Many smokers attempt to quit smoking but few are successful in the long term. The heritability of nicotine addiction and smoking relapse have been documented, and research is focused on identifying specific genetic influences on the ability to quit smoking and response to specific medications. Research in genetically modified cell lines and mice has identified nicotine acetylcholine receptor subtypes that mediate the pharmacological and behavioral effects of nicotine sensitivity and withdrawal. Human genetic association studies have identified single nucleotide polymorphisms (SNPs) in genes encoding nicotine acetylcholine receptor subunits and nicotine metabolizing enzymes that influence smoking cessation phenotypes. There is initial promising evidence for a role in smoking cessation for SNPs in the β2 and α5/α3/β4 nAChR subunit genes; however, effects are small and not consistently replicated. There are reproducible and clinically significant associations of genotypic and phenotypic measures of CYP2A6 enzyme activity and nicotine metabolic rate with smoking cessation as well as response to nicotine replacement therapies and bupropion. Prospective clinical trials to identify associations of genetic variants and gene–gene interactions on smoking cessation are needed to generate the evidence base for both medication development and targeted therapy approaches based on genotype.
Drug abuse and dependence are multifaceted disorders with complex genetic underpinnings. Identifying specific genetic correlates is challenging and may be more readily accomplished by defining endophenotypes specific for addictive disorders. Symptoms and syndromes, including acute drug response, consumption, preference, and withdrawal, are potential endophenotypes characterizing addiction that have been investigated using model organisms. We present a review of major genes involved in serotonergic, dopaminergic, GABAergic, and adrenoreceptor signaling that are considered to be directly involved in nicotine, opioid, cannabinoid, and ethanol use and dependence. The zebrafish genome encodes likely homologs of the vast majority of these loci. We also review the known expression patterns of these genes in zebrafish. The information presented in this review provides support for the use of zebrafish as a viable model for studying genetic factors related to drug addiction. Expansion of investigations into drug response using model organisms holds the potential to advance our understanding of drug response and addiction in humans.
Addiction; Zebrafish; Endophenotype; Genetics; Genomics; Behavior
Constitutional deletions of distal 9q34 encompassing the EHMT1 (euchromatic histone methyltransferase 1) gene, or loss-of-function point mutations in EHMT1, are associated with the 9q34.3 microdeletion, also known as Kleefstra syndrome [MIM#610253]. We now report further evidence for genomic instability of the subtelomeric 9q34.3 region as evidenced by copy number gains of this genomic interval that include duplications, triplications, derivative chromosomes and complex rearrangements. Comparisons between the observed shared clinical features and molecular analyses in 20 subjects suggest that increased dosage of EHMT1 may be responsible for the neurodevelopmental impairment, speech delay, and autism spectrum disorders revealing the dosage sensitivity of yet another chromatin remodeling protein in human disease. Five patients had 9q34 genomic abnormalities resulting in complex deletion-duplication or duplication-triplication rearrangements; such complex triplications were also observed in six other subtelomeric intervals. Based on the specific structure of these complex genomic rearrangements (CGR) a DNA replication mechanism is proposed confirming recent findings in C elegans telomere healing. The end-replication challenges of subtelomeric genomic intervals may make them particularly prone to rearrangements generated by errors in DNA replication.
chromosome 9q34.3; duplication; triplication; molecular mechanism; subtelomeric rearrangements; genomic disorder; telomere stabilization
Autosomal dominant conditions are known to be associated with advanced paternal age, and it has been suggested that retinoblastoma (Rb) also exhibits a paternal age effect due to the paternal origin of most new germline RB1 mutations. To further our understanding of the association of parental age and risk of de novo germline Rb mutations, we evaluated the effect of parental age in a cohort of Rb survivors in the United States. A cohort of 262 retinoblastoma patients was retrospectively identified at one institution, and telephone interviews were conducted with parents of 160 survivors (65.3%). We built two sets of hierarchical stepwise logistic regression models to detect an increased odds of a de novo germline mutation related to older parental age compared to sporadic and familial Rb. The modeling strategy evaluated effects of continuous increasing maternal and paternal age and five-year age increases adjusted for the age of the other parent. Mean maternal ages for patients with de novo germline mutations and sporadic Rb were similar (28.3 and 28.5 respectively) as were mean paternal ages (31.9 and 31.2 respectively), and all were significantly higher than the weighted general U.S. population means. In contrast, maternal and paternal ages for familial Rb did not differ significantly from the weighted U.S. general population means. Although we noted no significant differences between mean maternal and paternal ages between each of the three Rb classification groups, we found increased odds of having a de novo germline mutation for each five-year increase in paternal age, but these findings were not statistically significant (de novo versus sporadic ORs: 30-34 = 1.65 [0.69-4], ≥35 = 1.34 [0.54-3.3]; de novo versus familial ORs: 30-34 = 2.82 [0.95 – 8.4], ≥35 = 1.61 [0.57-4.6]). Our study suggests a weak paternal age effect for Rb resulting from de novo germline mutations consistent with the paternal origin of most of these mutations.
Trisomies 18 and 21 are the two most common live born autosomal aneuploidies in humans. While the anatomic abnormalities in affected fetuses are well documented, the dysregulated biological pathways associated with the development of the aneuploid phenotype are less clear. Amniotic fluid (AF) cell-free RNA is a valuable source of biological information obtainable from live fetuses. In this study, we mined gene expression data previously produced by our group from mid-trimester AF supernatant samples. We identified the euploid, trisomy 18 and trisomy 21 AF transcriptomes, and analyzed them with a particular focus on the nervous system. We used multiple bioinformatics resources, including DAVID, Ingenuity Pathway Analysis, and the BioGPS Gene Expression Atlas. Our analyses confirmed that AF supernatant from aneuploid fetuses is enriched for nervous system gene expression and neurological disease pathways. Tissue analysis showed that fetal brain cortex and Cajal–Retzius cells were significantly enriched for genes contained in the AF transcriptomes. We also examined AF transcripts known to be dysregulated in aneuploid fetuses compared with euploid controls and identified several brain-specific transcripts among them. Many of these genes play critical roles in nervous system development. NEUROD2, which was downregulated in trisomy 18, induces neurogenic differentiation. SOX11, downregulated in trisomy 21, is a transcription factor that is essential for pan-neuronal protein expression and axonal growth of sensory neurons. Our results show that whole transcriptome analysis of cell-free RNA in AF from live pregnancies permits discovery of biomarkers of abnormal human neurodevelopment and advances our understanding of the pathophysiology of aneuploidy.
Signaling by the glial cell line-derived neurotrophic factor (GDNF)-RET receptor tyrosine kinase and SPRY1, a RET repressor, is essential for early urinary tract development. Individual or a combination of GDNF, RET and SPRY1 mutant alleles in mice cause renal malformations reminiscent of congenital anomalies of the kidney or urinary tract (CAKUT) in humans and distinct from renal agenesis phenotype in complete GDNF or RET null mice. We sequenced GDNF, SPRY1 and RET in 122 unrelated living CAKUT patients to discover deleterious mutations that cause CAKUT. Novel or rare deleterious mutations in GDNF or RET were found in 6 unrelated patients. A family with duplicated collecting system had a novel mutation, RETR831Q, which showed markedly decreased GDNF dependent MAPK activity. Two patients with RET-G691S polymorphism harbored additional rare non-synonymous variants GDNF-R93W and RET-R982C. The patient with double RET-G691S/R982C genotype had multiple defects including renal dysplasia, megaureters and cryptorchidism. Presence of both mutations were necessary to affect RET activity. Targeted whole exome and next-generation sequencing revealed a novel deleterious mutation G443D in GFRα1, the co-receptor for RET, in this patient. Pedigree analysis indicated that the GFRα1 mutation was inherited from the unaffected mother and the RET mutations from the unaffected father. Our studies indicate that 5% of living CAKUT patients harbor deleterious rare variants or novel mutations in GDNF-GFRα1-RET pathway. We provide evidence for the coexistence of deleterious rare and common variants in genes in the same pathway as a cause of CAKUT and discovered novel phenotypes associated with the RET pathway.
