The current focus of this package is on implementing (either exactly or approximately) regression analyses using summary statistics instead of using subject-specific genotype and phenotype data. So far, functions exist to support three applications detailed below: Multi-SNP risk score analyses; multi-SNP conditional regression analyses; and multi-phenotype analyses.
In addition, there are helper functions for reading and
manipulating subject-specific genotype and phenotype data, which
provide a platform for calculating the necessary summary statistics,
and for performing exact analyses to validate some of the
approximate summary statistic based methods.
The first application is multi-SNP risk score analyses, and the main
functions provided for analysing summary statistics are
grs.summary
, grs.plot
and
grs.filter.Qrs
. The summary statistics necessary for
these analyses are single SNP association statistics, which can be
calculated using a wide variety of existing tools for GWAS analysis
and meta-analysis.
The second application is multi-SNP conditional or multiple regression
analyses. The main functions provided for performing multiple regression using
summary statistics are combine.moments2
,
est.moments2
, lm.moments2
and stepup.moments2
. The summary
statistics necessary for these analyses can be calculated from
subject-specific genotype and phenotype data, using the function
make.moments2
.
The third application is multi-phenotype analyses. So far, a single
function multipheno.T2
is provided.
The helper functions for reading and manipulating subject-specific
genotype and phenotype data provide a convenient interface from R to
genotype data exported from PLINK, and imputed genotype data generated
by MACH, minimac, or IMPUTE. The main functions provided are
read.snpdata.plink
, read.snpdata.mach
,
read.snpdata.minimac
, and
read.snpdata.impute
.