Starr, C. | bioRxiv Science

bioRxiv2023

flashfmZoom: a tool for joint fine-mapping and exploration of GWAS results in the UK Biobank

SummaryflashfmZoom is an all-in-one tool for analysis and interactive visualisation of potential causal genetic variants that underlie associations with quantitative traits from the UK Biobank. It offers a user-friendly interface and guides users in the selection of pleiotropic regions among subsets of 134 quantitative traits, such as cardiometabolic, hematologic, and respiratory traits. Users may then run single-trait fine-mapping, allowing for multiple causal variants, and leverage information between the traits using multi-trait fine-mapping to improve resolution. A series of interactive plots and downloadable tables are generated within flashfmZoom to identify potential causal variants that are shared or distinct between the traits; it also lists relevant literature for the traits and/or variants. Besides exploring traits that are well-known to be related, flashfmZoom encourages interactive exploration for the joint analysis of traits that may not often be considered together. This may reveal common aetiological pathways between traits related to different disorders. Availability and ImplementationflashfmZoom is an interactive open-source R shiny app software available online directly at https://mrc-bsu.shinyapps.io/flashfmZoomOnline/, with source code in GitHub under the MIT license https://github.com/fz-cambridge/flashfmZoom and at https://doi.org/10.5281/zenodo.7756205. Contactfeng.zhou@mrc-bsu.cam.ac.uk or jennifer.asimit@mrc-bsu.cam.ac.uk

genetics↗

bioRxiv2020

BaseQTL: a Bayesian method to detect eQTLs from RNA-seq data with or without genotypes

Available methods to detect molecular quantitative trait loci (QTL) require study individuals to be genotyped. Here, we describe BaseQTL, a Bayesian method that exploits allele-specific expression to map molecular QTL from sequencing reads even when no genotypes are available. When used with genotypes, BaseQTL has lower error rates and increased power compared with existing QTL mapping methods. Running without genotypes limits how many tests can be performed, but due to the proximity of QTL variants to gene bodies, the 2.8% of variants within a 100kB-window that could be tested, contained 26% of QTL variants detectable with genotypes. eQTL effect estimates were invariably consistent between analyses performed with and without genotypes. Often, sequencing data may be generated in absence of genotypes on patients and controls in differential expression studies, and we identified an apparent psoriasis-specific effect for GSTP1 in one such dataset, providing new insights into disease-dependent gene regulation.

genomics↗