Genetic Epidemiology最新文献

{"title":"Bayesian multivariant fine mapping using the Laplace prior","authors":"Kevin Walters,&nbsp;Hannuun Yaacob","doi":"10.1002/gepi.22517","DOIUrl":"10.1002/gepi.22517","url":null,"abstract":"<p>Currently, the only effect size prior that is routinely implemented in a Bayesian fine-mapping multi-single-nucleotide polymorphism (SNP) analysis is the Gaussian prior. Here, we show how the Laplace prior can be deployed in Bayesian multi-SNP fine mapping studies. We compare the ranking performance of the posterior inclusion probability (PIP) using a Laplace prior with the ranking performance of the corresponding Gaussian prior and FINEMAP. Our results indicate that, for the simulation scenarios we consider here, the Laplace prior can lead to higher PIPs than either the Gaussian prior or FINEMAP, particularly for moderately sized fine-mapping studies. The Laplace prior also appears to have better worst-case scenario properties. We reanalyse the iCOGS case–control data from the CASP8 region on Chromosome 2. Even though this study has a total sample size of nearly 90,000 individuals, there are still some differences in the top few ranked SNPs if the Laplace prior is used rather than the Gaussian prior. R code to implement the Laplace (and Gaussian) prior is available at https://github.com/Kevin-walters/lapmapr.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 3","pages":"249-260"},"PeriodicalIF":2.1,"publicationDate":"2023-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22517","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9120129","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":"Study of effect modifiers of genetically predicted CETP reduction","authors":"Marc-André Legault,&nbsp;Amina Barhdadi,&nbsp;Isabel Gamache,&nbsp;Audrey Lemaçon,&nbsp;Louis-Philippe Lemieux Perreault,&nbsp;Jean-Christophe Grenier,&nbsp;Marie-Pierre Sylvestre,&nbsp;Julie G. Hussin,&nbsp;David Rhainds,&nbsp;Jean-Claude Tardif,&nbsp;Marie-Pierre Dubé","doi":"10.1002/gepi.22514","DOIUrl":"10.1002/gepi.22514","url":null,"abstract":"<p>Genetic variants in drug targets can be used to predict the long-term, on-target effect of drugs. Here, we extend this principle to assess how sex and body mass index may modify the effect of genetically predicted lower CETP levels on biomarkers and cardiovascular outcomes. We found sex and body mass index (BMI) to be modifiers of the association between genetically predicted lower CETP and lipid biomarkers in UK Biobank participants. Female sex and lower BMI were associated with higher high-density lipoprotein cholesterol and lower low-density lipoprotein cholesterol for the same genetically predicted reduction in CETP concentration. We found that sex also modulated the effect of genetically lower CETP on cholesterol efflux capacity in samples from the Montreal Heart Institute Biobank. However, these modifying effects did not extend to sex differences in cardiovascular outcomes in our data. Our results provide insight into the clinical effects of CETP inhibitors in the presence of effect modification based on genetic data. The approach can support precision medicine applications and help assess the external validity of clinical trials.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 2","pages":"198-212"},"PeriodicalIF":2.1,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22514","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9406442","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":"Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies","authors":"Hongjing Xie,&nbsp;Xuewei Cao,&nbsp;Shuanglin Zhang,&nbsp;Qiuying Sha","doi":"10.1002/gepi.22513","DOIUrl":"10.1002/gepi.22513","url":null,"abstract":"<p>In genome-wide association studies (GWAS) for thousands of phenotypes in biobanks, most binary phenotypes have substantially fewer cases than controls. Many widely used approaches for joint analysis of multiple phenotypes produce inflated type I error rates for such extremely unbalanced case-control phenotypes. In this research, we develop a method to jointly analyze multiple unbalanced case-control phenotypes to circumvent this issue. We first group multiple phenotypes into different clusters based on a hierarchical clustering method, then we merge phenotypes in each cluster into a single phenotype. In each cluster, we use the saddlepoint approximation to estimate the <i>p</i> value of an association test between the merged phenotype and a single nucleotide polymorphism (SNP) which eliminates the issue of inflated type I error rate of the test for extremely unbalanced case-control phenotypes. Finally, we use the Cauchy combination method to obtain an integrated <i>p</i> value for all clusters to test the association between multiple phenotypes and a SNP. We use extensive simulation studies to evaluate the performance of the proposed approach. The results show that the proposed approach can control type I error rate very well and is more powerful than other available methods. We also apply the proposed approach to phenotypes in category IX (diseases of the circulatory system) in the UK Biobank. We find that the proposed approach can identify more significant SNPs than the other viable methods we compared with.