STAT4, ULK4, and KCNH5) at the genome-wide significance level, two of which (ULK4 and KCNH5) have not been found associated with PBC in any population previously. Notably, these genes could not be detected by using conventional single-SNP GWAS, highlighting the potential of the RHM method for the detection of new susceptibility loci in human diseases. These findings thereby provide strong empirical evidence that RHM is an effective and practical complementary approach to GWAS in this context. Also, liver tissue mRNA microarray analysis revealed higher gene expression levels in ULK4 in PBC patients (P < 0.01). Lastly, we estimated the common SNP heritability of PBC in the Japanese population (0.210 ± 0.026).While the advent of GWAS more than a decade ago has ushered in remarkable advances in our understanding of complex traits, the limitations of single-SNP analysis have also led to the development of several other approaches. Simulation studies have shown that the regional heritability mapping (RHM) method, which makes use of multiple adjacent SNPs jointly to estimate the genetic effect of a given region of the genome, generally has higher detection power than single-SNP GWAS. However, thus far its use has been mostly limited to agricultural settings, and its potential for the discovery of new genes in human diseases is yet to be fully exploited. In this study, by applying the RHM method to primary biliary cholangitis (PBC) in the Japanese population, we identified three novel loci ( Since the publication of the first GWAS study at the beginning of the twenty-first century, thousands of GWAS analyses have been performed and published; however, with the improvement of genotyping technologies, increased sophistication in study design, and the development of large-scale DNA biobanks and cohorts containing a wide range of clinical data/phenotypes on hundreds of thousands of individuals, the genome-wide association studies conducted nowadays are often much more elaborate than they were only a decade ago.In spite of the many successes of GWAS, this development has created a need for innovative analytical methods and statistical models to better make sense of this newly available data, for instance by accounting for population structure and relatedness, reducing error rates in unbalanced case/control traits, improving the detection power of rare variants, or analyzing complex immune-mediated diseases . These aIn this context, linear mixed models have received considerable attention; their flexibility enables among others estimating trait heritability while adjusting for environmental factors, or accounting for population stratification and cryptic relatedness in mixed-linear-model association studies. This trend is best illustrated by the development of a number of software applications catering for linear mixed model analysis, such as GCTA , BOLT-LMAmong these, it has been demonstrated that the regional heritability mapping (RHM) method , 10, whiSTAT4, ULK4, and KCNH5), two of which (ULK4 and KCNH5) have not been found associated with PBC in any population previously. These findings highlight the importance of polygenic model approaches like the RHM method for the discovery of new susceptibility genes alongside traditional GWAS analyses, given that these genes could not be detected with a conventional single-SNP GWAS approach. We tested these three new loci in an independent data set of 491 individuals for replication, and used liver tissue mRNA microarray data to analyze gene expression levels. We also carried out a univariate GCTA-GREML analysis [In practice, this means that in spite of the firm theoretical foundation and the simulation studies supporting the ability of the RHM method to identify QTLs that cannot be detected by single-SNP GWAS, its potential for the discovery of new susceptibility loci in human diseases is yet to be fully exploited. In this study, we applied the RHM method to primary biliary cholangitis (PBC) in 5643 Japanese individuals, and identified three new loci at the genome-wide significance level . Linkage disequilibrium pruning was also performed by using the --indep-pairwise 50 5 0.8 command. Identity by descent was calculated for each sample pair to remove related samples (pi-hat > 0.1). After these steps, there were 5643 individuals  and 1,022,240 autosomal SNPs remaining for analysis in the discovery data set. SNP cluster plots were used locally as a visual tool to confirm the genotyping quality of specific SNPs and remove those displaying genotyping issues. A small number of regions that displayed convergence problems or extreme values were also removed from the analysis. In the replication data set, 491 individuals  remained for analysis.In both the discovery and replication data sets, quality control procedures were performed per individual and per SNP, by using the following criteria in PLINK v1.90 : individu) = e) = y is a vector that represents the PBC phenotypes, β is a vector of fixed effects that includes the overall mean, sex, genotyping array, as well as the first ten eigenvectors from principal component analysis (PCA), u is a vector of random effects representing the whole (genome-wide) genomic additive effect, and e is a vector of residual effects. X is the design matrix for the fixed effects, G is the whole genomic relationship matrix (GRM) computed by using all SNPs, and I is a unit matrix; Linear mixed model methods were used for analysis. The following model was used to estimate the genome-wide SNP heritability:u) = w) = e) = w is a vector of random effects representing the regional genomic additive effect, Q is the regional GRM, and The RHM method, which consists in scanning the genome by using windows (regions) of a given number of adjacent SNPs , was usej and k was calculated as follows [xij and xik are the genotypes of the jth and kth individuals, respectively, at the ith SNP; pi is the frequency of the reference allele at the ith SNP; and N is the total number of SNPs.The GRMs were computed by using all of the SNPs to calculate the whole GRM, and by using the SNPs corresponding to each region of the genome to construct the regional GRMs. The genetic relationship between two individuals  follows :\documenAdditionally, a single-SNP GWAS analysis was performed to provide a point of comparison to the results obtained with the RHM method, using a logistic regression model with covariate adjustment for sex, genotyping array, and the first ten principal components. The replication data set was adjusted for sex and the first ten principal components.http://www.bioconductor.org/) was used in R to analyze the mRNA microarray data.The Genetic Complex Trait Analysis (GCTA v1.91.4) software application  was usedL0/L1), was used to test for the presence of regional variance, where L0 represents the likelihood for the null model , and L1 represents the likelihood for the alternative model . It was assumed that the distribution of the LRT for regional variance follows a 50–50 mixture of chi-square distributions with degrees of freedom equal to 0 and 1 [The likelihood ratio test (LRT) statistic, LRT = −2ln, chronic hepatitis C (CHC) , and metastatic liver cancer . Details about RNA extraction and preservation have been described previously . QuantitIn this study, we calculated the GREML-based common SNP heritability estimate of PBC (±standard error), which is generally thought of as the lower-bound estimate of narrow-sense heritability. It was estimated to be 0.210 (±0.026), adjusting for sex, genotyping array, and the first ten principal components.The results of the RHM analysis are presented in Fig. STAT4, ULK4, NFKB1/MANBA, HLA, TNFSF15/TNFSF8, KCNH5, and IKZF3. Information about each significant non-HLA region, as well as detail about the corresponding LRT and regional heritabilities, is provided in Table NFKB1/MANBA, TNFSF15/TNFSF8, and IKZF3 genes have already been examined in a number of publications on PBC in the Japanese population [The regions that were found to be significant at the genome-wide level LRT > 20.88) in this analysis are located on the following genes: 0.88 in tpulation , 30–32 aSTAT4, ULK4, and KCNH5) did not reach genome-wide significance (P < 5 × 10−8) in the GWAS analysis, and conversely two genes (POGLUT1 and IL7R) were identified by GWAS but not RHM.To provide a point of comparison to the results obtained with the RHM method, we performed a single-SNP GWAS analysis of the same data Fig. . AlthougSTAT4 and PBC in the Japanese population at the genome-wide level. Beside the discovery of this association, this finding is consequential given that it demonstrates the potential of the RHM method for the detection of new disease-related genes in human populations, as this association would have gone undetected had we only performed a conventional GWAS analysis.The RHM analysis provides evidence, for the first time, of a statistically significant association between ULK4 and KCNH5 genes. We performed additional analyses for validation and to confirm their involvement in disease pathogenesis by uncovering differences in gene expression through mRNA microarray data analysis (see below).RHM also identified two entirely new susceptibility loci, located on the IQCJ-SCHIP1/IL12A and PPARGC1A, at the suggestive level of significance (19.55 < LRT < 20.88). The IL12A gene is known to share a common pathogenic pathway with STAT4 and play an essential role in the development of PBC [PPARGC1A, at least two Japanese studies have provided evidence of its function in PBC progression through the regulation of bile acid synthesis [Additionally, the RHM analysis detected two other loci, t of PBC . Althougynthesis , 35.STAT4, ULK4, and KCNH5 genes. The results, presented in Table STAT4 and KCNH5 with PBC; while the replication of STAT4 was expected given that its role is known in PBC, the strongest replication was at KCNH5 (P = 5.83 × 10−25), a new candidate gene that only RHM detected. This KCNH5 locus corresponds to rs28608483 (chr14:g.63375059A > G), which had a P value of P = 7.33 × 10−8 (OR = 1.57) in the discovery data set. .We used an independent replication data set of 491 Japanese individuals  to validate the ULK4. Nevertheless, subsequent mRNA analysis performed to examine differences in gene expression in liver tissues provided promising results. While no gene expression was detected for KCNH5, for ULK4 gene expression levels were significantly higher in PBC patients (P < 0.01) , corresponds to the binding site for the Hand1:E47 and Smad3 transcription factors [This analysis, however, failed to replicate the findings for ) Table S. This fi factors .ULK4 and KCNH5 are necessary to uncover the mechanisms underlying disease etiology and