Kathryn Roeder

Google Scholar

Selected and Recent Publications

    Wang, Jiebiao and Roeder, Kathryn and Devlin, Bernie, Bayesian estimation of cell type-specific gene expression with prior derived from single-cell data, Genome Research, gr--268722, 2021.

    Katsevich, Eugene and Barry, Timothy and Roeder, Kathryn, Conditional resampling improves calibration and sensitivity in single cell CRISPR screen analysis, bioRxiv, 2020--08, 2021.

    Lin, Kevin Z and Lei, Jing and Roeder, Kathryn, Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data, Journal of the American Statistical Association, 1--14, 2021.

    Qiu, Yixuan and Wang, Jiebiao and Lei, Jing and Roeder, Kathryn, Identification of cell-type-specific marker genes from co-expression patterns in tissue samples, Bioinformatics, 37(19), 3228--3234, 2021.

    Peng, Minshi and Wamsley, Brie and Elkins, Andrew G and Geschwind, Daniel M and Wei, Yuting and Roeder, Kathryn, Cell type hierarchy reconstruction via reconciliation of multi-resolution cluster tree, bioRxiv, 2021.

    Wang, Xuran and Choi, David and Roeder, Kathryn, Constructing Local Cell Sepcific Networks from Single Cell Data, bioRxiv, 2021.

    Peng, Minshi and Li, Yue and Wamsley, Brie and Wei, Yuting and Roeder, Kathryn, Integration and transfer learning of single-cell transcriptomes via cFIT, Proceedings of the National Academy of Sciences, 118(10), 2021.

    Yurko, Ronald J and Roeder, Kathryn and Devlin, Bernie and G'Sell, Max, An approach to gene-based testing accounting for dependence of tests among nearby genes, bioRxiv, 2021.

    Cai, Zhanrui and Lei, Jing and Roeder, Kathryn, A Distribution-Free Independence Test for High Dimension Data, arXiv preprint arXiv:2110.07652, 2021.

    Satterstrom, F Kyle and Kosmicki, Jack A and Wang, Jiebiao and Breen, Michael S and De Rubeis, Silvia and An, Joon-Yong and Peng, Minshi and Collins, Ryan and Grove, Jakob and Klei, Lambertus and others, Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism, Cell, 180(3), 568--584, 2020.

    Yurko, Ronald and G’Sell, Max and Roeder, Kathryn and Devlin, Bernie, A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk, Proceedings of the National Academy of Sciences, 117(26), 15028--15035, 2020.

    Werling, Donna M and Pochareddy, Sirisha and Choi, Jinmyung and An, Joon-Yong and Sheppard, Brooke and Peng, Minshi and Li, Zhen and Dastmalchi, Claudia and Santpere, Gabriel and Sousa, Andre MM and others, Whole-genome and RNA sequencing reveal variation and transcriptomic coordination in the developing human prefrontal cortex, Cell reports, 31(1), 107489, 2020.

    Qiu, Yixuan and Lei, Jing and Roeder, Kathryn, Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning, arXiv preprint arXiv: 1911.08048, 2019.

    An, Joon-Yong and Lin, Kevin and Zhu, Lingxue and Werling, Donna M and Dong, Shan and Brand, Harrison and Wang, Harold Z and Zhao, Xuefang and Schwartz, Grace B and Collins, Ryan L and others, Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder Science, 362, 6420, eat6576, 2018.

    Chen, S. et al. An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders. Nat Genet, Jun 2018.

    Werling, D.M. et al. An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder. Nat Genet, 50(5):727-736, May 2018.

    Zhu, L. et al. A unified statistical framework for single cell and bulk RNA sequencing data. Ann Appl Stat, 12(1)609-632, Mar 2018.

    Zhu, L. et al. Semisoft clustering of single-cell data. Proc Natl Acad Sci USA Dec 2018.

    An, J-Y et al. Genomewide de novo risk score implicates promoter variation in autism spectrum disorder. Science, 362(6420), 12 2018.

    Liu, F, Choi, D, Xie, L and Roder, K. Global spectral clustering in dynamic networks. Proceedings of the National Academy, 2018.

    Kosmicki, J. et al. Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples. Nat Genet, Feb. 2017.

    Zhu, L, Lei, J, Devlin, B, and Roeder, K. Testing high dimensional covariance matrices, with application to detecting schizophrenia risk genes. Annals of Applied Statistics, 1(3):1810--1831, Sept. 2017.

    Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci, 19:1442–1453, 2016.

