Genetic and molecular knowledge of a species is crucial to its gene discovery and enhanced breeding. Here, we report the genetic and molecular dissection of ginseng, an important herb for healthy food and medicine. A mini-core collection consisting of 344 cultivars and landraces was developed for ginseng that represents the genetic variation of ginseng existing in its origin and diversity center. We sequenced the transcriptomes of all 344 cultivars and landraces; identified over 1.5 million genic SNPs, thereby revealing the genic diversity of ginseng; and analyzed them with 26,600 high-quality genic SNPs or a selection of them. Ginseng had a wide molecular diversity and was clustered into three subpopulations. Analysis of 16 ginsenosides, the major bioactive components for healthy food and medicine, showed that ginseng had a wide variation in the contents of all 16 ginsenosides and an extensive correlation of their contents, suggesting that they are synthesized through a single or multiple correlated pathways. Furthermore, we pair-wisely examined the relationships between the cultivars and landraces, revealing their relationships in gene expression, gene variation, and ginsenoside biosynthesis. These results provide new knowledge and new genetic and genic resources for advanced research and breeding of ginseng and related species. Panax ginseng Mey.) and American ginseng (P. quinquefolius L.), has long been used for healthy food and medicine  (CAS) (DS) (FPS) (SE) (SS) (UGT71A27) (UGT74AE2) (CYP716A53v2) (CYP716A52v2) (CYP716A47)  , cycloarS) (CAS) , dammareAS) (DS) , farnesyS) (FPS) , squalenPS) (SE) , squalenSE) (SS) , UDP-glyGT71A27) , UDP-glyGT74AE2) , cytochr16A53v2) , cytochr16A52v2) , and cytP716A47) . MoreoveP716A47) , differeP716A47) , differeP716A47) , and difP716A47)  to identThe germplasm collection of a species is the gene pool of the species containing most, if not all, of the genes necessary for its genetic improvement and adaptation to climate change. Therefore, genetic and molecular evaluation and dissection of a species’ germplasm collection is paramount to its efficient utilization for continued genetic improvement, gene discovery, and favorable allele mining and for deciphering the species’ speciation, genetic variation, and evolution . SeveralThe aims of the present study were to construct a mini-core germplasm collection representative for Jilin ginseng and characterize the collection at genetic and molecular levels. We first collected 1,168 germplasm lines from the origin and diversity center of Jilin ginseng, developed the mini-core germplasm collection, and analyzed its genetic variation using 16 ginsenosides. Then, we sequenced the 4-year-old plant root transcriptomes of the mini-core collection, identified the SNPs contained in functional genes, quantified the expressions of the genes and transcripts, and characterized it using high-density genic SNP markers and gene expression profiles to facilitate gene discovery, favorable allele mining, and application of the research result for enhanced breeding. Analysis of the genic SNPs and gene expressions provided a comprehensive insight into molecular diversity, population structure, and evolution of the ginseng species. In addition, a relationship of the ginsenosides was proposed in the biosynthesis of ginsenosides. These resources and findings promote ginseng research such as genome-wide identification of genes involved in ginsenoside biosynthesis and enhance ginseng breeding such as marker-assisted selection and gene-based breeding .A mini-core germplasm collection that is representative for the genetic variation and diversity of traits and genes in a species is necessary for its deeper research in genetics, genomics, and breeding. Therefore, to develop a mini-core germplasm collection for Jilin ginseng, we previously collected a total of 1,168 cultivars and landraces from 16 major ginseng-producing counties of Baishan, Jilin, Tonghua, and Yanbian, Jilin Province (43°42'N 126°12'E), China, in 2009 Figure 1During plant growth and development from 2010 to 2014, the cultivars and landraces were recorded for main quality and yield potential traits and phylogenetically analyzed with 50 SSR markers . The quar = 0.90 to 0.98 (P < 0.0001) between plants collected from a field trial replicate, different field trial replicates, and across years. Therefore, the mRNA of one plant root was sequenced for this study. Total RNA was extracted using the RNeasy Plant Mini Kit , following the manufacturer’s protocol. The integrity and quantity of the RNAs were estimated by Agilent 2100 Bioanalyzer . Poly(A) mRNA was purified from total RNA by NEBNext Oligo(dT)25 beads . cDNA libraries were constructed from the mRNAs using the NEBNext Ultra RNA Library Prep Kit for Illumina , as described by the manufacturer. Library sequencing was accomplished using the Illumina HiSeq 4000 platform with a 150-base paired-end (150PE) module at Gene Denovo Biotechnology Co . Raw reads of low quality were removed, including those with adapters, containing more than 10% unknown nucleotides, and containing more than 50% low-quality bases (<Q10).The root sample collected above from one root per cultivar or landrace was used for transcriptome sequencing. Single-nucleotide polymorphisms (SNPs) and nucleotide insertions/deletions (InDels) were called and genotyped among the mini-core collection cultivars and landraces by the SAMtools software with the default parameters  using thWe constructed the population structure of the mini-core collection using STRUCTURE 2.3.4 . BecauseFurthermore, the population structure of the mini-core collection was confirmed by phylogenetic analysis, followed by principal component analysis (PCA) using all high-quality genic SNPs. The phylogenetic tree was constructed using the TASSEL 5.2.79 software  with theThe molecular diversity of the mini-core collection was estimated using allele frequency, gene diversity, heterozygosity, and polymorphism information content (PIC). The allele frequency, gene diversity, heterozygosity, and PIC were determined with the PowerMarker software  using thBecause ginsenosides are the main bioactive components for healthy food and medicine, we assayed the contents of ginsenosides in the roots of the cultivars and landraces of the mini-core collection to further characterize it. The roots of the cultivars and landraces collected above, with two roots per cultivar or landrace and one root from each field replicate, were used for ginsenoside content quantification. The roots were dried in an oven at 35°C to consistent weight , and theWe performed a correlation analysis between the 16 ginsenosides using their contents in the 4-year-old plant roots of the entire mini-core collection, each subpopulation, and the cultivars and landraces collected from each geographical region. The correlation analyses between ginsenosides were based on the content variations of the ginsenosides in the cultivars and landraces of the entire mini-core collection, each subpopulation, or those collected from each geographical region. The results were then used to deduce their relationships in ginsenoside biosynthesis. The relationships of the ginsenosides by content were determined by Person’s correlation coefficient calculated using the SPSS package (IBM SPSS Statistics 23) and by network analysis based on ginsenoside contents using the BioLayout Express3D (Version 3.3) . Their pThe ginsenoside contents of the cultivars and landraces clustered into different subpopulations and admixture group or collected from different geographical regions were compared by ANOVA  with a single factor, followed by Tukey’s HSD (honestly significant difference) test using Excel. A two-tailed significance level was used for the analysis.As different transcripts spliced from a gene may have different biological functions , the expMoreover, we examined the expressions and interactions of the 11 genes previously cloned that encode the key enzymes involved in ginseng ginsenoside biosynthesis to further characterize the mini-core collection . The expressions of 15 transcripts of the 11 ginsenoside biosynthesis genes were extracted from the expression dataset of the mini-core collection. Expression heatmaps of the 15 gene transcripts were constructed and visualized for the cultivars and landraces collected from different geographical regions and for different subpopulations and the admixture group using an R language package . The co-Pearson’s correlation coefficients were calculated pair-wisely between cultivars and landraces of the mini-core collection to estimate their relationships using an R language package . The corA mini-core collection was constructed for Jilin ginseng from 344 cultivars and landraces Table S1A total of 16.3 billion 150-nucleotide clean reads or over 244 Gb transcriptome sequences were obtained for the transcriptomes of the 344 cultivars and landraces of the mini-core collection, with a range from 40.1 million to 51.5 million clean reads and an average of 47.2 million clean reads per cultivar or landrace. From the transcriptome sequences, 1,532,337 genic SNPs were identified, with an average of 25.6 SNPs per gene, suggesting a high variation of ginseng germplasm. These SNPs spanned 2,761.8 Mb (92.5%) of the 2,984.9 Mb ginseng genome assembly , with a The population structure of the mini-core collection was evaluated based on 6,232 SNPs that were chosen from the 26,600 high-quality SNPs, with one SNP per approximately 500 kb along the ginseng genome. The structure of the mini-core collection was estimated using the LnP(K) and ΔK values, respectively, at a number (K) of subpopulations varying from 2 to 10 Table S1Furthermore, we evaluated the molecular relationships among the 344 cultivars and landraces of the mini-core collection by phylogenic analysis using all 26,600 high-quality SNPs  based on all 26,600 high-quality SNPs also verified the population structure of the mini-core collection determined with the 6,232 SNPs  using 6,232 of the high-quality SNPs selected with one SNP in approximately 500 kb of the ginseng genome. Pairwise genetic distance analysis excluding the admixture group showed that the mini-core collection had a pairwise genetic distance varying from 0.0167 to 0.0707, with a variation of 4.4-fold and an average genetic distance of 0.0488. The pairwise mean genetic distance of the mini-core collection was larger than the within-sub1, within-sub2, and within-sub3 mean genetic distances of 0.0436, 0.0398, and 0.0296, respectively. It was apparent that the pairwise genetic distances between subpopulations were much larger than the within-subpopulation genetic distances , but their within-subpopulation or admixture group variations were dramatic among cultivars and landraces (P < 0.05). R0 had the highest content in sub2, followed by admixture group, sub1, and sub3. Rf was the most abundant in sub3, and then admixture group, sub1, and sub2 in descending order. The content of Rb2 was the highest in the admixture group, followed by sub2, sub1, and sub3. Rd was also the highest in content in the admixture group, but followed by sub2, sub3, and sub1  of the 120 possible pairwise correlations of the 16 ginsenosides were significant, 44 of which were significant at rrelated Table S5P ≤ 0.05, with each ginsenoside strongly correlated with one or more other ginsenosides. This result suggested that they were synthesized through a single complicated pathway or multiple pathways that are correlated in ginsenoside biosynthesis , including six PPD-typed ginsenosides, one PPT-typed ginsenoside, and one oleanane-typed ginsenoside, but with a lower correlation coefficient varying from 0.120 to 0.380, suggesting that these eight ginsenosides are likely in a more distant position or existence of one or more other ginsenosides between them in the ginsenoside biosynthesis pathway , thus distinguishing the origin of the cultivars and landraces among geographical regions. Among the four geographical regions, the cultivars and landraces from Jilin had the network with the largest number of gene nodes, the largest number of gene interaction edges, and the highest connectivity (P < 0.01), differing the cultivars and landraces among subpopulations and admixture group. Subpopulation 3 (sub3) had the largest number of gene interaction edges and the highest connectivity . To have a glimpse into variation of the expression activities and interactions of these genes in the mini-core collection, we constructed their expression heatmaps and co-expression networks in 4-year-old plant roots of different subpopulations and admixture group and cultivars and landraces collected from different geographical regions. Comparative analysis of the heatmaps showed that the genes expressed in low activity in almost all cultivars and landraces analyzed, regardless of their geographical regions and there was no tendency that the cultivars and landraces collected from a similar geographical region were grouped into a similar cluster , with an average of 0.78 and a CV of 12.66% (P < 0.01) , with an average of 0.76 and a CV of 31.29% (P < 0.01) , with an average of 0.73 and a CV of 25.66% (P < 0.01) , with an average of 0.56 and a CV of 18.13% (P < 0.01)  (P < 0.05) (P < 0.05) (P < 0.05)  of the mini-core collection using the genic SNPs. The mini-core collection has been clustered into three subpopulations and an admixture group using a selection of 6,232 SNPs, which is in consistency with the results of phylogenetic analysis and PCA determined with all 26,600 high-quality SNPs. This indicates that the population structure obtained in this study is highly reliable. Although the population structure of the mini-core collection was weaker with a low ΔK value, relative to other plant species such as rice , similarThe cultivars and landraces of the mini-core collection were collected from four geographical regions of the origin and diversity center of Jilin ginseng, but only a limited relationship was observed between the subpopulations and geographical regions from which they were collected. This result was previously observed by This study has examined the genetic diversity of the mini-core collection by major allele frequency, heterozygosity, gene diversity, and polymorphism information content (PIC) based on 6,232 genic SNPs. Ginsenosides are the most important bioactive components in ginseng for healthy food and medicine; therefore, they are widely used to evaluate ginseng quality. However, variation of ginsenoside contents across genotypes, environments, ages, seasons, and tissues  makes itThe present study has, for the first time, revealed that extensive correlations exist in content among the 16 ginsenosides in the mini-core collection, and when they were subjected to network analysis, they have formed only a single network consisting of two clusters and with each being correlated with at least one of the other ginsenosides. These results suggest that the ginsenosides are synthesized through a single or multiple related biological pathways and reflect their relationships in the pathway. However, variation of the correlations has been observed among the cultivars and landraces collected from different geographical regions. Because the samples used for the ginsenoside assay were collected from the cultivars and landraces grown from the same trials, the variation of the ginsenoside content correlations might result from genetic difference of the cultivars and landraces collected from different geographical regions. The correlations of the ginsenosides provide a clue useful for construction of the pathway of ginsenoside biosynthesis and information necessary for breeding varieties of high quality in multiple ginsenosides. Therefore, we have inferred the relationships of the ginsenosides in the pathway of ginsenoside biosynthesis, which is helpful for comprehensively deciphering the molecular mechanisms underlying ginsenoside biosynthesis.β-AS, CAS, DS, FPS, SE, SS, UGT71A27, UGT74AE2, CYP716A53v2, CYP716A52v2, and CYP716A47 , even though they had a wide variation among the cultivar and landrace pairs. These results indicate that the cultivars and landraces of Jilin ginseng currently used in production and breeding are substantially related with each other in both gene expression and mutations. Nevertheless, the patterns of their relationships revealed were not consistent between the gene transcript expressions and genic SNPs. Although the inconsistency could be partly attributed to the facts that gene expressions are consequences of the interactions of multiple factors, such as genotype-by-environment, gene-by-non gene elements, nucleotide methylation, chromatin modification, and small RNA regulation, whereas SNPs are relatively stable across these factors, it would be rather more likely to reflect different aspects of relationships between the cultivars and landraces. The relationships of 83%–89% of the cultivar and landrace pairs revealed by expressions of 15 ginsenoside biosynthesis gene transcripts or the contents of 16 ginsenosides were significant (P < 0.05), thus confirming the general conclusion that the cultivars and landraces of Jilin ginseng tended to be related. These results indicate that the expressions of the ginsenoside biosynthesis genes as well as the biosynthesis of the ginsenosides in which the genes are involved somehow are related or involved in a single or related processes. Moreover, it should be pointed out that the relationships do not seem to dent the robustness and applications of the mini-core collection for advanced ginseng research as this study did not identify significant kinships among the cultivars and landraces. Finally, the relationships of the cultivars and landraces revealed by any of genome-wide gene transcript expressions, ginsenoside biosynthesis gene transcript expressions, ginsenoside contents, and genic SNPs were significantly different between subpopulations of the mini-core collection, or the cultivars and landraces collected from different geographical regions.Knowledge of the relationships between cultivars, landraces, and germplasm lines is crucial to understanding and use of the germplasm collection for advanced ginseng research and breeding. The present study has, for the first time, examined the relationships between ginseng cultivars and landraces using genome-wide gene transcript expressions, ginsenoside biosynthesis gene transcript expressions, ginsenoside contents, and genic SNPs. Of these characters, the relationships of all pairs of the cultivars and landraces revealed by either expressions of 10,000 random gene transcripts or 6,232 high-quality genic SNPs were significant  can be found below: MPZ and YW conceived and designed the project, and MPZ revised and edited the manuscript. SL and YFW wrote the manuscript draft. HH, PC, JL, and YJ extracted and quantified ginsenosides. SL, LL, KW, and MZZ extracted, purified, and quantified the RNA. SL, MC, YH, and LZ conducted data analysis. All authors contributed to the article and approved the submitted version.


Goats with diverse economic phenotypic traits play an important role in animal husbandry. However, the genetic mechanisms underlying complex phenotypic traits are unclear in goats. Genomic studies of variations provided a lens to identify functional genes. The work aimed to search for candidate genes related to body measurements and body weight of Karachai goats based on genome-wide associations. Structural annotation of the identified genomic regions showed that they regulate the processes of transcription, cell proliferation, angiogenesis, body growth, fatty acid and lipid metabolism, nervous system development, and spermatogenesis. For further use in breeding, SNPs were selected that were significant for live weight at four and eight months. It was located in the vicinity of structural genes. An experimental PCR-RV test system was developed and tested for the genotyping of significant SNPs. Our study found a set of new DNA markers for genetic improvement in meat goats and provided novel insights into the genetic mechanisms of complex traits.CRADD, HMGA2, MSRB3, FUT8, MAX, and RAB15 were selected to create a test system. The study of meat productivity after slaughter and chemical analysis of muscle tissue in Karachai goats at the age of 8 months of different genotypes according to the identified SNPs revealed that rs268269710 is the most promising for further research and use in breeding. The GG genotype is associated with a larger live weight of animals, a larger carcass yield, the content of the boneless part in it, and the ratio of protein and adipose tissue in meat preferred for dietary nutrition. These results will contribute to the genetic improvement of Karachai goats.Goats with diverse economic phenotypic traits play an important role in animal husbandry. However, the genetic mechanisms underlying complex phenotypic traits are unclear in goats. Genomic studies of variations provided a lens to identify functional genes. The work aimed to search for candidate genes related to body measurements and body weight of Karachai goats and develop an experimental PCR-RV test system for genotyping significant SNPs. Comparison of GWAS results for ages 4 and 8 months revealed 58 common SNPs for significant genotypes. 11 common SNPs were identified for body weight, 4 SNPs—for group of traits withers height, rump height, body length, 2 SNPs—for withers height and rump height, 1 SNP—for body length and chest depth. Structural annotation of genomic regions covering a window of ±0.20 Mb showed the presence of 288 genes; 52 of them had the described functions in accordance with gene ontology. The main molecular functions of proteins encoded by these genes are the regulation of transcription, cell proliferation, angiogenesis, body growth, fatty acid and lipid metabolism, nervous system development, and spermatogenesis. SNPs common to body weight and localized within a window of ±200 kb from the structural genes  Local breeds are most widespread in Central, South and East Asia and Africa and are a valuable source of animal products for developing countries [Goats are known as one of the most common farm animals. Their high adaptive abilities to various, and sometimes extreme, living conditions have formed many breeds of different productivity trends in more than 195 countries around the world ,3. The iountries ,7.Long-term breeding to improve productivity while maintaining high adaptive qualities of goats affected changes in their genome, as evidenced by several studies of modern and ancient goat DNA ,8. The sPRDM6, PSTPIP2, SUN3, MANEA, CDKAL1, CDH9, CBLN4, SOX4, SGCG, FIG9, CNTFR, RPP25L, FGF9, SIPA1L and CCL19, are associated with the height and length of the body, with the width and depth of the chest, the width of the hips and the live weight of the young. Functional annotation of these genes has shown that they regulate the immune response, cell division processes, and skeletal muscle development [To increase meat productivity and include valuable genotypes in breeding programs for further replication, a search for genomic associations was performed, and candidate genes for body parameters and live weight of goats were established. Thus, in goats of the dazu black goat breed, genotyped using Goat 50K BeadChip and sequencing of the complete genome using GWAS, it was revealed that the genes elopment .KDM6A, AKAP12 and SNX29, which regulate the processes of proliferation of muscle tissue cells [In Kashmiri (downy) goats, a connection was found with the live weight and height, body length, depth, and chest width of the genes ue cells ,28,29.DKK2, TBCK, FGF, and ANK2, which are involved in the processes of embryonic development and regulation of cell proliferation and growth, has been established with the body size in goats of seven breeds bred in Pakistan [The association of the genes Pakistan .ATF3, ADAMTS14 and COL14A1), general growth and development , limb development (ECEL1 and PIEZO2), homeostasis and bone remodeling  [Candidate genes associated with body type in Mursiana granadina goats have been identified, including genes associated with collagen synthesis (d EPHA3) .BTAF1, NTM and GRID1 genes. In addition, some of the established SNPs were localized near the genes CEP78, ROBO1, ZFP36L2, SPTLC3, CTR 9 and ZFHX3. Functional annotation of these genes has shown that they are involved either in growth, development or in both basic cell functions from embryonic to adult life of an animal [Three SNPs associated with body weight were identified in Beetal goats, which were located within the n animal .PRLR, MYLK and CADM2 [IGF-I [Several studies performed on different breeds of goats have established a connection with the live weight and growth dynamics of such genes as nd CADM2 ,34,35, I2 [IGF-I , which aIn Russia, there has recently been a growing interest in breeding dairy and meat-dairy goats. Goats of double productivity are most widespread in Altai, Tyva, Khakassia and the North Caucasus . One of Taking into account the relevance of genome-wide associative analysis, the work aimed to search for candidate genes associated with body measurements and body weight of Karachai goats to develop an experimental PCR-RT test system for genotyping significant SNPs based on functional annotation of genes, as well as to study meat productivity indicators of different genotypes.To obtain a representative sample of young Karachai goats and exclude kinship, 287 clinically healthy animals  at four months were randomly selected in genetically isolated herds using unrelated producers and belonging to six different farmers. The location of the farm herds was chosen in such a way as to cover the entire region of breeding Karachai goats in the North Caucasus. Farms “Pyatigorsky” and “Maysky” were located at an altitude of 400–600 m above sea level, “Kyzyl-Kala”, “Storozhevaya”, “Darik”—900–1200 m, “Uchkulan”—1500 m. The distance between the farms did not exceed 350 km . The sysIn the selected animals at 4 and 8 months, body measurements such as rump height (RH), body length (BL), chest perimeter (CP), chest width (CW), chest depth (CD) and rump width (RW) were recorded using measuring sticks and tape, in addition, withers height (WH) were determined by weighing on an electronic scale. The data obtained are summarized in The fixed effect of each trait in the model was identified by the General Linear Model (GLM) procedure using the Statistical 10 program.1—the regression coefficient of the model; animalj—the fixed effect of i th individual (j = 1–269) weighed by covariance structure for genomic relationship matrix ) built from the genomic information using VanRaden’s method [To describe the data, a mixed linear model was used, the equation of which is presented as follows:s method ; e—residhttp://zzz.bwh.harvard.edu/plink/, accessed: 9 June 2022) [p-value < 10−6. The received data were processed by the GenomeStudio 2.0 software and converted into the Plink . The format of standard A/B genotypes has been converted to a nucleotide format corresponding to allelic variants. The data that did not pass the quality filter was not applied in further analysis. As a result, genotypes of 269 animals were used on 47,647 polymorphic sites.Genomic DNA was isolated from ear tissue and blood samples according to the protocol of the DNA-Extran kit . The concentration was determined on a Qubit device , and samples were taken with a ratio of A260/280 in the range from 1.7 to 2.0. Genotyping was performed using GoatSNP53K Beadchip . Processing of genotype data quality control was carried out using Plink 1.9 (ne 2022)  by the fTo stratify data from six herds, a Principal Component Analysis (PCA) was performed using the PLINK v1.9 program.p-value was higher than the quotient of dividing the significance level by the number of polymorphic sites whose p-value of the test was less than 0.05 were considered potentially significant . The quantile-quantile (Q-Q) graphs were constructed by distributing the received and expected log10  with coefficients λ. The distribution of associations and significant SNPs were visualized on a Manhattan graph constructed using R v. 3.5.2 R [The associative search was performed using Plink, a set of tools for analyzing associations of the entire genome, namely the calculation of linear regression dependence and determination coefficients. The significance of the regression coefficients of polymorphic sites was evaluated using the Bonferroni null hypothesis test. Sites whose  3.5.2 R .p value) with inflation factors (λ). The association map and the significant SNPs were visualized in the Manhattan plot with a threshold line. The Manhattan and Q–Q graphics were generated with R v. 3.5.2.The quantile–quantile (Q–Q) plots were visualized by plotting the distribution of obtained vs. expected log10  terms for structural candidate genes identified in a window of ±0.2 Mb from statistically significant SNPs were used by DAVID v6.8 . Signifiviations .The method of hybridization-fluorescence detection in “real-time” mode based on Taq-Man technology was chosen to construct a multiplex test system . Two all4)2SO4; 75 mM Tris-HCl; pH = 8.8; 0.1% (v/v) Tween 20; 2.5 mM MgCl2; 0.25 mM dNTP, 0.15 units Taq-DNA polymerase, and 1 µL (~10–200 ng) of the genomic DNA under study. After initial denaturation , 40 amplification cycles were performed .The polymerase chain reaction was carried out in a final volume of 20 µL: 19 µL of a reaction mixture consisting of 20 mM , muscle tissue samples of 100 g were taken to perform chemical analysis in a total sample of minced meat. The percentage of moisture, protein, fat, and ash was determined in accordance with accepted national methods ,48,49. TThe principal component analysis shows a distribution of the studied population between three clusters. The first principal component (PC1), which is responsible for 8.76% of genetic variability, clearly separated the Maysky herd from the five remaining herds, while the principal component two (PC2), which explained 4.86% of genetic variability, distinguished the Darick, Kyzyl Kala and Storozhevaya herds from the Uchkulan herd. The individuals of the Piatigorsky herd were distributed between two clusters .Considering the observed population stratification, we performed GWAS using the first two PCs as covariates.The descriptive GLM statistics for body weight and body conformation traits in Karachai goats at ages four and eight months are summarized in The growth and development indicators at the age of eight months were characterized by a slightly larger scale of variability compared to similar indicators at four months .A comparative analysis of the GWAS results for the actual phenotypic values of the studied traits at the ages of 4 and 8 months showed that for most of them, the observed distribution of deviations from the normal distribution for confidence values approached the expected one .Association analysis made it possible to identify genome-wide SNPs for eight studied traits at the age of 8 months, including live weight (5 SNP), height at the withers (10 SNP), height at the sacrum (9 SNP), oblique trunk length (6 SNP), chest circumference (4 SNP), chest width (30 SNP), chest depth (8 SNP) and width in rump (14 SNP). For comparison, at four months, genome-wide SNPs were identified for five studied traits, including live weight (33 SNP), height at the withers (2 SNP), height at the sacrum (4 SNP), oblique trunk length (2 SNP) and chest depth at four months (1 SNP) , Table 2Comparison of the GWAS results for the studied growth and development indicators of young Karachai goats aged 4 and 8 months revealed the presence of 58 common SNPs for different traits . 11 commFor three traits—the height at the withers, the height of the sacrum, and body length  there were four SNPs in total , for two types—height at the withers, height of the sacrum , body length and chest depth (BL and CD) correspond to 2 SNPs (snp15461-scaffold164-717536 and snp50508-scaffold725-933710) and 1 SNP (snp41877-scaffold546-944746).Structural annotation of genome regions using Ensemble web resource within ±0.20 Mb window to identify significant SNPs revealed 288 genes .http://geneontology.org/, accessed on 15 November 2022)  .The functions of the identified genes are mainly related to the regulation of transcription, angiogenesis, body growth, metabolism of fatty acids and lipids, development of the nervous system, and spermatogenesis.The following approach was used to select SNP candidates for designing a test system. At the first stage, SNPs common to 2 or more signs of growth and development at the ages of 4 and 8 months were selected, while at least one of the signs should achieve a genome-wide level of reliability of the identified associations. A total of 10 such SNPs were identified .Six SNPs out of ten SNP candidates were associated with the body weight at four and eight months of age in Karachai goats . TherefoThe presence of genes localized within the ±200 kb (0.20 Mb) window was established for three SNPs: T47480416C (rs268269710 A/G) and A23345368G (rs268270492 G/A) on Chr 5  and A25854668G (rs268234545 A/G) on Chr 10 (3 genes) . These tAnalysis of scientific information sources for the previously described functions of the genes presented in The obtained data served as a justification for the feasibility of developing an experimental multiplex test system for genotyping SNP candidates associated with the live weight of young Karachai goats. A detailed analysis of SNP candidates selected for the design of molecular genetic test systems is presented in Based on the generated list of SNP candidates, three test systems were created to determine the alleles of the following polymorphic loci in the goat genome: rs268234545, rs268270492, and rs268269710. The structural solutions underlying the determination of polymorphisms of candidate genes are presented in Primers and fluorescently labeled probes developed for genotyping polymorphism at rs268234545, rs268270492, and rs268269710 loci are presented in Validation of test systems was performed by comparing genotyping results on 46 goats obtained using Goat 60K BeadChip and developed test systems. The results of genotyping of all 46 animals obtained using the two above-mentioned methodological approaches completely coincided.The developed test systems were used for genotyping 35 goats and the subsequent study of post-slaughter signs of meat productivity in carriers of different genotypes. The selected animals had a live weight equal to the average for a group of animals of a certain genotype.Quantitative and qualitative traits of meat productivity after slaughter were studied in animals of different genotypes at the age of eight months, which are presented in This age is determined because up to this period, the most intensive growth in Karachai goats was observed, and there was the greatest transformation of feed nutrients into productivity, which is known to be effective for meat production .p < 0.05); 10.5% (p < 0.01) and 5.2% (p < 0.05), respectively.Analysis of the data obtained allowed us to establish that according to SNP rs268269710, the best indicators of meat productivity were characterized by goats of the GG genotype. So, in comparison with AA genotype animals, they had a significant advantage in pre-slaughter and the fresh carcass weight, slaughter yield, boneless meat weight and loin eye area by 8.9%; 13.6%; 4.3% (p < 0.01); 8.4% (p < 0.05); 18.3% and 15.7% (p < 0.01), respectively.Comparison of animals of different genotypes in terms of meat productivity by SNP rs268270492 revealed that GG genotype compared to AG genotype had higher pre-slaughter, slaughter yield, slaughter weight, boneless meat weight and loin eye area by 15.8%; 25.7%  from the Recently, numerous studies have been conducted to analyze the genetic architecture of goats, as one of the most common domestic species, well adapted to extreme environmental conditions and possessing phenotypic diversity . In the Further research is likely to be related to the identification of new genes, as well as their selection signatures (SS) and the study of multiple effects on economic and even non-economic traits that are important in the selection of desirable or elimination of undesirable goat genotypes, in the development of breeding programs in goat farming . HoweverThe present study was devoted to searching for candidate genes associated with body measurements and live weight of Karachai goats based on genome-wide associations and developing an experimental PCR-RV test system for genotyping significant SNPs. At the same time, we considered it especially important to identify areas of the genome that would be associated with live weight at four months of age, and they would continue to be associated with this indicator until eight months, i.e., until the optimal age of slaughter and obtaining meat with lower fat content.There is evidence of significant differences in the genetic structure of the same traits in different age periods. For example, in sheep, the genes associated with body weight at birth, at weaning, at six, 12 and 30 months were different ,58. AlsoFor the studied traits at the ages of four and eight months, 58 common SNPs were identified and annotated for 288 genes. Function descriptions in terms of gene ontology are available for 52 of these genes. Despite their significant number, no matches were found with genes for which associations with body size and weight were determined in other studies. At the same time, the generalization of the described functions of candidate genes identified for Karachai goats showed their similarity to those known for other breeds of goats and sheep. Thus, the analysis of ontology and signaling pathways showed that in goats of different breeds, they regulate proliferation, growth, development of cells of the whole organism, and myocytes in particular, cell transport, transcription and translation, reproduction functions, as well as the process of limb development, homeostasis and bone remodeling, collagen synthesis ,34,35,36One of the tasks of our research was to design a test system for genotyping Karachai goats according to the most significant SNPs, the selection of which was carried out in three stages: (1) common SNPs for two or more signs of growth and development at the age of 4 and 8 months, while at least one of the signs should have a genome-wide level of association confidence; (2) then common SNPs for live weight only; and (3) SNPs localized within a window ±200 kb of structural genes. In the first stage, ten were revealed. In the second—six, and the third—three SNPs were identified in the vicinity of six genes CRADD, HMGA2, MSRB3, FUT8, MAX, RAB15, and five of which functions in gene ontology were described.Thus, it was found that the CRADD gene, along with SOCS2 and PLXNC1 genes, is lo-calized within the so-called “high-growing region” on chromosome 10 in mice. Those genes are associated with the phenotype of high growth in peace and with no obesity . AnotherPrevious studies have revealed a high level of HER2 expression during embryogenesis and a key role in growth-determining and early postnatal development . CRISPR/Sufficiently convincing data on the connection of the above genes with signs of animal growth were the justification for their selection for developing a PCR-RV test system. It was designed, successfully validated, and used for genotyping young goats of the next generation and studying their meat productivity indicators after slaughter. The results showed that the SNP in the rs268269710 position is the most promising for further research and use in breeding Karachai goats. The GG genotype is associated with a larger live weight of animals, increased carcass yield, and the content of the pulp part in it, but also the ratio of protein and adipose tissue in meat preferred for dietary nutrition. The data obtained seem to be explained by the fact that the SNP at the rs268269710 position is located in close proximity to the HMGA2 and MSRB3 genes, for which a more pronounced association with growth energy in young animals has been described ,67,68,69Unfortunately, it is not possible to compare our data with the results of other studies. As a rule, such works demonstrate the result of GWAS, functional annotation of genes for identified SNPs, and description of protein products of candidate genes ,72,73,74In conclusion, we would like to emphasize that our results are preliminary, and to confirm the feasibility of using a PCR test for genotyping and selecting animals preferred for increasing goat meat production, further testing on a larger sample of animals with additional studies on the meat qualities of animals of different genotypes is necessary.Thus, for the first time, GWAS of Karachai goats was conducted to identify genes associated with body traits at different ages. We identified 58 common SNPs for different signs of body size (conformation) and body weight at 4 and 8 months. The genes CED, HMGA2, MSRB3, FUT8, MAX, and RUBI 15 are candidates for signs of body weight and meat productivity and are promising for further priority studies. These results will make a significant contribution to understanding the mechanisms underlying the signs of meat productivity and will contribute to the genetic improvement of Karachai goats.


The impact of the clinical high-risk for psychosis (CHR-P) construct is dependent on accurately predicting outcomes. Individuals with brief limited intermittent psychotic symptoms (BLIPS) have higher risk of developing a first episode of psychosis (FEP) compared to individuals with attenuated psychotic symptoms (APS). Supplementing subgroup stratification with information from candidate biomarkers based on neurobiological parameters, such as resting-state, regional cerebral blood flow (rCBF), may help refine risk estimates. Based on previous evidence, we hypothesized that individuals with BLIPS would exhibit increased rCBF compared to APS in key regions linked to dopaminergic pathways.n = 30 healthy controls [HCs], n = 80 APS, n = 20 BLIPS and n = 20 FEP). Global gray matter (GM) rCBF was examined in addition to region-of-interest (ROI) analyses in bilateral/left/right frontal cortex, hippocampus and striatum. Group differences were assessed using general linear models: (i) alone; (ii) with global GM rCBF as a covariate; (iii) with global GM rCBF and smoking status as covariates. Significance was set at p < 0.05.Data from four studies were combined using ComBat (to account for between-study differences) to analyse rCBF in 150 age- and sex-matched subjects  = 1,41, p = 0.24], bilateral frontal cortex , hippocampus  or striatum  rCBF. Similar null findings were observed in lateralized ROIs (p > 0.05). All results were robust to addition of covariates (p > 0.05). No significant clusters were identified in whole-brain voxel-wise analyses (p > 0.05FWE). Weak-to-moderate evidence was found for an absence of rCBF differences between APS and BLIPS in Bayesian ROI analyses.Whole-brain voxel-wise analyses and Bayesian ROI analyses were also conducted. No significant group differences were found in global [On this evidence, APS and BLIPS are unlikely to be neurobiologically distinct. Due to this and the weak-to-moderate evidence for the null hypothesis, future research should investigate larger samples of APS and BLIPS through collaboration across large-scale international consortia. Psychotic disorders are prevalent, with an estimated 23.6 million cases worldwide,  and are To target interventions for CHR-P individuals at the highest risk of developing psychosis, accurate prediction of FEP onset is essential. Clinical interviews can be used to determine whether individuals meet CHR-P criteria, but while these have high specificity  their sensitivity is sub-optimal  –7. For tFor more effective risk stratification, information is needed from additional sources, particularly from those directly relating to underlying neuropathology  as these will likely capture different elements of disorder progression. Alterations in frontal, hippocampal and striatal functioning reliably appear, persist and progress from the CHR-P state –15 to FEPseudo-continuous arterial spin labelling (ASL) allows for indirect measurement of regional, resting neuronal activity through regional cerebral blood flow (rCBF) , a potenCHR-P individuals display elevated dopamine signaling in the midbrain and striatum –28, as wThere are also distinct neurobiological differences between medicated and unmedicated FEP patients, with medicated patients showing increased rCBF in the striatum and reduced rCBF in the frontal cortex compared to clinical controls , while uDue to the low proportion of CHR-P individuals who meet BLIPS criteria in CHR-P samples, there has been difficulty in recruiting samples that are large enough to be sufficiently powered to investigate differences in rCBF between APS and BLIPS. Therefore, in this study, we combined data from four studies to investigate differences in global and regional rCBF between CHR-P subgroups. Due to the very small proportion of individuals meeting GRD criteria and this subgroup having psychosis risk that is not significantly different from HCs , analyseA total of 150 individuals were recruited across four studies conducted at the Institute of Psychiatry, Psychology & Neuroscience, King’s College London between 2008 and 2020  10, 37,,37, 38. N = 30) were recruited from the local community as part of three studies conducted at King’s College London (HCs (e London . ParticiN = 100) aged 18–35 were recruited from four specialist early detection services: Outreach and Support in South London (OASIS)  , Tower H (OASIS) . CHR-P s (OASIS) . CHR-P s (OASIS)  or manic (OASIS) . CHR-P in = 20) were recruited from first episode psychosis teams within South London and Maudsley NHS Foundation Trust and Central and West London NHS Trust. To avoid the confounding effects of antipsychotics on CBF  .n = 20, APS n = 59, BLIPS n = 14) were scanned with a General Electric Signa HDX 3 Tesla scanner , using an 8-channel head coil as part of the NEUTOP and PROD studies  were scanned with a General Electric Discovery MR750 3 Tesla scanner  using a 32-channel head coil as part of the BRC-UHR study  were scanned with a General Electric Discovery MR750 3 Tesla scanner , using a 12-channel head coil as part of the TR-FEP study  to remove the majority of extra-cerebral signal with the rest removed by multiplication of the “brain only” binary mask, obtained in step 2, with the rCBF map in the space of the T1 image; and (5) T1-weighted scans and skull-stripped rCBF maps were spatially normalized to MNI avg152 standard space. Finally, rCBF maps were spatially smoothed using a 6 mm Gaussian smoothing kernel.ASL data were pre-processed using the Automatic Software for ASL Processing (ASAP) 2.0 toolbox  running Because the rCBF data were acquired in four different studies across three scanners, ComBatDifferences between HC, APS, BLIPS and FEP were investigated using Fisher’s exact test for categorical variables due to small expected values in the contingency tables. General linear models were used for continuous variables to assess the effect of group on the variable of interest.To measure global rCBF signal, we used the ASAP toolbox to extract average rCBF values from a gray matter mask for each subject in standard space. The ICBM-152 mask was obtained from the DARTEL toolbox in SPM and thresholded to contain voxels with a > 0.25 probability of being gray matter. All subsequent analyses were conducted with and without global rCBF as covariate.n = 9 datapoints in HC; n = 17 APS; n = 0 BLIPS and n = 6 FEP) were replaced using multivariate imputation by chained equations (MICE) using the mice package (version 3.13.0)  , followiAnalyses of global gray matter and all ROIs  were conducted using general linear models to assess the effect of group on rCBF. All six contrasts  were of interest. As a second step, global rCBF smoking status, age and sex were added to the model as covariates . As a third step, as antipsychotic medication is known to affect rCBF , 45, supp < 0.05.All ROI analyses were conducted using R version 3.6.3, with the “Emmeans” package (version 1.5.2–1)  used to p < 0.005 uncorrected); clusters were reported as significant at p < 0.05 using FWE correction in SPM, with analyses restricted to gray matter tissue using a binarized gray matter mask.For completeness, we performed voxel-wise whole brain analyses in SPM12. Global gray matter rCBF was entered as a covariate. The following post-hoc contrasts were used: HC < APS; APS < BLIPS and BLIPS <FEP. Cluster-level inference was used  in our analyses corresponds to an increase in evidence in favor of the null hypothesis (no differences in rCBF between APS and BLIPS). We used Jeffreys’ classification scheme  to interN = 30 HCs were recruited alongside n = 80 APS subjects, n = 20 BLIPS subjects and n = 20 FEP subjects. There were no significant differences in distribution of ethnicities between the groups (p = 0.47). The groups differed in clinical characteristics (p < 0.001). All groups were significantly different from each other in respect to global functioning (p < 0.001) with FEP subjects having the lowest GAF scores, followed by APS, BLIPS and HC, respectively.eristics . Psychotn = 12 APS (15.0%) and n = 2 BLIPS (10.0%) were confirmed to have developed a psychotic disorder over follow-up . A higher proportion of APS subjects were daily tobacco smokers compared to HCs (p = 0.049). There were additionally significant differences in frequency of cannabis use between groups (p = 0.029). Sociodemographic, clinical and substance use descriptors are presented in F = 1.408, p = 0.243] between HCs, APS, BLIPS, and FEP  = 1.012, p = 0.389], bilateral hippocampus , or bilateral striatum  between HCs, APS, BLIPS and FEP  were excluded or when individuals with previous antidepressant exposure  were excluded  global rCBF alone; (ii) global rCBF, smoking status, age and sex as covariates (FWE > 0.05).No significant group effects were identified in whole-brain rCBF analyses (p01 = 3.05), right frontal cortex (BF01 = 3.36), right hippocampus (BF01 = 3.01) and left striatum , left frontal cortex (BF01 = 2.42), bilateral hippocampus (BF01 = 2.10), left hippocampus (BF01 = 1.83), bilateral striatum (BF01 = 2.87) and right striatum  was seen in bilateral frontal cortex  to moderate  so our findings may not be conclusive. The weakest evidence was for rCBF in the left hippocampus, which has been most strongly implicated in neurobiological contrasts between CHR-P and HCs –12. PrevSociodemographic differences between our sample and those of previous studies may account for some differences in the results. As BLIPS is a less common CHR-P subgroup compared to APS, all matching between subgroups was in relation to the BLIPS sample. This means that the characteristics of HC, APS and FEP included in this study were selected to match the BLIPS cohort and are therefore not necessarily representative of their underlying populations. For example, despite the proportion of males historically being generally similar across APS and BLIPS , 42, 64,Two of the most robust predictors of psychosis onset in CHR-P individuals are attenuated positive psychotic symptom severity and global functioning at baseline, but the pattern of presentation varies between APS and BLIPS . The acuLow rates of transition to FEP could also dilute potential differences in perfusion between APS and BLIPS subgroups. While expected transition rates are 24 and 38% in APS and BLIPS, respectively,  only 15%n = 27; 18%). Thus, there may be slightly different sensitivity to tissue perfusion in those data, although we used ComBat to mitigate against this. Moreover, due to a lack of harmonization between study protocols, we were limited in terms of available covariates   , 80. HowIn conclusion, we have found weak-to-moderate evidence for an absence of difference in global GM, frontal, hippocampal and striatal rCBF between APS and BLIPS. Moreover, we did not find differences in rCBF between BLIPS and HCs or FEP either in whole-brain voxel-wise analysis or ROIs. These results suggest that rCBF alone may not be suitable for risk stratification in CHR-P individuals.dominic.a.oliver@kcl.ac.uk).The datasets presented in this article are not readily available because at the time the data was collected it was not routine for participants to be asked for their consent to share data publicly, so this permission was not obtained. Whilst we are in favor of data being open access, no supporting data is available for this study. Requests to access the datasets should be directed to DO  and OH (094849/Z/10/Z), Medical Research Council (MRC) grants to PM (G0700995) and OH (MC_A656_5QD30_2135), a Maudsley Charity grant (No. 666) to OH, and the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust and King’s College London .SJ has received honoraria for educational talks given for Lundbeck, Janssen, and Sunovion. OH has received investigator-initiated research funding from and/or participated in advisory/speaker meetings organized by Angelini, Autifony, Biogen, Boehringer-Ingelheim, Eli Lilly, Heptares, Global Medical Education, Invicro, Janssen, Lundbeck, Neurocrine, Otsuka, Sunovion, Recordati, Roche and Viatris/Mylan. OH has a patent for the use of dopaminergic imaging. OH is a part-time employee and stockholder of Lundbeck A/S. PF-P has received research funds or personal fees from Lundbeck, Angelini, Menarini, Sunovion, Boehringer Ingelheim, Mindstrong, Proxymm Science, outside the current study.The remaining 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.


MinSNPs encompasses functions that facilitate rapid and flexible SNP mining, and clear and comprehensive presentation of the results. The minSNPs’ running time scales in a linear fashion with input data volume and the numbers of SNPs and SNPs sets specified in the output. MinSNPs was tested using a previously reported orthologous SNP matrix of Staphylococcus aureus and an orthologous SNP matrix of 3,279 genomes with 164,335 SNPs assembled from four S. aureus short read genomic data sets. MinSNPs was shown to be effective for deriving discriminatory SNP sets for potential surveillance targets and in identifying SNP sets optimised to discriminate isolates from different clonal complexes. MinSNPs was also tested with a large Plasmodium vivax orthologous SNP matrix. A set of five SNPs was derived that reliably indicated the country of origin within three south-east Asian countries. In summary, we report the capacity to assemble comprehensive SNP matrices that effectively capture microbial genomic diversity, and to rapidly and flexibly mine these entities for optimised marker sets.Here, we present the R package, minSNPs. This is a re-development of a previously described Java application named Minimum SNPs. MinSNPs assembles resolution-optimised sets of single nucleotide polymorphisms (SNPs) from sequence alignments such as genome-wide orthologous SNP matrices. MinSNPs can derive sets of SNPs optimised for discriminating any user-defined combination of sequences from all others. Alternatively, SNP sets may be optimised to determine all sequences from all other sequences,  This can be combined with machine learning  of polymorphic positions in the alignment. SNP sets are assembled iteratively on the basis of maximised combinatorial resolving power. In other words, the program scans all acceptable positions to identify the SNP that confers the maximum discriminatory power in combination with SNPs already in the SNP set (if any). This SNP is added to the set. Where more than one SNP confers the same increase in resolving power, the SNP nearest to position 1 of the alignment will be added to the set.There are two user-selectable algorithms for measuring resolving power. 1.Percent mode. The resolving power is the percentage of sequences in the alignment that are not discriminated from the user-selected sequence(s) (the group of interest). The SNP sets are constrained to 100% sensitivity. The first SNP identified is the 100% sensitive SNP with maximum possible specificity. Subsequent SNPs are selected on the basis of the maximum possible increase in specificity in combination with the previously selected SNP(s). All alignment positions that are variable within the group of interest can optionally be excluded from the analysis. This has no effect on the search algorithm for two-state SNPs but can affect searches involving three-state SNPs. We suggest that, where possible, the group of interest be composed of > 1 sequence to avoid the identification of spurious SNPs arising from sequencing errors. 2.Simpson mode. The resolving power is the power to discriminate “all from all”, as measured by the Simpsons index of diversity. In this context, the index of the diversity is the probability that any two sequences in the alignment will be discriminated from each other by the SNP set, as calculated by index of the diversity = j is the number of sequences defined by the class j  in/from the SNP set. Where positions are included, new SNPs are identified based on resolving power in combination with the included positions. This facilitates rapid exploration of SNP sets.minSNPs can identify alignment positions where at least one sequence has a non-standard DNA symbol, and these positions are optionally excluded from the analysis. Indels (dashes) default to being regarded as symbols equivalent to other symbols. Alternatively, the user can specify that indels trigger the exclusion of the relevant alignment positions from the analysis. There is also an optional function to exclude positions with SNPs with > 2 alleles.i.e., no additional SNPs are derived. This will reveal how the sequences assort in relation to allelic profiles of the “forced included” SNPs. Alternatively, this can be done in reverse to assess the performance of a Simpson’s index of diversity maximised SNP set to detect user-defined subsets of sequences with 100% sensitivity. These strategies provide considerable flexibility regarding the exploration of SNP sets.minSNPs provides a cumulative increase in resolving power as the sets are built, and the tabulated information indexing the sequences in the alignment as defined by each allelic profile. For percent mode analyses, this is within a “group of interest or non-group of interest” framework. The outputs are presented in the R console and optionally outputted to a tab-delimited format file. A facile method to fully define the informative power of a SNP set derived by percent analysis is to force the inclusion of the identified SNPs into a Simpson mode analysis, in which the user-defined SNP set size equals the number of included SNPs, Package access and documentation are described in the “Availability” section. An abbreviated user guide and the search algorithm are in To explore the potential utility of minSNPs, we: 1.Determined the relationship between input alignment dimensions and the number and size of output SNP sets, with running time; 2.S. aureus and P. vivax and;Generated SNP sets of potential relevance to surveillance from orthologous SNP matrices derived from genomic epidemiology studies in  3.Generated single orthologous SNP matrices from multiple short-read data sets to demonstrate the utility of minSNPs for analysing large-scale comparative genome data from multiple studies.https://doi.org/10.6084/m9.figshare.19579816.v1). The faster run-time on a laptop when compared to a high-performance cluster (HPC) was due to the simpler architecture of the machine; we note that when the dimension of the alignments increases, the HPC’s comparative performance improves. Therefore, given a higher number of cores and increased memory available, an HPC can outperform a laptop. Our general experience is that minSNPs can be readily used for substantial analyses on PCs with an Intel i5-7500T CPU running at 2.70 GHz,and with 8 Gb of RAM, and 236 Gb of storage.The relationships between the analysis time and dimensions of the input alignment, the number of SNPs in the output SNP set, and the number of SNP sets in the output were determined. The relationship was linear with respect to all three parameters. Examples of running time are shown in To demonstrate minSNPs’ functionality, we analysed genome-wide orthologous SNP matrices to identify SNP sets diagnostic for a conserved lineage that is a potential surveillance target, SNP sets diagnostic for a broader phylogenetic lineage that encompasses the potential surveillance target, and SNP sets optimised with respect to Simpson’s index of diversity. For the latter, our interests were in the resolving power (the Simpson’s index of diversity) and the concordance of the genotypes defined by the SNP sets with the phylogeny indicated by the orthologous SNP matrix.S. aureus isolates, four Staphylococcus argenteus isolates, and S. aureus Mu50, which was the reference genome for matrix construction   .https://doi.org/10.6084/m9.figshare.19579837.v1). A BLAST analysis demonstrated that for each of these SNPs, the alleles present in the ST762 isolates were not present in the public databases, suggesting that these SNPs have a generalised ability to discriminate ST762 from the remainder of the S. aureus complex (figshare: https://doi.org/10.6084/m9.figshare.19579831.v1).The STARRS study identified isolates of multilocus sequence typing (MLST) defined ST762  1) and were involved in transmission events leading to patient infections. ST762 is vanishingly rare globally but was prevalent in the STARRS study. We, therefore, used the ST762 lineage as a model for a potential surveillance target. Using minSNPs in percent mode, we determined that 12 SNPs each individually discriminated all the ST762 isolates from other isolates in the study, with 100% sensitivity and specificity (figshare: https://doi.org/10.6084/m9.figshare.19579837.v1). Similar to SNPs identified for ST762, a BLAST analysis returned 61 specimens from Genbank; out of these, 53 were CC1, with three false positives belonging to ST425 and five specimens untypeable by MLST (figshare: https://doi.org/10.6084/m9.figshare.19579831.v1).The same procedure was used to derive SNP sets that discriminate the CC1 (ST1 and ST762) STARRS isolates from the other isolates. It was found that there were 119 SNPs that each individually provided 100% sensitivity and specificity for CC1 isolates  that were selected based on having no SNPs in common. Both SNP sets discriminated the major lineages defined by the STARRS SNP matrix , Table 2Plasmodium epidemiological tracking  approaches the elimination of vivax malaria .Next, SNPs that discriminated all Malaysian specimens from all other specimens were derived. To streamline the analysis, only one of the K2 specimens was included. Initially, we confined the analysis to the 343,598 SNPs that do not encompass any ambiguity codes. This test was not successful. The maximum percent score obtained from five SNPs was 0.265, meaning that 73.5% of the non-Malaysian specimens were not discriminated from the Malaysian specimens. The complete results are in this figshare: . Two sets of two SNPs were identified, each of which discriminated all Malaysian specimens from all other typable specimens. For one SNP set, 20 specimens (7.72%) were untypeable, and for the other, 22 (8.49%). All the Malaysian specimens were typable with both SNP sets. The precise reason for the superior result from the matrix with ambiguity codes transformed has not been determined but is explicable from the minSNPs algorithm. The minSNPs’ requirement in percent mode that SNP sets provide 100% sensitivity for the group of interest is very stringent. A false negative defined by a single member of a group of interest disqualifies a position from inclusion in a SNP set. Being able to capture more diversity for the analysis by using the transformation procedure appears to have been critical, with this being explicable because positions with ambiguity codes will likely be the most diverse. A possible workaround for this constraint on SNP selection is to run separate analyses, each with subsets of the group of interest. This could yield SNPs that provide low but non-zero false negatives with respect to the entire data set.Transforming ambiguity codes to the major allele present at each relevant position proved a successful approach to identifying SNPs that discriminated against Malaysian specimens. The complete results are in this figshare: . The most discriminatory SNP set (index of diversity = 0.751) was investigated further. It was determined that the matrix defined eight allelic profiles. Although this number of profiles and the index of diversity do not indicate high discrimination, there was close concordance between allelic profile and country of origin . The concordances with country of origin were poor. Even with the larger number of allelic profiles, there were numerous instances of specimens from different countries having the same profile. A likely explanation is that positions that encompass ambiguity codes are polymorphic within countries. Such SNPs are more likely to generate ambiguity codes because both alleles may be present in a mixed infection. The exclusion of these positions will enrich for SNPs that separate specimens from different countries and are monomorphic within countries. This would be expected to facilitate the derivation of SNP sets that indicate the country of origin. The complete results are in this figshare: P. vivax, diversity-maximised SNPs without ambiguity codes are useful as minimal sets of markers for geographical tagging  were used to create an orthologous SNP matrix using a modified SPANDx pipeline . These pipeline . The modhttps://doi.org/10.6084/m9.figshare.19582885.v1. The mapping to the reference genome is in this figshare: https://doi.org/10.6084/m9.figshare.19582885.v1. We used this to validate the SNPs discriminating both ST762 and CC1 obtained earlier using only the STARRS dataset. Apart from one SNP set, all the previously identified single SNP sets retained 100% sensitivity and specificity for ST762 with this large data set. However, two of the SNPs were not present in the matrix. For CC1 , most of the previously identified SNP sets were not fully present in the matrix . For similar reasons, not all the members of the previously identified high Simpson’s index SNPs-sets were present in the new matrix, and no meaningful comparison between the previous analysis and current analysis could be made. The results are in this figshare: https://doi.org/10.6084/m9.figshare.19582885.v1.The matrix encompasses 3,279 isolates (including the reference genome Mu50) and 164,335 SNP positions, and is in figshare: https://doi.org/10.6084/m9.figshare.19582885.v1. We similarly identified 39 individual SNPs and 61 two-member SNP sets (100 SNPs sets) that discriminate all CC1 isolates from all others. The results are in this figshare: https://doi.org/10.6084/m9.figshare.19582885.v1.We re-ran the same tasks in previous STARRS datasets with the matrix. We identified 50 individual SNPs and 50 two-member SNP sets that discriminate all ST762 isolates from all others. The results are in this figshare: https://doi.org/10.6084/m9.figshare.19582885.v1.We then experimented with the Simpson mode analysis to accomplish two different tasks. First, we attempted to identify SNPs that discriminated all CCs from each other. To accomplish this, all the variant positions between isolates within the same CC were identified and recorded. A reduced matrix was then constructed that contained only a single isolate from each of the CCs. We then excluded from analysis all the previously recorded variant positions within CCs before running a Simpson mode search. It was found that a minimum of seven SNPs were required to discriminate all 33 CCs from each other. minSNPs was tasked to provide 200 alternative SNP sets that achieved a diversity value of 1.0. Of these, 165 of the sets had seven members; the remaining had eight members. The results are in figshare: D without reference to CC. Similarly, we identified five 10-SNP sets with a high Simpson’s index of diversity (figshare: https://doi.org/10.6084/m9.figshare.19582885.v1 Overview). Prior to running the minSNPs analysis, all but a subset of 100 CC22 isolates were randomly selected to be included in the input matrix to avoid overly biasing the analysis to include SNPs that discriminated within CC22. We obtained SNP sets with diversity values  ranging from 0.6314 to 0.6461. We selected the SNP set with the highest diversity value and constructed the allelic profile with the first five SNPs. As expected from the similar experiment performed with the smaller STARRS data set, there was close but imperfect correspondence between CC and allelic profile, even though there was no reference to CC in the SNP derivation procedure. The results are included in this figshare: https://doi.org/10.6084/m9.figshare.19582885.v1 Overview, from page 9. A graphical representation of the results is in this figshare: https://doi.org/10.6084/m9.figshare.19582885.v1.Next, we explored the resolving power of SNP sets identified simply to maximise S. aureus and P. vivax. minSNPs can facilitate the derivation of genetic marker sets customised for defined surveillance applications from global-scale genomic diversity data.minSNPs is a new R package. This software provides a flexible means for deriving from comparative genome data SNP sets that are optimised for lineage-specific or generalised resolving power. The functionality of minSNPs has been demonstrated using large genome-wide orthologous SNP matrices from


In modern plant breeding, genomic selection is becoming the gold standard to select superior genotypes in large breeding populations that are only partially phenotyped. Many breeding programs commonly rely on single-nucleotide polymorphism (SNP) markers to capture genome-wide data for selection candidates. For this purpose, SNP arrays with moderate to high marker density represent a robust and cost-effective tool to generate reproducible, easy-to-handle, high-throughput genotype data from large-scale breeding populations. However, SNP arrays are prone to technical errors that lead to failed allele calls. To overcome this problem, failed calls are often imputed, based on the assumption that failed SNP calls are purely technical. However, this ignores the biological causes for failed calls—for example: deletions—and there is increasing evidence that gene presence–absence and other kinds of genome structural variants can play a role in phenotypic expression. Because deletions are frequently not in linkage disequilibrium with their flanking SNPs, permutation of missing SNP calls can potentially obscure valuable marker–trait associations. In this study, we analyze published datasets for canola and maize using four parametric and two machine learning models and demonstrate that failed allele calls in genomic prediction are highly predictive for important agronomic traits. We present two statistical pipelines, based on population structure and linkage disequilibrium, that enable the filtering of failed SNP calls that are likely caused by biological reasons. For the population and trait examined, prediction accuracy based on these filtered failed allele calls was competitive to standard SNP-based prediction, underlying the potential value of missing data in genomic prediction approaches. The combination of SNPs with all failed allele calls or the filtered allele calls did not outperform predictions with only SNP-based prediction due to redundancy in genomic relationship estimates. Genotypic information utilized for genomic prediction normally comprises biallelic single-nucleotide polymorphisms (SNPs) that are enormously abundant in eukaryotic genomes . BesidesIn large-scale breeding populations, SNPs are usually assessed with SNP arrays; however, these platforms are prone to technical errors that result in failed allele calls. Markers with a very high failed call rate are commonly discarded from downstream genetic analyses . For thevia whole-genome long-read sequencing is economically not feasible. Targeted long-read sequencing of agronomically interesting genomic regions using ReadUntil  populations of Flint and Dent maize. The population consists of 10 Dent and 11 Flint half-sib DH families. The lines were evaluated as test crosses, the DH Dent lines were all crossed to a single Flint tester line (UH007), and all DH Flint lines were crossed to a single Dent tester (F353). All DH lines were genotyped with the Illumina MaizeSNP50 SNP array . This po2.2Brassica napus Express 617 genome v2 (SNP matrices were filtered to remove markers with non-unique positions (multiple BLASTn hits of flanking sequences) on reference genomes. In canola, we utilized the enome v2  and in menome v2 . The genThe copy corresponding to standard SNPs was then phased and imputed with the software “BEAGLE V5.2” . SubsequThe copy corresponding to the failed allele calls was recoded to successful call/successful call  and F/F . This matrix was then also filtered for minor allele frequency ≥0.05 (to rule out monomorphic markers) and then converted into numeric format .For canola, the processing resulted in 31,085 markers with successful allele calls and 7,169 markers with failed allele calls. In maize, we obtained 39,624 markers with successful allele calls and 8,024 markers with failed allele calls.2.3For both datasets, the population structure was assessed by calculating the Euclidean distance between genotypes based on standard SNP markers and failed allele calls, respectively. Subsequently, the genotypes of each species were clustered into two subpopulations each using k-means clustering. A principal component analysis based on the genetic distance was conducted, and the first two principal components were utilized to visualize population stratification.2.4In the following two sections, we introduce two pipelines designed to distinguish between random failed allele calls and non-random systematic failed allele calls. This is done to strengthen the confidence that those failed allele calls stem from some biological reason, which hinders an allele call. These pipelines only rely on population measures and statistical tests.2.4.1An important step in hybrid breeding is the creation of distinct genetic pools. Hence, the datasets assessed in this study naturally show a strong population structure corresponding to divergent genetic pools. In such populations, a proportion of alleles become pool-specific due to selection and genetic drift. On the other hand, technical errors can, by definition, not be pool specific; hence, they cannot show a bias between two different hybrid breeding pools. We thus assumed that there should be no relationship between subpopulation assignment and SNP call failure. In the breeding populations examined here, the populations for each species investigated split into two major gene pools. Hence, we expect that technical errors and successful allele calls should distribute equally in the two subpopulations. A H0: failed allele call versus successful marker call and pool assignment is not related in the populations.• H1: failed allele call versus successful marker call and pool assignment is related in the populations.• H0 is rejected, this is considered to be biological evidence for pool specificity of marker failure rather than a technical failure. Hence, we filter this failed allele call marker from the set of all failed allele calls and use it further in prediction models. After adjustment according to p-values were compared at a threshold of α = 0.05.When 2.4.22r (http://population-genetics.uni-giessen.de/~software/), treating each failed allele call as an independent marker with the same genome position as its corresponding standard SNP.Linkage disequilibrium (LD) between markers on the same chromosome was calculated as r2  in “Selet-test can be used to compare the LD patterns. If the LD of the failed marker with all other standard SNP calls on the same chromosome is considerably lower than its standard SNP counterpart, we can assume that the failure is due to a technical error. Specifically, for each failed marker call, we test the following hypotheses:If a failed marker call is purely due to a technical error, the failed call should not be in LD with any other marker. If the failed call is in considerable LD with markers on the same chromosome, we can assume that the failure is inherited together with other markers and the failure has a biological reason. Subsequently, a simple Student’s H0: failed allele call and successful marker call show the same average LD to all standard markers on the same chromosome.• H1: failed allele call and successful marker call show lower average LD to all standard markers on the same chromosome.• H0 is failed to reject, failed allele calls are considered to be in LD to markers on the same chromosome. Hence, we filter this failed allele call marker from the set of all failed allele calls and use it further in prediction models. After adjustment according to p-values were compared at a threshold of α = 0.05.When 2.5Six genomic prediction models were used to predict test cross performance. Two variations of GBLUP, two Bayesian methods, and two machine learning methods were used, covering parametric and non-parametric models. We applied standard GBLUP and extended GBLUP (EGBLUP) to account for second-order additive*additive epistasis . FurtherIn GBLUP and EGBLUP, the underlying mixed linear model is:where It is assumed that The additive genomic relationship matrix was calculated following i) for homozygous allele A, (1-2pi) for the heterozygous state, and (2-2pi) for homozygous allele B, with pi being the allele frequency of the B allele. For prediction based on all failed calls or filtered failed allele calls, the elements of i) for successful allele calls and (2-2pi) for failed allele calls, with pi being the allele frequency of the failed allele call. Furthermore, the combination of (i) SNPs and failed allele calls, (ii) SNPs and failed allele calls filtered by pool specificity, and (iii) SNPs and failed allele calls filtered by LD were considered.In the case of prediction based on standard SNPs, the elements of A second-order (additive*additive) epistatic relationship matrix can be approximated with peration .All the mixed linear models described in this section were implemented and solved with the r package “sommer” , which aThe formula describing the Bayesian LASSO model, following where e “BGLR” , which cFollowing withlth value of the bandwidth parameter {0.1, 0.5, 2.5}. where e “BGLR” , which cGradient boosting sequentially builds ensembles of decision trees. The algorithm starts with an intercept estimation. Subsequently, it sequentially fits models on the residual of its predecessor . The goavia Bayesian hyperparameter optimization using the r package “rBayesianOptimization”  between the observed and predicted phenotypic values of the validation set in each fold. To ensure robustness, this entire procedure was repeated 30 times.The prediction accuracy for the two datasets was evaluated using fivefold cross-validation. The population was randomly divided into five equal-sized sets. In each fold, the prediction models were trained on four sets (training population), and then these trained models were utilized to predict the remaining set  with masked phenotypic data. This process was repeated until each set served as the validation population once. The accuracy was measured using the Pearson correlation coefficient , a simulation was conducted. The basis of the simulation was the genotypic data described in Section 2.2. Here we took the imputed marker matrices as “true” genotypic data and simulated marker effects. In total, 100, 1,000, and 10,000 markers were sampled to serve as QTL. Subsequently, marker effects were sampled from a normal distribution with mean = 0 and variance = 1. The phenotype was then obtained by adding a random residual term to the total additive value of the individual. The residuals were sampled from a normal distribution with mean = 0 and variance = According to the number of failed calls observed before imputation, 658,730 entries of the marker matrix in canola and 3,712,821 entries of the marker matrix in maize were randomly sampled to be failed calls and treated as described in Sections 2.2. and 2.4. In each simulation, genomic prediction was conducted with the GBLUP model based on SNPs and failed allele calls. Prediction accuracy was then measured with fivefold cross-validation with 10 repetitions (see Section 2.6). For each combination of number of QTL and heritability, 100 simulations were conducted to obtain a robust result.33.1In canola, k-means clustering based on standard SNP markers revealed a considerable population stratification into two subpopulations/pools which we designated as pool A and pool B, respectively Figure 1Each possible failed allele call was tested for pool specificity. In canola, 1,989 failed allele calls showed significant pool specificity. The lines in pool A carry, on average, 302.26 (median = 283) pool-specific failed allele calls, and the lines in pool B carry, on average, 398.93 (median = 409) (r = 0.604) was observed between the SNP-based relationship and the relationship based on failed alleles  pool-specific failed allele calls, whereas the lines in the Flint pool carry, on average, 1,962.51  (r = 0.982) detected was observed between the SNP-based relationship and failed allele calls (r = 0.984) or failed allele calls filtered by LD (r = 0.983) was considerably lower than the corresponding differences in canola  has emerged as a promising alternative to SNP arrays for genotyping breeding populations . Unlike In the canola analysis, the genomic prediction accuracy based on all marker sets roughly corresponded to the original results of In the maize analysis, the genomic prediction accuracy obtained from all marker sets corresponded to the original results of Importantly, predictions based on one of the three marker sets including failed allele calls always gave prediction accuracies competitive with standard SNP-based predictions. The simulation study indicates that this prediction accuracy seems to be not occurring by chance as the randomly sampled failed allele calls in the simulations resulted in a prediction accuracy close to zero. While failed allele calls were observed to be equally predictive as standard SNPs, it is essential to note that this might not directly translate to the entire germplasm of the given crop. This is because SNP arrays usually undergo thorough validation before being released for use. Of course, SNPs are influenced and linked to structural variations like deletions and insertions . Our anan > p problems  and LD, we were able to distinguish random errors from systematic allele call failure, enabling the filtering of presence–absence marker data representing deletions with potential impacts on traits. In all examined traits and datasets, genomic prediction using presence–absence markers filtered from failed SNP calls was nearly as accurate as SNP-based prediction. This is likely due to the following: (a) capture of previously overlooked genomic regions, (b) accurate estimation of relationships (similar to SNP-based relationship), and (c) capture of dominance effects caused by deletions which differentiate between heterotic pools in hybrid breeding. However, prediction accuracy did not improve when combining SNP information with failed allele calls, which can be attributed to the high redundancy between estimates of genomic relationship. Nevertheless, we recommend the inclusion of information of allele call failure into genomic prediction, as it adds information that is potentially highly predictive for agronomic traits not always in LD with neighboring SNPs and is available to plant breeders using SNP array datasets for genotyping at no additional cost.The original contributions presented in the study are included in the article/SW and RS designed the study. SW conceived the analysis, MF developed the software for LD calculation and supervised the statistical analysis. LE assisted with the statistical analysis. SW wrote the manuscript. RS, LH, and HC revised the manuscript. All authors contributed to the article and approved the submitted version.


DYNC1I2, VPS35, SLC38A9 and CHMP1A. GO terms such as lysosomal membrane, mitochondrial inner membrane and sodium:proton antiporter activity were significantly enriched. Genes involved in these identified GO terms mediate multivesicular body (MVB) fusion with lysosome for degradation and control mitochondrial membrane permeability. This reveals the polygenic status of this trait and candidate genes for further studies on breeding for copper tolerance in sheep.Economic losses due to copper intoxication or deficiency is a problem encountered by sheep farmers. The aim of this study was to investigate the ovine genome for genomic regions and candidate genes responsible for variability in liver copper concentration. Liver samples were collected from slaughtered lambs of the Merinoland breed from two farms, and used for measurement of copper concentration and genome-wide association study (GWAS). A total of 45,511 SNPs and 130 samples were finally used for analysis, in which single-locus and several multi-locus GWAS  methods were employed. Gene enrichment analysis was performed for identified candidate genes to detect gene ontology (GO) terms significantly associated with hepatic copper levels. The SL-GWAS and a minimum of two ML-GWAS identified two and thirteen significant SNPs, respectively. Within genomic regions surrounding identified SNPs, we observed nine promising candidate genes such as  Sheep farming is continuously challenged with the risks of copper intoxication and deficiency. Copper is an essential trace element for many species. It serves as a cofactor for the proper functioning of some copper-containing enzymes, so-called ”cuproenzymes” such as copper-–zinc superoxide dismutase, cytochrome c oxidase and hephaestin which function in the anti-oxidation of free radicals, energy metabolism and iron metabolism, respectively ,2. HowevHepatic copper levels in sheep have been classified into diagnostic levels of deficient, marginal, adequate, high, and toxic ,6. DeficAssociation of genes such as copper transporter 1 (CTR1), chaperone for superoxide dismutase 1 (CCS), antioxidant 1 (ATOX1), metallothionein (MT), copper-transporting ATPase 1 (ATP7A) and copper-transporting ATPase 2 (ATP7B) with copper metabolism are well documented for humans ,15,16,17The Illumina Ovine 50K SNP chip is a powerful tool to analyze the sheep genome with respect to an association with diverse traits as confirmed in various single-locus genome-wide association studies (SL-GWAS) ,21,22,23To the best of our knowledge, a genome-wide analysis regarding liver copper concentration in sheep has not been conducted until now. Therefore, the aim of this study was to identify candidate genes associated with variability in hepatic copper concentration in sheep using both SL-GWAS and ML-GWAS methods. This analysis was performed using liver samples from slaughtered lambs of the Merinoland sheep breed, a breed of high economic importance in Germany.Lobus caudatus, packed separately in plastic tubes, labelled and stored at −20 °C prior to the determination of liver copper concentration and DNA extraction.The animals were kept under standard farming regulations on two private sheep farms in Bavaria. For this study, only male lambs were sampled after slaughter for human consumption. Documented information on the date of birth and date of slaughter was obtained from the farmers. Regarding farm 1, 89 lambs from the flock with ages between 4 and 5.5 months were sampled after slaughter in batches over a period of 46 days. They were kept indoors with unlimited access to their dams and fed ad libitum with grass silage and compound feed consisting of lucerne pellets (5.53%), concentrate pellets (58.84%), barley (22.93%) and beet pulp (12.70%). Concentrate feeding of lambs started 14 days after birth. All dams had unhindered access to grass silage with occasional input of the above-mentioned compound feed. A total of 45 lambs were sampled from the flock located at farm 2. These lambs were kept on pasture and under uniform feeding conditions until slaughter. In addition, fresh water was made available ad libitum for all animals by the farmers. The nutrient composition of the feed, and the mineral content of the grass silage, components of compound feed, and pasture grass were determined by a nutrient analysis laboratory  . Liver s2O, 5 mL HNO3 and 3 mL H2O2 with a StarT-1500 microwave oven . Conditions for the microwave digestion system are shown in Liver samples were freeze-dried, pulverized and stored in airtight tubes. Duplicate samples of approximately 0.5 g were digested in 5 mL deionized Hp-values were lower than 1.0 × 10−6. After QC a total of 45,511 SNPs and 130 samples were left for analysis.DNA was extracted from liver samples using the Macherey-Nagel NucleoSpin Tissue Kit  according to the manufacturer’s instructions. All 134 liver samples were genotyped with the Illumina Ovine 50k SNP BeadChip. Quality control (QC) was performed to exclude loci with minor allele frequency (<5%), SNPs with a low call rate (<95%), samples with more than 5% of missing genotypes, and SNPs for which Hardy–Weinberg equilibrium test 1 and Z2 are the incidence matrices relating observations to β, a and c, respectively. Due to the conservative nature of Bonferroni threshold [p-value threshold was used [p-value level of significance defined as p = N/M × 0.01, where N is the number of SNP markers with p-values below the FDR threshold and M represents the total number of SNPs [10 was constructed with the package “ggplot” in R [p-value threshold were considered significant and selected for further analysis. The proportion of total variability explained by SNPs (VG) and error variance (VE) were computed using “varCompCI” function in GENESIS R package [2 = VG / (VG + VE) according to Yang et al. [SL-GWAS and ML-GWAS were performed for all samples and data obtained after QC. All 130 samples were used to estimate the genome-wide association of markers with liver copper concentration. To perform a single-locus GWAS, genetic relatedness was estimated using “pcrelate” and “pcrelateToMatrix” functions in the GENESIS R package . Next, ahreshold  and posshreshold , a falsewas used . FDR was of SNPs . A Manhaot” in R . SNPs ab package . Estimatg et al. .p-values for the selection of SNP in the first stage were set at default. These values were employed in order to maximize power for SNP detection as suggested by the respective authors [Regarding ML-GWAS analysis, the mrMLM v4.0.2  software authors ,34,35,38 authors . The cri authors ,25. Covahttps://david.ncifcrf.gov; accessed on 23 February 2023) [p-value threshold of ≤0.05 were considered significant [http://www.genecards.org (accessed on 23 February 2023)) and Uniprot (http://www.uniprot.org (accessed on 23 February 2023)) databases were considered as functional candidate genes. Additionally, a second subset of all genes was considered as positional candidate genes when harbouring a significant SNP or located near such an SNP.Genes located within 0.5 megabase (Mb) regions upstream and 0.5 Mb downstream of putative SNPs after SL-GWAS and ML-GWAS were identified according to the UCSC genome browser  with thery 2023) ,50,51. Tnificant . A subseDescriptive statistics of hepatic copper concentration and age of slaughtered lambs whose samples were included in the SL-GWAS and ML-GWAS analyses are shown in p-value = 8.77 × 10−5; −log10 = 4.06; λ = 0.98) were observed  located on chromosome 2 and below the given threshold  confirmed that this trait is largely influenced by genetic factors. This value is in the same range as estimated in Merino sheep by Judson et al. [2 ± SE = 0.60 ± 0.32; n = 208). However, the high SE for h2 observed in our study indicates that the heritability value may be inflated due to cryptic relatedness since SNP-based heritability is reported to increase with a higher number of related samples [Copper homoeostasis is less efficient in sheep compared to other farm animals such as cattle and goats . This imn et al.  (h2 ± SE samples . TherefoDYNC1I2), retromer complex component (VPS35) and solute carrier family 38 member 9 (SLC38A9) located on chromosomes 2, 14 and 16 have been reported to indirectly influence cellular transport and excretion [DYNC1I2 gene expresses a subunit of the cytoplasmic dynein motor protein which is important for microtubule-based transport towards the minus end [SLC38A9 gene encodes a lysosomal amino acid transporter protein reported in the mechanistic target of rapamycin complex 1 (mTORC1) signalling [+-ATPase (V-ATPase) and metallothionein 1 (MT1) [Among the candidate genes identified in this study, three genes including dynein cytoplasmic 1 intermediate chain 2  ,77. The  1 (MT1) . This in 1 (MT1) . In thisVPS35, SLC38A9 and charged multivesicular body protein 1 A (CHMP1A) are reportedly involved in cellular transport and excretion [SPG7) and ATP synthase membrane subunit f (ATP5MF) were observed as functional genes involved in the mitochondrial inner membrane, which is a significant GO term observed in this study. The SPG7 and ATP5MF genes are implicated in the opening of the mitochondrial permeability transition pore (PTP) [SLC25A12)) identified in this GO term is also required for the calcium-dependent maintenance of ATP homoeostasis in the mitochondria [SLC9B1) and solute carrier family 9 member B2 (SLC9B2). These genes encode Na+/H+ exchangers (NHEs) important in cytoplasmic and organelle pH [SLC9B1 gene is predominantly expressed in the testis while SLC9B2 is highly expressed in the liver and brain. In earlier studies on yeast NHE (Nhx1p), it was observed that Nhx1p localized to the trans-Golgi network compartments, late endosomes, and recycling endosomes, is critical for protein sorting and endocytic pathway [Regarding GO analysis, our findings revealed that lysosomal membrane and midbody were enriched GO terms in this study. Genes observed in these GO terms including xcretion ,75,79. Gxcretion . The CHMxcretion . Additioxcretion . These fre (PTP) ,83,84. Tchondria ,86. The chondria . Earlierchondria . Accordichondria , mitochonelle pH . The SLC pathway ,92. In a pathway . These fDYNC1I2, VPS35, SLC38A9 and CHMP1A associated with endosomal cargo sorting and trafficking, as well as lysosomal transport. Additionally, genes such as SPG7, ATP5MF and SLC25A12 were identified as functional genes involved in mitochondrial membrane permeability which has been associated with copper toxicity. Likewise, the genes SLC9B1 and SLC9B2 which are involved in luminal and intraluminal pH and MVB fusion to lysosome, were observed as potential candidate genes influencing hepatic copper levels in Merinoland sheep. In total, nine promising candidate genes were observed in this study. These genes need to be further investigated to ascertain their involvement in liver copper variation in Merinoland sheep in particular, and other sheep breeds in general. This study provides evidence for a polygenic trait with high heritability and delivers promising clues for further studies to identify potential causal variants that may be associated with variation in copper tolerance in sheep and used for practical breeding.The results revealed a number of identified SNPs potentially contributing to variability in hepatic copper levels in Merinoland sheep. The identified regions surrounding these SNPs harbour some promising functional candidate genes such as