Mast Cells in Inflammation and Disease: Recent Progress and Ongoing Concerns.

Mast cells have existed long before the development of adaptive immunity, although they have been given different names. Thus, in the marine urochordate Styela plicata, they have been designated as test cells. However, based on their morphological characteristics (including prominent cytoplasmic granules) and mediator content (including heparin, histamine, and neutral proteases), test cells are thought to represent members of the lineage known in vertebrates as mast cells. So this lineage presumably had important functions that preceded the development of antibodies, including IgE. Yet mast cells are best known, in humans, as key sources of mediators responsible for acute allergic reactions, notably including anaphylaxis, a severe and potentially fatal IgE-dependent immediate hypersensitivity reaction to apparently harmless antigens, including many found in foods and medicines. In this review, we briefly describe the origins of tissue mast cells and outline evidence that these cells can have beneficial as well as detrimental functions, both innately and as participants in adaptive immune responses. We also discuss aspects of mast cell heterogeneity and comment on how the plasticity of this lineage may provide insight into its roles in health and disease. Finally, we consider some currently open questions that are yet unresolved.

Finding Needles in the Haystack: Strategies for Uncovering Noncoding Regulatory Variants.

Despite accumulating evidence implicating noncoding variants in human diseases, unraveling their functionality remains a significant challenge. Systematic annotations of the regulatory landscape and the growth of sequence variant data sets have fueled the development of tools and methods to identify causal noncoding variants and evaluate their regulatory effects. Here, we review the latest advances in the field and discuss potential future research avenues to gain a more in-depth understanding of noncoding regulatory variants.

Orthogonal SARS-CoV-2 Serological Assays Enable Surveillance of Low-Prevalence Communities and Reveal Durable Humoral Immunity.

We conducted a serological study to define correlates of immunity against SARS-CoV-2. Compared to those with mild coronavirus disease 2019 (COVID-19) cases, individuals with severe disease exhibited elevated virus-neutralizing titers and antibodies against the nucleocapsid (N) and the receptor binding domain (RBD) of the spike protein. Age and sex played lesser roles. All cases, including asymptomatic individuals, seroconverted by 2 weeks after PCR confirmation. Spike RBD and S2 and neutralizing antibodies remained detectable through 5-7 months after onset, whereas α-N titers diminished. Testing 5,882 members of the local community revealed only 1 sample with seroreactivity to both RBD and S2 that lacked neutralizing antibodies. This fidelity could not be achieved with either RBD or S2 alone. Thus, inclusion of multiple independent assays improved the accuracy of antibody tests in low-seroprevalence communities and revealed differences in antibody kinetics depending on the antigen. We conclude that neutralizing antibodies are stably produced for at least 5-7 months after SARS-CoV-2 infection.

Omnibus proteome-wide association study identifies 43 risk genes for Alzheimer disease dementia.

Transcriptome-wide association study (TWAS) tools have been applied to conduct proteome-wide association studies (PWASs) by integrating proteomics data with genome-wide association study (GWAS) summary data. The genetic effects of PWAS-identified significant genes are potentially mediated through genetically regulated protein abundance, thus informing the underlying disease mechanisms better than GWAS loci. However, existing TWAS/PWAS tools are limited by considering only one statistical model. We propose an omnibus PWAS pipeline to account for multiple statistical models and demonstrate improved performance by simulation and application studies of Alzheimer disease (AD) dementia. We employ the Aggregated Cauchy Association Test to derive omnibus PWAS (PWAS-O) p values from PWAS p values obtained by three existing tools assuming complementary statistical models-TIGAR, PrediXcan, and FUSION. Our simulation studies demonstrated improved power, with well-calibrated type I error, for PWAS-O over all three individual tools. We applied PWAS-O to studying AD dementia with reference proteomic data profiled from dorsolateral prefrontal cortex of postmortem brains from individuals of European ancestry. We identified 43 risk genes, including 5 not identified by previous studies, which are interconnected through a protein-protein interaction network that includes the well-known AD risk genes TOMM40, APOC1, and APOC2. We also validated causal genetic effects mediated through the proteome for 27 (63%) PWAS-O risk genes, providing insights into the underlying biological mechanisms of AD dementia and highlighting promising targets for therapeutic development. PWAS-O can be easily applied to studying other complex diseases.

JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression.

Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction, and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks, or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture, and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits, and microbiome abundances. It allows for covariates, ascertainment, and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, most of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.

Mailed fecal immunochemical test outreach for colorectal cancer screening: Summary of a Centers for Disease Control and Prevention-sponsored Summit.

Uptake of colorectal cancer screening remains suboptimal. Mailed fecal immunochemical testing (FIT) offers promise for increasing screening rates, but optimal strategies for implementation have not been well synthesized. In June 2019, the Centers for Disease Control and Prevention convened a meeting of subject matter experts and stakeholders to answer key questions regarding mailed FIT implementation in the United States. Points of agreement included: 1) primers, such as texts, telephone calls, and printed mailings before mailed FIT, appear to contribute to effectiveness; 2) invitation letters should be brief and easy to read, and the signatory should be tailored based on setting; 3) instructions for FIT completion should be simple and address challenges that may lead to failed laboratory processing, such as notation of collection date; 4) reminders delivered to initial noncompleters should be used to increase the FIT return rate; 5) data infrastructure should identify eligible patients and track each step in the outreach process, from primer delivery through abnormal FIT follow-up; 6) protocols and procedures such as navigation should be in place to promote colonoscopy after abnormal FIT; 7) a high-quality, 1-sample FIT should be used; 8) sustainability requires a program champion and organizational support for the work, including sufficient funding and external policies (such as quality reporting requirements) to drive commitment to program investment; and 9) the cost effectiveness of mailed FIT has been established. Participants concluded that mailed FIT is an effective and efficient strategy with great potential for increasing colorectal cancer screening in diverse health care settings if more widely implemented.

A Turing test of whether AI chatbots are behaviorally similar to humans.

We administer a Turing test to AI chatbots. We examine how chatbots behave in a suite of classic behavioral games that are designed to elicit characteristics such as trust, fairness, risk-aversion, cooperation, etc., as well as how they respond to a traditional Big-5 psychological survey that measures personality traits. ChatGPT-4 exhibits behavioral and personality traits that are statistically indistinguishable from a random human from tens of thousands of human subjects from more than 50 countries. Chatbots also modify their behavior based on previous experience and contexts "as if" they were learning from the interactions and change their behavior in response to different framings of the same strategic situation. Their behaviors are often distinct from average and modal human behaviors, in which case they tend to behave on the more altruistic and cooperative end of the distribution. We estimate that they act as if they are maximizing an average of their own and partner's payoffs.

A sensitive assay for measuring whole-blood responses to type I IFNs.

Human inborn errors of the type I IFN response pathway and auto-Abs neutralizing IFN-α, -ß, and/or -ω can underlie severe viral illnesses. We report a simple assay for the detection of both types of condition. We stimulate whole blood from healthy individuals and patients with either inborn errors of type I IFN immunity or auto-Abs against type I IFNs with glycosylated human IFN-α2, -ß, or -ω. As controls, we add a monoclonal antibody (mAb) blocking the type I IFN receptors and stimulated blood with IFN-γ (type II IFN). Of the molecules we test, IP-10 (encoded by the interferon-stimulated gene (ISG) CXCL10) is the molecule most strongly induced by type I and type II IFNs in the whole blood of healthy donors in an ELISA-like assay. In patients with inherited IFNAR1, IFNAR2, TYK2, or IRF9 deficiency, IP-10 is induced only by IFN-γ, whereas, in those with auto-Abs neutralizing specific type I IFNs, IP-10 is also induced by the type I IFNs not neutralized by the auto-Abs. The measurement of type I and type II IFN-dependent IP-10 induction therefore constitutes a simple procedure for detecting rare inborn errors of the type I IFN response pathway and more common auto-Abs neutralizing type I IFNs.

Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data.

