For most of its history, non-invasive prenatal testing has focused on a small set of common chromosomal conditions - trisomies 21, 18, and 13, along with sex chromosome aneuploidies. These conditions were the low-hanging fruit: they involve whole or large chromosomal changes that are relatively straightforward to detect from cell-free DNA in maternal blood.

That era is ending. The next phase of NIPT is about screening for rare conditions - single-gene disorders, microdeletions, and monogenic diseases that individually are uncommon but collectively affect a significant number of pregnancies. The technology is nearly there. The validation infrastructure is not.

What's Changing

Several recent developments signal the shift toward rare condition NIPT:

At the 2026 Society for Maternal-Fetal Medicine (SMFM) meeting, Natera presented data from their EXPAND trial on Fetal Focus, a single-gene NIPT that screens for 21 monogenic conditions using their LinkedSNP technology. The results were selected for an oral plenary presentation - a signal of the data's significance. Reported performance: 96% sensitivity and 98% specificity across the panel of inherited conditions.

BillionToOne has expanded their UNITY prenatal test, which uses a single-molecule counting approach to screen for conditions including cystic fibrosis, sickle cell disease, and spinal muscular atrophy. Their integration with Epic's electronic health record system is making ordering these expanded panels as routine as ordering a standard NIPT.

Meanwhile, the underlying technologies - sequencing chemistry, bioinformatics algorithms, and genomic AI models - continue to improve in ways that make detecting subtle genetic signals from the cfDNA background increasingly feasible.

Why Rare Conditions Are Hard

Screening for rare conditions from cfDNA is fundamentally more challenging than detecting common trisomies. Several factors contribute:

Signal size. Trisomy 21 involves an entire extra copy of chromosome 21, representing roughly 1.5% of the genome. A single-nucleotide variant causing a monogenic condition represents a change of one base pair out of three billion. Detecting that signal against the noisy background of cfDNA, where fetal DNA may comprise only 5-10% of the total, is orders of magnitude harder.

Genetic heterogeneity. Many rare conditions can be caused by hundreds of different variants in the same gene. Cystic fibrosis, for example, has over 2,000 known CFTR variants. A screening panel needs to account for this diversity, not just test for one or two common mutations.

De novo variants. Some of the most severe rare conditions arise from new mutations not inherited from either parent. These can't be predicted from parental carrier screening and may occur in genes or at positions not previously associated with disease. Detecting them requires genome-wide sensitivity rather than targeted panels.

Low prevalence. When a condition affects 1 in 15,000 births, even a test with 99.9% specificity will generate more false positives than true positives. The positive predictive value problem - already a challenge for common trisomy NIPT in low-risk populations - becomes even more acute for rare conditions. Communicating this to patients and clinicians is a significant clinical challenge.

The Validation Data Problem

Perhaps the most immediate bottleneck is validation. To demonstrate that a screening test works, you need samples - positive cases with confirmed outcomes. For a condition affecting 1 in 50,000 births, a single clinical laboratory might see one or two cases per year.

This creates a circular problem: you can't validate a test without positive samples, and you can't accumulate positive samples quickly enough because the conditions are rare. Traditional approaches to validation - collecting clinical samples prospectively - are inadequate for the scale of rare condition screening.

The field needs alternative approaches:

• Synthetic validation data - Computationally generated cfDNA samples that simulate specific rare conditions at controlled fetal fractions. These can be produced in any quantity for any condition, enabling systematic validation across the full range of a screening panel. This is the approach Eabha is taking - using genomic AI to generate the validation datasets that rare condition screening demands.
• Cell-line spike-ins - Mixing DNA from cell lines carrying known pathogenic variants into a cfDNA background. This creates physical reference materials, though with some limitations in reproducing the true fragment characteristics of fetal cfDNA.
• Collaborative data sharing - Multi-site consortia that pool rare positive samples across laboratories and geographies. This is happening but slowly, limited by privacy regulations, competitive dynamics, and logistical challenges.

What Needs to Happen

For rare condition NIPT to move from pioneering early offerings to standard of care, several things need to align:

Validation frameworks. Professional societies and regulators need to establish clear guidelines for how rare condition NIPT should be validated. The current frameworks were designed for common trisomy screening and don't adequately address the unique challenges of rare condition panels. What constitutes sufficient evidence for clinical use when prospective positive samples may number in the single digits?

Clinical reporting standards. How do you report a screening result for a condition most clinicians have never encountered? The genetic counseling infrastructure needs to scale alongside the technology. Reporting needs to include not just results but context - residual risk, condition-specific natural history, and clear guidance on next steps.

Health economics. Expanded panels cost more to develop and validate. Payers need evidence that screening for rare conditions is cost-effective - not just in terms of detection rates, but in terms of enabling earlier intervention, reducing diagnostic odysseys, and improving outcomes. This evidence base is still being built.

Equity. Current genomic databases and screening panels are heavily biased toward populations of European descent. As NIPT expands to cover rare conditions, ensuring equitable performance across diverse populations is critical. This requires validation data from underrepresented groups - another area where synthetic approaches can help by modeling population-specific genetic backgrounds.

The Trajectory

The direction is clear even if the timeline is not. NIPT will continue expanding its reach - from common trisomies to microdeletions to single-gene conditions, and eventually toward genome-wide prenatal screening. Each step outward brings greater clinical utility but also greater validation complexity.

The companies and institutions that solve the validation and reference materials challenges will be the ones that make rare condition screening a clinical reality. The technology to detect these conditions is arriving faster than the infrastructure to prove it works. Closing that gap is the real frontier.