ALDH2, ADH1B and alcohol expectancies: integrating genetic and learning perspectives.

Psychiatric epidemiology studies have established that genetic and environmental factors each account for substantial variability in the risk for alcohol use and dependence (reviewed in Heath, 1995; McGue, 1994; 1999). A consequent goal is to enhance understanding of how genetic and psychosocial variables jointly influence drinking behavior (Dick et al., 2006; Heath & Nelson, 2002). Although biological and behavioral research efforts have tended to progress independently (Young, Lawford, Feeney, Ritchie, & Noble, 2004), there is increasing theoretical and empirical justification for integrating biological and behavioral approaches (e.g., Beauchaine, Neuhaus, Brenner, & Gatzke-Kopp, 2008; Bryan, Hutchison, Seals, & Allen, 2007; Hutchison et al., 2007; Plomin & Crabbe, 2000). This goal will be facilitated by the use of theoretical frameworks capable of accommodating hypotheses from both perspectives. Alcohol expectancy theory (Goldman, Darkes, & Del Boca, 1999; Goldman, Darkes, Reich, & Brandon, 2006) implicates cognitive and learning mechanisms as intermediary processes that may partly account for genetic influences on alcohol use behavior. In offering a bio-behavioral account of drinking behavior, expectancy theory serves as a promising theoretical framework for translational studies of alcohol use. The notion of biological influences on alcohol-related learning is supported by preliminary evidence that genetic factors account for variability in alcohol expectancies. Relevant twin studies suggest that additive genetic factors explain a modest but significant proportion (up to 20-40%) of individual variability in self-reported expectancies, although heritable effects appear to differ by drinking status and expectancy subtype (Agrawal et al., 2007; Gabrielli & Plomin, 1985; Slutske et al., 2002; Vernon et al., 1996).1 Genetic influences on expectancies can also be inferred from their association with biologically based risk markers for alcohol dependence. For instance, expectancies are shown to partly mediate effects of dispositional risk factors (e.g., behavioral undercontrol, disinhibition and sensation seeking; Anderson, Smith, & Fischer, 2003; Finn, Sharkansky, Brandt, & Turcotte; 2000; Henderson et al., 1994; McCarthy, Kroll, & Smith, 2001; Schuckit & Smith, 2000) and familial risk for alcohol dependence (Sher, Walitzer, Wood, & Brent, 1991; Sher et al., 1996) on drinking outcomes. Among the most widely studied risk markers for alcohol dependence is level of response (LR) to alcohol (Schuckit, Smith, & Kalmijn, 2004), a heritable phenotype that predicts the risk for alcohol dependence prospectively (Heath et al., 1999; Schuckit & Smith, 1996) and may predict drinking in part via expectancies (Schuckit et al., 2005). Expectancy research has begun to incorporate molecular genetic approaches. Not surprisingly, early studies have focused on genes with direct implications for alcohol metabolism, in particular ALDH2 and ADH1B. ALDH2 encodes the mitochondrial aldehyde dehydrogenase (ALDH) enzyme, the primary catalyst for the removal of acetaldehyde, a toxic byproduct of ethanol metabolism. The ALDH2*2 allele, common in 30-50% of individuals of northeast Asian descent, encodes a functionally inactive ALDH enzyme subunit that leads to impaired acetaldehyde removal. ALDH2*2 is associated with increased sensitivity to alcohol, as indexed by symptoms like flushing, tachycardia and nausea (Thomasson & Li, 1993). In turn, ALDH2*2 is associated with a significantly reduced likelihood of heavy drinking and alcohol dependence (Luczak et al., 2006; Wall & Ehlers, 1995). ADH1B is one of several genes that encode the alcohol dehydrogenase (ADH) enzymes, which regulate oxidation of ethanol to acetaldehyde (Edenberg, 2007). The ADH1B*2 and ADH1B*3 variants, prevalent among people of Asian and African descent, respectively, encode ADH enzyme subunits with a substantially greater turnover rate for converting ethanol to acetaldehyde relative to the common ADH1B*1 variant. ADH1B*2 and ADH1B*3 have been associated with increases in some measures of acute sensitivity to alcohol (Cook et al., 2005; Duranceaux et al., 2006; McCarthy, Pedersen, Lobos, Todd, & Wall, under review) as well