Two papers centred around the UK Biobank genetics data are published this week in Nature. The two papers describe the full dataset, which contains genome-wide genetic data, clinical measurements, and health records for around 500,000 individuals, and reveal insights into the brain’s genetic architecture.
The UK Biobank is a resource of genetic and clinical data from around 500,000 UK individuals, aged between 40 and 69 years old when they were recruited, that facilitates research into the genetic basis of health and disease. Participants were recruited between 2006 and 2010 and will continue to be monitored. Among the Biobank’s largest datasets are genotypes and brain scans, which enable investigations into genes that influence brain structure and function.
Jonathan Marchini and colleagues analysed genetic and MRI brain scan data from 8,428 individuals in the Biobank, looking for associations between genetic variants and features identified from the MRI scans, including structural volumes, lesion size, and the connectivity and microstructure of the brain’s white matter. The authors report various genetic associations, including genes involved in the transport and storage of iron, which can be involved in neurodegenerative disorders such as Alzheimer’s disease and Parkinson’s disease. They also find associations with genes that code for proteins implicated in synaptic plasticity and the repair of nerve fibres, or are related to depression, multiple sclerosis, or stroke. Many of the traits identified in the MRI scans are heritable. The findings increase our understanding of how the brain develops and ages, as well as the biological underpinnings of various neurological and psychiatric disorders.
In the accompanying paper, data on all of the around 500,000 individuals in the Biobank are described for the first time, including biological measurements, lifestyle indicators, and imaging data. This resource is available for other researchers to use.
In an associated News & Views, Nancy Cox comments: “The biobank promises to aid the discovery of relationships between genome variation and common human diseases, and to improve our understanding of the mechanisms underlying those associations.”
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