Genetic susceptibility to obsessive-compulsive hoarding: the contribution of neurotrophic tyrosine kinase receptor type 3 gene

Recent work suggests that neurotrophic factors may contribute to the genetic susceptibility to obsessive-compulsive disorder (OCD). Among other clinical dimensions, the presence of hoarding obsessions and compulsions has been shown to be correlated with a number of clinical and neuroimaging findings, as well as with a different pattern of genetic inheritance. We used a linkage disequilibrium (LD)-mapping approach to investigate whether neurotrophic tyrosine kinase receptor type 3 (NTRK3), the high-affinity receptor of neurotrophin 3 (NT-3), plays a role in increasing susceptibility to hoarding in OCD. We performed an association study of 52 tag single nucleotide polymorphisms (tagSNPs) covering the whole NTRK3 gene in a sample comprising 120 OCD patients and 342 controls. Single nucleotide polymorphism association and haplotype analysis were performed. Thirty-six of our patients (30%) exhibited significant hoarding obsessions and compulsions. A significant association of two SNPs in the 3' downstream region of NTRK3 gene and obsessive-compulsive hoarding was identified: rs1017412 [odds ratio (OR) = 2.16; P = 0.001] and rs7176429 (OR = 2.78; P = 0.0001), although only the latter remained significant after Bonferroni correction. Although the haplotype analysis did not show significant results, a more extended block of LD in the OCD hoarders with respect to the control group was observed, suggesting a lower haplotype diversity in these individuals. Our findings suggest that NTRK3 may contribute to the genetic susceptibility to hoarding in OCD and may constitute an interesting gene to focus on in studies of the genetic basis of obsessive-compulsive hoarding.     

Bivariate Genome-Wide Association Analysis of the Growth and Intake Components of Feed Efficiency

Single nucleotide polymorphisms (SNPs) associated with average daily gain (ADG) and dry matter intake (DMI), two major components of feed efficiency in cattle, were identified in a genome-wide association study (GWAS). Uni- and multi-SNP models were used to describe feed efficiency in a training data set and the results were confirmed in a validation data set. Results from the univariate and bivariate analyses of ADG and DMI, adjusted by the feedlot beef steer maintenance requirements, were compared. The bivariate uni-SNP analysis identified (P-value <0.0001) 11 SNPs, meanwhile the univariate analyses of ADG and DMI identified 8 and 9 SNPs, respectively. Among the six SNPs confirmed in the validation data set, five SNPs were mapped to KDELC2, PHOX2A, and TMEM40. Findings from the uni-SNP models were used to develop highly accurate predictive multi-SNP models in the training data set. Despite the substantially smaller size of the validation data set, the training multi-SNP models had slightly lower predictive ability when applied to the validation data set. Six Gene Ontology molecular functions related to ion transport activity were enriched (P-value <0.001) among the genes associated with the detected SNPs. The findings from this study demonstrate the complementary value of the uni- and multi-SNP models, and univariate and bivariate GWAS analyses. The identified SNPs can be used for genome-enabled improvement of feed efficiency in feedlot beef cattle, and can aid in the design of empirical studies to further confirm the associations.     

Associations between candidate gene markers at a quantitative trait locus on equine chromosome 4 responsible for osteochondrosis dissecans in fetlock joints of South German Coldblood horses

A previously accomplished whole-genome scan for osteochondrosis (OC) and OC dissecans (OCD) in South German Coldblood horses using 250 microsatellite markers identified putative quantitative trait loci (QTL). A chromosome-wide significant QTL for fetlock OCD was found on Equus caballus chromosome (ECA) 4q at a relative position of 70.0-73.3 cM. The aim of this study was to analyze associations of single nucleotide polymorphisms (SNPs) in candidate genes for OC in this region. The association analysis included 32 affected and 64 unaffected horses. Three SNPs located in intron 8, intron 9, and 3'-untranslated region (UTR) of the acyloxyacyl hydrolase (AOAH) gene on ECA4q were significantly associated with OCD in fetlock joints. In order to control for systematic environmental and quantitative genetic effects, we employed a linear animal model. The association of the SNP (AJ543065:g.703A>G) in the 3'-UTR of exon 21 was confirmed in the animal model analysis and a significant additive genetic effect for fetlock OCD of 0.42 (P = 0.002) and a dominance effect of -0.32 (P = 0.03) was estimated. This is the first report on a marker in population-wide linkage disequilibrium with equine OCD in fetlock joints.     

Beyond missing heritability: prediction of complex traits

Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses of genome-wide association studies. Recently, a large proportion of the "missing heritability" for human height was statistically explained by modeling thousands of single nucleotide polymorphisms concurrently. However, it is currently unclear how gains in explained genetic variance will translate to the prediction of yet-to-be observed phenotypes. Using data from the Framingham Heart Study, we explore the genomic prediction of human height in training and validation samples while varying the statistical approach used, the number of SNPs included in the model, the validation scheme, and the number of subjects used to train the model. In our training datasets, we are able to explain a large proportion of the variation in height (h(2) up to 0.83, R(2) up to 0.96). However, the proportion of variance accounted for in validation samples is much smaller (ranging from 0.15 to 0.36 depending on the degree of familial information used in the training dataset). While such R(2) values vastly exceed what has been previously reported using a reduced number of pre-selected markers (<0.10), given the heritability of the trait (～ 0.80), substantial room for improvement remains.     

COMT and MAO-A Polymorphisms and Obsessive-Compulsive Disorder: A Family-Based Association Study

Objective

Obsessive-compulsive disorder (OCD) is a common and debilitating psychiatric illness. Although a genetic component contributes to its etiology, no single gene or mechanism has been identified to the OCD susceptibility. The catechol-O-methyltransferase (COMT) and monoamine oxidase A (MAO-A) genes have been investigated in previous OCD studies, but the results are still unclear. More recently, Taylor (2013) in a comprehensive meta-analysis of genetic association studies has identified COMT and MAO-A polymorphisms involved with OCD. In an effort to clarify the role of these two genes in OCD vulnerability, a family-based association investigation was performed as an alternative strategy to the classical case-control design.

Methods

Transmission disequilibrium analyses were performed after genotyping 13 single-nucleotide polymorphisms (eight in COMT and five in MAO-A) in 783 OCD trios (probands and their parents). Four different OCD phenotypes (from narrow to broad OCD definitions) and a SNP x SNP epistasis were also analyzed.

Results

OCD, broad and narrow phenotypes,were not associated with any of the investigated COMT and MAO-A polymorphisms. In addition, the analyses of gene-gene interaction did not show significant epistatic influences on phenotype between COMT and MAO-A.

Conclusions

The findings do not support an association between DSM-IV OCD and the variants of COMT or MAO-A. However, results from this study cannot exclude the contribution of these genes in the manifestation of OCD. The evaluation of broader spectrum phenotypes could help to understand the role of these and other genes in the pathophysiology of OCD and its spectrum disorders.     

Genomewide linkage analysis in Costa Rican families implicates chromosome 15q14 as a candidate region for OCD

Obsessive compulsive disorder (OCD) has a complex etiology that encompasses both genetic and environmental factors. However, to date, despite the identification of several promising candidate genes and linkage regions, the genetic causes of OCD are largely unknown. The objective of this study was to conduct linkage studies of childhood-onset OCD, which is thought to have the strongest genetic etiology, in several OCD-affected families from the genetically isolated population of the Central Valley of Costa Rica (CVCR). The authors used parametric and non-parametric approaches to conduct genome-wide linkage analyses using 5,786 single nucleotide repeat polymorphisms (SNPs) in three CVCR families with multiple childhood-onset OCD-affected individuals. We identified areas of suggestive linkage (LOD score ≥ 2) on chromosomes 1p21, 15q14, 16q24, and 17p12. The strongest evidence for linkage was on chromosome 15q14 (LOD = 3.13), identified using parametric linkage analysis with a recessive model, and overlapping a region identified in a prior linkage study using a Caucasian population. Each CVCR family had a haplotype that co-segregated with OCD across a ~7 Mbp interval within this region, which contains 18 identified brain expressed genes, several of which are potentially relevant to OCD. Exonic sequencing of the strongest candidate gene in this region, the ryanodine receptor 3 (RYR3), identified several genetic variants of potential interest, although none co-segregated with OCD in all three families. These findings provide evidence that chromosome 15q14 is linked to OCD in families from the CVCR, and supports previous findings to suggest that this region may contain one or more OCD susceptibility loci.     

Validation of whole genome linkage‐linkage disequilibrium and association results,and identification of markers to predict genetic merit for twinning

A previous genome‐wide search with a moderate density 10K marker set identified many marker associations with twinning rate, either through single‐marker analysis or combined linkage‐linkage disequilibrium (LLD; haplotype) analysis. The objective of the current study was to validate putative marker associations using an independent set of phenotypic data. Holstein bulls (n = 921) from 100 paternal half‐sib families were genotyped. Twinning rate predicted transmitting abilities were calculated using calving records from 1994 to 1998 (Data I) and 1999 to 2006 (Data II), and the underlying liability scores from threshold model analysis were used as the trait in marker association analyses. The previous analysis used 201 bulls with daughter records in Data I. In the current analysis, this was increased to 434, providing a revised estimate of effect and significance. Bulls with daughter records in Data II totaled 851, and analysis of this data provided the validation of results from analysis of Data I. Single nucleotide polymorphisms (SNPs) were selected to validate previously significant single‐marker associations and LLD results. Bulls were genotyped for a total of 306 markers. Nine of 13 LLD regions located on chromosomes 1, 2, 3, 6, 9, 22, 23(2) and 26 were validated, showing significant results for both Data I and II. Association analysis revealed 55 of 174 markers validated, equating to a single‐marker validation rate of 31%. Stepwise backward elimination and cross‐validation analyses identified 18 SNPs for use in a final reduced marker panel explaining 34% of the genetic variation, and to allow prediction of genetic merit for twinning rate.     

Peakwide mapping on chromosome 3q13 identifies the kalirin gene as a novel candidate gene for coronary artery disease

A susceptibility locus for coronary artery disease (CAD) has been mapped to chromosome 3q13-21 in a linkage study of early-onset CAD. We completed an association-mapping study across the 1-LOD-unit-down supporting interval, using two independent white case-control data sets (CATHGEN, initial and validation) to evaluate association under the peak. Single-nucleotide polymorphisms (SNPs) evenly spaced at 100-kb intervals were screened in the initial data set (N=468). Promising SNPs (P<.1) were then examined in the validation data set (N=514). Significant findings (P<.05) in the combined initial and validation data sets were further evaluated in multiple independent data sets, including a family-based data set (N=2,954), an African American case-control data set (N=190), and an additional white control data set (N=255). The association between genotype and aortic atherosclerosis was examined in 145 human aortas. The peakwide survey found evidence of association in SNPs from multiple genes. The strongest associations were found in three SNPs from the kalirin (KALRN) gene, especially in patients with early-onset CAD (P=.00001-00028 in the combined CATHGEN data sets). In-depth investigation of the gene found that an intronic SNP, rs9289231, was associated with early-onset CAD in all white data sets examined (P<.05). In the joint analysis of all white early-onset CAD cases (N=332) and controls (N=546), rs9289231 was highly significant (P=.00008), with an odds-ratio estimate of 2.1. Furthermore, the risk allele of this SNP was associated with atherosclerosis burden (P=.03) in 145 human aortas. KALRN is a protein with many functions, including the inhibition of inducible nitric oxide synthase and guanine-exchange-factor activity. KALRN and two other associated genes identified in this study (CDGAP and MYLK) belong to the Rho GTPase-signaling pathway. Our data suggest the importance of the KALRN gene and the Rho GTPase-signaling pathway in the pathogenesis of CAD.     

