低频变异关联研究与统计检验

在过去的10年间,基于"常见疾病-常见变异"的假设,全基因组关联研究被广泛应用于疾病和复杂性状的遗传学病因研究中。但是,全基因组关联分析发现的疾病相关常见变异,只能解释疾病小部分的遗传风险,造成"遗传度丢失"。"常见疾病-低频变异"的假设被提出。随着新一代测序技术的发展,低频变异关联研究陆续开展。本文主要对低频变异关联研究的研究设计以及关联分析方法进行综述。     

以家庭为基础的关联检验在遗传流行病学中的应用

简述以家庭为基础的关联检验(FBAT)分析遗传标记等位基因与疾病表型之间关联的方法 在遗传流行病学研究中的应用.介绍FBAT的设计原理、基本步骤、应用原则,并结合实例说明如何利用相应的软件有效分析核心家庭数据.分析表明,相对于其他传统的遗传分析方法 ,FBAT可以充分利用双亲基因型、受累子代基因型及其表型的家系数据,同时还以其他性状,如环境暴露数据作为协变量,进行双等位基因或多等位基因与疾病表型的关联分析,其设计优势为可消除病例和不相关对照之间由于种族差异产生的虚假关联,有效控制由于群体分层引起的偏倚,较其他方法 有更高的检验效率.适用于核心家庭或家系资料的遗传分析,是目前为数不多的进行疾病与遗传标志物关联检验的家系遗传分析方法之一.     

SPU测试及其在全基因组多元统计分析中的应用北大核心CSCD

全基因组关联分析(genome-wide association study,GWAS)在识别疾病的常见变异方面取得了巨大进展,目前已经报道上万个单核苷酸多态性(single nucleotide polymorphism,SNP)位点与数百种复杂疾病存在关联,这为表型变异的遗传基础提供了前所未有的视角[1-2]。但GWAS是基于个体水平基因型和表型数据的分析,因此需要更有效的方法基于汇总统计数据来识别复杂疾病中的罕见变异[3-4]。     

罕见遗传位点关联性分析的近似限制性似然比统计学习

目的 发展快速的似然比罕见遗传位点关联性分析方法。方法在精确分布的基础上采用混合分布作为替代。通过模拟研究来比较混合分布和精确分布;以及通过GAW17数据来验证近似的和精确的似然比检验。结果不同的混合分布参数估计方法基本获得一致的估计值。GAW17数据分析中似然比检验根据混合分布和精确分布获得p值基本一致。结论混合分布不但有效提高了似然比罕见遗传位点关联性分析方法的计算速度,也同时保持了其高的统计效能。     

基于基因水平主成分logistic回归模型在全基因组关联研究中的应用

探讨基于基因水平的主成分logistic回归模型分析方法及其在全基因组关联研究中的应用.以全基因组关联研究基因型模拟数据为例,介绍基于主成分的logistic回归模型在基因水平检测遗传变异与复杂性疾病之间关联的分析策略.模拟结果表明致病位点所在基因假设检验的P值在所有基因检验结果中为最小.研究结果提示在全基因组关联研究中,采用基于基因水平的主成分logistic回归模型一方面能够降低检验的自由度,另一方面能够处理单核苷酸多态性之间相关性问题,在检测致病基因与疾病关联时具有一定的效能.     

非综合征型唇腭裂病因学研究进展

非综合征型唇腭裂是人类最常见的结构性出生缺陷,对患儿、家庭和社会均造成沉重负担。由于致病机制复杂,到目前为止病因尚未完全阐明。全基因组关联研究有效地发现了多个新的易感基因位点,但由于GWAS只对常见SNPs进行研究,未检测罕见变异与疾病的关联,且未考虑基因的生物学功能,因此并不能完全解释疾病的遗传度。通路分析和外显子组测序研究能够在一定程度上弥补全基因组关联研究的不足,有望为解决复杂疾病的病因学问题提供有效方案。     

临床路径变异分析方法的现状与建议

科学的变异分析方法是发现问题及提出改进措施的重要依据.阐述了临床路径变异分析方法现状及存在的不足,提出了改进建议:选择典型病种做变异影响因素分析,增加对照组研究,综合运用多种方法分析变异及从关键指标的量化入手等.     

