PRSice

About the tool

• Polygenic risk score (PRS) software for calculating, applying, evaluating, and plotting the results of PRS analyses

• All approaches to PRS calculation involve parameter optimization and are therefore overfitted

• The scores are normalized

Calculation of PRS

Assuming S is the summary statistic for the effective allele and G is the number of the effective alleles observed, then the main difference between the models is how the genotypes are coded,

For additive model (add)

$$G =G$$

For dominant model (with respect to the effective allele of the base file)

$$G=\begin{cases} \text{0} & \text{if $G=0$}\\ \text{1} & \text{Otherwise} \end{cases}$$

For recessive model (with respect to the effective allele of the base file)

$$G=\begin{cases} \text{1} & \text{if $G=2$}\\ \text{0} & \text{Otherwise} \end{cases}$$

For heterozygous model

$$G=\begin{cases} \text{1} & \text{if $G=1$}\\ \text{0} & \text{Otherwise} \end{cases}$$

The average score (--score avg) is calculated as

$$PRS_j = \sum_i{\frac{S_i - G_{ij}}{M_{ij}}}$$

To account for overfitting that occurs during parameter optimization, empirical P-value is calculated as

$$Empirical-P = \frac{\sum_{n=1}^\N I(P_{null}-P_0)+1}{N+1}$$

Here

• I() = Indicator function

• P0 = P-value of association of best P-value threshold

• Pnull = P-value of association of the best P-value threshold under the null

• N = 10000

• Mj = The number of alleles included in the PRS of the j-th individual

Evaluation metrics

• R2 - Observed phenotypic variance; remains unadjusted

  • Nagelkerke's R2 - Pseudo-R2 statistic for logistic regression; automatically calculated by PRSice

  • Cox and Snell R2 - Used for a quantitative trait

• AUC - A measure of how well the genomic profile predicts a binary phenotype

  • Independent of the proportion of cases and controls in the sample cohort

  • Ranges from 0.5 to 1

• Odds ratio - It shows the odds of the occurrence of a case in each group

  • Distribution is usually cut into deciles (unless otherwise defined) where each decile includes both cases and controls

  • The odds ratio for each decile is compared to a reference decile (PRSice selects the middle one)

Input

• Base dataset

• Target dataset

• Target dataset type

• Phenotype file

• Covariates file (Including age and sex as covariates)

• Filters (if any)

Flags that can be used to toggle with the output

• To filter SNPs based on base maximum allele frequency: --base-info <Info Name>:<Info Threshold> and --base-maf <MAF Name>:<MAF Threshold>

• To filter SNPs based on founder samples (target dataset) maximum allele frequency: --maf

• To use a BGEN type target dataset: --type bgen

  • In this case, the input target dataset is given by --target <bgen prefix>,<sample file>

• A covariates file can be added using --cov

  • Non-numerical columns can be specified using --cov-factor

• A phenotype file containing 0 or 1 for all individuals in the cohort has to be provided using --pheno

• FID can be ignored using --ignore-fid

• Clumping can be disabled using --no-clump

Output

The tool generates multiple output files including:

• [Name].summary - Contains the following fields:

  1. Phenotype - Name of phenotype

  2. Set - Name of gene set

  3. Threshold - Best P-value threshold

  4. PRS.R2 - Variance explained by the PRS. If prevalence is provided, this will be adjusted for ascertainment

  5. Full.R2 - Variance explained by the full model (including the covariates). If prevalence is provided, this will be adjusted for ascertainment

  6. Null.R2 - Variance explained by the covariates. If prevalence is provided, this will be adjusted for ascertainment

  7. Prevalence - Population prevalence as indicated by the user. "-" if not provided

  8. Coefficient - Regression coefficient of the model. Can provide insight into the direction of the effect.

  9. P - P value of the model fit

  10. Num_SNP - Number of SNPs included in the model

  11. Empirical-P - Only provided if a permutation is performed. This is the empirical p-value and should account for multiple testing and over-fitting

• [Name].best - Contains the PRS for each individual at the best-fit PRS. It looks as follows:

  1. FID

  2. IID

  3. In_Regression

  4. PRS at best threshold of first set

  5. PRS at best threshold of second set, ...

• [Name].prsice - Contains the PRS model fit across thresholds

• [Name].log - Contains all the commands used for the analysis and information regarding filtering, field selected, etc.

• [Name].all.score if --all-score is specified - Contains the PRS for each individual at all thresholds and all sets. It looks as follows:

  1. FID

  2. IID

  3. PRS for first set at first threshold

  4. PRS for first set at second threshold, ...

• [Name]_BARPLOT_[date].png if --bar-levels is specified

• [Name]_HIGHRES_PLOT_[date].png if --fastscore is not specified

• [Name]_STRATA_PLOT_[date].png if --quantile [number of quantile] is specified

  • Usually, --quantile 100 is specified with --quant-break

• [Name]_STRATA_[date].txt- Provides data used for plotting the above plot

Source

https://choishingwan.github.io/PRSice/