scholarly journals GeneNetwork: a continuously updated tool for systems genetics analyses

2020 ◽  
Author(s):  
Pamela M. Watson ◽  
David G. Ashbrook
Keyword(s):  
Candidate Genes ◽  
Quantitative Trait ◽  
Predictive Medicine ◽  
Systems Genetics ◽  
Immune Phenotype ◽  
Plausible Candidate ◽  
Genetics Research ◽  
Omics Analysis

AbstractGeneNetwork and its earlier iteration, WebQTL, have now been an important database and toolkit for quantitative trait genetics research for two decades. Recent improvements to GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi-omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses, demonstrating how a small number of plausible candidate genes can be found for a typical immune phenotype.

Systems genetics: challenges and developing strategies

Biologia ◽  
2012 ◽  
Vol 67 (3) ◽  
Author(s):  
Hong Li ◽  
Ping Zhang
Keyword(s):  
Protein Interactions ◽  
Quantitative Trait ◽  
Dna Microarrays ◽  
Complex Disease ◽  
Transcript Abundance ◽  
Systems Genetics ◽  
Wide Field ◽  
Protein Protein Interactions ◽  
Genetics Research ◽  
Field Of Vision

AbstractSystems genetics is a new discipline based on the transcription mapping, which is also called “genetical genomics”. In recent years, systems genetics has become more practical because of advances in science and technology. Analysis of expression quantitative trait loci (eQTLs) is an emerging technique in which individuals are genotyped across a panel of genetic markers and, simultaneously, phenotyped using DNA microarrays. Depending on eQTL mapping, one can infer the underlying regulatory network responsible for complex diseases or quantitative trait phenotypes. Systems genetics approaches integrate DNA sequence variation, variation in transcript abundance and other molecular phenotypes and variation in organismal phenotypes in a linkage or association mapping population, and allow us to interpret quantitative genetic variation in terms of biologically meaningful causal networks of correlated transcripts. These approaches have been made possible due to the development of massively parallel technologies for quantifying genome-wide levels of transcript abundance. The predictive power of the networks could be enhanced by more systematically integrating protein-protein interactions, protein-DNA interactions, protein-RNA interactions, RNA-RNA interactions, protein state information, methylation state, and interactions with metabolites. Systems genetics research will change the traditional approaches based on reductionism, and allows us to reconsider the living phenomenon and complex disease mechanism. Systems genetics benefits from varied “omics” researches (such as transcriptomics, metabolomics, and phenomics) and the development of bioinformatics tools and mathematical modeling, and will become mature in the near future like many other branches of genetics. Systems genetics is leading researchers to understand genetics systems from holism’s viewpoint, and will open a wide field of vision for genetics researchers in systems biology era.

scholarly journals Transcriptomic profiling of wheat near-isogenic lines reveals candidate genes on chromosome 3A for pre-harvest sprouting resistance

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xingyi Wang ◽  
Hui Liu ◽  
Kadambot H. M. Siddique ◽  
Guijun Yan
Keyword(s):  
Candidate Genes ◽  
Quantitative Trait ◽  
Genomic Region ◽  
Nucleotide Polymorphisms ◽  
Near Isogenic Lines ◽  
Gene Effects ◽  
Isogenic Lines ◽  
Trait Locus ◽  
Grain Yield And Quality ◽  
Sprouting Resistance

Abstract Background Pre-harvest sprouting (PHS) in wheat can cause severe damage to both grain yield and quality. Resistance to PHS is a quantitative trait controlled by many genes located across all 21 wheat chromosomes. The study targeted a large-effect quantitative trait locus (QTL) QPhs.ccsu-3A.1 for PHS resistance using several sets previously developed near-isogenic lines (NILs). Two pairs of NILs with highly significant phenotypic differences between the isolines were examined by RNA sequencing for their transcriptomic profiles on developing seeds at 15, 25 and 35 days after pollination (DAP) to identify candidate genes underlying the QTL and elucidate gene effects on PHS resistance. At each DAP, differentially expressed genes (DEGs) between the isolines were investigated. Results Gene ontology and KEGG pathway enrichment analyses of key DEGs suggested that six candidate genes underlie QPhs.ccsu-3A.1 responsible for PHS resistance in wheat. Candidate gene expression was further validated by quantitative RT-PCR. Within the targeted QTL interval, 16 genetic variants including five single nucleotide polymorphisms (SNPs) and 11 indels showed consistent polymorphism between resistant and susceptible isolines. Conclusions The targeted QTL is confirmed to harbor core genes related to hormone signaling pathways that can be exploited as a key genomic region for marker-assisted selection. The candidate genes and SNP/indel markers detected in this study are valuable resources for understanding the mechanism of PHS resistance and for marker-assisted breeding of the trait in wheat.

scholarly journals A Fine mapping quantitative trait loci that underlie resistance to soybean sudden death syndrome using NILs and SNPs.

