beta_normalised_bmiq

Contains normalised beta values for each CpG which passed QC from 450K array. Each column is a sample and each row is CpG. The first column contains CpG identifiers

Artificial Intelligence for multiple long-term conditions (AIM): A consensus statement from the NIHR AIM consortia

Recent advances in causal machine learning and wider artificial intelligence (AI) methods could provide new insights into the natural histories and potential prevention of clusters of multiple long-term conditions or multimorbidity (MLTC-M). When combined with expertise in clinical practice, applied health research and social science, there is potential to systematically identify and map new clusters of disease, understand the trajectories of patients with these conditions throughout their life course, predict serious adverse outcomes, optimise therapies and consider the influence of wider determinants such as environmental, behavioural and psychosocial factors. The National Institute of Health Research (NIHR) recently funded multidisciplinary consortia to bring together AI specialists, experts in big data and MLTC-M in the first and second waves of this new programme. The so-called AIM consortia of researchers will spearhead the use of artificial intelligence methods and develop insights for the identification and subsequent prevention of MLTC-M. This consensus agreement is aimed at facilitating a community of learning within the AIM consortia, promoting cooperation, transparency and rigour in our approaches while maintaining high methodological standards and consistency in defining and reporting within our research. In bringing together these research collaborations, there is also an opportunity to foster shared learning, synergies and rapidly compare and validate new AI approaches across our respective studies. This step is critical to implementation on the pathway to patient and public benefit.</p

COVID-19 Host Genetics Initiative. A first update on mapping the human genetic architecture of COVID-19

The COVID-19 pandemic continues to pose a major public health threat, especially in countries with low vaccination rates. To better understand the biological underpinnings of SARS-CoV-2 infection and COVID-19 severity, we formed the COVID-19 Host Genetics Initiative1. Here we present a genome-wide association study meta-analysis of up to 125,584 cases and over 2.5 million control individuals across 60 studies from 25 countries, adding 11 genome-wide significant loci compared with those previously identified2. Genes at new loci, including SFTPD, MUC5B and ACE2, reveal compelling insights regarding disease susceptibility and severity.</p