Dataset Information

Pathway-analysis of published genome-wide association studies of lung cancer: A potential role for the CYP4F3 locus.

ABSTRACT: The fatty acids (FAs) metabolism is suggested to play a pivotal role in the development of lung cancer, and we explored that by conducting a pathway-based analysis. We performed a meta-analysis of published datasets of six genome wide association studies (GWASs) from the Transdisciplinary Research in Cancer of the Lung (TRICL) consortium, which included 12?160 cases with lung cancer and 16?838 cancer-free controls. A total of 30?722 single-nucleotide polymorphisms (SNPs) from 317 genes relevant to FA metabolic pathways were identified. An additional dataset from the Harvard Lung Cancer Study with 984 cases and 970 healthy controls was also added to the final meta-analysis. In the initial meta-analysis, 26 of 28 SNPs that passed false discovery rate multiple tests were mapped to the CYP4F3 gene. Among the 26 top ranked hits was a proxy SNP, CYP4F3 rs4646904 (P?=?8.65?×?10-6 , FDR?=?0.018), which is suggested to change splicing pattern/efficiency and to be associated with gene expression levels. However, after adding data of rs4646904 from the Harvard GWAS, the significance in the combined analysis was reduced to P?=?3.52?×?10-3 [odds ratio (OR)?=?1.07, 95% confidence interval (95%CI)?=?1.03-1.12]. Interestingly, the small Harvard dataset also pointed to the same direction of the association in subgroups of smokers (OR?=?1.07) and contributed to a combined OR of 1.13 (95% CI?=?1.06-1.20, P?=?6.70?×?10-5 ). The results suggest that a potentially functional SNP in CYP4F3 (rs4646904) may contribute to the etiology of lung cancer, especially in smokers. Additional mechanistic studies are warranted to unravel the potential biological significance of the finding.

SUBMITTER: Yin J

PROVIDER: S-EPMC5423820 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Pathway-analysis of published genome-wide association studies of lung cancer: A potential role for the CYP4F3 locus.

Yin Jieyun J Liu Hongliang H Liu Zhensheng Z Owzar Kouros K Han Younghun Y Su Li L Wei Yongyue Y Hung Rayjean J RJ Brhane Yonathan Y McLaughlin John J Brennan Paul P Bickeboeller Heike H Rosenberger Albert A Houlston Richard S RS Caporaso Neil N Landi Maria Teresa MT Heinrich Joachim J Risch Angela A Christiani David C DC Amos Christopher I CI Wei Qingyi Q

Molecular carcinogenesis 20170223 6

The fatty acids (FAs) metabolism is suggested to play a pivotal role in the development of lung cancer, and we explored that by conducting a pathway-based analysis. We performed a meta-analysis of published datasets of six genome wide association studies (GWASs) from the Transdisciplinary Research in Cancer of the Lung (TRICL) consortium, which included 12 160 cases with lung cancer and 16 838 cancer-free controls. A total of 30 722 single-nucleotide polymorphisms (SNPs) from 317 genes relevant ...[more]

PMID: 28150878

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Project description:BackgroundRecent association analyses in genome-wide association studies (GWAS) mainly focus on single-locus association tests (marginal tests) and two-locus interaction detections. These analysis methods have provided strong evidence of associations between genetics variances and complex diseases. However, there exists a type of association pattern, which often occurs within local regions in the genome and is unlikely to be detected by either marginal tests or interaction tests. This association pattern involves a group of correlated single-nucleotide polymorphisms (SNPs). The correlation among SNPs can lead to weak marginal effects and the interaction does not play a role in this association pattern. This phenomenon is due to the existence of unfaithfulness: the marginal effects of correlated SNPs do not express their significant joint effects faithfully due to the correlation cancelation.ResultsIn this paper, we develop a computational method to detect this association pattern masked by unfaithfulness. We have applied our method to analyze seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). The analysis for each data set takes about one week to finish the examination of all pairs of SNPs. Based on the empirical result of these real data, we show that this type of association masked by unfaithfulness widely exists in GWAS.ConclusionsThese newly identified associations enrich the discoveries of GWAS, which may provide new insights both in the analysis of tagSNPs and in the experiment design of GWAS. Since these associations may be easily missed by existing analysis tools, we can only connect some of them to publicly available findings from other association studies. As independent data set is limited at this moment, we also have difficulties to replicate these findings. More biological implications need further investigation.AvailabilityThe software is freely available at http://bioinformatics.ust.hk/hidden_pattern_finder.zip.

Project description:Vertebral fracture risk is a heritable complex trait. The aim of this study was to identify genetic susceptibility factors for osteoporotic vertebral fractures applying a genome-wide association study (GWAS) approach. The GWAS discovery was based on the Rotterdam Study, a population-based study of elderly Dutch individuals aged > 55 years; and comprising 329 cases and 2666 controls with radiographic scoring (McCloskey–Kanis) and genetic data. Replication of one top-associated SNP was pursued by de-novo genotyping of 15 independent studies across Europe, the United States, and Australia and one Asian study. Radiographic vertebral fracture assessment was performed using McCloskey–Kanis or Genant semi-quantitative definitions. SNPs were analyzed in relation to vertebral fracture using logistic regression models corrected for age and sex. Fixed effects inverse variance and Han–Eskin alternative random effects meta-analyses were applied. Genome-wide significance was set at p < 5 × 10? 8. In the discovery, a SNP (rs11645938) on chromosome 16q24 was associated with the risk for vertebral fractures at p = 4.6 × 10? 8. However, the association was not significant across 5720 cases and 21,791 controls from 14 studies. Fixed-effects meta-analysis summary estimate was 1.06 (95% CI: 0.98–1.14; p = 0.17), displaying high degree of heterogeneity (I2 = 57%; Qhet p = 0.0006). Under Han–Eskin alternative random effects model the summary effect was significant (p = 0.0005). The SNP maps to a region previously found associated with lumbar spine bone mineral density (LS-BMD) in two large meta-analyses from the GEFOS consortium. A false positive association in the GWAS discovery cannot be excluded, yet, the low-powered setting of the discovery and replication settings (appropriate to identify risk effect size > 1.25) may still be consistent with an effect size < 1.10, more of the type expected in complex traits. Larger effort in studies with standardized phenotype definitions is needed to confirm or reject the involvement of this locus on the risk for vertebral fractures.

Project description:Peach is one of the most important fruit crops in the world, with the global annual production about 24.6 million tons. The United States is the fourth-largest producer after China, Spain, and Italy. Peach consumption has decreased over the last decade, most likely due to inconsistent quality of the fruit on the market. Thus, marker-assisted selection for fruit quality traits is highly desired in fresh market peach breeding programs and one of the major goals of the RosBREED project. The ability to use DNA information to select for desirable traits would enable peach breeders to efficiently plan crosses and select seedlings with desired quality traits early in the selection process before fruiting. Therefore, we assembled a multi-locus genome wide association study (GWAS) of 620 individuals from three public fresh market peach breeding programs (Arkansas, Texas, and South Carolina). The material was genotyped using 9K SNP array and the traits were phenotyped for three phenological (bloom date, ripening date, and days after bloom) and 11 fruit quality-related traits (blush, fruit diameter, fruit weight, adherence, fruit firmness, redness around pit, fruit texture, pit weight, soluble solid concentration, titratable acidity, and pH) over three seasons (2010, 2011, and 2012). Multi-locus association analyses, carried out using mrMLM 4.0 and FarmCPU R packages, revealed a total of 967 and 180 quantitative trait nucleotides (QTNs), respectively. Among the 88 consistently reliable QTNs detected using multiple multi-locus GWAS methods and/or at least two seasons, 44 were detected for the first time. Fruit quality hotspots were identified on chromosomes 1, 3, 4, 5, 6, and 8. Out of 566 candidate genes detected in the genomic regions harboring the QTN clusters, 435 were functionally annotated. Gene enrichment analyses revealed 68 different gene ontology (GO) terms associated with fruit quality traits. Data reported here advance our understanding of genetic mechanisms underlying important fruit quality traits and further support the development of DNA tools for breeding.

Project description:BACKGROUND: Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs. RESULTS: We describe a SNP-based pathway enrichment method for GWAS studies. The method consists of the following two main steps: 1) for a given pathway, using an adaptive truncated product statistic to identify all representative (potentially more than one) SNPs of each gene, calculating the average number of representative SNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and 2) ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing if the set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one from European-American (EA) and the other from African-American (AA). In the EA data set, we found 22 pathways with nominal P-value less than or equal to 0.001 and corresponding false discovery rate (FDR) less than 5%. In the AA data set, we found 11 pathways by controlling the same nominal P-value and FDR threshold. Interestingly, 8 of these pathways overlap with those found in the EA sample. We have implemented our method in a JAVA software package, called SNP Set Enrichment Analysis (SSEA), which contains a user-friendly interface and is freely available at http://cbcl.ics.uci.edu/SSEA. CONCLUSIONS: The SNP-based pathway enrichment method described here offers a new alternative approach for analysing GWAS data. By applying it to schizophrenia GWAS studies, we show that our method is able to identify statistically significant pathways, and importantly, pathways that can be replicated in large genetically distinct samples.

Dataset Information

Pathway-analysis of published genome-wide association studies of lung cancer: A potential role for the CYP4F3 locus.

Publications

Pathway-analysis of published genome-wide association studies of lung cancer: A potential role for the CYP4F3 locus.

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