Identifying cryptic population structure in multigenerational pedigrees in a Mexican American sample.

Cryptic population structure can increase both type I and type II errors. This is particularly problematic in case-control association studies of unrelated individuals. Some researchers believe that these problems are obviated in families. We argue here that this may not be the case, especially if families are drawn from a known admixed population such as Mexican Americans. We use a principal component approach to evaluate and visualize the results of three different approaches to searching for cryptic structure in the 20 multigenerational families of the Genetic Analysis Workshop 18 (GAW18). Approach 1 uses all family members in the sample to identify what might be considered "outlier" kindreds. Because families are likely to differ in size (in the GAW18 families, there is about a 4-fold difference in the number of typed individuals), approach 2 uses a weighting system that equalizes pedigree size. Approach 3 concentrates on the founders and the "marry-ins" because, in principle, the entire pedigree can be reconstructed with knowledge of the sequence of these unrelated individuals and genome-wide association study (GWAS) data on everyone else (to identify the position of recombinations). We demonstrate that these three approaches can yield very different insights about cryptic structure in a sample of families.

Genotypic discrepancies arising from imputation.

The ideal genetic analysis of family data would include whole genome sequence on all family members. A strategy of combining sequence data from a subset of key individuals with inexpensive, genome-wide association study (GWAS) chip genotypes on all individuals to infer sequence level genotypes throughout the families has been suggested as a highly accurate alternative. This strategy was followed by the Genetic Analysis Workshop 18 data providers. We examined the quality of the imputation to identify potential consequences of this strategy by comparing discrepancies between GWAS genotype calls and imputed calls for the same variants. Overall, the inference and imputation process worked very well. However, we find that discrepancies occurred at an increased rate when imputation was used to infer missing data in sequenced individuals. Although this may be an artifact of this particular instantiation of these analytic methods, there may be general genetic or algorithmic reasons to avoid trying to fill in missing sequence data. This is especially true given the risk of false positives and reduction in power for family-based transmission tests when founders are incorrectly imputed as heterozygotes. Finally, we note a higher rate of discrepancies when unsequenced individuals are inferred using sequenced individuals from other pedigrees drawn from the same admixed population.

Genome-wide survival analysis of age at onset of alcohol dependence in extended high-risk COGA families.

BACKGROUND: The age at onset of alcohol dependence (AD) is a critical moderator of genetic associations for alcohol dependence. The present study evaluated whether single nucleotide polymorphisms (SNPs) can influence the age at onset of AD in large high-risk families from the Collaborative Study on the Genetics of Alcoholism (COGA). METHODS: Genomewide SNP genotyping was performed in 1788 regular drinkers from 118 large European American families densely affected with alcoholism. We used a genome-wide Cox proportional hazards regression model to test for association between age at onset of AD and SNPs. RESULTS: This family-based analysis identified an intergenic SNP, rs2168784 on chromosome 3 that showed strong evidence of association (P=5×10(-9)) with age at onset of AD among regular drinkers. Carriers of the minor allele of rs2168784 had 1.5 times the hazard of AD onset as compared with those homozygous for the major allele. By the age of 20 years, nearly 30% of subjects homozygous for the minor allele were alcohol dependent while only 19% of those homozygous for the major allele were. We also identified intronic SNPs in the ADP-ribosylation factor like 15 (ARL15) gene on chromosome 5 (P=1.11×10(-8)) and the UTP20 small subunit (UTP20) gene on chromosome 12 (P=4.32×10(-8)) that were associated with age at onset of AD. CONCLUSIONS: This extended family based genome-wide cox-proportional hazards analysis identified several loci that might be associated with age at onset of AD.

A meta-analysis of two genome-wide association studies to identify novel loci for maximum number of alcoholic drinks.

Maximum number of alcoholic drinks consumed in a 24-h period (maxdrinks) is a heritable (>50 %) trait and is strongly correlated with vulnerability to excessive alcohol consumption and subsequent alcohol dependence (AD). Several genome-wide association studies (GWAS) have studied alcohol dependence, but few have concentrated on excessive alcohol consumption. We performed two GWAS using maxdrinks as an excessive alcohol consumption phenotype: one in 118 extended families (N = 2,322) selected from the Collaborative Study on the Genetics of Alcoholism (COGA), and the other in a case-control sample (N = 2,593) derived from the Study of Addiction: Genes and Environment (SAGE). The strongest association in the COGA families was detected with rs9523562 (p = 2.1 × 10(-6)) located in an intergenic region on chromosome 13q31.1; the strongest association in the SAGE dataset was with rs67666182 (p = 7.1 × 10(-7)), located in an intergenic region on chromosome 8. We also performed a meta-analysis with these two GWAS and demonstrated evidence of association in both datasets for the LMO1 (p = 7.2 × 10(-7)) and PLCL1 genes (p = 4.1 × 10(-6)) with maxdrinks. A variant in AUTS2 and variants in INADL, C15orf32 and HIP1 that were associated with measures of alcohol consumption in a meta-analysis of GWAS studies and a GWAS of alcohol consumption factor score also showed nominal association in the current meta-analysis. The present study has identified several loci that warrant further examination in independent samples. Among the top SNPs in each of the dataset (p ≤ 10(-4)) far more showed the same direction of effect in the other dataset than would be expected by chance (p = 2 × 10(-3), 3 × 10(-6)), suggesting that there are true signals among these top SNPs, even though no SNP reached genome-wide levels of significance.

Escitalopram reduces attentional performance in anxious older adults with high-expression genetic variants at serotonin 2A and 1B receptors.

Older adults are among the most vulnerable to adverse cognitive effects of psychotropic medications and, therefore, the personalization of psychotropic treatment based on adverse drug reactions in this demographic is of great importance. We examined changes on neuropsychological tests of attention attributable to selective serotonin reuptake inhibitor (SSRI) treatment in anxious older adults. We also examined whether variation in serotonin receptor genes was associated with reduced attentional performance with SSRIs. We examined change from pre- to post-treatment in two attention measures - digit span and coding - in 133 adults aged ≥60 yr with generalized anxiety disorder in a 12-wk trial of escitalopram vs. placebo. We also examined attentional change in relation to genetic variability in four central serotonin receptors: the serotonin transporter and serotonin 1A, 2A and 1B receptors. Digit span scores were significantly lowered in patients receiving escitalopram relative to placebo, indicating reduced attentional performance attributable to the SSRI. Individuals with high-transcription variants in the receptors 5-HTR2A rs6311 and 5-HTR1B rs11568817 had greater reductions in attention with SSRI treatment compared to placebo. We conclude that SSRIs reduce attention in older adults, particularly in those with high-expression genetic variants at the serotonin 2A and 1B receptors. Analysing neuropsychological changes with SSRIs in relation to genetic variation in the serotonin system may be a useful strategy for detecting subgroups of older adults who are more susceptible to side-effects of SSRIs. These results, if confirmed, could lead to the personalization of SSRI use to reduce adverse neurocognitive effects.

Incorporating linkage information into a common disease/rare variant framework.

Recent developments in sequencing technology have allowed the investigation of the common disease/rare variant hypothesis. In the Genetic Analysis Workshop 17 data set, we have sequence data on both unrelated individuals and eight large extended pedigrees with simulated quantitative and qualitative phenotypes. Group 11, whose focus was incorporating linkage information, considered several different ways to use the extended pedigrees to identify causal genes and variants. The first issue was the use of standard linkage or identity-by-descent information to identify regions containing causal rare variants. We found that rare variants of large effect segregating through pedigrees were precisely the bailiwick of linkage analysis. For a common disease, we anticipate many risk loci, so a heterogeneity linkage analysis or an analysis of a single pedigree at a time may be useful. The second issue was using pedigree data to identify individuals for sequencing. If one can identify linked regions and even carriers of risk haplotypes, the sequencing will be substantially more efficient. In fact, sequencing only 2.5% of the genome in carefully selected individuals can detect 52% of the risk variants that would be detected through whole-exome sequencing in a large number of unrelated individuals. Finally, we found that linkage information from pedigrees can provide weights for case-control association tests. We also found that pedigree-based association tests have the same issues of binning variants and variant counting as those in tests of unrelated individuals. Clearly, when pedigrees are available, they can provide great assistance in the search for rare variants that influence common disorders.

The contribution of common CYP2A6 alleles to variation in nicotine metabolism among European-Americans.

