• Raffield, Laura M.
• Manning, Alisa
• Liu, Qing
• Madsen, Tracy
• North, Kari E.
• Lin, Xiaochen
• Flickinger, Matthew
• Reiner, Alex P.
• Vito, Roberta De
• Chan, Kei Hang Katie
• Consortium, Nhlbi Trans-Omics For Precision Medicine
• Meigs, James B.
• Liu, Simin
• Correa, Adolfo
• Cupples, L. Adrienne
• Lange, Leslie
• Manson, Joann E.
• Kooperberg, Charles
• Li, Jie
• Brody, Jennifer

Abstract

<jats:p><jats:bold>Background:</jats:bold>Diabesity defines the concurrent manifestation of type 2 diabetes (T2D) and obesity (BMI≥30 kg/m<jats:sup>2</jats:sup>) in the development of cardiovascular diseases, although the genetic basis for this joint phenotype remain poorly understood.</jats:p><jats:p><jats:bold>Objective:</jats:bold>This study aimed to identify the overlapping genetic patterns for diabesity incidence in 3,231 self-reported African American (AA) and 8,252 European Americans (EA) participated in four cohorts of the Trans-Omics for Precision Medicine (TOPMed) consortium.</jats:p><jats:p><jats:bold>Methods:</jats:bold>Using marker set enrichment analysis (MSEA) of whole genome sequencing data, specific gene sets (pathways) and key driver (KD) genes (important hub genes overrepresented in a network of pathways) were identified for diabesity incidence. Using multi-tissue and multi-species gene expression signatures as molecular indicators of drug functions, their potential drug signatures were also examined.</jats:p><jats:p><jats:bold>Results:</jats:bold>Testing genome-wide significance (P-value &lt; 10<jats:sup>-8</jats:sup>) identified seven independent loci, six of which were replicated in the T2D Knowledge Portal<jats:underline>(https://t2d.hugeamp.org/</jats:underline>)(<jats:italic>NPFFR1, TRIO, G6PD, BCL11A, IGF1,</jats:italic>and<jats:italic>TCF7L2,</jats:italic>P&lt;0.05) for diabetes and/or such obesity-related traits as blood pressure, lipids, glucose and insulin levels. One novel variant for diabesity, rs144540309, is an intronic region of<jats:italic>GPAT3</jats:italic>(G&gt;A, AA MAF = 0.004, beta=3.66 and P=1.00e-8) whose enzyme plays important roles in dietary lipid absorption, enteric and hepatic lipid homeostasis, and entero-endocrine hormone production, along with 12 KEGG/Reactome/Biocarta pathways enriched for diabesity in AAs and 11 for EAs. In AAs, the top three pathways (ranked by P-value and false discovery rate [FDR]) were mitotic spindle checkpoint, resolution of sister chromatid cohesion, and rho GTPases activate formins, along with six KD genes (<jats:italic>NCKAP1L, CDCA8</jats:italic>,<jats:italic>BUB1</jats:italic>,<jats:italic>IRF5</jats:italic>,<jats:italic>FYB</jats:italic>, and<jats:italic>C15orf23</jats:italic>); in EAs, colorectal cancer, prostate cancer, and beta Catenin independent WNT signaling were the top 3 pathways (FDR≤0.25) including<jats:italic>LEF1</jats:italic>. Top repositioned drugs derived from diabesity-related gene sets (FDR≤0.25) included Benzbromarone, Fenofibrate, Interleukin-1β, and antihypertensive.</jats:p><jats:p><jats:bold>Conclusion:</jats:bold>Our study supports the notion that both pathway and network-based analytical approaches may identify novel signals from gene sets for highly clustering clinical phenotypes such as diabetes and obesity and improve their target validation for intervention.</jats:p>

Topics

• impedance spectroscopy
• atomic absorpion spectrometry
• clustering
• elemental analysis