Choosing genomic tests by phenotype — CMA, WES, and WGS yields from real cohort data, and the reasoning behind test selection.
Tags: Neurogenetics · Clinical Decision-Making
The single most important habit in genomic medicine is to treat a published yield figure as a property of a cohort, not of a test. The same platform — say, trio WES — is reported at 10% in one paper and 72% in another. Neither number is wrong; they describe different populations. A yield is a fraction, and almost all of the variation lives in the denominator: who was selected, what was already ruled out, and how the genome was interrogated. Six variables account for nearly all of the spread.
Cohort selection — the dominant force. Diagnostic yield rises with the prior probability of a monogenic cause. A specialty leukodystrophy clinic enrolls children with an MRI pattern that already implies a Mendelian white-matter disease, so almost everyone in the denominator is genetically diagnosable — yields of 40–72% follow naturally. A population-level developmental-delay cohort sweeps in mild, multifactorial, and environmentally influenced phenotypes, diluting the Mendelian fraction to 15–25%. The test never changed; the pre-test probability did. This is why the first question about any yield figure is never "which platform?" but "who was in the denominator?"
Singleton vs. trio. Trio sequencing (proband + both parents) approximately doubles yield for de novo-enriched phenotypes (OR ~2.04, Clark et al. 2018). The mechanism is not deeper sequencing — it is interpretive power. Parental data lets the lab instantly flag a variant as de novo (absent in both parents), the genetic signature of most severe early-onset disease, and phase compound heterozygous variants to confirm they sit on opposite alleles. A singleton finds the same variants but cannot resolve their significance, so many land in the VUS pile. For severe DEE, GDD, or MCA, trio is essential.
Prior testing erodes the denominator. A post-CMA-negative WES cohort yields ~25–35%, while first-tier WES in the same phenotype yields ~30–45%. The CMA already removed the CNV-driven diagnoses, so WES is now working a harder, pre-filtered population. Sequential testing strategies must be read in this light: each negative test enriches the remainder for whatever the next test detects.
Database maturation makes yield dynamic. Reanalysis of older WES data yields new diagnoses in ~10–25% of unsolved cases — not because the sequence changed, but because the gene-disease knowledge base did. New gene-disease associations are validated continuously, so a variant that was uninterpretable in 2021 may be diagnostic in 2024 from the identical FASTQ file. A negative WES is therefore time-stamped, not permanent.
CMA platform. SNP arrays detect UPD and long stretches of autozygosity (AOH); oligo-only arrays miss both. This is not a minor footnote — UPD is the mechanism behind a subset of imprinting disorders (Prader-Willi, Angelman), and AOH flags consanguinity that raises the prior for recessive disease and points the analysis toward homozygous regions.
Severity and specificity. Younger onset, multi-system involvement, comorbid epilepsy, and distinctive examination or imaging features each independently raise yield, because each shifts the phenotype away from the common, multifactorial middle and toward the rare, single-gene tail. Mild isolated phenotypes (isolated ASD without ID) sit squarely in that multifactorial middle and consistently show the lowest yields.
Key Points
The three first-line genomic tests are not a quality ladder where more sequence is simply better — they are different windows onto the genome, each blind to what the others see best. Choosing well means matching the window to the kind of variant the phenotype predicts.
Chromosomal Microarray (CMA) — Pooled NDD yield: ~10%
CMA measures dosage: it counts copies of DNA across the genome and flags deletions and duplications down to ~50–200 kb, plus whole-chromosome aneuploidy. SNP arrays add a second dimension — genotype — letting them detect UPD and autozygosity that copy-number-only arrays cannot. What CMA fundamentally cannot do is read sequence: a single-base change that destroys a gene is invisible because the amount of DNA is unchanged. So CMA misses SNVs, small indels, balanced rearrangements (no net dosage change), and repeat expansions. Its enduring value is being cheap, fast, decades-validated, and the most sensitive tool for the CNV space — which is why it stays first-tier for MCA, IESS, and ID, where copy-number disease is common.
Whole Exome Sequencing (WES) — Pooled NDD yield: ~36%
WES reads the ~2% of the genome that codes for protein, where the large majority of known Mendelian disease lives. It excels at the variant class CMA is blind to — SNVs and small indels — which inverts CMA's blind spot and explains why the two are complementary rather than redundant. Its own blind spots follow directly from what it captures: it ignores deep intronic and regulatory variants outside the captured exons, calls CNVs less reliably than CMA (uneven capture depth degrades dosage inference), resolves balanced SVs poorly, and does not read repeat expansions. It is the standard first-tier test for undiagnosed NDD/ID at most academic centres.
