SPS
Winter School on Imaging Genetics

November 26-29, 2019
Verona

Imaging genetics is an emerging research field that embraces the neuroimaging and genetics communities and aims at blending the respective know-how and methodologies within a unified framework pursuing a holistic view of the human being. Concretely, Imaging Genetics refers to the use of anatomical or physiological imaging technologies as phenotypic assays to evaluate genetic variation. Genetic information and neuroimaging data (structural and functional) are integrated within a unified model enabling the assessment of the link between genes and brain structure and function in health and disease, paving the way to multi-modal multi-scale precision medicine. This summer school is devoted to construct new professional figures and researchers to work at the frontiers of directed at researchers who wish to develop their knowledge and skills on state-of-the- art developments in the field of neuroimaging genetics.


The School on Imaging genetics aims at gathering the knowledge in the different fields that are touched by this topics providing the students a comprehensive view of this research area as well as awareness about the cutting-edge methodological, experimental and clinical aspects that are involved. The students will acquire cutting-edge knowledge in the fields of Signal modeling in structural, microstructural and functional imaging, Omics data analysis, Biostatistics, Integrative data representations for multiple genomic experiments, Programming in R/Bioconductor.

The school provides theoretical and practical sections in each topic covered by the speakers. Moreover 2 hands-on sections are devoted to combine signals from images and genetics. Students must bring their laptops. They will receive by email, weeks before starting the school, the list of all software to be installed in their laptops before school starts. Moreover, organizers will meet students on November 25th from 3:00 to 6:00 pm at the location of the school to help on installation problems that could not be solved by remote tutoring.

SPEAKERS

Andre Altmann
André Altmann
University College London (UK)
Introduction to genome wide association studies. Genome-wide association studies (GWAS) are still the major work horse for identifying novel genes that are associated with disease risk or certain traits. In this talk I will cover the basics of genomics (genome organization, genetic variation, etc.) and introduce how genetic variation is measured at low cost today (sanger sequencing, genotyping chips, next generation sequencing, …). Next, I will introduce the underlying principles of genome-wide association studies, basic tests, genetic model (assumptions), power analysis and the rationale of ever growing studies. I will introduce the motivation behind ‘imaging genetics’, i.e., investigating the genetic basis of phenotypes that we can drive from images. Further, I will cover the practical aspects of actually conducting GWAS, this will entail basic steps like how the data actually looks like (popular file forms), cleaning data, quality control, imputation of missing SNPs (and why this is possible), confounding factors such as population structure (and how to address this), and checking your GWAS results for ‘correctness’.

Marco Lorenzi
Marco Lorenzi
Université Côte d'Azur, Inria (FR)
A Guided Tour to Statistical Association Models. This talk aims at covering the statistical background required to perform association analysis in typical imaging-genetics studies. We will introduce the notion of statistical association, and highlight the standard analysis paradigm in univariate modeling. We will then explore multivariate association models, generalizing to high-dimensional data the notion of statistical association. In particular, we will focus on standard paradigms such as Canonical Correlation Analysis (CCA), Partial Least Squares (PLS), and Reduced Rank Regression (RRR). We will finally introduce more advanced analysis frameworks, such as Bayesian and deep association methods. Within this context we will present the Multi-Channel Variational Autoencoder, recently developed by our group.

Fabrizio Pizzagalli
Fabrizio Pizzagalli
University of Southern California,
Los Angeles (US)
From MRI pre-processing to genetic analysis. Understanding the mechanisms underlying brain structural and functional variation is essential for advancing neuroscience. Magnetic resonance imaging (MRI) can be used to derive metrics of brain structure and function and offer a powerful method to assess disease burden in the brain.
Genetic drivers of brain differences are important to identify as potential risk factors for heritable brain diseases, and targets for their treatment. Imaging genetic studies have found that, as with other complex traits, a single common variant explains less than 1% of the population variance, despite accounting for a large fraction of the variance in aggregate. Therefore, successful studies require tens of thousands of scans, as well as an independent sample for replication. Large-scale consortia in the field of neuroimaging genetics, including the Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA) consortium, have identified common genetic variants that have small but significant associations with variations in brain structural morphometry. Large-scale biobanks have been amassed tens of thousands of MRI scans of individuals from a single scanner for genomic discoveries, yet, replicating effects and ensuring generalizability of findings to current scanned populations require assurance that the brain measures being studied are reliably extracted across a variety of possible MRI scanning paradigms.
We will show the most common techniques and tools used by the neuroimagers community to extract anatomical and functional features from MRI data. Using examples, we will provide instructions for quality control and statistical methods to assess the robustness of the extracted features that will be used as phenotype for the genetic studies.