OBJECTIVE: To study the association between cytochrome P450 2A6 (CYP2A6) genotype and metabolism of nicotine to cotinine, identify functional polymorphisms, and develop a predictive genetic model of nicotine metabolism. METHODS: 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, (i) single time point measures of D2-cotinine/(D2-cotinine+D2-nicotine) after oral administration were used as a metric of CYP2A6 activity; (ii) the impact of CYP2A6 haplotype was treated as acting multiplicatively; (iii) 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 (iv) a minimum number of predictive polymorphisms were justified to be included in the model based on statistical evidence of differences between haplotypes. RESULTS: The final model includes seven polymorphisms and fits the phenotype, 30-min after D2-nicotine oral administration, with R=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 R=0.758 and vice versa with R=0.617; estimates calculated in current smokers (n=102) predict the phenotype in former-smokers (n=86) with R=0.690 and vice versa with R=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'-untranslated region conversion has negligible impact on metabolism. After controlling for CYP2A6 genotype, modest associations were found between increased metabolism and both female sex (P=4.8×10) and current smoking (P=0.02). CONCLUSION: Among European-Americans, seven polymorphisms in the CYP2A6 gene explain the majority of variability in the metabolism of nicotine to cotinine after oral administration. Parameters determined from this in-vivo experiment can be used to predict nicotine metabolism based on CYP2A6 genotype.

Common polymorphisms in FMO1 are associated with nicotine dependence.

BACKGROUND: 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 to increase the success rate of cessation and to decrease the massive morbidity and mortality associated with smoking. METHODS: 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, flavin monooxygenases (FMOs), and UDP-glucuronosyl transferases. RESULTS: Several polymorphisms in FMO1 showed association in a discovery sample, and were tested in an independent replication sample. One polymorphism, rs10912765, showed an association that remained significant after Bonferroni correction (nominal P=0.0067, corrected P=0.0134). Several additional polymorphisms in linkage disequilibrium with this single nucleotide polymorphism 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. CONCLUSION: 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.

Association of TMEM106B gene polymorphism with age at onset in granulin mutation carriers and plasma granulin protein levels.

OBJECTIVE: To test whether rs1990622 (TMEM106B) is associated with age at onset (AAO) in granulin (GRN) mutation carriers and with plasma GRN levels in mutation carriers and healthy, elderly individuals. Rs1990622 (TMEM106B) was identified as a risk factor for frontotemporal lobar degeneration with TAR DNA-binding protein inclusions (FTLD-TDP) in a recent genome-wide association. DESIGN: Rs1990622 was genotyped in GRN mutation carriers and tested for association with AAO using the Kaplan-Meier method and a Cox proportional hazards model. SETTING: Alzheimer's Disease Research Center. Subjects  We analyzed 50 affected and unaffected GRN mutation carriers from 4 previously reported FTLD-TDP families (HDDD1, FD1, HDDD2, and the Karolinska family). The GRN plasma levels were also measured in 73 healthy, elderly individuals. MAIN OUTCOME MEASURES: Age at onset and GRN plasma levels. RESULTS: The risk allele of rs1990622 was associated with a mean decrease of the AAO of 13 years (P = 9.9 × 10(-7)) and with lower plasma GRN levels in both healthy older adults (P = 4 × 10(-4)) and GRN mutation carriers (P = .0027). Analysis of the HapMap database identified a nonsynonymous single-nucleotide polymorphism rs3173615 (T185S) in perfect linkage disequilibrium with rs1990622. CONCLUSIONS: The association of rs1990622 with AAO explains, in part, the wide range in the AAO of disease among GRN mutation carriers. We hypothesize that rs1990622 or another variant in linkage disequilibrium could act in a manner similar to APOE in Alzheimer disease, increasing risk for disease in the general population and modifying AAO in mutation carriers. Our results also suggest that genetic variation in TMEM106B may influence risk for FTLD-TDP by modulating secreted levels of GRN.

Linkage analysis merging replicate phenotypes: an application to three quantitative phenotypes in two African samples.

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.

Stratify or adjust? Dealing with multiple populations when evaluating rare variants.

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.

Validating predicted biological effects of Alzheimer's disease associated SNPs using CSF biomarker levels.

Recent large-scale genetic studies of late-onset Alzheimer's disease 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-ß (Aß) 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) Aß or tau levels in 602 samples from two independent CSF series. We failed to detect association between CSF Aß42 levels and single nucleotide polymorphisms 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 Aß42 levels. This association is consistent with previous reports that this non-synonymous coding substitution results in increased Aß levels in vitro and provides support for an Aß-related mechanism for modulating risk for Alzheimer's disease.

A genome-wide association study of alcohol dependence.

