LDAK-GenotypeData

LDAK-GenotypeData#

LDAK is an advanced tool similar to PLINK, GCTA, GCTB, and other genetic tools. It provides two ways to calculate Polygenic Risk Scores (PRS):

  1. Using Existing GWAS: You can use the existing GWAS data and genetic correlations for regions in high linkage disequilibrium, calculated using LDAK, to estimate the betas.

  2. Using Genotype Data: You can generate GWAS from LDAK using the training genotype data and then use that GWAS to generate the PRS for both the training and the test set.

You can download LDAK from this link, and the documentation for GWAS calculation is available here. The documentation provides detailed descriptions of all the steps.

LDAK Hyperparameters#

LDAK offers multiple hyperparameters, but we considered power as a key hyperparameter. LDAK also provides various models, and we considered the following:

  • powers = [-0.25]

  • ldakmodels = ["ridge", "bolt", "bayesr", "elastic"]

Note: The model may overfit on the data.

GWAS file processing for LDAK for Binary Phenotypes.#

When the effect size relates to disease risk and is thus given as an odds ratio (OR) rather than BETA (for continuous traits), the PRS is computed as a product of ORs. To simplify this calculation, take the natural logarithm of the OR so that the PRS can be computed using summation instead.

For continuous phenotype GWAS, the SampleData1/SampleData1.gz file should have BETAs, and for binary phenotypes, it should have OR instead of BETAs. If BETAs are not available, we convert OR to BETAs using BETA = np.log(OR) and convert BETAs to OR using OR = np.exp(BETA).

import numpy as np; np is the NumPy module.

import os
import pandas as pd
import numpy as np
import sys

#filedirec = sys.argv[1]

filedirec = "SampleData1"
#filedirec = "asthma_19"
#filedirec = "migraine_0"

def check_phenotype_is_binary_or_continous(filedirec):
    # Read the processed quality controlled file for a phenotype
    df = pd.read_csv(filedirec+os.sep+filedirec+'_QC.fam',sep="\s+",header=None)
    column_values = df[5].unique()
 
    if len(set(column_values)) == 2:
        return "Binary"
    else:
        return "Continous"



# Read the GWAS file.
GWAS = filedirec + os.sep + filedirec+".gz"
df = pd.read_csv(GWAS,compression= "gzip",sep="\s+")
print(df.head().to_markdown()) 


if "BETA" in df.columns.to_list():
    # For Continous Phenotype.
    df = df[['CHR', 'BP', 'SNP', 'A1', 'A2', 'N', 'SE', 'P', 'BETA', 'INFO', 'MAF']]
else:
    df["BETA"] = np.log(df["OR"])
    df["SE"] = df["SE"]/df["OR"]
    df = df[['CHR', 'BP', 'SNP', 'A1', 'A2', 'N', 'SE', 'P', 'BETA', 'INFO', 'MAF']]
    
print(df.head().to_markdown()) 

df_transformed = pd.DataFrame({
    'Predictor': df['CHR'].astype(str) + ":" + df['BP'].astype(str),
    'A1': df['A1'],
    'A2': df['A2'],
    'n': df['N'],
    'Z': df['BETA']/df['SE'],
    'SNP':df['SNP']
}) 
 
df_transformed.to_csv(filedirec + os.sep +filedirec+".ldak",sep="\t",index=False)

print(df_transformed.head().to_markdown())
 

|    |   CHR |     BP | SNP        | A1   | A2   |      N |         SE |        P |       OR |     INFO |      MAF |
|---:|------:|-------:|:-----------|:-----|:-----|-------:|-----------:|---------:|---------:|---------:|---------:|
|  0 |     1 | 756604 | rs3131962  | A    | G    | 388028 | 0.00301666 | 0.483171 | 0.997887 | 0.890558 | 0.36939  |
|  1 |     1 | 768448 | rs12562034 | A    | G    | 388028 | 0.00329472 | 0.834808 | 1.00069  | 0.895894 | 0.336846 |
|  2 |     1 | 779322 | rs4040617  | G    | A    | 388028 | 0.00303344 | 0.42897  | 0.997604 | 0.897508 | 0.377368 |
|  3 |     1 | 801536 | rs79373928 | G    | T    | 388028 | 0.00841324 | 0.808999 | 1.00204  | 0.908963 | 0.483212 |
|  4 |     1 | 808631 | rs11240779 | G    | A    | 388028 | 0.00242821 | 0.590265 | 1.00131  | 0.893213 | 0.45041  |
|    |   CHR |     BP | SNP        | A1   | A2   |      N |         SE |        P |        BETA |     INFO |      MAF |
|---:|------:|-------:|:-----------|:-----|:-----|-------:|-----------:|---------:|------------:|---------:|---------:|
|  0 |     1 | 756604 | rs3131962  | A    | G    | 388028 | 0.00302305 | 0.483171 | -0.00211532 | 0.890558 | 0.36939  |
|  1 |     1 | 768448 | rs12562034 | A    | G    | 388028 | 0.00329246 | 0.834808 |  0.00068708 | 0.895894 | 0.336846 |
|  2 |     1 | 779322 | rs4040617  | G    | A    | 388028 | 0.00304073 | 0.42897  | -0.00239932 | 0.897508 | 0.377368 |
|  3 |     1 | 801536 | rs79373928 | G    | T    | 388028 | 0.00839615 | 0.808999 |  0.00203363 | 0.908963 | 0.483212 |
|  4 |     1 | 808631 | rs11240779 | G    | A    | 388028 | 0.00242504 | 0.590265 |  0.00130747 | 0.893213 | 0.45041  |
|    | Predictor   | A1   | A2   |      n |         Z | SNP        |
|---:|:------------|:-----|:-----|-------:|----------:|:-----------|
|  0 | 1:756604    | A    | G    | 388028 | -0.699731 | rs3131962  |
|  1 | 1:768448    | A    | G    | 388028 |  0.208683 | rs12562034 |
|  2 | 1:779322    | G    | A    | 388028 | -0.789061 | rs4040617  |
|  3 | 1:801536    | G    | T    | 388028 |  0.24221  | rs79373928 |
|  4 | 1:808631    | G    | A    | 388028 |  0.539155 | rs11240779 |

Define Hyperparameters#

Define hyperparameters to be optimized and set initial values.

Extract Valid SNPs from Clumped File#

For Windows, download gwak, and for Linux, the awk command is sufficient. For Windows, GWAK is required. You can download it from here. Get it and place it in the same directory.

Execution Path#

At this stage, we have the genotype training data newtrainfilename = "train_data.QC" and genotype test data newtestfilename = "test_data.QC".

We modified the following variables:

  1. filedirec = "SampleData1" or filedirec = sys.argv[1]

  2. foldnumber = "0" or foldnumber = sys.argv[2] for HPC.

Only these two variables can be modified to execute the code for specific data and specific folds. Though the code can be executed separately for each fold on HPC and separately for each dataset, it is recommended to execute it for multiple diseases and one fold at a time. Here’s the corrected text in Markdown format:

P-values#

PRS calculation relies on P-values. SNPs with low P-values, indicating a high degree of association with a specific trait, are considered for calculation.

You can modify the code below to consider a specific set of P-values and save the file in the same format.

We considered the following parameters:

  • Minimum P-value: 1e-10

  • Maximum P-value: 1.0

  • Minimum exponent: 10 (Minimum P-value in exponent)

  • Number of intervals: 100 (Number of intervals to be considered)

The code generates an array of logarithmically spaced P-values:

import numpy as np
import os

minimumpvalue = 10  # Minimum exponent for P-values
numberofintervals = 100  # Number of intervals to be considered

allpvalues = np.logspace(-minimumpvalue, 0, numberofintervals, endpoint=True)  # Generating an array of logarithmically spaced P-values

print("Minimum P-value:", allpvalues[0])
print("Maximum P-value:", allpvalues[-1])

count = 1
with open(os.path.join(folddirec, 'range_list'), 'w') as file:
    for value in allpvalues:
        file.write(f'pv_{value} 0 {value}\n')  # Writing range information to the 'range_list' file
        count += 1

pvaluefile = os.path.join(folddirec, 'range_list')

In this code:

  • minimumpvalue defines the minimum exponent for P-values.

  • numberofintervals specifies how many intervals to consider.

  • allpvalues generates an array of P-values spaced logarithmically.

  • The script writes these P-values to a file named range_list in the specified directory.

from operator import index
import pandas as pd
import numpy as np
import os
import subprocess
import sys
import pandas as pd
import statsmodels.api as sm
import pandas as pd
from sklearn.metrics import roc_auc_score, confusion_matrix
from statsmodels.stats.contingency_tables import mcnemar

def create_directory(directory):
    """Function to create a directory if it doesn't exist."""
    if not os.path.exists(directory):  # Checking if the directory doesn't exist
        os.makedirs(directory)  # Creating the directory if it doesn't exist
    return directory  # Returning the created or existing directory

 
#foldnumber = sys.argv[1]
foldnumber = "0"  # Setting 'foldnumber' to "0"

folddirec = filedirec + os.sep + "Fold_" + foldnumber  # Creating a directory path for the specific fold
trainfilename = "train_data"  # Setting the name of the training data file
newtrainfilename = "train_data.QC"  # Setting the name of the new training data file

testfilename = "test_data"  # Setting the name of the test data file
newtestfilename = "test_data.QC"  # Setting the name of the new test data file

# Number of PCA to be included as a covariate.
numberofpca = ["6"]  # Setting the number of PCA components to be included

# Clumping parameters.
clump_p1 = [1]  # List containing clump parameter 'p1'
clump_r2 = [0.1]  # List containing clump parameter 'r2'
clump_kb = [200]  # List containing clump parameter 'kb'

# Pruning parameters.
p_window_size = [200]  # List containing pruning parameter 'window_size'
p_slide_size = [50]  # List containing pruning parameter 'slide_size'
p_LD_threshold = [0.25]  # List containing pruning parameter 'LD_threshold'

# Kindly note that the number of p-values to be considered varies, and the actual p-value depends on the dataset as well.
# We will specify the range list here.

minimumpvalue = 10  # Minimum p-value in exponent
numberofintervals = 20  # Number of intervals to be considered
allpvalues = np.logspace(-minimumpvalue, 0, numberofintervals, endpoint=True)  # Generating an array of logarithmically spaced p-values



count = 1
with open(folddirec + os.sep + 'range_list', 'w') as file:
    for value in allpvalues:
        file.write(f'pv_{value} 0 {value}\n')  # Writing range information to the 'range_list' file
        count = count + 1

pvaluefile = folddirec + os.sep + 'range_list'

# Initializing an empty DataFrame with specified column names
prs_result = pd.DataFrame(columns=["clump_p1", "clump_r2", "clump_kb", "p_window_size", "p_slide_size", "p_LD_threshold",
                                   "pvalue", "numberofpca","numberofvariants","Train_pure_prs", "Train_null_model", "Train_best_model",
                                   "Test_pure_prs", "Test_null_model", "Test_best_model"])

Define Helper Functions#

  1. Perform Clumping and Pruning

  2. Calculate PCA Using Plink

  3. Fit Binary Phenotype and Save Results

  4. Fit Continuous Phenotype and Save Results

import os
import subprocess
import pandas as pd
import statsmodels.api as sm
from sklearn.metrics import explained_variance_score


def perform_clumping_and_pruning_on_individual_data(traindirec, newtrainfilename,numberofpca, p1_val, p2_val, p3_val, c1_val, c2_val, c3_val,Name,pvaluefile):
    
    command = [
    "./plink",
    "--bfile", traindirec+os.sep+newtrainfilename,
    "--indep-pairwise", p1_val, p2_val, p3_val,
    "--out", traindirec+os.sep+trainfilename
    ]
    subprocess.run(command)
    # First perform pruning and then clumping and the pruning.

    command = [
    "./plink",
    "--bfile", traindirec+os.sep+newtrainfilename,
    "--clump-p1", c1_val,
    "--extract", traindirec+os.sep+trainfilename+".prune.in",
    "--clump-r2", c2_val,
    "--clump-kb", c3_val,
    "--clump", filedirec+os.sep+filedirec+".txt",
    "--clump-snp-field", "SNP",
    "--clump-field", "P",
    "--out", traindirec+os.sep+trainfilename
    ]    
    subprocess.run(command)

    # Extract the valid SNPs from th clumped file.
    # For windows download gwak for linux awk commmand is sufficient.
    ### For windows require GWAK.
    ### https://sourceforge.net/projects/gnuwin32/
    ##3 Get it and place it in the same direc.
    #os.system("gawk "+"\""+"NR!=1{print $3}"+"\"  "+ traindirec+os.sep+trainfilename+".clumped >  "+traindirec+os.sep+trainfilename+".valid.snp")
    #print("gawk "+"\""+"NR!=1{print $3}"+"\"  "+ traindirec+os.sep+trainfilename+".clumped >  "+traindirec+os.sep+trainfilename+".valid.snp")

    #Linux:
    command = f"awk 'NR!=1{{print $3}}' {traindirec}{os.sep}{trainfilename}.clumped > {traindirec}{os.sep}{trainfilename}.valid.snp"
    os.system(command)
    
    command = [
    "./plink",
    "--make-bed",
    "--bfile", traindirec+os.sep+newtrainfilename,
    "--indep-pairwise", p1_val, p2_val, p3_val,
    "--extract", traindirec+os.sep+trainfilename+".valid.snp",
    "--out", traindirec+os.sep+newtrainfilename+".clumped.pruned"
    ]
    subprocess.run(command)
    
    command = [
    "./plink",
    "--make-bed",
    "--bfile", traindirec+os.sep+testfilename,
    "--indep-pairwise", p1_val, p2_val, p3_val,
    "--extract", traindirec+os.sep+trainfilename+".valid.snp",
    "--out", traindirec+os.sep+testfilename+".clumped.pruned"
    ]
    subprocess.run(command)    
    
    
 
def calculate_pca_for_traindata_testdata_for_clumped_pruned_snps(traindirec, newtrainfilename,p):
    
    # Calculate the PRS for the test data using the same set of SNPs and also calculate the PCA.


    # Also extract the PCA at this point.
    # PCA are calculated afer clumping and pruining.
    command = [
        "./plink",
        "--bfile", folddirec+os.sep+testfilename+".clumped.pruned",
        # Select the final variants after clumping and pruning.
        "--extract", traindirec+os.sep+trainfilename+".valid.snp",
        "--pca", p,
        "--out", folddirec+os.sep+testfilename
    ]
    subprocess.run(command)


    command = [
    "./plink",
        "--bfile", traindirec+os.sep+newtrainfilename+".clumped.pruned",
        # Select the final variants after clumping and pruning.        
        "--extract", traindirec+os.sep+trainfilename+".valid.snp",
        "--pca", p,
        "--out", traindirec+os.sep+trainfilename
    ]
    subprocess.run(command)

# This function fit the binary model on the PRS.
def fit_binary_phenotype_on_PRS(traindirec, newtrainfilename,p,ldakmodel,power, p1_val, p2_val, p3_val, c1_val, c2_val, c3_val,Name,pvaluefile):
    threshold_values = allpvalues

    # Merge the covariates, pca and phenotypes.
    tempphenotype_train = pd.read_table(traindirec+os.sep+newtrainfilename+".clumped.pruned"+".fam", sep="\s+",header=None)
    phenotype_train = pd.DataFrame()
    phenotype_train["Phenotype"] = tempphenotype_train[5].values
    pcs_train = pd.read_table(traindirec+os.sep+trainfilename+".eigenvec", sep="\s+",header=None, names=["FID", "IID"] + [f"PC{str(i)}" for i in range(1, int(p)+1)])
    covariate_train = pd.read_table(traindirec+os.sep+trainfilename+".cov",sep="\s+")
    covariate_train.fillna(0, inplace=True)
    covariate_train = covariate_train[covariate_train["FID"].isin(pcs_train["FID"].values) & covariate_train["IID"].isin(pcs_train["IID"].values)]
    covariate_train['FID'] = covariate_train['FID'].astype(str)
    pcs_train['FID'] = pcs_train['FID'].astype(str)
    covariate_train['IID'] = covariate_train['IID'].astype(str)
    pcs_train['IID'] = pcs_train['IID'].astype(str)
    covandpcs_train = pd.merge(covariate_train, pcs_train, on=["FID","IID"])
    covandpcs_train.fillna(0, inplace=True)


    ## Scale the covariates!
    from sklearn.preprocessing import MinMaxScaler
    from sklearn.metrics import explained_variance_score
    scaler = MinMaxScaler()
    normalized_values_train = scaler.fit_transform(covandpcs_train.iloc[:, 2:])
    #covandpcs_train.iloc[:, 2:] = normalized_values_test 
    
    
    tempphenotype_test = pd.read_table(traindirec+os.sep+testfilename+".clumped.pruned"+".fam", sep="\s+",header=None)
    phenotype_test= pd.DataFrame()
    phenotype_test["Phenotype"] = tempphenotype_test[5].values
    pcs_test = pd.read_table(traindirec+os.sep+testfilename+".eigenvec", sep="\s+",header=None, names=["FID", "IID"] + [f"PC{str(i)}" for i in range(1, int(p)+1)])
    covariate_test = pd.read_table(traindirec+os.sep+testfilename+".cov",sep="\s+")
    covariate_test.fillna(0, inplace=True)
    covariate_test = covariate_test[covariate_test["FID"].isin(pcs_test["FID"].values) & covariate_test["IID"].isin(pcs_test["IID"].values)]
    covariate_test['FID'] = covariate_test['FID'].astype(str)
    pcs_test['FID'] = pcs_test['FID'].astype(str)
    covariate_test['IID'] = covariate_test['IID'].astype(str)
    pcs_test['IID'] = pcs_test['IID'].astype(str)
    covandpcs_test = pd.merge(covariate_test, pcs_test, on=["FID","IID"])
    covandpcs_test.fillna(0, inplace=True)
    normalized_values_test  = scaler.transform(covandpcs_test.iloc[:, 2:])
    #covandpcs_test.iloc[:, 2:] = normalized_values_test     
    
