Genetic Epidemiology最新文献

{"title":"Data-adaptive and pathway-based tests for association studies between somatic mutations and germline variations in human cancers","authors":"Zhongyuan Chen,&nbsp;Han Liang,&nbsp;Peng Wei","doi":"10.1002/gepi.22537","DOIUrl":"10.1002/gepi.22537","url":null,"abstract":"<p>Cancer is a disease driven by a combination of inherited genetic variants and somatic mutations. Recently available large-scale sequencing data of cancer genomes have provided an unprecedented opportunity to study the interactions between them. However, previous studies on this topic have been limited by simple, low statistical power tests such as Fisher's exact test. In this paper, we design data-adaptive and pathway-based tests based on the score statistic for association studies between somatic mutations and germline variations. Previous research has shown that two single-nucleotide polymorphism (SNP)-set-based association tests, adaptive sum of powered score (aSPU) and data-adaptive pathway-based (aSPUpath) tests, increase the power in genome-wide association studies (GWASs) with a single disease trait in a case–control study. We extend aSPU and aSPUpath to multi-traits, that is, somatic mutations of multiple genes in a cohort study, allowing extensive information aggregation at both SNP and gene levels. <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>p</mi>\u0000 </mrow>\u0000 <annotation> $p$</annotation>\u0000 </semantics></math>-values from different parameters assuming varying genetic architecture are combined to yield data-adaptive tests for somatic mutations and germline variations. Extensive simulations show that, in comparison with some commonly used methods, our data-adaptive somatic mutations/germline variations tests can be applied to multiple germline SNPs/genes/pathways, and generally have much higher statistical powers while maintaining the appropriate type I error. The proposed tests are applied to a large-scale real-world International Cancer Genome Consortium whole genome sequencing data set of 2583 subjects, detecting more significant and biologically relevant associations compared with the other existing methods on both gene and pathway levels. Our study has systematically identified the associations between various germline variations and somatic mutations across different cancer types, which potentially provides valuable utility for cancer risk prediction, prognosis, and therapeutics.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 8","pages":"617-636"},"PeriodicalIF":2.1,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41198905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ioSearch: An approach for identifying interacting multiomics biomarkers using a novel algorithm with application on breast cancer data sets","authors":"Sarmistha Das,&nbsp;Deo Kumar Srivastava","doi":"10.1002/gepi.22536","DOIUrl":"10.1002/gepi.22536","url":null,"abstract":"<p>Identification of biomarkers by integrating multiple omics together is important because complex diseases occur due to an intricate interplay of various genetic materials. Traditional single-omics association tests neither explore this crucial interomics dependence nor identify moderately weak signals due to the multiple-testing burden. Conversely, multiomics data integration imparts complementary information but suffers from an increased multiple-testing burden, data diversity inherent with different omics features, high-dimensionality, and so forth. Most of the available methods address subtype classification using dimension-reduction techniques to circumvent the sample size issue but interacting multiomics biomarker identification methods are unavailable. We propose a two-step model that first investigates phenotype-omics association using logistic regression. Then, selects disease-associated omics using sparse principal components which explores the interrelationship of multiple variables from two omics in a multivariate multiple regression framework. On the basis of this model, we developed a multiomics biomarker identification algorithm, interacting omics search (ioSearch), that jointly tests the effect of multiple omics with disease and between-omics associations by using pathway information that subsequently reduces the multiple-testing burden. Further, inference in terms of <i>p</i> values potentially makes it an easily interpretable biomarker identification tool. Extensive simulation demonstrates ioSearch as statistically powerful with a controlled Type-I error rate. Its application to publicly available breast cancer data sets identified relevant omics features in important pathways.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 8","pages":"600-616"},"PeriodicalIF":2.1,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41108946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical methods to detect mother–father genetic interaction effects on risk of infertility: A genome-wide approach","authors":"Siri N. Skodvin,&nbsp;Håkon K. Gjessing,&nbsp;Astanand Jugessur,&nbsp;Julia Romanowska,&nbsp;Christian M. Page,&nbsp;Elizabeth C. Corfield,&nbsp;Yunsung Lee,&nbsp;Siri E. Håberg,&nbsp;Miriam Gjerdevik","doi":"10.1002/gepi.22534","DOIUrl":"10.1002/gepi.22534","url":null,"abstract":"<p>Infertility is a heterogeneous phenotype, and for many couples, the causes of fertility problems remain unknown. One understudied hypothesis is that allelic interactions between the genotypes of the two parents may influence the risk of infertility. Our aim was, therefore, to investigate how allelic interactions can be modeled using parental genotype data linked to 15,789 pregnancies selected from the Norwegian Mother, Father, and Child Cohort Study. The newborns in 1304 of these pregnancies were conceived using assisted reproductive technologies (ART), and the remainder were conceived naturally. Treating the use of ART as a proxy for infertility, different parameterizations were implemented in a genome-wide screen for interaction effects between maternal and paternal alleles at the same locus. Some of the models were more similar in the way they were parameterized, and some produced similar results when implemented on a genome-wide scale. The results showed near-significant interaction effects in genes relevant to the phenotype under study, such as Dynein axonemal heavy chain 17 (<i>DNAH17</i>) with a recognized role in male infertility. More generally, the interaction models presented here are readily adaptable to the study of other phenotypes in which maternal and paternal allelic interactions are likely to be involved.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 7","pages":"503-519"},"PeriodicalIF":2.1,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22534","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10084980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inference of causal metabolite networks in the presence of invalid instrumental variables with GWAS summary data","authors":"Siyi Chen,&nbsp;Zhaotong Lin,&nbsp;Xiaotong Shen,&nbsp;Ling Li,&nbsp;Wei Pan","doi":"10.1002/gepi.22535","DOIUrl":"10.1002/gepi.22535","url":null,"abstract":"<p>We propose structural equation models (SEMs) as a general framework to infer causal networks for metabolites and other complex traits. Traditionally SEMs are used only for individual-level data under the assumption that all instrumental variables (IVs) are valid. To overcome these limitations, we propose both one- and two-sample approaches for causal network inference based on SEMs that can: (1) perform causal analysis and discover causal relationships among multiple traits; (2) account for the possible presence of some invalid IVs; (3) allow for data analysis using only genome-wide association studies (GWAS) summary statistics when individual-level data are not available; (4) consider the possibility of bidirectional relationships between traits. Our method employs a simple stepwise selection to identify invalid IVs, thus avoiding false positives while possibly increasing true discoveries based on two-stage least squares (2SLS). We use both real GWAS data and simulated data to demonstrate the superior performance of our method over the standard 2SLS/SEMs. For real data analysis, our proposed approach is applied to a human blood metabolite GWAS summary data set to uncover putative causal relationships among the metabolites; we also identify some metabolites (putative) causal to Alzheimer's disease (AD), which, along with the inferred causal metabolite network, suggest some possible pathways of metabolites involved in AD.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 8","pages":"585-599"},"PeriodicalIF":2.1,"publicationDate":"2023-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10158155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensitivity analyses gain relevance by fixing parameters observable during the empirical analyses","authors":"Gibran Hemani,&nbsp;Apostolos Gkatzionis,&nbsp;Kate Tilling,&nbsp;George Davey Smith","doi":"10.1002/gepi.22530","DOIUrl":"10.1002/gepi.22530","url":null,"abstract":"&lt;p&gt;In 2017 we presented the MR Steiger method, a sensitivity analysis in Mendelian randomization (MR) for inferring causal directions between variables (Hemani et al., &lt;span&gt;2017&lt;/span&gt;). We discussed many of its potential limitations including that unmeasured confounding under certain extreme circumstances could lead to the wrong inferred causal direction. Lutz et al. (&lt;span&gt;2022&lt;/span&gt;) propose an R package (UCRMS) for performing sensitivity analysis of the MR Steiger method, and use it in an illustration to suggest that the MR Steiger method has a ~90% chance of giving the wrong answer due to unmeasured confounding. In this note we will show that an error in their approach to sensitivity analysis leads to the wrong conclusion about the validity of the MR Steiger test. We provide a valid alternative which uses the observed data to investigate sensitivity to unmeasured confounding.&lt;/p&gt;&lt;p&gt;A sensitivity analysis aims to understand the degree to which a result can change due to uncertainties in the inputs (Saltelli, &lt;span&gt;2002&lt;/span&gt;). In this case for the MR Steiger test, we need to ask how sensitive is the inference of the causal direction between X and Y to possible values of unmeasured confounders influencing X and Y. Importantly, there is relative certainty in many of the parameters of this system because they are easily observed, for example, the variances of X, Y and the instrumental variables (IVs), the estimated effect of the IVs on X and Y, and therefore the IV estimate of the effect of X on Y. Often the ordinary least squares (OLS) association between X and Y is also available either due to the analysis being performed using individual level data, or by sourcing the estimate from other published results. Therefore, an appropriate sensitivity analysis must explore the extent to which the inferred causal direction between X and Y can change due to unmeasured confounding, without causing these observed parameters to change.&lt;/p&gt;&lt;p&gt;Lutz et al.'s proposed method does not attempt to fix all observable parameters. In the simple example provided by Lutz et al. the variance of Y varies between 28 and 39, and the OLS estimate varies between 1 and −1 across the parameter values used for the sensitivity analysis. This arises because the residual variance—which is unobserved—is fixed in their approach. Instead the phenotypic variance—which is observed—should be fixed. If they were presenting a simulation of the general performance of MR Steiger under unmeasured confounding then it would not matter that the simulated parameters are not tied to those observed in a particular empirical analysis. However in a sensitivity analysis, allowing observed parameters to vary provides no value to the analyst. To say that unmeasured confounding could reverse the causal direction, provided that the variance of Y also changes drastically, is of little use to the researcher who has a data set with an observed variance of Y. If some quantities are observed (i.e. the re","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 6","pages":"461-462"},"PeriodicalIF":2.1,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10001501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of regmed and BayesNetty for exploring causal models with many variables","authors":"Richard Howey,&nbsp;Heather J. Cordell","doi":"10.1002/gepi.22532","DOIUrl":"10.1002/gepi.22532","url":null,"abstract":"<p>Here we compare a recently proposed method and software package, <span>regmed</span>, with our own previously developed package, BayesNetty, designed to allow exploratory analysis of complex causal relationships between biological variables. We find that \u0000<span>regmed</span> generally has poorer recall but much better precision than BayesNetty. This is perhaps not too surprising as \u0000<span>regmed</span> is specifically designed for use with high-dimensional data. BayesNetty is found to be more sensitive to the resulting multiple testing problem encountered in these circumstances. However, as \u0000<span>regmed</span> is not designed to handle missing data, its performance is severely affected when missing data is present, whereas the performance of BayesNetty is only slightly affected. The performance of \u0000<span>regmed</span> can be rescued in this situation by first using BayesNetty to impute the missing data, and then applying \u0000<span>regmed</span> to the resulting “filled-in” data set.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 7","pages":"496-502"},"PeriodicalIF":2.1,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22532","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9689871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A gene-based association test of interactions for maternal–fetal genotypes identifies genes associated with nonsyndromic congenital heart defects","authors":"Manyan Huang,&nbsp;Chen Lyu,&nbsp;Nianjun Liu,&nbsp;Wendy N. Nembhard,&nbsp;John S. Witte,&nbsp;Charlotte A. Hobbs,&nbsp;Ming Li,&nbsp;the National Birth Defects Prevention Study","doi":"10.1002/gepi.22533","DOIUrl":"10.1002/gepi.22533","url":null,"abstract":"<p>The risk of congenital heart defects (CHDs) may be influenced by maternal genes, fetal genes, and their interactions. Existing methods commonly test the effects of maternal and fetal variants one-at-a-time and may have reduced statistical power to detect genetic variants with low minor allele frequencies. In this article, we propose a gene-based association test of interactions for maternal–fetal genotypes (GATI-MFG) using a case-mother and control-mother design. GATI-MFG can integrate the effects of multiple variants within a gene or genomic region and evaluate the joint effect of maternal and fetal genotypes while allowing for their interactions. In simulation studies, GATI-MFG had improved statistical power over alternative methods, such as the single-variant test and functional data analysis (FDA) under various disease scenarios. We further applied GATI-MFG to a two-phase genome-wide association study of CHDs for the testing of both common variants and rare variants using 947 CHD case mother–infant pairs and 1306 control mother–infant pairs from the National Birth Defects Prevention Study (NBDPS). After Bonferroni adjustment for 23,035 genes, two genes on chromosome 17, <i>TMEM107</i> (<i>p</i> = 1.64e−06) and <i>CTC1</i> (<i>p</i> = 2.0e−06), were identified for significant association with CHD in common variants analysis. Gene <i>TMEM107</i> regulates ciliogenesis and ciliary protein composition and was found to be associated with heterotaxy. Gene <i>CTC1</i> plays an essential role in protecting telomeres from degradation, which was suggested to be associated with cardiogenesis. Overall, GATI-MFG outperformed the single-variant test and FDA in the simulations, and the results of application to NBDPS samples are consistent with existing literature supporting the association of <i>TMEM107</i> and <i>CTC1</i> with CHDs.