The MaizeGDB Genome Browser tutorial: one example of database outreach to biologists via video

Harper, Lisa C.; Schaeffer, Mary L.; Thistle, Jordan; Gardiner, Jack M.; Andorf, Carson M.; Campbell, Darwin A.; Cannon, Ethalinda K. S.; Braun, Bremen L.; Birkett, Scott M.; Lawrence, Carolyn J.; Sen, Taner Z.

doi:10.1093/database/bar016

Cited by 16 publications

(15 citation statements)

References 13 publications

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“…The significantly associated SNPs were located on the B73 genome V3 reference sequence (RefGen_V3) available from MaizeGDB (Harper et al, 2011), and we investigated a window of 125 kb around each SNP for annotated genes. In total, 26 protein‐coding gene models were found in the 125‐kb window with the four significant SNPs associated with popping expansion (Supplemental Table S2).…”

Section: Resultsmentioning

confidence: 99%

“…All significant SNPs with positive minor allele effects had higher minor allele frequencies in popcorn inbred lines, whereas the SNP with negative minor allele effects (S3_1223876449) had higher minor allele frequency in field corn inbred lines (Table 4), highlighting the strong association of these SNPs with popping expansion. The significantly associated SNPs were located on the B73 genome V3 reference sequence (RefGen_V3) available from MaizeGDB (Harper et al, 2011), and we investigated a window of 125 kb around each SNP for annotated genes. In total, 26 protein-coding gene models were found in the 125-kb window with the four significant SNPs associated with popping expansion (Supplemental Table S2).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Genomic‐Wide Association Study of Popping Expansion in Tropical Popcorn and Field Corn Germplasm

Senhorinho¹,

Coan²,

Marino

et al. 2019

Crop Science

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Popcorn is a popular snack food with higher grain value compared with “field corn” maize (Zea mays L.). In general, popcorn germplasm is less improved than common maize cultivars, with less favorable agronomic performance and greater susceptibility to pests, diseases, and lodging, especially in tropical environments. One approach to expanding and improving popcorn germplasm is to introduce favorable alleles for agronomic performance from field corn. If markers tagging quantitative trait loci (QTLs) associated with popping expansion were identified, they could be selected with markers to maintain popping expansion in breeding populations derived from crosses between field and popcorn germplasm. The objectives of this study were to identify single nucleotide polymorphism (SNP) markers and putative candidate genes associated with higher popping expansion in a diverse sample of popcorn and field corns relevant to Brazilian breeding programs. We applied a comprehensive genome‐wide association study (GWAS) for popping expansion, using 165,089 SNP markers in 183 inbred lines with 2 yr of trial data. Four SNPs were significantly associated with popping expansion, three of which were colocalized to previously reported meta‐QTLs, and one that was novel. Annotated genes closely linked to the associated SNPs were identified with functions related with starch content, which plays an important role in popping expansion quality. If these associations can be validated independently, they can be useful for breeders to select agronomically superior genotypes with greater popping expansion.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Genomic‐Wide Association Study of Popping Expansion in Tropical Popcorn and Field Corn Germplasm

Senhorinho¹,

Coan²,

Marino

et al. 2019

Crop Science

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“…That supporting interval would comprise the surrounding region in LD (r 2 > 0.4). All genes contained in the supporting interval were considered as candidate genes and identified and characterized by the use of the MaizeGDB genome browser [83]. Although SNP positions were referenced to the maize B73 RefGen_v2, the genes flanking the region in LD were positioned in the maize B73 RefGen_v4 to perform the search for candidate genes in the latest version of the B73 sequence.…”

Section: Candidate Gene Selectionmentioning

confidence: 99%

Genome-wide association analysis for fumonisin content in maize kernels

et al. 2019

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Background: Plant breeding has been proposed as one of the most effective and environmentally safe methods to control fungal infection and to reduce fumonisin accumulation. However, conventional breeding can be hampered by the complex genetic architecture of resistance to fumonisin accumulation and marker-assisted selection is proposed as an efficient alternative. In the current study, GWAS has been performed for the first time for detecting high-resolution QTL for resistance to fumonisin accumulation in maize kernels complementing published GWAS results for Fusarium ear rot. Results: Thirty-nine SNPs significantly associated with resistance to fumonisin accumulation in maize kernels were found and clustered into 17 QTL. Novel QTLs for fumonisin content would be at bins 3.02, 5.02, 7.05 and 8.07. Genes with annotated functions probably implicated in resistance to pathogens based on previous studies have been highlighted. Conclusions: Breeding approaches to fix favorable functional variants for genes implicated in maize immune response signaling may be especially useful to reduce kernel contamination with fumonisins without significantly interfering in mycelia development and growth and, consequently, in the beneficial endophytic behavior of Fusarium verticillioides.

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“…The amplified RNA was used to generate cDNA libraries and 44basepair reads were generated using Illumina RNA-sequencing. The RNA sequencing reads generated were aligned to the maize gene space (Harper et al, 2011) and those reads that aligned to the gene space unambiguously were tabulated for each gene. In total, there were 66139 genes with at least one unambiguous read.…”

Section: Simulation Studymentioning

confidence: 99%

“…In total, there were 66139 genes with at least one unambiguous read. Further details regarding this experiment are available at http://dev.maizegdb.org/cgi-bin/ termdoclist.cgi?ref=9021713&type=32466 on the Maize Genetics and Genomics Database (Harper et al, 2011).…”

Section: Simulation Studymentioning

confidence: 99%

Statistical methods for identifying differentially expressed genes using hierarchical models

Lund

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When analyzing microarray data, hierarchical models are often used to share information across genes when estimating means and variances or identifying differential expression. Many methods utilize some form of the two-level hierarchical model structure suggested by Kendziorski et al. (2003) in which the first level describes the distribution of latent mean expression levels among genes and among differentially expressed (DE) treatments within a gene. The second level describes the conditional distribution, given a latent mean, of repeated observations for a single gene and treatment. Many of these models, including those used in Kendziorski et al. (2003)'s EBarrays package, assume that expression level changes due to treatment effects have the same distribution as expression level changes from gene to gene. We present empirical evidence that this assumption is often inadequate and propose three-level hierarchical models as extensions to the two-level log-normal based EBarrays models to address this inadequacy. We demonstrate that use of our three-level models dramatically changes analysis results for a variety of microarray data sets and verify the validity and improved performance of our suggested method in a series of simulation studies. We also illustrate the importance of accounting for the uncertainty of gene-specific error variance estimates when using hierarchical models to identify differentially expressed genes.

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The MaizeGDB Genome Browser tutorial: one example of database outreach to biologists via video

Cited by 16 publications

References 13 publications

Genomic‐Wide Association Study of Popping Expansion in Tropical Popcorn and Field Corn Germplasm

Genomic‐Wide Association Study of Popping Expansion in Tropical Popcorn and Field Corn Germplasm

Genome-wide association analysis for fumonisin content in maize kernels

Statistical methods for identifying differentially expressed genes using hierarchical models

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