Beyond the Variant: Interpreting Genetic Diseases in the AI Era : Episode 2 — GNAS-Related Disorders

The Phenotypic Spectrum Shaped by Genomic Imprinting

📌 Series Introduction

In an era where AI-based variant interpretation tools have become widely adopted, this series focuses on what human interpreters must still understand and decide after automated prioritization.

Each episode centers on a specific disease group, examining genetic mechanisms and clinical spectra together, with the goal of moving beyond simply “finding variants” toward explaining and interpreting them in a clinically meaningful way.

In Episode 2, we examine GNAS-related disorders.

Episode 2 — GNAS-Related Disorders

1. Why GNAS?

GNAS-related disorders represent a classic example of conditions that cannot be adequately explained using the traditional single-gene disease model.

In this disease spectrum, phenotype is not determined solely by the presence or absence of a coding variant. Nor can it be understood by the classical one-gene–one-protein concept.

The broad phenotypic variability observed — even with identical variants — reflects the combined influence of several biological layers:

• Complex locus structure and multiple transcripts
• Genomic imprinting
• Tissue-specific expression
  

The GNAS locus expresses multiple transcripts, including:

• Gsα
• XLαs
• NESP55
• A/B transcript
• GNAS-AS1
  

Among these, the most clinically relevant is Gsα, which plays a central role in multiple hormone signaling pathways.

This episode focuses specifically on disorders associated with Gsα transcript variants.

2. The GNAS Locus and Gsα Interpretation

GNAS is not a single-transcript gene.

It is a complex imprinted locus containing:

• Multiple promoters
• Alternative transcripts
• An imprinting control region

The gene produces multiple products, but the most clinically significant protein is Gsα (G-protein stimulatory alpha subunit).

Gsα serves as a critical mediator in GPCR signaling pathways and transduces signals from hormones such as:

• Parathyroid Hormone (PTH)
• Growth Hormone–Releasing Hormone (GHRH)
• Adrenocorticotropic Hormone (ACTH)

Mechanistic cascade:

Hormone binding → GPCR activation → GDP–GTP exchange on Gsα → Activated Gsα → Adenylyl cyclase stimulation → Increased intracellular cAMP → PKA activation → Phosphorylation of downstream targets → Cellular response

Through this pathway, Gsα regulates multiple developmental and physiological processes, including:

• Endocrine hormone signaling
• Metabolic processes
• Bone development
• Renal physiology

A key feature of GNAS is that Gsα exhibits allele-specific expression, and the imprinting pattern varies across tissues.

This tissue-specific imprinting is a central determinant of phenotypic variability.

3. Parent-of-Origin Effect — Same Variant, Different Disease

A fundamental concept in GNAS-related disorders:

The same variant can cause different diseases depending on parental origin.

Even identical loss-of-function variants may produce distinct clinical manifestations depending on whether the mutation is inherited from the mother or the father.

This occurs because of tissue-specific imprinting of Gsα.

For example:

• In the renal proximal tubule, Gsα is predominantly expressed from the maternal allele

→ Loss of maternal function leads to hormone resistance

• In other tissues, Gsα is biallelically expressed

→ Loss of the paternal allele alone may not cause hormone resistance

Thus, in GNAS, allelic origin is as important as variant type in determining phenotype.

This parent-of-origin effect underlies the distinction among the following GNAS-related disorders:

• Pseudohypoparathyroidism (PHP)
• Pseudopseudohypoparathyroidism (PPHP)
• Progressive Osseous Heteroplasia (POH)

4. Pseudohypoparathyroidism (PHP) — Maternal Allele Disorder

Pseudohypoparathyroidism includes several subtypes, such as:

• PHP1A
• PHP1B
• PHP1C
• PHP2

Among these, PHP1A is the most well-characterized form and results from a loss-of-function variant on the maternal GNAS allele.

Clinical features include:

• Albright hereditary osteodystrophy (AHO): short stature, brachydactyly, subcutaneous ossification, round face
• PTH resistance
• Hypocalcemia
• Hyperphosphatemia

5. Pseudopseudohypoparathyroidism (PPHP) — Paternal Allele Disorder

When a loss-of-function variant occurs on the paternal allele of GNAS, the clinical phenotype is Pseudopseudohypoparathyroidism (PPHP).

Although PPHP superficially resembles PHP1A, the clinical manifestations differ.

AHO features are also observed in PPHP, including:

• Short stature
• Brachydactyly
• Subcutaneous ossification
• Round face

These physical features are prominent in growth plates, skeletal, and soft tissues.

The reason is that in these tissues, Gsα is expressed from both alleles (biallelic expression). Therefore, functional impairment from either allele can result in reduced Gsα signaling and AHO morphology.

The key distinction between PPHP and PHP1A is the presence or absence of hormone resistance.

Resistance to hormones including PTH or GHRH occurs primarily in tissues such as:

• Renal proximal tubule
• Thyroid
• Pituitary

In these tissues, Gsα is preferentially expressed from the maternal allele due to imprinting.

Therefore:

• Maternal allele variant → Reduced Gsα expression → Hormone resistance
• Paternal allele variant only → Maternal allele remains functional → No hormone resistance

Thus, in PPHP, hormone resistance does not occur because maternal allele expression is preserved.

6. Progressive Osseous Heteroplasia (POH) — The Extreme End of the Disease Spectrum Associated with Paternal Allele GNAS variants

POH represents the most severe phenotype within the paternal GNAS loss-of-function spectrum.

Although both PPHP and POH arise from paternal GNAS variants, their clinical presentations differ significantly.

