Forest tree breeding efforts have focused mainly on improving traits of economic importance, selecting trees suited to new environments or generating trees that are more resilient to biotic and abiotic stressors. This review describes various methods of forest tree selection assisted by genomics and the main technological challenges and achievements in research at the genomic level. Due to the long rotation time of a forest plantation and the resulting long generation times necessary to complete a breeding cycle, the use of advanced techniques with traditional breeding have been necessary, allowing the use of more precise methods for determining the genetic architecture of traits of interest, such as genome-wide association studies (GWASs) and genomic selection (GS). In this sense, main factors that determine the accuracy of genomic prediction models are also addressed. In turn, the introduction of genome editing opens the door to new possibilities in forest trees and especially clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9). It is a highly efficient and effective genome editing technique that has been used to effectively implement targetable changes at specific places in the genome of a forest tree. In this sense, forest trees still lack a transformation method and an inefficient number of genotypes for CRISPR/Cas9. This challenge could be addressed with the use of the newly developing technique GRF-GIF with speed breeding. Forest trees are a main source of wood, energy, pulp for paper, soil protection, correction of erosion problems and silvopastoral plantations, among other things. In this sense, important efforts in forest tree breeding have been carried out to improve traits of economic interest, to select trees (or hybrids) adapted to new habitats, or to generate trees that are more tolerant to various biotic and abiotic stresses. From the point of view of wood productivity, conventional breeding programs have continuously been improving traits related to throughput, such as wood volume, tree height, diameter at breast height, stem straightness, survival and wood quality, under various environmental conditions ,4,5,6,7.Conventional forest tree breeding considers phenotypic evaluations based on kinship relationships between individuals to identify trees that are superior in a trait of interest. The Henderson mixed-model method has been traditionally used to determine the genetic merit of trees and best linear unbiased prediction (BLUP) has been the standard prediction method ,9. The gEucalyptus globulus Labill., Ballesta et al. (2019) [Eucalyptus cladocalyx F. Muell.Both in traditional selection methods and in GS, the predictive accuracy of the phenotype of an individual depends on how accurate the estimation of the genetic relationships between individuals is ,16,17. T. (2019)  emphasiz. (2019)  showed tOn the other hand, with the advent of genomic editing, great opportunities are visualized in terms of the future development of plant science, including in forest trees and in the rapid remodeling of crops. Genome editing is a relevant, versatile and preferred tool for the improvement of annual crops, as well as for functional genomics . Great aEucalyptus, Pinus, Populus, Picea and Cryptomeria [In conventional forest tree breeding, phenotypic evaluations are performed and kinship relationships are determined to identify the trees that are superior in a trait of interest ,45,46,47ptomeria ,53,54,55ptomeria ,56,57.Parental genotypes are evaluated by the throughput of their progeny, such that allelic combinations that generate offspring with superior throughput are considered genetically superior and are put into successive breeding cycles. Therefore, an accurate estimation or prediction of the “breeding value” of an individual is required. On the other hand, it is common that it is complicated to analyze the results of a forest competition trial because (1) usually only a subset of the parents is represented in each progeny trial; (2) the parents are represented in different number of progeny trials; (3) the trials are evaluated at different ages of the trees; and 4) the kinship background between individuals may be wrong due to the management of a seed orchard or even due to inbreeding processes ,58. The  the kinsMolecular markers have been widely used in genetic and plant breeding studies ,68,69,70http://phytozome.net, accessed on 1 September 2021), which was due to the increasing throughput of sequencing methods. Second- and third-generation sequencing platforms, such as Illumina’s various platforms, 454 pyrosequencing (Roche), SOLID (Invitrogen) and Ion Torrent (Invitrogen), have the ability to obtain results from many sequences, which can be used to discover new molecular markers in a viable way and at low cost [In 2011, the number of sequenced plant genomes doubled compared to the previous decade  H. Karst., can be applied to at least four species of Picea, while EUChip60K has been applied to more than 14 taxa of the genus Eucalyptus [Given the need for molecular tools that support genomic studies of species of interest in forestry, different SNP arrays that are transferable between taxa of the same genus, such as eveloped ,91,92,93calyptus . The numcalyptus , which iAfter the emergence of molecular markers on a large scale and the decrease in costs associated with genomic tools, breeders began to use DNA markers to support selection cycles. In general, genomic-assisted breeding can be based on MAS or genomic prediction/selection ,106,107.n corresponds to the total number of markers, ith marker and jth individual at the ith marker. The abbreviation GEBV stands for genomic estimated breeding value.GS was first proposed by Meuwissen et al. (2001)  in the cDE) distribution: DE distribution generates a strong contraction (close to 0) to estimate the effects of the markers. BRR is a Bayesian method that is based on the model’s regressors (whether SNPs or other markers), which have a common variance (im) is Gaussian, and Var(im) takes a single value of im) follows an independent normal prior distribution with mean 0 and variance 0, π0) is assumed, such that p0 > 0 and π0 can take values between 0 and 1 [In contrast, the Bayes A, Bayes B and Bayesian LASSO methods assume that each marker has its own variance, and the phenotypic variance is explained by loci with effects of different magnitudes . These Bn effect . The a p 0 and 1 . In bothIn practice, it is recommended to test all available GS methods , which sZea mays L. and Eucalyptus globulus Labill., including two Machine Learning (ML) methods, i.e., Deep Learning (DL) and Bayesian Regularized Neural Network (BRNN), both in combination with different hyperparameters. The results showed that DL had a superior performance than GBLUP, BayesA, BayesB, BayesCπ, BRR, BL, RKHS and BRNN, in terms of predictive ability for all traits , confirming the importance of deep learning models in genome-wide studies and crop/tree improvement, which holds promise for accelerating breeding progress. Moreover, Pérez-Rodríguez et al. (2012) [2 = 0.30) and dominance effects of 0 or 0.15. In this regard, Maldonado et al. (2020) [With computational advancement and algorithms enhancements, artificial neural networks (ANNs) have emerged as an alternative statistical framework and have gained increasing interest in genomic studies ,139,140.. (2012)  found th. (2012)  evaluate. (2012)  comparedPinus pinaster Aiton, Isik et al. (2015) [Eucalyptus, which consists of simultaneously considering genotyped and nongenotyped trees. According to the authors, the use of additional phenotypic information from nongenotyped trees provided greater predictive ability than GBLUP. Additionally, Ballesta et al. (2020) [Eucalyptus cladocalyx F. Muell.In the context of forest tree breeding, GS was originally proposed for the analysis of complex traits such as tree growth and wood-related traits. The main parameter that reveals whether a GS model is adequate for the estimation of genetic merits is the precision and predictive power, which expresses the degree of correlation that exists between the genetic values predicted by the GS model and the adjusted phenotypic values  ,146,147.. (2015)  reported. (2015)  utilized. (2020)  showed tEucalyptus pellita F. Muell was too low to calculate (extremely low) by the pedigree method; however, the genomic heritability based on SNPs reached a value of 0.55 (Bayes method B). On the other hand, genomic tools have allowed access to heritable components that cannot be examined through genealogical relationships between individuals ,151,152.Above, it was discussed how the accuracy of genomic prediction methods changes according to the analytical assumptions that support each method. However, genetic factors that are specific to breeding populations determine the effectiveness of genomic-assisted selection. The existing pattern of LD in a given breeding population is one of the main factors that determines the power of genomic tools to predict phenotypic traits because the extent of LD throughout the genome of a species determines the density of markers that is necessary for accurate prediction ,24. StriPopulus trichocarpa Torr. & A.Gray ex Hook. [Pinus taeda L. [The magnitude of LD in a population comes from the history and dynamics of the population . In geneex Hook.  and withtaeda L.  while intaeda L. ). Addititaeda L. ,178.Populus can decrease within 750–1000 bp [Eucalyptus, LD can decrease rapidly over 3000 bp or 25,000 bp [Natural populations of forest species have lower LD values than cultivated and improved populations ,180,181.–1000 bp ,180, whe–1000 bp . In the 5,000 bp ,158,183,5,000 bp .Another factor that affects the accuracy of GS is the effective population size ,185. AccEucalyptus hybrids could be favored by using a training population that is closely related to the validation population in terms of pedigree and population genetic structure. In contrast, if the training population is composed of individuals genetically distant from the validation population, the predictive ability can be reduced by up to 25%.In this same context, both in GS methods and in pedigree-based prediction, the predictive accuracy of the phenotype of an individual depends on how accurate the estimation of the genetic relationships between the individuals of the population is ,16. The The determination of kinship relationships between individuals or organisms has been an important aspect in several fields of knowledge, such as forensic science, conservation genetics and animal and plant breeding . EstimatSeveral studies have evaluated the predictive power of phenotypic traits in GS models that include haplotypes from SNP arrays ,205,206.2 < 0.1) in Eucalyptus globulus Labill.The predictive power of haplotypes and molecular markers  could depend on the trait that sought to be improved. In the context of animal breeding, some authors suggest that the haplotype approach may be especially beneficial for predicting traits of relatively high heritability ,212,213.Villumisen and Janss et al. (2009)  showed, Lolium perenne L. with respect to phenotypic selection, respectively. However, GS led to a higher rate of inbreeding per selection cycle than phenotypic selection. This compromise between genetic gain and loss of diversity has not been a focus of study in forest species; however, it is also expected that GS can cause a significant loss of genetic diversity compared to traditional schemes that use phenotypic selection [Some studies suggest that GS based purely on molecular markers can lead to a loss of genetic variability, which leads to an increase in the rate of inbreeding ,217,218.election . In thiselection ,220,221.election , GS baseE. cladocalyx F. Muell, the authors reported that the genomic predictive ability of cyanogenic glycoside and anthocyanin