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 exome is reported at 4% in isolated dystonia (Zech 2020) and at 83% in a dedicated leukodystrophy clinic (Zerem 2023). Neither number is wrong; they describe different populations. A yield is a fraction, and most of the variation lives in the denominator: who was selected, what was already ruled out, and how the result was interpreted.

Cohort selection — the dominant force. Diagnostic yield rises with the prior probability of a monogenic cause. A leukodystrophy clinic enrolls children whose MRI already implies a Mendelian white-matter disease, so most of the denominator is genetically diagnosable. A broad developmental-delay cohort sweeps in milder, multifactorial phenotypes and dilutes the Mendelian fraction. In the meta-regression of Clark et al. 2018, every additional 1,000 probands in a study lowered the odds of diagnosis by 28%: small cohorts tend to be hand-picked, large laboratory series take broad referrals. The first question about any yield figure is therefore "who was in the denominator?"

Singleton vs. trio. Trio sequencing (proband + both parents) roughly doubles the odds of a diagnosis (OR 2.04, 95% CI 1.62–2.56; Clark et al. 2018). Doubling the odds is not doubling the yield: a 30% singleton yield becomes about 47%. In the DDD study, recruitment as a trio was the strongest predictor of diagnosis (OR 4.70; Wright et al. 2023). The mechanism is interpretive power: parental data shows a variant is de novo, phases two variants in a recessive gene, and removes inherited variants from the candidate list.

Prior testing erodes the denominator. A cohort that already had microarray or a gene panel has lost the easiest diagnoses, so the next test works on a harder, pre-filtered population. Sequential testing studies must be read in this light.

What counts as a diagnosis. Most studies count pathogenic and likely pathogenic variants; some also count variants of uncertain significance. The GEMINI study's 49% genome "molecular diagnostic yield" included VUS (Maron et al. 2023).

Database maturation makes yield dynamic. In Clark's meta-regression the odds of diagnosis rose about 16% per year from 2013 to 2017. Reanalysis of unsolved exome or genome data finds new diagnoses in about 10% (95% CI 6–13%), mostly from newly published gene-disease links (Dai et al. 2022). A negative exome is time-stamped, not permanent.

CMA platform. SNP arrays detect isodisomy and long runs of homozygosity; oligonucleotide-only arrays miss both. UPD underlies a subset of imprinting disorders (Prader-Willi, Angelman), and runs of homozygosity flag parental relatedness, raising the prior for recessive disease.

Severity and specificity. Early onset, encephalopathy, developmental comorbidity, multi-system involvement and distinctive examination or imaging features each raise yield, because each moves the phenotype toward the rare, single-gene tail. Isolated autism without intellectual disability sits in the multifactorial middle and shows the lowest yields.

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. The pooled yields below are from Clark et al. 2018 (37 studies, 20,068 children with suspected genetic disease of any kind).

Chromosomal Microarray (CMA) — pooled yield ~10% (95% CI 8–12%); 15–20% in developmental delay/ID, autism and congenital anomalies (Miller et al. 2010)

CMA measures dosage: it counts copies of DNA across the genome and flags deletions and duplications plus aneuploidy. SNP arrays add genotype, which reveals isodisomy and runs of homozygosity. CMA cannot read sequence, so it misses single-nucleotide variants, small indels, balanced rearrangements, repeat expansions and low-level mosaicism. Its enduring value is the CNV space, which is why it stays first-tier in multiple congenital anomalies, infantile spasms and intellectual disability when an exome is sent.

Exome Sequencing — pooled yield ~36% (95% CI 33–40%); 36% in NDD overall, 31% in isolated NDD and 53% in NDD with associated conditions (Srivastava et al. 2019)

Exome reads the ~1–2% of the genome that codes for protein, where most interpretable Mendelian variants lie. It misses deep intronic and regulatory variants, calls CNVs less reliably than CMA (capture depth is uneven), resolves structural variants poorly, cannot size repeat expansions and does not read methylation.

Genome Sequencing — pooled yield ~41% (95% CI 34–48%)

Genome removes the capture step and sequences at even depth, adding noncoding variants, structural variants with breakpoints, CNVs and mitochondrial DNA. How much it adds over exome is debated: Clark 2018 found no significant difference; Nurchis et al. 2023 found genome ahead in a network meta-analysis (OR 1.54, 1.11–2.12) although pooled yields were nearly identical (38.6% vs 37.8%); Pandey et al. 2025 found 30.6% vs 23.2% in within-cohort comparisons (OR 1.7, 0.94–2.92, not significant). The gain concentrates where exome is structurally blind: after a negative exome, structural variants, deep intronic splice variants and noncoding genes such as RNU4-2, whose de novo variants explain about 0.4% of NDD (Chen et al. 2024).

