Solanum melongena L.; 2n = 24) is one of the most important Solanaceae vegetables and is primarily cultivated in China (approximately 42% of world production) and India (approximately 39%). Thousand-grain weight (TGW) is an important trait that affects eggplant breeding cost and variety promotion. This trait is controlled by quantitative trait loci (QTLs); however, no quantitative trait loci (QTL) has been reported for TGW in eggplant so far, and its potential genetic basis remain unclear. In this study, two eggplant lines, 17C01  and 17C02 , were crossed to develop F1, F2 (308 lines), BC1P1 (44 lines), and BC1P2 (44 lines) populations for quantitative trait association analysis. The TGWs of P1, P2 and F1 were determined as 3.00, 3.98 and 3.77 g, respectively. The PG-ADI (polygene-controlled additive-dominance-epistasis) genetic model was identified as the optimal model for TGW and the polygene heritability value in the F2 generation was as high as 80.87%. A high-quality genetic linkage bin map was constructed with resequencing analysis. The map contained 3,918 recombination bins on 12 chromosomes, and the total length was 1,384.62 cM. A major QTL (named as TGW9.1) located on chromosome 9 was identified to be strongly associated with eggplant TGW, with a phenotypic variance explanation of 20.51%. A total of 45 annotated genes were identified in the genetic region of TGW9.1. Based on the annotation of Eggplant genome V3 and orthologous genes in Arabidopsis thaliana, one candidate gene SMEL_009g329850  contains 4 SNPs and 2 Indels consecutive intron mutations in the flank of the same exon in P1. SmGTS1 displayed significantly higher expression in P1 and was selected as a potential candidate gene controlling TGW in eggplant. The present results contribute to shed light on the genetic basis of the traits exploitable in future eggplant marker-assisted selection (MAS) breeding.Eggplant ( Solanum melongena L., 2n = 24) are highly present in our daily lives and are important members of the Solanaceae family. The total global cultivation of eggplants utilized 1,857,664 ha in 2019; among this land, China utilized approximately 782,998 ha, and India harvested 727,000 ha . Eggplant seeds are important genetic propagules, and seed vigour, germination rate, colour and thousand-grain weight (TGW) are important targeted traits. TGW is especially important in eggplant seed production, as it affects eggplant breeding costs and the popularization of new varieties. There has been little research on the TGW of eggplant; in contrast, research on the genetic and molecular mechanisms of the TGW of other food crops has become increasingly important in plant science  Banfi, Galasso & Bartolucci), and the male parent (P1) was the cultivated eggplant “17C02” (S. melongena L.). The seeds were stored and supplied by the Eggplant Research Group of the Beijing Vegetable Research Center. “17C01” is a wild resource with many prickles, small round fruit , and small seeds, whereas “17C02” is an inbred line with few prickles, long rod fruit , and larger seeds. The P1, P2, F1, F2, BC1P1 and BC1P2 populations were used for the genetic analysis. The F1 population was generated from a cross between “17C01” and “17C02,” the F2 populations (308 seedlings) were generated from self-fertilization of F1, and the BC1P1 and BC1P2 populations, all containing 44 seedlings, were generated from F1 progeny backcrossed to P1 and P2, respectively. In the spring of 2018, all populations were grown in greenhouses at the experimental field of the Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry, Beijing, China, with 120 cm between rows and 50 cm between each plant. The eggplant plants were grown in a standard manner until the fruits totally matured, and the fruits of each seedling were harvested. One thousand seeds were randomly selected and weighed.The female parent (P1) was the wild eggplant “17C01” , BC1P1 (n = 44) and BC1P2 (n = 44) were tested via genetic analysis for TGW. After sowing, each eggplant seedling was individually numbered and shelfed separately, and the fruit were obtained 60 days after pollination. The seeds were harvested after 7 days of fruit post-maturation at room temperature. The TGW was measured by electronic scales . Each phenotyping process was repeated three times, and the average value was utilized for further analysis. Phenotypic data analysis was performed using IBM SPSS v19.0 . The significance level was 0.05.The six eggplant populations P1, P2, F1 (P1 × P2), F2 , were used to choose an optimal genetic model from the 24 genetic models. The 24 genetic models contained five types: the one-major-gene model, two-major-gene model, pure polygene model, one-major-gene plus polygene model, and two major-gene plus polygene model, and each type considered the additive, dominance, and epistasis effect of the major gene and polygene in various segregating generations. By using the method of least squares according to the relationship between the genetic parameters and the component distribution parameters of a given model, the estimates of genetic parameters, the additive, dominance, and epistasis effects of major gene(s), the total additive, dominance, and epistasis effects, the heritability of major genes, and the heritability of polygenes of the optimal genetic mode were calculated  completed the resequencing. DNA sequencing was performed on an Illumina HiSeq2500 . The clean reads were aligned to the S. melongena reference genome  (https://solgenomics.net/organism/Solanum_melongena/genome) assembly by using Burrows–Wheeler Aligner (BWA) software (p < 0.001 by chi-square test) were filtered.The research team constructed a genetic linkage map by using the two parents and 100 randomly selected F2 individuals . Biomarksoftware . One binQTLs related to TGW were identified by using a high-density genetic linkage map. QTLs with a logarithm of odds (LOD) value of 3.00 based on 3,000 replications at α = 0.05 and a walk speed of 1.0 cM were detected by using the forward and backward regression method. The intervals with two supported LODs were established as 95% confidence intervals . Loci wihttps://solgenomics.net/organism/Solanum_melongena/genome) (Arabidopsis orthologous gene information of candidate genes was obtained with TAIR (https://www.arabidopsis.org) and candidate genes were further analysed, including annotation of the biological process and molecular function.Functional annotation of candidate genes was compared with the Eggplant Genome Consortium V3 (/genome) , and AraTotal RNA was obtained from the seeds of “17C01” and “17C02” using an RNeasy kit . The seed coat was destroyed by using liquid nitrogen and then ground in liquid nitrogen. RNA was converted into cDNA using a Fast Quant RT Kit . qRT–PCR was performed in a 20-μl total volume using SYBR Green I Master Mix . The EF1α gene was used as an internal control, and all the primers are listed in https://solgenomics.net/organism/Solanum_melongena/genome) and BLAST searched in NCBI (https://www.ncbi.nlm.nih.gov/genome). The top 15 protein sequences with the closest homology relationship were screened out , and AtGTS1 in Arabidopsis was added for phylogenetic analysis. The cluster W method embedded in MEGA 6.0 software was used for sequence alignment, The phylogenetic analysis was inferred by using the Maximum Likelihood (ML) method with MEGA6 software , while 17C02, an inbred line with larger seeds, was used as the male parent (P2). The size of F1 seeds was intermediate to the parent seed sizes . The TGWTo clarify the inheritance of the TGW of eggplant, the six generations P1, P2, F1, BC1P1 (B1), BC1P2 (B2), and F2 were used for quantitative genetic analysis. The values of the log maximum likelihood functions and Akaike’s information criterion (AIC) of 24 genetic models were calculated through the IECM algorithm. The MX2-A-AD models had the lowest AIC value, and the AIC values of PG-ADI and MX1-AD-ADI were near those of MX2-A-AD. Therefore, the three models were selected for the candidate models . Tests fThe least square method was used to calculate the values of the first-order and second-order genetic parameters and the effect values and heritability of the major gene or polygene in the optimal genetic model . BecauseThese results indicated that the TGW of eggplant was inherited through polygenes and had high heritability in the offspring.The Illumina HiSeq platform was used to re-sequence the whole genome of the two parents and 100 randomly F2 individuals, and the data and the map have been published in previous research papers . A totalThe values of LOD, additive, dominance and phenotypic variation explained (PVE) for the QTLs of TGW in the whole genome are listed . As a reA QTL with PVE lower than 15% was defined as a minor QTL; otherwise, it was defined as a major QTL . The PVEThere were 45 genes in the candidate genomic regions on 60.26 cM chromosome 9; among these genes, 23, 22, 38 and 45 genes had annotation information in the GO, KEGG, Swiss-Prot and nr databases, respectively .Many studies have shown that plant seed size is controlled by some key genes and regulatory pathways, such as the ubiquitin–proteasome pathway, G protein signalling, some transcriptional regulators, mitogen-activated protein kinase (MAPK) signalling, phytohormone perception and homeostasis . AccordiTherefore, the sequence differences of these three genes between 17C01 and 17C02 were examined . The resp = 0.0057), and the higher expression level of SmGTS1 in eggplant probably conferred smaller seed sizes  regulates growth and development in plants and is a member of the transducin/WD40 protein superfamily . TransduIntrons are found across almost all species, and alternative splicing of introns can lead to an expansion of the protein repertoire of organisms, increase complexity and phenotypic diversity . Many stin vitro, and the da1-1 mutant forms in the Arabidopsis maternal integuments of ovules can increase seed size  and can bind ubiquitin eed size . In riceeed size . Togetheeed size . There wIn this study, we first performed quantitative genetic analysis and defined an optimal inheritance model of TGW. Additionally, we constructed a high-quality genetic linkage map by whole genome resequencing of 100 F2 individuals. The major QTLs conferring TGW were identified. The ubiquitin–proteasome-related gene SmGTS1 in QTLs (TGW9.1) on chromosome 9 was proposed as a key candidate gene controlling the TGW of eggplant. The inheritance model, map, QTLs and key candidate genes obtained by this study will provide references for future basic and applied research involving TGW traits in eggplant.


With the development of new data acquisition technologies, there will be more and more data collected from high-through-put technologies to assist agricultural breeding. It will be crucial to extract useful information among these large amounts of data and to face this challenge, more efficient algorithms need to be developed and utilized for analyzing these data. Such development will require knowledge from multiple disciplines of research.To feed the fast growing global population with sufficient food using limited global resources, it is urgent to develop and utilize cutting-edge technologies and improve efficiency of agricultural production. In this review, we specifically introduce the concepts, theories, methods, applications and future implications of association studies and predicting unknown genetic value or future phenotypic events using genomics in the area of breeding in agriculture. Genome wide association studies can identify the quantitative genetic loci associated with phenotypes of importance in agriculture, while genomic prediction utilizes individual genetic value to rank selection candidates to improve the next generation of plants or animals. These technologies and methods have improved the efficiency of genetic improvement programs for agricultural production  Genome wide association study (GWAS) is used to find associations between specific genotypes obtained from direct measurements on DNA level and phenotype using a specific statistical or mathematical method which can identify the correlation or connection between genotype and phenotype. It is methods of great importance in the areas of animal and plant breeding. Identifying areas on the genome with effects on the phenotype such as yield or physiological  can be uGWAS has commonly been used in identifying genes or genotypes affecting specific phenotypes (traits) in agriculture. Usually, researchers use general linear regression to identify the relation between genotypes and phenotypes and the general solutions can be found using least squares. In plant or animal breeding, the genes or genotypes associated with phenotypes can be utilized to study the function or classifying plants or animals into simple classes when the phenotypic effect of genes or genotypes is relatively large and the number of genes significantly associated with the phenotypes is relatively small . The genNotably, association between genotypes and phenotypes does not always reflect the causal relationship between genotype and phenotype because the correlated structures of genotypes and phenotypes are very complex and detected loci are mostly in linkage disequilibrium with the causal loci. In order to explore and utilize the complex structure of the genotype and phenotypes better, more complex model is used for GWAS, for example, random regression model, mixed linear models etc. So far, very many genes with small effects have been identified in many agricultural species and there are extensive databases classifying these effects into different categories . During When the effect sizes of the genotypes are estimated simultaneously for all genes with corresponding regularization methods and the effects are summed, the sums are efficient predictions of individual genetic values. Usually, plant or animal breeders practically utilize these individual genetic values to predict the future phenotypes of plants or animals. This results in a ranking of candidate animals or plants for selection, which help breeders using prediction ahead to select the best animal lines or plant varieties to mate to save cost. There are also other methods in predicting selection candidates’ genetic values. For example, in animal or plant breeding, a mixed linear model is usually used to calculate the effect sizes of genotypes simultaneously under certain model assumption and these effect sizes are summed up for each of the selection candidates ,b. DiffeGWAS and genomic prediction are utilized in different context in Agricultural breeding. As stated before, GWAS is more used for identify significantly associated markers to assist agricultural breeding, therefore, the markers selected from GWAS are the key information for producing SNP chips for specific species. In contrast, genomic prediction calculates the estimated breeding values in order to rank the selection candidates in practical breeding. The difference between GWAS and genomic prediction is that in GWAS, usually a single SNP is associated with the phenotype accordingly while in genomic prediction, all SNPs are simultaneously fitted in the model associating with the phenotype . It meanSNPs associated with phenotype that directly lead to structural changes in protein or changes and significant difference in gene expression are often called “quantitative trait loci” (QTL). However, most common SNPs are anonymous markers around the important QTLs and are in linkage disequilibrium with important QTL. Usually, there are limited numbers of significant QTLs associated with each phenotype. It is important to know how these currently detected QTLs with significant effects on phenotypes evolve during time and how the allele frequencies of QTLs are changing during time in an evolutionary perspective . GenomicThis review article will give an overview of GWAS and genomic prediction in the context of genomics from different angles and perspectives in agriculture. It will include the importance and background of GWAS and genomic prediction in different areas such as plant and animal breeding, the generalized methods and theories, specialized methods in terms of different types of variants, the extended knowledge about GWAS and genomic prediction, and finally the applications.Improving the production and performance of plants and animals with better disease resistance is the central goal for plant and animal breeding . DiffereHere we firstly introduce the models and theories commonly used in GWAS and genomic prediction. One of the most common models for GWAS or prediction is the mixed linear model. It has been used for long time in animal and plant breeding since it was proposed in the middle of last century. The model can be written in the following general format:y is a vector of observed phenotypes expected following a normal distribution, X is the assigning matrix for any fixed effect allocating for the specific class and a is covariates computed for the fixed effects, M is the matrix of genotypes consisting of 0, 1, and 2 corresponding to 0, 1, or 2 copies of the reference allele and b is the coefficients computed for the genotypic effects, g is the polygenic effect estimated from the pedigree or genomic relationship matrix, e is the random effects for random errors. We have implemented a software package for implementing the key algorithms of GWAS and genomic prediction . This model is the well-known mixed linear model that assumes a random effect for the genetic effect estimated from pedigree [also known as Best Linear Unbiased Prediction (BLUP)] or genotypes (GBLUP) with correcting on the fixed effects (n > > p) using in an animal model based on pedigree when no markers is available. However, the situation has changed a bit in the current age, as there are more and more genomic information collected due to the decrease of the genotyping and sequencing price. In GWAS studies, the coefficients for genetic makers are tested to determine whether it is significant or not using t-test or chi-square test .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.


Udder health, parasitic resistance, welfare, milk yield and quality, and body condition score were recorded monthly for two milking periods. Udder health records included somatic cell count (SCC) and total viable count (TVC). Based on combinations of SCC and TVC and thresholds set at >106 cells/mL and >2 × 104 cfu/mL, respectively, additional udder health phenotypes were defined. Parasitism included myiasis, tick infestation, gastrointestinal nematode (GIN) and cestode faecal egg count (FEC), and lungworm faecal larval count (FLC). Infection with each of the endoparasites was defined based on FEC/FLC. Welfare assessment parameters included the presence of ear and horn injuries, ocular and nasal discharge, body and udder abscesses, injury and lesions on the skin of different regions, diarrhoea, hernias, overgrown hooves, arthritis, lameness, and udder asymmetry. Trait repeatability and animal correlations were estimated. Significant (p < 0.05) repeatability was reported for all udder health and most welfare traits in all breeds, GIN and cestode FEC, and GIN and lungworm infection in Eghoria, and myiasis in Skopelos. Correlations of health and most of welfare traits with performance were non-significant or favourable. Overall, results demonstrate potential to improve health and welfare of the studied breeds without compromising performance.The objectives of the study were to estimate the repeatability of health and welfare traits and investigate their association with performance in three breeds of dairy goats reared under low-input farming systems in Greece. A total of 1210 goats of Eghoria ( Dairy goat farming is a significant animal husbandry activity with an important socioeconomic and environmental role in disadvantaged areas . Given tGrazing at natural pastures favours internal and external parasitism, which affects goat health and welfare status ,5,6. MorPreventive and treatment measures to mitigate the occurrence and consequences of the above health and welfare issues can be challenging. Specifically, the repeated use of anthelmintics for controlling gastrointestinal nematodes (GIN) has led to increasing anthelmintic resistance ,10,11. AGenetic selection, however, is often limited by the lack of reliable pedigree records in low-input pastoral dairy goat farms ,20. NeveIn goats, relevant studies are scarce. Previous research has investigated the repeatability of somatic cell count (SCC) ,16,17,21The objectives of the present study were to (i) estimate the repeatability of a wide range of health and welfare traits measured on goats of three breeds reared under low-input pastoral farming systems in Greece, and (ii) investigate their association with milk production and body condition score (BCS).n = 418 and Skopelos, n = 429) and one imported breed  were used. Animals were randomly selected from seven farms  located in northern and central Greece  were used for this study. The available dataset included individual monthly records for two consecutive milking periods  of animal health, welfare, and production traits.Specifically, a standard protocol for welfare trait assessment was developed based on Anzuino et al. . Prior tPrzhevalskiana silenus larvae. Additionally, a faecal sample was collected to measure FEC of GIN and cestodes, and faecal larval counts (FLC) of lungworms using the modified McMaster technique [All goats were also thoroughly examined individually for the presence of hard ticks throughout the body and warbles beneath the skin on the back caused by echnique .The BCS of each goat was assessed by palpation of the dorsal lumbar region and recorded on the 5-point scale  of Russel et al. . SubsequThe official AT method of the International Committee of Animal Recording  was usedMoreover, some additional health and welfare phenotypes were defined based on the measurements described above. Specifically, a subclinical mastitis index (SMI) was derived using the following formula:6 cells/mL and >2 × 104 cfu/mL [Thresholds were also set for SCC and TVC at >104 cfu/mL , respectFurthermore, FEC and FLC records were used to determine the presence or absence of infection with GIN and cestodes, and lungworms, respectively. In all cases, records of ≥50 FEC or FLC were indicative of infection . We alsoRegarding welfare traits, the locomotion score assigned to each animal was used to define the presence or absence of lameness. In addition, measurements on each body part were used to define the presence or absence of head, body, limb and udder problems, and skin lesions and injuries.Descriptive statistics of all animal traits considered in the study are shown in Milk production traits SCC and TVC were logarithmically transformed to ensure normality of distributions. FEC and FLC records, which were also significantly skewed, were transformed using Tukey’s Ladder of Powers with R package “rcompanion”.p < 0.05) effects on the studied traits. The effects of farm, period of kidding, age at kidding, days from kidding, and interactions between them were tested.Preliminary analyses were performed to identify environmental factors with statistically significant ; μ = overall population mean; FiPj = fixed effect of the interaction between farm (I = two levels for Skopelos and Eghoria and three for Damascus) and period of kidding (j =two levels); b1 = regression coefficient on age at kidding A (months); b2 = regression coefficient on days from kidding D; Am = random effect of the animal m; eijmn = random residual effect.(Co)variance components of health and welfare phenotypes with milk production traits and BCS were estimated within breed in a series of bivariate statistical analyses implemented with Bayesian Markov Chain Monte Carlo methods and the R software package “MCMCglmm” . All anaA probit link function for binomial distribution was fitted to model 2 for the analyses of binary traits.10 was used. For binary traits, residual variance was fixed to one [Weekly informative priors were used for the random animal and residual effects, whereas for the fixed effects the default normal prior distribution with null mean and a large variance  scale. Repeatability estimates of all studied traits were derived as the mean of estimates from multiple bivariate analyses.p < 0.05) estimates were derived for all udder health traits ranging from 0.08 to 0.50, 0.09 to 0.59, and 0.08 to 0.43 in Eghoria, Skopelos, and Damascus goats, respectively. In all breeds, the most repeatable udder health trait was UHP1  are shown in was UHP1 . Of the was UHP1 . Regardiwas UHP1 . All stuwas UHP1 . Signifiwas UHP1 .Statistically significant animal correlations of health and welfare traits with milk production traits and BCS within breed are shown in This study investigated the repeatability of a wide range of animal health and welfare traits and their association with milk production and BCS in three dairy goat breeds reared under low-input pastoral farming systems in Greece. Overall, significant between-animal variation was revealed for udder health, resistance to parasitism, and most studied welfare traits in Eghoria, Skopelos, and Damascus goats. Moreover, animal correlations of health and welfare with performance traits were mostly either non-significantly different from zero or favourable, implying that improving one would also benefit the other set of traits.Few previous studies have estimated the repeatability of milk SCC, an indicator of subclinical mastitis, and significant estimates (0.31–0.59) have been reported in Saanen, Alpine, and mixed populations of goats ,16,17,213 cells/mL and >2 × 104 cfu/mL, respectively). These thresholds were based on the study by Gelasakis et al. [The ability of milk SCC to predict subclinical mastitis is reportedly lower in goats compared to dairy sheep and cows since it may be influenced by many more physiological and environmental factors in the former ,37,38. Ns et al.  recentlyn = 84) and number of records (n = 584) available in our study. There are no previous reports on the repeatability of cestode FEC and lungworm FLC. In our study, significant estimates were found only for cestode FEC in Eghoria goats. However, cestode and lungworm load manifested in FEC and FLC was low in our data, which may have led to an underestimation of between-animal variation [Regarding endoparasites, the repeatability of GIN FEC has been widely studied in extensively reared dairy goats and significant medium to high estimates (0.25–0.84) have been reported ,10,22 inariation . Therefoariation ,22. SincPrzhevalskiana silenus larvae. Moreover, previous reports in cattle [To our knowledge, there are no previous reports on the repeatability of infestation with ectoparasites in goats. In beef cattle, significant estimates (ca. 0.50) have been reported for myiasis resulting from buffalo and horn flies ,40. Thesn cattle ,42,43,44n cattle ,46 have n cattle . The latAll studied welfare traits were significantly repeatable, except for those associated with problems in very low frequency . According to available literature, our study is the first to evaluate the repeatability of welfare traits in goats. Moreover, there is a notable shortage of relevant literature in other farm animal species for most welfare-related traits. Currently, there are several genetic studies of lameness in dairy cows and significant heritability estimates (0.10–0.54) have been reported ,49,50,51Overall, according to our results, between-animal variance accounts for a significant proportion of the total phenotypic variance of most of the studied traits in Eghoria, Skopelos, and Damascus goats. Such results suggest that there is potential to improve goat health and welfare through management and selection practices. Specifically, results indicate that it is possible to predict the future health and welfare status of individual animals and identify those most probable to (i) be repeatedly infected with GIN and, consequently, produce excretions responsible for pasture contamination in Eghoria breed, (ii) contribute to high incidence of myiasis in Skopelos breed, and (iii) be prone to udder health and welfare issues in all breeds. Based on the above, preventive and control strategies could be applied selectively to individual animals rather than in the whole herd. This may contribute to reducing anthelmintic and antibiotic resistance, while being cost-effective for the farmer. These findings are consistent with outcomes from previous studies on other dairy goat breeds ,10,22. MFurthermore, given that between-animal variation is partly genetic, the studied traits with significant repeatability could also be heritable. In our study, it was not possible to disentangle the additive genetic variance from the permanent environmental variance due to lack of pedigree data. Previous research in goats supports the possibility of selective breeding towards resistance to subclinical mastitis based on SCC ,15,16,17According to the findings of the present study, implementing management and selection practices to improve goat health is not expected to compromise performance of the studied breeds. All animal correlations between udder health and milk production traits were either non-significant or favourable. Given that part of these correlations has a genetic basis, our findings are consistent with previous genetic correlations of SCC with milk, fat and protein yield reported in Alpine and Saanen goats . HoweverIn Eghoria goats, animal correlations of GIN infection with milk production traits were favourable, in agreement with the results of Morris et al.  in SaaneOur results suggest that the welfare status of the studied dairy goat breeds could also be improved without compromising their overall performance since, in most cases, no significant relevant associations were reported. Moreover, we found that skin lesions and udder asymmetry could be reduced by improving milk quality, based on the favourable animal correlations with some milk composition traits in Eghoria and Damascus goats, respectively. However, it should be noted that some welfare and milk production traits were unfavourably associated. Specifically, Skopelos goats with higher milk production and better milk quality seem to be more vulnerable to limb problems (mostly overgrown claws) and udder problems (mostly udder abscesses). Such results could be partly attributed to environmental factors such as grazing on soft and wet soil, inadequate flooring, and/or high supplementation with concentrate feedstuffs ,25. NeveFuture research aiming to estimate genetic correlations between the studied traits would provide results to underpin comprehensive selective breeding and genetic improvement programmes. However, this would require the collection of accurate pedigree data. As previously indicated, in low-input pastoral farming systems, pedigree recording is hindered by poor infrastructure, uncontrolled natural mating, and absence of artificial insemination ,18,19,20Finally, when practices to improve goat performance, health, and welfare are systematically implemented, it is advisable to periodically carry out welfare and performance assessments to assess improvements. Selection, even at the phenotypic level, could result in allelic frequency changes in the populations over time ,65, whicResults of the present study indicate that there is significant between-animal variation for udder health, resistance to parasitism, and welfare traits to support management and selection practices aiming to improve the health and welfare status of Eghoria, Skopelos, and Damascus goats reared under low-input pastoral systems. Such practices could be implemented without compromising animal performance. Importantly, in most cases, subclinical mastitis, GIN infection, udder asymmetry, and skin lesions could be reduced when selecting for higher milk yield and quality. In some cases, arthritis and overall limb issues could be reduced by improving animal BCS. Few antagonistic trait associations revealed in our study, exemplified by the unfavourable correlation of certain welfare issues with milk production, could be addressed with appropriate management practices and possibly the implementation of a phenotypic selection index to underpin culling decisions. In all cases, accurate and systematic record keeping is essential for improving overall goat performance in low-input pastoral systems.


