Overview #
Genetic constraint describes how tolerant a gene is to changes (mutations) in its DNA sequence.
Genes that are highly constrained show far fewer variants than expected in healthy populations, indicating that changes in these genes are often deleterious and may cause disease.
On the SeqSMART platform, gene-level constraint data helps users understand each gene’s biological importance and the potential clinical impact of variants within it.
This information is particularly useful when prioritizing variants for pathogenicity assessment.
1. Types of Genetic Constraint #
Genetic constraint is evaluated separately for different types of sequence changes. SeqSMART follows gnomAD-derived constraint models, which categorize constraint into three main classes:
A. Loss-of-Function (LoF) Constraint #
Definition:
LoF variants truncate or disrupt a gene product, leading to reduced or absent protein function.
LoF constraint reflects how intolerant a gene is to such damaging mutations.
Key Metrics
| Metric | Description | Interpretation |
| Observed/Expected Ratio (o/e) | Ratio of observed LoF variants to expected ones under neutrality. | Lower ratios (<0.35) indicate strong constraint. |
| Probability of LoF Intolerance (pLI) | Probability that a gene is intolerant to LoF variants. | Values >0.9 suggest strong intolerance. |
| Z-score | Statistical measure of deviation from expected variant count. | High positive scores indicate significant constraint. |
Clinical Insight
Genes with strong LoF constraint are often dosage-sensitive and may cause dominant or haploinsufficient disorders when disrupted.
B. Missense Constraint #
Definition:
Missense variants change one amino acid in the protein sequence.
Missense constraint reflects how tolerant a gene is to amino acid substitutions.
Key Metrics
| Metric | Description | Interpretation |
| o/e Ratio | Observed vs. expected count of missense variants. | Lower values (<0.8) suggest constraint. |
| Z-score | Statistical deviation measure. | Values >3 indicate significant depletion of missense variants. |
Clinical Insight
Genes with strong missense constraint are typically intolerant to amino acid changes, and pathogenic missense variants in such genes often cause diseases related to protein structure or function.
C. Synonymous Constraint #
Definition:
Synonymous variants do not alter amino acids and are generally considered neutral.
However, some may influence splicing, regulatory motifs, or mRNA stability.
Key Metrics
| Metric | Description | Interpretation |
| o/e Ratio | Observed vs. expected synonymous variant frequency. | Usually near 1 (neutral). |
| Z-score | Statistical measure of deviation. | Typically close to zero; higher values may suggest selection at the nucleotide level. |
Clinical Insight
While most synonymous variants are benign, rare exceptions occur when variants affect splicing signals or regulatory regions.
2. How to Interpret Constraint Data #
SeqSMART provides constraint metrics to guide clinical and research interpretation.
| Indicator | Interpretation |
| LoF o/e < 0.35 | Strong LoF constraint → gene likely essential |
| LoF pLI > 0.9 | Gene is LoF-intolerant |
| Missense o/e < 0.8 | Gene shows missense constraint |
| High Z-score (>3) | Statistically significant depletion of observed variants |
Interpretation Principle:
- The closer the o/e ratio is to zero, the more constrained the gene.
- A high pLI value (>0.9) reinforces LoF intolerance, suggesting variants are more likely to be pathogenic if they disrupt the gene’s function.
3. Why Genetic Constraint Matters #
Genetic constraint metrics are valuable for:
- Prioritizing potentially pathogenic variants during genomic interpretation.
- Assessing disease association, especially for genes not yet fully characterized.
- Understanding dosage sensitivity and variant tolerance patterns across genes.
Highly constrained genes are often critical for normal development and cellular function, making them key candidates for disease involvement when disrupted.
4. Data Source and Quality #
SeqSMART derives constraint data from gnomAD (v2 and v3) — one of the largest population genomics resources.
Each metric is updated periodically to incorporate newly available population data and annotations.
Important Notes
- Constraint scores should not be interpreted in isolation.
- Always consider inheritance patterns, functional studies, and clinical phenotype alongside constraint data.
- Constraint metrics are gene-level indicators, not variant-level predictors.
Genetic constraint analysis helps bridge population-level genomic data with clinical variant interpretation.
By integrating gnomAD constraint metrics directly into the SeqSMART platform, users can rapidly assess whether a gene is intolerant to functional variation, enhancing the accuracy of variant prioritization and disease interpretation.
SeqSMART Constraint Principle:
Genes that resist change reveal where change matters most.