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Rare Variant Association Studies

Yippie, our new paper about rare variant association studies, RVAS, just appeared in Bioinformatics! In this work we describe strategies to optimise the probability to detect the disease-causing mutations in a cohort of patients. Obviously, the detection power depends on the size of your case group and the genetic variability of the true disease gene. We tested multiple Mendelian disorders and found that our approach outperformed the existing analysis strategies that are based on simple intersection filtering. In the figure below you can see three example studies from the literature, a cohort of 10 patients with Kabuki make-up syndrome, 7 patients with Catel-Manzke syndrome, 13 patients with Mabry syndrome, where the disease causing gene can be readily identified. A suitable matching technique for the controls helps to decrease spurious artifacts from heterogeneous data quality and population backgrounds.

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