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Improved two-step testing of genome-wide gene–environment interactions 改进了全基因组基因-环境相互作用的两步检测
IF 2.1 4区 医学 Q3 GENETICS & HEREDITY Pub Date : 2022-12-26 DOI: 10.1002/gepi.22509
Eric S. Kawaguchi, Andre E. Kim, Juan Pablo Lewinger, W. James Gauderman

Two-step tests for gene–environment (G� � ×� � E $Gtimes E$) 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 G� � ×� � E $Gtimes E$ 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 G × Sex interaction located near the SMAD7 gene.

基因-环境(G × E$ G乘以E$)相互作用的两步测试利用边际单核苷酸多态性(SNP)效应来提高全基因组相互作用扫描的能力。他们在第二步中结合了基于边际效应的筛选步骤,用于“bin”snp加权假设检验,以提供比单步检验更大的能力,同时保留全基因组I型误差。然而,许多snp的存在对感兴趣的性状具有可检测到的边际效应,可以通过用较弱的边际效应"取代"真正的相互作用,以及通过增加需要为多次测试纠正的测试数量,从而降低功率。我们在步骤2 G × E$ G × E$测试中引入了一种新的基于显著性的分配方法,克服了位移问题,并提出了一种计算效率高的方法来解释箱内的多个测试。仿真结果表明,在几种情况下,这些简单的改进可以提供比当前方法更大的功率。一项用于了解结直肠癌的多研究合作应用揭示了位于SMAD7基因附近的G × Sex相互作用。
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引用次数: 1
Efficient identification of trait-associated loss-of-function variants in the UK Biobank cohort by exome-sequencing based genotype imputation 通过基于外显子组测序的基因型插补,有效识别英国生物银行队列中与性状相关的功能丧失变异
IF 2.1 4区 医学 Q3 GENETICS & HEREDITY Pub Date : 2022-12-09 DOI: 10.1002/gepi.22511
Wen-Yuan Yu, Shan-Shan Yan, Shu-Han Zhang, Jing-Jing Ni,  Bin-Li, Yu-Fang Pei, Lei Zhang

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 r2 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 PLIN1 and ANGPTL3 for high-density-lipoprotein cholesterol and one gene PDE3B for triglycerides. Our results highlighted the strength of WES based genotype imputation as well as provided useful imputed data within the UKB cohort.

