Pub Date : 2026-01-22DOI: 10.1038/s41588-025-02468-9
Haochen Zhang (, ), Palash Sashittal, Elias-Ramzey Karnoub, Akhil Jakatdar, Shigeaki Umeda, Jungeui Hong, Anne Marie Noronha, Agustin Cardenas III, Amanda Erakky (, ), Caitlin A. McIntyre, Akimasa Hayashi, Nicolas Lecomte, Marc Hilmi, Wungki Park (, ), Nan Pang, Eileen M. O’Reilly, Alice C. Wei, Benjamin J. Raphael, Christine A. Iacobuzio-Donahue
Most evolutionary studies on pancreatic cancer rely on bulk sequencing, yet clonal evolution happens at the single-cell level. We used single-nucleus DNA sequencing to study 137,491 single nuclei from 24 pancreatic neoplasms reflecting various clinical scenarios. We found higher frequencies of somatic alterations to driver genes that bulk studies indicate; many manifest as copy number alterations and account for the majority of spatial heterogeneity. In pancreatic cancers with canonical KRAS oncogenic mutations, we found likely varied dependence on the genotype that may signify differential response to KRAS inhibition. In pancreatic cancers with germline heterozygous BRCA2 mutations, we discovered varied mechanisms and timing of inactivation of the wild-type allele that sculpted differential evolutionary trajectories. Inactivation of tumor-intrinsic response to transforming growth factor-β happens through various mechanisms, takes place after oncogenesis and coincides with invasion and metastasis, reflecting increasing selective pressure for the phenotype later in pancreatic ductal adenocarcinoma development. This study uses single-cell DNA sequencing to analyze genomic evolution in pancreatic cancer using a cohort of multiregionally and longitudinally sampled patients’ tissues across various clinical contexts.
{"title":"Genomic evolution of pancreatic cancer at single-cell resolution","authors":"Haochen Zhang \u0000 (, ), Palash Sashittal, Elias-Ramzey Karnoub, Akhil Jakatdar, Shigeaki Umeda, Jungeui Hong, Anne Marie Noronha, Agustin Cardenas III, Amanda Erakky \u0000 (, ), Caitlin A. McIntyre, Akimasa Hayashi, Nicolas Lecomte, Marc Hilmi, Wungki Park \u0000 (, ), Nan Pang, Eileen M. O’Reilly, Alice C. Wei, Benjamin J. Raphael, Christine A. Iacobuzio-Donahue","doi":"10.1038/s41588-025-02468-9","DOIUrl":"10.1038/s41588-025-02468-9","url":null,"abstract":"Most evolutionary studies on pancreatic cancer rely on bulk sequencing, yet clonal evolution happens at the single-cell level. We used single-nucleus DNA sequencing to study 137,491 single nuclei from 24 pancreatic neoplasms reflecting various clinical scenarios. We found higher frequencies of somatic alterations to driver genes that bulk studies indicate; many manifest as copy number alterations and account for the majority of spatial heterogeneity. In pancreatic cancers with canonical KRAS oncogenic mutations, we found likely varied dependence on the genotype that may signify differential response to KRAS inhibition. In pancreatic cancers with germline heterozygous BRCA2 mutations, we discovered varied mechanisms and timing of inactivation of the wild-type allele that sculpted differential evolutionary trajectories. Inactivation of tumor-intrinsic response to transforming growth factor-β happens through various mechanisms, takes place after oncogenesis and coincides with invasion and metastasis, reflecting increasing selective pressure for the phenotype later in pancreatic ductal adenocarcinoma development. This study uses single-cell DNA sequencing to analyze genomic evolution in pancreatic cancer using a cohort of multiregionally and longitudinally sampled patients’ tissues across various clinical contexts.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"58 2","pages":"355-365"},"PeriodicalIF":29.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41588-025-02468-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146030388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1038/s41588-025-02470-1
Yixuan He, Wenhan Lu, Yon Ho Jee, Mu-Yi Shih, Ying Wang, Kristin Tsuo, David C. Qian, James A. Diao, Hailiang Huang, Chirag J. Patel, Jinyoung Byun, Bogdan Pasaniuc, Elizabeth G. Atkinson, Christopher I. Amos, Yen-Chen Anne Feng, Matthew Moll, Michael H. Cho, Alicia R. Martin
While respiratory diseases such as chronic obstructive pulmonary disease (COPD) and asthma share many risk factors, most studies investigate them in isolation and in predominantly European-ancestry populations. Here, we conducted the most powerful multi-trait and multi-ancestry genetic analysis of respiratory diseases and auxiliary traits to date, identifying 25 new loci associated with lung function in individuals of East Asian ancestry. Using these results, we developed PRSxtra (cross-trait and cross-ancestry), a multi-trait and multi-ancestry polygenic risk score (PRS) approach that leverages shared components of heritable risk via pleiotropic effects. PRSxtra significantly improved the prediction of asthma, COPD and lung cancer compared to trait- and ancestry-matched PRSs in a multi-ancestry cohort from the All of Us Research Program, especially in diverse populations. Our results present a new framework for multi-trait and multi-ancestry studies of respiratory diseases to improve genetic discovery and polygenic prediction. Multi-trait genome-wide analyses identify variants associated with comorbid lung diseases. Polygenic scores leveraging shared components of heritable risk improve prediction of asthma, chronic obstructive pulmonary disease and lung cancer in a multi-ancestry cohort.
{"title":"Multi-trait and multi-ancestry genetic analysis of comorbid lung diseases and traits improves genetic discovery and polygenic risk prediction","authors":"Yixuan He, Wenhan Lu, Yon Ho Jee, Mu-Yi Shih, Ying Wang, Kristin Tsuo, David C. Qian, James A. Diao, Hailiang Huang, Chirag J. Patel, Jinyoung Byun, Bogdan Pasaniuc, Elizabeth G. Atkinson, Christopher I. Amos, Yen-Chen Anne Feng, Matthew Moll, Michael H. Cho, Alicia R. Martin","doi":"10.1038/s41588-025-02470-1","DOIUrl":"10.1038/s41588-025-02470-1","url":null,"abstract":"While respiratory diseases such as chronic obstructive pulmonary disease (COPD) and asthma share many risk factors, most studies investigate them in isolation and in predominantly European-ancestry populations. Here, we conducted the most powerful multi-trait and multi-ancestry genetic analysis of respiratory diseases and auxiliary traits to date, identifying 25 new loci associated with lung function in individuals of East Asian ancestry. Using these results, we developed PRSxtra (cross-trait and cross-ancestry), a multi-trait and multi-ancestry polygenic risk score (PRS) approach that leverages shared components of heritable risk via pleiotropic effects. PRSxtra significantly improved the prediction of asthma, COPD and lung cancer compared to trait- and ancestry-matched PRSs in a multi-ancestry cohort from the All of Us Research Program, especially in diverse populations. Our results present a new framework for multi-trait and multi-ancestry studies of respiratory diseases to improve genetic discovery and polygenic prediction. Multi-trait genome-wide analyses identify variants associated with comorbid lung diseases. Polygenic scores leveraging shared components of heritable risk improve prediction of asthma, chronic obstructive pulmonary disease and lung cancer in a multi-ancestry cohort.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"58 2","pages":"289-298"},"PeriodicalIF":29.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005986","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}
Pub Date : 2026-01-20DOI: 10.1038/s41588-025-02467-w
Judit García-González, Paul F. O’Reilly
A key goal of human genetics research is to understand how the effects of genetic variants combine to produce the risk of complex disease. Here we discuss and contrast three conceptual models developed to explain how multigenic risk is generated. The polygenic model, derived from the century-old infinitesimal model, has been the dominant framework for understanding the genetic inheritance of complex traits. More recently, two mechanistic models have been proposed: the omnigenic model, which hypothesizes core genes with direct effects on disease and peripheral genes with regulatory, indirect effects, and what we call the ‘stratagenic’ model, in which the genetic risk of disease is stratified across genomic pathways of functional relevance. There are key differences in the implications of these models for research, drug development and precision medicine. Therefore, it is essential to determine which model is most accurate for each disease or whether a single model is broadly optimal across complex diseases. This study discusses polygenic, omnigenic and stratagenic models developed to explain multigenic disease risk. It proposes means to test their validity, which has implications for research, drug development and precision medicine.
