EM Bayesian Adaptive LASSO Variational Inference Based GWAS

Performs Genome-Wide Association Study (GWAS) analysis using Expectation-Maximization Bayesian Adaptive LASSO with Variational Inference (emBALVI). Includes genotype preprocessing, genomic relationship matrix construction, GWAS analysis, Manhattan and QQ plotting.s.

emBALVI

The goal of emBALVI is to ...

Installation

You can install the development version of emBALVI like so:

# FILL THIS IN! HOW CAN PEOPLE INSTALL YOUR DEV PACKAGE?

Example

This is a basic example which shows you how to solve a common problem:

library(emBALVI)
data(phenotypes_potatoyield)
data(snp_NN_10_hmp)
# Prepare genotype
hmp <- as.data.frame(t(snp_NN_10_hmp),stringsAsFactors = FALSE)
colnames(hmp) <- hmp[1, ]
hmp <- hmp[-1, ]
hmp$Taxa <- rownames(hmp)
snp_matrix <- hmp[, -ncol(hmp)]
rownames(snp_matrix) <- hmp$Taxa
X <- convert_to_dosage(snp_matrix)
G <- buildGRM(X)
pheno <- phenotypes_potatoyield
Y <- pheno$y
B <- model.matrix(~1, pheno)

results <- emBALVI(Y, X, B, G, max_iter = 5)
plot_manhattan(results)
qq_plot <- plot_qq(results)
print(qq_plot)
head(results)