Multifactor dimensionality reduction-phenomics: a novel method to capture genetic heterogeneity with use of phenotypic variables

Bioinformatics approaches for studying molecular sex differences in complex diseases

Many complex diseases exhibit pronounced sex differences that can affect both the initial risk of developing the disease, as well as clinical disease symptoms, molecular manifestations, disease progression, and the risk of developing comorbidities. Despite this, computational studies of molecular data for complex diseases often treat sex as a confounding variable, aiming to filter out sex-specific effects rather than attempting to interpret them. A more systematic, in-depth exploration of sex-specific disease mechanisms could significantly improve our understanding of pathological and protective processes with sex-dependent profiles. This survey discusses dedicated bioinformatics approaches for the study of molecular sex differences in complex diseases. It highlights that, beyond classical statistical methods, approaches are needed that integrate prior knowledge of relevant hormone signaling interactions, gene regulatory networks, and sex linkage of genes to provide a mechanistic interpretation of sex-dependent alterations in disease. The review examines and compares the advantages, pitfalls and limitations of various conventional statistical and systems-level mechanistic analyses for this purpose, including tailored pathway and network analysis techniques. Overall, this survey highlights the potential of specialized bioinformatics techniques to systematically investigate molecular sex differences in complex diseases, to inform biomarker signature modeling, and to guide more personalized treatment approaches.

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

A perspective on molecular signalling dysfunction, its clinical relevance and therapeutics in autism spectrum disorder

Intellectual disability (ID) and autism spectrum disorder (ASD) are neurodevelopmental disorders that have become a primary clinical and social concern, with a prevalence of 2-3% in the population. Neuronal function and behaviour undergo significant malleability during the critical period of development that is found to be impaired in ID/ASD. Human genome sequencing studies have revealed many genetic variations associated with ASD/ID that are further verified by many approaches, including many mouse and other models. These models have facilitated the identification of fundamental mechanisms underlying the pathogenesis of ASD/ID, and several studies have proposed converging molecular pathways in ASD/ID. However, linking the mechanisms of the pathogenic genes and their molecular characteristics that lead to ID/ASD has progressed slowly, hampering the development of potential therapeutic strategies. This review discusses the possibility of recognising the common molecular causes for most ASD/ID based on studies from the available models that may enable a better therapeutic strategy to treat ID/ASD. We also reviewed the potential biomarkers to detect ASD/ID at early stages that may aid in diagnosis and initiating medical treatment, the concerns with drug failure in clinical trials, and developing therapeutic strategies that can be applied beyond a particular mutation associated with ASD/ID.

Integrin β3 in forebrain Emx1-expressing cells regulates repetitive self-grooming and sociability in mice

BACKGROUND

Autism spectrum disorder (ASD) is characterized by repetitive behaviors, deficits in communication, and overall impaired social interaction. Of all the integrin subunit mutations, mutations in integrin β3 (Itgb3) may be the most closely associated with ASD. Integrin β3 is required for normal structural plasticity of dendrites and synapses specifically in excitatory cortical and hippocampal circuitry. However, the behavioral consequences of Itgb3 function in the forebrain have not been assessed. We tested the hypothesis that behaviors that are typically abnormal in ASD-such as self-grooming and sociability behaviors-are disrupted with conditional Itgb3 loss of function in forebrain circuitry in male and female mice.

METHODS

We generated male and female conditional knockouts (cKO) and conditional heterozygotes (cHET) of Itgb3 in excitatory neurons and glia that were derived from Emx1-expressing forebrain cells during development. We used several different assays to determine whether male and female cKO and cHET mice have repetitive self-grooming behaviors, anxiety-like behaviors, abnormal locomotion, compulsive-like behaviors, or abnormal social behaviors, when compared to male and female wildtype (WT) mice.

RESULTS

Our findings indicate that only self-grooming and sociability are altered in cKO, but not cHET or WT mice, suggesting that Itgb3 is specifically required in forebrain Emx1-expressing cells for normal repetitive self-grooming and social behaviors. Furthermore, in cKO (but not cHET or WT), we observed an interaction effect for sex and self-grooming environment and an interaction effect for sex and sociability test chamber.

LIMITATIONS

While this study demonstrated a role for forebrain Itgb3 in specific repetitive and social behaviors, it was unable to determine whether forebrain Itgb3 is required for a preference for social novelty, whether cHET are haploinsufficient with respect to repetitive self-grooming and social behaviors, or the nature of the interaction effect for sex and environment/chamber in affected behaviors of cKO.

CONCLUSIONS

Together, these findings strengthen the idea that Itgb3 has a specific role in shaping forebrain circuitry that is relevant to endophenotypes of autism spectrum disorder.

Evidence that ITGB3 promoter variants increase serotonin blood levels by regulating platelet serotonin transporter trafficking

Elevated serotonin (5-HT) blood levels, the first biomarker identified in autism research, has been consistently found in 20-30% of patients with Autism Spectrum Disorder (ASD). Hyperserotonemia is mainly due to greater 5-HT uptake into platelets, mediated by the 5-HT transporter (SERT) located at the platelet plasma membrane. The protein complex involved in platelet SERT trafficking and externalization includes integrin β3, the beta subunit of the platelet membrane adhesive GP IIb/IIIa. Integrin β3 is encoded by the ITGB3 gene, previously identified as a quantitative trait locus (QTL) for 5-HT blood levels in ASD at single nucleotide polymorphism (SNP) rs2317385. The present study aims to identify the functional ITGB3 gene variants contributing to hyperserotonemia. ITGB3 gene sequencing in 20 individuals selected on the basis of rs2317385 genotypes defined four haplotypes encompassing six SNPs located in the ITGB3 gene promoter region, all in linkage disequilibrium with rs2317385. Luciferase assays in two hematopoietic cell lines, K-562 and HEL 92.1.7, demonstrate that ITGB3 gene promoter activity is enhanced by the presence of the C allele at rs55827077 specifically during differentiation into megakaryocytes (P < 0.01), with modulatory effects by flanking SNPs. This same allele is strongly associated with (a) higher 5-HT blood levels in 176 autistic individuals (P < 0.001), (b) greater platelet integrin β3 protein expression (P < 0.05) and (c) enhanced SERT trafficking from the cytosol toward the platelet plasma membrane (P = 4.05 × 10-11). Our results support rs55827077 as the functional ITGB3 gene promoter variant contributing to elevated 5-HT blood levels in ASD and define a mechanistic chain of events linking ITGB3 to hyperserotonemia.

Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits

The manifestation of complex traits is influenced by gene-gene and gene-environment interactions, and the identification of multifactor interactions is an important but challenging undertaking for genetic studies. Many complex phenotypes such as disease severity are measured on an ordinal scale with more than two categories. A proportional odds model can improve statistical power for these outcomes, when compared to a logit model either collapsing the categories into two mutually exclusive groups or limiting the analysis to pairs of categories. In this study, we propose a proportional odds model-based generalized multifactor dimensionality reduction (GMDR) method for detection of interactions underlying polytomous ordinal phenotypes. Computer simulations demonstrated that this new GMDR method has a higher power and more accurate predictive ability than the GMDR methods based on a logit model and a multinomial logit model. We applied this new method to the genetic analysis of low-density lipoprotein (LDL) cholesterol, a causal risk factor for coronary artery disease, in the Multi-Ethnic Study of Atherosclerosis, and identified a significant joint action of the CELSR2, SERPINA12, HPGD, and APOB genes. This finding provides new information to advance the limited knowledge about genetic regulation and gene interactions in metabolic pathways of LDL cholesterol. In conclusion, the proportional odds model-based GMDR is a useful tool that can boost statistical power and prediction accuracy in studying multifactor interactions underlying ordinal traits.

The interaction of NOS1AP, DISC1, DAOA, and GSK3B confers susceptibility of early-onset schizophrenia in Chinese Han population

Although many major breakthrough had identificated potential susceptibility genes for schizophrenia, the aetiology of schizophrenia is still unknown. In the present study, we focused on the N-methyl-Daspartate receptors related genes nitric oxide synthase 1 adaptor gene (NOS1AP), disrupted in schizophrenia 1 gene (DISC1), d-amino acid oxidase activator gene (DAOA), and glycogen synthase kinase 3-beta gene (GSK3B). A family-based genetic association study (459 Han Chinese subjects in 153 nuclear families) using 3 single nucleotide polymorphisms in NOS1AP, 2 in DISC1, 1 in DAOA and 1 in GSK3B was conducted. We found rs12742393 have just positive trend with schizophrenia (SCZ) (p=0.07) after FDR correction. NOS1AP mRNA and serum levels were significantly elevated in SCZ patients (p<0.001; p<0.001) compared with healthy control. However, expression Quantitative Trait Loci (eQTL) analysis have demonstrated that rs12742393 genotype were not significantly associated with the NOS1AP mRNA expression. GMDR identified a significant seven-locus interaction model involving (NOS1AP-rs348624, rs12742393, rs1415263, DISC1-rs821633, rs1000731, DAOA-rs2391191and GSK3B- rs6438552) with a good testing accuracy (0.72). Our finding suggested statistically significant role of interaction of NOS1AP, DISC1, DAOA, and GSK3B polymorphisms (NOS1AP-rs348624, rs12742393, rs1415263, DISC1-rs821633, rs1000731, DAOA-rs2391191and GSK3B-rs6438552) in EOS susceptibility.

Significance of Dopaminergic Gene Variants in the Male Biasness of ADHD

OBJECTIVE

ADHD is frequently detected in boys though there is no established cause. One possibility is that genes predisposing to ADHD have sexually dimorphic effects. With an aim to find out the reason for this male biasness, contribution of 14 functional polymorphisms was investigated in ADHD subjects.

METHOD

Genomic DNA of probands, their parents, and ethnically matched controls was subjected to analysis of single-nucleotide polymorphisms and variable number of tandem repeats (VNTRs).

RESULTS

Case-control analysis revealed significant higher occurrence of DAT1 intron 8 VNTR "5R" allele ( p = .028), DBH rs1108580 "A" allele ( p = .027), and MAOA-u VNTR-rs6323 3R-T haplotype ( p = .007) in male probands. Family-based analysis showed significant preferential transmission of Dopamine receptor D4 exon 3 VNTR-rs1800955 7R-T haplotype from parents to male probands ( p = .008). Interaction between DBH gene variants and low enzymatic activity was also noticed, especially in male probands.

CONCLUSION

Data obtained may partly answer the male biasness of ADHD.

Reverse Pathway Genetic Approach Identifies Epistasis in Autism Spectrum Disorders

Although gene-gene interaction, or epistasis, plays a large role in complex traits in model organisms, genome-wide by genome-wide searches for two-way interaction have limited power in human studies. We thus used knowledge of a biological pathway in order to identify a contribution of epistasis to autism spectrum disorders (ASDs) in humans, a reverse-pathway genetic approach. Based on previous observation of increased ASD symptoms in Mendelian disorders of the Ras/MAPK pathway (RASopathies), we showed that common SNPs in RASopathy genes show enrichment for association signal in GWAS (P = 0.02). We then screened genome-wide for interactors with RASopathy gene SNPs and showed strong enrichment in ASD-affected individuals (P < 2.2 x 10-16), with a number of pairwise interactions meeting genome-wide criteria for significance. Finally, we utilized quantitative measures of ASD symptoms in RASopathy-affected individuals to perform modifier mapping via GWAS. One top region overlapped between these independent approaches, and we showed dysregulation of a gene in this region, GPR141, in a RASopathy neural cell line. We thus used orthogonal approaches to provide strong evidence for a contribution of epistasis to ASDs, confirm a role for the Ras/MAPK pathway in idiopathic ASDs, and to identify a convergent candidate gene that may interact with the Ras/MAPK pathway.

Multi-variant study of obesity risk genes in African Americans: The Jackson Heart Study

OBJECTIVE

Genome-wide association study (GWAS) has been successful in identifying obesity risk genes by single-variant association analysis. For this study, we designed steps of analysis strategy and aimed to identify multi-variant effects on obesity risk among candidate genes.

METHODS

Our analyses were focused on 2137 African American participants with body mass index measured in the Jackson Heart Study and 657 common single nucleotide polymorphisms (SNPs) genotyped at 8 GWAS-identified obesity risk genes.

RESULTS

Single-variant association test showed that no SNPs reached significance after multiple testing adjustment. The following gene-gene interaction analysis, which was focused on SNPs with unadjusted p-value<0.10, identified 6 significant multi-variant associations. Logistic regression showed that SNPs in these associations did not have significant linear interactions; examination of genetic risk score evidenced that 4 multi-variant associations had significant additive effects of risk SNPs; and haplotype association test presented that all multi-variant associations contained one or several combinations of particular alleles or haplotypes, associated with increased obesity risk.

CONCLUSIONS

Our study evidenced that obesity risk genes generated multi-variant effects, which can be additive or non-linear interactions, and multi-variant study is an important supplement to existing GWAS for understanding genetic effects of obesity risk genes.

The serotonin system in autism spectrum disorder: From biomarker to animal models

Elevated whole blood serotonin, or hyperserotonemia, was the first biomarker identified in autism spectrum disorder (ASD) and is present in more than 25% of affected children. The serotonin system is a logical candidate for involvement in ASD due to its pleiotropic role across multiple brain systems both dynamically and across development. Tantalizing clues connect this peripheral biomarker with changes in brain and behavior in ASD, but the contribution of the serotonin system to ASD pathophysiology remains incompletely understood. Studies of whole blood serotonin levels in ASD and in a large founder population indicate greater heritability than for the disorder itself and suggest an association with recurrence risk. Emerging data from both neuroimaging and postmortem samples also indicate changes in the brain serotonin system in ASD. Genetic linkage and association studies of both whole blood serotonin levels and of ASD risk point to the chromosomal region containing the serotonin transporter (SERT) gene in males but not in females. In ASD families with evidence of linkage to this region, multiple rare SERT amino acid variants lead to a convergent increase in serotonin uptake in cell models. A knock-in mouse model of one of these variants, SERT Gly56Ala, recapitulates the hyperserotonemia biomarker and shows increased brain serotonin clearance, increased serotonin receptor sensitivity, and altered social, communication, and repetitive behaviors. Data from other rodent models also suggest an important role for the serotonin system in social behavior, in cognitive flexibility, and in sensory development. Recent work indicates that reciprocal interactions between serotonin and other systems, such as oxytocin, may be particularly important for social behavior. Collectively, these data point to the serotonin system as a prime candidate for treatment development in a subgroup of children defined by a robust, heritable biomarker.

A roadmap to multifactor dimensionality reduction methods

Complex diseases are defined to be determined by multiple genetic and environmental factors alone as well as in interactions. To analyze interactions in genetic data, many statistical methods have been suggested, with most of them relying on statistical regression models. Given the known limitations of classical methods, approaches from the machine-learning community have also become attractive. From this latter family, a fast-growing collection of methods emerged that are based on the Multifactor Dimensionality Reduction (MDR) approach. Since its first introduction, MDR has enjoyed great popularity in applications and has been extended and modified multiple times. Based on a literature search, we here provide a systematic and comprehensive overview of these suggested methods. The methods are described in detail, and the availability of implementations is listed. Most recent approaches offer to deal with large-scale data sets and rare variants, which is why we expect these methods to even gain in popularity.

The role of β3 integrin gene variants in Autism Spectrum Disorders--diagnosis and symptomatology

Autism Spectrum Disorders (ASDs) represent a group of very complex early-onset neurodevelopmental diseases. In this study, we analyzed 5 SNPs (rs2317385, rs5918, rs15908, rs12603582, rs3809865) at the β3 integrin locus (ITGB3), which has been suggested as a possible susceptibility gene, both as single markers and as part of haplotypes in 209 ASD children and their biological parents. We tested for association with the following: a) DSM-IV ASD diagnosis; b) clinical symptoms common in ASD patients (repetitive behaviors, echolalia, seizures and epilepsy, mood instability, aggression, psychomotor agitation, sleep disorders); and c) dimensional scores obtained with the Autism Screening Questionnaire and the Childhood Autism Rating Scale. These hypotheses were investigated using family-based tests, logistic regression models and analysis of covariance. The family-based tests showed an association with the H5 haplotype (composed by GTCGA alleles, the order of SNPs as above), which was transmitted less often than expected by chance (P=0.006; Pcorr=0.036). The analyses of the clinical symptoms showed a trend for an association with rs12603582 (P=0.008; Pcorr=0.064) and positive results for the haplotype composed of rs15908 and rs12603582 (Pglcorr=0.048; Pindcorr=0.015), both in symptoms of echolalia. Other nominal associations with different variants were found and involved epilepsy/seizures, aggression symptoms and higher ASQ scores. Although our positive results are not definitive, they suggest small effect associations of the ITGB3 gene with both ASD diagnosis and symptoms of echolalia. Other studies are nonetheless needed to fully understand the involvement of this locus on the etiology of ASDs and its different clinical aspects.

Multivariate dimensionality reduction approaches to identify gene-gene and gene-environment interactions underlying multiple complex traits

The elusive but ubiquitous multifactor interactions represent a stumbling block that urgently needs to be removed in searching for determinants involved in human complex diseases. The dimensionality reduction approaches are a promising tool for this task. Many complex diseases exhibit composite syndromes required to be measured in a cluster of clinical traits with varying correlations and/or are inherently longitudinal in nature (changing over time and measured dynamically at multiple time points). A multivariate approach for detecting interactions is thus greatly needed on the purposes of handling a multifaceted phenotype and longitudinal data, as well as improving statistical power for multiple significance testing via a two-stage testing procedure that involves a multivariate analysis for grouped phenotypes followed by univariate analysis for the phenotypes in the significant group(s). In this article, we propose a multivariate extension of generalized multifactor dimensionality reduction (GMDR) based on multivariate generalized linear, multivariate quasi-likelihood and generalized estimating equations models. Simulations and real data analysis for the cohort from the Study of Addiction: Genetics and Environment are performed to investigate the properties and performance of the proposed method, as compared with the univariate method. The results suggest that the proposed multivariate GMDR substantially boosts statistical power.

The gene-gene interaction of INSIG-SCAP-SREBP pathway on the risk of obesity in Chinese children

Background. Childhood obesity has become a global public health problem in recent years. This study aimed to explore the association of genetic variants in INSIG-SCAP-SREBP pathway with obesity in Chinese children. Methods. A case-control study was conducted, including 705 obese cases and 1,325 nonobese controls. We genotyped 15 single nucleotide polymorphisms (SNPs) of five genes in INSIG-SCAP-SREBP pathway, including insulin induced gene 1 (INSIG1), insulin induced gene 2 (INSIG2), SREBP cleavage-activating protein gene (SCAP), sterol regulatory element binding protein gene 1 (SREBP1), and sterol regulatory element binding protein gene 2 (SREBP2). We used generalized multifactor dimensionality reduction (GMDR) and logistic regression to investigate gene-gene interactions. Results. Single polymorphism analyses showed that SCAP rs12487736 and rs12490383 were nominally associated with obesity. We identified a 3-locus interaction on obesity in GMDR analyses (P = 0.001), involving 3 genetic variants of INSIG2, SCAP, and SREBP2. The individuals in high-risk group of the 3-locus combinations had a 79.9% increased risk of obesity compared with those in low-risk group (OR = 1.799, 95% CI: 1.475–2.193, P = 6.61 × 10⁻⁹). Conclusion. We identified interaction of three genes in INSIG-SCAP-SREBP pathway on risk of obesity, revealing that these genes affect obesity more likely through a complex interaction pattern than single gene effect.

Weighted risk score-based multifactor dimensionality reduction to detect gene-gene interactions in nasopharyngeal carcinoma

Determining the complex relationships between diseases, polymorphisms in human genes and environmental factors is challenging. Multifactor dimensionality reduction (MDR) has been proven to be capable of effectively detecting the statistical patterns of epistasis, although classification accuracy is required for this approach. The imbalanced dataset can cause seriously negative effects on classification accuracy. Moreover, MDR methods cannot quantitatively assess the disease risk of genotype combinations. Hence, we introduce a novel weighted risk score-based multifactor dimensionality reduction (WRSMDR) method that uses the Bayesian posterior probability of polymorphism combinations as a new quantitative measure of disease risk. First, we compared the WRSMDR to the MDR method in simulated datasets. Our results showed that the WRSMDR method had reasonable power to identify high-order gene-gene interactions, and it was more effective than MDR at detecting four-locus models. Moreover, WRSMDR reveals more information regarding the effect of genotype combination on the disease risk, and the result was easier to determine and apply than with MDR. Finally, we applied WRSMDR to a nasopharyngeal carcinoma (NPC) case-control study and identified a statistically significant high-order interaction among three polymorphisms: rs2860580, rs11865086 and rs2305806.

Blood serotonin levels in autism spectrum disorder: a systematic review and meta-analysis

Elevated blood serotonin (5-HT) levels were the first biomarker identified in autism research. Many studies have contrasted blood 5-HT levels in autistic patients and controls, but different measurement protocols, technologies, and biomaterials have been used through the years. We performed a systematic review and meta-analysis to provide an overall estimate of effect size and between-study heterogeneity, while verifying whether and to what extent different methodological approaches influence the strength of this association. Our literature search strategy identified 551 papers, from which 22 studies providing patient and control blood 5-HT values were selected for meta-analysis. Significantly higher 5-HT levels in autistic patients compared to controls were recorded both in whole blood (WB) [O.R.=4.6; (3.1-5.2); P=1.0×10(-12]), and in platelet-rich plasma (PRP) [O.R.=2.6 (1.8-3.9); P=2.7×10(-7)]. Predictably, studies measuring 5-HT levels in platelet-poor plasma (PPP) yielded no significant group difference [O.R.=0.54 (0.2-2-0); P=0.36]. Altogether, elevated 5-HT blood levels were recorded in 28.3% in WB and 22.5% in PRP samples of autistic individuals, as reported in 15 and 4 studies, respectively. Studies employing HPLC vs fluorometric assays yield similar cumulative effect sizes, but the former display much lower variability. In summary, despite some limitations mainly due to small study sample sizes, our results significantly reinforce the reliability of elevated 5-HT blood levels as a biomarker in ASD, providing practical indications potentially useful for its inclusion in multi-marker diagnostic panels for clinical use.

Neuroimaging endophenotypes in animal models of autism spectrum disorders: lost or found in translation?

RATIONALE

Autism spectrum disorder(s) (ASDs) is a neurodevelopmental disorder characterized by stereotyped behaviours and impairments in communication and social interactions. This heterogeneity has been a major obstacle in uncovering the aetiology and biomarkers of ASDs. Rodent models with genetic modifications or environmental insults have been created to study particular endophenotypes and bridge the gap between genetics and behavioural phenotypes. Translational neuroimaging modalities with their ability to screen the brain noninvasively and yield structural, biochemical and functional information provide a unique platform for discovery and evaluation of such endophenotypes in preclinical and clinical research.

OBJECTIVES

We reviewed literature on translational neuroimaging in rodent models of ASDs. The most prominent models will be described and the respective neuroimaging endophenotypes will be discussed with reference to human data. A perspective on future directions of translational neuroimaging in animal models of ASDs will be given.

RESULTS AND CONCLUSIONS

To date, we experience a proliferation of rodent models which recapitulate specific liabilities identified in ASDs patients. Translational neuroimaging in these models is emerging but is skewed towards magnetic resonance imaging (MRI) modalities. Volumetric and structural assessments of the brain are dominating and a host of endophenotypes have been reported that allude to findings in ASDs patients but with only few to converge among the models. Caveats of current studies are the diverging biological conditions related to genetic background and age of the animals. It is anticipated that longitudinal and functional assessments will gain much importance and will help elucidating mechanistic relationship between behavioural and structural endophenotypes.

Catecholaminergic gene variants: contribution in ADHD and associated comorbid attributes in the eastern Indian probands

Contribution of genes in attention deficit hyperactivity disorder (ADHD) has been explored in various populations, and several genes were speculated to contribute small but additive effects. We have assessed variants in four genes, DDC (rs3837091 and rs3735273), DRD2 (rs1800496, rs1801028, and rs1799732), DRD4 (rs4646984 and rs4646983), and COMT (rs165599 and rs740603) in Indian ADHD subjects with comorbid attributes. Cases were recruited following the Diagnostic and Statistical Manual for Mental Disorders-IV-TR after obtaining informed written consent. DNA isolated from peripheral blood leukocytes of ADHD probands (N = 170), their parents (N = 310), and ethnically matched controls (n = 180) was used for genotyping followed by population- and family-based analyses by the UNPHASED program. DRD4 sites showed significant difference in allelic frequencies by case-control analysis, while DDC and COMT exhibited bias in familial transmission (P < 0.05). rs3837091 “AGAG,” rs3735273 “A,” rs1799732 “C,” rs740603 “G,” rs165599 “G” and single repeat alleles of rs4646984/rs4646983 showed positive correlation with co-morbid characteristics (P < 0.05). Multi dimensionality reduction analysis of case-control data revealed significant interactive effects of all four genes (P < 0.001), while family-based data showed interaction between DDC and DRD2 (P = 0.04). This first study on these gene variants in Indo-Caucasoid ADHD probands and associated co-morbid conditions indicates altered dopaminergic neurotransmission in ADHD.

Genetic association and gene-gene interaction analyses suggest likely involvement of ITGB3 and TPH2 with autism spectrum disorder (ASD) in the Indian population

BACKGROUND

Serotoninergic dysfunction leads to neurodevelopmental abnormalities and behavioral impairments. Platelet hyperserotoninemia is reported as the best identified endophenotype for autism spectrum disorders. Therefore, in the present study we investigate the association of TPH2, the rate limiting enzyme in 5-HT biosynthesis and ITGB3, a serotonin quantitative trait locus with ASD in the Indian population.

METHODS

Population and family-based genetic association and gene-gene interaction analyses were performed to evaluate the role of ITGB3 and TPH2 markers in ASD etiology.

RESULTS

Association tests using ITGB3 markers revealed significant paternal overtransmission of T allele of rs5918 to male probands. Interestingly for TPH2, we observed significant overrepresentation of A-A (rs11179000-rs4290270), G-A (rs4570625-rs4290270), G-G-A (rs4570625-rs11179001-rs4290270) and A-G-A (rs11179000-rs11179001-rs4290270) haplotypes in the controls and maternal preferential transmission of A-A (rs11179001-rs7305115), T-A-A (rs4570625-rs11179001-rs7305115) and T-A-A (rs11179000-rs11179001-rs7305115) and nontransmission of G-G-A (rs4570625-rs11179001-rs7305115) haplotypes to the affected offspring. Moreover, interaction of ITGB3 marker, rs15908 with TPH2 markers was found to be significant and influenced by the sex of the probands. Predicted individual risk, which varied from very mild to moderate, supports combined effect of these markers in ASD.

CONCLUSION

Overall results of the present study indicate likely involvement of ITGB3 and TPH2 in the pathophysiology of ASD in the Indian population.

SVM-based generalized multifactor dimensionality reduction approaches for detecting gene-gene interactions in family studies

Gene-gene interaction plays an important role in the etiology of complex diseases, which may exist without a genetic main effect. Most current statistical approaches, however, focus on assessing an interaction effect in the presence of the gene's main effects. It would be very helpful to develop methods that can detect not only the gene's main effects but also gene-gene interaction effects regardless of the existence of the gene's main effects while adjusting for confounding factors. In addition, when a disease variant is rare or when the sample size is quite limited, the statistical asymptotic properties are not applicable; therefore, approaches based on a reasonable and applicable computational framework would be practical and frequently applied. In this study, we have developed an extended support vector machine (SVM) method and an SVM-based pedigree-based generalized multifactor dimensionality reduction (PGMDR) method to study interactions in the presence or absence of main effects of genes with an adjustment for covariates using limited samples of families. A new test statistic is proposed for classifying the affected and the unaffected in the SVM-based PGMDR approach to improve performance in detecting gene-gene interactions. Simulation studies under various scenarios have been performed to compare the performances of the proposed and the original methods. The proposed and original approaches have been applied to a real data example for illustration and comparison. Both the simulation and real data studies show that the proposed SVM and SVM-based PGMDR methods have great prediction accuracies, consistencies, and power in detecting gene-gene interactions.

Risk score modeling of multiple gene to gene interactions using aggregated-multifactor dimensionality reduction

BACKGROUND

Multifactor Dimensionality Reduction (MDR) has been widely applied to detect gene-gene (GxG) interactions associated with complex diseases. Existing MDR methods summarize disease risk by a dichotomous predisposing model (high-risk/low-risk) from one optimal GxG interaction, which does not take the accumulated effects from multiple GxG interactions into account.

RESULTS

We propose an Aggregated-Multifactor Dimensionality Reduction (A-MDR) method that exhaustively searches for and detects significant GxG interactions to generate an epistasis enriched gene network. An aggregated epistasis enriched risk score, which takes into account multiple GxG interactions simultaneously, replaces the dichotomous predisposing risk variable and provides higher resolution in the quantification of disease susceptibility. We evaluate this new A-MDR approach in a broad range of simulations. Also, we present the results of an application of the A-MDR method to a data set derived from Juvenile Idiopathic Arthritis patients treated with methotrexate (MTX) that revealed several GxG interactions in the folate pathway that were associated with treatment response. The epistasis enriched risk score that pooled information from 82 significant GxG interactions distinguished MTX responders from non-responders with 82% accuracy.

CONCLUSIONS

The proposed A-MDR is innovative in the MDR framework to investigate aggregated effects among GxG interactions. New measures (pOR, pRR and pChi) are proposed to detect multiple GxG interactions.

Gene-gene interaction and functional impact of polymorphisms on innate immune genes in controlling Plasmodium falciparum blood infection level

Genetic variations in toll-like receptors and cytokine genes of the innate immune pathways have been implicated in controlling parasite growth and the pathogenesis of Plasmodium falciparum mediated malaria. We previously published genetic association of TLR4 non-synonymous and TNF-α promoter polymorphisms with P.falciparum blood infection level and here we extend the study considerably by (i) investigating genetic dependence of parasite-load on interleukin-12B polymorphisms, (ii) reconstructing gene-gene interactions among candidate TLRs and cytokine loci, (iii) exploring genetic and functional impact of epistatic models and (iv) providing mechanistic insights into functionality of disease-associated regulatory polymorphisms. Our data revealed that carriage of AA (P = 0.0001) and AC (P = 0.01) genotypes of IL12B 3′UTR polymorphism was associated with a significant increase of mean log-parasitemia relative to rare homozygous genotype CC. Presence of IL12B+1188 polymorphism in five of six multifactor models reinforced its strong genetic impact on malaria phenotype. Elevation of genetic risk in two-component models compared to the corresponding single locus and reduction of IL12B (2.2 fold) and lymphotoxin-α (1.7 fold) expressions in patients'peripheral-blood-mononuclear-cells under TLR4Thr399Ile risk genotype background substantiated the role of Multifactor Dimensionality Reduction derived models. Marked reduction of promoter activity of TNF-α risk haplotype (C-C-G-G) compared to wild-type haplotype (T-C-G-G) with (84%) and without (78%) LPS stimulation and the loss of binding of transcription factors detected in-silico supported a causal role of TNF-1031. Significantly lower expression of IL12B+1188 AA (5 fold) and AC (9 fold) genotypes compared to CC and under-representation (P = 0.0048) of allele A in transcripts of patients' PBMCs suggested an Allele-Expression-Imbalance. Allele (A+1188C) dependent differential stability (2 fold) of IL12B-transcripts upon actinomycin-D treatment and observed structural modulation (P = 0.013) of RNA-ensemble were the plausible explanations for AEI. In conclusion, our data provides functional support to the hypothesis that de-regulated receptor-cytokine axis of innate immune pathway influences blood infection level in P. falciparum malaria.