英国生物银行(UK Biobank)约20万参与者的大规模开放获取全外显子组测序(WES)数据正在加速新一轮的遗传关联研究,旨在识别与复杂性状和疾病相关的罕见和功能性功能丧失(LoF)变异。我们建议将167,000名UKB同质欧洲参与者的WES基因型和GWAS基因型合并为一个联合参考面板,然后推算出241,911名只有GWAS基因型的UKB同质欧洲参与者。然后,我们使用输入的数据来复制发现WES样本中确定的关联。在所有次要等位基因频率区间,LoF变异的平均输入精度测量r2从中等到高:在MAF区间(0.01,0.5)0.942,在(1.0 × 10−3,0.01)0.807,在(1.0 × 10−4,1.0 × 10−3)0.805,在(1.0 × 10−5,1.0 × 10−4)0.664,在(0,1.0 × 10−5)0.410。作为应用,我们研究了LoF变异与估计的足跟骨密度和四种脂质性状的关系。除了复制数十个先前报道的基因外,我们还发现了三个新的关联,两个基因PLIN1和ANGPTL3与高密度脂蛋白胆固醇有关,一个基因PDE3B与甘油三酯有关。我们的结果强调了基于WES的基因型估算的强度,并在UKB队列中提供了有用的估算数据。
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引用次数: 2
Methods for large-scale single mediator hypothesis testing: Possible choices and comparisons 大规模单一中介假设检验的方法:可能的选择和比较
IF 2.1 4区 医学 Q3 GENETICS & HEREDITY Pub Date : 2022-12-05 DOI: 10.1002/gepi.22510
Jiacong Du, Xiang Zhou, Dylan Clark-Boucher, Wei Hao, Yongmei Liu, Jennifer A. Smith, Bhramar Mukherjee
<p>Mediation hypothesis testing for a large number of mediators is challenging due to the composite structure of the null hypothesis, <math> <semantics> <mrow> <msub> <mi>H</mi> <mn>0</mn> </msub> <mo>:</mo> <mi>α</mi> <mi>β</mi> <mo>=</mo> <mn>0</mn> </mrow> <annotation> ${H}_{0}:alpha beta =0$</annotation> </semantics></math> (<math> <semantics> <mrow> <mi>α</mi> </mrow> <annotation> $alpha $</annotation> </semantics></math>: effect of the exposure on the mediator after adjusting for confounders; <math> <semantics> <mrow> <mi>β</mi> </mrow> <annotation> $beta $</annotation> </semantics></math>: 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 <math> <semantics> <mrow> <mi>α</mi> <mi>β</mi> </mrow> <annotation> $alpha beta $</annotation> </semantics></math> 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) <math> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mi>β</mi> <mo>≠</mo> <mn>0</mn> </mrow> <annotation> $alpha =0,beta ne 0$</annotation> </semantics></math>; (2) <math> <semantics> <mrow> <mi>α</mi> <mo>≠</mo> <mn>0</mn> <mo>,</mo> <mi>β</mi> <mo>=</mo> <mn>0</mn> </mrow>
由于零假设h0的复合结构,对大量中介的中介假设检验具有挑战性:α β = 0 ${H}_{0}:alpha beta =0$ (α $alpha $:调整混杂因素后暴露对介质的影响;β $beta $:调整暴露和混杂因素后,介质对结果的影响)。在本文中,我们回顾了大规模一次中介假设检验的三种方法。这些方法通常用于连续结果和连续介质,假设没有暴露-介质相互作用,因此产品α β $alpha beta $作为间接效应具有因果解释。第一类方法忽略了复合零假设下不同结构的影响,即(1)α = 0;β≠0 $alpha =0,beta ne 0$;(2) α≠0,β = 0 $alpha ne 0,beta =0$;(3) α = β = 0 $alpha =beta =0$。第二类方法对每一种情况下的引用分布进行加权,形成混合引用分布。第三类利用在每一种null情况下得到的三个p值构造一个复合检验统计量,使复合统计量的参考分布近似为U (0);1) $U(0,1)$。在这些现有方法的基础上,我们开发了Sobel-comp方法,属于第二类,它使用一个修正的Sobel检验统计量的混合参考分布。我们进行了广泛的模拟研究,以比较属于这三种类别的所有六种方法在零假设下的假阳性率(fpr)和替代假设下的真阳性率。我们发现,在零假设下,使用混合参考分布的第二类方法可以最好地将fpr保持在名义水平上,并且在备择假设下具有最大的真阳性率。我们使用来自多种族动脉粥样硬化研究(MESA)的数据,应用所有方法研究DNA甲基化位点在成人社会经济地位到糖化血红蛋白水平通路中的中介机制。我们提供了在实践中选择最佳中介假设检验方法的指南,并在CRAN上开发了一个R包medScan,用于实现所有六种方法。
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引用次数: 2
Adaptive Bayesian variable clustering via structural learning of breast cancer data 基于乳腺癌数据结构学习的自适应贝叶斯变量聚类
IF 2.1 4区 医学 Q3 GENETICS & HEREDITY Pub Date : 2022-11-15 DOI: 10.1002/gepi.22507
Riddhi Pratim Ghosh, Arnab K. Maity, Mohsen Pourahmadi, Bani K. Mallick

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.

蛋白质的聚类在癌细胞生物学中引起了人们的兴趣。本文提出了一种基于相关结构的蛋白质(变量)聚类的层次贝叶斯模型。从多元正态似然开始,我们通过使用基于角度的无约束相关性重新参数化的先验建模来强制聚类,并假设截断泊松分布(惩罚大量聚类)作为聚类数量的先验。参数的后验分布不是显式的,我们使用可逆跳跃马尔可夫链蒙特卡罗技术从后验模拟参数。该方法的最终产物是蛋白质(变量)的估计簇配置以及簇的数量。贝叶斯方法足够灵活,可以对蛋白质进行聚类,也可以估计聚类的数量。所提出的方法的性能已经证实了广泛的模拟研究和一个蛋白质表达数据与乳腺癌的遗传倾向,其中蛋白质来自不同的途径。
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引用次数: 0
Multivariate analysis of a missense variant in CREBRF reveals associations with measures of adiposity in people of Polynesian ancestries 对CREBRF错义变体的多变量分析揭示了与波利尼西亚祖先人群肥胖测量的关联
IF 2.1 4区 医学 Q3 GENETICS & HEREDITY Pub Date : 2022-11-09 DOI: 10.1002/gepi.22508
Jerry Z. Zhang, Lacey W. Heinsberg, Mohanraj Krishnan, Nicola L. Hawley, Tanya J. Major, Jenna C. Carlson, Jennie Harré Hindmarsh, Huti Watson, Muhammad Qasim, Lisa K. Stamp, Nicola Dalbeth, Rinki Murphy, Guangyun Sun, Hong Cheng, Take Naseri, Muagututi'a S. Reupena, Erin E. Kershaw, Ranjan Deka, Stephen T. McGarvey, Ryan L. Minster, Tony R. Merriman, Daniel E. Weeks

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 (n = 1632), Aotearoa New Zealand cohort (n = 1419), and combined cohort (n = 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 (n = 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 < log10BF < 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.

CREB3调节因子错义变体rs373863828的次要等位基因与波利尼西亚人的几种心脏代谢表型相关。为了更好地了解该变异,我们在萨摩亚队列(n = 1632)、新西兰Aotearoa队列(n = 1419)和联合队列(n = 2976)中使用多变量贝叶斯关联和网络分析测试了rs373863828与一组相关表型(体重指数[BMI]、体重、身高、高密度脂蛋白胆固醇、甘油三酯和总胆固醇)的相关性。在萨摩亚队列(n = 1496)中测试了一组扩展的表型(增加了估计的脂肪和无脂肪质量、腹围、臀围和腹臀比)。在萨摩亚队列中,我们观察到rs373863828与总体表型面板(8.81)、体重(8.30)和BMI(6.42)之间存在显著相关性(log10贝叶斯因子[BF]≥5.0)。在Aotearoa新西兰队列中,我们观察到rs373863828与整体表型面板(4.60)、体重(3.27)和BMI(1.80)之间存在提示关联(1.5 < log10BF < 5)。在联合队列中,我们观察到具有较大log10BFs的一致信号。在萨摩亚特异性扩展表型分析中,我们还观察到rs373863828与脂肪量(5.65)、腹围(5.34)和臀围(5.09)之间存在显著关联。贝叶斯网络提供了rs373863828与体重直接相关,与身高和BMI间接相关的证据。
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引用次数: 0
Sparse prediction informed by genetic annotations using the logit normal prior for Bayesian regression tree ensembles 贝叶斯回归树集合的logit正态先验遗传注释稀疏预测
IF 2.1 4区 医学 Q3 GENETICS & HEREDITY Pub Date : 2022-11-09 DOI: 10.1002/gepi.22505
Charles Spanbauer, Wei Pan, ADNI, The Alzheimer's Disease Neuroimaging Initiative

Using high-dimensional genetic variants such as single nucleotide polymorphisms (SNP) to predict complex diseases and traits has important applications in basic research and other clinical settings. For example, predicting gene expression is a necessary first step to identify (putative) causal genes in transcriptome-wide association studies. Due to weak signals, high-dimensionality, and linkage disequilibrium (correlation) among SNPs, building such a prediction model is challenging. However, functional annotations at the SNP level (e.g., as epigenomic data across multiple cell- or tissue-types) are available and could be used to inform predictor importance and aid in outcome prediction. Existing approaches to incorporate annotations have been based mainly on (generalized) linear models. Bayesian additive regression trees (BART), in contrast, is a reliable method to obtain high-quality nonlinear out of sample predictions without overfitting. Unfortunately, the default prior from BART may be too inflexible to handle sparse situations where the number of predictors approaches or surpasses the number of observations. Motivated by our real data application, this article proposes an alternative prior based on the logit normal distribution because it provides a framework that is adaptive to sparsity and can model informative functional annotations. It also provides a framework to incorporate prior information about the between SNP correlations. Computational details for carrying out inference are presented along with the results from a simulation study and a genome-wide prediction analysis of the Alzheimer's Disease Neuroimaging Initiative data.

利用高维遗传变异如单核苷酸多态性(SNP)来预测复杂疾病和性状在基础研究和其他临床环境中具有重要应用。例如,在全转录组关联研究中,预测基因表达是确定(假定的)因果基因的必要的第一步。由于信号弱、高维和snp之间的连锁不平衡(相关性),建立这样的预测模型是具有挑战性的。然而,SNP水平上的功能注释(例如,作为跨多种细胞或组织类型的表观基因组数据)是可用的,可用于告知预测因子的重要性并帮助预测结果。现有的合并注释的方法主要基于(广义的)线性模型。相比之下,贝叶斯加性回归树(BART)是一种可靠的方法,可以在没有过拟合的情况下获得高质量的非线性样本外预测。不幸的是,BART的默认先验可能过于不灵活,无法处理预测器数量接近或超过观测值数量的稀疏情况。受实际数据应用程序的启发,本文提出了一种基于logit正态分布的替代先验,因为它提供了一个适应稀疏性的框架,可以对信息功能注释进行建模。它还提供了一个框架,以纳入有关SNP相关性之间的先验信息。执行推理的计算细节与模拟研究和阿尔茨海默病神经成像倡议数据的全基因组预测分析的结果一起提出。
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引用次数: 0
Statistical methods for cis-Mendelian randomization with two-sample summary-level data 双样本汇总水平数据顺式孟德尔随机化的统计方法
IF 2.1 4区 医学 Q3 GENETICS & HEREDITY Pub Date : 2022-10-23 DOI: 10.1002/gepi.22506
Apostolos Gkatzionis, Stephen Burgess, Paul J. Newcombe

Mendelian randomization (MR) is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data MR analyses with many correlated variants from a single gene region, particularly on cis-MR studies which use protein expression as a risk factor. Such studies must rely on a small, curated set of variants from the studied region; using all variants in the region requires inverting an ill-conditioned genetic correlation matrix and results in numerically unstable causal effect estimates. We review methods for variable selection and estimation in cis-MR with summary-level data, ranging from stepwise pruning and conditional analysis to principal components analysis, factor analysis, and Bayesian variable selection. In a simulation study, we show that the various methods have comparable performance in analyses with large sample sizes and strong genetic instruments. However, when weak instrument bias is suspected, factor analysis and Bayesian variable selection produce more reliable inferences than simple pruning approaches, which are often used in practice. We conclude by examining two case studies, assessing the effects of low-density lipoprotein-cholesterol and serum testosterone on coronary heart disease risk using variants in the HMGCR and SHBG gene regions, respectively.

孟德尔随机化(MR)是利用遗传变异来评估风险因素与目标结果之间是否存在因果关系。在这里,我们将重点放在双样本汇总数据MR分析上,其中包含来自单个基因区域的许多相关变体,特别是使用蛋白质表达作为风险因素的顺式MR研究。这类研究必须依赖于来自研究地区的一组经过精心策划的小变量;使用该地区的所有变异需要对病态遗传相关矩阵进行反转,并导致在数值上不稳定的因果效应估计。我们回顾了顺式mr中变量选择和估计的方法,从逐步修剪和条件分析到主成分分析、因子分析和贝叶斯变量选择。在模拟研究中,我们表明各种方法在大样本量和强大的遗传工具的分析中具有相当的性能。然而,当怀疑弱仪器偏差时,因子分析和贝叶斯变量选择比实践中经常使用的简单修剪方法产生更可靠的推断。我们通过两个案例研究得出结论,分别使用HMGCR和SHBG基因区域的变异来评估低密度脂蛋白-胆固醇和血清睾酮对冠心病风险的影响。
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引用次数: 23
Mediation analysis of multiple mediators with incomplete omics data 具有不完整组学数据的多种介质的中介分析。
IF 2.1 4区 医学 Q3 GENETICS & HEREDITY Pub Date : 2022-09-20 DOI: 10.1002/gepi.22504
John Kidd, Chelsea K. Raulerson, Karen L. Mohlke, Dan-Yu Lin

There is an increasing interest in using multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation, protein expressions, and metabolic profiles) to study how the relationships between phenotypes and genotypes may be mediated by other omics markers. Genotypes and phenotypes are typically available for all subjects in genetic studies, but typically, some omics data will be missing for some subjects, due to limitations such as cost and sample quality. In this article, we propose a powerful approach for mediation analysis that accommodates missing data among multiple mediators and allows for various interaction effects. We formulate the relationships among genetic variants, other omics measurements, and phenotypes through linear regression models. We derive the joint likelihood for models with two mediators, accounting for arbitrary patterns of missing values. Utilizing computationally efficient and stable algorithms, we conduct maximum likelihood estimation. Our methods produce unbiased and statistically efficient estimators. We demonstrate the usefulness of our methods through simulation studies and an application to the Metabolic Syndrome in Men study.

人们越来越感兴趣的是使用多种类型的组学特征(例如,DNA序列、RNA表达、甲基化、蛋白质表达和代谢谱)来研究表型和基因型之间的关系如何由其他组学标记物介导。基因型和表型通常适用于遗传研究中的所有受试者,但通常情况下,由于成本和样本质量等限制,一些受试者的一些组学数据会缺失。在本文中,我们提出了一种强大的中介分析方法,该方法可以容纳多个中介之间缺失的数据,并允许各种交互效果。我们通过线性回归模型建立遗传变异、其他组学测量和表型之间的关系。我们推导了具有两个中介的模型的联合似然性,考虑了缺失值的任意模式。利用计算高效和稳定的算法,我们进行了最大似然估计。我们的方法产生了无偏和统计有效的估计量。我们通过模拟研究和在男性代谢综合征研究中的应用证明了我们的方法的有用性。
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引用次数: 0
An empirical Bayes approach to improving population-specific genetic association estimation by leveraging cross-population data 利用跨种群数据改进种群特异性遗传关联估计的经验贝叶斯方法
IF 2.1 4区 医学 Q3 GENETICS & HEREDITY Pub Date : 2022-09-18 DOI: 10.1002/gepi.22501
Li Hsu, Anna Kooperberg, Alexander P. Reiner, Charles Kooperberg

Populations of non-European ancestry are substantially underrepresented in genome-wide association studies (GWAS). As genetic effects can differ between ancestries due to possibly different causal variants or linkage disequilibrium patterns, a meta-analysis that includes GWAS of all populations yields biased estimation in each of the populations and the bias disproportionately impacts non-European ancestry populations. This is because meta-analysis combines study-specific estimates with inverse variance as the weights, which causes biases towards studies with the largest sample size, typical of the European ancestry population. In this paper, we propose two empirical Bayes (EB) estimators to borrow the strength of information across populations although accounting for between-population heterogeneity. Extensive simulation studies show that the proposed EB estimators are largely unbiased and improve efficiency compared to the population-specific estimator. In contrast, even though the meta-analysis estimator has a much smaller variance, it yields significant bias when the genetic effect is heterogeneous across populations. We apply the proposed EB estimators to a large-scale trans-ancestry GWAS of stroke and demonstrate that the EB estimators reduce the variance of the population-specific estimator substantially, with the effect estimates close to the population-specific estimates.

