Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution

Functional and epigenetic phenotypes of humans and mice with DNMT3A Overgrowth Syndrome

Germline mutations in the DNMT3A gene can cause an overgrowth syndrome associated with behavioural and hematopoietic phenotypes. Here the authors describe a mouse model of this syndrome that recapitulates many of these features, including conserved alterations in DNA methylation in the blood cells of both species

Transcriptomic signatures across human tissues identify functional rare genetic variation

© 2020 American Association for the Advancement of Science. All rights reserved. INTRODUCTION: The human genome contains tens of thousands of rare (minor allele frequency 800 genomes matched with transcriptomes across 49 tissues, we were able to study RVs that underlie extreme changes in the transcriptome. To capture the diversity of these extreme changes, we developed and integrated approaches to identify expression, allele-specific expression, and alternative splicing outliers, and characterized the RV landscape underlying each outlier signal. We demonstrate that personal genome interpretation and RV discovery is enhanced by using these signals. This approach provides a new means to integrate a richer set of functional RVs into models of genetic burden, improve disease gene identification, and enable the delivery of precision genomics

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Transcriptomic signatures across human tissues identify functional rare genetic variation

Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.This work was supported by the Common Fund of the Office of the Director, U.S. National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, NIA, NIAID, and NINDS through NIH contracts HHSN261200800001E (Leidos Prime contract with NCI: A.M.S., D.E.T., N.V.R., J.A.M., L.S., M.E.B., L.Q., T.K., D.B., K.R., A.U.), 10XS170 (NDRI: W.F.L., J.A.T., G.K., A.M., S.S., R.H., G.Wa., M.J., M.Wa., L.E.B., C.J., J.W., B.R., M.Hu., K.M., L.A.S., H.M.G., M.Mo., L.K.B.), 10XS171 (Roswell Park Cancer Institute: B.A.F., M.T.M., E.K., B.M.G., K.D.R., J.B.), 10X172 (Science Care, Inc.), 12ST1039 (IDOX), 10ST1035 (Van Andel Institute: S.D.J., D.C.R., D.R.V.), HHSN268201000029C (Broad Institute: F.A., G.G., K.G.A., A.V.S., X.Li., E.T., S.G., A.G., S.A., K.H.H., D.T.N., K.H., S.R.M., J.L.N.), 5U41HG009494 (F.A., G.G., K.G.A.) and through NIH grants R01 DA006227-17 (Univ. of Miami Brain Bank: D.C.M., D.A.D.), Supplement to University of Miami grant DA006227 (D.C.M., D.A.D.), R01 MH090941 (Univ. of Geneva), R01 MH090951 and R01 MH090937 (Univ. of Chicago), R01 MH090936 (Univ. of North Carolina–Chapel Hill), R01MH101814 (M.M-A., V.W., S.B.M., R.G., E.T.D., D.G-M., A.V., A.B.), U01HG007593 (S.B.M.), R01MH101822 (C.D.B.), U01HG007598 (M.O., B.E.S.), U01MH104393 (A.P.F.), extension H002371 to 5U41HG002371 (W.J.K.) as well as other funding sources: R01MH106842 (T.L., P.M., E.F., P.J.H.), R01HL142028 (T.L., Si.Ka., P.J.H.), R01GM122924 (T.L., S.E.C.), R01MH107666 (H.K.I.), P30DK020595 (H.K.I.), UM1HG008901 (T.L.), R01GM124486 (T.L.), R01HG010067 (Y.Pa.), R01HG002585 (G.Wa., M.St.), Gordon and Betty Moore Foundation GBMF 4559 (G.Wa., M.St.), 1K99HG009916-01 (S.E.C.), R01HG006855 (Se.Ka., R.E.H.), BIO2015-70777-P, Ministerio de Economia y Competitividad and FEDER funds (M.M-A., V.W., R.G., D.G-M.), la Caixa Foundation ID 100010434 under agreement LCF/BQ/SO15/52260001 (D.G-M.), NIH CTSA grant UL1TR002550-01 (P.M.), Marie-Skłodowska Curie fellowship H2020 Grant 706636 (S.K-H.), R35HG010718 (E.R.G.), FPU15/03635, Ministerio de Educación, Cultura y Deporte (M.M-A.), R01MH109905, 1R01HG010480 (A.B.), Searle Scholar Program (A.B.), R01HG008150 (S.B.M., A.B.), 5T32HG000044-22, NHGRI Institutional Training Grant in Genome Science (N.R.G.), EU IMI program (UE7-DIRECT-115317-1) (E.T.D., A.V.), FNS funded project RNA1 (31003A_149984) (E.T.D., A.V.), DK110919 (F.H.), F32HG009987 (F.H.), Massachusetts Lions Eye Research Fund Grant (A.R.H.), Mr. and Mrs. Spencer T. Olin Fellowship for Women in Graduate Study (A.J.S.), P30DK20595 (H.K.I.), UL1 TR001114 (P.M.), R01AG066490 (S.B.M.), R01HL142015 (S.B.M.), U01HG009431 (S.B.M.), U01HG009080 (S.B.M.), NIMH 1R01MH109905 (A.B.), National Science Foundation Graduate Research Fellowship, grant no. DGE – 1656518 (N.M.F.), graduate fellowship from the Stanford Center for Computational, Evolutionary and Human Genomics (N.M.F.), New York Center for Collaborative Research in Common Disease Genomics grant UM1HG008901 (J.E.), National Science Foundation of China grant 31970554 (X.L.), Shanghai Science and Technology Major Project IHPC 2017SHZDZX01 (X.L.), NIH T32 LM012409 (C.S.), Hewlett-Packard Stanford Graduate Fellowship and a doctoral scholarship from the Natural Science and Engineering Council of Canada (E.K.T.), Lucille P. Markey Stanford Graduate Fellowship (J.R.D.). We used data from the MVP, Office of Research and Development, Veterans Health Administration, supported by award no. MVP000. This publication does not represent the views of the Department of Veterans Affairs, the U.S. Food and Drug Administration, or the U.S. Government. Molecular Data for the TOPMed program was supported by the National Heart, Lung and Blood Institute (NHLBI). Genome sequencing for “NHLBI TOPMed: The Jackson Heart Study” (phs000964.v1.p1) was performed at the Northwest Genomics Center (HHSN268201100037C). Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample-identity QC, and general program coordination were provided by the TOPMed Data Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I). This research was also supported by funding from: the Department of Veterans Affairs awards nos. I01-BX03340 and I01-BX003362 (T.L.A.). P.N. and G.M.P. are supported by R01HL142711 from the National Heart, Lung, and Blood Institute (NHLBI). The JHS is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I) and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute on Minority Health and Health Disparities (NIMHD)

Annexin A1 induces a pro-angiogenic macrophage phenotype to promote myocardial repair

Background Heart failure following myocardial infarction (MI) remains one of the major causes of death worldwide, and its treatment is a crucial challenge of cardiovascular medicine. An attractive therapeutic strategy is to stimulate endogenous mechanisms of myocardial regeneration. Objectives This study evaluates the potential therapeutic treatment with annexin A1 (AnxA1) to induce cardiac repair after MI. Methods AnxA1 knockout (AnxA1−/−) and wild-type mice underwent MI induced by ligation of the left anterior descending coronary artery. Cardiac functionality was assessed by longitudinal echocardiographic measurements. Histological, fluorescence-activated cell sorting, dot blot analysis, and in vitro/ex vivo studies were used to assess the myocardial neovascularization, macrophage content, and activity in response to AnxA1. Results AnxA1−/− mice showed a reduced cardiac functionality and an expansion of proinflammatory macrophages in the ischemic area. Cardiac macrophages from AnxA1−/− mice exhibited a dramatically reduced ability to release the proangiogenic mediator vascular endothelial growth factor (VEGF)–A. However, AnxA1 treatment enhanced VEGF-A release from cardiac macrophages, and its delivery in vivo markedly improved cardiac performance. The positive effect of AnxA1 treatment on cardiac performance was abolished in wild-type mice transplanted with bone marrow derived from Cx3cr1creERT2Vegfflox/flox or in mice depleted of macrophages. Similarly, cardioprotective effects of AnxA1 were obtained in pigs in which full-length AnxA1 was overexpressed by use of a cardiotropic adeno-associated virus. Conclusions AnxA1 has a direct action on cardiac macrophage polarization toward a pro-angiogenic, reparative phenotype. AnxA1 stimulated cardiac macrophages to release high amounts of VEGF-A, thus inducing neovascularization and cardiac repair

Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution