This is what your next doctor’s visit will sound like after you get your genome sequenced:
“Analysis of 2·6 million single nucleotide polymorphisms and 752 copy number variations showed increased genetic risk for myocardial infarction, type 2 diabetes, and some cancers. We discovered rare variants in three genes that are clinically associated with sudden cardiac death—TMEM43, DSP, and MYBPC3. A variant in LPA was consistent with a family history of coronary artery disease. The patient had a heterozygous null mutation in CYP2C19 suggesting probable clopidogrel resistance, several variants associated with a positive response to lipid-lowering therapy, and variants in CYP4F2 and VKORC1 that suggest he might have a low initial dosing requirement for warfarin. Many variants of uncertain importance were reported….”
It’s been obvious for some time that cost will soon be no obstacle to getting your genome sequenced as part of a routine clinical workup. What’s been less clear is just how useful that is going to be, and how physicians should go about incorporating a patient’s genome sequence into routine clinical decisions. (Check out a discussion of where costs are now here.)
We can argue about how to go about bringing sequence data into the clinic, but perhaps the best way to get started is to just give it a try – which is exactly what a group of researchers at Harvard and Stanford have done. They’re reporting in The Lancet their trial run of a first whole genome clinical workup:
We assessed a patient with a family history of vascular disease and early sudden death. Clinical assessment included analysis of this patient’s full genome sequence, risk prediction for coronary artery disease, screening for causes of sudden cardiac death, and genetic counselling. Genetic analysis included the development of novel methods for the integration of whole genome and clinical risk. Disease and risk analysis focused on prediction of genetic risk of variants associated with mendelian disease, recognised drug responses, and pathogenicity for novel variants. We queried disease-specific mutation databases and pharmacogenomics databases to identify genes and mutations with known associations with disease and drug response. We estimated post-test probabilities of disease by applying likelihood ratios derived from integration of multiple common variants to age-appropriate and sex-appropriate pre- test probabilities. We also accounted for gene-environment interactions and conditionally dependent risks.
The Finch and Pea 