Weighted Interaction SNP Hub (WISH) network method for building genetic networks for complex diseases and traits using whole genome genotype data

BACKGROUND: High-throughput genotype (HTG) data has been used primarily in genome-wide association (GWA) studies; however, GWA results explain only a limited part of the complete genetic variation of traits. In systems genetics, network approaches have been shown to be able to identify pathways and their underlying causal genes to unravel the biological and genetic background of complex diseases and traits, e.g., the Weighted Gene Co-expression Network Analysis (WGCNA) method based on microarray gene expression data. The main objective of this study was to develop a scale-free weighted genetic interaction network method using whole genome HTG data in order to detect biologically relevant pathways and potential genetic biomarkers for complex diseases and traits. RESULTS: We developed the Weighted Interaction SNP Hub (WISH) network method that uses HTG data to detect genome-wide interactions between single nucleotide polymorphism (SNPs) and its relationship with complex traits. Data dimensionality reduction was achieved by selecting SNPs based on its: 1) degree of genome-wide significance and 2) degree of genetic variation in a population. Network construction was based on pairwise Pearson's correlation between SNP genotypes or the epistatic interaction effect between SNP pairs. To identify modules the Topological Overlap Measure (TOM) was calculated, reflecting the degree of overlap in shared neighbours between SNP pairs. Modules, clusters of highly interconnected SNPs, were defined using a tree-cutting algorithm on the SNP dendrogram created from the dissimilarity TOM (1-TOM). Modules were selected for functional annotation based on their association with the trait of interest, defined by the Genome-wide Module Association Test (GMAT). We successfully tested the established WISH network method using simulated and real SNP interaction data and GWA study results for carcass weight in a pig resource population; this resulted in detecting modules and key functional and biological pathways related to carcass weight. CONCLUSIONS: We developed the WISH network method which is a novel 'systems genetics' approach to study genetic networks underlying complex trait variation. The WISH network method reduces data dimensionality and statistical complexity in associating genotypes with phenotypes in GWA studies and enables researchers to identify biologically relevant pathways and potential genetic biomarkers for any complex trait of interest

Potential role of lncRNA cyp2c91-protein interactions on diseases of the immune system

With unprecedented increase in next generation sequencing (NGS) technologies, there has been a persistent interest on transcript profiles of long noncoding RNAs (lncRNAs) and protein-coding genes forming an interaction network. Apart from protein-protein interaction (PPI), gene network models such as Weighted Gene Co-expression Network Analysis are used to functionally annotate lncRNAs in identifying their potential disease associations. To address this, studies have led to characterizing transcript structures and understanding expression profiles mediating regulatory roles. In the current exploratory analysis, we show how a lncRNA - cyp2c91 contributes to the transcriptional regulation localized to cytoplasm thereby making refractory environment for transcription. By applying network methods and pathway analyses on genes related to a disease such as obesity and systemic lupus erythematosus, we show that we can gain deeper insight in biological processes such as the perturbances in immune system, and get a better understanding of the systems biology of diseases

WISH-R- a fast and efficient tool for construction of epistatic networks for complex traits and diseases

Abstract Background Genetic epistasis is an often-overlooked area in the study of the genomics of complex traits. Genome-wide association studies are a useful tool for revealing potential causal genetic variants, but in this context, epistasis is generally ignored. Data complexity and interpretation issues make it difficult to process and interpret epistasis. As the number of interaction grows exponentially with the number of variants, computational limitation is a bottleneck. Gene Network based strategies have been successful in integrating biological data and identifying relevant hub genes and pathways related to complex traits. In this study, epistatic interactions and network-based analysis are combined in the Weighted Interaction SNP hub (WISH) method and implemented in an efficient and easy to use R package. Results The WISH R package (WISH-R) was developed to calculate epistatic interactions on a genome-wide level based on genomic data. It is easy to use and install, and works on regular genomic data. The package filters data based on linkage disequilibrium and calculates epistatic interaction coefficients between SNP pairs based on a parallelized efficient linear model and generalized linear model implementations. Normalized epistatic coefficients are analyzed in a network framework, alleviating multiple testing issues and integrating biological signal to identify modules and pathways related to complex traits. Functions for visualizing results and testing runtimes are also provided. Conclusion The WISH-R package is an efficient implementation for analyzing genome-wide epistasis for complex diseases and traits. It includes methods and strategies for analyzing epistasis from initial data filtering until final data interpretation. WISH offers a new way to analyze genomic data by combining epistasis and network based analysis in one method and provides options for visualizations. This alleviates many of the existing hurdles in the analysis of genomic interactions

