Ana Vazquez

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avazquez@msu.edu
517-884-6688
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Systems Biology Division

About

Ana I. Vazquez is an assistant professor of epidemiology and biostatistics and is a member of the QuantGen group.

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 the risk of disease.

Dr. Vazquez completed her postdoctoral work in the Section on Statistical Genetics and Office of Energetics at the University of Alabama after graduating with an M.S. and Ph.D. in quantitative genetics and a certificate in bioinformatics from the University of Wisconsin-Madison.

QuantGen Group

Ana Vazquez is part of the QuantGen group. The team’s research involves methods, software development and applications in human health, plant and animal breeding and is guided by pressing genetic and epidemiological questions. The research is centered on the analysis and prediction of complex traits and diseases using genetic (DNA sequence, pedigree and multiple omics) and environmental information.

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.

Another current project involves 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.

Other research includes the examination of shrinkage methods in models where dense molecular markers are used to predict phenotypic outcomes and focusing on marker selection strategies to improve the predictive ability of genetic predisposition to diabetes.

Project specifics

Genomic Analysis of Obesity and Response to Exercise.
The QuantGen group maintain an active collaboration with researchers from the TIGER (Training Interventions and Genetics of Exercise Response) study, developing and implementing methods for the identification of genetic factors influencing Body Composition and Response to Exercise Intervention.

Integration of Data from Multiple Omics Layers.
Models and software for integrating high-dimensional multi-layer omics data for analysis and prediction of disease outcomes (e.g., survival of breast cancer patients) (e.g., Vazquez et al., 2015 ; Gonzalez-Reymundez et al, 2017).

Genomic Analysis and Prediction of Complex Traits.
Development and evaluation of methods and software for analysis and prediction of complex traits using Big Data from biobanks and GWAS cohorts (e.g., Kim et al, 2017).

Genetics and Epidemiology of Hyperuricemia and Gout.
Together with University of Alabama at Birmingham we have contributed research focused on genetic and epidemiology aspects of gout and its comorbidities (Sun et al., 2018; Reynolds et al., 2016).

Software development for analysis of big omics data.
The QuantGen group research includes the development of several R packages for genetic analysis using pedigrees, genomes and other omics (see https://quantgen.github.io/#software).

Featured 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.