Associating broad and clinically defined polygenic scores for depression with depression-related phenotypes.

Twin studies indicate that 30-40% of the disease liability for depression can be attributed to genetic differences. Here, we assess the explanatory ability of polygenic scores (PGS) based on broad- (PGSBD) and clinical- (PGSMDD) depression summary statistics from the UK Biobank in an independent sample of adults (N = 210; 100% European Ancestry) who were extensively phenotyped for depression and related neurocognitive traits (e.g., rumination, emotion regulation, anhedonia, and resting frontal alpha asymmetry). The UK Biobank-derived PGSBD had small associations with MDD, depression severity, anhedonia, cognitive reappraisal, brooding, and suicidal ideation but only the association with suicidal ideation remained statistically significant after correcting for multiple comparisons. Similarly small associations were observed for the PGSMDD but none remained significant after correcting for multiple comparisons. These findings provide important initial guidance about the expected effect sizes between current UKB PGSs for depression and depression-related neurocognitive phenotypes.

Multi-Polygenic Analysis of Nicotine Dependence in Individuals of European Ancestry.

INTRODUCTION: Heritability estimates of nicotine dependence (ND) range from 40% to 70%, but discovery GWAS of ND are underpowered and have limited predictive utility. In this work, we leverage genetically correlated traits and diseases to increase the accuracy of polygenic risk prediction. METHODS: We employed a multi-trait model using summary statistic-based best linear unbiased predictors (SBLUP) of genetic correlates of DSM-IV diagnosis of ND in 6394 individuals of European Ancestry (prevalence = 45.3%, %female = 46.8%, µâ€Šage = 40.08 [s.d. = 10.43]) and 3061 individuals from a nationally-representative sample with Fagerström Test for Nicotine Dependence symptom count (FTND; 51.32% female, mean age = 28.9 [s.d. = 1.70]). Polygenic predictors were derived from GWASs known to be phenotypically and genetically correlated with ND (i.e., Cigarettes per Day [CPD], the Alcohol Use Disorders Identification Test [AUDIT-Consumption and AUDIT-Problems], Neuroticism, Depression, Schizophrenia, Educational Attainment, Body Mass Index [BMI], and Self-Perceived Risk-Taking); including Height as a negative control. Analyses controlled for age, gender, study site, and the first 10 ancestral principal components. RESULTS: The multi-trait model accounted for 3.6% of the total trait variance in DSM-IV ND. Educational Attainment (ß = -0.125; 95% CI: [-0.149,-0.101]), CPD (0.071 [0.047,0.095]), and Self-Perceived Risk-Taking (0.051 [0.026,0.075]) were the most robust predictors. PGS effects on FTND were limited. CONCLUSIONS: Risk for ND is not only polygenic, but also pleiotropic. Polygenic effects on ND that are accessible by these traits are limited in size and act additively to explain risk. IMPLICATIONS: These findings enhance our understanding of inherited genetic factors for nicotine dependence. The data show that genome-wide association study (GWAS) findings across pre- and comorbid conditions of smoking are differentially associated with nicotine dependence and that when combined explain significantly more trait variance. These findings underscore the utility of multivariate approaches to understand the validity of polygenic scores for nicotine dependence, especially as the power of GWAS of broadly-defined smoking behaviors increases. Realizing the potential of GWAS to inform complex smoking behaviors will require similar theory-driven models that reflect the myriad of mechanisms that drive individual differences.