Xinyi Lin

Test for interactions between a genetic marker set and environment in generalized linear models

We consider in this paper testing for interactions between a genetic marker set and an environmental variable. A common practice in studying gene-environment (GE) interactions is to analyze one single-nucleotide polymorphism (SNP) at a time. It is of significant interest to analyze SNPs in a biologically defined set simultaneously, e.g. gene or pathway. In this paper, we first show that if the main effects of multiple SNPs in a set are associated with a disease/trait, the classical single SNP-GE interaction analysis can be biased. We derive the asymptotic bias and study the conditions under which the classical single SNP-GE interaction analysis is unbiased. We further show that, the simple minimum p-value-based SNP-set GE analysis, can be biased and have an inflated Type 1 error rate. To overcome these difficulties, we propose a computationally efficient and powerful gene-environment set association test (GESAT) in generalized linear models. Our method tests for SNP-set by environment interactions using a variance component test, and estimates the main SNP effects under the null hypothesis using ridge regression. We evaluate the performance of GESAT using simulation studies, and apply GESAT to data from the Harvard lung cancer genetic study to investigate GE interactions between the SNPs in the 15q24-25.1 region and smoking on lung cancer risk.

Kernel machine SNP-set analysis for censored survival outcomes in genome-wide association studies

In this article, we develop a powerful test for identifying single nucleotide polymorphism (SNP)‐sets that are predictive of survival with data from genome‐wide association studies. We first group typed SNPs into SNP‐sets based on genomic features and then apply a score test to assess the overall effect of each SNP‐set on the survival outcome through a kernel machine Cox regression framework. This approach uses genetic information from all SNPs in the SNP‐set simultaneously and accounts for linkage disequilibrium (LD), leading to a powerful test with reduced degrees of freedom when the typed SNPs are in LD with each other. This type of test also has the advantage of capturing the potentially nonlinear effects of the SNPs, SNP‐SNP interactions (epistasis), and the joint effects of multiple causal variants. By simulating SNP data based on the LD structure of real genes from the HapMap project, we demonstrate that our proposed test is more powerful than the standard single SNP minimum P‐value‐based test for association studies with censored survival outcomes. We illustrate the proposed test with a real data application. Genet. Epidemiol. 2011.

HIF3A association with adiposity: the story begins before birth

Aim: Determine if the association of HIF3A DNA methylation with weight and adiposity is detectable early in life. Material & methods: We determined HIF3A genotype and DNA methylation patterns (on hybridization arrays) in DNA extracted from umbilical cords of 991 infants. Methylation levels at three CpGs in the HIF3A first intron were related to neonatal and infant anthropometry and to genotype at nearby polymorphic sites. Results & conclusion: Higher methylation levels at three previously described HIF3A CpGs were associated with greater infant weight and adiposity. The effect sizes were slightly smaller than those reported for adult BMI. There was also an interaction within cis-genotype. The association between higher DNA methylation at HIF3A and increased adiposity is present in neonates. In this study, no particular prenatal factor strongly influenced HIF3A hypermethylation. Our data nonetheless suggest shared prenatal influences on HIF3A methylation and adiposity.

Kernel Machine SNP-set Testing under Multiple Candidate Kernels

Joint testing for the cumulative effect of multiple single‐nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large‐scale genetic association studies. The kernel machine (KM)‐testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori because this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest P‐value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power vs. using the best candidate kernel.

Traffic-Related Air Pollution, Blood Pressure, and Adaptive Response of Mitochondrial Abundance

Background— Exposure to black carbon (BC), a tracer of vehicular-traffic pollution, is associated with increased blood pressure (BP). Identifying biological factors that attenuate BC effects on BP can inform prevention. We evaluated the role of mitochondrial abundance, an adaptive mechanism compensating for cellular-redox imbalance, in the BC-BP relationship. Methods and Results— At ≥1 visits among 675 older men from the Normative Aging Study (observations=1252), we assessed daily BP and ambient BC levels from a stationary monitor. To determine blood mitochondrial abundance, we used whole blood to analyze mitochondrial-to-nuclear DNA ratio (mtDNA/nDNA) using quantitative polymerase chain reaction. Every standard deviation increase in the 28-day BC moving average was associated with 1.97 mm Hg (95% confidence interval [CI], 1.23–2.72; P<0.0001) and 3.46 mm Hg (95% CI, 2.06–4.87; P<0.0001) higher diastolic and systolic BP, respectively. Positive BC-BP associations existed throughout all time windows. BC moving averages (5-day to 28-day) were associated with increased mtDNA/nDNA; every standard deviation increase in 28-day BC moving average was associated with 0.12 standard deviation (95% CI, 0.03–0.20; P=0.007) higher mtDNA/nDNA. High mtDNA/nDNA significantly attenuated the BC-systolic BP association throughout all time windows. The estimated effect of 28-day BC moving average on systolic BP was 1.95-fold larger for individuals at the lowest mtDNA/nDNA quartile midpoint (4.68 mm Hg; 95% CI, 3.03–6.33; P<0.0001), in comparison with the top quartile midpoint (2.40 mm Hg; 95% CI, 0.81–3.99; P=0.003). Conclusions— In older adults, short-term to moderate-term ambient BC levels were associated with increased BP and blood mitochondrial abundance. Our findings indicate that increased blood mitochondrial abundance is a compensatory response and attenuates the cardiac effects of BC.

