A C++ tool for Gene-based Analysis with oMniBus, Integrative Tests
- Implements several gene-based test forms (quadratic: weighted sum of Zsq, linear: weighted sum of Z, and maximum Zsq) to aggregate GWAS single-variant summary statistics cross-referenced with variant- or region-based functional annotations
- Calculates annotation-stratified gene-based tests (e.g., TWAS/PrediXcan tests using eSNPs, gene-based tests using only coding variants, and gene-based tests using enhancer-to-target-gene maps), and omnibus tests by combining p-values for each gene
- Inputs: GWAS association summary statistics file (chromosome, position, ref/alt allele, and z-score or beta-hat + se), annotation files, and LD reference panel
- GWAS summary statistics files can be specified via
--gwas my_summary_stats.txt.gz
. Input files must be ordered by chromosome and genomic position, with input fields as shown below:
#CHR POS REF ALT SNP_ID N ZSCORE ANNO
1 721290 C G rs12565286 58663.62 0.86661 Intergenic
1 752566 G A rs3094315 57135 0.5521 Intergenic
1 775659 A G rs2905035 54570 1.12098 Intron:LOC643837
1 777122 A T rs2980319 54570 1.11906 Exon:LOC643837
- The first four fields and
ZSCORE
are required, whileSNP_ID
,ANNO
andN
(effective sample size) are optional. - See
format_gwas_summary_stats.sh
for annotating GWAS summary statistics files usingEPACTS/TabAnno
.
- To compute gene-based tests using regulatory element annotations, specify an annotation bed file with regulatory-element-to-target-gene weights via
--anno-bed my_reg_elems.txt.gz
, formatted
#CHR START END CLASS ELEMENT_ID TARGET_GENES ANNO
chr1 567400 567600 Enhancer chr1:567400:567600 MIB2:4.12|CPTP:2.53|GLTPD1:2.53 .
chr1 568000 568200 Enhancer chr1:568000:568200 ATAD3A:2.75 .
chr1 758600 758800 Enhancer chr1:758600:758800 C1orf170:2.57|PERM1:2.57 .
chr1 769200 769400 Enhancer chr1:769200:769400 C1orf170:3.36|PERM1:3.36 .
-
Association tests for individual regulatory elements is reported in
*.stratified_out.txt
files, and gene-based p-values (aggregating across regulatory elements for each gene) in*.summary_out.txt
files. -
Aggregation Methods for Regulatory Elements. By default, GAMBIT aggregates test statistics across variants in regulatory elements using a weighted sum of single-variant chi-squared statistics (SKAT gene-based test). To instead use weighted ACAT or HMP to combine single-variant p-values, specify
--no-skat
and a p-value combination method via--pcomb
.
- To compute gene-based tests using coding and other variants, GAMBIT relies on the
ANNO
field in GWAS summary statistics and an annotation hierarchy definitions file specified via--anno-defs my_defs.txt
, formatted as below:
#CLASS SUBCLASS ANNO_TERMS
Coding Protein_Altering Nonsynonymous,Start_Loss,Stop_Gain,Stop_Loss,CodonGain,CodonLoss,Frameshift
Coding Splice_Site Essential_Splice_Site,Normal_Splice_Site
Coding Exon_Other Exon,Synonymous
UTR UTR3 Utr3
UTR UTR5 Utr5
-
The
ANNO_TERMS
field specifies a comma-separated list of annotation terms (matching terms from the GWAS summary statistics file'sANNO
field), andCLASS
andSUBCLASS
determine the annotation hierarchy and classes reported in output files. -
Gene-Based Test Output. Test statistics stratified by gene and annotation subclass are provided in
*.stratified_out.txt
files, and gene-based p-values (aggregating across annotation classes for each gene) in*.summary_out.txt
files. -
Variant Aggregation Methods. By default, GAMBIT aggregates test statistics across variants using a weighted sum of single-variant chi-squared statistics (SKAT gene-based test). To instead use weighted ACAT or HMP to combine single-variant p-values, specify
--no-skat
and a p-value combination method via--pcomb
.
