def ldpred_genomewide(data_file=None, ld_radius=None, ld_dict=None, out_file_prefix=None, summary_dict=None, ps=None, n=None, h2=None, num_iter=None, verbose=False, zero_jump_prob=0.05, burn_in=5): """ Calculate LDpred for a genome """ df = h5py.File(data_file, 'r') has_phenotypes = False if 'y' in df: 'Validation phenotypes found.' y = df['y'][...] # Phenotype num_individs = len(y) risk_scores_pval_derived = sp.zeros(num_individs) has_phenotypes = True ld_scores_dict = ld_dict['ld_scores_dict'] chrom_ld_dict = ld_dict['chrom_ld_dict'] chrom_ref_ld_mats = ld_dict['chrom_ref_ld_mats'] print('Applying LDpred with LD radius: %d' % ld_radius) results_dict = {} cord_data_g = df['cord_data'] #Calculating genome-wide heritability using LD score regression, and partition heritability by chromsomes herit_dict = ld.get_chromosome_herits(cord_data_g, ld_scores_dict, n, h2=h2, debug=verbose, summary_dict=summary_dict) LDpred_inf_chrom_dict = {} print('Calculating LDpred-inf weights') for chrom_str in util.chromosomes_list: if chrom_str in cord_data_g: print('Calculating SNP weights for Chromosome %s' % ((chrom_str.split('_'))[1])) g = cord_data_g[chrom_str] # Filter monomorphic SNPs snp_stds = g['snp_stds_ref'][...] snp_stds = snp_stds.flatten() ok_snps_filter = snp_stds > 0 pval_derived_betas = g['betas'][...] pval_derived_betas = pval_derived_betas[ok_snps_filter] h2_chrom = herit_dict[chrom_str] start_betas = LDpred_inf.ldpred_inf( pval_derived_betas, genotypes=None, reference_ld_mats=chrom_ref_ld_mats[chrom_str], h2=h2_chrom, n=n, ld_window_size=2 * ld_radius, verbose=False) LDpred_inf_chrom_dict[chrom_str] = start_betas convergence_report = {} for p in ps: convergence_report[p] = False print('Starting LDpred gibbs with f=%0.4f' % p) p_str = '%0.4f' % p results_dict[p_str] = {} if out_file_prefix: # Preparing output files raw_effect_sizes = [] ldpred_effect_sizes = [] ldpred_inf_effect_sizes = [] out_sids = [] chromosomes = [] out_positions = [] out_nts = [] chrom_i = 0 num_chrom = len(util.chromosomes_list) for chrom_str in util.chromosomes_list: chrom_i += 1 if chrom_str in cord_data_g: g = cord_data_g[chrom_str] if verbose and has_phenotypes: if 'raw_snps_val' in g: raw_snps = g['raw_snps_val'][...] else: raw_snps = g['raw_snps_ref'][...] # Filter monomorphic SNPs snp_stds = g['snp_stds_ref'][...] snp_stds = snp_stds.flatten() pval_derived_betas = g['betas'][...] positions = g['positions'][...] sids = (g['sids'][...]).astype(util.sids_u_dtype) log_odds = g['log_odds'][...] nts = (g['nts'][...]).astype(util.nts_u_dtype) ok_snps_filter = snp_stds > 0 if not sp.all(ok_snps_filter): snp_stds = snp_stds[ok_snps_filter] pval_derived_betas = pval_derived_betas[ok_snps_filter] positions = positions[ok_snps_filter] sids = sids[ok_snps_filter] log_odds = log_odds[ok_snps_filter] nts = nts[ok_snps_filter] if verbose and has_phenotypes: raw_snps = raw_snps[ok_snps_filter] if out_file_prefix: chromosomes.extend([chrom_str] * len(pval_derived_betas)) out_positions.extend(positions) out_sids.extend(sids) raw_effect_sizes.extend(log_odds) out_nts.extend(nts) h2_chrom = herit_dict[chrom_str] if 'chrom_ld_boundaries' in ld_dict: ld_boundaries = ld_dict['chrom_ld_boundaries'][chrom_str] res_dict = ldpred_gibbs( pval_derived_betas, h2=h2_chrom, n=n, p=p, ld_radius=ld_radius, verbose=verbose, num_iter=num_iter, burn_in=burn_in, ld_dict=chrom_ld_dict[chrom_str], start_betas=LDpred_inf_chrom_dict[chrom_str], ld_boundaries=ld_boundaries, zero_jump_prob=zero_jump_prob) else: res_dict = ldpred_gibbs( pval_derived_betas, h2=h2_chrom, n=n, p=p, ld_radius=ld_radius, verbose=verbose, num_iter=num_iter, burn_in=burn_in, ld_dict=chrom_ld_dict[chrom_str], start_betas=LDpred_inf_chrom_dict[chrom_str], zero_jump_prob=zero_jump_prob) updated_betas = res_dict['betas'] updated_inf_betas = res_dict['inf_betas'] sum_sqr_effects = sp.sum(updated_betas**2) if sum_sqr_effects > herit_dict['gw_h2_ld_score_est']: print( 'Sum of squared updated effects estimates seems too large: %0.4f' % sum_sqr_effects) print( 'This suggests that the Gibbs sampler did not convergence.' ) convergence_report[p] = True if verbose: print('Calculating SNP weights for Chromosome %s' % ((chrom_str.split('_'))[1])) else: sys.stdout.write('\b\b\b\b\b\b\b%0.2f%%' % (100.0 * (min(1, float(chrom_i + 1) / num_chrom)))) sys.stdout.flush() updated_betas = updated_betas / (snp_stds.flatten()) updated_inf_betas = updated_inf_betas / (snp_stds.flatten()) ldpred_effect_sizes.extend(updated_betas) ldpred_inf_effect_sizes.extend(updated_inf_betas) if verbose and has_phenotypes: prs = sp.dot(updated_betas, raw_snps) risk_scores_pval_derived += prs corr = sp.corrcoef(y, prs)[0, 1] r2 = corr**2 print( 'The R2 prediction accuracy of PRS using %s was: %0.4f' % (chrom_str, r2)) if verbose and has_phenotypes: num_indivs = len(y) results_dict[p_str]['y'] = y results_dict[p_str]['risk_scores_pd'] = risk_scores_pval_derived print('Prediction accuracy was assessed using %d individuals.' % (num_indivs)) corr = sp.corrcoef(y, risk_scores_pval_derived)[0, 1] r2 = corr**2 results_dict[p_str]['r2_pd'] = r2 print( 'The R2 prediction accuracy (observed scale) for the whole genome was: %0.4f (%0.6f)' % (r2, ((1 - r2)**2) / num_indivs)) if corr < 0: risk_scores_pval_derived = -1 * risk_scores_pval_derived auc = util.calc_auc(y, risk_scores_pval_derived) print('AUC for the whole genome was: %0.4f' % auc) # Now calibration denominator = sp.dot(risk_scores_pval_derived.T, risk_scores_pval_derived) y_norm = (y - sp.mean(y)) / sp.std(y) numerator = sp.dot(risk_scores_pval_derived.T, y_norm) regression_slope = (numerator / denominator) # [0][0] print( 'The slope for predictions with P-value derived effects is: %0.4f' % regression_slope) results_dict[p_str]['slope_pd'] = regression_slope weights_out_file = '%s_LDpred_p%0.4e.txt' % (out_file_prefix, p) with open(weights_out_file, 'w') as f: f.write( 'chrom pos sid nt1 nt2 raw_beta ldpred_beta\n' ) for chrom, pos, sid, nt, raw_beta, ldpred_beta in zip( chromosomes, out_positions, out_sids, out_nts, raw_effect_sizes, ldpred_effect_sizes): nt1, nt2 = nt[0], nt[1] f.write('%s %d %s %s %s %0.4e %0.4e\n' % (chrom, pos, sid, nt1, nt2, raw_beta, ldpred_beta)) weights_out_file = '%s_LDpred-inf.txt' % (out_file_prefix) with open(weights_out_file, 'w') as f: f.write( 'chrom pos sid nt1 nt2 raw_beta ldpred_inf_beta \n' ) for chrom, pos, sid, nt, raw_beta, ldpred_inf_beta in zip( chromosomes, out_positions, out_sids, out_nts, raw_effect_sizes, ldpred_inf_effect_sizes): nt1, nt2 = nt[0], nt[1] f.write('%s %d %s %s %s %0.4e %0.4e\n' % (chrom, pos, sid, nt1, nt2, raw_beta, ldpred_inf_beta)) summary_dict[2.0] = { 'name': 'Gibbs sampler fractions used', 'value': str(ps) } ['Yes' if convergence_report[p] else 'No' for p in ps] summary_dict[2.1] = { 'name': 'Convergence issues (for each fraction)', 'value': str(['Yes' if convergence_report[p] else 'No' for p in ps]) }
