What Is Variant Filtering and Why Is It Important?

A person’s genome, their complete set of DNA, contains millions of genetic variants, which are differences in the DNA sequence when compared to a reference. Most of these variations are harmless, contributing to the diversity that makes each person unique. However, a small fraction can influence the development of diseases or affect how a person responds to medications.

The challenge is to find these few meaningful variants among the millions of benign ones. Variant filtering is a methodical process of sifting through a vast list of genetic differences to isolate a smaller number of candidates that are most likely to be relevant to a specific condition. This process acts as a series of sieves, removing variants that are less likely to be important, allowing researchers to focus on the ones that matter most.

The Challenge of Many Genetic Variants

Modern genetic sequencing technologies like Whole Exome Sequencing (WES), which focuses on protein-coding regions, and Whole Genome Sequencing (WGS), which covers the entire genome, generate an enormous amount of data. A single individual’s WGS data can reveal approximately 3 million genetic variants, presenting a significant analytical hurdle. The overwhelming majority of these identified variants are common in the general population and have no negative impact on health, being part of normal genetic diversity.

Some variants may even be sequencing artifacts—errors introduced during the complex laboratory and computational processes used to read the DNA sequence. The central problem is distinguishing the rare, potentially disease-causing variants from this extensive background noise.

Key Information Used for Filtering Variants

To distinguish potentially impactful variants from the benign majority, scientists rely on several categories of information.

• Sequencing data quality is assessed to ensure a variant is a real biological difference and not a technical error. Metrics such as read depth, which measures how many times a specific piece of DNA was sequenced, and mapping quality help establish confidence in the variant call.
• Population frequency from large databases like the Genome Aggregation Database (gnomAD) is a powerful filter. If a variant appears frequently in the general population, it is less likely to be the cause of a rare disease, so researchers prioritize rare or novel variants.
• The predicted functional impact of a variant on a gene or its protein product is evaluated using computational tools. These tools analyze how a variant might affect protein function, such as a “nonsense” variant creating a premature stop signal. Variants predicted to have a more severe consequence are prioritized.
• Inheritance patterns in family studies provide important clues. For a dominant disease, researchers look for variants present in an affected parent and child. For recessive conditions, which require two copies of a faulty gene, they search for variants inherited from both parents.
• Clinical and conservation databases add context. Databases like ClinVar catalog variants previously classified by the scientific community. Additionally, if a variant occurs in a highly conserved region of DNA that is unchanged across species, it suggests that region is important.

The Process of Narrowing Down Variants

The application of filtering criteria is a strategic, multi-layered process designed to progressively narrow the field of candidate variants. The exact sequence of these steps can be tailored to the specific goals of the research, whether it’s diagnosing a rare disorder or studying a common disease. A typical filtering cascade begins by removing low-quality variants to ensure the starting list is technically robust. From there, the next step is to filter out common variants found in population databases like gnomAD, which can reduce the number of variants by more than 90%.

After reducing the list to high-quality, rare variants, the focus shifts to their predicted biological effect. Variants predicted to cause significant changes to a protein are prioritized over those with more subtle or unknown effects. In the final stages, the remaining candidates are scrutinized through inheritance patterns and existing knowledge from databases like ClinVar. This systematic funneling approach transforms a list of millions of variants into a small, manageable number that can be realistically investigated further.

How Filtered Variants Drive Discoveries

The successful identification of a single, significant variant through this rigorous filtering process can have major impacts on medicine. For individuals and families affected by rare genetic disorders, pinpointing the causative mutation can end a long and difficult diagnostic journey. A definitive genetic diagnosis provides answers, allows for more accurate genetic counseling regarding family planning, and can guide potential therapeutic strategies.

In the realm of common and complex diseases, such as heart disease, diabetes, or Alzheimer’s, variant filtering helps researchers identify genes that contribute to risk. While each individual variant may only have a small effect, discovering these associations across large populations helps to piece together the complex genetic architecture of these conditions. This knowledge is a foundation for developing new preventative measures and treatments.

This process is also central to personalized medicine, particularly in the field of pharmacogenomics. By filtering an individual’s genetic variants, clinicians can predict how they might respond to certain medications. This can help in selecting the most effective drug and dosage from the outset, minimizing the risk of adverse reactions and improving treatment outcomes. For instance, knowing a patient’s genetic profile allows for more precise prescriptions.

Ultimately, every filtered variant that is linked to a trait or disease adds to our fundamental knowledge of gene function. By studying how a specific “misspelling” in the DNA code leads to a particular outcome, scientists can uncover the intricate mechanisms that govern human health. These discoveries propel biomedical science forward, leading to better diagnostics, new therapies, and a deeper comprehension of life itself.