The accurate prediction of growth traits in genomic selection (GS) is essential for pig breeding. Here, we performed GS using variants identified with three genome-wide association study methods on four growth-related traits in Yorkshire and Landrace pigs. A total of 1485 loci related to these traits and 24 candidate genes were mapped. Compared with using 60K SNP-chip data, GS with the pre-selected variants significantly improved prediction accuracies by 4 to 46% in genomic best linear unbiased prediction (GBLUP) models, and 5 to 27% in a two-kernel based GBLUP model for the four traits.Improving the prediction accuracies of economically important traits in genomic selection (GS) is a main objective for researchers and breeders in the livestock industry. This study aims at utilizing potentially functional SNPs and QTLs identified with various genome-wide association study (GWAS) models in GS of pig growth traits. We used three well-established GWAS methods, including the mixed linear model, Bayesian model and meta-analysis, as well as 60K SNP-chip and whole genome sequence (WGS) data from 1734 Yorkshire and 1123 Landrace pigs to detect SNPs related to four growth traits: average daily gain, backfat thickness, body weight and birth weight. A total of 1485 significant loci and 24 candidate genes which are involved in skeletal muscle development, fatty deposition, lipid metabolism and insulin resistance were identified. Compared with using all SNP-chip data, GS with the pre-selected functional SNPs in the standard genomic best linear unbiased prediction (GBLUP), and a two-kernel based GBLUP model yielded average gains in accuracy by 4 to 46% (from 0.19 ± 0.07 to 0.56 ± 0.07) and 5 to 27% (from 0.16 ± 0.06 to 0.57 ± 0.05) for the four traits, respectively, suggesting that the prioritization of preselected functional markers in GS models had the potential to improve prediction accuracies for certain traits in livestock breeding. Genomic selection (GS) has been a routine method for genetic evaluation in animal breeding schemes . It can Genome-wide association analysis (GWAS) is a powerful tool to identify potential causal mutations associated with traits of interest ,15. It cPigs are a primary source of animal protein for humans . ImproviIn this study, a total of 1734 Yorkshire and 1123 Landrace pigs were sampled from the nucleus farm of Guangdong IPIG Technology Co., Ltd. . The 153 male and 2704 female pigs of two populations were born between 2015 and 2021. All fattening pigs were reared under uniform feeding conditions and consistent management. Phenotypic observations included 4 growth-related traits: average daily gain (ADG kg/d), backfat thickness (BFT mm), birth weight (BTHWT kg) and body weight (BW kg). When the body weight of pigs reached approximately 115 kg, backfat thickness and body weight were recorded in the test station. Backfat thickness was measured by ultrasound between the 10th and 11th rib in the live pigs. To standardize the phenotypic records, BFT and ADG were both adjusted to 100 kg and calculated by corrected equations . The equThe ADG was adjusted to 100 kg by the formula:Piglets were weighed within 24 h after the delivery of each individual to measure and record the birth weight. In Yorkshire and Landrace pigs, the numbers of phenotypic records for ADG, BFT and BW were 1734 and 1123, and for BTHWT were 887 and 405, respectively. Animals with missing records were excluded from the following analysis.Ear tissues collected from target swine were used for genomic DNA extraction with a standard phenol/chloroform method. The A260/A280 ratio of all DNA samples was qualified by electrophoresis and a light absorption. The samples with ratio in the range of 1.8–2.0 were diluted to a final concentration of 50 ng/μL. Genotyping was conducting with the KPS Porcine 60K SNP Chip , which contains 57,466 genome-wide SNPs across 18 autosomes and sex chromosomes. SNPs with call rate less than 95% and the minor allele frequency less than 1% were filtered out using PLINK . Individhttp://www.ncbi.nlm.nih.gov/sra/ accessed on 1 July 2020) and European Nucleotide Archive  under project PRJNA260763 (Sus Scrofa (v11.1.104) by BWA, and then the aligned reads were used to detect short variants [R2 was 0.71 and 0.69 for the two populations. The imputed SNPs with minor allele frequency (MAF) < 0.01 and Hardy–Weinberg Equilibrium < 1 × 10−6 were discarded. Finally, the remaining 1,468,003 and 2,221,629 SNPs for Yorkshire and Landrace pigs were available for the following analyses.To obtain high density SNP data, genotype phasing and imputation from 60K SNP to whole genome sequencing (WGS) data were performed by Beagle using two reference panels , includiNA260763 . SNPs cavariants . The rawvariants ,35. The variants . After tWe used three statistic models including the mixed linear model, Bayesian model, and meta-analysis for GWAS, including (1) linear model based on 60K chip data (CL_GWAS); (2) linear model based on imputed WGS data (IL_GWAS); (3) Bayesian model based on 60K chip data (CB_GWAS); and (4) meta-analysis based on 60K chip data.−6 and 2.25 × 10−5 for CL_GWAS, and 3.40 × 10−8 and 6.81 × 10−7 for imputed data-based GWAS.We performed CL_GWAS and IL_GWAS by fitting a univariate Mixed Linear Model (MLM) in GEMMA :(1)y=Wα+CTA tool ; α denotd values . The estp values and effects of common SNPs were used as input data. Bonferroni correction threshold for meta-analysis was identical to CL_GWAS.Combining the CL_GWAS results from two breeds by the same trait, a meta-analysis using an inverse variance weighting method was implemented by METAL , in whicBayes C model was implemented by GenSel, the prior assumption of which was that most markers were set as zero effect, with only a few SNPs explaining large variances . Prior vSus scrofa reference genome (version 11.1) which served as the gene location map was downloaded from Ensembl database [p < 0.05.database . Genes tdatabase . The annThe model of benchmark best linear unbiased prediction (GBLUP) is :(2)y=Wα+ies used .1 estimated with 2 estimated with 1 and GEBV2 were GEBV for the method.The model of two-kernel based GBLUP is:Prediction accuracy (GPA) was calculated as: BLUPF90 . The pre BLUPF90 . The avePhenotypic summary statistics and heritability based on the 60K SNP-chip for four traits across two breeds are listed in Principal component analysis (PCA) was performed using a 60K SNP panel. Almost all individuals were divided into two clear clusters representing two different genetic backgrounds of target animals, with only a few individuals overlapping . The firResults of significant genome-wide associations for four growth traits were shown in MDFIC, RPS12, PDE4D, AQP4) were putatively linked to ADG and BW in pigs, which played supporting roles in the biological process of physical or muscular growth. As a myogenic regulatory factor, the post-transcription downregulation of MDFIC (MyoD family inhibitor domain containing) controls the promotion of myogenic differentiation, which is beneficial for skeletal muscle development in bovines [MDFIC was putatively linked to the birth weight of piglets [RPS12), mapped by two SNPs on SSC1:30.88~30.92 Mb, was examined to regulate the process of nucleic acid translation and physical growth in Drosophila by controlling Xrp1 expressing levels [FGF1), phosphodiesterase 4D (PDE4D) was associated with growth and fertility impairment [AQP4) was identified to widely express in the digestive tract of guinea pigs, and the mediation of gastric acid secretion in mice [Four candidate genes  were found to potentially affect the trait of BFT in pigs, and are involved in pathways of fatty acid metabolism, adipogenesis and insulin signaling. Candidate gene PNPLA2 (patatin like phospholipase domain containing 2), overlapping a genomic region with GV of 2.88% on SSC2:0.01~0.96 Mb, controls the initial step in triglyceride hydrolysis of long-chain fatty acid esters in adipocyte and non-adipocyte, and regulates adiposomes size [PNPLA2 was embedded in a region on SSC2:0~12.76 Mb which was associated with the abundance of three fatty acids in backfat in pigs [DEAF1  mapped an identified variant on SSC2, is involved in fatty acid deposition, and regulated the expression of peripheral tissue antigen in lymph node stromal cells among diabetic mice and humans [PLCD3) played a key role in phosphatidylinositol catabolism by hydrolyzing phospholipids into fatty acids [ANKRD55 (ankyrin repeat domain 55) showed a high correlation with insulin resistance and lipid metabolism, which may result in adiposity and have a risk of Type 2 Diabetes in humans [RORB) positively regulated insulin secretion in pancreatic β cells [NDUFS4 (NADH: ubiquinone oxidoreductase subunit S4), harboring two identified markers on SSC16:23.93~23.97 Mb, was reported to highly express in high intramuscular fat in pigs [ALDH8A1  was shown to be significantly associated with fatness in large white pigs [Seven candidate genes  mediated organ development and histological differentiation and controlled erythroid development and differentiation in primitive cells or organs, such as fetal liver and fetal kidney [DHX32), overlapping the two most prominent SNPs, has an ability to unwind DNA and RNA, and is involved in embryogenesis or cellular growth and division by changing RNA structure [GMNN), closed to the BTHWT-linked marker on SSC7:19.60 Mb, plays a crucial role in the S and G2 phases of the cell cycle. The gene GMNN is involved in the regulation of DNA replication and cell fate control during embryonic development [ZNF300 (zinc finger protein 300) served as a zinc finger-domain protein regulating embryonic development and transcription. Gene TAF4B (TATA-box binding protein associated factor 4b) was essential for the maintenance of spermatogonia stem cells in mammals [The annotation of candidate genes supported that cellular processes in early stages may impact birth weight of piglets, and consequently affect many post-natal traits including growth performance and meat quality. For BTHWT in Yorkshire, three SNPs located in SSC14:135.03~135.18 Mb that were shared in two single-marker models completely coincided with the QTL identified in the birth weight of Yorkshire piglets . Closed l kidney . The gentructure . Gemininelopment . Moreove mammals .p-value was used to test the statistical significance of SNPs [p-value) > 8) within that region, suggesting that increased marker density might construct a more precise genomic relatedness matrix, and was able to highlight non-significant SNPs among genomic regions capturing large genetic variance. GWAS based on genomic regions with a Bayesian model was able to effectively discover putative QTLs and candidate genes [In our study, 48 SNPs related to the four traits were identified in the two populations. In each single-marker analysis, we found several significant SNPs that were in high linkage disequilibrium within a small region. For example, 13 ADG-associated SNPs on SSC18 from 31 Mb to 35.1 Mb were partitioned into four blocks with high pairwise LDs of 0.86, 0.58, 0.99 and 0.80. However, single-marker analysis only identified the association between a single SNP and traits of interest, neglecting the LD effect of adjoining SNPs . Since p of SNPs , markers of SNPs ,80, simu of SNPs . Howeverte genes ,83, but Performing GWAS with imputed genotypes is an effective way to enhance SNP density at a low cost . We idenPTPRZ1 (protein tyrosine phosphatase receptor type Z1), which encodes protein tyrosine phosphatase that regulates the cell proliferation on the embryonic spinal cord. Another ADG-associated SNP located in SSC15:0.17 Mb was closely adjacent to candidate gene signal transducing adaptor molecule 2 (STAM2) which plays a role in intracellular signal transduction induced by growth factors and cytokines [STAM2 gene and demonstrated that these variants had significant associations with growth performance in Chinese Wuchuan Black cattle [ADAMTS6  and BMP2K (bone morphogenetic protein 2 inducible protein kinase), were assigned to the biological pathway of skeletal development and patterning. The lack of gene ADAMTS6 leads to a drastic reduction in aggrecan and cartilage link protein, the impairment of bone morphogenetic proteins (BMP) signaling in cartilage and the detention of growth differentiation factors, which consequently result in impaired skeletal development [BMP2K is considered as a kinase potentially regulating the attenuation of osteoblast differentiation [ADAMTS6 and BMP2K may play roles in body weight in pigs.Meta-analysis uplifted the detection power by taking all informative SNPs into account, including those SNPs filtered out by quality control in single population GWAS . We usedytokines . Yang etk cattle . Moreoveelopment . BMP plantiation . Since bntiation , ADAMTS6Genomic prediction using GBLUP and a 60K chip panel (CHIP) was set as a benchmark A,C. GPAsCompared with using SNP chip data, the prediction accuracy using imputed WGS data had almost no improvement for ADG and BW in both breeds, which was contrary to the report for ADG in pigs , while fWe used significant variants  preselecIn this study, we employed three different statistical methods as well as 60K SNP-chip and whole genome sequence data  for GWAS, which allowed us to discover more candidate loci linked to the four growth traits. A total of 1485 candidate loci, and 24 candidate genes which are involved in skeletal muscle development, fatty deposition, lipid metabolism and insulin resistance, were identified. Using the pre-selected functional SNPs in GS outperformed using all 60K SNP-chip and imputed WGS data for some traits, suggesting that the prioritization of preselected functional markers in GS models has a potential to improve prediction accuracies in livestock breeding.


Pearl millet is a crucial nutrient-rich staple food in Asia and Africa and adapted to the climate of semi-arid topics. Since the genomic resources in pearl millet are very limited, we have developed a brand-new mid-density 4K SNP panel and demonstrated its utility in genetic studies. A set of 4K SNPs were mined from 925 whole-genome sequences through a comprehensive in-silico pipeline. Three hundred and seventy-three genetically diverse pearl millet inbreds were genotyped using the newly-developed 4K SNPs through the AgriSeq Targeted Genotyping by Sequencing technology. The 4K SNPs were uniformly distributed across the pearl millet genome and showed considerable polymorphism information content (0.23), genetic diversity (0.29), expected heterozygosity (0.29), and observed heterozygosity (0.03). The SNP panel successfully differentiated the accessions into two major groups, namely B and R lines, through genetic diversity, PCA, and structure models as per their pedigree. The linkage disequilibrium (LD) analysis showed Chr3 had higher LD regions while Chr1 and Chr2 had more low LD regions. The genetic divergence between the B- and R-line populations was 13%, and within the sub-population variability was 87%. In this experiment, we have mined 4K SNPs and optimized the genotyping protocol through AgriSeq technology for routine use, which is cost-effective, fast, and highly reproducible. The newly developed 4K mid-density SNP panel will be useful in genomics and molecular breeding experiments such as assessing the genetic diversity, trait mapping, backcross breeding, and genomic selection in pearl millet. Pennisetum glaucum (L) R. Br., syn. Cenchrus americanus (L.) Morrone) is a strategic climate-resilient C4 crop. It has an inherent ability to provide sustainable yield even in harsh ecologies, making it an economically secure and favorable crop for farmers in semi-arid and arid regions of the world. Pearl millet is also known for nutritional security as it is competent to address malnutrition issues (Pearl millet (n issues . In tradVarious marker genotyping methods such as expressed sequence tags- derived simple sequence repeats (EST-SSRs) , genomicde-novo sequencing of the pearl millet genome (1.79 Gb), (activities. The EST-SSRs were developed and utilized in the genetic mapping and MAB programs targeting the traits such as yield and drought resistance in pearl millet . Through.79 Gb),  provided® 1536 SNP chip and Illumina® MaizeSNP50 Beadchip , mid-density (a few thousand SNPs), and low-density (less than 100 SNPs), generally used for genotyping purposes. The high-density platforms are usually applicable for whole genome studies and trait mapping but are very tedious, expensive, laborious, and time-consuming. High-density platforms such as Illumina, PacBio, genotyping-by-sequencing (GBS), and restriction site-associated DNA sequencing (RAD) provide tens of thousands of genome-wide SNPs. The mid-density platforms, which include AgriSeq , DartTagThe mid-density genotyping approach is highly efficient, informative, and cost-effective as it can be used in various genomics and molecular breeding experiments such as diversity assessment, trait mapping, marker-assisted breeding, and genomic selection. Although a crop with immense economic and social importance, pearl millet has been neglected for a long time, and not enough efforts have been made to explore genomic resources. Hence, the objectives of the experiment were to mine a mid-density panel of 4000 SNPs from 925 whole genome sequences of pearl millet, to develop a functional, robust, and reproducible genotyping protocol through AgriSeq technology, to characterize the newly developed SNPs in a set of 373 genetically diverse pearl millet B and R lines and to demonstrate its utility in genetic studies.The whole-genome resequences (WGRS) from two sets of pearl millet accessions  were conhttps://bitbucket.org/jcignacio/purity/wiki/Home), for picking an initial set of 6000 markers each that can carry maximum genetic information. These two sets (from different iterations) filtered for high LD (LDBlockShow) and redundant markers, resulting in 2000 SNPs each for Group A (2K set I) and B genotypes (2K set II), respectively. The 2K sets I and II together formed a 4K mid-density SNP panel (4K set). The 4K panel were functionally annotated using SnpEff (v4.3t) with predicted pearl millet reference annotations (The variant data (32 million SNPs) from the Groups A and B genotypes were retrieved from the pearl millet genome project . These votations Figure 1in-silico checked for specificity and sensitivity of the intended target/marker regions using pearl millet reference genome. Finally, 4000 SNPs were selected to constitute the custom 4K SNP GBS pearl millet panel.The selected SNPs were passed through AgriSeq’s design quality control process. The quality check was performed using the pearl millet reference genome (accession: GCA_002174835.2) and then submitted to the primer design phase. The primer designs were via the Ion 540™ Kit-Chef (A43541-Life Technologies) and Ion Chef. Sequencing was performed on the Ion S5 system . After sequencing, genotyping was performed automatically by Torrent Variant Caller (TVC) on the Torrent Suite Server (TS).The AgriSeq targeted-GBS solution utilizes a highly efficient multiplexed PCR chemistry where hundreds to thousands of markers can be targeted and uniformly amplified in a single reaction. Three eighty-four samples were prepared for sequencing using the AgriSeq HTS Library Kit (A34143-Life Technologies). In short, DNA concentrations were normalized to 3.3 ng/µL for a total of 10 ng DNA per 10 µL reaction. Normalized DNA was combined with the AgriSeq custom primer panel and AgriSeq amplification master mix. For amplification of genomic targets, the following thermocycling programs were used; 99°C for 2 minutes, then 15 cycles of 99°C for 15s and 60°C for 4 minutes. Amplicons were prepared for ligation with pre-ligation enzyme digestion at 50°C for 10 minutes, 55°C for 10 minutes, and 60°C for 20 minutes. IonCode™ Barcode Adapters 385-768 Kit (A36546-Life Technologies) were ligated to the digested products with barcoding enzyme and buffer. Labeled amplicons were then pooled, cleaned up, amplified, and normalized. Following library preparation, libraries were loaded onto an Ion 540™ sequencing Chip Kit (A42849) https://github.com/iontorrent/TS/tree/master/Analysis/TMAP) followed by genotyping using TVC-Torrent Variant Caller . The genotypes were reported in different formats TOP, TOP/BOT, and actual alleles using AgriSum Toolkit, an AgriSeq TS plugin .The variant calling pipeline is fully automated and optimized for analyzing Ion Torrent sequencing data on Torrent Suite Server (Thermo Fisher Scientific). This workflow comprises several series of steps. First, signal processing files are automatically transferred from the sequencing platform to the S5 server and then converted to raw reads (FASTQ). After that, the sequenced reads were de-multiplexed to individual samples using the barcode sequences. For each sample, the sequenced reads from the targeted regions were mapped to the pearl millet reference genome using TMAP- Torrent Mapping Alignment Program   was used2), which measures the correlation between alleles at two loci, was computed along with its corresponding P-value. The SNPs with all MAF, 15% heterozygotes, and 20% missing were included in this analysis. LD values of all pair-wise SNPs were shown in triangle LD plots using TASSEL the genome-wide and chromosome-wise LD patterns.The extent of the LD in the 373 genotypes in all three set SNP markers was evaluated using TASSEL 5 . For eacThe population structure of the accessions was estimated using an MCMC (Markov Chain Monte Carlo) model implemented in STRUCTURE version 2.3.4 . The datFor the preliminary statistics, the genotypic data from the two SNP sets, 2K set I and 2K set II, were compared since they were derived from two different sets of genotypes. Then, the combined genotypic data, the 4K set, was used for the remaining statistical studies. The data were used to calculate the marker call rate, i.e., the percentage of samples for a particular marker that generate a genotype call. The mean and the median marker call rate for the 2K set I was 93.8% and 98.9%, respectively, while it was 82.9% and 98.9%, respectively, for the 2K set II. For the combined 4K Set, the mean and the median marker call rate was 88.2% and 98.1%, respectively.In 2K sets I and II, most SNPs were located on chromosomes 2, 1, and 3. Chr2 had comparatively more SNPs in both sets (set I-17.9% and set II-18.8%) Figure 2The PIC, Ho, and He values were determined using the SNPReady for the data 2K set I, 2K set II, and combined 4K set Table 1.The observed heterozygosity (Ho) in 2K set I and 2K set II and 4K set ranged from 0 to 0.21, 0 to 0.41 and 0 to 0.31, while the expected heterozygosity (He) ranged from 0 to 0.21, 0 to 0.38 and 0to 0.3, respectively. 2K set II showed the highest average observed and expected heterozygosity (Ho= 0.04 and He= 0.38). When comparing the B- and R-lines groups, the B-lines had more average Ho in 2K set II and 4K set than the R-lines, whereas the R lines had more average He over the B-lines among all three data sets Table 1.The grouping behavior of B and R lines was characterized using the 4K set data through genetic diversity, principal component analysis, and structure models.The SNP frequency-based genetic dissimilarity matrix available in DARwin-6.0 was employed to study the genetic diversity among the 373 genotypes, which included 195 B- and 182-R lines. The NJ-based statistics grouped all the B- and R-lines into two clear-cut major groups Figure 3We have also noticed cross-grouping of genotypes, where the R-lines, namely, ICMR 100258, ICMR 102545, ICMR 102497, ICMR 102499, ICMR 11555, ICMR 14111, ICMR 16444, ICMR 16333, ICMR 19888, ICMR 15111, ICMR 07333, ICMR 0755, ICMR 10111, ICMR 13666, ICMR 11888, ICMR 10222, ICMR 07666 and ICMR 15333 grouped with B- line clusters and B lines, ICMB 101839, ICMB 101912, ICMB 1502 and ICMA1 19888 grouped with R-line clusters Figure 3The average mean Nei’s genetic diversity (GD) among all 373 genotypes was 0.29. Comparing the B- and R-lines, the mean genetic diversity (0.28) of the R lines was higher than the B lines (0.26). Here, 57% of SNPs covered more than the mean GD value among all accessions.The pair-wise genetic dissimilarity analysis showed that the dissimilarity coefficient for the entire population ranged from 0.003 to 0.68. The minimum and maximum genetic dissimilarly between B- and R-line groups were 0.006 and 0.68, respectively. Within B- and R-line groups, the R-line pairs showed higher genetic dissimilarity (maximum 0.66) over the B-line pairs (maximum 0.58). The total dissimilarity measured in the population was classified into low (0.00 to 0.25), medium (0.25 to 0.50), and high (>0.50) to understand the frequency of pairs present in the respective dissimilarity group. It was observed that 817, 36, and 228 pairs from the B × R, B × B, and R × R groups fell in the high category. On the other hand, 158, 2431, and 1766 pairs were identified as having low genetic dissimilarity under the B × R, B × B, and R × R groups, respectively. It explained that the diversity among the R × R lines was much higher than the B × B lines.In the B × R group, genotypes namely, ICMR 100258, ICMR 16333, ICMR 102277, ICMA1 101813, ICMA1 101805, and ICMA1 1803 showed significant genetic diversity over others as they frequently occurred under the high-dissimilarity level. ICMB 92888, ICMB 92111, ICMR 13666, ICMA4 03111, ICMA1 18888, and ICMA1 11999 displayed a high level of genetic relatedness, as the dissimilarity coefficient was the lowest among other pairs. Based on the frequency of occurrence, ICMB 101791 and ICMA1 1803 (within B-lines) and ICMR 100258 and ICMR 16333 (within R-lines) were the genotypes showing a high level of genetic diversity with the other genotypes in their respective groups.Principal components were generated for the 373 genotypes using the function SnpgdsPCA available in the SNPRelate R package. The percentage of variation calculated for the first 15 principal components was 34%, and the three-dimensional plotting of genotypes was done for the first three components Figure 4We further investigated the presence of population structure in the 373 genotypes using Structure v2.3.4. The result showed that using mean LnP(K) and delta K values, population structure analysis reveals that the best-assumed group for the current population is 2 Figure 5AMOVA was performed to estimate the genetic differentiation of populations within and between B- and R-line groups. The results showed that a significant difference was available between the B- and R-line groups. The variation among and within the B- and R-line groups accounted for 13% and 87% of the total variation, respectively. The estimated pairwise PhiPT  value between the B- and R-line groups was 0.13. Table 2.r2 was used to estimate LD between all SNPs of the 4K set on each chromosome through TASSEL 5 , moderate (0.50-0.90), low (0.10-0.50), and very low (0.00-0.01). Around 278 pairwise SNP were classified as high LD (2r >0.90) across the genome. Around 113 SNP pairs showed high LD (0.90-1) at chr3, followed by Chr2, and chr6, which had 47 and 35 high LD SNP pairs, respectively. Clusters of SNP pairs in high LD were primarily found in Chr3, followed by Chr2 and Chr6. A high LD block spanning the length of ~1.5 Mb in the middle of Chr3  ranged from 3 bp to ~117 Mb. More than 2000 SNP pairs were observed in a moderate LD range (0.50 to 0.90). Of this, Chr3 had the highest pairs (1123) followed by Chr4 (349) and Chr2 (248). The length of the LD blocks was identified in the range of ~27 bp to ~150.93 Mb under the moderate LD category. Around 28K pairs of SNPs have demonstrated low LD (0.10-0.50). Among all, the highest number of pairs was found in Chr3  followed by Chr2  and Chr5 (7037 pairs). The length of the genomic region in the low LD range was extended from 1 Mb to ~300 Mb. More than 167K pairs of SNPs showed a very low LD of <0.10. Chr2 had a high number of LD pairs (~33K), followed by Chr1 (29K) and Chr4 (17K) under the low LD class.Based on the Plant breeding dynamics have changed since the 1980s with the development of molecular marker technologies . Marker-Over the last decade, SNPs have been generated in several crops and have become the most popular genetic marker in trait mapping, molecular breeding, and population genetics experiments. The real explanation for SNPs becoming the marker of choice is their high abundance, even distribution in coding and non-coding regions, and the bi-allelic nature corresponding to the models studied in population genetics and co-dominant mode of inheritance . AdditioRecent advances in NGS technologies have transformed the pace and precision of plant genomics, providing low-cost genotyping platforms that enable SNPs to be more readily used . GBS is https://excellenceinbreeding.org). In pearl millet, the de-novo genome sequencing, followed by reference-based sequencing of 925 accessions  and breeding lines (580 genotypes), respectively, were distributed uniformly across the pearl millet chromosomes (The polymorphism information content (PIC) is one of the important measures to calibrate the informativeness of the marker. The higher value indicated that a marker has more alleles and can discriminate most individuals in a population . Markersvice-versa due to the fact that they share some level of parentage with the respective neighbors. The cross-grouping of B and R lines was also found in previous studies in pearl millet , a modified form of Wright’s F(Fst). The genetic variation between individuals within a population and the population’s divergence from the Hardy-Weinberg proportions are measured by Fst . The AMOindica and japonica groups  is a squared value of the correlation between pairs of markers across the genome. The details of the LD pattern are used in mapping genes associated with complex quantitative traits. In association studies, it has frequently been discovered that markers directly related to the mutation exhibit less LD than those more distantly related. The test of LD is crucial because it will help to quickly and efficiently choose the SNP markers that can be used for trait mapping and selection studies.The potential response to both natural and artificial selection is constrained by the non-random association of alleles at two or more loci, which also offers information about past events. LD reflects the history of natural selection, gene conversion, mutation, and other forces influencing gene frequency and evolution. LD provides insight into previous evolutionary events and explains the co-evolution of linked sets of genes. LD-based on Pearson correlations (2r <0.90) SNP pairs, 63% were derived from PMiGAP, and 36% SNPs from breeding lines since the PMiGAP represented the accessions with greater genetic diversity over the cultivated breeding lines and was clearly captured by the newly developed 4K SNP panel. The knowledge of LD regions from the B and R lines of the pearl millet genomes characterized by the newly developed mid-density set provides the opportunity to exploit them in the genetic characterization of diverse germplasm, trait mapping, and genomic selection experiments.In our result, uniformly distributed markers showed the regions of high and low LD on various chromosomes of pearl millet Figure 6We have successfully identified and validated a mid-density marker set for routine genotyping of pearl millet lines and its usefulness for various genomic studies. By mining 925 pearl millet genomes comprising genetically diverse wild and breeding lines, a set of 4112 SNPs were identified. A panel of 373 B and R lines was genotyped by these SNPs using the Agri-Seq platform. The results showed that the newly developed SNPs were uniformly distributed across the genome and had significant PIC and gene diversity. The SNP panel was used to group the genotypes through diversity, PCA, and structure models. All three statistics showed consistent results where they separated the accessions into two major groups, B and R lines. The LD analysis showed the regions of high and low LD. The AMOVA revealed a significant distinction between the B- and R-line groups and the extent of genetic divergence within and across and R lines groups. This research demonstrated that pearl millet has a high degree of genetic diversity and variable levels of LD across the genome, which are highly beneficial in developing heterotic groups for hybrid breeding. The experiment revealed that our mid-density 4K SNP panel genotyped by AgriSeq technology had a high level of information, making them suitable for several uses, including trait mapping, marker-assisted backcrossing, and genomic selection for pearl millet improvement.The original contributions presented in the study are included in the article/NT conceptualized the experiment. JS, SS, AR, AC, JI performed the data analysis. SKG, RM, MG, SR contributed and maintained the genotypes. KG, SH, AV performed the genotyping experiments. All authors contributed to the final manuscript. All authors read and approved the manuscript. All authors contributed to the article and approved the submitted version.


