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Yuan K, Zeng T, Chen L. Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype–Phenotype Association Study. Front Cell Dev Biol 2022; 9:720321. [PMID: 35155440 PMCID: PMC8826544 DOI: 10.3389/fcell.2021.720321] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 12/13/2021] [Indexed: 12/18/2022] Open
Abstract
An enormous challenge in the post-genome era is to annotate and resolve the consequences of genetic variation on diverse phenotypes. The genome-wide association study (GWAS) is a well-known method to identify potential genetic loci for complex traits from huge genetic variations, following which it is crucial to identify expression quantitative trait loci (eQTL). However, the conventional eQTL methods usually disregard the systematical role of single-nucleotide polymorphisms (SNPs) or genes, thereby overlooking many network-associated phenotypic determinates. Such a problem motivates us to recognize the network-based quantitative trait loci (QTL), i.e., network QTL (nQTL), which is to detect the cascade association as genotype → network → phenotype rather than conventional genotype → expression → phenotype in eQTL. Specifically, we develop the nQTL framework on the theory and approach of single-sample networks, which can identify not only network traits (e.g., the gene subnetwork associated with genotype) for analyzing complex biological processes but also network signatures (e.g., the interactive gene biomarker candidates screened from network traits) for characterizing targeted phenotype and corresponding subtypes. Our results show that the nQTL framework can efficiently capture associations between SNPs and network traits (i.e., edge traits) in various simulated data scenarios, compared with traditional eQTL methods. Furthermore, we have carried out nQTL analysis on diverse biological and biomedical datasets. Our analysis is effective in detecting network traits for various biological problems and can discover many network signatures for discriminating phenotypes, which can help interpret the influence of nQTL on disease subtyping, disease prognosis, drug response, and pathogen factor association. Particularly, in contrast to the conventional approaches, the nQTL framework could also identify many network traits from human bulk expression data, validated by matched single-cell RNA-seq data in an independent or unsupervised manner. All these results strongly support that nQTL and its detection framework can simultaneously explore the global genotype–network–phenotype associations and the underlying network traits or network signatures with functional impact and importance.
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Affiliation(s)
- Kai Yuan
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
- Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Guangzhou Laboratory, Guangzhou, China
- *Correspondence: Tao Zeng, ; Luonan Chen,
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
- *Correspondence: Tao Zeng, ; Luonan Chen,
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Recent innovations and in-depth aspects of post-genome wide association study (Post-GWAS) to understand the genetic basis of complex phenotypes. Heredity (Edinb) 2021; 127:485-497. [PMID: 34689168 PMCID: PMC8626474 DOI: 10.1038/s41437-021-00479-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 10/13/2021] [Accepted: 10/13/2021] [Indexed: 12/13/2022] Open
Abstract
In the past decade, the high throughput and low cost of sequencing/genotyping approaches have led to the accumulation of a large amount of data from genome-wide association studies (GWASs). The first aim of this review is to highlight how post-GWAS analysis can be used make sense of the obtained associations. Novel directions for integrating GWAS results with other resources, such as somatic mutation, metabolite-transcript, and transcriptomic data, are also discussed; these approaches can help us move beyond each individual data point and provide valuable information about complex trait genetics. In addition, cross-phenotype association tests, when the loci detected by GWASs have significant associations with multiple traits, are reviewed to provide biologically informative results for use in real-time applications. This review also discusses the challenges of identifying interactions between genetic mutations (epistasis) and mutations of loci affecting more than one trait (pleiotropy) as underlying causes of cross-phenotype associations; these challenges can be overcome using post-GWAS analysis. Genetic similarities between phenotypes that can be revealed using post-GWAS analysis are also discussed. In summary, different methodologies of post-GWAS analysis are now available, enhancing the value of information obtained from GWAS results, and facilitating application in both humans and nonhuman species. However, precise methods still need to be developed to overcome challenges in the field and uncover the genetic underpinnings of complex traits.
