Failure to replicate a genetic association may provide important clues about genetic architecture

Meta-analysis of aldehyde dehydrogenase 2 gene polymorphism and Alzheimer's disease in East Asians

BACKGROUND

The association of genetic polymorphism of mitochondrial aldehyde dehydrogenase 2 (ALDH2) and Alzheimer's disease (AD) has been controversial and has been investigated only in several small-sample studies. In the present study, we performed a meta-analysis to evaluate the cross-sectional association of ALDH2 variants and AD risk in East Asian populations.

METHODS

Trials were retrieved through MEDLINE, EMBASE, J-STAGE and the China National Knowledge Internet databases (from January 1, 1994 to November 1, 2010) without any restriction on language. Data were abstracted by a standardized protocol.

RESULTS

We found four studies of 821AD patients and 1380 healthy controls that qualified for the analysis. The variant ALDH2 genotype GA/AA was not associated with increased AD risk (odds ratio (OR) = 1.35; 95% confidence interval (CI) = 0.75-2.42; p = 0.32), even after stratification for the status of apolipoprotein E epsilon 4 allele. However, in the subgroup analyses, the association was significant for men (OR = 1.72; 95% CI = 1.10-2.67; p = 0.02).

CONCLUSIONS

This study adds to the evidence that ALDH2 GA/AA genotype increases the risk of AD among East Asian men, although the effect size is moderate.

Validation in genetic association studies

Validation of genetic associations is understood to be a cornerstone for the scientific credibility of the results. To approach this topic, the general concept of genetic association studies is introduced briefly, followed by how the term 'validation' is used in the context of genetic association studies. As a central issue, reasons for the importance of validation and for failure of validation will be described.

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.

Evolving hard problems: Generating human genetics datasets with a complex etiology

Background

A goal of human genetics is to discover genetic factors that influence individuals' susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variants and the task of modeling interactions between them. We and others have previously developed algorithms to detect and model the relationships between these genetic factors and disease. Previously these methods have been evaluated with datasets simulated according to pre-defined genetic models.

Results

Here we develop and evaluate a model free evolution strategy to generate datasets which display a complex relationship between individual genotype and disease susceptibility. We show that this model free approach is capable of generating a diverse array of datasets with distinct gene-disease relationships for an arbitrary interaction order and sample size. We specifically generate eight-hundred Pareto fronts; one for each independent run of our algorithm. In each run the predictiveness of single genetic variation and pairs of genetic variants have been minimized, while the predictiveness of third, fourth, or fifth-order combinations is maximized. Two hundred runs of the algorithm are further dedicated to creating datasets with predictive four or five order interactions and minimized lower-level effects.

Conclusions

This method and the resulting datasets will allow the capabilities of novel methods to be tested without pre-specified genetic models. This allows researchers to evaluate which methods will succeed on human genetics problems where the model is not known in advance. We further make freely available to the community the entire Pareto-optimal front of datasets from each run so that novel methods may be rigorously evaluated. These 76,600 datasets are available from http://discovery.dartmouth.edu/model_free_data/.

A knowledge-driven interaction analysis reveals potential neurodegenerative mechanism of multiple sclerosis susceptibility

Gene-gene interactions are proposed as an important component of the genetic architecture of complex diseases, and are just beginning to be evaluated in the context of genome-wide association studies (GWAS). In addition to detecting epistasis, a benefit to interaction analysis is that it also increases power to detect weak main effects. We conducted a knowledge-driven interaction analysis of a GWAS of 931 multiple sclerosis (MS) trios to discover gene-gene interactions within established biological contexts. We identify heterogeneous signals, including a gene-gene interaction between CHRM3 (muscarinic cholinergic receptor 3) and MYLK (myosin light-chain kinase) (joint P=0.0002), an interaction between two phospholipase C-β isoforms, PLCβ1 and PLCβ4 (joint P=0.0098), and a modest interaction between ACTN1 (actinin alpha 1) and MYH9 (myosin heavy chain 9) (joint P=0.0326), all localized to calcium-signaled cytoskeletal regulation. Furthermore, we discover a main effect (joint P=5.2E-5) previously unidentified by single-locus analysis within another related gene, SCIN (scinderin), a calcium-binding cytoskeleton regulatory protein. This work illustrates that knowledge-driven interaction analysis of GWAS data is a feasible approach to identify new genetic effects. The results of this study are among the first gene-gene interactions and non-immune susceptibility loci for MS. Further, the implicated genes cluster within inter-related biological mechanisms that suggest a neurodegenerative component to MS.

Pathways of distinction analysis: a new technique for multi-SNP analysis of GWAS data

Genome-wide association studies (GWAS) have become increasingly common due to advances in technology and have permitted the identification of differences in single nucleotide polymorphism (SNP) alleles that are associated with diseases. However, while typical GWAS analysis techniques treat markers individually, complex diseases (cancers, diabetes, and Alzheimers, amongst others) are unlikely to have a single causative gene. Thus, there is a pressing need for multi–SNP analysis methods that can reveal system-level differences in cases and controls. Here, we present a novel multi–SNP GWAS analysis method called Pathways of Distinction Analysis (PoDA). The method uses GWAS data and known pathway–gene and gene–SNP associations to identify pathways that permit, ideally, the distinction of cases from controls. The technique is based upon the hypothesis that, if a pathway is related to disease risk, cases will appear more similar to other cases than to controls (or vice versa) for the SNPs associated with that pathway. By systematically applying the method to all pathways of potential interest, we can identify those for which the hypothesis holds true, i.e., pathways containing SNPs for which the samples exhibit greater within-class similarity than across classes. Importantly, PoDA improves on existing single–SNP and SNP–set enrichment analyses, in that it does not require the SNPs in a pathway to exhibit independent main effects. This permits PoDA to reveal pathways in which epistatic interactions drive risk. In this paper, we detail the PoDA method and apply it to two GWAS: one of breast cancer and the other of liver cancer. The results obtained strongly suggest that there exist pathway-wide genomic differences that contribute to disease susceptibility. PoDA thus provides an analytical tool that is complementary to existing techniques and has the power to enrich our understanding of disease genomics at the systems-level.

We present a novel method for multi–SNP analysis of genome-wide association studies. The method is motivated by the intuition that, if a set of SNPs is associated with disease, cases and controls will exhibit more within-group similarity than across-group similarity for the SNPs in the set of interest. Our method, Pathways of Distinction Analysis (PoDA), uses GWAS data and known pathway–gene and gene–SNP associations to identify pathways that permit the distinction of cases from controls. By systematically applying the method to all pathways of potential interest, we can identify pathways containing SNPs for which the cases and controls are distinguished and infer those pathways' role in disease. We detail the PoDA method and describe its results in breast and liver cancer GWAS data, demonstrating its utility as a method for systems-level analysis of GWAS data.

Multilocus analyses of seven candidate genes suggest interacting pathways for obesity-related traits in Brazilian populations

We investigated whether variants in major candidate genes for food intake and body weight regulation contribute to obesity-related traits under a multilocus perspective. We studied 375 Brazilian subjects from partially isolated African-derived populations (quilombos). Seven variants displaying conflicting results in previous reports and supposedly implicated in the susceptibility of obesity-related phenotypes were investigated: β2-adrenergic receptor (ADRB2) (Arg16Gly), insulin induced gene 2 (INSIG2) (rs7566605), leptin (LEP) (A19G), LEP receptor (LEPR) (Gln223Arg), perilipin (PLIN) (6209T > C), peroxisome proliferator-activated receptor-γ (PPARG) (Pro12Ala), and resistin (RETN) (-420 C > G). Regression models as well as generalized multifactor dimensionality reduction (GMDR) were employed to test the contribution of individual effects and higher-order interactions to BMI and waist-hip ratio (WHR) variation and risk of overweight/obesity. The best multilocus association signal identified in the quilombos was further examined in an independent sample of 334 Brazilian subjects of European ancestry. In quilombos, only the PPARG polymorphism displayed significant individual effects (WHR variation, P = 0.028). No association was observed either with the risk of overweight/obesity (BMI ≥ 25 kg/m2), risk of obesity alone (BMI ≥ 30 kg/m2) or BMI variation. However, GMDR analyses revealed an interaction between the LEPR and ADRB2 polymorphisms (P = 0.009) as well as a third-order effect involving the latter two variants plus INSIG2 (P = 0.034) with overweight/obesity. Assessment of the LEPR-ADRB2 interaction in the second sample indicated a marginally significant association (P = 0.0724), which was further verified to be limited to men (P = 0.0118). Together, our findings suggest evidence for a two-locus interaction between the LEPR Gln223Arg and ADRB2 Arg16Gly variants in the risk of overweight/obesity, and highlight further the importance of multilocus effects in the genetic component of obesity.

A quantitative association study of SLC25A12 and restricted repetitive behavior traits in autism spectrum disorders

Background

SLC25A12 was previously identified by a linkage-directed association analysis in autism. In this study, we investigated the relationship between three SLC25A12 single nucleotide polymorphisms (SNPs) (rs2056202, rs908670 and rs2292813) and restricted repetitive behavior (RRB) traits in autism spectrum disorders (ASDs), based on a positive correlation between the G allele of rs2056202 and an RRB subdomain score on the Autism Diagnostic Interview-Revised (ADI-R).

Methods

We used the Repetitive Behavior Scale-Revised (RBS-R) as a quantitative RRB measure, and conducted linear regression analyses for individual SNPs and a previously identified haplotype (rs2056202-rs2292813). We examined associations in our University of Illinois at Chicago-University of Florida (UIC-UF) sample (179 unrelated individuals with an ASD), and then attempted to replicate our findings in the Simons Simplex Collection (SSC) sample (720 ASD families).

Results

In the UIC-UF sample, three RBS-R scores (ritualistic, sameness, sum) had positive associations with the A allele of rs2292813 (p = 0.006-0.012) and with the rs2056202-rs2292813 haplotype (omnibus test, p = 0.025-0.040). The SSC sample had positive associations between the A allele of rs2056202 and four RBS-R scores (stereotyped, sameness, restricted, sum) (p = 0.006-0.010), between the A allele of rs908670 and three RBS-R scores (stereotyped, self-injurious, sum) (p = 0.003-0.015), and between the rs2056202-rs2292813 haplotype and six RBS-R scores (stereotyped, self-injurious, compulsive, sameness, restricted, sum)(omnibus test, p = 0.002-0.028). Taken together, the A alleles of rs2056202 and rs2292813 were consistently and positively associated with RRB traits in both the UIC-UF and SSC samples, but the most significant SNP with phenotype association varied in each dataset.

Conclusions

This study confirmed an association between SLC25A12 and RRB traits in ASDs, but the direction of the association was different from that in the initial study. This could be due to the examined SLC25A12 SNPs being in linkage disequilibrium with another risk allele, and/or genetic/phenotypic heterogeneity of the ASD samples across studies.

ADAMTSL3 as a candidate gene for schizophrenia: gene sequencing and ultra-high density association analysis by imputation

We previously reported an association with a putative functional variant in the ADAMTSL3 gene, just below genome-wide significance in a genome-wide association study of schizophrenia. As variants impacting the function of ADAMTSL3 (a disintegrin-like and metalloprotease domain with thrombospondin type I motifs-like-3) could illuminate a novel disease mechanism and a potentially specific target, we have used complementary approaches to further evaluate the association. We imputed genotypes and performed high density association analysis using data from the HapMap and 1000 genomes projects. To review all variants that could potentially cause the association, and to identify additional possible pathogenic rare variants, we sequenced ADAMTSL3 in 92 schizophrenics. A total of 71 ADAMTSL3 variants were identified by sequencing, many were also seen in the 1000 genomes data, but 26 were novel. None of the variants identified by re-sequencing was in strong linkage disequilibrium (LD) with the associated markers. Imputation analysis refined association between ADAMTSL3 and schizophrenia, and highlighted additional common variants with similar levels of association. We evaluated the functional consequences of all variants identified by sequencing, or showing direct or imputed association. The strongest evidence for function remained with the originally associated variant, rs950169, suggesting that this variant may be causal of the association. Rare variants were also identified with possible functional impact. Our study confirms ADAMTSL3 as a candidate for further investigation in schizophrenia, using the variants identified here. The utility of imputation analysis is demonstrated, and we recommend wider use of this method to re-evaluate the existing canon of suggestive schizophrenia associations.

Genetic architecture of cancer and other complex diseases: lessons learned and future directions

Genome-wide association studies have broadened our understanding of the genetic architecture of cancer to include common variants, in addition to the rare variants previously identified by linkage analysis. We review current knowledge on the genetic architecture of four cancers--breast, lung, prostate and colorectal--for which the balance of common and rare alleles identified ranges from fewer common alleles (lung cancer) to more common alleles (prostate cancer). Although most variants are cancer specific, pleiotropy has been observed for several variants, for example, variants at the 8q24 locus and breast, ovarian and prostate cancers or variants in KITLG in relation to hair color and testicular cancer. Although few studies have been adequately powered to investigate heterogeneity among ancestry groups, effect sizes associated with common variants have been reported to be fairly homogenous among ethnic groups. Some associations appear to be ancestry specific, such as HNF1B, which is associated with prostate cancer in European Americans and Latinos but not in African-Americans. Studies of cancer and other complex diseases suggest that a simple dichotomy between rare and common allelic architectures may be too simplistic and that future research is needed to characterize a fuller spectrum of allele frequency (common (>5%), uncommon (1-5%) and rare (<<1%) alleles) and effect size. In addition, a broadening of the concept of genetic architecture to encompass both population architecture, which reflects differences in exposures, genetic factors and population level risk among diverse groups of people, and genomic architecture, which includes structural, epigenomic and somatic variation, is envisioned.

Variation in the uric acid transporter gene SLC2A9 and its association with AAO of Parkinson's disease

Based on the observed inverse association between hyperuricemia and Parkinson's disease (PD) risk, the natural antioxidant activity of uric acid has been suggested to play a protective role. SLC2A9 has been indicated as the most effective of all uric acid transporters, and SLC2A9 variants have been shown to influence circulating uric acid levels. With this study, we aimed to test the association between such SLC2A9 polymorphisms and age at onset (AAO) of PD. Variants rs733175, rs737267, rs1014290, and rs6449213 within SLC2A9 were genotyped in 664 PD individuals from three European centers. The effect of each polymorphism on AAO was estimated within each center using a linear regression model adjusted for gender and genotype at the other SNPs and assuming an additive genetic model. Results across centers were combined using inverse-variance weighted fixed-effect meta-analysis. The minor allele of rs1014290, previously shown to be associated with lower serum uric acid levels, was found to be associated with a lower AAO of PD (pooled estimate -4.56 years; 95% CI -8.13, -1.00; p=0.012). The association remained significant after adjustment for multiple comparisons and was highly consistent across centers (heterogeneity, I (2) 0%). No gender differences were observed. Our study suggests that SLC2A9 genetic variants influence age of onset of Parkinson's disease.

Statistical Optimization of Pharmacogenomics Association Studies: Key Considerations from Study Design to Analysis

Research in human genetics and genetic epidemiology has grown significantly over the previous decade, particularly in the field of pharmacogenomics. Pharmacogenomics presents an opportunity for rapid translation of associated genetic polymorphisms into diagnostic measures or tests to guide therapy as part of a move towards personalized medicine. Expansion in genotyping technology has cleared the way for widespread use of whole-genome genotyping in the effort to identify novel biology and new genetic markers associated with pharmacokinetic and pharmacodynamic endpoints. With new technology and methodology regularly becoming available for use in genetic studies, a discussion on the application of such tools becomes necessary. In particular, quality control criteria have evolved with the use of GWAS as we have come to understand potential systematic errors which can be introduced into the data during genotyping. There have been several replicated pharmacogenomic associations, some of which have moved to the clinic to enact change in treatment decisions. These examples of translation illustrate the strength of evidence necessary to successfully and effectively translate a genetic discovery. In this review, the design of pharmacogenomic association studies is examined with the goal of optimizing the impact and utility of this research. Issues of ascertainment, genotyping, quality control, analysis and interpretation are considered.

Correcting away the hidden heritability

In the age of high-density genome-wide association (GWAS) data, correcting for multiple comparisons is a substantial issue for genetic epidemiological studies. However, the current manuscript review process generally requires both stringent correction and independent replication. The result of this stringency is that studies that are published suffer from inflated Type 2 error rates (false negatives), thereby removing many likely real signals from follow-up. Elimination of these alleles, if they are truly associated, from further study will slow research progress in studies of complex disease. We argue that this method of correction is overly conservative, especially in an age when high-density follow-up experiments are possible and reasonably inexpensive.

Interleukin 12B (IL12B) genetic variation and pulmonary tuberculosis: a study of cohorts from The Gambia, Guinea-Bissau, United States and Argentina

We examined whether polymorphisms in interleukin-12B (IL12B) associate with susceptibility to pulmonary tuberculosis (PTB) in two West African populations (from The Gambia and Guinea-Bissau) and in two independent populations from North and South America. Nine polymorphisms (seven SNPs, one insertion/deletion, one microsatellite) were analyzed in 321 PTB cases and 346 controls from Guinea-Bissau and 280 PTB cases and 286 controls from The Gambia. For replication we studied 281 case and 179 control African-American samples and 221 cases and 144 controls of European ancestry from the US and Argentina. First-stage single locus analyses revealed signals of association at IL12B 3' UTR SNP rs3212227 (unadjusted allelic p = 0.04; additive genotypic p = 0.05, OR = 0.78, 95% CI [0.61-0.99]) in Guinea-Bissau and rs11574790 (unadjusted allelic p = 0.05; additive genotypic p = 0.05, OR = 0.76, 95% CI [0.58-1.00]) in The Gambia. Association of rs3212227 was then replicated in African-Americans (rs3212227 allelic p = 0.002; additive genotypic p = 0.05, OR = 0.78, 95% CI [0.61-1.00]); most importantly, in the African-American cohort, multiple significant signals of association (seven of the nine polymorphisms tested) were detected throughout the gene. These data suggest that genetic variation in IL12B, a highly relevant candidate gene, is a risk factor for PTB in populations of African ancestry, although further studies will be required to confirm this association and identify the precise mechanism underlying it.

Association of ADIPOQ gene variants with body weight, type 2 diabetes and serum adiponectin concentrations: the Finnish Diabetes Prevention Study

BACKGROUND

Adiponectin, secreted mainly by mature adipocytes, is a protein with insulin-sensitising and anti-atherogenic effects. Human adiponectin is encoded by the ADIPOQ gene on the chromosomal locus 3q27. Variations in ADIPOQ are associated with obesity, type 2 diabetes (T2DM) and related phenotypes in several populations. Our aim was to study the association of the ADIPOQ variations with body weight, serum adiponectin concentrations and conversion to T2DM in overweight subjects with impaired glucose tolerance. Moreover, we investigated whether ADIPOQ gene variants modify the effect of lifestyle changes on these traits.

METHODS

Participants in the Finnish Diabetes Prevention Study were randomly assigned to a lifestyle intervention group or a control group. Those whose DNA was available (n = 507) were genotyped for ten ADIPOQ single nucleotide polymorphisms (SNPs). Associations between SNPs and baseline body weight and serum adiponectin concentrations were analysed using the univariate analysis of variance. The 4-year longitudinal weight data were analysed using linear mixed models analysis and the change in serum adiponectin from baseline to year four was analysed using Kruskal-Wallis test. In addition, the association of SNPs with the risk of developing T2DM during the follow-up of 0-11 (mean 6.34) years was analysed by Cox regression analysis.

RESULTS

rs266729, rs16861205, rs1501299, rs3821799 and rs6773957 associated significantly (p < 0.05) with body weight at baseline and in the longitudinal analyses. The rs266729 C allele and the rare minor alleles of rs2241766 and rs2082940 were associated with an increased adjusted hazard ratio of developing T2DM. The differences in baseline serum adiponectin concentrations were seen according to rs16861210, rs17366568, rs2241766, rs6773957 and rs2082940 and differences in the change of serum adiponectin levels from baseline to the four year examination were seen according to rs16861205, especially in subjects who were able to lose weight during the first year of intervention.

CONCLUSIONS

These results from the Finnish Diabetes Prevention Study support the concept that genetic variation in ADIPOQ locus contributes to variation in body size and serum adiponectin concentrations and may also modify the risk of developing T2DM.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov NCT00518167.

Gene × environment interaction models in psychiatric genetics

Gene-environment (G × E) interaction research is an emerging area in psychiatry, with the number of G × E studies growing rapidly in the past two decades. This article aims to give a comprehensive introduction to the field, with an emphasis on central theoretical and practical problems that are worth considering before conducting a G × E interaction study. On the theoretical side, we discuss two fundamental, but controversial questions about (1) the validity of statistical models for biological interaction and (2) the utility of G × E research for psychiatric genetics. On the practical side, we focus on study characteristics that potentially influence the outcome of G × E interaction studies and discuss strengths and pitfalls of different study designs, including recent approaches like Genome-Environment Wide Interaction Studies (GEWIS). Finally, we discuss recent developments in G × E interaction research on the most heavily investigated example in psychiatric genetics, the interaction between a serotonin transporter gene promoter variant (5-HTTLPR) and stress on depression.

Clustering by genetic ancestry using genome-wide SNP data

BACKGROUND

Population stratification can cause spurious associations in a genome-wide association study (GWAS), and occurs when differences in allele frequencies of single nucleotide polymorphisms (SNPs) are due to ancestral differences between cases and controls rather than the trait of interest. Principal components analysis (PCA) is the established approach to detect population substructure using genome-wide data and to adjust the genetic association for stratification by including the top principal components in the analysis. An alternative solution is genetic matching of cases and controls that requires, however, well defined population strata for appropriate selection of cases and controls.

RESULTS

We developed a novel algorithm to cluster individuals into groups with similar ancestral backgrounds based on the principal components computed by PCA. We demonstrate the effectiveness of our algorithm in real and simulated data, and show that matching cases and controls using the clusters assigned by the algorithm substantially reduces population stratification bias. Through simulation we show that the power of our method is higher than adjustment for PCs in certain situations.

CONCLUSIONS

In addition to reducing population stratification bias and improving power, matching creates a clean dataset free of population stratification which can then be used to build prediction models without including variables to adjust for ancestry. The cluster assignments also allow for the estimation of genetic heterogeneity by examining cluster specific effects.

Clustering by genetic ancestry using genome-wide SNP data

Background

Population stratification can cause spurious associations in a genome-wide association study (GWAS), and occurs when differences in allele frequencies of single nucleotide polymorphisms (SNPs) are due to ancestral differences between cases and controls rather than the trait of interest. Principal components analysis (PCA) is the established approach to detect population substructure using genome-wide data and to adjust the genetic association for stratification by including the top principal components in the analysis. An alternative solution is genetic matching of cases and controls that requires, however, well defined population strata for appropriate selection of cases and controls.

Results

We developed a novel algorithm to cluster individuals into groups with similar ancestral backgrounds based on the principal components computed by PCA. We demonstrate the effectiveness of our algorithm in real and simulated data, and show that matching cases and controls using the clusters assigned by the algorithm substantially reduces population stratification bias. Through simulation we show that the power of our method is higher than adjustment for PCs in certain situations.

Conclusions

In addition to reducing population stratification bias and improving power, matching creates a clean dataset free of population stratification which can then be used to build prediction models without including variables to adjust for ancestry. The cluster assignments also allow for the estimation of genetic heterogeneity by examining cluster specific effects.

CAG-repeat variant in the polymerase γ gene and male infertility in the Chinese population: a meta-analysis

Several studies have reported a relationship between the length of the CAG-repeat in the polymerase γ (POLG) gene and male infertility. However, other studies have not reproduced this result. In our study, the POLG-CAG-repeat length was analyzed in 535 healthy individuals from six Chinese Han populations living in different provinces. The frequencies of 10-CAG alleles and genotypes were high (97.38 and 94.13%, respectively), with no significant difference among the six Chinese Han populations. Furthermore, we determined the distribution of the POLG-CAG-repeat in 150 infertile men and 126 fertile men. Our study suggested that the distributions of POLG-CAG-repeat alleles and genotypes were not significantly different between infertile (95.67 and 92.67%, respectively) and fertile men (97.22 and 94.44%, respectively). In a subsequent meta-analysis, combining our data with data from previous studies, a comparison of the CAG-repeat alleles in fertile versus infertile men showed no obvious risk for male infertility associated with any particular allele (pooled odds ratio (OR)=0.94; 95% confidence interval (CI): 0.60-1.48). The significance level was not attained with any of the following genetic models: homozygote comparison (not 10/not 10 versus 10/10: OR=1.34; 95% CI: 0.66-2.72), heterozygote comparison (10/not 10 versus 10/10: OR=1.04; 95% CI: 0.78-1.38), dominant model comparison (not 10/not 10+10/not 10 versus 10/10: OR=1.08; 95% CI: 0.79-1.47) and recessive genetic comparison (not 10/not 10 versus 10/not 10+10/10: OR=1.31; 95% CI: 0.68-2.55). In conclusion, there is no significant difference of the frequencies of POLG-CAG-repeat variants among six Chinese Han populations, and this polymorphism may not be associated with Chinese male infertility. On the basis of a meta-analysis, there is no obvious association between CAG-repeat variants of the POLG gene and male infertility.

SNP-SNP interactions dominate the genetic architecture of candidate genes associated with left ventricular mass in African-Americans of the GENOA study

BACKGROUND

Left ventricular mass (LVM) is a strong, independent predictor of heart disease incidence and mortality. LVM is a complex, quantitative trait with genetic and environmental risk factors. This research characterizes the genetic architecture of LVM in an African-American population by examining the main and interactive effects of individual candidate gene single nucleotide polymorphisms (SNPs) and conventional risk factors for increased LVM.

METHODS

We used least-squares linear regression to investigate 1,878 SNPs from 234 candidate genes for SNP main effects, SNP-risk factor interactions, or SNP-SNP interactions associated with LVM in 1,328 African-Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. We reduced the probability of false positive results by implementing three analytic criteria: 1) the false discovery rate, 2) cross-validation, and 3) testing for internal replication of results.

RESULTS

We identified 409 SNP-SNP interactions passing all three criteria, while no SNP main effects or SNP-risk factor interactions passed all three. A multivariable model including four SNP-SNP interactions explained 11.3% of the variation in LVM in the full GENOA sample and 5.6% of LVM variation in independent test sets.

CONCLUSIONS

The results of this research underscore that context dependent effects, specifically SNP-SNP interactions, may dominate genetic contributions to variation in complex traits such as LVM.

Association of hydroxyprostaglandin dehydrogenase 15-(NAD) (HPGD) variants and colorectal cancer risk

A recent study examined associations of tagging single nucleotide polymorphisms (tagSNPs) in 43 fatty acid metabolism-related genes and risk of colorectal cancer (CRC), showing rs8752, rs2612656 and a haplotype [comprising both of the single nucleotide polymorphisms (SNPs)] in the hydroxyprostaglandin dehydrogenase 15-(NAD) (HPGD) gene to be positively associated with CRC risk. In the present study, we attempted to replicate these single marker and haplotype associations, using 1795 CRC cases and 1805 controls from the German Darmkrebs: Chancen der Verhütung durch Screening study (DACHS). In addition to rs8752 and rs2612656, HPGD tagSNPs rs9312555, rs17360144 and rs7349744 were genotyped for haplotype analyses. Except for a marginally significant inverse association of HPGD rs8752 with CRC risk [odds ratio (OR) = 0.85; 95% confidence interval (CI) = 0.74, 0.98; P = 0.03], none of the analyzed tagSNPs showed any association with CRC. Subset analyses for colon and rectal cancers yielded similar, yet non-significant risk estimates at all five loci. Also, none of the haplotypes was found to be associated with CRC, colon or rectal cancers. However, rs8752 was significantly associated with a decreased risk of CRC among individuals with a body mass index < 30 (OR = 0.82, 95% CI = 0.70, 0.95, P = 0.01) as well as among smokers (OR = 0.74, 95% CI = 0.61, 0.90, P = 0.003). Yet, our data do not support the previously reported associations of HPGD tagSNPs and risk of CRC.

Association of a common LAMA5 variant with anthropometric and metabolic traits in an Italian cohort of healthy elderly subjects

Laminins are large heterotrimeric glycoproteins found in basement membranes where they play an essential role in cell-matrix adhesion, migration, growth, and differentiation of various cell types. Previous work reported that a genetic variant located within the intron 1 of LAMA5 (rs659822) was associated with anthropometric traits and HDL-cholesterol levels in a cohort of premenopausal women. The present study aimed to investigate the effect of LAMA5 rs659822 on anthropometric traits, lipid profile, and fasting glucose levels in an Italian cohort of 667 healthy elderly subjects (aged 64-107years). We also tested for association between these traits and the single nucleotide polymorphism (SNP) rs13043313, which was previously shown to control variation in LAMA5 transcript abundance in the liver of Caucasians. In age- and gender-adjusted linear regression analyses, we did not find association of rs13043313 with any of the traits. However, under an additive model, the minor C-allele of LAMA5 rs659822 was associated with shorter stature (p = 0.007) and higher fasting glucose levels (p = 0.02). Moreover, subjects homozygous for the C-allele showed on average 6% and 10% lower total cholesterol (p = 0.034) and LDL-cholesterol (p = 0.016) levels, respectively, than those carrying at least one T allele, assuming a recessive model. Finally, in analyses stratified by age groups (age range 64-89 and 90-107 years), we found that the C-allele was additively associated with increased body weight (p = 0.018) in the age group 64-89 years, whereas no association was found in the age group 90-107 years. In conclusion, this study provides evidence that LAMA5 rs659822 regulates anthropometric and metabolic traits in elderly people. Future studies are warranted to replicate these findings in independent and larger populations and to investigate whether rs659822 is the causal variant responsible for the observed associations.

What can we learn from genetic studies of systemic lupus erythematosus? Implications of genetic heterogeneity among populations in SLE

Recent progress in genetics has expanded the number of the genes associated with SLE to more than 20 in the past 2 years. One might assign these candidate genetic factors into several pre-existing biological pathways: (i) innate immune response including TLR/interferon signaling pathways (IRF5, STAT4, TNFAIP3, and TREX1); (ii) adaptive immune response (HLA-DR, PTPN22, PDCD1, STAT4, LYN, BLK, and BANK1) including B, T cells, and antigen-presenting cells; and (iii) immune complex clearance mechanism (FCGRs, CRP, and ITGAM). In addition, there are also several genes and loci that could not be assigned into previous known pathways (KIAA1542, PXK, XKR6, ATG5, etc), providing possible novel mechanisms in SLE. It has also been evident that there are similarities and differences in SLE susceptibility loci across ethnic groups. Here we categorize the susceptible genes into four groups. The first group is the consistently associated genes with similar risk allele frequency between multiple ethnic populations such as STAT4, TNFAIP3, BANK1, and IRAK1/MECP2. The second group is the genes that are consistently associated but show marked difference in risk allele frequency (BLK, IRF5). The third group is the genes in which different risk variants exist within a gene or genetic loci (allelic heterogeneity) such as HLA-DR, FCGRs, and IRF5. The fourth group is the genes that show consistently discrepancy between populations such as PTPN22 and possibly ITGAM, PXK, and LYN (genetic heterogeneity). The possible explanations for differences of susceptible genetic factors between populations could be different genetic backgrounds, contribution of gene—gene or gene—environment interaction, and the relation between marker and causal variants. Therefore, efforts to identify ethnic-specific genetic factors or disease causing variants should be necessary for individualized therapy for SLE in future.

Model-based multifactor dimensionality reduction for detecting epistasis in case-control data in the presence of noise

Analyzing the combined effects of genes and/or environmental factors on the development of complex diseases is a great challenge from both the statistical and computational perspective, even using a relatively small number of genetic and nongenetic exposures. Several data-mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR) has proven its utility in a variety of theoretical and practical settings. Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new MDR-based technique that is able to unify the best of both nonparametric and parametric worlds, was developed to address some of the remaining concerns that go along with an MDR analysis. These include the restriction to univariate, dichotomous traits, the absence of flexible ways to adjust for lower order effects and important confounders, and the difficulty in highlighting epistatic effects when too many multilocus genotype cells are pooled into two new genotype groups. We investigate the empirical power of MB-MDR to detect gene-gene interactions in the absence of any noise and in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Power is generally higher for MB-MDR than for MDR, in particular in the presence of genetic heterogeneity, phenocopy, or low minor allele frequencies.

PCM1 and schizophrenia: a replication study in the Northern Swedish population

Previous studies implicated centrosomal dysfunction as a source of various neuropsychiatric disorders, including schizophrenia (SZ). Two recent reports [Gurling et al., 2006; Datta et al., 2008. Mol Psychiatry] described an association between polymorphisms in the PCM1 gene and SZ in a UK/Scottish population. In this study, we aimed to replicate these findings in a Northern Swedish association sample of 486 research subjects with SZ and 512 unrelated control individuals. We genotyped 12 previously described SNP markers and carried out haplotype analyses using the same multi-marker haplotypes previously reported. Though we could not replicate the association with SNPs rs445422 and rs208747, we did observe a significant protective association with intronic SNP rs13276297. Furthermore, we performed a meta-analysis comprising 1,794 SZ patients and 1,553 controls, which confirmed the previously reported association with rs445422 and rs208747. These data provide further evidence that PCM1-though certainly not a major risk factor in the Northern Swedish population-cannot be ruled out as a contributor to SZ risk and/or protection, and deserves further replication in larger populations to elucidate its role in disease etiology.

