General Framework for Meta-analysis of Rare Variants in Sequencing Association Studies

We propose a general statistical framework for meta-analysis of gene- or region-based multimarker rare variant association tests in sequencing association studies. In genome-wide association studies, single-marker meta-analysis has been widely used to increase statistical power by combining results via regression coefficients and standard errors from different studies. In analysis of rare variants in sequencing studies, region-based multimarker tests are often used to increase power. We propose meta-analysis methods for commonly used gene- or region-based rare variants tests, such as burden tests and variance component tests. Because estimation of regression coefficients of individual rare variants is often unstable or not feasible, the proposed method avoids this difficulty by calculating score statistics instead that only require fitting the null model for each study and then aggregating these score statistics across studies. Our proposed meta-analysis rare variant association tests are conducted based on study-specific summary statistics, specifically score statistics for each variant and between-variant covariance-type (linkage disequilibrium) relationship statistics for each gene or region. The proposed methods are able to incorporate different levels of heterogeneity of genetic effects across studies and are applicable to meta-analysis of multiple ancestry groups. We show that the proposed methods are essentially as powerful as joint analysis by directly pooling individual level genotype data. We conduct extensive simulations to evaluate the performance of our methods by varying levels of heterogeneity across studies, and we apply the proposed methods to meta-analysis of rare variant effects in a multicohort study of the genetics of blood lipid levels.     

On individual genome-wide association studies and their meta-analysis

Individual genome-wide association (GWA) studies and their meta-analyses represent two approaches for identifying genetic loci associated with complex diseases/traits. Inconsistent findings and non-replicability between individual GWA studies and meta-analyses are commonly observed, hence posing the critical question as to how to interpret their respective results properly. In this study, we performed a series of simulation studies to investigate and compare the statistical properties of the two approaches. Our results show that (1) as expected, meta-analysis of larger sample size is more powerful than individual GWA studies under the ideal setting of population homogeneity among individual studies; (2) under the realistic setting of heterogeneity among individual studies, detection of heterogeneity is usually difficult and meta-analysis (even with the random-effects model) may introduce elevated false positive and/or negative rates; (3) despite relatively small sample size, well-designed individual GWA study has the capacity to identify novel loci for complex traits; (4) replicability between meta-analysis and independent individual studies or between independent meta-analyses is limited, and thus inconsistent findings are not unexpected.     

The meta-analysis of genome-wide association studies

The pressure to publish novel genetic associations has meant that meta-analysis has been applied to genome-wide association studies without the time for a careful consideration of the methods that are used. This review distinguishes between the use of meta-analysis to validate previously reported genetic associations and its use for gene discovery, and advocates viewing gene discovery as an exploratory screen that requires independent replication instead of treating it as the application of hundreds of thousands of statistical tests. The review considers the use of fixed and random effects meta-analyses, the investigation of between-study heterogeneity, adjustment for confounding, assessing the combined evidence and genomic control, and comments on alternative approaches that have been used in the literature.     

PICALM rs3851179 Variant Confers Susceptibility to Alzheimer’s Disease in Chinese Population

The association between PICALM rs3851179 variant and Alzheimer’s disease (AD) has been well established by previous genome-wide association studies (GWAS) and candidate gene studies in European population. Recent studies investigated the association between PICALM rs3851179 and AD susceptibility in Chinese population. However, these studies reported consistent and inconsistent results. Here, we selected 9435 samples including 3704 AD cases and 5731 controls from previous studies and evaluated this association using a meta-analysis method for additive model. We did not observe significant genetic heterogeneity in Chinese population. Our results indicate significant association between PICALM rs3851179 and AD in Chinese population. The sensitivity analysis indicates that the association between rs3851179 and AD did not vary substantially. The regression analysis suggests no significant publication bias. In summary, this updated meta-analysis highlights the involvement of PICALM rs3851179 variant in Alzheimer’s disease susceptibility in Chinese population.     

