Morphological changes in aging brain structures are differentially affected by time-linked environmental influences despite strong genetic stability

This longitudinal study used the full twin model to estimate change and stability of genetic contributions to morphology of two brain structures, the corpus callosum and lateral ventricles. The 142 subjects were 34 monozygotic (MZ) and 37 dizygotic (DZ) elderly male twin pairs from the National Heart, Lung, and Blood Institute (NHLBI) Twin Study who underwent brain magnetic resonance imaging twice, separated by a 4-year interval. Genetic factors accounted for a substantial portion of individual differences in the size of the corpus callosum and its substructures and of lateral ventricular size. Longitudinal genetic analyses revealed no significant change in the heritability of these structures and no evidence for new genetic variance at Time 2 not present at Time 1. However, both the callosal and ventricular measures showed evidence for new environmental variance at Time 2 not present at Time 1. Confirming a previously posed hypothesis, the phenotypic correlation between absolute change in height of the corpus callosum and absolute change in ventricular volume was significant. Bivariate genetic analysis estimated a significant genetic correlation between the changes in these two structures and the genetic variance in the change of callosal height was entirely due to genes involved in the expansion of ventricles. Genetic stability was present even in old age when brain and other morphological changes can be rapid and highly variable across individuals, inconsistent with an hypothesis that random DNA damage is the cause of aging.     

Discovery and genetic localization of Down syndrome cerebellar phenotypes using the Ts65Dn mouse

Down syndrome (DS) is the most common genetic cause of mental retardation and affects many aspects of brain development. DS individuals exhibit an overall reduction in brain size with a disproportionately greater reduction in cerebellar volume. The Ts65Dn mouse is segmentally trisomic for the distal 12-15 Mb of mouse chromosome 16, a region that shows perfect conserved linkage with human chromosome 21, and therefore provides a genetic model for DS. In this study, high resolution magnetic resonance imaging and histological analysis demonstrate precise neuro- anatomical parallels between the DS and the Ts65Dn cerebellum. Cerebellar volume is significantly reduced in Ts65Dn mice due to reduction of both the internal granule layer and the molecular layer of the cerebellum. Granule cell number is further reduced by a decrease in cell density in the internal granule layer. Despite these changes in Ts65Dn cerebellar structure, motor deficits have not been detected in several tests. Reduction in granule cell density in Ts65Dn mice correctly predicts an analogous pathology in humans; a significant reduction in granule cell density in the DS cerebellum is reported here for the first time. The candidate region of genes on chromosome 21 affecting cerebellar development in DS is therefore delimited to the subset of genes whose orthologs are at dosage imbalance in Ts65Dn mice, providing the first localization of genes affecting a neuroanatomical phenotype in DS. The application of this model for analysis of developmental perturbations is extended by the accurate prediction of DS cerebellar phenotypes.     

Synergistic effects of the dopaminergic and glutamatergic system on hippocampal volume in alcohol-dependent patients

Several genes of the dopaminergic and glutamatergic neurotransmitter systems have been found to be associated with alcohol disease and related intermediate phenotypes. Here, we evaluated genetic variants of the catechol-O-methyltransferase (COMT) and the metabotropic glutamate receptor 3 (mGluR3) genes in alcohol-dependent patients and their association with volumetric measurements of brain structures. By combined analysis of imaging data and genotyping results, large numbers of variables are produced that overstrain conventional statistical methods based on tests for group differences. Limitations in assessment of epistatic effects and multiple testing problems are encountered. Therefore, we introduce a novel method for detecting associations between a set of genetic markers and phenotypical measurements based on machine learning techniques. Hippocampal volume was found to be associated with epistatic effects of the COMT-mGluR3 genes in alcohol-dependent patients but not in controls. These data are in line with prior studies supporting a role for dopamine-glutamate interaction in modulation of alcohol disease.     