contents, based on haplotypes, can be improved by the use of haplotypes significantly associated with these quantitative traitsHaplotype-based GS has been mainly implemented in agricultural crops and self-pollinated plants  [Heterobasidion parviporum in Picea abies (L.) H. Karst., which explained between 2 and 5% of the phenotypic variation of the studied trait. Additionally, the authors demonstrated that the PaLAC5 gene, one of the candidate genes related to resistance to H. parviporum, could be involved in the development of the length of the lesion, as an induced defense response. Bai et al. (2019) [Pinus massoniana Lamb. trees, which were evaluated for different phenotypic traits. They found a large number of SNPs significantly associated with resin production, wood volume, tree height and diameter. In general, many marker-trait associations with relatively large effect have been detected for wood properties, disease resistance and phenology [High-throughput genotyping technology and phenotyping platforms have enabled large-scale marker-trait association analysis, such as GWAS, to precisely dissect the genetic architecture of plant traits . In tree. (2020)  reported. (2019)  identifihenology .E. cladocalyx F. Muell under arid conditions, 11 of which were common between tree height, wood hardness and diameter; a result consistent with the trade-off between hydraulic safety and efficiency in Eucalyptus trees under drought conditions. Interestingly, the significant SNPs and haplotype blocks were located close to genes involved in the primary metabolism, biosynthesis of cell wall components and stress response genes, which could be related to the mechanisms of adaptation to stressful conditions. Lamara et al. (2016) [Picea glauca (Moench) Voss). In this study, the authors tested SNPs into ~2000 candidate genes for statistical associations with microfibril angle, wood density, stiffness and ring width. The co-expression networks revealed complex interactions and pleotropic effects between genes involved in wood stiffness and microfibril angle. Baison et al. (2019) [Picea abies (L.) H. Karst., using ~170 K SNPs generated from exome genotyping of mother trees. The authors identified more than 50 SNPs associated with 39 candidate genes, of which their role in wood formation and tree growth has previously been recognized. Moreover, to understand the genetic mechanisms underlying wood anatomical and morphological traits in Populus trichocarpa Torr. & A.Gray ex Hook., Chhetri et al. (2020) [Populus, which are important determinants of the suitability and sustainability of improved genotypes for lignocellulosic biofuel production.GWASs have been carried out considering different analysis strategies, which are often complemented with studies of gene co-expression networks, transcriptomic studies, heterologous expression assays, among others. For instance, Valenzuela et al. (2021)  identifi. (2016)  develope. (2019)  performe. (2020)  performeSphaerulina musiva in Populus trichocarpa Torr. & A.Gray ex Hook. About 90 SNPs encompassing 73 candidate genes were significantly associated to the number of cankers, number of cankers cm−1 and disease severity based on digital imagery. Interestingly, three loci were functionally validated by a transcriptomic study, allele analysis, binding assays and overexpression assays. Recently, Quan et al. (2021) [Populus tomentosa C.K. Schneid. The authors detected SNPs related to ~170 candidate genes for the studied traits, 74 epistatic relationships between the phenotypes and several pleiotropic loci. In addition, the heterologous expression of two pleiotropic genes in Arabidopsis thaliana (L.) Heynh.  demonstrated that these genes control the regulatory networks of photosynthesis and the components of the secondary wall, respectively, in P. tomentosa.Muchero et al. (2018)  employed. (2021)  performeEucalyptus, Populus and various coniferous species. The authors summarized the recent progress in GWAS-based functional genomics of these traits and concluded that the emerging high-throughput phenotyping technology will be broadly used in the future to collect data for quantitative studies of complex traits related to tree growth, adaptation, morphological and physiological traits.8. Genome editing in forest treesIn terms of new challenges to implementing the integration of different OMICs technologies, Du et al. (2018)  discusseThe anticipated expansion of the population in the coming decades will significantly boost demand for forest products. Due to the long juvenile period and genomic complexity of forests trees, the genetic improvement via conventional breeding is laborious and time-consuming. Therefore, genetic modification (GM) provides the potential for transformation in shorter timeframes but is challenged by existing genetically modified organism (GMO) laws. Genome editing (GE), which may generate mutations in sites, allows for the fast implementation of specific changes and is less restricted worldwide than genetically modified technology ,241. GenGE techniques such as Zinc Finger Nuclease (ZFN) and transcription activator-like effector nuclease (TALENs) may be used to modify the genome. However, these methods are either labor demanding or prohibitively costly since the targeting mechanisms are all dependent on protein-nucleic acid interactions, necessitating creating a unique protein for each gene locus of interest . Recent Parasponia andersonii Planch [This method enables editing single to many genes by knock-in or knock-out genes from the host genome. Thus, various traits may be introduced or metabolic pathways modified concurrently by inserting double-stranded breaks (DSBs) at multiple locations ,252,253.i Planch ,256. Alt4CL) gene family were targeted in Populus using CRISPR/Cas9 genome editing. One of the genes from the 4CL family is known as the 4CL1gene and it has been widely studied for its role in lignin production (Populus tomentosa Carr. It was shown that a protospacer-adjacent motif (PAM). is followed by four guide RNAs (gRNAs) that target the phytoene desaturase gene 8 (PtoPDS-8) in poplar through Agrobacterium-mediated transformation and albino phenotype were observed in homozygous plants. Researchers found that mutation efficiency at these target locations was assessed to be 51.7% based on RNA-guided genome editing events and suggesting that CRISPR/Cas9 is efficient method to edit the genome of woody plants [PtoMYB156 and PtrMYB57) or reduced the flow (PtoMYB115 and PtoMYB170), influencing the lignin deposition (PtoMYB156 and PtoMYB170) [Most significant progress has been achieved in woody species to date with poplar, which was the first stably transgenic tree to be genome-edited with high efficiency using the CRISPR/Cas9 system ,258. Theoduction . The ligy plants ,259. MosoMYB170) ,262. CRIoMYB170) .LEAFY(LFY) and AGAMOUS (AG) mutations through a transgenic approach. A distinct mutation spectrum was observed LFY and AG in sgRNA-gene combinations. While an AG-sgRNA construct containing two sgRNAs produced comparable mutation spectra between two poplar clones, an LFY-sgRNA construct containing a single sgRNA produced substantially different mutation spectra between the same two clones [Eucalyptus [Eucalyptus orthologue of LFY by converting a Eucalyptus grandis x urophylla wild-type hybrid and two Flowering Locus T (FT) overexpressing lines targeting the LFY orthologues of ELFY. The CRISPR-KO mutants achieved 100% transgenic insertion such as deletion, frameshift mutation and phenotypically transgenic plants as the absence of male and female gametes and indeterminacy in floral development due to floral alteration because of disruption of ELFY function. Similarly, Van Zeijl et al. (2018) [Agrobacterium tumefaciens-mediated transformation and CRISPR/Cas9 mutation within 03 months in the fast-growing tropical tree species Parasponia andersonii Planch. They edited the four genes PanHK4, PanEIN2, PanNSP1 and PanNSP2 that regulate cytokinin, ethylene, or strigolactone hormonal pathways and, in legumes, perform important symbiotic activities. CRISP-KO mutants of PanHK4 and PanEIN2 reduced the procambium activity and disturbed sex differentiation, respectively. In contrast, CRISP-KO mutants of PanNSP1 and PanNSP2 were essential for nodule formation. Using CRISPR/Cas9, forest tree genomics may be taken to the next level by evaluating gene function and its role in adapting trees to their environment [Additionally, CRISPR/Cas9 works very well and accurately in two poplar clones to produce o clones . Similarcalyptus . CRISPR . (2018)  presenteironment ,265. Mulironment ,258,266.Populus tremula L.) using CRISPR/Cas9. They selected twelve genes for three distinct study areas, including SOC1, FUL, their paralogs, four NFP-like genes and TOZ19. The sgRNAs were created for editing with Cas9 nuclease and transferred into P. tremula L. and regenerated plants showed different types of editing, with single nucleotide insertions being the most common occurrence. They attempted to establish a correlation between genome editing and gRNA efficiency by evaluating the genome editing effort. They suggested that the GC content, purine residues in the final four nucleotides of the gRNA and an at least partially unpaired seed region all affected the gRNAs effectiveness for target cleavage [Pinus radiata D. Don using CRISPR/Cas9. They explored the use of this system to edit the xylan 1 (GUX1) gene in P. radiata D. Don and showed genome editing using DNA and RNPs [P. radiata D. Don using DNA and RNPs, respectively. This study enables the use of genome editing in conifers to change the desired traits, or attributes, quickly.Furthermore, in recent years, Landstrasse and Grosshansdorf (2019)  checked cleavage ,268. Simand RNPs . They coOphiostoma novo-ulmi is another intriguing potential, in which it has been demonstrated that these genes are excellent candidates for CRISPR/Cas9 gene editing to generate knockout mutants with decreased capacity to switch the DED pathogen throughout the elm tree life cycle [PdbPDS1. Only one was found, which was confirmed by both phenotypic and genotypic analysis; in T0 generation, all transgenic plants were chimeric. Still, during the second round of shoot regeneration, about 27.0 percent or 19.1 percent of the regenerated shoots were homozygous mutants with or without kanamycin selection, respectively.Toxic diseases are causing just as much damage in forest plantations. They are a worldwide concern for forest ecosystems and must be handled as soon as possible. Ideally, CRISPR/Cas9 should be integrated into forest development projects to create more effective disease resistance methods for long-term forest sustainability . For exafe cycle ,271. DuePopulus alba L. and Populus glandulosa Moench were subjected to CRISPR/Cas12a to achieve targeted mutations using three nucleases AsCas12a, AsCas12a and LbCas12a. It has been utilized in using CRISPR/Cas12 to knock off various targets of the PDS gene. AsCas12a is a more appropriate and efficient method to edit the big part of the genome at editing sites with the most remarkable mutation. For multi-gene knockout mutation in forest trees, the advantages of CRISPR/Cas12a for the creation of transgenic tree species are only further amended. A method for forest genetics for developing transgenic tree species will be provided using CRISPR/Cas12 [Despite the advancement in genome editing, CRISPR/Cas9 has limited use in the formation of transgenic forest trees. Recently, CRISPR/Cas12a is a newly developed novel CRISPR effector protein supporting the CRISPR/Cas to edit the larger genome fragments. Two popular species, such as PR/Cas12 . There aPR/Cas12 .AP2L-A5). Finally, they demonstrated that a dicot GRF–GIF chimera increases regeneration efficiency in citrus, indicating that this approach may be applied to other dicot crops [Low plant regeneration efficiency and few transformable genotypes restrict the potential of genome editing to enhance the performance of crops and forest trees ,266. Genot crops .Triticum aestivum L.), durum wheat (T. durum Desf.), barley (Hordeum vulgare L.), chickpea (Cicer arietinum L.), pea (Pisum sativum L.) and canola (Brassica napus L.) may produce up to six generations each year instead of two or three under regular glasshouse circumstances  , we belimstances . For detDue to the long rotation time of a forest plantation and the resulting long generation times necessary to complete a breeding cycle, the use of advanced methods with traditional breeding, such as high-throughput genotyping techniques, have been necessary, allowing the use of more precise approaches for determining the genetic architecture of traits of interest, such as genome-wide association studies and genomic selection. Moreover, the introduction of genome editing opens the door to new possibilities and perspectives in theoretical genetics and breeding science of forest trees and the fast remodeling of varieties. In this sense, mutations have greatly enhanced genetic resources for forest trees throughout the globe. With the development of TILLING as a high-throughput mutant screening method, stable gene-specific mutations are now extremely efficient. TILLING screening allows for more accurate detection of mutations at particular loci or genes. On the other hand, the development of new techniques such as CRISPR-based method has the potential to sustain productivity with less effort and cost substantially. More sophisticated techniques must be used to further complicate matters in selecting genome editing reagents and procedures for the regeneration of mutant plants. According to this, we believe that such problems will be adequately handled in the future.


Polygenic scores (PGS) aim to genetically predict complex traits at an individual level. PGS are typically trained on genome-wide association summary statistics and require an independent test dataset to tune parameters. More recent methods allow parameters to be tuned on the training data, removing the need for independent test data, but approaches are computationally intensive. Based on fine-mapping principles, we present RápidoPGS, a flexible and fast method to compute PGS requiring summary-level Genome-wide association studies (GWAS) datasets only, with little computational requirements and no test data required for parameter tuning.r2 difference: -0.0092, -0.0042 and 0.0064, respectively, but up to 17 000-fold faster with reduced computational requirements. RápidoPGS is implemented in R and can work with user-supplied summary statistics or download them from the GWAS catalog.We show that RápidoPGS performs slightly less well than two out of three other widely used PGS methods  for case–control datasets, with median Our method is available with a GPL license as an R package from CRAN and GitHub.Bioinformatics online. Genome-wide association studies (GWAS) have been widely successful at identifying a large number of genetic variants  associated with a wide range of diseases and complex traits . HoweverS to use. If ignored, linkage disequilibrium (LD) between variants would mean double-counting the effects of causal variants in high LD with multiple other variants. In addition, error in the estimated effect sizes is most pronounced for small effects, for which true association may not be distinguishable from noise about a null value. Different approaches have been developed to deal with these. Initial approaches selected the most strongly associated variant in each genome wide-significant peak. However, it was soon realized that predictive accuracy could be improved by reducing the significance threshold, to capture more truly associated variants even at the cost of including some false associations , especially for highly polygenic diseases (Polygenic scores (PGS) estimate individual propensity to a phenotype (e.g. disease) by summing an individual’s genotypes , weighted by their effect sizes, estimated from GWAS datadiseases . MethodsMore statistically sophisticated approaches have since been developed to average over multiple variants in LD without double counting (which is more accurate than selecting just one), shrinking effect estimates by a continuous weight, wi . While unrealistic, it has been shown to perform well for fine-mapping and, of relevance to PGS, allows the definition of posterior probabilities of causality using predefined LD regions and without the burden of processing large LD matrices  method to compute PGS based on fine-mapping approaches that only requires a summary statistics dataset, with no need for an independent test dataset to adjust parameters, and that can generate weights for millions of SNPs in only a few seconds or minutes. RápidoPGS works fully in R, requiring few dependencies. A PGS can be quickly constructed from any GWAS summary statistic dataset, or any GWAS PubMed ID if harmonized datasets are available at GWAS catalog.We created PGS models for eight case–control and two quantitative traits using RápidoPGS and three widely used PGS methods for comparison: LDpred2 .We compute posterior probabilities of causality at each variant within LD-blocks defined by ldetect   distribution.Under a single causal variant assumption, given summary statistics at each SNP ociation  asBFi=li is causal in a region of n SNPs, isi is causal  and Under the at most one causal variant assumption , the posterior probability that SNP W for quantitative traits, and we propose to estimate W given the estimated heritability of the trait under study or a similar trait, which is often available , p is the number of variants, iN is the number of individuals used in the inference for variant i and i in the GWAS summary statistics for the trait of choice. See We set default parameter values for RápidoPGS according to currently widely adopted values: l traits . There iW) as the single causal variant approach, although W may be internally estimated. We call the two approaches to estimating W ‘informed’ or ‘auto’.Alternatively, we can relax the single causal variant assumption. SuSiE  αblock, the minimum P-value at least one variant in an LD block must have for the block to be considered for subsequent analyses , and (ii) αSNP, the minimum P-value a variant within a selected block must have to be considered.Finally, we considered pruning input SNPs according to their We downloaded ten publicly available GWAS summary statistics datasets on the following traits: eight case–control datasets: asthma , breast eff = 4/(1/N controls + 1/N cases)], the summary statistic standard deviation (SDss), defined as val), which here we computed as f is the allele frequency for the effect allele. When this frequency was not available in the dataset, we computed it using the CEU population in 1000 Genomes Phase III panel. Note that in the LDpred2 paper, SDval is computed using the test dataset, but since our aim is not to use any external dataset for QC or parameter tuning, we computed it as above. We then excluded SNPs for which SDss < 0.5 × SDval or SDss > SDval + 0.1 or SDss < 0.1 or SDval < 0.05, following LDpred2 guidelines , standard error], and from case/control sample size column, if provided. Following LDpred2 quality control guidance for summary statistic datasets, we computed the effective sample size [Nviations –S10.We further filtered SNPs to the subset overlapping the HapMap3 variants, as recommended by LDpred2, PRScs and SBayesR, the three methods we compared RápidoPGS to. To explore how RápidoPGS prediction ability changes depending on the selection of SNPs, we created another set of post-QC datasets, filtered by 1000 Genomes Phase III variants instead.W, which for case–control traits we set as W = 0.22. For quantitative traits, we estimated the informed W using estimates of heritability from LDhub: ‘BMI’ h2 = 0.246, and ‘Standing Height’ h2 = 0.462 which led to estimates of W of (0.127)2 for BMI, and (0.141)2 for height. RápidoPGS-multi allows for W to be automatically estimated, and it is our recommended choice.RápidoPGS comes in two flavours: RápidoPGS-single, and RápidoPGS-multi. As explained in Section 2.1, they correspond to two different approaches, which differ in their assumption of how many causal variants per LD block are considered in their respective models—hence ‘single’ and ‘multi’. RápidoPGS-single requires specifying https://figshare.com/articles/dataset/European_LD_reference/13034123.All methods included in this study (with the exception of RápidoPGS-single) require LD matrices. LD matrices inform the method about the correlations between SNPs in a reference population, which is ideally close to the population we create the PGS for. LDpred2, SBayesR and PRScs offer European HapMap3-filtered LD matrices, which are supplied with each method. RápidoPGS-multi can either use a reference panel to compute LD matrices or input pre-computed LD matrices supplied by the user. In this study, we employed UKBB-based LD matrices provided by LDpred2 authors, publicly avaliable at SNP = 0.1, or αSNP = 0.01. As only 10% of truly null SNPs achieve P values < 0.1 and only 1% < 0.01, this should remove 90% or 99% of null SNPs respectively. Assuming most blocks have 1000 SNPs or more, nearly all blocks containing only null SNPs should achieve a minimum P-value of 10−3, and only 10% a minimum P-value of 10−4, so we chose block-thinning parameters of αblock = 10−3 or αblock = 10−4. Of note, although we used 15 CPUs and 8 h as a standard across all methods, RápidoPGS-single and RápidoPGS-multi (using pre-computed LD matrices) do not require using multiple CPUs, and our tests showed that one CPU is sufficient to run the method, without speed lost compared with using multiple CPUs. This makes RápidoPGS especially suitable for computational environments with limited resources.To improve speed and reduce computational cost for RápidoPGS-multi, we thinned the input SNPs using αhttps://github.com/privefl/paper-ldpred2 and https://privefl.github.io/bigsnpr/articles/LDpred2.html). However, we omitted the prediction steps, as our approach does not consider a test dataset for PGS model generation (see companion code to this paper for implementation details).For PGS model generation using LDpred2-auto , and 2h (the heritability of the trait). LDpred2-auto can estimate both hyperparameters from the training data, thus not requiring a test dataset to tune them. For p, it can take multiple a number of initial values, from which Gibbs sampling chains run for a fixed number of iterations to find the optimal effect sizes for each SNP. After 4000a number of iterations (3000 after 1000 burn-in), we averaged betas across all chains. As recommended by LDpred2 authors in the LDpred2 tutorial (https://privefl.github.io/bigsnpr/articles/LDpred2.html), we used 15 initial values for p, ranging from 10−4 to 0.9. We computed initial 2h from the data using the snp_ldsc function. To ensure convergence, we used 4000 iterations (3000 iterations + 1000 burn-in). We skipped computation for chromosomes for which estimated 2h was below 10-4, as was done in the LDpred2 paper (r2 = 0.0031). Our LDpred2 per-chromosome are also very similar to the reported genome-wide results in LDpred2 paper , so we used case + control numbers for case–control datasets. MDD dataset had per-SNP N, so we used the sum of median cases and median controls . For BMI and height datasets, which also had per-column N, we used the median value of each dataset as its N (233 691 and 252 021 respectively). We ran PRScs for all chromosomes, and concatenated individual files together. Although it was unclear if PRScs would benefit from parallel computation, we ran it with 15 CPUs and 8 hours for consistency.We ran PRScs . For BMI and height, we used the UK Biobank codes ‘21001’ and ‘50’, respectively.We evaluated the predictive performance of our PGS models generated with the four methods using individual genotypes from the UK Biobank cohort. For all traits, we excluded SNPs with low imputation quality (info score <0.3) or multi-allelic SNPs. Moreover, we removed related individuals and restricted the analysis to individuals of European ancestry . The binary traits selected  were them is the mean of liability for cases, K is the population prevalence and t is the threshold on the normal distribution which truncates the proportion of disease prevalence.After computing the scores, for case–control phenotypes we estimated the area under the curve (AUC) adjusting for the first 40 genetic principal components , age (code ‘21003-0.0’) and sex (code ‘22001-0.0’)—the latter for all traits except breast and prostate cancer—using the R package ‘ROCnReg’. We then obtained K as the proportion of cases from the UKBB dataset.We estimated r2) adjusting or the first 40 genetic PCs (codes ‘22009-0.1-40’), age (code ‘21003-0.0’) and sex (code ‘22001-0.0’). Briefly, we regressed each trait against the covariates  and then correlated the residuals with the predictive score for the relevant trait. We constructed 95% confidence intervals for our estimates by bootstrapping 1000 times.For continuous traits, we assessed the squared correlation between the PGS and the measured trait . We used system.time function in R for timing RápidoPGS, LDpred2 and SBayesR wall clock runtime. For PRScs, we used the unix ‘time’ programme, as using system.time was unfeasible.Times for RápidoPGS includes full PGS generation procedure: check dataset integrity (for both single and multi), handling of pre-computed LD matrix or LD matrix computing from panel and α filtering (RápidoPGS-multi only), algorithm running and final weight computation. SBayesR and PRScs require transforming the input data into a specific format, a step we did not include in the timing.α parameters for all traits. The milder thinning , which discards fewer SNPs, showed slightly better performance than αSNP= 0.01, αblock= 10−4 setting. RápidoPGS-multi, which allows for multiple causal variants, achieved better performance than RápidoPGS-single, which is simpler and faster, but has the limitation of assuming a single causal variant per block, which is unrealistic in most scenarios. However, for certain traits (e.g. RA and T1D), differences in AUC and r2 for both approaches are small( W (prior variance on the true effect of a variant) can be provided by the user for RápidoPGS-multi, we found that letting RápidoPGS-multi compute W automatically provides better performance, at the expense of increased running time  to ∼64% (asthma) . Selecting (asthma) .We next compared RápidoPGS performance with those of LDpred2, PRScs and SBayesR.RápidoPGS achieved reasonably good prediction performance for most case–control traits, being superior to SBayesR in most instances and reaching prediction values close to PRScs although lower than LDpred2, which showed the best performance for most traits . SBayesRαSNP = 0.1, αblock = 10−3) is the best-performing RápidoPGS method, which shows the smallest median r2 difference and median r2 ratio to LDpred2  and PRScs , and outperforms SBayesR for case–control traits  (RápidoPGS-multi ( 0.6199)  and S5. W automatic estimation, and SuSiE algorithm running. All these steps make it inevitably slower than simpler RápidoPGS-single. Nonetheless, RápidoPGS-multi is generally much faster than any of the other methods (1.12–22.17 times faster). We only observed one instance that RápidoPGS-multi took unusually long to finish , 1000 Genomes Project Phase III is publicly available, and as we have shown, can be used for LD matrix computation and obtain good results. We provide a function to download and pre-process a 1000 Genomes-based reference panel from scratch, although users are free to use their own panel.iw. Other advances in fine-mapping methods may be transferable to the PGS setting. For example, PGS are known to have less predictive power in populations other than that used for training (We are not the first to suggest that fine-mapping approaches can be helpful for PGS construction, training . Alternatraining . Thus, wbtab456_Supplementary_DataClick here for additional data file.


Genetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases. However, existing approaches oversimplify the shared genetic architecture between different phenotypes and cannot effectively identify precise genetic regions contributing to the genetic correlation. In this work, we introduce LOGODetect, a powerful and efficient statistical method to identify small genome segments harboring local genetic correlation signals. LOGODetect automatically identifies genetic regions showing consistent associations with multiple phenotypes through a scan statistic approach. It uses summary association statistics from genome-wide association studies (GWAS) as input and is robust to sample overlap between studies. Applied to seven phenotypically distinct but genetically correlated neuropsychiatric traits, we identify 227 non-overlapping genome regions associated with multiple traits, including multiple hub regions showing concordant effects on five or more traits. Our method addresses critical limitations in existing analytic strategies and may have wide applications in post-GWAS analysis. Genetic correlation analyses give insight on complex disease, yet are limited by oversimplification. Here, the authors present LOGODetect, a method using summary statistics from genome-wide association studies to identify genomic regions with correlation signals across multiple phenotypes. Leveraging pervasive pleiotropy in the human genome, these methods enhanced the statistical power to identify genetic associations7, improved the accuracy of genetic risk prediction9, revealed novel genetic sharing across diverse phenotypes12, and provided great insights into the genetic basis of a variety of diseases and traits14.Genome-wide association studies (GWASs) have been carried out for numerous cosmplex traits and diseases, identifying tens of thousands of single-nucleotide polymorphisms (SNPs) associated with these phenotypes. However, our understanding of most traits’ genetic basis remains incomplete, in part due to the limited power and interpretability of the traditional GWAS approach that correlates one trait with one SNP at a time. Recently, statistical methods that jointly model multiple phenotypes have quickly gained popularity in human genetics research17. However, most complex human traits and their genetic overlaps are highly polygenic, with top SNPs showing weak to moderate effects20. Thus, single SNP-based methods modeling pleiotropy effects may not be sufficient to characterize the full landscape of genetic similarity of complex traits. An alternative approach is to estimate the genetic correlation between different traits22. These methods effectively utilize genome-wide genetic data, including SNPs that do not reach statistical significance in GWAS, to quantify the overall genetic sharing between two traits. In addition, recent methodological advances have enabled estimation of genetic correlation with GWAS summary statistics23, making these approaches widely applicable to a large number of complex phenotypes. With these advances, genetic correlation analysis has become a routine procedure in post-GWAS analysis and was implemented in almost all large-scale GWASs published in the past few years.Genetic similarity between traits can be modeled at different scales. Methods that identify SNPs associated with multiple phenotypes have achieved some success12 and annotation-stratified estimates11. However, these methods rely on strong prior evidence about which local region or functional annotation to investigate. When applied to hypothesis-free scans, statistical power is reduced. In this work, we introduce LOGODetect , a method that uses scan statistics to identify genome segments harboring local genetic correlation between two complex traits. Compared to other methods, LOGODetect does not pre-specify candidate regions of interest, and instead, automatically detects regions with shared genetic components with great resolution and statistical power. In addition, LOGODetect only uses GWAS summary statistics as input and is robust to sample overlap between GWASs. We demonstrate its performance through extensive simulations and analysis of well-powered GWASs for seven distinct but genetically correlated neuropsychiatric traits25. Our analysis implicates a collection of hub regions  in the genome that underlie the risk for several of these traits.However, despite improved statistical power and wide applications, genetic correlation approaches fail to provide detailed, mechanistic insights due to its oversimplification of complex genetic sharing into a single metric. Two recent methods improved genetic correlation analysis by providing localR is the index set for all SNPs in the region, zi1 and zi2 are the association z-scores for the ith SNP with two traits, li is the linkage disequilibrium (LD) score for the ith SNP10, and θ controls the impact of LD. Q(R) extends the scan statistic proposed for single trait analysis27 to the framework of detecting local genetic correlation. The scan statistic Q(R) is a LD score-weighted inner product of local z-scores from two GWASs and is conceptually similar to local genetic correlation—regions with high absolute values of Q(R) show concordant association patterns across multiple SNPs in the region and the sign of Q(R) shows if the correlation is positive or negative. Of note, when the candidate region is the whole genome and θ is equal to 1, the scan statistic is an estimator for the global genetic covariance11. In our framework, we do not assume that per-SNP genetic covariance is the same for all SNPs across genome, but assume that genetic covariance is localized in some small genome regions. Therefore, we use the scan statistic in a local region, as a metric to detect significant local genetic sharing. A full discussion of the functional form of the scan statistic Q(R) is provided in Q(R)| values by scanning the genome while allowing the segment size to vary  in our study is consistent with the traditional definition of covariance (correlation) of additive genetic effects under fixed-effect model10.We conducted simulations to compare the performance of LOGODetect with three existing methods: ρ-HESS29 to simulate genotypes for 100,000 samples based on 503 individuals with European ancestry from the 1000 Genomes Project Phase 3 data30, and assessed the type I error of the four approaches under a variety of settings  were used to quantify the statistical power (see the “Methods” section). Signal points detection rate and signal segments detection rate measure the sensitivity at the SNP level and segment level, respectively. However, they do not reflect specificity of the method, as both metrics will be 1 if the identified region is the entire genome. G-score is a more informative alternative, which can jointly quantify specificity and sensitivity. First, we evaluated the power of LOGODetect with different θ under a heritability enrichment model, where a higher level of heritability was attributed to correlated regions , schizophrenia , major depressive disorder , neuroticism , attention-deficit/hyperactivity disorder , autism spectrum disorder , and intelligence , using summary statistics from the latest GWASs45. We adaptively selected the best θ by maximizing the genetic covariance in all identified regions (Supplementary Table r = 0.23). We identified 56 shared genomic regions for BIP and SCZ , 53 regions for SCZ and IQ , 40 regions for MDD and NEU , and 261 regions for 16 other trait pairs, which is consistent with the strong genetic overlap between these traits49. Overall, we identified strong genetic sharing (higher genetic correlation and more shared genome segments) among BIP, SCZ, MDD, and NEU and among MDD, ADHD, ASD, and IQ. Sharing between these two clusters was relatively weaker, which is consistent with previous reports50.Previous studies have revealed pervasive pleiotropy IQ; n = 9,867, usTo benchmark the performance of LOGODetect with existing approaches, we also applied ρ-HESS, coloc, and gwas-pw to the same seven neuropsychiatric traits. We first assumed full sample overlaps as suggested in the original paper that introduced ρ-HESS. In total, ρ-HESS detected 778 regions for BIP and SCZ, and 304 regions for SCZ and IQ  and SCZ (h2 = 0.43) have high SNP heritability. As demonstrated in the simulations, regions identified by ρ-HESS may include a non-negligible proportion of false positive findings.There are two reasons why our method showed improved performance compared to the other methods. First, ρ-HESS and the colocalization methods pre-specify regions of interest, which are generally much larger than the signal regions harboring true genetic sharing  and SCZ  in the UKBB were collected . The summary statistics of BIP  and height, for which independent replication cohorts of large sample size are available . We iden51 to investigate the functional relevance of the genomic regions found to harbor local genetic correlations among seven neuropsychiatric traits (Supplementary Table p = 4.00e−4)  and pancreatic islets . Of note, annotated functional regions in mononuclear cells and pancreatic islets have substantial overlaps with annotations of brain tissues .We used 66 GenoSkyline-Plus tissue-specific functional annotations−4) Fig. . In addiues Fig. . After c52. After conditioning on these annotations, the enrichment in brain tissues remained significant , suggesting that the observed enrichment in functional genome in these brain tissues were not driven by generic annotations alone  in Finucane et al. 53. The 968 genes in regions detected by LOGODetect were significantly enriched for 83 GO terms (Supplementary Table p = 5.36e−13), nucleolus (p = 9.30e−6), and protein arginine deiminase activity (p = 7.35e−9).We also ran Gene Ontology-enrichment analysis using FUMANext, we investigated hub regions shared by five or more traits. Among the 227 non-overlapping genome regions identified in our analysis, 91 regions were identified in two or more different trait pairs , NEU45 , and IQ GWAS44 . Additionally, SNPs at this locus showed consistent associations with BIP . More importantly, this genome region covers the NTAD (NCAM1-TTC12-ANKK1-DRD2) gene cluster. Multiple variants of DRD2 and NCAM1 are reported to be associated with BIP, SCZ, MDD, and NEU56. Also, multiple eQTLs for DRD2  and NCAM1  are located in the region.The region showing significant correlation in nine pair-wise analyses is a locus spanning 711 KB on chromosome 11 Fig. . InteresIGSF9B, a potential risk gene for SCZ40 and IQ44, and its multiple eQTLs  are located in this genomic region. The third hub region is located on chromosome 14 spanning 694 KB and shows significant correlations in seven trait pairs  and FOXG1 , are located in the region. The fourth region on chromosome 3 spans 258 KB and was identified in seven pairs  are located in the hub region on chromosome 5  in principle, this remains a statistically challenging problem. As shown in our simulations, the estimation is inaccurate. Under the null hypothesis that local genetic correlation is zero, ρ-HESS underestimates the standard error of local genetic covariance when heritability is high and leads to inflated type I error rates, and it overestimates the standard error of local genetic covariance when heritability is low and leads to deflated statistical power. We note that the deflation of type I error observed for ρ-HESS is not contradictory to results published in ρ-HESS paper28 and genetic correlation estimation methods utilizing genome-wide data21, our method elucidates the shared genetic architecture between two traits by identifying local genomic segments that are concordant. The candidate genes and regions we identified may be tapping into a set of transdiagnostic mechanisms that underlie all of psychopathology . In practice, LOGODetect can be used in combination with other methods to further improve statistical power and biological interpretability. For example, it may be of interest to first screen the genome by identifying larger genetic regions82 or certain functional annotations11 enriched for the shared genetics between two traits. Then, LOGODetect can be applied to these candidate regions to identify the precise genetic segments that explain such sharing. Since high-dimensional sampling remains a challenge, a multi-tier analytical strategy would improve the statistical power and computational burden in the analysis. We believe that LOGODetect has addressed some key limitations in the current practice of cross-trait genetic correlation analysis and will benefit complex trait genetics research.Taken together, we have introduced LOGODetect, a scan statistic method to identify local genetic regions showing correlated effects with multiple neuropsychiatric traits. Complementary to single SNP-based approaches for pleiotropy mappingy1 and y1 follow the linear model with random effects:X and Z are fixed and standardized genotype matrices with M columns (i.e. the number of SNPs is M); ε and δ are non-genetic effects; β and γ are M-dimensional vectors denoting genetic effects. They follow the multivariate normal distribution:Suppose two standardized traits ρg is the global genetic covariance between two traits; ith diagonal element equals 1 if the effects of the ith SNP on two traits (i.e. βi and γi) are correlated and equals 0 if otherwise; K is the number of SNPs such that βi and γi are correlated, i.e., β and γ are independent from non-genetic effects ε and δ. The statistical model described here is similar to the polygenic model used in genetic correlation estimation10. The difference is that we allow local genetic sharing and do not assume the global genetic covariance to be equally attributed to all SNPs in the whole genome. Compared to the local genetic correlation estimation method in the literature12, we do not assume genetic effects to be fixed. Instead, our framework is a direct generalization of the model developed for global genetic correlation estimation11. Under the alternative hypothesis, we denote the non-overlapping genetic regions that contribute to multiple traits to be where 83. Suppose z-scores for two traits asWe use a scan statistics approach to identify regions showing correlated effects between different traits. This type of approach has been used for burden test in a single-trait settingR is the index set for SNPs in a genome region, 80 for the ith SNP computed within a 1 MB window, and Then, we can define the scan statistic:C is a pre-specified parameter that defines the upper boundary of the SNPs count in a region. In practice, C can be set based on the number of SNPs in the dataset (e.g. the average number of SNPs in 1 million bases). LOGODetect takes advantages of the flexible framework to scan local regions with varying sizes. Compared to a sliding-window approach based on a pre-specified window size, our method is more appealing since the size of signal region could vary substantially by locus and by trait. We use a Monte Carlo type approach to assess the distribution of Finally, we use the maximal scan statistic over all possible regions as the test statistic:θ affects the size of identified regions. A relatively long segment may not have a large absolute value of scan statistic, due to the penalty in the denominator θ implies stronger penalty, henceforth is more likely to detect smaller signal segments. In particular when θ equals 1, θ is between 0 and 1. In practice, it is important to consider the “best” θ adaptive to the data. We used the proportion of genetic covariance of the identified regions as the metric. We varied the value of θ in the candidate set, and chose the best θ such that the corresponding identified regions have the largest genetic covariance. In general, one can use any subset of  as the candidate set of θ values. However, extensively searching for θ substantially increases the computation time. In practice, we suggest the set of {0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7} would be sufficient. Denote the regions detected by LOGODetect under parameter θ as 10. Let π(θ) = 0 if θ values, and then we determine θ adaptive to data via the following optimization problem:Parameter z-score vectors. When two GWASs do not have sample overlap, it can be verified thatIn order to simulate the null distribution of H0, the combined z-score vector 30), XTX and ZTZ can be estimated as V, XTXXTX and ZTZZTZ can be estimated as n is the sample size of the reference panel and V is the LD matrix of the reference panel. And the genetic heritability for two traits 80. After plugging in the reference LD matrix, we haveAnd similarly for 7 in GWAS. We developed a computationally tractable method that leverages the LD structure in the genome. First, we split the high-dimensional vector z into sub-vectors z as Σ and it can be written as the block matrix form. Denote Σ, with rows indexed by the Σ by shrinking The random multivariate normal vectors have complex covariance structure, which is computationally challenging as the dimension of the vector can be as high as 1084 and use the top q singular values and their corresponding singular vectors to calculate the inverse matrix. For numerical stability, we choose q to be as large as possible such that the conditional number is <100085. Finally, we standardize each pseudo z-score vector so that it has the same mean and variance as the z-score vector in real data.In practice, 11. However, most genetic correlation methods, including LOGODetect, can be directly applied to GWAS summary statistics of binary outcomes11. It is known that under the liability threshold model, the following formulas hold10:P1 and P2 denote population prevalence for two traits; S1 and S2 denote sample prevalence for two traits; So far, we have based the derivation on the setting that the both input traits are continuous. This is a common approach to introducing genetic correlation methodologySuppose there are V. Therefore, under z-score vectors asymptotically follows multivariate normal distributionsWhile Note that the variance matrix can be split into two terms asz-score vector of interest. In particular, the parameters z-score null distribution are not of our interest, but we need their values while doing Monte Carlo sampling of 10 to estimate them. Note that LDSC is based on random effect random design model setup, which is incompatible with our model assumption, yet we believe it should yield little consequence.if 86. Each LD block spans 15 MB on average. We applied LOGODetect to each LD block separately and identified the local regions with p-value < 0.05 under a family-wise type I error control. We aggregated all the candidate regions across different LD blocks, and applied Benjamini–Hochberg procedure87 to control FDR with a cutoff of 0.05, accounting for the multiple testing problem concerning all LD blocks.We separated the genome into 204 LD blocks using ldetectThe major computation step in LOGODetect is to compute the maximal scan statistic in real data and in Monte Carlo samples. The computation time depends on the number of SNPs in GWAS. For a typical GWAS with 6 million SNPs, it takes about 12 h on a 2.5GHz cluster with 22 computation cores.29. 336,532 variants remained in the dataset after removing strand-ambiguous SNPs. Samples were randomly divided into two subsets with equal sample size, each with 50,000 individuals. We used each subset to simulate the phenotype data.Based on 503 individuals with European ancestry from the 1000 Genomes Project Phase 3 data, we simulated genotype data for 100,000 individuals with minor allele frequency (MAF) > 5% on chromosome 1 using HAPGEN2First, we performed simulations under the null hypothesis to see whether our approach would produce false positive findings. We follow the infinitesimal model, where the effect size level of all the normalized SNPs are the same, and the per-normalized-SNP genetic effect was drawn from a normal distribution N = 5 segments, each containing L = 1000 SNPs, as the signal regions shared between two traits. We attributed Next, we performed simulations to assess the statistical power under a heritability enrichment model. We randomly selected The genetic effect size for the SNPs outside the signal regions follows a different multivariate normal distribution without local genetic correlation.h2 was set to vary from 0.01 to 0.05, and the correlation of genetic effect size of two traits ρ was set to 0.9. Each simulation setting was repeated for 100 times. Further simulation settings are described in detail in the The trait heritability We adjusted the significance cutoff of different approaches to achieve the same type I error. For coloc and gwas-pw, in those heritability settings with empirical type I error >0.05, we increased the cutoff of the posterior probabilities so that the empirical type I error is controlled at 0.05.88 to define G-score with respect to a signal region G-score measure as G-score aims to measure the specificity and sensitivity together. The three metrics were also applied to quantify ρ-HESS, coloc, and gwas-pw.We use three different metrics to quantify the performance of our approach. Denote the true signal segments as 86 to pre-specify 1703 approximately LD-independent blocks (spanning 1.6 Mb on average) as candidate genomic regions, as suggested by ρ-HESS and gwas-pw. We also used these LD-independent blocks as candidate genomic regions for coloc. In simulation studies, we used 133 approximately LD-independent regions in chromosome 1 as the pre-specified genomic regions for ρ-HESS, coloc, and gwas-pw. For ρ-HESS, the 1000 Genomes Project Phase 3 data30 was used as the reference panel, the number of eigenvectors used in the truncated-SVD for LD matrix inversion is determined as 50 by default, and the minimum eigenvalue cut off in truncated-SVD is determined as 1.0 by default, as suggested by the ρ-HESS software (https://huwenboshi.github.io/hess/). ρ-HESS reported the estimate and significance of local genetic correlation for each candidate genomic region, and we applied Benjamini–Hochberg procedure87 to control FDR with a cutoff of 0.05, accounting for the multiple testing problem concerning all genomic regions. Coloc (https://CRAN.R-project.org/package=coloc) and gwas-pw (https://github.com/joepickrell/gwas-pw) estimated the posterior probability that two traits shared at least one causal SNP for each genomic region, and those genomic regions with posterior probability above 0.95 are determined as identified regions.We used ldetecthttps://github.com/bulik/ldsc) to estimate heritability in each chromosome. Stratified-LDSC was used to estimate genetic covariance of the identified regions. In detail, we manually created two annotations: the identified regions and the remaining genome, then we ran the standard LDSC software to calculate the genetic covariance and the proportion of genetic covariance of each annotation. For both LDSC and stratified-LDSC, LD scores were computed with the standard LDSC software from 503 individuals with European ancestry from the 1000 Genomes Project Phase 3 data. Both methods were applied with an unconstrained intercept, using all SNPs as observations in the dependent variable and LD scores as regression weights.We used LDSC . We used permutation test to assess the statistical significance of enrichment in conditional analyses.We also assessed whether the detected regions were enriched in non-brain tissue types after adjusting for the overlap of brain and non-brain annotations. Specifically, for the pancreatic islets cell type annotation, we removed the annotations that overlap with any of the eight significant brain cell type annotations to define the conditional annotation of pancreatic islets. The same procedure was taken to define the conditional annotation of mononuclear cells from peripheral blood. Afterwards, permutation tests were performed on these two conditional annotations. We performed conditional analysis on six generic annotations including coding regions, enhancers, introns, promoters, 5′UTRs and 3′UTRs (extended by a 500-bp window around each of the annotations) in Finucane et al. 53 to run the Gene Ontology enrichment analysis with these 968 genes.Using GENCODE V33lift37 on the UCSC genome browser, we extracted 968 genes with recognized Ensembl IDs in the genomic regions found to harbor local genetic correlations among seven neuropsychiatric traits. We used FUMAFurther information on research design is available in the Supplementary InformationPeer Review FileDescriptions of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Lolium perenne genotypes, the organic matter digestibility (OMD) and underlying traits such as cell wall digestibility (NDFD) and cell wall components  were investigated for 2 years. A high genotype × season interaction was found for OMD and NDFD, indicating differences in genetic control of these forage quality traits in spring versus autumn. OMD could be explained by both the quantity of cell wall content (NDF) and the quality of the cell wall content (NDFD). The variability in NDFD in spring was mainly explained by differences in hemicellulose. A 1% increase of the hemicellulose content in the cell wall (HC.NDF) resulted in an increase of 0.81% of NDFD. In autumn, it was mainly explained by the lignin content in the cell wall (ADL.NDF). A 0.1% decrease of ADL.NDF resulted in an increase of 0.41% of NDFD. The seasonal traits were highly heritable and showed a higher variation in autumn versus spring, indicating the potential to select for forage quality in the autumn cut. In a candidate gene association mapping approach, in which 503 genes involved in cell wall biogenesis, plant architecture, and phytohormone biosynthesis and signalling, identified significant quantitative trait loci (QTLs) which could explain from 29 to 52% of the phenotypic variance in the forage quality traits OMD and NDFD, with small effects of each marker taken individually (ranging from 1 to 7%). No identical QTLs were identified between seasons, but within a season, some QTLs were in common between digestibility traits and cell wall composition traits confirming the importance of hemicellulose concentration for spring digestibility and lignin concentration in NDF for autumn digestibility.Perennial ryegrass is an important forage crop in dairy farming, either for grazing or haying purposes. To further optimise the forage use, this study focused on understanding forage digestibility in the two most important cuts of perennial ryegrass, the spring cut at heading and the autumn cut. In a highly diverse collection of 592  Lolium perenne L.), which is one of the most used forage grass species in temperate regions thanks to its good tolerance to grazing and its overall good nutritive quality  and the concentration of crude protein . The DM An improvement of the forage nutritive value can be achieved by increasing the proportion of non-fibre constituents and/or by increasing the digestibility of the fibre constituents . ConcernThe composition of the cell walls has also a strong impact on the forage nutritive value. As Several studies have shown that in forage grasses DMD depends on several factors including species, genotype within species, plant organ/tissue, organ/tissue age, season, and environmental conditions . A largeO-methyltransferase) and CAD  in maize and tall fescue  in perennial ryegrass leaves harvested in spring and autumn on a wide collection of 580 genotypes. A large number of genotypes analysed allowed us to obtain interesting variations for all studied traits. A particular emphasis was placed on the variability of fibre digestibility and its link with the composition of fibre for a better comprehension of their links with the overall quality. We also investigated the genetic determinism of quality traits by a genome-wide association study (GWAS) approach based on candidate genes. The results obtained could help to improve breeding programs by focusing on major indicators for quality in spring and/or autumn.The plant material used in this study consisted of 580 genotypes from diverse origins  chosen tca. 4 cm). The soil in the pots was a combination of white peat and garden peat , fertilised with 0.3 kg/m3 nitrogen phosphorus potassium (NPK) 14-16-18. The pots were transferred to a container field in open-air in Melle, Belgium  and arranged according to a randomised complete block design with three clonal replicates. The same setup was used for the 2 years. In 2012, the plants were fertilised with NPK (16-8-22) at the end of April, at the end of June, in the middle of July, and in early August . In 2013, the plants were fertilised with NPK (16-8-22) at the beginning of May, in the middle of June, in the middle of July, and in the middle of August . The pots were watered using drip irrigation once to twice a day, depending on irradiance and air temperature.For each genotype, three clones were produced in trays by splitting tillers in September 2011 and 2012. The clones were moved to an unheated greenhouse (minimum temperature of 4°C) for vernalisation during the winter and then potted in 12 L containers in early March 2012 and 2013. Each plant consisted of three tillers, which were cut to the same height . This involved inspection of each individual plant every 2 or 3 days from the beginning of April to early July 2012 and from the beginning of May to the end of July 2013, to record the heading date of each individual plant. Plants were cut at 4 cm height in the time slot between 2:00 and 3:00 p.m. The biomass was dried in a ventilated oven for 48 h at 70°C, hand separated in the blade and “stem”  fractions, and milled in a Fritsch cutting mill using a 0.5-mm sieve. In this study, we focused only on the blade samples which were harvested in both spring and autumn to allow comparison of the same organ between seasons. Plants that were harvested with too many spikes (weight proportion of spikes larger than 5%) and plants with extreme heading dates were not considered for further analysis. We considered the extreme 0.6%  of the distribution as “extreme heading dates”, which corresponded to >17 days before and >28 days after the median heading date in both years.The summer cuts were performed on 9 July 2012, 6 August 2012, 8 July 2013, and 12 August 2013 to promote a good regrowth in September. On 7, 18, and 19 of September 2012 and on 17, 19, and 20 of September 2013, the autumn cuts were performed. Cuts were spread other 3 days because short time window (2:00 to 3:00 p.m). All plants were cut at 4 cm height, then dried and milled according to the same used protocol in spring. In this case, blades and sheaths were not separated because of the absence of stems.All samples from spring and autumn cuts were scanned by near-infrared spectroscopy (NIRS) with FOSS XDS spectrometer . Wet-chemical analyses were performed on a selection of samples for seven traits: ADM expressed in percentage of fresh material, OM expressed as a percentage of DM, OMD, Neutral Detergent Fibre (NDF), Acid Detergent Lignin (ADL), Cellulose, and HC expressed in percentage of OM. Because of lacking data, some genotypes (20) were