Graphstructured Sparse Mixed Models for Genetic Association with Confounding Factors Correction GenomeWide Association Study (GWAS) plays an essential role in understanding human genetics. While various methods have been introduced to increase the signals of GWAS with consideration of population stratification, polygenicity, or pleiotropy. There seems no existing methods that can consdier these three different aspects of genetic association studies together. In this paper, we introduce a new set of models that can utilize the relatedness of available phenotypes to help improve the signals regarding pleiotropy, calculate multivariate coefficients corresponds to polygenicity, and correct population stratification through modelling random effects. We first propose the sparse graphstructured linear mixed model (sGLMM). Then the treeguided sparse linear mixed model (TgSLMM) has further put forward to explore how specifically clusters are. Our method turns out to outperform other existing approaches after simulation experiments and be capable of exploring the correct genetic association and scales to the large dataset like human genome. Further, we validate, compare and use the effectiveness of both sGLMM and TgSLMM in the realworld genomic dataset on Human Alzheimeru0027s Disease discovered by our model, and justify a few of the most important genetic loci. Overlapping SNPs implies that association between each pair of traits follows only one path and is acyclic, which more corresponds to tree structure.

Predicting genedisease associations from the heterogeneous network using graph embedding The discovery of genedisease associations is important for the prevention, diagnosis and treatment of diseases. The studies on genedisease associations have produced diverse data, which can facilitate the genedisease association prediction. Integrating diverse information is critical for developing highaccuracy prediction models. In this paper, we propose a heterogeneous networkbased method that enhances genedisease association prediction by using graph embedding and ensemble learning, abbreviated as xe2x80x9cHNEEMxe2x80x9d. A heterogeneous network is constructed based on genedisease associations, genechemical associations and diseasechemical associations, to combine diverse information. The network uses genes, diseases and chemicals as nodes, and uses their associations as edges. The graph embedding methods are utilized to extract representation vectors of nodes in the heterogeneous network, and the feature vectors of genes and diseases are merged to represent genedisease pairs, and the random forest is employed to build the prediction model based on genedisease pairs. We consider six types of graph embedding methods, and take the individual graph embedding methodgenerated features to build prediction models and use them as base predictors, and then combine base predictors to develop the ensemble learning model HNEEM. We comprehensively compare different graph embedding methods, and results demonstrate that the graph embedding methods produce satisfying results in the genedisease association prediction, and integrating different graph embedding methods can make further improvements. In computational experiments, HNEEM produces better results compared to the stateoftheart genedisease perdition methods, and HNEEM is robust to the data richness as well. Moreover, the usefulness of the proposed method HNEEM is validated by the case studies. In conclusion, HNEEM is a promising method for predicting genedisease associations.