Major impacts of widespread structural variation on sorghum.

Genetic diversity is critical to crop breeding and improvement, and dissection of the genomic variation underlying agronomic traits can both assist breeding and give insight into basic biological mechanisms. Although recent genome analyses in plants reveal many structural variants (SVs), most current studies of crop genetic variation are dominated by single-nucleotide polymorphisms (SNPs). The extent of the impact of SVs on global trait variation, as well as their utility in genome-wide selection, is not yet understood. In this study, we built an SV data set based on whole-genome resequencing of diverse sorghum lines (n = 363), validated the correlation of photoperiod sensitivity and variety type, and identified SV hotspots underlying the divergent evolution of cellulosic and sweet sorghum. In addition, we showed the complementary contribution of SVs for heritability of traits related to sorghum adaptation. Importantly, inclusion of SV polymorphisms in association studies revealed genotype-phenotype associations not observed with SNPs alone. Three-way genome-wide association studies (GWAS) based on whole-genome SNP, SV, and integrated SNP + SV data sets showed substantial associations between SVs and sorghum traits. The addition of SVs to GWAS substantially increased heritability estimates for some traits, indicating their important contribution to functional allelic variation at the genome level. Our discovery of the widespread impacts of SVs on heritable gene expression variation could render a plausible mechanism for their disproportionate impact on phenotypic variation. This study expands our knowledge of SVs and emphasizes the extensive impacts of SVs on sorghum.

The genomic case against genetic determinism.

Animal studies reveal that the molecular wiring of the brain can be altered by heredity, the environment, and their interaction. A deeper molecular understanding of these interactions could be a potent antidote to societal concerns of genetic determinism for human behavior, but this requires a paradigm that extends beyond traditional genome-wide association study (GWAS).

Leveraging orthology within maize and Arabidopsis QTL to identify genes affecting natural variation in gravitropism.

Plants typically orient their organs with respect to the Earth's gravity field by a dynamic process called gravitropism. To discover conserved genetic elements affecting seedling root gravitropism, we measured the process in a set of Zea mays (maize) recombinant inbred lines with machine vision and compared the results with those obtained in a similar study of Arabidopsis thaliana. Each of the several quantitative trait loci that we mapped in both species spanned many hundreds of genes, too many to test individually for causality. We reasoned that orthologous genes may be responsible for natural variation in monocot and dicot root gravitropism. If so, pairs of orthologous genes affecting gravitropism may be present within the maize and Arabidopsis QTL intervals. A reciprocal comparison of sequences within the QTL intervals identified seven pairs of such one-to-one orthologs. Analysis of knockout mutants demonstrated a role in gravitropism for four of the seven: CCT2 functions in phosphatidylcholine biosynthesis, ATG5 functions in membrane remodeling during autophagy, UGP2 produces the substrate for cellulose and callose polymer extension, and FAMA is a transcription factor. Automated phenotyping enabled this discovery of four naturally varying components of a conserved process (gravitropism) by making it feasible to conduct the same large-scale experiment in two species.

An NBS-LRR protein in the Rpp1 locus negates the dominance of Rpp1-mediated resistance against Phakopsora pachyrhizi in soybean.

The soybean Rpp1 locus confers resistance to Phakopsora pachyrhizi, causal agent of rust, and resistance is usually dominant over susceptibility. However, dominance of Rpp1-mediated resistance is lost when a resistant genotype (Rpp1 or Rpp1b) is crossed with susceptible line TMG06_0011, and the mechanism of this dominant susceptibility (DS) is unknown. Sequencing the Rpp1 region reveals that the TMG06_0011 Rpp1 locus has a single nucleotide-binding site leucine-rich repeat (NBS-LRR) gene (DS-R), whereas resistant PI 594760B (Rpp1b) is similar to PI 200492 (Rpp1) and has three NBS-LRR resistance gene candidates. Evidence that DS-R is the cause of DS was reflected in virus-induced gene silencing of DS-R in Rpp1b/DS-R or Rpp1/DS-R heterozygous plants with resistance partially restored. In heterozygous Rpp1b/DS-R plants, expression of Rpp1b candidate genes was not significantly altered, indicating no effect of DS-R on transcription. Physical interaction of the DS-R protein with candidate Rpp1b resistance proteins was supported by yeast two-hybrid studies and in silico modeling. Thus, we conclude that suppression of resistance most likely does not occur at the transcript level, but instead probably at the protein level, possibly with Rpp1 function inhibited by binding to the DS-R protein. The DS-R gene was found in other soybean lines, with an estimated allele frequency of 6% in a diverse population, and also found in wild soybean (Glycine soja). The identification of a dominant susceptible NBS-LRR gene provides insight into the behavior of NBS-LRR proteins and serves as a reminder to breeders that the dominance of an R gene can be influenced by a susceptibility allele.

Promoter deletion in the soybean Compact mutant leads to overexpression of a gene with homology to the C20-gibberellin 2-oxidase family.

Height is a critical component of plant architecture, significantly affecting crop yield. The genetic basis of this trait in soybean remains unclear. In this study, we report the characterization of the Compact mutant of soybean, which has short internodes. The candidate gene was mapped to chromosome 17, and the interval containing the causative mutation was further delineated using biparental mapping. Whole-genome sequencing of the mutant revealed an 8.7 kb deletion in the promoter of the Glyma.17g145200 gene, which encodes a member of the class III gibberellin (GA) 2-oxidases. The mutation has a dominant effect, likely via increased expression of the GA 2-oxidase transcript observed in green tissue, as a result of the deletion in the promoter of Glyma.17g145200. We further demonstrate that levels of GA precursors are altered in the Compact mutant, supporting a role in GA metabolism, and that the mutant phenotype can be rescued with exogenous GA3. We also determined that overexpression of Glyma.17g145200 in Arabidopsis results in dwarfed plants. Thus, gain of promoter activity in the Compact mutant leads to a short internode phenotype in soybean through altered metabolism of gibberellin precursors. These results provide an example of how structural variation can control an important crop trait and a role for Glyma.17g145200 in soybean architecture, with potential implications for increasing crop yield.

Genomic regions influencing aggressive behavior in honey bees are defined by colony allele frequencies.

For social animals, the genotypes of group members affect the social environment, and thus individual behavior, often indirectly. We used genome-wide association studies (GWAS) to determine the influence of individual vs. group genotypes on aggression in honey bees. Aggression in honey bees arises from the coordinated actions of colony members, primarily nonreproductive "soldier" bees, and thus, experiences evolutionary selection at the colony level. Here, we show that individual behavior is influenced by colony environment, which in turn, is shaped by allele frequency within colonies. Using a population with a range of aggression, we sequenced individual whole genomes and looked for genotype-behavior associations within colonies in a common environment. There were no significant correlations between individual aggression and specific alleles. By contrast, we found strong correlations between colony aggression and the frequencies of specific alleles within colonies, despite a small number of colonies. Associations at the colony level were highly significant and were very similar among both soldiers and foragers, but they covaried with one another. One strongly significant association peak, containing an ortholog of the Drosophila sensory gene dpr4 on linkage group (chromosome) 7, showed strong signals of both selection and admixture during the evolution of gentleness in a honey bee population. We thus found links between colony genetics and group behavior and also, molecular evidence for group-level selection, acting at the colony level. We conclude that group genetics dominates individual genetics in determining the fatal decision of honey bees to sting.

CROPSR: an automated platform for complex genome-wide CRISPR gRNA design and validation.

BACKGROUND: CRISPR/Cas9 technology has become an important tool to generate targeted, highly specific genome mutations. The technology has great potential for crop improvement, as crop genomes are tailored to optimize specific traits over generations of breeding. Many crops have highly complex and polyploid genomes, particularly those used for bioenergy or bioproducts. The majority of tools currently available for designing and evaluating gRNAs for CRISPR experiments were developed based on mammalian genomes that do not share the characteristics or design criteria for crop genomes. RESULTS: We have developed an open source tool for genome-wide design and evaluation of gRNA sequences for CRISPR experiments, CROPSR. The genome-wide approach provides a significant decrease in the time required to design a CRISPR experiment, including validation through PCR, at the expense of an overhead compute time required once per genome, at the first run. To better cater to the needs of crop geneticists, restrictions imposed by other packages on design and evaluation of gRNA sequences were lifted. A new machine learning model was developed to provide scores while avoiding situations in which the currently available tools sometimes failed to provide guides for repetitive, A/T-rich genomic regions. We show that our gRNA scoring model provides a significant increase in prediction accuracy over existing tools, even in non-crop genomes. CONCLUSIONS: CROPSR provides the scientific community with new methods and a new workflow for performing CRISPR/Cas9 knockout experiments. CROPSR reduces the challenges of working in crops, and helps speed gRNA sequence design, evaluation and validation. We hope that the new software will accelerate discovery and reduce the number of failed experiments.

WI12Rhg1 interacts with DELLAs and mediates soybean cyst nematode resistance through hormone pathways.

The soybean cyst nematode (SCN) is one of the most important causes of soybean yield loss. The major source of genetic resistance to SCN is the Rhg1 repeat, a tandem copy number polymorphism of three genes. The roles of these genes are only partially understood. Moreover, nematode populations virulent on Rhg1-carrying soybeans are becoming more common, increasing the need to understand the most successful genetic resistance mechanism. Here, we show that a Rhg1-locus gene (Glyma.18G02270) encoding a wound-inducible protein (WI12Rhg1 ) is needed for SCN resistance. Furthermore, knockout of WI12Rhg1 reduces the expression of DELLA18, and the expression of WI12Rhg1 is itself induced by either JA, SA or GA. The content of the defence hormone SA is significantly lower whilst GA12 and GA53 are increased in WI12Rhg1 knockout roots compared with unedited hairy roots. We find that WI12Rhg1 directly interacts with DELLA18 (Glyma.18G040000) in yeast and plants and that double knockout of DELLA18 and its homeolog DELLA11 (Glyma.11G216500) significantly reduces SCN resistance and alters the root morphology. As DELLA proteins are implicated in hormone signalling, we explored the content of defence hormones (JA and SA) in DELLA knockout and unedited roots, finding reduced levels of JA and SA after the knockout of DELLA. Additionally, the treatment of DELLA-knockout roots with JA or SA rescues SCN resistance lost by the knockout. Meanwhile, the SCN resistance of unedited roots decreases after the treatment with GA, but increases with JA or SA. Our findings highlight the critical roles of WI12Rhg1 and DELLA proteins in SCN resistance through interconnection with hormone signalling.

Impact of multiple selective breeding programs on genetic diversity in soybean germplasm.

KEY MESSAGE: Independent soybean breeding programs shape genetic diversity from unimproved germplasm to modern cultivars in similar ways, but distinct breeding populations retain unique genetic variation, preserving additional diversity. From the domestication of wild soybean (Glycine soja Sieb. & Zucc.), over 3,000 years ago, to the modern soybean (Glycine max L. Merr) cultivars that provide much of the world's oil and protein, soybean populations have undergone fundamental changes. We evaluated the molecular impact of breeding and selection using 391 soybean accessions including US cultivars and their progenitors from the USDA Soybean Germplasm Collection (CGP), plus two new populations specifically developed to increase genetic diversity and high yield in two alternative gene pools: one derived from exotic G. max germplasm (AGP) and one derived from G. soja (SGP). Reduction in nucleotide genetic diversity (π) was observed with selection within gene pools, but artificial selection in the AGP maintained more diversity than in the CGP. The highest FST levels were seen between ancestral and elite lines in all gene pools, but specific nucleotide-level patterns varied between gene pools. Population structure analyses support that independent selection resulted in high-yielding elite lines with similar allelic compositions in the AGP and CGP. SGP, however, produced elite progeny that were well differentiated from, but lower yielding than, CGP elites. Both the AGP and SGP retained a significant number of private alleles that are absent in CGP. We conclude that the genomic diversity shaped by multiple selective breeding programs can result in gene pools of highly productive elite lines with similar allelic compositions in a genome-wide perspective. Breeding programs with different ancestral lines, however, can retain private alleles representing unique genetic diversity.

t-SNAREs bind the Rhg1 α-SNAP and mediate soybean cyst nematode resistance.

Soybean cyst nematode (SCN; Heterodera glycines) is the largest pathogenic cause of soybean yield loss. The Rhg1 locus is the most used and best characterized SCN resistance locus, and contains three genes including one encoding an α-SNAP protein. Although the Rhg1 α-SNAP is known to play an important role in vesicle trafficking and SCN resistance, the protein's binding partners and the molecular mechanisms underpinning SCN resistance remain unclear. In this report, we show that the Rhg1 α-SNAP strongly interacts with two syntaxins of the t-SNARE family (Glyma.12G194800 and Glyma.16G154200) in yeast and plants; importantly, the genes encoding these syntaxins co-localize with SCN resistance quantitative trait loci. Fluorescent visualization revealed that the α-SNAP and the two interacting syntaxins localize to the plasma membrane and perinuclear space in both tobacco epidermal and soybean root cells. The two syntaxins and their two homeologs were mutated, individually and in combination, using the CRISPR-Cas9 system in the SCN-resistant Peking and SCN-susceptible Essex soybean lines. Peking roots with deletions introduced into syntaxin genes exhibited significantly reduced resistance to SCN, confirming that t-SNAREs are critical to resisting SCN infection. The results presented here uncover a key step in the molecular mechanism of SCN resistance, and will be invaluable to soybean breeders aiming to develop highly SCN-resistant soybean varieties.

Recommendations for performance optimizations when using GATK3.8 and GATK4.

BACKGROUND: Use of the Genome Analysis Toolkit (GATK) continues to be the standard practice in genomic variant calling in both research and the clinic. Recently the toolkit has been rapidly evolving. Significant computational performance improvements have been introduced in GATK3.8 through collaboration with Intel in 2017. The first release of GATK4 in early 2018 revealed rewrites in the code base, as the stepping stone toward a Spark implementation. As the software continues to be a moving target for optimal deployment in highly productive environments, we present a detailed analysis of these improvements, to help the community stay abreast with changes in performance. RESULTS: We re-evaluated multiple options, such as threading, parallel garbage collection, I/O options and data-level parallelization. Additionally, we considered the trade-offs of using GATK3.8 and GATK4. We found optimized parameter values that reduce the time of executing the best practices variant calling procedure by 29.3% for GATK3.8 and 16.9% for GATK4. Further speedups can be accomplished by splitting data for parallel analysis, resulting in run time of only a few hours on whole human genome sequenced to the depth of 20X, for both versions of GATK. Nonetheless, GATK4 is already much more cost-effective than GATK3.8. Thanks to significant rewrites of the algorithms, the same analysis can be run largely in a single-threaded fashion, allowing users to process multiple samples on the same CPU. CONCLUSIONS: In time-sensitive situations, when a patient has a critical or rapidly developing condition, it is useful to minimize the time to process a single sample. In such cases we recommend using GATK3.8 by splitting the sample into chunks and computing across multiple nodes. The resultant walltime will be nnn.4 hours at the cost of $41.60 on 4 c5.18xlarge instances of Amazon Cloud. For cost-effectiveness of routine analyses or for large population studies, it is useful to maximize the number of samples processed per unit time. Thus we recommend GATK4, running multiple samples on one node. The total walltime will be ∼34.1 hours on 40 samples, with 1.18 samples processed per hour at the cost of $2.60 per sample on c5.18xlarge instance of Amazon Cloud.

