Set association analysis of SNP case-control and microarray data

Application of amino acid ionic liquids for increasing the stability of DNA in long term storage

The structural stability of DNA is important because of its biological activity. DNAs due to their inherent chemical properties are not stable in an aqueous solution, therefore, a long period of storage of DNA at the ambient condition in bioscience is of importance. Ionic liquids (ILs) as interesting alternatives compared to organic solvents and water due to their considerable properties can be used as new agents to increase the stability of DNA for a long period of storage. In this article, molecular dynamics (MD) simulations and quantum chemistry calculations were applied to investigate the effects of amino acid ionic liquids ([BMIM][Ala], [BMIM][Gly], [BMIM][Val], [BMIM][Pro] and [BMIM][Leu]) on the dynamical behavior and the structural stability of calf thymus DNA. Based on the obtained MD results ILs enter into the solvation shell of the DNA and push away the water molecules from the DNA surface. Structural analysis shows that [BMIM]⁺ cations can occupy the DNA minor groove without disturbing the double-helical structure of DNA. ILs due to strong electrostatic and van der Waals (vdW) interactions with the DNA structure contribute to the stability of the double-helical structure. Quantum chemistry calculations indicate that the interactions between the [BMIM]⁺ cation and DNA structure has an electrostatic character. Moreover, this cation forms a more stable complex with the CGCG region of the DNA in comparison with AATT base pairs. Overall, the results of this study can provide new insight into the application of ILs for maintaining DNA stability during long-term storage.Communicated by Ramaswamy H. Sarma.

Critical Assessment of the Interaction between DNA and Choline Amino Acid Ionic Liquids: Evidences of Multimodal Binding and Stability Enhancement

Long-term storage and stability of DNA is of paramount importance in biomedical applications. Ever since the emergence of ionic liquids (ILs) as alternate green solvents to aqueous and organic solvents, their exploration for the extraction and application of DNA need conscientious understanding of the binding characteristics and molecular interactions between IL and DNA. Choline amino acid ILs (CAAILs) in this regard seem to be promising due to their non-cytotoxic, completely biobased and environment-friendly nature. To unravel the key factors for the strength and binding mechanism of CAAILs with DNA, various spectroscopic techniques, molecular docking, and molecular dynamics simulations were employed in this work. UV-Vis spectra indicate multimodal binding of CAAILs with DNA, whereas dye displacement studies through fluorescence emission confirm the intrusion of IL molecules into the minor groove of DNA. Circular dichorism spectra show that DNA retains its native B-conformation in CAAILs. Both isothermal titration calorimetry and molecular docking studies provide an estimate of the binding affinity of DNA with CAAILs ≈ 4 kcal/mol. The heterogeneity in binding modes of CAAIL-DNA system with evolution of time was established by molecular dynamics simulations. Choline cation while approaching DNA first binds at surface through electrostatic interactions, whereas a stronger binding at minor groove occurs via van der Waals and hydrophobic interactions irrespective of anions considered in this study. We hope this result can encourage and guide the researchers in designing new bio-ILs for biomolecular studies in future.

Biological and nanotechnological applications using interactions between ionic liquids and nucleic acids

Nucleic acids have emerged as powerful biological and nanotechnological tools. In biological and nanotechnological experiments, methods of extracting and purifying nucleic acids from various types of cells and their storage are critical for obtaining reproducible experimental results. In nanotechnological experiments, methods for regulating the conformational polymorphism of nucleic acids and increasing sequence selectivity for base pairing of nucleic acids are important for developing nucleic acid-based nanomaterials. However, dearth of media that foster favourable behaviour of nucleic acids has been a bottleneck for promoting the biology and nanotechnology using the nucleic acids. Ionic liquids (ILs) are solvents that may be potentially used for controlling the properties of the nucleic acids. Here, we review researches regarding the behaviour of nucleic acids in ILs. The efficiency of extraction and purification of nucleic acids from biological samples is increased by IL addition. Moreover, nucleic acids in ILs show long-term stability, which maintains their structures and enhances nuclease resistance. Nucleic acids in ILs can be used directly in polymerase chain reaction and gene expression analysis with high efficiency. Moreover, the stabilities of the nucleic acids for duplex, triplex, and quadruplex (G-quadruplex and i-motif) structures change drastically with IL cation-nucleic acid interactions. Highly sensitive DNA sensors have been developed based on the unique changes in the stability of nucleic acids in ILs. The behaviours of nucleic acids in ILs detailed here should be useful in the design of nucleic acids to use as biological and nanotechnological tools.

New genetic signatures associated with cancer cachexia as defined by low skeletal muscle index and weight loss

BACKGROUND

Cachexia affects the majority with advanced cancer. Based on current demographic and clinical factors, it is not possible to predict who will develop cachexia or not. Such variation may, in part, be due to genotype. It has recently been proposed to extend the diagnostic criteria for cachexia to include a direct measure of low skeletal muscle index (LSMI) in addition to weight loss (WL). We aimed to explore our panel of candidate single nucleotide polymorphism (SNPs) for association with WL +/- computerized tomography-defined LSMI. We also explored whether the transcription in muscle of identified genes was altered according to such cachexia phenotype METHODS: A retrospective cohort study design was used. Analysis explored associations of candidate SNPs with WL (n = 1276) and WL + LSMI (n = 943). Human muscle transcriptome (n = 134) was analysed using an Agilent platform.

RESULTS

Single nucleotide polymorphisms in the following genes showed association with WL alone: GCKR, LEPR, SELP, ACVR2B, TLR4, FOXO3, IGF1, CPN1, APOE, FOXO1, and GHRL. SNPs in LEPR, ACVR2B, TNF, and ACE were associated with concurrent WL + LSMI. There was concordance between muscle-specific expression for ACVR2B, FOXO1 and 3, LEPR, GCKR, and TLR4 genes and LSMI and/or WL (P < 0.05).

CONCLUSIONS

The rs1799964 in the TNF gene and rs4291 in the ACE gene are new associations when the definition of cachexia is based on a combination of WL and LSMI. These findings focus attention on pro-inflammatory cytokines and the renin-angiotensin system as biomarkers/mediators of muscle wasting in cachexia.

Structure, stability and behaviour of nucleic acids in ionic liquids

Nucleic acids have become a powerful tool in nanotechnology because of their conformational polymorphism. However, lack of a medium in which nucleic acid structures exhibit long-term stability has been a bottleneck. Ionic liquids (ILs) are potential solvents in the nanotechnology field. Hydrated ILs, such as choline dihydrogen phosphate (choline dhp) and deep eutectic solvent (DES) prepared from choline chloride and urea, are 'green' solvents that ensure long-term stability of biomolecules. An understanding of the behaviour of nucleic acids in hydrated ILs is necessary for developing DNA materials. We here review current knowledge about the structures and stabilities of nucleic acids in choline dhp and DES. Interestingly, in choline dhp, A-T base pairs are more stable than G-C base pairs, the reverse of the situation in buffered NaCl solution. Moreover, DNA triplex formation is markedly stabilized in hydrated ILs compared with aqueous solution. In choline dhp, the stability of Hoogsteen base pairs is comparable to that of Watson-Crick base pairs. Moreover, the parallel form of the G-quadruplex is stabilized in DES compared with aqueous solution. The behaviours of various DNA molecules in ILs detailed here should be useful for designing oligonucleotides for the development of nanomaterials and nanodevices.

Genetic correlate of cognitive training response in schizophrenia

Intensive computerized auditory training results in improved cognition for schizophrenia patients, but participants show variation in their cognitive gains and the biological factors that affect the response to training are unknown. Single nucleotide polymorphisms (SNPs) in the catechol-O-methyltransferase (COMT) gene have been related to cognitive function. Here we asked if functional variation in this gene has an impact on the response of schizophrenia patients to cognitive training. We genotyped 48 schizophrenia patients who completed 50 h of computerized cognitive training and analyzed the association between DNA variants in the COMT gene and the improvement in global cognition. Although conventional analyses did not reveal any significant associations, a set-based analysis examining the aggregate effect of common variation in the COMT gene (42 SNPs) suggested association with improvement in global cognition. Eight SNPs, mostly located in the 3' end of the COMT gene, were nominally associated with improvement in cognition. These data suggest that genotype influences the response to intensive cognitive training in schizophrenia, and may indicate that cognitive training regimens need to be personalized to the underlying biosignatures of each individual patient. This article is part of a Special Issue entitled 'Cognitive Enhancers'.

