RAINBOW: Haplotype-based genome-wide association study using a novel SNP-set method

Cross potential selection: a proposal for optimizing crossing combinations in recurrent selection using the usefulness criterion of future inbred lines

In plant breeding programs, rapid production of novel varieties is highly desirable. Genomic selection allows the selection of superior individuals based on genomic estimated breeding values. However, it is worth noting that superior individuals may not always be superior parents. The choice of the crossing pair significantly influences the genotypic value of the resulting progeny. This study has introduced a new crossing strategy, termed cross potential selection, designed to expedite the production of novel varieties of inbred crops. Cross potential selection integrates fast recurrent selection and usefulness criterion to generate novel varieties. It considers the segregation of each crossing pair and computes the expected genotypic values of the top-performing individuals, assuming that the progeny distribution of genotypic values follows a normal distribution. It does not consider genetic diversity and focuses only on producing a novel variety as soon as possible. We simulated a 30-year breeding program in 2 scenarios, low heritability (h2=0.3) and high heritability (h2=0.6), to compare cross potential selection with 2 other selection strategies. Cross potential selection consistently demonstrated the highest genetic gains among the 3 strategies in early cycles. In the 3rd year of the breeding program with a high heritability (h2=0.6), cross potential selection exhibited the highest genetic gains, 138 times that of 300 independent breeding simulations. Regarding long-term improvement, the other selection strategies outperformed cross potential selection. Nevertheless, compared with the other 2 strategies, cross potential selection achieved significant short-term genetic improvements. Cross potential selection is a suitable breeding strategy for the rapid production of varieties within limited time and cost.

High-Throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models

High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles. In this study, we developed models to estimate the phenotypes of biomass-related traits in soybean (Glycine max) using unmanned aerial vehicle (UAV) remote sensing and deep learning models. In 2018, a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions: drought and control. We used a convolutional neural network (CNN) as a model to estimate the phenotypic values of 5 conventional biomass-related traits: dry weight, main stem length, numbers of nodes and branches, and plant height. We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models. The accuracy of the developed models was assessed through 10-fold cross-validation, which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously. Deep learning enabled us to extract features that exhibited strong correlations with the output (i.e., phenotypes of the target traits) and accurately estimate the values of the features from the input data. We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits. Furthermore, we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions. The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.

Genome-wide association studies from spoken phenotypic descriptions: a proof of concept from maize field studies

We present a novel approach to genome-wide association studies (GWAS) by leveraging unstructured, spoken phenotypic descriptions to identify genomic regions associated with maize traits. Utilizing the Wisconsin Diversity panel, we collected spoken descriptions of Zea mays ssp. mays traits, converting these qualitative observations into quantitative data amenable to GWAS analysis. First, we determined that visually striking phenotypes could be detected from unstructured spoken phenotypic descriptions. Next, we developed two methods to process the same descriptions to derive the trait plant height, a well-characterized phenotypic feature in maize: (1) a semantic similarity metric that assigns a score based on the resemblance of each observation to the concept of 'tallness' and (2) a manual scoring system that categorizes and assigns values to phrases related to plant height. Our analysis successfully corroborated known genomic associations and uncovered novel candidate genes potentially linked to plant height. Some of these genes are associated with gene ontology terms that suggest a plausible involvement in determining plant stature. This proof-of-concept demonstrates the viability of spoken phenotypic descriptions in GWAS and introduces a scalable framework for incorporating unstructured language data into genetic association studies. This methodology has the potential not only to enrich the phenotypic data used in GWAS and to enhance the discovery of genetic elements linked to complex traits but also to expand the repertoire of phenotype data collection methods available for use in the field environment.

Genomic prediction and genome-wide association study using combined genotypic data from different genotyping systems: application to apple fruit quality traits

With advances in next-generation sequencing technologies, various marker genotyping systems have been developed for genomics-based approaches such as genomic selection (GS) and genome-wide association study (GWAS). As new genotyping platforms are developed, data from different genotyping platforms must be combined. However, the potential use of combined data for GS and GWAS has not yet been clarified. In this study, the accuracy of genomic prediction (GP) and the detection power of GWAS increased for most fruit quality traits of apples when using combined data from different genotyping systems, Illumina Infinium single-nucleotide polymorphism array and genotyping by random amplicon sequencing-direct (GRAS-Di) systems. In addition, the GP model, which considered the inbreeding effect, further improved the accuracy of the seven fruit traits. Runs of homozygosity (ROH) islands overlapped with the significantly associated regions detected by the GWAS for several fruit traits. Breeders may have exploited these regions to select promising apples by breeders, increasing homozygosity. These results suggest that combining genotypic data from different genotyping platforms benefits the GS and GWAS of fruit quality traits in apples. Information on inbreeding could be beneficial for improving the accuracy of GS for fruit traits of apples; however, further analysis is required to elucidate the relationship between the fruit traits and inbreeding depression (e.g. decreased vigor).

