Basic statistical analysis in genetic case-control studies

This protocol describes how to perform basic statistical analysis in a population-based genetic association case-control study. The steps described involve the (i) appropriate selection of measures of association and relevance of disease models; (ii) appropriate selection of tests of association; (iii) visualization and interpretation of results; (iv) consideration of appropriate methods to control for multiple testing; and (v) replication strategies. Assuming no previous experience with software such as PLINK, R or Haploview, we describe how to use these popular tools for handling single-nucleotide polymorphism data in order to carry out tests of association and visualize and interpret results. This protocol assumes that data quality assessment and control has been performed, as described in a previous protocol, so that samples and markers deemed to have the potential to introduce bias to the study have been identified and removed. Study design, marker selection and quality control of case-control studies have also been discussed in earlier protocols. The protocol should take ~1 h to complete.     

Data quality control in genetic case-control association studies

This protocol details the steps for data quality assessment and control that are typically carried out during case-control association studies. The steps described involve the identification and removal of DNA samples and markers that introduce bias. These critical steps are paramount to the success of a case-control study and are necessary before statistically testing for association. We describe how to use PLINK, a tool for handling SNP data, to perform assessments of failure rate per individual and per SNP and to assess the degree of relatedness between individuals. We also detail other quality-control procedures, including the use of SMARTPCA software for the identification of ancestral outliers. These platforms were selected because they are user-friendly, widely used and computationally efficient. Steps needed to detect and establish a disease association using case-control data are not discussed here. Issues concerning study design and marker selection in case-control studies have been discussed in our earlier protocols. This protocol, which is routinely used in our labs, should take approximately 8 h to complete.     

复杂疾病全基因组关联研究进展——遗传统计分析

2005年, Science杂志首次报道了有关人类年龄相关性黄斑变性的全基因组关联研究, 此后有关肥胖、2型糖尿病、冠心病、阿尔茨海默病等一系列复杂疾病的全基因组关联研究被陆续报道, 这一阶段被称为人类全基因组关联研究的第一次浪潮。文章分别介绍了全基因组关联研究统计分析的方法、软件和应用实例; 比较了关联分析中多重检验的P值调整方法, 包括Bonferroni、递减的Bonferroni校正法、模拟运算法和控制错误发现率的方法; 还讨论了人群混杂对关联分析结果可能产生的影响及原理, 以及全基因组关联研究中控制人群混杂的方法的研究进展和应用实例。在全基因组关联研究的第一次浪潮中, 应用经典的遗传统计方法发现了许多基因-表型之间的关联并且能够对这些关联做出解释, 其中包括许多基因组中的未知基因和染色体区域。然而, 全基因组关联研究的继续发展需要进一步阐述基因组内基因之间相互作用、基因-基因之间的复杂作用网络与环境因素的相互作用在复杂疾病发生中的作用, 现有的统计分析方法肯定不能满足需要, 开发更为高级的统计分析方法势在必行。最后, 文章还给出了全基因组关联研究统计分析软件的相关网站信息。     

Strategies and issues in the detection of pathway enrichment in genome-wide association studies

A fundamental question in human genetics is the degree to which the polygenic character of complex traits derives from polymorphism in genes with similar or with dissimilar functions. The many genome-wide association studies now being performed offer an opportunity to investigate this, and although early attempts are emerging, new tools and modeling strategies still need to be developed and deployed. Towards this goal, we implemented a new algorithm to facilitate the transition from genetic marker lists (principally those generated by PLINK) to pathway analyses of representational gene sets in either threshold or threshold-free downstream applications (e.g. DAVID, GSEA-P, and Ingenuity Pathway Analysis). This was applied to several large genome-wide association studies covering diverse human traits that included type 2 diabetes, Crohn’s disease, and plasma lipid levels. Validation of this approach was obtained for plasma HDL levels, where functional categories related to lipid metabolism emerged as the most significant in two independent studies. From analyses of these samples, we highlight and address numerous issues related to this strategy, including appropriate gene based correction statistics, the utility of imputed versus non-imputed marker sets, and the apparent enrichment of pathways due solely to the positional clustering of functionally related genes. The latter in particular emphasizes the importance of studies that directly tie genetic variation to functional characteristics of specific genes. The software freely provided that we have called ProxyGeneLD may resolve an important bottleneck in pathway-based analyses of genome-wide association data. This has allowed us to identify at least one replicable case of pathway enrichment but also to highlight functional gene clustering as a potentially serious problem that may lead to spurious pathway findings if not corrected. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Association Studies of Sporadic Parkinson’s Disease in the Genomic Era

