Data description

File naming pattern and file structure

Summary association statistics

GWAS summary statistics (tab-delimited, bgzipped, genome build 38, tabix index files included) are named as {endpoint}.gz. For example, endpoint I9_CHD has I9_CHD.gz and I9_CHD.gz.tbi.

To learn more about the methods used, see section GWAS.

The {endpoint}.gz have the following structure:

Column name	Description
#chrom	chromosome on build GRCh38 (1-23)
pos	position in base pairs on build GRCh38
ref	reference allele
alt	alternative allele (effect allele)
rsids	variant identifier
nearest_genes	nearest gene(s) (comma separated) from variant
pval	p-value from regenie
mlogp	-log10(p-value)
beta	effect size (log(OR) scale) estimated with regenie for the alternative allele
sebeta	standard error of effect size estimated with regenie
af_alt	alternative (effect) allele frequency
af_alt_cases	alternative (effect) allele frequency among cases
af_alt_controls	alternative (effect) allele frequency among controls

Fine-mapping results

Two fine-mapping methods were used:

• SuSiE

• FINEMAP

Fine-mapping results are tab-delimited and bgzipped.

SuSiE results have the following filename pattern:

• {endpoint}.SUSIE.cred.bgz

• {endpoint}.SUSIE.cred_99.bgz

• {endpoint}.SUSIE.snp.bgz

FINEMAP results have the following filename pattern:

• {endpoint}.FINEMAP.config.bgz

• {endpoint}.FINEMAP.region.bgz

• {endpoint}.FINEMAP.snp.bgz

To learn more about the methods used, see section Fine-mapping.

{endpoint}.SUSIE.cred.bgz contain credible set summaries from SuSiE fine-mapping for all genome-wide significant regions. {endpoint}.SUSIE.cred_99.bgz contain the 99% credible set summaries while the default is 95%. They have the following structure:

Column name	Description
Column name	Description
trait	phenotype
region	region for which the fine-mapping was run
cs	running number for independent credible sets in a region
cs_log10bf	Log10 bayes factor of comparing the solution of this model (cs independent credible sets) to cs -1 credible sets
cs_avg_r2	Average correlation R2 between variants in the credible set
cs_min_r2	minimum r2 between variants in the credible set
low_purity
cs_size	how many snps does this credible set contain

{endpoint}.SUSIE.snp.bgz contain variant summaries with credible set information and have the following structure:

Column name	Description
trait	endpoint name
region	chr:start-end
v	variant identifier
rsid	rs variant identifier
chromosome	chromosome on build GRCh38 (1-22, X)
position	position in base pairs on build GRCh38
allele1	reference allele
allele2	alternative allele (effect allele)
maf	minor allele frequency
beta	effect size GWAS
se	standard error GWAS
p	p-value GWAS
mean	posterior expectation of true effect size
sd	posterior standard deviation of true effect size
prob	posterior probability of association
cs	identifier of 95% credible set (-1 = variant is not part of credible set)
lead_r2	r2 value to a lead variant (the one with maximum PIP) in a credible set
alphax	posterior inclusion probability for the x-th single effect (x := 1..L where L is the number of single effects (causal variants) specified; default: L = 10)

{endpoint}.FINEMAP.config.bgz contain summary fine-mapping variant configurations from FINEMAP method and have the following structure:

Column name	Description
Column name	Description
trait	phenotype
region	region for which the fine-mapping was run
rank	rank of this configuration within a region
config	causal variants in this configuration
prob	probability across all n independent signal configurations
log10bf	log10 bayes factor for this configuration
odds	odds of this configuration
k	how many independent signals in this configuration
prob_norm_k	probability of this configuration within k independent signals solution
h2	snp heritability of this solution
h2_0.95CI	95% confidence interval limits of snp heritability of this solution
mean	marginalized shrinkage estimates of the posterior effect size mean
sd	marginalized shrinkage estimates of the posterior effect standard deviation

{endpoint}.FINEMAP.region.bgz contain summary statistics on number of independent signals in each region and have the following structure:

Column name	Description
Column name	Description
trait	phenotype
region	region for which the fine-mapping was run
h2g	heritability of this region
h2g_sd	standard deviation of snp heritability of this region
h2g_lower95	lower limit of 95% CI for snp heritability
h2g_upper95	upper limit of 95% CI for snp heritability
log10bf	log bayes factor compared against null (no signals in the region)
prob_xSNP	columns for probabilities of different number of independent signals
expectedvalue	expectation (average) of the number of signals

{endpoint}.FINEMAP.snp.bgz has summary statistics of variants and into what credible set they may belong to. Columns:

Column name	Description
Column name	Description
trait	phenotype
region	region for which the fine-mapping was run
v	variant
index	running index
rsid	rs variant identifier
chromosome	chromosome
position	position
allele1	reference allele
allele2	alternative allele
maf	alternative allele frequency
beta	original marginal effect size
se	original standard error
z	original zscore
prob	post inclusion probability
log10bf	log10 bayes factor
mean	marginalized shrinkage estimates of the posterior effect size mean
sd	marginalized shrinkage estimates of the posterior effect standard deviation
mean_incl	conditional estimates of the posterior effect size mean
sd_incl	conditional estimates of the posterior effect size standard deviation
p	original p-value
csx	credible set index for given number of causal variants x

Variant annotation

The variant annotation has measures (HWE, INFO, ...) listed per batch.

Gene-based burden test results of LoF variants

Loss of function (LoF) variants were generated from vcf files with VEP (https://github.com/Ensembl/ensembl-vep). LoF variants are defined as having consequences in the list [frameshift_variant,splice_donor_variant,stop_gained,splice_acceptor_variant]. Also, a max_maf (0.01) and minimum info score (0.8) filters are applied. Then a bgen file is formed by filtering chromosome based vcfs and merging them into a single file, allowing us to run the whole analysis in one data set. Then the bgen is passed to step 2 of regenie in burden mode, which uses the nulls from the standard GWAS runs.

## File structure

### Data

| File | Description |

|---|---|

|finngen_R8_lof_txt.gz | Merged results, sorted by mglop. |

|finngen_R8_lof_variants.txt | A tsv file with variant/geno/lof data used in the run. |

|finngen_R8_lof_sig_hits.txt | A summary of the results only including hits for mlogp > 3 and sorted by difference between mlogp and max(mlogp) of its variants.|

### Documentation

| File | Description |

|---|---|

|finngen_R8_lof.log| Merged logs of all runs.|