Striga hermonthica is a widespread, destructive parasitic plant that causes substantial yield loss to maize productivity in sub-Saharan Africa. Under severe Striga infestation, yield losses can range from 60 to 100% resulting in abandonment of farmers’ lands. Diverse methods have been proposed for Striga management; however, host plant resistance is considered the most effective and affordable to small-scale famers. Thus, conducting a genome-wide association study to identify quantitative trait nucleotides controlling S. hermonthica resistance and mining of relevant candidate genes will expedite the improvement of Striga resistance breeding through marker-assisted breeding. For this study, 150 diverse maize inbred lines were evaluated under Striga infested and non-infested conditions for two years and genotyped using the genotyping-by-sequencing platform. Heritability estimates of Striga damage ratings, emerged Striga plants and grain yield, hereafter referred to as Striga resistance-related traits, were high under Striga infested condition. The mixed linear model (MLM) identified thirty SNPs associated with the three Striga resistance-related traits based on the multi-locus approaches . These SNPs explained up to 14% of the total phenotypic variation. Under non-infested condition, four SNPs were associated with grain yield, and these SNPs explained up to 17% of the total phenotypic variation. Gene annotation of significant SNPs identified candidate genes  with functions related to plant growth, development, and defense mechanisms. The marker-effect prediction was able to identify alleles responsible for predicting high yield and low Striga damage rating in the breeding panel. This study provides valuable insight for marker validation and deployment for Striga resistance breeding in maize. Zea mays L.) is an important cereal that plays a crucial role in alleviating food insecurity in sub-Saharan Africa (SSA) due to its high yield potential, ease in processing and low cost1. However, its production is constantly hampered by a plethora of biotic stresses, including the parasitic weed Striga. Among the numerous Striga species endemic to Africa, Striga hermonthica (Del.) Benth is the most destructive and widespread, affecting cereals, including maize and sorghum (Sorghum bicolor L.)2. Yield losses attributed to Striga infestation in maize range from 60 to 100% under severe field infestation, especially in marginal production areas where smallholder farmers cannot afford high inputs and other control measures3.Maize  for Striga resistance in maize using molecular markers. QTL mapping and genome-wide association study (GWAS) are two methods widely used to discover genetic loci controlling complex traits. Quantitative trait loci (QTLs) associated with Striga hermonthica have been identified in maize using QTL mapping approach11. Badu-Apraku et al.9 identified 12 QTLs associated with four Striga resistance/tolerance traits in maize and these QTLs explained 3.2 to 34.9% of the phenotypic variation. However, the QTL mapping approach have several limitations, for example, it has limited allelic diversity, and limited mapping resolution due to limited recombination events12. On the other hand, GWAS explores ancestral recombination in natural genetically diverse population to dissect complex traits13. GWAS is an improvement over QTL mapping in that it improves the resolution of QTLs due to accumulated meiotic events and reduces the time taken in developing mapping populations14. GWAS is a powerful tool for detecting QTLs associated with important complex quantitative traits, as well as predicting or identifying causative genes15.Several control methods have been proposed for 16, enriched MLM17, however, all these models perform single dimensional genome scan and require multiple correction. These models also have major limitations in mapping QTNs with small effects. Wang et al.18 proposed a new model based on multi-locus random-SNP-effect MLM (mrMLM). The methods include polygenic-background control-based least angle regression plus empirical Bayes (pLARmEB), fast multi-locus random-SNP-effect efficient mixed model association (FASTmrEMMA), iterative-sure independence screening expectation–maximization (EM)-Bayesian LASSO (ISIS EM-BLASSO) and fast multi-locus random-SNP-effect mixed linear model (FASTmrMLM)22. These methods can effectively detect small-effect QTNs and improve the efficiency and accuracy of GWAS. Recently, few studies have implemented the above GWAS methods to detect important loci controlling different traits in maize23.Many statistical models have been developed to improve the power of identifying QTNs with the GWAS approach. The single-locus mixed-linear model (MLM) is the most common method used for GWAS. The method uses several algorithms such as the compressed MLMS. hermonthica resistance has been conducted in maize. Adewale et al.24 identified 24 SNPs that were significantly associated with four Striga resistance-related traits in early maturing maize inbred lines using compressed MLM, these lines captured only the genetic variation existing in the extra early and early maturing maize germplasm developed at IITA. However, genomic regions governing Striga resistance in many intermediate and late-maturing maize inbred lines with consistent expression of polygenic resistance to S. hermonthica have not been identified. This germplasm offers an excellent resource for discovering functional genes underlying the genetic variation in the Striga resistance-related traits. This study was thus conducted; to evaluate diverse intermediate and late maturing maize inbred line for Striga resistance under Striga infested and non-infested conditions and identify genomic regions and candidate genes related to Striga resistance.Genome-wide association study (GWAS) for p < 0.001) effects on the three Striga resistance-related traits under Striga infested conditions and for grain yield under non-infested condition , environments and lines had significant p < 0.00 effects n Tables . The linp < 0.0001) correlations for the Striga resistance-related traits . These lines, on average, suffered 19% yield reduction relative to the non-infested conditions, produced significantly higher grain yields than the resistant benchmark (9450), and were categorized as resistant lines. In contrast, 44 inbred lines supported as many Striga plants as the susceptible benchmark (5057) but produced significantly higher grain yields than the susceptible line, and were categorized as tolerant. The remaining 20 inbred lines, supported as many Striga plants as the susceptible benchmark and did not differ significantly from the susceptible line in their grain yields, and were regarded as susceptible. All paired traits showed statistically significant differences at p-value < 0.01. A negative correlation was observed between grain yield and Striga damage rating and emerged Striga plants at 8 and 10 WAP. However, there was a positive correlation between Striga damage rating and emerged Striga plants at 8 and 10 WAP  showed a slight increasing and then consistent pattern of LD decay was observed with an increase in the physical distance of SNP markers mapped on the 10 chromosomes  were mapped on chromosome 10, whereas a maximum of 2532 SNPs (15.2%) were mapped on chromosome 1. The Admixture analysis using tenfold cross-validation from Striga resistance-related traits. These SNPs were distributed on all maize chromosomes but chromosome 8. The highest number of SNPs was found on Chromosome 1, and the least on chromosomes 3, 6 and 7  associated with grain yield was detected by two GWAS multi-locus methods (FASTmrMLM and mrMLM) and they explained 6.7 and 13.4% of the phenotypic variation. The LOD score of the significant SNPs ranged from 7.18 (S4_164335765) to 7.39 (S10_1784894).The GWAS multiple-locus models used in this study identified 30 significant SNPs that were significantly associated with the three  7 Table . This stStriga damage rating at 8 WAP. These SNPs are located on seven chromosomes and the proportion of phenotypic variation explained by each SNP ranged from 0.14 to 14.19%. The LOD values of the identified SNPs ranged from 6.06 (S4_160459526) to 11.80 (S10_68324912). Nine SNPs were associated with Striga damage rating at 10 WAP. These SNPs were located on five chromosomes, and they explained 4.13 to 11.60% of the phenotypic variation. Four SNPs  associated with Striga damage rating at 10 WAP were detected by two or more of the GWAS multi-locus  methods.Eleven SNPs were associated with Striga damage rating at 8 and 10 WAP.The LOD values of the identified SNPs ranged from 6.02 (S2_190557148) to 13.76 (S2_188120710). In general, two SNPs (S2_160791711 and S10_25285761) were simultaneously associated with Striga plants at 8 WAP. These SNPs are located on five chromosomes and the proportion of phenotypic variation explained by each SNP ranged from 5.03 to 13.58%. Five SNPs  associated with emerged Striga plants at 8 WAP were detected by two or more of the GWAS multi-locus  methods. The LOD values of the identified SNPs ranged from 6.14 (S1_298950342) to 9.28 (S10_125571525). Four SNPs were associated with emerged Striga damage plants at 10 WAP. These SNPs are located on three chromosomes and the proportion of phenotypic variation explained by each SNP ranged from 6.33 to 12.21%. Three SNPs  associated with emerged Striga plants at 10 WAP were detected by two GWAS multi-locus  methods. The LOD values of the identified SNPs ranged from 6.83 (S2_135038935) to 8.65 (S5_148751913). In general, three SNPs  were simultaneously associated with emerged Striga plants at 8 and 10 WAP.Eight SNPs were associated with emerged Under non-infested conditions, four SNPs were associated with grain yield Table . These SStriga resistance-related traits are presented in Table Striga infestation displayed high segregation among the inbred lines. For SNPs on chromosome 4, alleles AA and CA were associated with genotypes with higher grain yield, while alleles CC were associated with lower grain yield. For the second SNP on chromosome 9, alleles GG were associated with genotypes with higher grain yield, while alleles AA were associated with lower grain yield  displayed high segregation among the inbred lines. For two of the SNPs on chromosomes 1 and 4, alleles