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Cao J, Liu W, Yang Z, Qu G, Zhong C. Causal Relationship Between Body Mass Index and Risk of Otitis Media with Effusion in Children: A Mendelian Randomization Study. Indian J Otolaryngol Head Neck Surg 2024; 76:1410-1418. [PMID: 38440649 PMCID: PMC10909010 DOI: 10.1007/s12070-023-04161-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 08/19/2023] [Indexed: 03/06/2024] Open
Abstract
Background Body mass index(BMI) in children appears to be associated with Otitis media with effusion(OME) in observational studies, but the causal relationship is not clear. Methods A two-sample Mendelian randomization (MR) study was used to explore the causal relationship between childhood BMI and OME in people of European ancestry. Genome-wide association studies (GWAS) of childhood BMI were used as exposures (n = 61,111), while GWAS of OME were used as outcomes (n = 429,290). The weighted inverse variance method (IVW) was used as a baseline method to test for causality. In addition, MR-Egger, simple mode analysis, weighted median, and weighted mode were used as complementary methods.MR-PRESSO analysis, MR-Egger intercept analysis, and Cochran's Q statistical analysis were also used to detect possible directional heterogeneity and polymorphism. To assess this association, we used ratios (OR) with 95% confidence intervals (ci). All statistical analyses were performed in R. Results We selected 22 genome-wide significant single nucleotide polymorphisms (SNPs) from GWAS as instrumental variables (IVW). the IVW approach showed evidence supporting a causal relationship between BMI and OME in children (β = 0.265, SE = 0.113, P = 0.018). MR-Egger regression showed that targeted polymorphisms were unlikely to bias the results bias (intercept=-0.022; P = 0.488), but there was no causal relationship between BMI and OME (β = 0.584, SE = 0.465, P = 0.224). Although the results of the IVW and MR Egger analyses were not consistent, the IVW analysis maintained higher precision, and the Cochran Q test, heterogeneity and polymorphism tests showed no heterogeneity, no directionality and no polymorphism. Conclusions MR studies suggest that genetically predicted body mass index in childhood is associated with an increased risk of OME. Notably, given the limitations of this study, the mechanism of association between body mass index and OME in childhood needs further investigation. These results support the importance of effective management of obesity, which may reduce OME occurrence and decrease OME recurrence.
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Affiliation(s)
- Jingwen Cao
- Otolaryngology Head and Neck Surgery, The 940th Hospital of the Joint Logistics Support Force of the Chinese People’s Liberation Army, Lanzhou, China
- Ningxia Medical University, Yinchuan, China
| | - Wei Liu
- Ningxia Medical University, Yinchuan, China
- Key Laboratory of Stem Cells and Gene Province, Lanzhou, China
| | | | - Gaoya Qu
- Ningxia Medical University, Yinchuan, China
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Liang J, Cai Y, Zhang J, Jing Z, Lv L, Zhang G, Zhang R, Liu R, Nan K, Dang X. Metformin Treatment Reduces the Incidence of Rheumatoid Arthritis: A Two-Sample Mendelian Randomized Study. J Clin Med 2023; 12:jcm12072461. [PMID: 37048545 PMCID: PMC10095374 DOI: 10.3390/jcm12072461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/22/2023] [Accepted: 03/20/2023] [Indexed: 04/14/2023] Open
Abstract
Several studies have shown that rheumatologic patients can benefit from metformin, but it remains unclear whether metformin treatment is causally associated with the risk of rheumatoid arthritis (RA). A two-sample Mendelian randomization (MR) study was conducted to investigate the causal relationship between metformin treatment and the incidence of rheumatoid arthritis. The genome-wide significant (p < 5 × 10-8) single-nucleotide polymorphisms (SNPs) associated with metformin use were selected as instrumental variables (IVs). Summary statistics on RA were extracted from a large genome-wide association study (GWAS) meta-analysis. The inverse variance-weighted (IVW) method was used as the determinant of the causal effects of metformin treatment on RA. Cochran's Q was used to detect heterogeneity. Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test and MR-Egger regression were used to detect horizontal pleiotropy. A total of 34 SNPs significantly associated with metformin treatment were obtained. Thirty-two SNPs were selected as IVs after removing two SNPs for being palindromic with intermediate allele frequencies (rs11658063 and rs4930011). The IVW results showed a negative causal association between metformin treatment and RA (OR = 0.0232, 95% CI 1.6046 × 10-3 - 0.3368; p = 0.006). Meanwhile, no heterogeneity or pleiotropy was detected, indicating that the results were reliable. This study indicated a negative causality between metformin treatment and RA, indicating that the treatment of metformin can prevent the pathogenesis of RA.
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Affiliation(s)
- Jialin Liang
- Department of Orthopaedics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710006, China
| | - Yuanqing Cai
- Department of Orthopaedics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710006, China
| | - Jianan Zhang
- Zonglian College, Xi'an Jiaotong University, Xi'an 710054, China
| | - Zhaopu Jing
- Department of Orthopaedics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710006, China
| | - Leifeng Lv
- Department of Orthopaedics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710006, China
| | - Guangyang Zhang
- Department of Orthopaedics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710006, China
| | - Rupeng Zhang
- Department of Orthopaedics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710006, China
| | - Ruiyu Liu
- Department of Orthopaedics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710006, China
| | - Kai Nan
- Department of Osteonecrosis & Joint Reconstruction Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an 710054, China
| | - Xiaoqian Dang
- Department of Orthopaedics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710006, China
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Ratnasekera P, Graham J, McNeney B. Inference of gene-environment interaction from heterogeneous case-parent trios. Front Genet 2023; 13:1065568. [PMID: 36685810 PMCID: PMC9845406 DOI: 10.3389/fgene.2022.1065568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 11/29/2022] [Indexed: 01/05/2023] Open
Abstract
Introduction: In genetic epidemiology, log-linear models of population risk may be used to study the effect of genotypes and exposures on the relative risk of a disease. Such models may also include gene-environment interaction terms that allow the genotypes to modify the effect of the exposure, or equivalently, the exposure to modify the effect of genotypes on the relative risk. When a measured test locus is in linkage disequilibrium with an unmeasured causal locus, exposure-related genetic structure in the population can lead to spurious gene-environment interaction; that is, to apparent gene-environment interaction at the test locus in the absence of true gene-environment interaction at the causal locus. Exposure-related genetic structure occurs when the distributions of exposures and of haplotypes at the test and causal locus both differ across population strata. A case-parent trio design can protect inference of genetic main effects from confounding bias due to genetic structure in the population. Unfortunately, when the genetic structure is exposure-related, the protection against confounding bias for the genetic main effect does not extend to the gene-environment interaction term. Methods: We show that current methods to reduce the bias in estimated gene-environment interactions from case-parent trio data can only account for simple population structure involving two strata. To fill this gap, we propose to directly accommodate multiple population strata by adjusting for genetic principal components (PCs). Results and Discussion: Through simulations, we show that our PC adjustment maintains the nominal type-1 error rate and has nearly identical power to detect gene-environment interaction as an oracle approach based directly on population strata. We also apply the PC-adjustment approach to data from a study of genetic modifiers of cleft palate comprised primarily of case-parent trios of European and East Asian ancestry. Consistent with earlier analyses, our results suggest that the gene-environment interaction signal in these data is due to the self-reported European trios.
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Xiao G, Wang H, Hu J, Liu L, Zhang T, Zhou M, Li X, Qin C. Estimating the causal effect of frailty index on vestibular disorders: A two-sample Mendelian randomization. Front Neurosci 2022; 16:990682. [PMID: 36090295 PMCID: PMC9448900 DOI: 10.3389/fnins.2022.990682] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Frailty index and vestibular disorders appear to be associated in observational studies, but causality of the association remains unclear. Methods A two-sample Mendelian randomization (MR) study was implemented to explore the causal relationship between the frailty index and vestibular disorders in individuals of European descent. A genome-wide association study (GWAS) of frailty index was used as the exposure (n = 175, 226), whereas the GWAS of vestibular disorders was the outcome (n = 462,933). MR Steiger filtering method was conducted to investigate the causal effect of the frailty index on vestibular disorders. An inverse variance weighted (IVW) approach was used as the essential approach to examine the causality. Additionally, the MR-Egger methods, the simple mode analysis, the weighted median analysis, and the weighted mode analysis were used as supplementary methods. The MR-PRESSO analysis, the MR-Egger intercept analysis, and Cochran's Q statistical analysis also were used to detect the possible heterogeneity as well as directional pleiotropy. To evaluate this association, the odds ratio (OR) with 95% confidence intervals (CIs) was used. All statistical analyses were performed in R. The STROBE-MR checklist for the reporting of MR studies was used in this study. Results In total, 14 single nucleotide polymorphisms (SNPs) were identified as effective instrumental variables (IVs) in the two sample MR analyses. The significant causal effect of the frailty index on vestibular disorders was demonstrated by IVW method [OR 1.008 (95% CI 1.003, 1.013), p = 0.001]. Results from the various sensitivity analysis were consistent. The “leave-one-out” analysis indicated that our results were robust even without a single SNP. According to the MR-Egger intercept test [intercept = −0.000151, SE = 0.011, p = 0.544], genetic pleiotropy did not affect the results. No heterogeneity was detected by Cochran's Q test. Results of MR Steiger directionality test indicated the accuracy of our estimate of the potential causal direction (Steiger p < 0.001). Conclusion The MR study suggested that genetically predicted frailty index may be associated with an increased risk of vestibular disorders. Notably, considering the limitations of this study, the causal effects between frailty index and vestibular disorders need further investigation. These results support the importance of effectively managing frailty which may minimize vestibular disorders and improve the quality of life for those with vestibular disorders.
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Affiliation(s)
- Gui Xiao
- Department of Health Management, The Third Xiangya Hospital, Central South University, Changsha, China
- Xiangya School of Nursing, Central South University, Changsha, China
| | - Hu Wang
- Xiangya School of Nursing, Central South University, Changsha, China
| | - Jiaji Hu
- Xiangya School of Nursing, Central South University, Changsha, China
| | - Li Liu
- Department of Health Management, The Third Xiangya Hospital, Central South University, Changsha, China
- Xiangya School of Nursing, Central South University, Changsha, China
| | - Tingting Zhang
- Department of Health Management, The Third Xiangya Hospital, Central South University, Changsha, China
- Xiangya School of Nursing, Central South University, Changsha, China
| | - Mengjia Zhou
- Department of Health Management, The Third Xiangya Hospital, Central South University, Changsha, China
- Xiangya School of Nursing, Central South University, Changsha, China
| | - Xingxing Li
- Department of Health Management, The Third Xiangya Hospital, Central South University, Changsha, China
- Xiangya School of Nursing, Central South University, Changsha, China
| | - Chunxiang Qin
- Department of Health Management, The Third Xiangya Hospital, Central South University, Changsha, China
- Xiangya School of Nursing, Central South University, Changsha, China
- *Correspondence: Chunxiang Qin
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Wang J, Liu D, Tian E, Guo ZQ, Chen JY, Kong WJ, Zhang SL. Is Hearing Impairment Causally Associated With Falls? Evidence From a Two-Sample Mendelian Randomization Study. Front Neurol 2022; 13:876165. [PMID: 35547384 PMCID: PMC9082948 DOI: 10.3389/fneur.2022.876165] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 03/15/2022] [Indexed: 11/17/2022] Open
Abstract
Background Observational studies have suggested that hearing impairment (HI) was associated with the risk of falls, but it remains unclear if this association is of causal nature. Methods A two-sample Mendelian randomization (MR) study was conducted to investigate the causal association between HI and falls in individuals of European descent. Summary data on the association of single nucleotide polymorphisms (SNPs) with HI were obtained from the hitherto largest genome-wide association study (GWAS) (n = 323,978), and statistics on the association of SNPs with falls were extracted from another recently published GWAS (n = 461,725). MR Steiger filtering method was applied to determine the causal direction between HI and falls. Inverse-variance weighted (IVW) method was employed as the main approach to analyze the causal association between HI and falls, whereas weighted median, simple mode, weighted mode, and MR-Egger methods were used as complementary analyses. The MR-Egger intercept test, the MR-PRESSO test, and Cochran's Q statistic were performed to detect the potential directional pleiotropy and heterogeneity, respectively. The odds ratio (OR) with 95% confidence intervals (CIs) was used to evaluate this association. Results A total of 18 SNPs were identified as valid instrumental variables in our two-sample MR analysis. The positive causality between HI and risk of falls was indicated by IVW [OR 1.108 (95% CI 1.028, 1.194), p = 0.007]. The sensitivity analyses yielded comparable results. The “leave-one-out” analysis proved that lack of a single SNP did not affect the robustness of our results. The MR-Egger intercept test exhibited that genetic pleiotropy did not bias the results [intercept = −2.4E−04, SE = 0.001, p = 0.832]. Cochran's Q test revealed no heterogeneity. Conclusion Our MR study revealed a causal association between genetically predicted HI and falls. These results provide further evidence supporting the need to effectively manage HI to minimize fall risks and improve quality of life.
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Affiliation(s)
- Jun Wang
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dan Liu
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - E Tian
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhao-Qi Guo
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jing-Yu Chen
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei-Jia Kong
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Union Hospital, Institute of Otorhinolaryngology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Su-Lin Zhang
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Union Hospital, Institute of Otorhinolaryngology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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6
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Johnsen PV, Riemer-Sørensen S, DeWan AT, Cahill ME, Langaas M. A new method for exploring gene-gene and gene-environment interactions in GWAS with tree ensemble methods and SHAP values. BMC Bioinformatics 2021; 22:230. [PMID: 33947323 PMCID: PMC8097909 DOI: 10.1186/s12859-021-04041-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 02/22/2021] [Indexed: 01/08/2023] Open
Abstract
Background The identification of gene–gene and gene–environment interactions in genome-wide association studies is challenging due to the unknown nature of the interactions and the overwhelmingly large number of possible combinations. Parametric regression models are suitable to look for prespecified interactions. Nonparametric models such as tree ensemble models, with the ability to detect any unspecified interaction, have previously been difficult to interpret. However, with the development of methods for model explainability, it is now possible to interpret tree ensemble models efficiently and with a strong theoretical basis. Results We propose a tree ensemble- and SHAP-based method for identifying as well as interpreting potential gene–gene and gene–environment interactions on large-scale biobank data. A set of independent cross-validation runs are used to implicitly investigate the whole genome. We apply and evaluate the method using data from the UK Biobank with obesity as the phenotype. The results are in line with previous research on obesity as we identify top SNPs previously associated with obesity. We further demonstrate how to interpret and visualize interaction candidates. Conclusions The new method identifies interaction candidates otherwise not detected with parametric regression models. However, further research is needed to evaluate the uncertainties of these candidates. The method can be applied to large-scale biobanks with high-dimensional data. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04041-7.
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Affiliation(s)
- Pål V Johnsen
- SINTEF DIGITAL, Forskningsveien 1, 0373, Oslo, Norway. .,Department of Mathematical Sciences, Norwegian University of Science and Technology, A. Getz vei 1, 7491, Trondheim, Norway.
| | | | - Andrew Thomas DeWan
- Department of Chronic Disease Epidemiology and Center for Perinatal, Pediatric and Environmental Epidemiology, Yale School of Public Health, 1 Church Street, New Haven, CT, 06510, USA.,Gemini Center for Sepsis Research, Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, Prinsesse Kristinas gate 3, 7030, Trondheim, Norway
| | - Megan E Cahill
- Department of Chronic Disease Epidemiology and Center for Perinatal, Pediatric and Environmental Epidemiology, Yale School of Public Health, 1 Church Street, New Haven, CT, 06510, USA
| | - Mette Langaas
- Department of Mathematical Sciences, Norwegian University of Science and Technology, A. Getz vei 1, 7491, Trondheim, Norway
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7
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Cleary JM, Raghavan S, Wu Q, Li YY, Spurr LF, Gupta HV, Rubinson DA, Fetter IJ, Hornick JL, Nowak JA, Siravegna G, Goyal L, Shi L, Brais LK, Loftus M, Shinagare AB, Abrams TA, Clancy TE, Wang J, Patel AK, Brichory F, Vaslin Chessex A, Sullivan RJ, Keller RB, Denning S, Hill ER, Shapiro GI, Pokorska-Bocci A, Zanna C, Ng K, Schrag D, Janne PA, Hahn WC, Cherniack AD, Corcoran RB, Meyerson M, Daina A, Zoete V, Bardeesy N, Wolpin BM. FGFR2 Extracellular Domain In-Frame Deletions are Therapeutically Targetable Genomic Alterations that Function as Oncogenic Drivers in Cholangiocarcinoma. Cancer Discov 2021; 11:2488-2505. [PMID: 33926920 DOI: 10.1158/2159-8290.cd-20-1669] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 03/10/2021] [Accepted: 04/26/2021] [Indexed: 11/16/2022]
Abstract
We conducted next generation DNA sequencing on 335 biliary tract cancers and characterized the genomic landscape by anatomic site within the biliary tree. In addition to frequent FGFR2 fusions among patients with intrahepatic cholangiocarcinoma (IHCC), we identified FGFR2 extracellular domain in-frame deletions (EIDs) in 5 of 178 (2.8%) patients with IHCC, including two patients with FGFR2 p.H167_N173del. Expression of this FGFR2 EID in NIH3T3 cells resulted in constitutive FGFR2 activation, oncogenic transformation, and sensitivity to FGFR inhibitors. Three patients with FGFR2 EIDs were treated with Debio 1347, an oral FGFR-1/2/3 inhibitor, and all showed partial responses. One patient developed an acquired L618F FGFR2 kinase domain mutation at disease progression and experienced a further partial response for 17 months to an irreversible FGFR2 inhibitor, futibatinib. Together, these findings reveal FGFR2 EIDs as an alternative mechanism of FGFR2 activation in IHCC that predict sensitivity to FGFR inhibitors in the clinic.
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Affiliation(s)
- James M Cleary
- Department of Medical Oncology, Dana-Farber Cancer Institute
| | | | | | - Yvonne Y Li
- Department of Medical Oncology, Dana-Farber Cancer Institute
| | - Liam F Spurr
- Dana-Farber Cancer Institute, Harvard Medical School
| | - Hersh V Gupta
- Department of Medical Oncology, Dana-Farber Cancer Institute
| | | | | | - Jason L Hornick
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School
| | | | | | - Lipika Goyal
- Internal Medicine, Massachusetts General Hospital Cancer Center, Harvard Medical School
| | - Lei Shi
- Center for Cancer Research, Massachusetts General Hospital Cancer Center, Harvard Medical School
| | - Lauren K Brais
- Department of Medical Oncology, Dana-Farber Cancer Institute
| | | | - Atul B Shinagare
- Department of Radiology, Brigham and Women's Hospital/ Dana-Farber Cancer Institute
| | | | | | - Jiping Wang
- Department of Surgery, Brigham and Women's Hospital
| | - Anuj K Patel
- Department of Gastrointestinal Oncology, Dana-Farber Cancer Institute
| | | | | | - Ryan J Sullivan
- Center for Melanoma, Massachusetts General Hospital Cancer Center
| | | | | | - Emma R Hill
- Dana-Farber/Brigham and Women's Cancer Center
| | | | | | | | - Kimmie Ng
- Department of Medical Oncology, Dana-Farber Cancer Institute
| | | | - Pasi A Janne
- Lowe Center for Thoracic Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute
| | - William C Hahn
- Department of Medical Oncology, Dana-Farber Cancer Institute
| | - Andrew D Cherniack
- Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School
| | | | | | | | | | | | - Brian M Wolpin
- Department of Medical Oncology, Dana-Farber/Harvard Cancer Center
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Chatelain C, Durand G, Thuillier V, Augé F. Performance of epistasis detection methods in semi-simulated GWAS. BMC Bioinformatics 2018; 19:231. [PMID: 29914375 PMCID: PMC6006572 DOI: 10.1186/s12859-018-2229-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2017] [Accepted: 06/04/2018] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Part of the missing heritability in Genome Wide Association Studies (GWAS) is expected to be explained by interactions between genetic variants, also called epistasis. Various statistical methods have been developed to detect epistasis in case-control GWAS. These methods face major statistical challenges due to the number of tests required, the complexity of the Linkage Disequilibrium (LD) structure, and the lack of consensus regarding the definition of