What a Genetic Panel Tests For, and When Sequencing the Whole Genome Is the Better Buy

A genetic panel is a targeted sequencing assay: a fixed list of genes (usually 20 to 500) is captured with biotinylated probes or amplified with PCR primers, sequenced to high depth, and analyzed against that list. A “full panel” is a readout of whatever the lab chose to put on the panel, typically the coding exons plus 10 to 20 flanking intronic bases. The word “full” does not mean a full readout of your genome, and the report will only mention genes on that list. Prices in the US run roughly $250 to $1,000 for a hereditary cancer or cardiomyopathy panel through a commercial lab, higher through a hospital, and often $0 out of pocket when a clinician orders it and insurance covers it. Whole-genome sequencing at 30x now costs less than many mid-size clinical panels at list price, which changes the calculus considerably.

The short version of our view: if you have a specific clinical question and a clinician ordering the test, take the panel, because the interpretation, the CLIA-grade confirmation, and the variant classification are the product you are buying. If you want data you own and can re-analyze for the next thirty years, sequence the genome once at 30x or better, keep the CRAM, and run panel-style analysis in software as often as you like.

What is physically different between a panel and a genome

A hybrid-capture panel uses oligo probes tiled across target regions. Off-target reads get thrown away, and the on-target reads pile up: typical clinical panels report mean coverage of 500x to 1,500x, with a quality gate like “≥99% of target bases at ≥50x.” That depth is the point. It lets the lab call mosaic variants and low-frequency somatic variants that a 30x genome would miss, which is why panels dominate tumor profiling, where the variant of interest may be present in 5% of reads from a heterogeneous biopsy ¹.

A 30x whole genome gives you roughly 30 reads per position across 3.1 Gb, including introns, promoters, untranslated regions, and the intergenic space where GWAS hits live. You lose depth, you gain breadth, and you gain structural variant detection: a panel cannot see a 40 kb deletion whose breakpoints fall outside its probes, and it cannot see a balanced translocation at all. Genomes also give even, GC-insensitive coverage. That lets them resolve exons that capture kits chronically under-cover (GC-rich first exons and paralog-heavy regions like PMS2 exons 11-15 or SMN1/SMN2).

The practical consequence for coding regions: PCR-free WGS at 30x calls exonic SNVs and small indels about as well as exome capture, and better in the regions capture misses. In hypertrophic cardiomyopathy, whole-genome sequencing recovered the pathogenic variants found by a sarcomere panel and additionally detected variants outside panel coverage, though most of what it added was of uncertain significance ². In a racially and ethnically diverse pediatric cohort, genome sequencing produced a higher molecular diagnostic yield than targeted panel testing, with the gap driven largely by variants panels were not designed to see ³.

The real cost comparison

Panel list prices look low because the assay is cheap. The nationwide French costing study of NGS panels in routine oncology practice put the full per-patient cost of a targeted panel, including labor, amortized instrument time, and bioinformatics, at several hundred euros, with wide variation across labs driven mostly by throughput ⁴. Reagents are a minority of that. Interpretation and reporting are a large fraction, and they do not scale down with sequencing costs.

That matters for comparing to WGS. Sequencing chemistry for a 30x human genome has fallen below $200 in reagents at scale. The clinical cost of a genome is still dominated by the same fixed costs as a panel. Those costs are sample handling, confirmation Sanger, variant curation, genetic counseling and reporting. A systematic review of economic evaluations found that cost-effectiveness evidence for whole-exome and whole-genome sequencing was limited and methodologically inconsistent, with most studies not capturing downstream costs of follow-up testing and incidental findings ⁵. Do not assume the genome is cheaper end-to-end just because the flow cell is.

A middle path that works well in public systems: sequence the genome, analyze only a gene panel. Hocking and colleagues did exactly this for rare inherited conditions, running genome sequencing with panel-based filtered analysis, and argued that the genome data retained for reanalysis is where the long-run value sits ⁶. You get panel-like specificity and reporting burden today, and the option to widen the filter in five years without redrawing blood.

Running your own panel analysis over genome data

If you hold a 30x PCR-free genome as CRAM plus a joint-called VCF, panel analysis is a filtering exercise.

1. Define the region. Pull a gene list (ClinGen’s curated gene-disease validity table is the best starting point), map to MANE Select transcripts, and build a BED with bedtools slop -b 20 around exons to include canonical splice sites.
2. Check coverage before you trust a negative. Run mosdepth --by panel.bed --thresholds 10,20,30 sample sample.cram and look at the per-region threshold columns. Any exon where less than 100% of bases hit 20x is a region where “no variant found” means “no variant callable.” This is the single most common mistake in DIY panel analysis.
3. Restrict and annotate. bcftools view -R panel.bed sample.vcf.gz, then annotate with VEP (--assembly GRCh38 --pick_allele_gene --plugin SpliceAI --plugin AlphaMissense) and cross-reference ClinVar with a recent release date and a review-status filter of at least two stars.
4. Handle the structural variants separately. Small-variant callers will not find them. Run Manta or DELLY for breakpoint-based calls and a read-depth caller (CNVpytor, or GATK gCNV) for exon-level copy number, then check the two against each other.
5. Treat everything as a hypothesis. A variant found in your own analysis is not a clinical result. Clinical labs confirm by an orthogonal method, and classification follows ACMG/AMP criteria applied by people who do it full time. If you find something that looks pathogenic in a gene with real clinical consequences, take it to a genetics clinic and expect them to re-test from a fresh sample.

