Eigenvector metabolite analysis reveals dietary effects on the association among metabolite correlation patterns, gene expression, and phenotypes.

INTRODUCTION: 'Multi-omics' datasets obtained from an organism of interest reared under different environmental treatments are increasingly common. Identifying the links among metabolites and transcripts can help to elucidate our understanding of the impact of environment at different levels within the organism. However, many methods for characterizing physiological connections cannot address unidentified metabolites. OBJECTIVES: Here, we use Eigenvector Metabolite Analysis (EvMA) to examine links between metabolomic, transcriptomic, and phenotypic variation data and to assess the impact of environmental factors on these associations. Unlike other methods, EvMA can be used to analyze datasets that include unidentified metabolites and unannotated transcripts. METHODS: To demonstrate the utility of EvMA, we analyzed metabolomic, transcriptomic, and phenotypic datasets produced from 20 Drosophila melanogaster genotypes reared on four dietary treatments. We used a hierarchical distance-based method to cluster the metabolites. The links between metabolite clusters, gene expression, and overt phenotypes were characterized using the eigenmetabolite (first principal component) of each cluster. RESULTS: EvMA recovered chemically related groups of metabolites within the clusters. Using the eigenmetabolite, we identified genes and phenotypes that significantly correlated with each cluster. EvMA identifies new connections between the phenotypes, metabolites, and gene transcripts. Conclusion EvMA provides a simple method to identify correlations between metabolites, gene expression, and phenotypes, which can allow us to partition multivariate datasets into meaningful biological modules and identify under-studied metabolites and unannotated gene transcripts that may be central to important biological processes. This can be used to inform our understanding of the effect of environmental mechanisms underlying physiological states of interest.

An adaptive permutation approach for genome-wide association study: evaluation and recommendations for use.

BACKGROUND: Permutation testing is a robust and popular approach for significance testing in genomic research, which has the broad advantage of estimating significance non-parametrically, thereby safe guarding against inflated type I error rates. However, the computational efficiency remains a challenging issue that limits its wide application, particularly in genome-wide association studies (GWAS). Because of this, adaptive permutation strategies can be employed to make permutation approaches feasible. While these approaches have been used in practice, there is little research into the statistical properties of these approaches, and little guidance into the proper application of such a strategy for accurate p-value estimation at the GWAS level. METHODS: In this work, we advocate an adaptive permutation procedure that is statistically valid as well as computationally feasible in GWAS. We perform extensive simulation experiments to evaluate the robustness of the approach to violations of modeling assumptions and compare the power of the adaptive approach versus standard approaches. We also evaluate the parameter choices in implementing the adaptive permutation approach to provide guidance on proper implementation in real studies. Additionally, we provide an example of the application of adaptive permutation testing on real data. RESULTS: The results provide sufficient evidence that the adaptive test is robust to violations of modeling assumptions. In addition, even when modeling assumptions are correct, the power achieved by adaptive permutation is identical to the parametric approach over a range of significance thresholds and effect sizes under the alternative. A framework for proper implementation of the adaptive procedure is also generated. CONCLUSIONS: While the adaptive permutation approach presented here is not novel, the current study provides evidence of the validity of the approach, and importantly provides guidance on the proper implementation of such a strategy. Additionally, tools are made available to aid investigators in implementing these approaches.

Evaluation of genetic risk score models in the presence of interaction and linkage disequilibrium.

In the area of genetic epidemiology, genetic risk predictive modeling is becoming an important area of translational success. As an increasing number of genetic variants are successfully discovered, the use of multiple genetic variants in constructing a genetic risk score (GRS) for modeling has been widely applied using a variety of approaches. Previously, we compared the performance of a simple, additive GRS with weighted GRS approaches, but our initial simulation experiment assumed very simple models without many of the complications found in real genetic studies. In particular, interactions between variants and linkage disequilibrium (LD) (indirect mapping) remain important and challenging problems for GRS modeling. In the present study, we applied two simulation strategies to mimic various types of epistasis to evaluate their impact on the performance of the GRS models. We simulated a range of models demonstrating statistical interaction and linkage disequilibrium. Three genetic risk models were compared in terms of power, type I error, C-statistic and AIC, including a simple count GRS (SC-GRS), an odds ratio weighted GRS (OR-GRS) and an explained variance weighted GRS (EV-GRS). Simulation factors of interest included allele frequencies, effect sizes, strengths of interaction, degrees of LD and heritability. We extensively examined the extent to how these interactions could influence the performance of genetic risk models. Our results show that the weighted methods outperform simple count method in general even if interaction or LD is present, with well controlled type I error.

A new explained-variance based genetic risk score for predictive modeling of disease risk.

