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.

Genomewide linkage analysis of stature in multiple populations reveals several regions with evidence of linkage to adult height

Abstract: Genomewide linkage analysis has been extremely successful at identification of the genetic variation underlying single-gene disorders. However, linkage analysis has been less successful for common human diseases and other complex traits in which multiple genetic and environmental factors interact to influence disease risk. We hypothesized that a highly heritable complex trait, in which the contribution of environmental factors was relatively limited, might be more amenable to linkage analysis. We therefore chose to study stature (adult height), for which heritability is approximately 75%-90% (Phillips and Matheny 1990; Carmichael and McGue 1995; Preece 1996; Silventoinen et al. 2000). We reanalyzed genomewide scans from four populations for which genotype and height data were available, using a variance-components method implemented in GENEHUNTER 2.0 (Pratt et al. 2000). The populations consisted of 408 individuals in 58 families from the Botnia region of Finland, 753 individuals in 183 families from other parts of Finland, 746 individuals in 179 families from Southern Sweden, and 420 individuals in 63 families from the Saguenay-Lac-St.-Jean region of Quebec. Four regions showed evidence of linkage to stature: 6q24-25, multipoint LOD score 3.85 at marker D6S1007 in Botnia (genomewide P<.06), 7q31.3-36 (LOD 3.40 at marker D7S2195 in Sweden, P<.02), 12p11.2-q14 (LOD 3.35 at markers D12S10990-D12S398 in Finland, P<.05) and 13q32-33 (LOD 3.56 at markers D13S779-D13S797 in Finland, P<.05). In a companion article (Perola et al. 2001 [in this issue]), strong supporting evidence is obtained for linkage to the region on chromosome 7. These studies suggest that highly heritable complex traits such as stature may be genetically tractable and provide insight into the genetic architecture of complex traits.

Abdominal Aortic Aneurysm Is Associated with a Variant in Low-Density Lipoprotein Receptor-Related Protein 1

Abstract: Abdominal aortic aneurysm (AAA) is a common cause of morbidity and mortality and has a significant heritability. We carried out a genome-wide association discovery study of 1866 patients with AAA and 5435 controls and replication of promising signals (lead SNP with a p value < 1 × 10(-5)) in 2871 additional cases and 32,687 controls and performed further follow-up in 1491 AAA and 11,060 controls. In the discovery study, nine loci demonstrated association with AAA (p < 1 × 10(-5)). In the replication sample, the lead SNP at one of these loci, rs1466535, located within intron 1 of low-density-lipoprotein receptor-related protein 1 (LRP1) demonstrated significant association (p = 0.0042). We confirmed the association of rs1466535 and AAA in our follow-up study (p = 0.035). In a combined analysis (6228 AAA and 49182 controls), rs1466535 had a consistent effect size and direction in all sample sets (combined p = 4.52 × 10(-10), odds ratio 1.15 [1.10-1.21]). No associations were seen for either rs1466535 or the 12q13.3 locus in independent association studies of coronary artery disease, blood pressure, diabetes, or hyperlipidaemia, suggesting that this locus is specific to AAA. Gene-expression studies demonstrated a trend toward increased LRP1 expression for the rs1466535 CC genotype in arterial tissues; there was a significant (p = 0.029) 1.19-fold (1.04-1.36) increase in LRP1 expression in CC homozygotes compared to TT homozygotes in aortic adventitia. Functional studies demonstrated that rs1466535 might alter a SREBP-1 binding site and influence enhancer activity at the locus. In conclusion, this study has identified a biologically plausible genetic variant associated specifically with AAA, and we suggest that this variant has a possible functional role in LRP1 expression.

Comprehensive Association Testing of Common Mitochondrial DNA Variation in Metabolic Disease

Abstract: Many lines of evidence implicate mitochondria in phenotypic variation: (a) rare mutations in mitochondrial proteins cause metabolic, neurological, and muscular disorders; (b) alterations in oxidative phosphorylation are characteristic of type 2 diabetes, Parkinson disease, Huntington disease, and other diseases; and (c) common missense variants in the mitochondrial genome (mtDNA) have been implicated as having been subject to natural selection for adaptation to cold climates and contributing to "energy deficiency" diseases today. To test the hypothesis that common mtDNA variation influences human physiology and disease, we identified all 144 variants with frequency >1% in Europeans from >900 publicly available European mtDNA sequences and selected 64 tagging single-nucleotide polymorphisms that efficiently capture all common variation (except the hypervariable D-loop). Next, we evaluated the complete set of common mtDNA variants for association with type 2 diabetes in a sample of 3,304 diabetics and 3,304 matched nondiabetic individuals. Association of mtDNA variants with other metabolic traits (body mass index, measures of insulin secretion and action, blood pressure, and cholesterol) was also tested in subsets of this sample. We did not find a significant association of common mtDNA variants with these metabolic phenotypes. Moreover, we failed to identify any physiological effect of alleles that were previously proposed to have been adaptive for energy metabolism in human evolution. More generally, this comprehensive association-testing framework can readily be applied to other diseases for which mitochondrial dysfunction has been implicated.

The Case for Selection at CCR5-Δ32

Abstract: The C-C chemokine receptor 5, 32 base-pair deletion (CCR5-D32) allele confers strong resistance to infection by the AIDS virus HIV. Previous studies have suggested that CCR5-D32 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-D32 does not stand out as exceptional relative to other loci across the genome. Moreover using newer genetic maps, we estimated that the CCR5-D32 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 at CCR5-D32 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.

A temporally regulated, diffusible activity is required for rod photoreceptor development in vitro.

Abstract: The retina is a relatively simple and well-characterized CNS structure in which cell-cell interactions have been hypothesized to influence cell type determination. By manipulating cell density in serum-free cultures we show that rat rod photoreceptor development requires a diffusible activity produced by neonatal retinal cells. This effect is not mediated by changes in cell survival or mitosis. Production of the rod promoting activity varies with developmental stage and is temporally correlated with the timing of rod generation in vivo. In low density cultures, which do not support rod development, an increased fraction of cells stain with an antibody specific for another retinal neuron, the bipolar cell. Thus, the diffusible rod promoting activity may influence cell fate determination, and not only terminal differentiation. These results provide an approach for the molecular characterization of developmentally important signals in the vertebrate retina.

Fast gapped-read alignment with Bowtie 2

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.

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

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.

PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses

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.

Initial sequencing and analysis of the human genome.

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.

limma powers differential expression analyses for RNA-sequencing and microarray studies

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.