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Cross-sectional analysis of diagnostic yield from exome and genome sequencing in pediatric and prenatal casesGenetic Testing Success Varies by Clinic—Here’s Why

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Key Takeaway
Consider that diagnostic yield from sequencing varies with clinical indications, sex, and site, but causal claims are not supported.

This is a cross-sectional analysis from the NHGRI Clinical Sequencing Evidence-Generating Research (CSER) consortium. The scope was to evaluate diagnostic yield from exome and genome sequencing in a cohort of 3,008 prenatal, neonatal, and pediatric cases across five U.S. clinical sequencing sites.

The authors synthesized that the overall diagnostic yield was 19.0%. A key finding was that having multiple clinical indications was associated with a higher diagnostic yield (OR=1.47, 95% CI 1.20-1.80, p<0.001). Conversely, male sex was associated with a lower diagnostic yield (OR=0.80, 95% CI 0.66-0.96, p=0.017), as was prenatal status (OR=0.63, 95% CI 0.44-0.90, p=0.012). Site-level variance analysis attributed approximately 10% of variance to between-site differences without fixed effects, decreasing to 5.7% after adjusting for covariates.

The authors note key limitations, including the cross-sectional design, which limits causal inference, and residual site-level variation not fully explained by measured factors. Safety events were not reported.

Practice relevance is restrained; patient-level clinical features influence diagnostic yield, but site-level variation remains. Efforts to increase consistency in sequencing workflows may reduce inter-site differences. The authors emphasize that associations are reported, not causation, and findings may be influenced by unmeasured confounders.

  • Big Discovery: Clinic practices affect genetic diagnosis rates more than patient traits
  • Who it helps: Families seeking answers for rare childhood conditions
  • The Catch: Results depend on where you’re tested—standardization is still lacking

This study reveals that your odds of getting a genetic diagnosis may depend less on your DNA—and more on where you get tested.

You’ve waited months. Your child has delays, seizures, or a rare-looking face. Doctors suggest genetic testing. You hope for answers. But what if the clinic you choose changes your chances of finding them?

That’s exactly what this study uncovered.

More kids need answers

Thousands of children in the U.S. are born each year with unexplained health issues. Many have rare genetic conditions. Symptoms can include trouble learning, seizures, heart defects, or unusual physical traits. Without a diagnosis, families face stress, endless tests, and delayed care.

Genetic sequencing—reading a child’s DNA—can help. It’s become a key tool for finding the root cause. But not every test leads to answers. And until now, it wasn’t clear why some clinics find diagnoses more often than others.

Clinics don’t all follow the same playbook

We used to think that a patient’s symptoms, age, or genetic background were the biggest factors in whether a test worked. And yes—kids with multiple health issues are more likely to get a diagnosis. Babies tested before birth (prenatally) have lower odds.

But here’s the twist: the clinic itself plays a bigger role than expected.

Even after accounting for patient traits—like age, sex, or number of symptoms—diagnostic success still varied between clinics. That means two kids with the same condition could get different results just based on where they were tested.

This isn’t about equipment. It’s about how doctors and labs interpret the DNA data.

Like reading a book with missing words

Think of DNA as an instruction manual for the body. A typo in the text can cause disease. Sequencing finds those typos. But not every typo matters.

Geneticists must decide: Is this spelling mistake harmful? Or just a harmless variation?

It’s like finding a typo in a cookbook. Is it in the cake recipe—or the copyright page? That’s where human judgment comes in.

The surprising shift

The study looked at 3,008 kids and pregnant patients across five U.S. clinics from 2017 to 2023. All had exome or genome sequencing. These tests scan large parts of DNA to find errors.

Researchers checked whether factors like race, genetic ancestry, or type of test affected diagnosis rates. They used strict medical guidelines to confirm results.

Overall, 19 out of every 100 patients got a clear genetic diagnosis. That’s about 1 in 5—lower than some past reports.

Kids with more than one major symptom were more likely to be diagnosed. Boys were slightly less likely than girls. And prenatal cases—tests done before birth—had lower success rates.

But race, ethnicity, and genetic ancestry? They didn’t affect results. That’s good news—it means access to diagnosis wasn’t tied to background.

