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Human behavioral informatics in genetic studies of neuropsychiatric disease: multivariate profile-based analysis.

ABSTRACT: While genome-wide association (GWA) studies have yielded notable findings with regard to the identification of risk variants in diseases such as obesity and diabetes, similar studies of schizophrenia - and neuropsychiatric diseases in general - have failed to produce strong findings. One, plausible explanation for this relates to phenotypic heterogeneity and what may be inherent imprecision associated with diagnostic categories in neuropsychiatric disorders. In this review we discuss a general approach to addressing the problem of heterogeneity that draws on concepts in behavioral informatics and the use of multivariable behavioral profiles in genetic studies of neuropsychiatric disease. The use of behavioral profiles as phenotypes eliminates the need for categorizing individuals with different 'subtypes' of a disease into one group and provides a way to investigate genetic susceptibility to different neuropsychiatric disorders that share similar clinical characteristics, such as schizophrenia and bipolar disorder. Further, behavioral profiles are a direct, quantitative representation of the emotional, personality, and neurocognitive functioning of the individuals being studied, and as such, the use of these profiles may provide increased statistical power to detect genetic associations and linkages. We describe and discuss four general data analysis approaches that can be used to analyze and integrate multivariate behavioral profile data and high-dimensional genomic data. Ultimately, we propose that behavioral profile-based phenotypes provide a meaningful alternative to the use of single measures, such as diagnostic category, in genetic association studies of neuropsychiatric disease.

SUBMITTER: Bloss CS

PROVIDER: S-EPMC2941546 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

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Human behavioral informatics in genetic studies of neuropsychiatric disease: multivariate profile-based analysis.

Bloss Cinnamon S CS Schiabor Kelly M KM Schork Nicholas J NJ

Brain research bulletin 20100428 3-4

While genome-wide association (GWA) studies have yielded notable findings with regard to the identification of risk variants in diseases such as obesity and diabetes, similar studies of schizophrenia - and neuropsychiatric diseases in general - have failed to produce strong findings. One, plausible explanation for this relates to phenotypic heterogeneity and what may be inherent imprecision associated with diagnostic categories in neuropsychiatric disorders. In this review we discuss a general a ...[more]

PMID: 20433907

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Project description:BackgroundThe origin of sex differences in prevalence and presentation of neuropsychiatric and behavioral traits is largely unknown. Given established genetic contributions and correlations, we tested for a sex-differentiated genetic architecture within and between traits.MethodsUsing European ancestry genome-wide association summary statistics for 20 neuropsychiatric and behavioral traits, we tested for sex differences in single nucleotide polymorphism (SNP)-based heritability and genetic correlation (rg < 1). For each trait, we computed per-SNP z scores from sex-stratified regression coefficients and identified genes with sex-differentiated effects using a gene-based approach. We calculated correlation coefficients between z scores to test for shared sex-differentiated effects. Finally, we tested for sex differences in across-trait genetic correlations.ResultsWe observed no consistent sex differences in SNP-based heritability. Between-sex, within-trait genetic correlations were high, although <1 for educational attainment and risk-taking behavior. We identified 4 genes with significant sex-differentiated effects across 3 traits. Several trait pairs shared sex-differentiated effects. The top genes with sex-differentiated effects were enriched for multiple gene sets, including neuron- and synapse-related sets. Most between-trait genetic correlation estimates were not significantly different between sexes, with exceptions (educational attainment and risk-taking behavior).ConclusionsSex differences in the common autosomal genetic architecture of neuropsychiatric and behavioral phenotypes are small and polygenic and unlikely to fully account for observed sex-differentiated attributes. Larger sample sizes are needed to identify sex-differentiated effects for most traits. For well-powered studies, we identified genes with sex-differentiated effects that were enriched for neuron-related and other biological functions. This work motivates further investigation of genetic and environmental influences on sex differences.

Project description:ImportanceBehavioral disturbances are core features of frontotemporal dementia (FTD); however, symptom progression across the course of disease is not well characterized in genetic FTD.ObjectiveTo investigate behavioral symptom frequency and severity and their evolution and progression in different forms of genetic FTD.Design, setting, and participantsThis longitudinal cohort study, the international Genetic FTD Initiative (GENFI), was conducted from January 30, 2012, to May 31, 2019, at 23 multicenter specialist tertiary FTD research clinics in the United Kingdom, the Netherlands, Belgium, France, Spain, Portugal, Italy, Germany, Sweden, Finland, and Canada. Participants included a consecutive sample of 232 symptomatic FTD gene variation carriers comprising 115 with variations in C9orf72, 78 in GRN, and 39 in MAPT. A total of 101 carriers had at least 1 follow-up evaluation (for a total of 400 assessments). Gene variations were included only if considered pathogenetic.Main outcomes and measuresBehavioral and neuropsychiatric symptoms were assessed across disease duration and evaluated from symptom onset. Hierarchical generalized linear mixed models were used to model behavioral and neuropsychiatric measures as a function of disease duration and variation.ResultsOf 232 patients with FTD, 115 (49.6%) had a C9orf72 expansion (median [interquartile range (IQR)] age at evaluation, 64.3 [57.5-69.7] years; 72 men [62.6%]; 115 White patients [100%]), 78 (33.6%) had a GRN variant (median [IQR] age, 63.4 [58.3-68.8] years; 40 women [51.3%]; 77 White patients [98.7%]), and 39 (16.8%) had a MAPT variant (median [IQR] age, 56.3 [49.9-62.4] years; 25 men [64.1%]; 37 White patients [94.9%]). All core behavioral symptoms, including disinhibition, apathy, loss of empathy, perseverative behavior, and hyperorality, were highly expressed in all gene variant carriers (>50% patients), with apathy being one of the most common and severe symptoms throughout the disease course (51.7%-100% of patients). Patients with MAPT variants showed the highest frequency and severity of most behavioral symptoms, particularly disinhibition (79.3%-100% of patients) and compulsive behavior (64.3%-100% of patients), compared with C9orf72 carriers (51.7%-95.8% of patients with disinhibition and 34.5%-75.0% with compulsive behavior) and GRN carriers (38.2%-100% with disinhibition and 20.6%-100% with compulsive behavior). Alongside behavioral symptoms, neuropsychiatric symptoms were very frequently reported in patients with genetic FTD: anxiety and depression were most common in GRN carriers (23.8%-100% of patients) and MAPT carriers (26.1%-77.8% of patients); hallucinations, particularly auditory and visual, were most common in C9orf72 carriers (10.3%-54.5% of patients). Most behavioral and neuropsychiatric symptoms increased in the early-intermediate phases and plateaued in the late stages of disease, except for depression, which steadily declined in C9orf72 carriers, and depression and anxiety, which surged only in the late stages in GRN carriers.Conclusions and relevanceThis cohort study suggests that behavioral and neuropsychiatric disturbances differ between the common FTD gene variants and have different trajectories throughout the course of disease. These findings have crucial implications for counseling patients and caregivers and for the design of disease-modifying treatment trials in genetic FTD.

Project description:In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data in the endpoint are imputed with null effects and quite large variance.

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Human behavioral informatics in genetic studies of neuropsychiatric disease: multivariate profile-based analysis.

Publications

Human behavioral informatics in genetic studies of neuropsychiatric disease: multivariate profile-based analysis.

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