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models

Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates. The models can be used to analyze rare variants, common variants, or a combination of the two. Both likelihood-ratio test (LRT) and F-distributed statistics are introduced to test association between quantitative traits and multiple variants in one genetic region. Extensive simulations are performed to evaluate empirical type I error rates and power performance of the proposed tests. The proposed LRT and F-distributed statistics control the type I error very well and have higher power than the existing methods of the meta-analysis sequence kernel association test (MetaSKAT). We analyze four blood lipid levels in data from a meta-analysis of eight European studies. The proposed methods detect more significant associations than MetaSKAT and the P-values of the proposed LRT and F-distributed statistics are usually much smaller than those of MetaSKAT. The functional linear models and related test statistics can be useful in whole-genome and whole-exome association studies.     

Interpreting meta-analyses of genome-wide association studies

Meta-analysis is an increasingly popular tool for combining multiple genome-wide association studies in a single analysis to identify associations with small effect sizes. The effect sizes between studies in a meta-analysis may differ and these differences, or heterogeneity, can be caused by many factors. If heterogeneity is observed in the results of a meta-analysis, interpreting the cause of heterogeneity is important because the correct interpretation can lead to a better understanding of the disease and a more effective design of a replication study. However, interpreting heterogeneous results is difficult. The standard approach of examining the association p-values of the studies does not effectively predict if the effect exists in each study. In this paper, we propose a framework facilitating the interpretation of the results of a meta-analysis. Our framework is based on a new statistic representing the posterior probability that the effect exists in each study, which is estimated utilizing cross-study information. Simulations and application to the real data show that our framework can effectively segregate the studies predicted to have an effect, the studies predicted to not have an effect, and the ambiguous studies that are underpowered. In addition to helping interpretation, the new framework also allows us to develop a new association testing procedure taking into account the existence of effect.     

Meta-Analysis Indicates That the European GWAS-Identified Risk SNP rs1344706 within ZNF804A Is Not Associated with Schizophrenia in Han Chinese Population

Recent genetic association studies have implicated several candidate susceptibility variants for schizophrenia among general populations. Rs1344706, an intronic SNP within ZNF804A, was identified as one of the most compelling candidate risk SNPs for schizophrenia in Europeans through genome-wide association studies (GWASs) and replications as well as large-scale meta-analyses. However, in Han Chinese, the results for rs1344706 are inconsistent, and whether rs1344706 is an authentic risk SNP for schizophrenia in Han Chinese is inconclusive. Here, we conducted a systematic meta-analysis of rs1344706 with schizophrenia in Chinese population by combining all available case-control samples (N = 12), including a total of 8,982 cases and 12,342 controls. The results of our meta-analysis were not able to confirm an association of rs1344706 A-allele with schizophrenia (p = 0.10, odds ratio = 1.06, 95% confidence interval = 0.99–1.13). Such absence of association was further confirmed by the non-superiority test (p = 0.0003), suggesting that rs1344706 is not a risk SNP for schizophrenia in Han Chinese. Detailed examinations of individual samples revealed potential sampling bias in previous replication studies in Han Chinese. The absence of rs1344706 association in Han Chinese suggest a potential genetic heterogeneity in the susceptibility of schizophrenia on this locus and also demonstrate the difficulties in replicating genome-wide association findings of schizophrenia across different ethnic populations.     

