Statistical fine-mapping pipeline in FinnGen Fine-mapping pipeline using FINEMAP and SuSiE. Overview This repository provides a pipeline for statistical fine-mapping in FinnGen. We used two state-of-the-art methods, FINEMAP (Benner, C. et al., 2016; Benner, C. et al., 2018) and SuSiE (Wang, G. et al., 2020) to fine-map genome-wide significant loci in FinnGen endpoints. Briefly, there are three main steps: 1. Preprocessing For each genome-wide significant locus (default configuration: P < 5e-8), we define a fine-mapping region by taking a 3 Mb window around a lead variant (and merge regions if they overlap). We preprocess an input GWAS summary statistics into separate files per region for the following steps. 2. LD computation We compute in-sample dosage LD using LDstore2 for each fine-mapping region. 3. Fine-mapping With the inputs of summary statistics and in-sample LD from the steps 1-2, we conduct fine-mapping using FINEMAP and SuSiE with the maximum number of causal variants in a locus L = 10. Usage Please run finemap.wdl on a cromwell server with an appropriate json input (e.g., finemap_inputs.json) and a dependent sub workflow (finemap_sub.zip). Please pay attention to an important configuration below (finemap.ldstore_finemap.susie.min_cs_corr) controlling reliability of credible sets and variants! Configurable options include: finemap.sumstats_pattern: a path to GWAS summary statistics where {PHENO} is a magic keyword to be replaced by an actual phenotype name in finemap.phenolistfile finemap.phenolistfile: a list of phenotypes to fine-map finemap.phenotypes: a path to phenotype file. [IMPORTANT] The first column should contains sample IDs that match to bgen sample IDs. If a phenotype is binary, we expect it's coded as 0/1/NA, otherwise quantitative phenotype is automatically assumed. finemap.preprocess.rsid_col: a column name of rsid / variant id. If finemap.preprocess.set_variant_id is true, this option is disregarded. finemap.preprocess.chromosome_col: a column name of chromosome finemap.preprocess.position_col: a column name of position finemap.preprocess.allele1_col: a column name of reference allele (by default) finemap.preprocess.allele2_col: a column name of alternative allele (by default) finemap.preprocess.freq_col: a column name of allele frequency finemap.preprocess.beta_col: a column name of marginal beta finemap.preprocess.se_col: a column name of standard error of marginal beta finemap.preprocess.p_col: a column name of p-value finemap.preprocess.delimiter: a delimiter of summary statistics. Due to the limitation of non-supported escape characters in json, it supports the following magic keywords: WHITESPACE: \s+ SINGLE_WHITESPACE: '\s' TAB: '\t' SPACE: ' ' finemap.preprocess.scale_se_by_pval: an option to scale standard error based on p-value finemap.preprocess.x_chromosome: an option to include X-chromosome. It assumes females coded as 0/1/2 and males coded as 0/2. finemap.preprocess.set_variant_id: an option to specify whether to set variant ids as chr:pos:ref:alt. If false, finemap.preprocess.rsid_col is required. finemap.preprocess.max_region_width: Overlapping regions won't be merged if the resulting region exceeds this width finemap.preprocess.p_threshold: a p-value threshold to define a fine-mapping region finemap.ldstore_finemap.n_causal_snps: a maximum number of causal variants per locus finemap.ldstore_finemap.susie.min_cs_corr: [IMPORTANT] a minimum pairwise correlation value (r) for variants in a credible set for purity filter in SuSiE. In a minority of credible sets, there is a region with a few lead variants in tight LD and low LD to others in a region. In these occasions, if the sum of PIPs of those in tight LD is below 0.95, SuSiE adds low LD variants to get 95% credible sets. However, since those low LD variants are not part of "pure"/reliable credible set, purity filtering filters the whole credible set if minimum r2 between variants is lower than the given threshold. To enable a post-hoc purity filtering, it is set as 0 by default but users are strongly encouraged to do a purity filtering based on cs_min_r2 (default original SuSiE filter 0.5) value or low_purity flag. In many occasions, the lead tight LD variants form truly the credible set of variants, and one option, depending on