TT and TG were associated with high Striga damage ratings, while alleles GG and CC were associated with low Striga damage rating. For the SNP on chromosome 5, alleles AA and AC were associated with high Striga damage ratings, while alleles CC were associated with low Striga damage ratings at 8 and 10 WAP  displayed high segregation among the inbred lines. For the two SNPs on chromosomes 1 and 3, variants GG and AG supported the emergence of more Striga plants whereas, alleles AA supported little emerged Striga plants. For the SNP on chromosome 5, variants CC supported the emergence of more Striga plants, while alleles AA and AC supported little emerged Striga plants , U-box domain-containing protein 39, and NLR family CARD domain-containing protein 3. The remaining gene model on chromosome 10 encodes VQ proteins. These putative genes/proteins are mainly involved in developmental processes including responses to biotic and abiotic stress, seed development and photo-morphogenesis. The LD heat-map of significant SNPs (S9_1994432 and S10_1784894) identified on chromosomes 9 and 10 were highly correlated (0.5 to 0.8) with regions adjacent to the identified putative genes   transcription factors, knotted related homeobox 5, ubiquitin-protein ligase, and zinc-finger domain proteins. Most of these genes/proteins identified are involved in different development and plant defense mechanism. Plant resistance genes allow plants recognize the presence of specific pathogens and initiate defense responses. The LD heat-map of significant SNPs  identified on chromosomes 5, 7 and 10 were highly correlated (0.5 to 0.8) with regions adjacent to the identified putative genes   and WRKY14 transcription factor. Transcription factors are usually involved in diverse plant processes including, growth, development and stress signaling. In addition, protein accelerated cell death is a positive regulator of programmed cell death and it is a mechanism used by plants for defense against pathogen infection. The LD heat-map of significant SNPs (S3_74335447 and S9_7727167) identified on chromosomes 3 and 9 were highly correlated (0.4 to 0.8) with regions adjacent to the putative genes   for emerged Striga plants and Striga damage ratings. .Heritability estimates were high for the 29. The diversity panel used in this study are inbred lines derived from broad-based populations containing tropical and temperate germplasm, backcrosses containing Z. diploperennis adapted to tropical environments, and some lines that are tolerant to drought30. The two complementary approach used to infer the population structure grouped the inbred lines into three subpopulations based on their genetic backgrounds/pedigree and maturity information. It is worth noting that there was high agreement in the assignment of the inbred lines into the three subgroups based on the two approaches (Admixture and PCA). The phylogeny tree also grouped the inbred lines into three subgroups.The efficiency of association mapping largely depends on population size and population structure, which infers the ancestry of lines based on their genotypic information2 > 0.1. In addition, the high r2 value observed on chromosomes 4 and 8 in this study can be attributed to fewer recombination events on these chromosomes; this is consistent with the findings of Thirunavukkarasu et al.31 and Dinesh et al.32, who reported high r2 value on chromosome 4 and 8 of maize. Reports have shown that the effectiveness of recombination is limited by the high level of homozygosity33. In this study, faster decay of LD with increasing distance between markers was observed, which agrees with Doa et al.34 and Dinesh et al.32.LD is an important factor that determines the power of marker-trait association analysis. In this study, more than 60% of the SNP pairs across the genome exhibit LD at rStriga resistance in maize, and many QTLs associated with Striga resistance have been detected using bi-parental populations11. Badu-Apraku et al.10 identified 116 QTLs associated with four Striga resistance-related traits  using bi-parental (F2:3) population derived from two early maturing maize lines. In another study, Badu-Apraku et al.9 identified 14 QTLs that were associated with three Striga resistance-related traits . However, QTL mapping only exploits only a small fraction of available genetic diversity and exhibits limited capacity to detect polygenic resistance12. However, only a few GWAS has been conducted to identify genomic regions associated with Striga hermonthica resistance in maize. Genome-wide association study exhibits high mapping resolution and abundant genetic variation due to the high ancestral recombination events in natural populations35. Thus, it has been identified as a useful tool for detecting QTNs associated with complex quantitative traits, as well as predicting or identifying causative genes15.Several studies have been conducted to dissect the genetic architecture of 36. The adjustments of single-locus GWAS model improves its detection accuracy to an extent, however, the multiple-testing correction (Bonferroni correction) of significance thresholds for single-locus model is too strict. This leads to the exclusion of important loci, especially when large experimental errors occur in field trials of crop genetics. To solve this problem, the application of multi-locus mrMLM is essential. Previous study on GWAS have used the single-locus model to identify quantitative trait nucleotides (QTNs) controlling Striga resistance in maize. Adewale et al.24 identified 24 SNPs associated with four Striga resistance-related traits in early maturing maize inbred lines using single-locus GWAS model. Further annotation analysis identified three putative genes that explained 9 to 42% of the phenotypic variation. The high phenotypic variation explained can be attributed to the single-locus GWAS model used. However, the genomic regions identified by Adewale et al.24 differs from those discovered in this study.Different statistical models have been used for GWAS, the multi-locus model exhibits a higher distinctive power and a lower false-positive rate for detecting QTNs compared with the single-locus GWAS modelStriga resistance-related traits. This study is the first to use multi-locus GWAS model to identify SNPs associated with S. hermonthica resistance in maize. Comparing our GWAS results with previous studies on S. hermonithica resistance in maize11, there were no similar genomic regions detected, however, additional genomic regions associated with Striga hermonthica resistance were identified. The annotation analysis identified gene models with potential involvement in plant growth, development, and defense mechanism. Intriguingly, some of the gene models identified encode transcription factors (TFs) including WRKY14, basic helix-loop-helix (bHLH7), bHLH20, basic-domain leucine-zipper (bZIP46), JUNGBRUNNEN 1 and zinc finger proteins. Transcription factors (TFs) are critical regulators of gene expression in all living organisms. They are involved in plant development, cell signaling, and plant defense response.In this study, the four multi-locus GWAS models used identified thirty significant SNPs associated the three 37. Studies have associated WRKY TFs with S. hermonthica resistance mechanism in rice. Swarbrick et al.38 reported the up-regulation of genes encoding WRKY TFs in the roots of Nipponbare, a rice variety with resistance to S. hermonthica. Also, Mutuku et al.39 reported the knockdown of WRKY45 (WRKY45-kd) by RNA interference in rice plants resulted in susceptibility to S. hermonthica infestation. bHLH is another TF, they are commonly expressed in response to drought stress and they have been reported in rice40. The bHLH family TFs in Populus, PebHLH35 from Populus euphratica, has been reported as an essential gene in response to drought by regulating stomatal development and photosynthesis in Arabidopsis41.Studies have shown that most WRKY TFs respond to pathogen attack and act as both positive and negative regulators in complex defense response network42. Cao et al.43 indicated that Ring zinc-finger domain superfamily proteins are involved in resistance to blast fungus infection in rice. The DNA binding with one finger proteins (dofs) also regulate the expression of genes involved in plant development and defense processes44. In maize, ZmDof1 has been isolated and connected with C4 photosynthesis45, which makes it thrive more than the C3 plants under warmer harsh climates because they are known to be drought resilient. In maize, there are no information on the roles of most of these TFs in S. hermonthica resistance; thus, further transcriptomic study will give a better understanding on the role of these TFs in S. hermonthica resistance in maize.In addition, the Ring zinc-finger domain superfamily proteins are the most significant TFs known for their finger-like structure and ability to bind to zinc. These proteins have been reported in plants such as wheat (Triticum aestivum), soybean (Glycine max), and rice (Oryza sativa)46. One of its functions in plants is defense reaction against parasitic plants47. In tomato, xyloglucan endotransglycosylase/hydrolase (XTH) plays a major role in defense reactions against plant parasite Cuscuta reflexa47.Plants generally lack specific cells to defend themselves against attack, but they possess the necessary components for detecting invasion and building up defense response. Xyloglucan endotransglucosylase/hydrolases (XTHs) are cell wall enzymes that are able to graft xyloglucan chains to oligosaccharides48. NBS-LRR proteins have been suggested as the largest class of known R proteins that can either directly or indirectly recognize the presence of pathogens49. R gene proteins are involved in pathogen detection and disease resistance50. The Recognition of Peronospora Parasitica 13-like (RPP13-like) genes also play important roles in the resistance of various plant diseases including the downy mildew caused by Peronospora parasitica. In Arabidopsis, the RPP13-Nd, cloned from an ecotype (Niederzenz (Nd-1)), was characterized to resist the infection of various isolates of P. parasitica51.Plants have evolved a series of mechanisms to resist pathogens infection. Most plant disease resistance (R) genes contain nucleotide-binding site (NBS) and leucine-rich repeat (LRR) domains. NBS domains could bind and hydrolyze ATP or GTP, while LRR domains are critical for the formation of protein–protein interactions52. In Arabidopsis two LRR were identified to regulate cell death and innate immunity53. According to Yuan et al.54, LRR-RLK can positively regulate plant biotic resistance but negatively regulate plant abiotic tolerance in Arabidopsis . Interestingly, several RLKs were found to possess dual or multiple roles during plant growth and development. For example, ERECTA is involved in both plant development and pathogen defense responses55.Leucine-rich repeat receptor-like protein kinases (LRR-RLKs) are the largest group of receptor-like kinases in plants and play vital roles in development and defense-related processes including cell proliferation, hormone perception, host-specific defense response, wounding response and symbiosis56. The ubiquitination pathway regulates growth, development, and stress responses in plants, and the U-box protein family of ubiquitin ligases plays important role in this pathway. In higher plants, U-box-ARM proteins are associated with regulation of cell death and defense57. In addition, VQ proteins regulate diverse developmental processes, including responses to biotic and abiotic stresses, seed development, and photomorphogenesis58. Members of the VQ family either play a positive or negative role in plant immune response. Plants with loss of function of VQ23 lack resistance to both Botrytis cinerea and Pseudomonas syringae. While lines, which overexpresses VQ23, showed reduced disease symptoms upon infection with either pathogen58.U-box proteins significantly contribute to the ability of plants to respond to diverse environmental stresses, due to plant immobilityStriga plants, they are often involved in phytoalexin synthesis and the scavenging of toxins. Plants utilize a wide array of cytochrome P450 monoxygenases (P450s) in biosynthetic and detoxification pathways. Several genes encoding P450s were observed to be highly up-regulated during the resistance response to S. hermonthica in rice38. From this study, it was observed that the genomic regions controlling grain yield under Striga infestation differs from the genomic region controlling grain yield under non-infested condition. Genomic regions identified to be associated with plant defense mechanisms will be developed into kompetitive allele-specific PCR (KASP) genotyping assay and this will be validated in independent populations to improve Striga resistance breeding in maize before deployment for use in marker-assisted selection. Identified SNPs will also help expedite the use of molecular markers in Striga resistance improvement through the use of marker-assisted backcrossing (MABC) to advance the effectiveness of breeding for superior and desirable qualities but susceptible to Striga infestation.Cytochrome P450 superfamily proteins were also associated with emerged Striga resistance in maize from other studies. QTNs identified in this study can be potentially used to expedite the use of marker-assisted selection (MAS) in breeding for durable resistance to S. hermonthica in maize. The chromosomal regions controlling the Striga resistance-related traits can also be exploited for selection and effective pyramiding of favorable alleles in Striga improvement. Since most of the maize lines used in this study were developed at IITA and have a diverse response to Striga infestation, this study will contribute to molecular-marker based transformation of Striga resistance breeding in maize.In conclusion, most of the significant SNPs discovered in this study encode genes associated with plant defense mechanism. Most of the QTLs identified have not been documented in maize, indicating they are novel and are addition to the already identified QTLs for 7:9 stages of inbreeding and had varying reactions to S. hermonthica . The maize inbred lines in this panel were at SStriga infested and non-infested conditions at Abuja  and Mokwa  in Nigeria during the main rainy seasons of 2017 and 2018. The experiment was laid out in a 15 X 10 alpha lattice design with two replications planted in a crisscross arrangement. Each experimental unit was planted in adjacent infested and non-infested strips, located opposite each other and separated by a 1.5 m alley. An inbred line was planted within each strip in a 4 m long row, with 0.75 m inter-row spacing and 0.25 m intra-row spacing. Ethylene gas was sprayed two weeks before planting to induce suicidal germination of Striga seeds in the soil.The inbred lines were evaluated under Striga seeds. The sand-Striga mixture contains approximately 3000 germinable Striga seeds. The Striga seeds used in this study were collected from sorghum field from the previous planting season in Mokwa and Abuja with farmers’ consent before usage. Two weeks after planting, maize plants were thinned to one plant per hill to attain a population density of 53,333 plant/ha. Fertilizer was applied at the rate of 30 kg/ha of nitrogen, 60 kg/ha each of phosphorus and potassium at planting and an additional 30 kg/ha nitrogen was applied four weeks later. Weeds other than Striga were removed from plots manually throughout the planting season. Data were taken under both infested and non-infested conditions, except for Striga damage score and Striga emergence, recorded only under Striga infestation. Data recorded under the two environments included plant height, ear aspect, and grain yield , the variance components and broad-sense heritability estimates. In addition, Spearman rank correlations between pairs of environments were computed for the 60. Purified DNA was sent to Elshire facility in New Zealand for genotyping-by-sequencing (GBS)61. Genomic DNA was digested with the restriction enzyme (ApeK1), and genotyping-by-sequencing (GBS) of the libraries were constructed in 96-plex and sequenced on Illumina HiSeq2500 following manufacturer’s protocol. Raw flow cell output was processed to genotype calls using the TASSEL-GBS pipeline. Reads and tags found in each sequencing result were aligned with the Zea mays L. genome reference, version AGPV3 (B73 Ref Gen v4 assembly). SNPs with minor allele frequency (MAF) of < 0.05 and missing rate of > 10% were excluded from the genotyping dataset using PLINK 1.9 beta62.Genomic DNA was extracted from young leaf samples of the 150 maize inbred lines using the modified cetyltrimethylammonium bromide (CTAB) protocol63. The pairwise genetic distance was calculated through identity-by-state (IBS), and the phylogenetic tree was generated using analysis of phylogenetics and evolution (ape) R package64. The population stratification among the inbred lines was assessed using Admixture software65. The method uses maximum likelihood estimation on data from many loci to estimate individual ancestries among the inbred lines. The analysis was performed using a cross-validation error (k) varying from 2 to 10. The most appropriate k-value selected exhibit low cross-validation error compared to other k-values. LD among markers was calculated using PLINK software. The window size for LD calculation was set based on the number of SNPs located in the genome. Pairwise linkage disequilibrium was measured using the squared allele frequency correlations, according to Weir,66, and assessed by calculating r2 for pairs of SNP loci.To explore the genetic relationship among the inbred lines, principal component analysis (PCA) was conducted using factorMiner package in Rhttps://cran.r-project.org/web/packages/mrMLM.GUI/index.html). The unified parameter settings for the four methods were as follows; the Q + K model was used, where the population structure value Q was calculated by Admixture software65 and the kinship value K was analyzed by the “mrMLM” software package. The limit of detection (LOD) score was set to 6 for robust QTNs for all measured trait. The Manhattan and QQ plots for GWAS were displayed using the R package CMplot.All the phenotypic and genotypic information from the 150 diverse maize inbred lines were used to detect SNPs using four of the GWAS multi-locus models, multi-locus random-SNP-effect MLM (mrMLM), fast multi-locus random-SNP-effect mixed linear model (FASTmrMLM), fast multi-locus random-SNP-effect efficient mixed model association (FASTmrEMMA) and polygenic-background-control-based least angle regression empirical Bayes (pLARmEB), implemented in mrMLM v4.0  distance, using “ggplot2” package in R 67.SNPs detected for rstatix package implemented in R, and their effect were compared using ANOVA p < 0.05. The nature of the SNP marker (favorable and unfavorable) was defined based on the direct contribution to the traits using rstatix and visualized using ggplot2.Variants  associated with significant SNPs were identified using Supplementary Figures.Supplementary Tables.


Arabidopsis, which has been shown to play an important role in D-myo-inositol phosphates metabolism. Earlier, we suggested that genes involved in myo-inositol phosphate metabolism have significant impact on the early flowering of guar plants. Hence, we believe that the developed SNP set allows for the identification of confident molecular markers of important agrobiological traits.Guar gum, a polysaccharide derived from guar seeds, is widely used in a variety of industrial applications, including oil and gas production. Although guar is mostly propagated in India, interest in guar as a new industrial legume crop is increasing worldwide, demanding the development of effective tools for marker-assisted selection. In this paper, we report a wide-ranging set of 4907 common SNPs and 327 InDels generated from RADseq genotyping data of 166 guar plants of different geographical origin. A custom guar reference genome was assembled and used for variant calling. A consensus set of variants was built using three bioinformatic pipelines for short variant discovery. The developed molecular markers were used for genome-wide association study, resulting in the discovery of six markers linked to the variation of an important agronomic trait—percentage of pods matured to the harvest date under long light day conditions. One of the associated variants was found inside the putative transcript sequence homologous to an ABC transporter in  Cyamopsis tetragonoloba (L.) Taub.) is an annual legume crop that has been used by the local populations of India and Pakistan for food and as feed for cattle, providing a good source of nutrition both for humans and animals. Guar was further distributed around the world in the middle of the last century due to the discovery of a valuable polysaccharide—galactomannan (guar gum)—in its seed endosperm. Due to its ability to form gels with cold water, guar gum has found wide application in the food, textile, and cosmetic industries. However, the main demand for guar gum comes from the oil and gas companies, which are using guar-based fluids to improve the efficiency of hydraulic fracturing. India is responsible for ~80% of total guar production in the world  [Guar or cluster bean (The introduction of guar as a new legume crop to the Western agriculture requires significant breeding efforts aimed at the development of new high-yield varieties well adapted to new environments. Marker-assisted selection (MAS) based on the latest sequencing technologies can be effectively used to obtain disease/pest resistant guar cultivars with lower sensitivity to photoperiod and higher yield. However, genomic resources are limited in guar, and ‘omics’ technologies are rarely applied for this species .C. tetragonoloba. Such an analysis is important for the breeding and introduction of guar to various countries, as valuable rare alleles can be found in such a study. Genotyping of SNPs and InDels in a large set of guar samples of different origin might open the way for genome-wide association studies.The limitation is now gradually being overcome due to the recent transcriptomic studies of guar ,5. An RNIn the present study we aimed to construct a comprehensive set of SNPs and InDels by genotyping of 166 diverse guar plants introduced to Russia over the past few years from different sources. Our set of samples mainly comprises landraces brought in by breeders and industrial companies from India and Pakistan, as well as guar cultivars raised in the USA. Since a high-quality reference genome assembly of guar  is stillTo construct a comprehensive set of SNPs, we performed the genotype-by-sequencing restriction site-associated DNA sequencing (GBS RAD-Seq) of guar plants belonging to 45 accessions of different geographic origin  Table S1Due to the incompleteness of the reference genome, as well as due to the high genotyping error rates in RAD-Seq , we deciWe next used our constructed genotype data to obtain insights into the genetic structure of the guar population. To do so, we first used principal component analysis (PCA) to assess the general stratification of the population. Indeed, we observed that samples formed several distinct clusters in the space of the first two principal components a; at theSelfing may also contribute to the observed patterns of genetic variation and to the lack of clear separation between known varieties and geographical populations. High levels of selfing in a population increases the homozygosity of the individuals, which can be assessed directly using genotype frequencies. Indeed, we observed that the relationship between the non-reference allele frequency and the number of heterozygous genotypes in our dataset was significantly altered in our data compared to the Hardy–Weinberg equilibrium . For all2 between variants in our dataset decayed slowly with increasing the physical distance between markers, with a median r2 = 0.17 even for pairs of variants located at a physical distance of 1 million base pairs (1 Mbp)  . Taken tWe next went on to identify the loci associated with productivity-related traits in guar using the constructed genotype dataset. Several traits were selected for this analysis, including the plant height, the height of the first branch attachment, the total number of pods, the percentage of pods matured to the harvest date, and the total weight of seeds harvested from mature pods. As the raw trait distributions were significantly skewed, the outliers for all traits were removed, and the raw values were normalized prior to genome-wide association analysis (see Methods). A moderate correlation between normalized trait values was observed, with maturation-related traits such as the number of pods, percentage of mature pods, and seed weight from mature pods forming a densely correlated cluster .We applied two types of association analysis models to detect genome-wide association across our panel of phenotypes . GLM-based association testing did not identify any significant genome-wide associations ; howeverp < 1 × 10−4. The GWAS hits were curated to exclude repetitive sequences or variants with no adjacent transcript matches. As a result, six variants were found corresponding to six different contigs in the reference genome assembly , we performed PCR with the designed locus-specific primers. PCR products were directly used for Sanger sequencing. Sanger sequencing confirmed the physical presence of the target nucleotide substitutions in a subset of 12 plants , in a pep-value, determined by the Fixed and Random effect model . In addition to the successful experimental validation of the selected variants, we consider the moderate level of heterozygosity  observed in the estimated population of this obligatorily self-pollinated species as indirect evidence of the reliability of markers. In a typical RAD library, millions of reads are generated and then allocated to each multiplexed individual and to each genomic position in the reference genome . The incThe genotyping of the diverse guar samples stored in a large ex situ collection showed few to no correlations between the origin of accessions and their genetic variation profiles. While the possibility of errors during sample collection cannot be ruled out completely, plants from the same accessions (or even assigned to the same cultivar) were often observed in different genetic clusters. These results are consistent with previous reports about the discrepancies between phenotype-based and molecular-based clustering results in guar  and alsoThe possible reason for the lack of the obvious evolutionary lineages of guar is the relatively recent breeding history of the crop. It is frequently considered that guar has been domesticated in the northwestern region of the Indo-Pakistan subcontinent , and, asp < 0.0001, The development of the set of SNPs provides an opportunity for the further genome-wide association study aimed at finding the genetic loci linked to variation of agrobiological traits. The prolonged vegetation period of plants upon their introduction to countries of more northern latitudes presents a major problem for guar breeding. This causes a delay in the harvest before the onset of autumn rains and negatively affects the yield . Thus, tGlycine soja and to ATP-BINDING CASSETTE G40 (AT1G15520) in Arabidopsis. Earlier, we detected increased expression of the same transcript in early flowering guar plants grown under long day conditions compared with delayed flowering plants [AT1G15520 orthologous gene of guar and early maturing of guar plants. Since the structure of the corresponding gene in guar has not yet been investigated, there is no option to predict any amino acid substitutions caused by the discovered SNP. Nevertheless, we can assume the presence of tight linkage disequilibrium between SNPs in the gene exon and other polymorphisms in its promoter region that affect gene expression.Using the genome-guided guar transcriptome assembly, that was based on RNA-Seq data from 15 plants included in the present analysis , we wereg plants . Importag plants . TherefoAT1G15520) was recently shown to play a significant role in D-myo-inositol phosphates (IPs) metabolism in Arabidopsis [myo-inositol ring (Ins) resulting in the synthesis of a series of soluble Ins phosphates, as follows: Ins → Ins(3)P1→ InsP2 → InsP3 → InsP4 → InsP5 → InsP6 [myo-inositol in its free dephosphorylated form, was previously suggested as a biomarker of early flowering (and presumably early maturing) of guar plants [AT1G15520 in Arabidopsis) was co-expressed with the purple acid phosphatase (PAP)-like gene [Arabidopsis (AtPAP15) encodes phytase that hydrolyzes myo-inositol hexakisphosphate P6, also known as phytic acid) to yield free myo-inositol and free phosphate [An ABC transporter gene P6 . It is nr plants . In maizike gene , whose ohosphate . These dMYB20 and F-box/LRR-repeat protein 3, respectively  gene in the maize embryos, it was proposed that small molecules, e.g., auxin, can bind to F-box protein and mediate ubiquitination to regulate protein function at the posttranslational level [2+ signal transduction [PLC) gene, resulting in an exponential increase in the amount of myo-inositol in its free form [Two other associated SNPs (contig_59:14389378 and contig_188:3026232) were discovered near the guar coding sequences, showing homology to transcription factor ectively . Remarka content . F-box pal level . Phosphosduction . We receree form . Thus, fOverall, our results demonstrate the utility of the constructed set of SNPs and InDels for the analysis of genome-wide associations in guar and for the reconstruction of its population history. We believe that this dataset might be used for development of the genotyping methods and for further investigation of the guar genetics.Guar plants investigated in the present study were derived from 45 accessions of the collection of Vavilov Institute of Plant Genetic Resources (VIR)  Table S1Procedure of DNA isolation for RADseq genotyping as well as protocol for construction and sequencing of ddRAD libraries were described earlier ,35. SequFor the SNP calling procedure, the custom guar reference genome was employed. For its construction, DNA from leaves of guar variety ‘Vavilovskij 130′ was used . Two difShort and long reads data were preprocessed: base calling with included step of removing low quality data was performed for minION by Guppy v. 0.1.11. Then, for both types of data (short and long reads) the most common contaminants and chloroplast DNA were removed using Bbmap v. 38.62, low quality reads were trimmed by Trimmomatic v. 0.38 for Illumina.For the downstream assembly, different strategies were applied and compared: long-reads assembly, short reads assembly, and hybrid assembly combining short and long reads. For the short read assembly, Abyss v. 2.2.5, Soapdenovo2 v. 2.2.4, and SGA v. 0.10.13 tools were used. For the long reads assembly, Flye v. 2.8. and Canu v.1.9. were employed with additional polishing step using Racon v. 1.4.11. For the hybrid assembly we used MaSuRCA v.3.3.7., Abyss v. 2.2.1, and Spades (hybridspades) v.3.13.2. tools.All of the assemblies were compared using basic assembly metrics via Quast v. 5.0.1. As a result, MaSuRCA-derived assembly was chosen as the most resultative one based on the N50, and a percentage of the assembly made up of scaffolds larger than 200,000/300,000 bp.Variant calling using RAD-seq data is known to be complicated by several notable biases , and theFor GATK-HC and NGSEP pipelines, raw reads were demultiplexed using NGSEP. For GATK-HC analysis, reads were then aligned onto a reference genome assembly using BWA MEM v. 0.7.17 . Alignedhttps://github.com/hail-is/hail, accessed on 24 August 2021). Variant quality statistics were computed using the Hail built-in functionality. Variants with variant call rate (the proportion of genotyped samples) < 0.75, observed heterozygosity > 0.5, mean per sample genotype quality (GQ) < 15, or quality-to-depth (QD) ratio < 20 were excluded. Filtered variants were used to compute the sample quality metrics, and samples with high heterozygous-to-homozygous genotype (HET/HOM) ratio > 2 or call rate (the proportion of genotyped variants) < 0.8 were removed. Finally, variants with minor allele frequency (MAF) > 0.05 in the filtered dataset were selected for further analyses.Due to substantial differences in both the number of discovered variants  and the main quality metrics , we dec2 threshold < 0.2 were selected) and pre-processed using a custom script followed by conversion to the PLINK v.2.0 [Population stratification was analyzed using the principal component analysis (PCA) with Hail v.0.2. For ADMIXTURE  analysisNK v.2.0  binary fHo is the observed heterozygosity (the proportion of heterozygous genotypes per variant site), and He is the expected heterozygosity computed using the Hardy–Weinberg equilibrium.The inbreeding coefficient for each variant site in the final dataset was computed as follows:2 scores between SNPs located within the 10-Mb windows were computed using Hail v. 0.2.For linkage disequilibrium analysis, pairwise rFor each trait, the distribution of the values was examined, and points deviating more than 3 standard deviations from the median were removed. The raw values were then processed using the Inverse Rank-based Normalization (INT), a method that has been shown to increase the statistical power in genome-wide association studies . To analhttps://github.com/YinLiLin/CMplot) (Accessed on 30 September 2021).Genome-wide association analysis was performed using Hail v. 0.2. or fixed and random model Circulating Probability Unification (FarmCPU) . A generFor functional annotation of the genome-wide associations, the reference transcriptome assembly generated using Trinity V. 2.8.5  was alighttps://eu.idtdna.com/pages/tools/primerquest?returnurl=%2FPrimerquest%2FHome%2FIndex, accessed on 24 August 2021) with default settings. Then, primers were aligned against the reference genome using Bwa V. 0.7.3. to prove that they do not anneal anywhere else outside the target contig. For such an alignment, the minimum sequence identity of 85% was used as the threshold value. Next, the designed primers were further checked via primer design settings of UGENE v. 39.0. using the corresponding contig as a target sequence. Only primers with the unique sequence within the target contig were used for the downstream analysis. Next, PCR validations for 6 SNPs were performed by PCR with designed primers and further Sanger sequencing. The PCR mixture consisted of 2.5 μL of 10× Taq buffer with MgCl2, 1 μL of dNTPs (10 mM), 0.5 μL of each primer (10 mM), 0.5 μL of Taq polymerase (5 U/μL), and 2 μL of DNA (10–30 ng/μL). The volume was adjusted to 25 μL by deionized water. For primers gu59, gu229, gu182, gu37, and gu188, the amplification program was as follows: 1 cycle (3 min at 95 °C), 35 cycles , and 1 cycle (5 min at 72 °C). For primer gu88 the amplification program consists of the 1 cycle (3 min at 95 °C), 35 cycles , and 1 cycle (5 min at 72 °C).To validate SNPs in the final call set, the PCR-based assays were designed for those SNPs that were highlighted by the GWAS . The PCRFor sequencing, the PCR products were purified using AMPure XP magnetic beads according to the Agencourt AMPure XP protocol. Preparation of probes for sequencing was carried out with BigDye Terminator v3.1 (Thermo Fisher Scientific). The required concentration of the PCR product was adjusted according to the BigDyeTM Terminator v3.1 Cycle Sequencing Kit User Guide. The PCR program and further purification of the PCR product using 125 mM EDTA were carried out in accordance with the manufacturer’s recommendations.The SNP information is available from


The haplogrouping was largely consistent with the population structure derived from alternate clustering methods . Further, we present evidence confirming a new indicine subgroup  mainly consisting of breeds originating from China and characterised by two private mutations within the I1 haplogroup. The total genetic variation was attributed mainly to within-breed variance (96.9%). The accuracy of the imputation of missing genotypes was high (99.8%) except for the relatively rare heteroplasmic genotypes, suggesting the potential for trait association studies within a breed.Maternal diversity based on a sub-region of mitochondrial genome or variants were commonly used to understand past demographic events in livestock. Additionally, there is growing evidence of direct association of mitochondrial genetic variants with a range of phenotypes. Therefore, this study used complete bovine mitogenomes from a large sequence database to explore the full spectrum of maternal diversity. Mitogenome diversity was evaluated among 1883 animals representing 156 globally important cattle breeds. Overall, the mitogenomes were diverse: presenting 11 major haplogroups, expanding to 1309 unique haplotypes, with nucleotide diversity 0.011 and haplotype diversity 0.999. A small proportion of African  Bos primigenius) following two separate domestication events: one in the Fertile Cresent approximately 10,000 years ago and the second in the Indus Valley some 8000 years ago4. After domestication, cattle spread to Europe with human migration mainly along the Mediterranean coastline and the Danube River6 to reach the British Isles (6500 years ago). These cattle populations also expanded to the Iberian Peninsula following the northern coastal region of Africa7. Similarly, cattle from the Indus Valley spread to China and South-East Asia8 and Africa (~ 2500–3500 years ago)11. The two genetically distinct major cattle sub-species from these two early domestication sites still predominate in modern day cattle as Bos taurus taurus and Bos taurus indicus along with their widespread crossbreds.Based on archaeogenetic evidence, modern day cattle originated from at least two distinct wild aurochsen  and haplogroups (higher level of related groups among the haplotypes). Nucleotide diversity describes the average number of nucleotide differences per site in pairwise comparisons among DNA sequences13. Haplotype diversity is the probability that two randomly sampled alleles are different13. Mitochondrial haplotype clustering14 and mitochondrial haplogroups based on a set of known and pre-defined mutations point to plausible maternal origins and evolutionary history. The compiled haplogroup trees and the corresponding mutations were based on 233 cattle previously used for haplogrouping17 available from GenBank and are publicly available as a resource called DomeTree18. The mitochondrial haplogroups represent a group of haplotypes with a similar combination of SNPs arising from the ancestral mtDNA with sequential accumulation of mutations through maternal lineages19. That is, haplogroups define distinct maternal lineages in wild and domesticated cattle.An important part of the molecular evidence for the origin of cattle has been based on mitochondrial DNA (mtDNA) studies. The mitochondrial genome is small (16.34 kb), circular, haploid, non-recombining and maternally inherited20, there is increasing interest in the role of mitochondrial diversity on important traits in both humans and livestock animals. In humans, mitochondrial mutations have been associated with several conditions such as LHON (Leber hereditary optic neuropathy), MELAS , MIDD  as reviewed in21. In livestock, there is no clear evidence of causality, but mitochondrial haplotypes/mutations have been associated with meat quality22, litter size23, and reproductive capacity24 in pigs, as well as increased milk production in cattle25. At a cellular level, mitochondrial haplotypes have been shown to influence DNA methylation and gene expression in embryonic stem cells14, as well as metabolic traits in porcine and bovine cybrids (cybrids is a cytoplasmic hybrid cell lines containing different cytoplast against uniform nuclear background)27.While the mitochondrial genetic diversity of many cattle breeds has been previously characterized29. While the partial or whole D-loop region is informative for population genetics because it is hyper-variable, whole mitochondrial genome sequences are more likely to reflect the full range of mitochondrial genomic diversity30. Now that large sequence databases for cattle are available, it is timely to undertake a comprehensive study involving worldwide breeds, countries and continents for a holistic understanding of the mitochondrial genomic landscape in modern cattle. One such database available for cattle is from the 1000 Bull Genomes project31.To date, most mitochondrial molecular diversity studies in cattle are primarily evaluated based on the non-coding hypervariable control region (D-loop) or involved limited breeds either within a country or within a region36. The variants from autosomal chromosomes have also been used to determine population structure and ancestry of bulls37. On the other hand, the 1000 Bull Genomes project mitochondrial sequence variants have not been used in mitochondrial diversity studies. While the imputation of mitochondrial variants for population genetics studies is not recommended, it is clearly of interest to empirically test the accuracy of imputation of mitochondrial variants. Large scale data sets of imputed mitogenomes could contribute in predicting and associating phenotypes to the mitochondrial haplotypes in parallel to the variants from autosomal chromosomes.The nuclear DNA variants from the 1000 Bull Genomes project have been extensively used in genomic analyses, particularly for imputation, genome-wide association and genomic predictions in dairy and beef cattle38). Further, the format in which the 1000 Bull Genomes Project data is presented (VCF) is not a standard input format for most of the available mtDNA analysis tools. The format conversion must consider the attributes specific to mtDNA . Currently, there are tools (e.g. BCFtools)39 to derive a consensus sequence from VCF format to the routinely used fasta format, but for mitochondrial variants these tools lack description, particularly on the handling of heteroplasmy. Furthermore, the allocation of the allelic base call at heteroplasmic positions in haploid genotypes needs careful consideration because heteroplasmy is not generally considered in diversity analyses.The use of mitochondrial variants for mitochondrial diversity from the 1000 Bull Genomes project requires close attention to two key aspects of the data. First, the short-read sequence data are not specific to the mitochondrial genome only and some nuclear mitochondrial sequences (NUMTs) can potentially be wrongly aligned to the mitochondrial genome. This may manifest as heteroplasmy  but the expectation is that for most tissues, the MT allele reads will be more numerous than NUMT alleles. Thus, a read depth filter could help mitigate this issue. However, due to the low number of mitochondria in sperm cells, the wrongly aligned NUMTs will be harder to be distinguished from true mitochondrial reads due to more even read depth at heteroplasmic sites. This necessitates strict quality control and filters to minimize the impact of NUMTs on the analysis. It should also be noted that true heteroplasmy in MT genomes does exist due to multiple mitogenome copies sometimes carrying different mutations . Run 8 included 4931 animals representing over 200 taurine and indicine breeds and their crosses. Prior to our study, as part of the standardised processing for Run 8, all mitochondrial sequence data was aligned to the latest Bovine Reference Genome, ARS-UCD1.2_Btau5.0.1Y.fa. This reference combines ARS_UCD1.240 with the Y chromosome assembly from Btau5.0.141 because the ARS-UCD-1.2 animal was female and includes the mitochondrial (M) genome version from the ARS-UCD-1.2 assembly. The sequence processing for Run 8 from raw fastq sequence files through to variant calling and creation of the VCF files (that we used in this study) is described here  across all remaining sites were removed, such that 2176 remained out of 4931 animals. Further, to develop filters to remove animals that may have excessive and/or questionable heteroplasmy due to contamination from NUMTs, animals were evaluated in two groups as:males whose DNA samples were either from semen or from unknown tissue (Semen group), andfemales and males with DNA sampled from known tissues other than semen (Non-semen group).In order to obtain a high quality and reliable dataset for the analysis, we applied quality filters at both site and individual animal levels. The site quality control thresholds used were similar to those applied in nuclear sequences of the 1000 Bull Genomes Project  or a whole mitochondrial genome in prescribed formats. We adopted a genotype-based allele assignment approach for the conversion to a homoplasmic variant sequence. Homoplasmic variants  were directly assigned the corresponding alleles, while the heteroplasmic sites  were assigned a homoplasmic status for the most abundant allele based on read depth. In other words, the allele  with a higher read depth was chosen as the most representative base for a sequence at heteroplasmic positions. In cases where the allele read depth of REF and ALT alleles were equal, the ALT allele was chosen as the base for the position in the sequence. It was assumed that this strategy would be more informative of the existing allelic diversity and would help avoid reference bias. In addition, we also generated a complete genome length sequence  using bases for variant positions and inserting "N" (missing base) in non-variant positions because this full mitochondrial genome length sequence format was required to predict the haplogroups using traditional tools .43 for 15 selected breeds . We conducted an analysis of molecular variance (AMOVA) using Arlequin 3.544 to detect population differentiation: an approach based on a matrix of squared differences among all pairs of haplotypes within selected breeds. We used the maximum likelihood tree implemented in the MEGA X program45 to derive a phylogenetic relationship among the breeds. MEGA X conducts a statistical analysis of molecular evolution across the maternal lineages to construct the phylogenetic tree. The haplotype network within breed was constructed by employing the median joining tree approach in the PopART program46. This program enables construction and visualization the relationship between individual haplotypes but the network does not explicitly reconstruct the evolutionary history.The variant sequences (derived from VCF) were used for the description of the overall DNA polymorphism for all animals and the evaluation of nucleotide and haplotype diversity using the DnaSP program18 using the whole mitochondrial genome sequence (fasta) prepared as described above. At the time of development (2015) this tool captured a list of variant positions across the mitogenome that published studies had used to define distinct bovine haplogroups. The corresponding sequences identified to a haplogroup which were available in the public repositories are used as the reference. The aim of the tool was to automate assigning of haplogroups and to standardise the haplogroup definitions regardless of whether a study used a specific region or the entire mitogenome. The tool aligns the query sequences to the bovine reference sequence V00654 (hereafter referred to as BRS) which was generated using a shotgun DNA sequencing strategy12. It then compares the alleles at all variant sites that overlap the predetermined list to assign individuals to specific haplogroups. In total, MitoToolPy covers all previously defined haplogroups; I1, I2, T1 to T7, P1, P2, Q1, Q2, R1 and R2. The tool output also provides lists of missing variants in the query sequence for an assigned haplogroup and a list of new variants not in an assigned haplogroup, but present in query sequence as private variants. The private variant output from the tool provides the opportunity to infer a new subgroup within a haplogroup and to annotate the variants specific to a haplogroup and breed.Mitochondrial haplogroups were predicted using the MitoToolPy programAdditionally, because of the extensive use of D-loop sequences in determining mitochondrial diversity and haplogroups in the past, mitochondrial variant sequences from only the D-loop region were also used to predict the mitochondrial haplogroups in MitoToolPy as a comparison. The outputs from the MitotoolPy (private and missing variants) had slightly altered nucleotide positions due to being aligned to an older reference genome (BRS) incorporated in the software. To enable the annotation of variants to the latest reference , the haplogrouping variants in the MitoToolPy were lifted over (positions and bases) to ARS-UCD1.2_M, and the reference genome for alignment within the tool was changed to the ARS-UCD1.2_M.fa. The two reference genomes differed in their length  resulting from two deletions in the former as well as having nucleotide base differences at 12 positions Table . The pos47. The importance of the coding variants was predicted by SNPeff as being either high (e.g. stop gained), moderate (missense variants) or low (synonymous variant) and non-coding variants were annotated as modifier (e.g. upstream/downstream variants).We investigated private variants specific to certain individuals within a haplogroup and within individuals within a breed to investigate whether they may be biologically meaningful. The private variants specific to a particular group within a haplogroup/breed in this study were annotated using SNPeff48 and these genotypes were used to generate a haploid GRM (make-grm-xchr option in GCTA49) to use as input for a principal component analysis (PCA) also completed with GCTA. Principal components (PCs) 1, 2 and 3 were plotted using scatterplot3d50, and the clustering was interactively visualised using the rgl package in R51. Second, we determined the individual ancestry and population structure of the animals using Admixture52. Admixture software employs maximum likelihood to estimate the proportion of each individual’s alleles (using SNP genotypes) that are ancestral to one of k populations. Clearly then, from our filtered set of 3069 SNP on this haploid non-recombining mitogenome, we expected that Admixture would only be able to separate broad population clusters such as haplogroups, where there might be an adequate number of alleles to enable differentiation. The pruning of variants for strong LD in diploid genotype data is optional for Admixture and was not undertaken here (within haplogroups many variants are in strong to perfect linkage disequilibrium (LD) because there is no recombination). The estimate of k, the expected number of population (sub)groups, must be provided in advance to run Admixture. This was determined using the Admixture cross-validation errors approach, and also an a priori population structure was implemented with k ranging from 2 to 6. The third approach was hierarchical clustering implemented in the R package dendextend53 using a matrix of nucleotide differences between each pair of sequences . The hierarchical clusters were implemented at the highest (2 groups) as well as the lowest levels (0 nucleotide difference). To check for the concordance between these three unsupervised clustering methods, the resulting clusters/groups were annotated according to the individual's predicted haplogroups from MitoToolPy.The overall mitochondrial population structure was also investigated through three unsupervised clustering approaches. First, clustering based on principal components was derived from a genomic relationship matrix (GRM) generated between all animal pairs from all filtered polymorphic variants. Fasta files were converted to .bed format using Plink ver1.954 following the gt and ref options and providing Group I (as gt) and Group II (as ref) accordingly. The Beagle software phases genotypes and then imputes the ungenotyped markers or missing markers. Beagle will impute bi- and multi-allelic variants. The accuracy of imputation in Group I was evaluated as the proportion of imputed genotypes in agreement with original genotypes at the masked and unmasked positions separately. The Beagle estimate of alternate allele dose (DS) and genotype probabilities (GP) were used to define the most likely base call at heteroplasmic positions. For example, for an imputed heteroplasmic genotype with a reference and alternate allele of 0|1, 0|2, or 0|3 etc. , if the DS is < 1, a Ref allele is assigned while DS = 1 is assigned the Alt allele. In rare cases where heteroplasmy was imputed as two alternate alleles  where 1 is ALT1, 2 is ALT2 and DS = 2 , the base was then set to missing and when DS is < 2, the more frequent allele (summed across genotype probabilities) was assigned as the base for this position. These variant sequences with imputed sporadic missing genotypes were then reconstituted to a full genome sequence in fasta format by adding Ns at other non-variant positions and then used for re-predicting the haplogroups. The extent of agreement between an individual's haplogroup using the real and partially imputed genotypes was examined. The mean accuracies of imputation and the predicted haplogroup were calculated from 50 repeats of this cross-validation . The empirical accuracy of imputation was assessed as the concordance between real and imputed genotypes.The accuracy of imputation of sporadic missing mitochondrial genotypes and the effect of this imputation on haplogroup assignment were investigated. The empirical accuracy of imputation was tested using the filtered sequence dataset that had no missing genotypes. The 1,883 individuals in the filtered data were split into two random groups consisting of 333 (I) and 1,550 (II) animals. A random 10% of the genotypes of individuals in Group I were masked (set to missing) at random sites and then imputed using Beagle 4.0The raw variant call dataset was filtered to have high quality SNP genotypes, and both SNP and animals with missing genotypes were removed. This not only reduced the overall number of animals and sites substantially, but also reduced the number of heteroplasmic genotypes, improved the average read depth and retained higher quality sites Table , Fig. S1We therefore imposed a strong filter based on the maximum number of heteroplasmic sites per individual, to result in a similar distribution of heteroplasmy and allelic ratios in both the Semen and Non-semen groups Figs. , Fig. S3taurine breeds18. Generally, the indicine cattle breeds belonged to major haplogroup I and modern taurine cattle to haplogroup T, although there were some exceptions. As expected, the composite breeds mostly sourced from Australia were unpredictable. Notably, the haplogroups of Brahman cattle (N = 18) were mostly T1 (N = 12), T3 (N = 5) and one indicus (I). In some animals of European breed origin and their composites (N = 1302), the integration of T1 (3.5%), I1 and I2 (1.3%) haplogroups was also observed . Similarly, the I Haplogroup was present in Holstein animals from China (N = 5/12), Herefords from New Zealand (N = 3/4) and, as expected, in composite taurus x indicus breeds from Australia 4/12  but did not resolve haplogroup sub-levels. For example, the incidence of unresolved haplogroups was more than 60% of the animals between T1 and T3 (1280), and T3 and T4 (N 15). This indicates that the D-loop variants in our set played a key role in defining the sub-haplogroups when used together with the non-D-loop. This is not unexpected because the higher mutation rate in the D-loop region is more likely to resolve the sub-haplogroup levels (i.e. more recently diverged groups).In the past, sequences from D-loop region (910 bp long) have been extensively used in the prediction of haplogroupsThe PCA of the GRM derived from all filtered mitochondrial variants (whole sequence) revealed distinct clusters that corresponded to the I, T and Q major haplogroups after annotation with MitoToolPy results Fig. . HoweverA GRM of only D-loop variants was also used for PCA and revealed the same two major clusters . Depending on the k value used (2–6), the major haplogroups were progressively split   Fig. . When anThe nucleotide differences between each pair of whole mtDNA variant sequences was calculated using an in-house script. The mean nucleotide difference across all pair combinations was 36 but ranged from 0 to 224. Hierarchical clustering, based on the nucleotide differences matrix between individuals, again presented two broad and distinct clusters  within a haplogroup and/or breed(s). Some of the private variants were specific to a group of animals within a haplogroup Table . For theA maximum-likelihood tree was constructed for whole mitogenomes of only the animals belonging to I haplogroup using MEGA X. This analysis showed four distinct clusters, one cluster corresponded to the I2 haplogroup and the three other clusters were annotated to I1 haplogroup Fig. a. The suindicine breeds except for two Buryat animals (Russia), while I2, I1-Orig and I1b were mostly indicine breeds from the Indian subcontinent and Chinese indicine breeds  were reanalysed using PCA of the GRM, Admixture and hierarchical clustering. The PC plot also showed sub-grouping of the I1 haplogroup into three well-separated clusters that were distinct from I2 Fig. b. SimilaThe hierarchical clustering analysis  showed two main clusters  which is calculated based on the number of haplotypes and the proportion of the population under each haplotype was high . Of the 1309 haplotypes, 1010 were singletons  indicating considerable diversity. The remaining haplotypes (299) were shared by 2–23 animals  corresponding to the nucleotide difference of 0 between two haplotype pairs. This method resulted in approximately the same number of singleton haplotype clusters as singleton haplotypes (1032) determined from the DnaSP software. At least 132 clusters and haplotypes had substantial memberships in common  was 0.012, and the average nucleotide difference between the pair of sequences was 35.5. The nucleotide diversity within breed was high in Buryat and Hereford compared to other breeds contributing substantially to the overall nucleotide diversity. Other breeds had low but comparable nucleotide diversity ranging from 0.002 to 0.005. The analysis of molecular variance (AMOVA) showed that the percentage of genetic variation from among and within breed components was 3.1% and 96.9%, respectively, indicating high within breed genetic diversity.gt and ref option using the remaining 1550 animals with all genotypes present as a reference for imputation. The overall concordance of this imputation was 99.8%, although concordance for heteroplasmic sites was approximately 66%  in a random 20% of animals . Then we imputed the masked genotypes using Beagle (version 4.0) using the To evaluate the effect of imputation on haplogroup prediction, we re-analysed the animal haplogroups in the imputed dataset and compared these to their haplogroup prediction from the original dataset. This was replicated 50 times with a new random set of animals chosen for masking genotypes for imputation. The predicted haplogroups matched in 99.7% of the individuals when compared to their haplogroup predicted from the full set of real genotypes. The accuracy of imputation and the predicted haplogroup for the masked dataset showed little variation across the 50 replications Table . This su55. The low representation of P, Q, T4, T5 and T6 was also in line with a recent study by Cubric-Curik et al.30. Like us, Cubric-Curik and colleagues found that MitoToolpy assigned T6 to some Angus animals, but they reported no statistical evidence for T6 using BEAST software56 which assigned the same animals to the T3 group (agreement was good for most other haplogroups). In agreement with Cubric-Curik et al.30, we recommend using whole mitogenomes with MitoToolpy because some haplogroups failed to separate using only the D-loop region. In addition to the conventional diversity indices, we have investigated alternate ways of analysing population structure and haplotypes of the entire mitogenome, that do not rely on predefined haplogroups and therefore capture a broader spectrum of the diversity.Our study undertook a comprehensive analysis of mitochondrial genome sequence diversity in 1883 cattle, including the most important global cattle breeds and sub-species in a single study. This represents one of the single largest studies of this kind demonstrating the use of short read mitochondrial sequence data from general whole genome sequencing after stringent quality controls. Our use of the entire mitogenome enabled a more in-depth study of the full range of diversity, that may be important in future studies of the potential impact of mitochondrial variants on phenotypes. Our large sample size enabled identification of all major haplogroups  except for R, subgrouping within haplogroups and breeds as well as annotation of the private variants. The R haplogroup is relatively rare and previously has been identified occasionally in Italian breeds58 and America (Creole cattle)59. The detection of T1 haplogroups in Iberia60 and Sicily and southern Italy, according to61 may be the influence of migration of African cattle into southern Europe via the Mediterranean Sea coastline. The African T1 sequence was also found in Iberian Bronze age cattle62. Interestingly, although all African breeds were assigned as T1, we observed that their nuclear DNA was an admixture of taurus and indicus  and relatively few to indicus (I). This is not surprising, as the T1 haplogroup has been previously reported in European cattle breeds (1–30%) from France, Spain, Portugal, Italy, Balkan and Greececus Fig.  as repor64. Australia imported Jersey from the Channel Islands and