Association between miR-146a rs2910164 polymorphism and autoimmune diseases susceptibility: A meta-analysis

Published data on the rs2910164 in microRNA-146a (miR-146a) are shown to be associated with increased or decreased autoimmune diseases risk. To derive a more precise estimation of the relationship, we performed a meta-analysis to systematically summarize the possible. A meta-analysis including 11 studies with 3042 controls and 2197 cases was performed for genotypes CC (recessive effect), CC + CG (dominant effect) and C allele in fixed or random-effects models based on between-study heterogeneity. Overall, no significant association between miR-146a G/C rs2910164 polymorphism and autoimmune diseases risk was found in all genetic models when all studies were pooled into the meta-analysis. SLE (OR = 0.99, 95% CI: 0.90–1.10), RA (OR = 0.98, 95% CI: 0.85–1.14) did not yield statistical significance as for C allele pooled studies. In the subgroup analysis by ethnicity, still no significant association was detected in all genetic models. Our meta-analysis suggests that there is no association between miR-146a G/C rs2910164 polymorphism and the development of autoimmune diseases.     

A unification of multivariate methods for meta-analysis of genetic association studies

Methods for multivariate meta-analysis of genetic association studies are reviewed, summarized and presented in a unified framework. Modifications of standard models are described in detail in order to be applied in genetic association studies. The model based on summary data is uniformly defined for both discrete and continuous outcomes and analytical expressions for the covariance of the two jointly modeled outcomes are derived for both cases. The models based on the binary nature of the data are fitted using both prospective and retrospective likelihood. Furthermore, formal tests for assessing the genetic model of inheritance are developed based on standard normal theory. The general model is compared to the recently proposed genetic model-free bivariate approach (either using summary or binary data), and it is clearly shown that the estimates provided by this approach are nearly identical to the estimates derived by the general bivariate model using the aforementioned tests for the genetic model. The methods developed here as well as the tests, are easily implemented in all major statistical packages, escaping the need of self written software. The methods are applied in several already published meta-analyses of genetic association studies (with both discrete and continuous outcomes) and the results are compared against the widely used univariate approach as well as against the genetic model free approaches. Illustrative examples of code in Stata are given in the appendix. It is anticipated that the methods developed in this work will be widely applied in the meta-analysis of genetic association studies.     

The E-cadherin (CDH1) -C160A polymorphism and colorectal cancer susceptibility: a meta-analysis

E-cadherin, encoded by the CDH1 gene, involves in invasion and metastasis of cancer cells. CDH1 -C160A polymorphism was shown to contribute to genetic susceptibility to colorectal cancer (CRC). However, the results from different studies remain controversial. This study was conducted to further explore the association between CDH1 -C160A genetic polymorphism and CRC susceptibility by means of a meta-analysis. A comprehensive literature search was conducted to identify all case-control studies of CDH1 -C160A polymorphism and risk for CRC. A total of nine eligible studies, including 7954 CRC cases and 7369 controls, were identified to the meta-analysis. On the whole, the meta-analysis indicated that CDH1 -C160A genetic polymorphism could reduce the risk of CRC under AA versus CC contrast (odds ratio [OR]=0.86, 95% confidence interval [CI]=0.75-0.98, p(heterogeneity)=0.11), recessive model (OR=0.88, 95% CI=0.77-0.99, p(heterogeneity)=0.23), dominant model (OR=0.92, 95% CI=0.87-0.99, p(heterogeneity)=0.11), and allele A versus allele C contrast (OR=0.93, 95% CI=0.88-0.98, p(heterogeneity)=0.26). A conclusion could be drawn from the research that CDH1 -C160A polymorphism provides a possible protection against CRC, which is especially evident in Caucasian and hospital populations.     

Helicobacter pylori infection and colorectal cancer risk: a meta-analysis

BACKGROUND: Several studies suggested an association between Helicobacter pylori infection and colorectal carcinoma or adenoma risk. However, different authors reported quite varying estimates. We carried out a systematic review and meta-analysis of published studies investigating this association and paid special attention to the possibility of publication bias and sources of heterogeneity between studies. Materials and METHODS: An extensive literature search and cross-referencing were performed to identify all published studies. Summary estimates were obtained using random-effects models. The presence of possible publication bias was assessed using different statistical approaches. RESULTS: In a meta-analysis of the 11 identified human studies, published between 1991 and 2002, a summary odds ratio of 1.4 (95% CI, 1.1-1.8) was estimated for the association between H. pylori infection and colorectal cancer risk. The graphical funnel plot appeared asymmetrical, but the formal statistical evaluations did not provide strong evidence of publication bias. The proportion of variation of study results because of heterogeneity was small (36.5%). CONCLUSIONS: The results of our meta-analysis are consistent with a possible small increase in risk of colorectal cancer because of H. pylori infection. However, the possibility of some publication bias cannot be ruled out, although it could not be statistically confirmed. Larger, better designed and better controlled studies are needed to clarify the situation.     

