Browsing by Author "Kim, Dokyoon"
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Item A genetically informed brain atlas for enhancing brain imaging genomics(Springer Nature, 2025-04-14) Bao, Jingxuan; Wen, Junhao; Chang, Changgee; Mu, Shizhuo; Chen, Jiong; Shivakumar, Manu; Cui, Yuhan; Erus, Guray; Yang, Zhijian; Yang, Shu; Wen, Zixuan; The Alzheimer’s Disease Neuroimaging Initiative; Zhao, Yize; Kim, Dokyoon; Duong-Tran, Duy; Saykin, Andrew J.; Zhao, Bingxin; Davatzikos, Christos; Long, Qi; Shen, Li; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthBrain imaging genomics has manifested considerable potential in illuminating the genetic determinants of human brain structure and function. This has propelled us to develop the GIANT (Genetically Informed brAiN aTlas) that accounts for genetic and neuroanatomical variations simultaneously. Integrating voxel-wise heritability and spatial proximity, GIANT clusters brain voxels into genetically informed regions, while retaining fundamental anatomical knowledge. Compared to conventional (non-genetics) brain atlases, GIANT exhibits smaller intra-region variations and larger inter-region variations in terms of voxel-wise heritability. As a result, GIANT yields increased regional SNP heritability, enhanced polygenicity, and its polygenic risk score explains more brain volumetric variation than traditional neuroanatomical brain atlases. We provide extensive validation to GIANT and demonstrate its neuroanatomical validity, confirming its generalizability across populations with diverse genetic ancestries and various brain conditions. Furthermore, we present a comprehensive genetic architecture of the GIANT regions, covering their functional annotation at the molecular levels, their associations with other complex traits/diseases, and the genetic and phenotypic correlations among GIANT-defined imaging endophenotypes. In summary, GIANT constitutes a brain atlas that captures the complexity of genetic and neuroanatomical heterogeneity, thereby enhancing the discovery power and applicability of imaging genomics investigations in biomedical science.Item Addressing overfitting bias due to sample overlap in polygenic risk scoring(Wiley, 2025) Jeong, Seokho; Shivakumar, Manu; Jung, Sang-Hyuk; Won, Hong-Hee; Nho, Kwangsik; Huang, Heng; Davatzikos, Christos; Saykin, Andrew J.; Thompson, Paul M.; Shen, Li; Kim, Young Jin; Kim, Bong-Jo; Lee, Seunggeun; Kim, Dokyoon; Radiology and Imaging Sciences, School of MedicineIntroduction: Numerous studies on Alzheimer's disease polygenic risk scores (PRSs) overlook sample overlap between International Genomics of Alzheimer's Project (IGAP) and target datasets like Alzheimer's Disease Neuroimaging Initiative (ADNI). Methods: To address this, we developed overlap-adjusted PRS (OA PRS) and tested it on simulated data to assess biases from different scenarios by varying training, testing, and overlap proportions. OA PRS was used to adjust for sample bias in simulations; then, we applied OA PRS to IGAP and ADNI datasets and validated through visual diagnosis. Results: OA PRS effectively adjusted for sample overlap in all simulation scenarios, as well as for IGAP and ADNI. The original IGAP PRS showed an inflated area under the receiver operating characteristic (AUROC: 0.915) on overlapping samples. OA PRS reduced the AUROC to 0.726, closely aligning with the AUROC of non-overlapping samples (0.712). Further, visual diagnostics confirmed the effectiveness of our adjustments. Discussion: With OA PRS, we were able to adjust the IGAP summary-based PRS for the overlapped ADNI samples, allowing the dataset to be fully used without the risk of overfitting. Highlights: Sample overlap between large Alzheimer's disease (AD) cohorts poses overfitting bias when using AD polygenic risk scores (PRSs). This study highlighted the effectiveness of overlap-adjusted PRS (OA -PRS) in mitigating overfitting and improving the accuracy of PRS estimations. New PRSs based on adjusted effect sizes showed increased power in association with clinical features.Item An interpretable Alzheimer's disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification(Frontiers Media, 2023-10-26) Suh, Erica H.; Lee, Garam; Jung, Sang-Hyuk; Wen, Zixuan; Bao, Jingxuan; Nho, Kwangsik; Huang, Heng; Davatzikos, Christos; Saykin, Andrew J.; Thompson, Paul M.; Shen, Li; Kim, Dokyoon; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineIntroduction: Stratification of Alzheimer's disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors. Methods: Using whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (n = 1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores. Results: adORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature. Discussion: Compared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.Item Association analysis of rare variants near the APOE region with CSF and neuroimaging biomarkers of Alzheimer's disease(Springer Nature, 2017-05-24) Nho, Kwangsik; Kim, Sungeun; Horgusluoglu, Emrin; Risacher, Shannon L.; Shen, Li; Kim, Dokyoon; Lee, Seunggeun; Foroud, Tatiana; Shaw, Leslie M.; Trojanowski, John Q.; Aisen, Paul S.; Petersen, Ronald C.; Jack, Clifford R., Jr.; Weiner, Michael W.; Green, Robert C.; Toga, Arthur W.; Saykin, Andrew J.; Radiology and Imaging Sciences, School of MedicineBACKGROUND: The APOE ε4 allele is the most significant common genetic risk factor for late-onset Alzheimer's disease (LOAD). The region surrounding APOE on chromosome 19 has also shown consistent association with LOAD. However, no common variants in the region remain significant after adjusting for APOE genotype. We report a rare variant association analysis of genes in the vicinity of APOE with cerebrospinal fluid (CSF) and neuroimaging biomarkers of LOAD. METHODS: Whole genome sequencing (WGS) was performed on 817 blood DNA samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sequence data from 757 non-Hispanic Caucasian participants was used in the present analysis. We extracted all rare variants (MAF (minor allele frequency) < 0.05) within a 312 kb window in APOE's vicinity encompassing 12 genes. We assessed CSF and neuroimaging (MRI and PET) biomarkers as LOAD-related quantitative endophenotypes. Gene-based analyses of rare variants were performed using the optimal Sequence Kernel Association Test (SKAT-O). RESULTS: A total of 3,334 rare variants (MAF < 0.05) were found within the APOE region. Among them, 72 rare non-synonymous variants were observed. Eight genes spanning the APOE region were significantly associated with CSF Aβ1-42 (p < 1.0 × 10-3). After controlling for APOE genotype and adjusting for multiple comparisons, 4 genes (CBLC, BCAM, APOE, and RELB) remained significant. Whole-brain surface-based analysis identified highly significant clusters associated with rare variants of CBLC in the temporal lobe region including the entorhinal cortex, as well as frontal lobe regions. Whole-brain voxel-wise analysis of amyloid PET identified significant clusters in the bilateral frontal and parietal lobes showing associations of rare variants of RELB with cortical amyloid burden. CONCLUSIONS: Rare variants within genes spanning the APOE region are significantly associated with LOAD-related CSF Aβ1-42 and neuroimaging biomarkers after adjusting for APOE genotype. These findings warrant further investigation and illustrate the role of next generation sequencing and quantitative endophenotypes in assessing rare variants which may help explain missing heritability in AD and other complex diseases.Item Author Correction: Predicting Alzheimer’s disease progression using multi-modal deep learning approach(Springer Nature, 2023-08-01) Lee, Garam; Nho, Kwangsik; Kang, Byungkon; Sohn, Kyung‑Ah; Kim, Dokyoon; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineCorrection to: Scientific Reports 10.1038/s41598-018-37769-z, published online 13 February 2019 This Article contains errors. A Supplementary Information file was omitted from the original version of this Article. The Supplementary Information file is now linked to this correction notice.Item Codon bias among synonymous rare variants is associated with Alzheimer's disease imaging biomarker(Pacific Symposium on Biocomputing, 2018) Miller, Jason E.; Shivakumar, Manu K.; Risacher, Shannon L.; Saykin, Andrew J.; Lee, Seunggeun; Nho, Kwangsik; Kim, Dokyoon; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Radiology and Imaging Sciences, School of MedicineAlzheimer's disease (AD) is a neurodegenerative disorder with few biomarkers even though it impacts a relatively large portion of the population and is predicted to affect significantly more individuals in the future. Neuroimaging has been used in concert with genetic information to improve our understanding in relation to how AD arises and how it can be potentially diagnosed. Additionally, evidence suggests synonymous variants can have a functional impact on gene regulatory mechanisms, including those related to AD. Some synonymous codons are preferred over others leading to a codon bias. The bias can arise with respect to codons that are more or less frequently used in the genome. A bias can also result from optimal and