The Most Comprehensive Top-Down Genomic Analysis Available

As testing for single susceptibility genes is widely considered of limited value, each panel displays the “top ten” genetic variations shown to exert the greatest effect on disease susceptibility.  Risk scoring integrates weak effects of multiple single nucleotide polymorphisms (SNPs) and has been shown to enhance predictability of the most common diseases.

A weighted GRS is calculated based on the number of risk alleles carried at each locus, weighted by effect size (odds ratio).  Odds ratios for common diseases associated with each variant panel were derived from the peer-reviewed literature.  Where available, gene-wide association studies (GWAS) and meta-analyses were used. Where meta-analyses were not available, the odds ratios were taken from the largest well performed studies.

And this is just the first step.

A Health Care Process Enabled By an AI-Driven Clinical Decision Support System (CDSS)

There is little research on genomics-based practice protocols or clinical decision support systems (CDSS’s) that incorporate genomic information technologies at the point-of-care.

We developed a CDSS whose user interface combines raw DNA sequence variant data and gut microbial biomarkers to enable a high level, holistic interpretation of the holobiome. Our goal was to individualize therapeutic and preventive interventions, based on insights gleaned from two omics data sources and advanced laboratory biomarkers for oxidative stress and inflammation.

While several studies have shown that polygenic risk analysis accurately predicts the individual risk of developing a chronic disease, our model attempts to extend risk stratification to identify molecularly defined subsets of individuals. Specifically, our CDSS clusters polymorphisms in multiple genetic loci that contribute to the pathogenesis of many diseases: glucose and lipid dysregulation, endothelial dysfunction, mitochondrial stress, deficiencies in DNA repair capacity, circadian disruption, maladaptive emotion regulation, and a dysbiotic gut microbiome.

We believe that intelligent decision support tools are crucial to the integration of heterogenous inputs from ‘omic’ technologies and evidence-based clinical regimens. Towards that end, we have designed an application that allows for easy, reliable, and rapid assessment of genotype-phenotype relationships. Output is relevant and intelligible to both physicians and patients. In short, our vision of a genomic CDSS fully embraces the concept of the clinical holobiome.