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Understanding uptake and redistribution of essential minerals or sequestering of toxic elements is important for optimized crop production. Although the mechanisms controlling mineral transport have been elucidated in rice and other species, little is understood in sorghum—an important C4 cereal crop. Here, we assessed the genetic factors that govern grain ionome profiles in sorghum using recombinant inbred lines (RILs) derived from a cross between BTx623 and NOG (Takakibi). Pairwise correlation and clustering analysis of 22 elements, measured in sorghum grains harvested under greenhouse conditions, indicated that the parental lines, as well as the RILs, show different ionomes. In particular, BTx623 accumulated significantly higher levels of cadmium (Cd) than NOG, because of differential root-to-shoot translocation factors between the two lines. Quantitative trait locus (QTL) analysis revealed a prominent QTL for grain Cd concentration on chromosome 2. Detailed analysis identified SbHMA3a, encoding a P1B-type ATPase heavy metal transporter, as responsible for low Cd accumulation in grains; the NOG allele encoded a functional HMA3 transporter (SbHMA3a-NOG) whose Cd-transporting activity was confirmed by heterologous expression in yeast. BTx623 possessed a truncated, loss-of-function SbHMA3a allele. The functionality of SbHMA3a in NOG was confirmed by Cd concentrations of F2 grains derived from the reciprocal cross, in which the NOG allele behaved in a dominant manner. We concluded that SbHMA3a-NOG is a Cd transporter that sequesters excess Cd in root tissues, as shown in other HMA3s. Our findings will facilitate the isolation of breeding cultivars with low Cd in grains or in exploiting high-Cd cultivars for phytoremediation.

Key message Despite phenotyping the training set under unfavorable conditions on smallholder farms in Madagascar, we were able to successfully apply genomic prediction to select donors among gene bank accessions. Abstract Poor soil fertility and low fertilizer application rates are main reasons for the large yield gap observed for rice produced in sub-Saharan Africa. Traditional varieties that are preserved in gene banks were shown to possess traits and alleles that would improve the performance of modern variety under such low-input conditions. How to accelerate the utilization of gene bank resources in crop improvement is an unresolved question and here our objective was to test whether genomic prediction could aid in the selection of promising donors. A subset of the 3,024 sequenced accessions from the IRRI rice gene bank was phenotyped for yield and agronomic traits for two years in unfertilized farmers’ fields in Madagascar, and based on these data, a genomic prediction model was developed. This model was applied to predict the performance of the entire set of 3024 accessions, and the top predicted performers were sent to Madagascar for confirmatory trials. The prediction accuracies ranged from 0.10 to 0.30 for grain yield, from 0.25 to 0.63 for straw biomass, to 0.71 for heading date. Two accessions have subsequently been utilized as donors in rice breeding programs in Madagascar. Despite having conducted phenotypic evaluations under challenging conditions on smallholder farms, our results are encouraging as the prediction accuracy realized in on-farm experiments was in the range of accuracies achieved in on-station studies. Thus, we could provide clear empirical evidence on the value of genomic selection in identifying suitable genetic resources for crop improvement, if genotypic data are available.

Background OryzaGenome (http://viewer.shigen.info/oryzagenome21detail/index.xhtml), a feature within Oryzabase (https://shigen.nig.ac.jp/rice/oryzabase/), is a genomic database for wild Oryza species that provides comparative and evolutionary genomics approaches for the rice research community. Results Here we release OryzaGenome2.1, the first major update of OryzaGenome. The main feature in this version is the inclusion of newly sequenced genotypes and their meta-information, giving a total of 217 accessions of 19 wild Oryza species (O. rufipogon, O. barthii, O. longistaminata, O. meridionalis, O. glumaepatula, O. punctata, O. minuta, O. officinalis, O. rhizomatis, O. eichingeri, O. latifolia, O. alta, O. grandiglumis, O. australiensis, O. brachyantha, O. granulata, O. meyeriana, O. ridleyi, and O. longiglumis). These 19 wild species belong to 9 genome types (AA, BB, CC, BBCC, CCDD, EE, FF, GG, and HHJJ), representing wide genomic diversity in the genus. Using the genotype information, we analyzed the genome diversity of Oryza species. Other features of OryzaGenome facilitate the use of information on single nucleotide polymorphisms (SNPs) between O. sativa and its wild progenitor O. rufipogon in rice research, including breeding as well as basic science. For example, we provide Variant Call Format (VCF) files for genome-wide SNPs of 33 O. rufipogon accessions against the O. sativa reference genome, IRGSP1.0. In addition, we provide a new SNP Effect Table function, allowing users to identify SNPs or small insertion/deletion polymorphisms in the 33 O. rufipogon accessions and to search for the effect of these polymorphisms on protein function if they reside in the coding region (e.g., are missense or nonsense mutations). Furthermore, the SNP Viewer for 446 O. rufipogon accessions was updated by implementing new tracks for possible selective sweep regions and highly mutated regions that were potentially exposed to selective pressures during the process of domestication. Conclusion OryzaGenome2.1 focuses on comparative genomic analysis of diverse wild Oryza accessions collected around the world and on the development of resources to speed up the identification of critical trait-related genes, especially from O. rufipogon. It aims to promote the use of genotype information from wild accessions in rice breeding and potential future crop improvements. Diverse genotypes will be a key resource for evolutionary studies in Oryza, including polyploid biology.

According to Fisher's principles, an experimental field is typically divided into multiple blocks for local control. Although homogeneity is supposed within a block, this assumption may not be practical for large blocks, such as those including hundreds of plots. In line evaluation trials, which are essential in plant breeding, field heterogeneity must be carefully treated, because it can cause bias in the estimation of genetic potential. To more accurately estimate genotypic values in a large field trial, we developed spatial kernel models incorporating genome-wide markers, which consider continuous heterogeneity within a block and over the field. In the simulation study, the spatial kernel models were robust under various conditions. Although heritability, spatial autocorrelation range, replication number, and missing plots directly affected the estimation accuracy of genotypic values, the spatial kernel models always showed superior performance over the classical block model. We also employed these spatial kernel models for quantitative trait locus mapping. Finally, using field experimental data of bioenergy sorghum lines, we validated the performance of the spatial kernel models. The results suggested that a spatial kernel model is effective for evaluating the genetic potential of lines in a heterogeneous field.

Biological resources are the basic infrastructure of bioscience research. Rice (Oryza sativa L.) is a good experimental model for research in cereal crops and monocots and includes important genetic materials used in breeding. The availability of genetic materials, including mutants, is important for rice research. In addition, Oryza species are attractive to researchers for both finding useful genes for breeding and for understanding the mechanism of genome evolution that enables wild plants to adapt to their own habitats. NBRP-RICE contributes to rice research by promoting the usage of genetic materials, especially wild Oryza accessions and mutant lines. Our activity includes collection, preservation and distribution of those materials and the provision of basic information on them, such as morphological and physiological traits and genomic information. In this review paper, we introduce the activities of NBRP-RICE and our database, Oryzabase, which facilitates the access to NBRP-RICE resources and their genomic sequences as well as the current situation of wild Oryza genome sequencing efforts by NBRP-RICE and other institutes.

In sorghum [Sorghum bicolor (L.) Moench], hybrid cultivars for the biofuel industry are desired. Along with selection based on testcross performance, evaluation of the breeding population per se is also important for the success of hybrid breeding. In addition to additive genetic effects, non-additive (i.e., dominance and epistatic) effects are expected to contribute to the performance of early generations. Unfortunately, studies on early generations in sorghum breeding programs are limited. In this study, we analyzed a breeding population for bioenergy sorghum, which was previously developed based on testcross performance, to compare genomic selection models both trained on and evaluated for the per se performance of the 3rd generation S0 individuals. Of over 200 ancestral inbred accessions in the base population, only 13 founders contributed to the 3rd generation as progenitors. Compared to the founders, the performances of the population per se were improved for target traits. The total genetic variance within the S0 generation progenies themselves for all traits was mainly additive, although non-additive variances contributed to each trait to some extent. For genomic selection, linear regression models explicitly considering all genetic components showed a higher predictive ability than other linear and non-linear models. Although the number and effect distribution of underlying loci was different among the traits, the influence of priors for marker effects was relatively small. These results indicate the importance of considering non-additive effects for dissecting the genetic architecture of early breeding generations and predicting the performance per se.

Sorghum [Sorghum bicolor (L.) Moench] grown locally by Japanese farmers is generically termed Takakibi, although its genetic diversity compared with geographically distant varieties or even within Takakibi lines remains unclear. To explore the genomic diversity and genetic traits controlling biomass and other physiological traits in Takakibi, we focused on a landrace, NOG, in this study. Admixture analysis of 460 sorghum accessions revealed that NOG belonged to the subgroup that represented Asian sorghums, and it was only distantly related to American/African accessions including BTx623. In an attempt to dissect major traits related to biomass, we generated a recombinant inbred line (RIL) from a cross between BTx623 and NOG, and we constructed a high-density linkage map based on 3,710 single-nucleotide polymorphisms obtained by restriction-site-associated DNA sequencing of 213 RIL individuals. Consequently, 13 fine quantitative trait loci (QTLs) were detected on chromosomes 2, 3, 6, 7, 8 and 9, which included five QTLs for days to heading, three for plant height (PH) and total shoot fresh weight and two for Brix. Furthermore, we identified two dominant loci for PH as being identical to the previously reported dw1 and dw3. Together, these results corroborate the diversified genome of Japanese Takakibi, while the RIL population and high-density linkage map generated in this study will be useful for dissecting other important traits in sorghum.

With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for 3 years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing. Then, a dynamic model was fitted to the green fraction to extract the dynamic characteristics of the green fraction as five parameters. Using the estimated growth parameters, we developed models for genomic prediction of the growth process and tested whether the inclusion of the dynamic model contributed to better prediction of growth. Our proposed models consist of two steps: first, predicting the parameters of the dynamics model with genomic prediction, and then substituting the predicted values for the parameters of the dynamics model. By evaluating the heritability of the growth parameters, the dynamic model was able to effectively extract genetic diversity in the growth characteristics of the green fraction. In addition, the proposed prediction model showed higher prediction accuracy than conventional genomic prediction models, especially when the future growth of the test population is a prediction target given the observed values in the first half of growth as training data. This indicates that our model was able to successfully combine information from the early growth period with phenotypic data from the training population for prediction. This prediction method could be applied to selection at an early growth stage in crop breeding, and could reduce the cost and time of field trials.

Multi-spectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of above-ground biomass (AGB) and determined which developmental stages should be used for accurate prediction in soybean. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early to late growth stages. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multi-trait genomic prediction. The accuracy of the prediction model increased starting at an early stage of growth (31 days after sowing). To predict phenotypic values of AGB, we employed multi-kernel genomic prediction. Consequently, the prediction accuracy of phenotypic values reached a maximum at a relatively early growth stage (38 days after sowing). Hence, the optimal timing for MS imaging may depend on the irrigation levels.

The awn, a needle-like structure extending from the tip of the lemma in grass species, plays a role in environmental adaptation and fitness. In some crops, awns appear to have been eliminated during domestication. Although numerous genes involved in awn development have been identified, several dominant genes that eliminate awns are also known to exist. For example, in sorghum (Sorghum bicolor), the dominant awn inhibiting gene has been known since 1921; however, its molecular features remain uncharacterized. In this study, we conducted quantitative trait locus analysis and a genome-wide association study of awn-related traits in sorghum and identifiedDOMINANT AWN INHIBITOR(DAI), which encodes the ALOG family protein on chromosome 3.DAIappeared to be present in most awnless sorghum cultivars, likely because of its effectiveness. Detailed analysis of the ALOG protein family in cereals revealed thatDAIoriginated from duplication of its twin paralog (DAIori) on chromosome 10. Observations of immature awns in near-isogenic lines revealed that DAI inhibits awn elongation by suppressing both cell proliferation and elongation. We also found that onlyDAIgained a novel function to inhibit awn elongation through an awn-specific expression pattern distinct from that ofDAIori. Interestingly, heterologous expression ofDAIwith its own promoter in rice inhibited awn elongation in the awned cultivar Kasalath. We found thatDAIoriginated from gene duplication, providing an interesting example of gain-of-function that occurs only in sorghum but shares its functionality with rice and sorghum.

Non-additive (dominance and epistasis) effects have remarkable influences on hybrid performance, e.g., via heterosis. Nevertheless, only additive effects are often considered in genomic predictions (GP). In this study, we demonstrated the importance of dominance effects in the prediction of hybrid performance in bioenergy sorghum [Sorghum bicolor (L.) Moench]. The dataset contained more than 400 hybrids between 200 inbred lines and two testers. The hybrids exhibited considerable heterosis in culm length and fresh weight, and the degree of heterosis was consistent with the genetic distance from the corresponding tester. The degree of heterosis was further different among subpopulations. Conversely, Brix exhibited limited heterosis. Regarding GP, we examined three statistical models and four training dataset types. In most of the dataset types, genomic best linear unbiased prediction (GBLUP) with additive effects had lower prediction accuracy than GBLUP with additive and dominance effects (GBLUP-AD) and Gaussian kernel regression (GK). The superiority of GBLUP-AD and GK depended on the level of dominance variance, which was high for culm length and fresh weight, and low for Brix. Considering subpopulations, the influence of dominance was more complex. Our findings highlight the importance of considering dominance effects in GP models for sorghum hybrid breeding.

(1) Background: The genetic basis of local adaptation in conifers remains poorly understood because of limited research evidence and the lack of suitable genetic materials. Sakhalin fir (Abies sachalinensis) is an ideal organism for elucidating the genetic basis of local adaptation because its altitudinal adaptation has been demonstrated, and suitable materials for its linkage mapping are available. (2) Method: We constructed P336 and P236 linkage maps based on 486 and 516 single nucleotide polymorphisms, respectively, that were derived from double digest restriction site-associated DNA sequences. We measured the growth and eco-physiological traits associated with morphology, phenology, and photosynthesis, which are considered important drivers of altitudinal adaptation. (3) Results: The quantitative trait loci (QTLs) for growth traits, phenology, needle morphology, and photosynthetic traits were subsequently detected. Similar to previous studies on conifers, most traits were controlled by multiple QTLs with small or moderate effects. Notably, we detected that one QTL for the crown area might be a type-A response regulator, a nuclear protein responsible for the cytokinin-induced shoot elongation. (4) Conclusion: The QTLs detected in this study include potentially important genomic regions linked to altitudinal adaptation in Sakhalin fir.

With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for three years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing. Then, a dynamic model was fitted to the green fraction to extract the dynamic characteristics of the green fraction as five parameters. Using the estimated growth parameters, we developed models for genomic prediction of the growth process and tested whether the inclusion of the dynamic model contributed to better prediction of growth. Our proposed models consist of two steps: first, predicting the parameters of the dynamics model with genomic prediction, and then substituting the predicted values for the parameters of the dynamics model. By evaluating the heritability of the growth parameters, the dynamic model was able to effectively extract genetic diversity in the growth characteristics of the green fraction. In addition, the proposed prediction model showed higher prediction accuracy than conventional genomic prediction models, especially when the future growth of the test population is a prediction target given the observed values in the first half of growth as training data. This indicates that our model was able to successfully combine information from the early growth period with phenotypic data from the training population for prediction. This prediction method could be applied to selection at an early growth stage in crop breeding, and could reduce the cost and time of field trials.

We performed whole-genome Illumina resequencing of 198 accessions to examine the genetic diversity and facilitate the use of soybean genetic resources and identified 10 million single nucleotide polymorphisms and 2.8 million small indels. Furthermore, PacBio resequencing of 10 accessions was performed, and a total of 2,033 structure variants were identified. Genetic diversity and structure analysis congregated the 198 accessions into three subgroups (Primitive, World, and Japan) and showed the possibility of a long and relatively isolated history of cultivated soybean in Japan. Additionally, the skewed regional distribution of variants in the genome, such as higher structural variations on the R gene clusters in the Japan group, suggested the possibility of selective sweeps during domestication or breeding. A genome-wide association study identified both known and novel causal variants on the genes controlling the flowering period. Novel candidate causal variants were also found on genes related to the seed coat colour by aligning together with Illumina and PacBio reads. The genomic sequences and variants obtained in this study have immense potential to provide information for soybean breeding and genetic studies that may uncover novel alleles or genes involved in agronomically important traits.

Pith parenchyma cells store water in various plant organs. These cells are especially important for producing sugar and ethanol from the sugar juice of grass stems. In many plants, the death of pith parenchyma cells reduces their stem water content. Previous studies proposed that a hypothetical D gene might be responsible for the death of stem pith parenchyma cells in Sorghum bicolor, a promising energy grass, although its identity and molecular function are unknown. Here, we identify the D gene and note that it is located on chromosome 6 in agreement with previous predictions. Sorghum varieties with a functional D allele had stems enriched with dry, dead pith parenchyma cells, whereas those with each of six independent nonfunctional D alleles had stems enriched with juicy, living pith parenchyma cells. D expression was spatiotemporally coupled with the appearance of dead, air-filled pith parenchyma cells in sorghum stems. Among D homologs that are present in flowering plants, Arabidopsis ANAC074 also is required for the death of stem pith parenchyma cells. D and ANAC074 encode previously uncharacterized NAC transcription factors and are sufficient to ectopically induce programmed death of Arabidopsis culture cells via the activation of autolytic enzymes. Taken together, these results indicate that D and its Arabidopsis ortholog, ANAC074, are master transcriptional switches that induce programmed death of stem pith parenchyma cells. Thus, targeting the D gene will provide an approach to breeding crops for sugar and ethanol production.

A genome-wide association study (GWAS) needs to have a suitable population. The factors that affect a GWAS, e.g. population structure, sample size, and sequence analysis and field testing costs, need to be considered. Mixture populations containing subpopulations of different genetic backgrounds may be suitable populations. We conducted simulation experiments to see if a population with high genetic diversity, e.g., a diversity panel, should be added to a target population, especially when the target population harbors small genetic diversity. The target population was 112 accessions ofOryza sativasubsp.japonica, mainly developed in Japan. We combined the target population with three populations that had higher genetic diversities. These were 100indicaaccessions, 100japonicaaccessions, and 100 accessions with various genetic backgrounds. The results showed that the GWAS power with a mixture population was generally higher than with a separate population. Also, the GWAS optimal population varied depending on the fixation indexFSTof the quantitative trait nucleotide (QTN) and its polymorphism of QTN in each population. When a QTN is polymorphic in a target population, a target population combined with a higher diversity population improves the QTN detection power. InvestigatingFSTand the expected heterozygosityHeas factors influencing the detection power, we showed that SNPs with highFSTor lowHeare less likely to be detected by GWAS with mixture populations. Sequenced/genotyped germplasm collections can improve the GWAS detection power by using a subset of them with a target population.Core ideas (3-5 impact statements, 85 char max for each)- Genome-wide association studies with mixture populations are expected to improve the detection power of novel genes due to the increase of the sample size although the influence of population structure is a concern.- When a quantitative trait nucleotide (QTN) is polymorphic in a target population, a combination of the target population and a population with higher diversity than the target population improves the detection power of the QTN.- We found that the fixation index (FST) and the expected heterozygosity (He) were strongly related to the detection power of QTNs.- Germplasm collections which have been already sequenced/genotyped are useful for improving the detection power of GWAS without any addition of sequence costs by using a subset of them with a target population.

A genome-wide association study (GWAS) needs to have a suitable population. The factors that affect a GWAS (e.g. population structure, sample size, and sequence analysis and field testing costs) need to be considered. Mixed populations containing subpopulations of different genetic backgrounds may be suitable populations. We conducted simulation experiments to see if a population with high genetic diversity, such as a diversity panel, should be added to a target population, especially when the target population harbors small genetic diversity. The target population was 112 accessions of Oryza sativa L. subsp. japonica, mainly developed in Japan. We combined the target population with three populations that had higher genetic diversity. These were 100 indica accessions, 100 japonica accessions, and 100 accessions with various genetic backgrounds. The results showed that the GWAS's power with a mixed population was generally higher than with a separate population. Also, the optimal GWAS populations varied depending on the fixation index (FST ) of the quantitative trait nucleotides (QTNs) and the polymorphism of QTNs in each population. When a QTN was polymorphic in a target population, a target population combined with a higher diversity population improved the QTN's detection power. By investigating FST and the expected heterozygosity (He ) as factors influencing the detection power, we showed that single nucleotide polymorphisms with high FST or low He are less likely to be detected by GWAS with mixed populations. Sequenced or genotyped germplasm collections can improve the GWAS's detection power by using a subset of the collections with a target population.

