Oïdium lini, is progressing in France. Genetic resistance of varieties is critical for the control of this disease, but very few resistance genes have been identified so far. It is therefore necessary to identify new resistance genes to powdery mildew suitable to the local context of pathogenicity. For this purpose, we studied a worldwide diversity panel composed of 311 flax genotypes both phenotyped for resistance to powdery mildew resistance over 2 years of field trials in France and resequenced. Sequence reads were mapped on the CDC Bethune reference genome revealing 1,693,910 high-quality SNPs, further used for both population structure analysis and genome-wide association studies (GWASs). A number of four major genetic groups were identified, separating oil flax accessions from America or Europe and those from Asia or Middle-East and fiber flax accessions originating from Eastern Europe and those from Western Europe. A number of eight QTLs were detected at the false discovery rate threshold of 5%, located on chromosomes 1, 2, 4, 13, and 14. Taking advantage of the moderate linkage disequilibrium present in the flax panel, and using the available genome annotation, we identified potential candidate genes. Our study shows the existence of new resistance alleles against powdery mildew in our diversity panel, of high interest for flax breeding program.Powdery mildew is one of the most important diseases of flax and is particularly prejudicial to its yield and oil or fiber quality. This disease, caused by the obligate biotrophic ascomycete  Linum usitatissimum L.) is an important industrial crop providing both stem fibers and linseeds that are used to produce textiles, edible oil, animal feed, and other industrial products such as sound and thermal insulation panels or composite materials  between fiber and oil flax groups was observed (with a low representativeness of fiber flax in the panel studied: 22%). Genomic analysis between fiber and oil flax identified candidate genes involved in cell-wall biogenesis or modification, auxin regulation, and fatty acid biosynthesis. Moreover, the level of differentiation between sub-groups ranged from 0.02  to 0.16 . A more recent Chinese study, based on resequencing of 200 cultivated flax accessions, proposed a similar classification into three major groups according to the flax type  and identified a closer genetic relationship between fiber (F) flax and admixture (OxF) flax groups , as well as between the oil sub-population originated from North America and the fiber group originated from Europe (FST = 0.54).A previous genetic study, based on 407 flax accessions genotyped by 259 SSRs, identified three major genetic groups: an oil (O) group subdivided into three sub-groups representing accessions from South Asia, Western Europe, and South America; a fiber (F) group distributed into two sub-groups, namely, North America and Eastern Europe; and an admixured (OxF) group including accessions from North America and Europe  or Erysiphe polygoni (perfect stage)  for resistance to powdery mildew. A number of one dominant gene (designated Ol1) was identified in Indian oil cultivars by studying the segregation of resistance in F2 populations , and 19 unknown type flax were used in this study. Among fiber flax accessions, 7 originated from the current TERRE DE LIN breeding program. Sampled over 38 countries, the different accessions represent all continents and diverse worldwide climatic regions. Seeds of the evaluated panel were provided by the Plant Gene Resources of Canada (PGRC), the N.I.Vavilov Institute of Plant Genetic Resources (VIR), and the personal collection from TERRE DE LIN (A panel of 311 worldwide diverse flax accessions consisting of 173 fiber flax (F), 110 oil flax (O), 7 mixed oil-fiber flax (OxF), 2 wild flax (including one E DE LIN . To prevGenomic DNA was extracted from 80 mg of fresh leaves using a modified DNeasy Plant Mini Kit (Qiagen), adapted to produce high-quality and quantity DNA for PCR-free sequencing. The main modified steps were the addition of sodium bisulfite and RNAse A in the lysis buffer and an additional ethanol wash step before the DNA elution step. Integrity of genomic DNA was determined by running 2 μl of each sample in 1% agarose gel, 1X TAE buffer (Thermo Fisher Scientific) for 30 min at 100 V. The DNA was stained with 1X ethidium bromide solution (Sigma-Aldrich). The molecular weight of the genomic DNA was estimated by comparison with a 1-kb DNA ladder (Invitrogen). After this step, genomic DNA was checked for quality on NanoDrop ND8000 (Thermo Fisher Scientific) and quantified on Qubit (Thermo Fisher Scientific) by Picogreen dosage. Minimum conditions required for the preparation of PCR-free libraries were as follows: a 260/230 nm ratio over 1.8, a 260/280 nm ratio over 1.8, and at least 3 μg of genomic DNA. Illumina TruSeq PCR-free libraries were carried out using the DNA Shearing M220 (Covaris) and were checked by the Fragment Analyzer (Agilent). Sequencing was performed as 150-bp paired-end reads on NovaSeq6000 (Illumina). Both library preparation and sequencing were done by GeT-PlaGe platform, Genotoul, France.www.bioinformatics.babraham.ac.uk/projects/fastqc/). Then, short reads were aligned with Stampy v1.0.20  were excluded from the analysis with VCFtools v0.1.17  on two accessions (332 SNPs on CC109 accession and 337 SNPs on CC123 accession).https://github.com/YinLiLin/CMplot). Private SNPs belonging strictly to fiber, oil, and wild groups were tracked using BCFtools .Visualization of SNP density plots was carried out using the CMplot v3.6.2 R-package  using BCFtools for subsequent analyses based on a missing rate (MR) <10% and on a minor allele frequency (MAF) ≥ 5%.2 > 0.4  a PCA using the “prcomp” R-function; (ii) a discriminant analysis of principal components (DAPC) using the R-package adegenet v2.1.3  according to the following equation:Linkage disequilibrium distribution between pairwise SNPs across the whole genome was evaluated from a random sub-sample of 20,000 SNPs of the imputed matrix without the wild accessions using the estimator of Rogers and Huff .The flax panel, sown in May 2018 and 2019, was phenotyped for key agronomic traits under natural OïThe experimental setup was sown in three replicates according to a randomized complete block design (RCBD). In 2018, each block consisted of 10 columns and 33 rows with one susceptible  and one tolerant genotype  per column as controls for disease spread in the field (sowing in 2019 was done in 5 columns and 65 rows). Trial plots were composed of a 2-m-long double-row with around 100 plants per row, with a row spacing of 20 cm.Disease severity (DS) was scored as the percentage of infected leaf area  following a scale from 1 to 9 . DS was recorded three times in 7-day intervals over the three replicates during the flowering period. DS was used to calculate the area under the disease progress curve (AUDPC) using the following formula proposed by Shaner and Finney :i = powdery mildew severity on the ith date, ti = the ith day and n = number of times on which DS was recorded.where DSThe powdery mildew phenotyping data collected in 2018 and 2019 were analyzed after first checking for residual normality and error variance homogeneity at each date used for the calculation of AUDPC. We explored spatial and scoring effects on AUDPC through a mixed-model including genotype, row, and column factors modeled as random effects and the order of plot phenotyping in the field (modeling the bias in decreasing attentiveness) as a fixed effect. To correct for spatial heterogeneities, each year was analyzed separately using the SpATS v1.0–11 R-package  were excluded from GWAS. Analyses were performed by applying a mixed linear association model (MLM) with a leave-one-chromosome-out (LOCO) approach as implemented in GCTA software v1.93  with α = 0.05.” We defined as QTL chromosome regions where significant association signals were detected.The quantile–quantile (QQ) and Manhattan plots were visualized using the “qqman” v0.1.4 R-package Turner, . We applp-values, local linkage disequilibrium, and gene annotation using the GFF3 file of the Linum usitatissimum genome. This information was summarized for each candidate loci using the LocusZooms R-package (https://github.com/Geeketics/LocusZooms) and the LDBlockShow tool (https://github.com/BGI-shenzhen/LDBlockShow). An approximate flanking region of 20 to 100 kb was explored for each significant SNP and adjusted according to the extent of local linkage disequilibrium with the candidate SNP (considering regions with r2 ≥ 0.2 with the significant SNP).Candidate loci were explored using a combination of GWAS Sequencing of the flax panel generated approximately 2.5 Tb of high-quality paired-end reads using the Illumina NovaSeq6000 sequencer, of which 95.9% of the reads aligned with the CDC Bethune reference genome, including 69% of properly mapped reads. No significant difference was observed between fiber type and oil type both on mapped reads and on properly mapped reads: 95.8% (F) / 96.2% (O) and 68.6% (F) / 69.6% (O), respectively. The average genomic depth coverage obtained in our flax panel is about 15X.Single-nucleotide polymorphism discovery and scoring, carried out on a panel of 311 genotypes, allowed us to obtain a genotyping matrix containing 5,464,275 SNPs. To evaluate the SNP detection pipeline, we compared a set of 337 KASP genotyping assays  used at TERRE DE LIN with those newly detected by the pipeline on one fiber flax and one oil flax accessions. We found 92% of the KASP SNP markers using the bioinformatic pipeline, and all SNPs showed the expected alternative allele .Average SNP density contained in our flax panel is about 17.28 SNPs/kb. However, this density varied depending on chromosomes and positions along chromosomes with a higher density observed on the chromosomal arms than in the central positions (centromeres). Chromosomes 8 and 13 presented the extreme SNP densities with 13.27 and 23.65 SNPs/kb, respectively .Private SNPs were identified based on the flax type. We counted 722,861 private SNPs in fiber flax , 1,058,862 in oil flax (19.38%), and 746,115 in wild flax (13.65%)  with a gThe occurrences of bi-allelic SNP transition were greater than those transversions by a ratio of 1.96 . Both A/After filtering the SNPs using the MAF and missing data criteria, the genotyping matrix contained 1,693,910 high-quality SNPs (wild accessions were excluded).After imputation of missing values, the high-quality SNP matrix containing 1,693,910 SNPs was pruned for both population structure and clustering analysis, resulting in a matrix containing 103,249 SNPs. Based on the optimal a-score plot , we retaFST) was observed between fiber groups (FST = 0.0347), whereas the highest FST was observed between the Asian/Middle-Eastern oil group and the two fiber groups (FST = 0.1598)  .2 threshold of 0.2, with a lower LD extent in the oil cluster than in the fiber cluster . On the same principle, the decline of LD can be measured on the subpopulations identified previously: The extent of LD is the lowest in oil AM-EU cluster (11.26 kb) and the highest in oil AS-ME cluster (threshold 0.2 not reached)  . Based o2) values for