# Per-phenotype files

##### note

**Please note that p-values are now stored as -log10 p-values to avoid underflow.**

## #

OverviewThe data are released in 7,228 flat files, one for each phenotype, and a corresponding tabix index file for each. These files are available on Amazon AWS (for large-scale analysis, we recommend using the Hail format files on Google Cloud).

The files are named with respect to their `trait_type`

, `phenocode`

, and a combination of `pheno_sex`

, `coding`

, or `modifier`

. To find a specific phenotype, we suggest looking in the phenotype manifest (Google Sheets) (available for download on Amazon). Search for your phenotype(s) of interest and use the paths indicated to download the summary statistics. A description of fields in the manifest can be found here.

The per-phenotype files are summary statistics files containing meta-analyzed and single-ancestry GWAS results. We especially highlight the `low_confidence`

fields, which includes some (non-exhaustive) basic quality control filters (see below). These files each have 28,987,534 variants, but note that not all populations will have data for each variant.

Finally, the variant manifest file includes information on each variant in the dataset and has the same number of rows as each per-phenotype file. We highlight the `high_quality`

column which represents variants that are PASS variants in gnomAD and have consistent frequencies with each population in gnomAD (AFR, AMR, EAS, and EUR frequencies are within 2-fold or chi-squared p-value of the difference > 1e-6).

## #

Phenotype manifest filePan-UK Biobank phenotype manifest (Google Sheets) (download on Amazon)

### #

Phenotype ID fieldsThe first 5 fields are guaranteed to be unique.

`trait_type`

: One of the following: continuous, biomarkers, prescriptions, icd10, phecode, categorical

`phenocode`

: The code for the phenotype (for continuous, biomarkers, and categorical traits, this corresponds to the field ID as described by UKB, e.g. 21001 for BMI)

`pheno_sex`

: Indicating whether the phenotype was run for both sexes (`pheno_sex`

="both_sexes") or in just females (`pheno_sex`

="females") or males (`pheno_sex`

="males"). In 0.1, this is only differentiated for phecodes.

`coding`

: For categorical variables, this corresponds to the coding that was used (e.g. coding 2 for field 1747). For all other `trait_type`

s, this field is blank.

`modifier`

: Refers to any miscellaneous downstream modifications of the phenotype (e.g. `irnt`

for inverse-rank normal transformation). If the phenotype is updated, this field can be used to denote the update (e.g. the particular wave of COVID-19 data used).

`description`

: A shorter description of the phenotype (for continuous, biomarkers, and categorical variables, corresponds to the Description on the showcase). For phecodes, this is the "description" column in the phecodes definition file.

`description_more`

: A longer description of the phenotype (for continuous and categorical variables, corresponds to the Notes page on the showcase).

`coding_description`

: For categorical variables, a description of the particular coding that was used (the Meaning column on the showcase page for that coding).

`category`

: A categorization of the phenotype. For continuous, biomarkers, and categorical traits, this corresponds to the Category at the top of the showcase page. For ICD codes, this corresponds to the Chapter of the ICD code; for phecodes, this is the "group" column in the phecodes definition file; for prescriptions, this corresponds to a semi-manual categorization of prescription drugs.

`in_max_independent_set`

: If the phenotype is in our maximally indepdent set. This set of relatively uncorrelated phenotypes was constructed using a pairwise phenotypic correlation matrix of phenotypes with ancestries passing all QC filters (released here via `make_pairwise_ht`

). Of all phenotype pairs, we retained any with a pairwise correlation $r < 0.1$. For pairs with $r > 0.1$ , we used `hl.maximal_independent_set`

to identify indendent phenotypes for retention, imposing a tiebreaker of higher case count (or higher sample size for continuous phenotypes), producing 195 independent phenotypes.

### #

Case and ancestry fields##### note

If a trait is quantitative (`trait_type`

is "continuous" or "biomarkers"), all samples are considered to be "cases". Thus, the number of cases is equivalent to the number of samples.

`n_cases_full_cohort_both_sexes`

: Number of cases (or individuals phenotyped for quantitative traits) across all ancestry groups, females and males combined. Should be similar to the sum of per-ancestry n_cases for relevant ancestries, but may include ancestry outliers and samples that failed QC.

`n_cases_full_cohort_females`

: Number of female cases (or individuals phenotyped for quantitative traits) across all ancestry groups. May include ancestry outliers and samples that failed QC.

`n_cases_full_cohort_males`

: Number of male cases (or individuals phenotyped for quantitative traits) across all ancestry groups. May include ancestry outliers and samples that failed QC.

`n_cases_hq_cohort_both_sexes `

: Number of cases (or individuals phenotyped for quantitative traits) across ancestry groups passing stringent phenotype QC (see `pops_pass_qc`

), females and males combined. Should be similar to the sum of per-ancestry n_cases for relevant ancestries, but may include ancestry outliers and samples that failed QC.

`n_cases_hq_cohort_females`

: Number of female cases (or individuals phenotyped for quantitative traits) across ancestry groups passing stringent phenotype QC (see `pops_pass_qc`

). May include ancestry outliers and samples that failed QC.

`n_cases_hq_cohort_males`

: Number of male cases (or individuals phenotyped for quantitative traits) across ancestry groups passing stringent phenotype QC (see `pops_pass_qc`

). May include ancestry outliers and samples that failed QC.

`pops`

: Comma-delimited list of ancestry codes for which this phenotypes was GWASed.

`num_pops`

: Number of ancestry groups for which this phenotype was GWASed.

`pops_pass_qc`

: Comma-delimited list of ancestry codes for which this phenotype passes QC (see quality control, heritability manifest, and `phenotype_qc_{pop}`

field).

`num_pops_pass_qc`

: Number of ancestry groups for which this phenotype passes QC.

### #

Population-specific fields##### note

The variable `pop`

is a placeholder for a 3-letter ancestry code. For example, `n_cases_AFR`

is the number of cases with AFR ancestry.

##### note

If a trait is quantitative (`trait_type`

is "continuous" or "biomarkers"), all samples are considered to be "cases". Thus, the number of cases is equivalent to the number of samples.

##### note

The final heritability estimates for non-EUR traits use a randomized Haseman Elston estimator on genotype data with 5 MAF and 5 LD bins and 50 random variables (`rhemc_25bin_50rv`

), while for computational tractability for EUR we use S-LDSC with 5 MAF and 5 LD bins. In the phenotype manifest we provide estimates with the `sldsc_25bin_`

prefix for EUR and the `rhemc_25bin_50rv_`

prefix for other ancestry groups. See the heritability manifest for all heritability estimates computed, including `sldsc_25bin`

for all ancestry groups.

`n_cases_{pop}`

: Number of cases (or individuals phenotyped for quantitative traits) with `pop`

ancestry in the GWAS analysis. Excludes ancestry outliers and samples that failed QC.

`n_controls_{pop}`

: Number of controls with `pop`

ancestry in the GWAS analysis. Excludes ancestry outliers and samples that failed QC.

`{rhemc_25bin_50rv/sldsc_25bin}_h2_observed_{pop}`

: Observed scale heritability estimates using 25 MAF/LD bins with RHEmc (non-EUR) or SLDSC (EUR).

`{rhemc_25bin_50rv/sldsc_25bin}_h2_observed_se_{pop}`

: Observed scale heritability standard error using 25 MAF/LD bins with RHEmc (non-EUR) or SLDSC (EUR).

`{rhemc_25bin_50rv/sldsc_25bin}_h2_liability_{pop}`

: Libaility scale heritability estimates using 25 MAF/LD bins with RHEmc (non-EUR) or SLDSC (EUR), transformed using per-ancestry in-sample prevalence.

`{rhemc_25bin_50rv/sldsc_25bin}_h2_liability_se_{pop}`

: Liability scale heritability standard error using 25 MAF/LD bins with RHEmc (non-EUR) or SLDSC (EUR), transformed using per-ancestry in-sample prevalence.

`{rhemc_25bin_50rv/sldsc_25bin}_h2_z_{pop}`

: Heritability Z-scores (for test of h2 > 0) using per-ancestry-trait pair h2 estimates and standard errors.

`lambda_gc_{pop}`

: The genomic control (lambda GC) calculated from the summary statistics for `pop`

with low-confidence statistics removed and only considering high-quality variants.

`phenotype_qc_{pop}`

: Phenotype QC outcome for each ancestry-trait pair. Filters are described in the heritability manifest in more detail. Filters are applied sequentially; this field specifies either PASS or the reason for failure.

### #

File information##### note

For each field in this section there also exists a field with the suffix `_tabix`

, which contains the equivalent information for the tabix file. For instance, `filename_tabix`

contains the name of the tabix file.

`filename`

: Name of summary statistics file.

`aws_link`

: Link to download summary statistics file from Amazon AWS.

## #

Heritability manifest filePan-UK Biobank heritability manifest (Google Sheets) (download on Amazon)

### #

Phenotype ID fields##### note

The variable `pop`

is a placeholder for a 3-letter ancestry code. For example, `n_cases_AFR`

is the number of cases with AFR ancestry.

##### note

Unlike the phenotype manifest, the heritability manifest provides a row for each ancestry group. Thus the *first 6 fields* are guarenteed to be unique.

`trait_type`

: One of the following: continuous, biomarkers, prescriptions, icd10, phecode, categorical

`phenocode`

: The code for the phenotype (for continuous, biomarkers, and categorical traits, this corresponds to the field ID as described by UKB, e.g. 21001 for BMI)

`pheno_sex`

: Indicating whether the phenotype was run for both sexes (`pheno_sex`

="both_sexes") or in just females (`pheno_sex`

="females") or males (`pheno_sex`

="males"). In 0.1, this is only differentiated for phecodes.

`coding`

: For categorical variables, this corresponds to the coding that was used (e.g. coding 2 for field 1747). For all other `trait_type`

s, this field is blank.

`modifier`

: Refers to any miscellaneous downstream modifications of the phenotype (e.g. `irnt`

for inverse-rank normal transformation). If the phenotype is updated, this field can be used to denote the update (e.g. the particular wave of COVID-19 data used).

`heritability.pop`

: Ancestry group.

### #

Heritability methods##### note

Each of the below **Heritability methods** subheadings contain (a subset of) the fields described in the **Heritability estimates** section (except for SAIGE estimates, which are provided as-is).

##### note

`rhemc_8bin`

was run only for a subset of traits in the EUR ancestry group and for all traits across non-EUR ancestry groups. `rhemc_25bin`

and `rhemc_25bin_50rv`

were run only for non-EUR ancestry groups due to computational complexity. `sldsc_25bin`

and `ldsc`

were run for all ancestry-trait pairs.

##### note

`heritability.estimates.ldsc.*`

: Univariate LD score regression

`heritability.estimates.sldsc_25bin.*`

: Stratified LD score regression, 5 LD score bins x 5 MAF bins

`heritability.estimates.rhemc_25bin.*`

: RHEmc (HE regression), 5 LD score bins x 5 MAF bins

`heritability.estimates.rhemc_8bin.*`

: RHEmc (HE regression), 4 LD score bins x 2 MAF bins

`heritability.estimates.rhemc_25bin_50rv.*`

: RHEmc (HE regression), 5 LD score bins x 5 MAF bins; 50 random variables for improved power

`heritability.estimates.saige`

: The heritability as estimated by SAIGE: note that this is likely not well-calibrated for binary traits, or traits with high heritabilities.

`heritability.estimates.final.*`

: Final estimates; 25 bin SLDSC for EUR and 25 bin, 50 RV RHEmc for non-EUR (these are also present in the full manifest)

### #

Heritability estimates`heritability.estimates.*.h2_observed`

: Observed scale heritability point estimates.

`heritability.estimates.*.h2_liability`

: Liability scale results, using per-ancestry in-sample prevalence.

`heritability.estimates.*.h2_z`

: Heritability Z-scores for test of $h^2 > 0$.

`heritability.estimates.*.intercept`

: LDSC intercept. Only present for `ldsc`

and `sldsc_25bin`

`heritability.estimates.*.ratio`

: LDSC ratio, given by $\frac{intercept-1}{mean \chi^2 -1}$ as a measure of the proportion of test statistic inflation not attributed to polygenicity. Only present for `ldsc`

and `sldsc_25bin`

.

### #

QC flags##### note

The below QC flags were applied sequentially. See quality control for more information.

`heritability.N_ancestry_QC_pass`

: Number of ancestries passing all QC per trait.

`heritability.qcflags.GWAS_run`

: if the GWAS was performed for the ancestry-trait pair

`heritability.qcflags.ancestry_reasonable_n`

: if the ancestry has a reasonable sample size; has the effect of removing AMR

`heritability.qcflags.defined_h2`

: if the heritability estimate is non-missing

`heritability.qcflags.significant_z`

: if the ancestry-trait pair shows $h^2$ z-score $> 0$

`heritability.qcflags.in_bounds_h2`

: if, for all ancestries for a given trait, observed-scale heritability estimates $\in (0,1)$

`heritability.qcflags.normal_lambda`

: if, for all ancestries for a given trait, $\lambda_{GC} > 0.9$

`heritability.qcflags.normal_ratio`

: if, for the top three best powered ancestry groups (EUR, CSA, AFR), the S-LDSC ratio, given by $\frac{intercept-1}{mean \chi^2 -1}$, $< 0.3$ or the ratio z-score $< 4$

`heritability.qcflags.EUR_plus_1`

: if the trait passes all above filters in EUR and at least 1 other ancestry group

`heritability.qcflags.pass_all`

: if all QC flags pass for each ancestry-trait pair

## #

Per-phenotype files##### note

All p-values reported in the summary statistics flat files are now stored as log p-values to avoid underflow. This includes the per-ancestry association test p-value as well as `pval_meta`

, `pval_meta_hq`

, `pval_heterogeneity`

, and `pval_heterogeneity_hq`

.

The per-phenotype files are `tsv.bgz`

files are (b)gzipped: they can either be unzipped (`zcat file.tsv.bgz > file.txt`

), or read natively in R (`read_delim(gzfile('file.tsv.bgz'), delim='\t')`

) and Python (`gzip.open('file.tsv.bgz')`

).

Depending on whether a phenotype is quantitative (`trait_type`

is "continuous" or "biomarkers") or binary (`trait_type`

is "prescriptions", "icd10", "phecode" or "categorical"), the number of columns will change due to case/control-stratified statistics for binary phenotypes.

### #

Variant fields`chr`

: Chromosome of the variant.

`pos`

: Position of the variant in GRCh37 coordinates.

`ref`

: Reference allele on the forward strand.

`alt`

: Alternate allele (not necessarily minor allele). Used as effect allele for GWAS.

### #

Meta-analysis fields##### note

All meta-analyses were only performed on variants that were not flagged as `low_confidence`

for a given population. As described below in **Population-specific fields**, per-variant `low_confidence`

status is specific to each ancestry-trait pair.

`af_meta`

: Alternate allele frequency from meta-analysis across populations for which this phenotype was GWASed. **NOTE: This field only appears in files for quantitative phenotypes.**

`af_cases_meta`

: Alternate allele frequency in cases from meta-analysis across populations for which this phenotype was GWASed. **NOTE: This field only appears in files for binary phenotypes.**

`af_controls_meta`

: Alternate allele frequency in controls from meta-analysis across populations for which this phenotype was GWASed. **NOTE: This field only appears in files for binary phenotypes.**

`beta_meta`

: Estimated effect size of alternate allele from meta-analysis across populations for which this phenotype was GWASed.

`se_meta`

: Estimated standard error of `beta_meta`

.

`neglog10_pval_meta`

: -log10 p-value of `beta_meta`

significance test.

`neglog10_pval_heterogeneity`

: -log10 p-value from heterogeneity test of meta-analysis.

### #

High quality meta-analysis fields##### note

These fields are only present in flat files for traits that have any QC-pass ancestries. As a requirement for passing QC a trait must pass in EUR and at least 1 other ancestry (see quality control).

##### note

As above, meta-analyses were only performed on variants that were not flagged as `low_confidence`

for a given population. As described below in **Population-specific fields**, per-variant `low_confidence`

status is specific to each ancestry-trait pair.

`af_meta_hq`

: Alternate allele frequency from meta-analysis across populations for which this phenotype passes all QC filters. **NOTE: This field only appears in files for quantitative phenotypes.**

`af_cases_meta_hq`

: Alternate allele frequency in cases from meta-analysis across populations for which this phenotype passes all QC filters. **NOTE: This field only appears in files for binary phenotypes.**

`af_controls_meta_hq`

: Alternate allele frequency in controls from meta-analysis across populations for which this phenotype passes all QC filters. **NOTE: This field only appears in files for binary phenotypes.**

`beta_meta_hq`

: Estimated effect size of alternate allele from meta-analysis across populations for which this phenotype passes all QC filters.

