KASP markers were developed based on the data obtained. It will be possible to screen the breeding material and to breed the varieties more effectively using current markers associated with a shallow tuber’s eye depth.Potato (Solanum tuberosum L.) is one of the most important food crops in the world. The genome of this potato species is autotetraploid and has a high level of heterozygosity, also this potato species is a cross-pollinated plant. These characteristics complicate the genetic analysis and breeding process. The tuber’s eye depth is an important trait that affects the suitability of potato varieties for processing. Potato breeding for this trait is based on phenotypic assessment. Identification of the loci that control tuber eye depth would allow diagnostic markers for the marker-assisted selection to be created. The aim of this study is to search for loci associated with the eye depth by analyzing Solanum tuberosum varieties from the GenAgro collection of the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, genotyped using the Illumina 22K SNP potato array DNA chip. The 24 significant markers associated with the “eye depth” trait were identified using 15,214 SNP markers genotyped with the Illumina 22K SNP potato array chip and the general linear model (GLM) taking into account the population structure. Data obtained showed the presence of SNPs in four genomic regions: on chromosome 4 (1 marker in the 3.92 Mb area), 5 (1 marker in the 4.67 Mb area) and 10 (1 marker in the 4.87 Mb area and 21 markers in the region between 48.1–48.9 Mb). The results of localization in the region 48.1–48.9 Mb of chromosome 10 correspond to previously published studies, the remaining three regions were detected for the first time. DNA sections containing SNPs linked to the tuber’s eye depth were studied in the SolTub_3.0 potato genome assembly ( Potato Solanum tuberosum L. is the important food crop.The genetic analysis and breeding process of this crop iscomplicated by the autotetraploid nature and a high level ofheterozygosity of this species. In addition, S. tuberosum isa cross-pollinated species, that also makes genetic researchand breeding more difficult. . Potatovarieties are reproduced vegetatively in view of the low fertilityand the impossibility of ripening fruits in the climaticconditions of many countries. This type of reproductionallows this crop to maintain the identity of variety genomein different reproduction, despite the heterozygosity.Mapping of quantitative trait loci (QTL) and genes ofautotetraploid potato using biparental populations is a difficulttask. The biparental populations method for authotetraploidsrequires obtaining and analysis of numerousprogeny. However, the low fertility of most potato varietiesdoes not allow obtaining big mapping populations.Researches often overcome these limitations of mappingstudies by transition to diploid level, at which in additioninterspecific hybridization is possible. However this approachstill requires high level of fertility. All these featureslimit the use of S. tuberosum varieties in genetic mappingand QTL studies. However, the possibility of applyinga genome-wide association studies (GWAS) with thedevelopmentof high performance (HP) sequencing andother HP-genotyping methods (including application ofSNP-arrays) made it possible to intensify research aimedon identification of genomic loci related with quantitativetraits, avoiding all difficulties mentioned above for biparentalmapping and QTL analysis. The vegetative reproductionway of cultivated forms of potato facilitates GWAS methodcompared to other cross-pollinated plants, however someadaptation of the method is still needed taking into accountthe autotherapoid nature and heterozygosity .The depth of the tuber eyes is an important trait for thesuitability of potato varieties for processing. The volumeof losses during peeling, which should not be higher than15 % , depends on the eyedepth. Accordingly, this trait affects the cost of peelingduring processing. Based on the conditions for obtainingminimal waste during mechanized abrasive peeling, thetuber eye depth should be no more than 1.5 mm . Evaluation of potato tuber eye depth iscarried out using various scales. A number of studies usedscales divided into three to nine grades .The first genetic studies of tuber eye depth were performedat the beginning of the twentieth century. R.N. Salaman(1911) showed that deep eyes dominate shallow ones.Later W. Black (1930) also suggested that the eye depth iscontrolled by genetic factors, but he hypothesized that thistrait has an intermediate type of inheritance or incompletedominance when extreme phenotypes  are probably homozygous and intermediatephenotypes (medium depth of the eye) are heterozygous.B. Maris (1966) suggested that the eye depth is controlledby one major gene with a additive effect. Some authors havereported that shallow eyes are dominant .H. Kukimura (1972) and H.W. Howard (1974) indicatedthat when crossing two samples with shallow eyes, in segregationcan appear samples with deep and medium eyes.Studies that using genetic markers have shown existenceof the major locus that controls the eye depth trait . These studies have also shown that the deep eye(Eyd) dominates the shallow eye (eyd). The Eyd/eyd locus,which is responsible for the depth of the eye, is located onchromosome 10 .W. Black (1930) was first who suggested eye depth tobe quite dependent on environmental conditions. Later,B. Maris (1966) demonstrated a high susceptibility ofthis trait to changes under the influence of environmentalfactors. However, H.W. Howard (1974) argued that theeye depth is determined mainly by the genotype. Later, anumber of studies also showed a high heritability of the eyedepth  and an insignificant environmentalinfluence . Other authors show that thegenotype most strongly affects the trait, but the influenceof environmental factors is also quite high . A number of studies have shown the ability ofthe eye depth to change under the influence of somaclonalvariation .In traditional breeding, the depth of the eyes is estimatedin the first generation after crossing. However, the tubers ofhybrids grown from botanical seeds are too small in size andin this case it is difficult to adequately estimate the depth of the eyes. To solve this problem, DNA markers can beused that will allow effective selection at the first generationstage and reduce the volume of subsequent studies.The aim of this study was to search for loci associatedwith the eye depth by analyzing S. tuberosum potato varietiesfrom the GenAgro collection of the Institute of Cytologyand Genetics SB RAS, genotyped using the Illumina22K SNP potato array.The plant material. A total of 88 potato varieties fromthe collection of the GenAgro Institute of Cytology andGenetics SB RAS (Table 1) were phenotyped. Most of thepotato collection was represented by varieties and hybridsof domestic selection, some varieties studied were fromabroad .Plants were grown in the field in two field plots on theterritory of the Michurinsky village, Novosibirsk regionfrom May to August 2017.Field tests were carried out according to the followingscheme: the number of rows for each genotype was 2;number of plants in a row – 10; row length – 3 m; the distancebetween the rows – 0.75 m; the distance between theplants in the rows – 0.30 m; planting method – manually(by hand) on furrows, filling furrows with harrows; landingdate is the third decade of May.Agrochemical characteristics of the soil: the contentof exchanged potassium 110.00 mg/kg; the amount ofexchanged bases 24.19 mg-eq/100 g; hydrolytic acidity3.23 mg-eq/100 g; exchanged acidity 5.60 mg-eq/100 g;humuscontent 2.67 %; the content of mobile phosphorus5.14 mg/kg; the degree of saturation with bases (V)88.20 %.Meteorological conditions of the growing season:May. Air temperature: long-term average 10.90 °C;average monthly 12.60 °C; effective temperature sum197.60 °C. Precipitation: long-term average 37.00 mm;the amount for the month is 33.90 mm.June. Air temperature: long-term average 16.90 °C;average monthly 19.30 °C; effective temperature sum576.00 °C. Precipitation: long-term average 55.00 mm;the amount for the month is 71.90 mm.July. Air temperature: long-term average 19.40 °C;average monthly 18.50 °C; effective temperature sum1004.00 °C. Precipitation: long-term average 61.00 mm;the amount for the month is 99.50 mm.August. Air temperature: long-term average 16.20 °C;average monthly 16.80 °C; effective temperature sum1408.00 °C. Precipitation: long-term average 67.00 mm;the amount for the month is 65.60 mm.Evaluation of the eye depth was carried out in 2017 inaccordance with the VIR methodology .The eye depth was determined on a scale of 1 to 3: 1 – shallow(less than 1–1.3 mm), 2 – medium (1.4–1.6 mm),3 – deep (more than 1.7 mm). Five typical eyes of a potatotuber were measured. These data were compared with the data presented in the database of the State Register ofbreeding achievements approved for use (http://gossortrf.ru/gosreestr.html) and data from the European cultivatedpotato database (https://www.europotato.org/).For genotyping, DNA was isolated from the skin ofpotato tubers using the DNeasy Plant Mini kit  according to the manufacturer’s protocol. Theconcentration and purity of the test samples was determinedusing gel electrophoresis and using a Nanodrop 2000 apparatus.All 88 varieties were genotyped using the Illumina 22KSNP potato array (GGP Potato V3) DNA chip at TraitgeneticsGmbH .The 21,226 SNP dataset was filtered using Excel software.Low quality data was deleted, all monomorphicmarkers and markers containing more than 95 % of oneallele. For further analysis, 15,214 (71.7 %) SNPs weretaken. The chromosomal position for SNP has been determinedusing data of .An analysis of the associations between the eye depthand the genomic regions was carried out using the Tassel5.2.59 software package . A generalizedlinear model (GLM) was used taking into account thepopulation structure (Q). The population structure of thecollection was analyzed in previous works  using the STRUCTURE v 2.3.4 program , based on data from all 15,214 markers takenfor further analysis.Since the TASSEL software package was developed forthe analysis of the genomes of diploid species, for its applicationto the tetraploid genome, the data were transcodedinto a digital format taking into account the representationof the effector allele .–6, and (2) Benjamini–Hochberg test  . In this case, only those SNPs whosep-value (FDR) did not exceed the threshold value of 0.05were considered significant, taking into account the amendmentsto the Benjamini–Hochberg method.Two criteria were used to determine the significanceof associations: (1) the Bonferroni correction, which wasdefined as dividing the statistical significance level (0.05)by the total number of trials, in our case, by the number ofmarkers  and amounted to 3.28·10The development of KASP markers and genotypingof 86 varieties using them was carried out by LGC GenomicsLLC . Sections of 100 bp DNAcontaining SNP associated with the eye depth trait wereconverted to KASP markers (Supplementary 1)1. Dataabout the nucleotide composition of these sites are presentedin the SolTub_3.0 potato genome assembly (https://plants.ensembl.org/). In the future, it will be possible moreeffectively carry out of screening breeding material andselection varieties with a shallow eye depth using thesedeveloped KASP markers.https://vavilov.elpub.ru/jour/manager/files/SupplTotsky_engl.pdfSupplementary Materials are available in:Phenotyping conducted on 88 samples of the collectionshowed that most of the collection, 49 samples, had ashallow eye depth. Also, a large part of the collection wasrepresented by samples having a medium eye depth – thesewere 33 genotypes. Samples with deep eyes representeda small part of the sample and were represented by only6 samples (see Table 1).–6), only 15 of 24 SNPs remained significantfor the tuber’s eye depth, the remaining nine SNPs remainedsignificant only using the FDR criterion ( p < 0.05).Association studies using GLM and taking into accountthe population structure revealed 24 SNPs significantly associatedwith the tuber’s eye depth .After applying the Bonferroni multiple test correction at5 %  were located onone of the two loci of chromosome 10. SNPs that are significantwhen using FDR ( p < 0.05) are found at all fourloci on chromosomes 10, 4, and 5. On chromosomes 4and 5, only one significant SNP was located. Twenty-oneSNPs were located on chromosome 10 in a relatively smallarea between 48.1 and 48.9 Mb. Another SNP was located in another region of chromosome 10 at position 4.87 Mb(this SNP is significant when using FDR).The most significant SNP PotVar0111687 refers to a noncodingsequence. In total, among 18 significant SNPs, 6 arelocated in non-coding regions of the genome, and 11 arelocated in protein coding sequences. Annotation of genescontaining significant SNPs is given in Supplementary 2.Due to the lack of accurate information on the geneticcontrol of the eye depth, it turned out to be difficult todefinitely relate the identified genes to the formation of theeye depth.KASP genotyping was carried out using 11 KASP markersdeveloped on the basis of SNP that significant when usingBonferroni multiple test corrections, which were linked tothe eye depth and located on the 10th chromosome between48.1 and 48.9 Mb (Table 3). Eight markers correlated withthe eye depth, or rather, the homozygous state of one ofthe alleles of these markers correlates with a shallow eye.Haplotypes that are 100 % predictive for shallow eyesare: PotVar0111687 (homozygote for T), solcap_snp_c1_16351 (homozygote for T), solcap_snp_c2_55948, solcap_snp_c2_25522 (homozygousfor G), solcap_snp_c2_25528 (homozygous for G).The carriers of this haplotype are the Vasilyok, Gulliver,Lyuks, Matushka, Nayada, Dochka, Tuleevskij, Favorit,Charoit varieties.X.Q. Li et al. (2005) conducted genetic mapping of thelocus responsible for the eye depth. As a result, the Eydlocus was mapped on chromosome 10 proximal to theCT240 marker (12 cM) and distal to the STM0051 marker(13 cM). BLAST analysis of the sequences of these markersindicates the position of CT240 in the region of 51 Mb, andSTM0051 in the region of 23.4 Mb. It can be assumed thatthe locus that we identified on chromosome 10, which islocated between 48.1 and 48.9 Mb, corresponds to the Eydgene. In a number of works , a locus linkedto eye depth was regularly detected in this region.A. Prashar et al. (2014) using Infinium 8303 Potato Arraycompiled a genetic map of diploid potatoes. After selectingthe most valuable SNPs, the map contained 1355 differentloci and 2157 SNPs. In this work, it was found that themain locus for the tuber’s eye depth is located on chromosome10 (SNP: c1_8020) and linked to the tuber-shapedlocus. This SNP is located on chromosome 10 in the regionof 48.8 Mb, as well as locus 4 detected in the current study.Also A. Prashar et al. (2014) found two SNPs linked to theeye depth on chromosomes 2 (c2_7422) and 3 (c2_37119),which were not so significant and most likely could havean auxiliary effect.U.R. Rosyara et al. (2016), also used GWAS to searchfor SNPs linked to the eye depth. In their study, the DNAchip had 3.5 thousand markers. A highly significant SNP for the eye depth was located at the 48.9 Mb position onchromosome 10  and locus 4, identified in our work).The less significant SNP c2_11685, which was also linkedto the tuber’s eye depth, was located on chromosome 5 at2.3 Mb position. In our work, we also detected SNP associatedwith the eye depth, located on chromosome 5, butin a distinct region – 4.7 Mb.H. Lindqvist-Kreuze et al. (2015) also found potato tuberseye depth QTL on chromosome 10 at position 49.4 Mb,as well as another locus on chromosome 12. Researchershave identified a number of candidate genes that underliesignificant QTL and are linked to the eye depth. One ofthem is the BEL-1-like homeobox gene, found at a distanceof 1.37 Mb from the QTL marker toPt-437059 on chromosome10. The second is the α-expansion gene, which wasfound in the region of significant QTL on chromosome 10at a distance of 1.78 Mb from the QTL marker toPt-437059.Also, some genes associated with the production andmodification of pectins were found in the close proximityto toPt-437059 marker on chromosome 10.A. Hara-Skrzypiec et al. (2018) also performed mappingfor a number of potato traits, including the eye depth.Seven QTLs that were linked to the eye depth were found:one per each chromosome 1, 4, and 11, and two per eachchromosomes 3 and 5. However, unlike other studies, themajor QTL was located on chromosome 4 (at position68.8 Mb) and accounted for 22.6 % of the dispersion inthe average data set. We found in our study a significantSNP PotVar0106879 located on chromosome 4 at position3.9 Mb.Thus, the key locus responsible for the eye depth islocated on chromosome 10, while loci with a minor effectare located on chromosomes 1, 2, 3, 4, 5, 11, and 12, aswell as in other parts of chromosome 10.Despite a wide range of genetic studies of the potato tuberseye depth trait and the identification of genes and QTL associatedwith eye depth variability, DNA markers are stillnot used in the breeding of potatoes to this trait. Meanwhile,the use of diagnostic DNA markers allows to carry out moreefficient pre-breeding research  andmarker-assisted selection in breeding programs .We were able to develop a number of new PCR markers thatcan be convenient for screening the genetic resources andbreeding material of potato. After additional verification ofthese proposed markers on an extended sample, they can beused to select shallow-eyed plants by analysis at the DNAlevel. We selected PCR markers located on chromosome10 at positions between 48.62 and 48.98 Mb. These datacoincide with the data of other authors on the location ofthe supposed gene, which is responsible for the eye depth.However, due to the larger number of markers that wefound, these data allow us to clarify the localization of the gene of interest to us and suggest that it is located at positionsbetween 48.0 and 49.0 Mb. According to our data,selection according to the haplotype which includes 8 SNPslocated on chromosome 10 is optimal (PotVar0111687(homozygote of T), solcap_snp_c2_25526 (homozygoteof T), solcap_snp_c1_16351 (homozygote of T), solcap_snp_c2_55948 (homozygote of T), solcap_snp_c1_8019(homozygote of A), solcap_snp_c2_25471 (homozygoteof A), solcap_snp_c2_25522 (homozygote of G), solcap_snp_c2_25528 (homozygous for G)). The carriers ofthis haplotype are the varieties Vasilyok, Gulliver, Lyuks,Matushka, Nayada, Dochka, Tuleevskij, Favorit, Charoit,which are characterized by a shallow eye depth. The useof these varieties as donors of this trait in combination with PCR analysis of the offspring to selectcarriers of the corresponding haplotype will provide a moreeconomical and accelerated method of creating potatoeswith a shallow tuber eye depth.The authors declare no conflict of interest.Benjamini Y., Hochberg Y. Controlling the false discovery rate: apractical and powerful approach to multiple testing. J. R. Stat.Soc. Ser. B Stat. Methodol. 1995;57(1):289-300.Black W. Notes on the progenies of various potato hybrids. J. Genet.1930;22(1):27-43. DOI 10.1007/BF0298 3366.Bradbury P.J., Zhang Z., Kroon D.E., Casstevens T.M., RamdossY., Buckler E.S. TASSEL: Software for association mappingof complex traits in diverse samples. Bioinformatics. 2007;23:2633-2635. 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Breast cancer has a major disease burden in the female population, and it is a highly genome-associated human disease. However, in genetic studies of complex diseases, modern geneticists face challenges in detecting interactions among loci.This study aimed to investigate whether variations of single-nucleotide polymorphisms (SNPs) are associated with histopathological tumor characteristics in breast cancer patients.A hybrid Taguchi-genetic algorithm (HTGA) was proposed to identify the high-order SNP barcodes in a breast cancer case-control study. A Taguchi method was used to enhance a genetic algorithm (GA) for identifying high-order SNP barcodes. The Taguchi method was integrated into the GA after the crossover operations in order to optimize the generated offspring systematically for enhancing the GA search ability.The proposed HTGA effectively converged to a promising region within the problem space and provided excellent SNP barcode identification. Regression analysis was used to validate the association between breast cancer and the identified high-order SNP barcodes. The maximum OR was less than 1 (range 0.870-0.755) for two- to seven-order SNP barcodes.We systematically evaluated the interaction effects of 26 SNPs within growth factor–related genes for breast carcinogenesis pathways. The HTGA could successfully identify relevant high-order SNP barcodes by evaluating the differences between cases and controls. The validation results showed that the HTGA can provide better fitness values as compared with other methods for the identification of high-order SNP barcodes using breast cancer case-control data sets. Breast cancer has a major disease burden in the female population, with a growing incidence recently ,2. PreviSNPs are crucial genetic variants in genomic association analyses involving leukemia , cancersAn SNP barcode consists of SNPs, and each SNP includes three genotypes. The large space of suitable SNP barcode combinations complicates the statistical evaluation and identification of relevant SNP barcodes. Evolutionary algorithms have been proposed to facilitate statistical identification of SNP barcodes, and a genetic algorithm (GA) is one of the most frequently used algorithms in genomic studies ,25. A GABreast cancer is a major health issue, and machine learning algorithms are frequently employed to detect the complex genomic associations in breast cancer studies. Although previous machine learning approaches could effectively identify SNP associations in genomic studies, the detection rate of SNP barcodes remains challenging for high-order SNP barcodes. Thus, we proposed a hybrid Taguchi-genetic algorithm (HTGA) for high-order SNP barcode identification in a breast cancer case-control study.A GA is a machine learning algorithm inspired by biological evolutionary processes . The firnL (2n−1), where n=2k is the number of experimental runs, k (1) is a positive integer, base 2 represents two levels for each design parameter, and n−1 is the number of columns in the OA. “L” represents “Latin,” because the OA experimental design concept is associated with the Latin square. An example of an OA is shown in The methods proposed by Taguchi et al  are base1, y2, …, yn, in the smaller-the-better case, the SNR can be determined using the following equation:SNR (η) is used as the selection quality characteristic in the field of communications engineering; it can be used to optimize the parameters for a target. Taguchi methods can classify the parameter design problem into several categories according the problem. Both smaller-the-better and larger-the-better SNR types are used. Considering the set of characteristics yIn the larger-the-better case, the SNR can be determined using the following equation:The SNR evaluates the robustness of the levels of each design parameter. A high-quality result can be achieved for a particular target by controlling the parameters at