Browsing by Author "Wen, Zixuan"

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    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 Health
    Brain 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.
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    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 Medicine
    Introduction: 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.
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    Brain-wide genome-wide colocalization study for integrating genetics, transcriptomics and brain morphometry in Alzheimer’s disease
    (Elsevier, 2023) Bao, Jingxuan; Wen, Junhao; Wen, Zixuan; Yang, Shu; Cui, Yuhan; Yang, Zhijian; Erus, Guray; Saykin, Andrew J.; Long, Qi; Davatzikos, Christos; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases. However, the AD mechanism has not yet been fully elucidated to date, hindering the development of effective therapies. In our work, we perform a brain imaging genomics study to link genetics, single-cell gene expression data, tissue-specific gene expression data, brain imaging-derived volumetric endophenotypes, and disease diagnosis to discover potential underlying neurobiological pathways for AD. To do so, we perform brain-wide genome-wide colocalization analyses to integrate multidimensional imaging genomic biobank data. Specifically, we use (1) the individual-level imputed genotyping data and magnetic resonance imaging (MRI) data from the UK Biobank, (2) the summary statistics of the genome-wide association study (GWAS) from multiple European ancestry cohorts, and (3) the tissue-specific cis-expression quantitative trait loci (cis-eQTL) summary statistics from the GTEx project. We apply a Bayes factor colocalization framework and mediation analysis to these multi-modal imaging genomic data. As a result, we derive the brain regional level GWAS summary statistics for 145 brain regions with 482,831 single nucleotide polymorphisms (SNPs) followed by posthoc functional annotations. Our analysis yields the discovery of a potential AD causal pathway from a systems biology perspective: the SNP chr10:124165615:G>A (rs6585827) mutation upregulates the expression of BTBD16 gene in oligodendrocytes, a specialized glial cells, in the brain cortex, leading to a reduced risk of volumetric loss in the entorhinal cortex, resulting in the protective effect on AD. We substantiate our findings with multiple evidence from existing imaging, genetic and genomic studies in AD literature. Our study connects genetics, molecular and cellular signatures, regional brain morphologic endophenotypes, and AD diagnosis, providing new insights into the mechanistic understanding of the disease. Our findings can provide valuable guidance for subsequent therapeutic target identification and drug discovery in AD.
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    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 Medicine
    Brain 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.
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    Identifying imaging genetic associations via regional morphometricity estimation
    (World Scientific, 2022) Bao, Jingxuan; Wen, Zixuan; Kim, Mansu; Saykin, Andrew J.; Thompson, Paul M.; Zhao, Yize; Shen, Li; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Brain imaging genetics is an emerging research field aiming to reveal the genetic basis of brain traits captured by imaging data. Inspired by heritability analysis, the concept of morphometricity was recently introduced to assess trait association with whole brain morphology. In this study, we extend the concept of morphometricity from its original definition at the whole brain level to a more focal level based on a region of interest (ROI). We propose a novel framework to identify the SNP-ROI association via regional morphometricity estimation of each studied single nucleotide polymorphism (SNP). We perform an empirical study on the structural MRI and genotyping data from a landmark Alzheimer’s disease (AD) biobank; and yield promising results. Our findings indicate that the AD-related SNPs have higher overall regional morphometricity estimates than the SNPs not yet related to AD. This observation suggests that the variance of AD SNPs can be explained more by regional morphometric features than non-AD SNPs, supporting the value of imaging traits as targets in studying AD genetics. Also, we identified 11 ROIs, where the AD/non-AD SNPs and significant/insignificant morphometricity estimation of the corresponding SNPs in these ROIs show strong dependency. Supplementary motor area (SMA) and dorsolateral prefrontal cortex (DPC) are enriched by these ROIs. Our results also demonstrate that using all the detailed voxel-level measures within the ROI to incorporate morphometric information outperforms using only a single average ROI measure, and thus provides improved power to detect imaging genetic associations.
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    Preference Matrix Guided Sparse Canonical Correlation Analysis for Genetic Study of Quantitative Traits in Alzheimer’s Disease
    (IEEE, 2022-12) Sha, Jiahang; Bao, Jingxuan; Liu, Kefei; Yang, Shu; Wen, Zixuan; Cui, Yuhan; Wen, Junhao; Davatzikos, Christos; Moore, Jason H.; Saykin, Andrew J.; Long, Qi; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer’s disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetic-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlation as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.
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    Preference Matrix Guided Sparse Canonical Correlation Analysis for Mining Brain Imaging Genetic Associations in Alzheimer’s Disease
    (Elsevier, 2023) Sha, Jiahang; Bao, Jingxuan; Liu, Kefei; Yang, Shu; Wen, Zixuan; Wen, Junhao; Cui, Yuhan; Tong, Boning; Moore, Jason H.; Saykin, Andrew J.; Davatzikos, Christos; Long, Qi; Shen, Li; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetics-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlations as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.