Chromosome 7 has shown consistent evidence of linkage with a variety of phenotypes related to alcohol dependence in the Collaborative Study on the Genetics of Alcoholism (COGA) project. Using a sample of 262 densely affected families, a peak lod score for alcohol dependence of 2.9 was observed at D7S1799 (Wang et al., 2004, Hum Mol Genet). The lod score in the region increased to 4.1 when a subset of the sample was genotyped with the Illumina Linkage III panel for the Genetic Analysis Workshop 14 (GAW14; Dunn et al., 2005, BMC Genetics). To follow-up on this linkage region, we systematically screened SNPs across a 2 LOD support interval surrounding the alcohol dependence peak.
SNPs were selected from the HapMap Phase I CEPH data to tag linkage disequilibrium bins across the region. 1340 across the 18Mb region, genotyped by the Center for Inherited Disease Research (CIDR), were analyzed. Family-based association analyses were performed on a sample of 1172 individuals from 217 Caucasian families. Results: Eight SNPs showed association with alcohol dependence at p<0.01. Four of the eight most significant SNPs were located in or very near the ACN9 gene. We conducted additional genotyping across ACN9 and identified multiple variants with significant evidence of association with alcohol dependence.
These analyses suggest that ACN9 is involved in the predisposition to alcohol dependence. Data from yeast suggest that ACN9 is involved in gluconeogenesis and the assimilation of ethanol or acetate into carbohydrate.
genetics; association; linkage disequilibrium; alcohol dependence; ACN9
Cytochrome P450 2A6 (CYP2A6) is the primary catalyst of nicotine metabolism. To develop a predictive genetic model of nicotine metabolism, the conversion of deuterated (D2)-nicotine to D2-cotinine was quantified in 189 European Americans and the contribution of CYP2A6 genotype to variability in first-pass nicotine metabolism was assessed. Specifically, 1) single time-point measures of D2-cotinine/(D2-cotinine + D2-nicotine) following oral administration were used as a metric of CYP2A6 activity; 2) the impact of CYP2A6 haplotype was treated as acting multiplicatively; 3) parameter estimates were calculated for all haplotypes in the subject pool, defined by a set of polymorphisms previously reported to affect function, including gene copy number; and 4) a minimum number of predictive polymorphisms are justified to be included in the model based on statistical evidence of differences between haplotypes. The final model includes seven polymorphisms and fits the phenotype, 30 minutes following D2-nicotine oral administration, with R2=0.719. The predictive power of the model is robust: parameter estimates calculated in men (n=89) predict the phenotype in women (n=100) with R2=0.758 and vice versa with R2=0.617; estimates calculated in current smokers (n=102) predict phenotype in former smokers (n=86) with R2=0.690 and vice versa with R2=0.703. Comparisons of haplotypes also demonstrate that CYP2A6*12 is a loss of function allele indistinguishable from CYP2A6*4 and CYP2A6*2 and that the CYP2A6*1B 5′ UTR conversion has negligible impact on metabolism. After controlling for CYP2A6 genotype modest associations were found between increased metabolism and both female gender (p= 4.8×10−4) and current smoking (p=0.02).
CYP2A6; nicotine metabolism; cotinine
Cigarette smoking and other forms of tobacco use are the leading cause of preventable mortality in the world. A better understanding of the etiology of nicotine addiction may help increase the success rate of cessation and decrease the massive morbidity and mortality associated with smoking.
In order to identify genetic polymorphisms that contribute to nicotine dependence, our group undertook a genetic association study including three enzyme families that potentially influence nicotine metabolism: cytochrome P450 enzymes (CYP P450s), flavin monooxygenases (FMOs) and UDP-glucuronosyl transferases (UGTs).
