While awaiting the COVID-19 vaccines, researchers have been actively exploring the effectiveness of existing vaccines against the new virus, among which the BCG vaccine (Bacillus Calmette-Guérin) receives the most attention. While many reports suggest a potential role for BCG immunization in ameliorating SARS-CoV-2 infection, these findings remain controversial. With country-level COVID-19 outbreak data from Johns Hopkins University Coronavirus Resource Center, and BCG program data from World Atlas of BCG Policies and Practices and WHO/UNICE, we estimated a dynamic model to investigate the effect of BCG vaccination across time during the pandemic. Our results reconcile these varying reports regarding protection by BCG against COVID-19 in a variety of clinical scenarios and model specifications. We observe a notable protective effect of the BCG vaccine during the early stage of the pandemic. However, we do not see any strong evidence for protection during the later stages. We also see that a higher proportion of vaccinated young population may confer some level of communal protection against the virus in the early pandemic period, even when the proportion of vaccination in the older population is low. Our results highlight that while BCG may offer some protection against COVID-19, we should be cautious in interpreting the estimated effectiveness as it may vary over time and depend on the age structure of the vaccinated population.

Background: Recent Alzheimer’s disease (AD) genetics findings from genome-wide association studies (GWAS) span progressively larger and more diverse populations and outcomes. Currently, there is no up-to-date resource providing harmonized and searchable information on all AD genetic associations found by GWAS, nor linking the reported genetic variants and genes with functional and genomic annotations. Objective: Create an integrated/harmonized, and literature-derived collection of population-specific AD genetic associations. Methods: We developed the Alzheimer’s Disease Variant Portal (ADVP), an extensive collection of associations curated from >200 GWAS publications from Alzheimer’s Disease Genetics Consortium and other consortia. Genetic associations were systematically extracted, harmonized, and annotated from both the genome-wide significant and suggestive loci reported in these publications. To ensure consistent representation of AD genetic findings, all the extracted genetic association information was harmonized across specifically designed publication, variant, and association categories. Results: ADVP V1.0 (February 2021) catalogs 6,990 associations related to disease-risk, expression quantitative traits, endophenotypes, or neuropathology. This extensive harmonization effort led to a catalog containing >900 loci, >1,800 variants, >80 cohorts, and 8 populations. Besides, ADVP provides investigators with a seamless integration of genomic and publicly available functional annotations across multiple databases per harmonized variant and gene records, thus facilitating further understanding and analyses of these genetics findings. Conclusion: ADVP is a valuable resource for investigators to quickly and systematically explore high-confidence AD genetic findings and provides insights into population-specific AD genetic architecture. ADVP is continually maintained and enhanced by NIAGADS and is freely accessible at https://advp.niagads.org.

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To better capture the polygenic architecture of Alzheimer’s disease (AD), we developed a joint genetic score, MetaGRS. We incorporated genetic variants for AD and 24 other traits from two independent cohorts, NACC (n = 3,174, training set) and UPitt (n = 2,053, validation set). One standard deviation increase in the MetaGRS is associated with about 57% increase in the AD risk [hazard ratio (HR) = 1.577, p = 7.17 E-56], showing little difference from the HR for AD GRS alone (HR = 1.579, p = 1.20E-56), suggesting similar utility of both models. We also conducted APOE-stratified analyses to assess the role of the e4 allele on risk prediction. Similar to that of the combined model, our stratified results did not show a considerable improvement of the MetaGRS. Our study showed that the prediction power of the MetaGRS significantly outperformed that of the reference model without any genetic information, but was effectively equivalent to the prediction power of the AD GRS.