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A set-based association test identifies sex-specific gene sets associated with type 2 diabetes.

ABSTRACT: Single variant analysis in genome-wide association studies (GWAS) has been proven to be successful in identifying thousands of genetic variants associated with hundreds of complex diseases. However, these identified variants only explain a small fraction of inheritable variability in many diseases, suggesting that other resources, such as multilevel genetic variations, may contribute to disease susceptibility. In this work, we proposed to combine genetic variants that belong to a gene set, such as at gene- and pathway-level to form an integrated signal aimed to identify major players that function in a coordinated manner conferring disease risk. The integrated analysis provides novel insight into disease etiology while individual signals could be easily missed by single variant analysis. We applied our approach to a genome-wide association study of type 2 diabetes (T2D) with male and female data analyzed separately. Novel sex-specific genes and pathways were identified to increase the risk of T2D. We also demonstrated the performance of signal integration through simulation studies.

SUBMITTER: He T

PROVIDER: S-EPMC4228910 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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A set-based association test identifies sex-specific gene sets associated with type 2 diabetes.

He Tao T Zhong Ping-Shou PS Cui Yuehua Y

Frontiers in genetics 20141112

Single variant analysis in genome-wide association studies (GWAS) has been proven to be successful in identifying thousands of genetic variants associated with hundreds of complex diseases. However, these identified variants only explain a small fraction of inheritable variability in many diseases, suggesting that other resources, such as multilevel genetic variations, may contribute to disease susceptibility. In this work, we proposed to combine genetic variants that belong to a gene set, such ...[more]

PMID: 25429300

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Project description:This study aims to investigate the relationship between single nucleotide polymorphisms (SNPs) of sex hormone binding globulin (SHBG) and type 2 diabetes mellitus (T2DM) in an Uighur population. One hundred and fourteen T2DM male patients (with a history of diabetes or diagnosed as diabetic by the oral glucose tolerance test) and 173 healthy males from the Uighur ethnic group were included in the study to test the following SNPs of SHBG: rs727428, rs1799941, rs6259, rs6257, rs858521, rs858518, rs3760213, and rs11078701. The body mass index (BMI), blood pressure, and waist circumference, and lipid, glucose, HbA1c, insulin, HOMA-IR, testosterone, and SHBG levels of enrolled individuals were measured. We used the t-test or rank sum test and Chi-square test to analyze the difference and compare numeration data, respectively, between the case and control groups. Comparisons among multiple groups were carried out using analysis of variance, and the correlation between variables was determined by nonparametric Spearman rank correlation analysis; multivariate logistic regression analysis was used to assess the risk of abnormal glucose in the two groups. There was a significant difference (P < 0.05) in BMI, blood pressure, and waist circumference, and lipid, glucose, HbA1c, insulin, and HOMA-IR levels between the case and control groups. The risk factors for diabetes included testosterone (P = 0.042) and SHBG (P = 0.001). The distribution of rs858521 (P = 0.001), rs1799941 (2.3%, P = 0.032), rs6259 (2.5%, P = 0.040), and rs727428 (3.4%, P = 0.016) was significantly different between the case and control groups (P < 0.05). In the control group, there was linkage disequilibrium (LD) between rs727428 and rs6259, while in the case group LD was found among rs858518, rs3760213, rs1799941, and rs6257. The frequency of rs858518-rs3760213-rs1799941-rs6257 haplotype TCGC was significantly different between the two groups (P = 0.033). Both testosterone and SHBGwere found to be risk factors of diabetes in the Uighur population, and SNPs of SHBG may contribute to T2DM.

Project description:BACKGROUND: Until recently, genome-wide association study (GWAS)-based findings have provided a substantial genetic contribution to type 2 diabetes mellitus (T2DM) or related glycemic traits. However, identification of allelic heterogeneity and population-specific genetic variants under consideration of potential confounding factors will be very valuable for clinical applicability. To identify novel susceptibility loci for T2DM and glycemic traits, we performed a two-stage genetic association study in a Korean population. METHODS: We performed a logistic analysis for T2DM, and the first discovery GWAS was analyzed for 1,042 cases and 2,943 controls recruited from a population-based cohort (KARE, n=8,842). The second stage, de novo replication analysis, was performed in 1,216 cases and 1,352 controls selected from an independent population-based cohort (Health 2, n=8,500). A multiple linear regression analysis for glycemic traits was further performed in a total of 14,232 nondiabetic individuals consisting of 7,696 GWAS and 6,536 replication study participants. A meta-analysis was performed on the combined results using effect size and standard errors estimated for stage 1 and 2, respectively. RESULTS: A combined meta-analysis for T2DM identified two new (rs11065756 and rs2074356) loci reaching genome-wide significance in CCDC63 and C12orf51 on the 12q24 region. In addition, these variants were significantly associated with fasting plasma glucose and homeostasis model assessment of ?-cell function. Interestingly, two independent single nucleotide polymorphisms were associated with sex-specific stratification in this study. CONCLUSION: Our study showed a strong association between T2DM and glycemic traits. We further observed that two novel loci with multiple diverse effects were highly specific to males. Taken together, these findings may provide additional insights into the clinical assessment or subclassification of disease risk in a Korean population.

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A set-based association test identifies sex-specific gene sets associated with type 2 diabetes.

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A set-based association test identifies sex-specific gene sets associated with type 2 diabetes.

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