Ana Vazquez, PhD

Ana Vazquez completed her postdoctoral work in the Section on Statistical Genetics and Office of Energetics at UAB after graduating with an M.S. and PhD in Quantitative Genetics and a Certificate in Bioinformatics at the University of Wisconsin-Madison. Her areas of interest include the development and use of genomic-based statistical methods for human health, the genetic architecture of complex traits, and the investigation of the effects of obesity on risk of disease. Some of her current projects include the examination of shrinkage methods in models where dense molecular markers are used to predict phenotypic outcomes. One such project focuses on marker selection strategies to improve the predictive ability of genetic predisposition to diabetes. She is also involved in research examining the properties and performance of these models in unrelated human populations and has examined the effects of the number of markers in the predictive ability of dense arrays of variants for both human and animal populations, which has allowed her to evaluate which factors affect genomic predictions. Her most recent project involves the development of new multivariate analysis approaches of correlated phenotypes, including obesity and other body composition characteristics of humans using genomic data to uncover common genetic roots between these traits. In another current project, she is assessing predictive genomic-based models for the prediction of treatment response, cancer recurrence, and metastasis development in cancer patients, with a greater focus on cutting-edge statistical and computational methodologies.

 

SELECTED PUBLICATIONS

 A.I. Vazquez, Y. Veturi, M. Behring, S. Shrestha, M. Kirst, M.F.R. Resende Jr, G. de los Campos. Multi-Omic Prediction of Disease Risk and Progression Using Bayesian Generalized Additive Models. Genetics, 204, 2016. [PMID: 27129736], highlights of the issue article.

A.I. Vazquez, G. de los Campos, Y.C. Klimentidis, G.J.M. Rosa, D. Gianola, N. Yi, and D.B. Allison, 2012. A comprehensive genetic approach for improving prediction of skin cancer risk in humans, Genetics, Vol. 192, 1493–1502, [PMID: 23051645].

G. de los Campos, A.I. Vazquez, R. Fernando, Y.C. Klimentidis and D. Sorensen, 2013. Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor, PLoS Genet 9(7): e1003608. [PMID: 23874214].

A.I. Vazquez, D.M. Bates, G.J.M. Rosa, D. Gianola and K.A. Weigel, 2010. Technical note: An R package for fitting generalized linear mixed models in animal breeding. J. Anim. Sci., 88: 497-504.

H. Kim, A. Grueneberg, A.I. Vazquez, S. Hsu, G. de Los Campos. Will Big Data Close the Missing Heritability Gap? Genetics, 2017 [PMID: 28893854]. * This article was selected Highlights of the issue; **This article was selected Highlights of the year (Genetics 2017).

R. Makowsky, N.M. Pajewski, Y.C. Klimentidis, A.I. Vazquez, C.W. Duarte, D.B. Allison and G. de los Campos, 2011. Beyond missing heritability: Prediction of complex traits, PLoS Genetics, 7(4): e1002051. [DOI: 10.1371/journal.pgen.1002051, PMID: 21552331]

R.J. Reynolds, A.I. Vazquez, V. Srinivasasainagendra, Y.C. Klimentidis, S.L. Bridges Jr., D.B. Allison, J.A. Singh, 2016. Serum urate gene associations with incident gout, measured in the Framingham Heart Study, are modified by renal disease and not by body mass index. Rheumatology International. 36:263-70. [PMID: 26427508].

M. Sun, A.I. Vazquez, R.J. Reynolds, J. Singh, M. Reeves, G. le los Campos. “Untangling the complex relationship between incident gout risk, serum urate and its comorbidities.” In press.

A.I. Vazquez, Y.C. Klimentidis, E.J. Dhurandhar, V. Srinivasasainagendra, Y. Veturi, P. Perez, 2015. Assessment of Whole-Genome Regression for Type II Diabetes, Plos One, 10(4).

Y.C. Klimentidis, N.E. Wineinger, A.I. Vazquez, G. de los Campos, 2014. Multiple metabolic genetic risk scores and type 2 diabetes risk in three racial/ethnic groups. Journal of Clinical Endocrinology and Metabolism. 99(9): [PMID: 24905067].