在全基因组关联研究(GWAS)中,非欧洲血统人群的代表性不足。由于不同祖先之间的遗传效应可能由于不同的因果变异或连锁不平衡模式而不同,包括所有人群的GWAS的荟萃分析在每个人群中产生有偏差的估计,并且偏差不成比例地影响非欧洲血统的人群。这是因为荟萃分析结合了研究特定估计和逆方差作为权重,这导致了对样本量最大的研究的偏见,典型的欧洲血统人群。在本文中,我们提出了两个经验贝叶斯(EB)估计,尽管考虑了种群间的异质性,但借用了种群间信息的强度。大量的仿真研究表明,与种群特异性估计器相比,所提出的EB估计器在很大程度上是无偏的,并且提高了效率。相比之下,即使荟萃分析估计值的方差要小得多,但当遗传效应在人群中是异质的时,它会产生显著的偏差。我们将提出的EB估计器应用于卒中的大规模跨祖先GWAS,并证明EB估计器大大减少了人群特异性估计器的方差,其效果估计接近人群特异性估计。
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引用次数: 0
Multivariate analysis of a missense variant in CREBRF reveals associations with measures of adiposity in people of Polynesian ancestries 对CREBRF错义变体的多变量分析揭示了与波利尼西亚祖先人群肥胖测量的关联
IF 2.1 4区 医学 Q3 GENETICS & HEREDITY Pub Date : 2022-09-09 DOI: 10.1101/2022.09.08.22279720
Jerry Z. Zhang, L. W. Heinsberg, Mohanraj Krishnan, N. Hawley, Tanya J. Major, J. Carlson, J. Harré Hindmarsh, H. Watson, Muhammad Qasim, L. Stamp, N. Dalbeth, R. Murphy, Guangyun Sun, Hong Cheng, T. Naseri, M. Reupena, E. Kershaw, R. Deka, S. McGarvey, R. Minster, T. Merriman, D. Weeks
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 (n = 1632), Aotearoa New Zealand cohort (n = 1419), and combined cohort (n = 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 (n = 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 < log10BF < 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.
CREB3调节因子错义变体rs373863828的次要等位基因与波利尼西亚人的几种心脏代谢表型相关。为了更好地了解该变异,我们在萨摩亚队列(n = 1632)、新西兰Aotearoa队列(n = 1419)和联合队列(n = 2976)中使用多变量贝叶斯关联和网络分析测试了rs373863828与一组相关表型(体重指数[BMI]、体重、身高、高密度脂蛋白胆固醇、甘油三酯和总胆固醇)的相关性。在萨摩亚队列(n = 1496)中测试了一组扩展的表型(加上估计的脂肪和无脂肪质量、腹围、臀围和腹臀比)。在萨摩亚队列中,我们观察到rs373863828与总体表型面板(8.81)、体重(8.30)和BMI(6.42)之间存在显著相关性(log10贝叶斯因子[BF]≥5.0)。在Aotearoa新西兰队列中,我们观察到rs373863828与整体表型面板(4.60)、体重(3.27)和BMI(1.80)之间存在提示相关性(1.5 < log10BF < 5)。在联合队列中,我们观察到具有较大log10BFs的一致信号。在萨摩亚特异性扩展表型分析中,我们还观察到rs373863828与脂肪量(5.65)、腹围(5.34)和臀围(5.09)之间存在显著关联。贝叶斯网络提供了rs373863828与体重直接相关,与身高和BMI间接相关的证据。
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Genetic Epidemiology
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