Annual review of genomics and human genetics最新文献

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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 Q1 GENETICS & HEREDITY

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

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.

结核分枝杆菌被许多人认为是最致命的微生物，估计每年病例超过 1 000 万。包括非洲分枝杆菌在内的结核分枝杆菌被分为九大支系和数百个亚支系，每个支系都有不同的地理分布和毒力水平。这种系统地理学模式可能是近期和早期人类迁徙的结果，也可能是细菌与各种人类种群共同进化的结果，这或许可以解释为什么许多关于导致结核病的人类遗传因素的研究无法在不同地区复制。此外，一些研究揭示了人类基因变异和细菌基因型之间的相互作用在决定结核病发展方面的重要意义，这表明存在共同适应。随着人类和细菌全基因组序列数据的增加，人们能够更好地了解这些相互作用，从而为疫苗和其他控制措施的开发提供依据。

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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 Q1 GENETICS & HEREDITY

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

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.

过去五年，应用于蛋白质研究的深度学习模型取得了令人瞩目的进展。最值得注意的是，基于序列的结构预测以 AlphaFold2 和相关方法的形式取得了变革性的进展。人类群体中有数百万个错义蛋白质变体缺乏注释，这些计算方法是优先选择变体进行进一步分析的重要手段。在此，我们回顾了应用于预测蛋白质结构和蛋白质变异的深度学习模型的最新进展，并特别强调了它们对人类遗传学和健康的影响。改进蛋白质结构预测有助于注释变异对蛋白质稳定性、蛋白质-蛋白质相互作用界面和小分子结合口袋的影响。此外，它还有助于研究宿主与病原体之间的相互作用以及蛋白质功能的特征。随着大群体基因组测序的日益普及，我们认为将最先进的蛋白质信息学技术更好地融入人类遗传学研究至关重要。

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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 Q1 GENETICS & HEREDITY

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

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.

负责任地开展人类研究的伦理标准已经取得了长足的进步，但在人类遗传学和基因组学研究中，与公平有关的问题依然存在。解决这些问题将有助于社会充分发挥人类基因组学研究的潜力。一个突出的问题是如何公平公正地分享人类参与研究带来的利益。一些国际机构已经认识到，利益共享可以成为符合伦理的研究行为的有效工具，但包括《生物多样性公约》及其《获取和利益共享名古屋议定书》在内的国际法明确将人类基因和基因组资源排除在外。如果要在人类基因组学研究中应用类似的原则，就必须考虑和预见这些协议所面临的重大挑战。我们建议，人类基因组学研究的惠益分享可以是一种自下而上的努力，并纳入现有的研究过程。我们建议制定一个 "设计利益共享 "框架，以解决人类基因组资源和样本使用中的公平和公正问题，并从《名古屋议定书》的愿望和十年实施过程中吸取经验教训。

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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 Q1 GENETICS & HEREDITY

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

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。

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 Q1 GENETICS & HEREDITY

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

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。

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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 Q1 GENETICS & HEREDITY

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

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.

青光眼是一种临床异质性疾病，也是导致不可逆失明的全球主要原因。治疗干预可以预防失明，但有赖于早期诊断，而目前的临床风险因素在预测谁会患上危及视力的青光眼方面能力有限。青光眼的高遗传性使其成为遗传风险预测的理想基质，其中大部分风险具有多基因性质。在此，我们总结了青光眼遗传风险的基础、多基因风险预测工具的开发以及遗传风险分层的新机遇。尽管挑战依然存在，但遗传风险分层将大大改善青光眼筛查和管理。

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 Q1 GENETICS & HEREDITY

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

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。

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 Q1 GENETICS & HEREDITY

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

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.

群体规模的单细胞基因组学是揭示遗传和细胞变异之间错综复杂联系的变革性方法。前沿的实验方法，包括高通量单细胞多组学的发展以及多重环境和遗传扰动的进步，为这种方法提供了便利。通过研究天然或合成基因变异在不同细胞环境中的影响，可以深入了解遗传和环境在塑造细胞异质性方面的相互影响。计算方法的发展进一步实现了对分子变异的详细定量分析，为研究随机变异、细胞间变异和个体间变异各自的作用提供了机会。未来的机遇在于利用长读数测序、完善疾病相关细胞模型以及采用预测性和生成性机器学习模型。这些进展有可能加深对人类分子性状遗传结构的理解，进而对理解人类疾病的遗传原因产生重要影响。《基因组学与人类遗传学年度综述》第 25 卷的最终在线出版日期预计为 2024 年 8 月。修订后的预计日期请参见 http://www.annualreviews.org/page/journal/pubdates。

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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 Q1 GENETICS & HEREDITY

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

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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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 Q1 GENETICS & HEREDITY

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

The Human Cell Atlas (HCA) is striving to build an open community that is inclusive of all researchers adhering to its principles and as open as possible with respect to data access and use. However, open data sharing can pose certain challenges. For instance, being a global initiative, the HCA must contend with a patchwork of local and regional privacy rules. A notable example is the implementation of the European Union General Data Protection Regulation (GDPR), which caused some concern in the biomedical and genomic data-sharing community. We examine how the HCA's large, international group of researchers is investing tremendous efforts into ensuring appropriate sharing of data. We describe the HCA's objectives and governance, how it defines open data sharing, and ethico-legal challenges encountered early in its development; in particular, we describe the challenges prompted by the GDPR. Finally, we broaden the discussion to address tools and strategies that can be used to address ethical data governance.

人类细胞图谱(HCA)正在努力建立一个开放的社区，包括所有遵守其原则的研究人员，并在数据访问和使用方面尽可能开放。然而，开放数据共享可能会带来某些挑战。例如，作为一项全球倡议，HCA必须应对地方和地区隐私规则的拼凑。一个显著的例子是欧盟通用数据保护条例(GDPR)的实施，这引起了生物医学和基因组数据共享界的一些担忧。我们研究了HCA庞大的国际研究小组如何投入巨大的努力来确保适当的数据共享。我们描述了HCA的目标和治理，它如何定义开放数据共享，以及在其发展早期遇到的伦理法律挑战;特别是，我们描述了GDPR带来的挑战。最后，我们将扩大讨论范围，以解决可用于解决道德数据治理的工具和策略。

引用次数: 2

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