Diagnosis of schizophrenia in latino populations: a comparison of direct interview and consensus based multi-source methods

Genome-wide QTL analysis for anxiety trait in bipolar disorder type I

BACKGROUND

Genetic studies have been consistent that bipolar disorder type I (BPI) runs in families and that this familial aggregation is strongly influenced by genes. In a preliminary study, we proved that anxiety trait meets endophenotype criteria for BPI.

METHODS

We assessed 619 individuals from the Central Valley of Costa Rica (CVCR) who have received evaluation for anxiety following the same methodological procedure used for the initial pilot study. Our goal was to conduct a multipoint quantitative trait linkage analysis to identify quantitative trait loci (QTLs) related to anxiety trait in subjects with BPI. We conducted the statistical analyses using Quantitative Trait Loci method (Variance-components models), implemented in Sequential Oligogenic Linkage Analysis Routines (SOLAR), using 5606 single nucleotide polymorphism (SNPs).

RESULTS

We identified a suggestive linkage signal with a LOD score of 2.01 at chromosome 2 (2q13-q14).

LIMITATIONS

Since confounding factors such as substance abuse, medical illness and medication history were not assessed in our study, these conclusions should be taken as preliminary.

CONCLUSIONS

We conclude that region 2q13-q14 may harbor a candidate gene(s) with an important role in the pathophysiology of BPI and anxiety.

Quantitative genetic analysis of anxiety trait in bipolar disorder

BACKGROUND

Bipolar disorder type I (BPI) affects approximately 1% of the world population. Although genetic influences on bipolar disorder are well established, identification of genes that predispose to the illness has been difficult. Most genetic studies are based on categorical diagnosis. One strategy to overcome this obstacle is the use of quantitative endophenotypes, as has been done for other medical disorders.

METHODS

We studied 619 individuals, 568 participants from 61 extended families and 51 unrelated healthy controls. The sample was 55% female and had a mean age of 43.25 (SD 13.90; range 18-78). Heritability and genetic correlation of the trait scale from the Anxiety State and Trait Inventory (STAI) was computed by using the general linear model (SOLAR package software).

RESULTS

we observed that anxiety trait meets the following criteria for an endophenotype of bipolar disorder type I (BPI): 1) association with BPI (individuals with BPI showed the highest trait score (F = 15.20 [5,24], p = 0.009), 2) state-independence confirmed after conducting a test-retest in 321 subjects, 3) co-segregation within families 4) heritability of 0.70 (SE: 0.060), p = 2.33 × 10^-14 and 5) genetic correlation with BPI was 0.20, (SE = 0.17, p = 3.12 × 10^-5).

LIMITATIONS

Confounding factors such as comorbid disorders and pharmacological treatment could affect the clinical relationship between BPI and anxiety trait. Further research is needed to evaluate if anxiety traits are specially related to BPI in comparison with other traits such as anger, attention or response inhibition deficit, pathological impulsivity or low self-directedness.

CONCLUSIONS

Anxiety trait is a heritable phenotype that follows a normal distribution when measured not only in subjects with BPI but also in unrelated healthy controls. It could be used as an endophenotype in BPI for the identification of genomic regions with susceptibility genes for this disorder.

"Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases

The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the “psychiatric phenotype” is to provide an improved vision of the shape of the phenotype as it is visualized by “Gestalt” psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. Therefore, we propose the term “Gestaltomics” as a term from Systems Biology to integrate data coming from different sources of information (such as the genome, transcriptome, proteome, epigenome, metabolome, phenome, and microbiome). In addition to this biological complexity, the mind is integrated through multiple brain functions that receive and process complex information through channels and perception networks (i.e., sight, ear, smell, memory, and attention) that in turn are programmed by genes and influenced by environmental processes (epigenetic). Today, the approach of medical research in human diseases is to isolate one disease for study; however, the presence of an additional disease (co-morbidity) or more than one disease (multimorbidity) adds complexity to the study of these conditions. This review will present the challenge of integrating psychiatric disorders at different levels of information (Gestaltomics). The implications of increasing the level of complexity, for example, studying the co-morbidity with another disease such as cancer, will also be discussed.

The role of complex emotions in inconsistent diagnoses of schizophrenia

In the case of large-scale epidemiological studies, there is evidence of substantial disagreement when lay diagnoses of schizophrenia based on structured interviews are compared with expert diagnoses of the same patients. Reasons for this level of disagreement are investigated in the current study, which made use of advances in text-mining techniques and associated structural representations of language expressions. Specifically, the current study examined whether content analyses of transcribed diagnostic interviews obtained from 150 persons with serious psychiatric disorders yielded any discernable patterns that correlated with diagnostic inconsistencies of schizophrenia. In summary, it was found that the patterning or structure of spontaneous self-reports of emotion states in the diagnostic interview was associated with diagnostic inconsistencies of schizophrenia, irrespective of confounders; i.e., age of patient, gender, or ethnicity. In particular, complex emotion patterns were associated with greater disagreement between experts and trained lay interviewers than were simpler patterns.

Is subclinical anxiety an endophenotype for bipolar I patients? A study from a Costa Rican sample

BACKGROUND

Although genetic influences on bipolar I disorder are well established, localization of genes that predispose to the illness has been difficult. Some genes predisposing to bipolar I disorder may be transmitted without expression of the categorical clinical phenotype. One strategy to overcome this obstacle is the use of quantitative endophenotypes, as has been done for other medical disorders.

METHODS

We analyzed 30 bipolar I extended families (300 subjects, average family size 10.34 members, range: 2-31) and 20 unrelated healthy controls from a Costa Rican sample. Heritability and genetic correlation of the state and trait scale from the Anxiety State and Trait Inventory was computed by using the general linear model (SOLAR package software). We also assessed variation of both scores among groups (patients, relatives and controls) and tested independence of affection status.

RESULTS

Heritability for state is 0.45 (SE=0.11, p=0.0000001) and for trait is 0.89 (SE=0.06, p=6.22e-29). Genetic correlation for state and trait is 0.29, (SE=0.12, p=0.038-3.19e-8). Bipolar I patients showed the highest trait score (F=12.17 [5,24], p=0.002), (bipolar I patients>relatives with other pathologies, >healthy relatives>unrelated healthy controls) with normal distribution in healthy individuals and no difference regarding depression and mania current status, (F=0.230, df=1, p=0.632 and F=1.401, df=1, p=0.238, respectively), contrary to the state score.

LIMITATIONS

Confounding factors such as comorbid disorders could affect the interaction of subclinical anxiety with mania. Due to our limited budget we were not able to re-evaluate the subjects and conduct a test retest to assess the STAI reliability and mood state independence of anxiety traits over different times. Further research is needed to evaluate if anxiety traits are specially related to bipolar I disorder in comparison with other traits such as anger, attention or response inhibition deficit, pathological impulsivity or low self-directedness.

CONCLUSIONS

Anxiety state and trait are heritable and share some genetic factors but only trait showed normal distribution in healthy subjects, mood current status independence and significant liability for bipolar I disorder. A stair-step distribution of trait anxiety scores in the family members and controls based on their genetic proximity to affected individuals and diagnostic status suggests that trait anxiety could be an endophenotype in these bipolar I families.