Association of the angiotensin II type I receptor gene +1166 A>C polymorphism with hypertension risk: evidence from a meta-analysis of 16474 subjects

Mounting evidence suggests the potential susceptibility of individuals with a mutation in the angiotensin II type I receptor (AT1R) gene to hypertension. One polymorphism, +1166 A>C, has been extensively studied, but the results have often been irreproducible. We therefore aimed to meta-analyze all available case-control studies from the English language literature to explore the association of this polymorphism with hypertension. A total of 22 studies with 24 populations involving 8249 patients and 8225 controls were identified as of 25 February 2010. A random-effects model was performed regardless of the between-study heterogeneity. The study quality was assessed in duplicate. The data were analyzed using RevMan software (version 5.0.23). Overall, the presence of the +1166 C allele significantly conferred an increased risk of hypertension (odds ratio (OR)=1.14; 95% confidence interval, 1.00-1.30; P=0.05). Under the assumption of three genetic modes of inheritance, an elevated hypertension risk was observed for each comparison (codominant: AC vs. AA, OR=1.10 (P=0.20) and CC vs. AA, OR=1.21 (P=0.36); dominant: OR=1.13 (P=0.09); recessive: OR=1.21 (P=0.36)). Upon stratification by study design, more obvious associations were observed for the population-based design, whereas there were no changes in direction and only slight changes in magnitude upon stratification by sample size and geographical area. No publication biases were indicated by the fail-safe number. Our study pooled previous findings and showed that the AT1R +1166 C allele conferred an increased risk of hypertension. We suggest that confirmation in a large, well-designed study or from functional aspects of this polymorphism is critical.

Survival dimensionality reduction (SDR): development and clinical application of an innovative approach to detect epistasis in presence of right-censored data

BACKGROUND

Epistasis is recognized as a fundamental part of the genetic architecture of individuals. Several computational approaches have been developed to model gene-gene interactions in case-control studies, however, none of them is suitable for time-dependent analysis. Herein we introduce the Survival Dimensionality Reduction (SDR) algorithm, a non-parametric method specifically designed to detect epistasis in lifetime datasets.

RESULTS

The algorithm requires neither specification about the underlying survival distribution nor about the underlying interaction model and proved satisfactorily powerful to detect a set of causative genes in synthetic epistatic lifetime datasets with a limited number of samples and high degree of right-censorship (up to 70%). The SDR method was then applied to a series of 386 Dutch patients with active rheumatoid arthritis that were treated with anti-TNF biological agents. Among a set of 39 candidate genes, none of which showed a detectable marginal effect on anti-TNF responses, the SDR algorithm did find that the rs1801274 SNP in the Fc gamma RIIa gene and the rs10954213 SNP in the IRF5 gene non-linearly interact to predict clinical remission after anti-TNF biologicals.

CONCLUSIONS

Simulation studies and application in a real-world setting support the capability of the SDR algorithm to model epistatic interactions in candidate-genes studies in presence of right-censored data.

AVAILABILITY

http://sourceforge.net/projects/sdrproject/.

Review: Association between angiotensin converting enzyme G2350A polymorphism and hypertension risk: a meta-analysis

Background and objective: An exonic polymorphism G2350A (rs4343) in angiotensin converting enzyme (protein: ACE; gene: ACE) was shown to exert the most significant influence on plasma ACE levels. We therefore performed a meta-analysis to investigate association of ACE G2350A polymorphism with hypertension.

Methods: Published case-control studies in English were identified. A total of four studies with 1699 cases and 1274 controls were identified. A random-effects model was performed irrespective of the between-study heterogeneity. Study quality was assessed in duplicate.

Results: Compared with 2350G, the ACE 2350A allele conferred a protective effect on hypertension (odds ratio (OR) = 0.81; 95% confidence interval (CI), 0.56—1.18; p = .28). Similarly, comparisons of 2350AA and 2350GA with 2350GG generated a nonsignificant reduced risk, respectively. Under the dominant model, the ACE 2350A allele conferred a reduced hypertension risk and such associations were divergent between Han Chinese and Muslims from the Arab Gulf and Pakistan. Under the recessive model, this protective effect was totally reversed (OR = 1.01; 95% CI, 0.77—1.33; p = .94). Subgroup analyses indicated a significant protective effect of ACE 2350A compared with 2350G among Muslims from the Arab Gulf and Pakistan (OR = 0.55; 95% CI, 0.42—0.71; p < .00001). No publication biases were observed.

Conclusions: Our results demonstrate that the ACE 2350A allele is associated with a significantly reduced hypertension risk among Muslims from the Arab Gulf and Pakistan, yet an elevated risk among Han Chinese.

Functional variants of the sphingosine-1-phosphate receptor 1 gene associate with asthma susceptibility

BACKGROUND

The genetic mechanisms underlying asthma remain unclear. Increased permeability of the microvasculature is a feature of asthma, and the sphingosine-1-phosphate receptor (S1PR1) is an essential participant regulating lung vascular integrity and responses to lung inflammation.

OBJECTIVE

We explored the contribution of polymorphisms in the S1PR1 gene to asthma susceptibility.

METHODS

A combination of gene resequencing for single nucleotide polymorphism (SNP) discovery, case-control association, functional evaluation of associated SNPs, and protein immunochemistry studies was used.

RESULTS

Immunohistochemistry studies demonstrated significantly decreased S1PR1 protein expression in pulmonary vessels in lungs of asthmatic patients compared with those of nonasthmatic subjects (P < .05). Direct DNA sequencing of 27 multiethnic samples identified 39 S1PR1 variants (18 novel SNPs). Association studies were performed based on genotyping results from cosmopolitan tagging SNPs in 3 case-control cohorts from Chicago and New York totaling 1,061 subjects (502 cases and 559 control subjects). The promoter SNP rs2038366 (-1557G/T) was found to be associated with asthma (P = .03) in European Americans. In African Americans an association was found for both asthma and severe asthma for intronic SNP rs3753194 (c.-164+170A/G; P = .006 and P = .040, respectively) and for promoter SNP rs59317557 (-532C/G) with severe asthma (P = .028). Consistent with predicted in silico functionality, alleles of the promoter SNPs rs2038366 (-1557G/T) and rs59317557 (-532C/G) influenced the activity of a luciferase S1PR1 reporter vector in transfected endothelial cells exposed to growth factors (epidermal growth factor, platelet-derived growth factor, and vascular endothelial growth factor) known to be increased in asthmatic airways.

CONCLUSION

These data provide strong support for a role for S1PR1 gene variants in asthma susceptibility and severity.

Methods for investigating gene-environment interactions in candidate pathway and genome-wide association studies

Despite the considerable enthusiasm about the yield of novel and replicated discoveries of genetic associations from the new generation of genome-wide association studies (GWAS), the proportion of the heritability of most complex diseases that have been studied to date remains small. Some of this "dark matter" could be due to gene-environment (G x E) interactions or more complex pathways involving multiple genes and exposures. We review the basic epidemiologic study design and statistical analysis approaches to studying G x E interactions individually and then consider more comprehensive approaches to studying entire pathways or GWAS data. In addition to the usual issues in genetic association studies, particular care is needed in exposure assessment, and very large sample sizes are required. Although hypothesis-driven, pathway-based and agnostic GWA study approaches are generally viewed as opposite poles, we suggest that the two can be usefully married using hierarchical modeling strategies that exploit external pathway knowledge in mining genome-wide data.

Identification of fetal and maternal single nucleotide polymorphisms in candidate genes that predispose to spontaneous preterm labor with intact membranes

OBJECTIVE

The purpose of this study was to determine whether maternal/fetal single nucleotide polymorphisms (SNPs) in candidate genes are associated with spontaneous preterm labor/delivery.

STUDY DESIGN

A genetic association study was conducted in 223 mothers and 179 fetuses (preterm labor with intact membranes who delivered <37 weeks of gestation [preterm birth (PTB)]), and 599 mothers and 628 fetuses (normal pregnancy); 190 candidate genes and 775 SNPs were studied. Single locus/haplotype association analyses were performed; the false discovery rate was used to correct for multiple testing.

RESULTS

The strongest single locus associations with PTB were interleukin-6 receptor 1 (fetus; P=.000148) and tissue inhibitor of metalloproteinase 2 (mother; P=.000197), which remained significant after correction for multiple comparisons. Global haplotype analysis indicated an association between a fetal DNA variant in insulin-like growth factor F2 and maternal alpha 3 type IV collagen isoform 1 (global, P=.004 and .007, respectively).

CONCLUSION

An SNP involved in controlling fetal inflammation (interleukin-6 receptor 1) and DNA variants in maternal genes encoding for proteins involved in extracellular matrix metabolism approximately doubled the risk of PTB.

FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals

We propose a novel multifactor dimensionality reduction method for epistasis detection in small or extended pedigrees, FAM-MDR. It combines features of the Genome-wide Rapid Association using Mixed Model And Regression approach (GRAMMAR) with Model-Based MDR (MB-MDR). We focus on continuous traits, although the method is general and can be used for outcomes of any type, including binary and censored traits. When comparing FAM-MDR with Pedigree-based Generalized MDR (PGMDR), which is a generalization of Multifactor Dimensionality Reduction (MDR) to continuous traits and related individuals, FAM-MDR was found to outperform PGMDR in terms of power, in most of the considered simulated scenarios. Additional simulations revealed that PGMDR does not appropriately deal with multiple testing and consequently gives rise to overly optimistic results. FAM-MDR adequately deals with multiple testing in epistasis screens and is in contrast rather conservative, by construction. Furthermore, simulations show that correcting for lower order (main) effects is of utmost importance when claiming epistasis. As Type 2 Diabetes Mellitus (T2DM) is a complex phenotype likely influenced by gene-gene interactions, we applied FAM-MDR to examine data on glucose area-under-the-curve (GAUC), an endophenotype of T2DM for which multiple independent genetic associations have been observed, in the Amish Family Diabetes Study (AFDS). This application reveals that FAM-MDR makes more efficient use of the available data than PGMDR and can deal with multi-generational pedigrees more easily. In conclusion, we have validated FAM-MDR and compared it to PGMDR, the current state-of-the-art MDR method for family data, using both simulations and a practical dataset. FAM-MDR is found to outperform PGMDR in that it handles the multiple testing issue more correctly, has increased power, and efficiently uses all available information.

Gene--environment-wide association studies: emerging approaches

Despite the yield of recent genome-wide association (GWA) studies, the identified variants explain only a small proportion of the heritability of most complex diseases. This unexplained heritability could be partly due to gene--environment (G×E) interactions or more complex pathways involving multiple genes and exposures. This Review provides a tutorial on the available epidemiological designs and statistical analysis approaches for studying specific G×E interactions and choosing the most appropriate methods. I discuss the approaches that are being developed for studying entire pathways and available techniques for mining interactions in GWA data. I also explore methods for marrying hypothesis-driven pathway-based approaches with 'agnostic' GWA studies.

Association of TGFB1 -509 C>T polymorphism with breast cancer: evidence from a meta-analysis involving 23,579 subjects

Although a number of genetic studies have attempted to link transforming growth factor beta 1 gene (TGFB1) -509 C>T polymorphism to breast cancer, the results were often irreproducible. We therefore aimed to meta-analyze all available case-control studies from the English-published literature to explore the association of this polymorphism with breast cancer. A total of 6 studies with 9 populations involving 10,197 patients and 13,382 controls were identified as of February 20, 2010. A random-effects model was performed irrespective of the between-study heterogeneity. Study quality was assessed in duplicate. The frequencies of TGFB1 -509 T allele in patients and controls ranged from 21.72 to 51.74%, and 24.53 to 52.40%, respectively. The presence of -509 T allele conferred a nonsignificant protective effect on breast cancer [odds ratio (OR) = 0.99; 95% confidence interval (CI) 0.93-1.05; P = 0.72]. This lack of association persisted under co-dominant, dominant, and recessive models. However, exclusion of the initial study significantly strengthened the magnitude of this protective effect. For example, under the dominant assumption, carriers of -509 T allele had a moderate reduced risk for breast cancer compared with the -509 CC homozygous (OR = 0.94; 95% CI 0.88-1.00; P = 0.04). Subgroup analyses by study designs and geographic areas did not substantially affect the present associations. No publication biases were observed by the fail-safe number. Taken together, our results demonstrated that TGFB1 -509 T allele was associated with a reduced risk to develop breast cancer and this allele appeared to act in an additive mode.

A meta-analysis of genome-wide data from five European isolates reveals an association of COL22A1, SYT1, and GABRR2 with serum creatinine level

BACKGROUND

Serum creatinine (S CR) is the most important biomarker for a quick and non-invasive assessment of kidney function in population-based surveys. A substantial proportion of the inter-individual variability in S CR level is explicable by genetic factors.

METHODS

We performed a meta-analysis of genome-wide association studies of S CR undertaken in five population isolates ('discovery cohorts'), all of which are part of the European Special Population Network (EUROSPAN) project. Genes showing the strongest evidence for an association with SCR (candidate loci) were replicated in two additional population-based samples ('replication cohorts').

RESULTS

After the discovery meta-analysis, 29 loci were selected for replication. Association between SCR level and polymorphisms in the collagen type XXII alpha 1 (COL22A1) gene, on chromosome 8, and in the synaptotagmin-1 (SYT1) gene, on chromosome 12, were successfully replicated in the replication cohorts (p value = 1.0 x 10(-6) and 1.7 x 10(-4), respectively). Evidence of association was also found for polymorphisms in a locus including the gamma-aminobutyric acid receptor rho-2 (GABRR2) gene and the ubiquitin-conjugating enzyme E2-J1 (UBE2J1) gene (replication p value = 3.6 x 10(-3)). Previously reported findings, associating glomerular filtration rate with SNPs in the uromodulin (UMOD) gene and in the schroom family member 3 (SCHROOM3) gene were also replicated.

CONCLUSIONS

While confirming earlier results, our study provides new insights in the understanding of the genetic basis of serum creatinine regulatory processes. In particular, the association with the genes SYT1 and GABRR2 corroborate previous findings that highlighted a possible role of the neurotransmitters GABAA receptors in the regulation of the glomerular basement membrane and a possible interaction between GABAA receptors and synaptotagmin-I at the podocyte level.

Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS

MOTIVATION

Epistasis, the presence of gene-gene interactions, has been hypothesized to be at the root of many common human diseases, but current genome-wide association studies largely ignore its role. Multifactor dimensionality reduction (MDR) is a powerful model-free method for detecting epistatic relationships between genes, but computational costs have made its application to genome-wide data difficult. Graphics processing units (GPUs), the hardware responsible for rendering computer games, are powerful parallel processors. Using GPUs to run MDR on a genome-wide dataset allows for statistically rigorous testing of epistasis.

RESULTS

The implementation of MDR for GPUs (MDRGPU) includes core features of the widely used Java software package, MDR. This GPU implementation allows for large-scale analysis of epistasis at a dramatically lower cost than the standard CPU-based implementations. As a proof-of-concept, we applied this software to a genome-wide study of sporadic amyotrophic lateral sclerosis (ALS). We discovered a statistically significant two-SNP classifier and subsequently replicated the significance of these two SNPs in an independent study of ALS. MDRGPU makes the large-scale analysis of epistasis tractable and opens the door to statistically rigorous testing of interactions in genome-wide datasets.

AVAILABILITY

MDRGPU is open source and available free of charge from http://www.sourceforge.net/projects/mdr.

Maternal and fetal genetic associations of PTGER3 and PON1 with preterm birth

OBJECTIVE

The purpose of this study was to identify associations between maternal and fetal genetic variants in candidate genes and spontaneous preterm birth (PTB) in a Norwegian population and to determine the effect size of those associations that corroborate a previous study of PTB.

METHODS

DNA from 434 mother-baby dyads (214 cases and 220 controls) collected from the Norwegian Mother and Child Cohort (MoBa) was examined for association between 1,430 single nucleotide polymorphisms in 143 genes and PTB. These results were compared to a previous study on European Americans (EA) from Centennial Women's Hospital in Nashville, TN, USA. Odds ratios for SNPs that corroborated the Cenntennial study were determined on the combined MoBa and Centennial studies.

RESULTS

In maternal samples the strongest results that corroborated the Centennial study were in the prostaglandin E receptor 3 gene (PTGER3; rs977214) (combined genotype p = 3x10(-4)). The best model for rs977214 was the AG/GG genotypes relative to the AA genotype and resulted in an OR of 0.55 (95% CI = 0.37-0.82, p = 0.003), indicating a protective effect. In fetal samples the most significant association in the combined data was rs854552 in the paraoxonase 1 gene (PON1) (combined allele p = 8x10(-4)). The best model was the TT genotype relative to the CC/CT genotypes, and resulted in an OR of 1.32 (95% CI = 1.13-1.53, p = 4x10(-4)).

CONCLUSIONS

These studies identify single locus associations with preterm birth for both maternal and fetal genotypes in two populations of European ancestry.

Bioinformatics challenges for genome-wide association studies

Motivation: The sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype–phenotype relationship that is characterized by significant heterogeneity and gene–gene and gene–environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods.

Contact:jason.h.moore@dartmouth.edu

Angiotensin converting enzyme D allele is associated with an increased risk of type 2 diabetes: evidence from a meta-analysis

Associations of angiotensin converting enzyme gene (ACE) insertion/deletion (I/D) polymorphism with type 2 diabetes (T2D) have been inconsistent with both positive, null and negative results. We thereby performed a meta-analysis from all English-published reports to examine ACE I/D polymorphism in association with T2D risk. Case-control studies were identified from MEDLINE, EMBASE and Web of Science as of Dec 10, 2009. A total of 14 studies with 1985 patients with T2D and 4602 controls were finally identified. Random-effects model was applied irrespective of between-study heterogeneity. Study quality was assessed in duplicate. Compared with ACE I allele, presence of D allele conferred a significant increased risk for T2D (OR=1.33; 95% CI, 1.10-1.61; p=0.003). This trend was potentiated after comparing homozygotes of D allele with I allele with a 90% increased risk (p=0.0008). Carriers of D allele had a moderate increased risk for T2D compared with the II genotype carriers (OR=1.34; 95% CI, 1.04-1.72; p=0.02), whereas under recessive model this effect was significantly enhanced (OR=1.73; 95% CI, 1.26-2.38; p=0.0008). Subgroup analyses indicated significant association for population-based study design only, as well as among populations from Africa and Europe ancestries rather than from Asia ancestry. No publication bias was observed using the fail-safe number at the level of 0.05. Our results demonstrated that ACE D allele was significantly associated with an increased risk of T2D, and this effect appeared to be additive. Moreover, this association was more prominent for population-based studies and among Africans and Caucasians.

A meta-analysis of the bradykinin B2 receptor gene --58C/T polymorphism with hypertension

BACKGROUND AND OBJECTIVE

Numerous studies have attempted to associate -58C/T polymorphism of bradykinin B2 receptor gene (BDKRB2) with hypertension, whereas results were often irreproducible. We performed a meta-analysis aiming to provide a comprehensive evaluation of this polymorphism and hypertension.

METHODS

Case-control reports published in English were searched totaling four studies with six populations (823 cases and 916 controls). Random-effects model was applied irrespective of between-study heterogeneity, and study quality was assessed in duplicate.

RESULTS

Compared with -58C allele carriers, those with -58T allele had a lower yet nonsignificant risk for hypertension (OR=0.86; 95% CI: 0.68-1.09; P=0.21). Lack of significance persisted after combining those with genotypes -58TC and -58TT together (OR=0.87; 95% CI: 0.67-1.09; P=0.21) or with -58TC and -58CC together (OR=0.75; 95% CI: 0.48-1.18; P=0.22) in association with hypertension. Sensitivity analyses by race indicated that comparison of -58T versus -58C generated a protective effect for hypertension in Asians (OR=0.77; 95% CI: 0.58-1.02; P=0.07) and African-Americans (OR=0.65; 95% CI: 0.43-0.98; P=0.04), but a risk effect in Caucasians (OR=1.22; 95% CI: 0.92-1.61; P=0.17). No publication bias was observed.

CONCLUSIONS

Our results suggested that -58T allele exhibited a protective effect on hypertension in Asians and African-Americans, yet a risk effect in Caucasians.

Genome-wide association studies - A summary for the clinical gastroenterologist

Genome-wide association studies (GWAS) have been applied to various gastrointestinal and liver diseases in recent years. A large number of susceptibility genes and key biological pathways in disease development have been identified. So far, studies in inflammatory bowel diseases, and in particular Crohn’s disease, have been especially successful in defining new susceptibility loci using the GWAS design. The identification of associations related to autophagy as well as several genes involved in immunological response will be important to future research on Crohn’s disease. In this review, key methodological aspects of GWAS, the importance of proper cohort collection, genotyping issues and statistical methods are summarized. Ways of addressing the shortcomings of the GWAS design, when it comes to rare variants, are also discussed. For each of the relevant conditions, findings from the various GWAS are summarized with a focus on the affected biological systems.