def ldpred_genomewide(data_file=None, ld_radius=None, ld_dict=None, out_file_prefix=None, summary_dict=None, ps=None, n=None, h2=None, num_iter=None, verbose=False, zero_jump_prob=0.05, burn_in=5): """ Calculate LDpred for a genome """ df = h5py.File(data_file, 'r') has_phenotypes = False if 'y' in df: 'Validation phenotypes found.' y = df['y'][...] # Phenotype num_individs = len(y) risk_scores_pval_derived = sp.zeros(num_individs) has_phenotypes = True ld_scores_dict = ld_dict['ld_scores_dict'] chrom_ld_dict = ld_dict['chrom_ld_dict'] chrom_ref_ld_mats = ld_dict['chrom_ref_ld_mats'] print('Applying LDpred with LD radius: %d' % ld_radius) results_dict = {} cord_data_g = df['cord_data'] #Calculating genome-wide heritability using LD score regression, and partition heritability by chromsomes herit_dict = ld.get_chromosome_herits(cord_data_g, ld_scores_dict, n, h2=h2, debug=verbose,summary_dict=summary_dict) LDpred_inf_chrom_dict = {} print('Calculating LDpred-inf weights') for chrom_str in util.chromosomes_list: if chrom_str in cord_data_g: print('Calculating SNP weights for Chromosome %s' % ((chrom_str.split('_'))[1])) g = cord_data_g[chrom_str] # Filter monomorphic SNPs snp_stds = g['snp_stds_ref'][...] snp_stds = snp_stds.flatten() ok_snps_filter = snp_stds > 0 pval_derived_betas = g['betas'][...] pval_derived_betas = pval_derived_betas[ok_snps_filter] h2_chrom = herit_dict[chrom_str] start_betas = LDpred_inf.ldpred_inf(pval_derived_betas, genotypes=None, reference_ld_mats=chrom_ref_ld_mats[chrom_str], h2=h2_chrom, n=n, ld_window_size=2 * ld_radius, verbose=False) LDpred_inf_chrom_dict[chrom_str] = start_betas convergence_report = {} for p in ps: convergence_report[p] = False print('Starting LDpred gibbs with f=%0.4f' % p) p_str = '%0.4f' % p results_dict[p_str] = {} if out_file_prefix: # Preparing output files raw_effect_sizes = [] ldpred_effect_sizes = [] ldpred_inf_effect_sizes = [] out_sids = [] chromosomes = [] out_positions = [] out_nts = [] chrom_i = 0 num_chrom = len(util.chromosomes_list) for chrom_str in util.chromosomes_list: chrom_i+=1 if chrom_str in cord_data_g: g = cord_data_g[chrom_str] if verbose and has_phenotypes: if 'raw_snps_val' in g: raw_snps = g['raw_snps_val'][...] else: raw_snps = g['raw_snps_ref'][...] # Filter monomorphic SNPs snp_stds = g['snp_stds_ref'][...] snp_stds = snp_stds.flatten() pval_derived_betas = g['betas'][...] positions = g['positions'][...] sids = (g['sids'][...]).astype(util.sids_u_dtype) log_odds = g['log_odds'][...] nts = (g['nts'][...]).astype(util.nts_u_dtype) ok_snps_filter = snp_stds > 0 if not sp.all(ok_snps_filter): snp_stds = snp_stds[ok_snps_filter] pval_derived_betas = pval_derived_betas[ok_snps_filter] positions = positions[ok_snps_filter] sids = sids[ok_snps_filter] log_odds = log_odds[ok_snps_filter] nts = nts[ok_snps_filter] if verbose and has_phenotypes: raw_snps = raw_snps[ok_snps_filter] if out_file_prefix: chromosomes.extend([chrom_str] * len(pval_derived_betas)) out_positions.extend(positions) out_sids.extend(sids) raw_effect_sizes.extend(log_odds) out_nts.extend(nts) h2_chrom = herit_dict[chrom_str] if 'chrom_ld_boundaries' in ld_dict: ld_boundaries = ld_dict['chrom_ld_boundaries'][chrom_str] res_dict = ldpred_gibbs(pval_derived_betas, h2=h2_chrom, n=n, p=p, ld_radius=ld_radius, verbose=verbose, num_iter=num_iter, burn_in=burn_in, ld_dict=chrom_ld_dict[chrom_str], start_betas=LDpred_inf_chrom_dict[chrom_str], ld_boundaries=ld_boundaries, zero_jump_prob=zero_jump_prob, print_progress=False) else: res_dict = ldpred_gibbs(pval_derived_betas, h2=h2_chrom, n=n, p=p, ld_radius=ld_radius, verbose=verbose, num_iter=num_iter, burn_in=burn_in, ld_dict=chrom_ld_dict[chrom_str], start_betas=LDpred_inf_chrom_dict[chrom_str], zero_jump_prob=zero_jump_prob, print_progress=False) updated_betas = res_dict['betas'] updated_inf_betas = res_dict['inf_betas'] sum_sqr_effects = sp.sum(updated_betas ** 2) if sum_sqr_effects > herit_dict['gw_h2_ld_score_est']: print('Sum of squared updated effects estimates seems too large: %0.4f'% sum_sqr_effects) print('This suggests that the Gibbs sampler did not convergence.') convergence_report[p] = True if verbose: print('Calculating SNP weights for Chromosome %s' % ((chrom_str.split('_'))[1])) else: sys.stdout.write('\b\b\b\b\b\b\b%0.2f%%' % (100.0 * (min(1, float(chrom_i + 1) / num_chrom)))) sys.stdout.flush() updated_betas = updated_betas / (snp_stds.flatten()) updated_inf_betas = updated_inf_betas / (snp_stds.flatten()) ldpred_effect_sizes.extend(updated_betas) ldpred_inf_effect_sizes.extend(updated_inf_betas) if verbose and has_phenotypes: prs = sp.dot(updated_betas, raw_snps) risk_scores_pval_derived += prs corr = sp.corrcoef(y, prs)[0, 1] r2 = corr ** 2 print('The R2 prediction accuracy of PRS using %s was: %0.4f' % (chrom_str, r2)) if not verbose: sys.stdout.write('\b\b\b\b\b\b\b%0.2f%%\n' % (100.0)) sys.stdout.flush() if verbose and has_phenotypes: num_indivs = len(y) results_dict[p_str]['y'] = y results_dict[p_str]['risk_scores_pd'] = risk_scores_pval_derived print('Prediction accuracy was assessed using %d individuals.' % (num_indivs)) corr = sp.corrcoef(y, risk_scores_pval_derived)[0, 1] r2 = corr ** 2 results_dict[p_str]['r2_pd'] = r2 print('The R2 prediction accuracy (observed scale) for the whole genome was: %0.4f (%0.6f)' % (r2, ((1 - r2) ** 2) / num_indivs)) if corr < 0: risk_scores_pval_derived = -1 * risk_scores_pval_derived auc = util.calc_auc(y, risk_scores_pval_derived) print('AUC for the whole genome was: %0.4f' % auc) # Now calibration denominator = sp.dot(risk_scores_pval_derived.T, risk_scores_pval_derived) y_norm = (y - sp.mean(y)) / sp.std(y) numerator = sp.dot(risk_scores_pval_derived.T, y_norm) regression_slope = (numerator / denominator) # [0][0] print('The slope for predictions with P-value derived effects is: %0.4f' % regression_slope) results_dict[p_str]['slope_pd'] = regression_slope weights_out_file = '%s_LDpred_p%0.4e.txt' % (out_file_prefix, p) with open(weights_out_file, 'w') as f: f.write('chrom pos sid nt1 nt2 raw_beta ldpred_beta\n') for chrom, pos, sid, nt, raw_beta, ldpred_beta in zip(chromosomes, out_positions, out_sids, out_nts, raw_effect_sizes, ldpred_effect_sizes): nt1, nt2 = nt[0], nt[1] f.write('%s %d %s %s %s %0.4e %0.4e\n' % (chrom, pos, sid, nt1, nt2, raw_beta, ldpred_beta)) weights_out_file = '%s_LDpred-inf.txt' % (out_file_prefix) with open(weights_out_file, 'w') as f: f.write('chrom pos sid nt1 nt2 raw_beta ldpred_inf_beta \n') for chrom, pos, sid, nt, raw_beta, ldpred_inf_beta in zip(chromosomes, out_positions, out_sids, out_nts, raw_effect_sizes, ldpred_inf_effect_sizes): nt1, nt2 = nt[0], nt[1] f.write('%s %d %s %s %s %0.4e %0.4e\n' % (chrom, pos, sid, nt1, nt2, raw_beta, ldpred_inf_beta)) summary_dict[2.0]={'name':'Gibbs sampler fractions used','value':str(ps)} ['Yes' if convergence_report[p] else 'No' for p in ps] summary_dict[2.1]={'name':'Convergence issues (for each fraction)','value':str(['Yes' if convergence_report[p] else 'No' for p in ps])}
