Kathleen F. Kerr

Methodology for Evaluating Biomarkers and Risk Prediction

• Kerr KF and Janes H. First things first: risk model performance metrics shoudl reflect the clinical application. Statistics in Medicine, to appear 2017.
• Kerr KF, Wang Z, Janes H, McClelland R, Psaty B, Pepe M. Net Reclassification Indices for Evaluating Risk Prediction Instruments: A Critical Review. Epidemiology, 25:114-21, 2014. (Published with Invited Commentary.)
• Pepe MS, Kerr KF, Longton G, Wang Z: Testing for improvement in prediction model performance. Statistics in Medicine 32:1467-1482, 2013.
• Kerr KF, Bansal A, Pepe MS: Further Insight into the Incremental Value of New Markers: The Interpretation of Performance Measures and the Importance of Clinical Context. American Journal of Epidemiology, 2012.
• Kerr KF, Pepe MS: Joint Modeling, Covariate Adjustment, and Interaction: Contrasting Notions in Risk Prediction Models and Risk Prediction Performance. Epidemiology 22(6):805-812, 2011.
• Kerr KF, McClelland RL, Brown ER, Lumley T: Evaluating the Incremental Value of New Biomarkers with Integrated Discrimination Improvement. American Journal of Epidmiology, 2011.

Microarrays, Gene Expression, Bioinformatics

• Kerr KF: Comments on the Analysis of Unbalanced Microarray Data. Bioinformatics, 25: 2035-2041, 2009.
• Kerr KF: Extended analysis of benchmarker datasets for Agilent two-color microarrays. BMC Bioinformatics 8:371, 2007.
• Qin, L-X, Kerr KF, Contributing Members of the Toxicogenomics Research Consortium: Empirical evaluation of data transformations and ranking statistics for microarray analysis. Nucleic Acids Research 32:5471-5479, 2004.
• Kerr MK: Linear models for microarray data analysis: Hidden similarities and differences, Journal of Computational Biology 10:891-901, 2003.
• Kerr MK: Design considerations for efficient and effective microarray studies, Biometrics 59:822-828, 2003.
• Kerr MK, Churchill GA: Experimental design for gene expression microarrays. Biostatistics 2:183-201, 2001.
• Kerr MK, Churchill GA: Statistical design and the analysis of gene expression microarrays. Genetical Research 77:123-128, 2001.
• Kerr MK, Martin M, Churchill GA: Analysis of variance for gene expression microarray data. Journal of Computational Biology 7:819-837, 2000.

GWAS Methods

• Sofer T, Heller R, Bogomolov M, Avery C, Graff M, North K, Reiner A, Thornton T, Rice K, Banjamini Y, Laurie CC, Kerr KF. A powerful statistical framework for generalization testing in GWAS, with application to the HCHS/SOL. Genetic Epidemiology 41:251-250, 2017.
• Conomos MP, Laurie CA, Stilp AM, Gogarten SM, McHugh CP, Nelson SC, Sofer T, Fernández-Rhodes L, Justice AE, Graff M, Seyerle AA, Avery CL, Taylor KD, Rotter JI, Talavera GA, Daviglus ML, Wassertheil-Smoller S, Schneiderman N, Heiss G, Kaplan RC, Franceschini N, Reiner AP, Shaffer JR, Barr1 RG, Kerr KF, Browning SR, Browning BL, Weir BS, Avilés-Santa ML, Papanicolaou1 JG, Lumley T, Szpiro AA, North KE, Rice K, Thornton TA, and Laurie CC. Genetic Diversity and Association Studies in U.S. Hispanic/Latino Populations: Applications in the Hispanic Community Health Study/Study of Latinos. American Journal of Human Genetics, 2016.

