A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction

While linear mixed model (LMM) has shown a competitive performance in correcting spurious associations raised by population stratification, family structures, and cryptic relatedness, more challenges are still to be addressed regarding the complex structure of genotypic and phenotypic data. For example, geneticists have discovered that some clusters of phenotypes are more co-expressed than others. Hence, a joint analysis that can utilize such relatedness information in a heterogeneous data set is crucial for genetic modeling. We proposed the sparse graph-structured linear mixed model (sGLMM) that can incorporate the relatedness information from traits in a dataset with confounding correction. Our method is capable of uncovering the genetic associations of a large number of phenotypes together while considering the relatedness of these phenotypes. Through extensive simulation experiments, we show that the proposed model outperforms other existing approaches and can model correlation from both population structure and shared signals. Further, we validate the effectiveness of sGLMM in the real-world genomic dataset on two different species from plants and humans. In Arabidopsis thaliana data, sGLMM behaves better than all other baseline models for 63.4% traits. We also discuss the potential causal genetic variation of Human Alzheimer's disease discovered by our model and justify some of the most important genetic loci.Comment: Code available at https://github.com/YeWenting/sGLM

Sparse Variable Selection on High Dimensional Heterogeneous Data with Tree Structured Responses

We consider the problem of sparse variable selection on high dimension heterogeneous data sets, which has been taken on renewed interest recently due to the growth of biological and medical data sets with complex, non-i.i.d. structures and prolific response variables. The heterogeneity is likely to confound the association between explanatory variables and responses, resulting in a wealth of false discoveries when Lasso or its variants are na\"ively applied. Therefore, the research interest of developing effective confounder correction methods is growing. However, ordinarily employing recent confounder correction methods will result in undesirable performance due to the ignorance of the convoluted interdependency among the prolific response variables. To fully improve current variable selection methods, we introduce a model that can utilize the dependency information from multiple responses to select the active variables from heterogeneous data. Through extensive experiments on synthetic and real data sets, we show that our proposed model outperforms the existing methods

Tuning the Magnetic Ordering Temperature of Hexagonal Ferrites by Structural Distortion Control

To tune the magnetic properties of hexagonal ferrites, a family of magnetoelectric multiferroic materials, by atomic-scale structural engineering, we studied the effect of structural distortion on the magnetic ordering temperature (TN). Using the symmetry analysis, we show that unlike most antiferromagnetic rare-earth transition-metal perovskites, a larger structural distortion leads to a higher TN in hexagonal ferrites and manganites, because the K3 structural distortion induces the three-dimensional magnetic ordering, which is forbidden in the undistorted structure by symmetry. We also revealed a near-linear relation between TN and the tolerance factor and a power-law relation between TN and the K3 distortion amplitude. Following the analysis, a record-high TN (185 K) among hexagonal ferrites was predicted in hexagonal ScFeO3 and experimentally verified in epitaxially stabilized films. These results add to the paradigm of spin-lattice coupling in antiferromagnetic oxides and suggests further tunability of hexagonal ferrites if more lattice distortion can be achieved

Magnetism of novel rare-earth-free intermetallic compounds

Rare-earth-free magnets have drawn lots of interest because of their low cost, and the production is not limited by the shortage of rare-earth elements. This dissertation focuses on three rare-earth-free materials, Fe-Co-Ti alloys, Fe-Ni-B alloys, and Co-Si. All of them are synthesized by arc melting followed by melt-spinning. Fe3+xCo3−xTi2 (x = 0, 2, 3) alloys exhibit hexagonal crystal structures and show non-collinear spin structures according to neutron diffraction. The magnetic moments have projections on both the c-axis and basal plane, and the corresponding misalignment angle exhibits a nonlinear decrease with x, which we explain as a micromagnetic effect caused by Fe-Co site disorder. To increase the magnetic anisotropy of Fe2Ni alloy, we dope boron into Fe2Ni and analyze the structure with X-ray diffraction, which shows face center cubic or body center cubic structure according to different temperatures. Magnetic analysis with magnetometer shows that the presence of boron dramatically increases the anisotropy of Fe-Ni-B alloy. Neutron powder diffraction is employed to investigate the magnetism and spin structure in single-phase B20 Co1.043Si0.957. The magnetic contributions to the neutron powder diffraction data measured in zero fields are consistent with the helical order among the allowed spin structures derived from group theory. The magnitude of the magnetic moment is larger than the bulk magnetization determined from magnetometry, indicating the formation of a helical spin phase and the associated conical states in high magnetic fields. Advisor: Xiaoshan X

Deep Learning for Genomics: A Concise Overview

Advancements in genomic research such as high-throughput sequencing techniques have driven modern genomic studies into "big data" disciplines. This data explosion is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in a variety of fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning since we are expecting from deep learning a superhuman intelligence that explores beyond our knowledge to interpret the genome. A powerful deep learning model should rely on insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with a proper deep architecture, and remark on practical considerations of developing modern deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research, as well as pointing out potential opportunities and obstacles for future genomics applications.Comment: Invited chapter for Springer Book: Handbook of Deep Learning Application