Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer’s disease1,2 and is reduced in schizophrenia3, major depression4 and mesial temporal lobe epilepsy5. Whereas many brain imaging phenotypes are highly heritable6,7, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10−16) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10−12). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10−7).

Severe intellectual disability (IQ G50) affects about 0.5% of the population in Western countries and carries a high health burden. In developed countries, most severe forms of intellectual disability are thought to have a genetic cause, but the cause is still unknown in 55% to 60% of patients. A diagnosis and understanding of a genetic cause may offer information on the prognosis, preclude further unnecessary invasive testing, and lead to appropriate access to medical and supportive care. De novo point mutations in more than 1000 different genes may cause intellectual disability. This study

The sensitivity of physiological measures to mental workload was investigated in a flight simulator. Twelve pilots had to fly through a tunnel with varying levels of difficulty. Additionally, they had to perform a memory task with four levels of difficulty. The easiest memory task was combined with the easiest tunnel task and the most difficult memory task with the most difficult tunnel task. Between the tunnel tasks, subjects had to fly a pursuit task in which a target jet had to be followed. Rest periods before and after the experiment were used as a baseline for the physiological measures. Mental workload was measured with heart period, continuous blood pressure, respiration and eye blinks. Several respiratory parameters, heart rate variability, blood pressure variability and the gain between systolic blood pressure and heart period (modulus) were scored. All measures showed differences between rest and flight, and between the pursuit and the tunnel task. Only heart period was sensitive to difficulty levels in the tunnel task. Heart rate variability increased when respiratory activity around 0.10 Hz increased, which occurred often. The modulus was hardly influenced by respiration and therefore appears to be a better measure than heart rate variability. Among the respiratory parameters, the duration of a respiratory cycle was the most sensitive to changes in workload. The time in between two successive eye blinks (blink interval) increased and the blink duration decreased as more visual information had to be processed. Increasing the difficulty of the memory task led to a decrement in blink interval, probably caused by subvocal activity during rehearsal of target letters. The data show that physiological measures are sensitive to mental effort, whereas rating scales are sensitive to both mental effort and task difficulty.

The growing availability of human genetic variation has given rise to novel methods of measuring genetic tolerance that better interpret variants of unknown significance. We recently developed a concept based on protein domain homology in the human genome to improve variant interpretation. For this purpose, we mapped population variation from the Exome Aggregation Consortium (ExAC) and pathogenic mutations from the Human Gene Mutation Database (HGMD) onto Pfam protein domains. The aggregation of these variation data across homologous domains into meta‐domains allowed us to generate amino acid resolution of genetic intolerance profiles for human protein domains. Here, we developed MetaDome, a fast and easy‐to‐use web server that visualizes meta‐domain information and gene‐wide profiles of genetic tolerance. We updated the underlying data of MetaDome to contain information from 56,319 human transcripts, 71,419 protein domains, 12,164,292 genetic variants from gnomAD, and 34,076 pathogenic mutations from ClinVar. MetaDome allows researchers to easily investigate their variants of interest for the presence or absence of variation at corresponding positions within homologous domains. We illustrate the added value of MetaDome by an example that highlights how it may help in the interpretation of variants of unknown significance. The MetaDome web server is freely accessible at https://stuart.radboudumc.nl/metadome.