Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data

Novel QTL for Lateral Root Density and Length Improve Phosphorus Uptake in Rice (Oryza sativa L.)

The rice root system consists of two types of lateral roots, indeterminate larger L-types capable of further branching, and determinate, short, unbranched S-types. L-type laterals correspond to the typical lateral roots of cereals whereas S-type laterals are unique to rice. Both types contribute to nutrient and water uptake and genotypic variation for density and length of these laterals could be exploited in rice improvement to enhance adaptations to nutrient and water-limited environments. Our objectives were to determine how best to screen for lateral root density and length and to identify markers linked to genotypic variation for these traits. Using different growing media showed that screening in nutrient solution exposed genotypic variation for S-type and L-type density, but only the lateral roots of soil-grown plants varied for their lengths. A QTL mapping population developed from parents contrasting for lateral root traits was grown in a low-P field, roots were sampled, scanned and density and length of lateral roots measured. One QTL each was detected for L-type density (LDC), S-type density on crown root (SDC), S-type density on L-type (SDL), S-type length on L-type (SLL), and crown root number (RNO). The QTL for LDC on chromosome 5 had a major effect, accounting for 46% of the phenotypic variation. This strong positive effect was confirmed in additional field experiments, showing that lines with the donor parent allele at qLDC5 had 50% higher LDC. Investigating the contribution of lateral root traits to P uptake using stepwise regressions indicated LDC and RNO were most influential, followed by SDL. Simulating effects of root trait differences conferred by the main QTL in a P uptake model confirmed that qLDC5 was most effective in improving P uptake followed by qRNO9 for RNO and qSDL9 for S-type lateral density on L-type laterals. Pyramiding qLDC5 with qRNO9 and qSDL9 would be possible given that trade-offs between traits were not detected. Phenotypic selection for the RNO trait during variety development would be feasible, however, the costs of doing so reliably for lateral root density traits is prohibitive and markers identified here therefore provide the first opportunity to incorporate such traits into a breeding program.

Multispectral Phenotyping and Genetic Analyses of Spring Appearance in Greening Plant, Phedimus spp

The change in appearance during the seasonal transitions in ornamental greening plants is an important characteristic. In particular, the early onset of green leaf color is a desirable trait for a cultivar. In this study, we established a method for phenotyping leaf color change by multispectral imaging and performed genetic analysis based on the phenotypes to clarify the potential of the approach in breeding greening plants. We performed multispectral phenotyping and quantitative trait locus (QTL) analysis of an F₁ population derived from 2 parental lines of Phedimus takesimensis, known to be a drought and heat-tolerant rooftop plant species. The imaging was conducted in April of 2019 and 2020 when dormancy breakage occurs and growth extension begins. Principal component analysis of 9 different wavelength values showed a high contribution from the first principal component (PC1), which captured variation in the visible light range. The high interannual correlation in PC1 and in the intensity of visible light indicated that the multispectral phenotyping captured genetic variation in the color of leaves. We also performed restriction site-associated DNA sequencing and obtained the first genetic linkage map of Phedimus spp. QTL analysis revealed 2 QTLs related to early dormancy breakage. Based on the genotypes of the markers underlying these 2 QTLs, the F₁ phenotypes with early (late) dormancy break, green (red or brown) leaves, and a high (low) degree of vegetative growth were classified. The results suggest the potential of multispectral phenotyping in the genetic dissection of seasonal leaf color changes in greening plants.

QTL mapping for early root and shoot vigor of upland rice (Oryza sativa L.) under P deficient field conditions in Japan and Madagascar

Upland rice production is limited by the low phosphorus (P) availability of many highly weathered tropical soils and P deficiency is likely to become increasingly limiting in future drier climates because P mobility decreases sharply with soil moisture. Good seedling root development will be crucial to cope with the combined effects of low P and water availability. Upland rice genebank accession DJ123 was used as a donor for P efficiency and root vigor traits in a cross with inefficient local variety Nerica4 and a set of backcross lines were used to characterize the seedling stage response of upland rice to low P availability and to identify associated QTL in field trials in Japan and Madagascar. Ten QTL were detected for crown root number, root, shoot and total dry weight per plant in a highly P deficient field in Japan using the BC₁F₃ generation. Of these, qPef9 on chromosome 9 affected multiple traits, increasing root number, root weight and total biomass, whereas a neighboring QTL on chromosome 9 (qPef9-2) increased shoot biomass. Field trials with derived BC₁F₅ lines in a low-P field in Madagascar confirmed a highly influential region on chromosome 9. However, qPef9-2 appeared more influential than qPef9, as the shoot and root biomass contrast between lines carrying DJ123 or Nerica4 alleles at qPef9-2 was +23.8% and +13.5% compared to +19.2% and +14.4% at qPef9. This advantage increased further during the growing season, leading to 46% higher shoot biomass at the late vegetative stage. Results suggest an introgression between 8.0 and 12.9 Mb on chromosome 9 from P efficient donor DJ123 can improve plant performance under P-limited conditions. The QTL identified here have practical relevance because they were confirmed in the target genetic background of the local variety Nerica4 and can therefore be applied directly to improve its performance.