a particular level with a high SNR value.In the HTGA, a Taguchi method is added into GA crossover and mutation operations. cp; (5) Select a suitable two-level orthogonal array for the experiment; (6) Randomly choose two chromosomes at a time to execute a matrix experiment; (7) Calculate the function values and SNRs of n experiments in the orthogonal array nL (2n−1); (8) Calculate the effects of different factors and in the experiment; (9) An optimal chromosome is generated; (10) Repeat steps 5 through 8 until the expected number (1/2) × M × cp is reached; (11) New chromosomes are generated through the Taguchi method; (12) Mutation operation, mutation probability is determined by the mutation rate pm; (13) Add chromosomes from a pool into the population; (14) Sort the population by fitness; (15) Select the fittest chromosomes as the new population for the next generation; (16) If the stopping criterion is met, execute step 17; if not, go back to step 2; (17) The chromosome with the highest fitness value is the HTGA solution.The procedure involves the following 17 steps: (1) Population initialization, execute the algorithm and generate an initial population; (2) Fitness value evaluations, evaluate the population’s fitness values; (3) Selection operation, select candidates using the tournament approach; (4) Crossover operation, the probability of crossover is determined by the crossover rate Input: maximum iteration as T (termination criterion)M     population size cP     crossover rate mP     mutation rate Output: optimal chromosome beginInitialization# M chromosomes as population *//* Initialize foriteration ← 1 to TdoSelection operation     # fori ← 1 to Mdo     population as chromosome1 and         /* Randomly select two chromosomes from chromosome2 */         iffitness1 ≥ fitness2do         winner ← chromosome1             end if         else do         winner ← chromosome2             end if         winner into mating pool */             /* put end for     Crossover operation     # fori ← 1 to (M / 2) do     mating pool as chromosome1         /* Sequentially select two chromosomes from chromosome2 */         and if random < cPdo         chromosome1, chromosome2)             crossover(             /* generate two offspring */end if         offspring pool */         /* put two offspring into end for     # Taguchi operation     fori ← 1 to (0.5 × M × cP) do     offspring pool as chromosome1         /* Randomly select two chromosomes from chromosome2 */         and chromosome1, chromosome2)         Taguchi(         /* generate one offspring */offspring pool */         /* put one offspring into end for          # Mutation operationfori ← 1 to size of offspring pooldo     forj ← 1 to dimensions of chromosome do         if random < mPdo             j])                 mutation the indexes of the selected SNPs in the data set and (2) the genotypes of these selected SNPs. Thus, a chromosome iC is expressed as shown in equation 3.In the proposed GA, a suitable solution to a problem is denoted as chromosome iC =  (3)i = 1, 2, …, m, and is the population size. i,sSNP, where s = 1, 2, …, n/2, is a selected SNP dimension in which all SNPs are unrepeatable, and n is the SNP barcode order. i,gGenotype represents the three possible genotypes of the selected i,sSNP, where g = n/2 + 1, n/2 + 2, …, n is the selected genotype dimension. In the population initialization, all chromosomes are stochastically generated according to the encoding schemes.where The aim of SNP barcode identification is to detect relevant differences between cases and controls. To optimize the protective effect of the SNP combination, a fitness function is required for comparing cases and controls. A high difference between cases and controls indicates a high probability of detecting relevant SNP barcodes. In the proposed GA, a chromosome is measured by the fitness function shown in equation 4.F(iC) = number (control∩iC) − number (case∩iC) (4)number is the total number of elements in a set, control denotes the controls, case denotes the cases, and iC is the ith chromosome. Thus, the number of intersections between the ith chromosome and the controls is calculated by number (control∩iC), and the number of intersections between the ith chromosome and the cases is calculated by number (case∩iC). Thereafter, we calculate the difference between number (control∩iC) and number (case∩iC) as the fitness value at iC.where In the selection operation, a random tournament selection scheme is used to pick each pair of parents from the population . In tourInput:population, the list of chromosomes to select fromInput:chromosome1, the first randomly selected chromosome from populationInput:chromosome2, the second randomly selected chromosome from populationInput:fitness1, the fitness value of the first chromosomeInput:fitness2, the fitness value of the second chromosomeOutput:winner: the chromosome with better fitness value in tournamentOutput:mating pool: reserve the list of chromosomes to execute crossover operation# Tournament selectionbeginfori ← 1 to size of mating pool dopopulation     Randomly select two chromosomes from iffitness1 ≥ fitness2do     winner ← chromosome1         end if     else do     winner ← chromosome2         end else     winner into mating pool     put end forj 2 + 1 position, where j = 1, 2, …, n/2 is the index in the chromosome and n is the SNP barcode order. Therefore, n/2 bits are randomly generated, and both the j 2 + 1 genotype locus and jth bit representing an SNP are swapped in the parent organisms.After the selection operation, the crossover operation is implemented to create high-performing individuals. Two chromosomes are sequentially selected from the mating pool as a pair of parents, and then, the crossover operation is executed on them. The crossover operation uses a uniform crossover. Each bit in a chromosome is randomly generated as 0 or 1, and for 1, points are swapped between parent organisms; otherwise, points are not swapped. The encoding schemes establish a single point as an SNP locus with a corresponding genotype locus at the Q design factors. Each factor has two levels. An orthogonal array nL (2n−1) exhibits n−1 columns and n individual experiments corresponding to n rows, where n = 2k and Q ≤ n−1; k is a positive integer, defined as an integer >1, and it is used for adjusting the number of experimental runs.An orthogonal array exhibits η) is the mean square deviation of the fitness function. Let two values of η be iη = (iy)2 and iη = −(iy)2 (where is negative) in the case of a fitness function that is maximized (larger-the-better). Let iy be the function evaluation value of experiment i = 1, 2, 3, …, n, where n is the number of experiments. The effect of factor f is defined as follows:The SNR (flE = sum of iη for factor f at level l (5)i is the experiment number, f is the factor name, and l is the level number.where mp generates a random number in . If the number is less than mp at the ith element in an offspring specimen, the ith element will be mutated by a randomly generated possible value.The mutation operation aims to prevent the population from falling into local optima. In all suitable solutions, each offspring element has a chance to undergo a mutation operation. Each mutation position with a probability of mutation p chromosomes in the population size are selected as the new population for the next generation, where p is the population size.The replacement operation uses an individual to replace the weakest individual in the population. After the completion of the aforementioned operations, the offspring are added to the population, and then, all the parents and offspring are ranked based on their fitness values. Subsequently, top The HTGA operation is repeated in successive iterations until the stopping criterion is met. In this study, a maximum number of iterations was used to terminate HTGA operations.c1 and c2 were set to 2. In the tested GA and the proposed HTGA, the probability of crossover (cp) with an exchange probability was 0.3 and the probability of mutation (mp) with an exchange probability was 0.05.This study compared the search effectiveness of the HTGA with that of standard GA, particle swarm optimization (PSO) , and chaThe OR was used to evaluate the risk of an SNP barcode , and it TP × TN) / (FP × FN) (6)OR = , including 26 SNPs, were used as simulation data to evaluate existing algorithms and the proposed HTGA. The data set only provided the genotype frequencies of each SNP without the original raw data of genotypes. SNP1 , the total number of three genotypes  in the control was 2273 (2008 + 259 + 6). The percentage for each genotype in SNP1 was calculated as “original data*/sum (%)” , where the symbol “*” indicates that the original data were derived from the SNP data set before normalization. On the basis of this percentage, the modified data for SNP1 were calculated by multiplying the percentage with the sum of the complete data set (SNP number adjusted to 5000) . Therefore, the modified data for SNP1 were adjusted to a total of 5000 (4418 + 569 + 13 = 5000). Thus, 5000 simulation samples of SNP genotypes were randomly generated by following fixed distribution.A set of 26 SNPs related to growth factor genes was selected to simulate a data set. Several growth factor–related breast cancer genes (IGF1R] and rs2854744-CC [IGFBP3]) showed significant protection associations  for breast cancer. The minimum and maximum protection associations exhibited ORs of 0.82 and 0.88, respectively, and the other SNPs showed nonsignificant protection associations for breast cancer.P<.05 indicates a significant difference for the SNP barcode between cases and controls. The identified 3-SNP to 7-SNP barcodes showed that the HTGA provided values with a greater degree of difference as compared with PSO, CPSO, and the GA, indicating that the HTGA identified relevant SNP barcodes with protection associations more effectively (P=.003) for protection associations with breast cancer. The 2-SNP and 3-SNP barcodes in PSO, CPSO, and the GA showed significant differences between cases and controls . The 4-SNP barcodes in CPSO and the GA showed significant differences , and the 5-SNP barcode in the GA also showed a significant difference (P=.03). Although CPSO and the GA provided better ORs as compared with the HTGA in all SNP barcodes, the degrees of difference indicated that the SNP barcodes identified by the HTGA were superior to those identified by other methods, and P values >.05 indicated that these differences revealed by the models were not significant.We compared PSO , CPSO 3, and theectively . HTGA-idOptimization algorithms have been widely applied to detect relevant high-order SNP barcodes in disease and cancer studies ,25,34. DMany breast cancer studies have identified the associations among the effects of important related genes -42, inclAn HTGA was proposed to effectively identify relevant SNP barcodes among genes related to breast cancer. The study results demonstrated that the HTGA could effectively detect SNP barcodes for problems with numerous high-order SNP barcode combinations. The proposed Taguchi method can improve the GA via the identification of high-dimensional SNP barcodes, and hence, it is integrated following GA crossover operations to systematically optimize chromosomes and thus enhance their values. Moreover, the HTGA can effectively converge to a promising region within the problem space and provide excellent SNP barcode identification. In this study, large data sets were used to evaluate and compare the performances of the GA, PSO, CPSO, and the HTGA, and the results indicated that the HTGA can effectively identify relevant high-order SNP barcodes in breast cancer.


