Polygenic risk score (PRS) analyses have become an integral part of biomedical research, exploited to gain insights into shared aetiology among traits, to control for genomic profile in experimental studies, and to strengthen causal inference, among a range of applications. Substantial efforts are now devoted to biobank projects to collect large genetic and phenotypic data, providing unprecedented opportunity for genetic discovery and applications. To process the large-scale data provided by such biobank resources, highly efficient and scalable methods and software are required. Here we introduce PRSice-2, an efficient and scalable software program for automating and simplifying PRS analyses on large-scale data. PRSice-2 handles both genotyped and imputed data, provides empirical association P-values free from inflation due to overfitting, supports different inheritance models, and can evaluate multiple continuous and binary target traits simultaneously. We demonstrate that PRSice-2 is dramatically faster and more memory-efficient than PRSice-1 and alternative PRS software, LDpred and lassosum, while having comparable predictive power. PRSice-2's combination of efficiency and power will be increasingly important as data sizes grow and as the applications of PRS become more sophisticated, e.g., when incorporated into high-dimensional or gene set–based analyses. PRSice-2 is written in C++, with an R script for plotting, and is freely available for download from http://PRSice.info.

Polygenic scores (PGS) summarize the genetic contribution of a person's genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating PGS have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can use LD information available elsewhere to supplement such analyses. To answer this question, we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and P‐value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred.

Anxiety disorders are common, complex psychiatric disorders with twin heritabilities of 30–60%. We conducted a genome-wide association study of Lifetime Anxiety Disorder (n_case = 25 453, n_control = 58 113) and an additional analysis of Current Anxiety Symptoms (n_case = 19 012, n_control = 58 113). The liability scale common variant heritability estimate for Lifetime Anxiety Disorder was 26%, and for Current Anxiety Symptoms was 31%. Five novel genome-wide significant loci were identified including an intergenic region on chromosome 9 that has previously been associated with neuroticism, and a locus overlapping the BDNF receptor gene, NTRK2. Anxiety showed significant positive genetic correlations with depression and insomnia as well as coronary artery disease, mirroring findings from epidemiological studies. We conclude that common genetic variation accounts for a substantive proportion of the genetic architecture underlying anxiety.

In this paper, we report a novel heterozygous mutation of A285V codon conversion on exon 4 of the desmin (DES), using whole exome sequencing (WES) in an isolated proband with documented dilated cardiomyopathy (DCM). This mutation is predicted to cause three-dimensional structure changes of DES. Immunohistological and electron microscopy studies demonstrated diffuse abnormal DES aggregations in DCM-induced-pluripotent stem cell (iPSC)-derived cardiomyocytes, and control-iPSC-derived cardiomyocytes transduced with A285V-DES. DCM-iPSC-derived cardiomyocytes also exhibited functional abnormalities in vitro. This is the first demonstration that patient-specific iPSC-derived cardiomyocytes can be used to provide histological and functional confirmation of a suspected genetic basis for DCM identified by WES.