101 research outputs found

    Speech Communication

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    Contains table of contents for Part IV, table of contents for Section 1, an introduction, reports on seven research projects and a list of publications.C.J. Lebel FellowshipDennis Klatt Memorial FundNational Institutes of Health Grant T32-DC00005National Institutes of Health Grant R01-DC00075National Institutes of Health Grant F32-DC00015National Institutes of Health Grant R01-DC00266National Institutes of Health Grant P01-DC00361National Institutes of Health Grant R01-DC00776National Science Foundation Grant IRI 89-10561National Science Foundation Grant IRI 88-05680National Science Foundation Grant INT 90-2471

    Speech Communication

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    Contains reports on five research projects.C.J. Lebel FellowshipNational Institutes of Health (Grant 5 T32 NSO7040)National Institutes of Health (Grant 5 R01 NS04332)National Institutes of Health (Grant 5 R01 NS21183)National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Grant 1 PO1-NS23734)National Science Foundation (Grant BNS 8418733)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0254)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0341)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0290)National Institutes of Health (Grant RO1-NS21183), subcontract with Boston UniversityNational Institutes of Health (Grant 1 PO1-NS23734), subcontract with the Massachusetts Eye and Ear Infirmar

    Speech Communication

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    Contains table of contents for Part IV, table of contents for Section 1 and reports on five research projects.Apple Computer, Inc.C.J. Lebel FellowshipNational Institutes of Health (Grant T32-NS07040)National Institutes of Health (Grant R01-NS04332)National Institutes of Health (Grant R01-NS21183)National Institutes of Health (Grant P01-NS23734)U.S. Navy / Naval Electronic Systems Command (Contract N00039-85-C-0254)U.S. Navy - Office of Naval Research (Contract N00014-82-K-0727

    A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants.

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    This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/ng.3448Advanced age-related macular degeneration (AMD) is the leading cause of blindness in the elderly, with limited therapeutic options. Here we report on a study of >12 million variants, including 163,714 directly genotyped, mostly rare, protein-altering variants. Analyzing 16,144 patients and 17,832 controls, we identify 52 independently associated common and rare variants (P < 5 × 10(-8)) distributed across 34 loci. Although wet and dry AMD subtypes exhibit predominantly shared genetics, we identify the first genetic association signal specific to wet AMD, near MMP9 (difference P value = 4.1 × 10(-10)). Very rare coding variants (frequency <0.1%) in CFH, CFI and TIMP3 suggest causal roles for these genes, as does a splice variant in SLC16A8. Our results support the hypothesis that rare coding variants can pinpoint causal genes within known genetic loci and illustrate that applying the approach systematically to detect new loci requires extremely large sample sizes.We thank all participants of all the studies included for enabling this research by their participation in these studies. Computer resources for this project have been provided by the high-performance computing centers of the University of Michigan and the University of Regensburg. Group-specific acknowledgments can be found in the Supplementary Note. The Center for Inherited Diseases Research (CIDR) Program contract number is HHSN268201200008I. This and the main consortium work were predominantly funded by 1X01HG006934-01 to G.R.A. and R01 EY022310 to J.L.H

    Phylogeny of Parasitic Parabasalia and Free-Living Relatives Inferred from Conventional Markers vs. Rpb1, a Single-Copy Gene

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    Parabasalia are single-celled eukaryotes (protists) that are mainly comprised of endosymbionts of termites and wood roaches, intestinal commensals, human or veterinary parasites, and free-living species. Phylogenetic comparisons of parabasalids are typically based upon morphological characters and 18S ribosomal RNA gene sequence data (rDNA), while biochemical or molecular studies of parabasalids are limited to a few axenically cultivable parasites. These previous analyses and other studies based on PCR amplification of duplicated protein-coding genes are unable to fully resolve the evolutionary relationships of parabasalids. As a result, genetic studies of Parabasalia lag behind other organisms.Comparing parabasalid EF1α, α-tubulin, enolase and MDH protein-coding genes with information from the Trichomonas vaginalis genome reveals difficulty in resolving the history of species or isolates apart from duplicated genes. A conserved single-copy gene encodes the largest subunit of RNA polymerase II (Rpb1) in T. vaginalis and other eukaryotes. Here we directly sequenced Rpb1 degenerate PCR products from 10 parabasalid genera, including several T. vaginalis isolates and avian isolates, and compared these data by phylogenetic analyses. Rpb1 genes from parabasalids, diplomonads, Parabodo, Diplonema and Percolomonas were all intronless, unlike intron-rich homologs in Naegleria, Jakoba and Malawimonas.The phylogeny of Rpb1 from parasitic and free-living parabasalids, and conserved Rpb1 insertions, support Trichomonadea, Tritrichomonadea, and Hypotrichomonadea as monophyletic groups. These results are consistent with prior analyses of rDNA and GAPDH sequences and ultrastructural data. The Rpb1 phylogenetic tree also resolves species- and isolate-level relationships. These findings, together with the relative ease of Rpb1 isolation, make it an attractive tool for evaluating more extensive relationships within Parabasalia

    Insights into hominid evolution from the gorilla genome sequence.

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    Gorillas are humans' closest living relatives after chimpanzees, and are of comparable importance for the study of human origins and evolution. Here we present the assembly and analysis of a genome sequence for the western lowland gorilla, and compare the whole genomes of all extant great ape genera. We propose a synthesis of genetic and fossil evidence consistent with placing the human-chimpanzee and human-chimpanzee-gorilla speciation events at approximately 6 and 10 million years ago. In 30% of the genome, gorilla is closer to human or chimpanzee than the latter are to each other; this is rarer around coding genes, indicating pervasive selection throughout great ape evolution, and has functional consequences in gene expression. A comparison of protein coding genes reveals approximately 500 genes showing accelerated evolution on each of the gorilla, human and chimpanzee lineages, and evidence for parallel acceleration, particularly of genes involved in hearing. We also compare the western and eastern gorilla species, estimating an average sequence divergence time 1.75 million years ago, but with evidence for more recent genetic exchange and a population bottleneck in the eastern species. The use of the genome sequence in these and future analyses will promote a deeper understanding of great ape biology and evolution

    Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

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    Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

    New genetic loci link adipose and insulin biology to body fat distribution.

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    Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

    A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

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    A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.Xiang Zhu is supported by the Stein Fellowship from Stanford University and Institute for Computational and Data Sciences Seed Grant from The Pennsylvania State University. C.D.B. is supported by the NIH (R01-HL133218). Funding for the Global Lipids Genetics Consortium was provided by the NIH (R01-HL127564). This research was conducted using the UK Biobank Resource under application number 24460. This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by awards 2I01BX003362-03A1 and 1I01BX004821-01A1. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. We thank Bethany Klunder for administrative support. Study-specific acknowledgments are provided in the supplemental information
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