Breast and ovarian cancers are prevalent worldwide, with genetic factors such as BRCA1 and BRCA2 mutations playing a significant role. However, not all patients carry these mutations, making it challenging to identify risk factors. Researchers have turned to whole exome sequencing (WES) as a tool to identify genetic risk factors in BRCA-negative women. WES allows the sequencing of all protein-coding regions of an individual\'s genome, providing a comprehensive analysis that surpasses traditional gene-by-gene sequencing methods. This technology offers efficiency, cost-effectiveness and the potential to identify new genetic variants contributing to the susceptibility to the diseases. Interpreting WES data for disease-causing variants is challenging due to its complex nature. Machine learning techniques can uncover hidden genetic-variant patterns associated with cancer susceptibility. In this study, we used the extreme gradient boosting (XGBoost) and random forest (RF) algorithms to identify BRCA-related cancer high-risk genes specifically in the Saudi population. The experimental results exposed that the RF method scored superior performance with an accuracy of 88.16% and an area under the receiver-operator characteristic curve of 0.95. Using bioinformatics analysis tools, we explored the top features of the high-accuracy machine learning model that we built to enhance our knowledge of genetic interactions and find complex genetic patterns connected to the development of BRCA-related cancers. We were able to identify the significance of HLA gene variations in these WES datasets for BRCA-related patients. We find that immune response mechanisms play a major role in the development of BRCA-related cancer. It specifically highlights genes associated with antigen processing and presentation, such as HLA-B, HLA-A and HLA-DRB1 and their possible effects on tumour progression and immune evasion. In summary, by utilizing machine learning approaches, we have the potential to aid in the development of precision medicine approaches for early detection and personalized treatment strategies.

Genetic repeat expansions cause neuronal degeneration in amyotrophic lateral sclerosis as well as other neurodegenerative disorders such as spinocerebellar ataxia, Huntington\'s disease and Kennedy\'s disease. Repeat expansions in the same gene can cause multiple clinical phenotypes. We aimed to characterize repeat expansions in a Norwegian amyotrophic lateral sclerosis cohort. Norwegian amyotrophic lateral sclerosis patients (n = 414) and neurologically healthy controls adjusted for age and gender (n = 713) were investigated for repeat expansions in AR, ATXN1, ATXN2 and HTT using short read exome sequencing and the ExpansionHunter software. Five amyotrophic lateral sclerosis patients (1.2%) and two controls (0.3%) carried ≥36 repeats in HTT (P = 0.032), and seven amyotrophic lateral sclerosis patients (1.7%) and three controls (0.4%) carried ≥29 repeats in ATXN2 (P = 0.038). One male diagnosed with amyotrophic lateral sclerosis carried a pathogenic repeat expansion in AR, and his diagnosis was revised to Kennedy\'s disease. In ATXN1, 50 amyotrophic lateral sclerosis patients (12.1%) and 96 controls (13.5%) carried ≥33 repeats (P = 0.753). None of the patients with repeat expansions in ATXN2 or HTT had signs of Huntington\'s disease or spinocerebellar ataxia type 2, based on a re-evaluation of medical records. The diagnosis of amyotrophic lateral sclerosis was confirmed in all patients, with the exception of one patient who had primary lateral sclerosis. Our findings indicate that repeat expansions in HTT and ATXN2 are associated with increased likelihood of developing amyotrophic lateral sclerosis. Further studies are required to investigate the potential relationship between HTT repeat expansions and amyotrophic lateral sclerosis.

Apolipoprotein E (APOE) genetic variants are most notably known for their divergent impact on the risk of developing Alzheimer\'s disease. While APOE genotype has been consistently shown to modulate lipid metabolism in a variety of cellular contexts, the effect of APOE alleles on the lipidome in hepatocytes is unknown. In this study, we investigated the contribution of APOE alleles to lipidomic profiles of donor-derived primary human hepatocytes from 77 subjects. Lipidomic data obtained by liquid chromatography-mass spectrometry were analyzed across ε2/ε3, ε3/ε3, and ε3/ε4 genotypes to reveal how APOE modulates lipid relative levels over age and between groups. Hepatic APOE concentration, measured by ELISA, was assessed for correlation with lipid abundance in subjects grouped as per APOE genotype and sex. APOE genotype-specific differential lipidomic signatures associated with age for multiple lipid classes but did not differ between sexes. Compared to ε2/ε3, ε3/ε4 hepatocytes had higher abundance of acylcarnitines (AC) and acylphosphatidylglycerol (AcylPG) as a class, as well as higher medium and long-chain ACs, AcylPG, phosphatidylglycerol (PG), bis(monoacylglycerol)phosphate (BMP), monoacylglycerol (MG) and diacylglycerol (DG) species. The ε3/ε4 hepatocytes also exhibited a higher abundance of medium and long-chain ACs compared to the ε3/ε3 hepatocytes. Only in the ε3/ε4 hepatocytes, APOE concentration was lower and showed a negative correlation with BMP levels, specifically in females. APOE genotype dictates a differential lipidome in primary human hepatocytes. The lipids involved suggest mitochondrial dysfunction with accompanying alterations in neutral lipid storage, reflective of a general disturbance of free fatty acid metabolism in human hepatocytes with the ε4 allele.

