From Variants to Effects: What Does Functional Interpretation Mean in Genomics?

This article is part I of the Regulatory Variation & Functional Prediction series.

The variant bottleneck

Modern sequencing technologies make it possible to identify millions of genetic variants in a single individual. Large population studies now catalog variation at a scale that was not feasible a decade ago.

As a result, the limiting factor in genomics is no longer the ability to detect variants. It is the ability to understand what, if anything, those variants do.

Functional interpretation exists to address this gap between measurement and biological meaning.

What does it mean to interpret a variant?

Variant interpretation is often described loosely, but it refers to a specific question: how does a change in DNA sequence influence biological processes?

This is distinct from identifying where a variant occurs or how frequently it appears in a population. Interpretation focuses on downstream consequences rather than genomic location alone.

In practice, interpretation connects genotype to intermediate molecular effects, and only indirectly to phenotype.

Between genotype and phenotype

Genetic variants do not act on traits directly. Their effects are mediated through multiple layers of cellular organization, including chromatin structure, transcription, RNA processing, and protein abundance.

These layers form a cascade rather than a single step. A variant may subtly shift regulatory activity without producing an immediate or deterministic phenotypic outcome.

Functional interpretation therefore requires reasoning about intermediate biological mechanisms.

The classical focus on protein-coding variation

Early successes in human genetics were largely driven by variants that disrupted protein-coding sequences. Changes that introduced stop codons, altered amino acids, or destabilized proteins were comparatively straightforward to interpret.

This framing shaped many early tools for variant annotation and prioritization. Protein sequence provided a concrete and mechanistic reference point.

For certain diseases, particularly rare Mendelian disorders, this approach remains highly effective.

Most variants do not alter proteins

Genome-wide studies have since shown that the majority of common disease-associated variants lie outside protein-coding regions.

These variants fall within introns, regulatory elements, or intergenic regions. They do not change the amino acid sequence of proteins, yet they are reproducibly associated with phenotypic variation.

Their effects, when present, are often mediated through changes in gene regulation rather than protein structure.

Expanding the definition of function

Functional interpretation in a regulatory context refers to how variants influence when, where, and how strongly genes are expressed.

This includes effects on transcription factor binding, chromatin accessibility, RNA splicing, and other regulatory processes that shape cellular behavior.

A variant can be functionally relevant even if the encoded protein remains unchanged.

Why regulatory effects are harder to interpret

Regulatory effects are often context-dependent. A variant may influence gene expression in one cell type but have little effect in another.

These effects are also typically modest in magnitude. Rather than causing binary disruptions, regulatory variants tend to shift probabilities and expression levels.

Unlike coding variants, regulatory variation rarely follows simple, universal rules.

Functional effects are not binary

It is tempting to classify variants as either functional or non-functional. In practice, regulatory effects are better understood as continuous and probabilistic.

A variant may slightly increase expression under specific conditions or alter regulatory dynamics only in certain developmental stages.

Functional interpretation therefore involves uncertainty rather than definitive labels.

From annotation to prediction

Early approaches to regulatory interpretation relied on static annotations, such as overlap with known regulatory elements.

While useful, annotations do not directly model how sequence changes alter regulatory behavior. They describe context but do not predict effects.

This limitation has motivated a shift toward predictive models that estimate regulatory consequences directly from DNA sequence.

Why functional interpretation matters now

As genomic datasets continue to grow, experimental validation of every variant is not feasible. Computational interpretation plays an increasingly central role in prioritization and hypothesis generation.

Understanding regulatory effects is particularly important for complex traits, where many variants contribute small, distributed influences.

Functional interpretation does not end with identifying a variant’s location. It requires placing that variant within layers of regulation, cellular context, and biological systems. That broader framework is where modern genomics increasingly turns its attention.

Looking ahead

This article establishes the conceptual foundation for regulatory variant effect prediction. Later posts in this series explore how regulatory effects are modeled, why deep sequence-based approaches have become viable, and what their limitations remain.