Differential expression and co-expression gene networks reveal candidate biomarkers of boar taint in non-castrated pigs

Abstract Boar taint (BT) is an offensive odour or taste observed in pork from a proportion of non-castrated male pigs. Surgical castration is effective in avoiding BT, but animal welfare issues have created an incentive for alternatives such as genomic selection. In order to find candidate biomarkers, gene expression profiles were analysed from tissues of non-castrated pigs grouped by their genetic merit of BT. Differential expression analysis revealed substantial changes with log-transformed fold changes of liver and testis from −3.39 to 2.96 and −7.51 to 3.53, respectively. Co-expression network analysis revealed one module with a correlation of −0.27 in liver and three modules with correlations of 0.31, −0.44 and −0.49 in testis. Differential expression and co-expression analysis revealed candidate biomarkers with varying biological functions: phase I (COQ3, COX6C, CYP2J2, CYP2B6, ACOX2) and phase II metabolism (GSTO1, GSR, FMO3) of skatole and androstenone in liver to steroidgenesis (HSD17B7, HSD17B8, CYP27A1), regulation of steroidgenesis (STARD10, CYB5R3) and GnRH signalling (MAPK3, MAP2K2, MAP3K2) in testis. Overrepresented pathways included “Ribosome”, “Protein export” and “Oxidative phosphorylation” in liver and “Steroid hormone biosynthesis” and “Gap junction” in testis. Future work should evaluate the biomarkers in large populations to ensure their usefulness in genomic selection programs

An integrative systems genetics approach reveals potential causal genes and pathways related to obesity

Background: Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach. Methods: Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected differentially expressed genes, and previously detected co-expressed gene modules. Further data integration was performed by detecting co-expression patterns among eQTLs and integration with protein data. Results: Differential expression analysis of RNA sequencing data revealed 458 differentially expressed genes. The eQTL mapping resulted in 987 cis-eQTLs and 73 trans-eQTLs (false discovery rate <0.05), of which the cis-eQTLs were associated with metabolic pathways. We reduced the eQTL search space by focusing on differentially expressed and co-expressed genes and disease-associated single nucleotide polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we detected several obesity candidate genes, for example, ENPP1, CTSL, and ABHD12B. Conclusions: To our knowledge, this is the first study to perform an integrated genomics and transcriptomics (eQTL) study using, and modeling, genomic and subcutaneous adipose tissue RNA sequencing data on obesity in a porcine model. We detected several pathways and potential causal genes for obesity. Further validation and investigation may reveal their exact function and association with obesity

An unusual case of ambiguous genitalia

ABSTRACT Introduction: Mixed gonadal dysgenesis is most commonly associated with a mosaic 45X/46XY karyotype. The clinical phenotype may range from almost complete female or male external genitalia to ambiguous genitalia. The internal genitalia may be composed of dysgenetic testis on one side and a streak gonad on the contralateral side, however there can be varying configurations as well. The uterus and fallopian tubes are generally ipsilateral to the streak gonad and these retained Mullerian remnants are common. The presence of 45X may present with Turner like features including cardiac, renal anomalies and short stature. Case Report: We describe a case of ambiguous genitalia secondary to mixed gonadal dysgenesis where the sex assignment was made before the final diagnosis was reached. Conclusion: The diagnosis of mixed gonadal dysgenesis can be difficult for families. A multidisciplinary approach must be established for the patient and family while incorporating the psychosocial aspects also