Association Studies of BMI and Type 2 Diabetes in the Neuropeptide Y Pathway

Campbell, Catarina D.; Lyon, Helen N.; Nemesh, James; Drake, Jared A.; Tuomi, Tiinamaija; Gaudet, Daniel; Zhu, Xiaofeng; Cooper, Richard S.; Ardlie, Kristin; Groop, Leif; Hirschhorn, Joel N.

doi:10.2337/db06-1051

Cited by 52 publications

(39 citation statements)

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“…The earlier genetic and epidemiological studies indicate that the NPY Leu7Pro allele is a major risk factor for obesity (Ding et al, 2005;van Rossum et al, 2006). In Table 2 NPY gene polymorphisms and obesity (Campbell et al, 2007). Ding (2003) reported that the Pro7 allele might have originated in the north of Europe and then spread to the neighboring regions, and the frequency showed a decreasing north to south gradient and also stated that the highest allele frequencies were found in the Nordic countries.…”

Section: Discussionmentioning

confidence: 99%

Neuropeptide Y gene polymorphisms are not associated with obesity in a South Indian population

et al. 2010

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Background/Objectives: Neuropeptide Y (NPY) gene has been shown to have a critical role in the regulation of satiety, reproduction, central endocrine and cardiovascular systems. Among the primary functions associated with NPY are its acute effects on feeding behavior and energy expenditure. The aim of this study is to evaluate the relationship between obesity and NPY gene polymorphisms in a South Indian Population. Subjects/Methods: Three polymorphisms in NPY gene (Leu7Pro, Ser50Ser and A7735G) were analyzed in 263 individuals of an endogamous Kota population. On the basis of body mass index (BMI), they were divided into two groups. Associations were tested using logistic regression and haplotype analyses and linkage disequilibrium (LD). Results: There was no evidence of deviation from Hardy-Weinberg equilibrium. Logistic regression analysis did not reveal significant association with obesity and NPY single-nucleotide polymorphisms (SNPs) in the present study. All three SNPs were in weak LD with low r 2 values. Haplotype analysis also did not yield significant association between NPY gene and obesity (global P ¼ 0.756). Conclusions: Our study did not validate the association between previously implicated SNPs in NPY gene and obesity in an Indian population. Population-specific validation of putative associations has far reaching implications for the future personal genomics medicine applications.

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Section: Discussionmentioning

confidence: 99%

Neuropeptide Y gene polymorphisms are not associated with obesity in a South Indian population

et al. 2010

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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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“…Assays were multiplexed up to a maximum of five using the homologous Mass Extension (hME) protocol (Gabriel et al 2002;Campbell et al 2007). The iPLEX protocol was used to multiplex assays up to a maximum of 25 as follows.…”

Section: Replicatedmentioning

confidence: 99%

A survey of allelic imbalance in F1 mice

Campbell¹,

Kirby²,

Nemesh³

et al. 2008

Genome Res.

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There are widespread, genetically determined differences in gene expression. However, methods that compare transcript levels between individuals are subject to trans-acting effects and environmental differences. By looking at allele-specific expression in the F1 progeny of inbred mice, we can directly test for allelic imbalance (AI), which must be due to cis-acting variants in the parental strains. We tested over one hundred genes for AI between C57Bl/6J and A/J alleles in F1 mice, including a validation set of 23 genes enriched for cis-acting variants and a second set of 92 genes whose orthologs were previously examined for AI in humans. We assayed an average of two transcribed SNPs per gene in liver, spleen, and brain from three male and three female F1 mice. In the set of 92 genes, we observed 33 genes (36%) with significant AI including genes with AI that was specific to certain tissues or transcripts. We also observed extensive tissue-specific AI, with 11 out of 92 genes (12%) having differences in AI between tissues. Interestingly, several genes with alternate transcripts have transcript-specific AI. Finally, we observed that the presence of AI in human genes was correlated to the presence of AI in the mouse orthologs (one-tailed P = 0.003), suggesting that certain genes may be more tolerant of cis-acting variation across species.

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Association Studies of BMI and Type 2 Diabetes in the Neuropeptide Y Pathway

Cited by 52 publications

References 50 publications

Neuropeptide Y gene polymorphisms are not associated with obesity in a South Indian population

Neuropeptide Y gene polymorphisms are not associated with obesity in a South Indian population

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

A survey of allelic imbalance in F1 mice

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