Germline Genomics In Action: Importance & Scope

From rare pediatric diseases to inherited cancer syndromes, the secrets hidden in our DNA are guiding treatment, prevention, and family planning like never before. Here’s why understanding your genome has never been more appropriate—or more actionable!

Clinical genomics involves applying a person’s inherited genetic variants to medical decision-making.

As sequencing costs have dropped and computational methods have matured, genomic testing is increasingly integrated into routine care. Germline genomics examines variants inherited from parents for a wide spectrum of rare diseases, cardiomyopathies, neurological disorders, and reproductive risks, as well as accounts for about 10% of cancer predispositions. 

Rare & Inherited Diseases in Neonatal and Pediatric Care

In neonatal and pediatric intensive care, time is critical. Rapid whole-exome sequencing (rWES) or rapid whole-genome sequencing (rWGS) can shrink diagnostic time from weeks to merely days, enabling faster treatment decisions, reducing length of stay, and cutting costs.

In implementation studies, more than half of acutely ill infants received a molecular diagnosis via rWGS, and in roughly one-third of cases, management was altered as a result (National Rapid Genome Sequencing in Neonatal Intensive Care Study, 2023).

Project Baby Bear, a well-known pilot in California, enrolled 184 infants and delivered results in a median of 3 days. In that cohort, 40% of infants had a genetic diagnosis that explained their presentation, and 32% had a change in their clinical management. Overall, the program yielded net healthcare savings of between US$2.2 million and $2.9 million after factoring in test and care costs (Dimmock et al., 2021). These data demonstrate that early genomic diagnosis can offset its own expense.

Economic modeling also supports this:

• meta-analyses
• cost-effectiveness analyses

of early application of rWES/rWGS demonstrate that costs are saved through avoidance of uninformative tests and reduction in length-of-stay at hospital (Walsh et al., 2024).

In one Australian cohort, up-front exome testing led to a net reduction of >AU$500,000 across 21 critically ill children due mainly to reductions in duplicate workups and hospital days (Stark et al., 2018). Most recently, a substantial study calculated median per-child cost saving of ~$16,500 with rapid genomic sequencing compared to standard diagnostic routes (Walsh et al., 2024).

A helpful data analytic bioinformatics solution can help supplement rapid sequencing cases by prioritizing variants quickly and efficiently— turnaround times from VCF upload to ranked variants may be as fast as 3 minutes for WES & 15 minutes for WGS. (Find out how this works in practice with a closer look at InheriNext.)

At the clinical level, these tools provide insight into:

• acute metabolic emergencies
• syndromic diagnosis
• actionable disease states.

For instance, an inpatient with seizures or hypotonia entering the world might receive a diagnosis within 48 hours of testing, enabling targeted therapy (e.g., vitamin supplementation, modulation of metabolic pathways) to be implemented immediately rather than delayed and symptom-based treatment.

Outside of the hospital, families become clear about their situation, the psychological burden of uncertainty decreases, and they are in time for genetic counseling regarding future pregnancies.

Although there are infrastructure, logistical, and workforce constraints that have hampered more widespread implementation, international experience indicates rWES/rWGS are likely to change lives and do so in a cost-effective way for use in NICU/paediatric settings (National Rapid Genome Sequencing in Neonatal Intensive Care Study, 2023).

Oncology (Germline Risk Only—Today)

Germline cancer genetics is responsible for around 10% of all oncological cases, while somatic mutations—those acquired during a person’s lifetime—make up the remaining ~90%. The somatic landscape is an important area in its own right and will be explored in detail in a future discussion, along with dedicated solutions for somatic oncology interpretation.

This article, however, focuses on inherited risk. (In contrast, somatic mutations, those acquired during a person’s lifetime, account for approximately 90% of cancer cases and represent a critical area of genomic medicine. While this article focuses on inherited (germline) risk, the somatic landscape merits its own in-depth discussion and will be explored in a dedicated context and solutions aimed at advancing somatic oncology interpretation).

Key hereditary cancer syndromes are the following:

• BRCA1/2 (breast- and ovarian-cancer risk);
• Lynch syndrome (mismatch repair gene variants such as MLH1, MSH2, MSH6, PMS2);
• Li-Fraumeni syndrome (TP53).

It is estimated that 5–10% of breast cancer patients present a BRCA1/2 variant; one attempt to find the proportion among breast cancers was within, where a cohort of 60,000 cases showed an overall prevalence of 6%, for example (Lopes et al., 2024).

Lynch syndrome-associated variants are present in approximately 5% of colorectal cancer (CRC) patients (Ingles et al., 2020).

The identification of these germline variants prompts clinical intervention:

• increased surveillance (e.g., MRI, colonoscopy);
• risk-reducing surgery, chemoprevention;
• genetic testing in the family members.

Genetic counseling is critical to clarify these choices, evaluate penetrance, and provide family planning. 

Professional organizations such as ASCO and ESMO have issued guidelines recommending germline testing in patients with early-onset malignancies, >1 primary cancer, or a family history of significance. At present, germline genomics is primarily restricted to predisposition syndromes, but future hybrid models combining both germline and somatic data may enable true precision oncology.