Whole Genome Sequencing (WGS) — Pooled NDD yield: ~41%
WGS removes the capture step and sequences the whole genome at uniform depth. In principle it sees everything WES sees plus deep intronic/regulatory variants, balanced SVs, better CNV breakpoints, and improved mitochondrial coverage; modern pipelines (e.g., ExpansionHunter) can even screen some STR loci, though sensitivity is variable. In practice, the headline result is sobering: WGS yield is NOT significantly higher than WES overall (OR 1.13, p=0.50). The reason is that most currently interpretable disease variants are coding, so the extra non-coding territory WGS reads is largely uninterpretable today. The incremental gain is therefore concentrated where non-coding or structural pathology is enriched: post-WES-negative patients, leukodystrophies, and atypical CP.
The repeat-expansion blind spot. Short-read sequencing assembles the genome from ~150-bp fragments, which cannot span a long, monotonous tandem repeat — the reads simply pile up ambiguously, so the expansion is unmeasurable. This is a structural limitation of the chemistry, not a coverage problem, and it means standard WES (and largely short-read WGS) miss the most common repeat-expansion disorders: Friedreich ataxia, the SCAs, CANVAS, Fragile X/FXTAS, DM1/DM2, Huntington, and C9orf72. These require dedicated repeat-primed PCR, Southern blot, or long-read sequencing. Before declaring a genomic workup complete, always ask whether the phenotype points to a repeat disorder that the sequencing could never have seen.
Key Points
Within epilepsy, yield tracks a single organising principle: the more the seizures index brain that was built wrong rather than brain that fires wrong, the higher the monogenic fraction. Encephalopathic, early-onset, developmentally regressive epilepsies sit at the high-yield end; isolated seizures in a normally developing child sit at the low-yield end. The clinical sub-phenotype, more than the test, predicts the number.
The neonatal brain has limited ways to express distress, so a wide range of monogenic disorders converge on the same encephalopathic picture — making the phenotype non-specific but the underlying genetic causes abundant. That combination, plus a narrow therapeutic window where a diagnosis can redirect care within days, is why speed (rWGS/rWES returning in days, not weeks) is itself part of the clinical utility here.
The syndrome-specific spread is instructive: a sharply defined electroclinical syndrome (EIMFS, Dravet) behaves like a specialty cohort in miniature — the phenotype is so constrained that nearly everyone has a detectable variant, hence ~78%. Looser "DEE" labels pull in more heterogeneity and lower the number.
IESS is the phenotype where CMA earns its first-tier place even in the sequencing era — a 14% CNV yield is high enough that ordering CMA alongside WES, not after it, is justified.
Here normal development signals brain that fires wrong, not brain built wrong: the monogenic fraction is small and skewed toward ion-channel point mutations, so a focused panel is an efficient first pass and CMA contributes little.
Key Points
Across neurodevelopmental phenotypes, two levers move yield: how severe and syndromic the presentation is, and how much family structure the lab is given to interpret the data. Severity raises the monogenic prior; trio structure converts found variants into diagnoses. The phenotypes below are arranged roughly from high to low monogenic fraction, and the reasoning matters more than any single number.
The jump from ~35% singleton WES to 61% with trio + CNV-seq is the whole argument for first-tier trio in one cohort: severe early GDD is enriched for de novo SNVs and de novo CNVs, so pairing trio interpretation with parallel copy-number detection captures both de novo mechanisms at once rather than missing one.
The ACMG shift to WES/WGS as a first-tier test reflects accumulated evidence that exome/genome sequencing out-yields the older stepwise (karyotype → CMA → single-gene) pathway while reaching diagnosis faster — sequential testing mostly delays the answer.
Isolated ASD is the canonical low-yield, highly polygenic phenotype: the more it is just ASD, the more its architecture is common-variant and environmental rather than monogenic. Each added feature — ID, epilepsy, distinctive features — pulls it back toward the Mendelian tail and roughly doubles the yield.
Multiple malformations imply a disturbance early in development affecting several organ fields — frequently a contiguous-gene deletion or aneuploidy, which is exactly the CNV space CMA reads best, justifying CMA first. Consanguinity then shifts the prior toward homozygous recessive disease, and SNP-array AOH data directs the sequencing analysis to those regions.
CP is the phenotype whose yield rises as you subtract patients: the stricter the exclusion of perinatal and structural causes, the more the remaining "unexplained" denominator is enriched for genetic disease — 31% becomes 42%. This is the denominator principle running in reverse, and it reframes unexplained CP from a static-injury label into an actionable genetic diagnosis.
Overgrowth phenotypes carry a mechanistic trap: when the driver is a post-zygotic somatic variant, it may be absent or vanishingly rare in blood and present only in affected tissue. Standard blood WES can return falsely negative, so deep sequencing or a tissue biopsy of the overgrown region may be required to find a variant that is real but not in the sampled cells.
Key Points
Movement and white-matter disorders make the central lesson of this module concrete: the molecular architecture of a phenotype decides which test can ever find the answer. Ataxia is split down the middle — one half is point mutations that sequencing reads cleanly, the other half is repeat expansions that sequencing structurally cannot see. Leukodystrophy, by contrast, is where front-loaded phenotyping (the MRI) pushes yield to the top of all neurogenetics.
Because episodic ataxia is mechanistically a channelopathy — paroxysmal dysfunction of otherwise normal cerebellum — its variants are SNVs that both panels and WES detect well, so a tightly defined phenotype gains little from the broader exome.
The ~50% WES "ceiling" is not a technical failure to be solved by sequencing harder — it is the boundary of the variant class WES can read. The unsolved half is disproportionately repeat-expansion disease, so a negative WES in progressive ataxia is a positive prompt to send dedicated repeat testing, not evidence against a genetic cause. CANVAS (ataxia + sensory neuropathy + cough) is the trap case: a textbook-fit phenotype that short-read sequencing will report as negative every time.
Leukodystrophy proves that yield is engineered before the sample is drawn. The MRI is a free, high-resolution phenotyping step: categorising the white-matter signature (hypomyelination vs. demyelination vs. cystic vs. vacuolating) collapses a vast differential into a handful of candidate disorders, so the sequencing arrives with a near-monogenic prior already established — exactly the specialty-cohort effect from section one, generated by imaging rather than referral. The same logic explains the internal gradient: hypomyelination (85%) is a more constraining MRI pattern than "leukodystrophy" in general (72%), so it carries the higher yield. See the Hereditary Ataxias module for detailed ataxia clinical features.
Key Points
The pooled figures below are the anchors — but read them through the lens of the whole module: each is a cohort-specific number, and the reasoning behind it is what transfers to the patient in front of you, not the digit itself.
| Test | Yield | Key Source |
|---|---|---|
| CMA | ~10% | Clark 2018, n=20,068 |
| WES | ~36% | Clark 2018; Pandey 2025, n=24,631 |
| WGS | ~41% (NSD vs. WES) | Clark 2018 |
| rWGS (NICU) | 35–50% | Maron 2023 |
| All epilepsy | CMA 9%, WES 24%, WGS 48% | Sheidley 2022, n=39,094 |
Notice that the all-epilepsy row (9 / 24 / 48%) recapitulates the broad-NDD row (10 / 36 / 41%) but with a wider WGS-over-WES gap — a reminder that the WES-vs-WGS verdict is phenotype-dependent, not universal. Where non-coding and structural pathology is enriched, the genome's extra reach starts to pay off; where it is not, the two converge.
Clinical utility ≠ diagnostic yield. A negative test can still inform recurrence counseling, and a positive one earns its value only if it changes something. The strongest evidence for that change sits in the NICU, where a diagnosis alters management in 38–50% of cases, and in IESS, where it enables precision therapy in 61.6% of genetically explained cases — because here the diagnosis maps onto a specific, time-sensitive intervention. Examples: KCNQ2 → carbamazepine; GLUT1 → ketogenic diet; SLC6A1 → valproate first-line; SCN1A → avoid sodium channel blockers. The decision to test, and which test, should ultimately be argued from this end — what action a result would change — not from the yield percentage alone.
Key Points
1. A 6-month-old with severe developmental delay, seizures, and no family history presents for genetic evaluation. Both parents are available for testing. Which testing strategy is best supported by current evidence?
For severe early-onset neurodevelopmental phenotypes (DEE, GDD with seizures), trio sequencing is strongly supported by evidence. The key advantage is detecting de novo variants — spontaneous mutations present in the child but absent from both parents — which are the primary cause of many severe neurogenetic conditions. Meta-analysis shows trio sequencing approximately doubles yield compared to singleton testing (OR ~2.04). Trio analysis also enables phasing of compound heterozygous variants and parent-of-origin determination. While CMA remains valuable as a complementary test (detecting large CNVs, aneuploidy, UPD), it alone would miss the majority of diagnoses in this clinical context. Sequential testing delays diagnosis unnecessarily in severe early-onset presentations.