Holsteins from the Netherlands in 1850. It is possible that some of the first African cattle arriving in Australia were Afrikander (8 bulls and 2 cows) imported from South Africa in the early 1950s65 and other breeds  in the late 1980s. However, details on the sex of imported animals are not available making it difficult to confirm the most likely maternal route of T1 mitogenome transmission. In 1990, the embryos from Boran and Tuli (African) cattle were imported66. While the attributes of heat tolerance and tick resistance were sought after under the extensive tropical beef production system, the presence of the T1 haplogroup in dairy breeds (Holstein and Jersey) in Australia suggests these animals may be the result of upgrading from cows carrying the T1 mitochondrial lineages or sporadic cases of cross breeding to improve heat tolerance but this warrants further investigation.The breeds with no previous report of T1 haplogroup but found in our study are Jersey and Holstein: those showing the T1 haplotype were sourced mainly from Australia (9 out of 10). Australia has a recent history of crossbreeding European breeds with more heat-tolerant imported breeds to develop cattle better adapted to the tropical environment in northern Australiaindicine haplogroup (I) in Holsteins in this study were largely in female samples originating from China. This is not surprising as the I haplogroup has been previously reported in Chinese Holstein67 and at least three haplotypes were shared among Chinese Holstein and native cattle 68. Imported purebred Holsteins were used to grade-up local cows as well as for the development of the Chinese Black and White cattle breed69.The indicine and the African taurine maternal lines in these breeds are likely due to upgrading.There is a possibility that the breed origin was incorrectly labelled on some samples, therefore we undertook a PCA of all the taurus animals (N = 1451) based on a genomic relationship matrix derived from 45,000 autosomal SNPs. The PC plot of Holstein and Jersey breeds shows tight clustering of these animals regardless of the MT haplogroup Fig. , which staurine in this study, which is interesting because the breed's nuclear DNA is primarily of indicus origin in the 1000 Bull Genomes project31. The Australian Brahman cattle in this study were approximately 97% taurine (T1 47% and T3 50%) and about 3% indicus (I) haplogroups. Compared to our study, Brahman from China were reported with lower representation of T1 (35%) and T3 (26%), but higher in I (39%) haplogroups29, while American Brahman showed lower T1 (30%) but higher T3 (70%)70. Originally, Brahman cattle were introduced into Australia from the USA in 193364. In fact, in America, Brahmans were developed from the crossing/upgrading of B. taurus females (often Creole cattle) with Guzerat, Nellore, Gyr and Krishna valley cattle. As such, haplogroups in the indicine breeds in Americas were reported to be largely taurine  and rarely indicine (I) 71.In composite breeds such as Brahman, the mitochondrial haplotypes were mostly The inter-breed introgression of haplogroups was also supported by sharing of the diverse haplotypes among breeds. This again points to the common practice of upgrading. Mitochondrial haplotypes were also shared across countries, and to a higher degree between countries in close proximity, indicating the movement of female animals. However, the shared haplotypes between more distant countries  suggests the movement of foundation females or, more recently, embryos.74. The I1 haplogroup originated in the Indus valley, while the I2 haplogroup is believed to have originated in northern India75. Interestingly, within the large subcluster of I1, we consistently identified a sub-cluster (I1a) comprising mainly of Chinese indicine breeds (19/20 breeds)  for a breed not in the current study (Yunling cattle)29. The two specific mutations characterising the I1a subgroup in our study were also reported in the Yunling cattle subgroup, while the other four were found non-specific to I1a group in our study. These findings, together with results from our study, suggest the presence of a unique I1 subgroup (I1a) specific to breeds emanating from China. Further, five Holstein animals in our study that originated from China also had the I1a sub-haplogroup, indicating there may have been adhoc or controlled upgrading of indicus females that carry I1a.The presence of both I1 and I2 indicus haplogroups with the predominance of I1 recorded in the current study agrees with the previous studies76. However, their proposed new haplogroup C sequence did not match our I1a subgroup sequences. The presence of conventional I1and I2 haplogroups support the consensus among the published literature that the indicus cattle population in China is a result of migration and spread from India. The Bos indicus are reported to have been introduced into China between 2000 and 200 BC and currently there is no zooarchaeological or genetic evidence for the origin of domestic cattle in ancient China77 suggesting genetic drift as a contributing factor to the formation of the subgroup (I1a). Another possibility, considering the specificity to Chinese breeds (not present in Indian indicine breeds), is the potential restocking of auroch female lines from the wild in China and establishing nucleus or base for the indicine breeds in China. There is molecular evidence of aurochs in China's northeast during the Neolithic period78. Therefore, these hypotheses need further investigation. The subgroup (I1b) under I1 characterised by a specific mutation (5707 bp) did not exist in any breeds from China, while the entire I1 group included breeds from both India and China. There are shared haplogroups (I1 and I2) between breeds of the two countries and also sub-groups specific to the region.While I1a in this study may not be a separate haplogroup, a distinct cattle haplogroup "C" and a separate domestication event in north-eastern China during the early Holocene has been proposed byindicine haplogroups compared to taurine and previous studies classified them into only two broad haplogroups (I1 and I2). Despite several previous studies on mtDNA of the Chinese cattle, subgroupings under I1 were not reported except by29. One possible reason is that the location of the mutations defining subgroup under I1, is in the coding region of the mitochondrial genome, while most studies in the past were mainly based on the D-loop. Complete mitochondrial genome sequences better define the full spectrum of mitochondrial diversity compared to using the D-loop only, and may uncover mutations in coding regions that affect specific phenotypes.There are fewer studies within 81, metabolic disorders83 and athletic endurance85, is more advanced than in domestic animals. Although two recent studies examined the relationship between mitochondrial haplogroups and litter size23 and other phenotypic traits24 in pigs, these studies lack sufficient power to distinguish or pinpoint specific mutations  affecting the trait.Genetic variation was mainly within breed 97%, AMOVA), and high haplotype diversity and multiple haplogroups exist within breeds. For example, Holstein animals (N = 267) belonged to at least 15 subgroups and 210 haplotypes  was comparable to results in humans that used tools specifically for imputation of the mitochondrial genome such as MitoIMP87. This indicates that existing tools may be applied to mitochondrial genome imputation with customization. We are currently investigating the accuracy of imputation of mitochondrial SNPs from lower density SNP panels to mitochondrial sequence.Association studies of traits could include variants from the mitochondrial genome, if it is possible to sequence and impute large numbers of animals for MT variants, but this has not yet been done in livestock. While whole-genome sequences are now regularly imputed and exploited for association studies of production traits across livestock, mitochondrial genomes are excluded from these studies and literature on mitochondrial genome imputation is scarce or non-existent for livestock. In humans, the imputation of the mitogenome from ancient remains showed that the accuracy of mitogenome imputation, like the nuclear counterpart, benefitted from having a large and diverse reference sequenceThe current study utilised conventional tools for mtDNA analysis  but also compared these results with alternative tools such as GRM based PCA, Admixture, and hierarchical clustering based on nucleotide differences. Our primary interest in use of the alternative tools was to better quantify the full spectrum of genetic diversity across the entire mitogenome, rather than simply place animals into the higher level haplogroups. However, as expected, the results from the less conventional mitochondrial tools were mostly in agreement with haplogrouping. Therefore, alternative clustering methods, specifically the hierarchical clustering based on nucleotide differences, may be used as grouping techniques that is equivalent to haplotypes for use in trait/phenotypic association studies.taurine and indicine mitochondrial haplogroups into European taurine breeds occurred at low frequencies. The patterns of population structure and haplogroups from conventional tools were very similar to results of non-traditional mitochondrial methods developed for autosomal DNA. We provide additional evidence of a new indicus I1 haplogroup subgroup (I1a) in Chinese indicine breeds. Within breed mitochondrial diversity (haplotypes/ haplogroups) is likely at a level sufficient to conduct trait association studies. Imputation of sporadic missing genotypes in the mitochondrial genome was highly accurate with the exception of heteroplasmic sites. This could enable larger data sets to be used for population studies through recovery of sites or animals with low levels of missing genotypes. Potentially, it would also provide a diverse reference population for large scale imputation of mitogenomes from lower density panels with MT SNP.There is high mitochondrial genomic diversity among modern cattle and a large proportion of this genetic variation is within breeds. The introgression of African Supplementary Figures.Supplementary Information 1.Supplementary Information 2.Supplementary Tables.Supplementary Tables.Supplementary Tables.