CNTN1—which has been linked to weight loss in mice and is potentially influential for AGE100—and MC4R, which is associated with obesity and appetite and may impact both traits. Additionally, we identified other genes—namely, PDZRN4, LIPM, and ANKRD22—which play a partial role in fat growth. Our findings provide valuable insights into the genetic basis of these important traits in Large White pigs, which may inform breeding strategies for improved production efficiency and meat quality.The pig industry is significantly influenced by complex traits such as growth rate and fat deposition, which have substantial implications for economic returns. Over the years, remarkable genetic advancements have been achieved through intense artificial selection to enhance these traits in pigs. In this study, we aimed to investigate the genetic factors that contribute to growth efficiency and lean meat percentages in Large White pigs. Specifically, we focused on analyzing two key traits: age at 100 kg live weight (AGE100) and backfat thickness at 100 kg (BF100), in three distinct Large White pig populations—500 Canadian, 295 Danish, and 1500 American Large White pigs. By employing population genomic techniques, we observed significant population stratification among these pig populations. Utilizing imputed whole-genome sequencing data, we conducted single population genome-wide association studies (GWAS) as well as a combined meta-analysis across the three populations to identify genetic markers associated with the aforementioned traits. Our analyses highlighted several candidate genes, such as  The Large White pig breed holds a prominent place in pig production due to its favorable traits, such as its efficient feed conversion, rapid growth, high slaughter value, and having a significant proportion of lean meat. These characteristics have made it a preferred choice for breeding and meat production. Selective breeding over time has led to significant improvements in genetic traits—particularly in terms of rapid growth and enhanced leanness. Consequently, the Large White pig breed has gained a significant market share in the pig breeding industry in recent decades . Days ofGenome-wide association analysis (GWAS) is a comprehensive approach that explores genetic variation—particularly single nucleotide polymorphisms (SNPs)—across the entire genome to identify variants potentially associated with specific traits. In livestock breeding, GWAS serves as a valuable tool for identifying economically important traits such as growth, carcass ,6,7, repCNTN1 as a candidate gene that could influence AGE100, drawing from its association with weight loss in mice. Furthermore, the gene MC4R—which has been linked to obesity and appetite—emerged as a possible determinant affecting both traits. Additionally, our findings implicated PDZRN4, LIPM, and ANKRD22 as partial contributors to fat growth. The outcomes of this study provide valuable insights into the intricate genetic underpinnings of these complex traits—thereby enhancing our understanding of and paving the way for advancements in pig breeding and overall improvement strategies.This study employed a GWAS approach to investigate the genetic factors underlying AGE100 and BF100 in three distinct populations of Large White pigs, each characterized by unique genetic backgrounds. The GWAS analysis allowed for the identification of significant SNP loci associated with these traits. Subsequently, a comprehensive meta-analysis was conducted, integrating data from all three populations, to further validate and identify noteworthy SNPs and candidate genes associated with AGE100 and BF100. Through our analysis, we uncovered the potential involvement of The Animal Welfare Committee of Nanjing Agricultural University conducted a review of all the animal testing and sample collection techniques used in this research. This review process included a careful examination of the ethical considerations of the research, as well as the methods and procedures used to ensure the safety and welfare of the animals involved. The Committee approved the animal testing and sample collection techniques used in this research, ensuring that the animals were treated humanely, and that the data collected was accurate and reliable (Permit number: DK652).2 per pig depending on weight. Wet curtain fans and heat preservation lamps were used to control the building temperature at 19–21 °C; the relative humidity was kept at 40–70% and ventilation was 0.3 m/s; harmful gases such as NH3 were kept at 20 mg/m3 or less, H2S at 8 mg/m3 or less, and CO at 5 mg/m3 or less. Lighting conditions were 100 Ix and the lighting time was 8 h/d. Feeding was carried out by automatic troughs to ensure that there would be no break in feeding. Rodents were controlled by rodenticide to ensure that they did not come into contact with the pigs, and the pigs were enclosed by gauze nets to ensure that wild birds did not enter the barn. All experimental animal buildings were disinfected with the same procedure, and all experimental animals were vaccinated with the same swine fever vaccine.In total, there were 2295 individuals of three different pig breeds including 500 Canadian, 295 Danish, and 1500 American Large White pigs. The Large White pigs used in this research were all in the fattening stage and were fed in large columns, with a feeding density of about 0.8–1.2 mmale = female = male = female = Pigs from all the pig populations were selected for analysis, taking individuals of approximately 160 days of age with similar feeding conditions, both healthy and disease-free. These pigs were managed under standardized feeding conditions and measured individually at an average weight of 100 kg. To evaluate backfat thickness, ultrasound was utilized to measure between the 11th and 12th ribs at the same weighing time. Using the equation corrected for the measurement AGE100 : AGE100 or BF100 : BF100maSus scrofa version 10.2, the SNP chip consisted of 50,915 probes, and autosomes were further upgraded to the latest version of the porcine genome—Sus scrofa version 11.1. The final remaining autosomal SNPs were 34,150 Kb for the Canadian line, 34,543 Kb for the Danish line, and 34,497 Kb for the American line.This study utilized three distinct populations of Large White pigs—including 500 Canadian, 295 Danish, and 1500 American pigs—as experimental materials. All individuals with phenotypes were genotyped using the GeneSeek GGP porcine HD array. According to http://www.cog-genomics.org/; accessed on 16 March 2023) [Exclusion of individuals that did not meet Hardy-Weinberg equilibrium was carried out. Quality control was performed by PLINK  . Pigs wihttp://www.cog-genomics.org/; accessed on 16 March 2023), and principal component analysis (PCA) was performed using the remaining SNPs [2) was calculated in each line. In this study, SNeP software  was used to calculate the population effect size (Ne) [Eigenvalues and eigenvectors were obtained using PLINK  [The linear mixed model used for GWAS was as follows:ch 2023) .https://csg.sph.umich.edu/abecasis/Metal/download/; accessed on 16 March 2023) [p-values, the direction of the SNPs’ effects, and the number of individuals. In each case, threshold p-values were set to −log10 (1/SNPs) and −log10 (0.05/SNPs) for suggestive and Bonferroni-adjusted genome-wide significance, respectively. The Bonferroni threshold for significant SNPs were determined as 1.449 × 10−6, 1.465 × 10−6, and 1.448 × 10−6 for the Canadian, American, and Danish line, respectively. A quantile-quantile (QQ) plot of −log10  was examined to determine how well GCTA accounted for population structure and family relatedness.In addition, a meta-analysis was implemented through METAL  . In the http://www.ensembl.org/; accessed on 16 March 2023) for comprehensive annotation. In our study, candidate genes were selected within a 500 Kb genomic region surrounding the significant SNPs, taking into account both upstream and downstream regions. Additionally, we utilized the R package WebGestaltR [https://metascape.org/; accessed on 16 March 2023), which provides a valuable resource for exploring the functional characteristics of genes. Furthermore, to supplement our investigations, an extensive literature search was conducted to gather relevant information regarding the functions and roles of these candidate genes. This holistic approach allowed us to thoroughly explore and evaluate the potential functions and pathways associated with the identified candidate genes, providing a solid foundation for further exploratory studies in this field.To identify potential candidate genes associated with the significant SNP loci, we employed the BioMart database (2) at varying physical distances between two motifs on all autosomes of the three populations. The data indicated a strong linkage disequilibrium in all three populations at an SNP physical distance of approximately 0.5 Mb, averaged over the autosomes. Furthermore, A principal component analysis was conducted to assess the genetic variation indices of the three populations. The results demonstrated significant differences in the genetic backgrounds of the American, Canadian, and Danish line pigs, with PC1 effectively separating the three populations A. AdditiSus scrofa chromosomes 2 and 14 . Additionally, two potentially significant SNPs were detected in the Canadian lineage population, located on SSC1 and SSC6. Additionally, there was one significant SNP on SSC13. Furthermore, one SNP locus located on SSC5 (SSC5: 52888587) appeared at significant levels in the Danish line. Through the meta-analysis, a total of 10 potentially significant SNPs were identified, with seven located in SSC1 and the remaining three located in SSC2, SSC5, and SSC14, respectively  analysis revealed a significant enrichment of molecular functions associated with BF100 in the meta-analysis A. The caFor AGE100, the meta-analysis revealed a significant enrichment of biological processes based on Gene Ontology (GO) analysis C. The caNe). The Danish and Canadian lines demonstrated similarity only within the initial 100 generations; these findings were further corroborated by the principal component analysis, which clearly highlighted the presence of population stratification across the three populations. Thus, our comprehensive investigation provides compelling evidence of distinct population structures within the Large White pig breeds under study.The present study aimed to assess the population structure of Large White pigs originating from distinct genetic backgrounds—namely, the Canadian, American, and Danish lines. Through an extensive analysis, we observed consistent patterns of linkage disequilibrium (LD) among the three populations. However, notable population stratification was identified in all three groups based on the analysis of effective population size  in the Canadian population, specifically located on chromosome 6. Further investigation through Biomart annotation of this SNP locus revealed the presence of a rocesses . DAB1 acite pigs ,26.CNTN1 gene located 720,076 base pairs upstream from the SNP. The CNTN1 gene encodes a cell adhesion molecule known as contactin-1, which belongs to the immunoglobulin-superfamily. Studies in mice have associated CNTN1 with weight loss [CNTN1 is a glycosylphosphatidylinositol (GPI)-anchored neuronal membrane protein that acts as a ligand for Notch, thereby promoting oligodendrocyte maturation and myelin formation. Deficiencies in CNTN1 can lead to impairments in inhibitory synaptic development [CNTN1 affects the cell cycle and cell growth, potentially influencing the trait under investigation [PDZRN4 gene, which has been identified