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      Mutations in Kelch-like 3 and Cullin 3 cause hypertension and electrolyte abnormalities

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      1 , 1 , 2 , 1 , 1 , 3 , 4 , 4 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 5 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 35 , 36 , 36 , 37 , 1
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          Abstract

          Hypertension affects one billion people and is a principal reversible risk factor for cardiovascular disease. A rare Mendelian syndrome, pseudohypoaldosteronism type II (PHAII), featuring hypertension, hyperkalemia, and metabolic acidosis, has revealed previously unrecognized physiology orchestrating the balance between renal salt reabsorption versus K + and H + excretion 1 . We used exome sequencing to identify mutations in Kelch-like 3 ( KLHL3) or Cullin 3 ( CUL3) in 41 PHAII kindreds. KLHL3 mutations are either recessive or dominant, while CUL3 mutations are dominant and predominantly de novo. CUL3 and BTB-Kelch proteins such as KLHL3 are components of Cullin/RING E3 ligase complexes (CRLs) that ubiquitinate substrates bound to Kelch propeller domains 28 . Dominant KLHL3 mutations are clustered in short segments within the Kelch propeller and BTB domains implicated in substrate 9 and Cullin 5 binding, respectively. Diverse CUL3 mutations all result in skipping of exon 9, producing an in-frame deletion. Because dominant KLHL3 and CUL3 mutations both phenocopy recessive loss-of-function KLHL3 mutations, they may abrogate ubiquitination of KLHL3 substrates. Disease features are reversed by thiazide diuretics, which inhibit the Na-Cl cotransporter (NCC) in the distal nephron of the kidney; KLHL3 and CUL3 are expressed in this location, suggesting a mechanistic link between KLHL3/ CUL3 mutations, increased Na-Cl reabsorption, and disease pathogenesis. These findings demonstrate the utility of exome sequencing in disease gene identification despite combined complexities of locus heterogeneity, mixed models of transmission, and frequent de novo mutation, and establish a fundamental role for KLHL3/CUL3 in blood pressure, K +, and pH homeostasis.

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          Is Open Access

          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            PLINK: a tool set for whole-genome association and population-based linkage analyses.

            Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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              Integrative Genomics Viewer