Correction to: Recommendations for performance optimizations when using GATK3.8 and GATK4.

Following publication of the original article [1], the author explained that Table 2 is displayed incorrectly. The correct Table 2 is given below. The original article has been corrected.

Mapping of new quantitative trait loci for sudden death syndrome and soybean cyst nematode resistance in two soybean populations.

KEY MESSAGE: Novel QTL conferring resistance to both the SDS and SCN was detected in two RIL populations. Dual resistant RILs could be used in breeding programs for developing resistant soybean cultivars. Soybean cultivars, susceptible to the fungus Fusarium virguliforme, which causes sudden death syndrome (SDS), and to the soybean cyst nematode (SCN) (Heterodera glycines), suffer yield losses valued over a billion dollars annually. Both pathogens may occur in the same production fields. Planting of cultivars genetically resistant to both pathogens is considered one of the most effective means to control the two pathogens. The objective of the study was to map quantitative trait loci (QTL) underlying SDS and SCN resistances. Two recombinant inbred line (RIL) populations were developed by crossing 'A95-684043', a high-yielding maturity group (MG) II line resistant to SCN, with 'LS94-3207' and 'LS98-0582' of MG IV, resistant to both F. virguliforme and SCN. Two hundred F7 derived recombinant inbred lines from each population AX19286 (A95-684043 × LS94-3207) and AX19287 (A95-684043 × LS98-0582) were screened for resistance to each pathogen under greenhouse conditions. Five hundred and eighty and 371 SNP markers were used for mapping resistance QTL in each population. In AX19286, one novel SCN resistance QTL was mapped to chromosome 8. In AX19287, one novel SDS resistance QTL was mapped to chromosome 17 and one novel SCN resistance QTL was mapped to chromosome 11. Previously identified additional SDS and SCN resistance QTL were also detected in the study. Lines possessing superior resistance to both pathogens were also identified and could be used as germplasm sources for breeding SDS- and SCN-resistant soybean cultivars.

A comparison of genotyping-by-sequencing analysis methods on low-coverage crop datasets shows advantages of a new workflow, GB-eaSy.

BACKGROUND: Genotyping-by-sequencing (GBS), a method to identify genetic variants and quickly genotype samples, reduces genome complexity by using restriction enzymes to divide the genome into fragments whose ends are sequenced on short-read sequencing platforms. While cost-effective, this method produces extensive missing data and requires complex bioinformatics analysis. GBS is most commonly used on crop plant genomes, and because crop plants have highly variable ploidy and repeat content, the performance of GBS analysis software can vary by target organism. Here we focus our analysis on soybean, a polyploid crop with a highly duplicated genome, relatively little public GBS data and few dedicated tools. RESULTS: We compared the performance of five GBS pipelines using low-coverage Illumina sequence data from three soybean populations. To address issues identified with existing methods, we developed GB-eaSy, a GBS bioinformatics workflow that incorporates widely used genomics tools, parallelization and automation to increase the accuracy and accessibility of GBS data analysis. Compared to other GBS pipelines, GB-eaSy rapidly and accurately identified the greatest number of SNPs, with SNP calls closely concordant with whole-genome sequencing of selected lines. Across all five GBS analysis platforms, SNP calls showed unexpectedly low convergence but generally high accuracy, indicating that the workflows arrived at largely complementary sets of valid SNP calls on the low-coverage data analyzed. CONCLUSIONS: We show that GB-eaSy is approximately as good as, or better than, other leading software solutions in the accuracy, yield and missing data fraction of variant calling, as tested on low-coverage genomic data from soybean. It also performs well relative to other solutions in terms of the run time and disk space required. In addition, GB-eaSy is built from existing open-source, modular software packages that are regularly updated and commonly used, making it straightforward to install and maintain. While GB-eaSy outperformed other individual methods on the datasets analyzed, our findings suggest that a comprehensive approach integrating the results from multiple GBS bioinformatics pipelines may be the optimal strategy to obtain the largest, most highly accurate SNP yield possible from low-coverage polyploid sequence data.

