He) = 1 across six generations in inbred pigs, while only one was found in the non-inbred population. Among these SNPs, we observed nine SNPs located in swine RefSeq genes, which were found to be involved in signaling and immune processes. Together, our findings indicate that the inbred-specific pattern of homozygosity and heterozygosity in inbred pigs can offer valuable insights for elucidating the mechanisms of inbreeding in farm animals.The inbred strain of miniature pig is an ideal model for biomedical research due to its high level of homozygosity. Our results from multidimensional scaling, admixture, and phylogenetic analyses indicated that the inbred Wuzhishan pig (WZSP), with its unique genetic properties, can be utilized as a novel genetic resource for pig genome studies. Inbreeding depression and run of homozygosity (ROH) analyses revealed an average of 61 and 12 ROH regions in the inbred and non-inbred genomes of WZSPs, respectively. ROH number, length, and distribution across generations indicate the impacts of recombination and demography on ROHs in these WZSPs. Finally, we detected 56 single nucleotide polymorphisms (SNPs) showing constant heterozygosity with heterozygosity . Our results from multidimensional scaling, admixture, and phylogenetic analyses indicated that the inbred WZSP, with its unique genetic properties, can be utilized as a novel genetic resource for pig genome studies. Inbreeding depression and run of homozygosity (ROH) analyses revealed an average of 61 and 12 ROH regions in the inbred and non-inbred genomes of WZSPs, respectively. By investigating ROH number, length, and distribution across generations, we further briefly studied the impacts of recombination and demography on ROH in these WZSPs. Finally, we explored the SNPs with higher heterozygosity across generations and their potential functional implications in the inbred WZSP. We detected 56 SNPs showing constant heterozygosity with  Inbred animals are an ideal model for conservation genetics, and can be utilized as a valuable genetic resource for human medical research . StudyinRecently, high-throughput genotyping technologies have been successfully used to identify quantitative trait loci (QTL) that affect health, behavioral, meat quality, and production traits ,13,14,15SLA-3), nuclear element 1 (PANE1), and growth hormone receptor (GHR) to illustrate the mechanisms involved with immune response and dwarfism [The Wuzhishan pig (WZSP), characterized by its small adult body size, is a rare and endangered breed originating from the mountain area of Hainan province, China. From 1989, Shutang Feng and his colleagues have developed a highly inbred strain of WZSP based on the initial mating of one male with one female, followed by consanguineous mating for more than 24 generations ,34. A prdwarfism ,37,38. Rdwarfism . HoweverIn the current study, we performed genome-wide scan using the Porcine SNP60K Bead Chip in both inbred and non-inbred WZSPs. The non-inbred WZSP animals were sampled from a natural population, which was kept by random mating. After estimating their genetic structure, we determined and compared inbreeding coefficients based on the proportion of homozygotes in all pigs. We then investigated heterozygosity and ROH number, length, and distribution across generations. Finally, we explored their potential functional implications in the inbred WZSPs.All animal procedures were performed according to guidelines developed by the China Council on Animal Care, and protocols were approved by the Animal Care and Use Committee of Chinese Academy of Agricultural Sciences, Beijing, China. The approval ID/permit numbers are SYXK (Beijing) 2008-007 and SYXK (Beijing) 2008-008.Following the principles of inbred mice, an inbred WZSP strain was initiated from a pair of full-sibling WZSPs, an indigenous breed distributed in Hainan province, China. This strain was consanguineously bred and maintained up to the 24th generation by the Institute of Animal Science, Chinese Academy of Tropical Agricultural Sciences in Beijing. In this study a total of 96 inbred WZSPs, including 52 males and 44 females, were selected for analysis. They consisted of 16 animals for each of the six successive populations from the 17th (G17) to the 22nd (G22) generations. Another 12 animals of regular non-inbred WZSP maintained for conservation purposes in Hainan province were used as a control population. The population was re-created by mating several local types of mini-pigs in Hainan over the past two decades.http://www.animalgenome.org/repository/pig/).DNA was extracted from porcine blood leukocytes using the conventional phenol-chloroform protocol . DNA conp <= 1 × 10−5). Finally, a total of 46,936 autosomal SNPs shared by the six generations of the inbred WZSP line and the non-inbred WZSP population were used for population genetic analysis. For file format conversion, data management, and quality control, we used PLINK v1.0.7 [http://zzz.bwh.harvard.edu/plink/) and custom-made in-house R scripts.A total of 108 samples from inbred WZSPs and non-inbred pigs were genotyped using Illumina Porcine SNP60K. The samples showing a genotyping success rate of more than 98% were retained. A total of 52,258 raw SNPs were filtered with geno ≥ 0.01 (including only SNPs with a 99% genotyping rate or higher), and the Hardy–Weinberg equilibrium test  Inbreeding coefficient based on ROH, F(ROH); and (3) heterozygosity (eH). The three metrics were estimated using PLINK v1.07 software. To investigate the genetic relationships between inbred strains and non-inbred population, multidimensional scaling (MDS) analysis was also conducted. To prevent uncorrected linkage disequilibrium (LD) from distorting the population stratification estimation, we pruned SNPs for LD using pair-wise genotype correction in 50 SNP sliding widows with a sliding step of 10 SNPs across the genome (indep-pairwise 50 10 0.2). The data pruning step resulted in 7714 SNPs for all 108 individuals.Three metrics were used to estimate levels of breed genetic diversity: (1) Inbreeding coefficient based on the proportion of homozygotes, http://www.phylip.com/). The phylogenetic trees were visualized in FigTree 1.3.1 (http://tree.bio.ed.ac.uk/software/figtree/). Clustering of breeds into genetic groups was examined using STRUCTURE 2.3.4 [Pairwise genetic distance (D) between individuals was calculated using PLINK, where D = 1 − (IBS2 + 0.5IBS1)/N). IBS1 and IBS2 are the number of SNPs that share either one or two alleles identical by state (IBS), respectively, and the N is the number of SNPs. Neighbor joining (NJ) trees were then built based on these two types of pairwise distances using PHYLIP 3.69  with reference genome v10.2. Fisher’s exact test was used to select each significant pathway, and the significance threshold was defined by the p value (p < 0.05).Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were carried out from the database for Annotation, Visualization and Integrated Discovery  was clearly lower across six generations in the inbred WZSP compared to the non-inbred population (He ≈ 0.1) in the non-inbred population (He = 0.19). We observed 79 and 61 SNPs with heterozygosity greater than 0.5 (He > 0.5) and 0.8 (He > 0.8) in all six inbred generations (G17 to G22), respectively  across generations (F(ROH) is better at detecting both rare and common variants, we focused on F(ROH) for downstream analysis. We found the average F(ROH) value across inbred populations was 0.49. Among six generations, the highest average inbreeding coefficients F(ROH) was 0.52 in G22, while the relatively low F(ROH) was found in G20 and G21. We conducted Student’s t-test on F(ROH) values between generations and groups  using 7714 LD-filtered SNPs. In this study, we observed that the studied individuals displayed clear population stratification (inbred versus non-inbred) for the first component (C1) and the second component (C2) . This isWe further examined inbred and non-inbred WZSPs using admixture analysis in STRUCTURE 2.3.4 . We founWe observed significant differences of ROH occurrence between the inbred WZSP and non-inbred population. On average, 61 ROHs per individual  were detected in inbred WZSPs, whereas on average 12 ROHs per individual  were identified in the non-inbred population. In inbred WZSPs, we found the largest ROH proportion in G22, covering 1830 Mb in the pig genome (~70% of genome). In contrast, we observed a relatively small proportion of ROHs (<0.5 Mb) in the non-inbred population. To further explore the characteristics of ROHs for inbred and non-inbred WZSPs, we merged ROHs detected from individuals into nonredundant regions (ROHR) for each generation. We observed the estimated ROHR for each generation in inbred WZSPs was dramatically larger than that of the non-inbred population. Then, we merged the ROHs from each individual to a subset of ROHR across 108 individuals, and found a total of 22 ROHR distributed at 18 autosomes, with some regions occupying nearly the whole chromosome. The two largest regions were found on chromosomes 1 and 17, whereas the smallest region was observed on chromosome 11 at 5.3 Mb. Among 22 ROHRs, we observed that the count of ROHs for each generation in inbred WZSPs was about 2~4 fold of that of non-inbred population. Moreover, we found 70, 74, 71, 68, 77, and 72 ROHs at chromosome 1 in generations 17–22, respectively, while only there were 45 ROHs in the non-inbred population. Also, for the ROHR14 at chromosome 11, we found 10, 6, 8, 2, 2, and 3 ROHs in generations 17–22, and two ROHs in the non-inbred population, respectively . The detROHs were further separated into three size classes: (1) small (<100 Kb), (2) medium (0.1 to 5 Mb), and (3) large (>5 Mb) as described previously . We compHe = 1, including 56 SNPs in inbred WZSP and five SNPs in non-inbred pigs. In total, we observed nine SNPs located in known RefSeq gene (ESR1), receptor-interacting serine-threonine kinase 2 (RIPK2), FYVE, RhoGEF, and PH domain containing 2 (FGD2), ubiquitin specific peptidase 4 (proto-oncogene) (USP4), and Sus scrofa nuclear receptor corepressor 2 (NCOR2) which were involved in the pathway of G protein-coupled receptors; and three genes including ferredoxin 1 (FDX1), abhydrolase domain containing 5 (ABHD5), and 6-phosphofructo-2-kinase/fructose-2, 6-biphosphatase 4 (PFKFB4) which were involved in metabolism process. One gene, named protein kinase, cAMP-dependent regulatory type II alpha (PRKAR2A), was involved in platelet activation, signaling and aggregation, and sertoli–sertoli cell junction dynamics.In this study, we found some SNPs displayed complete heterozygosity with Seq gene . Among tOur previous study had revealed that genomic regions with extremely low rates of heterozygosis (<0.01%) account for 60% of the genome in one sequenced inbred WZSP . In thisESR1, RIPK2, FGD2, USP4, NCOR2, FDX1, ABHD5, PFKFB4, and PRKAR2A, which are involved in signaling and other important processes. This was in accordance with general observations that genes involved in pathogen resistance appear to comprise a significant proportion of the polymorphisms known to be maintained by the heterozygote advantage [On the other hand, we also investigated heterozygosity persistence across generations in inbred WZSPs. Our results shed light on the functional aspects of genes which remained heterozygous across generations. These genes included dvantage ,45. The dvantage . HoweverSince both recombination and selection have influence on pattern of ROHs, we studied ROH number, size, and distribution and provided some evidence for the correlation between ROH and recombination. The fact that cumulative ROH size was dominated by large ROHs in inbred and non-inbred WZSPs may be due to the limited numbers of small ROHs in WZSPs and/or due to the fact that small ROHs cannot be efficiently detected due to the limited resolution of the SNP chip. Even so, in non-inbred WZSPs we found that the large ROHs appeared more in the low recombination regions in the middle of the chromosome, and less towards the telomeric regions. The inbred WZSP, by contrast, showed a more uniform distribution of ROHs across chromosomal position. This may suggest that large ROHs were not yet broken down by recombination in inbred WZSPs due to their short history, while they were broken down by recombination at chromosomal terminals in a non-uniform distribution in non-inbred WZSPs. Therefore, it was revealed that ROHs might reflect both the recent past and current status of a population as well as distant population history, and are very susceptible to population dynamics.As genetic drift may occur in a population of limited size during the inbreeding process, we cannot totally exclude this possibility but believe the probability is low. A further population study of WZSPs will enable us to distinguish whether heterozygous genes resulted from selection or recombination.Sequencing of non-inbred WZSPs as an out-group and the inbreeding pedigree will be important for future high-resolution studies and should help us to understand how heterozygosis decreased or was maintained, and how chromosome structures reorganized during the inbreeding process from generation to generation.In summary, to our knowledge this study is the first attempt to explore the genetic characteristics of an inbred pig strain at a genome-wide level. We found significant genetic differences between inbred and non-inbred pigs. Moreover, we revealed and confirmed that several SNPs maintained higher heterozygosis even in inbred WZSPs. Our results offer some valuable insights into understanding of the pattern of homozygosity and heterozygosis during the inbreeding process.


Gallus gallus (chicken) is phenotypically diverse, with over 60 recognized breeds, among the myriad species within the Aves lineage. Domestic chickens have been under artificial selection by humans for thousands of years for agricultural purposes. The North American Araucana (NAA) breed arose as a cross between the Chilean “Collonocas” that laid blue eggs and was rumpless and the “Quetros” that had unusual tufts but with tail. NAAs were introduced from South America in the 1940s and have been kept as show birds by enthusiasts since then due to several distinctive traits: laying eggs with blue eggshells, characteristic ear-tufts, a pea comb, and rumplessness. The population has maintained variants for clean-faced and tufted, as well as tailed and rumplessness traits making it advantageous for genetic studies. Genome resequencing of six NAA chickens with a mixture of these traits was done to 71-fold coverage using Illumina HiSeq 2000 paired-end reads. Trimmed and concordant reads were mapped to the Gallus_gallus-5.0 reference genome , generated from a female Red Junglefowl (UCD001). To identify candidate genes that are associated with traits of the NAA, their genome was compared with the Korean Araucana, Korean Domestic and White Leghorn breeds. Genomic regions with significantly reduced levels of heterogeneity were detected on five different chromosomes in NAA. The sequence data generated confirm the identity of variants responsible for the blue eggshells, pea comb, and rumplessness traits of NAA and propose one for ear-tufts. Gallus gallus domesticus) [Gallus subspecies are phenotypically diverse, among the hugely diverse Galliform order that contains 290 species. Chickens are of worldwide agricultural importance due to their meat and eggs being consumed as food [IRX1 and IRX2 proneural genes, in a rare gain-of-function mechanism, results in disruption of the bipotential tailbud mesenchyme progenitor population, which drives cells toward a neural fate at the expense of the mesoderm lineage. This defect in axis elongation causes premature termination of caudal extension and the characteristic rumpless phenotype [IRX1/2/4 cluster.The Red Junglefowl is considered ancestral to the domestic chicken (esticus) . Throughesticus) , and theesticus) , making  as food . In addi as food –7. Compa as food ,9. Comme as food ,11. Huma as food . The NAA as food . Misexprhenotype . Here, wTBX1 and GNB1L was associated with the ear-tufted phenotype [Similarly, a heterozygous region (0.93–1.51 Mb) on chromosome 15 including henotype . ThroughTAGLN3) and cytoskeleton , metabolism (NAXD), vesicle trafficking associated genes , and many others. A flowchart of methods can be seen in To examine the genetic architecture of the NAA genome, whole-genome resequencing of six Araucana birds was performed: one tailed and five rumpless, of which four had ear-tufts and two were clean-faced . To idenThe number of raw reads produced per sample ranged from 103 to 215 million. Trimmed paired-end reads were mapped to the Gallus gallus-5.0 reference genome  , generatPutative variants were determined by comparing the aligned reads of all 15 birds with the reference sequence . More than 17.5 million variants were identified across the genome, including unmapped linkage groups. These variants were filtered to 9,422,906 single nucleotide polymorphis (SNPs) by including only biallelic SNPs on the autosomes, with 0% missing data across all 15 birds, and the following minimum qualities: read depth ≥ 3, MAPQ > 40, and QUAL > 40. This filtered list of SNPs was used as input for the variant effect predictor (VEP) analyses. These variants were on chromosomes 1–28, 30, 32, 33, and LGE64. Chromosomes Z and W were excluded due to inclusion of an unknown number of male and female birds.The first method used to identify genes associated with the NAA breed was to find SNPs that were significantly discriminatory when comparing the NAA breed to the others in this study. The dataset was further reduced from ~9.42 to 9.17 million SNPs using SNPRelate, a program that uses principal components analysis (PCA) to handle population structure and identity-by-decent to determine relatedness between samples to identify informative SNPs. This program removed 252,528 SNPs using the default parameters. A PCA plot showing the definitive separation of the six NAA and the three WL from the other chickens can be seen in The clustering patterns revealed by PCA are further delineated in the dendrogram where chickens that belong to the same group appear in clades . For exaTo identify the SNPs that explicitly discriminate NAAs from the other three breeds of chickens the SNPs identified by SNPRelate were input into a machine learning classifier based on decision trees called random forest (RF). The goal of this algorithm is first to construct a discriminant model that separates groups based on their variable values. In addition to the construction of the discriminant model, it also ranks the variables based on their contribution to the discrimination between groups. This is done by randomizing the values of each variable, reconstructing the discriminant model and assessing the loss in discriminatory power due to the randomization of the variable. This functionality was exploited to identify the SNPs that define NAA with reference to the other three breeds  in the comparison. The results of this analysis identified 203 out of 9,170,378 markers as the top discriminatory SNPs . These mAnnotation of each SNP from the separation of the NAA to the other three groups with its VEP results  found thTwenty-six candidate genes were selected for further analyses using the top, in order of importance for classification , NOC4L (nucleolar complex associated 4 homolog), ENSGALG00000037890, and SYAP1 (synapse associated protein 1).Reannotation of each of the 22 markers with snpEff was combined with the results from VEP . This taSince these significant markers were all non-coding variants, it can be considered that they might have regulatory functions over nearby genes. Linkage disequilibrium breaks down at about 40kb in chickens . To thisp distribution plot showed several regions with ZHp ≤ -4 in NAA and KA, indicating suggestive selection  and WL [p values as low as -4.33, which indicate only the presence of moderate selection. One explanation for the discrepancy in the range of ZHp values between the two groups is the difference in the number of individuals; the NAA group has six chickens, whereas the KA group has only three chickens. The ZHp calculation is influenced by the number of reads present and having twice as many birds increase the number of SNPs used in the calculation [p ≥ -4.8. Comprehensive descriptive statistics of the negative ZHp values for the KA and half set of NAA are provided in Another method for identifying genes associated with the NAA breed is to look for genomic regions where selection is occurring within the genome. A creeping window method was used to visualize the distribution of pooled heterozygosity within each breed . A totalelection . The NAA) and WL , had ZHpculation ,20. To op values for KD and WL were all greater than -4.0, indicating the lack of any significant selective sweeps based on this dataset  and of those 949 were at the strong selection threshold of -6.0 ZHp  on 16 chromosomes   . Violin ribution . ComparaRF analysis were able to identify 203 markers to classify NAA from the other three groups. The selective sweep analysis identified the presence of 8 strong selective sweeps in NAA, and only suggestive sweeps in the KA. Prior to combining the candidate gene sets from both analyses to identify genes not previously known to be associated with NAA phenotypes, the results were interrogated to confirm the presence of significant markers and sweeps containing or nearby the four previously identified genes/regions associated with characteristic traits of NAA: blue eggshell, ear-tufts, a pea comb and rumplessness.SLCO1B3 is a membrane transporter of amphipathic organic compounds, including bile salts. The blue eggshell color is associated with ectopic expression of SLCO1B3 in the shell glands of the uterus in birds with an EAV-HP insertion in the 5’ flanking region of the gene , and is thought to be responsible for blue eggshells in Araucana chickens [EAV-HP insertion in all six NAA birds. It was further confirmed that the three WL birds lacked the insertion [SLCO1B3, RF did not select any of these markers as significant classifiers in either analysis. This is likely due to the presence of blue eggshell laying KA, which posses the same insertion in the same position as NAA [SLCO1B3’s 4th and 5th exons and continuing beyond its sequence and into an adjacent novel gene (ENSGALG00000019276), but this occurs after the site of the EAV-HP insertion. There is also a suggestive selective sweep starting upstream of SLCO1B3, before the EAV-HP insertion site, and stretching to include the strong selective sweep creeping window and beyond it. We hypothesize that the suggestive selective sweep in conjunction with the strong selective sweep creeping window supports the selection of blue eggshell color in NAA. In the KA, the suggestive selective sweeps on chromosome 1 did not overlap the region upstream or including SLCO1B3.The breed standard for NAA requires a blue-colored eggshell . The genchickens . Direct nsertion . Whereasn as NAA . In NAA,TBX1 and a partial sequence of GNB1L are heterozygous for ear-tufted NAA birds [TBX1, 334 in GNB1L and 19 intergenic. Whereas there are some SNP markers that passed filtering within this region, none of them were significant in either RF or selective sweep analyses. This may be due to low coverage within this region and the inclusion of two clean-faced birds, making the NAA population heterogeneous. All of the KA, KD, and WL are clean-faced, and no sweeps exist on microchromosome 15 in KA. SvABA was used to examine the chromosome for structural variations. Twenty-one deletion breakpoints were identified on 15 in NAA, see TXNRD2 (thioredoxin reductase 2). This heterozygous deletion falls within the heterozygous region previously identified by genome-wide association study as associated with ear-tufts [TXNRD2 has been considered for contributing to the phenotype for DiGeorge syndrome [Another breed standard in NAA are ear-tufts . These aAA birds . The repar-tufts . A homolsyndrome ,23.SOX5 [2 ratios of 2.08–3.81, possess the chromosome 1 duplication in the first intron of SOX5  when compared to the WL  were identified proximal to IRX1 and IRX2 [Rp mutation. The single tailed NAA and all KA, KD, and WL were wildtype A/A at the Rp loci, as expected. The five rumpless NAA were A/C or C/C at both Rp loci. Neither SNP position was found to be significant in classifying the NAA using RF, but this is explained by the presence of the single tailed NAA preventing the Rp SNPs from fitting a discriminant pattern, which the RF models are sensitive to. With selective sweeps, the second Rp locus is present in 11 creeping windows in NAA that pass the threshold for suggestive selection; however, the first locus is absent from any windows for NAA. We hypothesize that the suggestive selective sweep including the second Rp locus confirms that Rp has been selected for in NAA. Neither locus exists in any sweeps in the KA consistent with KA being a tailed breed not having the Rp genotype.Confirming our earlier finding, the two candidate causative SNPs for rumplessness  were all wildtype having AA birds . A heterVEP analysis was conducted on the SNP dataset after removing markers within each group where all samples were homozygous for the reference allele. The NAA had the highest percentage of positions with the alternate allele present (81.28%), followed by the KA (68.84%), then the KD (62.02%), and finally the WL (52.02%) . The numThe 26 candidate genes associated with SNPs from RF analysis that classify NAA from the other three groups, and the 12 identified genes from the strong selective sweeps in NAA were combined, and GO terms for all genes were compiled. Seven novel genes lacking any annotation were identified and were therefore not included. GO terms were associated with each gene and their associated GO molecular function, and cellular component ontology is detailed in The North American Araucana (NAA) breed is the result of crossing the Chilean “Collonocas” breed that laid blue eggs and was rumpless and a second breed called the “Quetros” that had ear-tufts but was tailed and laid brown eggs ,11. CarbCareful breeding of these birds has continued to select for now characteristic blue eggshell color, ear-tufts, a pea comb, and rumpless phenotypes recognized in the breed standard . Over tiA 71-fold coverage of the NAA chicken genome was generated. The high coverage sequence data generated herein provides an additional resource for future studies in the NAA breed. The filtered marker set of 9,170,378 SNPs successfully showed clear separation of the NAA birds from the three other lines in this study, confirming the presence of two NAA genetic traits underlying characteristic phenotypes, and importantly, identified candidate genes and pathways suggesting potential additional unique variants. Within the markers, SNPs that separate and classify NAAs from the other three groups of birds were identified. Principal component analysis clusters NAA together, and they are more similar to members of this group than the other nine birds. The dendrogram, based on dissimilarity between individuals, confirms this finding. We suggest that SNPs within the dataset are within or adjacent to genes that are deterministic for additional NAA traits. To this end, RF and selective sweeps subset the SNP list and rank its members in order of significance to separation of these groups. The SNPs identified by RF do not appear to cause mutations in the gene coding regions; however, they link to putative regulatory regions of the nearby genes.TBX1 and surrounding genes, including TXNRD2, has been linked to DiGeorge syndrome (22q11.2 deletion syndrome), the most common microdeletion disorder in humans [In contrast, genes identified by selective sweeps, encompass whole or significant portions of gene(s) in addition to the surrounding regulatory regions. Therefore, priority was given to characterizing genes identified by selective sweeps. Several genes identified for meat quality or muscle growth and development have been previously studied in chickens and may point to additional NAA characteristic traits –31. Furtn humans . TherefoEAV-HP insertion that promotes expression of the transmembrane protein SLCO1B3 in the uterus. EAV-HP is an ancient retrovirus found in many loci in modern birds, but in this instance, it is thought that biliverdin deposition, which produces the blue eggshell color, is enhanced in animals with SLCO1B3 expression in the uterus. The pea comb trait is caused by duplication in the first intron of the SOX5 gene. The expansion of this region leads to ectopic expression of SOX5.Whole-genome resequencing of KA, developed by crossing a Golden Duckwing Araucana (NAA) and WL, a recently developed blue eggshell laying chicken breed, reveals its origin and genetic characteristics . The GolIRX1/2/4 cluster are likely causative for gain-of-function misexpression of IRX1 and IRX2 that result in the rumpless phenotype. Also of importance, direct analysis of the six NAA genomes identified a heterozygous deletion in only the four ear-tufted birds in a region significantly associated with the trait. This finding may help breeders understand why there appears to be a lethal effect in birds homozygous for the ear-tufts trait. The results in this study present the NAA as a unique breed with some still unconfirmed breed specific traits making future work appealing to breeders, the commercial poultry industry, and geneticists.In conclusion, this comparative genomic analysis demonstrates that the NAA possesses the genetic variants that give rise to the characteristic phenotypic traits associated with the breed. Breeders have maintained variants for clean-faced, as well as tailed traits, which are advantageous for future genetic studies. Additionally, the screening and direct sequencing of 141 tailed and rumpless chickens demonstrates that two SNPs upstream of the The use of chicken embryos up to E15 does not require IACUC approval. For embryos from E15—E21 and hatched birds, blood and tissue collection was approved by the Clemson University IACUC protocol number 2011–041. Processing of blood and tissue samples was approved by the Clemson University IBC protocol number 2010–041 and 2017–08.ATCCTTATGAACTCCACAGACCAAA reverse: AGAATGAATTGGTTTAGTATCATCCAGA) and SV2 (forward: TATTCATAGAGGAGAGGAAACAACC reverse: GTTGTTGAACTCAGTGATGTATCA). Amplicons were sequenced by Eton Biosciences.North American Araucana (NAA) samples were collected from geographically distinct flocks that had not been interbred. Members of the Araucana Club of America were contacted to solicit research participants. Whole blood was collected from the wing vein and DNA extracted using the DNeasy Blood and Tissue Kit . NAA samples were separated into fully or partially rumpless and tailed Araucana based on the morphology of the spine, and recorded at the time the blood sample was taken together with photographic images of each bird from our previous study . Among tGenomic DNA adjusted to a concentration of 50 ng/μL in 10 mM Tris-CL buffer pH 8.5, measured by optical density using a NanoDrop 1000 Spectrophotometer . One hundred fifty ng of DNA was used for 2x100 bp paired-end sequencing, on an Illumina HiSeq 2000 at The Roslin Institute at The University of Edinburgh . The following fragments were gel size