Abstract
Objective
We examined whether a cumulative genetic score (CGS) containing MAOA and 5-HTTLPR polymorphisms moderated drinking and intimate partner violence (IPV) treatment outcomes in hazardous drinking men receiving batterer intervention or batterer intervention plus a brief alcohol intervention.
Method
We conducted a randomized controlled trial with 97 hazardous drinking men who had a relationship partner and were in batterer intervention programs. Participants were randomized to receive 40 hours of standard batterer program (SBP) or the SBP plus a 90-minute alcohol intervention (SBP+BAI). Data were collected at baseline, 3-, 6-, and 12-month follow-up, with follow-up rates of 99.0%, 97.9%, and 93.8%, respectively. Genomic DNA was extracted from saliva. Substance use was measured with the Timeline Followback Interview; IPV was assessed with the Revised Conflict Tactics Scales. The primary outcomes were drinks per drinking day (DDD), percentage of days abstinent from alcohol (PDA), frequency of physical IPV, and injuries to partners.
Results
Consistent with hypotheses, analyses demonstrated significant treatment condition by CGS interactions for PDA, physical violence, and injuries, but not for DDD. At high levels of the CGS, men in SBP+BAI had greater PDA (B=.16, 95%CI=.04–.27, p=.01), less physical violence perpetration (B=−1.21, 95%CI=−2.21–−.21, p=.02), and fewer injuries to partners (B=−2.37, 95%CI=−3.19–−.82, p=.00) than men in SBP. No differences between the groups in PDA, physical violence, or injuries were observed at low levels of the CGS.
Conclusions
Findings demonstrate the potential importance of MAOA and 5-HTTLPR polymorphisms in the treatment of IPV and drinking in men in batterer intervention programs.
Keywords: genetics, intimate partner violence, batterer intervention, randomized controlled trial, brief alcohol intervention
INTRODUCTION
Standard batterer intervention programs (SBPs) for intimate partner violence (IPV) have limited efficacy (Feder & Wilson, 2005). Given the link between alcohol and IPV, addressing alcohol misuse in SBPs could improve outcomes. In prior work, we examined whether adding a brief motivational alcohol intervention to SBPs (SBP+BAI) improved outcomes relative to SBP alone (Stuart et al., 2013). Men receiving SBP+BAI reported superior IPV and drinking outcomes for up to 6 months, but no group differences remained at 12-months. Past researchers have noted the importance of examining genetics associated with IPV (Hines & Saudino, 2004) and alcohol use (McHugh, Hofmann, Asnaani, Sawyer, & Otto, 2010). Thus, in the current study we examined whether genes moderated the effects of our previous findings.
Two genes may be particularly relevant to IPV and alcohol treatment outcomes. The monoamine oxidase A (MAOA) gene, located on the X chromosome, codes for an enzyme that metabolizes neurotransmitters including serotonin, dopamine, and norephinephrine (McDermott, Tingley, Cowden, Frazzetto, & Johnson, 2009; Pinto et al., 2010). MAOA variation, particularly the 3-repeat variant of the 30bp repeat in the uVNTR polymorphism, is associated with aggressive behavior (McDermott et al., 2009; Widom & Brzustowicz, 2006), and alcohol dependence (e.g., Contini, Marques, Garcia, Hutz, & Bau, 2006). Popova (2006) theorized that variants in the serotonin transporter gene (SLC6A4) modulate violence. One such variant is 5-HTTLPR, a functional polymorphism in the promoter region of SLC6A4. Men with histories of violence were more likely than nonviolent men to have the short (S) allele and the S/S genotype at 5-HTTLPR (Retz, Retz-Junginger, Supprian, Thome, & Rosler, 2004). The 5-HTTLPR S allele was also linked with alcohol dependence (e.g., McHugh et al., 2010).