Test for rare variants by environment interactions in sequencing association studies

We consider in this article testing rare variants by environment interactions in sequencing association studies. Current methods for studying the association of rare variants with traits cannot be readily applied for testing for rare variants by environment interactions, as these methods do not effectively control for the main effects of rare variants, leading to unstable results and/or inflated Type 1 error rates. We will first analytically study the bias of the use of conventional burden-based tests for rare variants by environment interactions, and show the tests can often be invalid and result in inflated Type 1 error rates. To overcome these difficulties, we develop the interaction sequence kernel association test (iSKAT) for assessing rare variants by environment interactions. The proposed test iSKAT is optimal in a class of variance component tests and is powerful and robust to the proportion of variants in a gene that interact with environment and the signs of the effects. This test properly controls for the main effects of the rare variants using weighted ridge regression while adjusting for covariates. We demonstrate the performance of iSKAT using simulation studies and illustrate its application by analysis of a candidate gene sequencing study of plasma adiponectin levels.

Cardiac autonomic dysfunction: Particulate air pollution effects are modulated by epigenetic immunoregulation of Toll-like receptor 2 and dietary flavonoid intake

Background Short‐term fine particles (PM2.5) exposure is associated with reduced heart rate variability, a strong predictor of cardiac mortality among older people. Identifying modifiable factors that confer susceptibility is essential for intervention. We evaluated whether Toll‐like receptor 2 (TLR2) methylation, a reversible immune‐epigenetic process, and its dietary modulation by flavonoids and methyl nutrients, modify susceptibility to heart rate variability effects following PM2.5 exposure. Methods and Results We measured heart rate variability and PM2.5 repeatedly over 11 years (1275 total observations) among 573 elderly men from the Normative Aging Study. Blood TLR2 methylation was analyzed using pyrosequencing. Daily flavonoid and methyl nutrients intakes were assessed through the Food Frequency Questionnaire (FFQ). Every 10 μg/m3 increase in 48‐hour PM2.5 moving average was associated with 7.74% (95% CI: −1.21% to 15.90%; P=0.09), 7.46% (95% CI: 0.99% to 13.50%; P=0.02), 14.18% (95% CI: 1.14% to 25.49%; P=0.03), and 12.94% (95% CI: −2.36% to 25.96%; P=0.09) reductions in root mean square of successive differences, standard deviation of normal‐to‐normal intervals, low‐frequency power, and high‐frequency power, respectively. Higher TLR2 methylation exacerbated the root mean square of successive differences, standard deviation of normal‐to‐normal intervals, low‐frequency, and high‐frequency reductions associated with heightened PM2.5 (Pinteraction=0.006, 0.03, 0.05, 0.04, respectively). Every interquartile‐range increase in flavonoid intake was associated with 5.09% reduction in mean TLR2 methylation (95% CI: 0.12% to 10.06%; P=0.05) and counteracted the effects of PM2.5 on low frequency (Pinteraction=0.05). No significant effect of methyl nutrients on TLR2 methylation was observed. Conclusions Higher TLR2 methylation may confer susceptibility to adverse cardiac autonomic effects of PM2.5 exposure in older individuals. Higher flavonoid intake may attenuate these effects, possibly by decreasing TLR2 methylation.

