Omics Sciences for the Identification of Pathogenetic Mechanisms and Biomarkers in Neurodegenerative Diseases
Conditions: Alzheimer Disease, FTD, Young-onset Dementia, MCI, Parkinson Disease, ALS (Amyotrophic Lateral Sclerosis)
Sponsor: Ospedale Policlinico San Martino

  
 
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Study Protocol 
Study Title: Omics sciences for the identification of pathogenic mechanisms and biomarkers in 
neurodegenerative diseases 
Study Code: NeurOmics 
 
Sponsor: IRCCS Ospedale Policlinico San Martino 
 
Co-Sponsor: Fondazione Istituto Italiano di Tecnologia (IIT) 
 
Principal Investigator: Dr. Paola Mandich, Director of the Unit of Medical Genetics, 
IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, Genoa, Italy 
 
Principal Investigator: Prof. Stefano Gustincich, Scientific Director CMP3VdA, IIT, Via Lavoratori - Vittime 
del Col du Mont 28, Aosta, Italy 
 
Principal Investigator: Dr. Alessio Di Fonzo, Senior Neurologist, Department of Neurology, 
IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Via Sforza 28, Milan, Italy 
 
Version and Date: V. 1 30.11.2023 
 
 
 
 
 
 
CONFIDENTIAL 
The information contained in this document is confidential and the property of the Sponsor. It may not, in whole or in 
part, be transmitted, reproduced, published, or disclosed to any party other than the responsible Ethics Committee 
without prior written authorization from the Sponsor, except to the extent necessary to obtain informed consent from 
subjects who may participate in the study. 
 

  
 
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1. INTRODUCTION 
Neurodegenerative diseases are a heterogeneous group of disorders characterized by the progressive deterioration 
of structures and functions of the nervous system. The most relevant conditions in terms of incidence, impact on 
patients’ quality of life, and burden on the national healthcare system are Alzheimer’s disease (AD), Parkinson’s 
disease (PD), and Amyotrophic Lateral Sclerosis (ALS)¹,²,³. The prevalences of AD, PD, and ALS are estimated at 742, 
174, and 3 cases per 100,000, respectively⁴,⁵,⁶. Overall, approximately 1,000,000 individuals in Italy are affected by 
neurodegenerative diseases, largely represented by dementias and parkinsonian syndromes⁷. 
Neurodegenerative diseases are caused by the progressive loss of neurons within the nervous system and are generally 
associated with protein deposits or pathological protein conformations, leading to biochemical alterations, loss of 
function, and apoptosis in neurons and glial cells⁸. They are chronic, progressive, and disabling disorders. At present, 
only symptomatic treatments are available, while no curative or disease-modifying therapy has yet been established. 
These conditions may arise from acquired and environmental factors, genetic determinants, or—more likely—from 
their combined effect. Molecular genetics and pathology have played a pivotal role in identifying genes and proteins 
involved in the intracellular aggregation processes that lead to neurodegeneration. The most common proteins 
implicated in these pathological aggregates are amyloid-β, tau, α-synuclein, and TDP-43⁸,⁹. 
Currently, genetic diagnosis is possible only in a minority of cases, particularly in early-onset forms and/or with a 
positive family history. For example, in AD, approximately 1% of cases have a clear genetic etiology, whereas in about 
40% a genetic and/or environmental risk factor for disease development can be identified¹⁰. In PD, a genetic cause can 
be determined in approximately 10% of cases¹¹, while in ALS this proportion ranges from 10% to 15%¹². 
1.1 Neurodegenerative diseases: background 
Alzheimer’s disease (AD) is the most common form of dementia. Dementia is defined as a progressive and often 
irreversible decline of mental faculties, with diagnostic criteria well established by the DSM-V. From a 
neuropathological standpoint, AD is characterized by extracellular β-amyloid plaques and intracellular fibrillary 
tangles in cortical and limbic brain regions, resulting in progressive cognitive and functional decline¹³. Symptom 
onset generally occurs after the fifth decade of life, with variable progression. Three genes have been associated 
with early-onset AD (< 60 years), while numerous genomic loci have been linked to increased risk in late-onset 
forms¹⁰. Encouraging preliminary results have been reported for disease-modifying therapies that slow 
progression¹⁴,¹⁵. Consequently,  a precise diagnosis and accurate patient stratification, including genetic 
characterization, are crucial for the implementation of personalized therapeutic approaches. 
Frontotemporal dementia (FTD) is the second most common cause of early-onset dementia¹⁶ and it   encompasses a 
broad spectrum of neuropathological entities and clinical phenotypes. The disease primarily affects three domains: 
behavior, language, and motor function, due to progressive atrophy of the frontal and temporal lobes. Motor 
dysfunction is also observed in related disorders such as progressive  
supranuclear palsy (PSP), corticobasal syndrome (CBS), and FTD with amyotrophic lateral sclerosis (FTD-ALS)¹⁷. 
Executive dysfunction, a core clinical feature, is present across all clinical variants. 
All FTD phenotypes share a pathological frontotemporal lobar degeneration (FTLD), which can be classified according 
to the predominant protein composition of cellular inclusions¹⁸,¹⁹. Alongside clinical and pathological heterogeneity, 
a rapidly growing body of research highlights the genetic complexity of FTLD, with 20–30% of patients carrying 
variants in the progranulin (GRN), microtubule-associated protein tau (MAPT), or chromosome 9 open reading frame 
72 (C9orf72) genes²⁰. Some patients with FTD develop motor neuron disease characterized by generalized muscle 
atrophy, weakness, fasciculations, and bulbar symptoms. This heterogeneity, together with the lack of a strict  

  
 
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correlation between clinical, genetic, and neuropathological features²¹,²², represents a major challenge to the 
development of unified pathogenic models and, consequently, effective disease-modifying strategies. The rising 
prevalence of FTD and its escalating public health impact highlight the critical need for therapies that can prevent 
the disease, delay its onset, slow its progression, and ease its symptoms. 
Other forms of early-onset dementia (Young Onset Dementia, YOD), with onset before 65 years of age, have an 
estimated prevalence of 119 cases per 100,000, corresponding to approximately 3.9 million individuals worldwide²³. 
Although AD and FTD are the leading causes of YOD, this group comprises a wider range of disorders. Indeed, YOD 
can be associated with conditions such as Huntington’s disease and vascular dementia, while the accumulation of 
proteins (e.g., amyloid-β) may give rise to atypical AD variants, such as posterior cortical atrophy or the 
behavioral/frontal variant²⁴. This phenotypic heterogeneity is also observed in genetically determined forms of YOD. 
Biomarkers—including plasma and cerebrospinal fluid proteins, neuroimaging, and genetic markers—have shown 
promise for early identification of YOD and for improving understanding of overlapping psychiatric and 
neurodegenerative symptoms in younger patients. Therefore, clinical management of YOD should consider the 
specific age-related challenges of younger dementia patients and their families, and provide integrated social, 
healthcare, and financial support tailored to both age and type of disability²⁴. 
Mild Cognitive Impairment (MCI) represents an intermediate condition between physiological age-related cognitive 
decline and dementia, characterized by cognitive impairment greater than expected for age, yet with preserved 
functional independence in daily life. Unlike dementia, autonomy remains intact. The most recent definition of MCI 
was proposed by Petersen and colleagues25.  
A major challenge in MCI is the variability of clinical trajectories depending on the underlying etiology. Three main 
outcomes are possible: 
-stability: some individuals with MCI remain stable for extended periods without significant decline (“stable MCI”). 
-recovery: in certain cases, cognitive function improves and returns to normal or near-normal levels. 
-progression to dementia: MCI may evolve into more severe cognitive impairment with loss of functional 
independence, the hallmark of dementia. The annual progression rate from MCI to dementia is estimated at 10–
15%, depending on underlying causes (notably AD and FTD) and individual risk factors. 
Cognitive impairment may be restricted to a single domain (single-domain MCI) or involve multiple domains (multi-
domain MCI). Patients can thus be classified into four clinical subtypes: (i) amnestic single-domain MCI (aMCI-SD), (ii) 
amnestic multi-domain MCI (aMCI-MD), (iii) non-amnestic single-domain MCI (naMCI-SD), and (iv) non-amnestic 
multi-domain MCI (naMCI-MD). 
The MCI construct can further be staged clinically using the Clinical Dementia Rating (CDR) scale26, a widely used 
instrument to assess cognitive decline and dementia severity over time. A CDR score of 0.5 is consistent with MCI, 
reflecting subtle cognitive changes exceeding normal aging (CDR = 0), but not yet meeting dementia criteria. 
Dementia is stratified according to severity and functional impairment into mild (CDR = 1), moderate (CDR = 2), 
severe (CDR = 3), very severe (CDR = 4), and terminal (CDR = 5).  
Clinical characterization integrates laboratory and neuroimaging findings to guide clinicians in estimating risk and 
timing of progression. A central concept is that, by combining clinical subtypes with presumed etiologies, it may be 
possible to predict the specific type of dementia likely to emerge in patients with MCI. Current research is 
increasingly focused on genetic risk factors to identify individuals at higher risk of short-term progression. 
 
 
 

  
 
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Parkinson’s Disease (PD) is the second most common neurodegenerative disorder in terms of prevalence and 
incidence. Its estimated prevalence is approximately 0.3% in the general population and 1–3% in individuals over 65 
years of age. 
Disease progression leads to degeneration of the nigrostriatal dopaminergic pathway, with marked loss of neurons in 
the substantia nigra pars compacta (SNpc) and subsequent dopamine (DA) depletion. Non-motor dysfunctions such 
as mild cognitive impairment (MCI), dementia, hyposmia, and gastrointestinal abnormalities frequently accompany 
disease progression. 
The pathological hallmarks of PD are intracellular α-synuclein aggregates, referred to as Lewy bodies and Lewy 
neurites, which are found in multiple regions of the central nervous system, including the basal ganglia, dorsal motor 
nucleus of the vagus, olfactory bulb, locus coeruleus, intermediolateral nucleus of the spinal cord, celiac ganglia, and 
enteric nervous system.²7 Genetic factors are estimated to account for approximately 25% of PD risk, with associated 
genetic variants differing in both frequency and relative risk²8. Familial Parkinson’s disease (also referred to as 
Mendelian or monogenic PD) is characterized by rare but highly penetrant variants that significantly increase disease 
risk. Autosomal dominant forms (e.g., SNCA, VPS35) and autosomal recessive forms (PARK2, PINK1, DJ-1) represent 
approximately 5–10% of all PD cases²9. 
Genetic classification of PD may guide therapeutic approaches and thereby improve patient prognosis and quality of 
life, including that of caregivers. The presence of pathogenic variants, together with clinical features such as age of 
onset and family history, can be used to stratify patients into homogeneous subgroups. 
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disorder that typically begins with muscle weakness 
localized to one region, progressively spreading to other muscle groups. In some cases, dysarthria, dysphonia, and 
dysphagia may appear30. A considerable proportion of patients also exhibit cognitive impairment, sometimes 
nonspecific, and in other cases presenting as frontotemporal dementia. Disease progression is generally rapid, 
although it shows substantial interindividual variability, suggesting a significant role of genetic background in disease 
course. Epidemiological studies have shown a slightly higher incidence of ALS in women, and a greater prevalence in 
Europe compared to Asia and the Middle East31,32,33. Median survival is approximately 2–5 years from diagnosis, with 
respiratory failure representing the most common cause of death. ALS can be classified as familial (10% of cases) or 
sporadic (90% of cases). Environmental factors have also been implicated in disease onset³4. The genes most 
frequently associated with ALS include C9orf72, SOD1, TARDBP, FUS, and TBK132,34. 
The complexity of ALS is underscored by the failure of more than 40 randomized controlled trials aimed at 
identifying effective disease-modifying therapies. Currently, riluzole remains the only approved disease-modifying 
drug in most European countries; its antiglutamatergic properties prolong survival by approximately six months. 
More recently, the FDA approved tofersen for patients carrying SOD1 mutations. 
1.2. Potential therapeutic targets 
Neurodegenerative diseases remain incurable. In recent decades, extensive experimental evidence has deepened 
our understanding of the cellular and molecular mechanisms underlying their development and progression. 
However, current knowledge remains insufficient not only for prevention but also for the effective treatment of 
these disorders. Despite their diverse clinical manifestations, neurodegenerative diseases share common molecular 
and cellular mechanisms³5. This raises the prospect of identifying intracellular targets common to specific patient 
subgroups, thus enabling targeted therapeutic strategies. 
Shared pathological features across neurodegenerative diseases include protein aggregation, altered proteolysis, 
mitochondrial dysfunction, impaired intracellular transport (including neurotransmitter trafficking), and 
dysregulated protein expression. A key unresolved question is whether these cellular processes are causal in 
neurodegeneration or represent downstream consequences of disease. From a practical standpoint, identifying 
dysfunctional molecular mechanisms responsible for the development of multiple neurodegenerative disorders is 
crucial. The advent of “omics” sciences (genomics, transcriptomics, proteomics, metabolomics, etc.) holds the 
potential to expand our understanding in this field and to provide innovative diagnostic, prognostic, and therapeutic 
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1.3. Omics sciences 
In recent years, the substantial reduction in costs associated with omics technologies has enabled the collection of 
large-scale datasets, fostering the integration of multimodal data into disease-specific networks. Notably, the 
integration of clinical data with whole-genome sequencing and transcriptomic, proteomic, and metabolomic 
profiling—supported by increasingly powerful machine learning algorithms—has allowed the identification of (i) 
novel causal loci or risk-associated variants, thereby improving diagnostic yield, and (ii) rare variants not detectable 
in small-scale studies, broadening our understanding of pathogenetic mechanisms involved in 
neurodegeneration36,37. This progress has created new opportunities to identify, characterize, and stratify patient 
subgroups, with the aim of enhancing clinical, genetic, and prognostic assessments and advancing personalized 
medicine strategies tailored to each individual38. The ultimate aim is to optimize treatment, thereby improving both 
health outcomes and socio-economic conditions associated with these diseases. Nevertheless, challenges remain in 
the application and management of omics data. Technical issues include computational demands for big data 
analysis and requirements for storage and data management infrastructure, which often represent a limitation for 
medium-sized centers. The most significant challenges, however, are ethical. The accumulation of personal 
information increases the vulnerability of systems that must ensure individual privacy. Moreover, the availability of 
broad clinical, instrumental, and molecular data can lead to the discovery of unexpected findings. In this context, the 
scientific community has long been engaged in regulating the use of such data. One example is the management of 
secondary/incidental findings (SF/IFs) in genomic sequencing39. According to numerous scientific publications and 
guidelines from scientific societies such as the American College of Medical Genetics and the European Society of 
Human Genetics, incidental findings are defined as “genetic variants identified through genomic sequencing that are 
unrelated to the primary disease under investigation”40. These include numerous genetic and genomic variants 
present in every individual. For the purpose of this project, and in line with the current literature41, incidental 
findings are defined as “information relating to an individual’s health and/or relevant to their reproductive or 
existential choices—such as genetic data—that emerge during the course of a biomedical research project (such as 
the present one), without being among its primary or secondary objectives, as a result of commonly employed 
practices, analyses, or methodologies within that type of research” (CNR Research Ethics and Bioethics Commission 
Report, 2023). Among incidental findings that may emerge in omics-based research, a list of genes associated with 
highly penetrant disorders has been identified, for which early detection is linked to reduced morbidity and mortality 
(“actionable genes,” ACMG). In the present project, ACMG recommendations for reporting incidental findings 
identified during clinical exome and genome sequencing will be followed. This periodically updated list will serve as 
the reference throughout the project42. 

  
 
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2. STUDY RATIONALE  
Dementias, Parkinson’s disease (PD) and parkinsonisms, and motor neuron diseases are clinically and genetically 
highly heterogeneous disorders. To date, genomic studies have largely focused on coding regions, i.e., protein-
coding genes, which explain only a limited proportion of intra- and interindividual clinical variability. Furthermore, 
numerous cases with a family history strongly suggestive of a genetic basis but lacking a molecular diagnosis point to 
the involvement of other genomic regions—yet to be identified—in the etiology of these conditions. In this context, 
currently available treatments are often ineffective, and the list of failed pharmacological trials is long, with 
significant implications for both patient care and pharmaceutical investment. In recent years, a potential revolution 
in medical and healthcare practice has emerged through the interconnection and integrated analysis of data derived 
from multiple “omics” technologies. When combined with clinical and instrumental data, these data enable 
statistically meaningful correlations to be identified and novel molecular contributors to be revealed, which would 
be impossible to detect in small-scale studies. Ultimately, the systematic collection and accessible integration of 
clinical and genomic data aim to develop algorithms for stratifying patients into specific subgroups, thereby 
optimizing disease risk prediction, prognosis, and therapeutic response. 
Several examples illustrate this approach: 
Alzheimer’s disease (AD): Large-cohort genomic studies using Genome-Wide Association Studies (GWAS) have 
identified ~90 loci associated with increased AD risk, thereby expanding the list of AD-related genes43,44. The 
construction of multimodal, interconnected networks has further enabled risk stratification profiles to be calculated 
based on clinical, genomic, metabolomic, and other data. Recent studies have investigated polygenic risk scores 
(PRS) for penetrance and recurrence in familial AD, as well as PRS associated with microbiome composition or 
specific cognitive and functional decline trajectories. However, predictive performance has so far been limited45,46,47. 
Parkinson’s disease (PD): GWAS has extended the list of PD-associated genes, identified population-specific risk 
variants, and highlighted differences in genetic predisposition across populations48,49,50. Several studies have 
attempted to build risk prediction algorithms based on PRS, biochemical, clinical, and demographic profiles. While 
statistical performance has remained modest, these efforts provide proof-of-concept for the utility of integrating 
clinical, genomic, and metabolomic data51. 
Amyotrophic lateral sclerosis (ALS): Large-scale studies are fewer, owing to the lower prevalence of the disease. 
Nevertheless, GWAS have identified new risk loci and pathogenic mechanisms. For instance, DNA methylation 
studies have revealed alterations in several metabolic pathways52,53. 
Despite these advances, it remains unclear which factors influence disease progression in each neurodegenerative 
condition. Genetic studies have identified associations between certain phenotypes (slow versus aggressive 
progression) and single-gene variants. However, these findings cannot yet be applied reliably at the individual level, 
as (i) most patients lack identifiable genetic causes and (ii) the role of other factors in the metabolic context that may 
shape progression speed remains poorly understood. Moreover, the contribution of structural variants (CNVs and 
transposable element–related changes), noncoding genes (transcribed but untranslated sequences), and regulatory 
DNA regions (promoters, enhancers, repeat sequences) remains to be fully explored. 
In this regard, whole-genome sequencing (WGS) offers the potential to improve diagnostic yield in patients with 
neurodegenerative diseases who test negative through next-generation sequencing (NGS) approaches limited to the 
coding genome (WES, whole-exome sequencing). Comprehensive genome characterization—enabling the 
simultaneous analysis of monogenic and polygenic factors as well as structural variants—combined with other 
“omics” sciences (e.g., transcriptomics, proteomics) provides a fundamental strategy for more precise and 
personalized elucidation of pathogenetic mechanisms and disease progression. For example, the combined genome–
transcriptome approach allows investigation of genomic variants that drive aberrant splicing events (e.g., intron 
retention in mRNA), thereby uncovering novel disease-causing mutations. 
 

  
 
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Data generated through this integrated “omics” approach may ultimately enable the identification of subgroups of 
patients with distinct clinical profiles. At present, however, integrated genomic analyses are not routine practice. 
 
Therefore, the NeurOmics Study aims to exploit state-of-the-art omics technologies to identify pathogenic 
genomic variants, altered proteins, and/or dysregulated molecular pathways in neurodegenerative diseases, 
thereby providing a novel and comprehensive characterization of affected individuals. By integrating genomic, 
transcriptomic (including epigenomic), proteomic, metabolomic, and clinical data, the study seeks to identify new 
biomarkers for diagnosis, prognosis (including treatment response), and disease monitoring. These omics data will 
expand knowledge of the molecular basis of neurodegenerative diseases and constitute a valuable resource to be 
shared—fully anonymized—within the scientific community. Importantly, the omics analysis of hundreds of cases 
across different neurodegenerative disease categories, as planned in this study, will have a significant impact on 
both the scientific community and public health, given the limited number of omics studies currently available at the 
national level. Collecting omics and clinical data from such large cohorts will contribute to defining homogeneous 
patient subgroups, achieving greater diagnostic–etiological resolution, and enabling more accurate patient 
stratification. Ultimately, this will support improved phenotypic characterization and foster the development of 
precision medicine approaches for neurodegenerative disorders. 
3. STUDY DESIGN  
NeurOmics is a multicenter, prospective, observational study enrolling patients diagnosed with neurodegenerative 
diseases, including Parkinson’s disease and degenerative parkinsonisms, neurodegenerative cognitive disorders at 
the stage of mild cognitive impairment (MCI) or early/late-onset dementia, and motor neuron diseases (ALS). The 
study aims to identify pathogenic genomic variants and/or dysregulated molecular pathways in these patients by 
performing whole-genome sequencing and integrating these data with transcriptomic, epigenomic, and/or 
proteomic profiles. It further aims to identify novel biomarkers for the diagnosis and monitoring of 
neurodegenerative diseases through the analysis of biological samples (e.g., blood and derivatives), including liquid 
biopsy approaches using the most advanced technologies currently available. 
The study plans to enroll at least 1,000 individuals with neurodegenerative disease (see section 7.4 Statistical 
Analysis). 
Thanks to the availability of large public databases containing whole-genome sequences from healthy populations, 
such as gnomAD and the 1000 Genomes Project54, these datasets can be used as controls for variant identification in 
participants diagnosed with PD and parkinsonisms, early- and late-onset dementias (including MCI), and ALS. For 
example, the 1000 Genomes Project provides a catalog of common human genetic variants derived from self-
reported healthy individuals, which has become a widely used reference in biomedical genomics. 
To confirm the association of variants identified in this study with the relevant neurodegenerative disease, their 
presence will be checked against curated mutation repositories, including ALZFORUM and the Alzheimer’s Disease 
Neuroimaging Initiative (ADNI) (http://adni.loni.usc.edu/) for AD, and PPMI for PD55,56. For subjects with MCI, 
genomic data deposited in ADNI (including controls, AD cases, and MCI) will be used. For ALS, the study will leverage 
the Project MinE database and genomic resources. 
The case series and available data from each reference database are summarized in Table 1, while detailed statistical 
methodology is provided in section 7.4 Statistical Analysis. 
 
 
 

  
 
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Database Name 
Type of Sequencing 
Number of Genomes (WGS), 
Exomes (WES), Genes, SNPs, 
Indels, SVs or CNV 
Number of 
Subjects 
Type of Case 
gnomAD: genome 
aggregation 
Database, 
versione 3.1 
(GRCh38) 
WGS 
76.156 WGS 
76.156 
Controls 
Structural 
Variants 
(SVs) 
 
10,738  
10,738  
 
Controls 
1000 
Genomes 
Project (GRCh38) 
WGS 
3.202 WGS (30x) contenti 602 
trio padre, madre, figlio  
3.202 
Controls 
Alzheimer’s 
Disease 
Neuroimaging 
Initiative ADNI 
ADNI WGS 
SNPs: ~3.7 million 
Indels: ~700.000 
SVs: ~3.500 
 
818 
267 
128 
415  
8  
Total 
Controls 
AD 
MCI  
Uncertain Diagnosis 
 
Parkinson’s 
Progression 
Markers Initiative 
PPMI 
WGS and WES 
NA 
195 
184 
414 
287 
Controls 
Genetic PD 
Sporadic PD 
Prodromal  
Project MinE  
WGS 
6,198 WGS 
4,366  
1,832 
ALS 
Controls 
Table 1. List of the main databases to be used for variant analysis. 
WGS, Whole Genome Sequencing; WES, Whole Exome Sequencing; AD, Alzheimer’s Disease; PD, Parkinson’s 
Disease; MCI, Mild Cognitive Impairment; ALS, Amyotrophic Lateral Sclerosis; NA, not applicable. 
 
 
 
 

  
 
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Various types of biological samples will be collected from patients enrolled in the study, primarily peripheral blood, 
saliva (when blood sampling is not feasible), and urine. 
Omics analyses—mainly genomics, transcriptomics, epigenomics, etc.—will be performed on the collected biological 
samples and/or their derivatives. Analyses may be conducted on genetic material extracted from whole peripheral 
blood, saliva, PBMCs (Peripheral Blood Mononuclear Cells), plasma, serum, and/or urine. In selected subgroups of 
cases (e.g., patients carrying genetic variants involved in specific metabolic processes, such as lipoprotein 
metabolism), analyses may also include proteins and/or lipids from biological samples and their derivatives. 
The study does not involve the use of experimental drugs or interventions beyond those required for routine 
diagnostic practice. 
The study will also assess the feasibility of implementing genomic analysis into clinical practice, and the potential 
clinical relevance of identifying novel genetic alterations, including those in non-coding regions of the genome. 
 
 
 
 
 
 
 
 
 
 

  
 
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3.1 Sample Management 
• 
Molecular analyses will be performed using state-of-the-art technologies, in particular next-generation 
sequencing (NGS) of both second- and third-generation (short-read [SR] and long-read [LR]) platforms for 
DNA and/or RNA sequencing at the Genoa and Aosta (CMP3VdA) sites of the Istituto Italiano di Tecnologia 
Foundation. 
• 
NGS analyses of single genes, gene panels, clinical exome, and/or whole exome (WES)—when indicated in 
the clinical management of these patients—will be carried out at the laboratories of IRCCS Policlinico San 
Martino (Genoa) and Fondazione IRCCS Policlinico Cà Granda (Milan). When necessary, these may also be 
performed at the IIT sites in Genoa and Aosta (CMP3VdA). 
• 
Proteomics, metabolomics, and lipidomics analyses: Laboratory selection is ongoing to identify centers able 
to perform these investigations in accordance with the quality standards imposed by the project. Once 
identified, relevant ethical committees will be notified. 
• 
Data management and privacy: The study’s principal investigator is responsible for ensuring participant 
privacy. Study data will be recorded in a validated Data Management System via an electronic Case Report 
Form (eCRF), provided by the Scientific Directorate of IRCCS Policlinico San Martino (Genoa) and Fondazione 
IRCCS Policlinico Cà Granda (Milan). The REDCap (Research Electronic Data Capture) platform will be 
employed. The REDCap consortium comprises >1,000 institutional partners worldwide and supports secure 
web-based applications for research data collection. REDCap enables rapid and secure 
creation/management of online databases and is currently used for >110,000 projects with ~150,000 users 
across multiple research fields. 
For this study, REDCap will enable: 
a) user-level identification with role-based access restrictions; 
b) real-time data integrity validation; 
c) patient de-identification prior to data export; 
d) centralized data storage with daily backup on secure servers within the IT infrastructure of IRCCS 
Policlinico San Martino and Fondazione IRCCS Policlinico Cà Granda. 
• 
Biobanking: Within the NeurOmics project, the Biological Resource Center (CRB) of IRCCS Policlinico San 
Martino and the Telethon Biobank of Fondazione IRCCS Policlinico Cà Granda will coordinate the collection, 
processing, and short- and long-term storage of samples, as well as their redistribution. 
 
 
Specifically, the CRB will: 
1. Develop the workflow for biological material management at the Genoa center and harmonize all 
study SOPs in collaboration with the Telethon Biobank (Milan). 
2. Prepare blood collection kits to trace pre-analytical variables such as vacutainer batch number, order 
of tube collection, fill volume, and time between sampling and processing. Each kit, assigned at 
enrollment, will contain: 
a) a CRF for pre-analytical and storage data (completed by phlebotomist/nurse and CRB staff); 
b) a 5 mL vacutainer with clot activator and gel separator for serum (Ref: Vacuette®, Greiner 
456073); 
c) a 6 mL K3EDTA vacutainer for plasma (Ref: Vacuette®, Greiner 456036); 
d) a 9 mL K3EDTA vacutainer (Ref: Vacuette®, Greiner 455036) for PBMC isolation using Ficoll. 
3. Ensure sample quality (high-quality samples) through dedicated internal and/or external quality 
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4.  Trace all pre-analytical, analytical, and storage-related information via biobank software. These data 
will be pseudonymized and linked to laboratory, clinical, and follow-up data (well-annotated data). 
4. STUDY DURATION 
The study will have an overall duration of 10 years. Follow-up assessments will be conducted periodically, as per 
routine clinical practice for each condition under investigation. 
 
5. STUDY OBJECTIVES 
The study aims to collect omics characterization for at least 1,000 patients with neurodegenerative diseases, 
recruited from the Neurology and Geriatrics Units of IRCCS Policlinico San Martino (Genoa), Fondazione IRCCS 
Policlinico Cà Granda (Milan), and other units joining after ethics committee approval. Estimated recruitment: >400 
dementia/cognitive impairment cases, >450 PD/parkinsonism cases, ~150 ALS/motor neuron disease cases.  
The primary goal is to identify, through whole-genome analysis, both rare and common monogenic and polygenic 
variants—single nucleotide variants (SNVs) and structural variants (CNVs, insertions, translocations, inversions, 
mobile element insertions [MEIs])—that may be causative or confer increased risk for neurodegenerative diseases. 
Integration with other omics sciences will enable correlation of these variants with gene, protein, and metabolic 
expression patterns, thereby defining distinct molecular trajectories underlying neurodegeneration and their impact 
on disease progression. 
Primary Objectives 
1. Identify genomic variants not yet recognized as causes or susceptibility factors for neurodegenerative 
diseases, through WGS and dedicated bioinformatic pipelines (including protein-coding genes, noncoding 
RNA genes, regulatory DNA sequences—promoters, enhancers, repeats—and both SNVs and SVs). 
2. Assess correlations between genetic–molecular alterations and clinical phenotype (e.g., severity, 
comorbidities) for the most frequent variants. 
3. Identify splicing variants associated with neurodegenerative diseases using genomic and transcriptomic 
analyses. 
4. Detect SNVs and/or SVs affecting enhancer or promoter regions. 
5. Identify molecular mechanisms associated with neurodegenerative diseases and their impact on disease 
progression via multi-omics approaches. 
6. Identify novel diagnostic, prognostic, and predictive biomarkers in neurodegenerative diseases. 
Secondary Objectives 
1. Identify causative or susceptibility genomic variants and provide accurate genetic counseling to patients and 
families. 
2. Investigate associations between pathogenic variants in neurodegeneration-related genes and systemic 
comorbidities to inform prevention and early treatment57. 
3. Identify and characterize molecular markers (genes, transcripts, proteins, pathways) commonly dysregulated 
across different neurodegenerative diseases (e.g., cross-disorder gene overlap). 
4. Identify cross-talk between known causative/susceptibility variants and additional 
transcripts/proteins/pathways influencing penetrance/expressivity of neurodegenerative disorders. 
This study lays the foundation for defining homogeneous patient subgroups with combined clinical and genomic 
features, ultimately enabling personalized therapeutic strategies. 
 

  
 
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Given the sample size, disease-specific targeted studies may be conducted on selected subgroups. For example in 
Parkinson’s disease, transcriptomic analyses will be performed in early-onset PD cases to detect expression or 
splicing defects in known PD genes, thereby revealing candidate genes with cryptic pathogenic variants through 
genome–transcriptome integration. Genetic data from PD patients will also be used to investigate associations 
between excess rare variants in predefined gene clusters (e.g., protein misfolding, mitochondrial 
dysfunction/oxidative stress, endolysosomal defects) and clinical phenotype, stratifying patients by progression rate: 
rapid progression (Hoehn & Yahr ≥2.5 at 5 years) versus slow progression (Hoehn & Yahr ≤1 at 5 years). 
6. STUDY POPULATION AND SUBJECT SELECTION CRITERIA 
The study population consists of patients affected by neurodegenerative diseases, referred to IRCCS Ospedale 
Policlinico San Martino (Genoa), Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico (Milan), and any 
additional Units that may join the study following Ethics Committee (EC) approval. Eligible subjects will be identified 
during outpatient visits conducted as part of routine clinical follow-up at the participating centers. At these visits, 
patients will be invited to participate in the study. 
The clinical diagnosis will be established according to the criteria detailed below. 
Eligibility Criteria 
Subjects will be enrolled only if they fulfill all inclusion criteria and none of the exclusion criteria for each 
neurodegenerative condition under investigation, as described below. 
ALZHEIMER’S DISEASE (AD) 
Inclusion criteria 
- Signed written informed consent from the patient and/or legal representative (Appendix X); 
- Age ≥18 years; 
- Clinical diagnosis of AD in patients referred to the participating clinical centers, as follows: 
o 
Predominantly amnestic presentation based on primary complaint and comprehensive 
neuropsychological assessment; 
o 
MMSE >24, with instrumental activities of daily living preserved (MCI) or mildly impaired (mild 
dementia), corresponding to a CDR score of 0.5 and 1, respectively; 
o 
Absence of significant neurological/systemic comorbidities or structural abnormalities indicative of 
other causes of cognitive impairment on morphological imaging; 
o 
Absence of non-AD–related structural abnormalities on imaging review; 
o 
CSF biomarkers (Aβ42, Aβ40, p-Tau181, t-Tau) consistent with AD profile (A+T+) or amyloid-positive 
PET with fluorinated tracers, according to current diagnostic criteria for AD-related MCI and 
dementia58,59; 
o 
Availability of longitudinal clinical assessments, including MMSE and daily living activities, for at least 
24 months. 
Exclusion criteria 
- Failure to meet any of the above inclusion criteria. 
FRONTOTEMPORAL LOBAR DEGENERATION (FTLD) 
Inclusion criteria 
- Signed written informed consent from the patient and/or legal representative (Appendix X); 
- Age ≥18 years; 
- Clinical diagnosis of FTLD in patients referred to the participating centers, as follows: 
 

  
 
