Understanding quantitative genetics in the systems biology era

Role of genetic architecture in phenotypic plasticity

Phenotypic plasticity, the ability of an organism to display different phenotypes across environments, is widespread in nature. Plasticity aids survival in novel environments. Herein, we review studies from yeast that allow us to start uncovering the genetic architecture of phenotypic plasticity. Genetic variants and their interactions impact the phenotype in different environments, and distinct environments modulate the impact of genetic variants and their interactions on the phenotype. Because of this, certain hidden genetic variation is expressed in specific genetic and environmental backgrounds. A better understanding of the genetic mechanisms of phenotypic plasticity will help to determine short- and long-term responses to selection and how wide variation in disease manifestation occurs in human populations.

Effect of genotype and environment on agronomical characters of alfalfa (Medicago sativa L.) in a typical acidic soil environment in southwest China

Alfalfa (Medicago sativa L.), an important perennial legume forage crop with high nutritional value and forage yield, is widely used in animal husbandry. However, it is very sensitive to aluminum, which severely limits its growth in acidic soils. In this study, we analyzed the genotype variation of each agronomic trait in 44 alfalfa varieties in two acidic soil environments. Then, analysis of variance (ANOVA) of the variance components was performed using the Residual Maximum Likelihood (REML). The best linear unbiased predictor analysis was used to obtain the mean trait of each variety, and the mean values were used to construct the mean matrix of varieties × traits and interaction analysis of varieties × years. The results showed that there was significant (P < 0.05) genotypic variation for each trait of the 44 varieties and the genetic diversity was abundant. The average repeatability (R value) of interannual plant height (PH), stem thickness (ST), number of branches (NS), fresh weight (FW), total fresh weight (TFW), and total dry weight (TDW) was high (0.21–0.34), whereas the genetics were relatively stable. PH, NS, FW, TFW, and dry weight (DW) were positively correlated (P < 0.01) with TDW. Six alfalfa varieties (Algonquin, Xinjiang daye, Trifecta, Vernal, WL354HQ, and Boja) with excellent TDW and TFW were identified in different years, environmental regions, and climatic altitudes. Our research results can provide suggestions and critical information regarding the future improvement and development of new alfalfa strains and varieties that are resistant to acidic soil conditions.

OsbHLHq11, the Basic Helix-Loop-Helix Transcription Factor, Involved in Regulation of Chlorophyll Content in Rice

Photosynthesis is an important factor in determining the yield of rice. In particular, the size and efficiency of the photosynthetic system after the heading has a great impact on the yield. Research related to high-efficiency photosynthesis is essential to meet the growing demands of crops for the growing population. Chlorophyll is a key molecule in photosynthesis, a pigment that acts as an antenna to absorb light energy. Improvement of chlorophyll content characteristics has been emphasized in rice breeding for several decades. It is expected that an increase in chlorophyll content may increase photosynthetic efficiency, and understanding the genetic basis involved is important. In this study, we measured leaf color (CIELAB), chlorophyll content (SPAD), and chlorophyll fluorescence, and quantitative trait loci (QTL) mapping was performed using 120 Cheongcheong/Nagdong double haploid (CNDH) line after the heading date. A major QTL related to chlorophyll content was detected in the RM26981-RM287 region of chromosome 11. OsbHLHq11 was finally selected through screening of genes related to chlorophyll content in the RM26981-RM287 region. The relative expression level of the gene of OsbHLHq11 was highly expressed in cultivars with low chlorophyll content, and is expected to have a similar function to BHLH62 of the Gramineae genus. OsbHLHq11 is expected to increase photosynthetic efficiency by being involved in the chlorophyll content, and is expected to be utilized as a new genetic resource for breeding high-yield rice.

Identification and classification of differentially expressed genes in pyrethroid-resistant Culex pipiens pallens

Culex pipiens pallens is an important vector that transmits Bancroftian filariasis, Japanese encephalitis and other diseases that pose a serious threat to human health. Extensive and improper use of insecticides has caused insecticide resistance in mosquitoes, which has become an important obstacle to the control of mosquito-borne diseases. It is crucial to investigate the underlying mechanism of insecticide resistance. The aims of this study were to identify genes involved in insecticide resistance based on the resistance phenotype, gene expression profile and single-nucleotide polymorphisms (SNPs) and to screen for major genes controlling insecticide resistance. Using a combination of SNP and transcriptome data, gene expression quantitative trait loci (eQTLs) were studied in deltamethrin-resistant mosquitoes. The most differentially expressed pathway in the resistant group was identified, and a regulatory network was built using these SNPs and the differentially expressed genes (DEGs) in this pathway. The major candidate genes involved in the control of insecticide resistance were analyzed by qPCR, siRNA microinjection and CDC bottle bioassays. A total of 85 DEGs that encoded putative detoxification enzymes (including 61 P450s) were identified in this pathway. The resistance regulatory network was built using SNPs, and these metabolic genes, and a major gene, CYP9AL1, were identified. The functional role of CYP9AL1 in insecticide resistance was confirmed by siRNA microinjection and CDC bottle bioassays. Using the eQTL approach, we identified important genes in pyrethroid resistance that may aid in understanding the mechanism underlying insecticide resistance and in targeting new measures for resistance monitoring and management.

