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Allele-specific quantitative proteomics unravels molecular mechanisms modulated by cis-regulatory PPARG locus variation.

ABSTRACT: Genome-wide association studies identified numerous disease risk loci. Delineating molecular mechanisms influenced by cis-regulatory variants is essential to understand gene regulation and ultimately disease pathophysiology. Combining bioinformatics and public domain chromatin information with quantitative proteomics supports prediction of cis-regulatory variants and enabled identification of allele-dependent binding of both, transcription factors and coregulators at the type 2 diabetes associated PPARG locus. We found rs7647481A nonrisk allele binding of Yin Yang 1 (YY1), confirmed by allele-specific chromatin immunoprecipitation in primary adipocytes. Quantitative proteomics also found the coregulator RING1 and YY1 binding protein (RYBP) whose mRNA levels correlate with improved insulin sensitivity in primary adipose cells carrying the rs7647481A nonrisk allele. Our findings support a concept with diverse cis-regulatory variants contributing to disease pathophysiology at one locus. Proteome-wide identification of both, transcription factors and coregulators, can profoundly improve understanding of mechanisms underlying genetic associations.

SUBMITTER: Lee H

PROVIDER: S-EPMC5389726 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Allele-specific quantitative proteomics unravels molecular mechanisms modulated by cis-regulatory PPARG locus variation.

Lee Heekyoung H Qian Kun K von Toerne Christine C Hoerburger Lena L Claussnitzer Melina M Hoffmann Christoph C Glunk Viktoria V Wahl Simone S Breier Michaela M Eck Franziska F Jafari Leili L Molnos Sophie S Grallert Harald H Dahlman Ingrid I Arner Peter P Brunner Cornelia C Hauner Hans H Hauck Stefanie M SM Laumen Helmut H

Nucleic acids research 20170401 6

Genome-wide association studies identified numerous disease risk loci. Delineating molecular mechanisms influenced by cis-regulatory variants is essential to understand gene regulation and ultimately disease pathophysiology. Combining bioinformatics and public domain chromatin information with quantitative proteomics supports prediction of cis-regulatory variants and enabled identification of allele-dependent binding of both, transcription factors and coregulators at the type 2 diabetes associat ...[more]

PMID: 28334807

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Project description:The genetic basis of gene expression variation has long been studied with the aim to understand the landscape of regulatory variants, but also more recently to assist in the interpretation and elucidation of disease signals. To date, many studies have looked in specific tissues and population-based samples, but there has been limited assessment of the degree of inter-population variability in regulatory variation. We analyzed genome-wide gene expression in lymphoblastoid cell lines from a total of 726 individuals from 8 global populations from the HapMap3 project and correlated gene expression levels with HapMap3 SNPs located in cis to the genes. We describe the influence of ancestry on gene expression levels within and between these diverse human populations and uncover a non-negligible impact on global patterns of gene expression. We further dissect the specific functional pathways differentiated between populations. We also identify 5,691 expression quantitative trait loci (eQTLs) after controlling for both non-genetic factors and population admixture and observe that half of the cis-eQTLs are replicated in one or more of the populations. We highlight patterns of eQTL-sharing between populations, which are partially determined by population genetic relatedness, and discover significant sharing of eQTL effects between Asians, European-admixed, and African subpopulations. Specifically, we observe that both the effect size and the direction of effect for eQTLs are highly conserved across populations. We observe an increasing proximity of eQTLs toward the transcription start site as sharing of eQTLs among populations increases, highlighting that variants close to TSS have stronger effects and therefore are more likely to be detected across a wider panel of populations. Together these results offer a unique picture and resource of the degree of differentiation among human populations in functional regulatory variation and provide an estimate for the transferability of complex trait variants across populations.

Dataset Information

Allele-specific quantitative proteomics unravels molecular mechanisms modulated by cis-regulatory PPARG locus variation.

Publications

Allele-specific quantitative proteomics unravels molecular mechanisms modulated by cis-regulatory PPARG locus variation.

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