Rationale: Emphysema occurs in distinct pathologic patterns, but little is known about the epidemiologic associations of these patterns. Standard quantitative measures of emphysema from computed tomography (CT) do not distinguish between distinct patterns of parenchymal destruction.
Objectives: To study the epidemiologic associations of distinct emphysema patterns with measures of lung-related physiology, function, and health care use in smokers.
Methods: Using a local histogram-based assessment of lung density, we quantified distinct patterns of low attenuation in 9,313 smokers in the COPDGene Study. To determine if such patterns provide novel insights into chronic obstructive pulmonary disease epidemiology, we tested for their association with measures of physiology, function, and health care use.
Measurements and Main Results: Compared with percentage of low-attenuation area less than −950 Hounsfield units (%LAA-950), local histogram-based measures of distinct CT low-attenuation patterns are more predictive of measures of lung function, dyspnea, quality of life, and health care use. These patterns are strongly associated with a wide array of measures of respiratory physiology and function, and most of these associations remain highly significant (P < 0.005) after adjusting for %LAA-950. In smokers without evidence of chronic obstructive pulmonary disease, the mild centrilobular disease pattern is associated with lower FEV1 and worse functional status (P < 0.005).
Conclusions: Measures of distinct CT emphysema patterns provide novel information about the relationship between emphysema and key measures of physiology, physical function, and health care use. Measures of mild emphysema in smokers with preserved lung function can be extracted from CT scans and are significantly associated with functional measures.
emphysema; chronic obstructive pulmonary disease; spiral computed tomography; epidemiology
Rationale: Previous studies of chronic obstructive pulmonary disease (COPD) have suggested that genetic factors play an important role in the development of disease. However, single-nucleotide polymorphisms that are associated with COPD in genome-wide association studies have been shown to account for only a small percentage of the genetic variance in phenotypes of COPD, such as spirometry and imaging variables. These phenotypes are highly predictive of disease, and family studies have shown that spirometric phenotypes are heritable.
Objectives: To assess the heritability and coheritability of four major COPD-related phenotypes (measurements of FEV1, FEV1/FVC, percent emphysema, and percent gas trapping), and COPD affection status in smokers of non-Hispanic white and African American descent using a population design.
Methods: Single-nucleotide polymorphisms from genome-wide association studies chips were used to calculate the relatedness of pairs of individuals and a mixed model was adopted to estimate genetic variance and covariance.
Measurements and Main Results: In the non-Hispanic whites, estimated heritabilities of FEV1 and FEV1/FVC were both about 37%, consistent with estimates in the literature from family-based studies. For chest computed tomography scan phenotypes, estimated heritabilities were both close to 25%. Heritability of COPD affection status was estimated as 37.7% in both populations.
Conclusions: This study suggests that a large portion of the genetic risk of COPD is yet to be discovered and gives rationale for additional genetic studies of COPD. The estimates of coheritability (genetic covariance) for pairs of the phenotypes suggest considerable overlap of causal genetic loci.
missing heritability; pleiotropy; pulmonary function; imaging phenotypes; chromosomal partition
The investigation of complex disease heterogeneity has been challenging. Here, we introduce a network-based approach, using partial correlations, that analyzes the relationships among multiple disease-related phenotypes.
We applied this method to two large, well-characterized studies of chronic obstructive pulmonary disease (COPD). We also examined the associations between these COPD phenotypic networks and other factors, including case-control status, disease severity, and genetic variants. Using these phenotypic networks, we have detected novel relationships between phenotypes that would not have been observed using traditional epidemiological approaches.
Phenotypic network analysis of complex diseases could provide novel insights into disease susceptibility, disease severity, and genetic mechanisms.
Network medicine; Phenotypic networks; COPD; Genetic association analysis
We previously reported genome-wide significant evidence for linkage between chromosome 6q and bipolar I disorder (BPI) by performing a meta-analysis of original genotype data from 11 genome scan linkage studies. We now present follow-up linkage disequilibrium mapping of the linked region utilizing 3,047 single nucleotide polymorphism (SNP) markers in a case–control sample (N = 530 cases, 534 controls) and family-based sample (N = 256 nuclear families, 1,301 individuals). The strongest single SNP result (rs6938431, P=6.72× 10−5) was observed in the case–control sample, near the solute carrier family 22, member 16 gene (SLC22A16). In a replication study, we genotyped 151 SNPs in an independent sample (N = 622 cases, 1,181 controls) and observed further evidence of association between variants at SLC22A16 and BPI. Although consistent evidence of association with any single variant was not seen across samples, SNP-wise and gene-based test results in the three samples provided convergent evidence for association with SLC22A16, a carnitine transporter, implicating this gene as a novel candidate for BPI risk. Further studies in larger samples are warranted to clarify which, if any, genes in the 6q region confer risk for bipolar disorder.
bipolar disorder; genetic; association; SLC22A16; 6q
The revolution in next-generation sequencing has made obtaining both common and rare high-quality sequence variants across the entire genome feasible. Because researchers are now faced with the analytical challenges of handling a massive amount of genetic variant information from sequencing studies, numerous methods have been developed to assess the impact of both common and rare variants on disease traits. In this report, whole genome sequencing data from Genetic Analysis Workshop 18 was used to compare the power of several methods, considering both family-based and population-based designs, to detect association with variants in the MAP4 gene region and on chromosome 3 with blood pressure. To prioritize variants across the genome for testing, variants were first functionally assessed using prediction algorithms and expression quantitative trait loci (eQTLs) data. Four set-based tests in the family-based association tests (FBAT) framework--FBAT-v, FBAT-lmm, FBAT-m, and FBAT-l--were used to analyze 20 pedigrees, and 2 variance component tests, sequence kernel association test (SKAT) and genome-wide complex trait analysis (GCTA), were used with 142 unrelated individuals in the sample. Both set-based and variance-component-based tests had high power and an adequate type I error rate. Of the various FBATs, FBAT-l demonstrated superior performance, indicating the potential for it to be used in rare-variant analysis. The updated FBAT package is available at: http://www.hsph.harvard.edu/fbat/.
Anorexia nervosa and bulimia nervosa (BN) are rare, but eating disorders not otherwise specified (EDNOS) are relatively common among female participants. Our objective was to evaluate whether BN and subtypes of EDNOS are predictive of developing adverse outcomes.
This study comprised a prospective analysis of 8594 female participants from the ongoing Growing Up Today Study. Questionnaires were sent annually from 1996 through 2001, then biennially through 2007 and 2008. Participants who were 9 to 15 years of age in 1996 and completed at least 2 consecutive questionnaires between 1996 and 2008 were included in the analyses. Participants were classified as having BN (≥weekly binge eating and purging), binge eating disorder (BED; ≥weekly binge eating, infrequent purging), purging disorder (PD; ≥weekly purging, infrequent binge eating), other EDNOS (binge eating and/or purging monthly), or nondisordered.
BN affected ∼1% of adolescent girls; 2% to 3% had PD and another 2% to 3% had BED. Girls with BED were almost twice as likely as their nondisordered peers to become overweight or obese (odds ratio [OR]: 1.9 [95% confidence interval: 1.0–3.5]) or develop high depressive symptoms (OR: 2.3 [95% confidence interval: 1.0–5.0]). Female participants with PD had a significantly increased risk of starting to use drugs (OR: 1.7) and starting to binge drink frequently (OR: 1.8).
PD and BED are common and predict a range of adverse outcomes. Primary care clinicians should be made aware of these disorders, which may be underrepresented in eating disorder clinic samples. Efforts to prevent eating disorders should focus on cases of subthreshold severity.
adolescents; eating disorders; epidemiology; obesity; substance use
IL10 is an anti-inflammatory cytokine that has been found to have lower production in macrophages and mononuclear cells from asthmatics. Since reduced IL10 levels may influence the severity of asthma phenotypes, we examined IL10 single-nucleotide polymorphisms (SNPs) for association with asthma severity and allergy phenotypes as quantitative traits. Utilizing DNA samples from 518 Caucasian asthmatic children from the Childhood Asthma Management Program (CAMP) and their parents, we genotyped six IL10 SNPs: 3 in the promoter, 2 in introns, and one in the 3′ UTR. Using family-based association tests, each SNP was tested for association with asthma and allergy phenotypes individually. Population-based association analysis was performed with each SNP locus, the promoter haplotypes and the 6-loci haplotypes. The 3′ UTR SNP was significantly associated with FEV1 as a percent of predicted (FEV1PP) (P=0.0002) in both the family and population analyses. The promoter haplotype GCC was positively associated with IgE levels and FEV1PP (P=0.007 and 0.012, respectively). The promoter haplotype ATA was negatively associated with lnPC20 and FEV1PP (P=0.008 and 0.043, respectively). Polymorphisms in IL10 are associated with asthma phenotypes in this cohort. Further studies of variation in the IL10 gene may help elucidate the mechanism of asthma development in children.
interleukin 10 (IL10); single nucleotide polymorphism (SNP); genetic association; family-based association test (FBAT); haplotype; promoter; 3′; untranslated region (3′UTR)
Many Genome-Wide Association Studies (GWAS) have signals with unknown etiology. This paper addresses the question — is such an association signal caused by rare or common variants that lead to increased disease risk? For a genomic region implicated by a GWAS, we use Single Nucleotide Polymorphism (SNP) data in a case-control setting to predict how many common or rare variants there are, using a Bayesian analysis. Our objective is to compute posterior probabilities for configurations of rare and/or common variants. We use an extension of coalescent trees — the Ancestral Recombination Graphs (ARG) — to model the genealogical history of the samples based on marker data. As we expect SNPs to be in Linkage Disequilibrium (LD) with common disease variants, we can expect the trees to reflect on the type of variants. To demonstrate the application, we apply our method to candidate gene sequencing data from a German case-control study on nonsyndromic cleft lip with or without cleft palate (NSCL/P).
Coalescent Tree; Genetic Association; Rare Variant; Common Variant; Ancestral Recombination Graphs; Bayesian Modeling
To identify predictors of becoming eating disordered among adolescents.
Prospective cohort study.
Girls (n=6916) and boys (n=5618), aged 9 to 15 years at baseline, in the ongoing Growing Up Today Study (GUTS).
Parent, peer, and media influences.
Main Outcome Measures
Onset of starting to binge eat or purge (ie, vomiting or using laxatives) at least weekly.
During 7 years of follow-up, 4.3% of female subjects and 2.3% of male subjects (hereafter referred to as “females” and “males”) started to binge eat and 5.3% of females and 0.8% of males started to purge to control their weight. Few participants started to both binge eat and purge. Rates and risk factors varied by sex and age group (<14 vs ≥14 years). Females younger than 14 years whose mothers had a history of an eating disorder were nearly 3 times more likely than their peers to start purging at least weekly (odds ratio, 2.8; 95% confidence interval, 1.3–5.9); however, maternal history of an eating disorder was unrelated to risk of starting to binge eat or purge in older adolescent females. Frequent dieting and trying to look like persons in the media were independent predictors of binge eating in females of all ages. In males, negative comments about weight by fathers was predictive of starting to binge at least weekly.
Risk factors for the development of binge eating and purging differ by sex and by age group in females. Maternal history of an eating disorder is a risk factor only in younger adolescent females.
For genetic association studies in designs of unrelated individuals, current statistical methodology typically models the phenotype of interest as a function of the genotype and assumes a known statistical model for the phenotype. In the analysis of complex phenotypes, especially in the presence of ascertainment conditions, the specification of such model assumptions is not straight-forward and is error-prone, potentially causing misleading results.
In this paper, we propose an alternative approach that treats the genotype as the random variable and conditions upon the phenotype. Thereby, the validity of the approach does not depend on the correctness of assumptions about the phenotypic model. Misspecification of the phenotypic model may lead to reduced statistical power. Theoretical derivations and simulation studies demonstrate both the validity and the advantages of the approach over existing methodology. In the COPDGene study (a GWAS for Chronic Obstructive Pulmonary Disease (COPD)), we apply the approach to a secondary, quantitative phenotype, the Fagerstrom nicotine dependence score, that is correlated with COPD affection status. The software package that implements this method is available.
The flexibility of this approach enables the straight-forward application to quantitative phenotypes and binary traits in ascertained and unascertained samples. In addition to its robustness features, our method provides the platform for the construction of complex statistical models for longitudinal data, multivariate data, multi-marker tests, rare-variant analysis, and others.
Genetic associations studies; Secondary phenotypes; Case-control; Ascertainment; Semi-parametric
We previously reported that asthmatic children with GSTM1 null genotype may be more susceptible to the acute effect of ozone on the small airways and might benefit from antioxidant supplementation. This study aims to assess the acute effect of ozone on lung function (FEF25-75) in asthmatic children according to dietary intake of vitamin C and the number of putative risk alleles in three antioxidant genes: GSTM1, GSTP1 (rs1695), and NQO1 (rs1800566).
257 asthmatic children from two cohort studies conducted in Mexico City were included. Stratified linear mixed models with random intercepts and random slopes on ozone were used. Potential confounding by ethnicity was assessed. Analyses were conducted under single gene and genotype score approaches.
The change in FEF25-75 per interquartile range (60 ppb) of ozone in persistent asthmatic children with low vitamin C intake and GSTM1 null was −91.2 ml/s (p = 0.06). Persistent asthmatic children with 4 to 6 risk alleles and low vitamin C intake showed an average decrement in FEF25-75 of 97.2 ml/s per 60 ppb of ozone (p = 0.03). In contrast in children with 1 to 3 risk alleles, acute effects of ozone on FEF25-75 did not differ by vitamin C intake.
Our results provide further evidence that asthmatic children predicted to have compromised antioxidant defense by virtue of genetic susceptibility combined with deficient antioxidant intake may be at increased risk of adverse effects of ozone on pulmonary function.
Air pollution; Asthmatic children; Antioxidant genes; Mexico City; Vitamin C
Genome-wide association studies have been able to identify disease associations with many common variants; however most of the estimated genetic contribution explained by these variants appears to be very modest. Rare variants are thought to have larger effect sizes compared to common SNPs but effects of rare variants cannot be tested in the GWAS setting. Here we propose a novel method to test for association of rare variants obtained by sequencing in family-based samples by collapsing the standard family-based association test (FBAT) statistic over a region of interest. We also propose a suitable weighting scheme so that low frequency SNPs that may be enriched in functional variants can be upweighted compared to common variants. Using simulations we show that the family-based methods perform at par with the population-based methods under no population stratification. By construction, family-based tests are completely robust to population stratification; we show that our proposed methods remain valid even when population stratification is present.
Two recent metaanalyses of genome-wide association studies conducted by the CHARGE and SpiroMeta consortia identified novel loci yielding evidence of association at or near genome-wide significance (GWS) with FEV1 and FEV1/FVC. We hypothesized that a subset of these markers would also be associated with chronic obstructive pulmonary disease (COPD) susceptibility. Thirty-two single-nucleotide polymorphisms (SNPs) in or near 17 genes in 11 previously identified GWS spirometric genomic regions were tested for association with COPD status in four COPD case-control study samples (NETT/NAS, the Norway case-control study, ECLIPSE, and the first 1,000 subjects in COPDGene; total sample size, 3,456 cases and 1,906 controls). In addition to testing the 32 spirometric GWS SNPs, we tested a dense panel of imputed HapMap2 SNP markers from the 17 genes located near the 32 GWS SNPs and in a set of 21 well studied COPD candidate genes. Of the previously identified GWS spirometric genomic regions, three loci harbored SNPs associated with COPD susceptibility at a 5% false discovery rate: the 4q24 locus including FLJ20184/INTS12/GSTCD/NPNT, the 6p21 locus including AGER and PPT2, and the 5q33 locus including ADAM19. In conclusion, markers previously associated at or near GWS with spirometric measures were tested for association with COPD status in data from four COPD case-control studies, and three loci showed evidence of association with COPD susceptibility at a 5% false discovery rate.
It is useful to have robust gene-environment interaction tests that can utilize a variety of family structures in an efficient way. This paper 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.
Gene-Environment Interaction; Family-Based Association Tests; Candidate Gene Analysis; Binary Trait; COPD; Serpine2
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.
Published studies suggest associations between circadian gene polymorphisms and bipolar I disorder (BPI), as well as schizoaffective disorder (SZA) and schizophrenia (SZ). The results are plausible, based on prior studies of circadian abnormalities. As replications have not been attempted uniformly, we evaluated representative, common polymorphisms in all three disorders.
We assayed 276 publicly available ‘tag’ single nucleotide polymorphisms (SNPs) at 21 circadian genes among 523 patients with BPI, 527 patients with SZ/SZA, and 477 screened adult controls. Detected associations were evaluated in relation to two published genome-wide association studies (GWAS).
Using gene-based tests, suggestive associations were noted between EGR3 and BPI (p = 0.017), and between NPAS2 and SZ/SZA (p = 0.034). Three SNPs were associated with both sets of disorders (NPAS2: rs13025524 and rs11123857; RORB: rs10491929; p < 0.05). None of the associations remained significant following corrections for multiple comparisons. Approximately 15% of the analyzed SNPs overlapped with an independent study that conducted GWAS for BPI; suggestive overlap between the GWAS analyses and ours was noted at ARNTL.
Several suggestive, novel associations were detected with circadian genes and BPI and SZ/SZA, but the present analyses do not support associations with common polymorphisms that confer risk with odds ratios greater than 1.5. Additional analyses using adequately powered samples are warranted to further evaluate these results.
association; bipolar disorder; circadian; gene; schizoaffective disorder; schizophrenia
In clinical trials multiple outcomes are often used to assess treatment interventions. This paper presents an evaluation of likelihood-based methods for jointly testing treatment effects in clinical trials with multiple continuous outcomes. Specifically, we compare the power of joint tests of treatment effects obtained from joint models for the multiple outcomes with univariate tests based on modelling the outcomes separately. We also consider the power and bias of tests when data are missing, a common feature of many trials, especially in psychiatry. Our results suggest that joint tests capitalize on the correlation of multiple outcomes and are more powerful than standard univariate methods, especially when outcomes are missing completely at random. When outcomes are missing at random, test procedures based on correctly specified joint models are unbiased, while standard univariate procedures are not. Results of a simulation study are reported, and the methods are illustrated in an example from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) for schizophrenia.
joint tests; multiple outcomes; power; missing data; psychiatry
Investigators sometimes use information about a given variable obtained from multiple informants. We focus on estimating the effect of a predictor on a continuous outcome, when that predictor cannot be observed directly but is measured by two informants. We describe various approaches to using information from two informants to estimate a regression or correlation coefficient for the effect of the (true) predictor on the outcome. These approaches include methods we refer to as single informant, simple average, optimal weighted average, principal components analysis, and classical measurement error. Each of these five methods effectively uses a weighted average of the informants' reports as a proxy for the true predictor in calculating the correlation or regression coefficient. We compare the performance of these methods in simulation experiments that assume a rounded congeneric measurement model for the relationship between the informants' reports and a true predictor that is a mixture of zeros and positively-distributed continuous values. We also compare the methods' performance in a real data example -the relationship between vigorous physical activity (the predictor) and body mass index (the continuous outcome). The results of the simulations and the example suggest that the simple average is a reasonable choice when there are only two informants.
Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the workshop.
Linkage- and association-based methods have been proposed for mapping disease-causing rare variants. Based on the family information provided in the Genetic Analysis Workshop 17 data set, we formulate a two-pronged approach that combines both methods. Using the identity-by-descent information provided for eight extended pedigrees (n = 697) and the simulated quantitative trait Q1, we explore various traditional nonparametric linkage analysis methods; the best result is obtained by assuming between-family heterogeneity and applying the Haseman-Elston regression to each pedigree separately. We discover strong signals from two genes in two different families and weaker signals for a third gene from two other families. As an exploratory approach, we apply an association test based on a modified family-based association test statistic to all rare variants (frequency < 1% or < 3%) designated as causal for Q1. Family-based association tests correctly identified causal single-nucleotide polymorphisms for four genes (KDR, VEGFA, VEGFC, and FLT1). Our results suggest that both linkage and association tests with families show promise for identifying rare variants.
Recent advances in next-generation sequencing technologies have made it possible to generate large amounts of sequence data with rare variants in a cost-effective way. Statistical methods that test variants individually are underpowered to detect rare variants, so it is desirable to perform association analysis of rare variants by combining the information from all variants. In this study, we use a Bayesian regression method to model all variants simultaneously to identify rare variants in a data set from Genetic Analysis Workshop 17. We studied the association between the quantitative risk traits Q1, Q2, and Q4 and the single-nucleotide polymorphisms and identified several positive single-nucleotide polymorphisms for traits Q1 and Q2. However, the model also generated several apparent false positives and missed many true positives, suggesting that there is room for improvement in this model.
In this article, we propose and explore a multivariate logistic regression model for analyzing multiple binary outcomes with incomplete covariate data where auxiliary information is available. The auxiliary data are extraneous to the regression model of interest but predictive of the covariate with missing data. describe how the auxiliary information can be incorporated into a regression model for a single binary outcome with missing covariates, and hence the efficiency of the regression estimators can be improved. We consider extending the method of Horton and Laird (2001) to the case of a multivariate logistic regression model for multiple correlated outcomes, and with missing covariates and completely observed auxiliary information. We demonstrate that in the case of moderate to strong associations among the multiple outcomes, one can achieve considerable gains in efficiency from estimators in a multivariate model as compared to the marginal estimators of the same parameters.
Asymptotic relative efficiency; Auxiliary information; Incomplete data; Logistic regression model; Missing covariates; Multiple outcomes
The recent emergence of massively parallel sequencing technologies has enabled an increasing number of human genome re-sequencing studies, notable among them being the 1000 Genomes Project. The main aim of these studies is to identify the yet unknown genetic variants in a genomic region, mostly low frequency variants (frequency less than 5%). We propose here a set of statistical tools that address how to optimally design such studies in order to increase the number of genetic variants we expect to discover. Within this framework, the tradeoff between lower coverage for more individuals and higher coverage for fewer individuals can be naturally solved.
The methods here are also useful for estimating the number of genetic variants missed in a discovery study performed at low coverage.
We show applications to simulated data based on coalescent models and to sequence data from the ENCODE project. In particular, we show the extent to which combining data from multiple populations in a discovery study may increase the number of genetic variants identified relative to studies on single populations.
species problem; variant discovery studies; sequencing technologies
R 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.
GUI; interface; fgui
When testing for genetic effects, failure to account for a gene-environment interaction can mask the true association effects of a genetic marker with disease. Family-based association tests are popular because they are completely robust to population substructure and model misspecification. However, when testing for an interaction, failure to model the main genetic effect correctly can lead to spurious results. Here we propose a family-based test for interaction that is robust to model misspecification, but still sensitive to an interaction effect, and can handle continuous covariates and missing parents. We extend the FBAT-I gene-environment interaction test for dichotomous traits to using both trios and sibships. We then compare this extension to joint tests of gene and gene-environment interaction, and compare the joint test additionally to the main effects test of the gene. Lastly we apply these three tests to a group of nuclear families ascertained according to affection with Bipolar Disorder.
genetic association; genetic interaction; family-based test; FBAT-I