AUDPC were high and equal to 0.94 for both 2018 and 2019  with the most significant SNP . SNP density varies depending on chromosomes and their positions along the chromosome with a higher density observed on chromosomal arms than in central position. A similar SNP distribution along the 15 chromosomes was observed in other studies based on different flax diversity panels , the other is currently undetermined.The highest number of private SNPs identified in oil flax  suggests that this group could be the ancestor of cultivated flax, confirming previous analyses in flax  showed a new population structure for this flax type in comparison with previously published results. Indeed, analysis of the genetic structure of this panel reveals four distinct genetic clusters: two fiber clusters (with mostly flax originated from Eastern Europe and from Western Europe) and two oil clusters (with mostly flax from America-Europe and from Asia/Middle-East). At k = 4, the clustering highlights a geographical distribution linked to flax type. Note that for k = 6, the two additional groups highlight the recent breeding history  and a steeper decay in oil group than in fiber groups . In a recent Chinese study on a diversity panel, of similar composition , a 50% decrease of DL was reached at a physical distance equals to 6.4 kb for the oil group and 25.3 kb for the fiber group , thus confirming the robustness of this QTL and the reliability of the current GWAS analyses. In this study, QTL_13b and QTL_14b had robust effects over the 2 years, whereas the other 6 QTLs were detected only in one of the 2 years.A total of eight QTLs located on chromosomes 1, 2, 4, 13, and 14 were significantly associated with resistance to Oïdium lini are present in Canada and France. Consequently, we assume that the 3 QTLs reported in Asgarinia et al. . It has to be noted that QTL_2 was supported by only one SNP with a significant effect. The strong LD between this SNP and the SNPs supporting QTL_13a makes us believe that QTL_2 might not correspond to an actual QTL but either (i) to a wrong attribution of this SNP marker to chromosome 2 or (ii) to a long-range LD not sufficiently controlled in the GWAS model by the kinship and the population structure. Further investigations are therefore needed to verify this QTL.The analysis of DL between the 10 most significant SNPs revealed a strong correlation between QTL_2 and QTL_13a .linum RPW8 genes are the strong candidates for QTL_13b. We also identified other interesting candidate genes encoding LysM-RLK (QTL_13a), RLK (QTL_14a and QTL_14b), and PR (QTL_14b), known to be involved in the perception and defense against microorganisms  genes, most of them encoding nucleotide-binding and leucine-rich repeat proteins (NLR). NLR-mediated resistance is strong but is unfortunately often rapidly overcome by virulent strains of the pathogens. In that context, quantitative resistance is gaining increasing attention for breeding purpose. In this study, 8 QTLs were identified and none of the target region harbors NLR genes. Accordingly, resistance QTLs are known to encode a great diversity of function  distribution of the most significant SNPs varies according to the four genetic groups that we identified. If the alleles associated with resistance at snp1_4412249 (QTL_1), snp4_13878965 (QTL_4b), snp13_4776228 (QTL_13b), and snp13_4830850 (QTL_13b) are present in the four genetic groups, the other ones are absent either from the Western Europe fiber group or from the Asia/Middle East oil group, or both. The present work therefore provides the information needed to implement this strategy in breeding .The second strategy would be to pyramid in varieties powdery mildew resistance genes with the help of marker-assisted selection. In the case of an introgression of powdery mildew resistance genes from oil flax into fiber flax, back-crossing steps would be necessary to keep the favorable genetic background of the flax fiber. This strategy can be further extended by genomic selection, allowing to raise the background resistance of flax germplasm due to QTLs of minor effect (Poland and Rutkoski, The datasets presented in this article are not readily available due to commercial restrictions. Requests to access the datasets should be directed to the corresponding author.AS and J-PT performed the establishment of collections of flax germplasm and the phenotyping. AS analyzed data and wrote the manuscript with significant input from all authors. All authors read and approved the final manuscript.The authors declare that the study received funding from Terre De Lin Développement. The funder had the following involvement in the study: DNA extraction, libraries construction, sequencing, and field phenotyping.AS and J-PT were employed by Terre De Lin. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


These results may contribute to the understanding of ALS pathogenesis, its diagnosis, and therapeutic strategy improvement.Amyotrophic lateral sclerosis (ALS) is a multi-system neurodegenerative disease that affects both upper and lower motor neurons, resulting from a combination of genetic, environmental, and lifestyle factors. Usually, the association between single-nucleotide polymorphisms (SNPs) and this disease is tested individually, which leads to the testing of multiple hypotheses. In addition, this classical approach does not support the detection of interaction-dependent SNPs. We applied a two-step procedure to select SNPs and pairwise interactions associated with ALS. SNP data from 276 ALS patients and 268 controls were analyzed by a two-step group LASSO in 2000 iterations. In the first step, we fitted a group LASSO model to a bootstrap sample and a random subset of predictors ( Amyotrophic lateral sclerosis (ALS) is a multisystem neurodegenerative disease that affects both upper and lower motor neurons, causing progressive loss of muscle strength and paralysis ,2. Its pStudies dedicated to understanding the genetic characteristics of the disease can contribute not only to the comprehension of its etiology but also to the development of diagnostic tests and therapies. Genome-wide association studies (GWAS) are the most common approach to detecting relationships between genetic variants  and disease occurrence . Since SAnalyses focused on one genetic variant at a time lead to a great number of simultaneous hypothesis tests, requiring control for family-wise error rate or false-discovery rates, such as Bonferroni and Benjamin–Hochberg, respectively, although the former is too conservative and the latter, less stringent , both heWe aim to overcome these issues by iteratively applying a two-step group LASSO procedure to select SNPs and pairwise interactions in a case-control ALS study. This approach has additional benefits for high-dimensional data composed of multi-level factors and a binary response since group LASSO makes it possible to encode each level of a factor or a pairwise interaction using a dummy variable and generating coefficients with direct interpretation . By iterThe manuscript is organized as follows: first, we review the logistic regression models, LASSO and group LASSO methodologies, as well as pairwise interaction models with and without regularization. Then, we describe the implementation of our iterative approach, presenting the main results regarding variables and pairwise interaction selections, as well as their biological implications.We analyzed SNP data from ALS patients and controls from the National Institute of Neurological Disorders and Stroke Repository. The data set is available for download from the database of Genotypes and Phenotypes (dbGaP). Its first version includes genotype measurements of 555,352 SNPs from 276 individuals from the United States of America diagnosed with sporadic ALS  and 268 In our analysis, the response variable was represented by the presence or absence of ALS, i.e., A,Aa,aa} . We recoN observations, a response variable, Y, and a set of predictors, A logistic regression model can be used to develop a genetic model aiming to understand how a predictor, i.e, an SNP, is associated with a qualitative phenotype on a genome-wide scale. Given a collection of N observations, the log-likelihood function is given by:The unknown coefficients p, is much larger than the sample size, N, a relatively small number of predictors is expected to be associated with the response variable. Regularization methods can be used to modify the log-likelihood function in order to shrink to 0 those coefficients related to predictors that do not play an important role in the disease occurrence, resulting in a smaller subset of predictors [In a high-dimensional scenario, where the number of predictors, edictors .The least absolute shrinkage and selection operator (LASSO) is a popular technique for variable selection in high-dimensional scenarios . This apfunction ,minimizedictors .G groups of dummy variables, g. From this representation, a model for G group variables gth group. In such settings, for N observations, the maximum likelihood estimators of the coefficients In our application, we have a large number of multi-level factors as predictors, whose contribution to the linear model can be expressed through Aiming at selecting variables represented by groups of dummy variables, it is desirable to jointly select or omit all the coefficients within a group. Therefore, instead of using LASSO, which may select individual dummies rather than the entire factor , a groupenalized .The To evaluate not only main effects but also interactions between any two group variables, Variable selection using the group LASSO method described previously can be extended to incorporate pairwise interactions. Thus, the model is fitted by minimizing:In our application, we considered all pairwise interactions within the selected candidate set of SNPs through a logistic regression pairwise interaction model via hierarchical group LASSO regularization .Based on variable selection procedures described in the random LASSO , recursiAs the first step of each iteration, we used a bootstrap sample and a random subset of the predictors  and the correspondent criteria —definiteDescriptive results from the iterative process, such as the number of selected SNPs in each iteration and group LASSO estimated coefficients for the prioritized SNPs are presented. Analyses were performed using the R software, and the model fit was determined through group LASSO penalized learning using a unified blockwise-majorization-descent algorithm (gglasso) and learning interactions via hierarchical group LASSO regularization (glinternet) packages.We annotated the prioritized SNPs, both on the first and second steps, with UCSC  and Ensep-value < We performed quality control (QC) on genotype data to include SNPs with complete genotyping call rate, minor allele frequency The distribution of the number of selected variables in 2000 iterations is shown in interest .Variable selection reduces the computational burden for pairwise interaction searches. Bootstrap analysis, in turn, can be applied to assess the stability of selected variables, contributing to variable importance evaluation . We priors16984239, rs10459680, and rs1436918), having