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Takahashi Y, Maynard KR, Tippani M, Jaffe AE, Martinowich K, Kleinman JE, Weinberger DR, Hyde TM. Single molecule in situ hybridization reveals distinct localizations of schizophrenia risk-related transcripts SNX19 and AS3MT in human brain. Mol Psychiatry 2021; 26:3536-3547. [PMID: 33649454 DOI: 10.1038/s41380-021-01046-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 01/20/2021] [Accepted: 02/02/2021] [Indexed: 11/09/2022]
Abstract
Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) associated with schizophrenia risk. Integration of RNA-sequencing data from postmortem human brains with these risk SNPs identified transcripts associated with increased schizophrenia susceptibility, including a class of exon 9-spliced isoforms of Sorting nexin-19 (SNX19d9) and an isoform of Arsenic methyltransferase (AS3MT) splicing out exons 2 and 3 (AS3MTd2d3). However, the biological function of these transcript variants is unclear. Defining the cell types where these risk transcripts are dominantly expressed is an important step to understand function, in prioritizing specific cell types and/or neural pathways in subsequent studies. To identify the cell type-specific localization of SNX19 and AS3MT in the human dorsolateral prefrontal cortex (DLPFC), we used single-molecule in situ hybridization techniques combined with automated quantification and machine learning approaches to analyze 10 postmortem brains of neurotypical individuals. These analyses revealed that both pan-SNX19 and pan-AS3MT were more highly expressed in neurons than non-neurons in layers II/III and VI of DLPFC. Furthermore, pan-SNX19 was preferentially expressed in glutamatergic neurons, while pan-AS3MT was preferentially expressed in GABAergic neurons. Finally, we utilized duplex BaseScope technology, to delineate the localization of SNX19d9 and AS3MTd2d3 splice variants, revealing consistent trends in spatial gene expression among pan-transcripts and schizophrenia risk-related transcript variants. These findings demonstrate that schizophrenia risk transcripts have distinct localization patterns in the healthy human brains, and suggest that SNX19 transcripts might disrupt the normal function of glutamatergic neurons, while AS3MT may lead to disturbances in the GABAergic system in the pathophysiology of schizophrenia.
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Affiliation(s)
- Yoichiro Takahashi
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Legal Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Mental Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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Investigating Causality Between Blood Metabolites and Emotional and Behavioral Responses to Traumatic Stress: a Mendelian Randomization Study. Mol Neurobiol 2019; 57:1542-1552. [PMID: 31786776 DOI: 10.1007/s12035-019-01823-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 10/23/2019] [Indexed: 12/21/2022]
Abstract
To investigate the causal relationship between blood metabolites and traits related to trauma-response, we combined genome-wide and metabolome-wide datasets generated from large-scale cohorts. Five trauma-response traits ascertained in the UK Biobank (52,816 < N < 117,900 individuals) were considered: (i) "Avoided activities/situations because of previous stressful experience" (Avoidance); (ii) "Felt distant from other people" (Distant); (iii) "Felt irritable/had angry outbursts" (Irritable); (iv) "Felt very upset when reminded of stressful experience" (Upset); (v) "Repeated disturbing thoughts of stressful experience". These were investigated with respect to 52 blood metabolites tested in a previous genome-wide-association study (N = 24,925 European-ancestry individuals). Linkage disequilibrium score regression, polygenic risk scoring (PRS), and Mendelian randomization were applied to the datasets. We observed that 14 metabolites were genetically correlated with trauma-response traits (p < 0.05). High-resolution PRS of 4 metabolites (citrate; glycoprotein acetyls; concentration of large very-low-density lipoproteins (VLDL) particles (LVLDLP); total cholesterol in medium particles of VLDL (MVLDLC)) were associated with trauma-response traits (false discovery rate Q < 10%). These genetic associations were partially due to causal relationships (Citrate→Upset β = - 0.058, p = 9.1 × 10-4; Glycoproteins→Avoidance β = 0.008, p = 0.003; LVLDLP→Distant β = 0.008, p = 0.022; MVLDLC→Avoidance β = 0.019, p = 3 × 10-4). No reverse associations were observed. In conclusion, our study supports causal relationships between certain blood metabolites and emotional and behavioral responses to traumatic experiences.
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Ping J, Oyebamiji O, Yu H, Ness S, Chien J, Ye F, Kang H, Samuels D, Ivanov S, Chen D, Zhao YY, Guo Y. MutEx: a multifaceted gateway for exploring integrative pan-cancer genomic data. Brief Bioinform 2019; 21:1479-1486. [PMID: 31588509 DOI: 10.1093/bib/bbz084] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 06/03/2019] [Accepted: 06/17/2019] [Indexed: 12/11/2022] Open
Abstract
Somatic mutation and gene expression dysregulation are considered two major tumorigenesis factors. While independent investigations of either factor pervade, studies of associations between somatic mutations and gene expression changes have been sporadic and nonsystematic. Utilizing genomic data collected from 11 315 subjects of 33 distinct cancer types, we constructed MutEx, a pan-cancer integrative genomic database. This database records the relationships among gene expression, somatic mutation and survival data for cancer patients. MutEx can be used to swiftly explore the relationship between these genomic/clinic features within and across cancer types and, more importantly, search for corroborating evidence for hypothesis inception. Our database also incorporated Gene Ontology and several pathway databases to enhance functional annotation, and elastic net and a gene expression composite score to aid in survival analysis. To demonstrate the usability of MutEx, we provide several application examples, including top somatic mutations associated with the most extensive expression dysregulation in breast cancer, differential mutational burden downstream of DNA mismatch repair gene mutations and composite gene expression score-based survival difference in breast cancer. MutEx can be accessed at http://www.innovebioinfo.com/Databases/Mutationdb_About.php.
