FINEMAP: efficient variable selection using summary data from genome-wide association studies

By Christian Benner, Chris C. A. Spencer, Aki S. Havulinna, Veikko Salomaa, Samuli Ripatti, Matti Pirinen. bioRxiv, 2015

774 citations recorded by the source corpus.

Motivation The goal of fine-mapping in genomic regions associated with complex diseases and traits is to identify causal variants that point to molecular mechanisms behind the associations. Recent fine-mapping methods using summary data from genome-wide association studies rely on exhaustive search through all possible causal configurations, which is computationally expensive. Results We introduce FINEMAP, a software package to efficiently explore a set of the most important causal configurations of the region via a shotgun stochastic search algorithm. We show that FINEMAP produces accurate results in a fraction of processing time of existing approaches and is therefore a promising tool for analyzing growing amounts of data produced in genome-wide association studies. Availability FINEMAP v1.0 is freely available for Mac OS X and Linux at http://www.christianbenner.com. Contact: christian.benner@helsinki.fi, matti.pirinen@helsinki.fi

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ae0459f3af3d9d841d4f03c174507523010a408a

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Citations in the corpus

Functionally-informed fine-mapping and polygenic localization of complex trait heritability (2019, methodology): “Details of the PolyFun method are provided in Methods; we have released open-source software implementing PolyFun together with SuSiE 21 and FINEMAP 22 .”
The Polygenic and Monogenic Basis of Blood Traits and Diseases (2020, methodology): “Using a Bayesian method that accounts for multiple independent signals (Benner et al., 2016) (Method Details, Figure 1B), we fine-mapped 3,100 (19% of 16,643 autosomal) associations to a single putative causative variant (> 95% posterior probability [PPFM]) (Table S5), and more than half of the asso”
A more accurate method for colocalisation analysis allowing for multiple causal variants (2021, methodology): “A suite of Bayesian fine-mapping methods have been developed recently which calculate posterior probabilities of sets of causal variants for a given trait[5, 6, 7].”
The MR-Base platform supports systematic causal inference across the human phenome (2018, methodology): “…API and future work will see extensions to support other methods for the investigation of complex trait genetic architecture, such as fine-mapping ( Benner et al., 2016 ), colocalisation ( Zhu et al., 2016 ; Newcombe et al., 2016 ; Giambartolomei et al., 2014 ) and polygenic risk prediction (…”
Colocalization of GWAS and eQTL Signals Detects Target Genes (2016, methodology): “The third class of methods is CAVIAR [18, 30], CAVIARBF [46], and FINEMAP [36], which assumes a presence of more than one causal variant in a region.”
Fine-mapping from summary data with the “Sum of Single Effects” model (2021, methodology): “As might be expected, the performance of SuSiE-RSS , FINEMAP and DAP-G all degraded with out-of-sample LD compared with in-sample LD; see Figures 4 and 5 .”
Genomic dissection of bipolar disorder and schizophrenia including 28 subphenotypes (2018, methodology): “In an effort to prioritize genes for the two GWS loci from the GWAS, we performed fine-mapping (Benner et al., 2016) using a linkage disequilibrium (LD) map derived from a majority of the control samples.”
Mapping genomic loci implicates genes and synaptic biology in schizophrenia (2022, methodology): “For regions predicted to contain 3 or fewer causal variants (N=249; Figure 1 ; Supplementary Tables 11a, 11b ), we extracted from FINEMAP the posterior probabilities (PP) of being causal for every SNP across the region, and constructed credible sets of SNPs that cumulatively capture 95% of the regio”