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).

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.

Introduction: Lung cancer survivors are at high risk of developing a second primary lung cancer (SPLC). However, SPLC risk factors have not been established and the impact of tobacco smoking remains controversial. We examined the risk factors for SPLC across multiple epidemiologic cohorts and evaluated the impact of smoking cessation on reducing SPLC risk.Methods: We analyzed data from 7059 participants in the Multiethnic Cohort (MEC) diagnosed with an initial primary lung cancer (IPLC) between 1993 and 2017. Cause-specific proportional hazards models estimated SPLC risk. We conducted validation studies using the Prostate, Lung, *Corresponding author. Disclosure: Dr. Kurian reports receiving research funding to the institution from Myriad Genetics outside of the submitted work. Dr. Wakelee reports receiving personal consulting fees from Janssen, Daiichi Sankyo, Helsinn, Mirati, AstraZeneca, and Blueprint and grants to institution for clinical trial conduct from ACEA Biosciences, Arrys

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Background With advancing therapeutics, lung cancer (LC) survivors are rapidly increasing in number. While mounting evidence suggests LC survivors have high risk of second primary lung cancer (SPLC), there is no validated prediction tool available for clinical use to identify high-risk LC survivors for SPLC. Methods Using data from 6,325 ever-smokers in the Multiethnic Cohort (MEC) diagnosed with initial primary lung cancer (IPLC) in 1993–2017, we developed a prediction model for 10-year SPLC risk after IPLC diagnosis using cause-specific Cox regression. We evaluated the model’s clinical utility using decision curve analysis and externally validated it using two population-based data, PLCO and NLST, that included 2,963 and 2,844 IPLC (101 and 93 SPLC cases), respectively. Results Over 14,063 person-years, 145 (2.3%) developed SPLC in MEC. Our prediction model demonstrated a high predictive accuracy (Brier score = 2.9, 95% confidence interval [CI] = 2.4–3.3) and discrimination (AUC = 81.9%, 95% CI = 78.2%–85.5%) based on bootstrap validation in MEC. Stratification by the estimated risk quartiles showed that the observed SPLC incidence was statistically significantly higher in the 4th versus 1st quartile (9.5% versus 0.2%; P &lt; .001). Decision curve analysis indicated that in a wide range of 10-year risk thresholds from 1% to 20%, the model yielded a larger net-benefit versus hypothetical all-screening or no-screening scenarios. External validation using PLCO and NLST showed an AUC of 78.8% (95% CI = 74.6%–82.9%) and 72.7% (95% CI = 67.7%–77.7%), respectively. Conclusions We developed and validated a SPLC prediction model based on large population-based cohorts. The proposed prediction tool can help identify high-risk LC patients for SPLC and can be incorporated into clinical decision-making for SPLC surveillance and screening.