Genetic strategies to detect genes involved in alcoholism and alcohol-related traits

Researchers are using a variety of sophisticated approaches to identify genes that contribute to the development of alcoholism in humans or influence other alcohol-related traits. These strategies include linkage approaches, which can identify broad chromosomal regions that are likely to contain genes predisposing to the disorder, and association approaches, which test the association between a particular marker allele and a specific outcome. Animal studies using diverse strategies can also help identify genes or DNA regions that influence alcohol-related traits in humans. The results of these analyses are likely to have implications for fields such as genetic counseling, gene therapy, and pharmacogenetics

The genetics of dementia

Over the past decade, there has been a dramatic evolution of genetic methodologies that can be used to identify genes contributing to disease. Initially, the focus was primarily on classical linkage analysis; more recently, genomewide association studies, and high-throughput whole genome and whole exome sequencing have provided efficient approaches to detect common and rare variation contributing to disease risk. Application of these methodologies to dementias has led to the nomination of dozens of causative and susceptibility genes, solidifying the recognition that genetic factors are important contributors to the disease processes. In this review, the authors focus on current knowledge of the genetics of Alzheimer's disease and frontotemporal lobar degeneration. A working understanding of the genes relevant to common dementias will become increasingly critical, as options for genetic testing and eventually gene-specific therapeutics are developed

Meta-Analyses of Externalizing Disorders: Genetics or Prenatal Alcohol Exposure?

Background Externalizing disorders are heritable precursors to alcohol dependence, common in children of alcoholics (COA), and in children with prenatal alcohol exposure (PAE). Pregnancies involving alcohol exposure sufficient to affect the fetus may involve women with genetic risk for alcohol dependence. We hypothesized that known PAE will increase the odds of having an externalizing disorder compared to COA. Methods The odds ratios of 3 externalizing disorders (attention-deficit hyperactivity disorder [ADHD], conduct disorder [CD], and oppositional defiant disorder [ODD]) were obtained for 2 domains: (i) PAE and (ii) COA, by estimating the logged odds ratio (LOR) for each study. Permutation tests were implemented to compare LORs for PAE versus COA studies within each disorder, including PAE versus an alcohol dependent (AD) mother and PAE versus an AD father. Results In PAE studies, the odds of ADHD and CD were elevated. Rates of all 3 disorders were elevated in COA studies. Permutation tests revealed that the mean LOR for ADHD was significantly higher in PAE studies compared to: COA (p = 0.01), AD mother (p < 0.05), and AD father (p = 0.03). No differences were found for ODD (p = 0.09) or CD (p = 0.21). Conclusions These results provide compelling evidence of an increased risk of ADHD in those with PAE beyond that due to parental alcohol dependence or a genetic liability, consistent with a unique etiology most likely due to direct alcohol exposure during prenatal development

Standard linkage and association methods identify the mechanism of four susceptibility genes for a simulated complex disease

The simulated dataset of the Genetic Analysis Workshop 14 provided affection status and the presence or absence of 12 traits. It was determined that all affected individuals must have traits E, F and H (EFH phenotype) and they must also have either trait B (B subtype) or traits C, D, and G (CDG subtype). A genome screen was performed, and linkage peaks were identified on chromosomes 1, 3, 5, and 9 using microsatellite markers. Dense panels of single-nucleotide polymorphism (SNP) markers were ordered for each of the four linkage peaks. In each case, association analyses identified a single SNP that accounted for the linkage evidence. The SNP on chromosome 1 appeared to primarily influence the B subtype, while the SNPs on chromosomes 5 and 9 primarily influenced the CDG subtype. The chromosome 3 SNP had the strongest effect and influenced both subtypes, as well as the requisite EFH phenotype. Recognizing the two subtypes prior to linkage analysis was key to identifying these loci using only a single replicate. This highlights the need in real life situations for careful examination of the phenotypic data prior to genetic analysis