progression, as well as clarify the potential role of the eQTL effects that have been found for ULK4, through cell-specific eQTL analyses of immune cells, in B, CD4+ T cells, CD8+ T cells, monocytes, and natural killer cells in the Japanese population  is somewhat low given that previous studies suggest a strong genetic component to PBC, e.g., it is known to have much higher concordance rates in monozygotic twins  than dizygotic twins , and a sibling relative risk of 10.5 . HoweverSTAT4 is a well-known PBC risk locus in populations of European ancestry and its role in the pathogenesis of PBC as well as other autoimmune diseases has already been thoroughly examined [STAT4 at the genome-wide significance level, while RHM detected an association signal; it is therefore possible that the failure of Japanese GWAS to identify STAT4 is simply due to the smaller sample sizes of these analyses, or maybe to untagged causal variants.Although examined , previouexamined , 31. In STAT4 SNP (P = 2.75 × 10−5) in a recent study of 2886 Japanese individuals  [STAT4 and PBC in populations of European ancestry and that were of a more limited scale, such as those performed by Hirschfield et al. [STAT4 and PBC. While sample size is not the only factor at play—differences exist for instance in terms of minor allele frequency between European and Asian populations with respect to rs7574865, one can assume that future GWAS analyses of PBC with larger sample sizes will detect an association signal at the genome-wide significance level for STAT4 in the Japanese population. RHM is therefore in all likelihood an effective approach when sample size matters for the detection of a given locus.Although rs7574865 (chr2:g.191964633T > G), the top ontrols) , reachedontrols) , earlierd et al.  and Liu d et al. , only prSTAT4 was detected by the RHM method but did not reach genome-wide significance in the single-SNP GWAS analysis also corroborates the results from simulations studies demonstrating that RHM often has higher power than single-SNP mapping methods [IL7R (rs7717955 (chr5:g.35862841C > T); P = 3.91 × 10−8) and CD80/POGLUT1 (rs13092998 (chr3:g.119245044G > T); P = 2.57 × 10−10) [The fact that  methods , 11, and× 10−10) , but tha× 10−10) , our finSTAT4, for example, supports this idea, given that it reached genome-wide significance in our RHM analysis but not with GWAS, and results from previous GWAS analyses have revealed that STAT4 comprises several independent association signals with PBC [IL7R gene, which was detected with GWAS but not RHM, have been found to be associated with PBC and other autoimmune diseases, it appears that they are all linked to a single locus, rs6897932 (chr5:g.35874575C > T), known to affect the inclusion of exon 6 through splicing regulation. According to the 1000 Genomes Phase 3 database, the top IL7R SNP in our analysis, rs7717955, is in perfect linkage disequilibrium with rs6897932 in the Japanese population  [Regarding the characteristics of the loci detected by either one of the two methods, our findings seem to be in line with computer simulations showing that RHM has more power than GWAS when it comes to detecting regions with multiple QTLs, but suggest that single-SNP GWAS may be more efficient in some instances when the susceptibility locus contains only a single causal variant . The caswith PBC . This ma r2 = 1) . Our finAlthough more discovery studies using the RHM method are needed to confirm the results of our analysis, it seems that while RHM is an effective and practical method for the detection of susceptibility loci that are not easily identified by GWAS, it should be considered as a complementary approach to single-SNP GWAS analysis rather than a potential replacement.P value in the GWAS analysis—e.g., for KCNH5 this corresponds to rs28608483 (P = 7.33 × 10−8 in the discovery data set)—and used them for replication. While we acknowledge that applying different approaches in the discovery and replication data sets is an imperfect strategy, we deemed it the most appropriate in this case.From a methodological point of view, the process of discovery and replication in GWAS is fairly straightforward: (1) identify a statistically significant SNP in the discovery data set, and then (2) use the same SNP for replication in an independent cohort. The only major caveat is that the SNP to be replicated—or a near-perfect proxy—must exist in the replication cohort. With RHM, several questions arise. First, applying the same standard as single-SNP GWAS—i.e., using the same SNPs in the discovery and replication cohorts—is not realistic in most cases since in RHM a given window contains tens or hundreds of SNPs; replication would therefore entail that all of the SNPs contained in a given window in the discovery cohort must also exist in the replication cohort. A more practical alternative would be to choose windows that cover exactly the same genomic region, i.e. at least the first and last SNPs are the same, with the additional assumption that the SNPs in between capture the regional genomic variance to the same degree in both cohorts. Proceeding this way, however, raises several other issues, such as the difference in marker density between windows in the target cohorts. Another key issue to contend with is sample size, given that replication cohorts are usually much smaller than discovery cohorts. For unrelated individuals and common SNPs, univariate mixed model analyses using REML methodology typically require sample sizes of over 3000 to bring the standard error of the SNP heritability below 0.1 , and in On a different note, the findings of our study, by validating simulation results showing that the RHM method is able to identify QTLs that cannot be easily detected by single-SNP GWAS, and by demonstrating that RHM is a powerful tool for the discovery of new susceptibility loci, imply that analytical approaches that focus on the joint effect of multiple SNPs are effective in capturing genetic variation for highly polygenic traits and deserve a more prominent place alongside traditional GWAS methods for the detection of new loci. Our results also suggest that their systematic implementation is desirable whenever possible, especially when statistical power is an issue, for instance when the cost of gathering large amounts of data becomes a major hurdle and/or sample size is limited to begin with, such as in the case of rare diseases or for studies targeting people from a given genetic group for which large data sets are unavailable. More broadly, these findings can be extended to the application of a number of methods other than single-SNP GWAS; even though in this study we chose to focus on RHM for the analysis of common SNPs given the dearth of studies on the application of RHM to human traits for the discovery of new susceptibility loci, other approaches such as SKAT-O for the detection of rare variants are also necessary.In the specific case of PBC in Japan, the value of pursuing complementary analytic methods in parallel with conventional single-SNP GWAS analyses with larger sample sizes is gaining recognition , one of STAT4, ULK4, and KCNH5) and PBC in the Japanese population, two of which (ULK4 and KCNH5) have not been identified in any population previously; this result was obtained by applying RHM and could not be achieved with single-SNP GWAS only. At the same time, this approach is not a replacement for GWAS, as GWAS appears to have its own benefits regarding QTL discovery. Additional research is nonetheless required to deepen our understanding of the underlying mechanisms between these new genes and the development of PBC.In summary, this study provides strong empirical evidence that RHM is an effective tool for the identification of new susceptibility loci in human diseases with the ability to identify QTLs that cannot be detected with conventional single-SNP mapping methods. We identified associations at the genome-wide significance level between three new loci (https://www.cog-genomics.org/plink/1.9/. GCTA, https://cnsgenomics.com/software/gcta/. R, https://www.r-project.org/. limma, http://www.bioconductor.org/packages/release/bioc/html/limma.html.PLINK, Supplemental legendsSupplementary Figure 1Supplementary Table 1Supplementary Table 2Supplementary Table 3


Beta vulgaris L.) breeding. This study aimed to identify SNP markers associated with low bolting tendency by establishing a genome-wide association study. An elaborate 3-year field trial comprising 13 sugar beet lines identified L14 as the one exhibiting the lowest bolting tendency along with an increased survival rate after autumnal sowing. For SNP discovery following phenotyping, contrasting phenotypes of 24 non-bolting and 15 bolting plants of the L14 line were sequenced by restriction site-associated DNA sequencing (RAD-seq). An association model was established with a set of 10,924 RAD-based single nucleotide polymorphism (SNP) markers. The allelic status of the most significantly associated SNPs ranked based on their differential allelic status between contrasting phenotypes (p < 0.01) was confirmed on three different validation datasets comprising diverse sugar beet lines and varieties adopting a range of SNP detection technologies. This study has led to the identification of SNP_36780842 and SNP_48607347 linked to low bolting tendency and can be used for marker-assisted breeding and selection in sugar beet.The identification of efficient molecular markers related to low bolting tendency is a priority in sugar beet ( Sugar beet is an important crop in the temperate areas providing about 30% of total sugar  affects different plant species, such as sugar beet  bolt readily without vernalization, heterozygotes derived from crosses between annual and biennial beets exhibit a delay in bolting compared with annual (BB) beets  located in chromosomes 4 and 9 of sugar beet. In the same study, it was shown that late seasonal bolting alleles, SBT_4 and SBT-9 (BR1) in combination with biennial alleles at B might provide beet genotypes with bolting resistance  phenotyping 13 sugar beet lines for bolting tolerance after autumnal sowing from a 3-year field trial, (b) RAD-sequencing of the line L14 combining highest survival and the lowest bolting percentage, (c) establishing a Genome-wide association study (GWAS) to identify favorable alleles related to low bolting tendency, and (d) validating the identified SNP markers using a range of SNP genotyping methods on diverse sugar beet lines and varieties. The results from this study precede controlled bolting of sugar beet breeding through marker-assisted selection and provides insights into candidate genes involved in the complex process of flowering and cold tolerance in sugar