Novel DNA methylation signatures of tobacco smoking with trans-ethnic effects.

BACKGROUND: Smoking remains one of the leading preventable causes of death. Smoking leaves a strong signature on the blood methylome as shown in multiple studies using the Infinium HumanMethylation450 BeadChip. Here, we explore novel blood methylation smoking signals on the Illumina MethylationEPIC BeadChip (EPIC) array, which also targets novel CpG-sites in enhancers. METHOD: A smoking-methylation meta-analysis was carried out using EPIC DNA methylation profiles in 1407 blood samples from four UK population-based cohorts, including the MRC National Survey for Health and Development (NSHD) or 1946 British birth cohort, the National Child Development Study (NCDS) or 1958 birth cohort, the 1970 British Cohort Study (BCS70), and the TwinsUK cohort (TwinsUK). The overall discovery sample included 269 current, 497 former, and 643 never smokers. Replication was pursued in 3425 trans-ethnic samples, including 2325 American Indian individuals participating in the Strong Heart Study (SHS) in 1989-1991 and 1100 African-American participants in the Genetic Epidemiology Network of Arteriopathy Study (GENOA). RESULTS: Altogether 952 CpG-sites in 500 genes were differentially methylated between smokers and never smokers after Bonferroni correction. There were 526 novel smoking-associated CpG-sites only profiled by the EPIC array, of which 486 (92%) replicated in a meta-analysis of the American Indian and African-American samples. Novel CpG sites mapped both to genes containing previously identified smoking-methylation signals and to 80 novel genes not previously linked to smoking, with the strongest novel signal in SLAMF7. Comparison of former versus never smokers identified that 37 of these sites were persistently differentially methylated after cessation, where 16 represented novel signals only profiled by the EPIC array. We observed a depletion of smoking-associated signals in CpG islands and an enrichment in enhancer regions, consistent with previous results. CONCLUSION: This study identified novel smoking-associated signals as possible biomarkers of exposure to smoking and may help improve our understanding of smoking-related disease risk.

Somatic, positive and negative domains of the Center for Epidemiological Studies Depression (CES-D) scale: a meta-analysis of genome-wide association studies.

BACKGROUND: Major depressive disorder (MDD) is moderately heritable, however genome-wide association studies (GWAS) for MDD, as well as for related continuous outcomes, have not shown consistent results. Attempts to elucidate the genetic basis of MDD may be hindered by heterogeneity in diagnosis. The Center for Epidemiological Studies Depression (CES-D) scale provides a widely used tool for measuring depressive symptoms clustered in four different domains which can be combined together into a total score but also can be analysed as separate symptom domains. METHOD: We performed a meta-analysis of GWAS of the CES-D symptom clusters. We recruited 12 cohorts with the 20- or 10-item CES-D scale (32 528 persons). RESULTS: One single nucleotide polymorphism (SNP), rs713224, located near the brain-expressed melatonin receptor (MTNR1A) gene, was associated with the somatic complaints domain of depression symptoms, with borderline genome-wide significance (p discovery = 3.82 × 10-8). The SNP was analysed in an additional five cohorts comprising the replication sample (6813 persons). However, the association was not consistent among the replication sample (p discovery+replication = 1.10 × 10-6) with evidence of heterogeneity. CONCLUSIONS: Despite the effort to harmonize the phenotypes across cohorts and participants, our study is still underpowered to detect consistent association for depression, even by means of symptom classification. On the contrary, the SNP-based heritability and co-heritability estimation results suggest that a very minor part of the variation could be captured by GWAS, explaining the reason of sparse findings.

Rare, low frequency and common coding variants in CHRNA5 and their contribution to nicotine dependence in European and African Americans.

The common nonsynonymous variant rs16969968 in the α5 nicotinic receptor subunit gene (CHRNA5) is the strongest genetic risk factor for nicotine dependence in European Americans and contributes to risk in African Americans. To comprehensively examine whether other CHRNA5 coding variation influences nicotine dependence risk, we performed targeted sequencing on 1582 nicotine-dependent cases (Fagerström Test for Nicotine Dependence score⩾4) and 1238 non-dependent controls, with independent replication of common and low frequency variants using 12 studies with exome chip data. Nicotine dependence was examined using logistic regression with individual common variants (minor allele frequency (MAF)⩾0.05), aggregate low frequency variants (0.05>MAF⩾0.005) and aggregate rare variants (MAF<0.005). Meta-analysis of primary results was performed with replication studies containing 12 174 heavy and 11 290 light smokers. Next-generation sequencing with 180 × coverage identified 24 nonsynonymous variants and 2 frameshift deletions in CHRNA5, including 9 novel variants in the 2820 subjects. Meta-analysis confirmed the risk effect of the only common variant (rs16969968, European ancestry: odds ratio (OR)=1.3, P=3.5 × 10(-11); African ancestry: OR=1.3, P=0.01) and demonstrated that three low frequency variants contributed an independent risk (aggregate term, European ancestry: OR=1.3, P=0.005; African ancestry: OR=1.4, P=0.0006). The remaining 22 rare coding variants were associated with increased risk of nicotine dependence in the European American primary sample (OR=12.9, P=0.01) and in the same risk direction in African Americans (OR=1.5, P=0.37). Our results indicate that common, low frequency and rare CHRNA5 coding variants are independently associated with nicotine dependence risk. These newly identified variants likely influence the risk for smoking-related diseases such as lung cancer.

'Born in Michigan? You're in the biobank': engaging population biobank participants through Facebook advertisements.

BACKGROUND/AIMS: Despite a broad call for biobanks to use social media, data is lacking regarding the capacity of social media tools, especially advertising, to engage large populations on this topic. METHODS: We used Facebook advertising to engage Michigan residents about the BioTrust for Health. We conducted a low-budget (

Understandings of basic genetics in the United States: results from a national survey of black and white men and women.

AIM: This study examined understandings of basic genetic concepts among Americans. METHOD: In a national telephone survey of 1,200 Americans with equal representation among Black and White men and women, subjects responded to 8 items developed by a multidisciplinary team of experts that assessed understanding of basic concepts in multiple domains, including inheritance, genetics and race, and genetics and behavior. RESULTS: Over 70% of subjects responded correctly on items about the genetic similarity of identical twins and siblings. Less than half of subjects responded correctly on all other items. Understanding of genetics was lowest in three areas: types/locations of genes in the body (29% correct), a genetic basis for race (25% correct), and the influence of single genes on behaviors (24% correct). Logistic regression models controlling for age and education showed some differences by race and gender on specific items but also showed that understandings are generally similar across these groups. CONCLUSION: Misunderstandings about genetics are common among Black and White American men and women. Responses appear to reflect personal experiences, group values and interests. These findings emphasize the need for initiatives to improve the public's genetic literacy as well as a need for further investigation in this domain.

Considerations for designing a prototype genetic test for use in translational research.

BACKGROUND: Translational research is needed to explore how people will respond to personal genetic susceptibility information related to common health conditions. Maximizing the rigor of this research will require that genetic test results be returned to study participants. Currently, there is no established method that guides the selection of genetic variants to be used in research with these objectives. METHODS AND RESULTS: To address this question, we designed a process to identify gene variants and health conditions to be included in a prototype genetic test for use in a larger research effort, the Multiplex Initiative. The intention of this exploration was to facilitate research that generates individual genetic test results that are returned to study participants. Inclusion criteria were developed as part of a transdisciplinary and iterative process that considered the weight of evidential support for genetic association with common health conditions, the appropriateness of use in human subjects research, and the recommendations of expert peer reviewers. CONCLUSIONS: The selection process was designed to identify gene variants for the limited purpose of translational research and, therefore, should not be seen as producing a valid clinical test. However, this example of an applied selection process may provide guidance for researchers who are designing studies to evaluate the implications of genetic susceptibility testing through the return of personalized genetic information. As the rate of genomic discoveries increases, such research will be essential in steering the translation of this information towards the greatest public health benefit.

Linkage analysis of plasma ApoE in three ethnic groups: multiple genes with context-dependent effects.

We performed variance component-based linkage analysis in four samples (two of non-Hispanic European-Americans from Rochester, MN; African-Americans from Jackson, MS; and Mexican-Americans from Starr County, TX) to identify chromosomal regions containing genes influencing plasma apolipoprotein E (apoE) levels. The APOE gene region on chromosome (chr) 19 was identified with a LOD > or = 2.00 in both samples from Rochester and the sample from Jackson. Adjustment of apoE levels for differences among means of genotypes defined by the APOE epsilon2/3/4 alleles reduced evidence of linkage, indicating that the APOE gene was responsible for the majority of the linkage signal. In stratified linkage analyses, there was a LOD of 1.70 in the Starr County sibships with average total cholesterol (TC) above the median level for all sibships in that population. Adjustment for APOE genotype did not remove this LOD score, suggesting a second gene in this region may influence apoE variation. Evidence of linkage (LOD= 3.32) on chr 17 was observed in the Starr County sibships with average TC below the median. Inter-individual variation in plasma apoE level may be influenced by variations in the structural gene, and at least one other gene whose effects differ among populations and are dependent on the influence of unmeasured genetic and environmental factors indexed by correlated measures of lipid metabolism.

Combinations of glutathione S-transferase genotypes and risk of early-onset lung cancer in Caucasians and African Americans: a population-based study.

Polymorphisms in GSTM1, GSTT1 and GSTP1 genes in humans are associated with the reduction of enzymatic activity toward several substrates, including those in tobacco smoke. To investigate the potential role these polymorphisms have, as modulators of early-onset lung cancer risk, a population-based case-control study involving early-onset lung cancer cases was performed. Biological samples were available for 350 individuals diagnosed <50 years of age identified from the metropolitan Detroit Surveillance, Epidemiology and End Results (SEER) program and 410 cases of age, race and sex-matched controls ascertained through random digit dialing. African Americans carrying at least one G allele at the GSTP1 locus were 2.9-fold more likely to have lung cancer compared with African Americans without a G allele after adjustment for age, sex, pack years of smoking and history of lung cancer in a first-degree relative (95% CI 1.29-6.20). African Americans with either one or two risk genotypes at the GSTM1 and GSTP1 loci were at increased risk of having lung cancer compared with those having fully functional GSTM1 and GSTP1 genes (OR = 2.8, 95% CI 1.1-7.2 and OR = 4.0, 95% CI 1.3-12.2, respectively). No significant single gene associations between GSTM1, GSTT1 or GSTP1 and early-onset lung cancer were identified in Caucasians, after adjusting for age, sex, pack years and family history of lung cancer. However, our results suggest that specific combinations of glutathione S-transferase polymorphisms increase the risk of early-onset of lung cancer. Joint analysis of these genotypes may identify individuals who are at a higher risk of developing early-onset lung cancer with a greater certainty than single gene studies.

A comparative molecular analysis of developing mouse forelimbs and hindlimbs using serial analysis of gene expression (SAGE).

The analysis of differentially expressed genes is a powerful approach to elucidate the genetic mechanisms underlying the morphological and evolutionary diversity among serially homologous structures, both within the same organism (e.g., hand vs. foot) and between different species (e.g., hand vs. wing). In the developing embryo, limb-specific expression of Pitx1, Tbx4, and Tbx5 regulates the determination of limb identity. However, numerous lines of evidence, including the fact that these three genes encode transcription factors, indicate that additional genes are involved in the Pitx1-Tbx hierarchy. To examine the molecular distinctions coded for by these factors, and to identify novel genes involved in the determination of limb identity, we have used Serial Analysis of Gene Expression (SAGE) to generate comprehensive gene expression profiles from intact, developing mouse forelimbs and hindlimbs. To minimize the extraction of erroneous SAGE tags from low-quality sequence data, we used a new algorithm to extract tags from -analyzed sequence data and obtained 68,406 and 68,450 SAGE tags from forelimb and hindlimb SAGE libraries, respectively. We also developed an improved method for determining the identity of SAGE tags that increases the specificity of and provides additional information about the confidence of the tag-UniGene cluster match. The most differentially expressed gene between our SAGE libraries was Pitx1. The differential expression of Tbx4, Tbx5, and several limb-specific Hox genes was also detected; however, their abundances in the SAGE libraries were low. Because numerous other tags were differentially expressed at this low level, we performed a 'virtual' subtraction with 362,344 tags from six additional nonlimb SAGE libraries to further refine this set of candidate genes. This subtraction reduced the number of candidate genes by 74%, yet preserved the previously identified regulators of limb identity. This study presents the gene expression complexity of the developing limb and identifies candidate genes involved in the regulation of limb identity. We propose that our computational tools and the overall strategy used here are broadly applicable to other SAGE-based studies in a variety of organisms. [SAGE data are all available at GEO (http://www.ncbi.nlm.nih.gov/geo/) under accession nos. GSM55 and GSM56, which correspond to the forelimb and hindlimb raw SAGE data.]

Organ-specific molecular classification of primary lung, colon, and ovarian adenocarcinomas using gene expression profiles.

Molecular classification of tumors based on their gene expression profiles promises to significantly refine diagnosis and management of cancer patients. The establishment of organ-specific gene expression patterns represents a crucial first step in the clinical application of the molecular approach. Here, we report on the gene expression profiles of 154 primary adenocarcinomas of the lung, colon, and ovary. Using high-density oligonucleotide arrays with 7129 gene probe sets, comprehensive gene expression profiles of 57 lung, 51 colon, and 46 ovary adenocarcinomas were generated and subjected to principle component analysis and to a cross-validated prediction analysis using nearest neighbor classification. These statistical analyses resulted in the classification of 152 of 154 of the adenocarcinomas in an organ-specific manner and identified genes expressed in a putative tissue-specific manner for each tumor type. Furthermore, two tumors were identified, one in the colon group and another in the ovarian group, that did not conform to their respective organ-specific cohorts. Investigation of these outlier tumors by immunohistochemical profiling revealed the ovarian tumor was consistent with a metastatic adenocarcinoma of colonic origin and the colonic tumor was a pleomorphic mesenchymal tumor, probably a leiomyosarcoma, rather than an epithelial tumor. Our results demonstrate the ability of gene expression profiles to classify tumors and suggest that determination of organ-specific gene expression profiles will play a significant role in a wide variety of clinical settings, including molecular diagnosis and classification.

Identification and prevention of a GC content bias in SAGE libraries.

Serial Analysis of Gene Expression (SAGE) is becoming a widely used gene expression profiling method for the study of development, cancer and other human diseases. Investigators using SAGE rely heavily on the quantitative aspect of this method for cataloging gene expression and comparing multiple SAGE libraries. We have developed additional computational and statistical tools to assess the quality and reproducibility of a SAGE library. Using these methods, a critical variable in the SAGE protocol was identified that has the potential to bias the Tag distribution relative to the GC content of the 10 bp SAGE Tag DNA sequence. We also detected this bias in a number of publicly available SAGE libraries. It is important to note that the GC content bias went undetected by quality control procedures in the current SAGE protocol and was only identified with the use of these statistical analyses on as few as 750 SAGE Tags. In addition to keeping any solution of free DiTags on ice, an analysis of the GC content should be performed before sequencing large numbers of SAGE Tags to be confident that SAGE libraries are free from experimental bias.

Genome-wide linkage analysis reveals evidence of multiple regions that influence variation in plasma lipid and apolipoprotein levels associated with risk of coronary heart disease.

Results of genome-wide linkage analyses to identify chromosomal regions that influence interindividual variation in plasma lipid and apolipoprotein levels in the Rochester, Minn, population are reported. Analyses were conducted for total cholesterol (total-C), triglycerides (TGs), high density lipoprotein cholesterol (HDL-C), apolipoprotein A-I, apolipoprotein A-II, apolipoprotein B, apolipoprotein C-II, apolipoprotein C-III, apolipoprotein E, the total-C/HDL-C ratio, and the TG/HDL-C ratio. Genotypes were measured for 373 genome-wide marker loci on 1484 individuals distributed among 232 multigeneration pedigrees sampled without regard to health status. LOD scores and estimates of additive genetic variance associated with map locations were obtained by using the variance-component method of linkage analysis. No evidence of linkage with genes influencing variation in age served as a negative control. Plasma apolipoprotein E levels and the apolipoprotein E gene served as a positive control (LOD score 4.20). Evidence (LOD score >2.00) was provided that was suggestive of a gene or genes on chromosomes 4 and 5 influencing variation in the apolipoprotein A-II level, on chromosome 12 influencing variation in the apolipoprotein A-I level, and on chromosome 17 influencing variation of total-C/HDL-C. These analyses provide new information about genomic regions in humans that influence interindividual variation in plasma lipid and apolipoprotein levels and serve as a basis for further fine-mapping studies to identify new genes involved in lipid metabolism.

