A PRS for an individual is a summation of their genotypes at variants genome-wide, weighted by effect sizes on a trait of interest. Effect sizes are typically estimated from published GWAS results, and only variants exceeding a P-value threshold, PT, are included. Since even large GWAS achieve only marginal evidence for association for many causal variants, PRS are usually calculated at a set of P-value thresholds, e.g. PT=1×10−5,1×10−4,…,0.05,0.1,…,0.5
PRSice will automatically calculate the PRS for different p-value thresholds and perform a regression to test the level of association of the PRS with the target phenotype. This allow users to identify the PRS that "best" predicts the phenotype and can be used for downstream analysis.
The p-value thresholds are inclusive. That is, For a p-value threshold of 0.5, all SNPs with p-value of 0.5 will also be included in this threshold.
Level of barchart to be plotted. When
--fastscoreis set, PRSice will only calculate the PRS for threshold within the bar level. Levels should be comma separated without space
Only calculate threshold stated in
By default, PRSice will include the full model, i.e. p-value threshold = 1. Setting this flag will disable that behaviour
The step size of the threshold. Default: 0.00005
The starting p-value threshold. Default: 0.0001
Genetic model use for regression. The genetic encoding is based on the base data where the encoding represent number of the effective allele Available models include:
add- Additive model, code as 0/1/2 (default)
dom- Dominant model, code as 0/1/1
rec- Recessive model, code as 0/0/1
het- Heterozygous only model, code as 0/1/0
Method to handle missing genotypes. By default, final scores are averages of valid per-allele scores with missing genotypes contribute an amount proportional to imputed allele frequency. To throw out missing observations instead (decreasing the denominator in the final average when this happens), use the
no_mean_imputationmodifier. Alternatively, you can use the
centermodifier to shift all scores to mean zero.
Do not perform the regression analysis and simply output all PRS.
Method to calculate the polygenic score. Available methods include:
avg- Take the average effect size (default)
std- Standardize the effect size
sum- Direct summation of the effect size
The final p-value threshold. Default: 0.5