Blaz Zupan
Blaž Zupan
University of Ljubljana (SI)
Introduction to Clustering. Clustering is a crucial procedure in exploratory data analysis. Given some data, clustering, combined with some visualization, is probably where start to fish for any useful data patterns. I will carry out a hands-on workshop where we will dive into some of the most famous clustering approaches. These will include hierarchical clustering, k-means, DBSCAN, and network-based clustering. We will also combine clustering algorithms with dimensionality reduction and embedding approaches, and learn about principal component analysis, multidimensional scaling, and t-SNE. We will learn how to apply these techniques to images that we will profile with deep learning models. During the workshop, we will use Orange, a data mining framework, and participants are welcome to download and install it from http://orange.biolab.si to follow along.

Francesca Cordero
Francesca Cordero
University of Turin (IT)

Marco Beccuti
Marco Beccuti
University of Turin (IT)
Whole transcriptome data analysis. This course aims to facilitate the use of computing demanding applications in the field of NGS data analysis. The main feature to perform a correct experimental design will be explained. Then, the tools for RNA-seq data analysis will be detailed considering the following steps: quality control, normalisation and data reformatting, selection differentially regulated genes/microRNA, multiple testing and biological interpretation. All these steps will be explained from the theoretical point of view followed by a set of computational exercises to analysis a set of RNASeq data.
The analysis will be performed based on Docker4seq package. This package uses docker containers that embed demanding computing tasks (e.g. short reads mapping) into isolated containers. This approach provides multiple advantages:

Ilaria Boscolo Galazzo
Ilaria Boscolo Galazzo
University of Verona (IT)
Available soon.

PROGRAM

Tuesday 26 NovWednesday 27 NovThursday 28 NovFriday 29 Nov
08:00-09:00Registration
09:00-10:00PizzagalliAltmannZupanLorenzi
10:00-11:00PizzagalliAltmannZupanAltmann
11:00-11:30Coffee Break
11:30-12:30Boscolo GalazzoLorenziZupanPizzagalli
14:00-15:00Boscolo GalazzoLorenzi Imaging Genetics LabDeparture
15:00-16:00Beccuti/Cordero Imaging Genetics Lab
16:00-17:00Beccuti/Cordero
17:00-18:00Beccuti/CorderoSocial Event
Social Event

VENUE

The school will be held at the Dept. of Computer Science of the University of Verona (Verona, Italy). Facilities will be made available for hands-on laboratory sessions where the students will learn and experiment software tools.

Directions

By plane: The airport of Verona is connected to the main European and national cities. From the airport you can reach the city by taxi or by bus. A shuttle bus connects the airport to Verona Porta Nuova train station.

By train: The train station of Verona Porta Nuova is connected with all the main Italian cities by fast and local trains. For the train schedule, please check the Italian railway company. From the station you can reach the Department of Computer Science by bus or by taxi.

By bus: Bus line 21 (towards S. Giovanni Lupatoto) get off at the first bus stop after Borgo Roma hospital; from the bus stop, you can see the department’s buildings. You can also catch bus line 22 (towards Policlinico/San Giovanni Lupatoto) and line 93 (during the night and on Sundays), towards Cadidavid. For these last two bus lines, get off at Borgo Roma hospital, then follow the map above. You can find timetables and line maps at the ATV website.

By car: Take the A4 Milano-Venezia highway, exit Verona Sud then follow the direction "Ospedale Borgo Roma" (hospital), on the right. At the hospital, go straight, cross the small bridge on a river and take the second right. From here you can see the department buildings on your left.

COMMITTEES

School directors

Rosalba Giugno, Associate Professor at the Department of Computer Science, University of Verona
Gloria Menegaz, Full Professor at the Department of Computer Science, University of Verona, Senior IEEE
Carlo Combi, Full Professor at the Department of Computer Science, University of Verona


Organizing commitee

Rosalba Giugno, Associate Professor at the Department of Computer Science, University of Verona
Gloria Menegaz, Full Professor at the Department of Computer Science, University of Verona, Senior IEEE
Carlo Combi, Full Professor at the Department of Computer Science, University of Verona
Ilaria Boscolo Galazzo, Post-doc research associate, Department of Computer Science, University of Verona
Vincenzo Bonnici, Post-doc research associate, Department of Computer Science, University of Verona
Antonino Aparo, Ph.D. student, Department of Computer Science, University of Verona
Samuele Cancellieri, Ph.D. student, Department of Computer Science, University of Verona


Web chair

Vincenzo Bonnici, Post-doc research associate, Department of Computer Science, University of Verona

REGISTRATION

Registration fees:


The number of participants is limited to 40.
Priority will be given to Ph.D. students. If you are a post-doc or a researcher, please contact the organizers at imagenschool2019@gmail.com. Admission to the school is possible only if there are positions available.


Application requirements:

Applicants should send the necessary documentation via email at the following address: imagenschool2019@gmail.com

Deadline for applications: November 1, 2019
Notification of acceptance will be by November 10, 2019.

Applications after the deadline will be accepted until the maximum number of attendants will be reached.

AFTER the notification of acceptance, payments can be made via credit card at the following link: AVAILABLE SOON.

SPONSORS

IEEE SPS Department of Computer Science Ph.D. school

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