Systems genetics: From GWAS to disease pathways

Genetic control of root architectural traits under drought stress in spring barley (Hordeum vulgare L.)

Root architectural traits play pivotal roles in plant adaptation to drought stress, and hence they are considered promising targets in breeding programs. Here, we phenotyped eight root architecture traits in response to well-watered and drought stress conditions in 200 spring barley (Hordeum vulgare L.) inbred lines over two consecutive field seasons. Root architecture traits were less developed under drought in both seasons when compared with control treatments. Genetic variation in root architectural traits was dissected employing a genome-wide association study (GWAS) coupled with linkage disequilibrium mapping. GWAS uncovered a total of 186 significant single nucleotide polymorphism-trait associations for eight root traits under control, drought, and drought-related indices. Of these, a few loci for root traits were detected on chromosomes 3 and 5, which co-located with QTL identified in previous studies. Interestingly, 13 loci showed simultaneou associations with multiple root traits under drought and drought-related indices. These loci harbored candidate genes, which included a wide range of drought-responsive components such as transcription factors, binding proteins, protein kinases, nutrient and ion transporters, and stress signaling factors. For instance, two candidate genes, HORVU7Hr3G0713160 and HORVU6H r3G0626550, are orthologous to AtACX3 and AtVAMPs, which have reported functions in root length-mediated drought tolerance and as a key protein in abiotic stress tolerance, respectively. Interestingly, one of these loci underlying a high-confidence candidate gene NEW ENHANCER OF ROOT DWARFISM1 (NERD1) showed involvement with root development. An allelic variation of this locus in non-coding region was significantly associated with increased root length under drought. Collectively, these results offer promising multi-trait affecting loci and candidate genes underlying root phenotypic responses to drought stress, which may provide valuable resources for genetic improvement of drought tolerance in barley.

Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities

The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and performance in obesity prediction. Our study fills a crucial gap by bridging healthcare and urban planning domains, offering insights into data-driven approaches to promote healthier living within urban environments. The proposed ABi-LSTM model demonstrates exceptional performance, achieving a remarkable accuracy of 96.5% in predicting obesity levels. Comparative analysis showcases its superiority over conventional approaches, with superior precision, recall, and overall classification balance. This study highlights significant advancements in predictive accuracy and positions the ABi-LSTM model as a pioneering solution for accurate obesity prognosis. The implications extend beyond healthcare, offering a precise tool to address the global obesity epidemic and foster sustainable development in smart cities.

Single-Cell Transcriptomics of Bone Marrow Stromal Cells in Diversity Outbred Mice: A Model for Population-Level scRNA-Seq Studies

Genome-wide association studies (GWASs) have advanced our understanding of the genetics of osteoporosis; however, the challenge has been converting associations to causal genes. Studies have utilized transcriptomics data to link disease-associated variants to genes, but few population transcriptomics data sets have been generated on bone at the single-cell level. To address this challenge, we profiled the transcriptomes of bone marrow-derived stromal cells (BMSCs) cultured under osteogenic conditions from five diversity outbred (DO) mice using single-cell RNA-seq (scRNA-seq). The goal of the study was to determine if BMSCs could serve as a model to generate cell type-specific transcriptomic profiles of mesenchymal lineage cells from large populations of mice to inform genetic studies. By enriching for mesenchymal lineage cells in vitro, coupled with pooling of multiple samples and downstream genotype deconvolution, we demonstrate the scalability of this model for population-level studies. We demonstrate that dissociation of BMSCs from a heavily mineralized matrix had little effect on viability or their transcriptomic signatures. Furthermore, we show that BMSCs cultured under osteogenic conditions are diverse and consist of cells with characteristics of mesenchymal progenitors, marrow adipogenic lineage precursors (MALPs), osteoblasts, osteocyte-like cells, and immune cells. Importantly, all cells were similar from a transcriptomic perspective to cells isolated in vivo. We employed scRNA-seq analytical tools to confirm the biological identity of profiled cell types. SCENIC was used to reconstruct gene regulatory networks (GRNs), and we observed that cell types show GRNs expected of osteogenic and pre-adipogenic lineage cells. Further, CELLECT analysis showed that osteoblasts, osteocyte-like cells, and MALPs captured a significant component of bone mineral density (BMD) heritability. Together, these data suggest that BMSCs cultured under osteogenic conditions coupled with scRNA-seq can be used as a scalable and biologically informative model to generate cell type-specific transcriptomic profiles of mesenchymal lineage cells in large populations. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Neurodegenerative disorders: From clinicopathology convergence to systems biology divergence

Neurodegenerative diseases are multifactorial. This means that several genetic, epigenetic, and environmental factors contribute to their emergence. Therefore, for the future management of these highly prevalent diseases, it is necessary to change perspective. If a holistic viewpoint is assumed, the phenotype (the clinicopathological convergence) emerges from the perturbation of a complex system of functional interactions among proteins (systems biology divergence). The systems biology top-down approach starts with the unbiased collection of sets of data generated through one or more -omics techniques and has the aim to identify the networks and the components that participate in the generation of a phenotype (disease), often without any available a priori knowledge. The principle behind the top-down method is that the molecular components that respond similarly to experimental perturbations are somehow functionally related. This allows the study of complex and relatively poorly characterized diseases without requiring extensive knowledge of the processes under investigation. In this chapter, the use of a global approach will be applied to the comprehension of neurodegeneration, with a particular focus on the two most prevalent ones, Alzheimer's and Parkinson's diseases. The final purpose is to distinguish disease subtypes (even with similar clinical manifestations) to launch a future of precision medicine for patients with these disorders.

The Role of FXR-Signaling Variability in the Development and Course of Non-Alcoholic Fatty Liver Disease in Children

INTRODUCTION

Genetic mechanisms among many other factors play a crucial role in the development and progression of nonalcoholic fatty liver disease (NAFLD). The farnesoid X-receptor (FXR) regulates the expression of target genes involved in metabolic and energy homeostasis, so it can be assumed that genetic variations within the NR1H4 gene, encoding FXR, can affect the development or progression of associated diseases, including NAFLD.

THE AIM

To study the association of SNP rs11110390 NR1H4 gene with the probability of development and course of NAFLD in children.

MATERIALS AND METHODS

76 children aged 9-17 years and overweight were examined. According to controlled attenuated parameter (CAP) measurement (Fibroscan®502touch) children were divided into 2 groups: group 1 consisted of 40 patients with NAFLD, group 2 was composed by 36 patients without hepatic steatosis. According to genetic testing children were divided into 3 subgroups - children with CC-, CT-, TT-genotype SNP rs11110390 NR1H4 gene.

RESULTS

The frequency of TT-genotype SNP rs11110390 NR1H4 gene detection in children with NAFLD was 17.5% versus 2.8% in the control group (p NR1H4 gene the liver stiffness (p NR1H4 (p NR1H4 is associated with an increased probability of NAFLD development in children. An increase in the steatosis degree and liver stiffness in combination with increased taurine-conjugated bile acids fractions in the hepatic and gallbladder's bile, shift in cytokine balance due to a decrease in IL-10 level in children with TT-genotype SNP rs11110390 NR1H4 were observed.

Regulation of Human Endogenous Metabolites by Drug Transporters and Drug Metabolizing Enzymes: An Analysis of Targeted SNP-Metabolite Associations

Drug transporters and drug-metabolizing enzymes are primarily known for their role in the absorption, distribution, metabolism, and excretion (ADME) of small molecule drugs, but they also play a key role in handling endogenous metabolites. Recent cross-tissue co-expression network analyses have revealed a "Remote Sensing and Signaling Network" of multispecific, oligo-specific, and monospecific transporters and enzymes involved in endogenous metabolism. This includes many proteins from families involved in ADME (e.g., SLC22, SLCO, ABCC, CYP, UGT). Focusing on the gut-liver-kidney axis, we identified the endogenous metabolites potentially regulated by this network of ~1000 proteins by associating SNPs in these genes with the circulating levels of thousands of small, polar, bioactive metabolites, including free fatty acids, eicosanoids, bile acids, and other signaling metabolites that act in part via G-protein coupled receptors (GPCRs), nuclear receptors, and kinases. We identified 77 genomic loci associated with 7236 unique metabolites. This included metabolites that were associated with multiple, distinct loci, indicating coordinated regulation between multiple genes (including drug transporters and drug-metabolizing enzymes) of specific metabolites. We analyzed existing pharmacogenomic data and noted SNPs implicated in endogenous metabolite handling (e.g., rs4149056 in SLCO1B1) also affecting drug ADME. The overall results support the existence of close relationships, via interactions with signaling metabolites, between drug transporters and drug-metabolizing enzymes that are part of the Remote Sensing and Signaling Network, and with GPCRs and nuclear receptors. These analyses highlight the potential for drug-metabolite interactions at the interfaces of the Remote Sensing and Signaling Network and the ADME protein network.

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.

The impact of genomic variation on protein phosphorylation states and regulatory networks

Genomic variation impacts on cellular networks by affecting the abundance (e.g., protein levels) and the functional states (e.g., protein phosphorylation) of their components. Previous work has focused on the former, while in this context, the functional states of proteins have largely remained neglected. Here, we generated high‐quality transcriptome, proteome, and phosphoproteome data for a panel of 112 genomically well‐defined yeast strains. Genetic effects on transcripts were generally transmitted to the protein layer, but specific gene groups, such as ribosomal proteins, showed diverging effects on protein levels compared with RNA levels. Phosphorylation states proved crucial to unravel genetic effects on signaling networks. Correspondingly, genetic variants that cause phosphorylation changes were mostly different from those causing abundance changes in the respective proteins. Underscoring their relevance for cell physiology, phosphorylation traits were more strongly correlated with cell physiological traits such as chemical compound resistance or cell morphology, compared with transcript or protein abundance. This study demonstrates how molecular networks mediate the effects of genomic variants to cellular traits and highlights the particular importance of protein phosphorylation.

Bioinformatic approach for the discovery of cis-eQTL signals during fruit ripening of a woody species as grape (Vitis vinifera L.)

Expression quantitative trait loci (eQTLs) are associations between genetic variants, such as Single Nucleotide Polymorphisms (SNPs), and gene expression. eQTLs are an important tool to understand the genetic variance of gene expression of complex phenotypes. eQTLs analyses are common in biomedical models but are scarce in woody crop species such as fruit trees or grapes. In this study, a comprehensive bioinformatic analysis was conducted leveraging with expression data from two different growth stages, around ripening onset, of 10 genotypes of grape (Vitis vinifera L.). A total of 2170 cis-eQTL were identified in 212 gene modulated at ripening onset. The 48% of these DEGs have a known function. Among the annotated protein-coding genes, terpene synthase, auxin-regulatory factors, GRFS, ANK_REP_REGION domain-containing protein, Kinesin motor domain-containing protein and flavonol synthase were noted. This new inventory of cis-eQTLs influencing gene expression during fruit ripening will be an important resource to examine variation for this trait and will help to elucidate the complex genetic architecture underlying this process in grape.

Flavonoids Modulate the Accumulation of Toxins From Aspergillus flavus in Maize Kernels

Aspergillus flavus is an opportunistic fungal pathogen capable of producing aflatoxins, potent carcinogenic toxins that accumulate in maize kernels after infection. To better understand the molecular mechanisms of maize resistance to A. flavus growth and aflatoxin accumulation, we performed a high-throughput transcriptomic study in situ using maize kernels infected with A. flavus strain 3357. Three maize lines were evaluated: aflatoxin-contamination resistant line TZAR102, semi-resistant MI82, and susceptible line Va35. A modified genotype-environment association method (GEA) used to detect loci under selection via redundancy analysis (RDA) was used with the transcriptomic data to detect genes significantly influenced by maize line, fungal treatment, and duration of infection. Gene ontology enrichment analysis of genes highly expressed in infected kernels identified molecular pathways associated with defense responses to fungi and other microbes such as production of pathogenesis-related (PR) proteins and lipid bilayer formation. To further identify novel genes of interest, we incorporated genomic and phenotypic field data from a genome wide association analysis with gene expression data, allowing us to detect significantly expressed quantitative trait loci (eQTL). These results identified significant association between flavonoid biosynthetic pathway genes and infection by A. flavus. In planta fungal infections showed that the resistant line, TZAR102, has a higher fold increase of the metabolites naringenin and luteolin than the susceptible line, Va35, when comparing untreated and fungal infected plants. These results suggest flavonoids contribute to plant resistance mechanisms against aflatoxin contamination through modulation of toxin accumulation in maize kernels.

Galton's Quincunx: Probabilistic causation in developmental behavior genetics

In what sense are associations between particular markers and complex behaviors made by genome-wide association studies (GWAS) and related techniques discoveries of, or entries into the study of, the causes of those behaviors? In this paper, we argue that when applied to individuals, the kinds of probabilistic 'causes' of complex traits that GWAS-style studies can point towards do not provide the kind of causal information that is useful for generating explanations; they do not, in other words, point towards useful explanations of why particular individuals have the traits that they do. We develop an analogy centered around Galton's "Quincunx" machine; while each pin might be associated with outcomes of a certain sort, in any particular trial, that pin might be entirely bypassed even if the ball eventually comes to rest in the box most strongly associated with that pin. Indeed, in any particular trial, the actual outcome of a ball hitting a pin might be the opposite of what is usually expected. While we might find particular pins associated with outcomes in the aggregate, these associations will not provide causally relevant information for understanding individual outcomes. In a similar way, the complexities of development likely render impossible any moves from population-level statistical associations between genetic markers and complex behaviors to an understanding of the causal processes by which individuals come to have the traits that they in fact have.

Loci Associated With Antibody Response in Feral Swine (Sus scrofa) Infected With Brucella suis

Feral swine (Sus scrofa) are a destructive invasive species widespread throughout the United States that disrupt ecosystems, damage crops, and carry pathogens of concern for the health of domestic stock and humans including Brucella suis-the causative organism for swine brucellosis. In domestic swine, brucellosis results in reproductive failure due to abortions and infertility. Contact with infected feral swine poses spillover risks to domestic pigs as well as humans, companion animals, wildlife, and other livestock. Genetic factors influence the outcome of infectious diseases; therefore, genome wide association studies (GWAS) of differential immune responses among feral swine can provide an understanding of disease dynamics and inform management to prevent the spillover of brucellosis from feral swine to domestic pigs. We sought to identify loci associated with differential antibody responses among feral swine naturally infected with B. suis using a case-control GWAS. Tissue, serum, and genotype data (68,516 bi-allelic single nucleotide polymorphisms) collected from 47 feral swine were analyzed in this study. The 47 feral swine were culture positive for Brucella spp. Of these 47, 16 were antibody positive (cases) whereas 31 were antibody negative (controls). Single-locus GWAS were performed using efficient mixed-model association eXpedited (EMMAX) methodology with three genetic models: additive, dominant, and recessive. Eight loci associated with seroconversion were identified on chromosome 4, 8, 9, 10, 12, and 18. Subsequent bioinformatic analyses revealed nine putative candidate genes related to immune function, most notably phagocytosis and induction of an inflammatory response. Identified loci and putative candidate genes may play an important role in host immune responses to B. suis infection, characterized by a detectable bacterial presence yet a differential antibody response. Given that antibody tests are used to evaluate brucellosis infection in domestic pigs and for disease surveillance in invasive feral swine, additional studies are needed to fully understand the genetic component of the response to B. suis infection and to more effectively translate estimates of Brucella spp. antibody prevalence among feral swine to disease control management action.

The role of host genetics in susceptibility to severe viral infections in humans and insights into host genetics of severe COVID-19: A systematic review

BACKGROUND

Susceptibility to severe viral infections was reported to be associated with genetic variants in immune response genes using case reports and GWAS studies. SARS-CoV-2 is an emergent viral disease that caused millions of COVID-19 cases all over the world. Around 15 % of cases are severe and some of them are accompanied by dysregulated immune system and cytokine storm. There is increasing evidence that severe manifestations of COVID-19 might be attributed to human genetic variants in genes related to immune deficiency and or inflammasome activation (cytokine storm).

OBJECTIVE

Identify the candidate genes that are likely to aid in explaining severe COVID-19 and provide insights to understand the pathogenesis of severe COVID-19.

METHODS

In this article, we systematically reviewed genes related to viral susceptibility that were reported in human genetic studies (Case-reports and GWAS) to understand the role of host viral interactions and to provide insights into the pathogenesis of severe COVID-19.

RESULTS

We found 40 genes associated with viral susceptibility and 21 of them were associated with severe SARS-CoV disease and severe COVID-19. Some of those genes were implicated in TLR pathways, others in C-lectin pathways, and others were related to inflammasome activation (cytokine storm).

CONCLUSION

This compilation represents a list of candidate genes that are likely to aid in explaining severe COVID-19 which are worthy of inclusion in gene panels and during meta-analysis of different variants in host genetics studies of COVID-19. In addition, we provide several hypotheses for severe COVID-19 and possible therapeutic targets.

A systems genetics approach reveals environment-dependent associations between SNPs, protein coexpression, and drought-related traits in maize

The effect of drought on maize yield is of particular concern in the context of climate change and human population growth. However, the complexity of drought-response mechanisms makes the design of new drought-tolerant varieties a difficult task that would greatly benefit from a better understanding of the genotype–phenotype relationship. To provide novel insight into this relationship, we applied a systems genetics approach integrating high-throughput phenotypic, proteomic, and genomic data acquired from 254 maize hybrids grown under two watering conditions. Using association genetics and protein coexpression analysis, we detected more than 22,000 pQTLs across the two conditions and confidently identified 15 loci with potential pleiotropic effects on the proteome. We showed that even mild water deficit induced a profound remodeling of the proteome, which affected the structure of the protein coexpression network, and a reprogramming of the genetic control of the abundance of many proteins, including those involved in stress response. Colocalizations between pQTLs and QTLs for ecophysiological traits, found mostly in the water deficit condition, indicated that this reprogramming may also affect the phenotypic level. Finally, we identified several candidate genes that are potentially responsible for both the coexpression of stress response proteins and the variations of ecophysiological traits under water deficit. Taken together, our findings provide novel insights into the molecular mechanisms of drought tolerance and suggest some pathways for further research and breeding.

Novel Alzheimer risk genes determine the microglia response to amyloid-β but not to TAU pathology

Polygenic risk scores have identified that genetic variants without genome-wide significance still add to the genetic risk of developing Alzheimer's disease (AD). Whether and how subthreshold risk loci translate into relevant disease pathways is unknown. We investigate here the involvement of AD risk variants in the transcriptional responses of two mouse models: APPswe/PS1L166P and Thy-TAU22. A unique gene expression module, highly enriched for AD risk genes, is specifically responsive to Aβ but not TAU pathology. We identify in this module 7 established AD risk genes (APOE, CLU, INPP5D, CD33, PLCG2, SPI1, and FCER1G) and 11 AD GWAS genes below the genome-wide significance threshold (GPC2, TREML2, SYK, GRN, SLC2A5, SAMSN1, PYDC1, HEXB, RRBP1, LYN, and BLNK), that become significantly upregulated when exposed to Aβ. Single microglia sequencing confirms that Aβ, not TAU, pathology induces marked transcriptional changes in microglia, including increased proportions of activated microglia. We conclude that genetic risk of AD functionally translates into different microglia pathway responses to Aβ pathology, placing AD genetic risk downstream of the amyloid pathway but upstream of TAU pathology.