    Bodea CA, Ripke S, Daly, MJ, Devlin, B, and Roeder K. A method to exploit the structure of genetic ancestry space to enhance case-control studies. Am J Hum Genet, 98:857–868, 2016.

    Liu, L, Lei, J, and Roeder, K. Network assisted analysis to reveal the genetic basis of autism. Ann Appl Stat, 9:1571–1600, 2015.

    Sanders, S.J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron, 87(6):1215–33, Sep 2015.

    Cotney, J. et al. The autism-associated chromatin modifier chd8 regulates other autism risk genes during human neurodevelopment. Nat Commun, 6:6404, 2015.

    De Rubeis, S. et al. Synaptic, transciptional and chromatin genes disrupted in autism. Nature, Oct. 2014.

    Samocha, K. et al. A framework for the interpretation of de novo mutation in human disease. Nat Genet, 46(9):944-50, Sep 2014.

    Liu, L. et al. Dawn: A framework to identify autism genes and subnetworks using gene expression and genetics. Mol Autism, 5:22, 2014.

    Gaugler, T. et al. Most genetic risk for autism resides with common variation. Nat Genet, 46(8):881-5, Aug 2014.

    Cicek, A.E. et al. Mira mutual information based reporter algorithm. Bioinformatics, 15:175--184, 2014.

    Blumenthal, I. et al. Transcriptional consequences of 16p11.2 deletion and duplication in mouse coretex and multiplex autism families. Am J Hum Genet, 94:870--883, 2014.

    Zhao, T., Roeder, K. and Liu, H. Positive semidefinite rank-based correlation matrix estimation with application to semiparametric graph estimation. Journal of Computational and Graphical Statistics, (DOI: 10.1080/10618600.2013.858633), 2013.

    Willsey, A.J. et al. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell, 155(5):997--1007, Nov 2013.

    Schafer, C.M. et al. Whole exome sequencing reveals minimal differences between cell line and whole blood derived dna. Genomics, Jun 2013.

    Ringquist, S., Bellone, G., Lu, Y., Roeder, K., and Trucco, M. Clustering and alignment of polymorphic sequences for hla-drb1 genotyping. PLoS One, 8(3):e59835, 2013.

    Liu, L. et al. Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls. PLoS Genet, 9(4):e1003443, Apr 2013.

    Lim, E.T. et al. Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders. Neuron, 77(2):235--42, Jan 2013.

    He, X and Roeder, K. Integrated model of de novo and inherited genetic variants yields greater power to identify risk genes. PLoS Genet. (in press), 2013.

    Crossett, A., Lee, L., Klei, B., Devlin, B. and Roeder, K. Refining genetically inferred relationships using treelet covariance smoothing. Annals of Applied Statistics, 7:669-690, 2013.

    Zhao, T., Roeder, K., and Liu, H. Smooth-projected neighborhood pursuit for high-dimensional nonparanormal graph estimation. In Bartlett, P., Pereira, F.C.N., Burges, C.J.C., Bottou, L., and Weinberger, K.Q., editors, Advances in Neural Information Processing Systems 25, pages 162--170. 2012.

    Klei, L. et al. Common genetic variants, acting additively, are a major source of risk for autism. Mol Autism, 3(1):9, Oct 2012.

    Zhao, T., Roeder, K., and Liu, H. High-dimensional nonparanormal graph estimation via smooth-projected neighborhood pursuit. In review, 2012.

    Zhao, T., Liu, H. Roeder, K., Lafferty, J., and Wasserman, L. The HUGE package for high-dimensional undirected graph estimation in R. Journal of Machine Learning Research, 13(Apr):1059-1062, 2012.

    Mechanic, L. E. et al. Next generation analytic tools for large scale genetic epidemiology studies of complex diseases. Genet. Epidemiol., 36(1):22-35, Jan. 2012.

    Liu, Li, Liu, Han, and Roeder, Kathryn. Discrepancy pursuit: A nonparametric framework for high dimensional variable selection. JASA (submitted), 2012.

    Sanders, S.J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature, 485(7397):237-241, May 2012.

    Neale, B.M. et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature, 485(7397): 242-245, 2012.

    Achkar, J-P. et al. Amino acid position 11 of HLA-DRB1 is a major determinant of chromosome 6p association with ulcerative colitis. Genes Immmun, 13(3):245-252, Apr. 2012.

    Sanders, S. J. et al. Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron, 70:863–885, Jun 2011.

    Percival, D., Roeder, K., Rosenfeld, R., and Wasserman, L. Structured, sparse regression with application to HIV drug resistance. Ann Appl Stat, 5:628–644, Jun 2011.

    Neale, B. M. et al. Testing for an unusual distribution of rare variants. PLoS Genet., 7:e1001322, Mar 2011.

    Melhem, N. et al. Copy number variants for schizophrenia and related psychotic disorders in Oceanic Palau: risk and transmission in extended pedigrees. Biol. Psychiatry, 70:1115–1121, Dec 2011.

    Devlin, B., Melhem, N., and Roeder, K. Do common variants play a role in risk for autism? Evidence and theoretical musings. Brain Res., 1380:78–84, Mar 2011.

    Chu, S. H. et al. TOMM40 poly-T repeat lengths, age of onset and psychosis risk in Alzheimer disease. Neurobiol. Aging, 32:1–9, Dec 2011.

    Achkar, J. P. et al. Amino acid position 11 of HLA-DRB1 is a major determinant of chromosome 6p association with ulcerative colitis. Genes Immun, Dec 2011.

    Liu H, Roeder K and Wasserman L (2010) Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models

    Wu, J., Devlin, B., Ringquist, S., Trucco, M., and Roeder, K. Screen and clean: a tool for identifying interactions in genome-wide association studies. Genet. Epidemiol., 34:275–285, Apr 2010.

    Pinto, D. et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature, 466:368–372, Jul 2010.

    McGovern, D. P. et al. Genome-wide association identities multiple ulcerative colitis susceptibility loci. Nat. Genet., 42:332–337, Apr 2010.

    Lee, A. B., Luca, D., and Roeder, K. A spectral graph approach to discovering genetic ancestry. Ann Appl Stat, 4:179–202, 2010.

    Lee, A. B., Luca, D., Klei, L., Devlin, B., and Roeder, K. Discovering genetic ancestry using spectral graph theory. Genet. Epidemiol., 34:51–59, Jan 2010.

    Devlin, B., Melhem, N., and Roeder, K. Do common variants play a role in risk for autism? evidence and theoretical musings. Brain Res., Nov 2010.

    Crossett, A. et al. Using ancestry matching to combine family-based and unrelated samples for genome-wide association studies. Stat Med, 29:2932–2945, Dec 2010.

    Anney, R. et al. A genome-wide scan for common alleles affecting risk for autism. Hum. Mol. Genet., 19:4072–4082, Oct 2010.

    Wasserman, L. and Roeder, K. High dimensional variable selection. Ann Stat, 37:2178–2201, Jan 2009.

    Silverberg, M. S. et al. Ulcerative colitis-risk loci on chromosomes 1p36 and 12q15 found by genome-wide association study. Nat. Genet., 41:216–220, Feb 2009.

    Roeder, K. and Wasserman, L. Genome-Wide Significance Levels and Weighted Hypothesis Testing. Stat Sci, 24:398–413, Nov 2009.

    Roeder, K. and Luca, D. Searching for disease susceptibility variants in structured populations. Genomics, 93:1–4, Jan 2009.

    Luca, D. et al. On the use of general control samples for genome-wide association studies: genetic matching highlights causal variants. Am. J. Hum. Genet., 82:453–463, Feb 2008.

    Klei, L., Luca, D., Devlin, B., and Roeder, K. Pleiotropy and principal components of heritability combine to increase power for association analysis. Genet. Epidemiol., 32:9–19, Jan 2008.

    Roeder, K., Devlin, B., and Wasserman, L. Improving power in genome-wide association studies: weights tip the scale. Genet. Epidemiol., 31:741–747, Nov 2007.

    Klei, L. and Roeder, K. Testing for association based on excess allele sharing in a sample of related cases and controls. Hum. Genet., 121:549–557, Jun 2007.

    Devlin, B. et al. Genetic liability to schizophrenia in Oceanic Palau: a search in the affected and maternal generation. Hum. Genet., 121:675–684, Jul 2007.

    Roeder, K., Bacanu, S. A., Wasserman, L., and Devlin, B. Using linkage genome scans to improve power of association in genome scans. Am. J. Hum. Genet., 78:243–252, Feb 2006.

    Genovese, C., Roeder, K., and Wasserman, L. False discovery control with p-value weighting. Biometrika, 93:509–524, 2006.

    Roeder, K., Bacanu, S. A., Sonpar, V., Zhang, X., and Devlin, B. Analysis of single-locus tests to detect gene/disease associations. Genet. Epidemiol., 28:207–219, Apr 2005.

