Christoph Lippert

Summary

Affiliation: Max Planck Institute for Biological Cybernetics
Country: Germany

Publications

  1. doi request reprint Gene function prediction from synthetic lethality networks via ranking on demand
    Christoph Lippert
    Machine Learning and Computational Biology Research Group, Max Planck Institutes, Tubingen, Germany
    Bioinformatics 26:912-8. 2010
  2. doi request reprint FaST linear mixed models for genome-wide association studies
    Christoph Lippert
    Microsoft Research, Los Angeles, California, USA
    Nat Methods 8:833-5. 2011
  3. pmc A powerful and efficient set test for genetic markers that handles confounders
    Jennifer Listgarten
    eScience Group, Microsoft Research, Los Angeles, CA 90024, USA
    Bioinformatics 29:1526-33. 2013
  4. pmc The benefits of selecting phenotype-specific variants for applications of mixed models in genomics
    Christoph Lippert
    eScience Group, Microsoft Research, Los Angeles, CA 90024, United States
    Sci Rep 3:1815. 2013
  5. pmc An exhaustive epistatic SNP association analysis on expanded Wellcome Trust data
    Christoph Lippert
    Microsoft Research, Los Angeles, CA, USA
    Sci Rep 3:1099. 2013
  6. pmc Patterns of methylation heritability in a genome-wide analysis of four brain regions
    Gerald Quon
    eScience Group, Microsoft Research, 1100 Glendon Avenue, Suite PH1, Los Angeles, CA 90024, USA
    Nucleic Acids Res 41:2095-104. 2013

Collaborators

Detail Information

Publications6

  1. doi request reprint Gene function prediction from synthetic lethality networks via ranking on demand
    Christoph Lippert
    Machine Learning and Computational Biology Research Group, Max Planck Institutes, Tubingen, Germany
    Bioinformatics 26:912-8. 2010
    ..However, there is a lack of algorithms for predicting gene function from synthetic lethality interaction networks...
  2. doi request reprint FaST linear mixed models for genome-wide association studies
    Christoph Lippert
    Microsoft Research, Los Angeles, California, USA
    Nat Methods 8:833-5. 2011
    ..Our algorithm can analyze data for 120,000 individuals in just a few hours, whereas current algorithms fail on data for even 20,000 individuals (http://mscompbio.codeplex.com/)...
  3. pmc A powerful and efficient set test for genetic markers that handles confounders
    Jennifer Listgarten
    eScience Group, Microsoft Research, Los Angeles, CA 90024, USA
    Bioinformatics 29:1526-33. 2013
    ..Until now, these approaches did not address confounding by family relatedness and population structure, a problem that is becoming more important as larger datasets are used to increase power...
  4. pmc The benefits of selecting phenotype-specific variants for applications of mixed models in genomics
    Christoph Lippert
    eScience Group, Microsoft Research, Los Angeles, CA 90024, United States
    Sci Rep 3:1815. 2013
    ..For each application of the LMM, we review known effects and describe new effects showing how variable selection can be used to mitigate them...
  5. pmc An exhaustive epistatic SNP association analysis on expanded Wellcome Trust data
    Christoph Lippert
    Microsoft Research, Los Angeles, CA, USA
    Sci Rep 3:1099. 2013
    ..Our work suggests that carefully combining data from large repositories could reveal many new biological insights through increased power. As a community resource, all results have been made available through an interactive web server...
  6. pmc Patterns of methylation heritability in a genome-wide analysis of four brain regions
    Gerald Quon
    eScience Group, Microsoft Research, 1100 Glendon Avenue, Suite PH1, Los Angeles, CA 90024, USA
    Nucleic Acids Res 41:2095-104. 2013
    ..Finally, we show that the number of heritable loci depends on the window size parameter commonly used to identify candidate cis-acting single-nucleotide polymorphism variants...