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 2","pages":"185-197"},"PeriodicalIF":2.1,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9906667","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":"Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank","authors":"Zihuan Liu,&nbsp;Wei Dai,&nbsp;Shiying Wang,&nbsp;Yisha Yao,&nbsp;Heping Zhang","doi":"10.1002/gepi.22515","DOIUrl":"10.1002/gepi.22515","url":null,"abstract":"<p>Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). To reveal genetic determinants of susceptibility to COVID-19 related mortality, we train a deep learning model to identify groups of genetic variants and their interactions that contribute to the COVID-19 related mortality risk using the UK Biobank data (28,097 affected cases and 1656 deaths). We refer to such groups of variants as super variants. We identify 15 super variants with various levels of significance as susceptibility loci for COVID-19 mortality. Specifically, we identify a super variant (odds ratio [OR] = 1.594, <i>p</i> = 5.47 × 10⁻⁹) on Chromosome 7 that consists of the minor allele of rs76398985, rs6943608, rs2052130, 7:150989011_CT_C, rs118033050, and rs12540488. We also discover a super variant (OR = 1.353, <i>p</i> = 2.87 × 10⁻⁸) on Chromosome 5 that contains rs12517344, rs72733036, rs190052994, rs34723029, rs72734818, 5:9305797_GTA_G, and rs180899355.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 3","pages":"215-230"},"PeriodicalIF":2.1,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22515","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9184940","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":"Weak and pleiotropy robust sex-stratified Mendelian randomization in the one sample and two sample settings","authors":"Vasilios Karageorgiou,&nbsp;Jess Tyrrell,&nbsp;Trevelyan J. Mckinley,&nbsp;Jack Bowden","doi":"10.1002/gepi.22512","DOIUrl":"10.1002/gepi.22512","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Mendelian randomization (MR) leverages genetic data as an instrumental variable to provide estimates for the causal effect of an exposure <i>X</i> on a health outcome <i>Y</i> that is robust to confounding. Unfortunately, horizontal pleiotropy—the direct association of a genetic variant with multiple phenotypes—is highly prevalent and can easily render a genetic variant an invalid instrument.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Building on existing work, we propose a simple method for leveraging sex-specific genetic associations to perform weak and pleiotropy-robust MR analysis. This is achieved by constructing an MR estimator in which pleiotropy is perfectly removed by cancellation, while placing it within the powerful machinery of the robust adjusted profile score (MR-RAPS) method. Pleiotropy cancellation has the attractive property that it removes heterogeneity and therefore justifies a statistically efficient fixed effects model. We extend the method from the typical two-sample summary-data MR setting to the one-sample setting by adapting the technique of Collider-Correction. Simulation studies and applied examples are used to assess how the sex-stratified MR-RAPS estimator performs against other common approaches.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The sex-stratified MR-RAPS method is shown to be robust to pleiotropy even in cases where all genetic variants violated the standard Instrument Strength Independent of Direct Effect assumption. In some cases where the strength of the pleiotropic effect additionally varied by sex (and so perfect cancellation was not achieved), over-dispersed MR-RAPS implementations can still consistently estimate the true causal effect. In applied analyses, we investigate the causal effect of waist-hip ratio (WHR), an important marker of central obesity, on a range of downstream traits. While the conventional approaches suggested paradoxical links between WHR and height and body mass index, the sex-stratified approach obtained a more realistic null effect. Nonzero effects were also detected for systolic and diastolic blood pressure as well as high-density and low-density lipoprotein cholesterol.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>We provide a simple but attractive method for weak and pleiotropy robust causal estimation of sexually dimorphic traits on downstream outcomes, by combining several existing approaches in a novel fashion.</p>\u0000 </section>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 2","pages":"135-151"},"PeriodicalIF":2.1,"publicationDate":"2023-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22512","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10816122","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":"Improved two-step testing of genome-wide gene–environment interactions","authors":"Eric S. Kawaguchi,&nbsp;Andre E. Kim,&nbsp;Juan Pablo Lewinger,&nbsp;W. James Gauderman","doi":"10.1002/gepi.22509","DOIUrl":"10.1002/gepi.22509","url":null,"abstract":"<p>Two-step tests for gene–environment (<math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>G</mi>\u0000 \u0000 <mo>×</mo>\u0000 \u0000 <mi>E</mi>\u0000 </mrow>\u0000 <annotation> $Gtimes E$</annotation>\u0000 </semantics></math>) interactions exploit marginal single-nucleotide polymorphism (SNP) effects to improve the power of a genome-wide interaction scan. They combine a