Inferring linkage disequilibrium from non-random samples.

backgroundLinkage disequilibrium (LD) plays a fundamental role in population genetics and in the current surge of studies to screen for subtle genetic variants affecting complex traits. Methods widely implemented in LD analyses require samples to be randomly collected, which, however, are usually ignored and thus raise the general question to the LD community of how the non-random sampling affects statistical inference of genetic association. Here we propose a new approach for inferring LD using a sample un-randomly collected from the population of interest.

resultsSimulation study was conducted to mimic generation of samples with various degrees of non-randomness from the simulated populations of interest. The method developed in the paper outperformed its rivals in adequately estimating the disequilibrium parameters in such sampling schemes. In analyzing a 'case and control' sample with beta-thalassemia, the current method presented robustness to non-random sampling in contrast to two commonly used methods.

conclusionsThrough an intensive simulation study and analysis of a real dataset, we demonstrate the robustness of the proposed method to non-randomness in sampling schemes and the significant improvement of the method to provide accurate estimates of the disequilibrium parameter. This method provides a route to improve statistical reliability in association studies.