Two studies have examined genetic components of IPV. A twin study (Hines & Saudino, 2004) showed that heritability accounted for 16% of the variance in physical IPV. We (Stuart et al., 2014) examined whether a cumulative genetic score (CGS) containing the MAOA uVNTR and 5-HTTLPR polymorphisms was cross-sectionally related to IPV assessed at baseline in the sample of men investigated in the current study. We found that the CGS containing the sum of high-risk alleles was positively associated with frequency of physical IPV and injuries caused to partners, above and beyond alcohol and drug problems, age, and length of relationship. In the current study, we examined whether this same CGS moderated alcohol use and IPV treatment outcomes in the aforementioned randomized controlled trial (RCT) of men arrested for domestic violence. With a CGS, a sum score is created that is comprised of risk alleles across multiple polymorphisms to represent an index of risk that models the influence of the constituent variants in a single parameter (McGeary et al., 2012). We formed a CGS consisting of the MAOA uVNTR and 5-HTTPLR genes, due to their links to the serotonergic system and to violence and substance use, to examine whether this would moderate alcohol and IPV treatment outcomes. To the extent that this CGS may index perturbations in prefrontal serotonergic function (Buckholtz & Meyer-Lindenberg, 2008; Stuart et al., 2014) that may result in diminished executive control, individuals with higher CGS scores might be at particular risk for synergistic alcohol-related disinhibition that could result in more IPV. Accordingly, individuals with higher CGS scores might benefit preferentially from an intervention that prevented alcohol use from further exacerbating impaired executive function. We hypothesized that, among individuals with higher CGS scores (i.e., a greater number of high risk alleles), men in SBP+BAI would decrease IPV and alcohol use more than men in SBP alone.
Method
Trial Design
We conducted a RCT in which men in batterer intervention programs were randomly assigned to receive the standard 40-hour batterer intervention program alone (SBP) or the SBP plus a 90-minute, motivationally-based alcohol intervention (SBP+BAI) that targeted drinking and alcohol-related IPV. Participants were assessed at baseline, 3-, 6-, and 12-month follow-up.
Participants
Participants in the original RCT were 252 hazardous drinking men, 18 or older, attending one of five SBPs in Rhode Island. Hazardous drinking was defined as: in the past 6 months, meeting NIAAA’s (1995) clinical guidelines for “at risk” drinking (≥5 drinks per occasion) at least once per month or scoring 8 or higher (“hazardous drinking”) on the Alcohol Use Disorders Identification Test (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001). To be eligible, men had to be participants in the original RCT and in a current intimate relationship with a partner at baseline, to allow examination of genetic predictors of IPV outcomes. Participants were recruited from November 2003–May 2009. However, DNA collection began in August 2009, when participants were re-contacted for a saliva sample. Data collection concluded October 2011. We located 166 of the 252 RCT participants; 157/166 men provided DNA (94.6%). Men who provided DNA (n=157) did not significantly differ from men who did not provide DNA (n=95) on demographic characteristics, alcohol use, and violence at any time point. The final sample was 97 (Figure 1). Demographic characteristics for the sample are presented in Table 1. Participants were paid up to $325 for completing screening, all assessments, and providing DNA.
Figure 1.
Consort Flow Diagram.
Table 1.
Summary Statistics of Sample by Treatment Condition
| Total Sample (n=97) | Brief Alcohol Intervention (n = 45) | Standard Batterer Intervention (n = 52) | |
|---|---|---|---|
| Age, mean (SD), years | 31.64 (9.83) | 31.13 (9.01) | 32.08 (10.56) |
| Race, No. (%) | |||
| White | 75 (77.3) | 35 (77.7) | 40 (76.9) |
| Black | 13 (13.4) | 4 (8.8) | 9 (17.3) |
| Hispanic or Latino | 6 (6.2) | 4 (8.8) | 2 (3.8) |
| Other | 3 (3.1) | 2 (4.4) | 1 (1.9) |
| Education, mean, (SD), years | 11.78 (1.63) | 11.67 (1.61) | 11.88 (1.66) |
| Number of Children, mean (SD) | 1.66 (1.98) | 1.68 (2.04) | 1.63 (1.94) |
| Past Year Income, mean, (SD), US Dollars | 22,864 (18,490) | 20,871 (17,617) | 24,766 (19,295) |
| Relationship Length, mean, (SD), years | 5.28 (5.27) | 4.88 (4.59) | 5.63 (5.84) |
| CGS Score x | 1.36 (0.88) | 1.40 (0.94) | 1.33 (0.83) |
The two groups did not significantly differ on CGS Score (t(95)=0.41, p>.10).
