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The need to identify and effectively treat COVID-19 cases at highest risk for severe disease remains critical. We identified five common genetic loci (two novel) that modulate both COVID-19 susceptibility and severity, implicating TMPRSS2, IFNAR2, CCHCR1, TCF19 and SLC6A20 as potential targets. A high genetic burden was strongly associated with increased risk of hospitalization and severe disease among COVID-19 cases, especially among individuals with few known clinical risk factors.

INTRODUCTION PARAGRAPHMultiple large COVID-19 genome-wide association studies (GWAS) have identified reproducible genetic associations indicating that some infection susceptibility and severity risk is heritable.1-5 Most of these studies ascertained COVID-19 cases in medical clinics and hospitals, which can lead to an overrepresentation of cases with severe outcomes, such as hospitalization, intensive care unit admission, or ventilation. Here, we demonstrate the utility and validity of deep phenotyping with self-reported outcomes in a population with a large proportion of mild and subclinical cases. Using these data, we defined eight different phenotypes related to COVID-19 outcomes: four that align with previously studied COVID-19 definitions and four novel definitions that focus on susceptibility given exposure, mild clinical manifestations, and an aggregate score of symptom severity. We assessed replication of 13 previously identified COVID-19 genetic associations with all eight phenotypes and found distinct patterns of association, most notably related to the chr3/SLC6A20/LZTFL1 and chr9/ABO regions. We then performed a discovery GWAS, which suggested some novel phenotypes may better capture protective associations and also identified a novel association in chr11/GALNT18 that reproduced in two fully independent populations.

Multiple COVID-19 genome-wide association studies (GWASs) have identified reproducible genetic associations indicating that there is a genetic component to susceptibility and severity risk. To complement these studies, we collected deep coronavirus disease 2019 (COVID-19) phenotype data from a survey of 736,723 AncestryDNA research participants. With these data, we defined eight phenotypes related to COVID-19 outcomes: four phenotypes that align with previously studied COVID-19 definitions and four 'expanded' phenotypes that focus on susceptibility given exposure, mild clinical manifestations and an aggregate score of symptom severity. We performed a replication analysis of 12 previously reported COVID-19 genetic associations with all eight phenotypes in a trans-ancestry meta-analysis of AncestryDNA research participants. In this analysis, we show distinct patterns of association at the 12 loci with the eight outcomes that we assessed. We also performed a genome-wide discovery analysis of all eight phenotypes, which did not yield new genome-wide significant loci but did suggest that three of the four ' expanded' COVID-19 phenotypes have enhanced power to capture protective genetic associations relative to the previously studied phenotypes. Thus, we conclude that continued large-scale ascertainment of deep COVID-19 phenotype data would likely represent a boon for COVID-19 therapeutic target identification.

SARS-CoV-2 enters host cells by binding angiotensin-converting enzyme 2 (ACE2). Through a genome-wide association study, we show that a rare variant (MAF = 0.3%, odds ratio 0.60, P=4.5x10-13) that down-regulates ACE2 expression reduces risk of COVID-19 disease, providing human genetics support for the hypothesis that ACE2 levels influence COVID-19 risk. Further, we show that common genetic variants define a risk score that predicts severe disease among COVID-19 cases.

BackgroundThe enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analyzing population-scale datasets in real time to monitor and better understand the evolving pandemic.MethodsThe AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors, and exposures for over 563,000 adult individuals in the U.S. in just under four months, including over 4,700 COVID-19 cases as measured by a self-reported positive test.ResultsWe replicated previously reported associations between several risk factors and COVID-19 susceptibility and severity outcomes, and additionally found that differences in known exposures accounted for many of the susceptibility associations. A notable exception was elevated susceptibility for males even after adjusting for known exposures and age (adjusted odds ratio [aOR]=1.36, 95% confidence interval [CI] = (1.19, 1.55)). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualized COVID-19 susceptibility (area under the curve [AUC]=0.84) and severity outcomes including hospitalization and critical illness (AUC=0.87 and 0.90, respectively). The risk models achieved robust discriminative performance across different age, sex, and genetic ancestry groups within the study.ConclusionThe results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic.THUMBNAILWhat is already known on this subjectThe COVID-19 pandemic has exacted a historic toll on human lives, healthcare systems and global economies, with over 83 million cases and over 1.8 million deaths worldwide as of January 2021.COVID-19 risk factors for susceptibility and severity have been extensively investigated by clinical and public health researchers.Several groups have developed risk models to predict COVID-19 illness outcomes based on known risk factors.What this study addsWe performed association analyses for COVID-19 susceptibility and severity in a large, at-home survey and replicated much of the previous clinical literature.Associations were further adjusted for known COVID-19 exposures, and we observed elevated positive test odds for males even after adjustment for these known exposures.We developed risk models and evaluated them across different age, sex, and genetic ancestry cohorts, and showed robust performance across all cohorts in a holdout dataset.Our results establish large-scale, self-reported surveys as a potential framework for investigating and monitoring rapidly evolving pandemics.

Human infection with SARS-CoV-2, the causative agent of COVID-19, leads to a remarkably diverse spectrum of outcomes, ranging from asymptomatic to fatal. Recent reports suggest that both clinical and genetic risk factors may contribute to COVID-19 susceptibility and severity. To investigate genetic risk factors, we collected over 500,000 COVID-19 survey responses between April and May 2020 with accompanying genetic data from the AncestryDNA database. We conducted sex-stratified and meta-analyzed genome-wide association studies (GWAS) for COVID-19 susceptibility (positive nasopharyngeal swab test,ncases=2,407) and severity (hospitalization,ncases=250). The severity GWAS replicated associations with severe COVID-19 nearABOandSLC6A20(PP−8. The strongest association was nearIVNS1ABP, a gene involved in influenza virus replication1, and was associated only in males. The other two novel loci harbor genes with established roles in viral replication or immunity:SRRM1and the immunoglobulin lambda locus. We thus present new evidence that host genetic variation likely contributes to COVID-19 outcomes and demonstrate the value of large-scale, self-reported data as a mechanism to rapidly address a health crisis.

The last ten years have seen an exponential growth of direct-to-consumer genomics. One popular feature of these tests is the report of a distant ancestral inference profile-a breakdown of the regions of the world where the test-taker's ancestors may have lived. While current methods and products generally focus on the more distant past (e.g., thousands of years ago), we have recently demonstrated that by leveraging network analysis tools such as community detection, more recent ancestry can be identified. However, using a network analysis tool like community detection on a large network with potentially millions of nodes is not feasible in a live production environment where hundreds or thousands of new genotypes are processed every day. In this study, we describe a classification method that leverages network features to assign individuals to communities in a large network corresponding to recent ancestry. We recently launched a beta version of this research as a new product feature at AncestryDNA.

Humans are remarkably good at performing visual tasks, but experimental measurements reveal substantial biases in the perception of basic visual attributes. An appealing hypothesis is that these biases arise through a process of statistical inference, in which information from noisy measurements is fused with a probabilistic model of the environment. But such inference is optimal only if the observer’s internal model matches the environment. Here, we provide evidence that this is the case. We measured performance in an orientation-estimation task, demonstrating the well-known fact that orientation judgements are more accurate at cardinal (horizontal and vertical) orientations, along with a new observation that judgements made under conditions of uncertainty are strongly biased toward cardinal orientations. We estimate observers’ internal models for orientation and find that they match the local orientation distribution measured in photographs. We also show how a neural population could embed probabilistic information responsible for such biases.

As stereoscopic displays become more commonplace, it is more important than ever for those displays to create a faithful impression of the 3-D structure of the object or scene being portrayed. This article reviews current research on the ability of a viewer to perceive the 3-D layout specified by a stereo display.

