Egg laying rate (LR) is associated with a clutch, which is defined as consecutive days of oviposition. The clutch trait can be used as a selection indicator to improve egg production in poultry breeding. However, little is known about the genetic basis of clutch traits. In this study, our aim was to estimate genetic parameters and identify quantitative trait single nucleotide polymorphisms for clutch traits in 399 purebred Laiwu Black chickens (a native Chinese breed) using a genome-wide association study (GWAS).In this work, after estimating the genetic parameters of age at first egg, body weight at first egg, LR, longest clutch until 52 week of age, first week when the longest clutch starts, last week when the longest clutch ends, number of clutches, and longest number of days without egg-laying until 52 week of age, we identified single nucleotide polymorphisms (SNPs) and potential candidate genes associated with clutch traits in Laiwu Black chickens. The restricted maximum likelihood method was used to estimate genetic parameters of clutch pattern in 399 Laiwu Black hens, using the GCTA software.NELL2, SMYD9, SPTLC2, SMYD3 and PLCL1 were the most promising candidates for clutch traits in Laiwu Black chickens.The results showed that SNP-based heritability estimates of clutch traits ranged from 0.06 to 0.59. Genotyping data were obtained from whole genome re-sequencing data. After quality control, a total of 10,810,544 SNPs remained to be analyzed. The GWAS revealed that 421 significant SNPs responsible for clutch traits were scattered on chicken chromosomes 1–14, 17–19, 21–25, 28 and Z. Among the annotated genes, The findings of this research provide critical insight into the genetic basis of clutch traits. These results contribute to the identification of candidate genes and variants. Genes and SNPs potentially provide new avenues for further research and would help to establish a framework for new methods of genomic prediction, and increase the accuracy of estimated genetic merit for egg production and clutch traits.The online version contains supplementary material available at 10.1186/s12864-023-09798-0. Long-term selection has decreased the genetic variability in the egg LR , while the threshold for suggestive significance was 1/1,462,803 = 6.84E-7 [-log10 = 6.16]. The Manhattan and quantile–quantile (Q-Q) plots for traits were generated using the “CMplot” package in R.Briefly, we started by filtering out SNPs with an inheritance or genotyping error, a minor allele frequency < 5%, and a call rate < 95%. Eventually, after quality control of genotypes, a total of 10,810,544 SNPs and 399 individuals were left for analysis. Linkage disequilibrium (LD) pruning was then performed with a window size of 25 SNPs, a step of five SNPs, and r threshold of 0.2, identifying 1,462,803 independent SNPs and LD blocks. The sample structure was visualized by PCA using eigenvalues as coordinates. The Efficient Mixed-Model Association eXpedited (EMMAX) tool was then applied to all informative SNPs using the BN kinship matrix for genome-wide association mapping . The topThe phenotypic variation explained (PVE) was calculated using the following formula: where β represents the effect value from the GWAS results, MAF denotes the minimum allele frequency of the SNP, and N represents the number of individuals involved in the GWAS analysis.http://asia.ensembl.org/Tools/VEP), SNPs were assigned to genes if they were within the genomic sequence of a gene or within a flanking region of 5 kb up- or downstream of a gene, to include SNPs located in gene regulatory regions. The Ensembl GRCg6a database was used as a reference .We then identified the biological functions of these genes in PubMed (https://pubmed.ncbi.nlm.nih.gov). To elucidate the biological functions of the genes, we performed the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) enrichment analysis using the KOBAS 3.0 online platform [P-value of < 0.05 as significant. The simplify enrichment analysis of GO terms were generated using the “simplifyEnrichment” package in R environment [Using the VEP software    located on 24 chromosomes were identified , neural EGFL like 2 (NELL2), protein tyrosine phosphatase non-receptor type 20 (PTPN20), and mitogen-Activated protein kinase 8 (MAPK8). In addition, the candidate genes associated with the BWFE are listed in Table GPC5), cytoskeleton associated protein 2 (CKAP2), vacuolar protein sorting 36 homolog (VPS36), SH3 domain containing GRB2 like, endophilin B1 (SH3GLB1), and mitogen-activated protein kinase kinase 6 (MAP2K6).The candidate genes associated with the AFE are listed in Table SMAD9) and serine palmitoyltransferase long chain base subunit 2 (SPTLC2). Annotations of suggestive SNPs for clutch traits are shown in Table GPCPD1), SET and MYND domain containing 3 (SMYD3), solute carrier family 4 member 3 (SLC4A4), fibroblast growth factor 12 (FGF12), phospholipase D1 (PLD1), mitogen-activated protein kinase kinase 1 (MAP2K1), von Willebrand factor A domain containing 8 (VWA8), Myb/SANT DNA binding domain containing 1 (MSANTD1), huntingtin (HTT), pro-apoptotic WT1 regulator (PAWR), asparaginase like 1 (ASRGL1), phospholipase C like 1 (PLCL1), calcium voltage-gated channel auxiliary subunit alpha2delta 3 (CACNA2D3), and SKI family transcriptional corepressor 2 (SKOR2), which have been shown to be related to egg number, egg production, litter size, or reproductive traits, are worth a deeper exploration.The candidate genes associated with the LR are listed in Table Using VEP tools, 140 genes were annotated through 421 unique candidate SNPs, then the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and GO enrichment analysis was performed Tables S and S9. Clutch traits are one of the important reproductive performance indicators of hens, which are affected by follicle development. The growth of follicles is affected by factors, such as species, age, environment, hormone levels, nutrition, etc. When ovulation is interrupted for a day or a few days, hens experience intermittent egg-laying. Related research shows that clutch size can be used to describe the individual laying pattern and has been considered as a trait for selection, and clutch trait used as selection indicator of breeding can effectively extend the clutch length, reduce the intermission clutch, and improve egg production , 8. HoweEstimates of genetic parameters for clutch related traits revealed that the AFE had the highest heritability estimate (0.76). In the literature, the AFE has been reported as a trait with moderate heritability 0.36–0.55) , 25–27, 6–0.55 , Population stratification is a critical issue that can lead to false positive results in GWAS of complex traits. Previous studies have mainly used genomic control, stratification analysis, PCA, and mixed linear model (MLM) association to address this issue. However, in recent years, genomic control and stratification analysis have been less frequently used, and most studies have instead opted for PCA and MLM association for population stratification analysis. For instance, Tang et al.  used PCAIn our study, we calculated the PVE for QTS candidates, and some SNPs had PVE results exceeding 10%. Noteworthy, due to linkage disequilibrium (LD) between significant SNPs, the PVE method results in a sum of PVE values being significantly greater than 1 for all SNPs. Given that each SNP represents a gene, a high LD implies that the gene is represented multiple times, leading to an overestimation of the PVE. In theory, the upper limit of the PVE is the heritability (h2). Additionally, PVE values obtained using different models, such as the generalized linear model (GLM) and MLM, differ, indicating that the PVE values obtained in our study for each SNP are only relevant when compared to QTSs with the same phenotype.NELL2 and MAPK8 genes were associated with the AFE of the chicken. Some genes regulate hormone levels, including GNRH, oxytocin (OXT), growth hormone (GH), and thyroid hormone (TH). All these hormones play a crucial role in the female reproductive system [Lhb expression and slower frequencies favoring Fshb expression. The GNRH-induced Egr1 gene expression is mediated by MAPK8/9 and MAPK1/3, and both are critical for Lhb gene transcription [NELL2-GNRH-MAPK8 axis) regulates the AFE trait of chicken. The CKAP2 and MAP2K6 genes have been associated with the BWFE. CKAP2 encodes cytoskeleton associated protein 2, which is involved in cell proliferation and cell survival [CKAP2 may participate in the hyperplasia and hypertrophy of the oviduct before the laying of the first egg [The AFE of chicken is an indicator that is related to reproduction and growth . There ie system –41. NELLe system , but is e system . Pulsaticription . The intsurvival , and is survival . A previsurvival , and anoirst egg . MAP2K6 irst egg .SMAD9 and SPTLC2 genes were identified as candidate genes associated with the LR trait.The LR is a trait with high economic value in breeding programs for laying hens, as it reflects the number of eggs laid during days of oviposition. In this study, the SMAD9, an essential transcription factor in the BMP/SMAD pathway, plays a crucial role in follicular initiation, suggesting the involvement