Mimulus is an emerging model system for the integration of ecological and genomic studies

The plant genus Mimulus is rapidly emerging as a model system for studies of evolutionary and ecological functional genomics. Mimulus contains a wide array of phenotypic, ecological and genomic diversity. Numerous studies have proven the experimental tractability of Mimulus in laboratory and field studies. Genomic resources currently under development are making Mimulus an excellent system for determining the genetic and genomic basis of adaptation and speciation. Here, we introduce some of the phenotypic and genetic diversity in the genus Mimulus and highlight how direct genetic studies with Mimulus can address a wide spectrum of ecological and evolutionary questions. In addition, we present the genomic resources currently available for Mimulus and discuss future directions for research. The integration of ecology and genetics with bioinformatics and genome technology offers great promise for exploring the mechanistic basis of adaptive evolution and the genetics of speciation.     

Joint analysis of multiple phenotypes: summary of results and discussions from the Genetic Analysis Workshop 19

For Genetic Analysis Workshop 19, 2 extensive data sets were provided, including whole genome and whole exome sequence data, gene expression data, and longitudinal blood pressure outcomes, together with nongenetic covariates. These data sets gave researchers the chance to investigate different aspects of more complex relationships within the data, and the contributions in our working group focused on statistical methods for the joint analysis of multiple phenotypes, which is part of the research field of data integration. The analysis of data from different sources poses challenges to researchers but provides the opportunity to model the real-life situation more realistically.Our 4 contributions all used the provided real data to identify genetic predictors for blood pressure. In the contributions, novel multivariate rare variant tests, copula models, structural equation models and a sparse matrix representation variable selection approach were applied. Each of these statistical models can be used to investigate specific hypothesized relationships, which are described together with their biological assumptions.The results showed that all methods are ready for application on a genome-wide scale and can be used or extended to include multiple omics data sets. The results provide potentially interesting genetic targets for future investigation and replication. Furthermore, all contributions demonstrated that the analysis of complex data sets could benefit from modeling correlated phenotypes jointly as well as by adding further bioinformatics information.     

SASQuant: a SAS software program to estimate genetic effects and heritabilities of quantitative traits in populations consisting of 6 related generations

Plant breeders are interested in the analysis of phenotypic data to measure genetic effects and heritability of quantitative traits and predict gain from selection. Measurement of phenotypic values of 6 related generations (parents, F(1), F(2), and backcrosses) allows for the simultaneous analysis of both Mendelian and quantitative traits. In 1997, Liu et al. released a SAS software based program (SASGENE) for the analysis of inheritance and linkage of qualitative traits. We have developed a new program (SASQuant) that estimates gene effects (Hayman's model), genetic variances, heritability, predicted gain from selection (Wright's and Warner's models), and number of effective factors (Wright's, Mather's, and Lande's models). SASQuant makes use of traditional genetic models and allows for their easy application to complex data sets. SASQuant is freely available and is intended for scientists studying quantitative traits in plant populations.     

A simulation test bed for hypotheses of genome evolution

MOTIVATION: Microbial genomes undergo evolutionary processes such as gene family expansion and contraction, variable rates and patterns of sequence substitution and lateral genetic transfer. Simulation tools are essential for both the generation of data under different evolutionary models and the validation of analytical methods on such data. However, meaningful investigation of phenomena such as lateral genetic transfer requires the simultaneous consideration of many underlying evolutionary processes. RESULTS: We have developed EvolSimulator, a software package that combines non-stationary sequence and gene family evolution together with models of lateral genetic transfer, within a customizable birth-death model of speciation and extinction. Here, we examine simulated data sets generated with EvolSimulator using existing statistical techniques from the evolutionary literature, showing in detail each component of the simulation strategy. AVAILABILITY: Source code, manual and other information are freely available at www.bioinformatics.org.au/evolsim. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Performance of genomic selection in mice

Selection plans in plant and animal breeding are driven by genetic evaluation. Recent developments suggest using massive genetic marker information, known as "genomic selection." There is little evidence of its performance, though. We empirically compared three strategies for selection: (1) use of pedigree and phenotypic information, (2) use of genomewide markers and phenotypic information, and (3) the combination of both. We analyzed four traits from a heterogeneous mouse population (http://gscan.well.ox.ac.uk/), including 1884 individuals and 10,946 SNP markers. We used linear mixed models, using extensions of association analysis. Cross-validation techniques were used, providing assumption-free estimates of predictive ability. Sampling of validation and training data sets was carried out across and within families, which allows comparing across- and within-family information. Use of genomewide genetic markers increased predictive ability up to 0.22 across families and up to 0.03 within families. The latter is not statistically significant. These values are roughly comparable to increases of up to 0.57 (across family) and 0.14 (within family) in accuracy of prediction of genetic value. In this data set, within-family information was more accurate than across-family information, and populational linkage disequilibrium was not a completely accurate source of information for genetic evaluation. This fact questions some applications of genomic selection.     

The Homeodomain Resource: sequences, structures and genomic information.

The Homeodomain Resource is a comprehensive collection of sequence, structure and genomic information on the homeodomain protein family. Available through the Resource are both full-length and domain-only sequence data, as well as X-ray and NMR structural data for proteins and protein-DNA complexes. Also available is information on human genetic diseases and disorders in which proteins from the homeodomain family play an important role; genomic information includes relevant gene symbols, cytogenetic map locations, and specific mutation data. Search engines are provided to allow users to easily query the component databases and assemble specialized data sets. The Homeodomain Resource is available through the World Wide Web at http://genome.nhgri.nih.gov/homeodomain     

Identification of a QTL in Mus musculus for Alcohol Preference,Withdrawal, and Ap3m2 Expression Using Integrative Functional Genomics and Precision Genetics

Extensive genetic and genomic studies of the relationship between alcohol drinking preference and withdrawal severity have been performed using animal models. Data from multiple such publications and public data resources have been incorporated in the GeneWeaver database with >60,000 gene sets including 285 alcohol withdrawal and preference-related gene sets. Among these are evidence for positional candidates regulating these behaviors in overlapping quantitative trait loci (QTL) mapped in distinct mouse populations. Combinatorial integration of functional genomics experimental results revealed a single QTL positional candidate gene in one of the loci common to both preference and withdrawal. Functional validation studies in Ap3m2 knockout mice confirmed these relationships. Genetic validation involves confirming the existence of segregating polymorphisms that could account for the phenotypic effect. By exploiting recent advances in mouse genotyping, sequence, epigenetics, and phylogeny resources, we confirmed that Ap3m2 resides in an appropriately segregating genomic region. We have demonstrated genetic and alcohol-induced regulation of Ap3m2 expression. Although sequence analysis revealed no polymorphisms in the Ap3m2-coding region that could account for all phenotypic differences, there are several upstream SNPs that could. We have identified one of these to be an H3K4me3 site that exhibits strain differences in methylation. Thus, by making cross-species functional genomics readily computable we identified a common QTL candidate for two related bio-behavioral processes via functional evidence and demonstrate sufficiency of the genetic locus as a source of variation underlying two traits.     

Prediction of neuropeptide cleavage sites in insects

MOTIVATION: The production of neuropeptides from their precursor proteins is the result of a complex series of enzymatic processing steps. Often, the annotation of new neuropeptide genes from sequence information outstrips biochemical assays and so bioinformatics tools can provide rapid information on the most likely peptides produced by a gene. Predicting the final bioactive neuropeptides from precursor proteins requires accurate algorithms to determine which locations in the protein are cleaved. RESULTS: Predictive models were trained on Apis mellifera and Drosophila melanogaster precursors using binary logistic regression, multi-layer perceptron and k-nearest neighbor models. The final predictive models included specific amino acids at locations relative to the cleavage sites. Correct classification rates ranged from 78 to 100% indicating that the models adequately predicted cleaved and non-cleaved positions across a wide range of neuropeptide families and insect species. The model trained on D.melanogaster data had better generalization properties than the model trained on A. mellifera for the data sets considered. The reliable and consistent performance of the models in the test data sets suggests that the bioinformatics strategies proposed here can accurately predict neuropeptides in insects with sequence information based on neuropeptides with biochemical and sequence information in well-studied species.     

Unique patterns of evolutionary conservation of imprinted genes

During mammalian evolution, complex systems of epigenetic gene regulation have been established: Epigenetic mechanisms control tissue-specific gene expression, X chromosome inactivation in females and genomic imprinting. Studying DNA sequence conservation in imprinted genes, it becomes evident that evolution of gene function and evolution of epigenetic gene regulation are tightly connected. Furthermore, comparative studies allow the identification of DNA sequence features that distinguish imprinted genes from biallelically expressed genes. Among these features are CpG islands, tandem repeats and retrotransposed elements that are known to play major roles in epigenetic gene regulation. Currently, more and more genetic and epigenetic data sets become available. In future, such data sets will provide the basis for more complex investigations on epigenetic variation in human populations. Therein, an exciting topic will be the genetic and epigenetic variability of imprinted genes and its input on human disease.     