PTPN22 C1858T and the risk of psoriasis: a meta-analysis

Psoriasis is a chronic autoimmune skin disease with both environmental and genetic risk factors. Previous studies of the association between psoriasis and PTPN22 C1858T (rs2476601), a gain of function variant associated with a stronger inhibitory effect of T-lymphocytes, have produced inconsistent results. The purpose of the current study is to evaluate the association between PTPN22 C1858T and psoriasis using meta-analysis to: (1) have a sufficient sample size for detecting a weak association; and (2) to explore the heterogeneity between studies. A meta-analysis based on random-effects model was performed with ten studies (3,334 psoriasis cases and 5,753 controls) identified from a literature search. A non-significantly positive association between psoriasis and the PTPN22 T1858 was observed [summary allelic odds ratio (OR) = 1.15, 95 % confidence interval (CI): 1.00-1.33] and the association appears stronger among subjects with psoriatic arthritis (summary allelic OR = 1.23, 95 % CI: 1.00-1.52). A null association between PTPN22 T1858 and early-onset psoriasis was observed (summary allelic OR = 1.08, 95 % CI: 0.92-1.28). The current analysis showed a non-significantly positive association between psoriasis and the PTPN22 T1858 allele, and the association appeared stronger among subjects with psoriatic arthritis. Future studies of psoriasis should incorporate gene-environment interaction in the analysis and pay attention to the heterogeneity of psoriasis cases and bias associated with population stratification.     

A common SMAD7 variant is associated with risk of colorectal cancer: evidence from a case-control study and a meta-analysis

Background

A common genetic variant, rs4939827, located in SMAD7, was identified by two recent genome-wide association (GWA) studies to be strongly associated with the risk of colorectal cancer (CRC). However, the following replication studies yielded conflicting results.

Method and Findings

We conducted a case-control study of 641 cases and 1037 controls in a Chinese population and then performed a meta-analysis, integrating our and published data of 34313 cases and 33251 controls, to clarify the relationship between rs4939827 and CRC risk. In our case-control study, the dominant model was significant associated with increased CRC risk [Odds Ratio (OR) = 1.46; 95% confidence interval (95% CI), 1.19–1.80]. The following meta-analysis further confirmed this significant association for all genetic models but with significant between-study heterogeneity (all P for heterogeneity <0.1). By stratified analysis, we revealed that ethnicity, sample size, and tumor sites might constitute the source of heterogeneity. The cumulative analysis suggested that evident tendency to significant association was seen with adding study samples over time; whilst, sensitive analysis showed results before and after removal of each study were similar, indicating the highly stability of the current results.

Conclusion

Results from our case-control study and the meta-analysis collectively confirmed the significant association of the variant rs4939827 with increased risk of colorectal cancer. Nevertheless, fine-mapping of the susceptibility loci defined by rs4939287 should be imposed to reveal causal variant.     

