run.multireg: Local genetic multiple regression analysis

In josefin-werme/LAVA: Local Analysis of [co]Variant Association

Local genetic multiple regression analysis

Description

Will perform a local genetic multiple regression analysis, which models the genetic signal for a single outcome phenotype of interest using two or more predictor phenotypes. Here, the genetic correlations between all predictors will be accounted for, and their genetic relation with the outcome will be conditioned on one another.

Usage

run.multireg(
  locus,
  target,
  phenos = NULL,
  adap.thresh = c(1e-04, 1e-06),
  only.full.model = F,
  p.values = T,
  CIs = T,
  param.lim = 1.5,
  suppress.message = F
)

Arguments

locus	Locus object created using the the process.locus function. Contains all the relevant parameters and processed sum-stats for the phenotypes of interest
target	Outcome phenotype of interest (all other phenotypes will be considered predictors)
phenos	Subset of phenotypes to analyse. If NULL, all phenotypes in the locus object will be analysed.
adap.thresh	The thresholds at which to increase the number of iterations for the p-value generation. Default number of iterations is 1e+4, but will be increased to 1e+5, and 1e+6 as p-values fall below the respective thresholds. If set to NULL, the maximum number of iterations is capped at the default (Note: this significantly speeds up the analysis, but results in poor accuracy for low p-values)
p.values	Set to F to suppress p-values
CIs	Set to F to suppress 95% confidence intervals
param.lim	The +- threshold at which estimated parameters are considered to be too far out of bounds. If the estimated parameter exceeds this threshold, it is considered unreliable and will be set to NA.

Value

Data frame with the columns:

• predictors / outcome - analysed phenotypes

• gamma - standardised multiple regression coefficient

• gamma.lower / gamma.upper - 95% confidence intervals for gamma

• r2 - proportion of variance in genetic signal for the outcome phenotype explained by all predictor phenotypes simultaneously

• r2.lower / r2.upper - 95% confidence intervals for the r2

• p - simulation p-values for the gammas

josefin-werme/LAVA documentation built on July 4, 2024, 8:11 p.m.