one copy for the minor allele resulted in a positive coefficient estimate (increased odds of ALS). On the other hand, for the SNPs rs1037666, rs4552942, rs10773543, and rs2241493 having one copy for the minor allele presented a negative coefficient. Additionally, for rs1037666 and rs1436918 SNPs, having two copies for the minor allele also resulted in a negative coefficient. It is important to note that these estimates are adjusted for other SNPs considered together in the model fit in the corresponding iteration.The distribution of the group LASSO estimated coefficients for the categories of these SNPs is presented in rs16984239 A   highlighrs16984239 and the rs2241493. The former occurs in a genomic region near the potassium voltage-gated channel modifier subfamily S member 3 (KCNS3) gene. This SNP was also highlighted by other authors who analyzed the ALS SNP data set provided by Schymick et al. (2007) [transient receptor potential cation channel subfamily M member 1 (TRPM1) gene. Additionally, this SNP induces a substitution of serine (AGC) amino acid by isoleucine (ATC), threonine (ACC), or asparagine (AAC) according to the dbSNP database [Our analysis revealed two SNPs related to genes in the biological process of ion transport, . (2007) , as descdatabase   gene, which has an actin-binding molecular function, and the rs10773543 is located within intron 2 of the transmembrane protein 132C (TMEM132C) gene.In addition, two SNPs associated with actin cytoskeleton organization were prioritized for the in silico analysis. The The FMN2 protein plays an important role in the organization of the actin cytoskeleton and in cell polarity . It is hTransmembrane proteins are responsible for maintaining cell junctions in the central nervous system. The TMEM132 family of proteins has a cell adhesion function, connecting the extracellular environment with the intracellular actin cytoskeleton, thus playing an important role in the regulation of changes in the morphology, motility, and migration of neuronal cells . Howeverrs10459680 SNP is located within intron 4 of the LOC101927025 non-coding gene, and is genomically close to the repulsive-guidance molecule (RGM) bone morphogenetic protein (BMP) co-receptor A (RGMA) gene, according to the HaploReg webtool [Two other selected SNPs are located in intronic regions. The  webtool . The RGM webtool , and alt webtool .rs4552942 SNP is located within intron 1 of the long intergenic non-protein coding RNA 2055 (LINC02055) gene, according to the Ensembl genome browser [LINC02055 gene. Nonetheless, by analyzing the ALS SNP data set provided by Schymick et al. (2007) [rs12680546 SNP as an interaction pair  , Sha et . (2007)  prioritirs1436918 SNP, which is within a regulatory region genomically close to the golgin A8 family member B (GOLGA8B) gene, according to the HaploReg webtool [GOLGA8B gene is a member of the Golgin family and is part of the Golgin matrix [rs1436918 GG genotype was associated with increased GOLGA8B expression in skeletal muscle, according to eQTL expression level analysis [We also prioritized the  webtool . The GOLn matrix . It is hanalysis . Despiteanalysis ,55, the A genetic interaction refers to any type of interaction between segments of the genome, which can occur at different levels of the biological system, from the direct relation of genes to the physical interaction of proteins and the physiological interaction of different metabolic pathways, all of which can have beneficial or harmful implications for the overall expression of a phenotype .rs16984239:rs2118657 and rs16984239:rs3172469 interactions were both selected 37 times from 58 and 69 s step model fits, respectively, and were considered for the in silico analysis. The distribution of the number of selected pairwise interactions in 2000 iterations is presented in rs16984239 with ALS in the strata of its interaction pairs, based on the original data set. Such analysis was performed to describe the effect of each genotype combination on the odds of having ALS. Considering the absence of the minor allele as the reference category, the rs16984239 A allele can increase ALS susceptibility in the following strata: both rs2118657  to evaluate these interactions. Only the pairwise interaction rs2118657 and rs3172469 SNPs are located in chromosome 3  gene, which originated from an ancient duplication of acetylcholinesterase (ACHE) in vertebrate evolution [Both the volution . The chovolution . Reducedvolution . The gluvolution . Additiovolution .rs16984239:rs2118657 interaction pair could represent a relationship between BCHE-KCNS3 proteins, implying a cholinesterase–potassium balance involved in synapse function. BCHE is an important cholinergic regulator present in neurons and motor endplates, which are also rich in potassium-gate regulators, such as KCNS3. Glial protection against neurotoxicity was previously related to potassium balance control [The  control . Thus, irs3172469 SNP is located in intron 1 of the B-cell lymphoma 6 (BCL6) gene, and encodes a transcriptional repressor that may be involved in the modulation of several metabolic pathways, the most popular being related to B cell activation implied in lymphoma pathogenesis [BCL6 gene is highly expressed in whole blood, musculoskeletal, and tibial nerve tissues, according to the GTEx Portal [The ogenesis . The BCLx Portal , and wasx Portal .rs16984239:rs3172469 pair could, therefore, be related to a BCL6-KCNS3 protein-gene association, particularly due to the transcriptional repression capabilities of the BCL6 protein and its known presence in spinal motor neurons [KCNS3 gene might imply over-expression of KCNS3, leading to an overstimulation of neuronal synapses, which may contribute to the general loss of coordination and muscle tremor symptoms in the ALS phenotype [The  neurons . Wang et neurons  reportedhenotype .To assess whether the variability in clinical findings can modify the odds of ALS observed for the prioritized SNPs and pairwise interactions, we evaluated the association between these variables and the presence of ALS in subgroups defined according to El Escorial criteria, as well as age and site of symptom’s onset. In general, the estimated odds ratios showed the same direction as those observed considering the ALS cases together, but with differences in magnitude and statistical significance, possibly due to the smaller sample size in each ALS disease subgroup .p-value p-value correction for multiple comparisons by the Bonferroni method revealed that no SNP maintained statistical significance at the rs2241493 was not highlighted). On the other hand, neither the rs2118657 nor the rs3172469 variants were pointed out by the authors.Schymick et al. (2007)  were thep-values for the single-marker test were selected and two pairwise interaction terms were identified in this set: rs4363506:rs3733242 and rs4363506:rs16984239. We also selected pairwise interactions with SNP rs16984239 on the same data set. However, this pair was not evaluated by our approach, because the rs4363506 SNP was excluded in the quality control step   searchedIn addition to single-marker tests, two-step approaches involving multiple regression with regularization to select main ,18,19 anCombining these different ideas, we iteratively applied the group LASSO method, both for individual SNP (first step) and pairwise interaction (second step) selection, using a cross-validation process to choose the penalty parameter for each model fit. These procedures are integrated since the variables selected in the first step according to a group LASSO fit were evaluated for all possible interactions by a hierarchical group LASSO model at each iteration. Finally, variable importance measures were calculated from the bootstrap selection frequency in 2000 iterations.Our proposal has limitations. First, it is not possible to test all pairwise interactions in complete SNP data, and some mechanism to reduce its dimension is necessary. Thus, we randomly and iteratively included subsets of variables to fit a regularized model allowing greater variability in the selection of variables in the first step, and consequently, the evaluation of a greater number of pairwise interactions, in the second step. Nonetheless, as a random subset of variables was included in the regularization model in each iteration, a greater variability for the estimated coefficients is also to be expected, since in a regression model the effect of a variable depends on which other variables are considered together. However, the individual SNPs and the selected pairwise interactions presented consistent results for the estimated coefficients in the bootstrap analyses: even with different effect sizes, their signs were preserved, as evidenced in the distributions of the estimated coefficients shown in p-values from logistic regression fits, leave-one-out indices, as proposed by Wu et al. (2007) [p-values from statistical tests applied to genetic variant selection based on bootstrap results, as proposed by Park et al. (2015) [Our criterion for establishing a final set of variables for further in silico analysis was based on an ad hoc cut-off point for their bootstrap selection frequency. Importantly, all statistical measures have drawbacks, including . (2007) , and p-v. (2015)  and revi. (2015) . These dThe SNP data set analyzed provides limited data regarding the clinical characteristics of ALS patients. Additionally, environmental and lifestyle factors were not investigated. It is known that ALS is a heterogeneous multi-system neurodegenerative disease , and itsrs2241493 is the only SNP in a coding region and is also a missense polymorphism. Therefore, rs2241493 seems to be the most promising candidate for future functional studies. Two pairwise interactions were prioritized for in silico analyses: rs16984239:rs2118657 and rs16984239:rs3172469. Although rs16984239 has been identified by other studies that explored the same ALS SNP data we analyzed, neither rs2118657 nor rs3172469 was previously described as associated with the ALS phenotype. We believe our results may contribute to a better molecular understanding of the ALS phenotype, with the potential to be tested in diagnosis or in therapeutic strategy improvement.We selected seven SNPs and two pairwise interactions associated with the ALS phenotype by applying a two-step group LASSO approach and described their biological consequences through in silico analysis. In summary, the biological implications of the selected genetic variants revealed proteins related to membrane potential regulation, Golgi apparatus fragmentation, actin cytoskeleton and cell polarity organization, axon guidance, and neurotransmitter metabolism. ALS is known to be clinically and genetically highly variable, and future studies aiming to prioritize genetic variants capable of differentiating ALS subgroups can be explored using the approach proposed in the present study. Additionally, ALS is hypothesized to be related not only to genetics but also to environmental and lifestyle factors. Therefore, considering these characteristics as predictors of ALS in future studies may improve the holistic understanding of the mechanisms involved in disease pathogenesis.


Herein, we investigated Pb tolerance among a maize association panel using root bushiness (BSH) under Pb treatment as an indicator. Through a genome-wide association study of relative BSH, we identified four single nucleotide polymorphisms (SNPs) and 30 candidate genes associated with Pb tolerance in maize seedlings. Transcriptome analysis showed that four of the 30 genes were differentially responsive to Pb treatment between two maize lines with contrasting Pb tolerance. Among these, the ZmbZIP107 transcription factor was confirmed as the key gene controlling maize tolerance to Pb by using gene-based association studies. Two 5’ UTR_variants in ZmbZIP107 affected its expression level and Pb tolerance among different maize lines. ZmbZIP107 protein was specifically targeted to the nucleus and ZmbZIP107 mRNA showed the highest expression in maize seedling roots among different tissues. Heterologous expression of ZmbZIP107 enhanced rice tolerance to Pb stress and decreased Pb absorption in the roots. Our study provided the basis for revelation of the molecular mechanism underlying Pb tolerance and contributed to cultivation of Pb-tolerant varieties in maize.Lead (Pb) is a highly toxic contaminant to living organisms and the environment. Excessive Pb in soils affects crop yield and quality, thus threatening human health  Heavy metal pollution from industrial and agricultural activities has become one of the most important environmental concerns worldwide. Heavy metals can cause irreversible damages to plants and thus lead to serious harm to human health through the food chain . Among vFurthermore, excessive Pb in soils seriously affect the yield and quality of plants . On the Lemna minor root growth by inhibiting root tip cell division  content in rice grains and shoots   family was previously demonstrated to play important roles in absorption and transport of heavy metals in plants. Among the NRAMP members, d shoots . Howeverbidopsis . Yellow ese (Mn) . Metal tand rice . Moreove in rice . In addis plants . Expressstresses . PSE1 th2/ABCG40 . In our eedlings . Plant beedlings . Previoueedlings . These seedlings . To dateZmbZIP107 affected Pb tolerance of root BSH among maize lines. Finally, we investigated the role of ZmbZIP107 in vivo by heterologous expression of ZmbZIP107 in rice, which validated the effects of ZmbZIP107 on Pb tolerance and accumulation in rice seedlings. Collectively, these results suggest that ZmbZIP107 is a positive regulator of Pb tolerance in maize seedlings, which inhibits Pb absorption in the roots.Genome-wide association study (GWAS) is widely used to identify genetic variations and candidate genes of abiotic stress response-related traits in maize. A total of 21 single nucleotide polymorphisms (SNPs) for Pb accumulation were identified in a panel of 269 maize accessions using GWAS . In a ma3)2  and the other into a Hoagland solution without Pb(NO3)2 . pH of these two solutions were maintained at approximately 4.5 with titration of KOH or HCl when required  was measured for each line using an EPSON Expression 10000XL Root Scanner and assessed using the ARIA software , and non-Stiff Stalk (NSS) germplasms , was userequired . On the software . The 312software .For each line, the average of BSH across three replicates was used as the phenotype of BSH. The SPSS  software was used for descriptive statistical analysis of phenotypic data, including mean, standard deviation (SD), skewness, kurtosis, frequency distribution, and coefficient of variation (CV).-6) was used to detect SNPs significantly associated with BSH-LTC. Based on the B73 (RefGen V2) genome, all the genes located in the LD (Linkage disequilibrium) = 300 kb regions of these significant SNPs were excavated and identified as putative candidate genes for BSH-LTC (The genotypes of 312 association panel were constructed using a maize SNP chip (56K) in our previous study . The fol BSH-LTC .http://www.broad.mit.edu/mpg/haploview) was used to calculate the LD between the markers  was used as the reference sequence to align the amplified sequences, and the DNAMAN software  was employed to detect insertions/deletions (InDels) and SNPs . The ass markers . For eacth day of Pb treatment, rice seedlings were soaked in 20 mM Na2-EDTA and then rinsed with distilled water for three times. The roots were sampled and dried at 105°C for 2 h and 70°C to a constant weight. These samples were then digested with 10 mL nitric acid (HNO3) using the MARSX (CEM) microwave digester. Finally, Pb content was determined by the Inductively Coupled Plasma Mass Spectrometry (NexION 1000 ICP-MS). Pb concentration (mg/kg DW) was represented by Pb content per unit dry weight (DW).On the 14ZmbZIP107, including roots (three-leaf stage and silking stage), leaf (three-leaf stage and silking stage), stems (silking stage), female flowers, male flowers, grains on the 6th day after pollination (6 DAP) and 12 DAP. Moreover, maize seedlings were cultured in Hoagland solutions supplemented with 1mM Pb(NO3)2 and without Pb(NO3)2 for seven days, respectively. The shoots and roots were individually sampled for total RNA extraction by using TRIzol Reagent . The first-strand cDNA was synthesized from 2 µg total RNA using a reverse transcription kit  following the manufacturer’s instruction. The gene expression level of ZmbZIP107 was quantified by performing RT-qPCR with SYBR qPCR SuperMix Plus . The OsActin gene was used an internal reference, and the 2-ΔΔCT method was used to calculate the relative expression levels of ZmbZIP107 .To verify the subcellular localization of the ZmbZIP107 protein, we cloned the coding sequence (CDS) of ZmbZIP107 was cloned from the favorable-haplotype line (B73) and integrated into the pCUB vector under the control of Ubi promoter. The recombinant plasmid was transferred into the rice cultivar ZHONG HUA 11 through Agrobacterium-mediated transformation to produce ZmbZIP107-overexpressed lines. Positive transgenic plants were detected by using ZmbZIP107-based PCR. Seeds from the transgenic and wild-type plants were incubated on the germinating paper at 37°C in the darkness. Germinated seeds were transferred to a germinating box filled with distilled water and cultured for seven days. Then, the seedlings with uniform growth were transferred to nutrient solutions (pH = 4.5) with 50 mg/L Pb(NO3)2 and without Pb(NO3)2, respectively  (-7 (PZE-110032152) to 2.87×10-8 (PZE-108077298). The PVE of each SNP varied between 1.89% (PZE-108062009) and 4.80% (PZE-108077298)  showed a sharp deviation from the expected p value specially in the tail region (8077298) Table 2,Zm00001d011012 and Zm00001d007827 were differentially expressed under Pb treatment in both lines , Zm00001d024160was annotated as a TGA4 transcription factor (ZmbZIP107) whereas Zm00001d011134 encodes aputative AP2/EREBP transcription factor superfamily protein (WRI1). Zm00001d010404 andZm00001d040356 encode a putative cyclin superfamily protein and geranylgeranyl hydrogenase 2, respectively  and a Pb-tolerant line (SCL280) during the response to Pb stress . Finallyectively Table S2Zm00001d010404, Zm00001d040356, Zm00001d024160, and Zm00001d011134, respectively (Zm00001d010404 were significantly (p < 0.05) associated with BSH-LTC, which were situated in the seventh intron (Zm00001d040356 was significantly (p< 0.05) associated with BSH-LTC, which resided in the first exon and led to a synonymous mutation (Zm00001d011134 was significantly (p < 0.05) associated with BSH-LTC, nevertheless, no significant difference in BSH-LTC value was observed between the lines with allele C and those with allele A (Zm00001d024160 (ZmbZIP107) were significantly (p< 0.05) associated with BSH-LTC (t-test showed that the BSH-LTC (1.178) of Hap1 was significantly (p< 0.05) lower than that (1.417) of Hap 2 (ZmbZIP107 in the two contrasting haplotypes under Pb treatment by using RT-qPCR. The results showed that the expression level of ZmbZIP107 in Hap1 (AA) was significantly (p< 0.01) lower than that in Hap2 (CC) under Pb treatment , 8 (7 SNPs and 1 InDel), 25 (25 SNPs), and 5 (5 SNPs) polymorphisms were identified in ectively Table S3of Hap 2 . We thenZmbZIP107 with eGFP and transformed it into tobacco leaves. The eGFP fluorescence signal was specifically observed in the nucleus of the leaves transformed with the p35S:ZmbZIP107-eGFP fusion expression vector , followed by the leaves (three-leaf stage) and roots (silking stage). However, the female flower had the lowest expression level of ZmbZIP107. These findings supported the role of ZmbZIP107 in mediating root system architecture  overexpressing ZmbZIP107 with the favorable haplotype (Hap2)  and shoot length (SHL) in the OE lines and ZHONG HUA 11 under normal conditions and Pb treatment. Ultimately, ZmbZIP107 overexpression had no significant effect on TRL, TSA, PRL, SEL, MNR, and SHL in the early vegetative stage of rice under normal conditions  and 41.4% (OE2) relative to the wild-type ZHONG HUA 11 under Pb treatment , uncovering five SNPs acting on Cd accumulation in maize leaves  from the GWAS results by combining transcriptome analysis and gene-based association mapping, which suggested the high efficiency of using the integration strategy in excavating functional genes. On the other hand, the identified favorable haplotype in ZmbZIP107 can be used for developing functional markers in molecular marker-assisted selection.In the present study, four significant SNPs were identified using GWAS Table 2,ZmbZIP107 that was responsible for Pb tolerance and accumulation in the roots of maize seedlings. The heterologous expression of ZmbZIP107 enhanced Pb tolerance and inhibited Pb absorption in rice. In the future, using transgenic technologies to increase ZmbZIP107 expression in other crops is desirable to improve Pb tolerance of germplasm resources and cultivate varieties with low Pb accumulation. As a widely grown crop with large biomass, maize has a great potential of soil bioremediation , the Sichuan Science and Technology Program (2021JDTD0004 and 2021YJ0476), and the earmarked fund for China Agriculture Research System (CARS-02).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.