Differential effects of trisomy on brain shape and volume in related aneuploid mouse models

Down syndrome (DS) results from inheritance of three copies of human chromosome 21 (Hsa21). Individuals with DS have a significantly smaller brain size overall and a disproportionately small cerebellum. The small cerebellum is seen in Ts65Dn mice, which have segmental trisomy for orthologs of about half the genes on Hsa21 and provide a genetic model for DS. While small cerebellar size is well-established in mouse and humans, much less is known about the shape of the brain in trisomy. Here we conduct a morphometric analysis of the whole brain and cerebellum in Ts65Dn mice and show that the differences with euploid littermates are largely a function of volume and not of shape. This is not the case in two aneuploid mouse models that have fewer genes orthologous to Hsa21 than Ts65Dn. Ts1Rhr is trisomic for genes corresponding to the so-called Down syndrome critical region (DSCR), which was purported to contain a dosage sensitive gene or genes responsible for many phenotypes of DS. Ms1Rhr is monosomic for the same segment. These models show effects on cerebellum and overall brain that are different from each other and from Ts65Dn. These models can help to identify the contributions of genes from different regions of the chromosome on this and other aspects of brain development in trisomy.     

Genetic regulation of growth from birth to 18 years of age: The Swedish young male twins study

Growth is a complex process, and only little is known on the genetic regulation of it. We analyzed the effect of genetic and environmental factors on growth in a longitudinal Swedish cohort of 231 monozygotic and 144 dizygotic twin pairs born 1973–1979 with length or height measured annually from birth to age 18. The data were analyzed by two different multivariate variance component models for twin data using the Mx statistical package. At birth and 1 year of age, a substantial part of the variation in length was because of common environment (50 and 57%, respectively) and the effect of genetic factors was minor. After 2 years of age, 91–97% of the variation of height could be explained by genetic differences whereas the rest was because of environmental variation not shared by twins. The genetic correlation between heights at ages 2 and 18 was 0.73 (95% confidence intervals 0.68–0.77) showing that 53% of the genes affecting height at these ages are the same or closely linked; with increasing age the correlation with genetic effects at age 18 become subsequently stronger. Especially in mid‐childhood, growth was largely regulated by the same genetic factors. During puberty new genetic factors started to affect height, but also genetic variation affecting height at previous ages remained. These results suggest that genetic regulation of growth is rather uniform, which is encouraging for further efforts to identify genes affecting growth. Am. J. Hum. Biol., 2008. © 2008 Wiley‐Liss, Inc.     

An EM algorithm for obtaining maximum likelihood estimates in the multi-phenotype variance components linkage model

In recent years variance components models have been developed for localising genes that contribute to human quantitative variation. In typical applications one assumes a multivariate normal model for phenotypes and estimates model parameters by maximum likelihood. For the joint analysis of several correlated phenotypes, however, finding the maximum likelihood estimates for an appropriate multivariate normal model can be a difficult computational task due to complex constraints among the model parameters. We propose an algorithm for computing maximum likelihood estimates in a multi-phenotype variance components linkage model that readily accommodates these parameter constraints. Data simulated for Genetic Analysis Workshop 10 are used to demonstrate the potential increase in power to detect linkage that can be obtained if correlated phenotypes are analysed jointly rather than individually.     

Brain anatomy in non-affected parents of autistic probands: a MRI study

BACKGROUND: Autism is a neurodevelopmental disorder with an estimated genetic origin of 90%. Previous studies have reported an increase in brain volume of approximately 5% in autistic subjects, especially in children. If this increase in brain volume is genetically determined, biological parents of autistic probands might be expected to show brain enlargement, or at least intracranial enlargement, as well. Identifying structural brain abnormalities under genetic control is of particular importance as these could represent endophenotypes of autism. METHOD: Using quantitative anatomic brain magnetic resonance imaging, volumes of intracranial, total brain, frontal, parietal, temporal and occipital lobe, cerebral and cortical gray and white matter, cerebellum, lateral ventricle, and third ventricle were measured in biological, non-affected parents of autistic probands (19 couples) and in healthy, closely matched control subjects (20 couples). RESULTS: No significant differences were found between the parents of the autistic probands and healthy control couples in any of the brain volumes. Adding gender as a factor in a second analysis did not reveal a significant interaction effect of gender by group. CONCLUSIONS: The present sample of biological, non-affected parents of autistic probands did not show brain enlargements. As the intracranium is not enlarged, it is unlikely that the brain volumes of the parents of autistic probands have originally been enlarged and have been normalized. Thus, increased brain volume in autism might be caused by the interaction of paternal and maternal genes, possibly with an additional effect of environmental factors, or increased brain volumes might reflect phenotypes of autism.     

