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  • 1
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    Analysis of case-control association studies with known risk variants (2012)
    Oxford University Press
    Details
    Publication Date: 2012-06-24
    Description: Motivation: The question of how to best use information from known associated variants when conducting disease association studies has yet to be answered. Some studies compute a marginal P -value for each Several Nucleotide Polymorphisms independently, ignoring previously discovered variants. Other studies include known variants as covariates in logistic regression, but a weakness of this standard conditioning strategy is that it does not account for disease prevalence and non-random ascertainment, which can induce a correlation structure between candidate variants and known associated variants even if the variants lie on different chromosomes. Here, we propose a new conditioning approach, which is based in part on the classical technique of liability threshold modeling. Roughly, this method estimates model parameters for each known variant while accounting for the published disease prevalence from the epidemiological literature. Results: We show via simulation and application to empirical datasets that our approach outperforms both the no conditioning strategy and the standard conditioning strategy, with a properly controlled false-positive rate. Furthermore, in multiple data sets involving diseases of low prevalence, standard conditioning produces a severe drop in test statistics whereas our approach generally performs as well or better than no conditioning. Our approach may substantially improve disease gene discovery for diseases with many known risk variants. Availability: LTSOFT software is available online http://www.hsph.harvard.edu/faculty/alkes-price/software/ Contact: nzaitlen@hsph.harvard.edu ; aprice@hsph.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 2
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    Integrating common and rare genetic variation in diverse human populations (2010)
    Nature Publishing Group (NPG)
    Details
    Publication Date: 2010-09-03
    Description: Despite great progress in identifying genetic variants that influence human disease, most inherited risk remains unexplained. A more complete understanding requires genome-wide studies that fully examine less common alleles in populations with a wide range of ancestry. To inform the design and interpretation of such studies, we genotyped 1.6 million common single nucleotide polymorphisms (SNPs) in 1,184 reference individuals from 11 global populations, and sequenced ten 100-kilobase regions in 692 of these individuals. This integrated data set of common and rare alleles, called 'HapMap 3', includes both SNPs and copy number polymorphisms (CNPs). We characterized population-specific differences among low-frequency variants, measured the improvement in imputation accuracy afforded by the larger reference panel, especially in imputing SNPs with a minor allele frequency of 〈or=5%, and demonstrated the feasibility of imputing newly discovered CNPs and SNPs. This expanded public resource of genome variants in global populations supports deeper interrogation of genomic variation and its role in human disease, and serves as a step towards a high-resolution map of the landscape of human genetic variation.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3173859/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3173859/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉International HapMap 3 Consortium -- Altshuler, David M -- Gibbs, Richard A -- Peltonen, Leena -- Dermitzakis, Emmanouil -- Schaffner, Stephen F -- Yu, Fuli -- Bonnen, Penelope E -- de Bakker, Paul I W -- Deloukas, Panos -- Gabriel, Stacey B -- Gwilliam, Rhian -- Hunt, Sarah -- Inouye, Michael -- Jia, Xiaoming -- Palotie, Aarno -- Parkin, Melissa -- Whittaker, Pamela -- Chang, Kyle -- Hawes, Alicia -- Lewis, Lora R -- Ren, Yanru -- Wheeler, David -- Muzny, Donna Marie -- Barnes, Chris -- Darvishi, Katayoon -- Hurles, Matthew -- Korn, Joshua M -- Kristiansson, Kati -- Lee, Charles -- McCarrol, Steven A -- Nemesh, James -- Keinan, Alon -- Montgomery, Stephen B -- Pollack, Samuela -- Price, Alkes L -- Soranzo, Nicole -- Gonzaga-Jauregui, Claudia -- Anttila, Verneri -- Brodeur, Wendy -- Daly, Mark J -- Leslie, Stephen -- McVean, Gil -- Moutsianas, Loukas -- Nguyen, Huy -- Zhang, Qingrun -- Ghori, Mohammed J R -- McGinnis, Ralph -- McLaren, William -- Takeuchi, Fumihiko -- Grossman, Sharon R -- Shlyakhter, Ilya -- Hostetter, Elizabeth B -- Sabeti, Pardis C -- Adebamowo, Clement A -- Foster, Morris W -- Gordon, Deborah R -- Licinio, Julio -- Manca, Maria Cristina -- Marshall, Patricia A -- Matsuda, Ichiro -- Ngare, Duncan -- Wang, Vivian Ota -- Reddy, Deepa -- Rotimi, Charles N -- Royal, Charmaine D -- Sharp, Richard R -- Zeng, Changqing -- Brooks, Lisa D -- McEwen, Jean E -- 068545/Wellcome Trust/United Kingdom -- 068545/Z/02/Wellcome Trust/United Kingdom -- 076113/Wellcome Trust/United Kingdom -- 077011/Wellcome Trust/United Kingdom -- 077014/Wellcome Trust/United Kingdom -- 082371/Wellcome Trust/United Kingdom -- 089061/Wellcome Trust/United Kingdom -- 089062/Wellcome Trust/United Kingdom -- 091746/Wellcome Trust/United Kingdom -- G0000934/Medical Research Council/United Kingdom -- P30 DK043351/DK/NIDDK NIH HHS/ -- U54 HG003273/HG/NHGRI NIH HHS/ -- England -- Nature. 2010 Sep 2;467(7311):52-8. doi: 10.1038/nature09298.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Broad Institute, 7 Cambridge Center, Cambridge, Massachusetts 02138, USA. altshuler@molbio.mgh.harvard.edu〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/20811451" target="_blank"〉PubMed〈/a〉
    Keywords: *DNA Copy Number Variations ; *Genome, Human ; Human Genome Project ; Humans ; *Polymorphism, Single Nucleotide ; Population Groups/*genetics
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Published by Nature Publishing Group (NPG)