RET; CAKUT; kidney; exome; genetics; renal malformations
Adenocarcinoma of the pancreas is a significant cause of cancer mortality, and up to 10 % of cases appear to be familial. Heritable genomic copy number variants (CNVs) can modulate gene expression and predispose to disease. Here, we identify candidate predisposition genes for familial pancreatic cancer (FPC) by analyzing germline losses or gains present in one or more high-risk patients and absent in a large control group. A total of 120 FPC cases and 1,194 controls were genotyped on the Affymetrix 500K array, and 36 cases and 2,357 controls were genotyped on the Affymetrix 6.0 array. Detection of CNVs was performed by multiple computational algorithms and partially validated by quantitative PCR. We found no significant difference in the germline CNV profiles of cases and controls. A total of 93 non-redundant FPC-specific CNVs (53 losses and 40 gains) were identified in 50 cases, each CNV present in a single individual. FPC-specific CNVs overlapped the coding region of 88 RefSeq genes. Several of these genes have been reported to be differentially expressed and/or affected by copy number alterations in pancreatic adenocarcinoma. Further investigation in high-risk subjects may elucidate the role of one or more of these genes in genetic predisposition to pancreatic cancer.
Millions of genetic variants have been assessed for their effects on the trait of interest in genome-wide association studies (GWAS). The complex traits are affected by a set of inter-related genes. However, the typical GWAS only examine the association of a single genetic variant at a time. The individual effects of a complex trait are usually small, and the simple sum of these individual effects may not reflect the holistic effect of the genetic system. High-throughput methods enable genomic studies to produce a large amount of data to expand the knowledge base of the biological systems. Biological networks and pathways are built to represent the functional or physical connectivity among genes. Integrated with GWAS data, the network- and pathway-based methods complement the approach of single genetic variant analysis, and may improve the power to identify trait-associated genes. Taking advantage of the biological knowledge, these approaches are valuable to interpret the functional role of the genetic variants, and to further understand the molecular mechanism influencing the traits. The network- and pathway-based methods have demonstrated their utilities, and will be increasingly important to address a number of challenges facing the mainstream GWAS.
Heritability, the fraction of phenotypic variation explained by genetic variation, has been estimated for many phenotypes in a range of populations, organisms, and time points. The recent development of efficient genotyping and sequencing technology has led researchers to attempt to identify the genetic variants responsible for the genetic component of phenotype directly via GWAS. The gap between the phenotypic variance explained by GWAS results and those estimated by from classical heritability methods has been termed the “missing heritability problem”. In this work, we examine modern methods for estimating heritability, which use the genotype and sequence data directly. We discuss them in the context of classical heritability methods, the missing heritability problem, and describe their implications for understanding the genetic architecture of complex phentoypes.
heritability; genome-wide association study; prediction; polygenic models; linear mixed models
As whole genome sequence becomes a routine component of gene discovery studies in humans, we will have an exhaustive catalog of genetic variation and the challenge becomes understanding the phenotypic consequences of these variants. Statistical genetic methods and analytical approaches that are concerned with optimizing phenotypes for gene discovery for complex traits offer two general categories of advantages. They may increase power to localize genes of interest and also aid in interpreting associations between genetic variants and disease outcomes by suggesting potential mechanisms and pathways through which genes may affect outcomes. Such phenotype optimization approaches include use of allied phenotypes such as symptoms or ages of onset to reduce genetic heterogeneity within a set of cases, study of quantitative risk factors or endophenotypes, joint analyses of related phenotypes, and derivation of new phenotypes designed to extract independent measures underlying the correlations among a set of related phenotypes through approaches such as principal components. New opportunities are also presented by technological advances that permit efficient collection of hundreds or thousands of phenotypes on an individual, including phenotypes more proximal to the level of gene action such as levels of gene expression, microRNAs, or metabolic and proteomic profiles.
