Author
David Altshuler
Other affiliations: Vertex Pharmaceuticals, Massachusetts Institute of Technology, Broad Institute ...read more
Bio: David Altshuler is an academic researcher from University of Michigan. The author has contributed to research in topics: Genome-wide association study & Population. The author has an hindex of 162, co-authored 345 publications receiving 201782 citations. Previous affiliations of David Altshuler include Vertex Pharmaceuticals & Massachusetts Institute of Technology.
Papers published on a yearly basis
Papers
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TL;DR: This work describes a systematic method for using dense SNP genotype data to discover deletions and its application to data from the International HapMap Consortium to characterize and catalogue segregating deletion variants across the human genome.
Abstract: The locations and properties of common deletion variants in the human genome are largely unknown. We describe a systematic method for using dense SNP genotype data to discover deletions and its application to data from the International HapMap Consortium to characterize and catalogue segregating deletion variants across the human genome. We identified 541 deletion variants (94% novel) ranging from 1 kb to 745 kb in size; 278 of these variants were observed in multiple, unrelated individuals, 120 in the homozygous state. The coding exons of ten expressed genes were found to be commonly deleted, including multiple genes with roles in sex steroid metabolism, olfaction and drug response. These common deletion polymorphisms typically represent ancestral mutations that are in linkage disequilibrium with nearby SNPs, meaning that their association to disease can often be evaluated in the course of SNP-based whole-genome association studies.
730 citations
01 Sep 2011
TL;DR: The Lin28/let-7 pathway is established as a central regulator of mammalian glucose metabolism and enriched for genes containing SNPs associated with type 2 diabetes and control of fasting glucose in human genome-wide association studies.
Abstract: The let-7 tumor suppressor microRNAs are known for their regulation of oncogenes, while the RNA-binding proteins Lin28a/b promote malignancy by inhibiting let-7 biogenesis. We have uncovered unexpected roles for the Lin28/let-7 pathway in regulating metabolism. When overexpressed in mice, both Lin28a and LIN28B promote an insulin-sensitized state that resists high-fat-diet induced diabetes. Conversely, muscle-specific loss of Lin28a or overexpression of let-7 results in insulin resistance and impaired glucose tolerance. These phenomena occur, in part, through the let-7-mediated repression of multiple components of the insulin-PI3K-mTOR pathway, including IGF1R, INSR, and IRS2. In addition, the mTOR inhibitor, rapamycin, abrogates Lin28a-mediated insulin sensitivity and enhanced glucose uptake. Moreover, let-7 targets are enriched for genes containing SNPs associated with type 2 diabetes and control of fasting glucose in human genome-wide association studies. These data establish the Lin28/let-7 pathway as a central regulator of mammalian glucose metabolism.
727 citations
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726 citations
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Wellcome Trust Centre for Human Genetics1, Medical Research Council2, Broad Institute3, Harvard University4, Wellcome Trust Sanger Institute5, King's College London6, deCODE genetics7, Boston University8, University of Michigan9, Erasmus University Rotterdam10, National Institutes of Health11, VU University Amsterdam12, University of Oulu13, Lund University14, University of Virginia15, University Hospital of Lausanne16, University of Lausanne17, University of Southern California18, Imperial College London19, Ninewells Hospital20, University of California, Los Angeles21, University of Düsseldorf22, Novartis23, Swiss Institute of Bioinformatics24, European Bioinformatics Institute25, University of Eastern Finland26, GlaxoSmithKline27, University of North Carolina at Chapel Hill28, Oulu University Hospital29, University Medical Center Groningen30, University of Helsinki31, Ludwig Maximilian University of Munich32, University of Cambridge33, VU University Medical Center34, Leiden University Medical Center35, Brigham and Women's Hospital36, Massachusetts Institute of Technology37, University of Iceland38, University of Oxford39
TL;DR: Variants in the gene encoding melatonin receptor 1B (MTNR1B) were consistently associated with fasting glucose across all ten genome-wide association scans, and previous associations of fasting glucose with variants at the G6PC2 and GCK loci are confirmed.
Abstract: To identify previously unknown genetic loci associated with fasting glucose concentrations, we examined the leading association signals in ten genome-wide association scans involving a total of 36,610 individuals of European descent. Variants in the gene encoding melatonin receptor 1B (MTNR1B) were consistently associated with fasting glucose across all ten studies. The strongest signal was observed at rs10830963, where each G allele (frequency 0.30 in HapMap CEU) was associated with an increase of 0.07 (95% CI = 0.06-0.08) mmol/l in fasting glucose levels (P = 3.2 x 10(-50)) and reduced beta-cell function as measured by homeostasis model assessment (HOMA-B, P = 1.1 x 10(-15)). The same allele was associated with an increased risk of type 2 diabetes (odds ratio = 1.09 (1.05-1.12), per G allele P = 3.3 x 10(-7)) in a meta-analysis of 13 case-control studies totaling 18,236 cases and 64,453 controls. Our analyses also confirm previous associations of fasting glucose with variants at the G6PC2 (rs560887, P = 1.1 x 10(-57)) and GCK (rs4607517, P = 1.0 x 10(-25)) loci.
716 citations
01 Jan 2016
TL;DR: Large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes, but most fell within regions previously identified by genome-wide association studies.
Abstract: The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.
698 citations
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TL;DR: Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
Abstract: As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
37,898 citations
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TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
34,830 citations
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TL;DR: This work introduces PLINK, an open-source C/C++ WGAS tool set, and describes the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation, which focuses on the estimation and use of identity- by-state and identity/descent information in the context of population-based whole-genome studies.
Abstract: Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
26,280 citations
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TL;DR: The results of an international collaboration to produce and make freely available a draft sequence of the human genome are reported and an initial analysis is presented, describing some of the insights that can be gleaned from the sequence.
Abstract: The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.
22,269 citations
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TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
22,147 citations