Background: Neonatal respiratory distress syndrome (RDS), due to surfactant deficiency in preterm infants, is the most common cause of respiratory morbidity. The surfactant proteins (SFTP) genetic variants have been well-studied in association with RDS; however, the impact of SNP-SNP (single nucleotide polymorphism) interactions on RDS has not been addressed. Therefore, this study utilizes a newer statistical model to determine the association of SFTP single SNP model and SNP-SNP interactions in a two and a three SNP interaction model with RDS susceptibility.Methods: This study used available genotype and clinical data in the Floros biobank at Penn State University. The patients consisted of 848 preterm infants, born <36 weeks of gestation, with 477 infants with RDS and 458 infants without RDS. Seventeen well-studied SFTPA1, SFTPA2, SFTPB, SFTPC, and SFTPD SNPs were investigated. Wang's statistical model was employed to test and identify significant associations in a case-control study.Results: Only the rs17886395  of the SFTPA2 was associated with protection for RDS in a single-SNP model . The highest number of interactions (n = 27) in the three SNP interactions were among SFTPA1 and SFTPA2. The three SNP models showed intergenic and intragenic interactions among all SFTP SNPs except SFTPC.Conclusion: The single SNP model and SNP interactions using the two and three SNP interactions models identified SFTP-SNP associations with RDS. However, the large number of significant associations containing SFTPA1 and/or SFTPA2 SNPs point to the importance of SFTPA1 and SFTPA2 in RDS susceptibility. Neonatal respiratory distress syndrome (RDS) is the most common cause of respiratory failure in premature infants due to surfactant deficiency . HoweverMajor risk factors, such as prematurity and low birth weight (BW) along with sex and race –7 have bFurthermore, it has been noted that infants dying with RDS have low levels of surfactant proteins (SP) , 12. SP-SFTP) have been shown to associate with RDS  and exhibit sequence variability within coding and non-coding regions (SFTPA1 and/or SFTPA2 (SFTPB and/or SFTPD haplotypes associated with risk or protective effect in RDS (Multiple genetic variants and single nucleotide polymorphisms (SNP) of the surfactant protein gene  interactions among genetic variants of the surfactant proteins may alter disease susceptibility , 45, butSFTP genes. We hypothesized that epistatic interactions among SFTP gene variants are associated with RDS susceptibility in preterm infants.In the present study, we studied intergenic and intragenic SNP-SNP interactions of the This study used available genotype data and clinical information in the Floros biobank at Penn State University, College of Medicine. These were collected and processed under an approved protocol by the institutional review board from the human subject protection office of the Pennsylvania State University (PSU) College of Medicine as well as the institutional review board of the respective centers where samples were collected in other Institutions other than PSU, as described previously , 46, 47.SFTPA1, SFTPA2, SFTPB, SFTPC, and SFTPD were studied. These included five SNPs from SFTPA1: rs1059047, rs1136450, rs1136451, rs1059057, and rs4253527; four SNPs from SFTPA2: rs1059046, rs17886395, rs1965707, and 1965708; four SNPs from SFTPB: rs1130866, rs7316, rs2077079, and rs3024798; two SNPs from SFTPC: rs4715 and rs1124; and two SNPs from SFTPD: rs721917 and rs2243639. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was used to analyze the SFTP gene polymorphisms as described  was estimated to assess the magnitude of the dominant/additive effect.To estimate the additive effect, the size was compared as below,Odds of genotype for cases = number of cases with AA/number of cases with aaOdds of genotype for controls = number of controls with AA/number of controls with aaOR = odds for cases/odds for controls= For example-OR = 1: Genotype difference is not associated with the disease;OR > 1.0: Genotype AA is “more risky” OR < 1.0: Genotype aa is “more risky” for the disease than genotype AAA similar procedure was applied to analyze all other genetic effects.The significance of each effect was adjusted for multiple comparisons using the false discovery rate (FDR) controlled at 1%. Wang et al.'s simulation data indicate that a 100 × 100 sample size combination in an epistatic case-control model has a power of > 0.80 to detect significant associations in a 2 × 2 contingency table analysis . Thus, op-value 0.02). The two risk factors for RDS  were corrected in the analysis. Gestational age and birth weight are co-linear variables, and only one  was chosen to be corrected in the analysis. As expected, infants who developed RDS had increased use of surfactant and a higher incidence of chronic lung disease than infants who did not have RDS. These outcomes are related to RDS rather than predictors (surfactant use and chronic lung disease); therefore, we did not correct them in the SNP-SNP interaction model. The use of antenatal steroids was significantly different between the two groups. However, ~40% of the antenatal steroid data were missing and may have caused bias in estimating this parameter.SFTP genes and their effect, either additive (a) or dominant (d). The additive effect of the SNP indicates that one of the homozygous alleles (one or two copies) is associated with the disease compared to the other homozygous allele. The dominant effect of the SNP indicates that the heterozygous genotype is associated with the disease compared to the mean of either homozygous genotype. The numbers 1, 2, or 3 are for SNP1, SNP2, or SNP3, respectively. For example, (a) a1d2 (The associations of single SNP and intergenic/intragenic two and three SNP interactions with RDS are shown in (a) a1d2  interact(a) a1d2  interactSFTP genes, only the rs17886395 of the SFTPA2 was associated by itself with RDS (p = 0.03). This particular SNP is also noted to interact with other SNPs in the two and three SNP interactions models, as shown in SFTP SNP by itself was associated with RDS at the adjusted value p < 0.01.Out of the 17 SNPs of the five with RDS . This SNSFTPA1 SNPs; rs1136450 and rs4253527 (P = 0.0001), and the d1a2 was associated with protection for RDS   .SFTPA1 and two involved the SFTPA2 and SFTPB genes. The SFTPA2 SNPs: rs1059046, rs1965707, and rs1965708 exhibited an effect, d1d2d3, that was protective for RDS . The SFTPA1 gene variants: rs1059047 (SNP1), rs1136451 (SNP2), rs1059057 (SNP3) in a three-SNP interaction (d1a2d3) increased the risk for RDS  (SFTPB SNPs: rs2077079 (SNP1), rs3024798 (SNP2), and rs7316 (SNP3), as d1d2d3, and this was protective for RDS .There were five intragenic interactions associated with RDS. Three interactions were among SNPs of the  0.0012) . The othSFTPA2 rs17886395 (SNP1) with (i) SFTPA1 rs4253527 (SNP2) as d1d2, increased risk of RDS , and (ii) SFTPA1 rs1059047 (SNP2) as d2 without any epistatic effect from SNP1 was protective . The SFTPA2 SNP rs17886395 interaction with the SFTPD SNP rs721917 was protective when both had a dominant effect . Intergenic SNP-SNP interactions were also noted between each of the two of the SFTPB SNPs (rs2077079 or rs3024798) and one SFTPC SNP rs4715 associated with protection or risk against RDS, as shown in The two SNP interactions are shown in SFTP genes associated with RDS. There were a total of 28 intergenic interactions. There were four SFTPA2 SNPs studied. Among them, the rs17886395 SNP, found to have an additive effect and be protective for RDS by itself in the single SNP model, was present in 7 out of the 28 intergenic interactions and in 5 out of the 7 interactions were noted to be protective.SFTPA1 gene SNPs exhibited mainly a dominant effect. The rs1136450 was involved in the highest number of interactions (10 intergenic interactions), and the other SFTPA1 SNPs had fewer than 5 interactions showing either protective or risk effect. An example of a three intergenic SNP interaction is shown diagrammatically in SFTPA1 and SFTPA2. In this intergenic interaction, the additive effect of SNP1, rs17886395, G variant that codes for alanine interacts with SNP2 (rs1059047) and SNP3 (rs1059057) of SFTPA1 in a dominant effect. This interaction, based on odd's ratios, is associated with increased disease susceptibility. It has the highest odd's ratio  compared to the odd's ratios of the other three SNP interactions.The five SFTPB SNPs  were involved in 5 intergenic interactions, and the SFTPD SNPs  were involved in a total of 6 intergenic interactions, and they were mainly in a dominant effect.The SFTPB and SFTPC interacted with each other in the two-SNP model, and the SNPs of the hydrophilic SFTPA1, SFTPA2, and SFTPD SNPs also interacted with each other. There was no interaction between any of the hydrophobic and the hydrophilic SPs SNPs. The three-SNP model , involved either SFTPA1 or SFTPA2. This highlights the impact and importance of SFTPA1 and SFTPA2 in RDS.NP model  depictedSFTP variants have been implicated in RDS  the highest number of the two and three SNP interactions were among SNPs of the SFTPA1 and SFTPA2, and these were associated with risk or protection for RDS. (b) Only the rs17886395  of the SFTPA2 was protective for RDS in a single-SNP model. (c) In the two SNP models, there was no interaction between the hydrophilic SFTPA1, SFTPA2, SFTPD SNPs, and the hydrophobic SFTPB or SFTPC SNPs. (d) the three SNP models showed intricate intergenic and intragenic interactions among SNPs of the SFTPA1, SFTPA2, SFTPB, and SFTPD; however, SFTPC did not interact with any of the other SFTP SNPs. Thus, in the present study we show not only association of a single SNP but also of two and three SNP interactions to associate with RDS susceptibility.Although d in RDS , 27, 39,d in RDS  enables d in RDS  model hafibrosis , pediatrfibrosis , and hypfibrosis . In thisp < 0.01), none of the single SFTP SNPs was associated with RDS. When the FDR correction was set at 5% (p < 0.05), the rs17886395 G allele of the SFTPA2 gene exhibited an additive effect and increased risk for neonatal RDS compared to the C allele. The 1A3 haplotype that includes the G allele increased the risk of TB in Mexicans  is located in the collagen-like domain of codon 91 . It has codon 91 . Thus, tSFTPA1 with RDS susceptibility in the two-SNP model. The susceptibility of RDS changes based on the effect of rs4253527 in that interaction, i.e., dominant and additive effect of rs4253527 is associated with increased and decreased risk of RDS, respectively (SFTPA1 and changes the amino acid arginine (CGG) to tryptophan (TGG) at amino acid 219. This change may differentially affect innate immune processes under various conditions, including oxidative stress, because tryptophan is more sensitive to oxidation than arginine (SFTPA1 variants that differ in CRD at rs4253527 have been shown to differ in their ability to enhance phagocytosis (Pneumocystis carinii (CTC) to valine (CTC) substitution at amino acid 50 and together with rs4253527, may impact protein function, but direct experimental evidence is lacking. Moreover, SFTPA1 has been shown to more efficiently affect surfactant reorganization (than SFTPA2) in the alveolar space and inhibit surfactant inactivation by serum proteins  of the ectively . This inarginine , 67. SFTocytosis  and cytoocytosis . Moreove carinii , 71 and SFTPB and SFTPA1 and/or SFTPA2 with increased risk of neonatal RDS in case-control studies , and these were associated with either risk or protection for RDS. The rs1136450 (C/G) results in an amino acid change, Leu/Val (CTC/GTC) at codon 50 , and all of the interactions with a dominant effect were shown to be protective for RDS. This SNP changes the amino acid Asn/Thr at codon 9 (AAC/ACC). This amino acid is part of the signal peptide and may affect the processing of SP-A2. The A allele of this SNP of SFTPA2 was also noted to have a protective role in community-acquired pneumonia , five of them had a dominant effect with a protective role and the remaining two (dominant or additive) were associated with risk in RDS. These together highlight the complexity of SNP interactions and their important effect on disease susceptibility.The rs1059046 SNP of neumonia . Of noteneumonia , 76 as wneumonia . The rs1There was one significant intragenic interaction . Each SNP exhibited a dominant effect and this interaction was associated with decreased risk of RDS. The rs2077079 (C/A) is located 10 nt downstream of the TATAA box, 5′ regulatory region and may affect gene transcription. The rs3024798 (A/C) is located at the splice sequence of intron 2-exon 3 and may affect splicing. The rs7316 (A/G) is located in the 3′UTR, at 4 nt upstream of the TAATAAA polyadenylation signal and may affect polyadenylation . The locSFTPC SNPs have been associated with RDS  SNP changes Threonine (C) to Methionine (T) at position 11 in the mature protein. The C allele of the rs721917 SNP, is associated with O- linked glycosylation of threonine leading to a partial posttranslational modification and this may alter the tendency to form multimers . Another study limitation may be reduced generalizability as both study groups were predominantly whites. It is also possible that we have missed some significant interactions due to the use of stringent criteria such as those imposed by the FDR correction, set at 1% to avoid spurious associations. Nonetheless, the present findings need to be replicated. The SNP interactions and their association with the disease phenotype may be affected by the severity of RDS, which was not captured in this study. Around 40% of the data on important parameters such as antenatal use of steroids were missing and that may have introduced bias in the estimation of the difference between groups. The diagnosis of chronic lung disease included oxygen use at 28 days or oxygen at 36 weeks postmenstrual age. The definition for BPD has evolved over time and hence the study characteristic does not capture the current definition of BPD, consistently, as per NICHD 2019 .SFTPA1 and SFTPA2 in RDS susceptibility as they had the most interactions with SNPs of other SFTPs in the two and three-SNP models. Furthermore, the concern for infection in the setting of prematurity and chorioamnionitis sets up the SFTPA1 and SFTPA2 gene products, SP-A1 and SP-A2, as very important molecules for the first line of defense and regulation of various processes of the alveolar macrophage  at Penn State University, College of Medicine. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.SA: data curation. MY, LY, and RW: formal analysis. JF: funding acquisition. BN and JF: resources. RW and JF: supervision and writing—review and editing. SA, CG, and JF: writing—original draft. All authors read and approved the final manuscript.This study was supported by NIH grant R37 HL34788 to JF.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Whole-genome resequencing of eh1 and ZY9 produced 737.7 Gb sequencing data and identified a total of 23,537,117 homozygous single nucleotide polymorphism (SNP) and 1,608,468 Indel. Analysis of SNP distribution across the chromosome suggests that mutation hotspots existed in certain chromosomal regions. Among the three subgenomes, the variation frequency in subgenome D was significantly lower than in subgenomes A and B. A total of 27.8 Gb data were obtained by exome-capturing sequencing, while 217,948 SNP and 13,554 Indel were identified. Variation annotation in the gene-coding sequences demonstrated that 5.0% of the SNP and 5.3% of the Indel were functionally important. Characterization of exomic variations in 12 additional gamma-ray-induced mutant lines further provided additional insights into the mutagenic effects of this approach. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analysis suggested that genes with functional variations were enriched in several metabolic pathways, including plant–pathogen interactions and ADP binding. Kompetitive allele-specific PCR (KASP) genotyping with selected SNP within functional genes indicated that 85.7% of the SNPs were polymorphic between the eh1 and wild type. This study provides a basic understanding of the mechanism behind gamma-ray irradiation in hexaploid wheat.Induced mutagenesis is a powerful approach for the creation of novel germplasm and the improvement of agronomic traits. The evaluation of mutagenic effects and functional variations in crops is needed for breeding mutant strains. To investigate the mutagenic effects of gamma-ray irradiation in wheat, this study characterized genomic variations of wheat early heading mutant ( Induced mutagenesis is an effective method of generating novel genetic resources and improving important agronomic traits. Over the past few decades, induced mutagenesis has played a vital role in mining allelic variations from functional genes and breeding crop cultivars . GeneticGypsophila paniculate  and chemical  factors. There are several genomic mutation characteristics caused by these mutagenic factors. The chemical mutagen EMS is prone to interact with the phosphate, pyrimidine, and purine groups within the nucleotide and induce point mutations . Howeverniculate , and Tubniculate .st1 in wheat  decreased and the number of Indel mutations increased . This suin wheat .Arabidopsis and rice  (6 generation) derived from the Chinese winter cultivar Jing411 by gamma-ray irradiation at a dosage of 250 Gy were used for exome-capturing sequencing.The dry seeds of the inbred wheat line Zhongyuan 9  were treated with gamma-rays at a dosage of 284 Gy. After the treatment, the seeds were planted at the Zhongpuchang station of the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences . The mutants were continuously planted to M 1 (eh1) . A totaleh1 and wild-type (WT) at the seedling stage. Genomic DNA was extracted according to the PVP-40 method . The resulting image data were converted to raw sequencing data by Illumina bcl2fastq2 (v2.7). To obtain clean reads, low-quality raw reads were removed based on three criteria: reads containing more than five adapter-contaminated bases; reads with the number of low-quality bases  accounting for more than half of total bases; and reads with more than 5% unidentified bases.To eliminate the minimal variations derived from individuals, we mixed eight DNA samples from 1 Reads mapped to multiple loci were also removed. Based on the alignment, the GATK software (The clean reads were mapped to the Chinese Spring reference genome (IWGSC RefSeq assembly v1.0) using the Burrows-Wheeler aligner . SAMtoolsoftware  was usedsoftware  based oneh1 and WT were constructed by mixing 16 DNA samples equally. The genomic library was constructed using a KAPA HyperPlus Kit (cat number 7962380001), according to the manufacturer’s instructions. The length of exome-capturing probes ranged from 55 to 105 bp and covers approximately 200 Mb of the exonic regions in wheat. The probes were then added to the genomic library for liquid-phase hybridization, while streptavidin magnetic beads were added to the reaction solution to enrich biotin-tagged probes. After elution, the enriched DNA fragments were amplified by PCR to obtain exome-capturing sequencing libraries, which were then subjected to high-throughput sequencing on the NovaSeq 6000 platform , according to the manufacturer’s instructions, by Bioacme Biotechnology Co., Ltd. . The Fastp software2 was used to filter out the low-quality and unidentified reads. The clean reads were aligned to the Chinese Spring reference genome3 and then sorted using the SAMtools4 to yield BAM files. Repetitive sequences caused by PCR were identified and filtered out using Biobambam2 software.5 The statistics of mapping data, including mapping rate, sequencing depth, and coverage rate, were analyzed using the Qualimap software.6 BCFtools , GO terms were extracted based on the gene homology.9 For the two forward primers, each was added with standard FAM or HEX complementary tails (FAM 5′-GAAGGTGACCAAGTTCATGCTT-3′ and HEX 5′-GAAGGTCGGAGTCAACGGATT-3′) at the 5′ end. All primers were synthesized by Sangon Biotech . The PCR was performed as previously described  markers. The 150-bp DNA sequence flanking SNP loci were retrieved to align to the reference genome. Primers used for KASP assay were designed with the Galaxy online tool.escribed . Fluoreseh1 and ZY9, respectively. After filtering out low-quality and adapter-contaminated reads, we obtained 2,439,545,586  and 2,479,168,134  clean reads from the eh1 and ZY9, respectively. More than 360 Gb raw data were obtained for each sample. The Phred quality score Q30 (an error rate of 0.1%) of the clean reads was 94.2 and 94.3%, respectively  and 2,546,130,554  raw reads from the ectively , indicatur times . The avetely 25X .eh1 and 28.1% of them were heterozygous. To identify reliable polymorphic sites between the eh1 and WT, heterozygous SNPs or SNPs with sequencing depth >100 or <4 were removed. In total, 23,537,117 homozygous SNPs with high confidence were chosen for downstream analysis. To analyze the distribution patterns of SNPs in the genome, the SNP number was counted on each chromosome. Results demonstrated that the SNP frequency varies among different chromosomes  accounted for 29.44% of the total, suggesting that the frequency of transitions is higher than that of transversions  were obtained in each sample. The clean reads were aligned to the reference genome, and over 99.8% of them were successfully matched. Based on the alignment, approximately 73% of the target region was sequenced at least 5 times, whereas 43% of them with a sequencing depth of more than 30 X  and characterized SNP and Indel variation patterns. A total of 1,496,999 homozygous SNPs were identified in all mutant lines, with an average of 124,748 loci in each line. We observed a biased distribution of SNP numbers in different subgenomes and chromosomes. The Chr. 4D contained a relatively small number of SNPs whereas the highest amount of SNPs was identified on Chr. 2B -ion beams, and fast-neutron-irradiated rice mutants, suggesting that these physical treatments are efficient in inducing SNP variations  can be found below: LL and ZF conceived the project and revised the manuscript. YL and HX conducted most of the experiments. HG, JZ, and CZ participated in the data analyses and visualization. YX, JG, LZ, SZ, and YD assisted in the enrichment analyses and KASP assays. YL wrote the first draft of