Rare variation is the current frontier in human genetics. The large pedigree design is practical, efficient, and well-suited for investigating rare variation. In large pedigrees, specific rare variants that co-segregate with a trait will occur in sufficient numbers so that effects can be measured, and evidence for association can be evaluated, by making use of methods that fully use the pedigree information. Evidence from linkage analysis can focus investigation, both reducing the multiple testing burden and expanding the variants that can be evaluated and followed up, as recent studies have shown. The large pedigree design requires only a small fraction of the sample size needed to identify rare variants of interest in population-based designs, and many highly suitable, well-understood, and available statistical and computational tools already exist. Samples consisting of large pedigrees with existing rich phenotype and genome scan data should be prime candidates for high-throughput sequencing in the search of the determinants of complex traits.
The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies – when the number of environmental or genetic risk factors is relatively small – has been described before.
In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze Genome-Wide Environmental Interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for Genome-Wide Association gene-gene Interaction (GWAI) studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to “joining” two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.
Genome-wide association studies; gene-environment interaction; post-GWAS analysis; association tests; exploratory methods
Alcohol dependence (AD), a genetically influenced phenotype, is extremely costly to individuals and to society in the United States and throughout the world, contributing to morbidity and mortality and a host of economic, interpersonal, and societal problems. Although until recently the only genes established to affect risk for AD were those encoding several alcohol metabolizing enzymes, there are now several other genes that can be regarded as confirmed risk loci, discovered through linkage and candidate gene association studies. While the mechanism of action of the effects of alcohol-metabolizing enzymes on AD risk is thought to be well understood, we are still in the early stages of understanding the physiology of other risk loci. Further, it is clear that only a small number of the many genes that influence risk for AD have been identified. Newer methodologies (e.g., genomewide association, study of copy number variation, and deep sequencing of candidate loci to identify rare risk variants) that have improved our understanding of other complex traits hold the promise of identifying a greater set of AD susceptibility loci.
Two primary chitinases have been identified in humans – acid mammalian chitinase (AMCase) and chitotriosidase (CHIT1). Mammalian chitinases have been observed to affect the host’s immune response. The aim of this study was to test for association between genetic variation in the chitinases and phenotypes related to Chronic Obstructive Pulmonary Disease (COPD). Polymorphisms in the chitinase genes were selected based on previous associations with respiratory diseases. Polymorphisms that were associated with lung function level or rate of decline in the Lung Health Study (LHS) cohort were analyzed for association with COPD affection status in four other COPD case-control populations. Chitinase activity and protein levels were also related to genotypes. In the Caucasian LHS population, the baseline forced expiratory volume in one second (FEV1) was significantly different between the AA and GG genotypic groups of the AMCase rs3818822 polymorphism. Subjects with the GG genotype had higher AMCase protein and chitinase activity compared with AA homozygotes. For CHIT1 rs2494303, a significant association was observed between rate of decline in FEV1 and the different genotypes. In the African American LHS population, CHIT1 rs2494303 and AMCase G339T genotypes were associated with rate of decline in FEV1. Although a significant effect of chitinase gene alleles was found on lung function level and decline in the LHS, we were unable to replicate the associations with COPD affection status in the other COPD study groups.
Chronic Obstructive Pulmonary Disease; chitinase; polymorphism; lung function
ATP-sensitive K+ (KATP) channels maintain cardiac homeostasis under stress, as revealed by murine gene knockout models of the KCNJ11-encoded Kir6.2 pore. However, the translational significance of KATP channels in human cardiac physiology remains largely unknown. Here, the frequency of the minor K23 allele of the common functional Kir6.2 E23K polymorphism was found overrepresented in 115 subjects with congestive heart failure compared to 2,031 community-based controls (69 vs. 56%, P < 0.001). Moreover, the KK genotype, present in 18% of heart failure patients, was associated with abnormal cardiopulmonary exercise stress testing. In spite of similar baseline heart rates at rest among genotypic subgroups (EE: 72.2 ± 2.3, EK: 75.0 ± 1.8 and KK: 77.1 ± 3.0 bpm), subjects with the KK genotype had a significantly reduced heart rate increase at matched workload (EE: 32.8 ± 2.7%, EK: 28.8 ± 2.1%, KK: 21.7 ± 2.6%, P < 0.05), at 75% of maximum oxygen consumption (EE: 53.9 ± 3.9%, EK: 49.9 ± 3.1%, KK: 36.8 ± 5.3%, P < 0.05), and at peak VO2 (EE: 82.8 ± 6.0%, EK: 80.5 ± 4.7%, KK: 59.7 ± 8.1%, P < 0.05). Molecular modeling of the tetrameric Kir6.2 pore structure revealed the E23 residue within the functionally relevant intracellular slide helix region. Substitution of the wild-type E residue with an oppositely charged, bulkier K residue would potentially result in a significant structural rearrangement and disrupted interactions with neighboring Kir6.2 subunits, providing a basis for altered high-fidelity KATP channel gating, particularly in the homozygous state. Blunted heart rate response during exercise is a risk factor for mortality in patients with heart failure, establishing the clinical relevance of Kir6.2 E23K as a biomarker for impaired stress performance and underscoring the essential role of KATP channels in human cardiac physiology.
