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  • 1
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    Another explanation for apparent epistasis (2014)
    Nature Publishing Group (NPG)
    Details
    Publication Date: 2014-10-04
    Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Wood, Andrew R -- Tuke, Marcus A -- Nalls, Mike A -- Hernandez, Dena G -- Bandinelli, Stefania -- Singleton, Andrew B -- Melzer, David -- Ferrucci, Luigi -- Frayling, Timothy M -- Weedon, Michael N -- G0500070/Medical Research Council/United Kingdom -- England -- Nature. 2014 Oct 2;514(7520):E3-5. doi: 10.1038/nature13691.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, Exeter EX2 5DW, UK. ; Laboratory of Neurogenetics, National Institute of Aging, Bethesda, Maryland 20892, USA. ; 1] Laboratory of Neurogenetics, National Institute of Aging, Bethesda, Maryland 20892, USA [2] Department of Molecular Neuroscience and Reta Lila Laboratories, Institute of Neurology, UCL, London WC1N IPJ, UK. ; 1] Tuscany Regional Health Agency, Florence, Italy, I.O.T. and Department of Medical and Surgical Critical Care, University of Florence, Florence, Italy [2] Geriatric Unit, Azienda Sanitaria di Firenze, Florence, Italy. ; Longitudinal Studies Section, Clinical Research Branch, Gerontology Research Center, National Institute on Aging, Baltimore, Maryland 21225, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25279928" target="_blank"〉PubMed〈/a〉
    Keywords: Epistasis, Genetic/*genetics ; Female ; Gene Expression Regulation/*genetics ; Humans ; Male ; Transcription, Genetic/*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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  • 2
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    New quality measure for SNP array based CNV detection (2016)
    Oxford University Press
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    Publication Date: 2016-10-26
    Description: Motivation: Only a few large systematic studies have evaluated the impact of copy number variants (CNVs) on common diseases. Several million individuals have been genotyped on single nucleotide variation arrays, which could be used for genome-wide CNVs association studies. However, CNV calls remain prone to false positives and only empirical filtering strategies exist in the literature. To overcome this issue, we defined a new quality score (QS) estimating the probability of a CNV called by PennCNV to be confirmed by other software. Results: Out-of-sample comparison showed that the correlation between the consensus CNV status and the QS is twice as high as it is for any previously proposed CNV filters. ROC curves displayed an AUC higher than 0.8 and simulations showed an increase up to 20% in statistical power when using QS in comparison to other filtering strategies. Superior performance was confirmed also for alternative consensus CNV definition and through improving known CNV-trait associations. Availability and Implementation: http://goo.gl/T6yuFM Contact: zoltan.kutalik@unil.ch or aurelien@mace@unil.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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  • 3
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    Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes (2015)
    Oxford University Press
    Details
    Publication Date: 2015-02-10
    Description: Initial results from sequencing studies suggest that there are relatively few low-frequency (〈5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency–large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant–common phenotype associations—11 132 cis -gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (〈5%), respectively, low frequency–large effect associations comprised 7% of detectable cis -gene expression traits [89 of 1314 cis -eQTLs at P 〈 1 x 10 –06 (false discovery rate ~5%)] and one of eight biomarker associations at P 〈 8 x 10 –10 . Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect.
    Print ISSN: 0964-6906
    Electronic ISSN: 1460-2083
    Topics: Biology , Medicine
    Published by Oxford University Press
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