Mining genetic, transcriptomic, and imaging data in Parkinson’s disease

Parkinson’s disease (PD) is a brain disorder that leads to shaking, stiffness and difficulties with walking, balance, and coordination. Affected people may also have mental and behavioral changes, sleep problems, depression, memory difficulties and fatigue. PD is an age-related disease, with an increased prevalence in populations of subjects over the age of 60. About 5 to 10% of PD patients have an "early-onset" variant and it is often, but not always, inherited. PD is characterized by the loss of groups of neurons involved in the control of voluntary movements. Here we present a novel imaging-genetics workflow on Parkinson’s disease aimed to discover some new potential candidate biomarkers for Parkinson’s disease onset, by interpolating genotyping, transcriptomic, functional (Dopamine Transporter Scan) and morphological (Magnetic Resonance Imaging) imaging data. The proposed tutorial has the aim to encourage and stimulate the attendees on the biomedical research with the advantage of integration of heterogenous data. In the last decade the use of images together with genetics data has become widespread among the bioinformatics researchers. This has allowed to inspect and investigate in detail different specific diseases, to better understand their origin and cause. While in recent years many imaging genetics analyses have been developed and successfully applied to characterize brain functioning and neurodegenerative diseases such as Alzheimer’s disease, to our knowledge, no standard imaging genetics workflow has been proposed for PD. The novelty of our workflow can be summarized as follows: • We propose a domain free and easy-to-use workflow, integrating heterogenous data, such as genotyping, transcriptomic, and imaging data. • The workflow addresses the complexity of integrating real multi-source data when a limited number of data are available by proposing three step-based method, where the first step integrates genotyping and imaging features considering each feature individually, the second step summarizes imaging features in a single measure, and the last step focuses on linking potential functional effects caused by the biomarkers found during the two previous phases. • We propose a validation of the method on genetic and imaging data related to PD, showing our new results. The data used for this tutorial were obtained from the Parkinson’s Progression Marker Initiative (PPMI) data portal. Currently, PPMI is the most complete and comprehensive collection of PD-related data. The dataset that will be used in the tutorial consists in a set of polymorphisms, more specifically insertions and deletions (indels) or Single Nucleotide Polymorphisms (SNPs), and transcriptomic data retrieved by RNA sequencing. In addition, DaTSCAN and MRI data are used, which have been shown to be effective in providing potential biomarkers for PD onset and progression. The attendees will acquire an experience on how to conduct a complete imaging-genetics workflow, in a specific case study of Parkinsonian subjects. After the tutorial session the attendees will be able to conduct themselves an imaging-genetics pipeline, which could also be applied to study other neurological diseases. The tutorial will introduce the partecipants to the biological background, especially with the notion of DNA, RNA, Single-nucleotide polymorphism (SNP) and Genome-Wide Association Study (GWAS). The participants will have the opportunity to get familiar with PLINK, a free, open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyzes in a computationally efficient manner. It provides a large range of functionalities designed for data management, summary statistics, quality control, population stratification detection, association analysis, etc. for genotyping data analysis. The audience will also learn how to run code on the widely used R programming environment for statistical computing and graphics. They will also learn some notions about Python, especially how to deal efficiently, with genotyping data using Pandas library, which was designed for data manipulation and analysis. The tutorial code is wrapped in different Jupyter notebooks (formerly IPython Notebooks), that is a web-based and system-independent interactive computational environment for easy analysis reproducibility

Dynamic trajectories of connectome state transitions are heritable

The brain's functional connectome is dynamic, constantly reconfiguring in an individual-specific manner. However, which characteristics of such reconfigurations are subject to genetic effects, and to what extent, is largely unknown. Here, we identified heritable dynamic features, quantified their heritability, and determined their association with cognitive phenotypes. In resting-state fMRI, we obtained multivariate features, each describing a temporal or spatial characteristic of connectome dynamics jointly over a set of connectome states. We found strong evidence for heritability of temporal features, particularly, Fractional Occupancy (FO) and Transition Probability (TP), representing the duration spent in each connectivity configuration and the frequency of shifting between configurations, respectively. These effects were robust against methodological choices of number of states and global signal regression. Genetic effects explained a substantial proportion of phenotypic variance of these features (h2=0.39, 95% CI= [.24,.54] for FO; h2=0.43, 95% CI=[.29,.57] for TP). Moreover, these temporal phenotypes were associated with cognitive performance. Contrarily, we found no robust evidence for heritability of spatial features of the dynamic states (i.e., states’ Modularity and connectivity pattern). Genetic effects may therefore primarily contribute to how the connectome transitions across states, rather than the precise spatial instantiation of the states in individuals. In sum, genetic effects impact the dynamic trajectory of state transitions (captured by FO and TP), and such temporal features may act as endophenotypes for cognitive abilities

Re-Annotator: Annotation Pipeline for Microarray Probe Sequences

Microarray technologies are established approaches for high throughput gene expression, methylation and genotyping analysis. An accurate mapping of the array probes is essential to generate reliable biological findings. However, manufacturers of the microarray platforms typically provide incomplete and outdated annotation tables, which often rely on older genome and transcriptome versions that differ substantially from up-to-date sequence databases. Here, we present the Re-Annotator, a re-annotation pipeline for microarray probe sequences. It is primarily designed for gene expression microarrays but can also be adapted to other types of microarrays. The Re-Annotator uses a custom-built mRNA reference database to identify the positions of gene expression array probe sequences. We applied Re-Annotator to the Illumina Human-HT12 v4 microarray platform and found that about one quarter (25%) of the probes differed from the manufacturer's annotation. In further computational experiments on experimental gene expression data, we compared Re-Annotator to another probe re-annotation tool, ReMOAT, and found that Re-Annotator provided an improved re-annotation of microarray probes. A thorough re-annotation of probe information is crucial to any microarray analysis. The Re-Annotator pipeline is freely available at http://sourceforge.net/projects/reannotator along with re-annotated files for Illumina microarrays HumanHT-12 v3/v4 and MouseRef-8 v2

Nomograms of human hippocampal volume shifted by polygenic scores

Nomograms are important clinical tools applied widely in both developing and aging populations. They are generally constructed as normative models identifying cases as outliers to a distribution of healthy controls. Currently used normative models do not account for genetic heterogeneity. Hippocampal Volume (HV) is a key endophenotype for many brain disorders. Here, we examine the impact of genetic adjustment on HV nomograms and the translational ability to detect dementia patients. Using imaging data from 35,686 healthy subjects aged 44 to 82 from the UK BioBank (UKB), we built HV nomograms using gaussian process regression (GPR), which - compared to a previous method - extended the application age by 20 years, including dementia critical age ranges. Using HV Polygenic Scores (HV-PGS), we built genetically adjusted nomograms from participants stratified into the top and bottom 30% of HV-PGS. This shifted the nomograms in the expected directions by ~100 mm3 (2.3% of the average HV), which equates to 3 years of normal aging for a person aged ~65. Clinical impact of genetically adjusted nomograms was investigated by comparing 818 subjects from the AD neuroimaging (ADNI) database diagnosed as either cognitively normal (CN), having mild cognitive impairment (MCI) or Alzheimer's disease patients (AD). While no significant change in the survival analysis was found for MCI-to-AD conversion, an average of 68% relative decrease was found in intra-diagnostic-group variance, highlighting the importance of genetic adjustment in untangling phenotypic heterogeneity

What PLS can still do for Imaging Genetics in Alzheimer's disease

In this work we exploited Partial Least Squares (PLS) model for analyzing the genetic underpinning of grey matter atrophy in Alzheimer's Disease (AD). To this end, 42 features derived from T1-weighted Magnetic Resonance Imaging, including cortical thicknesses and subcortical volumes were considered to describe the imaging phenotype, while the genotype information consisted of 14 recently proposed AD related Polygenic Risk Scores (PRS), calculated by including Single Nucleotide Polymorphism passing different significance thresholds. The PLS model was applied on a large study cohort obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database including both healthy individuals and AD patients, and validated on an independent ADNI Mild Cognitive Impairment (MCI) cohort, including Early (EMCI) and Late MCI (LMCI). The experimental results confirm the existence of a joint dynamics between brain atrophy and genotype data in AD, while providing important generalization results when tested on a clinically heterogeneous cohort. In particular, less AD specific PRS scores were negatively correlated with cortical thicknesses, while highly AD specific PRSs showed a peculiar correlation pattern among specific subcortical volumes and cortical thicknesses. While the first outcome is in line with the well known neurodegeneration process in AD, the second could be revealing of different AD subtypes

Tackling the dimensions in imaging genetics with CLUB-PLS

A major challenge in imaging genetics and similar fields is to link high-dimensional data in one domain, e.g., genetic data, to high dimensional data in a second domain, e.g., brain imaging data. The standard approach in the area are mass univariate analyses across genetic factors and imaging phenotypes. That entails executing one genome-wide association study (GWAS) for each pre-defined imaging measure. Although this approach has been tremendously successful, one shortcoming is that phenotypes must be pre-defined. Consequently, effects that are not confined to pre-selected regions of interest or that reflect larger brain-wide patterns can easily be missed. In this work we introduce a Partial Least Squares (PLS)-based framework, which we term Cluster-Bootstrap PLS (CLUB-PLS), that can work with large input dimensions in both domains as well as with large sample sizes. One key factor of the framework is to use cluster bootstrap to provide robust statistics for single input features in both domains. We applied CLUB-PLS to investigating the genetic basis of surface area and cortical thickness in a sample of 33,000 subjects from the UK Biobank. We found 107 genome-wide significant locus-phenotype pairs that are linked to 386 different genes. We found that a vast majority of these loci could be technically validated at a high rate: using classic GWAS or Genome-Wide Inferred Statistics (GWIS) we found that 85 locus-phenotype pairs exceeded the genome-wide suggestive (P<1e-05) threshold.Comment: 12 pages, 4 Figures, 2 Table

Multi view based imaging genetics analysis on Parkinson disease

Longitudinal studies integrating imaging and genetic data have recently become widespread among bioinformatics researchers. Combining such heterogeneous data allows a better understanding of complex diseases origins and causes. Through a multi-view based workflow proposal, we show the common steps and tools used in imaging genetics analysis, interpolating genotyping, neuroimaging and transcriptomic data. We describe the advantages of existing methods to analyze heterogeneous datasets, using Parkinson\u2019s Disease (PD) as a case study. Parkinson's disease is associated with both genetic and neuroimaging factors, however such imaging genetics associations are at an early investigation stage. Therefore it is desirable to have a free and open source workflow that integrates different analysis flows in order to recover potential genetic biomarkers in PD, as in other complex diseases