Parkinson’s disease is a common age-related progressive neurodegenerative disorder. Over the last 10 years, advances have been made in our understanding of the etiology of the disease with the greatest insights perhaps coming from genetic studies, including genome-wide association approaches. These large scale studies allow the identification of genomic regions harboring common variants associated to disease risk. Since the first genome-wide association study on sporadic Parkinson’s disease performed in 2005, improvements in study design, including the advent of meta-analyses, have allowed the identification of ~21 susceptibility loci. The first loci to be nominated were previously associated to familial PD (SNCA, MAPT, LRRK2) and these have been extensively replicated. For other more recently identified loci (SREBF1, SCARB2, RIT2) independent replication is still warranted. Cumulative risk estimates of associated variants suggest that more loci are still to be discovered. Additional association studies combined with deep re-sequencing of known genome-wide association study loci are necessary to identify the functional variants that drive disease risk. As each of these associated genes and variants are identified they will give insight into the biological pathways involved the etiology of Parkinson’s disease. This will ultimately lead to the identification of molecules that can be used as biomarkers for diagnosis and as targets for the development of better, personalized treatment.     

The efficacy of detecting variants with small effects on the Affymetrix 6.0 platform using pooled DNA

Genome-wide genotyping of a cohort using pools rather than individual samples has long been proposed as a cost-saving alternative for performing genome-wide association (GWA) studies. However, successful disease gene mapping using pooled genotyping has thus far been limited to detecting common variants with large effect sizes, which tend not to exist for many complex common diseases or traits. Therefore, for DNA pooling to be a viable strategy for conducting GWA studies, it is important to determine whether commonly used genome-wide SNP array platforms such as the Affymetrix 6.0 array can reliably detect common variants of small effect sizes using pooled DNA. Taking obesity and age at menarche as examples of human complex traits, we assessed the feasibility of genome-wide genotyping of pooled DNA as a single-stage design for phenotype association. By individually genotyping the top associations identified by pooling, we obtained a 14- to 16-fold enrichment of SNPs nominally associated with the phenotype, but we likely missed the top true associations. In addition, we assessed whether genotyping pooled DNA can serve as an inexpensive screen as the second stage of a multi-stage design with a large number of samples by comparing the most cost-effective 3-stage designs with 80% power to detect common variants with genotypic relative risk of 1.1, with and without pooling. Given the current state of the specific technology we employed and the associated genotyping costs, we showed through simulation that a design involving pooling would be 1.07 times more expensive than a design without pooling. Thus, while a significant amount of information exists within the data from pooled DNA, our analysis does not support genotyping pooled DNA as a means to efficiently identify common variants contributing small effects to phenotypes of interest. While our conclusions were based on the specific technology and study design we employed, the approach presented here will be useful for evaluating the utility of other or future genome-wide genotyping platforms in pooled DNA studies.     

Rodent models of rheumatoid arthritis

The study of rheumatoid arthritis is greatly facilitated by animal models that enable investigation of a complex system involving inflammation, immunological tolerance, and autoimmunity. Although the models cover several species and pathogenetic mechanisms and can be classified as induced or spontaneous, all converge on arthritis. However, because each model features a different mechanism driving disease expression, the merits of each should be evaluated carefully in making the appropriate choice for the scientific question to be addressed. In addition, because the incidence and kinetics of disease vary by model, careful thought should be given to protocol design to minimize animal use.     

On the meta-analysis of genome-wide association studies: a robust and efficient approach to combine population and family-based studies

For the meta-analysis of genome-wide association studies, we propose a new method to adjust for the population stratification and a linear mixed approach that combines family-based and unrelated samples. The proposed approach achieves similar power levels as a standard meta-analysis which combines the different test statistics or p values across studies. However, by virtue of its design, the proposed approach is robust against population admixture and stratification, and no adjustments for population admixture and stratification, even in unrelated samples, are required. Using simulation studies, we examine the power of the proposed method and compare it to standard approaches in the meta-analysis of genome-wide association studies. The practical features of the approach are illustrated with a meta-analysis of three genome-wide association studies for Alzheimer's disease. We identify three single nucleotide polymorphisms showing significant genome-wide association with affection status. Two single nucleotide polymorphisms are novel and will be verified in other populations in our follow-up study.     

Practical aspects of genome-wide association interaction analysis

Large-scale epistasis studies can give new clues to system-level genetic mechanisms and a better understanding of the underlying biology of human complex disease traits. Though many novel methods have been proposed to carry out such studies, so far only a few of them have demonstrated replicable results. Here, we propose a minimal protocol for genome-wide association interaction (GWAI) analysis to identify gene–gene interactions from large-scale genomic data. The different steps of the developed protocol are discussed and motivated, and encompass interaction screening in a hypothesis-free and hypothesis-driven manner. In particular, we examine a wide range of aspects related to epistasis discovery in the context of complex traits in humans, hereby giving practical recommendations for data quality control, variant selection or prioritization strategies and analytic tools, replication and meta-analysis, biological validation of statistical findings and other related aspects. The minimal protocol provides guidelines and attention points for anyone involved in GWAI analysis and aims to enhance the biological relevance of GWAI findings. At the same time, the protocol improves a better assessment of strengths and weaknesses of published GWAI methodologies.     

Coarse-Grained Molecular Models of Water: A Review

Coarse-grained (CG) models have proven to be very effective tools in the study of phenomena or systems that involve large time- and length-scales. By decreasing the degrees of freedom in the system and using softer interactions than seen in atomistic models, larger timesteps can be used and much longer simulation times can be studied. CG simulations are widely used to study systems of biological importance that are beyond the reach of atomistic simulation, necessitating a computationally efficient and accurate CG model for water. In this review, we discuss the methods used for developing CG water models and the relative advantages and disadvantages of the resulting models. In general, CG water models differ with regards to how many waters each CG group or bead represents, whether analytical or tabular potentials have been used to describe the interactions, and how the model incorporates electrostatic interactions. Finally, how the models are parameterized depends on their application, so, while some are fitted to experimental properties such as surface tension and density, others are fitted to radial distribution functions extracted from atomistic simulations.     

Integrated analysis of genome-wide genetic and epigenetic association data for identification of disease mechanisms

Many human diseases are multifactorial, involving multiple genetic and environmental factors impacting on one or more biological pathways. Much of the environmental effect is believed to be mediated through epigenetic changes. Although many genome-wide genetic and epigenetic association studies have been conducted for different diseases and traits, it is still far from clear to what extent the genomic loci and biological pathways identified in the genetic and epigenetic studies are shared. There is also a lack of statistical tools to assess these important aspects of disease mechanisms. In the present study, we describe a protocol for the integrated analysis of genome-wide genetic and epigenetic data based on permutation of a sum statistic for the combined effects in a locus or pathway. The method was then applied to published type 1 diabetes (T1D) genome-wide- and epigenome-wide-association studies data to identify genomic loci and biological pathways that are associated with T1D genetically and epigenetically. Through combined analysis, novel loci and pathways were also identified, which could add to our understanding of disease mechanisms of T1D as well as complex diseases in general.     

Genome-wide association mapping in Arabidopsis identifies previously known flowering time and pathogen resistance genes

There is currently tremendous interest in the possibility of using genome-wide association mapping to identify genes responsible for natural variation, particularly for human disease susceptibility. The model plant Arabidopsis thaliana is in many ways an ideal candidate for such studies, because it is a highly selfing hermaphrodite. As a result, the species largely exists as a collection of naturally occurring inbred lines, or accessions, which can be genotyped once and phenotyped repeatedly. Furthermore, linkage disequilibrium in such a species will be much more extensive than in a comparable outcrossing species. We tested the feasibility of genome-wide association mapping in A. thaliana by searching for associations with flowering time and pathogen resistance in a sample of 95 accessions for which genome-wide polymorphism data were available. In spite of an extremely high rate of false positives due to population structure, we were able to identify known major genes for all phenotypes tested, thus demonstrating the potential of genome-wide association mapping in A. thaliana and other species with similar patterns of variation. The rate of false positives differed strongly between traits, with more clinal traits showing the highest rate. However, the false positive rates were always substantial regardless of the trait, highlighting the necessity of an appropriate genomic control in association studies.     

High Resolution Esophageal Manometry in Patients with Chagas Disease: A Cross-Sectional Evaluation

Introduction

Gastrointestinal involvement affects 30–40% of the patients with chronic Chagas disease. Esophageal symptoms appear once the structural damage is established. Little is known about the usefulness of high resolution manometry to early identification of esophageal involvement.

