Understanding immune responses to severe acute respiratory syndrome coronavirus 2 is crucial to understanding disease pathogenesis and the usefulness of bridge therapies, such as hyperimmune globulin and convalescent human plasma, and to developing vaccines, antivirals, and monoclonal antibodies. A mere 11 months ago, the canvas we call COVID-19 was blank. Scientists around the world have worked collaboratively to fill in this blank canvas. In this Review, we discuss what is currently known about human humoral and cellular immune responses to severe acute respiratory syndrome coronavirus 2 and relate this knowledge to the COVID-19 vaccines currently in phase 3 clinical trials.

Our understanding of the importance of CD4+ T cells in orchestrating immune responses has grown dramatically over the past decade. This lymphocyte family consists of diverse subsets ranging from interferon-gamma (IFN-gamma)-producing T-helper 1 (Th1) cells to transforming growth factor-beta (TGF-beta)-secreting T-regulatory cells, which have opposite roles in modulating immune responses to pathogens, tumor cells, and self-antigens. This review briefly addresses the various T-cell subsets within the CD4+ T-cell family and discusses recent research efforts aimed at elucidating the nature of the 'T-cell help' that has been shown to be essential for optimal immune function. Particular attention is paid to the role of Th cells in tumor immunotherapy. We review some of our own work in the field describing how CD4+ Th cells can enhance anti-tumor cytotoxic T-lymphocyte (CTL) responses by enhancing clonal expansion at the tumor site, preventing activation-induced cell death and functioning as antigen-presenting cells for CTLs to preferentially generate immune memory cells. These unconventional roles for Th lymphocytes, which require direct cell-to-cell communication with CTLs, are clear examples of how versatile these immunoregulatory cells are.

Two recently developed fine-mapping methods, CAVIAR and PAINTOR, demonstrate better performance over other finemapping methods. They also have the advantage of using only the marginal test statistics and the correlation among SNPs. Both methods leverage the fact that the marginal test statistics asymptotically follow a multivariate normal distribution and are likelihood based. However, their relationship with Bayesian fine mapping, such as BIMBAM, is not clear. In this study, we first show that CAVIAR and BIMBAM are actually approximately equivalent to each other. This leads to a fine-mapping method using marginal test statistics in the Bayesian framework, which we call CAVIAR Bayes factor (CAVIARBF). Another advantage of the Bayesian framework is that it can answer both association and fine-mapping questions. We also used simulations to compare CAVIARBF with other methods under different numbers of causal variants. The results showed that both CAVIARBF and BIMBAM have better performance than PAINTOR and other methods. Compared to BIMBAM, CAVIARBF has the advantage of using only marginal test statistics and takes about one-quarter to onefifth of the running time. We applied different methods on two independent cohorts of the same phenotype. Results showed that CAVIARBF, BIMBAM, and PAINTOR selected the same top 3 SNPs; however, CAVIARBF and BIMBAM had better consistency in selecting the top 10 ranked SNPs between the two cohorts. Software is available at https://bitbucket.org/Wenan/caviarbf. KEYWORDS Bayesian fine mapping; marginal test statistics; causal variants U NTIL recently, there have been .2000 genome-wide association studies (GWAS) published with different traits or disease status (Hindorff et al. 2014). Most of them reported only regions of association, represented by SNPs with the lowest P-values in each region. Only a few provide further information of likely underlying causal variants. A noted exception is refinement based on Bayesian methods (Maller et al. 2012). Fine mapping the causal variants from the verified association regions is an important step toward understanding the complex biological mechanisms linking the genetic code to various traits or phenotypes.Fine-mapping methods can be roughly divided into two groups. The first group was developed before the availability of high-density genotype data. These fine-mapping methods assume the causal variants are not genotyped in the data and aim to identify a region as close as possible to the causal variants (Morris et al. 2002; Durrant et al. 2004;Liang and Chiu 2005;Zollner and Pritchard 2005;Minichiello and Durbin 2006;Waldron et al. 2006). Because the causal variants are not observed in the data, these methods usually rely on various strong assumptions to model the relationship of the causal and the observed variants. Examples include models based on the coalescent theory (Morris et al. 2002;Zollner and Pritchard 2005;Minichiello and Durbin 2006) or statistical assumptions about the patterns of linkage disequilibrium (LD) (Liang and ...