WGS, SNP test and WES

If you are generating new genomic data and want to use Genome as broadly as possible later, WGS is the sensible choice. It keeps more information in one dataset, allows later reinterpretation and turns one sequencing run into a reusable local foundation. SNP tests and WES are not wrong, but they answer narrower questions.

An SNP test or microarray checks predefined positions. It is inexpensive and can be enough for ancestry, single known markers or simple rsID questions. The limitation is structural: anything not on the array was not measured. That is why 23andMe or Ancestry raw data are not a replacement for WGS when health questions, PGx, HLA context or later reanalysis matter.

Whole exome sequencing mainly reads protein-coding regions. That can be useful for exome questions, especially when the question clearly targets genes and protein changes. For Genome as a general workbench, WES remains narrower: non-coding regions, many regulatory contexts and some later analyses are missing or weaker.

Whole genome sequencing covers the genome more broadly. Genome can use it to look up known SNPs, contextualise variants, derive PGx context, use HLA-adjacent tools and ask later questions against the same dataset. The key advantage is not only more content, but reanalysis: when a new database, report or tool arrives, retesting is not necessarily required.

If a provider offers several exports, keep the most complete files. FASTQ contains raw reads, BAM or CRAM the alignment, VCF the variant list. A small text file with selected consumer markers is convenient, but it limits later analysis. For Genome, complete sequencing data are the more robust foundation.

WGS is the technical default recommendation for broad Genome use, but it is not a medical automatic answer. Quality, laboratory process, coverage, privacy, price, file access and the concrete question remain important. A WGS dataset does not replace a medical diagnosis or clinical confirmation when a finding could have medical consequences.