Method

We performed a cross-sectional study at the Vall d’Hebron University Hospital (Barcelona, Spain) between May 2011 and April 2012. Consecutive patients diagnosed with Chagas disease in the chronic phase were offered to participate. All patients underwent a structured questionnaire about digestive symptoms, a barium esophagogram (Rezende classification) and an esophageal high resolution manometry (HRM). A control group of patients with heartburn who underwent an esophageal HRM in our hospital was selected.

Results

62 out of 73 patients that were included in the study fulfilled the study protocol. The median age of the Chagas disease group (CG) was 37 (IQR 32–45) years, and 42 (67.7%) patients were female. Twenty-seven (43.5%) patients had esophageal symptoms, heartburn being the most frequent. Esophagogram was abnormal in 5 (8.77%). The esophageal HRM in the CG showed a pathological motility pattern in 14 patients (22.6%). All of them had minor disorders of the peristalsis (13 with ineffective esophageal motility and 1 with fragmented peristalsis). Hypotonic lower esophageal sphincter was found more frequently in the CG than in the control group (21% vs 3.3%; p<0.01). Upper esophageal sphincter was hypertonic in 22 (35.5%) and hypotonic in 1 patient. When comparing specific manometric parameters or patterns in the CG according to the presence of symptoms or esophagogram no statistically significant association were seen, except for distal latency.

Conclusion

The esophageal involvement measured by HRM in patients with chronic Chagas disease in our cohort is 22.6%. All the patients with esophageal alterations had minor disorders of the peristalsis. Symptoms and esophagogram results did not correlate with the HRM results.     

Genome-Wide Association Scan for Variants Associated with Early-Onset Prostate Cancer

Prostate cancer is the most common non-skin cancer and the second leading cause of cancer related mortality for men in the United States. There is strong empirical and epidemiological evidence supporting a stronger role of genetics in early-onset prostate cancer. We performed a genome-wide association scan for early-onset prostate cancer. Novel aspects of this study include the focus on early-onset disease (defined as men with prostate cancer diagnosed before age 56 years) and use of publically available control genotype data from previous genome-wide association studies. We found genome-wide significant (p<5×10⁻⁸) evidence for variants at 8q24 and 11p15 and strong supportive evidence for a number of previously reported loci. We found little evidence for individual or systematic inflated association findings resulting from using public controls, demonstrating the utility of using public control data in large-scale genetic association studies of common variants. Taken together, these results demonstrate the importance of established common genetic variants for early-onset prostate cancer and the power of including early-onset prostate cancer cases in genetic association studies.     

Investigating the Role of Mitochondrial Haplogroups in Genetic Predisposition to Meningococcal Disease

Background and Aims

Meningococcal disease remains one of the most important infectious causes of death in industrialized countries. The highly diverse clinical presentation and prognosis of Neisseria meningitidis infections are the result of complex host genetics and environmental interactions. We investigated whether mitochondrial genetic background contributes to meningococcal disease (MD) susceptibility.

Methodology/Principal Findings

Prospective controlled study was performed through a national research network on MD that includes 41 Spanish hospitals. Cases were 307 paediatric patients with confirmed MD, representing the largest series of MD patients analysed to date. Two independent sets of ethnicity-matched control samples (CG1 [N = 917]), and CG2 [N = 616]) were used for comparison. Cases and controls underwent mtDNA haplotyping of a selected set of 25 mtDNA SNPs (mtSNPs), some of them defining major European branches of the mtDNA phylogeny. In addition, 34 ancestry informative markers (AIMs) were genotyped in cases and CG2 in order to monitor potential hidden population stratification. Samples of known African, Native American and European ancestry (N = 711) were used as classification sets for the determination of ancestral membership of our MD patients. A total of 39 individuals were eliminated from the main statistical analyses (including fourteen gypsies) on the basis of either non-Spanish self-reported ancestry or the results of AIMs indicating a European membership lower than 95%. Association analysis of the remaining 268 cases against CG1 suggested an overrepresentation of the synonym mtSNP G11719A variant (Pearson''s chi-square test; adjusted P-value = 0.0188; OR [95% CI] = 1.63 [1.22–2.18]). When cases were compared with CG2, the positive association could not be replicated. No positive association has been observed between haplogroup (hg) status of cases and CG1/CG2 and hg status of cases and several clinical variants.

Conclusions

We did not find evidence of association between mtSNPs and mtDNA hgs with MD after carefully monitoring the confounding effect of population sub-structure. MtDNA variability is particularly stratified in human populations owing to its low effective population size in comparison with autosomal markers and therefore, special care should be taken in the interpretation of seeming signals of positive associations in mtDNA case-control association studies.     

Contribution of Genome-Wide Association Studies to Scientific Research: A Bibliometric Survey of the Citation Impacts of GWAS and Candidate Gene Studies Published during the Same Period and in the Same Journals

In genetic epidemiology, genome-wide association studies (GWAS) are used to rapidly scan a large set of genetic variants and thus to identify associations with a particular trait or disease. The GWAS philosophy is different to that of conventional candidate-gene-based approaches, which directly test the effects of genetic variants of potentially contributory genes in an association study. One controversial question is whether GWAS provide relevant scientific outcomes by comparison with candidate-gene studies. We thus performed a bibliometric study using two citation metrics to assess whether the GWAS have contributed a capital gain in knowledge discovery by comparison with candidate-gene approaches. We selected GWAS published between 2005 and 2009 and matched them with candidate-gene studies on the same topic and published in the same period of time. We observed that the GWAS papers have received, on average, 30±55 citations more than the candidate gene papers, 1 year after their publication date, and 39±58 citations more 2 years after their publication date. The GWAS papers were, on average, 2.8±2.4 and 2.9±2.4 times more cited than expected, 1 and 2 years after their publication date; whereas the candidate gene papers were 1.5±1.2 and 1.5±1.4 times more cited than expected. While the evaluation of the contribution to scientific research through citation metrics may be challenged, it cannot be denied that GWAS are great hypothesis generators, and are a powerful complement to candidate gene studies.     

Natural genetic variation in Drosophila melanogaster reveals genes associated with Coxiella burnetii infection

The gram-negative bacterium Coxiella burnetii is the causative agent of Query (Q) fever in humans and coxiellosis in livestock. Host genetics are associated with C. burnetii pathogenesis both in humans and animals; however, it remains unknown if specific genes are associated with severity of infection. We employed the Drosophila Genetics Reference Panel to perform a genome-wide association study to identify host genetic variants that affect host survival to C. burnetii infection. The genome-wide association study identified 64 unique variants (P < 10⁻⁵) associated with 25 candidate genes. We examined the role each candidate gene contributes to host survival during C. burnetii infection using flies carrying a null mutation or RNAi knockdown of each candidate. We validated 15 of the 25 candidate genes using at least one method. This is the first report establishing involvement of many of these genes or their homologs with C. burnetii susceptibility in any system. Among the validated genes, FER and tara play roles in the JAK/STAT, JNK, and decapentaplegic/TGF-β signaling pathways which are components of known innate immune responses to C. burnetii infection. CG42673 and DIP-ε play roles in bacterial infection and synaptic signaling but have no previous association with C. burnetii pathogenesis. Furthermore, since the mammalian ortholog of CG13404 (PLGRKT) is an important regulator of macrophage function, CG13404 could play a role in host susceptibility to C. burnetii through hemocyte regulation. These insights provide a foundation for further investigation regarding the genetics of C. burnetii susceptibility across a wide variety of hosts.     

Effects of cis and trans regulatory variations on the expression divergence of heat shock response genes between yeast strains

Recently, two genome-wide association studies in Asia identified gene polymorphisms known as rs4488809, rs9816619 in TP63 and rs2131877, rs952481 in C3orf21. It has been proposed that these polymorphisms are susceptibility loci for non-small cell lung cancer (NSCLC) development among Japanese and Korean populations. We ask whether susceptibility to NSCLC is limited to the Chinese population or whether the environment also affects genetic polymorphisms. We conducted a matched case-control study to explore this question. Results show that polymorphism of TP63 was not associated with NSCLC development, whereas variant genotypes of C3orf21 were nominally associated with a reduced risk of lung adenocarcinoma (OR=0.619, 95% CI=0.390-0.976). These results strongly suggest that environmental agents interact with human genetic polymorphism independent of ethnic background. In addition, the C3orf21 gene may be a potential susceptibility marker for lung adenocarcinoma independent of ethnic background and environmental agents.