beet.SNP183, linked to bolting tolerance generated in an earlier effort by our lab . The SNP markers had a high call rate of 97% across all the samples and were 100% reproducible s of L14 . The firp-value . These variants were mined using two likelihood ratio tests: (i) Fisher exact test and (ii) nbinom test . The resp-value .The molecular validation strategy involved three technologies: (1) high-resolution melting analysis (HRM), (2) Sanger sequencing, and (3) SNP genotyping using rhAmp based assays.T-test; We adopted a rapid and reliable approach to screening SNP markers identified from the bioinformatic association analyses using, as the first step, 384-well plate based high-resolution melting (HRM) assays. Thirty SNP targets were evaluated on nine individuals of bolters and non-bolters of the L14 line in triplicates. The difference in the melt curve profiles of the bolter and non-bolter phenotypes of L14 as a result of their sequence composition can be seen in Sanger sequencing-based validation was carried out for 10 promising SNP targets from HRM based validation . Figure p < 0.0001) as shown by the contingency table  annual vs. biennial habits, (2) bolters- vs. non-bolters of biennial habits, and (3) spring-sown and autumn-sown hybrid varieties. The results of the association test with chi-square and p-value are reported in cy table .The chi-square test of independence was used to analyze the contingency table formed by the evaluated alleles and phenotypes as categorical variables. Particularly, this test evaluated whether there is a significant association between the categories. As the output of this test, the mosaic plot in Arabidopsis was done and pointed to AT1G47410, which was still uncharacterized in terms of function and protein domains. In fact, orthologs in many species remained uncharacterized but provided a 75% overlap between the protein in question and Chaperone J-domain superfamily proteins. Further, PSI-BLAST resulted in its annotation as “DNAJ heat shock N-terminal domain-containing protein” in Prunus dulcis. The protein similarity and overlaps along with the ortholog tree specifically looking at orthologs of B. vulgaris, Arabidopsis, and Prunus can be observed in BvBTC, BvFT1, and BvFT2 . Mining the most useful SNPs discriminating the phenotypes involved the use of both (a) Fisher exact test and (b) nbinom test from DESeq2 to identify SNP signatures strongly linked to bolting tolerance post-winter . SeveralArabidopsis  sugar beet lines with high survival rate and bolting tolerance post-winter, (2) SNP alleles associated with low bolting tendency post-winter, and (3) description of candidate genes involved in bolting resistance in sugar beet.BTC1 locus in the homozygous state . The seeds of each genotype were sown on November 19–20, 2016 for the 1st year, November 20, 2017 for the 2nd year, and November 22, 2018 for the consecutive year. Sugar beet plants were thinned after germination in the field to obtain 250 individuals per line distributed in three randomized plots. The plots were managed following local recommendations until the bolters and survival count date. The final count was carried out in the middle of June across the 3 years. Plants displaying stem elongation were scored as bolting individuals, while plants that did not show stem elongation were labeled as non-bolting individuals .Genomic DNA was obtained from 15 bolting and 24 non-bolting individuals of the L14 line using a modified CTAB DNA extraction method  was selected as candidates for downstream molecular validation , bowtie2 (version 2.3.5.1), samtools (version 1.9), picard-tools (version 2.18.12), samtools (version 1.9) mpileup, and bcftools (version 1.9) to extract variants. The resulting vcf files were filtered using bcftools (version 1.9). The reference genome used was the EL10 Sugar Beet reference genome (NCBI PRJNA413079). The base file for all the bioinformatics analyses described was the merged vcf file containing genotypes of the 10,924 markers across 15 bolters and 24 non-bolters. The genotype matrix was analyzed using the SNPRelate package in R to build PCA plots and understand the genetic structure . For thelidation .The commonly resulting signatures from the association study and DESeq approach  were alsT-test; Flanking sequences of 150 bp around the target SNP were extracted using the bedtools getfasta tool (version 2.28.0). The list of sequences was given as input to the stand-alone version of the Primer3 software (version 4.0) to design primers with suitable amplicon lengths. The HRM primer sequences are available in http://www.ebi.ac.uk/Tools/msa/clustalw2/) and SnapGene 5.1.7  to generate multiple sequence alignments and chromatograms (Flanking sequences of 250 bp around the target SNP were extracted using the bedtools getfasta tool (version 2.28.0). The list of sequences was given as input to the stand-alone version of the Primer3 software (version 4.0) to design primers with suitable amplicon lengths. Five biological replicates of bolters and non-bolters of the L14 biennial line were tested. The reaction setup used comprised of 10 μL of 5X Reaction Buffer , 2 μL each of 10 μM forward and reverse primers, 2 μL of 2u/μl of HiFi Polymerase (PCR Biosystems), 5 μL of 50 ng/μL sample DNA made up to a 50 μL using nuclease-free water. The thermocycler program consisted of 1 min of initial denaturation at 95°C, followed by 30 cycles of 15 s at 95°C, 30 s at 60°C, and 30 s at 72°C. The amplicons were run on a 1.5% agarose gel to verify the amplification. PCR products were purified using NucleoSpin Gel and PCR Clean-up  and were sent for sequencing. The resulting sequences were analyzed using ClustalW2 . Their sequences are available upon request. Three hundred biological samples were subjected to rhAmp genotyping, which was performed in 5 μL using 384-well plates, and low Rox was used as a passive reference dye. 5 ng of DNA was mixed with 2.65 μL of rhAmp Genotyping Master Mix, 0.25 μL of rhAmp SNP assay mix, and 1 μL of nuclease-free water. The thermal cycle parameters were adopted from Broccanello et al. . The asshttps://www.ebi.ac.uk/ena/browser/view/PRJEB42403.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: SR, GCa, and PS made the experimental design. SR, GCa, MD, GB, and PS carried out the sampling. SR, CB, CC, LM, AE, and MM performed molecular analyses. SR conducted the statistical and bioinformatics analyses and wrote the study. AS, GCo, SS, and PS contributed to the critical writing and review of the manuscript. All the authors reviewed the manuscript and gave final approval for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Juglans regia L.) is one of the top four most consumed nuts in the world due to its health benefits and pleasant taste. Despite its economic importance, the evolutionary history and genetic control of its adaptation and agronomic traits remain largely unexplored.Common walnut (J. regia diverged from J. sigillata with extensive hybridizations after the split of the two species. In contrast to annual crops, the genetic diversity and heterozygous deleterious mutations of Chinese common walnut trees have continued to increase during the improvement process. Selective sweep analyses identify 902 genes uniquely selected in the improved common walnut compared to its progenitor population. Five major-effect loci are identified to be involved in walnut adaptations to temperature, precipitation, and altitude. Genome-wide association studies reveal 27 genomic loci responsible for 18 important agronomic traits, among which JrFAD2 and JrANR are the potentially major-effect causative genes controlling linoleic acid content and color of the endopleura of the nut, respectively.We report a comprehensive walnut genomic variation map based on whole-genome resequencing of 815 walnut accessions. Evolutionary analyses suggest that Chinese The largest genomic resource for walnuts to date has been generated and explored in this study, unveiling their evolutionary history and cracking the genetic code for agronomic traits and environmental adaptation of this economically crucial crop tree.The online version contains supplementary material available at 10.1186/s13059-021-02517-6. Juglans genus contains ~ 21 diploid species. Among them, common walnut (Juglans regia L.) is one of the top four most consumed nuts in the world. Rich in unsaturated fatty acids, proteins, minerals, and vitamins, the common walnut offers many benefits to human health. It is widely grown commercially in diverse temperate regions of Asia, Europe, North and South America, South Africa, Australia, and New Zealand. China is the world’s leading producer followed by the USA, Iran, and Turkey [J. regia, J. sigillata harbors some wild nut characteristics, which may represent a primitive form, and is mainly distributed in southwest China [J. regia and J. sigillata arose from an ancient hybridization that occurred between American black walnuts (Rhysocaryon) and Asian butternuts (Cardiocaryon) after a climate-driven range expansion in Eurasia during the Pliocene [14C-dated leaf fossils and carbonized nuts, J. regia has existed in China for more than 7000 years [The Pliocene . Accordi00 years . However00 years .J. regia by de novo assembly from short reads opened the genomic era for studies on walnut biology [J. regia reference genome has been improving, assisted by up-to-date long-read and Hi-C sequencing technologies [J. regia 700 K SNP array was developed, which has greatly facilitated research and development in terms of walnut genetics and breeding [The release of the first reference genome of  biology . The quanologies , 9, whicbreeding . Using tbreeding –13. Somebreeding . HoweverJ. sigillata and J. regia accessions from geographically diverse regions in China, as well as samples from Iran and Pakistan. Population structure, genetic diversity, and genetic load for Chinese walnut populations were investigated. Candidate genomic loci or genes involved in the improvement of Chinese walnuts have been identified based on genomic selective sweep analyses. In addition, potentially causative genes responsible for agronomic traits and their adaptations to multiple environmental factors were identified based on GWAS and genetic-environment association (GEA) analyses.Population genomic analyses of seed-propagated annual crops have greatly advanced our understanding of plant domestication and evolution . HoweverJ. regia and J. sigillata samples from China, Iran, and Pakistan. The Chinese accessions were sampled from 19 provinces covering most areas where common walnut is currently cultivated  [J. regia (JR) and J. sigillata (JS) accessions could be separated into different clades, while there is one intermediate