Browsing by Author "Pugalenthi, Pradeep Varathan"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation
    (Research Square, 2024-02-08) Pugalenthi, Pradeep Varathan; He, Bing; Xie, Linhui; Nho, Kwangsik; Saykin, Andrew J.; Yan, Jingwen; Radiology and Imaging Sciences, School of Medicine
    Alzheimer’s disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a significant set of SNPs associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed even with the strongest associations in GWASs, lead SNPs have historically been the focus of the field, with the remaining associations inferred to be redundant. Recent deep genome annotation tools enable the prediction of function from a segment of a DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits on chromatin functions and whether it will be altered by the genetic context (i.e., alleles of neighboring SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impacts on downstream functions. Although some GWAS lead SNPs showed dominant functional effects regardless of the neighborhood SNP alleles, several other SNPs did exhibit enhanced loss or gain of function under certain genetic contexts, suggesting potential additional information hidden in the LD blocks.
  • Thumbnail Image
    Item
    Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation
    (Springer Nature, 2024-11-13) Pugalenthi, Pradeep Varathan; He, Bing; Xie, Linhui; Nho, Kwangsik; Saykin, Andrew J.; Yan, Jingwen; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a set of SNPs significantly associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed even with the strongest associations in GWASs, lead SNPs have historically been the focus of the field, with the remaining associations inferred to be redundant. Recent deep genome annotation tools enable the prediction of function from a segment of a DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits around APOE region on chromatin functions and whether it will be altered by the genetic context (i.e., alleles of neighboring SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impacts on downstream functions. Although some GWAS lead SNPs showed dominant functional effects regardless of the neighborhood SNP alleles, several other SNPs did exhibit enhanced loss or gain of function under certain genetic contexts, suggesting potential additional information hidden in the LD blocks.
  • Thumbnail Image
    Item
    Integrating amyloid imaging and genetics for early risk stratification of Alzheimer's disease
    (Wiley, 2024) He, Bing; Wu, Ruiming; Sangani, Neel; Pugalenthi, Pradeep Varathan; Patania, Alice; Risacher, Shannon L.; Nho, Kwangsik; Apostolova, Liana G.; Shen, Li; Saykin, Andrew J.; Yan, Jingwen; Alzheimer’s Disease Neuroimaging Initiative; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Introduction: Alzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment. Methods: Given the genetic susceptibility of AD, a multi-factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk-stratified groups, focusing on patients with mild cognitive impairment (MCI). Results: Our risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI, APOE ε4- MCI, and amyloid+ MCI. Discussion: Our risk score holds great potential to improve the precision of early risk assessment. Highlights: Accurate early risk assessment is critical for the success of clinical trials. A new risk score was built from integrating amyloid imaging and genetic data. Our risk score demonstrated improved capability in early risk stratification.
  • Thumbnail Image
    Item
    Multi-omics Investigation into Alzheimer's Disease: Functional Mechanism and Early Detection
    (2024-08) Pugalenthi, Pradeep Varathan; Yan, Jingwen; Janga, Sarath Chandra; Nho, Kwangsik; Wang, Juexin
    Alzheimer’s disease (AD), a multi-factorial and highly heritable condition, stands as the foremost contributor to dementia. Despite its early discovery and extensive studies, the underlying pathogenesis of AD remains incomplete. This thesis addresses critical aspects of AD through multi-omics approach for improved understanding of underlying functional mechanisms and for improved precision in early detection. Multi-omics integration allows us to explore a wide spectrum of AD-related changes at different biological levels including genomics and metabolomics and how they associate with the biomarkers. In the first aim, I performed an integrative analysis of summary statistics from genome-wide association study (GWAS) and expression quantitative trait loci (eQTL) analysis. Results of this study confirmed the potential of integrative GWAS and eQTL analysis in estimating the transcriptomic changes when lack of tissue-specific expression data, and provided important insights into tissue-specific downstream biology of observed GWAS associations in AD. In the second aim, I took a step further and hypothesized the epistatic effect of GWAS findings and neighboring variants on the downstream functional mechanism. Leveraging the recent advances in sequence-based genome annotation, I investigated the tissue-specific effects of top AD GWAS variants on the chromatin profiles. With in-silico mutagenesis, GWAS variants were found to function via either lead effect or epistatic effect, pinpointing the limitation of existing focus on single-variant-based function annotation. In the last aim, I built a comprehensive bioinformatics pipeline to investigate the potential of metabolic age as an early indicator for AD progression, in which we also observed significant difference between sex groups. We identified strong associations of metabolic age with longitudinal changes of current diagnostic metrics in the ATN framework, suggesting the potential of metabolic age as early biomarkers. Collectively, results from these aims contribute to advancing our understanding of AD and provide valuable insights for future research and clinical applications.