Publications

A list of publications from previous students participating in the undergraduate research program. Student authors are denoted with an asterisk (*).

Note: In addition to the published papers and book chapters on this list, we have numerous other manuscripts in preparation.

Huisman J*, VanderBerg L*, VanderWoude J*, Veenstra J*, Bos A*, Kalsbeek A*, Koster K*, Ryder N*, Tintle NL. “Evaluating the performance of gene-based tests of genetic association when testing for association between methylation and change in triglyceride levels at Genetic Analysis Workshop 20.” Submitted Feb 2017. Revised July 2017. Proceedings of Genetic Analysis Workshop 20. Accepted August 2017.

Grinde K*, Arbet J*, Green A*, O’Connell M*, Valcarcel A*, Westra J, Tintle NL “Illustrating, quantifying and correcting for bias in post-hoc analysis of gene-based rare variant tests of association” Frontiers in Genetic Epidemiology. Accepted August 2017.

Veenstra J*, Kalsbeek A*, Koster K*, Ryder N*, Bos A*, Huisman J*, VanderBerg L*, VanderWoude J* and Tintle NL. “Epigenome wide association study of SNP-CpG interactions on changes in triglyceride levels after pharmaceutical intervention: A GAW20 analysis” Accepted August 2017. Proceedings of Genetic Analysis Workshop 20.

Veenstra J*, Kalsbeek A*, Westra J, Disselkeon C*, Smith C, Tintle NL. “Genome-wide interaction study of omega-3 PUFAs and other fatty acids on inflammatory biomarkers of cardiovascular health in the Framingham Heart Study.” Nutrients. Accepted August 2017.

Beck A*, Luedtke A*, Liu K*, Tintle NL. “A powerful method for including genotype uncertainty in tests of Hardy-Weinberg Equilibrium” Pacific Symposium on Biocomputing. Accepted September 2016.

Kamp T*, Adams M*, Disselkoen C*, Tintle NL “Improved performance of gene set analysis on genome-wide transcriptomics data when using gene activity state estimates” Pacific Symposium on Biocomputing. Accepted September 2016.

Bowerman N*, Tintle NL, DeJongh M, Best AA “Identification and analysis of bacterial genomic metabolic signatures” Pacific Symposium on Biocomputing. Accepted September 2016.

Disselkoen C*, Greco B*, Cook K*, Koch K*, Lerebours R*, Viss C*, Cape J*, Held E*, Ashenafi Y*, Fischer K*, Acosta A*, Cunningham M*, Best AA, DeJongh M, Tintle NL. “A Bayesian framework for the inference of microbial gene activity states” Frontiers in Microbiology. July 2016.

Konig I, Auerbach J, Deng Q, Gola D, Held E*, Holzinger E, Legault M, Sun R, Tintle NL and Yang H. “Machine learning and data mining in complex genetic data- a review on the lessons learned in Genetic Analysis Workshop 19” Accepted. To appear. BMC Genetics. Vol 17(Suppl 2):1 http://bmcgenet.biomedcentral.com/articles/10.1186/s12863-015-0315-8

Greco B*, Hainline A*, Arbet J*, Grinde K*, Benitez A* and Tintle NL. (2016) “A general approach for combining diverse rare variant association tests provides improved robustness across a wider range of genetic architecture” European Journal of Human Genetics. 24:774-778.

Powers S*, De Jongh M, Best A, Tintle NL (2015) “Cautions about the reliability of pairwise gene correlations based on expression data.” Frontiers in Microbiology. 6:650. http://dx.doi.org/10.3389/fmicb.2015.00650

Held E*, Cape J* and Tintle NL. “Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data” BMC Proceedings for Genetic Analysis Workshop 19, Vienna, Austria. 9 Suppl 8:S14.

Green A*, Cook K*, Grinde K*, Valcarcel A*, Tintle NL. “A general method for combining different family-based rare variant tests of association to improve power and robustness to a wide range of genetic architectures” BMC Proceedings for Genetic Analysis Workshop 19, Vienna, Austria. 9 Suppl 8:S18.

Valcarcel A*, Grinde K*, Cook K*, Green A*, Tintle NL. “A multi-step approach to SNP-set analysis: An evaluation of power and type I error of gene-based tests of association after pathway-based association tests” BMC Proceedings for Genetic Analysis Workshop 19, Vienna, Austria. 9 Suppl 8:S49.

Tintle NL, Pottala JV, Lacey S, Ramachandran V, Westra J*, Rogers A*, Clark J*, Olthoff B*, Larson M, Harris W, Shearer G. “A genome wide association study of fourteen red blood cell fatty acids in the Framingham Heart Study” (2015) Prostoglandins, Leukotrienes and Essential Fatty Acids. 94:65-72.