The FlyBase database of the Drosophila genome projects and community literature

FlyBase (http://flybase.bio.indiana.edu/) provides an integrated view of the fundamental genomic and genetic data on the major genetic model Drosophila melanogaster and related species. FlyBase has primary responsibility for the continual reannotation of the D. melanogaster genome. The ultimate goal of the reannotation effort is to decorate the euchromatic sequence of the genome with as much biological information as is available from the community and from the major genome project centers. A complete revision of the annotations of the now-finished euchromatic genomic sequence has been completed. There are many points of entry to the genome within FlyBase, most notably through maps, gene products and ontologies, structured phenotypic and gene expression data, and anatomy.     

Models for navigating biological complexity in breeding improved crop plants

Progress in breeding higher-yielding crop plants would be greatly accelerated if the phenotypic consequences of making changes to the genetic makeup of an organism could be reliably predicted. Developing a predictive capacity that scales from genotype to phenotype is impeded by biological complexities associated with genetic controls, environmental effects and interactions among plant growth and development processes. Plant modelling can help navigate a path through this complexity. Here we profile modelling approaches for complex traits at gene network, organ and whole plant levels. Each provides a means to link phenotypic consequence to changes in genomic regions via stable associations with model coefficients. A unifying feature of the models is the relatively coarse level of granularity they use to capture system dynamics. Much of the fine detail is not directly required. Robust coarse-grained models might be the tool needed to integrate phenotypic and molecular approaches to plant breeding.     

Functional and structural genomics using PEDANT

MOTIVATION: Enormous demand for fast and accurate analysis of biological sequences is fuelled by the pace of genome analysis efforts. There is also an acute need in reliable up-to-date genomic databases integrating both functional and structural information. Here we describe the current status of the PEDANT software system for high-throughput analysis of large biological sequence sets and the genome analysis server associated with it. RESULTS: The principal features of PEDANT are: (i) completely automatic processing of data using a wide range of bioinformatics methods, (ii) manual refinement of annotation, (iii) automatic and manual assignment of gene products to a number of functional and structural categories, (iv) extensive hyperlinked protein reports, and (v) advanced DNA and protein viewers. The system is easily extensible and allows to include custom methods, databases, and categories with minimal or no programming effort. PEDANT is actively used as a collaborative environment to support several on-going genome sequencing projects. The main purpose of the PEDANT genome database is to quickly disseminate well-organized information on completely sequenced and unfinished genomes. It currently includes 80 genomic sequences and in many cases serves as the only source of exhaustive information on a given genome. The database also acts as a vehicle for a number of research projects in bioinformatics. Using SQL queries, it is possible to correlate a large variety of pre-computed properties of gene products encoded in complete genomes with each other and compare them with data sets of special scientific interest. In particular, the availability of structural predictions for over 300 000 genomic proteins makes PEDANT the most extensive structural genomics resource available on the web.     

Accessing genomic information: alternatives to PCR

The growing abundance of genomic sequence data invites increasingly large-scale genetic analyses. Studies of genetic variation in large sets of genes can illuminate important disease mechanisms and serve to identify novel drug targets or predict therapeutic responses. At present mostly a concern in extensive research projects, large-scale genetic analyses will gradually also find their way into clinical practice as an aid to the physician. It is timely, therefore, to take stock of methods that are becoming available for analyses of large sets of gene sequences. Clearly PCR remains the workhorse for molecular genetic analysis, and several modifications such as homogenous amplification assays and parallel detection on DNA microarrays further increase throughput. Recent developments, however, also offer hope that other methods will become available for genomic investigations, providing substantially increased analytical capacity.     

Genetic algorithms applied to multi-class prediction for the analysis of gene expression data

MOTIVATION: An important challenge in the use of large-scale gene expression data for biological classification occurs when the expression dataset being analyzed involves multiple classes. Key issues that need to be addressed under such circumstances are the efficient selection of good predictive gene groups from datasets that are inherently 'noisy', and the development of new methodologies that can enhance the successful classification of these complex datasets. METHODS: We have applied genetic algorithms (GAs) to the problem of multi-class prediction. A GA-based gene selection scheme is described that automatically determines the members of a predictive gene group, as well as the optimal group size, that maximizes classification success using a maximum likelihood (MLHD) classification method. RESULTS: The GA/MLHD-based approach achieves higher classification accuracies than other published predictive methods on the same multi-class test dataset. It also permits substantial feature reduction in classifier genesets without compromising predictive accuracy. We propose that GA-based algorithms may represent a powerful new tool in the analysis and exploration of complex multi-class gene expression data. AVAILABILITY: Supplementary information, data sets and source codes are available at http://www.omniarray.com/bioinformatics/GA.