P-selectin genotype is associated with the development of cancer cachexia

The variable predisposition to cachexia may, in part, be due to the interaction of host genotype. We analyzed 129 single nucleotide polymorphisms (SNPs) in 80 genes for association with cachexia based on degree of weight loss (>5, >10, >15%) as well as weight loss in the presence of systemic inflammation (C-reactive protein, >10 mg/l). 775 cancer patients were studied with a validation association study performed on an independently recruited cohort (n = 101) of cancer patients. The C allele (minor allele frequency 10.7%) of the rs6136 (SELP) SNP was found to be associated with weight loss >10% both in the discovery study (odds ratio (OR) 0.52; 95% confidence intervals (CI), 0.29–0.93; p = 0.026) and the validation study (OR 0.09, 95% CI 0.01–0.98, p = 0.035). In separate studies, induction of muscle atrophy gene expression was investigated using qPCR following either tumour-induced cachexia in rats or intra-peritoneal injection of lipopolysaccharide in mice. P-selectin was found to be significantly upregulated in muscle in both models. Identification of P-selectin as relevant in both animal models and in cachectic cancer patients supports this as a risk factor/potential mediator in cachexia.

Systems biology data analysis methodology in pharmacogenomics

Pharmacogenetics aims to elucidate the genetic factors underlying the individual's response to pharmacotherapy. Coupled with the recent (and ongoing) progress in high-throughput genotyping, sequencing and other genomic technologies, pharmacogenetics is rapidly transforming into pharmacogenomics, while pursuing the primary goals of identifying and studying the genetic contribution to drug therapy response and adverse effects, and existing drug characterization and new drug discovery. Accomplishment of both of these goals hinges on gaining a better understanding of the underlying biological systems; however, reverse-engineering biological system models from the massive datasets generated by the large-scale genetic epidemiology studies presents a formidable data analysis challenge. In this article, we review the recent progress made in developing such data analysis methodology within the paradigm of systems biology research that broadly aims to gain a 'holistic', or 'mechanistic' understanding of biological systems by attempting to capture the entirety of interactions between the components (genetic and otherwise) of the system.

[Current status of studies on genome-wide gene-gene interactions]

Complex diseases have affected human's health throughout the world. Hundreds of studies show that complex diseases are caused by multiple loci. Currently, genome-wide association studies(GWAS) only focus on the single locus that contributes to the susceptibility of a certain disease. However, the interaction between genes could be one of the main factors that lead to complex traits. This fact has initiated scientists to propose some algorithms to detect these interactions, such as the penalized logistic regression model, multifactor dimensionality reduction method, set association analysis method, Bayesian networks analysis method and random forest. However, these algorithms are of high complexity, hypothesis-driven, causing over fitting of data, or not sensible of data at low dimensions. In this paper, we reviewed these algorithms, and then demonstrated a new algorithm based on GPU to provide a powerful strategy to analyze gene-gene interaction in genome-wide association datasets. This algorithm is of low computing complexity, free of hypothesis, not affected by single locus marginal effect, and also of high stability and speed.

A new expectation-maximization statistical test for case-control association studies considering rare variants obtained by high-throughput sequencing

Genome-wide association studies (GWAS) have been successful in identifying common genetic variation reproducibly associated with disease. However, most associated variants confer very small risk and after meta-analysis of large cohorts a large fraction of expected heritability still remains unexplained. A possible explanation is that rare variants currently undetected by GWAS with SNP arrays could contribute a large fraction of risk when present in cases. This concept has spurred great interest in exploring the role of rare variants in disease. As the cost of sequencing continue to plummet, it is becoming feasible to directly sequence case-control samples for testing disease association including rare variants. We have developed a test statistic that allows for association testing among cases and controls using data directly from sequencing reads. In addition, our method allows for random errors in reads. We determine the probability of a true genotype call based on the observed base pair reads using the expectation-maximization algorithm. We apply the SumStat procedure to obtain a single statistic for a group of multiple rare variant loci. We document the validity of our method through simulations. Our results suggest that our statistic maintains the correct type I error rate, even in the presence of differential misclassification for sequence reads, and that it has good power under a number of scenarios. Finally, our SumStat results show power at least as good as the maximum single locus results.

Lack of association of polymorphisms in elastin with pseudoexfoliation syndrome and glaucoma

PURPOSE

To evaluate the elastin gene (ELN) as a secondary risk factor for pseudoexfoliation syndrome (PXFS) and the associated glaucoma pseudoexfoliation glaucoma (PXFG).

METHODS

One hundred seventy-eight unrelated patients with PXFS, including 132 patients with PXFG, and 113 unrelated controls were recruited from the Massachusetts Eye and Ear Infirmary. All the patients and controls were white of European ancestry. Three tag SNPs (rs2071307, rs3823879, and rs3757587) that capture the majority of alleles in ELN were genotyped. Single-SNP association was analyzed using Fisher exact test. Haplotype analysis and the set-based test were used to assess the association for the whole gene. Interaction analysis was done between the ELN SNP rs2071307 and LOXL1 SNP rs2165241 using logistic regression. Multiple comparisons were corrected using the Bonferroni method.

