Quantitative Trait Nucleotides Research Articles

Loci and candidate gene identification for soybean resistance to Phytophthora root rot race 1 in combination with association and linkage mapping

As the second most destructive disease in soybean (Glycine max (L.) Merr), Phytophthora root or stem-rot (PRR) of soybean, which was caused by Phytophthora sojae (P. sojae), could lead to a 10–40% or complete yield loss annually. Researches were needed to perform to identify the P. sojae-resistant germplasm and to better understand the genetic basis of P. sojae resistance in soybean. A total of 225 diverse soybean cultivars and 109 recombinant inbred lines (RILs) derived from crossing ‘DongnongL-28’ (resistant to P. sojae race 1) and ‘Hefeng 25’ (susceptible to P. sojae race 1) were used to evaluate P. sojae race 1 resistance. These 225 soybean cultivars were sequenced using the Specific Locus Amplified Fragment Sequencing (SLAF-seq) approach, and 28,722 single-nucleotide polymorphisms (SNPs) were obtained from the mapping of resistant loci through genome-wide association. Eight quantitative trait nucleotides (QTNs) were associated with resistance to P. sojae race 1. The QTN on Chr.03 was colocalised according to linkage mapping of the RILs. A total of 18 candidate genes were predicted in the flanking region of the colocalised locus on Chr.03. There were stress response-related motifs, such as cis-acting regulatory elements involved in salicylic acid or MeJA responsiveness, in the 1-kb upstream region of sixteen genes. Quantitative RT-PCR showed that the Glyma.03G033700 was induced by P. sojae race 1. Association analysis for Rps loci showed that Glyma.03G033700 and Glyma.03G033800 were the candidates near peak SNP. The identified loci along with the candidate genes could be valuable for studying the molecular mechanisms underlying soybean resistance to P. sojae race 1 and breeding resistant varieties.

Identification of Loci and Candidate Genes Analyses for Tocopherol Concentration of Soybean Seed.

Tocopherol (Toc) occurs in soybean seeds and is extracted together with the soybean oil. Toc is utilized as an antioxidant in food and an additive in animal feed. A total of 180 representative accessions and 144 recombinant inbred lines (RILs) from the cross of ‘Hefeng 25’ and ‘OAC Bayfield’ were selected to evaluate individuals and total Toc concentrations in soybean seeds. The 180 soybean samples were sequenced by the approach of Specific Locus Amplified Fragment Sequencing (SLAF-seq). A total of 22,611 single nucleotide polymorphisms (SNPs) were developed. Nineteen quantitative trait nucleotides (QTNs) were identified associated with individual or total-Toc based on genome-wide association analysis (GWAS). Among them, three QTNs located near known QTLs, and 16 were novel. Eighteen QTLs and nine eQTLs were also detected by linkage mapping. The QTN rs9337368 on Chr.02 was colocalized according to the linkage mapping of the RILs and genome-wide association analysis and regarded as a stable locus for mining the candidate genes in association with Toc. A total of 42 candidate genes near the 200 kbp flanking region of this identified locus were found. Upon a gene-based association, 11 SNPs from five genes out of the 42 candidates were detected. Expression level analysis of five candidate genes revealed that two genes were significantly related to Toc content. The identified loci, along with the candidate genes, might be valuable for increasing the Toc concentration in soybean seeds and improving the nutritional value of soybean oil.

Identification of quantitative trait nucleotides and candidate genes for soybean seed weight by multiple models of genome-wide association study