epistasis. Their limited impact in terms of uncovering new biological knowledge might be explained in part by the limited amount of experimental data available to validate their statistical performances in a realistic GWAS context. In this paper, we introduce a simulation pipeline for generating real scale GWAS data, including epistasis and realistic LD structure. We evaluate five exhaustive bivariate interaction methods, fastepi, GBOOST, SHEsisEpi, DSS, and IndOR. Two hundred thirty four different disease scenarios are considered in extensive simulations. We report the performances of each method in terms of false positive rate control, power, area under the ROC curve (AUC), and computation time using a GPU. Finally we compare the result of each methods on a real GWAS of type 2 diabetes from the Welcome Trust Case Control Consortium. RESULTS GBOOST, SHEsisEpi and DSS allow a satisfactory control of the false positive rate. fastepi and IndOR present an increase in false positive rate in presence of LD between causal SNPs, with our definition of epistasis. DSS performs best in terms of power and AUC in most scenarios with no or weak LD between causal SNPs. All methods can exhaustively analyze a GWAS with 6.105 SNPs and 15,000 samples in a couple of hours using a GPU. CONCLUSION This study confirms that computation time is no longer a limiting factor for performing an exhaustive search of epistasis in large GWAS. For this task, using DSS on SNP pairs with limited LD seems to be a good strategy to achieve the best statistical performance. A combination approach using both DSS and GBOOST is supported by the simulation results and the analysis of the WTCCC dataset demonstrated that this approach can detect distinct genes in epistasis. Finally, weak epistasis between common variants will be detectable with existing methods when GWAS of a few tens of thousands cases and controls are available.
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Affiliation(s)
| | - Guillermo Durand
- Laboratoire de Probabilités et Modèles Aléatoires, Université Pierre et Marie Curie, 4, place Jussieu, Paris Cedex 05, 75252 France
| | - Vincent Thuillier
- SANOFI R&D, Biostatistics & Programming, Chilly Mazarin, 91385 France
| | - Franck Augé
- SANOFI R&D, Translational Sciences, Chilly Mazarin, 91385 France
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9
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Frånberg M, Strawbridge RJ, Hamsten A, de Faire U, Lagergren J, Sennblad B. Fast and general tests of genetic interaction for genome-wide association studies. PLoS Comput Biol 2017; 13:e1005556. [PMID: 28586362 PMCID: PMC5478145 DOI: 10.1371/journal.pcbi.1005556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2016] [Revised: 06/20/2017] [Accepted: 05/09/2017] [Indexed: 11/29/2022] Open
Abstract
A complex disease has, by definition, multiple genetic causes. In theory, these causes could be identified individually, but their identification will likely benefit from informed use of anticipated interactions between causes. In addition, characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease. Large-scale collaborative efforts are now paving the way for comprehensive studies of interaction. As a consequence, there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power. Another issue is that, currently, the relation between different methods for interaction inference is in many cases not transparent, complicating the comparison and interpretation of results between different interaction studies. In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models (GLMs). The tests can be applied for inference of single or multiple interaction parameters, but we show, by simulation, that jointly testing the full set of interaction parameters yields superior power and control of false positive rate. Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta-analysis. We investigate the impact of several assumptions commonly made when modeling interactions. We also show that, across the important class of models with a full set of interaction parameters, jointly testing the interaction parameters yields identical results. Further, we apply our method to genetic data for cardiovascular disease. This allowed us to identify a putative interaction involved in Lp(a) plasma levels between two ‘tag’ variants in the LPA locus (p = 2.42 ⋅ 10−09) as well as replicate the interaction (p = 6.97 ⋅ 10−07). Finally, our meta-analysis method is used in a small (N = 16,181) study of interactions in myocardial infarction. Interaction between organic molecules forms the basis of all biological systems. The availability of high-throughput genotyping and sequencing platforms enables us to cost-effectively genotype a large number of individuals. For sufficiently large datasets it is possible to reconstruct the genetic dependencies that underlie complex traits and diseases. However, there is a need for efficient statistical methodologies that can tackle the large sample size and computational resources required to study interaction. In this work we provide theory that reduces the required computational resources, and enable multiple research groups to effectively combine their results.
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Affiliation(s)
- Mattias Frånberg
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
- * E-mail:
| | - Rona J. Strawbridge
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
| | - Anders Hamsten
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
| | | | - Ulf de Faire
- Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Cardiology, Karolinska University Hospital, Stockholm, Sweden
| | - Jens Lagergren
- Science for Life Laboratory, Stockholm, Sweden
- The School of Computer Science and Communications, KTH Royal Institute of Technology, Stockholm, Sweden
- Swedish e-science Research Center (SeRC), Stockholm, Sweden
| | - Bengt Sennblad
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
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Dankowski T, Buck D, Andlauer TFM, Antony G, Bayas A, Bechmann L, Berthele A, Bettecken T, Chan A, Franke A, Gold R, Graetz C, Haas J, Hecker M, Herms S, Infante-Duarte C, Jöckel KH, Kieseier BC, Knier B, Knop M, Kümpfel T, Lichtner P, Lieb W, Lill CM, Limmroth V, Linker RA, Loleit V, Meuth SG, Moebus S, Müller-Myhsok B, Nischwitz S, Nöthen MM, Paul F, Pütz M, Ruck T, Salmen A, Stangel M, Stellmann JP, Strauch K, Stürner KH, Tackenberg B, Then Bergh F, Tumani H, Waldenberger M, Weber F, Wiendl H, Wildemann B, Zettl UK, Ziemann U, Zipp F, Hemmer B, Ziegler A. Successful Replication of GWAS Hits for Multiple Sclerosis in 10,000 Germans Using the Exome Array. Genet Epidemiol 2015; 39:601-8. [PMID: 26497834 DOI: 10.1002/gepi.21933] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Revised: 09/01/2015] [Accepted: 09/02/2015] [Indexed: 12/20/2022]
Abstract
Genome-wide association studies (GWAS) successfully identified various chromosomal regions to be associated with multiple sclerosis (MS). The primary aim of this study was to replicate reported associations from GWAS using an exome array in a large German study. German MS cases (n = 4,476) and German controls (n = 5,714) were genotyped using the Illumina HumanExome v1-Chip. Genotype calling was performed with the Illumina Genome Studio(TM) Genotyping Module, followed by zCall. Single-nucleotide polymorphisms (SNPs) in seven regions outside the human leukocyte antigen (HLA) region showed genome-wide significant associations with MS (P values < 5 × 10(-8) ). These associations have been reported previously. In addition, SNPs in three previously reported regions outside the HLA region yielded P values < 10(-5) . The effect of nine SNPs in the HLA region remained (P < 10(-5) ) after adjustment for other significant SNPs in the HLA region. All of these findings have been reported before or are driven by known risk loci. In summary, findings from previous GWAS for MS could be successfully replicated. We conclude that the regions identified in previous GWAS are also associated in the German population. This reassures the need for detailed investigations of the functional mechanisms underlying the replicated associations.