DOMINANT AWN INHIBITOR Encodes the ALOG Protein Originating from Gene Duplication and Inhibits AWN Elongation by Suppressing Cell Proliferation and Elongation in Sorghum

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 identified DOMINANT AWN INHIBITOR (DAI), which encodes the ALOG family protein on chromosome 3. DAI appeared to be present in most awnless sorghum cultivars, likely because of its effectiveness. Detailed analysis of the ALOG protein family in cereals revealed that DAI originated from a 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 only DAI gained a novel function to inhibit awn elongation through an awn-specific expression pattern distinct from that of DAIori. Interestingly, heterologous expression of DAI with its own promoter in rice inhibited awn elongation in the awned cultivar Kasalath. We found that DAI originated from gene duplication, providing an interesting example of gain-of-function that occurs only in sorghum but shares its functionality with rice and sorghum.

Genomic signatures of natural selection at phenology-related genes in a widely distributed tree species Fagus sylvatica L

BACKGROUND

Diversity among phenology-related genes is predicted to be a contributing factor in local adaptations seen in widely distributed plant species that grow in climatically variable geographic areas, such as forest trees. European beech (Fagus sylvatica L.) is widespread, and is one of the most important broadleaved tree species in Europe; however, its potential for adaptation to climate change is a matter of uncertainty, and little is known about the molecular basis of climate change-relevant traits like bud burst.

RESULTS

We explored single nucleotide polymorphisms (SNP) at candidate genes related to bud burst in beech individuals sampled across 47 populations from Europe. SNP diversity was monitored for 380 candidate genes using a sequence capture approach, providing 2909 unlinked SNP loci. We used two complementary analytical methods to find loci significantly associated with geographic variables, climatic variables (expressed as principal components), or phenotypic variables (spring and autumn phenology, height, survival). Redundancy analysis (RDA) was used to detect candidate markers across two spatial scales (entire study area and within subregions). We revealed 201 candidate SNPs at the broadest scale, 53.2% of which were associated with phenotypic variables. Additive polygenic scores, which provide a measure of the cumulative signal across significant candidate SNPs, were correlated with a climate variable (first principal component, PC1) related to temperature and precipitation availability, and spring phenology. However, different genotype-environment associations were identified within Southeastern Europe as compared to the entire geographic range of European beech.

CONCLUSIONS

Environmental conditions play important roles as drivers of genetic diversity of phenology-related genes that could influence local adaptation in European beech. Selection in beech favors genotypes with earlier bud burst under warmer and wetter habitats within its range; however, selection pressures may differ across spatial scales.

Genetic dissection of QTLs associated with spikelet-related traits and grain size in sorghum

Although spikelet-related traits such as size of anther, spikelet, style, and stigma are associated with sexual reproduction in grasses, no QTLs have been reported in sorghum. Additionally, there are only a few reports on sorghum QTLs related to grain size, such as grain length, width, and thickness. In this study, we performed QTL analyses of nine spikelet-related traits (length of sessile spikelet, pedicellate spikelet, pedicel, anther, style, and stigma; width of sessile spikelet and stigma; and stigma pigmentation) and six grain-related traits (length, width, thickness, length/width ratio, length/thickness ratio, and width/thickness ratio) using sorghum recombinant inbred lines. We identified 36 and 7 QTLs for spikelet-related traits and grain-related traits, respectively, and found that most sorghum spikelet organ length- and width-related traits were partially controlled by the dwarf genes Dw1 and Dw3. Conversely, we found that these Dw genes were not strongly involved in the regulation of grain size. The QTLs identified in this study aid in understanding the genetic basis of spikelet- and grain-related traits in sorghum.

Comparative Analysis of SNP Discovery and Genotyping in Fagus sylvatica L. and Quercus robur L. Using RADseq, GBS, and ddRAD Methods

Next-generation sequencing of reduced representation genomic libraries (RRL) is capable of providing large numbers of genetic markers for population genetic studies at relatively low costs. However, one major concern of these types of markers is the precision of genotyping, which is related to the common problem of missing data, which appears to be particularly important in association and genomic selection studies. We evaluated three RRL approaches (GBS, RADseq, ddRAD) and different SNP identification methods (de novo or based on a reference genome) to find the best solutions for future population genomics studies in two economically and ecologically important broadleaved tree species, namely F. sylvatica and Q. robur. We found that the use of ddRAD method coupled with SNP calling based on reference genomes provided the largest numbers of markers (28 k and 36 k for beech and oak, respectively), given standard filtering criteria. Using technical replicates of samples, we demonstrated that more than 80% of SNP loci should be considered as reliable markers in GBS and ddRAD, but not in RADseq data. According to the reference genomes’ annotations, more than 30% of the identified ddRAD loci appeared to be related to genes. Our findings provide a solid support for using ddRAD-based SNPs for future population genomics studies in beech and oak.

Dissecting the Genetic Architecture of Biofuel-Related Traits in a Sorghum Breeding Population

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 3^rd generation S₀ individuals. Of over 200 ancestral inbred accessions in the base population, only 13 founders contributed to the 3^rd 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.

Impacts of dominance effects on genomic prediction of sorghum hybrid performance

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.

RAD-seq-Based High-Density Linkage Map Construction and QTL Mapping of Biomass-Related Traits in Sorghum using the Japanese Landrace Takakibi NOG

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.

A Fast and Scalable Workflow for SNPs Detection in Genome Sequences Using Hadoop Map-Reduce

Next generation sequencing (NGS) technologies produce a huge amount of biological data, which poses various issues such as requirements of high processing time and large memory. This research focuses on the detection of single nucleotide polymorphism (SNP) in genome sequences. Currently, SNPs detection algorithms face several issues, e.g., computational overhead cost, accuracy, and memory requirements. In this research, we propose a fast and scalable workflow that integrates Bowtie aligner with Hadoop based Heap SNP caller to improve the SNPs detection in genome sequences. The proposed workflow is validated through benchmark datasets obtained from publicly available web-portals, e.g., NCBI and DDBJ DRA. Extensive experiments have been performed and the results obtained are compared with Bowtie and BWA aligner in the alignment phase, while compared with GATK, FaSD, SparkGA, Halvade, and Heap in SNP calling phase. Experimental results analysis shows that the proposed workflow outperforms existing frameworks e.g., GATK, FaSD, Heap integrated with BWA and Bowtie aligners, SparkGA, and Halvade. The proposed framework achieved 22.46% more efficient F-score and 99.80% consistent accuracy on average. More, comparatively 0.21% mean higher accuracy is achieved. Moreover, SNP mining has also been performed to identify specific regions in genome sequences. All the frameworks are implemented with the default configuration of memory management. The observations show that all workflows have approximately same memory requirement. In the future, it is intended to graphically show the mined SNPs for user-friendly interaction, analyze and optimize the memory requirements as well.

Identification and Comparison of Imprinted Genes Across Plant Species

Genomic imprinting is a phenomenon that occurs in flowering plants and mammals, whereby a gene is expressed in a parent-of-origin-specific manner. Although imprinting has now been examined genome-wide in a number of species using RNA-seq, the analyses used to assess imprinting vary between studies, making consistent comparisons between species difficult. Here we present a simple, easy-to-use bioinformatic pipeline for imprinting analyses suitable for any tissue, including plant endosperm. All relevant scripts can be downloaded. As an illustrative example, we reanalyze published data from A. thaliana and Z. mays endosperm using the pipeline and then demonstrate how to use the results to assess the conservation of imprinting between these species. We also introduce the Plant Imprinting Database, a repository for published imprinting datasets in plants that can be used to view, compare, and download data.

Comparison of shape quantification methods for genomic prediction, and genome-wide association study of sorghum seed morphology

Seed shape is an important agronomic trait with continuous variation among genotypes. Therefore, the quantitative evaluation of this variation is highly important. Among geometric morphometrics methods, elliptic Fourier analysis and semi-landmark analysis are often used for the quantification of biological shape variations. Elliptic Fourier analysis is an approximation method to treat contours as a waveform. Semi-landmark analysis is a method of superimposed points in which the differences of multiple contour positions are minimized. However, no detailed comparison of these methods has been undertaken. Moreover, these shape descriptors vary when the scale and direction of the contour and the starting point of the contour trace change. Thus, these methods should be compared with respect to the standardization of the scale and direction of the contour and the starting point of the contour trace. In the present study, we evaluated seed shape variations in a sorghum (Sorghum bicolor Moench) germplasm collection to analyze the association between shape variations and genome-wide single-nucleotide polymorphisms by genomic prediction (GP) and genome-wide association studies (GWAS). In our analysis, we used all possible combinations of three shape description methods and eight standardization procedures for the scale and direction of the contour as well as the starting point of the contour trace; these combinations were compared in terms of GP accuracy and the GWAS results. We compared the shape description methods (elliptic Fourier descriptors and the coordinates of superposed pseudo-landmark points) and found that principal component analysis of their quantitative descriptors yielded similar results. Different scaling and direction standardization procedures caused differences in the principal component scores, average shape, and the results of GP and GWAS.