RESULTS

All 3 ELN tag SNPs were not significantly associated with PXFS and PXFG (P>0.20). The minor allele frequencies in PXFS, PXFG, and controls were 40.7%, 39.8%, and 45.6%, respectively for rs2071307, 6.7%, 6.3%, and 5.4% for rs3823879, and 14.8%, 16.2%, and 13.6% for rs3757587. Haplotype analysis and the set-based test did not find significant association of ELN with PXFS (P=0.94 and 0.99, respectively). No significant interaction effects on PXFS were identified between the ELN and LOXL1 SNPs (P=0.55).

CONCLUSIONS

Our results suggest that common polymorphisms of ELN are not associated with PXFS and PXFG in white populations. Further studies are required to identify secondary genetic factors that contribute to PXFS.

Genetic and clinical predictors of sexual dysfunction in citalopram-treated depressed patients

Sexual dysfunction is a major contributor to treatment discontinuation and nonadherence among patients treated with selective serotonin reuptake inhibitors (SSRIs). The mechanisms by which depressive symptoms in general, as well as SSRI exposure in particular, may worsen sexual function are not known. We examined genetic polymorphisms, including those of the serotonin and glutamate systems, for association with erectile dysfunction, anorgasmia, and decreased libido during citalopram treatment. Clinical data were drawn from a nested case-control cohort derived from the STAR(*)D study, a multicenter, prospective, effectiveness trial in outpatients with nonpsychotic major depressive disorder (MDD). Self-reports of erectile dysfunction, decreased libido, or difficulty achieving orgasm based on the Patient-Rated Inventory of Side Effects were examined among Caucasian subjects (n=1473) for whom DNA and adverse effect measures were available, and who were treated openly with citalopram for up to 14 weeks. Of 1473 participants, 799 (54%) reported decreased libido; 525 (36%) reported difficulty achieving orgasm. Of 574 men, 211 (37%) reported erectile dysfunction. Using a set-based test for association, single nucleotide polymorphisms in glutamatergic genes were associated with decreased libido (GRIA3; GRIK2), difficulty achieving orgasm (GRIA1), and difficulty achieving erection (GRIN3A) (experiment-wide permuted p<0.05 for each). Evidence of association persisted after adjustment for baseline clinical and sociodemographic differences. Likewise, evidence of association was similar when the cohort was limited to those who did not report a given adverse event at the first post-baseline visit (ie, those whose adverse events were known to be treatment emergent). These hypothesis-generating analyses suggest the potential for glutamatergic treatment targets for sexual dysfunction during major depressive episodes.

Protocol for investigating genetic determinants of posttraumatic stress disorder in women from the Nurses' Health Study II

BACKGROUND

One in nine American women will meet criteria for the diagnosis of posttraumatic stress disorder (PTSD) in their lifetime. Although twin studies suggest genetic influences account for substantial variance in PTSD risk, little progress has been made in identifying variants in specific genes that influence liability to this common, debilitating disorder.

METHODS AND DESIGN

We are using the unique resource of the Nurses Health Study II, a prospective epidemiologic cohort of 68,518 women, to conduct what promises to be the largest candidate gene association study of PTSD to date. The entire cohort will be screened for trauma exposure and PTSD; 3,000 women will be selected for PTSD diagnostic interviews based on the screening data. Our nested case-control study will genotype 1000 women who developed PTSD following a history of trauma exposure; 1000 controls will be selected from women who experienced similar traumas but did not develop PTSD.The primary aim of this study is to detect genetic variants that predict the development of PTSD following trauma. We posit inherited vulnerability to PTSD is mediated by genetic variation in three specific neurobiological systems whose alterations are implicated in PTSD etiology: the hypothalamic-pituitary-adrenal axis, the locus coeruleus/noradrenergic system, and the limbic-frontal neuro-circuitry of fear. The secondary, exploratory aim of this study is to dissect genetic influences on PTSD in the broader genetic and environmental context for the candidate genes that show significant association with PTSD in detection analyses. This will involve: conducting conditional tests to identify the causal genetic variant among multiple correlated signals; testing whether the effect of PTSD genetic risk variants is moderated by age of first trauma, trauma type, and trauma severity; and exploring gene-gene interactions using a novel gene-based statistical approach.

