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Approved research

Identifying correlations between genetic ancestry and clinically relevant phenotypes.

Principal Investigator: Mr Danny Park
Approved Research ID: 32990
Approval date: October 10th 2017 | Completion date: January 15th 2019

Lay summary

We are interested in the correlation between genetic ancestry and clinically relevant phenotypes (such as diagnostic test results). These relationships can improve the accuracy of diagnostic measurements such as FEV1 in lung function or eGFR in kidney function. Our aims are: 1. Find correlations between genetic ancestry and clinically relevant phenotypes in healthy individuals. 2. Update standard of care reference equations for diagnostic tests to incorporate the effect of genetic ancestry upon healthy ranges. 3. Use the updated reference equations to determine if individuals with large changes in expected diagnostic results are more likely to have worse health outcomes. Improving the precision of diagnostic testing will lead to more effective treatment, fewer misdiagnoses, and improvement in health outcomes. By integrating genetic ancestry into reference equations, we will help doctors make better treatment decisions that are tailored to the genetic background of their patients. We hope our approach will help to improve health outcomes, especially in genetically diverse populations. We will use genetic ancestry algorithms similar to ADMIXTURE and PCA to infer the genetic background of individuals. We will then determine the correlation between components of ancestry and clinically relevant phenotypes, to generate new reference equations for standard diagnostics. Finally, we will identify individuals with common diseases who have large changes in expected diagnostic values and determine if they have worse disease outcomes than other individuals. Full cohort, 500,000 individuals