Selection of important variables by statistical learning in genome-wide association analysis

Genetic analysis of complex diseases demands novel analytical methods to interpret data collected on thousands of variables by genome-wide association studies. The complexity of such analysis is multiplied when one has to consider interaction effects, be they among the genetic variations (G × G) or with environment risk factors (G × E). Several statistical learning methods seem quite promising in this context. Herein we consider applications of two such methods, random forest and Bayesian networks, to the simulated dataset for Genetic Analysis Workshop 16 Problem 3. Our evaluation study showed that an iterative search based on the random forest approach has the potential in selecting important variables, while Bayesian networks can capture some of the underlying causal relationships

Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups

We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare clumping-and-thresholding (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals

Assistive VR Gym: Interactions with Real People to Improve Virtual Assistive Robots

Versatile robotic caregivers could benefit millions of people worldwide, including older adults and people with disabilities. Recent work has explored how robotic caregivers can learn to interact with people through physics simulations, yet transferring what has been learned to real robots remains challenging. Virtual reality (VR) has the potential to help bridge the gap between simulations and the real world. We present Assistive VR Gym (AVR Gym), which enables real people to interact with virtual assistive robots. We also provide evidence that AVR Gym can help researchers improve the performance of simulation-trained assistive robots with real people. Prior to AVR Gym, we trained robot control policies (Original Policies) solely in simulation for four robotic caregiving tasks (robot-assisted feeding, drinking, itch scratching, and bed bathing) with two simulated robots (PR2 from Willow Garage and Jaco from Kinova). With AVR Gym, we developed Revised Policies based on insights gained from testing the Original policies with real people. Through a formal study with eight participants in AVR Gym, we found that the Original policies performed poorly, the Revised policies performed significantly better, and that improvements to the biomechanical models used to train the Revised policies resulted in simulated people that better match real participants. Notably, participants significantly disagreed that the Original policies were successful at assistance, but significantly agreed that the Revised policies were successful at assistance. Overall, our results suggest that VR can be used to improve the performance of simulation-trained control policies with real people without putting people at risk, thereby serving as a valuable stepping stone to real robotic assistance.Comment: IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2020), 8 pages, 8 figures, 2 table

The genetic determinants of recurrent somatic mutations in 43,693 blood genomes

Nononcogenic somatic mutations are thought to be uncommon and inconsequential. To test this, we analyzed 43,693 National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine blood whole genomes from 37 cohorts and identified 7131 non-missense somatic mutations that are recurrently mutated in at least 50 individuals. These recurrent non-missense somatic mutations (RNMSMs) are not clearly explained by other clonal phenomena such as clonal hematopoiesis. RNMSM prevalence increased with age, with an average 50-year-old having 27 RNMSMs. Inherited germline variation associated with RNMSM acquisition. These variants were found in genes involved in adaptive immune function, proinflammatory cytokine production, and lymphoid lineage commitment. In addition, the presence of eight specific RNMSMs associated with blood cell traits at effect sizes comparable to Mendelian genetic mutations. Overall, we found that somatic mutations in blood are an unexpectedly common phenomenon with ancestry-specific determinants and human health consequences

Calorimetric Measurements of Magnetic-Field-Induced Inhomogeneous Superconductivity Above The Paramagnetic Limit

We report the first magneto-caloric and calorimetric observations of a magnetic-field-induced phase transition within a superconducting state to the long-sought exotic "FFLO" superconducting state first predicted over 50 years ago. Through the combination of bulk thermodynamic calorimetric and magnetocaloric measurements in the organic superconductor $\kappa$ - (BEDT-TTF)$_2$Cu(NCS)$_2$, as a function of temperature, magnetic field strength, and magnetic field orientation, we establish for the first time that this field-induced first-order phase transition at the paramagnetic limit $H_p$ for traditional superconductivity is to a higher entropy superconducting phase uniquely characteristic of the FFLO state. We also establish that this high-field superconducting state displays the bulk paramagnetic ordering of spin domains required of the FFLO state. These results rule out the alternate possibility of spin-density wave (SDW) ordering in the high field superconducting phase. The phase diagram determined from our measurements --- including the observation of a phase transition into the FFLO phase at $H_p$ --- is in good agreement with recent NMR results and our own earlier tunnel-diode magnetic penetration depth experiments, but is in disagreement with the only previous calorimetric report.Comment: 5 pages, 5 figure

Application of three-level linear mixed-effects model incorporating gene-age interactions for association analysis of longitudinal family data

Longitudinal studies that collect repeated measurements on the same subjects over time have long been considered as being more powerful and providing much better information on individual changes than cross-sectional data. We propose a three-level linear mixed-effects model for testing genetic main effects and gene-age interactions with longitudinal family data. The simulated Genetic Analysis Workshop 16 Problem 3 data sets were used to evaluate the method. Genome-wide association analyses were conducted based on cross-sectional data, i.e., each of the three single-visit data sets separately, and also on the longitudinal data, i.e., using data from all three visits simultaneously. Results from the analysis of coronary artery calcification phenotype showed that the longitudinal association tests were much more powerful than those based on single-visit data only. Gene-age interactions were evaluated under the same framework for detecting genetic effects that are modulated by age

Mendelian randomization supports bidirectional causality between telomere length and clonal hematopoiesis of indeterminate potential

Human genetic studies support an inverse causal relationship between leukocyte telomere length (LTL) and coronary artery disease (CAD), but directionally mixed effects for LTL and diverse malignancies. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by expansion of hematopoietic cells bearing leukemogenic mutations, predisposes both hematologic malignancy and CAD