The repeat-expansion blind spot. Short-read sequencing assembles the genome from ~150-bp fragments, which cannot span a long tandem repeat, so the expansion cannot be sized. This is a property of the chemistry, not a coverage problem. Standard exome misses the commonest repeat disorders: Friedreich ataxia (FXN GAA), the CAG SCAs, CANVAS (RFC1 AAGGG), SCA27B (FGF14 GAA), fragile X/FXTAS (FMR1 CGG), DM1, Huntington disease and C9orf72. Short-read genome pipelines (e.g., ExpansionHunter) can flag some known loci, but a flag needs confirmation by repeat-primed PCR, Southern blot or long-read sequencing. Before declaring a genomic work-up complete, ask whether the phenotype points to a repeat disorder the sequencing could never have seen.

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 epilepsies with developmental impairment sit at the high-yield end; seizures in a normally developing child sit at the low end.

By test (all epilepsy). Sheidley et al. 2022 pooled 154 studies: overall yield 17%; genome 48%, exome 24%, multigene panel 19%, CMA 9%. Each estimate pools different cohorts, so these are not head-to-head comparisons. The only phenotypic factors associated with higher yield were developmental and epileptic encephalopathy and neurodevelopmental comorbidity.

By phenotype. Stefanski et al. 2021 (103 studies, 32,331 individuals): epilepsy 24% overall, 27.9% with intellectual disability, 36.8% with early-onset seizures. In a population-based Scottish cohort of every child presenting with epilepsy before age 3, a genetic cause was found in 31% (Symonds et al. 2021). In a laboratory series of 9,769 individuals tested with epilepsy gene panels, yield was 14.9–24.4% depending on the panel, and about a third of definitive diagnoses had possible precision-medicine implications (Truty et al. 2019).

Infantile Epileptic Spasms Syndrome (IESS). Meta-analysis of 30 studies: exome 26% (21–31%), multigene panel 20% (15–27%), CMA 14% (11–16%); exome and panel were comparable (P = 0.34), and 61.6% of individuals with genetic IESS may benefit from the diagnosis in clinical management (Feng et al. 2025). IESS is the epilepsy phenotype where CMA earns its place in the sequencing era: a 14% CNV yield justifies sending it alongside sequencing, not after.

Neonates and infants in intensive care. See section 6: in GEMINI, genome found a molecular diagnostic variant (pathogenic or VUS) in 49% vs 27% with a targeted neonatal panel (Maron et al. 2023).

Normally developing child with focal or generalized epilepsy. The monogenic fraction is smaller and skewed toward ion-channel and synaptic genes; a well-designed panel is a reasonable first step, and CMA contributes little.

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.

Autism

• Lowest NDD yield: pooled sequencing yield 17.1% across all autism cohorts vs 28.2% in ID (Stefanski 2021); in a consecutively ascertained, unselected clinic sample of children with autism, microarray 9.3% and trio exome 8.4% (Tammimies 2015); within that sample, children without additional morphological features ("essential" autism) had the lowest yield and those with complex morphology the highest
• CMA retains value for recurrence counseling even without a diagnosis

Intellectual disability and global developmental delay

• Exome 31% in isolated NDD, 53% in NDD with associated conditions (Srivastava et al. 2019), the meta-analysis behind exome as a first-tier test
• ACMG 2021 recommends exome or genome as a first- or second-tier test for congenital anomalies or DD/ID (Manickam et al. 2021)
• DDD study: diagnosis in ~41% of 13,449 probands with severe, probably monogenic developmental disorders (exome + microarray; Wright et al. 2023)
• GDD aged 12–60 months, trio exome + CNV-seq: 61% reported positive detection rate in 434 children; craniofacial abnormalities, moderate-to-severe impairment and age 12–24 months predicted a positive result (Zhang 2024)

Multiple congenital anomalies (MCA)

• CMA 15–20% in DD/ID, autism or MCA (Miller et al. 2010); multiple malformations often reflect contiguous-gene deletions or aneuploidy, the CNV space CMA reads best
• Parental relatedness raises the prior for recessive disease; SNP-array runs of homozygosity point the sequencing analysis to those regions

Cerebral palsy (unexplained, no clear perinatal cause)

• Srivastava et al. 2022: exome 23% overall (15 cohorts, 2,419 individuals), 35% in cryptogenic CP, 7% with known risk factors; CMA 5% (2–12%)
• Gonzalez-Mantilla et al. 2023: exome 31.1% overall (13 studies, 2,612 individuals), 34.8% in pediatric cohorts, 42.1% when exclusion criteria were applied vs 20.7% when they were not

CP is the phenotype whose yield rises as you subtract patients: the stricter the exclusion of acquired causes, the more the remaining denominator is enriched for genetic disease. A known risk factor lowers but does not remove the chance of a genetic cause.

