Pub Date : 2026-01-23DOI: 10.1038/s41588-025-02454-1
Jin Li,Jun Wang,Ignacio L Ibarra,Xuesen Cheng,Malte D Luecken,Jiaxiong Lu,Aboozar Monavarfeshani,Wenjun Yan,Yiqiao Zheng,Zhen Zuo,Samantha Lynn Zayas Colborn,Berenice Sarahi Cortez,Leah A Owen,Brittney Wick,Xuan Bao,Maximilian Haeussler,Nicholas M Tran,Karthik Shekhar,Joshua R Sanes,J Timothy Stout,Shiming Chen,Yumei Li,Margaret M DeAngelis,Fabian J Theis,Rui Chen
Single-cell sequencing has revolutionized the scale and resolution of molecular profiling of tissues and organs. Here we present an integrated dual-modal reference atlas of the most accessible portion of the mammalian central nervous system, the retina. We compiled around 3.9 million cells from 125 donors of diverse ancestral backgrounds, including 8 published studies and 2.7 million unpublished data points, to create a comprehensive human retina cell atlas (HRCA) with more than 130 cell types identified. We annotated each cluster, identified marker genes and characterized cis-regulatory elements and gene regulatory networks. Our analysis uncovered differences in transcriptome, chromatin and gene regulatory networks across cell types. We modeled changes in gene expression and chromatin accessibility across age, ancestry and tissue region. This integrated atlas enhanced the fine-mapping of genome-wide association study and expression quantitative trait loci variants. Accessible through interactive browsers, this multimodal multidonor and multilab HRCA can facilitate a better understanding of retinal function and pathology.
{"title":"Single-cell atlas of the transcriptome and chromatin accessibility in the human retina.","authors":"Jin Li,Jun Wang,Ignacio L Ibarra,Xuesen Cheng,Malte D Luecken,Jiaxiong Lu,Aboozar Monavarfeshani,Wenjun Yan,Yiqiao Zheng,Zhen Zuo,Samantha Lynn Zayas Colborn,Berenice Sarahi Cortez,Leah A Owen,Brittney Wick,Xuan Bao,Maximilian Haeussler,Nicholas M Tran,Karthik Shekhar,Joshua R Sanes,J Timothy Stout,Shiming Chen,Yumei Li,Margaret M DeAngelis,Fabian J Theis,Rui Chen","doi":"10.1038/s41588-025-02454-1","DOIUrl":"https://doi.org/10.1038/s41588-025-02454-1","url":null,"abstract":"Single-cell sequencing has revolutionized the scale and resolution of molecular profiling of tissues and organs. Here we present an integrated dual-modal reference atlas of the most accessible portion of the mammalian central nervous system, the retina. We compiled around 3.9 million cells from 125 donors of diverse ancestral backgrounds, including 8 published studies and 2.7 million unpublished data points, to create a comprehensive human retina cell atlas (HRCA) with more than 130 cell types identified. We annotated each cluster, identified marker genes and characterized cis-regulatory elements and gene regulatory networks. Our analysis uncovered differences in transcriptome, chromatin and gene regulatory networks across cell types. We modeled changes in gene expression and chromatin accessibility across age, ancestry and tissue region. This integrated atlas enhanced the fine-mapping of genome-wide association study and expression quantitative trait loci variants. Accessible through interactive browsers, this multimodal multidonor and multilab HRCA can facilitate a better understanding of retinal function and pathology.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"56 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033862","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}
The host genetics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have previously been studied based on cases from the earlier waves of the pandemic in 2020 and 2021, identifying 51 genomic loci associated with infection and/or severity. SARS-CoV-2 has shown rapid sequence evolution, increasing transmissibility, particularly for Omicron variants, which raises the question of whether this affected the host genetic factors. We performed a genome-wide association study of SARS-CoV-2 infection with Omicron variants, including more than 150,000 cases from four cohorts. We identified 13 genome-wide significant loci, of which only five were previously described as associated with SARS-CoV-2 infection. The strongest signal was a single nucleotide polymorphism in an intron of ST6GAL1, a gene affecting immune development and function, connected to three other associated loci (harboring MUC1, MUC5AC and MUC16) through O-glycan biosynthesis. Our study provides robust evidence for individual genetic variation related to glycosylation, translating into susceptibility to SARS-CoV-2 infections with Omicron variants.
