KIT, MITF, PMEL), performance (MSTN) and locomotion (DMRT3) for a more detailed examination. In addition, we looked for, and found, associations with height at withers and SNPs located close to gene LCORL. Among the four breeding sections of Finnhorses , a single SNP located close to the DMRT3 gene was significantly differentiated and only between harness trotters and pony‐sized horses.Selection by breeders modifies the morphology, behaviour and performance of domesticated species. Here, we examined signs of selection in Finnhorse, the only native horse breed in Finland. We first searched divergent genomic regions between Finnhorses and other breeds, as well as between different breeding sections of the Finnhorse with data from Illumina Equine SNP70 BeadChip, and then studied several of the detected regions in more detail. We found altogether 35 common outlier SNPs between Finnhorses and other breeds using two different selection tests. Many of the SNPs were located close to genes affecting coat colour, performance, size, sugar metabolism, immune response and olfaction. We selected genes affecting coat colour ( This artificial selection is often directional, decreasing the amount of genetic diversity within breeds. Genetic diversity can decrease also because of fixation of breed‐specific traits through inbreeding, genetic drift and founder effect  have been selectively bred for centuries, especially for transportation, agriculture and warfare, nowadays mainly for leisure and sports activities. Schubert et al.  heavy horses used in agriculture; (b) lightly built and “leggy” horses used in harness racing; and (c) relatively light, pony‐sized horses, in addition to local landraces. The breed was strongly selected for colour and size in the early years. Only horses representing the heavy type were accepted in the studbook between 1907 and 1924, because agriculture and forestry required draft‐type horses. However, as trotting speed was desired in addition to weight pulling, most stallions represented a light‐draft phenotype. Accepted stallions had to be at least 148 cm tall at the withers , excluded from the studbook since 1907, were thus now accepted. However, favouring of draft‐type phenotypes since 1907 had already largely depleted suitable breeding material for light horses  from the nuclear SNP data was just about 50 for each of the breeding sections and for the breed as a whole  gene alleles have been found to affect speed in trotting horses and quality of gaits in riding horses  of the Finnhorse. Based on the results of the screenings, we then studied a selected set of loci previously connected to coat colour, performance, locomotion and height at withers to identify candidate loci for a set of specific traits.22.1N = 954) or from an equine veterinary hospital . DNA was extracted from these samples as in Kvist et al.  and linkage equilibrium  with plink 1.9  and 12 randomly sampled horses from other breeds  lower than 5%.We used two approaches to detect signs of selection in the horse genomes based on FST outlier‐based method used for detection of selection was the hierarchical island model implemented in program arlequin v.3.5.2.2.  for further inspection. These specific genes were selected because they have been found to be under selection in other breeds and several polymorphisms are known  with at least 32 variable sites , which is caused by a SNP g2350‐13 G > A in intron 16 leading to exon splitting and lack of exon 17. The existence of this SNP was examined as in Brooks and Bailey , we selected samples from Finnhorses that showed large white markings  and/or blue eyes (N = 59) and a control group with no white markings (N = 10).The second coat colour‐associated PMEL gene coding for premelanosome protein. This mutation fades the mane and tail of eumelanin (black pigment)‐producing horses to flaxen or silver and body colour to chocolate or reddish, but it does not have a fading effect on pheomelanin‐producing horses, that is chestnuts . We then examined the SNP g1457 C > T that changes an amino acid arginine to cysteine in exon 11 of the PMEL gene , had the allele 177 in preliminary analyses with a microsatellite locus TKY284 (N = 10) and/or had flaxen or faded coloured mane and tail (N = 17). We amplified and sequenced a 182‐bp fragment (PCR and sequencing protocols in Table seqscanner v.2 (Applied Biosystems).The third coat colour outlier was at the region, where a gene involved in a dilution colour called silver is located. Silver colour is connected with two strongly linked SNPs at the FST outliers was close to the myostatin gene MSTN, which has previously been connected with racetrack performance  producing SNP genotypes TT and CT has been associated with better performance in long distances and CC genotypes with better performance in short distances . The Hardy–Weinberg equilibrium and differences in genotype frequencies between the four Finnhorse breeding sections and horses not included in the breeding sections were tested with the G test. Note that here those horses, which were registered in two breeding sections, were included in both sections. Further, we tested differences in MSTN genotype distribution between Finnhorses classified into different endurance classes  and used for different purposes  using the G test. Additionally, we tested whether there were differences in records of harness racing competitions between the genotypes using the t test. Here, we included record times only of horses aged 6 years or more.One of the doublesex and mab‐3‐related transcription factor 3 (DMRT3). A nonsense mutation in this gene has been shown to affect the capability of horses to pace and amble .One of the detected outliers was located close to gene 33.1bayescan detected altogether 42 and arlequin 55 outlier loci with FST values ranging 0.11326–0.19392 and 0.5000–0.6802, respectively, in comparisons between Finnhorses and other breeds  in bayescan and 0.0370 (SD = 0.0825) in arlequin. The two different methods detected altogether 35 common loci. The common outlier loci were found in chromosomes 1 (1 SNPs), 2 (1 SNP), 3 (3 SNPs), 4 (4 SNPs), 5 (1 SNPs), 6 (4 SNPs), 7 (5 SNPs), 8 (1 SNP), 9 (1 SNPs), 10 (1 SNP), 12 (1 SNP), 13 (1 SNP), 14 (2 SNPs), 16 (2 SNPs), 18 (1 SNP), 19 (1 SNP), 21 (1 SNP), 22 (1 SNP), 23 (1 SNP), 30, (1 SNP) and 31 (1 SNP). Four of those loci were in regions where also genes involved in coat colour can be found . One locus in chromosome 3 was close to LCORL, ligand‐dependent nuclear receptor corepressor like, a gene that has been connected with body size, and one locus in chromosome 18 was close to MSTN, myostatin gene that has been connected with performance . Several loci are located close to genes, which regulate sugar metabolism . In addition, several SNPs were found close to genes involved in immune response . Two SNPs were in regions holding many genes of the olfactory receptor family in chromosomes 1 and 12. In comparisons between breeding sections of the Finnhorse, only one outlier was detected by bayescan and five by arlequin in comparisons between harness trotters and pony‐sized horses. The SNP BIEC_2_620406 located in chromosome 23, about 500 kb upstream of the DMRT3 gene, doublesex and mab‐3‐related transcription factor 3 (Table bayescanp < .022). A Venn diagram of genes located at the regions of the outlier SNPs detected with BAYESCAN and ARLEQUIN and an intersection detected by both approaches is shown in Figure  Figures . Mean F 3 Table , was foudavid found that the most important functional annotation clusters were involved in (a) signal transduction and transcriptional regulation, (b) endomembrane systems, (c) apoptosis, (d) cell cortex, (e) cellular responses (to a variety of stimuli) and (f) immunity  allele as heterozygotes and no sabino1 (SB1) carriers out of the 59 candidates having white markings and/or blue eyes. None of the control horses carried SW1 or SB1 genotypes. Studying the pedigrees of SW1 horses revealed that four of these horses were offspring of the same stallion, whose half‐sister was also tested positive for the SW1 mutation. Of the other two horses, one was offspring of another stallion, also registered as carrying SW1, and one did not have any ancestors registered as SW1.We found seven Finnhorses carrying the splashed white , there were in general much more TT homozygotes (long‐distance runners) and less CC homozygotes (sprinters) in Finnhorses than in other breeds. However, also warmblood trotters and PRE‐horses had more TT homozygotes and fewer CC homozygotes than the other breeds we studied , nor on the usage of horses . For trotters, there was no effect of the MSTN genotype on the records of the horses . All groups were in Hardy–Weinberg equilibrium, except the mixed‐breed group .The DMRT3 genotypes, there were significantly fewer CC homozygotes and more CA heterozygotes (both associated with good gaits for riding horses) than expected , but only the difference between riding horses versus horses not in the studbook remained significant after the Bonferroni correction, riding horses having more of the CC homozygotes and less of the AA homozygotes (the “gaited” genotype). All groups were in Hardy–Weinberg equilibrium, except the mixed‐breed group .Finnhorses differed from the mixed‐breed group in the frequency of the DMRT3 AA homozygotes and more CC homozygotes than horses showing pacy walk  nor on the liability of trotters to break into canter . Finnhorses with three‐beat canter and round movements had less AA homozygotes and more CC homozygotes and CA heterozygotes than horses that at least sometimes present four‐beat canter and have sharp movements  LCORL gene that codes for a ligand‐dependent nuclear receptor corepressor like protein , another SNP in chromosome 3 close to LCORL gene (about 70 Kb upstream).One SNP locus (BIEC2_849774) detected by both in Table a,c and iin Table . In addi4bayescan and arlequin, we detected 42 and 55 FST outlier SNPs, respectively, between Finnhorses and other breeds, of which several were of special interest as they were connected to traits, which (a) were strongly selected for at the dawn of the Finnhorse breed (coat colour and size), (b) are related to the usage of the breed (performance and gaits) or (c) are associated with metabolism and immunity system. Gene ontology analysis of genes located at the proximity to the SNPs proposed to be under selection classified the genes in functional clusters including signal transduction, transcriptional regulation, cellular responses and immunity.Using MC1R gene (Melanocortin‐1 Receptor), which would have been expected to have arisen due to selection for chestnut colour, as this gene is responsible for production of red or black pigment. One of the detected outlier SNPs related to colour was close to KIT, proto‐oncogene receptor tyrosine kinase gene, that is crucial in melanocyte development. This gene has been connected with white coat markings. Several polymorphisms have been described in this gene, producing a variety of different white patterns in coat colouration, from totally white horses to sabinos and roans or to small patches of white in legs, face and belly  causing white coat colour patches in Finnhorses, no sabinos were detected. At least three lineages were found to carry the SW1 mutation. Previously, the rare Finnhorses with white patches were believed to be sabinos  syndrome, which is a heritable eye disorder  were not accepted for breeding until the establishment of a separate breeding section for the pony‐sized Finnhorses in 1971. We detected one SNP outlier close to a transcription factor MSTN, myostatin gene. This gene has been connected with muscle mass, body conformation related to muscularity  and also performance. There are a couple of variants located in the promoter region of this gene that have been connected with heavy or light stature  appears in high frequency in Icelandic horses, famous for their ability to perform four or five gaits  genes, IL23A , CLEC5A (C‐type lectin domain containing 5A) and NPSR1 , an allergic skin condition thought to be caused by biting insects, especially of the genus FST outlier analyses detected SNPs located in genomic regions containing several olfactory receptor‐like genes. Previous studies have found large numbers of non‐synonymous variants in horse olfactory receptor (and immunity‐related) genes, and these loci have turned up in selection studies as well (e.g., Fawcett et al., Further, our 5DMRT3 gene is located, and only between two breeding sections, the harness trotters and pony‐sized horses. Thus, selection for over 100 years since the breed was founded has induced differentiation through selection. However, time since foundation of the breeding sections has been too short or selection too weak for differences to emerge. This can also be due to acceptance of new horses into the breeding sections from the common gene pool and acceptance of individuals to more than one breeding section. On the other hand, this practice helps to maintain inbreeding at a lower level and effective population sizes higher than they would be without this practice, which are both matters of concern in the breed.We found evidence of selection in the Finnhorse when compared to other breeds in several regions of the genome. Many of the genes at these regions affect height, movement, performance, colour, sugar metabolism, immune response and olfaction and are connected with signal transduction, transcriptional regulation, cell death and cellular responses. On the contrary, selection has not yet resulted in strong differentiation among the breeding sections within the breed. We found strong evidence of divergence in one region, where The authors declare no conflict of interest.Supplementary MaterialClick here for additional data file.


Pyrenophora tritici-repentis effector ToxB allowed development of a diagnostic genetic marker, and investigation of wheat pedigrees allowed transmission of sensitive alleles to be tracked.Genetic mapping of sensitivity to the Pyrenophora tritici-repentis, is a major disease of wheat (Triticum aestivum). Secretion of the P. tritici-repentis effector ToxB is thought to play a part in mediating infection, causing chlorosis of plant tissue. Here, genetic analysis using an association mapping panel (n = 480) and a multiparent advanced generation intercross (MAGIC) population  genotyped with a 90,000 feature single nucleotide polymorphism (SNP) array found ToxB sensitivity to be highly heritable (h2 ≥ 0.9), controlled predominantly by the Tsc2 locus on chromosome 2B. Genetic mapping of Tsc2 delineated a 1921-kb interval containing 104 genes in the reference genome of ToxB-insensitive variety ‘Chinese Spring’. This allowed development of a co-dominant genetic marker for Tsc2 allelic state, diagnostic for ToxB sensitivity in the association mapping panel. Phenotypic and genotypic analysis in a panel of wheat varieties post-dated the association mapping panel further supported the diagnostic nature of the marker. Combining ToxB phenotype and genotypic data with wheat pedigree datasets allowed historic sources of ToxB sensitivity to be tracked, finding the variety ‘Maris Dove’ to likely be the historic source of sensitive Tsc2 alleles in the wheat germplasm surveyed. Exploration of the Tsc2 region gene space in the ToxB-sensitive line ‘Synthetic W7984’ identified candidate genes for future investigation. Additionally, a minor ToxB sensitivity QTL was identified on chromosome 2A. The resources presented here will be of immediate use for marker-assisted selection for ToxB insensitivity and the development of germplasm with additional genetic recombination within the Tsc2 region.Tan spot, caused by the necrotrophic fungal pathogen The online version of this article (10.1007/s00122-019-03517-8) contains supplementary material, which is available to authorized users. Triticum aestivum L.). It is caused by the necrotrophic fungus Pyrenophora tritici-repentis (Died.) Drechs.  and typified by necrotic lesions as well as regions of chlorosis on infected leaves, which result in reduced leaf photosynthetic area. This typically results in a 5–10% reduction in grain yield, although losses can reach 50% under favourable conditions  spot, is a major fungal disease of wheat  domain and a serine/threonine protein kinase (S/TPK) domain , while the MAGIC data matrix consisted of 20,643 polymorphic SNPs across 643 progeny  Parents of wheat genetic mapping populations: the eight founders of the ‘NIAB Elite MAGIC’ population  as described in See et al. (CATATGAACTGCGTCGCCAATAT-3′) and ToxB_R (5′-CCGCTCGAGACAACGTCCTCCACTTTGCA-3′) that included the engineered NdeI and XhoI sites (underlined), respectively. The resulting construct contained the predicted ToxB protein fused to a poly-histidine tag at the C-terminal, and was used to transform the E. coli SHuffle strain. The SHuffle strain harbouring the ToxB expression construct was grown in Terrific Broth medium and induced with β-D-1-thiogalactopyranoside (IPTG) at a final concentration of 100 µM for the expression of ToxB. Cells were harvested by centrifugation and resuspended in 5 ml binding buffer  for the purpose of downstream his-tag purification. Cells were then lysed using sonication of 10-s pulses with 10-s intervals for 1 min . The cell debris was pelleted by centrifugation, and the ToxB protein was purified from the cell extract using HisPur Ni-NTA purification spin columns (Thermo Scientific) by gravity flow according to manufacturer’s instructions. Purified protein was dialysed in 20 mM sodium phosphate buffer, pH 7.4, quantified via the bicinchoninic acid assay . The experimental plan for each germplasm set comprised five replicates of each variety or accession, arrayed in a randomised block design using R/blocksdesign (cran.r-project.org). Seeds of the first germplasm set were grown in a growth chamber (Conviron) using a 16 h light (at 20 °C) and 8 h dark (15 °C) photoperiod. The AM and MAGIC populations were grown in a heated glasshouse  for 14 days with supplementary lighting to maintain photoperiod. ToxB infiltration was undertaken 14 days after sowing, following the method described by Moffat et al. . Brieflyh2 = σ2G/ (σ2G + σ2e) where σ2G is the genetic variance of line means and σ2e is the residual variance.Summary statistics  were calculated using the software GenStat . Best linear unbiased estimates (BLUEs) were calculated using a linear mixed approach in REML using GenStat  significance threshold was used.Genome-wide association scans (GWAS) using the AM panel were undertaken using the Efficient Mixed-Model Association algorithm . The pedigree was displayed using Helium v.1.17.08.14 . The subset of plants that post-dated the AM panel were grown and phenotyped for ToxB sensitivity and genotyped with the KASP marker, following the methods described above.To validate the conversion of selected SNPs from the Illumina 90 k array to the Kompetitive Allele-Specific PCR (KASP) genotyping platform , DNAs were extracted from the eight MAGIC founder varieties. Marker co-dominance was investigated using a 50:50 mix of DNAs from two varieties known from the 90 k SNP dataset to contrast for allele call. Additionally, DNA was extracted from a panel of 48 UK varieties that post-date the AM panel, released to the AHDB Recommended List between 2009 and 2017 (Supplementary Table 2). All DNAs were extracted from two week old leaves using a modified Tanksley protocol  The W7984 assembly scaffolds  (SAM flags 0 and 16) were extracted from the W7984 assembly and ordered and orientated based on their SAM file coordinates and mapping orientation flags before being joined together as one fragment with each contributing scaffold separated by 100 Ns. (2) The synthetic 2B assembled fragment and the corresponding region of CS42 (chr2B: 21536610..0.27030113 bp) were then repeat masked using Repeatmasker v4.0.8 (https://www.repeatmasker.org) using the command line option: ‘-species Triticum aestivum’. (3) The two repeat masked sequences were aligned using minimap2 v2.16, using the ‘Chinese Spring’ region as reference and with the command line options:‘–cs = long -c -t 20 -K 500 M –Y’. Methods for the liftover of the ‘Chinese Spring’ gene models are further described in Supplementary Text 1. (4) RNA-seq reads (> 8 billion) from BioProject accession numbers PRJDB2496, PRJEB12497, PRJEB25593, PRJEB25639, PRJEB25640, PRJNA213168, PRJNA243835 were obtained from the National Center for Biotechnology Information (NCBI) Short Read Archive (SRA). Further details on the processing of the RNA-seq data and subsequent transcript assembly, gene model and splice junction prediction are listed in Supplementary Text 1. (5) Protein datasets sourced from the genome assemblies of three cereal species (listed in Supplementary Text 1) were aligned to our synthetic 2B fragment from ‘W7984’ that spanned the Tsc2 region using GenomeThreader v1.7.1 , using the Portcullis junctions above as externally validated junctions and with further parameters: ‘-minI 20 -maxE 1000 -maxM 5000’, which excluded any hit with terminal introns longer than 1 kbps or internal introns longer than 5 kbps. (6) Transcript reconstruction: Mikado  in just two accessions Table . All eign = 643) was found to be highly heritable (h2 = 0.95). Mean ToxB sensitivity in the progeny ranged between 0 (insensitive) and 2.25, with 503 lines = 0, 70 lines > 0 ≤ 1 and 67 lines > 1. QTL mapping of ToxB sensitivity in the MAGIC population using log10 transformed phenotypic data and four genetic mapping approaches  all identified a major QTL on the short arm of chromosome 2B   identified a 15.11 cM region (from SNPs BS00034887_51 to BS00053520_51). Within this region, via systematic comparison of the P values along with the genetic and physical map positions of the markers, we define the peak of this QTL to be located in a 1.094 Mbp interval from 24.092 Mbp (BS00070051_51) to 25.186 Mbp (BS00002660_51), which includes all markers with −log10P > 90 from the IM and CIM analyses. This locus corresponds to be the major ToxB sensitivity locus Tsc2   and P = 0.01 (−log10P = 5.29) significance thresholds, using CIM-cov5 it was found to be insignificant (−log10P = 3.68).ToxB sensitivity in the MAGIC population . The majority of varieties (93%) were found to either be insensitive (ToxB sensitivity < 0.2) or slightly sensitive , while just 7% of varieties were classified as sensitive (≥ 1) (Supplementary Table 2). GWAS using the 22,237 SNPs after skimming the complete data matrix on minor allele frequency ≤ 0.06 identified 73 significant SNPs at the Bonferroni corrected P = 0.01 threshold (−log10P > 6.35)  Fig.  who trea35) Fig. . AccordiTsc2 locus in the AM panel allowed a condensed five-SNP haplotype across the region to be determined (Supplementary Table 4). This defined 10 haplotypes, six of which were associated with low ToxB sensitivity (the ‘group 1’ haplotypes: 1.1 to 1.6) and four with high sensitivity (the ‘group 2’ haplotypes: 2.1 to 2.4), with the differences in ToxB sensitivity between these two haplotype groups found to be highly significant  . Additionally, clear recombinations proximal (haplotype 2.2) and distal (haplotypes 2.3 and 2.4) to Tsc2 delimited the target region to a 1921-kb region between 231.063 Mbp (TraesCS2B01G046400) and 250.180 Mbp (TraesCS2B01G051000) in the AM panel. All three of the most significant SNPs identified by GWAS  originated from gene model TraesCS2B01G048500, and all three cosegregate on the ‘NIAB Elite MAGIC’ genetic map at 34.38 cM. Additionally, BS00070050_51 was also identified as the marker immediately proximal to the Tsc2 QTL peak in the MAGIC IM and CIM analyses. In the AM panel, this SNP perfectly predicts sensitivity to ToxB, with alleles A:A and G:G diagnostic for sensitivity (score ≥ 1) and insensitivity (score ≤ 0.6), respectively , and surrounded by a wider cluster of seven additional NBS-LRR genes.In summary, the genetic analyses defined a 1921-kb region defined in the AM panel (23.106 to 25.027 Mbp), extended proximally to 25.236 Mbp by the proximal end of the MAGIC peak region. This combined interval of 2129 kb contains 108 predicted genes in the ToxB-insensitive variety ‘Chinese Spring’ (Supplementary Table 5). Within the interval, the most significant SNPs from the AM panel and the MAGIC population were all located within gene model TraesCS2B01G048500, a glyoxylate reductase/hydroxypyruvate reductase located at 24.092 Mbp. Additional SNPs identified in the AM panel were contained within the interval: hit 6 (GENE-1343_556) originates from TraesCS2B01G048700 which encodes an arginase while hit 12 (Kukri_c63748_1453) is predicted to encode a cullin-associated NEDD8-dissociated protein 1. The wheat gene controlling sensitivity to the Tsc2 locus. Of the few additional hexaploid wheat varieties for which a genome sequence is currently available, only one has previously been shown to be ToxB sensitive: ‘W9784’ , spanning GWAS hit 15  through to hit 23 . This region comfortably includes the 2129-kb interval as defined from the combined AM and MAGIC analyses. The ‘W9784’ scaffolds were stitched together to form a single ‘super-contig’ . Details of all ‘W9784’ genes and candidate genes are summarised in Supplementary Table 6. As the contig size of the ‘W9784’ assembly is very low, with an N50 of 6.7 kb, we did not undertake further characterisation of its genic content. Nevertheless, this information provides a starting point with which to undertake future studies.‘Chinese Spring’, for which the wheat reference genome is available, is insensitive to ToxB. Based on this, as well as the existing evidence in the published literature and the identification of a single ToxB sensitivity locus via analysis of the association mapping panel undertaken here, Chinese Spring would be expected to contain a non-functional allele/deleted gene at the https://cereals.ahdb.org.uk/varieties/ahdb-recommended-lists.aspx) between 2009 and 2017 (Supplementary Table 7). Of these, 39 were found to be A:A homozygous alleles, while 8 were G:G . Phenotyping these 47 recent varieties for ToxB sensitivity found the KASP allele call to correctly predict phenotype in all cases (Supplementary Table 7).The highly significant SNP BS00072620_51 identified in both the AM and MAGIC populations was selected for conversion from the 90 k SNP array to the KASP genotyping platform. The KASP primers designed  were tested on DNAs extracted from all eight MAGIC founders, as well as a 50:50 mix of DNAs from ‘Brompton and ‘Xi19’, and found tight clustering of both homozygous allele classes, while robust calling of the artificially admixed DNA confirmed the KASP marker to be co-dominant Fig. . The marTsc2 locus. As the remaining hub variety ‘Aardvark’ has ‘Cadenza sib’ as one of its parents, and where data were available, none of the remaining parents for the 12 ToxB-sensitive ‘Aardvark’ descendants were found to possess sensitivity, we speculate that ‘Aardvark’ germplasm used for breeding was also segregating at Tsc2. The assumption of segregation at Tsc2 in ‘Cadenza’ and ‘Aardvark’ germplasm used during their early use in breeding results in all but eight of the 46 ToxB-sensitive varieties being linked in a single pedigree, within which ToxB sensitivity arose from ‘Maris Dove’.Overlaying ToxB sensitivity scores from the AM panel and post-AM panel onto an up-to-date wheat pedigree  the inability to trace the source of sensitivity in the immediate parents of all three hub varieties, (2) the observation that all hub varieties contained ‘Cadenza’ in their immediate parentage, (3) the ToxB-sensitive variety ‘Arriva’ represents a ‘Lynx’ (insensitive) × ‘Cadenza’ cross, and (4) ‘Cadenza’ has the sensitive variety ‘Axona’ as one of its parents. A similar assumption was made for the key hub variety ‘Aardvark’, whose parentage includes a sib of ‘Cadenza’ × ‘Lynx’ (insensitive), and which ToxB phenotyping found to be insensitive, despite being a parent of 12 varieties for which no other source of sensitivity was found. On the assumption of segregation at the Tsc2 locus for the ‘Cadenza’ and ‘Aardvark’ germplasm used during breeding, all but eight ToxB-sensitive lines could be traced via the pedigree to the spring wheat variety ‘Maris Dove’, released in the UK in 1971. Of the eight ToxB-sensitive lines not accounted for in the pedigree, one was a coded breeder’s line with unknown parentage, three contained coded breeders lines in their pedigrees preventing further investigation, one contained parents with unknown sensitivity , one had insensitive parents , one was the sensitive landrace ‘Thatcher’, and the last (‘Ardone’) had ‘Thatcher’ in its immediate parentage.Analysis of ToxB sensitivity within the context of the wheat pedigree found three varieties to represent hubs for frequent transmission of ToxB sensitivity: ‘Xi19’ , ‘Cordiale’ (‘Cadenza’ × ‘Reaper’) and ‘Aardvark’ ([‘Cadenza sib’ × ‘Lynx’] × ‘Lynx’). However, a good indication was found that the germplasm for ‘Cadenza’ and its sibs used during early breeding activities segregated for alleles at Tsc2-sensitive alleles in our European germplasm may reflect low occurrence of Ptr isolates carrying the ToxB gene. Indeed, ToxB was not present in any of 42 UK Ptr isolates we have recently surveyed (J. Cockram and J. Turner unpublished), indicating that sensitivity to this effector is not currently an issue in UK agricultural environments. Surveys of Ptr isolates from several countries have shown that presence or absence of the ToxB gene varies by region. For example, ToxB has been shown to be absent in Ptr from Australia  hexaploid wheat bi-parental mapping population, in which ToxB sensitivity is conferred by the synthetic hexaploid wheat line ‘W7984’ (Friesen and Faris T. durum variety ‘Altar 84’ (the AB sub-genome donor) and Aegilops tauschii accession ‘CI 18’ (D sub-genome donor), the ToxB sensitivity on chromosome 2BS identified in the ITMI population must have originated from T. durum. More recently, QTL analysis of ToxB sensitivity and Ptr race 5 isolates in an ‘Altar’ × ‘Langdon’ durum population has demonstrated the role of ToxB sensitivity conferred by the Tsc2 locus with tan spot susceptibility in T. durum  based on recombination events in ‘Orbit’ and ‘Cadogan’. The MAGIC marker immediately distal to the Tsc2 QTL peak corresponded to AM hit 12 (Kukri_c63748_1453), with analysis of haplotypes in the AM panel positioning this marker distal to Tsc2, based on recombination events in four varieties: ‘Selkirk’, ‘Elka’, ‘Thatcher’ and ‘Aronde’ (Supplementary Tables 2 and 4). Therefore, the genomic regions defined by GWAS in the AM panel and genetic analyses in the MAGIC population combined to localise Tsc2 to a 1921-kb region containing 104 gene models, based on the wheat cv. ‘Chinese Spring’ reference genome.QTL mapping using the MAGIC and AM panels found ToxB sensitivity to be predominantly controlled by Tsc2 region, SNP BS00072620_51 was found to be the most significant marker in both of the genetic mapping populations investigated. Furthermore, it was shown in the AM panel (n = 480) to be diagnostic for ToxB sensitivity, with A:A and G:G alleles perfectly predicting insensitivity (ToxB score < 1) or sensitivity (score ≥ 1), respectively. Similarly, SNP BS00072620_51 was also diagnostic for ToxB sensitivity in the 47 varieties investigated that post-dated the AM panel. As ToxB sensitivity has been shown to be correlated with susceptibility to tan spot  . However, we found none of these varieties to be sensitive to ToxB. Here we investigated the genome sequence of the synthetic hexaploid wheat accession ‘W7984’ , as well as an ongoing project to resequence all eight parents of the ‘NIAB Elite MAGIC’ population (https://gtr.ukri.org/projects?ref=BB%2FP010741%2F1). As MAGIC founder ‘Xi19’ is ToxB sensitive, we hope that the future completion of the ‘Xi19’ genome assembly will aid identification of the genetic variant(s) underlying Tsc2.The current consensus is that ToxB sensitivity is predominantly conferred by a single locus, Supplementary Figure 1. Boxplot of mean ToxB sensitivities for the 480 varieties of the association mapping panel, according to Tsc2 haplotype groups 1 (insensitive) and 2 (sensitive). Haplotype groups are as defined in Supplementary Table 4. (TIF 3 kb)Supplementary Figure 2. A high-resolution version of Figure 4 in which the variety names are included directly in the image. (TIF 3260 kb)Supplementary file3 (XLSX 1113 kb)Supplementary file4 (XLSX 54 kb)Supplementary file5 (DOCX 20 kb)Supplementary file6 (DOCX 14 kb)Supplementary file7 (DOCX 19 kb)Supplementary file8 (XLSX 47 kb)Supplementary file9 (DOCX 15 kb)Supplementary file10 (DOCX 25 kb)Below is the link to the electronic supplementary material.