group between the two species clades   , in which JR1 mainly consists of accessions from Middle and North China, JR2 accessions are mainly distributed in Northwest (Xinjiang) and Northeast (Liaoning) China, while the JR3 subgroup consists of accessions from Iran, Pakistan, and the Tibetan Plateau, China  than in JR1 (14/251), suggesting that JR2 seems to be subjected to more artificial selection and is likely a more derived group compared to JR1  for J. sigillata (JS) and J. regia (JR1) populations and found the Ne of the JR1 population rapidly decreased relative to JS around 20–30 k generations ago  years ago.Principal component analysis (PCA) based on the genome-wide SNPs supported the population structure evidenced in the phylogenetic tree Fig. d. The JS regia JR populatiθw and θπ values were calculated genome-wide to estimate the genetic diversity for different Juglans populations , we found the JS population had obviously higher genetic diversity than the two Chinese J. regia populations (JR1 and JR2). The overall reduction in nucleotide diversity supports that Chinese J. regia could have endured a genetic bottleneck during divergence from J. sigillata  of JR1 and the improved JR2 population, we observed an opposite pattern based on the two different summary statistics  . However, a significantly  higher θπ value was observed in the JR2 (3.113E−3) compared to JR1 (2.475E−3). The reduction in the θw value could be due to loss of segregation sites caused by genetic drift during the genetic bottleneck, while the heterogeneity across genome-wide loci was increased possibly due to artificial hybridizations during dispersal and immigration for improvement of walnut trees. We examined the diversity characteristics in some perennial tree species  among the Juglans populations suggested that the inter-species divergence between J. sigillata and J. regia was around 0.28 to 0.33, while intra-species divergence within J. regia was 0.11–0.21  for the entire population  between JS and JR1 decreased with the increased proportion of the JR1 ancestry  [J. sigillata than in J. regia , although the total genetic burden remained generally unchanged, the proportion of heterozygous deleterious mutations for JR2 increased substantially compared to its progenitor JR1   but lowee (< 5%)  and grape (< 5%) . The numWe observed that the deleterious SNPs segregated at a lower frequency compared to tolerant and synonymous SNPs in walnut genomes, suggesting a stronger effect of purifying selection acting on the dSNPs than other types of SNPs during walnut evolution Fig. c. We furJ. regia populations (JR1 and JR2), we used J. sigillata as a background population and adopted a combination of three strategies  > 3, Z(FST) > 3, and top 5% for μ statistic) to search for selective sweep regions  were located within the selective sweep regions, suggesting that those genes might play roles in adapting to high latitude environments for JR1 compared to JS  associated with dwarfism were also found in the target regions of JR1. Among them, we found the signature of positive selection of the region covering gid2 was stronger in the JR2  > 3) between JR1 and JS. The region could also be targeted by signatures based on reduction of diversity and μ statistics. This genomic region included functional genes involved in flowering time  and reproduction (gsl8-2 and tms)  was found in the region (Chr6: 28.1–28.4 Mb) only differentiated between JR2 and JS. In rice, BAK1 was reported to be involved in the developmental process of grain filling [AGPS2 (JreChr04G11920) was in a genomic region (Chr4: 37.4–37.7 Mb) with specific reduction of diversity in JR2 compared to JS. AGPS2 could be coordinated with starch accumulation in the developing rice seed [glu4a genes (JreChr03G12610 and JreChr03G12612) involved in seed glutelin quality  [th1 gene (JreChr03G12627) related to seed weight per panicle in rice [OsNIT1, which has been reported to be involved in root-growth responses to nitrate and ammonium [Compared to JR1, we found that some genes located in regions specifically selected in JR2 are homologous to important functional genes responsible for seed weight and starch accumulation  , and one in rice , were foammonium .OsMKK6) and in Arabidopsis   of the ATC in the Arabidopsis TERMINAL FLOWER 1 (TFL1) gene family was also located within the peak region on chromosome 13  involved in the flavonoid biosynthetic pathway. In addition, the gene was also under positive selection in the walnut population collected from Tibet at high altitude  with Pfam “PF00654” (Voltage gated chloride channel) is located within the peak region on Chr3. The CBSCLC gene family has been reported in response to various plant stresses including salinity, drought, cold, high temperature, UV, wounding, and genotoxicity, in both root and shoot tissues [UV-B radiation stress in high-altitude regions can enhance the production of reactive oxygen species (ROS), which could cause damage to plant growth and development. It has been documented that flavonoids, especially flavonols, are highly effective in mitigating ROS and UV-B radiation . Interes tissues . These gP < 1.16E−6, a total of 27 genomic loci were identified by both fastGWA and EMMAX software as significant GWAS peaks for 18 traits  was found within this region, of which one nonsynonymous SNP (13_27108275) showed a significant association with both C18:1 (5.75E−8) and C18:2 (5.17E−7)  is the causal gene responsible for linoleic acid biosynthesis in walnut. We further evaluated the conservation level based on SIFT (sorting intolerant from tolerant) score [The ratio of unsaturated fatty acid relative to saturated fatty acids is an important indicator of evaluating the quality of edible oil. Compared to a stable unsaturated fatty acid proportion in sesame , a highleticulum . There aogs Fig. c. In ourogs Fig. d, addingt) score   located within this region  showed a continuously increasing trend, and FPKM value could reach up to ~ 800, providing more support for a role of JrANR playing in the endopleura development   , using a similar strategy would involve a tremendous amount of time and efforts to investigate their flowering time and fruit traits. Considering the differences between long-lived perennial trees and annual crops, strategies to study their genetic basis of adaptation and agronomic traits may be modified accordingly. It would be more feasible to first identify one genetically diverse tree population and investigate the phenotypes of the same individuals in the population annually. Here, we constructed a genotyping panel of 815 walnut trees, among which annually continuous phenotyping could be performed for walnut individuals from China. This phenotyping strategy in a tree population has been proved to be capable of mapping candidate QTLs for some important traits investigated in this study. With accumulated annual phenotyping data, more GWAS analyses and evidences could refine these QTLs. This may also assist in determining the genetic basis of traits related to tree development. In addition to agronomic traits investigated here, many other traits like disease resistance, root morphology, and even microscopic phenotypes of walnut trees could also be studied based on this population and genotyping panel in the future.Marker-assisted selection (MAS) and genome selection (GS) have been successfully applied to accelerating breeding cycles for many annual crops. Due to the long breeding period of fruit trees, it is important to develop useful MAS and GS tools for the selection of individuals that have desirable traits and good fitness to the growing environments . Based oAlthough genomics has greatly accelerated the dissection of potential genes or networks related to fruit tree traits in the past decade, there remain many limitations which hinder further causative gene identification and in-depth gene function verification. For example, it is usually difficult to construct an artificial population for a perennial tree species. The lack of knowledge on gene function in fruit trees also presents an obstacle to narrow down candidate genes in the GWAS analysis. Functional analysis requires simple and effective transgenic technologies. While a mature transformation approach in walnuts (and other fruit trees) was developed some time ago , it is sJ. regia likely diverged from J. sigillata with a genetic bottleneck. Extensive hybridizations occurred after the divergence of the two species. The improvement in the walnut trees was accompanied with an increased level of heterozygosity and genetic diversity, which is distinct from the cultivation process of annual crops. The deleterious mutations in walnut tree genomes were shifted from a homozygous to a heterozygous form during its improvement. The signatures of environmental adaptation and improvement for walnut trees were uncovered by selective sweep scans and genetic-environmental associations. We also identified potentially causative genes for economically important traits including linoleic acid content and color of endopleura. These identified loci will contribute significantly to the genetic improvement and molecular breeding of walnuts.Based on the most comprehensive walnut genomic variation map to date, we found that Chinese Juglans accessions from Zhang et al. (2019) [J. sigillata (2), J. regia (1), J. cathayensis (2), J. mandshurica (2), J. microcarpa (2), and J. nigra (2) , Iran (76), and Pakistan (11). The Chinese walnut accessions were widely sampled from 19 provinces  , includiA total of 44 agronomic traits of walnut accessions collected from China were investigated in 2016 (44 traits) and in 2019 (14 traits) . SNPs with minor allele frequency (MAF) above 0.05 were used for LD estimation of each population. We estimated the inbreeding coefficient for each individual of all walnut samples by VCFtools  [F = (O(hom) − E(hom)) / (N − E(hom)), in which O(hom) is the observed number of homozygous SNPs, E(hom) refers to the expected number of homozygous SNPs, and N is the total number of genotyped loci of the individual.A phylogenetic tree was constructed using FastTree  based on v0.9.19 . The ancVariScan  based onthub.io) . The sofJ. sigillata and J. regia was inferred using SMC++ v1.13 [μ = 2.5 × 10−9 mutations×bp−1 × generation−1 [The divergence time between ++ v1.13 . For eacration−1 .π, FST) were estimated for each window by VCFtools  [J. sigillata population as background, the FST and relative genetic diversity change for the two J. regia populations (JR1 and JR2) were calculated and then were standardized and transformed into Z-scores. The genomic windows with a Z-score above 3 were determined as candidate regions. In addition, we used RAiSD [J. regia populations (JR1 and JR2). The scanning window size of RAiSD was set to 10 kb. RAiSD adopted a composite evaluation test that scores genomic regions by quantifying changes in the amount of genetic diversity, SFS, and the levels of LD and along a chromosome [The genome was scanned in a 100-kb window size with a step size of 10 kb, and the population parameters (thub.io) . Using ted RAiSD  to detecromosome . The genJ. regia reference genome. The SIFT score ranges from 0 to 1, with 0 being more conservative. For genes within GWAS significant signals, SIFT scores for SNPs at coding regions could be obtained.For measuring SNP conservation, we used SIFT software  to constJ. regia and J. sigillata accessions as well as one J. nigra sample (outgroup). (2) The software SnpEff was applied to determine nonsynonymous SNP sites. We further used the SIFT software to calculate the SIFT score, which could estimate the probability of each nonsynonymous SNP to be deleterious (SIFT score < 0.05) or tolerant (SIFT score ≥ 0.05). (3) Since we aimed to calculate the derived deleterious allele for each sample, the genotypes of the J. nigra accession (homozygous state) was used for allele polarization. For a deleterious SNP site, if the SNP genotype of a given sample is homozygous and different from J. nigra, we determined it as homozygous deleterious SNP, then the number of derived deleterious alleles is 2 × No. homozygous deleterious SNPs. If the SNP genotype of a given sample is heterozygous, we determined it as heterozygous deleterious SNP, then the number of derived heterozygous deleterious alleles is 1 × No. heterozygous deleterious SNPs. Finally, the total number of deleterious alleles for each sample is 2 × No. homozygous derived SNPs + 1 × No. heterozygous deleterious SNPs.For the deleterious variation analysis, we followed previous deleterious variation studies in plants  [r2 ≥ 0.5” [Genome-wide association study was performed for 44 traits by fastGWA  implemennt SNPs) . The ind2 ≥ 0.5” . TherefoJ. regia accessions (mainly in JR2) could have migrated from different places, we only explore genetic-environmental associations based on samples from populations of JS, JS-JR1 and JR1. Taking environmental data as phenotypes, genetic-environment associations were performed using mixed linear model by fastGWA [For genetic-environmental association analysis, the latitude, longitude, and altitude data for each individual were recorded with a GPS measuring instrument . Based on the latitude and longitude of each walnut sample, we extracted 19 bioclimatic variables (bio1 – bio19) related to temperature and precipitation from the WorldClim 2 database  using th fastGWA  with PCA fastGWA .J. regia genome reference [The RNA-seq data for walnut embryo were from our previous study  [J. regia annotation based on Interproscan v5 [P value criterion of < 0.05 was used for the considered enrichment GO terms.GO enrichment analysis was carried out using AgriGO (agriGO/) . The GO oscan v5 . The P vAdditional file 1: Table S1. Data information for all walnut accessions used in this study.Additional file 2: Fig. S1. Circos plot for the genome-wide SNP density. Fig. S2. Population structure of J. sigillata and J. regia accessions. Fig. S3. Clustering of 44 traits for walnut samples investigated in this study. Fig. S4. Comparison of traits related to walnut fruit weight, size and fatty acid content for different populations. Fig. S5. Mutation burden for different walnut subpopulations in the JR3 group. Fig. S6. Genomic signatures for selection of JR1 walnut population. Fig. S7. Phenotypic differences between J. sigillata (JS) and J. regia (JR). Fig. S8. Clustering of 20 environmental values for walnut samples investigated in this study. Fig. S9. Genomic signatures for the Tibetan walnut population. Fig. S10. Genomic loci associated with agronomic traits of walnut identified by GWAS. Fig. S11. Candidate gene associated with nut shell thickness.Additional file 3: Table S2. Comparison of called genotypes by NGS-based bioinformatic approach and Sanger-based method.Additional file 4: Table S3. Comparison of characteristics of genetic diversity during domestication and improvement of perennial trees.Additional file 5: Table S4. Putative functions of genes with high genetic burden during walnut evolution.Additional file 6: Table S5. Genes under putatively selected genomic regions for JR1 identified by three selective sweep scan approaches.Additional file 7: Table S6. Genes under putatively selected genomic regions for JR2 identified by three selective sweep scan approaches.Additional file 8: Table S7. Enriched GO terms for genes located in selected regions of JR1 and JR2.Additional file 9: Table S8. Environmental data values for 492 walnut samples used for genetic-environment association analysis.Additional file 10: Table S9. Genomic regions associated with environmental factors related to temperature, precipitation, and altitude.Additional file 11: Table S10. Genes located in genomic regions identified by genetic-environmental association studies.Additional file 12: Table S11. Genes under putatively selected genomic regions for the Tibetan walnut population identified by three selective sweep scan approaches.Additional file 13: Table S12. Phenotypic values for walnut samples collected and investigated in 2016 and in 2019.Additional file 14: Table S13. Genomic loci associated with agronomic traits investigated in 2016 in walnut identified by GWAS.Additional file 15: Table S14. List of significantly associated genic SNPs (identified by fastGWA) for traits investigated in 2016.Additional file 16: Table S15. Genomic loci associated with agronomic traits investigated in 2019 in walnut identified by GWAS.Additional file 17: Supplementary Note. Detailed descriptions for the phenotyped walnut traits.Additional file 18: Table S16. Designed primers for validation of called SNP genotypes.Additional file 19: Table S17. Summary of RNAseq data used in this study.Additional file 20. Review history.


Sebastes mentella displays a particular life-history as livebearers, for which existence of multiple ecotypes has been suspected to complicate the genetic population structure of the species. Double digest restriction-site associated DNA was used to investigate genetic population structure in S. mentella and to scan for evidence of selection. In total, 42,288 SNPs were detected in 277 fish, and 1,943 neutral and 97 tentatively adaptive loci were selected following stringent filtration. Unprecedented levels of genetic differentiation were found among the previously defined ‘shallow pelagic’, ‘deep pelagic’ and ‘demersal slope’ ecotypes, with overall mean FST = 0.05 and 0.24 in neutral and outlier SNPs, respectively. Bayesian computation estimated a concurrent and historical divergence among these three ecotypes and evidence of local adaptation was found in the S. mentella genome. Overall, these findings imply that the depth-defined habitat divergence of S. mentella has led to reproductive isolation and possibly adaptive radiation among these ecotypes. Additional sub-structuring was detected within the ‘shallow’ and ‘deep’ pelagic ecotypes. Population assignment of individual fish showed more than 94% agreement between results based on SNP and previously generated microsatellite data, but the SNP data provided a lower estimate of hybridization among the ecotypes than that by microsatellite data. We identified a SNP panel with only 21 loci to discriminate populations in mixed samples based on a machine-learning algorithm. This first SNP based investigation clarifies the population structure of S. mentella, and provides novel and high-resolution genomic tools for future investigations. The insights and tools provided here can readily be incorporated into the management of S. mentella and serve as a template for other exploited marine species exhibiting similar complex life history traits.The diverse biology and ecology of marine organisms may lead to complex patterns of intraspecific diversity for both neutral and adaptive genetic variation.  Disentangling complex genetic structure is needed for conservation of intraspecific biodiversity and future evolutionary potential of exploited species . EvidencSebastes mentella (beaked redfish), which is the most economically important species of the genus in the North Atlantic. The species has a complex life history, which has caused much debate regarding genetic population structure . All other loci were considered as neutral. Convergence of BayeScan runs was assessed in R using the CODA package following Geweke’s [Gelman.diag which is based on a comparison of between and within chain variances. The output of gelman.diag are the scale reduction factors for each parameter. A factor of 1 was found meaning that between- and within-chain variance are the same indicating no convergence problems.A finite-island model as implemented in BayeScan 2.1  was applGeweke’s  convergeAs BayeScan is sensitive to hierarchical structure in the data, and has low power in pairwise tests, we also tested OutFLANK  to detecS. norvegicus reference scaffolds [S. rubrivinctus and S. nigrocinctus scaffolds  using blastn [To further identify regions of the genome potentially under selection, we aligned all the neutral and outlier sequences to caffolds  and to Sadegenet R package [find.clusters’ was applied to determine the number of clusters whereas ‘optima.a.score’ was used to estimate the optimal number of PCs to retain. Finally, the identified clusters were named as the ‘shallow’, ‘deep’ and ‘slope’ ecotypes depending on the occurrence of reference fish, and were compared to the clusters identified by Saha et al. [Individuals were clustered by discriminant analysis of principal components (DAPC)  using ne package . The met package . The funa et al. .q values between 0.1 and 0.9 [q values based on microsatellite and SNP data for the 96 ‘shallow’ and ‘deep’ fish from Greenland waters.In an effort to identify possible hybrids between the ecotypes, a Bayesian clustering approach, as implemented in STRUCTURE 2.3.4 , was use and 0.9 , 41. We FST [Population divergences, before and after clustering see , were meFST  and testFST . ArlequiFST  to minimThe population history of the ecotypes were inferred by Approximate Bayesian Computation (ABC) in DIYABC V2.1.0 . The ABCS. mentella gene pools and to examine if a scenario similar to that of Shum et al. [17, et al. [S. mentella gene pools was determined, we added the ‘slope’ ecotype in the analysis and proceeded with stage-2 to determine the complete scenario  were excluded due to excessive missing data (>20%), retaining 14,412 SNPs. We removed 25 fish with incomplete genotypes in >20% of loci, retaining 