1
|
Wu Y, Hao X, Zhu K, Zheng C, Guan F, Zeng P, Wang T. Long-term adverse influence of smoking during pregnancy on height and body size of offspring at ten years old in the UK Biobank cohort. SSM Popul Health 2023; 24:101506. [PMID: 37692834 PMCID: PMC10492214 DOI: 10.1016/j.ssmph.2023.101506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 09/12/2023] Open
Abstract
Background To explore the long-term relationship between maternal smoking during pregnancy and early childhood growth in the UK Biobank cohort. Methods To estimate the effect of maternal smoking during pregnancy on offspring height and body size at ten years old, we performed binary logistic analyses and reported odds ratios (OR) as well as 95% confidence intervals (95%CIs). We also implemented the cross-contextual comparison study to examine whether such influence could be repeatedly observed among three different ethnicities in the UK Biobank cohort (n = 22,140 for White, n = 7094 for South Asian, and n = 5000 for Black). In particular, we conducted the sibling cohort study in White sibling cohort (n = 9953 for height and n = 7239 for body size) to control for unmeasured familial confounders. Results We discovered that children whose mothers smoked during pregnancy had greater risk of being shorter or plumper at age ten in the full UK Biobank White cohort, with 15.3% (95% CIs: 13.0%∼17.7%) higher risk for height and 32.4% (95%CIs: 29.5%∼35.4%) larger risk for body size. Similar associations were identified in the South Asian and Black ethnicities. These associations were robust and remained significant in the White sibling cohort (12.6% [95%CIs: 5.0%∼20.3%] for height and 36.1% [95%CIs: 26.3%∼45.9%] for body size) after controlling for family factors. Conclusion This study robustly confirms that maternal smoking during pregnancy can promote height deficit and obesity for offspring at ten years old. Our findings strongly encourage mothers to quit smoking during pregnancy for improving growth and development of offspring.
Collapse
Affiliation(s)
- Yuxuan Wu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Xingjie Hao
- Department of Biostatistics and Epidemiology, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Kexuan Zhu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Chu Zheng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Fengjun Guan
- Department of Pediatrics, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| |
Collapse
|
2
|
Xu H, Shao Z, Zhang S, Liu X, Zeng P. How can childhood maltreatment affect post-traumatic stress disorder in adult: Results from a composite null hypothesis perspective of mediation analysis. Front Psychiatry 2023; 14:1102811. [PMID: 36970281 PMCID: PMC10033829 DOI: 10.3389/fpsyt.2023.1102811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 02/20/2023] [Indexed: 03/11/2023] Open
Abstract
BackgroundA greatly growing body of literature has revealed the mediating role of DNA methylation in the influence path from childhood maltreatment to psychiatric disorders such as post-traumatic stress disorder (PTSD) in adult. However, the statistical method is challenging and powerful mediation analyses regarding this issue are lacking.MethodsTo study how the maltreatment in childhood alters long-lasting DNA methylation changes which further affect PTSD in adult, we here carried out a gene-based mediation analysis from a perspective of composite null hypothesis in the Grady Trauma Project (352 participants and 16,565 genes) with childhood maltreatment as exposure, multiple DNA methylation sites as mediators, and PTSD or its relevant scores as outcome. We effectively addressed the challenging issue of gene-based mediation analysis by taking its composite null hypothesis testing nature into consideration and fitting a weighted test statistic.ResultsWe discovered that childhood maltreatment could substantially affected PTSD or PTSD-related scores, and that childhood maltreatment was associated with DNA methylation which further had significant roles in PTSD and these scores. Furthermore, using the proposed mediation method, we identified multiple genes within which DNA methylation sites exhibited mediating roles in the influence path from childhood maltreatment to PTSD-relevant scores in adult, with 13 for Beck Depression Inventory and 6 for modified PTSD Symptom Scale, respectively.ConclusionOur results have the potential to confer meaningful insights into the biological mechanism for the impact of early adverse experience on adult diseases; and our proposed mediation methods can be applied to other similar analysis settings.
Collapse
Affiliation(s)
- Haibo Xu
- Center for Mental Health Education and Research, Xuzhou Medical University, Xuzhou, China
- School of Management, Xuzhou Medical University, Xuzhou, China
- *Correspondence: Haibo Xu,
| | - Zhonghe Shao
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuo Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Xin Liu
- Center for Mental Health Education and Research, Xuzhou Medical University, Xuzhou, China
- School of Management, Xuzhou Medical University, Xuzhou, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Ping Zeng,
| |
Collapse
|
3
|
Shao Z, Wang T, Qiao J, Zhang Y, Huang S, Zeng P. A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies. BMC Bioinformatics 2022; 23:359. [PMID: 36042399 PMCID: PMC9429742 DOI: 10.1186/s12859-022-04897-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/22/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Multilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods. RESULTS We herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow. CONCLUSION In conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at https://github.com/biostatpzeng/ .
Collapse
Affiliation(s)
- Zhonghe Shao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Jiahao Qiao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Yuchen Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shuiping Huang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
| |
Collapse
|
4
|
Wang T, Qiao J, Zhang S, Wei Y, Zeng P. Simultaneous test and estimation of total genetic effect in eQTL integrative analysis through mixed models. Brief Bioinform 2022; 23:6535679. [PMID: 35212359 DOI: 10.1093/bib/bbac038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 01/22/2022] [Accepted: 02/07/2021] [Indexed: 11/14/2022] Open
Abstract
Integration of expression quantitative trait loci (eQTL) into genome-wide association studies (GWASs) is a promising manner to reveal functional roles of associated single-nucleotide polymorphisms (SNPs) in complex phenotypes and has become an active research field in post-GWAS era. However, how to efficiently incorporate eQTL mapping study into GWAS for prioritization of causal genes remains elusive. We herein proposed a novel method termed as Mixed transcriptome-wide association studies (TWAS) and mediated Variance estimation (MTV) by modeling the effects of cis-SNPs of a gene as a function of eQTL. MTV formulates the integrative method and TWAS within a unified framework via mixed models and therefore includes many prior methods/tests as special cases. We further justified MTV from another two statistical perspectives of mediation analysis and two-stage Mendelian randomization. Relative to existing methods, MTV is superior for pronounced features including the processing of direct effects of cis-SNPs on phenotypes, the powerful likelihood ratio test for assessment of joint effects of cis-SNPs and genetically regulated gene expression (GReX), two useful quantities to measure relative genetic contributions of GReX and cis-SNPs to phenotypic variance, and the computationally efferent parameter expansion expectation maximum algorithm. With extensive simulations, we identified that MTV correctly controlled the type I error in joint evaluation of the total genetic effect and proved more powerful to discover true association signals across various scenarios compared to existing methods. We finally applied MTV to 41 complex traits/diseases available from three GWASs and discovered many new associated genes that had otherwise been missed by existing methods. We also revealed that a small but substantial fraction of phenotypic variation was mediated by GReX. Overall, MTV constructs a robust and realistic modeling foundation for integrative omics analysis and has the advantage of offering more attractive biological interpretations of GWAS results.
