Phenome-wide association studies (PheWAS) have been less focused on maternal diseases and maternal-newborn comorbidities, especially in the Chinese population. To enhance our understanding of the genetic basis of these related diseases, we conducted a PheWAS on 25,639 pregnant women and 14,151 newborns in the Chinese Han population using ultra-low-coverage whole-genome sequence (ulcWGS). We identified 2,883 maternal trait-associated SNPs associated with 26 phenotypes, among which 99.5% were near established genome-wide association study (GWAS) loci. Further refinement delineated these SNPs to 442 unique trait-associated loci (TALs) predicated on linkage disequilibrium R2 > 0.8, revealing that 75.6% demonstrated pleiotropy and 50.9% were located in genes implicated in analogous phenotypes. Notably, we discovered 21 maternal SNPs associated with 35 neonatal phenotypes, including two SNPs associated with identical complications in both mothers and children. These findings underscore the importance of integrating ulcWGS data to enrich the discoveries derived from traditional PheWAS approaches.
{"title":"Phenome-wide association study in 25,639 pregnant Chinese women reveals loci associated with maternal comorbidities and child health.","authors":"Jintao Guo, Qiwei Guo, Taoling Zhong, Chaoqun Xu, Zhongmin Xia, Hongkun Fang, Qinwei Chen, Ying Zhou, Jieqiong Xie, Dandan Jin, You Yang, Xin Wu, Huanhuan Zhu, Ailing Hour, Xin Jin, Yulin Zhou, Qiyuan Li","doi":"10.1016/j.xgen.2024.100632","DOIUrl":"10.1016/j.xgen.2024.100632","url":null,"abstract":"<p><p>Phenome-wide association studies (PheWAS) have been less focused on maternal diseases and maternal-newborn comorbidities, especially in the Chinese population. To enhance our understanding of the genetic basis of these related diseases, we conducted a PheWAS on 25,639 pregnant women and 14,151 newborns in the Chinese Han population using ultra-low-coverage whole-genome sequence (ulcWGS). We identified 2,883 maternal trait-associated SNPs associated with 26 phenotypes, among which 99.5% were near established genome-wide association study (GWAS) loci. Further refinement delineated these SNPs to 442 unique trait-associated loci (TALs) predicated on linkage disequilibrium R<sup>2</sup> > 0.8, revealing that 75.6% demonstrated pleiotropy and 50.9% were located in genes implicated in analogous phenotypes. Notably, we discovered 21 maternal SNPs associated with 35 neonatal phenotypes, including two SNPs associated with identical complications in both mothers and children. These findings underscore the importance of integrating ulcWGS data to enrich the discoveries derived from traditional PheWAS approaches.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":"4 10","pages":"100632"},"PeriodicalIF":11.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142402182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fish air breathing is crucial for the transition of vertebrates from water to land. So far, the genes involved in fish air breathing have not been well identified. Here, we performed gene enrichment analysis of positively selected genes (PSGs) in loach (Misgurnus anguillicaudatus, an air-breathing fish) in comparison to Triplophysa tibetana (a non-air-breathing fish), haplotype-resolved genome assembly of the loach, and gene evolutionary analysis of air-breathing and non-air-breathing fishes and found that the PSG mex3a originated from ancient air-breathing fish species. Deletion of Mex3a impaired loach air-breathing capacity by inhibiting angiogenesis through its interaction with T-box transcription factor 20. Mex3a overexpression significantly promoted angiogenesis. Structural analysis and point mutation revealed the critical role of the 201st amino acid in loach Mex3a for angiogenesis. Our findings innovatively indicate that the ancient mex3a is a fish air-breathing gene, which holds significance for understanding fish air breathing and provides a valuable resource for cultivating hypoxia-tolerant fish varieties.
