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David Altshuler

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
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Journal ArticleDOI
TL;DR: It is implied that the pattern of genetic variation seen at C-C chemokine receptor 5 (CCR5) is consistent with neutral evolution, which has general implications for the design of future studies to detect the signs of positive selection in the human genome.
Abstract: The C-C chemokine receptor 5, 32 base-pair deletion (CCR5-Δ32) allele confers strong resistance to infection by the AIDS virus HIV. Previous studies have suggested that CCR5-Δ32 arose within the past 1,000 y and rose to its present high frequency (5%–14%) in Europe as a result of strong positive selection, perhaps by such selective agents as the bubonic plague or smallpox during the Middle Ages. This hypothesis was based on several lines of evidence, including the absence of the allele outside of Europe and long-range linkage disequilibrium at the locus. We reevaluated this evidence with the benefit of much denser genetic maps and extensive control data. We find that the pattern of genetic variation at CCR5-Δ32 does not stand out as exceptional relative to other loci across the genome. Moreover using newer genetic maps, we estimated that the CCR5-Δ32 allele is likely to have arisen more than 5,000 y ago. While such results can not rule out the possibility that some selection may have occurred at C-C chemokine receptor 5 (CCR5), they imply that the pattern of genetic variation seen atCCR5-Δ32 is consistent with neutral evolution. More broadly, the results have general implications for the design of future studies to detect the signs of positive selection in the human genome.

175 citations

Journal ArticleDOI
10 Jun 2016-Science
TL;DR: This data-sharing effort has led to improved variant interpretation and development of treatments for rare diseases and some cancer types, but such benefits will only be available to the general population if researchers and clinicians can access and make comparisons across data from millions of individuals.
Abstract: Silos of genome data collection are being transformed into seamlessly connected, independent systems Early data-sharing efforts have led to improved variant interpretation and development of treatments for rare diseases and some cancer types (1–3). However, such benefits will only be available to the general population if researchers and clinicians can access and make comparisons across data from millions of individuals.

173 citations

Journal ArticleDOI
01 Apr 2011-Diabetes
TL;DR: A high GRS is associated with increased risk of developing diabetes and lower probability of returning to NGR in high-risk individuals, but a lifestyle intervention attenuates this risk.
Abstract: A high GRS is associated with increased risk of developing diabetes and lower probability of returning to NGR in high-risk individuals, but a lifestyle intervention attenuates this risk.

173 citations

Journal ArticleDOI
01 May 2002-Diabetes
TL;DR: In nondiabetic individuals from the Quebec City area and the Saguenay-Lac-St-Jean region of Quebec, the g.-537 mutation was significantly associated with an increase in BMI and in case/control and family-based study populations from Scandinavia, there was no effect on BMI with either of these promoter variants.
Abstract: Diabetes and obesity have long been known to be related. The recently characterized adipocyte hormone resistin (also called FIZZ3/ADSF) has been implicated as a molecular link between impaired glucose tolerance (IGT) and obesity in mice. A search for sequence variants at the human resistin locus identified nine single-nucleotide polymorphisms (SNPs) but no coding variants. An investigation into the association of these SNPs with diabetes and obesity revealed two 5′ flanking variants (g.-537 and g.-420), in strong linkage disequilibrium, that are associated with BMI. In nondiabetic individuals from the Quebec City area and the Saguenay-Lac-St-Jean region of Quebec, the g.-537 mutation (allelic frequency = 0.04) was significantly associated with an increase in BMI ( P = 0.03 and P = 0.01, respectively). When the data from these two populations were combined and adjusted for age and sex, both the g.-537 (odds ratio [OR] 2.72, 95% CI 1.28–5.81) and the g.-420 variants (1.58, 1.06–2.35) were associated with an increased risk for a BMI ≥30 kg/m 2 . In contrast, in case/control and family-based study populations from Scandinavia, we saw no effect on BMI with either of these promoter variants. No association was seen with diabetes in any of the population samples.

173 citations


Cited by
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Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
Eric S. Lander1, Lauren Linton1, Bruce W. Birren1, Chad Nusbaum1  +245 moreInstitutions (29)
15 Feb 2001-Nature
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

Journal ArticleDOI
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