Integrative Analysis of Metabolomic, Proteomic and Genomic Data to Reveal Functional Pathways and Candidate Genes for Drip Loss in Pigs

The aim of this study was to integrate multi omics data to characterize underlying functional pathways and candidate genes for drip loss in pigs. The consideration of different omics levels allows elucidating the black box of phenotype expression. Metabolite and protein profiling was applied in Musculus longissimus dorsi samples of 97 Duroc × Pietrain pigs. In total, 126 and 35 annotated metabolites and proteins were quantified, respectively. In addition, all animals were genotyped with the porcine 60 k Illumina beadchip. An enrichment analysis resulted in 10 pathways, amongst others, sphingolipid metabolism and glycolysis/gluconeogenesis, with significant influence on drip loss. Drip loss and 22 metabolic components were analyzed as intermediate phenotypes within a genome-wide association study (GWAS). We detected significantly associated genetic markers and candidate genes for drip loss and for most of the metabolic components. On chromosome 18, a region with promising candidate genes was identified based on SNPs associated with drip loss, the protein "phosphoglycerate mutase 2" and the metabolite glycine. We hypothesize that association studies based on intermediate phenotypes are able to provide comprehensive insights in the genetic variation of genes directly involved in the metabolism of performance traits. In this way, the analyses contribute to identify reliable candidate genes.

Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare

In the past years, there has been a remarkable development of high-throughput omics (HTO) technologies such as genomics, epigenomics, transcriptomics, proteomics and metabolomics across all facets of biology. This has spearheaded the progress of the systems biology era, including applications on animal production and health traits. However, notwithstanding these new HTO technologies, there remains an emerging challenge in data analysis. On the one hand, different HTO technologies judged on their own merit are appropriate for the identification of disease-causing genes, biomarkers for prevention and drug targets for the treatment of diseases and for individualized genomic predictions of performance or disease risks. On the other hand, integration of multi-omic data and joint modelling and analyses are very powerful and accurate to understand the systems biology of healthy and sustainable production of animals. We present an overview of current and emerging HTO technologies each with a focus on their applications in animal and veterinary sciences before introducing an integrative systems genomics framework for analysing and integrating multi-omic data towards improved animal production, health and welfare. We conclude that there are big challenges in multi-omic data integration, modelling and systems-level analyses, particularly with the fast emerging HTO technologies. We highlight existing and emerging systems genomics approaches and discuss how they contribute to our understanding of the biology of complex traits or diseases and holistic improvement of production performance, disease resistance and welfare.

Genetic regulatory network motifs constrain adaptation through curvature in the landscape of mutational (co)variance

Systems biology is accumulating a wealth of understanding about the structure of genetic regulatory networks, leading to a more complete picture of the complex genotype-phenotype relationship. However, models of multivariate phenotypic evolution based on quantitative genetics have largely not incorporated a network-based view of genetic variation. Here we model a set of two-node, two-phenotype genetic network motifs, covering a full range of regulatory interactions. We find that network interactions result in different patterns of mutational (co)variance at the phenotypic level (the M-matrix), not only across network motifs but also across phenotypic space within single motifs. This effect is due almost entirely to mutational input of additive genetic (co)variance. Variation in M has the effect of stretching and bending phenotypic space with respect to evolvability, analogous to the curvature of space-time under general relativity, and similar mathematical tools may apply in each case. We explored the consequences of curvature in mutational variation by simulating adaptation under divergent selection with gene flow. Both standing genetic variation (the G-matrix) and rate of adaptation are constrained by M, so that G and adaptive trajectories are curved across phenotypic space. Under weak selection the phenotypic mean at migration-selection balance also depends on M.

Systems genetics in "-omics" era: current and future development

The systems genetics is an emerging discipline that integrates high-throughput expression profiling technology and systems biology approaches for revealing the molecular mechanism of complex traits, and will improve our understanding of gene functions in the biochemical pathway and genetic interactions between biological molecules. With the rapid advances of microarray analysis technologies, bioinformatics is extensively used in the studies of gene functions, SNP-SNP genetic interactions, LD block-block interactions, miRNA-mRNA interactions, DNA-protein interactions, protein-protein interactions, and functional mapping for LD blocks. Based on bioinformatics panel, which can integrate "-omics" datasets to extract systems knowledge and useful information for explaining the molecular mechanism of complex traits, systems genetics is all about to enhance our understanding of biological processes. Systems biology has provided systems level recognition of various biological phenomena, and constructed the scientific background for the development of systems genetics. In addition, the next-generation sequencing technology and post-genome wide association studies empower the discovery of new gene and rare variants. The integration of different strategies will help to propose novel hypothesis and perfect the theoretical framework of systems genetics, which will make contribution to the future development of systems genetics, and open up a whole new area of genetics.

Novel clinico-genome network modeling for revolutionizing genotype-phenotype-based personalized cancer care

Although cancer heterogeneity, even within individual tumors with different treatment responses of subcloncal cells populations, suggests the need for personalized medicine, most funding and efforts go to conventional single gene-based research and comparative-effectiveness research. Cancer arises from changes in the DNA sequence in the genomes of cancer cells. These accelerating somatic mutations dysregulate signaling pathways, including EGFR, Wnt/Notch, Hedgehog and others, with a central role in cell growth, proliferation, survival, angiogenesis and metastasis. All of these genetic alterations can now be discovered using next-generation DNA sequencing technology. This high-throughput technology can achieve two major goals: first, to complete the catalogue of driver mutations, including point mutations, rearrangements and copy-number changes, by full and targeted sequencing; and second, to explore the functional role of cancer genes and their interactions by genome-wide RNA, serial analysis of gene expression, microRNAs, protein-DNA interactions, and comprehensive analyses of transcriptomes and interactomes. This review article discusses the challenges, including costs, in completing the catalogue of driver mutations for each cancer type and understanding how cancer genomes operate as whole biological systems. Now high-quality clinical treatment and outcomes (death or survival) data from biobanks, and extensive genetics and genomics data for some common tumors, including breast, colorectal and pancreatic cancer, are available. In this article, we will describe how all these clinical and genetics data could be integrated into reverse engineering-based network modeling to approach the extremely complex genotype-phenotype map. This clinico-genome systems model, published for the first time, opens the way for the discovery of new molecular innovations, both predictive markers and therapies, towards personalized treatment of cancer. Instead of the comparative-effectiveness research or personalized medicine debate, harmonization of both can revolutionize cancer management.

Pathway projector: web-based zoomable pathway browser using KEGG atlas and Google Maps API

Background

Biochemical pathways provide an essential context for understanding comprehensive experimental data and the systematic workings of a cell. Therefore, the availability of online pathway browsers will facilitate post-genomic research, just as genome browsers have contributed to genomics. Many pathway maps have been provided online as part of public pathway databases. Most of these maps, however, function as the gateway interface to a specific database, and the comprehensiveness of their represented entities, data mapping capabilities, and user interfaces are not always sufficient for generic usage.

Methodology/Principal Findings

We have identified five central requirements for a pathway browser: (1) availability of large integrated maps showing genes, enzymes, and metabolites; (2) comprehensive search features and data access; (3) data mapping for transcriptomic, proteomic, and metabolomic experiments, as well as the ability to edit and annotate pathway maps; (4) easy exchange of pathway data; and (5) intuitive user experience without the requirement for installation and regular maintenance. According to these requirements, we have evaluated existing pathway databases and tools and implemented a web-based pathway browser named Pathway Projector as a solution.

Conclusions/Significance

Pathway Projector provides integrated pathway maps that are based upon the KEGG Atlas, with the addition of nodes for genes and enzymes, and is implemented as a scalable, zoomable map utilizing the Google Maps API. Users can search pathway-related data using keywords, molecular weights, nucleotide sequences, and amino acid sequences, or as possible routes between compounds. In addition, experimental data from transcriptomic, proteomic, and metabolomic analyses can be readily mapped. Pathway Projector is freely available for academic users at http://www.g-language.org/PathwayProjector/.

A developmental systems perspective on epistasis: computational exploration of mutational interactions in model developmental regulatory networks

The way in which the information contained in genotypes is translated into complex phenotypic traits (i.e. embryonic expression patterns) depends on its decoding by a multilayered hierarchy of biomolecular systems (regulatory networks). Each layer of this hierarchy displays its own regulatory schemes (i.e. operational rules such as +/− feedback) and associated control parameters, resulting in characteristic variational constraints. This process can be conceptualized as a mapping issue, and in the context of highly-dimensional genotype-phenotype mappings (GPMs) epistatic events have been shown to be ubiquitous, manifested in non-linear correspondences between changes in the genotype and their phenotypic effects. In this study I concentrate on epistatic phenomena pervading levels of biological organization above the genetic material, more specifically the realm of molecular networks. At this level, systems approaches to studying GPMs are specially suitable to shed light on the mechanistic basis of epistatic phenomena. To this aim, I constructed and analyzed ensembles of highly-modular (fully interconnected) networks with distinctive topologies, each displaying dynamic behaviors that were categorized as either arbitrary or functional according to early patterning processes in the Drosophila embryo. Spatio-temporal expression trajectories in virtual syncytial embryos were simulated via reaction-diffusion models. My in silico mutational experiments show that: 1) the average fitness decay tendency to successively accumulated mutations in ensembles of functional networks indicates the prevalence of positive epistasis, whereas in ensembles of arbitrary networks negative epistasis is the dominant tendency; and 2) the evaluation of epistatic coefficients of diverse interaction orders indicates that, both positive and negative epistasis are more prevalent in functional networks than in arbitrary ones. Overall, I conclude that the phenotypic and fitness effects of multiple perturbations are strongly conditioned by both the regulatory architecture (i.e. pattern of coupled feedback structures) and the dynamic nature of the spatio-temporal expression trajectories displayed by the simulated networks.