Genome-Wide Association Study of the Genetic Determinants of Emphysema Distribution.

RATIONALE: Emphysema has considerable variability in the severity and distribution of parenchymal destruction throughout the lungs. Upper lobe-predominant emphysema has emerged as an important predictor of response to lung volume reduction surgery. Yet, aside from alpha-1 antitrypsin deficiency, the genetic determinants of emphysema distribution remain largely unknown. OBJECTIVES: To identify the genetic influences of emphysema distribution in non-alpha-1 antitrypsin-deficient smokers. METHODS: A total of 11,532 subjects with complete genotype and computed tomography densitometry data in the COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease [COPD]; non-Hispanic white and African American), ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints), and GenKOLS (Genetics of Chronic Obstructive Lung Disease) studies were analyzed. Two computed tomography scan emphysema distribution measures (difference between upper-third and lower-third emphysema; ratio of upper-third to lower-third emphysema) were tested for genetic associations in all study subjects. Separate analyses in each study population were followed by a fixed effect metaanalysis. Single-nucleotide polymorphism-, gene-, and pathway-based approaches were used. In silico functional evaluation was also performed. MEASUREMENTS AND MAIN RESULTS: We identified five loci associated with emphysema distribution at genome-wide significance. These loci included two previously reported associations with COPD susceptibility (4q31 near HHIP and 15q25 near CHRNA5) and three new associations near SOWAHB, TRAPPC9, and KIAA1462. Gene set analysis and in silico functional evaluation revealed pathways and cell types that may potentially contribute to the pathogenesis of emphysema distribution. CONCLUSIONS: This multicohort genome-wide association study identified new genomic loci associated with differential emphysematous destruction throughout the lungs. These findings may point to new biologic pathways on which to expand diagnostic and therapeutic approaches in chronic obstructive pulmonary disease. Clinical trial registered with www.clinicaltrials.gov (NCT 00608764).

A genome-wide association study identifies risk loci for spirometric measures among smokers of European and African ancestry.

BACKGROUND: Pulmonary function decline is a major contributor to morbidity and mortality among smokers. Post bronchodilator FEV1 and FEV1/FVC ratio are considered the standard assessment of airflow obstruction. We performed a genome-wide association study (GWAS) in 9919 current and former smokers in the COPDGene study (6659 non-Hispanic Whites [NHW] and 3260 African Americans [AA]) to identify associations with spirometric measures (post-bronchodilator FEV1 and FEV1/FVC). We also conducted meta-analysis of FEV1 and FEV1/FVC GWAS in the COPDGene, ECLIPSE, and GenKOLS cohorts (total n = 13,532). RESULTS: Among NHW in the COPDGene cohort, both measures of pulmonary function were significantly associated with SNPs at the 15q25 locus [containing CHRNA3/5, AGPHD1, IREB2, CHRNB4] (lowest p-value = 2.17 × 10(-11)), and FEV1/FVC was associated with a genomic region on chromosome 4 [upstream of HHIP] (lowest p-value = 5.94 × 10(-10)); both regions have been previously associated with COPD. For the meta-analysis, in addition to confirming associations to the regions near CHRNA3/5 and HHIP, genome-wide significant associations were identified for FEV1 on chromosome 1 [TGFB2] (p-value = 8.99 × 10(-9)), 9 [DBH] (p-value = 9.69 × 10(-9)) and 19 [CYP2A6/7] (p-value = 3.49 × 10(-8)) and for FEV1/FVC on chromosome 1 [TGFB2] (p-value = 8.99 × 10(-9)), 4 [FAM13A] (p-value = 3.88 × 10(-12)), 11 [MMP3/12] (p-value = 3.29 × 10(-10)) and 14 [RIN3] (p-value = 5.64 × 10(-9)). CONCLUSIONS: In a large genome-wide association study of lung function in smokers, we found genome-wide significant associations at several previously described loci with lung function or COPD. We additionally identified a novel genome-wide significant locus with FEV1 on chromosome 9 [DBH] in a meta-analysis of three study populations.

A Genome-Wide Association Study of Emphysema and Airway Quantitative Imaging Phenotypes.

RATIONALE: Chronic obstructive pulmonary disease (COPD) is defined by the presence of airflow limitation on spirometry, yet subjects with COPD can have marked differences in computed tomography imaging. These differences may be driven by genetic factors. We hypothesized that a genome-wide association study (GWAS) of quantitative imaging would identify loci not previously identified in analyses of COPD or spirometry. In addition, we sought to determine whether previously described genome-wide significant COPD and spirometric loci were associated with emphysema or airway phenotypes. OBJECTIVES: To identify genetic determinants of quantitative imaging phenotypes. METHODS: We performed a GWAS on two quantitative emphysema and two quantitative airway imaging phenotypes in the COPDGene (non-Hispanic white and African American), ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints), NETT (National Emphysema Treatment Trial), and GenKOLS (Genetics of COPD, Norway) studies and on percentage gas trapping in COPDGene. We also examined specific loci reported as genome-wide significant for spirometric phenotypes related to airflow limitation or COPD. MEASUREMENTS AND MAIN RESULTS: The total sample size across all cohorts was 12,031, of whom 9,338 were from COPDGene. We identified five loci associated with emphysema-related phenotypes, one with airway-related phenotypes, and two with gas trapping. These loci included previously reported associations, including the HHIP, 15q25, and AGER loci, as well as novel associations near SERPINA10 and DLC1. All previously reported COPD and a significant number of spirometric GWAS loci were at least nominally (P < 0.05) associated with either emphysema or airway phenotypes. CONCLUSIONS: Genome-wide analysis may identify novel risk factors for quantitative imaging characteristics in COPD and also identify imaging features associated with previously identified lung function loci.

Heritability of chronic obstructive pulmonary disease and related phenotypes in smokers.

RATIONALE: Previous studies of chronic obstructive pulmonary disease (COPD) have suggested that genetic factors play an important role in the development of disease. However, single-nucleotide polymorphisms that are associated with COPD in genome-wide association studies have been shown to account for only a small percentage of the genetic variance in phenotypes of COPD, such as spirometry and imaging variables. These phenotypes are highly predictive of disease, and family studies have shown that spirometric phenotypes are heritable. OBJECTIVES: To assess the heritability and coheritability of four major COPD-related phenotypes (measurements of FEV1, FEV1/FVC, percent emphysema, and percent gas trapping), and COPD affection status in smokers of non-Hispanic white and African American descent using a population design. METHODS: Single-nucleotide polymorphisms from genome-wide association studies chips were used to calculate the relatedness of pairs of individuals and a mixed model was adopted to estimate genetic variance and covariance. MEASUREMENTS AND MAIN RESULTS: In the non-Hispanic whites, estimated heritabilities of FEV1 and FEV1/FVC were both about 37%, consistent with estimates in the literature from family-based studies. For chest computed tomography scan phenotypes, estimated heritabilities were both close to 25%. Heritability of COPD affection status was estimated as 37.7% in both populations. CONCLUSIONS: This study suggests that a large portion of the genetic risk of COPD is yet to be discovered and gives rationale for additional genetic studies of COPD. The estimates of coheritability (genetic covariance) for pairs of the phenotypes suggest considerable overlap of causal genetic loci.

Combining disease models to test for gene-environment interaction in nuclear families.

It is useful to have robust gene-environment interaction tests that can utilize a variety of family structures in an efficient way. This article focuses on tests for gene-environment interaction in the presence of main genetic and environmental effects. The objective is to develop powerful tests that can combine trio data with parental genotypes and discordant sibships when parents' genotypes are missing. We first make a modest improvement on a method for discordant sibs (discordant on phenotype), but the approach does not allow one to use families when all offspring are affected, e.g., trios. We then make a modest improvement on a Mendelian transmission-based approach that is inefficient when discordant sibs are available, but can be applied to any nuclear family. Finally, we propose a hybrid approach that utilizes the most efficient method for a specific family type, then combines over families. We utilize this hybrid approach to analyze a chronic obstructive pulmonary disorder dataset to test for gene-environment interaction in the Serpine2 gene with smoking. The methods are freely available in the R package fbati.

A new testing strategy to identify rare variants with either risk or protective effect on disease.

Rapid advances in sequencing technologies set the stage for the large-scale medical sequencing efforts to be performed in the near future, with the goal of assessing the importance of rare variants in complex diseases. The discovery of new disease susceptibility genes requires powerful statistical methods for rare variant analysis. The low frequency and the expected large number of such variants pose great difficulties for the analysis of these data. We propose here a robust and powerful testing strategy to study the role rare variants may play in affecting susceptibility to complex traits. The strategy is based on assessing whether rare variants in a genetic region collectively occur at significantly higher frequencies in cases compared with controls (or vice versa). A main feature of the proposed methodology is that, although it is an overall test assessing a possibly large number of rare variants simultaneously, the disease variants can be both protective and risk variants, with moderate decreases in statistical power when both types of variants are present. Using simulations, we show that this approach can be powerful under complex and general disease models, as well as in larger genetic regions where the proportion of disease susceptibility variants may be small. Comparisons with previously published tests on simulated data show that the proposed approach can have better power than the existing methods. An application to a recently published study on Type-1 Diabetes finds rare variants in gene IFIH1 to be protective against Type-1 Diabetes.

Genome-wide association study on overall survival of advanced non-small cell lung cancer patients treated with carboplatin and paclitaxel.

PURPOSE: Our goal was to identify candidate polymorphisms that could influence overall survival (OS) in advanced non-small cell lung cancer (NSCLC) patients treated with carboplatin (CBDCA) and paclitaxel (PTX). METHODS: Chemotherapy-naïve stage IIIB or IV NSCLC patients treated with CBDCA (area under the curve = 6 mg/mL/min) and PTX (200 mg/m, 3-hour period) were eligible for this study. The DNA samples were extracted from peripheral blood mononuclear cells before treatment, and genotypes at approximately 110,000 gene-centric single-nucleotide polymorphisms (SNPs) were obtained by Illumina's Sentrix Human-1 Genotyping BeadChip. Statistical analyses were performed by the log-rank test and Cox proportional hazards model. RESULTS: From July 2002 to May 2004, 105 patients received a total of 308 cycles of treatment. The median survival time (MST) of 105 patients was 17.1 months. In the genome-wide association study, three SNPs were associated significantly with shortened OS after multiple comparison adjustment: rs1656402 in the EIF4E2 gene (MST was 18.0 and 7.7 months for AG [n = 50] + AA [n = 40] and GG [n = 15], respectively; p = 8.4 × 10), rs1209950 in the ETS2 gene (MST = 17.7 and 7.4 months for CC [n = 94] and CT [n = 11] + TT [n = 0]; p = 2.8 × 10), and rs9981861 in the DSCAM gene (MST = 17.1 and 3.8 months for AA [n = 75] + AG [n = 26] and GG [n = 4]; p = 3.5 × 10). CONCLUSION: Three SNPs were identified as new prognostic biomarker candidates for advanced NSCLC treated with CBDCA and PTX. The agnostic genome-wide association study may unveil unexplored molecular pathways associated with the drug response, but our findings should be replicated by other investigators.

Biostatistic tools in pharmacogenomics--advances, challenges, potential.

