The Swedish Twin Registry : establishment of a biobank and other recent developments

The Swedish Twin Registry (STR) today contains more than 194,000 twins and more than 75,000 pairs have zygosity determined by an intra-pair similarity algorithm, DNA, or by being of opposite sex. Of these, approximately 20,000, 25,000, and 30,000 pairs are monozygotic, same-sex dizygotic, and opposite-sex dizygotic pairs, respectively. Since its establishment in the late 1950s, the STR has been an important epidemiological resource for the study of genetic and environmental influences on a multitude of traits, behaviors, and diseases. Following large investments in the collection of biological specimens in the past 10 years we have now established a Swedish twin biobank with DNA from 45,000 twins and blood serum from 15,000 twins, which effectively has also transformed the registry into a powerful resource for molecular studies. We here describe the main projects within which the new collections of both biological samples as well as phenotypic measures have been collected. Coverage by year of birth, zygosity determination, ethnic heterogeneity, and influences of in vitro fertilization are also described.VetenskapsrådetNIHSSFHjärt- och LungfondenAstma- och AllergiförbundetAccepte

Avpr1a variant associated with preschoolers' lower altruistic behavior

10.1371/journal.pone.0025274PLoS ONE69

Caenorhabditis elegans behavioral genetics: where are the knobs?

Thousands of behavioral mutants of Caenorhabditis elegans have been studied. I suggest a set of criteria by which some genes important in the evolution of behavior might be recognized, and identify neuropeptide signaling pathways as candidates

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

<p>Abstract</p> <p>Background</p> <p>Bioactivity profiling using high-throughput <it>in vitro </it>assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex <it>in vitro/in vivo </it>datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.</p> <p>Results</p> <p>The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated <it>in vitro </it>assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.</p> <p>Conclusion</p> <p>We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.</p

Common marmoset (Callithrix jacchus) personality, subjective well-being, hair cortisol level and AVPR1a, OPRM1, and DAT genotypes

We studied personality, subjective well-being, and hair cortisol level, in common marmosets Callithrix jacchus, a small, cooperatively breeding New World monkey, by examining their associations with one another and genotypes. Subjects were 68 males and 9 females that lived in the RIKEN Center for Life Science Technologies. Personality and subjective well-being were assessed by keeper ratings on two questionnaires, hair samples were obtained to assay cortisol level and buccal swabs were used to assess AVPR1a, OPRM1 and DAT genotypes. Three personality domains—Dominance, Sociability, and Neuroticism—were identified. Consistent with findings in other species, Sociability and Neuroticism were related to higher and lower subjective well-being, respectively. Sociability was also associated with higher hair cortisol levels. The personality domains and hair cortisol levels were heritable and associated with genotypes: the short form of AVPR1a was associated with lower Neuroticism and the AA genotype of the A111T SNP of OPRM1 was related to lower Dominance, lower Neuroticism, and higher hair cortisol level. Some genetic associations were not in directions that one would expect given findings in other species. These findings provide insights into the proximate and ultimate bases of personality in common marmosets, other primates and humans

Generation of Induced Pluripotent Stem Cells from the Prairie Vole

The vast majority of animals mate more or less promiscuously. A few mammals, including humans, utilize more restrained mating strategies that entail a longer term affiliation with a single mating partner. Such pair bonding mating strategies have been resistant to genetic analysis because of a lack of suitable model organisms. Prairie voles are small mouse-like rodents that form enduring pair bonds in the wild as well as in the laboratory, and consequently they have been used widely to study social bonding behavior. The lack of targeted genetic approaches in this species however has restricted the study of the molecular and neural circuit basis of pair bonds. As a first step in rendering the prairie vole amenable to reverse genetics, we have generated induced pluripotent stem cell (IPSC) lines from prairie vole fibroblasts using retroviral transduction of reprogramming factors. These IPSC lines display the cellular and molecular hallmarks of IPSC cells from other organisms, including mice and humans. Moreover, the prairie vole IPSC lines have pluripotent differentiation potential since they can give rise to all three germ layers in tissue culture and in vivo. These IPSC lines can now be used to develop conditions that facilitate homologous recombination and eventually the generation of prairie voles bearing targeted genetic modifications to study the molecular and neural basis of pair bond formation