Macrocephaly and segmental overgrowth

• Germline causes include PTEN, NSD1 and RAS-MAPK genes; mosaic PIK3CA, AKT3 and MTOR variants cause megalencephaly and segmental overgrowth
• A post-zygotic variant may be absent or very low in blood: deep sequencing of affected tissue (skin, saliva or resected brain) may be needed

Movement and white-matter disorders make the central lesson of this module concrete: the molecular architecture of a phenotype decides which test can find the answer, and front-loaded phenotyping (the examination, the MRI) decides the denominator.

Dystonia. Exome sequencing of 764 people with dystonia at 33 specialty centres found a diagnosis in 19% of 728 families: 4% (16/388) with isolated dystonia, 19% (19/98) with combined dystonia, and 45% (100/222) when other neurological signs accompanied the dystonia; 65% of diagnostic variants were in genes also linked to neurodevelopmental disorders (Zech et al. 2020).

Progressive (hereditary) cerebellar ataxia. In 260 mostly adult, sporadic patients, exome gave a clinically relevant detection rate of 52%, with 25% classified as pathogenic; reanalysis of the first 76 exomes raised the pathogenic rate by 7% (Ngo et al. 2020). The authors described ~50% as a relative ceiling for exome-based methods. Much of the remainder lies in variant classes exome cannot see:

• Friedreich ataxia (FXN GAA, intron 1, biallelic) → sizing and repeat-primed PCR
• SCA1/2/3/6/7/17, DRPLA (coding CAG) → repeat sizing panel
• CANVAS (RFC1 AAGGG, intron 2, biallelic) → flanking and repeat-primed PCR, Southern blot or long-read sequencing to size
• SCA27B (FGF14 GAA, intron 1) → long-range and repeat-primed PCR
• FXTAS (FMR1 CGG premutation) → FMR1 PCR

After a negative exome in ataxia, ask whether dedicated repeat testing has been sent; in late-onset sporadic ataxia, send the repeat panel first or alongside sequencing. CANVAS (ataxia + sensory neuropathy + chronic cough) is the trap case: a textbook phenotype that exome will report as negative every time.

White-matter disease. The yield depends on how the cohort was assembled:

• Persistently unresolved white-matter abnormalities, trio exome: 35% pathogenic, 42% including potentially pathogenic variants (71 patients; Vanderver et al. 2016)
• Genetic white-matter disorders undiagnosed after standard work-up, singleton exome: 59%, rising to 68% after annual reanalysis and 72% after genome sequencing in 16 remaining cases (126 patients; Schlüter et al. 2022)
• Dedicated leukodystrophy clinic: pathogenic variants in 89.6% (60/67); exome diagnostic in 34/41 (82.9%), single-gene testing 13/24, panels 3/9, CMA 2/25 (Zerem et al. 2023)

MRI pattern recognition (hypomyelination vs. demyelination vs. cystic or vacuolating patterns) is the essential pre-test step: it collapses the differential before any DNA is drawn, generating the specialty-cohort effect from section one by imaging rather than referral. See the Hereditary Ataxias module for clinical features.

The pooled figures below are anchors, but each is a cohort-specific number; the reasoning behind it is what transfers to the patient in front of you.

Pooled yields in children with suspected genetic disease (Clark 2018, 37 studies, 20,068 children):

Other anchors

Six take-home points

• CMA is not obsolete: it owns the dosage window (aneuploidy, large CNVs, isodisomy) that exome reads poorly
• Trio is essential for severe early-onset phenotypes: it doubles the odds of a diagnosis (OR 2.04) through interpretive power (de novo calling, phasing), not deeper sequencing
• Genome adds a modest, phenotype-dependent increment over exome: largest after a negative exome and where structural or noncoding variants are likely
• Repeat expansions are a separate testing universe: Friedreich, SCAs, CANVAS, SCA27B, fragile X, DM1, HD and C9orf72 need dedicated tests
• Yield is dynamic: reanalysis finds about 10% more diagnoses as knowledge, not data, accrues
• White-matter disease is the special case: MRI-directed testing pushes exome yields above 80% in dedicated clinics

Clinical utility ≠ diagnostic yield. A negative test can still inform recurrence counseling, and a positive one earns its value when it changes something. The strongest evidence sits in infant intensive care: in Project Baby Bear, rapid genome sequencing changed care in 32% of infants and reduced net costs. In epilepsy, about a third of definitive diagnoses carry possible precision-medicine implications (Truty 2019), and 61.6% of genetically explained IESS may benefit in management (Feng 2025). Examples: SCN1A → avoid sodium-channel blockers; KCNQ2 → sodium-channel blockers such as carbamazepine; SLC2A1 (GLUT1) → ketogenic diet; ALDH7A1 → pyridoxine; SLC6A1 → valproate often effective. The decision to test, and which test, should be argued from what a result would change, not from the yield percentage alone.