def ldpred_inf_genomewide(data_file=None, ld_radius = None, ld_dict=None, out_file_prefix=None, n=None, h2=None, verbose=False): """ Calculate LDpred for a genome """ df = h5py.File(data_file,'r') has_phenotypes=False if 'y' in df: 'Validation phenotypes found.' y = df['y'][...] # Phenotype num_individs = len(y) risk_scores_pval_derived = sp.zeros(num_individs) has_phenotypes=True ld_scores_dict = ld_dict['ld_scores_dict'] chrom_ref_ld_mats = ld_dict['chrom_ref_ld_mats'] print('Applying LDpred-inf with LD radius: %d' % ld_radius) results_dict = {} cord_data_g = df['cord_data'] #Calculating genome-wide heritability using LD score regression, and partition heritability by chromsomes herit_dict = ld.get_chromosome_herits(cord_data_g, ld_scores_dict, n, h2=h2) if out_file_prefix: #Preparing output files raw_effect_sizes = [] ldpred_effect_sizes = [] sids = [] chromosomes = [] positions = [] nts = [] for chrom_str in util.chromosomes_list: if chrom_str in cord_data_g: g = cord_data_g[chrom_str] if has_phenotypes: if 'raw_snps_val' in g: raw_snps = g['raw_snps_val'][...] else: raw_snps = g['raw_snps_ref'][...] snp_stds = g['snp_stds_ref'][...] pval_derived_betas = g['betas'][...] if out_file_prefix: chromosomes.extend([chrom_str]*len(pval_derived_betas)) positions.extend(g['positions'][...]) sids_arr = (g['sids'][...]).astype(util.sids_u_dtype) sids.extend(sids_arr) raw_effect_sizes.extend(g['log_odds'][...]) nts_arr = (g['nts'][...]).astype(util.nts_u_dtype) nts.extend(nts_arr) h2_chrom = herit_dict[chrom_str] updated_betas = ldpred_inf(pval_derived_betas, genotypes=None, reference_ld_mats=chrom_ref_ld_mats[chrom_str], h2=h2_chrom, n=n, ld_window_size=2*ld_radius, verbose=False) print('Calculating scores for Chromosome %s'%((chrom_str.split('_'))[1])) updated_betas = updated_betas / (snp_stds.flatten()) ldpred_effect_sizes.extend(updated_betas) if has_phenotypes: prs = sp.dot(updated_betas, raw_snps) risk_scores_pval_derived += prs corr = sp.corrcoef(y, prs)[0, 1] r2 = corr ** 2 print('The R2 prediction accuracy of PRS using %s was: %0.4f' %(chrom_str, r2)) if has_phenotypes: num_indivs = len(y) results_dict['y']=y results_dict['risk_scores_pd']=risk_scores_pval_derived print('Prediction accuracy was assessed using %d individuals.'%(num_indivs)) corr = sp.corrcoef(y, risk_scores_pval_derived)[0, 1] r2 = corr ** 2 results_dict['r2_pd']=r2 print('The R2 prediction accuracy (observed scale) for the whole genome was: %0.4f (%0.6f)' % (r2, ((1-r2)**2)/num_indivs)) if corr<0: risk_scores_pval_derived = -1* risk_scores_pval_derived auc = util.calc_auc(y,risk_scores_pval_derived) print('AUC for the whole genome was: %0.4f'%auc) #Now calibration denominator = sp.dot(risk_scores_pval_derived.T, risk_scores_pval_derived) y_norm = (y-sp.mean(y))/sp.std(y) numerator = sp.dot(risk_scores_pval_derived.T, y_norm) regression_slope = (numerator / denominator) print('The slope for predictions with P-value derived effects is: %0.4f'%regression_slope) results_dict['slope_pd']=regression_slope weights_out_file = '%s.txt'%(out_file_prefix) with open(weights_out_file,'w') as f: f.write('chrom pos sid nt1 nt2 raw_beta ldpred_inf_beta\n') for chrom, pos, sid, nt, raw_beta, ldpred_beta in zip(chromosomes, positions, sids, nts, raw_effect_sizes, ldpred_effect_sizes): nt1,nt2 = nt[0],nt[1] f.write('%s %d %s %s %s %0.4e %0.4e\n'%(chrom, pos, sid, nt1, nt2, raw_beta, ldpred_beta))
