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The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.
Background: The potential anti-cancer effects of mammalian target of rapamycin (mTOR) inhibitors are being intensively studied. To date, however, few randomised clinical trials (RCT) have been performed to demonstrate anti-neoplastic effects in the pure oncology setting, and at present, no oncology endpoint-directed RCT has been reported in the high-malignancy risk population of immunosuppressed transplant recipients. Interestingly, since mTOR inhibitors have both immunosuppressive and anti-cancer effects, they have the potential to simultaneously protect against immunologic graft loss and tumour development. Therefore, we designed a prospective RCT to determine if the mTOR inhibitor sirolimus can improve hepatocellular carcinoma (HCC)-free patient survival in liver transplant (LT) recipients with a pre-transplant diagnosis of HCC. Methods: The study is an open-labelled, randomised, RCT comparing sirolimus-containing versus mTOR-inhibitor-free immunosuppression in patients undergoing LT for HCC. Patients with a histologically confirmed HCC diagnosis are randomised into 2 groups within 4-6 weeks after LT; one arm is maintained on a centre-specific mTOR-inhibitor-free immunosuppressive protocol and the second arm is maintained on a centre-specific mTOR-inhibitor-free immunosuppressive protocol for the first 4-6 weeks, at which time sirolimus is initiated. A 3-year recruitment phase is planned with a 5-year follow-up, testing HCC-free survival as the primary endpoint. Our hypothesis is that sirolimus use in the second arm of the study will improve HCC-free survival. The study is a non-commercial investigator-initiated trial (IIT) sponsored by the University Hospital Regensburg and is endorsed by the European Liver and Intestine Transplant Association; 13 countries within Europe, Canada and Australia are participating. Discussion: If our hypothesis is correct that mTOR inhibition can reduce HCC tumour growth while simultaneously providing immunosuppression to protect the liver allograft from rejection, patients should experience less post-transplant problems with HCC recurrence, and therefore could expect a longer and better quality of life. A positive outcome will likely change the standard of posttransplant immunosuppressive care for LT patients with HCC. (trial registered at www.clinicaltrials.gov: NCT00355862) (EudraCT Number: 2005-005362-36)
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
