scholarly journals Joint eQTL mapping and Inference of Gene Regulatory Network Improves Power of Detecting both cis- and trans-eQTLs

2020 ◽  
Author(s):  
Xin Zhou ◽  
Xiaodong Cai
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Complex Traits ◽  
Multiple Testing ◽  
Statistical Power ◽  
Critical Role ◽  
Equation Model ◽  
Effective Sample Size ◽  
Eqtl Mapping ◽  
Gene Regulatory

AbstractMotivationGenetic variations of expression quantitative trait loci (eQTLs) play a critical role in influencing complex traits and diseases development. Two main factors that affect the statistical power of detecting eQTLs are: 1) relatively small size of samples available, and 2) heavy burden of multiple testing due to a very large number of variants to be tested. The later issue is particularly severe when one tries to identify trans-eQTLs that are far away from the genes they influence. If one can exploit co-expressed genes jointly in eQTL-mapping, effective sample size can be increased. Furthermore, using the structure of the gene regulatory network (GRN) may help to identify trans-eQTLs without increasing multiple testing burden.ResultsIn this paper, we employ the structure equation model (SEM) to model both GRN and effect of eQTLs on gene expression, and then develop a novel algorithm, named sparse SEM, for eQTL mapping (SSEMQ) to conduct joint eQTL mapping and GRN inference. The SEM can exploit co-expressed genes jointly in eQTL mapping and also use GRN to determine trans-eQTLs. Computer simulations demonstrate that our SSEMQ significantly outperforms eight existing eQTL mapping methods. SSEMQ is further employed to analyze a real dataset of human breast tissues, yielding a number of cis- and trans-eQTLs.AvailabilityR package ssemQr is available on https://github.com/Ivis4ml/ssemQr.git.

scholarly journals Joint eQTL mapping and Inference of Gene Regulatory Network Improves Power of Detecting both cis- and trans-eQTLs

2021 ◽  
Author(s):  
Xin Zhou ◽  
Xiaodong Cai
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Complex Traits ◽  
Multiple Testing ◽  
Critical Role ◽  
Supplementary Information ◽  
Effective Sample Size ◽  
Eqtl Mapping ◽  
Gene Regulatory ◽  
Cis And Trans

Abstract Motivation Genetic variations of expression quantitative trait loci (eQTLs) play a critical role in influencing complex traits and diseases development. Two main factors that affect the statistical power of detecting eQTLs are: 1) relatively small size of samples available, and 2) heavy burden of multiple testing due to a very large number of variants to be tested. The later issue is particularly severe when one tries to identify trans-eQTLs that are far away from the genes they influence. If one can exploit co-expressed genes jointly in eQTL-mapping, effective sample size can be increased. Furthermore, using the structure of the gene regulatory network (GRN) may help to identify trans-eQTLs without increasing multiple testing burden. Results In this paper, we employ the structure equation model (SEM) to model both GRN and effect of eQTLs on gene expression, and then develop a novel algorithm, named sparse SEM for eQTL mapping (SSEMQ), to conduct joint eQTL mapping and GRN inference. The SEM can exploit co-expressed genes jointly in eQTL mapping and also use GRN to determine trans-eQTLs. Computer simulations demonstrate that our SSEMQ significantly outperforms nine existing eQTL mapping methods. SSEMQ is further employed to analyze two real datasets of human breast and whole blood tissues, yielding a number of cis- and trans-eQTLs. Availability R package ssemQr is available at https://github.com/Ivis4ml/ssemQr.git. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Deciphering the genetic links between NAFLD and co-occurring conditions using a liver gene regulatory network

2021 ◽  
Author(s):  
Sreemol Gokuladhas ◽  
William Schierding ◽  
Roan Eltigani Zaied ◽  
Tayaza Fadason ◽  
Murim Choi ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Complex Traits ◽  
Target Genes ◽  
Specific Gene ◽  
Fat Percentage ◽  
Alcoholic Fatty Liver ◽  
Regulatory Interactions ◽  
Hepatic Diseases ◽  
Gene Regulatory

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) is a multi-system metabolic disease that co-occurs with various hepatic and extra-hepatic diseases. The phenotypic manifestation of NAFLD is primarily observed in the liver. Therefore, identifying liver-specific gene regulatory interactions between variants associated with NAFLD and multimorbid conditions may help to improve our understanding of underlying shared aetiology. Methods: Here, we constructed a liver-specific gene regulatory network (LGRN) consisting of genome-wide spatially constrained expression quantitative trait loci (eQTLs) and their target genes. The LGRN was used to identify regulatory interactions involving NAFLD-associated genetic modifiers and their inter-relationships to other complex traits. Results and Conclusions: We demonstrate that MBOAT7 and IL32, which are associated with NAFLD progression, are regulated by spatially constrained eQTLs that are enriched for an association with liver enzyme levels. MBOAT7 transcript levels are also linked to eQTLs associated with cirrhosis, and other traits that commonly co-occur with NAFLD. In addition, genes that encode interacting partners of NAFLD-candidate genes within the liver-specific protein-protein interaction network were affected by eQTLs enriched for phenotypes relevant to NAFLD (e.g. IgG glycosylation patterns, OSA). Furthermore, we identified distinct gene regulatory networks formed by the NAFLD-associated eQTLs in normal versus diseased liver, consistent with the context-specificity of the eQTLs effects. Interestingly, genes targeted by NAFLD-associated eQTLs within the LGRN were also affected by eQTLs associated with NAFLD-related traits (e.g. obesity and body fat percentage). Overall, the genetic links identified between these traits expand our understanding of shared regulatory mechanisms underlying NAFLD multimorbidities.

scholarly journals Interpreting ruminant specific conserved non-coding elements by developmental gene regulatory network

2021 ◽  
Author(s):  
Xiangyu Pan ◽  
Zhaoxia Ma ◽  
Xinqi Sun ◽  
Hui Li ◽  
Tingting Zhang ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Complex Traits ◽  
Systematic Approach ◽  
Chromatin Accessibility ◽  
Specific Gene ◽  
Developmental Gene ◽  
Gene Regulatory ◽  
Evo Devo ◽  
Interpretation Method

Biologists long recognized that the genetic information encoded in DNA leads to trait innovation via gene regulatory network (GRN) in development. Here, we generated paired expression and chromatin accessibility data during rumen and esophagus development in sheep and revealed 1,601 active ruminant-specific conserved non-coding elements (active-RSCNEs). To interpret the function of these active-RSCNEs, we developed a Conserved Non-coding Element interpretation method by gene Regulatory network (CNEReg) to define toolkit transcription factors (TTF) and model its regulation on rumen specific gene via batteries of active-RSCNEs during development. Our developmental GRN reveals 18 TTFs and 313 active-RSCNEs regulating the functional modules of the rumen and identifies OTX1, SOX21, HOXC8, SOX2, TP63, PPARG and 16 active-RSCNEs that functionally distinguish the rumen from the esophagus. We argue that CNEReg is an attractive systematic approach to integrate evo-devo concepts with omics data to understand how gene regulation evolves and shapes complex traits.

scholarly journals Satb2 regulates the development of dopaminergic neurons in the arcuate nucleus by Dlx1

2021 ◽  
Vol 12 (10) ◽  
Author(s):  
Qiong Zhang ◽  
Lei Zhang ◽  
Ying Huang ◽  
Pengcheng Ma ◽  
Bingyu Mao ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Dopaminergic Neurons ◽  
Energy Homeostasis ◽  
Arcuate Nucleus ◽  
Critical Role ◽  
The Other ◽  
Serum Prolactin ◽  
Serum Prolactin Level ◽  
Gene Regulatory

AbstractDopaminergic (DA) neurons in the arcuate nucleus (ARC) of the hypothalamus play essential roles in the secretion of prolactin and the regulation of energy homeostasis. However, the gene regulatory network responsible for the development of the DA neurons remains poorly understood. Here we report that the transcription factor special AT-rich binding protein 2 (Satb2) is required for the development of ARC DA neurons. Satb2 is expressed in a large proportion of DA neurons without colocalization with proopiomelanocortin (POMC), orexigenic agouti-related peptide (AgRP), neuropeptide-Y (NPY), somatostatin (Sst), growth hormone-releasing hormone (GHRH), or galanin in the ARC. Nestin-Cre;Satb2flox/flox (Satb2 CKO) mice show a reduced number of ARC DA neurons with unchanged numbers of the other types of ARC neurons, and exhibit an increase of serum prolactin level and an elevated metabolic rate. The reduction of ARC DA neurons in the CKO mice is observed at an embryonic stage and Dlx1 is identified as a potential downstream gene of Satb2 in regulating the development of ARC DA neurons. Together, our study demonstrates that Satb2 plays a critical role in the gene regulatory network directing the development of DA neurons in ARC.

scholarly journals Reverse engineering gene regulatory network based on complex-valued ordinary differential equation model

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Bin Yang ◽  
Wenzheng Bao ◽  
Wei Zhang ◽  
Haifeng Wang ◽  
Chuandong Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Equation ◽  
Ordinary Differential Equation ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Equation Model ◽  
Expression Data ◽  
Gene Regulatory ◽  
Complex Valued

Abstract Background The growing researches of molecular biology reveal that complex life phenomena have the ability to demonstrating various types of interactions in the level of genomics. To establish the interactions between genes or proteins and understand the intrinsic mechanisms of biological systems have become an urgent need and study hotspot. Results In order to forecast gene expression data and identify more accurate gene regulatory network, complex-valued version of ordinary differential equation (CVODE) is proposed in this paper. In order to optimize CVODE model, a complex-valued hybrid evolutionary method based on Grammar-guided genetic programming and complex-valued firefly algorithm is presented. Conclusions When tested on three real gene expression datasets from E.coli and Human Cell, the experiment results suggest that CVODE model could improve 20–50% prediction accuracy of gene expression data, which could also infer more true-positive regulatory relationships and less false-positive regulations than ordinary differential equation.

Sign in / Sign up

Export Citation Format

Share Document