{"title":"The loach haplotype-resolved genome and the identification of Mex3a involved in fish air breathing.","authors":"Bing Sun, Qingshan Li, Xinxin Xiao, Jianwei Zhang, Ying Zhou, Yuwei Huang, Jian Gao, Xiaojuan Cao","doi":"10.1016/j.xgen.2024.100670","DOIUrl":"10.1016/j.xgen.2024.100670","url":null,"abstract":"<p><p>Fish air breathing is crucial for the transition of vertebrates from water to land. So far, the genes involved in fish air breathing have not been well identified. Here, we performed gene enrichment analysis of positively selected genes (PSGs) in loach (Misgurnus anguillicaudatus, an air-breathing fish) in comparison to Triplophysa tibetana (a non-air-breathing fish), haplotype-resolved genome assembly of the loach, and gene evolutionary analysis of air-breathing and non-air-breathing fishes and found that the PSG mex3a originated from ancient air-breathing fish species. Deletion of Mex3a impaired loach air-breathing capacity by inhibiting angiogenesis through its interaction with T-box transcription factor 20. Mex3a overexpression significantly promoted angiogenesis. Structural analysis and point mutation revealed the critical role of the 201st amino acid in loach Mex3a for angiogenesis. Our findings innovatively indicate that the ancient mex3a is a fish air-breathing gene, which holds significance for understanding fish air breathing and provides a valuable resource for cultivating hypoxia-tolerant fish varieties.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":"4 10","pages":"100670"},"PeriodicalIF":11.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142402184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.xgen.2024.100669
Siyang Liu, Yanhong Liu, Yuqin Gu, Xingchen Lin, Huanhuan Zhu, Hankui Liu, Zhe Xu, Shiyao Cheng, Xianmei Lan, Linxuan Li, Mingxi Huang, Hao Li, Rasmus Nielsen, Robert W Davies, Anders Albrechtsen, Guo-Bo Chen, Xiu Qiu, Xin Jin, Shujia Huang
Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. Its global adoption has established NIPT as a large human genetic resource for exploring genetic variations and their associations with phenotypes. Here, we present methods for analyzing large-scale, low-depth NIPT data, including customized algorithms and software for genetic variant detection, genotype imputation, family relatedness, population structure inference, and genome-wide association analysis of maternal genomes. Our results demonstrate accurate allele frequency estimation and high genotype imputation accuracy (R2>0.84) for NIPT sequencing depths from 0.1× to 0.3×. We also achieve effective classification of duplicates and first-degree relatives, along with robust principal-component analysis. Additionally, we obtain an R2>0.81 for estimating genetic effect sizes across genotyping and sequencing platforms with adequate sample sizes. These methods offer a robust theoretical and practical foundation for utilizing NIPT data in medical genetic research.
{"title":"Utilizing non-invasive prenatal test sequencing data for human genetic investigation.","authors":"Siyang Liu, Yanhong Liu, Yuqin Gu, Xingchen Lin, Huanhuan Zhu, Hankui Liu, Zhe Xu, Shiyao Cheng, Xianmei Lan, Linxuan Li, Mingxi Huang, Hao Li, Rasmus Nielsen, Robert W Davies, Anders Albrechtsen, Guo-Bo Chen, Xiu Qiu, Xin Jin, Shujia Huang","doi":"10.1016/j.xgen.2024.100669","DOIUrl":"https://doi.org/10.1016/j.xgen.2024.100669","url":null,"abstract":"<p><p>Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. Its global adoption has established NIPT as a large human genetic resource for exploring genetic variations and their associations with phenotypes. Here, we present methods for analyzing large-scale, low-depth NIPT data, including customized algorithms and software for genetic variant detection, genotype imputation, family relatedness, population structure inference, and genome-wide association analysis of maternal genomes. Our results demonstrate accurate allele frequency estimation and high genotype imputation accuracy (R<sup>2</sup>>0.84) for NIPT sequencing depths from 0.1× to 0.3×. We also achieve effective classification of duplicates and first-degree relatives, along with robust principal-component analysis. Additionally, we obtain an R<sup>2</sup>>0.81 for estimating genetic effect sizes across genotyping and sequencing platforms with adequate sample sizes. These methods offer a robust theoretical and practical foundation for utilizing NIPT data in medical genetic research.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":"4 10","pages":"100669"},"PeriodicalIF":11.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142402186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The emergence of perturbation transcriptomics provides a new perspective for drug discovery, but existing analysis methods suffer from inadequate performance and limited applicability. In this work, we present PertKGE, a method designed to deconvolute compound-protein interactions from perturbation transcriptomics with knowledge graph embedding. By considering multi-level regulatory events within biological systems that share the same semantic context, PertKGE significantly improves deconvoluting accuracy in two critical "cold-start" settings: inferring targets for new compounds and conducting virtual screening for new targets. We further demonstrate the pivotal role of incorporating multi-level regulatory events in alleviating representational biases. Notably, it enables the identification of ectonucleotide pyrophosphatase/phosphodiesterase-1 as the target responsible for the unique anti-tumor immunotherapy effect of tankyrase inhibitor K-756 and the discovery of five novel hits targeting the emerging cancer therapeutic target aldehyde dehydrogenase 1B1 with a remarkable hit rate of 10.2%. These findings highlight the potential of PertKGE to accelerate drug discovery.