the manuscript. All authors have read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Epha2 knock-in mutant mice homozygous for either a missense variant associated with age-related cataract in humans (Epha2-Q722) or a novel insertion-deletion mutation (Epha2-indel722) that were both located within the tyrosine-kinase domain of EPHA2. Confocal imaging of ex vivo lenses from Epha2-indel722 mice on a fluorescent reporter background revealed misalignment of epithelial-to-fiber cell meridional-rows at the lens equator and severe disturbance of Y-suture formation at the lens poles, whereas Epha2-Q722 lenses displayed mild disturbance of posterior sutures. Immunofluorescent labeling showed that EPHA2 was localized to radial columns of hexagonal fiber cell membranes in Epha2-Q722 lenses, whereas Epha2-indel722 lenses displayed disorganized radial cell columns and cytoplasmic retention of EPHA2. Immunoprecipitation/blotting studies indicated that EPHA2 formed strong complexes with Src kinase and was mostly serine phosphorylated in the lens. RNA sequencing analysis revealed differential expression of several cytoskeleton-associated genes in Epha2-mutant and Epha2-null lenses including shared downregulation of Lgsn and Clic5. Collectively, our data suggest that mutations within the tyrosine-kinase domain of EPHA2 result in lens cell patterning defects and dysregulated expression of several cytoskeleton-associated proteins.Genetic variations in ephrin type-A receptor 2 (EPHA2) have been associated with inherited and age-related forms of cataract in humans. Here, we have characterized the eye lens phenotype and transcript profile of germline  First identified as epithelial cell kinase (eck), ephrin type-A receptor 2 (EPHA2) belongs to the largest subfamily of receptor tyrosine kinases that were originally discovered in a human erythropoietin-producing-hepatoma (EPH) cell line ,2. EPH rEPHA2) underlie inherited, mostly autosomal dominant, forms of early-onset cataract often with a variable clinical morphology described as nuclear, cortical, and posterior polar/sub-capsular opacities depending on their location within the lens [https://cat–map.wustl.edu/; accessed on 30 July 2021). Most EPHA2 mutations underlying inherited cataract are missense or frameshift with the majority located in cytoplasmic regions of the receptor including the SAM and TK domains. In addition to relatively rare forms of inherited cataract, at least 12 common single nucleotide variants in EPHA2 (mostly non-coding) including one non-synonymous coding variant (p.R721Q) located in the TK domain have been associated with susceptibility to the much more prevalent forms of age-related nuclear, cortical, and posterior sub-capsular cataracts [https://cat–map.wustl.edu/; accessed on 30 July 2021). Further, in addition to such germline cataract-risk variants, EPHA2 coding variants predicted to be functionally deleterious have been found in genomic DNA from lenses of adults over 50 years of age raising the possibility that somatic EPHA2 variants may also contribute to the risk for age-related cataract [Besides cancer and infectious diseases, EPHA2 has been repeatedly linked with clouding of the eye lens or cataract(s)—a leading cause of visual impairment worldwide . Currentthe lens   has been associated with a variable lens phenotype ranging from severe progressive cataract formation and lens rupture to subtle nuclear opacities or clear lenses with translucent regions resulting from lens cell disorganization [The crystalline lens is a transparent, ellipsoidal, biomechanical structure that plays a critical role in anterior eye development and variable fine-focusing of images onto the photosensitive retina ,23. At tnization ,34,35,36Epha2-null mice (Stock no. 006028) [Epha2-mutant mice were generated by clustered regularly interspersed short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9) gene editing technology in our Genome Engineering and iPSC Center (GEiC) and Mouse Embryo Stem (ES) Cell Core facility using standard protocols as described [Epha2 to replicate that (p.R721Q) predicted to be associated with age-related cortical cataract in humans [ 006028) , transge 006028) , and C57escribed . Brieflyn humans . SequencEpha2.g2, 5′-gttcagtgtacttcagttggngg; Epha2.g3, 5′-ttcagtgtacttcagttggtngg; Epha2.ssODN-antisense, 5′-ggc cag gtc ccg gtg cac gta gtt cat gtt ggc cag gta ctt cat gcc gga tgc gat acc ctg cag cat gcc cac tag ctg aag tac act gaa ctc acc atc ctt ctc ctg cag aga tag gcc ctc agt gct gac cgg. Correctly edited and ‘off-target’ founder mice were identified by PCR-amplification and Sanger sequencing with gene-specific primers (Epha2-null mice (B6J background) were genotyped by PCR-amplification as described [Epha2-mutant mice were crossed with tdT-reporter mice (B6J background) to generate mutant and wild type littermates that constitutively express membrane-targeted tdT. Expression of tdT was detected in vivo by means of a Dual Fluorescent Protein Flashlight  and confirmed by PCR genotyping as described [2 asphyxiation followed by cervical dislocation or decapitation. Eyes were removed from age- and sex-matched littermates and lenses dissected and imaged as described [ primers  as descrescribed . Epha2-mescribed . Mice weescribed ,39,41. ALenses labeled with membrane-targeted tdT were mounted in agarose-coated petri-dishes overlaid with pre-warmed cell culture medium and imaged (‘multi-area time-lapse’ function) at various depths (10–400 µm) from the lens surface using a water immersion objective lens attached to a confocal, fluorescence microscope  as described .Enucleated eyes were processed using standard formaldehyde-fixed-paraffin-embedded (FFPE) and frozen section techniques followed by immunolabeling with antigen-specific primary antibodies  and specFollowing lens homogenization , lens post-nuclear lysates were quantified  and subjected to SDS-PAGE separation  and immunoblot analysis  using appropriate primary antibodies  and IRDyp-values were adjusted using the Benjamini–Hochberg Procedure. Heatmaps and other plots were generated using custom scripts and gplots, ggplot2, and ComplexHeatmap [http://geneontology.org/; accessed on 28 July 2021) [Lens total RNA was prepared in triplicate (6 lenses per sample) using the RNeasy Kit , quantified by spectrophotometry  and then sized for quality by electrophoresis  prior to next-generation sequencing in our Genome Technology Access Center (GTAC). Samples with RNA integrity number (RIN) values >8.0 were subjected to poly-A selection (oligo-dT), chemical fragmentation, random hexamer priming, cDNA synthesis, and adapter-ligation using the TruSeq RNA Library Prep Kit  followed by paired-end (2 × 101 nt), multiplexed sequencing  according to the manufacturer’s instructions. Raw data were mapped to the mouse mm10 genome build using STAR (v2.5.3a) . AlignedxHeatmap . Gene only 2021) .EPHA2 resulting in a conservative substitution of arginine-to-glutamine (p.Arg721Gln or p.R721Q) in the TK domain of EPHA2 that has been associated with age-related cortical cataract in humans [EPHA2 was phylogenetically conserved with the R722 codon (AGG) of mouse Epha2. A donor single-stranded oligonucleotide was designed to introduce a two base pair change (c.2164_2165delAG > CA) that converted R722 (AGG) to Q722 (CAG). Genomic PCR and Sanger sequencing confirmed the introduction of the correct A > C transversion and G > A transition in exon-13 of Epha2 in 9 of 47 (19%) founder (F0) mice. However, six of the nine F0 mice had acquired additional ‘off-target’ sequence changes including insertions and/or deletions (data not shown) leaving three (6%) correctly targeted mice  that were crossed to generate homozygous F1 offspring. Sequencing confirmed germline transmission of the p.R722Q substitution  in exon 13 of n humans . Amino atitution . Both heEpha2 mutation. At the DNA level this mutant allele comprised a single base insertion (c.2162_2163insT) causing a reading frame-shift that introduced three novel codons including the targeted 2-nucleotide change (GCA GGG TAT) followed by deletion of a 58-bp coding region before returning to the correct reading-frame 19 codons downstream (at codon R744). At the protein level, this compound mutation was predicted to cause an in-frame replacement of a 22-amino acid region (722-RGIASGMKYLANMNYVHRDLAA-743) from the TK domain of EPHA2 with three novel residues (722-AGY-724) resulting in an insertion-deletion mutation (p.R722_A743delinsAGY) that ‘truncates’ the wild type protein from 977 amino acids to 958 amino-acids  appeared indistinguishable from wild type and dissected lenses of heterozygous Epha2-mutants were grossly similar to wild type at 1–2 months of age (data not shown). By contrast, homozygous indel722 mutant lenses displayed translucent regions of internal refractive disturbance that were not observed in homozygous Q722 mutant or wild type lenses (Epha2-mutant lenses manifest cataract formation through 12 months of age (data not shown). All subsequent studies were performed on lenses from homozygous Epha2-mutants referred to as Epha2-Q722 and Epha2-indel722 throughout.Enucleated eyes from heterozygous and homozygous e lenses . HoweverEpha2-mutant mice, we generated Epha2-mutant and wild type littermates that constitutively express the red fluorescent protein tdT on cell membranes. First, we focused on the lens equator region where anterior epithelial cells begin terminal differentiation into highly-elongated fiber cells that form the refractive mass of the lens. In the wild type tdT lens , equatorial imaging near the surface (10–20 µm depth) revealed the precise alignment of elongating, hexagonal-shaped fiber cells (in cross section) into meridional rows , suture lines centered on the optical axis ,50,51. Iild type B,H. Howeild type K. By conY shapes C,I, wherY shapes F,L. OverTo determine the effects of the Q722 and indel722 mutations on the expression and distribution of EPHA2 and other lens cell membrane proteins, we performed immunoblot analysis and immunofluorescence confocal microscopy. Immunoblotting revealed that the Q722 mutant was expressed at levels similar to wild type EPHA2 in the lens, whereas the indel722 mutant was present at reduced levels compared to the Q722 mutant and migrated with a molecular mass slightly lower (~2 kDa) than wild type EPHA2 A. These Epha2-Q722 lens, immunolabeling revealed that EPHA2 was primarily localized to radial columns of hexagonal fiber cell membranes , β-catenin or catenin β1 (CTNNB1), and connexin-46 or gap-junction alpha-3 protein (GJA3). In wild type lenses, both CDH2 and CTNNB1 labeling outlined the radial columns of flattened hexagonal fiber cells particularly along the narrow membrane faces , whereas GJA3 was localized to gap-junction plaques particularly on the broad faces of fiber cell membranes  generating a ‘ladder-like’ appearance along the radial cell columns  and at several serine residues including S897 (S898 in mouse) in certain cancer cell lines [Epha2-Q722 lenses but not in Epha2-indel722 lenses—likely due to reduced levels of mutant protein in the latter (Epha2-mutant lenses (data not shown). However, we are cautious about interpreting such negative results as immunoblotting of whole lenses