DISCUSSION

Identification of liability genes for PTSD would represent a major advance in understanding the pathophysiology of the disorder. Such understanding could advance the development of new pharmacological agents for PTSD treatment and prevention. Moreover, the addition of PTSD assessment data will make the NHSII cohort an unparalleled resource for future genetic studies of PTSD as well as provide the unique opportunity for the prospective examination of PTSD-disease associations.

Variation in catechol-O-methyltransferase is associated with duloxetine response in a clinical trial for major depressive disorder

BACKGROUND

The study objective was to evaluate variations in genes implicated in antidepressant mechanism of action for association with response to duloxetine treatment in major depressive disorder (MDD).

METHODS

We assessed response over 6 weeks in 250 duloxetine-treated Caucasian patients in a randomized, double-blind study of patients with MDD. Single nucleotide polymorphisms (SNPs) were genotyped in 19 candidate genes selected based on evidence for involvement in antidepressant mechanism of action. Primary analysis examined baseline to end point reduction in the 17-item Hamilton Depression Rating Scale (HAMD17) total score, using a set-based test for association for each gene. Follow-up analyses examined individual SNPs within any significant gene for association with reduction in HAMD17 and 30-item Inventory of Depressive Symptomatology-Clinician Rated (IDS-C-30).

RESULTS

After correction for multiple comparisons, only COMT was associated with change in HAMD17 (experiment wide p = .018). Peak association was detected with rs165599 (p = .006), which accounted for approximately 3% of variance in HAMD17 change and >4% of variance in IDS-C-30 change (p = .001). The least-squared mean change (SE) in HAMD17 score by rs165599 genotype was -10.8 (1.2), -8.7 (.6), and -6.5 (.7) for patients with GG, GA, and AA genotypes, respectively. For SNPs in serotonin 2A receptor (HTR2A) previously associated with citalopram response, including rs7997012, no significant evidence of association with duloxetine response was identified.

CONCLUSIONS

Single nucleotide polymorphisms in COMT were associated with symptom change in duloxetine-treated patients with MDD. If replicated, the magnitude of the COMT genotype effect is of clinical relevance.

Pharmacogenetic analysis of genes implicated in rodent models of antidepressant response: association of TREK1 and treatment resistance in the STAR(*)D study

Recent rodent models of antidepressant response implicate a novel set of genes in mechanisms of antidepressant action. The authors examined variants in four such genes (KCNK2 (TREK1), SLC18A2 (VMAT2), S100A10, and HDAC5) for association with remission in a large effectiveness trial of antidepressant treatments. Subjects were drawn from the Sequenced Treatment Alternatives to Relieve Depression (STAR(*)D) study, a multicenter, prospective, effectiveness trial in major depressive disorder (MDD). Outpatients with nonpsychotic MDD were initially treated with citalopram for up to 14 weeks; those who did not remit with citalopram were sequentially randomized to a series of next-step treatments, each for up to 12 weeks. Single-nucleotide polymorphisms in four genes were examined for association with remission, defined as a clinician-rated Quick Inventory of Depressive Symptomatology (QIDS-C(16)) score < or =5. Of 1554 participants for whom DNA was available, 565 (36%) reached remission with citalopram treatment. No association with any of the four genes was identified. However, among the 751 who entered next-step treatment, variants in KCNK2 were associated with treatment response (Bonferroni-corrected, gene-based empirical p<0.001). In follow-up analyses, KCNK2 was also associated with effects of similar magnitude for third-step treatment among those with unsatisfactory benefit to both citalopram and one next-step pharmacotherapy (n=225). These findings indicate that genetic variation in KCNK2 may identify individuals at risk for treatment resistance. More broadly, they indicate the utility of animal models in identifying genes for pharmacogenetic studies of antidepressant response.

Confronting complexity in late-onset Alzheimer disease: application of two-stage analysis approach addressing heterogeneity and epistasis

Common diseases with a genetic basis are likely to have a very complex etiology, in which the mapping between genotype and phenotype is far from straightforward. A new comprehensive statistical and computational strategy for identifying the missing link between genotype and phenotype has been proposed, which emphasizes the need to address heterogeneity in the first stage of any analysis and gene-gene interactions in the second stage. We applied this two-stage analysis strategy to late-onset Alzheimer disease (LOAD) data, which included functional and positional candidate genes and markers in a region of interest on chromosome 10. Bayesian classification found statistically significant clusterings for independent family-based and case-control datasets, which used the same five markers in leucine-rich repeat transmembrane neuronal 3 (LRRTM3) as the most influential in determining cluster assignment. In subsequent analyses to detect main effects and gene-gene interactions, markers in three genes--urokinase-type plasminogen activator (PLAU), angiotensin 1 converting enzyme (ACE) and cell division cycle 2 (CDC2)--were found to be associated with LOAD in particular subsets of the data based on their LRRTM3 multilocus genotype. All of these genes are viable candidates for LOAD based on their known biological function, even though PLAU, CDC2 and LRRTM3 were initially identified as positional candidates. Further studies are needed to replicate these statistical findings and to elucidate possible biological interaction mechanisms between LRRTM3 and these genes.

Cholesterol-related genetic risk scores are associated with hypometabolism in Alzheimer's-affected brain regions

We recently implicated a cluster of nine single nucleotide polymorphisms from seven cholesterol-related genes in the risk of Alzheimer's disease (AD) in a European cohort, and we proposed calculating an aggregate cholesterol-related genetic score (CREGS) to characterize a person's risk. In a separate study, we found that apolipoprotein E (APOE) epsilon4 gene dose, an established AD risk factor, was correlated with fluorodeoxyglucose (FDG) positron emission tomography (PET) measurements of hypometabolism in AD-affected brain regions in a cognitively normal American cohort, and we proposed using PET as a presymptomatic endophenotype to help assess putative modifiers of AD risk. Thus, the objective in the present study is to determine whether CREGS is related to PET measurements of hypometabolism in AD-affected brain regions. DNA and PET data from 141 cognitively normal late middle-aged APOE epsilon4 homozygotes, heterozygotes and noncarriers were analyzed to evaluate the relationship between CREGS and regional PET measurements. Cholesterol-related genetic risk scores were associated with hypometabolism in AD-affected brain regions, even when controlling for the effects of APOE epsilon4 gene dose. The results support the role of cholesterol-related genes in the predisposition to AD and support the value of neuroimaging in the presymptomatic assessment of putative modifiers of AD risk.

Association study between Apolipoprotein L and schizophrenia by exhaustive and rule-based combination analysis for identification of multilocus interactions

Several single marker association and haplotypic analyses have been performed to identify susceptible genes for various common diseases, but these approaches using candidate genes did not provide accurate and consistent evidence in each analysis. This inconsistency is partly due to the fact that the common diseases are caused by complex interactions among various genetic factors. Therefore, in this study, to evaluate exhaustive genotype or allele combinations, we applied the binomial and random permutation test (BRP) proposed by Tomita et al. [IPSJ Digital Courier, 2, 691-709 (2006)] for the association analysis between an Apolipoprotein L gene cluster and schizophrenia. Using the seven selected representative single nucleotide polymorphisms (SNPs) based on the results of linkage disequilibrium evaluation, we analyzed 845 schizophrenic patients and 707 healthy controls, and investigated the validation of risk and protective factors with two randomly divided data sets. A comparative study of a method for analyzing the interactions was performed by conventional methods. Even if all the tested methods were used for analysis, the risk factor with a high significance was not commonly selected from both independent data sets. However, the significant interactions for the protective factor against disease development were commonly obtained from both data sets by BRP analysis. In conclusion, although it is considered that the causality of schizophrenia is too complex to identify a susceptible interaction using a small sample size, it was suggested that the healthy controls tend to have the same combination of certain alleles or genotypes for protection from disease development when BRP as a new exhaustive combination analytical method was used.

Association Rule Discovery Has the Ability to Model Complex Genetic Effects

Dramatic advances in genotyping technology have established a need for fast, flexible analysis methods for genetic association studies. Common complex diseases, such as Parkinson's disease or multiple sclerosis, are thought to involve an interplay of multiple genes working either independently or together to influence disease risk. Also, multiple underlying traits, each its own genetic basis may be defined together as a single disease. These effects - trait heterogeneity, locus heterogeneity, and gene-gene interactions (epistasis) - contribute to the complex architecture of common genetic diseases. Association Rule Discovery (ARD) searches for frequent itemsets to identify rule-based patterns in large scale data. In this study, we apply Apriori (an ARD algorithm) to simulated genetic data with varying degrees of complexity. Apriori using information difference to prior as a rule measure shows good power to detect functional effects in simulated cases of simple trait heterogeneity, trait heterogeneity and epistasis, and moderate power in cases of trait heterogeneity and locus heterogeneity. Also, we illustrate that bootstrapping the rule induction process does not considerably improve the power to detect these effects. These results show that ARD is a framework with sufficient flexibility to characterize complex genetic effects.