Genetic components of root architecture and anatomy adjustments to water-deficit stress in spring barley

Roots perform vital roles for adaptation and productivity under water-deficit stress, even though their specific functions are poorly understood. In this study, the genetic control of the nodal-root architectural and anatomical response to water deficit were investigated among diverse spring barley accessions. Water deficit induced substantial variations in the nodal root traits. The cortical, stele, and total root cross-sectional areas of the main-shoot nodal roots decreased under water deficit, but increased in the tiller nodal roots. Root xylem density and arrested nodal roots increased under water deficit, with the formation of root suberization/lignification and large cortical aerenchyma. Genome-wide association study implicated 11 QTL intervals in the architectural and anatomical nodal root response to water deficit. Among them, three and four QTL intervals had strong effects across seasons and on both root architectural and anatomical traits, respectively. Genome-wide epistasis analysis revealed 44 epistatically interacting SNP loci. Further analyses showed that these QTL intervals contain important candidate genes, including ZIFL2, MATE, and PPIB, whose functions are shown to be related to the root adaptive response to water deprivation in plants. These results give novel insight into the genetic architectures of barley nodal root response to soil water deficit stress in the fields, and thus offer useful resources for root-targeted marker-assisted selection.

Genome-wide association studies of severe P. falciparum malaria susceptibility: progress, pitfalls and prospects

Background

P. falciparum malaria has been recognized as one of the prominent evolutionary selective forces of human genome that led to the emergence of multiple host protective alleles. A comprehensive understanding of the genetic bases of severe malaria susceptibility and resistance can potentially pave ways to the development of new therapeutics and vaccines. Genome-wide association studies (GWASs) have recently been implemented in malaria endemic areas and identified a number of novel association genetic variants. However, there are several open questions around heritability, epistatic interactions, genetic correlations and associated molecular pathways among others. Here, we assess the progress and pitfalls of severe malaria susceptibility GWASs and discuss the biology of the novel variants.

Results

We obtained all severe malaria susceptibility GWASs published thus far and accessed GWAS dataset of Gambian populations from European Phenome Genome Archive (EGA) through the MalariaGen consortium standard data access protocols. We noticed that, while some of the well-known variants including HbS and ABO blood group were replicated across endemic populations, only few novel variants were convincingly identified and their biological functions remain to be understood. We estimated SNP-heritability of severe malaria at 20.1% in Gambian populations and showed how advanced statistical genetic analytic methods can potentially be implemented in malaria susceptibility studies to provide useful functional insights.

Conclusions

The ultimate goal of malaria susceptibility study is to discover a novel causal biological pathway that provide protections against severe malaria; a fundamental step towards translational medicine such as development of vaccine and new therapeutics. Beyond singe locus analysis, the future direction of malaria susceptibility requires a paradigm shift from single -omics to multi-stage and multi-dimensional integrative functional studies that combines multiple data types from the human host, the parasite, the mosquitoes and the environment. The current biotechnological and statistical advances may eventually lead to the feasibility of systems biology studies and revolutionize malaria research.

Exploring the involvement of Tac2 in the mouse hippocampal stress response through gene networking

Tachykinin 2 (Tac2) is expressed in a number of areas throughout the brain, including the hippocampus. However, knowledge about its function has been only well explored in the hypothalamus in the context of reproductive health. In this study, we identified and validated increased hippocampal Tac2 mRNA expression in response to chronic mild stress in mice. Expression quantitative trait locus (eQTL) analysis showed Tac2 is cis-regulated in the hippocampus. Using a systems genetics approach, we constructed a Tac2 co-expression network to better understand the relationship between Tac2 and the hippocampal stress response. Our network identified 69 total genes associated with Tac2, several of which encode major neuropeptides involved in hippocampal stress signaling as well as critical genes for producing neural plasticity, indicating that Tac2 is involved in these processes. Pathway analysis for the member of Tac2 gene network revealed a strong connection between Tac2 and neuroactive ligand-receptor interaction, calcium signaling pathway, as well as cardiac muscle contraction. In addition, we also identified 46 stress-related phenotypes, specifically fear conditioning response, that were significantly correlated with Tac2 expression. Our results provide evidence for Tac2 as a strong candidate gene who likely plays a role in hippocampal stress processing and neural plasticity.

A network-based conditional genetic association analysis of the human metabolome

Background

Genome-wide association studies have identified hundreds of loci that influence a wide variety of complex human traits; however, little is known regarding the biological mechanism of action of these loci. The recent accumulation of functional genomics (“omics”), including metabolomics data, has created new opportunities for studying the functional role of specific changes in the genome. Functional genomic data are characterized by their high dimensionality, the presence of (strong) statistical dependency between traits, and, potentially, complex genetic control. Therefore, the analysis of such data requires specific statistical genetics methods.

Results

To facilitate our understanding of the genetic control of omics phenotypes, we propose a trait-centered, network-based conditional genetic association (cGAS) approach for identifying the direct effects of genetic variants on omics-based traits. For each trait of interest, we selected from a biological network a set of other traits to be used as covariates in the cGAS. The network can be reconstructed either from biological pathway databases (a mechanistic approach) or directly from the data, using a Gaussian graphical model applied to the metabolome (a data-driven approach). We derived mathematical expressions that allow comparison of the power of univariate analyses with conditional genetic association analyses. We then tested our approach using data from a population-based Cooperative Health Research in the region of Augsburg (KORA) study (n = 1,784 subjects, 1.7 million single-nucleotide polymorphisms) with measured data for 151 metabolites.

Conclusions

We found that compared to single-trait analysis, performing a genetic association analysis that includes biologically relevant covariates can either gain or lose power, depending on specific pleiotropic scenarios, for which we provide empirical examples. In the context of analyzed metabolomics data, the mechanistic network approach had more power compared to the data-driven approach. Nevertheless, we believe that our analysis shows that neither a prior-knowledge-only approach nor a phenotypic-data-only approach is optimal, and we discuss possibilities for improvement.

