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,

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.

Software included in this pipeline are listed in the table below.

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.

Information on whole-genome analysis, which could be used to set up the regions, are described at the repository's wiki page.

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.

The pipeline itself can be installed in the usual way,

git clone https://github.com/jinghuazhao/FM-pipeline

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.

Implementations have been done for the finemapping software along with LocusZoom and GCTA; support for fgwas is still alpha tested. 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.

Before start, settings at the beginning of the script need to be changed and only minor change is expected after this. The syntax of pipeline is then simply

bash fmp.sh <input>

The input will be GWAS summary statistics described at SUMSTATS

This format is in line with joint/conditional analysis by GCTA.

The pipeline uses a reference panel in a .GEN 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 file is required for each region, named such that chr{chr}_{start}_{end}.gen, 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,

Note that unlike fmp.sh, the utility program uses qctool-1.4 for its more comprehensive options. In line with qctool -excl-samples option, it contains a list of individuals corresponding to ID_2 of the sample file rather than ID_1 and ID_2.

Given these, one can do away with Stata and work on a text version for instance SNPinfo.txt. 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

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

In addition, we have implemented clumping using PLINK with options comparable to those used in depict (e.g. description in PW-pipeline).

File bmi.txt and 97.snps are described in SUMSTATS, from which we can build st.bedas follows,

# 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

This was the default,

cp bmi.txt HRC
cp fmp.sh HRC.sh
# modify HRC.sh to use the HRC panel
HRC.sh HRC

and the results will be in HRC.out.

This is available as FUSION LD reference panel, with 1KG.sh to generate SNPinfo.dta.gz and st.do to generate the required data.

We then proceed with.

cp bmi.txt 1KG
cp fmp.sh 1KG.sh
# modify 1KG.sh to use the 1KG panel
1KG.sh 1KG

and the results will be in 1KG.out.

The wiki page has additional information, e.g.,

• WHOLE GENOME CONDITIONAL JOINT ANALYSIS

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.

FM-summary

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

LocusZoom

Pruim RJ, et al. (2010) LocusZoom: Regional visualization of genome-wide association scan results. Bioinformatics 2010 September 15; 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. bioRxiv 10.1101/000752

finemap

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

VEGAS (Versatile Gene-based Association Study)

Liu JZ, et al. (2010). A versatile gene-based test for genome-wide association studies. Am J Hum Genet 87:139–145.