{"title":"Central role of glycosylation processes in human genetic susceptibility to SARS-CoV-2 infections with Omicron variants.","authors":"Frank Geller,Xiaoping Wu,Vilma Lammi,Erik Abner,Jesse Tyler Valliere,Katerina Nastou,Angus Burns,Morten Rasmussen,Niklas Worm Andersson,Liam Quinn, ,Bitten Aagaard,Karina Banasik,Sofie Bliddal,Lasse Boding,Søren Brunak,Nanna Brøns,Jonas Bybjerg-Grauholm,Lea Arregui Nordahl Christoffersen,Maria Didriksen,Khoa Manh Dinh,Christian Erikstrup,Ulla Feldt-Rasmussen,Kirsten Grønbæk,Kathrine Agergård Kaspersen,Christina Mikkelsen,Claus Henrik Nielsen,Henriette Svarre Nielsen,Susanne Dam Nielsen,Janna Nissen,Celia Burgos Sequeros,Niels Tommerup,Henrik Ullum, , ,Lampros Spiliopoulos,Peter Bager,Anders Hviid,Erik Sørensen,Ole Birger Pedersen,Jacqueline M Lane,Ria Lassaunière,Hanna M Ollila,Sisse Rye Ostrowski,Bjarke Feenstra","doi":"10.1038/s41588-025-02484-9","DOIUrl":"https://doi.org/10.1038/s41588-025-02484-9","url":null,"abstract":"The host genetics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have previously been studied based on cases from the earlier waves of the pandemic in 2020 and 2021, identifying 51 genomic loci associated with infection and/or severity. SARS-CoV-2 has shown rapid sequence evolution, increasing transmissibility, particularly for Omicron variants, which raises the question of whether this affected the host genetic factors. We performed a genome-wide association study of SARS-CoV-2 infection with Omicron variants, including more than 150,000 cases from four cohorts. We identified 13 genome-wide significant loci, of which only five were previously described as associated with SARS-CoV-2 infection. The strongest signal was a single nucleotide polymorphism in an intron of ST6GAL1, a gene affecting immune development and function, connected to three other associated loci (harboring MUC1, MUC5AC and MUC16) through O-glycan biosynthesis. Our study provides robust evidence for individual genetic variation related to glycosylation, translating into susceptibility to SARS-CoV-2 infections with Omicron variants.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"35 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021635","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 : 2026-01-22DOI: 10.1038/s41588-025-02468-9
Haochen Zhang, Palash Sashittal, Elias-Ramzey Karnoub, Akhil Jakatdar, Shigeaki Umeda, Jungeui Hong, Anne Marie Noronha, Agustin Cardenas, 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.
{"title":"Genomic evolution of pancreatic cancer at single-cell resolution.","authors":"Haochen Zhang, Palash Sashittal, Elias-Ramzey Karnoub, Akhil Jakatdar, Shigeaki Umeda, Jungeui Hong, Anne Marie Noronha, Agustin Cardenas, 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","doi":"10.1038/s41588-025-02468-9","DOIUrl":"10.1038/s41588-025-02468-9","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":" ","pages":""},"PeriodicalIF":29.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","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.
{"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":"https://doi.org/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.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"53 1","pages":""},"PeriodicalIF":30.8,"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.
{"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":"https://doi.org/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.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"30 1","pages":""},"PeriodicalIF":30.8,"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.
{"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":"https://doi.org/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.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"6 1","pages":""},"PeriodicalIF":30.8,"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 : 2026-01-19DOI: 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":"https://doi.org/10.1038/s41588-025-02469-8","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":" ","pages":""},"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.
{"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":"https://doi.org/10.1038/s41588-025-02465-y","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":" ","pages":""},"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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