removed from the analysis. Only 550 were kept for further phenotypic analysis. The data for the 550 remaining genotypes are given in in vitro rumen digestibility method, corrected for ashes.The ADM was determined gravimetrically in duplicate by drying 2.5 g of the sample at 103°C for 24 h. This sub-sample was then ashed at 550°C, allowing calculation of OM as the proportion of ash-free ADM in ADM. OMD was determined following 2SO4 and CTAB (20 g/L) for 1 h at 100°C in the ANKOM. The residue was then washed with water at 100°C and acetone, dried at 103°C, and weighed. ADF was calculated as the proportion of ash-free ADF residue divided by the OM previously determined. ADL was determined by submerging the bags containing the ADF fraction in a beaker with 72% H2SO4 for 3 h. Ash content was determined by heating the ADL residue to 550°C in a muffle furnace. ADL was calculated as the proportion of ash-free ADL residue divided by the OM previously determined. HC was calculated as NDF – ADF and cellulose as ADF – ADL in % OM.The sequential van Soest method  was modiA NIRS calibration was built with all the wet-chemistry data including spring and autumn samples of the 2 years (2012 and 2013). To assess the extent to which a trait could be predicted for samples not included in the calibration model, a leave-one-out cross-validation was performed.Several traits were computed to study cell wall digestibility and its composition. Cell wall components  were expressed in percentage of NDF: C.NDF, HC.NDF, and ADL.NDF. Digestibility of NDF was computed using a formula derived from This formula is based on the assumption that all indigestible parts of the plant are contained in cell walls.lme4 , in year j , season k  and block l  ,where μ = overall mean,Gi = random effect of genotype i, Gi ∼ N,Tj = fixed effect of year j,Sk = fixed effect of season k,jk = interaction of year j and season k,TSij = interaction of genotype i and year j, GTij ∼ N,GTik = interaction of genotype i and season k, GSik ∼ N,GSl(TSjk) = fixed effect of block l nested within year j and season k,Beijkl = residual, assumed to be normally distributed, eijkl ∼ N.and The data set was further analysed by season using the following model for spring and autumn separately:Yijl = the phenotype of genotype i , in year j  and bloc l ,where Bl(Tj) = fixed effect of block l nested within year j,eijl = residual, assumed to be normally distributed, eijl ∼ N.and H2) for each trait was calculated as:For each season, the broad-sense heritability (Gi) per season over 2 years. Another model per season and per year was also used:For GWAS, Eq. 3 was used to calculate the Best Linear Unbiased Predictor (BLUP) of the genotype effect  and bloc l ,Where Bl = fixed effect of block l,eil = residual assumed to be normally distributed, eil ∼ N.and Gi) per season and per year.This model (Eq. 5) was used to calculate the BLUP of genotype effect ]. Imputation of missing genotype calls was performed independently for each group found by population structure analysis (see below). For each marker, a missing genotype call was replaced by the value of the most common allelic dose of its group. Minimal allele frequency (MAF) was set to 1% and markers with MAF below 1% were removed .The harvest date in spring was set at the heading date of each individual genotype, as this is also an agronomic important time point at which farmers tend to harvest perennial ryegrass. However, this individual harvest date had an impact on OMD, NDFD, and NDF . In partper season and per year:per season over the two years:HDil = heading date of genotype i in block l per year,where HDijl = heading date of genotype i in block l in year j.To determine population structure, we used fastSTRUCTURE  with defFour genetic groups were identified, based on 252,406 SNPs and 5,074 indels, using marginal likelihood . The firMulti-locus mixed-modelling (MLMM) was used for GWAS analysis . We usedG is the matrix of genotypes  coded as 0, 0.5, and 1 for the homozygote, the heterozygote, and the alternative homozygote state, respectively; K is the total number of markers, and G2 = 1 - G, where 1 is a matrix of ones.where p-value of 5% was 0.05/503 = 9.94e10–5.Multi-locus mixed-modelling was used for each trait with: (a) the matrix of genotype calls coded as 0, 1, and 2; (b) the BLUPs values; (c) the population structure; (d) the kinship matrix. The MLMM R function  was usedR2 of each studied marker was computed by a comparison of these models with the r.squaredLR function from the MuMIn R package  . Next, a package .For each trait, the effect of each QTL was estimated by the coefficient (slope) of the marker effect in the model.To evaluate the effect of cell wall components QTLs on OMD and NDFD, two models were applied. For the spring cut, we evaluated the variance explained for OMD and NDFD by the QTLs identified for HC.NDF. In autumn, the same method was applied but used QTLs identified for ADL.NDF.The distribution of all traits per season and per year are given in The heritability of OMD and NDFD was very low or zero when both seasons were analysed together , suggestThe values of the digestibility traits OMD and NDFD were significantly different between the spring and autumn seasons. OMD was higher in spring than in autumn (8% OM difference), but the range of OMD was broader in autumn than in spring . Similarly, NDFD was higher in spring than in autumn (6% NDF difference), but the range of NDFD was broader in autumn. Nevertheless, weak positive and significant correlations between seasons for OMD and NDFD were observed .p < 0.05); p < 0.05); We observed a significant and strongly positive correlation between OMD and NDFD for each season and year [0.79 in spring 2012, 0.87 in spring 2013, 0.92 in autumn 2012, and 0.92 in autumn 2013   than in To understand better which aspects had determined the variation in NDFD in spring and autumn, we analysed the cell wall components. In spring, a large part of NDFD variability could be explained by HC.NDF itself (or C.NDF). A 1% increase of HC.NDF resulted in an increase of 0.81% of NDFD. In autumn, NDFD was mainly explained by ADL.NDF . A 0.1% p-values, for all traits in spring and autumn, are presented in p-value, and the percentage of variance explained for all significant QTLs are presented in In spring, to identify QTLs different from the ones for heading date, and due to the correlation between traits and heading date, GWAS was performed on data corrected and uncorrected for heading date . All Comparing all identified genes, we noticed that two genes  were in common between HC.NDF, NDFD, and OMD in spring and six genes  were in common between ADL.NDF, NDFD, and OMD in autumn . In spriWe observed strong interactions between genotype and season for both OMD and NDFD, which leads to inversions of ranking between genotypes when autumn data versus spring data are considered. This observation indicates that selection should be realised in both spring and autumn simultaneously to improve the overall, annual digestibility. Interestingly, some genotypes showed high values of OMD and NDFD in both seasons, and are of particular relevance for breeding purposes.The OMD was higher in spring than in autumn. This cannot be explained by the fact that only blades were analysed in spring compared to blades and sheaths combined in autumn since OMD of blades and “stems” (sheaths and flowering stems) was also higher in spring . This dih2 = 0.82) determined by a few genes with large effect  and of its digestibility (NDFD), as previously reported by Indeed, variability of NDFD can be partially explained by fibre composition, with the main contribution of different components in spring and autumn. In spring, an increase of NDFD was associated with an increase of HC  but with almost no effect of lignin. In contrast, in autumn, an increase of NDFD was associated with a decrease of lignin concentration in the cell wall but with almost no effect of relative HC content. The link between cell wall lignin concentration and NDFD is normally observed in forage grasses , but notWe observed a strong effect of heading date on all traits determined in plant materials harvested in spring. OMD decreased with a later heading date, which was associated with a decrease of NDFD, cell wall HC, and lignin concentrations. In other words, leaves harvested on late-flowering genotypes were less digestible than leaves harvested on early flowering genotypes. Since leaves were harvested at the heading stage, the decrease in digestibility could be explained by the age of leaves on later heading plants. Indeed, it has previously been reported that digestibility decreases with the increasing age of the organs . It is tL. perenne, as expected on mapping populations (L. perenne.The SNP dataset used in this study tagged genes from the pathways involved in plant cell wall biogenesis, plant architecture and development, and phytohormone biosynthesis, signalling, and response . GWAS idulations , but alsulations . Severalulations . These iulations . Associaulations . Furtherulations . It has ulations . A more No identical QTLs were identified across seasons, except one QTL for ADL.NDF, C.NDF, and OMD, and four QTLs for NDF. Within each season, we identified some QTLs common between digestibility traits and cell wall composition traits. In spring, HC.NDF had three QTLs in common with OMD and/or NDFD, whereas no common QTLs were identified in autumn. In autumn, ADL.NDF had seven QTLs in common with OMD and/or NDFD whereas no common QTL was identified in spring. These GWAS results confirm the importance of HC concentration for the digestibility of the spring cut, and lignin concentration in NDF for the digestibility of the autumn cut. QTLs only linked to NDFD or OMD could suggest there are other components, not considered in this study, which have a link with NDFD or OMD.In conclusion, we found that NDF and NDFD are major components of OMD in spring and autumn, but we observed a high interaction between genotypes and seasons. In spring, NDFD was mainly explained by HC.NDF and C.NDF and their related QTLs. In autumn, NDFD was mainly explained by ADL.NDF and their related QTLs. This new knowledge suggests that breeding for OMD should be performed by selecting both NDF and NDFD in spring and autumn with an emphasis on cell wall HC content in spring and cell wall lignin content in autumn. In future work, the diversity of candidate genes identified in this study could be surveyed more in detail and other plant materials to identify interesting alleles useful for molecular-assisted breeding.The original contributions presented in the study are included in the article/FP, HM, TR, and IR-R designed and performed the research. VC, PB, FP, TR, IR-R, and HM performed the experiments and analyzed the data. VC, PB, AE, and HM wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Cereals are the main food and feed crops on our planet, with wheat, rice, and maize occupying three-quarters of the total acreage. The vast majority of plant breeders and plant geneticists around the world are engaged in cereal breeding. The genetic resources for crop genepools, including breeding and research materials, landraces, and wild crop relatives, which collectively are the pillars of modern plant breeding, are maintained ex situ in gene banks. The main challenges or bottlenecks in the advanced breeding techniques currently used in cereals are connected with concerns related to climate change, with breeding programs aiming to increase yield and tolerance to biotic and abiotic stresses . In the last few years, a trend has occurred in cereal crop breeding aimed at combining high agronomic and biochemical parameters in a single cultivar. Currently, traditional genetic and innovative molecular genetic methods are widely used in the breeding of grain crops. The success of