COVID-19 susceptibility and severity risks in a cross-sectional survey of over 500 000 US adults.

OBJECTIVES: The enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analysing population-scale datasets in real time to monitor and better understand the evolving pandemic. The objectives of this study were to examine the relationship of risk factors to COVID-19 susceptibility and severity and to develop risk models to accurately predict COVID-19 outcomes using rapidly obtained self-reported data. DESIGN: A cross-sectional study. SETTING: AncestryDNA customers in the USA who consented to research. PARTICIPANTS: The AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors and exposures for over 563 000 adult individuals in the USA in just under 4 months, including over 4700 COVID-19 cases as measured by a self-reported positive test. RESULTS: We 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 men even after adjusting for known exposures and age (adjusted OR=1.36, 95% CI=1.19 to 1.55). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualised COVID-19 susceptibility (area under the curve (AUC)=0.84) and severity outcomes including hospitalisation 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. CONCLUSIONS: The results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic.

Expanded COVID-19 phenotype definitions reveal distinct patterns of genetic association and protective effects.

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.

Genome-wide analysis provides genetic evidence that ACE2 influences COVID-19 risk and yields risk scores associated with severe disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters human host cells via angiotensin-converting enzyme 2 (ACE2) and causes coronavirus disease 2019 (COVID-19). Here, through a genome-wide association study, we identify a variant (rs190509934, minor allele frequency 0.2-2%) that downregulates ACE2 expression by 37% (P = 2.7 × 10-8) and reduces the risk of SARS-CoV-2 infection by 40% (odds ratio = 0.60, P = 4.5 × 10-13), providing human genetic evidence that ACE2 expression levels influence COVID-19 risk. We also replicate the associations of six previously reported risk variants, of which four were further associated with worse outcomes in individuals infected with the virus (in/near LZTFL1, MHC, DPP9 and IFNAR2). Lastly, we show that common variants define a risk score that is strongly associated with severe disease among cases and modestly improves the prediction of disease severity relative to demographic and clinical factors alone.

Visual-haptic adaptation is determined by relative reliability.

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.

Probabilistic combination of slant information: weighted averaging and robustness as optimal percepts.

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.

Why pictures look right when viewed from the wrong place.

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).

Perception of 3-D Layout in Stereo Displays.

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.

Vergence-accommodation conflicts hinder visual performance and cause visual fatigue.

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.

Consequences of Incorrect Focus Cues in Stereo Displays.

Conventional stereo displays produce images in which focus cues - blur and accommodation - are inconsistent with the simulated depth. We have developed new display techniques that allow the presentation of nearly correct focus. Using these techniques, we find that stereo vision is faster and more accurate when focus cues are mostly consistent with simulated depth; furthermore, viewers experience less fatigue when focus cues are correct or nearly correct.

Cardinal rules: visual orientation perception reflects knowledge of environmental statistics.

Humans are good at performing visual tasks, but experimental measurements have revealed 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. However, such inference is optimal only if the observer's internal model matches the environment. We found this to be the case. We measured performance in an orientation-estimation task and found that orientation judgments were more accurate at cardinal (horizontal and vertical) orientations. Judgments made under conditions of uncertainty were strongly biased toward cardinal orientations. We estimated observers' internal models for orientation and found that they matched the local orientation distribution measured in photographs. In addition, we determined how a neural population could embed probabilistic information responsible for such biases.