Recently genomic selection (GS) has emerged as an important tool for plant breeders to select superior genotypes. Multi-trait (MT) prediction model provides an opportunity to improve the predictive ability of expensive and labor-intensive traits. In this study, we assessed the potential use of a MT genomic prediction model by incorporating two physiological traits  to predict 5 complex primary traits  using two cross-validation schemes CV1 and CV2.In this study, we evaluated 236 wheat genotypes in two locations in 2 years. The wheat genotypes were genotyped with genotyping by sequencing approach which generated 27,466 SNPs. MT-CV2  model improved predictive ability by 4.8 to 138.5% compared to ST-CV1. However, the predictive ability of MT-CV1 was not significantly different compared to the ST-CV1 model.The study showed that the genomic prediction of complex traits such as HI, GN, and GY can be improved when correlated secondary traits (cheaper and easier phenotyping) are used. MT genomic selection could accelerate breeding cycles and improve genetic gain for complex traits in wheat and other crops.The online version contains supplementary material available at 10.1186/s12864-022-08487-8. Phenotypic selection is widely used in most of the conventional plant breeding programs. However, this method is both labor and time-intensive as it involves screening for traits of interest across several years and environments –3. MarkePhysiological traits (PT) such as normalized difference vegetation index (NDVI) and canopy temperature (CT) are indicative of stress-resilient genotypes with efficient photosynthesis and respiration processes , 26. PreA combined ANOVA showed significant genotypic and environmental effects on correlated secondary traits, CT and NDVI, but genotype-by-environment interaction (G × E) was not significant Table . However− 1 (BLUEQ18) to 4483 kg ha− 1 (BLUEC18)  is shown in Fig. 8) Table . SimilarFE Table , from 0.FE Table . The ranFE Table .Fig. 1DiNDVI showed positive correlations with HI (0.14* to 0.41***), GY (0.30** to 0.46**), GN (0.20** to 0.42**), SPI (0.11 to 0.19**) and FE (0.09 to 0.16**), whereas CT showed negative correlations with HI (− 0.05 to − 0.23**), GY (− 0.15** to − 0.34**), GN (− 0.18** to − 0.37**), SPI (− 0.01 to − 0.21**) and FE (− 0.03 to − 0.14*)  in BLUEQ18, and the lowest predictive ability was observed for FE (0.07) in BLUEQ17 Table , Fig. 3.9 in BLUEp > 0.05). In the MT-CV1 model, the predictive ability was highest for HI, from 0.29 (BLUEQ17) to 0.40 (BLUEQ18), but lowest for FE, from 0.07 (BLUEQ17) to 0.19 (BLUEQ18)  Table . The val8) Table . Likewis8) Table . For SPI8) Table .The MT-CV2 included two physiological traits, NDVI and CT, as secondary traits on both training and validation sets. This in general improved the predictive ability for all traits  compared to the models ST-CV1 and MT-CV1 with a single exception Table , Fig. 3.When we compared ST-CV1 with MT-CV2, FE had the lowest percentage increase (5.1) in predictive ability (BLUEQ18), while GN showed the highest percentage increase of 138.5 (BLUEC18) in predictive ability Table , Fig. 3.GS has been used to select superior genotypes in different plant breeding programs. It is being employed more now due to the availability and continuously reduced cost of advanced DNA sequencing techniques. In the past, ST-GS was a popular method to evaluate the performance of plant genotypes. However, plant breeders generally collect data for several traits for selection purposes, which provides an opportunity to use multiple traits in GS models. To determine whether incorporating physiological traits in the prediction model increases the predictive ability of traits of interest, we compared two MT-GS methods (MT-CV1 and MT-CV2) with ST-GS method (ST-CV1). In the ST-CV1, we evaluated the predictive ability of five primary traits  individually. In MT-CV1 and MT-CV2 models, we included CT and NDVI as secondary traits along with five primary traits.ANOVA data showed significant genotypic and environmental effects. The Genotype-by-environment effect was not significant for NDVI and CT. The larger influence of G × E on other primary traits resulted in a lower heritability as they are complex polygenic , 49. CT n = 236) used in this study might have limited the efficacy of MT-CV1.MT-CV1 and ST-CV1 models showed similar predictive ability in most cases, consistent with many other studies , 48, 51.The MT-CV2 model improved predictive abilities for all five primary traits in this study although the extent of improvement fluctuated across traits and environments, which agrees with previous reports , 51, 53.Phenotyping some traits are more expensive, time-consuming, and labor-intensive than others, which makes implementing GS for these traits burdensome. The GS becomes cost-efficient when phenotyping of primary traits is more expensive and difficult than secondary traits. In this case, we only phenotype the training set for primary traits, but both training and testing sets for secondary traits. For instance, the MT-CV2 model resembles a scenario in a breeding program where physiological data are taken when plots are yet to be harvested in a later stage . This coTo exploit genetic information from correlated traits using an MT-GS method, GS using two traits could be useful to improve the genomic prediction accuracy of a primary trait of interest. In a wheat breeding program, physiological traits such as CT and NDVI are measured routinely to evaluate stress tolerance along with other agronomic traits. We compared predictive ability among ST prediction model (ST-CV1) and two MT genomic prediction models (MT-CV1 and MT-CV2) and found that the phenotypically correlated secondary traits in both the training and testing sets (MT-CV2) improved predictive ability giving the high correlation between primary and secondary traits. Whereas improvement in predictive ability was not obvious when the secondary trait was incorporated only in the training set (MT-CV1). This result is highly useful in breeding programs where data for several traits are usually collected. Multi-trait genomic selection involves measuring laborious and expensive traits in a smaller training population, whereas phenotyping of inexpensive correlated traits in the testing population. With the increasing availability of the HTP platforms, the MT-GS methods can facilitate improvement in the genetic gain for many important traits in wheat.The genotypes used in this study consisted of 236 facultative soft wheat elite lines and varieties that were developed by different wheat breeding programs in the south and soueastern USA . The wheat lines used in the present study are mostly facultative in nature and vernalization requirements are generally low and are well adapted to the warm and humid southern and southeastern regions of the USA. The field