Showing papers in "American Journal of Human Genetics in 2017"
TL;DR: The remarkable range of discoveriesGWASs has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics are reviewed.
Abstract: Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.
2,669 citations
TL;DR: It is demonstrated that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable.
Abstract: The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.
1,073 citations
TL;DR: The utility of InterVar is demonstrated in significantly reducing the time to interpret the clinical significance of sequence variants, especially for addressing severe congenital or very early-onset developmental disorders with high penetrance.
Abstract: In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published updated standards and guidelines for the clinical interpretation of sequence variants with respect to human diseases on the basis of 28 criteria. However, variability between individual interpreters can be extensive because of reasons such as the different understandings of these guidelines and the lack of standard algorithms for implementing them, yet computational tools for semi-automated variant interpretation are not available. To address these problems, we propose a suite of methods for implementing these criteria and have developed a tool called InterVar to help human reviewers interpret the clinical significance of variants. InterVar can take a pre-annotated or VCF file as input and generate automated interpretation on 18 criteria. Furthermore, we have developed a companion web server, wInterVar, to enable user-friendly variant interpretation with an automated interpretation step and a manual adjustment step. These tools are especially useful for addressing severe congenital or very early-onset developmental disorders with high penetrance. Using results from a few published sequencing studies, we demonstrate the utility of InterVar in significantly reducing the time to interpret the clinical significance of sequence variants.
631 citations
TL;DR: Findings from a cohort of 722 individuals with inherited retinal disease, who have had whole-genome sequencing, whole-exomes sequencing, or both performed, are presented, as part of the NIHR-BioResource Rare Diseases research study.
Abstract: Inherited retinal disease is a common cause of visual impairment and represents a highly heterogeneous group of conditions. Here, we present findings from a cohort of 722 individuals with inherited retinal disease, who have had whole-genome sequencing (n = 605), whole-exome sequencing (n = 72), or both (n = 45) performed, as part of the NIHR-BioResource Rare Diseases research study. We identified pathogenic variants (single-nucleotide variants, indels, or structural variants) for 404/722 (56%) individuals. Whole-genome sequencing gives unprecedented power to detect three categories of pathogenic variants in particular: structural variants, variants in GC-rich regions, which have significantly improved coverage compared to whole-exome sequencing, and variants in non-coding regulatory regions. In addition to previously reported pathogenic regulatory variants, we have identified a previously unreported pathogenic intronic variant in CHM in two males with choroideremia. We have also identified 19 genes not previously known to be associated with inherited retinal disease, which harbor biallelic predicted protein-truncating variants in unsolved cases. Whole-genome sequencing is an increasingly important comprehensive method with which to investigate the genetic causes of inherited retinal disease.
325 citations
TL;DR: An evidence-based, systematic method to assess the strength of gene-disease relationships will facilitate more knowledgeable utilization of genomic variants in clinical and research settings.
Abstract: With advances in genomic sequencing technology, the number of reported gene-disease relationships has rapidly expanded. However, the evidence supporting these claims varies widely, confounding accurate evaluation of genomic variation in a clinical setting. Despite the critical need to differentiate clinically valid relationships from less well-substantiated relationships, standard guidelines for such evaluation do not currently exist. The NIH-funded Clinical Genome Resource (ClinGen) has developed a framework to define and evaluate the clinical validity of gene-disease pairs across a variety of Mendelian disorders. In this manuscript we describe a proposed framework to evaluate relevant genetic and experimental evidence supporting or contradicting a gene-disease relationship and the subsequent validation of this framework using a set of representative gene-disease pairs. The framework provides a semiquantitative measurement for the strength of evidence of a gene-disease relationship that correlates to a qualitative classification: "Definitive," "Strong," "Moderate," "Limited," "No Reported Evidence," or "Conflicting Evidence." Within the ClinGen structure, classifications derived with this framework are reviewed and confirmed or adjusted based on clinical expertise of appropriate disease experts. Detailed guidance for utilizing this framework and access to the curation interface is available on our website. This evidence-based, systematic method to assess the strength of gene-disease relationships will facilitate more knowledgeable utilization of genomic variants in clinical and research settings.
323 citations
Centre Hospitalier Universitaire Sainte-Justine1, University of Washington2, Université de Montréal3, Montreal Neurological Institute and Hospital4, McGill University5, Children's Mercy Hospital6, GeneDx7, Baylor College of Medicine8, University of Toronto9, Toronto Western Hospital10, University of Melbourne11, Western General Hospital12, University Hospitals Bristol NHS Foundation Trust13, London North West Healthcare NHS Trust14, Nuffield Orthopaedic Centre15, Central Manchester University Hospitals NHS Foundation Trust16, University Hospital of Wales17, Cardiff University18, Great Ormond Street Hospital19, Leeds Teaching Hospitals NHS Trust20, State University of New York Upstate Medical University21, University Medical Center Groningen22, University of South Dakota23, Columbia University Medical Center24, Baptist Memorial Hospital-Memphis25, Boston Children's Hospital26, Washington University in St. Louis27, Radboud University Nijmegen28, University of Burgundy29, Tartu University Hospital30, Broad Institute31, Nationwide Children's Hospital32, Children's Hospital of Eastern Ontario33, University of Tasmania34, Walter and Eliza Hall Institute of Medical Research35, Children's Hospital at Westmead36, Sapienza University of Rome37, Istituto Superiore di Sanità38, Wolfson Medical Center39, University of Oklahoma Health Sciences Center40, University of British Columbia41, Pierre-and-Marie-Curie University42, Paris Diderot University43, Joint Genome Institute44, University of Alabama at Birmingham45, University of Arkansas for Medical Sciences46, Centre Hospitalier Universitaire de Sherbrooke47, University of Otago48, Université du Québec à Chicoutimi49, Royal Children's Hospital50, Florey Institute of Neuroscience and Mental Health51, University of Texas Southwestern Medical Center52
TL;DR: De novo missense variants explained a larger proportion of individuals in the series than in other series that were primarily ascertained because of ID, indicating that the genetic landscape of DEE might be different from that of ID without epilepsy.
Abstract: Developmental and epileptic encephalopathy (DEE) is a group of conditions characterized by the co-occurrence of epilepsy and intellectual disability (ID), typically with developmental plateauing or regression associated with frequent epileptiform activity. The cause of DEE remains unknown in the majority of cases. We performed whole-genome sequencing (WGS) in 197 individuals with unexplained DEE and pharmaco-resistant seizures and in their unaffected parents. We focused our attention on de novo mutations (DNMs) and identified candidate genes containing such variants. We sought to identify additional subjects with DNMs in these genes by performing targeted sequencing in another series of individuals with DEE and by mining various sequencing datasets. We also performed meta-analyses to document enrichment of DNMs in candidate genes by leveraging our WGS dataset with those of several DEE and ID series. By combining these strategies, we were able to provide a causal link between DEE and the following genes: NTRK2, GABRB2, CLTC, DHDDS, NUS1, RAB11A, GABBR2, and SNAP25. Overall, we established a molecular diagnosis in 63/197 (32%) individuals in our WGS series. The main cause of DEE in these individuals was de novo point mutations (53/63 solved cases), followed by inherited mutations (6/63 solved cases) and de novo CNVs (4/63 solved cases). De novo missense variants explained a larger proportion of individuals in our series than in other series that were primarily ascertained because of ID. Moreover, these DNMs were more frequently recurrent than those identified in ID series. These observations indicate that the genetic landscape of DEE might be different from that of ID without epilepsy.
299 citations
Children's Hospital of Eastern Ontario1, French Institute of Health and Medical Research2, University of Washington3, King Abdulaziz City for Science and Technology4, University of Leicester5, University of Toronto6, University of Santiago de Compostela7, Leiden University Medical Center8, McGill University9, Qatar Airways10, National Institutes of Health11, Pompeu Fabra University12, Johns Hopkins University School of Medicine13, University of British Columbia14, Wellcome Trust Sanger Institute15, University of South Florida16, Charles University in Prague17, Katholieke Universiteit Leuven18, Broad Institute19, Charité20, University of Hong Kong21, Medical University Plovdiv22, Istituto Superiore di Sanità23, Maastricht University24, Radboud University Nijmegen25, Seattle Children's26, Newcastle University27
TL;DR: The current and future bottlenecks to gene discovery are reviewed and strategies for enabling progress are suggested for enabling precision medicine for this patient population.
Abstract: Provision of a molecularly confirmed diagnosis in a timely manner for children and adults with rare genetic diseases shortens their "diagnostic odyssey," improves disease management, and fosters genetic counseling with respect to recurrence risks while assuring reproductive choices. In a general clinical genetics setting, the current diagnostic rate is approximately 50%, but for those who do not receive a molecular diagnosis after the initial genetics evaluation, that rate is much lower. Diagnostic success for these more challenging affected individuals depends to a large extent on progress in the discovery of genes associated with, and mechanisms underlying, rare diseases. Thus, continued research is required for moving toward a more complete catalog of disease-related genes and variants. The International Rare Diseases Research Consortium (IRDiRC) was established in 2011 to bring together researchers and organizations invested in rare disease research to develop a means of achieving molecular diagnosis for all rare diseases. Here, we review the current and future bottlenecks to gene discovery and suggest strategies for enabling progress in this regard. Each successful discovery will define potential diagnostic, preventive, and therapeutic opportunities for the corresponding rare disease, enabling precision medicine for this patient population.
280 citations
TL;DR: How multiplex assays of variant effect (MAVEs) can be used to measure the functional consequences of all possible variants in disease-relevant loci for a variety of molecular and cellular phenotypes and how this data can be combined with machine learning and clinical knowledge for the development of "lookup tables" of accurate pathogenicity predictions is discussed.
Abstract: Classical genetic approaches for interpreting variants, such as case-control or co-segregation studies, require finding many individuals with each variant. Because the overwhelming majority of variants are present in only a few living humans, this strategy has clear limits. Fully realizing the clinical potential of genetics requires that we accurately infer pathogenicity even for rare or private variation. Many computational approaches to predicting variant effects have been developed, but they can identify only a small fraction of pathogenic variants with the high confidence that is required in the clinic. Experimentally measuring a variant's functional consequences can provide clearer guidance, but individual assays performed only after the discovery of the variant are both time and resource intensive. Here, we discuss how multiplex assays of variant effect (MAVEs) can be used to measure the functional consequences of all possible variants in disease-relevant loci for a variety of molecular and cellular phenotypes. The resulting large-scale functional data can be combined with machine learning and clinical knowledge for the development of "lookup tables" of accurate pathogenicity predictions. A coordinated effort to produce, analyze, and disseminate large-scale functional data generated by multiplex assays could be essential to addressing the variant-interpretation crisis.
257 citations
TL;DR: A method for estimating the local genetic correlation between gene expression and a complex trait and utilizes it to estimate the genetic correlation due to predicted expression between pairs of traits, providing insight into the role of gene expression in the susceptibility of complex traits and diseases.