Integrated Assays of Genome-Wide Association Study, Multi-Omics Co-Localization, and Machine Learning Associated Calcium Signaling Genes with Oilseed Rape Resistance to Sclerotinia sclerotiorum

Sclerotinia sclerotiorum (Ss) is one of the most devastating fungal pathogens, causing huge yield loss in multiple economically important crops including oilseed rape. Plant resistance to Ss pertains to quantitative disease resistance (QDR) controlled by multiple minor genes. Genome-wide identification of genes involved in QDR to Ss is yet to be conducted. In this study, we integrated several assays including genome-wide association study (GWAS), multi-omics co-localization, and machine learning prediction to identify, on a genome-wide scale, genes involved in the oilseed rape QDR to Ss. Employing GWAS and multi-omics co-localization, we identified seven resistance-associated loci (RALs) associated with oilseed rape resistance to Ss. Furthermore, we developed a machine learning algorithm and named it Integrative Multi-Omics Analysis and Machine Learning for Target Gene Prediction (iMAP), which integrates multi-omics data to rapidly predict disease resistance-related genes within a broad chromosomal region. Through iMAP based on the identified RALs, we revealed multiple calcium signaling genes related to the QDR to Ss. Population-level analysis of selective sweeps and haplotypes of variants confirmed the positive selection of the predicted calcium signaling genes during evolution. Overall, this study has developed an algorithm that integrates multi-omics data and machine learning methods, providing a powerful tool for predicting target genes associated with specific traits. Furthermore, it makes a basis for further understanding the role and mechanisms of calcium signaling genes in the QDR to Ss.

Genome-Enabled Analysis of Population Dynamics and Virulence-Associated Loci in the Oat Crown Rust Fungus Puccinia coronata f. sp. avenae

Puccinia coronata f. sp. avenae (Pca) is an important fungal pathogen causing crown rust that impacts oat production worldwide. Genetic resistance for crop protection against Pca is often overcome by the rapid virulence evolution of the pathogen. This study investigated the factors shaping adaptive evolution of Pca using pathogen populations from distinct geographic regions within the United States and South Africa. Phenotypic and genome-wide sequencing data of these diverse Pca collections, including 217 isolates, uncovered phylogenetic relationships and established distinct genetic composition between populations from northern and southern regions from the United States and South Africa. The population dynamics of Pca involve a bidirectional movement of inoculum between northern and southern regions of the United States and contributions from clonality and sexuality. The population from South Africa is solely clonal. A genome-wide association study (GWAS) employing a haplotype-resolved Pca reference genome was used to define 11 virulence-associated loci corresponding to 25 oat differential lines. These regions were screened to determine candidate Avr effector genes. Overall, the GWAS results allowed us to identify the underlying genetic factors controlling pathogen recognition in an oat differential set used in the United States to assign pathogen races (pathotypes). Key GWAS findings support complex genetic interactions in several oat lines, suggesting allelism among resistance genes or redundancy of genes included in the differential set, multiple resistance genes recognizing genetically linked Avr effector genes, or potentially epistatic relationships. A careful evaluation of the composition of the oat differential set accompanied by the development or implementation of molecular markers is recommended. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Extreme trait GWAS (Et-GWAS): Unraveling rare variants in the 3,000 rice genome

Identifying high-impact, rare genetic variants associated with specific traits is crucial for crop improvement. The 3,010 rice genome (3K RG) dataset offers a valuable resource for discovering genomic regions with potential applications in crop breeding. We used Extreme Trait GWAS (Et-GWAS), employing bulk pooling and allele frequency measurement to efficiently extract rare variants from the 3K RG. This innovative approach facilitates the detection of associations between genetic variants and target traits, concentrating and quantifying rare alleles. In our study, on grain yield under drought stress, Et-GWAS successfully identified five key genes (OsPP2C11, OsK5.2, OsIRO2, OsPEX1, and OsPWA1) known for enhancing yield under drought. In addition, we examined the overlap of our results with previously reported qDTY-QTLs and observed that OsUCH1 and OsUCH2 genes were located within qDTY2.2 We compared Et-GWAS with conventional GWAS, finding it effectively capturing most candidate genes associated with the target trait. Validation with resistant starch showed similar results. To enhance user-friendliness, we developed a GUI for Et-GWAS; https://et-gwas.shinyapps.io/Et-GWAS/.

Genome-wide association analysis identifies a consistent QTL for powdery mildew resistance on chromosome 3A in Nordic and Baltic spring wheat

KEY MESSAGE

QPm.NOBAL-3A is an important QTL providing robust adult plant powdery mildew resistance in Nordic and Baltic spring wheat, aiding sustainable crop protection and breeding. Powdery mildew, caused by the biotrophic fungal pathogen Blumeria graminis f. sp. tritici, poses a significant threat to bread wheat (Triticum aestivum L.), one of the world's most crucial cereal crops. Enhancing cultivar resistance against this devastating disease requires a comprehensive understanding of the genetic basis of powdery mildew resistance. In this study, we performed a genome-wide association study (GWAS) using extensive field trial data from multiple environments across Estonia, Latvia, Lithuania, and Norway. The study involved a diverse panel of recent wheat cultivars and breeding lines sourced from the Baltic region and Norway. We identified a major quantitative trait locus (QTL) on chromosome 3A, designated as QPm.NOBAL-3A, which consistently conferred high resistance to powdery mildew across various environments and countries. Furthermore, the consistency of the QTL haplotype effect was validated using an independent Norwegian spring wheat panel. Subsequent greenhouse seedling inoculations with 15 representative powdery mildew isolates on a subset of the GWAS panel indicated that this QTL provides adult plant resistance and is likely of race non-specific nature. Moreover, we developed and validated KASP markers for QPm.NOBAL-3A tailored for use in breeding. These findings provide a critical foundation for marker-assisted selection in breeding programs aimed at pyramiding resistance QTL/genes to achieve durable and broad-spectrum resistance against powdery mildew.

Bayesian LASSO for population stratification correction in rare haplotype association studies

Population stratification (PS) is one major source of confounding in both single nucleotide polymorphism (SNP) and haplotype association studies. To address PS, principal component regression (PCR) and linear mixed model (LMM) are the current standards for SNP associations, which are also commonly borrowed for haplotype studies. However, the underfitting and overfitting problems introduced by PCR and LMM, respectively, have yet to be addressed. Furthermore, there have been only a few theoretical approaches proposed to address PS specifically for haplotypes. In this paper, we propose a new method under the Bayesian LASSO framework, QBLstrat, to account for PS in identifying rare and common haplotypes associated with a continuous trait of interest. QBLstrat utilizes a large number of principal components (PCs) with appropriate priors to sufficiently correct for PS, while shrinking the estimates of unassociated haplotypes and PCs. We compare the performance of QBLstrat with the Bayesian counterparts of PCR and LMM and a current method, haplo.stats. Extensive simulation studies and real data analyses show that QBLstrat is superior in controlling false positives while maintaining competitive power for identifying true positives under PS.

FiMAP: A fast identity-by-descent mapping test for biobank-scale cohorts

Although genome-wide association studies (GWAS) have identified tens of thousands of genetic loci, the genetic architecture is still not fully understood for many complex traits. Most GWAS and sequencing association studies have focused on single nucleotide polymorphisms or copy number variations, including common and rare genetic variants. However, phased haplotype information is often ignored in GWAS or variant set tests for rare variants. Here we leverage the identity-by-descent (IBD) segments inferred from a random projection-based IBD detection algorithm in the mapping of genetic associations with complex traits, to develop a computationally efficient statistical test for IBD mapping in biobank-scale cohorts. We used sparse linear algebra and random matrix algorithms to speed up the computation, and a genome-wide IBD mapping scan of more than 400,000 samples finished within a few hours. Simulation studies showed that our new method had well-controlled type I error rates under the null hypothesis of no genetic association in large biobank-scale cohorts, and outperformed traditional GWAS single-variant tests when the causal variants were untyped and rare, or in the presence of haplotype effects. We also applied our method to IBD mapping of six anthropometric traits using the UK Biobank data and identified a total of 3,442 associations, 2,131 (62%) of which remained significant after conditioning on suggestive tag variants in the ± 3 centimorgan flanking regions from GWAS.

Assessing the Potential for Genome-Assisted Breeding in Red Perilla Using Quantitative Trait Locus Analysis and Genomic Prediction

Red perilla is an important medicinal plant used in Kampo medicine. The development of elite varieties of this species is urgently required. Medicinal compounds are generally considered target traits in medicinal plant breeding; however, selection based on compound phenotypes (i.e., conventional selection) is expensive and time consuming. Here, we propose genomic selection (GS) and marker-assisted selection (MAS), which use marker information for selection, as suitable selection methods for medicinal plants, and we evaluate the effectiveness of these methods in perilla breeding. Three breeding populations generated from crosses between one red and three green perilla genotypes were used to elucidate the genetic mechanisms underlying the production of major medicinal compounds using quantitative trait locus analysis and evaluating the accuracy of genomic prediction (GP). We found that GP had a sufficiently high accuracy for all traits, confirming that GS is an effective method for perilla breeding. Moreover, the three populations showed varying degrees of segregation, suggesting that using these populations in breeding may simultaneously enhance multiple target traits. This study contributes to research on the genetic mechanisms of the major medicinal compounds of red perilla, as well as the breeding efficiency of this medicinal plant.

Mining novel genomic regions and candidate genes of heading and flowering dates in bread wheat by SNP- and haplotype-based GWAS

Bread wheat (Triticum aestivum L.) is a global staple crop vital for human nutrition. Heading date (HD) and flowering date (FD) are critical traits influencing wheat growth, development, and adaptability to diverse environmental conditions. A comprehensive study were conducted involving 190 bread wheat accessions to unravel the genetic basis of HD and FD using high-throughput genotyping and multi-environment field trials. Seven independent quantitative trait loci (QTLs) were identified to be significantly associated with HD and FD using two GWAS methods, which explained a proportion of phenotypic variance ranging from 1.43% to 9.58%. Notably, QTLs overlapping with known vernalization genes Vrn-D1 were found, validating their roles in regulating flowering time. Moreover, novel QTLs on chromosome 2A, 5B, 5D, and 7B associated with HD and FD were identified. The effects of these QTLs on HD and FD were confirmed in an additional set of 74 accessions across different environments. An increase in the frequency of alleles associated with early flowering in cultivars released in recent years was also observed, suggesting the influence of molecular breeding strategies. In summary, this study enhances the understanding of the genetic regulation of HD and FD in bread wheat, offering valuable insights into crop improvement for enhanced adaptability and productivity under changing climatic conditions. These identified QTLs and associated markers have the potential to improve wheat breeding programs in developing climate-resilient varieties to ensure food security.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01422-z.