experiments were carried out in two locations: Plant Science Research and Education Unit (PSREU) in Citra, Florida in 2017–18 growing season and North Florida Research and Education Center (NFREC), Quincy for two growing seasons (2016–17 and 2017–2018). An augmented design was used with three repeated check varieties  that are widely grown wheat in the southern and southeastern US to control spatial variability. The size of six-row plot used for.2 (3.33 m long/1.52 m wide) with a seed rate of 100 kg ha− 1. Management and agronomic practices such as fertilizer and chemical application and irrigation were performed as recommended for optimum growth and yield potential. Fungicides were sprayed as needed at stem elongation, booting, and early grain filling to prevent different foliar and spike diseases. The weather data is listed in Table the study was 5.1 m2 area of each plot. The sample was oven-dried at 60 °C temperature for 72 h. The weight of the total dried sample was collected, and the fertile spike number was counted. Spikes and stems were separated, and weights were collected. Spike partitioning index was calculated as a ratio of total spike dry weight to the above-ground dry matter at anthesis plus 7 days. Traits such as GN, GY, and HI were recorded at physiological maturity . Days to physiological maturity for each plot was taken when the flag leaves and spikes turn yellow. Grain number m− 2 was calculated by dividing total grain weight by individual grain weight. Harvest index was measured as the ratio of grain weight m− 2 to total dry biomass m− 2. Likewise, GY (kg ha− 1) was measured as a total seed weight from each plot after adjustment with 12% moisture. FE was calculated as a ratio of GN (m− 2) at maturity and spike dry matter (m− 2) at anthesis plus 7 days. CT was collected at three growth stages, heading (H), mid-grain filling (MGF), and late-grain filling (LGF), between 1300 and 1500 h on sunny days when the temperature reached the daily high by using Fluke 572–2 IR thermometer . CT data were collected from both sides of each plot at a 50 cm distance from the edge and approximately 50 cm above the canopy at an angle of 30o to the horizontal. The mean value of two readings was calculated for each growth stage and the average of three values from the three growth stages was used for further statistical analysis. NDVI was measured at four growth stages: H, early-grain filling (EGF), MGF, and LGF using the GreenSeeker handheld crop sensor (Trimble Navigation Limited) by holding it 50 cm above the canopy facing the center of the plot. The mean value of those readings was used for statistical analysis.Five primary traits  and two physiological traits CT and NDVI were measured in the present study. Days to anthesis was taken for each plot as the days from planting to the day when 50% of plants were flowered . At 7 daThe genotyping method has been explained in detail in a previous paper . We obtaAnalysis of variance (ANOVA) was conducted using the “lme4” package  in R sofFor combined analysis across environments, the statistical model was as follows.i is the effect of ith block; Genk is the effect of kth genotypes; Checkl is the effect of the lth checks on each block; Envm is the effect of the mth environment. IDCheckj is the effect of jth IDCheck. IDCheck was used to differentiate the effects of one check over the other checks, as well as the number of checks present on each block; IDCheckj x Envm, Genk x Envm, and Checkl x Envm are the effects of check identifier by environment, genotype by environment, and check by environment interactions, respectively. Blocki(Envm) is the effect of ith block nested within mth environment and ε is the residual.where Y is the phenotype of a trait of interest; μ is the effect of the mean; BlockH2 is a broad-sense heritability estimate, 2GXE is genotype-by-environmental variance, Broad-sense heritability was calculated assuming genotype and other effects as random  and was Pearson’s correlation among traits was calculated from BLUEs in R using the “corrplot” package in R . PC biply is the vector of BLUE values for a single trait; μ is the vector of the overall mean; Z is a design matrix with random marker effects, α is a genotypic predictor with α ~N where K is the realized additive relationship matrix and σ2g is additive genetic variance and ε is the residual errors vector with ε ~N where I is the identity matrix. Prediction accuracies were estimated using a cross-validation approach CV1 [In the ST model, the prediction was obtained by using a Bayesian ridge regression (BRR) model with 2000 burn-ins and 12,000 iterations for the Gibbs sampler algorithm , 51 imploach CV1 , explainThe MT model was built using a Bayesian multivariate Gaussian model to estimate an unstructured variance-covariance matrix between traits (Σ) and residual matrix (R) with 2000 burn-ins and 12,000 iterations for the Gibbs sample algorithm , 51 imply is a vector of BLUE values for t traits; μ is the overall mean; Z is the incidence matrix; α is a genotypic predictor with α ~MVN  and ε is the residual errors vector with ε ~MVN , where Σ is the variance-covariance matrix across traits, K is the realized additive relationship matrix among individuals estimated from the markers, R is the variance-covariance matrix for the residual effects for each individual among traits, I is the identity matrix, and ⊗ is the Kronecker product of two matrices. Σ was estimated as an unstructured matrix and R as a diagonal matrix [where l matrix .The Monte-Carlo cross-validation scheme was used to estimate prediction accuracy , 51 Fig. The CV1n = 165) which have phenotypic (primary+secondary traits for MT-CV1) and genotypic data. The testing set (VP) consisted of the remaining 30% of genotypes (n = 71) that have genotypic data only. This process was repeated for 100 times, where each iteration included a different combination of genotypes in training and testing sets. Predictive ability was calculated as a mean of Pearson’s correlations between observed phenotypic values and predicted values.The first cross-validation scheme (CV1) used a training set (TP) of 70% of random genotypes  used the phenotypic and genotypic data from the training set of 165 lines. However, the genotypic data and phenotypic data of physiological traits from the testing set of 71 lines were used. In other words, the CV2 scheme not only used genotypic information from both TP and VP and phenotypic data of the primary traits (HI or GY or GN or SPI or FE) from the TP but also used phenotypic data of secondary correlated traits (NDVI and CT) from both TP and VP. This process was repeated 100 times, where each iteration included a different combination of genotypes in the TP and VP. Predictive ability was calculated as a mean of Pearson’s correlations between observed phenotypic values and predicted values.Additional file 1.


In recent years, the number of genome-wide association studies (GWAS) carried out for various economically important animal traits has been increasing. GWAS discoveries provide summary statistics that can be used both for targeted marker-oriented selection and for studying the genetic control of economically important traits of farm animals. In contrast to research in human genetics, GWAS on farm animals often does not meet generally acceptedstandards . Thisgreatly complicates the use of GWAS results for breeding needs. Within the framework of human genetics, there areseveral technological solutions for researching the harmonized results of GWAS, including one of the largest, theGWAS-MAP platform. For other types of living organisms, including economically important agricultural animals,there are no similar solutions. To our knowledge, no similar solution has been proposed to date for any of the speciesof economically important animals. As part of this work, we focused on creating a platform similar to GWAS-MAP forworking with the results of GWAS of sheep, since sheep breeding is one of the most important branches of agriculture.By analogy with the GWAS-MAP platform for storing, unifying and analyzing human GWAS, we have createdthe GWAS-MAP|ovis platform. The platform currently contains information on more than 34 million associations betweengenomic sequence variants and traits of meat production in sheep. The platform can also be used to conductcolocalization analysis, a method that allows one to determine whether the association of a particular locus withtwo different traits is the result of pleiotropy or whether these traits are associated with different variants that are inlinkage disequilibrium. This platform will be useful for breeders to select promising markers for breeding, as well asto obtain information for the introduction of genomic breeding and for scientists to replicate the results obtained. A genome-wide association study (GWAS) is moderntechnique to detect genome loci associated with differenttraits both quantitative and binary ones. GWASs have beenwidely used in human and animal genetics . Since 2007, an annual increase of GWAS-basedinvestigations to search for trait-associated loci has beenobserved. A GWAS results in the so-called summary statistics,a text tables where each line is a result of associationanalysis between a studied trait and a single nucleotidepolymorphism (SNP). In breeding, such summary statisticscan be used to devise marker-assisted and genomic selectionmodels and identify target candidates for genomeediting. Thanks to the new-generation technologies ofgenome-wide genotyping and next-generation sequencing,modern GWASs enable one to study hundreds of thousandsand oftentimes millions of SNP associations with dozensof traits in huge samples including some tens thousandsof animals. In other words, GWAS summary statistics arehuge datasets , whose processing isfar from being trivial and requires, apart from their collection,a platform to provide proper quality control, storage,access and processing tools.In the field of human genetics, such a platform, knownas GWAS-MAP , serves for unification,storing and analysis of millions of associations forthousands of human traits. However, to our knowledge,there is still no such a solution even for a single kind oflivestock farming. There are only databases to processquantitative trait loci significantly associated with studiedtraits. The biggest of such databases is AnimalQTLdb thatcontains information on quantitative-trait loci for differentkinds of livestock . There are similarsolutions for particular kinds of animals such as the iSheepdatabase that provides access to the known quantitative-traitloci, genotyping and sequencing data of sheep . Since none of the mentioned solutions providesopportunities for storing, unification and analysis of GWASsummary statistics, the objective of the presented study wasto create such a platform for sheep.Sheep breeding is an important branch of animal farmingwith mutton being its most popular product. Since 2013,more than 20 GWASs of economically important sheeptraits have been known. Here, S. Bolormaa et al. (2016)is of particular note being the biggest GWAS for a meatproductivity traits in sheep. The authors investigated acohort of more than 10,000 animals to study informationon 56 different genetic traits related to meat productivityand found more than 70 significantly associated SNPsincluded in 23 loci.Here we present the GWAS-MAP|ovis platform that isanalogous to GWAS-MAP and can be used for aggregation,unification, storing and analysis of the GWAS summarystatistics of the economically important traits affectingmeat production in sheep. Currently, the platform containsmore than 34 mln associations for 80 such traits. Breedersmay find it useful for selection based on the effects of thealleles associated with studied traits and on perspectiveselection markers. It may also be useful for the scientistsinvestigating sheep genetics. In particular, the platformenables for GWAS quality control; search for significantlyassociated SNPs; GWAS results visualization and colocalizationanalysis.Developing the GWAS-MAP|ovis platform. GWAS resultsare commonly summary statistics being big text tablesin which a single line is the results of association analysis to match a studied trait and SNP. Summary statistics caninclude different columns such as SNP identifier (if any);SNP position in a physical genome map (chromosome andposition), SNP imputation or genotyping quality controlmetrics; effective and reference alleles; effective allele fre-quency; SNP effect estimation; standard error and p- value;sample size and other technical variables specific forparticular research. The minimum set of the variablesnecessary for summary statistics processing includes anSNP identifier, effective and reference alleles, effect estimationand a standard error. Without loss of information,the standard error can be replaced by either the p-value orby z-statistic. Since the number of SNPs is huge, the sizeof the file containing a summary statistics table can reachseveral gigabytes in a case the number of characterizedSNPs exceeds 20 millionThe GWAS-MAP platform  to store GWAS results in the humanwas used as a basis to design the solution in question. Forpresentation and visualization of genetic data, the PheLiGeweb interface was introduced .The flow chart of GWAS-MAP|ovis can be seen in Fig. 1.To store and process data, GWAS-MAP uses two databasemanagement systems, ClickHouse and PostgreSQL.While ClickHouse serves to store matched summary statistics,PostgreSQL is used to store the metadata describing thesources summary statistics come from, a GWAS dataset anddata analysis features. They may include e. g., a reference tothe publication data come from; trait name; description ofthe model used for analysis; the number of studied animals,etc. Their full list is available in Supplementary material 11.The two systems are deployed in an LXC container andoperate using an Ubuntu 18.04 operating system image.One of the key steps in setting up the database was creatinga linkage disequilibrium catalogue and the so-calledreference table (see the description below). To provide convenient access to the database and enable data visualizationas interactive graphs, the PheLiGe-ovis web interface wasdeployed in another LXC container and operated using thesame operating system image. The web interface is availableat https://pheligeovis.icgbio.ru/.http://vavilov.elpub.ru/jour/manager/files/Suppl_Kirichenko_engl.pdfSupplementary Materials are available in the online version of the paper:The designed platform having two databases to storesummary statistics and metadata and a web interface wascalled GWAS-MAP|ovis and is available via public (web)and private (ssh) interfaces. The general principles of howto operate its web interface and main tools are describedin the on-line assistant program, whose window pops upwhen pressing the red button under the Associations menuitem in the top right corner of the platform’s main page.Linkage disequilibrium catalogue is an SNP list thatcontains the following information for each included SNP:identifier (rs_id); chromosome and position (base on theOAR_V3.1 genome assembly); effective and referenceallele, and effective allele frequency in referent population.To create the catalog were used the genome data ofa reference population (N = 96) including 18 Romanovbreed sheep, 6 Katahdin sheep, 10 argali, 48 F1 hybridsfrom crossing F1  and Romanovbreeds, and 14 F1 hybrids from crossing Romanov andKatahdin breeds. All the animal had been genotyped usingInfinium® HD SNP Bead-Chip  and used for replication inour recent paper . Preparing the referencetable involved removing all duplicated and monomorphicSNPs; the alleles were placed in lexicographicorder; sex chromosome data were removed. For the timebeing, the disequilibrium catalogue contains informationon 523.578 SNPs. Their autosomal distribution can befound in Supplementary material 2 and their minor allelefrequencies in Supplementary material 3.The linkage disequilibrium catalogue is files containingthe genotypes of the reference population animals forevery SNP included in the reference table. The files are inbinary format PLINK . The catalogueis used to calculate the linkage disequilibrium betweenSNPs for finding a proxy SNP in the PheLiGe-ovis webinterface.Module of unification and quality control GWAS results.This module is used to download summary statisticsin the database. Since these files are obtained from differentsources and often have different formats, they have to beunified first. At this stage, matches between rs_id and theposition in the chromosome and between the effective andreference alleles are checkedAs the next stage, the summary statistics pass qualitycontrol that includes three stages: (1) comparison of GWASallele frequencies against those in the linkage disequilibriumcatalogue; (2) checking effect-size distribution,and (3) verifying if the p-value corresponds to z-statistic(effect estimation divided by a standard estimation error). The module generates an html report that can be used todetect unification and quality-control errors. If data passthe control, the summary statistics are stored in the platform’sdatabase.Content managementTo fill the database, a search for published summarystatistics was carried out in an early published databaseof the QTL and genes associated with meat productivitytraits in sheep . Out of 46 publicationsanalyzed, only S. Bolormaa et al. (2016) provided accessto genome-wide summary statistics. Before being loadedin the GWAS-MAP|ovis database, all the data were unifiedand they passed the quality control. As a result, 488,074associations for each of the 56 traits listed in the paperwere downloaded inIn addition to the data provided by S. Bolormaa et al.(2016), the GWAS-MAP|ovis database also contains thesummary statistics obtained from the Russian sheep population(N = 50–108) early used for replication analysis . This summary statistics described 7 compositeindices related to the meat productivity measured for threedifferent time periods  and ananimal’s weight also measured for three time periods . As a result,the platform’s database has accumulated information onmore than 34 million SNP associations for 80 differenttraits related to meat productivity in sheep. The availabledataset and its short description are presented in the TablePleiotropy analysisOne of the advantages of the designed platform is that itcan be used to perform colocalization analysis (SMR-𝜃), which enables one to comparethe association patterns of a particular genome locus fordifferent traits and make conclusions whether this locushas a pleiotropic effect on a certain trait. In a nutshell, inpresence of this effect, the ratio of estimated SNP effects ontwo traits in a studied locus is expected to be insignificant.However, if the association patterns are different i. e., theeffect ratio changes significantly from one SNP to another, it is most likely that the locus contains different functionalpolymorphisms for each of the traits and they are being inlinkage disequilibrium.Statistic 𝜃 is a weighted correlation, whose computationrequires information on 𝑝-values and an effect direction.The high absolute value  means a locus has apleiotropic effect on investigated traits. If 𝜃 is positive, theSNPs of the locus have a similar effect direction towardsthe traits, and a divergent one if negative. An example ofcolocalization analysis is presented in Fig. 2.In the framework of the presented study, colocalizationanalysis was carried for the most significantly associatedSNP of the LCORL1 gene in the data by . For SNP rs401834107, 12 traits of genome-widesignificance level  associated with thislocus were indicated. The SMR-𝜃 method was applied tocompare association patterns for all the 12 traits, the resultsare demonstrated in Fig. 3. The heat map characterizing𝜃-values shows the traits form two clusters with differenteffect correlation directions, which means the locus had apleiotropic effect inside each of the clusters. At the sametime, the same strong effect was observed between traitsfrom different clusters with divergent SNP effect on thetraits . It is noteworthy that the slaughterweight trait was a dropout since some of its 𝜃-values turnedout to be low (possible pleiotropy absence). However, dueto the pleiotropic effect the locus had on the other traits,the low 𝜃-values in this particular case can be consideredeither as a statistic artifact or as a value produced due toinsignificant analysis capacity.Thus, it has been found that locus rs401834107 had apleiotropic effect on at least 12 different traits associatedwith meat production in sheep. The presented data is anexample of a first in the world colocalization analysisperformed using sheep GWAS results.Searching for candidate DNA markersfor marker-assisted selectionIn addition to the described pleiotropy analysis, anotheradvantage of the GWAS-MAP|ovis platform is that it allowsone to use previously loaded summary statistics tosearch for candidate markers to perform marker-assistedselection (MAS). In the presented study, such a search for the hot-carcass-weight (HCWT) trait was performed to estimatethe markers’ predictive potential in a Russian sheepcohort. To begin with, a clumping procedure was carriedout. To do this, the private (ssh) interface was used to selectthe summary statistics of interest and set up their significantthreshold of p-value < 1E–07. Four independent lociwere detected . A table containing information on SNPwith the highest associations for each of the loci, theirchromosomes, positions, effector and reference alleles andp-values was formed for further analysis.The found loci were used to estimate the breeding value(BV) of 94 animals  to be the backcrosshybrids of Romanov and argali sheep. Their BV wasestimated as:where b is the effect of the effective allele of the most associatedSNP in every locus; g is the genotypes of 94 animalsfor that SNP encoded as 0, 1, 2 in relation to the effectiveallele’s quantityCases then underwent linear regression to find how significantthe estimated BVs were for predicting a species’phenotype. As a trait, an animal’s weight in six days afterbirth was considered. The used model was p ~ BV + cov,where p is a phenotype’s value; cov is the covariates includinginformation about animal gender, yield number, and twofirst main components of their kinship matrix. As a result,the estimated BV was found to be significantly associated with the trait, which confirmed that theselected loci had a significant effect on an animal’s weightin six days after birth.In addition to the performed analysis, it was also estimatedwhat body weight increase could be expected in theanimals whose BV was included in the fourth (top) quartile(> 75 %). Student’s t-criterion was used to compare themean values of the phenotype corrected for the abovementionedcovariants for the whole cohort and the animalsincluded in top BV quartile. It was found that the weightof the top-quartile animals was 1.8 %  higher than that in the averaged population.Thus, the selected markers can be potentially used fordevelopment of MAS test systems in sheep.The GWAS-MAP|ovis platform has been designed foraggregation, unification, storage and analysis of GWASsummary statistics in sheep. Today, it contains more than34 million SNP associations for 80 different traits relatedto meat productivity in sheep. The platform enables oneto search for information on associations for a particularSNP, plot Manhattan graphs and carry out colocalization analysis. 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