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      Whole genome sequencing of one complex pedigree illustrates challenges with genomic medicine

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          Abstract

          Background

          Human Phenotype Ontology (HPO) has risen as a useful tool for precision medicine by providing a standardized vocabulary of phenotypic abnormalities to describe presentations of human pathologies; however, there have been relatively few reports combining whole genome sequencing (WGS) and HPO, especially in the context of structural variants.

          Methods

          We illustrate an integrative analysis of WGS and HPO using an extended pedigree, which involves Prader–Willi Syndrome (PWS), hereditary hemochromatosis (HH), and dysautonomia-like symptoms. A comprehensive WGS pipeline was used to ensure reliable detection of genomic variants. Beyond variant filtering, we pursued phenotypic prioritization of candidate genes using Phenolyzer.

          Results

          Regarding PWS, WGS confirmed a 5.5 Mb de novo deletion of the parental allele at 15q11.2 to 15q13.1. Phenolyzer successfully returned the diagnosis of PWS, and pinpointed clinically relevant genes in the deletion. Further, Phenolyzer revealed how each of the genes is linked with the phenotypes represented by HPO terms. For HH, WGS identified a known disease variant (p.C282Y) in HFE of an affected female. Analysis of HPO terms alone fails to provide a correct diagnosis, but Phenolyzer successfully revealed the phenotype-genotype relationship using a disease-centric approach. Finally, Phenolyzer also revealed the complexity behind dysautonomia-like symptoms, and seven variants that might be associated with the phenotypes were identified by manual filtering based on a dominant inheritance model.

          Conclusions

          The integration of WGS and HPO can inform comprehensive molecular diagnosis for patients, eliminate false positives and reveal novel insights into undiagnosed diseases. Due to extreme heterogeneity and insufficient knowledge of human diseases, it is also important that phenotypic and genomic data are standardized and shared simultaneously.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12920-017-0246-5) contains supplementary material, which is available to authorized users.

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          Most cited references51

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          Patterns of somatic mutation in human cancer genomes.

          Cancers arise owing to mutations in a subset of genes that confer growth advantage. The availability of the human genome sequence led us to propose that systematic resequencing of cancer genomes for mutations would lead to the discovery of many additional cancer genes. Here we report more than 1,000 somatic mutations found in 274 megabases (Mb) of DNA corresponding to the coding exons of 518 protein kinase genes in 210 diverse human cancers. There was substantial variation in the number and pattern of mutations in individual cancers reflecting different exposures, DNA repair defects and cellular origins. Most somatic mutations are likely to be 'passengers' that do not contribute to oncogenesis. However, there was evidence for 'driver' mutations contributing to the development of the cancers studied in approximately 120 genes. Systematic sequencing of cancer genomes therefore reveals the evolutionary diversity of cancers and implicates a larger repertoire of cancer genes than previously anticipated.
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            The mystery of missing heritability: Genetic interactions create phantom heritability.

            Human genetics has been haunted by the mystery of "missing heritability" of common traits. Although studies have discovered >1,200 variants associated with common diseases and traits, these variants typically appear to explain only a minority of the heritability. The proportion of heritability explained by a set of variants is the ratio of (i) the heritability due to these variants (numerator), estimated directly from their observed effects, to (ii) the total heritability (denominator), inferred indirectly from population data. The prevailing view has been that the explanation for missing heritability lies in the numerator--that is, in as-yet undiscovered variants. While many variants surely remain to be found, we show here that a substantial portion of missing heritability could arise from overestimation of the denominator, creating "phantom heritability." Specifically, (i) estimates of total heritability implicitly assume the trait involves no genetic interactions (epistasis) among loci; (ii) this assumption is not justified, because models with interactions are also consistent with observable data; and (iii) under such models, the total heritability may be much smaller and thus the proportion of heritability explained much larger. For example, 80% of the currently missing heritability for Crohn's disease could be due to genetic interactions, if the disease involves interaction among three pathways. In short, missing heritability need not directly correspond to missing variants, because current estimates of total heritability may be significantly inflated by genetic interactions. Finally, we describe a method for estimating heritability from isolated populations that is not inflated by genetic interactions.
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              Splicing in disease: disruption of the splicing code and the decoding machinery.

