Nature Reviews Genetics最新文献_第4页

Using genetics to mimic drug interactions 利用遗传学模拟药物相互作用。

IF 51.4 1区生物学

Nature Reviews Genetics Pub Date : 2026-08-17 DOI: 10.1038/s41576-026-01003-8

Pamela Yeh

引用次数: 0

Genetics and genomics of hybridization 杂交遗传学和基因组学。

IF 51.4 1区生物学

Nature Reviews Genetics Pub Date : 2026-08-11 DOI: 10.1038/s41576-026-00995-7

Molly Schumer, Loren H. Rieseberg

{"title":"Genetics and genomics of hybridization","authors":"Molly Schumer, Loren H. Rieseberg","doi":"10.1038/s41576-026-00995-7","DOIUrl":"10.1038/s41576-026-00995-7","url":null,"abstract":"Once considered rare in some species groups, we now appreciate that hybridization between distinct lineages is a feature of essentially all branches of the eukaryotic tree of life. Determining the drivers of hybridization across species and locally along genomes is not just a question of history; it is fundamental to understanding genomic variation, adaptation and even disease risk across diverse species. Here, we start by reviewing methodological advances that may improve our understanding of the frequency of hybridization and its evolutionary effects. Next, we compile results from traditional and genomic sources of evidence to estimate the incidence of hybridization in nature and discuss the causes and consequences of this key evolutionary process. Finally, we highlight open questions, including whether hybridization promotes diversification, and how such questions could be addressed in the future. Hybridization, the exchange of genes between divergent lineages, is now recognized as widespread across eukaryotes and essential to understanding genome evolution. In this Review, Schumer and Rieseberg overview methodological advances facilitating recent progress, reassess the frequency, causes and consequences of hybridization, and highlight key open questions.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"27 11","pages":"840-854"},"PeriodicalIF":51.4,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148713277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

AI-based multimodal integration of genomics and electronic health records 基于人工智能的基因组学和电子健康记录的多模式集成。

IF 51.4 1区生物学

Nature Reviews Genetics Pub Date : 2026-08-10 DOI: 10.1038/s41576-026-00992-w

Rasika Venkatesh, Marylyn D. Ritchie

{"title":"AI-based multimodal integration of genomics and electronic health records","authors":"Rasika Venkatesh, Marylyn D. Ritchie","doi":"10.1038/s41576-026-00992-w","DOIUrl":"10.1038/s41576-026-00992-w","url":null,"abstract":"The widespread adoption of electronic health records (EHRs), which capture patient-specific longitudinal information on diagnoses, laboratory tests, clinical procedures, and outcomes, has created unprecedented opportunities to study diseases at scale. Integrating EHR data with genomic information offers novel ways to understand disease heterogeneity, identify biomarkers and therapeutic targets, and predict disease risk to improve clinical decision-making at scale. Recent methodological advances in machine learning (ML) and artificial intelligence (AI) can handle data with high dimensionality, high levels of noise, and irregular temporality better than traditional statistical approaches. Progress in EHR-linked biobank development, data standardization pipelines, and architectures for modelling and implementation have accelerated the advancement of the field and warrant an assessment of current capabilities and limitations. This Review highlights AI and ML frameworks for integrating genomic, multi-omics, and EHR data, and discusses how these approaches are reshaping genomics research as well as clinical practice. This Review covers advances in artificial intelligence-based methods for multimodal modelling of genomics and electronic health record data, and the integration of these data into electronic-health-record-based research and clinical workflows to support genomics-enabled clinical decision, with an emphasis on governance and equitable implementation.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"27 11","pages":"821-839"},"PeriodicalIF":51.4,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148707167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

The genetics of complex gene expression phenotypes illuminated 阐明了复杂基因表达表型的遗传学。

IF 51.4 1区生物学

Nature Reviews Genetics Pub Date : 2026-08-07 DOI: 10.1038/s41576-026-00999-3

Christian R. Landry

引用次数: 0

Embracing non-linearity in human ageing. 拥抱人类衰老的非线性。

IF 51.4 1区生物学

Nature Reviews Genetics Pub Date : 2026-08-03 DOI: 10.1038/s41576-026-00993-9

Robin Grolaux, Andrew E Teschendorff, Nir Eynon

引用次数: 0

Bidirectional functional genomics using CRISPR-KOALA 使用crispr -考拉的双向功能基因组学。

IF 51.4 1区生物学

Nature Reviews Genetics Pub Date : 2026-07-31 DOI: 10.1038/s41576-026-00997-5

Khalid N. Al-Zahrani

引用次数: 0

Towards quantifying immune diversity through HLA genomics 通过HLA基因组学量化免疫多样性。

IF 51.4 1区生物学

Nature Reviews Genetics Pub Date : 2026-07-31 DOI: 10.1038/s41576-026-00996-6

Dinesh Velayutham, Zainab Jan, Puthen Veettil Jithesh, Hamdi Mbarek

引用次数: 0

Genomic, epigenomic and transcriptomic regulation of cellular senescence 细胞衰老的基因组、表观基因组和转录组调控。