              To the Editor Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole genome sequencing, epigenetic surveys, expression profiling of coding and non-coding RNAs, SNP and copy number profiling, and functional assays. Analysis of these large, diverse datasets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large datasets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data poses a significant challenge to the development of such tools. To address this challenge we developed the Integrative Genomics Viewer (IGV), a lightweight visualization tool that enables intuitive real-time exploration of diverse, large-scale genomic datasets on standard desktop computers. It supports flexible integration of a wide range of genomic data types including aligned sequence reads, mutations, copy number, RNAi screens, gene expression, methylation, and genomic annotations (Figure S1). The IGV makes use of efficient, multi-resolution file formats to enable real-time exploration of arbitrarily large datasets over all resolution scales, while consuming minimal resources on the client computer (see Supplementary Text). Navigation through a dataset is similar to Google Maps, allowing the user to zoom and pan seamlessly across the genome at any level of detail from whole-genome to base pair (Figure S2). Datasets can be loaded from local or remote sources, including cloud-based resources, enabling investigators to view their own genomic datasets alongside publicly available data from, for example, The Cancer Genome Atlas (TCGA) 1 , 1000 Genomes (www.1000genomes.org/), and ENCODE 2 (www.genome.gov/10005107) projects. In addition, IGV allows collaborators to load and share data locally or remotely over the Web. IGV supports concurrent visualization of diverse data types across hundreds, and up to thousands of samples, and correlation of these integrated datasets with clinical and phenotypic variables. A researcher can define arbitrary sample annotations and associate them with data tracks using a simple tab-delimited file format (see Supplementary Text). These might include, for example, sample identifier (used to link different types of data for the same patient or tissue sample), phenotype, outcome, cluster membership, or any other clinical or experimental label. Annotations are displayed as a heatmap but more importantly are used for grouping, sorting, filtering, and overlaying diverse data types to yield a comprehensive picture of the integrated dataset. This is illustrated in Figure 1, a view of copy number, expression, mutation, and clinical data from 202 glioblastoma samples from the TCGA project in a 3 kb region around the EGFR locus 1, 3 . The investigator first grouped samples by tumor subtype, then by data type (copy number and expression), and finally sorted them by median copy number over the EGFR locus. A shared sample identifier links the copy number and expression tracks, maintaining their relative sort order within the subtypes. Mutation data is overlaid on corresponding copy number and expression tracks, based on shared participant identifier annotations. Several trends in the data stand out, such as a strong correlation between copy number and expression and an overrepresentation of EGFR amplified samples in the Classical subtype. IGV’s scalable architecture makes it well suited for genome-wide exploration of next-generation sequencing (NGS) datasets, including both basic aligned read data as well as derived results, such as read coverage. NGS datasets can approach terabytes in size, so careful management of data is necessary to conserve compute resources and to prevent information overload. IGV varies the displayed level of detail according to resolution scale. At very wide views, such as the whole genome, IGV represents NGS data by a simple coverage plot. Coverage data is often useful for assessing overall quality and diagnosing technical issues in sequencing runs (Figure S3), as well as analysis of ChIP-Seq 4 and RNA-Seq 5 experiments (Figures S4 and S5). As the user zooms below the ~50 kb range, individual aligned reads become visible (Figure 2) and putative SNPs are highlighted as allele counts in the coverage plot. Alignment details for each read are available in popup windows (Figures S6 and S7). Zooming further, individual base mismatches become visible, highlighted by color and intensity according to base call and quality. At this level, the investigator may sort reads by base, quality, strand, sample and other attributes to assess the evidence of a variant. This type of visual inspection can be an efficient and powerful tool for variant call validation, eliminating many false positives and aiding in confirmation of true findings (Figures S6 and S7). Many sequencing protocols produce reads from both ends (“paired ends”) of genomic fragments of known size distribution. IGV uses this information to color-code paired ends if their insert sizes are larger than expected, fall on different chromosomes, or have unexpected pair orientations. Such pairs, when consistent across multiple reads, can be indicative of a genomic rearrangement. When coloring aberrant paired ends, each chromosome is assigned a unique color, so that intra- (same color) and inter- (different color) chromosomal events are readily distinguished (Figures 2 and S8). We note that misalignments, particularly in repeat regions, can also yield unexpected insert sizes, and can be diagnosed with the IGV (Figure S9). There are a number of stand-alone, desktop genome browsers available today 6 including Artemis 7 , EagleView 8 , MapView 9 , Tablet 10 , Savant 11 , Apollo 12 , and the Integrated Genome Browser 13 . Many of them have features that overlap with IGV, particularly for NGS sequence alignment and genome annotation viewing. The Integrated Genome Browser also supports viewing array-based data. See Supplementary Table 1 and Supplementary Text for more detail. IGV focuses on the emerging integrative nature of genomic studies, placing