Narrow Results

The popularization of biobanks provides an unprecedented amount of genetic and phenotypic information that can be used to research the relationship between genetics and human health. Despite the opportunities these datasets provide, they also pose many problems associated with computational time and costs, data size and transfer, and privacy and security. The publishing of summary statistics from these biobanks, and the use of them in a variety of downstream statistical analyses, alleviates many of these logistical problems. However, major questions remain about how to use summary statistics in all but the simplest downstream applications. Here, we present a novel approach to utilize basic summary statistics (estimates from single marker regressions on single phenotypes) to evaluate more complex phenotypes using multivariate methods. In particular, we present a covariate-adjusted method for conducting principal component analysis (PCA) utilizing only biobank summary statistics. We validate exact formulas for this method, as well as provide a framework of estimation when specific summary statistics are not available, through simulation. We apply our method to a real data set of fatty acid and genomic data.

Privacy and trust of biomedical solutions that capture and share data is an issue rising to the center of public attention and discourse. While large-scale academic, medical, and industrial research initiatives must collect increasing amounts of personal biomedical data from patient stakeholders, central to ensuring precision health becomes a reality, methods for providing sufficient privacy in biomedical databases and conveying a sense of trust to the user is equally crucial for the field of biocomputing to advance with the grace of those stakeholders. If the intended audience does not trust new precision health innovations, funding and support for these efforts will inevitably be limited. It is therefore crucial for the field to address these issues in a timely manner. Here we describe current research directions towards achieving trustworthy biomedical informatics solutions.

In order to establish data interaction between two physically isolated networks, this paper develops a horizontal joint robot system to automatically load and unload the CD disk for CD recording operations. A FPGA-based controller is constructed as the core control module of the system and the B-spline curve is used for the trajectory planning of the robot to improve the efficiency of the system. The experimental results demonstrate that the developed robot system adequately meets the demand of safe data interaction and the trajectory planning method improves the system’s control precision and efficiency.

As genetic sequencing becomes less expensive and data sets linking genetic data and medical records (e.g., Biobanks) become larger and more common, issues of data privacy and computational challenges become more necessary to address in order to realize the benefits of these datasets. One possibility for alleviating these issues is through the use of already-computed summary statistics (e.g., slopes and standard errors from a regression model of a phenotype on a genotype). If groups share summary statistics from their analyses of biobanks, many of the privacy issues and computational challenges concerning the access of these data could be bypassed. In this paper we explore the possibility of using summary statistics from simple linear models of phenotype on genotype in order to make inferences about more complex phenotypes (those that are derived from two or more simple phenotypes). We provide exact formulas for the slope, intercept, and standard error of the slope for linear regressions when combining phenotypes. Derived equations are validated via simulation and tested on a real data set exploring the genetics of fatty acids.