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ARS Home » Northeast Area » Boston, Massachusetts » Jean Mayer Human Nutrition Research Center On Aging » Research » Publications at this Location » Publication #334576
Title: Functional genomics analysis of big data identifies novel peroxisome proliferator–activated receptor gamma target single nucleotide polymorphisms showing association with cardiometabolic outcomes

Author
item RICHARDSON, KRIS - Jean Mayer Human Nutrition Research Center On Aging At Tufts University
item SCHNITZLER, GAVIN - Tufts University
item Lai, Chao Qiang
item ORDOVAS, JOSE - Jean Mayer Human Nutrition Research Center On Aging At Tufts University

Submitted to: Circulation: Cardiovascular Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/22/2015
Publication Date: 12/8/2015
Citation: Richardson, K., Schnitzler, G.R., Lai, C., Ordovas, J.M. 2015. Functional genomics analysis of big data identifies novel peroxisome proliferator–activated receptor gamma target single nucleotide polymorphisms showing association with cardiometabolic outcomes. Circulation: Cardiovascular Genetics. 8:842-851. doi: 10.1161/CIRCGENETICS.115.001174.

Interpretive Summary: Cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) are major age-related diseases and both of them share environmental and genetic components. However, most of the specific genetic factors involved in the individual predisposition to these diseases remain unknown. An important player in their pathogenesis is peroxisome proliferator-activated receptor gamma (PPARG), a key regulator of gene expression that is involved in lipid and glucose metabolism and maintenance of metabolic homeostasis. We used a functional genomics approach to interrogate all the genomic data available to identify functional polymorphisms related to the action of this master regulator. Our search identified 146 SNPs in regions of potential PPARG activity. A screen of these SNPs against genome-wide association studies for cardiometabolic traits revealed significant enrichment with 16 SNPs. And further filtering of the data revealed 8 of these were significantly associated with altered gene expression in human adipose. Several SNPs fall close, or are linked by expression of genes related to lipid-metabolism, including CYP26A1. In summary, we have demonstrated that the use of functional genomics and Big Data can identify genetic variants of functional significance that can be used to better predict an individual’s risk for CVD and T2DM.

Technical Abstract: Background Cardiovascular disease and type 2 diabetes mellitus represent overlapping diseases where a large portion of the variation attributable to genetics remains unexplained. An important player in their pathogenesis is peroxisome proliferator–activated receptor gamma (PPARgamma) that is involved in lipid and glucose metabolism and maintenance of metabolic homeostasis. We used a functional genomics methodology to interrogate human chromatin immunoprecipitation-sequencing, genome-wide association studies, and expression quantitative trait locus data to inform selection of candidate functional single nucleotide polymorphisms (SNPs) falling in PPARgamma motifs. Methods and Results We derived 27 328 chromatin immunoprecipitation-sequencing peaks for PPARgamma in human adipocytes through meta-analysis of 3 data sets. The PPARgamma consensus motif showed greatest enrichment and mapped to 8637 peaks. We identified 146 SNPs in these motifs. This number was significantly less than would be expected by chance, and Inference of Natural Selection from Interspersed Genomically coHerent elemenTs analysis indicated that these motifs are under weak negative selection. A screen of these SNPs against genome-wide association studies for cardiometabolic traits revealed significant enrichment with 16 SNPs. A screen against the MuTHER expression quantitative trait locus data revealed 8 of these were significantly associated with altered gene expression in human adipose, more than would be expected by chance. Several SNPs fall close, or are linked by expression quantitative trait locus to lipid-metabolism loci including CYP26A1. Conclusions We demonstrated the use of functional genomics to identify SNPs of potential function. Specifically, that SNPs within PPARgamma motifs that bind PPARgamma in adipocytes are significantly associated with cardiometabolic disease and with the regulation of transcription in adipose. This method may be used to uncover functional SNPs that do not reach significance thresholds in the agnostic approach of genome-wide association studies.