Bos taurus taurus and Bos taurus indicus) to form composite breeds, such as Brangus. These composite breeds provide an opportunity to identify recent selection signatures formed in the new population and evaluate the genomic composition of these regions of the genome. Using high-density genotyping, we first identified runs of homozygosity (ROH) and calculated genomic inbreeding. Then, we evaluated the genomic composition of the regions identified as selected (selective sweeps) using a chromosome painting approach. The genomic inbreeding increased at approximately 1% per generation after composite breed formation, showing the need of inbreeding control even in composite breeds. Three selected regions in Brangus were also identified as Angus selection signatures. Two regions (chromosomes 14 and 21) were identified as signatures of selection in Brangus and both founder breeds. Five of the 10 homozygous regions in Brangus were predominantly Angus in origin (probability >80%), and the other five regions had a mixed origin but always with Brahman contributing less than 50%. Therefore, genetic events, such as drift, selection, and complementarity, are likely shaping the genetic composition of founder breeds in specific genomic regions. Such findings highlight a variety of opportunities to better control the selection process and explore heterosis and complementarity at the genomic level in composite breeds.Cattle breeding routinely uses crossbreeding between subspecies ( The breed represents the complementarity between the tropically adapted Bos taurus indicus and the temperate high-valued carcass of Bos taurus taurus cattle . Therefore, artificial selection pressure, at varying levels of intensity, would have been employed on these traits. In this process, the inbreeding level can increase due the selection of few parents, especially bulls, and, consequently, decreasing heterosis. In this scenario, genomic selection signatures may arise after composite formation  for specific traits. The United States Brangus breeder association (IBBA) has developed EPDs for birth weight, weaning weight, yearling weight, milk production, total maternal, calving ease direct, calving ease maternal, scrotal circumference, ribeye area, and intramuscular fat  from 68 Brahman, 95 Angus, and 59 prominent Brangus sires born from 1970 to 2010 were evaluated in total. Of the animals genotyped, 36 Brahman and 20 Brangus samples were acquired from the National Animal Germplasm Program’s (NAGP-ARS-USDA) gene bank . The other samples were genotyped by the USMARC research center .proc cluster of SAS University Edition . Brangus were grouped into 17 clusters. The Brangus animals sampled for genotyping represented all 17 clusters. Sampled Brangus bulls were born in 12 states in the southern United States from 1970 to 2010, and these bulls had 43,393 progeny recorded by the IBBA.The Brangus pedigree, provided by the IBBA, consists of 1,152,050 individual animal records from which the genetic relationship coefficients were computed. The coefficient of genetic relationship was used to cluster the current Brangus population using Ward’s method in g = ∑(1/2)n, where g is the equivalent generation number and n is the number of generations separating the individual from each known ancestor. The method used is similar to the equation described by The pedigree file was evaluated using the optiSel package  in R 3.4A summary of the pedigree analysis of the Brangus bulls used is shown in the X and 1,043 in Y). Markers with minor allele frequency lower than 1% were removed. One Brangus sample with a call rate lower than 90% was removed.Markers with a call rate lower than 95% or not physically mapped to the bovine genome assembly UMD3.1 were removed from the analyses. The remaining genotypes were 698,282 SNP markers on the autosomes and 38,581 SNP on the sex chromosomes . The parameters were set to a minimum run length equal to 1000 kb with minimum of 70 SNP, allowing runs to contain up to two heterozygotes and five missing genotypes with a maximum gap equal to 50 kb and minimal SNP density of 1 SNP per 50 kb.The runs of homozygosity (ROH) analyses were conducted in SNP and Variation Suitel) was determined by the same method used by The minimal number of SNP to constitute a ROH , and het is the mean SNP heterozygosity across all SNP.where The incidence of common ROH was transformed to each breed’s frequency, dividing by the number of animals of each breed in the analysis. Normality tests were performed, and the frequency threshold defining the top 1% of the observations for each breed was determined. The homozygous regions above the frequency threshold of each breed  were considered as selected regions.E(LIBD−H|gcA) = 100/(2gcA), where E(LIBD–H | gcA) is the expected length of an identical by descendent (IBD) haplotype (in centiMorgans – cM), and gcA is the number of generations from the common ancestor. The conversion from the recombination rate metric to physical distance (from cM to Mb) was performed using the average of the results of According to the length of the ROH, it is possible to estimate the number of generations traced back to the common ancestor, which generates the homozygosity in that region. We classified the ROH into 4 classes  using the equation proposed by FROH) was calculated on each animal according to A genomic inbreeding coefficient based on ROH . For each animal, FROH was calculated based on each of the four classes explained before and for each chromosome using the total size of each chromosome as Ltotal (following the chromosome size estimated by the Btau5.0.1 genome assembly).where 3 was used.We used the copying model, implemented in ChromoPainter , to esti4. Beagle3.3  using the linked model. The recombination files were created using the Perl scripts provided on the ChromoPainter websiteeagle3.3  was used5. The initial parameters were set to an initial allele frequency of 62.5% ; 10 generations; effective population size of 100; no selection, mutation, migration, and inbreeding . We performed 50 simulations for each generation. The raw data were used to calculate the summary statistics (mean and standard deviation) and to determine the expected lower and upper value (within 99% of the Gaussian distribution) of the expected founder composition for each locus. These lower and upper values were applied as a threshold in the visualization of chromosome painting results to identify regions with significant enrichment of alleles coming from one of the founders.We performed a population genetics simulation using the online tool® visualization tool v2.1 . The genes were identified based on the NCBI Bos taurus annotation release 105 and Btau5.0.1 genome assembly. The genes list obtained was submitted in the NetworkAnalyst online tool7, aiming to characterize the biological process of these genes through the Enrichment Network tool using the PANTHER database. Thereafter, a search in the literature and in the Cattle QTL database (available online at8) was executed to identify traits related to genes located in each significant genomic region.Genes in the selected regions (ROH islands) were identified in the Golden Helix GenomeBrowseThe runs of homozygosity (ROH) were categorized into four classes according to the expected number of prior generations to a common ancestor . The ROH classified as coming from a common ancestor within the previous 3 generations (>13 Mb) was found in Brangus between the 4th and 5th generations, and the incidence increased thereafter for most chromosomes . That saFROH) was significantly (p < 0.0001) higher for Angus cattle compared to Brahman and/or Brangus , which was not expected and suggests a high effective population size for Brahman. For ROH coming from more than 10 previous generations (ROH < 3.9 Mb), Brangus cattle had higher FROH than Brahman cattle.The genomic inbreeding coefficient based on ROH . The increase in FROH was not observed for all chromosomes; only chromosomes 4, 10, 13, 15, 23, 26, and 29 had a positive FROH slope with generation number (Pedigree inbreeding had a positive and significant relationship with H length . Brangusn number . All theTen genomic regions had ROH with frequency higher than 25.9% in Brangus (the top 1% of ROH frequency). Two of the 10 regions were found to be ROH islands for both founder breeds, and three ROH islands were observed in Angus . ROH aboFST results show Brangus had a closer relationship with Angus than with Brahman in these ROH regions  . BrangusThe length of ROH agree with the generation criteria of F)  of 51.55 (Ne = 1/2ΔF) . Accordiesources , the desesources . TherefoThree selected regions in Brangus  were identified as Angus selection signatures, and two regions on chromosomes 14 and 21 were identified as selection signatures in both founder breeds . ChromosKHDRBS2 gene, which was previously associated with calving ease  with high ROH frequency in Angus and Brangus harbored the ing ease .LEP gene, which is associated with 96 traits in the Cattle QTL database. This gene is expressed in adipose tissue and codes for leptin, a hormone known to regulate feed intake and energy balance in mammals  was previously identified as a QTL for weaning weight in Brangus . Cánovasrecocity . PLAG1 hd weight . This ged weight . The assPLAG1 significantly increased hip height, weight, net food intake, age at puberty in males and females and decreased concentration of IGF-I in blood and fat depth  but 21.6 Mb long in Brahmans, here as well as in B. taurus and, likely, introduced into Brahman cattle during crossbreeding with taurine cattle when indicine cattle were introduced into the United States  near at depth . These ad States .Selection for growth and growth-related traits, such as average daily gain, feed conversion, and body size, has been conducted to improve beef productivity in both taurine and indicine breeds in the United States for several decades . TherefoThe high Angus contribution for the selected genomic regions in Brangus cattle could support the use of the Brangus data for genomic selection and QTL identification (fine mapping) for Angus. This reinforces previous simulation studies that a crossbred or an admixed population can be used as training data for genomic selection and can provide reasonably accurate estimates of genomic breeding values of purebred selection candidates . Marker The genetic composition of a composite breed is dynamic and changes across generations . Here, tThe majority of selection signatures in Brangus cattle came from Angus, which can be related to the traits of interest for genetic improvement and selection. These results demonstrate how quickly selection and drift can shift the genetic architecture of a population. Genomic inbreeding was found to be increasing in the composite population with advancing generations. Therefore, breeders should be aware of the need to manage inbreeding in this population. Moreover, composite cattle breeders need to be aware that selection for a set of specific traits that favor one of the progenitor breeds over the other can and will alter the original breed proportions and which, over the long term, may decrease the utility of the composite.https://nrrc.ars.usda.gov/A-GRIN).The datasets generated for this study can be found in the all genotypic data used for this study are available in the website of The Animal-Genetic Resources Information Network  (Ethical review and approval was not required for the animal study because no samples were collected for this study; rather they were collected as part of other studies or program activities not associated with this study.TP analyzed the data, interpreted the results, and wrote the manuscript. EH analyzed the data, discussed the results, and revised the text. CW maintained the datasets and analyzed the data. MT discussed the results and revised the text. LK was responsible for data curation, discussion of the results, and revision of the text. SP designed the study, discussed results, and revised the text. CM designed the study and revised the text. HB coordinated the study, discussed and interpreted the results, and wrote the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Most studies investigating human gut microbiome dynamics are conducted on humans living in an urban setting. However, few studies have researched the gut microbiome of the