Nature genetics最新文献_第10页

Aneuploidy as a driver of human cancer 非整倍体是人类癌症的驱动因素

IF 31.7 1区生物学

Nature genetics Pub Date : 2024-10-02 DOI: 10.1038/s41588-024-01916-2

Eran Sdeor, Hajime Okada, Ron Saad, Tal Ben-Yishay, Uri Ben-David

{"title":"Aneuploidy as a driver of human cancer","authors":"Eran Sdeor, Hajime Okada, Ron Saad, Tal Ben-Yishay, Uri Ben-David","doi":"10.1038/s41588-024-01916-2","DOIUrl":"10.1038/s41588-024-01916-2","url":null,"abstract":"Aneuploidy, an abnormal chromosome composition, is a major contributor to cancer development and progression and an important determinant of cancer therapeutic responses and clinical outcomes. Despite being recognized as a hallmark of human cancer, the exact role of aneuploidy as a ‘driver’ of cancer is still largely unknown. Identifying the specific genetic elements that underlie the recurrence of common aneuploidies remains a major challenge of cancer genetics. In this Review, we discuss recurrent aneuploidies and their function as drivers of tumor development. We then delve into the context-dependent identification and functional characterization of the driver genes underlying driver aneuploidies and examine emerging strategies to uncover these driver genes using cancer genomics data and cancer models. Lastly, we explore opportunities for targeting driver aneuploidies in cancer by leveraging the functional consequences of these common genetic alterations. This Review discusses recurrent aneuploidies driving human cancer, methods to identify them and strategies to uncover underlying driver genes. It highlights genomic and experimental approaches to study and ultimately target driver aneuploidies.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"56 10","pages":"2014-2026"},"PeriodicalIF":31.7,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Genetic and non-genetic HLA disruption is widespread in lung and breast tumors 遗传性和非遗传性 HLA 干扰广泛存在于肺癌和乳腺癌中

IF 31.7 1区生物学

Nature genetics Pub Date : 2024-10-02 DOI: 10.1038/s41588-024-01886-5

引用次数: 0

Multivariate genomic analysis of 5 million people elucidates the genetic architecture of shared components of the metabolic syndrome 对 500 万人进行的多变量基因组分析阐明了代谢综合征共有成分的基因结构

IF 31.7 1区生物学

Nature genetics Pub Date : 2024-09-30 DOI: 10.1038/s41588-024-01933-1

Sanghyeon Park, Soyeon Kim, Beomsu Kim, Dan Say Kim, Jaeyoung Kim, Yeeun Ahn, Hyejin Kim, Minku Song, Injeong Shim, Sang-Hyuk Jung, Chamlee Cho, Soohyun Lim, Sanghoon Hong, Hyeonbin Jo, Akl C. Fahed, Pradeep Natarajan, Patrick T. Ellinor, Ali Torkamani, Woong-Yang Park, Tae Yang Yu, Woojae Myung, Hong-Hee Won

{"title":"Multivariate genomic analysis of 5 million people elucidates the genetic architecture of shared components of the metabolic syndrome","authors":"Sanghyeon Park, Soyeon Kim, Beomsu Kim, Dan Say Kim, Jaeyoung Kim, Yeeun Ahn, Hyejin Kim, Minku Song, Injeong Shim, Sang-Hyuk Jung, Chamlee Cho, Soohyun Lim, Sanghoon Hong, Hyeonbin Jo, Akl C. Fahed, Pradeep Natarajan, Patrick T. Ellinor, Ali Torkamani, Woong-Yang Park, Tae Yang Yu, Woojae Myung, Hong-Hee Won","doi":"10.1038/s41588-024-01933-1","DOIUrl":"10.1038/s41588-024-01933-1","url":null,"abstract":"Metabolic syndrome (MetS) is a complex hereditary condition comprising various metabolic traits as risk factors. Although the genetics of individual MetS components have been investigated actively through large-scale genome-wide association studies, the conjoint genetic architecture has not been fully elucidated. Here, we performed the largest multivariate genome-wide association study of MetS in Europe (nobserved = 4,947,860) by leveraging genetic correlation between MetS components. We identified 1,307 genetic loci associated with MetS that were enriched primarily in brain tissues. Using transcriptomic data, we identified 11 genes associated strongly with MetS. Our phenome-wide association and Mendelian randomization analyses highlighted associations of MetS with diverse diseases beyond cardiometabolic diseases. Polygenic risk score analysis demonstrated better discrimination of MetS and predictive power in European and East Asian populations. Altogether, our findings will guide future studies aimed at elucidating the genetic architecture of MetS. Large-scale multivariate analyses across populations of European ancestry identify risk loci for the metabolic syndrome, improving polygenic prediction models and highlighting associations with diverse traits beyond cardiometabolic diseases.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"56 11","pages":"2380-2391"},"PeriodicalIF":31.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-024-01933-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329658","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}

