| Title: | Exome-wide association analysis of CT imaging-derived hepatic fat in a medical biobank |
| Journal: | Cell Reports Medicine |
| Published: | 12 Dec 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36513072/ |
| DOI: | https://doi.org/10.1016/j.xcrm.2022.100855 |
Publication 8043
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Abstract
Nonalcoholic fatty liver disease is common and highly heritable. Genetic studies of hepatic fat have not sufficiently addressed non-European and rare variants. In a medical biobank, we quantitate hepatic fat from clinical computed tomography (CT) scans via deep learning in 10,283 participants with whole-exome sequences available. We conduct exome-wide associations of single variants and rare predicted loss-of-function (pLOF) variants with CT-based hepatic fat and perform cross-modality replication in the UK Biobank (UKB) by linking whole-exome sequences to MRI-based hepatic fat. We confirm single variants previously associated with hepatic fat and identify several additional variants, including two (FGD5 H600Y and CITED2 S198_G199del) that replicated in UKB. A burden of rare pLOF variants in LMF2 is associated with increased hepatic fat and replicates in UKB. Quantitative phenotypes generated from clinical imaging studies and intersected with genomic data in medical biobanks have the potential to identify molecular pathways associated with human traits and disease.16 Authors
- Joseph Park
- Matthew T. MacLean
- Anastasia M. Lucas
- Drew A. Torigian
- Carolin V. Schneider
- Tess Cherlin
- Brenda Xiao
- Jason E. Miller
- Yuki Bradford
- Renae L. Judy
- Regeneron Genetics Center
- Anurag Verma
- Scott M. Damrauer
- Marylyn D. Ritchie
- Walter R. Witschey
- Daniel J. Rader
1 Application
| Application ID | Title |
|---|---|
| 32133 | Integration of multi-organ imaging phenotypes, clinical phenotypes, and genomic data |
Enabling scientific discoveries that improve human health