Annual review of genomics and human genetics最新文献_第3页

Deep Learning Sequence Models for Transcriptional Regulation 转录调控的深度学习序列模型

IF 8.7 2区生物学

Annual review of genomics and human genetics Pub Date : 2024-04-10 DOI: 10.1146/annurev-genom-021623-024727

Ksenia Sokolova, Kathleen M. Chen, Yun Hao, Jian Zhou, Olga G. Troyanskaya

{"title":"Deep Learning Sequence Models for Transcriptional Regulation","authors":"Ksenia Sokolova, Kathleen M. Chen, Yun Hao, Jian Zhou, Olga G. Troyanskaya","doi":"10.1146/annurev-genom-021623-024727","DOIUrl":"https://doi.org/10.1146/annurev-genom-021623-024727","url":null,"abstract":"Deciphering the regulatory code of gene expression and interpreting the transcriptional effects of genome variation are critical challenges in human genetics. Modern experimental technologies have resulted in an abundance of data, enabling the development of sequence-based deep learning models that link patterns embedded in DNA to the biochemical and regulatory properties contributing to transcriptional regulation, including modeling epigenetic marks, 3D genome organization, and gene expression, with tissue and cell-type specificity. Such methods can predict the functional consequences of any noncoding variant in the human genome, even rare or never-before-observed variants, and systematically characterize their consequences beyond what is tractable from experiments or quantitative genetics studies alone. Recently, the development and application of interpretability approaches have led to the identification of key sequence patterns contributing to the predicted tasks, providing insights into the underlying biological mechanisms learned and revealing opportunities for improvement in future models.","PeriodicalId":8231,"journal":{"name":"Annual review of genomics and human genetics","volume":"98 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140579567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Polygenic Risk Scores Driving Clinical Change in Glaucoma 驱动青光眼临床变化的多基因风险评分

IF 8.7 2区生物学

Annual review of genomics and human genetics Pub Date : 2024-04-10 DOI: 10.1146/annurev-genom-121222-105817

Antonia Kolovos, Mark M. Hassall, Owen M. Siggs, Emmanuelle Souzeau, Jamie E. Craig

引用次数: 0

Mapping Human Immunity and the Education of Waldeyer's Ring 绘制人类免疫图谱与 Waldeyer's Ring 教育

IF 8.7 2区生物学

Annual review of genomics and human genetics Pub Date : 2024-04-10 DOI: 10.1146/annurev-genom-120522-012938

Benjamin J. Talks, Michael W. Mather, Manisha Chahal, Matthew Coates, Menna R. Clatworthy, Muzlifah Haniffa

引用次数: 0

Population Diversity at the Single-Cell Level 单细胞水平的种群多样性

IF 8.7 2区生物学

Annual review of genomics and human genetics Pub Date : 2024-02-21 DOI: 10.1146/annurev-genom-021623-083207

M. Grace Gordon, Pooja Kathail, Bryson Choy, Min Cheol Kim, Thomas Mazumder, Melissa Gearing, Chun Jimmie Ye

{"title":"Population Diversity at the Single-Cell Level","authors":"M. Grace Gordon, Pooja Kathail, Bryson Choy, Min Cheol Kim, Thomas Mazumder, Melissa Gearing, Chun Jimmie Ye","doi":"10.1146/annurev-genom-021623-083207","DOIUrl":"https://doi.org/10.1146/annurev-genom-021623-083207","url":null,"abstract":"Population-scale single-cell genomics is a transformative approach for unraveling the intricate links between genetic and cellular variation. This approach is facilitated by cutting-edge experimental methodologies, including the development of high-throughput single-cell multiomics and advances in multiplexed environmental and genetic perturbations. Examining the effects of natural or synthetic genetic variants across cellular contexts provides insights into the mutual influence of genetics and the environment in shaping cellular heterogeneity. The development of computational methodologies further enables detailed quantitative analysis of molecular variation, offering an opportunity to examine the respective roles of stochastic, intercellular, and interindividual variation. Future opportunities lie in leveraging long-read sequencing, refining disease-relevant cellular models, and embracing predictive and generative machine learning models. These advancements hold the potential for a deeper understanding of the genetic architecture of human molecular traits, which in turn has important implications for understanding the genetic causes of human disease.Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 25 is August 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":8231,"journal":{"name":"Annual review of genomics and human genetics","volume":"9 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139926837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Methods for Assessing Population Relationships and History Using Genomic Data. 利用基因组数据评估种群关系和历史的方法。

IF 8.7 2区生物学

Annual review of genomics and human genetics Pub Date : 2023-08-25 Epub Date: 2023-05-23 DOI: 10.1146/annurev-genom-111422-025117

Priya Moorjani, Garrett Hellenthal

{"title":"Methods for Assessing Population Relationships and History Using Genomic Data.","authors":"Priya Moorjani, Garrett Hellenthal","doi":"10.1146/annurev-genom-111422-025117","DOIUrl":"10.1146/annurev-genom-111422-025117","url":null,"abstract":"Genetic data contain a record of our evolutionary history. The availability of large-scale datasets of human populations from various geographic areas and timescales, coupled with advances in the computational methods to analyze these data, has transformed our ability to use genetic data to learn about our evolutionary past. Here, we review some of the widely used statistical methods to explore and characterize population relationships and history using genomic data. We describe the intuition behind commonly used approaches, their interpretation, and important limitations. For illustration, we apply some of these techniques to genome-wide autosomal data from 929 individuals representing 53 worldwide populations that are part of the Human Genome Diversity Project. Finally, we discuss the new frontiers in genomic methods to learn about population history. In sum, this review highlights the power (and limitations) of DNA to infer features of human evolutionary history, complementing the knowledge gleaned from other disciplines, such as archaeology, anthropology, and linguistics.","PeriodicalId":8231,"journal":{"name":"Annual review of genomics and human genetics","volume":"24 ","pages":"305-332"},"PeriodicalIF":8.7,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11040641/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10090370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Open Data in the Era of the GDPR: Lessons from the Human Cell Atlas. GDPR时代的开放数据:来自人类细胞图谱的教训。

IF 8.7 2区生物学

Annual review of genomics and human genetics Pub Date : 2023-08-25 DOI: 10.1146/annurev-genom-101322-113255

Bartha Maria Knoppers, Alexander Bernier, Sarion Bowers, Emily Kirby

引用次数: 2

DECIPHER: Improving Genetic Diagnosis Through Dynamic Integration of Genomic and Clinical Data. DECIPHER：通过动态整合基因组和临床数据改进基因诊断。

IF 8.7 2区生物学

Annual review of genomics and human genetics Pub Date : 2023-08-25 Epub Date: 2023-06-07 DOI: 10.1146/annurev-genom-102822-100509

Julia Foreman, Daniel Perrett, Erica Mazaika, Sarah E Hunt, James S Ware, Helen V Firth

{"title":"DECIPHER: Improving Genetic Diagnosis Through Dynamic Integration of Genomic and Clinical Data.","authors":"Julia Foreman, Daniel Perrett, Erica Mazaika, Sarah E Hunt, James S Ware, Helen V Firth","doi":"10.1146/annurev-genom-102822-100509","DOIUrl":"10.1146/annurev-genom-102822-100509","url":null,"abstract":"DECIPHER (Database of Genomic Variation and Phenotype in Humans Using Ensembl Resources) shares candidate diagnostic variants and phenotypic data from patients with genetic disorders to facilitate research and improve the diagnosis, management, and therapy of rare diseases. The platform sits at the boundary between genomic research and the clinical community. DECIPHER aims to ensure that the most up-to-date data are made rapidly available within its interpretation interfaces to improve clinical care. Newly integrated cardiac case-control data that provide evidence of gene-disease associations and inform variant interpretation exemplify this mission. New research resources are presented in a format optimized for use by a broad range of professionals supporting the delivery of genomic medicine. The interfaces within DECIPHER integrate and contextualize variant and phenotypic data, helping to determine a robust clinico-molecular diagnosis for rare-disease patients, which combines both variant classification and clinical fit. DECIPHER supports discovery research, connecting individuals within the rare-disease community to pursue hypothesis-driven research.","PeriodicalId":8231,"journal":{"name":"Annual review of genomics and human genetics","volume":"24 ","pages":"151-176"},"PeriodicalIF":8.7,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7615097/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10298898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The p-Arms of Human Acrocentric Chromosomes Play by a Different Set of Rules. 人类顶中心染色体的p臂有一套不同的规则。

IF 8.7 2区生物学

Annual review of genomics and human genetics Pub Date : 2023-08-25 DOI: 10.1146/annurev-genom-101122-081642

Brian McStay

引用次数: 5

Methods and Insights from Single-Cell Expression Quantitative Trait Loci. 单细胞表达定量性状基因组的方法和见解。

IF 7.7 2区生物学

Annual review of genomics and human genetics Pub Date : 2023-08-25 Epub Date: 2023-05-17 DOI: 10.1146/annurev-genom-101422-100437

Joyce B Kang, Alessandro Raveane, Aparna Nathan, Nicole Soranzo, Soumya Raychaudhuri

{"title":"Methods and Insights from Single-Cell Expression Quantitative Trait Loci.","authors":"Joyce B Kang, Alessandro Raveane, Aparna Nathan, Nicole Soranzo, Soumya Raychaudhuri","doi":"10.1146/annurev-genom-101422-100437","DOIUrl":"10.1146/annurev-genom-101422-100437","url":null,"abstract":"Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.","PeriodicalId":8231,"journal":{"name":"Annual review of genomics and human genetics","volume":"24 ","pages":"277-303"},"PeriodicalIF":7.7,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10784788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10472932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Meiotic Chromosome Structure, the Synaptonemal Complex, and Infertility. 减数分裂染色体结构、突触复合体与不孕症。

IF 8.7 2区生物学

Annual review of genomics and human genetics Pub Date : 2023-08-25 DOI: 10.1146/annurev-genom-110122-090239

Ian R Adams, Owen R Davies

引用次数: 3