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Affiliation(s)
- Jie Ping
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, USA, 37232
| | | | - Hui Yu
- Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, USA, 87109
| | - Scott Ness
- Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, USA, 87109
| | - Jeremy Chien
- Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, USA, 87109
| | - Fei Ye
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, USA, 37232
| | - Huining Kang
- Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, USA, 87109
| | - David Samuels
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, USA, 37232
| | - Sergey Ivanov
- Department of Internal Medicine, Vanderbilt University, Nashville, USA, 37232
| | - Danqian Chen
- Key Laboratory of Resource Biology and Biotechnology in Western China, School of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China
| | - Ying-Yong Zhao
- Key Laboratory of Resource Biology and Biotechnology in Western China, School of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China
| | - Yan Guo
- Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, USA, 87109
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Xu T, Jin P, Qin ZS. Regulatory annotation of genomic intervals based on tissue-specific expression QTLs. Bioinformatics 2019; 36:690-697. [PMID: 31504167 PMCID: PMC8215915 DOI: 10.1093/bioinformatics/btz669] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 05/14/2019] [Accepted: 08/23/2019] [Indexed: 01/31/2023] Open
Abstract
MOTIVATION Annotating a given genomic locus or a set of genomic loci is an important yet challenging task. This is especially true for the non-coding part of the genome which is enormous yet poorly understood. Since gene set enrichment analyses have demonstrated to be effective approach to annotate a set of genes, the same idea can be extended to explore the enrichment of functional elements or features in a set of genomic intervals to reveal potential functional connections. RESULTS In this study, we describe a novel computational strategy named loci2path that takes advantage of the newly emerged, genome-wide and tissue-specific expression quantitative trait loci (eQTL) information to help annotate a set of genomic intervals in terms of transcription regulation. By checking the presence or the absence of millions of eQTLs in a set of input genomic intervals, combined with grouping eQTLs by the pathways or gene sets that their target genes belong to, loci2path build a bridge connecting genomic intervals to functional pathways and pre-defined biological-meaningful gene sets, revealing potential for regulatory connection. Our method enjoys two key advantages over existing methods: first, we no longer rely on proximity to link a locus to a gene which has shown to be unreliable; second, eQTL allows us to provide the regulatory annotation under the context of specific tissue types. To demonstrate its utilities, we apply loci2path on sets of genomic intervals harboring disease-associated variants as query. Using 1 702 612 eQTLs discovered by the Genotype-Tissue Expression (GTEx) project across 44 tissues and 6320 pathways or gene sets cataloged in MSigDB as annotation resource, our method successfully identifies highly relevant biological pathways and revealed disease mechanisms for psoriasis and other immune-related diseases. Tissue specificity analysis of associated eQTLs provide additional evidence of the distinct roles of different tissues played in the disease mechanisms. AVAILABILITY AND IMPLEMENTATION loci2path is published as an open source Bioconductor package, and it is available at http://bioconductor.org/packages/release/bioc/html/loci2path.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tianlei Xu
- Department of Mathematics and Computer Science, Emory University, Atlanta, GA 30322, USA
| | - Peng Jin
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
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Polimanti R, Kayser MH, Gelernter J. Local adaptation in European populations affected the genetics of psychiatric disorders and behavioral traits. Genome Med 2018; 10:24. [PMID: 29580271 PMCID: PMC5870256 DOI: 10.1186/s13073-018-0532-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 03/14/2018] [Indexed: 01/30/2023] Open
Abstract
Background Recent studies have used genome-wide data to investigate evolutionary mechanisms related to behavioral phenotypes, identifying widespread signals of positive selection. Here, we conducted a genome-wide investigation to study whether the molecular mechanisms involved in these traits were affected by local adaptation. Methods We performed a polygenic risk score analysis in a sample of 2455 individuals from 23 European populations with respect to variables related to geo-climate diversity, pathogen diversity, and language phonological complexity. The analysis was adjusted for the genetic diversity of European populations to ensure that the differences detected would reflect differences in environmental exposures. Results The top finding was related to the association between winter minimum temperature and schizophrenia. Additional significant geo-climate results were also observed with respect to bipolar disorder (sunny daylight), depressive symptoms (precipitation rate), major depressive disorder (precipitation rate), and subjective well-being (relative humidity). Beyond geo-climate variables, we also observed findings related to pathogen diversity and language phonological complexity: openness to experience was associated with protozoan diversity; conscientiousness and extraversion were associated with language consonants. Conclusions We report that common variation associated with psychiatric disorders and behavioral traits was affected by processes related to local adaptation in European populations. Electronic supplementary material The online version of this article (10.1186/s13073-018-0532-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Renato Polimanti
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, 950 Campbell Avenue, West Haven, CT, 06516, USA.
| | - Manfred H Kayser
- Department of Genetic Identification, Erasmus University Medical Center, Rotterdam, Rotterdam, the Netherlands
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, 950 Campbell Avenue, West Haven, CT, 06516, USA.,Departments of Genetics and Neuroscience, Yale School of Medicine, New Haven, CT, USA
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