    Rinaldo, A. et al. Characterization of multilocus linkage disequilibrium. Genet. Epidemiol., 28:193–206, Apr 2005.

    Devlin, B., Bacanu, S. A., and Roeder, K. Genomic Control to the extreme. Nat. Genet., 36:1129–1130, Nov 2004.

    Tzeng, J. Y., Devlin, B., Wasserman, L., and Roeder, K. On the identification of disease mutations by the analysis of haplotype similarity and goodness of fit. Am. J. Hum. Genet., 72:891–902, Apr 2003.

    Tzeng, J. Y., Byerley, W., Devlin, B., Roeder, K., and Wasserman, L. Outlier detection and false discovery rates for whole-genome dna matching. J. Amer. Statist. Assoc., 98:236–247, 2003.

    Seltman, H., Roeder, K., and Devlin, B. Evolutionary-based association analysis using haplotype data. Genet. Epidemiol., 25:48–58, Jul 2003.

    Devlin, B., Roeder, K., and Wasserman, L. False discovery or missed discovery? Heredity (Edinb), 91:537–538, Dec 2003.

    Devlin, B., Roeder, K., and Wasserman, L. Analysis of multilocus models of association. Genet. Epidemiol., 25:36–47, Jul 2003.

    Devlin, B., Jones, B. L., Bacanu, S. A., and Roeder, K. Mixture models for linkage analysis of affected sibling pairs and covariates. Genet. Epidemiol., 22:52–65, Jan 2002.

    Devlin, B., Jones, B. L., Bacanu, S. A., and Roeder, K. Mixture and linear models for linkage analysis with covariates. Genetic Epidemiology, 23:449–455, 2002.

    Bacanu, S. A., Devlin, B., and Roeder, K. Association studies for quantitative traits in structured populations. Genet. Epidemiol., 22:78–93, Jan 2002.

    Seltman, H., Roeder, K., and Devlin, B. Transmission/disequilibrium test meets measured haplotype analysis: family-based association analysis guided by evolution of haplotypes. Am. J. Hum. Genet., 68:1250–1263, May 2001. https://acis.as.cmu.edu:9903/isqlplus

    Lockwood, J. R., Roeder, K., and Devlin, B. A Bayesian hierarchical model for allele frequencies. Genet. Epidemiol., 20:17–33, Jan 2001.

    Jones, B., Nagin, D., and Roeder, K. A SAS procedure based on mixture model for estimating developmental trajectories. Sociological Methods and Research, 29(3):374–393, 2001.

    Devlin, B., Roeder, K., and Wasserman, L. Genomic control, a new approach to genetic-based association studies. Theor Popul Biol, 60:155–166, Nov 2001.

    Devlin, B., Roeder, K., and Bacanu, S. A. Unbiased methods for population-based association studies. Genet. Epidemiol., 21:273–284, Dec 2001.

    Bacanu, S. A., Devlin, B., and Roeder, K. The power of genomic control. Am. J. Hum. Genet., 66:1933–1944, Jun 2000.

    Devlin, B. and Roeder, K. Genomic control for association studies. Biometrics, 55:997–1004, Dec 1999.

    Devlin, B., Daniels, M., and Roeder, K. The heritability of IQ. Nature, 388:468–471, Jul 1997.

    Roeder, K., Carroll, R. J., and Lindsay, B.G. A nonparametric maximum likelihood approach to case-control studies with errors in covariables. J. Amer. Statist. Assoc., 91:722–732, 1996.

    Roeder, K. DNA Fingerprinting: A review of the controversy (with discussion). Statistical Science, 9:222–278, 1994.

    Devlin, B., Risch, N., and Roeder, K. Statistical evaluation of DNA Fingerprinting: a critique of the NRC's report. Science, 259:748–749, 1993.

    Devlin, B., Risch, N., and Roeder, K. NRC report on DNA typing. Science, 260:1057–1059, May 1993.

    Devlin, B., Risch, N., and Roeder, K. Response. Science, 253:1039–1041, Aug 1991.

    Roeder, K. Density estimation with confidence sets exemplified by superclusters and voids in the galaxies. J. Amer. Statist. Assoc., 85:616–624, 1990.

    Devlin, B., Risch, N., and Roeder, K. No excess of homozygosity at loci used for DNA fingerprinting. Science, 249:1416–1420, Sep 1990. j

UPMC Professor of Statistics and Life Sciences
Department of Statistics and Computational Biology
Carnegie Mellon University
Baker Hall 228B
Pittsburgh, PA 15213
Contact: kathryn.roeder (gmail)
Phone: (412) 268-5775