Triplophysa)—the largest component of the QTP ichthyofauna—we characterized population differentiation patterns and adaptive mechanisms to salinity change in two sympatric and phylogenetically closely related Tibetan loaches, T. stewarti and T. stenura, by integrating population genomic, transcriptomic, and electron probe microanalysis approaches. Based on millions of genome-wide SNPs, the two Tibetan loach species show contrasting population differentiation patterns, with highly geographically structured and clear genetic differentiation among T. stewarti populations, whereas there is no such observation in T. stenura, which is also supported by otolith microchemistry mapping. While limited genetic signals of parallel adaption to salinity changes between the two species are found from either genetic or gene expression variation perspective, a catalog of genes involved in ion transport, energy metabolism, structural reorganization, immune response, detoxification, and signal transduction is identified to be related to adaptation to salinity change in Triplophysa loaches. Together, our findings broaden our understanding of the population characteristics and adaptive mechanisms in sympatric Tibetan loach species and would contribute to biodiversity conservation and management of aquatic organisms on the QTP.Genetic differentiation in aquatic organisms is usually shaped by drainage connectivity. Sympatric aquatic species are thus expected to show similar population differentiation patterns and similar genetic responses to their habitats. Water bodies on the Qinghai–Tibet Plateau (QTP) have recently experienced dramatic physicochemical changes, threatening the biodiversity of aquatic organisms on the “roof of the world.” To uncover ecological genetics in Tibetan loaches ( Freshwater organisms are vulnerable to drastic climate changes because they have limited abilities to disperse; and availability of water–their habitat–are highly climate-dependent . The Qin the QTP  in the c the QTP . Water s the QTP . However the QTP . It sugg the QTP . The ong in 2017 . AquaticTriplophysa loaches (Cypriniformes: Nemacheilidae), Schizothoracinae carps (Cypriniformes), and Sisoridae catfishes (Siluriformes) are the only native ichthyofauna on the QTP, with Triplophysa loaches being the largest component. Triplophysa loaches are mainly distributed on the QTP and its adjacent areas (Triplophysa originated ∼23.5 million years ago (Mya) (https://www.gbif.org/). Triplophysa loaches could be found in freshwater   and rapiandensis  and 3.74 dalaica . Earliertaxonomy , systemataxonomy , and biotaxonomy . Next-getaxonomy , cave , it is possible to identify genetic variants underlying environmental adaptation . HoweverTriplophysa loaches, T. stewarti and T. stenura, and are particularly interested in whether the two Triplophysa species show similar population differentiation patterns and common adaptive mechanisms to salinity changes. Therefore, we incorporated population genomic, transcriptomic, and electron probe microanalysis (EPMA) approaches. First, we characterized spatial-temporal habitat changes of the two Triplophysa species from sympatric populations using the otolith EPMA technique. Then, we generated single nucleotide polymorphism datasets including 70 individuals of seven populations from five localities of the two Triplophysa species to explore their population genetic features by utilizing whole-genome re-sequencing data and identified loci that bear similar signals of population genetic differentiation with a newly developed unsupervised LD graph learning method. Finally, we carried out RNA-seq in sympatric populations to investigate the transcriptomic responses to salinity changes in the two Triplophysa species.In this study, we aim to investigate the population genetics and adaptive mechanisms to salinity changes in two Triplophysa species, T. stewarti and T. stenura  and calcium carbonate (CaCO3) were used as standards. For the line transect analyses, the accelerating voltage, beam current, and counting time were 15 kV, 2 × 10−8 A, and 15 s, respectively. The electron beam was 2 μm in diameter, spaced at 4 μm intervals. For the mapping analyses, the accelerating voltage, beam current, and counting time were 15 kV, 5 × 10−7 A, and 30 ms, with the electron beam of 2 μm in diameter. The pixel size for the mapping analyses was 3 × 3 μm.The asteriscus otoliths were extracted from 21 individuals, including 18 individuals used in RNA-seq . The expDNA and RNA extraction, library construction, and sequencing were done by Novogene Technology Co., Ltd. Briefly, DNA was extracted from the fin clips and then sheared into short fragments by ultrasonication. Short DNA fragments were end-repaired, phosphorylated, A-tailed, and ligated to index adapters. After purification and PCR amplification, the constructed libraries were sequenced on the Illumina HiSeq platform with a 150 bp paired-end strategy. In total, 408.84 G of raw data of 70 individuals from the seven populations were produced. Low-quality reads were removed according to the following criteria: 1) paired reads were removed when containing adapters; 2) paired reads were removed if the N content of either single-end read exceeds 10% of the total length; and 3) paired reads were removed if the number of low-quality bases (Q ≤ 5) of either single-end read exceeds 50% of the total length. Read quality control was double-checked using FastQC v0.11.9. Finally, 408.01 G of clean genomic data were obtained .Total RNA was first extracted, and magnetic beads with oligo (dT) were used for enriching mRNA. Then, the mRNA was fragmented into short fragments. cDNA was synthesized using these mRNA fragments as templates and random hexamers as primers. The short cDNA fragments were purified, end-repaired, A-tailed, and then ligated to index adaptors. Fragments of 250–300 bp were selected for PCR amplification, purification, and cDNA library construction. These libraries were sequenced on Illumina NovaSeq 6000 platform with a 150 bp paired-end strategy. In total, 299.6 million raw reads of 18 transcriptomes from four populations were obtained . The numT. tibetana genome was retrieved from GenBank  Version 0.6.5-r67 . Consensbleekeri  and visubleekeri .FST in non-overlapping 100 kb sliding windows between populations was calculated by VCFtools using “individual” SNP datasets. Windows with 15–205 SNPs, comprising ∼90% of genome-wide windows, were included in genetic diversity and FST calculations. Principal component analysis (PCA) was performed using PLINK Version 1.90b6.18 (n + 1 (n is the number of populations in the dataset) were applied. The optimal K was determined through a cross-validation procedure  was developed to identify loci showing similar genetic differentiation signals. The object of LD graph learning is first to build an undirected correlation network among SNPs on the basis of their pairwise LDs and then use a graph-based clustering approach to classify SNPs into LD blocks. The proposed LD graph learning comprises two steps: chromosome-wise LD network construction and genome-wide LD network construction.j  is considered a response variable, and all the SNPs other than SNP j are considered explanatory variables in a multinomial LASSO regression model , yik = 1; otherwise, yik = 0. xil, j, and λ is a shrinkage factor that decides the number of SNPs to have zero regression coefficients. In practice, λ was determined by maximizing the Akaike information criterion (AIC). From j and l are in LD with each other. If SNP j and l are presented as two nodes in an undirected graph, there is an edge between j and l. Consequently, on the basis of the output of pc × pc adjacency matrix A, with element Alj = 1 indicating SNP l and j are in LD and Alj = 0 indicating l and j are not in LD. The graph is expected to be sparse, meaning that most of the elements of A equal zero. We then applied a random-walk-based clustering approach  as the basic unit (like “SNP”), a group LASSO regression  represents the first PC within the cluster j, zilk  are the PCs defined in clusters other than j, αlk are the regression coefficients that define the level of correlation between the first PC in cluster j and all the PCs in cluster l, and j. The optimal shrinkage was again decided by AIC. Here, algorithm (2) generates a sparse undirected and unweighted graph of M nodes representing M chromosome-wise clusters, and the edge between a pair of clusters tells whether the two are correlated with each other. The genome-wide SNP clusters were then obtained using the same random-walk-based clustering approach as in chromosome-wise LD network construction.Next was to construct the genome-wide network based on the chromosome-wise clusters. To reduce the dimension of the data, in each chromosome-wise cluster, we calculated the principal components of the SNPs within that cluster and then took the first PCs that accumulatively explained 80% of the genetic variance to be used as covariates in the genome-wide LD network analysis. Considering each chromosome-wise cluster gression  was thenr2 statistic) between SNPs was first calculated using ngsLD v1.1.1  that exhibited the highest median LD with all other SNPs in the same window were chosen. Next, MCL were used to perform LDna within each chromosome. The parameters were set to λlim = 8 and |E|min = 50 to extract single outlier clusters (SOCs). Finally, only MCL in each SOC was selected to perform genome-wide LDna. SOCs were grouped into genome-wide LD clusters, and original SNPs were extracted. In T. stenura, only 4.513 windows were obtained in LDnClusteringEL, so genome-wide LDna was directly performed using the MCL of these 4,513 windows. Pairwise FST per site of these LD clusters was calculated using VCFtools. Absolute differentiation Dxy was calculated using the script popgenWindows.py .To further validate the robustness of our LD graph learning approach in detecting clusters of highly differentiated SNPs, an earlier developed LD-based network analysis, three-step LDna , was useD v1.1.1  within tD v1.1.1 . The desTriplophysa species, both the LD graph learning and three-step LDna were performed with “individual” SNP datasets by considering different scenarios of parallel adaptation according to salinity in population locations . SNPs showing the maximum/minimum 5% of average FST, which might indicate a directional or balancing selection, were also removed. The remaining neutral SNPs  were used to estimate covariance matrices from 100,000 iterations. Covariance matrix estimation was independently run three times. By using the mean matrix as the null model and original “individual” SNP datasets, correlations between genetic variation and salinity variation were detected. BAYENV2 was independently run three times using different random seeds for 50,000 iterations. Bayes factors were averaged across the three independent runs. SNPs associated with salinity adaptation using the above unsupervised and supervised methods were annotated using ANNOVAR , whereas the Sr:Ca ratio is not significantly different between populations of T. stenura . The Sr:Ca ratio in otolith is significantly different between T. stewarti and T. stenura populations either from different lakes or from the same lakes   in one cluster. The three populations of T. stenura were mixed together  . PSMC she period . Populattogether . Bayesiaeparated .T. stewarti populations, of which the genome-wide SNP cluster 2 containing 120,484 SNPs showed clear genetic differentiation between high- and mid-salinity groups  were in 45 highly differentiated chromosome-wide clusters of genome-wide cluster 2. The unsupervised three-step LDna with the “individual” SNP dataset of the 350,930 SNPs in the four T. stewarti populations identified 11 LD clusters, and LD cluster 2, containing 20,311 SNPs, showed similar genetic signatures between high- and mid-salinity populations as the genome-wide SNP cluster 2 identified by LD graph learning (HNF4A), facilitated glucose transporter member 1 (GLUT1), gamma-glutamyl hydrolase (GGH), and dipeptidyl aminopeptidase-like protein 6 (DPP6)  .T. stenura. Because PCA of genome-wide SNP clusters did not separate the low-salinity group (SNR-LHA) from the mid-salinity group (SNR-YAM and SNR-DOQ), FST instead of PCA was used as a benchmark in LD graph learning. 142 out of 368 genome-wide SNP clusters containing 77,730 SNPs showed higher weighted FST than those of all SNPs. Totally, 61,858 SNPs were commonly found to be in highly differentiated genome-wide clusters in LD graph learning in the two pairwise T. stenura populations, SNR-YAM and SNR-LHA, as well as SNR-DOQ and SNR-LHA. Moreover, 22,213 SNPs were commonly found to be in highly differentiated genome-wide SNP clusters when LD graph learning was performed with all populations and between population pairs in T. stenura. Using three-step LDna, 16 SOCs containing 2,265 SNPs were identified  between population SWT-YAM and SWT-DOQ, with 264 downregulated and 378 upregulated in the Lake Yamzhog Yumco population , C member 5 multi-specific organic anion transporter (ABCC5), and adenomatous polyposis coli protein (APC).The two species of seq data . The expseq data ; that ispulation . In T. spulation . Twenty pulation . Of thesTriplophysa loaches, whereas the two phylogenetically closely related Tibetan loach species show limited genetic signals of parallel adaption to salinity changes from either genetic or gene expression variation perspective. We will discuss these findings in detail below. Finally, we highlight the utilization of our newly developed unsupervised LD graph learning approach with large SNP datasets in the molecular ecological study of non-model organisms.The most salient finding in this study is that the two sympatric and phylogenetically closely related Tibetan loach species show contrasting population differentiation patterns, which broaden our understanding of the Tibetan loach diversification on the QTP. In addition, a catalog of genes involved in ion transport, energy metabolism, structural reorganization, immune response, detoxification, and signal transduction is found to be related to adaptation to salinity change in Triplophysa loaches on the QTP is thought to result from the occurrence of geological events associated with the QTP uplift , showing sympatric distributions with our sampling sites, are morphologically distinct to T. stewarti, with caudal peduncle laterally compressed and/or swim bladder reduced, whereas T. stewarti showed more slender caudal peduncle with roughly round-shaped cross-section and developed posterior chamber of the swim bladder. Moreover, the genetic distance among T. stewarti populations measured by Kimura 2-parameter (K2P) distance of mitochondrial COI gene sequences ranged from 0.0013 to 0.0170, within the range of intraspecific genetic distance in Triplophysa  are involved in structural reorganization during cell volume change under salinity stress , we modeled the correlation between one SNP and all the other SNPs simultaneously and used these correlation coefficient values to represent the correlation between SNPs. In addition, our LD graph learning method incorporates only two steps and uses fewer parameters . According to the LDna method (λlim and |E|min will affect the clustering of SNPs, though LD clusters are robust to these parameter settings. Compared with early developed LDna (Graph learning has rarely been used in population genomics data analysis but see . Howevera method , the valped LDna  and netwped LDna , our LD Triplophysa species: T. stewarti and T. stenura. Our results showed contrasting population differentiation patterns between the two species, which may be attributed to their different ecological characteristics and population histories. Using both unsupervised and supervised genetic differentiation methods, including our brand-new LD graph learning method, we found that the two Triplophysa species showed limited genetic signals of parallel adaption to salinity changes from either genetic or gene expression variation perspective. However, a catalog of genes involved in iron transport, energy metabolism, structural reorganization, immune response, and signal transduction may function in adaptation to salinity change in Triplophysa, of which several genes are also identified in other fishes. These findings help predict how aquatic organisms will respond to similar selective pressures on the QTP when facing challenges from climate changes.In conclusion, by integrating population genomic, transcriptomic, and electron probe microanalysis approaches, we unraveled the population differentiation patterns and possible adaptive mechanisms to salinity change in two


The SNP genotyping PCR revealed that the median values of the PVL were 1278 copies/50 ng of genomic DNA for the major homozygous, 843 for the heterozygous, and 621 for the minor homozygous genotypes in the Japanese Black cattle (p < 0.0001). A similar tendency was also observed in the Holstein cattle. We found that cattle with the minor allele for this SNP showed 20–25% lower PVLs. Although the mechanisms through which this SNP impacts the PVL remain unknown, we found a novel SNP related to BLV resistance located on the SPATA16 gene-coding region on bovine chromosome 1.Bovine leukemia virus (BLV) is an etiological agent of malignant lymphoma in cattle and is endemic in many cattle-breeding countries. Thus, the development of cattle genetically resistant to BLV is desirable. The purpose of this study was to identify novel single-nucleotide polymorphisms (SNPs) related to resistance to BLV. A total of 146 DNA samples from cattle with high BLV proviral loads (PVLs) and 142 samples from cattle with low PVLs were used for a genome-wide association study (GWAS). For the verification of the GWAS results, an additional 1342 and 456 DNA samples from BLV-infected Japanese Black and Holstein cattle, respectively, were used for an SNP genotyping PCR to compare the genotypes for the identified SNPs and PVLs. An SNP located on the spermatogenesis associated 16 (SPATA16)-coding region on bovine chromosome 1 was found to exceed the moderate threshold ( Retroviridae family and the Deltaretrovirus genus, and the disease is classed as an enzootic bovine leukosis (EBL). Although most BLV-infected cattle remain EBL-free for life, 1–5% develop EBL a few years after infection [Bovine leukemia virus (BLV) is an etiological agent of malignant lymphoma in cattle and water buffaloes. This virus is classified into the nfection . Australnfection ,4,5. AltAn animal’s BLV proviral load (PVL) fluctuates months after infection and becomes stable over time . Stable-An animal’s PVL is thought to be under the control of multiple host genes. The bovine leukocyte antigen (BoLA) class II gene, which is the major histocompatibility complex class II system in cattle, is an especially well-known factor strongly associated with PVL ,16,17,18All the blood samples used in this study were obtained from Japanese Black and Holstein cattle  in the Mie, Oita, Kumamoto, Miyazaki, and Kagoshima prefectures in Japan from January 2017 to March 2022 by local clinical veterinarians for BLV infection diagnosis. All the blood samples were confirmed positive for anti-BLV antibodies and BLV proviral DNA with an enzyme-linked immunosorbent assay (ELISA) and real-time PCR, respectively. The ELISA, extraction of genomic DNA, and real-time PCR are described in detail in our previous report .2 > 0.9). The Additive, Dominant, and Recessive models based on associations of the SNPs with case (low PVL) and control (high PVL) were analyzed using Fisher’s exact test. The genomic inflation factor (λ) was calculated in each model, and the genomic control method was used to control sample stratification [p = 1.449 × 10−6  because 34,491 SNPs passed the quality control filters and LD pruning. Additionally, a moderate threshold (p = 1.0 × 10−5) was set because the p-value controlled by the genomic control method is thought to be conservative [We previously reported that cattle with fewer than 100 BLV proviral copies/50 ng of genomic DNA were hardly able to transmit the virus to BLV-free cattle ,26. On tfication . The genervative .rhAmp SNP genotyping was used for a real-time PCR-based SNP genotyping assay . A totalp < 0.05 was considered statistically significant.The chi-square test was used for comparisons between the coloration of PVL groups and each genotype. The D’Agostino–Pearson normality test was used to verify the normal and lognormal PVL distribution. Because the PVL was not distributed normally or lognormally, Kruskal–Wallis and Dunn’s multiple-comparison tests were used for comparisons among the PVLs and each genotype. These analyses were performed using the GraphPad Prism 6 software . p-values are represented in Manhattan plots  and the quantile–quantile (QQ) plots indicated the presence of systematic bias a. Thus, an plots . No SNPsan plots . AX-1682an plots b. On thean plots c. Previop < 0.0001) and between the major and minor homozygous genotypes (p = 0.0003) were confirmed. On the other hand, in a total of 456 samples of BLV-infected Holstein cattle, the median (mean) values of the proviral loads were 2896 (3799) copies/50 ng for the major homozygous genotype, 2033 (3153) copies/50 ng for the heterozygous genotype, and 388 (2101) copies/50 ng for the minor homozygous genotype (p = 0.048). Of the Japanese Black and Holstein cattle, only 7.2% (97/1342) and 5.2% (24/456) had the minor homozygous genotype, respectively.To follow up on the result of the GWAS, we investigated the correlation between the PVL and the genotypes of the identified SNPs with an additional 1798 DNA samples using the rhAmp SNP genotyping system. This system discriminated 95 samples of the major (A_A) and minor (G_G) homozygous genotypes and the heterozygous (A_G) genotype in the identified SNP by one real-time PCR run . In the genotype b. A signp-values from the expected p-values in the QQ plots suggested sample stratifications  located on the bovine SPATA16-coding region between exons 4 and 5 was shown to possibly be associated with a low PVL. Then, we confirmed a correlation between the PVL and these genotypes via rhAmp SNP genotyping with an additional 1798 BLV-infected samples. Significant differences in the PVL between the Japanese Black cattle with the major homozygous genotype and the heterozygous genotype and between the Japanese Black cattle with the major and minor homozygous genotypes were confirmed. A similar tendency was also confirmed in the Holstein cattle. Thus, having the minor allele in this SNP would reduce the PVL via an unknown mechanism. Although further studies are needed, this SNP could be a novel biomarker correlated with resistance to BLV.


Epithelial-mesenchymal plasticity (EMP) involves bidirectional transitions between epithelial, mesenchymal and multiple intermediary hybrid epithelial/mesenchymal phenotypes. While the process of epithelial-mesenchymal transition (EMT) and its associated transcription factors are well-characterised, the transcription factors that promote mesenchymal-epithelial transition (MET) and stabilise hybrid E/M phenotypes are less well understood.Here, we analyse multiple publicly-available transcriptomic datasets at bulk and single-cell level and pinpoint ELF3 as a factor that is strongly associated with an epithelial phenotype and is inhibited during EMT. Using mechanism-based mathematical modelling, we also show that ELF3 inhibits the progression of EMT. This behaviour was also observed in the presence of an EMT inducing factor WT1. Our model predicts that the MET induction capacity of ELF3 is stronger than that of KLF4, but weaker than that of GRHL2. Finally, we show that ELF3 levels correlates with worse patient survival in a subset of solid tumour types.ELF3 is shown to be inhibited during EMT progression and is also found to inhibit the progression of complete EMT suggesting that ELF3 may be able to counteract EMT induction, including in the presence of EMT-inducing factors, such as WT1. The analysis of patient survival data indicates that the prognostic capacity of ELF3 is specific to cell-of-origin or lineage.The online version contains supplementary material available at 10.1186/s13036-023-00333-z. Phenotypic plasticity – the ability of cancer cells to reversibly change their phenotypes to adapt to changing environments – is crucial for cancer cell survival. It is a hallmark of metastasizing cancer cells that enables them to alter their cell–cell adhesion and migration traits, evade the immune system, and resist targeted therapies , 2. GiveEpithelial-mesenchymal transition (EMT) and its reverse—mesenchymal-epithelial transition (MET) – constitute a key axis of phenotypic plasticity, through bidirectional transitions between epithelial, mesenchymal, and one or more hybrid epithelial/mesenchymal (E/M) phenotype(s) , 4. OnceCdh1) expression and led to activation of Grhl3 [Among the potential candidate transcription factors that may promote MET, the transcription factor E74-like factor 3 (ELF3) belongs to the E26 transformation-specific (ETS) family of transcription factors. It is strongly expressed in epithelial tissues, such as the digestive tract, bladder, and lungs, where it plays key roles in differentiation and homeostasis . It has of Grhl3 , therebyAt a molecular level, ELF3 is inhibited by both the SNAI family members SNAI1 (SNAIL) and SNAI2 (SLUG) , 31, botHere, we utilize the experimental observations discussed above, along with multiple transcriptomic data sets to develop a mechanism-based mathematical model to delineate the impact of ELF3 on epithelial-mesenchymal plasticity. Our model predicts that ELF3 can delay or prevent the onset of EMT; consequently, its overexpression can induce a partial or complete MET. Analysis of publicly- available in vitro transcriptomics data, including that from the Cancer Cell Line Encyclopedia (CCLE), and The Cancer Genome Atlas (TCGA) revealed that ELF3 is negatively correlated with mesenchymal factors and positively correlated with epithelial factors. Further, analysis of time-course transcriptomic data shows that ELF3 levels decrease upon EMT induction, which further supports the hypothesis that ELF3 acts as a putative MET-TF. Finally, ELF3 levels are associated with cancer patient survival in a lineage- and cancer-specific manner, highlighting the clinical relevance of ELF3 in specific cancer types.Cdh1 and negative correlation with Vim [We first investigated the association between ELF3 expression levels and both epithelial and mesenchymal programs across cancer cell lines. In the CCLE cohort, we quantified the correlation coefficient for each individual gene with epithelial and mesenchymal scores using single-sample gene expression enrichment (ssGSEA)  Fig. 1AA1A. As ewith Vim , 29. NexWe next tabulated ELF3 expression levels with respect to the median epithelial ssGSEA scores in a given cancer type. We observed that an increase in ELF3 expression levels was concordant with that in the corresponding median epithelial scores Fig. D. ConverNext, we asked whether ELF3 levels are downregulated during EMT, using publicly- available transcriptomics datasets. We first examined changes in ELF3 expression levels in response to silencing of GRHL2 in OVCA4209 cells GSE118407) which led to induction of EMT  and redu18407 whir = -0.91, p < 0.001). Next, we interrogated ELF3 levels in LNCaP prostate cancer cells along the EMT trajectory upon SNAIL induction and a subsequent MET over 20 days after withdrawal of SNAIL induction [We next investigated temporal changes in ELF3 expression levels in time-course transcriptomic datasets. A549 lung adenocarcinoma cells treated with TGFβ to undergo EMT  accompanied by a shrinking of the {E}  and {H} (only hybrid E/M) regions  and Tenascin C (Tnc) [Given the proposed role of ELF3 in safeguarding an epithelial phenotype, we analysed whether ELF3 can prevent EMT induction when an additional factor is added to the abovementioned regulatory network. As an example of an additional EMT inducing factor, we focused on Wilms Tumour (WT1). WT1 can transcriptionally repress ivatives . Similarinvasion , 50. WT1invasion , 51. Sim C (Tnc) . Togethe C (Tnc) , while p C (Tnc)  and inhi C (Tnc) .Based on these experimental data, we expanded our network model to incorporate these interactions Fig. D. Next, We next mapped the temporal responses of these four circuits for a fixed value of I_ext signal. Among these four circuits, we found steady-state values of ZEB1/2 mRNA levels to be at a minimum in the presence of ELF3 and absence of WT1 (WT1 + /ELF3-), and to be at a maximum in the presence of WT1 and absence of ELF3 (ELF3 + /WT1-)  and with 76-gene signature (76GS)-based scoring of EMT in which higher values indicate an epithelial behavior [r = 0.92 for ELF3 vs. r = 0.45 for KLF4; DU145: r = 0.48 for ELF3 vs. r = 0.21 for KLF4)  with the epithelial signature, the correlation was positive in 25 datasets. Conversely, among 25 breast cancer datasets where ELF3 correlated significantly with the mesenchymal signature, the correlation was negative in 20 datasets  and mesenchymal (= ZEB + SNAIL + SLUG) factors individually, based on our simulation data. Again, ELF3 showed stronger correlations as compared to KLF4  breast cancer. Earlier work, including ours, has shown that in ER + breast cancer, EMT and tamoxifen resistance can promote each other –70. WithSimulation of this gene regulatory network Fig. C, ii usiWe propose ELF3 as a putative MET-TF, based on transcriptomic data analysis showcasing a strong association of ELF3 with an epithelial phenotype as seen in TCGA samples and CCLE cohort. Moreover, in multiple transcriptomic datasets analyzed, ELF3 expression was reduced with induction of EMT and rescued upon MET Figs. , 2. ThesBesides being a potential epithelial gatekeeper, ELF3 is also involved in maintaining cancer cell stemness. In high-grade serous ovarian cancer (HGSOC), ELF3 forms a positive feedback loop with LGR4, which is involved in stem-cell renewal. Knockdown of ELF3 reduced tumorsphere formation . HoweverIn addition to stemness, the role of ELF3 in conferring therapy resistance has been investigated. ELF3 has been reported to be upregulated in NSCLC cells resistant to the PARP inhibitor, olaparib . FurtherOur analysis also revealed that while ELF3 may show stronger association with an epithelial state when compared with KLF4, but its effects in inducing MET were found to be weaker than GRHL2. GRHL2 is a pioneering transcription factor that can bind to closed chromatin and initiate its opening , 96. AltX represents the basal rate of production, transcriptional/translational/post-translational regulations is represented by the terms multiplied by gX. – one or more shifted Hills function (XX. The complete set of equations and parameters are presented in Supplementary Information (SI) section ‘Mathematical model formulation’. Bifurcation diagrams were drawn in MATLAB (Math-Works Inc.) using the continuation software package MATCONT [A system of coupled ordinary differential equations were employed to understand the dynamics of the ELF3 coupled EMT circuit comprising of miR-200, SNAIL, ZEB, SLUG and KLF4  for each node in the network. The parameterised set of ODEs are then solved using the Eulers method to obtain one or many steady states that represent the attractors that are enabled by each parameter set. The steady state expression values are then z-normalised for principal component analysis (PCA) and hierarchical clustering analysis. The perturbation analysis was done by performing RACIPE analysis on a gene regulatory network by either over expressing (OE) or down expressing (DE) a specified node by x-fold . The Z-score normalisation of these perturbation data was done with respect to the control case where none of the production rates were altered. The proportion of phenotypes in each case were then computed over three replicates of in-silico perturbations to assess for statistical significance.Random Circuit Perturbation (RACIPE) is a simulation framework that extensively explore the possible multistable properties of a given gene regulatory network . Based oGene expression datasets were downloaded using the GEOquery R Bioconductor package . The datTo calculate Epithelial and/or mesenchymal scores for bulk RNA seq data, we used the ssGSEA functionality to estimate the activity of either the epithelial and/or the mesenchymal set of genes for each sample in the corresponding datasets. The ssGSEA scores are computed to estimate the collective activity of a gene set based on their expression values. The epithelial and mesenchymal gene lists were obtained from . The HalFor the single cell RNA seq dataset, GSE147405 , imputatThe methylation data for TCGA samples Fig S were dowThe KS and 76GS scores were calculated as previously described . The KS 76GS scores are calculated based on weighted gene expression values of a list of 76 genes known to be associated with an epithelial state. The higher the 76GS score, the more epithelial a sample is. The 76GS scores have no pre-defined range, unlike KS score, and can take both positive and negative values.Kaplan–Meier analysis for respective datasets was performed using ProgGene . The samAdditional file 1: Mathematical model formulation. Fig S1. ELF3 levels in TCGA. A) ELF3 expression levels in TCGA cancer samples ordered by low KS score to high KS score. B) Scatter plot for ELF3 expression and its methylation status in TCGA cancer types. Each dot (cancer type) colored by KS score. The higher the KS score, the more mesenchymal a sample is. Colorbar is given to the right; higher scores are denoted by red.Fig S2. ELF3 delays onset of EMT.(A) Phase diagrams for the ELF3 network driven by an external signal (l_ext) for varying strength of interactions along the ELF3-ZEB axis (B) Phase diagrams for the ELF3 network driven by an external signal (l_ext) for varying strength of ELF3 self-activation. (C) Phase diagrams for the ELF3 network driven by an external signal (l_ext) for varying strength of interactions along the Slug-ELF3 axis (D) Sensitivity analysis of parameters for the ELF3 coupled EMT circuit  indicating percent change in the I_ext interval for which the hybrid E/M state exists. The red dotted line indicates the percent change in the stable hybrid region in the absence of ELF3 (core network) when compared to the coupled network. FigureS3. ELF3 is an inducer of MET. (A) Gene regulatory network showing the regulation between epithelial and mesenchymal genes. Green arrows indicate activatory links and Red hammers indicate inhibitory links. (B) Kernel density estimate plots of EMT score (ZEB – miR200) (top panel) and ELF3 z-normalized expression (bottom panel). The red vertical line shows the approximate position of the minima of the largely bimodal distributions. PCA scatter plot of all steady states of RACIPE colored by (i) the EMT score defined as ZEB – miR200, (ii) SLUG expression (iii) ELF3 expression and (iv) KLF4 expression. (C) PCA scatter plot of all steady states of RACIPE colored by GRHL2 Expression (leftmost panel), miR200 expression (center panel) and ZEB expression (rightmost panel). (D) Scatterplot of EMT scores and ELF3 levels across steady state solutions obtained from RACIPE. Red lines indicate the position of minima in the bimodal distributions of EMT scores and ELF3 levels. Spearman correlation. coefficient and p-value are mentioned. (E) Scatterplot of Epithelial scores (GRHL2 + miR200) and Mesenchymal scores (ZEB + SNAIL +SLUG) with ELF3 levels of steady state solutions from RACIPE. The spearman correlation coefficient and the corresponding p-values have been mentioned. (F) Scatterplot of Epithelial scores (GRHL2 + miR200) and Mesenchymal scores (ZEB + SNAIL +SLUG) with KLF4 levels of steady state solutions from RACIPE. The spearman correlation coefficient and the corresponding p-values have been mentioned. (G) Fraction of steady state solutions resulting in Epithelial phenotype in control, 20-fold and 100-fold over expression of ELF3. * represents a statistically significant difference in the fraction of cases in the epithelial phenotype . (H) Fraction of steady state solutions resulting in the Epithelial (left panel) and Mesenchymal (right panel) phenotypes in control, 20-fold over expression of ELF3, GRHL2 and KLF4. *represents a statistically significant difference . FigS4. ELF3 shows stronger trends as compared to KLF4 with an epithelial behavior. Scatter plots showing correlations of imputed ELF3 and KLF4 expression with 76GS EMT score  in two cancer cell lines A) A549 (lung cancer) and B) DU145 (prostate cancer) when treated with TGFβ (GSE147405). Spearman correlation coefficient and the corresponding p-values have been mentioned. Imputed gene expression values were calculated using the MAGIC algorithm for different cell lines separately. C) Volcano plots showing correlation of GRHL2 expression levels with ssGSEA epithelial and mesenchymal scores in a meta-analysis of breast cancer datasets. Each dot represents a dataset. R < - 0.3, p< 0.05 or R > 0.3, p < 0.05 are counted as statistically significant cases. Nneg denotes number of datasets for which a negative correlation (blue dots) is observed, Npos denotes number of datasets for which a positive correlation (red dots) is observed between the two corresponding expression levels or ssGSEA scores. D) Same as C) but for bladder cancer. E) Same as C) but for prostate cancer. Panels D and E show results for KLF4, ELF3 and GRHL2. FigS5. ELF3 correlates with patient survival in a cancer-specific manner. Trends in breast cancer samples. (A) overall survival (GSE9893) (B) relapse-free survival (GSE4922) (C) metastasis-free survival (GSE6532). Trends in colorectal cancer samples. (D) overall survival (GSE39582) (E) relapse-free survival (GSE395824) (F) metastasis-free survival (GSE28722). FigS6. WT1 correlates with patient survival in a cancer-specific manner. (A) Overall survival in Breast cancer sample (GSE9893). (B) Relapse free survival in breast cancer sample (GSE9893). (C) (D) Relapse free survival in colorectal cancer samples  (E) (F) Overall survival in lung cancer samples  (G) Relapse free survival in lung cancer sample (GSE31210) (H) Overall survival in ovarian cancer sample (GSE73614) (I) Overall survival in pancreatic cancer sample (TCGA-PAAD)Additional file 2: Table S1.