as a highly expressed gene in human abdominal fat and a potential candidate gene associated with intramuscular fat content in pigs [CNTN1 and PDZRN4 may contribute to the regulation of AGE100 in Large White pigs, although further research is needed to elucidate the exact mechanisms underlying their effects.In our analysis of the Danish lines, we identified a SNP SSC 5_72888587) that exhibited potential significance in both the GWAS and meta-analysis. The location of this SNP on the genome revealed the presence of a ght loss  and anor88587 thaelopment . Furthertigation . The SNP in pigs . These fLIPM gene located at 463,324 base pairs upstream of a potentially significant SNP (SSC14_101189804). The LIPM gene belongs to the lipase I.3 subfamily [ANKRD22. ANKRD22 is a novel human N-myristoylated protein, and its expression product is specifically localized in lipid droplets [LIPM and ANKRD22 genes are likely to have an impact on lipid growth—a factor that may influence AGE100 in pigs. These findings suggest the potential involvement of LIPM and ANKRD22 in the regulation of AGE100 through their roles in lipid metabolism.Furthermore, the Biomart analysis of SNPs detected through the meta-analysis revealed the presence of the droplets . Both thMC4R) gene, based on Biomart gene annotation. The MC4R gene, known as a retinoid-like gene, exhibits expression in multiple tissues and has been implicated in the regulation of obesity-related metabolic diseases [MC4R gene is likely to exert an influence on weight gain and AGE100 in pigs by modulating feed intake. The identification of the MC4R gene as a candidate associated with AGE100 suggests its potential as a key regulatory element affecting growth characteristics in pigs.In our meta-analysis, a noteworthy SNP locus (SSC1_160773437) was identified that corresponds to the melanocortin 4 receptor  was identified at a location 205,846 bp upstream of the significant SNP (SSC1_53461852) in the Canadian population. This gene has been reported to have an important role in regulating appetite and maintaining energy homeostasis. MC4R can interact with melanocortin receptor accessory protein 2 (MRAP2) and form a modulation complex [MRAP2 have been associated with obesity [MRAP2 gene may contribute to the regulation of BF100 in Large White pigs. What is more, we annotated some of the SNP loci detected in the single population analysis and meta-analysis to genes related to cell proliferation and cell cycle—for example, ZC3HAV1 [CCND2 [MAP3K1 [ZC3HAV1 is described as a zinc finger antiviral protein, playing vital roles in immunity and microRNA-mediated stress responses [CCND2 gene is a strong candidate gene for backfat deposition in pigs, supported by changes in expression and its involvement in adipogenesis [CCND2 could have an influence through growth-related processes on conformation traits in Danish pigs [MAP3K1 is an important regulator of T cell proliferative expansion and cell cycle progression [The potentially significant SNP (SSC1_160773437) identified in the meta-analysis assay was annotated as being within the  complex . Additio obesity . This ob ZC3HAV1 , CCND2 [1 [CCND2 , and MAPesponses . The CCNogenesis . Additiogression . This geMC4R gene, which showed potential significance and potential effects on appetite and obesity regulation in both AGE100 and BF100 traits. We also identified candidate genes such as PDZRN4, LIPM, and ANKRD22 that were associated with fat growth. Additionally, we detected the CNTN1 gene, which exhibited significant associations in the Danish line population. However, further investigations are warranted to fully understand the role of CNTN1 in body weight regulation in Large White pigs. These findings contribute to our understanding of the genetic architecture of these traits and provide valuable insights for future research focused on enhancing pig breeding and genetic improvement strategies.In this study, our objective was to investigate the genetic factors underlying AGE100 and BF100 in purebred Large White pigs from Canadian, American, and Danish-line breeds. We conducted single-population GWAS and mixed-population meta-analysis to identify SNPs associated with these traits. Our analyses revealed significant associations between SNPs and candidate genes, including the


The GWAS was done using a mixed linear model (MLMA) and based on the Ovis aries v3.1 genome. The CHSE showed the lowest meat juice release and, consequently, the highest water-holding capacity (WHC = 30.6 ± 0.1), suggesting that this breed has better performance in the meat industry compared with CHSS (WHC = 41.7 ± 0.1) and CHSP (WHC = 36.8 ± 0.1), since there is a relationship between WHC and juiciness. For the character pH, it was not possible to annotate genes related to meat quality, while, for the WHC, they have obtained 11 candidate genes associated . Particularly, ELOVL2, ARAP2, IBN2, and TPM2 genes are involved in muscle contraction and fatty acid composition in sheep. In this study, we generated a baseline for GWAS related to meat quality traits in Colombian Creole hair sheep that can be used for future genomic selection plans.Genome-wide association studies (GWAS) allow identifying genomic regions related to traits of economic importance in animals of zootechnical interest. The objective of this research was to conduct a genome-wide association study on meat quality traits using the Illumina OvineSNPs50 BeadChip array. The animals were sampled in the departments of Córdoba, Cesar, and Valle del Cauca. The genotypes obtained with the Illumina OvineSNP50 BeadChip microarray were analyzed SNP (single-nucleotide polymorphism) data to conduct a GWAS for pH and water-holding capacity (WHC) traits measured after 7 days of maturation, in the Longissimus dorsi (LD) muscle, in 167 Creole hair sheep of 12 months old belonging to Pelibuey (CHSP, The online version contains supplementary material available at 10.1007/s11250-023-03688-z. In Colombia, Creole hair sheep (CHS) are considered a socially important genetic resource for rural economy-based communities due to the multiple benefits that these animals provide, especially, to indigenous communities   DANE , 31,978 ca) DANE .The increasing demand of sheep meat in the country , Ethiopian (CHSE), and Sudan (CHSS) Creole hair sheep.A genome-wide association study (GWAS) is a feasible and powerful tool to discover candidate genes and loci associated with quantitative traits . All procedures followed the guidelines and regulations established in the Colombian code of bioethics (resolution 8430 of 1993) and Law 84/1989 regarding the protection of animals. Blood samples were collected by qualified veterinarians during their routine practice within the framework of official programs aimed at the identification, health monitoring, and parentage confirmation of the breeds and populations included in this study. The use of animals and private land in this study was approved by the respective owners.E, we evaluated 44 individuals  from four farms located in the Department of Cordoba and one farm in the Department of Cesar (Table (Dichantium aristatum), Brachiaria (Brachiaria sp.), Colosuana (Bothriochloa pertusa), and Guinea (Megathyrsus maximus) pastures. For CHSS, we analyzed 63 animals  from five farms located in Cesar and one farm in Cordoba (Table Bothriochloa pertusa), Angleton (Dichantium aristatum), and Guinea (Megathyrsus maximus) pastures. For these farms, we were not able to obtain a family structure due to low technological levels. For CHSP, we evaluated 60 males from El Hatico Natural Reserve, located in the municipality of Cerrito   muscle from the left carcass, which was vacuum sealed and stored at 4 °C. After 7 days of maturation (aging), we measured the pH using a pH meter, with three readings per sample. Additionally, the WHC was determined according to the protocol described by Leal-Gutiérrez et al. ; those from Cesar at Echeverry Gutiérrez & CIA SC , and those from Valle del Cauca at Carnes y Derivados de Occidente S.A. (Yumbo). After slaughter and at 24-h z et al. . Brieflyz et al. a and a cz et al. b. The paA total of 10 mL of blood from each animal was collected in BD vacuum tubes  with k3-EDTA. Genomic DNA was extracted from 167 blood samples using the QIAamp® DNA Mini Kit . DNA quantity and quality were assessed by spectrophotometry using a ColibriTM equipment  and agarose gel electrophoresis using a standard protocol to evaluate sample degradation.p < 0.0001 (696). Furthermore, SNPs on sex chromosomes and the mitochondrial contig were also excluded. Finally, 42,701 SNPs remained for further analyses. The genetic structure of the population of CHS was evaluated by a multidimensional scaling analysis (MDS) based on pairwise identical-by-state (IBS) distances that were calculated and plotted using Bite .All animals were genotyped with the Illumina OvineSNP50 SNP chip, which allows the simultaneous characterization of up to 54,241 independent SNPs, according to the Infinium® Assay Super II Illumina® protocol for use on the HiScan®SQ System and BeadChips scanning. Quality control (QC) was performed using PLINK v1.9b5.2 . For the CHSE and CHSS breeds, the effects of different farms were considered, while for CHSP, controlled breeding was conducted on a single farm where 10 male offspring were produced per sire. For that reason, it was used two fixed models. First, model (M1) was used for CHSE and CHSS, where the following fixed effects were included: sex , farm , and lambing season (drought and rainy).Yijkl is the vector of traits WHC and pH, μ corresponds to the overall mean, Si is the fixed effects of the i-nth sex, Fj is the fixed effects of the j-nth farm, EPk is the fixed effects of the k-nth lambing season, (SF)ij is the fixed effects of the interaction between sex and farm, (FP)jk is the fixed effects of the interaction between sex and lambing season, (SFP)ijk is the fixed effects of the interaction between farm and lambing season, and Ɛijkl is the error.(M1)Second, model (M2) included only two individuals for CHSP and the fixed effects were as follows: father sire  and lambing season (drought 1 and rainy 2).Yijkl is the vector of traits WHC and pH, μ corresponds to the overall mean, Pi is the fixed effects of the i-nth father, EPk is the fixed effects of the k-nth lambing season, (SF)ij is the fixed effects of the interaction between father and lambing season, and Ɛikl is the error.