Collapse
Affiliation(s)
- Ting Wang
- Department of Biostatistics at Xuzhou Medical University, China
| | - Jiahao Qiao
- Department of Biostatistics at Xuzhou Medical University, China
| | - Shuo Zhang
- Department of Biostatistics at Xuzhou Medical University, China
| | - Yongyue Wei
- Department of Biostatistics at Nanjing Medical University, China
| | - Ping Zeng
- Department of Biostatistics, Center for Medical Statistics and Data Analysis and Key Laboratory of Human Genetics and Environmental Medicine at Xuzhou Medical University, China
| |
Collapse
|
5
|
Shao Z, Wang T, Zhang M, Jiang Z, Huang S, Zeng P. IUSMMT: Survival mediation analysis of gene expression with multiple DNA methylation exposures and its application to cancers of TCGA. PLoS Comput Biol 2021; 17:e1009250. [PMID: 34464378 PMCID: PMC8437300 DOI: 10.1371/journal.pcbi.1009250] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 09/13/2021] [Accepted: 07/06/2021] [Indexed: 02/07/2023] Open
Abstract
Effective and powerful survival mediation models are currently lacking. To partly fill such knowledge gap, we particularly focus on the mediation analysis that includes multiple DNA methylations acting as exposures, one gene expression as the mediator and one survival time as the outcome. We proposed IUSMMT (intersection-union survival mixture-adjusted mediation test) to effectively examine the existence of mediation effect by fitting an empirical three-component mixture null distribution. With extensive simulation studies, we demonstrated the advantage of IUSMMT over existing methods. We applied IUSMMT to ten TCGA cancers and identified multiple genes that exhibited mediating effects. We further revealed that most of the identified regions, in which genes behaved as active mediators, were cancer type-specific and exhibited a full mediation from DNA methylation CpG sites to the survival risk of various types of cancers. Overall, IUSMMT represents an effective and powerful alternative for survival mediation analysis; our results also provide new insights into the functional role of DNA methylation and gene expression in cancer progression/prognosis and demonstrate potential therapeutic targets for future clinical practice.
Collapse
Affiliation(s)
- Zhonghe Shao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Meng Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Zhou Jiang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Shuiping Huang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China
| |
Collapse
|
6
|
Lu H, Zhang J, Jiang Z, Zhang M, Wang T, Zhao H, Zeng P. Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics. Front Genet 2021; 12:656545. [PMID: 33815486 PMCID: PMC8012913 DOI: 10.3389/fgene.2021.656545] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 03/01/2021] [Indexed: 01/04/2023] Open
Abstract
Background Clinical and epidemiological studies have suggested systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) are comorbidities and common genetic etiologies can partly explain such coexistence. However, shared genetic determinations underlying the two diseases remain largely unknown. Methods Our analysis relied on summary statistics available from genome-wide association studies of SLE (N = 23,210) and RA (N = 58,284). We first evaluated the genetic correlation between RA and SLE through the linkage disequilibrium score regression (LDSC). Then, we performed a multiple-tissue eQTL (expression quantitative trait loci) weighted integrative analysis for each of the two diseases and aggregated association evidence across these tissues via the recently proposed harmonic mean P-value (HMP) combination strategy, which can produce a single well-calibrated P-value for correlated test statistics. Afterwards, we conducted the pleiotropy-informed association using conjunction conditional FDR (ccFDR) to identify potential pleiotropic genes associated with both RA and SLE. Results We found there existed a significant positive genetic correlation (rg = 0.404, P = 6.01E-10) via LDSC between RA and SLE. Based on the multiple-tissue eQTL weighted integrative analysis and the HMP combination across various tissues, we discovered 14 potential pleiotropic genes by ccFDR, among which four were likely newly novel genes (i.e., INPP5B, OR5K2, RP11-2C24.5, and CTD-3105H18.4). The SNP effect sizes of these pleiotropic genes were typically positively dependent, with an average correlation of 0.579. Functionally, these genes were implicated in multiple auto-immune relevant pathways such as inositol phosphate metabolic process, membrane and glucagon signaling pathway. Conclusion This study reveals common genetic components between RA and SLE and provides candidate associated loci for understanding of molecular mechanism underlying the comorbidity of the two diseases.
