Person:

Christiani, David

GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary. Several methods have been proposed based on grouping SNPs into SNP sets using biological knowledge and/or genomic features. In this article, we compare the linear kernel machine based test (LKM) and principal components analysis based approach (PCA) using simulated datasets under the scenarios of 0 to 3 causal SNPs, as well as simple and complex linkage disequilibrium (LD) structures of the simulated regions. Our simulation study demonstrates that both LKM and PCA can control the type I error at the significance level of 0.05. If the causal SNP is in strong LD with the genotyped SNPs, both the PCA with a small number of principal components (PCs) and the LKM with kernel of linear or identical-by-state function are valid tests. However, if the LD structure is complex, such as several LD blocks in the SNP set, or when the causal SNP is not in the LD block in which most of the genotyped SNPs reside, more PCs should be included to capture the information of the causal SNP. Simulation studies also demonstrate the ability of LKM and PCA to combine information from multiple causal SNPs and to provide increased power over individual SNP analysis. We also apply LKM and PCA to analyze two SNP sets extracted from an actual GWAS dataset on non-small cell lung cancer.

Background: Chronic inflammation from recurring trauma is an underlying pathophysiological basis of numerous diseases. Furthermore, it may result in cell death, scarring, fibrosis, and loss of tissue function. In states of inflammation, subsequent increases in oxidative stress and cellular division may lead to the accelerated erosion of telomeres, crucial genomic structures which protect chromosomes from decay. However, the association between plasma inflammatory marker concentrations and telomere length has been inconsistent in previous studies. Objective: The purpose of this study was to determine the longitudinal association between telomere length and plasma inflammatory biomarker concentrations including: CRP, SAA, sICAM-1, sVCAM-1, VEGF, TNF-α, IL-1β, IL-2, IL-6, IL-8, and IL-10. Methods: The longitudinal study population consisted of 87 subjects. The follow-up period was approximately 2 years. Plasma inflammatory biomarker concentrations were assessed using highly sensitive electrochemiluminescent assays. Leukocyte relative telomere length was assessed using Real-Time qPCR. Linear mixed effects regression models were used to analyze the association between repeated-measurements of relative telomere length as the outcome and each inflammatory biomarker concentration as continuous exposures separately. The analyses controlled for major potential confounders and white blood cell differentials. Results: At any follow-up time, each incremental ng/mL increase in plasma CRP concentration was associated with a decrease in telomere length of −2.6×10−2 (95%CI: −4.3×10−2, −8.2×10−3, p = 0.004) units. Similarly, the estimate for the negative linear association between SAA and telomere length was −2.6×10−2 (95%CI:−4.5×10−2, −6.1×10−3, p = 0.011). No statistically significant associations were observed between telomere length and plasma concentrations of pro-inflammatory interleukins, TNF-α, and VEGF. Conclusions: Findings from this study suggest that increased systemic inflammation, consistent with vascular injury, is associated with decreased leukocyte telomere length.

Background: Chronic exposure to arsenic in drinking water is associated with increased risk of type 2 diabetes mellitus (T2DM) but the underlying molecular mechanism remains unclear. Objectives: This study evaluated the interaction between single nucleotide polymorphisms (SNPs) in genes associated with diabetes and arsenic exposure in drinking water on the risk of developing T2DM. Methods: In 2009–2011, we conducted a follow up study of 957 Bangladeshi adults who participated in a case-control study of arsenic-induced skin lesions in 2001–2003. Logistic regression models were used to evaluate the association between 38 SNPs in 18 genes and risk of T2DM measured at follow up. T2DM was defined as having a blood hemoglobin A1C level greater than or equal to 6.5% at follow-up. Arsenic exposure was characterized by drinking water samples collected from participants' tubewells. False discovery rates were applied in the analysis to control for multiple comparisons. Results: Median arsenic levels in 2001–2003 were higher among diabetic participants compared with non-diabetic ones (71.6 µg/L vs. 12.5 µg/L, p-value <0.001). Three SNPs in ADAMTS9 were nominally associated with increased risk of T2DM (rs17070905, Odds Ratio (OR) = 2.30, 95% confidence interval (CI) 1.17–4.50; rs17070967, OR = 2.02, 95%CI 1.00–4.06; rs6766801, OR = 2.33, 95%CI 1.18–4.60), but these associations did not reach the statistical significance after adjusting for multiple comparisons. A significant interaction between arsenic and NOTCH2 (rs699780) was observed which significantly increased the risk of T2DM (p for interaction = 0.003; q-value = 0.021). Further restricted analysis among participants exposed to water arsenic of less than 148 µg/L showed consistent results for interaction between the NOTCH2 variant and arsenic exposure on T2DM (p for interaction = 0.048; q-value = 0.004). Conclusions: These findings suggest that genetic variation in NOTCH2 increased susceptibility to T2DM among people exposed to inorganic arsenic. Additionally, genetic variants in ADAMTS9 may increase the risk of T2DM.