Gene-environment interaction tests for dichotomous traits in trios and sibships

PILRA polymorphism modifies the effect of APOE4 and GM17 on Alzheimer's disease risk

PILRA (rs1859788 A > G) has been suggested to be a protective variant for Alzheimer's disease (AD) and is an entry co-receptor for herpes simplex virus-1. We conducted a nested case-control study of 360 1:1-matched AD subjects. Interactions between the PILRA-A allele, APOE risk variants (ε3/ε4 or ε4/ε4) and GM17 for AD risk were modelled. The associations were cross-validated using two independent whole-genome sequencing datasets. We found negative interactions between PILRA-A and GM17 (OR 0.72, 95% CI 0.52-1.00) and between PILRA-A and APOE risk variants (OR 0.56, 95% CI 0.32-0.98) in the discovery dataset. In the replication cohort, a joint effect of PILRA and PILRA × GM 17/17 was observed for the risk of developing AD (p .02). Here, we report a negative effect modification by PILRA on APOE and GM17 high-risk variants for future AD risk in two independent datasets. This highlights the complex genetics of AD.

A novel locus for exertional dyspnoea in childhood asthma

Most children diagnosed with asthma have respiratory symptoms such as cough, dyspnoea and wheezing, which are also important markers of overall respiratory function. A decade of genome-wide association studies (GWAS) have investigated genetic susceptibility to asthma itself, but few have focused on important respiratory symptoms that characterise childhood asthma.Using whole-genome sequencing (WGS) data for 894 asthmatic trios from a Costa Rican cohort, we performed family-based association tests (FBATs) to assess the association between genetic variants and multiple asthma-relevant respiratory phenotypes: cough, phlegm, wheezing, exertional dyspnoea and exertional chest tightness. We tested whether genome-wide significant associations were replicated in two additional studies: 1) 286 asthmatic trios from the Childhood Asthma Management Program (CAMP), and 2) 2691 African American current or former smokers from the COPDGene study.In the 894 Costa Rican trios, we identified a genome-wide significant association (p=2.16×10-9) between exertional dyspnoea and the single nucleotide polymorphism (SNP) rs10165869, located on chromosome 2q37.3, that was replicated in the CAMP cohort (p=0.023) with the same direction of association (combined p=3.28×10-10). This association was not found in the African American participants from COPDGene. We also found suggestive evidence for an association between SNP rs10165869 and the atypical chemokine receptor 3 (ACKR3).Our finding encourages the secondary association analysis of a wider range of phenotypes that characterise respiratory symptoms in other airway diseases/studies.

Identification of Novel Alzheimer's Disease Loci Using Sex-Specific Family-Based Association Analysis of Whole-Genome Sequence Data

With the advent of whole genome-sequencing (WGS) studies, family-based designs enable sex-specific analysis approaches that can be applied to only affected individuals; tests using family-based designs are attractive because they are completely robust against the effects of population substructure. These advantages make family-based association tests (FBATs) that use siblings as well as parents especially suited for the analysis of late-onset diseases such as Alzheimer's Disease (AD). However, the application of FBATs to assess sex-specific effects can require additional filtering steps, as sensitivity to sequencing errors is amplified in this type of analysis. Here, we illustrate the implementation of robust analysis approaches and additional filtering steps that can minimize the chances of false positive-findings due to sex-specific sequencing errors. We apply this approach to two family-based AD datasets and identify four novel loci (GRID1, RIOK3, MCPH1, ZBTB7C) showing sex-specific association with AD risk. Following stringent quality control filtering, the strongest candidate is ZBTB7C (Pinter = 1.83 × 10-7), in which the minor allele of rs1944572 confers increased risk for AD in females and protection in males. ZBTB7C encodes the Zinc Finger and BTB Domain Containing 7C, a transcriptional repressor of membrane metalloproteases (MMP). Members of this MMP family were implicated in AD neuropathology.

