Search

Background: SARS-CoV-2 is continuously evolving with the emergence of variants of interest (VOI) or with variants of concern (VOC). While Variants of High Consequence (VOHC) are well defined, no such variants have been formally documented. Here we propose an integrated strategy and application towards discovering VOHC. Methods: We utilized 7,137 viral sequences collected from COVID-19 cases in Washington State from January 19, 2020 to January 31, 2021, to identify genome-wide viral single nucleotide variants (SNVs). Utilizing a non-parametric regression model, we selected a subset of SNVs that had significant and substantial expansions over the collection period. Further, using unsupervised learning, we identified multiple SNVs forming haplotypes. To evaluate their clinical relevance, we assembled a discovery cohort of COVID-19 cases (388 inpatients and 295 outpatients) to identify SNVs and haplotypes associated with hospitalization status, a proxy for disease severity. A logistic regression model was used to assess associations of SNVs with hospitalization status in the discovery cohort. These results were validated on an independent cohort of 964 genome sequences derived from COVID-19 cases in Washington State from June 1, 2020 to March 31, 2021. Finding: The analysis of the 7,137 sequences led to identification of 107 SNVs that were statistically significant (false positive error rate q-value 0.10). Forty-one SNVs were considered urgent, because their SNV proportions persisted or expanded above 10% in January 2021, the last month of the current investigation period. Correlating with clinical data, eight SNVs were found to significantly associate with inpatient status (p-values

Background: DR4, a common antigen of HLA-DRB1, has multiple subtypes that are strongly associated with risk of type 1 diabetes (T1D); however, some are risk neutral or resistant. The pathobiological mechanism of DR4 subtypes remains to be elucidated. Methods: We used a population-based case-control study of T1D to decipher genetic associations of DR4 subtypes and specific residues with susceptibility to T1D, and a birth cohort of children with periodically measured islet autoantibodies (GADA, IAA or IA-2A) to investigate the same genetic associations with time-to-seroconversions prior to clinical onset of T1D. A novel analytic strategy hierarchically organized the HLA-DRB1 alleles by sequence similarity and identified critical amino acid residues by minimizing local genomic architecture and higher-order interactions. Finding: Three amino acid residues of HLA-DRB1 (β71, β74, β86) were found to be predictive of T1D risk in the population-based study. The “KAG” motif, corresponding to HLA-DRB1*04:01, was most strongly associated with T1D risk ([O]dds [R]atio=3.64, p=3.19x10 -64 ). Three less frequent motifs (“EAV”, OR=2.55, p=0.025; “RAG”, OR=1.93, p=0.043; and “RAV”, OR=1.56, p=0.003) were associated with T1D risk, while two motifs (“REG” and “REV”) were equally protective (OR=0.11, p=4.23x10 -4 ). In an independent birth cohort of HLA-DR3 and HLA-DR4 subjects, those having the “KAG” motif had increased risk for time-to-seroconversion (Hazard Ratio=1.74, p=6.51x10 -14 ). Interpretations: DNA sequence variation in HLA-DRB1 at positions b71, b74, and b86 alters antigen binding site anchor pockets p1 and p4 (and potentially neighboring regions such as anchor pocket p6) in both dramatic (b74 AaE) and subtle (b71 E vs K vs R; b86 G vs V) ways with major implications for antigen binding. The impact of these sequence substitutions is discussed in the context of two HLA-peptide model complexes for experimentally well-documented T1D autoantigens and may account for most of the HLA-DR4 contribution to T1D risk. Funding: National Institute of Diabetes and Digestive and Kidney Diseases and Swedish Child Diabetes Foundation Declaration of Interest: None to declare. Ethical Approval: This study was approved by the Swedish Better Diabetes Diagnosis (BDD) and the Karolinska Institute Ethics Board.