Discovering Genomic Regions Associated with Reproductive Traits and Frame Score in Mexican Simmental and Simbrah Cattle Using Individual SNP and Haplotype Markers

Reproductive efficiency stands as a critical determinant of profitability within beef production systems. The incorporation of molecular markers can expedite advancements in reproductive performance. While the use of SNPs in association analysis is prevalent, approaches centered on haplotypes can offer a more comprehensive insight. The study used registered Simmental and Simbrah cattle genotyped with the GGP Bovine 150 k panel. Phenotypes included scrotal circumference (SC), heifer fertility (HF), stayability (STAY), and frame score (FS). After quality control, 105,129 autosomal SNPs from 967 animals were used. Haplotype blocks were defined based on linkage disequilibrium. Comparison between haplotypes and SNPs for reproductive traits and FS was conducted using Bayesian and frequentist models. 23, 13, 7, and 2 SNPs exhibited associations with FS, SC, HF, and STAY, respectively. In addition, seven, eight, seven, and one haplotypes displayed associations with FS, SC, HF, and STAY, respectively. Within these delineated genomic segments, potential candidate genes were associated.

Haplotype based testing for a better understanding of the selective architecture

BACKGROUND

The identification of genomic regions affected by selection is one of the most important goals in population genetics. If temporal data are available, allele frequency changes at SNP positions are often used for this purpose. Here we provide a new testing approach that uses haplotype frequencies instead of allele frequencies.

RESULTS

Using simulated data, we show that compared to SNP based test, our approach has higher power, especially when the number of candidate haplotypes is small or moderate. To improve power when the number of haplotypes is large, we investigate methods to combine them with a moderate number of haplotype subsets. Haplotype frequencies can often be recovered with less noise than SNP frequencies, especially under pool sequencing, giving our test an additional advantage. Furthermore, spurious outlier SNPs may lead to false positives, a problem usually not encountered when working with haplotypes. Post hoc tests for the number of selected haplotypes and for differences between their selection coefficients are also provided for a better understanding of the underlying selection dynamics. An application on a real data set further illustrates the performance benefits.

CONCLUSIONS

Due to less multiple testing correction and noise reduction, haplotype based testing is able to outperform SNP based tests in terms of power in most scenarios.

Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data

Plant response to drought is an important yield-related trait under abiotic stress, but the method for measuring and modeling plant responses in a time series has not been fully established. The objective of this study was to develop a method to measure and model plant response to irrigation changes using time-series multispectral (MS) data. We evaluated 178 soybean (Glycine max (L.) Merr.) accessions under three irrigation treatments at the Arid Land Research Center, Tottori University, Japan in 2019, 2020 and 2021. The irrigation treatments included W5: watering for 5 d followed by no watering 5 d, W10: watering for 10 d followed by no watering 10 d, D10: no watering for 10 d followed by watering 10 d, and D: no watering. To capture the plant responses to irrigation changes, time-series MS data were collected by unmanned aerial vehicle during the irrigation/non-irrigation switch of each irrigation treatment. We built a random regression model (RRM) for each of combination of treatment by year using the time-series MS data. To test the accuracy of the information captured by RRM, we evaluated the coefficient of variation (CV) of fresh shoot weight of all accessions under a total of nine different drought conditions as an indicator of plant's stability under drought stresses. We built a genomic prediction model (MT RRM   model ) using the genetic random regression coefficients of RRM as secondary traits and evaluated the accuracy of each model for predicting CV. In 2020 and 2021,the mean prediction accuracies of MT RRM   models built in the changing irrigation treatments (r = 0.44 and 0.49, respectively) were higher than that in the continuous drought treatment (r = 0.34 and 0.44, respectively) in the same year. When the CV was predicted using the MT RRM   model across 2020 and 2021 in the changing irrigation treatment, the mean prediction accuracy (r = 0.46) was 42% higher than that of the simple genomic prediction model (r =0.32). The results suggest that this RRM method using the time-series MS data can effectively capture the genetic variation of plant response to drought.

Genome wide association analysis of root hair traits in rice reveals novel genomic regions controlling epidermal cell differentiation

BACKGROUND

Genome wide association (GWA) studies demonstrate linkages between genetic variants and traits of interest. Here, we tested associations between single nucleotide polymorphisms (SNPs) in rice (Oryza sativa) and two root hair traits, root hair length (RHL) and root hair density (RHD). Root hairs are outgrowths of single cells on the root epidermis that aid in nutrient and water acquisition and have also served as a model system to study cell differentiation and tip growth. Using lines from the Rice Diversity Panel-1, we explored the diversity of root hair length and density across four subpopulations of rice (aus, indica, temperate japonica, and tropical japonica). GWA analysis was completed using the high-density rice array (HDRA) and the rice reference panel (RICE-RP) SNP sets.

RESULTS

We identified 18 genomic regions related to root hair traits, 14 of which related to RHD and four to RHL. No genomic regions were significantly associated with both traits. Two regions overlapped with previously identified quantitative trait loci (QTL) associated with root hair density in rice. We identified candidate genes in these regions and present those with previously published expression data relevant to root hair development. We re-phenotyped a subset of lines with extreme RHD phenotypes and found that the variation in RHD was due to differences in cell differentiation, not cell size, indicating genes in an associated genomic region may influence root hair cell fate. The candidate genes that we identified showed little overlap with previously characterized genes in rice and Arabidopsis.

CONCLUSIONS

Root hair length and density are quantitative traits with complex and independent genetic control in rice. The genomic regions described here could be used as the basis for QTL development and further analysis of the genetic control of root hair length and density. We present a list of candidate genes involved in root hair formation and growth in rice, many of which have not been previously identified as having a relation to root hair growth. Since little is known about root hair growth in grasses, these provide a guide for further research and crop improvement.