Measures
Alcohol and Drug Use
The Timeline Followback (TLFB; L.C. Sobell & M.B. Sobell, 2003), a calendar-assisted structured interview, was used to assess substance use. We examined number of drinks per drinking day (DDD) and percentage of days abstinent from alcohol (PDA).
Intimate Partner Violence
The Revised Conflict Tactics Scales (CTS2; Straus, Hamby, & Warren, 2003) was used to assess physical assault and injury perpetration (primary outcomes).
Genotypes and Cumulative Genetic Score (CGS)
Genotyping was performed using salivary DNA isolated from Oragene kits following published methods (Beevers, Ellis, Wells, & McGeary, 2010). The CGS included the same genotypes of two polymorphisms relevant to violence and alcohol use (Stuart et al., 2014). For the 5-HTTLPR polymorphism, the short (S) and LG alleles are less transcriptionally efficient than the long (L) allele (Hu et al., 2005); thus, the LG allele and S allele are considered of equivalent risk (Zalsman et al., 2006). A participant was given a risk allele score of 0, 1, or 2 for their triallelic 5-HTTLPR genotype corresponding to the number of low expressing alleles they possess. For the MAOA polymorphism, an allele score of 1 corresponded to the presence of 2-repeat, 3-repeat, or 5-repeat base pairs, indicating a lower transcription rate. Participants with the 3.5 or 4-repeat variants of MAOA, indicating a more efficient transcription rate, were assigned an allele score of 0. The scores across each of the three candidate alleles were summed to create a CGS that ranged from 0–3. There were no missing genotype data. Table 2 presents Hardy Weinberg information.
Table 2.
Results of an Exact Test for Hardy Weinberg Proportions and Allele Frequencies for 5-HTTLPR and MAOA
| 5-HTTLPR (n=97) | |||
|---|---|---|---|
| LG (97bp) | S (138bp) | LA (181bp) | |
| LG (97bp) | 0 (0) | 7 (3) | 9 (9) |
| S (138bp) | 15 (22) | 37 (39) | |
| LA (181bp) | 29 (24) | ||
| Genotyping calls for MAOA | |
|---|---|
| 2 repeats (290bp) | 1 |
| 3 repeats (320bp) | 34 |
| 3.5 repeats (335bp) | 1 |
| 4 repeats (350bp) | 59 |
| 3 repeats/4 repeats (320bp/350bp) | 2 |
Note: Allele Frequencies for 5-HTTLPR are in parentheses. The Hardy Weinberg test examines the observed distribution of genotypes in the sample compared to the expected distribution of genotypes. Departures from Hardy Weinberg equilibrium (i.e., the expected genotype frequencies) may result from true association between the polymorphism and the selection criteria, evolutionary influences, or genotyping error. Results presented here using Markov chain–Monte Carlo implementation (Engels, 2009) indicated that observed genotype frequencies for 5-HTTLPR did not differ from Hardy Weinberg equilibrium (HWE; p=.722). We could not conduct a test for HWE for the MAOA polymorphism since it is an X-linked variant, females are not included in our sample, and men are hemizygous for the X chromosome (Falconer & Mackay, 1996).
Procedures
IRB approval was obtained from the relevant institutions. Men were recruited at their SBP intake or groups. They were paid for study assessments but their participation had no impact on their criminal justice system outcomes. All men provided informed consent.
Interventions
Men were randomly assigned to SBP alone or SBP+BAI. SBP included 40-hours of group batterer intervention. The BAI was administered by one of six doctoral-level therapists in a 90-minute individual audio taped session. BAI therapist adherence to the treatment manual was assessed with a checklist. Twenty percent (n=25) of the BAI sessions were coded for therapist adherence by a bachelor’s level research assistant trained by the first author. The coder rated the tapes for presence of 15 program elements; therapists covered 92% of the program ingredients. Therapist competence was assessed by a psychologist trained to administer the Motivational Interviewing Treatment Integrity 3.1.1, a global scale assessing therapist competence (Moyers, Martin, Manuel, Miller, & Ernst, 2010); 86% of the BAIs (n=38) were coded. The Global Motivational Interviewing Spirit variable (the average of scores for collaboration, evocation, and autonomy/support) was our index of therapist competence. Scores could range from 1 to 5, with higher scores reflecting greater competence. Therapists had a mean score of 4.10 (SD=0.39).
Data Analysis Plan
Analyses were conducted using Generalized Estimating Equations (GEE; Liang & Zeger, 1986) with PROC GENMOD in SAS (SAS Institute Inc., 1997). GEE uses all available data, consistent with an intent to treat analysis. Physical IPV, injuries sustained by partners, DDD, and PDA at follow-up were the dependent variables. A negative binomial distribution for physical IPV and injury, and a normal distribution for DDD and PDA, was specified. Variables with normal distributions were standardized so that model coefficients (B) represent effect sizes. Results of negative binomial models produce incidence rate ratios (IRR), which reflect the ratio of the expected count (rate) of the dependent variable in one group relative to the other group.
Treatment condition was dummy-coded with SBP as the reference. Time was included as a linear effect representing the number of months since baseline (3, 6, or 12). The CGS X Treatment interaction was used to determine whether the effects of the SBP+BAI compared to SBP varied as a function of CGS. We centered the CGS to aid in the interpretation of the CGS X Treatment condition interactions. For significant interactions, post-hoc analyses were conducted to determine treatment effects at high (+1SD) and low (−1SD) levels of the CGS (Aiken & West, 1991). In the GEE analyses for IPV [alcohol use], baseline indicators of the same IPV [alcohol] variable was entered as a covariate. We entered SBP treatment site into the models to test whether there were effects for specific batterer programs. Analyses revealed that outcomes did not vary across sites; thus, this variable was not included in the primary analyses.
Results
Tables 2 and 3 present allele frequencies and means and standard deviations of study variables. Upon inspecting MAOA allele frequencies, two men were heterozygous for the MAOA promoter polymorphism and were removed from analyses (Saito et al., 2002).
Table 3.
Means and Standard Deviations among Study Variables and Treatment Condition
| Brief Alcohol Intervention | Standard Batterer Intervention | |||||||
|---|---|---|---|---|---|---|---|---|
| Variable Mean (SD) | Baseline (n = 44) | 3-Month (n = 44) | 6-Month (n = 43) | 12-Month (n = 41) | Baseline (n = 51) | 3-Month (n = 50) | 6-Month (n = 50) | 12-Month (n = 48) |
| DDD | 10.78 (5.63) | 8.19 (4.96) | 7.52 (4.47) | 5.97 (4.77) | 8.96 (5.11) | 8.96 (9.31) | 7.69 (6.70) | 7.78 (6.35) |
| PDA, % | 59.3 (23.0) | 75.5 (24.4) | 70.7 (27.1) | 74.0 (28.4) | 56.4 (28.7) | 59.6 (35.1) | 65.2 (33.7) | 72.6 (28.8) |
| Revised Conflict Tactics Scales (CTS2) | (n = 44)* | (n = 40)* | (n = 36)* | (n = 25)* | (n = 51)* | (n = 47)* | (n = 41)* | (n = 35)* |
| Physical Total | 4.72 (6.02) | .42 (1.10) | 1.31 (3.06) | 2.08 (6.19) | 5.41 (12.07) | 2.85 (9.05) | 1.46 (3.63) | 1.31 (4.20) |
| Injury Total | 1.29 (3.95) | .02 (.15) | .11 (.39) | .16 (.55) | .92 (2.88) | .51 (1.86) | .29 (.78) | .08 (.37) |
Substance variables assessed with the Timeline Followback; DDD = Drinks per drinking day; PDA = Percentage of days abstinent from alcohol; Violence variables assessed with Revised Conflict Tactics Scales (CTS2). There were no significant group differences in PDA or DDD at baseline.
Had relationship partner at baseline and the time of the assessment, and was with the same relationship partner at each follow-up. Men who broke up with their partner by the 12-month assessment did not significantly differ from men who did not break up on violence or substance use at baseline.
