BioQ: tracing experimental origins in public genomic databases using a novel data provenance model.

UNLABELLED: Public genomic databases, which are often used to guide genetic studies of human disease, are now being applied to genomic medicine through in silico integrative genomics. These databases, however, often lack tools for systematically determining the experimental origins of the data. RESULTS: We introduce a new data provenance model that we have implemented in a public web application, BioQ, for assessing the reliability of the data by systematically tracing its experimental origins to the original subjects and biologics. BioQ allows investigators to both visualize data provenance as well as explore individual elements of experimental process flow using precise tools for detailed data exploration and documentation. It includes a number of human genetic variation databases such as the HapMap and 1000 Genomes projects. AVAILABILITY AND IMPLEMENTATION: BioQ is freely available to the public at http://bioq.saclab.net.

New tools and methods for direct programmatic access to the dbSNP relational database.

Genome-wide association studies often incorporate information from public biological databases in order to provide a biological reference for interpreting the results. The dbSNP database is an extensive source of information on single nucleotide polymorphisms (SNPs) for many different organisms, including humans. We have developed free software that will download and install a local MySQL implementation of the dbSNP relational database for a specified organism. We have also designed a system for classifying dbSNP tables in terms of common tasks we wish to accomplish using the database. For each task we have designed a small set of custom tables that facilitate task-related queries and provide entity-relationship diagrams for each task composed from the relevant dbSNP tables. In order to expose these concepts and methods to a wider audience we have developed web tools for querying the database and browsing documentation on the tables and columns to clarify the relevant relational structure. All web tools and software are freely available to the public at http://cgsmd.isi.edu/dbsnpq. Resources such as these for programmatically querying biological databases are essential for viably integrating biological information into genetic association experiments on a genome-wide scale.

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.

Chromosome 20 shows linkage with DSM-IV nicotine dependence in Finnish adult smokers.

INTRODUCTION: Chromosome 20 has previously been associated with nicotine dependence (ND) and smoking cessation. Our aim was to replicate and extend these findings. METHODS: First, a total of 759 subjects belonging to 206 Finnish families were genotyped with 18 microsatellite markers residing on chromosome 20, in order to replicate previous linkage findings. Then, the replication data were combined to an existing whole-genome linkage data resulting in a total of 1,302 genotyped subjects from 357 families. ND diagnosed by DSM-IV criteria, the Fagerström Test for Nicotine Dependence (FTND) score, and the lifetime maximum number of cigarettes smoked within a 24-hr period (MaxCigs24) were used as phenotypes in the nonparametric linkage analyses. RESULTS: We replicated previously reported linkage to DSM-IV ND, with a maximum logarithm of odd (LOD) score of 3.8 on 20p11, with females contributing more (maximum LOD [MLOD] score 3.4 on 20q11) than males (MLOD score 2.6 on 20p11). With the combined sample, a suggestive LOD score of 2.3 was observed for DSM-IV ND on 20p11. Sex-specific analyses revealed that the signal was driven by females with a maximum LOD score of 3.3 (on 20q11) versus LOD score of 1.3 in males (on 20q13) in the combined sample. No significant linkage signals were obtained for FTND or MaxCigs24. CONCLUSIONS: Our results provide further evidence that chromosome 20 harbors genetic variants influencing ND in adult smokers.

SPOT: a web-based tool for using biological databases to prioritize SNPs after a genome-wide association study.

SPOT (http://spot.cgsmd.isi.edu), the SNP prioritization online tool, is a web site for integrating biological databases into the prioritization of single nucleotide polymorphisms (SNPs) for further study after a genome-wide association study (GWAS). Typically, the next step after a GWAS is to genotype the top signals in an independent replication sample. Investigators will often incorporate information from biological databases so that biologically relevant SNPs, such as those in genes related to the phenotype or with potentially non-neutral effects on gene expression such as a splice sites, are given higher priority. We recently introduced the genomic information network (GIN) method for systematically implementing this kind of strategy. The SPOT web site allows users to upload a list of SNPs and GWAS P-values and returns a prioritized list of SNPs using the GIN method. Users can specify candidate genes or genomic regions with custom levels of prioritization. The results can be downloaded or viewed in the browser where users can interactively explore the details of each SNP, including graphical representations of the GIN method. For investigators interested in incorporating biological databases into a post-GWAS SNP selection strategy, the SPOT web tool is an easily implemented and flexible solution.

Risk for nicotine dependence and lung cancer is conferred by mRNA expression levels and amino acid change in CHRNA5.

Nicotine dependence risk and lung cancer risk are associated with variants in a region of chromosome 15 encompassing genes encoding the nicotinic receptor subunits CHRNA5, CHRNA3 and CHRNB4. To identify potential biological mechanisms that underlie this risk, we tested for cis-acting eQTLs for CHRNA5, CHRNA3 and CHRNB4 in human brain. Using gene expression and disease association studies, we provide evidence that both nicotine-dependence risk and lung cancer risk are influenced by functional variation in CHRNA5. We demonstrated that the risk allele of rs16969968 primarily occurs on the low mRNA expression allele of CHRNA5. The non-risk allele at rs16969968 occurs on both high and low expression alleles tagged by rs588765 within CHRNA5. When the non-risk allele occurs on the background of low mRNA expression of CHRNA5, the risk for nicotine dependence and lung cancer is significantly lower compared to those with the higher mRNA expression. Together, these variants identify three levels of risk associated with CHRNA5. We conclude that there are at least two distinct mechanisms conferring risk for nicotine dependence and lung cancer: altered receptor function caused by a D398N amino acid variant in CHRNA5 (rs16969968) and variability in CHRNA5 mRNA expression.

A genome-wide association study of DSM-IV cannabis dependence.

Despite twin studies showing that 50-70% of variation in DSM-IV cannabis dependence is attributable to heritable influences, little is known of specific genotypes that influence vulnerability to cannabis dependence. We conducted a genome-wide association study of DSM-IV cannabis dependence. Association analyses of 708 DSM-IV cannabis-dependent cases with 2346 cannabis-exposed non-dependent controls was conducted using logistic regression in PLINK. None of the 948 142 single nucleotide polymorphisms met genome-wide significance (P at E-8). The lowest P values were obtained for polymorphisms on chromosome 17 (rs1019238 and rs1431318, P values at E-7) in the ANKFN1 gene. While replication is required, this study represents an important first step toward clarifying the biological underpinnings of cannabis dependence.

Modeling complex genetic and environmental influences on comorbid bipolar disorder with tobacco use disorder.

BACKGROUND: Comorbidity of psychiatric and substance use disorders represents a significant complication in the clinical course of both disorders. Bipolar Disorder (BD) is a psychiatric disorder characterized by severe mood swings, ranging from mania to depression, and up to a 70% rate of comorbid Tobacco Use Disorder (TUD). We found epidemiological evidence consistent with a common underlying etiology for BD and TUD, as well as evidence of both genetic and environmental influences on BD and TUD. Therefore, we hypothesized a common underlying genetic etiology, interacting with nicotine exposure, influencing susceptibility to both BD and TUD. METHODS: Using meta-analysis, we compared TUD rates for BD patients and the general population. We identified candidate genes showing statistically significant, replicated, evidence of association with both BD and TUD. We assessed commonality among these candidate genes and hypothesized broader, multi-gene network influences on the comorbidity. Using Fisher Exact tests we tested our hypothesized genetic networks for association with the comorbidity, then compared the inferences drawn with those derived from the commonality assessment. Finally, we prioritized candidate SNPs for validation. RESULTS: We estimate risk for TUD among BD patients at 2.4 times that of the general population. We found three candidate genes associated with both BD and TUD (COMT, SLC6A3, and SLC6A4) and commonality analysis suggests that these genes interact in predisposing psychiatric and substance use disorders. We identified a 69 gene network that influences neurotransmitter signaling and shows significant over-representation of genes associated with BD and TUD, as well as genes differentially expressed with exposure to tobacco smoke. Twenty four of these genes are known drug targets. CONCLUSIONS: This work highlights novel bioinformatics resources and demonstrates the effectiveness of using an integrated bioinformatics approach to improve our understanding of complex disease etiology. We illustrate the development and testing of hypotheses for a comorbidity predisposed by both genetic and environmental influences. Consistent with our hypothesis, the selected network models multiple interacting genetic influences on comorbid BD with TUD, as well as the environmental influence of nicotine. This network nominates candidate genes for validation and drug testing, and we offer a panel of SNPs prioritized for follow-up.

A comparison of association statistics between pooled and individual genotypes.

BACKGROUND: Markers for individual genotyping can be selected using quantitative genotyping of pooled DNA. This strategy saves time and money. METHODS: To determine the efficacy of this approach, we investigated the bivariate distribution of association test statistics from pooled and individual genotypes. We used a sample of approximately 1,000 samples with individual and pooled genotyping on 40,000 SNPs. RESULTS AND CONCLUSIONS: We found that the distribution of the joint test statistics can be modelled as a mixture of two bivariate normal distributions. One distribution has a correlation of zero, and is probably due to SNPs whose pooled genotyping was unsuccessful. The other distribution has a correlation of approximately 0.65 in our data. This latter distribution is probably accounted for by SNPs whose pooled genotyping accurately predicts the underlying allele frequency. Approximately 87% of the data belongs to this distribution. We also derived a method to investigate the effect of both the correlation and selection cut-off on the relative power of pooling studies. We demonstrate that pooled genotyping has good power to detect SNPs that are truly associated with disease-causing variants for SNPs showing good correlation between pooled and individual genotyping. Therefore, this approach is a cost effective tool for association studies.

An investigation of candidate regions for association with bipolar disorder.

We performed a case-control study of 1,000 cases and 1,028 controls on 1,509 markers, 1,139 of which were located in a 8 Mb region on chromosome 6 (105-113 Mb). This region has shown evidence of involvement in bipolar disorder (BP) in a number of other studies. We find association between BP and two SNPs in the gene LACE1. SNP rs9486880 and rs11153113 (both have P-values of 2 × 10(-5)). Both P-values are in the top 5% of the distribution derived from null simulations (P = 0.02 and 0.01, respectively). LACE is a good candidate for BP; it is an ATPase. We genotyped 173 other markers in 17 other positional and/or functional loci but found no further evidence of association with BP.

Systematic biological prioritization after a genome-wide association study: an application to nicotine dependence.

MOTIVATION: A challenging problem after a genome-wide association study (GWAS) is to balance the statistical evidence of genotype-phenotype correlation with a priori evidence of biological relevance. RESULTS: We introduce a method for systematically prioritizing single nucleotide polymorphisms (SNPs) for further study after a GWAS. The method combines evidence across multiple domains including statistical evidence of genotype-phenotype correlation, known pathways in the pathologic development of disease, SNP/gene functional properties, comparative genomics, prior evidence of genetic linkage, and linkage disequilibrium. We apply this method to a GWAS of nicotine dependence, and use simulated data to test it on several commercial SNP microarrays. AVAILABILITY: A comprehensive database of biological prioritization scores for all known SNPs is available at http://zork.wustl.edu/gin. This can be used to prioritize nicotine dependence association studies through a straightforward mathematical formula-no special software is necessary. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic linkage findings for DSM-IV nicotine withdrawal in two populations.

Nicotine withdrawal (NW) is both an important contributor to difficulty quitting cigarettes and because of mood-related withdrawal symptoms a problem of particular relevance to psychiatry. Twin-studies suggest that genetic factors influence NW (heritability = 45%). Only one previous linkage study has published findings on NW [Swan et al. (2006); Am J Med Genet Part B 141B:354-360; LOD = 2.7; Chr. 6 at 159 cM]. As part of an international consortium, genome-wide scans (using over 360 autosomal microsatellite markers) and telephone diagnostic interviews were conducted on 289 Australian (AUS) and 161 Finnish (FIN, combined (COMB) N = 450 families) families ascertained from twin registries through index-cases with a lifetime history of cigarette smoking. The statistical approach used an affected-sib-pair design (at least two adult full siblings reported a history of DSM-IV NW) and conducted the linkage analyses using MERLIN. Linkage signals with LOD scores >1.5 were found on two chromosomes: 6 (FIN: LOD = 1.93 at 75 cM) and 11 at two different locations (FIN: LOD = 3.55 at 17 cM, and AUS: LOD = 1.68 with a COMB: LOD = 2.30 at 123 cM). The multipoint LOD score of 3.55 on chromosome 11p15 in FIN met genomewide significance (P = 0.013 with 1,000 simulations). At least four strong candidate genes lie within or near this peak on chromosome 11: DRD4, TPH, TH, and CHRNA10. Other studies have reported that chromosome 11 may harbor genes associated with various aspects of smoking behavior. This study adds to that literature by highlighting evidence for NW.

