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.     

Interplay of cis- and trans-regulatory mechanisms in the spliceosomal RNA helicase Brr2

RNA helicase Brr2 is implicated in multiple phases of pre-mRNA splicing and thus requires tight regulation. Brr2 can be auto-inhibited via a large N-terminal region folding back onto its helicase core and auto-activated by a catalytically inactive C-terminal helicase cassette. Furthermore, it can be regulated in trans by the Jab1 domain of the Prp8 protein, which can inhibit Brr2 by intermittently inserting a C-terminal tail in the enzyme's RNA-binding tunnel or activate the helicase after removal of this tail. Presently it is unclear, whether these regulatory mechanisms functionally interact and to which extent they are evolutionarily conserved. Here, we report crystal structures of Saccharomyces cerevisiae and Chaetomium thermophilum Brr2-Jab1 complexes, demonstrating that Jab1-based inhibition of Brr2 presumably takes effect in all eukaryotes but is implemented via organism-specific molecular contacts. Moreover, the structures show that Brr2 auto-inhibition can act in concert with Jab1-mediated inhibition, and suggest that the N-terminal region influences how the Jab1 C-terminal tail interacts at the RNA-binding tunnel. Systematic RNA binding and unwinding studies revealed that the N-terminal region and the Jab1 C-terminal tail specifically interfere with accommodation of double-stranded and single-stranded regions of an RNA substrate, respectively, mutually reinforcing each other. Additionally, such analyses show that regulation based on the N-terminal region requires the presence of the inactive C-terminal helicase cassette. Together, our results outline an intricate system of regulatory mechanisms, which control Brr2 activities during snRNP assembly and splicing.     

Genomic mapping of the MHC transactivator CIITA using an integrated ChIP-seq and genetical genomics approach

Background

The master transactivator CIITA is essential to the regulation of Major Histocompatibility Complex (MHC) class II genes and an effective immune response. CIITA is known to modulate a small number of non-MHC genes involved in antigen presentation such as CD74 and B2M but its broader genome-wide function and relationship with underlying genetic diversity has not been resolved.

Results

We report the first genome-wide ChIP-seq map for CIITA and complement this by mapping inter-individual variation in CIITA expression as a quantitative trait. We analyse CIITA recruitment for pathophysiologically relevant primary human B cells and monocytes, resting and treated with interferon-gamma, in the context of the epigenomic regulatory landscape and DNA-binding proteins associated with the CIITA enhanceosome including RFX, CREB1/ATF1 and NFY. We confirm recruitment to proximal promoter sequences in MHC class II genes and more distally involving the canonical CIITA enhanceosome. Overall, we map 843 CIITA binding intervals involving 442 genes and find 95% of intervals are located outside the MHC and 60% not associated with RFX5 binding. Binding intervals are enriched for genes involved in immune function and infectious disease with novel loci including major histone gene clusters. We resolve differentially expressed genes associated in trans with a CIITA intronic sequence variant, integrate with CIITA recruitment and show how this is mediated by allele-specific recruitment of NF-kB.

Conclusions

Our results indicate a broader role for CIITA beyond the MHC involving immune-related genes. We provide new insights into allele-specific regulation of CIITA informative for understanding gene function and disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0494-z) contains supplementary material, which is available to authorized users.     

Integrative analysis of genetical genomics data incorporating network structures

In a living organism, tens of thousands of genes are expressed and interact with each other to achieve necessary cellular functions. Gene regulatory networks contain information on regulatory mechanisms and the functions of gene expressions. Thus, incorporating network structures, discerned either through biological experiments or statistical estimations, could potentially increase the selection and estimation accuracy of genes associated with a phenotype of interest. Here, we considered a gene selection problem using gene expression data and the graphical structures found in gene networks. Because gene expression measurements are intermediate phenotypes between a trait and its associated genes, we adopted an instrumental variable regression approach. We treated genetic variants as instrumental variables to address the endogeneity issue. We proposed a two‐step estimation procedure. In the first step, we applied the LASSO algorithm to estimate the effects of genetic variants on gene expression measurements. In the second step, the projected expression measurements obtained from the first step were treated as input variables. A graph‐constrained regularization method was adopted to improve the efficiency of gene selection and estimation. We theoretically showed the selection consistency of the estimation method and derived the bound of the estimates. Simulation and real data analyses were conducted to demonstrate the effectiveness of our method and to compare it with its counterparts.     

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.     

Gene network inference via structural equation modeling in genetical genomics experiments

Our goal is gene network inference in genetical genomics or systems genetics experiments. For species where sequence information is available, we first perform expression quantitative trait locus (eQTL) mapping by jointly utilizing cis-, cis-trans-, and trans-regulation. After using local structural models to identify regulator-target pairs for each eQTL, we construct an encompassing directed network (EDN) by assembling all retained regulator-target relationships. The EDN has nodes corresponding to expressed genes and eQTL and directed edges from eQTL to cis-regulated target genes, from cis-regulated genes to cis-trans-regulated target genes, from trans-regulator genes to target genes, and from trans-eQTL to target genes. For network inference within the strongly constrained search space defined by the EDN, we propose structural equation modeling (SEM), because it can model cyclic networks and the EDN indeed contains feedback relationships. On the basis of a factorization of the likelihood and the constrained search space, our SEM algorithm infers networks involving several hundred genes and eQTL. Structure inference is based on a penalized likelihood ratio and an adaptation of Occam's window model selection. The SEM algorithm was evaluated using data simulated with nonlinear ordinary differential equations and known cyclic network topologies and was applied to a real yeast data set.     

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     

A genetical genomics approach to genome scans increases power for QTL mapping

We describe a method for integrating gene expression information into genome scans and show that this can substantially increase the statistical power of QTL mapping. The method has three stages. First, standard clustering methods identify small (size 5-20) groups of genes with similar expression patterns. Second, each gene group is tested for a causative genetic locus shared with the clinical trait of interest. This is done using an EM algorithm approach that treats genotype at the putative causative locus as an unobserved variable and combines expression information from all of the genes in the group to infer genotype information at the locus. Finally, expression QTL (eQTL) are mapped for each gene group that shares a causative locus with the clinical trait. Such eQTL are candidates for the causative locus. Simulation results show that this method has far superior power to standard QTL mapping techniques in many circumstances. We applied this method to existing data on mouse obesity. Our method identified 27 putative body weight QTL, whereas standard QTL mapping produced only one. Furthermore, most gene groups with body weight QTL included cis genes, so candidate genes could be immediately identified. Eleven body weight QTL produced 16 candidate genes that have been previously associated with body weight or body weight-related traits, thus validating our method. In addition, 15 of the 16 other loci produced 32 candidate genes that have not been associated with body weight. Thus, this method shows great promise for finding new causative loci for complex traits.     

Mapping determinants of gene expression plasticity by genetical genomics in C. elegans

Recent genetical genomics studies have provided intimate views on gene regulatory networks. Gene expression variations between genetically different individuals have been mapped to the causal regulatory regions, termed expression quantitative trait loci. Whether the environment-induced plastic response of gene expression also shows heritable difference has not yet been studied. Here we show that differential expression induced by temperatures of 16 °C and 24 °C has a strong genetic component in Caenorhabditis elegans recombinant inbred strains derived from a cross between strains CB4856 (Hawaii) and N2 (Bristol). No less than 59% of 308 trans-acting genes showed a significant eQTL-by-environment interaction, here termed plasticity quantitative trait loci. In contrast, only 8% of an estimated 188 cis-acting genes showed such interaction. This indicates that heritable differences in plastic responses of gene expression are largely regulated in trans. This regulation is spread over many different regulators. However, for one group of trans-genes we found prominent evidence for a common master regulator: a transband of 66 coregulated genes appeared at 24 °C. Our results suggest widespread genetic variation of differential expression responses to environmental impacts and demonstrate the potential of genetical genomics for mapping the molecular determinants of phenotypic plasticity.