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 7","pages":"475-495"},"PeriodicalIF":2.1,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22533","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9669966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phenotypic variance partitioning by transcriptomic gene expression levels and environmental variables for anthropometric traits using GTEx data","authors":"Pastor Jullian Fabres,&nbsp;S. Hong Lee","doi":"10.1002/gepi.22531","DOIUrl":"10.1002/gepi.22531","url":null,"abstract":"<p>Phenotypic variation in human is the results of genetic variation and environmental influences. Understanding the contribution of genetic and environmental components to phenotypic variation is of great interest. The variance explained by genome-wide single nucleotide polymorphisms (SNPs) typically represents a small proportion of the phenotypic variance for complex traits, which may be because the genome is only a part of the whole biological process to shape the phenotypes. In this study, we propose to partition the phenotypic variance of three anthropometric traits, using gene expression levels and environmental variables from GTEx data. We use the gene expression of four tissues that are deemed relevant for the anthropometric traits (two adipose tissues, skeletal muscle tissue and blood tissue). Additionally, we estimate the transcriptome–environment correlation that partly underlies the phenotypes of the anthropometric traits. We found that genetic factors play a significant role in determining body mass index (BMI), with the proportion of phenotypic variance explained by gene expression levels of visceral adipose tissue being 0.68 (SE = 0.06). However, we also observed that environmental factors such as age, sex, ancestry, smoking status, and drinking alcohol status have a small but significant impact (0.005, SE = 0.001). Interestingly, we found a significant negative correlation between the transcriptomic and environmental effects on BMI (transcriptome–environment correlation = −0.54, SE = 0.14), suggesting an antagonistic relationship. This implies that individuals with lower genetic profiles may be more susceptible to the effects of environmental factors on BMI, while those with higher genetic profiles may be less susceptible. We also show that the estimated transcriptomic variance varies across tissues, e.g., the gene expression levels of whole blood tissue and environmental variables explain a lower proportion of BMI phenotypic variance (0.16, SE = 0.05 and 0.04, SE = 0.004 respectively). We observed a significant positive correlation between transcriptomic and environmental effects (1.21, SE = 0.23) for this tissue. In conclusion, phenotypic variance partitioning can be done using gene expression and environmental data even with a small sample size (<i>n</i> = 838 from GTEx data), which can provide insights into how the transcriptomic and environmental effects contribute to the phenotypes of the anthropometric traits.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 7","pages":"465-474"},"PeriodicalIF":2.1,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22531","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9687294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ravages: An R package for the simulation and analysis of rare variants in multicategory phenotypes","authors":"Ozvan Bocher,&nbsp;Gaëlle Marenne,&nbsp;Emmanuelle Génin,&nbsp;Hervé Perdry","doi":"10.1002/gepi.22529","DOIUrl":"10.1002/gepi.22529","url":null,"abstract":"<p>Current software packages for the analysis and the simulations of rare variants are only available for binary and continuous traits. Ravages provides solutions in a single R package to perform rare variant association tests for multicategory, binary and continuous phenotypes, to simulate datasets under different scenarios and to compute statistical power. Association tests can be run in the whole genome thanks to C++ implementation of most of the functions, using either RAVA-FIRST, a recently developed strategy to filter and analyse genome-wide rare variants, or user-defined candidate regions. Ravages also includes a simulation module that generates genetic data for cases who can be stratified into several subgroups and for controls. Through comparisons with existing programmes, we show that Ravages complements existing tools and will be useful to study the genetic architecture of complex diseases. Ravages is available on the CRAN at https://cran.r-project.org/web/packages/Ravages/ and maintained on Github at https://github.com/genostats/Ravages.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 6","pages":"450-460"},"PeriodicalIF":2.1,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22529","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10385156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Brief History behind the journal Genetic Epidemiology and the International Genetic Epidemiology Society","authors":"Dabeeru C. Rao","doi":"10.1002/gepi.22528","DOIUrl":"10.1002/gepi.22528","url":null,"abstract":"<p>This commentary briefly describes the process and steps that underlie the launching of the journal Genetic Epidemiology in 1984 and the International Genetic Epidemiology Society (IGES, to be pronounced as “I guess”) in 1992.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 5","pages":"361-364"},"PeriodicalIF":2.1,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9673429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}