A picture viewed from its center of projection generates the same retinal image as the original scene, so the viewer perceives the scene correctly. When a picture is viewed from other locations, the retinal image specifies a different scene, but we normally do not notice the changes. We investigated the mechanism underlying this perceptual invariance by studying the perceived shapes of pictured objects viewed from various locations. We also manipulated information about the orientation of the picture surface. When binocular information for surface orientation was available, perceived shape was nearly invariant across a wide range of viewing angles. By varying the projection angle and the position of a stimulus in the picture, we found that invariance is achieved through an estimate of local surface orientation, not from geometric information in the picture. We present a model that explains invariance and other phenomena (such as perceived distortions in wide-angle pictures).

Accurate calibration of sensory estimators is critical for maintaining accurate estimates of the environment. Classically, it was assumed that sensory calibration occurs by one sense changing to become consistent with vision; this is visual dominance. Recently, it has been proposed that changes in estimators occur according to their relative reliabilities; this is the reliability-based model. We show that if cue combination occurs according to relative reliability, then reliability-based calibration assures minimum-variance sensory estimates over time. Recent studies are qualitatively consistent with the reliability-based model, but none have shown that the predictions are quantitatively accurate. We conducted an experiment in which the model could be assessed quantitatively. Subjects indicated whether visual, haptic, and visual–haptic planar surfaces appeared slanted positively or negatively from frontoparallel. In preadaptation, we determined the visual and haptic slants of perceived frontoparallel, and measured visual and haptic reliabilities. We varied visual reliability by adjusting the size of the viewable stimulus. Haptic reliability was fixed. During adaptation, subjects were exposed to visual–haptic surfaces with a discrepancy between the visual and haptic slants. After adaptation, we remeasured the visual and haptic slants of perceived frontoparallel. When vision was more reliable, haptics adapted to match vision. When vision was less reliable, vision adapted to match haptics. Most importantly, the ratio of visual and haptic adaptation was quantitatively predicted by relative reliability. The amount of adaptation of one sensory estimator relative to another depends strongly on the relative reliabilities of the two estimators.

Depth perception involves combining multiple, possibly conflicting, sensory measurements to estimate the 3D structure of the viewed scene. Previous work has shown that the perceptual system combines measurements using a statistically optimal weighted average. However, the system should only combine measurements when they come from the same source. We asked whether the brain avoids combining measurements when they differ from one another: that is, whether the system is robust to outliers. To do this, we investigated how two slant cues-binocular disparity and texture gradients-influence perceived slant as a function of the size of the conflict between the cues. When the conflict was small, we observed weighted averaging. When the conflict was large, we observed robust behavior: perceived slant was dictated solely by one cue, the other being rejected. Interestingly, the rejected cue was either disparity or texture, and was not necessarily the more variable cue. We modeled the data in a probabilistic framework, and showed that weighted averaging and robustness are predicted if the underlying likelihoods have heavier tails than Gaussians. We also asked whether observers had conscious access to the single-cue estimates when they exhibited robustness and found they did not, i.e. they completely fused despite the robust percepts.

Three-dimensional (3D) displays have become important for many applications including vision research, operation of remote devices, medical imaging, surgical training, scientific visualization, virtual prototyping, and more. In many of these applications, it is important for the graphic image to create a faithful impression of the 3D structure of the portrayed object or scene. Unfortunately, 3D displays often yield distortions in perceived 3D structure compared with the percepts of the real scenes the displays depict. A likely cause of such distortions is the fact that computer displays present images on one surface. Thus, focus cues-accommodation and blur in the retinal image-specify the depth of the display rather than the depths in the depicted scene. Additionally, the uncoupling of vergence and accommodation required by 3D displays frequently reduces one's ability to fuse the binocular stimulus and causes discomfort and fatigue for the viewer. We have developed a novel 3D display that presents focus cues that are correct or nearly correct for the depicted scene. We used this display to evaluate the influence of focus cues on perceptual distortions, fusion failures, and fatigue. We show that when focus cues are correct or nearly correct, (1) the time required to identify a stereoscopic stimulus is reduced, (2) stereoacuity in a time-limited task is increased, (3) distortions in perceived depth are reduced, and (4) viewer fatigue and discomfort are reduced. We discuss the implications of this work for vision research and the design and use of displays.

