Yann C Klimentidis

Associate Professor, Public Health

Assistant Professor, Genetics - GIDP

Associate Professor, BIO5 Institute

Primary Department

Epidemiology and Biostatistics

Department Affiliations

Epidemiology and Biostatistics

BIO5 Institute

Contact

(520) 621-0147

yann@arizona.edu

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

Association of physical activity with lower type 2 diabetes incidence is weaker among individuals at high genetic risk

Klimentidis, Y. C., Chen, Z., Arora, A., & Hsu, C. (2014). Association of physical activity with lower type 2 diabetes incidence is weaker among individuals at high genetic risk. Diabetologia, 57(12), 2530-4.

BIO5 Collaborators

Zhao Chen, Yann C Klimentidis

We examined whether or not the association of physical activity with type 2 diabetes incidence differs according to several types of genetic susceptibility.

High genetic risk individuals benefit less from resistance exercise intervention

Klimentidis, Y. C., Bea, J. W., Lohman, T., Hsieh, P., Going, S., & Chen, Z. (2015). High genetic risk individuals benefit less from resistance exercise intervention. International journal of obesity (2005), 39(9), 1371-5.

BIO5 Collaborators

Zhao Chen, Yann C Klimentidis

Genetic factors have an important role in body mass index (BMI) variation, and also likely have a role in the weight loss and body composition response to physical activity/exercise. With the recent identification of BMI-associated genetic variants, it is possible to investigate the interaction of these genetic factors with exercise on body composition outcomes.

Fractal complexity of daily physical activity patterns differs with age over the lifespan and predicts mortality in older adults

Raichlen, D. A., Klimentidis, Y., Hsu, C., & Alexander, G. E. (2017). Fractal complexity of daily physical activity patterns differs with age over the lifespan and predicts mortality in older adults. ..

BIO5 Collaborators

Gene E Alexander, Yann C Klimentidis

Multiple metabolic genetic risk scores and type 2 diabetes risk in three racial/ethnic groups

Klimentidis, Y. C., Wineinger, N. E., Vazquez, A. I., & de Los Campos, G. (2014). Multiple metabolic genetic risk scores and type 2 diabetes risk in three racial/ethnic groups. The Journal of clinical endocrinology and metabolism, 99(9), E1814-8.

CONTEXT/RATIONALE: Meta-analyses of genome-wide association studies have identified many single-nucleotide polymorphisms associated with various metabolic and cardiovascular traits, offering us the opportunity to learn about and capitalize on the links between cardiometabolic traits and type 2 diabetes (T2D).

Identification of allelic heterogeneity at type-2 diabetes loci and impact on prediction

Klimentidis, Y. C., Zhou, J., & Wineinger, N. E. (2014). Identification of allelic heterogeneity at type-2 diabetes loci and impact on prediction. PloS one, 9(11), e113072.

Although over 60 single nucleotide polymorphisms (SNPs) have been identified by meta-analysis of genome-wide association studies for type-2 diabetes (T2D) among individuals of European descent, much of the genetic variation remains unexplained. There are likely many more SNPs that contribute to variation in T2D risk, some of which may lie in the regions surrounding established SNPs--a phenomenon often referred to as allelic heterogeneity. Here, we use the summary statistics from the DIAGRAM consortium meta-analysis of T2D genome-wide association studies along with linkage disequilibrium patterns inferred from a large reference sample to identify novel SNPs associated with T2D surrounding each of the previously established risk loci. We then examine the extent to which the use of these additional SNPs improves prediction of T2D risk in an independent validation dataset. Our results suggest that multiple SNPs at each of 3 loci contribute to T2D susceptibility (TCF7L2, CDKN2A/B, and KCNQ1; p5×10(-8)). Using a less stringent threshold (p5×10(-4)), we identify 34 additional loci with multiple associated SNPs. The addition of these SNPs slightly improves T2D prediction compared to the use of only the respective lead SNPs, when assessed using an independent validation cohort. Our findings suggest that some currently established T2D risk loci likely harbor multiple polymorphisms which contribute independently and collectively to T2D risk. This opens a promising avenue for improving prediction of T2D, and for a better understanding of the genetic architecture of T2D.

Yann C Klimentidis