Across-breed genetic investigation of canine hip dysplasia, elbow dysplasia, and anterior cruciate ligament rupture using whole-genome sequencing

Here, we report the use of genome-wide association study (GWAS) for the analysis of canine whole-genome sequencing (WGS) repository data using breed phenotypes. Single-nucleotide polymorphisms (SNPs) were called from WGS data from 648 dogs that included 119 breeds from the Dog10K Genomes Project. Next, we assigned breed phenotypes for hip dysplasia (Orthopedic Foundation for Animals (OFA) HD, n = 230 dogs from 27 breeds; hospital HD, n = 279 dogs from 38 breeds), elbow dysplasia (ED, n = 230 dogs from 27 breeds), and anterior cruciate ligament rupture (ACL rupture, n = 279 dogs from 38 breeds), the three most important canine spontaneous complex orthopedic diseases. Substantial morbidity is common with these diseases. Previous within- and between-breed GWAS for HD, ED, and ACL rupture using array SNPs have identified disease-associated loci. Individual disease phenotypes are lacking in repository data. There is a critical knowledge gap regarding the optimal approach to undertake categorical GWAS without individual phenotypes. We considered four GWAS approaches: a classical linear mixed model, a haplotype-based model, a binary case-control model, and a weighted least squares model using SNP average allelic frequency. We found that categorical GWAS was able to validate HD candidate loci. Additionally, we discovered novel candidate loci and genes for all three diseases, including FBX025, IL1A, IL1B, COL27A1, SPRED2 (HD), UGDH, FAF1 (ED), TGIF2 (ED & ACL rupture), and IL22, IL26, CSMD1, LDHA, and TNS1 (ACL rupture). Therefore, categorical GWAS of ancestral dog populations may contribute to the understanding of any disease for which breed epidemiological risk data are available, including diseases for which GWAS has not been performed and candidate loci remain elusive.

Time-series multispectral imaging in soybean for improving biomass and genomic prediction accuracy

Multispectral (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 aboveground biomass (AGB) in soybean [Glycine max (L.) Merr.]. 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 vegetative to early reproductive stage. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multitrait genomic prediction. The prediction accuracy of the genotypic values of AGB from MS and genomic data largely outperformed that of the genomic data alone before the flowering stage (90% of accessions did not flower), suggesting that it would be possible to determine cross-combinations based on the predicted genotypic values of AGB. We compared the prediction accuracy of a model using the five VIs and a model using only one VI to predict the phenotypic values of AGB and found that the difference in prediction accuracy decreased over time at all irrigation levels except for the most severe drought. The difference in the most severe drought was not as small as that in the other treatments. Only the prediction accuracy of a model using the five VIs in the most severe droughts gradually increased over time. Therefore, the optimal timing for MS imaging may depend on the irrigation levels.

Optimizing genomic selection in soybean: An important improvement in agricultural genomics

Fast-paced yield improvement in strategic crops such as soybean is pivotal for achieving sustainable global food security. Precise genomic selection (GS), as one of the most effective genomic tools for recognizing superior genotypes, can accelerate the efficiency of breeding programs through shortening the breeding cycle, resulting in significant increases in annual yield improvement. In this study, we investigated the possible use of haplotype-based GS to increase the prediction accuracy of soybean yield and its component traits through augmenting the models by using sophisticated machine learning algorithms and optimized genetic information. The results demonstrated up to a 7% increase in the prediction accuracy when using haplotype-based GS over the full single nucleotide polymorphisms-based GS methods. In addition, we discover an auspicious haplotype block on chromosome 19 with significant impacts on yield and its components, which can be used for screening climate-resilient soybean genotypes with improved yield in large breeding populations.

Prioritized candidate causal haplotype blocks in plant genome-wide association studies

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects in many GWAS. In plant, the relatively small population size in GWAS and the high genetic diversity found in many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to prioritize the candidate causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, GMMAT, and BLINK in both simulated and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in high polygenicity simulation setting. Moreover, it resulted in smaller mapping intervals, especially in regions of high LD, achieved by prioritizing small candidate causal blocks in the larger haplotype blocks. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA’s results, and the average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved on mapping resolution to facilitate crop improvement.

Genome-wide association studies (GWAS) are commonly used in human and plant studies to identify genetic variants responsible for the phenotype of interest and provide foundations for studying disease mechanisms and crop improvement. Most GWAS models are developed and optimized using human datasets. However, the difference between human and plant datasets essentially limits their applications in plant studies, especially when mapping complex traits such as drought resistance and yield. In this study, we present a novel GWAS method, HapFM, tailored for plant datasets to overcome the difficulties of many conventional GWAS methods. HapFM resulted in higher statistical power than conventional GWAS methods for mapping complex traits in our simulation and real dataset analyses. In addition, HapFM reduced the mapping interval by prioritizing candidate causal regions in the genome, which benefits the downstream experimental studies. Last but not least, HapFM can incorporate biological annotations to increase statistical power further. Overall, HapFM balances statistical power, result interpretability, and downstream experimental verifiability.

Genome-wide association study identifies a gene responsible for temperature-dependent rice germination

Environment is an important determinant of agricultural productivity; therefore, crops have been bred with traits adapted to their environment. It is assumed that the physiology of seed germination is optimised for various climatic conditions. Here, to understand the genetic basis underlying seed germination, we conduct a genome-wide association study considering genotype-by-environment interactions on the germination rate of Japanese rice cultivars under different temperature conditions. We find that a 4 bp InDel in one of the 14-3-3 family genes, GF14h, preferentially changes the germination rate of rice under optimum temperature conditions. The GF14h protein constitutes a transcriptional regulatory module with a bZIP-type transcription factor, OREB1, and a florigen-like protein, MOTHER OF FT AND TFL 2, to control the germination rate by regulating abscisic acid (ABA)-responsive genes. The GF14h loss-of-function allele enhances ABA signalling and reduces the germination rate. This allele is found in rice varieties grown in the northern area and in modern cultivars of Japan and China, suggesting that it contributes to the geographical adaptation of rice. This study demonstrates the complicated molecular system involved in the regulation of seed germination in response to temperature, which has allowed rice to be grown in various geographical locations.

Marker-Trait Associations for Total Carotenoid Content and Individual Carotenoids in Durum Wheat Identified by Genome-Wide Association Analysis

Yellow pigment content is one of the main traits considered for grain quality in durum wheat (Triticum turgidum L.). The yellow color is mostly determined by carotenoid pigments, lutein being the most abundant in wheat endosperm, although zeaxanthin, α-carotene and β-carotene are present in minor quantities. Due to the importance of carotenoids in human health and grain quality, modifying the carotenoid content and profile has been a classic target. Landraces are then a potential source for the variability needed for wheat breeding. In this work, 158 accessions of the Spanish durum wheat collection were characterized for carotenoid content and profile and genotyped using the DArTSeq platform for association analysis. A total of 28 marker-trait associations were identified and their co-location with previously described QTLs and candidate genes was studied. The results obtained confirm the importance of the widely described QTL in 7B and validate the QTL regions recently identified by haplotype analysis for the semolina pigment. Additionally, copies of the Zds and Psy genes on chromosomes 7B and 5B, respectively, may have a putative role in determining zeaxanthin content. Finally, genes for the methylerythritol 4-phosphate (MEP) and isopentenyl diphosphate (IPPI) carotenoid precursor pathways were revealed as additional sources of untapped variation for carotenoid improvement.

Towards accurate, contiguous and complete alignment-based polyploid phasing algorithms

Phasing, and in particular polyploid phasing, have been challenging problems held back by the limited read length of high-throughput short read sequencing methods which can't overcome the distance between heterozygous sites and labor high cost of alternative methods such as the physical separation of chromosomes for example. Recently developed single molecule long-read sequencing methods provide much longer reads which overcome this previous limitation. Here we review the alignment-based methods of polyploid phasing that rely on four main strategies: population inference methods, which leverage the genetic information of several individuals to phase a sample; objective function minimization methods, which minimize a function such as the Minimum Error Correction (MEC); graph partitioning methods, which represent the read data as a graph and split it into k haplotype subgraphs; cluster building methods, which iteratively grow clusters of similar reads into a final set of clusters that represent the haplotypes. We discuss the advantages and limitations of these methods and the metrics used to assess their performance, proposing that accuracy and contiguity are the most meaningful metrics. Finally, we propose the field of alignment-based polyploid phasing would greatly benefit from the use of a well-designed benchmarking dataset with appropriate evaluation metrics. We consider that there are still significant improvements which can be achieved to obtain more accurate and contiguous polyploid phasing results which reflect the complexity of polyploid genome architectures.

Genomic prediction of growth in a commercially, recreationally, and culturally important marine resource, the Australian snapper (Chrysophrys auratus)

Growth is one of the most important traits of an organism. For exploited species, this trait has ecological and evolutionary consequences as well as economical and conservation significance. Rapid changes in growth rate associated with anthropogenic stressors have been reported for several marine fishes, but little is known about the genetic basis of growth traits in teleosts. We used reduced genome representation data and genome-wide association approaches to identify growth-related genetic variation in the commercially, recreationally, and culturally important Australian snapper (Chrysophrys auratus, Sparidae). Based on 17,490 high-quality single-nucleotide polymorphisms and 363 individuals representing extreme growth phenotypes from 15,000 fish of the same age and reared under identical conditions in a sea pen, we identified 100 unique candidates that were annotated to 51 proteins. We documented a complex polygenic nature of growth in the species that included several loci with small effects and a few loci with larger effects. Overall heritability was high (75.7%), reflected in the high accuracy of the genomic prediction for the phenotype (small vs large). Although the single-nucleotide polymorphisms were distributed across the genome, most candidates (60%) clustered on chromosome 16, which also explains the largest proportion of heritability (16.4%). This study demonstrates that reduced genome representation single-nucleotide polymorphisms and the right bioinformatic tools provide a cost-efficient approach to identify growth-related loci and to describe genomic architectures of complex quantitative traits. Our results help to inform captive aquaculture breeding programs and are of relevance to monitor growth-related evolutionary shifts in wild populations in response to anthropogenic pressures.

