Sequence-based variant-effect predictions reveal trait-specific regulatory-layer profiles of complex-trait heritability
- 1Korea Advanced Institute of Science and Technology
Abstract
Partitioned-heritability studies have shown which tissues carry the heritability of complex traits, but whether the regulatory layer whose annotations are most informative for heritability within a tissue differs between traits has not been tested, because measured annotations for different layers come from different sets of biosamples. Sequence-to-function models predict the allelic effect of every variant on several regulatory layers with a single model, so that layers can be compared on the same variants within the same tissues. We grouped AlphaGenome Atlas predictions into 12 lineages and six regulatory layers and partitioned the heritability of 11 complex traits with stratified LD score regression. Traits localized largely to the expected lineages, and within these lineages the most informative predicted layer differed between traits (P = 3.6 x 10-8). Predicted chromatin-accessibility (DNase) effects were the most informative for height and bone mineral density, whereas predicted transcription-initiation (CAGE) effects were the most informative for Alzheimers disease, most brain traits and cholesterol levels. The difference persisted across sensitivity analyses, and the predicted contrast held for three related traits whose layers had been predicted in advance (P = 2 x 10-7). Predicted CAGE effects also remained informative after conditioning on fine-mapped eQTLs and measured regulatory annotations. These results identify differences across traits in the heritability information captured by predicted regulatory layers and offer testable hypotheses for investigating the regulatory basis of complex traits.
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BibTeX
@misc{choi2026sequencebased,
title = {{Sequence-based variant-effect predictions reveal trait-specific regulatory-layer profiles of complex-trait heritability}},
author = {B. H. Choi and J. Jeong},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.16.26365786},
url = {https://www.medrxiv.org/content/10.64898/2026.10.16.26365786v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Sequence-based variant-effect predictions reveal trait-specific regulatory-layer profiles of complex-trait heritability
AU - B. H. Choi
AU - J. Jeong
PY - 2026
DA - 2026/10/16
PB - medRxiv
DO - 10.64898/2026.10.16.26365786
UR - https://www.medrxiv.org/content/10.64898/2026.10.16.26365786v1
AB - Partitioned-heritability studies have shown which tissues carry the heritability of complex traits, but whether the regulatory layer whose annotations are most informative for heritability within a tissue differs between traits has not been tested, because measured annotations for different layers come from different sets of biosamples. Sequence-to-function models predict the allelic effect of every variant on several regulatory layers with a single model, so that layers can be compared on the same variants within the same tissues. We grouped AlphaGenome Atlas predictions into 12 lineages and six regulatory layers and partitioned the heritability of 11 complex traits with stratified LD score regression. Traits localized largely to the expected lineages, and within these lineages the most informative predicted layer differed between traits (P = 3.6 x 10-8). Predicted chromatin-accessibility (DNase) effects were the most informative for height and bone mineral density, whereas predicted transcription-initiation (CAGE) effects were the most informative for Alzheimers disease, most brain traits and cholesterol levels. The difference persisted across sensitivity analyses, and the predicted contrast held for three related traits whose layers had been predicted in advance (P = 2 x 10-7). Predicted CAGE effects also remained informative after conditioning on fine-mapped eQTLs and measured regulatory annotations. These results identify differences across traits in the heritability information captured by predicted regulatory layers and offer testable hypotheses for investigating the regulatory basis of complex traits.
ER -