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PubMed2005

Genetic models of schizophrenia and bipolar disorder: overlapping inheritance or discrete genotypes?

Schizophrenia and affective disorder have been considered to be nosologically and etiologically distinct disorders. This postulate is challenged by progress in new biological research. Both disorders are strongly influenced by genetic factors; thus genetic research is a main contributor to this discussion. We review current evidence of the genetic relationship between schizophrenia and affective disorders, mainly bipolar disorder (the various genetic research methods have been particularly applied to bipolar disorder). Recent family and twin studies reveal a growing consistency in demonstrating cosegregation between both disorders which is difficult to detect with certainty given the low base rates. Systematic molecular genetic search for specific genes impacting on either disorder has now identified one gene which is apparently involved in both disorders (G72/G30); other candidate genes reveal some evidence to present as susceptibility genes with very modest effects for each of both disorders, although not consistently so (e. g., COMT, BDNF). There is room for speculation about other common susceptibility genes, given the overlap between candidate regions for schizophrenia and those for bipolar disorder emerging from linkage studies.

Bipolar Disorder↗
PubMed1999

The flinders sensitive line rats, a genetic model of depression, show abnormal serotonin receptor mRNA expression in the brain that is reversed by 17beta-estradiol.

The possible link between estrogen and serotonin (5-HT) in depression was investigated using a genetic animal model of depression, the Flinders Sensitive Line (FSL) rats, in comparison to control Flinders Resistant Line rats. The mRNA levels of the estrogen receptor (ER) alpha and beta subtypes and the 5-HT(1A) and 5-HT(2A) receptors were analyzed in several limbic-related areas of ovariectomized FSL and FRL rats treated with 17beta-estradiol (0.15 microg/g) or vehicle. The FSL animals were shown to express significantly lower levels of the 5-HT(2A) receptor transcripts in the perirhinal cortex, piriform cortex, and medial anterodorsal amygdala and higher levels in the CA 2-3 region of the hippocampus. The only significant difference between the rat lines in ER mRNA expression was found in the medial posterodorsal amygdala, where the FSL rats showed lower ERalpha expression levels. Overall, estradiol treatment increased 5-HT(2A) and decreased 5-HT(1A) receptor mRNA levels in several of the examined regions of both lines. Thus, in many areas, estradiol was found to regulate the 5-HT receptor mRNA expression in the opposite direction to the alterations found in the FSL rats. These findings further support the implication of 5-HT receptors, in particular the 5-HT(2A) subtype, in the etiology of affective disorders. Moreover, the ability of estradiol to regulate the expression of the 5-HT(1A) and 5-HT(2A) receptor genes might account for the reported influence of gonadal hormones in mood and depression.

Animals↗
PubMed2001

Quantitative genetic modeling of variation in human brain morphology.

The degree to which individual variation in brain structure in humans is genetically or environmentally determined is as yet not well understood. We studied the brains of 54 monozygotic (33 male, 21 female) and 58 dizygotic (17 male, 20 female, 21 opposite sex) pairs of twins and 34 of their full siblings (19 male, 15 female) by means of high resolution magnetic resonance imaging scans. Structural equation modeling was used to quantify the genetic and environmental contributions to phenotypic (co)variance in whole brain, gray and white matter volume of the cerebrum, lateral ventricle volume and associated variables such as intracranial volume and height. Because the cerebral cortex makes up more that two-thirds of the brain mass and almost three-quarters of its synapses, our data predominantly concerns the telencephalon. Genetic factors accounted for most of the individual differences in whole brain (90%), gray (82%) and white (88%) matter volume. Individual differences in lateral ventricle volume were best explained by a model containing common (58%) and unique (42%) environmental factors, indicating genes to be of no or minor influence. In our sample, genetic or environmental influences were not different for males and females. The same genes influenced brain volumes and intracranial volume and almost completely explained their high phenotypic correlation. Genes influencing gray and white matter overlapped to a large extent and completely determined their phenotypic correlation. The high heritability estimates that were found indicate that brain volumes may be useful as intermediate phenotypes in behavioral genetic research.

Adult↗
PubMed2002

Complexities of cancer research: mouse genetic models.

Cancer susceptibility is a complex interaction of an individual's genetic composition and environmental exposures. Huge strides have been made in understanding cancer over the past 100 yr, from recognition of cancer as a genetic disease, to identification of specific carcinogens, isolation of oncogenes, and recognition of tumor suppressors. A tremendous amount of knowledge has accumulated about the etiology of cancer. Cancer genetics has played a significant role in these discoveries. Analysis of high-risk familial cancers has led to the discovery of new tumor suppressor genes and important cancer pathways. These families, however, represent only a small fraction of cancer in the general population. Most cancer is instead probably the result of an intricate interaction of polymorphic susceptibility genes with the sea of environmental exposures that humans experience. Although the central cadre of cancer genes is known, little is understood about the peripheral genes that likely comprise the polymorphic susceptibility loci. The challenge for cancer genetics is therefore to move forward from the mendelian genetics of the rare familial cancer syndromes into the field of quantitative trait loci, susceptibility factors, and modifier genes. By identifying the genes that modulate an individual's susceptibility to cancer after an environmental exposure, researchers will be able to gain important insights into human biology, cancer prevention, and cancer treatment. This article summarizes the current state of quantitative trait genetic analysis and the tools, both proven and theoretical, that may be used to unravel one of the great challenges in cancer genetics.

Animals↗
PubMed1998

Empirical nonparametric bootstrap strategies in quantitative trait loci mapping: conditioning on the genetic model.

Several nonparametric bootstrap methods are tested to obtain better confidence intervals for the quantitative trait loci (QTL) positions, i.e., with minimal width and unbiased coverage probability. Two selective resampling schemes are proposed as a means of conditioning the bootstrap on the number of genetic factors in our model inferred from the original data. The selection is based on criteria related to the estimated number of genetic factors, and only the retained bootstrapped samples will contribute a value to the empirically estimated distribution of the QTL position estimate. These schemes are compared with a nonselective scheme across a range of simple configurations of one QTL on a one-chromosome genome. In particular, the effect of the chromosome length and the relative position of the QTL are examined for a given experimental power, which determines the confidence interval size. With the test protocol used, it appears that the selective resampling schemes are either unbiased or least biased when the QTL is situated near the middle of the chromosome. When the QTL is closer to one end, the likelihood curve of its position along the chromosome becomes truncated, and the nonselective scheme then performs better inasmuch as the percentage of estimated confidence intervals that actually contain the real QTL's position is closer to expectation. The nonselective method, however, produces larger confidence intervals. Hence, we advocate use of the selective methods, regardless of the QTL position along the chromosome (to reduce confidence interval sizes), but we leave the problem open as to how the method should be altered to take into account the bias of the original estimate of the QTL's position.

Chromosome Mapping↗
PubMed2006

Cortical glutamate metabolism is enhanced in a genetic model of absence epilepsy.

Disturbances in GABAergic and glutamatergic neurotransmission in the thalamocortical loop are involved in absence seizures. Here, we examined potential disturbances in metabolism and interactions between neurons and glia in 5-month-old genetic absence epilepsy rats from Strasbourg (GAERS) and nonepileptic rats (NER). Animals received [1-(13)C]glucose and [1,2-(13)C]acetate, the preferential substrates of neurons and astrocytes, respectively. Extracts from cerebral cortex, thalamus, and hippocampus were analyzed by (13)C nuclear magnetic resonance spectroscopy. Most changes were detected in the cortex. Pyruvate metabolism was enhanced as evidenced by increases of lactate, and labeled and unlabeled alanine. Neuronal mitochondrial metabolism was also enhanced as detected by elevated amounts of N-acetylaspartate and nicotinamide adenine dinucleotide as well as increased incorporation of label from [2-(13)C]acetyl CoA into glutamate, glutamine, and aspartate. Likewise, mitochondrial metabolism in astrocytes was increased. Changes in thalamus were restricted to increased concentration and labeling of glutamine. Changes in the hippocampus were similar to those in the cortex. This increase in glutamate-glutamine metabolism in cortical neurons and astrocytes accompanied by a decreased gamma aminobyturic acid level may lead to impaired thalamic filter function. Hence, reduced sensory input to cortex could allow the occurrence of spike-and-wave discharges in the thalamocortical loop. Increased glutamatergic output from the cortex to hippocampus may be the underlying cause of improved learning in GAERS.