screening step based on marginal effects used to “bin” SNPs for weighted hypothesis testing in the second step to deliver greater power over single-step tests while preserving the genome-wide Type I error. However, the presence of many SNPs with detectable marginal effects on the trait of interest can reduce power by “displacing” true interactions with weaker marginal effects and by adding to the number of tests that need to be corrected for multiple testing. We introduce a new significance-based allocation into bins for Step-2 <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>G</mi>\u0000 \u0000 <mo>×</mo>\u0000 \u0000 <mi>E</mi>\u0000 </mrow>\u0000 <annotation> $Gtimes E$</annotation>\u0000 </semantics></math> testing that overcomes the displacement issue and propose a computationally efficient approach to account for multiple testing within bins. Simulation results demonstrate that these simple improvements can provide substantially greater power than current methods under several scenarios. An application to a multistudy collaboration for understanding colorectal cancer reveals a <i>G</i> × Sex interaction located near the SMAD7 gene.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 2","pages":"152-166"},"PeriodicalIF":2.1,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22509","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10811874","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":"Efficient identification of trait-associated loss-of-function variants in the UK Biobank cohort by exome-sequencing based genotype imputation","authors":"Wen-Yuan Yu,&nbsp;Shan-Shan Yan,&nbsp;Shu-Han Zhang,&nbsp;Jing-Jing Ni,&nbsp; Bin-Li,&nbsp;Yu-Fang Pei,&nbsp;Lei Zhang","doi":"10.1002/gepi.22511","DOIUrl":"10.1002/gepi.22511","url":null,"abstract":"<p>The large-scale open access whole-exome sequencing (WES) data of the UK Biobank ~200,000 participants is accelerating a new wave of genetic association studies aiming to identify rare and functional loss-of-function (LoF) variants associated with complex traits and diseases. We proposed to merge the WES genotypes and the genome-wide genotyping (GWAS) genotypes of 167,000 UKB homogeneous European participants into a combined reference panel, and then to impute 241,911 UKB homogeneous European participants who had the GWAS genotypes only. We then used the imputed data to replicate association identified in the discovery WES sample. The average imputation accuracy measure <i>r</i>² is modest to high for LoF variants at all minor allele frequency intervals: 0.942 at MAF interval (0.01, 0.5), 0.807 at (1.0 × 10⁻³, 0.01), 0.805 at (1.0 × 10⁻⁴, 1.0 × 10⁻³), 0.664 at (1.0 × 10⁻⁵, 1.0 × 10⁻⁴) and 0.410 at (0, 1.0 × 10⁻⁵). As applications, we studied associations of LoF variants with estimated heel BMD and four lipid traits. In addition to replicating dozens of previously reported genes, we also identified three novel associations, two genes <i>PLIN1</i> and <i>ANGPTL3</i> for high-density-lipoprotein cholesterol and one gene <i>PDE3B</i> for triglycerides. Our results highlighted the strength of WES based genotype imputation as well as provided useful imputed data within the UKB cohort.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 2","pages":"121-134"},"PeriodicalIF":2.1,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10854554","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":"Methods for large-scale single mediator hypothesis testing: Possible choices and comparisons","authors":"Jiacong Du,&nbsp;Xiang Zhou,&nbsp;Dylan Clark-Boucher,&nbsp;Wei Hao,&nbsp;Yongmei Liu,&nbsp;Jennifer A. Smith,&nbsp;Bhramar Mukherjee","doi":"10.1002/gepi.22510","DOIUrl":"10.1002/gepi.22510","url":null,"abstract":"&lt;p&gt;Mediation hypothesis testing for a large number of mediators is challenging due to the composite structure of the null hypothesis, &lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;H&lt;/mi&gt;\u0000 \u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 \u0000 &lt;mo&gt;:&lt;/mo&gt;\u0000 \u0000 &lt;mi&gt;α&lt;/mi&gt;\u0000 \u0000 &lt;mi&gt;β&lt;/mi&gt;\u0000 \u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 \u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${H}_{0}:alpha beta =0$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; (&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;α&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $alpha $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;: effect of the exposure on the mediator after adjusting for confounders; &lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;β&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $beta $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;: effect of the mediator on the outcome after adjusting for exposure and confounders). In this paper, we reviewed three classes of methods for large-scale one at a time mediation hypothesis testing. These methods are commonly used for continuous outcomes and continuous mediators assuming there is no exposure-mediator interaction so that the product &lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;α&lt;/mi&gt;\u0000 \u0000 &lt;mi&gt;β&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $alpha beta $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; has a causal interpretation as the indirect effect. The