In the parent study, 3-, 6-, and 12-month follow-up rates were 95%, 89%, and 82%, respectively. In the current study, 3-, 6-, and 12-month follow-up rates were 99%, 98%, and 94%, respectively.
Analyses demonstrated no significant main effects for intervention condition or the CGS in predicting DDD and PDA (Table 4). In addition, there was no significant interaction between treatment condition and the CGS in predicting DDD. Analyses demonstrated a significant interaction for PDA. At high levels of the CGS, men in SBP+BAI had greater PDA than men in SBP. At low levels of the CGS, there was no difference between the groups in PDA.
Table 4.
Generalized Estimation Equations Analyses Examining CGS as a Moderator of Treatment Outcomes for Alcohol and Aggression at 3, 6, and 12 Months after Intervention
| Ba/IRRb | 95% CI | p value | |
|---|---|---|---|
| DDD a | |||
| Time | −.16 | −.30, −.03 | .00 |
| DDD (baseline) | .47 | .26, .68 | .02 |
| CGS | .31 | −.66, 1.28 | .53 |
| Intervention Group | −1.44 | −3.32, .44 | .13 |
| Intervention × CGS | −.44 | −2.40, 1.51 | .65 |
| PDA a | |||
| Time | .00 | .00, .01 | .00 |
| PDA (baseline) | .62 | .42, .83 | .00 |
| CGS | −.02 | −.07, .02 | .32 |
| Intervention Group | .07 | −.00, .16 | .07 |
| Intervention × CGS | .09 | .00, .19 | .05 |
| High CGS | .16 | .04, .27 | .01 |
| Low CGS | .00 | −.11, .11 | .97 |
| Physical Aggression b | |||
| Time | .06 | −.05, .18 | .28 |
| Aggression (baseline) | .06 | .02, .09 | .00 |
| CGS | .21 | −.39, .82 | .49 |
| Intervention Group | −.11 | −1.11, .87 | .82 |
| Intervention × CGS | −1.28 | −2.27, −.30 | .01 |
| High CGS | −1.21 | −2.21, −.21 | .02 |
| Low CGS | .89 | −.45, 2.25 | .19 |
| Injury of Partner a | |||
| Time | −.03 | −.16, .11 | .71 |
| Aggression (baseline) | −.01 | −.11, .09 | .91 |
| CGS | .49 | −.22, 1.21 | .18 |
| Intervention Group | −1.08 | −2.40, .23 | .11 |
| Intervention × CGS | −1.54 | −2.69, −.38 | .00 |
| High CGS | −2.37 | −3.19, −.82 | .00 |
| Low CGS | .15 | −1.12, 1.43 | .81 |
Note: n=95. Ba = model coefficient is for standardized normal variables and equivalent to effect size d; IRRb = Incidence Rate Ratio; Substance variables assessed with the Timeline Followback; DDD = Drinks per drinking day; PDA = Percentage of days abstinent from alcohol; Violence variables assessed with Revised Conflict Tactics Scales.
For violence outcomes, analyses demonstrated no significant main effects of treatment condition or the CGS in predicting IPV (Table 4). However, significant treatment condition by CGS interactions emerged in predicting physical IPV (Figure 2) and causing injuries to partners. For physical IPV, at high levels of the CGS, men in SBP+BAI reported less physical IPV over time than men in SBP. At low levels of the CGS, no difference in physical IPV was evident. The same pattern of findings emerged for injury, such that men in SBP+BAI reported less frequent injury perpetration than men in SBP at high levels of the CGS, but not low levels of the CGS.
Figure 2.
Interaction between intervention condition and CGS predicting Physical Intimate Parner Violence
To reduce the potential impact of population stratification (Devlin & Roeder, 1999) we reran all analyses with race as a covariate. Results remained consistent for DDD and PDA with race included in the models. For physical IPV, results were consistent with the overall finding, although the alpha level for intervention group predicting IPV at high levels of the CGS only approached significance (p=.06). The model for injury exceeded the number of iterations to converge, suggesting that we may have been underpowered with this covariate included.
