Author
Tarjinder Singh
Other affiliations: Harvard University, Williams College, Wellcome Trust Sanger Institute ...read more
Bio: Tarjinder Singh is an academic researcher from Broad Institute. The author has contributed to research in topics: Exome sequencing & Population. The author has an hindex of 20, co-authored 37 publications receiving 4318 citations. Previous affiliations of Tarjinder Singh include Harvard University & Williams College.
Papers
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Icahn School of Medicine at Mount Sinai1, Carnegie Mellon University2, Harvard University3, University of Toronto4, Wellcome Trust Sanger Institute5, University of Pittsburgh6, Nagoya University7, University of Freiburg8, King's College London9, Vanderbilt University10, King Abdulaziz University11, University of Santiago de Compostela12, University of Utah13, Duke University14, Memorial University of Newfoundland15, Trinity College, Dublin16, University of Pennsylvania17, University of Illinois at Chicago18, Boston Children's Hospital19, Columbia University20, German Cancer Research Center21, University College London22, Kaiser Permanente23, Broad Institute24, Cardiff University25, Complutense University of Madrid26, Newcastle University27, Baylor College of Medicine28, University of California, San Francisco29, RWTH Aachen University30, National Health Service31, McMaster University32, Saarland University33, Karolinska Institutet34, National Institutes of Health35, University of Helsinki36, Emory University37
TL;DR: Using exome sequencing, it is shown that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate of < 0.05, plus a set of 107 genes strongly enriched for those likely to affect risk (FDR < 0.30).
Abstract: The genetic architecture of autism spectrum disorder involves the interplay of common and rare variants and their impact on hundreds of genes. Using exome sequencing, here we show that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate (FDR) < 0.05, plus a set of 107 autosomal genes strongly enriched for those likely to affect risk (FDR < 0.30). These 107 genes, which show unusual evolutionary constraint against mutations, incur de novo loss-of-function mutations in over 5% of autistic subjects. Many of the genes implicated encode proteins for synaptic formation, transcriptional regulation and chromatin-remodelling pathways. These include voltage-gated ion channels regulating the propagation of action potentials, pacemaking and excitability-transcription coupling, as well as histone-modifying enzymes and chromatin remodellers-most prominently those that mediate post-translational lysine methylation/demethylation modifications of histones.
2,228 citations
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F. Kyle Satterstrom1, F. Kyle Satterstrom2, Jack A. Kosmicki, Jiebiao Wang3 +198 more•Institutions (53)
TL;DR: The largest exome sequencing study of autism spectrum disorder (ASD) to date, using an enhanced analytical framework to integrate de novo and case-control rare variation, identifies 102 risk genes at a false discovery rate of 0.1 or less, consistent with multiple paths to an excitatory-inhibitory imbalance underlying ASD.
1,169 citations
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F. Kyle Satterstrom1, Jack A. Kosmicki1, Jiebiao Wang2, Michael S. Breen3 +150 more•Institutions (45)
TL;DR: Using an enhanced Bayesian framework to integrate de novo and case-control rare variation, 102 risk genes are identified at a false discovery rate of ≤ 0.1, consistent with multiple paths to an excitatory/inhibitory imbalance underlying ASD.
Abstract: We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n=35,584 total samples, 11,986 with ASD). Using an enhanced Bayesian framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate ≤ 0.1. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained for severe neurodevelopmental delay, while 53 show higher frequencies in individuals ascertained for ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most of the risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In human cortex single-cell gene expression data, expression of risk genes is enriched in both excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory/inhibitory imbalance underlying ASD.
461 citations
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Wellcome Trust Sanger Institute1, University of Helsinki2, University College London3, Icahn School of Medicine at Mount Sinai4, National Institutes of Health5, University of Edinburgh6, King's College London7, Queen's University8, Chapel Allerton Hospital9, Cardiff University10, Broad Institute11, University of California, Los Angeles12, University of Glasgow13, Oulu University Hospital14, Karolinska Institutet15, University of Oulu16, University of Cambridge17, University Hospitals Bristol NHS Foundation Trust18, University of Aberdeen19, University of North Carolina at Chapel Hill20
TL;DR: In this article, the authors found a genome-wide significant association between rare loss-of-function (LoF) variants in SETD1A and risk for schizophrenia (P = 3.3 × 10−9).
Abstract: By analyzing the whole-exome sequences of 4,264 schizophrenia cases, 9,343 controls and 1,077 trios, we identified a genome-wide significant association between rare loss-of-function (LoF) variants in SETD1A and risk for schizophrenia (P = 3.3 × 10−9). We found only two heterozygous LoF variants in 45,376 exomes from individuals without a neuropsychiatric diagnosis, indicating that SETD1A is substantially depleted of LoF variants in the general population. Seven of the ten individuals with schizophrenia carrying SETD1A LoF variants also had learning difficulties. We further identified four SETD1A LoF carriers among 4,281 children with severe developmental disorders and two more carriers in an independent sample of 5,720 Finnish exomes, both with notable neuropsychiatric phenotypes. Together, our observations indicate that LoF variants in SETD1A cause a range of neurodevelopmental disorders, including schizophrenia. Combining these data with previous common variant evidence, we suggest that epigenetic dysregulation, specifically in the histone H3K4 methylation pathway, is an important mechanism in the pathogenesis of schizophrenia.
388 citations
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Wellcome Trust Sanger Institute1, University of Nottingham2, Katholieke Universiteit Leuven3, Harvard University4, King Abdulaziz Medical City5, Saarland University6, University of Manchester7, Central Manchester University Hospitals NHS Foundation Trust8, Charité9, University of Oxford10, Glenfield Hospital11, London North West Healthcare NHS Trust12, Leipzig University13, University of Cambridge14, Leeds Teaching Hospitals NHS Trust15, University of London16, Dresden University of Technology17, Princess Anne Hospital18, Salisbury NHS Foundation Trust19, University of Southampton20, Western General Hospital21, Hospital for Sick Children22, National Institutes of Health23, St. Michael's GAA, Sligo24, National Guard Health Affairs25, Cambridge University Hospitals NHS Foundation Trust26, Boston Children's Hospital27, Guy's and St Thomas' NHS Foundation Trust28, John Radcliffe Hospital29, NHS Blood and Transplant30, University of Freiburg31, University of Erlangen-Nuremberg32, Newcastle upon Tyne Hospitals NHS Foundation Trust33, Imperial College London34, Newcastle University35, King Saud bin Abdulaziz University for Health Sciences36, Medical Research Council37
TL;DR: Exome sequenced 1,891 probands and identified three genome-wide significant S-CHD disorders caused by DNMs in CHD4, CDK13 and PRKD1, finding evidence for distinct genetic architectures underlying the low sibling recurrence risk in S- CHD and NS-CHd.
Abstract: Congenital heart defects (CHDs) have a neonatal incidence of 0.8-1% (refs. 1,2). Despite abundant examples of monogenic CHD in humans and mice, CHD has a low absolute sibling recurrence risk (∼2.7%), suggesting a considerable role for de novo mutations (DNMs) and/or incomplete penetrance. De novo protein-truncating variants (PTVs) have been shown to be enriched among the 10% of 'syndromic' patients with extra-cardiac manifestations. We exome sequenced 1,891 probands, including both syndromic CHD (S-CHD, n = 610) and nonsyndromic CHD (NS-CHD, n = 1,281). In S-CHD, we confirmed a significant enrichment of de novo PTVs but not inherited PTVs in known CHD-associated genes, consistent with recent findings. Conversely, in NS-CHD we observed significant enrichment of PTVs inherited from unaffected parents in CHD-associated genes. We identified three genome-wide significant S-CHD disorders caused by DNMs in CHD4, CDK13 and PRKD1. Our study finds evidence for distinct genetic architectures underlying the low sibling recurrence risk in S-CHD and NS-CHD.
325 citations
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TL;DR: A catalogue of predicted loss-of-function variants in 125,748 whole-exome and 15,708 whole-genome sequencing datasets from the Genome Aggregation Database (gnomAD) reveals the spectrum of mutational constraints that affect these human protein-coding genes.
Abstract: Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases. A catalogue of predicted loss-of-function variants in 125,748 whole-exome and 15,708 whole-genome sequencing datasets from the Genome Aggregation Database (gnomAD) reveals the spectrum of mutational constraints that affect these human protein-coding genes.
4,913 citations
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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
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TL;DR: Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.
Abstract: The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies, because biological and technical differences are interspersed. We present Harmony (
https://github.com/immunogenomics/harmony
), an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms while requiring fewer computational resources. Harmony enables the integration of ~106 cells on a personal computer. We apply Harmony to peripheral blood mononuclear cells from datasets with large experimental differences, five studies of pancreatic islet cells, mouse embryogenesis datasets and the integration of scRNA-seq with spatial transcriptomics data. Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.
2,459 citations
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TL;DR: It is proposed that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways.
2,257 citations
01 Jan 2011
TL;DR: The sheer volume and scope of data posed by this flood of data pose a significant challenge to the development of efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data.
Abstract: Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.
2,187 citations