Inter-individual variations in drug response are all-too common and, throughout medical history have often posed problems, many of them serious ones. The variations could stem from multiple factors, which include those of both the host (age, genetic and environmental factors) and disease (pathophysiological phenotypes, somatic mutations in case of cancers). The complex interplay of these factors can influence pharmacodynamic responses, such as adverse effects and efficacy, as well as pharmacokinetic manifestations through variability in drug absorption, distribution, metabolism and excretion. Recently, several potentially powerful tools to decipher such intricacies are emerging in various fields of science, and the translation of such knowledge to personalized medicine, called, in general, pharmacogenomics, has been promoted and has occasioned strong expectations from almost every sector of health care. However, at present, few biomarkers can predict which group of patients will respond positively, which will be non-responders and who might experience adverse reactions from the same medication and dosage. This review highlights several important aspects related to the design and statistical analysis for pharmacogenomics studies or clinical trials, which incorporate biomarkers. First, we review biomarker development: how biomarkers may be used as targets and the difference between prognostic and predictive markers. Second, in confirmatory clinical trials, we focus on issues related to study design for evaluating biomarkers and how they can be used to determine which patients might optimally benefit from a specific therapy. Finally, we review exploratory statistical screening techniques for detecting biomarkers in Phase I or pharmacokinetics studies.

Genetic polymorphisms of estrogen metabolizing enzyme and breast cancer risk in Thai women.

Estrogen and its metabolites are believed to play important roles in breast cancer, and its determinants include both genetic and lifestyle factors. The objective of the study is to investigate the association of breast cancer risk in Thailand with genetic polymorphisms in several genes involved in estrogen synthesis and metabolism. Five hundred and seventy patients with histopathologically confirmed breast cancer and 497 controls were included in the present study. Forty single nucleotide polymorphisms (SNPs) in the CYP1A1, CYP1A2, CYP1B1, CYP17, CYP19, CYP2C9, CYP2C19, AhR, ESR1, PGR, ERRG, COMT, HSD17B1, HSD17B2, EPHX1 and NQO1 genes were genotyped. Association of genotypes with breast cancer risk was evaluated using multivariate logistic regression, which suggested an altered risk for the following SNPs [gene, odds ratio (OR) and 95% confidence interval are shown]: heterozygote carriers of rs4917623 [CYP2C19, OR = 1.38 (1.04-1.84)], rs2066853 [AhR, OR = 1.34 (1.02-1.76)] and rs1857407 [ERRG, (OR = 0.72 (0.55-0.96)]; homozygote carriers of rs762551 [CYP1A2, OR = 2.75 (1.47-5.14)], rs4917623 [CYP2C19, OR = 1.48 (1.00-2.19) and rs945453 [ERRG, OR = 1.66 (1.04-2.65)]. In addition, a stratified analysis by menopausal status indicated that the association of the CYP1A2 (rs762551) and CYP17 (rs743572) polymorphisms with breast cancer risk were mainly evident in premenopausal, while ERRG (rs1857407) was significant in postmenopausal women. These findings suggest that CYP1A2, CYP2C19, AhR, ERRG and CYP17 polymorphisms may play an important role in estrogen metabolism and modify individual susceptibility to breast cancer in Thai women.

Co-occurrence of binge eating disorder with psychiatric and medical disorders.

BACKGROUND: Prior studies suggest that certain psychiatric and medical disorders co-occur with binge eating disorder (BED). However, there has been no large, community-based study with diagnoses made by clinician interviewers. We used data from that type of study to assess the co-occurrence of various psychiatric and medical disorders with DSM-IV BED and with subthreshold BED. METHOD: From October 2002 to July 2004, we interviewed 150 probands with BED, 150 probands without BED, and 888 of their first-degree relatives (135 of whom had BED, and 54 of whom met specific partial criteria for BED that we defined as subthreshold BED). Study participants were interviewed using the Structured Clinical Interview for DSM-IV to assess BED and other psychiatric disorders and a supplemental structured interview to assess certain medical disorders; participants also completed a self-report questionnaire, the Bad Things Scale. For each psychiatric and medical disorder, we calculated the age- and sex-adjusted co-occurrence odds ratio: the odds of having that disorder in one's lifetime among individuals with (full or subthreshold) lifetime BED compared to individuals without lifetime BED. We also used subjects' responses to the Bad Things Scale to adjust for adversity over-reporting, a type of response bias that could result in spurious findings of co-occurrence. RESULTS: Full BED co-occurred significantly with bipolar disorder, major depressive disorder, bulimia nervosa but not anorexia nervosa, most anxiety disorders, substance use disorders, body dysmorphic disorder, kleptomania, irritable bowel syndrome, and fibromyalgia. These results changed little after correcting for adversity over-reporting. Subthreshold BED co-occurred significantly with many, but not all, of the significantly co-occurring disorders for full BED. CONCLUSION: BED and, to a lesser degree, subthreshold BED exhibit substantial lifetime co-occurrence with psychiatric and medical disorders.

