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}
Pub Date : 2026-01-13DOI: 10.1038/s41588-025-02492-9
Petra Gross
{"title":"Predicting in vivo chromatin states","authors":"Petra Gross","doi":"10.1038/s41588-025-02492-9","DOIUrl":"10.1038/s41588-025-02492-9","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"58 1","pages":"13-13"},"PeriodicalIF":29.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145966486","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 : 2026-01-12DOI: 10.1038/s41588-025-02466-x
Yvonne Walburga Joko Fru
{"title":"Journeys of hope.","authors":"Yvonne Walburga Joko Fru","doi":"10.1038/s41588-025-02466-x","DOIUrl":"https://doi.org/10.1038/s41588-025-02466-x","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":" ","pages":""},"PeriodicalIF":29.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145959647","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}
Pangenomics is an emerging field that uses collections of genomes, rather than a single reference, to reduce bias and capture intra-species diversity. However, existing pangenomic data formats face challenges in scaling to millions of genomes and primarily emphasize variation, often neglecting the underlying mutational events and evolutionary relationships. This work introduces Pangenome Mutation-Annotated Network (PanMAN), a lossless pangenome representation that achieves compression ratios ranging from 3.5–1,391× in file sizes compared to existing variation-preserving formats, with performance generally improving on larger datasets. In addition to compression, PanMAN increases representational capacity by encoding detailed mutational and evolutionary histories inferred across genomes, thereby enabling new biological insights. Using PanMAN, a comprehensive SARS-CoV-2 pangenome was constructed from 8 million publicly available sequences, requiring only 366 MB of disk space. We also present ‘panmanUtils’, a toolkit that supports common analyses and ensures interoperability with existing software. PanMAN is poised to greatly improve the scale, speed, resolution and scope of pangenomic analysis and data sharing.
{"title":"Compressive pangenomics using mutation-annotated networks","authors":"Sumit Walia, Harsh Motwani, Yu-Hsiang Tseng, Kyle Smith, Russell Corbett-Detig, Yatish Turakhia","doi":"10.1038/s41588-025-02478-7","DOIUrl":"https://doi.org/10.1038/s41588-025-02478-7","url":null,"abstract":"Pangenomics is an emerging field that uses collections of genomes, rather than a single reference, to reduce bias and capture intra-species diversity. However, existing pangenomic data formats face challenges in scaling to millions of genomes and primarily emphasize variation, often neglecting the underlying mutational events and evolutionary relationships. This work introduces Pangenome Mutation-Annotated Network (PanMAN), a lossless pangenome representation that achieves compression ratios ranging from 3.5–1,391× in file sizes compared to existing variation-preserving formats, with performance generally improving on larger datasets. In addition to compression, PanMAN increases representational capacity by encoding detailed mutational and evolutionary histories inferred across genomes, thereby enabling new biological insights. Using PanMAN, a comprehensive SARS-CoV-2 pangenome was constructed from 8 million publicly available sequences, requiring only 366 MB of disk space. We also present ‘panmanUtils’, a toolkit that supports common analyses and ensures interoperability with existing software. PanMAN is poised to greatly improve the scale, speed, resolution and scope of pangenomic analysis and data sharing.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"250 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956263","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 : 2026-01-09DOI: 10.1038/s41588-025-02451-4
