Much evidence from studies in humans and animals supports the hypothesis that alcohol addiction is a complex disease with both hereditary and environmental influences. Molecular determinants of excessive alcohol consumption are difficult to study in humans. However, several rodent models show a high or low degree of alcohol preference, which provides a unique opportunity to approach the molecular complexities underlying the genetic predisposition to drink alcohol. Microarray analyses of brain gene expression in three selected lines, and six isogenic strains of mice known to differ markedly in voluntary alcohol consumption provided >4.5 million data points for a meta-analysis. A total of 107 arrays were obtained and arranged into six experimental data sets, allowing the identification of 3,800 unique genes significantly and consistently changed between all models of high or low amounts of alcohol consumption. Several functional groups, including mitogen-activated protein kinase signaling and transcription regulation pathways, were found to be significantly overrepresented and may play an important role in establishing a high level of voluntary alcohol drinking in these mouse models. Data from the general meta-analysis was further filtered by a congenic strain microarray set, from which cis-regulated candidate genes for an alcohol preference quantitative trait locus on chromosome 9 were identified: Arhgef12, Carm1, Cryab, Cox5a, Dlat, Fxyd6, Limd1, Nicn1, Nmnat3, Pknox2, Rbp1, Sc5d, Scn4b, Tcf12, Vps11, and Zfp291 and four ESTs. The present study demonstrates the use of (i) a microarray meta-analysis to analyze a behavioral phenotype (in this case, alcohol preference) and (ii) a congenic strain for identification of cis regulation.alcoholism ͉ gene expression ͉ microarray

Phenome-wide association is a novel reverse genetic strategy to analyze genome-to-phenome relations in human clinical cohorts. Here we test this approach using a large murine population segregating for ∼5 million sequence variants, and we compare our results to those extracted from a matched analysis of gene variants in a large human cohort. For the mouse cohort, we amassed a deep and broad open-access phenome consisting of ∼4,500 metabolic, physiological, pharmacological and behavioural traits, and more than 90 independent expression quantitative trait locus (QTL), transcriptome, proteome, metagenome and metabolome data sets—by far the largest coherent phenome for any experimental cohort (www.genenetwork.org). We tested downstream effects of subsets of variants and discovered several novel associations, including a missense mutation in fumarate hydratase that controls variation in the mitochondrial unfolded protein response in both mouse and Caenorhabditis elegans, and missense mutations in Col6a5 that underlies variation in bone mineral density in both mouse and human.

Highlights d There are now 140 fully inbred BXD strains available, with high-quality genotypes d More strains, new genotypes, and new models have improved power and precision d We have high power even for traits with low heritability or small effect sizes d A phenome of >100 omics datasets and >7,500 classic phenotypes is freely available

Background Eating disorders are lethal and heritable; however, the underlying genetic factors are unknown. Binge eating is a highly heritable trait associated with eating disorders that is comorbid with mood and substance use disorders. Therefore, understanding its genetic basis will inform therapeutic development that could improve several comorbid neuropsychiatric conditions. Methods We assessed binge eating in closely related C57BL/6 mouse substrains and in an F2 cross to identify quantitative trait loci (QTL) associated with binge eating. We used gene targeting to validated candidate genetic factors. Finally, we used transcriptome analysis of the striatum via mRNA sequencing (RNA-seq) to identify the premorbid transcriptome and the binge-induced transcriptome to inform molecular mechanisms mediating binge eating susceptibility and establishment. Results C57BL/6NJ but not C57BL/6J mice showed rapid and robust escalation in palatable food consumption. We mapped a single genome-wide significant QTL on chromosome 11 (LOD=7.4) to a missense mutation in cytoplasmic FMR1-interacting protein 2 (Cyfip2). We validated Cyfip2 as a major genetic factor underlying binge eating in heterozygous knockout mice on a C57BL/6N background that showed reduced binge eating toward a wild-type C57BL/6J-like level. Transcriptome analysis of premorbid genetic risk identified the enrichment terms “morphine addiction” and “retrograde endocannabinoid signaling” whereas binge eating resulted in the downregulation of a gene set enriched for decreased myelination, oligodendrocyte differentiation, and expression. Conclusions We identified Cyfip2 as a major significant genetic factor underlying binge eating and provide a behavioral paradigm for future genome-wide association studies in populations with increased genetic complexity.

The goal of systems genetics is to understand the impact of genetic variation across all levels of biological organization, from mRNAs, proteins, and metabolites, to higher-order physiological and behavioral traits. This approach requires the accumulation and integration of many types of data, and also requires the use of many types of statistical tools to extract relevant patterns of covariation and causal relations as a function of genetics, environment, stage, and treatment. In this protocol we explain how to use the GeneNetwork web service, a powerful and free online resource for systems genetics. We provide workflows and methods to navigate massive multiscalar data sets and we explain how to use an extensive systems genetics toolkit for analysis and synthesis. Finally, we provide two detailed case studies that take advantage of human and mouse cohorts to evaluate linkage between gene variants, addiction, and aging.