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.

The age-related dysregulation and decline of the immune system—collectively termed “immunosenescence”—has been generally associated with an increased susceptibility to infectious pathogens and poor vaccine responses in older adults. While numerous studies have reported on the clinical outcomes of infected or vaccinated individuals, our understanding of the mechanisms governing the onset of immunosenescence and its effects on adaptive immunity remains incomplete. Age-dependent differences in T and B lymphocyte populations and functions have been well-defined, yet studies that demonstrate direct associations between immune cell function and clinical outcomes in older individuals are lacking. Despite these knowledge gaps, research has progressed in the development of vaccine and adjuvant formulations tailored for older adults in order to boost protective immunity and overcome immunosenescence. In this review, we will discuss the development of vaccines for older adults in light of our current understanding—or lack thereof—of the aging immune system. We highlight the functional changes that are known to occur in the adaptive immune system with age, followed by a discussion of current, clinically relevant pathogens that disproportionately affect older adults and are the central focus of vaccine research efforts for the aging population. We conclude with an outlook on personalized vaccine development for older adults and areas in need of further study in order to improve our fundamental understanding of adaptive immunosenescence.

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 ...