An efficient method for measuring copy number variation applied to improvement of nematode resistance in soybean.

Copy number variation (CNV) is implicated in important traits in multiple crop plants, but can be challenging to genotype using conventional methods. The Rhg1 locus of soybean, which confers resistance to soybean cyst nematode (SCN), is a CNV of multiple 31.2-kb genomic units each containing four genes. Reliable, high-throughput methods to quantify Rhg1 and other CNVs for selective breeding were developed. The CNV genotyping assay described here uses a homeologous gene copy within the paleopolyploid soybean genome to provide the internal control for a single-tube TaqMan copy number assay. Using this assay, CNV in breeding populations can be tracked with high precision. We also show that extensive CNV exists within Fayette, a released, inbred SCN-resistant soybean cultivar with a high copy number at Rhg1 derived from a single donor parent. Copy number at Rhg1 is therefore unstable within a released variety over a relatively small number of generations. Using this assay to select for individuals with altered copy number, plants were obtained with both increased copy number and increased SCN resistance relative to control plants. Thus, CNV genotyping technologies can be used as a new type of marker-assisted selection to select for desirable traits in breeding populations, and to control for undesirable variation within cultivars.

Genome-wide association mapping of resistance to a Brazilian isolate of Sclerotinia sclerotiorum in soybean genotypes mostly from Brazil.

BACKGROUND: Sclerotinia Stem Rot (SSR), caused by the fungal pathogen Sclerotinia sclerotiorum, is ubiquitous in cooler climates where soybean crops are grown. Breeding for resistance to SSR remains challenging in crops like soybean, where no single gene provides strong resistance, but instead, multiple genes work together to provide partial resistance. In this study, a genome-wide association study (GWAS) was performed to dissect the complex genetic architecture of soybean quantitative resistance to SSR and to provide effective molecular markers that could be used in breeding programs. A collection of 420 soybean genotypes were selected based on either reports of resistance, or from one of three different breeding programs in Brazil, two commercial, one public. Plant genotype sensitivity to SSR was evaluated by the cut stem inoculation method, and lesion lengths were measured at 4 days post inoculation. RESULTS: Genotyping-by-sequencing was conducted to genotype the 420 soybean lines. The TASSEL 5 GBSv2 pipeline was used to call SNPs under optimized parameters, and with the extra step of trimming adapter sequences. After filtering missing data, heterozygosity, and minor allele frequency, a total of 11,811 SNPs and 275 soybean genotypes were obtained for association analyses. Using a threshold of FDR-adjusted p-values <0.1, the Compressed Mixed Linear Model (CMLM) with Genome Association and Prediction Integrated Tool (GAPIT), and the Fixed and Random Model Circulating Probability Unification (FarmCPU) methods, both approaches identified SNPs with significant association to disease response on chromosomes 1, 11, and 18. The CMLM also found significance on chromosome 19, whereas FarmCPU also identified significance on chromosomes 4, 9, and 16. CONCLUSIONS: These similar and yet different results show that the computational methods used can impact SNP associations in soybean, a plant with a high degree of linkage disequilibrium, and in SSR resistance, a trait that has a complex genetic basis. A total of 125 genes were located within linkage disequilibrium of the three loci shared between the two models. Their annotations and gene expressions in previous studies of soybean infected with S. sclerotiorum were examined to narrow down the candidates.

Identification of Multiple Phytotoxins Produced by Fusarium virguliforme Including a Phytotoxic Effector (FvNIS1) Associated With Sudden Death Syndrome Foliar Symptoms.