Gene-based association tests aggregate multiple SNP-trait associations into sets defined by gene boundaries and are widely used in post-GWAS analysis. A common approach for gene-based tests is to combine SNPs associations by computing the sum of χ2 statistics. However, this strategy ignores the directions of SNP effects, which could result in a loss of power for SNPs with masking effects, e.g., when the product of two SNP effects and the linkage disequilibrium (LD) correlation is negative. Here, we introduce "mBAT-combo," a set-based test that is better powered than other methods to detect multi-SNP associations in the context of masking effects. We validate the method through simulations and applications to real data. We find that of 35 blood and urine biomarker traits in the UK Biobank, 34 traits show evidence for masking effects in a total of 4,273 gene-trait pairs, indicating that masking effects is common in complex traits. We further validate the improved power of our method in height, body mass index, and schizophrenia with different GWAS sample sizes and show that on average 95.7% of the genes detected only by mBAT-combo with smaller sample sizes can be identified by the single-SNP approach with a 1.7-fold increase in sample sizes. Eleven genes significant only in mBAT-combo for schizophrenia are confirmed by functionally informed fine-mapping or Mendelian randomization integrating gene expression data. The framework of mBAT-combo can be applied to any set of SNPs to refine trait-association signals hidden in genomic regions with complex LD structures.

An allelic-series rare-variant association test for candidate-gene discovery.

Allelic series are of candidate therapeutic interest because of the existence of a dose-response relationship between the functionality of a gene and the degree or severity of a phenotype. We define an allelic series as a collection of variants in which increasingly deleterious mutations lead to increasingly large phenotypic effects, and we have developed a gene-based rare-variant association test specifically targeted to identifying genes containing allelic series. Building on the well-known burden test and sequence kernel association test (SKAT), we specify a variety of association models covering different genetic architectures and integrate these into a Coding-Variant Allelic-Series Test (COAST). Through extensive simulations, we confirm that COAST maintains the type I error and improves the power when the pattern of coding-variant effect sizes increases monotonically with mutational severity. We applied COAST to identify allelic-series genes for four circulating-lipid traits and five cell-count traits among 145,735 subjects with available whole-exome sequencing data from the UK Biobank. Compared with optimal SKAT (SKAT-O), COAST identified 29% more Bonferroni-significant associations with circulating-lipid traits, on average, and 82% more with cell-count traits. All of the gene-trait associations identified by COAST have corroborating evidence either from rare-variant associations in the full cohort (Genebass, n = 400,000) or from common-variant associations in the GWAS Catalog. In addition to detecting many gene-trait associations present in Genebass by using only a fraction (36.9%) of the sample, COAST detects associations, such as that between ANGPTL4 and triglycerides, that are absent from Genebass but that have clear common-variant support.

Significance tests for R2 of out-of-sample prediction using polygenic scores.

The coefficient of determination (R2) is a well-established measure to indicate the predictive ability of polygenic scores (PGSs). However, the sampling variance of R2 is rarely considered so that 95% confidence intervals (CI) are not usually reported. Moreover, when comparisons are made between PGSs based on different discovery samples, the sampling covariance of R2 is required to test the difference between them. Here, we show how to estimate the variance and covariance of R2 values to assess the 95% CI and p value of the R2 difference. We apply this approach to real data calculating PGSs in 28,880 European participants derived from UK Biobank (UKBB) and Biobank Japan (BBJ) GWAS summary statistics for cholesterol and BMI. We quantify the significantly higher predictive ability of UKBB PGSs compared to BBJ PGSs (p value 7.6e-31 for cholesterol and 1.4e-50 for BMI). A joint model of UKBB and BBJ PGSs significantly improves the predictive ability, compared to a model of UKBB PGS only (p value 3.5e-05 for cholesterol and 1.3e-28 for BMI). We also show that the predictive ability of regulatory SNPs is significantly enriched over non-regulatory SNPs for cholesterol (p value 8.9e-26 for UKBB and 3.8e-17 for BBJ). We suggest that the proposed approach (available in R package r2redux) should be used to test the statistical significance of difference between pairs of PGSs, which may help to draw a correct conclusion about the comparative predictive ability of PGSs.

BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases.

Genome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT (https://github.com/wangjr03/BayesKAT), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases.

A high-dimensional omnibus test for set-based association analysis.

Set-based association analysis is a valuable tool in studying the etiology of complex diseases in genome-wide association studies, as it allows for the joint testing of variants in a region or group. Two common types of single nucleotide polymorphism (SNP)-disease functional models are recognized when evaluating the joint function of a set of SNP: the cumulative weak signal model, in which multiple functional variants with small effects contribute to disease risk, and the dominating strong signal model, in which a few functional variants with large effects contribute to disease risk. However, existing methods have two main limitations that reduce their power. Firstly, they typically only consider one disease-SNP association model, which can result in significant power loss if the model is misspecified. Secondly, they do not account for the high-dimensional nature of SNPs, leading to low power or high false positives. In this study, we propose a solution to these challenges by using a high-dimensional inference procedure that involves simultaneously fitting many SNPs in a regression model. We also propose an omnibus testing procedure that employs a robust and powerful P-value combination method to enhance the power of SNP-set association. Our results from extensive simulation studies and a real data analysis demonstrate that our set-based high-dimensional inference strategy is both flexible and computationally efficient and can substantially improve the power of SNP-set association analysis. Application to a real dataset further demonstrates the utility of the testing strategy.

A Targeted LC-MRM3 Proteomic Approach for the Diagnosis of SARS-CoV-2 Infection in Nasopharyngeal Swabs.

Since its first appearance, severe acute respiratory syndrome coronavirus 2 quickly spread around the world and the lack of adequate PCR testing capacities, especially during the early pandemic, led the scientific community to explore new approaches such as mass spectrometry (MS). We developed a proteomics workflow to target several tryptic peptides of the nucleocapsid protein. A highly selective multiple reaction monitoring-cubed (MRM3) strategy provided a sensitivity increase in comparison to conventional MRM acquisition. Our MRM3 approach was first tested on an Amsterdam public health cohort (alpha-variant, 760 participants) detecting viral nucleocapsid protein peptides from nasopharyngeal swabs samples presenting a cycle threshold value down to 35 with sensitivity and specificity of 94.2% and 100.0%, without immunopurification. A second iteration of the MS-diagnostic test, able to analyze more than 400 samples per day, was clinically validated on a Leiden-Rijswijk public health cohort (delta-variant, 2536 participants) achieving 99.9% specificity and 93.1% sensitivity for patients with cycle threshold values up to 35. In this manuscript, we also developed and brought the first proof of the concept of viral variant monitoring in a complex matrix using targeted MS.

Predicting implicit attitudes with natural language data.

Large-scale language datasets and advances in natural language processing offer opportunities for studying people's cognitions and behaviors. We show how representations derived from language can be combined with laboratory-based word norms to predict implicit attitudes for diverse concepts. Our approach achieves substantially higher correlations than existing methods. We also show that our approach is more predictive of implicit attitudes than are explicit attitudes, and that it captures variance in implicit attitudes that is largely unexplained by explicit attitudes. Overall, our results shed light on how implicit attitudes can be measured by combining standard psychological data with large-scale language data. In doing so, we pave the way for highly accurate computational modeling of what people think and feel about the world around them.

Cleaner fish recognize self in a mirror via self-face recognition like humans.

