GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Background: The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence-and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results: GANN ( available at http://bioinformatics.org.au/gann) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion: GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.

The genetic basis of academic achievement on the Queensland Core Skills Test and its shared genetic variance with IQ

First, this study examined genetic and environmental sources of variation in performance on a standardised test of academic achievement, the Queensland Core Skills Test (QCST) (Queensland Studies Authority, 2003a). Second, it assessed the genetic correlation among the QCST score and Verbal and Performance IQ measures using the Multidimensional Aptitude Battery (MAB), [Jackson, D. N. (1984) Multidimensional Aptitude Battery manual. Port Huron, MI:Research Psychologist Press, Inc.]. Participants were 256 monozygotic twin pairs and 326 dizygotic twin pairs aged from 15 to 18 years (mean 17 years +/- 0.4 [SD]) when achievement tested, and from 15 to 22 years (mean 16 years +/- 0.4 [SD]) when IQ tested. Univariate analysis indicated a heritability for the QCST of 0.72. Adjustment to this estimate due to truncate selection (downward adjustment) and positive phenotypic assortative mating (upward adjustment) suggested a heritability of 0.76 The phenotypic (0.81) and genetic (0.91) correlations between the QCST and Verbal IQ (VIQ) were significantly stronger than the phenotypic (0.57) and genetic (0.64) correlations between the QCST and Performance IQ (PIQ). The findings suggest that individual variation in QCST performance is largely due to genetic factors and that common environmental effects may be substantially accounted for by phenotypic assortative mating. Covariance between academic achievement on the QCST and psychometric IQ (particularly VIQ) is to a large extent due to common genetic influences.

Predictive genetic test decisions for Huntington's disease: Elucidating the test/no-test dichotomy

Predictive genetic testing for serious, mature-onset genetic illness represents a unique context in health decision making. This article presents findings from an exploratory qualitative Australian-based study into the decision making of individuals at risk for Huntington's disease (HD) with regard to predictive genetic testing. Sixteen in-depth interviews were conducted with a range of at-risk individuals. Data analysis revealed four discrete decision-making positions rather than a 'to test' or not to test' dichotomy. A conceptual dimension of (non-)openness and (non-)engagement characterized the various decisions. Processes of decision making and a concept of 'test readiness' were identified. Findings from this research, while not generalizable, are discussed in relation to theoretical frameworks and stage models of health decision making, as well as possible clinical implications.

Unlocking pathology archives for molecular genetic studies: a reliable method to generate probes for chromogenic and fluorescent in situ hybridization

Chromogenic (CISH) and fluorescent ( FISH) in situ hybridization have emerged as reliable techniques to identify amplifications and chromosomal translocations. CISH provides a spatial distribution of gene copy number changes in tumour tissue and allows a direct correlation between copy number changes and the morphological features of neoplastic cells. However, the limited number of commercially available gene probes has hindered the use of this technique. We have devised a protocol to generate probes for CISH that can be applied to formalin-fixed, paraffin-embedded tissue sections (FFPETS). Bacterial artificial chromosomes ( BACs) containing fragments of human DNA which map to specific genomic regions of interest are amplified with phi 29 polymerase and random primer labelled with biotin. The genomic location of these can be readily confirmed by BAC end pair sequencing and FISH mapping on normal lymphocyte metaphase spreads. To demonstrate the reliability of the probes generated with this protocol, four strategies were employed: (i) probes mapping to cyclin D1 (CCND1) were generated and their performance was compared with that of a commercially available probe for the same gene in a series of 10 FFPETS of breast cancer samples of which five harboured CCND1 amplification; (ii) probes targeting cyclin-dependent kinase 4 were used to validate an amplification identified by microarray-based comparative genomic hybridization (aCGH) in a pleomorphic adenoma; (iii) probes targeting fibroblast growth factor receptor 1 and CCND1 were used to validate amplifications mapping to these regions, as defined by aCGH, in an invasive lobular breast carcinoma with FISH and CISH; and (iv) gene-specific probes for ETV6 and NTRK3 were used to demonstrate the presence of t(12; 15)(p12; q25) translocation in a case of breast secretory carcinoma with dual colour FISH. In summary, this protocol enables the generation of probes mapping to any gene of interest that can be applied to FFPETS, allowing correlation of morphological features with gene copy number.

A framework for the study of genetic variation in migratory behaviour

Evolutionary change results from selection acting on genetic variation. For migration to be successful, many different aspects of an animal's physiology and behaviour need to function in a co-coordinated way. Changes in one migratory trait are therefore likely to be accompanied by changes in other migratory and life-history traits. At present, we have some knowledge of the pressures that operate at the various stages of migration, but we know very little about the extent of genetic variation in various aspects of the migratory syndrome. As a consequence, our ability to predict which species is capable of what kind of evolutionary change, and at which rate, is limited. Here, we review how our evolutionary understanding of migration may benefit from taking a quantitative-genetic approach and present a framework for studying the causes of phenotypic variation. We review past research, that has mainly studied single migratory traits in captive birds, and discuss how this work could be extended to study genetic variation in the wild and to account for genetic correlations and correlated selection. In the future, reaction-norm approaches may become very important, as they allow the study of genetic and environmental effects on phenotypic expression within a single framework, as well as of their interactions. We advocate making more use of repeated measurements on single individuals to study the causes of among-individual variation in the wild, as they are easier to obtain than data on relatives and can provide valuable information for identifying and selecting traits. This approach will be particularly informative if it involves systematic testing of individuals under different environmental conditions. We propose extending this research agenda by using optimality models to predict levels of variation and covariation among traits and constraints. This may help us to select traits in which we might expect genetic variation, and to identify the most informative environmental axes. We also recommend an expansion of the passerine model, as this model does not apply to birds, like geese, where cultural transmission of spatio-temporal information is an important determinant of migration patterns and their variation.

Why are there so few genetic markers available for coral population analyses?

Coral reefs are in serious decline, and research in support of reef management objectives is urgently needed. Reef connectivity analyses have been highlighted as one of the major future research avenues necessary for implementing effective management initiatives for coral reefs. Despite the number of new molecular genetic tools and the wealth of information that is now available for population-level processes in many marine disciplines, scleractinian coral population genetic information remains surprisingly limited. Here we examine the technical problems and approaches used, address the reasons contributing to this delay in understanding, and discuss the future of coral population marker development. Considerable resources are needed to target the immediate development of an array of relevant genetic markers coupled with the rapid production of management focused data in order to help conserve our globally threatened coral reef resources.