Sudden death syndrome (SDS) of soybean is caused by a soilborne pathogen, Fusarium virguliforme. Phytotoxins produced by F. virguliforme are translocated from infected roots to leaves, in which they cause SDS foliar symptoms. In this study, additional putative phytotoxins of F. virguliforme were identified, including three secondary metabolites and 11 effectors. While citrinin, fusaric acid, and radicicol induced foliar chlorosis and wilting, Soybean mosaic virus (SMV)-mediated overexpression of F. virguliforme necrosis-inducing secreted protein 1 (FvNIS1) induced SDS foliar symptoms that mimicked the development of foliar symptoms in the field. The expression level of fvnis1 remained steady over time, although foliar symptoms were delayed compared with the expression levels. SMV::FvNIS1 also displayed genotype-specific toxicity to which 75 of 80 soybean cultivars were susceptible. Genome-wide association mapping further identified three single nucleotide polymorphisms at two loci, where three leucine-rich repeat receptor-like protein kinase (LRR-RLK) genes were found. Culture filtrates of fvnis1 knockout mutants displayed a mild reduction in phytotoxicity, indicating that FvNIS1 is one of the phytotoxins responsible for SDS foliar symptoms and may contribute to the quantitative susceptibility of soybean by interacting with the LRR-RLK genes.

Evolutionary divergence of phytochrome protein function in Zea mays PIF3 signaling.

Two maize phytochrome-interacting factor (PIF) basic helix-loop-helix (bHLH) family members, ZmPIF3.1 and ZmPIF3.2, were identified, cloned and expressed in vitro to investigate light-signaling interactions. A phylogenetic analysis of sequences of the maize bHLH transcription factor gene family revealed the extent of the PIF family, and a total of seven predicted PIF-encoding genes were identified from genes encoding bHLH family VIIa/b proteins in the maize genome. To investigate the role of maize PIFs in phytochrome signaling, full-length cDNAs for phytochromes PhyA2, PhyB1, PhyB2 and PhyC1 from maize were cloned and expressed in vitro as chromophorylated holophytochromes. We showed that ZmPIF3.1 and ZmPIF3.2 interact specifically with the Pfr form of maize holophytochrome B1 (ZmphyB1), showing no detectable affinity for the Pr form. Maize holophytochrome B2 (ZmphyB2) showed no detectable binding affinity for PIFs in either Pr or Pfr forms, but phyB Pfr from Arabidopsis interacted with ZmPIF3.1 similarly to ZmphyB1 Pfr. We conclude that subfunctionalization at the protein-protein interaction level has altered the role of phyB2 relative to that of phyB1 in maize. Since the phyB2 mutant shows photomorphogenic defects, we conclude that maize phyB2 is an active photoreceptor, without the binding of PIF3 seen in other phyB family proteins.

Transposable elements, mRNA expression level and strand-specificity of small RNAs are associated with non-additive inheritance of gene expression in hybrid plants.

BACKGROUND: Gene expression inheritance patterns in Arabidopsis hybrid plants were investigated for correlation with the presence of transposable elements (TEs) and small RNA profile. RESULTS: The presence of TEs in a gene and the expression of small RNA matching a gene were both found to be associated with non-additive mRNA inheritance patterns in hybrids. Expression levels below mid-parent values in the hybrids were associated with low mRNA expression in parents, with the presence of small RNA from both strands, and with the presence of TEs. High-parent dominance of mRNA levels was found to be associated with high parental mRNA expression levels, the absence of TEs, and for some genes, with small RNA fragments that are predominantly from the sense strand. These small RNAs exhibit a broader size distribution than siRNA and reduced nucleotide end bias, which are consistent with an origin from degraded mRNA. Thus, increased as well as decreased gene expression in hybrids relative to the parental mean is associated with gene expression levels, TE presence and small RNA fragments with differing characteristics. CONCLUSIONS: The data presented here is consistent with a role for differential mRNA decay kinetics as one mechanism contributing to high-parent dominance in gene expression. Our evidence is also consistent with trans repression by siRNA and TEs as the cause of low-parent dominance.