Yann C Klimentidis

Yann C Klimentidis

Associate Professor, Public Health
Assistant Professor, Genetics - GIDP
Associate Professor, BIO5 Institute
Primary Department
Contact
(520) 621-0147

Work Summary

I use human genetic data to find associations of genetic markers with complex traits and diseases, to shed light on disease pathophysiology, causal pathways, and health disparities, and to inform precision medicine.

Research Interest

Yann C. Klimentidis, PhD, is an Associate Professor in the Department of Epidemiology and Biostatistics in the Mel and Enid Zuckerman College of Public Health at the University of Arizona. His research centers on improving our understanding of the links between genetic variation, lifestyle factors, metabolic disease, and health disparities. In the past, he has used measures of genetic admixture and genomic tests of natural selection to understand the genetic basis of population differences in disease susceptibility. His most recent work examines the use various statistical approaches for the analysis of high-dimensional genetic data for improving prediction of genetic susceptibility to type-2 diabetes. In addition, his work examines gene-by-lifestyle interactions in type-2 diabetes, as well as understanding the causal links between metabolic traits such as dyslipidemia and type-2 diabetes. Keywords: Genetics, epidemiology, Cardiometabolic disease, Physical activity

Publications

Chen, G., Liu, N., Klimentidis, Y. C., Zhu, X., Zhi, D., Wang, X., & Lou, X. (2013). A unified GMDR method for detecting gene-gene interactions in family and unrelated samples with application to nicotine dependence. Human genetics.

Gene-gene and gene-environment interactions govern a substantial portion of the variation in complex traits and diseases. In convention, a set of either unrelated or family samples are used in detection of such interactions; even when both kinds of data are available, the unrelated and the family samples are analyzed separately, potentially leading to loss in statistical power. In this report, to detect gene-gene interactions we propose a generalized multifactor dimensionality reduction method that unifies analyses of nuclear families and unrelated subjects within the same statistical framework. We used principal components as genetic background controls against population stratification, and when sibling data are included, within-family control were used to correct for potential spurious association at the tested loci. Through comprehensive simulations, we demonstrate that the proposed method can remarkably increase power by pooling unrelated and offspring's samples together as compared with individual analysis strategies and the Fisher's combining p value method while it retains a controlled type I error rate in the presence of population structure. In application to a real dataset, we detected one significant tetragenic interaction among CHRNA4, CHRNB2, BDNF, and NTRK2 associated with nicotine dependence in the Study of Addiction: Genetics and Environment sample, suggesting the biological role of these genes in nicotine dependence development.

Lebrón-Aldea, D., Dhurandhar, E. J., Pérez-Rodríguez, P., Klimentidis, Y. C., Tiwari, H. K., & Vazquez, A. I. (2015). Integrated genomic and BMI analysis for type 2 diabetes risk assessment. Frontiers in genetics, 6, 75.

Type 2 Diabetes (T2D) is a chronic disease arising from the development of insulin absence or resistance within the body, and a complex interplay of environmental and genetic factors. The incidence of T2D has increased throughout the last few decades, together with the occurrence of the obesity epidemic. The consideration of variants identified by Genome Wide Association Studies (GWAS) into risk assessment models for T2D could aid in the identification of at-risk patients who could benefit from preventive medicine. In this study, we build several risk assessment models, evaluated with two different classification approaches (Logistic Regression and Neural Networks), to measure the effect of including genetic information in the prediction of T2D. We used data from to the Original and the Offspring cohorts of the Framingham Heart Study, which provides phenotypic and genetic information for 5245 subjects (4306 controls and 939 cases). Models were built by using several covariates: gender, exposure time, cohort, body mass index (BMI), and 65 SNPs associated to T2D. We fitted Logistic Regressions and Bayesian Regularized Neural Networks and then assessed their predictive ability by using a ten-fold cross validation. We found that the inclusion of genetic information into the risk assessment models increased the predictive ability by 2%, when compared to the baseline model. Furthermore, the models that included BMI at the onset of diabetes as a possible effector, gave an improvement of 6% in the area under the curve derived from the ROC analysis. The highest AUC achieved (0.75) belonged to the model that included BMI, and a genetic score based on the 65 established T2D-associated SNPs. Finally, the inclusion of SNPs and BMI raised predictive ability in all models as expected; however, results from the AUC in Neural Networks and Logistic Regression did not differ significantly in their prediction accuracy.