2. Two published studies report WES diagnostic yields for intellectual disability: Study A reports 45% yield, while Study B reports 20% yield. Both used trio WES with comparable bioinformatics. What is the most likely explanation for the difference?
Cohort selection is the single largest driver of yield variation across published genomic studies. Referral-centre or specialty-clinic cohorts are enriched for complex, treatment-resistant, or multi-system disease — these patients are more likely to have identifiable Mendelian conditions, driving yields of 40–60%+. Population-level or primary-care cohorts that include milder, less specific phenotypes yield 15–25% for the same test. Before citing any yield number, always ask: 'Who was in the denominator?' Other variables — severity, syndromic burden, younger age at onset, and comorbid epilepsy — all independently predict higher yield. Platform and bioinformatics differences exist but introduce smaller variations (5–10% relative).
3. A teenager with progressive ataxia and sensory neuropathy has whole exome sequencing that returns negative. Which critical next step should the clinician consider?
This is one of the most important 'testing blind spots' in clinical neurogenetics. The most common hereditary ataxias worldwide — Friedreich ataxia (FXN GAA repeat), spinocerebellar ataxias (SCA types with CAG repeats), and CANVAS (RFC1 AAGGG repeat) — are all caused by trinucleotide or pentanucleotide repeat expansions. Standard whole exome sequencing does NOT detect these expansions, and even standard short-read whole genome sequencing has variable sensitivity. Dedicated testing (repeat-primed PCR, Southern blot, or long-read sequencing) is required. This presentation — progressive ataxia with sensory neuropathy — is particularly suggestive of Friedreich ataxia or CANVAS. A negative WES in an ataxia patient should always prompt the question: 'Has dedicated repeat expansion testing been ordered?'
4. A colleague argues that chromosomal microarray (CMA) is obsolete now that whole exome sequencing is widely available. Which statement best counters this argument?
CMA is NOT obsolete — it provides information that WES does not. CMA detects copy number variants (CNVs) at higher resolution and sensitivity than WES-based CNV calling. Critically, SNP-array CMA detects uniparental disomy (UPD) and regions of autozygosity (AOH) — essential for diagnosing imprinting disorders (Angelman syndrome, Prader-Willi syndrome) and identifying consanguinity. CMA yields ~10% across broad NDD cohorts and is particularly productive in multiple congenital anomalies (15–25%), infantile spasms (14%), and ID. Many centres now run CMA + WES simultaneously rather than sequentially. While WES can detect some CNVs, its sensitivity for CNVs in segmental duplications and complex regions is lower than dedicated CMA.
5. Among the following pediatric neurogenetics phenotypes, which consistently shows the HIGHEST diagnostic yield from exome/genome sequencing?
Leukodystrophy in MRI-selected cohorts achieves among the highest diagnostic yields in all of clinical neurogenetics: WES yields 50–72%, and dedicated WGS programmes report 72–90%+. The GWMD cohort (Neurology 2022, n=126) achieved 72% overall, 77% for onset <3 years, and 85% in the hypomyelination subgroup. The key reason: MRI pattern recognition dramatically narrows the differential diagnosis before sequencing begins. Categorizing the white matter abnormality (hypomyelination vs. demyelination vs. cystic vs. vacuolating) directs testing to the appropriate gene-disease context. By contrast, isolated ASD without ID has the lowest NDD yield (~10–15% WES), and mild GDD yields less than broad NDD cohorts. This illustrates a general principle: the more phenotypically specific and severe the presentation, the higher the diagnostic yield.
6. Rapid whole genome sequencing in NICU patients with unexplained encephalopathy has a diagnostic yield of 35–50%. Beyond identifying a diagnosis, what makes this one of the most impactful applications of genomic testing in pediatrics?
The NICU setting demonstrates the strongest evidence for clinical utility of genomic testing. Multiple studies, including the Maron et al. 2023 JAMA RCT, show that a molecular diagnosis changes clinical management in 38–50% of diagnosed neonatal cases. Management changes include: initiation of targeted therapy (e.g., ketogenic diet for GLUT1 deficiency, enzyme replacement for Pompe disease), avoidance of harmful medications, redirection of care when a devastating prognosis is confirmed, and surgical planning decisions. These are among the highest clinical utility figures in any medical context. Speed matters — rapid WGS (results in days rather than weeks) allows these management changes during the critical neonatal period. Approximately 18% of NICU admissions are estimated to carry a Mendelian disease, making this a high-impact target population.