non-optimal codons, which have stronger and weaker codon anti-codon interactions, respectively. Although association tests have been utilized before to identify genes associated with AD, it remains unclear how codon bias plays a role and if it can improve rare variant analysis. In this work, rare variants from whole-genome sequencing from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were binned into genes using BioBin. An association analysis of the genes with AD-related neuroimaging biomarker was performed using SKAT-O. While using all synonymous variants we did not identify any genomewide significant associations, using only synonymous variants that affected codon frequency we identified several genes as significantly associated with the imaging phenotype. Additionally, significant associations were found using only rare variants that contains an optimal codon in among minor alleles and a non-optimal codon in the major allele. These results suggest that codon bias may play a role in AD and that it can be used to improve detection power in rare variant association analysis.Item Genetic variation affecting exon skipping contributes to brain structural atrophy in Alzheimer's disease(American Medical Informatics Association, 2018-05-18) Lee, Younghee; Han, Seonggyun; Kim, Dongwook; Kim, Dokyoon; Horgousluoglu, Emrin; Risacher, Shannon L.; Saykin, Andrew J.; Nho, Kwangsik; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineGenetic variation in cis-regulatory elements related to splicing machinery and splicing regulatory elements (SREs) results in exon skipping and undesired protein products. We developed a splicing decision model to identify actionable loci among common SNPs for gene regulation. The splicing decision model identified SNPs affecting exon skipping by analyzing sequence-driven alternative splicing (AS) models and by scanning the genome for the regions with putative SRE motifs. We used non-Hispanic Caucasians with neuroimaging, and fluid biomarkers for Alzheimer's disease (AD) and identified 17,088 common exonic SNPs affecting exon skipping. GWAS identified one SNP (rs1140317) in HLA-DQB1 as significantly associated with entorhinal cortical thickness, AD neuroimaging biomarker, after controlling for multiple testing. Further analysis revealed that rs1140317 was significantly associated with brain amyloid-f deposition (PET and CSF). HLA-DQB1 is an essential immune gene and may regulate AS, thereby contributing to AD pathology. SRE may hold potential as novel therapeutic targets for AD.Item A graph-based integration of multimodal brain imaging data for the detection of early mild cognitive impairment (E-MCI)(Springer, 2013) Kim, Dokyoon; Kim, Sungeun; Risacher, Shannon L.; Shen, Li; Ritchie, Marylyn D.; Weiner, Michael W.; Saykin, Andrew J.; Nho, Kwangsik; Radiology and Imaging Sciences, School of MedicineAlzheimer's disease (AD) is the most common cause of dementia in older adults. By the time an individual has been diagnosed with AD, it may be too late for potential disease modifying therapy to strongly influence outcome. Therefore, it is critical to develop better diagnostic tools that can recognize AD at early symptomatic and especially pre-symptomatic stages. Mild cognitive impairment (MCI), introduced to describe a prodromal stage of AD, is presently classified into early and late stages (E-MCI, L-MCI) based on severity. Using a graph-based semi-supervised learning (SSL) method to integrate multimodal brain imaging data and select valid imaging-based predictors for optimizing prediction accuracy, we developed a model to differentiate E-MCI from healthy controls (HC) for early detection of AD. Multimodal brain imaging scans (MRI and PET) of 174 E-MCI and 98 HC participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were used in this analysis. Mean targeted region-of-interest (ROI) values extracted from structural MRI (voxel-based morphometry (VBM) and FreeSurfer V5) and PET (FDG and Florbetapir) scans were used as features. Our results show that the graph-based SSL classifiers outperformed support vector machines for this task and the best performance was obtained with 66.8% cross-validated AUC (area under the ROC curve) when FDG and FreeSurfer datasets were integrated. Valid imaging-based phenotypes selected from our approach included ROI values extracted from temporal lobe, hippocampus, and amygdala. Employing a graph-based SSL approach with multimodal brain imaging data appears to have substantial potential for detecting E-MCI for early detection of prodromal AD warranting further investigation.Item