first class of methods ignores the impact of different structures under the composite null hypothesis, namely, (1) &lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;α&lt;/mi&gt;\u0000 \u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 \u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 \u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 \u0000 &lt;mi&gt;β&lt;/mi&gt;\u0000 \u0000 &lt;mo&gt;≠&lt;/mo&gt;\u0000 \u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $alpha =0,beta ne 0$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;; (2) &lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;α&lt;/mi&gt;\u0000 \u0000 &lt;mo&gt;≠&lt;/mo&gt;\u0000 \u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 \u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 \u0000 &lt;mi&gt;β&lt;/mi&gt;\u0000 \u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 \u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 ","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 2","pages":"167-184"},"PeriodicalIF":2.1,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22510","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9762740","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":"Adaptive Bayesian variable clustering via structural learning of breast cancer data","authors":"Riddhi Pratim Ghosh,&nbsp;Arnab K. Maity,&nbsp;Mohsen Pourahmadi,&nbsp;Bani K. Mallick","doi":"10.1002/gepi.22507","DOIUrl":"10.1002/gepi.22507","url":null,"abstract":"<p>The clustering of proteins is of interest in cancer cell biology. This article proposes a hierarchical Bayesian model for protein (variable) clustering hinging on correlation structure. Starting from a multivariate normal likelihood, we enforce the clustering through prior modeling using angle-based unconstrained reparameterization of correlations and assume a truncated Poisson distribution (to penalize a large number of clusters) as prior on the number of clusters. The posterior distributions of the parameters are not in explicit form and we use a reversible jump Markov chain Monte Carlo based technique is used to simulate the parameters from the posteriors. The end products of the proposed method are estimated cluster configuration of the proteins (variables) along with the number of clusters. The Bayesian method is flexible enough to cluster the proteins as well as estimate the number of clusters. The performance of the proposed method has been substantiated with extensive simulation studies and one protein expression data with a hereditary disposition in breast cancer where the proteins are coming from different pathways.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 1","pages":"95-104"},"PeriodicalIF":2.1,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10718634","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":"Multivariate analysis of a missense variant in CREBRF reveals associations with measures of adiposity in people of Polynesian ancestries","authors":"Jerry Z. Zhang,&nbsp;Lacey W. Heinsberg,&nbsp;Mohanraj Krishnan,&nbsp;Nicola L. Hawley,&nbsp;Tanya J. Major,&nbsp;Jenna C. Carlson,&nbsp;Jennie Harré Hindmarsh,&nbsp;Huti Watson,&nbsp;Muhammad Qasim,&nbsp;Lisa K. Stamp,&nbsp;Nicola Dalbeth,&nbsp;Rinki Murphy,&nbsp;Guangyun Sun,&nbsp;Hong Cheng,&nbsp;Take Naseri,&nbsp;Muagututi'a S. Reupena,&nbsp;Erin E. Kershaw,&nbsp;Ranjan Deka,&nbsp;Stephen T. McGarvey,&nbsp;Ryan L. Minster,&nbsp;Tony R. Merriman,&nbsp;Daniel E. Weeks","doi":"10.1002/gepi.22508","DOIUrl":"10.1002/gepi.22508","url":null,"abstract":"<p>The minor allele of rs373863828, a missense variant in CREB3 Regulatory Factor, is associated with several cardiometabolic phenotypes in Polynesian peoples. To better understand the variant, we tested the association of rs373863828 with a panel of correlated phenotypes (body mass index [BMI], weight, height, HDL cholesterol, triglycerides, and total cholesterol) using multivariate Bayesian association and network analyses in a Samoa cohort (<i>n</i> = 1632), Aotearoa New Zealand cohort (<i>n</i> = 1419), and combined cohort (<i>n</i> = 2976). An expanded set of phenotypes (adding estimated fat and fat-free mass, abdominal circumference, hip circumference, and abdominal-hip ratio) was tested in the Samoa cohort (<i>n</i> = 1496). In the Samoa cohort, we observed significant associations (log₁₀ Bayes Factor [BF] ≥ 5.0) between rs373863828 and the overall phenotype panel (8.81), weight (8.30), and BMI (6.42). In the Aotearoa New Zealand cohort, we observed suggestive associations (1.5 &lt; log₁₀BF &lt; 5) between rs373863828 and the overall phenotype panel (4.60), weight (3.27), and BMI (1.80). In the combined cohort, we observed concordant signals with larger log₁₀BFs. In the Samoa-specific expanded phenotype analyses, we also observed significant associations between rs373863828 and fat mass (5.65), abdominal circumference (5.34), and hip circumference (5.09). Bayesian networks provided evidence for a direct association of rs373863828 with weight and indirect associations with height and BMI.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 1","pages":"105-118"},"PeriodicalIF":2.1,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22508","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9162994","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}