Some animals have the remarkable capacity for mirror self-recognition (MSR), yet any implications for self-awareness remain uncertain and controversial. This is largely because explicit tests of the two potential mechanisms underlying MSR are still lacking: mental image of the self and kinesthetic visual matching. Here, we test the hypothesis that MSR ability in cleaner fish, Labroides dimidiatus, is associated with a mental image of the self, in particular the self-face, like in humans. Mirror-naive fish initially attacked photograph models of both themselves and unfamiliar strangers. In contrast, after all fish had passed the mirror mark test, fish did not attack their own (motionless) images, but still frequently attacked those of unfamiliar individuals. When fish were exposed to composite photographs, the self-face/unfamiliar body were not attacked, but photographs of unfamiliar face/self-body were attacked, demonstrating that cleaner fish with MSR capacity recognize their own facial characteristics in photographs. Additionally, when presented with self-photographs with a mark placed on the throat, unmarked mirror-experienced cleaner fish demonstrated throat-scraping behaviors. When combined, our results provide clear evidence that cleaner fish recognize themselves in photographs and that the likely mechanism for MSR is associated with a mental image of the self-face, not a kinesthetic visual-matching model. Humans are also capable of having a mental image of the self-face, which is considered an example of private self-awareness. We demonstrate that combining mirror test experiments with photographs has enormous potential to further our understanding of the evolution of cognitive processes and private self-awareness across nonhuman animals.

All human social groups are human, but some are more human than others: A comprehensive investigation of the implicit association of "Human" to US racial/ethnic groups.

All human groups are equally human, but are they automatically represented as such? Harnessing data from 61,377 participants across 13 experiments (six primary and seven supplemental), a sharp dissociation between implicit and explicit measures emerged. Despite explicitly affirming the equal humanity of all racial/ethnic groups, White participants consistently associated Human (relative to Animal) more with White than Black, Hispanic, and Asian groups on Implicit Association Tests (IATs; experiments 1-4). This effect emerged across diverse representations of Animal that varied in valence (pets, farm animals, wild animals, and vermin; experiments 1-2). Non-White participants showed no such Human=Own Group bias (e.g., Black participants on a White-Black/Human-Animal IAT). However, when the test included two outgroups (e.g., Asian participants on a White-Black/Human-Animal IAT), non-White participants displayed Human=White associations. The overall effect was largely invariant across demographic variations in age, religion, and education but did vary by political ideology and gender, with self-identified conservatives and men displaying stronger Human=White associations (experiment 3). Using a variance decomposition method, experiment 4 showed that the Human=White effect cannot be attributed to valence alone; the semantic meaning of Human and Animal accounted for a unique proportion of variance. Similarly, the effect persisted even when Human was contrasted with positive attributes (e.g., God, Gods, and Dessert; experiment 5a). Experiments 5a-b clarified the primacy of Human=White rather than Animal=Black associations. Together, these experiments document a factually erroneous but robust Human=Own Group implicit stereotype among US White participants (and globally), with suggestive evidence of its presence in other socially dominant groups.

Testing for SARS-CoV-2: lessons learned and current use cases.

SUMMARYThe emergence and worldwide dissemination of SARS-CoV-2 required both urgent development of new diagnostic tests and expansion of diagnostic testing capacity on an unprecedented scale. The rapid evolution of technologies that allowed testing to move out of traditional laboratories and into point-of-care testing centers and the home transformed the diagnostic landscape. Four years later, with the end of the formal public health emergency but continued global circulation of the virus, it is important to take a fresh look at available SARS-CoV-2 testing technologies and consider how they should be used going forward. This review considers current use case scenarios for SARS-CoV-2 antigen, nucleic acid amplification, and immunologic tests, incorporating the latest evidence for analytical/clinical performance characteristics and advantages/limitations for each test type to inform current debates about how tests should or should not be used.

Powerful Rare-Variant Association Analysis of Secondary Phenotypes.

Most genome-wide association studies are based on case-control designs, which provide abundant resources for secondary phenotype analyses. However, such studies suffer from biased sampling of primary phenotypes, and the traditional statistical methods can lead to seriously distorted analysis results when they are applied to secondary phenotypes without accounting for the biased sampling mechanism. To our knowledge, there are no statistical methods specifically tailored for rare variant association analysis with secondary phenotypes. In this article, we proposed two novel joint test statistics for identifying secondary-phenotype-associated rare variants based on prospective likelihood and retrospective likelihood, respectively. We also exploit the assumption of gene-environment independence in retrospective likelihood to improve the statistical power and adopt a two-step strategy to balance statistical power and robustness. Simulations and a real-data application are conducted to demonstrate the superior performance of our proposed methods.