Abstract Background Next-generation sequencing is widely used to identify disease-causing variants in patients with rare genetic disorders. Identifying those variants from whole-genome or exome data can be both scientifically challenging and time consuming. A significant amount of time is spent on variant annotation, and interpretation. Fully or partly automated solutions are therefore needed to streamline and scale this process. Results We describe Phenotype Driven Ranking (PDR), an algorithm integrated into Ingenuity Variant Analysis, that uses observed patient phenotypes to prioritize diseases and genes in order to expedite causal-variant discovery. Our method is based on a network of phenotype-disease-gene relationships derived from the QIAGEN Knowledge Base, which allows for efficient computational association of phenotypes to implicated diseases, and also enables scoring and ranking. Conclusions We have demonstrated the utility and performance of PDR by applying it to a number of clinical rare-disease cases, where the true causal gene was known beforehand. It is also shown that PDR compares favorably to a representative alternative tool.

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algorithm method nextgeneration sequencing ingenuity variant analysis scoring rare genetic observed patient phenotypes diseasecausing variants wholegenome or exome data clinical raredisease cases phenotype driven ranking pdr implicated diseases network of phenotypediseasegene relationships computational association of phenotypes qiagen knowledge

,.Leveraging network analytics to infer patient syndrome and identify causal genes in rare disease cases. 18 (S5),.

,"Leveraging network analytics to infer patient syndrome and identify causal genes in rare disease cases" 18

,"Leveraging network analytics to infer patient syndrome and identify causal genes in rare disease cases"