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

A logical relationship for schizophrenia, bipolar, and major depressive disorder. Part 1: Evidence from chromosome 1 high density association screen

Familial clustering of schizophrenia (SCZ), bipolar disorder (BPD), and major depressive disorder (MDD) was investigated systematically (Aukes et al., Genetics in Medicine, 2012, 14, 338-341) and any two or even three of these disorders could coexist in some families. Furthermore, evidence from symptomatology and psychopharmacology also imply the existence of intrinsic connections between these three major psychiatric disorders. A total of 71,445 SNPs on chromosome 1 were genotyped on 119 SCZ, 253 BPD (type-I), 177 MDD cases and 1000 controls and further validated in 986 SCZ patients in the population of Shandong province of China. Outstanding psychosis genes are systematically revealed( ATP1A4, ELTD1, FAM5C, HHAT, KIF26B, LMX1A, NEGR1, NFIA, NR5A2, NTNG1, PAPPA2, PDE4B, PEX14, RYR2, SYT6, TGFBR3, TTLL7, and USH2A). Unexpectedly, flanking genes for up to 97.09% of the associated SNPs were also replicated in an enlarged cohort of 986 SCZ patients. From the perspective of etiological rather than clinical psychiatry, bipolar, and major depressive disorder could be subtypes of schizophrenia. Meanwhile, the varied clinical feature and prognosis might be the result of interaction of genetics and epigenetics, for example, irreversible or reversible shut down, and over or insufficient expression of certain genes, which may gives other aspects of these severe mental disorders.

Comorbidity classes and associated impairment, demographics and 9/11-exposures in 8,236 children and adolescents

The extensive comorbidity of psychiatric disorders in children and adolescents leads to clinical heterogeneity, and is an often-overlooked issue in etiopathogenic and treatment studies in developmental psychopathology. In a representative sample (N=8236) of New York City public school students assessed six months after 9/11, latent class analysis was applied to 48 symptoms across seven disorders: posttraumatic stress, agoraphobia, separation anxiety, panic disorder, generalized anxiety (GAD), major depression (MDD) and conduct disorder (CD). Our objective was to identify classes defined by homogenous symptom profiles, and to examine the association between class membership and gender, age, race, different types of exposure to 9/11, and impairment. Eight homogenous comorbidity patterns were identified, including four severe disturbance classes: a multimorbid internalizing class (INT), a class with a high probability of CD, MDD, and GAD symptoms (Distress/EXT), a non-comorbid externalizing class, and a non-comorbid MDD class. Demographic and 9/11-related exposures showed some degree of specificity in their association with severe symptom profiles. Impairment was particularly high in the INT and Distress/EXT classes. A better characterization of phenomic data, that takes comorbidity into account, is essential to understand etiopathogenic processes, and to move psychiatric research forward towards personalized medicine. The high probability of endorsing symptoms of multiple disorders in the INT and Distress/EXT classes supports the use of treatments focusing on multimorbidity. Clinical trials should evaluate the effectiveness of disorder-specific versus transdiagnostic interventions. The association between class membership and demographic and exposure variables suggests that interventions may be improved by considering specific predictors of class membership.

Receptiveness to participation in genetic research: A pilot study comparing views of people with depression, diabetes, or no illness

BACKGROUND

Genetic research in human health relies on the participation of individuals with or at-risk for different types of diseases, including health conditions that may be stigmatized, such as mental illnesses. This preliminary study examines the differences in attitudes toward participation in genetic research among individuals with a psychiatric disorder, individuals with a physical disorder, and individuals with no known illness.

METHODS

Seventy-nine individuals with a history of diabetes or depression, or no known illness, underwent a simulated consent process for a hypothetical genetic research study. They were then surveyed about their willingness to participate in the hypothetical study and their attitudes about future and family participation in genetic research.

RESULTS

Participants with and without a history of depression ranked participating in genetic and medical research as very important and indicated that they were likely to participate in the hypothetical genetics study. Expressed willingness to participate was generally stable and consistent with future willingness. Individuals less strongly endorsed willingness to ask family members to participate in genetic research.

CONCLUSION

Individuals with and without a history of mental illness viewed genetic and medical research favorably and expressed willingness to participate in real-time and in the future. Informed consent processes ideally include an exploration of influences upon volunteers' enrollment decisions. Additional empirical study of influences upon genetic research participation is important to ensure that volunteers' rights are respected and that conditions that greatly affect the health of the public are not neglected scientifically.

Evaluation of the relationship between the ZNF804A single nucleotide polymorphism rs1344706 A/C variant and schizophrenia subtype in Han Chinese patients

OBJECTIVE

Recent genome wide association studies (GWASs) assessing the relationship between schizophrenia (SZ) and the ZNF804A gene, particularly the single nucleotide polymorphism (SNP) rs1344706, have yielded conflicting results. Schizophrenia is a heterogeneous disorder, so it is possible that an association may be restricted to specific SZ subtypes and that population heterogeneity may obscure a contribution of ZNF804A allelic variation to SZ risk. We thus evaluated the association between rs1344706 and different clinical SZ subtypes in a large Han Chinese patient population.

METHOD

The rs1344706 genotype was determined in 1,025 SZ patients and 977 healthy controls using polymerase chain reaction restriction fragment length polymorphisms (PCR-RFLPs). The clinical SZ subtypes included paranoid, catatonic, disintegrated, and undifferentiated, diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition IV (DSM-IV).

RESULTS

No significant differences in genotype and allele frequencies were found between controls and either the total SZ population (A > C, chi2 = 4.339, 2.994; p = 0.227, 0.087, respectively) or paranoid SZ patients (chi2 = 2.053, 0.002; p = 0.562, 0.973, respectively). However, there was a significant association between genotype frequency and SZ subtype (chi2 = 12.632, p = 0.049).

CONCLUSIONS

We found no evidence that the ZNF804A SNP rs1344706 is a susceptibility locus for SZ. However, conflicting results from previous association studies may be due to genetic heterogeneity between different patient SZ subtypes.

TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies

To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype–phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests based on composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype–phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor.

The genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS methods are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score, which frequently results in a considerable loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. We present a new multivariate method called TATES (Trait-based Association Test that uses Extended Simes procedure). Extensive simulations show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests of composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES uncovers both genetic variants that are common to multiple phenotypes as well as phenotype specific variants. TATES thus provides a more complete view of the genetic architecture of complex traits and constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants.

A latent modeling approach to genotype-phenotype relationships: maternal problem behavior clusters, prenatal smoking, and MAOA genotype

This study illustrates the application of a latent modeling approach to genotype-phenotype relationships and gene × environment interactions, using a novel, multidimensional model of adult female problem behavior, including maternal prenatal smoking. The gene of interest is the monoamine oxidase A (MAOA) gene which has been well studied in relation to antisocial behavior. Participants were adult women (N = 192) who were sampled from a prospective pregnancy cohort of non-Hispanic, white individuals recruited from a neighborhood health clinic. Structural equation modeling was used to model a female problem behavior phenotype, which included conduct problems, substance use, impulsive-sensation seeking, interpersonal aggression, and prenatal smoking. All of the female problem behavior dimensions clustered together strongly, with the exception of prenatal smoking. A main effect of MAOA genotype and a MAOA × physical maltreatment interaction were detected with the Conduct Problems factor. Our phenotypic model showed that prenatal smoking is not simply a marker of other maternal problem behaviors. The risk variant in the MAOA main effect and interaction analyses was the high activity MAOA genotype, which is discrepant from consensus findings in male samples. This result contributes to an emerging literature on sex-specific interaction effects for MAOA.

Translational bioinformatics in psychoneuroimmunology: methods and applications

Translational bioinformatics plays an indispensable role in transforming psychoneuroimmunology (PNI) into personalized medicine. It provides a powerful method to bridge the gaps between various knowledge domains in PNI and systems biology. Translational bioinformatics methods at various systems levels can facilitate pattern recognition, and expedite and validate the discovery of systemic biomarkers to allow their incorporation into clinical trials and outcome assessments. Analysis of the correlations between genotypes and phenotypes including the behavioral-based profiles will contribute to the transition from the disease-based medicine to human-centered medicine. Translational bioinformatics would also enable the establishment of predictive models for patient responses to diseases, vaccines, and drugs. In PNI research, the development of systems biology models such as those of the neurons would play a critical role. Methods based on data integration, data mining, and knowledge representation are essential elements in building health information systems such as electronic health records and computerized decision support systems. Data integration of genes, pathophysiology, and behaviors are needed for a broad range of PNI studies. Knowledge discovery approaches such as network-based systems biology methods are valuable in studying the cross-talks among pathways in various brain regions involved in disorders such as Alzheimer's disease.

Genomics for disease treatment and prevention

The enormous advances in genetics and genomics of the past decade have the potential to revolutionize health care, including mental health care, and bring about a system predominantly characterized by the practice of genomic and personalized medicine. This article briefly reviews the history of genetics and genomics and assesses the extent to which the results of genetic and genomic studies are currently being leveraged clinically for disease treatment and prevention. Relevant social, economic, and policy issues relevant to genomic medicine are also reviewed, and priority research areas in which further work is needed are identified.

Neurocognitive phenotypes and genetic dissection of disorders of brain and behavior

Elucidating the molecular mechanisms underlying quantitative neurocognitive phenotypes will further our understanding of the brain's structural and functional architecture and advance the diagnosis and treatment of the psychiatric disorders that these traits underlie. Although many neurocognitive traits are highly heritable, little progress has been made in identifying genetic variants unequivocally associated with these phenotypes. A major obstacle to such progress is the difficulty in identifying heritable neurocognitive measures that are precisely defined and systematically assessed and represent unambiguous mental constructs, yet are also amenable to the high-throughput phenotyping necessary to obtain adequate power for genetic association studies. In this perspective we compare the current status of genetic investigations of neurocognitive phenotypes to that of other categories of biomedically relevant traits and suggest strategies for genetically dissecting traits that may underlie disorders of brain and behavior.