MULTI-FINELD2

Genome-wide association studies (GWAS) have identified countless genetic loci associated with hundreds of health traits, but fine-mapping those loci to pinpoint the causal variants remains challenging due to computational limitations in existing methodologies. The MULTI-FINELD2 project will implement innovative mathematical solutions to lighten linkage disequilibrium (LD) matrix estimation, enabling the utilization of fine-mapping methods to exploit whole genome sequencing (WGS) data across diverse ancestries. This will increase genomic coverage compared to current GWAS and will allow for the first-time extension to genetic
studies from more than two ancestries improving fine-mapping precision. Structured around two specific objectives, MULTI-FINELD2 will: 1) develop methods for constructing low-dimensional LD matrices,
reducing computational costs; 2) graft the low-dimensional LD matrix into state-of-the-art fine-mapping methods, using summary statistics from WGS association studies from diverse ancestries. The method will be developed and tested on large, publicly available biobank data, such as the UK Biobank, All of Us, and the Tehran Cardiometabolic Genetic Study, encompassing hundreds of thousands of participants from five ancestries. The project will capitalize on: my expertise in advanced statistical methods in genomics; the experience and extensive network of Dr C. Pattaro, Biostatistics and Epidemiology group leader at the Eurac Research Institute for Biomedicine (Italy), leader of an international genetic consortium and a national large population-based study; a secondment to embed the calculation method into modern Workflow Management Systems at the Medical University Innsbruck Genetic Epidemiology Institute, where Nextflow
pipelines for fast whole-genome analyses were recently developed. I will promote the method through an extensive dissemination plan, developed following expression of interest by numerous academic genomic consortia and industries.