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

**LocusZoom plot showing Regional association for chr1:39114617-39614617**

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,

  1. Extraction of effect (beta)/z statistics from GWAS summary statistics (.sumstats),
  2. Extraction of correlation from the reference panel among overlapped SNPs from 1 and the reference panel containing individual level data.
  3. 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.

It is common to use PLINK and GCTA for identification of independent variants, see ADDTIONAL TOPICS below. Although this pipeline focuses on regional association, these vehicles would offer corroborative information on the regional level, especially when approximately independent LD blocks are used.

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


An fmp.ini needs to be present at the working directory,

The pipeline is then called with

bash <input>

where <input> is a file containing GWAS summary statistics as described at the SUMSTATS repository, The input is in line with joint/conditional analysis by GCTA involving chromosomal positions.

The pipeline uses a reference panel in a .gen.gz format, allowing for imputed genotypes and taking into account directions of effect in both the GWAS summary statistics and the reference panel. A .gen.gz file is required for each region, named such that chr{chr}_{start}_{end}.gen.gz, together with an info file and a single .sample file for all regions, see example below.

An auxiliary file called st.bed contains chr, start, end, rsid, pos, r, p corresponding to the lead SNPs specified and r is a sequence number of region while p is a phenotype for GWASs involving multiple phenotyps such as different proteins.


The output will involve counterpart(s) from individual software, i.e., .set/.post, .caviarbf, .snp/.config/.cred, .jam/.top/.cs

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/.cred SNPs with largest log10(BF), configurations with their log10(BF) and credible sets

It is helpful to examine directions of effects together with their correlation which is now embedded when finemap is involved.


— GWAS summary statistics —

File bmi.tsv.gz is described in the SUMSTATS repository,

— 1000Genomes panel —

The .gen.gz and .info files for all approximately independent LD blocks are available from 1KG/FUSION, the .sample file is FUSION.sample, all derived from FUSION LD reference panel, with and

— The lead SNPs —

From the 97 SNPs described in the SUMSTATS repository, the st.bed is generated as follows,

# 97 SNPs in approximately independent LD blocks
sed -i 's/rs12016871/rs9581854/g' 97.snps
  echo -e "chrom\tstart\tend\trsid\tpos\tr"
  grep -w -f 97.snps snp150.txt | \
  sort -k1,1n -k2,2n | \
  awk -vOFS="\t" '{print "chr" $1,$2-1,$2,$3,$2,NR}'
# awk -vflanking=250000 '{l=$2-flanking;u=$2+flanking;if(l<0) l=0;print $1,l,u,$3,$2,NR}'
) | \
bedtools intersect -a 1KG/EUR.bed -b - -loj | \
sed 's/chr//g;s/region//g' | \
  echo "chr start end rsid pos r"
  awk '$5!="."{print $1,$2,$3,$8,$9,$4}'
) > st.bed

Note rs12016871 in build 36 became rs9581854 in build 37 and a recent version of bedtools is required to recoggnise standard input. Should we not use approximately independent LD blocks, we would use a flanking region around each SNP as st.bed.

We then proceed with

# modify fmp.ini to use the 1KG panel
gunzip -c bmi.tsv.gz > BMI BMI

and the results will be in BMI.out.


We describe use of PLINK and GCTA to establish regions of interest by first returning to the GIANT BMI example as used above. For PLINK, we do

gunzip -c bmi.tsv.gz | \
sort -k9,9n -k10,10n | \
awk '
   if (NR==1) print "SNP","P"
   print $1, $7
}' | \
gzip -f > BMI.P.gz
if [ -f BMI.clumped ]; then rm BMI.clumped; fi
plink --bfile 1KG/FUSION \
      --clump BMI.P.gz \
      --clump-snp-field SNP \
      --clump-field P \
      --clump-kb 500 \
      --clump-p1 5e-8 \
      --clump-p2 0.01 \
      --clump-r2 0.1 \
      --mac 50 \
      --out BMI

where –bfile FUSION contains the LD reference data as from here. Note that only fields for SNP and p value are required, and for GCTA, we use

gunzip -c bmi.tsv.gz | \
sort -k9,9n -k10,10n | \
awk '
   if (NR==1) print "SNP","A1","A2","freq","b","se","p","N"
   print $1, $2, $3, $4, $5, $6, $7, $8
}' >
if [ -f BMI.jma.cojo ]; then rm BMI.jma.cojo BMI.ldr.cojo; fi
gcta64 --bfile 1KG/FUSION \
       --cojo-file \
       --cojo-slct \
       --cojo-p 5e-8 \
       --cojo-collinear 0.01 \
       --cojo-wind 500 \
       --maf 0.0001 \
       --thread-num 3 \
       --out BMI

Since it does not yet accept a compressed file as input.

GCTA is also described on wiki page,

The 1000Genomes data above are largely HapMap II SNPs, and a counterpart based on LocusZoom 1.4 and built from increases the number of SNPs from ~1M to ~22M.

Credible sets are often described, see


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, was adapted from (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


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