Genotyping is the process of identifying the specific genetic makeup of an individual, while the transcriptome represents the complete set of RNA molecules in a cell, indicating which genes are active. Genotyping of transcriptomes is a technique that leverages RNA sequencing data to accomplish both tasks simultaneously. It uses the expressed gene information, captured in RNA, to infer the underlying genetic variants in the DNA. This approach provides a snapshot revealing which genes are being used by a cell and the genetic blueprint of those active genes.
The Underlying Mechanism
The process is based on RNA sequencing (RNA-Seq), beginning with the extraction of all RNA molecules from a biological sample. These RNA molecules, which are copies of expressed genes, are converted into a more stable form for sequencing. Machines then read the specific order of nucleotide bases in each molecule, generating millions of short data fragments called reads.
These reads are then computationally mapped, or aligned, to a well-established reference genome. This alignment process is like assembling a puzzle, where each read is a piece and the reference genome is the guide. Bioinformatics software scans for consistent discrepancies between the sequenced reads and the reference. When a position in the RNA reads consistently shows a different nucleotide base than the reference DNA, it is flagged as a potential genetic variant.
The most common type of variant identified this way is a single nucleotide polymorphism (SNP). For example, if the reference genome has a Guanine (G) at a location, but the RNA reads from the sample consistently show an Adenine (A), the software identifies this difference. Analyzing these patterns allows scientists to determine an individual’s genotype, such as being homozygous (two identical copies) or heterozygous (two different copies), at numerous locations within expressed genes.
Key Analytical Applications
A primary application is expression Quantitative Trait Loci (eQTL) mapping. This analysis connects genetic variants identified from the RNA data to the quantity of gene expression. An eQTL is a region of the genome containing a variant that influences how much a particular gene is turned on or off. Using this integrated data, researchers can test if individuals with a “T” at a specific SNP have higher or lower expression of a nearby gene compared to individuals with a “G”.
This approach allows scientists to build regulatory maps that reveal how genetic differences translate into functional changes in gene activity. These connections are important for understanding the genetic basis of complex diseases. For instance, an eQTL might explain a predisposition to a disease by showing that a specific variant leads to lower expression of a protective gene.
Another application is the analysis of Allele-Specific Expression (ASE). An individual inherits two copies, or alleles, of most genes—one from each parent. While these alleles are often expressed at similar levels, sometimes one is more active than the other. ASE analysis uses the genotypic information in the transcriptome to compare the expression levels of the two parental alleles.
By identifying SNPs within a gene’s transcribed region, researchers can distinguish the RNA produced by the maternal allele from the paternal allele. If sequencing reads show a significantly higher number of transcripts from one allele over the other, it indicates allele-specific expression. This reveals how genetic variation can directly impact gene function, often in a cell-specific manner.
Advantages Over Traditional Methods
A key advantage of genotyping transcriptomes is resource efficiency. Traditionally, scientists performed two separate experiments: a DNA analysis to determine genotype and an RNA-Seq experiment to measure gene expression. Combining these into a single workflow saves time and money. It also conserves biological sample material, which is a major consideration when studying rare cells or using small clinical biopsies.
This method also offers a more direct linkage between genetic information and gene expression data. Because both genotype and expression levels are derived from the same RNA molecules, the association between a variant and its effect on gene activity is unambiguous. This eliminates confounding variables that can arise when comparing DNA and RNA data from different batches, resulting in a more robust dataset for drawing conclusions.
Limitations and Considerations
A primary constraint is the technique’s reliance on active gene expression. Genetic variants can only be detected if they are located within a gene that is being transcribed in the tissue being studied. If a gene is turned off, any variants within it will be invisible, meaning the resulting genotype data is incomplete and represents only the active subset of the genome.
Accuracy can also be affected by biological processes like RNA editing, which alters an RNA molecule’s sequence after transcription. This can create a base in the RNA that does not match the underlying DNA, which an analysis pipeline might misinterpret as a genetic variant. Bioinformatics tools are needed to differentiate true genetic variants from these modifications.
Confidence in a genotype call is dependent on the level of gene expression. Genes expressed at very low levels may not produce enough RNA for sufficient sequencing depth, known as coverage. When coverage is low, there may be too few reads to reliably distinguish a true variant from sequencing errors, leading to uncertainty in the genotype.