Oryza sativa L. ‘Takanari’ is one of the most productive indica cultivars [1,2]. Reciprocal chromosome segment substitution lines (CSSLs) derived from a cross between ‘Koshihikari’ and ‘Takanari’ are useful tools for the detection and precise mapping of target quantitative trait loci (QTL) in ‘Takanari’. Although the available Os-Nipponbare-Reference-IRGSP-1.0 reference genome is available and useful for evaluating genetic diversity among japonica cultivars, it is not always useful for evaluating genetic diversity harbored by indica cultivars such as ‘Takanari’. To reveal sequence variants in ‘Takanari’ and to exploit these variants in rice breeding programs, the whole genome of ‘Takanari’ was sequenced using a combination of Illumina HiSeq X Ten (20,983,495 reads and %GC 43) and PacBio (2,847,220 high-quality subreads). NGS data obtained have been deposited in the DNA Data Bank of Japan (DDBJ) under accession number DRA007557. Keywords: Oryza sativa L., Rice, Next-generation sequencing, Long-read sequencing

Genomic selection (GS) has proven to be an efficient tool for predicting crop-rank performance of untested genotypes; however, when the traits have intermediate optima (phenology stages) this implementation might not be the most convenient. GS might deliver high rank correlations but incurring in serious bias. Days to heading (DTH) is a crucial development stage in rice for regional adaptability with significant impact in yield potential. The objectives of this research consisted in the development of a method that accurately predicts time related traits like DTH in unobserved environments. For this, we developed an implementation that incorporate day length information (DL) in the prediction process for two relevant scenarios: CV0, predicting tested genotypes in unobserved environments (C method); and CV00, predicting untested genotypes in unobserved environments (CB method). The use of DL has advantages over weather data since it can be determined in advance just by knowing the location and planting date. The proposed methods showed that DL information significantly helps to improve the predictive ability of DTH in unobserved environments. Under CV0, the C method returned an root-mean-square-error (RMSE) of 3.9 days, a Pearson Correlation (PC) of 0.98 and the differences between the predicted and observed environmental means (EMD) ranged between −4.95 and 4.67 days. For CV00, the CB method returned an RMSE of 7.3 days, a PC of 0.93 and the EMD ranged between −6.4 and 4.1 days while the conventional GS implementation produced an RMSE of 18.1 days, a PC of 0.41 and the EMD ranged between −31.5 and 28.7 days.

Breeding of fruit trees is hindered by their large size and long juvenile period. Genome-wide association study (GWAS) and genomic selection (GS) are promising methods for circumventing this hindrance, but preparing new large datasets for these methods may not always be practical. Here, we evaluated the potential of breeding populations evaluated routinely in breeding programs for GWAS and GS. We used a pear parental population of 86 varieties and breeding populations of 765 trees from 16 full-sib families, which were phenotyped for 18 traits and genotyped for 1,506 single nucleotide polymorphisms (SNPs). The power of GWAS and accuracy of genomic prediction were improved when we combined data from the breeding populations and the parental population. The accuracy of genomic prediction was improved further when full-sib data of the target family were available. The results suggest that phenotype data collected in breeding programs can be beneficial for GWAS and GS when they are combined with genome-wide marker data. The potential of GWAS and GS will be further extended if we can build a system for routine collection of the phenotype and marker genotype data for breeding populations.

