ALBERT

Description: Accurate prediction of the functional effect of genetic variation is critical for clinical genome interpretation. We systematically characterized the transcriptome effects of protein-truncating variants, a class of variants expected to have profound effects on gene function, using data from the Genotype-Tissue Expression (GTEx) and Geuvadis projects. We quantitated tissue-specific and positional effects on nonsense-mediated transcript decay and present an improved predictive model for this decay. We directly measured the effect of variants both proximal and distal to splice junctions. Furthermore, we found that robustness to heterozygous gene inactivation is not due to dosage compensation. Our results illustrate the value of transcriptome data in the functional interpretation of genetic variants.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4537935/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4537935/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Rivas, Manuel A -- Pirinen, Matti -- Conrad, Donald F -- Lek, Monkol -- Tsang, Emily K -- Karczewski, Konrad J -- Maller, Julian B -- Kukurba, Kimberly R -- DeLuca, David S -- Fromer, Menachem -- Ferreira, Pedro G -- Smith, Kevin S -- Zhang, Rui -- Zhao, Fengmei -- Banks, Eric -- Poplin, Ryan -- Ruderfer, Douglas M -- Purcell, Shaun M -- Tukiainen, Taru -- Minikel, Eric V -- Stenson, Peter D -- Cooper, David N -- Huang, Katharine H -- Sullivan, Timothy J -- Nedzel, Jared -- GTEx Consortium -- Geuvadis Consortium -- Bustamante, Carlos D -- Li, Jin Billy -- Daly, Mark J -- Guigo, Roderic -- Donnelly, Peter -- Ardlie, Kristin -- Sammeth, Michael -- Dermitzakis, Emmanouil T -- McCarthy, Mark I -- Montgomery, Stephen B -- Lappalainen, Tuuli -- MacArthur, Daniel G -- 090532/Wellcome Trust/United Kingdom -- 090532/Z/09/Z/Wellcome Trust/United Kingdom -- 095552/Wellcome Trust/United Kingdom -- 095552/Z/11/Z/Wellcome Trust/United Kingdom -- 098381/Wellcome Trust/United Kingdom -- DA006227/DA/NIDA NIH HHS/ -- HHSN261200800001E/CA/NCI NIH HHS/ -- HHSN261200800001E/PHS HHS/ -- HHSN268201000029C/HL/NHLBI NIH HHS/ -- HHSN268201000029C/PHS HHS/ -- MH090936/MH/NIMH NIH HHS/ -- MH090937/MH/NIMH NIH HHS/ -- MH090941/MH/NIMH NIH HHS/ -- MH090948/MH/NIMH NIH HHS/ -- MH090951/MH/NIMH NIH HHS/ -- P30 DK020595/DK/NIDDK NIH HHS/ -- R01 GM104371/GM/NIGMS NIH HHS/ -- R01 MH090941/MH/NIMH NIH HHS/ -- R01 MH101810/MH/NIMH NIH HHS/ -- R01 MH101814/MH/NIMH NIH HHS/ -- R01 MH101820/MH/NIMH NIH HHS/ -- R01GM104371/GM/NIGMS NIH HHS/ -- R01MH090941/MH/NIMH NIH HHS/ -- R01MH101810/MH/NIMH NIH HHS/ -- R01MH101814/MH/NIMH NIH HHS/ -- U01 HG007593/HG/NHGRI NIH HHS/ -- U01HG007593/HG/NHGRI NIH HHS/ -- New York, N.Y. -- Science. 2015 May 8;348(6235):666-9. doi: 10.1126/science.1261877.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Wellcome Trust Centre for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. rivas@well.ox.ac.uk tlappalainen@nygenome.org macarthur@atgu.mgh.harvard.edu. ; FInstitute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. ; Washington University in St. Louis, St. Louis, MO, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA, USA. Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. ; Department of Genetics, Stanford University, Stanford, CA, USA. Department of Pathology, Stanford University, Stanford, CA, USA. Biomedical Informatics Program, Stanford University, Stanford, CA, USA. ; Department of Genetics, Stanford University, Stanford, CA, USA. Department of Pathology, Stanford University, Stanford, CA, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA, USA. Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. Department of Psychiatry, Mt. Sinai Hospital, NY, USA. ; Department of Genetic Medicine and Development,University of Geneva, Geneva, Switzerland. Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland. Swiss Institute of Bioinformatics, Geneva, Switzerland. ; Department of Genetics, Stanford University, Stanford, CA, USA. ; Department of Psychiatry, Mt. Sinai Hospital, NY, USA. Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA, USA. Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. Department of Psychiatry, Mt. Sinai Hospital, NY, USA. Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY, USA. ; Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, UK. ; Center for Genomic Regulation (CRG), Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain. ; Wellcome Trust Centre for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Department of Statistics, University of Oxford, Oxford, UK. ; Center for Genomic Regulation (CRG), Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain. National Institute for Scientific Computing (LNCC), Petropolis, Rio de Janeiro, Brazil. ; Wellcome Trust Centre for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Oxford Center for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK. ; Department of Genetics, Stanford University, Stanford, CA, USA. Department of Genetic Medicine and Development,University of Geneva, Geneva, Switzerland. Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland. Swiss Institute of Bioinformatics, Geneva, Switzerland. New York Genome Center, New York, NY, USA. Department of Systems Biology, Columbia University, New York, NY, USA. rivas@well.ox.ac.uk tlappalainen@nygenome.org macarthur@atgu.mgh.harvard.edu. ; Broad Institute of MIT and Harvard, Cambridge, MA, USA. Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. Department of Medicine, Harvard Medical School, Boston, MA, USA. rivas@well.ox.ac.uk tlappalainen@nygenome.org macarthur@atgu.mgh.harvard.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25954003" target="_blank"〉PubMed〈/a〉

Description: Genome-wide association studies have discovered many genetic loci associated with disease traits, but the functional molecular basis of these associations is often unresolved. Genome-wide regulatory and gene expression profiles measured across individuals and diseases reflect downstream effects of genetic variation and may allow for functional assessment of disease-associated loci. Here,...

Description: : Site-directed mutagenesis is frequently used by scientists to investigate the functional impact of amino acid mutations in the laboratory. Over 10 000 such laboratory-induced mutations have been reported in the UniProt database along with the outcomes of functional assays. Here, we explore the performance of state-of-the-art computational tools (Condel, PolyPhen-2 and SIFT) in correctly annotating the function-altering potential of 10 913 laboratory-induced mutations from 2372 proteins. We find that computational tools are very successful in diagnosing laboratory-induced mutations that elicit significant functional change in the laboratory (up to 92% accuracy). But, these tools consistently fail in correctly annotating laboratory-induced mutations that show no functional impact in the laboratory assays. Therefore, the overall accuracy of computational tools for laboratory-induced mutations is much lower than that observed for the naturally occurring human variants. We tested and rejected the possibilities that the preponderance of changes to alanine and the presence of multiple base-pair mutations in the laboratory were the reasons for the observed discordance between the performance of computational tools for natural and laboratory mutations. Instead, we discover that the laboratory-induced mutations occur predominately at the highly conserved positions in proteins, where the computational tools have the lowest accuracy of correct prediction for variants that do not impact function (neutral). Therefore, the comparisons of experimental-profiling results with those from computational predictions need to be sensitive to the evolutionary conservation of the positions harboring the amino acid change. Contact: s.kumar@asu.edu