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  • 3
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    Sequential transcriptional waves direct the differentiation of newborn neurons in the mouse neocortex (2016)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2016-03-05
    Description: During corticogenesis, excitatory neurons are born from progenitors located in the ventricular zone (VZ), from where they migrate to assemble into circuits. How neuronal identity is dynamically specified upon progenitor division is unknown. Here, we study this process using a high-temporal-resolution technology allowing fluorescent tagging of isochronic cohorts of newborn VZ cells. By combining this in vivo approach with single-cell transcriptomics in mice, we identify and functionally characterize neuron-specific primordial transcriptional programs as they dynamically unfold. Our results reveal early transcriptional waves that instruct the sequence and pace of neuronal differentiation events, guiding newborn neurons toward their final fate, and contribute to a road map for the reverse engineering of specific classes of cortical neurons from undifferentiated cells.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Telley, Ludovic -- Govindan, Subashika -- Prados, Julien -- Stevant, Isabelle -- Nef, Serge -- Dermitzakis, Emmanouil -- Dayer, Alexandre -- Jabaudon, Denis -- New York, N.Y. -- Science. 2016 Mar 25;351(6280):1443-6. doi: 10.1126/science.aad8361. Epub 2016 Mar 3.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Basic Neurosciences, University of Geneva, Switzerland. Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Switzerland. ; Department of Genetic Medicine and Development, University of Geneva, Switzerland. Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Switzerland. ; Department of Genetic Medicine and Development, University of Geneva, Switzerland. Biomedical Research Foundation Academy of Athens, Greece. Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Saudi Arabia. Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Switzerland. ; Department of Basic Neurosciences, University of Geneva, Switzerland. Department of Psychiatry, Geneva University Hospital, Switzerland. Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Switzerland. ; Department of Basic Neurosciences, University of Geneva, Switzerland. Clinic of Neurology, Geneva University Hospital, Switzerland. Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Switzerland. denis.jabaudon@unige.ch.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26940868" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Basic Helix-Loop-Helix Transcription Factors/genetics ; Cerebral Ventricles/cytology/embryology ; DNA-Binding Proteins/genetics ; Female ; GPI-Linked Proteins/genetics ; Green Fluorescent Proteins/genetics ; Male ; Mice ; Neocortex/cytology/*embryology ; Nerve Tissue Proteins/genetics ; Neural Stem Cells/cytology ; Neurogenesis/*genetics ; Neurons/*cytology ; Neuropeptides/genetics ; SOXB1 Transcription Factors/genetics ; *Transcription, Genetic ; Transcriptome
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 4
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    Comparison of mouse and human genomes followed by experimental verification yields an estimated 1,019 additional genes (2003)
    National Academy of Sciences
    Details
    Publication Date: 2003-01-27
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
    Published by National Academy of Sciences
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  • 5
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    Demographic history and rare allele sharing among human populations (2011)
    National Academy of Sciences
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    Publication Date: 2011-07-05
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
    Published by National Academy of Sciences
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  • 6
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    Genome-wide association analysis of self-reported events in 6135 individuals and 252 827 controls identifies 8 loci associated with thrombosis (2016)
    Oxford University Press
    Details
    Publication Date: 2016-07-06
    Description: Thrombotic diseases are among the leading causes of morbidity and mortality in the world. To add insights into the genetic regulation of thrombotic disease, we conducted a genome-wide association study (GWAS) of 6135 self-reported blood clots events and 252 827 controls of European ancestry belonging to the 23andMe cohort of research participants. Eight loci exceeded genome-wide significance. Among the genome-wide significant results, our study replicated previously known venous thromboembolism (VTE) loci near the F5, FGA-FGG, F11, F2, PROCR and ABO genes, and the more recently discovered locus near SLC44A2 . In addition, our study reports for the first time a genome-wide significant association between rs114209171, located upstream of the F8 structural gene, and thrombosis risk. Analyses of expression profiles and expression quantitative trait loci across different tissues suggested SLC44A2 , ILF3 and AP1M2 as the three most plausible candidate genes for the chromosome 19 locus, our only genome-wide significant thrombosis-related locus that does not harbor likely coagulation-related genes. In addition, we present data showing that this locus also acts as a novel risk factor for stroke and coronary artery disease (CAD). In conclusion, our study reveals novel common genetic risk factors for VTE, stroke and CAD and provides evidence that self-reported data on blood clots used in a GWAS yield results that are comparable with those obtained using clinically diagnosed VTE. This observation opens up the potential for larger meta-analyses, which will enable elucidation of the genetics of thrombotic diseases, and serves as an example for the genetic study of other diseases.
