Conditional analysis results

This page has last been updated for R14.

Conditional analysis algorithm

All regions with genome-wide significant results were subjected to Regenie conditional analysis (i.e. exactly the same regions that are also finemapped). The most significant variant is added as a covariate and all the other variants in the region are tested for association, conditionally on the top variant. This process is continued, adding new variants to the list of covariates, until there are no variants with conditional p-value < 1e-6. The same covariates were used as in the main FinnGen core GWAS analysis.

Independent snps file

All independent top SNPs in the region tested. CHR_POS_REF_ALT is the variant id for the most significant SNP in the region in unconditional analysis.

independent.snps file structure

Column
Description

VARIANT

Top SNP of the iteration

BETA

unconditional beta of top SNP

SE

unconditional standard error of top SNP

MLOG10P

unconditional -log10 p-value of top SNP

BETA_cond

conditional beta of top SNP

SE_cond

conditional standard error of top SNP

MLOG10P_cond

conditional -log10 p-value of top SNP

VARIANT_cond

list of variants that were used as conditioning variants

Conditional file

Conditional results for all variants in each iteration in the above summary file. #ITER corresponds to each iteration in the independent snps file. CHR_POS_REF_ALT is the variant id for the most significant SNP in the region in unconditional analysis.

conditional file structure

Column
Description

CHROM

chromosome of tested variant

GENPOS

position of tested variant

ID

CHROM_POS_REF_ALT of tested variant

ALLELE0

Reference allele

ALLELE1

Effect allele

A1FREQ

Effect allele frequency

INFO

Imputation INFO score (IMPUTE method formula as output by REGENIE)

N

sample size

TEST

test performed

BETA

condititional beta

SE

conditional standard error

CHISQ

chisq statistic of association

LOG10P

-log10 p-value

EXTRA

Additional notes by Regenie

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