Interval estimation of disease loci: development and applications of new linkage methods

Three variants of the confidence set inference (CSI) procedure were proposed and applied to both the simulated and the Collaborative Study on the Genetics of Alcoholism (COGA) data. For each of the two applications, we first performed a preliminary genome scan study based on the microsatellite markers using the GENEHUNTER+ software to identify regions that potentially harbor disease loci. For each such region, we estimated the sibling identity-by-descent sharing probability distribution at the putative disease locus. Based on these estimated probabilities, the CSI procedures were employed to further localize the disease loci using the single-nucleotide polymorphism markers, leading to confidence intervals/regions for their locations. For our analysis with the simulated data, we had knowledge of the simulating models at the time we performed the analysis

A LASSO penalized regression approach for genome-wide association analyses using related individuals: application to the Genetic Analysis Workshop 19 simulated data

We propose a novel LASSO (least absolute shrinkage and selection operator) penalized regression method used to analyze samples consisting of (potentially) related individuals. Developed in the context of linear mixed models, our method models the relatedness of individuals in the sample through a random effect whose covariance structure is a linear function of known matrices with elements combinations of the condensed coefficients of identity between the individuals in the sample. We implement our method to analyze the simulated family data provided by the 19th Genetic Analysis Workshop in an effort to identify loci regulating the simulated trait of systolic blood pressure. The analyses were performed with full knowledge of the simulation model. Our findings demonstrate that we can significantly reduce the rate of false positive signals by incorporating the relatedness of the study participants

Comparative Assessment of Grit in the Allopathic versus Osteopathic Physician

Matriculating into medical school is a strenuous process, including numerous roadblocks unique to both allopathic and osteopathic schools, with osteopathic applicants often jumping more hurdles in the journey. Grit: passion and perseverance towards long term goals, especially in the setting of failure/adversity High levels of grit correlate with low levels of burnout Purpose: examine grit in allopathic versus osteopathic physician to help identify resources to prevent physician burnou

Linkage analysis of the simulated data – evaluations and comparisons of methods

The goal of this study is to evaluate, compare, and contrast several standard and new linkage analysis methods. First, we compare a recently proposed confidence set approach with MAPMAKER/SIBS. Then, we evaluate a new Bayesian approach that accounts for heterogeneity. Finally, the newly developed software SIMPLE is compared with GENEHUNTER. We apply these methods to several replicates of the Genetic Analysis Workshop 13 simulated data to assess their ability to detect the high blood pressure genes on chromosome 21, whose positions were known to us prior to the analyses. In contrast to the standard methods, most of the new approaches are able to identify at least one of the disease genes in all the replicates considered

Confidence set of putative quantitative trait loci in whole genome scans with application to the Genetic Analysis Workshop 17 simulated data

As genetic maps become more highly dense, the ability to sufficiently localize putative disease loci becomes an achievable goal. This has prompted an increased interest in methods for constructing confidence intervals for the location of variants that contribute to a trait. Such intervals are important because, by reducing the number of candidate loci, they can help in the design of cost-effective and time-efficient follow-up studies. We introduce a new approach that can be used in whole-genome scans to obtain a confidence set of loci that contribute at least a predetermined percentage h to the overall genetic variation of a quantitative phenotype. The method is developed in the framework of generalized linear mixed models and can accommodate families of arbitrary size and structure. We apply our method to the Genetic Analysis Workshop 17 simulated data where we scan chromosomes 6, 15, 20, 21, and 22 to uncover loci regulating the simulated phenotype Q2. For the analyses we had prior knowledge of the simulation model used to generate the phenotype

Design and Experimental Evaluation of a Haptic Robot-Assisted System for Femur Fracture Surgery

In the face of challenges encountered during femur fracture surgery, such as the high rates of malalignment and X-ray exposure to operating personnel, robot-assisted surgery has emerged as an alternative to conventional state-of-the-art surgical methods. This paper introduces the development of Robossis, a haptic system for robot-assisted femur fracture surgery. Robossis comprises a 7-DOF haptic controller and a 6-DOF surgical robot. A unilateral control architecture is developed to address the kinematic mismatch and the motion transfer between the haptic controller and the Robossis surgical robot. A real-time motion control pipeline is designed to address the motion transfer and evaluated through experimental testing. The analysis illustrates that the Robossis surgical robot can adhere to the desired trajectory from the haptic controller with an average translational error of 0.32 mm and a rotational error of 0.07 deg. Additionally, a haptic rendering pipeline is developed to resolve the kinematic mismatch by constraining the haptic controller (user hand) movement within the permissible joint limits of the Robossis surgical robot. Lastly, in a cadaveric lab test, the Robossis system assisted surgeons during a mock femur fracture surgery. The result shows that Robossis can provide an intuitive solution for surgeons to perform femur fracture surgery.Comment: This paper is to be submitted to an IEEE journa