Methods for meta-analyses of genome-wide association studies: critical assessment of empirical evidence

Exploring Factors Underlying Poorly-Controlled Asthma in Adults by Integrating Phenotypes and Genotypes Associated with Obesity and Asthma: A Case-Control Study

Background

Uncontrolled asthma in adults leads to poor clinical outcome, while the clinical heterogeneity of phenotypes interferes the applicable genetic determinants. This study aimed to identify phenotypes and genetic impact on poorly-controlled asthma to optimize individualized treatment strategies.

Methods

This propensity score-matched case-control study included 340 and 1020 asthmatics with poorly-controlled asthma and well-controlled asthma, respectively. Data were obtained from the 2008-2015 Taiwan Biobank Database and linked to the National Health Insurance Research Database. All asthmatics were aged ≥30 years, without cancer history, and each completed a questionnaire, physical examination, and genome-wide single nucleotide polymorphisms (SNPs). Multivariate adjusted odds ratios (ORs) for genetic risk scores were calculated using conditional logistic regression, stratified by age and sex. A model integrating obesity- and asthma-associated phenotypes and genotypes was applied for poorly-controlled asthma risk prediction.

Results

General obesity with body mass index (BMI) ≥27 kg/m² (OR:1.49, 95% confidence interval (CI) 1.09-2.03), central obesity with waist-to-height ratio (WHtR) ≥0.5 (OR:1.62, 95% CI 1.22-2.15), and parental history of asthma (OR:1.65, and 1.68; for BMI model and WHtR model, respectively) were significantly associated with poorly-controlled asthma in adults, and the combination effect of both obesity phenotypes was 1.66 (95% CI 1.17-2.35). A total of 16 obesity-associated SNPs and 9 asthma-associated SNPs were converted into genetic scores, and the aforementioned phenotypes were incorporated into the risk prediction model for poorly-controlled asthma, with an area under curve 0.72 in the receiver operating characteristic curve. The potential biological functions of genes are involved in immunity pathways.

Conclusion

The prediction model integrating obesity-asthma phenotypes and genotypes for poorly-controlled asthma can facilitate the prediction of high-risk asthma and provide potential targets for novel treatment.

Relationship among genetic variants, obesity traits and asthma in the Taiwan Biobank

BACKGROUND AND OBJECTIVE

Obesity and asthma impose a heavy health and economic burden on millions of people around the world. The complex interaction between genetic traits and phenotypes caused the mechanism between obesity and asthma is still vague. This study investigates the relationship among obesity-related polygenic risk score (PRS), obesity phenotypes and the risk of having asthma.

METHODS

This is a matched case-control study, with 4 controls (8288 non-asthmatic) for each case (2072 asthmatic). Data were obtained from the 2008-2015 Taiwan Biobank Database and linked to the 2000-2016 National Health Insurance Research Database. All participants were ≥30 years old with no history of cancer and had a complete questionnaire, as well as physical examination, genome-wide single nucleotide polymorphisms and clinical diagnosis data. Environmental exposure, PM_2.5, was also considered. Multivariate adjusted ORs and 95% CIs were calculated using conditional logistic regression stratified by age and sex. Mediation analysis was also assessed, using a generalised linear model.

RESULTS

We found that the obese phenotype was associated with significantly increased odds of asthma by approximately 26%. Four obesity-related PRS, including body mass index (OR=1.07 (1.01-1.13)), waist circumference (OR=1.10 (1.04-1.17)), central obesity as defined by waist-to-height ratio (OR=1.09 (1.03-1.15)) and general-central obesity (OR=1.06 (1.00-1.12)), were associated with increased odds of asthma. Additional independent risk factors for asthma included lower educational level, family history of asthma, certain chronic diseases and increased PM_2.5 exposure. Obesity-related PRS is an indirect risk factor for asthma, the link being fully mediated by the trait of obesity.

CONCLUSIONS

Obese phenotypes and obesity-related PRS are independent risk factors for having asthma in adults in the Taiwan Biobank. Overall, genetic risk for obesity increases the risk of asthma by affecting the obese phenotype.

Quality of methods and reporting in association studies of chronic periodontitis and IL1A -889 and IL1B +3953/4 SNPs: A systematic review

OBJECTIVE

The aim of this systematic review was to evaluate the quality of reporting and methodology in genetic association studies between IL1A -889 and IL1B +3954 polymorphisms and chronic periodontitis.

BACKGROUND

Evidence provided by periodontal research on genetic risk factors is of uttermost importance in clinical practice as a possible diagnostic and prognostic tool for periodontitis. Inadequate reporting of results as well as high risk of bias due to methodological inconsistency hampers the integration of evidence in terms of clinical applicability.

METHODS

This review includes case-control studies in humans published between 1997 and July 2017. Searching was conducted through MEDLINE, EMBASE, and search handing. Specific scoring systems have been developed to evaluate the quality of methods and reporting. Each article was scored according to its adequacy, and then, the total number and the percentage of items positively qualified for both methods and reporting were calculated. The quality of methods in studies scoring 0-6, 7-12, and 13-16 was, respectively, considered poor, moderate, and good. For reporting, scores of 0-9, 10-18, and 19-26 were deemed of poor, moderate, and good quality, respectively. Pearson's correlation coefficient was calculated to explore the correlation between the year of publication and the quality in terms of methods and reporting.

RESULTS

From the 531 screened studies, 52 met the inclusion criteria and were thus included in the study. The quality of methods and reporting of published genetic association papers on IL1 and chronic periodontitis is moderate. On a scale from 0 to 16, the mean score for methods of the reviewed studies was 8.19 ± 1.93. The items more frequently considered inadequate concerned the handling of confounders in statistical analysis, especially oral hygiene habits, socioeconomic status, subgingival colonization of specific periodontal pathogens, and stress. A significant positive correlation was found between the year of publication and the quality scores in terms of method (r = 0.401, P = 0.003). In terms of reporting, the mean score was 14.83 ± 3.04 on a scale from 0 to 26 and it was considered overall moderate. No statistically significant correlation was found between the year of publication and the quality of reporting (P = 0.266).

CONCLUSIONS

The association between IL1A -889 and IL1B +3954 polymorphisms and chronic periodontitis is questionable due to methodological inconsistency. Evidence arising from meta-analysis is unreliable due to high risk of bias and moderate quality in terms of reporting.

A Bayesian approach to Mendelian randomisation with dependent instruments

Mendelian randomisation (MR) is a method for establishing causality between a risk factor and an outcome by using genetic variants as instrumental variables. In practice, the association between individual genetic variants and the risk factor is often weak, which may lead to a lack of precision in the MR and even biased MR estimates. Usually, the most significant variant within a genetic region is selected to represent the association with the risk factor, but there is no guarantee that this variant will be causal or that it will capture all of the genetic association within the region. It may be advantageous to use extra variants selected from the same region in the MR. The problem is to decide which variants to select. Rather than selecting a specific set of variants, we investigate the use of Bayesian model averaging (BMA) to average the MR over all possible combinations of genetic variants. Our simulations demonstrate that the BMA version of MR outperforms classical estimation with many dependent variants and performs much better than an MR based on variants selected by penalised regression. In further simulations, we investigate robustness to violations in the model assumptions and demonstrate sensitivity to the inclusion of invalid instruments. The method is illustrated by applying it to an MR of the effect of body mass index on blood pressure using SNPs in the FTO gene.

Mitochondrial DNA variants and pulmonary function in older persons

BACKGROUND

We provide the first examination of mitochondrial DNA (mtDNA) variants and pulmonary function in older persons.

METHODS

Cross-sectional associations between mtDNA variants and pulmonary function were evaluated as a combined p-values meta-analysis, using data from two independent cohorts of older persons. The latter included white and black participants, aged ≥70 years, from the Lifestyle Interventions and Independence for Elders study (LIFE) (N = 1247) and the Health, Aging and Body Composition study (Health ABC) (N = 731), respectively. Pulmonary function included the forced expiratory volume in one-second as a Z-score (FEV1z) and the maximal inspiratory pressure (MIP) in cm of water.

RESULTS

In black participants, significant associations were found between mtDNA variants and MIP: m.7146A > G, COI (p = 3E-5); m.7389 T > C, COI (p = 2E-4); m.15301G > A, CYB (p = 9E-5); m.16265A > G, HV1 (p = 9E-5); meta-analytical p-values <0.0002. Importantly, these mtDNA variants were unique to black participants and were not present in white participants. Moreover, in black participants, aggregate genetic effects on MIP were observed across mutations in oxidative phosphorylation complex IV (p = 0.004), complex V (p = 0.0007), and hypervariable (p = 0.003) regions. The individual and aggregate variant results were significant after adjustment for multiple comparisons. Otherwise, no significant associations were detected for MIP in whites or for FEV1z in whites or blacks.

CONCLUSIONS

We have shown that mtDNA variants of African origin are cross-sectionally associated with MIP, a measure of respiratory muscle strength. Thus, our results establish the rationale for longitudinal studies to evaluate whether mtDNA variants of African origin identify those at risk of subsequently developing a respiratory muscle impairment (lower MIP values).

Meta-analysis identifies mitochondrial DNA sequence variants associated with walking speed

Declines in walking speed are associated with a variety of poor health outcomes including disability, comorbidity, and mortality. While genetic factors are putative contributors to variability in walking, few genetic loci have been identified for this trait. We examined the role of mitochondrial genomic variation on walking speed by sequencing the entire mitochondrial DNA (mtDNA). Data were meta-analyzed from 1758 Lifestyle Interventions and Independence for Elders (LIFE) Study and replication data from 730 Health, Aging, and Body Composition (HABC) Study participants with baseline walking speed information. Participants were 69+ years old of diverse racial backgrounds (African, European, and other race/ethnic groups) and had a wide range of mean walking speeds [4-6 m (0.78-1.09 m/s) and 400 m (0.83-1.24 m/s)]. Meta-analysis across studies and racial groups showed that m.12705C>T, ND5 variant was significantly associated (p < 0.0001) with walking speed at both short and long distances. Replication and meta-analysis also identified statistically significant walking speed associations (p < 0.0001) between the m.5460.G>A, ND2 and m.309C>CT, HV2 variants at short and long distances, respectively. All results remained statistically significant after multiple comparisons adjustment for 499 mtDNA variants. The m.12705C>T variant can be traced to the beginnings of human global migration and that cells carrying this variant display altered tRNA expression. Significant pooled effects related to stopping during the long-distance walk test were observed across OXPHOS complexes I (p = 0.0017) and III (p = 0.0048). These results suggest that mtDNA-encoded variants are associated with differences in walking speed among older adults, potentially identifying those at risk of developing mobility impairments.

CDH1 rs9929218 variant at 16q22.1 contributes to colorectal cancer susceptibility

Colorectal cancer (CRC) is the third most common cancer. Large-scale genome-wide association studies (GWAS) have been performed and reported some novel CRC susceptibility variants in European ancestry including the CDH1 rs9929218. Following GWAS and candidate studies evaluated the association between the CDH1 rs9929218 polymorphism and CRC in European, Asian and American populations. However, these studies reported inconsistent associations. Evidence shows that rs9929218 may regulate different gene expressions in different human tissues. Here, we reevaluated this association using large-scale samples from 16 studies (n=131768) using a meta-analysis method. In heterogeneity test, we did not identify significant heterogeneity among these studies. Meta-analysis using fixed effect model showed significant association between rs9929218 and CRC (P=6.16E-21, odds ratio (OR) =0.92, 95% confidence interval (CI) 0.91-0.94). In order to validate the effect of rs9929218 variant on CDH1 expression, we further performed a functional analysis using two large-scale expression datasets. We identified significant regulation relation between rs9929218 variant and the expression of CDH1, ZFP90, RP11-354M1.2 and MCOLN2 by both cis-effect and trans-effect. In summary, our analysis highlights significant association between rs9929218 polymorphism and CRC susceptibility.

Genome-wide association studies of albuminuria: towards genetic stratification in diabetes?

Genome-wide association studies (GWAS) have been very successful in unraveling the polygenic structure of several complex diseases and traits. In the case of albuminuria, despite the large sample size achieved by some studies, results look sparse with a limited number of loci reported so far. This review searched for GWAS studies of albumin excretion, albuminuria, and proteinuria. The resulting picture sets elements of uniqueness for albuminuria GWAS with respect to other complex traits. So far, very few loci associated with albuminuria have been validated by means of genome-wide significant evidence or formal replication. With rare exceptions, the validated loci are ethnicity specific. Within a given ethnicity, variants are common and have relatively large effects, especially in the presence of diabetes. In most cases, the identified variants were functional and a biological involvement of the target genes in renal damage was established. Recently reported variants associated with albuminuria in diabetes may be potentially combined into a genetic risk score, making it possible to rank diabetic patients by increasing risk of albuminuria. Validation of this model is required. To expand the understanding of the biological basis of albumin excretion regulation, future initiatives should achieve larger sample sizes and favor a transethnic study design.

Using family members to augment genetic case-control studies of a life-threatening disease

Survival bias is difficult to detect and adjust for in case-control genetic association studies but can invalidate findings when only surviving cases are studied and survival is associated with the genetic variants under study. Here, we propose a design where one genotypes genetically informative family members (such as offspring, parents, and spouses) of deceased cases and incorporates that surrogate genetic information into a retrospective maximum likelihood analysis. We show that inclusion of genotype data from first-degree relatives permits unbiased estimation of genotype association parameters. We derive closed-form maximum likelihood estimates for association parameters under the widely used log-additive and dominant association models. Our proposed design not only permits a valid analysis but also enhances statistical power by augmenting the sample with indirectly studied individuals. Gene variants associated with poor prognosis can also be identified under this design. We provide simulation results to assess performance of the methods. Copyright © 2016 John Wiley & Sons, Ltd.

Understanding and evaluating meta-analysis

BACKGROUND

Meta-analysis refers to statistical methodology used to combine data from many studies to obtain an overall assessment of disease risk or treatment outcomes. In this article, the authors review basic methods, interpretation, and limitations of meta-analysis.

METHODS

Investigators use meta-analysis approaches to combine data from available studies to obtain an answer to a specific question. An investigator uses a fixed model if there is homogeneity among the combined studies and a random-effects model if there is heterogeneity. The random-effects model results in wider confidence limits and more conservative estimates of overall results. A meta-analysis can be biased because studies with negative results (no differences in treatment outcomes) are less likely to be published (publication bias).

RESULTS

A meta-analysis should include a well-specified and reproducible set of procedures, including description of data abstraction procedures, attempts to include unpublished studies, and appropriate statistical analysis that includes thorough consideration of heterogeneity and potential bias.

CONCLUSIONS

Meta-analysis cannot correct shortcomings of existing studies or data. However, if potential pitfalls are recognized, meta-analysis can be a useful tool for summarizing existing studies, providing a means to address conflicting reports. Meta-analysis can lead to increased precision, providing greater power to detect existing relationships or treatment effects. Furthermore, meta-analysis may make it possible to address questions that cannot be answered by means of individual studies.

PRACTICAL IMPLICATIONS

Meta-analysis provides an objective, quantitative synthesis of available studies but needs to be understood and assessed critically by those who use it to assess risk or make treatment decisions.

Association of CD33 and MS4A cluster variants with Alzheimer's disease in East Asian populations

CD33 and MS4A cluster variants have been identified to modulate the risk of Alzheimer's disease (AD) in several recent genome-wide association studies (GWAS) in Caucasians. In the present study, we first conducted a case-control study to investigate the CD33 single nucleotide polymorphisms (SNPs) rs3865444 and rs3826656 and the MS4A cluster SNPs rs610932 and rs670139 in a cohort from eastern China that comprised 126 late-onset Alzheimer's disease (LOAD) patients and 129 healthy controls. The results revealed that the frequency of rs3826656 major (G) allele carriers was higher among the LOAD patients than among the controls [P=0.005; odds ratio (OR), 1.760; 95% confidence interval (CI), 1.185-2.615]. In apolipoprotein E (APOE) ε4 allele carriers, the G allele of the SNP rs3865444 was found to be associated with an increased risk of LOAD (P=0.002; OR, 3.391; 95% CI, 1.512-7.605). Next, we re-evaluated the association between these variants and LOAD by conducting a meta-analysis using data from studies of East Asian populations, including the present case-control study, and confirmed that rs3826656 increased the risk of LOAD. In addition, we identified a significant association between rs610932 and LOAD (P=0.035; OR, 0.79; 95% CI, 0.63-0.98). Note that heterogeneity should be considered during the interpretation of these results; significant heterogeneity was identified among studies on rs3865444, even in a subgroup analysis based on stratification of studies by the country of origin. In summary, our results suggest that CD33 and MS4A cluster variants are associated with LOAD susceptibility in East Asian populations.

The Cancer Genomics Resource List 2014

CONTEXT

Genomic sequencing for cancer is offered by commercial for-profit laboratories, independent laboratory networks, and laboratories in academic medical centers and integrated health networks. The variability among the tests has created a complex, confusing environment.

OBJECTIVE

To address the complexity, the Personalized Health Care (PHC) Committee of the College of American Pathologists proposed the development of a cancer genomics resource list (CGRL). The goal of this resource was to assist the laboratory pathology and clinical oncology communities.

DESIGN

The PHC Committee established a working group in 2012 to address this goal. The group consisted of site-specific experts in cancer genetic sequencing. The group identified current next-generation sequencing (NGS)-based cancer tests and compiled them into a usable resource. The genes were annotated by the working group. The annotation process drew on published knowledge, including public databases and the medical literature.

RESULTS

The compiled list includes NGS panels offered by 19 laboratories or vendors, accompanied by annotations. The list has 611 different genes for which NGS-based mutation testing is offered. Surprisingly, of these 611 genes, 0 genes were listed in every panel, 43 genes were listed in 4 panels, and 54 genes were listed in 3 panels. In addition, tests for 393 genes were offered by only 1 or 2 institutions. Table 1 provides an example of gene mutations offered for breast cancer genomic testing with the annotation as it appears in the CGRL 2014.

CONCLUSIONS

The final product, referred to as the Cancer Genomics Resource List 2014, is available as supplemental digital content.

Genetic Variant rs10757278 on Chromosome 9p21 Contributes to Myocardial Infarction Susceptibility

Large-scale genome-wide association studies (GWAS) have revealed that rs10757278 polymorphism (or its proxy rs1333049) on chromosome 9p21 is associated with myocardial infarction (MI) susceptibility in individuals of Caucasian ancestry. Following studies in other populations investigated this association. However, some of these studies reported weak or no significant association. Here, we reevaluated this association using large-scale samples by searching PubMed and Google Scholar databases. Our results showed significant association between rs10757278 polymorphism and MI with p = 6.09 × 10-22, odds ratio (OR) = 1.29, 95% confidence interval (CI) 1.22-1.36 in pooled population. We further performed a subgroup analysis, and found significant association between rs10757278 polymorphism and MI in Asian and Caucasian populations. We identified that the association between rs10757278 polymorphism and MI did not vary substantially by excluding any one study. However, the heterogeneity among the selected studies varies substantially by excluding the study from the Pakistan population. We found even more significant association between rs10757278 polymorphism and MI in pooled population, p = 3.55 × 10-53, after excluding the study from the Pakistan population. In summary, previous studies reported weak or no significant association between rs10757278 polymorphism and MI. Interestingly, our analysis suggests that rs10757278 polymorphism is significantly associated with MI susceptibility by analyzing large-scale samples.

Analyzing large-scale samples confirms the association between rs16892766 polymorphism and colorectal cancer susceptibility

Colorectal cancer (CRC) is a common complex disease caused by the combination of genetic variants and environmental factors. Genome-wide association studies (GWAS) have been performed and reported some novel CRC susceptibility variants. The rs16892766 (8q23.3) polymorphism was first identified to be significantly associated with CRC in European ancestry. The following studies investigated this association in Chinese, Japanese, Romanian, Swedish, African American, European American, and Croatian populations. These studies reported consistent and inconsistent results. Here, we reevaluated this association using the relatively large-scale samples from 13 studies (N = 59737, 26237 cases and 33500 controls) using a meta-analysis by searching the PubMed, Google Scholar and CRCgene databases. We observed no significant heterogeneity among the included studies. Our results showed significant association between rs16892766 polymorphism and CRC (P = 1.33E-35, OR = 1.23, 95% CI 1.20-1.27). Collectively, our analysis further supports previous findings that the rs16892766 polymorphism is significantly associated with CRC susceptibility. We believe that our findings will be very useful for future genetic studies on CRC.

CD33 rs3865444 Polymorphism Contributes to Alzheimer's Disease Susceptibility in Chinese, European, and North American Populations

The CD33 rs3865444 polymorphism was first identified to be associated with Alzheimer's disease (AD) in European population. However, the following studies reported weak or no significant association in Chinese, Japanese, Korean, American, and Canadian populations. We think that these negative results may have been caused by either relatively small sample sizes compared with those used for the previous GWAS in European ancestry or the genetic heterogeneity of the rs3865444 polymorphism in different populations. Here, we reevaluated this association using the relatively large-scale samples from previous 27 studies (N = 86,759; 31,106 cases and 55,653 controls) by searching the PubMed, AlzGene, and Google Scholar databases. We identified significant heterogeneity and observed no significant association between the rs3865444 polymorphism and AD in pooled populations (P = 0.264, odds ratio (OR) = 0.97, 95% confidence interval (CI) 0.93-1.02). In subgroup analysis, we identified significant heterogeneity only in East Asian population and observed no significant association between the rs3865444 polymorphism and AD. We further identified significant heterogeneity and observed significant association between the rs3865444 polymorphism and AD in Chinese population. We identified no significant heterogeneity and significant association in North American and European populations. Collectively, our analysis shows that the CD33 rs3865444 polymorphism is associated with AD susceptibility in Chinese, European, and North American populations. We believe that our findings will be very useful for future genetic studies on AD.

Analyzing 54,936 Samples Supports the Association Between CD2AP rs9349407 Polymorphism and Alzheimer's Disease Susceptibility

The CD2-associated protein (CD2AP) rs9349407 polymorphism was first identified to be significantly associated with Alzheimer's disease (AD) in European ancestry by genome-wide association studies (GWAS). However, the following studies reported no association in Chinese, Japanese, African-American, Canadian, and European populations. We think that these negative results may have been caused by either relatively small sample sizes compared with those used for the previous GWAS in European ancestry or the genetic heterogeneity of the rs9349407 polymorphism in different populations. Here, we reevaluated this association using the relatively large-scale samples from 15 previous studies (N = 54,936; 23,777 cases and 31,159 controls) by searching the PubMed, AlzGene, and Google Scholar databases. Using an additive genetic model, we did not identify a significant heterogeneity among the 15 studies. Using meta-analysis, we observed a significant association between the rs9349407 polymorphism and AD with P = 8.78E-07, odds ratio (OR) = 1.08, 95% confidence interval (CI) 1.05-1.12. To our knowledge, this is the first meta-analysis to investigate the association between rs9349407 polymorphism and AD in East Asian, American, Canadian, and European populations. Our analysis further supports previous findings that the CD2AP rs9349407 polymorphism contributes to AD susceptibility. We believe that our findings will be very useful for future genetic studies on AD.

An Updated Analysis with 85,939 Samples Confirms the Association Between CR1 rs6656401 Polymorphism and Alzheimer's Disease

The complement receptor 1 (CR1) rs6656401 polymorphism was first identified to be associated with Alzheimer's disease (AD) in European ancestry. However, the following studies reported weak or no significant association in Chinese, Japanese, Korean, African-American, Polish, and Canadian populations. We think that these negative results may have been caused by either relatively small sample sizes compared with those used for the previous genome-wide association studies (GWAS) in European ancestry or the genetic heterogeneity of the rs6656401 polymorphism in different populations. Here, we reevaluated this association using the relatively large-scale samples from previous 24 studies (N = 85,939, 30,100 cases and 55,839 controls) by searching the PubMed, AlzGene, and Google Scholar databases. Using additive model, we did not identify significant heterogeneity among the 24 studies. We observed significant association between the rs6656401 polymorphism and AD in pooled populations (P = 1.82E-26, odds ratio (OR) = 1.18, 95 % confidence interval (CI) 1.15-1.22). In subgroup analysis, we identified significant results in East Asian population with P = 5.00E-04, OR = 1.31, 95 % CI 1.13-1.52. To our knowledge, this is the first meta-analysis to investigate the association between rs6656401 polymorphism and AD in East Asian, African-American, Canadian, and European populations. Our analysis further supports previous findings that the CR1 rs6656401 polymorphism contributes to AD susceptibility. We believe that our findings will be very useful for future genetic studies on AD.

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.

Radiogenomics: using genetics to identify cancer patients at risk for development of adverse effects following radiotherapy

Normal-tissue adverse effects following radiotherapy are common and significantly affect quality of life. These effects cannot be accounted for by dosimetric, treatment, or demographic factors alone, and evidence suggests that common genetic variants are associated with radiotherapy adverse effects. The field of radiogenomics has evolved to identify such genetic risk factors. Radiogenomics has two goals: (i) to develop an assay to predict which patients with cancer are most likely to develop radiation injuries resulting from radiotherapy, and (ii) to obtain information about the molecular pathways responsible for radiation-induced normal-tissue toxicities. This review summarizes the history of the field and current research.

SIGNIFICANCE

A single-nucleotide polymorphism–based predictive assay could be used, along with clinical and treatment factors, to estimate the risk that a patient with cancer will develop adverse effects from radiotherapy. Such an assay could be used to personalize therapy and improve quality of life for patients with cancer.

Importance of different types of prior knowledge in selecting genome-wide findings for follow-up

Biological plausibility and other prior information could help select genome-wide association (GWA) findings for further follow-up, but there is no consensus on which types of knowledge should be considered or how to weight them. We used experts' opinions and empirical evidence to estimate the relative importance of 15 types of information at the single-nucleotide polymorphism (SNP) and gene levels. Opinions were elicited from 10 experts using a two-round Delphi survey. Empirical evidence was obtained by comparing the frequency of each type of characteristic in SNPs established as being associated with seven disease traits through GWA meta-analysis and independent replication, with the corresponding frequency in a randomly selected set of SNPs. SNP and gene characteristics were retrieved using a specially developed bioinformatics tool. Both the expert and the empirical evidence rated previous association in a meta-analysis or more than one study as conferring the highest relative probability of true association, whereas previous association in a single study ranked much lower. High relative probabilities were also observed for location in a functional protein domain, although location in a region evolutionarily conserved in vertebrates was ranked high by the data but not by the experts. Our empirical evidence did not support the importance attributed by the experts to whether the gene encodes a protein in a pathway or shows interactions relevant to the trait. Our findings provide insight into the selection and weighting of different types of knowledge in SNP or gene prioritization, and point to areas requiring further research.

An updated meta-analysis of endothelial nitric oxide synthase gene: three well-characterized polymorphisms with ischemic stroke

Polymorphisms in the endothelial nitric oxide synthase (eNOS) gene may influence the risk of ischemic stroke (IS), but the results are still debatable. A meta-analysis was performed to investigate the association between the eNOS gene polymorphisms in IS risk. Case-control studies on the association between the G894T, T-786C, and 4b/a polymorphisms and IS were searched up to July 2012, and the genotype frequencies in the control group were found to be consistent with the Hardy-Weinberg equilibrium (HWE). The effect summary odds ratio (OR) and 95% confidence intervals (CIs) were obtained. Meta-regression was used to explore the potential sources of heterogeneity. Funnel plots and Egger's test was used to estimate small study biases, and heterogeneity was assessed by chi-square-based Q-test and I(2) test. There were total 6537/6475 cases/controls for G894T, 3459/3951 cases/controls for 4b/a, and 2125/2673 cases/controls for T-786C polymorphism. For G894T and 4b/a, a significant association of 894T allele and 4a allele with increased risk of IS was found in Asians (TT+GT vs. GG: p<0.00001, OR=1.60, 95% CI=1.38-1.79, Pheterogeneity=0.11; aa+ba vs. bb: P<0.00001, OR=1.60, 95% CI=1.30-1.97, Pheterogeneity=0.02), but not in Caucasians (TT+GT vs. GG: P=0.60, OR=0.94, 95% CI=0.75-1.19, Pheterogeneity=0.002; aa+ba vs. bb: P=0.13, OR=0.81, 95% CI=0.62-1.06, Pheterogeneity=0.63). For T-786C polymorphism, there were no significant differences in genotype distribution between IS and control in Asians (CC+TC vs. TT: P=0.15, OR=1.14, 95% CI=0.95-1.37, Pheterogeneity=0.94) and in Caucasians (CC+TC vs. TT: P=0.72, OR=0.96, 95% CI=0.75-1.22, Pheterogeneity=0.53). This analysis provides strong evidence that the eNOS T-786C gene polymorphism is not associated with IS, the G894T and 4b/a polymorphisms might be associated with IS, at least in Asians.

The power of meta-analysis in genome-wide association studies

Meta-analysis of multiple genome-wide association (GWA) studies has become common practice over the past few years. The main advantage of this technique is the maximization of power to detect subtle genetic effects for common traits. Moreover, one can use meta-analysis to probe and identify heterogeneity in the effect sizes across the combined studies. In this review, we systematically appraise and evaluate the characteristics of GWA meta-analyses with 10,000 or more subjects published up to June 2012. We provide an overview of the current landscape of variants discovered by GWA meta-analyses, and we discuss and assess with extrapolations from empirical data the value of larger meta-analyses for the discovery of additional genetic associations and new biology in the future. Finally, we discuss some emerging logistical and practical issues related to the conduct of meta-analysis of GWA studies.

Contribution of rare and common variants determine complex diseases-Hirschsprung disease as a model

Finding genes for complex diseases has been the goal of many genetic studies. Most of these studies have been successful by searching for genes and mutations in rare familial cases, by screening candidate genes and by performing genome wide association studies. However, only a small fraction of the total genetic risk for these complex genetic diseases can be explained by the identified mutations and associated genetic loci. In this review we focus on Hirschsprung disease (HSCR) as an example of a complex genetic disorder. We describe the genes identified in this congenital malformation and postulate that both common 'low penetrant' variants in combination with rare or private 'high penetrant' variants determine the risk on HSCR, and likely, on other complex diseases. We also discuss how new technological advances can be used to gain further insights in the genetic background of complex diseases. Finally, we outline a few steps to develop functional assays in order to determine the involvement of these variants in disease development.

Identification of Genetic Susceptibility Loci for Colorectal Tumors in a Genome-Wide Meta-analysis

BACKGROUND & AIMS

Heritable factors contribute to the development of colorectal cancer. Identifying the genetic loci associated with colorectal tumor formation could elucidate the mechanisms of pathogenesis.

METHODS

We conducted a genome-wide association study that included 14 studies, 12,696 cases of colorectal tumors (11,870 cancer, 826 adenoma), and 15,113 controls of European descent. The 10 most statistically significant, previously unreported findings were followed up in 6 studies; these included 3056 colorectal tumor cases (2098 cancer, 958 adenoma) and 6658 controls of European and Asian descent.

RESULTS

Based on the combined analysis, we identified a locus that reached the conventional genome-wide significance level at less than 5.0 × 10(-8): an intergenic region on chromosome 2q32.3, close to nucleic acid binding protein 1 (most significant single nucleotide polymorphism: rs11903757; odds ratio [OR], 1.15 per risk allele; P = 3.7 × 10(-8)). We also found evidence for 3 additional loci with P values less than 5.0 × 10(-7): a locus within the laminin gamma 1 gene on chromosome 1q25.3 (rs10911251; OR, 1.10 per risk allele; P = 9.5 × 10(-8)), a locus within the cyclin D2 gene on chromosome 12p13.32 (rs3217810 per risk allele; OR, 0.84; P = 5.9 × 10(-8)), and a locus in the T-box 3 gene on chromosome 12q24.21 (rs59336; OR, 0.91 per risk allele; P = 3.7 × 10(-7)).

CONCLUSIONS

In a large genome-wide association study, we associated polymorphisms close to nucleic acid binding protein 1 (which encodes a DNA-binding protein involved in DNA repair) with colorectal tumor risk. We also provided evidence for an association between colorectal tumor risk and polymorphisms in laminin gamma 1 (this is the second gene in the laminin family to be associated with colorectal cancers), cyclin D2 (which encodes for cyclin D2), and T-box 3 (which encodes a T-box transcription factor and is a target of Wnt signaling to β-catenin). The roles of these genes and their products in cancer pathogenesis warrant further investigation.

SNP prioritization using a Bayesian probability of association

Prioritization is the process whereby a set of possible candidate genes or SNPs is ranked so that the most promising can be taken forward into further studies. In a genome-wide association study, prioritization is usually based on the P-values alone, but researchers sometimes take account of external annotation information about the SNPs such as whether the SNP lies close to a good candidate gene. Using external information in this way is inherently subjective and is often not formalized, making the analysis difficult to reproduce. Building on previous work that has identified 14 important types of external information, we present an approximate Bayesian analysis that produces an estimate of the probability of association. The calculation combines four sources of information: the genome-wide data, SNP information derived from bioinformatics databases, empirical SNP weights, and the researchers' subjective prior opinions. The calculation is fast enough that it can be applied to millions of SNPS and although it does rely on subjective judgments, those judgments are made explicit so that the final SNP selection can be reproduced. We show that the resulting probability of association is intuitively more appealing than the P-value because it is easier to interpret and it makes allowance for the power of the study. We illustrate the use of the probability of association for SNP prioritization by applying it to a meta-analysis of kidney function genome-wide association studies and demonstrate that SNP selection performs better using the probability of association compared with P-values alone.

Genome-wide search for gene-gene interactions in colorectal cancer

Genome-wide association studies (GWAS) have successfully identified a number of single-nucleotide polymorphisms (SNPs) associated with colorectal cancer (CRC) risk. However, these susceptibility loci known today explain only a small fraction of the genetic risk. Gene-gene interaction (GxG) is considered to be one source of the missing heritability. To address this, we performed a genome-wide search for pair-wise GxG associated with CRC risk using 8,380 cases and 10,558 controls in the discovery phase and 2,527 cases and 2,658 controls in the replication phase. We developed a simple, but powerful method for testing interaction, which we term the Average Risk Due to Interaction (ARDI). With this method, we conducted a genome-wide search to identify SNPs showing evidence for GxG with previously identified CRC susceptibility loci from 14 independent regions. We also conducted a genome-wide search for GxG using the marginal association screening and examining interaction among SNPs that pass the screening threshold (p<10⁻⁴). For the known locus rs10795668 (10p14), we found an interacting SNP rs367615 (5q21) with replication p = 0.01 and combined p = 4.19×10⁻⁸. Among the top marginal SNPs after LD pruning (n = 163), we identified an interaction between rs1571218 (20p12.3) and rs10879357 (12q21.1) (nominal combined p = 2.51×10⁻⁶; Bonferroni adjusted p = 0.03). Our study represents the first comprehensive search for GxG in CRC, and our results may provide new insight into the genetic etiology of CRC.

Knowledge integration in cancer: current landscape and future prospects

Knowledge integration includes knowledge management, synthesis, and translation processes. It aims to maximize the use of collected scientific information and accelerate translation of discoveries into individual and population health benefits. Accumulated evidence in cancer epidemiology constitutes a large share of the 2.7 million articles on cancer in PubMed. We examine the landscape of knowledge integration in cancer epidemiology. Past approaches have mostly used retrospective efforts of knowledge management and traditional systematic reviews and meta-analyses. Systematic searches identify 2,332 meta-analyses, about half of which are on genetics and epigenetics. Meta-analyses represent 1:89-1:1162 of published articles in various cancer subfields. Recently, there are more collaborative meta-analyses with individual-level data, including those with prospective collection of measurements [e.g., genotypes in genome-wide association studies (GWAS)]; this may help increase the reliability of inferences in the field. However, most meta-analyses are still done retrospectively with published information. There is also a flurry of candidate gene meta-analyses with spuriously prevalent "positive" results. Prospective design of large research agendas, registration of datasets, and public availability of data and analyses may improve our ability to identify knowledge gaps, maximize and accelerate translational progress or-at a minimum-recognize dead ends in a more timely fashion.