SummaryTo understand the genetic variation underlying atrial fibrillation (AF), the most common cardiac arrhythmia, we performed a genome-wide association study (GWAS) of > 1 million people, including 60,620 AF cases and 970,216 controls. We identified 163 independent risk variants at 111 loci and prioritized 165 candidate genes likely to be involved in AF. Many of the identified risk variants fall near genes where more deleterious mutations have been reported to cause serious heart defects in humans or mice (MYH6, NKX2-5, PITX2, TBC1D32, TBX5),1,2 or near genes important for striated muscle function and integrity (e.g. MYH7, PKP2, SSPN, SGCA). Experiments in rabbits with heart failure and left atrial dilation identified a heterogeneous distributed molecular switch from MYH6 to MYH7 in the left atrium, which resulted in contractile and functional heterogeneity and may predispose to initiation and maintenance of atrial arrhythmia.

To facilitate scientific collaboration on polygenic risk scores (PRS) research, we created an extensive PRS online repository for 49 common cancer traits integrating freely available genome-wide association studies (GWAS) summary statistics from three sources: published GWAS, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWAS. Our framework condenses these summary statistics into PRS using various approaches such as linkage disequilibrium pruning / p-value thresholding (fixed or dataadaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRS in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB).For each PRS construct, we provide measures on predictive performance, calibration, and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRS. We expect this integrated platform to accelerate PRS-related cancer research. Leukemia (204) 545 (1.42%) 1,665 (0.41%) Cancer of brain (191.11) 483 (1.26%) 525 (0.13%) Cervical cancer (180.1) 430 (1.12%) 272 (0.12%) * ICD9/10-CM codes S.D. standard deviation

Clinicians have historically used family history and other risk prediction algorithms to guide patient care and preventive treatment such as statin therapeutics for coronary artery disease. As polygenic scores move towards clinical use, we have begun to consider the interplay of these scores with other predictors for optimal second generation risk prediction. Here, we assess the use of family history and polygenic scores as independent predictors of coronary artery disease and type 2 diabetes. We highlight considerations for use of family history as a predictor of these two diseases after evaluating their effectiveness in the Trøndelag Health Study and the UK Biobank. From these, we advocate for collection of high resolution family history variables in biobanks for future prediction models.