Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping

Progressive Genomic Approaches to Explore Drought- and Salt-Induced Oxidative Stress Responses in Plants under Changing Climate

Drought and salinity are the major environmental abiotic stresses that negatively impact crop development and yield. To improve yields under abiotic stress conditions, drought- and salinity-tolerant crops are key to support world crop production and mitigate the demand of the growing world population. Nevertheless, plant responses to abiotic stresses are highly complex and controlled by networks of genetic and ecological factors that are the main targets of crop breeding programs. Several genomics strategies are employed to improve crop productivity under abiotic stress conditions, but traditional techniques are not sufficient to prevent stress-related losses in productivity. Within the last decade, modern genomics studies have advanced our capabilities of improving crop genetics, especially those traits relevant to abiotic stress management. This review provided updated and comprehensive knowledge concerning all possible combinations of advanced genomics tools and the gene regulatory network of reactive oxygen species homeostasis for the appropriate planning of future breeding programs, which will assist sustainable crop production under salinity and drought conditions.

Robust regression based genome-wide multi-trait QTL analysis

In genome-wide quantitative trait locus (QTL) mapping studies, multiple quantitative traits are often measured along with the marker genotypes. Multi-trait QTL (MtQTL) analysis, which includes multiple quantitative traits together in a single model, is an efficient technique to increase the power of QTL identification. The two most widely used classical approaches for MtQTL mapping are Gaussian Mixture Model-based MtQTL (GMM-MtQTL) and Linear Regression Model-based MtQTL (LRM-MtQTL) analyses. There are two types of LRM-MtQTL approach known as least squares-based LRM-MtQTL (LS-LRM-MtQTL) and maximum likelihood-based LRM-MtQTL (ML-LRM-MtQTL). These three classical approaches are equivalent alternatives for QTL detection, but ML-LRM-MtQTL is computationally faster than GMM-MtQTL and LS-LRM-MtQTL. However, one major limitation common to all the above classical approaches is that they are very sensitive to outliers, which leads to misleading results. Therefore, in this study, we developed an LRM-based robust MtQTL approach, called LRM-RobMtQTL, for the backcross population based on the robust estimation of regression parameters by maximizing the β-likelihood function induced from the β-divergence with multivariate normal distribution. When β = 0, the proposed LRM-RobMtQTL method reduces to the classical ML-LRM-MtQTL approach. Simulation studies showed that both ML-LRM-MtQTL and LRM-RobMtQTL methods identified the same QTL positions in the absence of outliers. However, in the presence of outliers, only the proposed method was able to identify all the true QTL positions. Real data analysis results revealed that in the presence of outliers only our LRM-RobMtQTL approach can identify all the QTL positions as those identified in the absence of outliers by both methods. We conclude that our proposed LRM-RobMtQTL analysis approach outperforms the classical MtQTL analysis methods.

The Effect of the Graphic Structures of Humanoid Robot on N200 and P300 Potentials

Humanoid robots are widely used in brain computer interface (BCI). Using a humanoid robot stimulus could increase the amplitude of event-related potentials (ERPs), which improves BCI performance. Since a humanoid robot contains many human elements, the element that increases the ERPs amplitude is unclear, and how to test the effect of it on the brain is a problem. This study used different graphic structures of an NAO humanoid robot to design three types of robot stimuli: a global robot, its local information, and its topological action. Ten subjects first conducted an odd-ball-based BCI (OD-BCI) by applying these stimuli. Then, they accomplished a delayed matching-to-sample task (DMST) that was used to specialize the encoding and retrieval phases of the OD-BCI task. In the retrieval phase of the DMST, the global stimulus induces the largest N200 and P300 potentials with the shortest latencies in the frontal, central, and occipital areas. This finding is in accordance with the P300 and classification performance of the OD-BCI task. When induced by the local stimulus, the subjects responded faster and more accurately in the retrieval phase of the DMST than in the other two conditions, indicating that the local stimulus improved the subject's responses. These results indicate that the OD-BCI task causes subject's retrieval work when the subject recognizes and outputs the stimulus. The global stimulus that contains topological and local elements could make brain react faster and induce larger ERPs, this finding could be used during the development of visual stimuli to improve BCI performance.

Comparison of F-tests for Univariate and Multivariate Mixed-Effect Models in Genome-Wide Association Mapping

Genome-wide association mapping (GWA) has been widely applied to a variety of species to identify genomic regions responsible for quantitative traits. The use of multivariate information could enhance the detection power of GWA. Although mixed-effect models are frequently used for GWA, the utility of F-tests for multivariate mixed-effect models is not well-recognized. Thus, we compared the F-tests for univariate and multivariate mixed-effect models with simulations. The superiority of the multivariate F-test over the univariate test varied depending on three parameters: phenotypic correlation between variates (r), relative size of quantitative trait locus effects between variates (a_d), and missing proportion of phenotypic records (m_prop). Simulation results showed that, when m_prop was low, the multivariate F-test outperformed the univariate test as r and a_d differ, and as m_prop increased, the multivariate F-test outperformed as a_d increased. These observations were consistent with results of the analytical evaluation of the F-value. When m_prop was at the maximum, i.e., when no individual had phenotypic values for multiple variates, as in the case of meta-analysis, the multivariate F-test gained more detection power as a_d increased. Although using multivariate information in mixed-effect model contexts did not always ensure more detection power than with univariate tests, the multivariate F-test will be a method applied when multivariate data are available because it does not show inflation of signals and could lead to new findings.

A Dual Stimuli Approach Combined with Convolutional Neural Network to Improve Information Transfer Rate of Event-Related Potential-Based Brain-Computer Interface

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.