use case, would be post-hoc filtering variants in a credible set by r2 values (which results in < 95% credible set). finemap.ldstore_finemap.combine.good_cred_r2: good quality credible set minimum r2 between variants. Controls how filtered summary of credible sets and their variants are reported. See output of filtered credible set report , finemap.ldstore_finemap.combine.snp_annot_file: optional file for arbitrary variant annotations to be included in filtered summary credible set reports. Must be bgzipped and tabixed. Remove the whole row if no annotations are to be used. First four columns must be chr pos ref alt -finemap.ldstore_finemap.combine.snp_annot_file_tbi: tabix index file for the above file. Must be given if annot_file is specified finemap.ldstore_finemap.combine.snp_annot_fields: comma separated string of column names to include from the annot_file finemap.set_variant_id_map_chr: comma separated list of chromosome name mappings to perform for summary stat files. Can be useful if bgen file and summary file are coded differently. In output the chr are reported in the original format given in the summary stat file. Example 23=X,24=MT to code 23 to X and 24 to MT finemap.ldstore_finemap.filter_and_summarize.good_cred_r2: r2 value threshold of minimum pairwise r2 between cs variants to call good CS in summary SUSIE files. finemap.ldstore_finemap.filter_and_summarize.snp_annot_file: Optional annotation file to annotate variants in SUSIE summarized results. Has to be tbi indexed and have column chr pos ref alt matching those in summary stats and any additional annotation columns finemap.ldstore_finemap.filter_and_summarize.snp_annot_file_tbi: must be given if annotation file is given. finemap.ldstore_finemap.filter_and_summarize.snp_annotation_fields: which fields to add from annotation file to variant output finemap.ldstore_finemap.filter_and_summarize.cpu: number of CPUs to use for filtering and annotation finemap.ldstore_finemap.filter_and_summarize.mem: amount of RAM to use for filtering and annotation Output descriptions SuSiE outputs PHENONAME.SUSIE.cred.bgz Contains credible set summaries from SuSiE fine-mapping for all genome-wide significant regions. Columns: region: region for which the fine-mapping was run. cs: running number for independent credible sets in a region cs_log10bf: Log10 bayes factor of comparing the solution of this model (cs independent credible sets) to cs -1 credible sets. cs_avg_r2: Average correlation R2 between variants in the credible set cs_min_r2: minimum r2 between variants in the credible set cs_size: how many snps does this credible set contain PHENONAME.SUSIE.cred.summary.tsv Summary of credible sets where top variant for each CS is included. Columns: trait: phenotype region: region for which the fine-mapping was run. cs: running number for independent credible sets in a region cs_log10bf: Log10 bayes factor of comparing the solution of this model (cs independent credible sets) to cs -1 credible sets cs_avg_r2: Average correlation R2 between variants in the credible set cs_min_r2: minimum r2 between variants in the credible set low_purity: boolean (TRUE,FALSE) indicator if the CS is low purity (low min r2) cs_size: how many snps does this credible set contain good_cs: boolean (TRUE,FALSE) indicator if this CS is considered reliable. IF this is FALSE then top variant reported for the CS will be chosen based on minimum p-value in the credible set, otherwise top variant is chosen by maximum PIP cs_id: v: top variant (chr:pos:ref:alt) p: top variant p-value beta: top variant beta sd: top variant standard deviation prob: overall PIP of the variant in the region cs_specific_prob: PIP of the variant in the current credible set (this and previous are typically almost identical) 0..n: configured annotation columns. Typical default most_severe,gene_most_severe giving consequence and gene of top variant PHENONAME.SUSIE_99.cred.summary.tsv The same file as PHENONAME.SUSIE.cred.summary.tsv except 99% credible set computed instead of 95%. PHENONAME.SUSIE_extend.cred.summary.tsv The same file as PHENONAME.SUSIE.cred.summary.tsv (i.e. 95% CS results) except the corresponding SNPs (see below) contain variants up to 99% credible set. PHENONAME.SUSIE.snp.bgz Contains variant summaries with credible