GWAS / Manhattan track
The GWAS track renders results as a Manhattan plot: -log₁₀(p-value) on the Y axis, genomic position on the X axis. Points support LocusZoom-style r² LD coloring to show linkage to an index SNP.
For a genome-wide example, the embedded Pan-UKB GWAS example browses the full Pan-UK Biobank catalog of ~7,200 phenotypes, loading each trait's summary statistics straight from the Pan-UKBB public S3 bucket.
Example file
To try the workflow without preparing anything, paste this hg19 BMI GWAS summary
statistics file (with neg_log_pvalue as the score column) into the Add GWAS
track form:
https://s3.amazonaws.com/jbrowse.org/genomes/hg19/gwas/summary_stats.txt.gzIts peaks are the ones a BMI scan is known for, FTO at chr16:53.8 Mb first by a wide margin, then TMEM18, MC4R and SEC16B, so a view that lands on any of them has something to show.
That file carries no LD of its own. The LD-colored demo below is a second pair,
gwas_giant-bmi_meta_women-only.gz with a PLINK table beside it, both under
jbrowse.org/demos/gwas/ and wired together by the demo config.
Public data sources
Most public GWAS repositories use their own TSV column layout rather than the BED format GWASAdapter requires, so a conversion step is needed before loading. See the config guide for the preprocessing pipeline.
| Source | Notes |
|---|---|
| GWAS Catalog | Harmonized TSV; needs BED conversion + tabix |
| Pan-UKB | bgzipped TSV; loads directly with scoreColumn: neglog10_pval_meta_hq |
| FinnGen | TSV with raw pval; needs -log₁₀ transform + BED conversion |
| Open GWAS | VCF format; not directly compatible |
LD data
With an LD file loaded, points are colored by r² to an index SNP, revealing which nearby variants are in linkage with the lead signal (see Add GWAS track workflow for how the index is chosen).
The embedded LocusZoom-style LD example shows this r² coloring running live in a React app.
The LD triangle is a separate feature: it draws r² between every pair of nearby variants as a heatmap, read from a PLINK LD file. See the linkage disequilibrium tutorial.
LD data must be in PLINK LD table format, either 1.9's .ld or 2.0's .vcor,
bgzipped or not. Generate it from:
- Your own cohort -
plink2 --r2-unphased --ld-window-kb 1000 --ld-window-r2 0, or 1.9'splink --r2 --ld-window-kb 1000 --ld-window-r2 0 - Reference panel - 1000 Genomes phase 3 at
ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/(choose EUR, AFR, EAS, AMR, or SAS by population). Pointbcftoolsat the.bcffiles undersupporting/bcf_files/rather than the.vcf.gzbeside them: the BCF and its.csianswer a remote region query in seconds, while the.vcf.gzindex sends htslib to an offset that fails with "Illegal seek"
Clumping the peak
A peak is one signal smeared across every variant in LD with the causal one.
plink2 --clump writes that down rather than leaving it to be read off the
colors: each clump is an index variant plus the variants whose signal it
accounts for, which loads as an interval track under the Manhattan lane.
The reference panel is a region of 1000 Genomes, fetched over HTTP without downloading the file:
BCF=https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/bcf_files/ALL.chr16.phase3_shapeit2_mvncall_integrated_v5.20130502.genotypes.bcf
curl -fsSLO https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/integrated_call_samples_v3.20130502.ALL.panel
awk 'NR>1 && $3=="EUR"{print $1}' integrated_call_samples_v3.20130502.ALL.panel > eur.samples
# -r is a region request, so 900 kb costs 900 kb rather than the 450 MB chromosome
bcftools view -r 16:53400000-54300000 -S eur.samples -m2 -M2 -v snps \
-q 0.01:minor -Oz -o fto.vcf.gz "$BCF"
# --set-missing-var-ids is not optional: plink2 refuses a dataset where the ID
# '.' appears twice, and an unnamed variant is common in a reference panel
plink2 --vcf fto.vcf.gz --double-id --set-missing-var-ids @:# \
--rm-dup force-first --make-bed --out fto--clump then reads the summary statistics themselves. The columns are named
rather than positional, and --clump-log10 input-only tells plink2 the p-value
column is already -log10, as neg_log_pvalue is:
# plink2 reads the header, so the leading '#' on '#chrom' has to go
gzip -dc summary_stats.txt.gz | sed '1s/^#//' > stats.txt
plink2 --bfile fto --clump stats.txt --clump-log10 input-only \
--clump-p-field neg_log_pvalue --clump-id-field rsid \
--clump-log10-p1 7.3 --clump-log10-p2 2 --clump-r2 0.2 --clump-kb 500 --out fto
# clump to BED: SP2 names the members, and the .bim says where each one is, so
# the span is their lowest and highest position
awk 'NR==FNR{pos[$2]=$4; next} FNR>1 {n=split($11,m,","); lo=$2; hi=$2
for(i=1;i<=n;i++){p=pos[m[i]]; if(p){if(p<lo)lo=p; if(p>hi)hi=p}}
printf "%s\t%d\t%d\t%s (%d variants)\n", $1, lo-1, hi, $3, $5}' \
fto.bim fto.clumps | sort -k2,2n > clumps.bedOver the example file that gives three clumps at FTO, and the big one shows what
the peak is tagging: index rs1121980, 51 variants, spanning 51 kb of FTO's
first intron. Load clumps.bed through a
BedAdapter beside the GWAS track.
Add GWAS track workflow
Open the track selector, click Add track, and choose Add GWAS / Manhattan track.
GWAS data (required)
- GWAS file - the
.bed.gzor.txt.gz - GWAS tabix index - omit if the index is at
<file>.tbi - Score column - column name for the -log₁₀(p) values (the
scoreColumnslot)
LD coloring (optional)
Select a PLINK .ld or .ld.gz file. For bgzipped files a second field appears
for the tabix index.
Click Submit. In LD mode JBrowse auto-picks the highest-scoring loaded SNP as the index; right-click any point or use the track menu to change it.