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Chen Q, Aguirre L, Liang G, Zhao H, Dong T, Borrego F, de Rojas I, Hu Q, Reyes C, Su LY, Zhang B, Lechleiter JD, Göring HHH, De Jager PL, Kleinman JE, Hyde TM, Li PP, Ruiz A, Weinberger DR, Seshadri S, Ma L. Identification of a specific APOE transcript and functional elements associated with Alzheimer's disease. Mol Neurodegener 2024; 19:63. [PMID: 39210471 PMCID: PMC11361112 DOI: 10.1186/s13024-024-00751-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND The APOE gene is the strongest genetic risk factor for late-onset Alzheimer's Disease (LOAD). However, the gene regulatory mechanisms at this locus remain incompletely characterized. METHODS To identify novel AD-linked functional elements within the APOE locus, we integrated SNP variants with multi-omics data from human postmortem brains including 2,179 RNA-seq samples from 3 brain regions and two ancestries (European and African), 667 DNA methylation samples, and ChIP-seq samples. Additionally, we plotted the expression trajectory of APOE transcripts in human brains during development. RESULTS We identified an AD-linked APOE transcript (jxn1.2.2) particularly observed in the dorsolateral prefrontal cortex (DLPFC). The APOE jxn1.2.2 transcript is associated with brain neuropathological features, cognitive impairment, and the presence of the APOE4 allele in DLPFC. We prioritized two independent functional SNPs (rs157580 and rs439401) significantly associated with jxn1.2.2 transcript abundance and DNA methylation levels. These SNPs are located within active chromatin regions and affect brain-related transcription factor-binding affinities. The two SNPs shared effects on the jxn1.2.2 transcript between European and African ethnic groups. CONCLUSION The novel APOE functional elements provide potential therapeutic targets with mechanistic insight into the disease etiology.
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Affiliation(s)
- Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, USA
| | - Luis Aguirre
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, USA
| | - Guoming Liang
- College of Animal Science, Shanxi Agricultural University, Taigu, Shanxi, China
| | - Huanhuan Zhao
- Bioinformatics Program, University of Texas at El Paso, El Paso, TX, USA
| | - Tao Dong
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Felix Borrego
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, USA
| | - Itziar de Rojas
- Research Center and Memory Clinic, Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain
- Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Qichan Hu
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, USA
| | - Christopher Reyes
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, USA
| | - Ling-Yan Su
- College of Food Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Bao Zhang
- College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - James D Lechleiter
- Department of Cell Systems and Anatomy, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Harald H H Göring
- South Texas Diabetes and Obesity Institute and Division of Human Genetics, University of Texas Rio Grande Valley School of Medicine, San Antonio, TX, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Pan P Li
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Agustín Ruiz
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, USA
- Research Center and Memory Clinic, Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain
- Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Departments of Neurology, Neuroscience, and Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, USA.
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA.
| | - Liang Ma
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, USA.
- Department of Pharmacology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
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Chen Q, Aguirre L, Zhao H, Borrego F, de Rojas I, Su L, Li PP, Zhang B, Kokovay E, Lechleiter JD, Göring HH, De Jager PL, Kleinman JE, Hyde TM, Ruiz A, Weinberger DR, Seshadri S, Ma L. Identification of a specific APOE transcript and functional elements associated with Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.30.23297431. [PMID: 37961425 PMCID: PMC10635228 DOI: 10.1101/2023.10.30.23297431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
INTRODUCTION The APOE gene is the strongest genetic risk factor for late-onset Alzheimer's Disease (LOAD). However, the gene regulatory mechanisms at this locus have not been fully characterized. METHODS To identify novel AD-linked functional elements within the APOE locus, we integrated SNP variants with RNA-seq, DNA methylation, and ChIP-seq data from human postmortem brains. RESULTS We identified an AD-linked APOE transcript (jxn1.2.2) observed in the dorsolateral prefrontal cortex (DLPFC). The APOE jxn1.2.2 transcript is associated with brain neuropathological features in DLPFC. We prioritized an independent functional SNP, rs157580, significantly associated with jxn1.2.2 transcript abundance and DNA methylation levels. rs157580 is located within active chromatin regions and predicted to affect brain-related transcriptional factors binding affinity. rs157580 shared the effects on the jxn1.2.2 transcript between European and African ethnic groups. DISCUSSION The novel APOE functional elements provide potential therapeutic targets with mechanistic insight into the disease's etiology.