Genetic Variation at Chromosome 2q13 and Its Potential Influence on Endometriosis Susceptibility Through Effects on the IL-1 Family

Endometriosis is characterized by the growth of epithelial and stromal cells outside the uterine cavity. It has a complex etiology and affects ∼10% of reproductive age women. It is accompanied by a chronic inflammatory response with substantial evidence to indicate genetic susceptibility. The causal genes and their pathways leading to endometriosis, however, are still unknown. Recently, genomewide association studies on endometriosis identified 14 genomic risk loci in women of European and Japanese ancestry. It is becoming increasingly clear that these risk regions are intergenic and thus contribute to disease susceptibility through regulatory mechanisms, most likely mediated through regulation of genes within a restricted distance from the risk variants. One endometriosis risk locus has been detected at chromosome 2q13 within an inflammatory-rich region of gene transcripts and thus may play a role in the inflammation component of the disease. We carried out detailed analysis of the genomic region 250 kb on either side of sentinel SNP rs10167914 and identified 21 transcripts which contained 6 interleukin (IL)-1 family genes, 3 previously reported coding genes that have a relationship to inflammation, 4 novel coding, or pseudogenes, and 8 noncoding RNA transcripts. Through an extensive literature search, we examined the roles these genes and their resultant proteins play in endometriosis pathogenesis. The results suggest alteration in the expression the IL-1 family transcripts either alone or as a complex milieu could have a significant influence on endometriosis and should be prioritized for future study on the implications of inflammation on endometriotic lesions.

Invited review: Reproductive and genomic technologies to optimize breeding strategies for genetic progress in dairy cattle

Abstract. Dairy cattle breeders have exploited technological advances that have emerged in the past in regards to reproduction and genomics. The implementation of such technologies in routine breeding programs has permitted genetic gains in traditional milk production traits as well as, more recently, in low-heritability traits like health and fertility. As demand for dairy products increases, it is important for dairy breeders to optimize the use of available technologies and to consider the many emerging technologies that are currently being investigated in various fields. Here we review a number of technologies that have helped shape dairy breeding programs in the past and present, along with those potentially forthcoming. These tools have materialized in the areas of reproduction, genotyping and sequencing, genetic modification, and epigenetics. Although many of these technologies bring encouraging opportunities for genetic improvement of dairy cattle populations, their applications and benefits need to be weighed with their impacts on economics, genetic diversity, and society.

Advances in the genome-wide association study of chronic hepatitis B susceptibility in Asian population

Chronic hepatitis B (CHB) is the most common chronic liver disease resulting from viral infection and has become a serious threat to human health. Each year, about 1.2 million people in the world die from diseases caused by chronic infection of hepatitis B virus. The genetic polymorphism is significantly associated with the susceptibility to chronic hepatitis B. Genome-wide association study was recently developed and has become an important tool to detect susceptibility genes of CHB. To date, a number of CHB-associated susceptibility loci and regions have been identified by scientists over the world. To clearly understand the role of susceptibility loci in the occurrence of CHB is important for the early diagnosis and prevention of CHB.

Exhaustive search for epistatic effects on the human methylome

Studies assessing the existence and magnitude of epistatic effects on complex human traits provide inconclusive results. The study of such effects is complicated by considerable increase in computational burden, model complexity, and model uncertainty, which in concert decrease model stability. An additional source introducing significant uncertainty with regard to the detection of robust epistasis is the biological distance between the genetic variation and the trait under study. Here we studied CpG methylation, a genetically complex molecular trait that is particularly close to genomic variation, and performed an exhaustive search for two-locus epistatic effects on the CpG-methylation signal in two cohorts of healthy young subjects. We detected robust epistatic effects for a small number of CpGs (N = 404). Our results indicate that epistatic effects explain only a minor part of variation in DNA-CpG methylation. Interestingly, these CpGs were more likely to be associated with gene-expression of nearby genes, as also shown by their overrepresentation in DNase I hypersensitivity sites and underrepresentation in CpG islands. Finally, gene ontology analysis showed a significant enrichment of these CpGs in pathways related to HPV-infection and cancer.

Why do Individuals Differ in Viral Susceptibility? A Story Told by Model Organisms

Viral susceptibility and disease progression is determined by host genetic variation that underlies individual differences. Genetic polymorphisms that affect the phenotype upon infection have been well-studied for only a few viruses, such as HIV-1 and Hepatitis C virus. However, even for well-studied viruses the genetic basis of individual susceptibility differences remains elusive. Investigating the effect of causal polymorphisms in humans is complicated, because genetic methods to detect rare or small-effect polymorphisms are limited and genetic manipulation is not possible in human populations. Model organisms have proven a powerful experimental platform to identify and characterize polymorphisms that underlie natural variations in viral susceptibility using quantitative genetic tools. We summarize and compare the genetic tools available in three main model organisms, Mus musculus, Drosophila melanogaster, and Caenorhabditis elegans, and illustrate how these tools can be applied to detect polymorphisms that determine the viral susceptibility. Finally, we analyse how candidate polymorphisms from model organisms can be used to shed light on the underlying mechanism of individual variation. Insights in causal polymorphisms and mechanisms underlying individual differences in viral susceptibility in model organisms likely provide a better understanding in humans.

Disease Risk Assessment Using a Voronoi-Based Network Analysis of Genes and Variants Scores

Much effort has been devoted to assess disease risk based on large-scale protein-protein network and genotype-phenotype associations. However, the challenge of risk prediction for complex diseases remains unaddressed. Here, we propose a framework to quantify the risk based on a Voronoi tessellation network analysis, taking into account the disease association scores of both genes and variants. By integrating ClinVar, SNPnexus, and DISEASES databases, we introduce a gene-variant map that is based on the pairwise disease-associated gene-variant scores. This map is clustered using Voronoi tessellation and network analysis with a threshold obtained from fitting the background Voronoi cell density distribution. We define the relative risk of disease that is inferred from the scores of the data points within the related clusters on the gene-variant map. We identify autoimmune-associated clusters that may interact at the system-level. The proposed framework can be used to determine the clusters that are specific to a subtype or contribute to multiple subtypes of complex diseases.

Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver

The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.