Cardiology: Inherited Heart Diseases

Inherited cardiovascular disorders are major causes of Sudden Cardiac Death (SCD), particularly in young and apparently healthy persons. Disorders like hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), long QT syndrome (LQTS), or Brugada syndrome are frequently caused by monogenic mutations. Thus, genetics is rightly a key player in cardiology.

Pathogenic sarcomere gene mutations underlie HCM in 30–40% of the cases (Lopes et al., 2024). In DCM, testing panels frequently reveal causative mutations in >60 genes: even if many patients still miss a molecular diagnosis, variant detection is informative on prognosis and therapy (Heliö et al., 2023). In the arrhythmia syndromes, impacting applicable pharmacological therapy (see drug-related restrictions and lifestyle advice in Brugada after SCN5A variants, beta blocker treatment in LQTS by KCNQ1 or KCNH2).

When a causative variant is found in one case, screening first-degree relatives provides early detection of carriers who may be asymptomatic. This allows for close surveillance (e.g., ECGs, imaging) and preventative measures including recipient ICD (implantable cardioverter-defibrillator) where appropriate.

Genetic counseling is essential to communicate risk, significance of the genotype–phenotype range, and the role of shared decision-making. In a multicenter series of >5000 individuals with suspected cardiomyopathy or arrhythmia, genetic yield was approximately ~49% for combined gene panel testing, highlighting the utility by which genome evaluation has in cardiology (Dellefave-Castillo et al., 2022).

These findings highlight this evolution from managing symptoms to a preventive or proactive treatment in cardiology, based on genomics.

Carrier & Prenatal Genomics

Reproductive genomics empowers would-be moms and dads to know and reduce their kids’ risk of inherited disease. Preconceptional carrier testing examines whether couples may be  at risk of having a child affected with recessive or X-linked diseases, including:

• cystic fibrosis;
• spinal muscular atrophy (SMA);
• thalassemia.

Carrier screening expanded to panels also of hundreds of genes with an increased scope and detection (Stafford et al., 2022).

NIPT, non-invasive prenatal testing, is performed on DNA fragments from the fetus found in maternal plasma to screen for chromosomal anomalies (21, 18, and 13 triso-mies) with a high sensitivity and specificity. However, NIPT is a screen, and a positive NIPT result must be confirmed by amniocentesis or chorionic villus sampling.

Preimplantation genetic testing (PGT) is employed to screen embryos for single-gene disorders (PGT-M), structural chromosomal rearrangements (PGT-SR), or aneuploidy (PGT-A) in couples undergoing IVF. This allows choosing embryos with lower genetic risk and therefore decreasing miscarriage rates and increasing success.

Moral considerations are of paramount importance. Prior to testing, couples are required to receive counseling and give their informed consent with an understanding of secondary findings, uncertain results, and the limitations of prediction. Family communication guidelines exist and ensure that incriminating findings are appropriately communicated, including the privacy of any relevant disclosures. Genetic counseling is critical for couples to provide help interpreting results and making reproductive choices consistent with their beliefs.

But did you know carrier screening is invaluable, even beyond prenatal genomics?

In the context of genetic disease diagnostics, when paired with family Trio data—sequencing the patient along with both parents—it provides clinicians with essential context to determine if a causative variant may be arisen de novo in symptomatic proband, but not inherited from asymptomatic parents.

This distinction is critical: inherited variants may explain family history or predisposition, while de novo variants often underlie rare, unexpected, or severe conditions. By clarifying this, carrier screening helps clinicians prioritize likely causative variants, reduce the number of variants of uncertain significance (VUS) to investigate, and guide targeted follow-up testing or interventions. The result is faster, more precise, and reliable diagnoses, enabling informed treatment decisions and better patient outcomes. 

Data Pipelines, AI & Reporting

A good clinical genomics service, such as a variant interpretation bioinformatics platform, has dedicated robust infrastructure for support. Broadly, the clinical flow would involve:

• secondary and tertiary bioinformatic analysis of sequenced data;
• variant classification with ACMG/AMP guidelines;
• multidisciplinary case review;
• report sign-out (Richards et al., 2015).

Variants are categorized in standardized terms: pathogenic/likely pathogenic, variant of uncertain significance (VUS), benign, or likely benign (Richards et al., 2015; Davis-Turak et al., 2017).

QC, traceability and version control are crucial to operational integrity—each analytical step needs to be well documented, reproducible, and auditable (Ménard et al., 2021). In high-risk environments like neonatal intensive care, TAT can mean the difference between life and death; indeed, some leading centers have turned around genomic results within 24–48 hours, allowing for critically needed clinical decision-making (Sundercombe et al., 2021).

An additional optimization to enable clinical utility, would be to embed genomic results into both EMR (Electronic Medical Record) and LIS (Laboratory Information System). It allows clinicians to access variant-association data in the context of clinical history, facilitates longitudinal follow-up, and keeps the patient’s records updated (Elhussein et al., 2024). The connection of genomic pipelines to the wider health informatics systems becomes more important as penetration of precision medicine depends on sustainability (Elhussein et al., 2024).