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Multi-omics research in sarcopenia: Current progress and future prospects. Ageing Res Rev 2022; 76:101576. [PMID: 35104630 DOI: 10.1016/j.arr.2022.101576] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 12/13/2021] [Accepted: 01/26/2022] [Indexed: 12/17/2022]
Abstract
Sarcopenia is a systemic disease with progressive and generalized skeletal muscle dysfunction defined by age-related low muscle mass, high content of muscle slow fibers, and low muscle function. Muscle phenotypes and sarcopenia risk are heritable; however, the genetic architecture and molecular mechanisms underlying sarcopenia remain largely unclear. In recent years, significant progress has been made in determining susceptibility loci using genome-wide association studies. In addition, recent advances in omics techniques, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, offer new opportunities to identify novel targets to help us understand the pathophysiology of sarcopenia. However, each individual technology cannot capture the entire view of the biological complexity of this disorder, while integrative multi-omics analyses may be able to reveal new insights. Here, we review the latest findings of multi-omics studies for sarcopenia and provide an in-depth summary of our current understanding of sarcopenia pathogenesis. Leveraging multi-omics data could give us a holistic understanding of sarcopenia etiology that may lead to new clinical applications. This review offers guidance and recommendations for fundamental research, innovative perspectives, and preventative and therapeutic interventions for sarcopenia.
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Brotman SM, Raulerson CK, Vadlamudi S, Currin KW, Shen Q, Parsons VA, Iyengar AK, Roman TS, Furey TS, Kuusisto J, Collins FS, Boehnke M, Laakso M, Pajukanta P, Mohlke KL. Subcutaneous adipose tissue splice quantitative trait loci reveal differences in isoform usage associated with cardiometabolic traits. Am J Hum Genet 2022; 109:66-80. [PMID: 34995504 PMCID: PMC8764203 DOI: 10.1016/j.ajhg.2021.11.019] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 11/23/2021] [Indexed: 01/13/2023] Open
Abstract
Alternate splicing events can create isoforms that alter gene function, and genetic variants associated with alternate gene isoforms may reveal molecular mechanisms of disease. We used subcutaneous adipose tissue of 426 Finnish men from the METSIM study and identified splice junction quantitative trait loci (sQTLs) for 6,077 splice junctions (FDR < 1%). In the same individuals, we detected expression QTLs (eQTLs) for 59,443 exons and 15,397 genes (FDR < 1%). We identified 595 genes with an sQTL and exon eQTL but no gene eQTL, which could indicate potential isoform differences. Of the significant sQTL signals, 2,114 (39.8%) included at least one proxy variant (linkage disequilibrium r2 > 0.8) located within an intron spanned by the splice junction. We identified 203 sQTLs that colocalized with 141 genome-wide association study (GWAS) signals for cardiometabolic traits, including 25 signals for lipid traits, 24 signals for body mass index (BMI), and 12 signals for waist-hip ratio adjusted for BMI. Among all 141 GWAS signals colocalized with an sQTL, we detected 26 that also colocalized with an exon eQTL for an exon skipped by the sQTL splice junction. At a GWAS signal for high-density lipoprotein cholesterol colocalized with an NR1H3 sQTL splice junction, we show that the alternative splice product encodes an NR1H3 transcription factor that lacks a DNA binding domain and fails to activate transcription. Together, these results detect splicing events and candidate mechanisms that may contribute to gene function at GWAS loci.
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Affiliation(s)
- Sarah M Brotman
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Chelsea K Raulerson
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | | | - Kevin W Currin
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Qiujin Shen
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Victoria A Parsons
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Apoorva K Iyengar
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Tamara S Roman
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Terrence S Furey
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Biology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Kuopio University Hospital, University of Eastern Finland, Kuopio 70210, Finland
| | - Francis S Collins
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Markku Laakso
- Institute of Clinical Medicine, Kuopio University Hospital, University of Eastern Finland, Kuopio 70210, Finland
| | - Päivi Pajukanta
- Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.
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Clark KC, Kwitek AE. Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome. Compr Physiol 2021; 12:3045-3084. [PMID: 34964118 PMCID: PMC9373910 DOI: 10.1002/cphy.c210010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.