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o 
Predominant behavioral/dysexecutive presentation or progressive language impairment based on 
primary complaint and full neuropsychological evaluation, according to current diagnostic 
criteria60,61; 
o 
MMSE >24, with preserved (MCI) or mildly impaired (mild dementia) instrumental daily living 
activities (CDR score 0.5–1); 
o 
Absence of significant neurological/systemic comorbidities or structural abnormalities indicative of 
other causes of cognitive impairment; 
o 
Imaging findings consistent with bvFTD 60[one of the following required]: 
a) Prominent frontal and/or anterior temporal atrophy on structural imaging; 
b) Frontal/anterior temporal hypoperfusion or hypometabolism on PET or SPECT; 
OR 
Imaging consistent with primary progressive aphasia (PPA) 61 [one of the following required]: 
a) Prominent left posterior fronto-insular/parietal atrophy (non-fluent variant, nfvPPA) or anterior 
temporal lobe atrophy (semantic variant, svPPA) on structural imaging; 
b) Hypoperfusion or hypometabolism in the same regions on PET or SPECT; 
o 
Availability of longitudinal clinical assessments, including MMSE and daily living activities, for at least 
24 months. 
Exclusion criteria 
- Failure to meet any of the above inclusion criteria. 
DEMENTIA WITH LEWY BODIES (DLB) 
Inclusion criteria 
- Signed written informed consent from the patient and/or legal representative (Appendix X); 
- Age ≥18 years; 
- Clinical diagnosis of DLB in patients referred to the participating centers, as follows: 
o 
Predominantly non-amnestic cognitive impairment (visuospatial, dysexecutive, attentional) based on 
patient report or full neuropsychological assessment, plus characteristic clinical features according 
to current diagnostic criteria for DLB or MCI-LB62,63; 
o 
MMSE >24, with preserved (MCI) or mildly impaired (mild dementia) instrumental daily living 
activities (CDR 0.5–1); 
o 
Absence of significant neurological/systemic comorbidities or structural abnormalities indicative of 
other causes of cognitive impairment; 
o 
Supportive instrumental or imaging findings consistent with probable DLB62,63, at least one of: 
a) Polysomnographic confirmation of REM sleep behavior disorder without atonia; 
b) Reduced striatal dopamine transporter uptake on SPECT/PET; 
c) Abnormal cardiac [123i]-MIBG scintigraphy (reduced uptake); 
o 
Availability of longitudinal clinical assessments, including MMSE and daily living activities, for at least 
24 months. 
Exclusion criteria 
- Failure to meet any of the above inclusion criteria. 
MILD COGNITIVE IMPAIRMENT (MCI) 
Inclusion criteria 
- Signed written informed consent from the patient and/or legal representative (Appendix X); 
- Age ≥18 years; 
- Clinical diagnosis of MCI in patients referred to the participating centers, defined as: 

  
 
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o 
Subjective memory complaint (preferably corroborated by an informant); 
o 
Intact ability to perform daily activities (e.g., meal preparation); 
o 
Preserved general cognitive abilities; 
o 
Objective evidence of memory deficit; 
o 
Absence of dementia. 
Exclusion criteria 
- Failure to meet any of the above inclusion criteria. 
AMYOTROPHIC LATERAL SCLEROSIS (ALS) 
Inclusion criteria 
- Signed written informed consent from the patient and/or legal representative (Appendix X); 
- Age ≥18 years; 
- Clinical diagnosis of motor neuron disease in patients referred to participating centers, as follows: 
o 
Diagnosis of definite or probable ALS according to revised El Escorial criteria64; 
o 
EMG must demonstrate: 
a) Active denervation (fibrillation potentials, positive sharp waves, complex fasciculations); 
b) Chronic denervation (prolonged, often polyphasic motor unit potentials, reduced recruitment 
with high firing frequency at maximal effort); 
c) Nerve conduction studies typically show reduced motor potential amplitude with preserved or 
minimally reduced motor conduction velocity, normal sensory conduction, and absence of 
conduction block; 
o 
Transcranial magnetic stimulation (TMS) may demonstrate UMN involvement (reduced 
corticobulbar/spinal conduction velocity, decreased cortical motor neuron excitability); 
o 
Neuroimaging and laboratory investigations must exclude compressive, infiltrative, or inflammatory 
conditions mimicking UMN/LMN signs. 
Exclusion criteria 
- Failure to meet any of the above inclusion criteria. 
PARKINSON’S DISEASE (PD) 
Inclusion criteria 
- Signed written informed consent from the patient and/or legal representative (Appendix X); 
- Age ≥18 years; 
- Clinical diagnosis of PD according to revised MDS criteria, as follows: 
o 
Predominant motor presentation with bradykinesia plus resting tremor and/or rigidity; 
o 
MMSE >24, with preserved (MCI) or mildly impaired (mild dementia) instrumental daily living 
activities (CDR 0.5–1); 
o 
Absence of significant neurological/systemic comorbidities or structural abnormalities indicative of 
other causes of cognitive impairment; 
o 
Imaging findings: 
a) Normal structural brain imaging; 
b) Striatal DAT binding reduction on SPECT DAT-SCAN; 
o 
Availability of longitudinal clinical assessments, including motor scales (e.g., UPDRS-III, Hoehn & 
Yahr), MMSE/MOCA, and daily living activities, for at least 24 months. 
Exclusion criteria 
- Failure to meet any of the above inclusion criteria. 

  
 
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7. LABORATORY PROCEDURES: GENETICS, OMICS, BIOMARKERS 
7.1 Enrollment and Follow-up 
- Patient selection 
Eligible subjects will be identified during outpatient visits at IRCCS Policlinico San Martino (Genoa), Fondazione IRCCS 
Cà Granda (Milan), and other centers that may later join the study. 
- Baseline visit and informed consent 
The Investigator will invite patients to participate after verifying inclusion/exclusion criteria. Patients (or legal 
representatives in case of cognitive impairment) will receive detailed information on study aims, benefits, and 
potential risks, along with written information sheets and informed consent forms. Adequate time will be allowed 
for discussion with the general practitioner before consent. Patients will also be asked whether they agree to be re-
contacted for future research studies. 
Investigators at the Genoa and Milan centers are responsible for obtaining and archiving signed informed consent 
before any study-specific procedures. 
- Visit 1 
At this visit, informed consent will be obtained, and subjects will be enrolled with assignment of a pseudonymized 
code generated in REDCap using the Mainzelliste algorithm (PMID: 25656224). The algorithm processes identifying 
attributes (e.g., name, surname, date of birth) to generate a unique, anonymous personal identifier (PID), ensuring 
record linkage across multiple databases while tolerating spelling variations. 
Peripheral blood (20–25 mL) will be collected for preparation of PBMCs, serum, and/or plasma, and urine will be 
collected for omics analyses. DNA will be extracted from whole blood, RNA preferably from PBMCs; saliva may be 
used as an alternative genetic source. Sampling will be integrated into routine clinical workflow. 
Clinical and biological data will be collected at IRCCS Policlinico San Martino (Prof. Paola Mandich), Fondazione IRCCS 
Cà Granda (Dr. Alessio Di Fonzo), and other participating centers. 
Clinical data collection 
Following consent, clinical data will be collected at participating Units and managed through REDCap. Case report 
forms (CRFs) will specify disease-specific data. At Visit 1, the following will be recorded: 
• 
Sex 
• 
Age 
• 
Medical history 
• 
Neurological examination 
• 
Neuropsychological profile 
• 
Neuroimaging findings 
• 
Previous treatments 
• 
Planned treatments 
- Visit 2 
If consent is given, patients/legal representatives will be informed of clinically relevant genetic variants identified. 
Variants associated with risk of developing other conditions (subject to confirmatory testing) will be disclosed during 
genetic counseling with psychological support. Patients will also decide whether information should be extended to 
relatives. 
- Follow-up visits 
Subsequent follow-up visits will coincide with routine clinical visits in Neurology and Geriatrics Units at Genoa and 
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7.2 Sample Collection and Processing 
- Genomic data collection 
Molecular and genetic diagnostic data generated at Genoa and Milan centers will be entered into the CRF and 
REDCap. Genomic data generated by IIT-CMP3VdA will be stored at the Engineering D.Hub data center in Pont Saint 
Martin (Aosta), with backup in Vicenza. 
- Biological sample processing 
Samples will be collected at Neurology and Geriatrics Units of IRCCS Policlinico San Martino (Prof. Paola Mandich) 
and Fondazione IRCCS Cà Granda (Dr. Alessio Di Fonzo), frozen, and stored in local biobanks (Genoa Biobank; 
Telethon Biobank, Milan). DNA/RNA will be extracted from PBMCs and/or peripheral blood and derivatives according 
to omics analyses to be performed at the respective hospital laboratories. Part of the pseudonymized DNA/RNA 
sample will be transferred to IIT (Genoa and Aosta CMP3VdA) for subsequent genomic analyses. 
- Biobanking  
Serum and plasma isolation 
Serum and plasma isolation will be performed following standard procedures: blood samples will be collected in 
dedicated tubes (K2/K3 EDTA for plasma and clot activator for serum and/or with stabilization solutions for nucleic 
acids) and centrifuged once or multiple times (ranging from 1600 to 3000g) at room temperature and/or +4°C, 
depending on whether serum or plasma is being prepared. Cryovials containing aliquots of serum and plasma will be 
stored at –80°C. 
PBMC collection and culture 
Two EDTA or Lithium-Heparin tubes will be layered onto Ficoll and centrifuged at 2000g for 20 minutes to obtain the 
PBMC fraction, which will be isolated and frozen in FBS + 10% DMSO at –80°C. 
Biomarkers 
Biomarkers will be analyzed using standardized methodologies. For biomarkers already assessed in clinical practice, 
values will be retrieved from the patients’ medical records. Should new diagnostic biomarkers become available over 
time, these will also be analyzed according to validated and standardized procedures. 
- Sequencing 
NGS sequencing will be performed in collaboration with IIT using state-of-the-art sequencing technologies. The 
laboratory is equipped with both second- and third-generation sequencing platforms, enabling short- and long-read 
sequencing, respectively, with the possibility of integrating both technologies when advantageous for research 
purposes. 
Short-read Illumina sequencing allows highly accurate analysis of single-nucleotide variants (SNVs) and small 
insertions/deletions (indels), while long-read sequencing facilitates the analysis and identification of structural 
variants (SVs). These NGS platforms represent the state of the art for transcriptomic (RNA-Seq), epigenomic, and 
metagenomic studies. 
The Illumina NovaSeq 6000, currently available at Fondazione Istituto Italiano di Tecnologia in Genoa and Aosta 
(CMP3VdA), ensures high throughput, generating up to 6 terabases of sequencing and 20 billion reads per run. 
Sequencing quality is high, with over 75% of bases reaching a quality score ≥30. Sequencing outputs FASTQ files 
stored directly in the cluster’s analysis storage. A minimum sequencing depth of 30X will be applied, enabling 
comprehensive variant identification, classification, and association with neurodegenerative diseases. 
 
 

  
 
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The PromethION 24 system (Oxford Nanopore Technologies, ONT), also available at CMP3VdA, provides flexible, 
ultra-high throughput third-generation sequencing, with 24 independent flow cells generating terabases of data in 
real time. This technology enables direct sequencing of native DNA/RNA strands using nanopores embedded in an 
electro-resistant membrane, through which ionic current alterations induced by nucleic acid passage are converted 
into genomic sequences via basecalling software. 
Third-generation sequencers, due to their ability to process long fragments, offer superior resolution in detecting 
short repeat units (<1 kb), repeat-rich DNA regions, and large-scale inversions (>10 kb), while also allowing haplotype 
phasing and improved resolution of compound heterozygosity. Sequencing depth typically ranges between 2–5X, 
depending on the study design. 
7.3 Data Analysis 
Bioinformatic analyses 
Bioinformatic analyses of the various omics datasets will be conducted in collaboration with Fondazione Istituto 
Italiano di Tecnologia (CMP3VdA, Genoa and Aosta). 
- DNA analysis using Illumina technology 
For genomics, DNA variants identified will be analyzed jointly by CMP3VdA and the clinical units in Genoa and Milan. 
The analytical pipeline will generate the following files: 
1. FASTQ – containing raw sequencing reads and per-base quality scores; the starting point of bioinformatic 
analyses. 
2. BAM – containing mapped reads aligned to the human reference genome. 
3. VCF/BCF – containing identified variants for each individual. 
Pipeline steps: 
a) Quality assessment, filtering, and trimming of raw reads. 
b) Read alignment to the reference genome. 
c) Identification of SNVs, indels, structural variations (SVs), rearrangements, and copy number variations (CNVs). 
d) Generation of a comprehensive variant database. 
e) Compilation of control population variants and allele frequency tables. 
f) Comparison of patient-specific variants and allele frequencies with control populations and public cohorts. 
g) Prediction of variant effects and association with genomic features. 
h) Prioritization of patient-specific variants and their association with neurodegenerative disorders, leading to 
genomic-based stratification. 
The pipeline employs standard tools, notably GATK (Genome Analysis ToolKit)89 from the Broad Institute, widely 
considered the gold standard for variant analysis. As certain GATK tools are computationally intensive, IIT uses 
NVIDIA GPUs and Clara™ Parabricks software for accelerated processing, achieving a 15–20× reduction in runtime 
for mapping and variant calling. 
- Variant annotation 
VCFs will be annotated using tools such as SnpEff66,67, leveraging multiple public databases that catalog variant 
effects, including: 
• 
ALZFORUM55 (AD mutations): https://www.alzforum.org/ 
• 
PPMI56 (PD mutations): https://www.ppmi-info.org/ 
• 
Alzheimer’s Disease Neuroimaging Initiative (ADNI): http://adni.loni.usc.edu/about/ 
• 
Gene4PD69 http://genemed.tech/gene4pd/   

  
 
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• 
Parkinson’s Disease DNA Variant Browser70 : https://pdgenetics.shinyapps.io/variantBrowser/  
• 
ClinVar71 (variant–phenotype relationships) 
• 
Databases containing allele frequencies of known variants gnomAD72, 1000 Genomes, dbSNP 73  
• 
ALSoD (ALS mutations): https://alsod.ac.uk/ 
Variants will be classified according to SIGU and ACMG international guidelines. 
- Structural variant analysis 
Given the high false-positive rate of short-read SV detection, a consensus approach integrating multiple tools 
(Manta, Lumpy, CNVnator, BreakDancer)77 will be applied. Variants will then undergo consensus filtering with 
SURVIVOR, re-genotyping with Smoove, and false-positive reduction with Duphold, maintaining >99% true positives 
while removing ~61% false positives. Additionally, MELT will be used for transposable element insertions78. SV 
frequency will be estimated using an in-house algorithm, and annotations added with AnnotSV and SVAFotate. CNVs 
will also be evaluated using CNVkit. 
All results will populate a central database for variant prioritization, disease association, and data mining. 
- Reproducibility and updates 
All analyses will be performed within standardized virtual environments (containers) ensuring reproducibility across 
samples. Updates to software and databases (e.g., genome builds, annotation tools) will be periodically 
implemented, reprocessing prior datasets to maintain comparability. Variant annotation will be continuously 
updated as novel pathogenic variants emerge in the scientific literature. 
In summary, the pipelines employed for genomic data analysis utilize a sequence of widely adopted software tools 
for variant identification (including PARABRICKS, with BWA and GATK adapted for GPU usage, and ANNOVAR and 
SnpEff for annotation) applied to both coding genes and non-coding regions of the genome. Typically, the most 
recent human reference genome is used, currently version Hg38. Several databases containing known variants are 
employed to filter out neutral variants and to identify those associated with the disease of interest. In particular, 
gnomAD and the 1000 Genomes Project (Table 1) are used, as they are representative of the global population in 
terms of both neutral and deleterious mutations. This strategy allows us to exclude variants currently lacking 
functional impact and to identify those likely responsible for disease. 
It is important to note that, regardless of the software employed, variant definition and classification are carried out 
according to internationally recognized standards such as ACMG guidelines. 
This approach ensures that all samples are analyzed using the same software and database versions stored within 
the container (Table 2). As soon as new software updates become available, the virtual machine will be updated 
accordingly, and all previously processed samples will be reanalyzed. This guarantees reproducibility across different 
samples. Furthermore, the variant annotation software will be updated frequently, as mutations currently of 
unknown significance may, in the future, be classified as pathogenic owing to continuous advances in scientific 
research. 
 
 
 
 
 

  
 
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Software or Database 
Version 
Clara™ Parabrick 
4.1.0 
SnpEFF 
5.0e 
ANNOVAR 
20221123 
Cosmic 
v94 
GnomAD 
v3.1.2 
dbSNP 
155 
PolyPhen 
v2 
Sift 
6 Dec 2019 
CADD 
v1.6 
FATHMM 
v2.3 
Genoma 
Hg38 
 
 
ADNI 
 
NA 
PPMI 
 
NA 
ALZFORUM 
NA 
Gene4PD 
 
NA 
Parkinsons's Disease Variant Browser 
0.2.1 
ClinVar 
NA 
1000 Genomes Project 
 
NA 
MELT 
2.2.2 
ALSoD 
NA 
BreakDancer 
1.4.5 
Manta 
1.6.0 
CNVKit 
0.9.9 
Lumpy 
0.2.13 

  
 