Effectively Identifying eQTLs from Multiple Tissues by Combining Mixed Model and Meta-analytic Approaches

Gene expression data, in conjunction with information on genetic variants, have enabled studies to identify expression quantitative trait loci (eQTLs) or polymorphic locations in the genome that are associated with expression levels. Moreover, recent technological developments and cost decreases have further enabled studies to collect expression data in multiple tissues. One advantage of multiple tissue datasets is that studies can combine results from different tissues to identify eQTLs more accurately than examining each tissue separately. The idea of aggregating results of multiple tissues is closely related to the idea of meta-analysis which aggregates results of multiple genome-wide association studies to improve the power to detect associations. In principle, meta-analysis methods can be used to combine results from multiple tissues. However, eQTLs may have effects in only a single tissue, in all tissues, or in a subset of tissues with possibly different effect sizes. This heterogeneity in terms of effects across multiple tissues presents a key challenge to detect eQTLs. In this paper, we develop a framework that leverages two popular meta-analysis methods that address effect size heterogeneity to detect eQTLs across multiple tissues. We show by using simulations and multiple tissue data from mouse that our approach detects many eQTLs undetected by traditional eQTL methods. Additionally, our method provides an interpretation framework that accurately predicts whether an eQTL has an effect in a particular tissue.     

Replication Study in Chinese Population and Meta-Analysis Supports Association of the 11q23 Locus with Colorectal Cancer

Background

A common single nucleotide polymorphism (SNP), rs3802842, located at 11q23, was identified by genome-wide association studies (GWAS) to be significantly associated with the risk of colorectal cancer (CRC); however, the results of following replication studies were not always concordant. Thus, a case-control study and a meta-analysis were performed to clearly discern the effect of this variant in CRC.

Method and Findings

We determined the genotypes of rs3802842 in 641 unrelated Chinese patients with CRC and 1037 cancer-free controls. Additionally, a meta-analysis comprising current and previously published studies was conducted. In our case-control study, significant associations between the polymorphism and CRC risk were observed in all genetic models, with an additive OR being 1.45 (95% CI = 1.26–1.67). The meta-analysis of 38534 cases and 39446 controls further confirmed the significant associations in all genetic models but with obvious between-study heterogeneity. Nevertheless, ethnicity, study type and whether subjects affected by Lynch syndrome could synthetically accounted for the heterogeneity. Besides, the cumulative and sensitivity analyses indicated the robust stability of the results.

Conclusion

The results from our case-control study and meta-analysis provided convincing evidence that rs3802842 significantly contributed to CRC risk.     

Association of angiotensin-converting enzyme I/D polymorphism with heart failure: a meta-analysis

Heart failure (HF) is a complex clinical syndrome and is thought to have a genetic basis. Numerous case-control studies have investigated the association between heart failure and polymorphisms in candidate genes. Most studies focused on the angiotensin-converting enzyme insertion/deletion (ACE I/D) polymorphism, however, the results were inconsistent because of small studies and heterogeneous samples. The objective was to assess the association between the ACE I/D polymorphism and HF. We performed a meta-analysis of all case-control studies that evaluated the association between ACE I/D polymorphism and HF in humans. Studies were identified in the PUBMED and EMBASE databases, reviews, and reference lists of relevant articles. Two reviewers independently assessed the studies. Seventeen case-control studies with a total of 5576 participants were included in the meta-analysis, including 2453 cases with HF and 3123 controls. The heterogeneity between studies was significant. No association was found under all the four genetic models (D vs. I, DD vs. ID and II, DD and ID vs. II, DD vs. ID). Subgroup analyses for ischemic HF (IHF) and HF because of dilated cardiomyopathy (DHF) also showed no significant association between ACE I/D polymorphism and HF. No significant association between the ACE I/D polymorphism and risk of HF was found in this meta-analysis. The future studies should focus on large-scale prospective and case-control studies which designed to investigate gene-gene and gene-environment interactions to shed light on the genetics of HF.     