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Affiliation(s)
- Karen C Clark
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Anne E Kwitek
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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Hoskins JW, Chung CC, O’Brien A, Zhong J, Connelly K, Collins I, Shi J, Amundadottir LT. Inferred expression regulator activities suggest genes mediating cardiometabolic genetic signals. PLoS Comput Biol 2021; 17:e1009563. [PMID: 34793442 PMCID: PMC8639061 DOI: 10.1371/journal.pcbi.1009563] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 12/02/2021] [Accepted: 10/15/2021] [Indexed: 12/12/2022] Open
Abstract
Expression QTL (eQTL) analyses have suggested many genes mediating genome-wide association study (GWAS) signals but most GWAS signals still lack compelling explanatory genes. We have leveraged an adipose-specific gene regulatory network to infer expression regulator activities and phenotypic master regulators (MRs), which were used to detect activity QTLs (aQTLs) at cardiometabolic trait GWAS loci. Regulator activities were inferred with the VIPER algorithm that integrates enrichment of expected expression changes among a regulator's target genes with confidence in their regulator-target network interactions and target overlap between different regulators (i.e., pleiotropy). Phenotypic MRs were identified as those regulators whose activities were most important in predicting their respective phenotypes using random forest modeling. While eQTLs were typically more significant than aQTLs in cis, the opposite was true among candidate MRs in trans. Several GWAS loci colocalized with MR trans-eQTLs/aQTLs in the absence of colocalized cis-QTLs. Intriguingly, at the 1p36.1 BMI GWAS locus the EPHB2 cis-aQTL was stronger than its cis-eQTL and colocalized with the GWAS signal and 35 BMI MR trans-aQTLs, suggesting the GWAS signal may be mediated by effects on EPHB2 activity and its downstream effects on a network of BMI MRs. These MR and aQTL analyses represent systems genetic methods that may be broadly applied to supplement standard eQTL analyses for suggesting molecular effects mediating GWAS signals.
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Affiliation(s)
- Jason W. Hoskins
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail: (JWH); (LTA)
| | - Charles C. Chung
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- Cancer Genome Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Aidan O’Brien
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Jun Zhong
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Katelyn Connelly
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Irene Collins
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Jianxin Shi
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Laufey T. Amundadottir
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail: (JWH); (LTA)
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Garrido-Martín D, Borsari B, Calvo M, Reverter F, Guigó R. Identification and analysis of splicing quantitative trait loci across multiple tissues in the human genome. Nat Commun 2021; 12:727. [PMID: 33526779 PMCID: PMC7851174 DOI: 10.1038/s41467-020-20578-2] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 12/02/2020] [Indexed: 12/13/2022] Open
Abstract
Alternative splicing (AS) is a fundamental step in eukaryotic mRNA biogenesis. Here, we develop an efficient and reproducible pipeline for the discovery of genetic variants that affect AS (splicing QTLs, sQTLs). We use it to analyze the GTEx dataset, generating a comprehensive catalog of sQTLs in the human genome. Downstream analysis of this catalog provides insight into the mechanisms underlying splicing regulation. We report that a core set of sQTLs is shared across multiple tissues. sQTLs often target the global splicing pattern of genes, rather than individual splicing events. Many also affect the expression of the same or other genes, uncovering regulatory loci that act through different mechanisms. sQTLs tend to be located in post-transcriptionally spliced introns, which would function as hotspots for splicing regulation. While many variants affect splicing patterns by altering the sequence of splice sites, many more modify the binding sites of RNA-binding proteins. Genetic variants affecting splicing can have a stronger phenotypic impact than those affecting gene expression.
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Affiliation(s)
- Diego Garrido-Martín
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Catalonia, Spain.
| | - Beatrice Borsari
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Catalonia, Spain
| | - Miquel Calvo
- Section of Statistics, Faculty of Biology, Universitat de Barcelona (UB), Av. Diagonal 643, Barcelona, 08028, Spain
| | - Ferran Reverter
- Section of Statistics, Faculty of Biology, Universitat de Barcelona (UB), Av. Diagonal 643, Barcelona, 08028, Spain
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Catalonia, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain.