Etiological overlap between obsessive‐compulsive disorder and anorexia nervosa: a longitudinal cohort,multigenerational family and twin study

Obsessive‐compulsive disorder (OCD) often co‐occurs with anorexia nervosa (AN), a comorbid profile that complicates the clinical management of both conditions. This population‐based study aimed to examine patterns of comorbidity, longitudinal risks, shared familial risks and shared genetic factors between OCD and AN at the population level. Participants were individuals with a diagnosis of OCD (N=19,814) or AN (N=8,462) in the Swedish National Patient Register between January 1992 and December 2009; their first‐, second‐ and third‐degree relatives; and population‐matched (1:10 ratio) unaffected comparison individuals and their relatives. Female twins from the population‐based Swedish Twin Register (N=8,550) were also included. Females with OCD had a 16‐fold increased risk of having a comorbid diagnosis of AN, whereas males with OCD had a 37‐fold increased risk. Longitudinal analyses showed that individuals first diagnosed with OCD had an increased risk for a later diagnosis of AN (risk ratio, RR=3.6), whereas individuals first diagnosed with AN had an even greater risk for a later diagnosis of OCD (RR=9.6). These longitudinal risks were about twice as high for males than for females. First‐ and second‐degree relatives of probands with OCD had an increased risk for AN, and the magnitude of this risk tended to increase with the degree of genetic relatedness. Bivariate twin models revealed a moderate but significant degree of genetic overlap between self‐reported OCD and AN diagnoses (r_a=0.52, 95% CI: 0.26‐0.81), but most of the genetic variance was disorder‐specific. The moderately high genetic correlation supports the idea that this frequently observed comorbid pattern is at least in part due to shared genetic factors, though disorder‐specific factors are more important. These results have implications for current gene‐searching efforts and for clinical practice.     

Association between 5-HT2A receptor polymorphisms and risk of obstructive sleep apnea and hypopnea syndrome: A systematic review and meta-analysis

Obstructive sleep apnea and hypopnea syndrome (OSAHS) is a common disorder with several predisposing factors, which may include genetic causes. Studies of the association of susceptibility to and severity of OSAHS with the polymorphisms of the 5-HTR 2A/2C genes have had low statistical power and have yielded inconsistent results. To clarify the association we perform a meta-analysis that combines the genotyping data from all eligible published studies.     

Gender Differences in Neuropsychological Performance across Psychotic Disorders – a Multi-Centre Population Based Case-Control Study

Background

Patients with schizophrenia and other psychoses exhibit a wide range of neuropsychological deficits. An unresolved question concerns whether there are gender differences in cognitive performance.

Methods

Data were derived from a multi-centre population based case-control study of patients with first-episode psychosis. A neuropsychological test battery was administered to patients with a diagnosis of schizophrenia or schizoaffective disorder (N=70, 36% females), bipolar/mania (N=34, 60% females), depressive psychosis (N=36, 58% females) and healthy controls (N=148, 55% females). Generalized and specific cognitive deficits were compared.

Results

There was strong evidence for disorder-specific gender differences in neuropsychological performance. Males and females with schizophrenia showed similar pervasive neuropsychological impairments. In psychotic depressive disorder females performed worse than males across neuropsychological measures. Differences in neuropsychological performance between males and females with bipolar/manic disorder were restricted to language functions. Symptom severity did not contribute to the observed gender differences.

Conclusions

Early in the course of psychotic illness, gender related factors appear to moderate the severity of cognitive deficits in depressive psychosis and bipolar/mania patients.     

Fitting and validating the genomic evaluation model to Polish Holstein-Friesian cattle

The aim of the study was to fit the genomic evaluation model to Polish Holstein-Friesian dairy cattle. A training data set for the estimation of additive effects of single nucleotide polymorphisms (SNPs) consisted of 1227 Polish Holstein-Friesian bulls. Genotypes were obtained by the use of Illumina BovineSNP50 Genotyping BeadChip. Altogether 29 traits were considered: milk-, fat- and protein- yields, somatic cell score, four female fertility traits, and 21 traits describing conformation. The prediction of direct genomic values was based on a mixed model containing deregressed national proofs as a dependent variable and random SNP effects as independent variables. The correlations between direct genomic values and conventional estimated breeding values estimated for the whole data set were overall very high and varied between 0.98 for production traits and 0.78 for non return rates for cows. For the validation data set of 232 bulls the corresponding correlations were 0.38 for milk-, 0.37 for protein-, and 0.32 for fat yields, while the correlations between genomic enhanced breeding values and conventional estimated breeding values for the four traits were: 0.43, 0.44, 0.31, and 0.35. This model was able to pass the interbull validation criteria for genomic selection, which indicates that it is realistic to implement genomic selection in Polish Holstein-Friesian cattle.     

Alterations of Gray and White Matter Networks in Patients with Obsessive-Compulsive Disorder: A Multimodal Fusion Analysis of Structural MRI and DTI Using mCCA+jICA

Many of previous neuroimaging studies on neuronal structures in patients with obsessive-compulsive disorder (OCD) used univariate statistical tests on unimodal imaging measurements. Although the univariate methods revealed important aberrance of local morphometry in OCD patients, the covariance structure of the anatomical alterations remains unclear. Motivated by recent developments of multivariate techniques in the neuroimaging field, we applied a fusion method called “mCCA+jICA” on multimodal structural data of T1-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) of 30 unmedicated patients with OCD and 34 healthy controls. Amongst six highly correlated multimodal networks (p < 0.0001), we found significant alterations of the interrelated gray and white matter networks over occipital and parietal cortices, frontal interhemispheric connections and cerebella (False Discovery Rate q ≤ 0.05). In addition, we found white matter networks around basal ganglia that correlated with a subdimension of OC symptoms, namely ‘harm/checking’ (q ≤ 0.05). The present study not only agrees with the previous unimodal findings of OCD, but also quantifies the association of the altered networks across imaging modalities.     

Quantitative trait nucleotide analysis using Bayesian model selection

Although much attention has been given to statistical genetic methods for the initial localization and fine mapping of quantitative trait loci (QTLs), little methodological work has been done to date on the problem of statistically identifying the most likely functional polymorphisms using sequence data. In this paper we provide a general statistical genetic framework, called Bayesian quantitative trait nucleotide (BQTN) analysis, for assessing the likely functional status of genetic variants. The approach requires the initial enumeration of all genetic variants in a set of resequenced individuals. These polymorphisms are then typed in a large number of individuals (potentially in families), and marker variation is related to quantitative phenotypic variation using Bayesian model selection and averaging. For each sequence variant a posterior probability of effect is obtained and can be used to prioritize additional molecular functional experiments. An example of this quantitative nucleotide analysis is provided using the GAW12 simulated data. The results show that the BQTN method may be useful for choosing the most likely functional variants within a gene (or set of genes). We also include instructions on how to use our computer program, SOLAR, for association analysis and BQTN analysis.     

Predicting genotype environmental range from genome–environment associations

Genome–environment association methods aim to detect genetic markers associated with environmental variables. The detected associations are usually analysed separately to identify the genomic regions involved in local adaptation. However, a recent study suggests that single‐locus associations can be combined and used in a predictive way to estimate environmental variables for new individuals on the basis of their genotypes. Here, we introduce an original approach to predict the environmental range (values and upper and lower limits) of species genotypes from the genetic markers significantly associated with those environmental variables in an independent set of individuals. We illustrate this approach to predict aridity in a database constituted of 950 individuals of wild beets and 299 individuals of cultivated beets genotyped at 14,409 random single nucleotide polymorphisms (SNPs). We detected 66 alleles associated with aridity and used them to calculate the fraction (I) of aridity‐associated alleles in each individual. The fraction I correctly predicted the values of aridity in an independent validation set of wild individuals and was then used to predict aridity in the 299 cultivated individuals. Wild individuals had higher median values and a wider range of values of aridity than the cultivated individuals, suggesting that wild individuals have higher ability to resist to stress‐aridity conditions and could be used to improve the resistance of cultivated varieties to aridity.     