ROH), excess of homozygosity (FHOM), correlation between uniting gametes (FUNI), and diagonal elements of the genomic relationship matrix (FGRM); 2) to identify frequently occurring ROH (i.e. ROH islands) for our selection signature and gene enrichment studies.Consecutive homozygous fragments of a genome inherited by offspring from a common ancestor are known as runs of homozygosity (ROH). ROH can be used to calculate genomic inbreeding and to identify genomic regions that are potentially under historical selection pressure. The dataset of our study consisted of 254 Azeri (AZ) and 115 Khuzestani (KHZ) river buffalo genotyped for ~ 65,000 SNPs for the following two purposes: 1) to estimate and compare inbreeding calculated using ROH . In the KHZ breed, the genes located in ROH islands were enriched for multiple Gene Ontology (GO) terms (P ≤ 0.05). The genes located in ROH islands were associated with diverse biological functions and traits such as body size and muscle development (BMP2), immune response (CYP27B1), milk production and components , coat colour and pigmentation (PMEL and MYO1A), reproductive traits , and bone development (SUOX).In this study, 9102 ROH were identified, with an average number of 21.2 ± 13.1 and 33.2 ± 15.9 segments per animal in AZ and KHZ breeds, respectively. On average in AZ, 4.35% (108.8 ± 120.3 Mb), and in KHZ, 5.96% (149.1 ± 107.7 Mb) of the genome was autozygous. The estimated inbreeding values based on FROH was in line with expected higher inbreeding in KHZ than in AZ because of the smaller effective population size of KHZ. Thus, we find that FROH can be used as a robust estimate of genomic inbreeding. Further, the majority of ROH peaks were overlapped with or in close proximity to the previously reported genomic regions with signatures of selection. This tells us that it is likely that the genes in the ROH islands have been subject to artificial or natural selection.The calculated F Bubalus bubalis) and the African wild buffalo (Syncerus caffer), the second of which is also known as the cape buffalo [B. bubalis, including of the river  and swamp  subspecies, occurred approximately 3000–6000 years ago [There are two main species of buffalo: the Asian water buffalo  has been the most common parameter for describing the level of inbreeding since Wright [PED depends on the completeness and correctness of pedigree. With the availability of high-density SNP-chip markers, inbreeding can also be defined according to genomic information such as genome-wide autozygosity [ROH) can discriminate between homozygous  and non-autozygous  positions in the genome [ROH. Thus, using genetic markers instead of pedigree information to calculate inbreeding can produce more robust estimates [The inbreeding coefficient measured from pedigree information  in 40 GO terms. These GO terms belonged to 23 biological processes (BP), 12 cellular component (CC) and 5 molecular function (MF) groups  between SNPs with a distance up to 100 Kb , short he) of AZ and KHZ breeds and the consequent increased rate of inbreeding since 20 generations ago [As presented in Table ions ago .The portion of the genome that was autozygote in the AZ and KHZ breeds was lower than the reported ROH coverage in the Marchigiana beef breed (7%) , AustriaHOM, FUNI and FGRM were higher in AZ than they were in KHZ. However, the previously reported Ne for AZ (477) was larger than it was for KHZ (212) [ROH, which showed lower inbreeding for AZ (0.043) than for KHZ (0.059).On average, FHZ (212) . TherefoUNI and FROH (AZ = 0.98 and KHZ = 0.94). Literature has reported different correlation coefficients between FUNI and FROH (0.15–0.80) [HOM and FROH (0.06–0.95) [GRM and FROH (0.17–0.81) [HOM, FUNI and FGRM on allelic frequencies [The highest correlation was observed between F15–0.80) , between06–0.95) , 21, 27,17–0.81) , 38. Thequencies .PED of 0.03 previously reported in Iranian buffalo [ROH in the current study. Given that pedigree data were not available for our study, we could not calculate FPED and compare it with FROH. However, previous studies reported moderate to high (0.47–0.82) and low to moderate (0.12–0.76) correlations between FPED and FROH in cattle and sheep, respectively [PED and FROH was also reported by Peripolli et al. [PED may not accurately capture small IBD segments that result from ancient inbreeding. Further, accurate and in-depth pedigree records are required to measure FPED. Additionally, methods based on allelic frequency have demonstrated considerable variation among different breeds [The F buffalo  was loweectively , 17. A li et al.  in Gyr ct breeds . Given tP > 0.05). In the KHZ breed, however, the genes located in the ROH islands were significantly enriched (P ≤ 0.05) in 40 GO terms  in the BP group. Among these genes, STAT6  has been reported to have large effects on the growth efficiency and the quality of carcass in cattle . AdditioTwelve genes in ROH islands were associated with lipid metabolic process (GO:0006629) in the BP group. Of these genes, BMP2  plays a major role in rebuilding hair follicles in goats . FurtherCYP27B1 (cytochrome P450 family 27 subfamily B member 1) located on BTA5 was also one of the genes enriched in the lipid metabolic process (GO:0006629). This gene is important for making 1-α-hydroxylase, which is required in vitamin D bio-activation, and has been reported to be up-regulated as a result of bacterial infection, suggesting that this gene plays a role in modulating innate immune responses [In ROH islands on BTA13, three genes were associated with the positive regulation of DNA replication (GO:0045740) in the BP group. Proliferating cell nuclear antigen (PCNA) has been reported to be associated with follicular development and growth in buffalo ovaries , and mayP = 0.05) in ROH islands, including MARS , and ADRA1D . MARS has been reported to influence milk and protein production in Chinese [Single-organism cellular process GO:0044763) with 64 genes was significantly enriched  between adjacent SNPs in ROH islands was higher than the r2 of adjacent SNPs located on the same chromosome , which are north-western provinces of Iran. Samples of the KHZ breed were obtained from Kermanshah  and Khuzestan , which are the south and south-western provinces of Iran, respectively  were stored. According to the filtration criteria, samples with more than 5% missing genotype and SNPs with 5% missing rate were eliminated from further analyses. We also filtered out SNPs with unidentified position in the UMD3.1 assembly, positioned on the sex chromosomes, with minor allele frequency of < 2%, and with p-value for the Hardy–Weinberg equilibrium chi-square test < 10− 6. In total, 62,122 SNPs and 369 samples with an average call rate of 99.6% passed the quality-control filters.The SNPs in the 90 K SNPChip were selected using buffalo DNA sequence, but similar to methods used in previous studies , 72–75, http://www.R-project.org/). According to the first and second PCs, we removed four samples: two from each breed that were placed outside their expected breed cluster.Genetic distance, which is based on the IBS matrix, was estimated through the --ibs-matrix command in PLINK v1.9 . PrincipROH can be detected in the genome through two main approaches: 1) genotype-counting algorithms in which the genome is scanned to identify long stretches of consecutive homozygous genotypes like the one implemented in PLINK v1.9 , and 2) n = 252) and KHZ (n = 113) breeds, separately. For each individual, ROH segments with the following attributes were identified: 1) each ROH stretch was at least 1 Mb in length; 2) there was at the most only one heterozygous and one missing SNP in each ROH; 3) there was a minimum number of SNPs that could form ROH in each breed, calculated according to Eq. l is the minimum number of SNPs in ROH, α is the false positive rate of the identified ROH (set at 0.5); na and ns are the number of individuals and the number of SNPs per individual, respectively; and het is the average heterozygosity across individuals. l was calculated to be 40 and 38 in AZ and KHZ breeds, respectively; 4) each ROH contained at least one SNP over 100 Kb; and 5) the maximum gap between two neighbouring SNPs in ROH had to be less than 1 Mb.A genome scan for ROH was conducted for the AZ ( KHZ n = 13 breedshttp://www.R-project.org/).The ROH that had these five attributes were divided into the following five groups: 1–2, 2–4, 4–8, 8–16 and > 16 Mb, as suggested in the literature , 15, 25.ROH), excess of homozygosity (FHOM), correlation between uniting gametes (FUNI) and diagonal elements of the genomic relationship matrix (FGRM).The coefficient of inbreeding was estimated using ROH .FGRM ; and n is the total number of SNPs. The Pearson’s correlation coefficient between FROH and the other genomic inbreeding estimates was also calculated.We also calculated the following three different genomic inbreeding estimations: FGRM Eq. , FHOM  were nominated ROH islands, as suggested in previous studies , 67. Furhttps://www.ncbi.nlm.nih.gov/mapview/). Additionally, to find significantly enriched Gene Ontology (GO) terms (P ≤ 0.05) of the genes located in ROH peaks, we used DAVID v6.8 tool [To identify genes in ROH islands, we used UMD3.1 map viewer from the NCBI website  that had already been published for AZ and KHZ breeds . An iHS Additional file 1. List of the detected runs of homozygosity (ROH) in Azeri (AZ) and Khuzestani (KHZ) breeds.Additional file 2. The estimated inbreeding coefficient using runs of homozygosity (ROH) > 1 Mb (FROH), diagonal elements of genomic relationship matrix (FGRM), excess of homozygosity (FHOM) and correlation between uniting gametes (FUNI) in Azeri (AZ) and Khuzestani (KHZ) breeds.Additional file 3. Frequently occurring runs of homozygosity (ROH) regions (i.e. ROH islands) in Azeri (AZ) and Khuzestani (KHZ) breeds. The last column represents the average r2 of ROH islands divided by the average r2 of each chromosome.Additional file 4. The genes located in detected runs of homozygosity (ROH) islands in the Khuzestani (KHZ) breed that were significantly enriched (P ≤ 0.05) in biological processes (BP), cellular component (CC) and molecular function (MF) Gene Ontology (GO) terms.Additional file 5. List of integrated haplotype homozygosity scores (iHS) for all SNPs in Azeri (AZ) and Khuzestani (KHZ) breeds.Additional file 6. Manhattan plot of integrated haplotype homozygosity score (iHS) across the genome.


Bemisia tabaci. We identified 38 neuropeptide precursor genes and 35 neuropeptide receptors and constructed a phylogenetic tree using additional data from other insects. As temperature adaptability enables B. tabaci to colonize a diversity of habitats, we performed quantitative polymerase chain reaction with two temperature stresses (low = 4 °C and high = 40 °C) to screen for temperature‐sensitive neuropeptides. We found many neuropeptides and receptors that may be involved in the temperature adaptability of B. tabaci. This study is the first to identify B. tabaci neuropeptides and their receptors, and it will help to reveal the roles of neuropeptides in temperature adaptation of B. tabaci.Insect neuropeptides play an important role in regulating physiological functions such as growth, development, behavior and reproduction. We identified temperature‐sensitive neuropeptides and receptor genes of the cotton whitefly,  Bemisia tabaci  is distributed in tropical, subtropical and some temperate regions of the world. It is an insect pest  approach, we confirmed the presence of mature neuropeptides encoded by some of these precursors in B. tabaci. We analyzed the expression profiles of neuropeptides and receptor genes in B. tabaci under different temperatures.In this study, we identified neuropeptide precursor genes and their receptors by analyzing the published genome and transcriptome of B. tabaci were collected from cotton plants in a greenhouse in Jinan, Shandong Province, China in 2012. The laboratory population of B. tabaci MEAM1 was reared inside a cage (400 mm × 500 mm × 450 mm) in a greenhouse maintained at 27 °C ± 0.5 °C and a 16 : 8 (L : D) photoperiod. In the temperature treatments, B. tabaci were separately reared on tomato seedlings at 4 °C, 27 °C and 40 °C for 1 h and 4 h, respectively.Acyrthosiphon pisum  and the prediction of mature peptides. The predicted neuropeptide receptors of B. tabaci were based on the predicted receptor genes of Diaphorina citri  and the Whitefly Genome Database (http://www.whiteflygenomics.org/cgi-bin/bta/index.cgi). Upon comparing the sequences of the genome alignment, we selected the high hit scaffold sequence and used the Softberry website (http://linux1.softberry.com/) to predict the protein sequence of target neuropeptide genes.Based on data from xus Ons, , TriboliB. tabaci receptors were constructed with B. mori, D. melanogaster, D. citri, N. lugens, T. castaneum and Z. nevadensis data. The sequence name used is the same name as used in the literature and the amino acid sequences are shown in Supplementary data S1. We used the TMHMM Server, v.2.05 (http://www.cbs.dtu.dk/services/TMHMM//) website to predict the transmembrane regions and then deleted the non‐transmembrane regions before aligning the different sequences. Then, all processed sequences were aligned using ClustalX2 software with default settings. A neighbor‐joining tree was constructed in MEGA 5.2 with 1000 bootstrap replicates .Phylogenetic trees of B. tabaci adults were taken from the laboratory population and transferred to a grinding tube with a protein lysing solution (8 mol/L urea containing a protease inhibitor cocktail). The whiteflies were ground using a high throughput tissue grinder  and triturated three times for 40 s each. The mixture was left on ice for 30 min, centrifuged once to obtain the supernatant, and then centrifuged a second time for ultrafiltration. The collected flow‐through solution was desalted using a Waters Oasis HLB μElution Plate 30 μm desalting column , followed by drying in a freeze‐concentration dryer . Ten percent of the samples were dissolved in 60 μL of distilled water and their protein concentrations were estimated using a NanoDrop 2000 spectrophotometer .μElution Plate 30 μm (Waters) and then applied to EASY‐nLC 1200 and Q‐Exactive (Thermo Fisher). The data acquisition software used was Thermo Xcalibur 4.0 (Thermo Fisher). The chromatographic separation time was 90 min and flow rate was 300 nL/min. MS scan range (m/z) was 350–1300, acquisition mode data‐dependent acquisition and the 20 strongest signals were selected in the parent ion for secondary fragmentation. Primary MS resolution was 70 000; the fragmentation method was high‐energy collision‐induced dissociation; the secondary resolution was 17 500; the dynamic exclusion time was 18 s. We used PEAKS Studio 8.5 to search for parameters. Dynamic modification selects oxidation (M), and acetyl (Protein N Terminus). The value of the enzyme name was set to unspecific. Precursor mass and fragment mass tolerance were set to 10 ppm and 0.05 Da, respectively. The results of MS were obtained directly from the neuropeptide gene database of the whitefly by the BLAST program.The polypeptide samples were purified by Oasis MCX B. tabaci adults. The quantitative polymerase chain reaction (qPCR) primers (Supplementary data http://bioinfo.ut.ee/primer3-0.4.0/) and synthesized by Sangon Biotech . The primer efficiencies were between 90% and 110%. Succinate dehydrogenase complex subunit A (SDHA) was selected as the housekeeping gene for the qPCR  were determined based on three biological replications.Three biological replicates, taken as three independent samples, were performed for each treatment and analyzed using Excel 2016 and SPSS 20. Student's B. tabaci genome and transcriptome data, we predicted and annotated 38 neuropeptide precursors , which was type A . However, in The stress responses of whitefly neuropeptides, under different temperature treatments, were measured using qPCR , and its function includes cardiostimulation and the inhibition of synthesis of RNA, fatty acids and proteins in the fat body  used by B. tabaci to adapt to temperature stress.In conclusion, 38 neuropeptide genes were predicted and identified in The authors declare they have no conflicts of interest.Supplementary data S1 Data of neuropeptide G‐protein‐coupled receptor (GPCR) genes. Bt, B. tabaci; Bm, B. mori; Dc, D. citri; Dm, D. melanogaster; Nl, N. lugens; Tc, T. castaneum; Zn, Z. nevadensis.Click here for additional data file.Supplementary data S2 Primers used for real‐time quantitative polymerase chain reaction.Click here for additional data file.Supplementary data S3 Predicted structures of neuropeptide precursors of Bemisia tabaci. Predicted signalpeptides (highlighted in yellow), cleavage signals (red), putative bioactive mature peptides (light blue), amidation signals (pink), N‐terminal Glutamate (Q) to Pyroglutamate (pQ) conversion (green) and cysteine residues (deep yellow) are indicated.Click here for additional data file.Supplementary data S4 The expression of neuropeptide precursor and receptor genes under temperature treatment in Bemisia tabaci. The quantitative real‐time polymerase chain reaction analysis results of neuropeptides in B. tabaci. Data are presented as means ± SE based on three independent experiments .Click here for additional data file.Table S1 Neuropeptide G‐protein‐coupled receptor (GPCR) genes putative identified from Bemisia tabaci comparison with Diaphorina citri, Drosophila melanogaster and Zootermopsis nevadensis.Click here for additional data file.


S. Pullorum infection in chickens. The results suggested that the gut microbial structure is altered in S. Pullorum-infected chickens.The present study investigated the association among the host genome, the gut microbiome, and  Salmonella enterica subspecies enterica serovar Gallinarum biovar Pullorum (S. Pullorum); it negatively affects the poultry industry. However, limited studies have explored the association between the gut microbiota and S. Pullorum infection in chickens. In the present study, we performed a microbiome comparison and a microbiome genome-wide association study (mGWAS) to investigate the association among the host genetics, the gut microbiota, and pullorum disease in chickens. We found that S. Pullorum infection in chickens could alter the abundance of 39 bacterial genera (P < 0.05). The altered structure and composition of the gut microbiota were also detected in the offspring. mGWAS results revealed host genetic variants to be prominently associated with gut microbial diversity and individual microbes. The pathogens Pelomonas and Brevundimonas, which had a high abundance in positive parent chickens and their offspring, were significantly associated with several genetic mutations in immunity-related genes, such as TGIF1, TTLL12, and CCR7. This finding explained why Pelomonas and Brevundimonas were heritable in S. Pullorum-infected chickens. The heritable gut microbes and identified genetic variants could provide references for the selection of resistant chickens and the elimination of pullorum disease.Pullorum disease is one of the most common diarrhea-related diseases caused by IMPORTANCE The present study investigated the association among the host genome, the gut microbiome, and S. Pullorum infection in chickens. The results suggested that the gut microbial structure is altered in S. Pullorum-infected chickens. The diversity and abundance of the gut microbiota remarkably differed between the offspring coming from S. Pullorum-positive and S. Pullorum-negative chickens. Heritable gut microbiota were detected in the offspring. Moreover, host genetic variants were associated with microbial diversity and individual gut microbes. The pathogens Pelomonas and Brevundimonas, which exhibited a high heritability in S. Pullorum-positive parents and their offspring, were associated with several genetic mutations in immunity-related genes. Salmonella enterica subspecies enterica serovar Gallinarum biovar Pullorum (S. Pullorum)  , 2. Sincullorum) , 4. Thisullorum) . PulloruPullorum . Some inPullorum . In 1927ividuals . Althougachieved , the outindustry –14. Thisndustry –. TherefoSalmonella, the pathogens compete with the gut microbiota and overcome the host immune defenses  to explore the interaction between the microbiota and host genetic variants  and their respective offspring  was carried out to assess the association between the gut microbial composition and S. Pullorum infection. mGWAS was used to evaluate the contribution of host genomic loci to microbial beta diversity and the abundance of individual microbes. Our discovery provides more information to identify heritable gut microbiota and potential genetic loci associated with S. Pullorum infection and could help in the elimination of infected chickens and the selection of resistant chickens.In the present study, we performed a microbiome comparison and mGWAS to investigate the association among the host genetics, the gut microbiota, and pullorum disease in chickens . MicrobiS. Pullorum infection on the gut microbiome of chickens, the microbial composition in groups N and P was compared. Nineteen phyla were detected in the two groups. The dominant phyla were Firmicutes (65.5% in group N and 62.1% in group P), Fusobacteria (16.3% in group N and 18.7% in group P), and Proteobacteria (9.37% in group N and 9.95% in group P) (Lactobacillus (Firmicutes), Fusobacterium (Fusobacteria), Peptoclostridium (Firmicutes), and Gallibacterium (Proteobacteria) (P < 0.05), with the difference being significant in the case of 33 out of 39 genera (P < 0.01) (Klebsiella (Proteobacteria), Neisseria (Proteobacteria), Enhydrobacter (Proteobacteria), Leuconostoc (Firmicutes), Faecalibaculum (Firmicutes), Enterococcus (Firmicutes), and Mobilitalea (Firmicutes) were enriched in group N, while Anaerobiospirillum (Proteobacteria), Deinococcus (Deinococcus-Thermus), Phascolarctobacterium (Firmicutes), Brevundimonas (Proteobacteria), Pelomonas (Proteobacteria), Oscillibacter (Firmicutes), and Serratia (Proteobacteria) were more abundant in group P than in group N. The metabolic pathways of Staphylococcus aureus infection, beta-lactam resistance, and penicillin and cephalosporin biosynthesis (related to infectious diseases and the biosynthesis of other secondary metabolites) were more enriched in group N than in group P (P < 0.05) (To examine the effect of group P) . The preacteria)  and d. M < 0.01) . Klebsie < 0.05)  and f.S. Pullorum infection on the gut microbiota of the offspring, we compared the gut microbial composition between the offspring in groups OP and ON. The alpha diversity indices abundance-based coverage estimator (ACE) and Chao1 suggested that the community richness of group OP was remarkably lower than that of group ON (P < 0.05)  (Lactobacillus was more abundant in group ON (62%) than in group OP (53%), while the percentage of Enterococcus, Fusobacterium, and Helicobacter (Proteobacteria) was greater in group OP than in group ON (P < 0.05) (Corynebacterium (Actinobacteria), Novosphingobium (Proteobacteria), Vibrionimonas (Bacteroidetes), Aeribacillus (Firmicutes), and Enterococcus, was higher in group OP than in group ON (P < 0.05) (Kurthia (Firmicutes), Acidovorax (Proteobacteria), and Comamonas (Proteobacteria), were abundant in group ON  . By appl < 0.05) . Of the  < 0.01) . At the group ON . Forty-o < 0.05) . The abu < 0.05) . On the group ON . Pelomongroup ON .10.1128/mSystems.01192-20.1FIG S1FIG S1, TIF file, 0.9 MB.Gut microbial characteristics of groups OP and ON. Download Copyright © 2021 Ding et al.2021Ding et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.01192-20.2TABLE S1Table S1, DOCX file, 0.01 MB.Gut microbiota with significant difference between groups OP and ON at the phylum level. Download Copyright © 2021 Ding et al.2021Ding et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.01192-20.3TABLE S2Table S2, DOCX file, 0.02 MB.Gut microbiota with significant difference between groups OP and ON at the genus level. Download Copyright © 2021 Ding et al.2021Ding et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P < 3.1 × 10−7) were identified in group P  27, which is located at the intergenic region of IKZF3 and ZPBP2   ( group P . The mosnd ZPBP2 . Seventend ZPBP2 . Of thesMAPKKK3L . In grou group P . Meanwhi × 10−7) . In this × 10−7) . Accordi × 10−7) .10.1128/mSystems.01192-20.4TABLE S3P < 3.1 × 10−7) in groups P and N. Download Table S3, XLSX file, 0.03 MB.Significant SNPs  with 177 bacterial genera (Facklamia (Firmicutes). This SNP was close to NUDT14 (P < 0.05) (Bilophila (Proteobacteria) was associated with 25 genes, such as SRPRB, ZP3, and ACAA1  and Phascolarctobacterium were correlated with 16 and 11 genes, respectively , and genera used for mGWAS is provided in l genera . The moso NUDT14 . Ten of  < 0.05) . Of thesnd ACAA1 . Victivaectively . Pelomonf PSMD14 . Furthercillales .10.1128/mSystems.01192-20.6TABLE S5Table S5, DOCX file, 0.01 MB.Numbers of experimental animals, SNPs, OTUs, and genera and the significant thresholds used for mGWAS. Download Copyright © 2021 Ding et al.2021Ding et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.01192-20.7TABLE S6Table S6, XLSX file, 0.2 MB.Identification of host genetic variants associated with the gut microbiota. Download Copyright © 2021 Ding et al.2021Ding et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Brachybacterium (Actinobacteria) (P = 1.29 × 10−11). This SNP was located at the intergenic region between RFC3 and PDS5B .In group N, 1,118 SNPs were prominently linked to 181 genera . The mosnd PDS5B . Moreoveilitalea . The abu of PBX3 . The rem PLEKHJ1 . Further PLEKHJ1 . Among tS. Pullorum infection in chickens. Microbiome comparison revealed that S. Pullorum infection in chickens altered the gut microbial composition, resulting in variation of the microbial metabolic function. The abundance of 39 bacterial genera differed between groups P and N. Moreover, compared to group ON, group OP showed a remarkable difference in microbial composition and a high abundance of potentially harmful bacteria. Pelomonas and Brevundimonas exhibited heritability in the offspring coming from S. Pullorum-infected chickens. Pelomonas has been reported to be the dominant bacterium in patients with serious inflammatory bowel disease , by stimulating the Toll-like receptor , is associated with susceptibility to S. Pullorum infection  used in the present study were obtained from the Animal Husbandry and Veterinary Research Institute, Shanghai Academy of Agricultural Science, Shanghai, China. None of the chickens had been treated with antibiotics. The wing venous blood and feces of 275 hens  were collected. Ten positive roosters were mated with 10 positive hens, and 10 negative roosters were mated with 10 negative hens to obtain their respective offspring. All the chickens were maintained at the same location and fed the same diets. Eighty fecal samples of the offspring were collected at the age of 10 days (40 in group ON and 40 in group OP). In total, 355 fecal samples were collected and stored at −80°C. The protocols in the present study were approved by the Laboratory Animal Research (ILAR) guide for the care and use of laboratory animals at Shanghai Jiao Tong University, China.S. Pullorum infections in chickens were diagnosed by S. Pullorum and S. Gallinarum polyvalent antigen rapid slide agglutination test reagents . In brief, 50 μl of polyvalent antigen and 50 μl of venous blood were placed on a clean glass slide. The antigen and blood were thoroughly mixed and smeared into a circle of 2 cm on the glass slide. The samples were considered positive if 50% or more agglutination occurs in the mixture within 2 min. Samples without agglutination were deemed negative.ACTCCTACGGGAGGCAGCA; reverse primer 806R, GGACTACHVGGGTWTCTAAT). The PCR conditions were 98°C for 2 min; 98°C for 15 s; 55°C for 30 s and 72°C for 30 s, repeated for 30 cycles; and 72°C for 5 min. PCR amplicons were excised from a 1.5% agarose gel and purified using the QIAquick gel extraction kit . Purified PCR products were combined at equal concentrations and used to construct a metagenomic library using the Illumina TruSeq sample preparation kit (Illumina) according to the manufacturer's protocol. Sequencing was performed by Shanghai Personal Biotechnology Limited Company  using the Illumina MiSeq sequencing platform (Illumina).Microbiome DNA was isolated from the fecal samples using the Tiangen DNA stool minikit  by following the manufacturer’s instructions. The extracted DNA was quantified on a NanoDrop spectrophotometer (Thermo Scientific). The DNA samples were stored at −20°C for further analysis. The V3-V4 regions of the 16S rRNA of all the fecal samples were amplified by PCR using barcoded fusion primers  pipeline was employed to process the sequencing data, as described previously . In brieThe filtered high-quality sequences were clustered into operational taxonomic units (OTUs) at 97% sequence identity using UCLUST . A repret test and Benjamini-Hochberg false discovery rate (FDR) (P < 0.05)   correcti < 0.05) . OTUs we < 0.05) .P value of >1 × 10−6 using PLINK. A final set of 159,272 SNPs that passed the quality control assessment were used for further analyses.The genomic DNA of 135 chickens in group P and 140 chickens in group N was extracted from their venous blood using the TIANamp blood DNA kit . The DNA was used to construct double-digest genotyping-by-sequencing (dd-GBS) libraries , which wP values that passed the genome-wide significance threshold (0.05/SNP number) were considered significant. SNPs were annotated using ANNOVAR  using logistic regression with chi-square-based P value estimation. The binary and quantitative models were used for groups N and P, respectively. The genome-wide significance threshold for the association was set at 0.05/SNP number. The suggestive significance level was determined by 1/SNP number.To identify the genetic variants in groups P and N that were associated with the abundance of individual gut bacteria, a statistical test was performed for each association between SNPs and the taxa. The analysis was performed using the MiBioGen miQTL pipeline . In briePRJNA679403.Raw read sequences are publicly available in the Sequence Read Archive at National Center for Biotechnology Information (NCBI) under the BioProject accession number


Genetic variability among native cattle breeds can disclose the important features that make a population adapted to harsh environments. The Aosta cattle breeds have been raised and selected for centuries to be farmed in a mountain environment, characterized by a semi-intensive system, i.e., summer pasture with winter recovery on the farms. To disclose the genomic variation and its association with known genes, it is important to genetically characterize these breeds.ST values, ADMIXTURE and Principal Component Analysis (PCA) results. Runs of Homozygosity (ROH) were obtained for the three populations to disclose recent autozygosity. The genomic inbreeding based on the ROH was calculated and coupled with information derived from the F (inbreeding coefficient) and FST parameters. The mean FROH values were low: CAS = 0.06, VBP = 0.05 and VRP = 0.07, while the average F values were −0.003, −0.01 and −0.003, respectively. The annotation and enrichment analysis, performed in the identified most frequent ROH (TOP_ROH), showed genes that can be linked to the resilience capacity of these populations to harsh environmental farming conditions, and to the peculiar characteristics searched for by farmers in each breed.The Aosta Red Pied ), the Aosta Black Pied ) and the Aosta Chestnut ) are dual-purpose cattle breeds (meat and milk), very well adapted to the harsh environmental conditions of alpine territories: their farming is in fact characterized by summer pasture at very high altitude. A total of 728 individuals were genotyped with the GeenSeek Genomic Profiler® (GGP) Bovine 150K Illumina SNP chip as a part of the DUALBREEDING-PSRN Italian-funded research project. The genetic diversity among populations showed that the three breeds are distinct populations based on the F Animal genetic resources play an important role in local economies and in maintenance of territories and landscapes . Among tBattailes des Reines” has a central role for farmers and the Aosta valley’s cultural value and sociality [Aosta breeds are the Aosta Red Pied ), the Aosta Black Pied ) and the Aosta Chestnut ). All three are dual-purpose cattle breeds and possess a considerable milk production (2019 average production per lactation: 3000 kg of milk for CAS and VBP and 4000 for VRP) in proportion to their body size . Even if their production selection goal includes milk and meat, their capacity to adapt to the harsh alpine environment, i.e., their functionality, has been strongly pursued by farmers for decades , as the ociality .ROH) and the inbreeding coefficient calculated by genealogical information. Several authors used ROH to explore genomic variation in cattle [ROH to identify hotspot regions , where the frequency of ROH is high, and coldspot regions, where no ROH is found .The availability of wide genome SNP genotypes on a large scale in cattle made it possible to obtain a detailed picture of the breeds’ genetic diversity across the genome, to investigate genomic variation among populations and to relate their genomic structure to the occurring selection that is undergoing in cattle populations. Runs of Homozygosity (ROH) are long tracts of homozygous DNA that were firstly identified in human populations by . McQuilln cattle ,11,12,13n cattle  discusseThe nationally funded project DUALBREEDING  had among its objectives the disclosure of genomic variation across Aosta breeds and its relationship with occurring selection. Among the activities of DUALBREEDING, all males and a part of the female population have been analyzed with SNP chips at a 150K density providing as such a solid reference genotype database.ROH based on the ROH and compare it with information derived from F (the inbreeding coefficient). Additionally, we aimed to investigate the genetic variability across breeds using Wright’s F statistics, a Principal Component Analysis and ADMIXTURE analysis. Finally, we aimed to annotate the genes mapped in the ROH in order to disclose the common and proprietary regions under selection in the three Aosta breeds.The aim of this study was to map ROH in the three Aosta breeds using the 150K SNP chip genotypes available from DUALBREEDING. A further objective was to calculate the FThis study did not require approval from the Animal Care and Use Committee.The Associazione Nazionale Allevatori Bovini di Razza Valdostana (A.N.A.Bo.Ra.Va.) provided genotypes of 728 individuals—male and females —obtained with the GGP Bovine 150K Illumina SNP chip. The sampling of the individuals was homogeneous across breeds regarding their population structure and reflects their actual population size. The genotyping is part of the national project DUALBREEDING, a pluriannual effort funded by the EU EAFRD and by the Ministry of Agriculture of Italy aimed at maintaining the biodiversity in cattle populations. The SNP genotypes were mapped on the ARS-UCD1.2 bovine reference genome. Out of the genotypes available on the SNP chip, those with a sample and marker call rate ≤0.90, without chromosomal position and on non-autosomal chromosomes, were deleted, leaving a total of 128,180 SNP markers for subsequent analyses.ST—is a measure of the total genetic variance that can be explained by the population structure; in other words, FST estimates the genetic divergence among subpopulations. The FST was estimated for three pairs of breed combinations . The expected (He) and observed (Ho) heterozygosity values were also calculated for each breed.Genetic diversity within and among breeds was determined through a Principal Component Analysis (PCA) and estimating the pairwise Fixation Index through the pipelines implemented in Golden Helix (SVS) 8.8.4 software . The Fixation Index—equivalent to Wright’s F-statistic F2  >  0.5 in a 50-SNP sliding windows in SVS software) in order to reduce the impact of the SNP ascertainment bias from linkage between loci.Determination of the most probable number of ancestral populations was obtained with ADMIXTURE v. 1.3.0 software . ADMIXTUST estimated for each SNP (marker-based FST), was also used and obtained with SVS on the same three breed pair combinations. Genomic regions can be considered being under different (positive) selection in a pair comparison if they contained a high proportion of highly differentiated SNPs based on the FST values  [ST > 0.5 were considered to be under different selection.In order to identify the genome-wide patterns under selection, the outlier loci approach, based on the F of 0.5) . DiffereROH analyses were performed separately for each population, using SVS software. No linkage disequilibrium (LD)-based pruning was performed and, as in , the minBos taurus: Annotation Release 105) was downloaded from NCBI [The full gene set , were catalogued into the TOP_ROH by overlapping . The samIdentity-by-descent (IBD) was obtained in order to assess the quality of the dataset, to identify the potential sample replicates and to estimate pair-wise comparison of relatedness (comprising first-degree relatives). IBD was calculated using SVS software on pruned SNPs.F [F values ranged from −1 to +1, representing an excess of heterozygosity and an excess of homozygosity, respectively, where an F value equal to 0 denotes the Hardy–Weinberg equilibrium across all markers. To compare inbreeding across breeds, the average F value was calculated for each breed.SVS software also provided the individual’s inbreeding coefficient F , which wROH) for each sample and for the three breeds was calculated separately, considering the following formula [ROH is the total length of all the proper ROH of an individual, and LAUT is the specified length of the autosomal genome covered by SNPs .In addition, the inbreeding coefficient based on the ROH  represented up the 94% of the total SNPs of all three breeds. The He and Ho values were similarly low among breeds: CAS (0.344 and 0.342), VBP (0.345 and 0.341) and VRP (0.339 and 0.338). The Principal Component Analysis (PCA) result is displayed in ST, showing that although there is a very low level of genetic differentiation, there is a clear and similar distinction between VRP vs. CAS (FST = 0.052) and VRP vs. VBP (FST = 0.050), and a close relationship between CAS vs. VBP (FST = 0.019). The average lower and upper 95% confidence interval differences from the pairwise breed FST values were 0.0003 and 0.0008, respectively.These results are confirmed by the pairwise breed comparisons using FTo investigate the ancestry composition of the Aosta breeds, ADMIXTURE analysis was run for values of possible ancestors (K) ranging from 2 to 6. The lowest CV value was obtained at K = 3 B. At thiST values, calculated for each marker across all chromosomes for each pair of comparison, is shown in ST value is >0.5  and CLOCK (n = 3) genes (The distribution of F3) genes , Sheet 2The ROH were identified in all 728 individuals of the three Aosta breeds, for a total of 36,400 homozygous regions. At the individual level, the average numbers of ROH per animal were 50, 45 and 53 for CAS, VBP and VRP, respectively , with a Differences among animals were also found considering the total length of the genome covered by the ROH : 6.10–381.55 Mb for CAS, 19.02–403.27 Mb for VBP and 7.31–557.46 Mb for VRP samples (data not shown).The ROH were identified for all classes of length and, generally, the lower coverages were in concordance with a low number of regions per samples. Although shorter regions (<2 Mb) are the most frequent classes of length  were identified in CAS (above the redline in KHDRBS2 (n = 33), CTNNA3 (n = 32), ADGRL3 (n = 27), CCSER1 (n = 26) and CACNA2D1 (n = 25) genes. In addition, three homozygote SNPs were annotated in missense positions of the PCDHA13 (Hapmap42803-BTA-110000-BTA7), KRBA2 (BovineHD1900008383-BTA19) and DNA2 (BTB-00981633-BTA28) genes.One thousand three hundred and fifty-five SNPs are involved in the definition of TOP_ROH and the summary indication of their annotated position on genome are shown in KHDRBS2 (n = 33) and CTNNA3 (n = 32), as for CAS. Missense SNP positions were annotated also in the VBP breed: MAP3K19 (BovineHD0200018075–BTA2), CYP4F2 (BovineHD0700002200–BTA7), KRBA2 (BovineHD1900008383–BTA19) and DNA2 (BTB-00981633-BTA28).In VBP, the genome regions that were the richest in TOP_ROH were identified on 13 autosomes, mainly on BTA6 and BTA23 . These 1MAP3K19 (BovineHD0200018075–BTA2), CFAP221 (ARS-BFGL-NGS-16745–BTA2) and GALNT6 (ARS-BFGL-NGS-110943–BTA5) genes. The highest number of homozygous SNPs were harbored within the THSD7B (n = 48) and the KHDRBS2 (n = 33) genes.The proper TOP_ROH of VRP were 11, identified on 8 chromosomes . SNPs deC5H12orf50, C5H12orf29, CEP290, TMTC3, KITLG and KHDRBS2) lied within the three regions in common among the three breeds (CAS–VBP–VRP). In addition, other 12 TOP_ROH were shared by two breeds: n = 5, CAS–VBP; n = 4, VBP–VRP; and n = 3, CAS–VRP.Within the CAS, VBP and VRP’s TOP_ROH, a total of 312, 212 and 162 genes were annotated, respectively. Among them, 6 genes  with respect to CAS_VRP and VBP_VRP, while the lowest one was between CAS and VRP (n = 11). Not all common TOP_ROH harbored genes. The annotation, performed with the DAVID database, is presented in The No sample duplication (pairwise IBD > 0.95) was identified for each breed. However, some samples belonging to CAS, VBP and VRP, with a genetic similarity greater than 50%, were found, suggesting a first-degree relationship between pairs of individuals A. The avF), were slightly negative (close to 0) in each population  to 0.224 (VRP), with the highest average value for FROH (0.067) in the VRP , as shown in Among The three Aosta breeds have been sharing the same environment and farming practices for centuries. Nowadays, after the structuring of the breeding activities in herd books, even if they are part of the same herd book association, they are managed as three different populations with different selection indexes. A common denominator is that their milk is used for the Fontina cheese production, a DPO product that, in its manufacturing specifications, includes the rule that only the milk from Aosta cattle breeds can be used.In term of selection, CAS and VBP are sharing a similar selection goal accounting for milk, meat and fighting ability. Differently, the dual-purpose selection of VRP is more oriented toward milk production . The difST (both at population and at marker levels), PCA and ADMIXTURE results.Even if the three breeds share the same environment and farming structure, they appear to be genetically different. The genetic relationship between CAS and VBP, previously reported using microsatellite markers , was conST statistic at the population level showed a value of genetic differentiation among the three breeds, which was around 0.05 when the comparison (both) involved VRP, but a lower one (FST = 0.019) between CAS and VBP. At the single-marker level, no differentiated SNPs (FST > 0.5) were identified in the CAS_VBP breed comparison, but several ones were found for the comparison of VRP with the other two breeds. These different values of FST can be affected by the origin of the three Aosta breeds: as known by historical evidences, the origin of VRP is independent from VBP and CAS. In fact, historical findings indicate that VRP was introduced in the area by the Burgundians in the 5th century AC.The FCLOCK) [CORIN) [LNX1) [CBFA2T3) [KIT and PDGFRA genes involved in melanogenesis and in coat color (spotting) (KIT) [PDGFRA) [It is interesting to mention that these genomic regions are harboring genes involved in the mammalian circadian rhythms regulation (CLOCK) , feed ef [CORIN) , marblin) [LNX1)  and immuCBFA2T3) . It is wg) (KIT) , as well[PDGFRA) , respect[PDGFRA) ,33.The genetic distinctness of the three populations is supported also by the PCA, showing clearly that the three breeds cluster separately: VBP and CAS are both placed on the same spatial position according to PC_1 A, explaiThe ROH analysis is particularly interesting in these three populations as it allows to disclose recent inbreeding caused by the managing of the populations, for example, by performing artificial insemination and with structured breeding plans, even if the selection scheme was carefully evaluated and planned to minimize the increasing of inbreeding .The recent inbreeding’s loop produces small numbers of long ROH, influencing the sum of ROH much more than the total number of ROH itself. In the VRP, the regions linked to recent inbreeding are more frequent than the ones found in the other two breeds. In VRP, in fact, an ROH > 16 Mb has been found in 34% of samples , while in CAS and VBP, it was found in 26% and 30% of animals, respectively. Among the latter, 15 CAS and 12 VBP individuals had an ROH longer than 30 Mb.Although a similar proportion of homozygous SNPs was identified among the three breeds (around 67%—F and FROH).In VRP, a higher number of ROH and a longer size with respect to CAS and VBP were identified, mainly as a consequence of no introduction of animals from other regions. This is not the case for CAS, who has been recently recognized as genetically similar to the Hérens breed. The possibility to enroll progeny of the two breeds in the studbook of any of them was recently approved by the two breeder’s associations. This recent advance in reciprocal recognition occurred after some generation of known exchange of reproducers. In fact, already in 1929 , the clon = 94—31%) that shared the same TOP_ROH was identified. This TOP_ROH, defined by 18 homozygous SNPs (of which 5 are intronic), lies within the TECRL gene encoding for an enzyme involved in chemical reactions and pathways involving lipids, also reported to possibly play a role in puberty and female fertility in cattle [HOXA13, HOXA11, HOXA10, HOXA9, HOXA7, HOXA5, HOXA3 and HOXA4 are involved in the reproductive tract and in development and fertility in males and females [The ROH are not randomly distributed across the genome and there are regions with a high prevalence of ROH. In the CAS breed, on the BTA5 located around 79–80 Mb, the highest number of samples  and , respectively, shared intergenic and intragenic (KHDRBS2 gene) homozygous regions. The KHDRBS2 gene has been associated with fertility traits in goats [n = 150, 53.6%), 5  and 6 . Except for the most representative regions on BTA2, for which an SNP maps in the intronic position of the DARS1 gene, the other ones include SNPs lying closely to the KITLG (BTA5) and KIT (BTA6) genes, respectively.The second highest peak (TOP_ROH) found in CAS is located on chromosome 5 , where the e breeds ,41 and ie breeds ,43. In Vin goats  and in B and n = , 29.4%, in goats . This fiTHSD7B gene of the VRP’s TOP_ROH has 48 intronic SNPs annotated (https://genomics.neogen.com/en/ggp-hd150k-dairy). For this gene, classified as an integral component of membranes (GO:0016021), no association study, at the best of our knowledge, is available. Another example is represented by the KHDRBS2 gene on BTA23 (described above in the text), in which 33 homozygous intronic SNPs are annotated for all the three Aosta breeds at an average distance of 20.5 kb. Lastly, CTNNA3 (n = 32 intronic SNPs spacing 20 kb), both in CAS and VBP, and ADGRL3 (n = 27 intronic SNPs spacing 20.2 kb), CCSER1 (n = 26 intronic SNPs spacing 17.9 kb) and CACNA2D1 (n = 25 intronic SNPs plus a synonymous variant spacing 18.8 kb) in CAS are genes mostly annotated with homozygous SNPs. These genes were previously shown to be under positive selection and associated with marbling score in Korean cattle (CTNNA3) [ADGRL3) [CCSER1) [CACNA2D1) [We would like to underline the impact of selection on highly homozygous regions: several interesting genes in TOP_ROH include a large number of SNPs annotated within the gene itself. Although this information may be affected by the density of SNPs on the chip and by the length of the genes, it would be worth mentioning them. The nnotated , with an(CTNNA3) , with pr[CCSER1)  and withGABRA2, GABRA4, GABRB1 and GABRG1), was identified. These genes being the major inhibitory neurotransmitters in the mammalian brain, mediating anxiolytic activity and playing a key role in emotional and behavioral control in humans [Battailes des Reines”. We may assume that, during the non-ferocious match, the reaction to the visual and physical view of the opponent may be mediated by these groups of genes. The behaviors exhibited during the matches is the same that cows show in pastures when unfamiliar individuals from different herds meet at the beginning of the summer season.In VRP and VBP, a homozygous cluster on BTA6, involving GABA-A receptor subunit genes  on a large number of individuals and on a medium density SNP chip (150K), is indicating that farmers in their mating decisions still prioritized adaptation to the environment and thus the farming system envisaging summer pasture, a practice that have characterized these breeds for centuries. All three breeds were shown to have a large number of regions in autozygosity, harboring genes that appear to be linked to efficiency and functional traits, i.e., characteristics for adapting to the environment. As such, with respect to other populations, e.g., Holstein, where breeding plans have strongly acted on genomic regions linked to milk production, the genomic status of these populations shows that, for decades, breeders pursued among the selection objectives the adaptation to the environment, making these breeds resilient and efficient producers in a changing environment. The low extent of recent autozygosity found in this study is a tangible measure of the success of the reproductive scheme to implement selection in the Aosta breeds, as pursued in the last 30 years, aimed also at the maintenance of the genetic variability in these three breeds.