{"title":"The polygenic, omnigenic and stratagenic models of complex disease risk","authors":"Judit García-González, Paul F. O’Reilly","doi":"10.1038/s41588-025-02467-w","DOIUrl":"10.1038/s41588-025-02467-w","url":null,"abstract":"A key goal of human genetics research is to understand how the effects of genetic variants combine to produce the risk of complex disease. Here we discuss and contrast three conceptual models developed to explain how multigenic risk is generated. The polygenic model, derived from the century-old infinitesimal model, has been the dominant framework for understanding the genetic inheritance of complex traits. More recently, two mechanistic models have been proposed: the omnigenic model, which hypothesizes core genes with direct effects on disease and peripheral genes with regulatory, indirect effects, and what we call the ‘stratagenic’ model, in which the genetic risk of disease is stratified across genomic pathways of functional relevance. There are key differences in the implications of these models for research, drug development and precision medicine. Therefore, it is essential to determine which model is most accurate for each disease or whether a single model is broadly optimal across complex diseases. This study discusses polygenic, omnigenic and stratagenic models developed to explain multigenic disease risk. It proposes means to test their validity, which has implications for research, drug development and precision medicine.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"58 2","pages":"253-263"},"PeriodicalIF":29.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005409","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}
Pub Date : 2026-01-20DOI: 10.1038/s41588-025-02497-4
Jonathan Mitchel, Teng Gao, Viktor Petukhov, Eli Cole, Peter V. Kharchenko
Spatial transcriptomics aims to elucidate how cells coordinate within tissues by connecting cellular states to their native microenvironments. Imaging-based assays are especially promising, capturing molecular and cellular features at subcellular resolution in three dimensions. Interpretation of such data, however, hinges on accurate cell segmentation. Assigning individual molecules to the correct cells remains challenging. Here we re-analyze data from multiple tissues and platforms to find that segmentation errors currently confound most downstream analysis of cellular state, including differential expression, neighbor influence and ligand–receptor interactions. The extent to which misassigned molecules impact the results can be striking, frequently dominating the results. Thus, we show that matrix factorization of local molecular neighborhoods can effectively identify and isolate such molecular admixtures, thereby reducing their impact on downstream analyses, in a manner analogous to doublet filtering in single-cell RNA sequencing. As the applications of spatial transcriptomics assays become more widespread, accounting for segmentation errors will be important for resolving molecular mechanisms of tissue biology. This study finds that cell segmentation errors affect numerous downstream applications of spatial transcriptomics data and provides a method to correct these errors by factorizing molecular neighborhoods.
{"title":"Impact and correction of segmentation errors in spatial transcriptomics","authors":"Jonathan Mitchel, Teng Gao, Viktor Petukhov, Eli Cole, Peter V. Kharchenko","doi":"10.1038/s41588-025-02497-4","DOIUrl":"10.1038/s41588-025-02497-4","url":null,"abstract":"Spatial transcriptomics aims to elucidate how cells coordinate within tissues by connecting cellular states to their native microenvironments. Imaging-based assays are especially promising, capturing molecular and cellular features at subcellular resolution in three dimensions. Interpretation of such data, however, hinges on accurate cell segmentation. Assigning individual molecules to the correct cells remains challenging. Here we re-analyze data from multiple tissues and platforms to find that segmentation errors currently confound most downstream analysis of cellular state, including differential expression, neighbor influence and ligand–receptor interactions. The extent to which misassigned molecules impact the results can be striking, frequently dominating the results. Thus, we show that matrix factorization of local molecular neighborhoods can effectively identify and isolate such molecular admixtures, thereby reducing their impact on downstream analyses, in a manner analogous to doublet filtering in single-cell RNA sequencing. As the applications of spatial transcriptomics assays become more widespread, accounting for segmentation errors will be important for resolving molecular mechanisms of tissue biology. This study finds that cell segmentation errors affect numerous downstream applications of spatial transcriptomics data and provides a method to correct these errors by factorizing molecular neighborhoods.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"58 2","pages":"434-444"},"PeriodicalIF":29.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005410","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}
Pub Date : 2026-01-20DOI: 10.1038/s41588-025-02487-6
Bingxin Zhao
AI co-scientists can act as virtual research collaborators in statistical genetics, accelerating genetic discovery and translation. Realizing this potential depends on robust domain-specific data and infrastructures, together with interdisciplinary teamwork to build community standards that ensure rigor and responsible deployment.