Animals↗
PubMed1980

Genetic models of reading disability.

Test data collected on 133 reading-disabled (RD) children and their nuclear families who participated in the Colorado Family Reading Study were subjected to segregation analysis utilizing the technique of Elston and Yelverton (1975) for a continuous phenotypic measure. The possibility of genetic heterogeneity of RD was investigated by analyzing four subsets of data: all families, families with male probands, families with female probands, and families with severely affected probands. Furthermore, an analysis of the children's data was compared to that of all family members to investigate the possibility that the disorder may be manifested differently in adults. Results from the four subsets of data show that RD is etiologically heterogeneous. Compatibility with a major recessive gene for RD was demonstrated for families with female probands. Analyses of the children's data alone give results consistent with both environmental and genetic determination of RD.

Child↗
PubMed2004

Quantitative genetic models for describing simultaneous and recursive relationships between phenotypes.

Multivariate models are of great importance in theoretical and applied quantitative genetics. We extend quantitative genetic theory to accommodate situations in which there is linear feedback or recursiveness between the phenotypes involved in a multivariate system, assuming an infinitesimal, additive, model of inheritance. It is shown that structural parameters defining a simultaneous or recursive system have a bearing on the interpretation of quantitative genetic parameter estimates (e.g., heritability, offspring-parent regression, genetic correlation) when such features are ignored. Matrix representations are given for treating a plethora of feedback-recursive situations. The likelihood function is derived, assuming multivariate normality, and results from econometric theory for parameter identification are adapted to a quantitative genetic setting. A Bayesian treatment with a Markov chain Monte Carlo implementation is suggested for inference and developed. When the system is fully recursive, all conditional posterior distributions are in closed form, so Gibbs sampling is straightforward. If there is feedback, a Metropolis step may be embedded for sampling the structural parameters, since their conditional distributions are unknown. Extensions of the model to discrete random variables and to nonlinear relationships between phenotypes are discussed.

Bayes Theorem↗
PubMed2006

Long-term memory for aversive training is impaired in Idua(-/-) mice, a genetic model of mucopolysaccharidosis type I.

Mucopolysaccharidosis type I (MPS I) is a lysosomal storage disease that leads to neurodegeneration and neurological deficits, among other pathological and clinical consequences. The aim of the present study was to evaluate neurobehavioral parameters in a genetic mouse model of mucopolysaccharidosis type I (MPS I). During exploration of an open field, adult MPS I (Idua(-/-)) mice showed normal locomotion and anxiety but reduced number of rearings. Idua(-/-) mice performed normally in a novel object recognition memory task and showed normal short-term retention of inhibitory avoidance training. By contrast, long-term retention of inhibitory avoidance was impaired in Idua(-/-) mice. The deficit in inhibitory avoidance memory could not be attributed to reduced footshock reactivity. The results indicate that Idua(-/-) mice present deficits in long-term memory for aversive training and reduced exploratory behavior.

Animals↗
PubMed2006

A general population-genetic model for the production by population structure of spurious genotype-phenotype associations in discrete, admixed or spatially distributed populations.

In linkage disequilibrium mapping of genetic variants causally associated with phenotypes, spurious associations can potentially be generated by any of a variety of types of population structure. However, mathematical theory of the production of spurious associations has largely been restricted to population structure models that involve the sampling of individuals from a collection of discrete subpopulations. Here, we introduce a general model of spurious association in structured populations, appropriate whether the population structure involves discrete groups, admixture among such groups, or continuous variation across space. Under the assumptions of the model, we find that a single common principle--applicable to both the discrete and admixed settings as well as to spatial populations--gives a necessary and sufficient condition for the occurrence of spurious associations. Using a mathematical connection between the discrete and admixed cases, we show that in admixed populations, spurious associations are less severe than in corresponding mixtures of discrete subpopulations, especially when the variance of admixture across individuals is small. This observation, together with the results of simulations that examine the relative influences of various model parameters, has important implications for the design and analysis of genetic association studies in structured populations.

Computer Simulation↗
PubMed2002

Adiposity and the development of diabetes in mouse genetic models.

