2 Variant Filtering
Transition to Transversion Ratio
The transition to transversion ratio (Ti/Tv) compares the number of transition mutations to the number of transversion mutations observed within a sequence. Due to there being twice as many possible transversions to transitions, we would expect a Ti/Tv of 0.5. However, this changes when we take into consideration biological context. In human genomes, this expected Ti/Tv can range anywhere from ~2.0 to ~3.3. The ratio is also highly depenedant on genomic region, ancestry, analysis pipelines, filtering etc. As a QC, it can be used to evaluate the quality of a sequence of SNPs for technical errors. A Ti/Tv closer to the theroretical 0.5 value can indicate sequencing errors (as these errors mimic random mutation rates) and extremely high Ti/Tv values (>4) can indicate high bias in analysis. [@gatk2024qc; @cdgenomics2026; @wang2015; @carson2014]
Mutational Spectrum Plot
Here, we break down all mutations in a sample according to each type of mutation. There are six possible mutations. [@griffithlab2017]
References:
Broad Institute GATK Documentation: https://gatk.broadinstitute.org/hc/en-us/articles/360035531572-Evaluating-the-quality-of-a-germline-short-variant-callset
CD Genomics Technical Resource: https://www.cd-genomics.com/resource-quality-control-whole-exome-sequencing.html
PMID: 25297068, 24884706
Principal Component Analysis (PCA)
PCA is a statistical method used to simplify high-dimensional genetic data (such as millions of SNPs) into a small number of continuous axes called principal components (PCs). This is useful to infer ancestry and investigate population structure. It can also be used to check for batch effects in your dataset. [@wickland2021]
References:
PMID: 33861770