Applications

Childhood Disease

For customers interested in identifying potentially causative rare variants underlying a disease or trait, RTG delivers accurate genome analytics by integrating familial relationships during the analytical process.

You can't interpret what you don't see.

A key contributor to the recent success of exome sequencing in elucidating causal variants responsible for Mendelian diseases is the analysis of parent/affected-offspring trios. In this context, Real Time Genomics technology delivers less false negative results by jointly and simultaneously integrating data from the proband with its parents, resulting in more effective coverage at each site and thus more power to identify true variants. Real Time Genomics delivers higher sensitivity in identifying medically relevant mutations as compared with alternative open source methods, with no tradeoff in specificity or time-to-result. Our technology delivers high sensitivity for de novo dominant, inherited dominant, homozygous recessive, compound heterozygous, and X-linked variants. The result is the most powerful analytical platform for delivering accurate and comprehensive variants in the study of early childhood disease.

de novo mutations 

The identification of de novo mutations is crucial when analyzing data from affected offspring with healthy parents that are not closely related. In fact, it is estimated that nearly half of all early neurodevelopmental childhood disorders are a result of de novo mutations and likely a similar fraction of neonatal/prenatal cases. The problems of variant identification from high-throughput sequence data are magnified when looking for de novo mutations as this enriches for sequencing artifacts. RTG technology uses Mendelian segregation to accurately detect these critical de novo events while reducing false positive candidates by up to 70-fold during the analytical process. 

Less hay in the haystack

The identification of causal variants in Mendelian diseases using whole genome or exome sequence data revolves around complexity reduction and the avoidance of spurious results from false positives. Spurious, false positive variants generate an unnecessary workload by clinicians, sometimes appearing more deleterious than the real variants. rtgVariant scores variant genotypes using advanced Bayesian techniques with priors based in human polymorphism rates, sequencing platform error rates, and Mendelian segregation. In addition, a complementary adaptive rescoring system takes into account context effects correlated with false positives though a machine learning approach. These more accurate variant scores allow the user to assess the likelihood of a variant being a false positive during analysis and, if desired, filter these from downstream analysis without sacrificing true positives. The result is a 70-fold reduction in Mendelian inconsistent candidate causal variants.