BackgroundSeed weight is a complex yield-related trait with a lot of quantitative trait loci (QTL) reported through linkage mapping studies. Integration of QTL from linkage mapping into breeding program is challenging due to numerous limitations, therefore, Genome-wide association study (GWAS) provides more precise location of QTL due to higher resolution and diverse genetic diversity in un-related individuals.ResultsThe present study utilized 573 breeding lines population with 61,166 single nucleotide polymorphisms (SNPs) to identify quantitative trait nucleotides (QTNs) and candidate genes for seed weight in Chinese summer-sowing soybean. GWAS was conducted with two single-locus models (SLMs) and six multi-locus models (MLMs). Thirty-nine SNPs were detected by the two SLMs while 209 SNPs were detected by the six MLMs. In all, two hundred and thirty-one QTNs were found to be associated with seed weight in YHSBLP with various effects. Out of these, seventy SNPs were concurrently detected by both SLMs and MLMs on 8 chromosomes. Ninety-four QTNs co-localized with previously reported QTL/QTN by linkage/association mapping studies. A total of 36 candidate genes were predicted. Out of these candidate genes, four hub genes (Glyma06g44510, Glyma08g06420, Glyma12g33280 and Glyma19g28070) were identified by the integration of co-expression network. Among them, three were relatively expressed higher in the high HSW genotypes at R5 stage compared with low HSW genotypes except Glyma12g33280. Our results show that using more models especially MLMs are effective to find important QTNs, and the identified HSW QTNs/genes could be utilized in molecular breeding work for soybean seed weight and yield.ConclusionApplication of two single-locus plus six multi-locus models of GWAS identified 231 QTNs. Four hub genes (Glyma06g44510,Glyma08g06420,Glyma12g33280 & Glyma19g28070) detected via integration of co-expression network among the predicted candidate genes.

Detection of QTL and QTN and candidate genes for oil content in soybean using a combination of four-way-RIL and germplasm populations

Soybean is a source of edible oil for humans and provides a third of the vegetable oil consumed worldwide. Increasing seed oil content in seeds is thus a key objective in soybean breeding. In the present study, a four-way recombinant inbred line (FW-RIL) population comprising 144 lines, planted in 10 environments, and a germplasm panel of 455 accessions, planted in two environments, were used to collect oil-content phenotypes. First, 59 quantitative trait loci (QTL) were detected in the FW-RIL population by inclusive complete interval mapping on a linkage map consisting of 2232 single-nucleotide polymorphism (SNP) markers. Also in the FW-RILs, 44 quantitative trait nucleotides (QTNs) were detected by association analysis using 109,676 SNP markers and five methods of multi-locus genome-wide association study. Second, 77 QTN were detected by association analysis in the germplasm panel using 63,306 markers. Comparison of the QTL and QTN suggested four QTN controlling oil content. Pathway analysis was performed on genes in attenuation regions of these four QTN, and two candidate genes involved in the synthesis or metabolism of soybean oil were identified. These findings provide useful information about the genetics of oil content and may contribute to its genetic improvement by marker-assisted selection.

QTL mapping and GWAS for field kernel water content and kernel dehydration rate before physiological maturity in maize

Kernel water content (KWC) and kernel dehydration rate (KDR) are two main factors affecting maize seed quality and have a decisive influence on the mechanical harvest. It is of great importance to map and mine candidate genes related to KWCs and KDRs before physiological maturity in maize. 120 double-haploid (DH) lines constructed from Si287 with low KWC and JiA512 with high KWC were used as the mapping population. KWCs were measured every 5 days from 10 to 40 days after pollination, and KDRs were calculated. A total of 1702 SNP markers were used to construct a linkage map, with a total length of 1,309.02 cM and an average map distance of 0.77 cM. 10 quantitative trait loci (QTLs) and 27 quantitative trait nucleotides (QTNs) were detected by genome-wide composite interval mapping (GCIM) and multi-locus random-SNP-effect mixed linear model (mrMLM), respectively. One and two QTL hotspot regions were found on Chromosome 3 and 7, respectively. Analysis of the Gene Ontology showed that 2 GO terms of biological processes (BP) were significantly enriched (P ≤ 0.05) and 6 candidate genes were obtained. This study provides theoretical support for marker-assisted breeding of mechanical harvest variety in maize.

Multi-Locus GWAS of Quality Traits in Bread Wheat: Mining More Candidate Genes and Possible Regulatory Network.

In wheat breeding, improved quality traits, including grain quality and dough rheological properties, have long been a critical goal. To understand the genetic basis of key quality traits of wheat, two single-locus and five multi-locus GWAS models were performed for six grain quality traits and three dough rheological properties based on 19, 254 SNPs in 267 bread wheat accessions. As a result, 299 quantitative trait nucleotides (QTNs) within 105 regions were identified to be associated with these quality traits in four environments. Of which, 40 core QTN regions were stably detected in at least three environments, 19 of which were novel. Compared with the previous studies, these novel QTN regions explained smaller phenotypic variation, which verified the advantages of the multi-locus GWAS models in detecting important small effect QTNs associated with complex traits. After characterization of the function and expression in-depth, 67 core candidate genes involved in protein/sugar synthesis, histone modification and the regulation of transcription factor were observed to be associated with the formation of grain quality, which showed that multi-level regulations influenced wheat grain quality. Finally, a preliminary network of gene regulation that may affect wheat quality formation was inferred. This study verified the power and reliability of multi-locus GWAS methods in wheat quality trait research, and increased the understanding of wheat quality formation mechanisms. The detected QTN regions and candidate genes in this study could be further used for gene cloning and marker-assisted selection in high-quality breeding of bread wheat.

Identification of QTNs Controlling 100-Seed Weight in Soybean Using Multilocus Genome-Wide Association Studies.

Hundred-seed weight (HSW) is an important measure of yield and a useful indicator to monitor the inheritance of quantitative traits affected by genotype and environmental conditions. To identify quantitative trait nucleotides (QTNs) and mine genes useful for breeding high-yielding and high-quality soybean (Glycine max) cultivars, we conducted a multilocus genome-wide association study (GWAS) on HSW of soybean based on phenotypic data from 20 different environments and genotypic data for 109,676 single-nucleotide polymorphisms (SNPs) in 144 four-way recombinant inbred lines. Using five multilocus GWAS methods, we identified 118 QTNs controlling HSW. Among these, 31 common QTNs were detected by various methods or across multiple environments. Pathway analysis identified three potential candidate genes associated with HSW in soybean. We used allele information to study the common QTNs in 20 large-seed and 20 small-seed lines and identified a higher percentage of superior alleles in the large-seed lines than in small-seed lines. These observations will contribute to construct the gene networks controlling HSW in soybean, which can improve the genetic understanding of HSW, and provide assistance for molecular breeding of soybean large-seed varieties.

Identification of QTNs and Their Candidate Genes for 100-Seed Weight in Soybean (Glycine max L.) Using Multi-Locus Genome-Wide Association Studies.

100-seed weight (100-SW) in soybeans is a yield component trait and controlled by multiple genes with different effects, but limited information is available for its quantitative trait nucleotides (QTNs) and candidate genes. To better understand the genetic architecture underlying the trait and improve the precision of marker-assisted selection, a total of 43,834 single nucleotide polymorphisms (SNPs) in 250 soybean accessions were used to identify significant QTNs for 100-SW in four environments and their BLUP values using six multi-locus and one single-locus genome-wide association study methods. As a result, a total of 218 significant QTNs were detected using multi-locus methods, whereas eight QTNs were identified by a single-locus method. Among 43 QTNs or QTN clusters identified repeatedly across various environments and/or approaches, all of them exhibited significant trait differences between their corresponding alleles, 33 were found in the genomic region of previously reported QTLs, 10 were identified as new QTNs, and three (qHSW-4-1, qcHSW-7-3, and qcHSW-10-4) were detected in all the four environments. The number of seed weight (SW) increasing alleles for each accession ranged from 8 (18.6%) to 36 (83.72%), and three accessions (Yixingwuhuangdou, Nannong 95C-5, and Yafanzaodou) had more than 35 SW increasing alleles. Among 36 homologous seed-weight genes in Arabidopsis underlying the above 43 stable QTNs, more importantly, Glyma05g34120, GmCRY1, and GmCPK11 had known seed-size/weight-related genes in soybean, and Glyma07g07850, Glyma10g03440, and Glyma10g36070 were candidate genes identified in this study. These results provide useful information for genetic foundation, marker-assisted selection, genomic prediction, and functional genomics of 100-SW.

Genetic Basis of Maize Resistance to Multiple Insect Pests: Integrated Genome-Wide Comparative Mapping and Candidate Gene Prioritization.

Several species of herbivores feed on maize in field and storage setups, making the development of multiple insect resistance a critical breeding target. In this study, an association mapping panel of 341 tropical maize lines was evaluated in three field environments for resistance to fall armyworm (FAW), whilst bulked grains were subjected to a maize weevil (MW) bioassay and genotyped with Diversity Array Technology’s single nucleotide polymorphisms (SNPs) markers. A multi-locus genome-wide association study (GWAS) revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits on all 10 maize chromosomes, of which, 47 and 31 were discovered at stringent Bonferroni genome-wide significance levels of 0.05 and 0.01, respectively, and located within or close to multiple insect resistance genomic regions (MIRGRs) concerning FAW, SB, and MW. Sixteen QTNs influenced multiple traits, of which, six were associated with resistance to both FAW and MW, suggesting a pleiotropic genetic control. Functional prioritization of candidate genes (CGs) located within 10–30 kb of the QTNs revealed 64 putative GWAS-based CGs (GbCGs) showing evidence of involvement in plant defense mechanisms. Only one GbCG was associated with each of the five of the six combined resistance QTNs, thus reinforcing the pleiotropy hypothesis. In addition, through in silico co-functional network inferences, an additional 107 network-based CGs (NbCGs), biologically connected to the 64 GbCGs, and differentially expressed under biotic or abiotic stress, were revealed within MIRGRs. The provided multiple insect resistance physical map should contribute to the development of combined insect resistance in maize.

Genome wide association mapping for tocopherol concentration in soybean seeds across multiple environments

Tocopherol (Toc) in soybean seeds has value for foods, medicines, cosmetics, and animal husbandry. Soybean breeders and soybean-derived food industries have thus focused on breeding new soybean cultivars with higher seed Toc content. In this study, an association panel consisting of 180 diverse soybean accessions were used to detect quantitative trait nucleotides (QTNs) and scan possible candidate genes based on genome-wide association analysis (GWAS) and high-throughput single-nucleotide polymorphisms (SNP). Approximately 22,611 SNPs were identified using the specific locus amplified fragment sequencing (SLAF-seq) approach and based on minor allele frequencies (MAFs) > 5 %, which spanned 84.10 % of the soybean whole genome. For the three individuals including α-tocopherol (α-Toc), γ- tocopherol (γ-Toc), δ- tocopherol (δ-Toc), and total tocopherol (total Toc) levels, a total of 18 quantitative trait nucleotides (QTNs) distributed on the 18 genomic regions of 10 chromosomes were verified via GWAS. Seven QTNs (rs55811336, rs7975863, rs37094897, rs7097743, rs17125409, rs330000, and rs4730199) could be identified in more than three environments, and the other eleven could be identified in least two environments. A total of 6, 8, 4, and 10 QTNs were novel loci for α-Toc, γ-Toc, δ-Toc, and total Toc, respectively. Nine QTNs (rs37505279, rs37686178, rs36359429, rs6898494, rs7097743, rs17125409, rs10200201, rs21080726, and rs5680781) were associated with more than one Toc component. A total of 216 candidate genes near the 200-kb flanking region of these identified QTNs were identified as potential candidate genes associated with soybean seed Toc levels. Of these genes, Glyma.17G061900 (located near QTN rs4730199 on Chr.17), a homogentisate phytyltransferase 1 that catalytic homogentisate to 2-methyl-6-phyl-1,4-benzoquinol in Toc biosynthesis, was detected through association analysis, haplotype analysis, and expression pattern analysis. The identified QTNs with beneficial alleles and the candidate genes would be valuable for improving Toc levels in soybean seeds and improving the nutritional value of soybean oil.

Genome-wide association study of partial resistance to sclerotinia stem rot of cultivated soybean based on the detached leaf method.

Sclerotinia stem rot (SSR) is a devastating fungal disease that causes severe yield losses of soybean worldwide. In the present study, a representative population of 185 soybean accessions was selected and utilized to identify the quantitative trait nucleotide (QTN) of partial resistance to soybean SSR via a genome-wide association study (GWAS). A total of 22,048 single-nucleotide polymorphisms (SNPs) with minor allele frequencies (MAF) > 5% and missing data < 3% were used to assess linkage disequilibrium (LD) levels. Association signals associated with SSR partial resistance were identified by two models, including compressed mixed linear model (CMLM) and multi-locus random-SNP-effect mixed linear model (mrMLM). Finally, seven QTNs with major effects (a known locus and six novel loci) via CMLM and nine novel QTNs with minor effects via mrMLM were detected in relation to partial resistance to SSR, respectively. One of all the novel loci (Gm05:14834789 on Chr.05), which was co-located by these two methods, might be a stable one that showed high significance in SSR partial resistance. Additionally, a total of 71 major and 85 minor candidate genes located in the 200-kb genomic region of each peak SNP detected by CMLM and mrMLM were found, respectively. By using a gene-based association, a total of six SNPs from three major effects genes and eight SNPs from four minor effects genes were identified. Of them, Glyma.18G012200 has been characterized as a significant element in controlling fungal disease in plants.