This doesn’t mean this treatment is available yet.

But there’s a catch. Even after adjusting for all patient traits, clinics still differed in their success rates. At first, 10% of the variation came from where the test was done. After adjusting for patient traits, 5.7% of the difference remained.

That’s not small. It means clinic habits—how they review DNA, classify variants, or define a “diagnosis”—still shape outcomes.

What scientists didn’t expect

Sequencing method (exome vs. genome) didn’t change results. Neither did isolated cancer cases. That suggests the analysis, not the test type, is the key variable.

Experts say this highlights the need for more consistent rules. Right now, one lab might call a DNA change “harmful,” while another calls it “uncertain.”

That confusion delays answers.

Where the real problem lies

Imagine two mechanics diagnosing the same car. One says the engine is broken. The other says it’s fine. That’s the challenge in genetic testing today.

Differences in training, software, or internal checklists can lead to different calls—even with the same data.

This study shows those differences are real and measurable.

If you or your child are getting genetic testing, ask:

  • What kind of sequencing will be used?
  • How do they interpret uncertain results?
  • Do they follow national guidelines?

Not all clinics do. And this study shows it can affect your results.

But don’t panic. A negative result doesn’t mean there’s no answer—just that it wasn’t found this time.

Talk to your doctor about whether reanalysis later—or testing at a different center—might help.

It’s not perfect

The study only looked at five sites. All were part of a research network, so results may not reflect every hospital. Also, most data came from academic centers, which may have more expertise than community labs.

And while race and ancestry didn’t affect outcomes here, access to testing still does. Many families face delays due to cost, location, or lack of referrals.

Researchers now urge labs to standardize how they review DNA results. National efforts are underway to create shared tools and checklists.

Future studies will track whether these changes reduce clinic-to-clinic differences. For now, awareness is the first step.

Better consistency could mean faster answers—for more families.

Study Details

EvidenceLevel 5
PublishedApr 2026
View Original Abstract ↓
Purpose: Diagnostic yield from exome and genome sequencing varies widely across studies. It remains unclear how much of this variation reflects patient-level factors (e.g., sex, clinical features, race/ethnicity, genetic ancestry) versus site-level practices such as sequencing modality or variant interpretation workflows. We aimed to quantify the contributions of these factors to diagnostic outcomes across five U.S. clinical sequencing sites. Methods: We performed a cross-sectional analysis of 3,008 prenatal, neonatal, and pediatric cases from the NHGRI Clinical Sequencing Evidence-Generating Research (CSER) consortium (2017-2023). Clinical indications spanned neurodevelopmental, neurological, immunological, metabolic, craniofacial, skeletal, cardiac, prenatal, and oncologic presentations. Genetic ancestry was inferred from sequencing data, and variants were interpreted using ACMG/AMP guidelines to classify DNA-based diagnoses. Generalized linear mixed models were used to estimate associations between diagnostic yield and fixed effects (sex, prenatal status, isolated cancer, number of clinical indications, sequencing modality, race/ethnicity, and genetic ancestry), while modeling study site as a random effect to quantify between-site variation. Results: The overall diagnostic yield was 19.0%. Multiple clinical indications (OR=1.47, 95% CI 1.20-1.80, p<0.001) were associated with higher diagnostic yield, and male sex (OR=0.80, 95% CI 0.66-0.96, p=0.017) and prenatal status (OR=0.63, 95% CI 0.44-0.90, p=0.012) were associated with lower yield. Sequencing modality, race/ethnicity, genetic ancestry, and isolated cancer were not statistically significantly associated with diagnostic outcomes. A model without fixed effects attributed ~10% of variance in diagnostic yield to between-site differences. After adjusting for covariates, site-level variance decreased to 5.7%, indicating consistent variation across sites not explained by measured patient factors. Conclusion: Across five sites, patient-level clinical features influenced diagnostic yield, but substantial site-level variation remained even after adjustment. Differences in variant interpretation, or case-classification practices may contribute to this residual variability. Further efforts to increase consistency in exome and genome-sequencing diagnostic workflows may help reduce inter-site differences.
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