FineMapping analysis using GWAS summary statistics
This is a pipeline for finemapping using GWAS summary statistics, implemented in Bash as a series of steps to furnish an incremental analysis. As depicted in the diagram below
where our lead SNP rs4970634 is in LD with many others, the procedure attempts to identify causal variants from region(s) showing significant SNP-trait association.
The process involves the following steps,
- Extraction of effect (beta)/z statistics from GWAS summary statistics (.sumstats),
- Extraction of correlation from the reference panel among overlapped SNPs from 1 and the reference panel containing individual level data.
- Information from 1 and 2 above is then used as input for finemapping.
The measure of evidence is typically (log10) Bayes factor (BF) and associate SNP probability in the causal set.
Information on whole-genome analysis, which could be used to set up the regions, are described at the wiki page. Clumping using PLINK is also included analogous to those used in depict (e.g. description in PW-pipeline).
Software options included in this pipeline are listed in the table below.
| Option | Name | Function | Input | Output | Reference |
|---|---|---|---|---|---|
| CAVIAR | CAVIAR | finemapping | z, correlation matrix | causal sets and probabilities | Hormozdiari, et al. (2014) |
| CAVIARBF | CAVIARBF | finemapping | z, correlation matrix | BF and probabilities for all configurations | Chen, et al. (2015) |
| GCTA | GCTA | joint/conditional analysis | .sumstats, reference data | association results | Yang, et al. (2012) |
| FM_summary | FM-summary | finemapping | .sumstats | posterior probability & credible set | Huang, et al. (2017) |
| JAM | JAM | finemapping | beta, individual reference data | Bayes Factor of being causal | Newcombe, et al. (2016) |
| LocusZoom | LocusZoom | regional plot | .sumstats | .pdf/.png plots | Pruim, et al. (2010) |
| fgwas | fgwas | functional GWAS | .sumstats | functional significance | Pickrell (2014) |
| finemap | finemap | finemapping | z, correlation matrix | causal SNPs and configuration | Benner, et al. (2016) |
so they range from regional association plots via LocusZoom, joint/conditional analysis via GCTA, functional annotation via fgwas to dedicated finemapping software including CAVIAR, CAVIARBF, an adapted version of FM-summary, R2BGLiMS/JAM and finemap. One can optionally use a subset of these for a particular analysis by specifying relevant flags from the pipeline's settings.
On many occasions, the pipeline takes advantage of the GNU parallel. Besides (sub)set of software listed in the table above, the pipeline requires qctool 2.0, PLINK 1.9, and the companion program LDstore from finemap's website need to be installed. To facilitate handling of grapahics, e.g., importing them into Excel, pdftopng from XpdfReader is used. We use Stata and Sun grid engine (sge) for some of the data preparation, which would become handy when available.
The pipeline itself can be installed in the usual way,
git clone https://github.com/jinghuazhao/FM-pipeline
An fmp.ini needs to be present at the working directory,
The pipeline is then called with
bash fmp.sh <input>
The input will be GWAS summary statistics described at https://github.com/jinghuazhao/SUMSTATS, in line with joint/conditional analysis by GCTA involving chromosomal positions.
The pipeline uses a reference panel in a .gen.gz format, taking into account directions of effect in both the GWAS summary statistics and the reference panel. Its development will facilitate summary statistics from a variety of consortiua as with reference panels such as the HRC and 1000Genomes.
A .gen.gz file is required for each region, named such that chr{chr}_{start}_{end}.gen.gz, together with a sample file. For our own data, st.do is written to generate such files from their whole chromosome counterpart using SNPinfo.dta.gz which has the following information,
| chr | rsid | RSnum | pos | FreqA2 | info | type | A1 | A2 |
|---|---|---|---|---|---|---|---|---|
| 1 | 1:54591_A_G | rs561234294 | 54591 | .0000783 | .33544 | 0 | A | G |
| 1 | 1:55351_T_A | rs531766459 | 55351 | .0003424 | .5033 | 0 | T | A |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
We may also work on a text version for instance SNPinfo.txt.
The setup is in line with summary statistics from consortia where only RSid are given for the fact that their chromosomal position may be changed
over different builds. An auxiliary file called st.bed contains chr, start, end, rsid, pos, r corresponding to the lead SNPs specified and r is
a sequence number of region.