Several polymorphisms in FMO1 showed association in a discovery sample and were tested in an independent replication sample. One polymorphism, rs10912765, showed association that remained significant after Bonferroni correction (nominal p=0.0067, corrected p=0.0134). Several additional polymorphisms in linkage disequilibrium with this SNP also showed association. Subsequent in vitro experiments characterized FMO1 as a more efficient catalyst of nicotine N-oxidation than FMO3. In adult humans, FMO1 is primarily expressed in the kidney and is likely to be a major contributor to the renal metabolism and clearance of therapeutic drugs. FMO1 is also expressed in the brain and could contribute to the nicotine concentration in this tissue.
These findings suggest that polymorphisms in FMO1 are significant risk factors in the development of nicotine dependence and that the mechanism may involve variation in nicotine pharmacology.
FMO1; nicotine dependence; nicotine metabolism
Several genome-wide association and candidate gene studies have linked chromosome 15q24–q25.1 (a region including the CHRNA5-CHRNA3-CHRNB4 gene cluster) with alcohol dependence, nicotine dependence and smoking-related illnesses such as lung cancer and chronic obstructive pulmonary disease. To further examine the impact of these genes on the development of substance use disorders, we tested whether variants within and flanking the CHRNA5-CHRNA3-CHRNB4 gene cluster affect the transition to daily smoking (individuals who smoked cigarettes 4 or more days per week) in a cross sectional sample of adolescents and young adults from the COGA (Collaborative Study of the Genetics of Alcoholism) families. Subjects were recruited from families affected with alcoholism (either as a first or second degree relative) and the comparison families. Participants completed the SSAGA interview, a comprehensive assessment of alcohol and other substance use and related behaviors. Using the Quantitative trait disequilibrium test (QTDT) significant association was detected between age at onset of daily smoking and variants located upstream of CHRNB4. Multivariate analysis using a Cox proportional hazards model further revealed that these variants significantly predict the age at onset of habitual smoking among daily smokers. These variants were not in high linkage disequilibrium (0.28
Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the workshop.
The unrelated individuals sample from Genetic Analysis Workshop 17 consists of a small number of subjects from eight population samples and genetic data composed mostly of rare variants. We compare two simple approaches to collapsing rare variants within genes for their utility in identifying genes that affect phenotype. We also compare results from stratified analyses to those from a pooled analysis that uses ethnicity as a covariate. We found that the two collapsing approaches were similarly effective in identifying genes that contain causative variants in these data. However, including population as a covariate was not an effective substitute for analyzing the subpopulations separately when only one subpopulation contained a rare variant linked to the phenotype.
We report two approaches for linkage analysis of data consisting of replicate phenotypes. The first approach is specifically designed for the unusual (in human data) replicate structure of the Genetic Analysis Workshop 17 pedigree data. The second approach consists of a standard linkage analysis that, although not specifically tailored to data consisting of replicate genotypes, was envisioned as providing a sounding board against which our novel approach could be assessed. Both approaches are applied to the analysis of three quantitative phenotypes (Q1, Q2, and Q4) in two sets of African families. All analyses were carried out blind to the generating model (i.e., the “answers”). Using both methods, we found numerous significant linkage signals for Q1, although population colocalization was absent for most of these signals. The linkage analysis of Q2 and Q4 failed to reveal any strong linkage signals.
Although the importance of selecting cases and controls from the same population has been recognized for decades, the recent advent of genome-wide association studies has heightened awareness of this issue. Because these studies typically deal with large samples, small differences in allele frequencies between cases and controls can easily reach statistical significance. When, unbeknownst to a researcher, cases and controls have different substructures, the number of false-positive findings is inflated. There have been three recent developments of purely statistical approaches to assessing the ancestral comparability of case and control samples: genomic control, structured association, and multivariate reduction analyses. The widespread use of high-throughput technology has allowed the quick and accurate genotyping of the large number of markers required by these methods.
