Acid mucopolysaccharides of chick-embryo cartilage in osteolathyrism

1. In view of the suggested association between collagen and acid mucopolysaccharides in the connective tissues, this study was designed to see whether the lathyrus factor, beta-aminopropionitrile, affected the acid mucopolysaccharides as well as inhibiting the normal polymerization of collagen. The changing pattern of these two components of cartilage from normal and lathyritic chick embryos aged 14-20 days is described. The chondroitin sulphates and their protein complexes have been isolated from these cartilages, characterized and compared, particularly with respect to their sulphate content; no significant differences in quality or quantity were detectable. 2. Saline extracts of normal and lathyritic cartilages were also compared; whereas the collagen content of lathyritic extracts was increased to ten times that in the normal, the acid mucopolysaccharide content of both extracts was always the same. 3. It therefore appears that in the chick embryo beta-aminopropionitrile does not affect the acid mucopolysaccharides of the developing cartilage.     

Ordered subset linkage analysis based on admixture proportion identifies new linkage evidence for alcohol dependence in African-Americans

Genetic heterogeneity could reduce the power of linkage analysis to detect risk loci for complex traits such as alcohol dependence (AD). Previously, we performed a genomewide linkage analysis for AD in African-Americans (AAs) (Biol Psychiatry 65:111–115, 2009). The power of that linkage analysis could have been reduced by the presence of genetic heterogeneity owing to differences in admixture among AA families. We hypothesized that by examining a study sample whose genetic ancestry was more homogeneous, we could increase the power to detect linkage. To test this hypothesis, we performed ordered subset linkage analysis in 384 AA families using admixture proportion as a covariate to identify a more homogeneous subset of families and determine whether there is increased evidence for linkage with AD. Statistically significant increases in lod scores in subsets relative to the overall sample were identified on chromosomes 4 (P = 0.0001), 12 (P = 0.021), 15 (P = 0.026) and 22 (P = 0.0069). In a subset of 44 families with African ancestry proportions ranging from 0.858 to 0.996, we observed a genomewide significant linkage at 180 cM on chromosome 4 (lod = 4.24, pointwise P < 0.00001, empirical genomewide P = 0.008). A promising candidate gene located there, GLRA3, which encodes a subunit of the glycine neurotransmitter receptor. Our results demonstrate that admixture proportion can be used as a covariate to reduce genetic heterogeneity and enhance the detection of linkage for AD in an admixed population such as AAs. This approach could be applied to any linkage analysis for complex traits conducted in an admixed population.     

An Effective Method to Identify Heritable Components from Multivariate Phenotypes

Multivariate phenotypes may be characterized collectively by a variety of low level traits, such as in the diagnosis of a disease that relies on multiple disease indicators. Such multivariate phenotypes are often used in genetic association studies. If highly heritable components of a multivariate phenotype can be identified, it can maximize the likelihood of finding genetic associations. Existing methods for phenotype refinement perform unsupervised cluster analysis on low-level traits and hence do not assess heritability. Existing heritable component analytics either cannot utilize general pedigrees or have to estimate the entire covariance matrix of low-level traits from limited samples, which leads to inaccurate estimates and is often computationally prohibitive. It is also difficult for these methods to exclude fixed effects from other covariates such as age, sex and race, in order to identify truly heritable components. We propose to search for a combination of low-level traits and directly maximize the heritability of this combined trait. A quadratic optimization problem is thus derived where the objective function is formulated by decomposing the traditional maximum likelihood method for estimating the heritability of a quantitative trait. The proposed approach can generate linearly-combined traits of high heritability that has been corrected for the fixed effects of covariates. The effectiveness of the proposed approach is demonstrated in simulations and by a case study of cocaine dependence. Our approach was computationally efficient and derived traits of higher heritability than those by other methods. Additional association analysis with the derived cocaine-use trait identified genetic markers that were replicated in an independent sample, further confirming the utility and advantage of the proposed approach.     

Aerobic training increases the expression of adiponectin receptor genes in the peripheral blood mononuclear cells of young men

Little is known about the effect of exercise training on the expression of adiponectin receptor genes in peripheral blood mononuclear cells (PBMCs). In this study, we investigated the effects of aerobic training on the expression of AdipoR1 and AidpoR2 mRNAs in PBMCs, whole body insulin sensitivity, and circulating adiponectins in men. Thirty young men were randomly assigned to either a control (n=15) or an exercise (n=15) group. Subjects assigned to the exercise group underwent a 12-week jogging and/or running programme on a motor-driven treadmill at an intensity of 60%-75% of the age-based maximum heart rate with duration of 40 minutes per session and a frequency of 5 days per week. Two-way mixed ANOVA with repeated measures was used to test any significant time-by-group interaction effects for the measured variables at p=0.05. We found significant time-by-group interaction effects for waist circumference (p=0.001), VO_2max (p<0.001), fasting insulin (p=0.016), homeostasis model assessment for insulin resistance (HOMA-IR) (p=0.010), area under the curve (AUC) for insulin response during the 75-g oral glucose tolerance test (p=0.002), high-molecular weight (HMW) adiponectin (p=0.016), and the PBMC mRNA levels of AdipoR1 (p<0.001) and AdipoR2 (p=0.001). The exercise group had significantly increased mRNA levels of AdipoR1 and AdipoR2 in PBMCs, along with increased whole body insulin sensitivity and HMW adiponectin, decreased waist circumference, and increased VO_2max compared with the control group. In summary, the current findings suggest that exercise training modulates the expression of AdipoR1 and AdipoR2 mRNAs in PBMCs, implying that manipulation of the expression of these genes could be a potential surrogate for lifestyle intervention-mediated improvements of whole body insulin sensitivity and glucose homeostasis.     