Abstract: Although genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. Here, we introduce a method for estimating the local genetic correlation between gene expression and a complex trait and utilize it to estimate the genetic correlation due to predicted expression between pairs of traits. We integrated gene expression measurements from 45 expression panels with summary GWAS data to perform 30 multi-tissue transcriptome-wide association studies (TWASs). We identified 1,196 genes whose expression is associated with these traits; of these, 168 reside more than 0.5 Mb away from any previously reported GWAS significant variant. We then used our approach to find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, eight were not found through genetic correlation at the SNP level. Finally, we used bi-directional regression to find evidence that BMI causally influences triglyceride levels and that triglyceride levels causally influence low-density lipoprotein. Together, our results provide insight into the role of gene expression in the susceptibility of complex traits and diseases.
256 citations
TL;DR: In this paper, the causal roles of cytokine signaling and upstream inflammation in immune-related and other chronic diseases were clarified using a genome-wide association study (GWAS) of up to 8,293 Finns.
Abstract: Circulating cytokines and growth factors are regulators of inflammation and have been implicated in autoimmune and metabolic diseases. In this genome-wide association study (GWAS) of up to 8,293 Finns we identified 27 genome-widely significant loci (p −9 ) for one or more cytokines. Fifteen of the associated variants had expression quantitative trait loci in whole blood. We provide genetic instruments to clarify the causal roles of cytokine signaling and upstream inflammation in immune-related and other chronic diseases. We further link inflammatory markers with variants previously associated with autoimmune diseases such as Crohn disease, multiple sclerosis, and ulcerative colitis and hereby elucidate the molecular mechanisms underpinning these diseases and suggest potential drug targets.
239 citations
TL;DR: π-HESS is introduced, a technique to quantify the correlation between pairs of traits due to genetic variation at a small region in the genome, requiring GWAS summary data only and making no distributional assumption on the causal variant effect sizes while accounting for linkage disequilibrium (LD) and overlapping GWAS samples.
Abstract: Although genetic correlations between complex traits provide valuable insights into epidemiological and etiological studies, a precise quantification of which genomic regions disproportionately contribute to the genome-wide correlation is currently lacking. Here, we introduce ρ-HESS, a technique to quantify the correlation between pairs of traits due to genetic variation at a small region in the genome. Our approach requires GWAS summary data only and makes no distributional assumption on the causal variant effect sizes while accounting for linkage disequilibrium (LD) and overlapping GWAS samples. We analyzed large-scale GWAS summary data across 36 quantitative traits, and identified 25 genomic regions that contribute significantly to the genetic correlation among these traits. Notably, we find 6 genomic regions that contribute to the genetic correlation of 10 pairs of traits that show negligible genome-wide correlation, further showcasing the power of local genetic correlation analyses. Finally, we report the distribution of local genetic correlations across the genome for 55 pairs of traits that show putative causal relationships.
TL;DR: The authors' experimental observations on human subjects and animal models strongly suggest that biallelic mutations in either CFAP43 or CFAP44 can cause sperm flagellar abnormalities and impair sperm motility.
Abstract: Sperm motility is vital to human reproduction Malformations of sperm flagella can cause male infertility Men with multiple morphological abnormalities of the flagella (MMAF) have abnormal spermatozoa with absent, short, coiled, bent, and/or irregular-caliber flagella, which impair sperm motility The known human MMAF-associated genes, such as DNAH1, only account for fewer than 45% of affected individuals Pathogenic mechanisms in the genetically unexplained MMAF remain to be elucidated Here, we conducted genetic analyses by using whole-exome sequencing and genome-wide comparative genomic hybridization microarrays in a multi-center cohort of 30 Han Chinese men affected by MMAF Among them, 12 subjects could not be genetically explained by any known MMAF-associated genes Intriguingly, we identified compound-heterozygous mutations in CFAP43 in three subjects and a homozygous frameshift mutation in CFAP44 in one subject All of these recessive mutations were parentally inherited from heterozygous carriers but were absent in 984 individuals from three Han Chinese control populations CFAP43 and CFAP44, encoding two cilia- and flagella-associated proteins (CFAPs), are specifically or preferentially expressed in the testis Using CRISPR/Cas9 technology, we generated two knockout models each deficient in mouse ortholog Cfap43 or Cfap44 Notably, both Cfap43- and Cfap44-deficient male mice presented with MMAF phenotypes, whereas the corresponding female mice were fertile Our experimental observations on human subjects and animal models strongly suggest that biallelic mutations in either CFAP43 or CFAP44 can cause sperm flagellar abnormalities and impair sperm motility Further investigations on other CFAP-encoding genes in more genetically unexplained MMAF-affected individuals could uncover novel mechanisms underlying sperm flagellar formation
TL;DR: Somatic mutations in MAP2K1 are a common cause of extracranial AVM, and the likely mechanism is endothelial cell dysfunction due to increased MEK1 activity.
Abstract: Arteriovenous malformation (AVM) is a fast-flow, congenital vascular anomaly that may arise anywhere in the body. AVMs typically progress, causing destruction of surrounding tissue and, sometimes, cardiac overload. AVMs are difficult to control; they often re-expand after embolization or resection, and pharmacologic therapy is unavailable. We studied extracranial AVMs in order to identify their biological basis. We performed whole-exome sequencing (WES) and whole-genome sequencing (WGS) on AVM tissue from affected individuals. Endothelial cells were separated from non-endothelial cells by immune-affinity purification. We used droplet digital PCR (ddPCR) to confirm mutations found by WES and WGS, to determine whether mutant alleles were enriched in endothelial or non-endothelial cells, and to screen additional AVM specimens. In seven of ten specimens, WES and WGS detected and ddPCR confirmed somatic mutations in mitogen activated protein kinase kinase 1 (MAP2K1), the gene that encodes MAP-extracellular signal-regulated kinase 1 (MEK1). Mutant alleles were enriched in endothelial cells and were not present in blood or saliva. 9 of 15 additional AVM specimens contained mutant MAP2K1 alleles. Mutations were missense or small in-frame deletions that affect amino acid residues within or adjacent to the protein's negative regulatory domain. Several of these mutations have been found in cancers and shown to increase MEK1 activity. In summary, somatic mutations in MAP2K1 are a common cause of extracranial AVM. The likely mechanism is endothelial cell dysfunction due to increased MEK1 activity. MEK1 inhibitors, which are approved to treat several forms of cancer, are potential therapeutic agents for individuals with extracranial AVM.
TL;DR: The occurrence of clonal hematopoiesis-associated mutations as a widespread mechanism linked with aging is supported, suggesting that mosaicism as a result ofClonal evolution of cells harboring somatic mutations is a universal mechanism occurring at all ages in healthy humans.
Abstract: Clonal hematopoiesis results from somatic mutations in hematopoietic stem cells, which give an advantage to mutant cells, driving their clonal expansion and potentially leading to leukemia. The acquisition of clonal hematopoiesis-driver mutations (CHDMs) occurs with normal aging and these mutations have been detected in more than 10% of individuals ≥65 years. We aimed to examine the prevalence and characteristics of CHDMs throughout adult life. We developed a targeted re-sequencing assay combining high-throughput with ultra-high sensitivity based on single-molecule molecular inversion probes (smMIPs). Using smMIPs, we screened more than 100 loci for CHDMs in more than 2,000 blood DNA samples from population controls between 20 and 69 years of age. Loci screened included 40 regions known to drive clonal hematopoiesis when mutated and 64 novel candidate loci. We identified 224 somatic mutations throughout our cohort, of which 216 were coding mutations in known driver genes (DNMT3A, JAK2, GNAS, TET2, and ASXL1), including 196 point mutations and 20 indels. Our assay's improved sensitivity allowed us to detect mutations with variant allele frequencies as low as 0.001. CHDMs were identified in more than 20% of individuals 60 to 69 years of age and in 3% of individuals 20 to 29 years of age, approximately double the previously reported prevalence despite screening a limited set of loci. Our findings support the occurrence of clonal hematopoiesis-associated mutations as a widespread mechanism linked with aging, suggesting that mosaicism as a result of clonal evolution of cells harboring somatic mutations is a universal mechanism occurring at all ages in healthy humans.
TL;DR: Evidence is presented that, compared with a meta-analysis, using individual-level data results in an increase of approximately 50% in power to detect eQTL, and approximately half the genetic variance for gene expression is not tagged by common SNPs.
Abstract: We analyzed the mRNA levels for 36,778 transcript expression traits (probes) from 2,765 individuals to comprehensively investigate the genetic architecture and degree of missing heritability for gene expression in peripheral blood. We identified 11,204 cis and 3,791 trans independent expression quantitative trait loci (eQTL) by using linear mixed models to perform genome-wide association analyses. Furthermore, using information on both closely and distantly related individuals, heritability was estimated for all expression traits. Of the set of expressed probes (15,966), 10,580 (66%) had an estimated narrow-sense heritability (h2) greater than zero with a mean (median) value of 0.192 (0.142). Across these probes, on average the proportion of genetic variance explained by all eQTL (hCOJO2) was 31% (0.060/0.192), meaning that 69% is missing, with the sentinel SNP of the largest eQTL explaining 87% (0.052/0.060) of the variance attributed to all identified cis- and trans-eQTL. For the same set of probes, the genetic variance attributed to genome-wide common (MAF > 0.01) HapMap 3 SNPs (hg2) accounted for on average 48% (0.093/0.192) of h2. Taken together, the evidence suggests that approximately half the genetic variance for gene expression is not tagged by common SNPs, and of the variance that is tagged by common SNPs, a large proportion can be attributed to identifiable eQTL of large effect, typically in cis. Finally, we present evidence that, compared with a meta-analysis, using individual-level data results in an increase of approximately 50% in power to detect eQTL.
TL;DR: This paper showed that a reference panel of 1,000 individuals from the target population is adequate for a GWAS cohort of up to 10, 000 individuals, whereas smaller panels, such as those from the 1000 Genomes Project, should be avoided.
Abstract: During the past few years, various novel statistical methods have been developed for fine-mapping with the use of summary statistics from genome-wide association studies (GWASs). Although these approaches require information about the linkage disequilibrium (LD) between variants, there has not been a comprehensive evaluation of how estimation of the LD structure from reference genotype panels performs in comparison with that from the original individual-level GWAS data. Using population genotype data from Finland and the UK Biobank, we show here that a reference panel of 1,000 individuals from the target population is adequate for a GWAS cohort of up to 10,000 individuals, whereas smaller panels, such as those from the 1000 Genomes Project, should be avoided. We also show, both theoretically and empirically, that the size of the reference panel needs to scale with the GWAS sample size; this has important consequences for the application of these methods in ongoing GWAS meta-analyses and large biobank studies. We conclude by providing software tools and by recommending practices for sharing LD information to more efficiently exploit summary statistics in genetics research.
Icahn School of Medicine at Mount Sinai1, Great Ormond Street Hospital2, University College London3, Vanderbilt University4, Cincinnati Children's Hospital Medical Center5, Northwestern University6, Marshfield Clinic7, Children's Hospital of Philadelphia8, Group Health Research Institute9, University of Washington10, Boston Children's Hospital11, National Institutes of Health12, Mayo Clinic13, Geisinger Health System14, Vanderbilt University Medical Center15
TL;DR: Willingness to participate was associated with self-identified white race, higher educational attainment, lower religiosity, perceiving more research benefits, fewer concerns, and fewer information needs, and the concern that the use of broad consent and open data sharing could adversely affect participant recruitment is not supported.
Abstract: Individuals participating in biobanks and other large research projects are increasingly asked to provide broad consent for open-ended research use and widespread sharing of their biosamples and data. We assessed willingness to participate in a biobank using different consent and data sharing models, hypothesizing that willingness would be higher under more restrictive scenarios. Perceived benefits, concerns, and information needs were also assessed. In this experimental survey, individuals from 11 US healthcare systems in the Electronic Medical Records and Genomics (eMERGE) Network were randomly allocated to one of three hypothetical scenarios: tiered consent and controlled data sharing; broad consent and controlled data sharing; or broad consent and open data sharing. Of 82,328 eligible individuals, exactly 13,000 (15.8%) completed the survey. Overall, 66% (95% CI: 63%–69%) of population-weighted respondents stated they would be willing to participate in a biobank; willingness and attitudes did not differ between respondents in the three scenarios. Willingness to participate was associated with self-identified white race, higher educational attainment, lower religiosity, perceiving more research benefits, fewer concerns, and fewer information needs. Most (86%, CI: 84%–87%) participants would want to know what would happen if a researcher misused their health information; fewer (51%, CI: 47%–55%) would worry about their privacy. The concern that the use of broad consent and open data sharing could adversely affect participant recruitment is not supported by these findings. Addressing potential participants’ concerns and information needs and building trust and relationships with communities may increase acceptance of broad consent and wide data sharing in biobank research.
TL;DR: MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research.
Abstract: One major challenge encountered with interpreting human genetic variants is the limited understanding of the functional impact of genetic alterations on biological processes. Furthermore, there remains an unmet demand for an efficient survey of the wealth of information on human homologs in model organisms across numerous databases. To efficiently assess the large volume of publically available information, it is important to provide a concise summary of the most relevant information in a rapid user-friendly format. To this end, we created MARRVEL (model organism aggregated resources for rare variant exploration). MARRVEL is a publicly available website that integrates information from six human genetic databases and seven model organism databases. For any given variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER. Importantly, it curates model organism-specific databases to concurrently display a concise summary regarding the human gene homologs in budding and fission yeast, worm, fly, fish, mouse, and rat on a single webpage. Experiment-based information on tissue expression, protein subcellular localization, biological process, and molecular function for the human gene and homologs in the seven model organisms are arranged into a concise output. Hence, rather than visiting multiple separate databases for variant and gene analysis, users can obtain important information by searching once through MARRVEL. Altogether, MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research.
Stanford University1, Vanderbilt University2, American Society of Human Genetics3, University of Toronto4, National Institutes of Health5, Geisinger Health System6, National Society of Genetic Counselors7, University of Washington8, Seattle Children's9, Sarah Lawrence College10, University of Miami11, University of Pennsylvania12, University of British Columbia13
TL;DR: It is inappropriate to perform germline gene editing that culminates in human pregnancy and future clinical application of human germline genome editing should not proceed unless there is a compelling medical rationale, an evidence base that supports its clinical use, and an ethical justification.
Abstract: With CRISPR/Cas9 and other genome-editing technologies, successful somatic and germline genome editing are becoming feasible. To respond, an American Society of Human Genetics (ASHG) workgroup developed this position statement, which was approved by the ASHG Board in March 2017. The workgroup included representatives from the UK Association of Genetic Nurses and Counsellors, Canadian Association of Genetic Counsellors, International Genetic Epidemiology Society, and US National Society of Genetic Counselors. These groups, as well as the American Society for Reproductive Medicine, Asia Pacific Society of Human Genetics, British Society for Genetic Medicine, Human Genetics Society of Australasia, Professional Society of Genetic Counselors in Asia, and Southern African Society for Human Genetics, endorsed the final statement. The statement includes the following positions. (1) At this time, given the nature and number of unanswered scientific, ethical, and policy questions, it is inappropriate to perform germline gene editing that culminates in human pregnancy. (2) Currently, there is no reason to prohibit in vitro germline genome editing on human embryos and gametes, with appropriate oversight and consent from donors, to facilitate research on the possible future clinical applications of gene editing. There should be no prohibition on making public funds available to support this research. (3) Future clinical application of human germline genome editing should not proceed unless, at a minimum, there is (a) a compelling medical rationale, (b) an evidence base that supports its clinical use, (c) an ethical justification, and (d) a transparent public process to solicit and incorporate stakeholder input.
University of Texas Health Science Center at Houston1, Erasmus University Medical Center2, University of North Carolina at Chapel Hill3, University of Michigan4, University of Washington5, University of Alabama at Birmingham6, University of Edinburgh7, University of Queensland8, University of Utah9, King's College London10, University of Maryland, Baltimore11, Los Angeles Biomedical Research Institute12, University of Toronto13, Stanford University14, Baylor College of Medicine15, University of Minnesota16, University of Mississippi Medical Center17, Boston University18, Boston Children's Hospital19, Northwestern University20, Icahn School of Medicine at Mount Sinai21, Harvard University22, Columbia University23, Wake Forest University24, University of California, Los Angeles25, University of Kentucky26, Mayo Clinic27, Regeneron28, MIND Institute29, Imperial College London30
TL;DR: A two-stage meta-analysis of the cross-sectional associations of systolic and diastolic BP with blood-derived genome-wide DNA methylation measured on the Infinium HumanMethylation450 BeadChip suggests that heritableDNA methylation plays a role in regulating BP independently of previously known genetic variants.
Abstract: Genome-wide association studies have identified hundreds of genetic variants associated with blood pressure (BP), but sequence variation accounts for a small fraction of the phenotypic variance. Epigenetic changes may alter the expression of genes involved in BP regulation and explain part of the missing heritability. We therefore conducted a two-stage meta-analysis of the cross-sectional associations of systolic and diastolic BP with blood-derived genome-wide DNA methylation measured on the Infinium HumanMethylation450 BeadChip in 17,010 individuals of European, African American, and Hispanic ancestry. Of 31 discovery-stage cytosine-phosphate-guanine (CpG) dinucleotides, 13 replicated after Bonferroni correction (discovery: N = 9,828, p 30%) and independent of known BP genetic variants, explaining an additional 1.4% and 2.0% of the interindividual variation in systolic and diastolic BP, respectively. Bidirectional Mendelian randomization among up to 4,513 individuals of European ancestry from 4 cohorts suggested that methylation at cg08035323 (TAF1B-YWHAQ) influences BP, while BP influences methylation at cg00533891 (ZMIZ1), cg00574958 (CPT1A), and cg02711608 (SLC1A5). Gene expression analyses further identified six genes (TSPAN2, SLC7A11, UNC93B1, CPT1A, PTMS, and LPCAT3) with evidence of triangular associations between methylation, gene expression, and BP. Additional integrative Mendelian randomization analyses of gene expression and DNA methylation suggested that the expression of TSPAN2 is a putative mediator of association between DNA methylation at cg23999170 and BP. These findings suggest that heritable DNA methylation plays a role in regulating BP independently of previously known genetic variants.
TL;DR: Analysis of the DNAm targets in each gene-specific signature identified both common gene targets, including homeobox A5 (HOXA5), which could account for some of the clinical overlap in CHARGE and Kabuki syndromes, as well as distinct gene targets.
Abstract: Epigenetic dysregulation has emerged as a recurring mechanism in the etiology of neurodevelopmental disorders. Two such disorders, CHARGE and Kabuki syndromes, result from loss of function mutations in chromodomain helicase DNA-binding protein 7 (CHD7LOF) and lysine (K) methyltransferase 2D (KMT2DLOF), respectively. Although these two syndromes are clinically distinct, there is significant phenotypic overlap. We therefore expected that epigenetically driven developmental pathways regulated by CHD7 and KMT2D would overlap and that DNA methylation (DNAm) alterations downstream of the mutations in these genes would identify common target genes, elucidating a mechanistic link between these two conditions, as well as specific target genes for each disorder. Genome-wide DNAm profiles in individuals with CHARGE and Kabuki syndromes with CHD7LOF or KMT2DLOF identified distinct sets of DNAm differences in each of the disorders, which were used to generate two unique, highly specific and sensitive DNAm signatures. These DNAm signatures were able to differentiate pathogenic mutations in these two genes from controls and from each other. Analysis of the DNAm targets in each gene-specific signature identified both common gene targets, including homeobox A5 (HOXA5), which could account for some of the clinical overlap in CHARGE and Kabuki syndromes, as well as distinct gene targets. Our findings demonstrate how characterization of the epigenome can contribute to our understanding of disease pathophysiology for epigenetic disorders, paving the way for explorations of novel therapeutics.
TL;DR: Deep hybrid capture and amplicon sequencing of five important mTOR pathway genes shows that brain somatic mutations in TSC1 and TSC2 cause FCD and that in utero application of the CRISPR-Cas9 system is useful for generating neurodevelopmental disease models of somatic mutation in the brain.
Abstract: Focal cortical dysplasia (FCD) is a major cause of the sporadic form of intractable focal epilepsies that require surgical treatment. It has recently been reported that brain somatic mutations in MTOR account for 15%–25% of FCD type II (FCDII), characterized by cortical dyslamination and dysmorphic neurons. However, the genetic etiologies of FCDII-affected individuals who lack the MTOR mutation remain unclear. Here, we performed deep hybrid capture and amplicon sequencing (read depth of 100×–20,012×) of five important mTOR pathway genes—PIK3CA, PIK3R2, AKT3, TSC1, and TSC2—by using paired brain and saliva samples from 40 FCDII individuals negative for MTOR mutations. We found that 5 of 40 individuals (12.5%) had brain somatic mutations in TSC1 (c.64C>T [p.Arg22Trp] and c.610C>T [p.Arg204Cys]) and TSC2 (c.4639G>A [p.Val1547Ile]), and these results were reproducible on two different sequencing platforms. All identified mutations induced hyperactivation of the mTOR pathway by disrupting the formation or function of the TSC1-TSC2 complex. Furthermore, in utero CRISPR-Cas9-mediated genome editing of Tsc1 or Tsc2 induced the development of spontaneous behavioral seizures, as well as cytomegalic neurons and cortical dyslamination. These results show that brain somatic mutations in TSC1 and TSC2 cause FCD and that in utero application of the CRISPR-Cas9 system is useful for generating neurodevelopmental disease models of somatic mutations in the brain.
TL;DR: It is estimated that Postzygotic mosaic mutations potentially contribute risk to 3%-4% of simplex ASD case subjects and future studies of PMMs in ASD and related disorders are warranted.
Abstract: Genetic risk factors for autism spectrum disorder (ASD) have yet to be fully elucidated. Postzygotic mosaic mutations (PMMs) have been implicated in several neurodevelopmental disorders and overgrowth syndromes. By leveraging whole-exome sequencing data on a large family-based ASD cohort, the Simons Simplex Collection, we systematically evaluated the potential role of PMMs in autism risk. Initial re-evaluation of published single-nucleotide variant (SNV) de novo mutations showed evidence consistent with putative PMMs for 11% of mutations. We developed a robust and sensitive SNV PMM calling approach integrating complementary callers, logistic regression modeling, and additional heuristics. In our high-confidence call set, we identified 470 PMMs in children, increasing the proportion of mosaic SNVs to 22%. Probands have a significant burden of synonymous PMMs and these mutations are enriched for computationally predicted impacts on splicing. Evidence of increased missense PMM burden was not seen in the full cohort. However, missense burden signal increased in subcohorts of families where probands lacked nonsynonymous germline mutations, especially in genes intolerant to mutations. Parental mosaic mutations that were transmitted account for 6.8% of the presumed de novo mutations in the children. PMMs were identified in previously implicated high-confidence neurodevelopmental disorder risk genes, such as CHD2, CTNNB1, SCN2A, and SYNGAP1, as well as candidate risk genes with predicted functions in chromatin remodeling or neurodevelopment, including ACTL6B, BAZ2B, COL5A3, SSRP1, and UNC79. We estimate that PMMs potentially contribute risk to 3%-4% of simplex ASD case subjects and future studies of PMMs in ASD and related disorders are warranted.