set information. Columns: trait: phenotype region: region for which the fine-mapping was run. v, rsid: variant ids chromosome position allele1 allele2 maf: minor allele frequency beta: original marginal beta se: original se p: original p mean: posterior mean beta after fine-mapping sd: posterior standard deviation after fine-mapping. prob: posterior inclusion probability cs: credible set index within region lead_r2: r2 value to a lead variant (the one with maximum PIP) in a credible set alphax: posterior inclusion probability for the x-th single effect (x := 1..L where L is the number of single effects (causal variants) specified; default: L = 10). PHENONAME.SUSIE.snp.filter.tsv snps that are part of good quality credible sets as reported in PHENONAME.cred.summary.tsv file. trait phenotype region region for which the fine-mapping was run v variantid (chr:pos:ref:alt) cs running credible set id within region cs_specific_prob posterior inclusion probability for this CS. chromosome position allele1 allele2 maf beta original association beta p original pvalue se original se most_severe most severe consequence of the variant gene_most_severe gene corresponding to most severe consequence PHENONAME.SUSIE_99.snp.filter.tsv Same as PHENONAME.SUSIE.snp.filter.tsv but corresponding to 99% CS results. PHENONAME.SUSIE_extend.snp.filter.tsv Same as PHENONAME.SUSIE.snp.filter.tsv but may contain extra variants on top of 95% CS up to forming 99% CS. Contains 2 additional columns: cs_99 indicator which CS this variant is part of. cs_specific_prob_99 PIP in 99% CS solution Note that in case variant is not part of 95% CS but belongs to 99% CS, cs field=-1 and cs_99 contains the CS id. FINEMAP outputs PHENONAME.FINEMAP.config.bgz Summary fine-mapping variant configurations from FINEMAP method. Columns: trait: phenotype region: region for which the fine-mapping was run. rank: rank of this configuration within a region config: causal variants in this configuration prob: probability across all n independent signal configurations log10bf: log10 bayes factor for this configuration odds: odds of this configuration k: how many independent signals in this configuration prob_norm_k: probability of this configuration within k independent signals solution h2: snp heritability of this solution. 95% confidence interval limits of snp heritability of this solution. mean: marginalized shrinkage estimates of the posterior effect size mean sd: marginalized shrinkage estimates of the posterior effect standard deviation FINEMAP.region.bgz Summary statistics on number of independent signals in each region Columns: trait: phenotype region: region for which the fine-mapping was run. h2g snp: heritability of this region h2g_sd: standard deviation of snp heritability of this region h2g_lower95: lower limit of 95% CI for snp heritability h2g_upper95: upper limit of 95% CI for snp heritability log10bf: log bayes factor compared against null (no signals in the region) prob_xSNP: columns for probabilities of different number of independent signals expectedvalue: expectation (average) of the number of signals PHENOTYPE.FINEMAP.snp.bgz Summary statistics of variants and into what credible set they may belong to. Columns: trait: phenotype region: region for which the fine-mapping was run. v: variant index: running index rsid: variant id chromosome position allele1 allele2 maf: minor allele frequency beta: original marginal beta se: original se z: original z prob: posterior inclusion probability log10bf: log10 bayes factor mean: marginalized shrinkage estimates of the posterior effect size mean sd: marginalized shrinkage estimates of the posterior effect standard deviation mean_incl: conditional estimates of the posterior effect size mean sd_incl: conditional estimates of the posterior effect size standard deviation p: original p csx: credible set index for given number of causal variants x PHENOTYPE.REGION.credx files Posterior inclusion probabilities of SNPs in x number of signals solution Columns: index running index credn variant in credible set n probn posterior inclusion probability of variant into credible set n Authors Masahiro Kanai (mkanai@broadinstitute.org) with significant inputs from Hilary Finucane, Juha Karjalainen, Mitja Kurki, Sina Rüeger, and Jacob Ulirsch.