252 fish . A totalFIS . However, 139 SNPs had FIS>0.60 or FIS<-0.60. Ninety-six of those loci were homozygous in three to six of the seven collections and had MAF<0.05 in one to four other collections, and so were excluded from the dataset. At that stage, we retained 4,277 SNPs in 2,040 RAD tags . The mean sequencing depth for the 2,040 RAD tags was 21  as a final dataset. Descriptive statistics for each selected locus are available in Neither individual multilocus heterozygosity nor locus-specific FIS  was corrs was 21 . The meaectively . There wS and HE . After tAmong the seven collections (before clustering), BayeScan detected 90 SNPs under directional selection in the dataset containing 4,277 SNPs (FDR = 0.05). After clustering of individuals into three ecotypes , BayeScan identified in total 109 outliers from 4,277 SNPs . No outlSebastes scaffolds  while 92 and 89 RAD tags aligned to Pacific  Sebastes scaffolds  and two RAD tags aligned to the same scaffold in S. rubrivinctus genome .In total, 82 RAD tags (containing one outlier each) aligned to Atlantic S. norvegicus, S. rubrivinctus and S. nigrocinctus scaffolds, respectively . Of the 97 outliers, only 15 aligned to the same scaffold as neutral SNPs  methods), using data from both the neutral and outlier SNP panels . The maiS. mentella samples using data from both neutral  and outlier  SNPs. Likewise, pairwise FST estimates were highly significant among the three ecotypes  of the ecotypes . DiffereFSC = 0.008, P < 0.00001) and outlier  SNPs. Using neutral SNPs, no differentiation between the Faroe Islands  and Norwegian  or Greenland ‘shallow’ was apparent  by microsatellites. When the observed q values based on microsatellites and SNP data for the ‘shallow’ and ‘deep’ ecotypes from Greenland waters were compared, microsatellite data provided more ‘intermediate’ q values  waters, which also includes ‘shallow’ fish north of the Faroe Islands. Roques et al. [S. mentella. A distinctive Northeast Arctic population within the ‘shallow’ ecotype is supported by the existence of independent larval extrusion and nursery grounds along the Norwegian shelf [et al. [Our data clearly supported an isolated genetic population within the ‘shallow’ s et al.  found ons et al. , 22. Sahs et al.  suggestean shelf , 9, 13.  [et al.  found noet al. [Our SNP data strongly supported the presence of ‘slope’ fish in the continental slope off east Greenland. The ‘slope’ ecotype identified by Stefansson et al.  in Icelaet al.  as they et al. , 18.FST marine fish populations [One ‘shallow’ fish from the Faroe Islands water clustered with the ‘slope’ ecotype in the present study Figs  and 4. Iulations  and misculations .S. mentella complex. We identified in total 109 outliers in our dataset of seven geographical collections belonging to the three ecotypes of S. mentella. In line with the results based on neutral SNPs, the outliers precisely differentiated the ‘shallow’, ‘deep’ and ‘slope’ ecotypes of the species. It is therefore possible that along with genetic drift, adaptive processes are at play within the species complex, also suspected by Cadrin et al. [et al. [et al. [Results from the analyses of genome-wide SNPs suggested adaptive divergence in the n et al. , Shum et [et al.  and Shum [et al. . The rea [et al. .S. mentella genomic region under selection. Similar divergence in immune related genes has been revealed in Atlantic salmon [Alignment analyses of the tentative adaptive sequences revealed that many outliers are linked with functional parts of the genome serving immunology, growth and/or sexual development, as well as complex cellular and molecular functions. Fish from the ‘deep’ ecotype are known to be larger in size at sexual maturity and have less parasitic infestation compared to their ‘shallow’ counterparts see , and outc salmon  and sticc salmon . More spc salmon .S. mentella genome was indicated, as some of our outliers were on same scaffolds of the Sebastes genomes and multiple outliers aligned to the same locations of the Dicentrarchus labrax genome. Furthermore, these scaffolds contained no neutral loci and only a few outliers aligned to the same scaffold as neutral SNPs  could assign over 90% of individuals to ecotype and even to population of origin. In Pacific salmon, where population assignment is extensively used in management, a 90% precision in population assignment is considered as the ‘gold standard’ [S. mentella fishery or even to design a handled device for on-sight discrimination of S. mentella ecotypes and populations [e.g. The present study provides improved tools for investigating genetic population structure in tandard’ . Our SNPtandard’ , particuons e.g. .S1 File(PDF)Click here for additional data file.S2 File(ZIP)Click here for additional data file.S3 FileTotal 4,277 SNPs were found suitable for the downstream analyses. For 1 SNP per RAD tag, 1,943 neutral and 97 outlier loci were selected in the final panels.(ZIP)Click here for additional data file.S4 File(XLSX)Click here for additional data file.S5 File(XLSX)Click here for additional data file.S6 File(XLSX)Click here for additional data file.S7 File(XLSX)Click here for additional data file.S8 FileIn this figure, each circle represents a fish individual.(PDF)Click here for additional data file.S9 File(XLSX)Click here for additional data file.


In Tibet, the two most important breeds are Tibetan chicken and Lhasa white chicken, and the duo exhibit specific adaptations to the high altitude thereby supplying proteins for humans living in the plateau. These breeds are partly included in the conservation plans because they represent important chicken genetic resources. However, the genetic diversity of these chickens is rarely investigated. Based on whole-genome sequencing data of 113 chickens from 4 populations of Tibetan chicken including Shigatse (SH), Nyemo (NM), Dagze (DZ) and Nyingchi (LZ), as well as Lhasa white (LW) chicken breed, we investigated the genetic diversity of these chicken breeds by genetic differentiation, run of homozygosity (ROH), genomic inbreeding and selection signature analyses.ROH) of 0.11 was observed in LZ. In other populations, the FROH ranged from 0.04 to 0.06. In total, 74, 111, 62, 42 and 54 ROH islands containing SNPs ranked top 1% for concurrency were identified in SH, NM, DZ, LZ and LW, respectively. Genes common to the ROH islands in the five populations included BDNF, CCDC34, LGR4, LIN7C, GLS, LOC101747789, MYO1B, STAT1 and STAT4. This suggested their essential roles in adaptation of the chickens. We also identified a common candidate genomic region harboring AMY2A, NTNG1 and VAV3 genes in all populations. These genes had been implicated in digestion, neurite growth and high-altitude adaptation.Our results revealed high genetic diversity across the five chicken populations. The linkage disequilibrium decay was highest in LZ, while subtle genetic differentiation was found between LZ and other populations (Fst ranging from 0.05 to 0.10). Furthermore, the highest ROH-based inbreeding estimate (FHigh genetic diversity is observed in Tibetan native chickens. Inbreeding is more intense in the Nyingchi population which is also genetically distant from other chicken populations. Candidate genes in ROH islands are likely to be the drivers of adaptation to high altitude exhibited by the five Tibetan native chicken populations. Our findings contribute to the understanding of genetic diversity offer valuable insights for the genetic mechanism of adaptation, and provide veritable tools that can help in the design and implementation of breeding and conservation strategies for Tibetan native chickens.The online version contains supplementary material available at 10.1186/s12864-021-08280-z. The Tibetan plateau is the largest high-altitude area on earth with an average altitude exceeding 4000 m, representing 25% of the landmass of China. This high-altitude environment becomes a habitat for many unique animal genetic resources. Tibetan chicken is one of the chicken breeds found in this area dating back to the seventh century A.C . The chiThe availability of high-throughput affordable sequencing techniques has allowed genome-wide analysis of the genetic structure and relationships in animal populations. Large scale omics data have opened new perspectives for a more accurate animal genetic analysis. In addition to SNP and gene expression data, runs of homozygosity (ROH) has joined the assembly of omics data available in biological databases for livestock gene discovery and diversity assessment. ROH is a kind of long continuous homozygous stretches in the genome that are formed by the combination of two identical haplotypes in an individual . These rIn chickens, analysis of ROH has been deployed to assess the genome-wide diversity in local and imported genetic resources. Results from such analysis help in the design and review of effective conservation program for endangered populations –13. The Within-breed diversity and the population structure analyses in livestock species are fundamental for understanding environmental adaptation, implementing conservation programs and designing selection plans , 17, 18.2 values were 0.08, 0.07, 0.08, 0.20 and 0.09 for SH, NM, DZ, LZ and LW populations, respectively. This indicated a stronger LD between SNPs and a clear difference in LZ compared to the other four chicken populations. Furthermore, the analysis of population relatedness (Table SThe genetic diversity for Shigatse (SH), Neymo (NM), Dagze (DZ), Nyingchi (LZ) and Lhasa white (LW) chicken populations was evaluated by observed heterozygosity (Ho), expected heterozygosity (He) and multiple-locus heterozygosity (MLH) using eligible SNPs under Hardy–Weinberg equilibrium. Ignoring the minor allele frequency (MAF) of SNPs, the Ho ranged from 0.15 to 0.18. Using filtered SNPs with MAF ≥ 5%, Ho increased ranging from 0.27 to 0.31. The estimates of He were similar to Ho for each population. MLH ranged from 0.22 to 0.31 when all SNPs were used, and it went up from 0.27 to 0.31 when only SNPs with MAF ≥ 5% were used. The MAF ranged from 0.11 to 0.13 when its filter was set at 0, and it went up from 0.21 to 0.22 when only SNPs with MAF ≥ 5% were used. The diversity indices of the five populations were shown in Table The current study identified 1269, 2438, 1366, 1284 and 1342 ROHs in SH, NM, DZ, LZ and LW chicken population, respectively. ROHs were identified in each sampled bird. Population-wise, the average number of ROH segments was highest in the LZ (128.4 ROH/bird) compared to the other populations. The lowest and highest average length of ROHs were observed in DZ (54.64 Mb) and LZ (102.54 Mb), respectively. The number of SNPs harbored in the ROHs varied between the studied populations, and the maximum number  was located on GGA1 of the SH population. Similarly, the minimum number of SNPs in the ROHs (50) was located on GGA1 found in the LZ chicken population (Table ROH), genomic SNP-by-SNP inbreeding coefficient (FGRM), excess of homozygosity (FHOM) and correlation between uniting gametes (FUNI). As shown in Fig. ROH (0.110 ± 0.016) and low FHOM (− 0.029 ± 0.055), low FGRM (− 0.027 ± 0.046) and low FUNI (− 0.027 ± 0.052). We further analyzed the correlation among the four inbreeding coefficients which was 0.55 between FROH and FHOM, 0.51 between FROH and FUNI, 0.91 between FHOM and FUNI, 0.39 between FGRM and FUNI, respectively  that harbored signatures of selection in SH, NM, LZ and LW population, respectively. Further mining of this region revealed that only three genes  were located within it. The information of ROH islands, SNPs and genes harbored within the studied region were listed in Table Notably, the genomic region containing common QTLs spanned from 0.03 Mb to 1.13 Mb of GGA8, and harbored six top 0.1% ROH islands across the five populations. By focusing on GGA8, we calculated integrated haplotype homozygosity (iHS) for each population. In all the populations except for the DZ population, SNPs harbored in the top 0.1% ROH islands were strongly selected for Fig. , and theThis study used whole genome sequences of four Tibetan chicken populations reared on the Tibetan plateau to analyze genetic diversity, run of homozygosity, genomic inbreeding and selection signatures. A composite Tibetan local breed, Lhasa white was also included in the analyses to compare results among populations. Lhasa white is a synthetic breed generated by crossing male White Leghorn and female Tibetan chickens, which has been reared for over sixty years on the Tibetan plateau.Observed (Ho) and expected (He) heterozygosity values for SNPs with MAF ≥ 0.05 close to 0.3 were found in all populations. Similar values were reported in modern chicken populations using sequence data  but loweROH), genomic SNP-by-SNP inbreeding coefficient (FGRM), excess of homozygosity (FHOM) and correlation between uniting gametes (FUNI) were commonly accepted indicators for inbreeding assessment [ROH and FHOM, as well as FUNI were significantly high, similar to those reported in cattle [ROH as an accurate estimate of identity by descent (IBD).In our present study, the number and the distribution of ROHs identified in Tibetan native chickens were comparable to those reported in broiler chickens . Furthersessment . Herein,sessment , but mucsessment , 16 and sessment . Therefon cattle , pigs [2n cattle , horses n cattle  and moden cattle . This fuLGR4), enriched in the biological process of osteoblast in GO database, contributes to the regulation of energy metabolism including food intake and energy expenditure [BDNF), enriched in the positive regulation of synapse assembly is considered important for the temperature perception in chickens [STAT1 and STAT4 are members of Janus kinase (JAK)-signal transducer and activators of transcription (STAT) pathway that plays critical roles in facilitating various cellular reactions to cellular stress including hypoxia, ultraviolet light and hyperosmolarity [VAV3, NOS2, COL3A1 and PRKD1 were putative candidate genes associated with highland adaptation [Whole-genome sequencing allows detection of the ROH and its analysis enables reliable inference of the demographic history of animal populations. It also provides a new approach for the exploration and discovery of complex traits . The ROHenditure . Brain-dchickens . In ratschickens . STAT1 amolarity . Moreovemolarity , suggestaptation , implyinAMY2A, NTNG1 and VAV3 genes on GGA8. AMY2A encodes a member of the alpha-amylase family of proteins, which is involved in carbohydrates and glycogen metabolism, affecting growth, carcass traits and feed intake efficiency in chicken [AMY2A was under selection for metabolism, energy availability and response to thermal stress in African chickens [NTNG1 is a responsible gene for axon and neurite growth [VAV3 is a member of the VAV gene family that plays vital role as guanosine nucleotide exchange factors for Rho GTPases and signaling adaptors downstream of protein tyrosine kinases [VAV3 had been identified as a candidate gene associated with highland adaptation in Ethiopians [VAV3 also functions putatively in the adaptation to the high altitude of Tibetan native chicken. Given that different chicken populations were reared in the Tibetan plateau for many decades, we speculated that this genomic region and the candidate genes on GGA8 might be under natural or artificial selection for adaptation to the high-altitude environment.ROH islands might be the representative genomic regions under natural and artificial selection . The iHS chicken . The prechickens . Similare growth . It was e growth . VAV3 is kinases . Specifihiopians  and Ethihiopians . This prhiopians . We therAMY2A, NTNG1 and VAV3. This region is suggested as a candidate genomic region for adaptation to the high-altitude environment, which should be further validated in the following studies. Our findings contribute to the understanding of genetic diversity, population inbreeding and the underlying genetic mechanism of the high-altitude adaptation, and may help in the design and implementation of breeding and conservation strategies for the chickens.In the present study, we used whole genome sequence data to characterize the genetic diversity and investigate the distribution of ROH across the genomes of five Tibetan indigenous chicken populations. Different LD, diversity and ROH patterns were observed in the five populations. Genetic diversity evaluated by observed heterozygosity was high in the five populations. The Nyingchi (LZ) population, which was distant from other populations had the highest proportion of long ROH fragments and ROH-based genomic inbreeding coefficient, reflecting recent inbreeding events. We identified a total of 343 ROH islands harboring 112 QTLs and 1429 genes. Five of such genes were involved in energy metabolism and STATs pathway. Specifically, ROH islands on GGA8 harbored genes including All birds were handled following the guidelines established by the Council for Animal Welfare of China. The experimental protocols were approved by the Science Research Department of the Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS).p-value < 10− 5. After these quality control steps, a total of 20,385,015 SNPs distributed across 33 autosomes in 105 birds were obtained for subsequent analyses. Among these birds, 20, 32, 25, 10 and 18 belonged to Shigatse, Nyemo, Dagze, Nyingchi and Lhasa white population, respectively.Genomic DNA was extracted from the blood of 114 female chickens. After purification and integrity verification of the DNA, whole-genome sequencing was performed using the Illumina HiSeq 2500 Sequencer  to generate 150 bp paired-end reads. To minimize mapping errors, low-quality reads were removed using FastQC software following Yan et al. . The cle2 threshold of 0.2. Principal components (PC) analysis was carried out with the pruned data in PLINK, and the first 2 PCs were extracted and plotted using R software. The observed heterozygosity (Ho) and the expected heterozygosity (He) were estimated based on allele frequency of all eligible SNPs, SNPs with minor allele frequency (MAF) ≥0.01 and SNPs with MAF ≥ 0.05, respectively, using ‘-hardy’ option in PLINK. Multiple-locus heterozygosity (MLH) was also calculated for each bird with formula O is the number of observed homozygotes, and N is the number of non-missing autosomal SNPs. The O and N were calculated using ‘-het’ option in PLINK. Further, PopLDdecay [The filtered SNPs were further pruned to obtain independent SNP markers using the ‘-indep-pairwise’ option, with a window size of 50 SNPs, a step of 10 SNPs, and rent}N-ON , where OpLDdecay  was usedpLDdecay , and thePrior to the ROH detection, the eligible SNPs with MAF ≥0.01 were separately filtered for each population. Long homozygous fragments were scanned in the pruned data using PLINK according to the following parameters: the minimum number of 50 SNPs in a ROH (−homozyg-snp 50), sliding windows of 50 SNPs (−homozyg-window-snp 50), allowance for not more than 5 missing SNPs (−homozyg-window-missing 5) and three heterozygous SNPs per window (−homozyg-window-het 3). The minimum length of an ROH segment was 300 kb (−homozyg-kb 300). The minimum SNP density was 1 SNP per 50 kb (−homozyg-density 50), and the maximum gap between two consecutive SNPs was 1000 kb (−homozyg-gap 1000). Finally, the rate in which a SNP was included in the total of sliding windows was at least 0.05 (−homozyg-window-threshold 0.05). After the run, the identified ROHs were classified according to their size into small (< 1 Mb), medium (1 ~ 3 Mb) and large (> 3 Mb) as previously delineated in chickens [ROH) was estimated using PLINK according to previous methods [ROH for each bird was calculated as i th individual, and Laut is the genome length of autosome covered by the SNPs in the sequence data. The inbreeding coefficient for an individual based on homozygous SNPs (FHOM) was computed as O is the number of observed homozygotes, E is the number of expected homozygotes by chance, and L is the number of non-missing autosomal SNPs. Genomic SNP-by-SNP inbreeding coefficient (FGRM) and the correlation between uniting gametes (FUNI) were computed in GCTA software as previously reported [Genomic inbreeding based on ROH (F methods . The FROreported . Pair-wiP < 0.05) GO terms and KEGG pathways.To identify the genomic regions that harbored common ROHs across the five chicken populations, we estimated the occurrences of SNPs in ROHs by counting the number of times when the SNP was detected in those ROHs using detectRUN package  implemenP value with the following formula piHS = −log10(1 − 2 | ∅(iHS) − 0.5| ), where ∅(iHS) represents the Gaussian cumulative distribution function, and piHS is the two sided P value associated with the neutral hypothesis of no selection [piHS higher than a threshold of 0.1% were considered as putative signatures of selection. Due to the limit of computing time and lack of accurate recombination rate of studied chickens, we only ran the selection signature analysis for candidate regions on GGA5 and GGA8 in the present study.To detect selection signatures in each ROH island, the integrated haplotype score (iHS) was calculated within each population. The iHS is a measure of the amount of extended haplotype homozygosity at a given SNP, that uses phased genotypes to identify putative regions of recent or ongoing positive selection in genomes . Herein,election . The piHAdditional file 1.


