Analysis of genes for alcoholism using two-disease-locus models

The Role of Neuropeptide Y (NPY) in Alcohol and Drug Abuse Disorders

Neuropeptide Y (NPY) is a neuromodulator that is widely expressed throughout the central nervous system (CNS) and which is cosecreted with classic neurotransmitters including GABA and glutamate. There is a long history of research implicating a role for NPY in modulating neurobiological responses to alcohol (ethanol) as well as other drugs of abuse. Both ethanol exposure and withdrawal from chronic ethanol have been shown to produce changes in NPY and NPY receptor protein levels and mRNA expression in the CNS. Importantly, manipulations of NPY Y1 and Y2 receptor signaling have been shown to alter ethanol consumption and self-administration in a brain region-specific manner, with Y1 receptor activation and Y2 receptor blockade in regions of the extended amygdala promoting robust reductions of ethanol intake. Similar observations have been made in studies examining neurobiological responses to nicotine, psychostimulants, and opioids. When taken together with observations of potential genetic linkage between the NPY system and the human alcohol abuse disorders, NPY represents a promising target for treating problematic alcohol and drug use, and in protecting individuals from relapse during abstinence.

Epigenetics and biomarkers in the staging of neuropsychiatric disorders

Epigenetics, or alterations in the phenotype or gene expression due to mechanisms other than changes in the underlying DNA sequence, reflects the sensitivity and responsiveness of human and animal brains in constantly varying circumstances regulating gene expression profiles that define the biomarkers and present the ultimate phenotypical outcomes, such as cognition and emotion. Epigenetics is associated with functionally relevant alterations to the genome in such a fashion that under the particular conditions of early, adolescent, and adult life, environmental signals may activate intracellular pathways that remodel the "epigenome," triggering changes in gene expression and neural function. Thus, genetic influences in neuropsychiatric disorders that are subject to clinical staging, epigenetics in schizophrenia, epigenetic considerations in the expression of sensorimotor gating resulting from disease conditions, biomarkers of drug use and addiction, current notions on the role of dopamine in schizophrenia spectrum disorders, and the discrete interactions of biomarkers in persistent memory were to greater or lesser extents reflected upon. The relative contributions of endophenotypes and epistasis for mediating epigenetic phenomena and the outcomes as observed in the analysis of biomarkers appear to offer a multitude of interactive combinations to further complicate the labyrinthine machinations of diagnosis, intervention, and prognosis.

Approaches to detecting gene × gene interaction in Genetic Analysis Workshop 14 pedigrees

Whether driven by the general lack of success in finding single-gene contributions to complex disease, by increased knowledge about the potential involvement of specific biological interactions in complex disease, or by recent dramatic increases in computational power, a large number of approaches to detect locus x locus interactions were recently proposed and implemented. The six Genetic Analysis Workshop 14 (GAW14) papers summarized here each applied either existing or refined approaches with the goal of detecting gene x gene, or locus x locus, interactions in the GAW14 data. Five of six papers analyzed the simulated data; the other analyzed the Collaborative Study on the Genetics of Alcoholism data. The analytic strategies implemented for detecting interactions included multifactor dimensionality reduction, conditional linkage analysis, nonparametric linkage correlation, two-locus parametric linkage analysis, and a joint test of linkage and association. Overall, most of the groups found limited success in consistently detecting all of the simulated interactions due, in large part, to the nature of the generating model.