of SMAD9 in both avian follicular development and ovulation processes [SPTLC2) infection leads to the downregulation of gluconeogenic genes, resulting in a decrease in plasma glucose levels. Conversely, the same infection leads to increased VLDL secretion, which in turn causes elevated plasma triglyceride levels [Follicular development and selection are critical factors that affect egg production in poultry. According to recent studies, rocesses . Efficierocesses . Researce levels .ASRGL1 gene was identified as a candidate gene associated with clutch traits. ASRGL1 is a ubiquitously-expressed asparaginase present in the mammalian uterus and is highly expressed in the brain and testes [ASRGL1 knockout mice exhibited impaired visual function and showed degeneration and apoptosis of photoreceptor cells [As the main factor affecting the reproductive performance of birds, a lighting regime has been applied to modern poultry production. By adopting a suitable light regime, producers can make the start of production of poultry earlier or later, thereby adjusting the egg production rate and laying time. Hens have regular laying intervals under continuous light conditions, have high production efficiency for a certain period of time, and show no laying interruptions. In this study, the d testes , 54. ASRor cells . This geMEGF11 and SUCLG2 genes were identified as LC-related genes. According the GWAS and functional annotations, the MEGF11 gene is associated with feed efficiency [SUCLG2 gene is involved in the propanoate metabolic pathway and has been significantly associated with growth in pigs [SUCLG2 plays a key role in the regulation of POU1F1, which is known to be involved in the expression of growth hormone and prolactin genes [The change of the clutch cycle Is not only directly affected by the external environmental photoperiod and temperature, but the nutrient intake also affects the clutch cycle change. Delayed ovulation time may be affected by the different ways in which laying hens metabolize material and energy throughout the day. Hens mainly focus on energy metabolism during the day and protein synthesis at night, and thus hens need a sufficient amount of protein throughout the day for optimal egg-laying performance . The MEGficiency . and the in pigs . Moreovein genes , 60.SMYD3, PAWR, PLCL1 have been shown to be related to the HPG axis. SMYD3 is a SET domain-containing protein with histone methyltransferase activity on histone H3-K4. RNA interference-directed down-regulation of SMYD3 revealed that SMYD3 is required for endoplasmic reticulum-regulated gene transcription in the estrogen signaling pathway [Plcl1 and Plcl2 results in subfertility, abnormal estrous cycle, and smaller uteri [Plcl1 and Plcl2 double-knockout mice apparently grew normally and became fertile, but mutant female mice had an apparently smaller uterus by gross anatomical observation and had more estrous days during the cycles. They also measured serum levels of LH and FSH for 5–6 consecutive days and found them to be significantly higher in the mutant [The reproduction of birds is regulated by a series of complex internal and external environments. Seasons, lighting, and environmental stress all have an impact on their reproduction. The internal reproductive Hypothalamic-Pituitary–Gonadal (HPG) axis regulates their reproductive cycle. A related study found that prolactin (PRL) levels in hens were low in the early part of the clutch cycle, while PRL levels increased in the later part of the cycle. PRL levels were inversely correlated with clutch cycle . In gene pathway . It has  pathway , 65. The pathway . In femaer uteri . Plcl1 ae mutant .The regulation of the clutch traits may be mediated through complex interactions among multiple pathways. According to the KEGG pathway enrichment analysis, several well-known pathways associated with reproduction were found to be enriched, including GNRH signaling pathway, ErbB signaling pathway and Oocyte meiosis pathway, etc. GNRH, a hypothalamic neuropeptide, is a main regulator of male and female reproductive function, which is required for reproduction. GNRH is secreted from the hypothalamus and acts on G-protein coupled receptors in the anterior pituitary. GNRH regulates the production and release of LH and FSH gonadotropins, controlling gametogenesis and steroidogenesis , 69. TheWASH1, NPVF, and FOXO3 as candidate genes [P < 0.05) and/or increasing the average clutch length (P < 0.05) [Previous studies have identified numerous QTLs associated with AFE and clutch traits. For instance, a GWAS revealed seven and twelve regions linked to egg production in the Rhode Island Red (RIR) and White Leghorn (WL) lines, respectively, with te genes . However < 0.05) . This fiNELL2 gene may affect the AFE through the NELL2-GNRH-MAPK8 axis in chickens. Moreover, the SMYD9, SPTLC2, SMYD3 and PLCL1 genes, which were identified by annotating genome-wide significant SNPs, can be considered as candidates genes associated with LR and clutch traits. The identified candidate genes and SNPs provide new avenues for further research. Further validation of SNPs and genes identified in this study in a larger cohort would help to establish a framework in methods of genomic prediction, increasing the accuracy of estimated genetic merit for egg production and clutch traits and helping the chicken breeding industry.We performed genetic parameter estimation and a GWAS in a Chinese native chicken population to examine genetic architectures underlying clutch traits. The clutch traits showed moderate to high heritability. The Additional file 1: Figure S1. SNP density plot across the 30 chromosomes of chicken showing the number of SNPs within a 1-Mb window size. The horizontal axis represents the chromosome length in Mb. Different colors correspond to SNP density. Figure S2. Principal component analysis (PCA). Figure S3. Q-Q plot derived from GWASs for clutch traits. The Q-Q plot contains expected -log10-transformed P-values plotted against the observed -log10-transformed P-values. Figure S4. Manhattan plot derived from GWASs for the LC. Each dot on this figure corresponds to a SNP within the dataset, and the horizontal red and blue lines denote the genome-wide significance (3.42E-8) and suggestive significance thresholds (6.84E-7), respectively. The Manhattan plot contains -log10 observed P-values for genome-wide SNPs (y-axis) plotted against their corresponding position on each chromosome (x-axis). The horizontal axis represents the chromosome length in Mb. Different colors correspond to SNP density. Figure S5. Manhattan plot derived from GWASs for the FWLCS. Each dot on this figure corresponds to a SNP within the dataset, and the horizontal red and blue lines denote the genome-wide significance (3.42E-8) and suggestive significance thresholds (6.84E-7), respectively. The Manhattan plot contains -log10 observed P-values for genome-wide SNPs (y-axis) plotted against their corresponding position on each chromosome (x-axis). The horizontal axis represents the chromosome length in Mb. Different colors correspond to SNP density. Figure S6. Manhattan plot derived from GWASs for the LWLCE. Each dot on this figure corresponds to a SNP within the dataset, and the horizontal red and blue lines denote the genome-wide significance (3.42E-8) and suggestive significance thresholds (6.84E-7), respectively. The Manhattan plot contains -log10 observed P-values for genome-wide SNPs (y-axis) plotted against their corresponding position on each chromosome (x-axis). The horizontal axis represents the chromosome length in Mb. Different colors correspond to SNP density. Figure S7. Manhattan plot derived from GWASs for the NUMC. Each dot on this figure corresponds to a SNP within the dataset, and the horizontal red and blue lines denote the genome-wide significance (3.42E-8) and suggestive significance thresholds (6.84E-7), respectively. The Manhattan plot contains -log10 observed P-values for genome-wide SNPs (y-axis) plotted against their corresponding position on each chromosome (x-axis). The horizontal axis represents the chromosome length in Mb. Different colors correspond to SNP density. Figure S8. Manhattan plot derived from GWASs for the LNDWEL. Each dot on this figure corresponds to a SNP within the dataset, and the horizontal red and blue lines denote the genome-wide significance (3.42E-8) and suggestive significance thresholds (6.84E-7), respectively. The Manhattan plot contains -log10 observed P-values for genome-wide SNPs (y-axis) plotted against their corresponding position on each chromosome (x-axis). The horizontal axis represents the chromosome length in Mb. Different colors correspond to SNP density.Additional file 2: Table S1. The clean data details of resequencing samples. Table S2. Mapping details of resequencing samples. Table S3. Distribution of SNPs after LD pruned. Table S4. Estimates of genetic correlations between clutch traits g using the relationship matrix that blends genomic information. Table S5. GWAS significant SNPs. Table S6. Annotations of GWAS significant SNPs. Table S7. The relationship between SNP genotypes with phenotypes. Table S8. The results of KEGG analysis. Table S9. The results of GO analysis.


Trifolium pratense L.). In areas with cold winters, lack of persistency is often due to poor winter survival, of which low freezing tolerance (FT) is an important component. We conducted a genome wide association study (GWAS) to identify loci associated with freezing tolerance in a collection of 393 red clover accessions, mostly of European origin, and performed analyses of linkage disequilibrium and inbreeding. Accessions were genotyped as pools of individuals using genotyping-by-sequencing (pool-GBS), generating both single nucleotide polymorphism (SNP) and haplotype allele frequency data at accession level. Linkage disequilibrium was determined as a squared partial correlation between the allele frequencies of pairs of SNPs and found to decay at extremely short distances (< 1 kb). The level of inbreeding, inferred from the diagonal elements of a genomic relationship matrix, varied considerably between different groups of accessions, with the strongest inbreeding found among ecotypes from Iberia and Great Britain, and the least found among landraces. Considerable variation in FT was found, with LT50-values (temperature at which 50% of the plants are killed) ranging from -6.0°C to -11.5°C. SNP and haplotype-based GWAS identified eight and six loci significantly associated with FT (of which only one was shared), explaining 30% and 26% of the phenotypic variation, respectively. Ten of the loci were found within or at a short distance (<0.5 kb) from genes possibly involved in mechanisms affecting FT. These include a caffeoyl shikimate esterase, an inositol transporter, and other genes involved in signaling, transport, lignin synthesis and amino acid or carbohydrate metabolism. This study paves the way for a better understanding of the genetic control of FT and for the development of molecular tools for the improvement of this trait in red clover through genomics assisted breeding.Improvement of persistency is an important breeding goal in red clover ( A draftGenotyping-by-sequencing (GBS) of DNA pools is a time- and cost-effective method for genotyping a large number of accessions, which is useful when genetic characterization is more relevant at population level than at the constituent individual level, such as in plant breeding of self-incompatible forage species . Genome-The goal of this work was to characterize the level of FT of a diverse panel of red clover accessions and to identify genomic regions and candidate genes associated with FT through a GWAS.22.1-2s−1 photosynthetic photon flux density (PPFD) and expressed as LT50 (temperature required to kill 50% of the plants), in a similar way as described by A total of 393 red clover accessions were phenotyped for FT under controlled experimental conditions. These accessions are cultivars, breeding material, landraces and ecotypes mostly of European origin . FT was 2.2PstI and MseI for digestion, ii) a methodological adaptation that involves molecular normalization of read depth across loci within samples and size selection , and iii) paired-end sequencing. The 393 accessions were genotyped at the accession level using pool-GBS  of DNA eSNP calling and allele frequency estimation was done as described in Haplotype variants within GBS loci were called and their relative frequencies estimated with the SMAP package  as expla2.3Fij, with i indexing the samples and j indexing the markers) was centered by the mean allele frequencies G, as followsThe population structure among the 392 accessions was investigated with a principal component analysis (PCA) based on the reference allele frequencies of the SNPs after filtration and imputation. SNP- and haplotype-based genomic relationship matrices (GRM) were calculated following the method described by m represents the number of markers, thj marker, and n represents the total ploidy number, which is the sum of the ploidy number of the parents used to generate the populations as demonstrated by n = 16, as suggested by n values, and a negligible effect was found in the GWAS results using these different GRMs (data not shown), therefore a value of n = 16 was used throughout this study.where r2s;  a linear regression between the reference allele frequency and FT was calculated to estimate the allele effect on the phenotype (slope) and the phenotypic variance explained (R2). Furthermore, to characterize the effect of kinship on the R2, two different regression models were fitted, one without and one with kinship (as a random genetic effect), with the “lmekin” and the “lm” functions of the “coxme”  between SNP markers was very low (0.005-0.01) at distances ranging from 1 Mb to 1 kb and increased to 0.16 at distances shorter than 0.5 kb  by the GWAS models with the lowest e-BIC values was 0.30 and 0.26 for the SNP- and haplotype-based analysis, respectively. When kinship was not accounted for, these values were 0.48 and 0.45  of a gene, while for the remaining three loci the marker was located on scaffolds that contained no genes. Functional annotations and the best BLAST hits to the 44.1There was considerable phenotypic variation in FT among the studied red clover accessions, with LT50 values ranging from –6.0 to –11.5°C. For comparison, 48 Nordic gene bank accessions and 6 Nordic cultivars tested using the same protocol ranged from -6.7 to -13.1°C , while aPstI and ApeKI were used as restriction enzymes. In the present study, using similar calling and filtering criteria, we found only 2.8 haplotype variants per HTP on average. The true haplotype diversity is likely to be higher, but as the sequencing depth and consequently the SNP density was much lower in the present study , and because a haplotype must be present in many accessions to reach a MAF > 0.05, a large number of rare polymorphisms are likely to be either not detected or filtered out. This implies that we here explore the genetic variation defined by the most common markers and are likely to miss variation that is due to less common markers. The extremely low LD that we observed is typical in outbreeding species with a high genetic diversity and in line with those found previously in studies considering wide and diversified plant material of red clover  are 1.0 plus the inbreeding coefficient . The hig4.2L. multiflorum) and red clover  had a clearly different average allele frequency than those from the other countries, suggesting that the former material has a very low frequency for an allele which likely provides a better level of FT. Interestingly, both GBS loci which were shared across SNPs and haplotype-based GWAS  were among the loci with the highest 4.3Because of the very rapid LD decay we considered only the closest gene to significant loci as candidate genes, since the likelihood of markers located further away being in LD with FT-associated alleles is low. Ten of the GBS loci significantly associated with variation of FT that mapped within gene sequences, and two more loci were found very close to a gene (< 0.5kb).M. truncatula , is located in a gene encoding caffeoyl shikimate esterase. This enzyme is essential for lignin synthesis and secondary cell wall formation in uncatula , which muncatula . Additiod clover . Allele myo-inositol from the vacuole into the cytoplasm in A. thaliana  protein. INT1 transports thaliana . Inositon plants . The metn plants . Pinitoln plants . It alson plants . Allele One candidate gene encodes a peroxidase, which may be involved in both lignin synthesis and/or oxidative stress response. Plants exposed to low temperatures combined with light are subjected to oxidative stress because of an energy imbalance in the photosystem . Several4.4Considering the current and future need for a more self-sufficient and sustainable production of plant-based protein in Europe, the availability of red clover cultivars with improved persistency is of primary importance to guarantee higher and more stable yields for this species. We have here described the variation in FT across a broad European collection of red clover accessions and identified loci and possible candidate genes associated with this trait. The genetic polymorphisms that were identified may, after validation in relevant germplasm, be transformed into molecular markers and used in selection for this trait. Furthermore, the integration of these markers with markers related to disease resistance and other traits of interest in a genomic selection model will contribute to a powerful tool to accelerate breeding of improved red clover.https://dataverse.no/dataset.xhtml?persistentId=doi:10.18710/WF1AGU. The GBS reads are available at NCBI under project number PRJNA842231.The original contributions presented in the study are publicly available. This data can be found here: NMBU Open Research Data CG and RK were responsible for establishing a comprehensive red clover germplasm collection and for collecting and distributing red clover seeds. LS was responsible for DNA extractions and genotyping. LS and TR designed the genotyping strategy. TR performed SNP and haplotype calling. ÅE designed the phenotyping strategy and SZ performed the phenotyping. SZ performed all analyses of phenotypic and marker data with supervision from MP and ÅE. SZ wrote the paper with supervision from ÅE. All authors contributed to the article and approved the submitted version.


Pyricularia oryzae that are responsible for two new plant diseases: wheat blast and grey leaf spot of ryegrasses. We provide evidence that wheat blast/grey leaf spot evolved through two distinct mating episodes: the first occurred ~60 years ago, when a fungal individual adapted to Eleusine mated with another individual from Urochloa. Then, about 10 years later, a single progeny from this cross underwent a series of matings with a small number of individuals from three additional host-specialized populations. These matings introduced non-functional alleles of two key host-specificity factors, whose recombination in a multi-hybrid swarm probably facilitated the host jump. We show that very few mutations have arisen since the founding event and a majority are private to individual isolates. Thus, adaptation to the wheat or Lolium hosts appears to have been instantaneous, and driven entirely by selection on repartitioned standing variation, with no obvious role for newly formed mutations.Most plant pathogens exhibit host specificity but when former barriers to infection break down, new diseases can rapidly emerge. For a number of fungal diseases, there is increasing evidence that hybridization plays a major role in driving host jumps. However, the relative contributions of existing variation versus new mutations in adapting to new host(s) is unclear. Here we reconstruct the evolutionary history of two recently emerged populations of the fungus  Analysing the evolutionary history of two recent populations of a pathogenic fungus that infects wheat and ryegrasses, the authors show how recombination and selection on standing genetic variation contributed to adaptation to the new hosts. For phytopathogenic fungi, however, interspecific hybridization and admixture are emerging as common themes in the evolution and spread of new diseases6 and these processes alone can produce genetic radiations. Consequently, the relative contributions of pre-jump, standing variation versus post-jump, adaptive mutations in driving pathogen diversification are often unclear. Factors that can contribute to this uncertainty are knowledge gaps about a hybrid’s immediate ancestors8 or if recombination events were ancient and/or repeated over many years9. At the same time, the genetic basis for host specificity is poorly understood for many fungal pathogens, so the mechanisms by which hybridization promotes host jumps and range expansions are unknown.Many new plant diseases emerge when pathogen populations specialized on one or more other host plants overcome genetic barriers that once prevented infection of a former non-host. It is generally believed that new diseases start with a host jump, followed by an adaptive period that results in a gradual increase of pathogen fitness and dispersalPyricularia oryzae (synonymous with Magnaporthe oryzae) each caused major epidemics shortly after their detection. The initial outbreak on wheat (Triticum aestivum)   in Mississippi and Louisiana occurred in late 1971 (refs. 19) but initially appeared unrelated to the later outbreaks because the disease was localized and sporadic.The wheat blast (WB) and grey leaf spot (GLS) diseases caused by the haploid fungus um) Fig.  occurredne) Fig.  in PennsP. oryzae is best known as a global pathogen of rice (Oryza sativa) but also infects other crops such as oats, barley, millets, as well as a range of turf and weedy grasses20. Normally, the fungus is highly host specific and, although infection of multiple hosts is often seen in inoculation assays21, the phylogenetic clustering of isolates by host of origin24  appear to be less host restricted, with members having been found on 11 other Gramineae27 24, whose estimated existences span hundreds and thousands of years, respectively29. High genetic and phenotypic diversity is often associated with recombining populations. However, in the pathogenic phase, P. oryzae propagates primarily through the production of asexual spores and the sexual phase has never been observed in nature. For this reason, field populations are usually considered asexual30, although recent studies implicate the sexual cycle in the structuring of some host-specialized populations, including WB and GLS31.PoT and PoL1 exhibit far greater nucleotide diversity than other host-specialized populations of ths Fig.  that greEleusine pathogen to jump hosts—first onto Lolium, then onto wheat lacking the RWT3 resistance gene and, finally, onto RWT3 wheat33. However, this could not explain how such high levels of genetic diversity arose in a population that had no obvious existence before 1985. In this Article, accordingly, we reasoned that understanding the high genetic diversity in WB/GLS would provide important new insights into their evolution.The prevailing model for WB evolution holds that it involved sequential loss/mutation of key host specificity genes, which allowed an 23. This pointed to the introgression of DNA from several sources. We used 20 kb of sequence surrounding two markers showing the highest diversity to perform phylogenetic analyses and, in both cases, four alleles were identified and each showed perfect/near-perfect nucleotide identity to an allele from another P. oryzae lineage. CH7BAC7 matched alleles found in lineages infecting Urochloa (PoU1 and PoU3), while MPG1 identified with alleles found in Eleusine pathogens (PoE1). Both loci also showed alleles most closely related to those in PoO (Oryza), PoS (Setaria) and PoP (Panicum)  data were acquired for 96 candidate donor isolates (PoT/PoL1 excluded) that were assigned to 16 discrete populations based on discriminant analysis of principle components35  between B71 and a representative isolate from each of the candidate donor populations. The resulting plots revealed that the isolate with the lowest divergence relative to B71 often showed perfect haplotype identity (sequence identity) over multiple consecutive windows ) Fig. . OverallThe B71 chromosome segments showing the highest similarity to the PoO and PoS populations routinely showed higher divergence relevant to the best donor candidate, than did the rest of the B71 genome Fig. . Considex axis. This told us that all chromosome segments—no matter their origin—have accumulated similar (and very low) numbers of mutations since the population’s foundation. This was true also for the regions with PoO/PoS-like haplotypes and, therefore, confirms the existence of an unsampled PoX donor.To understand the unusually high nucleotide diversity within PoT/PoL1, we use sliding window haplotype comparisons to compare B71 with 116 other PoT/PoL1 isolates. As expected, this revealed many chromosome regions where B71 showed substantial divergence relative to other PoT/PoL1 members. However, these regions were unevenly distributed across the genome because long stretches of chromosomes 1, 2, 4, 5 and 6, spanning a total of 7 Mb (~15% of the genome), showed almost perfect identity across all isolates Fig. . ImportaLuziola sp. (PoLu), with most of these sequences being present on chromosome 4.The logical extension from the above findings is that nearly all of the nucleotide diversity within PoT/PoL1 must have arisen through the variable repartitioning of standing variation. More specifically, this points to a scenario in which PoT/PoL1 arose in a hybrid swarm, where a series of admixture events introduced genetic material from several formerly diverged donor populations. Matings between swarm members then led to extensive recombination and re-assortment of the various contributions, while still retaining substantial portions of each donor genome within the newfound population. To test this ‘multi-hybrid’ swarm hypothesis, we first used SplitsTree to explore recombination within the PoT and PoL1 lineages. The resulting network contained deep reticulations that pointed to extensive recombination among population members   Fig. . Second,T7) Fig. . Particueat Fig. .P. oryzae mates via hyphal fusion between strains of opposite mating type. Therefore, to explain the chromosomal constitutions of PoT/PoL1, we hypothesized that evolution must have occurred in a stepwise fashion, starting with a mating between a pair of fungal individuals from two of the formerly diverged donor lineages. A recombinant individual from this founding haplotype would have then mated with a member of a third diverged lineage, and so on. Furthermore, because chromosome segments with common ancestry showed so little variation within the new population, we surmised that the founding cross must have occurred very recently, so that isolates collected soon after the first disease outbreak might provide insights into early population structure and could be intermediate admixtures. Most PoL1 isolates were collected during the mid-1990s epidemics, except for ATCC64557 , which was isolated in 1980. Chromopainting revealed ATCC64557 to be a two-way admixture  and T3-1 (1986), appear to be clones of one another and represent the PoT1 haplotype. PoT1 is a three‑way admixture, with about 27% of the genome having come from PoX, and the remainder essentially coming from PoL1-1 Fig. . The PoXSeveral PoL1 members contained PoE1 introgressions that are also not present in PoT1 and were presumably acquired by backcrossing with PoL1-1. A good example is a diagnostic PoE1 to PoU1 crossover that was regained on the left arm of chromosome 7 Fig. . Other s−7 SNPs per site per year for the average nucleotide substitution rate and a time to most recent common ancestor (TMRCA) of ~50 years . These estimates should not have been greatly affected by recombination because only 15 of the 422 SNPs (3.5%) were shared between different haplotypes  to increase our chances of detecting recombination within the dataset. A total of 422 high-confidence SNPs were identified among 73 isolates, and these had an acceptable temporal signal Fig.  that wasars Fig. —effectivars Fig. . Values Of the 31 PoL1/PoT haplotypes represented in the tree, 18 are rooted at nodes dated between 1978 and 1980 Fig. —a patterP. oryzae’s host jump to wheat is correlated with mutation and/or loss of the PWT3 and PWT6 host-specificity genes, whose products trigger resistance in wheat37. To understand how the swarm activity might have driven the host jump, we used BLAST searches to trace PWT3 and PWT6 alleles through the WB pedigree. Importantly, we found that WB/GLS contained null/non-functional alleles of these genes that already existed as standing variation within P. oryzae and the chromosomal patterns of inheritance seen in Fig. PWT6 resides on chromosome 5 in PoE1, but the PoL1-1 founder inherited the relevant chromosome segment from the PoU1 lineage whose members lack the gene  are present in the PoT1 founder isolates and, therefore, with no evidence of a secondary donation from PoU1, both must have arisen de novo. pwt3Atm is found in multiple haplotypic backgrounds consistent with segregation in the swarm, while pwt3Atp was found in just a single isolate, WBKY11.Prior work has shown that oU1 Fig. . PWT3A wLolium-infecting lineages of P. oryzae23. Here, we provide evidence they co-evolved in a multi-hybrid swarm that repartitioned standing variation present in at least eight individuals from five P. oryzae populations individually specialized on Eleusine, Luziola, Stenotaphrum, Urochloa and an unknown host related to Oryza/Setaria pathogens . Allopolyploid hybrids formed via vegetative fusion have also been linked to host range expansions in pathogens such as Verticillium45 and in mutualistic Epichloë symbionts46. With the resolution afforded by phylogenomic approaches, hybridization between divergent lineages within a single species (admixture) has recently been identified as a potential driver of pathogen diversification and new disease emergence49. For most pathosystems, however, the genetic barriers that prevent colonization by non-pathogens and the mechanism(s) by which hybridization breaks these down are unknown.The idea that hybridization drives the evolution of plant pathogens is well established. Interspecific hybrids have been associated with the emergences of several new plant diseasesP. oryzae avirulence on wheat50. After surveying allele distributions for two cloned genes, they proposed an evolutionary model where functional losses of PWT6 and currently unknown PWT genes allowed an Eleusine pathogen to colonize rwt3 wheat, probably via an intermediate Lolium host32. A population increase on wheat was then proposed to have been a springboard for the evolution of pwt3 mutants with virulence on RWT3 cultivars33. By tracing PWT3/6 alleles through the PoT/PoL1 pedigree  were capable of infecting non-wheat/Lolium hosts. Moreover, with some haplotypes being found on multiple genera, and one (PoL1-9) that so far has only been found on oat, it appears that WB/GLS evolution occurred within the context of a broader adaptive radiation that also produced some haplotypes with unusually wide host ranges, and others that are preferentially adapted to other grasses. Interestingly, the fungal pathogens Zymoseptoria pseudotritici and Blumeria graminis triticale also have recent evolutionary origins in hybrid swarms and high haplotype diversity within the emergent populations furnished to them with expanded host ranges, compared with related species (Z. pseudotritici), or their immediate forebears .Resurrection of ancestral virulence alleles has been proposed as a mechanism by which the apple scab pathogen, pwt alleles could have facilitated host jumps onto wheat and other grasses, but it is not clear how strains of P. oryzae adapted to so many different hosts could have come together in the first place, or how progeny of differentially adapted parents avoided epistatic interactions that typically cause losses of virulence on either parental host43. One possibility is that suitably adapted strains suppress host immunity, allowing co-colonization by less-adapted relatives—a demonstrated avenue for gene flow among different races of the obligate oomycete pathogen, Albugo candida9. However, recombination in Albugo occurred over a considerable period of time and, given the short-lived nature of the WB/GLS swarm and the widespread sharing of recombination blocks  and associated weeds (Eleusine/Luziola). Plant tissues in this form would have similarly compromised, or inactive, immunity and should, therefore, have been minimally restrictive to fungal mating and growth.In hindsight it is understandable that shuffling of cks Fig. , we favo4. The exact opposite appears to be true for WB/GLS because diversification was driven primarily by the recombination of admixture variation, which then led to the host jump, and it would take an estimated 25,000 years to accumulate diversity equivalent to that generated by the brief swarm activity. Moreover, because so few of the mutations that arose after admix 1 were shared among different haplotypes (Supplementary Data File 45), which only experienced host-driven selection after asexual spores escaped the swarm.It has been proposed that host jumping is a ‘cornerstone in the evolution of plant pathogens, as it leads to pathogen diversification’, which is then ‘followed by radiation, specialization and speciation’R0 > 1)—a process known as ‘fine-tuning’3. For WB/GLS, the diseases exploded onto the scene so soon after the swarm’s formation, it seems unlikely that much fine-tuning would have occurred, especially considering the limited number of new mutations to fuel adaptation. This leads us to propose that the repartitioning of standing variation not only drove the initial host jumps onto wheat and Lolium (and other grasses), but produced a recombinant population with individuals sufficiently ‘pre-adapted’ to the new hosts that they were inherently highly pathogenic. This is unexpected considering the potential for variable epistatic interactions among loci donated by individuals adapted to divergent host species  for 7 days with shaking.The origins of fungal strains used in this study are listed in Supplementary Data File 56. Most libraries were prepared using the Nextera kit  and were constructed according to the manufacturer’s instructions, but using a tagmentation step of 60 min. A small number of libraries were prepared using the Kapa HyperPlus kit , according to the manufacturer’s protocol. Sequences were acquired using MiSeq  and using HiSeq (150 PE) at Novogene Corp Inc.DNA was prepared from freeze-dried mycelium using previously described methodsk-mer range 59–129.Raw reads were trimmed to remove adaptor sequences and poor-quality regions using Trimmomatic (PE mode: ILLUMINACLIP: NexteraPE-PE.fa:2:30:10 SLIDINGWINDOW:20:20 MINLEN:80). Paired-end reads passing these filters were assembled with Newbler 2.9 or Velvet 1.2.10 using VelvetOptimiser to optimize the assembly over the 57, which performs two passes of a repeat-masking algorithm originally developed for TruMatch58 to ensure that SNPs are not called in regions affected by repeat-induced point mutation. Briefly, repeat-masked genomes are aligned using blast , a second repeat-filtering step is then performed, and SNPs are then called only in uniquely aligned regions. iSNPcaller accuracy was tested by identifying SNPs in fully independent genome assemblies derived from the same raw read datasets. This revealed a median false SNP call error rate of ~1.1 × 10−5 . The final dataset comprised 471,202 SNPs in unique chromosome regions.We employed a custom perl script59). Tree plots were generated using the radial output format in MEGA X, circular format in ggtree60 and neighbour-net network in Splitstree5 (ref. 61). Phylogenetic analyses of MPG1 and CH7BAC7 were performed by aligning the sequences with MUSCLE 3.8.31 (ref. 62) and generating maximum likelihood trees with RAXML8 (ref. 63). A co-phylogeny plot was created in R using the phytools cophylo function with parameters: assoc = NULL, rotate = TRUE64. Within-population divergence values were calculated in MEGA X after partitioning the pairwise distance data based on population memberships. Resolution of distinct donor haplotypes from the same source population was accomplished by using the binary haplotype data to build phylogenetic trees in RAxML under the bingamma substitution model.Pairwise distance data from iSNPcaller were used to build neighbour-joining trees using default parameters in MEGA X . DAPC was performed over a K-value range from 10 to 25, after retaining 100 principal components. Populations used for subsequent analyses were defined according to the partitioning at K = 16, as this value had the lowest Bayesian information criterion value. Similar population divisions were identified with STRUCTURE66, although the latter programme failed to resolve the PoO and PoS lineages, which were clearly separated, both by DAPC  module from Poppr−9 across all chromosomes. Run parameters included the haploid (-j) and print copyprobsperlocus files (-b) options, with each expectation maximization being performed over ten iterations, while maximizing over copy proportions (-ip option). Donor probability plots and chromosome haplotype plots were generated from the resulting copyprobs files, using custom R programs. To speed up plotting and generate plots with manageable sizes, the copyprobs file can be downsampled by rounding chromosome coordinates to the nearest 10 kb and retaining just one data point per interval. The inferred donor contributions were obtained by using the write.csv function to print out the data frame used for the ChromoPainter stacked bar chart plots. Overall donor contributions to each haplotype/population were obtained by parsing the ChromoPainter chunklengths.out file, using custom R scripts.First, we used a custom script to call chromosomal haplotypes  for each strain, based on the SNP data from iSNPcaller. The dataset was filtered to remove all SNP positions where any one strain had a missing data point. ChromoPainter was run separately for a single representative of each PoT/PoL1 haplotype, using all non-PoT/PoL1 strains as potential donors, and an even recombination rate of 7 × 10The ChromoPainter input files were used as inputs to the perl script SlideCompare.pl, which compares haplotypes in a pairwise fashion in sliding windows of 2,000 variant sites and a step size of 400. Haplotype divergence was calculated as number of SNPs per variant site. The output files were then read by the ShinyHaplotypes.R code for interactive and static plotting.A custom perl script was used to interrogate the data frame used for the ChromoPainter plots. Briefly, the script identifies chromosome positions showing reciprocal swaps in parentage across the same inter-SNP interval. It then reports the positions of the crossover, which haplotypes exhibit the crossover and the respective heritages of the chromosome segments on each side of the exchange.67) was used to filter the resulting VCF files using the following schema: SNPs should not be repeated regions of the B71 reference, SNPs should not be heterozygous , the alternate:reference allele ratio must be ≥20:1, and there must be a minimum of ten reads covering the site in question. SNPs passing these pre-qualifiers were then cross-referenced with the variants identified by iSNPcaller and inconsistencies were resolved by interrogating the raw read data to determine true presence/absence of the SNP.For this analysis, we used only sequences where all strains were predicted to have inherited the respective chromosome regions from the original founder isolate . By restricting our analysis to sequence data from these regions we could be reasonably confident that the substitution rate was inferred using only new variants that arose between the founder event and each strain’s collection date, thereby avoiding inflation due to pre-existing SNPs acquired by admixture. In addition, because we suspected that the new populations had very recent evolutionary origins, we sought to minimize ‘noise’ from false SNP calls by using a dataset that represents the intersection of calls generated using two completely different SNP calling methods, both of which were pre-filtered to avoid false calls in repeated sequences. In addition to using the data from iSNPcaller, we aligned raw read data to the B71 reference genome using bowtie and then recalled SNPs in the target regions using the Genome Analysis Toolkit with the parameters: HaplotypeCaller, -ploidy 1; -emit-ref-confidence Genomic variant call format (VCF); GenotypeGenomicVCFs default settings; and SelectVariants -select-type SNP. A custom script , versus randomizing the sampling dates . The phylogenetic signal operations were performed using a custom script modified from69.The strength of the phylogenetic signal in the dataset was assessed by using RAxML to build a maximum likelihood tree under the GTRgamma model, with 1,000 bootstrap replications. Patristic distances to ATCC64557 (the earliest sampled isolate) were then calculated using the cophenetic function in ape70). Constant sites were determined by using a custom perl script to count the invariant nucleotides in the queried portions  portions of the surveyed chromosomes. Both strict clock and relaxed log normal clock models were employed, and their rates were estimated in separate Markov chain Monte Carlo runs. Coalescent Extended Bayesian Skyline was selected as the tree prior and 20 independent Markov chain Monte Carlo samplings were performed for each parameter set, with each incorporating 10 million pre-burnin iterations, 100 million sampled iterations and data logging every 5,000 iterations. The resulting logfiles were inspected in Tracer v1.7.1 and the five with the highest effective sample size values were combined using LogCombiner, along with the corresponding tree files71. TreeAnnotator was used to create a maximum clade credibility tree which was plotted using the ape and phyloch packages for R (ref. 72).For molecular dating, the dataset was analysed as a single partition due to the small number of variant sites. The HKY + I + G site substitution model was utilized as determined to be optimal by PartitionFinder2 (ref. PWT3 and PWT6 gene sequences were used to search genome assemblies for all available strains using BLAST and allele assignments were made based on nucleotide mismatches and gaps. For transposon-disrupted alleles, the positions of transposon insertions and target site duplications were used to define alleles. pwt6null alleles were characterized by first aligning the B71 chromosomes with the PWT6 locus in the PoE1 isolate, CD156, to identify the deletion breakpoints and then by comparing the structure of the breakpoint and flanking sequence between PoT/PoL1 members and isolates from the inferred donor populations. Finally, we verified that the allele designation for a given isolate was consistent with the inferred heritage of the corresponding chromosome segment, as determined by chromopainting.The Further information on research design is available in the Reporting SummaryPeer Review FileSupplementary Data 1Fungal isolate information.Supplementary Data 2SNPs used for molecular dating.


GC), calcium and potassium channels (JPH3 and KCNK13), energy and lipid metabolism , and immune response (IL12RB2 and CXCL8), among other functions. This work provides the foundation for the development of novel breeding and management tools for reducing the incidence of periparturient hypocalcemia in dairy cattle.Periparturient hypocalcemia is a complex metabolic disorder that occurs at the onset of lactation because of a sudden irreversible loss of Ca incorporated into colostrum and milk. Some cows are unable to quickly adapt to this demand and succumb to clinical hypocalcemia, commonly known as milk fever, whereas a larger proportion of cows develop subclinical hypocalcemia. The main goal of this study was to identify causative mutations and candidate genes affecting postpartum blood calcium concentration in Holstein cows. Data consisted of blood calcium concentration measured in 2513 Holstein cows on the first three days after parturition. All cows had genotypic information for 79 k SNP markers. Two consecutive rounds of imputation were performed: first, the 2513 Holstein cows were imputed from 79 k to 312 k SNP markers. This imputation was performed using a reference set of 17,131 proven Holstein bulls with 312 k SNP markers. Then, the 2513 Holstein cows were imputed from 312 k markers to whole-genome sequence data. This second round of imputation used 179 Holstein animals from the 1000 Bulls Genome Project as a reference set. Three alternative phenotypes were evaluated: (1) total calcium concentration in the first 24 h postpartum, (2) total calcium concentration in the first 72 h postpartum calculated as the area under the curve; and (3) the recovery of total calcium concentration calculated as the difference in total calcium concentration between 72 and 24 h. The identification of genetic variants associated with these traits was performed using a two-step mixed model-based approach implemented in the R package MixABEL. The most significant variants were located within or near genes involved in calcium homeostasis and vitamin D transport ( A larger proportion of cows develop subclinical hypocalcemia which increases the risk of the cow to develop other peripartum diseases, such as retained placenta, uterine prolapse, metritis, displaced abomasum, ketosis, and mastitis2. The economic losses due to periparturient hypocalcemia are substantial due to on-farm death, premature culling, extended days open, and increased veterinary and treatment costs, among others3.Periparturient hypocalcemia is a complex metabolic disorder that occurs at the onset of lactation because of a sudden irreversible loss of calcium incorporated into colostrum and milk. Some cows are unable to quickly adapt to this demand and succumb to clinical hypocalcemia, commonly known as milk fever, which affects around 5% of dairy cows4. The magnitude of these heritability estimates suggests that genetic selection can be effective to reduce the incidence of periparturient hypocalcemia in dairy cattle. Little is known, however, about the genes underlying cow’s susceptibility to periparturient hypocalcemia. Previous genomic studies in Holstein cows identified genes implicated in vitamin D metabolism, calcium, and potassium homeostasis, such as GC, LRRC38, KCNK9, and CYP27A6. In addition, Sasaki et al.7 reported genes PKIB, DDIT4, PER1, and NUAK1, as potential biomarkers for milk fever predisposition based on gene expression of peripheral blood mononuclear cells.Periparturient hypocalcemia is a heritable trait with heritability estimates between 0.01 and 0.35 depending on the breed and the methodologyThe identification of causal mutations and individual genes affecting periparturient hypocalcemia could have multiple benefits, including better understanding of the molecular mechanisms underlying this complex metabolic disorder, promote the development of new drugs, therapies, and prevention strategies, and contribute to the design of novel breeding strategies. As such, the aim of this study was to use imputed whole-genome sequence data to identify genetic variants associated with three alternative periparturient hypocalcemia traits, namely (1) total blood calcium concentration in the first 24 h postpartum, (2) total blood calcium concentration in the first 72 h postpartum, and (3) the difference in total blood calcium concentration between 72 and 24 h postpartum. We also characterized genes located near the most significant variants.The data consisted of 2513 Holstein cows (938 primiparous and 1575 multiparous) with blood calcium concentrations measured on the first, second, and third day after parturition. The samples were collected in five different experiments performed in 2 different dairy herds in the US from December 2015 to June 2020. Records with blood calcium concentration ≥ 3.0 mmol/L were removed from the analysis because they were considered lab errors. After data editing, the mean (± SD) for blood calcium concentration was 2.13 (± 0.28) mmol/L, with a minimum and maximum of 0.68 and 2.99, respectively. All cows with blood calcium records had genotypic information for 79,060 SNP markers.Three alternative postpartum blood calcium concentration traits were analyzed Fig. : 1) tot tot1 sho8. The 79 k and 312 k sets of SNP markers are both available in the BovineHD Genotyping BeadChip 9.The 2513 Holstein cows with postpartum blood calcium records and 79,060 SNP genotypes were imputed to whole-genome sequence in order to identify causal mutations associated with periparturient hypocalcemia. The imputation was performed in two steps, first from 79 k to 312 k markers, and then from 312 k markers to whole-genome sequence10 using the default parameters.The first imputation step aimed to provide a bridge between a medium density SNP chip and whole-genome sequence. As such, the 79 k SNP genotypes were imputed to 312,615 SNP genotypes using 17,131 Holstein bulls born between 1995 and 2008 as reference population. The imputation process was performed using FImpute311. The imputation was performed again using Fimpute3, and each chromosome was processed separately.After the first imputation step, the imputed 312 k markers were used as a new target for the imputation to whole-genome sequence. A total of 179 US Holstein bulls born in United States and part of the 1000 Bulls Genome project were used as reference panel12. Note that the following two steps were performed for each of the 3 phenotypes and each of the 3 SNP densities .Only autosomal markers with a call rate > 0.9 and a minor allele frequency ≥ 1% for SNP densities 79 k and 312 k, and ≥ 0.01% for whole-genome sequence were retained for this analysis. After quality control, a total of 76,389, 300,621 and 11.6 million markers were available for the alternative genomic scans. The three alternative periparturient hypocalcemia phenotypes were analyzed using a two-step mixed-model-based approachIn the first step, the following model was fitted:13. The variance–covariance matrix for this first model was estimated as Where 12 (version 0.1–3). The P-values were adjusted for multiple comparisons using the Benjamini–Hochberg procedure14. Statistical significance was declared using an adjusted P-values smaller than 0.05.In the second step, the following model was fitted for every SNP:15. Genetic variants located upstream, downstream, or within annotated genes were considered.The assignment of significant genetic variants to bovine genes was based on the latest bovine genome reference ARS-UCD 1.2 using EnsemblSREBP1, a major gene implicated in lipid metabolism. The most significant variant on BTA6 is localized between the genes GC and NPFFR2, which are implicated in vitamin D signaling pathway and regulation of MAPK cascade, respectively. This genomic region on BTA6 also harbors gene CXCL8, which encodes for a pro-inflammatory cytokine, and gene ADAMTS3, which encodes a protease involved in the biosynthesis of collagen. Interestingly, both peaks on BTA3 and BTA6 were also detected using 79 k and 312 k SNP chips. Moreover, the most significant variant on BTA10 is located in an intron of gene KCNK13, a two-pore domain potassium channel that is regulated by extracellular calcium concentrations. The most significant variant on BTA12 is located upstream gene NDFIP2, which is involved in metal ion transport and positive regulation of protein ubiquitination. Finally, the most significant variant on BTA21 is located in an intron of gene PRORP, a member of the mitochondrial ribonuclease P complex involved in mitochondrial tRNA 5′-end processing. The major peaks detected on BTA10, BTA12, and BTA21 were detected only using whole-genome sequence data.The heritability estimate for total calcium concentration in the first 24 h postpartum was 0.186. Five different genomic regions located on BTA3, BTA6, BTA10, BTA12 and BTA21 showed significant associations with calcium concentration in the first 24 h postpartum  and untranslated regions (5’UTR and 3’UTR) typically affect the level of gene expression, whereas mutations on introns could affect both level of gene expression but also the structure of the protein. Interestingly, The Encyclopedia of DNA Elements (ENCODE) project, launched as a follow-up to the Human Genome Project with the goal of identifying and annotating functional elements of the human genome, revealed that many DNA variants associated with diseases lie within non-coding elementsWe performed a comprehensive genomic analysis of three alternative postpartum blood calcium concentration traits in dairy cattle. The most significant variants were located within or near genes involved in calcium homeostasis and vitamin D transport, calcium and potassium channels, energy and lipid metabolism, and immune response, among other functions. These findings can contribute to the development of novel breeding and management strategies for reducing periparturient hypocalcemia in dairy cattle.


Setaria italica (foxtail millet), a founder crop of East Asian agriculture, is a model plant for C4 photosynthesis and developing approaches to adaptive breeding across multiple climates. Here we established the Setaria pan-genome by assembling 110 representative genomes from a worldwide collection. The pan-genome is composed of 73,528 gene families, of which 23.8%, 42.9%, 29.4% and 3.9% are core, soft core, dispensable and private genes, respectively; 202,884 nonredundant structural variants were also detected. The characterization of pan-genomic variants suggests their importance during foxtail millet domestication and improvement, as exemplified by the identification of the yield gene SiGW3, where a 366-bp presence/absence promoter variant accompanies gene expression variation. We developed a graph-based genome and performed large-scale genetic studies for 68 traits across 13 environments, identifying potential genes for millet improvement at different geographic sites. These can be used in marker-assisted breeding, genomic selection and genome editing to accelerate crop improvement under different climatic conditions. Setaria pan-genome constructed using genome assemblies of 110 representative accessions and variation analyses provides insights into foxtail millet domestication and the genetic basis for crop improvement. Setaria italica), one of the oldest domesticated grain crops in the world, is considered to have provided the foundation for the formation of early Chinese civilization. Recent archeological evidence suggests that this species was domesticated starting ~11,000 years ago from its progenitor, green foxtail (Setariaviridis)1, making it contemporaneous with barley and wheat in the early agricultural transitions of human Neolithic societies. Foxtail millet is the only present-day crop species in the genus Setaria and has excellent drought and low soil-nutrient tolerance. Since its domestication, foxtail millet has spread across Eurasia and Africa, and more recently to the Americas, and grows in temperate, tropical and arid environments.Foxtail millet , short life cycles (~70 d) and highly efficient transformation systems5. Despite the favorable features of foxtail millet as a C4 photosynthetic model crop, which may prove pivotal in ensuring global food security6, relatively less is known about its genomic diversity and potential for genetic improvement.Critically, 8, soybean9, wheat10, barley11, tomato12 and potato13 indicate that structural variants (SVs) have critical roles in crop domestication as well as trait determination14 and genetic improvement. To date, two draft genomes15 and three relatively high-quality genomes18 of green foxtail and foxtail millet have been released. Coupled with population-scale short-read sequencing data, previous studies have revealed population structure in foxtail millet and green foxtail, as well as the genetic basis of several key agronomic traits21. However, the full spectrum of genetic variants that underlie Setaria domestication and its broad ecological adaptability, including the role of pan-genomic diversity, remains largely unknown.Recently, pan-genome studies in riceSetaria accessions, and examined genome evolution in the context of foxtail millet domestication and improvement. By incorporating the foxtail millet pan-genome, we constructed the first graph-based genome sequence of Setaria across these multiple accessions and performed large-scale genetic studies across 13 different environments, which could serve as a foundation for foxtail millet research and breeding, providing an example for ‘breeding by design’ in other crops , 829 landrace and 385 modern cultivated accessions from the Setaria genus with an average sequencing depth of ~15×, of which 1,004 were newly generated and 840 were from previous studies21  genetically differentiated subpopulations —is broadly distributed worldwide, which suggests that C3 may have better adaptation to a wider range of climates than the other two subgroups  exceeding 20. Over 99% of Illumina short reads and 97% of embryophyte BUSCO genes could be properly mapped, suggesting high completeness. K-mer-based analysis also showed that all assemblies have high completeness (99.56% ± 0.04%) and quality (40.81 ± 0.52), and low false duplications (0.52 ± 0.13) (Supplementary Table Three representative accessions—Me34V (wild), Ci846 (landrace) and Yugu18 (modern cultivar)—were further selected to build high-quality reference genome assemblies for K-mer-based analysis showed that the assembled genome quality of cultivated accessions  is higher than that of wild accessions . Assessing genome assembly quality using long-terminal repeat retrotransposons (LTR-RTs) indicated that all 107 assemblies reached the ‘reference’ level (LAI > 10), of which 17 reached the ‘gold standard’ level  of assembled sequences were annotated as transposable elements , xiaomi18 and A10 , plus three previously released genomes—Yugu1 ref. , xiaomi1ons Fig. . The panons Fig. . We idenons Fig. .Fig. 3PaBy leveraging the high-quality genome assemblies, we performed pair-wise genome alignment with ‘Yugu1’ and identified 24.3 million SNPs and 3.8 million indels (<50 bp) in the 112 accessions, 1.5% of which are nonsynonymous and may impact gene function  and absence  variants overlapped with TEs, which are significantly higher than the proportion of TEs genome wide .We find that most presence  suggest that PAVs underlie gene expression differences between populations, further strengthening the possibility that PAVs had a role in crop domestication and improvement  using an RIL population  controlling seed shattering in Setaria were identified , we also fine-mapped and narrowed qSH9.1 to an 87.3-kb region between markers M2 and M3, which contained Seita.9G154300  involved in the regulation of green foxtail shattering.Haplotype analysis of both let Fig. . Interesail Fig. , which s27 , and the second most significant association (P = 7.3 × 10−9) with GW  for TGW and GW; this locus has 73% sequence similarity with the rice domestication-related GW5/GSE5 gene, which regulates rice grain size by influencing cell proliferation in spikelet hulls31.We screened gene expression patterns in ten tissues from ‘A10’ (wild) and ‘Yugu1’ (cultivar). The 200-kb interval around this SV harbored 27 genes, eight of which showed differential expression patterns in seeds at the grain-filling stage between ‘A10’ and ‘Yugu1’ Fig. . We thenSiGW3 function, we overexpressed this gene in foxtail millet (accession ‘Ci846’). Compared to wild-type plants, transgenic plants showed higher SiGW3 gene expression, reduced TGW and GW and increased GL  cosegregated with the phenotype  including yield, plant architecture, growth time, biomass, grain quality, coloration and disease resistance-related traits. To identify genes that operate across a broad set of climatic environments, we studied these traits at 13 distinct locations from 18.3°N (Sanya) to 47.3°N (Qiqihar) and 87.7°E (Urumqi) to 123.9°E (Qiqihar) across 11 years   Fig. , which iGBSS/Waxy)33. We directly identified the AAC-associated lead SV  located 1.6 kb downstream from the Seita.4G022400 (GBSSI) gene, while the lead SNP (P < 5.64 × 10−9) is found to be 398 kb away from the GBSSI gene  is a key factor that affects eating and cooking quality in different crops, as determined by the granule-bound starch synthase gene  and peduncle length  through SV-GWAS, while no associated SNPs could be detected within a 50-kb interval of these SVs , encoding a homolog of rice Ghd7, which has crucial roles in rice production and adaptation34, and was only detected by SV-GWAS. Lead SVs are also substantially associated with heading date , leaf length , primary branch number  and straw weight , and based on the amount of drift  and used to construct sequencing libraries following the manufacturer’s instructions (Illumina Inc.). Libraries were paired-end (NGS) sequenced on Illumina NovaSeq 6000 at Novogene. For three representative accessions, long-read library construction followed standard protocol (Pacbio Inc.) and was sequenced on the Pacbio RSII platform at Nextomics Bioscience. Long-read library construction and sequencing for the other 107 de novo assembled accessions were performed by Berry Genomics with the Pacbio Sequel II platform  from different tissues and sequenced by the NovaSeq 6000 platform. For BioNano, fresh leaf tissues from 10-d-old seedlings of three accessions  were collected and high-molecular-weight DNA was extracted and labeled according to standard protocols from BioNano Genomics. All labeled samples were loaded and analyzed using the BioNano Genomics SAPHYR system.41 with default parameters, and filtered reads were mapped to the Yugu1 reference genome with BWA (v0.7.12-r1039)42 using default parameters. Nonunique mapped and duplicated reads were excluded using SAMtools (v1.7)43 and Genome Analysis Toolkit (GATK v4.1.4)44, respectively. SNP calling was performed by GATK (v4.1.4)44. SnpEff (v5.0)45 was used for annotating and predicting the effects of identified SNPs and indels. To identify structural variation in the 1,844 accessions, we mapped filtered Illumina short reads to the Setaria graph-based reference genome and genotyped SVs using vg toolkit (v1.28.0)46 with default parameters.Low-quality sequencing reads of the 1,844 accessions were removed using fastp (v0.23.0)47 and SNPhylo (v2018-09-01)48 with standard settings and 1,000 bootstrap values. SV-based maximum-likelihood phylogenetic tree was constructed based on binary PAV data with 1,000 bootstraps using IQ-TREE (v2.1.2)49. Phylogenetic trees were drawn using ggtree50, an R package. We performed a population structure analysis using the ADMIXTURE (v1.3.0)51 software, initially with k ranging from 2 to 20. Here k = 7 was subsequently chosen because it was the minimal value of k that separated all previously known groups of green foxtail16. We then ran ADMIXTURE ten times with varying random seeds at k = 7.Biallelic SNPs or PAVs with missing frequency <10% and minor allele frequency >0.05 were kept for phylogenetic analysis. SNP-based neighbor-joining phylogenetic tree was inferred using MEGA-CC (v10.1.8)https://github.com/qiangh06/Setaria-pan-genome/tree/main/Population%20genomic%20and%20Demographic%20inference. For demographic history analysis, we aimed at estimating the formation process of three subgroups of foxtail millet. For these analyses, we filtered SNPs with heterozygosity >0.05, minimum allele frequency <0.05 and genotyping rate <90% using PLINK (v.1.90)52. To reconstruct the evolutionary relationships between domesticated subpopulations C1–C3 and the closest wild population W1, we used Admixtools (v2.0)23 on R v4.13 to construct an admixture graph with no migration edges. We used a maximum