A.I. Vazquez, M.A. Perez-Cabal, B. Heringstad, M. Rodrigues-Motta, G.J.M. Rosa, D. Gianola and K.A. Weigel, 2012. Predictive ability of alternative models for genetic analysis of clinical mastitis. Journal of Animal Breeding and Genetics, Journal Animal Breeding and Genetics. [doi: 10.1111/j.1439-0388.2011.00950.x, PMID: 22394234]

. A.I. Vazquez, K.A. Weigel, D. Gianola, D.M. Bates, M.A. Perez-Cabal, G.J.M. Rosa, and Y.M. Chang, 2009. Poisson versus threshold models for genetic analysis of clinical mastitis in US Holsteins. J. Dairy Sci., 92:5239-5247. [DOI: 10.3168/jds.2009-2085, PMID: 19762842]

A.I. Vazquez, D. Gianola, D.M. Bates, K.A. Weigel, and B. Heringstad, 2009. Assessment of Poisson, logit and linear models for genetic analysis of clinical mastitis in Norwegian Red Cows. J. Dairy Sci., 92: 739-748. [DOI: 10.3168/jds.2008-1325, PMID: 19164686]

I.F. Sørensen, A.I. Vazquez, M.R. Irvin, P. Sørensen, D.R. Barry, F.E. Charles, E. Boerwinkle, J.H. Eckfeldt, D.K. Arnett, 2014. Pharmacogenetic effects of “candidate gene complexes” on cardiovascular disease outcomes in response to antihypertensive treatments in the GenHAT study. Pharmacogenetics and Genomics, 24:556-63. [DOI: 10.1097, PMID: 2517170].

A.I. Vazquez, H. Wiener, S. Shrestha, H.K. Tiwari, G. de los Campos, 2014. Integration of Multi-Layer Omic Data for Prediction of Disease Risk in Human. WCGALP. 1-6.

A. Gonzalez, G. de las Campos, L. Gutierrez, A.I. Vazquez. Prediction Accuracy of Survival of Breast Cancer Patients Integrating Omics and Omic-by-Treatment Interactions. Accepted in: Eur. J. Hum. Genet, 2016.

E.F. Libby, M. Azrad, L. Novak, A.I. Vazquez, T.R. Wilson, W. Demark-Wahnefried, 2014. Obesity is associated with higher 4E-BP1 expression in endometrial cancer. Current Biomarker Findings, 4, PMID:24639918].

Vazquez A.I., Wiener H, Shrestha S, Tiwari H.K., de los Campos, G., 2014. Integration of Multi-Layer Omic Data for Prediction of Disease Risk. WCGALP. 1-6.

2.S. Aslibekyan, H.W. Wiener, G. Wu, D. Zhi, S. Shrestha, G. de los Campos, A.I. Vazquez. Estimating Proportions of Explained Variance: a Comparison of Whole Genome Subsets, In: The Genetic Analysis Workshops 18, BMC Preceding, 8 (Suppl 1): S102. 2014. [PMCID: In Process].

3.I.F. Sørensen, A.I. Vazquez, M.R. Irvin, P. Sørensen, D.R. Barry, F.E. Charles, E. Boerwinkle, J.H. Eckfeldt, D.K. Arnett, 2014. Pharmacogenetic effects of “candidate gene complexes” on cardiovascular disease outcomes in response to antihypertensive treatments in the GenHAT study. Pharmacogenetics and Genomics, [doi: 10.1097/FPC.0000000000000088], In press.

4.E.J. Dhurandhar, A. I. Vazquez, G. Argyropoulos, and D.B. Allison. 2014. Prediction of Complex Traits With Genetic Information: Even Modest Prediction Accuracy Can Have Substantial Utility. Frontiers in Genetics, In Press.

5.A. Shendre, M.R. Irvin, B.E. Aouizerat, H.W. Wiener, A.I. Vazquez, K. Anastos, J. Lazar, C. Liu, R. Karim, N.A. Limdi, M.H. Cohen, E.T. Golub, D. Zhi, R.C. Kaplan, S. Shrestha. RYR3 gene variants in subclinical atherosclerosis among HIV-infected women in the Women’s Interagency HIV Study (WIHS), 2014. Artheriosclerosis; 233(2): 666-672.

6.Shendre, H.W. Wiener, D. Zhi, A.I. Vazquez, M.A. Portman, S. Shrestha. High-density Genotyping of Immune Loci in Kawasaki Disease and IVIG Treatment Response in European-American Case-parent Trio Study. Genes and immunity, 2014.

7.E.F. Libby, M. Azrad, L. Novak, A.I. Vazquez, T.R. Wilson, W. Demark-Wahnefried. Obesity is associated with higher 4E-BP1 expression in endometrial cancer, Current Biomarker Findings, Current Biomarker Findings 2014 4:1-7. [doi: 10.2147/CBF.S53530]

8.Y.C. Klimentidis, N.E. Wineinger, A.I. Vazquez, G. de los Campos. Multiple metabolic genetic risk scores and type 2 diabetes risk in three racial/ethnic groups. Journal of Clinical Endocrinology and Metabolism. 2014. J Clin Endocrinol Metab. 99(9):E1814-8. [doi: 10.1210/jc.2014-1818]

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