Brain structure in men remains highly heritable in the seventh and eighth decades of life

The midsagittal cross-sectional dimensions of the corpus callosum, the coronal cross-sectional area of the lateral ventricles at the level of the pons, and a three-dimensional estimate of intracranial volume were derived from magnetic resonance brain images obtained from 45 monozygotic and 40 dizygotic male twin pairs aged 68 to 78. Univariate genetic analyses indicated strong genetic influences contributing significantly to the variability of each brain structure. The estimated proportion of genetic variance (i.e. heritability) was 81% for intracranial volume, 79% for the midline cross-sectional area of the corpus callosum, and 79% for lateral ventricle size. There was no evidence that shared environmental influences contributed significantly to twin-pair similarities. We further used bivariate genetic modeling to estimate the genetic and environmental correlation between correlated brain structures. Intracranial volume and corpus callosum area was highly correlated, and this relationship was entirely due to shared genetic effects between these two brain structures. By contrast, the relationship between the height of the corpus callosum and the size of the lateral ventricles was due to both genetic and environmental influences in common. Corresponding genetic and environmental correlations were 0.68 and 0.58, respectively, indicating that more than half of the genetic and environmental influences on these two brain structures were shared. The manner in which the brain responds to the environment with advancing age is highly genetically determined, both for the lateral ventricles, which dilate with aging and disease, and for the corpus callosum, which is deformed in shape by age-related ventricular enlargement, whereas its midline cross-sectional area remains unchanged.     

A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible correlations in variant effects

For region-based sequencing data, power to detect genetic associations can be improved through analysis of multiple related phenotypes. With this motivation, we propose a novel test to detect association simultaneously between a set of rare variants, such as those obtained by sequencing in a small genomic region, and multiple continuous phenotypes. We allow arbitrary correlations among the phenotypes and build on a linear mixed model by assuming the effects of the variants follow a multivariate normal distribution with a zero mean and a specific covariance matrix structure. In order to account for the unknown correlation parameter in the covariance matrix of the variant effects, a data-adaptive variance component test based on score-type statistics is derived. As our approach can calculate the P-value analytically, the proposed test procedure is computationally efficient. Broad simulations and an application to the UK10K project show that our proposed multivariate test is generally more powerful than univariate tests, especially when there are pleiotropic effects or highly correlated phenotypes.     

Chipping away at complex behavior: transcriptome/phenotype correlations in the mouse brain

Highly parallel gene expression profiling has the potential to provide new insight into the molecular mechanisms of complex brain diseases and behavioral traits. We review how gene expression profiling in various brain regions of inbred mouse strains has been used to identify genes that may contribute to strain-specific phenotypes. New data, which demonstrate the use of gene expression profiling in combination with behavioral testing to identify candidate genes involved in mediating variation in running wheel activity, are also presented. These and other studies suggest that a combination of gene expression profiling and more traditional genetic approaches, such as quantitative trait locus analysis, can be used to identify genes responsible for specific neurobehavioral phenotypes.     