Mortality associated with depression: a forty-year perspective from the Stirling County Study.

BACKGROUND: This report concerns long-term mortality risks associated with depression, and the potentially confounding factors of alcoholism and cigarette smoking, as experienced by a general population assessed at a baseline in 1952, followed for re-assessment of survivors in 1968, and for death by 1992. METHODS: Self-report and physician-report information was gathered in 1952 and again in 1968 about a sample of 1,079 adults. At the end of follow-up in 1992, the vital status of all subjects was known. Comorbidity among depression, alcoholism, and smoking was investigated. Cox regression models were employed to estimate hazard ratios (HRs) as indicators of mortality risk. Models including age, gender, and depression were fit for the complete sample at baseline as well as for re-assessed survivors. Models simultaneously controlling for the mortality risks associated with depression, alcoholism, and heavy smoking were fit for men. RESULTS: At the baseline in 1952, depression was somewhat more common among women than men (4% compared to 6%) but was found to carry a significant mortality risk only among men (HR 2.7, 95% CI 1.6-4.7). Based on re-assessments made in 1968, depression was associated with mortality risk among both men (HR 2.2, 95% CI 1.0-4.5) and women (HR 2.1, 95% CI 1.2-3.8). In 1952, more than 20% of men smoked cigarettes excessively and 8% abused alcohol, but very few of these groups of men were also depressed. In the original sample and also among the survivors, depression, alcoholism, and heavy smoking were separately associated with mortality among men. Depression and alcoholism carried a more immediate mortality risk while heavy smoking a more delayed one. CONCLUSIONS: At the baseline of the Stirling County Study, the mortality risk associated with depression among men was not enhanced or explained by abuse of alcohol or nicotine, mainly because comorbidity was rare at that time. The longitudinal research of the study has pointed to a number of psychiatrically-relevant time-trends such as the fact that an association between depression and cigarette smoking did not appear until the 1990s. It is hypothesized that a similar trend may emerge over time regarding the comorbidity of depression and alcoholism. A trend reported here was that, while depressed women in the original sample did not carry a significant mortality risk, the surviving women who were depressed at the time of re-assessment exhibited a mortality risk that was as significant as that for men. Such information may provide a useful back-drop for future investigations.

Penalized solutions to functional regression problems.

Recent technological advances in continuous biological monitoring and personal exposure assessment have led to the collection of subject-specific functional data. A primary goal in such studies is to assess the relationship between the functional predictors and the functional responses. The historical functional linear model (HFLM) can be used to model such dependencies of the response on the history of the predictor values. An estimation procedure for the regression coefficients that uses a variety of regularization techniques is proposed. An approximation of the regression surface relating the predictor to the outcome by a finite-dimensional basis expansion is used, followed by penalization of the coefficients of the neighboring basis functions by restricting the size of the coefficient differences to be small. Penalties based on the absolute values of the basis function coefficient differences (corresponding to the LASSO) and the squares of these differences (corresponding to the penalized spline methodology) are studied. The fits are compared using an extension of the Akaike Information Criterion that combines the error variance estimate, degrees of freedom of the fit and the norm of the bases function coefficients. The performance of the proposed methods is evaluated via simulations. The LASSO penalty applied to the linearly transformed coefficients yields sparser representations of the estimated regression surface, while the quadratic penalty provides solutions with the smallest L(2)-norm of the basis functions coefficients. Finally, the new estimation procedure is applied to the analysis of the effects of occupational particulate matter (PM) exposure on the heart rate variability (HRV) in a cohort of boilermaker workers. Results suggest that the strongest association between PM exposure and HRV in these workers occurs as a result of point exposures to the increased levels of particulate matter corresponding to smoking breaks.