Genetic and environmental heterogeneity of residual variance of weight traits in Nellore beef cattle

Background

Many studies have provided evidence of the existence of genetic heterogeneity of environmental variance, suggesting that it could be exploited to improve robustness and uniformity of livestock by selection. However, little is known about the perspectives of such a selection strategy in beef cattle.

Methods

A two-step approach was applied to study the genetic heterogeneity of residual variance of weight gain from birth to weaning and long-yearling weight in a Nellore beef cattle population. First, an animal model was fitted to the data and second, the influence of additive and environmental effects on the residual variance of these traits was investigated with different models, in which the log squared estimated residuals for each phenotypic record were analyzed using the restricted maximum likelihood method. Monte Carlo simulation was performed to assess the reliability of variance component estimates from the second step and the accuracy of estimated breeding values for residual variation.

Results

The results suggest that both genetic and environmental factors have an effect on the residual variance of weight gain from birth to weaning and long-yearling in Nellore beef cattle and that uniformity of these traits could be improved by selecting for lower residual variance, when considering a large amount of information to predict genetic merit for this criterion. Simulations suggested that using the two-step approach would lead to biased estimates of variance components, such that more adequate methods are needed to study the genetic heterogeneity of residual variance in beef cattle.     

Population admixture modulates risk for alcohol dependence

The admixture of different ancestral populations in America may have important implications for the risk for psychiatric disorders, as it appears to have for other medical disorders. The present study investigated the role of population admixture in risk for several psychiatric disorders in European-Americans (EAs) and African-Americans (AAs). This is a multisite study with 3,792 subjects recruited from across the United States, including 3,119 EAs and 673 AAs. These subjects included healthy controls and those with substance dependence (SD) [including alcohol dependence (AD), cocaine dependence, and opioid dependence], social phobia, affective disorders, and schizophrenia. In addition, DNA was included from 78 West Africans. The degree of admixture for each subject was estimated by analysis of a set of ancestry-informative genetic markers using the program STRUCTURE, and was compared between cases and controls. As noted previously, the degree of admixture in AAs was higher than EAs. In EAs, the degree of admixture (with African ancestry) was significantly lower in patients with SD (mainly AD) than controls (P = 0.009 for SD; P = 0.008 for AD). This finding suggests that population admixture may modulate risk for alcohol dependence. Population admixture might protect against alcohol dependence by increasing average heterozygosity and reducing the risk of deleterious recessive alleles. We cannot exclude the possibility that the results might have been influenced by selection bias due to the multisite nature of the study.     

Inferring phenotypes from substance use via collaborative matrix completion

Background

Although substance use disorders (SUDs) are heritable, few genetic risk factors for them have been identified, in part due to the small sample sizes of study populations. To address this limitation, researchers have aggregated subjects from multiple existing genetic studies, but these subjects can have missing phenotypic information, including diagnostic criteria for certain substances that were not originally a focus of study. Recent advances in addiction neurobiology have shown that comorbid SUDs (e.g., the abuse of multiple substances) have similar genetic determinants, which makes it possible to infer missing SUD diagnostic criteria using criteria from another SUD and patient genotypes through statistical modeling.

Results

We propose a new approach based on matrix completion techniques to integrate features of comorbid health conditions and individual’s genotypes to infer unreported diagnostic criteria for a disorder. This approach optimizes a bi-linear model that uses the interactions between known disease correlations and candidate genes to impute missing criteria. An efficient stochastic and parallel algorithm was developed to optimize the model with a speed 20 times greater than the classic sequential algorithm. It was tested on 3441 subjects who had both cocaine and opioid use disorders and successfully inferred missing diagnostic criteria with consistently better accuracy than other recent statistical methods.

Conclusions

The proposed matrix completion imputation method is a promising tool to impute unreported or unobserved symptoms or criteria for disease diagnosis. Integrating data at multiple scales or from heterogeneous sources may help improve the accuracy of phenotype imputation.

     

An Exchange Assay for the Measurement of Cell Nuclear Estrogen Receptors in Microdissected Brain Regions

An exchange assay is described for the measurement of nuclear estrogen receptors (ERn) in microdissected brain regions. The distribution of ERn in the hypothalamus and amygdala of the rat 1 h after an injection of estradiol (E) is presented. Combining the exchange assay with a previously described method for measurement of cytosol estrogen receptors (ERc) in microdissected brain samples, gonadectomized male and female rats were compared for ERc and ERn. While ERc concentrations tended to be higher in females than in males in all regions of the hypothalamus, with a significant sex difference in the arcuate-median eminence, no sex difference in ERn concentrations was observed after E injection. These results suggest that ERc measurements alone are not sufficient to establish the capacity of the E receptor system: ERn measurements are also necessary to establish the relationship between receptor levels and physiologic estrogen responsiveness.