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Ma L, Shcherbina A, Chetty S. Variations and expression features of CYP2D6 contribute to schizophrenia risk. Mol Psychiatry 2021; 26:2605-2615. [PMID: 32047265 PMCID: PMC8440189 DOI: 10.1038/s41380-020-0675-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 01/27/2020] [Accepted: 01/30/2020] [Indexed: 12/18/2022]
Abstract
Genome-wide association studies (GWAS) have successfully identified 145 loci implicated in schizophrenia (SCZ). However, the underlying mechanisms remain largely unknown. Here, we analyze 1497 RNA-seq data in combination with their genotype data and identify SNPs that are associated with expression throughout the genome by dissecting expression features to genes (eGene) and exon-exon junctions (eJunction). Then, we colocalize eGene and eJunction with SCZ GWAS using SMR and fine mapping. Multiple ChIP-seq data and DNA methylation data generated from brain were used for identifying the causal variants. Finally, we used a hypothesis-free (no SCZ risk loci considered) enrichment analysis to determine implicated pathways. We identified 171 genes and eight splicing junctions located within four genes (SNX19, ARL6IP4, APOPT1, and CYP2D6) that potentially contribute to SCZ susceptibility. Among the genes, CYP2D6 is significantly associated with SCZ SNPs in eGene and eJunction. In-depth examination of the CYP2D6 region revealed that a nonsynonymous single nucleotide variant rs16947 is strongly associated with a higher abundance of CYP2D6 exon 3 skipping junctions. While we found rs133377 and other functional SNPs in high linkage disequilibrium with rs16947 (r2 = 0.9539), histone acetylation analysis showed they are located within active transcription start sites. Furthermore, our data-driven enrichment analysis showed that CYP2D6 is significantly involved in drug metabolism of codeine, tamoxifen, and citalopram. Our study facilitates an understanding of the genetic architecture of SCZ and provides new drug targets.
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Affiliation(s)
- Liang Ma
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94305, USA.
| | - Anna Shcherbina
- grid.168010.e0000000419368956Department of Biomedical Informatics, Stanford University, Stanford, CA 94305 USA
| | - Sundari Chetty
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94305, USA. .,Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
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Zhang T, Gao H, Sahana G, Zan Y, Fan H, Liu J, Shi L, Wang H, Du L, Wang L, Zhao F. Genome-wide association studies revealed candidate genes for tail fat deposition and body size in the Hulun Buir sheep. J Anim Breed Genet 2019; 136:362-370. [PMID: 31045295 DOI: 10.1111/jbg.12402] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 03/25/2019] [Accepted: 03/28/2019] [Indexed: 01/01/2023]
Abstract
Fat-tailed sheep have a unique characteristic of depositing fat in their tails. In the present study, we conducted genome-wide association studies (GWAS) on traits related to tail fat deposition and body size in the Hulun Buir sheep. A total number of 300 individuals belonging to two fat-tailed lines of the Hulun Buir sheep breed genotyped with the Ovine Infinium HD SNP BeadChip were included in the current study. Two mixed models, one for continuous and one for binary phenotypic traits, were employed to analyse ten traits, that is, body length (BL), body height (BH), chest girth (CG), tail length (TL), tail width (TW), tail circumference (TC), carcass weight (CW), tail fat weight (TF), ratio of CW to TF (RCT) and tail type (TT). We identified 7, 6, 7, 2, 10 and 1 SNPs significantly associated with traits TF, CW, RCT, TW, TT and CG, respectively. Their associated genomic regions harboured 42 positional candidate genes. Out of them, 13 candidate genes including SMURF2, FBF1, DTNBP1, SETD7 and RBM11 have been associated with fat metabolism in sheep. The RBM11 gene has already been identified in a previous study on signatures of selection in this specific sheep population. Two more genes, that is, SMARCA5 and GAB1 were associated with body size in sheep. The present study has identified candidate genes that might be implicated in tail fat deposition and body size in sheep.
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Affiliation(s)
- Tongyu Zhang
- Key Laborary of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hongding Gao
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Goutam Sahana
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Yanjun Zan
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Hongying Fan
- Key Laborary of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jiaxin Liu
- Key Laborary of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Liangyu Shi
- Key Laborary of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hongwei Wang
- Beijing Compass Biotechnology Co., Ltd, Beijing, China
| | - Lixin Du
- Key Laborary of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lixian Wang
- Key Laborary of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Fuping Zhao
- Key Laborary of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
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