Quantitative Assessment of the Association Between MS Gene Polymorphism and Colorectal Cancer Risk

Accumulating genetic association studies have investigated the risk of colorectal cancer (CRC) in relation to MS gene polymorphism with uncertain conclusions. In the current study, we sought to assess the association between MS gene and CRC. We performed an updated meta-analysis including 18 case-control studies with a total of 10, 303 CRC patients and 15, 389 CRC-free controls to estimate the strength of the association using odds ratios with the corresponding 95 % confidence intervals. Overall, no CRC risk associated with the genotypes of MS gene polymorphism was indicated in our meta-analysis. Similarly, the stratified analysis according to ethnicity and control source did not show any evident association either. The results of our updated meta-analysis suggest that MS gene polymorphism may not serve as a biomarker for the CRC risk. Future large-scale and well-designed studies are required to clarify the association identified in the present meta-analysis.     

The Impact of Phenotypic and Genetic Heterogeneity on Results of Genome Wide Association Studies of Complex Diseases

Phenotypic misclassification (between cases) has been shown to reduce the power to detect association in genetic studies. However, it is conceivable that complex traits are heterogeneous with respect to individual genetic susceptibility and disease pathophysiology, and that the effect of heterogeneity has a larger magnitude than the effect of phenotyping errors. Although an intuitively clear concept, the effect of heterogeneity on genetic studies of common diseases has received little attention. Here we investigate the impact of phenotypic and genetic heterogeneity on the statistical power of genome wide association studies (GWAS). We first performed a study of simulated genotypic and phenotypic data. Next, we analyzed the Wellcome Trust Case-Control Consortium (WTCCC) data for diabetes mellitus (DM) type 1 (T1D) and type 2 (T2D), using varying proportions of each type of diabetes in order to examine the impact of heterogeneity on the strength and statistical significance of association previously found in the WTCCC data. In both simulated and real data, heterogeneity (presence of “non-cases”) reduced the statistical power to detect genetic association and greatly decreased the estimates of risk attributed to genetic variation. This finding was also supported by the analysis of loci validated in subsequent large-scale meta-analyses. For example, heterogeneity of 50% increases the required sample size by approximately three times. These results suggest that accurate phenotype delineation may be more important for detecting true genetic associations than increase in sample size.     

The SNP rs1883832 in CD40 Gene and Risk of Atherosclerosis in Chinese Population: A Meta-Analysis

Background

The complications of atherosclerosis such as coronary and cerebrovascular disease, are the most prevalent causes of mortality and morbidity worldwide. A single nucleotide polymorphism (SNP) rs1883832 (-1C/T) in CD40 gene has been recently suggested to contribute to the susceptibility to atherosclerosis in Chinese population; however, previous genetic association studies yielded inconsistent results.

Methods

A meta-analysis of eligible studies reporting the association between rs1883832 and atherosclerosis in Chinese population was carried out.

Results

Pooling 7 eligible case-control studies involving 2129 patients and 1895 controls demonstrated a significant association between rs1883832 and atherosclerosis under dominant model [odds ratio (OR) = 1.631, 95% confidence interval [CI] [1.176, 2.260] in Chinese population with evident heterogeneity. Meta-regression analysis indicated that the heterogeneity could be completely explained by disease category. In subgroup analysis, rs1883832 conferred ORs of 2.866 (C/C versus T/T, 95%CI [2.203, 3.729]) and 1.680 (C/T versus T/T, 95%CI [1.352, 2.086]) for coronary artery disease (CAD) under co-dominant model without heterogeneity. Similar results were obtained for acute coronary syndrome (ACS) (C/C versus T/T, 3.674, 95%CI [2.638, 5.116]; C/T versus T/T, 1.981, 95%CI [1.483, 2.646]). The other genetic models including dominant, recessive and additive models, yielded consistent results without heterogeneity for CAD and ACS, respectively. However, a protective role was found for C allele in ischemic stroke (IS) under recessive model (0.582, 95%CI [0.393, 0.864]) and additive model (0.785, 95%CI [0.679, 0.909]) with reduced heterogeneity.

Conclusions

This meta-analysis provided evidence of association of rs1883832 C allele with an overall increased risk of atherosclerosis but distinct effect of C allele on CAD (including ACS) and IS in Chinese population, respectively.