Fine-mapping

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). If a merged window exceeds 10MB, we iteratively shrink the window by 10%, until the merged window fits into 10MB or is split into merged windows that each fit into 10MB. 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 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 and with the maximum number of causal variants in a locus L = 10.

Notes

Due to too many variants and too large LD regions the following endpoints were run with a refined region selection algorithm that limits the region's size to a set maximum length:

• G6_MUSDYST

• G6_MYONEU

• Q17_CONGEN_MALFO_GALLB_BILE_DUCTS_LIVER

Integration to PheWeb

The "Credible Sets"-table on a phenotype page in the shows the SuSiE-fine-mapped credible sets of that phenotype. The variant shown per credible set is the maximum PIP (posterior inclusion probability) variant of that credible set. In addition to the causal variants, variants that were in sufficient LD (Pearson r^2 > 0.05), had a small enough p-value (pval < 0.01), and were close enough to the lead variant (distance to lead variant < 1.5 megabases) were clumped together with the credible set. Variants have been compared against GWAS Catalog and annotated. The LD grouping, annotation and GWAS Catalog comparison were done using the autoreporting pipeline.

The columns of the table are explained below: