Functional evaluation of epilepsy-associated KCNT1 variants in multiple cellular systems reveals a predominant gain of function impact on channel properties.

OBJECTIVE: Gain of function variants in the sodium-activated potassium channel KCNT1 have been associated with pediatric epilepsy disorders. Here, we systematically examine a spectrum of KCNT1 variants and establish their impact on channel function in multiple cellular systems. METHODS: KCNT1 variants identified from published reports and genetic screening of pediatric epilepsy patients were expressed in Xenopus oocytes and HEK cell lines. Variant impact on current magnitude, current-voltage relationships, and sodium ion modulation were examined. RESULTS: We determined basic properties of KCNT1 in Xenopus oocyte and HEK systems, including the role of extra- and intracellular sodium in regulating KCNT1 activity. The most common six KCNT1 variants demonstrated strong gain of function (GOF) effects on one or more channel properties. Analysis of 36 total variants identified phenotypic heterogeneity but a strong tendency for pathogenic variants to exert GOF effects on channel properties. By controlling intracellular sodium, we demonstrate that multiple pathogenic KCNT1 variants modulate channel voltage dependence by altering the sensitivity to sodium ions. SIGNIFICANCE: This study represents the largest systematic functional examination of KCNT1 variants to date. We both confirm previously reported GOF channel phenotypes and expand the number of variants with in vitro GOF effects. Our data provide further evidence that novel KCNT1 variants identified in epilepsy patients lead to disease through generalizable GOF mechanisms including increases in current magnitude and/or current-voltage relationships.

Nicotine, alcohol, and caffeine use among individuals with untreated obstructive sleep apnea.

BACKGROUND: Psychoactive substance use (i.e., nicotine, alcohol, and caffeine) has substantial effects on sleep architecture in healthy individuals, but their effects in those with obstructive sleep apnea (OSA) have not been well described. We aimed to describe the association between psychoactive substance use and sleep characteristics and daytime symptoms in individuals with untreated OSA. METHODS: We performed a secondary, cross-sectional analysis of The Apnea Positive Pressure Long-term Efficacy Study (APPLES). Exposures included current smoking, alcohol and caffeine use in individuals with untreated OSA. Outcome domains included subjective and objective sleep characteristics, daytime symptoms, and comorbid conditions. Linear or logistic regression assessed the association between substance use and each domain (e.g., self-reported sleep duration, total polysomnographic sleep time, sleepiness, and anxiety). RESULTS: Of the 919 individuals with untreated OSA, 116 (12.6%) were current cigarette smokers, 585 (63.7%) were moderate or heavy alcohol users, and 769 (83.7%) were moderate or heavy caffeine users. Participants were on average 52.2±11.9 years old, 65.2% were male with a median BMI of 30.6 (IQR: 27.2, 35.9, kg/m2). Current smokers exhibited lower sleep duration (0.3 h), longer sleep latency (5 min) compared with non-smokers (all p-values < 0.05). People with heavy or moderate alcohol use exhibited more REM sleep (2.5 and 5% of total sleep time respectively), as did those with moderate caffeine use (2%, p-values < 0.05). The combined smoker plus caffeine group exhibited shorter sleep duration (0.4 h, p-value < 0.05) and higher risk for chronic pain [Odds Ratio (95%CI) = 4.83 (1.57, 14.9) compared with non-users. CONCLUSIONS: Psychoactive substance use is associated with sleep characteristics and clinically relevant correlates in people with untreated OSA. Further investigation into the effects that various substances have on this population may present opportunities to understand disease mechanisms more fully and increase the effectiveness of treatment in OSA.

Physiological Traits and Adherence to Sleep Apnea Therapy in Individuals with Coronary Artery Disease.

Rationale: Untreated obstructive sleep apnea (OSA) is associated with adverse outcomes in patients with coronary artery disease (CAD). Continuous positive airway pressure (CPAP) is the most common treatment, but despite interventions addressing established adherence determinants, CPAP use remains poor. Objectives: To determine whether physiological traits that cause OSA are associated with long-term CPAP adherence in patients with CAD. Methods: Participants in the RICCADSA (Randomized Intervention with CPAP in CAD and OSA) trial with objective CPAP adherence (h/night) over 2 years and analyzable raw polysomnography data were included (N = 249). The physiological traits-loop gain, arousal threshold (ArTH), pharyngeal collapsibility (Vpassive), and pharyngeal muscle compensation (Vcomp)-were measured by using polysomnography. Linear mixed models were used to assess the relationship between the traits and adherence. We also compared actual CPAP adherence between those with physiologically predicted "poor" adherence (lowest quartile of predicted adherence) and those with physiologically predicted "good" adherence (all others). Measurements and Main Results: The median (interquartile range) CPAP use declined from 3.2 (1.0-5.8) h/night to 3.0 (0.0-5.6) h/night over 24 months (P < 0.001). In analyses adjusted for demographics, anthropometrics, OSA characteristics, and clinical comorbidities, a lower ArTH was associated with worse CPAP adherence (0.7 h/SD of the ArTH; P = 0.021). Both high and low Vcomp were associated with lower adherence (P = 0.008). Those with predicted poor adherence exhibited markedly lower CPAP use than those with predicted good adherence for up to 2 years of follow-up (group differences of 2.0-3.2 h/night; P < 0.003 for all). Conclusions: A low ArTH, as well as a very low and high Vcomp, are associated with worse long-term CPAP adherence in patients with CAD and OSA. Physiological traits-alongside established determinants-may help predict and improve CPAP adherence. Clinical trial registered with www.clinicaltrials.gov (NCT00519597).

Gene coexpression networks reveal novel molecular endotypes in alpha-1 antitrypsin deficiency.

BACKGROUND: Alpha-1 antitrypsin deficiency (AATD) is a genetic condition that causes early onset pulmonary emphysema and airways obstruction. The complete mechanisms via which AATD causes lung disease are not fully understood. To improve our understanding of the pathogenesis of AATD, we investigated gene expression profiles of bronchoalveolar lavage (BAL) and peripheral blood mononuclear cells (PBMCs) in AATD individuals. METHODS: We performed RNA-Seq on RNA extracted from matched BAL and PBMC samples isolated from 89 subjects enrolled in the Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study. Subjects were stratified by genotype and augmentation therapy. Supervised and unsupervised differential gene expression analyses were performed using Weighted Gene Co-expression Network Analysis (WGCNA) to identify gene profiles associated with subjects' clinical variables. The genes in the most significant WGCNA module were used to cluster AATD individuals. Gene validation was performed by NanoString nCounter Gene Expression Assay. RESULT: We observed modest effects of AATD genotype and augmentation therapy on gene expression. When WGCNA was applied to BAL transcriptome, one gene module, ME31 (2312 genes), correlated with the highest number of clinical variables and was functionally enriched with numerous immune T-lymphocyte related pathways. This gene module identified two distinct clusters of AATD individuals with different disease severity and distinct PBMC gene expression patterns. CONCLUSIONS: We successfully identified novel clusters of AATD individuals where severity correlated with increased immune response independent of individuals' genotype and augmentation therapy. These findings may suggest the presence of previously unrecognised disease endotypes in AATD that associate with T-lymphocyte immunity and disease severity.

Involvement of fine particulate matter exposure with gene expression pathways in breast tumor and adjacent-normal breast tissue.

BACKGROUND: Fine particulate matter (PM2.5) has been associated with breast cancer specific mortality, particularly for women with Stage I cancer. We examined the biological pathways that are perturbed by PM2.5 exposures by analyzing gene expression measurements from breast tissue specimens. METHODS: The Nurses' Health Studies (NHS and NHSII) are prospective cohorts with archival breast tissue specimens from breast cancer cases. Global gene expression data were ascertained with the Affymetrix Glue Human Transcriptome Array 3.0. PM2.5 was estimated using spatio-temporal models linked to participants' home addresses. All analyses were performed separately in tumor (n = 591) and adjacent-normal (n = 497) samples, and stratified by estrogen receptor (ER) status and stage. We used multivariable linear regression, gene-set enrichment analyses (GSEA), and the least squares kernel machine (LSKM) to assess whether 3-year cumulative average pre-diagnosis PM2.5 exposure was associated with breast-tissue gene expression pathways among predominately Stage I and II women (90.7%) and postmenopausal (81.2%) women. Replication samples (tumor, n = 245; adjacent-normal, n = 165) were measured on Affymetrix Human Transcriptome Array (HTA 2.0). RESULTS: Overall, no pathways in the tumor area were significantly associated with PM2.5 exposure. Among 272 adjacent-normal samples from Stage I ER-positive women, PM2.5 was associated with perturbations in the oxidative phosphorylation, protein secretion, and mTORC1 signaling pathways (GSEA and LSKM p-values <0.05); however, results were not replicated in a small set of replication samples (n = 80). CONCLUSIONS: PM2.5 was generally not associated with breast tissue gene expression though was suggested to perturb oxidative phosphorylation and regulation of proteins and cellular signaling in adjacent-normal breast tissue. More research is needed on the biological role of PM2.5 that influences breast tumor progression.

S100A12 as a marker of worse cardiac output and mortality in pulmonary hypertension.

BACKGROUND AND OBJECTIVE: Molecular biomarkers are needed to refine prognostication and phenotyping of pulmonary hypertension (PH) patients. S100A12 is an emerging biomarker of various inflammatory diseases. This study aims to determine the prognostic value of S100A12 in PH. METHODS: Exploratory microarray analysis performed on peripheral blood mononuclear cells (PBMC) collected from idiopathic pulmonary fibrosis (IPF) patients suggested an association between S100A12 and both PH and mortality. So the current study was designed to evaluate for an association between S100A12 in peripheral blood collected from two well-phenotyped PH cohorts in two other centres to derive and validate an association between S100A12 protein serum concentrations and mortality. RESULTS: The majority of the patients in the discovery and validation cohorts were either World Health Organization (WHO) group 1 (pulmonary arterial hypertension (PAH)) or 3 (lung disease-associated) PH. In the discovery PH cohort, S100A12 was significantly increased in patients with PH (n = 51) compared to controls (n = 22) (29.8 vs 15.7 ng/mL, P < 0.001) and negatively correlated with cardiac output (r = -0.58, P < 0.001) in PH patients. When S100A12 data were pooled from both cohorts, PAH and non-PAH PH patients had higher S100A12 compared to healthy external controls (32.6, 30.9, 15.7 ng/mL; P < 0.001). S100A12 was associated with an increased risk in overall mortality in PH patients in both the discovery (n = 51; P = 0.008) and validation (n = 40; P < 0.001) cohorts. CONCLUSION: S100A12 levels are increased in PH patients and are associated with increased mortality.

Validation of the prognostic value of MMP-7 in idiopathic pulmonary fibrosis.

BACKGROUND AND OBJECTIVE: Idiopathic pulmonary fibrosis (IPF) is a progressive disease with poor prognosis and variable clinical course. Although matrix metalloproteinase-7 (MMP-7) is emerging as an important IPF biomarker, reproducibility across studies is unclear. We aimed to determine whether a previously reported prognostic threshold for MMP-7 was predictive of mortality in an independent cohort of IPF patients. METHODS: MMP-7 concentrations obtained from heparinized plasma samples were determined by ELISA in 97 patients with IPF and 41 healthy controls. The association of the previously published heparin plasma MMP-7 threshold of 12.1 ng/mL with all-cause mortality or transplant-free survival (TFS) was determined, either as an independent biomarker or as part of the modified personal clinical and molecular mortality index (m-PCMI). RESULTS: MMP-7 plasma concentrations were significantly higher in IPF patients compared to healthy controls (14.40 ± 6.55 ng/mL vs 6.03 ± 2.51 ng/mL, P < 0.001). The plasma MMP-7 threshold of 12.1 ng/mL was significantly associated with both all-cause mortality and TFS (unadjusted Cox proportional hazard ratio (HR) = 25.85 and 15.49, 95% CI: 10.91-61.23 and 5.41-44.34, respectively, P < 0.001). MMP-7 concentrations, split by 12.1 ng/mL, were significantly (P < 0.05) predictive of mortality and TFS after adjusting for age, gender, smoking and baseline pulmonary function parameters, in a multivariate Cox proportional hazards model. MMP-7 concentrations were negatively correlated with diffusing lung capacity of carbon monoxide (DLCO ) (r = -0.21, P = 0.02), and positively with a mortality risk scoring system (GAP) that combines age, gender, forced vital capacity (FVC) and DLCO (r = 0.32, P = 0.001). CONCLUSION: This study confirms that MMP-7 concentrations could be used to accurately predict outcomes across cohorts and centres, when similar collection protocols are applied.

Lung Metabolic Activation as an Early Biomarker of Acute Respiratory Distress Syndrome and Local Gene Expression Heterogeneity.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is an inflammatory condition comprising diffuse lung edema and alveolar damage. ARDS frequently results from regional injury mechanisms. However, it is unknown whether detectable inflammation precedes lung edema and opacification and whether topographically differential gene expression consistent with heterogeneous injury occurs in early ARDS. The authors aimed to determine the temporal relationship between pulmonary metabolic activation and density in a large animal model of early ARDS and to assess gene expression in differentially activated regions. METHODS: The authors produced ARDS in sheep with intravenous lipopolysaccharide (10 ng ⋅ kg ⋅ h) and mechanical ventilation for 20 h. Using positron emission tomography, the authors assessed regional cellular metabolic activation with 2-deoxy-2-[(18)F]fluoro-D-glucose, perfusion and ventilation with NN-saline, and aeration using transmission scans. Species-specific microarray technology was used to assess regional gene expression. RESULTS: Metabolic activation preceded detectable increases in lung density (as required for clinical diagnosis) and correlated with subsequent histologic injury, suggesting its predictive value for severity of disease progression. Local time courses of metabolic activation varied, with highly perfused and less aerated dependent lung regions activated earlier than nondependent regions. These regions of distinct metabolic trajectories demonstrated differential gene expression for known and potential novel candidates for ARDS pathogenesis. CONCLUSIONS: Heterogeneous lung metabolic activation precedes increases in lung density in the development of ARDS due to endotoxemia and mechanical ventilation. Local differential gene expression occurs in these early stages and reveals molecular pathways relevant to ARDS biology and of potential use as treatment targets.

Gene expression network analyses in response to air pollution exposures in the trucking industry.

BACKGROUND: Exposure to air pollution, including traffic-related pollutants, has been associated with a variety of adverse health outcomes, including increased cardiopulmonary morbidity and mortality, and increased lung cancer risk. METHODS: To better understand the cellular responses induced by air pollution exposures, we performed genome-wide gene expression microarray analysis using whole blood RNA sampled at three time-points across the work weeks of 63 non-smoking employees at 10 trucking terminals in the northeastern US. We defined genes and gene networks that were differentially activated in response to PM2.5 (particulate matter ≤ 2.5 microns in diameter) and elemental carbon (EC) and organic carbon (OC). RESULTS: Multiple transcripts were strongly associated (padj < 0.001) with pollutant levels (48, 260, and 49 transcripts for EC, OC, and PM2.5, respectively), including 63 that were statistically significantly correlated with at least two out of the three exposures. These genes included many that have been implicated in ischemic heart disease, chronic obstructive pulmonary disease (COPD), lung cancer, and other pollution-related illnesses. Through the combination of Gene Set Enrichment Analysis and network analysis (using GeneMANIA), we identified a core set of 25 interrelated genes that were common to all three exposure measures and were differentially expressed in two previous studies assessing gene expression attributable to air pollution. Many of these are members of fundamental cancer-related pathways, including those related to DNA and metal binding, and regulation of apoptosis and also but include genes implicated in chronic heart and lung diseases. CONCLUSIONS: These data provide a molecular link between the associations of air pollution exposures with health effects.

Noninvasive analysis of the sputum transcriptome discriminates clinical phenotypes of asthma.

RATIONALE: The airway transcriptome includes genes that contribute to the pathophysiologic heterogeneity seen in individuals with asthma. OBJECTIVES: We analyzed sputum gene expression for transcriptomic endotypes of asthma (TEA), gene signatures that discriminate phenotypes of disease. METHODS: Gene expression in the sputum and blood of patients with asthma was measured using Affymetrix microarrays. Unsupervised clustering analysis based on pathways from the Kyoto Encyclopedia of Genes and Genomes was used to identify TEA clusters. Logistic regression analysis of matched blood samples defined an expression profile in the circulation to determine the TEA cluster assignment in a cohort of children with asthma to replicate clinical phenotypes. MEASUREMENTS AND MAIN RESULTS: Three TEA clusters were identified. TEA cluster 1 had the most subjects with a history of intubation (P = 0.05), a lower prebronchodilator FEV1 (P = 0.006), a higher bronchodilator response (P = 0.03), and higher exhaled nitric oxide levels (P = 0.04) compared with the other TEA clusters. TEA cluster 2, the smallest cluster, had the most subjects that were hospitalized for asthma (P = 0.04). TEA cluster 3, the largest cluster, had normal lung function, low exhaled nitric oxide levels, and lower inhaled steroid requirements. Evaluation of TEA clusters in children confirmed that TEA clusters 1 and 2 are associated with a history of intubation (P = 5.58 × 10(-6)) and hospitalization (P = 0.01), respectively. CONCLUSIONS: There are common patterns of gene expression in the sputum and blood of children and adults that are associated with near-fatal, severe, and milder asthma.

TDP-43-stratified single-cell proteomics of postmortem human spinal motor neurons reveals protein dynamics in amyotrophic lateral sclerosis.

A limitation of conventional bulk-tissue proteome studies in amyotrophic lateral sclerosis (ALS) is the confounding of motor neuron (MN) signals by admixed non-MN proteins. Here, we leverage laser capture microdissection and nanoPOTS single-cell mass spectrometry-based proteomics to query changes in protein expression in single MNs from postmortem ALS and control tissues. In a follow-up analysis, we examine the impact of stratification of MNs based on cytoplasmic transactive response DNA-binding protein 43 (TDP-43)+ inclusion pathology on the profiles of 2,238 proteins. We report extensive overlap in differentially abundant proteins identified in ALS MNs with or without overt TDP-43 pathology, suggesting early and sustained dysregulation of cellular respiration, mRNA splicing, translation, and vesicular transport in ALS. Together, these data provide insights into proteome-level changes associated with TDP-43 proteinopathy and begin to demonstrate the utility of pathology-stratified trace sample proteomics for understanding single-cell protein dynamics in human neurologic diseases.

Copy number variation genotyping using family information.

BACKGROUND: In recent years there has been a growing interest in the role of copy number variations (CNV) in genetic diseases. Though there has been rapid development of technologies and statistical methods devoted to detection in CNVs from array data, the inherent challenges in data quality associated with most hybridization techniques remains a challenging problem in CNV association studies. RESULTS: To help address these data quality issues in the context of family-based association studies, we introduce a statistical framework for the intensity-based array data that takes into account the family information for copy-number assignment. The method is an adaptation of traditional methods for modeling SNP genotype data that assume Gaussian mixture model, whereby CNV calling is performed for all family members simultaneously and leveraging within family-data to reduce CNV calls that are incompatible with Mendelian inheritance while still allowing de-novo CNVs. Applying this method to simulation studies and a genome-wide association study in asthma, we find that our approach significantly improves CNV calls accuracy, and reduces the Mendelian inconsistency rates and false positive genotype calls. The results were validated using qPCR experiments. CONCLUSIONS: In conclusion, we have demonstrated that the use of family information can improve the quality of CNV calling and hopefully give more powerful association test of CNVs.

Morning Chronotype Is Associated with Improved Adherence to Continuous Positive Airway Pressure among Individuals with Obstructive Sleep Apnea.

Rationale: Poor adherence limits the effectiveness of continuous positive airway pressure (CPAP) therapy for obstructive sleep apnea (OSA). A better understanding of CPAP adherence is needed to develop novel strategies to improve it. Objectives: To determine if the chronotype (morning, evening, or intermediate) of patients with OSA is associated with differences in CPAP adherence. If such an association exists, determine the mechanisms underlying this association. Methods: We performed a secondary analysis of the APPLES (Apnea Positive Pressure Long-term Efficacy Study) clinical trial. We assessed chronotype using the Morningness-Eveningness Questionnaire (MEQ) among participants randomized to the CPAP arm with daily adherence data (n = 469). Evening (MEQ ⩽ 41), intermediate (41 < MEQ < 59), and morning type (MEQ ⩾ 59) categories were the exposures. We modeled daily CPAP use (hours per night) over a 6-month period, using a linear mixed model, adjusted for covariates (e.g., age, sex, marital status). To assess mechanisms of the association, we performed mediation analyses using sleep duration, weekend catch-up sleep, depression, and other factors. Results: Most participants were obese men with severe OSA (body mass index of 32.3 ± 7.3 kg/m2, 65% male, and apnea-hypopnea index 39.8 ± 24.6/h). Participants were 44% morning, 47% intermediate, and 8% evening chronotype. Participants with the morning chronotype reported the shortest sleep duration on weekends (7.3 vs. 7.6 and 7.9 h/night) compared with the intermediate and evening types. Participants with the morning chronotype exhibited a 40-min/night higher CPAP use (P = 0.001) than persons with the intermediate chronotype. This relationship was mildly attenuated (32.8 min/night; P = 0.011) after adjustment for covariates. None of the selected factors (e.g., sleep duration, weekend catch-up sleep) exhibited a significant mediation effect. Conclusions: Morning chronotype is associated with a clinically meaningful increase in CPAP adherence compared with other chronotypes. Mechanisms of this association require further study. Chronotype may be a novel predictor of CPAP adherence. Clinical trial registered with www.clinicaltrials.gov (NCT00051363).

TDP-43-stratified single-cell proteomic profiling of postmortem human spinal motor neurons reveals protein dynamics in amyotrophic lateral sclerosis.

Unbiased proteomics has been employed to interrogate central nervous system (CNS) tissues (brain, spinal cord) and fluid matrices (CSF, plasma) from amyotrophic lateral sclerosis (ALS) patients; yet, a limitation of conventional bulk tissue studies is that motor neuron (MN) proteome signals may be confounded by admixed non-MN proteins. Recent advances in trace sample proteomics have enabled quantitative protein abundance datasets from single human MNs (Cong et al., 2020b). In this study, we leveraged laser capture microdissection (LCM) and nanoPOTS (Zhu et al., 2018c) single-cell mass spectrometry (MS)-based proteomics to query changes in protein expression in single MNs from postmortem ALS and control donor spinal cord tissues, leading to the identification of 2515 proteins across MNs samples (>900 per single MN) and quantitative comparison of 1870 proteins between disease groups. Furthermore, we studied the impact of enriching/stratifying MN proteome samples based on the presence and extent of immunoreactive, cytoplasmic TDP-43 inclusions, allowing identification of 3368 proteins across MNs samples and profiling of 2238 proteins across TDP-43 strata. We found extensive overlap in differential protein abundance profiles between MNs with or without obvious TDP-43 cytoplasmic inclusions that together point to early and sustained dysregulation of oxidative phosphorylation, mRNA splicing and translation, and retromer-mediated vesicular transport in ALS. Our data are the first unbiased quantification of single MN protein abundance changes associated with TDP-43 proteinopathy and begin to demonstrate the utility of pathology-stratified trace sample proteomics for understanding single-cell protein abundance changes in human neurologic diseases.

The impact of self-identified race on epidemiologic studies of gene expression.

Although population differences in gene expression have been established, the impact on differential gene expression studies in large populations is not well understood. We describe the effect of self-reported race on a gene expression study of lung function in asthma. We generated gene expression profiles for 254 young adults (205 non-Hispanic whites and 49 African Americans) with asthma on whom concurrent total RNA derived from peripheral blood CD4(+) lymphocytes and lung function measurements were obtained. We identified four principal components that explained 62% of the variance in gene expression. The dominant principal component, which explained 29% of the total variance in gene expression, was strongly associated with self-identified race (P<10(-16)). The impact of these racial differences was observed when we performed differential gene expression analysis of lung function. Using multivariate linear models, we tested whether gene expression was associated with a quantitative measure of lung function: pre-bronchodilator forced expiratory volume in one second (FEV(1)). Though unadjusted linear models of FEV(1) identified several genes strongly correlated with lung function, these correlations were due to racial differences in the distribution of both FEV(1) and gene expression, and were no longer statistically significant following adjustment for self-identified race. These results suggest that self-identified race is a critical confounding covariate in epidemiologic studies of gene expression and that, similar to genetic studies, careful consideration of self-identified race in gene expression profiling studies is needed to avoid spurious association.

Mapping of numerous disease-associated expression polymorphisms in primary peripheral blood CD4+ lymphocytes.

Genome-wide association studies of human gene expression promise to identify functional regulatory genetic variation that contributes to phenotypic diversity. However, it is unclear how useful this approach will be for the identification of disease-susceptibility variants. We generated gene expression profiles for 22 184 mRNA transcripts using RNA derived from peripheral blood CD4+ lymphocytes, and genome-wide genotype data for 516 512 autosomal markers in 200 subjects. We screened for cis-acting variants by testing variants mapping within 50 kb of expressed transcripts for association with transcript abundance using generalized linear models. Significant associations were identified for 1585 genes at a false discovery rate of 0.05 (corresponding to P-values ranging from 1 × 10(-91) to 7 × 10(-4)). Importantly, we identified evidence of regulatory variation for 119 previously mapped disease genes, including 24 examples where the variant with the strongest evidence of disease-association demonstrates strong association with specific transcript abundance. The prevalence of cis-acting variants among disease-associated genes was 63% higher than the genome-wide rate in our data set (P = 6.41 × 10(-6)), and although many of the implicated loci were associated with immune-related diseases (including asthma, connective tissue disorders and inflammatory bowel disease), associations with genes implicated in non-immune-related diseases including lipid profiles, anthropomorphic measurements, cancer and neurologic disease were also observed. Genetic variants that confer inter-individual differences in gene expression represent an important subset of variants that contribute to disease susceptibility. Population-based integrative genetic approaches can help identify such variation and enhance our understanding of the genetic basis of complex traits.

Integrated molecular response of exposure to traffic-related pollutants in the US trucking industry.

Exposure to traffic-related pollutants, including diesel exhaust, is associated with increased risk of cardiopulmonary disease and mortality; however, the precise biochemical pathways underlying these effects are not known. To investigate biological response mechanisms underlying exposure to traffic related pollutants, we used an integrated molecular response approach that included high-resolution metabolomic profiling and peripheral blood gene expression to identify biological responses to diesel exhaust exposure. Plasma samples were collected from 73 non-smoking males employed in the US trucking industry between February 2009 and October 2010, and analyzed using untargeted high-resolution metabolomics to characterize metabolite associations with shift- and week-averaged levels of elemental carbon (EC), organic carbon (OC) and particulate matter with diameter ≤ 2.5 µm (PM2.5). Metabolic associations with EC, OC and PM2.5 were evaluated for biochemical processes known to be associated with disease risk. Annotated metabolites associated with exposure were then tested for relationships with the peripheral blood transcriptome using multivariate selection and network correlation. Week-averaged EC and OC levels, which were averaged across multiple shifts during the workweek, resulted in the greatest exposure-associated metabolic alterations compared to shift-averaged exposure levels. Metabolic changes associated with EC exposure suggest increased lipid peroxidation products, biomarkers of oxidative stress, thrombotic signaling lipids, and metabolites associated with endothelial dysfunction from altered nitric oxide metabolism, while OC exposures were associated with antioxidants, oxidative stress biomarkers and critical intermediates in nitric oxide production. Correlation with whole blood RNA gene expression provided additional evidence of changes in processes related to endothelial function, immune response, inflammation, and oxidative stress. We did not detect metabolic associations with PM2.5. This study provides an integrated molecular assessment of human exposure to traffic-related air pollutants that includes diesel exhaust. Metabolite and transcriptomic changes associated with exposure to EC and OC are consistent with increased risk of cardiovascular diseases and the adverse health effects of traffic-related air pollution.

On the genome-wide analysis of copy number variants in family-based designs: methods for combining family-based and population-based information for testing dichotomous or quantitative traits, or completely ascertained samples.

We propose a new approach for the analysis of copy number variants (CNVs)for genome-wide association studies in family-based designs. Our new overall association test combines the between-family component and the within-family component of the family-based data so that the new test statistic is fully efficient and, at the same time, maintains robustness against population-admixture and stratification, like classical family-based association tests that are based only on the within-family component. Although all data are incorporated into the test statistic, an adjustment for genetic confounding is not needed, even for the between-family component. The new test statistic is valid for testing either quantitative or dichotomous phenotypes. If external CNV data are available, the approach can also be applied to completely ascertained samples. Similar to the approach by Ionita-Laza et al. ([2008]. Genet Epidemiol 32:273-284), the proposed test statistic does not require a CNV-calling algorithm and is based directly on the CNV probe intensities. We show, via simulation studies, that our methodology increases the power of the FBAT statistic to levels comparable to those of population-based designs. The advantages of the approach in practice are demonstrated by an application to a genome-wide association study for body mass index.