Development and Genetic Characterization of Peanut Advanced Backcross Lines That Incorporate Root-Knot Nematode Resistance From Arachis stenosperma

Crop wild species are increasingly important for crop improvement. Peanut (Arachis hypogaea L.) wild relatives comprise a diverse genetic pool that is being used to broaden its narrow genetic base. Peanut is an allotetraploid species extremely susceptible to peanut root-knot nematode (PRKN) Meloidogyne arenaria. Current resistant cultivars rely on a single introgression for PRKN resistance incorporated from the wild relative Arachis cardenasii, which could be overcome as a result of the emergence of virulent nematode populations. Therefore, new sources of resistance may be needed. Near-immunity has been found in the peanut wild relative Arachis stenosperma. The two loci controlling the resistance, present on chromosomes A02 and A09, have been validated in tetraploid lines and have been shown to reduce nematode reproduction by up to 98%. To incorporate these new resistance QTL into cultivated peanut, we used a marker-assisted backcrossing approach, using PRKN A. stenosperma-derived resistant lines as donor parents. Four cycles of backcrossing were completed, and SNP assays linked to the QTL were used for foreground selection. In each backcross generation seed weight, length, and width were measured, and based on a statistical analysis we observed that only one generation of backcrossing was required to recover the elite peanut's seed size. A populating of 271 BC3F1 lines was genome-wide genotyped to characterize the introgressions across the genome. Phenotypic information for leaf spot incidence and domestication traits (seed size, fertility, plant architecture, and flower color) were recorded. Correlations between the wild introgressions in different chromosomes and the phenotypic data allowed us to identify candidate regions controlling these domestication traits. Finally, PRKN resistance was validated in BC3F3 lines. We observed that the QTL in A02 and/or large introgression in A09 are needed for resistance. This present work represents an important step toward the development of new high-yielding and nematode-resistant peanut cultivars.

Bayesian optimization of multivariate genomic prediction models based on secondary traits for improved accuracy gains and phenotyping costs

We propose a novel approach to the Bayesian optimization of multivariate genomic prediction models based on secondary traits to improve accuracy gains and phenotyping costs via efficient Pareto frontier estimation. Multivariate genomic prediction based on secondary traits, such as data from various omics technologies including high-throughput phenotyping (e.g., unmanned aerial vehicle-based remote sensing), has attracted much attention because it offers improved accuracy gains compared with genomic prediction based only on marker genotypes. Although there is a trade-off between accuracy gains and phenotyping costs of secondary traits, no attempt has been made to optimize these trade-offs. In this study, we propose a novel approach to optimize multivariate genomic prediction models for secondary traits measurable at early growth stages for improved accuracy gains and phenotyping costs. The proposed approach employs Bayesian optimization for efficient Pareto frontier estimation, representing the maximum accuracy at a given cost. The proposed approach successfully estimated the optimal secondary trait combinations across a range of costs while providing genomic predictions for only about [Formula: see text] of all possible combinations. The simulation results reflecting the characteristics of each scenario of the simulated target traits showed that the obtained optimal combinations were reasonable. Analysis of real-time target trait data showed that the proposed multivariate genomic prediction model had significantly superior accuracy compared to the univariate genomic prediction model.

Features and applications of haplotypes in crop breeding

Climate change with altered pest-disease dynamics and rising abiotic stresses threatens resource-constrained agricultural production systems worldwide. Genomics-assisted breeding (GAB) approaches have greatly contributed to enhancing crop breeding efficiency and delivering better varieties. Fast-growing capacity and affordability of DNA sequencing has motivated large-scale germplasm sequencing projects, thus opening exciting avenues for mining haplotypes for breeding applications. This review article highlights ways to mine haplotypes and apply them for complex trait dissection and in GAB approaches including haplotype-GWAS, haplotype-based breeding, haplotype-assisted genomic selection. Improvement strategies that efficiently deploy superior haplotypes to hasten breeding progress will be key to safeguarding global food security.

Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics

Safeguarding crop yields in a changing climate requires bioinformatics advances in harnessing data from vast phenomics and genomics datasets to translate research findings into climate smart crops in the field. Climate change and an additional 3 billion mouths to feed by 2050 raise serious concerns over global food security. Crop breeding and land management strategies will need to evolve to maximize the utilization of finite resources in coming years. High-throughput phenotyping and genomics technologies are providing researchers with the information required to guide and inform the breeding of climate smart crops adapted to the environment. Bioinformatics has a fundamental role to play in integrating and exploiting this fast accumulating wealth of data, through association studies to detect genomic targets underlying key adaptive climate-resilient traits. These data provide tools for breeders to tailor crops to their environment and can be introduced using advanced selection or genome editing methods. To effectively translate research into the field, genomic and phenomic information will need to be integrated into comprehensive clade-specific databases and platforms alongside accessible tools that can be used by breeders to inform the selection of climate adaptive traits. Here we discuss the role of bioinformatics in extracting, analysing, integrating and managing genomic and phenomic data to improve climate resilience in crops, including current, emerging and potential approaches, applications and bottlenecks in the research and breeding pipeline.

Genome-Wide Association Analysis for Stem Cross Section Properties, Height and Heading Date in a Collection of Spanish Durum Wheat Landraces

Durum wheat landraces have a high potential for breeding but they remain underexploited due to several factors, including the insufficient evaluation of these plant materials and the lack of efficient selection tools for transferring target traits into elite backgrounds. In this work, we characterized 150 accessions of the Spanish durum wheat collection for stem cross section, height and heading date. Continuous variation and high heritabilities were recorded for the stem area, pith area, pith diameter, culm wall thickness, height and heading date. The accessions were genotyped with DArTSeq markers, which were aligned to the durum wheat 'Svevo' genome. The markers corresponding to genes, with a minor allele frequency above 5% and less than 10% of missing data, were used for genome-wide association scan analysis. Twenty-nine marker-trait associations (MTAs) were identified and compared with the positions of previously known QTLs. MTAs for height and heading date co-localized with the QTLs for these traits. In addition, all the MTAs for stem traits in chromosome 2B were located in the corresponding synteny regions of the markers associated with lodging in bread wheat. Finally, several MTAs for stem traits co-located with the QTL for wheat stem sawfly (WSS) resistance. The results presented herein reveal the same genomic regions in chromosome 2B are involved in the genetic control of stem traits and lodging tolerance in both durum and bread wheat. In addition, these results suggest the importance of stem traits for WSS resistance and the potential of these landraces as donors for lodging tolerance and WSS resistance enhancement. In this context, the MTAs for stem-related traits identified in this work can serve as a reference for further development of markers for the introgression of target traits into elite material.

