A strategy to search for common obesity and type 2 diabetes genes

Elbers, Clara C.; Onland‐Moret, N. Charlotte; Franke, Lude; Niehoff, Anne G.; Schouw, Yvonne T. van der; Wijmenga, Cisca

doi:10.1016/j.tem.2006.11.003

Cited by 43 publications

(38 citation statements)

References 78 publications

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“…Neuropeptide Y (NPY) interacts with the Y1 receptor (NPY1R) to control adrenergic activity and blood pressure (BP) [47]. Furthermore, NPY1R was identified as a positional candidate gene for both obesity and T2D [48]. A recent study demonstrated that the Y1 receptor plays an essential role in glucose transporter (GLUT4) translocation and may be a potential therapeutic target for type 2 diabetes [49].…”

Section: Discussionmentioning

confidence: 99%

Aberrant expression of long noncoding RNAs in cumulus cells isolated from PCOS patients

Huang

Hao

Bao

et al. 2015

J Assist Reprod Genet

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Purpose To describe the long noncoding RNA (lncRNA) profiles in cumulus cells isolated from polycystic ovary syndrome (PCOS) patients by employing a microarray and in-depth bioinformatics analysis. This information will help us understand the occurrence and development of PCOS. Methods In this study, we used a microarray to describe lncRNA profiles in cumulus cells isolated from ten patients (five PCOS and five normal women). Several differentially expressed lncRNAs were chosen to validate the microarray results by quantitative RT-PCR (qRT-PCR). Then, the differentially expressed lncRNAs were classified into three subgroups (HOX loci lncRNA, enhancer-like lncRNA, and lincRNA) to deduce their potential features. Furthermore, a lncRNA/mRNA co-expression network was constructed by using the Cytoscape software (V2.8.3, http://www.cytoscape.org/). Results We observed that 623 lncRNAs and 260 messenger RNAs (mRNAs) were significantly up-or down-regulated (≥2-fold change), and these differences could be used to discriminate cumulus cells of PCOS from those of normal pat i e n t s . F i v e d i f f e r e n t i a l l y e x p r e s s e d l n c R N A s (XLOC_011402, ENST00000454271, ENST00000433673, ENST00000450294, and ENST00000432431) were selected to validate the microarray results using quantitative RT-PCR (qRT-PCR). The qRT-PCR results were consistent with the microarray data. Further analysis indicated that many differentially expressed lncRNAs were transcribed from chromosome 2 and may act as enhancers to regulate their neighboring protein-coding genes. Forty-three lncRNAs and 29 mRNAs were used to construct the coding-non-coding gene coexpression network. Most pairs positively correlated, and one mRNA correlated with one or more lncRNAs. Conclusions Our study is the first to determine genome-wide lncRNA expression patterns in cumulus cells isolated from PCOS patients by microarray. The results show that clusters of lncRNAs were aberrantly expressed in cumulus cells of PCOS patients compared with those of normal women, which revealed that lncRNAs differentially expressed in PCOS and normal women may contribute to the occurrence of PCOS and affect oocyte development.

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Section: Discussionmentioning

confidence: 99%

Aberrant expression of long noncoding RNAs in cumulus cells isolated from PCOS patients

Huang

Hao

Bao

et al. 2015

J Assist Reprod Genet

View full text Add to dashboard Cite

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“…For example, Prioritizer predicted the NPY2R gene as being associated with obesity and T2D [Elbers et al, 2007], and a few months later Campbell et al published an article stating they had found association of NPY2R with both traits in 2,800 Caucasian individuals [Campbell et al, 2007].…”

Section: Network Analysesmentioning

confidence: 99%

Using genome‐wide pathway analysis to unravel the etiology of complex diseases

Elbers

Eijk

Franke

et al. 2009

Genetic Epidemiology

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Several genome-wide association studies (GWAS) have been published on various complex diseases. Although, new loci are found to be associated with these diseases, still only very little of the genetic risk for these diseases can be explained. As GWAS are still underpowered to find small main effects, and gene-gene interactions are likely to play a role, the data might currently not be analyzed to its full potential. In this study, we evaluated alternative methods to study GWAS data. Instead of focusing on the single nucleotide polymorphisms (SNPs) with the highest statistical significance, we took advantage of prior biological information and tried to detect overrepresented pathways in the GWAS data. We evaluated whether pathway classification analysis can help prioritize the biological pathways most likely to be involved in the disease etiology. In this study, we present the various benefits and limitations of pathway-classification tools in analyzing GWAS data. We show multiple differences in outcome between pathway tools analyzing the same dataset. Furthermore, analyzing randomly selected SNPs always results in significantly overrepresented pathways, large pathways have a higher chance of becoming statistically significant and the bioinformatics tools used in this study are biased toward detecting well-defined pathways. As an example, we analyzed data from two GWAS on type 2 diabetes (T2D): the Diabetes Genetics Initiative (DGI) and the Wellcome Trust Case Control Consortium (WTCCC). Occasionally the results from the DGI and the WTCCC GWAS showed concordance in overrepresented pathways, but discordance in the corresponding genes. Thus, incorporating gene networks and pathway classification tools into the analysis can point toward significantly overrepresented molecular pathways, which cannot be picked up using traditional single-locus analyses. However, the limitations discussed in this study, need to be addressed before these methods can be widely used. Genet. Epidemiol. 33:419-431, 2009.

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“…A great deal of effort has been expended in identifying genes underlying the risk for type 2 diabetes, including genome linkage scans (see 12,13), candidate gene studies (e.g., 14), and, more recently, genome-wide association studies (GWASs) (15)(16)(17)(18)(19). To date, such studies have yielded several replicated type 2 diabetes-associated risk genes including CAPN10, CDKAL1, CDKN2A, HHEX, HNF4A, IGF2BP2, KCNJ11, PPARG, SLC30A8, and TCF7L2 (20 -25), but none account for a large proportion of the risk of developing type 2 diabetes in the particular population under study nor are any seen universally across all populations.…”

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confidence: 99%

Identification of Type 2 Diabetes Genes in Mexican Americans Through Genome-Wide Association Studies

et al. 2007

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OBJECTIVE-The objective of this study was to identify DNA polymorphisms associated with type 2 diabetes in a MexicanAmerican population.RESEARCH DESIGN AND METHODS-We genotyped 116,204 single nucleotide polymorphisms (SNPs) in 281 Mexican Americans with type 2 diabetes and 280 random Mexican Americans from Starr County, Texas, using the Affymetrix GeneChip Human Mapping 100K set. Allelic association exact tests were calculated. Our most significant SNPs were compared with results from other type 2 diabetes genome-wide association studies (GWASs). Proportions of African, European, and Asian ancestry were estimated from the HapMap samples using structure for each individual to rule out spurious association due to population substructure.RESULTS-We observed more significant allelic associations than expected genome wide, as empirically assessed by permutation (14 below a P of 1 ϫ 10 Ϫ4 [8.7 expected]). No significant differences were observed between the proportion of ancestry estimates in the case and random control sets, suggesting that the association results were not likely confounded by substructure. A query of our top ϳ1% of SNPs (P Ͻ 0.01) revealed SNPs in or near four genes that showed evidence for association (P Ͻ 0.05) in multiple other GWAS interrogated: rs979752 and rs10500641 near UBQLNL and OR52H1 on chromosome 11, rs2773080 and rs3922812 in or near RALGPS2 on chromosome 1, and rs1509957 near EGR2 on chromosome 10. CONCLUSIONS-We

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A strategy to search for common obesity and type 2 diabetes genes

Cited by 43 publications

References 78 publications

Aberrant expression of long noncoding RNAs in cumulus cells isolated from PCOS patients

Aberrant expression of long noncoding RNAs in cumulus cells isolated from PCOS patients

Using genome‐wide pathway analysis to unravel the etiology of complex diseases

Identification of Type 2 Diabetes Genes in Mexican Americans Through Genome-Wide Association Studies

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