Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules

Motivation: Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity. Results: We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network. Our method includes three steps: (i) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re-prioritized genes; and (iii) identify phenotype-relevant modules enriched by top GWAS findings. We demonstrate our method on the GWAS of [18F]FDG-PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala-specific functional interaction network. The proposed network-based GWAS method can effectively detect densely connected modules enriched by top GWAS findings. Tissue-specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype

A Fractal Analysis of the HI Emission from the Large Magellanic Cloud

A composite map of HI in the LMC using the ATCA interferometer and the Parkes multibeam telescope was analyzed in several ways in an attempt to characterize the structure of the neutral gas and to find an origin for it. Fourier transform power spectra in 1D, 2D, and in the azimuthal direction were found to be approximate power laws over 2 decades in length. Delta-variance methods also showed the same power-law structure. Detailed models of these data were made using line-of-sight integrals over fractals that are analogous to those generated by simulations of turbulence with and without phase transitions. The results suggested a way to measure directly for the first time the line-of-sight thickness of the cool component of the HI disk of a nearly face-on galaxy. The signature of this thickness was found to be present in all of the measured power spectra. The character of the HI structure in the LMC was also viewed by comparing positive and negative images of the integrated emission. The geometric structure of the high-emission regions was found to be filamentary, whereas the geometric structure of the low-emission (intercloud) regions was found to be patchy and round. This result suggests that compressive events formed the high-emission regions, and expansion events, whether from explosions or turbulence, formed the low-emission regions. The character of the structure was also investigated as a function of scale using unsharp masks. All of these results suggest that most of the ISM in the LMC is fractal, presumably the result of pervasive turbulence, self-gravity, and self-similar stirring.Comment: 30 pages, 21 figures, scheduled for ApJ Vol 548n1, Feb 10, 200

Network-guided sparse learning for predicting cognitive outcomes from MRI measures

Alzheimer's disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from MRI measures. In particular, sparse models have been proposed to identify the optimal imaging markers with high prediction power. However, the complex relationship among imaging markers are often overlooked or simplified in the existing methods. To address this issue, we present a new sparse learning method by introducing a novel network term to more flexibly model the relationship among imaging markers. The proposed algorithm is applied to the ADNI study for predicting cognitive outcomes using MRI scans. The effectiveness of our method is demonstrated by its improved prediction performance over several state-of-the-art competing methods and accurate identification of cognition-relevant imaging markers that are biologically meaningful

Hippocampal transcriptome-guided genetic analysis of correlated episodic memory phenotypes in Alzheimer's disease

As the most common type of dementia, Alzheimer's disease (AD) is a neurodegenerative disorder initially manifested by impaired memory performances. While the diagnosis information indicates a dichotomous status of a patient, memory scores have the potential to capture the continuous nature of the disease progression and may provide more insights into the underlying mechanism. In this work, we performed a targeted genetic study of memory scores on an AD cohort to identify the associations between a set of genes highly expressed in the hippocampal region and seven cognitive scores related to episodic memory. Both main effects and interaction effects of the targeted genetic markers on these correlated memory scores were examined. In addition to well-known AD genetic markers APOE and TOMM40, our analysis identified a new risk gene NAV2 through the gene-level main effect analysis. NAV2 was found to be significantly and consistently associated with all seven episodic memory scores. Genetic interaction analysis also yielded a few promising hits warranting further investigation, especially for the RAVLT list B Score

Genome-wide association study of language performance in Alzheimer's disease

Language impairment is common in prodromal stages of Alzheimer's disease (AD) and progresses over time. However, the genetic architecture underlying language performance is poorly understood. To identify novel genetic variants associated with language performance, we analyzed brain MRI and performed a genome-wide association study (GWAS) using a composite measure of language performance from the Alzheimer's Disease Neuroimaging Initiative (ADNI; n=1560). The language composite score was associated with brain atrophy on MRI in language and semantic areas. GWAS identified GLI3 (GLI family zinc finger 3) as significantly associated with language performance (p<5×10-8). Enrichment of GWAS association was identified in pathways related to nervous system development and glutamate receptor function and trafficking. Our results, which warrant further investigation in independent and larger cohorts, implicate GLI3, a developmental transcription factor involved in patterning brain structures, as a putative gene associated with language dysfunction in AD

Multimodal Neuroimaging Predictors for Cognitive Performance Using Structured Sparse Learning

poster abstractRegression models have been widely studied to investigate whether multimodal neuroimaging measures can be used as effective biomarkers for predicting cognitive outcomes in the study of Alzheimer's Disease (AD). Most existing models overlook the interrelated structures either within neuroimaging measures or between cognitive outcomes, and thus may have limited power to yield optimal solutions. To address this issue, we propose to incorporate an L21 norm and/or a group L21 norm (G21 norm) in the regression models. Using ADNI-1 and ADNI-GO/2 data, we apply these models to examining the ability of structural MRI and AV-45 PET scans for predicting cognitive measures including ADAS and RAVLT scores. We focus our analyses on the participants with mild cognitive impairment (MCI), a prodromal stage of AD, in order to identify useful patterns for early detection. Compared with traditional linear and ridge regression methods, these new models not only demonstrate superior and more stable predictive performances, but also identify a small set of imaging markers that are biologically meaningful

The role of apolipoprotein E (APOE) genotype in early mild cognitive impairment (E-MCI)

Objective: Our goal was to evaluate the association of APOE with amyloid deposition, cerebrospinal fluid levels (CSF) of Aβ, tau, and p-tau, brain atrophy, cognition and cognitive complaints in E-MCI patients and cognitively healthy older adults (HC) in the ADNI-2 cohort. Methods: Two-hundred and nine E-MCI and 123 HC participants from the ADNI-2 cohort were included. We evaluated the impact of diagnostic status (E-MCI vs. HC) and APOE ε4 status (ε4 positive vs. ε4 negative) on cortical amyloid deposition (AV-45/Florbetapir SUVR PET scans), brain atrophy (structural MRI scans processed using voxel-based morphometry and Freesurfer version 5.1), CSF levels of Aβ, tau, and p-tau, and cognitive performance and complaints. Results: E-MCI participants showed significantly impaired cognition, higher levels of cognitive complaints, greater levels of tau and p-tau, and subcortical and cortical atrophy relative to HC participants (p < 0.05). Cortical amyloid deposition and CSF levels of Aβ were significantly associated with APOE ε4 status but not E-MCI diagnosis, with ε4 positive participants showing more amyloid deposition and lower levels of CSF Aβ than ε4 negative participants. Other effects of APOE ε4 status on cognition and CSF tau levels were also observed. Conclusions: APOE ε4 status is associated with amyloid accumulation and lower CSF Aβ, as well as increased CSF tau levels in early prodromal stages of AD (E-MCI) and HC. Alternatively, neurodegeneration, cognitive impairment, and increased complaints are primarily associated with a diagnosis of E-MCI. These findings underscore the importance of considering APOE genotype when evaluating biomarkers in early stages of disease

Sub-millimeter Observations of Giant Molecular Clouds in the Large Magellanic Cloud: Temperature and Density as Determined from J=3-2 and J=1-0 transitions of CO

We have carried out sub-mm 12CO(J=3-2) observations of 6 giant molecular clouds (GMCs) in the Large Magellanic Cloud (LMC) with the ASTE 10m sub-mm telescope at a spatial resolution of 5 pc and very high sensitivity. We have identified 32 molecular clumps in the GMCs and revealed significant details of the warm and dense molecular gas with n(H2) $\sim$ 10$^{3-5}$ cm$^{-3}$ and Tkin $\sim$ 60 K. These data are combined with 12CO(J=1-0) and 13CO(J=1-0) results and compared with LVG calculations. We found that the ratio of 12CO(J=3-2) to 12CO(J=1-0) emission is sensitive to and is well correlated with the local Halpha flux. We interpret that differences of clump propeties represent an evolutionary sequence of GMCs in terms of density increase leading to star formation.Type I and II GMCs (starless GMCs and GMCs with HII regions only, respectively) are at the young phase of star formation where density does not yet become high enough to show active star formation and Type III GMCs (GMCs with HII regions and young star clusters) represents the later phase where the average density is increased and the GMCs are forming massive stars. The high kinetic temperature correlated with \Halpha flux suggests that FUV heating is dominant in the molecular gas of the LMC.Comment: 74 pages, including 41 figures, accepted for publication in ApJ

Genomic Copy Number Analysis in Alzheimer's Disease and Mild Cognitive Impairment: An ADNI Study

Copy number variants (CNVs) are DNA sequence alterations, resulting in gains (duplications) and losses (deletions) of genomic segments. They often overlap genes and may play important roles in disease. Only one published study has examined CNVs in late-onset Alzheimer's disease (AD), and none have examined mild cognitive impairment (MCI). CNV calls were generated in 288 AD, 183 MCI, and 184 healthy control (HC) non-Hispanic Caucasian Alzheimer's Disease Neuroimaging Initiative participants. After quality control, 222 AD, 136 MCI, and 143 HC participants were entered into case/control association analyses, including candidate gene and whole genome approaches. Although no excess CNV burden was observed in cases (AD and/or MCI) relative to controls (HC), gene-based analyses revealed CNVs overlapping the candidate gene CHRFAM7A, as well as CSMD1, SLC35F2, HNRNPCL1, NRXN1, and ERBB4 regions, only in cases. Replication in larger samples is important, after which regions detected here may be promising targets for resequencing

Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-Learning Predictive Model

With rapid progress in high-throughput genotyping and neuroimaging, imaging genetics has gained significant attention in the research of complex brain disorders, such as Alzheimer's Disease (AD). The genotype-phenotype association study using imaging genetic data has the potential to reveal genetic basis and biological mechanism of brain structure and function. AD is a progressive neurodegenerative disease, thus, it is crucial to look into the relations between SNPs and longitudinal variations of neuroimaging phenotypes. Although some machine learning models were newly presented to capture the longitudinal patterns in genotype-phenotype association study, most of them required fixed longitudinal structures of prediction tasks and could not automatically learn the interrelations among longitudinal prediction tasks. To address this challenge, we proposed a novel temporal structure auto-learning model to automatically uncover longitudinal genotype-phenotype interrelations and utilized such interrelated structures to enhance phenotype prediction in the meantime. We conducted longitudinal phenotype prediction experiments on the ADNI cohort including 3,123 SNPs and 2 types of biomarkers, VBM and FreeSurfer. Empirical results demonstrated advantages of our proposed model over the counterparts. Moreover, available literature was identified for our top selected SNPs, which demonstrated the rationality of our prediction results. An executable program is available online at https://github.com/littleq1991/sparse_lowRank_regression