An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data
Genomic studies have been a major approach to elucidating disease etiology and to exploring potential targets for treatments of many complex diseases. Statistical analyses in these studies often face the challenges of multiplicity, weak signals, and the nature of dependence among genetic markers. Th...
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doaj-26dec9d359274d64b573f8c6a96382802021-09-08T06:01:52ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-09-011210.3389/fgene.2021.709555709555An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank DataQi-You Yu0Tzu-Pin Lu1Tzu-Pin Lu2Tzu-Hung Hsiao3Ching-Heng Lin4Chi-Yun Wu5Chi-Yun Wu6Jung-Ying Tzeng7Jung-Ying Tzeng8Chuhsing Kate Hsiao9Chuhsing Kate Hsiao10Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, TaiwanInstitute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, TaiwanDepartment of Public Health, National Taiwan University, Taipei, TaiwanDepartment of Medical Research, Taichung Veterans General Hospital, Taichung, TaiwanDepartment of Medical Research, Taichung Veterans General Hospital, Taichung, TaiwanGraduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, United StatesDepartment of Statistics, University of Pennsylvania, Philadelphia, PA, United StatesInstitute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, TaiwanDepartment of Statistics and Bioinformatics Research Center, North Carolina State University, Raleigh, NC, United StatesInstitute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, TaiwanDepartment of Public Health, National Taiwan University, Taipei, TaiwanGenomic studies have been a major approach to elucidating disease etiology and to exploring potential targets for treatments of many complex diseases. Statistical analyses in these studies often face the challenges of multiplicity, weak signals, and the nature of dependence among genetic markers. This situation becomes even more complicated when multi-omics data are available. To integrate the data from different platforms, various integrative analyses have been adopted, ranging from the direct union or intersection operation on sets derived from different single-platform analysis to complex hierarchical multi-level models. The former ignores the biological relationship between molecules while the latter can be hard to interpret. We propose in this study an integrative approach that combines both single nucleotide variants (SNVs) and copy number variations (CNVs) in the same genomic unit to co-localize the concurrent effect and to deal with the sparsity due to rare variants. This approach is illustrated with simulation studies to evaluate its performance and is applied to low-density lipoprotein cholesterol and triglyceride measurements from Taiwan Biobank. The results show that the proposed method can more effectively detect the collective effect from both SNVs and CNVs compared to traditional methods. For the biobank analysis, the identified genetic regions including the gene VNN2 could be novel and deserve further investigation.https://www.frontiersin.org/articles/10.3389/fgene.2021.709555/fullco-localizationgene-levelintegrative analysisTaiwan BiobankCNVSNV |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qi-You Yu Tzu-Pin Lu Tzu-Pin Lu Tzu-Hung Hsiao Ching-Heng Lin Chi-Yun Wu Chi-Yun Wu Jung-Ying Tzeng Jung-Ying Tzeng Chuhsing Kate Hsiao Chuhsing Kate Hsiao |
spellingShingle |
Qi-You Yu Tzu-Pin Lu Tzu-Pin Lu Tzu-Hung Hsiao Ching-Heng Lin Chi-Yun Wu Chi-Yun Wu Jung-Ying Tzeng Jung-Ying Tzeng Chuhsing Kate Hsiao Chuhsing Kate Hsiao An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data Frontiers in Genetics co-localization gene-level integrative analysis Taiwan Biobank CNV SNV |
author_facet |
Qi-You Yu Tzu-Pin Lu Tzu-Pin Lu Tzu-Hung Hsiao Ching-Heng Lin Chi-Yun Wu Chi-Yun Wu Jung-Ying Tzeng Jung-Ying Tzeng Chuhsing Kate Hsiao Chuhsing Kate Hsiao |
author_sort |
Qi-You Yu |
title |
An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data |
title_short |
An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data |
title_full |
An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data |
title_fullStr |
An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data |
title_full_unstemmed |
An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data |
title_sort |
integrative co-localization (inco) analysis for snv and cnv genomic features with an application to taiwan biobank data |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2021-09-01 |
description |
Genomic studies have been a major approach to elucidating disease etiology and to exploring potential targets for treatments of many complex diseases. Statistical analyses in these studies often face the challenges of multiplicity, weak signals, and the nature of dependence among genetic markers. This situation becomes even more complicated when multi-omics data are available. To integrate the data from different platforms, various integrative analyses have been adopted, ranging from the direct union or intersection operation on sets derived from different single-platform analysis to complex hierarchical multi-level models. The former ignores the biological relationship between molecules while the latter can be hard to interpret. We propose in this study an integrative approach that combines both single nucleotide variants (SNVs) and copy number variations (CNVs) in the same genomic unit to co-localize the concurrent effect and to deal with the sparsity due to rare variants. This approach is illustrated with simulation studies to evaluate its performance and is applied to low-density lipoprotein cholesterol and triglyceride measurements from Taiwan Biobank. The results show that the proposed method can more effectively detect the collective effect from both SNVs and CNVs compared to traditional methods. For the biobank analysis, the identified genetic regions including the gene VNN2 could be novel and deserve further investigation. |
topic |
co-localization gene-level integrative analysis Taiwan Biobank CNV SNV |
url |
https://www.frontiersin.org/articles/10.3389/fgene.2021.709555/full |
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