(M2)Duncan’s pairwise comparisons test was used to determine significant differences, using S.A.S. 9.1.3.Before conducting the GWAS, linear models were fitted to each phenotype to identify the fixed effects models that provided the best fit . A mixedy is the vector of phenotypes , μ is the overall mean for the trait, β is the vector of fixed effects, u is the vector of random effects, X and Z are the design matrices mapping vectors of fixed or random effects, G is the genomic relationship matrix, and Ɛ is the vector of residual effects.The model was:y = μ + u + e, and is fixed while the association between each SNP and the trait is tested. In the null model, the variables indicate the following: u =  ~ N , where G is the genomic relationships matrix (GRM) and σ2u = polygenic variance estimated by the null model. Table To facilitate this calculation, the genetic variance, var (G), is estimated based on the null model, QQ-plots (Quantile–Quantile plots) were utilized to visualize the test statistics under the hypothesis of no association (null hypothesis) to identify if there was inflation.p) ≥ 5.93, for genome-wide significance threshold. The chromosome-wide significance threshold had a value of 9.97 × 10^ − 4, which in the Manhattan plots corresponds to a value of 3.01. The Manhattan plots were generated using GWAS-tools package  to visualize the level of significance of the GWAS, the chromosomal locations, and SNPs. The Bonferroni correction, which is a statistical technique to determine the threshold, was used. This technique divides the 0.05 significance level by the number of markers used in the GWAS . Therefore, the significance threshold after applying the Bonferroni correction (1.17 × 10^ − 6) is − log10(http://www.r-project.org) by assigning functions and GO terms reported in Gene Ontology (GO), Kyoto Encyclopedia of Genes (KEGG) https://www.genome.jp/kegg/), and PATHWAY. Additionally, gene networks and gene enrichment analyses were done using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and its integrated databases .The functional information and biological processes of the mapped genes were retrieved using AnnotationHub, Mygene, and ReactomePA libraries  Yu  of Biocop < 0.01). The average WHC value of the LD in CHSE was 30.63% ± 10.9 and no significant differences were observed between sexes (Table p < 0.05), specifically, for farm 2C. This farm is the only one located in the Department of Cesar  and for the interaction farm*lambing season  and the interaction farm*lambing season ; accordingly, the lowest values were observed for individuals from fathers 1 and 2. Moreover, for WHC, ram lambs showed an average value of 36.8% ± 8.8  vs CHSE (red) and CHSS (yellow) along the first axis, and a secondary split of CHSS vs CHSE along the second axis  that was sampled at La Playa farm in Valledupar. This group of sheep shares a common genetic background with CHSE (n = 4), which were sampled at Caracas farm in Valledupar. Consequently, the overlap indicates that Sudan Blanco sheep are a mix between CHSE and CHSS. We observed population stratification due to the presence of the three breeds from the different farms with contrasting genetic backgrounds. Individual kinship and population stratification in GWAS are the main causes of false positive correlations; therefore, we performed a GWAS per breed to identify candidate SNPs and genes.The first two components of the bidimensional PCA plot explained 11.8% and 6.1% of the total variance, respectively. Furthermore, we observed a separation between CHSxis Fig. . Howeverp < 0.001, Table p < 0.001, Table A GWAS for each of the three breeds was tested using mixed linear models (MLMA) with fixed effects. The criterion applied to select the genomic association model was based on previous results of an ANOVA on phenotypic data Table . FurtherE, 20 GO terms were assigned to the ARAP2 gene . Additionally, a pathway enrichment analysis using DAVID significantly associated the term “nucleus” (p = 5.7E − 2) to four genes, namely AIPL1, CSRNP3, IFRD1, and KDM8. Finally, for CHSP, 50 GO terms were associated with two genes  compared to normal meat (5.4 to 5.9) turns the meat dark, firm, and dry (DFD) with a high WHC  indicated that the meats were red, firm, and non-exudative (RFN), which are optimal meat quality parameters  was reported for individuals of Pelibuey × Katadin × BlackBell that were slaughtered at an average weight of 40 kg at 10 months old  in Pelibuey × Katadin × BlackBell sheep raised in an intensive system and slaughtered at an average weight of 40 kg at 10 months old. Moreover, Frías et al.  and 2P (5.85 ± 0.09). Conversely, we did not find significant effects of farm on pH for CHSE. For this breed, the pH values ranged from 5.7 to 5.8 and corresponded to the optimal values for meat quality. These findings are attributed to an adequate management of the animals before and after slaughter, according to standardized protocols.Regarding pH, we did not find significant differences between sexes for CHSz et al. , who repE, we found two SNPs potentially associated with WHC. The first SNP, OAR20_48109414.1, is located in the ELOVL2 (fatty acid elongation) gene, which encodes the enzyme that catalyzes the synthesis of long-chain polyunsaturated fatty acids (C20- and C22-PUFA), acting specifically on polyunsaturated acyl-CoA and with greater activity towards acyl-CoA C20: 4 (n-6). This enzyme participates in the production of polyunsaturated VLCFA of different chain lengths that are involved in several biological processes as membrane lipid precursors and lipid mediators  in 10,613 in nine sheep breeds . ELOVL2, along with PNPLA3 and FADS2 genes, participate in fatty acid synthesis and metabolism. Moreover, the EVOVL6 gene was found as a candidate affecting the composition of palmitic and palmitoleic fatty acids  (UniProt Consortium ARAP2 as a candidate gene associated with meat color and concentration of polyunsaturated fatty acids (omega-3 and omega-6).The second candidate SNP found in CHSS, we propose eight candidate SNPs, including five that are associated with WHC, namely s55275.1 located in PRKCQ; OAR11_26036763.1 in AIPL1; OAR2_151938109.1 in CSRNP3; OAR4_59276586.1 in IFRD1; and OAR24_27265846.1 in KDM8. Altogether, these genes participate in the metabolic pathway of “nucleus processes.” Particularly, PRKCQ encodes a protein kinase C theta type, which belongs to the family of serine/threonine-specific kinase proteins that are activated by calcium and diacylglycerol. These protein family members phosphorylate a wide variety of protein targets and are involved in several cell signaling pathways -tropomyosin, which regulates muscle fiber tension (muscular contraction) by controlling myosin and actin binding. In non-muscle cells, tropomyosin proteins play a role in controlling cell shape . Regarding metabolic pathways associated with striated muscle contraction, we found that gluconeogenesis, glycolysis, the citric acid cycle, and the electron transport chain were enriched. Finally, SNP OAR2_56383269.1 is located in the NANS gene that encodes an enzyme involved in sialic acid biosynthesis. This protein uses N-Acetyl-mannosamine 6-phosphate and mannose 6-phosphate as substrates to produce phosphorylated forms of N-acetylneuraminic acid (Neu5Ac) and 2-keto-3-deoxy-D-glycero-D-galacto-nononic acid (KDN)  and are involved in muscle contraction, metabolism, and fatty acid composition.We generated a baseline for sheep farmers who seek to improve meat and carcass quality based on a genome-wide association study of meat quality traits in Colombian Creole hair sheep. We found that the LD muscle of CHSSupplementary file1 (XLSX 12 KB)Supplementary file2 (XLSX 32 KB)Supplementary file3 (XLSX 14 KB)Supplementary file4 (DOCX 1359 KB)Supplementary file5 (DOCX 51 KB)Below is the link to the electronic supplementary material.


Rice is the second-largest food crop in the world and vulnerable to bacterial leaf streak disease. A thorough comprehension of the genetic foundation of agronomic traits was essential for effective implementation of molecular marker-assisted selection.Xanthomonas oryzae pv. oryzicola (Xoc). In order to accomplish this, we first analyzed the population structure of 747 accessions and subsequently assessed their phenotypes 20 days after inoculation with a strain of Xoc, GX01. We conducted genome-wide association studies (GWAS) on a population of 747 rice accessions, consisting of both indica and japonica subpopulations, utilizing phenotypic data on resistance to bacterial leaf streak (RBLS) and sequence data. We identified a total of 20 QTLs associated with RBLS in our analysis. Through the integration of linkage mapping, sequence analysis, haplotype analysis, and transcriptome analysis, we were able to identify five potential candidate genes (OsRBLS1—OsRBLS5) that possess the potential to regulate RBLS in rice. In order to gain a more comprehensive understanding of the genetic mechanism behind resistance to bacterial leaf streak, we conducted tests on these genes in both the indica and japonica subpopulations, ultimately identifying superior haplotypes that suggest the potential utilization of these genes in breeding disease-resistant rice varieties.Our study aimed to evaluate the vulnerability of rice to bacterial leaf streak disease (BLS) induced by the gram-negative bacterium The findings of our study broaden our comprehension of the genetic mechanisms underlying RBLS in rice and offer significant insights that can be applied towards genetic improvement and breeding of disease-resistant rice in rapidly evolving environmental conditions.The online version contains supplementary material available at 10.1186/s12870-023-04412-7. Xanthomonas oryzae pv. oryzicola (Xoc), a pathogen that specifically targets rice [Rice, which is considered as the representative species of the Oryza genus, is a cereal crop that originated in China and India. It has been cultivated for over 7,000 years in the Yangtze River basin of China and has played a fundamental role in the development of human civilization , 2. Riceets rice , 4. BLS ets rice . The majets rice , 9. Whil2 generation using the varieties H359 and Jiannong 8 revealed that resistance to BLS was a quantitative trait controlled by multiple genes [qBlsr5a, was subsequently mapped to 30 kb using a sub-chromosome segment substitution line, suggesting that the most likely candidate gene was LOC_Os05g01710 [Oryza rufipogon Griff resistant source 'DY19' with indica rice variety 9311. The results showed that 'DY19' was controlled by a pair of new major recessive genes, bls2, located between SL03 and SL04 on chromosome 2, within a range of about 4 cM [The initial step towards identifying resistance genes and developing resistant rice varieties was the precise characterization of resistance to bacterial leaf streak (BLS) in rice. Here, they evaluated four different inoculation methods for rice BLS disease  across 10 diverse rice accessions. The results indicated that both acupuncture and infiltration methods were effective in differentiating the varying degrees of disease resistance or susceptibility among the 10 rice accessions [le genes . Identifle genes . Using cle genes . Using tle genes . One QTL05g01710 . A mappiout 4 cM . A mappiout 4 cM , so moreNRRB, was found to play a role in rice-Xoc interactions [AnkB, was identified in Xoc and can invade host leaves via stomata and wounds. The type three secretion system (T3SS) of Xoc was found to be pivotal to its pathogenic lifestyle [AvrRxo1, an ATP-dependent protease, can enhance the virulence of Xoc and inhibit stomatal immunity in rice by targeting and degrading the rice OsPDX1, which was involved in pyridoxal phosphate synthesis, thereby reducing vitamin B6 levels in rice [indica varieties, allowing identification of 12 quantitative trait loci (QTLs) on chromosomes 1, 2, 3, 4, 5, 8, 9 and 11, that conferred resistance to five representative isolates of Thai Xoc [In recent years, advances in genomic and transcriptomic technologies had provided powerful tools to investigate the molecular basis of plant-pathogen interactions. Microarray experiments, RNA sequencing (RNA-seq), and other high-throughput methods had enabled researchers to analyze the global gene expression patterns of both the host plant and the pathogen during infection, revealing key genes and pathways involved in the disease process. A microarray experiment was conducted to analyze changes in genome-wide gene expression in response to Xoc at the early stage of infection in both the rice transgenic line and its wild type . One of ractions . A novelifestyle . The eff in rice . A rice  in rice . A genomThai Xoc . GWAS maThai Xoc . DespiteIn this study, we conducted a GWAS to identify resistance to bacterial leaf streak (RBLS) using 747 cultivated rice accessions from the 3,000 Rice Genome (3 K-RG) project , 26. We Oryza sativa ssp. indica (comprising 458 accessions) and japonica (comprising 289 accessions). The first two principal components (PCs) accounted for almost half of the genetic variation, with PC1 and PC2 accounting for 38% and 7% of the total genetic variation, respectively. Furthermore, the neighbor-joining tree also demonstrated a clear subpopulation structure within the 747 accessions , mixed linear model (MLM), and fixed and random model circulating probability unification (FarmCPU) implemented in rMVP software [P) = 5 to detect significant association signals. For each GWAS result, we identified SNPs with P-values lower than the threshold  [Osaba1 (Zeaxanthin epoxidase) [To confirm our findings, we first checked whether the reported genes were located in the candidate QTLs. Subsequently, we extracted all genes that contained significant SNPs within each QTL and evaluated their annotations, homologous gene functions, linkage mapping in two bi-parental crosses, sequencing analysis, and distances from peak SNPs to identify the candidate genes in QTLs. Our GWAS identified two cloned genes related to RBLS located in QTLs, namely, eptor 1)  and Osaboxidase)   located in the promoter region of OsPSKR1   was higher than that for hap2 . However, no significant difference was found between the four haplotypes in the indica subpopulation. Therefore, we concluded that OsPSKR1 was a functional gene that regulates RBLS, and hap2 of OsPSKR1 was identified as the superior genotype. Increasing the frequency of hap2 in the japonica subpopulation could enhance resistance to rice bacterial leaf streak. In summary, hap2 of OsPSKR1 could be utilized to improve resistance to RBLS in rice  in both the indica and japonica subpopulations  being higher than that for hap1  and hap2 . Therefore, we tentatively identified this gene as a functional gene that controls bacterial leaf streak in rice, with hap1 and hap2 of Osaba1 being identified as the superior genotypes. However, we also observed a higher proportion of superior haplotypes for this gene in both indica and japonica rice, indicating that these superior haplotypes have already been widely utilized in modern breeding processes  within the promoter region of the gene. Haplotype analysis was conducted for LOC_Os08g10260 using the genotypes of these four SNPs for all 747 accessions, ultimately leading to the identification of seven haplotypes  and hap3  compared to hap5 , hap6 , and hap7 . Hap2 and hap3 exhibited higher mean RBLS values than hap5, hap6, and hap7. In japonica, we observed a significant difference in RBLS between hap1 , and hap4 , hap7 , with the mean RBLS value for hap1 greater than that of hap4 and hap7. Based on our findings, we concluded that LOC_Os08g10260, which we named OsRBLS1, is a functional gene that controls RBLS. Hap4, hap5, hap6, and hap7 of OsRBLS1 were identified as the superior genotypes. Thus, increasing their frequency in both the indica and japonica subpopulations could enhance resistance to rice bacterial leaf streak , located in qRBLS2-7, encoded an abscisic stress-ripening gene. We found four SNPs (P ≤ 10–2) within the promoter region of ASR1. Prior research had demonstrated that overexpression of ASR1 in transgenic rice plants led to increased tolerance to both drought and cold stress. Furthermore, ASR1 and ASR5 had been shown to function complementarily in the regulation of gene expression in response to aluminium (Al) toxicity [ASR1 using the genotypes of these four SNPs for all 747 accessions, ultimately leading to the identification of three haplotypes  was lower than those for hap1  and hap3 . In the japonica subspecies, a significant difference in RBLS was observed between hap1 and hap2, with hap1 exhibiting a higher mean RBLS value than hap2. Based on our results, we concluded that ASR1 (LOC_Os02g33820), which we named OsRBLS2, was a functional gene that controls RBLS. Regarding OsRBLS2, hap2 was identified as the superior genotype. Thus, increasing the frequency of hap2 in the indica subpopulation could enhance resistance to rice bacterial leaf streak -related defense by directly reducing SA, and promotes susceptibility to both Xoc and Xoo in rice [OsHsp18.0-CI showed enhanced resistance to RS105, whereas repression lines showed compromised resistance to RS105 [DEPG1 exhibited enhanced susceptibility to Xoc [OsPSKR1 and Osaba1, were located in the QTLs detected in our GWAS.Efforts to develop BLS-resistant rice varieties necessitated the exploration and utilization of genes associated with RBLS. To date, several genes related to bacterial leaf streak disease in rice have been cloned , 35–38.  in rice . Compareto RS105 . Comparey to Xoc . Two clo2 generations were constructed using H359 and Jiannong 8, and resistance to bacterial leaf streak disease was found to be controlled by multiple genes as a quantitative trait [2 population was constructed using the moderately resistant rice variety Minghui 86 and the highly resistant rice variety Jiafu as parents and a resistance quantitative trait locus (QTL) was detected between the second chromosome markers RM279-RM154 [qBlsr5a was shown to be located on the fifth chromosome [qBlsr3d in rice was verified to be controlled by quantitative inheritance by constructing a H359-BLSR3D single-chromosome segment substitution line [bls2 was inherited in a recessive manner and is preliminarily mapped between chromosome 2 markers SL03 and SL04, with a range of approximately 4 cM based on the wild rice resistant DY19 and SSR molecular markers [BLS tended to thrive in hot and humid conditions, particularly during periods of frequent rainfall, and can cause significant yield losses in rice. In years with frequent storms, these losses can average up to 50% , 9, 40. ve trait . Similarve trait . Four reve trait . A highlve trait . An F2 p79-RM154 . A mappiromosome . The BLSion line . The res markers . The BLS markers . A doubl markers .OsPSKR1 and Osaba1. However, many genes previously identified using mutants and gene differential expression as related to BLS resistance [Resequencing-based genotyping revealed a wide range of natural variations, enabling the exploration of functional genes and elite alleles (superior haplotypes) in natural populations through GWAS –52. In osistance , 35–38 dOsPSKR1 and Osaba1, located in the QTLs detected in our GWAS. We identified 20 QTLs and five candidate genes associated with resistance to BLS in rice through a combination of linkage mapping, sequence analysis, haplotype analysis, and transcriptome analysis. Moreover, we determined the superior haplotype and breeding potential of these genes in both indica and japonica rice varieties. These genes could be employed in future investigations aimed at elucidating the genetic mechanisms underlying resistance to bacterial leaf streak in rice.While naturally immune rice varieties for BLS had yet to be discovered, identifying bacterial leaf streak resistance genes provided an opportunity to develop highly resistant varieties capable of reducing losses in rice yield and quality caused by the disease. Consequently, identifying resistance genes can facilitate the breeding of plants for the scientific, effective, and rational control of BLS disease. Although many BLS resistance genes were previously identified using mutants and gene differential expression , 35–38, indica and japonica subpopulations based on both the phenotypic resistance to rice bacterial leaf streak and the sequence data of the 747 rice accessions. Our analysis led to the identification of 20 QTLs associated with RBLS in rice. Subsequently, we used a combination of linkage mapping, sequence analysis, haplotype analysis, and transcriptome analysis to identify five candidate genes that control resistance to BLS in rice. We also determined their superior haplotypes and breeding potential in both indica and japonica rice varieties. These findings suggested that these genes could be employed in future studies aimed at elucidating the genetic mechanisms underlying resistance to bacterial leaf streak in rice.To summarize, we analyzed the population structure of 747 rice accessions and evaluated their phenotypes 20 days after inoculation with Xoc strain GX01. We then conducted GWAS on the full population and the https://rstudio.github.io/leaflet/), and the map was based on “Esri.WorldStreetMap” provided by OpenStreetMap, with latitude and longitude of based on their clear geographic distribution. Transcriptomic data were obtained from the NCBI (https://www.ncbi.nlm.nih.gov/gds) with series accession ID: PRJNA280380 [The rice diversity panel comprised 747 cultivated rice accessions sourced from the 3 K Rice Genome (3 K-RG) , 26. ThiNA280380 .A total of 747 varieties were grown under field conditions in Nanning, Guangxi, southern China, with a plant spacing of 20 cm x 30 cm, and each variety was replicated twice. The bacterial strains of rice bacterial leaf streak were obtained from GX01, which was isolated at the Guangxi Academy of Agricultural Sciences. At the maximum tillering stage, the 747 varieties were inoculated with Xoc using the acupuncture method . PhenotyThe genetic variation data for the 747 accessions, in the form of single nucleotide polymorphisms (SNPs), were obtained from the publicly available 3 K-RG database. The database comprised approximately 17 million highly credible SNPs and 2.4 million indels, which were aligned to the Nipponbare IRGSP 1.0 reference genome .indica, and japonica populations, we identified 2,860,820 SNPs as a credible SNP set. To account for population structure, we performed principal component (PC) and kinship matrix analyses for the 747 accessions. To infer the basal group of rice, we used a total of 2,860,820 SNPs with a missing rate of ≤ 50% to construct a phylogenetic tree using the unweighted neighbor-joining (NJ) method with the 'phangorn' R package. For constructing the PC matrix, we used the first three principal components (PCs). To conduct PCA, we used R (version 4.0.3) and plotted the first three eigenvectors. We further called independent SNPs using Plink v1.90b4 with the parameter '–indep-pairwise 50 5 0.3'. This resulted in the effective numbers of independent SNPs being 113,223, 95,164, and 45,752 for the full, indica, and japonica populations, respectively [After removing SNPs with missing rates > 30% and minor allele frequencies < 5% in the full, ectively . To analectively . To esti2 ≥ 0.3. The effective numbers of independent SNPs were found to be 113,223, 95,164, and 45,752 in the full population, and the indica and japonica subpopulations, respectively. To identify significant associations, we set the threshold at -log (P) = 5. We used genome structure annotation information from MSU-RGAP 7.0 to annotate nonsynonymous SNPs using SnpEff [We obtained a total of 2,860,820 SNPs with a missing rate of ≤ 50% and minor allele frequencies of ≥ 5%. To account for population structure, we performed principal component (PC) and kinship matrix analyses, using the first three PCs to construct the PC matrix. We performed a genome-wide association study (GWAS) to identify genetic variants associated with resistance to rice bacterial leaf streak (BLS) in the 747 accessions. And we used three different models: general linear model (GLM), mixed linear model (MLM), and Fixed and random model Circulating Probability Unification (FarmCPU). The GWAS was conducted using the SNP set and default settings in the rMVP software . The strg SnpEff . An in-hP ≤ 10–2 in the 2 k promoter and exons of the gene. Haplotypes that contained at least five varieties were used for statistical testing. We calculated the differences in phenotypic value between haplotypes of each gene using one-way ANOVA or Student's t-tests with an R script [P < 0.01).For haplotype analysis, we used SNPs with a significance level of R script , 52. DunO. sativa as a control (PRJNA280380) [Xoc/RPKMmock between inoculation with 10 geographically diverse strains of Xoc and mock inoculation, as well as the ratio of RPKM > 1.5 or < 0.67 in our transcriptomic analysis [Xoc/RPKMmock > 1.5 were defined as up-regulated genes, and those with RPKMXoc/RPKMmock < 0.67 were defined as down-regulated genes.To identify candidate genes within the QTL associated with resistance to rice bacterial leaf streak, we performed transcriptome analysis on leaves of Nipponbare at 48 h after inoculation with 10 geographically diverse strains of Xoc, the causal agent of bacterial leaf streak. We used three biological replicates for each, and three replicates of mock-inoculated A280380) . We perfanalysis , 60. GenAdditional file 1:Fig. S1. Population structure analyses of 747 rice accessions. Fig. S2. Quantile–quantile plots for the GWAS in full population using GLM, MLM and FarmCPU. Fig. S3. Quantile–quantile plots for the GWAS in indica population using GLM, MLM and FarmCPU. Fig. S4. Quantile–quantile plots for the GWAS in japonica population using GLM, MLM and FarmCPU. Fig. S5. GWAS results of RBLS in different populations using MLM. Fig. S6. GWAS results of RBLS in different populations using GLM. Fig. S7. Pathway enrichment of the cloned genes for RBLS. Fig. S8. Pathway enrichment of the candidate genes for RBLS.Additional file 2:Table S1. Description of 747 rice accessions.Additional file 3:Table S2. Significant association signals for RBLS in the indica and japonica populations detected by rMVP using FarmCPU model.


As the continent of origin for our species, Africa harbours the highest levels of diversity anywhere on Earth. However, many regions of Africa remain under-sampled genetically. Here we present 350 whole genomes from Angola and Mozambique belonging to ten Bantu ethnolinguistic groups, enabling the construction of a reference variation catalogue including 2.9 million novel SNPs. We investigate the emergence of Bantu speaker population structure, admixture involving migrations across sub-Saharan Africa and model the demographic histories of Angolan and Mozambican Bantu speakers. Our results bring together concordant views from genomics, archaeology, and linguistics to paint an updated view of the complexity of the Bantu Expansion. Moreover, we generate reference panels that better represents the diversity of African populations involved in the trans-Atlantic slave trade, improving imputation accuracy in African Americans and Brazilians. We anticipate that our collection of genomes will form the foundation for future African genomic healthcare initiatives. African human genome variation remains under-sampled. Here, the authors present a collection of 350 whole genome sequences from Angola and Mozambique and model the timing and extent of significant demographic events in African history. This vast distribution has been largely attributed to the Bantu Expansion, a succession of dispersals originating in the inland Savannahs of Central-West Africa some 6000–5000 years before the present-day (BP)3, spanning the African Iron Age, and likely driven by the development of agriculture5 and periods of habitat change7. Historical records (www.slavevoyages.org) show that Bantu speaking communities were also heavily affected by the forced movement of peoples to the Americas during the trans-Atlantic slave trade, contributing over half of all slaves to have disembarked across the New World. Despite this significant role in the histories of both Africa and the Americas, Bantu speaking communities remain under-represented in human genomics research.With over 300 million speakers  spanning a region of sub-Saharan Africa of 10 million km9. Such endeavours are critical next steps towards understanding how genetic diversity is structured globally and providing reference variation catalogues for a broad range of medical genetics initiatives11. Although Bantu speaking communities have been involved in several WGS projects to date, such as the 1000 Genomes Project (1000G)12, African Genome Variation Project (AGVP)13, Ugandan Genome Resource14 and H3Africa-Baylor dataset (H3AB)15, gaps remain including a scarcity of data from populations on the edge of the Bantu Expansion such as those from Angola and Mozambique.Today, advances in next-generation sequencing technologies have begun to facilitate the curation of whole-genome sequencing (WGS) data representing the full spectrum of variation across diverse human populations17 and linguistic data18 from regions surrounding Angola and Mozambique have proved crucial in forming our understanding of major dispersal routes undertaken during the Bantu Expansion. This includes favouring the so-called late-split model concerning the diversification of Western and Eastern Bantu languages19. Furthermore, archaeological data21 has hinted at additional, more complex patterns including multi-step dispersals involving Bantu speaking communities across sub-Saharan Africa, suggesting our understanding of migrations into and out of Angola and Mozambique remains incomplete. Methods leveraging additional WGS data have the potential to shed further light on these events.Prior analyses of autosomal SNP arraywww.slavevoyages.org). Indeed, 96% of all slaves to arrive in south-east Brazil left from ports located in Angola and Mozambique, whilst 25% of all slaves to arrive in the USA originated from Angola in addition to those that disembarked from ports across much of coastal west Africa. With limited genomic data available from these important embarkation regions, current reference variation panels lack the complete diversity of African populations that have contributed ancestry to populations throughout the Americas22 potentially leading to asymmetries in our ability to impute variation for analysis using genome-wide association studies23.Moreover, as former Portuguese colonies, Angola and Mozambique are recorded as being the origin of over 5 million and 500,000 slaves to have crossed the Atlantic respectively from 1526–1875  and further into east and south Africa. We infer distinct periods and intensity of admixture during the Bantu dispersal. During the initial stages of the Bantu dispersals into Cabinda and Angola in the west, and into Mozambique in the east, admixture with local populations was limited; this was followed by more extensive admixture in later stages of the Bantu dispersals in south-west (in Namibia and south-western Botswana) and south-east (south-eastern Botswana and South Africa) Africa. Our results bring together concordant views from genomics, archaeology, and linguistics to paint an updated view of the complexity of the Bantu Expansion.Moreover, we generate reference panels that better represents the diversity of African populations involved in the Atlantic slave trade, improving imputation accuracy in African Americans and Brazilians over the 1000 Genomes Project. Overall, this dataset represents a timely addition to the growing number of whole-genome sequences from Africa, provides insights into the history of Bantu speaking migrant communities, and takes another step towards ensuring the potential benefits of genomics extends to all parts of the globe.X from 300 individuals sampled in Cabinda and 50 individuals sampled in Maputo , 91% of which were singletons. Modest differences in genetic diversity between CAB and MOZ were apparent, with CAB showing an increased coverage-adjusted average heterozygosity ratio compared to MOZ   < 0.05). Upon examining shared 26), and with <5% European ancestry (as estimated using ADMIXTURE27)  , MOZ (north); Zulu, Chimp), Z = 4.4)  and suggesting population structure within MOZ likely also reflects the genetic differentiation of north Mozambican from south Mozambican and South African Bantu speakers over time in addition to any subtle differences in east African or Khoe/San admixture components.Within MOZ, population structure is apparent on a PCA largely separating the northern Makua (MOZ (north)) from the southern Tsonga and Chopi peoples (MOZ (south)). 34 shared across sequenced Niger-Congo speaking groups in the dataset, we observe geographic stratification of recent ancestries. Longer, more recent haplotypes 35) , supporting inference that Zambia was an intermediate location for Bantu speaker migrations into the region surrounding present-day Mozambique15.Examining Identical-By-Descent (IBD) haplotypesp < 0.0001), corroborating the recent discovery of north to south serial founder events in the genetic history of the region17. This pattern is similarly observed when analysing short runs of homozygosity (ROH)36  .Examining ancient IBD (2–4 cM) sharing within groups Fig. , clear d43 set Fig. . CAB andset Fig.  that larset Fig.  and PC6 set Fig.  instead set Fig. , fineSTRset Fig. . Makua pset Fig. , aligned42, and supporting inference using PCA  in Tsonga and Chopi peoples among MOZ (south) are derived from single-date admixture events at ~1300 BP  1050–1550 BP), aligned with dates and ancestry proportions observed in previously genotyped south Mozambican groups17 and Tsonga peoples from South Africa55. We also find evidence of admixture involving a small 1–5% western rainforest hunter-gatherer component (best represented by the Cameroonian Bakola) in the Kongo peoples among CAB estimated to have occurred ~2050 BP (CI 1800–2150 BP), identical to those previously estimated in this group16. Ovimbundu and Kimbundu peoples among CAB and Makua peoples among MOZ (north) are modelled by SOURCEFIND as having as little as <1% rainforest hunter-gatherer or Khoe/San related ancestry respectively.Using fastGLOBETROTTER17. Importantly, the flexibility afforded by our WGS datasets now enables such models of migration and admixture developed in these earlier studies to be tested using largely independent datasets . To reconstruct dispersal into and out of the regions surrounding Angola and Mozambique over recent millennia, we therefore used SOURCEFIND and fastGLOBETROTTER to characterise ancestries and date admixture in all present-day African groups across our extended HOA dataset , significantly later than those estimated in Tsonga and Chopi peoples among MOZ (south)  Fig. .43 was recently modelled using the Ovambo , MOZ (north), or BSZ similarly provide working fits , Fig. 3  relative to those from closer to the Bantu heartland in Cameroon 3 Fig.  are wideure Fig. , CAB andNe) and separation histories of CAB and MOZ, we used the non-parametric Multiple Sequentially Markovian Coalescent (MSMC2)62 and genome-wide genealogies estimated using Relate63. We also investigate the demographic and separation histories under an Approximate Bayesian Computation (ABC) framework64, simulating 135,000 whole-chromosomes65 (chromosome 1) using realistic recombination rates and error rates typical of WGS data34 under a clean-split model , the split between CAB and MOZ is estimated to have occurred at ~5900 BP with MSMC2 and 4,800 BP with Relate 70, including 1,171 individuals from São Paulo, Brazil. ADMIXTURE clustering shows an average of 72% West African-like ancestry in individuals from the AASP  and 11% in individuals from SABE   and very rare  variants.To test this, we used two target datasets: The 23&Me African American Sequencing Project (AASP)zil Fig. , especiaAnalysing genomic data from under-represented human populations has the potential to shed light on major events in our species’ history and to fill important gaps in the current record of global diversity. Here, we present a collection of whole genomes from Angola and Mozambique—CAB and MOZ—expanding the coverage of catalogued genetic variation from sub-Saharan Africa, enabling insights into genetic history and potentially improving the imputation of African and African-derived ancestry in the Americas Fig. .18. Intriguingly, such estimates overlap with the period spanning the earliest archaeological evidence of the Bantu speaker inhabitation of the western Congo basin around 2800 BP71, whilst notably predate the earliest evidence of Bantu speaker-associated artefacts east of Lake Tanganyika ~2600 BP72. However, split times inferred using genomic data should be treated with caution as they can be influenced by sequencing and phasing errors or unmodelled admixture62. Moreover, we emphasize these estimates largely predict the genetic split between Kongo peoples and Bantu speakers from north and south Mozambique. Further analyses using a greater diversity of sequenced groups under more complex demographic scenarios are therefore necessary to comprehensively assess the separation of Western and Eastern Bantu speaking populations.Leveraging the power of WGS data and the well-placed diversity of ethnolinguistic groups among CAB and MOZ, we recover estimates of a genetic split between Eastern Bantu speakers from Western Bantu speakers south of the equatorial rainforests occurring around 3200 BP (CI 2700–3700 BP) Fig.  using ABNe relative to MOZ . These eastern branching dispersals through central Africa then likely further differentiated before reaching the Indian Ocean coast either moving further east towards the Great Lakes region or likely continuing east of Malawi before southward dispersals through Mozambique and subsequently into South Africa and southern Botswana , Cabinda, Angola (REf: UoN/2016), Pedagogic University (“Universidade Pedagógica”), Maputo, Mozambique (REf: UP/2017), and the University of Leicester ethics committee (REf: 11334-sdsb1-genetics). After obtaining full participant consent, saliva samples of individuals of both sexes and >18 years were collected from Cabinda, Angola (CAB) and Maputo, Mozambique (MOZ) and isolated at the University of Leicester. The participants provided their, their parents’, and their grandparents’ ethnolinguistic affiliation. Individuals who reported speaking the same language as their parents and grandparents were classified into major linguistic groups using the Ethnologue database (www.ethnologue.com)24. Isolated DNA from 300 Angolan participants and 50 Mozambicans were shipped for 15X target WGS. Reads of length 150 base pairs (bp) were generated by Illumina HiSeq X™. Four individuals from Cabinda and four individuals from Mozambique were additionally selected for high-coverage PCR-free 40X sequencing  was used to map FASTQ formatted paired-end reads generated from each newly sequenced sample against the GRCh37 reference genome (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.13/) to align with the coordinate system used by the majority of comparative datasets used in this study  and the dbSNP155 (https://ftp.ncbi.nih.gov/snp/) aligned to hg19/GRCh37.minimap2 v2.11-r797f2 alleles25 were identified using a custom R script  for 40 randomly selected samples from either CAB, MOZ, and all other sequenced groups within the 1000 Genomes Project Phase 3 (1000G)12 and the AGVP13. Variants within low-complexity regions (https://github.com/lh3/varcmp/raw/master/scripts/LCR-hs37d5.bed.gz) and regions of known segmental duplications  were ignored.Shared 15, Simons Genome Diversity Project (SGDP)28 and three high-coverage ancient African whole genomes31 84. Ten independent, unsupervised replicates of the software were run for values K = 2,..,12. For each value K, we retain the run with the highest log-likelihood after convergence. Ancient genomes  was performed to overlay data from ancient genomes present in the WGS and HOA datasets, with shrinkmode: YES used to mitigate errors associated with aDNA degradation patterns. We additionally performed PCA on the WGS dataset after down sampling each group to a maximum of ten randomly selected individuals.We performed PCA on genotype matrices generated from our WGS, HOA, and ILLUMINA datasets using the smartpca programme  from the EIGENSOFT v7.2.1 tool-suitef4 using the R package admixr85 across all possible (non-redundant) three-population arrangements of groups in our WGS dataset with panTro5 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001515.7/) set as the outgroup. Groups were sub-sampled to a maximum of ten randomly selected individuals. Significance (Z-scores) and standard errors are estimated using a weighted block-jacknife over segments of 5-centimorgans (cM).We calculate 34 with default parameters, after filtering for SNPs segregating with a MAF > 0.01 in each group. We removed gaps between IBD segments that have at most one discordant homozygote and are <0.6 cM in length as well as IBD segments in regions of low SNP-density. Significant  differences in the mean pairwise IBD sharing between groups were inferred using a one-tailed permutation test. Correlations between geographic distance and within-population IBD sharing among Bantu speakers outside of CAM were performed with CAB selected as the reference point owing to its proximity to original staging location for the dispersal of Eastern Bantu speakers proposed under a late-split model19. Distances (km) from Cabinda were calculated with approximate latitude and longitude coordinates for each group .IBD haplotypes were estimated across all Niger-Congo speakers in our WGS dataset using IBDSeq83 with parameters --homozyg-snp 50 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --homozyg-window-snp 50 --homozyg-window-threshold 0.05.ROH were estimated across all Niger-Congo speakers in our WGS dataset after filtering for SNPs segregating with a MAF > 0.01 in each group using PLINK 1.986 alongside the 1000 G reference panel (https://mathgen.stats.ox.ac.uk/impute/1000GP_Phase3/), and following the stepwise procedure to estimate global mutation/emission (-M) and switch rate (-n) parameters as outlined in previous studies87 CHROMOPAINTERv250 was run on all diploid individuals within either our HOA or ILLUMINA datasets using two distinct donor-recipient population configurations: (i) all individuals and/or groups in the dataset are included as both recipients and donors of shared haplotypes  and (ii) , as in (i), but with all Bantu speaking groups other than the Cameroonian Lemande (used to describe Bantu speaker related ancestry in previous studies41) excluded as donors (HOA no-Bantu-copying model).After phasing genotypes using SHAPEITv250 was run independently on both (a) Niger-Congo speakers within our HOA dataset , MOZ (south), with South_Africa_12000BP38 and Ballito Bay A31 as source groups, with Mende (MSL), Mbuti, Khomani, Dinka, Iran_Neolithic, Levant_Neolithic, Ami, Karitiana, Punjabi, Onge, French, Sardinian groups present in our extended HOA dataset  and cross-population  coalescence rates using high-coverage  genomes representing individuals from both the CAB and MOZ  and subsequently phased using SHAPEITv286 alongside 1000 G reference panel (https://mathgen.stats.ox.ac.uk/impute/1000GP_Phase3/). Following recommendations, we use sample-specific masks to exclude genotypes present in regions of low coverage relative to the genome-wide average with coverage statistics generated using mosdepth81. We also exclude genotypes across all samples using Heng Li’s universal mask28. The mutation rate used to scale time was 1.25 ×10−8 per base-pair per generation88.MSMC263, was used estimate genome-wide genealogies using 40 subsampled genomes from CAB, 13 from MOZ (north), and 27 from MOZ (south). Genotypes were phased using SHAPEITv286 and filtered at sites marked as “not passing” in the 1000G accessible genome pilot mask (ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/accessible_genome_masks/StrictMask/). The 6-EPO multiple alignment estimation of the human ancestral genome  was used to identify the most likely ancestral allele for each locus. Within-population and cross-population coalescence rates were calculated using the EstimatePopulationSize.sh script from the Relate GitHub repository (https://myersgroup.github.io/relate/).Relate v1.1PCAB and PMOZ represent populations of Bantu speakers from the CAB and MOZ datasets respectively. Moving backwards-in-time, for each population PCAB and PMOZ, population Pn is initialised with a diploid effective population size of Nnat Generation Present and an exponential growth/decay rate of αn=log(Nn/Nn’)/Generation Split, where Generation Split is defined as the generation at which populations PCAB and PMOZ merge  to form PAncestral and N’n is the diploid effective population size of Pn at Generation Split.We first define a simple, two-population split model  at generation 7586. Values were based on the model of human population history presented in Tennessen et al.89. Using the coalescent simulator msprime65, we generated 135,000 simulations of chromosome with values for each parameter randomly drawn from their corresponding prior distributions . As our observed data, we used 40 randomly sub-sampled genomes from CAB and 40 randomly sub-sampled genomes from MOZ. This data was subsequently restricted to ~1.5 million biallelic SNPs present on chromosome 1, segregating in these 80 individuals and phased using SHAPEITv286. For every simulated and observed VCF, we calculate a set of 46 summary statistics as described in Supplementary Table 67).To reduce computation time, we additionally include two fixed parameters wherein the merged population abc64 with a logit transformation applied to each parameter. We assess the accuracy of the median points of the posterior distributions by calculating the Mean Absolute Error, Mean Squared Error and Root Mean Squared Error using the Metrics R-package (https://github.com/mfrasco/Metrics) by comparing with pseudo-observed parameter values from 1000 randomly selected simulations  to convert AASP and SABE SNP coordinates from hg38 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26/) to hg19/GRCh37. For a third analysis, we also used 50 randomly sub-sampled individuals from CAB and 10 randomly subsampled individuals from MOZ as additional target dataset to test improvements in imputation accuracy afforded by the addition of CAB and MOZ to reference panels. Prior to imputation, SNPs across all three target datasets were phased using SHAPEITv286 and masked at all-but ~2.5 million autosomal loci present in the Illumina Omni 2.5 array panel . African ancestry proportions in SABE and the AASP were estimated using ADMIXTURE after merging data from either cohort with genotype data from the 1000G individuals from our newly sequenced CAB and MOZ datasets and applying the same procedure as outlined in Methods section “ADMIXTURE”. Haplotypes were split into 5 Mb chunks and provided in parallel to the IMPUTE290 software to impute reference panel genotypes using either (a) the 1000G Panel (b) the 1000G reference panel merged with 340 CAB and MOZ genomes  were filtered out. As a metric of imputation accuracy, for each reference panel across the set of loci present across both panels, we calculate Pearson’s Correlation Coefficient (Dosage R2) using imputed genotype dosages across the target dataset and the original, unmasked genotypes as a function of non-reference allele frequency.As our target datasets for imputing genotypes, we use phased, biallelic, autosomal SNPs from 2301 self-reported African Americans from the USA sequenced as part of the AASPFurther information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1-5Reporting Summary


Dasypus novemcinctus). We investigated the blood transcriptome of five wild monozygotic quadruplets over time to explore the influence of developmental stochasticity on gene expression. We identify an enduring signal of autosomal allelic variability that distinguishes individuals within a quadruplet despite their genetic similarity. This stochastic allelic variation, akin to X-inactivation but broader, provides insight into non-genetic influences on phenotype. The presence of stochastically canalized allelic signatures represents a novel axis for characterizing organismal variability, complementing traditional approaches based on genetic and environmental factors. We also developed a model to explain the inconsistent penetrance associated with these stochastically canalized allelic expressions. By elucidating mechanisms underlying the persistence of allele-specific expression, we enhance understanding of development’s role in shaping organismal diversity.Genetic and environmental variation are key contributors during organism development, but the influence of minor perturbations or noise is difficult to assess. This study focuses on the stochastic variation in allele-specific expression that persists through cell divisions in the nine-banded armadillo ( Minor perturbations or noise can significantly impact development but are difficult to assess. Here, the authors detail allelic expression imbalance across the development of nine-banded armadillos that indicate stochastic developmental canalization. The clearest example of this happening in nature is X-chromosome inactivation which is an epigenetic process that regulates gene dosage in females4. Occurring as a random coin-flip in each cell early in development, the status of inactivation is then stably inherited down cell lineages 6. Unusually, this is an example where the lineage relationship between cells can be observed by eye, as in the calico cat, where color alleles are X-linked and create obvious patterning3. But X-inactivation (XCI) is only one inherited epigenetic mark; cells likely have thousands, progressively defining cell-type as the cells move down Waddington landscapes in complex relationships creating stable developmental trajectories8. Further discovery and characterization of the shared marks, mechanisms, and impact of cell lineage relationships remain a central goal of modern biology.Cells in a fully developed organism share life histories traced back through their divisions, defining lineages. Epigenetic marks left on a single cell early in development can be inherited down through cell divisions, leaving shared features across cells, and barcoding their lineages11. Monoallelism is interesting because it could reflect regulatory noise from differentiation that is propagated forward epigenetically9. While some studies have reported this inherited effect in cell lines12, more recent work assessing individual cells in tissues has suggested some effect but the exact degree of impact has been challenging to ascertain13 with studies variously suggesting abundant monoallelism without lineage-dependency10 and a major role for cell intrinsic noise such as bursting14. One possibility is that early lineage marks are broadly encoded but invisible since, unlike XCI, they are not aligned across chromosomes in a simple-to-observe way. If so, these marks will be confounded with the effect of eQTLs in studies of wild populations, or with genetic background in crosses of inbred strains.Attempts to ascertain the existence of these permanent shared markings in previous work have mainly focused on the strongest events – monoallelism – in the simplest systems – cell lines – with mixed resultsDasypus novemcinctus (the nine-banded armadillo) which has a polyembryonic reproductive strategy, producing litters of identical quadruplets (quad)  and a small number of colonies exist where they are reared in captivity.One solution to the challenge of controlling for genetic background is to exploit identical twinning. Identical twins will frequently have maternal and paternal alleles that are derived from the same genetic background while still being distinct enough to permit allelic analyses. While genetics can be well controlled in identical twin studies, environment is more of a challenge. This is particularly critical for functional genomics studies, which generally measure properties that are responsive to environmental variation. To ensure both genetics and environment are shared in an outbred organism, we turned to ad) Fig. . The splIn this work, we study allelic imbalances from blood samples collected at 3 time points over the course of 18 months from 5 wild-caught armadillo quadruplets  imbalances to estimate our power to resolve cell lineages barcoded on the autosomes. We find that for our read coverage and number of armadillos, we are powered to detect events arising as late as the 10,000 cell stage. We show that autosomal allelic ratios varying between individuals are also enriched for stability over time, consistent with an early developmental origin. We test the ability of these autosomal ratios to barcode individuals and find that, in combination, they define approximately a half X-chromosome worth of additional epigenetic signal in terms of their contribution to organismal transcriptional variability. We close by suggesting this is likely to have an important impact on disease variability by rendering otherwise haplosufficient genetic variation penetrant.In order to better understand the range of expression variability within and across the quadruplets, we first assessed the similarity of the expression profiles. Similarity of the overall gene expression within and across quadruplets at a given time point is extremely strong Fig. . The min4. During development, epigenetic marks  are deposited, and then maintained along a cell’s lineage . The high predictive scores of XCI indicates that individuals within a quadruplet set have distinct XCIs, at least with respect to the cell populations sampled in this study.Having validated that the timing and inheritance of XCI leaves a well-defined permanent impact in allelic signatures across individual SNPs or at the chromosomal level, we looked for a more general measure that allows SNPs to be combined, as on the X, but might generalize to autosomal signatures not necessarily shared on a whole-chromosome basis. This measure of individuality can be quantified by the degree to which knowing it permanently defines the individual relative to siblings. Our basic strategy is to compute confusion matrices for each SNP’s allelic ratio across individuals within quads between time points , which we refer to as ASE-SNPs. To measure our capacity to recover ASE-SNPs, we ordered SNPs by deviation from the null expectation that allelic ratios are identical across individuals within a quad , then measured the predictive power using the area under the Receiver-Operator Characteristic curve (AUROC). The AUROC measures the probability of ranking an ASE-SNP in front of a non-ASE-SNP, with an AUROC of 0.5 indicating random predictions, and 1 indicating perfect recovery of all ASE-SNPs.The results suggest that ASE-SNPs can be readily identified at the read coverage observed in our data. ASE induced by early lineage events (≤64 cells) created high-variance allelic imbalance distributions that could be detected with read coverage as low as 12 reads . In magnitude of impact, this is approximately equivalent to an additional half an X-chromosome worth of imbalance distributed across the genome of both females and males. Looking across all quadruplets, we can find an average of 700 genes exhibiting strong imbalances. This is much larger than an estimated germline de novo mutation rate of 19 to 21 per generation based on data from 36 mammals18, consistent with our model that these allelic imbalances arise epigenetically, like XCI. Using these imbalanced genes as the feature set was predictive of individuality and gives approximately the same score as the genome in aggregate , implying a significant fraction of these genes are epigenetically differentially canalized. Because of the relationship between cell population and variance, greater imbalance implies a smaller number of cells present when our signal originated, which can be explicitly evaluated as in our earlier model.Simply combining all genes (excluding those on the X chromosome) into a single confusion matrix yields highly significant predictability scores  within specific cell lineages, so even an exact number of cells does not define an exact developmental stage without broader assessment across tissues to observe shared and distinct relationships. Functionally, the signature of the allelic imbalanced genes was unique to each sibling cohort of quadruplets, but did include enrichment for a common immune component . However, random effects provide an extremely useful record of shared history, a fact exploited in lineage analyses of all types27. To discover the permanent imprint of noise on cells in the organism, our study design exploited the shared environment of alleles within cells. In armadillos, this cellular control is particularly strong since the genetic background is perfectly shared. Thus, extrinsic noise  is shared by the two alleles, leaving variability in expression of alleles potentially driven by intrinsic noise29. Because this intrinsic noise is inherited down cell lineages, it leaves a permanent mark shared across many cells. While other study designs have, for the same reason, also focused on engineering or exploiting cases where allelic output can be carefully measured31, armadillos offer unusual robustness and replicability by sampling across identical sets of independent outbred genetic backgrounds. That they are mammals also provides a useful reference for the epigenetic signals we uncovered in the form of XCI, where the results of intrinsic variability are clear. Our key finding in armadillos is that similar allelic imbalances exist on the autosomes and are stably preserved and substantial in aggregate effect, totaling approximately half an X-chromosome worth of signal in both males and females in this species.In human studies, expression variability is most simply thought of as being due to some combination of environmental and genetic factors, leaving “noise” purely as a nuisance term. More complex models may include epigenetic associations of phenotypic and behavioral interactions32. Although the number of quadruplets used in our study was relatively small, our experimental design, which involved sampling at different time points within each individual of a quad, provided an unusually well-structured framework in which many genetic and environmental effects are controlled. Our experimental design was enabled by our use of armadillos, but leveraging this non-model organism does create challenges with respect to genome annotation. We resolved this by sequencing the individual genomes and assembling the X-chromosome; this yields much higher quality data but at a much higher expense. In contrast to the environmental control within quadruplet sets, we did have variability between quads due to the alternate use of the armadillo colony to study Hansen’s disease (at later ages). Since virtually all our analyses were within quads, this has little impact on our results, other than showing they are robust to shared environmental changes which would normally occur. However, this may explain one of our key findings, which was limited functional overlap in the ASE signatures across quads.Our study has a number of limitations due to our use of a non-model organism and our interest in subtle inter-individual effects. Key limitations include our use of peripheral blood mononuclear cells (PBMC) as our sample source, reference genome quality in the armadillo, the number quadruplet sets and variability between quadruplet sets. PBMCs offered the advantage of sampling over time without sacrificing the animals. This is key in studying natural heterogeneity over developmental time in contrast to pseudo-development in a functionally clonal group . Of course, the use of PBMCs potentially limits the generality of the results. However, in humans, we have shown that XCI ratios are shared across tissuesLack of overlap could reflect fundamental biological differences between the quadruplet litters, or it could reflect our ability to detect distinct ASE. We think of this as being a question of whether the Waddington landscape is somewhat distinct between litters. If each litter has a subtly distinct landscape , it is very easy to imagine minor perturbations creating unique signals in each litter. On the other hand, if all armadillos share a landscape , lack of overlap is more surprising. However, it could be explained by a broad signature such as our data otherwise supports, and technical limitations being the key driver of which subset of that broad signature we detect. Technical limitations in this context could include anything affecting our power to detect canalized allelic signatures such as quite plausibly different factors such as read depth, gene expression level, the allelic ratio itself, or even reference bias. We suspect that our results reflect a combination of both models.31. While genomic data has permitted valuable progress in our understanding of both heritable and non-heritable phenotypic variation, this progress has been more piecemeal in sources of non-heritable variation. All studies of genetic or environmental influences on phenotype are affected by this unexplained, non-heritable variability or ‘noise’33. One possibility is that ‘noise’ can be partitioned into well-defined categories of its own, based on underlying mechanisms. Development has long been thought to be a potential driver of unexplained phenotypic variability34 it is a time when small initial changes can permanently propagate forward to large later effect . This diminishes the impact of, e.g., transcriptional bursting, which has led to substantial controversy about the presence of monoallelism46. Our results reconcile previous observations by showing that while such permanent allelic effects exist, they are far weaker and more graded than individual cellular measures would easily reveal. However, they represent an important marker of lineage decisions within the organism since they appear to mark early events in development that are permanently inherited, marking classical epigenetic events.The degree of skewing toward one chromosome over the other has been researched intensively47. After 35 days pna, the diet was gradually transitioned to that of adult by 49–56 days pna. The wild-caught females were administered 0.15 ml Ivermectin SC, and Exceed antibiotic if they showed any wounds or abscesses. Adults were dewormed every 6–8 weeks with Panacure on chow for three consecutive days, or with 0.2 ml Ivermectin on food. The babies were treated once with Panacure on chow for three consecutive days.This study complied with the NHDP Institutional Animal Care and Use Committee (IACUC) guidelines under the approved protocol “Leprosy research support and maintenance of an armadillo colony” A-102). Five sets of armadillo quadruplets  were used in this study and dewormed with Ivermectin (0.1 mL) and Praziquantel (0.4 mL). Prednisone (10 mg/mL) was also given at this time.At four to five months of age, the animals were delivered to the National Hansen’s Disease Program (NHDP) facility in Baton Rouge, LA where siblings were placed in pairs in modified rabbit cages9Mycobacterium leprae derived from athymic nude mice49 – both after the first time point was collected. Blood was collected at different time points throughout the course of disease and at 18–24 months post-infection, the animals were humanely sacrificed when they developed heavy M. leprae dissemination with severe hypochromic microcytic anemia. The bacteria will locate in the bone marrow and the animals will eventually succumb to secondary complications of persistent bacteremia if not sacrificed50.Blood samples were collected at three time points per quadruplet staggered over the course of a year, starting from March 2017 until August 2018  and Dexdomitor (0.1 mg/kg). All animals were screened for leprosy and their health (CBC and blood chemistry) evaluated at tri-monthly blood screenings. RNA was extracted from the PBMC using an automated Maxwell 16 Instrument (Promega) and a Total RNA purification kit (Promega). Library preparation was done with a poly(A) selection kit (KAPA mRNA HyperPrep) to enrich for mRNAs. Multiplexed paired end sequencing (PE76) was done using an Illumina NextSeq500 on multiple flow cells. We blocked for lane batch effects by splitting the quadruplet samples into pairs and ran two pairs of each set per flow cell (Supplementary Table Ensembl (v95)52, and generated an index file for use within STAR53. We mapped reads with STAR and standardized counts to counts per million (CPM) by summing the counts and dividing by 1e6  tubes , and PBMC were isolated following standard protocols54. Variants were called from the BAM files using the GATK Unified Genotyper55 following best practices for DNA variant calling56  to generate a personal quadruplet genome for each quadruplet set. We first created VCI files of the SNPs and INDELs using the g2gtools vcf2vci with the –pass and –quality tags. This is an indexed version of the VCF file required by g2gtools. Homozygous  SNPs and INDELs that passed quality control were kept. SNPs were incorporated into reference genome FASTA file using the g2gtools patch command. INDELs were then incorporated into the patched genome with the g3gtools transform command. A chain file was generated using the g2gtools vcf2chain command. We updated the genome annotation file (liftover) using the new genome with the g2gtools convert command. As the genome of the armadillo is not assembled beyond a large number of scaffolds, the patches and transformations were done per scaffold. Once completed, we concatenated all the scaffold FASTA files back into one. With these five personal genomes, we generated individual STAR indices. Using samtools (v.1.9), we generated index files for the new genomes, and dictionary files with picard from GATK (v3.6.0)55.We used g2gtools 57. We then generated a VCF file with the heterozygous and homozygous  SNPs for each quadruplet. This VCF file was converted to a BED file, and then liftover to update the coordinates to the personal genome. This was then converted back to a VCF file. The SNPs (VCF) and counts (WIG) were then overlapped to obtain allele specific counts. Once again these were all performed on individual scaffolds, and recombined at the end of the analysis, which allowed for parallelization of the pipeline.Following quality control, we mapped reads from each quadruplet to their personal genome with STAR (v2.7)58 chain/liftover files between the armadillo genome and the human (hg38) and mouse (mm10) genomes. We extracted the scaffolds from these files that align to the respective Xs of the species. There were over a million human alignments  to around 2 K armadillo scaffolds , we performed a BLAST59 search on the armadillo genome. Of the 16 hits that were to annotated armadillo genes, we then performed a reverse BLAST on the human genome to find the reciprocal top hits. The two genes (ENSDNOG00000033080 and ENSDNOG00000047775) match to two XIST exons, and both these genes belong on the same armadillo scaffold (JH583104.1) and are within a few 100 Kbp  as the representative SNP and calculated the allelic ratio asTo detect SNPs with significant allelic imbalance, we modeled the ASE of the specific SNP p-values computed for each individual as follows:ith individual. While Fisher’s method is known to follow a p-value. To test the time-invariance of ASE ratios within the quadruplet, we computed the Pearson and Spearman correlation of a quad’s ASE ratios between the first and later time points , then this gene is selected as a feature gene. We then test for consistency in the third time point. As the first two time points are perfectly correlated, these genes form the training set, and the left out time point is the test set. A gene scoring matrix (4 by 4) is built per gene by comparing the ordering of the test and training data. Each individual gives a score of 1 to the test data individual it thinks it is , and a 0 otherwise. We then sum all the feature gene scoring matrices to produce an aggregate scoring matrix. Then, in a winner takes all strategy, we calculate a score which represents the number of armadillos that correctly predict themselves. The final score is between 0 and 4, with 4 as perfect predictability i.e., each armadillo correctly identifies its future (or past) self. We repeat this three times, using the first and second time points as training, the first and third, and finally the second and third, and then testing in the left out time point. We average this across time points to get quadruplet specific scores, and also across all to get a final overall score for the analysis. We calculate an analytic N), number of armadillo quadruplets, and number of cells at the timing of canalization (cell). In our simulations, each SNP is assigned to be either canalized or randomly sampled from both alleles. For canalized SNPs, we first sampled the background  ASE level for each individual from a binomial distribution with p = 0.5 and n = cell. We then sampled haplotype specific reads from a binomial distribution with p as determined in the previous step and n = coverage. Next, for each quad, we computed a p-value by extracting the two individuals with the minimum and maximum ASE ratios, then testing for equal ASE ratios using the chi-squared test (and Fisher’s exact test if the minimum read count is 5 or fewer). For non-canalized SNPs, we sampled p-values directly from the uniform distribution on the  interval. The p-values were then converted into False Discovery Rates using the Benjamini and Hochberg procedure63. The significance of the ASE bias for each SNP was judged according to the minimum adjusted p-values across multiple quads. Finally, we summarized performance as an AUROC by framing results in a binary classification setting, asking how well the minimum adjusted p-values were able to predict the ground truth SNP status .To determine the requirements for detecting SNPs with canalized allelic imbalance, we performed an in silico assessment of the SNP discovery with a wide variety of experimental conditions. We used five parameters as controlled variables; coverage of total read counts for each SNP of each individual (coverage), ratio of canalized SNPs (ratio), statistical replicates to reduce the randomness of AUROCs , or differentially . We also assume that this is persistent across time, such that once a cell is committed to expressing an allele, its lineage will continue to express this allele at a similar or equal amount. We also assume that this choice is random in a pool of cells at the same time. With these in mind, we can estimate the number of cells and the fraction of the genome that gives rise to the performance observed.To model the relationships between hetero- and homozygous mutations and disease appearance, we used the combination of a binomial distribution and a cumulative beta distribution. Let vels Fig.. Note thFurther information on research design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data