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.

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.

Genetic analysis of the maximum drinks phenotype.

Using data provided by the Collaborative Study on the Genetics of Alcoholism we studied the genetics of a quantitative trait: the maximum number of drinks consumed in a 24-hour period. A two-stage method was used. First, linkage analysis was performed, followed by association analysis in regions where linkage was detected. Additionally, the extent of linkage disequilibrium among single-nucleotide polymorphisms (SNP) associated with the phenotype was assessed. Linkage to chromosomes 2 and 7 was detected, and follow-up association analysis found multiple trait-associated SNPs in the chromosome 7 linkage region. Chromosome 4, which has been implicated in previous studies of the maximum drinks phenotype, did not pass our threshold for linkage evidence in stage 1, but secondary analyses of this chromosome indicated modest evidence for both linkage and association. The evidence suggests that chromosome 7 may harbor an additional locus influencing the maximum drinks consumption phenotype.

Genetic analysis of maximum cigarette-use phenotypes.

BACKGROUND: Using the Framingham Heart Study data set provided for Genetic Analysis Workshop 13, we defined the cigarette-use phenotype M for smokers to be the maximum number of cigarettes-per-day (MAXCIG) reported over the longitudinal course of the study. Adjustments were made for the significant covariates of gender and year of birth, and sib-pair based linkage analysis was performed. RESULTS: The primary analyses, in which individuals with MAXCIG = 0 were considered to have missing phenotype, resulted in modest linkage evidence, with LOD scores over 1 on chromosomes 5, 9, 13, 14, and 22. CONCLUSIONS: While the results reported here do not indicate definitive evidence for linkage to specific chromosomal regions, future studies may find it useful to include direct assessments of maximum and quantitative cigarette use. In defining and analyzing quantitative or "maximum use" phenotypes, the choice of how to handle individuals with MAXCIG = 0, or alternatively, individuals who are substance-naive, is a crucial one for genetic studies of nicotine and other substance use. In this study, the linkage results vary greatly depending on whether or not these "unexposed" individuals are included in the analyses.

Applying in silico integrative genomics to genetic studies of human disease.

As genome-wide association studies using common single nucleotide polymorphism microarrays transition to whole-genome sequencing and the study of rare variants, new approaches will be required to viably interpret the results given the surge in data. A common strategy is to focus on biological hypotheses derived from sources of functional evidence ranging from the nucleotide to the biochemical process level. The accelerated development of biotechnology has led to numerous sources of functional evidence in the form of public databases and tools. Here, we review current methods and tools for integrating genomic data, particularly from the public domain, into genetic studies of human disease.

A ν-support vector regression based approach for predicting imputation quality.

BACKGROUND: Decades of genome-wide association studies (GWAS) have accumulated large volumes of genomic data that can potentially be reused to increase statistical power of new studies, but different genotyping platforms with different marker sets have been used as biotechnology has evolved, preventing pooling and comparability of old and new data. For example, to pool together data collected by 550K chips with newer data collected by 900K chips, we will need to impute missing loci. Many imputation algorithms have been developed, but the posteriori probabilities estimated by those algorithms are not a reliable measure the quality of the imputation. Recently, many studies have used an imputation quality score (IQS) to measure the quality of imputation. The IQS requires to know true alleles to estimate. Only when the population and the imputation loci are identical can we reuse the estimated IQS when the true alleles are unknown. METHODS: Here, we present a regression model to estimate IQS that learns from imputation of loci with known alleles. We designed a small set of features, such as minor allele frequencies, distance to the nearest known cross-over hotspot, etc., for the prediction of IQS. We evaluated our regression models by estimating IQS of imputations by BEAGLE for a set of GWAS data from the NCBI GEO database collected from samples from different ethnic populations. RESULTS: We construct a ν-SVR based approach as our regression model. Our evaluation shows that this regression model can accomplish mean square errors of less than 0.02 and a correlation coefficient close to 0.75 in different imputation scenarios. We also show how the regression results can help remove false positives in association studies. CONCLUSION: Reliable estimation of IQS will facilitate integration and reuse of existing genomic data for meta-analysis and secondary analysis. Experiments show that it is possible to use a small number of features to regress the IQS by learning from different training examples of imputation and IQS pairs.