A novel structure-aware sparse learning algorithm for brain imaging genetics

Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings

GN-SCCA: GraphNet based Sparse Canonical Correlation Analysis for Brain Imaging Genetics

Identifying associations between genetic variants and neuroimaging quantitative traits (QTs) is a popular research topic in brain imaging genetics. Sparse canonical correlation analysis (SCCA) has been widely used to reveal complex multi-SNP-multi-QT associations. Several SCCA methods explicitly incorporate prior knowledge into the model and intend to uncover the hidden structure informed by the prior knowledge. We propose a novel structured SCCA method using Graph constrained Elastic-Net (GraphNet) regularizer to not only discover important associations, but also induce smoothness between coefficients that are adjacent in the graph. In addition, the proposed method incorporates the covariance structure information usually ignored by most SCCA methods. Experiments on simulated and real imaging genetic data show that, the proposed method not only outperforms a widely used SCCA method but also yields an easy-to-interpret biological findings

Identification of Cancer Cell-Line Origins Using Fluorescence Image-Based Phenomic Screening

Universal phenotyping techniques that can discriminate among various states of biological systems have great potential. We applied 557 fluorescent library compounds to NCI's 60 human cancer cell-lines (NCI-60) to generate a systematic fluorescence phenotypic profiling data. By the kinetic fluorescence intensity analysis, we successfully discriminated the organ origin of all the 60 cell-lines

Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease

Scanning the entire genome in search of variants related to imaging phenotypes holds great promise in elucidating the genetic etiology of neurodegenerative disorders. Here we discuss the application of a penalized multivariate model, sparse reduced-rank regression (sRRR), for the genome-wide detection of markers associated with voxel-wise longitudinal changes in the brain caused by Alzheimer’s disease (AD). Using a sample from the Alzheimer’s Disease Neuroimaging Initiative database, we performed three separate studies that each compared two groups of individuals to identify genes associated with disease development and progression. For each comparison we took a two-step approach: initially, using penalized linear discriminant analysis, we identified voxels that provide an imaging signature of the disease with high classification accuracy; then we used this multivariate biomarker as a phenotype in a genome-wide association study, carried out using sRRR. The genetic markers were ranked in order of importance of association to the phenotypes using a data re-sampling approach. Our findings confirmed the key role of the APOE and TOMM40 genes but also highlighted some novel potential associations with AD

Joint Modeling of Imaging and Genetics

Abstract. We propose a unified Bayesian framework for detecting genetic variants associated with a disease while exploiting image-based features as an intermediate phenotype. Traditionally, imaging genetics methods comprise two separate steps. First, image features are selected based on their relevance to the disease phenotype. Second, a set of genetic variants are identified to explain the selected features. In contrast, our method performs these tasks simultaneously to ultimately assign probabilistic measures of relevance to both genetic and imaging markers. We derive an efficient approximate inference algorithm that handles high dimensionality of imaging genetic data. We evaluate the algorithm on synthetic data and show that it outperforms traditional models. We also illustrate the application of the method on ADNI data

The frequency of central nervous system complications in the Cypriot cohort of ATTRV30M neuropathy transplanted patients

Background: Hereditary transthyretin amyloidosis (ATTR) is a hereditary, sensorimotor and autonomic neuropathy caused by deposits of mutated transthyretin (TTR). The commonest TTR mutation is V30M (ATTRV30M) with patients usually living for about 10 years after disease onset. Liver transplantation (LT) until recently was considered the standard treatment. Objective and methods: This study aims to assess the frequency of CNS complications in post-LT patients from the Cypriot cohort. Epidemiological data were collected for all genetically confirmed ATTRV30M neuropathy patients diagnosed at CING since 1992, and CNS-associated symptoms were assessed and evaluated by two neurology specialists. Results: Out of the 48 transplanted patients, 10 (20.8%) presented with a CNS complication. All patients had ocular involvement, mainly glaucoma (7/10). Eight presented with transient focal neurological episodes (TFNEs), with expressive dysphasia being reported by four of them. The mean time of TFNE-emergence was 16.6 years after the LT. Three died from cerebral hemorrhage. Conclusions: CNS complications in post-LT ATTRV30M patients are not rare and usually manifest themselves at a time that surpasses the mean time the patients would have survived without a LT. CNS involvement is associated with increased mortality, due to cerebral hemorrhage. © 2020, Fondazione Società Italiana di Neurologia