Dataset Information

Discovering Precision AD Biomarkers with Varying Prognosis Effects in Genetics Driven Subpopulations.

ABSTRACT: Alzheimer's Disease (AD) is a highly heritable neurodegenerative disorder characterized by memory impairments. Understanding how genetic factors contribute to AD pathology may inform interventions to slow or prevent the progression of AD. We performed stratified genetic analyses of 1,574 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants to examine associations between levels of quantitative traits (QT's) and future diagnosis. The Chow test was employed to determine if an individual's genetic profile affects identified predictive relationships between QT's and future diagnosis. Our chow test analysis discovered that cognitive and PET-based biomarkers differentially predicted future diagnosis when stratifying on allelic dosage of AD loci. Post-hoc bootstrapped and association analyses of biomarkers confirmed differential effects, emphasizing the necessity of stratified models to realize individualized AD diagnosis prediction. This novel application of the Chow test allows for the quantification and direct comparison of genetic-based differences. Our findings, as well as the identified QT-future diagnosis relationships, warrant future investigation from a biological context.

SUBMITTER: Lee BN

PROVIDER: S-EPMC10283147 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Discovering Precision AD Biomarkers with Varying Prognosis Effects in Genetics Driven Subpopulations.

Lee Brian N BN Wang Junwen J Nho Kwangsik K Saykin Andrew J AJ Shen Li L

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science 20230616

Alzheimer's Disease (AD) is a highly heritable neurodegenerative disorder characterized by memory impairments. Understanding how genetic factors contribute to AD pathology may inform interventions to slow or prevent the progression of AD. We performed stratified genetic analyses of 1,574 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants to examine associations between levels of quantitative traits (QT's) and future diagnosis. The Chow test was employed to determine if an individual ...[more]

PMID: 37350892

Similar Datasets

Project description:BackgroundNext Generation Sequencing (NGS) is playing a key role in therapeutic decision making for the cancer prognosis and treatment. The NGS technologies are producing a massive amount of sequencing datasets. Often, these datasets are published from the isolated and different sequencing facilities. Consequently, the process of sharing and aggregating multisite sequencing datasets are thwarted by issues such as the need to discover relevant data from different sources, built scalable repositories, the automation of data linkage, the volume of the data, efficient querying mechanism, and information rich intuitive visualisation.ResultsWe present an approach to link and query different sequencing datasets (TCGA, COSMIC, REACTOME, KEGG and GO) to indicate risks for four cancer types - Ovarian Serous Cystadenocarcinoma (OV), Uterine Corpus Endometrial Carcinoma (UCEC), Uterine Carcinosarcoma (UCS), Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) - covering the 16 healthy tissue-specific genes from Illumina Human Body Map 2.0. The differentially expressed genes from Illumina Human Body Map 2.0 are analysed together with the gene expressions reported in COSMIC and TCGA repositories leading to the discover of potential biomarkers for a tissue-specific cancer.ConclusionWe analyse the tissue expression of genes, copy number variation (CNV), somatic mutation, and promoter methylation to identify associated pathways and find novel biomarkers. We discovered twenty (20) mutated genes and three (3) potential pathways causing promoter changes in different gynaecological cancer types. We propose a data-interlinked platform called BIOOPENER that glues together heterogeneous cancer and biomedical repositories. The key approach is to find correspondences (or data links) among genetic, cellular and molecular features across isolated cancer datasets giving insight into cancer progression from normal to diseased tissues. The proposed BIOOPENER platform enriches mutations by filling in missing links from TCGA, COSMIC, REACTOME, KEGG and GO datasets and provides an interlinking mechanism to understand cancer progression from normal to diseased tissues with pathway components, which in turn helped to map mutations, associated phenotypes, pathways, and mechanism.

Project description:In clinical routine, the diagnosis of cystic fibrosis (CF) is still challenging regardless of international consensus on diagnosis guidelines and tests. For decades, the classical Gibson and Cooke test measuring sweat chloride concentration has been a keystone, yet, it may provide normal or equivocal results. As of now, despite the combination of sweat testing, CFTR genotyping, and CFTR functional testing, a small fraction (1-2%) of inconclusive diagnoses are reported and justifies the search for new CF biomarkers. More importantly, in the context of precision medicine, with a view to early diagnosis, better prognosis, appropriate clinical follow-up, and new therapeutic development, discovering companion biomarkers of CF severity and phenotypic rescue are of utmost interest. To date, previous sweat proteomic studies have already documented disease-specific variations of sweat proteins (e.g., in schizophrenia and tuberculosis). In the current study, sweat samples from 28 healthy control subjects and 14 patients with CF were analyzed by nanoUHPLC-Q-Orbitrap-based shotgun proteomics, to look for CF-associated changes in sweat protein composition and abundance. A total of 1057 proteins were identified and quantified at an individual level, by a shotgun label-free approach. Notwithstanding similar proteome composition, enrichment, and functional annotations, control and CF samples featured distinct quantitative proteome profiles significantly correlated with CF, accounting for the respective inter-individual variabilities of control and CF sweat. All in all: (i) 402 sweat proteins were differentially abundant between controls and patients with CF, (ii) 68 proteins varied in abundance between F508del homozygous patients and patients with another genotype, (iii) 71 proteins were differentially abundant according to the pancreatic function, and iv) 54 proteins changed in abundance depending on the lung function. The functional annotation of pathophysiological biomarkers highlighted eccrine gland cell perturbations in: (i) protein biosynthesis and trafficking, (ii) CFTR proteostasis and membrane stability, and (iii) cell-cell adherence, membrane integrity, and cytoskeleton crosstalk. Cytoskeleton-related biomarkers were of utmost interest because of the consistency between variations observed here in CF sweat and variations previously documented in other CF tissues. From a clinical stance, nine candidate biomarkers of CF diagnosis (CUTA, ARG1, EZR, AGA, FLNA, MAN1A1, MIA3, LFNG, SIAE) and seven candidate biomarkers of CF severity (ARG1, GPT, MDH2, EML4 (F508del homozygous), MGAT1 (pancreatic insufficiency), IGJ, TOLLIP (lung function impairment)) were deemed suitable for further verification.

Dataset Information

Discovering Precision AD Biomarkers with Varying Prognosis Effects in Genetics Driven Subpopulations.

Publications

Discovering Precision AD Biomarkers with Varying Prognosis Effects in Genetics Driven Subpopulations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets