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.     

Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies

Meta-analysis is an increasingly popular tool for combining multiple different genome-wide association studies (GWASs) in a single aggregate analysis in order to identify associations with very small effect sizes. Because the data of a meta-analysis can be heterogeneous, referring to the differences in effect sizes between the collected studies, what is often done in the literature is to apply both the fixed-effects model (FE) under an assumption of the same effect size between studies and the random-effects model (RE) under an assumption of varying effect size between studies. However, surprisingly, RE gives less significant p values than FE at variants that actually show varying effect sizes between studies. This is ironic because RE is designed specifically for the case in which there is heterogeneity. As a result, usually, RE does not discover any associations that FE did not discover. In this paper, we show that the underlying reason for this phenomenon is that RE implicitly assumes a markedly conservative null-hypothesis model, and we present a new random-effects model that relaxes the conservative assumption. Unlike the traditional RE, the new method is shown to achieve higher statistical power than FE when there is heterogeneity, indicating that the new method has practical utility for discovering associations in the meta-analysis of GWASs.     

Meta-analysis of genetic association studies

Meta-analysis, a statistical tool for combining results across studies, is becoming popular as a method for resolving discrepancies in genetic association studies. Persistent difficulties in obtaining robust, replicable results in genetic association studies are almost certainly because genetic effects are small, requiring studies with many thousands of subjects to be detected. In this article, we describe how meta-analysis works and consider whether it will solve the problem of underpowered studies or whether it is another affliction visited by statisticians on geneticists. We show that meta-analysis has been successful in revealing unexpected sources of heterogeneity, such as publication bias. If heterogeneity is adequately recognized and taken into account, meta-analysis can confirm the involvement of a genetic variant, but it is not a substitute for an adequately powered primary study.     

The impact of imputation on meta-analysis of genome-wide association studies

Genotype imputation is often used in the meta-analysis of genome-wide association studies (GWAS), for combining data from different studies and/or genotyping platforms, in order to improve the ability for detecting disease variants with small to moderate effects. However, how genotype imputation affects the performance of the meta-analysis of GWAS is largely unknown. In this study, we investigated the effects of genotype imputation on the performance of meta-analysis through simulations based on empirical data from the Framingham Heart Study. We found that when fix-effects models were used, considerable between-study heterogeneity was detected when causal variants were typed in only some but not all individual studies, resulting in up to ～25% reduction of detection power. For certain situations, the power of the meta-analysis can be even less than that of individual studies. Additional analyses showed that the detection power was slightly improved when between-study heterogeneity was partially controlled through the random-effects model, relative to that of the fixed-effects model. Our study may aid in the planning, data analysis, and interpretation of GWAS meta-analysis results when genotype imputation is necessary.     

Nonparametric evaluation of quantitative traits in population-based association studies when the genetic model is unknown

Statistical association between a single nucleotide polymorphism (SNP) genotype and a quantitative trait in genome-wide association studies is usually assessed using a linear regression model, or, in the case of non-normally distributed trait values, using the Kruskal-Wallis test. While linear regression models assume an additive mode of inheritance via equi-distant genotype scores, Kruskal-Wallis test merely tests global differences in trait values associated with the three genotype groups. Both approaches thus exhibit suboptimal power when the underlying inheritance mode is dominant or recessive. Furthermore, these tests do not perform well in the common situations when only a few trait values are available in a rare genotype category (disbalance), or when the values associated with the three genotype categories exhibit unequal variance (variance heterogeneity). We propose a maximum test based on Marcus-type multiple contrast test for relative effect sizes. This test allows model-specific testing of either dominant, additive or recessive mode of inheritance, and it is robust against variance heterogeneity. We show how to obtain mode-specific simultaneous confidence intervals for the relative effect sizes to aid in interpreting the biological relevance of the results. Further, we discuss the use of a related all-pairwise comparisons contrast test with range preserving confidence intervals as an alternative to Kruskal-Wallis heterogeneity test. We applied the proposed maximum test to the Bogalusa Heart Study dataset, and gained a remarkable increase in the power to detect association, particularly for rare genotypes. Our simulation study also demonstrated that the proposed non-parametric tests control family-wise error rate in the presence of non-normality and variance heterogeneity contrary to the standard parametric approaches. We provide a publicly available R library nparcomp that can be used to estimate simultaneous confidence intervals or compatible multiplicity-adjusted p-values associated with the proposed maximum test.     

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.     

Identification of cancer genomic markers via integrative sparse boosting

In high-throughput cancer genomic studies, markers identified from the analysis of single data sets often suffer a lack of reproducibility because of the small sample sizes. An ideal solution is to conduct large-scale prospective studies, which are extremely expensive and time consuming. A cost-effective remedy is to pool data from multiple comparable studies and conduct integrative analysis. Integrative analysis of multiple data sets is challenging because of the high dimensionality of genomic measurements and heterogeneity among studies. In this article, we propose a sparse boosting approach for marker identification in integrative analysis of multiple heterogeneous cancer diagnosis studies with gene expression measurements. The proposed approach can effectively accommodate the heterogeneity among multiple studies and identify markers with consistent effects across studies. Simulation shows that the proposed approach has satisfactory identification results and outperforms alternatives including an intensity approach and meta-analysis. The proposed approach is used to identify markers of pancreatic cancer and liver cancer.     

The SNP rs931794 in 15q25.1 Is Associated with Lung Cancer Risk: A Hospital-Based Case-Control Study and Meta-Analysis

Background

Lung cancer is one of the most common human malignant diseases and the leading cause of cancer death worldwide. The rs931794, a SNP located in 15q25.1, has been suggested to be associated with lung cancer risk. Nevertheless, several genetic association studies yielded controversial results.

Methods and Findings

A hospital-based case-control study involving 611 cases and 1062 controls revealed the variant of rs931794 was related to increased lung cancer risk. Stratified analyses revealed the G allele was significantly associated with lung cancer risk among smokers. Following meta-analysis including 6616 cases and 7697 controls confirmed the relevance of rs931794 variant with increased lung cancer risk once again. Heterogeneity should be taken into account when interpreting the consequences. Stratified analysis found ethnicity, histological type and genotyping method were not the sources of between-study heterogeneity. Further sensitivity analysis revealed that the study “Hsiung et al (2010)” might be the major contributor to heterogeneity. Cumulative meta-analysis showed the trend was increasingly obvious with adding studies, confirming the significant association.

Conclusions

Results from our current case-control study and meta-analysis offered insight of association between rs931794 and lung cancer risk, suggesting the variant of rs931794 might be related with increased lung cancer risk.     

Endothelial nitric oxide synthase gene polymorphisms and essential hypertension in Han Chinese

We examined the effect of polymorphisms in the endothelial nitric oxide synthase gene on the risk for essential hypertension in a Han Chinese population through a meta-analysis of data from 15 studies. Associations between increased risk for essential hypertension and 4b/a were obtained in a dominant model and allele contrast (aa + ab vs bb: odds ratio (OR)(FE) = 1.26, 95% confidence interval (CI) = 1.10-1.44; a vs b allele: OR(FE) = 1.23, 95%CI: 1.09-1.40). Four studies with sample sizes over 500 produced similar results. No evidence of publication bias was found. Also, no significant heterogeneity was observed among these studies. When we examined the G894T polymorphism, we found a marginally significant association for allele contrast and the recessive model when all the eligible studies were pooled together. However, there was no evidence for a significant association after the exclusion of two studies deviating from Hardy-Weinberg equilibrium in the control group. Heterogeneity among studies was observed. Results of cumulative and recursive cumulative meta-analysis indicated that more studies are needed to objectively determine the effects of these two polymorphisms.     

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.     

Microarray-based maps of copy-number variant regions in European and sub-Saharan populations

The genetic basis of phenotypic variation can be partially explained by the presence of copy-number variations (CNVs). Currently available methods for CNV assessment include high-density single-nucleotide polymorphism (SNP) microarrays that have become an indispensable tool in genome-wide association studies (GWAS). However, insufficient concordance rates between different CNV assessment methods call for cautious interpretation of results from CNV-based genetic association studies. Here we provide a cross-population, microarray-based map of copy-number variant regions (CNVRs) to enable reliable interpretation of CNV association findings. We used the Affymetrix Genome-Wide Human SNP Array 6.0 to scan the genomes of 1167 individuals from two ethnically distinct populations (Europe, N=717; Rwanda, N=450). Three different CNV-finding algorithms were tested and compared for sensitivity, specificity, and feasibility. Two algorithms were subsequently used to construct CNVR maps, which were also validated by processing subsamples with additional microarray platforms (Illumina 1M-Duo BeadChip, Nimblegen 385K aCGH array) and by comparing our data with publicly available information. Both algorithms detected a total of 42669 CNVs, 74% of which clustered in 385 CNVRs of a cross-population map. These CNVRs overlap with 862 annotated genes and account for approximately 3.3% of the haploid human genome.We created comprehensive cross-populational CNVR-maps. They represent an extendable framework that can leverage the detection of common CNVs and additionally assist in interpreting CNV-based association studies.     

Replication Study in Chinese Population and Meta-Analysis Supports Association of the 5p15.33 Locus with Lung Cancer

Background

Common genetic polymorphisms on chromosome 5p15.33, including rs401681 in cleft lip and palate transmembrane 1-like gene (CLPTM1L), have been implicated in susceptibility to lung cancer through genome-wide association studies (GWAS); however, subsequent replication studies yielded controversial results.

Methodology and Findings

A hospital-based case-control study in a Chinese population was conducted to replicate the association, and then a meta-analysis combining our non-overlapping new data and previously published data was performed to clearly discern the real effect of lung cancer susceptibility. In our study with 611 cases and 1062 controls, the minor allele T carrier (TT plus CT) group conferred an OR of 0.801 (95% CI = 0.654–0.981) under the dominant model. The meta-analysis comprising 9111 cases and 11424 controls further confirmed the significant association in the dominant model (OR = 0.842, 95% CI = 0.795–0.891). By stratified analysis, we revealed that ethnicity and study design might constitute the source of between-study heterogeneity. Besides, the sensitivity and cumulative analyses indicated the high stability of the results.