Multi-Trait Genome-Wide Association Studies Reveal Loci Associated with Maize Inflorescence and Leaf Architecture.

Maize inflorescence is a complex phenotype that involves the physical and developmental interplay of multiple traits. Given the evidence that genes could pleiotropically contribute to several of these traits, we used publicly available maize data to assess the ability of multivariate genome-wide association study (GWAS) approaches to identify pleiotropic quantitative trait loci (pQTL). Our analysis of 23 publicly available inflorescence and leaf-related traits in a diversity panel of n = 281 maize lines genotyped with 376,336 markers revealed that the two multivariate GWAS approaches we tested were capable of identifying pQTL in genomic regions coinciding with similar associations found in previous studies. We then conducted a parallel simulation study on the same individuals, where it was shown that multivariate GWAS approaches yielded a higher true-positive quantitative trait nucleotide (QTN) detection rate than comparable univariate approaches for all evaluated simulation settings except for when the correlated simulated traits had a heritability of 0.9. We therefore conclude that the implementation of state-of-the-art multivariate GWAS approaches is a useful tool for dissecting pleiotropy and their more widespread implementation could facilitate the discovery of genes and other biological mechanisms underlying maize inflorescence.

A Two-Stage Mutual Information Based Bayesian Lasso Algorithm for Multi-Locus Genome-Wide Association Studies.

Genome-wide association study (GWAS) has turned out to be an essential technology for exploring the genetic mechanism of complex traits. To reduce the complexity of computation, it is well accepted to remove unrelated single nucleotide polymorphisms (SNPs) before GWAS, e.g., by using iterative sure independence screening expectation-maximization Bayesian Lasso (ISIS EM-BLASSO) method. In this work, a modified version of ISIS EM-BLASSO is proposed, which reduces the number of SNPs by a screening methodology based on Pearson correlation and mutual information, then estimates the effects via EM-Bayesian Lasso (EM-BLASSO), and finally detects the true quantitative trait nucleotides (QTNs) through likelihood ratio test. We call our method a two-stage mutual information based Bayesian Lasso (MBLASSO). Under three simulation scenarios, MBLASSO improves the statistical power and retains the higher effect estimation accuracy when comparing with three other algorithms. Moreover, MBLASSO performs best on model fitting, the accuracy of detected associations is the highest, and 21 genes can only be detected by MBLASSO in Arabidopsis thaliana datasets.

In silico simulation of future hybrid performance to evaluate heterotic pool formation in a self-pollinating crop

Hybrid vigour has the potential to substantially increase the yield of self-pollinating crops such as wheat and rice, but future hybrid performance may depend on the initial strategy to form heterotic pools. We used in silico stochastic simulation of future hybrid performance in a self-pollinating crop to evaluate three strategies of forming heterotic pools in the founder population. The model included either 500, 2000 or 8000 quantitative trait nucleotides (QTN) across 10 chromosomes that contributed to a quantitative trait with population mean 100 and variance 10. The average degree of dominance at each QTN was either 0.2, 0.4 or 0.8 with variance 0.2. Three strategies for splitting the founder population into two heterotic pools were compared: (i) random split; (ii) split based on genetic distance according to principal component analysis of SNP genotypes; and (iii) optimized split based on F1 hybrid performance in a diallel cross among the founders. Future hybrid performance was stochastically simulated over 30 cycles of reciprocal recurrent selection based on true genetic values for additive and dominance effects. The three strategies of forming heterotic pools produced similar future hybrid performance, and superior future hybrids to a control population selected on inbred line performance when the number of quantitative trait nucleotides was ≥2000 and/or the average degree of dominance was ≥0.4.