Gliomas account for approximately 80% of all primary malignant brain tumors, and despite improvements in clinical care over the last 20 years remain among the most lethal tumors, underscoring the need for gaining new insights that could translate into clinical advances. Recent genome-wide association studies (GWAS) have identified seven new susceptibility regions. We conducted a new independent GWAS of glioma using 1,856 cases and 4,955 controls (from 14 cohort studies, 3 casecontrol studies, and 1 population-based case only study) and found evidence of strong replication for three of the seven previously reported associations at 20q13.33 (RTEL), 5p15.33 (TERT), and 9p21.3 (CDKN2BAS), and consistent association signals for the remaining four at 7p11.2 (EGFR both loci), 8q24.21 (CCDC26) and 11q23.3 (PHLDB1). The direction and magnitude of the signal were consistent for samples from cohort and case-control studies, but the strength of the association was more pronounced for loci rs6010620 (20q,13.33; RTEL) and rs2736100 (5p15.33, TERT) in cohort studies despite the smaller number of cases in this group, likely due to relatively more higher grade tumors being captured in the cohort studies. We further examined the 85 most promising single nucleotide polymorphism (SNP) markers identified in our study in three replication sets (5,015 cases and 11,601 controls), but no new markers reached genome-wide significance. Our findings suggest that larger studies focusing on novel approaches as well as specific tumor subtypes or subgroups will be required to identify additional common susceptibility loci for glioma risk.
Many common human diseases are complex and are expected to be highly heterogeneous, with multiple causative loci and multiple rare and common variants at some of the causative loci contributing to the risk of these diseases. Data from the genome-wide association studies (GWAS) and metadata such as known gene functions and pathways provide the possibility of identifying genetic variants, genes and pathways that are associated with complex phenotypes. Single-marker based tests have been very successful in identifying thousands of genetic variants for hundreds of complex phenotypes. However, these variants only explain very small percentages of the heritabilities. To account for the locus- and allelic-heterogeneity, gene-based and pathway-based tests can be very useful in the next stage of the analysis of GWAS data. U-statistics, which summarize the genomic similarity between pair of individuals and link the genomic similarity to phenotype similarity, have proved to be very useful for testing the associations between a set of single nucleotide polymorphisms (SNPs) and the phenotypes. Compared to single marker analysis, the advantages afforded by the U-statistics-based methods is large when the number of markers involved is large. We review several formulations of U-statistics in genetic association studies and point out the links of these statistics with other similarity-based tests of genetic association. Finally, potential application of U-statistics in analysis of the next generation sequencing data and rare variants association studies are discussed.
Alcohol dependence (AD) is a common neuropsychiatric disorder with high heritability. A number of studies have analyzed the association between the Taq1A polymorphism (located in the gene cluster ANKK1/DRD2) and AD. In the present study, we conducted a large-scale meta-analysis to confirm the association between the Taq1A polymorphism and the risk for AD in over 18,000 subjects included in 61 case-control studies that were published up to August 2012. Our meta-analysis demonstrated both allelic and genotypic association between the Taq1A polymorphism and AD susceptibility [allelic: P(Z)=1.1×10−5, OR=1.19; genotypic: P(Z)=3.2×10−5, OR=1.24]. The association remained significant after adjustment for publication bias using the trim and fill method. Sensitivity analysis showed that the effect size of the Taq1A polymorphism on AD risk was moderate and not influenced by any individual study. The pooled odds ratio from published studies decreased with the year of publication but stabilized after the year 2001. Subgroup analysis indicated that publication bias could be influenced by racial ancestry. In summary, this large-scale meta-analysis confirmed the association between the Taq1A polymorphism and AD. Future studies are required to investigate the functional significance of the ANKK1/DRD2 Taq1A polymorphism in AD.