def ldpred_inf_genomewide(data_file=None, ld_radius = None, ld_dict=None, out_file_prefix=None, n=None, h2=None, verbose=False): """ Calculate LDpred for a genome """ df = h5py.File(data_file,'r') has_phenotypes=False if 'y' in df: 'Validation phenotypes found.' y = df['y'][...] # Phenotype num_individs = len(y) risk_scores_pval_derived = sp.zeros(num_individs) has_phenotypes=True ld_scores_dict = ld_dict['ld_scores_dict'] chrom_ref_ld_mats = ld_dict['chrom_ref_ld_mats'] print('Applying LDpred-inf with LD radius: %d' % ld_radius) results_dict = {} cord_data_g = df['cord_data'] #Calculating genome-wide heritability using LD score regression, and partition heritability by chromsomes herit_dict = ld.get_chromosome_herits(cord_data_g, ld_scores_dict, n, h2=h2) if out_file_prefix: #Preparing output files raw_effect_sizes = [] ldpred_effect_sizes = [] sids = [] chromosomes = [] positions = [] nts = [] for chrom_str in util.chromosomes_list: if chrom_str in cord_data_g: g = cord_data_g[chrom_str] if has_phenotypes: if 'raw_snps_val' in g: raw_snps = g['raw_snps_val'][...] else: raw_snps = g['raw_snps_ref'][...] snp_stds = g['snp_stds_ref'][...] pval_derived_betas = g['betas'][...] if out_file_prefix: chromosomes.extend([chrom_str]*len(pval_derived_betas)) positions.extend(g['positions'][...]) sids_arr = (g['sids'][...]).astype(util.sids_u_dtype) sids.extend(sids_arr) raw_effect_sizes.extend(g['log_odds'][...]) nts_arr = (g['nts'][...]).astype(util.nts_u_dtype) nts.extend(nts_arr) h2_chrom = herit_dict[chrom_str] updated_betas = ldpred_inf(pval_derived_betas, genotypes=None, reference_ld_mats=chrom_ref_ld_mats[chrom_str], h2=h2_chrom, n=n, ld_window_size=2*ld_radius, verbose=False) print('Calculating scores for Chromosome %s'%((chrom_str.split('_'))[1])) updated_betas = updated_betas / (snp_stds.flatten()) ldpred_effect_sizes.extend(updated_betas) if has_phenotypes: prs = sp.dot(updated_betas, raw_snps) risk_scores_pval_derived += prs corr = sp.corrcoef(y, prs)[0, 1] r2 = corr ** 2 print('The R2 prediction accuracy of PRS using %s was: %0.4f' %(chrom_str, r2)) if has_phenotypes: num_indivs = len(y) results_dict['y']=y results_dict['risk_scores_pd']=risk_scores_pval_derived print('Prediction accuracy was assessed using %d individuals.'%(num_indivs)) corr = sp.corrcoef(y, risk_scores_pval_derived)[0, 1] r2 = corr ** 2 results_dict['r2_pd']=r2 print('The R2 prediction accuracy (observed scale) for the whole genome was: %0.4f (%0.6f)' % (r2, ((1-r2)**2)/num_indivs)) if corr<0: risk_scores_pval_derived = -1* risk_scores_pval_derived auc = util.calc_auc(y,risk_scores_pval_derived) print('AUC for the whole genome was: %0.4f'%auc) #Now calibration denominator = sp.dot(risk_scores_pval_derived.T, risk_scores_pval_derived) y_norm = (y-sp.mean(y))/sp.std(y) numerator = sp.dot(risk_scores_pval_derived.T, y_norm) regression_slope = (numerator / denominator) print('The slope for predictions with P-value derived effects is: %0.4f'%regression_slope) results_dict['slope_pd']=regression_slope weights_out_file = '%s.txt'%(out_file_prefix) with open(weights_out_file,'w') as f: f.write('chrom pos sid nt1 nt2 raw_beta ldpred_inf_beta\n') for chrom, pos, sid, nt, raw_beta, ldpred_beta in zip(chromosomes, positions, sids, nts, raw_effect_sizes, ldpred_effect_sizes): nt1,nt2 = nt[0],nt[1] f.write('%s %d %s %s %s %0.4e %0.4e\n'%(chrom, pos, sid, nt1, nt2, raw_beta, ldpred_beta))