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450 results for “Candidate Genes”

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dryad28/100

Petal size in rapeseed: novel QTL and candidate genes detected through genome-wide association study and transcriptome comparison

Petal size determines the value of ornamental plants, and thus their economic worth. However, the molecular mechanisms controlling petal size remain unclear in most non-model species. To identify quantitative trait loci and candidate genes regulating petal size in rapeseed (Brassica napus), we performed a genome-wide association study (GWAS) using data from 588 accessions over three consecutive years. We detected 17 significant single nucleotide polymorphisms (SNPs) associated with petal size, with the most significant SNPs located on chromosomes A05 and C06. A combination of GWAS and transcriptomic sequencing based on two accessions with extreme differences in petal size identified 11 differentially expressed genes (DEGs) that may control petal size variation in rapeseed. In particular, BnaA05.RAP2.2 homologous to RAP2.2 in rapeseed may be a critical gene negatively influencing petal size through the ethylene signaling pathway. In addition, a comparison of petal epidermal cells indicated that petal size differences between the two extreme accessions were determined mainly by cell number differences. Finally, we propose a preliminary model for the control of petal size in rapeseed. Our results provide insights into the genetic mechanisms regulating petal size, and also lay the foundation for a better understanding of petal development in plants.

opencc-zeroDec 2019View details →

zenodo28/100

Supplementary Files - AutoCaSc: Prioritizing candidate genes for neurodevelopmental disorders

These files contain scored candidate variants. For more information please refer to our manuscript.

opencc-by-4.0Feb 2022View details →

zenodo28/100

Figure 6 in Identification of candidate genes involved with dicamba resistance in waterhemp (Amoronthus tuberculotus) via transcriptomics analyses

Figure 6. Differentially expressed (DE) genes' regulatory prediction analysis results: (A) total number of transcription factors (TFs) enriched per family; (B) number of DE genes with motifs specific for each enriched TF family. TF family names in the x axis are according to the nomenclature in the Plant Transcription Factor Database (PlantTFDB).

opencc-by-4.0Dec 2023View details →

dryad28/100

Data from: Candidate genes mediating magnetoreception in rainbow trout (Oncorhynchus mykiss)

Diverse animals use Earth's magnetic field in orientation and navigation, but little is known about the molecular mechanisms that underlie magnetoreception. Recent studies have focused on two possibilities: (i) magnetite-based receptors; and (ii) biochemical reactions involving radical pairs. We used RNA sequencing to examine gene expression in the brain of rainbow trout (Oncorhynchus mykiss) after exposure to a magnetic pulse known to disrupt magnetic orientation behaviour. We identified 181 differentially expressed genes, including increased expression of six copies of the frim gene, which encodes a subunit of the universal iron-binding and trafficking protein ferritin. Functions linked to the oxidative effects of free iron (e.g. oxidoreductase activity, transition metal ion binding, mitochondrial oxidative phosphorylation) were also affected. These results are consistent with the hypothesis that a magnetic pulse alters or damages magnetite-based receptors and/or other iron-containing structures, which are subsequently repaired or replaced through processes involving ferritin. Additionally, some genes that function in the development and repair of photoreceptive structures (e.g. crggm3, purp, prl, gcip, crabp1 and pax6) were also differentially expressed, raising the possibility that a magnetic pulse might affect structures and processes unrelated to magnetite-based magnetoreceptors.

opencc-zeroDec 2016View details →

dryad28/100

Code and resulting candidate gene datasets from Anopheles genome environment association testing

The concept of a fundamental ecological niche is central to questions of geographic distribution, population demography, species conservation, and evolutionary potential. But robust inference of genomic regions associated with evolutionary adaptation to particular environmental conditions remains difficult due to the myriad of potential confounding processes that can generate heterogeneous patterns of variation across the genome. Here, we interrogate the potential role of genome environment association (GEA) testing as an initial step in building an understanding of the genetic basis of ecological niche. We leverage publicly available genomic data from the Anopheles gambiae 1000 Genomes (Ag1000g) Consortium to test the ability of multiple, unique analytical GEA methods to handle confounding genetic variation, control false positive rates, and discern associations with broadly relevant climate variables from randomly correlated allele frequency patterns throughout the genome. We find evidence supporting the ability of commonly implemented GEA methods to account for confounding patterns of spatial and genetic variation, and control false positive rates. But we subsequently fail to find evidence supporting the ability of GEA tests to reject signals of adaptation to randomly simulated environmental variables, indicating that discerning between true signals of genome environment adaptation and genome environment correlations resulting from alternative evolutionary processes remains challenging. Because signals of environmental adaptation are so diffuse and confounded throughout the genome, we argue that genomic adaptation to ecological niche is likely best understood under an omnigenic model wherein highly interconnected, genome-wide gene regulatory networks shape genomic adaptation to key environmental conditions.

opencc-zeroJul 2021View details →

dryad28/100

Rice grain weight and candidate gene marker dataset

It is hypothesized that the genome-wide genic markers may increase the prediction accuracy of genomic selection for quantitative traits. To test this hypothesis, a set of candidate gene based markers for yield and grain traits-related genes cloned across the rice genome were custom-designed. A multi-model, multi-locus genome-wide association study (GWAS) was performed using new genic markers developed to test their effectiveness for gene discovery. Two multi-locus models, FarmCPU and mrMLM, along with a single-locus mixed linear model (MLM), identified 28 significant marker trait associations. These associations revealed novel causative alleles for grain weight and pleiotropic associations with other traits. For instance, the marker YD91 derived from the gene OsAAP3 on chromosome 1 was consistently associated with grain weight, while the gene has a significant effect on grain yield. Furthermore, nine genomic selection methods, including regression-based and machine learning-based models, were used to predict grain weight using a leave-one-out five-fold cross validation approach to optimize the genomic selection model with genic markers. Among nine prediction models, Kernel Hilbert Space Regression (RKHS) is the best among regression-based models, and Random Forest Regression (RFR) is the best among machine learning-based models. Genomic prediction accuracies with and without GWAS significant markers were compared to assess the effectiveness of markers. The rapid decreases in prediction accuracy upon dropping GWAS significant markers indicate the effectiveness of new genic markers in genomic selection. Apart from that, the candidate gene-based markers were found to be more effective in genomic selection programs for better accuracy.

opencc-zeroFeb 2023View details →

ClinicalTrials.gov28/100

Analysis of Mother-child Interaction and Regulation of Candidate Genes of Stress Signaling Pathways in Mature Infants

ClinicalTrials.gov study NCT03926923. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

dryad28/100