B vitamins attenuate the epigenetic effects of ambient fine particles in a pilot human intervention trial

Significance Air pollution is a major public health concern worldwide. The molecular mechanistic underpinnings of the health effects of air pollution are not fully understood, and the lack of individual-level preventative options represent a critical knowledge gap. Our study demonstrated the epigenetic effects of air pollution and suggested that B vitamins might be used as prevention to complement regulations to attenuate the impact of air pollution on the epigenome. Our study inaugurated a line of research for the development of preventive interventions to minimize the adverse effects of air pollution on potential mechanistic markers. Because of the central role of epigenetic modifications in mediating environmental effects, our findings might be extended to other toxicants and environmental diseases. Acute exposure to fine particle (PM2.5) induces DNA methylation changes implicated in inflammation and oxidative stress. We conducted a crossover trial to determine whether B-vitamin supplementation averts such changes. Ten healthy adults blindly received a 2-h, controlled-exposure experiment to sham under placebo, PM2.5 (250 μg/m3) under placebo, and PM2.5 (250 μg/m3) under B-vitamin supplementation (2.5 mg/d folic acid, 50 mg/d vitamin B6, and 1 mg/d vitamin B12), respectively. We profiled epigenome-wide methylation before and after each experiment using the Infinium HumanMethylation450 BeadChip in peripheral CD4+ T-helper cells. PM2.5 induced methylation changes in genes involved in mitochondrial oxidative energy metabolism. B-vitamin supplementation prevented these changes. Likewise, PM2.5 depleted 11.1% [95% confidence interval (CI), 0.4%, 21.7%; P = 0.04] of mitochondrial DNA content compared with sham, and B-vitamin supplementation attenuated the PM2.5 effect by 102% (Pinteraction = 0.01). Our study indicates that individual-level prevention may be used to complement regulations and control potential mechanistic pathways underlying the adverse PM2.5 effects, with possible significant public health benefit in areas with frequent PM2.5 peaks.

Comparison of Methyl-capture Sequencing vs. Infinium 450K methylation array for methylome analysis in clinical samples

ABSTRACT Interindividual variability in the epigenome has gained tremendous attention for its potential in pathophysiological investigation, disease diagnosis, and evaluation of clinical intervention. DNA methylation is the most studied epigenetic mark in epigenome-wide association studies (EWAS) as it can be detected from limited starting material. Infinium 450K methylation array is the most popular platform for high-throughput profiling of this mark in clinical samples, as it is cost-effective and requires small amounts of DNA. However, this method suffers from low genome coverage and errors introduced by probe cross-hybridization. Whole-genome bisulfite sequencing can overcome these limitations but elevates the costs tremendously. Methyl-Capture Sequencing (MC Seq) is an attractive intermediate solution to increase the methylome coverage in large sample sets. Here we first demonstrate that MC Seq can be employed using DNA amounts comparable to the amounts used for Infinium 450K. Second, to provide guidance when choosing between the 2 platforms for EWAS, we evaluate and compare MC Seq and Infinium 450K in terms of coverage, technical variation, and concordance of methylation calls in clinical samples. Last, since the focus in EWAS is to study interindividual variation, we demonstrate the utility of MC Seq in studying interindividual variation in subjects from different ethnicities.

Gender differences in the effect of occupational endotoxin exposure on impaired lung function and death: the Shanghai Textile Worker Study

Objective Airborne endotoxin exposure has adverse and protective health effects. Studies show men have augmented acute inflammatory responses to endotoxin. In this longitudinal cohort study we investigated the effect of long-term exposure to endotoxin in cotton dust on health, and determined whether these effects differ by gender. Methods In the Shanghai Textile Worker Study, 447 cotton and 472 control silk textile workers were followed from 1981 to 2011 with repeated measures of occupational endotoxin exposure, spirometry and health questionnaires. Impaired lung function was defined as a decline in forced expiratory volume in one second to less than the 5th centile of population predicted. Death was ascertained by death registries. We used Cox proportional hazards models to assess the effect of endotoxin exposure on the time to development of impaired lung function and death. Results 128 deaths and 164 diagnoses of impaired lung function were ascertained between 1981 and 2011. HRs for the composite end point of impaired lung function or death was 1.47 (95% CI 1.09 to 1.97) for cotton vs silk workers and 1.04 (95% CI 1.01 to 1.07) per 10 000 endotoxin units (EU)/m3-years increase in exposure. HRs for all-cause mortality was 1.36 (95% CI 0.93 to 1.99) for cotton vs silk workers and 1.04 (95% CI 0.99 to 1.08) per 10 000 EU/m3-years. The risk associated with occupational endotoxin exposure was elevated only in men. Conclusions Occupational endotoxin exposure is associated with an increase in the risk of impaired lung function and all-cause mortality in men.