Detection of reciprocal quantitative trait loci for acute ethanol withdrawal and ethanol consumption in heterogeneous stock mice

Department of Behavioral Neuroscience, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Road, Portland, OR 97239-3098, USA.
Psychopharmacology (Impact Factor: 3.88). 05/2009; 203(4):713-22. DOI: 10.1007/s00213-008-1418-y
Source: PubMed


Previous studies have suggested that there is an inverse genetic relationship between ethanol consumption (two-bottle choice, continuous access) and ethanol withdrawal (e.g., Metten et al., Behav Brain Res 95:113-122, 1998a).
The current study used short-term selective breeding from heterogeneous stock (HS) animals to examine this relationship. The primary goal of the current study was to determine if reciprocal quantitative trait loci (QTLs) could be found in the selectively bred lines. The advantage of detecting QTLs in HS animals is that it is possible to extract a haplotype signature for the QTL, which in turn can be used to narrow the number of candidate genes generated from gene expression and sequence databases (see, e.g., Hitzemann et al., Mamm Genome 14:733-747, 2003).
Seven reciprocal QTLs were detected on chromosomes (Chr) 1 (two), 3, 6, 11, 16, and 17 that exceeded the nominal LOD threshold of 10; genetic drift, which occurs during selection, dramatically increases the LOD threshold. The proximal Chr 1 QTL was examined in some detail. The haplotype structure of the QTL was such that the LP/J allele was associated with low withdrawal and high consumption. The QTL appears to be located in a gene-poor region between 170 and 173 Mbp. Based on available sequence data, two plausible candidate genes emerge-Nos1ap and Atf6alpha.
The data presented here confirm some aspects of the negative genetic relationship between acute ethanol withdrawal and ethanol consumption. The QTL data point to the potential involvement of NO signaling and/or the unfolded protein response.

