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

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Testing for genetic effects on mean values of a quantitative trait has been a very successful strategy. However, most studies to date have not explored genetic effects on the variance of quantitative traits as a relevant consequence of genetic variation. In this report, we demonstrate that, under plausible scenarios of genetic interaction, the variance of a quantitative trait is expected to differ among the three possible genotypes of a biallelic SNP. Leveraging this observation with Levene's test of equality of variance, we propose a novel method to prioritize SNPs for subsequent gene–gene and gene–environment testing. This method has the advantageous characteristic that the interacting covariate need not be known or measured for a SNP to be prioritized. Using simulations, we show that this method has increased power over exhaustive search under certain conditions. We further investigate the utility of variance per genotype by examining data from the Women's Genome Health Study. Using this dataset, we identify new interactions between the LEPR SNP rs12753193 and body mass index in the prediction of C-reactive protein levels, between the ICAM1 SNP rs1799969 and smoking in the prediction of soluble ICAM-1 levels, and between the PNPLA3 SNP rs738409 and body mass index in the prediction of soluble ICAM-1 levels. These results demonstrate the utility of our approach and provide novel genetic insight into the relationship among obesity, smoking, and inflammation.

Vyšlo v časopise: 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. PLoS Genet 6(6): e32767. doi:10.1371/journal.pgen.1000981
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1000981

Souhrn

Zdroje

1. CordellHJ

2002 Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. Hum Mol Genet 11 2463 2468

2. NelsonMR

KardiaSL

FerrellRE

SingCF

2001 A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Res 11 458 470

3. RitchieMD

HahnLW

MooreJH

2003 Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genet Epidemiol 24 150 157

4. RitchieMD

WhiteBC

ParkerJS

HahnLW

MooreJH

2003 Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases. BMC Bioinformatics 4 28

5. BochdanovitsZ

SondervanD

PerillousS

van BeijsterveldtT

BoomsmaD

2008 Genome-wide prediction of functional gene-gene interactions inferred from patterns of genetic differentiation in mice and men. PLoS ONE 3 e1593 doi:10.1371/journal.pone.0001593

6. MillsteinJ

ContiDV

GillilandFD

GaudermanWJ

2006 A testing framework for identifying susceptibility genes in the presence of epistasis. Am J Hum Genet 78 15 27

7. MarchiniJ

DonnellyP

CardonLR

2005 Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 37 413 417

8. EvansDM

MarchiniJ

MorrisAP

CardonLR

2006 Two-stage two-locus models in genome-wide association. PLoS Genet 2 e157 doi:10.1371/journal.pgen.0020157

9. PuttW

PalmenJ

NicaudV

TregouetDA

Tahri-DaizadehN

2004 Variation in USF1 shows haplotype effects, gene : gene and gene : environment associations with glucose and lipid parameters in the European Atherosclerosis Research Study II. Hum Mol Genet 13 1587 1597

10. ChoYM

RitchieMD

MooreJH

ParkJY

LeeKU

2004 Multifactor-dimensionality reduction shows a two-locus interaction associated with Type 2 diabetes mellitus. Diabetologia 47 549 554

11. MurcrayCE

LewingerJP

GaudermanWJ

2009 Gene-environment interaction in genome-wide association studies. Am J Epidemiol 169 219 226

12. LeveneH

1960 Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling; Olkin et al., editor: Stanford University Press

13. NIST/SEMATECH e-Handbook of Statistical Methods

14. RidkerPM

ChasmanDI

ZeeRY

ParkerA

RoseL

2008 Rationale, design, and methodology of the Women's Genome Health Study: a genome-wide association study of more than 25,000 initially healthy american women. Clin Chem 54 249 255

15. RidkerPM

CookNR

LeeIM

GordonD

GazianoJM

2005 A randomized trial of low-dose aspirin in the primary prevention of cardiovascular disease in women. N Engl J Med 352 1293 1304

16. LouisGW

MyersMGJr

2007 The role of leptin in the regulation of neuroendocrine function and CNS development. Rev Endocr Metab Disord 8 85 94

17. FarooqiS

O'RahillyS

2006 Genetics of obesity in humans. Endocr Rev 27 710 718

18. ChenK

LiF

LiJ

CaiH

StromS

2006 Induction of leptin resistance through direct interaction of C-reactive protein with leptin. Nat Med 12 425 432

19. RidkerPM

PareG

ParkerA

ZeeRY

DanikJS

2008 Loci related to metabolic-syndrome pathways including LEPR,HNF1A, IL6R, and GCKR associate with plasma C-reactive protein: the Women's Genome Health Study. Am J Hum Genet 82 1185 1192

20. PareG

ChasmanDI

KelloggM

ZeeRY

RifaiN

2008 Novel association of ABO histo-blood group antigen with soluble ICAM-1: results of a genome-wide association study of 6,578 women. PLoS Genet 4 e1000118 doi:10.1371/journal.pgen.1000118

21. PonthieuxA

LambertD

HerbethB

DroeschS

PfisterM

2003 Association between Gly241Arg ICAM-1 gene polymorphism and serum sICAM-1 concentration in the Stanislas cohort. Eur J Hum Genet 11 679 686

22. PuthothuB

KruegerM

BernhardtM

HeinzmannA

2006 ICAM1 amino-acid variant K469E is associated with paediatric bronchial asthma and elevated sICAM1 levels. Genes Immun 7 322 326

23. Sarecka-HujarB

ZakI

KrauzeJ

2009 Interactions between rs5498 polymorphism in the ICAM1 gene and traditional risk factors influence susceptibility to coronary artery disease. Clin Exp Med 9 117 124

24. RomeoS

KozlitinaJ

XingC

PertsemlidisA

CoxD

2008 Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease. Nat Genet 40 1461 1465

25. JohanssonLE

LindbladU

LarssonCA

RastamL

RidderstraleM

2008 Polymorphisms in the adiponutrin gene are associated with increased insulin secretion and obesity. Eur J Endocrinol 159 577 583