Dataset Information

Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease.

ABSTRACT:

Introduction

Genome-wide association studies (GWAS) in late onset Alzheimer's disease (LOAD) provide lists of individual genetic determinants. However, GWAS do not capture the synergistic effects among multiple genetic variants and lack good specificity.

Methods

We applied tree-based machine learning algorithms (MLs) to discriminate LOAD (>700 individuals) and age-matched unaffected subjects in UK Biobank with single nucleotide variants (SNVs) from Alzheimer's disease (AD) studies, obtaining specific genomic profiles with the prioritized SNVs.

Results

MLs prioritized a set of SNVs located in genes PVRL2, TOMM40, APOE, and APOC1, also influencing gene expression and splicing. The genomic profiles in this region showed interaction patterns involving rs405509 and rs1160985, also present in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. rs405509 located in APOE promoter interacts with rs429358 among others, seemingly neutralizing their predisposing effect.

Discussion

Our approach efficiently discriminates LOAD from controls, capturing genomic profiles defined by interactions among SNVs in a hot-spot region.

SUBMITTER: Arnal Segura M

PROVIDER: S-EPMC8984091 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease.

Arnal Segura Magdalena M Bini Giorgio G Fernandez Orth Dietmar D Samaras Eleftherios E Kassis Maya M Aisopos Fotis F Rambla De Argila Jordi J Paliouras George G Garrard Peter P Giambartolomei Claudia C Tartaglia Gian Gaetano GG

Alzheimer's & dementia (Amsterdam, Netherlands) 20220405 1

<h4>Introduction</h4>Genome-wide association studies (GWAS) in late onset Alzheimer's disease (LOAD) provide lists of individual genetic determinants. However, GWAS do not capture the synergistic effects among multiple genetic variants and lack good specificity.<h4>Methods</h4>We applied tree-based machine learning algorithms (MLs) to discriminate LOAD (>700 individuals) and age-matched unaffected subjects in UK Biobank with single nucleotide variants (SNVs) from Alzheimer's disease (AD) studies ...[more]

PMID: 35415203

Similar Datasets

Project description:Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality. Due to the lack of effective preventive measures, its prediction is essential to its prompt management. This study aimed to develop models using machine learning to predict late-onset preeclampsia using hospital electronic medical record data. The performance of the machine learning based models and models using conventional statistical methods were also compared. A total of 11,006 pregnant women who received antenatal care at Yonsei University Hospital were included. Maternal data were retrieved from electronic medical records during the early second trimester to 34 weeks. The prediction outcome was late-onset preeclampsia occurrence after 34 weeks' gestation. Pattern recognition and cluster analysis were used to select the parameters included in the prediction models. Logistic regression, decision tree model, naïve Bayes classification, support vector machine, random forest algorithm, and stochastic gradient boosting method were used to construct the prediction models. C-statistics was used to assess the performance of each model. The overall preeclampsia development rate was 4.7% (474 patients). Systolic blood pressure, serum blood urea nitrogen and creatinine levels, platelet counts, serum potassium level, white blood cell count, serum calcium level, and urinary protein were the most influential variables included in the prediction models. C-statistics for the decision tree model, naïve Bayes classification, support vector machine, random forest algorithm, stochastic gradient boosting method, and logistic regression models were 0.857, 0.776, 0.573, 0.894, 0.924, and 0.806, respectively. The stochastic gradient boosting model had the best prediction performance with an accuracy and false positive rate of 0.973 and 0.009, respectively. The combined use of maternal factors and common antenatal laboratory data of the early second trimester through early third trimester could effectively predict late-onset preeclampsia using machine learning algorithms. Future prospective studies are needed to verify the clinical applicability algorithms.

Project description:Base excision repair (BER) defects and concomitant oxidative DNA damage accumulation play a role in the etiology and progression of late-onset Alzheimer's disease (LOAD). However, it is not known whether genetic variant(s) of specific BER genes contribute to reduced BER activity in LOAD patients and whether they are associated with risk, development and/or progression of LOAD. Therefore, we performed targeted next generation sequencing for three BER genes, uracil glycosylase (UNG), endonuclease VIII-like DNA glycosylase 1 (NEIL1) and polymerase β (POLβ) including promoter, exonic and intronic regions in peripheral blood samples and postmortem brain tissues (temporal cortex, TC and cerebellum, CE) from LOAD patients, high-pathology control and cognitively normal age-matched controls. In addition, the known LOAD risk factor, APOE was included in this study to test whether any BER gene variants associate with APOE variants, particularly APOE ε4. We show that UNG carry five significant variants (rs1610925, rs2268406, rs80001089, rs1018782 and rs1018783) in blood samples of Turkish LOAD patients compared to age-matched controls and one of them (UNG rs80001089) is also significant in TC from Brazilian LOAD patients (p<0.05). The significant variants present only in CE and TC from LOAD are UNG rs2569987 and POLβ rs1012381950, respectively. There is also significant epistatic relationship (p = 0.0410) between UNG rs80001089 and NEIL1 rs7182283 in TC from LOAD subjects. Our results suggest that significant BER gene variants may be associated with the risk of LOAD in non-APOE ε4 carriers. On the other hand, there are no significant UNG, NEIL1 and POLβ variants that could affect their protein level and function, suggesting that there may be other factors such as post-transcriptional or-translational modifications responsible for the reduced activities and protein levels of these genes in LOAD pathogenesis. Further studies with increased sample size are needed to confirm the relationship between BER variants and LOAD risk.

Project description:BACKGROUND:New genetic and genomic resources have identified multiple genetic risk factors for late-onset Alzheimer's disease (LOAD) and characterized this common dementia at the molecular level. Experimental studies in model organisms can validate these associations and elucidate the links between specific genetic factors and transcriptomic signatures. Animal models based on LOAD-associated genes can potentially connect common genetic variation with LOAD transcriptomes, thereby providing novel insights into basic biological mechanisms underlying the disease. METHODS:We performed RNA-Seq on whole brain samples from a panel of six-month-old female mice, each carrying one of the following mutations: homozygous deletions of Apoe and Clu; hemizygous deletions of Bin1 and Cd2ap; and a transgenic APOE?4. Similar data from a transgenic APP/PS1 model was included for comparison to early-onset variant effects. Weighted gene co-expression network analysis (WGCNA) was used to identify modules of correlated genes and each module was tested for differential expression by strain. We then compared mouse modules with human postmortem brain modules from the Accelerating Medicine's Partnership for AD (AMP-AD) to determine the LOAD-related processes affected by each genetic risk factor. RESULTS:Mouse modules were significantly enriched in multiple AD-related processes, including immune response, inflammation, lipid processing, endocytosis, and synaptic cell function. WGCNA modules were significantly associated with Apoe-/-, APOE?4, Clu-/-, and APP/PS1 mouse models. Apoe-/-, GFAP-driven APOE?4, and APP/PS1 driven modules overlapped with AMP-AD inflammation and microglial modules; Clu-/- driven modules overlapped with synaptic modules; and APP/PS1 modules separately overlapped with lipid-processing and metabolism modules. CONCLUSIONS:This study of genetic mouse models provides a basis to dissect the role of AD risk genes in relevant AD pathologies. We determined that different genetic perturbations affect different molecular mechanisms comprising AD, and mapped specific effects to each risk gene. Our approach provides a platform for further exploration into the causes and progression of AD by assessing animal models at different ages and/or with different combinations of LOAD risk variants.

Dataset Information

Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease.

Introduction

Methods

Results

Discussion

Publications

Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets