Collaborative Study On The Genetics Of Alcoholism Data Research Articles

The collaborative study on the genetics of alcoholism: Brain function.

Alcohol use disorder (AUD) and related health conditions result from a complex interaction of genetic, neural and environmental factors, with differential impacts across the lifespan. From its inception, the Collaborative Study on the Genetics of Alcoholism (COGA) has focused on the importance of brain function as it relates to the risk and consequences of alcohol use and AUD, through the examination of noninvasively recorded brain electrical activity and neuropsychological tests. COGA's sophisticated neurophysiological and neuropsychological measures, together with rich longitudinal, multi-modal family data, have allowed us to disentangle brain-related risk and resilience factors from the consequences of prolonged and heavy alcohol use in the context of genomic and social-environmental influences over the lifespan. COGA has led the field in identifying genetic variation associated with brain functioning, which has advanced the understanding of how genomic risk affects AUD and related disorders. To date, the COGA study has amassed brain function data on over 9871 participants, 7837 with data at more than one time point, and with notable diversity in terms of age (from 7 to 97), gender (52% female), and self-reported race and ethnicity (28% Black, 9% Hispanic). These data are available to the research community through several mechanisms, including directly through the NIAAA, through dbGAP, and in collaboration with COGA investigators. In this review, we provide an overview of COGA's data collection methods and specific brain function measures assessed, and showcase the utility, significance, and contributions these data have made to our understanding of AUD and related disorders, highlighting COGA research findings.

The collaborative study on the genetics of alcoholism: Sample and clinical data.

The collaborative study on the genetics of alcoholism (COGA) is a multi-site, multidisciplinary project with the goal of identifying how genes are involved in alcohol use disorder and related outcomes, and characterizing how genetic risk unfolds across development and in conjunction with the environment and brain function. COGA is a multi-generational family-based study in which probands were recruited through alcohol treatment centers, along with a set of community comparison families. Nearly 18,000 individuals from >2200 families have been assessed over a period of over 30 years with a rich phenotypic battery that includes semi-structured psychiatric interviews and questionnaire measures, along with DNA collection and electrophysiological data on a large subset. Participants range in age from 7 to 97, with many having longitudinal assessments, providing a valuable opportunity to study alcohol use and problems across the lifespan. Here we provide an overview of data collection methods for the COGA sample, and details about sample characteristics and comorbidity. We also review key research findings that have emerged from analyses of the COGA data. COGA data are available broadly to researchers, and we hope this overview will encourage further collaboration and use of these data to advance the field.

Whole-genome variance components linkage analysis using single-nucleotide polymorphisms versus microsatellites on quantitative traits of derived phenotypes from factor analysis of electroencephalogram waves

Alcohol dependence is a serious public health problem. We studied data from families participating in the Collaborative Study on the Genetics of Alcoholism (COGA) and made available to participants in the Genetic Analysis Workshop 14 (GAW14) in order to search for genes predisposing to alcohol dependence. Using factor analysis, we identified four factors (F1, F2, F3, F4) related to the electroencephalogram traits. We conducted variance components linkage analysis with each of the factors. Our results using the Affymetrix single-nucleotide polymorphism dataset showed significant evidence for a novel linkage of F3 (factor comprised of the three midline channel EEG measures from the target case of the Visual Oddball experiment ttdt2, 3, 4) to chromosome 18 (LOD = 3.45). This finding was confirmed by analyses of the microsatellite data (LOD = 2.73) and Illumina SNP data (LOD = 3.30). We also demonstrated that, in a sample like the COGA data, a dense single-nucleotide polymorphism map provides better linkage signals than low-resolution microsatellite map with quantitative traits.

A genome-wide scanning and fine mapping study of COGA data

A thorough genetic mapping study was performed to identify predisposing genes for alcoholism dependence using the Collaborative Study on the Genetics of Alcoholism (COGA) data. The procedure comprised whole-genome linkage and confirmation analyses, single locus and haplotype fine mapping analyses, and gene × environment haplotype regression. Stratified analysis was considered to reduce the ethnic heterogeneity and simultaneously family-based and case-control study designs were applied to detect potential genetic signals. By using different methods and markers, we found high linkage signals at D1S225 (253.7 cM), D1S547 (279.2 cM), D2S1356 (64.6 cM), and D7S2846 (56.8 cM) with nonparametric linkage scores of 3.92, 4.10, 4.44, and 3.55, respectively. We also conducted haplotype and odds ratio analyses, where the response was the dichotomous status of alcohol dependence, explanatory variables were the inferred individual haplotypes and the three statistically significant covariates were age, gender, and max drink (the maximum number of drinks consumed in a 24-hr period). The final model identified important AD-related haplotypes within a candidate region of NRXN1 at 2p21 and a few others in the inter-gene regions. The relative magnitude of risks to the identified risky/protective haplotypes was elucidated.

Investigation of altering single-nucleotide polymorphism density on the power to detect trait loci and frequency of false positive in nonparametric linkage analyses of qualitative traits

Genome-wide linkage analysis using microsatellite markers has been successful in the identification of numerous Mendelian and complex disease loci. The recent availability of high-density single-nucleotide polymorphism (SNP) maps provides a potentially more powerful option. Using the simulated and Collaborative Study on the Genetics of Alcoholism (COGA) datasets from the Genetics Analysis Workshop 14 (GAW14), we examined how altering the density of SNP marker sets impacted the overall information content, the power to detect trait loci, and the number of false positive results. For the simulated data we used SNP maps with density of 0.3 cM, 1 cM, 2 cM, and 3 cM. For the COGA data we combined the marker sets from Illumina and Affymetrix to create a map with average density of 0.25 cM and then, using a sub-sample of these markers, created maps with density of 0.3 cM, 0.6 cM, 1 cM, 2 cM, and 3 cM. For each marker set, multipoint linkage analysis using MERLIN was performed for both dominant and recessive traits derived from marker loci. Our results showed that information content increased with increased map density. For the homogeneous, completely penetrant traits we created, there was only a modest difference in ability to detect trait loci. Additionally, as map density increased there was only a slight increase in the number of false positive results when there was linkage disequilibrium (LD) between markers. The presence of LD between markers may have led to an increased number of false positive regions but no clear relationship between regions of high LD and locations of false positive linkage signals was observed.

A genome-wide linkage analysis of alcoholism on microsatellite and single-nucleotide polymorphism data, using alcohol dependence phenotypes and electroencephalogram measures

The Collaborative Study on the Genetics of Alcoholism (COGA) is a large-scale family study designed to identify genes that affect the risk for alcoholism and alcohol-related phenotypes. We performed genome-wide linkage analyses on the COGA data made available to participants in the Genetic Analysis Workshop 14 (GAW 14). The dataset comprised 1,350 participants from 143 families. The samples were analyzed on three technologies: microsatellites spaced at 10 cM, Affymetrix GeneChip® Human Mapping 10 K Array (HMA10K) and Illumina SNP-based Linkage III Panel. We used ALDX1 and ALDX2, the COGA definitions of alcohol dependence, as well as electrophysiological measures TTTH1 and ECB21 to detect alcoholism susceptibility loci. Many chromosomal regions were found to be significant for each of the phenotypes at a p-value of 0.05. The most significant region for ALDX1 is on chromosome 7, with a maximum LOD score of 2.25 for Affymetrix SNPs, 1.97 for Illumina SNPs, and 1.72 for microsatellites. The same regions on chromosome 7 (96–106 cM) and 10 (149–176 cM) were found to be significant for both ALDX1 and ALDX2. A region on chromosome 7 (112–153 cM) and a region on chromosome 6 (169–185 cM) were identified as the most significant regions for TTTH1 and ECB21, respectively. We also performed linkage analysis on denser maps of markers by combining the SNPs datasets from Affymetrix and Illumina. Adding the microsatellite data to the combined SNP dataset improved the results only marginally. The results indicated that SNPs outperform microsatellites with the densest marker sets performing the best.

The effect of linkage disequilibrium on linkage analysis of incomplete pedigrees

Dense SNP maps can be highly informative for linkage studies. But when parental genotypes are missing, multipoint linkage scores can be inflated in regions with substantial marker-marker linkage disequilibrium (LD). Such regions were observed in the Affymetrix SNP genotypes for the Genetic Analysis Workshop 14 (GAW14) Collaborative Study on the Genetics of Alcoholism (COGA) dataset, providing an opportunity to test a novel simulation strategy for studying this problem. First, an inheritance vector (with or without linkage present) is simulated for each replicate, i.e., locations of recombinations and transmission of parental chromosomes are determined for each meiosis. Then, two sets of founder haplotypes are superimposed onto the inheritance vector: one set that is inferred from the actual data and which contains the pattern of LD; and one set created by randomly selecting parental alleles based on the known allele frequencies, with no correlation (LD) between markers. Applying this strategy to a map of 176 SNPs (66 Mb of chromosome 7) for 100 replicates of 116 sibling pairs, significant inflation of multipoint linkage scores was observed in regions of high LD when parental genotypes were set to missing, with no linkage present. Similar inflation was observed in analyses of the COGA data for these affected sib pairs with parental genotypes set to missing, but not after reducing the marker map until r2 between any pair of markers was ≤ 0.05. Additional simulation studies of affected sib pairs assuming uniform LD throughout a marker map demonstrated inflation of significance levels at r2 values greater than 0.05. When genotypes are available only from two affected siblings in many families in a sample, trimming SNP maps to limit r2 to 0–0.05 for all marker pairs will prevent inflation of linkage scores without sacrificing substantial linkage information. Simulation studies on the observed pedigree structures and map can also be used to determine the effect of LD on a particular study.

