Overview

Genome-wide Association Studies (GWAS) are a promising approach for uncovering genetic variants related to complex diseases. Although GWAS have allowed the identification of thousands of trait-disease associations, the current methodology is far from being able to explain a small fraction of the estimated heritability of each trait, even when analyzing hundreds of thousands of samples. To allow an efficient analysis of the current and upcoming GWAS datasets we propose a novel strategy, called GUIDANCE, to make the most of the information available in GWAS dataset and taking advantage of novel imputation reference panels.

GUIDANCE is an integrated framework that is able to perform haplotype phasing, genotype imputation, association testing assuming different models of inheritance and phenome-wide association analysis (PheWAS) analysis of large GWAS datasets. Moreover, this application allows performing all these steps in a single execution, as well as in a modular way with optional user intervention.

The GUIDANCE strength is based on the possibility of increasing the potential of GWAS by means of the analysis of the X chromosome, as well as the autosomes analysis, using one or multiple reference panels, do the association testing using several models (additive, dominant, recessive, heterodominant and genotypic inheritance models) and performing a cross-phenotype association analysis when more than one disease is available in the cohort under study.

GUIDANCE is implemented on top of COMPSs, a programming framework that aims to facilitate the development and execution of applications for distributed infrastructures, balancing and organizing internally all the necessary subtasks to ensuring an efficient usage of the computing resources. GUIDANCE can run in High Performance Computing facilities, and in a cloud environment.

Reference

1. M. Guindo-Martínez, R. Amela et al. Nat Commun. 2021.

• Marta Guindo Martinez - R scripts, Java code and Scientific Methodology - ORCID
• Ramon Amela Milian - R scripts and Java code - ORCID, github
• Cristian Ramón-Cortés Vilarrodona - Java code - ORCID, github
• Montserrat Puiggròs - R scripts and Java code - ORCID
• Josep Maria Mercader - Scientific Methodology - ORCID
• David Torrents - Scientific Methodology - ORCID