Gene / Disease prioritisation
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- Python
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Description
See also #154 - Variant Ranking / Prioritisation
Sarah raised an issue https://github.com/SACGF/variantgrid_com/issues/12
Background
In some cases it is not possible to reduce variants for investigation to a reasonable number. In these cases it would be extremely helpful to be able to rank variants for investigation.
Describe the solution you'd like
Suggestion 1: Implementation of an existing ranking tool, although I haven't found a suitable option as yet.
Suggestion 2: In-house ranking tool, something along the lines of:
Variant rank = scaled population frequency score (0-1) x prediction score (0-1) x HPO based gene relevance score (0-1) x scaled publication score (0-1)
Where scores are calculated as:
1. Population
gnomad highest sub-pop maf frequency
absent = 1, scaled to 0 at a certain % (20%?, scale based on probability score?)
Pathogenicity prediction
Maximum of:
LOF no LOFTEE tag = 1
Splice = Highest score of SpliceAI. dbscSNV RF, dbscSNA Ada, MAXEntScan %diff (scaled 0-1),
Missense = highest of REVEL or CADD (0-1 scale)
Intron = ?? which score. Conservation? TFBS?
Gene relevance from HPO inputs
hiPHIVE or phen2gene 2
Scaled mastermind publications (0-1)
Additional context
Potential master's project?
Only raising for discussion - testing & modelling required prior to implementation. Having said that, a rough priority score (say just taking into account pop & pathogenicity) would be extremely helpful at this stage.
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