Collapse
Affiliation(s)
- Haojie Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Jinhui Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Zhou Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Meng Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Ting Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.,Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Huashuo Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.,Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Ping Zeng
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.,Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, China
| |
Collapse
|
7
|
Zhang Z, Braun TM, Peterson KE, Hu H, Téllez-Rojo MM, Sánchez BN. Extending Tests of Random Effects to Assess for Measurement Invariance in Factor Models. STATISTICS IN BIOSCIENCES 2019; 10:634-650. [PMID: 30805035 DOI: 10.1007/s12561-018-9222-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Factor analysis models are widely used in health research to summarize hard to measure predictor or outcome variable constructs. For example, in the ELEMENT study, factor models are used to summarize lead exposure biomarkers which are thought to indirectly measure prenatal exposure to lead. Classic latent factor models are fitted assuming that factor loadings are constant across all covariate levels (e.g., maternal age in ELEMENT); that is, measurement invariance (MI) is assumed. When the MI is not met, measurement bias is introduced. Traditionally, MI is examined by defining subgroups of the data based on covariates, fitting multi-group factor analysis, and testing differences in factor loadings across covariate groups. In this paper, we develop novel tests of measurement invariance by modeling the factor loadings as varying coeffcients, i.e., letting the factor loading vary across continuous covariate values instead of groups. These varying coeffcients are estimated using penalized splines, where spline coeffcients are penalized by treating them as random coeffcients. The test of MI is then carried out by conducting a likelihood ratio test for the null hypothesis that the variance of the random spline coeffcients equals zero. We use a Monte-Carlo EM algorithm for estimation, and obtain the likelihood using Monte-Carlo in tegration. Using simulations, we compare the Type I error and power of our testing approach and the multi-group testing method. We apply the proposed methods to to summarize data on prenatal biomarkers of lead exposure from the ELEMENT study and find violations of MI due to maternal age.
Collapse
Affiliation(s)
- Zhenzhen Zhang
- Department of Biostatistics, University of Michigan, Ann Arbor, U.S.A
| | - Thomas M Braun
- Department of Biostatistics, University of Michigan, Ann Arbor, U.S.A
| | - Karen E Peterson
- Department of Nutritional Sciences, University of Michigan, Ann Arbor, U.S.A
| | - Howard Hu
- Department of Environmental Health Sciences, University of Washington
| | - Martha M Téllez-Rojo
- Center for Nutrition and Health Research, National Institute of Public Health, Cuernavaca, Mexico
| | - Brisa N Sánchez
- Department of Biostatistics, University of Michigan, Ann Arbor, U.S.A
| |
Collapse
|
8
|
Schweiger R, Fisher E, Weissbrod O, Rahmani E, Müller-Nurasyid M, Kunze S, Gieger C, Waldenberger M, Rosset S, Halperin E. Detecting heritable phenotypes without a model using fast permutation testing for heritability and set-tests. Nat Commun 2018; 9:4919. [PMID: 30464216 PMCID: PMC6249264 DOI: 10.1038/s41467-018-07276-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 10/26/2018] [Indexed: 01/08/2023] Open
Abstract
Testing for association between a set of genetic markers and a phenotype is a fundamental task in genetic studies. Standard approaches for heritability and set testing strongly rely on parametric models that make specific assumptions regarding phenotypic variability. Here, we show that resulting p-values may be inflated by up to 15 orders of magnitude, in a heritability study of methylation measurements, and in a heritability and expression quantitative trait loci analysis of gene expression profiles. We propose FEATHER, a method for fast permutation-based testing of marker sets and of heritability, which properly controls for false-positive results. FEATHER eliminated 47% of methylation sites found to be heritable by the parametric test, suggesting a substantial inflation of false-positive findings by alternative methods. Our approach can rapidly identify heritable phenotypes out of millions of phenotypes acquired via high-throughput technologies, does not suffer from model misspecification and is highly efficient. Standard approaches for heritability and set testing in statistical genetics rely on parametric models that might not hold in reality and give inflated p-values. Here, the authors develop a fast method for permutation-based testing of marker sets and of heritability that does not suffer from model misspecification.
Collapse
Affiliation(s)
- Regev Schweiger
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 6997801, Israel.
| | - Eyal Fisher
- School of Mathematical Sciences, Department of Statistics, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Omer Weissbrod
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA
| | - Elior Rahmani
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, 85764, Germany.,Department of Medicine I, Ludwig-Maximilians-Universität, Munich, 80539, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, 80636, Germany
| | - Sonja Kunze
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764, Neuherberg, Germany.,Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Christian Gieger
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764, Neuherberg, Germany.,Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Melanie Waldenberger
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, 80636, Germany.,Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764, Neuherberg, Germany.,Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Saharon Rosset
- School of Mathematical Sciences, Department of Statistics, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Eran Halperin
- Los Angeles, University of California Los Angeles, Los Angeles, 90095, CA, USA.,Department of Anesthesiology and Perioperative Medicine, University of California, Los Angeles, 90095, CA, USA
| |
Collapse
|
9
|
Chernyavskiy P, Little MP, Rosenberg PS. A unified approach for assessing heterogeneity in age-period-cohort model parameters using random effects. Stat Methods Med Res 2017; 28:20-34. [PMID: 28589750 DOI: 10.1177/0962280217713033] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Age-period-cohort models are a popular tool for studying population-level rates; for example, trends in cancer incidence and mortality. Age-period-cohort models decompose observed trends into age effects that correlate with natural history, period effects that reveal factors impacting all ages simultaneously (e.g. innovations in screening), and birth cohort effects that reflect differential risk exposures that vary across birth years. Methodology for the analysis of multiple population strata (e.g. ethnicity, cancer registry) within the age-period-cohort framework has not been thoroughly investigated. Here, we outline a general model for characterizing differences in age-period-cohort model parameters for a potentially large number of strata. Our model incorporates stratum-specific random effects for the intercept, the longitudinal age trend, and the model-based estimate of annual percent change (net drift), thereby enabling a comprehensive analysis of heterogeneity. We also extend the standard model to include quadratic terms for age, period, and cohort, along with the corresponding random effects, which quantify possible stratum-specific departures from global curvature. We illustrate the utility of our model with an application to metastatic prostate cancer incidence (2004-2013) in non-Hispanic white and black men, using 17 population-based cancer registries in the Surveillance, Epidemiology, and End Results Program.
Collapse
Affiliation(s)
- Pavel Chernyavskiy
- 1 Radiation Epidemiology Branch, National Cancer Institute, USA.,2 Biostatistics Branch, National Cancer Institute, USA
| | - Mark P Little
- 1 Radiation Epidemiology Branch, National Cancer Institute, USA
| | | |
Collapse
|
10
|
Zhou JJ, Hu T, Qiao D, Cho MH, Zhou H. Boosting Gene Mapping Power and Efficiency with Efficient Exact Variance Component Tests of Single Nucleotide Polymorphism Sets. Genetics 2016; 204:921-931. [PMID: 27646141 PMCID: PMC5105869 DOI: 10.1534/genetics.116.190454] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Accepted: 09/07/2016] [Indexed: 11/18/2022] Open
Abstract
Single nucleotide polymorphism (SNP) set tests have been a powerful method in analyzing next-generation sequencing (NGS) data. The popular sequence kernel association test (SKAT) method tests a set of variants as random effects in the linear mixed model setting. Its P-value is calculated based on asymptotic theory that requires a large sample size. Therefore, it is known that SKAT is conservative and can lose power at small or moderate sample sizes. Given the current cost of sequencing technology, scales of NGS are still limited. In this report, we derive and implement computationally efficient, exact (nonasymptotic) score (eScore), likelihood ratio (eLRT), and restricted likelihood ratio (eRLRT) tests, ExactVCTest, that can achieve high power even when sample sizes are small. We perform simulation studies under various genetic scenarios. Our ExactVCTest (i.e., eScore, eLRT, eRLRT) exhibits well-controlled type I error. Under the alternative model, eScore P-values are universally smaller than those from SKAT. eLRT and eRLRT demonstrate significantly higher power than eScore, SKAT, and SKAT optimal (SKAT-o) across all scenarios and various samples sizes. We applied these tests to an exome sequencing study. Our findings replicate previous results and shed light on rare variant effects within genes. The software package is implemented in the open source, high-performance technical computing language Julia, and is freely available at https://github.com/Tao-Hu/VarianceComponentTest.jl Analysis of each trait in the exome sequencing data set with 399 individuals and 16,619 genes takes around 1 min on a desktop computer.
Collapse
Affiliation(s)
- Jin J Zhou
- Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona 85724
| | - Tao Hu
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695
- Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695
| | - Dandi Qiao
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115
| | - Hua Zhou
- Department of Biostatistics, University of California, Los Angeles, California 90095
| |
Collapse
|