The goal of association mapping is to identify genetic variants that predict disease, and as the field of human genetics matures, the number of successful association studies is increasing. Many such studies have shown that for many diseases, risk is explained by a reasonably large number of variants that each explains a very small amount of disease risk. This is prompting the use of genetic risk scores in building predictive models, where information across several variants is combined for predictive modeling. In the current study, we compare the performance of four previously proposed genetic risk score methods and present a new method for constructing genetic risk score that incorporates explained variance information. The methods compared include: a simple count Genetic Risk Score, an odds ratio weighted Genetic Risk Score, a direct logistic regression Genetic Risk Score, a polygenic Genetic Risk Score, and the new explained variance weighted Genetic Risk Score. We compare the methods using a wide range of simulations in two steps, with a range of the number of deleterious single nucleotide polymorphisms (SNPs) explaining disease risk, genetic modes, baseline penetrances, sample sizes, relative risks (RR) and minor allele frequencies (MAF). Several measures of model performance were compared including overall power, C-statistic and Akaike's Information Criterion. Our results show the relative performance of methods differs significantly, with the new explained variance weighted GRS (EV-GRS) generally performing favorably to the other methods.

Positive association between GRIN2B gene and bipolar disorder in the Chinese Han Population.

In the present work we genotyped three single-nucleotide polymorphisms (SNPs) (rs7301328, rs1805247, and rs1805502) of the GRIN2B gene in a set of 480 unrelated bipolar disorder patients and 480 unrelated genetically matched normal controls in Chinese Han population by either allelic-specific multiplex ligation-detection reaction (AMLR) technology or direct sequencing. Rs1805247 and the haplotype consisting of rs1805502 and rs1805247 were significantly associated, suggesting GRIN2B as having a role in the etiology of bipolar disorder.

Association study between RGS4 and bipolar disorder in the Chinese Han population.

This abstract concerns a brief association study between RGS4 and bipolar disorder, by an association study of five single nucleotide polymorphisms, rs951436, rs951439, rs2842030, rs2661319, and rs2344671, in 484 patients and 288 controls from the Chinese Han population. In our case-control study, the T allele of the single nucleotide polymorphism rs951436 tended to be protective (P=0.0078), and in addition, a haplotype containing this T allele was also protective for bipolar disorder (P=0.02). Our results provide further evidence to support RGS4 as a potential susceptible gene for bipolar disorder.

No association between the FXYD6 gene and schizophrenia in the Chinese Han population.

Choudhury et al. identified FXYD6 as a susceptible gene for schizophrenia in the London and Aberdeen populations. We genotyped D11S1998 and 8 SNPs (rs869789, rs11216567, rs10790212, rs876797, rs4938445, rs497768, rs11216598, rs11605223) in a Chinese sample consisting of 1514 schizophrenia patients and 1514 healthy controls. We also compared the expression levels of FXYD6 in lymphocytes in 86 schizophrenia patients and 94 controls. No association was detected either in D11S1998 or the 8 SNPs. No difference was found in expression level between patients and controls. Our study suggests that FXYD6 does not play a role in schizophrenia in the Chinese Han population.

Apoptotic engulfment pathway and schizophrenia.

BACKGROUND: Apoptosis has been speculated to be involved in schizophrenia. In a previously study, we reported the association of the MEGF10 gene with the disease. In this study, we followed the apoptotic engulfment pathway involving the MEGF10, GULP1, ABCA1 and ABCA7 genes and tested their association with the disease. METHODOLOGY/PRINCIPAL FINDINGS: Ten, eleven and five SNPs were genotyped in the GULP1, ABCA1 and ABCA7 genes respectively for the ISHDSF and ICCSS samples. In all 3 genes, we observed nominally significant associations. Rs2004888 at GULP1 was significant in both ISHDSF and ICCSS samples (p = 0.0083 and 0.0437 respectively). We sought replication in independent samples for this marker and found highly significant association (p = 0.0003) in 3 Caucasian replication samples. But it was not significant in the 2 Chinese replication samples. In addition, we found a significant 2-marker (rs2242436 * rs3858075) interaction between the ABCA1 and ABCA7 genes in the ISHDSF sample (p = 0.0022) and a 3-marker interaction (rs246896 * rs4522565 * rs3858075) amongst the MEGF10, GULP1 and ABCA1 genes in the ICCSS sample (p = 0.0120). Rs3858075 in the ABCA1 gene was involved in both 2- and 3-marker interactions in the two samples. CONCLUSIONS/SIGNIFICANCE: From these data, we concluded that the GULP1 gene and the apoptotic engulfment pathway are involved in schizophrenia in subjects of European ancestry and multiple genes in the pathway may interactively increase the risks to the disease.

The Val/Met functional polymorphism in COMT confers susceptibility to bipolar disorder: evidence from an association study and a meta-analysis.