Neuroanatomical Assessment of the Integrin β3 Mouse Model Related to Autism and the Serotonin System Using High Resolution MRI

The integrinβ3 (ITGβ3) gene has been associated with both autism and the serotonin system. The purpose of this study was to examine the volumetric differences in the brain of an ITGβ3 homozygous knockout mouse model compared with a corresponding wild-type mouse using high resolution magnetic resonance imaging and detailed statistical analyses. The most striking difference found was an 11% reduction in total brain volume. Moreover, 32 different regions were found to have significantly different relative volumes (percentage total brain volume) in the ITGβ3 mouse. A number of interesting differences relevant to autism were discovered including a smaller corpus callosum volume and bilateral decreases in the hippocampus, striatum, and cerebellum. Relative volume increases were also found in the frontal and parieto-temporal lobes as well as in the amygdala. Particularly intriguing were the changes in the lateral wings of the dorsal raphe nuclei since that nucleus is so integral to the development of many different brain regions and the serotonin system in general.

Analysis of gene-gene interactions

The goal of this unit is to introduce gene-gene interactions (epistasis) as a significant complicating factor in the search for disease susceptibility genes. This unit begins with an overview of gene-gene interactions and why they are likely to be common. Then, it reviews several statistical and computational methods for detecting and characterizing genes with effects that are dependent on other genes. The focus of this unit is genetic association studies of discrete and quantitative traits because most of the methods for detecting gene-gene interactions have been developed specifically for these study designs.

Role of gene-gene/gene-environment interaction in the etiology of eastern Indian ADHD probands

Associations between attention deficit hyperactivity disorder (ADHD) and genetic polymorphisms in the dopamine receptors, transporter and metabolizing enzymes have been reported in different ethnic groups. Gene variants may affect disease outcome by acting synergistically or antagonistically and thus their combined effect becomes an important aspect to study in the disease etiology. In the present investigation, interaction between ten functional polymorphisms in DRD4, DAT1, MAOA, COMT, and DBH genes were explored in the Indo-Caucasoid population. ADHD cases were recruited based on DSM-IV criteria. Peripheral blood samples were collected from ADHD probands (N=126), their parents (N=233) and controls (N=96) after obtaining informed written consent for participation. Genomic DNA was subjected to PCR based analysis of single nucleotide polymorphisms and variable number of tandem repeats (VNTRs). Data obtained was examined for population as well as family-based association analyses. While case-control analysis revealed higher occurrence of DAT1 intron 8 VNTR 5R allele (P=0.02) in cases, significant preferential transmission of the 7R-T (DRD4 exon3 VNTR-rs1800955) and 3R-T (MAOA-u VNTR-rs6323) haplotypes were noticed from parents to probands (P=0.02 and 0.002 respectively). Gene-gene interaction analysis revealed significant additive effect of DBH rs1108580 and DRD4 rs1800955 with significant main effects of DRD4 exon3 VNTR, DAT1 3'UTR and intron 8 VNTR, MAOA u-VNTR, rs6323, COMT rs4680, rs362204, DBH rs1611115 and rs1108580 thereby pointing towards a strong association of these markers with ADHD. Correlation between gene variants, high ADHD score and low DBH enzymatic activity was also noticed, especially in male probands. From these observations, an impact of the studied sites on the disease etiology could be speculated in this ethnic group.

Gene variation in resistant hypertension: multilocus analysis of the angiotensin 1-converting enzyme, angiotensinogen, and endothelial nitric oxide synthase genes

Resistant hypertension, a complex multifactorial hypertensive disease, is triggered by genetic and environmental factors and involves multiple physiological pathways. Single genetic variants may not reveal significant associations with resistant hypertension because their effects may be dependent on gene-gene or gene-environment interactions. We examined the interaction of angiotensin I-converting enzyme (ACE), angiotensinogen (AGT), and endothelial nitric oxide synthase (NOS3) polymorphisms with environmental factors (gender, age, body mass index, glycemia, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, estimated glomerular filtration rate, and urinary sodium excretion) in 70 resistant, 80 well-controlled hypertensive patients, and 70 normotensive controls. All subjects were genotyped for ACE insertion/deletion (rs1799752); AGT M235T (rs699), and NOS3 Glu298Asp (rs 1799983). Multifactorial associations were tested using two statistical methods: the traditional parametric method (adjusted logistic regression analysis) and gene-gene and gene-environment interactions evaluated by multifactor dimensionality reduction analyses. While adjusted logistic regression found no significant association between the studied polymorphisms and controlled or resistant hypertension, the multifactor dimensionality reduction analyses showed that carriers of the AGT 235T allele were at increased risk for resistant hypertension, especially if they were older than 50 years. The AGT 235T allele constituted an independent risk factor for resistant hypertension.

Absence of preference for social novelty and increased grooming in integrin β3 knockout mice: initial studies and future directions

Elevated whole blood serotonin 5-HT, or hyperserotonemia, is a common biomarker in autism spectrum disorder (ASD). The integrin β3 receptor subunit gene (ITGB3) is a quantitative trait locus for whole blood 5-HT levels. Recent work shows that integrin β3 interacts with the serotonin transporter (SERT) in both platelets and in the midbrain. Furthermore, multiple studies have now reported gene-gene interaction between the integrin β3 and SERT genes in association with ASD. Given the lack of previous data on the impact of integrin β3 on brain or behavioral phenotypes, we sought to compare mice with decreased or absent expression of the integrin β3 receptor subunit (Itgb3 +/- and -/-) with wildtype littermate controls in behavioral tasks relevant to ASD. These mice did not show deficits in activity level in the open field or anxiety-like behavior on the elevated plus maze, two potential confounds in the evaluation of mouse social behavior. In the three-chamber social test, mice lacking integrin β3 were shown to have normal sociability but did not show a preference for social novelty. Importantly, the absence of integrin β3 did not impair olfaction or the ability to recall familiar social odors. Additionally, mice lacking integrin β3 showed increased grooming behavior in novel environments. These preliminary studies reveal altered social and repetitive behavior in these mice, which suggests that the integrin β3 subunit may be involved in brain systems relevant to ASD. Further work is needed to fully characterize these behavioral changes and the underlying brain mechanisms.

Cell adhesion molecules and their involvement in autism spectrum disorder

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders characterized by abnormalities in social interaction, language development and behavior. Recent genetic studies demonstrate that alterations in synaptic genes including those encoding cell adhesion molecules and their interaction partners play important roles in the pathogenesis of ASD. Systematic analyses of different cell adhesion molecule genes will help elucidate their normal functions and regulatory mechanisms in the establishment and maintenance of normal neural circuits and uncover genetic aberrations contributing to ASD.

Family-based association study of ITGB3 in autism spectrum disorder and its endophenotypes

The integrin-β 3 gene (ITGB3), located on human chromosome 17q21.3, was previously identified as a quantitative trait locus (QTL) for 5-HT blood levels and has been implicated as a candidate gene for autism spectrum disorder (ASD). We performed a family-based association study in 281 simplex and 12 multiplex Caucasian families. ITGB3 haplotypes are significantly associated with autism (HBAT, global P=0.038). Haplotype H3 is largely over-transmitted to the affected offspring and doubles the risk of an ASD diagnosis (HBAT P=0.005; odds ratio (OR)=2.000), at the expense of haplotype H1, which is under-transmitted (HBAT P=0.018; OR=0.725). These two common haplotypes differ only at rs12603582 located in intron 11, which reaches a P-value of 0.072 in single-marker FBAT analyses. Interestingly, rs12603582 is strongly associated with pre-term delivery in our ASD patients (P=0.008). On the other hand, it is SNP rs2317385, located at the 5' end of the gene, that significantly affects 5-HT blood levels (Mann-Whitney U-test, P=0.001; multiple regression analysis, P=0.010). No gene-gene interaction between ITGB3 and SLC6A4 has been detected. In conclusion, we identify a significant association between a common ITGB3 haplotype and ASD. Distinct markers, located toward the 5' and 3' ends of the gene, seemingly modulate 5-HT blood levels and autism liability, respectively. Our results also raise interest into ITGB3 influences on feto-maternal immune interactions in autism.

A robust multifactor dimensionality reduction method for detecting gene-gene interactions with application to the genetic analysis of bladder cancer susceptibility

A central goal of human genetics is to identify susceptibility genes for common human diseases. An important challenge is modelling gene-gene interaction or epistasis that can result in nonadditivity of genetic effects. The multifactor dimensionality reduction (MDR) method was developed as a machine learning alternative to parametric logistic regression for detecting interactions in the absence of significant marginal effects. The goal of MDR is to reduce the dimensionality inherent in modelling combinations of polymorphisms using a computational approach called constructive induction. Here, we propose a Robust Multifactor Dimensionality Reduction (RMDR) method that performs constructive induction using a Fisher's Exact Test rather than a predetermined threshold. The advantage of this approach is that only statistically significant genotype combinations are considered in the MDR analysis. We use simulation studies to demonstrate that this approach will increase the success rate of MDR when there are only a few genotype combinations that are significantly associated with case-control status. We show that there is no loss of success rate when this is not the case. We then apply the RMDR method to the detection of gene-gene interactions in genotype data from a population-based study of bladder cancer in New Hampshire.

Association and gene-gene interaction of SLC6A4 and ITGB3 in autism

Autism is a heritable neurodevelopmental disorder with substantial genetic heterogeneity. Studies point to possible links between autism and two serotonin related genes: SLC6A4 and ITGB3 with a sex-specific genetic effect and interaction between the genes. Despite positive findings, inconsistent results have complicated interpretation. This study seeks to validate and clarify previous findings in an independent dataset taking into account sex, family-history (FH) and gene-gene effects. Family-based association analysis was performed within each gene. Gene-gene interactions were tested using extended multifactor dimensionality reduction (EMDR) and MDR-phenomics (MDR-P) using sex of affecteds and FH as covariates. No significant associations with individual SNPs were found in the datasets stratified by sex, but associations did emerge when we stratified by family history. While not significant in the overall dataset, nominally significant association was identified at RS2066713 (P = 0.006) within SLC6A4 in family-history negative (FH-) families, at RS2066713 (P = 0.038) in family-history positive (FH+) families but with the opposite risk allele as in the FH- families. For ITGB3, nominally significant association was identified at RS3809865 overall (P = 0.040) and within FH+ families (P = 0.031). However, none of the associations survived the multiple testing correction. MDR-P confirmed gene-gene effects using sex of affecteds (P = 0.023) and family history (P = 0.014, survived the multiple testing corrections) as covariates. Our results indicate the extensive heterogeneity within these two genes among families. The potential interaction between SLC6A4 and ITGB3 may be clarified using family history as an indicator of genetic architecture, illustrating the importance of covariates as markers of heterogeneity in genetic analyses.

Systematic analysis, comparison, and integration of disease based human genetic association data and mouse genetic phenotypic information

Background

The genetic contributions to human common disorders and mouse genetic models of disease are complex and often overlapping. In common human diseases, unlike classical Mendelian disorders, genetic factors generally have small effect sizes, are multifactorial, and are highly pleiotropic. Likewise, mouse genetic models of disease often have pleiotropic and overlapping phenotypes. Moreover, phenotypic descriptions in the literature in both human and mouse are often poorly characterized and difficult to compare directly.

Methods

In this report, human genetic association results from the literature are summarized with regard to replication, disease phenotype, and gene specific results; and organized in the context of a systematic disease ontology. Similarly summarized mouse genetic disease models are organized within the Mammalian Phenotype ontology. Human and mouse disease and phenotype based gene sets are identified. These disease gene sets are then compared individually and in large groups through dendrogram analysis and hierarchical clustering analysis.

Results

Human disease and mouse phenotype gene sets are shown to group into disease and phenotypically relevant groups at both a coarse and fine level based on gene sharing.

Conclusion

This analysis provides a systematic and global perspective on the genetics of common human disease as compared to itself and in the context of mouse genetic models of disease.

Gene-gene interactions in the folate metabolic pathway and the risk of conotruncal heart defects

Conotruncal and related heart defects (CTRD) are common, complex malformations. Although there are few established risk factors, there is evidence that genetic variation in the folate metabolic pathway influences CTRD risk. This study was undertaken to assess the association between inherited (i.e., case) and maternal gene-gene interactions in this pathway and the risk of CTRD. Case-parent triads (n = 727), ascertained from the Children's Hospital of Philadelphia, were genotyped for ten functional variants of nine folate metabolic genes. Analyses of inherited genotypes were consistent with the previously reported association between MTHFR A1298C and CTRD (adjusted P = .02), but provided no evidence that CTRD was associated with inherited gene-gene interactions. Analyses of the maternal genotypes provided evidence of a MTHFR C677T/CBS 844ins68 interaction and CTRD risk (unadjusted P = .02). This association is consistent with the effects of this genotype combination on folate-homocysteine biochemistry but remains to be confirmed in independent study populations.

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

Detecting, characterizing, and interpreting nonlinear gene-gene interactions using multifactor dimensionality reduction

Human health is a complex process that is dependent on many genes, many environmental factors and chance events that are perhaps not measurable with current technology or are simply unknowable. Success in the design and execution of population-based association studies to identify those genetic and environmental factors that play an important role in human disease will depend on our ability to embrace, rather that ignore, complexity in the genotype to phenotype mapping relationship for any given human ecology. We review here three general computational challenges that must be addressed. First, data mining and machine learning methods are needed to model nonlinear interactions between multiple genetic and environmental factors. Second, filter and wrapper methods are needed to identify attribute interactions in large and complex solution landscapes. Third, visualization methods are needed to help interpret computational models and results. We provide here an overview of the multifactor dimensionality reduction (MDR) method that was developed for addressing each of these challenges.

Positive association between ALDH1A2 and schizophrenia in the Chinese population

Vitamin A (retinol), in the biologically active form of retinoic acid (RA), has been proposed as involved in the pathogenesis of schizophrenia. We hypothesized that genetic basis of genes encoding RA metabolism enzymes, which control the cellular RA level, might be associated with this disease. This cascade genetic association model, using markers in genes of synthesis and degradation enzymes within the retinoid cascade, would better fit the biological character of the retinoid hypothesis than the single gene strategy. In the present study we chose to investigate 7 genes involved in the synthesis, degradation and transportation of RA, ALDH1A1, ALDH1A2, ALDH1A3, CYP26A1, CYP26B1, CYP26C1 and Transthyretin (TTR), for their roles in the development of schizophrenia. We genotyped 18 single nucleotide polymorphisms (SNPs) in the regulatory and coding regions of these 7 genes using LDR technology in the 617 Chinese Han subjects. Case-control analyses were performed to detect association of these 7 genes with schizophrenia. Association analyses using both allelic and genotypic single-locus tests revealed no significant association between the risk for each of investigated gene and schizophrenia. However, analyses of multiple-locus haplotypes indicated that the overall frequency of rs4646642-rs4646580 of ALDH1A2 gene showed significant difference between patients and control subjects (p=0.0055). We also employed multifactor dimensionality reduction method to detect multilocus effects. In summary, in this work we show multiple candidate genes involved in retinoid cascade in schizophrenics. In addition, our results suggest a positive association between ALDH1A2 and schizophrenics in the Chinese population and support the retinoid hypothesis of schizophrenia.

Epistasis and its implications for personal genetics

The widespread availability of high-throughput genotyping technology has opened the door to the era of personal genetics, which brings to consumers the promise of using genetic variations to predict individual susceptibility to common diseases. Despite easy access to commercial personal genetics services, our knowledge of the genetic architecture of common diseases is still very limited and has not yet fulfilled the promise of accurately predicting most people at risk. This is partly because of the complexity of the mapping relationship between genotype and phenotype that is a consequence of epistasis (gene-gene interaction) and other phenomena such as gene-environment interaction and locus heterogeneity. Unfortunately, these aspects of genetic architecture have not been addressed in most of the genetic association studies that provide the knowledge base for interpreting large-scale genetic association results. We provide here an introductory review of how epistasis can affect human health and disease and how it can be detected in population-based studies. We provide some thoughts on the implications of epistasis for personal genetics and some recommendations for improving personal genetics in light of this complexity.

Modeling rare gene variation to gain insight into the oldest biomarker in autism: construction of the serotonin transporter Gly56Ala knock-in mouse

Alterations in peripheral and central indices of serotonin (5-hydroxytryptamine, 5-HT) production, storage and signaling have long been associated with autism. The 5-HT transporter gene (HTT, SERT, SLC6A4) has received considerable attention as a potential risk locus for autism-spectrum disorders, as well as disorders with overlapping symptoms, including obsessive-compulsive disorder (OCD). Here, we review our efforts to characterize rare, nonsynonymous polymorphisms in SERT derived from multiplex pedigrees carrying diagnoses of autism and OCD and present the initial stages of our effort to model one of these variants, Gly56Ala, in vivo. We generated a targeting vector to produce the Gly56Ala substitution in the Slc6a4 locus by homologous recombination. Following removal of a neomycin resistance selection cassette, animals exhibiting germline transmission of the Ala56 variant were bred to establish a breeding colony on a 129S6 background, suitable for initial evaluation of biochemical, physiological and behavioral alterations relative to SERT Gly56 (wild-type) animals. SERT Ala56 mice were achieved and exhibit a normal pattern of transmission. The initial growth and gross morphology of these animals is comparable to wildtype littermate controls. The SERT Ala56 variant can be propagated in 129S6 mice without apparent disruption of fertility and growth. We discuss both the opportunities and challenges that await the physiological/behavioral analysis of Gly56Ala transgenic mice, with particular reference to modeling autism-associated traits.

Analysis of gene-gene interactions

The goal of this unit is to introduce gene-gene interactions (epistasis) as a significant complicating factor in the search for disease susceptibility genes. This unit begins with an overview of gene-gene interactions and why they are likely to be common. Then, it reviews several statistical and computational methods for detecting and characterizing genes with effects that are dependent on other genes. The focus of this unit is genetic association studies of discrete and quantitative traits because most of the methods for detecting gene-gene interactions have been developed specifically for these study designs.