Discussion
This is the first study to examine the association between specific candidate genes and IPV treatment outcomes. In addition, this research adds to studies examining genetic predictors of alcohol outcomes after a psychosocial alcohol treatment (Feldstein Ewing, LaChance, Bryan, & Hutchison, 2009). Although the CGS was not related to DDD, the CGS was significantly associated with IPV and PDA. Specifically, there was a treatment condition by CGS interaction showing that at high levels of the CGS, men in SBP+BAI reported less physical IPV perpetration, fewer injuries to their partners, and greater PDA over time than men in SBP.
The present study underscores the potential importance of two polymorphisms theorized to be related to aggression and drinking, possibly by altering serotonergic function (Buckholtz & Meyer-Lindenberg, 2008). Of significance, the CGS was associated with IPV and alcohol treatment outcomes over time. If these findings can be replicated and extended in future RCTs, this study may provide preliminary clues toward possible patient-treatment matching on the basis of genetic background, thereby improving IPV and substance use outcomes.
The findings from this study should be viewed as preliminary. The challenge remains to translate genetic influences on treatment outcome in a way that they can be used at the level of the individual entering treatment. CGS strategies, though still under development, remain a promising approach towards this end. This study adds to a growing body of literature demonstrating the potential for the use of genetics to inform psychosocial addiction treatment approaches (e.g., Feldstein Ewing et al., 2009). In terms of clinical utility, this study could be viewed as the first of many steps toward the ultimate goal of patient-treatment matching. Clearly, much more work needs to be conducted prior to even considering any practical application. In the future, the use of a systems-biology framework that identifies additional genes related to serotonin and/or relevant neuroanatomical regions could further increase the value of a CGS approach in predicting treatment outcomes. Although the current study is too preliminary to be useful for on-the-ground clinicians, the long-term goal of this program of research is to match participants to the most efficacious treatment for their individual needs, potentially increasing our effectiveness in treating and reducing drinking and violent behavior.
There are numerous limitations to this study. Given the small sample size and the preliminary nature of our data, we did not adjust our alpha level. Also, we lacked collateral reports of men’s drinking and IPV from partners, and we did not collect DNA from men at the onset of the RCT. Thus, we were unable to locate some RCT participants. Although men who provided DNA did not differ from men who did not provide DNA on study variables, the work would have been improved by collecting DNA from men at baseline. Data from the study are relatively old, as study enrollment began in 2003. The CGS score assumed equal weights for both candidate genes, as data on the relative weighting of genetic predictors of IPV and drinking were not available. Although combining genes in this study is an improvement over a single polymorphism approach, future work would be improved by adding other genes in biologically relevant pathways to the CGS. The CGS approach used in this study assumes additive effects and does not allow for epistasis. The utility of such a score is further challenged when it is examined in interactions with the environment. It is possible that individual variants that comprise such a score may interact differently with treatment condition to predict outcomes; however, post hoc analyses indicated that the two polymorphisms contained in our CGS did not interact differently with treatment. Further work needs to be done to consider the possible role of gene-environment correlation in CGS approaches. IPV was assessed in the same relationship dyad throughout the study; future work could examine genetic predictors of IPV across time and new relationships.
The primitive CGS used in our study combines two relatively common polymorphisms into a risk score. The utility of the CGS strategy depends upon the frequency of the levels of the CGS. A CGS comprised of rare variants might be highly statistically predictive but would be infrequent enough to be of limited public health impact, whereas a CGS comprised of common variants might be more relevant in the general population.
Public Significance Statement.
This preliminary study demonstrated that a cumulative genetic risk score predicted intimate partner violence and alcohol use treatment outcomes in a randomized controlled trial of men attending batterer intervention programs. Men with higher cumulative genetic risk scores had better outcomes when they received a brief alcohol intervention relative to men who did not receive the brief alcohol intervention. This research highlights the potential importance of examining genetic factors related to intimate partner violence treatment outcomes.
Acknowledgments
This work was supported by grants from the National Institute on Alcohol Abuse and Alcoholism (R01AA014193 and K24AA019707, Stuart, Principal Investigator) and shared equipment grants (1S10RR023457-01A1) from the National Center for Research Resources and the Department of Veteran Affairs (McGeary, Principal Investigator).
