Risk score analysis

CWAS-Plus utilizes categorized results to estimate the optimal predictor for the phenotype. It trains a Lasso regression model using the number of variants within each category across samples. After training the model with a subset of samples, the remaining test set is employed to calculate the R2. The significance of the R2 value is determined by calculating it from samples with a randomly shuffled phenotype. The number of regressions (-n_reg) can be set to obtain the average R2 value from all regressions.

  • -i, –input_file: Path to the categorized zarr directory, resulted from categorization process.

  • -o_dir, –output_directory: Path to the directory where the output files will be saved. By default, outputs will be saved at $CWAS_WORKSPACE.

  • -s, –sample_info: Path to the txt file containing the sample information for each sample. This file must have three columns (SAMPLE, FAMILY, PHENOTYPE) with the exact name.

    SAMPLE

    FAMILY

    PHENOTYPE

    11000.p1

    11000

    case

    11000.s1

    11000

    ctrl

    11002.p1

    11002

    case

    11002.s1

    11002

    ctrl

  • -a, –adjustment_factor: Path to the txt file containing the adjust factors for each sample. This is optional. With this option, CWAS-Plus multiplies the number of variants (or carriers, in -u option) with the adjust factor per sample.

    SAMPLE

    AdjustFactor

    11000.p1

    0.932

    11000.s1

    1.082

    11002.p1

    0.895

    11002.s1

    1.113

  • -c_info, –category_info: Path to a text file category information (*.category_info.txt).

  • -d, –domain_list: Domain list to filter categories based on GENCODE domain. If ‘run_all’ is given, all available options will be tested. Available options are run_all,all,coding,noncoding,ptv,missense,damaging_missense,promoter,noncoding_wo_promoter,intron,intergenic,utr,lincRNA. By default, all.

  • -t, –tag: Tag used for the name of the output files. By default, None.

  • –do_each_one: Use each annotation from functional annotation to calculate risk score. By default, False.

  • –leave_one_out: Calculate risk score while excluding one annotation from functional annotation. This option is not used when the –do_each_one flag is enabled. By default, False.

  • -u, –use_n_carrier: Enables the sample-level analysis (the use of the number of samples with variants in each category for burden test instead of the number of variants). With this option, CWAS-Plus counts the number of samples that carry at least one variant of each category.

  • -thr, –threshold: The number of variants in controls (or the number of control carriers) used to select rare categories. For example, if set to 3, categories with less than 3 variants in controls will be used for training. By default, 3.

  • -tf, –train_set_fraction: The fraction of the training set. For example, if set to 0.7, 70% of the samples will be used as training set and 30% will be used as test set. By default, 0.7.

  • -n_reg, –num_regression: Number of regression trials to calculate a mean of R squares. By default, 10.

  • -f, –fold: Number of folds for cross-validation.

  • -n, –n_permute: The number of permutations used to calculate the p-value. By default, 1,000.

  • –predict_only: If set, only predict the risk score and skip the permutation process. By default, False.

  • -S, –seed: Seed of random state. By default, 42.

  • -p, –num_proc: Number of worker processes that will be used for the permutation process. By default, 1.

cwas risk_score -i INPUT.categorization_result.txt.gz \
-o_dir OUTPUT_DIR \
-s SAMPLE_LIST.txt \
-a ADJUST_FACTOR.txt \
-c_info CATEGORY_SET.txt \
-thr 3 \
-tf 0.7 \
-n_reg 10 \
-f 5 \
-n 1000 \
-p 8

Users can perform two types of risk score analyses in a loop to identify annotations with the best predictive performance and composition within the annotation set.

  1. Risk score analysis for categories containing a single annotation within a specific domain

    cwas risk_score -i INPUT.categorization_result.txt.gz -o_dir OUTPUT_DIR -s SAMPLE_LIST.txt -a ADJUST_FACTOR.txt -c_info CATEGORY_SET.txt -thr 3 -tf 0.7 -n_reg 10 -f 5 -n 1000 -p 8 –do_each_one

  2. Risk score analysis for categories with one annotation excluded from the total annotations

    cwas risk_score -i INPUT.categorization_result.txt.gz -o_dir OUTPUT_DIR -s SAMPLE_LIST.txt -a ADJUST_FACTOR.txt -c_info CATEGORY_SET.txt -thr 3 -tf 0.7 -n_reg 10 -f 5 -n 1000 -p 8 –leave_one_out