Genome-wide Association Study and Meta-Analysis Identify ISL1 as Genome-wide Significant Susceptibility Gene for Bladder Exstrophy

The bladder exstrophy-epispadias complex (BEEC) represents the severe end of the uro-rectal malformation spectrum, and is thought to result from aberrant embryonic morphogenesis of the cloacal membrane and the urorectal septum. The most common form of BEEC is isolated classic bladder exstrophy (CBE). To identify susceptibility loci for CBE, we performed a genome-wide association study (GWAS) of 110 CBE patients and 1,177 controls of European origin. Here, an association was found with a region of approximately 220kb on chromosome 5q11.1. This region harbors the ISL1 (ISL LIM homeobox 1) gene. Multiple markers in this region showed evidence for association with CBE, including 84 markers with genome-wide significance. We then performed a meta-analysis using data from a previous GWAS by our group of 98 CBE patients and 526 controls of European origin. This meta-analysis also implicated the 5q11.1 locus in CBE risk. A total of 138 markers at this locus reached genome-wide significance in the meta-analysis, and the most significant marker (rs9291768) achieved a P value of 2.13 × 10⁻¹². No other locus in the meta-analysis achieved genome-wide significance. We then performed murine expression analyses to follow up this finding. Here, Isl1 expression was detected in the genital region within the critical time frame for human CBE development. Genital regions with Isl1 expression included the peri-cloacal mesenchyme and the urorectal septum. The present study identified the first genome-wide significant locus for CBE at chromosomal region 5q11.1, and provides strong evidence for the hypothesis that ISL1 is the responsible candidate gene in this region.     

生物信息学分析方法I: 全基因组关联分析概述

全基因组关联分析(GWAS)是动植物复杂性状相关基因定位的常用手段。高通量基因分型技术的应用极大地推动了GWAS的发展。在植物中, 利用GWAS不仅能够以较高的分辨率在全基因组水平鉴定出各种自然群体特定性状相关的基因或区间, 而且可揭示表型变异的遗传架构全景图。目前, 人们利用GWAS分析方法已在拟南芥(Arabidopsis thaliana)、水稻(Oryza sativa)、小麦(Triticum aestivum)、玉米(Zea mays)和大豆(Glycine max)等模式植物和重要农作物品系中发掘出与各种性状显著相关的数量性状座位(QTL)及其候选基因位点, 阐明了这些性状的遗传基础, 并为揭示这些性状背后的分子机理提供候选基因, 也为作物高产优质品种的选育提供了理论依据。该文对GWAS的方法、影响因素及数据分析流程进行了详细描述, 以期为相关研究提供参考。     

银川番茄斑萎病毒的分子鉴定

为明确银川番茄(Lycopersicon esculentum)是否遭受了番茄斑萎病毒(TSWV)的危害, 采用国家标准TSWV RT- PCR检测技术对银川番茄上采集的14份疑似感染TSWV病叶样本进行分子鉴定, 对克隆得到的核衣壳蛋白基因N (Nucleocapsid)序列进行多序列比对和系统进化树分析, 随后对PCR阳性样本进行蛋白检测。结果表明, 14份病叶样本中有8份扩增出长度为394 bp的TSWV N基因序列, 且8条序列完全一致; 获得的银川番茄TSWV分离物与云南番茄、中国莴苣(Lactuca sativa)、中国鸢尾(Iris tectorum)和重庆辣椒(Capsicum annuum) TSWV分离物相对近缘, 与山东、黑龙江和北京等地及国外TSWV分离物相对远缘; 利用TSWV的抗体通过Western blot对8个PCR阳性样本进一步检测, 结果也证实8个阳性样本中存在TSWV感染。该研究首次通过分子鉴定及蛋白检测证明银川番茄上存在TSWV感染, 需要加快抗TSWV番茄品种的选育工作。     

Powerful SNP-Set Analysis for Case-Control Genome-wide Association Studies

GWAS have emerged as popular tools for identifying genetic variants that are associated with disease risk. Standard analysis of a case-control GWAS involves assessing the association between each individual genotyped SNP and disease risk. However, this approach suffers from limited reproducibility and difficulties in detecting multi-SNP and epistatic effects. As an alternative analytical strategy, we propose grouping SNPs together into SNP sets on the basis of proximity to genomic features such as genes or haplotype blocks, then testing the joint effect of each SNP set. Testing of each SNP set proceeds via the logistic kernel-machine-based test, which is based on a statistical framework that allows for flexible modeling of epistatic and nonlinear SNP effects. This flexibility and the ability to naturally adjust for covariate effects are important features of our test that make it appealing in comparison to individual SNP tests and existing multimarker tests. Using simulated data based on the International HapMap Project, we show that SNP-set testing can have improved power over standard individual-SNP analysis under a wide range of settings. In particular, we find that our approach has higher power than individual-SNP analysis when the median correlation between the disease-susceptibility variant and the genotyped SNPs is moderate to high. When the correlation is low, both individual-SNP analysis and the SNP-set analysis tend to have low power. We apply SNP-set analysis to analyze the Cancer Genetic Markers of Susceptibility (CGEMS) breast cancer GWAS discovery-phase data.     

