Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a devastating inherited disorder characterized by episodic syncope and/or sudden cardiac arrest during exercise or acute emotion in individuals without structural cardiac abnormalities. Although rare, CPVT is suspected to cause a substantial part of sudden cardiac deaths in young individuals. Mutations in RYR2, encoding the cardiac sarcoplasmic calcium channel, have been identified as causative in approximately half of all dominantly inherited CPVT cases. Applying a genome-wide linkage analysis in a large Swedish family with a severe dominantly inherited form of CPVT-like arrhythmias, we mapped the disease locus to chromosome 14q31-32. Sequencing CALM1 encoding calmodulin revealed a heterozygous missense mutation (c.161A>T [p.Asn53Ile]) segregating with the disease. A second, de novo, missense mutation (c.293A>G [p.Asn97Ser]) was subsequently identified in an individual of Iraqi origin; this individual was diagnosed with CPVT from a screening of 61 arrhythmia samples with no identified RYR2 mutations. Both CALM1 substitutions demonstrated compromised calcium binding, and p.Asn97Ser displayed an aberrant interaction with the RYR2 calmodulin-binding-domain peptide at low calcium concentrations. We conclude that calmodulin mutations can cause severe cardiac arrhythmia and that the calmodulin genes are candidates for genetic screening of individual cases and families with idiopathic ventricular tachycardia and unexplained sudden cardiac death.

Genome-wide association studies (GWASs) have revolutionized human genetics, allowing researchers to identify thousands of disease-related genes and possible drug targets. However, case-control status does not account for the fact that not all controls may have lived through their period of risk for the disorder of interest. This can be quantified by examining the age-of-onset distribution and the age of the controls or the age of onset for cases. The age-of-onset distribution may also depend on information such as sex and birth year. In addition, family history is not routinely included in the assessment of control status. Here, we present LT-FH++, an extension of the liability threshold model conditioned on family history (LT-FH), which jointly accounts for age of onset and sex as well as family history. Using simulations, we show that, when family history and the age-of-onset distribution are available, the proposed approach yields statistically significant power gains over LT-FH and large power gains over genome-wide association study by proxy (GWAX). We applied our method to four psychiatric disorders available in the iPSYCH data and to mortality in the UK Biobank and found 20 genome-wide significant associations with LT-FH++, compared to ten for LT-FH and eight for a standard case-control GWAS. As more genetic data with linked electronic health records become available to researchers, we expect methods that account for additional health information, such as LT-FH++, to become even more beneficial.

The accuracy of polygenic risk scores (PRSs) to predict complex diseases increases with the training sample size. PRSs are generally derived based on summary statistics from large meta-analyses of multiple genome-wide association studies (GWASs). However, it is now common for researchers to have access to large individual-level data as well, such as the UK Biobank data. To the best of our knowledge, it has not yet been explored how best to combine both types of data (summary statistics and individual-level data) to optimize polygenic prediction. The most widely used approach to combine data is the meta-analysis of GWAS summary statistics (meta-GWAS), but we show that it does not always provide the most accurate PRS. Through simulations and using 12 real case-control and quantitative traits from both iPSYCH and UK Biobank along with external GWAS summary statistics, we compare meta-GWAS with two alternative data-combining approaches, stacked clumping and thresholding (SCT) and meta-PRS. We find that, when large individual-level data are available, the linear combination of PRSs (meta-PRS) is both a simple alternative to meta-GWAS and often more accurate.