The COMT gene is considered as one of the prominent candidate genes for susceptibility to BP, and most studies focused a functional polymorphism in the gene: the Val/Met polymorphism (rs4680). However, results from these studies are sometimes contradictory, due to small sample size or heterogeneity. In this study, we first investigate the possible association between the Val/Met polymorphism in COMT and bipolar disorder in the Han population, which has never been done before. Then a systematic meta-analysis was conducted to determine if the low-activity allele (Met) increases the risk of BP in different ethnic groups. A total of 478 BP patients and 469 healthy subjects were recruited in our case/control study. MIX software package was employed to perform the meta-analysis on 19 studies after careful search and selection. We observed statistically-significant differences in allele (p = 0.00060) and genotype (p = 0.00203) frequencies between patients and controls in our samples. The meta-analysis also provided a significant pooled OR for association of the Met allele in rs4680 with BP in the total population (p = 0.0223) and in the Asian population (p = 0.0232). Although a significant pooled OR was also found for the Caucasian population (p = 0.0409) after one of the studies as discussed below was removed, the role for Val/Met polymorphism in BP in Caucasian ethnicity was not yet to be confirmed. In conclusion, the low-activity allele (Met) of rs4680 in COMT gene possibly confers risk for bipolar disorder in the Han population, while it needs further evidence for concluding its association with BP in the Caucasian population.

An association study of the SLC26A4 gene in children with mental retardation.

It is generally considered that iodine deficiency is the single most common cause of preventable mental retardation (MR) and brain damage. The SLC26A4 gene is expressed at the apical surface of thyrocytes and its product forms an efficient iodide-trapping mechanism. To investigate whether variability in the SLC26A4 gene influences the risk of iodine-deficiency based MR, we undertook an association study between SLC26A4 and MR. Participants were recruited from a relatively isolated and traditionally iodine-deficient region with a high prevalence of MR. The SNPs we selected from the dbSNP and HapMap were identified using ARMS-PCR and sequencing methods. Singular-locus and haplotype association analysis indicated no association between the SLC26A4 gene and MR (p>0.05). The negative results suggest that the SLC26A4 gene has no measurable impact on iodine-deficiency based MR. In view of the characteristics of our samples, our study may provide a good reference for research into the transport features of pendrin in the thyrocyte apical surface.

No relationship between 2',3'-cyclic nucleotide 3'-phosphodiesterase and schizophrenia in the Chinese Han population: an expression study and meta-analysis.

BACKGROUND: 2',3'-Cyclic nucleotide 3'-phosphodiesterase (CNP), one of the promising candidate genes for schizophrenia, plays a key part in the oligodendrocyte function and in myelination. The present study aims to investigate the relationship between CNP and schizophrenia in the Chinese population and the effect of different factors on the expression level of CNP in schizophrenia. METHODS: Five CNP single nucleotide polymorphisms (SNPs) were investigated in a Chinese Han schizophrenia case-control sample set (n = 180) using direct sequencing. The results were included in the following meta-analysis. Quantitative real-time polymerase chain reaction (PCR) was conducted to examine CNP expression levels in peripheral blood lymphocytes. RESULTS: Factors including gender, genotype, sub-diagnosis and antipsychotics-treatment were found not to contribute to the expression regulation of the CNP gene in schizophrenia. Our meta-analysis produced similar negative results. CONCLUSION: The results suggest that the CNP gene may not be involved in the etiology and pathology of schizophrenia in the Chinese population.

Association analysis of serotonin receptor 7 gene (HTR7) and risperidone response in Chinese schizophrenia patients.

Several lines of evidence suggest that the human 5-HT(7) receptor may be involved in the pharmacodynamics of risperidone and may influence clinical response of the drug. A pharmocogenetics study of this receptor may therefore be useful in developing individualized therapy. But few studies about it have been done. In this study, we genotyped ten single nucleotide polymorphisms (SNPs) distributed throughout the HTR7 gene and analyzed six of them for association with the reduction of Brief Psychiatric Rating Scale (BPRS) scores in drug-naive Chinese schizophrenia patients, following an eight-week period of risperidone monotherapy. The confounding effects of nongenetic factors were estimated and the baseline symptom score as well as the duration of illness were included as covariates for adjustment. No significant correlation of HTR7 with antipsychotic efficacy was detected in either genotype or haplotype analysis. These results demonstrate that variations in the HTR7 gene may not be good genetic markers for predicting the therapeutic efficacy of risperidone.

No association found between the promoter variations of QKI and schizophrenia in the Chinese population.

BACKGROUND: Schizophrenia is a chronic psychiatric disorder with a strong genetic component. Several recent published studies have reported that the mRNA expression level of quaking homolog, KH domain RNA binding (QKI) is down regulated in individuals diagnosed with schizophrenia. METHODS: We were interested in the genetic variants around the promoter region of QKI and selected seven variants in this region, namely rs4263561, rs3904720, rs387504, rs3763197, rs7772756, rs7758706 and rs4709716. For the study we recruited 288 individuals diagnosed with schizophrenia and 288 control subjects. All the recruits were from Shanghai and were Han Chinese in origin. RESULTS: No individual SNP nor any haplotype was found to be associated with schizophrenia. CONCLUSIONS: These results suggest that the variants within the promoter region of QKI gene are unlikely to play a major role in susceptibility to schizophrenia in the Chinese population.