Using hidden genealogical structure to study the architecture of human disease

Project Information

ARGPHENO

Grant agreement ID: 850869

DOI

10.3030/850869

Project closed

EC signature date 30 October 2019

Start date 1 January 2020

End date 31 December 2025

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 499 665,00

EU contribution

€ 1 499 665,00

Coordinated by

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)

Reporting period: 2023-01-01 to 2024-06-30

Summary of the context and overall objectives of the project

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.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

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.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

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.