Understanding the genetic bases of local adaptation in dominant conifer species is critical in predicting the impacts of rapid climate change on forest ecosystems. However, the genetic basis of adaptation is not yet fully understood due to the huge and complex genomes of conifers and the unavailability to date of suitable crossing material. In this study, we constructed a linkage map for Abies sachalinensis (2n = 24) and investigated quantitative trait loci (QTLs) associated with local adaptation along an altitudinal gradient. A segregating population of 239 seedlings was produced from a cross between two F1 hybrids (high-altitude × low-altitude genotypes). QTL mapping of phenological and growth traits was performed using a pseudo-testcross strategy with linkage maps based on 1251 single-nucleotide polymorphism (SNP) and three simple sequence repeat (SSR) markers. Two maps consisting of 12 linkage groups with an average marker interval of ca. 3 cM were constructed for each parent. The total lengths of the maps were 1861 and 1949 cM. A permutation test identified four significant QTLs and 11 additional suggestive QTLs, with high logarithm of odds (LOD) scores (> 3.0). This is the first highly saturated linkage map produced for Abies taxa. Our results suggest that spring bud phenology is controlled by several QTLs with moderate effects. The use of the mapping population created by crossing two hybrids (high × low altitude genotypes) and numerous SNP markers enabled us to investigate the genetic basis of adaptive traits in conifer species.

To improve several agronomic traits in crisphead lettuce (Lactuca sativaL.) under high-temperature growth conditions, we investigated the correlation among those traits in multiple cultivars and performed genetic mapping of their causal genes. In a field cultivation test of Empire type (serrated leaf) and Salinas type (wavy leaf) cultivars, Empire type cultivars showed increased tipburn susceptibility and late bolting compared with Salinas type cultivars. We attempted genetic mapping of leaf shape and bolting time by ddRAD-seq using the F2population derived from a cross between ‘VI185’ (Empire type) and ‘ShinanoGreen’ (Salinas type). These analyses suggested that both traits are controlled by a single locus in LG5. Genotyping of 51 commercial lettuce cultivars with a tightly linked marker (LG5_v8_252.743Mbp) at this locus showed an association between its genotype and the serrated leaf phenotype. By further fine mapping and transcriptome analysis, a gene encoding putative CIN-like TCP transcription factor was determined to be a candidate gene at this locus and was designated asLsTCP4. An insertion of retrotransposable element was found in the allele of ‘VI185’, and its transcript level in the leaves was lower than that in ‘ShinanoGreen’. Because shapes of leaf epidermal cells in ‘VI185’ were similar to those in the TCP family mutant ofArabidopsis thaliana, the leaf shape phenotype was likely caused by reduced expression ofLsTCP4. Furthermore, because it is known that the TCP family protein also controls flowering time via interaction with FT inA. thaliana, it was highly possible thatLsTCP4gave pleiotropic effects on both leaf shape and bolting time in lettuce.

Bradyrhizobium sp. S23321 is an oligotrophic bacterium isolated from paddy field soil. Although S23321 is phylogenetically close to Bradyrhizobium japonicum USDA110, a legume symbiont, it is unable to induce root nodules in siratro, a legume often used for testing Nod factor-dependent nodulation. The genome of S23321 is a single circular chromosome, 7,231,841 bp in length, with an average GC content of 64.3%. The genome contains 6,898 potential protein-encoding genes, one set of rRNA genes, and 45 tRNA genes. Comparison of the genome structure between S23321 and USDA110 showed strong colinearity; however, the symbiosis islands present in USDA110 were absent in S23321, whose genome lacked a chaperonin gene cluster (groELS3) for symbiosis regulation found in USDA110. A comparison of sequences around the tRNA-Val gene strongly suggested that S23321 contains an ancestral-type genome that precedes the acquisition of a symbiosis island by horizontal gene transfer. Although S23321 contains a nif (nitrogen fixation) gene cluster, the organization, homology, and phylogeny of the genes in this cluster were more similar to those of photosynthetic bradyrhizobia ORS278 and BTAi1 than to those on the symbiosis island of USDA110. In addition, we found genes encoding a complete photosynthetic system, many ABC transporters for amino acids and oligopeptides, two types (polar and lateral) of flagella, multiple respiratory chains, and a system for lignin monomer catabolism in the S23321 genome. These features suggest that S23321 is able to adapt to a wide range of environments, probably including low-nutrient conditions, with multiple survival strategies in soil and rhizosphere.

