Revisiting gene-sex interaction patterns in complex traits
Monday, March 30 at 10:00 am
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11:00 am
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2026-03-30 10:00:00
2026-03-30 11:00:00
Revisiting gene-sex interaction patterns in complex traits
We invite you to join our seminar at UCSF Mission Bay campus with Hakhamanesh Mostafavi from NYU Grossman School of Medicine. Although you can access a Zoom option via our event website, we highly encourage in-person attendance!
Abstract:
Hakhamanesh Mostafavi, PhD
Assistant Professor, Center for Human Genetics and Genomics, Department of Population Health, NYU Grossman School of Medicine
https://www.mostafavilab.org/
Most complex traits differ between men and women, yet how these differences interact with genetic effects remains unclear. Many traits show genetic correlations below one (e.g., SHBG, waist-to-hip ratio), suggesting differences in genetic effects between sexes, but the underlying loci are largely unknown.
Here, we systematically analyze gene-by-sex interactions across quantitative traits in UK Biobank. Consistent with earlier work, we observe pervasive interactions following an amplification pattern, where genetic effects change proportionally between sexes. These patterns are sensitive to phenotype scale, as recently suggested, though this sensitivity varies by trait and is not trivially removed by standard transformations (e.g., log or IRNT). Similar patterns are observed for other environmental factors.
We remain agnostic about whether amplification is biological or a statistical artifact. Nevertheless, we argue that it obscures the loci driving imperfect genetic correlation between sexes, a point largely overlooked in studies of interaction. Under this model, standard interaction tests or sex-stratified GWAS largely rediscover main effects as interactions. Instead, we propose identifying biologically informative loci as outliers from the genome-wide correlation pattern, rather than deviations from equality between sexes. We illustrate this framework using traits including SHBG, LDL, and urate.
Overall, this work refines the interpretation of gene-by-sex effects and suggests a more general framework for studying genetic interactions in complex traits.
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1550 4th Street
Rock Hall, 102
San Francisco, CA 94158
United States
View on Map
We invite you to join our seminar at UCSF Mission Bay campus with Hakhamanesh Mostafavi from NYU Grossman School of Medicine. Although you can access a Zoom option via our event website, we highly encourage in-person attendance!
Abstract:
Hakhamanesh Mostafavi, PhD
Assistant Professor, Center for Human Genetics and Genomics, Department of Population Health, NYU Grossman School of Medicine
https://www.mostafavilab.org/
Most complex traits differ between men and women, yet how these differences interact with genetic effects remains unclear. Many traits show genetic correlations below one (e.g., SHBG, waist-to-hip ratio), suggesting differences in genetic effects between sexes, but the underlying loci are largely unknown.
Here, we systematically analyze gene-by-sex interactions across quantitative traits in UK Biobank. Consistent with earlier work, we observe pervasive interactions following an amplification pattern, where genetic effects change proportionally between sexes. These patterns are sensitive to phenotype scale, as recently suggested, though this sensitivity varies by trait and is not trivially removed by standard transformations (e.g., log or IRNT). Similar patterns are observed for other environmental factors.
We remain agnostic about whether amplification is biological or a statistical artifact. Nevertheless, we argue that it obscures the loci driving imperfect genetic correlation between sexes, a point largely overlooked in studies of interaction. Under this model, standard interaction tests or sex-stratified GWAS largely rediscover main effects as interactions. Instead, we propose identifying biologically informative loci as outliers from the genome-wide correlation pattern, rather than deviations from equality between sexes. We illustrate this framework using traits including SHBG, LDL, and urate.
Overall, this work refines the interpretation of gene-by-sex effects and suggests a more general framework for studying genetic interactions in complex traits.
