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

Klimentidis, Y. C., Arora, A., Chougule, A., Zhou, J., & Raichlen, D. A. (2015). FTO association and interaction with time spent sitting. International journal of obesity (2005).

Multiple studies have revealed an interaction between a variant in the FTO gene and self-reported physical activity on body mass index (BMI). Physical inactivity, such as time spent sitting (TSS) has recently gained attention as an important risk factor for obesity and related diseases. It is possible that FTO interacts with TSS to affect BMI, and/or that FTO's putative effect on BMI is mediated through TSS.

Duarte, C. W., Klimentidis, Y. C., Harris, J. J., Cardel, M., & Fernández, J. R. (2015). Discovery of phenotypic networks from genotypic association studies with application to obesity. International journal of data mining and bioinformatics, 12(2), 129-43.

Genome-wide Association Studies (GWAS) have resulted in many discovered risk variants for several obesity-related traits. However, before clinical relevance of these discoveries can be achieved, molecular or physiological mechanisms of these risk variants needs to be discovered. One strategy is to perform data mining of phenotypically-rich data sources such as those present in dbGAP (database of Genotypes and Phenotypes) for hypothesis generation. Here we propose a technique that combines the power of existing Bayesian Network (BN) learning algorithms with the statistical rigour of Structural Equation Modelling (SEM) to produce an overall phenotypic network discovery system with optimal properties. We illustrate our method using the analysis of a candidate SNP data set from the AMERICO sample, a multi-ethnic cross-sectional cohort of roughly 300 children with detailed obesity-related phenotypes. We demonstrate our approach by showing genetic mechanisms for three obesity-related SNPs.

Klimentidis, Y. C., & Arora, A. (2015). Interaction of Insulin Resistance and Related Genetic Variants with Triglyceride-Associated Genetic Variants. Circulation: Cardiovascular Genetics.
Klimentidis, Y. C., Lemas, D. J., Wiener, H. H., O'Brien, D. M., Havel, P. J., Stanhope, K. L., Hopkins, S. E., Tiwari, H. K., & Boyer, B. B. (2013). CDKAL1 and HHEX are associated with type-2 diabetes-related traits among Yup'ik people. Journal of diabetes.

Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with type-2 diabetes (T2D), mainly among individuals of European ancestry. We examined the frequency of these SNPs and their association with T2D-related traits in an Alaska Native study population with a historically low prevalence of T2D. We also investigated whether dietary characteristics that may protect against T2D, such as n-3 polyunsaturated fatty acid (n-3 PUFA) intake, modify these associations.

Klimentidis, Y. C., Vazquez, A. I., de Los Campos, G., Allison, D. B., Dransfield, M. T., & Thannickal, V. J. (2013). Heritability of pulmonary function estimated from pedigree and whole-genome markers. Frontiers in genetics, 4.

Asthma and chronic obstructive pulmonary disease (COPD) are major worldwide health problems. Pulmonary function testing is a useful diagnostic tool for these diseases, and is known to be influenced by genetic and environmental factors. Previous studies have demonstrated that a substantial proportion of the variation in pulmonary function phenotypes can be explained by familial relationships. The availability of whole-genome single nucleotide polymorphism (SNP) data enables us to further evaluate the extent to which genetic factors account for variation in pulmonary function and to compare pedigree- to SNP-based estimates of heritability. Here, we employ methods developed in the animal breeding field to estimate the heritability of forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and the ratio of these two measures (FEV1/FVC) among subjects in the Framingham Heart Study dataset. We compare heritability estimates based on pedigree-based relationships to those based on genome-wide SNPs. We find that, in a family-based study, estimates of heritability using SNP data are nearly identical to estimates based on pedigree information, and range from 0.50 for FEV1 to 0.66 for FEV1/FVC. Therefore, we conclude that genetic factors account for a sizable proportion of inter-individual differences in pulmonary function, and that estimates of heritability based on SNP data are nearly identical to estimates based on pedigree data. Finally, our findings suggest a higher heritability for FEV1/FVC compared to either FEV1 or FVC.