BACKGROUND: Parkinson\'s disease (PD) has a complex genetic background involving both rare and common genetic variants. Although a small percentage of cases show a clear Mendelian inheritance pattern, it is much more relevant to identify patients who present with a complex genetic profile of risk variants with different severity. The ß-glucocerebrosidase coding gene (GBA1) is recognized as the most frequent genetic risk factor for PD and Lewy body dementia, irrespective of reduction of the enzyme activity due to genetic variants.
METHODS: In a selected cohort of 190 Hungarian patients with clinical signs of PD and suspected genetic risk, we performed the genetic testing of the GBA1 gene. As other genetic hits can modify clinical features, we also screened for additional rare variants in other neurodegenerative genes and assessed the APOE-ε genotype of the patients.
RESULTS: In our cohort, we identified 29 GBA1 rare variant (RV) carriers. Out of the six different detected RVs, the highly debated E365K and T408M variants are composed of the majority of them (22 out of 32). Three patients carried two GBA1 variants, and an additional three patients carried rare variants in other neurodegenerative genes (SMPD1, SPG11, and SNCA). We did not observe differences in age at onset or other clinical features of the patients carrying two GBA1 variants or patients carrying heterozygous APOE-ε4 allele.
CONCLUSIONS: We need further studies to better understand the drivers of clinical differences in these patients, as this could have important therapeutic implications.

This study aimed to investigate the influence of the overall Alzheimer\'s disease (AD) genetic architecture on Down syndrome (DS) status, cognitive measures, and cerebrospinal fluid (CSF) biomarkers.
AD polygenic risk scores (PRS) were tested for association with DS-related traits.
The AD risk PRS was associated with disease status in several cohorts of sporadic late- and early-onset and familial late-onset AD, but not in familial early-onset AD or DS. On the other hand, lower DS Mental Status Examination memory scores were associated with higher PRS, independent of intellectual disability and APOE (PRS including APOE, PRSAPOE , p = 2.84 × 10-4 ; PRS excluding APOE, PRSnonAPOE , p = 1.60 × 10-2 ). PRSAPOE exhibited significant associations with Aβ42, tTau, pTau, and Aβ42/40 ratio in DS.
These data indicate that the AD genetic architecture influences cognitive and CSF phenotypes in DS adults, supporting common pathways that influence memory decline in both traits.
Examination of the polygenic risk of AD in DS presented here is the first of its kind. AD PRS influences memory aspects in DS individuals, independently of APOE genotype. These results point to an overlap between the genes and pathways that leads to AD and those that influence dementia and memory decline in the DS population. APOE ε4 is linked to DS cognitive decline, expanding cognitive insights in adults.

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Genetic risk factors such as APOE ε4 and MAPT (rs242557) A allele are associated with amyloid and tau pathways and grey matter changes at both early and established stages of Alzheimer\'s disease, but their effects on cortical morphology in young healthy adults remain unclear. A total of 144 participants aged from 18 to 24 underwent 3T MRI and genotyping for APOE and MAPT to investigate unique impacts of these genetic risk factors in a cohort without significant comorbid conditions such as metabolic and cardiovascular diseases. We segmented the cerebral cortex into 68 regions and calculated the cortical area, thickness, curvature and folding index for each region. Then, we trained machine learning models to classify APOE and MAPT genotypes using these morphological features. In addition, we applied a growing hierarchical self-organizing maps algorithm, which clustered the 68 regions into 4 subgroups representing different morphological patterns. Then, we performed general linear model analyses to estimate the interaction between APOE and MAPT on cortical patterns. We found that the classifiers using all cortical features could accurately classify individuals carrying genetic risks of dementia outperforming each individual feature alone. APOE ε4 carriers had a more convoluted and thinner cortex across the cerebral cortex. A similar pattern was found in MAPT A allele carriers only in the regions that are vulnerable for early tau pathology. With the clustering analysis, we found a synergetic effect between APOE ε4 and MAPT A allele, i.e. carriers of both risk factors showed the most deviation of cortical pattern from the typical pattern of that cluster. Genetic risk factors of dementia by APOE ε4 and MAPT (rs242557) A allele were associated with variations of cortical morphology, which can be observed in young healthy adults more than 30 years before Alzheimer\'s pathology is likely to occur and 50 years before dementia symptoms may begin.

Current hypothesis of Alzheimer\'s disease (AD) postulates that amyloid β (Aβ) deposition in the brain causes tau inclusion in neurons and leads to cognitive decline. The discovery of the genetic association between triggering receptor expressed on myeloid cells 2 (TREM2) with increased AD risk points to a causal link between microglia and AD pathogenesis, and revealed a crucial role of TREM2-dependent clustering of microglia around amyloid plaques that prevents Aβ toxicity to facilitate tau deposition near the plaques. Here we review the physiological and pathological roles of another AD risk gene expressed in microglia, inositol polyphosphate-5-polyphosphatase D (INPP5D), which encodes a phosphoinositide phosphatase. Evidence suggests that its risk polymorphisms alter the expression level and/or function of INPP5D, while concomitantly affecting tau levels in cerebrospinal fluids. In β-amyloidosis mice, INPP5D was upregulated upon Aβ deposition and negatively regulated the microglial clustering toward amyloid plaques. INPP5D seems to exert its function by acting antagonistically at downstream of the TREM2 signaling pathway, suggesting that it is a novel regulator of the protective barrier by microglia. Further studies to elucidate INPP5D\'s role in AD may help in developing new therapeutic targets for AD treatment.

Until now, next generation sequencing (NGS) data has not been incorporated into any prognostic stratification of multiple myeloma (MM) and no therapeutic considerations are based upon it. In this work, we correlated NGS data with (1) therapy response and survival parameters in newly diagnosed multiple myeloma, treated by VRd * and (2) MM disease stage: newly diagnosed multiple myeloma (ndMM) versus relapsed and/or refractory (relapsed/refractory multiple myeloma). We analyzed 126 patients, with ndMM and relapsed refractory multiple myeloma (rrMM), treated at the University Hospital of Bern (Inselspital). Next generation sequencing was performed on bone marrow, as part of routine diagnostics. The NGS panel comprised eight genes CCND1, DIS3, EGR1, FAM46C (TENT5C), FGFR3, PRDM1, TP53, TRAF3 and seven hotspots in BRAF, IDH1, IDH2, IRF4, KRAS, NRAS. The primary endpoint was complete remission (CR) after VRd in ndMM, in correlation with mutational profile. Mutational load was generally higher in rrMM, with more frequently mutated TP53: 11/87 (13%) in ndMM versus 9/11 (81%) in rrMM (OR 0.0857, p = 0.0007). In ndMM, treated by VRd, mutations in MAPK-pathway members (NRAS, KRAS or BRAF) were associated with reduced probability of CR (21/38, 55%), as compared with wild type NRAS, KRAS or BRAF (34/40, 85%; OR 0.2225, p = 0.006). NRAS c.181C > A (p.Q61K) as a single mutation event showed a trend to reduced probability of achieving CR (OR 0.0912, p = 0.0247). Activation of MAPK pathway via mutated NRAS, KRAS and BRAF genes seems to have a negative impact on outcome in ndMM patients receiving VRd therapy. VRd* - bortezomib (Velcade®), lenalidomide (Revlimid®) and dexamethasone.

Identifying genetic risk factors for Alzheimer\'s disease (AD) is an important research topic. To date, different endophenotypes, such as imaging-derived endophenotypes and proteomic expression-derived endophenotypes, have shown the great value in uncovering risk genes compared to case-control studies. Biologically, a co-varying pattern of different omics-derived endophenotypes could result from the shared genetic basis. However, existing methods mainly focus on the effect of endophenotypes alone; the effect of cross-endophenotype (CEP) associations remains largely unexploited. In this study, we used both endophenotypes and their CEP associations of multi-omic data to identify genetic risk factors, and proposed two integrated multi-task sparse canonical correlation analysis (inMTSCCA) methods, i.e., pairwise endophenotype correlation-guided MTSCCA (pcMTSCCA) and high-order endophenotype correlation-guided MTSCCA (hocMTSCCA). pcMTSCCA employed pairwise correlations between magnetic resonance imaging (MRI)-derived, plasma-derived, and cerebrospinal fluid (CSF)-derived endophenotypes as an additional penalty. hocMTSCCA used high-order correlations among these multi-omic data for regularization. To figure out genetic risk factors at individual and group levels, as well as altered endophenotypic markers, we introduced sparsity-inducing penalties for both models. We compared pcMTSCCA and hocMTSCCA with three related methods on both simulation and real (consisting of neuroimaging data, proteomic analytes, and genetic data) datasets. The results showed that our methods obtained better or comparable canonical correlation coefficients (CCCs) and better feature subsets than benchmarks. Most importantly, the identified genetic loci and heterogeneous endophenotypic markers showed high relevance. Therefore, jointly using multi-omic endophenotypes and their CEP associations is promising to reveal genetic risk factors. The source code and manual of inMTSCCA are available at https://ngdc.cncb.ac.cn/biocode/tools/BT007330.