What We Mean by Genetic Data: DNA and RNA

This article is part of the Foundations of Genomic Data series.

What is meant by genetic data?

In biological research, genetic data usually refers to measurements derived from molecules involved in storing or expressing genetic information. Most often, this includes DNA and RNA.

Although closely related, DNA and RNA correspond to different aspects of biological systems. They are therefore measured using different technologies and used to address different kinds of questions.

Clarifying this distinction helps frame later discussions about sequencing technologies, genotyping assays, and downstream analysis.

DNA and RNA as distinct biological layers

DNA encodes inherited genetic information. With limited exceptions, an individual’s DNA sequence remains stable across cells and over time. Variation at the DNA level captures differences such as single-nucleotide changes and larger structural alterations.

RNA reflects gene expression. RNA molecules are transcribed from DNA, and their abundance varies across tissues, cell types, developmental stages, and environmental conditions. RNA measurements therefore provide a snapshot of cellular activity under specific conditions.

One way to think about the distinction is that DNA constrains what is possible, while RNA reflects which possibilities are realized in a given context.

Because these layers capture different signals, neither is sufficient on its own for most biological questions.

Why multiple measurement approaches exist

There is no single method that captures all aspects of genetic information. Different research goals require access to different biological layers.

Studies focused on inherited risk emphasize DNA variation, while studies of cellular response, development, or disease progression often rely on RNA-based measurements.

In practice, genetic data technologies fall into a few broad categories:

Methods that read DNA sequence. Methods that quantify RNA abundance. Methods that assay selected genetic variants without sequencing entire genomes.

Each approach involves tradeoffs among resolution, cost, scale, and interpretability.

Sequencing and targeted measurement

DNA and RNA sequencing are often discussed together, but they serve different purposes.

DNA sequencing reads the underlying genetic sequence and is well suited for identifying variants, rare mutations, and structural changes.

RNA sequencing measures transcripts derived from DNA and provides quantitative information about gene expression and splicing. It does not directly capture most inherited genetic variation.

Genotyping arrays form a third category. Rather than sequencing, they measure predefined variants across the genome. While limited in scope, they scale efficiently to large populations and are widely used in association studies.

Why these distinctions matter

Confusion can arise when different data types are treated as interchangeable. In practice, they address different questions.

DNA-based data supports inference about inherited differences. RNA-based data informs functional activity. Genotyping enables population-scale analysis.

Choosing among these data types shapes what kinds of biological conclusions can reasonably be drawn.

Looking ahead

Later posts in this series examine DNA sequencing, RNA sequencing, and genotyping in more detail, followed by discussions of how these data types are combined in integrative analyses.

Understanding what genetic data represents provides context for all of these approaches.

Continue Reading →

Next: DNA Sequencing Explained