Persistent insomnia is among the most frequent complaints in general practice. To identify genetic factors for insomnia complaints, we performed a genome-wide association study (GWAS) and a genome-wide gene-association study (GWGAS) in 113,006 individuals. We identify three loci and seven genes of which one locus and five genes are supported by joint analysis with an independent sample (n=7,565). Our top association (MEIS1, P<5×10-8) has previously been implicated in Restless Legs Syndrome (RLS). Additional analyses favor the hypothesis that MEIS1 shows pleiotropy for insomnia and RLS, and that the observed association with insomnia complaints cannot be explained only by the presence of an RLS subgroup. Sex-specific analyses suggested different genetic architectures across sexes in addition to common genetic factors. We show substantial positive genetic overlap with internalizing and metabolic traits and negative overlap with subjective well-being and educational attainment. These findings provide novel insight into the genetic architecture of insomnia.

Meta-analyses and systematic reviews have reported surprisingly few consistent insomnia-characteristics with respect to cognitions, mood, traits, history of life events and family history. One interpretation of this limited consistency is that different subtypes of insomnia exist, each with its own specific multivariate profile of characteristics. Because previously unrecognized subtypes will be differentially represented in individual studies and dilute effect sizes of subtype-dependent characteristics of importance, they are unlikely to be reported consistently in individual studies, let alone in meta-analyses. This review therefore aims to complement meta-analyses by listing previously reported psychometric characteristics of insomnia, irrespective of the degree of consistency over studies. The review clearly indicates that characteristics of insomnia may not be limited to sleep. Reports suggest that at least some individuals with insomnia may deviate from people without sleep complaints with respect to demographics, mental and physical health, childhood trauma, life events, fatigue, sleepiness, hyperarousal, hyperactivity, other sleep disorders, lifetime sleep history, chronotype, depression, anxiety, mood, quality of life, personality, happiness, worry, rumination, self-consciousness, sensitivity, dysfunctional beliefs, self-conscious emotion regulation, coping, nocturnal mentation, wake resting-state mentation, physical activity, food intake, temperature perception and hedonic evaluation. The value of this list of characteristics is that 1) internet has now made it feasible to asses them all in a large sample of people suffering from insomnia, and 2) statistical methods like latent class analysis and community detection can utilize them for a truly bottom-up data-driven search for subtypes. The supplement to this review provides a blueprint of this multivariate approach as implemented in the Sleep registry platform (www.sleepregistry.nl), that allows for bottom-up subtyping and fosters cross-cultural comparison and worldwide collaboration on insomnia subtype finding - and beyond.

The algorithm allows for continuity of outcome parameters in ongoing actigraphy studies that consider switching to MEMS-accelerometers. Its implementation makes backward compatibility feasible, while collecting raw data that, in time, could provide better sleep estimates and promote cross-study data pooling.