Bi-allelic genetic variants in the translational GTPases GTPBP1 and GTPBP2 cause a distinct identical neurodevelopmental syndrome.

The homologous genes GTPBP1 and GTPBP2 encode GTP-binding proteins 1 and 2, which are involved in ribosomal homeostasis. Pathogenic variants in GTPBP2 were recently shown to be an ultra-rare cause of neurodegenerative or neurodevelopmental disorders (NDDs). Until now, no human phenotype has been linked to GTPBP1. Here, we describe individuals carrying bi-allelic GTPBP1 variants that display an identical phenotype with GTPBP2 and characterize the overall spectrum of GTP-binding protein (1/2)-related disorders. In this study, 20 individuals from 16 families with distinct NDDs and syndromic facial features were investigated by whole-exome (WES) or whole-genome (WGS) sequencing. To assess the functional impact of the identified genetic variants, semi-quantitative PCR, western blot, and ribosome profiling assays were performed in fibroblasts from affected individuals. We also investigated the effect of reducing expression of CG2017, an ortholog of human GTPBP1/2, in the fruit fly Drosophila melanogaster. Individuals with bi-allelic GTPBP1 or GTPBP2 variants presented with microcephaly, profound neurodevelopmental impairment, pathognomonic craniofacial features, and ectodermal defects. Abnormal vision and/or hearing, progressive spasticity, choreoathetoid movements, refractory epilepsy, and brain atrophy were part of the core phenotype of this syndrome. Cell line studies identified a loss-of-function (LoF) impact of the disease-associated variants but no significant abnormalities on ribosome profiling. Reduced expression of CG2017 isoforms was associated with locomotor impairment in Drosophila. In conclusion, bi-allelic GTPBP1 and GTPBP2 LoF variants cause an identical, distinct neurodevelopmental syndrome. Mutant CG2017 knockout flies display motor impairment, highlighting the conserved role for GTP-binding proteins in CNS development across species.

Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases.

BACKGROUND: Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25-30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome. METHODS: We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants. RESULTS: Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving. CONCLUSIONS: Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing.

CHARR efficiently estimates contamination from DNA sequencing data.

DNA sample contamination is a major issue in clinical and research applications of whole-genome and -exome sequencing. Even modest levels of contamination can substantially affect the overall quality of variant calls and lead to widespread genotyping errors. Currently, popular tools for estimating the contamination level use short-read data (BAM/CRAM files), which are expensive to store and manipulate and often not retained or shared widely. We propose a metric to estimate DNA sample contamination from variant-level whole-genome and -exome sequence data called CHARR, contamination from homozygous alternate reference reads, which leverages the infiltration of reference reads within homozygous alternate variant calls. CHARR uses a small proportion of variant-level genotype information and thus can be computed from single-sample gVCFs or callsets in VCF or BCF formats, as well as efficiently stored variant calls in Hail VariantDataset format. Our results demonstrate that CHARR accurately recapitulates results from existing tools with substantially reduced costs, improving the accuracy and efficiency of downstream analyses of ultra-large whole-genome and exome sequencing datasets.

CHARR efficiently estimates contamination from DNA sequencing data.

DNA sample contamination is a major issue in clinical and research applications of whole genome and exome sequencing. Even modest levels of contamination can substantially affect the overall quality of variant calls and lead to widespread genotyping errors. Currently, popular tools for estimating the contamination level use short-read data (BAM/CRAM files), which are expensive to store and manipulate and often not retained or shared widely. We propose a new metric to estimate DNA sample contamination from variant-level whole genome and exome sequence data, CHARR, Contamination from Homozygous Alternate Reference Reads, which leverages the infiltration of reference reads within homozygous alternate variant calls. CHARR uses a small proportion of variant-level genotype information and thus can be computed from single-sample gVCFs or callsets in VCF or BCF formats, as well as efficiently stored variant calls in Hail VDS format. Our results demonstrate that CHARR accurately recapitulates results from existing tools with substantially reduced costs, improving the accuracy and efficiency of downstream analyses of ultra-large whole genome and exome sequencing datasets.

Genome-wide association studies on Northern Italy isolated populations provide further support concerning genetic susceptibility for major depressive disorder.

OBJECTIVES: Major depressive disorder (MDD) is a psychiatric disorder with pathogenesis influenced by both genetic and environmental factors. To date, the molecular-level understanding of its aetiology remains unclear. Thus, we aimed to identify genetic variants and susceptibility genes for MDD with a genome-wide association study (GWAS) approach. METHODS: We performed a meta-analysis of GWASs and a gene-based analysis on two Northern Italy isolated populations (cases/controls n = 166/472 and 33/320), followed by replication and polygenic risk score (PRS) analyses in Italian independent samples (cases n = 464, controls n = 339). RESULTS: We identified two novel MDD-associated genes, KCNQ5 (lead SNP rs867262, p = 3.82 × 10-9) and CTNNA2 (rs6729523, p = 1.25 × 10-8). The gene-based analysis revealed another six genes (p < 2.703 × 10-6): GRM7, CTNT4, SNRK, SRGAP3, TRAPPC9, and FHIT. No replication of the genome-wide significant SNPs was found in the independent cohort, even if 14 SNPs around CTNNA2 showed association with MDD and related phenotypes at the nominal level of p (<0.05). Furthermore, the PRS model developed in the discovery cohort discriminated cases and controls in the replication cohort. CONCLUSIONS: Our work suggests new possible genes associated with MDD, and the PRS analysis confirms the polygenic nature of this disorder. Future studies are required to better understand the role of these findings in MDD.

The Bartter-Gitelman Spectrum: 50-Year Follow-up With Revision of Diagnosis After Whole-Genome Sequencing.

Bartter syndrome (BS) and Gitelman syndrome (GS) are renal tubular disorders affecting sodium, potassium, and chloride reabsorption. Clinical features include muscle cramps and weakness, in association with hypokalemia, hypochloremic metabolic alkalosis, and hyperreninemic hyperaldosteronism. Hypomagnesemia and hypocalciuria are typical of GS, while juxtaglomerular hyperplasia is characteristic of BS. GS is due to SLC12A3 variants, whereas BS is due to variants in SLC12A1, KCNJ1, CLCNKA, CLCNKB, BSND, MAGED2, or CASR. We had the opportunity to follow up one of the first reported cases of a salt-wasting tubulopathy, who based on clinical features was diagnosed with GS. The patient had presented at age 10 years with tetany precipitated by vomiting or diarrhea. She had hypokalemia, a hypochloremic metabolic alkalosis, hyponatremia, mild hypercalcemia, and normomagnesemia, and subsequently developed hypocalciuria and hypomagnesemia. A renal biopsy showed no evidence for juxtaglomerular hyperplasia. She developed chronic kidney failure at age 55 years, and ocular sclerochoroidal calcification, associated with BS and GS, at older than 65 years. Our aim was therefore to establish the genetic diagnosis in this patient using whole-genome sequencing (WGS). Leukocyte DNA was used for WGS analysis, and this revealed a homozygous c.226C > T (p.Arg76Ter) nonsense CLCNKB mutation, thereby establishing a diagnosis of BS type-3. WGS also identified 2 greater than 5-Mb regions of homozygosity that suggested likely mutational heterozygosity in her parents, who originated from a Greek island with fewer than 1500 inhabitants and may therefore have shared a common ancestor. Our results demonstrate the utility of WGS in establishing the correct diagnosis in renal tubular disorders with overlapping phenotypes.

GREEN-DB: a framework for the annotation and prioritization of non-coding regulatory variants from whole-genome sequencing data.

