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      Autosomal dominant tubulointerstitial kidney disease-UMOD is the most frequent non polycystic genetic kidney disease

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          Abstract

          Background

          Autosomal dominant tubulointerstitial kidney disease (ADTKD) caused by mutations in the UMOD gene (ADTKD-UMOD) is considered rare and often remains unrecognised. We aimed to establish the prevalence of genetic kidney diseases, ADTKD and ADTKD-UMOD in adult chronic kidney disease (CKD) patients, and to investigate characteristic features.

          Methods

          We sent questionnaires on family history to all patients with CKD stages 3–5 in our tertiary renal centre to identify patients with inherited renal disease. Details on clinical and family history were obtained from patient interviews and clinical records. Sanger sequencing of the UMOD gene was performed from blood or saliva samples.

          Results

          2027 of 3770 sent questionnaires were returned. 459 patients reported a family history, which was consistent with inherited kidney disease in 217 patients. 182 non-responders with inherited kidney diseases were identified through a database search. Of these 399 individuals, 252 had autosomal dominant polycystic kidney disease (ADPKD), 28 had ADTKD, 25 had Alports, and 44 were unknown, resulting in 11% of CKD 3–5 patients and 19% of end-stage renal disease patients with genetic kidney diseases. Of the unknown, 40 were genotyped, of whom 31 had findings consistent with ADTKD. 30% of unknowns and 39% of unknowns with ADTKD had UMOD mutations. Altogether, 35 individuals from 18 families were found to have ten distinct UMOD mutations (three novel), making up 1% of patients with CKD 3–5, 2% of patients with end-stage renal disease, 9% of inherited kidney diseases and 56% with ADTKD. ADTKD-UMOD was the most common genetic kidney disease after ADPKD with a population prevalence of 9 per million. Less proteinuria and haematuria, but not hyperuricaemia or gout were predictive of ADTKD-UMOD. The main limitations of the study are the single-centre design and a predominantly Caucasian population.

          Conclusions

          The prevalence of genetic kidney diseases and ADTKD-UMOD is significantly higher than previously described. Clinical features poorly predicted ADTKD-UMOD, highlighting the need for genetic testing guided by family history alone.

          Electronic supplementary material

          The online version of this article (10.1186/s12882-018-1107-y) contains supplementary material, which is available to authorized users.

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          Most cited references41

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          A method and server for predicting damaging missense mutations

          To the Editor: Applications of rapidly advancing sequencing technologies exacerbate the need to interpret individual sequence variants. Sequencing of phenotyped clinical subjects will soon become a method of choice in studies of the genetic causes of Mendelian and complex diseases. New exon capture techniques will direct sequencing efforts towards the most informative and easily interpretable protein-coding fraction of the genome. Thus, the demand for computational predictions of the impact of protein sequence variants will continue to grow. Here we present a new method and the corresponding software tool, PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), which is different from the early tool PolyPhen1 in the set of predictive features, alignment pipeline, and the method of classification (Fig. 1a). PolyPhen-2 uses eight sequence-based and three structure-based predictive features (Supplementary Table 1) which were selected automatically by an iterative greedy algorithm (Supplementary Methods). Majority of these features involve comparison of a property of the wild-type (ancestral, normal) allele and the corresponding property of the mutant (derived, disease-causing) allele, which together define an amino acid replacement. Most informative features characterize how well the two human alleles fit into the pattern of amino acid replacements within the multiple sequence alignment of homologous proteins, how distant the protein harboring the first deviation from the human wild-type allele is from the human protein, and whether the mutant allele originated at a hypermutable site2. The alignment pipeline selects the set of homologous sequences for the analysis using a clustering algorithm and then constructs and refines their multiple alignment (Supplementary Fig. 1). The functional significance of an allele replacement is predicted from its individual features (Supplementary Figs. 2–4) by Naïve Bayes classifier (Supplementary Methods). We used two pairs of datasets to train and test PolyPhen-2. We compiled the first pair, HumDiv, from all 3,155 damaging alleles with known effects on the molecular function causing human Mendelian diseases, present in the UniProt database, together with 6,321 differences between human proteins and their closely related mammalian homologs, assumed to be non-damaging (Supplementary Methods). The second pair, HumVar3, consists of all the 13,032 human disease-causing mutations from UniProt, together with 8,946 human nsSNPs without annotated involvement in disease, which were treated as non-damaging. We found that PolyPhen-2 performance, as presented by its receiver operating characteristic curves, was consistently superior compared to PolyPhen (Fig. 1b) and it also compared favorably with the three other popular prediction tools4–6 (Fig. 1c). For a false positive rate of 20%, PolyPhen-2 achieves the rate of true positive predictions of 92% and 73% on HumDiv and HumVar, respectively (Supplementary Table 2). One reason for a lower accuracy of predictions on HumVar is that nsSNPs assumed to be non-damaging in HumVar contain a sizable fraction of mildly deleterious alleles. In contrast, most of amino acid replacements assumed non-damaging in HumDiv must be close to selective neutrality. Because alleles that are even mildly but unconditionally deleterious cannot be fixed in the evolving lineage, no method based on comparative sequence analysis is ideal for discriminating between drastically and mildly deleterious mutations, which are assigned to the opposite categories in HumVar. Another reason is that HumDiv uses an extra criterion to avoid possible erroneous annotations of damaging mutations. For a mutation, PolyPhen-2 calculates Naïve Bayes posterior probability that this mutation is damaging and reports estimates of false positive (the chance that the mutation is classified as damaging when it is in fact non-damaging) and true positive (the chance that the mutation is classified as damaging when it is indeed damaging) rates. A mutation is also appraised qualitatively, as benign, possibly damaging, or probably damaging (Supplementary Methods). The user can choose between HumDiv- and HumVar-trained PolyPhen-2. Diagnostics of Mendelian diseases requires distinguishing mutations with drastic effects from all the remaining human variation, including abundant mildly deleterious alleles. Thus, HumVar-trained PolyPhen-2 should be used for this task. In contrast, HumDiv-trained PolyPhen-2 should be used for evaluating rare alleles at loci potentially involved in complex phenotypes, dense mapping of regions identified by genome-wide association studies, and analysis of natural selection from sequence data, where even mildly deleterious alleles must be treated as damaging. Supplementary Material 1
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            An integrated map of genetic variation from 1,092 human genomes

            Summary Through characterising the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help understand the genetic contribution to disease. We describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methodologies to integrate information across multiple algorithms and diverse data sources we provide a validated haplotype map of 38 million SNPs, 1.4 million indels and over 14 thousand larger deletions. We show that individuals from different populations carry different profiles of rare and common variants and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways and that each individual harbours hundreds of rare non-coding variants at conserved sites, such as transcription-factor-motif disrupting changes. This resource, which captures up to 98% of accessible SNPs at a frequency of 1% in populations of medical genetics focus, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations.
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              Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.

              The effect of genetic mutation on phenotype is of significant interest in genetics. The type of genetic mutation that causes a single amino acid substitution (AAS) in a protein sequence is called a non-synonymous single nucleotide polymorphism (nsSNP). An nsSNP could potentially affect the function of the protein, subsequently altering the carrier's phenotype. This protocol describes the use of the 'Sorting Tolerant From Intolerant' (SIFT) algorithm in predicting whether an AAS affects protein function. To assess the effect of a substitution, SIFT assumes that important positions in a protein sequence have been conserved throughout evolution and therefore substitutions at these positions may affect protein function. Thus, by using sequence homology, SIFT predicts the effects of all possible substitutions at each position in the protein sequence. The protocol typically takes 5-20 min, depending on the input. SIFT is available as an online tool (http://sift.jcvi.org).
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                Author and article information

                Contributors
                +44 2392 286000 , Christine.gast@doctors.org.uk
                tony.marinaki@kcl.ac.uk
                monica.arenas-hernandez@gsts.com
                campbsar@gmail.com
                e.seaby@soton.ac.uk
                r.j.pengelly@soton.ac.uk
                d.gale@ucl.ac.uk
                Thomas.connor@ouh.nhs.uk
                dave.bunyan@salisbury.nhs.uk
                khod@lf1.cuni.cz
                mzivna@lf1.cuni.cz
                skmoch@lf1.cuni.cz
                s.ennis@soton.ac.uk
                gvr1951@gmail.com
                Journal
                BMC Nephrol
                BMC Nephrol
                BMC Nephrology
                BioMed Central (London )
                1471-2369
                30 October 2018
                30 October 2018
                2018
                : 19
                : 301
                Affiliations
                [1 ]ISNI 0000 0004 0392 0072, GRID grid.415470.3, Wessex Kidney Centre, Queen Alexandra Hospital, Portsmouth Hospitals NHS Trust, ; Southwick Hill Road, Cosham, Portsmouth, PO6 3LY UK
                [2 ]ISNI 0000 0004 1936 9297, GRID grid.5491.9, Human Genetics and Genomic Medicine, Faculty of Medicine, , University of Southampton, ; Southampton, UK
                [3 ]GRID grid.420545.2, Purine Research Laboratory, Guys and St Thomas’ NHS Foundation Trust, ; London, UK
                [4 ]ISNI 0000 0004 0417 012X, GRID grid.426108.9, UCL Centre for Nephrology, Royal Free Hospital, ; London, UK
                [5 ]ISNI 0000 0004 0488 9484, GRID grid.415719.f, Oxford Kidney Unit, Churchill Hospital, ; Oxford, UK
                [6 ]GRID grid.433814.9, Wessex Regional Genetics Laboratory, Salisbury NHS Foundation Trust, ; Salisbury, UK
                [7 ]ISNI 0000 0004 1937 116X, GRID grid.4491.8, Research Unit for Rare Diseases, Department of Pediatrics and Adolescent Medicine, First Faculty of Medicine, , Charles University Prague, ; Prague, Czech Republic
                Author information
                http://orcid.org/0000-0003-0200-4913
                Article
                1107
                10.1186/s12882-018-1107-y
                6208030
                30376835
                aa70e7cb-6ece-4a97-89c0-3a6faaf93f6a
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 9 August 2018
                : 19 October 2018
                Funding
                Funded by: Wessex Kidney Centre Charitable Research Fund
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2018

                Nephrology
                genetic kidney disease,autosomal dominant tubulointerstitial kidney disease,umod,prevalence

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