populations living traditional lifestyles. These understudied populations are arguably better subjects in answering human-gut microbiome evolution because of their lower exposure to antibiotics and higher dependence on natural resources. Hadza hunter-gatherers in Tanzania have exhibited high biodiversity and seasonal patterns in their gut microbiome composition at the family level, where some taxa disappear in one season and reappear later. Such seasonal changes have been profiled, but the nucleotide changes remain unexplored at the genome level. Thus, it is still elusive how microbial communities change with seasonal changes at the genome level.Ruminococcus obeum, and mainly participated in metabolic pathways including carbon metabolism, pyruvate metabolism, and glycolysis.In this study, we performed a strain-level single nucleotide polymorphism (SNP) analysis on 40 Hadza fecal metagenome samples spanning three seasons. With more SNP presented in the wet season, eight prevalent species have significant SNP enrichment with the increasing number of SNP calling by VarScan2, among which only three species have relatively high abundances. Eighty-three genes have the most SNP distributions between the wet season and dry season. Many of these genes are derived from Eubacterium biforme, Eubacterium hallii and Ruminococcus obeum have relatively high species abundances. Many genes in the microbiomes also presented characteristic SNP distributions between the wet season and the dry season. This implies that the seasonal changes might indirectly impact the mutation patterns for specific species and functions for the gut microbiome of the population that lives in traditional lifestyles through changing the diet in wet and dry seasons, indicating the role of these variants in these species’ adaptation to the changing environment and diets.Eight prevalent species have significant SNP enrichments with the increasing number of SNP, among which only The online version contains supplementary material available at 10.1186/s12864-021-07372-0. The advancement of next-generation sequencing and bioinformatics techniques has made accessible the genetic information of the entire microbial community. The human gut microbiome has gained increasing research interest because of its critical role in metabolism, host nutrition, immune function, and central nervous system , 2. HoweThe genome-level variations of the gut microbial communities can be examined by SNP calling tools , Prevotella copri , Methanobrevibacter smithii , Eubacterium biforme , Treponema succinifaciens ) was used to generate a mutated reference genome set . Comparing the SNP identified by the three tools with the true SNP  and Ruminococcus lactaris , which belong to Firmicutes. Since a previous report has found that Firmicutes showed relatively stable abundance across seasonal succession [From 116 identified species, we selected 33 species , Catenibacterium mitsuokai , Coprococcus comes , Eubacterium biforme, Eubacterium hallii , Roseburia inulinivorans , Ruminococcus bromii  and Ruminococcus obeum) showed SNP enrichment  in the wet season compared to both dry seasons.To explore SNP changes with the seasonal succession, we selected 55 most prevalent species from 116 species to be included in the reference genome set. After threshold filtering , we identified 765,106 SNP  in samples collected in the dry season of 2013 (2013dry samples), 3,647,990 SNP  in samples collected in the wet season of 2014 (2014wet samples), and 1,892,342 SNP  in samples collected in the dry season of 2014 (2014dry samples)  were shown in Supplementary Fig. We also extracted the SNP sites with the mutated allele frequency > 0.5, and calculated the pairwise distance between samples using Manhattan distance. Hierarchical clustering was then used to cluster all the samples. The results for P < 0.05, Wilcoxon rank-sum test), but no difference between adjacent dry seasons. Considering multiple comparison, we focused on the 83 genes, which achieved a significance at P < 0.01 level  showed higher SNP density in the wet season   Fig.  had relaFaecalibacterium prausnitzii, Prevotella copri, Methanobrevibacter smithii, Eubacterium biforme, Treponema succinifaciens), which commonly reside in human gut and play specific functions [For simulated data, we first generated a list containing 10,786 SNP sites from five major species , we assessed their performance from two aspects which were calculated as follows.Here, the ‘match’ represents the number of actual SNP identified by the tool, while the ‘false positive’ represents the number of SNP identified by the tool were non-existent. ‘mismatch’ represents the number of actual SNP identified as a wrong base by the tool. ‘all’ represents the number of all actual SNP.Clean reads of 40 samples were aligned to the reference genome set via BWA MEM with the parameter -R to specify the header group in output sam files. SAMtools view command was used with parameter -bS to transform sam files into bam files, and its sort command was used to sort bam files by the order of chromosomes. Picard MarkDuplicates command was used with parameter REMOVE_DUPLICATES = true to remove PCR duplicates. Samtools mpileup command was used with parameter -Bf to specify the reference genome set and output mpileup files. VarScan mpileup2SNP command was used with parameter --min-coverage 10 --output-vcf 1 --variants --min-avg-qual 15 to identify SNP from mpileup files and output vcf format files. The variant calling procedure was illustrated in Fig. Faecalibacterium prausnitzii , Prevotella copri , Methanobrevibacter smithii , Eubacterium biforme , Treponema succinifaciens ) was used to generate a mutated reference genome set. Then, BBMap was applied to generate paired-end simulated reads of length 75 with the sequencing error: SNPrate = 0.002, insrate = 0.00002, delrate = 0.00002. Different amounts of reads were generated to understand the effect of different sequencing depth, with 1,000,000 reads for 5x, 200,000 reads for 10x, 4,000,000 reads for 20x and 8,000,000 reads for 40x. We then used Burrows-Wheeler Aligner (BWA) to index the original reference genome set and MEM algorithm to align the simulated reads with the reference. The result files (in sam format) were converted to bam files by SAMtools [To select the most suitable variant-calling tool for the strain-level SNP analysis, we first evaluated three representative tools  SNP density between wet and dry seasons but indistinct (P>0.05) between adjacent dry seasons are shown in boldface. Supplementary Table 3. Information of genes with characteristic SNP distribution pattern . Supplementary Table 4. Pathway information of 36 genes annotated to KEGG database. Supplementary Figure 1. Phylogenic trees based on whole genome SNP sites of remaining 6 species . Supplementary Figure 2. Clustering results of remaining six species based on whole genome SNP sites .


The Atlantic salmon is the most economically important species of the family Salmonidae. As a group, salmonid species hold considerable economic, social, and environmental importance in Europe, the Americas, and Australia . CompareSingle-nucleotide polymorphisms (SNP) are highly abundant markers broadly distributed in animal genomes. High density SNP arrays are used for collecting large amount of genome-wide genotype data. This information is useful for dissecting the genetic basis of quantitative traits in agriculture and for implementing models of genomic selection, which has revolutionized the field of selective breeding over the past decade . High deRecently, we initiated efforts to generate genomic research resources for the breeding program at the USDA-ARS National Cold Water Marine Aquaculture Center (NCWMAC). We used high coverage whole genome Illumina resequencing for SNP discovery in 80 NA Atlantic salmon individuals from three aquaculture stocks that are propagated in the NCWMAC. Sequences of four doubled haploid (DH) European Atlantic salmon were added to the analysis to detect putative paralogous sequence variants and multi-sequence variants following the SNP discovery analysis methods we previously described . OverallThe number of SNPs we identified using the freebayes, mpileup, and GATK pipelines were 10,110,286, 14,670,596, and 11,620,241, respectively. More specific information about each SNP can be found online in the European Variation Archive (EVA) in the Variant Call format (vcf) . A summary of how many SNPs are shared among the three pipelines is displayed as a Venn diagram in Genomic DNA was extracted from the fin clips of 80 NA Atlantic salmon selected from three aquaculture stocks that are currently propagated at the NCWMAC. These three aquaculture stocks represent three distinctive strains of NA Atlantic salmon. The GNB strain is a landlocked strain of fish. The SJR strain is of great economic importance as the fish produced by the Aquaculture industry in Northeast US and Canada were originated from this strain and it is also the primary species used in the selective breeding program in NCWMAC. The PR strain was originated from an endangered NA Atlantic salmon population. Of the 80 selected fish for DNA extraction, 16 are from GNB, 53 are from SJR, and 11 are from PR. Whole genome shotgun sequencing was performed using Illumina NovaSeq (paired-end 2 x 150 nucleotides) providing an average of 18.5x genome coverage per sample (minimum 13.7x and maximum 28x). The library preps and DNA sequencing were performed by a commercial sequencing service vendor . The information on each sample including population affiliation, number of sequence-reads, and genome sequence coverage is found in http://www.repeatmasker.org/); Third, we removed the sites that were covered by more than 3,000 reads as these sites are very likely to have excess number of reads caused by repeat sequences or genome locus duplications; Fourth, we required no more than one sample with missing genotype per SNP. To call a SNP genotype for an individual sample, we required at least two reads to support calling the alternate allele and at least one read to support the reference allele; Finally, we filtered out SNP sites with heterozygous genotypes in any of the four DH lines. A workflow chart of the bioinformatic pipeline we used for SNP is found in All raw sequence reads were mapped to the Atlantic salmon genome assembly (GCF_000233375.1), using BWA mem with the default parameters . At leasTo find the SNPs that allow for identifying the continental of origin of the Atlantic salmon, Fst between the NA and European Atlantic salmon were calculated at each selected SNP sites using the program vcftools , and tho2 > 0.1 was used to reduce SNP redundancy. The tree was built with the maximum likelihood method from the DNAML program within the PHYLIP package. A larger view of the phylogenetic tree is provided in The SNPhylo pipeline  was usedThe datasets generated for this study can be found in the DNA sequences are in NCBI SRA accession PRJNA559280. The three VCF files for the database of all the SNPs identified in this study are available online in the European Variation Archive (EVA) under project PRJEB34225 with Accessions ERZ107004, ERZ107005 and ERZ107006.The fish were sampled according to our Standard Operating Procedures: Care and Use of Research Animals. Publication 4, November 2018, USDA, ARS National Cold Water Marine Aquaculture Center, 25 Salmon Farm Road, Franklin, ME 04634. Approved by the Institutional Animal Care and Use Committee on November 14, 2018.GG planned, designed, and performed the bioinformatics and data analyses and wrote the manuscript draft. CR, BP, and YP conceived the study and research plan. MP, GB, and BP designed the samples collection and collected the samples. YP, MP, and BP coordinated and co-directed the work. YP also contributed to the data analyses design and the manuscript draft. All authors reviewed the manuscript draft and approved its content.This study was supported by the USDA Agricultural Research Service in-house projects 8030-31000-004 and 8082-31000-012.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Parentage misassignments directly affect genetic gain in traditional breeding programs. The use of genetic markers facilitates parentage verification. In sheep, microsatellite markers and single nucleotide polymorphism (SNP) markers have been proposed by the International Society of Animal Sciences (ISAG) for parentage testing. Since the implementation of genomic selection, the microsatellite information used for parental testing in previous generations is gradually being replaced by SNPs. However, parentage verifications should all be performed using the same technology. A strategy for transitioning from microsatellites to SNP markers, while avoiding extra genotyping costs, is the imputation of microsatellite alleles from SNP haplotypes. This study aims to identify the optimum approach, using a minimum number of SNPs to accurately impute microsatellite markers and developing a low-density SNP chip for parentage verification in the Assaf sheep breed. The imputation approach described here reached high accuracies using a low number of SNP markers, which supports the development of a low-density SNP chip that could avoid the problems of genotyping with both technologies, being a cost-effective method for parentage testing. This study will help sheep breeders to perform parentage verification when different genotyping platforms have been used across generations.2), and allelic dosage , for all imputation scenarios tested (0.5–10 Mb microsatellite flanking SNP windows). The imputation accuracy for the three metrics analyzed for all haplotype lengths tested was higher than 0.90 (C), 0.80 (length r2), and 0.75 , indicating strong genotype concordance. The window with 2 Mb length provides the best accuracy for the imputation procedure and the design of an affordable low-density SNP chip for parentage testing. We additionally evaluated imputation performance under two null models, naive  and random , which in comparison showed weak genotype concordances . Therefore, we describe a precise methodology in the present article to impute multiallelic microsatellite genotypes from a low-density SNP chip in sheep and solve the problem of parentage verification when different genotyping platforms have been used across generations.Transitioning from traditional to new genotyping technologies requires the development of bridging methodologies to avoid extra genotyping costs. This study aims to identify the optimum number of single nucleotide polymorphisms (SNPs) necessary to accurately impute microsatellite markers to develop a low-density SNP chip for parentage verification in the Assaf sheep breed. The accuracy of microsatellite marker imputation was assessed with three metrics: genotype concordance (C), genotype dosage (length r Parentage misassignments directly affect genetic gain when pedigree information is used in breeding programs by biasing heritability estimates, genetic parameters, breeding values, and the identification of superior animals for selection ,2,3. TheThe use of molecular markers, specifically genetic markers, facilitates parentage verification and individual identification by indicating the putative relatedness between individuals through different approaches  ,5. In thAt present, microsatellite information for parentage verification tests is being gradually replaced by single nucleotide polymorphisms (SNPs). Although SNPs are less informative due to their biallelic nature, which determines the range of markers required for parentage testing (200–700 SNPs compared to 14–20 microsatellites) ,9,10. MoIn the case of sheep, there are two strategies for parentage testing proposed by the International Society of Animal Sciences (ISAG): a panel of 19 microsatellites  and a paThe genetic profiles of 4423 animals from 94 different flocks included in the breeding program of Spanish Assaf dairy sheep were obtained from the Association of Spanish Assaf Sheep Breeders . This dataset is composed of animals born between 1997 and 2019, of which 349 were artificial insemination rams, 2071 were natural mating rams, and 2003 were ewes. These animals were genotyped for the 19 microsatellites recommended by ISAG for paternity control . A SNP pn) based on the microsatellite allele length. SNP-chip quality control was performed using PLINK [Prior to the imputation process, quality control was applied to both sets of markers. We filtered out microsatellites with call rates below 80% and expected heterozygosity below a Hardy-Weinberg equilibrium value of 0.095. This value corresponds to a minor allele frequency (MAF) of 5% for biallelic markers. After quality control, microsatellite alleles were recoded to fit the variant call format (VCF), following the VCFtools software specifications . The mosng PLINK , and SNPhttps://www.ensembl.org/Ovis_aries/Info/Index), and they were verified through the alignment of the primer sequences to the sheep reference genome using BLAST [The positions of the microsatellite markers in the ovine genome (Oar_v3.1) were obtained from the sheep database from Ensembl v.95 . The process was repeated for ten rounds, using different animals in the validation dataset in each round, following a nonparametric bootstrap of 10% of the total samples using a custom Fortran source code.2) was defined as the Pearson correlation between the sum of the two alleles at a specific locus (2) was calculated as the Pearson correlation for each microsatellite allele length n the number of samples, where j is the number of alleles per sample. In addition, Pearson’s correlations between the frequencies of the reference microsatellite alleles and the imputed microsatellite alleles were calculated. Furthermore, for each microsatellite, the imputation performance was evaluated by computing the expected value for each metric under two null models: (1) a naive model in which the imputed genotype was selected as the most common allele per microsatellite and (2) a random model in which the imputed genotype was randomly selected from the available genotypes at each marker, depending indirectly on the allelic frequencies [To assess the accuracy of the microsatellite imputation, we used the metrics of genotype concordance, genotype dosage, and allelic dosage, which were previously defined by Saini et al. . The genFinally, we used SNP chip information to evaluate different factors affecting imputation accuracies, such as population structure, effective population size, and parental–progeny pedigree conflicts. To assess the population structure, we estimated the genomic relationship matrix (GRM) following VanRaden et al. , and theAll microsatellite markers passed the quality control settings fixed in the analysis. Regarding SNP markers, a total of 3537 markers showed a call rate lower than 95% and were filtered out. Therefore, a total of 19 microsatellites and 42,665 SNPs were considered for the imputation procedure. The microsatellites were located along the sheep autosomes and on average each microsatellite included in this study had 12.73 alleles, ranging from 81 to 297 bp. The information for the microsatellites considered in this work is summarized in To impute the whole population considered in this study, we performed a 10-fold cross-validation approach, as explained in the Materials and Methods section. Therefore, ten imputation procedures were necessary to estimate the microsatellite information in the whole population. Moreover, we assessed the accuracy  of the imputed microsatellite markers in the proposed imputation scenarios (window lengths from 0.5 Mb to 50 Mb) to determine the best haplotype length for the imputation procedure .2 = 0.890, allelic r2 = 0.788), 1 Mb  and 2 Mb  window lengths were compared , whereas a stabilization in the imputation accuracy was observed for wider windows  with observed genotypes, and random imputation, which had an average concordance of 0.15 (ranging from 0.06 to 0.60). Both validation procedures revealed considerably fewer concordance values using the two null imputation models than the imputation method proposed in this study, which validates our approach.The pedigree records available for the Assaf sheep population under study integrated 1450 parental–progeny relationships that could be confirmed with the SNP information located in the 2 Mb around each microsatellite to detect parental–progeny conflicts. A total of 24 misassignments were found in the pedigree, representing a total of 1.66% of all the parental relationships analyzed. To ensure that the information from the imputed microsatellites can help verify the relationship between parents and offspring, we contrasted the parental–progeny relationships confirmed through the 19 microsatellites with the imputed microsatellite information, considering the window length scenarios proposed above (1–50 Mb). A total of 86.50% of these relationships were confirmed through analysis of the 19 imputed microsatellite alleles obtained regarding the SNPs located in the 2-Mb surroundings of each microsatellite. Similar values were reached through the 1 Mb (77.50%) and 3 Mb (85.70%) window lengths, with the 2 Mb scenario, described above as the best haplotype for microsatellite imputation, being the best rate in terms of parentage confirmation. The distribution of the compatible microsatellite alleles between parents and offspring is represented in This study presents a precise methodology to impute multiallelic genotypes from biallelic information in sheep. Traditional and new genotyping technologies must be aligned by applying bridge methodologies, which allow breeders to avoid the additional costs of re-genotyping historical data. Our study combines microsatellite and SNP markers in an efficient approach to impute microsatellite markers through SNP haplotypes, achieving high concordance rates. Therefore, the imputation procedure developed represents a useful and inexpensive approach to performing parentage verification when different genotyping platforms have been used across generations. The results from this study will undoubtedly have a great impact on Assaf sheep breeders, allowing them to perform a transition from microsatellite maker kinship verification to the use of SNP panels . In addi2, and allelic r2) in the different scenarios tested (SNP windows ranged between 0.5 and 10 Mb) was higher than 0.90 (C), 0.80 (length r2), and 0.75  for all haplotype lengths. The accuracy results presented in this study were higher than those found in a previous study performed in cattle by Sharma et al. [2 = 0.941, allelic r2 = 0.878). However, the imputation performance reached high accuracy values at an SNP haplotype length of 2 Mb: 0.97 (C), 0.95 (length r2), and 0.90 , with all accuracy metrics higher than 0.90. The SNPs located in the 2-Mb window distance used in the imputation procedure have been summarized in In general, as shown in a et al. , which ra et al. . AccordiThe number of haplotypes per microsatellite and the frequency of these haplotypes did not significantly affect the allele dosage, with correlations of 0.33 and 0.18, respectively. Therefore, as the number of alleles and their frequency increases, the concordance tends to rise. However, the correlations between the number of haplotypes of each microsatellite and the concordance rate of the naive model (−0.45) or the concordance rate of the naive model (−0.70) were both negatives, meaning that the naive and random models’ concordance rates decreased as the number of alleles increased because they depended on the number of haplotypes of each microsatellite.The imputation accuracies obtained may be overestimated due to (i) a highly structured and related population  or (ii) In summary, the optimum window distance (2 Mb) achieved a high concordance rate 0.97) in the microsatellite imputation procedure and the highest accuracy in parentage testing (>0.99). The parental–offspring relationships confirmed by 17 or more imputed microsatellite markers would ensure a 99.55% success rate, with no risk of parentage misassignments . However7 in the This study presents an effective methodology to overcome the problems presented by the transition from multiallelic markers (microsatellites) to biallelic markers (SNPs) for pedigree verification analyses in sheep. The use of a flanking 2-Mb SNP window for each microsatellite has been shown to achieve high accuracy in the imputation procedure while providing a cost-effective, low-density SNP chip for breeders. The microsatellite-imputed information could be used for individual identification and parentage verification in sheep, representing a useful approach in the sheep industry to avoid double genotyping.