selected: 440–489 bp.Six samples were pooled per lane, and 6 lanes of data were generated. Raw sequence data is available at the NCBI sequence read archive, BioProject PRJNA524911. Additionally, nine chicken samples from PRJNA291174 were included in this project’s meta-analyses. The reads from these samples were also 2x100 bp paired-end and sequenced on an Illumina HiSeq 2000. These nine samples consisted of three Gyeongbuk/Korean Araucana (KA), three Korean Domestics (KD), and three White Leghorns (WL).The quality of each set of reads was examined using FastQC 0.10.1 . TrimminGallus gallus reference (GCF 000002315.4) was downloaded from NCBI [The assembly sequence for rom NCBI ,36 and from NCBI . Bowtie2rom NCBI ,39 was urom NCBI  was usedGATK 3.8–0  was usedTo compare the variation present between the four groups of samples, the previously filtered and merged VCF file was used as input to Bioconductor’s SNPRelate package  in R 3.5The genofile from SNPRelate was used as input for discriminant analysis using RF. RF was run in the R package randomForest  using thThe top 22 SNPs were all non-coding variants within a gene, and in order to address the possibility that these might be regulatory variants, a modified version of the galGal5 GFF file, containing only the entries for full genes, the fourth column for each row contained “gene”, and bedtools closest were used to identify the nearest ten genes to each marker . The genp values were plotted in R using modified code from the qqman package [p values instead of -log10(p) values and the suggestive and genome-wide lines were moved to suggestive and strong sweep cutoffs. All creeping windows with a ZHp ≤ -6 were considered to be under strong selection and were reviewed for the presence of genes and SNPs. Creeping windows with a ZHp ≤ -4 were considered to be under suggestive selection and were checked for the presence of known genes/markers for North American Araucana phenotypic traits  [To identify selective sweeps, the creeping window method described by Qanbari and coworkers was implemented . VCF fil package . The man tufted) ,21,24.All putatively identified genes from RF and selective sweeps analyses were annotated with their GO terms, and sorted for the most represented terms. The top 10 most common terms were reviewed, and their associated genes were analyzed for relevance to NAA phenotypes. Pathway information was initially collected from KEGG GENES –55, and Vcftools was used to remove all positions within each group where all individuals were homozygous for the reference allele. Then the VCF file for each group was processed by Ensembl’s VEP script release 93.0  with datFor a secondary method to predict the possible implications of the SNPs identified as classifying for NAA by RF, snpEff 4.3t  was usedThe presence of structural variations in the 15 birds was determined using SvABA FH version: 134 . The ‘as≤ 0.001 and a log2 value ≥ 0.6 for a minimum of four overlapping windows. As stated in Wright, et al. 2009, the critical region containing the duplication for a pea comb is upstream of the first annotated exon in chickens, and their 5’ RACE analysis in the chicken showed the presence of the upstream exon 1 in chickens showing 90% identity to human SOX5 exon 1. Using BLAT [To get valid copy number variation results, the trimmed fastq files were all downsampled to the sample with the fewest reads using the sample command with seed 100 for seqtk 1.3-r106 . Then, ting BLAT  on theirCode and commands to perform all analyses within this manuscript are available in the S1 Fig(PPTX)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 TableThe SNPs identified by RF may harbor regulatory elements that affect nearby genes. Listed all genes within 40kb upstream (negative distance from gene) or downstream (positive distance from gene) of the 22 identified SNPs.(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S1 File(TXT)Click here for additional data file.


PCP027962 and PCP027967), were identified in the quantitative trait locus region on LG4 and await functional validation.Red skin colour is an important target trait in various pear breeding programmes. In this study, the genetic control of red skin colour was investigated in an interspecific population derived using the descendants of the red sport European pear cultivar ‘Max Red Bartlett’ (MRB) and the red-blushed Chinese pear cultivar ‘Huobali’. Approximately 550 seedlings from nine families were phenotyped for red skin over-colour coverage (Ocolcov) and the intensity of red over-colour (Ocolint) on a 0–9 scale, and genotyped using genotyping-by-sequencing. Genome-wide association analyses were conducted using 7500 high-quality single nucleotide polymorphisms (SNPs). Genomic regions on linkage groups (LG) 4 and 5 were found to be associated, and the best SNP (S578_25116) on LG4 accounted for ~15% of phenotypic variation in Ocolcov and Ocolint. The association of S578_25116 with Ocolcov and Ocolint was successfully validated in a sample of ~200 European and Asian pear accessions. The association with red skin at locus S578_25116 was not present in Asian pear accessions, suggesting its close proximity to the MRB’s Cardinal gene. Several putative candidate genes, including MYB transcription factors ( Researchers in New Zealand have produced a map of genetic variants linked with red skin color in pears, opening the door to identifying the genes responsible. Satish Kumar and others at the New Zealand Institute for Plant and Food Research Limited measured the skin color of 550 hybrid pear seedlings and sequenced their genomes. Combining these data produced a map of 7,500 variants throughout the genome and identified those associated with red skin color. The most significant variant accounted for about 15% of the color variation. Further analysis of that genomic region revealed several genes which might be related to red skin color. The genomic map produced by this study will improve breeding efficiency by making it possible to screen seedlings for fruit color, but further research is necessary to characterize the candidate genes. Pyrus sp.) has been cultivated for at least 2000–3000 years, and is currently grown commercially in more than 50 countries in Europe, Northern Africa, Asia, Australasia and North America1. Red skin colour, which enhances the appearance of pears and can make them more attractive to potential customers, is considered an important trait to target in breeding programmes around the world, including South Africa, New Zealand, Italy, Spain, China and Australia. Interspecific hybrids are sometimes used in pear breeding programmes to produce new cultivars with novel combinations of colour and texture2. Red skin expression in pear results mainly from the concentration and composition of anthocyanin; environmental factors including temperature and UV light can affect anthocyanin concentrations and perceived intensity of colour3.Pear (6. Regulation of the anthocyanin pathway in pear is most likely primarily controlled by the MYB-bHLH-WD40 (MBW) transcription factor complex, as it has been demonstrated to be conserved across a broad range of species7. For example, in apple, MYB10 and its allelic counterparts have been shown to control anthocyanin expression4. In pear, two MYB transcription factors have been associated with anthocyanin production, MYB10 and MYB114, and these appear to act synergistically to enhance pigment concentrations8. However, as has been demonstrated across a range of species, anthocyanin biosynthesis may not be regulated by the MBW complex alone9. Other transcription factors, such as MADS-box and ethylene response factors (ERF), have also been implicated in pear anthocyanin production10.The underlying genetics of anthocyanin biosynthesis and the metabolic pathway have been investigated in various studies and this topic has been extensively reviewed recently11. Dondini et al.12 reported that the red-skinned phenotype of MRB was controlled by a single dominant gene located on linkage group (LG) 4. Genome-wide dense genotyping, facilitated by the publication of draft genome of Asian13 and European14 pears, has contributed to the fine mapping of quantitative trait loci (QTL) for colour and fruit quality traits15. Using bulked-segregant analysis next-generation sequencing of pooled DNA from red- and green-skinned pear seedlings derived from a cross between red-skinned Asian pear cultivars, Xue et al.16 mapped the red/green (R/G) locus to the bottom of LG5.Genetic mechanism behind red skin or blush colour of various European and Asian pear cultivars have been investigated in segregating families. The red skin colour derived from ‘Max Red Bartlett’ (MRB) appears to be controlled by a single dominant gene, whereas the blush colour for ‘Huobali’ may be controlled by complimentary actions of at least two dominant genes19. In this study, we applied GWA analysis in combination with genotyping-by-sequencing (GBS) on a population of genetically related interspecific hybrid families to fine-map the genetic control of red skin colour. Genotype-phenotype associations identified from this GWA approach were validated in an independent population. The identified candidate genes within the QTL region lay a solid base for further functional genomics studies. Furthermore, it is expected that the single nucleotide polymorphic (SNP) markers identified in this study could improve efficiency of pear breeding programmes via marker-assisted selection.Previous studies that mapped QTL for pear red skin used a single bi-parental mapping population of small size (~100), and hence lacked statistical power. To address this deficiency, association mapping in multiple family populations, where large numbers of progeny derived from controlled crosses in various mating schemes, has been used for genome-wide association (GWA) studiesFirst-generation families were created at the New Zealand Institute for Plant and Food Research Limited (PFR) in 1986 and 1989 using commercial cultivars of European, Chinese and Japanese pear as parents. MRB, ‘Huobali’ and ‘Worden Seckel’ were the primary sources of red skin/blush. Second-generation families were created in 1996 using the best selections  produced from first-generation families as parents. Nine third-generation families were created in 2007–2008 using 12 second-generation selections as parents. All 12 parents had ‘PremP45’ (Maxie), a selection from a first-generation family (‘Nijisseiki’ × MRB), in their pedigree. In addition to varying degrees of genetic relatedness among the 12 parents, some of these parents were represented in more than one family  were stored for 4 weeks at 3 °C, then a further 1 day at 20 °C before assessment. Red skin over-colour coverage (Ocolcov) was scored on a scale from 0 (no red colour) to 9 (completely red). The intensity of red over-colour (Ocolint) was scored on a scale from 0 (none) to 9 (highest).20. Briefly, GBS libraries were sequenced on the Illumina HiSeq2000 platform and the sequence data were analysed using TASSEL/GBS21 pipeline. The fastq files were mapped separately to the P. × bretschneideri (cultivar ‘Suli’)13 and P. communis (cultivar ‘Bartlett’)14 reference genomes using Bowtie2 (version 2.2.1)22 to maximise the number of SNP calls in interspecific hybrid progenies. The various data quality filters used for SNP calling were described earlier by Kumar et al.20.Protocols for DNA extraction, GBS library preparation and variant discovery were the same as those reported earlier by Kumar et al.Joinmap v4.1 software. Then, common markers shared by at least three different paternal  maps were selected as a bridge to merge the maps using R package MergeMap23. Linkage group (LG) identification was determined based on those SNPs common with the previously published map of ‘Old Home’ × ‘Louise Bon of Jersey’24.Resulting SNPs from the previous step were used to construct a consensus linkage map. Genotypes of the parents of one family (P449) were not available, hence data from eight families were used for constructing a consensus linkage map. Paternal and maternal maps  were constructed using 25 and principal components (PCs) derived from the genotypic data matrix were used to account for cryptic relatedness and population stratification, respectively. Studies have shown that using genomic  relationship matrix is more efficient than the pedigree-based relationship matrix to account for Mendelian sampling and segregation distortion27. Estimate of narrow-sense heritability (h2) of each trait and genetic correlation between Ocolcov and Ocolint were also obtained. GAPIT28 software was used to conduct the above described genetic analyses.Genome-wide tests for marker-trait associations were conducted using a mixed-linear model approach. A genomic relationship matrix20. Searches for potential candidate genes in the vicinity of significant SNPs were also conducted using the Asian13 and European pear  genome assemblies. Candidate gene sequences were then compared to Arabidopsis genome with the Basic Local Alignment Search Tool (BLAST) from The Arabidopsis Information Resource (TAIR)29 to determine sequence homologs.The most significant SNP markers associated with Ocolcov and Ocolint were validated in a germplasm set of about 200 accessions comprising Asian and European pears and their hybrids. More details about this germplasm can be found in Kumar et al.A total of 550 seedlings with phenotypic and genotypic information were available for genetic analysis Table . The numNearly 50,000 SNPs were identified, across nine families, following the earlier described SNP calling criteria. After some additional quality controls , over 10,000 SNPs were retained for map construction. A total of about 7500 SNPs could reliably be mapped onto a consensus linkage map spanning about 4100 cM in total. The length of the 17 LGs showed a wide range, from 54 (LG16) to 362 cM (LG13). The average number of markers per LG and the average marker interval were 441 and 0.60 cM, respectively ) for all tested SNPs for both traits are illustrated in Fig. p-value of <0.01, which roughly equates to a comparison-wise p-values of p < 1 × 10−6 as we tested over 7500 SNPs, was used as a significance threshold for individual SNP testing. The ten most significant SNPs were common to both Ocolcov and Ocolint, and were clustered within a genomic region between 196 and 256 cM on LG4  values over most of the range (results not shown). Although not statistically significant, a couple of SNPs (S210_30053 and S210_30038) mapped at the bottom of LG5 and accounted for about 5% of phenotypic variation in Ocolint and Ocolcov, respectively  at SNP locus S578_25116 Table . The aveSeparate analyses of various subsets of families identified S39_462595 and S161_15221, which are in close proximity of the overall best SNP S578_25116, as the most significant markers. Joint analysis of families P507 and P508, which did not segregate for S578_25116 , and the estimated frequency of the positive allele in these samples was about 0.10. The number of accessions with genotype CC, CT and TT were 130, 17 and 13, respectively. Accessions with only Asian ancestry did not carry allele T irrespective of their Ocolcov and Ocolint scores. Although, the average Ocolcov and Ocolint of accessions carrying allele T (i.e. with genotype CT or TT) was significantly higher compared with CC genotypes, the pattern of additive association between marker and phenotype was possibly diluted by the small number of accessions with CT and TT genotypes Fig. . The secP. communis and P. bretschneideri genomes. A list of potential candidate genes in the vicinity of some of the most significant SNPs on LG4 and LG5 are listed in Supplementary Table PCP027962 and PCP027967) and three E3 Ubiquitin genes  were identified as potential candidates (Supplementary Table PCP027962 and PCP027967 exhibited sequence similarity to the Arabidopsis transcription factor genes At5G47390 (MYB-like transcription domain) and At5G35550 , respectively. The potential candidate genes aligned with the three other scaffolds  of significance included a family of basic helix-loop-helix (bHLH) transcription factors   and the subsequent, but necessary, time for reliable tree/fruit performance (phenotyping), testing and selection. Decreasing that time can be achieved through reducing the time to fruit, breeding for increased precocity, or using molecular toolsIn our study, a large number of multi-generation hybrid seedlings with varying degrees of genome sharing with the European and Asian pear cultivars MRB and ‘Huobali’, respectively, presented a mosaic of major and minor genes imparting variation in skin colour. Using advanced genotyping and analysis methods, a cluster of SNPs on LG4 displayed significant association with skin colour. The same set of SNPs were associated with Ocolcov and Ocolint, supporting the observed high genetic correlation (about 0.90) between these traits. We independently validated the additive effect of allele T at SNP locus S578_25116 in a large sample (about 200) of European and Asian pear accessions, adding further credibility to the marker-trait association reported in this study. A moderate performance of P507 and P508 families, which did not segregate for allele T at SNP locus S578_25116, highlighted the contribution of minor genes, especially those inherited from ‘Huobali’, but that might also have come from ‘Worden Seckel’ or ‘Sanguinole’.30.The maximum observed frequency of allele T was about 32% despite both parents of a family being heterozygous (CT × CT) Table . The abs12 using 90 F1 seedlings of cross ‘Abbé Fétel’ × MRB, mapped the red fruit colour as a morphological marker on LG4 of MRB, which is consistent with results from this study. This dominant gene in MRB is also associated with the red foliage, and was named C, the Cardinal red colour gene32. In ‘Bayuehong’, a red-blushed cultivar derived from the European pear cultivar ‘Clapp’s Favorite’, a major QTL for red phenotype was mapped to LG5, and a gene PyMYB114 was identified within the QTL region15. Xue et al.16, using a cross between Asian pear cultivars ‘Mantianhong’ and ‘Hongxiangsu’ , identified a QTL for red phenotype at the bottom of LG5. A genomic region at the bottom of LG5 identified in our study is consistent with these previous studies16, and suggests that LG5 could potentially harbour QTLs underpinning red blush development in Asian and European pears as well as interspecific hybrids.Unlike previous studies, interspecific hybrid seedlings in our study inherited genes for red fruit colour from both European (MRB) and Asian cultivars . A previous studyMdMYB10 gene, the pear PcMYB10 gene was mapped by Pierantoni et al.33 to the bottom of LG9, and the importance of MYB10 transcription factors in the expression of red skin phenotype has been widely reported in functional genomics studies35. However, there was no significant marker-trait association detected on LG9 in our study, which is consistent with other QTL mapping reports36. The molecular basis for this disconnect between QTL mapping and the major anthocyanin-controlling MYB transcription factor is as yet unknown.Similar to the apple 37. The other gene, PCP027967, is similar to Arabidopsis gene (At5G35550) TT2, which encodes an R2R3 MYB domain protein that acts as a key determinant for proanthocyanidin accumulation38. In addition to these MYB transcription factors, the bHLH, E3 Ubiquitin and ABC transporter classes were among the candidate genes identified within the QTL mapping region on LG4 and LG5. Functions of some of these candidate genes have been investigated in various studies10. For example, Yao et al.8 identified a gene PyMYB114 within the QTL region on LG5, and the transient assays demonstrated its role in biosynthesis of anthocyanins in pear fruits. Taken together with our results, we hypothesise that LG5 also harbours genes underpinning red skin colour development on both Asian and European pears. SNP markers identified in this study will help screening of young seedlings for red fruit colour and enhance the efficiency of pear breeding programmes. More research is required to test the function of candidate genes, especially PCP027962 and PCP027967, identified on LG4 in this study.Two MYB transcription factors (PCP027962 and PCP027967) were aligned with the scaffold S578 where the most significant SNP (S578_25116) resides. In particular, PCP027962 appears to be homologous to the Arabidopsis MYB-like domain transcription factor (At5G47390), that is reported to be a positive regulator of anthocyanin biosynthesis and is itself regulated by the anthocyanin-associated elongated hypocotyl (HY5) geneSupplementary Tables S1, S2 and Fig S1Supplementary Table S3


Plant domestication provides a unique model to study genome evolution. Many studies have been conducted to examine genes, genetic diversity, genome structure, and epigenome changes associated with domestication. Interestingly, domesticated accessions have significantly higher [A] and [T] values across genome-wide polymorphic sites than accessions sampled from the corresponding progenitor species. However, the relative contributions of different genomic regions to this genome divergence pattern and underlying mechanisms have not been well characterized.Here, we investigate the genome-wide base-composition patterns by analyzing millions of SNPs segregating among 100 accessions from a teosinte-maize comparison set and among 302 accessions from a wild-domesticated soybean comparison set. We show that non-genic part of the genome has a greater contribution than genic SNPs to the [AT]-increase observed between wild and domesticated accessions in maize and soybean. The separation between wild and domesticated accessions in [AT] values is significantly enlarged in non-genic and pericentromeric regions. Motif frequency and sequence context analyses show the motifs (PyCG) related to solar-UV signature are enriched in these regions, particularly when they are methylated. Additional analysis using population-private SNPs also implicates the role of these motifs in relatively recent mutations. With base-composition across polymorphic sites as a genome phenotype, genome scans identify a set of putative candidate genes involved in UV damage repair pathways.The [AT]-increase is more pronounced in genomic regions that are non-genic, pericentromeric, transposable elements; methylated; and with low recombination. Our findings establish important links among UV radiation, mutation, DNA repair, methylation, and genome evolution.The online version of this article (10.1186/s13059-019-1683-6) contains supplementary material, which is available to authorized users. Domestication is a special mode of evolution. Extensive studies have been carried out to understand the domestication process and genes associated with morphological changes –4. MeanwDNA base composition is an essential genomic feature. Remarkable research progress has been made in several areas, including codon usage bias , isochorMutation is a fundamental factor that generates the genetic variation upon which selection, drift, and recombination act. Point mutations are the most common type of mutations with a universal bias toward high AT, primarily due to the high rate of transition mutations . Recent Organisms have evolved a complex system to monitor and repair DNA damage caused by various exogenous mutagens, such as solar-ultraviolet (UV) radiation, reactive oxygen species, excess boron or aluminum, and pathogenic microorganisms . For plaDNA methylation is a major form of epigenetic modification in many eukaryotic genomes. It not only regulates gene expression and silences transposons and repeat sequences, but also affects mutation rates –25. DNA In this study, we report findings from the analysis of millions of SNPs segregating among 100 accessions from a teosinte-maize comparison set and among 302 accessions from a wild-domesticated soybean comparison set. First, we show that higher [AT] values in domesticated accessions relative to wild accessions, or [AT]-increase, are consistently observed for SNPs found in either genic or non-genic portions of the genome, with non-genic SNPs having a greater contribution to the [AT]-increase. Interestingly, we also find that the divergence in [AT] is much higher in pericentromeric regions than in other regions. All 4 sequence motifs related to solar-UV signature consistently have higher frequencies in methylated regions than unmethylated regions. With a different set of population-private SNPs, we also discover the enrichment of mutations related to the solar-UV signature in domesticated accessions. Using base-composition across polymorphic sites as the phenotype, genome-wide scans identify a set of putative candidate genes involved in UV damage repair pathways. Together, these findings seem to suggest that solar-UV radiation and differential mutation repair are critical components in the genome divergence process that resulted in domesticated accessions’ greater numbers of nucleotides A and T.P value is 1.49e−14 for maize and 1.02e−44 for soybean). Domesticated accessions have more nucleotides A and T at the polymorphic sites  and a ratio of 1.000 between the frequency of motif at SNP sites and that at random sites if SNPs occurred randomly in the genome. We detected 14 common motifs between maize and soybean with both frequencies and ratios greater than the expected, and 11 out of 14 were from A/G and C/T transition types Fig. , these SmCG-3′), termed as solar-UV signature  values obtained from the common SNP sets as a genome phenotype, GWAS identified a series of loci significantly associated with base-composition across polymorphic sites  is significantly associated with base-composition across polymorphic sites. The ATR encodes a putative ATR protein which functions in a wide range of responses to DNA damage, including sensing and activating a cell cycle arrest in response to UV-B-caused DNA damage  variation. Lig1 in soybean encodes a putative DNA ligase 1 protein which functions in sealing the nick of DNA at the last step of the repairing process. Besides one nonsense and two nonsynonymous SNPs, we also detected a 1.8-kb deletion at the fifth intron in wild soybean accessions  in both genes are shared by most of the domesticated accessions and a small number of wild accessions. Haplotype2 in ATR is shared among 86.7% of maize and 17.6% of teosinte, and haplotype2 in Lig1 is shared by 86.7% of domesticated soybean and 2% of wild soybean. Considering that domestication largely involved selection of favorable alleles from standing allelic variation in wild ancestors -increase in domesticated over wild accessions is consistently observed with the overall genome-wide SNPs, SNPs within major genomic annotation sets, and SNPs from different genomic regions. These findings indicate the presence of common underlying mechanisms that drive the domesticated accessions to build their genomes with more A and T nucleotides.parviglumis experienced an increase in effective population size which also lasts until the recent past (≈ 1100–1800 years ago)  in domesticated accessions, one natural question to ask is: What is the consequence of building genomes with more A and T nucleotides? One possibility will be more efficient energy usage. Energy usage efficiency is a trait under universal selection that has shaped various genomic aspects. For example, highly expressed proteins use cheaper amino acids –60 and amCG-3′ context, which is also supported by our findings of higher frequencies of motifs related to the solar-UV signature from methylated regions than unmethylated regions. DNA methylation is highly enriched within transposable elements and repetitive sequences -increase  segregating only at the regions surrounding mutation repair genes as starting materials will be preferable to demonstrate the connection between UV-induced mutation and base composition change. Sequencing lines that derived from starting materials carrying mutations at UV-damaged DNA repair gene regions may also provide additional support.Molecular experiments need to be carried out to provide evidence supporting the function of these UV-related genes and their connection to the base-composition pattern. Although it is beyond the scope of this study to address the functional difference between wild and domesticated alleles and the molecular mechanisms affecting the repair efficiency, this study pointed to a new direction for addressing some fundamental questions about the genome itself. We think that mutation repair genes, like Base-composition difference between domesticated accessions and wild accessions at the dynamic part of the genome suggests the important role of AT-bias mutation in shaping the overall pattern of base-composition variation. Regional variations of base-composition pattern indicate that non-genic SNPs and pericencentromeric regions have greater contributions to the observed pattern. This finding together with the discovery of solar UV’s potential role in driving the genome divergence establishes the connection between DNA methylation and base-composition variation. By focusing on the evolutionary outcome, our genome scans in maize and soybean identified a set of UV damage repair genes. Rapidly improved genomics and epigenomics capacity would further facilitate our efforts to probe potential connections among base composition, mutation, methylation, DNA repair, and genome evolution.ftp://ftp.ensemblgenomes.org/pub/plants/release-39/assembly_chain/zea_mays/AGPv2_to_AGPv4.chain.gz. And in soybean, the assembly chain file is available at ftp://ftp.ensemblgenomes.org/pub/plants/release-39/assembly_chain/glycine_max/V1.0_to_Glycine_max_v2.0.chain.gz.In maize, the original SNP set with B73 genome (AGPv2) as references was obtained from 103 maize genomes of Maize Hapmap2  . Three lThen, for each species, we obtained 2 sets of SNPs (common SNP set and population-private SNP set) from the original SNP sets by applying different filtering criteria  that are segregating in the wild group but fixed ancestral allele in the landrace and improved cultivar groups; 1,514,058 private domesticated SNPs (PD) that are segregating in either the landrace or improved group but fixed ancestral allele in the wild group; 270,390 private landrace SNPs (PL) that are segregating in the landrace group but fixed ancestral allele in the wild and improved cultivar groups; and 537,259 private improved cultivar SNPs (PI) that are segregating in the improved cultivar group but fixed ancestral allele in the wild and landrace groups. In soybean, we obtained 571,756 PW, 110,035 PD, 20,543 PL, and 1798 PI. The total numbers of SNPs  in private SNP sets are obtained by summing up PW and PD because there are no overlapping SNPs between the two population-private SNP sets by definition.For population-private SNP sets, we followed the procedure laid out in a previous study  to obtairipsacum . To inferipsacum  (versionsequence  with an Medicago truncatula genome sequence (version Mt4.0) was downloaded from Phytozome. Short reads from representative soybean accessions were downloaded from GenBank.For maize, all analyses were done using maize B73 genome (version AGPv4) as references. For soybean, all analyses were done using soybean Williams 82 genome (version 2.0) as references. Rsamtools  for each accession with a moving