Group 13 dealt with four population stratification issues: single-nucleotide polymorphism marker selection, association testing, non-standard methods, and linkage disequilibrium calculations in stratified or mixed ethnicity samples. We demonstrated that there are continuous axes of ethnic variation in both datasets of Genetic Analysis Workshop 16. Furthermore, ignoring this structure created p-value inflation for a variety of phenotypes. Principal-components analysis (or multidimensional scaling) can control inflation as covariates in a logistic regression. One can weight for local ancestry estimation and allow the use of related individuals. Problems arise in the presence of extremely high association or unusually strong linkage disequilibrium (e.g., in chromosomal inversions). Our group also reported a method for performing an association test controlling for substructure when genome-wide markers are not available to explicitly compute stratification.
genetic association; genome-wide association study; principal components; multidimensional scaling; ethnic substructure
Alcohol dependence is a complex disease, and although linkage and candidate gene studies have identified several genes associated with the risk for alcoholism, these explain only a portion of the risk.
We carried out a genome-wide association study (GWAS) on a case-control sample drawn from the families in the Collaborative Study on the Genetics of Alcoholism. The cases all met diagnostic criteria for alcohol dependence according to the Diagnostic and Statistical Manual of the American Psychiatric Association Fourth Edition (DSM-IV); controls all consumed alcohol but were not dependent on alcohol or illicit drugs. To prioritize among the strongest candidates, we genotyped most of the top 199 SNPs (p ≤ 2.1 × 10−4) in a sample of alcohol dependent families and performed pedigree-based association analysis. We also examined whether the genes harboring the top SNPs were expressed in human brain or were differentially expressed in the presence of ethanol in lymphoblastoid cells.
Although no single SNP met genome-wide criteria for significance, there were several clusters of SNPs that provided mutual support. Combining evidence from the case-control study, the followup in families, and gene expression provided strongest support for the association of a cluster of genes on chromosome 11 (SLC22A18, PHLDA2, NAP1L4, SNORA54, CARS, and OSBPL5) with alcohol dependence. Several SNPs nominated as candidates in earlier GWAS studies replicated in ours, including CPE, DNASE2B, SLC10A2,ARL6IP5, ID4, GATA4, SYNE1 and ADCY3.
We have identified several promising associations that warrant further examination in independent samples.
alcohol dependence; genome-wide association study; case-control study; family study; gene expression
To date, all known Alzheimer's disease genes influence amyloid beta (Aβ). The development of in vivo imaging of Aβ deposition in the human brain using Pittsburgh compound B (PIB) offers the possibility of using cortical PIB binding as a quantitative endophenotype for genetic studies of late-onset Alzheimer's disease (LOAD).
Heritability of Aβ deposition was determined using 82 elderly siblings from 35 families. Correlation with other Aβ related traits was determined using an unrelated sample of 112 individuals. For both samples, APOE ε4 was genotyped and PET imaging was performed using the PIB ligand. Mean cortical binding potential (MCBP) was computed from several regions-of-interest.
MCBP has a high heritability (0.61, p=0.043). Furthermore, most of the heritable component (74%) cannot be explained by APOE ε4 genotype. Analysis of the unrelated sample reveals that a third of the variance of MCBP cannot be predicted by other biological traits, including CSF Aβ42 levels.
These findings demonstrate that MCBP is a genetic trait and that other more easily measured Aβ related traits such as CSF Aβ42 do not fully explain the variance in MCBP. Thus, mean cortical PIB binding is a useful trait for large-scale genetic studies of LOAD.
Recent large-scale genetic studies of late-onset Alzheimer’s disease (LOAD) have identified risk variants in CALHM1, GAB2 and SORL1. The mechanisms by which these genes might modulate risk are not definitively known. CALHM1 and SORL1 may alter amyloid-beta (Aβ) levels and GAB2 may influence phosphorylation of the tau protein. In this study we have analyzed disease associated genetic variants in each of these genes for association with cerebrospinal fluid (CSF) Aβ or tau levels in 602 samples from two independent CSF series. We failed to detect association between CSF Aβ42 levels and SNPs in SORL1 despite substantial statistical power to detect association. While we also failed to detect association between variants in GAB2 and CSF tau levels, power to detect this association was limited. Finally, our data suggest that the minor allele of rs2986017, in CALHM1, is marginally associated with CSF Aβ42 levels. This association is consistent with previous reports that this non-synonymous coding substitution results in increased Aβ levels in vitro and provides support for an Aβ-related mechanism for modulating risk for AD.