may lack the sensitivity required to detect tyrosine phosphorylation that may be restricted to a specific sub-region of the lens . Overall, these data suggest that EPHA2 forms complexes with Src and is abundantly serine phosphorylated in the lens.As EPHA2 has been reported to form complexes with CTNNB1 and CDH2 ,53 and tll lines ,10,54. Ee latter D. By conEpha2-Q722 and Epha2-indel722 mutations on lens gene expression, we performed RNA-seq analysis to compare global transcriptional changes in Epha2-mutant versus Epha2-null and wild type lenses at an early stage of phenotype development (P7). RNA-seq data files have been deposited in the Gene Expression Omnibus (GEO) database under accession no. GSE181358. Biological triplicate samples were sequenced to a depth of >20 M reads and aligned to the mouse mm10 genome build with >98% alignment rate. Differential expression analysis using EdgeR  was performed for each Epha2 genotype compared to wild-type  and found that they displayed a largely unique set of dysregulated genes compared to wild type  enrichment analyses of each list of up or down regulated genes within an individual Epha2 genotype failed to yield any enriched categories or common pathways (data not shown). GO analysis of the combined upregulated gene set  epithelial-to-fiber cell alignment  at the lens equator, (2) radial cell column formation throughout the lens cortex, and (3) Y-suture branching at the lens poles—similar to those described for Epha2-null lenses [Epha2-Q722 lenses, whereas fiber cell columns were severely disorganized in Epha2-indel722 lenses along with cytoplasmic retention of EPHA2—consistent with failed targeting to the cell surface. EPHA2 formed strong immuno-complexes with Src kinase in vitro supporting a role for EPHA2/Src signaling during lens development [Epha2-Q722 mutant lenses (P21), whereas EPHA2 tyrosine588/589 phosphorylation was not detected using similar immunoblot analysis of whole lenses. The relative abundance of serine-897/898 phosphorylation in the lens suggests that ephrin-independent or non-canonical EPHA2 signaling [Lgsn in both Epha2-mutant and Epha2-null lenses and Clic5 in Epha2-indel722 and Epha2-null lenses. Combined, our imaging and transcript data support a role for EPHA2 signaling—potentially via the cytoskeleton—in generating the precise cellular patterning underlying the refractive properties and optical quality of the crystalline lens.In this study, we have demonstrated that mice homozygous for mutations (Q722 or indel722) in the tyrosine kinase domain of EPHA2 underwent variable changes in lens cell organization and gene expression. l lenses . As merielopment . Howeverelopment ,53. EPHAignaling  may partEpha2-Q722), homozygous expression of the equivalent variant protein at constitutive levels resulted in mild disturbance of the posterior Y-sutures but not in early-onset or age-related cataract , and morphological differences between the relatively small, almost spherical mouse lens with Y-suture branching versus the much larger, ellipsoidal human lens with more complex star-suture branching [Functional (over)expression studies in cultured (transfected) cell-lines have been used to predict diverse pathogenic mechanisms underlying EPHA2-related forms of human cataract. A non-coding risk allele for age-related cataract rs6603883) located in a paired-box-2 (PAX2) binding-site within the EPHA2 gene promoter suggested that it acts by down-regulating EPHA2 expression in cultured lens cells . Several3883 locacataract . Similar quality . While tranching .Epha2-mutant  or Epha2-null lenses [Epha2 genotypes as early as P7. Among the most upregulated genes (>4-fold) in both Epha2-Q722 and Epha2-indel722 mutant lenses were those for tubulin alpha 1C (TUBA1C) and alkaline ceramidase-2 (ACER2). TUBA1C serves as a prognostic biomarker for a variety of cancers [Epha2-Q722 and Epha2-null lenses, the gene for steroidogenic acute regulatory protein-related lipid transfer (START) domain-containing protein 9 (STARD9) was strongly upregulated, whereas that for doublecortin domain-containing 2a (DCDC2a) was strongly upregulated in Epha2-indel722 and Epha2-null lenses. STARD9 functions as a centrosomal protein that regulates both interphase and mitotic spindle microtubules [Epha2-mutant and Epha2-null lenses was that for WD-repeat and FYVE-domain-containing protein-1 (WDFY1), which serves as an adapter protein in toll-like receptor signaling [Epha2-indel722 lenses and that for actin, alpha 2, smooth muscle, aorta (ACTA2) was moderately upregulated in Epha2-null lenses. While ACTA2 serves as a marker for epithelial–mesenchymal transition during cataract formation [While we did not observe cataract formation in l lenses , there w cancers  and ACER cancers . In Ephaotubules , whereasotubules . The mosignaling . Finallyormation  and seveEpha2-Q722 (>−4-fold), Epha2-indel722 (>−100-fold), and Epha2-null (>−3-fold) lenses was that for lens glutamine synthase-like or lengsin (LGSN), also known as glutamate-ammonia ligase (glutamine synthase) domain containing 1 (GLULD1), a lens-specific protein with a glutamine synthase domain lacking glutamine synthase activity [Among the most downregulated genes, two have been directly implicated in lens-specific cytoskeleton biology. The most consistently downregulated gene in activity . LGSN isactivity ,70.Epha2-indel722 mutant lenses (<−1000-fold), and to a lesser extent in Epha2-null lenses (<−2-fold), was that for chloride intracellular channel 5 (CLIC5). Mutations in the human CLIC5 gene have been linked with progressive autosomal recessive, non-syndromic sensorineural hearing impairment with or without vestibular dysfunction and CLIC5 was found to be abundantly expressed in the fetal inner ear [jitterbug (jbg) mice a spontaneous deletion mutation in Clic5 underlies hearing loss with vestibular and renal dysfunction and CLIC5 was localized to the base of hair cell stereocilia where it complexes with radixin, taperin, and myosin VI to stabilize cell membrane–actin cytoskeleton attachments [Clic5-mutant (jtb) lenses were found to exhibit defective suture formation [The most downregulated gene in nner ear ,72. Simiachments . Recentlormation . Furtherormation . CollectIn conclusion, our data suggest that EPHA2 signaling is required for lens cell pattern recognition and support a role for EPHA2 in cytoskeleton dynamics during lens cell differentiation.


Sorghum bicolor (L.) Moench], where the distinct agro-ecological zones significantly contributed to the genetic diversity of the crops. A large number of sorghum landrace accessions have been conserved ex situ. Molecular characterization of this diverse germplasm can contribute to its efficient conservation and utilization in the breeding programs. This study aimed to investigate the genetic diversity of Ethiopian sorghum using gene-based single nucleotide polymorphism (SNP) markers. In total, 359 individuals representing 24 landrace accessions were genotyped using 3,001 SNP markers. The SNP markers had moderately high polymorphism information content (PIC = 0.24) and gene diversity (H = 0.29), on average. This study revealed 48 SNP loci that were significantly deviated from Hardy–Weinberg equilibrium with excess heterozygosity and 13 loci presumed to be under selection (P < 0.01). The analysis of molecular variance (AMOVA) determined that 35.5% of the total variation occurred within and 64.5% among the accessions. Similarly, significant differentiations were observed among geographic regions and peduncle shape-based groups. In the latter case, accessions with bent peduncles had higher genetic variation than those with erect peduncles. More alleles that are private were found in the eastern region than in the other regions of the country, suggesting a good in situ conservation status in the east. Cluster, principal coordinates (PCoA), and STRUCTURE analyses revealed distinct accession clusters. Hence, crossbreeding genotypes from different clusters and evaluating their progenies for desirable traits is advantageous. The exceptionally high heterozygosity observed in accession SB4 and SB21 from the western geographic region is an intriguing finding of this study, which merits further investigation.Ethiopia is the center of origin for sorghum [ Sorghum bicolor (L.) Moench] is the fifth most important cereal crop in the world next to maize, rice, wheat, and barley in terms of both production and harvested area . The accessions were selected to represent three agro-ecological zones according to the classification by SB10, which was represented by 14 individuals; hence 359 genotypes were sampled in total. The samples were then sent to LGC Genomics  where genomic DNA extraction was conducted for subsequent genotyping. High-quality genomic DNA, suitable for next-generation sequencing (NGS), was extracted using the Sbeadex plant kit.3Sorghum seeds representing the 24 accessions were planted using plastic pots filled with soil in a greenhouse at the Department of Plant Breeding, SLU, Sweden. Two weeks after planting, the leaf tissues from individual plants were collected using a sample collection kit provided by LGC-Genomics , as described by 4 a web-portal that provides genome-wide SNP markers for diverse sorghum genetic resources (Cherekit (an Ethiopian sorghum landrace accession) was targeted for selecting the SNPs. For genotyping, SeqSNP method (an advanced NGS method for genotyping target SNPs) was used. Initially, 12,316 SNPs were targeted for high-specificity  assay design, using Sorghum bicolor v3.1.1 genome in Phytozome 12.15 as a reference , 1,631 were partially covered (one oligo probe), whereas 1,190 failed.The vast majority of SNPs (97%) used in this study were selected from sorghum genome SNP database SorGSD,esources . Among deference . AdditioFor the seqSNP genotyping, 5,000 SNPs were selected among the totally covered SNPs, based on their distribution across the sorghum genome. The number of SNPs targeted on chromosome-1 to chromosome-10 included in the order, 532, 521, 572, 506, 497, 515, 437, 446, 465, and 509 refer to . One hunThe site frequency spectra were analyzed for each accession using DnaSP version 6 . The nucThe mean effective number of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), Shannon information index (I), and gene flow (Nm) for each SNP marker and accession were estimated using GenAlEx 6.5 , and theST) and heterozygosity (heterozygosity within populations)/(1 – FST) were obtained according to ST significance level of P < 0.01.Analysis of Molecular Variance (AMOVA) within and among the accessions as well as at higher hierarchical levels were done using the software, Arlequin ver. 3.5.2.2 . ArlequiThe principal coordinates analysis (PCoA) was done using GenAlEx 6.5. The bootstrap-supported unweighted pair group method with arithmetic mean (UPGMA) clustering based on Nei’s genetic distance  was perfIn the data matrix of 5,000 SNP loci for the 359 sorghum genotypes, missing data accounted for 2.8% and, of those with data, 94.1% were homozygous. Among the 5,000 target SNP loci, 4,301 (86%) were polymorphic, whereas 699 loci (14%) were monomorphic across the 359 genotypes. Among the 4,301 SNP loci, 4,256, 42, and 1 were bi-, tri- and tetra-allelic, respectively, whereas two loci had the combination of SNP and length variants. Tri- and tetra-allelic loci were excluded from further analysis. The filtering of the 4,256 bi-allelic SNP data, based on the percentage of missing data and minimum allele frequency (MAF) resulted in different numbers of loci varying from 2,259 loci with less than1% missing and greater than10% MAF to 3,089 loci with less than 5% missing and greater than 5% MAF. For the population genetics analyses, 3,001 bi-allelic SNP loci with missing data less than 2% and MAF greater than 5% were used . About 2SB2, SB7, and SB19 while it was inversely related to the observed frequency distributionsin accession SB4  per locus showed wide variation, ranging from 0.01 to 20.23, with a mean of 0.20 . The candidate genes containing SNP markers showing excess heterozygosity and their annotated functions were retrieved from SorGSD6 and further evaluated. Among the 48 SNP loci that showed excess heterozygosity, nine SNPs lacked one of the three possible genotypes expected in a bi-allelic polymorphic locus under the assumption of HWE. The change in amino acid sequences of the corresponding genes was obtained in all SNPs, except three SNPs   .SB14 and SB15) to 0.42 (SB21). The lowest and the highest Ho values varied from 0.01  to 0.25 (SB21) with a mean of 0.07. Likewise, the He and unbiased expected heterozygosity (uHe) of the accessions ranged from 0.0 to 0.27 and 0.0 to 0.28, respectively with a mean of 0.15  of the accessions varied from 0.8 to 91.4% with a mean of 47.7%. The fixation index (F) showed wide variation with values ranging from –0.76 (SB14) to 0.84 (SB3). Overall, accession SB21 showed the highest value for Ne, Ho, He, uHe, I, and the number of locally common alleles (NLCA) and PPL while SB14, SB15, and SB16 showed the lowest values for all genetic diversity parameters analyzed  . Similarly, significant differentiation was found among peduncle shape groups with 4.3% of the total variation between them  (CT = 0.02 and P = 0.17)  . Additio < 0.05) . However = 0.17) .ST values ranging from 0.18 to 0.99 . The pairs of accessions with the highest FST value (0.99) were SB14 vs. SB15 and SB15 vs. SB16, corresponding to the lowest estimate of gene flow   .xy) and the pairwise net number of allele differences  between the accessions revealed a wide variation with πxy ranging from 1.2 (SB12 vs. SB16) to 1,197.2 (SB6 vs. SB15) and d ranging from 0.001 (SB12 v.s SB16) to 0.56 (SB21 vs. SB6 and SB1 vs. SB14)  with other accessions  to 840 (SB21). This parameter was very low for SB15 and SB16, as with SB14  . Accessicessions . In lineith SB14 .ST values among each pair of the three groups were significant (P < 0.001). However, accessions from the western region were the most distinct with higher differentiation from those from the northern and eastern regions . Among the three pairs, accessions from the eastern vs. northern regions were the least differentiated (FST = 0.12) . The accessions belonging to the cool/subhumid group were the most differentiated having FST values of 0.13 and 0.10 against warm/semiarid and cool/semiarid groups, respectively. The warm/semiarid vs. cool/semiarid groups were the least differentiated (FST = 0.08) among the three pairs  . Similaree pairs . The aveectively .ST and heterozygosity among accessions to detect loci under selection revealed 74 SNP loci that were highly significant (P < 0.01). Among them, 61 loci had low FST value (ranging from –0.02 to 0.35), and hence were considered as candidates for balanced selection. Whereas 13 loci , and hence considered as under directional selection. The MAF of these loci ranged from 0.05 to 0.34. The markers were distributed on chromosomes 1, 5, 6, 7, and 9 with over 50% of them located on chromosome 7  were homozygous across the 359 individual samples genotyped, which is not surprising as sorghum is a self-pollinating crop.Genetic diversity analysis of crop species is an important step in detecting alleles that could be used for their improvement through breeding. The Ethiopian sorghum gene pool has been used as a novel source of biotic and abiotic stress tolerance, greatly contributing to the improvement of sorghum, globally . The genamylose extender1 (ae1), brittle2 (bt2), Opaque2 (O2), shrunken1 (sh1), shrunken2 (sh2), sugary1 (su1), and waxy1 (wx1), previously identified as candidates of domestication loci  are found at the genomic regions with notably low diversity on chromosomes 2, 4, and 7, respectively  of 1.2 recorded in this study is in agreement with the result of a previous study on sorghum landraces . Howeverion loci , three oectively . The bt2nd maize . The lownd maize .The average Ho of 0.06 obtained in the present study was in line with the results of previous studies on sorghum employing SNP markers  and SSR Selections, both natural and artificial, as well as inbreeding, contribute to the deviation of populations from HWE. In this study, 98% of the loci showed heterozygote deficiency while 1.60% of the loci showed excess heterozygosity. Since sorghum is a predominately self-pollinating species, heterozygote deficiency at the vast majority of the loci can be attributed to inbreeding. However, the small proportion of loci showing excess heterozygosity suggests that they could be under selection or linked to loci under selection. Among the SNP loci that showed excess heterozygosity, nine loci lacked one of the two homozygous genotypes. The data suggest that one of the two alleles at each locus reduces the fitness of homozygous genotypes, or the locus is linked to another locus within its corresponding gene or the nearby gene that has a significant fitness value. Most of these SNP markers are within the coding region of genes. For instance, snp_sb001000687053, snp_sb001000723312, snp_sb042060543510, snp_sb042060515233, and snp_sb042060517985 are within the coding region of senescence-related gene 1, tetratricopeptide repeat (TPR)-like superfamily protein, cysteine proteinases superfamily protein, C-terminal domain phosphatase-like 4, and hydroxyproline-rich glycoprotein family protein, respectively. These genes had major roles in the growth, development, physiology, and biotic and abiotic stress tolerances in plants. For instance, hydroxyproline-rich glycoproteins (HRGPs) play a major role in the growth and development of plants  while cySB21 being the most diverse accession followed by SB4. Accessions from this region  are characterized by bent peduncle and light brown seeds with the exception of the red seed color of SB1.The average He (0.15), I (0.25), and PPL (47.7%) obtained in the present study suggest low genetic variation within the sorghum accessions. In general, the relatively low genetic variation within landrace accessions in the present and previous studies on sorghum  is likelSB4 and SB21) from the western regions as compared to all other accessions. Higher Ho suggests a higher outcrossing rate in these accessions, which might have allowed for gene flow through pollen and hence increased the variation within the accessions. The results suggest the western region as an important source of sorghum genotypes with desirable traits, such as tolerance to biotic and abiotic stresses. On the other hand, most accessions from Northern Ethiopia (Tigray and Amhara) had very low variation within accessions. Their average Ho was 0.03, indicating that the vast majority of the loci in the genotypes of these accessions were homozygous. In this group, accessions SB5, SB14, SB15, and SB16 can be regarded as pure lines, as individuals within each accession are almost identical across the whole loci. On the other hand, other accessions in this group  are more diverse although their heterozygosity is still very low. Since the loss of heterozygosity increases the chance of deleterious recessive alleles being expressed in the progeny  were observed among the accessions than within the accessions (35.5%). The lower genetic variation within the accessions is expected in self-pollinating crops like sorghum . In addiectively . Similarectively . Motlhaoectively  and SSR ectively , suggestEragrostis tef)  , which mtis tef) .in situ conservation status of sorghum in that location. Hence, further studies that explore the region for highly desirable traits need to be conducted, especially for use in sorghum-breeding programs.Private alleles represent a unique genetic variability at certain loci of a particular population or hierarchically grouped populations. In this study, private alleles were not detected at the population level, but were recorded in all geographic regions. The Eastern region had a higher number of private alleles as compared to the western and northern regions, and hence it may serve as a rich source of desirable alleles for sorghum improvement. Private alleles generally support the potential to respond to a selection or have evolutionary significance . InformaSorghum genotypes showed a significant genetic differentiation based on their peduncle shape, possibly because the shape of the peduncle influences the mating system, with the architecture of very bent peduncle obstructing pollination with outcrossed pollen. A more interesting finding was that accessions with bent peduncles exhibited higher genetic variation on average than those with erect peduncles. Unlike previous studies on Ethiopian sorghum , sorghumST and heterozygosity. More than 50% of these SNP loci are located on chromosome 7 of the sorghum genome, suggesting that this chromosome carries many genes under natural selection or targeted by farmers directly or indirectly during and after domestication. These SNPs include those located in genes coding for zinc finger CCCH type family protein, DEAD-box ATP-dependent RNA helicase 42 and pentatricopeptide (PPR) repeat-containing proteins, which play a crucial role in plant responses to biotic and abiotic stresses  originate from the first genetic cluster of STRUCTURE analysis (shown orange in SB14 and SB15), and evaluate the progeny generations for desirable traits.Unweighted pair group method with arithmetic mean clustering based on Nei’s genetic distance  placed tIn this study, the significant differentiation among geographic groups but not among agro-ecological groups revealed through AMOVA was also evident in the cluster analysis at the level of accessions. Based on redundancy analysis in their recent study on sorghum, SB4 and SB21 from the western geographic region, is an interesting result of this study, and should be further investigated.In this study, SeqSNP method was used to genotype diverse sorghum accessions using a combination of previously developed and newly identified gene-based SNP markers. Despite the fact that they were gene-based, the SNP markers revealed a comparable genetic variation from the previous studies using SNP markers in sorghum. About half of the SNP markers can be regarded as highly informative and can be prioritized for future population genetics studies. A significant number of loci exhibited excess heterozygosity and/or were presumed to be under selection, some of which are located within genes playing crucial roles in plant responses to biotic and abiotic stresses. Further research on these loci