GeneSIS: enhancing transferability of polygenic scores with variant-level gene-by-sex interaction effects
- 1UCLA: University of California Los Angeles
Abstract
Advancing precision medicine requires accurate prediction of disease liability across populations and contexts. A major challenge is the limited transferability of polygenic scores (PGS) across genetic ancestry groups. We present GeneSIS (GENE and Sex Interaction Score), a supervised statistical learning framework for jointly modeling additive and context-dependent genetic effects at single-variant resolution directly from individual-level data. We analyze 406,659 individuals, including admixed individuals, in the UK Biobank and 1.3 million variants to develop predictive models for 99 complex traits. We report that ~8% of selected variables capture gene-by-sex (GxS) effects, validated by sex-stratified analyses. Modeling GxS effects improves prediction across 32 traits in non-European individuals. For predicting hip circumference in Africans, GeneSIS achieves a 3.7-fold improvement (p=8.0x10-7) over linear-only PGS and highlights biologically plausible hypotheses, such as pleiotropic GxS effects of GCKR (rs1260326) on anthropometry and menopause, as well as GxS pathway enrichments for interleukin-4 regulation. Overall, our results highlight the benefits of integrating context-dependent effects in human genetics studies.
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BibTeX
@misc{tanigawa2026genesis,
title = {{GeneSIS: enhancing transferability of polygenic scores with variant-level gene-by-sex interaction effects}},
author = {Y. Tanigawa and M. Kellis},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.14.751452},
url = {https://www.biorxiv.org/content/10.64898/2026.09.14.751452v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - GeneSIS: enhancing transferability of polygenic scores with variant-level gene-by-sex interaction effects
AU - Y. Tanigawa
AU - M. Kellis
PY - 2026
DA - 2026/09/14
PB - bioRxiv
DO - 10.64898/2026.09.14.751452
UR - https://www.biorxiv.org/content/10.64898/2026.09.14.751452v1
AB - Advancing precision medicine requires accurate prediction of disease liability across populations and contexts. A major challenge is the limited transferability of polygenic scores (PGS) across genetic ancestry groups. We present GeneSIS (GENE and Sex Interaction Score), a supervised statistical learning framework for jointly modeling additive and context-dependent genetic effects at single-variant resolution directly from individual-level data. We analyze 406,659 individuals, including admixed individuals, in the UK Biobank and 1.3 million variants to develop predictive models for 99 complex traits. We report that ~8% of selected variables capture gene-by-sex (GxS) effects, validated by sex-stratified analyses. Modeling GxS effects improves prediction across 32 traits in non-European individuals. For predicting hip circumference in Africans, GeneSIS achieves a 3.7-fold improvement (p=8.0x10-7) over linear-only PGS and highlights biologically plausible hypotheses, such as pleiotropic GxS effects of GCKR (rs1260326) on anthropometry and menopause, as well as GxS pathway enrichments for interleukin-4 regulation. Overall, our results highlight the benefits of integrating context-dependent effects in human genetics studies.
ER -