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Approved research

Genomic and evolutionary analyses of common disease in a large cohort

Principal Investigator: Professor Molly Przeworski
Approved Research ID: 11138
Approval date: August 1st 2015

Lay summary

Objectives of the proposed research: 1. To use genomic approaches to identify risk factors for a set of common diseases of particular importance in development and aging. In particular, we will focus on metabolic phenotypes (disease like type 2 diabetes, and phenotypes like BMI), reproductive phenotypes (like number of children and age at menarche), and common causes of death (cancer, heart disease, and neurological disease). 2. To determine the influence of natural selection (if any) on the frequencies of alleles influencing disease risk. The purpose of the UK Biobank is to improve the prevention, diagnosis, and treatment of illness. We aim to discover genetic and environmental risk factor that have causal influences on disease risk, and to measure the impact of these alleles on evolutionary fitness. Genomic research has identified thousands of variants that influence human traits like cholesterol levels, platelet counts, and blood pressure, but the influence of these traits on disease remains controversial. We will use novel statistical approaches to identify causal relationships between traits. We will also test whether these alleles influence overall mortality at different ages. The full cohort.

Scope extension:

Objectives of the proposed research:

  1. To use genomic approaches to identify risk factors for a set of common diseases of particular importance in development and aging. In particular, we will focus on metabolic phenotypes (disease like type 2 diabetes, and phenotypes like BMI), reproductive phenotypes (like number of children and age at menarche), and common causes of death (cancer, heart disease, and neurological disease).
  2. To determine the influence of natural selection (if any) on the frequencies of alleles influencing disease risk.
  3. To examine the generalizability of GWAS results and in particular of polygenic scores, considering individuals of different genetic ancestries or in different environments to the GWAS set.
  4. To infer the fitness consequences of de novo mutations from evolutionary constraint in large human samples.
  5. To identify the mechanisms by which de novo mutations arise, and the reasons why mutation rates vary among individuals.