My research is focused on machine learning and statistics in genomics and genetics. I am especially interested in method development and applications of Gaussian processes and linear mixed models.
In this space I have mostly been working on methods for genome-wide association studies (GWAS).
Microsoft offers a highly competitive internship program for PhD students with a strong background in machine learning, statistics, and bioinformatics. If you are interested in applying for an internship in our office in Los Angeles, write me an e-mail.
Lectures and tutorials (material can be found at mixed-models.org):
- July 2014: EMBO Practical course on Genotype to Phenotype Mapping of Complex Traits (material for practical sessions)
- UCLA, winter quarter 2014: Machine learning and statistics for genetics and genomics
- Christoph Lippert, Jing Xiang, Danilo Horta, Christian Widmer, Carl Kadie, David Heckerman, and Jennifer Listgarten, Greater power and computational efficiency for kernel-based association testing of sets of genetic variants, in Bioinformatics, pp. btu504, Oxford University Press, September 2014.
- Nicolo Fusi, Christoph Lippert, Neil D Lawrence, and Oliver Stegle, Warped linear mixed models for the genetic analysis of transformed phenotypes, in Nature communications, vol. 5, Nature Publishing Group, September 2014.
- Christoph Lippert, Francesco Paolo Casale, Barbara Rakitsch, and Oliver Stegle, LIMIX: genetic analysis of multiple traits, in bioRxiv, Cold Spring Harbor Labs Journals, May 2014.
- James Zou, Christoph Lippert, David Heckerman, Martin Ayree, and Jennifer Listgarten, Epigenome-wide association studies without the need for cell-type composition, in Nature Methods, Nature Publishing Group, 26 January 2014.
- Nicholas Furlotte, David Heckerman, and Christoph Lippert, Quantifying the Uncertainty in Heritability, in Journal of Human Genetics, Nature Publishing Group, 2014.
- Istvan Bartha, Jonathan M Carlson, Chanson J Brumme, Paul J McLaren, Zabrina L Brumme, Mina John, David W Haas, Javier Martinez-Picado, Judith Dalmau, Cecilio Lopez-Galindez, Concepcion Casado, Andri Rauch, Huldrych F Günthard, Enos Bernasconi, Pietro Vernazza, Thomas Klimkait, Sabine Yerly, Stephen J O'Brien, Jennifer Listgarten, Nico Pfeifer, Christoph Lippert, Nicolo Fusi, Zoltan Kutalik, Todd M Allen, Viktor Müller, P Richard Harrigan, David Heckerman, Amalio Telenti, and Jacques Fellay, A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control, in eLife, , 29 October 2013.
- C. Lippert, G. Quon, E. Y. Kang, C. M. Kadie, J. Listgarten, and D. Heckerman, The benefits of selecting phenotype-specific variants for applications of mixed models in genomics, in Scientific Reports, Nature Publishing Group, 9 May 2013.
- J. Listgarten, C. Lippert, and D. Heckerman, FaST-LMM-Select for addressing confounding from spatial structure and rare variants, in Nature Genetics, Nature Publishing Group, 26 April 2013.
- G. Quon, C. Lippert, D. Heckerman, and J. Listgarten, Patterns of methylation heritability in a genome-wide analysis of four brain regions, in Nucleic Acids Research, Oxford Univ Press, 2013.
- Nicoló Fusi, Christoph Lippert, Karsten Borgwardt, Neil D Lawrence, and Oliver Stegle, Detecting regulatory gene-environment interactions with unmeasured environmental factors, in Bioinformatics, Oxford Univ Press, 2013.
- C. Lippert, J. Listgarten, R.I. Davidson, J. Baxter, H. Poon, C. Kadie, and D. Heckerman, An Exhaustive Epistatic SNP Association Analysis on Expanded Wellcome Trust Data, in Scientific Reports, vol. 3, Nature Publishing Group, 2013.
- Barbara Rakitsch, Christoph Lippert, Karsten Borgwardt, and Oliver Stegle, It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals, in Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2013.
- B. Rakitsch, C. Lippert, O. Stegle, and K. Borgwardt, A Lasso Multi-Marker Mixed Model for Association Mapping with Population Structure Correction, in Bioinformatics, Oxford Univ Press, 2013.
- Jennifer Listgarten, Christoph Lippert, Eun Yong Kang, Jing Xiang, Carl M. Kadie, and David Heckerman, A powerful and efficient set test for genetic markers that handles confounders, in Bioinformatics, Oxford University Press, 2013.
- Christoph Lippert, Gerald Quon, Jennifer Listgarten, and David Heckerman, Extraneous markers used for genetic similarity leads to loss of power in GWAS and heritability determination, no. MSR-TR-2012-120, 3 December 2012.
- Jennifer Listgarten, Christoph Lippert, Carl M. Kadie, Robert I. Davidson, Eleazar Eskin, and David Heckerman, Improved linear mixed models for genome-wide association studies, in Nature Methods, vol. 9, no. 6, pp. 3–4, 2012.
- C Lippert, J Listgarten, Y Liu, CM Kadie, RI Davidson, and D Heckerman, FaST linear mixed models for genome-wide association studies, in Nature Methods, vol. 8, no. 10, pp. 833–835, October 2011.
- J. Cao, K. Schneeberger, S. Ossowski, T. Günther, S. Bender, J. Fitz, D. Koenig, C. Lanz, O. Stegle, C. Lippert, and others, Whole-genome sequencing of multiple Arabidopsis thaliana populations, in Nature genetics, vol. 43, no. 10, pp. 956–963, Nature Publishing Group, 2011.
- O. Stegle, C. Lippert, J. M. Mooij, N. D. Lawrence, and K. Borgwardt, Efficient inference in matrix-variate Gaussian models with iid observation noise, in Advances in Neural Information Processing Systems 24, 2011.
- C. Lippert, Z. Ghahramani, and K.M. Borgwardt, Gene function prediction from synthetic lethality networks via ranking on demand, in Bioinformatics, vol. 26, no. 7, pp. 912–918, Oxford Univ Press, 2010.
Past organized events:
- Jointly with Bjarni Vilhjalmsson I am organizing a workshop on Heritability analysis and genetic trait prediction at ISMB in Berlin. See you there on Sunday, July 21, 2013.