Pub Date : 2024-09-26DOI: 10.1038/s41588-024-01909-1
Laurie Rumker, Saori Sakaue, Yakir Reshef, Joyce B. Kang, Seyhan Yazar, Jose Alquicira-Hernandez, Cristian Valencia, Kaitlyn A. Lagattuta, Annelise Mah-Som, Aparna Nathan, Joseph E. Powell, Po-Ru Loh, Soumya Raychaudhuri
Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype–Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of single-cell RNA sequencing peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (P = 1.96 × 10−11) associates with increased abundance of natural killer cells expressing tumor necrosis factor response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-tumor necrosis factor treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk. GeNA identifies cell-state abundance quantitative trait loci (csaQTLs) in single-cell RNA sequencing data. Applied to OneK1K, GeNA identifies natural killer cell and myeloid csaQTLs and implicates interferon-α-related cell states using a polygenic risk score for systemic lupus erythematosus.
{"title":"Identifying genetic variants that influence the abundance of cell states in single-cell data","authors":"Laurie Rumker, Saori Sakaue, Yakir Reshef, Joyce B. Kang, Seyhan Yazar, Jose Alquicira-Hernandez, Cristian Valencia, Kaitlyn A. Lagattuta, Annelise Mah-Som, Aparna Nathan, Joseph E. Powell, Po-Ru Loh, Soumya Raychaudhuri","doi":"10.1038/s41588-024-01909-1","DOIUrl":"10.1038/s41588-024-01909-1","url":null,"abstract":"Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype–Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of single-cell RNA sequencing peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (P = 1.96 × 10−11) associates with increased abundance of natural killer cells expressing tumor necrosis factor response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-tumor necrosis factor treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk. GeNA identifies cell-state abundance quantitative trait loci (csaQTLs) in single-cell RNA sequencing data. Applied to OneK1K, GeNA identifies natural killer cell and myeloid csaQTLs and implicates interferon-α-related cell states using a polygenic risk score for systemic lupus erythematosus.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":null,"pages":null},"PeriodicalIF":31.7,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142321138","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 : 2024-09-26DOI: 10.1038/s41588-024-01937-x
Samuel A. Lambert, Benjamin Wingfield, Joel T. Gibson, Laurent Gil, Santhi Ramachandran, Florent Yvon, Shirin Saverimuttu, Emily Tinsley, Elizabeth Lewis, Scott C. Ritchie, Jingqin Wu, Rodrigo Cánovas, Aoife McMahon, Laura W. Harris, Helen Parkinson, Michael Inouye
Polygenic scores (PGSs) have transformed human genetic research and have numerous potential clinical applications. Here we present a series of recent enhancements to the PGS Catalog and highlight the PGS Catalog Calculator, an open-source, scalable and portable pipeline for reproducibly calculating PGSs that democratizes equitable PGS applications.
{"title":"Enhancing the Polygenic Score Catalog with tools for score calculation and ancestry normalization","authors":"Samuel A. Lambert, Benjamin Wingfield, Joel T. Gibson, Laurent Gil, Santhi Ramachandran, Florent Yvon, Shirin Saverimuttu, Emily Tinsley, Elizabeth Lewis, Scott C. Ritchie, Jingqin Wu, Rodrigo Cánovas, Aoife McMahon, Laura W. Harris, Helen Parkinson, Michael Inouye","doi":"10.1038/s41588-024-01937-x","DOIUrl":"10.1038/s41588-024-01937-x","url":null,"abstract":"Polygenic scores (PGSs) have transformed human genetic research and have numerous potential clinical applications. Here we present a series of recent enhancements to the PGS Catalog and highlight the PGS Catalog Calculator, an open-source, scalable and portable pipeline for reproducibly calculating PGSs that democratizes equitable PGS applications.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":null,"pages":null},"PeriodicalIF":31.7,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142321137","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 : 2024-09-26DOI: 10.1038/s41588-024-01915-3
This study presents a spatial transcriptomic analysis of matched primary tumors, liver metastases and lymph node metastases from patients with pancreatic ductal adenocarcinoma. Using a tumor ecosystem approach, we uncovered notable tumor microenvironmental heterogeneity and marked differences between primary and metastatic samples, providing key insights into metastatic pancreatic cancer.
{"title":"Spatial mapping of primary and metastatic pancreatic tumor ecosystems","authors":"","doi":"10.1038/s41588-024-01915-3","DOIUrl":"https://doi.org/10.1038/s41588-024-01915-3","url":null,"abstract":"This study presents a spatial transcriptomic analysis of matched primary tumors, liver metastases and lymph node metastases from patients with pancreatic ductal adenocarcinoma. Using a tumor ecosystem approach, we uncovered notable tumor microenvironmental heterogeneity and marked differences between primary and metastatic samples, providing key insights into metastatic pancreatic cancer.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":null,"pages":null},"PeriodicalIF":30.8,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142321134","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 : 2024-09-26DOI: 10.1038/s41588-024-01922-4
Nadia Nasreddin, Owen J. Sansom
Somatic mutations accrue with age as patches of mutant clones arise in otherwise histologically normal tissue. The clones’ persistence, expansion and roles in physiology and tumorigenesis are unclear. New work on the behavior of Pik3caH1047R mutant esophageal clones shows that host-dependent metabolic features underpin their expansion.
{"title":"Host physiology shapes the mutational landscape of normal and carcinogenic tissue","authors":"Nadia Nasreddin, Owen J. Sansom","doi":"10.1038/s41588-024-01922-4","DOIUrl":"10.1038/s41588-024-01922-4","url":null,"abstract":"Somatic mutations accrue with age as patches of mutant clones arise in otherwise histologically normal tissue. The clones’ persistence, expansion and roles in physiology and tumorigenesis are unclear. New work on the behavior of Pik3caH1047R mutant esophageal clones shows that host-dependent metabolic features underpin their expansion.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":null,"pages":null},"PeriodicalIF":31.7,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142321136","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 : 2024-09-25DOI: 10.1038/s41588-024-01930-4
Sheila M. Gaynor, Tyler Joseph, Xiaodong Bai, Yuxin Zou, Boris Boutkov, Evan K. Maxwell, Olivier Delaneau, Robin J. Hofmeister, Olga Krasheninina, Suganthi Balasubramanian, Anthony Marcketta, Joshua Backman, Jeffrey G. Reid, John D. Overton, Luca A. Lotta, Jonathan Marchini, William J. Salerno, Aris Baras, Goncalo R. Abecasis, Timothy A. Thornton
Whole-genome sequencing (WGS), whole-exome sequencing (WES) and array genotyping with imputation (IMP) are common strategies for assessing genetic variation and its association with medically relevant phenotypes. To date, there has been no systematic empirical assessment of the yield of these approaches when applied to hundreds of thousands of samples to enable the discovery of complex trait genetic signals. Using data for 100 complex traits from 149,195 individuals in the UK Biobank, we systematically compare the relative yield of these strategies in genetic association studies. We find that WGS and WES combined with arrays and imputation (WES + IMP) have the largest association yield. Although WGS results in an approximately fivefold increase in the total number of assayed variants over WES + IMP, the number of detected signals differed by only 1% for both single-variant and gene-based association analyses. Given that WES + IMP typically results in savings of lab and computational time and resources expended per sample, we evaluate the potential benefits of applying WES + IMP to larger samples. When we extend our WES + IMP analyses to 468,169 UK Biobank individuals, we observe an approximately fourfold increase in association signals with the threefold increase in sample size. We conclude that prioritizing WES + IMP and large sample sizes rather than contemporary short-read WGS alternatives will maximize the number of discoveries in genetic association studies.
{"title":"Yield of genetic association signals from genomes, exomes and imputation in the UK Biobank","authors":"Sheila M. Gaynor, Tyler Joseph, Xiaodong Bai, Yuxin Zou, Boris Boutkov, Evan K. Maxwell, Olivier Delaneau, Robin J. Hofmeister, Olga Krasheninina, Suganthi Balasubramanian, Anthony Marcketta, Joshua Backman, Jeffrey G. Reid, John D. Overton, Luca A. Lotta, Jonathan Marchini, William J. Salerno, Aris Baras, Goncalo R. Abecasis, Timothy A. Thornton","doi":"10.1038/s41588-024-01930-4","DOIUrl":"https://doi.org/10.1038/s41588-024-01930-4","url":null,"abstract":"<p>Whole-genome sequencing (WGS), whole-exome sequencing (WES) and array genotyping with imputation (IMP) are common strategies for assessing genetic variation and its association with medically relevant phenotypes. To date, there has been no systematic empirical assessment of the yield of these approaches when applied to hundreds of thousands of samples to enable the discovery of complex trait genetic signals. Using data for 100 complex traits from 149,195 individuals in the UK Biobank, we systematically compare the relative yield of these strategies in genetic association studies. We find that WGS and WES combined with arrays and imputation (WES + IMP) have the largest association yield. Although WGS results in an approximately fivefold increase in the total number of assayed variants over WES + IMP, the number of detected signals differed by only 1% for both single-variant and gene-based association analyses. Given that WES + IMP typically results in savings of lab and computational time and resources expended per sample, we evaluate the potential benefits of applying WES + IMP to larger samples. When we extend our WES + IMP analyses to 468,169 UK Biobank individuals, we observe an approximately fourfold increase in association signals with the threefold increase in sample size. We conclude that prioritizing WES + IMP and large sample sizes rather than contemporary short-read WGS alternatives will maximize the number of discoveries in genetic association studies.</p>","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":null,"pages":null},"PeriodicalIF":30.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317021","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 : 2024-09-25DOI: 10.1038/s41588-024-01919-z
Brian Clarke, Eva Holtkamp, Hakime Öztürk, Marcel Mück, Magnus Wahlberg, Kayla Meyer, Felix Munzlinger, Felix Brechtmann, Florian R. Hölzlwimmer, Jonas Lindner, Zhifen Chen, Julien Gagneur, Oliver Stegle
Rare genetic variants can have strong effects on phenotypes, yet accounting for rare variants in genetic analyses is statistically challenging due to the limited number of allele carriers and the burden of multiple testing. While rich variant annotations promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here we propose deep rare variant association testing (DeepRVAT), a model based on set neural networks that learns a trait-agnostic gene impairment score from rare variant annotations and phenotypes, enabling both gene discovery and trait prediction. On 34 quantitative and 63 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT yields substantial gains in gene discoveries and improved detection of individuals at high genetic risk. Finally, we demonstrate how DeepRVAT enables calibrated and computationally efficient rare variant tests at biobank scale, aiding the discovery of genetic risk factors for human disease traits. Deep rare variant association testing (DeepRVAT) is a deep set neural network model that flexibly integrates rare variant annotations into a trait-agnostic gene impairment score. These scores improve association testing and polygenic risk prediction.
{"title":"Integration of variant annotations using deep set networks boosts rare variant association testing","authors":"Brian Clarke, Eva Holtkamp, Hakime Öztürk, Marcel Mück, Magnus Wahlberg, Kayla Meyer, Felix Munzlinger, Felix Brechtmann, Florian R. Hölzlwimmer, Jonas Lindner, Zhifen Chen, Julien Gagneur, Oliver Stegle","doi":"10.1038/s41588-024-01919-z","DOIUrl":"10.1038/s41588-024-01919-z","url":null,"abstract":"Rare genetic variants can have strong effects on phenotypes, yet accounting for rare variants in genetic analyses is statistically challenging due to the limited number of allele carriers and the burden of multiple testing. While rich variant annotations promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here we propose deep rare variant association testing (DeepRVAT), a model based on set neural networks that learns a trait-agnostic gene impairment score from rare variant annotations and phenotypes, enabling both gene discovery and trait prediction. On 34 quantitative and 63 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT yields substantial gains in gene discoveries and improved detection of individuals at high genetic risk. Finally, we demonstrate how DeepRVAT enables calibrated and computationally efficient rare variant tests at biobank scale, aiding the discovery of genetic risk factors for human disease traits. Deep rare variant association testing (DeepRVAT) is a deep set neural network model that flexibly integrates rare variant annotations into a trait-agnostic gene impairment score. These scores improve association testing and polygenic risk prediction.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":null,"pages":null},"PeriodicalIF":31.7,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-024-01919-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317020","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 : 2024-09-24DOI: 10.1038/s41588-024-01920-6
Joshua S. Schiffman, Andrew R. D’Avino, Tamara Prieto, Yakun Pang, Yilin Fan, Srinivas Rajagopalan, Catherine Potenski, Toshiro Hara, Mario L. Suvà, Charles Gawad, Dan A. Landau
Single-cell sequencing has characterized cell state heterogeneity across diverse healthy and malignant tissues. However, the plasticity or heritability of these cell states remains largely unknown. To address this, we introduce PATH (phylogenetic analysis of trait heritability), a framework to quantify cell state heritability versus plasticity and infer cell state transition and proliferation dynamics from single-cell lineage tracing data. Applying PATH to a mouse model of pancreatic cancer, we observed heritability at the ends of the epithelial-to-mesenchymal transition spectrum, with higher plasticity at more intermediate states. In primary glioblastoma, we identified bidirectional transitions between stem- and mesenchymal-like cells, which use the astrocyte-like state as an intermediary. Finally, we reconstructed a phylogeny from single-cell whole-genome sequencing in B cell acute lymphoblastic leukemia and delineated the heritability of B cell differentiation states linked with genetic drivers. Altogether, PATH replaces qualitative conceptions of plasticity with quantitative measures, offering a framework to study somatic evolution. Phylogenetic analysis of trait heritability (PATH) applies phylogenetic correlations to single-cell lineage tracing data, quantifying cell state plasticity and transition probabilities. PATH offers insights into cell state heritability and transition dynamics in cancers.
单细胞测序技术描述了各种健康和恶性组织的细胞状态异质性。然而，这些细胞状态的可塑性或遗传性在很大程度上仍然未知。为了解决这个问题，我们引入了 PATH（性状遗传性系统发育分析），这是一个量化细胞状态遗传性与可塑性的框架，可以从单细胞系追踪数据推断细胞状态的转变和增殖动态。我们将 PATH 应用于小鼠胰腺癌模型，观察到上皮细胞向间质细胞转变谱两端的遗传性，而中间状态的可塑性更高。在原发性胶质母细胞瘤中，我们发现了干细胞和间充质样细胞之间的双向转变，这种转变以星形胶质细胞样状态为中介。最后，我们从 B 细胞急性淋巴细胞白血病的单细胞全基因组测序中重建了系统发育，并描述了与遗传驱动因素相关的 B 细胞分化状态的遗传性。总之，PATH 以定量测量取代了可塑性的定性概念，为研究体细胞进化提供了一个框架。
{"title":"Defining heritability, plasticity, and transition dynamics of cellular phenotypes in somatic evolution","authors":"Joshua S. Schiffman, Andrew R. D’Avino, Tamara Prieto, Yakun Pang, Yilin Fan, Srinivas Rajagopalan, Catherine Potenski, Toshiro Hara, Mario L. Suvà, Charles Gawad, Dan A. Landau","doi":"10.1038/s41588-024-01920-6","DOIUrl":"10.1038/s41588-024-01920-6","url":null,"abstract":"Single-cell sequencing has characterized cell state heterogeneity across diverse healthy and malignant tissues. However, the plasticity or heritability of these cell states remains largely unknown. To address this, we introduce PATH (phylogenetic analysis of trait heritability), a framework to quantify cell state heritability versus plasticity and infer cell state transition and proliferation dynamics from single-cell lineage tracing data. Applying PATH to a mouse model of pancreatic cancer, we observed heritability at the ends of the epithelial-to-mesenchymal transition spectrum, with higher plasticity at more intermediate states. In primary glioblastoma, we identified bidirectional transitions between stem- and mesenchymal-like cells, which use the astrocyte-like state as an intermediary. Finally, we reconstructed a phylogeny from single-cell whole-genome sequencing in B cell acute lymphoblastic leukemia and delineated the heritability of B cell differentiation states linked with genetic drivers. Altogether, PATH replaces qualitative conceptions of plasticity with quantitative measures, offering a framework to study somatic evolution. Phylogenetic analysis of trait heritability (PATH) applies phylogenetic correlations to single-cell lineage tracing data, quantifying cell state plasticity and transition probabilities. PATH offers insights into cell state heritability and transition dynamics in cancers.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":null,"pages":null},"PeriodicalIF":31.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313756","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}
Studying the genetic regulation of protein expression (through protein quantitative trait loci (pQTLs)) offers a deeper understanding of regulatory variants uncharacterized by mRNA expression regulation (expression QTLs (eQTLs)) studies. Here we report cis-eQTL and cis-pQTL statistical fine-mapping from 1,405 genotyped samples with blood mRNA and 2,932 plasma samples of protein expression, as part of the Japan COVID-19 Task Force (JCTF). Fine-mapped eQTLs (n = 3,464) were enriched for 932 variants validated with a massively parallel reporter assay. Fine-mapped pQTLs (n = 582) were enriched for missense variations on structured and extracellular domains, although the possibility of epitope-binding artifacts remains. Trans-eQTL and trans-pQTL analysis highlighted associations of class I HLA allele variation with KIR genes. We contrast the multi-tissue origin of plasma protein with blood mRNA, contributing to the limited colocalization level, distinct regulatory mechanisms and trait relevance of eQTLs and pQTLs. We report a negative correlation between ABO mRNA and protein expression because of linkage disequilibrium between distinct nearby eQTLs and pQTLs. Statistical fine-mapping of mRNA and protein quantitative trait loci in blood samples from the Japan COVID-19 Task Force sheds light on regulatory mechanisms and disease associations.
{"title":"Statistically and functionally fine-mapped blood eQTLs and pQTLs from 1,405 humans reveal distinct regulation patterns and disease relevance","authors":"Qingbo S. Wang, Takanori Hasegawa, Ho Namkoong, Ryunosuke Saiki, Ryuya Edahiro, Kyuto Sonehara, Hiromu Tanaka, Shuhei Azekawa, Shotaro Chubachi, Yugo Takahashi, Saori Sakaue, Shinichi Namba, Kenichi Yamamoto, Yuichi Shiraishi, Kenichi Chiba, Hiroko Tanaka, Hideki Makishima, Yasuhito Nannya, Zicong Zhang, Rika Tsujikawa, Ryuji Koike, Tomomi Takano, Makoto Ishii, Akinori Kimura, Fumitaka Inoue, Takanori Kanai, Koichi Fukunaga, Seishi Ogawa, Seiya Imoto, Satoru Miyano, Yukinori Okada, Japan COVID-19 Task Force","doi":"10.1038/s41588-024-01896-3","DOIUrl":"10.1038/s41588-024-01896-3","url":null,"abstract":"Studying the genetic regulation of protein expression (through protein quantitative trait loci (pQTLs)) offers a deeper understanding of regulatory variants uncharacterized by mRNA expression regulation (expression QTLs (eQTLs)) studies. Here we report cis-eQTL and cis-pQTL statistical fine-mapping from 1,405 genotyped samples with blood mRNA and 2,932 plasma samples of protein expression, as part of the Japan COVID-19 Task Force (JCTF). Fine-mapped eQTLs (n = 3,464) were enriched for 932 variants validated with a massively parallel reporter assay. Fine-mapped pQTLs (n = 582) were enriched for missense variations on structured and extracellular domains, although the possibility of epitope-binding artifacts remains. Trans-eQTL and trans-pQTL analysis highlighted associations of class I HLA allele variation with KIR genes. We contrast the multi-tissue origin of plasma protein with blood mRNA, contributing to the limited colocalization level, distinct regulatory mechanisms and trait relevance of eQTLs and pQTLs. We report a negative correlation between ABO mRNA and protein expression because of linkage disequilibrium between distinct nearby eQTLs and pQTLs. Statistical fine-mapping of mRNA and protein quantitative trait loci in blood samples from the Japan COVID-19 Task Force sheds light on regulatory mechanisms and disease associations.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":null,"pages":null},"PeriodicalIF":31.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-024-01896-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313755","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 : 2024-09-23DOI: 10.1038/s41588-024-01910-8
V. Kartik Chundru, Zhancheng Zhang, Klaudia Walter, Sarah J. Lindsay, Petr Danecek, Ruth Y. Eberhardt, Eugene J. Gardner, Daniel S. Malawsky, Emilie M. Wigdor, Rebecca Torene, Kyle Retterer, Caroline F. Wright, Hildur Ólafsdóttir, Maria J. Guillen Sacoto, Akif Ayaz, Ismail Hakki Akbeyaz, Dilşad Türkdoğan, Aaisha Ibrahim Al Balushi, Aida Bertoli-Avella, Peter Bauer, Emmanuelle Szenker-Ravi, Bruno Reversade, Kirsty McWalter, Eamonn Sheridan, Helen V. Firth, Matthew E. Hurles, Kaitlin E. Samocha, Vincent D. Ustach, Hilary C. Martin
Autosomal recessive coding variants are well-known causes of rare disorders. We quantified the contribution of these variants to developmental disorders in a large, ancestrally diverse cohort comprising 29,745 trios, of whom 20.4% had genetically inferred non-European ancestries. The estimated fraction of patients attributable to exome-wide autosomal recessive coding variants ranged from ~2–19% across genetically inferred ancestry groups and was significantly correlated with average autozygosity. Established autosomal recessive developmental disorder-associated (ARDD) genes explained 84.0% of the total autosomal recessive coding burden, and 34.4% of the burden in these established genes was explained by variants not already reported as pathogenic in ClinVar. Statistical analyses identified two novel ARDD genes: KBTBD2 and ZDHHC16. This study expands our understanding of the genetic architecture of developmental disorders across diverse genetically inferred ancestry groups and suggests that improving strategies for interpreting missense variants in known ARDD genes may help diagnose more patients than discovering the remaining genes. Analysis of autosomal recessive coding variants in 29,745 trios from the DDD study and GeneDx provides insights into the genetic architecture of developmental disorders across ancestrally diverse populations.
{"title":"Federated analysis of autosomal recessive coding variants in 29,745 developmental disorder patients from diverse populations","authors":"V. Kartik Chundru, Zhancheng Zhang, Klaudia Walter, Sarah J. Lindsay, Petr Danecek, Ruth Y. Eberhardt, Eugene J. Gardner, Daniel S. Malawsky, Emilie M. Wigdor, Rebecca Torene, Kyle Retterer, Caroline F. Wright, Hildur Ólafsdóttir, Maria J. Guillen Sacoto, Akif Ayaz, Ismail Hakki Akbeyaz, Dilşad Türkdoğan, Aaisha Ibrahim Al Balushi, Aida Bertoli-Avella, Peter Bauer, Emmanuelle Szenker-Ravi, Bruno Reversade, Kirsty McWalter, Eamonn Sheridan, Helen V. Firth, Matthew E. Hurles, Kaitlin E. Samocha, Vincent D. Ustach, Hilary C. Martin","doi":"10.1038/s41588-024-01910-8","DOIUrl":"10.1038/s41588-024-01910-8","url":null,"abstract":"Autosomal recessive coding variants are well-known causes of rare disorders. We quantified the contribution of these variants to developmental disorders in a large, ancestrally diverse cohort comprising 29,745 trios, of whom 20.4% had genetically inferred non-European ancestries. The estimated fraction of patients attributable to exome-wide autosomal recessive coding variants ranged from ~2–19% across genetically inferred ancestry groups and was significantly correlated with average autozygosity. Established autosomal recessive developmental disorder-associated (ARDD) genes explained 84.0% of the total autosomal recessive coding burden, and 34.4% of the burden in these established genes was explained by variants not already reported as pathogenic in ClinVar. Statistical analyses identified two novel ARDD genes: KBTBD2 and ZDHHC16. This study expands our understanding of the genetic architecture of developmental disorders across diverse genetically inferred ancestry groups and suggests that improving strategies for interpreting missense variants in known ARDD genes may help diagnose more patients than discovering the remaining genes. Analysis of autosomal recessive coding variants in 29,745 trios from the DDD study and GeneDx provides insights into the genetic architecture of developmental disorders across ancestrally diverse populations.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":null,"pages":null},"PeriodicalIF":31.7,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-024-01910-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142276880","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 : 2024-09-23DOI: 10.1038/s41588-024-01875-8