Identification of genes for complex disease using longitudinal phenotypes

Using the simulated data set from Genetic Analysis Workshop 13, we explored the advantages of using longitudinal data in genetic analyses. The weighted average of the longitudinal data for each of seven quantitative phenotypes were computed and analyzed. Genome screen results were then compared for these longitudinal phenotypes and the results obtained using two cross-sectional designs: data collected near a single age (45 years) and data collected at a single time point. Significant linkage was obtained for nine regions (LOD scores ranging from 5.5 to 34.6) for six of the phenotypes. Using cross-sectional data, LOD scores were slightly lower for the same chromosomal regions, with two regions becoming nonsignificant and one additional region being identified. The magnitude of the LOD score was highly correlated with the heritability of each phenotype as well as the proportion of phenotypic variance due to that locus. There were no false-positive linkage results using the longitudinal data and three false-positive findings using the cross-sectional data. The three false positive results appear to be due to the kurtosis in the trait distribution, even after removing extreme outliers. Our analyses demonstrated that the use of simple longitudinal phenotypes was a powerful means to detect genes of major to moderate effect on trait variability. In only one instance was the power and heritability of the trait increased by using data from one examination. Power to detect linkage can be improved by identifying the most heritable phenotype, ensuring normality of the trait distribution and maximizing the information utilized through novel longitudinal designs for genetic analysis

A multivariate finite mixture latent trajectory model with application to dementia studies

Dementia patients exhibit considerable heterogeneity in individual trajectories of cognitive decline, with some patients showing rapid decline following diagnoses while others exhibiting slower decline or remaining stable for several years. Dementia studies often collect longitudinal measures of multiple neuropsychological tests aimed to measure patients’ decline across a number of cognitive domains. We propose a multivariate finite mixture latent trajectory model to identify distinct longitudinal patterns of cognitive decline simultaneously in multiple cognitive domains, each of which is measured by multiple neuropsychological tests. EM algorithm is used for parameter estimation and posterior probabilities are used to predict latent class membership. We present results of a simulation study demonstrating adequate performance of our proposed approach and apply our model to the Uniform Data Set from the National Alzheimer's Coordinating Center to identify cognitive decline patterns among dementia patients

Expression profiling and QTL analysis: a powerful complementary strategy in drug abuse research

Alcoholism is a complex disease exhibiting a multifactorial mode of transmission. To simplify the genetic and phenotypic complexity of the alcoholic phenotype, alcohol-preferring (P) and -non-preferring (NP) rats were developed on the basis of alcohol preference and consumption as an animal model of alcoholism. Total gene expression analysis (TOGA) and quantitative trait loci (QTL) analysis were applied to selectively bred, inbred P and NP rats as complementary studies to identify genetic factors that contribute to alcohol preference and consumption. TOGA analysis was utilized to screen for differential expression in several brain regions involved in the mesocorticolimbic dopamine (DA) system. Genes exhibiting differences in expression were then screened for an association to the alcohol preference phenotype, the quantitative trait of a previously identified QTL. By evaluating differences in gene expression for linkage to a quantitative trait, this combined approach was implemented to identify alpha-synuclein, a candidate gene for alcohol preference

Is there a genetic relationship between alcoholism and depression?

The Collaborative Study on the Genetics of Alcoholism (COGA) seeks to identify genes contributing to alcoholism and related traits (i.e., phenotypes), including depression. Among alcoholic subjects the COGA study found an increased prevalence of depressive syndrome (i.e., depression that may or may not occur in conjunction with increased drinking). This combination of alcoholism and depression tends to run in families. Comorbid alcoholism and depression occurred substantially more often in first-degree relatives of COGA participants with alcoholism than in relatives of control participants. Based on these data, COGA investigators defined three phenotypes—“alcoholism,” “alcoholism and depression,” and “alcoholism or depression”—and analyzed whether these phenotypes were linked to specific chromosomal regions. These analyses found that the “alcoholism or depression” phenotype showed significant evidence for genetic linkage to an area on chromosome 1. This suggests that a gene or genes on chromosome 1 may predispose some people to alcoholism and others to depression (which may be alcohol induced)