nPhase: an accurate and contiguous phasing method for polyploids

While genome sequencing and assembly are now routine, we do not have a full, precise picture of polyploid genomes. No existing polyploid phasing method provides accurate and contiguous haplotype predictions. We developed nPhase, a ploidy agnostic tool that leverages long reads and accurate short reads to solve alignment-based phasing for samples of unspecified ploidy (https://github.com/OmarOakheart/nPhase). nPhase is validated by tests on simulated and real polyploids. nPhase obtains on average over 95% accuracy and a contiguous 1.25 haplotigs per haplotype to cover more than 90% of each chromosome (heterozygosity rate ≥ 0.5%). nPhase allows population genomics and hybrid studies of polyploids.

Exploring Efficient Linear Mixed Models to Detect Quantitative Trait Locus-by-Environment Interactions

Genotype-by-environment (G × E) interactions are important for understanding genotype–phenotype relationships. To date, various statistical models have been proposed to account for G × E effects, especially in genomic selection (GS) studies. Generally, GS does not focus on the detection of each quantitative trait locus (QTL), while the genome-wide association study (GWAS) was designed for QTL detection. G × E modeling methods in GS can be included as covariates in GWAS using unified linear mixed models (LMMs). However, the efficacy of G × E modeling methods in GS studies has not been evaluated for GWAS. In this study, we performed a comprehensive comparison of LMMs that integrate the G × E modeling methods to detect both QTL and QTL-by-environment (Q × E) interaction effects. Model efficacy was evaluated using simulation experiments. For the fixed effect terms representing Q × E effects, simultaneous scoring of specific and nonspecific environmental effects was recommended because of the higher recall and improved genomic inflation factor value. For random effects, it was necessary to account for both G × E and genotype-by-trial (G × T) effects to control genomic inflation factor value. Thus, the recommended LMM includes fixed QTL effect terms that simultaneously score specific and nonspecific environmental effects and random effects accounting for both G × E and G × T. The LMM was applied to real tomato phenotype data obtained from two different cropping seasons. We detected not only QTLs with persistent effects across the cropping seasons but also QTLs with Q × E effects. The optimal LMM identified in this study successfully detected more QTLs with Q × E effects.

Tracing founder haplotypes of Japanese apple varieties: application in genomic prediction and genome-wide association study

Haplotypes provide useful information for genomics-based approaches, genomic prediction, and genome-wide association study. As a small number of superior founders have contributed largely to the breeding history of fruit trees, the information of founder haplotypes may be relevant for performing the genomics-based approaches in these plants. In this study, we proposed a method to estimate 14 haplotypes from 7 founders and automatically trace the haplotypes forward to apple parental (185 varieties) and breeding (659 F1 individuals from 16 full-sib families) populations based on 11,786 single-nucleotide polymorphisms, by combining multiple algorithms. Overall, 92% of the single-nucleotide polymorphisms information in the parental and breeding populations was characterized by the 14 founder haplotypes. The use of founder haplotype information improved the accuracy of genomic prediction in 7 traits and the resolution of genome-wide association study in 13 out of 27 fruit quality traits analyzed in this study. We also visualized the significant propagation of the founder haplotype with the largest genetic effect in genome-wide association study over the pedigree tree of the parental population. These results suggest that the information of founder haplotypes can be useful for not only genetic improvement of fruit quality traits in apples but also for understanding the selection history of founder haplotypes in the breeding program of Japanese apple varieties.

Whole-genome sequence diversity and association analysis of 198 soybean accessions in mini-core collections

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.

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.

Hybrid of Restricted and Penalized Maximum Likelihood Method for Efficient Genome-Wide Association Study

In genome-wide association studies, linear mixed models (LMMs) have been widely used to explore the molecular mechanism of complex traits. However, typical association approaches suffer from several important drawbacks: estimation of variance components in LMMs with large scale individuals is computationally slow; single-locus model is unsatisfactory to handle complex confounding and causes loss of statistical power. To address these issues, we propose an efficient two-stage method based on hybrid of restricted and penalized maximum likelihood, named HRePML. Firstly, we performed restricted maximum likelihood (REML) on single-locus LMM to remove unrelated markers, where spectral decomposition on covariance matrix was used to fast estimate variance components. Secondly, we carried out penalized maximum likelihood (PML) on multi-locus LMM for markers with reasonably large effects. To validate the effectiveness of HRePML, we conducted a series of simulation studies and real data analyses. As a result, our method always had the highest average statistical power compared with multi-locus mixed-model (MLMM), fixed and random model circulating probability unification (FarmCPU), and genome-wide efficient mixed model association (GEMMA). More importantly, HRePML can provide higher accuracy estimation of marker effects. HRePML also identifies 41 previous reported genes associated with development traits in Arabidopsis, which is more than was detected by the other methods.