Pub Date : 2024-04-25DOI: 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
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.
{"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}
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.
{"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":"<jats:italic>Mycobacterium tuberculosis</jats:italic> is considered by many to be the deadliest microbe, with the estimated annual cases numbering more than 10 million. The bacteria, including <jats:italic>Mycobacterium africanum</jats:italic>, 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}
Pub Date : 2024-04-15DOI: 10.1146/annurev-genom-120622-020615
Miguel Correa Marrero, Jürgen Jänes, Delora Baptista, Pedro Beltrao
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.
{"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}
Pub Date : 2024-04-12DOI: 10.1146/annurev-genom-021623-104241
Ann M. Mc Cartney, Amber Hartman Scholz, Mathieu Groussin, Ciara Staunton
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.
{"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}
Pub Date : 2024-04-11DOI: 10.1146/annurev-genom-120822-105708
Andrea J. Betancourt, Kevin H.-C. Wei, Yuheng Huang, Yuh Chwen G. Lee
Transposable elements (TEs) are genomic parasites found in nearly all eukaryotes, including humans. This evolutionary success of TEs is due to their replicative activity, involving insertion into new genomic locations. TE activity varies at multiple levels, from between taxa to within individuals. The rapidly accumulating evidence of the influence of TE activity on human health, as well as the rapid growth of new tools to study it, motivated an evaluation of what we know about TE activity thus far. Here, we discuss why TE activity varies, and the consequences of this variation, from an evolutionary perspective. By studying TE activity in nonhuman organisms in the context of evolutionary theories, we can shed light on the factors that affect TE activity. While the consequences of TE activity are usually deleterious, some have lasting evolutionary impacts by conferring benefits on the host or affecting other evolutionary processes.
可转座元件（Transposable elements，TEs）是一种基因组寄生虫，几乎存在于包括人类在内的所有真核生物中。可转座元件之所以能在进化过程中取得成功，是因为它们具有复制活性，能插入新的基因组位置。从类群之间到个体内部，TE 的活性在多个层面上存在差异。TE 活性对人类健康影响的证据在迅速积累，研究 TE 活性的新工具也在快速发展，这促使我们对迄今所知的 TE 活性进行评估。在这里，我们从进化的角度讨论了 TE 活动变化的原因以及这种变化的后果。通过在进化理论的背景下研究非人类生物的 TE 活动，我们可以揭示影响 TE 活动的因素。虽然TE活动的后果通常是有害的，但有些TE活动会给宿主带来益处或影响其他进化过程，从而对进化产生持久的影响。
{"title":"Causes and Consequences of Varying Transposable Element Activity: An Evolutionary Perspective","authors":"Andrea J. Betancourt, Kevin H.-C. Wei, Yuheng Huang, Yuh Chwen G. Lee","doi":"10.1146/annurev-genom-120822-105708","DOIUrl":"https://doi.org/10.1146/annurev-genom-120822-105708","url":null,"abstract":"Transposable elements (TEs) are genomic parasites found in nearly all eukaryotes, including humans. This evolutionary success of TEs is due to their replicative activity, involving insertion into new genomic locations. TE activity varies at multiple levels, from between taxa to within individuals. The rapidly accumulating evidence of the influence of TE activity on human health, as well as the rapid growth of new tools to study it, motivated an evaluation of what we know about TE activity thus far. Here, we discuss why TE activity varies, and the consequences of this variation, from an evolutionary perspective. By studying TE activity in nonhuman organisms in the context of evolutionary theories, we can shed light on the factors that affect TE activity. While the consequences of TE activity are usually deleterious, some have lasting evolutionary impacts by conferring benefits on the host or affecting other evolutionary processes.","PeriodicalId":8231,"journal":{"name":"Annual review of genomics and human genetics","volume":null,"pages":null},"PeriodicalIF":8.7,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140579428","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}
Pub Date : 2024-04-10DOI: 10.1146/annurev-genom-021623-024727
Ksenia Sokolova, Kathleen M. Chen, Yun Hao, Jian Zhou, Olga G. Troyanskaya
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.
破译基因表达的调控密码和解读基因组变异的转录效应是人类遗传学面临的关键挑战。现代实验技术产生了大量的数据，使基于序列的深度学习模型得以开发，这些模型将嵌入 DNA 的模式与有助于转录调控的生化和调控特性联系起来，包括表观遗传标记建模、三维基因组组织和基因表达，并具有组织和细胞类型特异性。这些方法可以预测人类基因组中任何非编码变异的功能性后果，甚至是罕见的或从未观察到的变异，并系统地描述其后果的特征，而不仅仅是通过实验或定量遗传学研究来描述。最近，通过开发和应用可解释性方法，确定了有助于完成预测任务的关键序列模式，深入了解了潜在的生物学机制，并揭示了改进未来模型的机会。
{"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}
Pub Date : 2024-04-10DOI: 10.1146/annurev-genom-121222-105817
Antonia Kolovos, Mark M. Hassall, Owen M. Siggs, Emmanuelle Souzeau, Jamie E. Craig
Glaucoma is a clinically heterogeneous disease and the world's leading cause of irreversible blindness. Therapeutic intervention can prevent blindness but relies on early diagnosis, and current clinical risk factors are limited in their ability to predict who will develop sight-threatening glaucoma. The high heritability of glaucoma makes it an ideal substrate for genetic risk prediction, with the bulk of risk being polygenic in nature. Here, we summarize the foundations of glaucoma genetic risk, the development of polygenic risk prediction instruments, and emerging opportunities for genetic risk stratification. Although challenges remain, genetic risk stratification will significantly improve glaucoma screening and management.
{"title":"Polygenic Risk Scores Driving Clinical Change in Glaucoma","authors":"Antonia Kolovos, Mark M. Hassall, Owen M. Siggs, Emmanuelle Souzeau, Jamie E. Craig","doi":"10.1146/annurev-genom-121222-105817","DOIUrl":"https://doi.org/10.1146/annurev-genom-121222-105817","url":null,"abstract":"Glaucoma is a clinically heterogeneous disease and the world's leading cause of irreversible blindness. Therapeutic intervention can prevent blindness but relies on early diagnosis, and current clinical risk factors are limited in their ability to predict who will develop sight-threatening glaucoma. The high heritability of glaucoma makes it an ideal substrate for genetic risk prediction, with the bulk of risk being polygenic in nature. Here, we summarize the foundations of glaucoma genetic risk, the development of polygenic risk prediction instruments, and emerging opportunities for genetic risk stratification. Although challenges remain, genetic risk stratification will significantly improve glaucoma screening and management.","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":"140579554","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}
Pub Date : 2024-04-10DOI: 10.1146/annurev-genom-120522-012938
Benjamin J. Talks, Michael W. Mather, Manisha Chahal, Matthew Coates, Menna R. Clatworthy, Muzlifah Haniffa
The development and deployment of single-cell genomic technologies have driven a resolution revolution in our understanding of the immune system, providing unprecedented insight into the diversity of immune cells present throughout the body and their function in health and disease. Waldeyer's ring is the collective name for the lymphoid tissue aggregations of the upper aerodigestive tract, comprising the palatine, pharyngeal (adenoids), lingual, and tubal tonsils. These tonsils are the first immune sentinels encountered by ingested and inhaled antigens and are responsible for mounting the first wave of adaptive immune response. An effective mucosal immune response is critical to neutralizing infection in the upper airway and preventing systemic spread, and dysfunctional immune responses can result in ear, nose, and throat pathologies. This review uses Waldeyer's ring to demonstrate how single-cell technologies are being applied to advance our understanding of the immune system and highlight directions for future research.
单细胞基因组技术的开发和应用推动了我们对免疫系统认识的分辨率革命，为我们提供了前所未有的洞察力，让我们了解到存在于人体各处的免疫细胞的多样性及其在健康和疾病中的功能。Waldeyer's ring 是上消化道淋巴组织聚集的总称，包括腭、咽（腺样体）、舌和输卵管扁桃体。这些扁桃体是摄入和吸入抗原遇到的第一个免疫哨兵，负责启动第一波适应性免疫反应。有效的粘膜免疫反应对中和上呼吸道感染和防止全身扩散至关重要，而功能失调的免疫反应可导致耳鼻喉病变。本综述利用 Waldeyer's ring 展示了单细胞技术如何应用于促进我们对免疫系统的了解，并强调了未来的研究方向。
{"title":"Mapping Human Immunity and the Education of Waldeyer's Ring","authors":"Benjamin J. Talks, Michael W. Mather, Manisha Chahal, Matthew Coates, Menna R. Clatworthy, Muzlifah Haniffa","doi":"10.1146/annurev-genom-120522-012938","DOIUrl":"https://doi.org/10.1146/annurev-genom-120522-012938","url":null,"abstract":"The development and deployment of single-cell genomic technologies have driven a resolution revolution in our understanding of the immune system, providing unprecedented insight into the diversity of immune cells present throughout the body and their function in health and disease. Waldeyer's ring is the collective name for the lymphoid tissue aggregations of the upper aerodigestive tract, comprising the palatine, pharyngeal (adenoids), lingual, and tubal tonsils. These tonsils are the first immune sentinels encountered by ingested and inhaled antigens and are responsible for mounting the first wave of adaptive immune response. An effective mucosal immune response is critical to neutralizing infection in the upper airway and preventing systemic spread, and dysfunctional immune responses can result in ear, nose, and throat pathologies. This review uses Waldeyer's ring to demonstrate how single-cell technologies are being applied to advance our understanding of the immune system and highlight directions for future research.","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":"140579550","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}
Pub Date : 2024-02-21DOI: 10.1146/annurev-genom-021623-083207
M. Grace Gordon, Pooja Kathail, Bryson Choy, Min Cheol Kim, Thomas Mazumder, Melissa Gearing, Chun Jimmie Ye
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.
{"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}
Pub Date : 2023-08-25Epub Date: 2023-05-23DOI: 10.1146/annurev-genom-111422-025117
Priya Moorjani, Garrett Hellenthal
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.
遗传数据包含了人类进化史的记录。来自不同地理区域和时间尺度的大规模人类种群数据集的可用性，以及分析这些数据的计算方法的进步，改变了我们利用基因数据了解人类进化历史的能力。在此，我们将回顾一些广泛使用的统计方法，以便利用基因组数据探索和描述种群关系和历史。我们将介绍常用方法背后的直觉、解释以及重要的局限性。为了说明问题，我们将其中一些技术应用于人类基因组多样性项目（Human Genome Diversity Project）中代表全球 53 个种群的 929 个个体的全基因组常染色体数据。最后，我们讨论了基因组学方法在了解种群历史方面的新前沿。总之，这篇综述强调了 DNA 在推断人类进化史特征方面的能力（和局限性），是对考古学、人类学和语言学等其他学科知识的补充。
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