Why Do Different Databases Show Different Allele Frequencies?

Not all genetic databases are built the same way, and that is why the same variant can have a different reported frequency depending on which resource you check.

The differences come down to three main factors: who was included in the study, how their DNA was tested, and what the database was designed to do in the first place.

Who was included?

Some databases draw from large, diverse groups of people, while others focus on participants from studies with specific health conditions, such as heart or lung disease. A variant that is common in one group may be rare or not found among the people studied in another group, so the population behind the data shapes the frequency estimate significantly.

How DNA was the tested?

Some databases use whole genome sequencing, some use exome sequencing (which focuses on protein-coding regions), and others combine data from multiple technologies, including genotyping arrays. These methods differ in which parts of DNA they examine, how well they detect genetic changes, and how the results are checked for accuracy.

What was the database designed for?

Some resources are built specifically to estimate population-level variant frequencies, while others, like ClinVar, are primarily designed to collect assessments of whether genetic variants are linked to health conditions. Frequencies shown in ClinVar are often imported from other sources rather than independently generated, which is why going to the original population database directly helps explain how the frequency was calculated and which population it represents.

Because of these differences, it is important to be consistent about which database you rely on and to understand the strengths and limitations of each one when interpreting allele frequency data.