Choosing the optimal population for a genome-wide association study: A simulation of whole-genome sequences from rice.

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

Genomic Prediction Accuracy of Seven Breeding Selection Traits Improved by QTL Identification in Flax.

Molecular markers are one of the major factors affecting genomic prediction accuracy and the cost of genomic selection (GS). Previous studies have indicated that the use of quantitative trait loci (QTL) as markers in GS significantly increases prediction accuracy compared with genome-wide random single nucleotide polymorphism (SNP) markers. To optimize the selection of QTL markers in GS, a set of 260 lines from bi-parental populations with 17,277 genome-wide SNPs were used to evaluate the prediction accuracy for seed yield (YLD), days to maturity (DTM), iodine value (IOD), protein (PRO), oil (OIL), linoleic acid (LIO), and linolenic acid (LIN) contents. These seven traits were phenotyped over four years at two locations. Identification of quantitative trait nucleotides (QTNs) for the seven traits was performed using three types of statistical models for genome-wide association study: two SNP-based single-locus (SS), seven SNP-based multi-locus (SM), and one haplotype-block-based multi-locus (BM) models. The identified QTNs were then grouped into QTL based on haplotype blocks. For all seven traits, 133, 355, and 1208 unique QTL were identified by SS, SM, and BM, respectively. A total of 1420 unique QTL were obtained by SS+SM+BM, ranging from 254 (OIL, LIO) to 361 (YLD) for individual traits, whereas a total of 427 unique QTL were achieved by SS+SM, ranging from 56 (YLD) to 128 (LIO). SS models alone did not identify sufficient QTL for GS. The highest prediction accuracies were obtained using single-trait QTL identified by SS+SM+BM for OIL (0.929 ± 0.016), PRO (0.893 ± 0.023), YLD (0.892 ± 0.030), and DTM (0.730 ± 0.062), and by SS+SM for LIN (0.837 ± 0.053), LIO (0.835 ± 0.049), and IOD (0.835 ± 0.041). In terms of the number of QTL markers and prediction accuracy, SS+SM outperformed other models or combinations thereof. The use of all SNPs or QTL of all seven traits significantly reduced the prediction accuracy of traits. The results further validated that QTL outperformed high-density genome-wide random markers, and demonstrated that the combined use of single and multi-locus models can effectively identify a comprehensive set of QTL that improve prediction accuracy, but further studies on detection and removal of redundant or false-positive QTL to maximize prediction accuracy and minimize the number of QTL markers in GS are warranted.

Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes

BackgroundTransforming large amounts of genomic data into valuable knowledge for predicting complex traits has been an important challenge for animal and plant breeders. Prediction of complex traits has not escaped the current excitement on machine-learning, including interest in deep learning algorithms such as multilayer perceptrons (MLP) and convolutional neural networks (CNN). The aim of this study was to compare the predictive performance of two deep learning methods (MLP and CNN), two ensemble learning methods [random forests (RF) and gradient boosting (GB)], and two parametric methods [genomic best linear unbiased prediction (GBLUP) and Bayes B] using real and simulated datasets.MethodsThe real dataset consisted of 11,790 Holstein bulls with sire conception rate (SCR) records and genotyped for 58k single nucleotide polymorphisms (SNPs). To support the evaluation of deep learning methods, various simulation studies were conducted using the observed genotype data as template, assuming a heritability of 0.30 with either additive or non-additive gene effects, and two different numbers of quantitative trait nucleotides (100 and 1000).ResultsIn the bull dataset, the best predictive correlation was obtained with GB (0.36), followed by Bayes B (0.34), GBLUP (0.33), RF (0.32), CNN (0.29) and MLP (0.26). The same trend was observed when using mean squared error of prediction. The simulation indicated that when gene action was purely additive, parametric methods outperformed other methods. When the gene action was a combination of additive, dominance and of two-locus epistasis, the best predictive ability was obtained with gradient boosting, and the superiority of deep learning over the parametric methods depended on the number of loci controlling the trait and on sample size. In fact, with a large dataset including 80k individuals, the predictive performance of deep learning methods was similar or slightly better than that of parametric methods for traits with non-additive gene action.ConclusionsFor prediction of traits with non-additive gene action, gradient boosting was a robust method. Deep learning approaches were not better for genomic prediction unless non-additive variance was sizable.

