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.

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.

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.

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.

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.

Nicotinic receptor gene variants influence susceptibility to heavy smoking.

Heavy smoking is a strong predictor of nicotine dependence, which is a major impediment to smoking cessation. Although both heavy smoking and nicotine dependence are highly heritable, previous attempts to identify genes influencing these phenotypes have been largely unsuccessful until very recently. We studied 1,452 heavy smokers (defined as smoking at least 30 cigarettes per day for at least 5 years) and 1,395 light smokers (defined as smoking <5 cigarettes per day for at least 1 year) to investigate the association of common variants in nicotinic receptor subunit genes with smoking behavior. Compared with the most common allele, two separate groups of single nucleotide polymorphisms (SNP) in the CHRNA5-CHRNA3-CHRNB4 gene cluster were associated with heavy smoking with a very high statistical significance. One group of eight SNPs, which included a nonsynonymous SNP in the CHRNA5 gene, was in strong linkage disequilibrium and associated with increased risk of heavy smoking. A second group of SNPs not strongly correlated with the first was associated with decreased risk of heavy smoking. Analyses that combined both groups of SNPs found associations with heavy smoking that varied by >2-fold. Our findings identify two loci in the CHRNA5-CHRNA3-CHRNB4 gene cluster that predict smoking behavior and provide strong evidence for the involvement of the alpha5 nicotinic receptor in heavy smoking.

In search of causal variants: refining disease association signals using cross-population contrasts.

BACKGROUND: Genome-wide association (GWA) using large numbers of single nucleotide polymorphisms (SNPs) is now a powerful, state-of-the-art approach to mapping human disease genes. When a GWA study detects association between a SNP and the disease, this signal usually represents association with a set of several highly correlated SNPs in strong linkage disequilibrium. The challenge we address is to distinguish among these correlated loci to highlight potential functional variants and prioritize them for follow-up. RESULTS: We implemented a systematic method for testing association across diverse population samples having differing histories and LD patterns, using a logistic regression framework. The hypothesis is that important underlying biological mechanisms are shared across human populations, and we can filter correlated variants by testing for heterogeneity of genetic effects in different population samples. This approach formalizes the descriptive comparison of p-values that has typified similar cross-population fine-mapping studies to date. We applied this method to correlated SNPs in the cholinergic nicotinic receptor gene cluster CHRNA5-CHRNA3-CHRNB4, in a case-control study of cocaine dependence composed of 504 European-American and 583 African-American samples. Of the 10 SNPs genotyped in the r2 > or = 0.8 bin for rs16969968, three demonstrated significant cross-population heterogeneity and are filtered from priority follow-up; the remaining SNPs include rs16969968 (heterogeneity p = 0.75). Though the power to filter out rs16969968 is reduced due to the difference in allele frequency in the two groups, the results nevertheless focus attention on a smaller group of SNPs that includes the non-synonymous SNP rs16969968, which retains a similar effect size (odds ratio) across both population samples. CONCLUSION: Filtering out SNPs that demonstrate cross-population heterogeneity enriches for variants more likely to be important and causative. Our approach provides an important and effective tool to help interpret results from the many GWA studies now underway.

Gamma-aminobutyric acid receptor genes and nicotine dependence: evidence for association from a case-control study.

AIMS: The gamma-aminobutyric acid receptor A (GABRA) gene clusters on chromosomes 4 and 5 have been examined previously for their association with alcohol and drug dependence phenotypes. Compelling evidence suggests that GABRA2 is associated with alcohol and drug dependence. However, no study has investigated whether genes in the GABA(A) gene clusters are associated with nicotine dependence, an important phenotype with a high correlation to persistent smoking, the single most preventable cause of mortality world-wide. DESIGN: Using data on 1050 nicotine-dependent cases and 879 non-dependent smoking controls, we used logistic regression to examine the association between single nucleotide polymorphisms (SNPs) in 13 genes in the GABA(A) receptor system as well as GABBR2 (a GABA(B) gene). FINDINGS: We found evidence for association between four SNPs in GABRA4, two SNPs in GABRA2 and one SNP in GABRE with nicotine dependence. These included a synonymous polymorphism in GABRA2 (rs279858), lying in a highly conserved region, which has been shown previously to be associated with alcohol and drug dependence. A non-synonymous polymorphism (rs16859834/rs2229940) in GABRA4, also highly conserved, was associated at P-value of 0.03. Significant haplotypes associated with nicotine dependence were found for GABRA2. No evidence for epistatic interactions were noted. Our study did not find evidence for an association between GABBR2 gene and nicotine dependence. CONCLUSIONS: Given the potential role of compounds that enhance GABAergic neurotransmission in smoking cessation research, these findings have enormous potential for informing the wider field of addiction research.

Linkage scan for quantitative traits identifies new regions of interest for substance dependence in the Collaborative Study on the Genetics of Alcoholism (COGA) sample.

Dependence on alcohol and illicit drugs frequently co-occur. Results from a number of twin studies suggest that heritable influences on alcohol dependence and drug dependence may substantially overlap. Using large, genetically informative pedigrees from the Collaborative Study on the Genetics of Alcoholism (COGA), we performed quantitative linkage analyses using a panel of 1717 SNPs. Genome-wide linkage analyses were conducted for quantitative measures of DSM-IV alcohol dependence criteria, cannabis dependence criteria and dependence criteria across any illicit drug (including cannabis) individually and in combination as an average score across alcohol and illicit drug dependence criteria. For alcohol dependence, LOD scores exceeding 2.0 were noted on chromosome 1 (2.0 at 213 cM), 2 (3.4 at 234 cM) and 10 (3.7 at 60 cM). For cannabis dependence, a maximum LOD of 1.9 was noted at 95 cM on chromosome 14. For any illicit drug dependence, LODs of 2.0 and 2.4 were observed on chromosome 10 (116 cM) and 13 (64 cM) respectively. Finally, the combined alcohol and/or drug dependence symptoms yielded LODs >2.0 on chromosome 2 (3.2, 234 cM), 10 (2.4 and 2.6 at 60 cM and 116 cM) and 13 (2.1 at 64 cM). These regions may harbor genes that contribute to the biological basis of alcohol and drug dependence.

Association studies between common variants in prolyl isomerase Pin1 and the risk for late-onset Alzheimer's disease.

Alzheimer's disease (AD) pathology is associated with two proteins, the microtubule-binding protein tau and the beta-amyloid-precursor protein (APP). When tau becomes hyperphosphorylated, it forms neuritic aggregates, called neurofibrillary tangles. APP is cleaved by several enzymes to generate Abeta peptides, which are - depending on their length - more or less amyloidogenic and form senile plaques. Pin1, a peptidyl-propyl cis/trans-isomerase, seems to be involved in both pathologies. Pin1 may facilitate dephosphorylation of tau by PP2A phosphatase, while cellular overexpression of Pin1 causes a reduction in the amyloidogenic processing of APP, making this enzyme an interesting target for pharmaceutical intervention. The gene encoding Pin1 maps to 19p13.2, a region previously linked to late-onset Alzheimer's disease (LOAD). Therefore, Pin1 is an excellent positional and functional candidate for LOAD. In this study, we investigated whether common single nucleotide polymorphisms (SNPs) in Pin1 can influence the risk for developing late-onset Alzheimer's disease. No association was observed with any of six polymorphisms or their resulting haplotypes. A meta-analysis of two promoter SNPs, which combined the data from this study with two previous ones, did not show any association either suggesting that common SNPs in Pin1 do not increase the risk for LOAD.

Microsatellites versus single-nucleotide polymorphisms in linkage analysis for quantitative and qualitative measures.

BACKGROUND: Genetic maps based on single-nucleotide polymorphisms (SNP) are increasingly being used as an alternative to microsatellite maps. This study compares linkage results for both types of maps for a neurophysiology phenotype and for an alcohol dependence phenotype. Our analysis used two SNP maps on the Illumina and Affymetrix platforms. We also considered the effect of high linkage disequilibrium (LD) in regions near the linkage peaks by analysing a "sparse" SNP map obtained by dropping some markers in high LD with other markers in those regions. RESULTS: The neurophysiology phenotype at the main linkage peak near 130 MB gave LOD scores of 2.76, 2.53, 3.22, and 2.68 for the microsatellite, Affymetrix, Illumina, and Illumina-sparse maps, respectively. The alcohol dependence phenotype at the main linkage peak near 101 MB gave LOD scores of 3.09, 3.69, 4.08, and 4.11 for the microsatellite, Affymetrix, Illumina, and Illumina-sparse maps, respectively. CONCLUSION: The linkage results were stronger overall for SNPs than for microsatellites for both phenotypes. However, LOD scores may be artificially elevated in regions of high LD. Our analysis indicates that appropriately thinning a SNP map in regions of high LD should give more accurate LOD scores. These results suggest that SNPs can be an efficient substitute for microsatellites for linkage analysis of both quantitative and qualitative phenotypes.

An analysis of identical single-nucleotide polymorphisms genotyped by two different platforms.

The overlap of 94 single-nucleotide polymorphisms (SNP) among the 4,720 and 11,120 SNPs contained in the linkage panels of Illumina and Affymetrix, respectively, allows an assessment of the discrepancy rate produced by these two platforms. Although the no-call rate for the Affymetrix platform is approximately 8.6 times greater than for the Illumina platform, when both platforms make a genotypic call, the agreement is an impressive 99.85%. To determine if disputed genotypes can be resolved without sequencing, we studied recombination in the region of the discrepancy for the most discrepant SNP rs958883 (typed by Illumina) and tsc02060848 (typed by Affymetrix). We find that the number of inferred recombinants is substantially higher for the Affymetrix genotypes compared to the Illumina genotypes. We illustrate this with pedigree 10043, in which 3 of 7 versus 0 of 7 offspring must be double recombinants using the genotypes from the Affymetrix and the Illumina platforms, respectively. Of the 36 SNPs with one or more discrepancies, we identified a subset that appears to cluster in families. Some of this clustering may be due to the presence of a second segregating SNP that obliterates a XbaI site (the restriction enzyme used in the Affymetrix platform), resulting in a fragment too long (>1,000 bp) to be amplified.

Multipoint identity-by-descent computations for single-point polymorphism and microsatellite maps.

We used the LOKI software to generate multipoint identity-by-descent matrices for a microsatellite map (with 31 markers) and two single-nucleotide polymorphism (SNP) maps to examine information content across chromosome 7 in the Collaborative Study on the Genetics of Alcoholism dataset. Despite the lower information provided by a single SNP, SNP maps overall had higher and more uniform information content across the chromosome. The Affymetrix map (578 SNPs) and the Illumina map (271 SNPs) provided almost identical information. However, increased information has a computational cost: SNP maps require 100 times as many iterations as microsatellites to produce stable estimates.

The efficacy of short tandem repeat polymorphisms versus single-nucleotide polymorphisms for resolving population structure.

Accurately resolving population structure in a sample is important for both linkage and association studies. In this study we investigated the power of single-nucleotide polymorphisms (SNPs) in detecting population structure in a sample of 286 unrelated individuals. We varied the number of SNPs to determine how many are required to approach the degree of resolution obtained with the Collaborative Study on the Genetics of Alcoholism (COGA) short tandem repeat polymorphisms (STRPs). In addition, we selected SNPs with varying minor allele frequencies (MAFs) to determine whether low or high frequency SNPs are more efficient in resolving population structure. We conclude that a set of at least 100 evenly spaced SNPs with MAFs of 40-50% is required to resolve population structure in this dataset. If SNPs with lower MAFs are used, then more than 250 SNPs may be required to obtain reliable results.

A family study of alcohol dependence: coaggregation of multiple disorders in relatives of alcohol-dependent probands.

BACKGROUND: Alcohol dependence tends to aggregate within families. We analyzed data from the family collection of the Collaborative Study on the Genetics of Alcoholism to quantify familial aggregation using several different criterion sets. We also assessed the aggregation of other psychiatric disorders in the same sample to identify areas of possible shared genetic vulnerability. DESIGN: Age-corrected lifetime morbid risk was estimated in adult first-degree relatives of affected probands and control subjects for selected disorders. Diagnostic data were gathered by semistructured interview (the Semi-Structured Assessment for the Genetics of Alcoholism), family history, and medical records. Rates of illness were corrected by validating interview and family history reports against senior clinicians' all sources best estimate diagnoses. Sex, ethnicity, comorbidity, cohort effects, and site of ascertainment were also taken into account. RESULTS: Including data from 8296 relatives of alcoholic probands and 1654 controls, we report lifetime risk rates of 28.8% and 14.4% for DSM-IV alcohol dependence in relatives of probands and controls, respectively; respective rates were 37.0% and 20.5% for the less stringent DSM-III-R alcohol dependence, 20.9% and 9.7% for any DSM-III-R diagnosis of nonalcohol nonnicotine substance dependence, and 8.1% and 5.2% for antisocial personality disorder. Rates of specific substance dependence were markedly increased in relatives of alcohol-dependent probands for cocaine, marijuana, opiates, sedatives, stimulants, and tobacco. Aggregation was also seen for panic disorder, obsessive-compulsive disorder, posttraumatic stress disorder, and major depression. CONCLUSIONS: The risk of alcohol dependence in relatives of probands compared with controls is increased about 2-fold. The aggregation of antisocial personality disorder, drug dependence, anxiety disorders, and mood disorders suggests common mechanisms for these disorders and alcohol dependence within some families. These data suggest new phenotypes for molecular genetic studies and alternative strategies for studying the heterogeneity of alcohol dependence.

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.