While it is certain from the analysis of transgenic models of lipodystrophy that a critical mass of adipose tissue is necessary to prevent the development of diabetes, the reasons why diabetes develops in one obese individual and not in another need to be further investigated. This is also one of the major questions associated with the current obesity epidemic and the development of the metabolic syndrome. The severe lipodystrophic models do not really address this big problem. In this presentation, we point out that models exist among inbred strains of mice that can contribute towards finding answers to the diabetes found in the metabolic syndrome. The differences responsible for the phenotypic variations are undoubtedly multifactorial and involve many genes, but there are powerful genetic resources to investigate these problems.

Adipose Tissue↗
PubMed2006

Comparison of genetic models for analysing the effects of a PvuII polymorphism in the oestrogen receptor 1 (ESR1) gene on prolificacy in an Iberian x Meishan pig population.

The effect of a previously reported PvuII polymorphism in oestrogen receptor 1 (ESR1) was analysed in an F(2) population of Iberian x Meishan pigs. We tested three hypotheses: (1) that a causal mutation was fixed in the parental populations, (2) that a causal mutation existed that was in complete linkage disequilibrium with the alleles of the PvuII polymorphism and (3) that a causal mutation existed in linkage disequilibrium within each parental population. The third model was the most plausible based on the available data. ESR1 alleles displayed different patterns of linkage disequilibrium with the causal mutation in each of the parental populations and the PvuII polymorphism was clearly not the causal mutation. As a consequence, the use of the ESR1 mutation for selection must be evaluated for a particular pig population before it is applied.

Alleles↗
PubMed1997

Analysis of genomic alterations in benign, atypical, and anaplastic meningiomas: toward a genetic model of meningioma progression.

Nineteen benign [World Health Organization (WHO) grade I; MI], 21 atypical (WHO grade II; MII), and 19 anaplastic (WHO grade III; MIII) sporadic meningiomas were screened for chromosomal imbalances by comparative genomic hybridization (CGH). These data were supplemented by molecular genetic analyses of selected chromosomal regions and genes. With increasing malignancy grade, a marked accumulation of genomic aberrations was observed; i.e., the numbers (mean +/- SEM) of total alterations detected per tumor were 2.9 +/- 0.7 for MI, 9.2 +/- 1.2 for MII, and 13.3 +/- 1.9 for MIII. The most frequent alteration detected in MI was loss on 22q (58%). In MII, aberrations most commonly identified were losses on 1p (76%), 22q (71%), 14q (43%), 18q (43%), 10 (38%), and 6q (33%), as well as gains on 20q (48%), 12q (43%), 15q (43%), 1q (33%), 9q (33%), and 17q (33%). In MIII, most of these alterations were found at similar frequencies. However, an increase in losses on 6q (53%), 10 (68%), and 14q (63%) was observed. In addition, 32% of MIII demonstrated loss on 9p. Homozygous deletions in the CDKN2A gene at 9p21 were found in 4 of 16 MIII (25%). Highly amplified DNA sequences were mapped to 12q13-q15 by CGH in 1 MII. Southern blot analysis of this tumor revealed amplification of CDK4 and MDM2. By CGH, DNA sequences from 17q were found to be amplified in 1 MII and 8 MIII, involving 17q23 in all cases. Despite the high frequency of chromosomal aberrations in the MII and MIII investigated, none of these tumors showed mutations in exons 5-8 of the TP53 gene. On the basis of the most common aberrations identified in the various malignancy grades, a model for the genomic alterations associated with meningioma progression is proposed.

Adult↗
PubMed1977

Some population genetic models combining artificial and natural selection pressures in the presence of assortative mating.

We have attempted quantitatively through a series of assortative mating models to gain insight into the interaction between the usually antagonistic tendencies of artifical and natural selection pressures. We summarize some of the robust conclusions. In cases where natural selection is expressed only through the phenotype and acts in the opposite direction to the culling incline, then fixation of the dominant or recessive type can be achieved and which occurs depends critically on the initial composition of the population and the magnitude of the degree of culling compared to the selection coefficients. With traits determined at two loci in the case that the double heterozygote is the desired kind, the effect of selfing can only be overcome by very strong artificial selection pressures (high culling order). The degree of culling to achieve its objective can be relaxed with weakening of linkage. The relevant comparison is r2+(1-r)2less than 2(1-c) indicating the precise extent of culling needed, to prevent fixation. The relationships are more complex when natural selection forces are also involved (see Model IV).

Alleles↗