A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data

Background: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data. Conclusions: Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants

A Dual-Process Approach To Understanding Human-Robot Interaction

Human-robot interaction (HRI) research needs to leverage the theories and findings from multiple disciplines to inform subsequent empirical investigation and robot design. Utilizing evidence and suggestions from social cognitive and neurocognitive disciplines for human-human interaction, we propose an approach for conceptualizing HRI. Comparing HRI to human-human interaction at the surface level and deeper levels allows for the generation and evaluation of testable hypotheses in multiple disciplines to inform the design of future robotic systems. Copyright 2013 by Human Factors and Ergonomics Society, Inc

No Time, No Problem: Mental State Attributions Made Quickly Or After Reflection Do Not Differ

We describe an experiment that examined mental state attributions as a function of manipulated response speed. Based upon dual-process theories of cognition, the purpose was to examine the degree to which rapid versus reflective judgment might alter these attributions. Participants were presented with a theory of mind task and instructed to either respond as quickly as possible or to reflect on the stimuli before answering. Although, instructions did produce significantly different response times, there were no significant differences in the attributions made by participants. These results are interpreted as supporting a view of social cognition positing that people immediately interpret perceived social cues in the environment to produce social signals that inform the attributions made of another\u27s mental state. We discuss this in the context of socio-cognitive theories and their relevance to interdisciplinary approaches to understand and improve human-machine interactions and the development of social intelligence in machines

Leveraging Social Judgment Theory To Examine The Relationship Between Social Cues And Signals In Human-Robot Interactions

Human-robot interaction (HRI) research requires new techniques for understanding the social dynamics that occur at the interface between humans and robots. Prior work has focused on incorporating the social cues and social signals distinction from the field of social signal processing and complementing this with recent advances in understanding human social cognition that specify two primary types of cognitive processes. A related account, stemming from Social Judgment Theory (SJT), specifies a Lens Model for which cues can be interpreted as well as the task conditions that would induce either of the types of cognitive processes. Surprisingly, SJT-based research has not yet examined the social cue and signal relationship. We argue it provides an ideal path forward for such research and we integrate these related disciplines of study to provide a theoretically derived account that can be useful for both the design of humanhuman and HRI experiments focused on social interaction dynamics

Varying Social Cue Constellations Results In Different Attributed Social Signals In A Simulated Surveillance Task

A better understanding of human mental states in social contexts holds the potential to pave the way for implementation of robotic systems capable of more natural and intuitive interaction. In working toward such a goal, this paper reports on a study examining human perception of social signals based on manipulated sets of social cues in a simulated socio-cultural environment. Participants were presented with video vignettes of a simulated marketplace environment in which they took the perspective of an observing robot and were asked to make mental state attributions of a human avatar based on the avatar\u27s expression of a range of social cues. Results indicated that subtly varying combinations of social cues led to participants\u27 perception of different social signals. The different mental state attributions made were also significantly associated with what participants considered an appropriate behavioral response for the robot to exhibit in relation to the avatar. We discuss these results in the context of the development of computational-based perceptual systems to be implemented in socially intelligent robots

Effects Of Robotic Social Cues On Interpersonal Attributions And Assessments Of Robot Interaction Behaviors

We discuss an experiment investigating the influence of social cues expressed by a robot on human attributions of interpersonal characteristics towards a robot and assessments of its interaction behaviors. During a hallway navigation scenario, participants were exposed to varying expressions of proxemic behavior and gaze cues over repeated interactions with a robot. Analysis of participant perceptions of the robot\u27s personality revealed that cues indicative of socially mindful behavior expressed by the robot promote positive interpersonal attributions and perceptions of safe robot behavior. Results of the present study contribute to the scholarly discussion on robotic design for encouraging natural and effective interactions between humans and robots