Behavioral and physiological sensitivity to natural sick faces.

The capacity to rapidly detect and avoid sick people may be adaptive. Given that faces are reliably available, as well as rapidly detected and processed, they may provide health information that influences social interaction. Prior studies used faces that were manipulated to appear sick (e.g., editing photos, inducing inflammatory response); however, responses to naturally sick faces remain largely unexplored. We tested whether adults detected subtle cues of genuine, acute, potentially contagious illness in face photos compared to the same individuals when healthy. We tracked illness symptoms and severity with the Sickness Questionnaire and Common Cold Questionnaire. We also checked that sick and healthy photos were matched on low-level features. We found that participants (N = 109) rated sick faces, compared to healthy faces, as sicker, more dangerous, and eliciting more unpleasant feelings. Participants (N = 90) rated sick faces as more likely to be avoided, more tired, and more negative in expression than healthy faces. In a passive-viewing eye-tracking task, participants (N = 50) looked longer at healthy than sick faces, especially the eye region, suggesting people may be more drawn to healthy conspecifics. When making approach-avoidance decisions, participants (N = 112) had greater pupil dilation to sick than healthy faces, and more pupil dilation was associated with greater avoidance, suggesting elevated arousal to threat. Across all experiments, participants' behaviors correlated with the degree of sickness, as reported by the face donors, suggesting a nuanced, fine-tuned sensitivity. Together, these findings suggest that humans may detect subtle threats of contagion from sick faces, which may facilitate illness avoidance. By better understanding how humans naturally avoid illness in conspecifics, we may identify what information is used and ultimately improve public health.

Perturbational phenotyping of human blood cells reveals genetically determined latent traits associated with subsets of common diseases.

Although genome-wide association studies (GWAS) have successfully linked genetic risk loci to various disorders, identifying underlying cellular biological mechanisms remains challenging due to the complex nature of common diseases. We established a framework using human peripheral blood cells, physical, chemical and pharmacological perturbations, and flow cytometry-based functional readouts to reveal latent cellular processes and performed GWAS based on these evoked traits in up to 2,600 individuals. We identified 119 genomic loci implicating 96 genes associated with these cellular responses and discovered associations between evoked blood phenotypes and subsets of common diseases. We found a population of pro-inflammatory anti-apoptotic neutrophils prevalent in individuals with specific subsets of cardiometabolic disease. Multigenic models based on this trait predicted the risk of developing chronic kidney disease in type 2 diabetes patients. By expanding the phenotypic space for human genetic studies, we could identify variants associated with large effect response differences, stratify patients and efficiently characterize the underlying biology.