As usual: “It depends’. Data on gene variants related to the first steps in drug metabolism can be quite useful both at home and clinically—e.g, your own responses to ethanol, caffeine, and many over-the-counter and prescribed drugs.
St Jude Children’s Research Hospital routinely genotypes/sequences children before drug treatments to optimize initial doses. It makes a huge difference in outcomes for most cancer patients.
But chronic age-related diseases that older individuals care about most are too complicated and too strongly affected by environmental factors to be well predicted by low coverage sequencing or genotyping platforms. Even deep sequencing and perfect telomere-to-telomere personal genome assemblies (still about a $10,000 to 20,000 effort) will not be sufficient. You really need the patient’s full history and deep omics data. Michael P Snyder and colleagues at Stanford are getting close to this type of “future preventive health care” with a focus on type 2 diabetes.
Polygenic risk scores based in simple GWAS results and additive genetic models are uninformative (or minimally useful) wrt clinical care for complex diseases—even those that have moderate heritability. There are simple way too many variables, too many undefined gene-by-environmental effects, and too many non-additive effects (epistasis). Polygenic risk scores typically account for less than 20% of variance in disease traits.
Coming around full circle though—-these platforms ARE useful for pharmacogenetic predictions of initial metabolic processing of drugs—- getting us closer to the right dose the first time.
And the SNP genotypes generated by 23andMe are also valuable predictors for a subset of variants that contribute to nearly monogenic disorders.
As usual: “It depends’. Data on gene variants related to the first steps in drug metabolism can be quite useful both at home and clinically—e.g, your own responses to ethanol, caffeine, and many over-the-counter and prescribed drugs.
St Jude Children’s Research Hospital routinely genotypes/sequences children before drug treatments to optimize initial doses. It makes a huge difference in outcomes for most cancer patients.
But chronic age-related diseases that older individuals care about most are too complicated and too strongly affected by environmental factors to be well predicted by low coverage sequencing or genotyping platforms. Even deep sequencing and perfect telomere-to-telomere personal genome assemblies (still about a $10,000 to 20,000 effort) will not be sufficient. You really need the patient’s full history and deep omics data. Michael P Snyder and colleagues at Stanford are getting close to this type of “future preventive health care” with a focus on type 2 diabetes.
https://pubmed.ncbi.nlm.nih.gov/?term=michael+mo+stanford&so...
Polygenic risk scores based in simple GWAS results and additive genetic models are uninformative (or minimally useful) wrt clinical care for complex diseases—even those that have moderate heritability. There are simple way too many variables, too many undefined gene-by-environmental effects, and too many non-additive effects (epistasis). Polygenic risk scores typically account for less than 20% of variance in disease traits.
Coming around full circle though—-these platforms ARE useful for pharmacogenetic predictions of initial metabolic processing of drugs—- getting us closer to the right dose the first time.
And the SNP genotypes generated by 23andMe are also valuable predictors for a subset of variants that contribute to nearly monogenic disorders.