病例对照家系研究中发病年龄的家庭相关分析方法

目的 探讨失效时间的关联测量和病例对照家系研究中发病年龄家庭相关的分析方法。方法 分层Cox模型用于估计病例对照家系研究中肝癌发病年龄的交叉比。结果 显示先证者和父亲、母亲、同胞间发病年龄的家庭相关有统计学意义，而先证者和配偶间发病年龄的家庭相关没有统计学意义。结论 分层Cox模型可用于病例对照家系研究中发病年龄的家庭相关估计。     

GABRA4基因多态性与儿童孤独症的相关性研究

目的:通过对GABRA4基因上单核苷酸多态性位点rs2229940的检测,探讨GABRA4基因多态性与儿童孤独症的关系。方法:在中国汉族孤独症49个核心家系(孤独症患者49人,父母94人)及正常对照人群(158人)中,应用聚合酶链反应-限制性酶切片段多态性(PCR-RFLP)技术,测定了本位点的等位基因和基因型,进行以核心家系为基础的传递不平衡检验(TDT)及病例对照的关联研究。结果:病例对照关联研究中,rs2229940多态位点等位基因频率(χ2=4.128,P=0.040)及基因型(χ2=9.438,P=0.009)比较均发现差异有统计学意义。结论:提示GABRA4基因可能与孤独症有关联。     

应用全外显子组测序发现1例肺型BHD综合征家系FLCN基因c.1579＿1580insA位点变异

目的 应用全外显子组测序对1例遗传性肺囊肿家系进行遗传基因研究。方法 遴选出2018年8月—2021年8月我科临床上发现的具有家族聚集现象的肺囊肿家系,对家系成员进行全外显子组测序确定候选基因,并通过生物信息分析、遗传变异解读及Sanger测序验证分析该基因的致病性。结果 共纳入1例肺囊肿家系,在家系先证者检出FLCN基因c.1579＿1580insA位点变异,对其阳性家系成员采用Sanger测序验证,同样检出相同基因变异,经生物信息学分析及ACMG指南致病性评级显示检出变异可分类为致病性的基因变异。结论 该家系肺囊肿的病因为FLCN基因c.1579＿1580insA位点变异;应用全外显子组测序能够快速发现罕见遗传疾病的致病基因,建议临床上若出现不明原因肺囊肿,应进行全外显子组测序检测以明确是否存在遗传因素,从而进一步辅助临床诊断,研究为后期肺囊肿的临床诊疗指南的制定提供了借鉴内容。     

Efficient unified rare variant association test by modeling the population genetic distribution in case‐control studies

Recent advancements in next‐generation DNA sequencing technologies have made it plausible to study the association of rare variants with complex diseases. Due to the low frequency, rare variants need to be aggregated in association tests to achieve adequate power with reasonable sample sizes. Hierarchical modeling/kernel machine methods have gained popularity among many available methods for testing a set of rare variants collectively. Here, we propose a new score statistic based on a hierarchical model by additionally modeling the distribution of rare variants under the case‐control study design. Results from extensive simulation studies show that the proposed method strikes a balance between robustness and power and outperforms several popular rare‐variant association tests. We demonstrate the performance of our method using the Dallas Heart Study.     

A Novel Test for Testing the Optimally Weighted Combination of Rare and Common Variants Based on Data of Parents and Affected Children

With the development of sequencing technologies, the direct testing of rare variant associations has become possible. Many statistical methods for detecting associations between rare variants and complex diseases have recently been developed, most of which are population‐based methods for unrelated individuals. A limitation of population‐based methods is that spurious associations can occur when there is a population structure. For rare variants, this problem can be more serious, because the spectrum of rare variation can be very different in diverse populations, as well as the current nonexistence of methods to control for population stratification in population‐based rare variant associations. A solution to the problem of population stratification is to use family‐based association tests, which use family members to control for population stratification. In this article, we propose a novel test for Testing the Optimally Weighted combination of variants based on data of Parents and Affected Children (TOW‐PAC). TOW‐PAC is a family‐based association test that tests the combined effect of rare and common variants in a genomic region, and is robust to the directions of the effects of causal variants. Simulation studies confirm that, for rare variant associations, family‐based association tests are robust to population stratification although population‐based association tests can be seriously confounded by population stratification. The results of power comparisons show that the power of TOW‐PAC increases with an increase of the number of affected children in each family and TOW‐PAC based on multiple affected children per family is more powerful than TOW based on unrelated individuals.     

Rare‐variant association tests in longitudinal studies,with an application to the Multi‐Ethnic Study of Atherosclerosis (MESA)

Over the past few years, an increasing number of studies have identified rare variants that contribute to trait heritability. Due to the extreme rarity of some individual variants, gene‐based association tests have been proposed to aggregate the genetic variants within a gene, pathway, or specific genomic region as opposed to a one‐at‐a‐time single variant analysis. In addition, in longitudinal studies, statistical power to detect disease susceptibility rare variants can be improved through jointly testing repeatedly measured outcomes, which better describes the temporal development of the trait of interest. However, usual sandwich/model‐based inference for sequencing studies with longitudinal outcomes and rare variants can produce deflated/inflated type I error rate without further corrections. In this paper, we develop a group of tests for rare‐variant association based on outcomes with repeated measures. We propose new perturbation methods such that the type I error rate of the new tests is not only robust to misspecification of within‐subject correlation, but also significantly improved for variants with extreme rarity in a study with small or moderate sample size. Through extensive simulation studies, we illustrate that substantially higher power can be achieved by utilizing longitudinal outcomes and our proposed finite sample adjustment. We illustrate our methods using data from the Multi‐Ethnic Study of Atherosclerosis for exploring association of repeated measures of blood pressure with rare and common variants based on exome sequencing data on 6,361 individuals.     

Methods for Association Analysis and Meta‐Analysis of Rare Variants in Families

Advances in exome sequencing and the development of exome genotyping arrays are enabling explorations of association between rare coding variants and complex traits. To ensure power for these rare variant analyses, a variety of association tests that group variants by gene or functional unit have been proposed. Here, we extend these tests to family‐based studies. We develop family‐based burden tests, variable frequency threshold tests and sequence kernel association tests. Through simulations, we compare the performance of different tests. We describe situations where family‐based studies provide greater power than studies of unrelated individuals to detect rare variants associated with moderate to large changes in trait values. Broadly speaking, we find that when sample sizes are limited and only a modest fraction of all trait‐associated variants can be identified, family samples are more powerful. Finally, we illustrate our approach by analyzing the relationship between coding variants and levels of high‐density lipoprotein (HDL) cholesterol in 11,556 individuals from the HUNT and SardiNIA studies, demonstrating association for coding variants in the APOC3, CETP, LIPC, LIPG, and LPL genes and illustrating the value of family samples, meta‐analysis, and gene‐level tests. Our methods are implemented in freely available C++ code.     

A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next‐generation sequencing

In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F‐distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high‐dimensional genotype data. It is shown that approximate F‐distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models that perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods.     

Testing Genetic Association With Rare Variants in Admixed Populations

Recent studies suggest that rare variants play an important role in the etiology of many traits. Although a number of methods have been developed for genetic association analysis of rare variants, they all assume a relatively homogeneous population under study. Such an assumption may not be valid for samples collected from admixed populations such asAfricanAmericans andHispanicAmericans as there is a great extent of local variation in ancestry in these populations. To ensure valid and more powerful rare variant association tests performed in admixed populations, we have developed a local ancestry‐based weighted dosage test, which is able to take into account local ancestry of rare alleles, uncertainties in rare variant imputation when imputed data are included, and the direction of effect that rare variants exert on phenotypic outcome. We used simulated sequence data to show that our proposed test has controlled typeIerror rates, whereas naïve application of existing rare variants tests and tests that adjust for global ancestry lead to inflated type I error rates. We showed that our test has higher power than tests without proper adjustment of ancestry. We also applied the proposed method to a candidate gene study on low‐density lipoprotein cholesterol. Our results suggest that it is important to appropriately control for potential population stratification induced by local ancestry difference in the analysis of rare variants in admixed populations.     

Combining Family‐ and Population‐Based Imputation Data for Association Analysis of Rare and Common Variants in Large Pedigrees

In the last two decades, complex traits have become the main focus of genetic studies. The hypothesis that both rare and common variants are associated with complex traits is increasingly being discussed. Family‐based association studies using relatively large pedigrees are suitable for both rare and common variant identification. Because of the high cost of sequencing technologies, imputation methods are important for increasing the amount of information at low cost. A recent family‐based imputation method, Genotype Imputation Given Inheritance (GIGI), is able to handle large pedigrees and accurately impute rare variants, but does less well for common variants where population‐based methods perform better. Here, we propose a flexible approach to combine imputation data from both family‐ and population‐based methods. We also extend the Sequence Kernel Association Test for Rare and Common variants (SKAT‐RC), originally proposed for data from unrelated subjects, to family data in order to make use of such imputed data. We call this extension “famSKAT‐RC.” We compare the performance of famSKAT‐RC and several other existing burden and kernel association tests. In simulated pedigree sequence data, our results show an increase of imputation accuracy from use of our combining approach. Also, they show an increase of power of the association tests with this approach over the use of either family‐ or population‐based imputation methods alone, in the context of rare and common variants. Moreover, our results show better performance of famSKAT‐RC compared to the other considered tests, in most scenarios investigated here.     

A Variational Bayes Discrete Mixture Test for Rare Variant Association

Recently, many statistical methods have been proposed to test for associations between rare genetic variants and complex traits. Most of these methods test for association by aggregating genetic variations within a predefined region, such as a gene. Although there is evidence that “aggregate” tests are more powerful than the single marker test, these tests generally ignore neutral variants and therefore are unable to identify specific variants driving the association with phenotype. We propose a novel aggregate rare‐variant test that explicitly models a fraction of variants as neutral, tests associations at the gene‐level, and infers the rare‐variants driving the association. Simulations show that in the practical scenario where there are many variants within a given region of the genome with only a fraction causal our approach has greater power compared to other popular tests such as the Sequence Kernel Association Test (SKAT), the Weighted Sum Statistic (WSS), and the collapsing method of Morris and Zeggini (MZ). Our algorithm leverages a fast variational Bayes approximate inference methodology to scale to exome‐wide analyses, a significant computational advantage over exact inference model selection methodologies. To demonstrate the efficacy of our methodology we test for associations between von Willebrand Factor (VWF) levels and VWF missense rare‐variants imputed from the National Heart, Lung, and Blood Institute's Exome Sequencing project into 2,487 African Americans within the VWF gene. Our method suggests that a relatively small fraction (～10%) of the imputed rare missense variants within VWF are strongly associated with lower VWF levels in African Americans.     

Generalized Functional Linear Models for Gene‐Based Case‐Control Association Studies

By using functional data analysis techniques, we developed generalized functional linear models for testing association between a dichotomous trait and multiple genetic variants in a genetic region while adjusting for covariates. Both fixed and mixed effect models are developed and compared. Extensive simulations show that Rao's efficient score tests of the fixed effect models are very conservative since they generate lower type I errors than nominal levels, and global tests of the mixed effect models generate accurate type I errors. Furthermore, we found that the Rao's efficient score test statistics of the fixed effect models have higher power than the sequence kernel association test (SKAT) and its optimal unified version (SKAT‐O) in most cases when the causal variants are both rare and common. When the causal variants are all rare (i.e., minor allele frequencies less than 0.03), the Rao's efficient score test statistics and the global tests have similar or slightly lower power than SKAT and SKAT‐O. In practice, it is not known whether rare variants or common variants in a gene region are disease related. All we can assume is that a combination of rare and common variants influences disease susceptibility. Thus, the improved performance of our models when the causal variants are both rare and common shows that the proposed models can be very useful in dissecting complex traits. We compare the performance of our methods with SKAT and SKAT‐O on real neural tube defects and Hirschsprung's disease datasets. The Rao's efficient score test statistics and the global tests are more sensitive than SKAT and SKAT‐O in the real data analysis. Our methods can be used in either gene‐disease genome‐wide/exome‐wide association studies or candidate gene analyses.     

Meta‐Analysis of Sequencing Studies With Heterogeneous Genetic Associations

Recent advances in sequencing technologies have made it possible to explore the influence of rare variants on complex diseases and traits. Meta‐analysis is essential to this exploration because large sample sizes are required to detect rare variants. Several methods are available to conduct meta‐analysis for rare variants under fixed‐effects models, which assume that the genetic effects are the same across all studies. In practice, genetic associations are likely to be heterogeneous among studies because of differences in population composition, environmental factors, phenotype and genotype measurements, or analysis method. We propose random‐effects models which allow the genetic effects to vary among studies and develop the corresponding meta‐analysis methods for gene‐level association tests. Our methods take score statistics, rather than individual participant data, as input and thus can accommodate any study designs and any phenotypes. We produce the random‐effects versions of all commonly used gene‐level association tests, including burden, variable threshold, and variance‐component tests. We demonstrate through extensive simulation studies that our random‐effects tests are substantially more powerful than the fixed‐effects tests in the presence of moderate and high between‐study heterogeneity and achieve similar power to the latter when the heterogeneity is low. The usefulness of the proposed methods is further illustrated with data from National Heart, Lung, and Blood Institute Exome Sequencing Project (NHLBI ESP). The relevant software is freely available.