Non-coding variants have long been recognized as important contributors to common disease risks, but with the expansion of clinical whole genome sequencing, examples of rare, high-impact non-coding variants are also accumulating. Despite recent advances in the study of regulatory elements and the availability of specialized data collections, the systematic annotation of non-coding variants from genome sequencing remains challenging. Here, we propose a new framework for the prioritization of non-coding regulatory variants that integrates information about regulatory regions with prediction scores and HPO-based prioritization. Firstly, we created a comprehensive collection of annotations for regulatory regions including a database of 2.4 million regulatory elements (GREEN-DB) annotated with controlled gene(s), tissue(s) and associated phenotype(s) where available. Secondly, we calculated a variation constraint metric and showed that constrained regulatory regions associate with disease-associated genes and essential genes from mouse knock-outs. Thirdly, we compared 19 non-coding impact prediction scores providing suggestions for variant prioritization. Finally, we developed a VCF annotation tool (GREEN-VARAN) that can integrate all these elements to annotate variants for their potential regulatory impact. In our evaluation, we show that GREEN-DB can capture previously published disease-associated non-coding variants as well as identify additional candidate disease genes in trio analyses.

Clinical validation of a combinatorial PharmAcogeNomic approach in major Depressive disorder: an Observational prospective RAndomized, participant and rater-blinded, controlled trial (PANDORA trial).

BACKGROUND: Major depressive disorder (MDD) is a common, chronic, debilitating mood disorder that causes serious functional impairment and significantly decreased quality of life. Pharmacotherapy represents the first-line treatment option; however, only approximately one third of patients respond to the first treatment because of the ineffectiveness or side effects of antidepressants. Precision medicine in psychiatry might offer clinicians the possibility to tailor treatment according to the best possible evidence of efficacy and tolerability for each subject. In this context, our study aims to carry out a clinical validation of a combinatorial pharmacogenomics (PGx) test in an Italian MDD patient cohort with advocacy license independence. METHODS: Our study is a prospective participant- and rater-blinded, randomized, controlled clinical observational trial enrolling 300 MDD patients who are referred to psychiatric services to receive a new antidepressant due to the failure of their current treatment and/or the onset of adverse effects. Eligible participants are randomized to the TGTG group (Treated with Genetic Test Guide) or TAU group (Treated as Usual). For all subjects, DNA is collected with a buccal brush. The primary outcome is the reduction in depressive symptomatology. The secondary outcomes involve a range of scales that assess MDD symptoms and social functioning outcomes. The assessment is performed at four timepoints: baseline and 4, 8, and 12 weeks. DISCUSSION: This project represents the first randomized controlled clinical trial to investigate whether a non-commercial PGx test improves outcomes in an MDD naturalistic cohort. Moreover, the identification of new genetic variants associated with non-response or side effects will improve the efficacy of the test, leading to further cost-saving. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov NCT04615234. Registered on November 4, 2020.

Estrogen-Like Effect of Mitotane Explained by Its Agonist Activity on Estrogen Receptor-α.

Mitotane is the cornerstone of medical treatment of adrenocortical carcinoma. Estrogenic-like side effects frequently occur in patients, and previous studies explored the chemical nature of the interaction between estrogen receptor-α (ER-α) and toxic compounds, including the DDD derivatives. We used molecular docking and molecular dynamics (MD) simulations to explore the possible interaction between mitotane and the ER-α receptor and the induced conformational changes. The ER-α expressing MCF-7 cells were exposed to mitotane with/without tamoxifen, and the cell viability/proliferation was evaluated by MTT assay and direct count. The transient ER-α silencing was performed using two ER-α siRNA (50 nM) and verified by Western blot. MDA-MB-231 cells were used as a negative control. Mitotane showed a similar docking configuration to 17ß-estradiol and bisphenol A (BPA) and a significant binding affinity to ER-α. MD simulations showed that mitotane preserves the active conformation of ER-α more than both BPA and Bisphenol C, classifying it as an agonist. Exposure of MCF-7 cells to mitotane led to the concentration-dependent increase of cell viability and proliferation, which was reduced in the presence of tamoxifen and nullified by the transient ER-α knock-down. Integrating bioinformatics approaches with cell biology and pharmacological methods, we demonstrated that mitotane directly binds and activates ER-α.

Whole Blood Transcriptome Characterization of 3xTg-AD Mouse and Its Modulation by Transcranial Direct Current Stimulation (tDCS).

