Function reference • genstats

Generate simulated subjects

Functions used to generate a fbm of simulated subjects

gen_sim(): Simulate genetic data

G_func_fam(): Simulate SNP for all subjects and liabilities for family

G_func_simple(): Simulate SNP for all subjects

MAF_func(): Simulate vector containing minor allele frequencies (MAF)

beta_func(): Simulate beta

GWAS

Functions used to perform GWAS on genetic data

GWAS(): Computes causal SNP's

LT-FH

Functions used to perform LT-FH on genetic data

get_cov(): Create covariance matrix

gibbs_sampl(): Create LTFH estimations for a configuration.

LTFH(): Estimates liabilities for every subject

Prediction

Functions used to perform compute and use polygenic risk score

PRS_cross(): Uses cross validation to train and estimate PRS for each fold

pred_model(): Trains model on train data given the target vector.

prediction(): Predicts the probability of having the given trait

Data visulisation

Functions to visualise methods and results

GWAS

scatter_plot(): Plots true causal effects against estimated effects

manhattan_plot(): Creates a Manhattan plot

power_plot(): Creates a power plot

LT-FH

control_plot(): Plotting estimated liabilities agianst distribution

LTFH_plot(): Plots estimated liabilities against true liabilities

plot_gibbs(): Plot the convergence of gibb_sampl

Prediction

prs_plot(): Creates evaluation plot for MSE, AUC or r squared

decision_cross(): Creates evaluation plot for decision boundary

Controls for distributions sampled from

Functions which can be used to check if result follow the expected

dist_check(): Check distribution of liabilities

Helper Functions

Functions to ease the use of genstats

get_names(): Adds a postfix to given names for each family member

snp_attach(): Attach rds_file