Annual review of genomics and human genetics最新文献_第2页

Beyond the Human Genome Project: The Age of Complete Human Genome Sequences and Pangenome References. 超越人类基因组计划：完整人类基因组序列和 Pangenome 参考文献的时代。

IF 8.7 2区生物学

Annual review of genomics and human genetics Pub Date : 2024-04-25 DOI: 10.1146/annurev-genom-021623-081639

Dylan J Taylor, Jordan M. Eizenga, Qiuhui Li, Arun Das, Katharine M. Jenike, E. Kenny, Karen H. Miga, Jean Monlong, R. McCoy, B. Paten, Michael C Schatz

{"title":"Beyond the Human Genome Project: The Age of Complete Human Genome Sequences and Pangenome References.","authors":"Dylan J Taylor, Jordan M. Eizenga, Qiuhui Li, Arun Das, Katharine M. Jenike, E. Kenny, Karen H. Miga, Jean Monlong, R. McCoy, B. Paten, Michael C Schatz","doi":"10.1146/annurev-genom-021623-081639","DOIUrl":"https://doi.org/10.1146/annurev-genom-021623-081639","url":null,"abstract":"The Human Genome Project was an enormous accomplishment, providing a foundation for countless explorations into the genetics and genomics of the human species. Yet for many years, the human genome reference sequence remained incomplete and lacked representation of human genetic diversity. Recently, two major advances have emerged to address these shortcomings: complete gap-free human genome sequences, such as the one developed by the Telomere-to-Telomere Consortium, and high-quality pangenomes, such as the one developed by the Human Pangenome Reference Consortium. Facilitated by advances in long-read DNA sequencing and genome assembly algorithms, complete human genome sequences resolve regions that have been historically difficult to sequence, including centromeres, telomeres, and segmental duplications. In parallel, pangenomes capture the extensive genetic diversity across populations worldwide. Together, these advances usher in a new era of genomics research, enhancing the accuracy of genomic analysis, paving the path for precision medicine, and contributing to deeper insights into human biology.","PeriodicalId":8231,"journal":{"name":"Annual review of genomics and human genetics","volume":null,"pages":null},"PeriodicalIF":8.7,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140653477","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

Genomic Interactions Between Mycobacterium tuberculosis and Humans 结核分枝杆菌与人类之间的基因组相互作用

IF 8.7 2区生物学

Annual review of genomics and human genetics Pub Date : 2024-04-19 DOI: 10.1146/annurev-genom-021623-101844

Prasit Palittapongarnpim, Pornpen Tantivitayakul, Pakorn Aiewsakun, Surakameth Mahasirimongkol, Bharkbhoom Jaemsai

{"title":"Genomic Interactions Between Mycobacterium tuberculosis and Humans","authors":"Prasit Palittapongarnpim, Pornpen Tantivitayakul, Pakorn Aiewsakun, Surakameth Mahasirimongkol, Bharkbhoom Jaemsai","doi":"10.1146/annurev-genom-021623-101844","DOIUrl":"https://doi.org/10.1146/annurev-genom-021623-101844","url":null,"abstract":"Mycobacterium tuberculosis is considered by many to be the deadliest microbe, with the estimated annual cases numbering more than 10 million. The bacteria, including Mycobacterium africanum, are classified into nine major lineages and hundreds of sublineages, each with different geographical distributions and levels of virulence. The phylogeographic patterns can be a result of recent and early human migrations as well as coevolution between the bacteria and various human populations, which may explain why many studies on human genetic factors contributing to tuberculosis have not been replicable in different areas. Moreover, several studies have revealed the significance of interactions between human genetic variations and bacterial genotypes in determining the development of tuberculosis, suggesting coadaptation. The increased availability of whole-genome sequence data from both humans and bacteria has enabled a better understanding of these interactions, which can inform the development of vaccines and other control measures.","PeriodicalId":8231,"journal":{"name":"Annual review of genomics and human genetics","volume":null,"pages":null},"PeriodicalIF":8.7,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140623985","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