absolute f4-statistic z-score (|z-score|) threshold of <3.0 for accepting models and added the remaining wild subpopulations W2–W4 sequentially to explore whether they could be incorporated with no migration edges. Population admixture graphs including all seven subpopulations were also inferred using TreeMix (v1.13)22, with W3 as an outgroup. We used the GRoSS method53 to scan the genome for positive selection along each branch of our four-population admixture graph that comprised W1, C1, C2 and C3.Scripts for our population genomic analyses are deposited at Setaria accessions using two approaches. For three high-quality reference genomes , we used Illumina NovaSeq 6000 and PacBio RSII platforms 25. After polishing with Illumina reads and further correction with BioNano physical maps, we obtained 75, 114 and 103 contigs for Me34V , Ci846  and Yugu18 , respectively. For the other 107 accessions, we sequenced using Illumina NovaSeq 6000 at >40× short-read data (except Zhaogu1 with 37.5× data) for each accession. We examined genome size and heterozygosity using Jellyfish (v2.3.0)54 and GenomeScope (v2.0)55. Based on examined genome heterozygosity, we generated >50× and >80× long-read data for low heterozygosity (<0.3%) and high heterozygosity (≥0.3%) accessions by the Pacbio Sequel II platforms, respectively 42, and heterozygous sequences were filtered with Redundans  and Purge Haplotigs (with default parameters). Overlaps between contig sequences were merged using the results of BWA-MEM self-alignment.We assembled 110 diverse 42, and the results were filtered with Q30 by SAMtools (v1.7)43. Finally, the genome sequence was corrected using Pilon (v1.22)56 based on filtered alignments. Three rounds of genome correction were performed by Pilon. Finally, contigs were aligned to the reference genome to construct pseudo-chromosomes using Mummer (v4.0)57 with the parameters ‘-mum -mincluster = 1000’.NGS data were mapped to the genome using BWA-MEM (v0.7.12-r1039)58 with 1,440 embryophyte genes. To assess the assembly completeness of intergenic regions, we used the LAI using LTR_retriever (v2.9.0)59. We also assessed genome completeness by mapping high-quality Illumina short reads to the corresponding assembly using BWA (v0.7.12-r1039)42 with default parameters. K-mer-based completeness, quality and false duplication evaluation were performed by Merqury (v1.3)60.We assessed the completeness of the genic region of assemblies using BUSCO (v5.2.0)61 and RepeatModeler (v4.0.6)62 with default parameters. The predicted repeat library was aligned with the PGSB repeater database63 to assign repeats into distinct families. Next, Repbase (v20.11) was used to conduct homology-based annotation using RepeatMasker (v1.0.10)64. Finally, overlapping repeat sequences that belong to the same repeat class were combined. For overlapping repeats belonging to different repeat classes, overlapping regions were divided. In addition, Tandem Repeats Finder65 was used to annotate tandem repeats.A combination of ab initio and homology-based methods was used to annotate repeats in the assembled genomes. First, we constructed an ab initio repeat library using LTR_FINDER (v1.05)66 with default parameters. Assembled transcripts of Me34V, Ci846 and Yugu18 were used for annotation of wild, landrace and modern cultivars, respectively. Each genome was annotated to obtain gene models using UniProt SwissProt (v2020_01)67 protein database and MAKER (v3.01.03)68. These genes were used to train Augustus (v3.2.3)69 and SNAP (v2006-07-28)70, and the resulting training sets were used for annotation of corresponding genomes. Assembled transcripts were used as EST evidence, and protein sequences of rice (MSU v7)71, Arabidopsis thaliana (TAIR10)72, maize (B73 RefGen_v4)73, sorghum (v3.1.1)74, foxtail millet (v2.2)18, green foxtail (v2.1)16 and UniProt SwissProt database (release-2017_01) were used as protein evidence. Using models trained by SNAP and Augustus, the second round of gene annotation was performed for all repeat-masked genomes, and genes with AED < 0.4 were kept. Functional annotation of predicted genes was performed using InterProScan 5.0 (ref. 75) to assign Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. Based on the results of functional annotation, TE-related genes were filtered.We used transcriptome data from whole plants of three representative accessions . RNA-seq data from each accession were separately assembled using Trinity (v2.8.5)76. If a gene was aligned with >99% coverage and identity, it was considered present in the corresponding genome. We performed a pan-genome analysis based on a Markov clustering approach77. All-versus-all comparisons were performed using diamond (v0.9.25)78 with an E-value cutoff of 1 × 10−5. Subsequently, all paired genes were clustered using OrthoFinder (v2.3.12)77. Based on their frequency, we classified genes into the following four categories: core , soft core , dispensable  and private (present in only one accession).We aligned the CDS of all annotated genes to the 108 genomes from cultivated and wild (W1) foxtail millet using GMAP (v2015-09-21)79 pipeline for structural variation  identification in the 112 genomes. We first aligned each assembled genome to the Yugu1 reference genome using Minimap2 (v2.21-r1071)80. Raw alignment results were further used for variation calling using the SyRI (v1.2)79 software with default parameters. We then filtered SVs with variant size of over 50 bp. From filtered results, insertions and deletions were treated as PAVs. We used the vg toolkit (v1.28.0)46 for graph-based genome construction. First, we identified large PAVs and inversions with MUMmer (v4.0)57. Then, PAVs together with inversions detected by SyRI were integrated into the Yugu1 linear reference genome using the vg toolkit46.We used the SyRIFST and XPCLR, for identifying selective sweeps based on high-quality SNP markers (MAF ≥ 0.05 and missing <0.1). For nucleotide diversity and FST analysis, we used VCFtools (v0.1.17)81 with 20-kb sliding and 2-kb step size. We performed XPCLR analysis using the XPCLR program (https://github.com/hardingnj/xpclr).We used three different strategies, nucleotide diversity, α = 0.01).We performed GWAS for 226 phenotypes in 680 accessions using high-quality SV and SNP markers (MAF ≥ 0.05 and missing <0.1) using the Mixed-Model Association eXpedited program  with the first ten PCAs as a random effect matrix. An effective number of independent makers (SNP and SVs) were estimated to be 640,288, and we defined the significance threshold by Bonferroni-corrected genome-wide significance ) of each phenotype into one cluster if the distance between the SNPs/SVs and the leading SNPs/SVs is ≤50 kb and the LD R2 ≥ 0.3. The grouped SNPs/SVs were defined as associated loci and represented by the leading SNPs/SVs. Second, we selected candidate genes in ±50 kb interval of leading SNPs/SV if their homologous gene was functionally related to corresponding phenotypes in rice or maize.For candidate gene identification, we used the following strategies: first, we grouped all associated SNPs/SVs (82 software to estimate feature gain (FG)/marker effect of each SNP and SVs via information gain analysis. Second, highly effective markers were identified if their reduction of FG  was less than 0.99. Next, for each trait, we grouped markers into the following six panels: SNPcg panel contained highly effective SNP makers selected with ROF ≤ 0.99; SNPcg_gwas panel was the union set of highly effective SNP makers selected with ROF ≤ 0.99 and significantly associated SNP markers from GWAS (P ≤ 7.81 × 10−8); SVcg panel contained highly effective SV makers selected with ROF ≤ 0.99; SVcg_gwas panel was the union set of highly effective SV makers selected with ROF ≤ 0.99 and substantially associated SV markers from GWAS (P ≤ 7.81 × 10−8); SNPSVcg panel contained highly effective SNP and SV makers selected with ROF ≤ 0.99; and SNPSVcg_gwas panel was the union set of highly effective SNP and SV makers selected with ROF ≤ 0.99 and substantially associated SV markers from GWAS ).First, we performed a feature selection analysis of three different marker panels  for each of the 226 phenotype datasets independently using the CropGBM (v1.1.2)The predictive precision of models was assessed for each marker panel and corresponding phenotypes using Pearson’s correlation between observed phenotypes and predicated GEBVs. We randomly divided the dataset into 580 and 100 lines for validation. The 580 lines were used as training sets to estimate marker effects, which were then used to predict GEBVs for the remaining 100 lines; this was replicated 100 times for each dataset.max_haplotype represents the highest GEBV of simulated haplotypes, and GEBVmax_cultivated denotes the highest GEBV of cultivated foxtail millet.We used 63 datasets  for breeding potential prediction. The marker panel with the highest prediction precision for the corresponding phenotype was selected. We then simulated 1.04 million haplotype combinations using the top 20 high-effective markers of accessions with the highest GEBVs. The improvement percentage of each phenotype was calculated by SiGW3 was amplified from green foxtail accession ‘A10’ and cloned into pCAMBIA1305 under the control of the ubiquitin (UBI) promoter. Primers OE-GW3-F and OE-GW3-R were used  reporter gene driven by the minimal 35S promoter was used as an internal control, and firefly luciferase (LUC) driven by the target 366-bp insertion promoter and the target 366-bp deletion promoter was amplified from Setaria wild species ‘A10’ and cultivar ‘Yugu1’, respectively. Primers used for amplifying the SV in SiGW3 promoter sequences are listed in Supplementary Table Agrobacterium GV3101 and co-infiltrated into leaves of 4-week-old N. benthamiana. Luciferase signals were imaged using Tanon 5200 and measured using Dual-Luciferase Reporter Assay System (E1910) kit (Promega) and Varioskan LUX (Thermo Fisher Scientific). Each measurement was conducted with five biological replicates. All reagents used in this study are listed in Supplementary Table To validate the effect of 366-bp SV in the promoter of 83) package in R (v4.1.0) and QGIS (v3.16)84 software. The elevation map source data are collected from the National Earth System Science Data Center, National Science and Technology Infrastructure of China (http://www.geodata.cn/data/datadetails.html?dataguid=78789&docid=4850).The geographical location information of the collection sites of all varieties and phenotypes in this study are marked on the map using ggplot2 (ref. Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-023-01423-w.Supplementary InformationSupplementary Notes 1–5 and Supplementary Figs. 1–17.Reporting SummaryPeer Review FileSupplementary TablesSupplementary Tables 1–18.