Genomics and predictive medicine

Progress in understanding of structural and functional human genome organization and deciphering primary DNA sequence in human cells allowed for hitherto unreachable new capabilities of medical genetics in identifying the causes and mechanisms of inherited and inborn pathology. Implementation of genetics into medicine is progressively advancing along with improvement of molecular analysis of genome. Knowledge of genome and its functions allows to provide more accurate diagnosis, predict, to a considerable extent, the presence of genetic predisposition of a person to pathology, and to assess the chances for developing one or another disease. This approach became the basis for a new area of medical genetics named predictive medicine. The progress of predictive medicine refl ects success in tremendous upgrowth of molecular genetic methods and new capabilities of studying structure and functions of genome. Within less than 15 years after deciphering genome, medical genetics has travelled a long way from a single gene analysis to whole genome studies, from screening of genetic associations to systems genetics of multifactorial diseases, from translational to high-precision genetics, and from genetic passport idea to electronic genetic health records. The development of a genetic passport, prognostic genetic testing, and genomic chart of reproductive health is especially relevant for current practical medicine.

Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science

Mendel proposed an experimentally verifiable paradigm of particle-based heredity that has been influential for over 150 years. The historical arguments have been reflected in the near past as Mendel’s concept has been diversified by new types of omics data. As an effect of the accumulation of omics data, a virtual gene concept forms, giving rise to genetical data science. The concept integrates genetical, functional, and molecular features of the Mendelian paradigm. I argue that the virtual gene concept should be deployed pragmatically. Indeed, the concept has already inspired a practical research program related to systems genetics. The program includes questions about functionality of structural and categorical gene variants, about regulation of gene expression, and about roles of epigenetic modifications. The methodology of the program includes bioinformatics, machine learning, and deep learning. Education, funding, careers, standards, benchmarks, and tools to monitor research progress should be provided to support the research program.

qtlXplorer: an online systems genetics browser in the Eucalyptus Genome Integrative Explorer (EucGenIE)

Background Affordable high-throughput DNA and RNA sequencing technologies are allowing genomic analysis of plant and animal populations and as a result empowering new systems genetics approaches to study complex traits. The availability of intuitive tools to browse and analyze the resulting large-scale genetic and genomic datasets remain a significant challenge. Furthermore, these integrative genomics approaches require innovative methods to dissect the flow and interconnectedness of biological information underlying complex trait variation. The Plant Genome Integrative Explorer (PlantGenIE.org) is a multi-species database and domain that houses online tools for model and woody plant species including Eucalyptus. Since the Eucalyptus Genome Integrative Explorer (EucGenIE) is integrated within PlantGenIE, it shares genome and expression analysis tools previously implemented within the various subdomains (ConGenIE, PopGenIE and AtGenIE). Despite the success in setting up integrative genomics databases, online tools for systems genetics modelling and high-resolution dissection of complex trait variation in plant populations have been lacking. Results We have developed qtlXplorer (https://eucgenie.org/QTLXplorer) for visualizing and exploring systems genetics data from genome-wide association studies including quantitative trait loci (QTLs) and expression-based QTL (eQTL) associations. This module allows users to, for example, find co-located QTLs and eQTLs using an interactive version of Circos, or explore underlying genes using JBrowse. It provides users with a means to build systems genetics models and generate hypotheses from large-scale population genomics data. We also substantially upgraded the EucGenIE resource and show how it enables users to combine genomics and systems genetics approaches to discover candidate genes involved in biotic stress responses and wood formation by focusing on two multigene families, laccases and peroxidases. Conclusions qtlXplorer adds a new dimension, population genomics, to the EucGenIE and PlantGenIE environment. The resource will be of interest to researchers and molecular breeders working in Eucalyptus and other woody plant species. It provides an example of how systems genetics data can be integrated with functional genetics data to provide biological insight and formulate hypotheses. Importantly, integration within PlantGenIE enables novel comparative genomics analyses to be performed from population-scale data.