{"title":"Engineering AI co-scientists for statistical genetics applications","authors":"Bingxin Zhao","doi":"10.1038/s41588-025-02487-6","DOIUrl":"10.1038/s41588-025-02487-6","url":null,"abstract":"AI co-scientists can act as virtual research collaborators in statistical genetics, accelerating genetic discovery and translation. Realizing this potential depends on robust domain-specific data and infrastructures, together with interdisciplinary teamwork to build community standards that ensure rigor and responsible deployment.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"58 2","pages":"236-239"},"PeriodicalIF":29.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005491","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}
Pub Date : 2026-01-19DOI: 10.1038/s41588-025-02469-8
Justin Bellavance, Hongyu Xiao, Le Chang, Mehrdad Kazemi, Seyla Wickramasinghe, Alexandra J. Mayhew, Parminder Raina, Peter VandeHaar, Daniel Taliun, Sarah A. Gagliano Taliun
{"title":"Exploring and visualizing stratified GWAS results with PheWeb2","authors":"Justin Bellavance, Hongyu Xiao, Le Chang, Mehrdad Kazemi, Seyla Wickramasinghe, Alexandra J. Mayhew, Parminder Raina, Peter VandeHaar, Daniel Taliun, Sarah A. Gagliano Taliun","doi":"10.1038/s41588-025-02469-8","DOIUrl":"10.1038/s41588-025-02469-8","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"58 2","pages":"233-235"},"PeriodicalIF":29.0,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146003722","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}
Pub Date : 2026-01-13DOI: 10.1038/s41588-025-02465-y
Na Cai, Andy Dahl, Richard Border, Aditya Gorla, Jolien Rietkerk, Joel Mefford, Noah Zaitlen, Morten Dybdahl Krebs, Andrew J. Schork, Kenneth Kendler, Jonathan Flint
Identifying significant associations between genetic loci and psychiatric disorders is dependent on very large sample sizes. Methods for diagnosing diseases on this scale, such as the use of self-assessment questionnaires and data from electronic health records, incorporate heritable variation unrelated to the disease of interest into the diagnosis. Consequently, genetic mapping will identify loci unrelated to the target disease while missing some that are related, and genetic correlations cannot be used to infer the genetic relationships between diseases and between cohorts. Furthermore, shared biases between different disorders appear as shared etiology. As sample sizes grow, such confounders propagate, and findings based on their presence are replicated and extended. Here, we draw attention to the problem, make suggestions for flagging affected cohorts, and discuss future data collection and machine learning approaches to mitigate the effects of heritable confounders in psychiatric disorders. This Perspective argues that diagnoses derived from self-reports, electronic health records and self-administered questionnaires introduce heritable bias that confounds the interpretation of data from genome-wide association studies, resulting in false-positive and disease-irrelevant genetic signals.
{"title":"The predicament of heritable confounders","authors":"Na Cai, Andy Dahl, Richard Border, Aditya Gorla, Jolien Rietkerk, Joel Mefford, Noah Zaitlen, Morten Dybdahl Krebs, Andrew J. Schork, Kenneth Kendler, Jonathan Flint","doi":"10.1038/s41588-025-02465-y","DOIUrl":"10.1038/s41588-025-02465-y","url":null,"abstract":"Identifying significant associations between genetic loci and psychiatric disorders is dependent on very large sample sizes. Methods for diagnosing diseases on this scale, such as the use of self-assessment questionnaires and data from electronic health records, incorporate heritable variation unrelated to the disease of interest into the diagnosis. Consequently, genetic mapping will identify loci unrelated to the target disease while missing some that are related, and genetic correlations cannot be used to infer the genetic relationships between diseases and between cohorts. Furthermore, shared biases between different disorders appear as shared etiology. As sample sizes grow, such confounders propagate, and findings based on their presence are replicated and extended. Here, we draw attention to the problem, make suggestions for flagging affected cohorts, and discuss future data collection and machine learning approaches to mitigate the effects of heritable confounders in psychiatric disorders. This Perspective argues that diagnoses derived from self-reports, electronic health records and self-administered questionnaires introduce heritable bias that confounds the interpretation of data from genome-wide association studies, resulting in false-positive and disease-irrelevant genetic signals.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"58 2","pages":"264-270"},"PeriodicalIF":29.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145966443","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}
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