biotechnology approaches has expanded the breeding possibilities and allowed interspecies and intergenus hybrids to be obtained. The development of molecular biology and genomics has completely overcome the barriers limiting the breeding of living organisms, while methods for genome editing of agricultural crops are still being improved to achieve higher levels of accuracy. Studies aimed at finding genes and quantitative traits loci (QTLs) that affect the main breeding traits and at identifying the desired allelic variants are currently relevant. In the field of genetic sequencing, genotyping by sequencing, also called GBS, is a method used to discover single-nucleotide polymorphisms (SNP) in order to perform genotyping studies, such as genome-wide association studies (GWAS).The acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances have provided potential solutions to relieve such bottlenecks and to explore advanced methods for large-scale phenotyping, data acquisition, and data processing in the coming years. The phenomics data generated are already beginning to be used to identify genes and QTL through QTL mapping, association mapping, and genome-wide association studies (GWAS), in order to achieve crop improvements through genomics-assisted breeding (GAB). There is no doubt that accurate high-throughput phenotyping platforms will accelerate improvements in plant genetics.This Special Issue on ‘Advances in Cereal Crops Breeding’ comprises 9 papers covering a wide array of aspects, ranging from the expression-level investigation of genes in terms of salinity stress adaptations and their relationships with proteomics in rice, the use of genetic analysis to assess the general combining ability (GCA) and specific combining ability (SCA) in promising hybrids of maize, the use of DNA markers based on PCR in rice, the identification of quantitative trait loci (QTLs) in wheat and simple sequence repeats (SSR) in rice, the use of single-nucleotide polymorphisms (SNP) in a genome-wide association study (GWAS) in cereals, and Nanopore direct RNA sequencing of related with LTR RNA retrotransposon in triticale prior to genomic selection of heterotic maize hybrids.In order to better understand the mechanisms involved in salinity stress adaptations in rice, two contrasting rice cultivars were compared in a recent study—Luna Suvarna, a salt-tolerant cultivar, and IR64, a salt-sensitive cultivar. The expression-level investigation of auxin signaling pathway genes revealed increases in the transcript levels of several auxin homeostasis genes in Luna Suvarna compared with IR64 under salinity stress. Furthermore, protein profiling showed 18 proteins that were differentially regulated between the roots of two cultivars, some of which were salinity-stress-responsive proteins found exclusively in the proteome of Luna Suvarna roots, revealing the critical role of these proteins in imparting salinity stress tolerance. The results show that Luna Suvarna involves a combination of morphological and molecular traits of the root system that could prime the plant to better tolerate salinity stress [The tolerance of rice to salinity stress involves diverse and complementary mechanisms, such as the regulation of genome expression, activation of specific ion transport systems to manage excess sodium at the cell or plant level, and anatomical changes that mitigate sodium penetration into the inner tissues of the plant. The identification of salinity tolerance QTLs associated with different mechanisms involved in salinity tolerance requires the greatest possible genetic diversity to be explored. In the investigation of genotyped rice landraces, SNP markers were used, with the aim of identifying new QTLs involved in salinity stress tolerance via a genome-wide association study (GWAS). Twenty-one identified QTLs colocalized with known QTLs. Several genes within these QTLs have functions related to salinity stress tolerance and are mainly involved in gene regulation, signal transduction, and hormone signaling. This study provides promising QTLs for breeding programs to enhance salinity tolerance and identifies candidate genes that should be further functionally studied to better understand salinity tolerance mechanisms in rice .In addition to water flooding and salinity, rice growers in some parts of the world are also facing drought; thus, developing new rice genotypes tolerant to water scarcity is one of the best strategies to maximize yield potential and achieve water savings. In a recent study, rice genotypes were characterized for grain and agronomic parameters under normal and drought stress conditions and genetic differentiation was determined via specific DNA markers related to drought tolerance using simple sequence repeats (SSR) and cultivar grouping, establishing their genetic relationships with different traits. All genotypes were grouped into two major clusters with 66% similarity based on Jaccard’s similarity index. As a result of the study, genotypes were identified that could be included as appropriate materials for developing a drought-tolerant breeding program. Genetic diversity is needed to grow new rice cultivars that combine drought tolerance with high grain yields, which is essential to maintaining food security .Recent studies on the tolerance to biotic and abiotic stressors in rice hybrids with donor lines of the genes of interest showed the effectiveness of such hybrids. As a result of the studies carried out using molecular marking based on PCR in combination with traditional breeding, early-maturing rice lines with genes resistant to salinity  and flooding (Sub1A) were obtained, which are suitable for cultivation in southern Russia. The development of resistant rice varieties and their introduction into production will allow us to avoid the epiphytotic development of the disease, preserving the biological productivity of rice and resulting in environmentally friendly agricultural products .The combining ability and genetic diversity of plants are important prerequisites for the development of outstanding hybrids that are tolerant to high plant density. A recent study was carried out to assess general combining ability (GCA) and specific combining ability (SCA), identify promising hybrids, estimate genetic diversity among the inbred lines, and correlate genetic distance (GD) to hybrid performance and SCA across different plant densities. As a result, no significant correlation was found between GD and either hybrid performance or SCA for grain yield and other traits, proving to be of no predictive value. Nevertheless, SCA could be used to predict hybrid performance across all plant densities. Overall, this study presents useful information regarding the inheritance of maize grain yield and other important traits under high plant density .In addition to studying the productivity of plants and genes associated with general adaptability, the genetic improvement of root systems is of interest as an efficient approach to improve the yield potential and nitrogen use efficiency (NUE) of crops. QMrl-7B is a major stable quantitative trait locus (QTL) controlling the maximum root length in wheat. Two types of near isogenic lines  were used to specify the effects of QMrl-7B on root, grain output, and nitrogen-related traits under both low-nitrogen (LN) and high-nitrogen (HN) environments. The QMrl-7B A-NILs manifested larger root systems compared to the B-NILs, which is favorable to N uptake and accumulation, and eventually enhanced grain production. This study provides valuable information for the genetic improvement of root traits and breeding of elite wheat varieties with high yield potential .Traditional plant breeding approaches supplemented with SNP markers used for genome-wide associative studies (GWASs) and genetic editing, as well as high-throughput chemotyping techniques, are exploited to speed up the breeding of desired genotypes. To enrich cereal grains with functional components, the new breeding programs need a source of genes in order to improve the contents of the beneficial components. The sources of these valuable genes are plant genetic resources deposited in genebanks, including landraces, rare crop species, and even wild relatives of cultivated plants. Correlations between the contents of certain bioactive compounds and the resistance to diseases or tolerance to certain abiotic stressors suggest that breeding programs aimed at increasing the levels of health-benefiting components in cereal grain might at the same time allow the development of cultivars adapted to unfavorable environmental conditions .Using Nanopore long-term forward RNA sequencing, functionally important but unexplored RNA molecules have been identified, including long non-coding RNAs (lncRNAs), as they are often associated with repeat-rich regions of genomes and transposon-derived transcripts expressed during early stages of seed development in triticale. Detailed analysis of the protein-coding potential of the RTE-RNAs showed that 75% of them carry open reading frames (ORFs) for a diverse set of GAG proteins, the main components of virus-like particles of LTR retrotransposons. This demonstrated experimentally that certain RTE-RNAs originate from autonomous LTR retrotransposons, with ongoing transposition activity during early stages of triticale seed development. Overall, these results provide a framework for further exploration of the newly discovered lncRNAs and RTE-RNAs in functional and genome-wide association studies in triticale and wheat. The results also demonstrate that Nanopore direct RNA sequencing is an indispensable tool for the elucidation of lncRNA and retrotransposon transcripts .Genomic selection (GS) shows great promise in terms of strongly increasing rates of genetic improvement in plant breeding programs. It allows for comparative larger gains from selection by estimating all marker effects simultaneously, while the subsequent selection of genetically superior individuals is based on their genomic estimated breeding value (GEBV) instead of using a few significant markers, as is the case in classical marker-assisted selection (MAS). GS is ideal for complex traits with lower heritability and complex genetic architectures.Genomic selection (GS) can accelerate variety improvement when the training set (TS) size and its relationship with the breeding set (BS) are optimized for the prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and best linear unbiased estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. Random-based training sets (RBTS) and pedigree-based training sets (PBTSs) were designed to study biotic resistance. For PBTS, the FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits, whilst the TS designation was determinant, since a positive correlation between TS size and PAs was observed for RBTS, while for the PBTS, this correlation was negative. The resulting population could be of interest in future breeding activities targeted at improving insect resistance in maize and could be potentially useful for GS of complex traits with low to moderate heritability. This study has pioneered the use of GS for maize resistance to insect pests .Advances in cereals breeding to develop new improved cultivars are some of the most important factors in agricultural production, playing an essential role in ensuring sustainable agriculture. Along with classical breeding goals, innovative, modern plant breeding methodologies are applied here to create new cultivars of crops for current and future agriculture applications. This endeavor includes the development of cultivars for stress cultivation conditions to achieve sustainable agricultural production, increased food quality, and increased security, and to supply raw materials for innovative industrial products and to meet the needs of mankind.