Genetic variance in age‐related changes in running performance and growth during adolescence: A longitudinal twin study

The purpose of this study was to assess an effect of genotype on the patterns of age‐related changes in running performance and physical growth during adolescence. The total sample was 14 pairs of male and 25 pairs of female monozygotic (MZ) twins and 19 pairs of male and 15 pairs of female dizygotic (DZ) twins. Performance on the 50‐m dash and endurance run (1,500 m for boys and 1,000 m for girls, respectively) and stature and body mass were measured at yearly intervals from 12 to 17 years of age. A principal component analysis was applied to the longitudinal data, and within‐pair resemblance in the scores on the first, the second, and the third principal components was compared between MZ and DZ twins. The analysis was conducted with both sexes pooled because of the limited number of twin pairs. The first two principal components explained at least 87% of the total variance, while the third principal component (PC3) explained a smaller portion of the total variance (more than 5%) in the dash and the endurance run. However, the total variance was explained almost entirely (more than 96%) by the first two components in height and mass. The first principal component (PC1), which explained 67–84% of the total variance, was a good indicator of average performance/body size of individuals during the period of observation. The second (PC2) and the third components (PC3) could be considered as indicators of the “shape” of developmental/growth curves, which were not related to inter‐individual differences in the average performance/body size. Intraclass correlations (r_i) of the scores on PC1 were consistently higher for MZ than for DZ twins, and estimated genetic variance was significant in endurance run, stature and body mass. The situation was the same for the scores on PC2 and/or PC3. However, such a between‐genotype difference in r_i and a significant genetic variance were not observed in the dash. It was concluded that MZ twins are more similar in the patterns of age‐related changes in the performance of endurance run and physical growth during adolescence than DZ twins, suggesting a genetic contribution, while there was no evidence of a genetic contribution in the dash. Am. J. Hum. Biol. 13:71–80, 2001. © 2001 Wiley‐Liss, Inc.     

Volumetric neuroimaging in Usher syndrome: Evidence of global involvement

Usher syndrome is a group of genetic disorders consisting of congenital sensorineural hearing loss and retinitis pigmentosa of variable onset and severity depending on the genetic type. It was suggested that the psychosis of Usher syndrome might be secondary to a metabolic degeneration involving the brain more diffusely. There have been reports of focal and diffuse atrophic changes in the supratentorial brain as well as atrophy of some of the structures of the posterior fossa. We previously performed quantitative analysis of magnetic resonance imaging studies of 19 Usher syndrome patients (12 with type I and 7 with type II) looking at the cerebellum and various cerebellar components. We found atrophy of the cerebellum in both types and sparing of cerebellar vermis lobules I–V in type II Usher syndrome patients only. We now have studied another group of 19 patients (with some overlap in the patients studied from the previous report) with Usher syndrome (8 with type I, 11 with type II). We performed quantitative volumetric measurements of various brain structures compared to age- and sex-matched controls. We found a significant decrease in intracranial volume and in size of the brain and cerebellum with a trend toward an increase in the size of the subarachnoid spaces. These data suggest that the disease process in Usher syndrome involves the entire brain and is not limited to the posterior fossa or auditory and visual systems. Am. J. Med. Genet. 79:1–4, 1998. © 1998 Wiley-Liss, Inc.     

Genes and body fat

This paper reviews recent data concerning the role of inherited differences in body fat content. The heritability of percent body fat or fat mass reaches about 25% of the age and gender adjusted phenotypic variance. One study has reported a significant major gene effect accounting for almost one half of the variance in body fat content. Experimental overfeeding studies suggest that body weight and fat gains are influenced by undefined genetic characteristics. Significant heritability estimates have been reported for major determinants of body fat content, including fat content of the diet, resting metabolic rate, thermic response to food, and level of habitual physical activity. Animal genetic studies and other experimental approaches indicate that the number of genes affecting body fat content, and associated with the susceptibility to obesity, is likely to be high. Currently available research strategies along with more extensive intermediate phenotyping will advance our knowledge about the genetic basis of human obesity. © 1993 Wiley-Liss, Inc.     

Genetic overlap between P300, P50, and duration mismatch negativity.