Cook K*, Benitez A*, Fu C*, Tintle NL (2014) “Evaluating the impact of genotype errors on rare variant tests of association” Frontiers in Statistical Genetics and Methodology. http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00062/abstract

Rogers A*, Beck A*, Tintle NL (2014) “Evaluating the concordance between sequencing, imputation and microarray genotype calls in the GAW18 data” BMC Proceedings. 8(Suppl 2):S20. http://www.biomedcentral.com/1753-6561/8/S1/S22

Hainline A*, Alvarez C*, Luedtke A*, Greco B*, Beck A*, Tintle NL (2014) “Evaluation of the power and type I error of recently proposed family-based tests of association for rare variants” BMC Proceedings. 8(Suppl 2):S36. http://www.biomedcentral.com/1753-6561/8/S1/S36

Greco B*, Luedtke A*, Hainline A*, Alvarez C*, Beck A* and Tintle NL (2014) “Application of family-based tests of association for rare variants to pathways” BMC Proceedings. 8(Suppl 2):S105. http://www.biomedcentral.com/1753-6561/8/S1/S105

Petersen A*, Alvarez C*, DeClaire S*, Tintle NL “Assessing methods for assigning SNPs to genes in gene-based tests of association using common variants. PLoS One. May 31, 2013. http://dx.plos.org/10.1371/journal.pone.0062161

Liu K*, Fast S*, Zawistowski M, Tintle NL (2013) “A geometric framework for the evaluation of rare variant tests of association” Genetic Epidemiology. 37(4): 345-357.

Mayer-Jochimsen M*, Fast S*, Tintle NL (2013) “Assessing the impact of differential genotyping errors on rare variant tests of association” PLoS One. March 5, 2013. http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0056626

Liu K*, Luedtke A*, Tintle NL (2013) “Optimal methods for using posterior probabilities in association testing” Human Heredity. 75(1): 2-11.

Tintle NL, Sitarik A*, Boerema B*, Young K*, Best AA and DeJongh M. (2012) “Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data” BMC Bioinformatics. 13:193. http://www.biomedcentral.com/1471-2105/13/193

Bekmetjev A, Van Bruggen D*, McLellan B*, DeWinkle B*, Lunderberg E*, Tintle NL. (2012) “Reclassification as a cost-effective method of estimating disease prevalence.” PLoS One. 7(2):e32058.

Powers S* and Tintle NL. (2011) “Assessing the impact of non-differential genotyping errors on rare variant tests of association” Human Heredity. 72(3):152-159.

Luedtke A*, Powers S*, Petersen A*, Sitarik A*, Bekmetjev A, Tintle NL (2011) “Evaluating Methods for the Analysis of Rare Variants in Sequence Data.” BMC Proceedings, 5(9):S119 http://www.biomedcentral.com/1753-6561/5/S9/S119

Petersen A*, Sitarik A*, Luedtke A*, Powers S*, Bekmetjev A, Tintle NL (2011) “Evaluating methods for combining rare variant data in pathway-based tests of genetic association” BMC Proceedings. 5(9):S48. http://www.biomedcentral.com/1753-6561/5/S9/S48

Tintle, N.L., Borchers, B.*, Brown, M.*, Bekmetjev A. “Comparing gene set analysis methods on SNP data from GAW16.” (2009) Proceedings of Genetic Analysis Workshop 16, St. Louis, MO. BMC Proceedings, 3(Suppl 7):S96. http://www.biomedcentral.com/1753-6561/3/S7/S96

Borchers, B.*, Brown, M.*, McLellan, B.*, Bekmetjev, A., Tintle, N.L. (2009) “Incorporating duplicate genotype data into linear trend tests of genetic association: methods and cost-effectiveness” Statistical Applications in Genetics and Molecular Biology. 8(1):24. http://www.bepress.com/sagmb/vol8/iss1/art24

Tintle, N.L., Gordon D., Van Bruggen D.*, Finch, S.J. (2009) “The cost effectiveness of duplicate genotyping for testing genetic association.” Annals of Human Genetics. 73, 370-378.

Tintle, N.L., Best A.A., De Jongh M., Van Bruggen, D.*, Heffron F., Porwollik S., Taylor R.C. (2008) “Gene set analyses for interpreting microarray experiments on prokaryotic organisms.” BMC Bioinformatics. 9:469. http://www.biomedcentral.com/1471-2105/9/469