Alcohol dependence; meta-analysis; ANKK1 and DRD2 gene cluster; the Taq1A polymorphism
The risk of glioma has consistently been shown to be increased two-fold in relatives of patients with primary brain tumors (PBT). A recent genome-wide linkage study of glioma families provided evidence for a disease locus on 17q12-21.32, with the possibility of four additional risk loci at 6p22.3, 12p13.33-12.1, 17q22-23.2, and 18q23.
To identify the underlying genetic variants responsible for the linkage signals, we compared the genotype frequencies of 5,122 SNPs mapping to these five regions in 88 glioma cases with and 1,100 cases without a family history of PBT (discovery study). An additional series of 84 familial and 903 non-familial cases were used to replicate associations.
In the discovery study, 12 SNPs showed significant associations with family history of PBT (P < 0.001). In the replication study, two of the 12 SNPs were confirmed: 12p13.33-12.1 PRMT8 rs17780102 (P = 0.031) and 17q12-21.32 SPOP rs650461 (P = 0.025). In the combined analysis of discovery and replication studies, the strongest associations were attained at four SNPs: 12p13.33-12.1 PRMT8 rs17780102 (P = 0.0001), SOX5 rs7305773 (P = 0.0001) and STKY1 rs2418087 (P = 0.0003), and 17q12-21.32 SPOP rs6504618 (P = 0.0006). Further, a significant gene-dosage effect was found for increased risk of family history of PBT with these four SNPs in the combined data set (Ptrend < 1.0 ×10−8).
The results support the linkage finding that some loci in the 12p13.33-12.1 and 17q12-q21.32 may contribute to gliomagenesis and suggest potential target genes underscoring linkage signals.
Association; Polymorphisms; Glioma; Family history of primary brain tumor; Linkage analysis
Vitamin D deficiency is becoming more apparent in many populations. Genetic factors may play a role in the maintenance of vitamin D levels. The objective of this study was to perform a genome-wide analysis (GWAS) of vitamin D levels, including replication of prior GWAS results. We measured 25-hydroxyvitamin D (25(OH)D) levels in serum collected at the time of enrollment and at year 4 in 572 Caucasian children with asthma, who were part of a multi-center clinical trial, the Childhood Asthma Management Program. Replication was performed in a second cohort of 592 asthmatics from Costa Rica and a third cohort of 516 Puerto Rican asthmatics. In addition, we attempted replication of three SNPs that were previously identified in a large GWAS of Caucasian individuals. The setting included data from a clinical trial of childhood asthmatics and two cohorts of asthmatics recruited for genetic studies of asthma. The main outcome measure was circulating 25(OH)D levels. The 25(OH)D levels at the two time-points were only modestly correlated with each other (intraclass correlation coefficient = 0.33) in the CAMP population. We identified SNPs that were nominally associated with 25(OH)D levels at two time-points in CAMP, and replicated four SNPs in the Costa Rican cohort: rs11002969, rs163221, rs1678849, and rs4864976. However, these SNPs were not significantly associated with 25(OH)D levels in a third population of Puerto Rican asthmatics. We were able to replicate the SNP with the strongest effect, previously reported in a large GWAS: rs2282679 (GC), and we were able to replicate another SNP, rs10741657 (CYP2R1), to a lesser degree. We were able to replicate two of three prior significant findings in a GWAS of 25(OH)D levels. Other SNPs may be additionally associated with 25(OH)D levels in certain populations.