A picture viewed from its center of projection (CoP) generates the same retinal image as the original scene. When a picture is viewed from other locations, the retinal image specifies a different layout and shapes, but we normally do not notice the changes. The mechanism underlying this is unknown. We studied the perceived shapes of pictured ovoids and planes while varying viewing angle and the angle by which the pictures were projected. We also varied the viewer's information about the orientation of the picture surface. Viewers compensated nearly veridically for oblique viewing when binocular information for surface orientation was available. In so doing, they used an estimate of local surface orientation and not prior information for object shape nor geometric information in the picture. We present a model that explains invariance for incorrect viewing positions, and other phenomena like perceived distortions with wide fields of view.

Typical stereo displays provide incorrect focus cues because the light comes from a single surface. We describe a prototype stereo display comprising two independent fixed-viewpoint volumetric displays. Like autostereoscopic volumetric displays, fixed-viewpoint volumetric displays generate near-correct focus cues without tracking eye position, because light comes from sources at the correct focal distances. (In our prototype, from three image planes at different physical distances.) Unlike autostereoscopic volumetric displays, however, fixed-viewpoint volumetric displays retain the qualities of modern projective graphics: view-dependent lighting effects such as occlusion, specularity, and reflection are correctly depicted; modern graphics processor and 2-D display technology can be utilized; and realistic fields of view and depths of field can be implemented. While not a practical solution for general-purpose viewing, our prototype display is a proof of concept and a platform for ongoing vision research. The design, implementation, and verification of this stereo display are described, including a novel technique of filtering along visual lines using 1-D texture mapping.

Introduction and Related Work Computer-generated line drawings [4,7] are becoming an increasingly popular method of non-photorealistic rendering (NPR). While many factors contribute to an effective line drawing-quality of line, line placement and density-line direction is among the most critical for conveying surface shape. The principal directions hold particular promise in this respect [2]. In a human perceptual study, subjects displayed consistent biases toward perceiving surface contours (lines drawn on the surface) as being aligned with the principal directions [5]. Despite the potential for principal directions and their suggestion by previous authors [1,2,7], there are currently no available techniques for using them to render viewpoint-independent line drawings of arbitrary 3D surfaces. The line drawing algorithm of Markosian et al. [4] handled arbitrary 3D surfaces, but line direction was chosen in a viewpoint-dependent manner and lines tended to cluster on silhouette edges with little interior curvature information.

When people are exposed repeatedly to a conflict in visually and haptically specified shapes, they adapt and the apparent conflict is eventually eliminated. The inter-modal adaptation literature suggests that the conflict is resolved by adapting the haptic shape estimator. Another possibility is that both estimators adapt by amounts that depend on their relative variances. Thus, the visual estimator could adapt if its variance were high enough. Is relative reliability the better predictor of visual-haptic adaptation? We examined this by manipulating the variance of the visual signal during inter-modal adaptation and then measuring changes in the within-modal (vision-alone and haptics-alone) shape percepts. The stimulus was a 3D object with a rectangular front surface. It was specified visually by random-dot stereograms and haptically by PHANToM force-feedback devices. In pre- and post-tests, observers judged whether its front surface was taller or shorter than it was wide. For each modality, we found the aspect ratio that was perceptually a square. During adaptation, a conflict was created between the visually and haptically specified shapes by independently altering the visual and haptic aspect ratios of the front surface. The variance of the visual estimator (determined by dot number) was either low or high. We assessed the amount of visual and haptic adaptation by comparing pre- and post-test shape estimates. When the visual estimators variance was low, essentially all of the adaptation occurred in the haptic estimator. When the visual estimators variance was high, we observed visual and haptic adaptation. These results suggest that the relative reliability of visual and haptic estimators determines the relative amounts of visual and haptic adaptation.

Nat. Neurosci. 8, 1401–1410 (2005) On page 1402, the first two sentences of the second full paragraph in the second column were omitted. The paragraph should have begun as follows: “An alternative explanation, the local-slant hypothesis, suggests that location of the CoP is not recovered. Instead, the observed invariance is due to an adjustment of the retinal-image shape based on measurements of the local slant of the picture surface at the point of interest.