The goal of diabetes care is to achieve and maintain good glycemic control over time, so as to prevent or delay the development of micro- and macrovascular complications in type 1 (T1D) and type 2 diabetes (T2D). However, numerous barriers hinder the achievement of this goal, first of all the frequent episodes of hypoglycemia typical in patients treated with insulin as T1D patients, or sulphonylureas as T2D patients. The prevention strategy and treatment of hypoglycemia are important for the well-being of patients with diabetes. Hypoglycemia is strongly associated with an increased risk of cardiovascular disease in diabetic patients, due probably to the release of inflammatory markers and prothrombotic effects triggered by hypoglycemia. Treatment of hypoglycemia is traditionally based on administration of carbohydrates or of glucagon via intramuscular (IM) or subcutaneous injection (SC). The injection of traditional glucagon is cumbersome, such that glucagon is an under-utilized drug. In 1983, it was shown for the first time that intranasal (IN) glucagon increases blood glucose levels in healthy volunteers, and in 1989–1992 that IN glucagon is similar to IM glucagon in resolving hypoglycemia in normal volunteers and in patients with diabetes, both adults and children. IN glucagon was developed in 2010 and continued in 2015; in 2019 IN glucagon obtained approval in the US, Canada, and Europe for severe hypoglycemia in children and adults. In the 2010s, two ready-to-use injectable formulations, a stable non-aqueous glucagon solution and the glucagon analog dasiglucagon, were developed, showing an efficacy similar to traditional glucagon, and approved in the US in 2020 and in 2021, respectively, for severe hypoglycemia in adults and in children. Fast-acting glucagon  appears to represent a major breakthrough in the treatment of severe hypoglycemia in insulin-treated patients with diabetes, both adults and children. It is anticipated that the availability of fast-acting glucagon will expand the use of glucagon, improve overall metabolic control, and prevent hypoglycemia-related complications, in particular cardiovascular complications and cognitive impairment. Today, management of hyperglycemia is possible for nearly all patients with type 1 (T1D) and type 2 (T2D) diabetes mellitus, since the chances of treatment have dramatically changed for both kinds of patients. Oral agents such as metformin are used together with new oral DPP-IV antagonists and SGLT-2 inhibitors, as well as injectable GLP-1 agonists. Insulin analogs with an extremely short or an extremely long duration of action are now in use, and in T2D older drugs such as sulfonylureas are less and less in use, due primarily to their significant risk of hypoglycemia.Prevention and treatment of hypoglycemia still represents an unmet need; euglycemia without acute metabolic complications still remains a major goal of management in patients with diabetes.Hypoglycemia is defined as a concentration of glucose in the blood that is lower than normal, traditionally defined biochemically as a plasma glucose < 3.5 mmol/L . HypoglyThe frequency of hypoglycemia in people with T1D is estimated to be two events per week of non-severe hypoglycemia and one episode of severe hypoglycemia per year .Among insulin-treated patients the frequency of hypoglycemia is greater in T1D than in T2D patients, and depends on intensive insulin treatment, regimens of insulin administration, and age ,7,8,9,10Under normal conditions, a sudden decrease of blood glucose levels is followed by inhibition of insulin release and by sympathetic activation (catecholamines) and hormonal counter-regulation . Symptoms of hypoglycemia are due to sympathetic activation  and to neuroglycopenia . In people on insulin for long periods, all the responses are usually impaired, and recurrent exposure to hypoglycemia may result in lowering of the plasma glucose concentration, at which point stress responses are initiated, sometimes below the level at which cognitive deterioration starts, leading to impaired awareness of hypoglycemia .Frequent and recurrent hypoglycemia (under 3 mmol/L) in patients with T1D can lead to hypoglycemia unawareness, which puts individuals (moreso adult than pediatric patients) at greater risk of continued and severe hypoglycemia; there is evidence that avoidance of such exposures can restore awareness.Acute effects include tachy- and bradycardia, widened arterial pulse pressure and arrhythmias. The arrhythmias have been associated with adrenergic responses and the fall in plasma potassium associated with hypoglycemia. Hypoglycemia produces endothelial dysfunction, an inflammatory response, and creates a coagulopathy, which can last for several days with unknown long-term impacts on cardiovascular health ,13. Incr1c value, also depending on age, comorbidities, diabetes duration, and treatment regimens. In spite of these differences and concerns, T1D and T2D share the same “common soil” and hypoglycemia seem to induce oxidative stress, inflammation, and endothelial dysfunction in both. Currently there is debate on whether hypoglycemia is an independent risk factor for atherosclerosis [Evidence reporting the link between hypoglycemia and cardiovascular disease has been emerged from large prospective randomized clinical trials ,24 in whclerosis . Regardiclerosis . Severe clerosis . In oldeclerosis  and thesclerosis . Both thclerosis ,32.Although the mechanism remains unclear, increased inflammatory cytokines and leukocytosis are reported after hypoglycemia, suggesting a link between hypoglycemia and inflammation . Also, hIn T1D, and in particular T1D-unaware patients, there is a progressive blunting of brain responses in the cortico-striatal and fronto-parietal neurocircuits in response to mild-moderate hypoglycemia ,36; thesThese aspects have been shown in experimental research. It is supposed that prior to glycemic control, hypoglycemia and the degree of rebound hyperglycemia interact synergistically to accelerate oxidative stress and inflammation, which may explain why increased glycemic variability is now increasingly considered a risk factor for the complications of diabetes .Not only severe, but also symptomatic hypoglycemia negatively affects patient QoL, especially in T2DM; addressing fear of hypoglycemia should be a goal of diabetes education . Falls aIn addition, the experience of hypoglycemia leads to fear of hypoglycemia, which in turn can limit optimal glycemic control in many children and adolescents, as well as in adults with T1D. Finally, consideration should be given to societal impacts in the form of loss of driving privileges, restricted employment and campus experiences, high stress between partners, breakdown of family relationships and restricted access to children, as well as economic impacts such as time lost from work as costs beyond the medical costs for healthcare providers) .Both prevention and treatment of hypoglycemia are crucial to a normal life for patients with diabetes . PreventIn 1923, what was then merely a “component” of the pancreatic extracts was found to induce transient hyperglycemia . Cross-cIn the 1950s the term glucagon was coined, and in the 1950s, Lilly Research Laboratories were successfully able to extract a pure crystallized form of glucagon ; in 1957Glucagon is a 29-amino-acid peptide biologically synthesized by pancreatic alpha cells. It is considered a relevant counterregulatory hormone with a key role in the maintenance of normoglycemia, as it counteracts the effect of insulin in glucose metabolism regulation .Glucagon mainly exerts its hyperglycemic effects through stimulation of hepatic glycogenolysis. In contrast to the role of the insulin signaling pathway, the glucagon signaling pathway promotes catabolism; through its receptor, glucagon stimulates and releases glucose. The glucagon signaling pathway activates hepatocyte phosphorylase and accelerates glycogenolysis through the cAMP-PK system.sα and Gq. These receptors are linked to the activation of the adenylate cyclase-dependent pathway, which increases intracellular cyclic AMP and activates protein kinase A, and in some cases, stimulation of inositol trisphosphate and calcium influx. Activating Gq activates phospholipase C, increases production of inositol 1,4,5-triphosphate, and releases intracellular calcium. Protein kinase A phosphorylates glycogen phosphorylase kinase, which phosphorylates glycogen phosphorylase, which phosphorylates glycogen, causing its breakdown  gene, localized to human chromosome 17q25 .The main indication for glucagon is hypoglycemia in insulin-treated diabetic patients; however, congenital hyperinsulinism and alimentary hypoglycemia in infants are also amenable to glucagon. In contrast to other peptide hormones  glucagon does not show a clear dose–response relationship, suggesting that the glycemic response to glucagon is saturable; after IV or IM glucagon administration, there is a clear relationship between doses administered and pharmacokinetic parameters , but progressively increasing doses do not result in dose-related responses of glucose . This has opened the way to the use of glucagon mini-doses that have recently been administered at doses of 100–150 µg instead of 1 mg, both in adults and in