Albert Herms, David Fernandez-Antoran, Maria P. Alcolea, Argyro Kalogeropoulou, Ujjwal Banerjee, Gabriel Piedrafita, Emilie Abby, Jose Antonio Valverde-Lopez, Inês S. Ferreira, Irene Caseda, Maria T. Bejar, Stefan C. Dentro, Sara Vidal-Notari, Swee Hoe Ong, Bartomeu Colom, Kasumi Murai, Charlotte King, Krishnaa Mahbubani, Kourosh Saeb-Parsy, Alan R. Lowe, Moritz Gerstung, Philip H. Jones
Aging epithelia are colonized by somatic mutations, which are subjected to selection influenced by intrinsic and extrinsic factors. The lack of suitable culture systems has slowed the study of this and other long-term biological processes. Here, we describe epithelioids, a facile, cost-effective method of culturing multiple mouse and human epithelia. Esophageal epithelioids self-maintain without passaging for at least 1 year, maintaining a three-dimensional structure with proliferative basal cells that differentiate into suprabasal cells, which eventually shed and retain genomic stability. Live imaging over 5 months showed that epithelioids replicate in vivo cell dynamics. Epithelioids support genetic manipulation and enable the study of mutant cell competition and selection in three-dimensional epithelia, and show how anti-cancer treatments modulate competition between transformed and wild-type cells. Finally, a targeted CRISPR–Cas9 screen shows that epithelioids recapitulate mutant gene selection in aging human esophagus and identifies additional drivers of clonal expansion, resolving the genetic networks underpinning competitive fitness. Epithelioids are genetically stable, self-sustaining three-dimensional cultures. They may be used to investigate various aspects of epithelial biology over several months without need for passaging. In this paper, mouse epithelioids are used to identify drivers of clonal expansion in the esophagus.
{"title":"Self-sustaining long-term 3D epithelioid cultures reveal drivers of clonal expansion in esophageal epithelium","authors":"Albert Herms, David Fernandez-Antoran, Maria P. Alcolea, Argyro Kalogeropoulou, Ujjwal Banerjee, Gabriel Piedrafita, Emilie Abby, Jose Antonio Valverde-Lopez, Inês S. Ferreira, Irene Caseda, Maria T. Bejar, Stefan C. Dentro, Sara Vidal-Notari, Swee Hoe Ong, Bartomeu Colom, Kasumi Murai, Charlotte King, Krishnaa Mahbubani, Kourosh Saeb-Parsy, Alan R. Lowe, Moritz Gerstung, Philip H. Jones","doi":"10.1038/s41588-024-01875-8","DOIUrl":"10.1038/s41588-024-01875-8","url":null,"abstract":"Aging epithelia are colonized by somatic mutations, which are subjected to selection influenced by intrinsic and extrinsic factors. The lack of suitable culture systems has slowed the study of this and other long-term biological processes. Here, we describe epithelioids, a facile, cost-effective method of culturing multiple mouse and human epithelia. Esophageal epithelioids self-maintain without passaging for at least 1 year, maintaining a three-dimensional structure with proliferative basal cells that differentiate into suprabasal cells, which eventually shed and retain genomic stability. Live imaging over 5 months showed that epithelioids replicate in vivo cell dynamics. Epithelioids support genetic manipulation and enable the study of mutant cell competition and selection in three-dimensional epithelia, and show how anti-cancer treatments modulate competition between transformed and wild-type cells. Finally, a targeted CRISPR–Cas9 screen shows that epithelioids recapitulate mutant gene selection in aging human esophagus and identifies additional drivers of clonal expansion, resolving the genetic networks underpinning competitive fitness. Epithelioids are genetically stable, self-sustaining three-dimensional cultures. They may be used to investigate various aspects of epithelial biology over several months without need for passaging. In this paper, mouse epithelioids are used to identify drivers of clonal expansion in the esophagus.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":null,"pages":null},"PeriodicalIF":31.7,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-024-01875-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142276877","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}
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