    
    
    
    tempalphas = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
    l1weights = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]

    tempalphas = [0.1]
    l1weights = [0.1]

    phenotype_train["Phenotype"] = phenotype_train["Phenotype"].replace({1: 0, 2: 1}) 
    phenotype_test["Phenotype"] = phenotype_test["Phenotype"].replace({1: 0, 2: 1})
      
    for tempalpha in tempalphas:
        for l1weight in l1weights:

            
            try:
                null_model =  sm.Logit(phenotype_train["Phenotype"], sm.add_constant(covandpcs_train.iloc[:, 2:])).fit_regularized(alpha=tempalpha, L1_wt=l1weight)
                #null_model =  sm.Logit(phenotype_train["Phenotype"], sm.add_constant(covandpcs_train.iloc[:, 2:])).fit()
            
            except:
                print("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX")
                continue

            train_null_predicted = null_model.predict(sm.add_constant(covandpcs_train.iloc[:, 2:]))
            
            from sklearn.metrics import roc_auc_score, confusion_matrix
            from sklearn.metrics import r2_score
            
            test_null_predicted = null_model.predict(sm.add_constant(covandpcs_test.iloc[:, 2:]))
            
           
            
            global prs_result 
            for i in threshold_values:
                try:
                    prs_train = pd.read_table(traindirec+os.sep+Name+os.sep+"train_data.pv_"+f"{i}.profile", sep="\s+", usecols=["FID", "IID", "SCORE"])
                except:
                    continue

                prs_train['FID'] = prs_train['FID'].astype(str)
                prs_train['IID'] = prs_train['IID'].astype(str)
                try:
                    prs_test = pd.read_table(traindirec+os.sep+Name+os.sep+"test_data.pv_"+f"{i}.profile", sep="\s+", usecols=["FID", "IID", "SCORE"])
                except:
                    continue
                prs_test['FID'] = prs_test['FID'].astype(str)
                prs_test['IID'] = prs_test['IID'].astype(str)
                pheno_prs_train = pd.merge(covandpcs_train, prs_train, on=["FID", "IID"])
                pheno_prs_test = pd.merge(covandpcs_test, prs_test, on=["FID", "IID"])
        
                try:
                    model = sm.Logit(phenotype_train["Phenotype"], sm.add_constant(pheno_prs_train.iloc[:, 2:])).fit_regularized(alpha=tempalpha, L1_wt=l1weight)
                    #model = sm.Logit(phenotype_train["Phenotype"], sm.add_constant(pheno_prs_train.iloc[:, 2:])).fit()
                
                except:
                    continue


                
                train_best_predicted = model.predict(sm.add_constant(pheno_prs_train.iloc[:, 2:]))    
 

                test_best_predicted = model.predict(sm.add_constant(pheno_prs_test.iloc[:, 2:])) 
 
        
                from sklearn.metrics import roc_auc_score, confusion_matrix

                prs_result = prs_result._append({
                    "clump_p1": c1_val,
                    "clump_r2": c2_val,
                    "clump_kb": c3_val,
                    "p_window_size": p1_val,
                    "p_slide_size": p2_val,
                    "p_LD_threshold": p3_val,
                    "pvalue": i,
                    "numberofpca":p, 

                    "tempalpha":str(tempalpha),
                    "l1weight":str(l1weight),
                    "numberofvariants": len(pd.read_csv(traindirec+os.sep+newtrainfilename+".clumped.pruned.bim")),
                    
                    "ldakmodel":ldakmodel, 
                    "ldakpower":str(power), 
                    
                    "Train_pure_prs":roc_auc_score(phenotype_train["Phenotype"].values,prs_train['SCORE'].values),
                    "Train_null_model":roc_auc_score(phenotype_train["Phenotype"].values,train_null_predicted.values),
                    "Train_best_model":roc_auc_score(phenotype_train["Phenotype"].values,train_best_predicted.values),
                    
                    "Test_pure_prs":roc_auc_score(phenotype_test["Phenotype"].values,prs_test['SCORE'].values),
                    "Test_null_model":roc_auc_score(phenotype_test["Phenotype"].values,test_null_predicted.values),
                    "Test_best_model":roc_auc_score(phenotype_test["Phenotype"].values,test_best_predicted.values),
                    
                }, ignore_index=True)

          
                prs_result.to_csv(traindirec+os.sep+Name+os.sep+"Results.csv",index=False)
     
    return

# This function fit the binary model on the PRS.
def fit_continous_phenotype_on_PRS(traindirec, newtrainfilename,p,ldakmodel,power, p1_val, p2_val, p3_val, c1_val, c2_val, c3_val,Name,pvaluefile):
    threshold_values = allpvalues

    # Merge the covariates, pca and phenotypes.
    tempphenotype_train = pd.read_table(traindirec+os.sep+newtrainfilename+".clumped.pruned"+".fam", sep="\s+",header=None)
    phenotype_train = pd.DataFrame()
    phenotype_train["Phenotype"] = tempphenotype_train[5].values
    pcs_train = pd.read_table(traindirec+os.sep+trainfilename+".eigenvec", sep="\s+",header=None, names=["FID", "IID"] + [f"PC{str(i)}" for i in range(1, int(p)+1)])
    covariate_train = pd.read_table(traindirec+os.sep+trainfilename+".cov",sep="\s+")
    covariate_train.fillna(0, inplace=True)
    covariate_train = covariate_train[covariate_train["FID"].isin(pcs_train["FID"].values) & covariate_train["IID"].isin(pcs_train["IID"].values)]
    covariate_train['FID'] = covariate_train['FID'].astype(str)
    pcs_train['FID'] = pcs_train['FID'].astype(str)
    covariate_train['IID'] = covariate_train['IID'].astype(str)
    pcs_train['IID'] = pcs_train['IID'].astype(str)
    covandpcs_train = pd.merge(covariate_train, pcs_train, on=["FID","IID"])
    covandpcs_train.fillna(0, inplace=True)


    ## Scale the covariates!
    from sklearn.preprocessing import MinMaxScaler
    from sklearn.metrics import explained_variance_score
    scaler = MinMaxScaler()
    normalized_values_train = scaler.fit_transform(covandpcs_train.iloc[:, 2:])
    #covandpcs_train.iloc[:, 2:] = normalized_values_test 
    
    tempphenotype_test = pd.read_table(traindirec+os.sep+testfilename+".clumped.pruned"+".fam", sep="\s+",header=None)
    phenotype_test= pd.DataFrame()
    phenotype_test["Phenotype"] = tempphenotype_test[5].values
    pcs_test = pd.read_table(traindirec+os.sep+testfilename+".eigenvec", sep="\s+",header=None, names=["FID", "IID"] + [f"PC{str(i)}" for i in range(1, int(p)+1)])
    covariate_test = pd.read_table(traindirec+os.sep+testfilename+".cov",sep="\s+")
    covariate_test.fillna(0, inplace=True)
    covariate_test = covariate_test[covariate_test["FID"].isin(pcs_test["FID"].values) & covariate_test["IID"].isin(pcs_test["IID"].values)]
    covariate_test['FID'] = covariate_test['FID'].astype(str)
    pcs_test['FID'] = pcs_test['FID'].astype(str)
    covariate_test['IID'] = covariate_test['IID'].astype(str)
    pcs_test['IID'] = pcs_test['IID'].astype(str)
    covandpcs_test = pd.merge(covariate_test, pcs_test, on=["FID","IID"])
    covandpcs_test.fillna(0, inplace=True)
    normalized_values_test  = scaler.transform(covandpcs_test.iloc[:, 2:])
    #covandpcs_test.iloc[:, 2:] = normalized_values_test     
    
    
    
    
    tempalphas = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
    l1weights = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]

    tempalphas = [0.1]
    l1weights = [0.1]

    #phenotype_train["Phenotype"] = phenotype_train["Phenotype"].replace({1: 0, 2: 1}) 
    #phenotype_test["Phenotype"] = phenotype_test["Phenotype"].replace({1: 0, 2: 1})
      
    for tempalpha in tempalphas:
        for l1weight in l1weights:

            
            try:
                #null_model =  sm.OLS(phenotype_train["Phenotype"], sm.add_constant(covandpcs_train.iloc[:, 2:])).fit_regularized(alpha=tempalpha, L1_wt=l1weight)
                null_model =  sm.OLS(phenotype_train["Phenotype"], sm.add_constant(covandpcs_train.iloc[:, 2:])).fit()
                #null_model =  sm.OLS(phenotype_train["Phenotype"], sm.add_constant(covandpcs_train.iloc[:, 2:])).fit()
            except:
                print("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX")
                continue

            train_null_predicted = null_model.predict(sm.add_constant(covandpcs_train.iloc[:, 2:]))
            
            from sklearn.metrics import roc_auc_score, confusion_matrix
            from sklearn.metrics import r2_score
            
            test_null_predicted = null_model.predict(sm.add_constant(covandpcs_test.iloc[:, 2:]))
            
            
            
            global prs_result 
            for i in threshold_values:
                try:
                    prs_train = pd.read_table(traindirec+os.sep+Name+os.sep+"train_data.pv_"+f"{i}.profile", sep="\s+", usecols=["FID", "IID", "SCORE"])
                except:
                    continue

                prs_train['FID'] = prs_train['FID'].astype(str)
                prs_train['IID'] = prs_train['IID'].astype(str)
                try:
                    prs_test = pd.read_table(traindirec+os.sep+Name+os.sep+"test_data.pv_"+f"{i}.profile", sep="\s+", usecols=["FID", "IID", "SCORE"])
                except:
                    continue
                prs_test['FID'] = prs_test['FID'].astype(str)
                prs_test['IID'] = prs_test['IID'].astype(str)
                pheno_prs_train = pd.merge(covandpcs_train, prs_train, on=["FID", "IID"])
                pheno_prs_test = pd.merge(covandpcs_test, prs_test, on=["FID", "IID"])
        
                try:
                    #model = sm.OLS(phenotype_train["Phenotype"], sm.add_constant(pheno_prs_train.iloc[:, 2:])).fit_regularized(alpha=tempalpha, L1_wt=l1weight)
                    model = sm.OLS(phenotype_train["Phenotype"], sm.add_constant(pheno_prs_train.iloc[:, 2:])).fit()
                
                except:
                    continue


                
                train_best_predicted = model.predict(sm.add_constant(pheno_prs_train.iloc[:, 2:]))    
                test_best_predicted = model.predict(sm.add_constant(pheno_prs_test.iloc[:, 2:])) 
 
        
                from sklearn.metrics import roc_auc_score, confusion_matrix

                prs_result = prs_result._append({
                    "clump_p1": c1_val,
                    "clump_r2": c2_val,
                    "clump_kb": c3_val,
                    "p_window_size": p1_val,
                    "p_slide_size": p2_val,
                    "p_LD_threshold": p3_val,
                    "pvalue": i,
                    "numberofpca":p, 

                    "tempalpha":str(tempalpha),
                    "l1weight":str(l1weight),
                    "numberofvariants": len(pd.read_csv(traindirec+os.sep+newtrainfilename+".clumped.pruned.bim")),
                     
                    "ldakmodel":ldakmodel, 
                    "ldakpower":str(power),    
                    "Train_pure_prs":explained_variance_score(phenotype_train["Phenotype"],prs_train['SCORE'].values),
                    "Train_null_model":explained_variance_score(phenotype_train["Phenotype"],train_null_predicted),
                    "Train_best_model":explained_variance_score(phenotype_train["Phenotype"],train_best_predicted),
                    
                    "Test_pure_prs":explained_variance_score(phenotype_test["Phenotype"],prs_test['SCORE'].values),
                    "Test_null_model":explained_variance_score(phenotype_test["Phenotype"],test_null_predicted),
                    "Test_best_model":explained_variance_score(phenotype_test["Phenotype"],test_best_predicted),
                    
                }, ignore_index=True)

          
                prs_result.to_csv(traindirec+os.sep+Name+os.sep+"Results.csv",index=False)
     
    return

Execute LDAK#

# Define a global variable to store results
prs_result = pd.DataFrame()
def transform_ldak_data(traindirec, newtrainfilename,numberofpca,ldakmodel,power, p1_val, p2_val, p3_val, c1_val, c2_val, c3_val,Name,pvaluefile):     
    #perform_clumping_and_pruning_on_individual_data(traindirec, newtrainfilename,p, p1_val, p2_val, p3_val, c1_val, c2_val, c3_val,Name,pvaluefile)
    
    #newtrainfilename = newtrainfilename+".clumped.pruned"
    #testfilename = testfilename+".clumped.pruned"
    
    
    #clupmedfile = traindirec+os.sep+newtrainfilename+".clump"
    #prunedfile = traindirec+os.sep+newtrainfilename+".clumped.pruned"

        
    # Also extract the PCA at this point for both test and training data.
    #calculate_pca_for_traindata_testdata_for_clumped_pruned_snps(traindirec, newtrainfilename,p)

    #Extract p-values from the GWAS file.
    # Command for Linux.
    os.system("awk "+"\'"+"{print $3,$8}"+"\'"+" ./"+filedirec+os.sep+filedirec+".txt >  ./"+traindirec+os.sep+"SNP.pvalue")
    files_to_remove = [
        traindirec+os.sep+ldakmodel+".effects",
        allgwasdirec + "_ldak_genotype_gwas_final",
    ]

    # Loop through the files and remove them if they exist
    for file_path in files_to_remove:
        if os.path.exists(file_path):
            os.remove(file_path)
            print(f"Removed: {file_path}")
        else:
            print(f"File does not exist: {file_path}")
            
            
            
    #if ldakmodel=="elastic":
    print(ldakmodel)
    command1 = [ 

        './ldak',
        '--'+ldakmodel, traindirec+os.sep+ldakmodel,
        '--bfile', traindirec+os.sep+newtrainfilename+".clumped.pruned",
        '--pheno',traindirec+os.sep+"train_data.PHENO",
        '--LOCO','NO'
    ]
    subprocess.run(command1)

    
    
    allgwasdirec = traindirec+os.sep+ldakmodel+".effects"
    print(allgwasdirec)
    
    temp = pd.read_csv(allgwasdirec,sep="\s+" )
    if check_phenotype_is_binary_or_continous(filedirec)=="Binary":
        
        temp["Effect"] = np.exp(temp["Effect"])
        
    else:
        pass
    temp.iloc[:, [0, 1, 4]].to_csv(allgwasdirec + "_ldak_genotype_gwas_final", sep="\t", index=False)

  
    
    
    command = [
        "./plink",
        "--bfile", traindirec+os.sep+newtrainfilename,
        ### SNP column = 3, Effect allele column 1 = 4, OR column=9
        "--score", allgwasdirec+"_ldak_genotype_gwas_final", "1", "2","3", "header",
        "--q-score-range", traindirec+os.sep+"range_list",traindirec+os.sep+"SNP.pvalue",
        "--extract", traindirec+os.sep+trainfilename+".valid.snp",
        "--out", traindirec+os.sep+Name+os.sep+trainfilename
    ]
    #exit(0)
    subprocess.run(command)

    # Calculate the PRS for the test data using the same set of SNPs and also calculate the PCA.
 
    command = [
        "./plink",
        "--bfile", folddirec+os.sep+testfilename,
        ### SNP column = 3, Effect allele column 1 = 4, OR column=9
        "--score", allgwasdirec+"_ldak_genotype_gwas_final", "1", "2", "3", "header",
        "--q-score-range", traindirec+os.sep+"range_list",traindirec+os.sep+"SNP.pvalue",
        "--extract", traindirec+os.sep+trainfilename+".valid.snp",
        "--out", folddirec+os.sep+Name+os.sep+testfilename
    ]
    subprocess.run(command)


    if check_phenotype_is_binary_or_continous(filedirec)=="Binary":
        print("Binary Phenotype!")
        fit_binary_phenotype_on_PRS(traindirec,newtrainfilename, p,ldakmodel,power,str(p1_val), str(p2_val), str(p3_val), str(c1_val), str(c2_val), str(c3_val), Name, pvaluefile)
    else:
        print("Continous Phenotype!")
        fit_continous_phenotype_on_PRS(traindirec, newtrainfilename, p,ldakmodel,power,str(p1_val), str(p2_val), str(p3_val), str(c1_val), str(c2_val), str(c3_val), Name, pvaluefile)



 
powers = [-0.25]
ldakmodels =["ridge","bolt","bayesr","elastic"]
ldakmodels =["ridge" ] 
result_directory = "LDAK-GenotypeData"

# Nested loops to iterate over different parameter values
create_directory(folddirec+os.sep+result_directory)
for p1_val in p_window_size:
 for p2_val in p_slide_size: 
  for p3_val in p_LD_threshold:
   for c1_val in clump_p1:
    for c2_val in clump_r2:
     for c3_val in clump_kb:
      for p in numberofpca:
       for ldakmodel in ldakmodels:
        for power in powers: 
         transform_ldak_data(folddirec, newtrainfilename, p,ldakmodel,power,str(p1_val), str(p2_val), str(p3_val), str(c1_val), str(c2_val), str(c3_val), result_directory, pvaluefile)

ridge
-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
LDAK - Software for obtaining Linkage Disequilibrium Adjusted Kinships and Loads More
Version 5.2 - Help pages at http://www.ldak.org
-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --

There are 4 pairs of arguments:
--ridge SampleData1/Fold_0/ridge
--bfile SampleData1/Fold_0/train_data.QC.clumped.pruned
--pheno SampleData1/Fold_0/train_data.PHENO
--LOCO NO

Warning, the predictor weightings have been set to one (equivalent to adding "--ignore-weights YES"); if you wish to specify different weightings, use "--weights"

-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --

Constructing a ridge regression prediction model

Will use a fast, approximate algorithm; to revert to the original algorithm (which is much slower, but slightly more accurate), use "--fast NO"

Will consider five values for the predictor scaling (alpha = -1, -0.75, -0.5, -0.25 and 0); to instead specify the value, use "--power"

When constructing the prediction model, will scan the data at most 10 times (change this using "--num-scans")

If the SD of the estimated grammar-gamma scaling factor is above 0.01, will set the scaling factor to one (to stop this, use "--force-calibration YES")

Consider using "--covar" to provide covariates

It appears this system has multiple processors available; to run the parallel version of LDAK, use "--max-threads" (this will only reduce runtime for some commands)

-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --

Reading IDs for 380 samples from SampleData1/Fold_0/train_data.QC.clumped.pruned.fam

Checking responses for 380 samples from SampleData1/Fold_0/train_data.PHENO

Reading details for 172878 predictors from SampleData1/Fold_0/train_data.QC.clumped.pruned.bim

Data contain 380 samples and 172878 predictors

Reading responses for 380 samples from SampleData1/Fold_0/train_data.PHENO

Will use 342 samples to train and 38 to test

Estimating per-predictor heritabilities using Randomized Haseman-Elston Regression
Will divide the predictors into (approximately) 20 partitions and use 100 random vectors (change these using "--num-divides" and "--repetitions"
Will exclude a chunk if its estimated variance explained is greater than 0.0500 (change this using "--max-cor")

After allowing for chromosome boundaries, there are 31 partitions

Calculating traces for Chunk 1 of 688
Warning, will exclude Chunk 12 because its estimated variance explained is 0.0543
Warning, will exclude Chunk 23 because its estimated variance explained is 0.0694
Warning, will exclude Chunk 55 because its estimated variance explained is 0.0570
Warning, will exclude Chunk 59 because its estimated variance explained is 0.0776
Warning, will exclude Chunk 63 because its estimated variance explained is 0.0787
Calculating traces for Chunk 201 of 688
Calculating traces for Chunk 401 of 688
Calculating traces for Chunk 601 of 688
In total, 55 chunks were excluded

Best power is -0.2500, estimated heritability is 1.6796
Warning, the estimated heritability is very high (1.6796), so has been reduced to 0.95


dougvar is 0 (if 1, will regress covariates from normal predictors)

Constructing 1 prediction models using training samples

Scan 1: estimating training effect sizes for Chunk 1 of 676
Scan 1: estimating training effect sizes for Chunk 201 of 676
Scan 1: estimating training effect sizes for Chunk 401 of 676
Scan 1: estimating training effect sizes for Chunk 601 of 676
Average number of iterations per chunk: 2.01

Scan 2: estimating training effect sizes for Chunk 1 of 674
Scan 2: estimating training effect sizes for Chunk 201 of 674
Scan 2: estimating training effect sizes for Chunk 401 of 674
Scan 2: estimating training effect sizes for Chunk 601 of 674
Average number of iterations per chunk: 1.27

Measuring accuracy of each model
Model 1: heritability 0.9500, mean squared error 0.9399

Constructing final prediction model (heritability 0.9500) using all samples

Scan 1: estimating final effect sizes for Chunk 1 of 676
Scan 1: estimating final effect sizes for Chunk 201 of 676
Scan 1: estimating final effect sizes for Chunk 401 of 676
Scan 1: estimating final effect sizes for Chunk 601 of 676
Average number of iterations per chunk: 2.00

Scan 2: estimating final effect sizes for Chunk 1 of 675
Scan 2: estimating final effect sizes for Chunk 201 of 675
Scan 2: estimating final effect sizes for Chunk 401 of 675
Scan 2: estimating final effect sizes for Chunk 601 of 675
Average number of iterations per chunk: 1.37

Scan 3: estimating final effect sizes for Chunk 1 of 249
Scan 3: estimating final effect sizes for Chunk 201 of 249
Average number of iterations per chunk: 1.90

Scan 4: estimating final effect sizes for Chunk 1 of 225
Scan 4: estimating final effect sizes for Chunk 201 of 225
Average number of iterations per chunk: 1.72

Scan 5: estimating final effect sizes for Chunk 1 of 162
Average number of iterations per chunk: 1.75

Scan 6: estimating final effect sizes for Chunk 1 of 122
Average number of iterations per chunk: 1.15

Scan 7: estimating final effect sizes for Chunk 1 of 18
Average number of iterations per chunk: 1.17

Scan 8: estimating final effect sizes for Chunk 1 of 3
Average number of iterations per chunk: 1.00

Best-fitting model saved in SampleData1/Fold_0/ridge.effects, with posterior probabilities in SampleData1/Fold_0/ridge.probs, and in-sample PRS in SampleData1/Fold_0/ridge.prs

-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
Mission completed. All your basepair are belong to us :)
-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
SampleData1/Fold_0/ridge.effects
PLINK v1.90b7.2 64-bit (11 Dec 2023)           www.cog-genomics.org/plink/1.9/
(C) 2005-2023 Shaun Purcell, Christopher Chang   GNU General Public License v3
Logging to SampleData1/Fold_0/LDAK-GenotypeData/train_data.log.
Options in effect:
  --bfile SampleData1/Fold_0/train_data.QC
  --extract SampleData1/Fold_0/train_data.valid.snp
  --out SampleData1/Fold_0/LDAK-GenotypeData/train_data
  --q-score-range SampleData1/Fold_0/range_list SampleData1/Fold_0/SNP.pvalue
  --score SampleData1/Fold_0/ridge.effects_ldak_genotype_gwas_final 1 2 3 header

63761 MB RAM detected; reserving 31880 MB for main workspace.
491952 variants loaded from .bim file.
380 people (183 males, 197 females) loaded from .fam.
380 phenotype values loaded from .fam.
--extract: 172878 variants remaining.
Using 1 thread (no multithreaded calculations invoked).
Before main variant filters, 380 founders and 0 nonfounders present.
Calculating allele frequencies... 10111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697989 done.
Total genotyping rate is 0.999891.
172878 variants and 380 people pass filters and QC.
Phenotype data is quantitative.
--score: 172878 valid predictors loaded.

Warning: 326740 lines skipped in --q-score-range data file.

--score: 20 ranges processed.
Results written to SampleData1/Fold_0/LDAK-GenotypeData/train_data.*.profile.
PLINK v1.90b7.2 64-bit (11 Dec 2023)           www.cog-genomics.org/plink/1.9/
(C) 2005-2023 Shaun Purcell, Christopher Chang   GNU General Public License v3
Logging to SampleData1/Fold_0/LDAK-GenotypeData/test_data.log.
Options in effect:
  --bfile SampleData1/Fold_0/test_data
  --extract SampleData1/Fold_0/train_data.valid.snp
  --out SampleData1/Fold_0/LDAK-GenotypeData/test_data
  --q-score-range SampleData1/Fold_0/range_list SampleData1/Fold_0/SNP.pvalue
  --score SampleData1/Fold_0/ridge.effects_ldak_genotype_gwas_final 1 2 3 header

63761 MB RAM detected; reserving 31880 MB for main workspace.
551892 variants loaded from .bim file.
95 people (44 males, 51 females) loaded from .fam.
95 phenotype values loaded from .fam.
--extract: 172878 variants remaining.
Using 1 thread (no multithreaded calculations invoked).
Before main variant filters, 95 founders and 0 nonfounders present.
Calculating allele frequencies... 10111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697989 done.
Total genotyping rate is 0.999891.
172878 variants and 95 people pass filters and QC.
Phenotype data is quantitative.
--score: 172878 valid predictors loaded.
--score: 20 ranges processed.
Results written to SampleData1/Fold_0/LDAK-GenotypeData/test_data.*.profile.
Continous Phenotype!

Warning: 326740 lines skipped in --q-score-range data file.

Check the results file for each fold.#

Repeat the process for each fold.#

Change the foldnumber variable.