Comparison of marker types and map assumptions using Markov chain Monte Carlo-based linkage analysis of COGA data

We performed multipoint linkage analysis of the electrophysiological trait ECB21 on chromosome 4 in the full pedigrees provided by the Collaborative Study on the Genetics of Alcoholism (COGA). Three Markov chain Monte Carlo (MCMC)-based approaches were applied to the provided and re-estimated genetic maps and to five different marker panels consisting of microsatellite (STRP) and/or SNP markers at various densities. We found evidence of linkage near the GABRB1 STRP using all methods, maps, and marker panels. Difficulties encountered with SNP panels included convergence problems and demanding computations.

Comparison of single-nucleotide polymorphisms and microsatellite markers for linkage analysis in the COGA and simulated data sets for Genetic Analysis Workshop 14: Presentation Groups 1, 2, and 3

The papers in presentation groups 1-3 of Genetic Analysis Workshop 14 (GAW14) compared microsatellite (MS) markers and single-nucleotide polymorphism (SNP) markers for a variety of factors, using multiple methods in both data sets provided to GAW participants. Group 1 focused on data provided from the Collaborative Study on the Genetics of Alcoholism (COGA). Group 2 focused on data simulated for the workshop. Group 3 contained analyses of both data sets. Issues examined included: information content, signal strength, localization of the signal, use of haplotype blocks, population structure, power, type I error, control of type I error, the effect of linkage disequilibrium, and computational challenges. There were several broad resulting observations. 1) Information content was higher for dense SNP marker panels than for MS panels, and dense SNP markers sets appeared to provide slightly higher linkage scores and slightly higher power to detect linkage than MS markers. 2) Dense SNP panels also gave higher type I errors, suggesting that increased test thresholds may be needed to maintain the correct error rate. 3) Dense SNP panels provided better trait localization, but only in the COGA data, in which the MS markers were relatively loosely spaced. 4) The strength of linkage signals did not vary with the density of SNP panels, once the marker density was approximately 1 SNP/cM. 5) Analyses with SNPs were computationally challenging, and identified areas where improvements in analysis tools will be necessary to make analysis practical for widespread use.

Assessment and implications of linkage disequilibrium in genome-wide single-nucleotide polymorphism and microsatellite panels

Linkage disequilibrium (LD) between markers is more likely to exist in dense genome-wide single-nucleotide polymorphism (SNP) panels than in microsatellite panels. As part of Genetic Analysis Workshop 14 (GAW14), the extent of LD in the Illumina linkage panel III and the Affymetrix Genechip 10 K mapping array was assessed, using data from the Collaborative Study on the Genetics of Alcoholism (COGA). The impact of LD on linkage results was examined in COGA and simulated data, and characteristics of SNPs were assessed for their ability to detect population substructure and predict haplotypes. The authors of the papers summarized here observed greater LD in the Affymetrix than in the Illumina panel, possibly due to increased marker density in the Affymetrix panel, and found greater LD on chromosome X than on the autosomes. Simulation analyses suggest that intermarker LD can cause an upward bias in linkage statistics; however, the impact of LD on linkage analysis depends on the proportion of ungenotyped founders and the extent of LD. No large effect of LD on linkage peaks was observed in COGA analyses. In addition, the papers summarized here found that SNPs with high minor allele frequencies were the most informative compared with microsatellites for the detection of population substructure, and that SNPs in higher LD, and small numbers of SNPs, were the most reliable for haplotype prediction. As ease of genotyping continues to increase, study design and SNP selection for linkage and association studies (including genome-wide association studies) will be improved with consideration of LD in the particular populations studied.

Fine mapping by linkage and association in nuclear family and case-control designs

This report summarizes the Genetic Analysis Workshop 14 contributions related to fine-mapping strategies, in which examining smaller regions by association with single-nucleotide polymorphisms (SNPs) can yield savings in genotyping and multiple-testing penalties. The aim of the analyses conducted in Group 7 contributions was to localize disease susceptibility loci from either the simulated or the Collaborative Study on the Genetics of Alcoholism (COGA) data within identified regions of linkage. Among the 10 contributions, most groups analyzed the simulated data, one group analyzed the COGA data only, and one group analyzed both data sets. The research questions included evaluation of new methods of analysis, as well as comparisons among alternative methods, analytic strategies, and study designs. Methods of interest included an algorithm for SNP marker ordering, a locally weighted transmission disequilibrium test statistic, a likelihood-ratio test statistic for family-based association in nuclear families, a robust test statistic for case-control association studies, and Bayesian spatial modeling methods for haplotype clustering and association. Evaluations included comparisons among confidence intervals for loci detected via linkage, effects of multiple testing adjustments and trade-offs between type I error and power, comparisons among haplotype-based (multilocus) and genotype-based (multilocus and single-locus) association analyses, and design of fine-mapping and replication studies. While several promising new approaches were identified, further development and evaluation of methods for multiple testing, regression modeling of association with multiple markers and haplotypes, and combined treatment of linkage and association data are necessary if we are to identify many of the genes that contribute to complex traits.

Sib-pair linkage analysis of alcohol dependence taking into account covariates and age-of-onset variability: evaluation of the residual approach.

The construction of residuals allowed us to consider a phenotype related to alcohol dependence (ALDX1) adjusted for age, gender and current smoking status. Using the Haseman and Elston regression test, we compared genome scan results for detection of loci for the unadjusted and adjusted ALDX1 phenotypes in the Collaborative Study on the Genetics of Alcoholism (COGA) data. More markers were significant at the 1% level for the binary than for the residual analyses. Overall, our residual analyses do not show substantial improvement in their ability to detect linkage for alcohol dependence in the COGA data. This may be due to the relatively small sample size of discordant sib pairs.

The effect of phenotype variation on detection of linkage in the COGA data.

Error in phenotypic measurement can significantly compromise ability to detect linkage. We assessed the impact of introducing phenotypic measurement error on our ability to detect a quantitative trait locus in the Collaborative Study on the Genetics of Alcoholism (COGA) data. The impact of introducing three different types of errors was evaluated: 1) errors generated by sampling from a normal distribution; 2) errors generated by permuting phenotype values between subjects; and 3) errors generated by sampling from a uniform error distribution.

Asymptotic power of likelihood-ratio tests for detecting quantitative trait loci using the COGA data.

We investigated the asymptotic power of the likelihood-ratio test for detecting linkage to a quantitative trait locus (QTL) using the data set from the Collaborative Study on the Genetics of Alcoholism (COGA). Assuming a total trait heritability of 50% as determined for the COGA Cz P300 phenotype, we determined the minimum QTL heritability required at each point in the genome to achieve 80% power to detect linkage with a lod of 3.0. We find that there are regions of the genome where it is not possible to detect a QTL of any effect with 80% power, and that the overall minimum detectable QTL heritability for the COGA data is 0.35-0.40.

Cleaning genotype data.

The identification of genes contributing to variation in complex phenotypes requires genetic data of high fidelity. Thus, the identification of pedigree and genotyping errors is a crucial prerequisite to the analysis of data from a genome scan for disease genes. The problem has been given little attention in most gene hunting papers; the focus has often been on eliminating mendelian inconsistencies in order that the analysis may proceed, rather than on achieving the best possible data. Though a number of computer programs are available to assist in the identification of genotyping and pedigree errors, the process is still not completely automated. While the Collaborative Study on the Genetics of Alcoholism (COGA) data set for GAW11 is completely compatible with Mendel's rules, there are still some errors present. We inspected the COGA data for the presence of additional errors, and identified five possible pedigree errors.

Analyses of the COGA data set in one ethnic group with examinations of alternative definitions of alcoholism

We used GENEHUNTER and GENEHUNTER-PLUS to search for linkage with the markers in the Collaborative Study on the Genetics of Alcoholism (COGA) data set in a single ethnic group. Analyses of a complex disorder such as alcoholism depend on the definition of affection status. The COGA study provides two definitions of alcoholism (variables ALDX1 and ALDX2). To identify more severely affected alcoholics that might be more homogeneous genetically, we developed two other ways of characterizing subjects as alcohol dependent: (1) by combining the symptom variable values equally into a 24-point scale and (2) by weighting optimally the symptoms and other descriptive variables into a single score using logistic regression. We applied these definitions within a single ethnic group to map alcoholism-related loci. We found two regions on chromosome 1 that have adjacent markers significant at p-values < or = 0.05. ALDX1 provided the highest Z-scores compared to the alternatives.