In this paper we propose a workflow for studying the genetic architecture of ischemic stroke outcomes. It develops further the candidate gene approach. The workflow is based on the animal model of brain ischemia, comparative genomics, human genomic variations, and algorithms of selection of tagging single nucleotide polymorphisms (tagSNPs) in genes which expression was changed after ischemic stroke. The workflow starts from a set of rat genes that changed their expression in response to brain ischemia and results in a set of tagSNPs, which represent other SNPs in the human genes analyzed and influenced on their expression as well. The ischemic stroke (IS) is a multifactorial disease, where the genetic factors contribute substantially . The samGWAS does not require the prior knowledge on the importance of the specific functional features of the trait under consideration. At the same time, it is less precise in revealing causal loci (genes) generally located in particular chromosomal regions that can contain no genes or alternatively be abundant with them . The usaThe idea of this research originates from the models of brain ischemia in laboratory animals that were developed to understand the biological processes underlying cerebral ischemic injury . Studiesp-value < 0.01) and were chosen for further analysis.The main steps of the workflow proposed are shown in The human orthologues of the rat genes were comparatively identified by querying several resources: Ensembl , PANTHERThe next step was the identification of SNPs within the human genes, including their 5’ and 3’ flanking regions of 5000 bp length. To be relevant to the SNP frequencies in the potential case–control study, the genotypic data should be taken from an appropriate population . To chooThen, we explored the associations between the alleles of selected loci using the correlation coefficient r2 and revealed patterns of linkage disequilibrium (LD) in each of the region considered. To do this, we applied the CLUSTAG tool , Tagger The input files were generated from vcf files obtained in the previous step with the custom scripts. All of the tools were able to reveal patterns of LD (LD blocks) using distinct algorithms but only CLUSTAG and Haploview allowed to compute tagSNPs which represented the groups of highly correlated SNPs in a chromosomal region. Thus, they were used for revealing tagSNPs in the gene regions studied (the threshold of squared correlation between SNPs r2 ≥ 0.8). For both tools, we estimated the tagging effectiveness (TE) as the ratio of the number of tagSNPs to the number of SNPs they tagged. Because of large number of potential tagSNPs and taking into account that not all of them could mark functionally important SNPs, the subsequent step was to annotate all the possible tagSNPs from high-LD regions with expression quantitative trait loci (eQTLs). For each gene, we downloaded the Significant Single-Tissue eQTLs using the web-interface of Genotype-Tissue Expression (GTEx) project (Release V8) . The eQTAt the final step the tagSNPs from the Haploview’s Tagger runs with the maximal capture efficiency  and defying as eQTLs were selected to form a list of markers for studying in case–control associations using an appropriate genotyping approach .https://github.com/inzilico/tagSNP (accessed on 9 August 2020). The scripts used in this research are freely available at the repository GLYCAM1 is pseudogene. The genes extracted from Ensembl are presented in We extracted 23 of 24 human orthologues in rat using such projects as Ensembl, PANTHER, PhylomeDB, and MetaPhOrs. Different repositories resulted in the same list of orthologues that showed a one-to-one relationship between human and rat genes. The exception was Glycam1 gene, which orthologue was not identified. The human BCL3, CCL22, FOSL1, GLYCAM1, GPR6, HMOX1, IL6, and LCN2 genes. After checking the identified sets of eQTLs, nine tagSNPs were determined as potential candidates for further analysis in case–control study using real-time PCR with TaqMan probes. Eight of them were associated with the changes of expression in brain tissues and thus to be the first-priority markers. The ninth locus—the SNP in CCR1 gene—had the greatest absolute values of eQTL-related statistics, particularly, p-value and normalized effect size .In this paper we proposed a workflow to identify the genetic markers associated with the outcomes of ischemic stroke. It is based on candidate gene approach that requires a prior knowledge about the system under consideration. We hypothesized that such information, particularly, a list of gene-candidates, can be taken from the model studies of brain ischemia in rat. Namely, we took 24 genes exhibited substantial changes in their expression in brain rat after tMCAO and using the workflow proposed obtained a list of the SNPs (tagSNPs with eQTLs abilities) that can be potentially applied in case–control studies.In the line of workflow, we additionally compared four different sources of human orthologues in rat and three different methods for identification of high-LD regions and selection of tagSNPs. Ensembl, PANTHER, PhylomeDB, and MetaPhOrs were chosen because of the best accuracy and call rate of orthologues inference . They alTo explore patterns of LD and identify tagSNPs we used CLUSTAG, Tagger, and gpart tools. These methods were chosen because they represent three different approaches to the problem of identifying groups of highly correlated SNPs. Although they all exploit the LD-based approach and MAF to split the list of SNPs into high-LD regions (blocks), their algorithms differ. Tagger is based on the analysis of single markers and multi-marker haplotypes, CLUSTAG—on the analysis of clusters, while gpart—on graph analysis. gpart can effectively identify LD blocks of different range but cannot tag SNPs. In terms of TE, Tagger outperformed CLUSTAG and thus its tagSNPs were used for further analysis. However, the number of tagSNPs computed was still high for practical usage, which is why we annotated the SNPs from high-LD regions with eQTLs and subset the appropriate tagSNPs manually. Because the expression of a particular gene can be potentially affected not only the loci located inside the gene (cis-eQTLs) but the loci lied outside the gene (trans-eQTLs)  the workLike other studies pointed to establish genomic landscape of complex traits, our approach is also based on exploration of data of different types  ,34. HoweThe limitation of the proposed approach is that it has not been experimentally validated in a cohort of patients. Nevertheless, we believe that the created workflow will help both in studying of genomics of individual variability in ischemic stroke outcomes and looking inside the black box of polygenicity in their control.


Bemisia tabaci is a cryptic species complex that requires the use of molecular tools for identification. The most widely used approach for achieving this is the partial sequencing of the mitochondrial DNA cytochrome oxidase I gene (COI). A more reliable single nucleotide polymorphism (SNP)-based genotyping approach, using Nextera restriction-site-associated DNA (NextRAD) sequencing, has demonstrated the existence of six major haplogroups of B. tabaci on cassava in Africa. However, NextRAD sequencing is costly and time-consuming. We, therefore, developed a cheaper and more rapid diagnostic using the Kompetitive Allele-Specific PCR (KASP) approach. Seven sets of primers were designed to distinguish the six B. tabaci haplogroups based on the NextRAD data. Out of the 152 whitefly samples that were tested using these primer sets, 151 (99.3%) produced genotyping results consistent with NextRAD. The KASP assay was designed using NextRAD data on whiteflies from cassava in 18 countries across sub-Saharan Africa. This assay can, therefore, be routinely used to rapidly diagnose cassava B. tabaci by laboratories that are researching or monitoring this pest in Africa. This is the first study to develop an SNP-based assay to distinguish B. tabaci whiteflies on cassava in Africa, and the first application of the KASP technique for insect identification. Bemisia tabaci , with a host range of over 1000 plant species, is considered one of the most damaging crop pests worldwide. The greatest damage caused is by the vectoring of over 300 plant viruses [B. tabaci transmits viruses that cause cassava mosaic disease (CMD) and cassava brown streak disease (CBSD) [The whitefly, e (CBSD) ,3. The ce (CBSD) . Bemisia tabaci is genetically complex [COI) gene [B. tabaci was described in the 1950s after the discovery that morphologically indistinguishable populations of B. tabaci differed with respect to host range, host–plant adaptability, and plant virus transmission capabilities [B. tabaci relied on morphological characterization involving the examination of slide-mounted specimens of fourth instar nymphs [Bemisia, and ultimately resulted in many putative species of Bemisia being synonymised under the single species name, Bemisia tabaci [ complex , with maOI) gene ,7. The obilities . At thatr nymphs ,11. ThisBemisia led to the exploration of new molecular techniques to characterise B. tabaci groups, such as the use of esterase isozyme polymorphisms [COI) gene portions in the mitochondrial genome, and the nuclear ribosomal intergenic spacer 1 (ITS1), a non-coding sequence, for genetic characterization of whiteflies [The inconsistency in relying on morphological characteristics to classify orphisms , as wellorphisms . Advanceiteflies ,6,12,13.iteflies . COI marker has become the most commonly used marker for phylogenetic studies of B. tabaci [B. tabaci complex is composed of at least 24 distinct species [COI has the disadvantage that it is a single locus that is maternally inherited; therefore, it is unlikely to produce an adequate genetic resolution to differentiate populations, and it does not provide a full delineation of phylogenetic history [B. tabaci revealed the existence of fewer putative species as opposed to the much larger number reported with COI [COI for delimiting B. tabaci cryptic species [The . tabaci ,6,12,13, history . In some history ,17. In awith COI . These r species .Several studies have reported that single nucleotide polymorphisms (SNPs) are informative and affordable genetic markers ,20,21. LB. tabaci in Africa was occasioned by the severe outbreak of cassava mosaic disease (CMD) in Uganda in the 1990s [B. tabaci were reported in West Africa, the cassava-associated strain designated “cassava” and a strain associated with other crops [B. tabaci were reported based on esterase profiles—those found on cassava and those on other crops such as sweet potato and cotton [COI sequencing that were designated Ug1 and Ug2, with the latter shown to be associated with the severe CMD epidemics of the 1990s [B. tabaci from sub-Saharan Africa (SSA) reported the existence of five genetically distinct groups (SSA1–5) based on COI sequencing [COI [B. tabaci from Africa revealed the existence of six haplogroups, designated as SSA2, SSA4, SSA-CA, SSA-ESA, SSA-WA and SSA-ECA [Interest in cassava colonizing he 1990s . Previouer crops . In Ugand cotton . Host-asd cotton , while td cotton . The cashe 1990s . Furtherquencing . SSA1 waing [COI ,35. Most SSA-ECA ,25. Thes SSA-ECA ,25 repreB. tabaci in Africa. There is, therefore, a need to develop an alternative rapid, accurate and affordable SNP-based tool for whitefly identification. In this study, we developed a diagnostic Kompetitive Allele-Specific PCR (KASP) assay to distinguish the six haplogroups of cassava B. tabaci. KASP is a technique that was developed by LGC Genomics Teddington-UK, and it is based on allele-specific oligo extension and fluorescent resonance energy transfer (FRET) for signal generation. The assay uses allele-specific fluorescent labelled primers that allow for end-point fluorescent reads enabling the bi-allelic scoring of single nucleotide polymorphisms at specific loci. (https://biosearch-cdn.azureedge.net/assetsv6/KASP-genotyping-chemistry-User-guide.pdf). KASP assays are low cost, high-throughput and have high specificity and sensitivity relative to other markers [The high cost and time required