2018 ◽  
pp. 583-591
Author(s):  
Yi Chen Lee ◽  
M Javed Iqbal ◽  
Victor N Njiti ◽  
Stella Kantartzi ◽  
David A. Lightfoot
Keyword(s):  
Quantitative Trait Loci ◽  
Sudden Death ◽  
Candidate Genes ◽  
Quantitative Trait ◽  
Soybean Cyst Nematode ◽  
Snp Markers ◽  
Sudden Death Syndrome ◽  
Isogenic Line ◽  
Trait Loci ◽  
Glycine Max L

Soybean (Glycine max (L.) Merr.) cultivars differ in their resistance to sudden death syndrome (SDS), caused by Fusarium virguliforme. Breeding for improving SDS response has been challenging, due to interactions among the 18-42 known resistance loci. Four quantitative trait loci (QTL) for resistance to SDS (cqRfs–cqRfs3) were clustered within 20 cM of the rhg1 locus underlying resistance to soybean cyst nematode (SCN) on Chromosome (Chr.) 18. Another locus on Chr. 20 (cqRfs5) was reported to interact with this cluster. The aims here were to compare the inheritance of resistance to SDS in a near isogenic line (NIL) population that was fixed for resistance to SCN but segregated at two of the four loci (cqRfs1 and cqRfs) for SDS resistance; to examine the interaction with the locus on Chr. 20; and to identify candidate genes underlying QTL. Used were; a NIL population derived from residual heterozygosity in an F5:7 recombinant inbred line EF60 (lines 1-38); SDS response data from two locations and years; four segregating microsatellite and 1,500 SNP markers. Polymorphic regions were found from 2,788 Kbp to 8,938 Kbp on Chr. 18 and 33,100 Kbp to 34,943 Kbp on Chr. 20 that were significantly (0.005 < P > 0.0001) associated with resistance to SDS. The QTL fine maps suggested that the two loci on Chr. 18 were three loci (cqRfs1, cqRfs, and cqRfs19). Candidate genes were inferred.  An epistatic interaction was inferred between Chr. 18 and Chr. 20 loci. Therefore, SDS resistance QTL were both complex and interacting.

scholarly journals Meta-Analysis of Quantitative Trait Loci Associated with Seedling-Stage Salt Tolerance in Rice (Oryza sativa L.)

Plants ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 33 ◽  
Author(s):  
Md. Islam ◽  
John Ontoy ◽  
Prasanta Subudhi
Keyword(s):  
Quantitative Trait Loci ◽  
Salt Tolerance ◽  
Candidate Genes ◽  
Quantitative Trait ◽  
Oryza Sativa L ◽  
Meta Analysis ◽  
Seedling Stage ◽  
Phenotypic Variance ◽  
A Genome ◽  
Trait Loci

Soil and water salinity is one of the major abiotic stresses that reduce growth and productivity in major food crops including rice. The lack of congruence of salt tolerance quantitative trait loci (QTLs) in multiple genetic backgrounds and multiple environments is a major hindrance for undertaking marker-assisted selection (MAS). A genome-wide meta-analysis of QTLs controlling seedling-stage salt tolerance was conducted in rice using QTL information from 12 studies. Using a consensus map, 11 meta-QTLs for three traits with smaller confidence intervals were localized on chromosomes 1 and 2. The phenotypic variance of 3 meta-QTLs was ≥20%. Based on phenotyping of 56 diverse genotypes and breeding lines, six salt-tolerant genotypes (Bharathy, I Kung Ban 4-2 Mutant, Langmanbi, Fatehpur 3, CT-329, and IARI 5823) were identified. The perusal of the meta-QTL regions revealed several candidate genes associated with salt-tolerance attributes. The lack of association between meta-QTL linked markers and the level of salt tolerance could be due to the low resolution of meta-QTL regions and the genetic complexity of salt tolerance. The meta-QTLs identified in this study will be useful not only for MAS and pyramiding, but will also accelerate the fine mapping and cloning of candidate genes associated with salt-tolerance mechanisms in rice.

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