Both nicotine and alcohol addictions are common chronic brain disorders that are of great concern to individuals and society. Although genetics contributes significantly to these disorders, the susceptibility genes and variants underlying them remain largely unknown. Many years of genome-wide linkage and association studies have implicated a number of genes and pathways in the etiology of nicotine and alcohol addictions. In this communication, we focus on current evidence, primarily from human genetic studies, supporting the involvement of genes and variants in the GABAergic signaling system in the etiology of nicotine dependence and alcoholism based on linkage, association, and gene-by-gene interaction studies. Current efforts aim not only to replicate these findings in independent samples, but also to identify which variant contributes to the detected associations and through what molecular mechanisms.
X-chromosome inactivation is an epigenetic hallmark of mammalian development. Chromosome-wide regulation of the X-chromosome is essential in embryonic and germ cell development. In the male germline, the X-chromosome goes through meiotic sex chromosome inactivation, and the chromosome-wide silencing is maintained from meiosis into spermatids before the transmission to female embryos. In early female mouse embryos, X-inactivation is imprinted to occur on the paternal X-chromosome, representing the epigenetic programs acquired in both parental germlines. Recent advances revealed that the inactive X-chromosome in both females and males can be dissected into two elements: repeat elements versus unique coding genes. The inactive paternal X in female preimplantation embryos is reactivated in the inner cell mass of blastocysts in order to subsequently allow the random form of X-inactivation in the female embryo, by which both Xs have an equal chance of being inactivated. X-chromosome reactivation is regulated by pluripotency factors and also occurs in early female germ cells and in pluripotent stem cells, where X-reactivation is a stringent marker of naive ground state pluripotency. Here we summarize recent progress in the study of X-inactivation and X-reactivation during mammalian reproduction and development as well as in pluripotent stem cells.
In vitro studies have shown that p53 mediates a protective response against DNA damage by causing either cell-cycle arrest and DNA repair, or apoptosis. These responses have not yet been demonstrated in humans. A common source of DNA damage in humans is cigarette smoke, which should activate p53 repair mechanisms. The level of p53 is regulated by HDM2, which targets p53 for degradation. The G-allele of a polymorphism in intron 1 of HDM2 (rs2279744:G/T) results in higher HDM2 levels, and should be associated with a reduced p53 response and hence more DNA damage and corresponding tissue destruction. Similarly, the alleles of a polymorphism (rs1042522) in TP53 that encode arginine (G-allele) or proline (C-allele) at codon 72 cause increased pro-apoptotic (G-allele) or cell-cycle arrest activities (C-allele), respectively, and may moderate p53's ability to prevent DNA damage. To test these hypotheses we examined lung function in relation to cumulative history of smoking in a population-based cohort. The G-alleles in HDM2 and TP53 were found to be associated with accelerated smoking-related decline in lung function. These data support the hypothesis that p53 protects from DNA damage in humans and provides a potential explanation for variation in lung function impairment amongst smokers.
The μ-opioid receptor is the site of action of many endogenous opioids as well as opiates. We hypothesize that differences in DNA methylation of specific CpG dinucleotides between former severe heroin addicts in methadone maintenance treatment and control subjects will depend, in part, upon ethnicity. DNA methylation analysis of the μ-opioid receptor gene (OPRM1) promoter region was performed on African-Americans (118 cases, 80 controls) and Hispanics (142 cases, 61 controls) and these were compared with a similar Caucasian cohort from our earlier study. In controls, a higher methylation level was found in the African-Americans compared with the Hispanics or Caucasians. Significant experiment-wise differences in methylation levels were found at the −25 and +12 CpG sites in the controls among the three ethnicities. The overall methylation level of the CpG sites were significantly higher in the former heroin addicts when compared with the controls (point-wise P = 0.0457). However, in the African-Americans, the degree of methylation was significantly decreased experiment-wise in the former heroin addicts at the +12 CpG site (P = 0.0032, Bonferroni corrected general estimating equations). In Hispanics, the degree of methylation was increased in the former heroin addicts at the −25 (P < 0.001, experiment-wise), −14 (P = 0.001, experiment-wise), and +27 (P < 0.001, experiment-wise) CpG sites. These changes in methylation of the OPRM1 promoter region may lead to altered expression of the μ-opioid receptor gene in the lymphocytes of former heroin addicts who are stabilized in methadone maintenance treatment.