#foldnumber = sys.argv[1]
foldnumber = "0"  # Setting 'foldnumber' to "0"

Or uncomment the following line:

# foldnumber = sys.argv[1]
python LDAK-GenotypeData.py 0
python LDAK-GenotypeData.py 1
python LDAK-GenotypeData.py 2
python LDAK-GenotypeData.py 3
python LDAK-GenotypeData.py 4

The following files should exist after the execution:

  1. SampleData1/Fold_0/LDAK-GenotypeData/Results.csv

  2. SampleData1/Fold_1/LDAK-GenotypeData/Results.csv

  3. SampleData1/Fold_2/LDAK-GenotypeData/Results.csv

  4. SampleData1/Fold_3/LDAK-GenotypeData/Results.csv

  5. SampleData1/Fold_4/LDAK-GenotypeData/Results.csv

import os
result_directory = "LDAK-GenotypeData"
 
# List of file names to check for existence
f = [
    "./"+filedirec+"/Fold_0"+os.sep+result_directory+"Results.csv",
    "./"+filedirec+"/Fold_1"+os.sep+result_directory+"Results.csv",
    "./"+filedirec+"/Fold_2"+os.sep+result_directory+"Results.csv",
    "./"+filedirec+"/Fold_3"+os.sep+result_directory+"Results.csv",
    "./"+filedirec+"/Fold_4"+os.sep+result_directory+"Results.csv",
]

 

# Loop through each file name in the list
for loop in range(0,5):
    # Check if the file exists in the specified directory for the given fold
    if os.path.exists(filedirec+os.sep+"Fold_"+str(loop)+os.sep+result_directory+os.sep+"Results.csv"):
        temp = pd.read_csv(filedirec+os.sep+"Fold_"+str(loop)+os.sep+result_directory+os.sep+"Results.csv")
        print("Fold_",loop, "Yes, the file exists.")
        #print(temp.head())
        print("Number of P-values processed: ",len(temp))
        # Print a message indicating that the file exists
    
    else:
        # Print a message indicating that the file does not exist
        print("Fold_",loop, "No, the file does not exist.")

Fold_ 0 Yes, the file exists.
Number of P-values processed:  80
Fold_ 1 Yes, the file exists.
Number of P-values processed:  80
Fold_ 2 Yes, the file exists.
Number of P-values processed:  80
Fold_ 3 Yes, the file exists.
Number of P-values processed:  80
Fold_ 4 Yes, the file exists.
Number of P-values processed:  80

Sum the results for each fold.#

print("We have to ensure when we sum the entries across all Folds, the same rows are merged!")

def sum_and_average_columns(data_frames):
    """Sum and average numerical columns across multiple DataFrames, and keep non-numerical columns unchanged."""
    # Initialize DataFrame to store the summed results for numerical columns
    summed_df = pd.DataFrame()
    non_numerical_df = pd.DataFrame()
    
    for df in data_frames:
        # Identify numerical and non-numerical columns
        numerical_cols = df.select_dtypes(include=[np.number]).columns
        non_numerical_cols = df.select_dtypes(exclude=[np.number]).columns
        
        # Sum numerical columns
        if summed_df.empty:
            summed_df = pd.DataFrame(0, index=range(len(df)), columns=numerical_cols)
        
        summed_df[numerical_cols] = summed_df[numerical_cols].add(df[numerical_cols], fill_value=0)
        
        # Keep non-numerical columns (take the first non-numerical entry for each column)
        if non_numerical_df.empty:
            non_numerical_df = df[non_numerical_cols]
        else:
            non_numerical_df[non_numerical_cols] = non_numerical_df[non_numerical_cols].combine_first(df[non_numerical_cols])
    
    # Divide the summed values by the number of dataframes to get the average
    averaged_df = summed_df / len(data_frames)
    
    # Combine numerical and non-numerical DataFrames
    result_df = pd.concat([averaged_df, non_numerical_df], axis=1)
    
    return result_df

from functools import reduce

import os
import pandas as pd
from functools import reduce

def find_common_rows(allfoldsframe):
    # Define the performance columns that need to be excluded
    performance_columns = [
        'Train_null_model', 'Train_pure_prs', 'Train_best_model',
        'Test_pure_prs', 'Test_null_model', 'Test_best_model'
    ]
    important_columns = [
        'clump_p1',
        'clump_r2',
        'clump_kb',
        'p_window_size',
        'p_slide_size',
        'p_LD_threshold',
        'pvalue',
        #'h2model',
        #'gctb_ld_model',
        #'BayesModel',
        
        "ldakmodel", 
        "ldakpower",  

        'numberofpca',
        'tempalpha',
        'l1weight',
         
       
    ]
    # Function to remove performance columns from a DataFrame
    def drop_performance_columns(df):
        return df.drop(columns=performance_columns, errors='ignore')
    
    def get_important_columns(df ):
        existing_columns = [col for col in important_columns if col in df.columns]
        if existing_columns:
            return df[existing_columns].copy()
        else:
            return pd.DataFrame()

    # Drop performance columns from all DataFrames in the list
    allfoldsframe_dropped = [drop_performance_columns(df) for df in allfoldsframe]
    
    # Get the important columns.
    allfoldsframe_dropped = [get_important_columns(df) for df in allfoldsframe_dropped]    
    
    # Iteratively find common rows and track unique and common rows
    common_rows = allfoldsframe_dropped[0]
    #print(common_rows.head())
    
    for i in range(1, len(allfoldsframe_dropped)):
        # Get the next DataFrame
        next_df = allfoldsframe_dropped[i]

        # Count unique rows in the current DataFrame and the next DataFrame
        unique_in_common = common_rows.shape[0]
        unique_in_next = next_df.shape[0]

        # Find common rows between the current common_rows and the next DataFrame
        common_rows = pd.merge(common_rows, next_df, how='inner')
        #print(common_rows.head())
    
        # Count the common rows after merging
        common_count = common_rows.shape[0]

        # Print the unique and common row counts
        print(f"Iteration {i}:")
        print(f"Unique rows in current common DataFrame: {unique_in_common}")
        print(f"Unique rows in next DataFrame: {unique_in_next}")
        print(f"Common rows after merge: {common_count}\n")
    # Now that we have the common rows, extract these from the original DataFrames
 
    extracted_common_rows_frames = []
    for original_df in allfoldsframe:
        # Merge the common rows with the original DataFrame, keeping only the rows that match the common rows
        extracted_common_rows = pd.merge(common_rows, original_df, how='inner', on=common_rows.columns.tolist())
        
        # Add the DataFrame with the extracted common rows to the list
        extracted_common_rows_frames.append(extracted_common_rows)

    # Print the number of rows in the common DataFrames
    for i, df in enumerate(extracted_common_rows_frames):
        print(f"DataFrame {i + 1} with extracted common rows has {df.shape[0]} rows.")

    # Return the list of DataFrames with extracted common rows
    return extracted_common_rows_frames



# Example usage (assuming allfoldsframe is populated as shown earlier):
allfoldsframe = []

# Loop through each file name in the list
for loop in range(0, 5):
    # Check if the file exists in the specified directory for the given fold
    file_path = os.path.join(filedirec, "Fold_" + str(loop), result_directory, "Results.csv")
    if os.path.exists(file_path):
        allfoldsframe.append(pd.read_csv(file_path))
        # Print a message indicating that the file exists
        print("Fold_", loop, "Yes, the file exists.")
    else:
        # Print a message indicating that the file does not exist
        print("Fold_", loop, "No, the file does not exist.")

# Find the common rows across all folds and return the list of extracted common rows
extracted_common_rows_list = find_common_rows(allfoldsframe)
 
# Sum the values column-wise
# For string values, do not sum it the values are going to be the same for each fold.
# Only sum the numeric values.

divided_result = sum_and_average_columns(extracted_common_rows_list)
  
print(divided_result)

 

We have to ensure when we sum the entries across all Folds, the same rows are merged!
Fold_ 0 Yes, the file exists.
Fold_ 1 Yes, the file exists.
Fold_ 2 Yes, the file exists.
Fold_ 3 Yes, the file exists.
Fold_ 4 Yes, the file exists.
Iteration 1:
Unique rows in current common DataFrame: 80
Unique rows in next DataFrame: 80
Common rows after merge: 80

Iteration 2:
Unique rows in current common DataFrame: 80
Unique rows in next DataFrame: 80
Common rows after merge: 80

Iteration 3:
Unique rows in current common DataFrame: 80
Unique rows in next DataFrame: 80
Common rows after merge: 80

Iteration 4:
Unique rows in current common DataFrame: 80
Unique rows in next DataFrame: 80
Common rows after merge: 80

