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.

Clarifying the PROGINS Allele Association in Ovarian and Breast Cancer Risk: A Haplotype-Based Analysis

Abstract: Background: The PROGINS allele of the progesterone receptor (PGR) gene has been associated with an increased risk of ovarian cancer and a decreased risk of breast cancer. We set out to refine the association between common variation at the PGR gene locus and these two diseases. Methods: We characterized the haplotype structure of the PGR gene by genotyping 54 single-nucleotide polymorphisms (SNPs) in 349 women. We then selected a subset of 17 haplotypetagging SNPs that captured variation across the locus and typed them in 267 ovarian cancer case patients and 397 control subjects from two case‐control studies and in 1715 breast cancer case patients and 2505 control subjects from a cohort study. Results: The PGR locus was characterized by four blocks of strong linkage disequilibrium. Two SNPs in block 4 were associated with an increased risk of ovarian cancer among homozygous carriers as compared with noncarriers: rs1042838 (PROGINS allele; odds ratio [OR] 3.23, 95% confidence interval [CI] 1.19 to 8.75, P .022) and rs608995 (minor allele; OR 3.10, 95% CI 1.63 to 5.89, P<.001). The PROGINS allele was observed on a subset of chromosomes carrying the minor allele at rs608995, and its association with ovarian cancer was fully explained by its association with rs608995. In addition, rs608995 fell on two common haplotypes (4-D and 4-E), whose association with ovarian cancer was the same as that of rs608995. These same two haplotypes were associated with a non‐statistically significantly reduced risk of breast cancer. Conclusions: Variation in PGR was associated with ovarian cancer risk, although the strongest result was not with the PROGINS allele. Instead, any causal allele(s) are likely in or downstream of block 4 and carried on haplotypes 4-D and 4-E. There was some evidence that the same variation was associated with a reduced risk of breast cancer, but the association was not statistically significant. [J Natl Cancer Inst

CYP17 Genetic Variation and Risk of Breast and Prostate Cancer from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3)

Abstract: CYP17 encodes cytochrome p450c17 alpha, which mediates activities essential for the production of sex steroids. Common germ line variation in the CYP17 gene has been related to inconsistent results in breast and prostate cancer, with most studies focusing on the nonsynonymous single nucleotide polymorphism (SNP) T27C (rs743572). We comprehensively characterized variation in CYP17 by direct sequencing of exons followed by dense genotyping across the 58 kb region around CYP17 in five racial/ethnic populations. Two blocks of strong linkage disequilibrium were identified and nine haplotype-tagging SNPs, including T27C, were chosen to predict common haplotypes (R-h(2) >= 0.85). These haplotype-tagging SNPs were genotyped in 8,138 prostate cancer cases and 9,033 controls, and 5,333 breast cancer cases and 7,069 controls from the Breast and Prostate Cancer Cohort Consortium. We observed borderline significant associations with prostate cancer for rs2486758 [TC versus TT, odds ratios (OR), 1.07; 95% confidence intervals (95% Cl), 1.00-1.14; CC versus TT, OR, 1.09; 95% CI, 0.95-1.26; P trend = 0.04] and rs6892 (AG versus AA, OR, 1.08; 95% CI, 1.00-1.15; GG versus AA, OR, 1.11; 95% CI, 0.95-1.30; P trend = 0.03). We also observed marginally significant associations with breast cancer for rs4919687 (GA versus GG, OR, 1.04; 95% CI, 0.97-1.12, AA versus GG, OR, 1.17; 95% CI, 1.03-1.34; P trend = 0.03) and rs4919682 (CT versus CC, OR, 1.04; 95% CI, 0.97-1.12; TT versus CC, OR, 1.16; 95% CI, 1.01-1.33; P trend = 0.04). Common variation at CYP17 was not associated with circulating sex steroid hormones in men or postmenopausal women. Our findings do not support the hypothesis that common germ line variation in CYP17 makes a substantial contribution to postmenopausal breast or prostate cancer susceptibility.

Underlying Genetic Models of Inheritance in Established Type 2 Diabetes Associations

Abstract: For most associations of common single nucleotide polymorphisms (SNPs) with common diseases, the genetic model of inheritance is unknown. The authors extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations with type 2 diabetes. For 13 SNPs, the data fitted very well to an additive model of inheritance for the diabetes risk allele; for 4 SNPs, the data were consistent with either an additive model or a dominant model; and for 2 SNPs, the data were consistent with an additive or recessive model. Results were robust to the use of different priors and after exclusion of data for which index SNPs had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that were very similar to those previously reported based on fixed- or random-effects models, but uncertainty about several of the effects was substantially larger. The authors also examined the extent of between-study heterogeneity in the genetic model and found generally small between-study deviation values for the genetic model parameter. Heterosis could not be excluded for 4 SNPs. Information on the genetic model of robustly replicated association signals derived from genome-wide association studies may be useful for predictive modeling and for designing biologic and functional experiments.

Current state and future prospects of immunotherapy for glioma.

Abstract: There is a large unmet need for effective therapeutic approaches for glioma, the most malignant brain tumor. Clinical and preclinical studies have enormously expanded our knowledge about the molecular aspects of this deadly disease and its interaction with the host immune system. In this review we highlight the wide array of immunotherapeutic interventions that are currently being tested in glioma patients. Given the molecular heterogeneity, tumor immunoediting and the profound immunosuppression that characterize glioma, it has become clear that combinatorial approaches targeting multiple pathways tailored to the genetic signature of the tumor will be required in order to achieve optimal therapeutic efficacy.

Genetic Variation in the HSD17B1 Gene and Risk of Prostate Cancer

Abstract: Steroid hormones are believed to play an important role in prostate carcinogenesis, but epidemiological evidence linking prostate cancer and steroid hormone genes has been inconclusive, in part due to small sample sizes or incomplete characterization of genetic variation at the locus of interest. Here we report on the results of a comprehensive study of the association between HSD17B1 and prostate cancer by the Breast and Prostate Cancer Cohort Consortium, a large collaborative study. HSD17B1 encodes 17β-hydroxysteroid dehydrogenase 1, an enzyme that converts dihydroepiandrosterone to the testosterone precursor Δ5-androsterone-3β,17β-diol and converts estrone to estradiol. The Breast and Prostate Cancer Cohort Consortium researchers systematically characterized variation in HSD17B1 by targeted resequencing and dense genotyping; selected haplotype-tagging single nucleotide polymorphisms (htSNPs) that efficiently predict common variants in U.S. and European whites, Latinos, Japanese Americans, and Native Hawaiians; and genotyped these htSNPs in 8,290 prostate cancer cases and 9,367 study-, age-, and ethnicity-matched controls. We found no evidence that HSD17B1 htSNPs (including the nonsynonymous coding SNP S312G) or htSNP haplotypes were associated with risk of prostate cancer or tumor stage in the pooled multiethnic sample or in U.S. and European whites. Analyses stratified by age, body mass index, and family history of disease found no subgroup-specific associations between these HSD17B1 htSNPs and prostate cancer. We found significant evidence of heterogeneity in associations between HSD17B1 haplotypes and prostate cancer across ethnicity: one haplotype had a significant (p < 0.002) inverse association with risk of prostate cancer in Latinos and Japanese Americans but showed no evidence of association in African Americans, Native Hawaiians, or whites. However, the smaller numbers of Latinos and Japanese Americans in this study makes these subgroup analyses less reliable. These results suggest that the germline variants in HSD17B1 characterized by these htSNPs do not substantially influence the risk of prostate cancer in U.S. and European whites.

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.