Browsing by Author "Chen, Feng"

Now showing 1 - 10 of 11
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    Genetic Interactions Explain Variance in Cingulate Amyloid Burden: An AV-45 PET Genome-Wide Association and Interaction Study in the ADNI Cohort
    (Hindawi, 2015-09-03) Li, Jin; Zhang, Qiushi; Chen, Feng; Yan, Jingwen; Kim, Sungeun; Wang, Lei; Feng, Weixing; Saykin, Andrew J.; Liang, Hong; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    Alzheimer's disease (AD) is the most common neurodegenerative disorder. Using discrete disease status as the phenotype and computing statistics at the single marker level may not be able to address the underlying biological interactions that contribute to disease mechanism and may contribute to the issue of "missing heritability." We performed a genome-wide association study (GWAS) and a genome-wide interaction study (GWIS) of an amyloid imaging phenotype, using the data from Alzheimer's Disease Neuroimaging Initiative. We investigated the genetic main effects and interaction effects on cingulate amyloid-beta (Aβ) load in an effort to better understand the genetic etiology of Aβ deposition that is a widely studied AD biomarker. PLINK was used in the single marker GWAS, and INTERSNP was used to perform the two-marker GWIS, focusing only on SNPs with p ≤ 0.01 for the GWAS analysis. Age, sex, and diagnosis were used as covariates in both analyses. Corrected p values using the Bonferroni method were reported. The GWAS analysis revealed significant hits within or proximal to APOE, APOC1, and TOMM40 genes, which were previously implicated in AD. The GWIS analysis yielded 8 novel SNP-SNP interaction findings that warrant replication and further investigation.
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    Genome-wide association and interaction studies of CSF T-tau/Aβ42 ratio in ADNI cohort
    (Elsevier, 2017) Li, Jin; Zhang, Qiushi; Chen, Feng; Meng, Xianglian; Liu, Wenjie; Chen, Dandan; Yan, Jingwen; Kim, Sungeun; Wang, Lei; Feng, Weixing; Saykin, Andrew J.; Liang, Hong; Shen, Li; Department of Radiology and Imaging Sciences, IU School of Medicine
    The pathogenic relevance in Alzheimer’s disease (AD) presents a decrease of cerebrospinal fluid (CSF) amyloid-ß42 (Aß42) burden and an increase in CSF total-tau (T-tau) levels. In this work, we performed genome-wide association study (GWAS) and genome-wide interaction study (GWIS) of T-tau/Aß42 ratio as an AD imaging quantitative trait (QT) on 843 subjects and 563,980 single nucleotide polymorphisms (SNPs) in ADNI cohort. We aim to identify not only SNPs with significant main effects but also SNPs with interaction effects to help explain “missing heritability”. Linear regression method was used to detect SNP-SNP interactions among SNPs with uncorrected p-value≤0.01 from the GWAS. Age, gender and diagnosis were considered as covariates in both studies. The GWAS results replicated the previously reported AD-related genes APOE, APOC1 and TOMM40, as well as identified 14 novel genes, which showed genome-wide statistical significance. GWIS revealed 7 pairs of SNPs meeting the cell-size criteria and with bonferroni-corrected p-value≤0.05. As we expect, these interaction pairs all had marginal main effects but explained a relatively high-level variance of T-tau/Aß42, demonstrating their potential association with AD pathology.
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    Genome-wide Network-assisted Association and Enrichment Study of Amyloid Imaging Phenotype in Alzheimer's Disease
    (Bentham Science, 2019) Li, Jin; Chen, Feng; Zhang, Qiushi; Meng, Xianglian; Yao, Xiaohui; Risacher, Shannon L.; Yan, Jingwen; Saykin, Andrew J.; Liang, Hong; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    Background: The etiology of Alzheimer's disease remains poorly understood at the mechanistic level, and genome-wide network-based genetics have the potential to provide new insights into the disease mechanisms. Objective: The study aimed to explore the collective effects of multiple genetic association signals on an AV-45 PET measure, which is a well-known Alzheimer's disease biomarker, by employing a network assisted strategy. Methods: First, we took advantage of a dense module search algorithm to identify modules enriched by genetic association signals in a protein-protein interaction network. Next, we performed statistical evaluation to the modules identified by dense module search, including a normalization process to adjust the topological bias in the network, a replication test to ensure the modules were not found randomly , and a permutation test to evaluate unbiased associations between the modules and amyloid imaging phenotype. Finally, topological analysis, module similarity tests and functional enrichment analysis were performed for the identified modules. Results: We identified 24 consensus modules enriched by robust genetic signals in a genome-wide association analysis. The results not only validated several previously reported AD genes (APOE, APP, TOMM40, DDAH1, PARK2, ATP5C1, PVRL2, ELAVL1, ACTN1 and NRF1), but also nominated a few novel genes (ABL1, ABLIM2) that have not been studied in Alzheimer's disease but have shown associations with other neurodegenerative diseases. Conclusion: The identified genes, consensus modules and enriched pathways may provide important clues to future research on the neurobiology of Alzheimer's disease and suggest potential therapeutic targets.
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    Genome-wide network-based pathway analysis of CSF t-tau/Aβ1-42 ratio in the ADNI cohort
    (Springer Nature, 2017-05-30) Cong, Wang; Meng, Xianglian; Li, Jin; Zhang, Qiushi; Chen, Feng; Liu, Wenjie; Wang, Ying; Cheng, Sipu; Yao, Xiaohui; Yan, Jingwen; Kim, Sungeun; Saykin, Andrew J.; Liang, Hong; Shen, Li; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    BACKGROUND: The cerebrospinal fluid (CSF) levels of total tau (t-tau) and Aβ1-42 are potential early diagnostic markers for probable Alzheimer's disease (AD). The influence of genetic variation on these CSF biomarkers has been investigated in candidate or genome-wide association studies (GWAS). However, the investigation of statistically modest associations in GWAS in the context of biological networks is still an under-explored topic in AD studies. The main objective of this study is to gain further biological insights via the integration of statistical gene associations in AD with physical protein interaction networks. RESULTS: The CSF and genotyping data of 843 study subjects (199 CN, 85 SMC, 239 EMCI, 207 LMCI, 113 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed. PLINK was used to perform GWAS on the t-tau/Aβ1-42 ratio using quality controlled genotype data, including 563,980 single nucleotide polymorphisms (SNPs), with age, sex and diagnosis as covariates. Gene-level p-values were obtained by VEGAS2. Genes with p-value ≤ 0.05 were mapped on to a protein-protein interaction (PPI) network (9,617 nodes, 39,240 edges, from the HPRD Database). We integrated a consensus model strategy into the iPINBPA network analysis framework, and named it as CM-iPINBPA. Four consensus modules (CMs) were discovered by CM-iPINBPA, and were functionally annotated using the pathway analysis tool Enrichr. The intersection of four CMs forms a common subnetwork of 29 genes, including those related to tau phosphorylation (GSK3B, SUMO1, AKAP5, CALM1 and DLG4), amyloid beta production (CASP8, PIK3R1, PPA1, PARP1, CSNK2A1, NGFR, and RHOA), and AD (BCL3, CFLAR, SMAD1, and HIF1A). CONCLUSIONS: This study coupled a consensus module (CM) strategy with the iPINBPA network analysis framework, and applied it to the GWAS of CSF t-tau/Aβ1-42 ratio in an AD study. The genome-wide network analysis yielded 4 enriched CMs that share not only genes related to tau phosphorylation or amyloid beta production but also multiple genes enriching several KEGG pathways such as Alzheimer's disease, colorectal cancer, gliomas, renal cell carcinoma, Huntington's disease, and others. This study demonstrated that integration of gene-level associations with CMs could yield statistically significant findings to offer valuable biological insights (e.g., functional interaction among the protein products of these genes) and suggest high confidence candidates for subsequent analyses.
