| File | Description | Size |
|---|---|---|
| pcg_and_lncrna.tsv | Ensembl gene IDs for the subset of genes that are protein-coding or lncRNA. We include all genes in the Ensembl annotations in Pantry phenotyping and genetic analyses, and filter to this set of protein-coding and lncRNA genes for the Pantry and LaDDR study analyses. The aggregated results below have not been filtered, though LaDDR phenotyping and genetic analyses do only include protein-coding and lncRNA genes. | 3.0 MB |
| geuvadis.separate_kdp.assoc.tsv.gz |
Top cis-QTL associations per gene
(*.cis_qtl.txt.gz), not necessarily significant,
for each of six knowledge-driven modalities for Geuvadis.
|
7.7 MB |
| geuvadis.ddp.assoc.tsv.gz | Top cis-QTL associations per gene (not necessarily significant) for data-driven phenotypes for Geuvadis. | 2.5 MB |
| geuvadis.separate_kdp.qtls.tsv.gz |
Conditionally independent cis-QTLs
(*.cis_independent_qtl.txt.gz) mapped separately
for each of six knowledge-driven modalities for Geuvadis.
|
2.9 MB |
| geuvadis.cross_modality_kdp.qtls.tsv.gz | cis-QTLs conditionally independent across six knowledge-driven modalities for Geuvadis. | 1.6 MB |
| geuvadis.cross_modality_hybrid.qtls.tsv.gz | cis-QTLs conditionally independent across six knowledge-driven modalities plus residual data-driven phenotypes for Geuvadis. | 2.2 MB |
| geuvadis.ddp.qtls.tsv.gz | Conditionally independent cis-QTLs for data-driven phenotypes for Geuvadis. | 1.9 MB |
| geuvadis.kdp_rddp.hsq.tsv.gz | TWAS transcriptomic model summary table for six knowledge-driven modalities plus residual data-driven phenotypes for Geuvadis. | 2.4 MB |
| geuvadis.ddp.hsq.tsv.gz | TWAS transcriptomic model summary table for data-driven phenotypes for Geuvadis. | 1.3 MB |
| geuvadis.kdp_rddp.twas_hits.tsv.gz | TWAS hits for six knowledge-driven modalities plus residual data-driven phenotypes against all traits for Geuvadis. | 3.6 MB |
| geuvadis.ddp.twas_hits.tsv.gz | TWAS hits for data-driven phenotypes against all traits for Geuvadis. | 1.9 MB |
| gtex.separate_kdp.assoc.tsv.gz |
Top cis-QTL associations per gene
(*.cis_qtl.txt.gz), not necessarily significant,
for six knowledge-driven modalities for 49 GTEx tissues.
|
464 MB |
| gtex.ddp.assoc.tsv.gz | Top cis-QTL associations per gene (not necessarily significant) for data-driven phenotypes for 49 GTEx tissues. | 141 MB |
| gtex.separate_kdp.qtls.tsv.gz |
Conditionally independent cis-QTLs
(*.cis_independent_qtl.txt.gz) mapped separately
for each of six knowledge-driven modalities for 49 GTEx
tissues.
|
106 MB |
| gtex.cross_modality_kdp.qtls.tsv.gz | cis-QTLs conditionally independent across six knowledge-driven modalities for 49 GTEx tissues. | 60 MB |
| gtex.cross_modality_hybrid.qtls.tsv.gz | cis-QTLs conditionally independent across six knowledge-driven modalities plus residual data-driven phenotypes for 49 GTEx tissues. | 130 MB |
| gtex.ddp.qtls.tsv.gz | Conditionally independent cis-QTLs for data-driven phenotypes for 49 GTEx tissues. | 99 MB |
| gtex.kdp_rddp.hsq.tsv.gz | TWAS transcriptomic model summary table for six knowledge-driven modalities plus residual data-driven phenotypes for 49 GTEx tissues. | 121 MB |
| gtex.ddp.hsq.tsv.gz | TWAS transcriptomic model summary table for data-driven phenotypes for 49 GTEx tissues. | 83 MB |
| gtex.kdp_rddp.twas_hits.tsv.gz | TWAS hits for six knowledge-driven modalities plus residual data-driven phenotypes against all traits for 49 GTEx tissues. | 176 MB |
| gtex.ddp.twas_hits.tsv.gz | TWAS hits for data-driven phenotypes against all traits for 49 GTEx tissues. | 100 MB |
RNA phenotypes were quantified for Geuvadis and 49 GTEx tissues. The
tables are in BED format, with the first three columns specifying
the TSS coordinate of each phenotype's gene, i.e., the input format
for tensorQTL. They have been normalized by
quantile-normalizing samples to the average empirical distribution,
followed by rank-based inverse-normal transformation per phenotype.
For GTEx tissues, we included only samples with genotypes and
converted sample IDs to individual IDs to match the genotypes.
| File | Modality |
|---|
For each tissue-modality RNA phenotype table, the covariates include
the first 20 principal components (PCs) of the phenotype table and
the first 5 PCs of the genotype alternative allele count matrix. We
include two formats for each covariate table, one compatible with
tensorQTL (*.covar.tsv), and one in PLINK format for
compatibility with FUSION (*.covar.plink.tsv).
We include covariates and cis-QTL results for a concatenation of
phenotypes from six knowledge-driven modalities
(cross_modality_KDP) and for a concatenation of
phenotypes from the six knowledge-driven modalities plus residual
data-driven phenotypes (cross_modality_hybrid).
Covariates for these are computed from the
combined phenotype tables, and are not concatenations
of each modality's covariates. We used them for cross-modality
mapping of cis-QTLs, but not for xTWAS.
| File | Modality | Format |
|---|
Output files from tensorQTL run in cis mode
(*.cis_qtl.txt.gz, top association per gene, even if
not significant) and cis_independent mode
(*.cis_independent_qtl.txt.gz, all
conditionally-independent cis-QTLs). Files for
cross_modality_kdp are the result of running tensorQTL
on phenotype tables and group files containing all six
knowledge-driven modalities, so that cis-QTLs are conditionally
independent across modalities per gene (cross-modality mapping), and
phenotypes used for cross_modality_hybrid included the
six knowledge-driven modalities plus residual data-driven
phenotypes.
| File | Modality |
|---|
A set of TWAS transcriptomic models was generated for each
tissue-modality pair using FUSION. The weights and summary files are
provided in compressed archives, and the outputs of the FUSION
script FUSION.profile_wgt.R, i.e. tables giving
cis-heritability and model stats per phenotype, are also included
separately (*.twas_weights.profile).
| File | Modality |
|---|
TWAS associations were calculated for each set of models against
summary stats for each of
114 GWAS traits previously harmonized to GTEx v8 variant
reference. Outputs of FUSION.assoc_test.R were concatenated
across chromosomes to get one table per trait-tissue-modality
combination. Tables are provided in compressed archives per trait
and include, for each tissue, results for the six knowledge-driven
modalities, residual data-driven phenotypes, and full data-driven
phenotypes. It is recommended to analyze either the knowledge-driven
and residual data-driven phenotypes together or just the full
data-driven phenotypes.