A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation.

Recent advances in genome research have accelerated the process of locating candidate genes and the variable sites within them and have simplified the task of genotype measurement. The development of statistical and computational strategies to utilize information on hundreds -- soon thousands -- of variable loci to investigate the relationships between genome variation and phenotypic variation has not kept pace, particularly for quantitative traits that do not follow simple Mendelian patterns of inheritance. We present here the combinatorial partitioning method (CPM) that examines multiple genes, each containing multiple variable loci, to identify partitions of multilocus genotypes that predict interindividual variation in quantitative trait levels. We illustrate this method with an application to plasma triglyceride levels collected on 188 males, ages 20--60 yr, ascertained without regard to health status, from Rochester, Minnesota. Genotype information included measurements at 18 diallelic loci in six coronary heart disease--candidate susceptibility gene regions: APOA1--C3--A4, APOB, APOE, LDLR, LPL, and PON1. To illustrate the CPM, we evaluated all possible partitions of two-locus genotypes into two to nine partitions (approximately 10(6) evaluations). We found that many combinations of loci are involved in sets of genotypic partitions that predict triglyceride variability and that the most predictive sets show nonadditivity. These results suggest that traditional methods of building multilocus models that rely on statistically significant marginal, single-locus effects, may fail to identify combinations of loci that best predict trait variability. The CPM offers a strategy for exploring the high-dimensional genotype state space so as to predict the quantitative trait variation in the population at large that does not require the conditioning of the analysis on a prespecified genetic model.

Context-dependent genetic effects in hypertension.

There is a growing frustration with the limitations and inconsistencies of single locus candidate gene association and linkage studies. This frustration is exacerbated by the knowledge that a large influx of genotypic and gene expression data is expected to emerge over the next 5 years, and we are not prepared for the type of multigenic conceptual framework that will be necessary to analyze that data. A review of the hypertension genetic literature reveals substantial evidence for the importance of both genetic and environmental contexts on the mapping between single locus polymorphisms and risk of disease. These trends indicate that the current reliance on simple single gene studies to elucidate the complex etiology of hypertension needs to undergo some kind of transformation. It is suggested that even a minor shift to a more systematic investigation of context-dependent effects will increase our understanding of the multidimensional genetic and environmental realities underneath current studies. This shift is also necessary if we are to gain a deeper understanding of the specific ways in which an individual's risk of hypertension is a consequence of the inter- actions among genes and environments.

Association and linkage analysis of the alpha-adducin gene and blood pressure.

In Milan hypertensive rats, a variant in the alpha-adducin gene has been shown to account for approximately 50% of the interindividual variation in blood pressure levels between these animals and their normotensive counterparts. Additional studies have suggested that a polymorphism within exon 10 of the human alpha-adducin gene (Gly-460-Trp) may be associated with hypertension and salt sensitivity. On the basis of these observations, we investigated variation within or near the human alpha-adducin gene for linkage and association with a locus influencing blood pressure levels in 281 nuclear families (774 siblings aged 5 to 37 years; 380 parents aged 26 to 57 years), selected from the white population of Rochester, Minnesota, without regard to health. Sib pair linkage analyses (n = 852 sibling pairs) using a dinucleotide repeat marker (D4S43) that maps approximately 660 kb from the alpha-adducin gene provided no evidence of linkage between this marker locus and a locus influencing systolic, diastolic, or mean blood pressure levels. Allele frequencies for the Gly-460-Trp polymorphism were similar to those reported in other white populations (Gly = 0.812, Trp = 0.188); however, this polymorphism was not associated with any measure of blood pressure level in either parents or siblings. Therefore, variation within the alpha-adducin gene does not appear to have a major influence on measures of blood pressure in white families from Rochester, Minnesota.

The glutathione S-transferase-mu and -theta genotypes in the etiology of prostate cancer: genotype-environment interactions with smoking.

It has been reported that individuals who express GSTT1, the gene coding for the theta class of the glutathione S-transferases (GSTs), have an elevated risk of prostate cancer (CaP). This result is supported by studies that show glutathione conjugation of some xenobiotics by the GSTs can produce mutagenic intermediates. However, the potential role of environmental factors in modifying the risk of CaP conferred by GSTT1 is not known. We investigated whether there was an interaction between smoking and the non-deleted genotypes of the mu (GSTM1) and theta (GSTT1) GST genes using a clinic-based study of 276 CaP cases and 499 controls. We observed no main effect of smoking (odds ratio, 0.95; confidence interval, 0.69-1.29) or GSTM1 (odds ratio, 1.00; confidence interval, 0.73-1.36) with CaP, but did observe a statistically significant main effect of GSTT1 with CaP (odds ratio, 1.61; confidence interval, 1.14-2.28) as reported previously. No interaction between smoking and GSTM1 was observed. A significant increase in the probability of having CaP was observed in men who were both smokers and carried a non-deleted GSTT1 genotype compared with men who had neither or only one of these risk factors (P = 0.049). Approximately 30.9% of CaP cases in this study could be attributed to the smoking x GSTT1 interaction. Whereas the mechanism of this interaction is not known, it is plausible that the metabolism of carcinogenic intermediates or the response to chronic inflammation associated with smoking may be modulated by the GSTT1 genotype and may modify CaP risk.

The relationship between risk factor levels and presence of coronary artery calcification is dependent on apolipoprotein E genotype.

An important research question in the study of the genetics of coronary artery disease (CAD) is whether information about genetic variation will improve our ability to predict CAD beyond established risk factors. This question is especially relevant to the goal of identifying young, asymptomatic adults with coronary atherosclerosis who would benefit most from interventions to reduce risk. Coronary artery calcification (CAC) detected by electron-beam computed tomography is a relatively new method for detecting coronary atherosclerosis in asymptomatic individuals that has been shown to be a more accurate indicator of coronary atherosclerosis in asymptomatic individuals than other noninvasive techniques. In a study of asymptomatic women (n=169) and men (n=160) between the ages of 20 and 59 representative of the Rochester, Minnesota population, we used logistic regression to ask whether the most common Apolipoprotein (Apo) E genotypes (epsilon3/2, epsilon3/3, and epsilon4/3) predict the presence of CAC. The addition of information about ApoE genotypes to logistic models containing each separate risk factor did not improve prediction of CAC (P>0.10 in both women and men). However, there was significant evidence (P<0.10) that associations between variation in the probability of having CAC and variation in body mass index, plasma total cholesterol, and plasma ApoB in men and body mass index, plasma triglycerides, plasma ApoA1, and plasma ApoE in women were dependent on ApoE genotype. Thus, variation in the gene coding for ApoE may play a role in determining the contribution of established risk factors to risk of CAC.

Influence of apolipoprotein E genotype variation on the means, variances, and correlations of plasma lipids and apolipoproteins in children.

The impact of the three most common apolipoprotein E (APOE) genotypes (epsilon 32, epsilson 33, and epsilon 43) on means, variances, and correlations of nine plasma lipid and apolipoprotein traits (total cholesterol, InTriglycerides, HDL cholesterol, and apolipoproteins AI, AII, B, CII, CIII, and InE) was studied in 212 unrelated female and 219 unrelated male children aged 5-21.5 years from 278 pedigrees ascertained without regard to health status from Rochester, Minnesota. There was significant heterogeneity (p < or = 0.05) among genotypes for the mean plasma levels of InApo E, Apo CII, Apo CIII, and InTriglycerides (InTrig) in females, and for the means of InApo E, Apo B, and total cholesterol (Total-C) in males. Significant heterogeneity of intragenotypic variance was observed in males for Apo CII, InTrig, and HDL-C; no significant heterogeneity was observed in females. Pairwise correlations between traits differed significantly among APOE genotypes in both females (6 of 36 pairs) and males (5 of 36 pairs). These results differ from those obtained from studies of the parental generation from the same sample of pedigrees. Our study further demonstrates that, with the exception of mean InApo E levels, the univariate and bivariate distributions of traits that are measures of lipoprotein metabolism are influenced by variation in the APOE gene in a gender- and generation-dependent manner.