Panicum miliaceum L.) is a climate‐resilient and human health‐promoting cereal crop. The genome size of this self‐pollinated allotetraploid is 923 Mb. Proso millet seeds are an important part of the human diet in many countries. In the USA, its use is restricted to the birdseed and pet food market. Proso millet is witnessing gradual demand in the global human health and wellness food market owing to its health‐promoting properties such as low glycemic index and gluten‐free. The breeding efforts for developing improved proso millet cultivars are hindered by the dearth of genomic resources available to researchers. The publication of the reference genome and availability of cost‐effective NGS methodologies could lead to the identification of high‐quality genetic variants, which can be incorporated into breeding pipelines. Here, we report the identification of single‐nucleotide polymorphisms (SNPs) by low‐pass (1×) genome sequencing of 85 diverse proso millet accessions from 23 different countries. The 2 × 150 bp Illumina paired‐end reads generated after sequencing were aligned to the proso millet reference genome. The resulting sequence alignment information was used to call SNPs. We obtained 972,863 bi‐allelic SNPs after quality filtering of the raw SNPs. These SNPs were used to assess the population structure and phylogenetic relationships among the accessions. Most of the accessions were found to be highly inbred with heterozygosity ranging between .05 and .20. Principal component analysis (PCA) showed that PC1  and PC2 explained 19% of the variability in the population. PCA also clustered all the genotypes into three groups. A neighbor‐joining tree clustered the genotypes into four distinct groups exhibiting diverse representation within the population. The SNPs identified in our study could be used for molecular breeding and genetics research  in proso millet after proper validation.Domesticated ~10,000 years ago in northern China, Proso millet ( It was domesticated approximately 10,000 years ago in the semiarid regions of northern China  and high water‐use efficiency  , expected heterozygosity (HE), and nucleotide diversity (π), of the cultivated proso millet were significantly lower than the weedy types. The wild and feral types of weedy proso millet could also be distinguished using the SNPs  identify segregating markers across within the representative sample of global proso millet germplasm (~700 genotypes), including all the US cultivars, which were not sampled in previous studies, and (2) quantify patterns of population structure and phylogenetic relationships among the proso millet lines using the identified SNPs.22.1Panicum hallii (used as an outgroup) were planted in an experimental plot at the UNL Panhandle Research and Extension Center, Scottsbluff, Nebraska, USA . The genotypes were planted on a conventionally tilled ground with Tripp very fine sandy loam  type soil . Library construction for sequencing was conducted using the iGenomX RIPTIDE high throughput rapid library prep kit  following the manufacturer's instructions (https://igenomx.com/product/riptide/). The samples were subsequently sequenced to an average coverage depth of 1× utilizing 2 × 150 bp paired‐end reads on the Illumina HiSeq X platform by Psomagen Inc., Rockville, Maryland, USA.Leaf samples for DNA extraction were harvested at phenological stage 9 on the BCCH scale after 4 weeks of planting . Low‐quality sequences and adapters were removed from the sequence data using Trimmomatic software (v .33) with parameter settings “PE Illuminaclip: ~dir/contaminants.fasta: 2:30:10 SLIDINGWINDOW:4:15 HEADCROP:8 MINLEN:101 CROP:100”  of proso millet with higher SNP densities observed in the gene‐rich chromosome arms than in the more repeat‐rich chromosome arms. The number of SNPs per 10 Mb bins ranged from 0  to 1,850 (chromosome 6). The frequency distribution of the homozygous major, homozygous minor, and heterozygous allele for each SNP over all genotypes is shown in Figure A large proportion of the apparent heterozygosity may be explained by “paralogous” SNPs generated from incorrect alignments between duplicated sequences  of the filtered SNPs generated 10 principal components (PCs), and the total variation explained by the 10 PCs is ~33% ranging from 15% (PC1) to ~1% (PC10) Figure . The firA phylogenetic tree demonstrated the relatedness of the 85 proso millet accessions Figure . The majBoth the score‐plot (PCA) based cluster analysis Figure  and neigTwenty‐seven of 34 South Asian genotypes were grouped together in Group I. The IDs of these 27 genotypes are: PS29, PS30, PS31, PS32, PS33, PS34, PS37, PS38, PS41, PS42, PS43, PS44, PS45, PS47, PS48, PS51, PS52, PS54, PS55, PS56, PS57, PS58, PS59, PS60, PS61, PS64, and PS70 Table .All the six West Asian genotypes  were placed together in Group IV.Five of seven East Asian genotypes  were grouped together in Group III and other two (PS24 and PS26) were placed in Group II.Sixteen of 19 North American genotypes  were together in Group II and the remaining three  were together in Group III. It is important to note that the duplicated genotypes of the cultivar “Cerise” (PS116 and PS117) were placed in the same cluster (Group III).P. hallii (PS114), one of the diploid progenitors, was completely separated from all the 85 P. miliaceum genotypes .To verify the reliability of the SNPs, we analyzed the neighbor‐joining phylogenetic tree and a heatmap depicting relatedness among the genotypes in detail. We observed that almost all the genotypes of the same gene pool  were grouped together in the phylogenetic tree Figure , and theThe five genotypes in cluster A were PS91 (Abarr), PS94 (Minco), PS95 (Minsum), PS97 (Cope), and PS93 (Panhandle). Interestingly, all of them are single plant selection of the same landrace “Common white” , PS96 (Dawn), PS101 (Huntsman), and PS103 (Sunrise). Each of Rise and Huntsman has 50% Dawn, and Sunrise has 37.5% Dawn based on their pedigree  was the male parent of PS105, cultivar “Plateau”  to support this additional experiment because the project has ended, and no budget is left. Therefore, we decided not to conduct the additional sequencing.4The sequencing and assembly of the proso millet genome have enabled a new era of proso millet breeding and genetics by enabling the use of high throughput sequencing‐based strategies for discovering genetic markers, which can enhance molecular breeding, quantitative genetic, and population genetic analyses. Here, we identified and scored 972,863 SNPs from 85 accessions of proso millet originated from 23 countries spanning different regions of the globe.In the current report, the first 10 PCs were found to explain ~33% of the total variation. This finding is consistent with the observations reported by Miao et al.  in severA similar clustering of the North American genotypes based on SSR marker analysis was also reported in our previous study  after proper validation. The relationships among the accessions depicted by the phylogenetic tree could be used for selecting parents for QTL mapping and cultivar development. The findings from this study may encourage researchers working on other minor crops to adopt this approach to identify genetic variants cost‐effectively. Further studies can be conducted on the efficiency of this approach in identifying high‐quality SNPs and other genetic variants in comparison to high‐coverage sequencing.James Schnable has a significant equity interest in Dryland Genetics, a company seeking to develop new proso millet cultivars. The authors declare no other conflict of interest.Figure S1: Total number of reads (in million) per individual accession retained after preprocessing.Figure S2: Sequence alignment rates of the 85 proso millet accessions.Click here for additional data file.Table S1: Number of sequenced read bases (Mb), sequence yield and overall alignment rates of 85 proso millet accessionsClick here for additional data file.Data S1 Supporting InformationClick here for additional data file.


This technology is widely used in genome-wide association studies (GWAS) at bio-bank scales, and more recently, in polygenic score (PGS) analysis to predict and stratify disease risk. Over the last decade, human genotyping arrays have undergone a tremendous growth in both number and content making a comprehensive evaluation of their performances became more important. Here, we performed a comprehensive performance assessment for 23 available human genotyping arrays in 6 ancestry groups using diverse public and in-house datasets. The analyses focus on performance estimation of derived imputation (in terms of accuracy and coverage) and PGS (in terms of concordance to PGS estimated from whole-genome sequencing data) in three different traits and diseases. We found that the arrays with a higher number of SNPs are not necessarily the ones with higher imputation performance, but the arrays that are well-optimized for the targeted population could provide very good imputation performance. In addition, PGS estimated by imputed SNP array data is highly correlated to PGS estimated by whole-genome sequencing data in most cases. When optimal arrays are used, the correlations of PGS between two types of data are higher than 0.97, but interestingly, arrays with high density can result in lower PGS performance. Our results suggest the importance of properly selecting a suitable genotyping array for PGS applications. Finally, we developed a web tool that provides interactive analyses of tag SNP contents and imputation performance based on population and genomic regions of interest. This study would act as a practical guide for researchers to design their genotyping arrays-based studies. The tool is available at:  To date, thousands of DNA loci that are significantly associated with complex traits and diseases have been discovered3. Among numerous possible applications of GWAS results, disease risk prediction is rapidly gaining broad interest recently6. A polygenic score (PGS) or polygenic risk score (PRS) is an estimate of an individual’s genetic liability to a trait or disease, calculated based on their genotype profile and relevant GWAS data7. In its most common form, a PGS is computed as the sum of allele count of risk alleles  that are weighted by its effect size (i.e. log odd ratio or beta coefficient) of hundreds to thousands of associated SNPs. The outcome is a single score that aggregates each individual’s genetic loading proportional to the risk of a given disease or a quantitative trait6. Although the clinical utility of PGS has yet to be established, recent works have suggested that PGS may be used for disease risk stratification that potentially facilitates early disease detection, assists in diagnosis, or informs treatment choices5. For example, PGS of coronary artery disease, type 2 diabetes, and breast cancer at the top 8, 3.5, and 1.5% are risks equivalent to a monogenic mutation risk that confers an odds ratio of 38.Over the last decade, low-cost, robust genotyping platforms and large-scale genome variation projects such as the 1000 Genomes Project9. Because the coverage of SNP arrays is typically limited to lower than a million SNPs, a procedure involving haplotype phasing and genotype imputing of missing sites is usually employed to add more genotyping information that can increase the power of these genetic studies11. The imputation performance is affected by three main factors, including algorithms of choice12, imputation reference panels14, and the SNP array designs15.Similar to GWAS analysis, PGS can be derived from various types of genotyping data such as those obtained by single-nucleotide polymorphism (SNP) microarrays or whole-genome sequencing (WGS). While WGS is attractive of the ability to interrogate variations across the entire human genome, SNP arrays are the dominant assays to obtain genetic data for PGS calculation. They come up with several advantages such as cost-effectiveness and light computational requirement which are preferable for population-scale screening, where PGS would be most useful17. Based on the target population, human genotyping SNP arrays can be classified into three categories optimized for global, super population, or specific to targeted populations. In the early phase of development, genotyping SNP arrays were focused on common genetic variations of the whole world population  based on the HapMap catalog18. The second generation of SNP arrays was designed to cover variants with MAF as low as 0.01 by providing SNP arrays specifically for European, East Asian, African American, and Latino race/ethnicity populations based on the 1000 Genomes Project (1KGP) catalog20. However, the fact that the majority of human genetic variants are rare and population-specific demands customizing SNP arrays to improve over those designed for global or super populations22. Indeed, population-specific genotyping arrays such as the UK Biobank Axiom Array2, the Axiom-NL Array23, the Japonica and Japonica NEO Arrays25, and the Axiom KoreanChip26 have been developed on top of the many existing commercial arrays. These arrays are not only optimized for genomic coverage based on their unique variant catalogs but also include a large number of functional variants. For example, the Axiom KoreanChip contains more than 200,000 nonsynonymous loci and the new Japonica NEO Arrays were designed with abundant disease risk variants26.In principle, genotyping SNP arrays are designed by selecting a set of SNPs, commonly referred to as “tag SNPs”, which maximize coverage of ungenotyped DNA variants through associations between these alleles in the population 27, or limited in only testing for a small set of populations, and some studies focused on LD coverage28 that may not be relevant to current imputation practice for use in association studies and PGS analysis11. Moreover, although PGS is gaining increasing attention, practical evaluation of performance for PGS applications by current genotyping arrays is still lacking. Here, we provide a comprehensive evaluation of imputation-based genomic coverage29 and PGS performance of 23 human genotyping arrays in diverse populations. These analyses are intended to be a practical guide for researchers in selecting the most suitable genotyping array for their genetic studies.The development of customized arrays accompanied by commercial arrays provided by genotyping platform producers results in a large number of genotyping arrays. Each of these arrays has specific properties and contents, and thus, there is an urgent demand for a systematic guideline to determine which array best suits specific research questions and populations. Although there are SNP array comparative studies, they are either not updated with the many recent arrays30. Details and component statistics of these arrays are shown in Table In this study, we benchmarked 23 different human genotyping arrays including 14 arrays from Illumina and 9 arrays from Affymetrix. The examined arrays contain the numbers of tag SNPs (array size) ranging from approximately 300,000 (Infinium HumanCytoSNP-12 v2.1) up to more than 4,300,000 (Infinium Omni5 v1.2). They can be classified as old arrays such as the Genome-Wide Human SNP Array 6.0; population-specific optimized arrays such as Axiom UK Biobank Array and Axiom Japonica Array NEO; multiple populations optimized arrays such as Infinium Multi-Ethnic Global v1.0 and Infinium Global Diversity Array v1.0; cytogenetics and cancer applications optimized arrays such as Infinium CytoSNP-850K v1.2. Recently developed arrays include Infinium Global Screening Array v3.0, Axiom Precision Medicine Research Array, and Axiom Precision Medicine Diversity Array. Manifests of the 23 examined arrays were obtained from respective manufacturers’ websites. Genomic positions were further harmonized to the UCSC hg38 reference genome coordinate with CrossMap v0.2.6 for those requiring lifted over31 and the 1000 Vietnamese Genomes Project (1KVG)32, respectively, were used to estimate imputation-based coverage and PGS performance of 23 different genotyping arrays by the tenfold cross-validation approach. In the 1KGP dataset, 26 populations were grouped into 5 supper-populations according to their continental groups including East Asian (EAS), European (EUR), South Asian (SAS), African (AFR), and American (AMR). For consistent naming throughout the text, these continental groups are hereafter considered as a population. This dataset was randomly divided into 10 batches equally distributed across populations (4 batches with 251 samples and 6 batches with 250 samples). Similarly, the Vietnamese population (VNP) was processed separately with 8 batches of 101 and 2 batches of 100 samples. In each turn, one batch was used as the test set and the remaining samples as the reference set. For each array, variants in the test set with the same position as variants on the array were extracted with vcftools v0.1.1733 and phasing information was removed to generate the pseudo SNP array genotyped data, while variants in reference data were used as the pre-phasing and imputation reference panel. The pre-phasing and imputation were performed with SHAPEIT v4.1.334 and Minimac4 v1.0.212 respectively. Finally, the imputed genotyping data of 10 batches were combined to estimate imputation and PGS performance according to their populations, including 504, 503, 489, 661, 347, and 1,008 individuals in EAS, EUR, SAS, AFR, AMR, and VNP, respectively. This approach is similar to the strategy used previously to estimate imputation-based genomic coverage35.An overview of our evaluation pipeline is presented in Fig. 11, computation of PGS is conducted by summing the product of risk allele count  and its effect size derived from the GWAS. Thus, imputation performance is expected to play a key role in PGS derivation. Here, we focus on imputation 15, imputation quality28, LD coverage36. We choose imputation 40. This leads to the interpretation that an increase in mean imputation 40. Second, it is less sensitive to allele frequency than concordance15. Third, it incorporates imputation uncertainty by using expected allele dosage rather than the most likely genotype15. Finally, imputation 40. In this evaluation setting, we treated genotypes derived from WGS datasets as gold standard. Imputation performance is measured as imputation 41.Both GWAS and PGS often require genotype imputation that involves the prediction of untyped variants in the genome. While GWAS benefits from boosting the number of imputed SNPs that can be tested for association40, PGS was computed with a standard P+T (Prunning and Thresholding) approach implemented in PRSice-242 in this study. The main reason for using this approach is that we tried to mimic the real-life practice of PGS analysis that involves running a PGS computational method with multiple parameters and selecting the best one7. Another reason is that using pre-built PGS models may introduce a potential bias for some specific arrays as they were used in tuning in these established PGS model, i.e., we tried to avoid training using the same array twice. Using summary statistics for three phenotypes, namely height, body mass index (BMI), and type 2 diabetes (T2D), obtained from previous GWAS meta analyses44, a PGS for an individual i was calculated as:M is number of SNPs after clumping with “–clump-kb 250kb” and “–clump-r2 0.1”; Instead of using pre-tuned PGS models as in other studiesSimilar to imputation performance evaluation, we treated PGS derived from WGS as the “gold standard”. PGS derived from 23 different SNP arrays were evaluated using Pearson’s correlation to PGS derived from WGS data under the same PRSice-2 parameter settings. In addition, absolute differences in PGS percentile ranking generated by array-imputed and the WGS data were also evaluated.The study did not generated new dataset. Ethics approval and consent to participate were applied according to corresponding orginal works. In the 1KVG study, subjects provided informed consent and the study was approved by the Vinmec International Hospital Institutional Review Board with number 543/2019/QD-VMEC in accordance with the relevant guidelines and regulations (e.g. Helsinki Declaration). In the 1KGP-NYGC study, genetic data are publicly available according to the original ethics approval.Overall, we found two main factors affecting the imputation accuracy and imputation coverage that are array sizes and population optimization. The two densest arrays that are the Infinium Omni2.5 v1.5 and Infinium Omni5 v1.2 with approximately 2.4 and 4.3 minion tag SNPs yielded the highest imputation performance. In contrast, the two sparsest SNP arrays with approximately 300,000 tag SNPs that are Infinium HumanCytoSNP-12 v2.1 and Infinium Core-24 v1.2 obtained the poorest imputation performance in all six examined populations. At the MAF bin of . In detail, at the MAF bin of , Axiom Precision Medicine Diversity Array (922k), Genome-Wide Human SNP Array 6.0 (932k), Infinium Multi-Ethnic Global v1.0 (1784k), and Infinium Global Diversity Array v1.0 (1905k), with imputation coverage of 0.7814, 0.8078, 0.7513, 0.8228, 0.8277, respectively and lower 0.8409, and 0.8885 that were obtained by Infinium Omni2.5 v1.5 and Infinium Omni5 v1.2 arrays with 2373k and 4327k SNPs. Similarly, the Axiom Japonica Array NEO (671k) which was designed for the Japanese population also performed well against global optimized, higher-density arrays. These two arrays yielded mean imputation accuracy of 0.831, 0.9333; and imputation coverage of 0.7642, 0.9024 in EAS and VNP populations. These performances were higher than those of multi-ethics SNP arrays, even with higher density including Axiom Precision Medicine Research Array (919k), Axiom Precision Medicine Diversity Array (922k), Genome-Wide Human SNP Array 6.0 (932k) as showed in Fig. We evaluated PGS performance of these arrays based on two criteria: (i) Pearson’s correlation of PGS estimated by using imputed SNP array data compared to the PGS estimated by using WGS data—hereafter we refer as PGS correlation for short, (ii) absolute difference of percentile ranking (ADPR) between PGS generated by array-imputed and gold standard WGS. Both comparisons are set under various p-value cutoffs that allow us unbiased evaluate PGS performance of these arrays. In general, we found that PGS performance was highly concordant with imputation performance, i.e. SNP arrays with better imputation performance showed higher PGS correlation and less ADPR than the arrays with poor imputation performances.42. Notably, we also observed higher standard deviations of PGS correlation in EAS than in other populations.The summary results of Pearson’s correlation values of PGS from 23 genotyping SNP arrays for three different phenotypes are shown in Fig. In agreement with imputation performance, SNP arrays optimized specifically for targeted populations showed superior PGS correlation in the targeted/closely related populations. For instance, Axiom Japonica Array NEO and Infinium OmniZhongHua v1.4 which were optimized for Japanese, and Chinese showed clear advantages in the populations of EAS, and VNP while Axiom UK Biobank Array yielded higher PGS correlation in the EUR population than the other size-comparable genotyping arrays. Taking height as a typical trait of interest, PGS correlations of the Japonica Array NEO were 0.9760, and 0.9847, while the Infinium OmniZhongHua v1.4 had 0.9879, and 0.9914 in EAS and VNP respectively. Interestingly, we observed that the Infinium CytoSNP-850K v1.2 was the array with superior PGS correlations in all populations, for all the three evaluated traits. For example, the PGS correlation for this array for height phenotype in AFR, AMR, EAS, EUR, SAS and VNP were 0.9679, 0.9876, 0.9789, 0.9908, 0.9844, 0.988, respectively.Regarding the ADPR metric, the performance of arrays was in an agreement with the trend from comparing PGS correlation i.e. ADPRs were also affected by array sizes and optimization population. ADPR measurements in different PRSice-2 p-values settings are shown in Figs. https://genome.vinbigdata.org/ with no further filtering and quality control applied. We then applied the same pipeline to compare the imputation performance of the simulated genotyping data against the results obtained from the real genotyping data. In details, both simulated and real genotyping data were phased with SHAPEIT v4.1.334, and imputed with Minimac4 v1.0.212. Reference data for both phasing and imputation were the remaining 984 WGS samples. Finally, imputation performance of both simulated and real arrays were estimated as described in the “We further utilized the availability of real genotyping data in the 1KVG dataset with 24 out of the 1008 samples also genotyped by the Axiom Precision Medicine Research Array and the Infinium Global Screening Array v3.0 to investigate how our simulated array data performed relative to the real array data. In brief, we generated pseudo genotyping data (termed simulated data) of 24 samples by extracting variants from WGS data that matched with the Axiom Precision Medicine Research Array and the Infinium Global Screening Array v3.0 manifests before excluding phasing information. Regarding real genotyping data, processed VCF  files of 24 out of 1008 samples were obtained from 26. Moreover, genotyping by SNP arrays produce the exact information typically required for PGS analysis, which is based on summarizing effect sizes from individual SNPs. A promising application of genomic research that is gaining increasing interest recently across the healthcare system, and direct-to-consumer genomic services based on polygenic scoring like 23andMe46. SNP arrays are clearly economical in data generation and analysis, an important factor in designing projects with large sample sizes and/or limited budget. Given that there are many available human genotyping arrays optimized for various purposes, a comprehensive guideline for choosing the most suitable SNP arrays in multiple ancestry groups is still lacking. To address this gap, we have introduced a systematic approach to assess a large range of SNP arrays across multiple datasets. We performed imputation and PGS performance assessments for 23 human available genotyping arrays in six ancestry groups using both public and in-house datasets by various metrics. By comparing the relative performance of SNP arrays to WGS with 4 metrics including imputation accuracy, imputation coverage, PGS correlation, and ADPR, we discovered important insights that can be used to suggest suitable arrays for genotyping-based studies on a specific population, especially under-represented populations.Even in a booming time of next-generation sequencing technologies, current big genotyping projects are still using SNP arrays as the work-horse for generating valuable data, especially for bio-bank scale projects47. We also observed that there were no specific arrays with moderate sizes that had superior imputation performances in AFR, and SAS, suggesting the need for genotyping arrays optimized for these populations. PGS performances were concordant to imputation performances in general. However, CytoSNP-850K v1.2 was an interesting array that showed superior PGS performances in all populations. This superior performance may be explained by the enrichment of cytogenetic regions in the design of the Infinium CytoSNP-850K v1.2 array48. The analyses also showed that underrepresented populations such as AFR, and SAS exhibited lower PGS performances  than other well-studied populations regardless of sample sizes were not significantly different in these populations. A possible explanation for these lower performances is due to the use of meta-analysis GWAS summary statistics in the current study. The strong bias in GWAS participants toward populations of European descent could be a reason for lower PGS in other populations as described previously50. In addition, PGS performances of small-sized arrays were significant lower in AFR which was possibly due to the higher number of genetic variations in this population1.Overall, we found that all 23 assessed arrays had high performances in both imputation and PGS. These commercial arrays differ markedly in designs, i.e. the number of markers on the arrays and targeted ancestry groups that would cause performance deviations. An important finding in our analysis was that in order to obtain high imputation performances, the choice of an array is not necessarily about getting higher density, but small to moderately-sized arrays (approximately 650k–850k tag SNPs), accompanied by well optimization for the targeted population could also produce high imputation and PGS performances. For example, the Japonica Array NEO, and the UK Biobank Array showed the highest performance when compared with other arrays with the same sizes for EAS, and EUR populations respectively. This indicates that using customized, small-size SNP arrays at the population-specific level can be a cost-effective genotyping solution without losing PGS performance44 that were limited to detect rare associated markers. This indicates that using WGS for PGS analysis does not provide significant improvement in term of disease risk stratification at this time although this trend can change in the future when GWAS summary statistics at higher resolution become widely available51.Notably, PGS constructed from imputed genotypes were very high in comparison with the original WGS PGS. The majority PGS correlations ranged from 0.90 to 0.99. In cases of optimal arrays for targeted populations in used , the PGS correlation to WGS was higher than 0.97. In addition, PGS ranking differences between WGS and imputed array genotypes were not high with the majority of differences were under 5 percentile when optimal arrays were used. The possible reason for this observation was that current GWAS summary statistics were mostly generated by imputed array genotypesFinally, to make this analysis capability available to broad audiences, we have developed a web tool that provides interactive analyses of SNP array contents and performances. As researchers may be interested in specific variants or regions, the tool aimed to support researchers to analyze SNP array contents and imputation performance based on population and genomic regions of interest. We hope this tool could facilitate researchers in designing their SNP array-based studies.Supplementary Figures.Supplementary Tables.


The detection of selective traits in different populations can not only reveal current mechanisms of artificial selection for breeding, but also provide new insights into phenotypic variation in new varieties and the search for genes associated with important traits. Panou sheep is a cultivated breed of Tibetan sheep in China with stable genetic performance, consistent appearance and fast growth and development after decades of artificial selection and cultivation. Due to long-term adaptation to the high altitude, cold and hypoxic environment in the plateau area, they may have formed a unique gene pool that is different from other Tibetan sheep breeds. To explore the genetic resources of Panou sheep, we used next-generation sequencing technology for the first time to investigate the genome-wide population structure, genetic diversity, and candidate signatures of positive selection in Panou sheep.HDAC9), protein tyrosine kinase 2 (PTK2), microphthalmia-related transcription factor (MITF), vesicular amine transporter 1 (VAT1), trichohyalin-like 1 (TCHHL1), amine oxidase, copper containing 3 (AOC3), interferon-inducible protein 35 (IFI35).Comparative genomic analysis with the closely related species Oula sheep (a native breed of Tibetan sheep in China) was used to screen the population selection signal of Panou sheep. Principal component analysis and neighbor joining tree showed that Panou sheep and Oula sheep had differences in population differentiation. Furthermore, analyses of population structure, they came from the same ancestor, and when K = 2, the two populations could be distinguished. Panou sheep exhibit genetic diversity comparable to Oula sheep, as shown by observed heterozygosity, expected heterozygosity and runs of homozygosity. Genome-wide scanning using the Fst and π ratio methods revealed a list of potentially selected related genes in Panou sheep compared to Oula sheep, including histone deacetylase 9 (The results suggest that traits related to growth and development and plateau adaptation may be selection targets for the domestication and breeding improvement of Tibetan sheep. This study provides the fundamental footprints for Panou sheep breeding and management.The online version contains supplementary material available at 10.1186/s12864-023-09146-2. In China, according to the geographical distribution of sheep and their genetic origin, the native sheep are divided into three ancestral populations: Mongolian sheep, Kazakh sheep and Tibetan sheep . TibetanThe extensive variation in local and improved breeds was the basis for genomic variation, adaptive traits and important agronomic traits in domestic sheep , 8. ThusBy comparing with the genome of its close relative Oula sheep, the genome characteristics of Panou sheep can be determined and whether it has become an independent Tibetan sheep breed. The population differentiation index (Fst) is a measure of the degree of differentiation between two populations . Fst detMITF was an important gene in the adaptive progressive penetration of Argali sheep to Tibetan sheep [Although SNP arrays have been widely used to identify genetic variants, their results are limited by the low density of probes . High-than sheep . Furtheran sheep , and thian sheep .However, to our knowledge, the genome-wide genetic characterization of Panou sheep has not been investigated. In this study, we obtained the whole genome sequencing data of Panou sheep for the first time to deeply understand its genetic diversity and population structure. Comparative genomic analysis with the closely related species Oula sheep was used to screen the population selection signal of Panou sheep, and it could provide theoretical basis for future improvement of economic traits and development of new breeding strategies in Panou sheep., Magu County, Gannan Tibetan Autonomous Prefecture, Gansu Province, China The genomic DNA samples were subjected to biological digestion with the restriction endonuclease, followed the Paired-End DNA Sample Prep kit , with a process of end repaired, dA-tailed, ligated to paired-end adaptors and PCR amplification with 300-bp inserts. The constructed libraries were sequenced using Illumina HiSeq X Ten  NGS platform at Genedenovo company  to obtain 150 bp (PE150) paired-end read data. All samples were sequenced at 10× coverage. Quality trimming was an essential step to generate high confidence of variant calling. Raw reads were processed to get high quality clean reads according to three stringent filtering standards: 1) removing reads with ≥ 10% unidentified nucleotides (N); 2) removing reads with > 50% bases having phred quality scores of ≤ 20; 3) removing reads aligned to the barcode adapter .https://www.ncbi.nlm.nih.gov/assembly/GCA_017524585.1) with the default parameters (−mem 4 -k 32 -M). The mapped reads were further filtered by removing multiply aligned reads, and the remaining high-quality reads were kept for variant calling. We used the software Genome Analysis Toolkit  [The program Burrows-Wheeler Aligner    with theThe markers obtained from the process analysis need to be further filtered. VCFtools-0.1.14  with theROH is an important parameter to evaluate the degree of inbreeding among populations. The calculation method is as follows: ROH refers to the total length of ROH of each individual in the genome, and LAUTO is the specific length of the autosomal genome covered by the SNPs of whole-genome sequencing. The incidence of adjacent SNPs and ROHs exceeds a threshold in genomic regions forming so-called Island [We used the -homozyg function in the PLINK 1.9 program  with thed Island . On a pod Island  was takeWe identified the most common genomic regions and candidate genes associated with ROH in two Tibetan sheep breeds by assessing the ROH Island shared between individuals, and plotted Manhattan plots against the ROH ratio for each SNP locus.