Self-pay pharmacogenomic panels run roughly $200-$500, Medicare and commercial plans cover them only under narrow conditions, and a single whole-genome sequence answers the same questions permanently. Here is the pricing, the billing codes, and how to run the interpretation yourself.
A technical comparison of consumer genotyping arrays, clinical panels, and whole-genome sequencing, including the raw file formats each returns and what you can do with them.
A direct look at what a maximal blood panel contains and which assays add real information. Covers marketing claims and getting the raw data.
Pharmacogenomic panels like myDNA and GeneSight genotype a short list of variants and return a color-coded report. Here is what those genes do, how to call the same star alleles yourself from whole-genome sequencing, and where the evidence stops.
What the post-meal glucose numbers are for people without diabetes, where the thresholds come from, and how to compute peak, time-to-peak, and incremental AUC from your own CGM export.
A working pipeline for analyzing your own DNA with open source tools, from array raw data to whole-genome FASTQ, including the annotation databases, the commands, and the failure modes that produce wrong answers.
What annual bloodwork is worth drawing, why vendor 'optimal ranges' are weaker than they look, and how to build a personal baseline using biological variation and reference change values.
You can run a nanopore sequencer on your kitchen table and get real reads, but you cannot get clinical-grade whole-genome coverage that way. Here is what home sequencing produces, what it costs, and where sending a sample out is the better call.
A working end-to-end guide to going from FASTQ to a filtered, benchmarked VCF on a personal 30x human genome, with the specific tools, flags, reference files, and failure modes we would use.
What a WGS service delivers, what the files look like, what coverage and platform choices change, and how to tell a real provider from a reseller.
A technical answer to what makes genetic testing private: who holds the raw files, what the consent terms permit, how re-identifiable the data is, and how to store and analyze a genome yourself.
A technical guide to choosing and using a biomarker testing service: what analytes are worth measuring, what the analytical error looks like, how to store and version the results, and where a clinician has to be involved.
The best blood test tracker is a long-format table you control plus fifty lines of Python. No app is needed. Covers the schema and LOINC codes.
How to genotype ABO and RhD from whole-genome sequencing data, and where short reads fail. A genomic call is not a transfusion-grade answer.
A working guide to running DESeq2 on bulk RNA-seq counts. Covers the count matrix, dispersion, shrinkage and the sanity checks that catch most mistakes.
A worked walkthrough of what real DNA results look like: array text files, VCF lines field by field, coverage and callability checks, annotation with VEP, and what a 'positive' result does and does not mean.
A practical protocol for estimating functional age from physical performance tests, blood chemistry algorithms, and DNA methylation clocks, with the reliability problems that make most single-timepoint results uninterpretable.
A breakdown of the genomic testing market by category — clinical labs, direct-to-consumer arrays, research-grade sequencing providers, and tumor profiling — with the file formats, coverage depths, and failure modes that decide whether the data is useful to you.
No single lifespan blood test exists. A small set of analytes together predicts all-cause mortality. How to order, store and recompute them.
What direct-access lab testing is, how self-ordered blood work is priced, and how to store and interpret your own results as structured data rather than PDFs.