              Human genes contain a dense array of diverse cis-acting elements that make up a code required for the expression of correctly spliced mRNAs. Alternative splicing generates a highly dynamic human proteome through networks of coordinated splicing events. Cis- and trans-acting mutations that disrupt the splicing code or the machinery required for splicing and its regulation have roles in various diseases, and recent studies have provided new insights into the mechanisms by which these effects occur. An unexpectedly large fraction of exonic mutations exhibit a primary pathogenic effect on splicing. Furthermore, normal genetic variation significantly contributes to disease severity and susceptibility by affecting splicing efficiency.
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                Author and article information

                Contributors
                hanfang.cshl@gmail.com
                wuyiyang.cshl@gmail.com
                yanghui@usc.edu
                margaretsyoon@gmail.com
                laurajimenezb33@gmail.com
                david.a.mittelman@gmail.com
                rjrobison@gmail.com
                kaichop@gmail.com
                gholsonjlyon@gmail.com
                Journal
                BMC Med Genomics
                BMC Med Genomics
                BMC Medical Genomics
                BioMed Central (London )
                1755-8794
                23 February 2017
                23 February 2017
                2017
                : 10
                : 10
                Affiliations
                [1 ]ISNI 0000 0004 0387 3667, GRID grid.225279.9, , Stanley Institute for Cognitive Genomics, One Bungtown Road, Cold Spring Harbor Laboratory, ; Cold Spring Harbor, NY USA
                [2 ]ISNI 0000 0001 2216 9681, GRID grid.36425.36, , Stony Brook University, ; 100 Nicolls Rd, Stony Brook, NY USA
                [3 ]ISNI 0000 0004 0387 3667, GRID grid.225279.9, , Simons Center for Quantitative Biology, One Bungtown Road, Cold Spring Harbor Laboratory, ; Cold Spring Harbor, NY USA
                [4 ]Centro de Ciencias Genomicas, Universidad Nacional Autonoma de Mexico, Cuernavaca, Morelos, MX Mexico
                [5 ]Tute, Genomics Inc., 150 S 100 W, Provo, UT USA
                [6 ]Utah Foundation for Biomedical Research, Salt Lake City, UT USA
                [7 ]ISNI 0000 0001 2156 6853, GRID grid.42505.36, , Zilkha Neurogenetic Institute, University of Southern California, ; Los Angeles, CA USA
                [8 ]ISNI 0000 0001 2156 6853, GRID grid.42505.36, , Neuroscience Graduate Program, University of Southern California, ; Los Angeles, CA USA
                [9 ]ISNI 0000 0001 2156 6853, GRID grid.42505.36, Department of Psychiatry, , University of Southern California, ; Los Angeles, CA USA
                [10 ]ISNI 0000 0001 2156 6853, GRID grid.42505.36, Division of Bioinformatics, Department of Preventive Medicine, , University of Southern California, ; Los Angeles, CA USA
                [11 ]ISNI 0000 0001 2285 2675, GRID grid.239585.0, Present Address: Department of Biomedical Informatics and Institute for Genomic Medicine, , Columbia University Medical Center, ; New York, 10032 NY USA
                Article
                246
                10.1186/s12920-017-0246-5
                5322674
                28228131
                2ecab116-a9aa-44a7-a31d-9f32839c8622
                © The Author(s). 2017

                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
                : 28 September 2016
                : 14 February 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: HG006465
                Award ID: CA045508
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Genetics
                whole genome sequencing,precision medicine,human phenotype ontology,phenolyzer,variant calling,prader–willi syndrome,dysautonomia,hemochromatosis

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