The output will involve counterpart(s) from individual software, i.e., .set/post, caviarbf, .snp/.config, .jam/.top
| Software | Output type | Description |
|---|---|---|
| CAVIAR | .set/.post | causal set and probabilities in the causal set/posterior probabilities |
| CAVIARBF | .caviarbf | causal configurations and their BFs |
| FM-summary | .txt | additional information to the GWAS summary statistics |
| GCTA | .jma.cojo | joint/conditional analysis results |
| JAM | .jam/.top/.cs | posterior summary table, top models containing selected SNPs and credible sets |
| finemap | .snp/.config | top SNPs with largest log10(BF) and top configurations as with their log10(BF) |
It is helpful to examine directions of effects together with their correlation which is now embedded when finemap is involved.
Files bmi.txt and 97.snps are described in https://github.com/jinghuazhao/SUMSTATS.
This is available as FUSION LD reference panel, with
1KG.sh to generate SNPinfo.dta.gz and st.do to generate the script Extract.sh for the required data.
We then proceed with
awk '{gsub(/chr/,"",$0);if(NR==1) {print "chr","start","end","region"} else print $1,$2,$3,$4}' 1KG/EUR.bed > st.bed
ln -s bmi.txt BMI_1KG
# modify fmp.ini to use the 1KG panel
fmp.sh BMI_1KGand the results will be in BMI_1KG.out.
Assuming an HRC panel is ready, file 97.snps is used to build st.bed and the analysis proceeds as follows,
# st.bed
echo "chr start end rsid pos r" > st.bed
grep -w -f 97.snps snp150.txt | \
sort -k1,1n -k2,2n | \
awk -vflanking=250000 '{print $1,$2-flanking,$2+flanking,$3,$2,NR}' >> st.bed
ln -s bmi.txt BMI_HRC
# modify fmp.ini to use the HRC panel
# export GEN_location=/scratch/tempjhz22/LDcalc/HRC
fmp.sh BMI_HRCand the results will be in BMI_HRC.out.
The wiki page has the following information,
- Whole-genome conditional/joint analysis
- Whole genome analysis using approxmiately independent LD blocks.
Credible sets are often described, see https://github.com/statgen/gwas-credible-sets
The work was motivated by finemapping analysis at the MRC Epidemiology Unit and inputs from authors of GCTA, finemap, JAM, FM-summary as with participants in the
Physalia course Practical GWAS Using Linux and R are greatly appreciated. In particular, the utility program in Stata was adapted from
p0.do (which is still used when LD_MAGIC is enabled) originally written by Dr Jian'an Luan and
computeCorrelationsImpute2forFINEMAP.r by Ji Chen from the MAGIC consortium who also provides code calculating the
credible set based on finemap configurations. Earlier version of the pipeline also used GTOOL.
CAVIAR (Causal Variants Identification in Associated Regions)
Hormozdiari F, et al. (2014) Identifying causal variants at loci with multiple signals of association. Genetics 44:725–731
CAVIARBF (CAVIAR Bayes Factor)
Chen W, et al. (2015) Fine mapping causal variants with an approximate Bayesian method using marginal test statistics. Genetics 200:719-736.
Huang H, et al (2017) Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547:173–178, doi:10.1038/nature22969
GCTA (Genome-wide Complex Trait Analysis)
Yang J, et al. (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet 44:369-375
JAM (Joint Analysis of Marginal statistics)
Newcombe PJ, et al. (2016) JAM: A scalable Bayesian framework for joint analysis of marginal SNP effects. Genet Epidemiol 40:188–201
Pruim RJ, et al. (2010) LocusZoom: Regional visualization of genome-wide association scan results. Bioinformatics 26(18): 2336-2337
fgwas (Functional genomics and genome-wide association studies)
Pickrell JK (2014) Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am J Hum Genet 94(4):559-573.
Benner C, et al. (2016) FINEMAP: Efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493-1501.
Benner C, et al. (2017) Prospects of fine-mapping trait-associated genomic regions by using summary statistics from genome-wide association studies. Am J Hum Genet 101(4):539-551