{"title":"Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics.","authors":"Shengkun Ni, Xiangtai Kong, Yingying Zhang, Zhengyang Chen, Zhaokun Wang, Zunyun Fu, Ruifeng Huo, Xiaochu Tong, Ning Qu, Xiaolong Wu, Kun Wang, Wei Zhang, Runze Zhang, Zimei Zhang, Jiangshan Shi, Yitian Wang, Ruirui Yang, Xutong Li, Sulin Zhang, Mingyue Zheng","doi":"10.1016/j.xgen.2024.100655","DOIUrl":"10.1016/j.xgen.2024.100655","url":null,"abstract":"<p><p>The emergence of perturbation transcriptomics provides a new perspective for drug discovery, but existing analysis methods suffer from inadequate performance and limited applicability. In this work, we present PertKGE, a method designed to deconvolute compound-protein interactions from perturbation transcriptomics with knowledge graph embedding. By considering multi-level regulatory events within biological systems that share the same semantic context, PertKGE significantly improves deconvoluting accuracy in two critical \"cold-start\" settings: inferring targets for new compounds and conducting virtual screening for new targets. We further demonstrate the pivotal role of incorporating multi-level regulatory events in alleviating representational biases. Notably, it enables the identification of ectonucleotide pyrophosphatase/phosphodiesterase-1 as the target responsible for the unique anti-tumor immunotherapy effect of tankyrase inhibitor K-756 and the discovery of five novel hits targeting the emerging cancer therapeutic target aldehyde dehydrogenase 1B1 with a remarkable hit rate of 10.2%. These findings highlight the potential of PertKGE to accelerate drug discovery.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":" ","pages":"100655"},"PeriodicalIF":11.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09Epub Date: 2024-09-23DOI: 10.1016/j.xgen.2024.100659
Canhui Cao, Miaochun Xu, Ye Wei, Ting Peng, Shitong Lin, Xiaojie Liu, Yashi Xu, Tian Chu, Shiyi Liu, Ping Wu, Bai Hu, Wencheng Ding, Li Li, Ding Ma, Peng Wu
Evidence from clinical trials suggests that CXCR4 antagonists enhance immunotherapy effectiveness in several cancers. However, the specific mechanisms through which CXCR4 contributes to immune cell phenotypes are not fully understood. Here, we employed single-cell transcriptomic analysis and identified CXCR4 as a marker gene in T cells, with CD8+PD-1high exhausted T (Tex) cells exhibiting high CXCR4 expression. By blocking CXCR4, the Tex phenotype was attenuated in vivo. Mechanistically, CXCR4-blocking T cells mitigated the Tex phenotype by regulating the JAK2-STAT3 pathway. Single-cell RNA/TCR/ATAC-seq confirmed that Cxcr4-deficient CD8+ T cells epigenetically mitigated the transition from functional to exhausted phenotypes. Notably, clinical sample analysis revealed that CXCR4+CD8+ T cells showed higher expression in patients with a non-complete pathological response. Collectively, these findings demonstrate the mechanism by which CXCR4 orchestrates CD8+ Tex cells and provide a rationale for combining CXCR4 antagonists with immunotherapy in clinical trials.