using genotypes carrying different alleles may shed light on their significance in terms of desirable traits. The observed highly significant genetic differentiation among the sorghum accessions will be beneficial to the sorghum breeders in selecting desirable parents for crossbreeding. The sorghum accessions formed three distinct clusters, and it is therefore interesting to crossbreed genotypes from different clusters to evaluate their progeny for desirable traits. In this study, highly significant variations were observed among the geographic regions and peduncle-shaped groups. Compared to the western and northern regions, the eastern region had a higher number of private alleles, and hence it may serve as a rich source of desirable alleles for improving sorghum. Lastly, given that sorghum is generally regarded as a self-pollinating species, an exceptionally high heterozygosity observed in accessions, namely, The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/MG and ME designed the experiment and analyzed the data. ME conducted the experiment and wrote the draft manuscript. AC, CH, KT, MG, and TF reviewed the manuscript. All authors conceived the study and read and approved the submission of the manuscript for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Most traits in livestock, crops and humans are polygenic, that is, a large number of loci contribute to genetic variation. Effects at these loci lie along a continuum ranging from common low-effect to rare high-effect variants that cumulatively contribute to the overall phenotype. Statistical methods to calculate the effect of these loci have been developed and can be used to predict phenotypes in new individuals. In agriculture, these methods are used to select superior individuals using genomic breeding values; in humans these methods are used to quantitatively measure an individual’s disease risk, termed polygenic risk scores. Both fields typically use SNP array genotypes for the analysis. Recently, genotyping-by-sequencing has become popular, due to lower cost and greater genome coverage . Oxford Nanopore Technologies’ (ONT) portable sequencers have the potential to combine the benefits genotyping-by-sequencing with portability and decreased turn-around time. This introduces the potential for in-house clinical genetic disease risk screening in humans or calculating genomic breeding values on-farm in agriculture. Here we demonstrate the potential of the later by calculating genomic breeding values for four traits in cattle using low-coverage ONT sequence data and comparing these breeding values to breeding values calculated from SNP arrays. At sequencing coverages between 2X and 4X the correlation between ONT breeding values and SNP array-based breeding values was > 0.92 when imputation was used and > 0.88 when no imputation was used. With an average sequencing coverage of 0.5x the correlation between the two methods was between 0.85 and 0.92 using imputation, depending on the trait. This suggests that ONT sequencing has potential for in clinic or on-farm genomic prediction, however, further work to validate these findings in a larger population still remains. Complex traits in livestock, crop and human genetics are primarily polygenic, that is, a large number of variants contribute to genetic variation. These traits, which are often continuous  and are used to increase the accuracy and intensity of selection in a process referred to as genomic selection . The benToday, almost all livestock species have SNP arrays designed to enable genomic selection. In Australia, the turnaround time between sample collection and GEBV results is between 6–8 weeks. This turnaround time has prevented the adoption of genomic selection in Australia’s northern beef industry, where cattle are generally only handled once a year for a handful of days. This means management decisions based on the GEBV results from SNP array genotyping cannot be implemented until the following year, when the cattle are handled again. There is also significant demand for faster GEBV turnaround time in the sheep and southern beef industries to allow point of management decisions, for example, allocation of feeding regimes based on genetic potential.In human genetics, complex disease traits have been the focus of this type of quantitative genetics, although pharmacogenetics also holds some potential. For complex diseases such as bipolar disorder, Type II Diabetes, and Crohn’s disease  polygeniHowever, in some cases the complex genetic architecture surrounding the disease means family history information has limited utility for determining relative risk. For example, in schizophrenia a family history of the disease is reported in less than a third of cases . A SwediStudies exploring the polygenic nature of pharmacological response to chemotherapeutics  and asthGenomic prediction in both medicine and agriculture has previously relied heavily on SNP array technology. SNP arrays are a low-cost method to genotype thousands to hundreds-of-thousands of genetic markers. Once costing $400 USD for 10,000 SNP genotypes, SNP arrays now cost less than $50 USD for 50,000–800,000 SNP genotypes. However, the technology requires large, expensive laboratory equipment. Therefore, the turnaround time is, at a very optimistic minimum, the time for a sample to reach the laboratory.Recently, there has been a rise in the popularity of genotyping-by-sequencing. A widely applied approach is to use restriction enzymes to reduce the complexity of genomes for low-coverage sequencing on short-read sequencing platforms . This mein-situ for genomic prediction in livestock and human settings. Despite initial reports of poor sequencing accuracy and yield, steady developments in flow cell chemistry, library preparation and base calling algorithms has seen reported sequencing yields increase from less than 3 GB to greater than 40 GB and sequencing accuracy increase from 68.4% [The MinION could be used to sequence samples om 68.4%  to over om 68.4% ,21. A maom 68.4% . This isom 68.4%  and elimin-situ genomic prediction, with human clinical or on-farm applications. Specifically, we tested the hypothesis that genomic predictions from low-coverage Nanopore sequence data are equivalent to genomic predictions from SNP array genotypes.The aim of this study was to evaluate the accuracy of genomic predictions calculated from low-coverage ONT sequence data and compare the results to SNP array-based predictions. We calculated correlations and prediction bias for ONT genomic predictions against the SNP array predictions for various sequencing depths  and tested three different methods of imputing missing genotypes. Our results suggest ONT’s MinION sequencer could be a useful tool for ONT sequencing of 19 Droughtmaster heifers  from taiA minimum allele observation based genotyping approach was used to genotype 641,163 SNP markers. This method accounted for the random probability of sampling alleles in a diploid species by grouping loci with similar coverage together and calling genotypes using a minimum allele count specific to each coverage group. Three methods  were tested to genotype loci with less than one overlapping ONT read . Although the first two methods are obviously not accurate, they are very fast which could be useful on farm or in a clinical setting. Before imputation, at 0.5x sequencing coverage 64% of ONT genotypes were correctly called, 34% were incorrect in one allele  and 0.02% were incorrect in both alleles  for predicting GEBV were derived from 26,145 female cattle genotyped for 641,163 SNP markers from Hayes, Fordyce & Landmark  for eachThe non-imputed genotyping method, where uncalled loci were assigned a homozygous reference genotype, was as expected the least accurate for genomic prediction across all traits . The ONTThe correlation coefficients for the imputed-AF method, which used the population allele frequency to genotype missing loci, were the highest of the three methods. At 4x sequencing coverage the correlations were 0.99 for body condition score & CL score, 0.96 for body weight and 0.97 for hip height. This dropped slightly at 2x sequencing coverage, down to 0.95 for body weight, 0.98 for CL score, 0.97 for body condition score and 0.95 for hip height. The correlations remained above 0.9 for all traits at 1x sequencing coverage except for hip height, which was 0.88. At 0.5x sequencing coverage the correlations for this method remained above 0.8 with the lowest being 0.84 for body condition score. The prediction bias for this method was consistently greater than 1, ranging between 1.65 for hip height and 1.96 for body condition score at 1x sequencing coverage. This increased to between 2.2 and 3.1 for hip height and CL score respectively at the lowest sequencing coverage.The final genotyping method, which used the imputation package Beagle v5.1 , had corIn this study we sequenced 19 Droughtmaster heifers on ONT’s portable MinION sequencer, in order to compare genomic predictions from ONT data to SNP array genotyping, the current standard for genomic prediction. We investigated the accuracy of the ONT genomic predictions at various sequencing coverages  as well as using three different methods to genotype loci with no sequencing coverage: non-imputed, imputed-AF and imputed-Beagle. Beagle  imputatiin-situ genomic prediction more achievable, as the requirement to wait for data acquisition would be drastically reduced.The average sequencing yield of 22.57 Gb represents a significant improvement on flow cell yield from previous ONT studies in cattle  and otheThe average read length from these tail hair samples was significantly shorter than the average read length from previously sequenced tissues ,28. The in-situ.Although the sequencing effects of read length, base quality, effective mapping percentage and number of reads mapping with quality 0 had no significant effect on the ONT genomic prediction accuracy, this should still be investigated further. It is possible that a lack of variability between samples in this study made it difficult to model the true effect of these factors. Calculating genomic predictions using sequence data from different tissues or library preparation kits could help to investigate these different factors. It is particularly critical to test these effects under field conditions which reflect the real-world variation that may be experienced by either clinicians or producers applying this technology The non-imputed genotyping method performed significantly worse than either of the two imputation methods. This is likely because the missing genotypes were assigned as homozygous reference and therefore received no marker effect, because a homozygous reference is coded as a 0 in the genotype matrix, thereby giving 0 × the alternative allele effect. As the average sequencing coverage decreased the number of missing loci increased and therefore the breeding values were under-estimated at low coverages. This is supported by the decrease in the regression coefficient of SNP array GEBV on ONT GEBV for the non-imputed method as the sequencing coverage decreased. While not unexpected, this phenomenon has important implications: individuals with deeper sequencing coverage will have higher genomic prediction values, as more of their loci will be represented as non-zero in the genotype matrix. Therefor this method is unsuitable for any circumstance where the sequencing coverage is not tightly controlled.The allele frequency method of imputation had the highest correlations with the SNP array breeding values across the range of coverages. However, overestimation of breeding values in animals at the lower end for each trait and under estimation of breeding values for animals at the high end was observed at low coverages . This tyThe Beagle  imputatiHere we tested the effect of a single imputation program, Beagle . FurtherOther methods to increase the accuracy of the genomic prediction also include using sequence trimming quality control tools such as Prowler . These tin-situ applications is ONT’s adaptive sequencing, which can target loci within a genome for sequencing without molecular intervention [Targeted sequencing methods also hold potential to increase the accuracy of genomic prediction using ONT sequencing. A promising method for rvention ,42. Thisrvention ,42, therin-situ sequencing [in-situ genomic diagnosis.Although each flow cell was run for 96-hrs, the data used here for genomic prediction represent a small subset of the data acquired from each flow cell. For example, given the size of the bovine genome, 1x and 0.5x sequencing coverage represent 3 Gb and 1.5 Gb of sequence data respectively. This means that accurate genomic prediction for each animal would have been achievable in a fraction of the 96-hr sequencing run. Xu, Ge  reportedquencing –46, genoASIP gene controlling coat colour in Nellore cattle [Studies have demonstrated the ability for ONT data to characterise structural variants ,48, prove cattle . More ree cattle . Using cTail hair samples from 19 Droughtmaster animals were collected from The University of Queensland cattle research farm under the ethics approval number SVS/301/18.DNA for this study was extracted from tail hairs collected from 19 Droughtmaster heifers. The hairs were pulled and stored at room temperature for more than a year prior to DNA extraction. A subset of the hairs was used for genotyping on the 777k BovineHD BeadChip . The remaining hairs were used for ONT sequencing. Genomic DNA for sequencing was extracted using the Gentra Puregene Tissue Kit (Qiagen) according to the manufacturer’s instructions with modifications. Briefly, 20–30 hair samples were lysed in 300 μl of Cell lysis solution (Gentra® Puregene® Tissue Kit) and 1.5 μl of Proteinase K solution (20mg/ml) for 5 hours at 55°C. RNA was then digested by addition of 1.5 μl of RNase A Solution, following 1-hour incubation at 37°C. Samples were placed on ice for 5 minutes after adding 100 μl Protein Precipitation Solution (Gentra® Puregene® Tissue Kit) and spun at 14000 x g for 3 minutes. 300 μl of Isopropanol was used to precipitate DNA. Samples were centrifuged at 14000 x g for 3 minutes. DNA pellets were washed in 300 μl of 70% ethanol, air-dried for 5 minutes and resuspended in 55 μl of DNA Hydration Solution (Gentra® Puregene® Tissue Kit).DNA concentrations were measured using the Qubit dsDNA Broad Range assay kit (Thermo Fisher Scientific). The purity of the extracted DNA was determined with the NanoDropND 1000 , assessing the 260/280 nm and 260/230 nm ratios. The size of extracted DNA was examined using pulsed-field gel electrophoresis  with a 0.75% Seakem Gold agarose gel  in 0.5X Tris/Borate/EDTA (TBE) running buffer, run for 16 hours at 75 V. The gel was stained after the electrophoresis with SYBR Safe dye (10000x) and visualized using Quantity One analysis software (Bio-rad).Extracted DNA samples were prepared using a ligation kit  based on the manufacturer’s instruction with some modifications. Starting with 4–8 μg of DNA produced enough sequencing library to provide up to 4 flow cell loads from a single library. Samples were diluted at the clean-up points with nuclease-free water to prevent bead clumping during 0.4x AmPureXP purifications. End-prep reaction and ligation incubation times were increased to 30 minutes and 1 hour respectively. During the 96-hour sequencing run the flow cell was washed three times using the nuclease-flush kit (Oxford Nanopore Technologies) and then reloaded with the same library. The use of DNase in flushing and flow-cell re-fuelling helps to remove blocking DNA and increases the sequencing output.Bos taurus reference genome ARS-UCDv1.2 [-x ont-aln. Samtools mpileup v1.3 [Base calls were made from the raw current disruption data using GUPPY  on the University of Queensland high performance computing infrastructure. The sequence data (fastq files) from each animal were randomly subsampled down to 4x, 2x, 1x & 0.5x sequencing coverage using a bash script. Minimap2 v2.17  was used-UCDv1.2 , using teup v1.3  was usedA variable allele count genotyping method was used to genotype the SNP loci. Briefly, this method grouped loci with similar coverage together and called genotypes using a separate minimum allele count for each group . The minBos indicus content. The effective population and window parameters in Beagle were set to 100,000 and 100 respectively based on Pook, Mayer [Three methods were used to assign genotypes at loci with less than 2x coverage: non-imputed, imputed-AF and imputed-Beagle. The non-imputed approach assigned missing loci as homozygous reference, coded as 0 in the genotype matrix. The imputed-AF method used the allele frequency within the population, calculated from SNP array data, to genotype the missing loci. The sum of the probabilities of each genotype given Hardy-Weinberg equilibrium multiplied by the genotype codes, 0 for homozygous reference, 1 for heterozygous and 2 or homozygous alternate were used to fill missing genotypes. Using continuous genotype values between 0 and 2 for this method allowed some accounting for the uncertainty of genotyping from low coverage sequence data. Finally, the imputed-Beagle method used the imputation package Beagle v5.1  to imputk, Mayer . For impGenomic BLUP solutions from  were bacn by m design matrix M, where n is number of animals and m is number of markers . The matrix contained 0, 1 or 2, designating homozygous, heterozygous and homozygous alternative genotype calls from the SNP array. The estimated phenotypes . The matrix contained genotype values between 0 and 2, according to the genotyping method used , and consistent with the nomenclature in matrix M. The matrix contained only 0, 1 or 2 for the non-imputed and imputed-Beagle methods, however contained continuous genotypes between 0 and 2 for loci imputed using the allele frequency. The estimated phenotypes  as a random effect.Where S1 Fig(TIF)Click here for additional data file.


However, gene expression can be highly variable, and filters for SNP detection using the popular GATK toolkit are not yet standardized, making SNP detection and genotype calling by RNA-seq a challenging endeavor. We compared SNP calling results using GATK suggested filters, on two chicken populations for which both RNA-seq and DNA-seq data were available for the same samples of the same tissue. We showed, in expressed regions, a RNA-seq precision of 91% (SNPs detected by RNA-seq and shared by DNA-seq) and we characterized the remaining 9% of SNPs. We then studied the genotype (GT) obtained by RNA-seq and the impact of two factors  on the concordance of GT with DNA-seq; we proposed thresholds for them leading to a 95% concordance. Applying these thresholds to 767 multi-tissue RNA-seq of 382 birds of 11 chicken populations, we found 9.5 M SNPs in total, of which ∼550,000 SNPs per tissue and population with a reliable GT  and among them, ∼340,000 with a MAF ≥ 10%. We showed that such RNA-seq data from one tissue can be used to (i) detect SNPs with a strong predicted impact on proteins, despite their scarcity in each population , (ii) study, on a large scale, cis-regulations of gene expression, with ∼81% of protein-coding and 68% of long non-coding genes (TPM ≥ 1) that can be analyzed for ASE, and with ∼29% of them that were cis-regulated, and (iii) analyze population genetic using such SNPs located in expressed regions. This work shows that RNA-seq data can be used with good confidence to detect SNPs and associated GT within various populations and used them for different analyses as GTEx studies.In addition to their common usages to study gene expression, RNA-seq data accumulated over the last 10 years are a yet-unexploited resource of SNPs in numerous individuals from different populations. SNP detection by RNA-seq is particularly interesting for livestock species since whole genome sequencing is expensive and exome sequencing tools are unavailable. These SNPs detected in expressed regions can be used to characterize variants affecting protein functions, and to study  Fourth, the transcribed regions are well spread over the genome and much more numerous than previously thought. Thousands of novel long non-coding genes exist across the genome, as highlighted by the ENCODE project , making mapping of RNA-seq reads that overlap exon-exon junctions, more difficult, compared to DNA-seq read alignment . HoweverIn this context, this work aims at detecting SNPs from RNA-seq data in chicken. The first goal was to set up a procedure allowing SNP detection and genotype (GT) calling from RNA-seq data using reference tools (STAR for read mapping and GATK for SNP detection). We tested the SNP reliability according to three filters suggested by the GATK team and compared the detected SNPs with those obtained using DNA-seq data. This comparison was performed in two independent chicken populations for which RNA-seq and DNA-seq data were available on the same biological samples . In this paper, the workflow was used at the tissue level to provide results for RNA-seq experimental settings with only one analyzed tissue which represent a quite common case. This, however, corresponds to the least favorable case compared to multi-tissue experimental projects, since it does not allow cumulating the sequences from tissues per individual. We then analyzed the effects on the number of detected SNPs by this workflow performed at the tissue level when using additional tissues of a same population.i) provide an estimation of the number of SNPs and GT that can be detected using RNA-seq data per tissue and population, (ii) present an overview of the predicted consequences of the SNPs located in coding regions, in particular, the number of high-confidence predicted loss-of-function variants, as defined in the work of gnomAD, and finally (iii) give an overview of the potential of RNA-seq for allele-specific expression (ASE) analysis by estimating the number of genes that could be analyzed for ASE with the number of SNPs detected per gene. We then identified the cis-regulated genes in the liver of 2 of the 11 populations using the phASER tool ; PRJEB43829 ; PRJNA330615 and PRJNA248570 ; PRJEB26695 ; PRJEB34341 ; PRJEB34310 ; PRJEB27455 ; PRJEB43662 ; PRJNA612882  (Raw data of both DNA-seq and/or RNA-seq are available on the ENA and SRA archives under accession numbers: RNA-seq) . RNA seqFor the comparison of SNPs detected by RNA-seq versus DNA-seq, we used two