GWAS Report

Overview

These are the GWAS results for job run_all with 337.475 samples, run for the phenotype Current .

Methods

The association between Current and each variant with a minor allele count \(\ge\) 30 and an imputation quality metric \(r^2 \ge\) 0.8 was tested using the logistic regression model:

       Current ~ marker + PC1 + PC2 + PC3 + PC4 + PC5 + PC6 + PC7 + PC8 + PC9 + PC10 + PC11 + PC12 + PC13 + PC14 + PC15 + PC16 + PC17 + PC18 + PC19 + PC20 + array + sex + age

The association tests were performed using plink2/2.00-alpha-2-20190429, including a total of 19.064.366 markers (after filtering). Markers having a Hardy-Weinberg equilibrium exact test p-value below 1.0e-6 were filtered out.
861 significant markers have been found. The genomic inflation factor was \(\lambda =\) 1.1111664.

GWAS parameters

The plink2 program was run with the following parameters:

• Minor allele count cutoff: 30
• Minor allele frequency cutoff: 0.00
• Maximum variance inflation factor: 50
• Sample missing call rate cutoff: 0.1
• Variant missing call rate cutoff: 0.1
• Minimum imputation quality: 0.8
• Maximum imputation quality: 2.0
• Hardy-Weinberg exact test p-value threshold: 1.0e-6

Filtered samples and variants

The table displays the number of samples/variants that have been removed due to different filters:

• SGENO: Samples removed due to missing genotype. Cutoff: 0.1
• VGENO: Variants removed due to missing genotype. Cutoff: 0.1
• VHWE: Variants removed after Hardy-Weinberg exact test. P-value cutoff: 1.0e-6
• VFREQ: Variants removed due to allele frequency threshold(s). Cutoffs: mac = 30 ; maf = 0.00
• VIMP: Variants removed due to imputation quality filter (MACH-R2). Cutoff: 0.8 - 2.0

Genome-wide significant markers

• Markers with \(p \le 5 \cdot 10^{-8}\) are displayed, no matter what the corresponding \(\beta\)-value is.
• Spreadsheet with significant markers (only available if copied separately)
• ID: link to Phenoscanner
  
• POS: link to marker position at UCSC Genome Brower
• genes: link to nearest gene (UCSC Genome Brower)
• Note : A1_FREQ, BETA, SE, and P are not available if only one significant marker resides on a whole chromosome.

GCTA-COJO results

GCTA cojo was not conducted.

CLUMP results

Plink clumping was not conducted.

Unpruned significant markers

QQ-plot for \(p\)-values

Manhattan plot

SNP density plot

Histogram of \(\beta\) values

Kernel density plot of \(\beta\) values

Regression diagnostics

A regression was conducted using the linear model

       Current ~ PC1 + PC2 + PC3 + PC4 + PC5 + PC6 + PC7 + PC8 + PC9 + PC10 + PC11 + PC12 + PC13 + PC14 + PC15 + PC16 + PC17 + PC18 + PC19 + PC20 + array + sex + age

(Note that no marker genotype has been included). The dataframe used as regression input is stored in run_all_Current_regression_frame.RData, while the residuals are in run_all_Current_regression_resid.RData.

Regression coefficients obtained with glm():

Variance inflation factors

Variance inflation factors (VIF) are calculated in order to discover multicollinearity. VIF can be obtained by regressing a single independent variable against all other independent variables. As a rule of thumb, no variance inflation factor should be bigger than 10. Otherwise, highly correlated variables should be removed from the model.

Technical information

• GWAS workfolder /castor/project/proj/GWAS_TEST/run_all
• Parameter file: /castor/project/proj/GWAS_TEST/run_all/run_all_gwas_params.txt