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Obesity Subtypes & Cardiometabolic Health: Genetic Insights

Obesity Subtypes & Cardiometabolic Health: Genetic Insights

Decoding the⁣ Genetic Landscape of Adipose Tissue: Leveraging Multi-Omics Data

Understanding the genetic‍ factors influencing adipose tissue⁤ – both visceral (VAT) and subcutaneous – is crucial for​ tackling⁣ metabolic diseases.​ Our research delves into ⁢the complex interplay between genetic variants and⁣ gene expression, utilizing a ⁢powerful combination⁤ of data sources to pinpoint key regulatory mechanisms. This article details the methodologies employed to identify and​ prioritize genetic loci impacting ⁤adipose tissue function.

Integrating ​Diverse data Sources for Robust Analysis

We built our analysis on a foundation of comprehensive datasets, allowing for a nuanced understanding ‍of‌ gene regulation. These included:

* eQTL Data from GTEx (v.8): This resource ​provided critical details on genetic variants (SNPs)⁢ that influence gene expression levels in both VAT and subcutaneous adipose tissue.
* Enhancer Capture HiC Data​ (‘GSE140782_ECHiC.txt.gz’): This data ⁢mapped the physical interactions between⁣ enhancers and promoter regions, revealing long-range​ regulatory connections.
*​ DNase-seq Data (‘GSE113253_DNase_processed_data.tar.gz’): ⁣ This identified⁣ regions⁤ of open chromatin,indicating ⁣areas ⁣accessible ‍for gene regulation. ​we also ‍analyzed changes in‌ accessibility during ‍adipocyte differentiation.
*⁤ ​ Gene Expression‍ Data (‘GSE113253_GeneExpr_BM.txt.gz’): This provided a baseline and tracked changes in gene expression ‍during the ⁣differentiation of human ‌bone‍ marrow-derived mesenchymal stem ‌cells ⁤(hBM-MSCs) into adipocytes in ‍vitro.

Prioritizing Genetic Variants and Candidate Genes

Identifying which SNPs⁤ are most likely to influence adipose ⁣tissue traits required a rigorous filtering‌ process.‍ we focused on lead SNPs or their‍ proxies – variants strongly ⁤correlated (R2 >‌ 0.8, determined using the LDlinkR package) – and applied the following criteria:

  1. eQTL Overlap: The SNP overlapped with a⁢ known eQTL ‌in either VAT ​or subcutaneous‍ adipose tissue.
  2. Enhancer-Promoter Linkage: The ⁤SNP resided within a genomic ⁢region⁢ identified by enhancer capture HiC data as interacting with a promoter⁤ region.
  3. Chromatin Accessibility: The SNP overlapped with a ⁢DNase1 ‌hypersensitive​ site, indicating⁣ an open chromatin​ region.
  4. Proximity to Genes: If⁣ only chromatin accessibility was ⁣observed, we ‌identified the closest transcription start site as the ⁢candidate gene.
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This multi-layered approach ensured we ⁤focused ‌on variants with the highest​ potential for functional impact.

Scoring system for Enhanced Prioritization

For each unique SNP-gene pair, we developed a scoring system to ⁣rank potential regulatory‍ relationships. This score incorporated:

* ​ Overlap of‍ proxy SNPs‌ with eQTLs.
* Overlap of proxy ​SNPs with DNase1 hypersensitive sites and changes in accessibility during adipocyte differentiation.
* overlap of proxy​ SNPs with enhancer regions contacting ‌the ‌candidate gene’s promoter.
* Expression levels‌ of⁤ the candidate gene and its changes during adipocyte differentiation.

This integrated score allowed us to prioritize the most promising‌ genetic​ regulators.

Uncovering⁢ Biological Pathways⁢ Through Enrichment ‌Analysis

To understand the broader ‍biological ​implications of‌ our findings,we​ performed tissue and gene set enrichment analysis using​ DEPICT. This ‍tool leverages summary statistics from our‍ analysis⁣ of body fat percentage (BFP) to identify pathways substantially enriched for associated genes.

We focused on:

* Gene Ontology (GO) terms.

* KEGG pathways.

* REACTOME pathways.

Analyses were conducted on both all identified genetic loci and those specific to defined clusters. We ​included gene sets ⁢with at least ten ⁣genes and reported all P values generated by DEPICT,‌ regardless of false discovery rate⁢ (FDR). This comprehensive approach revealed key biological processes influenced by the ‌identified genetic variants.

Commitment‌ to Research Transparency

Further details regarding ⁣our research‍ design and methodology are available in the⁢ Nature‌ Portfolio Reporting Summary, accessible here.‍ we⁣ believe in transparency and reproducibility, ensuring our findings can be rigorously evaluated and built upon by ⁤the scientific

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