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A multiple-phenotyp...
A multiple-phenotype imputation method for genetic studies
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- Dahl, Andrew (författare)
- Univ Oxford, Wellcome Trust Ctr Human Genet, Oxford, England.
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- Iotchkova, Valentina (författare)
- Wellcome Trust Sanger Inst, Human Genet, Wellcome Trust Genome Campus, Hinxton, England.;European Bioinformat Inst EMBL EBI, Wellcome Trust Genome Campus, Hinxton, England.
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- Baud, Amelie (författare)
- European Bioinformat Inst EMBL EBI, Wellcome Trust Genome Campus, Hinxton, England.
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- Johansson, Åsa (författare)
- Uppsala universitet,Institutionen för immunologi, genetik och patologi,Science for Life Laboratory, SciLifeLab
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- Gyllensten, Ulf (författare)
- Uppsala universitet,Institutionen för immunologi, genetik och patologi,Science for Life Laboratory, SciLifeLab
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- Soranzo, Nicole (författare)
- Wellcome Trust Sanger Inst, Human Genet, Wellcome Trust Genome Campus, Hinxton, England.
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- Mott, Richard (författare)
- Univ Oxford, Wellcome Trust Ctr Human Genet, Oxford, England.
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- Kranis, Andreas (författare)
- Aviagen Ltd, Newbridge, England.;Univ Edinburgh, Roslin Inst, Edinburgh EH8 9YL, Midlothian, Scotland.
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- Marchini, Jonathan (författare)
- Univ Oxford, Wellcome Trust Ctr Human Genet, Oxford, England.;Univ Oxford, Dept Stat, Oxford OX1 3TG, England.
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Univ Oxford, Wellcome Trust Ctr Human Genet, Oxford, England Wellcome Trust Sanger Inst, Human Genet, Wellcome Trust Genome Campus, Hinxton, England.;European Bioinformat Inst EMBL EBI, Wellcome Trust Genome Campus, Hinxton, England. (creator_code:org_t)
- 2016-02-22
- 2016
- Engelska.
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 48:4, s. 466-472
- Relaterad länk:
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https://europepmc.or...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Genetic association studies have yielded a wealth of biological discoveries. However, these studies have mostly analyzed one trait and one SNP at a time, thus failing to capture the underlying complexity of the data sets. Joint genotype-phenotype analyses of complex, high-dimensional data sets represent an important way to move beyond simple genome-wide association studies (GWAS) with great potential. The move to high-dimensional phenotypes will raise many new statistical problems. Here we address the central issue of missing phenotypes in studies with any level of relatedness between samples. We propose a multiple-phenotype mixed model and use a computationally efficient variational Bayesian algorithm to fit the model. On a variety of simulated and real data sets from a range of organisms and trait types, we show that our method outperforms existing state-of-the-art methods from the statistics and machine learning literature and can boost signals of association.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Medicinsk genetik (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Medical Genetics (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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