{"title":"CXCR4 orchestrates the TOX-programmed exhausted phenotype of CD8<sup>+</sup> T cells via JAK2/STAT3 pathway.","authors":"Canhui Cao, Miaochun Xu, Ye Wei, Ting Peng, Shitong Lin, Xiaojie Liu, Yashi Xu, Tian Chu, Shiyi Liu, Ping Wu, Bai Hu, Wencheng Ding, Li Li, Ding Ma, Peng Wu","doi":"10.1016/j.xgen.2024.100659","DOIUrl":"10.1016/j.xgen.2024.100659","url":null,"abstract":"<p><p>Evidence from clinical trials suggests that CXCR4 antagonists enhance immunotherapy effectiveness in several cancers. However, the specific mechanisms through which CXCR4 contributes to immune cell phenotypes are not fully understood. Here, we employed single-cell transcriptomic analysis and identified CXCR4 as a marker gene in T cells, with CD8<sup>+</sup>PD-1<sup>high</sup> exhausted T (T<sub>ex</sub>) cells exhibiting high CXCR4 expression. By blocking CXCR4, the T<sub>ex</sub> phenotype was attenuated in vivo. Mechanistically, CXCR4-blocking T cells mitigated the T<sub>ex</sub> phenotype by regulating the JAK2-STAT3 pathway. Single-cell RNA/TCR/ATAC-seq confirmed that Cxcr4-deficient CD8<sup>+</sup> T cells epigenetically mitigated the transition from functional to exhausted phenotypes. Notably, clinical sample analysis revealed that CXCR4<sup>+</sup>CD8<sup>+</sup> T cells showed higher expression in patients with a non-complete pathological response. Collectively, these findings demonstrate the mechanism by which CXCR4 orchestrates CD8<sup>+</sup> T<sub>ex</sub> cells and provide a rationale for combining CXCR4 antagonists with immunotherapy in clinical trials.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":" ","pages":"100659"},"PeriodicalIF":11.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genetic factors significantly influence the concentration of metabolites in adults. Nevertheless, the genetic influence on neonatal metabolites remains uncertain. To bridge this gap, we employed genotype imputation techniques on large-scale low-pass genome data obtained from non-invasive prenatal testing. Subsequently, we conducted association studies on a total of 75 metabolic components in neonates. The study identified 19 previously reported associations and 11 novel associations between single-nucleotide polymorphisms and metabolic components. These associations were initially found in the discovery cohort (8,744 participants) and subsequently confirmed in a replication cohort (19,041 participants). The average heritability of metabolic components was estimated to be 76.2%, with a range of 69%-78.8%. These findings offer valuable insights into the genetic architecture of neonatal metabolism.
{"title":"A genome-wide association study of neonatal metabolites.","authors":"Quanze He, Hankui Liu, Lu Lu, Qin Zhang, Qi Wang, Benjing Wang, Xiaojuan Wu, Liping Guan, Jun Mao, Ying Xue, Chunhua Zhang, Xinye Cao, Yuxing He, Xiangwen Peng, Huanhuan Peng, Kangrong Zhao, Hong Li, Xin Jin, Lijian Zhao, Jianguo Zhang, Ting Wang","doi":"10.1016/j.xgen.2024.100668","DOIUrl":"https://doi.org/10.1016/j.xgen.2024.100668","url":null,"abstract":"<p><p>Genetic factors significantly influence the concentration of metabolites in adults. Nevertheless, the genetic influence on neonatal metabolites remains uncertain. To bridge this gap, we employed genotype imputation techniques on large-scale low-pass genome data obtained from non-invasive prenatal testing. Subsequently, we conducted association studies on a total of 75 metabolic components in neonates. The study identified 19 previously reported associations and 11 novel associations between single-nucleotide polymorphisms and metabolic components. These associations were initially found in the discovery cohort (8,744 participants) and subsequently confirmed in a replication cohort (19,041 participants). The average heritability of metabolic components was estimated to be 76.2%, with a range of 69%-78.8%. These findings offer valuable insights into the genetic architecture of neonatal metabolism.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":"4 10","pages":"100668"},"PeriodicalIF":11.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142402201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.xgen.2024.100673
Yuerong Wang, Xian Fu, Yue Shen
The molecular mechanisms underlying the paradoxical effects1 of aneuploidy are still not completely understood. In this issue, Rojas et al.2 systematically analyzed the associated costs of aneuploidy and the molecular drivers involved, which revealed that aneuploidy stress is primarily driven by the cumulative effects of genes per chromosome. Notably, gene length was predicted as the most significant indicator of aneuploidy toxicity by machine learning.
