Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.
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.
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