populations for which both data types were obtained from same liver samples collected on the same birds. The population A was composed of 15 birds from an experimental layer population  composed of birds diverging for feed efficiency (Rp and Rm) after a 40-year diverging selection . The popFor the rest of the work, we used RNA-seq data from 11 populations see  with 19 http://www.fragencode.org (LNChickenAtlas); Section: Galgal5—Ensembl v94; Genome annotation: LNCextendedEns94.gtf.gz;  and increased with coverage (see result section). Therefore, for the rare cases of discordance, we kept the genotype of the tissue with the highest coverage when they were different. However, outside from this study, for projects in which RNA-seq of different tissues per animal are available when the SNP detection analysis is started, we advise users of our pipeline to define in the first step a sample as a specific individual. This strategy allows to gain power in SNP detection by gathering all BAM tissue files per animal.DNA-seq read mapping and variant detection were performed using standard tools. The BWA-MEM algorithm  from BWAhttp://www.fragencode.org  and corresponding to the genes from the Ensembl annotation used as reference, extended with lncRNAs loci available in other public databases  (10(RpKb + 1) value of 0.5, corresponding to the first quartile of expression in both RpRm and FLLL  . To compE, etc.)  with optE, etc.) . These aFLLL see .Variant Effect Predictor (VEP) v92  with theRegions with 5 or more repeated nucleotides (homopolymers) and regions spanning 5 bp of each extremity of a junction were detected using home-made scripts.The hierarchical clustering was performed on a set of 67,341 SNPs obtained using liver RNA-seq data from the 10 populations presented in Prior to the quantification of allele specific expression, sequences need to be aligned against masked version of the genome to avoid favoring reference alleles. At the population level, polymorphic  and bi-allelic filtered (GATK—FS and QD criteria) SNP were extracted using the GATK “SelectVariants” tool. These last variants were then used to mask the reference genome using “maskfasta” tool from the BEDTools suite v2.29. Tissue sample sequence were aligned to this masked version of the genome using the multi-sample 2-pass mapping procedure of STAR 2.6. Non-duplicated  properly paired (if paired sequences) uniquely mapped reads (samtools 1.9 with –f 2 and –q 255 options) were selected. “SplitNCigar” tool from GATK were finally used to split alignment overlapping exon/intron junction and rescaled mapping quality. The phASER tool  and its binom.test R function) with the null hypothesis that, for a given gene, each super-allele had the same number of associated reads. P-values were corrected using the Benjamini-Hochberg method . As shown in i) 5.09% were located at 5 bp or less of an exon-exon junction, versus 3.55% for those detected only by DNA-seq; the corresponding ratio, that is significantly greater than 1 , was expected since RNA-seq deals with spliced transcripts (ii) 3.1% were located in low complexity regions, defined as repetition of at least 5 identical nucleotides, versus 3.4% for the ones detected only by DNA-seq (iii) 2.7 and 5.5 SNPs per gene for RNA-specific SNPs were observed in 5′UTR and 3′UTR regions, respectively, with a fewer 3′UTR SNPs compared to those detected by DNA-seq only  (iv) Last, another factor that could be responsible for these RNA-seq specific SNPs is RNA editing, however, according to the literature, it is unlikely that most of the remaining SNPs are due to this mechanism. In mammals, in which RNA editing is well studied, Adenosine-to-Inosine (A-to-I) editing due to ADAR1 and ADAR2 enzymes is the most common editing form and mostly occur in inverted pairs of Alu interspersed repeats  2χ2 test) . This maχ2 test) . (iv) La repeats . In chiclled CR1 . These elled CR1 , that arlled CR1  and chiclled CR1 , with lelled CR1  with leslled CR1 . Finallylled CR1 .p = 3 × 10–3) by cumulating the information on all tissues in which a same gene is more or less expressed. Note that here, we have used our pipeline in a sub-optimal manner, by analysing RNA-seq data per tissue instead of combining the tissues together to increase detection power and reliability. For projects in which RNA-seq from different tissues per animal are all available before SNP detection analysis, we advise users to pool for each animal the RNA-seq files. For SNPs detected in more than one tissue, the concordance between genotypes detected in different tissues was very high, 98.9% without read filtering. Considering genotypes supported by at least 5 reads (respectively 10 reads) the concordance raised to 99.5% (respectively 99.9%).Using blood and hypothalamus samples collected on the same 15 animals (population A), we studied the effect of detecting the SNPs in more than one tissue. RNA-seq from each tissue was not generated at the same time and have been analyzed separately at different occasions. Results are displayed in While reliable SNPs can be detected in the population thanks to some individuals that bear them, it does not necessarily mean that there are enough reads for each individual to produce a genotype (GT). This was exemplified in x-axis) versus the CR with a DP ≥ 5 reads (y-axis), on the genotype concordance between RNA-seq and DNA-seq (solid green isoclines) and on the number of SNPs selected according to the different criteria (dashed blue isoclines)  .x-axis) comprised between 0 and 50%, as shown by the horizontal isoclines. Hence, we propose for our subsequent analysis on different RNA-seq datasets to select SNPs within the red surface of Interestingly, only the CR with DP ≥ 5 reads have an effect on the genotype concordance and the percentage of selected SNPs, while no such effect is observed for the no DP filtering CR , suggesting an important role for the genes associated to these variants with severe-predicted impact. Such variants obtained using RNA-seq data constitute a new complementary resource to Ensembl dbSNP allowing to explore variants (deleterious or not) according to their genotypic and allelic frequencies in different populations of a farm species. For example, two deleterious missense SNPs (SIFT-score = 0) are presented in Among all these predictions, we focused on the predicted consequences with the most severe putative impacts as defined by the gnomAD consortium, which only considers the PCG (Protein Coding Genes) : variantThe second SNP, not reported in dbSNP, affects the SERGEF protein  by changing an aromatic, hydrophobic and positive charged amino acid  into an unchanged amino acid  . This SNAllele-specific expression (ASE) analysis requires a heterozygous SNP in the expressed feature, to test an eventual imbalance in the expression between the two parental chromosomes. Usually, the expression is evaluated using RNA-seq and the SNPs are detected using DNA-seq, which is expensive when working on a dozen or more individuals. Since we have shown that RNA-seq allows detecting a large number of reliable SNPs in expressed regions, we studied in this section, the potential of RNA-seq data for performing ASE analysis. To this end, the The first key result is that the number of genes with at least one SNP are similar in both cases (exons only versus exons + introns), meaning that there are enough SNPs to study ASE in exonic regions only, i.e., mature transcript, despite a much lower number of SNPs per gene when SNPs are only selected in exons . When wocis-regulated genes in one tissue, we performed an ASE analysis of the liver samples of the RpRm and the FLLL populations using phASER and its downstream tool, phASER Gene AE, that phase SNPs at the gene level . Using exonic and intronic SNPs and selecting genes having one haplotype with at least 10 reads, we found for genes with an hepatic expression ≥ 1 TPM, that in average 29% of the expressed PCG or lncRNA genes were cis-regulated (∼34% for RpRm and ∼23% for FLLL) (cis-regulated genes (21%) because they are less expressed and some of them did not have more than 10 reads for at least one “super-allele” analyzed by phASER . Interestingly, among these cis-regulated genes, ∼50% and 37% are shared by both populations for the protein-coding genes and long non-coding genes, respectively  . For lncectively . Two exaFinally, we explored genetic links between populations using the genotypic frequencies of SNPs detected by RNA-seq, which represent a set of SNPs, which may be under a larger selective pressure than those used in genotyping SNP chips. Indeed, the latter are considered as having a neutral effect, while most the SNPs present in our data are located in expressed regions and affect proteins to some extent .The classification in cis-regulated genes depending on the analyzed population and the gene biotype (PCG versus lncRNA), these results were consistent with other studies conducted in other species.We show here that RNA-seq data, which are cheaper to generate and store compared to DNA-seq data, can be a reliable resource for performing different analyses based on polymorphism detection. By comparing DNA-seq and RNA–seq results generated from the same animals in two independent chicken populations, this study provides a workflow to produce reliable SNPs and genotypes from RNA–seq data. We ran through this pipeline 767 RNA–seq of 382 birds from 11 populations and provided a per-population estimation of the average genotyped SNPs count per tissue  and an overview of the predicted consequences of SNPs located in coding regions. In particular, thanks to this large RNA-seq dataset, we identified 440 genes containing a stop-gained impact, known to be rare because of their potentially severe impact, especially when located in the first third of the coding sequences (133 genes). In a companion study , we checked the possible existence of more than one SNP in a given codon, that could “rescue” a stop-gained situation. We then gave an overview across 11 populations of genes that could be analyzed for ASE, i.e., having at least one SNP allowing to distinguish expression from both chromosomes. We applied phASER on liver RNA-seq data of two populations and identified around 21 to 30% of cis- and trans- genetic associations with gene expression, as previously performed in human  can be found in the article/Ethical review and approval was not required for the animal study because all data have been obtained from public databases as described in the materials and methods, section “DNA-Seq and RNA-Seq Data.”CK and SLa conceived the study and coordinated the study. FJ, LL, CD, BA, MT-B, BB, TB, DG, HA, SF, SD, EG, FP, TZ, and SLa participated to the set-up of the experimental design and sample collection. LL, CD, and SLe carried out all RNA extractions. OB generated RNA-seq libraries and sequencing. FJ, FD, MB, CK, and SLa performed bioinformatic processing of the RNA-seq data and carried out the whole analyses. FL, PB, and MC participated to bioinformatic analyses. FJ, FD, and SLa drafted the manuscript. MB, FL, BA, MT-B, BB, HA, SF, SD, EG, TZ, FP, and CK helped to improve the manuscript. All authors read and approved the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.