Summary Personality is influenced by genetic and environmental factors1, and associated with mental health. However, the underlying genetic determinants are largely unknown. We identified six genetic loci, including five novel loci2,3, significantly associated with personality traits in a meta-analysis of genome-wide association studies (N=123,132–260,861). Of these genome-wide significant loci, extraversion was associated with variants in WSCD2 and near PCDH15, and neuroticism with variants on chromosome 8p23.1 and in L3MBTL2. We performed a principal component analysis to extract major dimensions underlying genetic variations among five personality traits and six psychiatric disorders (N=5,422–18,759). The first genetic dimension separated personality traits and psychiatric disorders, except that neuroticism and openness to experience were clustered with the disorders. High genetic correlations were found between extraversion and attention-deficit/hyperactivity disorder (ADHD), and between openness and schizophrenia/bipolar disorder. The second genetic dimension was closely aligned with extraversion-introversion and grouped neuroticism with internalizing psychopathology (e.g., depression/anxiety).

This proof of concept study provides evidence of a link between the gut-microbiome and 3-(4-hydroxyphenyl)lactate that shares gene-effect with hepatic steatosis and fibrosis. (Hepatology 2018).

Introduction Nonalcoholic fatty liver disease (NAFLD) is associated with metabolic risk factors including hypertension and dyslipidemia, and may progress to liver fibrosis. Previous studies have shown that hepatic steatosis and fibrosis are heritable but whether they have a significant shared gene effect is unknown. This study aimed to examine the shared gene effects between hepatic steatosis, fibrosis, and their associations with metabolic risk factors. Methods This is a cross-sectional analysis of a prospective cohort of well-characterized, community-dwelling twins (45 monozygotic, 20 dizygotic twin pairs, 130 total subjects) from Southern California. Hepatic steatosis was assessed with MRI-proton density fat fraction (MRI-PDFF) and hepatic fibrosis was assessed with magnetic resonance elastography (MRE). A standard bivariate twin AE model was used to estimate the proportion of phenotypic variance between two phenotypes accounted for by additive genetic effects (A) and individual-specific environmental effects (E). Genetic correlations (rG) estimated from this model represent the degree to which the genetic determinants of two phenotypes overlap. Results The mean (±SD) age and BMI were 47.1 (±21.9) years and 26.9 (±6.5) kg/m2, respectively. 20% (26/130) of the cohort had hepatic steatosis (MRI-PDFF ≥5%) and 8.2% (10/122) had hepatic fibrosis (MRE ≥3Kpa). Blood pressure (systolic and diastolic), triglycerides, glucose, homeostatic model assessment of insulin resistance (HOMA-IR), insulin, hemoglobin A1c (HbA1c), and low high-density lipoprotein (HDL) had significant shared gene effects with hepatic steatosis. Triglycerides, glucose, HOMA-IR, insulin, HbA1c, and low HDL had significant shared gene effects with hepatic fibrosis. Hepatic steatosis and fibrosis had a highly significant shared gene effect of 0.756 (95% CI: 0.716–1, p<0.0001). Conclusions Genes involved with steatosis pathogenesis may also be involved with fibrosis pathogenesis.

The risk of APOE for Alzheimer's Disease (AD) is modified by age. Beyond APOE, the polygenic architecture may also be heterogeneous across age. We aim to investigate age-related genetic heterogeneity of AD and identify genomic loci with differential effects across age. Stratified genebased genome-wide association studies (GWAS) and polygenic variation analyses were performed in the younger (60-79 years, N = 14,895) and older (≥ 80 years, N = 6,559) age-at-onset groups using Alzheimer's Disease Genetics Consortium data. We showed a moderate genetic correlation (r g = 0.64) between the two age groups, supporting genetic heterogeneity. Heritability explained by variants on chromosome 19 (harboring APOE) was significantly larger in younger than in older onset group (P < 0.05). APOE region, BIN1, OR2S2, MS4A4E and PICALM were identified at the gene-based genome-wide significance (P < 2.73×10 −6) with larger effects at younger age (except MS4A4E). For the novel gene OR2S2, we further performed leave-one-out analyses, which showed consistent effects across subsamples. Our results suggest using genetically more homogeneous individuals may help detect additional susceptible loci.