{"title":"The hidden costs of aneuploidy: New insights from yeast.","authors":"Yuerong Wang, Xian Fu, Yue Shen","doi":"10.1016/j.xgen.2024.100673","DOIUrl":"https://doi.org/10.1016/j.xgen.2024.100673","url":null,"abstract":"<p><p>The molecular mechanisms underlying the paradoxical effects<sup>1</sup> of aneuploidy are still not completely understood. In this issue, Rojas et al.<sup>2</sup> systematically analyzed the associated costs of aneuploidy and the molecular drivers involved, which revealed that aneuploidy stress is primarily driven by the cumulative effects of genes per chromosome. Notably, gene length was predicted as the most significant indicator of aneuploidy toxicity by machine learning.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":"4 10","pages":"100673"},"PeriodicalIF":11.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142402183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.xgen.2024.100676
Claudia Nussbaum, Sarah Kim-Hellmuth
Human milk has long been recognized for its critical role in infant and maternal health. In this issue of Cell Genomics, Johnson et al.1 apply a human genetics and genomics approach to shed light on the complex relationship between maternal genetics, milk variation, and the infant gut microbiome.
{"title":"Unlocking the genetic influence on milk variation and its potential implication for infant health.","authors":"Claudia Nussbaum, Sarah Kim-Hellmuth","doi":"10.1016/j.xgen.2024.100676","DOIUrl":"https://doi.org/10.1016/j.xgen.2024.100676","url":null,"abstract":"<p><p>Human milk has long been recognized for its critical role in infant and maternal health. In this issue of Cell Genomics, Johnson et al.<sup>1</sup> apply a human genetics and genomics approach to shed light on the complex relationship between maternal genetics, milk variation, and the infant gut microbiome.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":"4 10","pages":"100676"},"PeriodicalIF":11.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142402185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.xgen.2024.100657
Siyang Liu, Jilong Yao, Liang Lin, Xianmei Lan, Linlin Wu, Xuelian He, Nannan Kong, Yan Li, Yuqing Deng, Jiansheng Xie, Huanhuan Zhu, Xiaoxia Wu, Zilong Li, Likuan Xiong, Yuan Wang, Jinghui Ren, Xuemei Qiu, Weihua Zhao, Ya Gao, Yuanqing Chen, Fengxia Su, Yun Zhou, Weiqiao Rao, Jing Zhang, Guixue Hou, Liping Huang, Linxuan Li, Xinhong Liu, Chao Nie, Liqiong Luo, Mei Zhao, Zengyou Liu, Fang Chen, Shengmou Lin, Lijian Zhao, Qingmei Fu, Dan Jiang, Ye Yin, Xun Xu, Jian Wang, Huanming Yang, Rong Wang, Jianmin Niu, Fengxiang Wei, Xin Jin, Siqi Liu
Metabolites are key indicators of health and therapeutic targets, but their genetic underpinnings during pregnancy-a critical period for human reproduction-are largely unexplored. Using genetic data from non-invasive prenatal testing, we performed a genome-wide association study on 84 metabolites, including 37 amino acids, 24 elements, 13 hormones, and 10 vitamins, involving 34,394 pregnant Chinese women, with sample sizes ranging from 6,394 to 13,392 for specific metabolites. We identified 53 metabolite-gene associations, 23 of which are novel. Significant differences in genetic effects between pregnant and non-pregnant women were observed for 16.7%-100% of these associations, indicating gene-environment interactions. Additionally, 50.94% of genetic associations exhibited pleiotropy among metabolites and between six metabolites and eight pregnancy phenotypes. Mendelian randomization revealed potential causal relationships between seven maternal metabolites and 15 human traits and diseases. These findings provide new insights into the genetic basis of maternal plasma metabolites during pregnancy.
