Publications: Genomics


·         D. Heckerman, D. Gurdasani, C. Kadie, C. Pomilla, T. Carstensen, H. Martin, K. Ekoru, R.N. Nsubuga, G. Ssenyomo A. Kamali, P. Kaleebu, C. Widmer, and M.S. Sandhu.  Linear mixed model for heritability estimation that explicitly addresses environmental variation.  PNAS, 113: 7377–7382, July 2016 (doi: 10.1073/pnas.1510497113).


·         C. Lippert and D. Heckerman.  Computational and statistical issues in personalized medicine.  XRDS 21, 24-27, Summer 2015 (doi:10.1145/2788502).


·         O. Weissbrod, C. Lippert, D. Geiger, and D. Heckerman.  Accurate liability estimation improves power in ascertained case-control studiesNature Methods, Feb 2015 (doi:10.1038/nmeth.3285).


·         C. Widmer, C. Lippert, O. Weissbrod, N. Fusi, C.M. Kadie, R.I. Davidson, J. Listgarten, and D. Heckerman. Further Improvements to Linear Mixed Models for Genome-Wide Association Studies. Scientific Reports 4, 6874, Nov 2014 (doi:10.1038/srep06874).


·         C. Lippert, J. Xiang, D. Horta, C. Widmer, C.M. Kadie, D. Heckerman, J. Listgarten. Greater Power and Computational Efficiency for Kernel-Based Association Testing of Sets of Genetic Variants. Bioinformatics 30 , July 2014 (doi: 10.1093/bioinformatics/btu504).


·        N. Furlotte, D. Heckerman, and C. Lippert.  Quantifying the uncertainty in heritabilityJournal of Human Genetics 27, March 2014 (doi: 10.1038/jhg.2014.15).


·         J. Zou, C. Lippert, D. Heckerman, M. Aryee, and J. Listgarten.  Epigenome-wide association studies without the need for cell-type compositionNature Methods 11:309–311, Jan 2014 (doi:10.1038/nmeth.2815).


·         I. Bartha, J.Carlson, C. Brumme, P. McLaren, Z. Brumme, M. John, D. Haas, J. Martinez-Picado, J. Dalmau, C. López-Galíndez, C. Casado, A. Rauch, H. Günthard, E. Bernasconi, P. Vernazza, T. Klimkait, S. Yerly, S. O’Brien, J. Listgarten, N. Pfeifer, C. Lippert, N. Fusi, Z. Kutalik, T. Allen, V. Müller, P. Harrigan, D. Heckerman, A. Telenti, and J. Fellay.  A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral controleLife 2013;2:e01123, 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 genomicsScientific Reports, 3, May 2013 (doi:10.1038/srep01815).


·         J. Listgarten, C. Lippert, E.Y. Kang, J. Xiang, C.M. Kadie, and D. Heckerman.  A powerful and efficient set test for genetic markers that handles confoundersBioinformatics, May 2013 (doi: 10.1093/bioinformatics/btt177).


·         J. Listgarten, C. Lippert, and D. Heckerman. FaST-LMM-Select for addressing confounding from spatial structure and rare variantsNature Genetics, 45: 470-471, April 2013 (doi:10.1038/ng.2620).


·         G. Quon, C. Lippert, D. Heckerman, and J. Listgarten.  Patterns of methylation heritability in a genome-wide analysis of four brain regionsNucleic Acids Research, March 2013 (doi: 10.1093/nar/gks1449).


·         C. Lippert, J. Listgarten, R.I. Davidson, J. Baxter, H. Poon, C.M. Kadie, and  D. Heckerman.  An Exhaustive Epistatic SNP Association Analysis on Expanded Wellcome Trust DataScientific Reports, 3, Jan 2013 (doi:10.1038/srep01099).


·         J. Listgarten, C. Lippert, C.M. Kadie, R.I. Davidson, E. Eskin, and D. Heckerman.  Improved linear mixed models for genome-wide association studiesNature Methods, 9: 525-526, June 2012 (doi:10.1038/nmeth.2037).


·         X. Zhang, W. Cheng, J. Listgarten, C. Kadie, S. Huang, W. Wang, and D. Heckerman.  Learning transcriptional regulatory relationships using sparse graphical modelsPLoS One 7(5): e35762, May 2012 (doi:10.1371/journal.pone.0035762).


·         A. Renton, et al.  A Hexanucleotide Repeat Expansion in C9ORF72 Is the Cause of Chromosome 9p21-Linked ALS-FTDNeuron 72(2): 257-268, Oct 2011.


·         C. Lippert, J. Listgarten, Y. Liu, C.M. Kadie, R.I. Davidson, and D. Heckerman.  FaST linear mixed models for genome-wide association studiesNature Methods, 8: 833-835, Oct 2011 (doi:10.1038/nmeth.1681).


·         J. Listgarten, C. Kadie, E. Schadt, D. Heckerman.  Correction for hidden confounders in the genetic analysis of gene expression. PNAS, 107 (38): 16465-16470, September 2010 (doi: 10.1073/pnas.1002425107).


 ·         J. Carlson, Z. Brumme, C. Rousseau, C. Brumme, P. Matthews, C. Kadie, J. Mullins, B. Walker, P. Harrigan, P. Goulder, D. Heckerman.  Phylogenetic dependency networks: Inferring patterns of CTL escape and codon covariation in HIV-1 Gag. PLoS Computational Biology, 4(11): e1000225, November 2008.


·         H. Kang, N. Zaitlen, C. Wade, A. Kirby, D. Heckerman, M. Daly, and E. Eskin, Efficient Control of Population Structure in Model Organism Association Mapping, Genetics, 178:1709-1723, March, 2008 (doi: 10.1534/genetics.107.080101).


·         J. Listgarten, Z. Brumme, C. Kadie, G. Xiaojiang, B. Walker, M. Carrington, P. Goulder, and D. Heckerman.  Statistical resolution of ambiguous HLA typing dataPLoS Computational Biology, 4(2): e1000016, February, 2008.


·         J. Listgarten, N. Frahm, C. Kadie, C. Brander, D. Heckerman.  A statistical framework for modeling HLA-dependent T cell response data, PLoS Computational Biology, 3(10): e188, October 2007.


·         D. Heckerman, C. Kadie, and J. Listgarten.  Leveraging information across HLA alleles/supertypes improves epitope prediction.  J. of Comp. Bio, 14(6): 736-746, August 2007.  Also appears as MSR-TR-05-127, Microsoft Research, September, 2005.


·         J. Carlson, C. Kadie, S. Mallal, and D. Heckerman.  Leveraging hierarchical population structure in discrete association studies. PLoS ONE, 2(7): e591, July 2007.


·         T. Bhattacharya, M. Daniels, D. Heckerman, B. Foley, N. Frahm, C. Kadie, J. Carlson, K. Yusim, B. McMahon, B. Gaschen, S. Mallal, J. Mullins, D. Nickle, J. Herbeck, C. Rousseau, G. Learn, T. Miura, C. Brander, B. Walker, B. Korber.  Founder effects in the assessment of HIV polymorphisms and HLA allele associations, Science, 315, 1583-1586, March 16 2007.