Excessive alcohol consumption is one of the leading causes of preventable death in the United States. Approximately 14% of those who use alcohol meet criteria during their lifetime for alcohol dependence, which is characterized by tolerance, withdrawal, inability to stop drinking, and continued drinking despite serious psychological or physiological problems. We explored genetic influences on alcohol dependence among 1,897 European-American and African-American subjects with alcohol dependence compared with 1,932 unrelated, alcohol-exposed, nondependent controls. Constitutional DNA of each subject was genotyped using the Illumina 1M beadchip. Fifteen SNPs yielded P < 10(-5), but in two independent replication series, no SNP passed a replication threshold of P < 0.05. Candidate gene GABRA2, which encodes the GABA receptor alpha2 subunit, was evaluated independently. Five SNPs at GABRA2 yielded nominal (uncorrected) P < 0.05, with odds ratios between 1.11 and 1.16. Further dissection of the alcoholism phenotype, to disentangle the influence of comorbid substance-use disorders, will be a next step in identifying genetic variants associated with alcohol dependence.

Cortical binding of pittsburgh compound B, an endophenotype for genetic studies of Alzheimer's disease.

BACKGROUND: To date, all known Alzheimer's disease genes influence amyloid beta (Abeta). Imaging of Abeta 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). METHODS: Heritability of Abeta deposition was determined using 82 elderly siblings from 35 families. Correlation with other Abeta related traits was determined using an unrelated sample of 112 individuals. For both samples, apolipoprotein E (APOE) epsilon4 was genotyped and positron emission tomography imaging was performed using the PIB ligand. Mean cortical binding potential (MCBP) was computed from several regions of interest. RESULTS: MCBP has a high heritability (.61, p = .043). Furthermore, 74% of the heritable component cannot be explained by APOE epsilon4 genotype. The unrelated sample reveals that a third of the variance of MCBP cannot be predicted by other biological traits, including cerebrospinal fluid (CSF) amyloid beta 42 (Abeta42) levels. CONCLUSIONS: These findings demonstrate that MCBP is a genetic trait and that other more easily measured Abeta related traits such as CSF Abeta42 do not fully explain the variance in MCBP. Thus, MCBP is a useful trait for large-scale genetic studies of LOAD.

Detecting population stratification using related individuals.

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.

A search for non-chromosome 6 susceptibility loci contributing to rheumatoid arthritis.

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.

Power and false-positive rates for the restricted partition method (RPM) in a large candidate gene data set.

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.

Population stratification and patterns of linkage disequilibrium.

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, nonstandard methods, and linkage disequilibrium calculations in stratified or mixed ethnicity samples. We demonstrated that there are continuous axes of ethnic variation in both data sets 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 weigh 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.

The CHRNA5-CHRNA3-CHRNB4 nicotinic receptor subunit gene cluster affects risk for nicotine dependence in African-Americans and in European-Americans.

Genetic association studies have shown the importance of variants in the CHRNA5-CHRNA3-CHRNB4 cholinergic nicotinic receptor subunit gene cluster on chromosome 15q24-25.1 for the risk of nicotine dependence, smoking, and lung cancer in populations of European descent. We have carried out a detailed study of this region using dense genotyping in both European-Americans and African-Americans. We genotyped 75 known single nucleotide polymorphisms (SNPs) and one sequencing-discovered SNP in an African-American sample (N = 710) and in a European-American sample (N = 2,062). Cases were nicotine-dependent and controls were nondependent smokers. The nonsynonymous CHRNA5 SNP rs16969968 is the most significant SNP associated with nicotine dependence in the full sample of 2,772 subjects [P = 4.49 x 10(-8); odds ratio (OR), 1.42; 95% confidence interval (CI), 1.25-1.61] as well as in African-Americans only (P = 0.015; OR, 2.04; 1.15-3.62) and in European-Americans only (P = 4.14 x 10(-7); OR, 1.40; 1.23-1.59). Other SNPs that have been shown to affect the mRNA levels of CHRNA5 in European-Americans are associated with nicotine dependence in African-Americans but not in European-Americans. The CHRNA3 SNP rs578776, which has a low correlation with rs16969968, is associated with nicotine dependence in European-Americans but not in African-Americans. Less common SNPs (frequency

Multiple distinct risk loci for nicotine dependence identified by dense coverage of the complete family of nicotinic receptor subunit (CHRN) genes.

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 1,050 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.