TL;DR: Insight is revealed into the genetic control of human growth and exome sequencing in OGID has a high diagnostic yield, and functional network analyses demonstrated that epigenetic regulation is a prominent biological process dysregulated in individuals with OGID.
Abstract: To explore the genetic architecture of human overgrowth syndromes and human growth control, we performed experimental and bioinformatic analyses of 710 individuals with overgrowth (height and/or head circumference ≥+2 SD) and intellectual disability (OGID). We identified a causal mutation in 1 of 14 genes in 50% (353/710). This includes HIST1H1E, encoding histone H1.4, which has not been associated with a developmental disorder previously. The pathogenic HIST1H1E mutations are predicted to result in a product that is less effective in neutralizing negatively charged linker DNA because it has a reduced net charge, and in DNA binding and protein-protein interactions because key residues are truncated. Functional network analyses demonstrated that epigenetic regulation is a prominent biological process dysregulated in individuals with OGID. Mutations in six epigenetic regulation genes—NSD1, EZH2, DNMT3A, CHD8, HIST1H1E, and EED—accounted for 44% of individuals (311/710). There was significant overlap between the 14 genes involved in OGID and 611 genes in regions identified in GWASs to be associated with height (p = 6.84 × 10−8), suggesting that a common variation impacting function of genes involved in OGID influences height at a population level. Increased cellular growth is a hallmark of cancer and there was striking overlap between the genes involved in OGID and 260 somatically mutated cancer driver genes (p = 1.75 × 10−14). However, the mutation spectra of genes involved in OGID and cancer differ, suggesting complex genotype-phenotype relationships. These data reveal insights into the genetic control of human growth and demonstrate that exome sequencing in OGID has a high diagnostic yield.
TL;DR: Trans-eQTL signals confirmed and extended the previously reported KLF14-mediated network to 55 target genes, validated the CIITA regulation of class II MHC genes, and identified ZNF800 as a candidate master regulator.
Abstract: Subcutaneous adipose tissue stores excess lipids and maintains energy balance. We performed expression quantitative trait locus (eQTL) analyses by using abdominal subcutaneous adipose tissue of 770 extensively phenotyped participants of the METSIM study. We identified cis-eQTLs for 12,400 genes at a 1% false-discovery rate. Among an approximately 680 known genome-wide association study (GWAS) loci for cardio-metabolic traits, we identified 140 coincident cis-eQTLs at 109 GWAS loci, including 93 eQTLs not previously described. At 49 of these 140 eQTLs, gene expression was nominally associated (p 5 Mb away. These trans-eQTL signals confirmed and extended the previously reported KLF14-mediated network to 55 target genes, validated the CIITA regulation of class II MHC genes, and identified ZNF800 as a candidate master regulator. Finally, we observed similar expression-clinical trait correlations of genes associated with GWAS loci in both humans and a panel of genetically diverse mice. These results provide candidate genes for further investigation of their potential roles in adipose biology and in regulating cardio-metabolic traits.
Rachel B. Ramoni1, Rachel B. Ramoni2, John J. Mulvihill3, David R. Adams3 +176 more•Institutions (8)
TL;DR: The Undiagnosed Diseases Network is extended nationally to meld clinical and research objectives, improve patient outcomes, and contribute to medical science.
Abstract: Diagnosis at the edges of our knowledge calls upon clinicians to be data driven, cross-disciplinary, and collaborative in unprecedented ways. Exact disease recognition, an element of the concept of precision in medicine, requires new infrastructure that spans geography, institutional boundaries, and the divide between clinical care and research. The National Institutes of Health (NIH) Common Fund supports the Undiagnosed Diseases Network (UDN) as an exemplar of this model of precise diagnosis. Its goals are to forge a strategy to accelerate the diagnosis of rare or previously unrecognized diseases, to improve recommendations for clinical management, and to advance research, especially into disease mechanisms. The network will achieve these objectives by evaluating patients with undiagnosed diseases, fostering a breadth of expert collaborations, determining best practices for translating the strategy into medical centers nationwide, and sharing findings, data, specimens, and approaches with the scientific and medical communities. Building the UDN has already brought insights to human and medical geneticists. The initial focus has been on data sharing, establishing common protocols for institutional review boards and data sharing, creating protocols for referring and evaluating patients, and providing DNA sequencing, metabolomic analysis, and functional studies in model organisms. By extending this precision diagnostic model nationally, we strive to meld clinical and research objectives, improve patient outcomes, and contribute to medical science.
TL;DR: This study identifies a previously unknown protective SMA modifier in humans, demonstrates modifier impact in three different SMA animal models, and suggests a potential combinatorial therapeutic strategy to efficiently treat SMA.
