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.

Whole-exome imputation of sequence variants identified two novel alleles associated with adult body height in African Americans

Abstract: Adult body height is a quantitative trait for which genome-wide association studies (GWAS) have identified numerous loci, primarily in European populations. These loci, comprising common variants, explain <10% of the phenotypic variance in height. We searched for novel associations between height and common (minor allele frequency, MAF ≥5%) or infrequent (0.5% < MAF < 5%) variants across the exome in African Americans. Using a reference panel of 1692 African Americans and 471 Europeans from the National Heart, Lung, and Blood Institute's (NHLBI) Exome Sequencing Project (ESP), we imputed whole-exome sequence data into 13 719 African Americans with existing array-based GWAS data (discovery). Variants achieving a height-association threshold of P < 5E−06 in the imputed dataset were followed up in an independent sample of 1989 African Americans with whole-exome sequence data (replication). We used P < 2.5E−07 (=0.05/196 779 variants) to define statistically significant associations in meta-analyses combining the discovery and replication sets (N = 15 708). We discovered and replicated three independent loci for association: 5p13.3/C5orf22/rs17410035 (MAF = 0.10, β = 0.64 cm, P = 8.3E−08), 13q14.2/SPRYD7/rs114089985 (MAF = 0.03, β = 1.46 cm, P = 4.8E−10) and 17q23.3/GH2/rs2006123 (MAF = 0.30; β = 0.47 cm; P = 4.7E−09). Conditional analyses suggested 5p13.3 (C5orf22/rs17410035) and 13q14.2 (SPRYD7/rs114089985) may harbor novel height alleles independent of previous GWAS-identified variants (r2 with GWAS loci <0.01); whereas 17q23.3/GH2/rs2006123 was correlated with GWAS-identified variants in European and African populations. Notably, 13q14.2/rs114089985 is infrequent in African Americans (MAF = 3%), extremely rare in European Americans (MAF = 0.03%), and monomorphic in Asian populations, suggesting it may be an African-American-specific height allele. Our findings demonstrate that whole-exome imputation of sequence variants can identify low-frequency variants and discover novel variants in non-European populations.

Genetic association analysis of LARS2 with type 2 diabetes

Abstract: LARS2 has been previously identified as a potential type 2 diabetes susceptibility gene through the low-frequency H324Q (rs71645922) variant (minor allele frequency [MAF] 3.0%). However, this association did not achieve genome-wide levels of significance. The aim of this study was to establish the true contribution of this variant and common variants in LARS2 (MAF > 5%) to type 2 diabetes risk. We combined genome-wide association data (n = 10,128) from the DIAGRAM consortium with independent data derived from a tagging single nucleotide polymorphism (SNP) approach in Dutch individuals (n = 999) and took forward two SNPs of interest to replication in up to 11,163 Dutch participants (rs17637703 and rs952621). In addition, because inspection of genome-wide association study data identified a cluster of low-frequency variants with evidence of type 2 diabetes association, we attempted replication of rs9825041 (a proxy for this group) and the previously identified H324Q variant in up to 35,715 participants of European descent. No association between the common SNPs in LARS2 and type 2 diabetes was found. Our replication studies for the two low-frequency variants, rs9825041 and H324Q, failed to confirm an association with type 2 diabetes in Dutch, Scandinavian and UK samples (OR 1.03 [95% CI 0.95-1.12], p = 0.45, n = 31,962 and OR 0.99 [0.90-1.08], p = 0.78, n = 35,715 respectively). In this study, the largest study examining the role of sequence variants in LARS2 in type 2 diabetes susceptibility, we found no evidence to support previous data indicating a role in type 2 diabetes susceptibility.

Association testing of common variants in the insulin receptor substrate-1 gene (IRS1) with type 2 diabetes.

Abstract: Aims/hypothesis Activation of the insulin receptor substrate-1 (IRS1) is a key initial step in the insulin signalling pathway. Despite several reports of association of the G972R polymorphism in its gene IRS1 with type 2 diabetes, we and others have not observed this association in well-powered samples. However, other nearby variants might account for the putative association signal. Subjects and methods We characterised the haplotype map of IRS1 and selected 20 markers designed to capture common variations in the region. We genotyped this comprehensive set of markers in several family-based and case-control samples of European descent totalling 12,129 subjects. Results In an initial sample of 2,235 North American and Polish case-control pairs, the minor allele of the rs934167 polymorphism showed nominal evidence of association with type 2 diabetes (odds ratio [OR] 1.25, 95% CI 1.03-1.51, p=0.03). This association showed a trend in the same direction in 7,659 Scandinavian samples (OR 1.16, 95% CI 0.96-1.39, p=0.059). The combined OR was 1.20 (p=0.008), but statistical correction for the number of variants examined yielded a p value of 0.086. We detected no differences across rs934167 genotypes in insulin-related quantitative traits. Conclusion/interpretation Our data do not support an association of common variants in IRS1 with type 2 diabetes in populations of European descent.

European admixture on the Micronesian island of Kosrae: lessons from complete genetic information

Abstract: The architecture of natural variation present in a contemporary population is a result of multiple population genetic forces, including population bottleneck and expansion, selection, drift, and admixture. We seek to untangle the contribution of admixture to genetic diversity on the Micronesian island of Kosrae. Toward this goal, we used a complete genetic approach by combining a dense genome-wide map of 100 000 single-nucleotide polymorphisms (SNPs) with data from uniparental markers from the mitochondrial genome and the nonrecombining portion of the Y chromosome. These markers were typed in ∼3200 individuals from Kosrae, representing 80% of the adult population of the island. We developed novel software that uses SNP data to delineate ancestry for individual segments of the genome. Through this analysis, we determined that 39% of Kosraens have some European ancestry. However, the vast majority of admixed individuals (77%) have European alleles spanning less than 10% of their genomes. Data from uniparental markers show most of this admixture to be male, introduced in the late nineteenth century. Furthermore, pedigree analysis shows that the majority of European admixture on Kosrae is because of the contribution of one individual. This approach shows the benefit of combining information from autosomal and uniparental polymorphisms and provides new methodology for determining ancestry in a population.

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.