Mathieu Quinodoz, Kim Rodenburg, Zuzana Cvackova, Karolina Kaminska, Suzanne E. de Bruijn, Ana Belén Iglesias-Romero, Erica G. M. Boonen, Mukhtar Ullah, Nick Zomer, Marc Folcher, Jacques Bijon, Lara K. Holtes, Stephen H. Tsang, Zelia Corradi, K. Bailey Freund, Stefanida Shliaga, Daan M. Panneman, Rebekkah J. Hitti-Malin, Manir Ali, Ala’a AlTalbishi, Sten Andréasson, Georg Ansari, Gavin Arno, Galuh D. N. Astuti, Carmen Ayuso, Radha Ayyagari, Sandro Banfi, Eyal Banin, Tahsin Stefan Barakat, Mirella T. S. Barboni, Miriam Bauwens, Tamar Ben-Yosef, Virginie Bernard, David G. Birch, Pooja Biswas, Fiona Blanco-Kelly, Beatrice Bocquet, Camiel J. F. Boon, Kari Branham, Dominique Bremond-Gignac, Alexis Ceecee Britten-Jones, Kinga M. Bujakowska, Cyril Burin des Roziers, Elizabeth L. Cadena, Giacomo Calzetti, Francesca Cancellieri, Luca Cattaneo, Naomi Chadderton, Peter Charbel Issa, Luísa Coutinho-Santos, Stephen P. Daiger, Elfride De Baere, Marieke De Bruyne, Berta de la Cerda, John N. De Roach, Julie De Zaeytijd, Ronny Derks, Claire-Marie Dhaenens, Lubica Dudakova, Jacque L. Duncan, G. Jane Farrar, Nicolas Feltgen, Beau J. Fenner, Lidia Fernández-Caballero, Juliana M. Ferraz Sallum, Simone Gana, Alejandro Garanto, Jessica C. Gardner, Christian Gilissen, Roser Gonzàlez-Duarte, Kensuke Goto, Sam Griffiths-Jones, Tobias B. Haack, Lonneke Haer-Wigman, Alison J. Hardcastle, Takaaki Hayashi, Elise Héon, Lies H. Hoefsloot, Alexander Hoischen, Josephine P. Holtan, Carel B. Hoyng, Manuel Benjamin B. Ibanez IV, Chris F. Inglehearn, Takeshi Iwata, Brynjar O. Jensson, Kaylie Jones, Vasiliki Kalatzis, Smaragda Kamakari, Marianthi Karali, Ulrich Kellner, Caroline C. W. Klaver, Krisztina Knézy, Robert K. Koenekoop, Susanne Kohl, Taro Kominami, Laura Kühlewein, Tina M. Lamey, Rina Leibu, Bart P. Leroy, Petra Liskova, Irma Lopez, Victor R. de J. López-Rodríguez, Quinten Mahieu, Omar A. Mahroo, Gaël Manes, Luke Mansard, M. Pilar Martín-Gutiérrez, Nelson Martins, Laura Mauring, Martin McKibbin, Terri L. McLaren, Isabelle Meunier, Michel Michaelides, José M. Millán, Kei Mizobuchi, Rajarshi Mukherjee, Zoltán Zsolt Nagy, Kornelia Neveling, Monika Ołdak, Michiel Oorsprong, Yang Pan, Anastasia Papachristou, Antonio Percesepe, Maximilian Pfau, Eric A. Pierce, Emily Place, Raj Ramesar, Francis Ramond, Florence Andrée Rasquin, Gillian I. Rice, Lisa Roberts, María Rodríguez-Hidalgo, Javier Ruiz-Ederra, Ataf H. Sabir, Ai Fujita Sajiki, Ana Isabel Sánchez-Barbero, Asodu Sandeep Sarma, Riccardo Sangermano, Cristina M. Santos, Margherita Scarpato, Hendrik P. N. Scholl, Dror Sharon, Sabrina G. Signorini, Francesca Simonelli, Ana Berta Sousa, Maria Stefaniotou, Kari Stefansson, Katarina Stingl, Akiko Suga, Patrick Sulem, Lori S. Sullivan, Viktória Szabó, Jacek P. Szaflik, Gita Taurina, Alberta A. H. J. Thiadens, Carmel Toomes, Viet H. Tran, Miltiadis K. Tsilimbaris, Pavlina Tsoka, Veronika Vaclavik, Marie Vajter, Sandra Valeina, Enza Maria Valente, Casey Valentine, Rebeca Valero, Sophie Valleix, Joseph van Aerschot, L. Ingeborgh van den Born, Mattias Van Heetvelde, Virginie J. M. Verhoeven, Andrea L. Vincent, Andrew R. Webster, Laura Whelan, Bernd Wissinger, Georgia G. Yioti, Kazutoshi Yoshitake, Juan C. Zenteno, Roberta Zeuli, Theresia Zuleger, Chaim Landau, Allan I. Jacob, Siying Lin, Frans P. M. Cremers, Winston Lee, Jamie M. Ellingford, David Stanek, Susanne Roosing, Carlo Rivolta
Small nuclear RNAs (snRNAs) combine with specific proteins to generate small nuclear ribonucleoproteins (snRNPs), the building blocks of the spliceosome. U4 snRNA forms a duplex with U6 and, together with U5, contributes to the tri-snRNP spliceosomal complex. Variants in RNU4-2, which encodes U4, have recently been implicated in neurodevelopmental disorders. Here we show that heterozygous inherited and de novo variants in RNU4-2 and in four RNU6 paralogs (RNU6-1, RNU6-2, RNU6-8 and RNU6-9), which encode U6, recur in individuals with nonsyndromic retinitis pigmentosa (RP), a genetic disorder causing progressive blindness. These variants cluster within the three-way junction of the U4/U6 duplex, a site that interacts with tri-snRNP splicing factors also known to cause RP (PRPF3, PRPF8, PRPF31), and seem to affect snRNP biogenesis. Based on our cohort, deleterious variants in RNU4-2 and RNU6 paralogs may explain up to ~1.4% of otherwise undiagnosed RP cases. This study highlights the contribution of noncoding RNA genes to Mendelian disease and reveals pleiotropy in RNU4-2, where distinct variants underlie neurodevelopmental disorder and retinal degeneration. De novo and inherited dominant variants in genes encoding U4 and U6 small nuclear RNAs are identified in individuals with retinitis pigmentosa. The variants cluster at nucleotide positions distinct from those implicated in neurodevelopmental disorders.
{"title":"De novo and inherited dominant variants in U4 and U6 snRNA genes cause retinitis pigmentosa","authors":"Mathieu Quinodoz, Kim Rodenburg, Zuzana Cvackova, Karolina Kaminska, Suzanne E. de Bruijn, Ana Belén Iglesias-Romero, Erica G. M. Boonen, Mukhtar Ullah, Nick Zomer, Marc Folcher, Jacques Bijon, Lara K. Holtes, Stephen H. Tsang, Zelia Corradi, K. Bailey Freund, Stefanida Shliaga, Daan M. Panneman, Rebekkah J. Hitti-Malin, Manir Ali, Ala’a AlTalbishi, Sten Andréasson, Georg Ansari, Gavin Arno, Galuh D. N. Astuti, Carmen Ayuso, Radha Ayyagari, Sandro Banfi, Eyal Banin, Tahsin Stefan Barakat, Mirella T. S. Barboni, Miriam Bauwens, Tamar Ben-Yosef, Virginie Bernard, David G. Birch, Pooja Biswas, Fiona Blanco-Kelly, Beatrice Bocquet, Camiel J. F. Boon, Kari Branham, Dominique Bremond-Gignac, Alexis Ceecee Britten-Jones, Kinga M. Bujakowska, Cyril Burin des Roziers, Elizabeth L. Cadena, Giacomo Calzetti, Francesca Cancellieri, Luca Cattaneo, Naomi Chadderton, Peter Charbel Issa, Luísa Coutinho-Santos, Stephen P. Daiger, Elfride De Baere, Marieke De Bruyne, Berta de la Cerda, John N. De Roach, Julie De Zaeytijd, Ronny Derks, Claire-Marie Dhaenens, Lubica Dudakova, Jacque L. Duncan, G. Jane Farrar, Nicolas Feltgen, Beau J. Fenner, Lidia Fernández-Caballero, Juliana M. Ferraz Sallum, Simone Gana, Alejandro Garanto, Jessica C. Gardner, Christian Gilissen, Roser Gonzàlez-Duarte, Kensuke Goto, Sam Griffiths-Jones, Tobias B. Haack, Lonneke Haer-Wigman, Alison J. Hardcastle, Takaaki Hayashi, Elise Héon, Lies H. Hoefsloot, Alexander Hoischen, Josephine P. Holtan, Carel B. Hoyng, Manuel Benjamin B. Ibanez IV, Chris F. Inglehearn, Takeshi Iwata, Brynjar O. Jensson, Kaylie Jones, Vasiliki Kalatzis, Smaragda Kamakari, Marianthi Karali, Ulrich Kellner, Caroline C. W. Klaver, Krisztina Knézy, Robert K. Koenekoop, Susanne Kohl, Taro Kominami, Laura Kühlewein, Tina M. Lamey, Rina Leibu, Bart P. Leroy, Petra Liskova, Irma Lopez, Victor R. de J. López-Rodríguez, Quinten Mahieu, Omar A. Mahroo, Gaël Manes, Luke Mansard, M. Pilar Martín-Gutiérrez, Nelson Martins, Laura Mauring, Martin McKibbin, Terri L. McLaren, Isabelle Meunier, Michel Michaelides, José M. Millán, Kei Mizobuchi, Rajarshi Mukherjee, Zoltán Zsolt Nagy, Kornelia Neveling, Monika Ołdak, Michiel Oorsprong, Yang Pan, Anastasia Papachristou, Antonio Percesepe, Maximilian Pfau, Eric A. Pierce, Emily Place, Raj Ramesar, Francis Ramond, Florence Andrée Rasquin, Gillian I. Rice, Lisa Roberts, María Rodríguez-Hidalgo, Javier Ruiz-Ederra, Ataf H. Sabir, Ai Fujita Sajiki, Ana Isabel Sánchez-Barbero, Asodu Sandeep Sarma, Riccardo Sangermano, Cristina M. Santos, Margherita Scarpato, Hendrik P. N. Scholl, Dror Sharon, Sabrina G. Signorini, Francesca Simonelli, Ana Berta Sousa, Maria Stefaniotou, Kari Stefansson, Katarina Stingl, Akiko Suga, Patrick Sulem, Lori S. Sullivan, Viktória Szabó, Jacek P. Szaflik, Gita Taurina, Alberta A. H. J. Thiadens, Carmel Toomes, Viet H. Tran, Miltiadis K. Tsilimbaris, Pavlina Tsoka, Veronika Vaclavik, Marie Vajter, Sandra Valeina, Enza Maria Valente, Casey Valentine, Rebeca Valero, Sophie Valleix, Joseph van Aerschot, L. Ingeborgh van den Born, Mattias Van Heetvelde, Virginie J. M. Verhoeven, Andrea L. Vincent, Andrew R. Webster, Laura Whelan, Bernd Wissinger, Georgia G. Yioti, Kazutoshi Yoshitake, Juan C. Zenteno, Roberta