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

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

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

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>CHRNA5-CHRNA3-CHRNB4</it>, 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≥ 0.8 bin for <it>rs16969968</it>, three demonstrated significant cross-population heterogeneity and are filtered from priority follow-up; the remaining SNPs include <it>rs16969968 </it>(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.</p> <p>Conclusion</p> <p>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.</p

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

Abstract 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.http://deepblue.lib.umich.edu/bitstream/2027.42/112449/1/12881_2009_Article_575.pd

A New Statistic to Evaluate Imputation Reliability

As the amount of data from genome wide association studies grows dramatically, many interesting scientific questions require imputation to combine or expand datasets. However, there are two situations for which imputation has been problematic: (1) polymorphisms with low minor allele frequency (MAF), and (2) datasets where subjects are genotyped on different platforms. Traditional measures of imputation cannot effectively address these problems.We introduce a new statistic, the imputation quality score (IQS). In order to differentiate between well-imputed and poorly-imputed single nucleotide polymorphisms (SNPs), IQS adjusts the concordance between imputed and genotyped SNPs for chance. We first evaluated IQS in relation to minor allele frequency. Using a sample of subjects genotyped on the Illumina 1 M array, we extracted those SNPs that were also on the Illumina 550 K array and imputed them to the full set of the 1 M SNPs. As expected, the average IQS value drops dramatically with a decrease in minor allele frequency, indicating that IQS appropriately adjusts for minor allele frequency. We then evaluated whether IQS can filter poorly-imputed SNPs in situations where cases and controls are genotyped on different platforms. Randomly dividing the data into "cases" and "controls", we extracted the Illumina 550 K SNPs from the cases and imputed the remaining Illumina 1 M SNPs. The initial Q-Q plot for the test of association between cases and controls was grossly distorted (lambda = 1.15) and had 4016 false positives, reflecting imputation error. After filtering out SNPs with IQS<0.9, the Q-Q plot was acceptable and there were no longer false positives. We then evaluated the robustness of IQS computed independently on the two halves of the data. In both European Americans and African Americans the correlation was >0.99 demonstrating that a database of IQS values from common imputations could be used as an effective filter to combine data genotyped on different platforms.IQS effectively differentiates well-imputed and poorly-imputed SNPs. It is particularly useful for SNPs with low minor allele frequency and when datasets are genotyped on different platforms

Supplementing High-Density SNP Microarrays for Additional Coverage of Disease-Related Genes: Addiction as a Paradigm

Commercial SNP microarrays now provide comprehensive and affordable coverage of the human genome. However, some diseases have biologically relevant genomic regions that may require additional coverage. Addiction, for example, is thought to be influenced by complex interactions among many relevant genes and pathways. We have assembled a list of 486 biologically relevant genes nominated by a panel of experts on addiction. We then added 424 genes that showed evidence of association with addiction phenotypes through mouse QTL mappings and gene co-expression analysis. We demonstrate that there are a substantial number of SNPs in these genes that are not well represented by commercial SNP platforms. We address this problem by introducing a publicly available SNP database for addiction. The database is annotated using numeric prioritization scores indicating the extent of biological relevance. The scores incorporate a number of factors such as SNP/gene functional properties (including synonymy and promoter regions), data from mouse systems genetics and measures of human/mouse evolutionary conservation. We then used HapMap genotyping data to determine if a SNP is tagged by a commercial microarray through linkage disequilibrium. This combination of biological prioritization scores and LD tagging annotation will enable addiction researchers to supplement commercial SNP microarrays to ensure comprehensive coverage of biologically relevant regions

Joint Endeavor Toward Sustainable Mountain Development: Research at the Institute for Interdisciplinary Mountain Research of the Austrian Academy of Sciences

The sustainable development of mountain regions requires inter-and transdisciplinary knowledge. The Institute for Interdisciplinary Mountain Research contributes to this global endeavor as part of the Austrian Academy of Sciences and as a member of international scientific networks, together with local partners and stakeholders. As a joint effort of individual researchers covering multiple fields, this article highlights our views on mountains as research objects, the phenomena we investigate as parts of entire mountain systems, and the synergies and differences of the disciplinary frames within which we work