Using hidden genealogical structure to study the architecture of human disease

Informations projet

ARGPHENO

N° de convention de subvention: 850869

DOI

10.3030/850869

Projet clôturé

Date de signature de la CE 30 Octobre 2019

Date de début 1 Janvier 2020

Date de fin 31 Decembre 2025

Financé au titre de

• EXCELLENT SCIENCE - European Research Council (ERC)

Coût total

€ 1 499 665,00

Contribution de l’UE

€ 1 499 665,00

Coordonné par

THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
United Kingdom

Periodic Reporting for period 3 - ARGPHENO (Using hidden genealogical structure to study the architecture of human disease)

Période du rapport: 2023-01-01 au 2024-06-30

Résumé du contexte et des objectifs généraux du projet

The objective of this project is to develop new algorithms and statistical methodology to reconstruct and analyze the deep genealogical structures that connect putatively unrelated individuals in large genotyping array and sequencing data sets. These algorithms will allow utilizing these genealogical structures to analyze evolutionary properties of both common and rare genomic variation and to increase the statistical power to detect associations to traits and diseases. Overall, this suite of methods will create new avenues for the study of human evolutionary history and disease.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

We have developed algorithms to infer the presence of hidden relatedness and to infer the ancestral recombination graph (ARG) in large genotyping and sequencing data sets. We have applied these algorithms to the UK Biobank data set, studying fine-scale population structure, recent evolutionary history, and association between heritable traits and unobserved genomic variation.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

These statistical methods improve both accuracy and scalability of analyses of fine-scale evolutionary history, and complement current complex trait analyses that are based on genotype imputation from sequenced reference panels.