What panels miss, and what genomes hand you that you may not want

Panels miss deep intronic splice variants, promoter and UTR variants, repeat expansions, mitochondrial heteroplasmy below the assay’s threshold, and anything in a gene added to the field’s knowledge after your panel was designed. In hematologic malignancy, Kuo and colleagues laid out the tradeoff clearly: gene panels give the depth and turnaround clinical decisions need, while genome-wide approaches give discovery breadth that most individual cases do not require ⁷.

Genomes hand you secondary findings. Sequence a healthy person’s genome and you will find variants of uncertain significance by the dozen, plus a small but real chance of a medically actionable finding you were not looking for ⁸. The ethical and practical load of that has been argued about since panels started expanding ⁹. Decide in advance which secondary findings you want surfaced. Reading a VUS in a cancer predisposition gene and then deciding what to do with it is a conversation for a genetic counselor. A Saturday afternoon is not the setting for it.

Questions people also ask

How much does a full panel genetic test cost? Commercial hereditary cancer and cardiac panels list at roughly $250 to $1,000 cash-pay in the US, often far less with insurance. Hospital-ordered testing is billed higher. The cost of running a panel in routine practice, once labor, instrumentation, and bioinformatics are included, has been measured in the several-hundred-euro range in a nationwide French study, with throughput the biggest driver of variation ⁴.

What is a genetic testing panel? A curated set of genes sequenced together at high depth, with results reported only for that set. Panels are built around a clinical question. Examples are hereditary breast and ovarian cancer, cardiomyopathy, epilepsy, hearing loss and myeloid malignancy.

What does a full genetic panel test for? Small variants (SNVs and short indels) in the coding exons and immediate splice sites of the listed genes, plus exon-level copy number changes if the lab includes a CNV caller. It does not cover the rest of your genome, and it does not cover variants in the listed genes that fall outside the captured intervals.

Is whole-genome sequencing better than a panel? For breadth and reusability, yes. For depth, turnaround, and cost of interpretation today, panels still win in many clinical settings. A useful compromise is sequencing the genome and analyzing it panel-first, keeping the raw data for reanalysis as gene-disease knowledge improves ⁶.

Footnotes

1. Masayuki Nagahashi, Yoshifumi Shimada, Hiroshi Ichikawa, et al. Next generation sequencing‐based gene panel tests for the management of solid tumors. Cancer Science, 2018. https://doi.org/10.1111/cas.13837 ↩

2. Allison L. Cirino, Neal K. Lakdawala, Barbara McDonough, et al. A Comparison of Whole Genome Sequencing to Multigene Panel Testing in Hypertrophic Cardiomyopathy Patients. Circulation Cardiovascular Genetics, 2017. https://doi.org/10.1161/circgenetics.117.001768 ↩

3. Noura S. Abul‐Husn, Priya N. Marathe, Nicole R. Kelly, et al. Molecular diagnostic yield of genome sequencing versus targeted gene panel testing in racially and ethnically diverse pediatric patients. Genetics in Medicine, 2023. https://doi.org/10.1016/j.gim.2023.100880 ↩

4. On behalf of NGSEco Group:, Patricia Marino, Rajae Touzani, et al. Cost of cancer diagnosis using next-generation sequencing targeted gene panels in routine practice: a nationwide French study. European Journal of Human Genetics, 2018. https://doi.org/10.1038/s41431-017-0081-3 ↩ ↩²

5. Katharina Schwarze, James Buchanan, Jenny C. Taylor, et al. Are whole-exome and whole-genome sequencing approaches cost-effective? A systematic review of the literature. Genetics in Medicine, 2018. https://doi.org/10.1038/gim.2017.247 ↩

6. Lynne J. Hocking, Claire M. Andrews, Christine Armstrong, et al. Genome sequencing with gene panel-based analysis for rare inherited conditions in a publicly funded healthcare system: implications for future testing. European Journal of Human Genetics, 2022. https://doi.org/10.1038/s41431-022-01226-3 ↩ ↩²

7. Frank C. Kuo, Brenton G. Mar, R. Coleman Lindsley, et al. The relative utilities of genome-wide, gene panel, and individual gene sequencing in clinical practice. Blood, 2017. https://doi.org/10.1182/blood-2017-03-734533 ↩

8. Noralane M. Lindor, Stephen N. Thibodeau, Wylie Burke. Whole-Genome Sequencing in Healthy People. Mayo Clinic Proceedings, 2017. https://doi.org/10.1016/j.mayocp.2016.10.019 ↩

9. Eva C. Winkler, Stefan Wiemann. Findings made in gene panel to whole genome sequencing: data, knowledge, ethics – and consequences?. Expert Review of Molecular Diagnostics, 2016. https://doi.org/10.1080/14737159.2016.1212662 ↩