as a reduced risk of alcohol dependence (Ehlers et al., 2007; Hasin et al., 2002; Luczak et al., 2006; Whitfield, 1997) compared to ADH1B*1. Presumably, the protection afforded by ALDH2*2, ADH1B*2 and ADH1B*3 occurs via a pathway involving increases in transient acetaldehyde during alcohol metabolism and greater sensitivity to alcohol, although empirical support for acetaldehyde as a mediating mechanism exists only for ALDH2*2 (Wall, 2005). Differences in alcohol sensitivity can theoretically influence how drinking events are experienced, interpreted and stored in memory (Brown, Tate, Vik, Haas & Aarons, 1999; Goldman et al., 2006). It follows that functional variations in alcohol metabolizing genes are reasonable candidates for studies of genetic influences on expectancy processes. In an initial study of ALDH2 and alcohol expectancies, ALDH2*2 was associated with lower endorsement of positive expectancies, which partially accounted for ALDH2 effects on drinking quantity in women (McCarthy, Wall, Brown and Carr, 2000). In a follow-up study using an alcohol challenge paradigm, ALDH2*2 predicted greater expectancies for cognitive and behavioral impairment among men, an association that was fully explained by response to alcohol in the laboratory (McCarthy, Brown, Carr & Wall, 2001). Associations of ALDH2 with expectancies have also been reported in a study of alcohol-dependent patients in Taiwan (Hahn et al., 2006). These initial studies did not examine the additional influence of ADH1B. Others, however, have found that ADH1B*2 was associated with retrospectively reported physiological symptoms (Carr et al., 2002; Wall et al., 2005). Also, ADH1B*3 was associated with alcohol expectancies in a study of African Americans (Ehlers et al., 2003). 2 Theory and research therefore suggest that genetic differences in alcohol metabolism and sensitivity could influence expectancy development. An imperative in genetic association studies is maximal precision in the definition and measurement of phenotypes (e.g., Hines, Ray, Hutchison, & Tabakoff, 2005; Hutchison, Stallings, McGeary, & Bryan, 2004). Similarly, expectancy theorists have stressed the importance of optimal measurement specificity (Goldman et al., 1999). Notably, previous attempts to study genetic influences on expectancies have used established expectancy instruments (e.g., Brown et al., 1980; Fromme et al., 1993) with item content spanning several social and behavioral domains. Depending on the genetic variable in question and its proposed mechanism of influence, traditional expectancy measures may lack sensitivity for detecting genetic influences. For example, although ALDH2 and ADH1B variations are proposed to influence drinking via differences in physiological responses to alcohol, the most popular expectancy instruments lack substantive item content in this domain. Tailoring expectancy measures to the proposed pathway of genetic influence would theoretically increase the likelihood of detecting genetic associations. The current study examined genetic influences on alcohol-related learning by evaluating associations of ALDH2 and ADH1B with alcohol expectancies and drinking behavior among individuals of northeast Asian descent. In addition to using a traditional alcohol expectancy instrument, we developed a measure of physiological alcohol expectancies (Study 1) in order to evaluate a relatively narrower expectancy phenotype with theoretical relevance for the mechanism by which ALDH2 and ADH1B variations are presumed to influence drinking behavior. Study 2 aimed to examine joint influences of genetic, expectancy and demographic variables on drinking outcomes and to evaluate mediating effects of genetic variables on drinking outcomes via expectancies. We predicted that ALDH2*2 and ADH1B*2 would each be associated with lower positive expectancies and higher negative expectancies as measured with an established instrument (Fromme et al., 1993). It was also anticipated that ALDH2*2 and ADH1B*2 would predict higher expectancies for physiological drinking outcomes and lower rates of drinking. We expected that genetic, expectancy and demographic factors would each explain unique variance in drinking outcomes and that expectancies would partly mediate the effects of ALDH2 and ADH1B on alcohol use.