Abstract: Homozygous SMN1 loss causes spinal muscular atrophy (SMA), the most common lethal genetic childhood motor neuron disease. SMN1 encodes SMN, a ubiquitous housekeeping protein, which makes the primarily motor neuron-specific phenotype rather unexpected. SMA-affected individuals harbor low SMN expression from one to six SMN2 copies, which is insufficient to functionally compensate for SMN1 loss. However, rarely individuals with homozygous absence of SMN1 and only three to four SMN2 copies are fully asymptomatic, suggesting protection through genetic modifier(s). Previously, we identified plastin 3 (PLS3) overexpression as an SMA protective modifier in humans and showed that SMN deficit impairs endocytosis, which is rescued by elevated PLS3 levels. Here, we identify reduction of the neuronal calcium sensor Neurocalcin delta (NCALD) as a protective SMA modifier in five asymptomatic SMN1-deleted individuals carrying only four SMN2 copies. We demonstrate that NCALD is a Ca2+-dependent negative regulator of endocytosis, as NCALD knockdown improves endocytosis in SMA models and ameliorates pharmacologically induced endocytosis defects in zebrafish. Importantly, NCALD knockdown effectively ameliorates SMA-associated pathological defects across species, including worm, zebrafish, and mouse. In conclusion, our study identifies a previously unknown protective SMA modifier in humans, demonstrates modifier impact in three different SMA animal models, and suggests a potential combinatorial therapeutic strategy to efficiently treat SMA. Since both protective modifiers restore endocytosis, our results confirm that endocytosis is a major cellular mechanism perturbed in SMA and emphasize the power of protective modifiers for understanding disease mechanism and developing therapies.
TL;DR: A CRISPR-based system that uses pairs of guide RNAs to program thousands of kilobase-scale deletions that deeply scan across a targeted region in a tiling fashion is described, suggesting that no singular distal regulatory element is required for HPRT1 expression and that distal mutations are unlikely to contribute substantially to Lesch-Nyhan syndrome burden.
Abstract: The extent to which non-coding mutations contribute to Mendelian disease is a major unknown in human genetics. Relatedly, the vast majority of candidate regulatory elements have yet to be functionally validated. Here, we describe a CRISPR-based system that uses pairs of guide RNAs (gRNAs) to program thousands of kilobase-scale deletions that deeply scan across a targeted region in a tiling fashion ("ScanDel"). We applied ScanDel to HPRT1, the housekeeping gene underlying Lesch-Nyhan syndrome, an X-linked recessive disorder. Altogether, we programmed 4,342 overlapping 1 and 2 kb deletions that tiled 206 kb centered on HPRT1 (including 87 kb upstream and 79 kb downstream) with median 27-fold redundancy per base. We functionally assayed programmed deletions in parallel by selecting for loss of HPRT function with 6-thioguanine. As expected, sequencing gRNA pairs before and after selection confirmed that all HPRT1 exons are needed. However, HPRT1 function was robust to deletion of any intergenic or deeply intronic non-coding region, indicating that proximal regulatory sequences are sufficient for HPRT1 expression. Although our screen did identify the disruption of exon-proximal non-coding sequences (e.g., the promoter) as functionally consequential, long-read sequencing revealed that this signal was driven by rare, imprecise deletions that extended into exons. Our results suggest that no singular distal regulatory element is required for HPRT1 expression and that distal mutations are unlikely to contribute substantially to Lesch-Nyhan syndrome burden. Further application of ScanDel could shed light on the role of regulatory mutations in disease at other loci while also facilitating a deeper understanding of endogenous gene regulation.
TL;DR: This work introduces a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics and demonstrates that this method provides accurate covariance estimates, thereby enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies.
Abstract: Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits' genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses, we demonstrate that our method provides accurate covariance estimates, thereby enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (Ntotal≈ 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimer disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in single-nucleotide polymorphisms (SNPs) with high minor allele frequencies and in SNPs located in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOAD's correlation with cognitive traits and hints at an autoimmune component for ALS.
Erasmus University Medical Center1, Boston Children's Hospital2, GeneDx3, University of California, San Francisco4, University of Washington5, Baylor College of Medicine6, Stanford University7, French Institute of Health and Medical Research8, Arkansas Children's Hospital9, University of Dundee10, Radboud University Nijmegen11, Children's Hospital of Philadelphia12, University of Illinois at Chicago13, Belfast Health and Social Care Trust14, Nottingham University Hospitals NHS Trust15, Utrecht University16, Washington University in St. Louis17, Leiden University Medical Center18, Seattle Children's Research Institute19, Heidelberg University20, German Cancer Research Center21, University of Nantes22, University of Strasbourg23, University of Poitiers24, Baylor University25, Seattle Children's26, University of Limerick27, University of Rennes28, Wellcome Trust Sanger Institute29, University of Melbourne30
TL;DR: The importance of CAMK 2A and CAMK2B and their auto-phosphorylation in human brain function is established and the phenotypic spectrum of the disorders caused by variants in key players of the glutamatergic signaling pathway is expanded.
Abstract: Calcium/calmodulin-dependent protein kinase II (CAMK2) is one of the first proteins shown to be essential for normal learning and synaptic plasticity in mice, but its requirement for human brain development has not yet been established. Through a multi-center collaborative study based on a whole-exome sequencing approach, we identified 19 exceedingly rare de novo CAMK2A or CAMK2B variants in 24 unrelated individuals with intellectual disability. Variants were assessed for their effect on CAMK2 function and on neuronal migration. For both CAMK2A and CAMK2B, we identified mutations that decreased or increased CAMK2 auto-phosphorylation at Thr286/Thr287. We further found that all mutations affecting auto-phosphorylation also affected neuronal migration, highlighting the importance of tightly regulated CAMK2 auto-phosphorylation in neuronal function and neurodevelopment. Our data establish the importance of CAMK2A and CAMK2B and their auto-phosphorylation in human brain function and expand the phenotypic spectrum of the disorders caused by variants in key players of the glutamatergic signaling pathway.