Integrating Large-Scale Protein Structure Prediction into Human Genetics Research 将大规模蛋白质结构预测纳入人类遗传学研究

IF 8.7 2区生物学

Annual review of genomics and human genetics Pub Date : 2024-04-15 DOI: 10.1146/annurev-genom-120622-020615

Miguel Correa Marrero, Jürgen Jänes, Delora Baptista, Pedro Beltrao

{"title":"Integrating Large-Scale Protein Structure Prediction into Human Genetics Research","authors":"Miguel Correa Marrero, Jürgen Jänes, Delora Baptista, Pedro Beltrao","doi":"10.1146/annurev-genom-120622-020615","DOIUrl":"https://doi.org/10.1146/annurev-genom-120622-020615","url":null,"abstract":"The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health. Improved prediction of protein structures facilitates annotations of the impact of variants on protein stability, protein–protein interaction interfaces, and small-molecule binding pockets. Moreover, it contributes to the study of host–pathogen interactions and the characterization of protein function. As genome sequencing in large cohorts becomes increasingly prevalent, we believe that better integration of state-of-the-art protein informatics technologies into human genetics research is of paramount importance.","PeriodicalId":8231,"journal":{"name":"Annual review of genomics and human genetics","volume":null,"pages":null},"PeriodicalIF":8.7,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140579683","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

Benefit-Sharing by Design: A Call to Action for Human Genomics Research 利益共享设计：人类基因组研究行动呼吁书

IF 8.7 2区生物学

Annual review of genomics and human genetics Pub Date : 2024-04-12 DOI: 10.1146/annurev-genom-021623-104241

Ann M. Mc Cartney, Amber Hartman Scholz, Mathieu Groussin, Ciara Staunton

{"title":"Benefit-Sharing by Design: A Call to Action for Human Genomics Research","authors":"Ann M. Mc Cartney, Amber Hartman Scholz, Mathieu Groussin, Ciara Staunton","doi":"10.1146/annurev-genom-021623-104241","DOIUrl":"https://doi.org/10.1146/annurev-genom-021623-104241","url":null,"abstract":"The ethical standards for the responsible conduct of human research have come a long way; however, concerns surrounding equity remain in human genetics and genomics research. Addressing these concerns will help society realize the full potential of human genomics research. One outstanding concern is the fair and equitable sharing of benefits from research on human participants. Several international bodies have recognized that benefit-sharing can be an effective tool for ethical research conduct, but international laws, including the Convention on Biological Diversity and its Nagoya Protocol on Access and Benefit-Sharing, explicitly exclude human genetic and genomic resources. These agreements face significant challenges that must be considered and anticipated if similar principles are applied in human genomics research. We propose that benefit-sharing from human genomics research can be a bottom-up effort and embedded into the existing research process. We propose the development of a “benefit-sharing by design” framework to address concerns of fairness and equity in the use of human genomic resources and samples and to learn from the aspirations and decade of implementation of the Nagoya Protocol.","PeriodicalId":8231,"journal":{"name":"Annual review of genomics and human genetics","volume":null,"pages":null},"PeriodicalIF":8.7,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140579547","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

Causes and Consequences of Varying Transposable Element Activity: An Evolutionary Perspective 可转座元件活性变化的原因和后果：进化的视角

IF 8.7 2区生物学

Annual review of genomics and human genetics Pub Date : 2024-04-11 DOI: 10.1146/annurev-genom-120822-105708

Andrea J. Betancourt, Kevin H.-C. Wei, Yuheng Huang, Yuh Chwen G. Lee

引用次数: 0

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":null,"pages":null},"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":null,"pages":null},"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":"<p><p>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.</p>","PeriodicalId":8231,"journal":{"name":"Annual review of genomics and human genetics","volume":null,"pages":null},"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