Using SVA Analysis functions - Variant Prioritization

 

The "Variant Prioritization" function is similar to "Gene Prioritization", however it prioritizes specific individual variants instead of entire genes. The variant types are limited to: stop gain, stop lost, and non-synonymous SNPs. This means that this function produces a list of coding variants that responsible for 'knocking out' genes in case genomes. This analysis prioritizes variants by the number of cases containing homozygous genetic variants, absent in control genomes. This analysis tries to answer this simple question: do you see enrichment of protein-truncating or non-synonymous variants in the cases, when compared to control genomes?

Your SVA project must contain at least one case and one control for this function to work. Your SVA project must contain at least two cases for this function to reliably prioritize knocked out genes.

To access the variant prioritization function, go to the menu 'Analysis ->Variant Prioritization'. The Genome Browser must be initilized to run this function.

This will open a 'Prioritization analysis' window. This window will allow you to customize this function in four different ways, explained below. The 'Prioritization analysis' window looks like this:

Function of genetic variant

This section allows you to select the types of 'knock out' variants you wish to include. The three options are:

1. "Protein truncation only". This is the default setting and includes all homozygous variants that result in a premature trunction of a protein. Protein truncating mutations will always be included in the output.
2. "Including non-synonymous SNPs". This setting includes both protein truncating variants and homozygous SNPs resulting in the change of an amino acid residue in the resulting protein.
3. "For non-synonymous SNPs: including only intolerable NS change". This setting is supplementary to option number #2. When checked, an additional filter is applied to the non-synonymous SNPs by eliminating any "tolerable" non-synonymous SNPs. Note: The status of an 'intolerable' non-synonymous SNP is determined using the MAPP software (http://mendel.stanford.edu/SidowLab/downloads/MAPP/index.html).

Criteria for homozygous variants

This section allows you to adjust the coverage you want to require for a homozygous variant. This will impact the number of variants that show up on your gene prioritization output. If you do not care about coverage (i.e. confidence) of the homozygous variants, you can uncheck 'Minimum coverage for a homozygous variant'. Otherwise the default parameters are set as follows:

	Number of sequencing reads
Autosomes (case):	10
Sex chromosomes (case):	5
Autosomes (control):	10
Sex chromosomes (control):	5

A brief explanation of default parameters for homozygous variants:
In order to determine reliable default parameters, a number of whole genome sequence samples were analyzed for the coverage (read depth) distribution for confident 'homozygous' SNPs called by SAMtools. The coverage data was compared to the 1 million SNP chip data for the same sample. This analysis revealed the following:

1. Mistakenly called homozygous SNPs (defined by mismatch with 1M chip), had an average read depth of 7.03, with a median of 5.
2. Correctly called homozygous SNPs (defined by match with 1M chip), had an average read depth of 23.48, with a median of 24.
3. When a read depth threshold is not specified, 97.2% homozygous SNPs can be correctly called.
4. When a read depth threshold of >=10X is specified, then 99.30% homozygous SNPs can be correctly called.
5. These data suggest that a read depth threshold needs to be considered when you define a SNP as homozygous.  
1. If you want a lower false negative, but a relatively higher false positive (sensitivity is high), then use a lower threshold in cases but higher threshold in controls.
2. If you want a higher false negative, but a relatively lower false positive (specificity is high), then use a higher threshold in cases but lower threshold in controls.

Genetic model

This section allows you to select the genotype of the variant (heterozygous or homozygous) that you will allow to be present in your control genomes. The target variations in your cases are unaffected by this setting (still homozygous knock out variants).

1. 'Recessive model". This model targets variant functioning in its homozygous form (alternative allele), allowing its heterozygous form in control genomes.
2. 'Dominant model". This model targets variant functioning in its homozygous (alternative allele) or heterozygous form, not allowing its heterozygous form in control genomes.

Control genomes

1. If 'check control samples for reference sequence' is unchecked, then all homozygous variants are listed, not just those unique to the cases. This function is disabled since you always want this function to check control samples for the reference sequence.
2. You may choose to 'Treat Venter's sequence as a control genome' by clicking the check box next to this option.

After selecting your desired settings in the 'Prioritization analysis' window, click 'Output to . . . ' select the file you wish to save your file to, name your file by entering a name into the 'File Name' field and select 'Save'.

After clicking "Save", the path to this file will be displayed in the 'Prioritization analysis' window, then select 'Analyze'.

Sample output (.txt file):

Rank	Type	SN	GeneSym	Count
1	NS_SNP	448982	NUCKS1	1