Genome-Wide Association and Transcriptome Analyses Reveal Candidate Genes Underlying Yield-determining Traits in Brassica napus

Yield is one of the most important yet complex crop traits. To improve our understanding of the genetic basis of yield establishment, and to identify candidate genes responsible for yield improvement in Brassica napus, we performed genome-wide association studies (GWAS) for seven yield-determining traits [main inflorescence pod number (MIPN), branch pod number (BPN), pod number per plant (PNP), seed number per pod (SPP), thousand seed weight, main inflorescence yield (MIY), and branch yield], using data from 520 diverse B. napus accessions from two different yield environments. In total, we detected 128 significant single nucleotide polymorphisms (SNPs), 93 of which were revealed as novel by integrative analysis. A combination of GWAS and transcriptome sequencing on 21 haplotype blocks from samples pooled by four extremely high-yielding or low-yielding accessions revealed the differential expression of 14 crucial candiate genes (such as Bna.MYB83, Bna.SPL5, and Bna.ROP3) associated with multiple traits or containing multiple SNPs associated with the same trait. Functional annotation and expression pattern analyses further demonstrated that these 14 candiate genes might be important in developmental processes and biomass accumulation, thus affecting the yield establishment of B. napus. These results provide valuable information for understanding the genetic mechanisms underlying the establishment of high yield in B. napus, and lay the foundation for developing high-yielding B. napus varieties.

Taking Bioinformatics to Systems Medicine

Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.

Advances in Integrating Traditional and Omic Biomarkers When Analyzing the Effects of the Mediterranean Diet Intervention in Cardiovascular Prevention

Intervention with Mediterranean diet (MedDiet) has provided a high level of evidence in primary prevention of cardiovascular events. Besides enhancing protection from classical risk factors, an improvement has also been described in a number of non-classical ones. Benefits have been reported on biomarkers of oxidation, inflammation, cellular adhesion, adipokine production, and pro-thrombotic state. Although the benefits of the MedDiet have been attributed to its richness in antioxidants, the mechanisms by which it exercises its beneficial effects are not well known. It is thought that the integration of omics including genomics, transcriptomics, epigenomics, and metabolomics, into studies analyzing nutrition and cardiovascular diseases will provide new clues regarding these mechanisms. However, omics integration is still in its infancy. Currently, some single-omics analyses have provided valuable data, mostly in the field of genomics. Thus, several gene-diet interactions in determining both intermediate (plasma lipids, etc.) and final cardiovascular phenotypes (stroke, myocardial infarction, etc.) have been reported. However, few studies have analyzed changes in gene expression and, moreover very few have focused on epigenomic or metabolomic biomarkers related to the MedDiet. Nevertheless, these preliminary results can help to better understand the inter-individual differences in cardiovascular risk and dietary response for further applications in personalized nutrition.

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.

Use of "big data" in drug discovery and clinical trials

Oncology is undergoing a data-driven metamorphosis. Armed with new and ever more efficient molecular and information technologies, we have entered an era where data is helping us spearhead the fight against cancer. This technology driven data explosion, often referred to as "big data", is not only expediting biomedical discovery, but it is also rapidly transforming the practice of oncology into an information science. This evolution is critical, as results to-date have revealed the immense complexity and genetic heterogeneity of patients and their tumors, a sobering reminder of the challenge facing every patient and their oncologist. This can only be addressed through development of clinico-molecular data analytics that provide a deeper understanding of the mechanisms controlling the biological and clinical response to available therapeutic options. Beyond the exciting implications for improved patient care, such advancements in predictive and evidence-based analytics stand to profoundly affect the processes of cancer drug discovery and associated clinical trials.

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.

Deciphering the Dark Matter of Complex Genetic Inheritance

A multifaceted "systems genetics" strategy illuminates the relationship between genotype and coronary artery disease.

Stroke genomics in people of African ancestry: charting new paths

One in six people worldwide will experience a stroke in his/her lifetime. While people in Africa carry a disproportionately higher burden of poor stroke outcomes, compared to the rest of the world, the exact contribution of genomic factors to this disparity is unknown. Despite noteworthy research into stroke genomics, studies exploring the genetic contribution to stroke among populations of African ancestry in the United States are few. Furthermore, genomics data in populations living in Africa are lacking. The wide genomic variation of African populations offers a unique opportunity to identify genomic variants with causal relationships to stroke across different ethnic groups. The Stroke Investigative Research and Educational Network (SIREN), a component of the Human Health and Heredity in Africa (H3Africa) Consortium, aims to explore genomic and environmental risk factors for stroke in populations of African ancestry in West Africa and the United States. In this article, we review the literature on the genomics of stroke with particular emphasis on populations of African origin.

Systems Genetic Validation of the SNP-Metabolite Association in Rice Via Metabolite-Pathway-Based Phenome-Wide Association Scans

In the post-GWAS (Genome-Wide Association Scan) era, the interpretation of GWAS results is crucial to screen for highly relevant phenotype-genotype association pairs. Based on the single genotype-phenotype association test and a pathway enrichment analysis, we propose a Metabolite-pathway-based Phenome-Wide Association Scan (M-PheWAS) to analyze the key metabolite-SNP pairs in rice and determine the regulatory relationship by assessing similarities in the changes of enzymes and downstream products in a pathway. Two SNPs, sf0315305925 and sf0315308337, were selected using this approach, and their molecular function and regulatory relationship with Enzyme EC:5.5.1.6 and with flavonoids, a significant downstream regulatory metabolite product, were demonstrated. Moreover, a total of 105 crucial SNPs were screened using M-PheWAS, which may be important for metabolite associations.

Lessons from Toxicology: Developing a 21st-Century Paradigm for Medical Research

Biomedical developments in the 21st century provide an unprecedented opportunity to gain a dynamic systems-level and human-specific understanding of the causes and pathophysiologies of disease. This understanding is a vital need, in view of continuing failures in health research, drug discovery, and clinical translation. The full potential of advanced approaches may not be achieved within a 20th-century conceptual framework dominated by animal models. Novel technologies are being integrated into environmental health research and are also applicable to disease research, but these advances need a new medical research and drug discovery paradigm to gain maximal benefits. We suggest a new conceptual framework that repurposes the 21st-century transition underway in toxicology. Human disease should be conceived as resulting from integrated extrinsic and intrinsic causes, with research focused on modern human-specific models to understand disease pathways at multiple biological levels that are analogous to adverse outcome pathways in toxicology. Systems biology tools should be used to integrate and interpret data about disease causation and pathophysiology. Such an approach promises progress in overcoming the current roadblocks to understanding human disease and successful drug discovery and translation. A discourse should begin now to identify and consider the many challenges and questions that need to be solved.

Functions of noncoding sequences in mammalian genomes

Most of the mammalian genome consists of nucleotide sequences not coding for proteins. Exons of genes make up only 3% of the human genome, while the significance of most other sequences remains unknown. Recent genome studies with high-throughput methods demonstrate that the so-called noncoding part of the genome may perform important functions. This hypothesis is supported by three groups of experimental data: 1) approximately 10% of the sequences, most of which are located in noncoding parts of the genome, is evolutionarily conserved and thus can be of functional importance; 2) up to 99% of the mammalian genome is being transcribed forming short and long noncoding RNAs in addition to common mRNA; and 3) mutations in noncoding parts of the genome can be accompanied by progression of pathological states of the organism. In the light of these data, in the review we consider the functional role of numerous known sequences of noncoding parts of the genome including introns, DNA methylation regions, enhancers and locus control regions, insulators, S/MAR sequences, pseudogenes, and genes of noncoding RNAs, as well as transposons and simple repeats of centromeric and telomeric regions of chromosomes. The assumption is made that the intergenic noncoding sequences without definite/clear functions can be involved in spatial organization of genetic loci in interphase nuclei.

Allelic Variants in Arhgef11 via the Rho-Rock Pathway Are Linked to Epithelial-Mesenchymal Transition and Contributes to Kidney Injury in the Dahl Salt-Sensitive Rat

Previously, genetic analyses identified that variants in Arhgef11 may influence kidney injury in the Dahl salt-sensitive (S) rat, a model of hypertensive chronic kidney disease. To understand the potential mechanism by which altered expression and/or protein differences in Arhgef11 could play a role in kidney injury, stably transduced Arhgef11 knockdown cell lines as well as primary cultures of proximal tubule cells were studied. Genetic knockdown of Arhgef11 in HEK293 and NRK resulted in reduced RhoA activity, decreased activation of Rho-ROCK pathway, and less stress fiber formation versus control, similar to what was observed by pharmacological inhibition (fasudil). Primary proximal tubule cells (PTC) cultured from the S exhibited increased expression of Arhgef11, increased RhoA activity, and up regulation of Rho-ROCK signaling compared to control (small congenic). The cells were also more prone (versus control) to TGFβ-1 induced epithelial-mesenchymal transition (EMT), a hallmark feature of the development of renal interstitial fibrosis, and characterized by development of spindle shape morphology, gene expression changes in EMT markers (Col1a3, Mmp9, Bmp7, and Ocln) and increased expression of N-Cadherin and Vimentin. S derived PTC demonstrated a decreased ability to uptake FITC-albumin compared to the small congenic in vitro, which was confirmed by assessment of albumin re-uptake in vivo by infusion of FITC-albumin and immunofluorescence imaging. In summary, these studies suggest that genetic variants in the S form of Arhgef11 via increased expression and/or protein activity play a role in promoting kidney injury in the S rat through changes in cell morphology (Rho-Rock and/or EMT) that impact the function of tubule cells.

Yeast model identifies ENTPD6 as a potential non-obstructive azoospermia pathogenic gene

Approximately ten percent of male infertility is caused by non-obstructive azoospermia (NOA), but the etiologies of many NOA remain elusive. Recently, a genome-wide association study (GWAS) of NOA in Han Chinese men was conducted, and only a few genetic variants associated with NOA were found, which might have resulted from genetic heterogeneity. However, those variants that lack genome-wide significance might still be essential for fertility. Functional analysis of genes surrounding these variants inDrosophilaidentified some spermatogenesis-essential genes. As a complementary method ofDrosophilascreening, SK1 backgroundSaccharomvces cerevisiaewas used as a model to screen meiosis-related genes from the NOA GWAS data in this study. After functional screening,GDA1(orthologous to humanENTPD6) was found to be a novel meiosis-related gene. The deletion ofGDA1resulted in the failure of yeast sporulation. Further investigations showed that Gda1p was important for pre-meiotic S phase entry. Interestingly, the meiotic role of Gda1p was dependent on its guanosine diphosphatase activity, but not it’s cytoplasmic, transmembrane or stem domains. These yeast data suggest thatENTPD6may be a novel meiosis-associated NOA-related gene, and the yeast model provides a good approach to analyze GWAS results of NOA.

Temporal expression profiling identifies pathways mediating effect of causal variant on phenotype

Even with identification of multiple causal genetic variants for common human diseases, understanding the molecular processes mediating the causal variants’ effect on the disease remains a challenge. This understanding is crucial for the development of therapeutic strategies to prevent and treat disease. While static profiling of gene expression is primarily used to get insights into the biological bases of diseases, it makes differentiating the causative from the correlative effects difficult, as the dynamics of the underlying biological processes are not monitored. Using yeast as a model, we studied genome-wide gene expression dynamics in the presence of a causal variant as the sole genetic determinant, and performed allele-specific functional validation to delineate the causal effects of the genetic variant on the phenotype. Here, we characterized the precise genetic effects of a functional MKT1 allelic variant in sporulation efficiency variation. A mathematical model describing meiotic landmark events and conditional activation of MKT1 expression during sporulation specified an early meiotic role of this variant. By analyzing the early meiotic genome-wide transcriptional response, we demonstrate an MKT1-dependent role of novel modulators, namely, RTG1/3, regulators of mitochondrial retrograde signaling, and DAL82, regulator of nitrogen starvation, in additively effecting sporulation efficiency. In the presence of functional MKT1 allele, better respiration during early sporulation was observed, which was dependent on the mitochondrial retrograde regulator, RTG3. Furthermore, our approach showed that MKT1 contributes to sporulation independent of Puf3, an RNA-binding protein that steady-state transcription profiling studies have suggested to mediate MKT1-pleiotropic effects during mitotic growth. These results uncover interesting regulatory links between meiosis and mitochondrial retrograde signaling. In this study, we highlight the advantage of analyzing allele-specific transcriptional dynamics of mediating genes. Applications in higher eukaryotes can be valuable for inferring causal molecular pathways underlying complex dynamic processes, such as development, physiology and disease progression.

The causal path from a genetic variant to a complex phenotype such as disease progression is often not known. Studying gene expression variation is one approach to identify the mediating genes, however, it is difficult to distinguish causative from correlative genes. This becomes a challenge especially when studying developmental and physiological traits, since they involve dynamic processes contributing to the variation and only single static expression profiling is performed. As a proof of concept, we addressed this challenge here in yeast, by studying genome-wide gene expression in the presence of the causative polymorphism of MKT1 as the sole genetic variant, during the time phase when it contributes to sporulation efficiency variation. Our analysis during early sporulation identified mitochondrial retrograde signaling and nitrogen starvation as novel regulators, acting additively to regulate sporulation efficiency. Furthermore, we showed that PUF3, a known interactor of MKT1 had an independent role in sporulation. Our results highlight the role of differential mitochondrial signaling for efficient meiosis, providing insights into the factors regulating infertility. In addition, our study has implications for characterizing the molecular effects of causal genetic variants on dynamic biological processes during development and disease progression.