人工神经网络模型在水稻群体分蘖动态模拟中外推能力的测试

研究利用人工神经网络模型 ,以水稻群体分蘖动态为例 ,采用交互验证和独立验证的方式 ,对水稻生长 BP网络模型进行了训练与模拟 ,其结果与水稻群体分蘖的积温统计模型、基本动力学模型和复合分蘖模型进行了比较。研究结果表明 ,神经网络模型具有一定的外推能力 ,但其外推能力依赖于大量的训练样本。神经网络模型具有较好的拟合能力 ,是因为有较多的模型参数 ,因此对神经网络模型的训练需要大量的参数来保证其参数不致过度吻合。具有外推能力神经网络模型的最少训练样本数应大于 6 .75倍于神经网络参数数目 ,小于 13.5倍于神经网络参数数目。因此在应用神经网络模型时 ,如果神经网络模型包括较多的输入变量时 ,可考虑采用主成分分析、对应分析等技术对输入变量进行信息综合 ,相应地减少网络模型的参数。另一方面 ,当训练样本不足时 ,最好只用神经网络模型对同一系统的情况进行模拟 ,应谨慎使用神经网络模型进行外推。神经网络模型给作物模拟研究的科学工作者提供了一个“傻瓜”式工具 ,对数学建模不熟悉的农业研究人员 ,人工神经网络可以替代数学建模进行仿真实验 ;对于精通数学建模的研究人员来说 ,它至少是一种补充和可作为比较的非线性数据处理方法     

Sequence variants of the brain-derived neurotrophic factor (BDNF) gene are strongly associated with obsessive-compulsive disorder

We evaluated a possible association between the brain-derived neurotrophic factor (BDNF) gene and susceptibility to obsessive-compulsive disorder (OCD) by genotyping a number of single-nucleotide polymorphisms (SNPs) and one microsatellite marker from the extended BDNF locus in 164 triads with OCD. Extensive background linkage disequilibrium was observed at this locus. Single-locus transmission-distortion tests revealed significant evidence of association with the disease for all the BDNF gene markers tested, including a Val66Met variation affecting the sequence of the proBDNF protein. Analysis of multi-SNP haplotypes provided similar results. Haplotype transmission comparisons in this and previous studies point to a functionally distinct BDNF haplotype uniquely marked by the rare Met66 allele, which is undertransmitted and likely confers a protective effect in OCD and other psychiatric disorders.     

Validation of outlier loci through replication in independent data sets: a test on Arabis alpina

Outlier detection and environmental association analysis are common methods to search for loci or genomic regions exhibiting signals of adaptation to environmental factors. However, a validation of outlier loci and corresponding allele distribution models through functional molecular biology or transplant/common garden experiments is rarely carried out. Here, we employ another method for validation, namely testing outlier loci in specifically designed, independent data sets. Previously, an outlier locus associated with three different habitat types had been detected in Arabis alpina. For the independent validation data set, we sampled 30 populations occurring in these three habitat types across five biogeographic regions of the Swiss Alps. The allele distribution model found in the original study could not be validated in the independent test data set: The outlier locus was no longer indicative of habitat‐mediated selection. We propose several potential causes of this failure of validation, of which unaccounted genetic structure and technical issues in the original data set used to detect the outlier locus were most probable. Thus, our study shows that validating outlier loci and allele distribution models in independent data sets is a helpful tool in ecological genomics which, in the case of positive validation, adds confidence to outlier loci and their association with environmental factors or, in the case of failure of validation, helps to explain inconsistencies.