average approach of a 5-Mb window size and a 4-Mb step size on each of the maize and soybean chromosomes with both genic and non-genic SNPs. Indeed, we examined the [AT] distribution with a series of window size including 1 Mb, 2 Mb, 5 Mb, and 10 MB. The patterns for all of those window sizes are similar. We decided to go with the 5 Mb for the analyses because it contains a good amount of SNPs in each window and the line of [AT] distribution is smoother than the smaller window size.The position of crossovers (COs) in maize was referred from . Then, . [AT]-diTransposable element (TE) regions in maize and soybean are referred from , 94. TheSelective sweep regions in maize and soybean are referred from , 30. TheThe maize methylation data was generated from whole-genome bisulfite sequencing (WGBS) of the leaf tissue of maize B73 seedling . Genome MethylC-seq reads of GsojaD were first mapped to its own genome assembly to get methylation call. Then, the genome coordinates of GsojaD methylation were converted to the coordinates in Williams 82 genome version 2. Genome coordinates of Williams 82 methylation data in Williams 82 version 1.1 were converted to those in version 2.0 with the CrossMap v0.2.5 . Then, tFor each SNP site, the directly adjacent upstream and downstream bases were extracted from reference genomes; meanwhile, the adjacent sequences of 1 randomly selected site from 1 kb flanking region were also extracted. For each of the 96 possible tri-nucleotide motifs , an empirical threshold at the 95th percentile was drawn from 100 random sample scenarios. A motif is considered as enriched if the ratio of its frequency at SNP site over the 95th percentile at random site is greater than 1.Cp(m) of type m mutations in set P for each mutation type m = P were obtained. Then, with a χ2 test, fPI(m) and fPL(m) were compared with fPW(m). For the χ2 test, we used χ2 value instead of P value to indicate the significance of difference because P value cannot be obtained for very large χ2 value in our data.We used the procedure laid out in a previous study  to test f(TCG → T) and f(CCG → T), private SNP sets PL, PI, and PW in maize and PD and PW in soybean was partitioned into non-overlapping bins of 1000 consecutive SNPs. Then, f(TCG → T) and f(CCG → T) for each bin were calculated.To assess the variance of P value was determined by Bonferroni correction.Following our earlier study in human , [AT] vaArabidopsis genes [The 334 maize genes and 107 soybean genes associated with repairing UV-damaged DNA were compiled based on either the sequence similarity of rice genes or is genes . We condis genes . The proAdditional file 1:Figure S1. Diagram of SNP filtering process. Figure S2. Frequency of SNP substitution types. Figure S3. Base-composition distribution for randomly sampled genic and non-genic SNPs. Figure S4. Comparison of base-composition distribution between different regions of the genome. Figure S5. Distribution of MAF for genome-wide genic and non-genic SNPs. Figure S6-S8. Base-composition distribution for genic and non-genic SNPs across chromosomes. Figure S9. Base-composition distribution at TE and non-TE regions. Figure S10. Base-composition distribution between domesticated and wild accessions and crossover rate for maize chromosomes. Figure S11-S12. Base-composition distribution between domesticated and wild accessions and recombination rate for soybean chromosomes. Figure S13-S15. Distribution of MAF calculated with genic and non-genic SNPs across chromosomes. Figure S16. Base-composition distribution at selective sweep and non-selective-sweep regions. Figure S17. Frequencies of motifs related to solar-UV signature among genic and non-genic SNPs conditional on methylated and unmethylated regions. Figure S18. Frequencies of motifs related to solar-UV signature among SNPs from pericentromeric and non-pericentromeric regions under methylated and unmethylated conditions. Figure S19. Enrichment test of mutations related to solar-UV signature with population-private SNPs. Figure S20. Distribution of f(TCG) and f(CCG) across population-private SNPs. Figure S21. Distribution of f(TCG) and f(CCG) at different genomic regions. Figure S22. GWAS-identified genomic regions underlying base-composition variation. Figure S23. GWAS tagged genes in NER pathway. Figure S24. Polymorphisms in soybean DNA ligase1. Table S1. UV-related genes are enriched near the associated loci in maize. Table S2. UV-related genes tagged by the associated SNPs in maize. Table S3. UV-related genes are enriched near the associated loci in soybean. Table S4. UV-related genes tagged by the associated SNPs in soybean. Table S5. Summary of 100 maize accessions. Table S6. Summary of 302 soybean accessions. (PDF 3092 kb)


Sorghum bicolor L. [Moench]) to the precipitation gradient in West Africa using a panel (n = 607) of sorghum accessions from diverse agroclimatic zones of Nigeria. Significant correlations were observed between common-garden phenotypes of three putative climate-adaptive traits  and climatic variables. The panel was characterized at >400,000 single nucleotide polymorphisms (SNPs) using genotyping-by-sequencing (GBS). Redundancy analysis indicated that a small proportion of SNP variation can be explained by climate (1%), space (1%), and climate collinear with space (3%). Discriminant analysis of principal components identified three genetic groups that are distributed differently along the precipitation gradient. Genome-wide association studies were conducted with phenotypes and three climatic variables . There was no overall enrichment of associations near a priori candidate genes implicated in flowering time, height, and inflorescence architecture in cereals, but several significant associations were found near a priori candidates including photoperiodic flowering regulators SbCN12 and Ma6. Together, the findings suggest that a small (3%) but significant proportion of nucleotide variation in Nigerian sorghum landraces reflects clinal adaptation along the West African precipitation gradient.Evolution of plants under climatic gradients may lead to clinal adaptation. Understanding the genomic basis of clinal adaptation in crops species could facilitate breeding for climate resilience. We investigated signatures of clinal adaptation in the cereal crop sorghum ( Adaptation to environmental gradients can lead to evolution of clines, a continuous form of local adaptation  is an essential staple cereal crop in dryland regions of the world  and genotype (population structure and SNPs). Further, we expect that trait-associated and climate-associated genome regions will colocalize with genes involved in putative climate-adaptive traits. We investigated these predictions in a large panel of georeferenced Nigerian genebank accessions, which were previously phenotyped and which we genotyped at high-density using GBS. We characterized patterns of association among climatic, phenotypic, and genotypic variables and tested colocalization of associated genomic regions. Overall, the patterns are consistent with a small contribution of clinal adaptation shaping genomic variation in Nigerian sorghum.https://www.ars-grin.gov/). Seedlings were raised in a greenhouse for two weeks and 50 mg of fresh leaf tissue was collected from each accession into 96-well plates. A control well was left empty on each plate. Leaf tissue was lyophilized  for two days and then ground using 96-well plate plant tissue grinder . Genomic DNA was extracted using BioSprint 96 DNA Plant Kit , quantified using Quant-iTTM PicoGreen dsDNA Assay Kit , then normalized to 10 ng/μl.Seeds for 553 Nigerian accessions were obtained from the USDA National Plant Germplasm System (NPGS) (ApeKI restriction enzyme (NEB R0643L) followed by ligation of barcode and common adapters ligation using T4 DNA ligase (NEB M0202L). Ligated libraries were pooled (96-plex libraries) then amplified by polymerase chain reaction (PCR). Purification of libraries was performed using QIAquick PCR purification kit . Library size distribution was obtained using a Bioanalyzer . Four 96-plex libraries were pooled to generate 384-plex sequencing libraries. Libraries were sequenced using single end 100-cycle sequencing using Illumina HiSeq2500  at the University of Kansas Medical Center, Kansas City MO, USA.GBS was conducted on 553 Nigerian accessions using methods previously described  in the NPGS database were removed from the Nigerian germplasm. Thus, 607 Nigerian accessions . The passport data were based on evaluations in one or more common garden experiments in tropical latitudes , so best linear unbiased predictors (BLUPs) were estimated for each trait for each accession (File S1). A term for common garden was fit as random in the BLUP estimation model using lmer function in LME4 package in R  were obtained from WorldClim 1.4 using the Raster package in R  was performed separately for global and Nigerian germplasm sets using the find clusters function in Adegenet package in R (ST) between DAPC groups was estimated using –weir-fst-pop parameter (Weir and Cockerham’s FST) in VCFtools (–window-pi (1kb) in VCFtools. LD decay analysis for each DAPC group was performed by PopLDdecay (BGI-shenzhen 2017). For comparison with Nigerian germplasm, West African accessions were identified from the published global GBS data  was conducted with the ST) between Nigerian, West African and global germplasm was evaluated using –weir-fst-pop parameter (Weir and Cockerham’s FST) in VCFtools (–window-pi (1kb window), –het, and –hardy respectively, in VCFtools.Linkage disequilibrium decay for the genomic data for Nigerian, West African, and global germplasm was estimated by PopLDdecay (BGI-shenzhen 2017), with minor allele frequency parameter set at 0.05 and smoothing by the spline function in R. Principal component analysis (PCA) was performed using SNPrelate package in R .Genome-wide association studies (GWAS) were performed using BLUPs of traits . After filtering the Nigerian data for a minimum minor allele frequency (MAF) of 0.03, a total of 149,342 SNPs were used in the GWAS analysis. First, a multi-locus mixed linear model (MLMM) , West African germplasm (DWestAfrica), and Nigerian germplasm (DNigeria). Enrichment analysis for a priori genes was performed by testing whether the DNigeria 100 kb windows were significantly enriched for our a priori candidate genes relative to a set of random genes derived from the sorghum genome version 3 gff3 gene file from Phytozome  were used as proxies for the precipitation gradient (n = 443). A GLM, which does not include population structure and kinship terms, was used to perform an association scan for climatic variables to reduce false negatives (doi:10.5061/dryad.g0141g7). All data are publicly available. File S1 contains detailed descriptions of Nigerian accessions, their passport data, georeference information, the BLUPs of phenotypes, climatic data, and DAPC groups. File S2 contains detailed descriptions of global accessions, their georeference information, and climatic data. File S3 contains a priori candidate genes list and literature sources. File S4 contains ANOVA and Tukey test results for race by precipitation analysis. File S5 contains detailed descriptions of a priori candidate genes associated with significant SNPs for MLMM and GLM GWAS results for the phenotypes. File S6 contains detailed descriptions of a priori candidate genes associated with outlier SNPs for GLM of environmental variables. File S7 contains detailed descriptions of a priori candidate genes associated with Nigerian germplasm Tajima’s D (DNigeria) windows. Supplemental material available at Figshare: https://doi.org/10.25387/g3.6942986.Raw sequencing data are available from the NCBI Sequence Read Archive under project accession SRP132525 SNP genotype, phenotype, and geographic data are available at Dryad . Significant differences were found between guinea and caudatum types  and between guinea and durra-caudatum types  (File S4) in precipitation differences among sites of origin.The georeferenced sorghum accessions from Nigeria originated across a wide precipitation gradient . Annual r = 0.21, P-value < 0.001) and plant height  but not flowering time . Latitude had negative relationships with panicle length  and plant height , but no relationship with flowering time . Among the environmental variables, annual mean precipitation had a significant negative relationship with latitude  and a weaker but significant negative correlation with annual mean temperature .The correlation of annual precipitation with common garden phenotypes of georeferenced Nigerian germplasm was investigated for three traits  . For coming time . By conting time  or planting time  but a siing time . Among tTo investigate genomic variation we developed a data set consisting of 431,698 SNPs genotyped across 2392 accessions . Most of the SNP variation in the three panels was rare; about 51% of the Nigerian genomic data were composed of SNPs with minor allele frequencies (MAF) < 0.01, 46% of the West African SNPs have MAF < 0.01, 36% of the global reference SNPs have MAF < 0.01, and 37% of global SNPs have MAF < 0.01 . The mean observed heterozygosity across loci in each of the germplasm is 0.02 (2%) . SNP density was higher in sub-telomeric regions and lower in sub-centromeric regions . In the Nigerian germplasm  and West African germplasm (πWestAfrica) was 4.5 × 10−4. The nucleotide diversity in Nigerian germplasm (πNigeria) was somewhat lower at 4.0 × 10−4. The average inbreeding coefficients were 0.83, 0.82, and 0.80 for global germplasm, West African germplasm, and Nigerian germplasm, respectively  . The FST between Nigerian and West African germplasm was 0.007  and FST between Nigeria and global germplasm was 0.07 . The genome-wide average rate of linkage disequilibrium decay differed among the panels. Of the three germplasm sets, the global germplasm had the fastest LD decay rate by reducing to r2 = 0.1 at 20 kb . LD decayed to half its initial value at 12 kb and r2 = 0.1 at 50 kb in the Nigerian germplasm. The West African germplasm had a slowest LD decay rate among the three sets, with r2 = 0.1 at 100 kb.The nucleotide diversity of Nigerian germplasm was similar to the West African and global germplasm. The average nucleotide diversity for global germplasm (πvs. geographic distance (space) we carried out a redundancy analysis, a form of multivariate regression. The proportion of SNP variation explained by climate and space in global germplasm was substantially greater than in Nigerian germplasm. In global germplasm, the proportion of variation explained by the 10 climate variables, space (latitude and longitude), and their combination together are 4%, 8%, and 5%, respectively (P < 0.001) than the null distribution from geographically permuted data . Neighbor joining analysis also showed that Nigerian accessions and West African accessions cluster together, separately from the rest of the global germplasm . Clustering by botanical race was also observed in the Nigerian germplasm .vs. Group 2 (FST of 0.21), Group 1 vs. Group 3 (FST of 0.18), and Group 2 vs. Group 3 (FST of 0.22). Accessions in Group 2 originate from locations with higher precipitation than the accessions in Group 1  and Group 3   level decayed to 0.1 at 30 kb in in Group 1, 80 kb in Group 2, and 90 kb in Group 3 . Caudatum types are more predominant in Group 1, Guinea types are more predominant in Group 2, and Durra types are more predominant in Group 3  . The ave Group 3 .P-value < 0.01) and Groups 2 and 3 . Also, there were significant differences between Groups 2 and 1  and Groups 2 and 3 . We also found significant differences for putative adaptive traits between DAPC groups. For panicle length, all DAPC groups comparisons were different at P-value < 0.01. Significant differences were found for flowering time distribution between Groups 1 and 2 . For plant height, significant differences were found between Groups 1 and 2  and Groups 2 and 3 .In order to characterize variation among genetic groups identified by DAPC, ANOVA, and Tukey test were performed for putative adaptive traits and environmental variables. Significant difference in the distribution of precipitation and temperature gradient were found between DAPC Groups 1 and 3   .To identify genomic regions associated with phenotypic variation, we conducted MLMM GWAS for panicle length, plant height, and flowering time using BLUPs of common-garden phenotypes. Several genomic regions associated with the traits were identified and two n 100 kb , File S5n 100 kb . For plan 100 kb . The sinkb away) . The mosa priori candidate genes were localized within 100 kb of a 1% outlier SNP for “annual precipitation” and “precipitation in the driest quarter”, respectively . Therefore, there is no enrichment of a priori candidate genes near environmental association outliers. A few of the a priori candidate genes that localize near 1% outlier SNPs are as follows. The SNP S9_54870238  associated with annual precipitation was 90 kb away from the regulator of photoperiodic flowering SbCN8 . S9_8022437  associated with annual precipitation is 94 kb from the sorghum ortholog (Sobic.009G069700) of maize barren inflorescence4 (bif4)  associated with annual precipitation and precipitation in the driest quarter were 100 kb from Sbra2 (Sobic.003G052900), the sorghum ortholog of maize inflorescence gene ramosa2 (ra2)  were identified on all chromosomes for “annual precipitation”, “precipitation in the driest quarter,” and “annual temperature” (D in the Nigerian germplasm (DNigeria) across 1 kb windows ranged between -2.0 to 4.0 . The average Tajima’s D value in the Nigerian germplasm (DNigeria) was -0.2 while the average genome wide Tajima’s D (across 1 kb windows) in the global germplasm  and West African germplasm (DWestAfrica) were 0.1 and 0.2, respectively. Positive DNigeria windows were significantly enriched for a priori candidate genes compared to the expectation under a null distribution . Some of the DNigeria windows  contain genes that control for flowering time and inflorescence development (File S7). For instance, the sorghum flowering time gene Maturity6  colocalized with the genome wide DNigeria scan window at 0.697 Mb  on chromosome 6. Maturity1  colocalized with the genome wide DNigeria scan window  around 40 Mb on chromosome 6. The sorghum ortholog of branched silkless1 (Sobic.002G411000), a maize spikelet meristem identity gene (DNigeria scan window (Tajima’s D = 2.2 in the 94th percentile) around 75.9 Mb on chromosome 2.Genome-wide pattern of Tajima’s Genome-wide studies of nucleotide variation can provide insights into patterns of genetic variation in crop landraces and the role of clinal adaptation in shaping this variation , Fig. S1For plant height, sorghum accessions from lower latitudes associated with high precipitation were taller than sorghum accessions from higher latitudes associated with less precipitation , 2H. ThiGlycine soja) (Hordeum vulgare) landraces  and caudatum-durra races .Genome-wide nucleotide variation in Nigeria is also structured according to precipitation zones. The DAPC analysis identified genetic groups within the sorghum botanical races. These genetic groups showed differences in precipitation distributions , Table 1Despite some evidence of clinal adaptation, we should note that the redundancy analysis  and theoa priori candidate genes related to adaptive traits colocalized with GWAS signals (SbCN12 and Ma6) with plant height and flowering time associations suggests that photoperiod sensitivity contributes to plant height and flowering time adaptation in Nigerian sorghum germplasm. This is consistent with previous studies that identified SbCN12 and Ma6/Ghd7 as major genes underlying natural variation in photoperiodic flowering in sorghum  with associations for annual precipitation . Further functional studies of candidate genes will be needed to establish if these candidate genes have a role in climate adaptation (Shattering1)  , the dema priori genes with environment associations  underlie clinal variation, then we expect significant enrichment of the approach  simultanNigeriavs. πglobal), consistent with West Africa as a center of diversity for sorghum . Low level of differentiation among regional germplasm in sorghum has been attributed to gene flow from human migration and agricultural trade (The Nigerian germplasm harbors abundant nucleotide polymorphism (90% of the global nucleotide polymorphism based on the π sorghum . The higCharacterization of LD patterns is critical for interpretation of genome scans since the local extent of LD decay determines resolution of mapping and long-range LD creates spurious associations (The application of genomics for crop improvement and plant genetic resource management is still in the early stage for most national agricultural research systems in sub-Saharan Africa, including Nigeria and neighboring countries (