Zeuli, Theresia Zuleger, Chaim Landau, Allan I. Jacob, Siying Lin, Frans P. M. Cremers, Winston Lee, Jamie M. Ellingford, David Stanek, Susanne Roosing, Carlo Rivolta","doi":"10.1038/s41588-025-02451-4","DOIUrl":"10.1038/s41588-025-02451-4","url":null,"abstract":"Small nuclear RNAs (snRNAs) combine with specific proteins to generate small nuclear ribonucleoproteins (snRNPs), the building blocks of the spliceosome. U4 snRNA forms a duplex with U6 and, together with U5, contributes to the tri-snRNP spliceosomal complex. Variants in RNU4-2, which encodes U4, have recently been implicated in neurodevelopmental disorders. Here we show that heterozygous inherited and de novo variants in RNU4-2 and in four RNU6 paralogs (RNU6-1, RNU6-2, RNU6-8 and RNU6-9), which encode U6, recur in individuals with nonsyndromic retinitis pigmentosa (RP), a genetic disorder causing progressive blindness. These variants cluster within the three-way junction of the U4/U6 duplex, a site that interacts with tri-snRNP splicing factors also known to cause RP (PRPF3, PRPF8, PRPF31), and seem to affect snRNP biogenesis. Based on our cohort, deleterious variants in RNU4-2 and RNU6 paralogs may explain up to ~1.4% of otherwise undiagnosed RP cases. This study highlights the contribution of noncoding RNA genes to Mendelian disease and reveals pleiotropy in RNU4-2, where distinct variants underlie neurodevelopmental disorder and retinal degeneration. De novo and inherited dominant variants in genes encoding U4 and U6 small nuclear RNAs are identified in individuals with retinitis pigmentosa. The variants cluster at nucleotide positions distinct from those implicated in neurodevelopmental disorders.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"58 1","pages":"169-179"},"PeriodicalIF":29.0,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41588-025-02451-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938176","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 : 2026-01-08DOI: 10.1038/s41588-025-02421-w
Opeyemi Soremekun, Young-Chan Park, Mauro Tutino, Ana Luiza Arruda, Allan Kalungi, N. William Rayner, Moffat Nyirenda, Segun Fatumo, Eleftheria Zeggini
Individuals of African ancestry remain largely underrepresented in genetic and proteomic studies. Here we measure the levels of 2,873 proteins in plasma samples from 163 individuals with type 2 diabetes (T2D) or prediabetes and 362 normoglycemic controls from the Ugandan population. We identify 88 differentially expressed proteins between the two groups. We link genome-wide data to protein expression levels and construct a protein quantitative trait locus (pQTL) map for this population. We identify 399 independent associations with 346 (86.7%) cis-pQTLs and 53 (13.3%) trans-pQTLs; 16.7% of the cis-pQTLs and all of the trans-pQTLs have not been previously reported in individuals of African ancestry. Of these, 37 pQTLs have not been previously reported in any population. We find evidence for colocalization between a pQTL and T2D genetic risk. Our findings reveal proteins causally implicated in the pathogenesis of T2D, which may be leveraged for personalized medicine tailored to individuals of African ancestry. This study uses a high-dimensional proteomics panel to explore protein-level genetic associations with type 2 diabetes in a Ugandan cohort.
