Mo Li, Xue Zeng, Chentian Jin, S. Jin, W. Dong, Martina Brueckner, R. Lifton, Q. Lu, Hongyu Zhao
{"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":"9 2 1","pages":"216-227"},"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}
{"title":"Early bioinformatics research in China","authors":"Runsheng Chen","doi":"10.15302/j-qb-021-0255","DOIUrl":"https://doi.org/10.15302/j-qb-021-0255","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67351037","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}
{"title":"Adaptive total variation constraint hypergraph regularized NMF for single-cell RNA-seq data analysis","authors":"","doi":"10.15302/j-qb-021-0261","DOIUrl":"https://doi.org/10.15302/j-qb-021-0261","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67351244","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}
{"title":"Transcriptome wide association studies: general framework and methods","authors":"Yu-Xiao Xie, N. Shan, Hongyu Zhao, Lin Hou","doi":"10.15302/J-QB-020-0228","DOIUrl":"https://doi.org/10.15302/J-QB-020-0228","url":null,"abstract":"Background : Genome-wide association studies (GWAS) have succeeded in identifying tens of thousands of genetic variants associated with complex human traits during the past decade, however, they are still hampered by limited statistical power and dif fi culties in biological interpretation. With the recent progress in expression quantitative trait loci (eQTL) studies, transcriptome-wide association studies (TWAS) provide a framework to test for gene-trait associations by integrating information from GWAS and eQTL studies. Results : In this review, we will introduce the general framework of TWAS, the relevant resources, and the computational tools. Extensions of the original TWAS methods will also be discussed. Furthermore, we will brie fl y introduce methods that are closely related to TWAS, including MR-based methods and colocalization approaches. Connection and difference between these approaches will be discussed. Conclusion : Finally, we will summarize strengths, limitations, and potential directions for TWAS. Author summary: Transcriptome-wide association studies (TWAS) provide an important framework to test for gene-trait associations by integrating information from GWAS and eQTL studies. In this review, we systematically review the general framework and methods of transcriptome-wide association studies, and discuss their strengths, limitations, and potential future directions.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67351219","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}