- To compute TWAS/PrediXcan gene-based tests using GAMBIT, specify an eWeight file via
--eweights my_eWeights.txt.gz
, formatted
##TISSUE_IDS=0:Adipose_Subcutaneous,1:Adipose_Visceral_Omentum,2:Adrenal_Gland,3:Artery_Aorta
#CHR POS RSID REF ALT BETAS
1 752566 rs3094315 G A C1orf159=3.92e-02@0|UBE2J2=-1.49e-02@0|FAM87B=2.75e-01@1;1.25e-01@2;1.17e-01@3
1 752721 rs3131972 A G LINC00115=1.15e-01@0;1.75e-02@3;4.90e-02@4|RP11-206L10.8=3.21e-02@1
1 754182 rs3131969 A G LINC00115=-2.1e-02@1|RP5-857K21.2=-8.27e-02@2|RP11-206L10.9=-1.11e-01@2
1 760912 rs1048488 C T C1orf159=3.35e-04@0|TTLL10=-1.4e-02@3|FAM87B=1.75e-01@1;1.12e-01@2;9.51e-02@3|SAMD11=-1.27e-02@2
- The
BETAS
field format iseGene_A=Weight_A1@Tissue_A1;Weight_A2@Tissue_A2|eGene_B=Weight_B1@Tissue_B1
, and labels for tissue IDs can be specified in the header. - Subsetting tissues. To restrict analysis to a subset of tissues/cell-types, specify a comma-separated list of tissues following the
--tissues
flag. By default, GAMBIT includes all tissues/cell-types present in the eWeight file. - Tissue Aggregation for Omnibus tests. GAMBIT reports both single-tissue TWAS/PrediXcan analysis results, and omnibus tests results aggregating across all specified tissues/cell-types for each eGene. Omnibus p-values for multi-tissue TWAS/PrediXcan analysis can be calculated in GAMBIT using either 1) the maximum single-tissue test statistic based on the joint distribution of single-tissue statistics, 2) the sum of squared single-tissue z-scores (analogous to SKAT), or 3) PCOMB for ACAT or HMP [default]. Omnibus test method for multi-tissue analysis can be specified via
--tissue-aggreg
(PCOMB
,MinP
,SKAT
, orALL
). P-value combination method can be specified via--pcomb
(ACAT
orHMP
). - Single-tissue and omnibus test output. Gene-based tests and p-values for each eGene-tissue pair are reported in
*.stratified_out.txt
files, and omnibus p-values (aggregating across all tissues for each eGene) in*.summary_out.txt
files.
- To incorporate un-annotated regulatory variants in gene-based analysis, GAMBIT implements a dTSS (distance to Transcription Start Site) weighted gene-based test, which aggregates all single-variant p-values within a specified window from each gene's TSS using weighted ACAT or HMP and assigns higher weight to variants nearer the TSS using an exponential decay function.
- To compute dTSS-weighted gene-based tests, specify a TSS bed file via
--tss-bed my_tss_bed.bed.gz
, fomatted
#CHR START END SYMBOL GENE GENE_ANNO
1 11868 11869 DDX11L1 ENSG00000223972 transcribed_unprocessed_pseudogene
1 62947 62948 OR4G11P ENSG00000240361 transcribed_unprocessed_pseudogene
1 69090 69091 OR4F5 ENSG00000186092 protein_coding
1 131024 131025 CICP27 ENSG00000233750 processed_pseudogene
- Window size. The window size for dTSS-weighted gene-based tests can be modified by specifying
--tss-window BASEPAIRS
(500 Kbp by default). - dTSS decay function. The relative weight assigned to variants nearer/farther from the TSS can be modified by specifying
--tss-alpha ALPHA
, where alpha=0 implies all variants receive equal weight, and larger values confer more weight to variants nearer the TSS.--tss-alpha
also accepts comma-separated lists of alpha values, in which case GAMBIT computes global test p-values across all specified values (individual p-values are reported inINFO
output field). By default, GAMBIT uses dTSS alpha values1e-4,5e-5,1e-5,5e-6
.
Statistical methods implemented in GAMBIT:
- Sequence Kernel Association Test (SKAT): Wu et al. (2011), AJHG
- TWAS: Gusev et al. (2016), Nat Genet
- PrediXcan: Gamazon et al. (2015), Nat Genet and Barbeira et al. (2018), Nat Comm
- Aggregated Cauchy Association Test (ACAT): Liu et al. (2019), AJHG and Liu and Xie (2018), arXiv
- Asymptotically exact Harmonic Mean P-value (HMP): Wilson (2019), PNAS
Libraries and resources used or adapted in GAMBIT:
- PDF, CDF, and quantile functions
- Hopscotch hashing
- Tabix and HTSLIB
- Matrix libraries
- Feel free to contact Corbin Quick ([email protected]) with questions, bug reports, or feedback