MaizeGDB 2018: the maize multi-genome genetics and genomics database

Portwood, John L.; Woodhouse, Margaret R.; Cannon, Ethalinda K. S.; Gardiner, Jack M.; Harper, Lisa C.; Schaeffer, Mary L.; Walsh, Jesse R.; Sen, Taner Z.; Cho, Kyoung Tak; Schott, David A.; Braun, Bremen L.; Dietze, Miranda; Dunfee, Brittney; Elsik, Christine G.; Manchanda, Nancy; Coe, E. H.; Sachs, Martin M.; Stinard, P. S.; Tolbert, J. L.; Zimmerman, Shane; Andorf, Carson M.

doi:10.1093/nar/gky1046

Cited by 272 publications

(223 citation statements)

References 44 publications

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“…Subsequently, these sequences were combined by the evidential gene pipeline (http://arthropods.eugenes.org/EvidentialGene) to obtain the final EP1 and F7 transcriptome assemblies. Additional candidate loci for the initial gene sets of EP1 and F7, and the original representative gene models of B73 and PH207 [68] were searched in all six genome sequences (including PE0075 and DK105) by blat alignments [69]. We denote gene models used for the mapping as 'informants', and those derived from informant mappings to another genome sequence as 'target' models.…”

Section: Discussionmentioning

confidence: 99%

European maize genomes unveil pan-genomic dynamics of repeats and genes

Haberer

Bauer

Kamal

et al. 2019

Preprint

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Section: Discussionmentioning

confidence: 99%

European maize genomes unveil pan-genomic dynamics of repeats and genes

Haberer

Bauer

Kamal

et al. 2019

Preprint

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“…Phenotypic data for 252 traits measured for these accessions were downloaded from Panzea ( https://www.panzea.org ) (Zhao et al, 2006) . Two of theses phenotypes were constant over more than 90% of the 150 accessions, these two were removed from further analysis ("NumberofTilleringPlants_env_07A", "TilleringIndex-BorderPlant_env_07A" (Portwood et al, 2019) , that was used to create the VCF file was downloaded from MaizeGDB and used for short read and k -mer alignments (Portwood et al, 2019) .…”

Section: Whole Genome Sequencing Data and Variant Calls Of Maizementioning

confidence: 99%

Finding genetic variants in plants without complete genomes

Voichek¹,

Weigel²

2019

Preprint

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Structural variants and presence/absence polymorphisms are common in plant genomes, yet they are routinely overlooked in genome-wide association studies (GWAS). Here, we expand the genetic variants detected in GWAS to include major deletions, insertions, and rearrangements. We first use raw sequencing data directly to derive short sequences, k -mers, that mark a broad range of polymorphisms independently of a reference genome. We then link k -mers associated with phenotypes to specific genomic regions. Using this approach, we re-analyzed 2,000 traits measured in Arabidopsis thaliana, tomato , and maize populations. Associations identified with k -mers recapitulate those found with single-nucleotide polymorphisms (SNPs), however, with stronger statistical support. Moreover, we identified new associations with structural variants and with regions missing from reference genomes.Our results demonstrate the power of performing GWAS before linking sequence reads to specific genomic regions, which allow detection of a wider range of genetic variants responsible for phenotypic variation.Here, we present an efficient method for k -mer-based GWAS and compare it directly to the conventional SNP-based approach on more than 2,000 phenotypes from three plant species with different genome and population characteristics -A. thaliana , maize and tomato. Most variants identified by SNPs can be detected with k -mers (and vice versa), but k -mers having stronger statistical support.For k -mer-only hits, we demonstrate how different strategies can be used to infer their genomic context, including large structural variants, sequences missing from the reference genome, and organeller variants. Lastly, we compute population structure directly from k -mers, enabling the analysis of species with poor quality or without a reference genome. In summary, we have inverted the conventional approach of building a genome, using it to find population variants, and only then associating variants with phenotypes. In contrast, we begin by associating sequencing reads with phenotypes, and only then infer the genomic context of these sequences. We posit that this change of order is especially effective in plant species, for which defining the full population-level genetic variation based on reference genomes remains highly challenging. Schneeberger et al., 2009) . While traditional GWAS methods will benefit from these technological improvements, so will k -mer based approaches, which will be able to use tags spanning larger genomic distances. Therefore, we posit that for GWAS purposes, k -mer based approaches are ideal because they minimize arbitrary choices when classifying alleles and because they capture more, almost optimal, information from raw sequencing data. 578

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“…Now, Maize GDB contains 12 complete maize genomes and is expected to increase to 40 by 2020. MaizeGDB has also developed a variety of tools to improve the retrieval, display and utilization of data (Portwood et al 2019). The progress of genome research has greatly promoted the research of maize functional genomics, and a large number of functional genes affecting various traits including agronomic, yield, quality, biotic and abiotic traits and so on have been cloned and applied for breeding (Xiao et al 2017).…”

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confidence: 99%

“…Qin et al () identified two transporters ZmHAK5 and ZmHAK1 which play crucial roles in K + homeostasis and cell growth. ZmHAK5 acts as a high‐affinity K + transporter that mediates K + uptake in roots under K + ‐limited conditions, while ZmHAK1 may regulate K + distribution in shoots.…”

mentioning

confidence: 99%