Browsing by Subject "Whole‐genome sequencing"

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    LD‐informed deep learning for Alzheimer's gene loci detection using WGS data
    (Wiley, 2025-01-16) Jo, Taeho; Bice, Paula; Nho, Kwangsik; Saykin, Andrew J.; Alzheimer’s Disease Sequencing Project; Radiology and Imaging Sciences, School of Medicine
    Introduction: The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk. Methods: The Deep-Block was applied to a large-scale whole genome sequencing (WGS) dataset from the Alzheimer's Disease Sequencing Project (ADSP), comprising 7416 non-Hispanic white (NHW) participants (3150 cognitively normal older adults (CN), 4266 AD). Results: 30,218 LD blocks were identified and then ranked based on their relevance with Alzheimer's disease. Subsequently, the Deep-Block identified novel SNPs within the top 1500 LD blocks and confirmed previously known variants, including APOE rs429358 and rs769449. Expression Quantitative Trait Loci (eQTL) analysis across 13 brain regions provided functional evidence for the identified variants. The results were cross-validated against established AD-associated loci from the European Alzheimer's and Dementia Biobank (EADB) and the GWAS catalog. Discussion: The Deep-Block framework effectively processes large-scale high throughput sequencing data while preserving SNP interactions during dimensionality reduction, minimizing bias and information loss. The framework's findings are supported by tissue-specific eQTL evidence across brain regions, indicating the functional relevance of the identified variants. Additionally, the Deep-Block approach has identified both known and novel genetic variants, enhancing our understanding of the genetic architecture and demonstrating its potential for application in large-scale sequencing studies. Highlights: Growing genomic datasets require advanced tools to identify genetic loci in sequencing. Deep-Block, a novel AI framework, was used to process large-scale ADSP WGS data. Deep-Block identified both known and novel AD-associated genetic loci.rs429358 (APOE) was key; rs11556505 (TOMM40), rs34342646 (NECTIN2) were significant. The AI framework uses biological knowledge to enhance detection of Alzheimer's loci.
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    The Frequency and Potential Implications of HFE Genetic Variants in Children With Cystic Fibrosis
    (Wiley, 2025) Huang, Leslie; Lai, HuiChuan J.; Furuya, Katryn N.; Antos, Nicholas J.; Asfour, Fadi; Boyne, Kathleen L.; Howenstine, Michelle; Rock, Michael J.; Sawicki, Gregory S.; Gaffin, Jonathan M.; Worthey, Elizabeth A.; Farrell, Philip M.; Pediatrics, School of Medicine
    Background: Genetic modifiers have been identified that increase the risks of lung disease and other complications, such as diabetes in people with cystic fibrosis (CF). Variants in the hemochromatosis gene (HFE) were reported in a study of adults to be associated with worse lung disease. Objectives: To ascertain the frequency of HFE variants, particularly C282Y (c.845G > A) and H63D (c.187C > G) and to determine if they are associated with variations in the onset and early severity of CF lung disease as well as abnormalities in iron status. Design: We studied with whole genome sequencing and clinical outcome measures in a cohort of 104 children with CF at 5-6 years old who were previously found to show an association between aggregated genetic modifiers and an earlier onset and a more severe lung disease phenotype. Results: In our cohort, 23% have H63D and 11% have C282Y. Lung function at age 6 years and Pseudomonas aeruginosa infections did not differ by HFE variants, but having C282Y was associated with more pulmonary exacerbations in the first 6 years of life. Three patients have H63D/C282Y genotype, and all showed phenotypic expression of hemochromatosis with abnormal iron indices. Conclusion: Our study revealed that the presence of HFE variant C282Y in people with CF may lead to more severe lung disease manifestations beginning in early childhood. There is a risk of hemochromatosis in CF patients with two HFE variants, and thus they should be followed for evidence of iron overload.