Preprint Review Version 1 This version is not peer-reviewed

Population Genetic Considerations for Using Biobanks as International Resources in the Pandemic Era and Beyond

Version 1 : Received: 21 April 2020 / Approved: 22 April 2020 / Online: 22 April 2020 (07:39:22 CEST)

How to cite: Carress, H.; Lawson, D.; Elhaik, E. Population Genetic Considerations for Using Biobanks as International Resources in the Pandemic Era and Beyond. Preprints 2020, 2020040394 (doi: 10.20944/preprints202004.0394.v1). Carress, H.; Lawson, D.; Elhaik, E. Population Genetic Considerations for Using Biobanks as International Resources in the Pandemic Era and Beyond. Preprints 2020, 2020040394 (doi: 10.20944/preprints202004.0394.v1).

Abstract

The past years saw the rise of genomic biobanks and mega-scale meta-analysis of genomic data that promise to reveal the genetic underpinnings of health and disease. However, the over-representation of Europeans in genomic studies not only limit the global understanding of disease risk and intervention efficacy, but also inhibit viable research into the genomic differences between carriers and patients. Whilst the community has agreed that more diverse samples are required, it is not enough to blindly increase diversity; the diversity must be quantified, compared, and annotated to lead to insight. Genetic annotations from separate biobanks need to be comparable, computable, operate without access to raw data due to privacy concerns. But they must be comparable, both for regular research and to allow international comparison in response to pandemics. Here, we evaluate the appropriateness of commonly used genomic tools used to depict population structure in a standardized and comparable manner. The end goal is to reduce the effects of confounding and learn from genuine variation in genetic effects on phenotypes across populations, which will improve the value of biobanks, locally and internationally, increase the accuracy of association analyses, and inform developmental efforts.

Subject Areas

bioinformatics; population structure; population stratification bias; genomic medicine; biobanks

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