for NextRAD sequencing means it cannot be used for routine monitoring of cassava-colonizing  markers . B. tabaci whiteflies using a KASP PCR technique and to validate these markers by comparing them with genotyping by NextRAD sequence data.The objective of the study described here was to develop SNP markers to distinguish the six major haplogroups of cassava http://snpsaurus.com/) [B. tabaci cassava whiteflies that produced quality sequences out of the combined 285 samples. We previously collected 95 cassava-colonizing whitefly samples from eight African countries , Democratic Republic of Congo (DRC), Madagascar, Nigeria, Rwanda and Tanzania) . An addius.com/) . Raw reaus.com/)  were firus.com/) . The finB. tabaci haplogroups designated as sub-Saharan Africa East and Central Africa , sub-Saharan Africa East and Southern Africa , sub-Saharan Africa Central Africa , sub-Saharan Africa West Africa , sub-Saharan Africa 2  and sub-Saharan Africa 4  [Downstream analyses revealed the existence of six major cassava  on COI) ,25. The B. tabaci haplogroups  were identified (Bemisia tabaci isolate SSA1 (SSA-ECA) whole-genome assembly (GenBank accession PGTP01000000). Seven sets of primers were designed  . A total of 152 whitefly samples from Nigeria, Ghana, Benin, Sierra Leone, Liberia, Cameroon, Malawi, Mozambique, Kenya, Tanzania, Uganda and the Democratic Republic of Congo (DRC) were tested using KASP. These were selected from the same DNA extracts that were used for SNP genotyping (NextRAD) [SNPs to distinguish the six newly designated cassava-colonising entified , and genBTS1161)  and optiBTS1161) . PCR proBTS1161) . The KASNextRAD)  with the2 (25 mM) solution, and 3.36 µL of sterile water. A total of 35 cycles of amplification were carried out, and conditions were the same for all set of primers: denaturation at 95 °C for 5 min and 94 °C for 40 s, annealing temperature at 58 °C for 30 s, and extension at 72 °C for 45 s, and a final extension at 72 °C for 10 min and held at 10 °C.The conventional PCR reaction mixture (10 µL) contained 1 µL template DNA, 5 µL OneTaq Quick-Load 2X Master Mix with Standard Buffer, 0.24 µL of primer (0.25 mM), 0.4 µL MgClThe KASP reaction mixture (10 µL) contained 5 µL 2X KASP master mix, 0.14 µL KASP primer assay mix and 5 µL DNA template (1 µL of PCR product/DNA extract + 4 µL of sterile water). KASP genotyping was performed in a Strategene MX 3000P . The following cycling conditions were used: Stage 1: 30 °C 60 s (pre-read); Stage 2: 94 °C for 15 min hot-start Taq activation (1 cycle); Stage 3: 94 °C for 20 s, 61 °C  for 60 s (10 cycles); Stage 4: 94 °C for 20 s, 55 °C for 60 s (29 cycles); Stage 5: 94 °C for 20 s, 57 °C for 60 s (3 cycles); Stage 6: 37 °C for 60 s  followed by an end-point fluorescent read. These conditions were used for five primers , while Stage 3: 94 °C for 20 s, 68 °C  was used for two primers, BTS613 and BTS46-203. The quality of genotyping cluster plots was visually assessed, and only samples in distinct clusters were considered for manual SNP calling, using the MxPro software incorporated in the Strategene MX 3000P unit and KlusterCaller . Genotyping profiles obtained from KASP assays were compared to genotype data from NextRAD to ensure only matching genotypes were considered. Bemisia tabaci (MED and IO) and B. afer that were tested in our laboratory. This primer is therefore used to first split African cassava B. tabaci from other species in addition to providing PCR templates for subsequent KASP assays. The KASP diagnostic flow chart (The KASP genotyping results were consistent with NextRAD genotyping (99.3%) with the exception of one sample. The SNP genotyping clusters for the selected six primers for representative samples are presented . The conow chart  shows thSeparating SSA-ECA and SSA-WA from all other cassava B. tabaci haplogroups. The SNP primers BTS99-319 used in KASP gave genotyping results for the 152 samples that were consistent with NextRAD haplogroups and that corresponded with NextRAD alleles in 151 (99.3%) of the samples. The SSA-ECA and SSA-WA haplogroups (73 samples) clustered as A:A and those in the other four haplogroups  (78 samples) clustered as G:G. Only one sample from Malawi—designated as SSA-ESA with NextRAD alleles (G:G)—was designated as A:A by KASP genotyping.Splitting SSA-ECA and SSA-WA. The conventional primers BTS22-762F/R amplified a fragment of ~880 bp. KASP genotyping with these primers was consistent with NextRAD for all 41 (100%) samples that were designated as SSA-WA (G:G) and 31 out of 32 (97%) samples that were designated as SSA-ECA (A:A). A single sample from Kenya designated as SSA-ECA by NextRAD was identified as SSA-WA by KASP. These primers were designed to separate SSA-ECA from SSA-WA; the other four haplogroups have mixed alleles at this SNP position, as confirmed by both NextRAD and KASP results. The samples in the SSA-ECA group were from DRC, Kenya, Tanzania and Uganda, while those in SSA-WA were from Benin, Ghana, Liberia, Nigeria and Sierra Leone.Distinguishing SSA2 and SSA4 from other cassava B. tabaci haplogroups. The conventional primers BTS141F/R amplified a ~1150-bp fragment. Genotyping of the 152 samples with KASP primers gave consistent results with NextRAD in 151 (99.3%) cases. KASP identified 34 samples as SSA2 or SSA4 (T:T) and 117 as other haplogroups (C:C). A single mismatched sample from DRC typed as SSA4 by NextRAD was heterozygous by KASP. The 117 samples belonging to the other four haplogroups had consistent results for both genotyping methods. Two samples grouped as SSA2 by NextRAD and typed in the same way with KASP had missing NextRAD alleles.Splitting SSA2 and SSA4. Conventional primers BTS55-473F/R amplified a ~815-bp fragment. KASP genotyping of the 152 samples was 100% consistent with NextRAD. Twenty-six SSA2 samples (T:T) were separated from SSA4 (8 samples) and also from the rest of the groups. These primers can be used to directly separate SSA2 from the other five haplogroups. The samples in the SSA2 group were from Cameroon, DRC, Ghana, Kenya and Sierra Leone, while those in the SSA4 group were from Cameroon and DRC.Distinguishing SSA-ESA and SSA-CA from other cassava B. tabaci haplogroups. The conventional primers BTS613F/R amplified a ~730-bp fragment. KASP genotyping of the 152 samples was 100% consistent with NextRAD. There were 45 samples in the SSA-ESA and SSA-CA group (G:G) and 107 samples in the group combining the other four haplogroups (A:A). Distinguishing SSA-ESA from SSA-CA. Conventional primers BTS46-203F/R amplified a ~390-bp fragment. These primers were tested only to separate SSA-ESA from SSA-CA and were not used with the other four haplogroups. KASP genotyping of 45 of these samples identified 39 as SSA-ESA and two as SSA-CA, which is consistent with NextRAD. The remaining four samples, two each from SSA-ESA and SSA-CA based on NextRAD grouping and typed homozygous by NextRAD alleles, were shown to be heterozygous when using KASP. These heterozygous samples were tested by an alternative primer set BTS1161 that also separates SSA-ESA from SSA-CA, and the samples were consistent with NextRAD as either SSA-ESA or SSA-CA. Amplification results using this primer for SSA-ESA samples from East Africa and some from Southern Africa were consistent with NextRAD, but 8 samples  out of 41 were designated as heterozygous and one sample from Mozambique was identified as SSA-CA although NextRAD identified it as SSA-ESA. SSA-ESA samples originated from Kenya, Malawi, Mozambique and Tanzania, whilst SSA-CA samples were from eastern DRC and western Tanzania. COI identity, NextRAD grouping and KASP result is provided for all 152 samples in Summarised results show that overall there was a very high level of concordance between NextRAD and KASP diagnoses . DetaileBemisia tabaci is a cryptic species complex with diverse members that have different biological properties. Accurate identification of these species is critical for the effective management of whiteflies, both as pests and as virus vectors. Most recently, an analysis of >63,000 genome-wide single nucleotide polymorphisms (SNPs) obtained from cassava-colonizing B. tabaci from Africa revealed the existence of six haplogroups, designated as SSA2, SSA4, SSA-CA, SSA-ESA, SSA-WA and SSA-ECA [B. tabaci whiteflies is required. The SSA-ECA haplogroup is of particular interest as it is predominant in regions currently affected by the severe CMD and CBSD pandemics [ SSA-ECA ,25. ThesCOI reveals that 18.4% (28 out of 152) of the samples were misidentified by COI. The SSA1-SG1 samples were predominantly placed in SSA-ECA, but 8.5% (13 out of 152) of these were actually SSA-WA. This indicates that authors still using COI will often be lumping two distinct haplogroups (SSA-ECA and SSA-WA) together as SSA1-SG1. Other samples that were misidentified include 7 SSA-ECA that were designated as SSA1-SG2, 3 SSA2 as SSA3, 2 SSA-CA as SSA1-SG1, 1 SSA-WA as SSA2, 1 SSA2 as SSA4 and 1 SSA-ECA as SSA1-SG1/SG2. This high misidentification rate of samples using COI indicates that it is unreliable for identifying the major genetic groups of cassava B. tabaci whiteflies, as has been highlighted in previous studies [COI sequencing. The single sample (0.7%) that was scored heterozygous by KASP but homozygous by NextRAD is an expected infrequent occurrence because allelic dropout can occur during RAD sequencing and it increases for low read loci [KASP results were consistent with NextRAD (99.3%), with only one sample out of the 152 not matching. A comparison with  studies ,25. The ead loci ,39. B. tabaci whiteflies from all of the major cassava-growing regions of the continent. The current protocol is designed to include conventional PCR to generate the DNA template for KASP with conventional primers. This step is necessary for whiteflies because individual insects produce low amounts of DNA that may not be of sufficiently good quality for the KASP assay. Attempts to use directly-extracted DNA as a template for KASP produced inconsistent results with some samples producing distinct clusters while others failed. Using a DNA template from a specific region with distinguishing SNPs can also eliminate the incidence of unspecific primer binding to non-target genome regions. This KASP assay set up did not present any challenges that required optimisation, and samples were resolved into the expected clusters just by following through the provided LGC Genomics protocol for all the SNP primers that were tested. The whitefly samples directly used in KASP assays were from 12 countries in all major cassava-growing regions in Africa, indicating that this tool will be reliable in identifying cassava B. tabaci occurring at a low frequency that have not currently been sampled, additional NextRAD sequencing will be required at occasional intervals to allow for updates to be made to the KASP diagnostic in order to ensure that it remains as accurate and comprehensive as possible.The availability of the cassava whitefly reference genome  made it Bemisia tabaci in sub-Saharan Africa. Since this assay has demonstrated results consistent with NextRAD sequencing, it can provide a rapid and reliable analysis of cassava B. tabaci that will allow for same-day in-house genotyping in local laboratories that have limited access to sequencing technologies. This study presents a KASP assay for the routine monitoring of cassava B. tabaci populations. Finally, the KASP diagnostic represents an important application of comprehensive genomics data for Bemisia on cassava in Africa. As other datasets become available for Bemisia populations elsewhere in the world, there are likely to be similar opportunities to develop and apply KASP diagnostics more widely for Bemisia identification and monitoring as part of pest and vectored virus disease management programmes.The KASP assay developed here has important practical developmental applications. Since there remains a concern that the haplogroup SSA-ECA may pose a risk for the continued spread of severe CMD and CBSD pandemics, the KASP diagnostic described could be an important component within early warning systems to track the spread of potentially dangerous