Cigarette smoking in relation to depression: historical trends from the Stirling County Study.

OBJECTIVE: Building on findings about the prevalence and incidence of depression over a 40-year period, the authors provide data on trends in cigarette smoking and associations with depression. METHOD: Data come from interviews with adult population samples (1952, 1970, and 1992) and followed cohorts (1952-1970 and 1970-1992). Logistic regression models and survival regressions were used to analyze the data. RESULTS: The associations between smoking and depression were small and nonsignificant in 1952 and 1970. In 1992, however, the odds that a smoker would be depressed were three times the odds that a nonsmoker would be depressed. The interaction between smoking and study year was significant, indicating that the association was limited to the most recent sample. In the cohort analysis, smoking at baseline did not predict the onset of depression, but subjects who became depressed were more likely to start or continue smoking and less likely to quit than those who never had a depression. CONCLUSIONS: In terms of population trends, the association between depression and cigarette smoking became prominent as the use of tobacco declined because of awareness of the risks involved. The findings about individuals followed over time suggest that those who became depressed were more involved with nicotine than those who never had a depression. The authors discuss hypotheses involving "self-medication," risk-taking, and changes in the social climate but conclude that the relationships between smoking and depression are probably multiple and complex.

Regression methods for assessing familial aggregation of disease.

This paper reviews methods for assessing familial aggregation of disease based on simple logistic regression models. Studies are based on a case-control sampling design, where the disease status of the first degree relatives of both cases and controls are obtained. Both 'proband predictive' and 'family predictive' models are discussed, and an example is given using a case-control sample from a lung cancer study in non-smokers. The methods are extended to characterize co-aggregation of two disorders, that is, presence of one disorder in the proband increases the risk of a second disorder in the relative. An example involving eating disorders and depression is given.

Family study of affective spectrum disorder.

BACKGROUND: Affective spectrum disorder (ASD) represents a group of psychiatric and medical conditions, each known to respond to several chemical families of antidepressant medications and hence possibly linked by common heritable abnormalities. Forms of ASD include major depressive disorder (MDD), attention-deficit/hyperactivity disorder, bulimia nervosa, cataplexy, dysthymic disorder, fibromyalgia, generalized anxiety disorder, irritable bowel syndrome, migraine, obsessive-compulsive disorder, panic disorder, posttraumatic stress disorder, premenstrual dysphoric disorder, and social phobia. Two predictions of the ASD hypothesis were tested: that ASD, taken as a single entity, would aggregate in families and that MDD would coaggregate with other forms of ASD in families. METHODS: Probands with and without MDD, together with their first-degree relatives, were interviewed using the Structured Clinical Interview for DSM-IV and a supplemental interview for other forms of ASD. The familial aggregation and coaggregation of disorders were analyzed using proband predictive logistic regression models, including a novel bivariate model for the presence or absence of each of 2 disorders in a relative as predicted by the presence or absence of each of 2 disorders in the associated proband. RESULTS: In the 178 interviewed relatives of 64 probands with MDD and 152 relatives of 58 probands without MDD, the estimated odds ratio (95% confidence interval) for the familial aggregation of ASD as a whole was 2.5 (1.4-4.3; P =.001) and for the familial coaggregation of MDD with at least one other form of ASD was 1.9 (1.1-3.2; P =.02). CONCLUSIONS: Affective spectrum disorder aggregates strongly in families, and MDD displays a significant familial coaggregation with other forms of ASD, taken collectively. These results suggest that forms of ASD may share heritable pathophysiologic features.