ABSTRACT
RT-PCR testing data provides opportunities to explore regional and individual determinants of test positivity and surveillance infrastructure. Using Generalized Additive Models, we explored 222,515 tests of a random sample of individuals with COVID-19 compatible symptoms in the Brazilian state of Bahia during 2020. We found that age and male gender were the most significant determinants of test positivity. There was evidence of an unequal impact among socio-demographic strata, with higher positivity among those living in areas with low education levels during the first epidemic wave, followed by those living in areas with higher education levels in the second wave. Our estimated probability of testing positive after symptom onset corroborates previous reports that the probability decreases with time, more than halving by about two weeks and converging to zero by three weeks. Test positivity rates generally followed state-level reported cases, and while a single laboratory performed ~90% of tests covering ~99% of the state's area, test turn-around time generally remained below four days. This testing effort is a testimony to the Bahian surveillance capacity during public health emergencies, as previously witnessed during the recent Zika and Yellow Fever outbreaks.
Subject(s)
COVID-19 , Zika Virus Infection , Zika Virus , Brazil/epidemiology , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19 Testing , Clinical Laboratory Techniques , Delivery of Health Care , Humans , Male , Reverse Transcriptase Polymerase Chain Reaction , SARS-CoV-2/geneticsABSTRACT
BACKGROUND: Our goal was to identify genetic risk factors for cutaneous leishmaniasis (CL) caused by Leishmania braziliensis. METHODS: Genotyping 2066 CL cases and 2046 controls using Illumina HumanCoreExomeBeadChips provided data for 4 498 586 imputed single-nucleotide variants (SNVs). A genome-wide association study (GWAS) using linear mixed models took account of genetic diversity/ethnicity/admixture. Post-GWAS positional, expression quantitative trait locus (eQTL) and chromatin interaction mapping was performed in Functional Mapping and Annotation (FUMA). Transcriptional data were compared between lesions and normal skin, and cytokines measured using flow cytometry and Bioplex assay. RESULTS: Positional mapping identified 32 genomic loci associated with CL, none achieving genome-wide significance (P < 5 × 10-8). Lead SNVs at 23 loci occurred at protein coding or noncoding RNA genes, 15 with eQTLs for functionally relevant cells/tissues and/or showing differential expression in lesions. Of these, the 6 most plausible genetic risk loci were SERPINB10 (Pimputed_1000G = 2.67 × 10-6), CRLF3 (Pimputed_1000G = 5.12 × 10-6), STX7 (Pimputed_1000G = 6.06 × 10-6), KRT80 (Pimputed_1000G = 6.58 × 10-6), LAMP3 (Pimputed_1000G = 6.54 × 10-6), and IFNG-AS1 (Pimputed_1000G = 1.32 × 10-5). LAMP3 (Padjusted = 9.25 × 10-12; +6-fold), STX7 (Padjusted = 7.62 × 10-3; +1.3-fold), and CRLF3 (Padjusted = 9.19 × 10-9; +1.97-fold) were expressed more highly in CL biopsies compared to normal skin; KRT80 (Padjusted = 3.07 × 10-8; -3-fold) was lower. Multiple cis-eQTLs across SERPINB10 mapped to chromatin interaction regions of transcriptional/enhancer activity in neutrophils, monocytes, B cells, and hematopoietic stem cells. Those at IFNG-AS1 mapped to transcriptional/enhancer regions in T, natural killer, and B cells. The percentage of peripheral blood CD3+ T cells making antigen-specific interferon-γ differed significantly by IFNG-AS1 genotype. CONCLUSIONS: This first GWAS for CL identified multiple genetic risk loci including a novel lead to understanding CL pathogenesis through regulation of interferon-γ by IFNG antisense RNA 1.
