Quality control and analytic best practices for testing genetic models of sex differences in large populations

Khramtsova, Ekaterina A., Wilson, Melissa A., Martin, Joanna ORCID: https://orcid.org/0000-0002-8911-3479, Winham, Stacey J., He, Karen Y., Davis, Lea K. and Stranger, Barbara E. 2023. Quality control and analytic best practices for testing genetic models of sex differences in large populations. Cell 186 (10) , pp. 2044-2061. 10.1016/j.cell.2023.04.014 Item availability restricted.

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Abstract

Phenotypic sex-based differences exist for many complex traits. In other cases, phenotypes may be similar, but underlying biology may vary. Thus, sex-aware genetic analyses are becoming increasingly important for understanding the mechanisms driving these differences. To this end, we provide a guide outlining the current best practices for testing various models of sex-dependent genetic effects in complex traits and disease conditions, noting that this is an evolving field. Insights from sex-aware analyses will not only teach us about the biology of complex traits but also aid in achieving the goals of precision medicine and health equity for all.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine
Publisher:	Elsevier
ISSN:	0092-8674
Date of First Compliant Deposit:	10 May 2023
Date of Acceptance:	7 April 2023
Last Modified:	05 Dec 2023 20:25
URI:	https://orca.cardiff.ac.uk/id/eprint/159401

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