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CNVnator 
0.3.3 
Smoove 
0.2.6 
SVAFotate 
0.0.1 
AnnotSV 
3.2.3 
Table 2. List of software and databases employed, with the corresponding version currently in use, where available. 
NA, Not Applicable. 
DNA Analysis with Nanopore Technology 
Depending on research outcomes and project requirements, a subset of cases within this study may be sequenced 
using third-generation Oxford Nanopore Technologies (ONT). This approach may be particularly useful for improved 
characterization of structural variants, methylation profiling, validation purposes, and other specific analyses. 
ONT provides a suite of analysis tools. For DNA sequencing, ONT currently recommends the Guppy basecaller, which 
is the most performant for initial basecalling. For alignment of sequencing reads to the reference genome, Lra will be 
employed, while SNV calling will be performed using Clair3. 
For structural variant detection, alignment will be performed with Lra, and variant calling with CuteSV. 
ONT technology also enables haplotype phasing, allowing determination of the allele on which a variant is located. 
This will be performed using the WhatsHap tool. 
Base Quality Estimation of Sequenced Reads 
In sequencing-by-synthesis (SBS) technology (Illumina), the quality of each base in a read is quantified by assigning a 
Phred Quality Score (PQS), computed by a dedicated algorithm79,80. This score represents the probability that a base 
has been miscalled. A PQS of 10 corresponds to an expected error rate of 1 in 10 bases (90% accuracy), a PQS of 20 
to 1 in 100 (99% accuracy), and a PQS of 30 to 1 in 1000 (99.9% accuracy). When sequencing quality reaches Q30—
the benchmark for next-generation sequencing (NGS)—virtually all reads are expected to be error-free and 
unambiguous. 
RNA Analysis 
RNA sequencing (RNA-Seq) data, generated using commercially available platforms and those installed at CMP3VdA 
laboratories (currently Illumina and/or ONT), may be used to investigate gene expression and potential correlations 
between non-coding variants and expression changes. Expression quantitative trait loci (eQTLs) highlight variants 
that, although not altering protein structure, may affect transcriptional regulation and gene function. Additional 
analyses of transcript modifications, such as RNA methylation, may also be performed. 
Gene expression analyses will be carried out using the DESeq2 package in R81. 
RNA sequencing with ONT technology further enables characterization of transcript isoforms generated by 
alternative splicing, in addition to quantifying expression. For instance, in Alzheimer’s disease, certain TAU isoforms 
resulting from alternative splicing have been associated with increased disease risk. 
Epigenomic Analysis 
For the study of molecular modifications regulating gene expression—such as DNA methylation, one of the most 
common epigenetic modifications—state-of-the-art technologies available both commercially and at CMP3VdA will 
be employed. Both Illumina and ONT sequencing platforms can be applied for this purpose. In particular, the 
PromethION 24 platform directly extracts methylation information during DNA sequencing, thereby enabling 
simultaneous characterization of both the genetic and epigenetic landscape in a single sequencing run. Identification 
of disease-specific methylation signatures (episignatures) will be performed using the most up-to-date bioinformatic 
tools available in the scientific community for this class of analyses. 
 

  
 
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Pathway Analysis 
For unresolved cases where the diagnostic solution is not readily identifiable, secondary analyses will be conducted. 
Variants, including common ones with potential functional effects, will be aggregated and investigated at the 
pathway level. Specifically, the involvement of patient-specific gene pathways will be compared with the individual’s 
clinical status. 
Causative variant identification will be supported by annotation tools such as ANNOVAR and SnpEff, which classify 
variants as loss-of-function (LoF) or missense. Genes implicated will then be analyzed using available platforms such 
as STRING and GeneMANIA to assess associations between pathways, gene lists, and variant frequencies. 
Gene network and pathway enrichment analyses will be performed to identify biological processes linked to the 
studied disorders. Commonly used bioinformatic tools such as Gene Set Enrichment Analysis (GSEA)82 and g:Profiler83 
will be applied for this purpose. 
Blood Biomarkers of Neurodegenerative Diseases 
Several blood biomarkers currently available and associated with cognitive impairment and other neurodegenerative 
disorders will be analyzed. These include amyloid-beta 1-42 (Aβ1-42), amyloid-beta 1-40 (Aβ1-40), tau protein 
phosphorylated at threonine 181 (pTau181), and neurofilament light chain (NFL), a sensitive indicator of 
neuroaxonal damage in various degenerative conditions. These molecules are key elements in the identification and 
understanding of pathological mechanisms specifically associated with Alzheimer’s disease (Aβ1-42, Aβ1-40, 
pTau181) and, more broadly, with neurodegenerative diseases (NFL). They also provide information regarding 
disease progression and patient staging84,85. Quantification of these biomarkers will be performed using a 
chemiluminescent enzyme immunoassay (CLEIA) on the Lumipulse G600 II platform (Fujirebio, Ghent, Belgium). 
Additional biomarkers, once validated and available, will also be analyzed. 
Experimental Validation of Data 
From a bioinformatics perspective, NGS-based sequencing methods—particularly Illumina short-read platforms—
present a very low error rate (~10⁻⁵)86. However, results obtained through sequencing technologies may be validated 
using Sanger sequencing, which has a per-base accuracy of 99.999%87, and/or other conventional approaches, 
thereby reducing the likelihood of error to a negligible level. 
7.4 Statistical Analysis 
 
Sample Size 
Given the nature of this study, sample size is not determined by a formal statistical power calculation but rather by 
projections of i) the number of patients with neurodegenerative diseases expected to be referred to the two 
experimental centers during the 10-year project period, and ii) an estimated compliance rate of approximately 80%. 
We anticipate enrolling at least 1,000 patients with neurodegenerative diseases, distributed approximately as 
follows: 
a) Parkinson’s disease (PD): ~30% of cases 
b) Parkinsonism: ~5% of cases 
c) Dementias: 
• Alzheimer’s disease (AD): ~30% of cases 
• Frontotemporal dementia (FTD): ~10% of cases 
• Young Onset Dementia (YOD): ~5% of cases 
• Mild Cognitive Impairment (MCI): ~5% of cases 
d) Amyotrophic lateral sclerosis (ALS): ~15% of cases. 
Additionally, ~200 geriatric controls with negative neurological evaluation and negative family history of 
neurodegeneration will be enrolled as internal controls. 

  
 
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The projected cohort of ~1,000 patients is based on the epidemiology of neurodegenerative diseases observed at 
IRCCS Policlinico San Martino (Genoa) and Fondazione IRCCS Policlinico Ca’ Granda (Milan). The expected 
distribution across disease categories should allow reliable characterization of clinical and molecular subgroups. 
Variant Analysis and Validation 
In genomic studies, initial evaluation focuses on determining whether identified variants are: 
i) previously established as causative of neurodegenerative disease, 
ii) novel variants potentially acting as disease-driving mutations, or 
iii) benign polymorphisms catalogued as non-pathogenic. 
Variant assessment will rely on open-access reference databases widely used as benchmarks in clinical and 
population genomics. To validate associations between identified variants and neurodegenerative diseases, analyses 
will include: 
a) Control genomes: Databases such as the 1000 Genomes Project, the International Parkinson’s Disease Genomics 
Consortium (IPDGC), and PPMI will be used to determine the minor allele frequency (MAF) of each variant in 
European and Italian populations, with a particular focus on the 107 Tuscan genomes sequenced for the 1000 
Genomes Project. Considering the prevalence of rare variants in neurodegenerative diseases and the MAF of 
common variants linked to late-onset AD and PD, the cut-off for rare variants will be set at 0.01. This analysis will 
allow prioritization of variants absent from control databases. 
b) Disease-specific databases: Variants will also be analyzed against disease-focused resources such as ALZFORUM, 
ADNI, PPMI, and Project MinE, which contain genomic data from thousands of patients with AD, PD, and ALS. This 
will support validation of both individual variant–disease associations and combinations of rare variants enriched in 
patient genomes. Custom databases will be built from ADNI, PPMI, and MinE to provide allele frequencies and 
enable systematic comparison between enrolled individuals and these large patient cohorts. 
Rare variants will be further assessed for potential disease susceptibility effects in the presence of other variants 
within the same genome. To this end, the number of variants per sample will be analyzed in both patient and control 
populations. Odds ratios (ORs) with corresponding 95% confidence intervals (95% CIs) will be calculated. Q-value 
correction (an extension of the False Discovery Rate) will be applied, and receiver operating characteristic (ROC) 
curves will be computed to assess test accuracy. 
Statistical Approaches 
Descriptive statistics will be generated to report the percentage of variants relative to the total enrolled cohort and 
to the control populations (European and Italian), stratified by disease (PD, parkinsonism, AD, FTD, YOD, ALS, and 
MCI), individual genes, and specific mutations. 
Molecular–genetic data (single variants or variant groups) and clinicopathological data (diagnosis, disease stage, 
prognostic/predictive parameters, survival) will be analyzed using both numerical and graphical approaches. 
Frequency and contingency tables will be constructed, and statistical indices (means, percentiles, standard deviation) 
will be computed. Data will be visualized through histograms, boxplots, and scatterplots. Appropriate statistical 
tests—including t-tests, Fisher’s exact test, and others depending on the formulated hypotheses—will be applied. 
Enrolled patients will be monitored through active follow-up as part of routine diagnostic care. Data regarding 
disability progression, disease course, and overall survival will be analyzed by diagnosis, mutation, or mutation 
combinations, as well as treatment received, using Kaplan–Meier survival analysis. 
 

  
 
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8. CLINICAL RISK 
Blood samples will be collected from these patients (San Martino and Milan) or, if necessary, through an ad hoc 
collection (IRCCS Foundation, Milan). No additional risks are expected for enrolled patients, since the study does not 
foresee further procedures but is limited to the collection of clinical data. 
Screening Failure 
If it is determined that a subject is not eligible for enrollment despite having provided informed consent (e.g., due to 
an incorrect diagnosis), the patient will be classified as a screening failure. In such cases, all collected data will be 
deleted and any biological samples destroyed. 
Withdrawal from the Study and Early Discontinuation 
Participants are free to withdraw from the study at any time without providing any explanation. 
Early withdrawal may also occur if the Sponsor decides to terminate the study prematurely, following discussion 
with the Ethics Committee. 
The Sponsor may decide to discontinue the study early under specific circumstances, such as inadequacy of the site 
or investigators, financial reasons, insufficient recruitment, or intervention by the Ethics Committee. 
Management of Adverse Events 
As this is not an interventional study, no adverse events are expected. Should an adverse event occur during the 
collection of biological samples, it will be managed according to standard clinical practice. 
9. ETHICAL ASPECTS 
The study will be conducted in accordance with the principles of the Declaration of Helsinki and with international 
standards of Good Clinical Practice. The protocol and the patient information/informed consent form must be 
approved by the Ethics Committee before enrollment of patients at the Clinical Center. 
The investigator is responsible for notifying the Ethics Committee of any protocol amendments, in compliance with 
current regulations, and for retaining copies in their records. 
Projects involving genomic studies raise several challenges related to the increasing use of data. First, technical 
challenges arise from the need for computational resources for big data analysis, as well as for data storage and 
management. The growing volume of data produced by research studies requires high-performance computational 
systems and large storage capacity, which often becomes a limiting factor for small to medium-sized centers. 
The most critical challenges, however, are ethical in nature. The accumulation of personal information inevitably 
makes systems responsible for ensuring privacy vulnerable. Furthermore, the availability of a wide range of 
information (both clinical and genetic) increases the likelihood of incidental or unexpected findings. For this reason, 
the scientific community has been working for years on developing regulations for the use of such data. 
An example is that of secondary/incidental findings (SF/IF) in genomic sequencing. The literature includes numerous 
publications and guidelines from Scientific Societies (e.g., the American College of Medical Genetics, the European 
Society of Human Genetics) that define SF/IF as “genetic variants identified through genomic sequencing that are 
unrelated to the investigated condition” (ACMG, 2013). These findings therefore include many genetic and genomic 
variants present in every individual. 
For the purposes of this project, and according to the relevant literature, secondary findings are defined as 
“information concerning an individual’s health and/or relevant to their reproductive or existential choices, 
particularly genetic data emerging in the course of a biomedical research project such as the present one, but not 
included among its primary or secondary objectives, as a result of practices, analyses, and/or investigative 
methodologies commonly used in that type of study” (CNR Research Ethics and Bioethics Commission, 2023). 
Among the possible secondary findings that may emerge in omics-based research projects, a list of genes has been 
identified that are associated with highly penetrant diseases, for which early detection is linked to reduced morbidity 
and mortality (actionable genes, ACMG 2023)91. This periodically updated list will serve as a reference throughout 
the present project. 

  
 
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10. PRIVACY AND CONFIDENTIALITY 
The Sponsor, Co-Sponsor, and Investigator act as independent data controllers for the processing activities under 
their respective responsibility, in compliance with current data protection regulations (Regulation (EU) 2016/679, 
GDPR, as amended), as well as in accordance with the provisions of the Italian Data Protection Authority (“Garante”) 
ruling of July 2019 on the processing of special categories of data pursuant to Article 21, paragraph 1 of Legislative 
Decree 10 August 2018, no. 101. 
Researchers and designated personnel involved in the study and authorized to process data operate in accordance 
with the “Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016.” 
In particular, each participant is informed, through an appropriate information sheet, of the purposes, conditions, 
and modalities of data processing. Participants will be assigned an alphanumeric identification code specific to the 
study, which will enable pseudonymized registration of their data. Throughout the study, participant data will be 
handled exclusively in association with the assigned study code, including for scientific congress presentations or 
publications, whether as individual cases or in aggregated form. 
Similarly, codes assigned by the healthcare facility to biological samples and patients will not be shared with 
collaborating centers. Only the Principal Investigator, or a designated member of the study team (clinical staff 
directly involved), will be able to link such codes to the study ID. 
For data verification purposes, authorized representatives of the Sponsor and Competent Authorities may request 
direct access to parts of the study documentation, including participant-related documents. 
Privacy compliance is supervised by the Data Protection Officers (DPOs) of the individual project partners. Their role 
is to ensure the correct application of privacy regulations, confirm that information systems comply with legal 
requirements, conduct risk assessments, and recommend any additional technical safeguards, as well as define the 
data management policies to be followed by operational staff. 
The Data Controllers are: 
• 
IRCCS Ospedale Policlinico San Martino, Largo Rosanna 10, 16132 Genoa, Italy. 
• 
Fondazione Istituto Italiano di Tecnologia (IIT), Via Morego 30, 16163 Genoa, Italy (Tel. +39 010 28961). 
• 
Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Via Francesco Sforza 28, 20122 Milan, Italy 
(Tel. +39 0255033802). 
All data processing will follow the provisions outlined in the Information Sheet and Consent Form for the Processing 
of Personal Data attached to this Protocol. 
11. DATA SECURITY AND DATA BREACH 
The risk of a data breach—i.e., unauthorized access to data with potential consequences for participants providing 
their health information and for the project’s outcomes and financial sustainability—requires that data protection be 
addressed both technically and organizationally. 
At the technical level, the entire IT infrastructure of the research center has been designed to ensure substantial 
isolation of the research laboratory from the external environment, thereby minimizing the risk of unauthorized 
access. All data exchange points with external entities are secured by dedicated firewalls and security appliances 
(Fortinet Fortigate 101s physical appliances and Fortinet Fortigate VM virtual appliances on ESX), which monitor and 
restrict network traffic strictly to the flows essential for the center’s functioning. 
 

  
 
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Connections between the different nodes of the IT infrastructure are encrypted to prevent interception, and storage 
systems are equipped with encryption mechanisms to protect data at rest, ensuring confidentiality even in the event 
of disk repair at external maintenance facilities. Central systems maintain access logs, in compliance with sector 
regulations, collected centrally in a dedicated system accessible only to the security monitoring team. 
The use of administrative “super-accounts” (root access) is restricted to emergency situations requiring system 
recovery, while routine administrative activities are performed through personal technician accounts, with privilege 
escalation applied only when necessary. The infrastructure undergoes weekly vulnerability assessments using the 
Tenable Nessus platform managed by the SOC Engineering team. Issues identified are resolved within scheduled 
monthly maintenance windows unless urgent intervention is required. 
The entire data center infrastructure is ISO 27001 and ISO 27017 certified, and compliant with ISO 27018 best 
practices for cloud and data center data security. Security events are monitored 24/7 by the SOC Engineering team, 
with specific countermeasures in place to mitigate denial-of-service (DDoS) and flooding attacks. A dedicated 
Security Officer is responsible for ensuring the enforcement of security policies and coordinating with public 
authorities in the event of cybersecurity incidents. 
The local network of the Aosta laboratory is composed of laptops and workstations with preconfigured, centrally 
managed software platforms controlled by the parent company’s IT department. Users are not authorized to install 
unauthorized software, and all administrative and functional system activities are managed by dedicated IT support 
staff. 
All software solutions developed in this context are designed and implemented according to the principles of Privacy 
by Design and Privacy by Default, which translate technically into Security by Design and Security by Default. This 
includes secure operator credential validation, application-level encryption of data, and authentication 
mechanisms—also between software modules—based on dual-key systems and digital certificates. 
At the organizational level, all staff involved in the project across the partner institutions have signed formal letters 
of appointment committing to compliance with the GDPR. Staff have also undergone dedicated training on GDPR 
regulations and the operational procedures applicable within the project, which are published and accessible via the 
project’s document repository. 
Particular attention is drawn to the Co-Sponsor’s Data Breach Management Procedure, which defines the steps for 
handling suspected personal data breaches: 
• 
Internal reporting of the suspected breach 
• 
Identification of the breach 
• 
Risk assessment and qualification 
• 
Notification to the Supervisory Authority 
• 
Communication to affected data subjects 
• 
Registration of the Data Breach, reporting to Management, and archiving of documentation 
12. PUBLICATION AND DISSEMINATION POLICY 
The Investigators and the Sponsor commit to making the data generated by the study available to the scientific 
community. The results of the Study may be published in anonymized form in scientific journals and presented at 
scientific congresses, events, or seminars, exclusively for scientific, educational, and/or institutional purposes. 
Funding, Support, and Activities 
This is an investigator-initiated project, the costs of which will, for the time being, be covered partly by IIT and partly 
through the budgetary resources of the participating units. 

  
 
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IIT will be primarily responsible for the NGS sequencing of the samples, big data analysis, and data storage within the 
Engineering D.HUB HPC infrastructure. 
13. INSURANCE COVERAGE 
Not applicable. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

  
 
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