The 3xTg-AD mouse is a widely used model in the study of Alzheimer's Disease (AD). It has been extensively characterized from both the anatomical and behavioral point of view, but poorly studied at the transcriptomic level. For the first time, we characterize the whole blood transcriptome of the 3xTg-AD mouse at three and six months of age and evaluate how its gene expression is modulated by transcranial direct current stimulation (tDCS). RNA-seq analysis revealed 183 differentially expressed genes (DEGs) that represent a direct signature of the genetic background of the mouse. Moreover, in the 6-month-old 3xTg-AD mice, we observed a high number of DEGs that could represent good peripheral biomarkers of AD symptomatology onset. Finally, tDCS was associated with gene expression changes in the 3xTg-AD, but not in the control mice. In conclusion, this study provides an in-depth molecular characterization of the 3xTg-AD mouse and suggests that blood gene expression can be used to identify new biomarkers of AD progression and treatment effects.

Characterization of three sialidases from Danio rerio.

Zebrafish encodes several sialidases belonging to the NEU3 group, the plasma membrane-associated member of the family with high specificity toward ganglioside substrates. Neu3.1, Neu3.2 and Neu 3.3 have been expressed in E. coli and purified using the pGEX-2T expression system. Although all the enzymes are expressed by bacterial cells, Neu3.1 formed insoluble aggregates that hampered its purification. Neu3.2 and Neu3.3 formed oligomers as demonstrated by gel filtration chromatography experiments. Actually, the first formed a trimer whereas the second a pentamer. Intriguingly, despite relevant degree of sequence identity and similarity, the two enzymes showed peculiar substrate specificities toward gangliosides other than GM3, two glycoproteins and two forms of sialyllactose. Using molecular modelling and the crystal structure of the human cytosolic sialidase NEU2 as a template, the 3D models of the sialidases from zebrafish have been generated. As expected, the 3D models showed the typical six blade beta-propeller typical of sialidases, with an overall highly conserved active site architecture. The differences among the three zebrafish enzymes and human NEU2 are mainly located in the loops connecting the antiparallel beta strands of the propeller core. These portions of the proteins are probably responsible for the differences observed in substrate specificities, as well as in the different subcellular localization and aggregation features observed in solution. Finally, the in silico analysis of RNA-Seq data evidenced a peculiar expression profile of the three genes during embryogenesis, suggesting different roles of these sialidases during development.

Alterations observed in the interferon α and ß signaling pathway in MDD patients are marginally influenced by cis-acting alleles.

Major depressive disorder (MDD) is a common psychiatric disorder with a multifactorial aetiology determined by the interaction between genetic and environmental risk factors. Pieces of evidence indicate that inflammation and immune activation may contribute to the onset of MDD playing a role in the pathogenetic mechanism. To date, it is not known to which extent the association between MDD and inflammation is shaped by the genetic background or by the presence of environmental factors. To clarify this issue, we analyzed genotype and blood RNA profiles of 463 MDD cases and 459 controls (NIMH-Study 88/Site621) estimating the Genetic and Environmental Regulated eXpression component of gene expression (GReX and EReX respectively). Both components were tested for association with MDD. Many genes belonging to the α/ß interferon signaling pathway showed an association between MDD and EReX, only two between MDD and GReX. Also other MDD differentially expressed genes were more influenced by the EReX than by GReX. These results suggest that impact of the genetic background on MDD blood gene expression alterations is much lower than the contribution of environmental factors and almost absent for the genes of the interferon pathway.

Investigating an in silico approach for prioritizing antidepressant drug prescription based on drug-induced expression profiles and predicted gene expression.