Systems genetics uncovers microbe-lipid-host connections in the murine gut

The molecular bases of how host genetic variation impact gut microbiome remain largely unknown. Here, we used a genetically diverse mouse population and systems genetics strategies to identify interactions between molecular phenotypes, including microbial functions, intestinal transcripts and cecal lipids that influence microbe-host dynamics. Quantitative trait loci (QTL) analysis identified genomic regions associated with variations in bacterial taxa, bacterial functions, including motility, sporulation and lipopolysaccharide production, and levels of bacterial- and host-derived lipids. We found overlapping QTL for the abundance of Akkermansia muciniphila and cecal levels of ornithine lipids (OL). Follow-up studies revealed that A. muciniphila is a major source of these lipids in the gut, provided evidence that OL have immunomodulatory effects and identified intestinal transcripts co-regulated with these traits. Collectively, these results suggest that OL are key players in A. muciniphila-host interactions and support the role of host genetics as a determinant of responses to gut microbes.

A systems genetics approach reveals PbrNSC as a regulator of lignin and cellulose biosynthesis in stone cells of pear fruit

Background Stone cells in fruits of pear (Pyrus pyrifolia) negatively influence fruit quality because their lignified cell walls impart a coarse and granular texture to the fruit flesh. Results We generate RNA-seq data from the developing fruits of 206 pear cultivars with a wide range of stone cell contents and use a systems genetics approach to integrate co-expression networks and expression quantitative trait loci (eQTLs) to characterize the regulatory mechanisms controlling lignocellulose formation in the stone cells of pear fruits. Our data with a total of 35,897 expressed genes and 974,404 SNPs support the identification of seven stone cell formation modules and the detection of 139,515 eQTLs for 3229 genes in these modules. Focusing on regulatory factors and using a co-expression network comprising 39 structural genes, we identify PbrNSC as a candidate regulator of stone cell formation. We then verify the function of PbrNSC in regulating lignocellulose formation using both pear fruit and Arabidopsis plants and further show that PbrNSC can transcriptionally activate multiple target genes involved in secondary cell wall formation. Conclusions This study generates a large resource for studying stone cell formation and provides insights into gene regulatory networks controlling the formation of stone cell and lignocellulose.

Systems Genetics Analysis Defines Importance Of TMEM43/LUMA For Cardiac And Metabolic Related Pathways

Background: Broad cellular functions and diseases including muscular dystrophy, arrhythmogenic right ventricular cardiomyopathy (ARVC5) and cancer are associated with transmembrane protein43 (TMEM43/LUMA). Objective: The study aimed to investigate biological roles of TMEM43 through genetic regulation, gene pathways and gene networks, candidate interacting genes and up- or down-stream regulators. Methods: Cardiac transcriptomes from 40 strains of recombinant inbred BXD mice and two parental strains representing murine genetic reference population (GRP) was applied for genetic correlation, functional enrichment and co-expression network analysis using systems genetics approach. The results were validated in a newly created knock-in Tmem43-S358L mutation mouse model (Tmem43S358L) that displayed signs of cardiac dysfunction, resembling ARVC5 phenotype seen in humans. Results: We found high Tmem43 levels among BXDs with broad variability in expression. Expression of Tmem43 highly negatively correlated with heart mass and heart rate among BXDs, while levels of Tmem43 highly positively correlated with plasma high density lipoproteins (HDL). Through finding differentially expressed genes (DEGs) between Tmem43S358L mutant and wild type (Tmem43WT) lines, 18 pathways (out of 42 found in BXDs GRP) that are involved in ARVC, Hypertrophic cardiomyopathy, Dilated cardiomyopathy, Non-alcoholic fatty liver disease, Alzheimer disease, Parkinson disease and Huntington disease were verified. We further constructed Tmem43-mediated gene network, in which Ctnna1, Adcy6, Gnas, Ndufs6 and Uqcrc2 were significantly altered in Tmem43S358L mice vs Tmem43WT controls. Conclusions: Our study defined the importance of Tmem43 for cardiac and metabolism related pathways, suggesting that cardiovascular disease-relevant risk factors may also increase risk of metabolic and neurodegenerative diseases via TMEM43-mediated pathways.

Abstract P414: Systems Genetics Analysis Reveals Significant Eqtls Associated With Echocardiography Parameters In Genetic Reference Population Of Bxd Strains.

