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- Chikowore, Tinashe, et al.
(författare)
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GWAS transethnic meta-analysis of BMI in similar to 700k individuals reveals novel gene-smoking interaction in African populations
- 2020
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Ingår i: Genetic Epidemiology. - : John Wiley & Sons. - 0741-0395 .- 1098-2272. ; 44:5, s. 475-476
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Sixty two percent of the 1.12 billion obese people globally reside in low‐middle income countries, 77% of which are in Africa. There is paucity of data on gene‐lifestyle interactions associated with the increasing prevalence of obesity among Africans. We hypothesised that gene‐environment interacting (GEI) variants exhibit heterogenous effects on obesity in transethnic meta‐analysis of marginal SNP associations as a result of modification by an unknown exposure that varies across populations.Body mass index (BMI) genome‐wide association study (GWAS) summary statistics for 678,671 individuals representative of the major global ancestries were aggregated at 21,338,816 SNPs via fixed‐effects meta‐analysis. Lead SNPs attaining genome‐wide significance (P < 5 × 10−8) were tested for heterogeneity in effects between GWAS. Lead SNPs with significant evidence of heterogeneity after Bonferroni correction were then selected for interaction analysis with selected lifestyle factors in an independent AWI‐Gen study of 10,500 African participants. Significant interaction findings were then replicated in 3,177 individuals of African ancestry in the UK Biobank.Of 881 lead SNPs, five had significant heterogenous effects on BMI (P < 5.7 × 10−5). Rs471094, at the CDKAL1 locus had significant interaction with smoking status, which reduced the effect of the BMI raising allele in current smokers (Betaint = −0.949 kg/m2; P int = .002) compared with non‐smokers in AWI‐Gen. This finding was validated in the UK Biobank (Betaint = −1.471 kg/m2, P int = .020; meta‐analysis Betaint = −1.050 kg/m2, P int = .0002). Our results highlight the first gene‐lifestyle interaction on BMI in Africans and demonstrate the utility of transethnic meta‐analysis of GWAS for identifying GEI effects.
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| 3. |
- Feng, Helian, et al.
(författare)
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Cross-cancer cross-tissue Transcriptome-wide Association Study (TWAS) of 11 cancers identifies 56 novel genes
- 2020
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Ingår i: Genetic Epidemiology. - : John Wiley & Sons. - 0741-0395 .- 1098-2272. ; 44:5, s. 481-481
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Though heterogeneous, multiple tumor types share hallmark mechanisms. Thus, identifying genes associated with multiple cancer types may shed light on general oncogenic mechanisms and identify genes missed in single‐cancer analyses. TWAS have been successful in testing whether genetically‐predicted tissue‐specific gene expression is associated with cancer risk. Although cross‐cancer genome‐wide association studies (GWAS) analyses have been performed previously, no cross‐cancer TWAS has been conducted to date. Here, we implement a pipeline to perform cross‐cancer, cross‐tissue TWAS analysis. We use newly‐developed multi‐trait TWAS test statistics to integrate the TWAS results for association between 11 separated cancers and predicted gene expression in 43 GTEx tissues, including a “sum” test and a “variance components” test, analogous to fixed‐ and random‐effects meta‐analyses. We then integrated the results across different tissues using the Aggregated Cauchy Association Test (ACAT) combined test.A total of 403 genes were significantly associated with at least one cancer type for at least one tissue; 96 additional genes were identified when combining test results across cancers; and 35 additional genes when further combining test results across tissue. Among these significant genes, 70 were not near previously‐published GWAS index variants. 14 of the 70 novel genes were identified from the single‐cancer single‐tissue test; an additional 43 were identified with the cross‐cancer test; and another 13 were identified when further combined across tissues. The newly identified genes, including RBBP8 and TP53BP , are involved in chromatin structure, tumorigenesis, apoptosis, transcriptional regulation, DNA repair, immune system, oxidative damage and cell‐cycle, proliferation, progression, shape, structure, and migration.
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| 6. |
- Sun, Ryan, et al.
(författare)
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Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer
- 2021
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Ingår i: Genetic Epidemiology. - : John Wiley & Sons. - 0741-0395 .- 1098-2272. ; 45:1, s. 99-114
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Tidskriftsartikel (refereegranskat)abstract
- Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1 beta pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-kappa B signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies.
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| 7. |
- Yang, Tianzhong, et al.
(författare)
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Incorporating multiple sets of eQTL weights into gene-by-environment interaction analysis identifies novel susceptibility loci for pancreatic cancer
- 2020
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Ingår i: Genetic Epidemiology. - : Wiley-Blackwell. - 0741-0395 .- 1098-2272. ; 44:8, s. 880-892
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Tidskriftsartikel (refereegranskat)abstract
- It is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene-by-environment interaction (G x E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular mechanisms, we incorporate functional genomics information, specifically, expression quantitative trait loci (eQTLs), into a data-adaptive G x E test, called aGEw. This test adaptively chooses the best eQTL weights from multiple tissues and provides an extra layer of weighting at the genetic variant level. Extensive simulations show that the aGEw test can control the Type 1 error rate, and the power is resilient to the inclusion of neutral variants and noninformative external weights. We applied the proposed aGEw test to the Pancreatic Cancer Case-Control Consortium (discovery cohort of 3,585 cases and 3,482 controls) and the PanScan II genome-wide association study data (replication cohort of 2,021 cases and 2,105 controls) with smoking as the exposure of interest. Two novel putative smoking-related pancreatic cancer susceptibility genes,TRIP10andKDM3A, were identified. The aGEw test is implemented in an R package aGE.
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