IF 51.4 1区生物学

Nature Reviews Genetics Pub Date : 2026-07-29 DOI: 10.1038/s41576-026-00982-y

Rahagir Salekeen, Tra L. Kieu, Paul D. Robbins, Laura J. Niedernhofer, Linshan Laux

{"title":"Genomic, epigenomic and transcriptomic regulation of cellular senescence","authors":"Rahagir Salekeen, Tra L. Kieu, Paul D. Robbins, Laura J. Niedernhofer, Linshan Laux","doi":"10.1038/s41576-026-00982-y","DOIUrl":"10.1038/s41576-026-00982-y","url":null,"abstract":"Cellular senescence is a complex, highly regulated cell state induced by cellular damage and stress. Senescence is central to many areas of biology, with roles in tumour suppression, tissue regeneration, antiviral defence and diverse age-related pathologies. Senescence is characterized by stable cell cycle arrest, metabolic alterations, chromatin remodelling and the secretion of pro-inflammatory and tissue-modifying factors that are collectively termed the senescence-associated secretory phenotype. Recent technological advances, including new genetic models, single-cell and spatial multi-omics platforms and machine-learning approaches, promise to enable the phenotyping, tracing and manipulation of senescent cells with unprecedented precision and resolution. This Review defines our current understanding of the genetic pathways that regulate senescence induction, maintenance, propagation and heterogeneity, including the DNA damage response, non-genotoxic stress pathways, epigenetic changes and cell–cell communication. We also emphasize key challenges in distinguishing senescence from other cell fates and the need for next-generation biomarkers to capture the varied phenotypes and functions of senescent cells. Senescence — a complex, dynamic and heterogeneous stress-responsive cell state — shapes ageing biology and tissue homeostasis. This Review examines the molecular regulation of senescence initiation, maintenance and propagation, highlighting the need for revised mechanistic frameworks and biomarkers to define and target senescent cells.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"27 11","pages":"855-876"},"PeriodicalIF":51.4,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148620767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Human genetics across levels of biological organization. 跨越生物组织层次的人类遗传学。

IF 51.4 1区生物学

Nature Reviews Genetics Pub Date : 2026-07-23 DOI: 10.1038/s41576-026-00991-x

Diederik S Laman Trip, Pedro Beltrao

{"title":"Human genetics across levels of biological organization.","authors":"Diederik S Laman Trip, Pedro Beltrao","doi":"10.1038/s41576-026-00991-x","DOIUrl":"https://doi.org/10.1038/s41576-026-00991-x","url":null,"abstract":"<p><p>Genetic variation influences human physiology across biological scales from molecules to cells, tissues, organs and the whole organism. Unravelling how variants and their genetic effects propagate across these levels, through molecular interactions, cellular programmes and tissue architectures, to shape phenotypes remains a central challenge in human genetics. Resolving this challenge requires deciphering the genetic architecture of each biological layer and developing systems-level analyses that aim to integrate across scales. Network-based and computational approaches, including artificial intelligence, offer opportunities to move beyond statistical associations towards a context-aware, mechanistic understanding of the genetics underlying human traits and disease, although integration across layers remains limited. Here we review recent advances in mapping genetic effects across biological scales, from intracellular networks that capture molecular interactions, through single-cell and spatial omics approaches that define cellular and tissue contexts, to population-scale imaging genomics that links genetic variation to organ-level and organismal phenotypes. We discuss emerging strategies and remaining challenges for integrating these layers into mechanistic models of genotype-phenotype relationships.</p>","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":" ","pages":""},"PeriodicalIF":51.4,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148579525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Stomata — guarding the gates against bacterial invasion 气孔-防止细菌入侵

IF 51.4 1区生物学

Nature Reviews Genetics Pub Date : 2026-07-16 DOI: 10.1038/s41576-026-00994-8

Tatsuya Nobori (, )

引用次数: 0