Completed Genetics & Molecular Biology Pregnancy, Children & Inherited Conditions

The Genetic Analysis of Populations.

Funder Wellcome Charity-funded
Recipient organisation University of Oxford
Funding £1.9M
Period Jan 2014 — Jun 2020

In plain English

AI plain-English summary

A single DNA sample contains thousands of hidden clues about ancestry, disease risk, and immune response—but current tools routinely miss or misinterpret them. This project tackles a fundamental gap in genetic analysis: most statistical methods were designed for simple, single-reference genomes and fail when applied to highly diverse species like malaria parasites or complex immune regions like the HLA system. The HLA region controls how the immune system recognises threats, yet standard sequencing cannot accurately resolve its extreme variability. The researcher will build new computational tools that integrate multiple reference genomes, detect rare structural changes such as gene conversions and non-allelic homologous recombination, and reconstruct the full genealogical history of a person's DNA. If successful, the work will improve the accuracy of genetic association studies in diverse populations—particularly people of African ancestry, who remain underrepresented in genomic research. It will also enable better tracking of malaria parasite evolution and more precise matching for organ transplants. This is fundamental science: the methods developed here will not directly change clinical practice tomorrow, but they will provide the statistical infrastructure that future discoveries in immunology, infectious disease, and population genetics depend on.

View original technical description
The proposed research is to develop methods for detecting, typing and inferring the history and functional consequences of genetic variants. The specific goals of the project are: i. To develop statistical and computational tools for integrating de novo assembly and the use of multiple reference sequences to enable accurate inference of diverse species (such as malarial parasites) and genomic regions (such as the human leukocyte antigen [HLA] region) from sequencing data. ii. To develop a robust statistical methodology for identifying diverse genome-changing events (point mutation, insertion, deletion, cross-over, gene conversion, non-allelic homologous recombination [NAHR], cross-over) from whole-genome sequence data of individuals in extended pedigrees and to collect and analyse such data to study how such processes differ between species and individuals. iii. To develop a resource for analysing classical HLA and SNP genotype data in the context of diverse immune phenoty pes in individuals of African ancestry, by typing seven classical loci (HLA-A, -B, -C, DRB1, -DQA1, -DQB1 and -DPB1) in 1,000 people from existing GWAS collections and integrating the results into the HLA*IMP software. iv. To develop methods for non-parametric analysis of population and gene history that make use of the detailed information on allele shared afforded by whole-genome sequencing. Specifically, to infer local (along the genome) genealogical histories and to develop methods, usi ng these structures, to account for population history in the association of genetic variation with phenotype.

View the original record at the funder ↗

Researchers

Gil McVean (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Statistical methods for understanding the genetics of human disease phenotypes.
Inference and Applications of Genetic Relatedness in Human Populations
Developing methods and software to analyse and interpret Normal Human Genome Variation and Disease
Inferring human colonization history using genetic data.
Haplotype-based inference of hidden structure in the human genome

Original classification

Investigator Award in Science

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