玉米出籽率全基因组关联分析

出籽率与玉米单穗产量密切相关,其遗传机制的解析对玉米高产育种具有重要意义.本研究利用309份玉米自交系为关联群体,利用固定和随机模型交替概率统一(FarmCPU)、压缩混合线性模型(CMLM)和多位点混合线性模型(MLMM)对2017年和2019年河南新乡原阳、周口郸城、海南三亚以及最佳线性无偏估计值(BLUE)的出籽...     

Genome-wide Epigenetic Data Facilitate Understanding of Disease Susceptibility Association Studies

Complex traits such as susceptibility to diseases are determined in part by variants at multiple genetic loci. Genome-wide association studies can identify these loci, but most phenotype-associated variants lie distal to protein-coding regions and are likely involved in regulating gene expression. Understanding how these genetic variants affect complex traits depends on the ability to predict and test the function of the genomic elements harboring them. Community efforts such as the ENCODE Project provide a wealth of data about epigenetic features associated with gene regulation. These data enable the prediction of testable functions for many phenotype-associated variants.     

Gene-Gene and Gene-Environment Interactions in Meta-Analysis of Genetic Association Studies

Extensive genetic studies have identified a large number of causal genetic variations in many human phenotypes; however, these could not completely explain heritability in complex diseases. Some researchers have proposed that the “missing heritability” may be attributable to gene–gene and gene–environment interactions. Because there are billions of potential interaction combinations, the statistical power of a single study is often ineffective in detecting these interactions. Meta-analysis is a common method of increasing detection power; however, accessing individual data could be difficult. This study presents a simple method that employs aggregated summary values from a “case” group to detect these specific interactions that based on rare disease and independence assumptions. However, these assumptions, particularly the rare disease assumption, may be violated in real situations; therefore, this study further investigated the robustness of our proposed method when it violates the assumptions. In conclusion, we observed that the rare disease assumption is relatively nonessential, whereas the independence assumption is an essential component. Because single nucleotide polymorphisms (SNPs) are often unrelated to environmental factors and SNPs on other chromosomes, researchers should use this method to investigate gene–gene and gene–environment interactions when they are unable to obtain detailed individual patient data.     

Significant Association of Glutathione S-Transferase T1 Null Genotype with Prostate Cancer Risk: A Meta-Analysis of 26,393 Subjects

Background

Recent studies on the association between Glutathione S-transferase T1 (GSTT1) polymorphism and risk of prostate cancer showed inconclusive results. To clarify this possible association, we conducted a meta-analysis of published studies.

Methods

Data were collected from the following electronic databases: Pubmed, Embase, and Chinese Biomedical Database (CBM). The odds ratio (OR) and its 95% confidence interval (95%CI) was used to assess the strength of the association. We summarized the data on the association between GSTT1 null genotype and risk of prostate cancer in the overall population, and performed subgroup analyses by ethnicity, adjusted ORs, and types of controls.

Results

Ultimately, a total of 43 studies with a total of 26,393 subjects (9,934 cases and 16,459 controls) were eligible for meta-analysis. Overall, there was a significant association between GSTT1 null genotype and increased risk of prostate cancer (OR = 1.14, 95%CI 1.01–1.29, P = 0.034). Meta-analysis of adjusted ORs also showed a significant association between GSTT1 null genotype and increased risk of prostate cancer (OR = 1.34, 95%CI 1.09–1.64, P = 0.006). Similar results were found in the subgroup analyses by ethnicity and types of controls.

Conclusion

This meta-analysis demonstrates that GSTT1 null genotype is associated with prostate cancer susceptibility, and GSTT1 null genotype contributes to increased risk of prostate cancer.     

HYST: A Hybrid Set-Based Test for Genome-wide Association Studies, with Application to Protein-Protein Interaction-Based Association Analysis

The extended Simes’ test (known as GATES) and scaled chi-square test were proposed to combine a set of dependent genome-wide association signals at multiple single-nucleotide polymorphisms (SNPs) for assessing the overall significance of association at the gene or pathway levels. The two tests use different strategies to combine association p values and can outperform each other when the number of and linkage disequilibrium between SNPs vary. In this paper, we introduce a hybrid set-based test (HYST) combining the two tests for genome-wide association studies (GWASs). We describe how HYST can be used to evaluate statistical significance for association at the protein-protein interaction (PPI) level in order to increase power for detecting disease-susceptibility genes of moderate effect size. Computer simulations demonstrated that HYST had a reasonable type 1 error rate and was generally more powerful than its parents and other alternative tests to detect a PPI pair where both genes are associated with the disease of interest. We applied the method to three complex disease GWAS data sets in the public domain; the method detected a number of highly connected significant PPI pairs involving multiple confirmed disease-susceptibility genes not found in the SNP- and gene-based association analyses. These results indicate that HYST can be effectively used to examine a collection of predefined SNP sets based on prior biological knowledge for revealing additional disease-predisposing genes of modest effects in GWASs.     

mrMLM v4.0.2:An R Platform for Multi-locus Genome-wide Association Studies

Previous studies have reported that some important loci are missed in single-locus genome-wide association studies (GWAS), especially because of the large phenotypic error in field experiments. To solve this issue, multi-locus GWAS methods have been recommended. However, only a few software packages for multi-locus GWAS are available. Therefore, we developed an R software named mrMLM v4.0.2. This software integrates mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO methods developed by our lab. There are four components in mrMLM v4.0.2, including dataset input, parameter setting, software running, and result output. The fread function in data.table is used to quickly read datasets, especially big datasets, and the doParallel package is used to conduct parallel computation using multiple CPUs. In addition, the graphical user interface software mrMLM.GUI v4.0.2, built upon Shiny, is also available. To confirm the correctness of the aforementioned programs, all the methods in mrMLM v4.0.2 and three widely-used methods were used to analyze real and simulated datasets. The results confirm the superior performance of mrMLM v4.0.2 to other methods currently available. False positive rates are effectively controlled, albeit with a less stringent significance threshold. mrMLM v4.0.2 is publicly available at BioCode (https://bigd.big.ac.cn/biocode/tools/BT007077) or R (https://cran.r-project.org/web/packages/mrMLM.GUI/index.html) as an open-source software.     

Association of PDE4B Polymorphisms with Susceptibility to Schizophrenia: A Meta-Analysis of Case-Control Studies

Background

The PDE4B single nucleotide polymorphisms (SNPs) have been reported to be associated with schizophrenia risk. However, current findings are ambiguous or even conflicting. To better facilitate the understanding the genetic role played by PDE4B in susceptibility to schizophrenia, we collected currently available data and conducted this meta-analysis.

Methods

A comprehensive electronic literature searching of PubMed, Embase, Web of Science and Cochrane Library was performed. The association between PDE4B SNPs and schizophrenia was evaluated by odds ratios (ORs) and 95% confidence intervals (CIs) under allelic, dominant and recessive genetic models. The random effects model was utilized when high between-study heterogeneity (I² > 50%) existed, otherwise the fixed effects model was used.

Results

Five studies comprising 2376 schizophrenia patients and 3093 controls were finally included for meta-analysis. The rs1040716 was statistically significantly associated with schizophrenia risk in Asian and Caucasian populations under dominant model (OR = 0.87, 95% CI: 0.76–0.99, P = 0.04). The rs2180335 was significantly related with schizophrenia risk in Asian populations under allelic (OR = 0.82, 95% CI: 0.72–0.93, P = 0.003) and dominant (OR = 0.75, 95% CI: 0.64–0.88, P < 0.001) models. A significant association was also observed between rs4320761 and schizophrenia in Asian populations under allelic model (OR = 0.87, 95% CI: 0.75–1.00, P = 0.048). In addition, a strong association tendency was found between rs6588190 and schizophrenia in Asian populations under allelic model (OR = 0.87, 95% CI: 0.76–1.00, P = 0.055).

Conclusion

This meta-analysis suggests that PDE4B SNPs are genetically associated with susceptibility to schizophrenia. However, due to limited sample size, more large-scale, multi-racial association studies are needed to further clarify the genetic association between various PDE4B variants and schizophrenia.     

Maximizing the Power of Principal-Component Analysis of Correlated Phenotypes in Genome-wide Association Studies

Many human traits are highly correlated. This correlation can be leveraged to improve the power of genetic association tests to identify markers associated with one or more of the traits. Principal component analysis (PCA) is a useful tool that has been widely used for the multivariate analysis of correlated variables. PCA is usually applied as a dimension reduction method: the few top principal components (PCs) explaining most of total trait variance are tested for association with a predictor of interest, and the remaining components are not analyzed. In this study we review the theoretical basis of PCA and describe the behavior of PCA when testing for association between a SNP and correlated traits. We then use simulation to compare the power of various PCA-based strategies when analyzing up to 100 correlated traits. We show that contrary to widespread practice, testing only the top PCs often has low power, whereas combining signal across all PCs can have greater power. This power gain is primarily due to increased power to detect genetic variants with opposite effects on positively correlated traits and variants that are exclusively associated with a single trait. Relative to other methods, the combined-PC approach has close to optimal power in all scenarios considered while offering more flexibility and more robustness to potential confounders. Finally, we apply the proposed PCA strategy to the genome-wide association study of five correlated coagulation traits where we identify two candidate SNPs that were not found by the standard approach.