Genotype

For finding sample matches and mismatches.

Compares each sample against each other to verify expected sample matches and identify any unexpected matches or mismatches. Running these comparisons requires the extracted pileup information to compute a discordance score between each pair of samples. The documentation below details the different ways to run this analysis, the output, and the methods behind them.

How to run the tool

You need one or more samples to run this analysis. However, if you supply just one sample then it is assumed you have samples already in the database to compare with. There are two required inputs: (1) the names of the sample(s) you want to compare (referred to as input samples below), and (2) the database (biometrics will automatically load all sample data from the database). Moreover, there are two types of comparisons that are performed when running the tool:

(1) Compares your input samples with each other

This only runs if you supplied two or more input samples. The easiest way to indicate which samples you want to compare is to give the sample names via the --sample-name flag. Below is an example command:

biometrics genotype \
  -sn C-48665L-N001-d C-PCYP90-N001-d C-MH6AL9-N001-d \
  -db /path/to/store/extract/output

You can also indicate your input samples via a CSV file, which has the same format as what you provided to the extraction tool, but you only need the sample_name column:

biometrics genotype \
  -i samples.csv \
  -db /path/to/store/extract/output

(2) Compares your input samples with remaining database samples

The second analysis will compare each of your input samples with all remaining samples in the database. However, if you wish to disable this step and not do the comparison then you can supply the --no-db-compare flag:

biometrics genotype \
  -sn C-48665L-N001-d C-PCYP90-N001-d C-MH6AL9-N001-d \
  --no-db-compare \
  -db /path/to/store/extract/output

Output

All analyses output a CSV file containing the metrics from comparing each sample. An interactive heatmap can also optionally be produced by supplying the --plot flag. These outputs are saved either to the current working directory or to a folder you specify via --outdir.

CSV file

The CSV file contains metrics for each pair of samples compared (one on each line). Table 1 below provides a description on each column.

Column Name	Description
ReferenceSample	First sample in the comparison.
QuerySample	Second sample in the comparison.
DatabaseComparison	True if the comparison was between one of your input samples and one in the database.
HomozygousInRef	Number of homozygous sites in the ReferenceSample.
TotalMatch	Total sites that match (homozygous and heterozygous).
HomozygousMatch	Number of homozygous sites that match.
HeterozygousMatch	Number of heterozygous sites that match.
HomozygousMismatch	Number of mismatching homozygous sites.
HeterozygousMismatch	Number of mismatching heterozygous sites.
DiscordanceRate	Discordance rate metric.
Matched	True if ReferenceSample and QuerySample have DiscordanceRate less than the threshold (default 0.05).
ExpectedMatch	True if the sample pair is expected to match.
Status	Takes one of the following: Expected Match, Unexpected Match, Unexpected Mismatch, or Expected Mismatch.

Interactive plot

Below are the two figures that are outputted from the two types of comparisons that are done. Samples that are unexpected matches or mismatches will be marked with a red star in the heatmap.

Algorithm details

Any samples with a discordance rate of 5% or higher are considered mismatches.

$$Discordance\ Rate = \frac{Number\ of\ matching\ homozygous\ SNPs\ in\ Reference\ but\ not\ Query}{Number\ of\ homozygous\ SNPs\ in\ Reference}\\$$

If there are <10 common homozygous sites, the discordance rate can not be calculated since this is a strong indication that coverage is too low and the samples failed other QC.