Recent advancements in genomic analysis technologies have opened up new avenues to promote the efficiency of plant breeding. Novel genomics-based approaches for plant breeding and genetics research, such as genome-wide association studies (GWAS) and genomic selection (GS), are useful, especially in fruit tree breeding. The breeding of fruit trees is hindered by their long generation time, large plant size, long juvenile phase, and the necessity to wait for the physiological maturity of the plant to assess the marketable product (fruit). In this article, we describe the potential of genomics-assisted breeding, which uses these novel genomics-based approaches, to break through these barriers in conventional fruit tree breeding. We first introduce the molecular marker systems and whole-genome sequence data that are available for fruit tree breeding. Next we introduce the statistical methods for biparental linkage and quantitative trait locus (QTL) mapping as well as GWAS and GS. We then review QTL mapping, GWAS, and GS studies conducted on fruit trees. We also review novel technologies for rapid generation advancement. Finally, we note the future prospects of genomics-assisted fruit tree breeding and problems that need to be overcome in the breeding.

Bradyrhizobium sp. S23321 is an oligotrophic bacterium isolated from paddy field soil. Although S23321 is phylogenetically close to Bradyrhizobium japonicum USDA110, a legume symbiont, it is unable to induce root nodules in siratro, a legume often used for testing Nod factor-dependent nodulation. The genome of S23321 is a single circular chromosome, 7,231,841 bp in length, with an average GC content of 64.3%. The genome contains 6,898 potential protein-encoding genes, one set of rRNA genes, and 45 tRNA genes. Comparison of the genome structure between S23321 and USDA110 showed strong colinearity; however, the symbiosis islands present in USDA110 were absent in S23321, whose genome lacked a chaperonin gene cluster (groELS3) for symbiosis regulation found in USDA110. A comparison of sequences around the tRNA-Val gene strongly suggested that S23321 contains an ancestral-type genome that precedes the acquisition of a symbiosis island by horizontal gene transfer. Although S23321 contains a nif (nitrogen fixation) gene cluster, the organization, homology, and phylogeny of the genes in this cluster were more similar to those of photosynthetic bradyrhizobia ORS278 and BTAi1 than to those on the symbiosis island of USDA110. In addition, we found genes encoding a complete photosynthetic system, many ABC transporters for amino acids and oligopeptides, two types (polar and lateral) of flagella, multiple respiratory chains, and a system for lignin monomer catabolism in the S23321 genome. These features suggest that S23321 is able to adapt to a wide range of environments, probably including low-nutrient conditions, with multiple survival strategies in soil and rhizosphere.

We have isolated five cDNA clones (osk1–5) for protein kinases from rice which are related to SNF1 protein kinase of Saccharomyces cerevisiae. Based on the sequence homology, these cDNAs can be classified into two groups, group 1 (osk1) and group 2 (osk2–5). The products of these genes were demonstrated to be functional SNF1-related protein kinases by in vitro and in vivo experiments. Recombinant proteins expressed from both groups of genes were fully active as protein kinases and could phosphorylate SAMS peptide, a substrate specific for the SNF1/AMPK family, as well as themselves (autophosphorylation). Moreover, expression of osk3 cDNA in yeast snf1 mutants restored SNF1 function. Northern blot analyses showed differential expression of these two gene groups; group 1 is expressed uniformly in growing tissues (young roots, young shoots, flowers, and immature seeds), whereas group 2 is strongly expressed in immature seeds. SNF1-related protein kinases have been reported from different plant species, such as rye, barley, Arabidopsis, tobacco, and potato, while the type of gene strongly expressed in immature seeds is known only in cereals such as rye, barley, and, from our findings, in rice. Expression levels of the group 2 genes were further analyzed in seeds during seed maturation. Expression is transiently increased in the early stages of seed maturation and then decreases. The expression peak precedes those of the sbe1 and waxy genes, which are involved in starch synthesis in rice. Taken together, these findings suggest that group 2 OSK genes play important roles in the early stages of endosperm development in rice seeds.