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    • "All of these approaches, including the use of AIL, HS and strain panels, require statistical corrections for population structure that can affect the power of mapping analysis (Kang et al. 2008; Cheng et al. 2011). The Collaborative Cross (CC) (Churchill et al. 2004; Collaborative Cross Consortium 2012), Diversity Outbred (DO) (Svenson et al. 2012) and CC-heterogenous stock (Hitzemann et al. 2009) provide alternative mapping populations that encompass a greater level of genetic variation, relatively small haplotype blocks and a uniform population structure that eliminates spurious linkages and provides better power to detect QTL. Early studies with the CC (Aylor et al. 2011; Durrant et al. 2011; Philip et al. 2011) and DO (Svenson et al. 2012) demonstrate the wide range of phenotypic diversity and precision of QTL that are obtained using these new resource populations. "
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    ABSTRACT: Historically our ability to identify genetic variants underlying complex behavioral traits in mice has been limited by low mapping resolution of conventional mouse crosses. The newly developed Diversity Outbred (DO) population promises to deliver improved resolution that will circumvent costly fine mapping studies. The DO is derived from the same founder strains as the Collaborative Cross (CC), including three wild derived strains. Thus the DO provides more allelic diversity and greater potential for new discovery compared to crosses involving standard mouse strains. We have characterized 283 male and female DO mice using open-field, light-dark box, tail-suspension, and visual-cliff avoidance tests to generate 38 behavioral measures. We identified several quantitative trait loci (QTL) for these traits with support intervals ranging from 1 to 3 Mb in size. These intervals contain relatively few genes (ranging from 5 to 96). For a majority of QTL, using the founder allelic effects together with whole genome sequence data, we could further narrow the positional candidates. Several QTL replicate previously published loci. Novel loci were also identified for anxiety- and activity-related traits. Half of the QTLs are associated with wild-derived alleles, confirming the value to behavioral genetics of added genetic diversity in the DO. In the presence of wild-alleles we sometimes observe behaviors that are qualitatively different from the expected response. Our results demonstrate that high-precision mapping of behavioral traits can be achieved with moderate numbers of DO animals, representing a significant advance in our ability to leverage the mouse as a tool for behavioral genetics.
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    • "severity of ethanol withdrawal (low and high, respectively), while conversely, selection for high and low withdrawal results in differential ethanol preference (Metten et al., 1998). While these results are consistent with the inverse genetic correlation between ethanol preference and withdrawal seen among inbred mouse strains (Metten & Crabbe, 2005; Metten et al., 1998), the effects of selection for withdrawal severity on ethanol preference have been somewhat inconsistent across studies (Ford et al., 2011; Hitzemann et al., 2009; Kosobud, Bodor, & Crabbe, 1988). A similar approach can also be employed to explore possible genetic correlations between ethanol-related phenotypes and neurobehavioral traits other than those directly related to ethanol responsiveness. "
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    ABSTRACT: Previous studies in mice and rats have shown that selective breeding for high and low ethanol preference results in divergence of circadian phenotype in the selected lines. These results indicate that some alleles influencing ethanol preference also contribute to circadian rhythm regulation. Selective breeding has also been used to produce lines of mice differing in a number of other ethanol-related traits, while studies of phenotypic and genetic correlation indicate that diverse ethanol-related traits are influenced by both shared and unshared genetics. In the present study, we examined several features of circadian activity rhythms in a mouse line selected for binge-like drinking and in mouse lines selected for high and low severity of ethanol withdrawal convulsions. Specifically, Experiment 1 compared High Drinking in the Dark (HDID-1) mice to their genetically heterogeneous progenitor line (HS/Npt), and Experiment 2 compared Withdrawal Seizure-Prone (WSP-2) and Withdrawal Seizure-Resistant (WSR-2) mice. Both line pairs displayed differences in their daily activity patterns under light-dark conditions. In addition, HDID-1 mice showed shorter free-running periods in constant light and less coherent activity rhythms across lighting conditions relative to HS/Npt controls, while WSP-2 mice showed longer free-running periods in constant darkness relative to WSR-2 mice. These results strengthen the evidence for genetic linkages between responsiveness to ethanol and circadian regulation, and extend this evidence to include ethanol-related phenotypes other than preference drinking. However, the present results also indicate that the nature of genetic correlations between and within phenotypic domains is highly complex.
    Full-text · Article · Feb 2013 · Alcohol (Fayetteville, N.Y.)
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    • "For example, researchers have used such models to detect and map quantitative trait loci (QTLs)—chromosomal regions containing or linked to the genes that underlie a quantitative, complex trait. These approaches have identified significant and suggestive QTLs for ethanol sensitivity (e.g., Bennett et al. 2006; Downing et al. 2006; Palmer et al. 2006), consumption (e.g., Belknap and Atkins 2001; Boyle and Gill 2008; Hitzemann et al. 2009; Phillips et al. 1998, 2010;Tarantino et al. 1998), withdrawal (Buck et al. 1997, 2002), conditioned aversion (Risinger and Cunningham 1998), conditioned place preference (Cunningham 1995), and tolerance (e.g., Bennett et al. 2007; Crabbe et al. 1994; Drews et al. 2010; Kirstein et al. 2002). The identification of specific genes (i.e., quantitative trait genes [QTGs], which carry allelic variations in the DNA that affect their expression and/or the structure of the product that they code for) that underlie QTL phenotypic effects and elucidation of their mechanisms of action is a crucial next step in the translation of such preclinical research. "
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    ABSTRACT: The genetic determinants of alcoholism still are largely unknown, hindering effective treatment and prevention. Systematic approaches to gene discovery are critical if novel genes and mechanisms involved in alcohol dependence are to be identified. Although no animal model can duplicate all aspects of alcoholism in humans, robust animal models for specific alcohol-related traits, including physiological alcohol dependence and associated withdrawal, have been invaluable resources. Using a variety of genetic animal models, the identification of regions of chromosomal DNA that contain a gene or genes which affect a complex phenotype (i.e., quantitative trait loci [QTLs]) has allowed unbiased searches for candidate genes. Several QTLs with large effects on alcohol withdrawal severity in mice have been detected, and fine mapping of these QTLs has placed them in small intervals on mouse chromosomes 1 and 4 (which correspond to certain regions on human chromosomes 1 and 9). Subsequent work led to the identification of underlying quantitative trait genes (QTGs) (e.g., Mpdz) and high-quality QTG candidates (e.g., Kcnj9 and genes involved in mitochondrial respiration and oxidative stress) and their plausible mechanisms of action. Human association studies provide supporting evidence that these QTLs and QTGs may be directly relevant to alcohol risk factors in clinical populations.
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