Pub Date : 2022-01-01DOI: 10.1007/978-981-16-5018-5_3
A. Kimura
{"title":"Mechanics of the Cell","authors":"A. Kimura","doi":"10.1007/978-981-16-5018-5_3","DOIUrl":"https://doi.org/10.1007/978-981-16-5018-5_3","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51118936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1007/978-981-16-5018-5_7
A. Kimura
{"title":"Diversity of the Cell","authors":"A. Kimura","doi":"10.1007/978-981-16-5018-5_7","DOIUrl":"https://doi.org/10.1007/978-981-16-5018-5_7","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51119511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1007/978-981-16-5018-5_11
A. Kimura
{"title":"Development of the Cell over Time (Perspectives)","authors":"A. Kimura","doi":"10.1007/978-981-16-5018-5_11","DOIUrl":"https://doi.org/10.1007/978-981-16-5018-5_11","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51118820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-13Epub Date: 2020-10-21DOI: 10.1007/s40484-020-0216-3
Xiaofeng Zhu
Background: Mendelian randomization (MR) analysis has become popular in inferring and estimating the causality of an exposure on an outcome due to the success of genome wide association studies. Many statistical approaches have been developed and each of these methods require specific assumptions.
Results: In this article, we review the pros and cons of these methods. We use an example of high-density lipoprotein cholesterol on coronary artery disease to illuminate the challenges in Mendelian randomization investigation.
Conclusion: The current available MR approaches allow us to study causality among risk factors and outcomes. However, novel approaches are desirable for overcoming multiple source confounding of risk factors and an outcome in MR analysis.
{"title":"Mendelian randomization and pleiotropy analysis.","authors":"Xiaofeng Zhu","doi":"10.1007/s40484-020-0216-3","DOIUrl":"10.1007/s40484-020-0216-3","url":null,"abstract":"<p><strong>Background: </strong>Mendelian randomization (MR) analysis has become popular in inferring and estimating the causality of an exposure on an outcome due to the success of genome wide association studies. Many statistical approaches have been developed and each of these methods require specific assumptions.</p><p><strong>Results: </strong>In this article, we review the pros and cons of these methods. We use an example of high-density lipoprotein cholesterol on coronary artery disease to illuminate the challenges in Mendelian randomization investigation.</p><p><strong>Conclusion: </strong>The current available MR approaches allow us to study causality among risk factors and outcomes. However, novel approaches are desirable for overcoming multiple source confounding of risk factors and an outcome in MR analysis.</p>","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8356909/pdf/nihms-1674208.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39306543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mo Li, Xue Zeng, Chentian Jin, S. Jin, W. Dong, Martina Brueckner, R. Lifton, Q. Lu, Hongyu Zhao
Background Whole-exome sequencing (WES) studies have identified multiple genes enriched for de novo mutations (DNMs) in congenital heart disease (CHD) probands. However, risk gene identification based on DNMs alone remains statistically challenging due to heterogenous etiology of CHD and low mutation rate in each gene. Methods In this manuscript, we introduce a hierarchical Bayesian framework for gene-level association test which jointly analyzes de novo and rare transmitted variants. Through integrative modeling of multiple types of genetic variants, gene-level annotations, and reference data from large population cohorts, our method accurately characterizes the expected frequencies of both de novo and transmitted variants and shows improved statistical power compared to analyses based on DNMs only. Results Applied to WES data of 2,645 CHD proband-parent trios, our method identified 15 significant genes, half of which are novel, leading to new insights into the genetic bases of CHD. Conclusion These results showcase the power of integrative analysis of transmitted and de novo variants for disease gene discovery.
{"title":"Integrative modeling of transmitted and de novo variants identifies novel risk genes for congenital heart disease.","authors":"Mo Li, Xue Zeng, Chentian Jin, S. Jin, W. Dong, Martina Brueckner, R. Lifton, Q. Lu, Hongyu Zhao","doi":"10.15302/j-qb-021-0248","DOIUrl":"https://doi.org/10.15302/j-qb-021-0248","url":null,"abstract":"Background\u0000Whole-exome sequencing (WES) studies have identified multiple genes enriched for de novo mutations (DNMs) in congenital heart disease (CHD) probands. However, risk gene identification based on DNMs alone remains statistically challenging due to heterogenous etiology of CHD and low mutation rate in each gene.\u0000\u0000\u0000Methods\u0000In this manuscript, we introduce a hierarchical Bayesian framework for gene-level association test which jointly analyzes de novo and rare transmitted variants. Through integrative modeling of multiple types of genetic variants, gene-level annotations, and reference data from large population cohorts, our method accurately characterizes the expected frequencies of both de novo and transmitted variants and shows improved statistical power compared to analyses based on DNMs only.\u0000\u0000\u0000Results\u0000Applied to WES data of 2,645 CHD proband-parent trios, our method identified 15 significant genes, half of which are novel, leading to new insights into the genetic bases of CHD.\u0000\u0000\u0000Conclusion\u0000These results showcase the power of integrative analysis of transmitted and de novo variants for disease gene discovery.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47316490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Genome-wide association studies (GWAS) have been widely adopted in studies of human complex traits and diseases.
Results: This review surveys areas of active research: quantifying and partitioning trait heritability, fine mapping functional variants and integrative analysis, genetic risk prediction of phenotypes, and the analysis of sequencing studies that have identified millions of rare variants. Current challenges and opportunities are highlighted.
Conclusion: GWAS have fundamentally transformed the field of human complex trait genetics. Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.
{"title":"Advances and challenges in quantitative delineation of the genetic architecture of complex traits.","authors":"Hua Tang, Zihuai He","doi":"10.15302/j-qb-021-0249","DOIUrl":"10.15302/j-qb-021-0249","url":null,"abstract":"<p><strong>Background: </strong>Genome-wide association studies (GWAS) have been widely adopted in studies of human complex traits and diseases.</p><p><strong>Results: </strong>This review surveys areas of active research: quantifying and partitioning trait heritability, fine mapping functional variants and integrative analysis, genetic risk prediction of phenotypes, and the analysis of sequencing studies that have identified millions of rare variants. Current challenges and opportunities are highlighted.</p><p><strong>Conclusion: </strong>GWAS have fundamentally transformed the field of human complex trait genetics. Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.</p>","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9053444/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41875328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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