Genetical genomics in livestock: potentials and pitfalls

Genetical genomics combines gene mapping and gene expression approaches to identify loci controlling gene expression (eQTLs) that may underlie functional trait variation. The combination of genomic tools has great potential to facilitate dissection of complex traits, but studies need careful design and interpretation. Here we explore both the potential and the pitfalls of this approach with illustrations from actual studies. There are now an appreciable number of studies in model species and even humans demonstrating the feasibility of genetical genomics. However, most studies are too limited in size and design to unlock the full potential of the approach. Limited statistical power of studies exacerbates the problem of detection of false-positive eQTL and some reported results should be interpreted with caution. As one approach to more successful implementation of genetical genomics, we propose to combine expression studies with fine mapping of functional trait loci. This synergistic approach facilitates the implementation of genetical genomics for species without inbred resources but is equally applicable to model species. These properties make it particularly suitable for livestock populations where many QTL are already in the public domain and potentially very large pedigreed populations can be accessed.     

Complex traits analysis of chicken growth using targeted genetical genomics

Dissecting the genetic control of complex trait variation remains very challenging, despite many advances in technology. The aim of this study was to use a major growth quantitative trait locus (QTL) in chickens mapped to chromosome 4 as a model for a targeted approach to dissect the QTL. We applied a variant of the genetical genomics approach to investigate genome-wide gene expression differences between two contrasting genotypes of a marked QTL. This targeted approach allows the direct quantification of the link between the genotypes and the genetic responses, thus narrowing the QTL-phenotype gap using fewer samples (i.e. microarrays) compared with the genome-wide genetical genomics studies. Four differentially expressed genes were localized under the region of the QTL. One of these genes is a potential positional candidate gene (AADAT) that affects lysine and tryptophan metabolism and has alternative splicing variants between the two genotypes. In addition, the lysine and glycolysis metabolism pathways were significantly enriched for differentially expressed genes across the genome. The targeted approach provided a complementary route to fine mapping of QTL by characterizing the local and the global downstream effects of the QTL and thus generating further hypotheses about the action of that QTL.     

Learning Gene Networks under SNP Perturbations Using eQTL Datasets

The standard approach for identifying gene networks is based on experimental perturbations of gene regulatory systems such as gene knock-out experiments, followed by a genome-wide profiling of differential gene expressions. However, this approach is significantly limited in that it is not possible to perturb more than one or two genes simultaneously to discover complex gene interactions or to distinguish between direct and indirect downstream regulations of the differentially-expressed genes. As an alternative, genetical genomics study has been proposed to treat naturally-occurring genetic variants as potential perturbants of gene regulatory system and to recover gene networks via analysis of population gene-expression and genotype data. Despite many advantages of genetical genomics data analysis, the computational challenge that the effects of multifactorial genetic perturbations should be decoded simultaneously from data has prevented a widespread application of genetical genomics analysis. In this article, we propose a statistical framework for learning gene networks that overcomes the limitations of experimental perturbation methods and addresses the challenges of genetical genomics analysis. We introduce a new statistical model, called a sparse conditional Gaussian graphical model, and describe an efficient learning algorithm that simultaneously decodes the perturbations of gene regulatory system by a large number of SNPs to identify a gene network along with expression quantitative trait loci (eQTLs) that perturb this network. While our statistical model captures direct genetic perturbations of gene network, by performing inference on the probabilistic graphical model, we obtain detailed characterizations of how the direct SNP perturbation effects propagate through the gene network to perturb other genes indirectly. We demonstrate our statistical method using HapMap-simulated and yeast eQTL datasets. In particular, the yeast gene network identified computationally by our method under SNP perturbations is well supported by the results from experimental perturbation studies related to DNA replication stress response.     

Sequence polymorphisms cause many false cis eQTLs

Many investigations have reported the successful mapping of quantitative trait loci (QTLs) for gene expression phenotypes (eQTLs). Local eQTLs, where expression phenotypes map to the genes themselves, are of especially great interest, because they are direct candidates for previously mapped physiological QTLs. Here we show that many mapped local eQTLs in genetical genomics experiments do not reflect actual expression differences caused by sequence polymorphisms in cis-acting factors changing mRNA levels. Instead they indicate hybridization differences caused by sequence polymorphisms in the mRNA region that is targeted by the microarray probes. Many such polymorphisms can be detected by a sensitive and novel statistical approach that takes the individual probe signals into account. Applying this approach to recent mouse and human eQTL data, we demonstrate that indeed many local eQTLs are falsely reported as "cis-acting" or "cis" and can be successfully detected and eliminated with this approach.     

Generalizing genetical genomics: getting added value from environmental perturbation

Genetical genomics is a useful approach for studying the effect of genetic perturbations on biological systems at the molecular level. However, molecular networks depend on the environmental conditions and, thus, a comprehensive understanding of biological systems requires studying them across multiple environments. We propose a generalization of genetical genomics, which combines genetic and sensibly chosen environmental perturbations, to study the plasticity of molecular networks. This strategy forms a crucial step toward understanding why individuals respond differently to drugs, toxins, pathogens, nutrients and other environmental influences. Here we outline a strategy for selecting and allocating individuals to particular treatments, and we discuss the promises and pitfalls of the generalized genetical genomics approach.     

Integrating biological information into the statistical analysis and design of microarray experiments

Microarray technology is a powerful tool for animal functional genomics studies, with applications spanning from gene identification and mapping, to function and control of gene expression. Microarray assays, however, are complex and costly, and hence generally performed with relatively small number of animals. Nevertheless, they generate data sets of unprecedented complexity and dimensionality. Therefore, such trials require careful planning and experimental design, in addition to tailored statistical and computational tools for their appropriate data mining. In this review, we discuss experimental design and data analysis strategies, which incorporate prior genomic and biological knowledge, such as genotypes and gene function and pathway membership. We focus the discussion on the design of genetical genomics studies, and on significance testing for detection of differential expression. It is shown that the use of prior biological information can improve the efficiency of microarray experiments.     

Expression Quantitative Trait Loci Are Highly Sensitive to Cellular Differentiation State

Genetical genomics is a strategy for mapping gene expression variation to expression quantitative trait loci (eQTLs). We performed a genetical genomics experiment in four functionally distinct but developmentally closely related hematopoietic cell populations isolated from the BXD panel of recombinant inbred mouse strains. This analysis allowed us to analyze eQTL robustness/sensitivity across different cellular differentiation states. Although we identified a large number (365) of “static” eQTLs that were consistently active in all four cell types, we found a much larger number (1,283) of “dynamic” eQTLs showing cell-type–dependence. Of these, 140, 45, 531, and 295 were preferentially active in stem, progenitor, erythroid, and myeloid cells, respectively. A detailed investigation of those dynamic eQTLs showed that in many cases the eQTL specificity was associated with expression changes in the target gene. We found no evidence for target genes that were regulated by distinct eQTLs in different cell types, suggesting that large-scale changes within functional regulatory networks are uncommon. Our results demonstrate that heritable differences in gene expression are highly sensitive to the developmental stage of the cell population under study. Therefore, future genetical genomics studies should aim at studying multiple well-defined and highly purified cell types in order to construct as comprehensive a picture of the changing functional regulatory relationships as possible.     

Biological control and the species status of two host-associated populations of Trissolcus basalis (Wollaston) (Hymenoptera: Scelionidae).

Abstract Populations of the morphologically defined taxon Trissolcus basalis (Wollaston) from two major hosts (Nezara viridula [L.] and Agonoscelis rutila [F.]) were tested, using biological criteria, to determine whether they represented populations of one genetical species, or whether they represented populations of two host-associated sibling species. Individuals from both hosts exhibited no obvious courtship differences and they selected mates at random in mate-choice experiments. In addition, female wasps produced offspring of both sexes after mating with a male from either population. We conclude that the individuals of the two populations tested constitute a single genetical species.     

Organ specificity and transcriptional control of metabolic routes revealed by expression QTL profiling of source-sink tissues in a segregating potato population

Background  

With the completion of genome sequences belonging to some of the major crop plants, new challenges arise to utilize this data for crop improvement and increased food security. The field of genetical genomics has the potential to identify genes displaying heritable differential expression associated to important phenotypic traits. Here we describe the identification of expression QTLs (eQTLs) in two different potato tissues of a segregating potato population and query the potato genome sequence to differentiate between cis- and trans-acting eQTLs in relation to gene subfunctionalization.     

The influence of genetics on future crop production strategies: from traits to genes, and genes to traits

This paper discusses how a genetical approach to plant physiology can contribute to research underpinning the production of new crop varieties. It highlights the interactions between genetics and plant breeding and how the current advances in genetics and the new science of genomics can contribute to our understanding of the genetical control of key agronomic traits ‐ the process of ‘translating’ traits to identified and mapped genes. Advances in genomics, such as the sequencing of whole genomes and expressed sequence tags, are producing information on genes and gene structures, but without knowing their function. A great deal more biology will be necessary to translate gene structure to function ‐ the process of translating genes to traits. Combining these ‘forward’ and ‘reverse’ genetic approaches will allow us to get comprehensive knowledge of the biology of agronomic traits at the physiological, biochemical and molecular levels, so that the ‘circuitry’ of our crop plants can be elucidated. This will enable plant breeders to manipulate crop phenotype using marker‐assisted breeding or genetic engineering approaches with a precision not previously possible.     

Qxpak: a versatile mixed model application for genetical genomics and QTL analyses

MOTIVATION: Current methodology and software for quantitative trait loci (QTL) analyses do not use all available information and are inadequate to deal with the huge amount of QTL analyses to be needed in forecoming genetical genomics' studies. RESULTS: We show that a mixed model statistical framework provides a very flexible tool for QTL modeling in a variety of populations, be it a cross between inbred lines, a within population study, or experiments involving a mixture of populations or crosses. The software allows multitrait and multiQTL analyses, inclusion of infinitesimal genetic value and a batch multitrait option suitable for genetical genomics studies. It also allows massive association studies between single nucleotide polymorphisms and the trait(s) of interest. AVAILABILITY: A software (Qxpak), together with a manual and example files, is freely available for research purposes. So far, the compiled program is available for linux systems, the windows version will follow soon. See http://www.icrea.es/pag.asp?id=Miguel.Perez     

Genetic complexity and quantitative trait loci mapping of yeast morphological traits

Functional genomics relies on two essential parameters: the sensitivity of phenotypic measures and the power to detect genomic perturbations that cause phenotypic variations. In model organisms, two types of perturbations are widely used. Artificial mutations can be introduced in virtually any gene and allow the systematic analysis of gene function via mutants fitness. Alternatively, natural genetic variations can be associated to particular phenotypes via genetic mapping. However, the access to genome manipulation and breeding provided by model organisms is sometimes counterbalanced by phenotyping limitations. Here we investigated the natural genetic diversity of Saccharomyces cerevisiae cellular morphology using a very sensitive high-throughput imaging platform. We quantified 501 morphological parameters in over 50,000 yeast cells from a cross between two wild-type divergent backgrounds. Extensive morphological differences were found between these backgrounds. The genetic architecture of the traits was complex, with evidence of both epistasis and transgressive segregation. We mapped quantitative trait loci (QTL) for 67 traits and discovered 364 correlations between traits segregation and inheritance of gene expression levels. We validated one QTL by the replacement of a single base in the genome. This study illustrates the natural diversity and complexity of cellular traits among natural yeast strains and provides an ideal framework for a genetical genomics dissection of multiple traits. Our results did not overlap with results previously obtained from systematic deletion strains, showing that both approaches are necessary for the functional exploration of genomes.