Bayesian multilocus association mapping on ordinal and censored traits and its application to the analysis of genetic variation among Oryza sativa L. germplasms

Association mapping can be a powerful tool for detecting quantitative trait loci (QTLs) without requiring line-crossing experiments. We previously proposed a Bayesian approach for simultaneously mapping multiple QTLs by a regression method that directly incorporates estimates of the population structure. In the present study, we extended our method to analyze ordinal and censored traits, since both types of traits are common in the evaluation of germplasm collections. Ordinal-probit and tobit models were employed to analyze ordinal and censored traits, respectively. In both models, we postulated the existence of a latent continuous variable associated with the observable data, and we used a Markov-chain Monte Carlo algorithm to sample the latent variable and determine the model parameters. We evaluated the efficiency of our approach by using simulated- and real-trait analyses of a rice germplasm collection. Simulation analyses based on real marker data showed that our models could reduce both false-positive and false-negative rates in detecting QTLs to reasonable levels. Simulation analyses based on highly polymorphic marker data, which were generated by coalescent simulations, showed that our models could be applied to genotype data based on highly polymorphic marker systems, like simple sequence repeats. For the real traits, we analyzed heading date as a censored trait and amylose content and the shape of milled rice grains as ordinal traits. We found significant markers that may be linked to previously reported QTLs. Our approach will be useful for whole-genome association mapping of ordinal and censored traits in rice germplasm collections.     

Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops

Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an “island model” inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of genomic selection in autogamous crops, especially bringing long-term improvement.     

Bayesian association mapping of multiple quantitative trait loci and its application to the analysis of genetic variation among <Emphasis Type="Italic">Oryza sativa</Emphasis> L. germplasms

One way to use a crop germplasm collection directly to map QTLs without using line-crossing experiments is the whole genome association mapping. A major problem with association mapping is the presence of population structure, which can lead to both false positives and failure to detect genuine associations (i.e., false negatives). Particularly in highly selfing species such as Asian cultivated rice, high levels of population structure are expected and therefore the efficiency of association mapping remains almost unknown. Here, we propose an approach that combines a Bayesian method for mapping multiple QTLs with a regression method that directly incorporates estimates of population structure. That is, the effects due to both multiple QTLs and population structure were included in our statistical model. We evaluated the efficiency of our approach in simulated- and real-trait analyses of a rice germplasm collection. Simulation analyses based on real marker data showed that our model could suppress both false-positive and false-negative rates and the error of estimation of genetic effects over single QTL models, indicating that our model has statistically desirable attributes over single QTL models. As real traits, we analyzed the size and shape of milled rice grains and found significant markers that may be linked to QTLs reported previously. Association mapping should have good prospects in highly selfing species such as rice if proper methods are adopted. Our approach will be useful for the whole genome association mapping of various selfing crop species.     

Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis

A comprehensive and large‐scale metabolome quantitative trait loci (mQTL) analysis was performed to investigate the genetic backgrounds associated with metabolic phenotypes in rice grains. The metabolome dataset consisted of 759 metabolite signals obtained from the grains of 85 lines of rice (Oryza sativa, Sasanishiki × Habataki back‐crossed inbred lines). Metabolome analysis was performed using four mass spectrometry pipelines to enhance detection of different classes of metabolites. This mQTL analysis of a wide range of metabolites highlighted an uneven distribution of 802 mQTLs on the rice genome, as well as different modes of metabolic trait (m‐trait) control among various types of metabolites. The levels of most metabolites within rice grains were highly sensitive to environmental factors, but only weakly associated with mQTLs. Coordinated control was observed for several groups of metabolites, such as amino acids linked to the mQTL hotspot on chromosome 3. For flavonoids, m‐trait variation among the experimental lines was tightly governed by genetic factors that alter the glycosylation of flavones. Many loci affecting levels of metabolites were detected by QTL analysis, and plausible gene candidates were evaluated by in silico analysis. Several mQTLs profoundly influenced metabolite levels, providing insight into the control of rice metabolism. The genomic region and genes potentially responsible for the biosynthesis of apigenin‐6,8‐di‐C‐α‐l‐ arabinoside are presented as an example of a critical mQTL identified by the analysis.     

Inference of the japonica rice domestication process from the distribution of six functional nucleotide polymorphisms of domestication-related genes in various landraces and modern cultivars

Crop domestication can serve as a model of plant evolutionary processes. It involves a series of selection events from standing natural variation and newly occurring mutations and combinations of mutations as a result of natural crossings in populations during local adaptation and propagation of plant lines to other cultivation areas. Our earlier identification of three functional nucleotide polymorphisms (FNPs) of distinct genes involved in the rice domestication process led us to propose a model of the japonica rice domestication process. Here, we examined three more FNPs in two domestication-related genes involved in pigment synthesis during the development of seed pericarp color (Rc and Rd) in 91 landraces (and some modern cultivars) of japonica rice collected from throughout the area of distribution of rice. These polymorphisms were assigned by using genome-wide patterns of restriction fragment length polymorphisms (RFLPs) and the local origins of the landraces. The results led us to infer the process of japonica rice domestication in more detail and propose a more refined model of the japonica domestication process. In this model, the critical role of the Rc FNP at an early step of the domestication process was highlighted. Independent artificial selections of two defective Rd alleles were found, suggesting a role for Rd other than in pigment synthesis during rice domestication.     

