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      Identification of IGF1, SLC4A4, WWOX, and SFMBT1 as Hypertension Susceptibility Genes in Han Chinese with a Genome-Wide Gene-Based Association Study

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          Abstract

          Hypertension is a complex disorder with high prevalence rates all over the world. We conducted the first genome-wide gene-based association scan for hypertension in a Han Chinese population. By analyzing genome-wide single-nucleotide-polymorphism data of 400 matched pairs of young-onset hypertensive patients and normotensive controls genotyped with the Illumina HumanHap550-Duo BeadChip, 100 susceptibility genes for hypertension were identified and also validated with permutation tests. Seventeen of the 100 genes exhibited differential allelic and expression distributions between patient and control groups. These genes provided a good molecular signature for classifying hypertensive patients and normotensive controls. Among the 17 genes, IGF1, SLC4A4, WWOX, and SFMBT1 were not only identified by our gene-based association scan and gene expression analysis but were also replicated by a gene-based association analysis of the Hong Kong Hypertension Study. Moreover, cis-acting expression quantitative trait loci associated with the differentially expressed genes were found and linked to hypertension. IGF1, which encodes insulin-like growth factor 1, is associated with cardiovascular disorders, metabolic syndrome, decreased body weight/size, and changes of insulin levels in mice. SLC4A4, which encodes the electrogenic sodium bicarbonate cotransporter 1, is associated with decreased body weight/size and abnormal ion homeostasis in mice. WWOX, which encodes the WW domain-containing protein, is related to hypoglycemia and hyperphosphatemia. SFMBT1, which encodes the scm-like with four MBT domains protein 1, is a novel hypertension gene. GRB14, TMEM56 and KIAA1797 exhibited highly significant differential allelic and expressed distributions between hypertensive patients and normotensive controls. GRB14 was also found relevant to blood pressure in a previous genetic association study in East Asian populations. TMEM56 and KIAA1797 may be specific to Taiwanese populations, because they were not validated by the two replication studies. Identification of these genes enriches the collection of hypertension susceptibility genes, thereby shedding light on the etiology of hypertension in Han Chinese populations.

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          Most cited references75

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Mice carrying null mutations of the genes encoding insulin-like growth factor I (Igf-1) and type 1 IGF receptor (Igf1r).

            Newborn mice homozygous for a targeted disruption of insulin-like growth factor gene (Igf-1) exhibit a growth deficiency similar in severity to that previously observed in viable Igf-2 null mutants (60% of normal birthweight). Depending on genetic background, some of the Igf-1(-/-) dwarfs die shortly after birth, while others survive and reach adulthood. In contrast, null mutants for the Igf1r gene die invariably at birth of respiratory failure and exhibit a more severe growth deficiency (45% normal size). In addition to generalized organ hypoplasia in Igf1r(-/-) embryos, including the muscles, and developmental delays in ossification, deviations from normalcy were observed in the central nervous system and epidermis. Igf-1(-/-)/Igf1r(-/-) double mutants did not differ in phenotype from Igf1r(-/-) single mutants, while in Igf-2(-)/Igf1r(-/-) and Igf-1(-/-)/Igf-2(-) double mutants, which are phenotypically identical, the dwarfism was further exacerbated (30% normal size). The roles of the IGFs in mouse embryonic development, as revealed from the phenotypic differences between these mutants, are discussed.
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              Common regulatory variation impacts gene expression in a cell type-dependent manner.

              Studies correlating genetic variation to gene expression facilitate the interpretation of common human phenotypes and disease. As functional variants may be operating in a tissue-dependent manner, we performed gene expression profiling and association with genetic variants (single-nucleotide polymorphisms) on three cell types of 75 individuals. We detected cell type-specific genetic effects, with 69 to 80% of regulatory variants operating in a cell type-specific manner, and identified multiple expressive quantitative trait loci (eQTLs) per gene, unique or shared among cell types and positively correlated with the number of transcripts per gene. Cell type-specific eQTLs were found at larger distances from genes and at lower effect size, similar to known enhancers. These data suggest that the complete regulatory variant repertoire can only be uncovered in the context of cell-type specificity.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                29 March 2012
                : 7
                : 3
                Affiliations
                [1 ]Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
                [2 ]Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
                [3 ]Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
                [4 ]National Yang-Ming University School of Medicine and Taipei Veterans General Hospital, Taipei, Taiwan
                [5 ]School of Public Health, National Medical Defense Center, Taipei, Taiwan
                [6 ]Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan
                [7 ]Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
                [8 ]Department of Internal Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
                [9 ]Division of Cardiology, Cheng-Hsin Rehabilitation Medical Center, Taipei, Taiwan
                [10 ]Division of Cardiology, Min-Sheng General Hospital, Taoyuan, Taiwan
                [11 ]Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
                [12 ]Department of Psychiatry, The University of Hong Kong, Hong Kong, China
                [13 ]The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
                [14 ]School of Public Health, The University of Hong Kong, Hong Kong, China
                [15 ]Public Health, Epidemiology and Biostatistics, School of Health and Population Sciences, University of Birmingham, Birmingham, United Kingdom
                [16 ]Division of Preventive Medicine and Health Services Research, Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
                National Institutes of Health, United States of America
                Author notes

                Conceived and designed the experiments: HCY WHP. Performed the experiments: JWC HYH CTT T. Lin SHS WCT JHC HBL WHY TYC CIC SJL. Analyzed the data: HCY YJL KMC CMC YG. Contributed reagents/materials/analysis tools: HCY JWC HYH CTT T. Lin SHS WCT JHC HBL WHY TYC CIC SJL GNT BT PCS SSC T. Lam WHP. Wrote the paper: HCY WHP.

                Article
                PONE-D-11-03399
                10.1371/journal.pone.0032907
                3315540
                22479346
                133dea4d-1bae-4682-9f5d-54b4a9c2ba89
                Yang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Page count
                Pages: 14
                Categories
                Research Article
                Biology
                Computational Biology
                Genomics
                Genome Analysis Tools
                Molecular Genetics
                Genetics
                Human Genetics
                Mathematics
                Statistics
                Medicine
                Cardiovascular

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                Uncategorized

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