    Print ISSN: 0964-6906
    Electronic ISSN: 1460-2083
    Topics: Biology , Medicine
    Published by Oxford University Press
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  • 7
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    Identification and removal of low-complexity sites in allele-specific analysis of ChIP-seq data (2014)
    Oxford University Press
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    Publication Date: 2014-01-16
    Description: Motivation : High-throughput sequencing technologies enable the genome-wide analysis of the impact of genetic variation on molecular phenotypes at unprecedented resolution. However, although powerful, these technologies can also introduce unexpected artifacts. Results : We investigated the impact of library amplification bias on the identification of allele-specific (AS) molecular events from high-throughput sequencing data derived from chromatin immunoprecipitation assays (ChIP-seq). Putative AS DNA binding activity for RNA polymerase II was determined using ChIP-seq data derived from lymphoblastoid cell lines of two parent–daughter trios. We found that, at high-sequencing depth, many significant AS binding sites suffered from an amplification bias, as evidenced by a larger number of clonal reads representing one of the two alleles. To alleviate this bias, we devised an amplification bias detection strategy, which filters out sites with low read complexity and sites featuring a significant excess of clonal reads. This method will be useful for AS analyses involving ChIP-seq and other functional sequencing assays. Availability : The R package absfilter for library clonality simulations and detection of amplification-biased sites is available from http://updepla1srv1.epfl.ch/waszaks/absfilter Contact : sebastian.waszak@epfl.ch or bart.deplancke@epfl.ch Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 8
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    Fast and efficient QTL mapper for thousands of molecular phenotypes (2016)
    Oxford University Press
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    Publication Date: 2016-05-14
    Description: Motivation: In order to discover quantitative trait loci, multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing. Results: We have developed FastQTL, a method that implements a popular cis -QTL mapping strategy in a user- and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to control for multiple testing. The outcome of permutations is modeled using beta distributions trained from a few permutations and from which adjusted P -values can be estimated at any level of significance with little computational cost. The Geuvadis & GTEx pilot datasets can be now easily analyzed an order of magnitude faster than previous approaches. Availability and implementation: Source code, binaries and comprehensive documentation of FastQTL are freely available to download at http://fastqtl.sourceforge.net/ Contact: emmanouil.dermitzakis@unige.ch or olivier.delaneau@unige.ch Supplementary information : Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 9
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    Assessing allele-specific expression across multiple tissues from RNA-seq read data (2015)
    Oxford University Press
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    Publication Date: 2015-07-26
    Description: Motivation : RNA sequencing enables allele-specific expression (ASE) studies that complement standard genotype expression studies for common variants and, importantly, also allow measuring the regulatory impact of rare variants. The Genotype-Tissue Expression (GTEx) project is collecting RNA-seq data on multiple tissues of a same set of individuals and novel methods are required for the analysis of these data. Results : We present a statistical method to compare different patterns of ASE across tissues and to classify genetic variants according to their impact on the tissue-wide expression profile. We focus on strong ASE effects that we are expecting to see for protein-truncating variants, but our method can also be adjusted for other types of ASE effects. We illustrate the method with a real data example on a tissue-wide expression profile of a variant causal for lipoid proteinosis, and with a simulation study to assess our method more generally. Availability and implementation : http://www.well.ox.ac.uk/~rivas/mamba/ . R-sources and data examples http://www.iki.fi/mpirinen/ Contact : matti.pirinen@helsinki.fi or rivas@well.ox.ac.uk Supplementary information : Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 10
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    Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies (2010)
    Oxford University Press
    Details
    Publication Date: 2010-08-10
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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