{"title":"Linking the plasma proteome to genetics in individuals from continental Africa provides insights into type 2 diabetes pathogenesis","authors":"Opeyemi Soremekun, Young-Chan Park, Mauro Tutino, Ana Luiza Arruda, Allan Kalungi, N. William Rayner, Moffat Nyirenda, Segun Fatumo, Eleftheria Zeggini","doi":"10.1038/s41588-025-02421-w","DOIUrl":"10.1038/s41588-025-02421-w","url":null,"abstract":"Individuals of African ancestry remain largely underrepresented in genetic and proteomic studies. Here we measure the levels of 2,873 proteins in plasma samples from 163 individuals with type 2 diabetes (T2D) or prediabetes and 362 normoglycemic controls from the Ugandan population. We identify 88 differentially expressed proteins between the two groups. We link genome-wide data to protein expression levels and construct a protein quantitative trait locus (pQTL) map for this population. We identify 399 independent associations with 346 (86.7%) cis-pQTLs and 53 (13.3%) trans-pQTLs; 16.7% of the cis-pQTLs and all of the trans-pQTLs have not been previously reported in individuals of African ancestry. Of these, 37 pQTLs have not been previously reported in any population. We find evidence for colocalization between a pQTL and T2D genetic risk. Our findings reveal proteins causally implicated in the pathogenesis of T2D, which may be leveraged for personalized medicine tailored to individuals of African ancestry. This study uses a high-dimensional proteomics panel to explore protein-level genetic associations with type 2 diabetes in a Ugandan cohort.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"58 1","pages":"39-46"},"PeriodicalIF":29.0,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41588-025-02421-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145919986","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 : 2026-01-07DOI: 10.1038/s41588-025-02449-y
Jinghui Li, Yang I. Li, Xuanyao Liu
Most genetic variants influence complex traits by affecting gene regulation. Yet, despite comprehensive catalogs of molecular quantitative trait loci (QTLs), linking trait-associated variants to biological functions remains difficult. By re-analyzing large maps of protein QTLs (pQTLs), we found that genes with trans-pQTLs but no cis-pQTLs are under strong selective constraints and are particularly informative in interpreting genome-wide association study (GWAS) loci. We observed that trans-pQTLs and their target proteins are frequently involved in protein–protein interactions (PPIs). Notably, trans-pQTLs are enriched in missense variants and at PPI interfaces, suggesting a key role of PPIs in the trans-regulation of proteome. Using PPI annotations to guide trans-pQTL mapping, we identified 17,662 trans-pQTLs affecting 961 PPI clusters after accounting for blood cell composition effects. These trans-pQTLs colocalized with 36% GWAS loci per trait on average for 27 complex traits, helping in many cases to link GWAS loci to cellular function. Finally, we identified trans-pQTL effects at multiple autoimmune GWAS loci that converge to the same PPIs, pinpointing protein complexes and signaling pathways that show promising therapeutic target potential. Protein quantitative trait loci show enrichment of trans effects among proteins in the same interaction networks and among missense variants at interaction interfaces, highlighting pathways impacted by trait-associated variants.
{"title":"Protein–protein interactions shape trans-regulatory impact of genetic variation on protein expression and complex traits","authors":"Jinghui Li, Yang I. Li, Xuanyao Liu","doi":"10.1038/s41588-025-02449-y","DOIUrl":"10.1038/s41588-025-02449-y","url":null,"abstract":"Most genetic variants influence complex traits by affecting gene regulation. Yet, despite comprehensive catalogs of molecular quantitative trait loci (QTLs), linking trait-associated variants to biological functions remains difficult. By re-analyzing large maps of protein QTLs (pQTLs), we found that genes with trans-pQTLs but no cis-pQTLs are under strong selective constraints and are particularly informative in interpreting genome-wide association study (GWAS) loci. We observed that trans-pQTLs and their target proteins are frequently involved in protein–protein interactions (PPIs). Notably, trans-pQTLs are enriched in missense variants and at PPI interfaces, suggesting a key role of PPIs in the trans-regulation of proteome. Using PPI annotations to guide trans-pQTL mapping, we identified 17,662 trans-pQTLs affecting 961 PPI clusters after accounting for blood cell composition effects. These trans-pQTLs colocalized with 36% GWAS loci per trait on average for 27 complex traits, helping in many cases to link GWAS loci to cellular function. Finally, we identified trans-pQTL effects at multiple autoimmune GWAS loci that converge to the same PPIs, pinpointing protein complexes and signaling pathways that show promising therapeutic target potential. Protein quantitative trait loci show enrichment of trans effects among proteins in the same interaction networks and among missense variants at interaction interfaces, highlighting pathways impacted by trait-associated variants.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"58 1","pages":"77-87"},"PeriodicalIF":29.0,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41588-025-02449-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145907949","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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