It has been shown that βHB is a natural inhibitor of HDAC1 and knockdown of HDAC1 upregulated Trx1 in cardiomyocytes, suggesting that βHB may upregulate Trx1 through HDAC inhibition. βHB induced Trx1 acetylation and inhibited Trx1 degradation, suggesting that βHB-induced inhibition of HDAC1 may stabilize Trx1 through protein acetylation. These results suggest that βHB potentiates the antioxidant defense in cardiomyocytes through the inhibition of HDAC1 and the increased acetylation and consequent stabilization of Trx1. Thus, modest upregulation of ketone bodies in diabetic hearts may protect the heart through the upregulation of Trx1.Thioredoxin 1 (Trx1) is a major antioxidant that acts adaptively to protect the heart during the development of diabetic cardiomyopathy. The molecular mechanism(s) responsible for regulating the Trx1 level and/or activity during diabetic cardiomyopathy is unknown. β-hydroxybutyrate (βHB), a major ketone body in mammals, acts as an alternative energy source in cardiomyocytes under stress, but it also appears to be involved in additional mechanisms that protect the heart against stress. βHB upregulated Trx1 in primary cultured cardiomyocytes in a dose- and a time-dependent manner and a ketogenic diet upregulated Trx1 in the heart. βHB protected cardiomyocytes against H The prevalence of obesity and metabolic syndrome has increased tremendously in the past few decades . Individ2O2 through reduction of peroxiredoxins (Prdxs), Trx1-dependent peroxidases  at 95 °C for 5–20 min). For SDS-PAGE under non-reducing conditions, the lysates were prepared with lysis buffer containing 100 mM N-ethylmaleimide and SDS sample buffer without 2-mercaptoethanol. For immunoprecipitation, the lysates were incubated with Flag-agarose. Immunocomplexes were washed with lysis buffer three times and eluted with 2 × SDS sample buffer. Antibodies used for this study were Acetylated lysine , 9814), Trx1 , mTOR , Phospho-mTOR (Ser2481) , p70 S6 Kinase , Phospho-p70 S6 Kinase , 4EBP1 , Phospho-4EBP1 , AMPKα2 , Phospho-AMPKα , ACC , Phospho-ACC , Gapdh , Prdx1 , Sirt1 , Nampt , Namnat1 , Tubulin , α-cardiac actin , and HDAC1 . The signal intensity of the Western blot signals was quantified using the ImageJ program . The signal intensity of the non-phosphorylated proteins was normalized by a loading control (Tubulin). The signal intensity of the phosphorylated or acetylated proteins was normalized by that of the relevant total protein. Normalized signal intensity relative to the control was used for statistical analyses of one or more Western blot membranes.Adenovirus vectors expressing Flag- and HA-Tagged Trx1 (Flag-Trx1-HA) and shTrx1 were generated with the AdMax system as described previously . Ad-shHDC57BL/6 mice (11-week-old) were fed a control (D10070802) or ketogenic diet (D10070801) for 5 days. Diets were purchased from Research Diets, Inc . The control diet consisted of 10% protein, 80% carbohydrates and 10% fat, whereas ketogenic diet consisted of 10% protein and 90% fat . All prot test for pairwise and one-way ANOVA for multiple comparisons. p < 0.05 was defined as statistically significant and is indicated by a filled asterisk. All error bars represent S.E.M.Statistical comparisons were made using Student’s To examine the effect of βHB upon Trx1 expression, primary cultured cardiomyocytes were treated with 0.1 to 10 mM βHB for 16 h. The circulating level of βHB is 0.4–0.5 mM in non-diabetic patients at baseline . This is2O2 in the presence of βHB with or without short-hairpin RNA (shRNA)-mediated downregulation of Trx1. We confirmed that βHB-induced Trx1 upregulation was abolished in the presence of adenovirus harboring Trx1 shRNA (shTrx1) but did not control shRNA   A 6]. Th. Th2O2 tnockdown B. βHB alnockdown C. These 2O2 treatment. The band shifts were abolished when the SDS-PAGE analyses were conducted in the presence of 2ME (2O2 treatment. βHB prevented the H2O2-induced AMPKα band shifts, an effect that was abolished in the presence of Trx1 knockdown (2O2-induced decreases in the phosphorylation of ACC, an AMPK substrate . Th. Th2O2 tnockdown B. βHB alubstrate , but the2O2 did not significantly upregulate HDAC1, whereas 300 µM H2O2 downregulated HDAC1 (We then investigated the mechanism through which βHB upregulates Trx1 in cardiomyocytes. Since βHB inhibits the HDAC activity of class I HDACs , βHB mayed HDAC1 B. To inved HDAC1 C. To tesed HDAC1 D. Since ed HDAC1 E, suggesKetone bodies have emerged as beneficial nutrients in cardiac pathophysiology. A modest increase in circulating ketone bodies resulting from ketogenic diet consumption mitigates oxidative stress in the heart, as evidenced by decreases in oxidized glutathione, 3-nitrotyrosine, and malondialdehyde levels . The cur2O2-induced oxidative inhibition of mTOR via Trx1. mTOR is inhibited by the direct oxidation at Cys1483, whereas Trx1 normalizes mTOR activity via the reduction of Cys1483 [We showed that βHB normalizes the H Cys1483 . The Trx Cys1483 , which m Cys1483 . Phenyle Cys1483 ,22. ThusTrx1 tended to be upregulated at 0.1 mM βHB, whereas it was significantly upregulated at 0.3 mM βHB in cardiomyocytes A. The plCalorie restriction and fasting promote ketone body synthesis, which potentially mediates the salutary effects of calorie restriction, including lifespan extension. In rats, calorie restriction induces a modest elevation of βHB, approximately 0.7 mM, in blood plasma . βHB is Our results show that βHB induces Trx1 acetylation and inhibits Trx1 degradation. Besides inhibiting HDACs, βHB may induce protein acetylation via an increase in acetyl-CoA . It has We assessed the activity of mTOR and AMPK to evaluate the oxidoreductase activity of Trx1 in this study. It should be noted, however, that mTOR and AMPK may be only one of the many effects of βHB, and we have not evaluated how βHB affects cellular function through mTOR and AMPK in this study. Thus, further investigation is needed to elucidate how the βHB–Trx1 axis protects the heart against diabetic cardiomyopathy.βHB, a major ketone body in mammals, confers resistance against oxidative stress to cardiomyocytes through Trx1 induction. βHB upregulates Trx1, possibly through class I HDAC inhibition and through the inhibition of Trx1 degradation.