or better at detecting the disease single nucleotide polymorphism-or in indirect association analysis its flanking markers-than the Mantel statistics without selection of markers prior to haplotype estimation and the Mantel statistics using sliding windows of size five. It is therefore appealing to apply our selection approach for haplotype-based association analysis, since marker selection driven by the observed data avoids both the arbitrary choice of markers when using a fixed window size, as well as the estimation of haplotype block structure.