Conclusion

The results from our case-control study and meta-analysis provide convincing evidence that rs401681 is significantly associated with lung cancer risk.     

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.     

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.     

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.     

An evolutionary framework for association testing in resequencing studies

Sequencing technologies are becoming cheap enough to apply to large numbers of study participants and promise to provide new insights into human phenotypes by bringing to light rare and previously unknown genetic variants. We develop a new framework for the analysis of sequence data that incorporates all of the major features of previously proposed approaches, including those focused on allele counts and allele burden, but is both more general and more powerful. We harness population genetic theory to provide prior information on effect sizes and to create a pooling strategy for information from rare variants. Our method, EMMPAT (Evolutionary Mixed Model for Pooled Association Testing), generates a single test per gene (substantially reducing multiple testing concerns), facilitates graphical summaries, and improves the interpretation of results by allowing calculation of attributable variance. Simulations show that, relative to previously used approaches, our method increases the power to detect genes that affect phenotype when natural selection has kept alleles with large effect sizes rare. We demonstrate our approach on a population-based re-sequencing study of association between serum triglycerides and variation in ANGPTL4.     

A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility

Detecting, characterizing, and interpreting gene-gene interactions or epistasis in studies of human disease susceptibility is both a mathematical and a computational challenge. To address this problem, we have previously developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension (i.e. constructive induction) thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe a comprehensive and flexible framework for detecting and interpreting gene-gene interactions that utilizes advances in information theory for selecting interesting single-nucleotide polymorphisms (SNPs), MDR for constructive induction, machine learning methods for classification, and finally graphical models for interpretation. We illustrate the usefulness of this strategy using artificial datasets simulated from several different two-locus and three-locus epistasis models. We show that the accuracy, sensitivity, specificity, and precision of a na?ve Bayes classifier are significantly improved when SNPs are selected based on their information gain (i.e. class entropy removed) and reduced to a single attribute using MDR. We then apply this strategy to detecting, characterizing, and interpreting epistatic models in a genetic study (n = 500) of atrial fibrillation and show that both classification and model interpretation are significantly improved.     

Microfragmentation concept explains non-positive environmental heterogeneity–diversity relationships

Although recent studies have revealed that the relationship between diversity and environmental heterogeneity is not always positive, as classical niche theory predicts, scientists have had difficulty interpreting these results from an ecological perspective. We propose a new concept—microfragmentation—to explain how small-scale heterogeneity can have neutral or even negative effect on species diversity. We define microfragmentation as a community level process of splitting habitat into a more heterogeneous environment that can have non-positive effects on the diversity through habitat loss and subsequent isolation. We provide support for the microfragmentation concept with results from spatially explicit heterogeneity–diversity model simulations, in which varying sets of species (with different ratios of specialist and generalist species) were modeled at different levels of configurational heterogeneity (meaning that only the habitat structure was changed, not its composition). Our results indicate that environmental heterogeneity can affect community diversity in the same way as fragmentation at the landscape level. Although generalist species might not be seriously affected by microfragmentation, the persistence of specialist species can be seriously disturbed by small-scale patchiness. The microfragmentation concept provides new insight into community level diversity dynamics and can influence conservation and management strategies.     

The SNP rs961253 in 20p12.3 is associated with colorectal cancer risk: a case-control study and a meta-analysis of the published literature

Background

Colorectal cancer (CRC) is the third common cancer and the fourth leading cause of cancer death worldwide. A single nucleotide polymorphism (SNP), rs961253 located in 20p12, was firstly described to be associated with the increased risk of CRC in a genome-wide association study; however, more recent replication studies yielded controversial results.

Methodology/Principal Findings

A hospital-based case-control study in a Chinese population was firstly performed, and then a meta-analysis combining the current and previously published studies were conducted to explore the real effect of rs961253 in CRC susceptibility. In the Chinese population including 641 cases and 1037 controls, per-A-allele conferred an OR of 1.60 (95% CI = 1.26–2.02) under additive model. In the meta-analysis including 29859 cases and 29696 controls, per-A-allele have an OR of 1.13 (95% CI = 1.09–1.18) under a random-effects model due to heterogeneity (P = 0.019). Nevertheless, the heterogeneity can be totally explained by ethnicity, with the tau²reduced to 0 after including ethnicity in meta-regression model. In stratified analysis by ethnicity, per-A-allele had ORs of 1.34 (95% CI = 1.20–1.50) and 1.11 (95% CI = 1.08–1.14) for Asian and European, respectively, without heterogeneity. Modest influence of each study was observed on overall estimate in sensitive analysis, and evident tendency to significant association was seen in cumulative analysis over time, together indicating the robust stability of the current results.

Conclusions/Significance

The results from our study and the meta-analysis provided firm evidence that rs961253 significantly contributed to CRC risk in both Asian and European population.