`se_meta_hq`

: Estimated standard error of `beta_meta_hq`

.

`neglog10_pval_meta_hq`

: -log10 p-value of `beta_meta_hq`

significance test.

`neglog10_pval_heterogeneity_hq`

: -log10 p-value from heterogeneity test of meta-analysis.

### #

Population-specific fields##### note

The variable `pop`

used in this section is a placeholder for a 3-letter ancestry code. For example, `af_AFR`

is the alternate allele frequency for AFR samples included in the GWAS of this phenotype.

##### note

An ancestry-specific column is only included in the file if a GWAS was run for that ancestry. For example, a trait that was only GWASed in AMR and CSA samples will only have the fields `af_AMR`

, `af_CSA`

, `beta_AMR`

, `beta_CSA`

, etc.

`af_{pop}`

: Alternate allele frequency for `pop`

samples included in the GWAS of this phenotype. **NOTE: This field only appears in files for quantitative phenotypes.**

`af_cases_{pop}`

: Alternate allele frequency for `pop`

cases included in the GWAS of this phenotype. **NOTE: This field only appears in files for binary phenotypes.**

`af_controls_{pop}`

: Alternate allele frequency for `pop`

controls included in the GWAS of this phenotype. **NOTE: This field only appears in files for binary phenotypes.**

`beta_{pop}`

: Estimated effect size of alternate allele from GWAS of `pop`

samples.

`se_{pop}`

: Estimated standard error of `beta_{pop}`

.

`neglog10_pval_{pop}`

: -log10 p-value of `beta_{pop}`

significance test.

`low_confidence_{pop}`

: Boolean flag indicating low confidence for `pop`

based on the following heuristics:

- Alternate allele count in cases <= 3
- Alternate allele count in controls <= 3
- Minor allele count (cases and controls combined) <= 20

## #

Variant manifest fileVariant manifest (download from Amazon AWS)

### #

Variant fieldsAs in per-phenotype files.

`chr`

: Chromosome of the variant.

`pos`

: Position of the variant in GRCh37 coordinates.

`ref`

: Reference allele on the forward strand.

`alt`

: Alternate allele (not necessarily minor allele). Used as effect allele for GWAS.

`rsid`

: The RSID for the variant (from the BGEN file from UK Biobank).

`varid`

: The variant ID for the variant (from the BGEN file from UK Biobank).

`pass_gnomad_genomes`

: A boolean corresponding to the PASS status in gnomAD (`NA`

if variant is not in gnomAD).

`n_passing_populations`

: The number of populations (max 4: AFR, AMR, EAS, EUR) where the frequency in UKB is less than twice the frequency in gnomAD for the corresponding population (see below), and the p-value of a chi-squared test assessing the difference is > 1e-6.

`high_quality`

: A boolean corresponding to a high-quality variant based on these filters (`pass_gnomad_genomes & n_passing_populations == 4`

).

`nearest_genes`

: The nearest genes for this variant based on Gencode v19.

`info`

: The Info score for this variant (from the `ukb_mfi_chrN_v2.txt`

files).

### #

Population-specific fields##### note

The variable `pop`

used in this section is a placeholder for a 3-letter ancestry code. For example, `af_AFR`

is the alternate allele frequency for AFR individuals in the whole dataset.

`ac_{pop}`

: The alternate allele count for this variant across all individuals in `pop`

. Defined as `af_{pop} * an_{pop}`

.

`af_{pop}`

: The alternate allele frequency for this variant across all individuals in `pop`

. The mean `dosage`

divided by two.

`an_{pop}`

: The alternate allele number for this variant. This is twice the number of `pop`

individuals with a defined genotype at this site.

`gnomad_genomes_ac_{pop}`

: The alternate allele count for this variant in the nearest gnomAD population: AFR, EAS, and AMR are matched as-is, while EUR is matched to the "North-West European" subset of gnomAD (not available for CSA or MID, as these populations are not in gnomAD v2 genomes).

`gnomad_genomes_af_{pop}`

: The alternate allele frequency for this variant in the nearest gnomAD population.

`gnomad_genomes_an_{pop}`

: The alternate allele number for this variant in the nearest gnomAD population. This is twice the number of individuals with a defined genotype at this site.