The Genetics of IgA Nephropathy: An Overview from Western Countries

BACKGROUND

IgA nephropathy (IgAN) is the commonest primary glomerulonephritis worldwide and a significant cause of chronic kidney disease and end-stage renal disease. It is widely accepted that genetic factors play a role in the pathogenesis of IgAN. However, the identity of these genetic factors remains uncertain.

SUMMARY

Critical to all genetic studies is a precise phenotypic definition of the disease. It is well recognised that IgAN displays striking phenotypic variation, raising the possibility that it may not be a single disease and it may not be the same disease in different parts of the world. In this review, we discuss the challenges that this phenotypic variation poses to interpreting genetic data and the current evidence for specific gene involvement in IgAN, focusing particularly on data from European IgAN cohorts.

KEY MESSAGE

With advances in genetic techniques, in particular next-generation sequencing, and an increased understanding of the importance of copy number variations, epigenetics and transcriptomics, it is likely that we will gain a greater understanding of the genetic basis for IgAN. However, due to the lack of consistency in epidemiological clinicopathological studies both within and between continents, this will only be achieved if we are able to more precisely phenotype IgAN populations.

FACTS FROM EAST AND WEST

The reported prevalence of IgAN is higher in Asia than in Europe and North America. However, differences in use of biopsy for the diagnosis of IgAN should be taken into account in analysing data from both East and West. In Europe, IgAN affects men more frequently than women; this is not the case in Asia. Familial IgAN has been more frequently reported in Europe than in Asia. Within Europe, familial IgAN is more evident in southern than in northern populations. Changes in the pattern of serum IgA1 O-glycosylation is a common finding in IgAN patients in the East and West. SNPs within the gene coding for the enzyme C1GALT1 have been reported in Chinese and European patients. However, there is no evidence for a role of gene polymorphism of the C1GALT1 chaperone cosmc in Europeans. Genetic variants in the HLA gene family have been observed in populations from the East and West. Associations between IgAN and variants of the TAP1/PSMB and DEFA genes were observed in Asian but not in Western patients. Association with the angiotensin-converting enzyme gene was seen only in Asian patients.

Genetic diversity of disease-associated loci in Turkish population

Many consortia and international projects have investigated the human genetic variation of a large number of ethno-geographic groups. However, populations with peculiar genetic features, such as the Turkish population, are still absent in publically available datasets. To explore the genetic predisposition to health-related traits of the Turkish population, we analyzed 34 genes associated with different health-related traits (for example, lipid metabolism, cardio-vascular diseases, hormone metabolism, cellular detoxification, aging and energy metabolism). We observed relevant differences between the Turkish population and populations with non-European ancestries (that is, Africa and East Asia) in some of the investigated genes (that is, AGT, APOE, CYP1B1, GNB3, IL10, IL6, LIPC and PON1). As most complex traits are highly polygenic, we developed polygenic scores associated with different health-related traits to explore the genetic diversity of the Turkish population with respect to other human groups. This approach showed significant differences between the Turkish population and populations with non-European ancestries, as well as between Turkish and Northern European individuals. This last finding is in agreement with the genetic structure of European and Middle East populations, and may also agree with epidemiological evidences about the health disparities of Turkish communities in Northern European countries.

An epidemiological perspective of personalized medicine: the Estonian experience

The Estonian Biobank and several other biobanks established over a decade ago are now starting to yield valuable longitudinal follow-up data for large numbers of individuals. These samples have been used in hundreds of different genome-wide association studies, resulting in the identification of reliable disease-associated variants. The focus of genomic research has started to shift from identifying genetic and nongenetic risk factors associated with common complex diseases to understanding the underlying mechanisms of the diseases and suggesting novel targets for therapy. However, translation of findings from genomic research into medical practice is still lagging, mainly due to insufficient evidence of clinical validity and utility. In this review, we examine the different elements required for the implementation of personalized medicine based on genomic information. First, biobanks and genome centres are required and have been established for the high-throughput genomic screening of large numbers of samples. Secondly, the combination of susceptibility alleles into polygenic risk scores has improved risk prediction of cardiovascular disease, breast cancer and several other diseases. Finally, national health information systems are being developed internationally, to combine data from electronic medical records from different sources, and also to gradually incorporate genomic information. We focus on the experience in Estonia, one of several countries with national goals towards more personalized health care based on genomic information, where the unique combination of elements required to accomplish this goal are already in place.

Association of a common genetic variant within ANKK1 with six-month cognitive performance after traumatic brain injury

Genetic association analyses suggest that certain common single nucleotide polymorphisms (SNPs) may adversely impact recovery from traumatic brain injury (TBI). Delineating their causal relationship may aid in development of novel interventions and in identifying patients likely to respond to targeted therapies. We examined the influence of the (C/T) SNP rs1800497 of ANKK1 on post-TBI outcome using data from two prospective multicenter studies: the Citicoline Brain Injury Treatment (COBRIT) trial and Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot). We included patients with ANKK1 genotyping results and cognitive outcomes at six months post-TBI (n = 492: COBRIT n = 272, TRACK-TBI Pilot n = 220). Using the California Verbal Learning Test Second Edition (CVLT-II) Trial 1-5 Standard Score, we found a dose-dependent effect for the T allele, with T/T homozygotes scoring lowest on the CVLT-II Trial 1-5 Standard Score (T/T 45.1, C/T 51.1, C/C 52.1, ANOVA, p = 0.008). Post hoc testing with multiple comparison-correction indicated that T/T patients performed significantly worse than C/T and C/C patients. Similar effects were observed in a test of non-verbal processing (Wechsler Adult Intelligence Scale, Processing Speed Index). Our findings extend those of previous studies reporting a negative relationship of the ANKK1 T allele with cognitive performance after TBI. In this study, we demonstrate the value of pooling shared clinical, biomarker, and outcome variables from two large datasets applying the NIH TBI Common Data Elements. The results have implications for future multicenter investigations to further elucidate the role of ANKK1 in post-TBI outcome.