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Using hidden genealogical structure to study the architecture of human disease

Periodic Reporting for period 2 - ARGPHENO (Using hidden genealogical structure to study the architecture of human disease)

Reporting period: 2021-07-01 to 2022-12-31

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.
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.
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.