Three mutually informative ways to understand the genetic relationships among behavioral disinhibition, alcohol use, drug use, nicotine use/dependence, and their co-occurrence: twin biometry, GCTA, and genome-wide scoring.
- Authors
- Vrieze, Scott I; McGue, Matt; Miller, Michael B; Hicks, Brian M; Iacono, William G
- Year
- 2013
- Journal
- Behavior genetics
- PMID
- 23362009
- DOI
- 10.1007/s10519-013-9584-z
- PMCID
- PMC3579160
Behavioral disinhibition is a trait hypothesized to represent a general vulnerability to the development of substance use disorders. We used a large community-representative sample (N = 7,188) to investigate the genetic and environmental relationships among measures of behavioral disinhibition, Nicotine Use/Dependence, Alcohol Consumption, Alcohol Dependence, and Drug Use. First, using a subsample of twins (N = 2,877), we used standard twin models to estimate the additive genetic, shared environmental, and non-shared environmental contributions to these five traits. Heritabilities ranged from .42 to .58 and shared environmental effects ranged from .12 to .24. Phenotypic correlations among the five traits were largely attributable to shared genetic effects. Second, we used Genome-wide Complex Trait Analysis (GCTA) to estimate as a random effect the aggregate genetic effect attributable to 515,384 common SNPs. The aggregated SNPs explained 10-30 % of the variance in the traits. Third, a genome-wide scoring approach summed the actual SNPs, creating a SNP-based genetic risk score for each individual. After tenfold internal cross-validation, the SNP sumscore correlated with the traits at .03 to .07 (p < .05), indicating small but detectable effects. SNP sumscores generated on one trait correlated at approximately the same magnitude with other traits, indicating detectable pleiotropic effects among these traits. Behavioral disinhibition thus shares genetic etiology with measures of substance use, and this relationship is detectable at the level of measured genomic variation.
Phenotypic correlations and biometric decomposition (leading decimals were removed). Shown here are the phenotypic correlation matrix, as well as the additive genetic, shared environmental, and non-shared environmental component matrices. In parentheses are the 95% maximum likelihood confidence intervals. The component matrices are scaled such that they sum elementwise to produce the full phenotypic matrix. All entries are significant at p < .05. Estimates are based solely on the twins, who have an average age of 17 years. NIC = Nicotine Use/Dependence; CON = Alcohol Consumption; DEP = Alcohol Dependence; DRG = Drug Use; BD = Behavioral Disinhibition.
LLM interpretation
This figure consists of four correlation matrices (Phenotypic, Additive Genetic, Shared Environment, and Non-shared Environment) comparing five traits: Nicotine Use/Dependence (NIC), Alcohol Consumption (CON), Alcohol Dependence (DEP), Drug Use (DRG), and Behavioral Disinhibiton (BD). The matrices use a color gradient to represent correlation strength, with 95% maximum likelihood confidence intervals provided in parentheses for each entry. All entries are noted as significant at p < .05, with the phenotypic matrix showing the strongest positive correlations across all trait pairs.
GCTA results with Biometric Comparison. The GCTA results are provided for each phenotype in a variety of samples. In grey are the additive genetic heritability estimates from the biometric twin analysis (also in Figure 1). In black is the sum of the additive genetic and shared environment estimates from the biometric analysis (also in Figure 1). Unrelated individuals were defined as those having a genetic relatedness estimated by GCTA to be < .025 (more distantly related than third cousins). The samples are: A) All unrelated parents (N = 3542), B) unrelated youths (N = 1784), C) All youths (N = 3336), and D) the full sample (N = 7188). Error bars are 95% confidence intervals. NIC = Nicotine Use/Dependence; CON = Alcohol Consumption; DEP = Alcohol Dependence; DRG = Drug Use; BD = Behavioral Disinhibition.
LLM interpretation
This bar chart compares the proportion of phenotypic variance for five phenotypes (NIC, CON, DEP, DRG, BD) across different sample groups using Genome-Wide Complex Trait Analysis (GCTA) and Biometric Twin Analysis. GCTA results are shown for four sample groups (Unrelated Parents, Unrelated Offspring, All Offspring, and Full Sample), while Biometric Twin Analysis provides additive genetic (grey) and combined additive genetic plus shared environment (black) estimates. Error bars represent 95% confidence intervals, with the highest variance proportions generally observed in the "All Offspring" GCTA group and the combined Biometric Twin Analysis.
Cross-validated Genome-wide Scoring Results. The top panel of three graphs provides the empirical results for the four substance use phenotypes and behavioral disinhibition. Each graph provides the seven p-value thresholds under consideration. The three top graphs only differ in the LD cutoff imposed (1.0, .50, and .05). The bottom row provides results from three kinds of simulated phenotypes. First, a simulated phenotype with a normal distribution of SNP regression coefficients, for each of the 7 p-value thresholds and three different polygenetic scenarios (100,000, 50,000, and 10,000 associated SNPs). Second, the same scenario except with uniformly distributed effects. Both of these simulated phenotypes were simulated such that the SNPs in aggregate accounted for 17% of the variance in the phenotype. Third, a completely random phenotype with no SNP associations. The bold horizontal line in each graph is zero. The dotted line represents a correlation that would be significant at p < .05, conservatively assuming an effective sample size of 5000. NIC = Nicotine Use/Dependence; CON = Alcohol Consumption; DEP = Alcohol Dependence; DRG = Drug Use; BD = Behavioral Disinhibition.
LLM interpretation
This figure consists of six line graphs showing cross-validated correlations across different proportions of SNPs in a score (x-axis). The top row displays empirical results for five phenotypes (BD, DRG, NIC, DEP, CON) under three different LD cutoffs ($r^2 = 1.0, .50, .05$), generally showing an increase in correlation as the proportion of SNPs increases. The bottom row shows simulated results for normally distributed effects, uniformly distributed effects (both across three SNP counts), and a random phenotype, with the latter remaining near zero. A bold horizontal line marks zero, and a dotted horizontal line indicates the significance threshold for $p < .05$.
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