Experimental Design

• Kerr KF: Optimality Criteria for the Design of 2-Color Microarray Studies. Statistical Applications in Genetics and Molecular Biology 11:1-9, 2012.
• Kerr KF: 2^k Factorials in Blocks of Size 2, with Application to Two-Color Microarray Experiments. Journal of Quality Technology 38:349-364, 2006.
• Kerr MK: Design considerations for efficient and effective microarray studies, Biometrics 59:822-828, 2003.
• Kerr MK: Churchill GA: Experimental design for gene expression microarrays. Biostatistics 2:183-201, 2001.
• Kerr MK: Bayesian optimal fractional factorials. Statistica Sinica 11:605-630, 2001.

Select Scientific Collaborations

• Smith JG, Luk K, Schulz C-A, Engert JC, Do R, Hindy G, Rukh G, Dufresne L, Almgren P, Owens DS, Harris TB, Peloso GM, Kerr KF, Wong Q, Smith AV, Rotter JI, Cupples A, Rich S, Kathiresan S, Orho-Melander M, Gudnason V, O’Donnell CJ, Post WS, Thanassoulis G. Association of Low-Density Lipoprotein Cholesterol–Related Genetic Variants With Aortic Valve Calcium and Incident Aortic Stenosis. Journal of the American Medical Association, 2014.
• den Hoed M, Eijgelsheim M, Esko T, Brundel B, Peal DS, Evans DM, Nolte IM, Segrè AV, Holm H, Handsaker RE, Westra H-J, Johnson T, Isaacs A, Yang J, Lundby A, Kim YJ, Go MJ, Almgren P, Bochud M, Boucher G, Cornelis MC, Gudbjartsson D, Hadley D, van der Harst P, Hayward C, den Heijer M, Igl W, Jackson AU, Kutalik Z, Luan J, Kemp JP, Kristiansson K, Ladenval C, Lorentzon M, Montasser ME, Njajou OT, O’Reilly PF, Padmanabhan S, St Pourcain B, Rankinen T, Salo P, Tanaka T, Timpson NJ, Vitart V, Waite L, Wheeler W, Zhao JH, Zhang W, Draisma HHM, Feitosa MF, Kerr KF, et al: Heart rate-associated loci and their effects on cardiac conduction and rhythm disorders. Nature Genetics 45:621-631, 2013.
• Thanassoulis G, Campbell CY, Owens DS, Smith JG, Smith AV, Peloso GM, Kerr KF, Pechlivanis S, Budoff MJ, Harris TB, Malhotra R, O’Brien KD, Allison MA, Aspelund T, Carr J, Criqui MH, Heckbert SR, Hwang S-J, Kathiresan S, Liu Y, Sjogren M, Van Der Pals J, Kälsch S, Cupples LA, Caslake M, Di Angelantonio E, Danesh J, Rotter JI, Sigurdsson S, Wong Q, Erbel R, Melander O, Gudnason V, O’Donnell CJ, Post WS for the CHARGE Extracoronary Calclum Working Group: Genetic associations with valvular calcification and aortic stenosis. New England Journal of Medicine 368:503-512, 2013.
• Schnabel RB, Kerr KF, Lubitz SA, Alkylbekova EL, Marcus GM, Sinner MF, Magnani, JW, Wolf PA, Deo R, Lloyd-Jones DM, Lunetta KL, Mehra R, Levy D, Fox ER, Arking DE, Mosley TH, Mueller M, Young T, Wichmann E, Seshadri S, Farlow DN, Rotter JI, Soliman EZ, Glazer NL, Wilson JG, Breteler MMB, Sotoodehnia N, Newton-Cheh C, Kääb S, Ellinor PT, Alonso A, Benjamin EJ, Heckbert SR: Large-scale candidate gene analysis in whites and African-Americans identifies IL6R polymorphism in relation to atrial fibrillation: the NHLBI CARe project. Circulation Cardiovascular Genetics, 4:557-564, 2011.
• Yuan Z-C, Edlind MP, Liu P, Saenkham P, Banta LM, Wise AA, Ronzone E, Binns AN, Kerr K, Nester EW: The plant signal salicylic acid shuts down expression of the vir regulon and activates quormone-quenching gene in Agrobacterium. Proceedings of the National Academy of Sciences of the USA 104:11790-11795, 2007.

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