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    Alcohol Metabolizing Polygenic Risk for Alcohol Consumption in European American College Students
    (Alcohol Res Documentation Inc Cent Alcohol Stud Rutgers Univ, 2018) Thomas, Nathaniel S.; Adkins, Amy; Aliev, Fazil; Edwards, Alexis C.; Webb, Bradley T.; Tiarsmith, E. Clare; Kendler, Kenneth S.
    Objective: Evidence suggests that the nature and magnitude of some genetic effects on alcohol use vary by age. We tested for moderation in the effect of an alcohol metabolizing polygenic score by time across the college years. Method: Participants (total n = 2,214) were drawn from three cohorts of undergraduate college students, who were assessed annually for up to 4 years starting in their freshman year. Polygenic risk scores (PRSs) were calculated from genes involved in the metabolism of alcohol, as many of these markers are among the best replicated in association studies examining alcohol use phenotypes. Linear mixed effects models were fit by maximum likelihood to test the main effects of time and the PRS on alcohol consumption, as well as moderation of the PRS effect on alcohol consumption by time. Results: In the main effects model, the fixed effects for time and the PRS were positively associated with alcohol consumption. The interaction term testing moderation of the PRS effect by time reached statistical significance and remained statistically significant after other relevant interaction effects were controlled for. The main effect of the PRS accounted for 0.2% of the variance in alcohol consumption, whereas the interaction of PRS effect and time accounted for 0.05%. Conclusions: Alcohol metabolizing genetic effects on alcohol use appear to be more influential in later years of college than in earlier years. Shifting environmental contexts, such as increased access to alcohol as individuals approach the legal age to purchase alcohol, may account for this association.
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    Polygenic Risk Score Prediction of Alcohol Dependence Symptoms Across Population-Based and Clinically Ascertained Samples
    (Wiley, 2018) Savage, Jeanne E.; Salvatore, Jessica E.; Aliev, Fazil; Edwards, Alexis C.; Hickman, Matthew; Kendler, Kenneth S.; Macleod, John
    BackgroundDespite consistent evidence of the heritability of alcohol use disorders (AUDs), few specific genes with an etiological role have been identified. It is likely that AUDs are highly polygenic; however, the etiological pathways and genetic variants involved may differ between populations. The aim of this study was thus to evaluate whether aggregate genetic risk for AUDs differed between clinically ascertained and population-based epidemiological samples. MethodsFour independent samples were obtained: 2 from unselected birth cohorts (Avon Longitudinal Study of Parents and Children [ALSPAC], N=4,304; FinnTwin12 [FT12], N=1,135) and 2 from families densely affected with AUDs, identified from treatment-seeking patients (Collaborative Study on the Genetics of Alcoholism, N=2,097; Irish Affected Sib Pair Study of Alcohol Dependence, N=706). AUD symptoms were assessed with clinical interviews, and participants of European ancestry were genotyped. Genomewide association was conducted separately in each sample, and the resulting association weights were used to create polygenic risk scores in each of the other samples (12 total discovery-validation pairs), and from meta-analyses within sample type. We then tested how well these aggregate genetic scores predicted AUD outcomes within and across sample types. ResultsPolygenic scores derived from 1 population-based sample (ALSPAC) significantly predicted AUD symptoms in another population-based sample (FT12), but not in either clinically ascertained sample. Trend-level associations (uncorrected p<0.05) were found for polygenic score predictions within sample types but no or negative predictions across sample types. Polygenic scores accounted for 0 to 1% of the variance in AUD symptoms. ConclusionsThough preliminary, these results provide suggestive evidence of differences in the genetic etiology of AUDs based on sample characteristics such as treatment-seeking status, which may index other important clinical or demographic factors that moderate genetic influences. Although the variance accounted for by genomewide polygenic scores remains low, future studies could improve gene identification efforts by amassing very large samples, or reducing genetic heterogeneity by informing analyses with other phenotypic information such as sample characteristics. Multiple complementary approaches may be needed to make progress in gene identification for this complex disorder.