It is only recently that explainable AI (XAI) has been brought to germline genomics. AI has become pervasive in variant prioritization, but exploratory and clinical use-cases require transparent and interpretable AI—particularly those that are built using explicit decision criteria, versioned knowledge bases, or logic as these enable users to audit recommendations from the AI model and verify its evidence (Kim et al., 2024). For example, Abe et al. propose an XAI system that couples a knowledge graph and variant estimation in order to achieve high performance as well as interpretability (Abe et al., 2023).

A third general practice is the reanalysis of existing genetic data on an ongoing basis. Newly categorized variants that the scientific community now knows to be “uncertain” or “benign,” as databases and knowledge continue to accumulate, can also shift. Studies suggest that 10–15% of cases that were not resolved at first can be elucidated when new data/reanalysis is available (Wenger et al., 2021; Ménard et al., 2021).

Lastly, the sensitivity of the genomic data necessitates strict consideration towards security and privacy. Best practice guidelines include:

• de-identification;
• controlled access through biobanks or variant repositories;
• adherence to international ethical and legal frameworks such as GDPR or HIPAA.

These measures are key to retaining patient trust while facilitating accountability for genomic research (Elhussein et al., 2024).

Conclusion

Today, clinical genomics revolves around germline risk and informs the treatment of diseases like pediatrics, oncology, cardiology, and reproductive health. Early diagnosis, family-based screening, personal prevention, and robust data infrastructure are turning us from “reactive” toward “proactive” medicine. Explainable AI, end-to-end pipelines, and always-on reanalysis drive reliability and trust. This paradigm is already transforming medical care and family life across generations.

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References

1. Abe, S., Tago, S., Yokoyama, K., Ogawa, M., Takei, T., Imoto, S., & Fuji, M. (2023). Explainable AI for estimating pathogenicity of genetic variants using large-scale knowledge graphs. Cancers, 15(4), 1118.
2. Alimohamed, M. Z., et al. (2021). Diagnostic yield of targeted next-generation sequencing in cardiomyopathy patients: Real-world outcomes. International Journal of Cardiology, 337, 17–22.
3. Davis-Turak, J., Wu, T., Dominguez, C., Guo, L., Bai, Y., et al. (2017). Genomics pipelines and data integration: Challenges and approaches. BMC Genomics, 18, 1–17.
4. Dellefave-Castillo, L. M., et al. (2022). Diagnostic yield of combined cardiomyopathy and arrhythmia gene testing in a large clinical cohort. JAMA Cardiology, 7(11), 1100–1108.
5. Dimmock, D., et al. (2021). Project Baby Bear: Rapid precision care incorporating rWGS leads to clinical utility and cost savings. American Journal of Human Genetics, 108(7), 1231–1246.
6. Elhussein, A., Baymuradov, U., et al. (2024). A framework for sharing of clinical and genetic data for precision medicine applications. Nature Medicine, 30, 3578–3589.
7. Heliö, K., et al. (2023). Diagnostic yield of genetic testing in dilated cardiomyopathy: Multinational perspectives. BMC Medicine, 21, 85.
8. Ingles, J., et al. (2020). Revisiting the diagnostic yield of hypertrophic cardiomyopathy genetic testing. Circulation: Genomic and Precision Medicine.
9. Kim, H. H., Cho, M. Y., Lee, K., et al. (2024). Explicable prioritization of genetic variants by integration of ACMG/AMP criteria: The 3ASC model. Human Genomics, 18, 5.
10. Lopes, L. R., et al. (2024). Genetics of hypertrophic cardiomyopathy: Established and emerging insights. European Heart Journal, 45(30), 2727–2741.
11. Ménard, T., Mazure, N. M., & Delahaye, A. (2021). Clinical quality considerations when using next-generation sequencing in oncology. Current Protocols in Human Genetics, 109, e123.
12. National Rapid Genome Sequencing in Neonatal Intensive Care Study. (2023). JAMA Network Open, 6(5), e238676.
13. Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., … & ACMG Laboratory Quality Assurance Committee. (2015). Standards and guidelines for the interpretation of sequence variants: A joint consensus recommendation. Genetics in Medicine, 17(5), 405–424.
14. Stafford, F., Tarailo-Graovac, M., & Shyr, D. (2022). The role of genetic testing in diagnosis and care of inherited cardiomyopathies and arrhythmias. Genome Medicine, 14, 117.
15. Stark, Z., et al. (2018). Cost and utility of rapid whole exome sequencing in critically ill infants in Australia. Pediatrics, 143(5).
16. Sundercombe, S. L., Pejovic, N., Turner, A. J., & others. (2021). Clinically responsive genomic analysis pipelines. Journal of Molecular Diagnostics, 23(1), 133–142.
17. Walsh, M., et al. (2024). Rapid genomic sequencing for genetic disease diagnosis and health care cost implications. NPJ Genomic Medicine, 9, 50.
18. Wenger, A. M., Guturu, H., Bernstein, J. A., & Bejerano, G. (2021). Systematic reanalysis of clinical exome and genome sequencing data yields additional diagnoses: A systematic meta-analysis. Genetics in Medicine, 23(2), 297–304.