{"title":"Genome-wide association study of maternal plasma metabolites during pregnancy.","authors":"Siyang Liu, Jilong Yao, Liang Lin, Xianmei Lan, Linlin Wu, Xuelian He, Nannan Kong, Yan Li, Yuqing Deng, Jiansheng Xie, Huanhuan Zhu, Xiaoxia Wu, Zilong Li, Likuan Xiong, Yuan Wang, Jinghui Ren, Xuemei Qiu, Weihua Zhao, Ya Gao, Yuanqing Chen, Fengxia Su, Yun Zhou, Weiqiao Rao, Jing Zhang, Guixue Hou, Liping Huang, Linxuan Li, Xinhong Liu, Chao Nie, Liqiong Luo, Mei Zhao, Zengyou Liu, Fang Chen, Shengmou Lin, Lijian Zhao, Qingmei Fu, Dan Jiang, Ye Yin, Xun Xu, Jian Wang, Huanming Yang, Rong Wang, Jianmin Niu, Fengxiang Wei, Xin Jin, Siqi Liu","doi":"10.1016/j.xgen.2024.100657","DOIUrl":"https://doi.org/10.1016/j.xgen.2024.100657","url":null,"abstract":"<p><p>Metabolites are key indicators of health and therapeutic targets, but their genetic underpinnings during pregnancy-a critical period for human reproduction-are largely unexplored. Using genetic data from non-invasive prenatal testing, we performed a genome-wide association study on 84 metabolites, including 37 amino acids, 24 elements, 13 hormones, and 10 vitamins, involving 34,394 pregnant Chinese women, with sample sizes ranging from 6,394 to 13,392 for specific metabolites. We identified 53 metabolite-gene associations, 23 of which are novel. Significant differences in genetic effects between pregnant and non-pregnant women were observed for 16.7%-100% of these associations, indicating gene-environment interactions. Additionally, 50.94% of genetic associations exhibited pleiotropy among metabolites and between six metabolites and eight pregnancy phenotypes. Mendelian randomization revealed potential causal relationships between seven maternal metabolites and 15 human traits and diseases. These findings provide new insights into the genetic basis of maternal plasma metabolites during pregnancy.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":"4 10","pages":"100657"},"PeriodicalIF":11.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142402180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.xgen.2024.100667
Tian Yu, James D Fife, Vineel Bhat, Ivan Adzhubey, Richard Sherwood, Christopher A Cassa
Deep mutational scanning enables high-throughput functional assessment of genetic variants. While phenotypic measurements from screening assays generally align with clinical outcomes, experimental noise may affect the accuracy of individual variant estimates. We developed the FUSE (functional substitution estimation) pipeline, which leverages measurements collectively within screening assays to improve the estimation of variant impacts. Drawing data from 115 published functional assays, FUSE assesses the mean functional effect per amino acid position and makes estimates for individual allelic variants. It enhances the correlation of variant functional effects from different assay platforms and increases the classification accuracy of missense variants in ClinVar across 29 genes (area under the receiver operating characteristic [ROC] curve [AUC] from 0.83 to 0.90). In UK Biobank patients with rare missense variants in BRCA1, LDLR, or TP53, FUSE improves the classification accuracy of associated phenotypes. FUSE can also impute variant effects for substitutions not experimentally screened. This approach improves accuracy and broadens the utility of data from functional screening.
{"title":"FUSE: Improving the estimation and imputation of variant impacts in functional screening.","authors":"Tian Yu, James D Fife, Vineel Bhat, Ivan Adzhubey, Richard Sherwood, Christopher A Cassa","doi":"10.1016/j.xgen.2024.100667","DOIUrl":"https://doi.org/10.1016/j.xgen.2024.100667","url":null,"abstract":"<p><p>Deep mutational scanning enables high-throughput functional assessment of genetic variants. While phenotypic measurements from screening assays generally align with clinical outcomes, experimental noise may affect the accuracy of individual variant estimates. We developed the FUSE (functional substitution estimation) pipeline, which leverages measurements collectively within screening assays to improve the estimation of variant impacts. Drawing data from 115 published functional assays, FUSE assesses the mean functional effect per amino acid position and makes estimates for individual allelic variants. It enhances the correlation of variant functional effects from different assay platforms and increases the classification accuracy of missense variants in ClinVar across 29 genes (area under the receiver operating characteristic [ROC] curve [AUC] from 0.83 to 0.90). In UK Biobank patients with rare missense variants in BRCA1, LDLR, or TP53, FUSE improves the classification accuracy of associated phenotypes. FUSE can also impute variant effects for substitutions not experimentally screened. This approach improves accuracy and broadens the utility of data from functional screening.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":"4 10","pages":"100667"},"PeriodicalIF":11.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142402203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}