Genome-wide association studies suggest that APOL1-environment interactions more likely trigger kidney disease in African Americans with nondiabetic nephropathy than strong APOL1–second gene interactions

Among the myriad of statistical methods that identify gene–gene interactions in the realm of qualitative genome-wide association studies, gene-based interactions are not only powerful statistically, but also they are interpretable biologically. However, they have limited statistical detection by making assumptions on the association between traits and single nucleotide polymorphisms. Thus, a gene-based method (GGInt-XGBoost) originated from XGBoost is proposed in this article. Assuming that log odds ratio of disease traits satisfies the additive relationship if the pair of genes had no interactions, the difference in error between the XGBoost model with and without additive constraint could indicate gene–gene interaction; we then used a permutation-based statistical test to assess this difference and to provide a statistical p-value to represent the significance of the interaction. Experimental results on both simulation and real data showed that our approach had superior performance than previous experiments to detect gene–gene interactions.

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Abstract P231: Gene-by-smoking Interaction Analyses of Kidney Traits in the NHLBI Candidate-gene Association Resources Care Cohorts

Circulation ◽

10.1161/circ.125.suppl_10.ap231 ◽

2012 ◽

Vol 125 (suppl_10) ◽

Author(s):

Nora Franceschini ◽

Ching-Ti Liu ◽

W Linda Kao ◽

Leslie Lange ◽

Kari E North ◽

...

Keyword(s):

Kidney Disease ◽

Association Studies ◽

Snp Array ◽

Genetic Effects ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Current Smoking ◽

Genome Wide ◽

Smoking Exposure ◽

Smoking Interaction

Smoking is a known risk factor for progression of chronic kidney disease (CKD) but little is known of the role of smoking exposure on genetic effects of variants influencing kidney traits in the general population. We examined the evidence for effect modification of current smoking on the association of single nucleotide polymorphisms (SNP) with estimated glomerular filtration rate (eGFR) and urine albumin to creatinine ratio (UACR), two well established markers of kidney disease, in 23,767 white and 8,110 African American individuals from five studies genotyped using the custom SNP array ITMAT-Broad-CARe (IBC array) in the CARe consortium. We obtained study- and race-specific residuals from linear regression models of natural log-transformed eGFR or UACR regressed on age, sex and study site. We then stratified residuals by current smoking exposure and performed genome wide association analyses using additive genetic models adjusted for 10 principal components, and accounting for family structure using mixed models, if needed. Meta-analyses across smoking-specific strata within each self-reported race were performed using the inverse variance weighted fixed effect models. We assessed smoking interaction using a heterogeneity test (P<0.10) and I 2 metric. Among SNPs reaching the array wide specific significance threshold (2.0x10 -6 ) for association with eGFR or UACR, there was significant between smoking-strata heterogeneity for rs7422339 ( CPS1 , P=0.03, I 2 =77.7%) and rs13333226 ( UMOD , P=0.06, I 2 =71.1%) for eGFR in whites, with larger decreases in eGFR among current smokers compared to past/never smokers. For UACR, rs1801239 (missense variant of CUBN , between smoking-strata heterogeneity P=0.09, I 2 =64.8%) T allele showed less protective effect among current smokers than non-smokers in whites only. These loci have been previously identified in genome wide association studies. Our findings, if replicated, suggest possible important interactions of smoking exposure on the genetic effects of known loci associated with kidney traits. Funding(This research has received full or partial funding support from the American Heart Association, National Center)

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