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Quertermous, Thomas

https://orcid.org/0000-0002-7645-9067

Mayo Clinic

Stanford Medicine

Citations 3

A Bivariate Genome-Wide Approach to Metabolic Syndrome
Kraja, Aldi T., Vaidya, Dhananjay , Pankow, James S. , Goodarzi, Mark O., Assimes, Themistocles L. , Sovio, Ulla , Mathias, Rasika A. , Sun, Yan V. , Franceschini, Nora , Absher, Devin , Zhang, Qunyuan, Feitosa, Mary F. , Glazer, Nicole L. , Hartikainen, Anna-Liisa, Knowles, Joshua W. , Iribarren, Carlos , Kral, Brian G. , Yanek, Lisa R. , O’Reilly, Paul F. , McCarthy, Mark I. , Jaquish, Cashell E. , Couper, David , Chakravarti, Aravinda , Becker, Lewis C. , Province, Michael A. , Boerwinkle, Eric , Quertermous, Thomas , Palotie, L, Järvelin, Marjo‐Riitta , Becker, Diane M. , Kardia, Sharon L. R., Rotter, Jerome I. , Chen, Yii-Der Ida , Borecki, Ingrid B. , Goodarzi, Mark O. , Kullo, Iftikhar J. , Guo, Li , Haritunians, Talin , Hartikainen, Anna‐Liisa, North, Kari E. , Psaty, Bruce M. , McCarthy, Mark I , Assimes, Themistocles L , Rotter, Jerome I , Mathias, Rasika A , Pankow, James S , Couper, David , Province, Mike

Diabetes | March 9, 2011 | Volume: 60 | Issue: 4 | Pages: 1329-1339

10.2337/db10-1011 Get It

OBJECTIVE The metabolic syndrome (MetS) is defined as concomitant disorders of lipid and glucose metabolism, central obesity, and high blood pressure, with an increased risk of type 2 diabetes and cardiovascular disease. This study tests whether common genetic variants with pleiotropic effects account for some of the correlated architecture among five metabolic phenotypes that define MetS. RESEARCH DESIGN AND METHODS Seven studies of the STAMPEED consortium, comprising 22,161 participants of European ancestry, underwent genome-wide association analyses of metabolic traits using a panel of ∼2.5 million imputed single nucleotide polymorphisms (SNPs). Phenotypes were defined by the National Cholesterol Education Program (NCEP) criteria for MetS in pairwise combinations. Individuals exceeding the NCEP thresholds for both traits of a pair were considered affected. RESULTS Twenty-nine common variants were associated with MetS or a pair of traits. Variants in the genes LPL, CETP, APOA5 (and its cluster), GCKR (and its cluster), LIPC, TRIB1, LOC100128354/MTNR1B, ABCB11, and LOC100129150 were further tested for their association with individual qualitative and quantitative traits. None of the 16 top SNPs (one per gene) associated simultaneously with more than two individual traits. Of them 11 variants showed nominal associations with MetS per se. The effects of 16 top SNPs on the quantitative traits were relatively small, together explaining from ∼9% of the variance in triglycerides, 5.8% of high-density lipoprotein cholesterol, 3.6% of fasting glucose, and 1.4% of systolic blood pressure. CONCLUSIONS Qualitative and quantitative pleiotropic tests on pairs of traits indicate that a small portion of the covariation in these traits can be explained by the reported common genetic variants.
Two Major QTLs and Several Others Relate to Factors of Metabolic Syndrome in the Family Blood Pressure Program
Kraja, Aldi T., Weder, Alan B., Cooper, Richard, Curb, J. David, Hanis, Craig L. , Hsiung, Chao A. , Quertermous, Thomas , Zhu, Xiaofeng , Province, Michael A. , Rao, D. C. , Cooper, Richard, Turner, Stephen T. , Andrade, Mariza de , Province, Mike

Hypertension | September 20, 2005 | Volume: 46 | Issue: 4 | Pages: 751-757

10.1161/01.hyp.0000184249.20016.bb Get It

Genome-wide variance components linkage analysis was performed on 4 latent factors underlying metabolic syndrome derived from 10 risk factors. The latent factors represent obesity and insulin, blood pressure, lipids and insulin, and central obesity. The metabolic syndrome factor scores were derived in 4 ethnic groups recruited in 3 Networks of the Family Blood Pressure Program: GENOA (blacks, Hispanics, and whites), HyperGEN (blacks and whites), SAPPHIRe (Asians). Heritabilities of metabolic syndrome factors ranged from 66% for obesity and insulin to 11% for blood pressure factor. We observed higher heritabilities for obesity and insulin, and lipids and insulin, whereas those for blood pressure and central obesity were smaller. Linkage analysis detected two major quantitative trait loci. One of them linked to the obesity and insulin factor with a lod score of 3.94 ( P =0.00001, marker GATA11A06, D18S53, 41.24 cM) at marker positions linkage (lod 4.71, at 46.84 cM at 1-cM-apart distances linkage), located on chromosome 18p11.21 in GENOA black. The other linked to the blood pressure factor with a lod score of 3.22 ( P =0.000059, marker GATA49C09, D17S1290, 82 cM) at marker positions linkage (lod 3.56, at 84.63 cM for 1 cM apart distances linkage) located on chromosome 17q23.1 in Hispanics. These quantitative trait loci, together with 4 additional ones with lod scores >2.5, and 30 additional ones with lod score >1.7, offer hope for dissecting the genetic architecture of metabolic syndrome with beneficial implications for molecular diagnosis, prognosis, and in potential medical intervention.