Footnotes
Clinical Trial Registration: ClinicalTrials.gov; registration number NCT00539955; http://clinicaltrials.gov/ct2/show/NCT00539955
The content is solely the responsibility of the authors and does not necessarily represent the official views of the Department of Veterans Affairs, NIAAA, or the National Institutes of Health.
References
- Aiken LS, West SG. Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA: Sage Publications; 1991. [Google Scholar]
- Babor TF, Higgins-Biddle JC, Saunders JG, Monteiro MG. The Alcohol Use Disorders Identification Test: Guidelines for use in primary care. 2. World Health Organization; Geneva, Switzerland: 2001. [Google Scholar]
- Beevers CG, Ellis AJ, Wells TT, McGeary JE. Serotonin transporter gene promoter region polymorphism and selective processing of emotional images. Biological Psychology. 2009;83(3):260–265. doi: 10.1016/j.biopsycho.2009.08.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Buckholtz JW, Meyer-Lindenberg A. MAOA and the neurogenetic architecture of human aggression. Trends in Neurosciences. 2008;31(3):120–129. doi: 10.1016/j.tins.2007.12.006. [DOI] [PubMed] [Google Scholar]
- Contini V, Marques FZC, Garcia CED, Hutz MH, Bau CHD. MAOA-uVNTR polymorphism in a Brazilian sample: Further support for the association with impulsive behaviors and alcohol dependence. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics. 2006;141B(3):305–308. doi: 10.1002/ajmg.b.30290. [DOI] [PubMed] [Google Scholar]
- Devlin B, Roeder K. Genomic control for association studies. Biometrics. 1999;55(4):997–1004. doi: 10.1111/j.0006-341X.1999.00997.x. [DOI] [PubMed] [Google Scholar]
- Engels WR. Exact tests for Hardy-Weinberg proportions. Genetics. 2009;183:1431–41. doi: 10.1534/genetics.109.108977. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Falconer DS, Mackay TF. Quantitative Genetics. 4. Longman Scientific Publishing; 1996. [Google Scholar]
- Feder L, Wilson D. A meta-analytic review of court-mandated batterer intervention programs: Can courts affect abusers’ behavior? Journal of Experimental Criminology. 2005;1:239–262. [Google Scholar]
- Feldstein Ewing SW, LaChance HA, Bryan A, Hutchison KE. Do genetic and individual risk factors moderate the efficacy of motivational enhancement therapy? Drinking outcomes with an emerging adult sample. Addiction Biology. 2009;14:356–365. doi: 10.1111/j.1369-1600.2009.00149.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hines DA, Saudino KJ. Genetic and environmental influences on intimate partner aggression: A preliminary study. Violence and Victims. 2004;19(6):701–718. doi: 10.1891/vivi.19.6.701.66341. [DOI] [PubMed] [Google Scholar]
- Hu X, Oroszi G, Chun J, Smith TL, Goldman D, Schuckit MA. An expanded evaluation of the relationship of four alleles to the level of response to alcohol and the alcoholism risk. Alcoholism, Clinical and Experimental Research. 2005;29(1):8–16. doi: 10.1097/01.alc.0000150008.68473.62. [DOI] [PubMed] [Google Scholar]
- Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73(1):13–22. [Google Scholar]
- McDermott R, Tingley D, Cowden J, Frazzetto G, Johnson DDP. Monoamine oxidase A gene (MAOA) predicts behavioral aggression following provocation. Proceedings of the National Academy of Sciences of the United States of America. 2009;106(7):2118–2123. doi: 10.1073/pnas.0808376106. [DOI] [PMC free article] [PubMed] [Google Scholar]
- McGeary JE, Knopik VS, Hayes JE, Palmer RH, Monti PM, Kalman D. Predictors of relapse in a bupropion trial for smoking cessation in recently-abstinent alcoholics: Preliminary results using an aggregate genetic risk score. Substance Abuse: Research and Treatment. 2012;6:107–114. doi: 10.4137/SART.S8866. [DOI] [PMC free article] [PubMed] [Google Scholar]
