Dataset Information

Evidence for large-scale gene-by-smoking interaction effects on pulmonary function.

ABSTRACT:

Background

Smoking is the strongest environmental risk factor for reduced pulmonary function. The genetic component of various pulmonary traits has also been demonstrated, and at least 26 loci have been reproducibly associated with either FEV 1 (forced expiratory volume in 1 second) or FEV 1 /FVC (FEV 1 /forced vital capacity). Although the main effects of smoking and genetic loci are well established, the question of potential gene-by-smoking interaction effect remains unanswered. The aim of the present study was to assess, using a genetic risk score approach, whether the effect of these 26 loci on pulmonary function is influenced by smoking.

Methods

We evaluated the interaction between smoking exposure, considered as either ever vs never or pack-years, and a 26-single nucleotide polymorphisms (SNPs) genetic risk score in relation to FEV 1 or FEV 1 /FVC in 50?047 participants of European ancestry from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) and SpiroMeta consortia.

Results

We identified an interaction ( ?int ?=?-0.036, 95% confidence interval, -0.040 to -0.032, P ?=?0.00057) between an unweighted 26 SNP genetic risk score and smoking status (ever/never) on the FEV 1 /FVC ratio. In interpreting this interaction, we showed that the genetic risk of falling below the FEV /FVC threshold used to diagnose chronic obstructive pulmonary disease is higher among ever smokers than among never smokers. A replication analysis in two independent datasets, although not statistically significant, showed a similar trend in the interaction effect.

Conclusions

This study highlights the benefit of using genetic risk scores for identifying interactions missed when studying individual SNPs and shows, for the first time, that persons with the highest genetic risk for low FEV 1 /FVC may be more susceptible to the deleterious effects of smoking.

SUBMITTER: Aschard H

PROVIDER: S-EPMC5837518 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evidence for large-scale gene-by-smoking interaction effects on pulmonary function.

Aschard Hugues H Tobin Martin D MD Hancock Dana B DB Skurnik David D Sood Akshay A James Alan A Vernon Smith Albert A Manichaikul Ani W AW Campbell Archie A Prins Bram P BP Hayward Caroline C Loth Daan W DW Porteous David J DJ Strachan David P DP Zeggini Eleftheria E O'Connor George T GT Brusselle Guy G GG Boezen H Marike HM Schulz Holger H Deary Ian J IJ Hall Ian P IP Rudan Igor I Kaprio Jaakko J Wilson James F JF Wilk Jemma B JB Huffman Jennifer E JE Hua Zhao Jing J de Jong Kim K Lyytikäinen Leo-Pekka LP Wain Louise V LV Jarvelin Marjo-Riitta MR Kähönen Mika M Fornage Myriam M Polasek Ozren O Cassano Patricia A PA Barr R Graham RG Rawal Rajesh R Harris Sarah E SE Gharib Sina A SA Enroth Stefan S Heckbert Susan R SR Lehtimäki Terho T Gyllensten Ulf U Jackson Victoria E VE Gudnason Vilmundur V Tang Wenbo W Dupuis Josée J Soler Artigas María M Joshi Amit D AD London Stephanie J SJ Kraft Peter P

International journal of epidemiology 20170601 3

<h4>Background</h4>Smoking is the strongest environmental risk factor for reduced pulmonary function. The genetic component of various pulmonary traits has also been demonstrated, and at least 26 loci have been reproducibly associated with either FEV 1 (forced expiratory volume in 1 second) or FEV 1 /FVC (FEV 1 /forced vital capacity). Although the main effects of smoking and genetic loci are well established, the question of potential gene-by-smoking interaction effect remains unanswered. The a ...[more]

PMID: 28082375

Similar Datasets

Project description:Background:In Genome-Wide Association Studies (GWAS), the concept of linkage disequilibrium is important as it allows identifying genetic markers that tag the actual causal variants. In Genome-Wide Association Interaction Studies (GWAIS), similar principles hold for pairs of causal variants. However, Linkage Disequilibrium (LD) may also interfere with the detection of genuine epistasis signals in that there may be complete confounding between Gametic Phase Disequilibrium (GPD) and interaction. GPD may involve unlinked genetic markers, even residing on different chromosomes. Often GPD is eliminated in GWAIS, via feature selection schemes or so-called pruning algorithms, to obtain unconfounded epistasis results. However, little is known about the optimal degree of GPD/LD-pruning that gives a balance between false positive control and sufficient power of epistasis detection statistics. Here, we focus on Model-Based Multifactor Dimensionality Reduction as one large-scale epistasis detection tool. Its performance has been thoroughly investigated in terms of false positive control and power, under a variety of scenarios involving different trait types and study designs, as well as error-free and noisy data, but never with respect to multicollinear SNPs. Results:Using real-life human LD patterns from a homogeneous subpopulation of British ancestry, we investigated the impact of LD-pruning on the statistical sensitivity of MB-MDR. We considered three different non-fully penetrant epistasis models with varying effect sizes. There is a clear advantage in pre-analysis pruning using sliding windows at r 2 of 0.75 or lower, but using a threshold of 0.20 has a detrimental effect on the power to detect a functional interactive SNP pair (power <?25%). Signal sensitivity, directly using LD-block information to determine whether an epistasis signal is present or not, benefits from LD-pruning as well (average power across scenarios: 87%), but is largely hampered by functional loci residing at the boundaries of an LD-block. Conclusions:Our results confirm that LD patterns and the position of causal variants in LD blocks do have an impact on epistasis detection, and that pruning strategies and LD-blocks definitions combined need careful attention, if we wish to maximize the power of large-scale epistasis screenings.

Dataset Information

Evidence for large-scale gene-by-smoking interaction effects on pulmonary function.

Background

Methods

Results

Conclusions

Publications

Evidence for large-scale gene-by-smoking interaction effects on pulmonary function.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets