You are here

Biblio

Search

Show only items where

Author

Type

Term

Year

Keyword

Export 39 results:

Author Title [ Type

] Year

Filters: Author is Province, Michael A [Clear All Filters]

Journal Article

Kraja AT, Vaidya D, Pankow JS, Goodarzi MO, Assimes TL, Kullo IJ, Sovio U, Mathias RA, Sun YV, Franceschini N, et al. A bivariate genome-wide approach to metabolic syndrome: STAMPEED consortium. Diabetes. 2011 ;60(4):1329-39.

Wuttke M, Li Y, Li M, Sieber KB, Feitosa MF, Gorski M, Tin A, Wang L, Chu AY, Hoppmann A, et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet. 2019 ;51(6):957-972.

Fretts AM, Follis JL, Nettleton JA, Lemaitre RN, Ngwa JS, Wojczynski MK, Kalafati IPanagiota, Varga TV, Frazier-Wood AC, Houston DK, et al. Consumption of meat is associated with higher fasting glucose and insulin concentrations regardless of glucose and insulin genetic risk scores: a meta-analysis of 50,345 Caucasians. Am J Clin Nutr. 2015 ;102(5):1266-78.

Winkler TW, Rasheed H, Teumer A, Gorski M, Rowan BX, Stanzick KJ, Thomas LF, Tin A, Hoppmann A, Chu AY, et al. Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals. Commun Biol. 2022 ;5(1):580.

Sung YJu, Winkler TW, Manning AK, Aschard H, Gudnason V, Harris TB, Smith AV, Boerwinkle E, Brown MR, Morrison AC, et al. An Empirical Comparison of Joint and Stratified Frameworks for Studying G × E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene-Lifestyle Interactions Working Group. Genet Epidemiol. 2016 ;40(5):404-15.

Yang J, Loos RJF, Powell JE, Medland SE, Speliotes EK, Chasman DI, Rose LM, Thorleifsson G, Steinthorsdottir V, Mägi R, et al. FTO genotype is associated with phenotypic variability of body mass index. Nature. 2012 ;490(7419):267-72.

Wang S, Zhao JH, An P, Guo X, Jensen RA, Marten J, Huffman JE, Meidtner K, Boeing H, Campbell A, et al. General Framework for Meta-Analysis of Haplotype Association Tests. Genet Epidemiol. 2016 ;40(3):244-52.

Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, Mandla R, Huerta-Chagoya A, Melloni GEM, Kanoni S, et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature. 2024 .

Yaghootkar H, Zhang Y, Spracklen CN, Karaderi T, Huang LOpal, Bradfield J, Schurmann C, Fine RS, Preuss MH, Kutalik Z, et al. Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity. Diabetes. 2020 .

Levy D, Neuhausen SL, Hunt SC, Kimura M, Hwang S-J, Chen W, Bis JC, Fitzpatrick AL, Smith E, Johnson AD, et al. Genome-wide association identifies OBFC1 as a locus involved in human leukocyte telomere biology. Proc Natl Acad Sci U S A. 2010 ;107(20):9293-8.

Wilk JB, Shrine NRG, Loehr LR, Zhao JH, Manichaikul A, Lopez LM, Smith AVernon, Heckbert SR, Smolonska J, Tang W, et al. Genome-wide association studies identify CHRNA5/3 and HTR4 in the development of airflow obstruction. Am J Respir Crit Care Med. 2012 ;186(7):622-32.

Minster RL, Sanders JL, Singh J, Kammerer CM, M Barmada M, Matteini AM, Zhang Q, Wojczynski MK, E Daw W, Brody JA, et al. Genome-Wide Association Study and Linkage Analysis of the Healthy Aging Index. J Gerontol A Biol Sci Med Sci. 2015 ;70(8):1003-8.

Smith CE, Follis JL, Dashti HS, Tanaka T, Graff M, Fretts AM, Kilpeläinen TO, Wojczynski MK, Richardson K, Nalls MA, et al. Genome-Wide Interactions with Dairy Intake for Body Mass Index in Adults of European Descent. Mol Nutr Food Res. 2017 .

Berndt SI, Gustafsson S, Mägi R, Ganna A, Wheeler E, Feitosa MF, Justice AE, Monda KL, Croteau-Chonka DC, Day FR, et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat Genet. 2013 ;45(5):501-12.

Mangino M, Hwang S-J, Spector TD, Hunt SC, Kimura M, Fitzpatrick AL, Christiansen L, Petersen I, Elbers CC, Harris T, et al. Genome-wide meta-analysis points to CTC1 and ZNF676 as genes regulating telomere homeostasis in humans. Hum Mol Genet. 2012 ;21(24):5385-94.

Allen HLango, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, Willer CJ, Jackson AU, Vedantam S, Raychaudhuri S, et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010 ;467(7317):832-8.

Scott RA, Lagou V, Welch RP, Wheeler E, Montasser ME, Luan J'an, Mägi R, Strawbridge RJ, Rehnberg E, Gustafsson S, et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet. 2012 ;44(9):991-1005.

Nagarajan P, Winkler TW, Bentley AR, Miller CL, Kraja AT, Schwander K, Lee S, Wang W, Brown MR, Morrison JL, et al. A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations. medRxiv. 2024 .

Sung YJ, Winkler TW, Fuentes Lde Las, Bentley AR, Brown MR, Kraja AT, Schwander K, Ntalla I, Guo X, Franceschini N, et al. A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure. Am J Hum Genet. 2018 ;102(3):375-400.

Wessel J, Chu AY, Willems SM, Wang S, Yaghootkar H, Brody JA, Dauriz M, Hivert M-F, Raghavan S, Lipovich L, et al. Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat Commun. 2015 ;6:5897.

Jackson VE, Latourelle JC, Wain LV, Smith AV, Grove ML, Bartz TM, Obeidat M'en, Province MA, Gao W, Qaiser B, et al. Meta-analysis of exome array data identifies six novel genetic loci for lung function. Wellcome Open Res. 2018 ;3:4.

Manning AK, LaValley M, Liu C-T, Rice K, An P, Liu Y, Miljkovic I, Rasmussen-Torvik L, Harris TB, Province MA, et al. Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients. Genet Epidemiol. 2011 ;35(1):11-8.

C Y Ng M, Shriner D, Chen BH, Li J, Chen W-M, Guo X, Liu J, Bielinski SJ, Yanek LR, Nalls MA, et al. Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes. PLoS Genet. 2014 ;10(8):e1004517.

Mahajan A, Spracklen CN, Zhang W, C Y Ng M, Petty LE, Kitajima H, Yu GZ, Rüeger S, Speidel L, Kim YJin, et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet. 2022 ;54(5):560-572.

de Vries PS, Brown MR, Bentley AR, Sung YJ, Winkler TW, Ntalla I, Schwander K, Kraja AT, Guo X, Franceschini N, et al. Multi-Ancestry Genome-Wide Association Study of Lipid Levels Incorporating Gene-Alcohol Interactions. Am J Epidemiol. 2019 .

Pages