equal emphasis on array-based platforms, such as expression and copy-number arrays, next-generation sequencing, as well as clinical and other sample metadata. Indeed, an important and unique feature of IGV is the ability to view all these different data types together and to use the sample metadata to dynamically group, sort, and filter datasets (Figure 1 above). Another important characteristic of IGV is fast data loading and real-time pan and zoom – at all scales of genome resolution and all dataset sizes, including datasets comprising hundreds of samples. Finally, we have placed great emphasis on the ease of installation and use of IGV, with the goal of making both the viewing and sharing of their data accessible to non-informatics end users. IGV is open source software and freely available at http://www.broadinstitute.org/igv/, including full documentation on use of the software. Supplementary Material 1
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                0028-0836
                1476-4687
                5 January 2012
                22 January 2012
                2 August 2012
                : 482
                : 7383
                : 98-102
                Affiliations
                [1 ]Department of Genetics and Howard Hughes Medical Institute, Yale University School of Medicine, New Haven, Connecticut, USA
                [2 ]Department of Dermatology, Yale University School of Medicine, New Haven, Connecticut, USA
                [3 ]Renal Division, Brigham and Women’s Hospital, Boston, Massachusetts, USA
                [4 ]Yale Center for Genome Analysis, Yale University, New Haven, Connecticut, USA
                [5 ]Nephrology Unit, Niguarda-Ca’ Granda Hospital, Milan, Italy
                [6 ]Department of Medicine, Laval University, Québec, Canada
                [7 ]Endocrine Hypertension Research Centre, University of Queensland School of Medicine, Brisbane, Australia
                [8 ]Department of Pediatrics, Canisius Wilhelmina Hospital, Nijmegen, Netherlands
                [9 ]Department of Pediatrics, Pays d’Aix Hospital, Aix-en-Provence, France
                [10 ]Division of Endocrinology, Department of Medicine, Helsinki University Central Hospital, Helsinki, Finland
                [11 ]Department of Pediatrics and Adolescent Medicine, American University Medical Center, Beirut, Lebanon
                [12 ]Hypertension Research Center, University of Medicine and Dentistry of New Jersey, Newark, New Jersey, USA
                [13 ]Department of Medical Genetics, University of Cambridge, Cambridge, United Kingdom
                [14 ]Division of Endocrinology, Roger Williams Medical Center, Providence, Rhode Island, USA
                [15 ]Department of Endocrinology and Diabetes, Ewen Downie Metabolic Unit, Alfred Hospital, Melbourne, Australia
                [16 ]Division of Medical Genetics, Department of Pediatrics, University of Iowa Children’s Hospital, Iowa City, Iowa, USA
                [17 ]Division of Nephrology, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
                [18 ]Department of Pediatrics, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
                [19 ]Duncan Guthrie Institute of Medical Genetics, Royal Hospital for Sick Children, Glasgow, United Kingdom
                [20 ]Baxter Healthcare Corporation, McGaw Park, Illinois, USA
                [21 ]Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
                [22 ]Division of Pediatrics, Mandic Hospital, Merate, Italy
                [23 ]Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
                [24 ]Division of Nephrology, Department of Pediatrics, Saint Louis University Health Sciences Center, St. Louis, Missouri, USA
                [25 ]Division of Nephrology, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
                [26 ]Division of Nephrology, Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA
                [27 ]Division of Nephrology, Cohen Children’s Medical Center of New York, New Hyde Park, New York, USA
                [28 ]West Midlands Regional Genetics Service, Birmingham Women’s Hospital, Birmingham, United Kingdom
                [29 ]Division of Nephrology, Department of Pediatrics, University of Alberta, Edmonton, Canada
                [30 ]Renal Unit, University College London Institute of Child Health, London, United Kingdom
                [31 ]Department of Nephrology, Royal Manchester Children’s Hospital, Manchester, United Kingdom
                [32 ]Department of Endocrinology, Children’s National Medical Center, Washington, DC, USA
                [33 ]Department of Pediatrics, Duke University Medical Center, Durham, North Carolina, USA
                [34 ]Division of Nephrology, Connecticut Children’s Medical Center, Hartford, Connecticut, USA
                [35 ]Division of Nephrology, Department of Medicine, University of Texas Health Science Center, San Antonio, Texas, USA
                [36 ]Department of Medicine, Columbia University College of Physicians and Surgeons, New York, New York, USA
                [37 ]Division of Nephrology, Children’s Hospital at Montefiore, Bronx, New York, USA
                Author notes
                Correspondence and requests for materials should be addressed to R.P.L. ( richard.lifton@ 123456yale.edu )
                [*]

                These authors contributed equally to this work.

                Article
                nihpa345981
                10.1038/nature10814
                3278668
                22266938
                81d40cb9-a80a-435d-ba82-ad32789e95bf

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                History
                Funding
                Funded by: National Center for Research Resources : NCRR
                Award ID: KL2 RR024138-07 || RR
                Funded by: Howard Hughes Medical Institute :
                Award ID: || HHMI_
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