Alzheimer’s disease; genetics; association; endophenotypes; amyloid; tau; CALHM1; SORL1; GAB2
Several studies have found replicable associations between nicotine dependence and specific variants in the nicotinic receptor genes CHRNA5(rs16969968) and CHRNA3(rs3743078). How these newly identified genetic risks combine with known environmental risks is unknown. This study examined whether the level of parent monitoring during early adolescence modified the risk of nicotine dependence associated with these genetic variants.
In a cross-sectional case control study of US-based community sample of 2027 subjects, we use a systematic series of regression models to examine the effect of parent monitoring on risk associated with two distinct variants in the nicotinic receptor genes CHRNA5(rs16969968) and CHRNA3(rs3743078).
Low parent monitoring as well as the previously identified genetic variants were associated with an increased risk of nicotine dependence. An interaction was found between the SNP(rs16969968) and parent monitoring (p=0.034). The risk for nicotine dependence increased significantly with the risk genotype of SNP(rs16969968) when combined with lowest quartile parent monitoring. In contrast, there was no evidence of an interaction between SNP(rs3743078) and parent monitoring (p=0.80).
The genetic risk of nicotine dependent associated with rs16969968 was modified by level of parent monitoring, while the genetic risk associated with rs3743078 was not, suggesting that the increased risk due to some genes may be mitigated by environmental factors such as parent monitoring.
nicotine dependence; parent monitoring; phenotype; gene-environmental interaction; nicotinic receptor genes; case control study
Genetic association studies have demonstrated the importance of variants in the CHRNA5-CHRNA3-CHRNB4 cholinergic nicotinic receptor subunit gene cluster on chromosome 15q24-25.1 in risk for nicotine dependence, smoking, and lung cancer in populations of European descent. We have now carried out a detailed study of this region using dense genotyping in both European- and African-Americans.
We genotyped 75 known single-nucleotide-polymorphisms (SNPs) and one sequencing-discovered SNP in an African-American (AA) sample (N = 710) and European-American (EA) sample (N = 2062). Cases were nicotine-dependent and controls were non-dependent smokers.
The non-synonymous CHRNA5 SNP rs16969968 is the most significant SNP associated with nicotine dependence in the full sample of 2772 subjects (p = 4.49×10−8, OR 1.42 (1.25–1.61)) as well as in AAs only (p = 0.015, OR = 2.04 (1.15–3.62)) and EAs only (p = 4.14×10−7, OR = 1.40 (1.23–1.59)). Other SNPs that have been shown to affect mRNA levels of CHRNA5 in EAs are associated with nicotine dependence in AAs but not in EAs. The CHRNA3 SNP rs578776, which has low correlation with rs16969968, is associated with nicotine dependence in EAs but not in AAs. Less common SNPs (frequency ≤ 5%) also are associated with nicotine dependence.
In summary, multiple variants in this gene cluster contribute to nicotine dependence risk, and some are also associated with functional effects on CHRNA5. The non-synonymous SNP rs16969968, a known risk variant in European-descent populations, is also significantly associated with risk in African-Americans. Additional SNPs contribute in distinct ways to risk in these two populations.
genetic association; smoking; cholinergic nicotinic receptors; nicotinic acetylcholine receptors
Genomic studies of cannabis use disorders have been limited. The cannabinoid receptor 1 gene (CNR1) on chromosome 6q14–15 is an excellent candidate gene for cannabis dependence due to the important role of the G-protein coupled receptor encoded by this gene in the rewarding effects of Δ9-tetrahydrocannabinol. Previous studies have found equivocal evidence for an association between SNPs in CNR1 and a general vulnerability to substance use disorders. We investigate the association between 9 SNPs spanning CNR1 and cannabis dependence in 1,923 individuals. Two SNPs that were previously associated with cannabis dependence in other studies were also significant with this phenotype in our analyses [rs806368 (p = 0.05) and rs806380 (p = 0.009)]. Haplotype analyses revealed the association to be largely driven by the SNP rs806380. These results suggest a role for the cannabinoid receptor 1 gene in cannabis dependence.
CNR1; cannabis dependence; COGA; pedigree disequilibrium test; association
Tobacco smoking continues to be a leading cause of preventable death. Recent research has underscored the important role of specific cholinergic nicotinic receptor subunit (CHRN) genes in risk for nicotine dependence and smoking. To detect and characterize the influence of genetic variation on vulnerability to nicotine dependence, we analyzed 226 SNPs covering the complete family of 16 CHRN genes, which encode the nicotinic acetylcholine receptor (nAChR) subunits, in a sample of 1050 nicotine-dependent cases and 879 non-dependent controls of European descent. This expanded SNP coverage has extended and refined the findings of our previous large scale genome-wide association and candidate gene study. After correcting for the multiple tests across this gene family, we found significant association for two distinct loci in the CHRNA5-CHRNA3-CHRNB4 gene cluster, one locus in the CHRNB3-CHRNA6 gene cluster, and a fourth, novel locus in the CHRND-CHRNG gene cluster. The two distinct loci in CHRNA5-CHRNA3-CHRNB4 are represented by the non-synonymous SNP rs16969968 in CHRNA5 and by rs578776 in CHRNA3, respectively, and joint analyses show that the associations at these two SNPs are statistically independent. Nominally significant single-SNP association was detected in CHRNA4 and CHRNB1. In summary, this is the most comprehensive study of the CHRN genes for involvement with nicotine dependence to date. Our analysis reveals significant evidence for at least four distinct loci in the nicotinic receptor subunit genes that each influence the transition from smoking to nicotine dependence and may inform the development of improved smoking cessation treatments and prevention initiatives.
cholinergic nicotinic receptors; nicotinic acetylcholine receptors; smoking; genetic association
A previous association analysis identified polymorphisms in GABRA4 and GABRA2 to be associated with nicotine dependence, as assessed by a score of 4 or more on the Fagerström Test for Nicotine Dependence (FTND). In the present report, we extend the previous study by significantly expanding our genotyping efforts for these two genes.
In 1,049 cases (FTND of 4 or more) and 872 controls (smokers with FTND of 0), from the U.S. and Australia, we examine the association between 23 GABRA4 and 39 GABRA2 recently genotyped single nucleotide polymorphisms (SNPs) and nicotine dependence using logistic regression-based association analyses in PLINK.
Two and 18 additional SNPs in GABRA4 and GABRA2 respectively were associated with nicotine dependence. The SNPs identified in GABRA4 (p value = 0.002) were restricted to introns 1 and 2, exon 1 and the 5’ end of the gene, while those in GABRA2 localized to the 3’ end of the gene and spanned introns 9 through 3, and were in moderate to high linkage disequilibrium (as measured by r2) with each other and with previously studied polymorphisms.
Our findings consistently demonstrate the role of GABRA4 and GABRA2 in nicotine dependence. However, further research is needed to identify the biological influence of these intronic variations and to isolate functionally relevant polymorphisms neighboring them.
Association; nicotine dependence; GABRA2; NICSNP
Familial autosomal dominant frontotemporal dementia with ubiquitin-positive, tau-negative inclusions in the brain linked to 17q21-22 recently has been reported to carry null mutations in the progranulin gene (PGRN). Hereditary dysphasic disinhibition dementia (HDDD) is a frontotemporal dementia with prominent changes in behavior and language deficits. A previous study found significant linkage to chromosome 17 in a HDDD family (HDDD2), but no mutation in the MAPT gene. Longitudinal follow-up has enabled us to identify new cases and to further characterize the dementia in this family. The goals of this study were to develop research criteria to classify the different clinical expressions of dementia observed in this large kindred, to identify the causal mutation in affected individuals and correlate this with phenotypic characteristics in this pedigree, and to assess the neuropathological characteristics using immunohistochemical techniques.