Genome-Wide Analysis of Gene-Gene and Gene-Environment Interactions Using Closed-Form Wald Tests

Despite the successful discovery of hundreds of variants for complex human traits using genome-wide association studies, the degree to which genes and environmental risk factors jointly affect disease risk is largely unknown. One obstacle toward this goal is that the computational effort required for testing gene-gene and gene-environment interactions is enormous. As a result, numerous computationally efficient tests were recently proposed. However, the validity of these methods often relies on unrealistic assumptions such as additive main effects, main effects at only one variable, no linkage disequilibrium between the two single-nucleotide polymorphisms (SNPs) in a pair or gene-environment independence. Here, we derive closed-form and consistent estimates for interaction parameters and propose to use Wald tests for testing interactions. The Wald tests are asymptotically equivalent to the likelihood ratio tests (LRTs), largely considered to be the gold standard tests but generally too computationally demanding for genome-wide interaction analysis. Simulation studies show that the proposed Wald tests have very similar performances with the LRTs but are much more computationally efficient. Applying the proposed tests to a genome-wide study of multiple sclerosis, we identify interactions within the major histocompatibility complex region. In this application, we find that (1) focusing on pairs where both SNPs are marginally significant leads to more significant interactions when compared to focusing on pairs where at least one SNP is marginally significant; and (2) parsimonious parameterization of interaction effects might decrease, rather than increase, statistical power.

Detecting gene-environment interactions in human birth defects: Study designs and statistical methods

BACKGROUND

The National Birth Defects Prevention Study (NBDPS) contains a wealth of information on affected and unaffected family triads, and thus provides numerous opportunities to study gene-environment interactions (G×E) in the etiology of birth defect outcomes. Depending on the research objective, several analytic options exist to estimate G×E effects that use varying combinations of individuals drawn from available triads.

METHODS

In this study, we discuss important considerations in the collection of genetic data and environmental exposures.

RESULTS

We will also present several population- and family-based approaches that can be applied to data from the NBDPS including case-control, case-only, family-based trio, and maternal versus fetal effects. For each, we describe the data requirements, applicable statistical methods, advantages, and disadvantages.

CONCLUSION

A range of approaches can be used to evaluate potentially important G×E effects in the NBDPS. Investigators should be aware of the limitations inherent to each approach when choosing a study design and interpreting results.

The logistic regression model for gene-environment interactions using both case-parent trios and unrelated case-controls

One of the greatest challenges in genetic studies is the determination of gene-environment interactions due to underlying complications and inadequate statistical power. With the increased sample size gained by using case-parent trios and unrelated cases and controls, the performance may be much improved. Focusing on a dichotomous trait, a two-stage approach was previously proposed to deal with gene-environment interaction when utilizing mixed study samples. Theoretically, the two-stage association analysis uses likelihood functions such that the computational algorithms may not converge in the maximum likelihood estimation with small study samples. In an effort to avoid such convergence issues, we propose a logistic regression framework model, based on the combined haplotype relative risk (CHRR) method, which intuitively pools the case-parent trios and unrelated subjects in a two by two table. A positive feature of the logistic regression model is the effortless adjustment for either discrete or continuous covariates. According to computer simulations, under the circumstances in which the two-stage test converges in larger sample sizes, we discovered that the performances of the two tests were quite similar; the two-stage test is more powerful under the dominant and additive disease models, but the extended CHRR is more powerful under the recessive disease model.

A Tutorial on Interaction

In this tutorial, we provide a broad introduction to the topic of interaction between the effects of exposures. We discuss interaction on both additive and multiplicative scales using risks, and we discuss their relation to statistical models (e.g. linear, log-linear, and logistic models). We discuss and evaluate arguments that have been made for using additive or multiplicative scales to assess interaction. We further discuss approaches to presenting interaction analyses, different mechanistic forms of interaction, when interaction is robust to unmeasured confounding, interaction for continuous outcomes, qualitative or “crossover” interactions, methods for attributing effects to interactions, case-only estimators of interaction, and power and sample size calculations for additive and multiplicative interaction.

Glutamatergic GRIN2B and polyaminergic ODC1 genes in suicide attempts: associations and gene-environment interactions with childhood/adolescent physical assault

The complex etiology of suicidal behavior has frequently been investigated in relation to monoaminergic neurotransmission, but other neurosystems have shown alterations as well, involving excitatory glutamatergic and inhibitory γ-aminobutyric acid (GABA) molecular components, together with the modulating polyamines. Sufficiently powered and family-based association studies of glutamatergic and GABAergic genes with suicidal behavior are nonexistent, but several studies have been reported for polyamines. We therefore conducted, for the first time ever, an extensive family-based study of 113 candidate single-nucleotide polymorphisms (SNPs) located in 24 glutamatergic and GABA genes, in addition to interrelated polyaminergic genes, on the outcome of severe suicide attempts (SAs). The family-based analysis (n=660 trios) was supplemented with gene-environment interaction (G × E), case-control (n=519 controls) and subgroup analyses. The main observations were the previously unreported association and linkage of SNPs rs2268115 and rs220557 in GRIN2B, as well as of SNPs rs1049500 and rs2302614 in ODC1 (P<10(-2)). Furthermore, GRIN2B haplotypic associations were observed, in particular with a four-SNP AGGC haplotype (rs1805247-rs1806201-rs1805482-rs2268115; P<10(-5)), and a third SNP rs7559979 in ODC1 showed G × E with serious childhood/adolescent physical assault (P<10(-4)). SA subjects were characterized by transdiagnostic trait anger and past year alcohol-drug use disorders, but not by alcohol-drug use at SA, depression, anxiety or psychosis diagnoses. We also discuss a first ever confirmatory observation of SNP rs6526342 (polyaminergic SAT1) in SA, originally identified in completed suicides. The results suggest that specific genetic variants in a subset of glutamatergic (GRIN2B) and polyaminergic (ODC1) neurosystem genes may be of importance in certain suicidal subjects.

Family-based study of AVPR1B association and interaction with stressful life events on depression and anxiety in suicide attempts

The cortisol response to psychosocial stress may become dysregulated in stress-related disorders. It is potentiated by pituitary secretion of adrenocorticotropic hormone (ACTH), which is, in part, regulated by arginine vasopressin receptor-1B (AVPR1B). AVPR1B variants were previously reported to associate with mood and anxiety disorders. This study aims, for the first time, to investigate association of AVPR1B genetic variants with mood and anxiety outcomes in suicidal behavior.Using a family-based study design of 660 complete nuclear family trios with offspring who have made a suicide attempt (SA), we tested the direct association and linkage of AVPR1B single nucleotide polymorphisms (SNPs) with SA, as well as with depression and anxiety in SA. Main findings were the association and linkage of AVPR1B exon 1 SNP rs33990840 and a major 6-SNP haplotype representative of all common AVPR1B-SNPs, on the outcome of high Beck Depression Inventory scores in SA. By contrast, genetic associations with lifetime diagnoses of depression and anxiety in SA or gene-environment interactions between AVPR1B variants and stressful life events (SLEs) were not significant. An exploratory screen of interactions between AVPR1B and CRHR1 (corticotropin-releasing hormone receptor-1), the principal pituitary regulator of ACTH secretion, showed no support for gene-gene interactions on the studied outcomes. The results suggest that AVPR1B genetic variation, eg, non-synonymous SNP rs33990840 mediating putative consequences on ligand binding, has a role in SA etiology characterized by elevated depression symptoms, without involving AVPR1B-moderation of SLEs.

Family-based study of HTR2A in suicide attempts: observed gene, gene × environment and parent-of-origin associations

While suicidal behavior is frequently accompanied by serotonergic system alterations, specific associations with genetic variation in the serotonin 2A receptor (HTR2A) gene have been inconsistent. Using a family-based study design of 660 offspring who have made a suicide attempt (SA) and both parents, we conducted an association and linkage analysis using single-nucleotide polymorphisms (SNPs) with extensive gene coverage, and included the study of parent-of-origin (POE) and gene-environment interaction (G × E), also using previously unstudied exposures. The main finding was a G × E between the exon 1 SNP rs6313 and exposure to cumulative types of lifetime stressful life events (SLEs), driven by overtransmission of CT and undertransmission of TT, both in relation to other genotypes. Further exploratory analysis revealed a significant POE in this G × E in female subjects, which followed a polar overdominant inheritance pattern. In addition, rs6310 and rs6305 were found to significantly associate with SA in the total sample. A G × E in female subjects (rs7322347 × physical assault in childhood/adolescence) confirmed features of a previously observed association with SA. Other potentially interesting nominally significant findings were observed, but like the G × E of rs7322347 did not pass a false-discovery rate cutoff. Taken together, this study found multiple associations of HTR2A SNPs on SA, with strongest statistical evidence for a G × E involving rs6313, and further suggested the importance of taking into account different inheritance patterns and G × Es with regard to HTR2A.

Challenges and opportunities in genome-wide environmental interaction (GWEI) studies

The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies-when the number of environmental or genetic risk factors is relatively small-has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene-gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to "joining" two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.

Gene-environment interaction testing in family-based association studies with phenotypically ascertained samples: a causal inference approach

We propose a method for testing gene-environment (G × E) interactions on a complex trait in family-based studies in which a phenotypic ascertainment criterion has been imposed. This novel approach employs G-estimation, a semiparametric estimation technique from the causal inference literature, to avoid modeling of the association between the environmental exposure and the phenotype, to gain robustness against unmeasured confounding due to population substructure, and to acknowledge the ascertainment conditions. The proposed test allows for incomplete parental genotypes. It is compared by simulation studies to an analogous conditional likelihood-based approach and to the QBAT-I test, which also invokes the G-estimation principle but ignores ascertainment. We apply our approach to a study of chronic obstructive pulmonary disorder.

Variants in genes that encode muscle contractile proteins influence risk for isolated clubfoot

Isolated clubfoot is a relatively common birth defect that affects approximately 4,000 newborns in the US each year. Calf muscles in the affected leg(s) are underdeveloped and remain small even after corrective treatment. This observation suggests that variants in genes that influence muscle development are priority candidate risk factors for clubfoot. This contention is further supported by the discovery that mutations in genes that encode components of the muscle contractile complex (MYH3, TPM2, TNNT3, TNNI2, and MYH8) cause congenital contractures, including clubfoot, in distal arthrogryposis (DA) syndromes. Interrogation of 15 genes encoding proteins that control myofiber contractility in a cohort of both non-Hispanic White (NHW) and Hispanic families, identified positive associations (P < 0.05) with SNPs in 12 genes; only 1 was identified in a family-based validation dataset. Six SNPs in TNNC2 deviated from Hardy-Weinberg equilibrium in mothers in our NHW discovery dataset. Relative risk and likelihood ratio tests showed evidence for a maternal genotypic effect with TNNC2/rs383112 and an inherited/child genotypic effect with two SNPs, TNNC2/rs4629 and rs383112. Associations with multiple SNPs in TPM1 were identified in the NHW discovery (rs4075583, P = 0.01), family-based validation (rs1972041, P = 0.000074), and case-control validation (rs12148828, P = 0.04) datasets. Gene interactions were identified between multiple muscle contraction genes with many of the interactions involving at least one potential regulatory SNP. Collectively, our results suggest that variation in genes that encode contractile proteins of skeletal myofibers may play a role in the etiology of clubfoot.

Testing gene-gene interactions in the case-parents design

The case-parents design has been widely used to detect genetic associations as it can prevent spurious association that could occur in population-based designs. When examining the effect of an individual genetic locus on a disease, logistic regressions developed by conditioning on parental genotypes provide complete protection from spurious association caused by population stratification. However, when testing gene-gene interactions, it is unknown whether conditional logistic regressions are still robust. Here we evaluate the robustness and efficiency of several gene-gene interaction tests that are derived from conditional logistic regressions. We found that in the presence of SNP genotype correlation due to population stratification or linkage disequilibrium, tests with incorrectly specified main-genetic-effect models can lead to inflated type I error rates. We also found that a test with fully flexible main genetic effects always maintains correct test size and its robustness can be achieved with negligible sacrifice of its power. When testing gene-gene interactions is the focus, the test allowing fully flexible main effects is recommended to be used.

Combining disease models to test for gene-environment interaction in nuclear families

It is useful to have robust gene-environment interaction tests that can utilize a variety of family structures in an efficient way. This article focuses on tests for gene-environment interaction in the presence of main genetic and environmental effects. The objective is to develop powerful tests that can combine trio data with parental genotypes and discordant sibships when parents' genotypes are missing. We first make a modest improvement on a method for discordant sibs (discordant on phenotype), but the approach does not allow one to use families when all offspring are affected, e.g., trios. We then make a modest improvement on a Mendelian transmission-based approach that is inefficient when discordant sibs are available, but can be applied to any nuclear family. Finally, we propose a hybrid approach that utilizes the most efficient method for a specific family type, then combines over families. We utilize this hybrid approach to analyze a chronic obstructive pulmonary disorder dataset to test for gene-environment interaction in the Serpine2 gene with smoking. The methods are freely available in the R package fbati.

Smoking, the xenobiotic pathway, and clubfoot

BACKGROUND

Isolated clubfoot is a common orthopedic birth defect that affects approximately 135,000 newborns worldwide. It is characterized by ankle equinus, hindfoot varus, and forefoot adductus. Although numerous studies suggest a multifactorial etiology, the specific genetic and environmental components have yet to be delineated. Maternal smoking during pregnancy is the only common environmental factor consistently shown to increase the risk for clubfoot. Moreover, a positive family history of clubfoot, in conjunction with maternal smoking, increases the risk 20-fold. These findings suggest that genetic variation in smoking metabolism (xenobiotic) genes may increase susceptibility to clubfoot. Based on this reasoning, we interrogated eight candidate genes from the xenobiotic metabolism.

METHODS

Twenty-two single-nucleotide polymorphisms and two null alleles in these genes (CYP1A1, CYP1A2, CYP1B1, CYP2A6, EPHX1, NAT2, GSTM1, and GSTT1) were genotyped in a dataset composed of non-Hispanic white and Hispanic multiplex and simplex families.

RESULTS

Only rs1048943/CYP1A1 had significantly altered transmission in the aggregate and multiplex non-Hispanic white datasets (p = 0.003 and p = 0.009, respectively). Perturbation of CYP1A1 can cause an increase in harmful, adduct-forming metabolic intermediates. A significant interaction between EPHX1 and NAT2 was also found (p = 0.007). Importantly, for CYP1A2, significant maternal (p = 0.03; relative risk [RR] = 1.24; 95% confidence interval [CI], 1.04-1.44) and fetal (p = 0.01; RR = 1.33; 95% CI, 1.13-1.54) genotypic effects were identified, suggesting that both maternal and fetal genotypes can negatively impact limb development. No association was found between maternal smoking status and variation in xenobiotic metabolism genes.

CONCLUSION

Together, these results suggest that xenobiotic metabolism genes are unlikely to play a major role in clubfoot; however, perturbation of this pathway may still play a contributory role.

Folate pathway and nonsyndromic cleft lip and palate

BACKGROUND

Nonsyndromic cleft lip with or without cleft palate (NSCLP) is a common complex birth defect. Periconceptional supplementation with folic acid, a key component in DNA synthesis and cell division, has reduced the birth prevalence of neural tube defects and may similarly reduce the birth prevalence of other complex birth defects including NSCLP. Past studies investigating the role of two common methylenetetrahydrofolate reductase (MTHFR) single-nucleotide polymorphisms (SNPs), C677T (rs1801133) and A1298C (rs1801131), in NSCLP have produced conflicting results. Most studies of folate pathway genes have been limited in scope, as few genes/SNPs have been interrogated. Here, we asked whether variations in a more comprehensive group of folate pathway genes were associated with NSCLP, and were there detectable interactions between these genes and environmental exposures?

METHODS

Fourteen folate metabolism-related genes were interrogated using 89 SNPs in multiplex and simplex non-Hispanic white and Hispanic NSCLP families.

RESULTS

Evidence for a risk association between NSCLP and SNPs in NOS3 and TYMS was detected in the non-Hispanic white group, whereas associations with MTR, BHMT2, MTHFS, and SLC19A1 were detected in the Hispanic group. Evidence for over-transmission of haplotypes and gene interactions in the methionine arm was detected.

CONCLUSIONS

These results suggest that perturbations of the genes in the folate pathway may contribute to NSCLP. There was evidence for an interaction between several SNPs and maternal smoking, and for one SNP with gender of the offspring. These results provide support for other studies that suggest that high maternal homocysteine levels may contribute to NSCLP and should be further investigated.

Tests for compositional epistasis under single interaction-parameter models

Compositional epistasis is said to be present when the effect of a genetic factor at one locus is masked by a variant at another locus. Although such compositional epistasis is not equivalent to the presence of an interaction in a statistical model, non-standard tests can sometimes be used to detect compositional epistasis. In this paper we consider empirical tests for compositional epistasis under models for the joint effect of two genetic factors which place no restrictions on the main effects of each factor but constrain the interactive effects of the two factors so as to be captured by a single parameter in the model. We describe the implications of these tests for cohort, case-control, case-only and family-based study designs and we illustrate the methods using an example of gene-gene interaction already reported in the literature.

Identifying gene regulatory networks in schizophrenia

The imaging genetics approach to studying the genetic basis of disease leverages the individual strengths of both neuroimaging and genetic studies by visualizing and quantifying the brain activation patterns in the context of genetic background. Brain imaging as an intermediate phenotype can help clarify the functional link among genes, the molecular networks in which they participate, and brain circuitry and function. Integrating genetic data from a genome-wide association study (GWAS) with brain imaging as a quantitative trait (QT) phenotype can increase the statistical power to identify risk genes. A QT analysis using brain imaging (DLPFC activation during a working memory task) as a quantitative trait has identified unanticipated risk genes for schizophrenia. Several of these genes (RSRC1, ARHGAP18, ROBO1-ROBO2, GPC1, TNIK, and CTXN3-SLC12A2) have functions related to progenitor cell proliferation, migration, and differentiation, cytoskeleton reorganization, axonal connectivity, and development of forebrain structures. These genes, however, do not function in isolation but rather through gene regulatory networks. To obtain a deeper understanding how the GWAS-identified genes participate in larger gene regulatory networks, we measured correlations among transcript levels in the mouse and human postmortem tissue and performed a gene set enrichment analysis (GSEA) that identified several microRNA associated with schizophrenia (448, 218, 137). The results of such computational approaches can be further validated in animal experiments in which the networks are experimentally studied and perturbed with specific compounds. Glypican 1 and FGF17 mouse models for example, can be used to study such gene regulatory networks. The model demonstrates epistatic interactions between FGF and glypican on brain development and may be a useful model of negative symptom schizophrenia.

A doubly robust test for gene-environment interaction in family-based studies of affected offspring

We develop a locally efficient test for (multiplicative) gene-environment interaction in family studies that collect genotypic information and environmental exposures for affected offspring along with genotypic information for their parents or relatives. The proposed test does not require modeling the effects of environmental exposures and is doubly robust in the sense of being valid if either a model for the main genetic effect holds or a model for the expected environmental exposure (given the offspring affection status and parental mating types) but not necessarily both. It extends the FBAT-I to allow for missing parental mating types and families of arbitrary size. Simulation studies and the analysis of an Alzheimer's disease study confirm the adequate performance of the proposed test.

Epistatic interactions

The term "epistasis" is sometimes used to describe some form of statistical interaction between genetic factors and is alternatively sometimes used to describe instances in which the effect of a particular genetic variant is masked by a variant at another locus. In general statistical tests for interaction are of limited use in detecting "epistasis" in the sense of masking. It is, however, shown that there are relations between empirical data patterns and epistasis that have not been previously noted. These relations can sometimes be exploited to empirically test for "epistatic interactions" in the sense of the masking of the effect of a particular genetic variant by a variant at another locus.

Genome-wide strategies for discovering genetic influences on cognition and cognitive disorders: methodological considerations

INTRODUCTION

Genes play a well-documented role in determining normal cognitive function. This paper focuses on reviewing strategies for the identification of common genetic variation in genes that modulate normal and abnormal cognition with a genome-wide association scan (GWAS). GWASs make it possible to survey the entire genome to discover important but unanticipated genetic influences.

METHODS

The use of a quantitative phenotype in combination with a GWAS provides many advantages over a case-control design, both in power and in physiological understanding of the underlying cognitive processes. We review the major features of this approach, and show how, using a General Linear Model method, the contribution of each Single Nucleotide Polymorphism (SNP) to the phenotype is determined, and adjustments then made for multiple tests. An example of the strategy is presented, in which fMRI measures of cortical inefficiency while performing a working memory task are used as the quantitative phenotype. We estimate power under different effect sizes (10-30%) and variations in allelic frequency for a Quantitative Trait (QT) (10-20%), and compare them to a case-control design with an Odds Ratio (OR) of 1.5, showing how a QT approach is superior to a traditional case-control. In the presented example, this method identifies putative susceptibility genes for schizophrenia which affect prefrontal efficiency and have functions related to cell migration, forebrain development and stress response.

CONCLUSION

The use of QT as phenotypes provide increased statistical power over categorical association approaches and when combined with a GWAS creates a strategy for identification of unanticipated genes that modulate cognitive processes and cognitive disorders.

fgui: A Method for Automatically Creating Graphical User Interfaces for Command-Line R Packages

The fguiR package is designed for developers of R packages, to help rapidly, and sometimes fully automatically, create a graphical user interface for a command line R package. The interface is built upon the Tcl/Tk graphical interface included in R. The package further facilitates the developer by loading in the help files from the command line functions to provide context sensitive help to the user with no additional effort from the developer. Passing a function as the argument to the routines in the fgui package creates a graphical interface for the function, and further options are available to tweak this interface for those who want more flexibility.