Literature DB >> 35260800

Quantitative trait loci, G×E and G×G for glycemic traits: response to metformin and placebo in the Diabetes Prevention Program (DPP).

Taylor J Maxwell1, Paul W Franks2, Steven E Kahn3, William C Knowler4, Kieren J Mather5, Jose C Florez6,7,8, Kathleen A Jablonski9.   

Abstract

The complex genetic architecture of type-2-diabetes (T2D) includes gene-by-environment (G×E) and gene-by-gene (G×G) interactions. To identify G×E and G×G, we screened markers for patterns indicative of interactions (relationship loci [rQTL] and variance heterogeneity loci [vQTL]). rQTL exist when the correlation between multiple traits varies by genotype and vQTL occur when the variance of a trait differs by genotype (potentially flagging G×G and G×E). In the metformin and placebo arms of the DPP (n = 1762) we screened 280,965 exomic and intergenic SNPs, for rQTL and vQTL patterns in association with year one changes from baseline in glycemia and related traits (insulinogenic index [IGI], insulin sensitivity index [ISI], fasting glucose and fasting insulin). Significant (p < 1.8 × 10-7) rQTL and vQTL generated a priori hypotheses of individual G×E tests for a SNP × metformin treatment interaction and secondarily for G×G screens. Several rQTL and vQTL identified led to 6 nominally significant (p < 0.05) metformin treatment × SNP interactions (4 for IGI, one insulin, and one glucose) and 12G×G interactions (all IGI) that exceeded experiment-wide significance (p < 4.1 × 10-9). Some loci are directly associated with incident diabetes, and others are rQTL and modify a trait's relationship with diabetes (2 diabetes/glucose, 2 diabetes/insulin, 1 diabetes/IGI). rs3197999, an ISI/insulin rQTL, is a possible gene damaging missense mutation in MST1, is associated with ulcerative colitis, sclerosing cholangitis, Crohn's disease, BMI and coronary artery disease. This study demonstrates evidence for context-dependent effects (G×G & G×E) and the complexity of these T2D-related traits.
© 2022. The Author(s), under exclusive licence to The Japan Society of Human Genetics.

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Year:  2022        PMID: 35260800     DOI: 10.1038/s10038-022-01027-y

Source DB:  PubMed          Journal:  J Hum Genet        ISSN: 1434-5161            Impact factor:   3.755


  29 in total

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Authors:  Trudy F C Mackay
Journal:  Curr Opin Genet Dev       Date:  2004-06       Impact factor: 5.578

2.  Evolution of adaptive phenotypic variation patterns by direct selection for evolvability.

Authors:  Mihaela Pavlicev; James M Cheverud; Günter P Wagner
Journal:  Proc Biol Sci       Date:  2010-11-24       Impact factor: 5.349

3.  Genetic variation in pleiotropy: differential epistasis as a source of variation in the allometric relationship between long bone lengths and body weight.

Authors:  Mihaela Pavlicev; Jane P Kenney-Hunt; Elizabeth A Norgard; Charles C Roseman; Jason B Wolf; James M Cheverud
Journal:  Evolution       Date:  2007-11-12       Impact factor: 3.694

4.  Genetic architecture of complex traits: large phenotypic effects and pervasive epistasis.

Authors:  Haifeng Shao; Lindsay C Burrage; David S Sinasac; Annie E Hill; Sheila R Ernest; William O'Brien; Hayden-William Courtland; Karl J Jepsen; Andrew Kirby; E J Kulbokas; Mark J Daly; Karl W Broman; Eric S Lander; Joseph H Nadeau
Journal:  Proc Natl Acad Sci U S A       Date:  2008-12-09       Impact factor: 11.205

5.  A fast algorithm to optimize SNP prioritization for gene-gene and gene-environment interactions.

Authors:  Wei Q Deng; Guillaume Paré
Journal:  Genet Epidemiol       Date:  2011-09-15       Impact factor: 2.135

6.  A versatile omnibus test for detecting mean and variance heterogeneity.

Authors:  Ying Cao; Peng Wei; Matthew Bailey; John S K Kauwe; Taylor J Maxwell
Journal:  Genet Epidemiol       Date:  2014-01       Impact factor: 2.135

7.  Evolution of pleiotropy: epistatic interaction pattern supports a mechanistic model underlying variation in genotype-phenotype map.

Authors:  Mihaela Pavlicev; Elizabeth A Norgard; Gloria L Fawcett; James M Cheverud
Journal:  J Exp Zool B Mol Dev Evol       Date:  2011-04-01       Impact factor: 2.656

8.  APOE modulates the correlation between triglycerides, cholesterol, and CHD through pleiotropy, and gene-by-gene interactions.

Authors:  Taylor J Maxwell; Christie M Ballantyne; James M Cheverud; Cameron S Guild; Chiadi E Ndumele; Eric Boerwinkle
Journal:  Genetics       Date:  2013-10-04       Impact factor: 4.562

9.  On the use of variance per genotype as a tool to identify quantitative trait interaction effects: a report from the Women's Genome Health Study.

Authors:  Guillaume Paré; Nancy R Cook; Paul M Ridker; Daniel I Chasman
Journal:  PLoS Genet       Date:  2010-06-17       Impact factor: 5.917

10.  Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability.

Authors:  Lars Rönnegård; William Valdar
Journal:  BMC Genet       Date:  2012-07-24       Impact factor: 2.797

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