Linkage Analysis and Multi-Locus Genome-Wide Association Studies Identify QTNs Controlling Soybean Plant Height.

Plant height is an important target for soybean breeding. It is a typical quantitative trait controlled by multiple genes and is susceptible to environmental influences. Here, we carried out phenotypic analysis of 156 recombinant inbred lines derived from “Dongnong L13” and “Henong 60” in nine environments at four locations over 6 years using interval mapping and inclusive composite interval mapping methods. We performed quantitative trait locus (QTL) analysis by applying pre-built simple-sequence repeat maps. We detected 48 QTLs, including nine significant QTLs detected by multiple methods and in multiple environments. Meanwhile, genotyping of all lines using the SoySNP660k BeadChip produced 54,836 non-redundant single-nucleotide polymorphism (SNP) genotypes. We used five multi-locus genome-wide association analysis methods to locate 10 quantitative trait nucleotides (QTNs), four of which overlap with previously located QTLs. Five candidate genes related to plant height are predicted to lie within 200 kb of these four QTNs. We identified 19 homologous genes in Arabidopsis, two of which may be associated with plant height. These findings further our understanding of the multi-gene regulatory network and genetic determinants of soybean plant height, which will be important for breeding high-yielding soybean.

Genome-wide association study of agronomic and quality traits in a world collection of the wild wheat relative Triticum urartu.

Triticum urartu (2n=2x=14, subgenome Au Au ), a wild diploid wheat progenitor, features broad allelic diversity for a number of traits of agronomic relevance. A thorough characterization of the diversity of T.urartu natural accessions may provide wheat breeders with new alleles potentially contributing to wheat improvement. In this study, we performed an extensive genotypic and phenotypic characterization of a world collection of 299 T.urartu ex situ accessions, developing 441327 single nucleotide polymorphisms and recording trait values for agronomic and quality traits. The collection was highly diverse, with broad variation in phenology and plant architecture traits. Seed features were also varied, and analyses of flour quality reported 18 distinct patterns of glutenins, and carotenoid concentrations and sedimentation volumes in some cases surpassing those of cultivated materials. The genome-wide molecular markers developed on the collection were used to conduct a genome-wide association study reporting 25 highly significant quantitative trait nucleotides for the traits under examination, only partially overlapping loci already reported in wheat. Our data show that T.urartu may be considered a valuable allele pool to support the improvement of wheat agronomy and quality.

Drought response of flax accessions and identification of quantitative trait nucleotides (QTNs) governing agronomic and root traits by genome-wide association analysis

Flaxʼs fiber yield and quality can be severely impaired due to water deprivation. Herein, 41 diverse flax accessions were evaluated for four agronomic and four root traits under drought stress (DS) and irrigated (IR) conditions. In order to identify quantitative trait nucleotides (QTNs) positively influencing the stability of these traits under DS, the stress tolerance index (STI) and trait stability index were calculated, which were analyzed using two single-locus and three multi-locus genome-wide association (GWA) methods with 170,534 single-nucleotide polymorphisms (SNPs). Significant genotype and treatment effects (p < 0.001) were observed for the traits assessed. A total of 118 QTNs were identified by multiple GWA methods. Fifteen QTNs were commonly detected by two or more methods. One QTN for STI was consistently identified by four methods and explained between 45 and 65% of the phenotypic variation (R2). A maximum of 12 out of 15 favorable QTNs were observed in flax accessions. Genotypes F_UNK_C_CN33393 and F_NLD_C_CN18987 showed superior plant height and root trait stability under DS, each one harboring 12 and 10 favorable QTNs, respectively. QTNs were further validated in an independent set of accessions under DS and IR conditions. Most of accessions performed as expected based on their corresponding haplotypes, confirming the robustness of the QTNs indentified by multiple GWA methods. Candidate genes involved in drought-responsive pathways and root and vascular tissue development were identified nearby QTNs. Collectively, our results should facilitate marker-assisted breeding toward the goal of improving flax production under water scarcity.