Identification of exon skipping events associated with Alzheimer's disease in the human hippocampus(Biomed Central, 2019-01-31) Han, Seonggyun; Miller, Jason E.; Byun, Seyoun; Kim, Dokyoon; Risacher, Shannon L.; Saykin, Andrew J.; Lee, Younghee; Nho, Kwangsik; Radiology and Imaging Sciences, School of MedicineBACKGROUND: At least 90% of human genes are alternatively spliced. Alternative splicing has an important function regulating gene expression and miss-splicing can contribute to risk for human diseases, including Alzheimer's disease (AD). METHODS: We developed a splicing decision model as a molecular mechanism to identify functional exon skipping events and genetic variation affecting alternative splicing on a genome-wide scale by integrating genomics, transcriptomics, and neuroimaging data in a systems biology approach. In this study, we analyzed RNA-Seq data of hippocampus brain tissue from Alzheimer's disease (AD; n = 24) and cognitively normal elderly controls (CN; n = 50) and identified three exon skipping events in two genes (RELN and NOS1) as significantly associated with AD (corrected p-value < 0.05 and fold change > 1.5). Next, we identified single-nucleotide polymorphisms (SNPs) affecting exon skipping events using the splicing decision model and then performed an association analysis of SNPs potentially affecting three exon skipping events with a global cortical measure of amyloid-β deposition measured by [18F] Florbetapir position emission tomography (PET) scan as an AD-related quantitative phenotype. A whole-brain voxel-based analysis was also performed. RESULTS: Two exons in RELN and one exon in NOS1 showed significantly lower expression levels in the AD participants compared to CN participants, suggesting that the exons tend to be skipped more in AD. We also showed the loss of the core protein structure due to the skipped exons using the protein 3D structure analysis. The targeted SNP-based association analysis identified one intronic SNP (rs362771) adjacent to the skipped exon 24 in RELN as significantly associated with cortical amyloid-β levels (corrected p-value < 0.05). This SNP is within the splicing regulatory element, i.e., intronic splicing enhancer. The minor allele of rs362771 conferred decreases in cortical amyloid-β levels in the right temporal and bilateral parietal lobes. CONCLUSIONS: Our results suggest that exon skipping events and splicing-affecting SNPs in the human hippocampus may contribute to AD pathogenesis. Integration of multiple omics and neuroimaging data provides insights into possible mechanisms underlying AD pathophysiology through exon skipping and may help identify novel therapeutic targets.Item Identifying highly heritable brain amyloid phenotypes through mining Alzheimer's imaging and sequencing biobank data(World Scientific, 2022) Bao, Jingxuan; Wen, Zixuan; Kim, Mansu; Zhao, Xiwen; Lee, Brian N.; Jung, Sang-Hyuk; Davatzikos, Christos; Saykin, Andrew J.; Thompson, Paul M.; Kim, Dokyoon; Zhao, Yize; Shen, Li; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineBrain imaging genetics, an emerging and rapidly growing research field, studies the relationship between genetic variations and brain imaging quantitative traits (QTs) to gain new insights into the phenotypic characteristics and genetic mechanisms of the brain. Heritability is an important measurement to quantify the proportion of the observed variance in an imaging QT that is explained by genetic factors, and can often be used to prioritize brain QTs for subsequent imaging genetic association studies. Most existing studies define regional imaging QTs using predefined brain parcellation schemes such as the automated anatomical labeling (AAL) atlas. However, the power to dissect genetic underpinnings under QTs defined in such an unsupervised fashion could be negatively affected by heterogeneity within the regions in the partition. To bridge this gap, we propose a novel method to define highly heritable brain regions. Based on voxelwise heritability estimates, we extract brain regions containing spatially connected voxels with high heritability. We perform an empirical study on the amyloid imaging and whole genome sequencing data from a landmark Alzheimer’s disease biobank; and demonstrate the regions defined by our method have much higher estimated heritabilities than the regions defined by the AAL atlas. Our proposed method refines the imaging endophenotype constructions in light of their genetic dissection, and yields more powerful imaging QTs for subsequent detection of genetic risk factors along with better interpretability.