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    Genome-wide variant-based study of genetic effects with the largest neuroanatomic coverage
    (BMC, 2021-04-30) Li, Jin; Liu, Wenjie; Li, Huang; Chen, Feng; Luo, Haoran; Bao, Peihua; Li, Yanzhao; Jiang, Hailong; Gao, Yue; Liang, Hong; Fang, Shiaofen; Computer and Information Science, School of Science
    Background: Brain image genetics provides enormous opportunities for examining the effects of genetic variations on the brain. Many studies have shown that the structure, function, and abnormality (e.g., those related to Alzheimer's disease) of the brain are heritable. However, which genetic variations contribute to these phenotypic changes is not completely clear. Advances in neuroimaging and genetics have led us to obtain detailed brain anatomy and genome-wide information. These data offer us new opportunities to identify genetic variations such as single nucleotide polymorphisms (SNPs) that affect brain structure. In this paper, we perform a genome-wide variant-based study, and aim to identify top SNPs or SNP sets which have genetic effects with the largest neuroanotomic coverage at both voxel and region-of-interest (ROI) levels. Based on the voxelwise genome-wide association study (GWAS) results, we used the exhaustive search to find the top SNPs or SNP sets that have the largest voxel-based or ROI-based neuroanatomic coverage. For SNP sets with >2 SNPs, we proposed an efficient genetic algorithm to identify top SNP sets that can cover all ROIs or a specific ROI. Results: We identified an ensemble of top SNPs, SNP-pairs and SNP-sets, whose effects have the largest neuroanatomic coverage. Experimental results on real imaging genetics data show that the proposed genetic algorithm is superior to the exhaustive search in terms of computational time for identifying top SNP-sets. Conclusions: We proposed and applied an informatics strategy to identify top SNPs, SNP-pairs and SNP-sets that have genetic effects with the largest neuroanatomic coverage. The proposed genetic algorithm offers an efficient solution to accomplish the task, especially for identifying top SNP-sets.
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    MoNET: an R package for multi-omic network analysis
    (Oxford Academic, 2022-02) Li, Jin; Chen, Feng; Liang, Hong; Yan, Jingwen; BioHealth Informatics, School of Informatics and Computing
    The increasing availability of multi-omic data has enabled the discovery of disease biomarkers in different scales. Understanding the functional interaction between multi-omic biomarkers is becoming increasingly important due to its great potential for providing insights of the underlying molecular mechanism.Leveraging multiple biological network databases, we integrated the relationship between single nucleotide polymorphisms (SNPs), genes/proteins and metabolites, and developed an R package Multi-omic Network Explorer Tool (MoNET) for multi-omic network analysis. This new tool enables users to not only track down the interaction of SNPs/genes with metabolome level, but also trace back for the potential risk variants/regulators given altered genes/metabolites. MoNET is expected to advance our understanding of the multi-omic findings by unveiling their transomic interactions and is likely to generate new hypotheses for further validation.The MoNET package is freely available on https://github.com/JW-Yan/MONET.Supplementary data are available at Bioinformatics online.
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    Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease
    (BMC, 2020-12-29) Meng, Xianglian; Li, Jin; Zhang, Qiushi; Chen, Feng; Bian, Chenyuan; Yao, Xiaohui; Xu, Zhe; Risacher, Shannon L.; Saykin, Andrew J.; Liang, Hong; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    Background: Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism. Results: In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer's disease, Legionellosis, Pertussis, and Serotonergic synapse. Conclusions: The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer's Disease and will be of value to novel gene discovery and functional genomic studies.
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    The Predictive Role of Analyst Coverage Intensity: Evidence from Cross-Listing in the U.S.
    (2007) Chen, Feng; Weiss, Ira S.; Zheng, Lin
    This paper investigates financial analysts’ predictive power of future performance and earnings quality, based on their selective coverage of firms that have recently cross-listed into the U.S. This setting is useful for examining these questions because, following cross-listing, firms often experience an increase in analyst coverage and an improvement in their information environment. We find that analyst coverage is positively related to analysts’ expectation about firms’ future performance and negatively related to analysts’ concern over firms’ earnings quality. Furthermore, country-level legal origin and disclosure index are two significant determinants of analyst coverage of cross-listed firms. In addition, the intensity of analyst coverage can predict future abnormal stock price performance. Our latter finding augments those in Das, Guo, and Zhang (2006), who investigate the predictive role of analyst coverage following IPOs. The benefit of our setting is that, unlike the post-IPO period, cross-listing firms do not appear to experience mis-pricing that might confound the results. Overall, our study further documents the substantial informational benefits to cross-listing, but suggests that these informational benefits may not be complete, since financial analysts appear to have predictive power and selectively provide coverage for firms with favorable future prospects.
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    Religiosity and Cross-Country Differences in Trade Credit Use
    (2020) Chen, Feng; Chen, Xiaolin; Tan, Weiqiang; Zheng, Lin
    Using the firm-level data over 1989-2012 from 53 countries, we find religiosity in a country is positively associated with trade credit use by local firms. Specifically, after controlling for firm- and country-level factors as well as industry and year effects, we show that trade credit use is higher in more religious countries. Moreover, both creditor rights and social trust in a country enhance the positive association between religiosity and trade credit use, while the quality of national-level disclosure mitigates the aforementioned positive association. These results are robust to alternative measures of religiosity, alternative sampling requirements, and potential endogeneity concerns.