infants, to prevent or to treat mild hypoglycemia ,50,51. IEven though it has been available for long time and approved by ADA for resolution of hypoglycemia, injected glucagon has been under-utilized in the US, in Europe, and in Japan. Glucagon is as effective as glucose in the resolution of hypoglycemia , but itsGlucagon is a peptide hormone and, as such, carries the challenges associated with stability and with the limitations common to administration of most peptides. Reasons for under-utilization are mainly represented by the difficulty of administering glucagon in its traditional form. Glucagon solutions are not stable, owing to the propensity of glucagon to form fibrils once in an aqueous solution .https://myglu.org/polls/1303 accessed on 1 September 2021).As a result, currently available glucagon emergency kits require reconstitution of lyophilized powder in a diluent immediately prior to IM injection by family members or others who may not be well trained in or comfortable with giving injections , approximately 75% of people interviewed declared they rarely or never carried a glucagon emergency kit.Injection of IM glucagon is so difficult for untrained individuals that some U.S. states allow only trained nurses or health professionals to administer glucagon while children and adolescents with T1D are in school . In a suThe story of nasal glucagon is very long, starting in the 1980s, when a group of investigators demonstrated that IN glucagon drops can raise blood glucose levels in normal subjects  and thatIN glucagon without promoters is not absorbed through the nose and is without any effects whatsoever ,61. SeveDuring the 1980s, several authors showed that IN glucagon was not different from IM glucagon in resolving hypoglycemia in normal subjects and in adults and children with diabetes ,63. In sOnly in 2010 did a Canadian pharmaceutical company  re-examine old data and start development of glucagon for IN administration. The company hypothesized that patients and caregivers might be more comfortable with a needle-free formulation and, on that basis, conducted safety, efficacy, and human factor studies to support registration of the product ,66,67,68The project has been continued by Eli Lilly, a U.S. pharmaceutical company that obtained registration for IN glucagon as a powder (with beta-cyclodextrin plus dodecyl-phospho-choline as the promoter) ,70 for sThe glucagon formulation is packaged in a ready-to-deliver auto-injector similar to the epinephrine rescue pens used for anaphylaxis. A freeze-dried powder that contains glucagon along with trehalose, hydroxyethyl, or a similar starch with a glycine buffer (maintaining a pH of 2.0–3.5), a surfactant, an antioxidant, and a chelating agent is combined with a polar aprotic liquid comprising triacetin, thereby preventing the aggregation and fibrillation of the glucagon that is normally propagated by aqueous solutions. This process creates a low-volume, highly-concentrated solution, effective in raising blood glucose levels ,72,73.www.xerispharma.com/images/xeris-annual-report-2018.pdf, accessed on 1 September 2021). Three studies demonstrated the similarity of this glucagon molecule and of traditional glucagon kits in insulin-induced hypoglycemia in adults and children. Human factors were repeatedly evaluated for the easiness and completeness of administration of this stable solution, also in comparison with traditional glucagon, and found to be superior to traditional glucagon kits [https://scholar.google.com/scholar Christiansen, Cummins, Prestrelski, Junaidi, Poster presented at Diabetes Technology Society’s Diabetes Technology 8–10 November 2018, Bethesda).The auto-injector is available in two premeasured doses: a 0.5 mg/0.1 mL dose for pediatric administration and a 1 mg/0.2 mL dose for adolescent and adult administration (Xeris Pharmaceuticals, Inc. Available from gon kits ,75. The The glucagon analog dasiglucagon is the first glucagon product to be provided as an aqueous formulation, ready-to use. Similar to endogenous glucagon, dasiglucagon is composed of 29 amino acids, and carries seven amino acid substitutions to increase the physical and chemical stability in aqueous media. The aqueous formulation does not require reconstitution before injection.Dasiglucagon has specificity for the glucagon receptor and potency comparable to that of native glucagon. In T1D patients, dasiglucagon restored euglycemia after insulin-induced hypoglycemia at doses from 0.1 to 1.0 mg , and proFor all formulations, real world situations were evaluated to understand the usability of the new drugs. In particular, studies were based on comparisons between the new formulations and the traditional emergency glucagon kits, to assess easiness and therefore completeness of administration. For all three drugs, positive results were obtained, showing that caregivers did not have to be familiar with these drugs, in contrast to traditional emergency glucagon kits. For nasal glucagon, additional studies evaluated drug administration under situations of common cold and of use of nasal decongestants, showing that the effectiveness of nasal glucagon was not affected by these situations. An interesting difference between nasal glucagon and the two other formulations is the time employed for development. For nasal glucagon it took from 1983 to 2019 to be developed and approved; for the other two drugs it took approximately six years. At present, in the absence of comparative studies, all three formulations appear valid for emergency treatment of severe hypoglycemia. A common aspect is that the cost-effectiveness ratio is favorable for fast-acting glucagon compared to traditional glucagon preparations .Severe hypoglycemia still represents a serious problem in children, even though new insulin analogs, better insulin administration regimes, and close glucose monitoring have reduced its frequency . In contWe can foresee that because of ease of administration of the three new formulations the use of glucagon to resolve severe hypoglycemia will likely expand dramatically. This will open the path to innovative uses of nasal glucagon, of self-injected stable glucagon, and of dasiglucagon. Of course, such increased use might also reveal other as yet unknown metabolic effects and side effects. For instance, even though not approved, one might think of their use as a self-remedy, or with the aid of a caregiver, to prevent severe hypoglycemia when under mild symptomatic hypoglycemia; fully randomized studies are necessary to evaluate this possibility. In addition, the use of these drugs in other types of hypoglycemia such as congenital hyperinsulinism, post-bariatric surgery hypoglycemia, alimentary hypoglycemia, and exercise-induced hypoglycemia, deserves greater attention.With the exception of one study dealing with spontaneous hypoglycemia , the majAs said before, an unmet need is still represented by prevention and treatment of hypoglycemia; euglycemia without acute metabolic complications still remains a major goal of management in patients with diabetes. Even though nasal glucagon, self-injected stable glucagon, and dasiglucagon are effective in resolving severe hypoglycemia, it would be reassuring to show that overall metabolic control improves as well in patients using these emergency drugs. Moreover, it would be reassuring to show that use of these drugs helps to prevent hypoglycemia-related complications such as cardiovascular disease and cognitive impairment. Long term studies are required to show these possible benefits.Glucagon still represents the fastest and most effective remedy to hypoglycemia, provided it is used by a trained person. The complexity of preparing glucagon for administration is the main reason why alternative routes of administration and other formulations have been actively searched for. In addition to the three drugs approved, other approaches to effective glucagon delivery have been attempted; some of them are still under development, while other routes have been abandoned. For instance, Biochaperone Glucagon is made of polymers, oligomers, and organic compounds that can form a complex with glucagon and improve its stability in aqueous solution. BIOD-961 is a lyophilized glucagon formulation that, while it does require reconstitution, has an auto-reconstitution device which has been developed. Another glucagon agonist, SAR438544, was developed for subcutaneous delivery; however, this medication is no longer under development ,89,90. TGlucagon remains the recommended remedy for hypoglycemia in out-of-hospital environments; however, its use is less frequent than expected due to the complexity of administration. The recent development of alternative routes of administration and of new fast-acting stable solutions for injection raises the necessity of making glucagon able to be easily administered by untrained persons, whether patients or caregivers. It can be anticipated that doing this will lead to expansion of the use of glucagon in patients with hypoglycemia, thus making possible the attainment of euglycemia without hypoglycemia. At present, in the absence of comparative head-to-head studies, all of the new formulations appear to be valid remedies; however, patients’ expectations are in favor of nasal administration .


Two individuals with a first-degree relationship share about 50 percent of their alleles. Parent–offspring relationships cannot be homozygous for alternative alleles (genetic exclusion).Applying the concept of genetic exclusion to HD arrays typed in animals for experimental purposes or genomic selection allows estimation of the rate of rejection of first-degree relationships as the rate at which two individuals typed for a large number of Single Nucleotide Polymorphisms (SNPs) do not share at least one allele. An Expectation–Maximization algorithm is applied to estimate parentage. In addition, genotyping errors are estimated in true parent–offspring relationships. Samples from nine candidate Duroc sires and 55 Iberian dams producing 214 Duroc × Iberian barrows were typed for the HD porcine Affymetrix array.We were able to establish paternity and maternity of 75 and 85 piglets, respectively. Rate of rejection in true parent–offspring relationships was estimated as 0.000735. This is a lower bound of the genotyping error since rate of rejection depends on allele frequencies. After accounting for allele frequencies, our estimate of the genotyping error is 0.6%. A total of 7,744 SNPs were rejected in five or more true parent–offspring relationships facilitating identification of “problematic” SNPs with inconsistent inheritance in multiple parent–offspring relationships.This study shows that animal experiments and routine genotyping in genomic selection allow to establish or to verify first-degree relationships as well as to estimate genotyping errors for each batch of animals or experiment.The online version contains supplementary material available at 10.1186/s12863-022-01025-1. Next Generation Sequencing (NGS), a term used for massive parallel sequencing of several hundred thousand to millions of DNA fragments simultaneously, has enabled massive discovery of novel single nucleotide polymorphism (SNP) genetic markers . Today, In livestock production, genotyped individuals in a population are often much more related than in human research. In most cases, individuals with first-degree relationships, i.e., parent–offspring or full-sib relationships, are part of the same selection candidates in genomic selection or crossbreeding programs. For optimum contribution selection (OCS), restricting the relationship between selected parents is crucial to restrict inbreeding in the progeny and maximize genetic gain . TherefoThe objective of this study is to investigate the rate of rejection of first-degree relationships of individuals in a population using an HD SNP array. The second objective of this study is to demonstrate the use of HD SNP arrays in detecting genotyping errors after parent–offspring relationships are resolved. The third objective is to identify SNPs that repeatedly result in genotyping errors in multiple true parent–offspring relationships.A first-degree relative is an individual who shares approximately 50 percent of their alleles with a particular other individual. There are two first-degree relationships: parent-offspring, and full-sibs. Under strict Mendelian inheritance, individuals with a first-degree parent–offspring relationship cannot be homozygous for alternative alleles at a given locus. For example, an individual offspring cannot be homozygous CC at a locus, when a parent is GG at the same locus. Applying this concept to a population in which individuals are genotyped with an HD SNP array allows the rejection of parent–offspring first-degree relationships based on this rule. Full-sibs, on the other hand, can be homozygous for alternative alleles when both parents are heterozygous, but this probability is small, at 0.125.i and j is thus defined as:We define genome-wide rate of rejection of first-degree relationships as the rate at which two individuals genotyped for a large number of SNPs have alternatives alleles, i.e., the number of markers for which two individuals are homozygous for alternative alleles divided by the total number of SNPs genotyped with an HD SNP array. The rate of rejection of a first-degree relationship between two individuals Nsnp is the total number of SNPs in the array; for each SNP, i-th marker ; it is 0 otherwise.where τij corresponding to each relationship can be used to detect or confirm first-degree relationships and genotyping errors. In Eq. /2. The binomial density models the probability of each outcome according to:Values of τp(τ) is the probability of the rate of rejection, τ, at the number of SNPs rejecting first-degree relationships, where τ, when τij > 0, mixing of several distinct distributions can be identified: one corresponding to binomial probabilities τg resulting from genotyping errors, and others corresponding to binomial probabilities τr resulting from rejections of first-degree relationships. Here, the rate of genotyping errors is a lower bound of the true rate of genotyping errors, since genotyping errors are only identified in SNP markers for which the individuals are homozygous for alternative alleles; genotyping errors in individuals that do not lead to a rejection of a first-degree relationship will remain undetected. In the dataset it is assumed that estimates of rates of rejection following Eq.  vs. a first-degree relationship is rejected because it does not exist . In order to determine to which of the two distributions a relationship belongs, the dataset has to be first subdivided into known relationship groups. In our example, we consider a crossbreeding experiment with candidate sires, dams and offspring. The data can now be subdivided into the following relationship groups: dam-offspring, sire-offspring, offspring-offspring, dam-dam, sire-sire, and sire-dam. First, we are interested in identifying dam-offspring, or sire-offspring relationships to establish maternities and paternities of our experimental crossbred population. A given pair of individuals from the dam-offspring or the sire-offspring group has a probability γ of belonging to the distribution of τg of true parent–offspring relationships. The likelihood function of the i-th relationship is:Given the distribution of the rate of rejection of first-degree relationships in a population, wing Eq.  when τijnr, between all pairs of individuals within the dam-offspring or within the sire-offspring group is:where γ, τg, and τr. Maximizing this equation is not straightforward because Eq. . One of the samples failed. Best Practices Workflow was applied with the following conditions in the Axiom Analysis Suite version 5.1.1.1: DQC: ≥ 0.82; QC call rate: ≥ 97; Average call rate for passing samples: ≥ 98.5; Percent of passing samples: ≥ 95. Of the 287 samples only 281 passed the QC-Call rate. There were 603,809 SNPs that passed the condition for the call rate. From those, only 546,220 autosomal SNPs mapped to SScroffa v11.1 were used for estimating rate of rejection of first-degree relationships. The genotyping was carried out at Centro Nacional de Genotipado (CeGen) at the University of Santiago de Compostela, Spain.http://www.r-project.org/) is provided as an additional file The methods to estimate paternities, maternities, genotyping errors were as described in the Methods section. Source code in R language , and between rates of rejection of first-degree relationships τij with higher values (τij > 0). In the parent–offspring groups, when the probability that two individuals are homozygous for alternative alleles at a given locus is very close to zero, their values correspond to genotyping errors , while higher τij values correspond to true rejection of first-degree relationships . The distributions of τg and τr are more overlapping in the offspring-offspring group. This is expected from the observation that true full-sibs can be homozygous for alternative alleles when both parents are heterozygous, albeit at a low probability of 0.125. A clear distinction between values of τij very close to zero and those farther away from zero can also be seen in the distribution of τij in the dam-dam and sire-sire groups  or to distribution τr . Relationships with values of τij that belong to distribution τg are true first-degree relationships. Figure τg and τr were chosen such that they were close to the peak of the corresponding distributions in Fig. τg and τr were not close to the corresponding peak of the distribution, parameters were not converging.Because due to the existence of genotyping errors Applying the Expectation–Maximization algorithm, we established paternity of 75 and maternity of 99 piglets. We identified that 14 offspring out of 99 appeared to have two mothers. We could establish that the two mothers were the same individual, and blood for DNA extraction had been sent twice and with different identification to our lab. Therefore, we established 85 dam-offspring relationships. The reason why only 75 and 85 piglets were associated to sire or dam candidates, respectively, was that DNA of the true parents was not included in the dataset for all offspring, since those DNA samples had not been supplied by the farm. In addition, we established 3,802 full-sib relationships among the offspring-offspring relationship group. This figure is too large which indicates that the method cannot separate properly full-sibs from half-sibs singularly in a pig breeding farm, when sires or dams are close relatives . This is illustrated by the overlap in the histogram of the offspring-offspring group in Fig. In summary, we analyzed samples of 288 individuals. Genotyping of one of the samples did not work, and six samples failed the threshold set for the calling rate. Statistical analysis detected 14 duplicated samples of dams. Of 9 candidate sires and 55 candidate dams, 7 sires and 38 dams were parents of the offspring. The sire with the largest number of offspring had 22 offspring; the dam with the largest number of offspring had 6 offspring. There were 27 piglets with paternity and maternity simultaneously identified.ij ≈ 0 that belong to distribution τg, we established that the genotyping error estimated jointly in sire-offspring and dam-offspring relationship groups was 0.000735. This is a lower bound of the true number of genotyping errors since genotyping errors are only identified for SNP markers in parent–offspring relationships for which individuals are homozygous for alternative alleles; other genotyping errors involving heterozygous animals in either parent or offspring remain undetected. The true genotyping error depends on the allele frequency of the marker. For markers with a very low allele frequency, the rate of rejection approaches the genotyping error. In this situation, true rejection is difficult to occur because the homozygote corresponding to the allele with very low frequency is very scarce; therefore, rejection can only be attributed to genotyping error. In our experiment, the genotyping error is estimated at 0.006 (0.6%).After establishing relationships with τIn our dataset, a total of 69,882 rejections of true relationships corresponding to 7744 SNP markers were observed in the parent–offspring groups. The genome-wide distribution of those SNPs is given in Fig. For the analyses performed in the present study, SNPs were not filtered for departure of Hardy–Weinberg equilibrium (HW) and/or Minimum Allele Frequency (MAF). Since the offspring are a result of a cross between Iberian × Duroc, it is expected that SNPs are not necessarily in HW equilibrium. There were 122,615 autosomal and mapped SNPs with MAF < 0.05 in the total data set, corresponding to a proportion of 0.224 . Only 165 out of a total of 7,744 SNPs that rejected true parent–offspring relationships had a MAF < 0.05, corresponding to a proportion of 0.021 . As aforementioned, there is a reduced capacity of markers with low allele frequency to reject true parent–offspring relationships because the chances of being heterozygous for alternative alleles is very low (one of the two homozygotes is at very low frequency). SNPs at intermediate frequencies are more likely to reject first-degree relationships.The present study shows the successful application of an Expectation–Maximization (EM) algorithm to establish or to verify first-degree parent–offspring relationships when relationships are unknown or uncertain, in an animal population that is genotyped with a High-Density (HD) SNP Array. This method has a wide range of applications. In livestock populations, first-degree relationships are nearly always present. Although the advent of genomic methods meant that pedigree information necessary for breeding value estimation can now be replaced by genomic relationships in genomic best linear unbiased prediction (GBLUP) , and theWhile the first parentage verification methods used blood groups , this waBecause microsatellites are more polymorphic than the bi-allelic SNPs, parentage verification is accomplished by a larger number of SNPs than microsatellites in a panel. Between approximately 40 and 100 SNPs are equivalent to between 14 and 20 microsatellites; ISAG recommends a minimum number of 100 SNPs for parentage testing . Microsaτg or τr can be approximated by computing the statistical power using the normal approximation to the binomial distribution (https://www.stat.ubc.ca/~rollin/stats/ssize/b1.html). For example, for a group of 300 individuals in which no assumptions can be made about candidate parents or offspring, the number of combinations (tests) of all possible relationship pairs is 300 × 299 / 2 = 44,850. This figure is needed for adjusting the significance level to multiple testing. Thus, a significance level of 0.01 is adjusted to 0.01/44,850 using the Bonferroni adjustment. For a statistical power of 0.99 at a global significance level of 0.01 with τg = 0.000735 and τr = 0.01, the number of SNPs required is 1,611 for 300 individuals. Therefore, detection of parent–offspring relationships should be possible with arrays of a low, medium or a high density unless the number of individuals is very high.The method described in this study can be easily applied to animal populations for which a SNP array is available for the species in question. Although the present study was performed with an HD SNP array, arrays at lower densities are expected to be capable to draw similar conclusions and will be cheaper. Routine application of genomic selection requires at least a low-density array. Verification or detection of first-degree relationships, together with a measure of the genotyping errors of each batch, may facilitate detection of errors and assure the quality of genotyping. The required number of SNPs needed to detect if any given relationship in a group of individuals belongs to either A simple statistic proposed in this study, rate of rejection of first-degree relationships, is helpful to verify or to detect paternities and maternities when testing a large number of SNPs. It is also shown that the rate of rejection allows estimation of different types of relationships, and even genetic differences between groups of animals when sires and dams belong to different breeds. This statistic is based on the simple exclusion rule that two individuals that are homozygous for alternative alleles cannot have a parent–offspring relationship. A more complete assessment including likelihoods of all possible first, second and third-degree relationships has been proposed by Huisman . That woSNP arrays can be used to test or to verify paternities using the rate of rejection of first-degree relationships. The same material can be used to estimate genotyping errors due to the large number of SNPs tested in parent–offspring relationships. This approach helps to identify SNPs inconsistent with Mendelian rules of inheritance in multiple parent–offspring relationships.Additional file 1. Additional file 2.


Phaseolus vulgaris L.) production globally. The study of the genotypic, phenotypic, and bio-climatic variables in a broad set of accessions may assist the identification of genomic regions involved in the climatic adaptation of the common bean. We conducted a genotyping-by-sequencing analysis using 28,823 SNPs on 110 georeferenced common bean accessions from Brazil to discover associations between SNPs and bio-climatic indexes. The population structure analysis clustered the accessions into two groups corresponding to the Andean and Mesoamerican gene pools. Of the 19 bioclimatic variables, 17 exhibited a significant association with SNPs on chromosomes Pv01, Pv02, Pv03, Pv04, Pv06, Pv09, Pv10, and Pv11 of common bean. Ten candidate genes were associated with specific bio-climatic variables related to temperature and precipitation. The candidate genes associated with this significant Pv09 region encode a Platz transcription factor family protein previously reported to be an essential regulator of drought stress. The SNP markers and candidate genes associated with the bio-climatic variables should be validated in segregating populations for water stress, which could further be used for marker-assisted selection. As a result, bean breeding programs may be able to provide advances in obtaining drought-tolerant cultivars.Abiotic stress is a limiting factor for common bean ( Phaseolus vulgaris L.) is one of the most important legume crops, providing 15% of the total daily calories and 36% of the complete daily protein in parts of Africa and the Americas [P. vulgaris. Common bean production in Brazil is challenging because of the wide range of different climates, soils, cultivars, and levels of agricultural technology [Common bean  can potentially predict factors involved in abiotic stress adaptation in plants. In this study, we sought to identify genome-wide single nucleotide polymorphisms (SNPs) that are associated with climate parameters at the accessions’ sampling site . Phaseolus vulgaris that were related to distribution and associated environmental changes. These authors identified five subpopulations distributed in different dry forests and observed distinct distributions of temperature and rainfall, resulting in decreased local potential evapotranspiration.The assumption is that genome–environment associations reflect the adaptation of common bean to abiotic stresses at the sampling sites. This approach can be used to efficiently identify loci that control drought tolerance in bean germplasm ,29. For In the present study, we evaluated common bean accessions from smallholder farmers settled in different Brazilian regions. These accessions are cultivated annually and are exposed to adverse climatic conditions, such as high temperature and rainfall shortages during the critical growth phase, such as flowering and pod fill. We characterized these common bean genotypes using genotyping-by-sequencing, evaluated their genetic structure, and identified QTLs associated with 19 bio-climatic variables obtained from the WorldClim database.A total of 180,515 SNPs identified in 175 common bean accessions were used for imputation, and were processed and analyzed using NGSEP software. Markers with more than 95% absence data and an allelic frequency <0.05% were excluded. After this filtering process, 28,823 SNPs identified in 110 bean accessions were used for association mapping. The markers were distributed on all 11 common bean chromosomes .n = 3310 SNPs) and Pv02 (n = 3651 SNPs) exhibited the highest number of SNPs, whereas Pv10 (n = 1660 SNPs) and Pv07 (n = 2266 SNPs) showed the fewest. Patterns of nucleotide substitution via the GBS technique classified SNPs as either transition (Ts) or transversions (Tv) (The SNP distribution across the 11 chromosomes showed an average of 2620 SNPs (57 SNPs per Megabase—Mb). Chromosomes Pv09 (ons (Tv) . We obsePrincipal component analysis (PCA) data revealed that the accessions were clustered into two distinct groups, which corresponded to either the Andean (33 accessions) or Mesoamerican (77 accessions) gene pool . The firBased on K = 2 groups, the population structure analysis showed that the common bean accessions were distributed between two gene pools, Andean and Mesoamerican , with a The results of the phylogenetic analysis for the 110 common bean accessions genotyped through GBS were composed of two large groups, one of them Andean and the other Mesoamerican . The clu−6 for α = 0.05 and 28,823 SNP markers). The SNPs associated with these variables reside on chromosomes Pv02 and Pv09  provided a better fit for all climatic variables in association analysis. Of the 19 bio-climatic