Schedule-Induced Polydipsia:Searching for the Endophenotype of Compulsive Behavior

The development of excessive and persistent drinking under intermittent food-reinforcement schedules, known Schedule-induced polydipsia (SIP), has been proposed as a successful animal model to study compulsive behaviors. On the last decade, we have been working in our laboratory on the stratification of the compulsive rats on SIP in order to know whether differences in the acquisition of compulsive drinking behavior could predict alterations in other behavioral measures as well as in the neurochemical function typically associated with compulsive spectrum disorders. The aim of this review is to collate the main findings relevant to the characterization and use of the high compulsive drinking rats (HD) in SIP as a possible compulsive endophenotype. The review of the genetic, behavioral and neurochemical differences found in the selection allows us to conclude that HD rats could be a valid model for studying the compulsive phenotype and modelling psychopathology common to a variety of compulsivity spectrum disorders such as obsessivecompulsive disorder (OCD), schizophrenia and alcohol abuse.

The norepinephrine transporter gene is associated with the retardation symptoms of major depressive disorder in the Han Chinese population

The norepinephrine transporter plays an important role in the pathophysiology and pharmacological treatment of major depressive disorder. Consequently, the norepinephrine transporter gene is an attractive candidate in major depressive disorder research. In the present study, we evaluated the depression symptoms of subjects with major depressive disorder, who were all from the North of China and of Hart Chinese origin, using the Hamilton Depression Scale. We examined the relationship between two single nucleotide polymorphisms in the norepinephrine transporter, rs2242446 and rs5569, and the retardation symptoms of major depressive disorder using quantitative trait testing with the UNPHASED program, rs5569 was associated with depressed mood, and the GG genotype may be a risk factor for this; rs2242446 was associated with work and interest, and the TT genotype may be a risk factor for loss of interest. Our findings suggest that rs2242446 and rs5569 in the norepinephrine transporter gene are associated with the retardation symptoms of depression in the Hart Chinese population.

New findings in the genetics of schizophrenia

New findings in schizophrenia genetics are based on genome-wide association studies(GWAS), research into DNA copy number variations(CNVs), and endophenotypes. More than 70 genes have recently been suspected to be involved in the genetic background of schizophrenia based on the GWAS′s results. They are typically related to neurodevelopment/neuroplasticity, immunology and neuroendocrinology. Nevertheless, for many detected genes their possible relationship to schizophrenia etiopathogenesis is still unknown. The CNVs at genome loci 1q21.1(candidate gene e.g., PRKAB2), 2p16.3(candidate gene e.g., NRXN1), 3q29(candidate genes e.g., BDH1, DLG1, PAK2 or TFRC), 15q11.2(candidate gene e.g., CYFIP1), 15q13.3(candidate gene e.g., CHRNA7), 16p13.1(candidate genes e.g.,NTAN1 or NDE1) and 22q11.2(candidate genes e.g., COMT, GSTT2 or PRODH) were associated with schizophrenia most frequently. Genetic research of schizophrenia endophenotypes, usually neurophysiological, neuromotoric, neurocognitive, neuroanatomical, neurological orpersonality-related, will help us to discover the role of relevant genes in the pathogenesis of schizophrenia. It is also necessary to integrate knowledge from other research platforms in schizophrenia, like epigenetics, studies of gene-environment interactions, transcriptomics, proteomics, metabolomics, neuroimaging and psychopathology. A better knowledge of the genetic background of schizophrenia can lead to changes in the treatment, prevention and genetic counselling. It may also reduce stigma in this severe mental disorder.

Translating Interdisciplinary Research on Language Learning into Identifying Specific Learning Disabilities in Verbally Gifted and Average Children and Youth

The current research was grounded in prior interdisciplinary research that showed cognitive ability (verbal ability for translating cognitions into oral language) and multiple-working memory endophenotypes (behavioral markers of genetic or brain bases of language learning) predict reading and writing achievement in students with and without specific learning disabilities in written language (SLDs-WL). Results largely replicated prior findings that verbally gifted with dyslexia score higher on reading and writing achievement than those with average verbal ability but not on endophenotypes. The current study extended that research by comparing those with and without SLDs-WL with assessed verbal ability held constant. The verbally gifted without SLDs-WL (n = 14) scored higher than the verbally gifted with SLDs-WL (n = 27) on six language skills (oral sentence construction, best and fastest handwriting in copying, single real word oral reading accuracy, oral pseudoword reading accuracy and rate) and four endophenotypes (orthographic and morphological coding, orthographic loop, and switching attention). The verbally average without SLDs-WL (n = 6) scored higher than the verbally average with SLDs-WL (n = 22) on four language skills (best and fastest hand-writing in copying, oral pseudoword reading accuracy and rate) and two endophenotypes (orthographic coding and orthographic loop). Implications of results for translating interdisciplinary research into flexible definitions for assessment and instruction to serve students with varying verbal abilities and language learning and endophenotype profiles are discussed along with directions for future research.

inMTSCCA:An Integrated Multi-task Sparse Canonical Correlation Analysis for Multi-omic Brain Imaging Genetics

Identifying genetic risk factors for Alzheimer's disease(AD)is an important research topic.To date,different endophenotypes,such as imaging-derived endophenotypes and proteomic expression-derived endophenotypes,have shown the great value in uncovering risk genes compared to case-control studies.Biologically,a co-varying pattern of different omics-derived endophenotypes could result from the shared genetic basis.However,existing methods mainly focus on the effect of endophenotypes alone;the effect of cross-endophenotype(CEP)associations remains largely unexploited.In this study,we used both endophenotypes and their CEP associations of multi-omic data to identify genetic risk factors,and proposed two integrated multi-task sparse canonical correlation analysis(inMTSCCA)methods,i.e.,pairwise endophenotype correlationguided MTSCCA(pcMTSCCA)and high-order endophenotype correlation-guided MTSCCA(hocMTSCCA).pcMTSCCA employed pairwise correlations between magnetic resonance imaging(MRI)-derived,plasma-derived,and cerebrospinal fluid(CSF)-derived endophenotypes as an additional penalty.hocMTSCCA used high-order correlations among these multi-omic data for regularization.To figure out genetic risk factors at individual and group levels,as well as altered endophenotypic markers,we introduced sparsity-inducing penalties for both models.We compared pcMTSCCA and hocMTSCCA with three related methods on both simulation and real(consisting of neuroimaging data,proteomic analytes,and genetic data)datasets.The results showed that our methods obtained better or comparable canonical correlation coefficients(CCCs)and better feature subsets than benchmarks.Most importantly,the identified genetic loci and heterogeneous endophenotypic markers showed high relevance.Therefore,jointly using multi-omic endophenotypes and their CEP associations is promising to reveal genetic risk factors.