Discovery of genome-wide DNA polymorphisms in a landrace cultivar of Japonica rice by whole-genome sequencing

Molecular breeding approaches are of growing importance to crop improvement. However, closely related cultivars generally used for crossing material lack sufficient known DNA polymorphisms due to their genetic relatedness. Next-generation sequencing allows the identification of a massive number of DNA polymorphisms such as single nucleotide polymorphisms (SNPs) and insertions-deletions (InDels) between highly homologous genomes. Using this technology, we performed whole-genome sequencing of a landrace of japonica rice, Omachi, which is used for sake brewing and is an important source for modern cultivars. A total of 229 million reads, each comprising 75 nucleotides of the Omachi genome, was generated with 45-fold coverage and uniquely mapped to 89.7% of the Nipponbare genome, a closely related cultivar. We identified 132,462 SNPs, 16,448 insertions and 19,318 deletions between the Omachi and Nipponbare genomes. An SNP array was designed to validate 731 selected SNPs, resulting in validation rates of 95 and 88% for the Omachi and Nipponbare genomes, respectively. Among the 577 SNPs validated in both genomes, 532 are entirely new SNP markers not previously reported between related rice cultivars. We also validated InDels on a part of chromosome 2 as DNA markers and successfully genotyped five japonica rice cultivars. Our results present the methodology and extensive data on SNPs and InDels available for whole-genome genotyping and marker-assisted breeding. The polymorphism information between Omachi and Nipponbare is available at NGRC_Rice_Omachi (http://www.nodai-genome.org/oryza_sativa_en.html).     

Genome-Wide DNA Polymorphisms in Seven Rice Cultivars of Temperate and Tropical Japonica Groups

Elucidation of the rice genome is expected to broaden our understanding of genes related to the agronomic characteristics and the genetic relationship among cultivars. In this study, we conducted whole-genome sequencings of 6 cultivars, including 5 temperate japonica cultivars and 1 tropical japonica cultivar (Moroberekan), by using next-generation sequencing (NGS) with Nipponbare genome as a reference. The temperate japonica cultivars contained 2 sake brewing (Yamadanishiki and Gohyakumangoku), 1 landrace (Kameji), and 2 modern cultivars (Koshihikari and Norin 8). Almost >83% of the whole genome sequences of the Nipponbare genome could be covered by sequenced short-reads of each cultivar, including Omachi, which has previously been reported to be a temperate japonica cultivar. Numerous single nucleotide polymorphisms (SNPs), insertions, and deletions were detected among the various cultivars and the Nipponbare genomes. Comparison of SNPs detected in each cultivar suggested that Moroberekan had 5-fold more SNPs than the temperate japonica cultivars. Success of the 2 approaches to improve the efficacy of sequence data by using NGS revealed that sequencing depth was directly related to sequencing coverage of coding DNA sequences: in excess of 30× genome sequencing was required to cover approximately 80% of the genes in the rice genome. Further, the contigs prepared using the assembly of unmapped reads could increase the value of NGS short-reads and, consequently, cover previously unavailable sequences. These approaches facilitated the identification of new genes in coding DNA sequences and the increase of mapping efficiency in different regions. The DNA polymorphism information between the 7 cultivars and Nipponbare are available at NGRC_Rices_Build1.0 (http://www.nodai-genome.org/oryza_sativa_en.html).     

Construction of Pseudomolecule Sequences of the aus Rice Cultivar Kasalath for Comparative Genomics of Asian Cultivated Rice

Having a deep genetic structure evolved during its domestication and adaptation, the Asian cultivated rice (Oryza sativa) displays considerable physiological and morphological variations. Here, we describe deep whole-genome sequencing of the aus rice cultivar Kasalath by using the advanced next-generation sequencing (NGS) technologies to gain a better understanding of the sequence and structural changes among highly differentiated cultivars. The de novo assembled Kasalath sequences represented 91.1% (330.55 Mb) of the genome and contained 35 139 expressed loci annotated by RNA-Seq analysis. We detected 2 787 250 single-nucleotide polymorphisms (SNPs) and 7393 large insertion/deletion (indel) sites (>100 bp) between Kasalath and Nipponbare, and 2 216 251 SNPs and 3780 large indels between Kasalath and 93-11. Extensive comparison of the gene contents among these cultivars revealed similar rates of gene gain and loss. We detected at least 7.39 Mb of inserted sequences and 40.75 Mb of unmapped sequences in the Kasalath genome in comparison with the Nipponbare reference genome. Mapping of the publicly available NGS short reads from 50 rice accessions proved the necessity and the value of using the Kasalath whole-genome sequence as an additional reference to capture the sequence polymorphisms that cannot be discovered by using the Nipponbare sequence alone.