A Systems Genetics Approach Implicates USF1, FADS3, and Other Causal Candidate Genes for Familial Combined Hyperlipidemia

Abstract

We hypothesized that a common SNP in the 3' untranslated region of the upstream transcription factor 1 (USF1), rs3737787, may affect lipid traits by influencing gene expression levels, and we investigated this possibility utilizing the Mexican population, which has a high predisposition to dyslipidemia. We first associated rs3737787 genotypes in Mexican Familial Combined Hyperlipidemia (FCHL) case/control fat biopsies, with global expression patterns. To identify sets of co-expressed genes co-regulated by similar factors such as transcription factors, genetic variants, or environmental effects, we utilized weighted gene co-expression network analysis (WGCNA). Through WGCNA in the Mexican FCHL fat biopsies we identified two significant Triglyceride (TG)-associated co-expression modules. One of these modules was also associated with FCHL, the other FCHL component traits, and rs3737787 genotypes. This USF1-regulated FCHL-associated (URFA) module was enriched for genes involved in lipid metabolic processes. Using systems genetics procedures we identified 18 causal candidate genes in the URFA module. The FCHL causal candidate gene fatty acid desaturase 3 (FADS3) was associated with TGs in a recent Caucasian genome-wide significant association study and we replicated this association in Mexican FCHL families. Based on a USF1-regulated FCHL-associated co-expression module and SNP rs3737787, we identify a set of causal candidate genes for FCHL-related traits. We then provide evidence from two independent datasets supporting FADS3 as a causal gene for FCHL and elevated TGs in Mexicans.

Genetic causes of high and low serum HDL-cholesterol

“…A particularly powerful analysis for complex traits involves the integration of common DNA variation, global expression, and clinical phenotypes ( 238 ). Currently, this type of analysis is mainly applied to model systems, such as mice, but recent analyses of liver and adipose biopsies clearly demonstrate that these network approaches are applicable to complex traits in humans ( 235,239,240 ). Furthermore, by integrating cross-species network data of human and mice, Schadt et al ( 235 ) were able to validate novel GWA susceptibility genes for LDL-C levels: PSRC1, CELSR2, and SORT1.…”

Elucidating Molecular Networks That Either Affect or Respond to Plasma Cortisol Concentration in Target Tissues of Liver and Muscle

“…Candidate gene screening from eQTL and causality modeling Recently, causality modeling was successfully applied in a variety of different settings (Farber et al 2009a,b;Plaisier et al 2009;Park et al 2011). With the causality modeling algorithms, we integrated GWA and eQTL markers to identify up-and downstream molecules related to cortisol concentration.…”

“…Recently, causality modeling algorithms have been developed by using genetic markers as causal anchors for orienting the edges of a trait network. These algorithms proved effective in establishing causality by integration of gene expression, QTL, and association (Farber et al 2009a,b;Plaisier et al 2009). Using this approach, we determined the biological function of genes in liver and muscle correlated with plasma cortisol concentrations.…”

New Directions in Networks and Systems Approaches to Cardiovascular Disease

“…There are several examples of WGCNA analysis identifying transcript networks in animal and human studies [30][31][32][33]. In our own previous study, we constructed weighted gene co-expression networks in adipose RNA samples from Mexican familial combined hyperlipidmia (FCHL) family members who underwent a subcutaneous fat biopsy [33].…”

“…In our own previous study, we constructed weighted gene co-expression networks in adipose RNA samples from Mexican familial combined hyperlipidmia (FCHL) family members who underwent a subcutaneous fat biopsy [33]. By integrating the upstream transcription factor 1 (USF1) SNP rs3737787 with the gene expression levels in adipose tissue, we were able to attribute function to this SNP in USF1 associated with FCHL in European origin and Mexican families [34,35].…”