- McHugh RK, Hofmann SG, Asnaani A, Sawyer AT, Otto MW. The serotonin transporter gene and risk for alcohol dependence: a meta-analytic review. Drug and Alcohol Dependence. 2010;108(1–2):1–6. doi: 10.1016/j.drugalcdep.2009.11.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Moyers TB, Martin T, Manuel JK, Miller WR, Ernst D. Revised Global Scales: Motivational Interviewing Treatment Integrity. 2010;3.1.1:MITI 3.1.1. [Google Scholar]
- NIAAA. The Physician’s Guide to Helping Patients With Alcohol Problems. Washington, DC: National Institutes of Health; 1995. NIH publication Report No.:95-3769. [Google Scholar]
- Pinto LA, Sullivan EL, Rosenbaum A, Wyngarden N, Umhau JC, Miller MW, Taft CT. Biological correlates of intimate partner violence perpetration. Aggression and Violent Behavior. 2010;15(5):387–398. doi: 10.1016/j.avb.2010.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Popova NK. From genes to aggressive behavior: The role of serotonergic system. BioEssays: News & Reviews in Molecular, Cellular & Developmental Biology. 2006;28:495–503. doi: 10.1002/bies.20412. [DOI] [PubMed] [Google Scholar]
- Retz W, Retz-Junginger P, Supprian T, Thome J, Rösler M. Association of serotonin transporter promoter gene polymorphism with violence: Relation with personality disorders, impulsivity, and childhood ADHD psychopathology. Behavioral Science & the Law. 2004;22(3):415–425. doi: 10.1002/bsl.589. [DOI] [PubMed] [Google Scholar]
- Saito T, Lachman HM, Diaz L, Hallikainen T, Kauhanen J, Salonen JT, … Tiihonen J. Analysis of monoamine oxidase A (MAOA) promoter polymorphism in Finnish male alcoholics. Psychiatry Research. 2002;109(2):113–119. doi: 10.1016/S0165-1781(02)00013-6. [DOI] [PubMed] [Google Scholar]
- SAS Institute Inc. SAS/STAT software: Changes and enhancements through release 6.12. Cary: SAS Institute; 1997. [Google Scholar]
- Sobell LC, Sobell MB. Alcohol consumption measures. In: Allen JP, Wilson VB, editors. Assessing alcohol problems: A guide for clinicians and researchers. 2. Bethesda: National Institute on Alcohol Abuse & Alcoholism; 2003. pp. 75–99. [Google Scholar]
- Straus MA, Hamby SL, Warren WL. The Conflict Tactics Scales Handbook. Western Psychological Services; Los Angeles, CA: 2003. [Google Scholar]
- Stuart GL, McGeary J, Shorey RC, Knopik V, Beaucage K, Temple JR. Genetic associations with intimate partner violence in a sample of hazardous drinking men in batterer intervention programs. Violence Against Women. 2014;20:385–400. doi: 10.1177/1077801214528587. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Stuart GL, Shorey RC, Moore TM, Ramsey SE, Kahler CW, O’Farrell TJ, Strong DR, Temple JR, Monti PM. Randomized clinical trial examining the incremental efficacy of a 90-minute motivational alcohol intervention as an adjunct to standard batterer intervention for men. Addiction. 2013;108:1376–1384. doi: 10.1111/add.12142. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Widom CS, Brzustowicz LM. MAOA and the “cycle of violence:” Childhood abuse and neglect, MAOA genotype, and risk for violent and antisocial behavior. Biological Psychiatry. 2006;60(7):684–689. doi: 10.1016/j.biopsych.2006.03.039. [DOI] [PubMed] [Google Scholar]
- Zalsman G, Huang Y, Oquendo MA, Burke AK, Hu X, Brent DA, … Mann JJ. Association of a triallelic serotonin transporter gene promoter region (5-HTTLPR) polymorphism with stressful life events and severity of depression. The American Journal of Psychiatry. 2006;163(9):1588–1593. doi: 10.1176/appi.ajp.163.9.1588. [DOI] [PubMed] [Google Scholar]