引用次数: 0

Valid inference for machine learning-assisted genome-wide association studies 机器学习辅助全基因组关联研究的有效推断

IF 31.7 1区生物学

Nature genetics Pub Date : 2024-09-30 DOI: 10.1038/s41588-024-01934-0

Jiacheng Miao, Yixuan Wu, Zhongxuan Sun, Xinran Miao, Tianyuan Lu, Jiwei Zhao, Qiongshi Lu

{"title":"Valid inference for machine learning-assisted genome-wide association studies","authors":"Jiacheng Miao, Yixuan Wu, Zhongxuan Sun, Xinran Miao, Tianyuan Lu, Jiwei Zhao, Qiongshi Lu","doi":"10.1038/s41588-024-01934-0","DOIUrl":"10.1038/s41588-024-01934-0","url":null,"abstract":"Machine learning (ML) has become increasingly popular in almost all scientific disciplines, including human genetics. Owing to challenges related to sample collection and precise phenotyping, ML-assisted genome-wide association study (GWAS), which uses sophisticated ML techniques to impute phenotypes and then performs GWAS on the imputed outcomes, have become increasingly common in complex trait genetics research. However, the validity of ML-assisted GWAS associations has not been carefully evaluated. Here, we report pervasive risks for false-positive associations in ML-assisted GWAS and introduce Post-Prediction GWAS (POP-GWAS), a statistical framework that redesigns GWAS on ML-imputed outcomes. POP-GWAS ensures valid and powerful statistical inference irrespective of imputation quality and choice of algorithm, requiring only GWAS summary statistics as input. We employed POP-GWAS to perform a GWAS of bone mineral density derived from dual-energy X-ray absorptiometry imaging at 14 skeletal sites, identifying 89 new loci and revealing skeletal site-specific genetic architecture. Our framework offers a robust analytic solution for future ML-assisted GWAS. Post-prediction genome-wide association study (POP-GWAS) is a statistical framework that uses summary statistics from labeled samples with both observed and imputed phenotypes to debias single-nucleotide polymorphism effect size estimates for unlabeled samples with imputed phenotypes only, leading to valid and powerful inference.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"56 11","pages":"2361-2369"},"PeriodicalIF":31.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structural remodeling of the inactive X chromosome during early mouse development 小鼠早期发育过程中无活性 X 染色体的结构重塑

IF 31.7 1区生物学

Nature genetics Pub Date : 2024-09-27 DOI: 10.1038/s41588-024-01936-y

Edda G. Schulz, Alexandra Martitz

引用次数: 0

Methylated GCC repeat expansion in AFF3 associates with intellectual disability AFF3 中甲基化的 GCC 重复扩增与智力残疾有关

IF 31.7 1区生物学

Nature genetics Pub Date : 2024-09-27 DOI: 10.1038/s41588-024-01918-0

引用次数: 0

Long-term 3D primary epithelioid cultures reveal genes that regulate esophageal cell fitness 长期三维原代上皮细胞培养揭示调控食管细胞健康的基因

IF 31.7 1区生物学

Nature genetics Pub Date : 2024-09-27 DOI: 10.1038/s41588-024-01887-4

引用次数: 0

Publisher Correction: Statistically and functionally fine-mapped blood eQTLs and pQTLs from 1,405 humans reveal distinct regulation patterns and disease relevance 出版商更正：来自1405名人类的血液eQTL和pQTL的统计和功能精细映射揭示了不同的调控模式和疾病相关性

IF 31.7 1区生物学

Nature genetics Pub Date : 2024-09-27 DOI: 10.1038/s41588-024-01959-5

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

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Context-specific targeting of the androgen receptor in prostate cancer 前列腺癌中雄激素受体的特异性靶向作用

IF 31.7 1区生物学

Nature genetics Pub Date : 2024-09-27 DOI: 10.1038/s41588-024-01935-z

Cory Abate-Shen

引用次数: 0

Identifying genetic variants that influence the abundance of cell states in single-cell data 识别影响单细胞数据中细胞状态丰度的基因变异

IF 31.7 1区生物学

Nature genetics Pub Date : 2024-09-26 DOI: 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

{"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":"56 10","pages":"2068-2077"},"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}

引用次数: 0