In this study we describe a detailed clinical, pathological and mutation analysis of the HDDD2 kindred.
Neuropathologically, HDDD2 represents a familial frontotemporal lobar degeneration with ubiquitin-positive, tau-negative inclusions (FTLD-U). We developed research classification criteria and identified three distinct diagnostic thresholds, which helped localize the disease locus. The chromosomal region with the strongest evidence of linkage lies within the minimum critical region for FTLD-U. Sequencing of each exon of the PGRN gene led to the identification of a novel missense mutation, Ala-9 Asp, within the signal peptide.
HDDD2 is an FTLD-U caused by a missense mutation in the PGRN gene that cosegregates with the disease and with the disease haplotype in at-risk individuals. This mutation is the first reported pathogenic missense mutation in the signal peptide of the PGRN gene causing FTLD-U. In light of the previous reports of null mutations and its position in the gene, two possible pathological mechanisms are proposed: (1) the protein may accumulate within the endoplasmic reticulum due to inefficient secretion; and (2) mutant RNA may have a lower expression because of degradation via nonsense-mediated decay.
Many phenotypes of public health importance (e.g., diabetes, coronary artery disease, major depression, obesity, and addictions to alcohol and nicotine) involve complex pathways of action. Interactions between genetic variants or between genetic variants and environmental factors likely play important roles in the functioning of these pathways. Unfortunately, complex interacting systems are likely to have important interacting factors that may not readily reveal themselves to univariate analyses. Instead, detecting the role of some of these factors may require analyses that are sensitive to interaction effects.
In this study, we evaluate the sensitivity and specificity of the restricted partition method (RPM) to detect signals related to coronary artery disease in the Genetic Analysis Workshop 16 Problem 3 data using the 50,000 k candidate gene single-nucleotide polymorphism set. Power and false-positive rates were evaluated using the first 100 replicate datasets. This included an exploration of the utility of using of all genotyped family members compared with selecting one member per family.
We conducted a search for non-chromosome 6 genes that may increase risk for rheumatoid arthritis (RA). Our approach was to retrospectively ascertain three "extreme" subsamples from the North American Rheumatoid Arthritis Consortium. The three subsamples are: 1) RA cases who have two low-risk HLA-DRB1 alleles (N = 18), 2) RA cases who have two high-risk HLA-DRB1 alleles (N = 163), and 3) controls who have two low-risk HLA-DRB1 alleles (N = 652). We hypothesized that since Group 1's RA was likely due to non-HLA related risk factors, and because Group 3, by definition, is unaffected, comparing Group 1 with Group 2 and Group 1 with Group 3 would result in the identification of candidate susceptibility loci located outside of the MHC region. Accordingly, we restricted our search to the 21 non-chromosome 6 autosomes. The case-case comparison of Groups 1 and 2 resulted in the identification of 17 SNPs with allele frequencies that differed at p < 0.0001. The case-control comparison of Groups 1 and 3 identified 23 SNPs that differed in allele frequency at p < 0.0001. Eight of these SNPs (rs10498105, rs2398966, rs7664880, rs7447161, rs2793471, rs2611279, rs7967594, and rs742605) were common to both lists.
Although identification of cryptic population stratification is necessary for case/control association analyses, it is also vital for linkage analyses and family-based association tests when founder genotypes are missing. However, including related individuals in an analysis such as EIGENSTRAT can result in bias; using only founders or one individual per pedigree results in loss of data and inaccurate estimates of stratification. We examine a generalization of principal-component analyses to allow for the inclusion of related individuals by down-weighting the significance of individual comparisons.