In clinical practice, an antidepressant prescription is a trial and error approach, which is time consuming and discomforting for patients. This study investigated an in silico approach for ranking antidepressants based on their hypothetical likelihood of efficacy. We predicted the transcriptomic profile of citalopram remitters by performing an in silico transcriptomic-wide association study on STAR*D GWAS data (N = 1163). The transcriptional profile of remitters was compared with 21 antidepressant-induced gene expression profiles in five human cell lines available in the connectivity-map database. Spearman correlation, Pearson correlation, and the Kolmogorov-Smirnov test were used to determine the similarity between antidepressant-induced profiles and remitter profiles, subsequently calculating the average rank of antidepressants across the three methods and a p value for each rank by using a permutation procedure. The drugs with the top ranks were those having a high positive correlation with the expression profiles of remitters and that may have higher chances of efficacy in the tested patients. In MCF7 (breast cancer cell line), escitalopram had the highest average rank, with an average rank higher than expected by chance (p = 0.0014). In A375 (human melanoma) and PC3 (prostate cancer) cell lines, escitalopram and citalopram emerged as the second-highest ranked antidepressants, respectively (p = 0.0310 and 0.0276, respectively). In HA1E (kidney) and HT29 (colon cancer) cell types, citalopram and escitalopram did not fall among top antidepressants. The correlation between citalopram remitters' and (es)citalopram-induced expression profiles in three cell lines suggests that our approach may be useful and with future improvements, it can be applicable at the individual level to tailor treatment prescription.

Author Correction: A novel homozygous mutation in GAD1 gene described in a schizophrenic patient impairs activity and dimerization of GAD67 enzyme.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Identification of Circulating Genomic and Metabolic Biomarkers in Intrahepatic Cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (ICC) is an aggressive cancer arising from the bile ducts with a need for earlier diagnosis and a greater range of treatment options. KRAS/NRAS mutations are common in ICC tumours and 6-32% of patients also have isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) gene mutations associated with metabolic changes. This feasibility study investigated sequencing circulating tumour DNA (ctDNA) combined with metabolite profiling of plasma as a method for biomarker discovery in ICC patients. Plasma was collected from four ICC patients receiving radio-embolisation and healthy controls at multiple time points. ctDNA was sequenced using Ampliseq cancer hotspot panel-v2 on Ion Torrent PGM for single nucleotide variants (SNV) detection and with Illumina whole genome sequencing for copy number variants (CNV) and further targeted examination for SNVs. Untargeted analysis of metabolites from patient and control plasma was performed using liquid chromatography coupled with high-resolution tandem mass spectrometry (LC-MS/MS). Metabolite identification was performed using multi-parameter comparisons with analysis of authentic standards, and univariate statistical analysis was performed to identify differences in metabolite abundance between patient and control samples. Recurrent somatic SNVs and CNVs were identified in ctDNA from three out of four patients that included both NRAS and IDH1 mutations linked to ICC. Plasma metabolite analysis revealed biomarker metabolites associated with ICC and in particular 2-hydroxyglutarate (2-HG) levels were elevated in both samples from the only patient showing a variant allele in IDH1. A reduction in the number of CNVs was observed with treatment. This study demonstrates that ctDNA and metabolite levels can be identified and correlated in ICC patient blood samples and differentiated from healthy controls. We conclude that combining genomic and metabolic analysis of plasma offers an effective approach to biomarker identification with potential for disease stratification and early detection studies.

Further Defining the Phenotypic Spectrum of B3GAT3 Mutations and Literature Review on Linkeropathy Syndromes.

The term linkeropathies (LKs) refers to a group of rare heritable connective tissue disorders, characterized by a variable degree of short stature, skeletal dysplasia, joint laxity, cutaneous anomalies, dysmorphism, heart malformation, and developmental delay. The LK genes encode for enzymes that add glycosaminoglycan chains onto proteoglycans via a common tetrasaccharide linker region. Biallelic variants in XYLT1 and XYLT2, encoding xylosyltransferases, are associated with Desbuquois dysplasia type 2 and spondylo-ocular syndrome, respectively. Defects in B4GALT7 and B3GALT6, encoding galactosyltransferases, lead to spondylodysplastic Ehlers-Danlos syndrome (spEDS). Mutations in B3GAT3, encoding a glucuronyltransferase, were described in 25 patients from 12 families with variable phenotypes resembling Larsen, Antley-Bixler, Shprintzen-Goldberg, and Geroderma osteodysplastica syndromes. Herein, we report on a 13-year-old girl with a clinical presentation suggestive of spEDS, according to the 2017 EDS nosology, in whom compound heterozygosity for two B3GAT3 likely pathogenic variants was identified. We review the spectrum of B3GAT3-related disorders and provide a comparison of all LK patients reported up to now, highlighting that LKs are a phenotypic continuum bridging EDS and skeletal disorders, hence offering future nosologic perspectives.

