In:
Diabetes, American Diabetes Association, Vol. 73, No. Supplement_1 ( 2024-06-14)
Abstract:
Introduction & Objective: High-risk single-nucleotide polymorphisms (SNPs) account for only ~20% of type 2 diabetes heritability, implying islet genes greatly influence one another to alter diabetes risk. Correlative analysis does not reveal conditional dependencies between key players regulating islet function whereas machine learning (ML) may do so. Methods: Using data from 374 genetically diverse mice, we derived models predicting islet function from protein abundance with gradient-boosted decision tree algorithms. Some mice (70%) were used to train the models and the rest were used to validate them. We also applied our models to similar data from other mouse studies and to data from humans to see if the models’ accuracies replicated. We then determined if the models included any known islet regulators, proteins with human orthologues possessing glycemia-related SNPs, and enriched for islet-relevant functional pathways. Finally, using the proteins’ predictive influences as meta-traits, we ran quantitative trait locus (QTL) scans to identify genomic regions altering the influence of proteins on islet function. Results: Our analysis revealed & lt; 150 of the ~ 5000 detected proteins sufficiently modeled any one functional trait, with & gt; 90% of a model’s prediction influenced by & lt; 50 proteins. Some models predicted as well or better in the other mouse sets (R & gt; 0.6) and reasonably well in human data (R & gt; 0.5). ML analysis correctly identified the direction of effect for candidates (e.g. DPP8) where prior studies using correlation alone failed. Models for islet traits enriched for pathways potentially relevant for islet function and several unstudied proteins (e.g. CRELD2) present in multiple models have SNPs for glycemia-related traits. Finally, QTL scans revealed genomic regions that may alter the influence of key proteins on islet function and are far from those proteins’ genomic positions. Conclusions: ML can be used to identify conditional dependencies regulating islet function. Disclosure C. Emfinger: None. E. Freiberger: Employee; AbbVie Inc. M. Rabaglia: None. J. Kolic: None. S. Simonett: Employee; Exact Sciences. M. Shortreed: None. L. Clark: None. D. Stapleton: None. K. Schueler: None. K. Mitok: None. T.R. Price: None. J.J. Coon: Research Support; Agilent, Eli Lilly and Company, Merck & Co., Inc. L. Smith: None. M.J. Merrins: None. J.D. Johnson: None. M. Keller: None. A.D. Attie: None. Funding American Diabetes Association (7-21-PDF-157); National Institutes of Health (R01-DK101573-06); National Institutes of Health (GM070683); National Institutes of Health (P41-GM108538); National Institutes of Health (R35GM126914); National Institutes of Health (R35-GM118110); National Institutes of Health (T32-HL007936); National Institutes of Health (R01-DK113103); National Institutes of Health (R01DK127637); Canadian Institutes for Health Research (CIHR) operating grant 168857 CIHR Team Grant (ASD-179092/5-SRA-2021-1149-S-B); CIHR-JDRF Team (ASD-173663/5-SRA-2020-1059-S-B); CIHR Banting fellowship United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service (I01BX005113)
Type of Medium:
Online Resource
ISSN:
0012-1797
Language:
English
Publisher:
American Diabetes Association
Publication Date:
2024
detail.hit.zdb_id:
1501252-9
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