Mismatch Negativity (MMN), P300, and P50 suppression event-related potential (ERP) components measure intermediate stages of information processing but little is known of how they relate to each other genetically. The present study used multivariate genetic model fitting analytic techniques in 46 monozygotic and 32 dizygotic twin pairs. P300, P50 suppression, and MMN were recorded using a 19-channel electroencephalograph (EEG). Zygosity was determined using DNA genotyping. Little evidence for either genetic or environmental association between each of the three ERP paradigms was found. This result suggests that P300, MMN, and P50 suppression serve to evaluate different brain information processing functions that may be mediated by distinct neurobiological mechanisms which in turn are influenced by different sets of genes. Within paradigm, P300 amplitude and latency shared about half of their genetic effects.     

Univariate and Multivariate Genetic Analysis of Subcutaneous Fatness and Fat Distribution in Early Adolescence

Univariate and multivariate analyses of the genetic and environmental contributions to variance in adipose tissue and adipose tissue distribution were carried out in early adolescents. Stature, weight, body mass index (BMI), and five subcutaneous skinfolds were measured at half-yearly intervals in 105 MZ and DZ twin pairs from 10 to 14 years. The most parsimonious model, which provided an adequate explanation for variation in the BMI, five skinfolds, and the T/E ratio, included additive genetic and specific environmental factors. Multivariate analyses of the genetic architecture of subcutaneous fat indicated a general skinfold genetic factor, an extremity skinfold genetic factor, and skinfold specific genetic factors. This implies that all skinfolds are under control of the same set of genes, that a different set of genes partly controls extremity skinfolds, and that other genes have a small skinfold specific impact. Environmental contributions included a general skinfold environmental factor and skinfold specific environmental factors. BMI is under control of the same set of genes as skinfolds and shows high genetic correlations with trunk skinfolds, which implies that nearly the same genes may influence trunk skinfolds and the BMI. All models were fairly consistent across the age range.     

Genetics of pre-pubertal growth: A longitudinal study of Japanese twins

Background: Genetic factors explain a major part of the variation of adult stature, but little is still known on the genetics of growth, especially in non-Caucasian populations.

Aim: To analyse the quantitative genetics of pre-pubertal growth in Japanese children.

Subjects and methods: Data from birth until 11 years of age were collected on 349 complete twin pairs based on previously recorded height measures. The data were analysed using two different multivariate models by the Mx statistical package.

Results: No major sex differences were found and thus boys and girls were analysed together. Since 1 year of age, genetic factors explained from 42–71% and environmental factors shared by co-twins from 14–33% of the variation of height. Genetic continuity of height was high and 75% of the genetic variance was shared since 1 year of age. Environmental factors affecting height showed weaker correlations between early and late childhood than genetic factors.

Conclusion: Growth from early to late childhood is largely regulated by the same set of genes. However, also environmental factors shared by co-twins are important for growth. Identifying specific environmental factors affecting growth has potentially important public health implications, even in an affluent society such as Japan.     

Genetically Informed Regression Analysis: Application to Aggression Prediction by Inattention and Hyperactivity in Children and Adults

We present a procedure to simultaneously fit a genetic covariance structure model and a regression model to multivariate data from mono- and dizygotic twin pairs to test for the prediction of a dependent trait by multiple correlated predictors. We applied the model to aggressive behavior as an outcome trait and investigated the prediction of aggression from inattention (InA) and hyperactivity (HA) in two age groups. Predictions were examined in twins with an average age of 10 years (11,345 pairs), and in adult twins with an average age of 30 years (7433 pairs). All phenotypes were assessed by the same, but age-appropriate, instruments in children and adults. Because of the different genetic architecture of aggression, InA and HA, a model was fitted to these data that specified additive and non-additive genetic factors (A and D) plus common and unique environmental (C and E) influences. Given appropriate identifying constraints, this ADCE model is identified in trivariate data. We obtained different results for the prediction of aggression in children, where HA was the more important predictor, and in adults, where InA was the more important predictor. In children, about 36% of the total aggression variance was explained by the genetic and environmental components of HA and InA. Most of this was explained by the genetic components of HA and InA, i.e., 29.7%, with 22.6% due to the genetic component of HA. In adults, about 21% of the aggression variance was explained. Most was this was again explained by the genetic components of InA and HA (16.2%), with 8.6% due to the genetic component of InA.