Background: Causal and modifier genes, genetic background and environment underlie clinical heterogeneity in cardiomyopathy (CM). The BXD recombinant inbred (RI) family represents a murine genetic reference population (GRP) that are descendants from crosses between C57BL/6J (B6) and DBA/2J (D2) mice. The parental D2 mouse is a natural model of hypertrophic CM (HCM). The study aimed to dissect genetic architecture of cardiac traits in BXD GRP using systems genetics analysis. Methods: Echocardiography was performed in 88 strains of male (M) and female (F) BXDs (N>5/sex) at 4-5 months of age. Cardiac traits were then associated with heart transcriptome, and expression quantitative trait loci ( eQTL) mapping was performed. Results: More than 2-fold variance in ejection fraction (EF%), fractional shortening (FS%), left ventricular (LV) volumes at end-systole and end-diastole (Vol;s and Vol;d), internal dimensions (ID;s and ID;d), posterior wall (PW), and interventricular septum (IVS) thickness was found among BXDs. Traits seen in dilated CM (DCM) patients such as reduced EF%, FS%, and LVPW and increased Vol;s and ID;s are found in BXD78 (M, F), BXD32, 111, and 68 (F) strains. Strains D2, BXD90 and 155 (M, F), BXD44 and 65 (M), and BXD113, 16, 77 (F) had significantly greater LV mass, LVPW and IVS thickness compared to sex-matched controls, suggestive for traits seen in HCM patients. A 6.4 Mb QTL (peak LRS=18.50) was identified on chromosome (Chr) 8 to be significantly associated with ID;s, ID;d, Vol;s and Vol;d among male BXDs. eQTL mapping for each of 131 genes on Chr8 QTL identified 6 genes ( Coq9 , Ndrg4 , Crnde, Irx3, Rpgrip1l, and Rbl2 ) being cis -regulated and Ndrg, Slc6a2 and Ces1d being significantly (p < 0.05) correlated with LV volumes. In female BXDs, a significant QTL on Chr7 (40.2 Mb) with 9 genes that significantly correlated with LVPW;d was identified. A suggestive 92.6 Mb QTL on Chr3 with Snapin , Tpm3 , and Wars2 correlated with EF% and FS% (p < 0.05). Conclusions: Our study found cardiomyopathy-associated traits are segregated among BXD family and these traits vary among BXD lines. Multiple associated QTLs demonstrate that the BXD family is suitable to map gene variants and identify genetic factors and modifiers that influence cardiomyopathy phenotypes.

A Comparison Analysis for Protein-Protein Interaction Network-Based Methods in Prioritizing Arabidopsis Functional Genes

Background: Connecting genes to phenotypes is still a great challenge in genetics. Research related to gene-phenotype associations has made remarkable progress recently due to high-throughput sequencing technology and genome-wide association study (GWAS). However, these genes, which are considered to be significantly associated with a target phenotype according to traditional GWAS, are less precise or subject to greater confounding. Objective: The present study is an attempt to prioritize functional genes for complex phenotypes employing protein-protein interaction (PPI) network-based systems genetics methods on available GWAS results. Method: In this paper, we calculated the functional gene enrichment ratios of the trait ontology of A. thaliana for three common systems genetics methods (i.e. GeneRank, K-shell and HotNet2). Then, comparison of gene enrichment ratios obtained by PPI network-based methods was performed. Finally, a hybrid model was proposed, integrating GeneRank, comprehensive score algorithm and HotNet diffusion-oriented subnetworks (HotNet2) to prioritize functional genes. Results: These PPI network-based systems genetics methods were indeed useful for prioritizing phenotype-associated genes. And functional gene enrichment ratios calculated from the top 20% of GeneRank-identified genes were higher than these ratios of K-shell and these ratios of HotNet2 for most phenotypes. However, the hybrid model can improve the efficiency of functional gene enrichment for A. thaliana (up to 40%). Conclusion: The present study provides a hybrid method integrating GeneRank, comprehensive score algorithm and HotNet2 to prioritize functional genes. The method will contribute to functional genomics in plants. The source data and codes are freely available at http://47.242.161.60/Plant/.