Genotype-specific Preventative Care
At The Harlin Center, we are practicing medicine with AI (artificial intelligence) today — that is genomics-based primary care medicine. To determine which of an individual’s genetic variants might have functional consequences, we developed a computer application that matches each patient’s unique genomic data with clinically significant variants published in the The National Human Genome Research Institute (NHGRI) Catalog of Genome-Wide Association Studies (GWAS) and genetic research curated within the NCBI ClinVar database.
Clinical Genetic Testing Analytics and Genetic Risk Scoring
Thousands of studies have examined the relationship between genetic variations and the risk of disease. But among all of that research exists a subset of genetic variants that appear to exert a moderate to large effect on disease causation. And since the NCBI evidence base derives from well-conducted systematic reviews and meta-analyses, it serves as a key resource in the evidence-based practice of precision medicine. Notably, it is that parcel of genetic risk factors that appear modifiable by explicit changes in lifestyle that we seek to target. For our overarching goal is to help patients avoid disease and enjoy life to its fullest.
Our Application Allows Physicians to Easily Perform Genetic Risk Assessment
Our application allows us to easily perform genetic risk assessment from a widely available DNA genetic testing service and a gut microbiome sequencing service.
Our AI engine enables us to apply cutting-edge research pertaining to the molecular basis for genetic risk in a clinical setting with just three button clicks. With just one button click, physicians can screen patients for susceptibility genes in 16 of the most common cardiometabolic diseases, neurodegenerative disorders, and cancer.
Click again and see the molecular mechanisms driving those risks. Click again and get a physician modifiable automated prescription based on the patient’s at-risk genes.
Gene profiles are composed of variants that appear to play an important role in disease pathogenesis. Columns are sorted based on the number of risk alleles carried at each locus weighted by effect size (odds ratio).
Run a query.
Seen above are the top ten risk alleles for Alzheimer’s disease, identified by HuGENet, the Centers for Disease Control and Prevention’s genomic project. (1,2)
Point-of-Care Risk Stratification
Precision medicine provides individualized health care on the basis of each patient’s genetic profile. Understanding the underlying molecular mechanisms contributing to disease risk is a core element in making better clinical decisions. Our expert system integrates genomic knowledge and molecular mechanisms with clarity and speed.
Our ability to rapidly generate genetic risk profiles through computer-assisted data mining of large-scale and gene-wide association studies provides unprecedented opportunities for personalized healthcare.
Artificial Intelligence, Not Just Software
Our AI engine uses a carefully curated research database that ensures the lead variants for each disease or mechanism of disease are identified and provide for an accurate clinical assessment.
- match multiple genetic variants, including pleiotropic genetic variants, to Gene-wide Association Studies (GWAS) and large-scale meta-analyses
- cluster lead variants by disease and provides references for two or more GWAS studies for multiple SNPs
- assemble a polygenic screen for mechanisms of disease (e.g. glucose dysregulation, dyslipidemia, genome instability)
- calculate genetic risk scores for each gene panel using the weighted sum of the risk allele counts where the weight for each individual variant is determined by the log-odds-ratio of its association with the disease (3,4)
- display aberrant genetic variants, ranked by effect size and zygosity
- provide preventive health care recommendations automatically defined from each individual’s genetic susceptibility
To see samples and descriptions of our genomic reports, see gene reports.
To see a sample and description of our microbiome report, see microbiome reports.
- Bertram L, McQueen MB, Mullin K, Blacker D, Tanzi RE. Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database. Nat Genet. 2007 Jan;39(1):17-23.
- Olgiati P, Politis AM, Papadimitriou GN, De Ronchi D, Serretti A. Genetics of late-onset Alzheimer’s disease: update from the alzgene database and analysis of shared pathways. Int J Alzheimers Dis. 2011;2011:832379.
- Pearson TA, Manolio TA. How to Interpret a Genome-wide Association Study. JAMA. 2008 Mar 19;299(11):1335-44.
- Janssens AC, Aulchenko YS, Elefante S, Borsboom GJ, Steyerberg EW, van Duijn CM. Predictive testing for complex diseases using multiple genes: fact or fiction? Genet Med. 2006 Jul;8(7):395-400.