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Affiliation(s)
- Theresa Dankowski
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Dorothea Buck
- Department of Neurology, Technische Universität München, Munich, Germany
| | | | - Gisela Antony
- Central Information Office (CIO), Philipps University Marburg, Marburg, Germany
| | - Antonios Bayas
- Department of Neurology, Klinikum Augsburg, Augsburg, Germany
| | - Lukas Bechmann
- Department of Neurology, University of Leipzig, Leipzig, Germany.,Institute of Medical Microbiology, Otto-von-Guericke University, Magdeburg, Germany
| | - Achim Berthele
- Department of Neurology, Technische Universität München, Munich, Germany
| | | | - Andrew Chan
- Department of Neurology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology (IKMB), Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Ralf Gold
- Department of Neurology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Christiane Graetz
- Department of Neurology, University Medical Center Mainz, Mainz, Germany
| | - Jürgen Haas
- Department of Neurology, University Hospital Heidelberg, Heidelberg, Germany
| | - Michael Hecker
- Department of Neurology, University of Rostock, Rostock, Germany
| | - Stefan Herms
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany.,Division of Medical Genetics, University Hospital, Basel, Switzerland.,Human Genetics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
| | | | - Karl-Heinz Jöckel
- Institute of Medical Informatics, Biometry and Epidemiology, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Bernd C Kieseier
- Department of Neurology, Heinrich Heine University, Düsseldorf, Germany
| | - Benjamin Knier
- Department of Neurology, Technische Universität München, Munich, Germany
| | - Matthias Knop
- Department of Neurology, MPI of Psychiatry, Munich, Germany
| | - Tania Kümpfel
- Institute of Clinical Neuroimmunology, Ludwigs-Maximilians-Universität, Munich, Germany
| | - Peter Lichtner
- Institute of Human Genetics, Technische Universität München, Munich, Germany.,Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology and Biobank popgen, Christian-Albrechts-Universität Kiel, Kiel, Germany
| | - Christina M Lill
- Department of Neurology, University Medical Center Mainz, Mainz, Germany.,Platform for Genome Analytics, Institutes of Neurogenetics, & for Integrative and Experimental Genomics, University of Lübeck, Lübeck, Germany
| | - Volker Limmroth
- Department of Neurology, Hospital Köln-Merheim, Köln, Germany
| | - Ralf A Linker
- Department of Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Verena Loleit
- Department of Neurology, Technische Universität München, Munich, Germany
| | - Sven G Meuth
- Department für Neurologie, Klinik für Allgemeine Neurologie, Münster, Germany
| | - Susanne Moebus
- Institute of Medical Informatics, Biometry and Epidemiology, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | | | | | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Friedemann Paul
- Department of Neurology, Charité University Medicine Berlin, Berlin, Germany.,NeuroCure Clinical Research Center, Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Pütz
- Department of Neurology, Philipps-University of Marburg, Marburg, Germany
| | - Tobias Ruck
- Department für Neurologie, Klinik für Allgemeine Neurologie, Münster, Germany
| | - Anke Salmen
- Department of Neurology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Martin Stangel
- Clinical Neuroimmunology and Neurochemistry, Department of Neurology, Hannover Medical School, Hannover, Germany
| | - Jan-Patrick Stellmann
- Department of Neurology and, Institute of Neuroimmunology and MS (INIMS), University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
| | - Klarissa H Stürner
- Department of Neurology and, Institute of Neuroimmunology and MS (INIMS), University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Björn Tackenberg
- Department of Neurology, Philipps-University of Marburg, Marburg, Germany
| | - Florian Then Bergh
- Department of Neurology and Translational Center for Regenerative Medicine, University of Leipzig, Leipzig, Germany
| | | | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Frank Weber
- Department of Neurology, MPI of Psychiatry, Munich, Germany.,Neurological Clinic, Medical Park, Bad Camberg, Germany
| | - Heinz Wiendl
- Department für Neurologie, Klinik für Allgemeine Neurologie, Münster, Germany
| | - Brigitte Wildemann
- Department of Neurology, University Hospital Heidelberg, Heidelberg, Germany
| | - Uwe K Zettl
- Department of Neurology, University of Rostock, Rostock, Germany
| | - Ulf Ziemann
- Department of Neurology, University Hospital, Eberhard-Karls-Universität Tübingen, Tübingen, Germany
| | - Frauke Zipp
- Department of Neurology, University Medical Center Mainz, Mainz, Germany
| | - Bernhard Hemmer
- Department of Neurology, Technische Universität München, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Andreas Ziegler
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany.,Zentrum für Klinische Studien, Universität zu Lübeck, Lübeck, Germany.,School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa
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