Treatment-Resistant Schizophrenia: Genetic and Neuroimaging Correlates.

Schizophrenia is a severe neuropsychiatric disorder that affects approximately 0.5-1% of the population. Response to antipsychotic therapy is highly variable, and it is not currently possible to predict those patients who will or will not respond to antipsychotic medication. Furthermore, a high percentage of patients, approximately 30%, are classified as treatment-resistant (treatment-resistant schizophrenia; TRS). TRS is defined as a non-response to at least two trials of antipsychotic medication of adequate dose and duration. These patients are usually treated with clozapine, the only evidence-based pharmacotherapy for TRS. However, clozapine is associated with severe adverse events. For these reasons, there is an increasing interest to identify better targets for drug development of new compounds and to establish better biomarkers for existing medications. The ability of antipsychotics to improve psychotic symptoms is dependent on their antagonist and reverse agonist activities at different neuroreceptors, and some genetic association studies of TRS have focused on different pharmacodynamic factors. Some genetic studies have shown an association between antipsychotic response or TRS and neurodevelopment candidate genes, antipsychotic mechanisms of action (such as dopaminergic, serotonergic, GABAergic, and glutamatergic) or pharmacokinetic factors (i.e., differences in the cytochrome families). Moreover, there is a growing body of literature on the structural and functional neuroimaging research into TRS. Neuroimaging studies can help to uncover the underlying neurobiological reasons for such resistance and identify resistant patients earlier. Studies examining the neuropharmacological mechanisms of antipsychotics, including clozapine, can help to improve our knowledge of their action on the central nervous system, with further implications for the discovery of biomarkers and the development of new treatments. The identification of the underlying mechanisms of TRS is a major challenge for developing personalized medicine in the psychiatric field for schizophrenia treatment. The main goal of precision medicine is to use genetic and brain-imaging information to improve the safety, effectiveness, and health outcomes of patients via more efficiently targeted risk stratification, prevention, and tailored medication and treatment management approaches. The aim of this review is to summarize the state of art of pharmacogenetic, pharmacogenomic and neuroimaging studies in TRS.

Next Generation Sequencing Analysis in Early Onset Dementia Patients.

BACKGROUND: Early onset dementias (EOD) are rare neurodegenerative dementias that present before 65 years. Genetic factors have a substantially higher pathogenetic contribution in EOD patients than in late onset dementia. OBJECTIVE: To identify known and/or novel rare variants in major candidate genes associated to EOD by high-throughput sequencing. Common-risk variants of apolipoprotein E (APOE) and prion protein (PRNP) genes were also assessed. METHODS: We studied 22 EOD patients recruited in Memory Clinics, in the context of studies investigating genetic forms of dementia. Two methodological approaches were applied for the target-Next Generation Sequencing (NGS) analysis of these patients. In addition, we performed progranulin plasma dosage, C9Orf72 hexanucleotide repeat expansion analysis, and APOE genotyping. RESULTS: We detected three rare known pathogenic mutations in the GRN and PSEN2 genes and eleven unknown-impact mutations in the GRN, VCP, MAPT, FUS, TREM2, and NOTCH3 genes. Six patients were carriers of only common risk variants (APOE and PRNP), and one did not show any risk mutation/variant. Overall, 69% (n = 9) of our early onset Alzheimer's disease (EAOD) patients, compared with 34% (n = 13) of sporadic late onset Alzheimer's disease (LOAD) patients and 27% (n = 73) of non-affected controls (ADNI, whole genome data), were carriers of at least two rare/common risk variants in the analyzed candidate genes panel, excluding the full penetrant mutations. CONCLUSION: This study suggests that EOD patients without full penetrant mutations are characterized by higher probability to carry polygenic risk alleles that patients with LOAD forms. This finding is in line with recently reported evidence, thus suggesting that the genetic risk factors identified in LOAD might modulate the risk also in EOAD.