DataFrame 1 with extracted common rows has 80 rows.
DataFrame 2 with extracted common rows has 80 rows.
DataFrame 3 with extracted common rows has 80 rows.
DataFrame 4 with extracted common rows has 80 rows.
DataFrame 5 with extracted common rows has 80 rows.
    clump_p1  clump_r2  clump_kb  p_window_size  p_slide_size  p_LD_threshold  \
0        1.0       0.1     200.0          200.0          50.0            0.25   
1        1.0       0.1     200.0          200.0          50.0            0.25   
2        1.0       0.1     200.0          200.0          50.0            0.25   
3        1.0       0.1     200.0          200.0          50.0            0.25   
4        1.0       0.1     200.0          200.0          50.0            0.25   
..       ...       ...       ...            ...           ...             ...   
75       1.0       0.1     200.0          200.0          50.0            0.25   
76       1.0       0.1     200.0          200.0          50.0            0.25   
77       1.0       0.1     200.0          200.0          50.0            0.25   
78       1.0       0.1     200.0          200.0          50.0            0.25   
79       1.0       0.1     200.0          200.0          50.0            0.25   

          pvalue  ldakpower  numberofpca  tempalpha  l1weight  \
0   1.000000e-10      -0.25          6.0        0.1       0.1   
1   3.359818e-10      -0.25          6.0        0.1       0.1   
2   1.128838e-09      -0.25          6.0        0.1       0.1   
3   3.792690e-09      -0.25          6.0        0.1       0.1   
4   1.274275e-08      -0.25          6.0        0.1       0.1   
..           ...        ...          ...        ...       ...   
75  7.847600e-03      -0.25          6.0        0.1       0.1   
76  2.636651e-02      -0.25          6.0        0.1       0.1   
77  8.858668e-02      -0.25          6.0        0.1       0.1   
78  2.976351e-01      -0.25          6.0        0.1       0.1   
79  1.000000e+00      -0.25          6.0        0.1       0.1   

    numberofvariants  Train_pure_prs  Train_null_model  Train_best_model  \
0           173107.8        0.000005           0.23001          0.511922   
1           173107.8        0.000005           0.23001          0.543687   
2           173107.8        0.000005           0.23001          0.589045   
3           173107.8        0.000005           0.23001          0.618895   
4           173107.8        0.000005           0.23001          0.655406   
..               ...             ...               ...               ...   
75          173107.8        0.000006           0.23001          0.964152   
76          173107.8        0.000006           0.23001          0.977628   
77          173107.8        0.000005           0.23001          0.987844   
78          173107.8        0.000005           0.23001          0.994897   
79          173107.8        0.000005           0.23001          0.999379   

    Test_pure_prs  Test_null_model  Test_best_model ldakmodel  
0    4.399381e-07         0.118692        -0.069904     ridge  
1    5.539094e-07         0.118692        -0.060869     ridge  
2    7.727895e-07         0.118692        -0.051053     ridge  
3    4.972825e-07         0.118692        -0.061832     ridge  
4    3.458053e-07         0.118692        -0.034566     ridge  
..            ...              ...              ...       ...  
75   1.757785e-07         0.118692         0.028814   elastic  
76   8.850651e-08         0.118692         0.009546   elastic  
77   1.315723e-07         0.118692         0.026812   elastic  
78   8.840679e-08         0.118692         0.023042   elastic  
79   4.419980e-08         0.118692         0.008853   elastic  

[80 rows x 19 columns]

/tmp/ipykernel_2344298/2044088675.py:24: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  non_numerical_df[non_numerical_cols] = non_numerical_df[non_numerical_cols].combine_first(df[non_numerical_cols])
/tmp/ipykernel_2344298/2044088675.py:24: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  non_numerical_df[non_numerical_cols] = non_numerical_df[non_numerical_cols].combine_first(df[non_numerical_cols])
/tmp/ipykernel_2344298/2044088675.py:24: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  non_numerical_df[non_numerical_cols] = non_numerical_df[non_numerical_cols].combine_first(df[non_numerical_cols])
/tmp/ipykernel_2344298/2044088675.py:24: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  non_numerical_df[non_numerical_cols] = non_numerical_df[non_numerical_cols].combine_first(df[non_numerical_cols])

Results#

1. Reporting Based on Best Training Performance:#

  • One can report the results based on the best performance of the training data. For example, if for a specific combination of hyperparameters, the training performance is high, report the corresponding test performance.

  • Example code:

    df = divided_result.sort_values(by='Train_best_model', ascending=False)
print(df.iloc[0].to_markdown())

Binary Phenotypes Result Analysis#

You can find the performance quality for binary phenotype using the following template:

PerformanceBinary

This figure shows the 8 different scenarios that can exist in the results, and the following table explains each scenario.

We classified performance based on the following table:

Performance Level

Range

Low Performance

0 to 0.5

Moderate Performance

0.6 to 0.7

High Performance

0.8 to 1

You can match the performance based on the following scenarios:

Scenario

What’s Happening

Implication

High Test, High Train

The model performs well on both training and test datasets, effectively learning the underlying patterns.

The model is well-tuned, generalizes well, and makes accurate predictions on both datasets.

High Test, Moderate Train

The model generalizes well but may not be fully optimized on training data, missing some underlying patterns.

The model is fairly robust but may benefit from further tuning or more training to improve its learning.

High Test, Low Train

An unusual scenario, potentially indicating data leakage or overestimation of test performance.

The model’s performance is likely unreliable; investigate potential data issues or random noise.

Moderate Test, High Train

The model fits the training data well but doesn’t generalize as effectively, capturing only some test patterns.

The model is slightly overfitting; adjustments may be needed to improve generalization on unseen data.

Moderate Test, Moderate Train

The model shows balanced but moderate performance on both datasets, capturing some patterns but missing others.

The model is moderately fitting; further improvements could be made in both training and generalization.

Moderate Test, Low Train

The model underperforms on training data and doesn’t generalize well, leading to moderate test performance.

The model may need more complexity, additional features, or better training to improve on both datasets.

Low Test, High Train

The model overfits the training data, performing poorly on the test set.

The model doesn’t generalize well; simplifying the model or using regularization may help reduce overfitting.

Low Test, Low Train

The model performs poorly on both training and test datasets, failing to learn the data patterns effectively.

The model is underfitting; it may need more complexity, additional features, or more data to improve performance.
Recommendations for Publishing Results#

When publishing results, scenarios with moderate train and moderate test performance can be used for complex phenotypes or diseases. However, results showing high train and moderate test, high train and high test, and moderate train and high test are recommended.

For most phenotypes, results typically fall in the moderate train and moderate test performance category.

Continuous Phenotypes Result Analysis#

You can find the performance quality for continuous phenotypes using the following template:

PerformanceContinous

This figure shows the 8 different scenarios that can exist in the results, and the following table explains each scenario.

We classified performance based on the following table:

Performance Level

Range

Low Performance

0 to 0.2

Moderate Performance

0.3 to 0.7

High Performance

0.8 to 1

You can match the performance based on the following scenarios:

Scenario

What’s Happening

Implication

High Test, High Train

The model performs well on both training and test datasets, effectively learning the underlying patterns.

The model is well-tuned, generalizes well, and makes accurate predictions on both datasets.

High Test, Moderate Train

The model generalizes well but may not be fully optimized on training data, missing some underlying patterns.

The model is fairly robust but may benefit from further tuning or more training to improve its learning.

High Test, Low Train

An unusual scenario, potentially indicating data leakage or overestimation of test performance.

The model’s performance is likely unreliable; investigate potential data issues or random noise.

Moderate Test, High Train

The model fits the training data well but doesn’t generalize as effectively, capturing only some test patterns.

The model is slightly overfitting; adjustments may be needed to improve generalization on unseen data.

Moderate Test, Moderate Train

The model shows balanced but moderate performance on both datasets, capturing some patterns but missing others.

The model is moderately fitting; further improvements could be made in both training and generalization.

Moderate Test, Low Train

The model underperforms on training data and doesn’t generalize well, leading to moderate test performance.

The model may need more complexity, additional features, or better training to improve on both datasets.

Low Test, High Train

The model overfits the training data, performing poorly on the test set.

The model doesn’t generalize well; simplifying the model or using regularization may help reduce overfitting.

Low Test, Low Train

The model performs poorly on both training and test datasets, failing to learn the data patterns effectively.

The model is underfitting; it may need more complexity, additional features, or more data to improve performance.
Recommendations for Publishing Results#

When publishing results, scenarios with moderate train and moderate test performance can be used for complex phenotypes or diseases. However, results showing high train and moderate test, high train and high test, and moderate train and high test are recommended.

For most continuous phenotypes, results typically fall in the moderate train and moderate test performance category.

2. Reporting Generalized Performance:#

  • One can also report the generalized performance by calculating the difference between the training and test performance, and the sum of the test and training performance. Report the result or hyperparameter combination for which the sum is high and the difference is minimal.

  • Example code:

    df = divided_result.copy()
df['Difference'] = abs(df['Train_best_model'] - df['Test_best_model'])
df['Sum'] = df['Train_best_model'] + df['Test_best_model']

sorted_df = df.sort_values(by=['Sum', 'Difference'], ascending=[False, True])
print(sorted_df.iloc[0].to_markdown())

3. Reporting Hyperparameters Affecting Test and Train Performance:#

  • Find the hyperparameters that have more than one unique value and calculate their correlation with the following columns to understand how they are affecting the performance of train and test sets:

    • Train_null_model

    • Train_pure_prs

    • Train_best_model

    • Test_pure_prs

    • Test_null_model

    • Test_best_model

4. Other Analysis#

  1. Once you have the results, you can find how hyperparameters affect the model performance.

  2. Analysis, like overfitting and underfitting, can be performed as well.

  3. The way you are going to report the results can vary.

  4. Results can be visualized, and other patterns in the data can be explored.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib notebook

import matplotlib
import numpy as np
import matplotlib.pyplot as plt

df = divided_result.sort_values(by='Train_best_model', ascending=False)
print("1. Reporting Based on Best Training Performance:\n")
print(df.iloc[0].to_markdown())


 
df = divided_result.copy()

# Plot Train and Test best models against p-values
plt.figure(figsize=(10, 6))
plt.plot(df['pvalue'], df['Train_best_model'], label='Train_best_model', marker='o', color='royalblue')
plt.plot(df['pvalue'], df['Test_best_model'], label='Test_best_model', marker='o', color='darkorange')

# Highlight the p-value where both train and test are high
best_index = df[['Train_best_model']].sum(axis=1).idxmax()
best_pvalue = df.loc[best_index, 'pvalue']
best_train = df.loc[best_index, 'Train_best_model']
best_test = df.loc[best_index, 'Test_best_model']

# Use dark colors for the circles
plt.scatter(best_pvalue, best_train, color='darkred', s=100, label=f'Best Performance (Train)', edgecolor='black', zorder=5)
plt.scatter(best_pvalue, best_test, color='darkblue', s=100, label=f'Best Performance (Test)', edgecolor='black', zorder=5)

# Annotate the best performance with p-value, train, and test values
plt.text(best_pvalue, best_train, f'p={best_pvalue:.4g}\nTrain={best_train:.4g}', ha='right', va='bottom', fontsize=9, color='darkred')
plt.text(best_pvalue, best_test, f'p={best_pvalue:.4g}\nTest={best_test:.4g}', ha='right', va='top', fontsize=9, color='darkblue')

# Calculate Difference and Sum
df['Difference'] = abs(df['Train_best_model'] - df['Test_best_model'])
df['Sum'] = df['Train_best_model'] + df['Test_best_model']

# Sort the DataFrame
sorted_df = df.sort_values(by=['Sum', 'Difference'], ascending=[False, True])
#sorted_df = df.sort_values(by=[ 'Difference','Sum'], ascending=[  True,False])

# Highlight the general performance
general_index = sorted_df.index[0]
general_pvalue = sorted_df.loc[general_index, 'pvalue']
general_train = sorted_df.loc[general_index, 'Train_best_model']
general_test = sorted_df.loc[general_index, 'Test_best_model']

plt.scatter(general_pvalue, general_train, color='darkgreen', s=150, label='General Performance (Train)', edgecolor='black', zorder=6)
plt.scatter(general_pvalue, general_test, color='darkorange', s=150, label='General Performance (Test)', edgecolor='black', zorder=6)

# Annotate the general performance with p-value, train, and test values
plt.text(general_pvalue, general_train, f'p={general_pvalue:.4g}\nTrain={general_train:.4g}', ha='left', va='bottom', fontsize=9, color='darkgreen')
plt.text(general_pvalue, general_test, f'p={general_pvalue:.4g}\nTest={general_test:.4g}', ha='left', va='top', fontsize=9, color='darkorange')

# Add labels and legend
plt.xlabel('p-value')
plt.ylabel('Model Performance')
plt.title('Train vs Test Best Models')
plt.legend()
plt.show()
 




print("2. Reporting Generalized Performance:\n")
df = divided_result.copy()
df['Difference'] = abs(df['Train_best_model'] - df['Test_best_model'])
df['Sum'] = df['Train_best_model'] + df['Test_best_model']
sorted_df = df.sort_values(by=['Sum', 'Difference'], ascending=[False, True])
print(sorted_df.iloc[0].to_markdown())


print("3. Reporting the correlation of hyperparameters and the performance of 'Train_null_model', 'Train_pure_prs', 'Train_best_model', 'Test_pure_prs', 'Test_null_model', and 'Test_best_model':\n")

print("3. For string hyperparameters, we used one-hot encoding to find the correlation between string hyperparameters and 'Train_null_model', 'Train_pure_prs', 'Train_best_model', 'Test_pure_prs', 'Test_null_model', and 'Test_best_model'.")

print("3. We performed this analysis for those hyperparameters that have more than one unique value.")

correlation_columns = [
 'Train_null_model', 'Train_pure_prs', 'Train_best_model',
 'Test_pure_prs', 'Test_null_model', 'Test_best_model'
]

hyperparams = [col for col in divided_result.columns if len(divided_result[col].unique()) > 1]
hyperparams = list(set(hyperparams+correlation_columns))
 
# Separate numeric and string columns
numeric_hyperparams = [col for col in hyperparams if pd.api.types.is_numeric_dtype(divided_result[col])]
string_hyperparams = [col for col in hyperparams if pd.api.types.is_string_dtype(divided_result[col])]


# Encode string columns using one-hot encoding
divided_result_encoded = pd.get_dummies(divided_result, columns=string_hyperparams)

# Combine numeric hyperparams with the new one-hot encoded columns
encoded_columns = [col for col in divided_result_encoded.columns if col.startswith(tuple(string_hyperparams))]
hyperparams = numeric_hyperparams + encoded_columns
 

# Calculate correlations
correlations = divided_result_encoded[hyperparams].corr()
 
# Display correlation of hyperparameters with train/test performance columns
hyperparam_correlations = correlations.loc[hyperparams, correlation_columns]
 
hyperparam_correlations = hyperparam_correlations.fillna(0)

# Plotting the correlation heatmap
plt.figure(figsize=(12, 8))
ax = sns.heatmap(hyperparam_correlations, annot=True, cmap='viridis', fmt='.2f', cbar=True)
ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha='right')

# Rotate y-axis labels to horizontal
#ax.set_yticklabels(ax.get_yticklabels(), rotation=0, va='center')

plt.title('Correlation of Hyperparameters with Train/Test Performance')
plt.show() 

sns.set_theme(style="whitegrid")  # Choose your preferred style
pairplot = sns.pairplot(divided_result_encoded[hyperparams],hue = 'Test_best_model', palette='viridis')

# Adjust the figure size
pairplot.fig.set_size_inches(15, 15)  # You can adjust the size as needed

for ax in pairplot.axes.flatten():
    ax.set_xlabel(ax.get_xlabel(), rotation=90, ha='right')  # X-axis labels vertical
    #ax.set_ylabel(ax.get_ylabel(), rotation=0, va='bottom')  # Y-axis labels horizontal

# Show the plot
plt.show()

1. Reporting Based on Best Training Performance:

|                  | 79                    |
|:-----------------|:----------------------|
| clump_p1         | 1.0                   |
| clump_r2         | 0.1                   |
| clump_kb         | 200.0                 |
| p_window_size    | 200.0                 |
| p_slide_size     | 50.0                  |
| p_LD_threshold   | 0.25                  |
| pvalue           | 1.0                   |
| ldakpower        | -0.25                 |
| numberofpca      | 6.0                   |
| tempalpha        | 0.1                   |
| l1weight         | 0.1                   |
| numberofvariants | 173107.8              |
| Train_pure_prs   | 4.607490665664571e-06 |
| Train_null_model | 0.23001030414198947   |
| Train_best_model | 0.9993789859474654    |
| Test_pure_prs    | 4.419979755532211e-08 |
| Test_null_model  | 0.11869244971793831   |
| Test_best_model  | 0.0088525879628383    |
| ldakmodel        | elastic               |

2. Reporting Generalized Performance:

|                  | 78                     |
|:-----------------|:-----------------------|
| clump_p1         | 1.0                    |
| clump_r2         | 0.1                    |
| clump_kb         | 200.0                  |
| p_window_size    | 200.0                  |
| p_slide_size     | 50.0                   |
| p_LD_threshold   | 0.25                   |
| pvalue           | 0.2976351441631313     |
| ldakpower        | -0.25                  |
| numberofpca      | 6.0                    |
| tempalpha        | 0.1                    |
| l1weight         | 0.1                    |
| numberofvariants | 173107.8               |
| Train_pure_prs   | 5.0190226413393544e-06 |
| Train_null_model | 0.23001030414198947    |
| Train_best_model | 0.9948971783108636     |
| Test_pure_prs    | 8.840678573651673e-08  |
| Test_null_model  | 0.11869244971793831    |
| Test_best_model  | 0.023041979644543      |
| ldakmodel        | elastic                |
| Difference       | 0.9718551986663205     |
| Sum              | 1.0179391579554065     |
3. Reporting the correlation of hyperparameters and the performance of 'Train_null_model', 'Train_pure_prs', 'Train_best_model', 'Test_pure_prs', 'Test_null_model', and 'Test_best_model':

3. For string hyperparameters, we used one-hot encoding to find the correlation between string hyperparameters and 'Train_null_model', 'Train_pure_prs', 'Train_best_model', 'Test_pure_prs', 'Test_null_model', and 'Test_best_model'.
3. We performed this analysis for those hyperparameters that have more than one unique value.