This difference can be explained by:

• The developmental timing of Gsα dysfunction
• The specific cellular lineage affected

In PPHP, reduced Gsα signaling primarily affects already differentiated skeletal cells, resulting in:

• Brachydactyly
• Short stature

Thus, the core pathophysiology of PPHP is signaling dysfunction in post-differentiation cells.

In contrast, POH pathophysiology begins earlier.

Current evidence suggests that Gsα dysfunction occurs at the level of mesenchymal progenitor cells.

Reduced Gsα signaling → Decreased cAMP → Dysregulation of Hedgehog and Wnt–β-catenin pathways → Osteogenic lineage bias

As this dysregulation accumulates, mesenchymal progenitor cells fail to differentiate into adipocytes, myocytes, chondrocytes, or connective tissue cells, and instead undergo abnormal differentiation into osteoblast lineage cells.

Clinically, this results in progressive heterotopic ossification extending from subcutaneous tissue into muscle and connective tissue.

POH therefore represents not merely a severe form of PPHP, but a distinct pathophysiological context defined by developmental stage–specific GNAS dysfunction.

7. Critical Elements in Clinical Interpretation

Identifying a GNAS variant does not justify a one-to-one mapping between variant and diagnosis.

GNAS is characterized by:

• Tissue-specific imprinting
• Allele-specific expression
• Epigenetic regulation

Therefore, integrated interpretation incorporating clinical findings, family history, and molecular genetic results is essential.

Precise phenotypic matching based on clinical presentation and imaging findings, combined with methylation analysis to infer parent-of-origin, enables more accurate and refined diagnosis.

Automated tools can prioritize candidate variants.

However, determining whether a variant truly explains the patient’s phenotype still requires integrative clinical reasoning.

8. Beyond “Finding” — Toward “Explaining”

AI-based analytical tools such as GEBRA play a powerful role in genomic interpretation.

In narrowing down candidate variants from among countless possibilities and presenting prioritization, computational tools have already become indispensable.

Yet one question remains:

Does this variant truly explain this patient’s phenotype?

GNAS-related disorders provide a representative example of this interpretive tension.

In this episode, we examined this question through the lens of GNAS biology.

Beyond Pathogenicity Classification — Toward Biological Causality

Classifying variants according to ACMG guidelines is essential.

However, in GNAS-related disorders, it is not sufficient.

Even the same loss-of-function variant can lead to entirely different diseases depending on:

• Which allele is affected
• In which tissue the gene is expressed
• At what developmental stage functional impairment occurs

Interpretation therefore requires a multilayered framework linking:

Variant → Signaling pathway → Tissue-specific expression → Phenotype

Only by additionally considering imprinting status and developmental cellular context can the full disease mechanism be understood.

Why Prioritization Alone Is Not Enough

Automated systems can filter candidate variants and provide statistical, data-driven evidence.

However, answering the following questions remains the responsibility of the human interpreter:

• Is the specific signaling alteration caused by the variant in the affected tissue consistent with the patient’s clinical presentation?
• Is the signaling alteration consistent with the patient’s clinical findings?
• Is there logical coherence between the phenotype and biological plausibility?

Within the individual patient’s clinical context, is this explanation the most valid?

GNAS-related disorders are not diseases defined by a single variant.

They represent a biological spectrum in which:

• Signaling dysfunction
• Imprinting dysregulation
• Differentiation-stage effects

operate simultaneously.

What Is Integrated Interpretation?

Interpreting such disorders goes beyond reading sequencing results.

It requires viewing genetics, epigenetics, tissue-specific expression, cellular differentiation, and physiological response as interconnected layers within a single biological framework.

Precise genetic interpretation is less about processing data and more about integrating molecular mechanisms with physiological context.

Computational tools can refine candidate variants.

However, explaining the significance of a variant — and determining how it leads to disease in a particular patient — still requires deep biological understanding and integrative clinical reasoning.

References

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2. Jüppner, H. “Pseudohypoparathyroidism: Complex Disease Variants with Unfortunate Names.” Journal of Molecular Endocrinology, vol. 72, no. 1, 12 Dec. 2023, e230104. https://doi.org/10.1530/JME-23-0104.

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4. Pignolo, R. J., et al. “Progressive Osseous Heteroplasia: Diagnosis, Treatment, and Prognosis.” Applied Clinical Genetics, vol. 8, 30 Jan. 2015, pp. 37–48. https://doi.org/10.2147/TACG.S51064.

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6. St-Jean, M., et al. “Aberrant G-Protein Coupled Hormone Receptor in Adrenal Diseases.” Best Practice & Research Clinical Endocrinology & Metabolism, vol. 32, no. 2, Apr. 2018, pp. 165–187. https://doi.org/10.1016/j.beem.2018.01.003.

7. Turan, S., and M. Bastepe. “GNAS Spectrum of Disorders.” Current Osteoporosis Reports, vol. 13, no. 3, June 2015, pp. 146–58. https://doi.org/10.1007/s11914-015-0268-x.

8. Weinstein, L. S., T. Xie, Q. H. Zhang, and M. Chen. “Studies of the Regulation and Function of the Gs Alpha Gene GNAS Using Gene Targeting Technology.” Pharmacology & Therapeutics, vol. 115, no. 2, Aug. 2007, pp. 271–91. https://doi.org/10.1016/j.pharmthera.2007.03.013.

9. Yang, W., et al. “GNAS Locus: Bone Related Diseases and Mouse Models.” Frontiers in Endocrinology, vol. 14, 18 Oct. 2023, article 1255864. https://doi.org/10.3389/fendo.2023.1255864.

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