Genetic epidemiologic studies of preterm birth: guidelines for research

Over the last decade, it has become increasingly apparent that the cause of preterm birth is multifactorial, involving both genetic and environmental factors. With the development of new technologies capable of probing the genome, exciting possibilities now present themselves to gain new insight into the mechanisms leading to preterm birth. This review aims to develop research guidelines for the conduct of genetic epidemiology studies of preterm birth with the expectation that this will ultimately facilitate the comparison of data sets between study cohorts, both nationally and internationally. Specifically, the 4 areas addressed in this review includes: (1) phenotypic criteria, (2) study design, (3) considerations in the selection of control populations, and (4) candidate gene selection. This article is the product of discussions initiated by the authors at the 3rd International Workshop on Biomarkers and Preterm Birth held at the University of California, Los Angeles, Los Angeles, CA, in March 2005.

Genetic associations in preterm birth: a primer of marker selection, study design, and data analysis

Spontaneous preterm birth (PTB; delivery before 37 weeks gestation) is a primary risk factor for infant morbidity and mortality. The etiology is unclear, but there is evidence that there is a genetic predisposition to PTB. Armed with the suggestion of genetic risk factors and the failure to identify useful biomarkers, investigators are starting to actively pursue the role of genetic predisposition in PTB. Several studies have been done to date assessing the role of single gene variants. However, positive findings have failed to replicate. We argue that heterogeneity in study designs, definition of phenotype, single-nucleotide polymorphism (SNP) selection, population selection, and sample size makes data interpretation difficult in complex phenotypes such as PTB. In this review, we introduce general concepts of study designs in genetic epidemiology, selection of candidate genes and markers for analysis, and analytical methodologies. We also introduce how the concept of gene-gene interactions (biologic epistasis) and gene-environment interactions may affect the predisposition to PTB.

The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases

Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analyzing the relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies. In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimized neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted. Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a useful strategy to find the important genes and interaction patterns involved in complex diseases.

Dissecting trait heterogeneity: a comparison of three clustering methods applied to genotypic data

Background

Trait heterogeneity, which exists when a trait has been defined with insufficient specificity such that it is actually two or more distinct traits, has been implicated as a confounding factor in traditional statistical genetics of complex human disease. In the absence of detailed phenotypic data collected consistently in combination with genetic data, unsupervised computational methodologies offer the potential for discovering underlying trait heterogeneity. The performance of three such methods – Bayesian Classification, Hypergraph-Based Clustering, and Fuzzy k-Modes Clustering – appropriate for categorical data were compared. Also tested was the ability of these methods to detect trait heterogeneity in the presence of locus heterogeneity and/or gene-gene interaction, which are two other complicating factors in discovering genetic models of complex human disease. To determine the efficacy of applying the Bayesian Classification method to real data, the reliability of its internal clustering metrics at finding good clusterings was evaluated using permutation testing.

Results

Bayesian Classification outperformed the other two methods, with the exception that the Fuzzy k-Modes Clustering performed best on the most complex genetic model. Bayesian Classification achieved excellent recovery for 75% of the datasets simulated under the simplest genetic model, while it achieved moderate recovery for 56% of datasets with a sample size of 500 or more (across all simulated models) and for 86% of datasets with 10 or fewer nonfunctional loci (across all simulated models). Neither Hypergraph Clustering nor Fuzzy k-Modes Clustering achieved good or excellent cluster recovery for a majority of datasets even under a restricted set of conditions. When using the average log of class strength as the internal clustering metric, the false positive rate was controlled very well, at three percent or less for all three significance levels (0.01, 0.05, 0.10), and the false negative rate was acceptably low (18 percent) for the least stringent significance level of 0.10.

Conclusion

Bayesian Classification shows promise as an unsupervised computational method for dissecting trait heterogeneity in genotypic data. Its control of false positive and false negative rates lends confidence to the validity of its results. Further investigation of how different parameter settings may improve the performance of Bayesian Classification, especially under more complex genetic models, is ongoing.

Identification of a genetic cluster influencing memory performance and hippocampal activity in humans

Experimental work in animals has shown that memory formation depends on a cascade of molecular events. Here we show that variability of human memory performance is related to variability in genes encoding proteins of this signaling cascade, including the NMDA and metabotrobic glutamate receptors, adenylyl cyclase, CAMKII, PKA, and PKC. The individual profile of genetic variability in these signaling molecules correlated significantly with episodic memory performance (P < 0.00001). Moreover, functional MRI during memory formation revealed that this genetic profile correlated with activations in memory-related brain regions, including the hippocampus and parahippocampal gyrus. The present study indicates that genetic variability in the human homologues of memory-related signaling molecules contributes to interindividual differences in human memory performance and memory-related brain activations.

Genetic and post-mortem mRNA analysis of the 14-3-3 genes that encode phosphoserine/threonine-binding regulatory proteins in schizophrenia and bipolar disorder

BACKGROUND

Previous work with animal models of psychosis, human genetic studies, and human post-mortem gene expression studies implicate the 14-3-3 family of genes in schizophrenia. The 14-3-3 genes code for a family of proteins that bind to and regulate other proteins, and they modulate neurodevelopment, cell-division, signal transduction and gene transcription.

OBJECTIVE

To explore the role of five 14-3-3 isoforms (beta, gamma, epsilon, zeta, and eta) in schizophrenia by: (1) comparing mRNA levels in post-mortem brain from schizophrenic, bipolar and control subjects and (2) assessing genetic association with schizophrenia in both case-control and nuclear family samples.

METHODS

Quantitative PCR (q-PCR) was used to determine relative mRNA levels in dorsolateral prefrontal cortex (Brodmann's area 46) samples donated by the Stanley Medical Research Institute (SMRI). Selected SNPs were genotyped in all five isoforms for association analysis in both family and case-control samples.

RESULTS

No significant differences in 14-3-3 mRNA expression levels between the diagnostic groups were found. A significant genetic association with schizophrenia was found for the 14-3-3zeta isoform in a subset of nuclear families of British ancestry (TDT: chi(2)=7.2; df=1; p=0.0073), in the case-control sample overall (p=0.011), and in a subset of the case-control sample.

CONCLUSION

The results, in combination with other published evidence, suggest that further work is necessary to clarify what role the 14-3-3 genes may play in the etiology and pathogenesis of schizophrenia.

Genetics, statistics and human disease: analytical retooling for complexity

Molecular biologists and geneticists alike now acknowledge that most common human diseases with a genetic component are likely to have complex etiologies. Yet despite this belief, many statistical geneticists continue applying, in slightly new and different ways, methodologies that were developed to dissect much simpler etiologies. In this article, we characterize, with examples, the various factors that can complicate genetic analysis and demonstrate their shared features and how they affect genetic analysis. We describe a variety of approaches that are currently available, revealing methodological gaps and suggesting new directions for method development. Finally, we propose a comprehensive two-step approach to analysis that systemically addresses the different genetic factors that are likely to underlie complex diseases.

Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis

Epistasis plays an important role in the genetic architecture of common human diseases and can be viewed from two perspectives, biological and statistical, each derived from and leading to different assumptions and research strategies. Biological epistasis is the result of physical interactions among biomolecules within gene regulatory networks and biochemical pathways in an individual such that the effect of a gene on a phenotype is dependent on one or more other genes. In contrast, statistical epistasis is defined as deviation from additivity in a mathematical model summarizing the relationship between multilocus genotypes and phenotypic variation in a population. The goal of this essay is to review definitions and examples of biological and statistical epistasis and to explore the relationship between the two. Specifically, we present and discuss the following two questions in the context of human health and disease. First, when does statistical evidence of epistasis in human populations imply underlying biomolecular interactions in the etiology of disease? Second, when do biomolecular interactions produce patterns of statistical epistasis in human populations? Answers to these two reciprocal questions will provide an important framework for using genetic information to improve our ability to diagnose, prevent and treat common human diseases. We propose that systems biology will provide the necessary information for addressing these questions and that model systems such as bacteria, yeast and digital organisms will be a useful place to start.

Rational design of DNA sequences for nanotechnology, microarrays and molecular computers using Eulerian graphs

Nucleic acids are molecules of choice for both established and emerging nanoscale technologies. These technologies benefit from large functional densities of 'DNA processing elements' that can be readily manufactured. To achieve the desired functionality, polynucleotide sequences are currently designed by a process that involves tedious and laborious filtering of potential candidates against a series of requirements and parameters. Here, we present a complete novel methodology for the rapid rational design of large sets of DNA sequences. This method allows for the direct implementation of very complex and detailed requirements for the generated sequences, thus avoiding 'brute force' filtering. At the same time, these sequences have narrow distributions of melting temperatures. The molecular part of the design process can be done without computer assistance, using an efficient 'human engineering' approach by drawing a single blueprint graph that represents all generated sequences. Moreover, the method eliminates the necessity for extensive thermodynamic calculations. Melting temperature can be calculated only once (or not at all). In addition, the isostability of the sequences is independent of the selection of a particular set of thermodynamic parameters. Applications are presented for DNA sequence designs for microarrays, universal microarray zip sequences and electron transfer experiments.