− 8 and a generation time of 2 years. To assess genomic linkage disequilibrium (LD) patterns in different populations, we utilized Haploviewb 4.2 [Using PSMC  , and the number of ancestral clusters (K) was set from 1 to 9, and five-fold cross-validation was run to determine the cross-validation error Minimum K value. (2) Principal Component Analysis (PCA) was performed using PLINK 1.9 [Using the filtered SNP sets, performing population structure analysis: (1) Population structure analysis based on all SNP information using ADMIXTURE V 1.3.0 software   and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on these genes to understand the approximate functions of these genes and provide clues for further screening and verification. All candidate genes were mapped to terms in the GO and KEGG databases, and the number of significant genes for each term was determined with a ). A total of 65,218 non-synonymous sites (38.96%) and 96,252 synonymous sites (57.50%) were located within exons, resulting in a non-synonymous/synonymous ratio of 0.68 . All raw data were deposited in the NCBI BioProject chapter with accession number PRJNA797957.We used Illumina Hiseq X10 PE150 sequencing platform to perform whole-genome resequencing of 20 Tibetan sheep individuals, and obtained more than 600 Gb, 2 × 150 bp end sequences. The average sequencing depth of each species was 11.57× (OL) and 11.73× (PO) using Burrows-Wheeler Aligner (BWA)  softwareROH and LD decay. According to the set parameters, a total of 740 ROHs were detected in the 2 sheep breeds, with an average of 37 detected per sample. On average, ROH (392) was higher in OL than PO (348) . The minor allele frequencies were almost similar between the two breeds . More than 96% of ROH lengths in both varieties were in 0-1 Mb range, and the relative number distribution of ROH with different lengths was shown in Fig. ). Linkage disequilibrium (LD) decay analysis showed that the breeding histories of the two Tibetan sheep breeds were significantly different. Within the genetic distance interval, LD decay was higher in OL sheep and slower in PO sheep .Despite the relatively low valuation of Ne  , the SMC). The maximum percentage of SNPs detected in ROH was 65%. To identify the genomic regions associated with ROH in all individuals, the top 1% of SNPs most common in ROH (occurring in more than 35% of samples) were considered candidate SNPs. A total of 66 ROH islands were obtained, as shown see Additional file With loss of nucleotide diversity and increased homozygosity, selected genomic regions tend to give rise to ROH islands compared to the rest of the genome. ROH islands are not randomly distributed across the genome, but are shared by all individuals in a population. ROH islands were evident throughout the genome, and their distribution varied in length and location on chromosomes. The most common genomic regions associated with high frequency of ROHs were detected in both sheep breeds and the percentage of SNPs was assessed by calculating the frequency of SNPs in these ROHs on autosomes, plotting a Manhattan plot Fig. . The maxSPTA1, GALNTL6, SPP1, ABCG2, CDH1 and PDE2A genes , observed heterozygosity (He), and minor allele frequency (MAF) based on genotype frequencies Table . The hig tree and admixture analyses were performed. PCA results show that the first eigenvector clearly distinguished PO sheep varieties from OL sheep Fig. A. The fi. To determine the proportion and/or admixture levels of common genetic ancestors, we used ADMIXTURE V 1.3.0 [. In addition, PO-2, PO-4, PO-6, PO-9 in the PO sheep population showed greater genetic differences than the other samples, and the genetic background of the rest of the PO sheep individuals was similar to that of the OL sheep . The admixture analysis was consistent with the NJ results, revealing a mixed genetic composition of the Panou sheep breed. It is not difficult to see that due to the influence of living environment or artificial selection, there is differentiation phenomenon in Panou sheep.The results of the NJ tree and admixture analysis showed that the two studied varieties were divided into different genetic groups, confirming their genetic differences. The results of the NJ tree and admixture analysis showed that the two studied varieties were divided into different genetic groups, confirming their genetic differences. Due to the geographical proximity and the low degree of differentiation of PO sheep population, there is a close kinship or gene flow between the two breeds Fig. B. To det V 1.3.0  to explost and π ratio were used to identify the selection characteristics. The results of the selection elimination analysis of Fst and π ratio , AP-type membrane-coated adapter complex , canonical Wnt signaling pathway , etc. , pyruvate metabolism , other types of O-glycan biosynthesis  correlation  between loci. This result is consistent with Zhang et al. [The results of LD analysis showed that the average rg et al. . Therefog et al. . It may HBB and MITF) and associated with morphological features such as horn size and shape (e.g. RXFP2). Gene infiltration was associated with adaptation to the extreme environment of the Tibetan Plateau [Genome-wide sequence analysis revealed genes associated with different origins and domestication in sheep European and Asian Europe and Asia. It was found that the splitting time between Argali and domestic sheep at ~ 0.12–0.15 Mya was closer than previous estimates . Li et a Plateau . Theoret Plateau .RXFP2, GHR and ASIP were associated with horn shape, growth and lipid metabolism [P < 0.05) for PO sheep . The GO clusters were primarily associated with acetylgalactosaminyltransferase activity，pigment cell differentiation，canonical Wnt signaling pathway. These functional clusters are biologically relevant to the plateau adaptations because they are involved in energy metabolism, oxidative responses and stress responses, all of which are important regulators of the animal’s response to the high altitude, low oxygen environment. We found two positively selected genes (GALNT16 and C1GALT1C1) regulating the acetylgalactosaminyltransferase activity, two genes (MITF and AP1G2) affecting the pigment cell differentiation, one gene (MITF) influencing the canonical Wnt signaling pathway. And also, MITF and GALNT16 were significantly enriched in the KEGG pathway of melanoma and mucin type O-glycan biosynthesis, respectively . In addition, 7 candidate genes (VAT1、IFI35、HDAC9、MITF、TCHHL1、AOC3 and PTK2) provide additional evidence for the evolution of selection in the adaptation of PO sheep to the plateau environment.It is known that detection of common selection signatures by more than one method can provide stronger evidence for selection of specific genomic regions. Various statistical tools, such as Fst and π ratio, are recognized tools in the identification of livestock selection traits because they can point to the direction of selection required to identify a range of new regions as potential selection targets. Using Fst analysis, Zhang et al. examined genome-wide selection signals in five sheep breeds and identified genes associated with important traits. For example, tabolism . Plateautabolism . We alsoely Fig. C. In addVAT1) [IFI35) [AOC3) [MITF) is a basic helix-loop-helix-leucine zipper transcription factor that regulates melanocyte differentiation and development and pigment cell-specific transcription of melanogenesis enzyme genes [MITF transcriptionally activates HIF-1ɑ and thus promotes tumor angiogenesis. Hypoxia inducible factor-1ɑ (HIF-1ɑ) has not only anti-apoptotic effects in melanoma, but also stimulates the production of vascular endothelial growth factor (VEGF) [MITF gene could be regulated by G protein coupled receptor 143 (GPR143) and soluble guanylate cyclase (SGC), and participate in the formation of sheep coat color [MITF in hair follicles, and MITF may play an important role in the formation of Tibetan sheep fur color [Tibetan sheep are exposed to strong ultraviolet radiation for a long time, which will inevitably cause some damage to their own skin and organism health. Previous studies have found that vesicular amine transporter 1 (VAT1) , interfe [IFI35) , and ami) [AOC3)  were assme genes . Cheli er (VEGF) . MITF geat color . The blaur color .TCHHL1 gene was enhanced in UV-irradiated-damaged epidermal proliferating keratinocytes [TCHHL1 is involved in the proliferation of keratinocytes, and that TCHHL1 plays an important role in the homeostasis of normal epidermis, reducing damage to basal cells and squamous cells of skin hair follicles caused by ultraviolet rays [Studies had shown that ultraviolet radiation will produce a large number of free radicals in the skin, which will lead to the peroxidation of the cell membrane, so that the melanocytes will produce more melanin, which will be distributed to the stratum corneum of the epidermis, causing black spots. The study found that the expression of inocytes . These flet rays . It can PTK2 gene may be involved in the development of follicles and ovaries in the final stage of Hu sheep [HDAC9) is related to the development of muscle structure in sheep [HDAC9 is strongly expressed in skeletal muscle during embryogenesis but is downregulated in skeletal muscle after birth [HDAC9 weakens the transcriptional loop of muscle differentiation through a negative feedback loop by inhibiting the transcriptional activity of MEF2 [HDAC9 expression initially increased but then decreased to basal levels [HDAC9, a key negative regulator of the muscle gene program.Generally speaking, Tibetan sheep are small individuals and belong to low lambing sheep breed with seasonal estrus . NeverthHu sheep . Meat quin sheep . HDAC9 ier birth . Previou of MEF2 . Furtherl levels . These fThe above results suggest that these genes may play a dominant role in the regulation of immune response, disease resistance, fecundity, growth and developmental body size of PO sheep. Thus, the complex genetic mechanism of artificial selection for breeding Tibetan sheep in the highland environment is further demonstrated.MITF, a common gene adapted to ultraviolet radiation in the plateau. And the other identified candidate genes in PO sheep associated with hypoxia adaptation, growth and development, disease resistance and reproductive traits, providing opportunities to further explore the genetic diversity and genetic basis behind different phenotypes in Tibetan sheep, as well as providing valuable information for future studies on genotype-phenotype relationships and improved breeding in PO sheep.This study provides a comprehensive understanding of the phylogenetic relationship between PO sheep and OL sheep. PCA, Structure and NJ-tree analysis showed that PO sheep is different from OL sheep. After long-term artificial selection and breeding, this breed has gradually formed a unique gene pool that is different from other Tibetan sheep. Strong selection by Fst and π ratio enriched canonical Wnt signaling pathway (GO:0060070) and Melanoma (ko05218), identifying Additional file 1: Table S1. Sampling information of Tibetan sheep breeds (Ovis aries).Additional file 2: Table S2. Numbers and distribution of SNPs-InDel in the resequenced sheep breeds.Additional file 3: Table S3. The numbers and distribution of SNPs-InDel in the exons.Additional file 4: Figure S1. Pairwise sequential Markovian coalescent (PSMC) analysis results for the Tibetan sheep inferred variations in Ne over the last 106 years.Additional file 5: Table S4. Distribution of ROH islands throughout the genome.Additional file 6: Table S5. Distribution and annotation information of 35 shared genes in ROH island.Additional file 7: Figure S2. Analysis of the signatures of positive selection in the genome of samples and genomic landscape of the Fst values. Figure S3. Analysis of the signatures of positive selection in the genome of samples and genomic landscape of the π ratio values.Additional file 8: Table S6. The list of 78 genes under selection across the two breeds.Additional file 9: Table S7. GO top 20:GO enrichment analysis for candidate genes as determined by top 1% Fst and π ratio value methods (p < 0.05).Additional file 10: Table S8. KEGG top 20: KEGG enrichment analysis for candidate genes as determined by top 1% Fst and π ratio value methods (p < 0.05).