Indels are now considered when computed the median adjusted CADD
scores in each CADD region
Indels can be annotated and analysed using the RAVA-FIRST
strategy
v1.0.0
RAVA-FIRST
Use RAVA.FIRST() to run the whole RAVA-FIRST strategy
Three files with adjusted CADD scores, CADD regions and Functional
categories are available at https://lysine.univ-brest.fr/RAVA-FIRST/ and
directly downoladed in Ravages repository if RAVA-FIRST functions are
used
Variants can be annotated into CADD regions and genomic categories
(set.CADDregions)
Possilibity to annotate variants with the adjusted CADD score and to
filter them based on the median observed in each CADD region
(filter.adjustedCADD)
Possibility to perform burden tests with subscores in the regression
to take into account the genomic categories (burden.subscores)
Simulations
rbm.haplos.power() and rbm.GRR.power() are available to directly
compute power of CAST, WSS and SKAT on the corresponding
simulations
random.bed.matrix() is now rbm.GRR()
CAST power can be computed using theoretical computations in
rbm.GRR.power()
Other
get.effect.size replaces get.OR.values in burden() and enables to
get the beta estimate for continuous phenotypes
Add the possibility to filter genomic regions based on the
cumulative MAF (min.cumulative.maf)
Parallelisation of burden on continuous phenotypes
v0.1.5:
Fix bugs with tinythreads and RcppParallel in SKAT
v0.1.2:
Parallelisation of SKAT on continuous phenotypes
Minor bugs corrected :
SKAT() with continuous phenotypes can be run only if at least 2
variants in the genomic region
Cleaning temporary files in functions called by mclapply to optimise
memory usage
Checks added in multiple functions to verify the fit between
functions and arguments