variables evaluated, 17 exhibited a significant association with SNPs distributed on chromosomes Pv01, Pv02, Pv03, Pv04, Pv06, Pv09, Pv10, and Pv11 of common bean. Only the variables BIO5 and BIO11 did not reveal significant associations. The genomic regions associated with the variables temperature seasonality (BIO4), precipitation of driest month (BIO14), precipitation seasonality (BIO15), and precipitation of driest quarter (BIO17) were selected with a significance greater than that established by the Bonferroni correction , BIO7 , and BIO18   . This map = 8.96 × 10−4), BIO10—mean temperature of warmest quarter (p = 7.02 × 10−4), BIO13 , and BIO16 , as shown in p = 6.98 × 10−4) and BIO13 (p = 5.63 × 10−4), and explained 11.3 and 13.6% of the variation, respectively , BIO10 , and BIO12 . Furthermore, the phenotypic variation explained by this SNP was 13.6, 12.7, and 13.1%, respectively. The S03_13038972 marker was close to the Phvul.003G081100 gene model on Pv03, which encodes a protein with no annotation.As shown in p = 4.15 × 10−4) and BIO 19 , and explained 14.5 and 14.0% of the phenotypic variation, respectively (p = 1.37 × 10−4) and variable BIO19 (p = 2.56 × 10−4)  . This SNp = 8.76 × 10−4), which explains 11.1% of the phenotypic variation  and BIO17   . This SN × 10−6) . This SNp = 7.86 × 10−4) (p = 4.20 × 10−5), BIO 4 , and BIO 9   , which e × 10−4) , which eGenome–environment association studies were performed to identify genomic loci that are associated with climatic traits and to define the potential genetic architecture of these associations, assuming that it is the result of genetic adaptation to the respective climatic traits of the locations or regions of origin. In addition to increasing our understanding of the genetics of adaptation, this approach could be useful in developing an effective, climate-stress-oriented breeding strategy via marker-assisted selection . NeverthP. vulgaris [CviAII enzyme, showed the power of this enzyme in detecting genetic divergence among common bean accessions pools.GBS analysis based on the reference genome of vulgaris  allowed vulgaris ,30, usinOur results show that the common bean accessions in our sample included members of the Andean and Mesoamerican gene pools. In addition, subdivisions within these groups were observed, confirming previously published work ,36,37,38Admixture is a characteristic feature of the genetic structure among Brazilian bean accessions, especially within the Mesoamerican gene pool. The current results confirm those of Burle et al.  and, subIf associations between single-nucleotide polymorphism (SNP) alleles and the environment of origin in crop landraces reflect adaptation, these could be used to predict phenotypic variation for adaptive traits . The aveP. vulgaris is situated in Neotropical seasonally dry forest areas, one of the most threatened biomes of the world [Previous studies have identified an association between SNPs and significant bio-climatic variables such as temperature and precipitation ,43. Assohe world . BraziliThe Mulatinho type of beans has seeds that are dark cream and almost brown in color; they are Mesoamerican, small-seeded beans, which are more widely accepted in the Northeast region of Brazil , an areahttps://www.ncbi.nlm.nih.gov, accessed on 20 June 2021), and functional annotation was verified in the Phytozome database. The SNP S01_43225566 associated with the traits at Pv01 is close to the gene model Phvul.001G169300, which encodes a cation/H(+) antiporter 20. Cation/H+ antiporters are essential regulators of intracellular ion homeostasis and are critical for cell expansion and plant stress acclimation [In the present study, 11 novel SNPs were significantly associated with common bean responses to 17 bio-climatic variables. These SNPs were located on chromosomes Pv01, Pv02, Pv03, Pv04, Pv06, Pv09, and Pv11. We searched for candidate genes close to these SNPs at NCBI  help plants cope with adverse conditions caused by stresses such as drought, salt stress, exposure to chemicals, and exposure to highly variable temperature (heat and cold) stress [A previous study  reported) stress . HSPs he) stress  observedGEA results identified the S03_13038972 SNP marker in Pv03 associated with BIO8, BIO10, and BIO12. This SNP is located in a different position from previously reported QTLs. Mukeshimana et al.  found thSNPs S04_36754502 and S04_36771335 were close to the candidate gene Phvul.004G111600, which encodes a solute carrier family of 13 members. This gene model is a homolog of an Arabidopsis thaliana divalent ion symporter. AtNIP5;1 (Arabidopsis) and OsNIP2;1 (rice) are located in the plasma membrane in plants and perform physiologically significant roles in the uptake of the nutritionally essential metalloids boron and silicon, respectively [The SNP S06_18985703 is close to the gene model Phvul.006G071300, which encodes a basic region leucine zipper (bZIP_2). bZIPs are among the most prominent families of transcription factors, which play diverse roles in plant growth development, including regulating cellular responses in plants under stress conditions . The autBrassica PLATZ genes vary, including in tissue-specific, stress-responsive, and hormone-responsive expression [SNP S09_35475381 was close to the candidate gene Phvul.009G241300, which encodes a PLATZ transcription factor (TF) protein. PLATZ is a plant AT-rich sequence and involves zinc-binding proteins that constitute a plant-specific transcription factor family with two conserved zinc-dependent DNA-binding domains. The PLATZ proteins perform significant functions in regulating plant development and resistance. pression . TFs arepression  observedpression  found thMedicago truncatula.The SNP S09_30671474 was close to the gene model Phvul.009G207800, which encodes a leucine-rich repeat (LRR) protein; some of these proteins contain an F-box. The leucine-rich repeat (LRR)-containing domain is well known to act in innate immunity in plants, serving as the first line of defense against biotic stresses. LRRs also promote interactions between LRR proteins, as observed in receptor–coreceptor complexes . HoweverThe gene model Phvul.011G091600 located close to SNP S11_9167649 encodes NTKL-binding protein 1. Limited information is available regarding the function of this protein; however, it is known to act as a transcriptional activator, playing a role in the stress response in plants. Based on real-time quantitative PCR analyses, He et al.  showed tAiming to identify loci putatively involved in common bean environmental adaptation, Ariani and Gepts  searchedIn the current study, whole-genome genotyping of 110 common bean accessions from Brazil resulted in wide genetic diversity compared to previous studies. In addition, the association between genetic and geoecological data allowed the identification of 11 SNPs significantly associated with common bean response to 17 bio-climatic variables. These SNPs are located on chromosomes Pv01, Pv02, Pv03, Pv04, Pv06, Pv09, and Pv11. Furthermore, we identified candidate genes encoding different proteins for each SNP. Of particular note is SNP S09_35475381 on chromosome Pv09, which was associated with nine of the nineteen bio-climatic variables. This SNP is linked to the candidate gene model Phvul.009G241300, which encodes a PLATZ transcription factor family protein previously known to be an essential regulator of drought stress. The SNP markers and gene models identified should be validated, and may efficiently reduce the cost and valuable time required in common bean breeding programs using marker-assisted selection.According to Singh , traits P. vulgaris and other species related to Phaseolus is essential to guarantee the adaptation of common bean to environments that do not fit those previously described [Variations in specific bio-climatic traits, such as the mean minimum temperature of the coldest month and seasonal temperature, favor Andean accessions because they are more adapted to regions of mesic temperature, which is also associated with intermediate altitudes . The varescribed ,66. Moreescribed . These an = 86), Paraíba (n = 3), Sergipe (n = 2), Mato Grosso (n = 13), and Paraná (n = 6) were investigated  (P. vulgaris genotype (G19833) were used as quality controls in each of the two libraries.Genomic DNA isolation and library preparation were performed following the GBS method based on the methylation-insensitive restriction enzyme CviAII . DNA quane 2021) . Sample The Experion DNA analysis kit  verified the presence of adapter dimer contamination in sequencing libraries. A total of two genomic libraries were sequenced using the Illumina HiSeq4000 platform to generate 50 bp single-end reads using the QB3 Vincent J. Coates Genomics Sequencing Laboratory at the University of California, Berkeley, USA. Raw sequencing reads are available in the NCBI Sequence Read Archive.P. vulgaris G19833 accession [http://www.phytozome.net/commonbean, accessed on 30 June 2021). In the filtering process, we included, for downstream analysis, only SNPs that showed minor allele frequency (MAF) >0.05; minimum quality >10; and mean read depth across all lines, ranging from 5 to 1000 (-maf 0.05-minQf 10-min-meanDP 5-max-meanDP 1000). VCFtools was used to estimate SNP and InDel statistics. The SNP density and transition/transversion ratio (Ts/Tv) were determined for nonoverlapping bins of 1 Mb. The ggplot2 package in R was used to construct graphs with the SNP density in each common bean chromosome.The sequence read alignment and SNP calling steps were conducted as previously described . For reaPopulation structure analysis was performed using the package LEA, an R package for landscape and ecological association studies. Parameters for population structure analysis included the number K from 2 to 10 groups and 10 iterations, as described by Frichot and François . The delP. vulgaris [For the phylogenetic analysis, we followed the methodology previously proposed by Ariani et al. . First, vulgaris . Only vavulgaris . A multivulgaris .http://www.worldclim.org, accessed on 30 June 2021) [www.r-project.org, accessed on 30 June 2021) (30 arc-second resolution). Bio-climatic variables were calculated based on monthly temperature and precipitation values, which are biologically more significant than simple average values because they represent annual trends such as seasonal effects and extreme weather conditions  , to evalnditions .Y is the phenotype of one genotype; X is the fixed effect of a SNP; P is the fixed effect of the population structure; K is the random effect of the relative kinship; and ε is the error term, which is assumed to be normally distributed with a mean of zero. The percentage contribution of each SNP to the total phenotypic variation was calculated using the marker R2 (regression coefficient) through TASSEL and multiplied by 100 [p ≤ 0.001%, or smaller than the Bonferroni threshold .Genome–environment associations were identified with TASSEL software . Populatd by 100 . Moreoved by 100 . The sigP. vulgaris reference genome v.1.0, available in Phytozome . Related to the bio-climatic variables, the predicted genes were identified using BLASTp in NCBI , and their putative homologs in Arabidopsis thaliana were observed. The Arabidopsis thaliana protein with the lowest E-value (<0.0) and highest identity (>40%) with each bean protein was considered to be a putative homolog and used to infer its molecular function.The putative functional annotation of each candidate gene was based on the descriptions available in the


Actinidia eriantha) is a dioecious vine, and the pollen of its male cultivars has a direct effect on the quality of its fruits. In this study, to facilitate molecular breeding and gene identification, we performed genome-wide association studies (GWAS) on 11 traits of flower and leaf. A total of 946,337 highly consistent SNP markers were obtained in the whole genome. Phylogenetic tree analysis and population structure analysis showed that the 143 germplasms can be divided into two groups. The linkage disequilibrium analysis showed that A. eriantha have a relatively fast attenuation rate, and that the average attenuation distance of LD was 0.1–0.3 Kb. The MLM (QK) model was determined as best for correlation analysis, and eight and three SNPs associated with flower- and leaf-related traits were identified, respectively, at 0.01 significance level. However, SNP markers associated with stamen number per flower, pollen viability, total chlorophyll content, and total flavonoid content were not identified at the 0.01 significant level, although it is worth noting that one, one, five, and two SNPs were identified to be associated with these traits at the 0.05 significant level. This study provides insights into the complex flower- and leaf-related biology, and identifies genes controlling important traits in A. eriantha through GWAS, which extends the genetic resources and basis for facilitating molecular breeding in kiwifruits.Kiwifruit ( Actinidia eriantha.- Simplified genome sequencing technology was used for the first time to mine genes related to some important traits in 143 male Actinidia eriantha is a unique source of kiwifruit germplasm. It is mainly distributed in the Yangtze River basin, the Yunnan-Guizhou plateau, and the Sichuan basin at altitudes of 200–2,000 m . In addition, because its flowers look good, A. eriantha is used as an ornament in parks and corridors. Furthermore, A. eriantha fruits can be easily peeled.In China, A. eriantha , a total of 37 quantitative trait loci (QTLs) related to eight fruit quality traits and five QTLs related to three fruit phenological stage traits were detected through GWAS analysis , Jinggang mountain (JGS), Wugong mountain (WGS), and Wufu mountain (WFS) in Jiangxi Province, China . Based oin vitro germination methodology . Library construction and computer sequencing were completed by Gene Denovo Co. . After the removal of low-quality reads, clean reads were mapped to the reference genome using the BWA v0.7.12 software , and the quality was tested according to previous methods  was obtained for each SNP locus, and the significant P value was corrected according to the previous methods .The genome-wide association study was analyzed for 11 quantitative traits with the GEMMA V0.98.1 software , number of stamens per flower (NSF), corolla diameter (CD), amount of pollen per flower (APF), amount of pollen per anther (APA), pollen viability (PV), anthocyanin content in petals (ACP) in flower and AsA content (AL), total chlorophyll content (TCL), total phenols content (TPL), and total flavonoids content (TFL) in leaf of test materials were determined statistically . In the ectively .A total of 164.84 Gb data were obtained by sequencing, with an average of 1.15 Gb per sample. Q20 and Q30 of sequencing data were 96.94 and 90.12%, respectively. The mapping rate to the reference genome was between 76.9 and 97.25%, and the sequencing depth was between 4.90 and 11.11 X .A total of 6,022,072 SNP markers were detected by the GATK software, and 946,337 high-quality SNP markers were obtained after filtration, distributed on different chromosomes, among which most SNP markers were 45,123 on Chr03 .A phylogenetic tree was constructed based on high-quality SNP markers obtained after filtration. The phylogenetic tree showed that there were some genetic differences among the samples, so the test materials were divided into two groups: 105 germplasm from the MGS group and 11 germplasm from the WFS group, 14 germplasm from the WGS group and 13 germplasm from the JGS group . The inf2. Generally, the attenuation distance of linkage imbalance was taken as the distance traveled when R2 dropped to half of the maximum value. As can be seen from A. eriantha from different geographical sources is between 0.1 and 0.3 Kb.In order to develop highly consistent single nucleotide polymorphism markers, the linkage situation of single nucleotide polymorphisms in the all samples was analyzed, which was represented by RIn this study, in order to select the optimal association model, the number of significant single nucleotide polymorphisms (SNPs) for the 11 quantitative traits under different association models was compared. Data analysis showed that the GLM model was associated with the largest number of SNPs , followeA. eriantha are shown in P) > 6.67], but there was an SNP significantly associated with PV and NSF at the 0.05 significant level [–log10(P) > 5.97], located on chromosomes 6 and 13. At the 0.01 significant level [–log10(P) > 6.67], two SNPs significantly associated with APA were detected, all of which were located on chromosome 5. Among the two SNPs associated with APA, one of them was also associated with APF at the 0.01 level. Subsequently, a total of 24 candidate functional genes that may be related to pollination traits were found at five SNPs, and 14, four and six candidate genes related to APA, PV, and NSF, respectively, were found (P) > 6.67], a total of two SNPs significantly associated with NPF were detected on chromosomes 19 and 22, respectively (P) > 6.67], but at the 0.05 significant level [–log10(P) > 5.97], there were five and two SNPs significantly associated with TCL and TFL on chromosomes 9, 12, and 18  of male re found . As for ectively . One SNPectively . Two SNPectively . A totalectively . There wectively . One SNPectively . No SNPs, and 18 . A total, and 18 .Two single nucleotide polymorphism sites related to ascorbic acid content in the leaf were selected for verification. Based on the re-sequencing data and SNP positions, primers were designed for verification. The verification results of Sanger sequencing and the re-sequencing screening results have a matching rate of 98.95% . Re-sequA. eriantha were studied by ISSR, SSR, and other molecular marker techniques  (Myles et al., A. eriantha has about 691 million bases, and a total of 946,337 SNPs were developed in this study, with about one SNP per 0.73 Kb DNA sequence length, which could be used for subsequent genetic evolution and association analysis. Similar to results reported previously (Lorenzis et al., The linkage disequilibrium decay distance in the germplasm material determines the marker density in genome-wide association study analysis. Based on the value of LD attenuation distances, A. eriantha, and has identified many analytical markers for related traits, which also indicates that part of the phenotypic quantitative traits in A. eriantha may be co-regulated by multiple genes (Tang et al., A. eriantha-related traits at the genetic level.The team of the authors has previously performed a lot of association analyses between traits and markers in The original contributions presented in the study are publicly available. This data can be found here: NCBI repository, accession number: PRJNA742212.GL: writing, original draft preparation. MZ, JT, XQ, and XX: collected materials. ZJ: data analysis. QL, DJ, and CH: measured quantitative traits. XX: writing, reviewing and editing. All the 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Ticks are obligate haematophagous ectoparasites considered second to mosquitos as vectors and reservoirs of multiple pathogens of global concern. Individual variation in tick infestation has been reported in indigenous sheep, but its genetic control remains unknown.ST, of 600 K SNP genotype data from 165 Tunisian sheep showing high and low levels of tick infestations and piroplasm infections. We consider 45 signatures that are detected by consensus results of at least two methods as high-confidence selection regions. These spanned 104 genes which included immune system function genes, solute carriers and chemokine receptor. One region spanned STX5, that has been associated with tick resistance in cattle, implicating it as a prime candidate in sheep. We also observed RAB6B and TF in a high confidence candidate region that has been associated with growth traits suggesting natural selection is enhancing growth and developmental stability under tick challenge. The analysis also revealed fine-scale genome structure indicative of cryptic divergence in Tunisian sheep.Here, we report 397 genome-wide signatures of selection overlapping 991 genes from the analysis, using ROH, LR-GWAS, XP-EHH, and FOur findings provide a genomic reference that can enhance the understanding of the genetic architecture of tick resistance and cryptic divergence in indigenous African sheep.The online version contains supplementary material available at 10.1186/s12864-022-08321-1. Bos taurus and B. taurus x B. indicus crossbred cattle [Ticks are among the most ancient acari and possibly the earliest to evolve blood-feeding capacity through the development of immune-active proteins and lipids –5. Thesed cattle , respectd cattle . Prophylactic use of acaricides is the most common strategy of controlling and eradicating ticks , 16. TheLUM) was identified as a potential biomarker for tick resistance in cattle [ELTD1 associated with tick burdens in both dairy and beef cattle, and a haplotype of nine tag SNPs and two others associated with tick counts in dairy cattle. The authors also reported haplotypes spanning ITGA11 associated with tick burdens. Class I antigens of the bovine major histocompatibility complex have also been associated with tick loads [Rhipicephalus (Boophilus) microplus infestation [Genetic variation in resistance to parasites has been demonstrated in many livestock species with investigations on individual variation in resistance to T-TBI being the subject of intense research in cattle (see review by ). Most on cattle , 31 repock loads , 33 and estation –36.Ovis aries) are the second most abundant ruminant livestock after cattle [Mycoplasma ovis, Anaplasma ovis, Borrelia burgdorferi s.l., Francisella tularensis, Dermatophilus congolensis, Coxiella burnetti) and protozoa (Babesia spp. and Theileria spp.) [Q fever and Human granulocytic anaplasmosis [STX5 being the possible prime, and RAB6B, TF, SLCO2A1 and STXBP6 being the likely potential, candidate genes driving genetic variation for tick infestation in sheep.Domestic sheep (r cattle  and are ia spp.) –44. Somelasmosis  resultinlasmosis –49, littGenetic structure and relationship were investigated with principal component analysis (PCA) and ADMIXTURE tool. The first and second PCs of the PCA explained respectively, 7.63 and 6.04% of the total genetic variation Fig. . They seK = 4  and expected (HE) heterozygosity averaged 0.3305 ± 0.0305 and 0.3470 ± 0.00006, respectively. The individual values for each statistic were in general higher than 0.3286 ± 0.0344. The mean length of ROH was 3.406 ± 0.4072 and ranged between 3.032 ± 0.5213 Mb (G1) and 3.728 ± 1.004 Mb (G4). The mean value of FROH was 0.0549 ± 0.0842 and ranged between 0.0239 ± 0.0527 (G1) and 0.1612 ± 0.1029 (G3). The average value of F was 0.0476 ± 0.0881 with a range of between -0.0866 ± 0.1339 (G4) and 0.0525 ± 0.0991 (HR). The LR cohort showed higher values of HO and HE compared to HR. At the opposite, the LR cohort showed lower values of ROH size, FROH and F. For the four genetic groups, G4 had the highest values of HO, HE and mean length of ROH while the lowest values were observed in G1, G2 and G1, respectively. The FROH was highest in G3 and lowest in G1. Except G1, the other three genetic groups showed negative values of F.Descriptive statistics, providing insights on genetic diversity were estimated for the overall dataset, the two breeds (B/Q), the two cohorts (HR/LR) and the four genetic groups   and thus higher LD compared to the overall dataset, HR, LR and G1 (r2 < 0.15). The trends in Ne over generation time showed different profiles for the data classes analysed. The overall dataset and G1 group showed the highest Ne which increased gradually reaching maxima around 350 generations ago and then declined rapidly to the present time. The HR and LR cohorts showed the next lowest estimate of Ne which increased gradually up to around 350 generations ago and was followed by a rapid decline to the present. The G2, G3 and G4, had the lowest Ne across all generations which declined gradually to the present time.We assessed the decay in linkage disequilibrium LD; Fig. a and theST) to gain insights on the regions of the genome correlating with resistance to ticks (HR verses LR) considering equal sample size of 74 animals in both cohorts.We assessed selection signatures using runs of homozygosity (ROH), Logistic regression- genome-wide association analysis (LR-GWAS), cross-population extended haplotype homozygosity (XP-EHH) and genetic differentiation , solute carriers  and a chemokine receptor (CCIN). Of these genes, SLCO2A1 (OAR1), MYO (OAR8), SLC24A3 (OAR13), CD180 (OAR16) and CDH18 (OAR16) occurred closest to the most significant SNP as identified by LR-GWAS in the respective candidate regions. Four genes (RAB6B (OAR1), TF (OAR1), SLCO2A1 (OAR1), STXBP6 (OAR18) and STX5 (OAR21)) found in our candidate regions are of particular interest with regard to the objectives of this study. They have been observed to be directly or indirectly associated with body weight traits in various livestock species and resistance to ticks in cattle.We used the ROH approach to identify cohort-specific selection signatures. We therefore compared the ROH results of HR and LR Fig. a and b aed Table . Among tST and XP-EHH, after excluding 56 genes that remain uncharacterized  having roles in innate immunity and disease-related inflammation [Functional enrichment analysis was performed in the pool of 104 putative genes found in the 45 candidate regions that overlapped between at least one HR-specific ROH region and the ones identified by LR-GWAS, Fed Table . The anaed Table . The twoammation , 51.ST, to investigate signatures of selection associated with variation in tick infestation and thus possible resistance. As noted by [T-TBI result in negative impacts on ruminant livestock production. Therefore, the control and ultimate elimination of T-TBI should be prioritized to minimize impacts not only on animal health and production but also on human and environmental wellbeing. Large variations in susceptibility to T-TBI points to genome-wide variability that underpins inter-animal variation in tick infestation and the pathogens they transmit. Here, we generated ovine 600 K SNP genotype data, from 165 animals of two breeds of Tunisian indigenous sheep that graze natural communal pastures and with no history of anti-tick prevention intervention. The sheep comprised of individuals showing high and low resistance (HR/LR) to tick infestation and piroplasm infection . We analnoted by , we alsonoted by .Compared to the HR, the LR cohort showed comparatively higher levels of genetic diversity but lower levels of inbreeding, though the differences were not significant (P > 0.05). The variability in the four genetic groups revealed by PCA and ADMIXTURE was of the same magnitude as that of the two cohorts. Comparable levels of genetic variation have also been reported in Egyptian , Algeriaa priori classification of the study individuals based on their susceptibility/resistance to tick infestation/piroplasm infection, geographic sampling regions and breeds. The lack of genetic differentiation corresponding to susceptibility/resistance to tick infestation/piroplasm infection is not surprising. A similar lack of genetic stratification corresponding to a priori classification of sheep based on levels of prolificacy [The PCA and ADMIXTURE provided corroborating evidence suggesting the absence of genetic stratification that is consistent with the lificacy  or resislificacy , 58 has lificacy  also obslificacy  resultinlificacy . The faclificacy .r2 metric plotted as a function of increasing genomic distance. The overall pattern of LD decay is the same for all the classes of datasets analysed. It reveals higher LD at shorter distances which declines rapidly and plateaus off at around 100 Kb. This differs from what has been observed in commercial breeds whose LD plateaus off at around 150 Kb [Ne. Although the two cohorts showed differences in tick infestation [2 values (r2 > 0.15) and thus highest LD over short genomic distances and a decay curve with persistently elevated values compared to G1. A similar pattern was observed in the primitive Soay sheep [Ne which declines gradually over all generations. The possible reason(s) for the gradual decline in Ne in the three groups remains unknown and is worth investigating. In contrast, LD was lower (r2 < 0.15) in G1 across all genomic distance intervals and the group also showed the highest Ne over all generations. A similar pattern and magnitude of LD was reported in the Iranian Qezel sheep and was attributed to high genetic diversity in the breed [Ne in G1, and similar magnitude of genetic diversity is unlikely to reflect any biological phenomenon.LD was estimated for all marker pairs using the d 150 Kb  and is mestation , this reay sheep  and was he breed . This exST approaches to detect selection signatures and SNPs that are likely associated with variation and thus resistance to tick infestation.Since millennia, ticks have parasitized animals for blood-meal to the extent of developing, through co-evolution, a sophisticated armoury that guarantees their biological success. To explore genomic signatures encoding individual variation in tick infestation and thus host resistance to ticks, we segregated Tunisian sheep into two extreme groups comprising animals showing high- and low-resistance (HR/LR) to tick infestations and piroplasm infections following the results of a previous study on the same animals . The HR ST identified a selection signature on OAR21 spanning nine genes, one of which was STX5 (Syntaxin-5). In a study of Belmont Red cattle, STX5 was amongst 11 of 14 genes that showed a significant increase in its level of expression in the skin of animals that were highly resistant to ticks at time zero post infestation compared to animals of low resistance [STX5 regulates the endoplasmic reticulum channel-release properties of polycystin-2, a member of the transient receptor potential cation channel family that can function as an intracellular calcium (Ca2+) release channel [2+ dependent genes) out of 14 genes that they tested, [2+ signaling genes in skin of HR animals may explain their increased resistance to ticks. Previous tick exposure may prime animals that exhibit the HR phenotype to resist further infestations via increased expression of Ca2+ signaling proteins and based on these results, we suggest that STX5 could be a prime candidate gene driving tick resistance in sheep.The four approaches revealed 45 candidate regions, that overlapped between at least two approaches, and which spanned 104 characterised genes. The LR-GWAS and Fsistance . The exp channel . Based o tested,  suggesteSTXBP6 (Syntaxin Binding Protein 6) and another one that was revealed by LR-GWAS, XP-EHH and FST on OAR1 spanned SLCO2A1 (OATP2A1). Both genes were closest to the most significant SNP as revealed by LR-GWAS. STXBP6 was one among a cluster of genes found to be upregulated in the testes of roosters exposed to acute heat stress [STXBP6 were also found to be among eight of other genes that were correlated with the modified Rodnan skin thickness score and forced vital capacity in humans suffering from scleroderma and systemic sclerosis [SLCO2A1, a prostaglandin transporter, maintains an increased interstitial concentration of PGE2, a major chemical mediator of febrile response, in the hypothalamus which plays a key role in thermoregulation [GNE, the gene closest to the top-most SNP as identified by LR-GWAS on a candidate region on OAR2, that likely plays a role in adaptation to climate-mediated selective pressures in sheep [STXBP6 could have a potential pleiotropic effect on skin thickness and thermoregulation in sheep that enhances tick and heat stress tolerance. SLCO2A1 on the other hand, can enhance tick resistance by regulating fever during infestation with T-TBI as well as thermoregulation. However, these hypotheses need to be validated with more data on appropriate phenotypes such as skin and coat characteristics.Environmental changes can exert positive or negative effects on mechanisms of thermoregulation that can influence tick burdens . It has t stress . Transcrclerosis , a condiclerosis , 70, 71.clerosis , and at gulation . The funin sheep , 74. TakST that spanned among others the RAB6B and TF genes. This region was observed, following GWAS analysis, to be significantly associated with birth weight in sheep [STXBP6 had potential pleiotropic effect on bone tissue and fecundity traits in chickens [RAB6B, TF and STXBP6 to enhance growth and development stability early in life as an adaptive strategy for survival in the context of high T-TBI challenge.Birth weight is the earliest available body weight trait with considerable impact on lamb survival and growth performance . Our anain sheep , 77. Grochickens  and was chickens  and layechickens  suggestia priori defined two groups of animals presenting contrasting phenotypes of high- and low-resistance to ticks, based on tick counts and piroplasm infections. The two cohorts were characterized by similar levels of genetic variation and a fine scale genomic structure that could not be explained by tick resistance status, geographic sampling region and breeds. Four methods of detecting selection signatures identified regions of the genome that were most likely associated with differences in tick infestation and thus resistance; with our results suggesting that STX5 could be a prime candidate driving tick resistance in sheep. We further hypothesized that STXBP6 and SLCO2A1 could be potential candidates for tick resistance in indigenous sheep and should be investigated further. The occurrence of RAB6B and TF in a candidate region that was significantly associated with body weight traits indicates that natural selection may be enhancing growth and development stability as an adaptive strategy to tick infestation. For these genes to qualify as candidates for enhancing genetic resistance to ticks in sheep through precision breeding (genomic selection and/or gene editing), their potential effects should be quantified through gene expression studies involving resistance and susceptible animals and identify actionable variants encoding the trait. Such quantification would benefit from the inclusion of variables, such as skin and coat characteristics, to investigate their influence on tick infestation and resistance. Overall, our findings provide a genomic reference for understanding the genetic architecture of tick resistance and cryptic divergence in indigenous sheep.In this study, we investigated selection signatures in Tunisian indigenous sheep using an The blood samples used in this study were collected by veterinary surgeons from farmers flocks. The sampling was done during routine animal health monitoring and surveillance following standard veterinary procedures that complied with animal welfare regulations as detailed in the guidelines for the care and use of animals of the Tunisian National Council of Veterinary Surgeons (TNCVS). Prior to blood sampling, verbal permission was sought and granted by animal owners who witnessed the procedure. Therefore, permission from the Ethics Committee of the TNCVS was not required.Two breeds, Barbarine (B) and Queue Fine de l’Ouest (Q) from Tunisia were sampled. The animals graze natural communal pastures throughout the year except during summer when they are released to forage on crop residues. Prophylactic treatment is rare, but vaccinations against Brucellosis, Bluetongue, Foot and Mouth disease and Sheep-pox are done annually by the National Veterinary Services. The animals selected for this study were sampled as part of a larger cross-sectional study on T-TBI carried out between 2018 and 2020  in the nFor the cross-sectional study, 439 mature ewes were tagged and monitored through eight rounds of sampling as detailed by . Whole bFrom the results of population structure analysis, which showed no genetic differentiation between the two breeds, the 165 samples were assigned to two cohorts, irrespective of the breed based on extreme values of tick load and piroplasm infections viz.: (i) high tick-resistant (HR) - animals with no tick infestation(s) (score = 0) and/or no piroplasm infection(s) and (ii) low tick-resistant (LR) - animals that were highly infested with ticks (score > 81) and/or infected by piroplasms  with PLINK v1.9 and the first two PC’s were plotted to visualize genetic relationships. We also inferred fine-scale genome structure and shared genome ancestry with the unsupervised mode of ADMIXTURE tool v1.30 [K range of 1-8 clusters and five replicate runs were performed for each K to generate cross-validation (CV) errors. The lowest CV error was used to infer the optimal number of distinct genetic clusters in the dataset.Although the study individuals were classified ol v1.30 , indepenHE) and observed (HO) heterozygosity, patterns of LD decay across the genome and effective population size (Ne) across generation time were investigated for each breed, the HR and LR cohorts and the genetic groups revealed by PCA and ADMIXTURE. HE and HO were calculated with PLINK v1.9. The extent of LD between pairwise SNPs was evaluated with the r2 statistic calculated with PLINK v1.9. The Ne over generation time was estimated with the equation Ne t = (1/4c) (1/r2 − 1) [Ne t is the Ne t generations ago (t = 1/2c); r2 is the LD between pairwise SNPs; and c is the genetic distance in Morgans between pairs of SNPs.Expected (/r2 − 1) , where N“F” and the runs of homozygosity (ROH) based “FROH”. The latter was computed as the ratio of the total length of ROH to the length of autosomes (2.45 Gb) [Two coefficients of inbreeding were calculated with PLINK v1.9; the SNP based 2.45 Gb) . The ROHTo detect selection signatures spanning regions associated with genomic targets for resistance to ticks, we first investigated the distribution of ROH across the genomes of the HR and LR cohorts after standardizing the sample sizes to 74 animals per cohort. The frequency of a SNP within an ROH region was determined and a Manhattan plot visualising all the tested SNPs against their autosomal positions was generated. The most frequent SNPs occurring above the 50% cut-off threshold of the empirical distribution were taken as the most significant loci underlying an ROH under selection. To identify the ROH streatches that are associated with tick resistance, we contrasted the HR and LR ROH regions and identified the ones that were specific to HR.ST, to investigate regions of the genome showing divergence between HR and LR. The unbiased pairwise FST [ST values were standardized by Z transformation following [ST values in each chromosome and representing the most divergent regions between the two cohorts were considered putative candidates under divergent selection.We used the population differentiation statistic, Fwise FST  was compwise FST  using a ollowing . To miniP-value < 0.05) within a distance of less than 100 kb from the top-most SNP.We used the software developed by  to estimWe performed the logistic regression (LR) GWAS with PLINK v1.9 to explore further, the possible genomic regions and SNPs associated with variation in tick resistance. The HR and LR cohorts were used as the test and control groups, respectively. To obtain the 99% confidence intervals for the estimated parameters, the “--ci 0.99” and “--covar” options were invoked, and Fisher’s exact test was used to generate the p-values considering age, sampling region and breed as covariates. The generated p-values were Bonferroni corrected to minimize the likelihood of false positive results. The corrected p-values were standardized and the -log10  of 4.25 (the top 0.001) was set as the cut-off threshold to identify the candidate regions and associated SNPs. The estimations were summarized in 100 Kb window sizes and the genes and top-most SNP found within the candidate regions identified. The Manhattan plots showing the genome-wide distribution of the SNPs were generated with the R package “qqman” v3.5.1.ST were analysed, and the ones that overlapped between at least two methods identified and merged using Bedtools v.2.28.0 [http://www.ensembl.org/biomart) tool based on the Ovine v3.1 reference genome. These set of genes were assessed for biological enrichment gene ontology (GO) and KEGG Pathway (www.kegg.jp/kegg/kegg1.html) terms compared to the full list of O. aries autosomal protein-coding genes with the functional annotation tool in DAVID v6.8 [http://www.ncbi.nlm.nih.gov/gene/) and review of literature.The candidate regions revealed by ROH were analysed and the ones that were specific to HR identified. These HR-specific ROH regions and the ones identified by LR-GWAS, XP-EHH and Fv.2.28.0 . The genVID v6.8 . We furtAdditional file 1.