Drosophila nephrocyte model. More broadly, this study demonstrates that tissue-specific eQTL maps informed by single-nucleus open chromatin data have enhanced utility for diverse downstream analyses.Expression quantitative trait locus (eQTL) studies illuminate genomic variants that regulate specific genes and contribute to fine-mapped loci discovered via genome-wide association studies (GWAS). Efforts to maximize their accuracy are ongoing. Using 240 glomerular (GLOM) and 311 tubulointerstitial (TUBE) micro-dissected samples from human kidney biopsies, we discovered 5371 GLOM and 9787 TUBE genes with at least one variant significantly associated with expression (eGene) by incorporating kidney single-nucleus open chromatin data and transcription start site distance as an “integrative prior” for Bayesian statistical fine-mapping. The use of an integrative prior resulted in higher resolution eQTLs illustrated by (1) smaller numbers of variants in credible sets with greater confidence, (2) increased enrichment of partitioned heritability for GWAS of two kidney traits, (3) an increased number of variants colocalized with the GWAS loci, and (4) enrichment of computationally predicted functional regulatory variants. A subset of variants and genes were validated experimentally in vitro and using a  Here, the authors discover over 15,000 eQTLs in human kidney samples by integrating single-nucleus open chromatin data, resulting in high resolution eQTLs, increased enrichment of GWAS heritability and colocalization, followed by downstream validation. More recently, genome-wide association studies (GWASs) have demonstrated that the heritability of diverse kidney traits and diseases are polygenic and primarily non-coding8. Thus, whether to deeply understand a single disease-related gene or to fine-map GWAS loci, it is necessary to have as precise an understanding of the genetic control of gene expression as possible. A high-resolution expression quantitative trait loci (eQTL) map of the kidney can contribute greatly to this need, where high-resolution in this context is defined by smaller credible sets and more confidence in prioritized variants.The genomic contributors to kidney diseases and traits extend well beyond rare, pathogenic, exonic variants with large effect sizes that typified the initial discoveries in this area. Focused analyses of individual genes have long illuminated common, non-coding variants whose regulatory effects contribute to their proper function10. In particular, GWAS fine-mapping of diseases and traits has been aided by these eQTL maps given that the non-coding nature of most GWAS variants and linkage disequilibrium (LD) preclude their direct interpretation11. Beyond eQTLs, annotations from complementary genomic experiments  can provide further refinement by identifying functional regions within the disease- and trait-associated loci. Previously, prioritizing GWAS SNPs using eQTL and other functional annotations was achieved by simply overlapping these datasets (post hoc lookups). However, investigators have begun to develop new approaches to empower more precise GWAS fine-mapping, particularly by building more high-resolution eQTL datasets11.eQTLs can identify variants associated with gene expression (eSNPs) and their target genes (eGenes) across individuals in a tissue-, and more recently cell-, informed manner12. This cell-type-informed eQTL data was then co-analyzed with assay for transposase-accessible chromatin using sequencing (ATAC-seq) data post hoc to identify overlaps between specific eSNPs and open chromatin.One method to improve eQTL mapping is to incorporate single-cell data. In a recent study, investigators predicted kidney cell-type interacting eQTLs by applying in silico deconvolution methods using a reference single-cell gene expression dataset and subsequently built a cell fraction-informed eQTL model from bulk tissue13. As a proof of principle, early studies showed the enrichment of regulatory annotations in eQTLs from lymphoblastoid cell lines. Then, they showed that integrating this information into a Bayesian fine-mapping framework could improve the eQTL discovery and fine-mapping resolution14. Similarly, a recent study demonstrated increased statistical fine-mapping accuracy of eQTLs by assigning weights to SNPs using priors derived from diverse functional annotations for the subsequent eQTL analysis15. This approach increases eQTL discovery and loci for downstream consideration that would have been missed using the post hoc lookup strategy.Another approach to improve the resolution of eQTL maps is to incorporate functionally-informed SNP annotations in the fine-mapping procedure13, we hypothesized that weighting SNPs by this parameter would increase eQTL discovery and fine-mapping resolution of putative functional SNPs that otherwise would not be found due to high LD or low allele frequencies. In doing so, we hypothesized that we would gain (1) greater statistical and functional confidence in putative causal eSNPs, (2) additional insight into the regulatory landscape for specific genes related to kidney diseases and traits, and (3) increased discovery and fine-mapping resolution for genome-wide, integrative analyses.In this study, we further extend this approach by incorporating single-nucleus open chromatin data from kidney tissue to fine-map kidney eQTLs. Given that SNPs within open chromatin peaks are more likely to impact transcriptional regulation7. A subset of candidate eQTLs were validated experimentally in vitro and using a Drosophila nephrocyte model. Altogether, we demonstrated improved precision in discerning putative functional SNPs within eSNP haploblocks, which subsequently increased discovery and provided biological insight in downstream analyses. Our interactive resource is available to the public at www.nephqtl2.org.To test this hypothesis, we created a workflow to discover high-resolution eQTLs by using single-nucleus open chromatin data from kidney tissue to generate priors for use in a Bayesian multi-SNP eQTL detection and fine-mapping algorithm Fig. . We appl16 individuals with paired RNA-seq and whole-genome sequencing (WGS) data, including 240 glomerular samples (GLOM) and 311 tubulointerstitial samples  maps that we recently created through the development and application of a new method to optimize the discovery of rare cell-type specific peaks using underlying sequence signatures - gkmQC17.The eQTL analysis consisted of 332 NEPTUNE17 to bulk kidney data to show that the single-nucleus data detected 62% additional open chromatin regions not detected in bulk kidney data18 , which includes peaks specific for podocytes and parietal epithelial cells. eGFR heritability was enriched in SNPs within C2 and C3-specific CREs, which include proximal tubule (P = 0.05) and loop of Henle (P = 0.04) specific peaks, respectively 22 and non-NS23 samples  and the enrichment based on the distance between each SNP and the corresponding gene’s transcription start site (TSS). We refer to this SNP Prior as the “integrative prior” (see Methods). For our GLOM CRE annotation, all SNPs within podocyte, parietal epithelial, endothelial, mesangial/fibroblast and leukocyte open chromatin peaks were combined. For TUBE, we combined all SNPs within CRE from all proximal tubule clusters, loop of Henle, distal convoluted tubule, connecting tubule, principal cells, type A and B intercalated cells, in addition to the same endothelial and leukocytes annotations used for GLOM Fig. . We detecis-eQTLs allowing for multiple independent SNPs per gene using the method DAP-G28. After Bayesian FDR control, we identified 5,371 GLOM and 9,787 TUBE eGenes  using our fine-mapping technique compared to ranking of SNPs from the single-SNP eQTL analysis with MatrixEQTL , the most significant GLOM-specific eGene  is a TUBE-specific eGene with one of the top-ranked signals  uniform prior—all SNPs having equal prior probability of being an eSNP, and (2) TSS prior—only including the distance from the TSS.The first metric was the posterior probability of each cluster’s lead SNP (snpPIP), where an increase would indicate more confidence being shifted to the lead SNPs. The second metric was the number of SNPs in the eQTL’s 95% credible sets, where a decrease would indicate an improvement. On both metrics, the integrative prior showed the highest fine-mapping resolution, followed by the TSS prior alone, then a uniform prior Fig. .Fig. 4Sp30 and negative control phenotypes31  versus low confidence (LC) eSNPs as a function of prior choice. We defined HC-SNPs as those whose posterior inclusion probability (PIP) within a haploblock cluster is higher than would be observed if the PIPs were equally distributed among all SNPs in a locus. LC-eSNPs have snpPIPs smaller than expected Fig. . There wP = 2.22 × 10−9 for GLOM; P = 4.64 × 10−6 for TUBE; Wilcoxon rank-sum test; Fig. P = 5.65 × 10−7 for GLOM; P = 1.49 × 10−17 for TUBE; Supplementary Fig. The final metric was a change in the computationally-predicted functionality of fine-mapped eSNPs defined using the different priors. To do this, we compared the deltaSVM score of lead eSNPs to random SNPs in the CREs, controlling for allelic frequency, distance from TSS, and the signal strength of open chromatin peaks. Lead eSNPs from the integrative prior had significantly higher deltaSVM scores than the random SNPs  ≥ 0.5. For UACR, there were 9 TUBE and 21 GLOM colocalization signals  were in open chromatin. In contrast, we only identified 69 colocalized loci with the uniform prior. Finally, from a systematic comparison of extended sets of colocalized SNPs (RCP ≥ 0.2) across the different priors, we confirmed a significant increase in colocalization probability when high-resolution eQTLs are incorporated 35. Thus, we hypothesized that if a specific prior better captured the functional GWAS variants, then lead colocalized SNPs weighted by that prior would be enriched in CREs more than expected. Indeed, we found that colocalized SNPs found using our high-resolution eQTLs were more enriched for SNPs in open chromatin when compared to the uniform prior. This enrichment was most significant when colocalizing TUBE eQTLs with primary kidney phenotypes30  = 0.2), which does not fall within any CRE, to rs6787229 (SCP = 0.79), which resides in a podocyte-specific peak . For eGFR, at a false discovery rate (FDR) ≤ 5%, we identified 601 significant gene-trait pairs in GLOM and 1,074 in TUBE. For UACR at an FDR ≤ 5%, we identified 137 significant gene-trait pairs in GLOM  between colocalization and PTWAS signals LOM Fig.  and 179 LOM Fig. . We alsoals Fig. , demonstMYL3 gene expression and UACR also validated by PTWAS  promoter directs muscle cell expression of a rat atrium natriuretic factor (ANF)–red fluorescent protein (RFP) fusion protein (ANF-RFP) that is secreted into the hemolymph40. ANF-RFP is typically filtered and endocytosed by healthy, wild-type nephrocytes, and the intracellular red fluorescence can be readily visualized and quantitated in vivo. We found nephrocyte-specific knockdown of five genes impacted nephrocyte function—Fkbp12 (FKBP1A), Larp4B (LARP4B), Mlc-c (MYL3), mtd (NCOA7), and svr (CPXM1)  were assessed as pleiotropic in the PTWAS and were not found to impact nephrocyte function when knocked down. To further validate TUBE associations identified in LARP4B and NCOA7, we generated luciferase reporter constructs in both forward and reverse orientations . The rs11154336-A allele demonstrated 198% increased reporter activity compared to the G allele, consistent with NCOA7 eQTL results (P = 1.30 × 10−16)  significant in both colocalization and PTWAS analyses with UACR and/or eGFR, (2) contain colocalized SNPs in CREs, and (3) had gene homolog expression in M1) Fig. . In an icis-regulatory elements, their target genes, and their cells of action. Conventional fine-mapping approaches, depending on the population size and haplotype structure, may be suboptimal in specifying the putative functional variants with low allelic frequency or multiple indistinguishable tag SNPs within the same haplotype block. Given that the functional characteristics of variants in CREs are orthogonal to LD patterns, diverse functional annotations have been used for fine-mapping of GWAS and eQTL variants. To this point, we created a workflow that used single-nucleus open chromatin data to generate priors for use in Bayesian multi-SNP eQTL detection algorithms. In doing so, we demonstrated improved precision in discerning putative functional SNPs within eSNP haploblocks (fine-mapping), which subsequently increased discovery and biologic insight of downstream analyses.In mapping the non-Mendelian genomic basis of kidney traits and diseases, we are challenged to maximize the detection of regulatory circuits - functional genetic variants in The CRE-informed fine-mapping for eQTLs can recover underpowered variants with a Bayesian approach that augments the enrichment of eQTLs across tissue-relevant cell-type CREs, reflecting the underlying biology of transcriptional regulation. Corroborating this, we found that the gain in heritability and deltaSVM score enrichment, when comparing our integrative approach to results from a uniform prior, was larger in the lower-powered tissue, GLOM, compared to our higher-powered TUBE analysis Fig. . In sum,A particularly important aspect of this study was our ability to use our newly developed method, gkmQC, to characterize CREs in rare cell types at a coverage and level of resolution not previously attained. For example, it was critical to comprehensively map (putatively)-casual regulatory variants of podocytes, a key rare (<1%) cell type involved in kidney filtration function. By nearly doubling the podocyte-specific peaks, we increased the statistical power of enrichment between podocyte peaks and glomerular eQTLs, which in turn improved fine-mapping efforts. This directly led to our ability to discover, via LD score regression analysis, a significant contribution of podocyte regulatory elements to the heritability of urine albumin-to-creatinine ratio (UACR) from GWAS studies. Interestingly, we also found enrichment of eGFR-associated SNPs among proximal tubule, loop of Henle, and distal convoluted tubule open chromatin. Together, these underscore the ability of our optimized CRE maps to provide biological insights into multiple kidney phenotypes and should serve as a resource to investigators seeking to discern the specific regulatory circuits of these diseases and traits.41. We found that weighing putative functional SNPs in our eQTL analysis resulted in an enrichment of colocalized SNPs within CREs and increased discovery of novel loci, thus partially overcoming this power limitation. These complementary analyses not only highlight the utility of our eQTL resource but also allow for new biological insights into associations between tissue and cell-specific gene regulation and kidney function. This was illustrated by a high confidence eSNP within a podocyte-specific CRE that was associated with both MYL3 glomerular expression and UACR.As intended, our integrative approach allowed us to increase fine-mapping resolution, marked by smaller credible set sizes and increased statistical confidence of lead SNPs. To highlight the utility of our eQTLs in mapping variants to their function, we performed colocalization and transcriptome-wide association studies with functional kidney outcomes UACR and eGFR. Colocalization analyses are hindered by low power, especially when LD matrices are not perfectly matched44 which can have a pleiotropic, tissue-agnostic impact on gene expression45. In contrast, high-powered eQTL studies can identify more tissue-specific regulatory SNPs46. Thus, an eQTL analysis with increased GLOM samples may identify more tissue-specific eQTL that robustly discriminate heritability/colocalization signals from UACR/eGFR and the negative-control traits. Given that statistical power differentially impacts GWAS and eQTL studies47, differences in statistical power should be considered when interpreting the cell-type relevance of a trait using colocalization/heritability analyses.We demonstrated CRE-informed fine-mapped GLOM/TUBE eQTLs had an enriched heritability and colocalization for GWAS traits with cell-type relevance Figs. , 5. HoweDrosophila nephrocyte, we were able to further validate selected genes of interest. For example, we replicated the association between a single intronic LARP4B eSNP, rs80282103, and both UACR and eGFR, previously discovered by Wuttke et al. and Morris et al.48 and identified a novel association between an intronic NCOA7 eSNP rs11154336 and eGFR. We found that knockdown of Larp4b and Mtd, an NCOA7 ortholog, in the Drosophila nephrocyte had the most statistically significant reductions in nephrocyte function, providing orthogonal support for the functional role of LARP4B and NCOA7 in the kidney. Using a luciferase assay, we also validated the functional impact of rs80282103 and rs11154336. In addition to LARP4B and NCOA7, three novel colocalized genes —FKBP1A, CPXM1, and MYL3 — impacted secretion and reabsorption by the nephrocyte. In our colocalization analysis, we identified a single SNP associated with both MYL3 and PTH1R expression and UACR. Interestingly, only MYL3 knockdown impacted nephrocyte function, providing support for the role of MYL3 (vs. PTH1R) in kidney function. We selected follow-up genes independent of their predicted horizontal pleiotropic effects. Interestingly, we found that all six genes predicted to be pleiotropic did not impact Drosophila nephrocyte function . This portal will be a novel resource to narrow down potential mechanisms and elucidate the regulatory landscape of kidney phenotypes.The eQTLs augmented by cell-type CREs make the results of downstream analyses  interpretable in terms of (1) mechanistic insight into transcriptional regulation and (2) contributing cell-types or ts Figs. , 6. The 41. Additionally, the comparison of colocalized loci with previous studies is imperfect due to the use of different methods. (3) Our eQTL dataset was built from a heterogenous nephrotic syndrome cohort from multiple ancestries. While this may allow us to improve fine-mapping14 and capture disease-specific eQTLs, the heterogeneity had to be properly controlled. To this end, we adjusted our eQTL analysis with PEER factors, which account for hidden technical and biological structure, and principal components, which account for population stratification. Interpretation of the eQTLs should take these factors into account. Of note, we found strong concordance of our eQTL effect sizes with compartment-matched eQTLs from healthy European samples23  Although we maximized the discovery of open chromatin peaks of rare cell types, the capability of peak discovery is still limited by the mappability of ATAC-seq reads, which depends on absolute cell counts. By harmonizing this data with data from future assays, we will be able to increase the cell counts and enhance the sensitivity of peak calling. (2) fastENLOC analysis, as well as other colocalization methods, tend to yield few highly confident findings. This is partially because the GWAS and the eQTL data are from different cohorts (the two-sample design), and their LD patterns do not match exactly. In addition, when working with summary statistics, an LD matrix from a third orthogonal population is used, which may not perfectly match the GWAS and eQTL datasets. However, while LD pattern mismatches reduce enrichment estimates and power, false positives are rare AlthoughThis research was conducted with the informed consent of all study participants and had ethical approval from by the NEPTUNE study and the IRBs at Boston Children’s and Washington University.For a detailed list of all data and tools used, see Supplementary Data 17 using publicly available human kidney snATAC-seq data from non-tumor kidney cortex samples from 5 patients undergoing partial or radical nephrectomy (GSE151302)49. Briefly, our optimized pipeline for snATAC-seq data processing includes (1) preprocess and quality control of reads with the Cell Ranger ATAC pipeline (v1.1.0) with default options, (2) harmonizing samples with Harmony (v0.1)50, (3) cell QC, clustering and type identification with snapATAC 51, (4) post hoc peak calling and optimization in a cell-type resolved manner with MACS2 (v2.2.7.1)52 and gkmQC (v1.0)17. Specifically, we sorted out cells based on read counts (103.5 ≤ UMI ≤ 105) and the fraction of promoter reads (0.1≤ FRiP ≤ 0.6) with excluding cells in a cluster of potential doublets. A total of 35,286 cells were analyzed to call the peaks. Regarding snRNA-seq, we downloaded count matrices and cell-type labels for the five snRNA-seq samples (GSE151302) to measure gene expression and the cell type identification of snATAC-seq datasets. Specifically, cells were sorted out using Seurat  as shown in Muto et al. Consequently, we profiled open chromatin peaks and gene expression for 16 known kidney cell types. The list of peaks and UMAP plots are available in the original gkmQC paper17.We used our optimized kidney CRE maps generated from a previous study18. We obtained representative ENCODE DNase-seq samples of seven bulk tissues  with the best quality chosen by gkmQC17. To process bulk DNase-seq and ATAC-seq, we adapted the previously established framework for DNase-seq and bulk ATAC-seq analyses as used in Lee et al.53 and Nandakumar et al.54. The pipeline includes cutadapt v4.1 for preprocessing reads, Picard 2.26.10 for duplicates removal, bowtie2 v2.4.4 for the alignment with GRCh38, and MACS2 v2.2.4 for calling peaks. We used the same post hoc peak calling and optimization as used in snATAC-seq analysis gkmQC17.For the kidney bulk ATAC-seq data, we used the processed data obtained from Dr. Chakravarti’s laboratory19 to estimate the proportion and enrichment of heritability from GWAS summary statistics. The proportion of the heritability contributed by a SNP set (C) in open chromatin peaks from a sample is calculated as follows:We used stratified LD-score regression C(h2)/PrC(M), where PrC(M) is the proportion of SNPs in C among the total SNP set (M). The standard error and statistical significance of the enrichment is estimated using block jackknife method implemented in the LDSC software19. For reference LD scores, European ancestry population and the corresponding allele frequencies in 1000 Genomes Phase 3 data were used . We considered open chromatin regions to be regions that extend ±1 kb from the peak summits17, as this includes the set of potentially associated regulatory variants. When comparing multiple functional annotations , we conducted S-LDSC regression jointly with the annotations, along with the full set of the baseline annotations.The enrichment of proportional heritability then is calculated by Pr30 and GWAS of 19 diseases and traits from the UK Biobank (negative controls)31. For negative controls, we selected UKBB phenotypes with insignificant genetic correlation  and with at least as many significant LD blocks as the lower powered GWAS of UACR (a proxy for power based on EUR LD blocks with at least one SNP having posterior probability > 0.2).To validate the cell-type convergence of heritability/colocalization with kidney GWASs, we repeated the analyses for 7 additional kidney phenotypes16 were prepared using the SMART-Seq v4 RNA kit . Samples underwent sequencing using Illumina HiSeq 2500, resulting in 150 bp unstranded, paired-end reads. Fastq files underwent quality control filtering and trimming using fastQC (v 0.11.5), fastQScreen (v0.11.4)55, and Picard Tools . Trimmed reads were aligned to the human genome (GRCh37) with STAR 2.6.0a56. Gene expression counts were quantified using StringTie (v2.1.4)57. Gene expression was normalized across samples using TMM normalization with edgeR (v3)58, only keeping genes with greater than 0.1 cpm  in 20% of the samples. Transformed expression values were then rank-based inverse normalized.Total RNA from microdissected biopsies  from the NEPTUNE study59. Variants underwent the following quality control filters using VCFtools (v0.1)60, PLINK (v1.9)61 and the HardyWeinberg R(v3.5.1) package62: multi-allelic variants were converted to bi-allelic, variants with GQ < 20 and AB < 0.2 or > 0.8 were set to missing, variants with genotyping rate <0.85, MAF < 0.01 and inbreeding coefficient < −0.3 were removed, and variants failing HWE, p < 10−6, in either European or African subsamples were removed. As a proxy for population stratification, we calculated principal components in PLINK using LD-pruned WGS.Whole genome sequencing (30x) was done using the Illumina HiSeq system. Alignment and variant calling were performed using default settings of GotCloud (v1.12.3) with the GrCh37 reference of the human genomecis-eQTL ( ± 1 Mb) analysis was performed with MatrixEQTL (v2.3)20 adjusting for age, sex, batch, 4 genotype PCs, and PEER factors (v1.3)64. The optimal number of PEER factors was selected based on the maximum number of eGenes, as determined by TORUS26, resulting in a variable number of PEER factors. Of note, the single-SNP eQTL results were used to estimate enrichment of the CRE annotations in TORUS and were not used for eGene calling. We validated our eQTLs by comparing the effect size and direction of associations and not the overlap of eGenes, since calling of eGenes can be influenced by sample size and methods. Pearson correlation of SNP effect sizes for the top-ranked 5,000 genes to other eQTL analyses including GLOM and TUBE from Gillies et al.21 and Qiu et al.23 and GTEx kidney cortex46. To globally compare our GLOM and TUBE analyses to all GTEx tissues, we calculated principal components of z-scores from GLOM, TUBE and GTEx V8 eQTL analyses. For each gene, the SNP with the largest z-score across all studies was selected, resulting in one strong SNP association per gene.Single-SNP26 excluding the distance to TSS annotation. For this analysis, the SNP annotations were generated for each cell type. To compare the enrichment of eSNPs in CREs across different cell types, we controlled several potential confounding factors. First, we used peaks called from the same number of subsampled cells (N = 300). Second, we used the results of MatrixEQTL from the matched samples (N = 219) between GLOM and TUBE. To analyze the baseline level of enrichment scores, we constructed two different matched control sets for the cell-type peaks: (1) peaks with the same number of target cell-type peaks randomly selected from the union of peaks covered in our kidney CRE map, and (2) randomly chosen genomic regions that have similar GC-contents and repeat fractions with the target cell-type peaks. To compare with different tissues, we applied our (optimized) pipeline to call the peaks from ENCODE DNase-seq datasets.Enrichment of epigenetic annotations was estimated using TORUSGSE151302), where genes with non-zero expression in at least 2% of the cells were considered expressed for a given cell type. We performed a permutation analysis to compute cell-type specific gene enrichment of the top 5000 MatrixEQTL eGenes. To generate the null distribution, we randomized gene expression for each cell type, assuming a uniform distribution, and calculated the number of MatrixEQTL eGenes expressed for each cell type, repeating 1000 times. We calculated a z-score to quantify cell-type-specific gene enrichment in the snRNA-seq data set.We binarized snRNA-seq gene expression  and biological relevance were considered when selecting cell-types to use for each tissue prior.Integrative prior: We use two SNP annotations to generate the integrative prior: (1) distance of the SNP to the given genes TSS (which gets binned in TORUS), and (2) binary indicator for the SNP being in open chromatin (from the union of tissue-relevant cell types). For GLOM and TUBE separately, we generated genome-wide base-pair resolution annotation files, where SNPs within ±300 bp from a summit of ATAC-seq peaks from the union of selected cell-types (pseudo-bulk) were coded with a binary indicator . Using TORUS and the single-SNP results from MatrixEQTL, we calculated enrichment of the CRE annotation jointly with the enrichment of the distance to the given gene’s TSS using default setting. The enrichment estimates are then used to weight SNPs appropriately and generate SNP priors for each gene. Given the positive enrichment of our annotations, SNPs closer the TSS and SNPs in open chromatin have higher prior probabilities than other SNPs.The following priors were generated for comparative purposes only:TSS prior: includes only the distance to the TSS, not the CRE annotations.Uniform prior: All SNPs are equally probable and have identical weights.28 using genotype and expression data from NEPTUNE and each of the priors  generated by TORUS (Methods: SNP Prior generation). We adjusted for PEER factors , age, sex, 4 genotype PCs, and RNA-seq batch. Using the genotype data to calculate LD, eQTL clusters were formed with SNPs in a cluster having an R2 > 0.25. Gene-level Bayesian FDR methods were used to identify eGenes in each tissue and 95% credible sets were formed by summing ranked SNPs for each gene cluster. The uniform and TSS priors are only for comparative purposes; results on nephqtl2.org are from the integrative prior.We performed our integrative eQTL analyses with Deterministic Approximation of Posteriors (DAP-G)GLOM_discovery = 96, NGLOM_replication = 144, NTUBE_discovery = 122, NTUBE_replication = 189). For each group, we conducted eQTL analysis as described above. Overlap of eGenes was assessed with upset plots. To compare prioritization of fine-mapped SNPs, we first identified all significant clusters with eGene FDR < 0.05 and cluster posterior probabilities > 0.2 in the discovery analyses. SNPs from each cluster were then matched with the replication analysis. Spearman rank correlation was used to compare SNP rankings between analyses. To show the increase in SNP prioritization with our integrative prior, we compared SNP rank correlations from fine-mapping with DAP to Spearman rank correlations of SNPs from single-SNP eQTL analysis with MatrixEQTL. The distribution of rank correlations was compared with a Wilcoxon rank sum test.To evaluate the increase in eGene discovery (independent of increased sample size and different samples) and assess fine-mapping replication, we conducted multiple eQTL analyses by splitting GLOM and TUBE samples into two independent subsets each. Our discovery samples consisted of samples used for NephQTL , and our replication analysis included all other samples  the maximum snpPIP and (2) the number of SNPs in the credible set. Distributions from the uniform and integrative prior were compared with Wilcoxon rank sum tests.65. Open chromatin peaks of each cell type were used as a positive training set to build gkm-SVM models66 as previously described, with some modifications. The LS-GKM (v0.1.1)67 software with default parameter settings was used for training. To calculate comparable scores across cell-type models, (1) the top 100,000 peaks were used to train each model, and (2) deltaSVM scores were normalized per cell type using z-score based normalization of the distribution created by common SNPs with MAF > 1% in European ancestry from the 1000 Genemies project68. Z-scores were transformed to probability scores for being functional variants using a logistic model trained by dsQTLs of lymphoblastoid cell line (LCL)69 as a positive set and random SNPs with the control of GC contents and repeat fractions as a negative set. To aggregate the deltaSVM scores for GLOM and TUBE, we used the transformed scores of SNPs in peaks of the corresponding cell type and chose the best score per SNP among cell types whose CREs were used as the prior of corresponding eQTLs. We regarded the SNPs with aggregated score >0.5 as deltaSVM-positive in GLOM or TUBE compartments.We generated deltaSVM scores to computationally predict the functional impact of SNPsTo test the significance of deltaSVM scores of the lead eSNPs, we identified random SNPs controlling for allelic frequency, distance from TSS, and the signal strength of open chromatin peaks . For per-SNP random control, we allowed 1%, 1000 bp, and 1.0 as the residual error of the corresponding controlling variables, respectively.Z-scores were extracted from trans-ethnic GWAS summary statistics (eGFR/UACR)7 and European 1000 Genomes project phase 3 version 5 samples68 were used for the LD reference panel in all analyses. Of note, colocalization analysis with the European-only GWAS summary statistics yielded similar results.To test for colocalization of phenotype-associated SNPs and eSNPs from our tissue-specific eQTL analysis, we used fast enrichment estimation aided colocalization analysis, fastENLOC (v1.0), with default settings. The fine-mapped DAP results were converted to vcf format using the provided script summarize_dap2enloc.pl. To assess enrichment of colocalized SNPs in our CRE annotations used in the integrative priors, we expanded our colocalization analyses to multiple GWAS of primary kidney and UK Biobank phenotypes (see “Positive and negative-control phenotypes” above for phenotype selection). Colocalization analysis was conducted as described above for each GWAS with both GLOM and TUBE eQTLs and the uniform and integrative prior. For each GWAS-eQTL-prior pairing, we identified colocalized loci with RCP ≥ 0.2. For each cluster, we selected a single SNP with the highest colocalization probability. When there were multiple top SNPs, we prioritized SNPs that were in both the uniform and integrative prior and SNPs within CRE annotations. To focus on the effect of each prior on colocalization, we removed loci that prioritized the same top SNP independent of prior selection. To generate the expected number of SNPs within CRE annotations for each analysis, we randomly selected SNPs  controlling for the rank of the colocalized SNPs in the eQTL model. We calculated the mean overlap from 10 simulations for each GWAS-eQTL-prior pairing. The enrichment significance was tested with a one-sided Binomial test of the observed overlap using the estimated expectations.36) was used to test for causal relationships between GLOM and TUBE gene expression and complex kidney phenotypes—trans-ethnic meta-analyses of UACR6 and eGFR7. Using the fine-mapped DAP results, glomerular and tubulointerstitial eQTL gene-SNP weights were calculated and formatted for GAMBIT gene-based testing using PTWAS helper scripts provided by the program authors . 1000 Genomes project phase 3 version 5 samples were used for the LD reference panel in all analyses. PTWAS_scan was run using default settings. Gene-level significance was adjusted to account for the multiple testing burden using two methods; q-value70 and a more conservative Bonferroni threshold. Genes with predicted pleiotropic effects or no strong instruments were excluded from our count of significant loci. We tested for pleiotropic effects and estimated effect sizes using the ptwas_est function.Probabilistic transcriptome-wide association analysis  were used in this experiment. Ethical approval was not needed given no vertebrate animals were used. Genes for functional validation were selected based on the following criteria (1) causal association between gene and kidney phenotype in PTWAS analysis (FDR ≤ 0.05); (2) colocalization of eSNP associated with eGene and GWAS variants (RCP ≥ 0.5); (3) colocalization of eSNPs and relevant cell-type open chromatin; and (4) relative high expression levels of Drosophila homologs in the nephrocyte. A random subset of the qualifying genes was selected for functional follow up.Three young female Drosophila hemolymph to assay RFP accumulation detected by fluorescence microscopy. For quantification of relative ANF-RFP fluorescence, 20 nephrocytes were analyzed from each of 3 flies per indicated genotype. T-tests were used to indicate significance differences from the control.Briefly, 10 virgin female flies from the MHC-ANF-RFP, Hand-GFP and Klf15-Gal4 transgenic lines were crossed to 5 male flies from UAS-RNAi transgenic lines of the targeted genes at 25 °C. Pericardial nephrocytes of newly emerged adult flies (within 24 h of eclosion) were dissected and kept in artificial Drosophila hemolymph and examination of the cells by fluorescence microscopy after a 20 min incubation with Texas Red labeled Dextran . For quantification of relative Dextran dye fluorescence, 20 nephrocytes were analyzed from each of 3 flies per indicated genotype. T-tests were used to indicate significance differences from the control.Flies carrying Hand-GFP and Klf15-Gal4 transgenes were crossed with flies carrying the UAS-RNAi transgenes at 25 °C. Dextran uptake was assessed in adult flies one-day post-emergence by dissection of pericardial nephrocyte in artificial Renilla luciferase control reporter, in HK-2 human proximal tubule cells  cultured in DMEM/F-12  supplemented with 10% FBS  at approximately 70% confluency in 96-well plates by using TransIT-2020 Reagent , following the manufacturer’s protocol. Three separate transfections were performed with four technical replicates in each plate. Empty luciferase vector, pGL4.23[luc2/minP], was also transfected in quadruplicate as a control. Luciferase activity was quantified 48 hours after transfection using the Dual-Glo Reporter Assay System  according to the manufacturer’s protocol. Luminescence signals were captured using a GloMax®-Multi+ Detection System (Promega) and normalized to Renilla luciferase readings for each well. We used linear regression with log-transformed normalized luminescence adjusting for batch and orientation to test the allele effect on enhancer activity.Approximately 500-bp regions of DNA containing rs80282103 and rs11154336 were amplified from purified human genomic DNA  by PCR using engineered restriction sites to allow directional cloning into the multiple cloning region of the pGL4.23[luc2/minP] luciferase reporter vector . The resulting plasmids containing the insert in either forward or reverse orientation were confirmed by Sanger sequencing. Constructs containing the alternate alleles were obtained by performing Q5 site-directed mutagenesis . Primers used to amplify targets and perform site-directed mutagenesis are listed in Supplementary Data R 3.5 and Python 3.7 with 3rd-party package (scipy) were used to perform statistical analysis. IGV (2.12.3), LocusZoom, ggplot2 were used for visualizing open chromatin, GWAS, eQTL datasets.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1-12Reporting Summary


ST) and locus-specific branch length (LSBL). By applying a top 1% threshold for both parameters, a total of 888 candidate selective windows were identified, harbouring 1571 genes. Secondly, the investigation of regions of homozygosity (ROH) was performed utilizing the PLINK software. In total, 25 genomic regions exhibiting a high frequency of ROHs were detected, leading to the identification of 1216 genes. Finally, the identified potential functional genes from candidate genomic regions were annotated, and several important candidate genes associated with reproductive traits  and meat quality traits  were identified. The findings of this study provide valuable insights into the genetic basis of economic traits in Large White pigs and may have potential use in future pig breeding programs.Large White pigs are extensively utilized in China for their remarkable characteristics of rapid growth and the high proportion of lean meat. The economic traits of pigs, comprising reproductive and meat quality traits, play a vital role in swine production. In this study, 2295 individuals, representing three different genetic backgrounds Large White pig populations were used: 500 from the Canadian line, 295 from the Danish line, and 1500 from the American line. The GeneSeek 50K GGP porcine HD array was employed to genotype the three pig populations. Firstly, genomic selective signature regions were identified using the pairwise fixation index (F ST) and locus-specific branch length (LSBL) methodologies have emerged as a valuable approach for discerning selection signatures and distinguishing traits across diverse breeds and geographic regions [Large White pig breeds have gained recognition for their exceptional performance, characterized by rapid growth, efficient feed conversion, and high-carcass yield . Through regions ,3,4.In addition to SNP and gene expression data, runs of homozygosity (ROH) have emerged as valuable components of omics data available in biological databases, serving as powerful tools for gene discovery and diversity assessment in livestock. ROH refers to contiguous segments of the genome where an individual inherits identical haplotypes from both parents ,6. Long ST and LSBL were employed as methods to select signatures associated with specific traits. Additionally, homozygosity analysis was conducted to further investigate the genetic characteristics of the three pig populations. The findings of this research contribute novel and valuable insights into the population history and genetic structure of Large White pigs with diverse genetic backgrounds.In this study, we utilized the GeneSeek 50K GGP porcine HD array to characterize Canadian, American, and Danish Large White pigs. FPrincipal component analysis was conducted to assess the genetic variation indices of the three pig populations. The results demonstrated significant differences in the genetic backgrounds of the American, Canadian, and Danish line Large White pigs, with PC1 effectively separating the three populations .ST and LSBL, for selection signature detection. Through a comprehensive genome-wide selective sweep analysis, we identified a total of 1434 enriched genes after filtering the intersection of the top 1% windows obtained from FST  per individual and the total number of ROH per individual was examined and presented in To identify the genomic regions most associated with ROH in the three pig populations, we calculated the frequency of SNP occurrence within the ROH segments. From these calculations, we selected the top 1% of SNPs with the highest frequency and plotted their positions along the respective chromosomes A–C. Our The 1216 candidate genes identified within ROH in pigs were subjected to functional enrichment analyses. Gene Ontology (GO) analysis revealed significant enrichment of specific biological processes A. Kyoto The KEGG pathway analysis demonstrated the enrichment of specific pathways associated with the candidate genes identified in each pig population. In the Danish line, candidate genes were significantly enriched in the PPAR signalling pathway and the cGMP-PKG signalling pathway D. In theMYL4 was found to exhibit differential expression in the longest muscle tissue of the pig’s back, which correlated with variations in the number of muscle fibres within the tissue [FGFR2, which has been recognized as a crucial regulator of myogenesis during skeletal muscle development and regeneration [SFRP1, predicted to be targeted by miRNA-1/206, and implicated in muscle cell proliferation and prenatal skeletal muscle development [SHANK2, a member of the Shank protein family, was found to be associated with childhood obesity, and to influence oestradiol blood concentration [MYH3 gene, encoding myosin heavy chain 3, was identified as a regulator of myofiber-type specification and adipogenesis in skeletal muscle [SHANK2 gene was identified as likely to affect the backfat thickness in pigs. FLVCR1 deficiency results in Diamond–Blackfan anaemia, often associated with skeletal malformations [STYX, the main signalling regulator in the ERK1/2 MAPK signalling pathway, increased pre-adipocyte adipogenesis by promoting pre-adipocyte proliferation [LRRK2 displayed lipid accumulation in the liver and kidney of rodents. LRRK2 was identified as an important gene for Intramuscular fat content (IFC) using GWAS in Suhuai pigs [FGF12 exhibits significant and localized expression in midgestation mouse embryos and plays a crucial role in inducing the differentiation of mouse embryonic stem cells [CD247 is T-cell receptor zeta, which has an important role in antigen recognition and signal transduction [The identification of numerous candidate genes associated with economic traits in this study contributes to our understanding of the genetic factors influencing these traits in pigs . Among te tissue . Other gneration , and SFRelopment . SHANK2,ntration ,20. Induntration . The MYHl muscle . In a stl muscle , the SHArmations . STYX, tferation . Knockouuai pigs . FGF12 eem cells . The prosduction .In this study, the GeneSeek 50K GGP porcine HD array was utilized to investigate the frequency and distribution of ROH in the genome of three distinct pig breeds. The formation of ROH patterns is primarily influenced by various factors, including population bottlenecks, inbreeding, genetic drift, and selective pressures arising from both natural and artificial selection . Our finADCYAP1 global knockout has decreased fertility and affects spermatogenesis [U2 and U6 play an important role in snRNP assembly and pre-mRNA splicing in oocytes. An essential role for CETN1 is in the late steps of spermiogenesis and spermatid maturation, and this gene plays a role in the reproductive capacity of the Danish line [THOC1 required, which means that this gene also has an effect on the ability of the Danish line to reproduce [USP14 is required for spermatid differentiation during spermiogenesis [GATA6 resulted in a loss of the normal steroidogenic testis function [GREB1L plays a major role in genital development [GREB1L were potential candidate genes for controlling the expression of the rib number. Some genes associated with specific traits related to meat quality were detected: MiR-133 repressed ERK1/2 activity by targeting FGFR1 and PP2AC to repress myoblast proliferation and promote its differentiation [PLEKHO1 depletion drastically impairs C2C12 myoblast fusion in vitro and in vivo during zebrafish muscle development [LPIN2 is one member of the lipid gene family associated with backfat thickness in pigs [The present study identified distinct sets of candidate genes from both selection signature analysis and ROH analysis. In pigs, inbreeding represents a combination of natural and artificial events, and our results demonstrate the complementary nature of different methods in investigating complex traits. There is some difference in the focus of artificial selection in these three distinct Large White Pigs discussed in this study. The Danish line focuses on reproductive performance, such as litter size; the American line focuses on growth performance, such as growth rate and meat ratio; and the Canadian line is similar to the American line in that it also focuses on growth performance. In order to improve the populations, all three populations were subjected to accurate phenotypic data collection and were regularly monitored, and data were analysed to identify the best-performing breeding individuals. The identification of candidate genes associated with economic traits was based on genomic regions exhibiting a high frequency of ROH. Functional analysis and previous studies support the association of most candidate genes with economic traits . Severalogenesis ,31. U2 aish line . Loss ofeproduce . USP14 iogenesis . The knofunction . GREB1L elopment . In addielopment  claim thntiation . PLEKHO1elopment . LPIN2 i in pigs . This geThe Animal Welfare Committee of Nanjing Agricultural University conducted a review of all animal testing and sample collection techniques used in this research. This review process included a careful examination of the ethical considerations of the research, as well as the methods and procedures used to ensure the safety and welfare of the animals involved. The Committee approved the animal testing and sample collection techniques in this research, ensuring that the animals were treated humanely, and that the data collected were accurate and reliable. (Permit number: DK652).http://www.cog-genomics.org/; accessed on 16 March 2023) [Sus scrofa 11.1 porcine genome reference assembly.This study utilized three distinct populations of Large White pigs, including 500 Canadian , 295 Danish , and 1500 American  Large White pigs, as experimental materials. Genomic DNA was extracted from ear tissue and genotyped with the GeneSeek 50K GGP porcine HD array. The software PLINK (V1.90) (ch 2023)  was usedPrincipal component analysis (PCA) was performed using PLINK1.9 , and theST method based on population differentiation was used to analyse the selection signature of the data of the three pig populations. The FST was calculated with VCFtools [CLW, LDLW, LALW) were calculated from single locus pairwise FST distances, where LCLW = (CLW-DLW FST + CLW-ALW FST − DLW-ALW FST)/2, LDLW = (CLW-DLW FST + DLW-ALW FST − CLW-ALW FST)/2 and LALW = (CLW-ALW FST + DLW-ALW FST − CLW-DLW FST)/2 [The FVCFtools  (—fst-wiW FST)/2 . Based oROH were detected with the detect RUNS package of R software version 4.0.5; we defined ROH according to the following criteria: (i) the minimum number of SNPs in a sliding window was 50; (ii) one heterozygous genotype and no more than two missing SNPs were allowed per window; (iii) the minimum ROH length was set to 1 Mb to eliminate the impact of strong linkage disequilibrium (LD); (iv) the minimum SNP density was 1 SNP every 500 kb and the maximum gap between consecutive SNPs was set to 1 Mb to avoid affecting the length of ROH with a low SNP density; and (v) to minimize the number of the false-positive ROH, the minimum number of SNPs that constituted the ROH (l) was calculated with the method proposed by , I=lnα/nIn this study, the detected ROHs were divided into three categories for further analysis: 1–5, 5–10, and >10 Mb. We computed the frequency of ROH numbers and the average length of an ROH per breed.We calculated the frequency of occurrences within the ROH regions of each SNP across the individuals and made a Manhattan figure by plotting these values in conformity with the position of each SNP on chromosomes. The SNPs in the top 1% of the frequency of occurrence were selected as a hint of a potential ROH.Sus scrofa 11.1, http://www.ensemble.org/; accessed on 16 March 2023) at 100-kb regions (upstream 50 kb and downstream 50 kb) flanking the SNPs of ROH hotspots. The Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analysed for all candidate genes by the Metascape database .Candidate genes were annotated via the Ensembl database (http://www.ensembl.org/) for annotating significant SNPs loci. In our study, candidate genes were identified within a 500-kb genomic region upstream and downstream of the significant SNPs. Furthermore, functional annotation of genes within the regions of interest was conducted using the R package WebGestaltR [To determine positional candidate genes, we utilized the BioMart database  and meat quality traits  located within genomic regions exhibiting a high frequency of ROH and selection signatures. Our findings suggest that GREB1L may play a role in controlling the expression of rib numbers. These results provide valuable insights into the genetic basis of reproductive and meat quality traits in Large White pigs and contribute to our understanding of the molecular mechanisms underlying these economically important traits.In this study, we investigated the selection signatures and runs of homozygosity (ROH) in three Large White pig populations  from the porcine 50 K SNPs chip. Our analysis revealed several candidate genes associated with reproductive traits (


MITF gene that were significantly associated with hoof color in the Australian White (AUW) sheep. The results showed that the three genotypes of this SNP were linked to black, grey, and amber hoof color. These findings provide molecular markers that can be used in marker-assisted selection to select for hoof color of purebred AUW sheep.In this research, we used RAD-seq genotyping data and GWAS to identify SNPs in the MITF) gene was significantly associated with the hoof color in AUW sheep (p = 9.40 × 10−36). The MITF gene plays a key role in the development, differentiation, and functional regulation of melanocytes. Furthermore, the association between this locus and hoof color was validated in a cohort of 212 individuals . The results indicated that the hoof color of AUW sheep with GG, AG, and AA genotypes tended to be black, grey, and amber, respectively. This study provided novel insights into hoof color genetics in AUW sheep, enhancing our comprehension of the genetic mechanisms underlying the diverse range of hoof colors. Our results agree with previous studies and provide molecular markers for marker-assisted selection for hoof color in sheep.Studying the characteristics of mammalian hoof colors is important for genetic improvements in animals. A deeper black hoof color is the standard for breeding purebred Australian White (AUW) sheep and this phenotype could be used as a phenotypic marker of purebred animals. We conducted a genome-wide association study (GWAS) analysis using restriction site associated DNA sequencing (RAD-seq) data from 577 Australian White sheep  and performed association analysis utilizing the mixed linear model in EMMAX. The results of GWAS demonstrated that a specific single-nucleotide polymorphism  in intron 14 of the microphthalmia-associated transcription factor ( Different mammal species exhibit striking and varied coat colors, which are a frequent and obvious biological occurrence . This vaThe Australian White (AUW) breed of sheep originated in Australia. It is a coarse-wool and specialized mutton breed. It integrates the genes of four breeds of sheep; namely, Dorper, Van Rooy, Polled Dorset, and Texel. The AUW breed has the advantages of strong stress resistance, a fast growth rate, and easy management. It is used for both meat and wool production. This indicates that AUW sheep are suitable for crossbreeding with local sheep breeds, thereby improving the meat quality and production efficiency. In the case of AUW sheep, the hoof color can vary depending on the strain or pedigree. The hoof color can be black, grey, amber, or striped due to a combination of different pigments . A deeper black hoof color is the standard for breeding purebred AUW sheep. Animal color traits, including hoof color, are qualitative traits controlled by a few genetic motifs. The color primarily depends on the type of melanin and its relative amount [Since 2005, following the publication of the first GWAS paper on human macular degeneration, an increasing number of researchers have used GWAS to investigate various complex human diseases . GWAS stMITF gene.In this study, we sequenced the genomes of AUW sheep by RAD-seq. We aimed to excavate genes and loci influencing hoof color in AUW sheep according to the GWAS analysis. The GWAS results indicate that the highest signal was located within the The AUW sheep analyzed in this study were reared on the sheep farm of the Aoqun Animal Husbandry Co., Ltd., Tianjin, China. A total of 789 AUW sheep were randomly selected from this farm. The sheep population consisted of 486 rams and 303 ewes. The hoof colors of the rams and ewes included in this study are presented in We collected the ear tissues from 789 AUW sheep for genomic DNA extraction. We used the high-salt extraction method to extract the DNA . Sequenchttps://www.ncbi.nlm.nih.gov/genome/?term=sheep, (accessed on 10 January 2023)) using BWA (version 0.7.17) software. We used Samtools (version 1.7) to sort the bam files and remove duplicates . The next step was to call SNPs by the GATK (version 4.1.9.0) software  [After obtaining sequencing data, individual data were extracted based on tag sequences, low-quality sequencing fragments were filtered out, and high-quality fragments were aligned to the sheep reference genome  .−6. The fastPHASE software (version 1.4) was used to fill in the missing genotypes, and the filled SNPs were filtered again using the same quality control standards [We used Vcftools (version 0.1.13) to control the quality of the raw data. The initial SNP data were quality controlled with the following criteria: call rate > 0.7, SNP quality > 20, minor allele frequency > 0.05, heterozygosity < 0.9, and HWE > 1 × 10tandards . We usedhttp://genetics.cs.ucla.edu/emmax/index.html, accessed on 10 January 2023). The mixed linear model (MLM) was used as follows: y = Xb + Zu + m + e.We performed GWAS analysis using the genotypes resulting from the variant calling analysis previously performed for the RAD-seq data of 577 AUW sheep. GWAS analysis of the hoof color was performed using the EMMAX program  and chromosome-wide  significance thresholds [In the model, resholds .https://asia.ensembl.org/info/docs/tools/vep/index.html, accessed on 5 June 2023).The SNPs within a 200 Kb region centered around the top SNP identified by the GWAS were extracted by Bcftools (version 1.8). Subsequently, the linkage disequilibrium between these SNPs was calculated. We used LDblockshow (version 1.40) for data visualization. Annotation of SNP loci was performed using Variant Effect Predictor (VEP) software of the Ensembl database  to design the primers of PCR amplification. We used the touchdown-PCR to amplify the target DNA fragment. We used a 50 µL system for amplification which included 2.5 μL of DNA template, 1.25 μL of each primer, 25 μL of PCR Mix reagent, and 20 μL of double-distilled water [We used the NCBI Primer Blast online analysis software . The PCR product was detected using 1.5% agarose gel electrophoresis. Sanger Sequencing was performed by the Tsingke Biotechnology Company  to verify mutations. The sequencing results were compared and analyzed with the sequence of the relevant gene fragment of sheep in GenBank using SnapGene software . We used Chi-square tests to analyze the relationship between the variant in the MITF gene and the hoof color of AUW sheep. The linear model used was: Yij = µ + Si + Gi + eij. . All results were considered significant when p < 0.05. All tests of significance were two-sided tests.An association analysis was performed using the phenotypic data of the hoof color of 212 AUW sheep with Sanger sequencing data. The association analysis of the SNP in the MITF gene from six different species (Ovis aries (XP_004018379.2), Capra hircus (XP_013829095.2), Bos taurus (XP_024838185.1), Homo sapiens (NP_001341533.1), Mus musculus (NP_001106669.1) and Sus scrofa (XP_020924242.1)) in the NCBI database. The similarity of the protein sequences was calculated using Protein Blast . We used MEGA (Version 6.06) to align the multiple protein sequences and construct the phylogenetic tree [We downloaded amino acid sequences of the ns NP_0011533.1, MMITF gene in different tissues and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways  [https://string-db.org/, accessed on 30 March 2023). Genomic regions and candidate genes were recognized by a genome browser (NCBI). The corresponding SNP loci were obtained by GWAS. In this study, we used relevant data from the Ruminant Genome Database of Animal Omics Database to explore the expression of the ch 2023) . The proThe alignment was conducted using the BWA software, followed by data sorting and duplicate removal using Samtools. SNP calling was performed using GATK, resulting in a total of 175,902 SNPs. The fastPHASE software was used to impute the missing genotypes, and the imputed SNPs were filtered again using the same quality control standards. After quality control, a total of 577 AUW sheep and 171,842 SNPs were used in the GWAS analysis. The filtered SNPs were spread across all 26 chromosomes. Chromosome 1 had the highest number of SNPs and chromosome 24 had the lowest. The minor allele frequency (MAF) for all SNPs was recalculated following the quality control process, and only SNPs with an MAF of 5% or greater were retained.−7, and the chromosome-wide significant threshold was 5.819−6. The accuracy and reliability of the results were improved by incorporating factors  in the model. The top SNP locus was mined by GWAS analysis (p = 9.40−36)  . This SNilibrium . We founThe primers were designed using NCBI online software. The forward primer was GGCCACCTGATGTGAAGAAC and the reverse primer was TTGACAGTGTTGTGGGGCA . The PCRp < 0.01). The more G alleles an individual had, the darker the hoof color  were associated with pigmentation in sheep. This SNP (s26449) was located near the MC1R gene; mutations in this SNP were significantly associated with the coat colors of sheep [MITF gene is located in the QTL region of chromosome 22 which could affect the spotting traits [ASIP gene could lead to the white-hair phenotype of swamp buffalo [MITF gene that was associated with hoof colors, including amber, grey, and black.As animal breed characteristics or protective colors, color traits are often used in breed identification and species evolution, and are important phenotypes in the field of genetic breeding . Severalof sheep . As earlg traits . Researcg traits . Using w gene s269 were as buffalo . Based oTYR). The MITF gene, a transcription factor involved in melanogenesis, codes for the MITF protein, which regulates the tyrosine gene family  [MITF is a member of the CANNTG superfamily. Previous research has shown that the MITF gene plays a role in the development, differentiation, and functional regulation of melanocytes [MITF gene plays a critical role in the formation of true melanin and indirectly affects color traits in animals. Coat colors in sheep and other mammalian species can display various shades. The MITF gene is known to play a crucial role in the process of melanin deposition, which significantly contributes to the observed coat color. The promoter of the MITF gene, located directly upstream of the melanocyte isoform, is regulated by various transcription factors and signaling pathways that are involved in melanocyte and melanoma biology. MITF is a gene of significant interest that exerts pleiotropic effects on various fundamental cellular processes such as cell survival, invasion, senescence, differentiation, metabolism, proliferation, and DNA damage repair [MITF gene is conserved, it plays a key role in pigmentation in other species. A 10 bp insertion in the melanocyte-specific promoter of the MTIF gene could explain a proportion of horses with white-spotting phenotypes [p = 9.40−36) with the hoof colors of AUW sheep. We observed that this SNP locus was located in intron 14 of the MITF gene using gene annotation. Melanin is a protein byproduct produced by melanosomes in melanocytes that is not soluble in water or most organic solvents. The ratio of true to brown melanin is strongly correlated with the activity of the enzyme tyrosinase (d TYRP2) . The MITd TYRP2) . MITF isanocytes , uveal eanocytes , and ostanocytes . As a ree repair . As the enotypes . Few stup < 0.01). Our association experiments confirmed the results of the GWAS analysis, revealing a significant association between the SNP locus (g.31603203A/G) and the hoof color of AUW sheep. According to an LD analysis, a 200 kb region around this SNP locus was highly linked. This suggested that this region had received a high degree of selection and that changes in this region may have resulted in different hoof colors of AUW sheep. Related studies identified SNP loci within a region of the RXFP2 gene on chromosome 10; this peak was significantly associated with the horn phenotype [In this study, we employed a combination of bioinformatics software analysis and experimental verification. We selected six additional sheep from the AUW sheep breed for validation purposes. The PCR products of these sheep were analyzed via agarose gel electrophoresis and were observed to have a size of 529 bp. The sequencing results were then compared and analyzed with the sequence of the relevant gene fragment of sheep from GenBank using SnapGene software. These results ensure the accuracy of subsequent Sanger sequencing. We associated the sequencing data with the hoof colors of 212 additional sheep from the AUW population. Our analysis results indicate that sex is not significantly associated with hoof color in AUW sheep. The association analysis indicated that the number of G alleles at the g.31603203A/G locus was highly significantly associated with the black hoof color , all within two steps of this network. The proteins that interact with MITF may also influence the expression of melanogenesis. Melanogenesis is a complex process that involves the synthesis of melanin in melanocytes and its subsequent transport to keratinocytes. This process is regulated by multiple genes and signaling pathways. DNA methylation regulates the expression of key genes that influence the expression of melanin, such as tyrosinase (TYR), TYRP1, dopachrome tautomerase (DCT), and MITF. Potential DNA methylation sites have been identified in the genes associated with melanogenesis-related signaling pathways such as Wnt, PI3K/Akt/CREB, and MAPK [TYR [TYR and TYRP1 genes in the head feather barbules of males were higher than in females. These genes significantly regulated the expression of melanin, which affected the color of the head in the different sexes [TYR in white crucian carp (WCC) and its hybrid progeny (WR), derived from the cross of WCC and red crucian carp. Their study observed that the level of TYR protein was significantly reduced in the mutant WCC compared with the wild-type sibling control fish, resulting in varying degrees of melanin reduction in both the mutant WCC and mutant WR; the mutation efficiency ranged from 60% to 90%. A series of pivotal pigment synthesis genes, including TYRP1, MITFA, MITFB, DCT, and SOX10, were downregulated in the TYR–CRISPR WCC, ultimately resulting in a reduction in melanin synthesis [The hoof color of sheep may be a complex trait that is determined by multiple genes, pathways, or networks. The and MAPK . The IFNAPK [TYR . The expnt sexes . Liu et ynthesis . With thynthesis . Mutatioynthesis . The resMITF gene that was associated with the hoof color trait in AUW sheep. Currently, the selection of the hoof colors of AUW sheep is mainly based on the subjective evaluation of hoof colors by human observers, which can lead to differences in interpretation and reduce the accuracy of the selection. In contrast, MAS uses the genetic information of the animals themselves, rather than a subjective evaluation to improve the accuracy of the selection. However, there is a lack of molecular markers that are available for hoof color selection. Identifying and screening the molecular markers related to hoof colors aids the accelerated breeding of AUW sheep strains with a consistent hoof color. This research may assist with the identification of valuable genes and mutations in the AUW sheep, improving the existing genetic resources and preparing for future large-scale meat-sheep breeding. Studying the genetic basis of the hoof color may lead to the discovery of genes related to pigment-associated diseases , which could aid in the development of new treatment methods. Such studies could help in the understanding of the mechanisms of a range of human diseases caused by melanin deposition using sheep as an animal model. The hoof color serves as a tool to discern purebred AUW sheep populations. In this research, we discovered that the MITF gene is likely to affect the hoof color in AUW sheep. These findings contribute to a more comprehensive understanding of the genetic foundations underlying the hoof colors of sheep and affirm the association of this locus with the phenotype. These results can be applied in future MAS breeding strategies for purebred AUW sheep. They can also be used in sheep breeding, ultimately enhancing the economic efficiency of the sheep industry. Further research is required to refine these results and to expand upon the molecular mechanisms governing the inheritance of the hoof colors of sheep.From the GWAS results, we were able to identify an SNP (rs417454260) located within the MITF gene that is associated with hoof color in the AUW sheep breed. The most significant SNP identified, located in an intron of this gene (rs417454260), could be considered a candidate genetic marker in relation to hoof variation in AUW sheep. This study offers an opportunity for MAS breeding in the future. These findings provide molecular markers that can be used in marker-assisted selection to select and breed the hoof colors of purebred AUW sheep, contributing to a better understanding of the genetic basis of this trait.This study identified a SNP marker in the color-related


In order to achieve optimal glycemic control, intensive insulin regimes are needed for individuals with Type 1 Diabetes (T1D) and insulin-dependent Type 2 Diabetes (T2D). Unfortunately, intensive glycemic control often results in insulin-induced hypoglycemia. Moreover, recurrent episodes of hypoglycemia result in both the loss of the characteristic warning symptoms associated with hypoglycemia and an attenuated counterregulatory hormone responses. The blunting of warning symptoms is known as impaired awareness of hypoglycemia (IAH). Together, IAH and the loss of the hormonal response is termed hypoglycemia associated autonomic failure (HAAF). IAH is prevalent in up to 25% in people with T1D and up to 10% in people with T2D. IAH and HAAF increase the risk of severe hypoglycemia 6-fold and 25-fold, respectively. To reduce this risk for severe hypoglycemia, multiple different therapeutic approaches are being explored that could improve awareness of hypoglycemia. Current therapies to improve awareness of hypoglycemia include patient education and psychoeducation, the use of novel glycemic control technology, pancreas/islet transplantation, and drug therapy. This review examines both existing therapies and potential therapies that are in pre-clinical testing. Novel treatments that improve awareness of hypoglycemia, via improving the counterregulatory hormone responses or improving hypoglycemic symptom recognition, would also shed light on the possible neurological mechanisms that lead to the development of IAH. To reduce the risk of severe hypoglycemia in people with diabetes, elucidating the mechanism behind IAH, as well as developing targeted therapies is currently an unmet need for those that suffer from IAH. For people with diabetes, hypoglycemia is caused by excess insulin action in the setting of impaired counterregulation. In people who rely on insulin therapy to control their blood sugar levels, episodes of hypoglycemia increase the risk for subsequent episodes of hypoglycemia as part of a vicious cycle . With reWith better glycemic control, patients with Type 1 (T1D) and insulin-dependent Type 2 Diabetes (T2D) have been able to reduce the risk for diabetes complications  . Yet, asIn addition to recurrent episodes of hypoglycemia, other factors can impair counterregulation and/or induce IAH and thus contribute to the risk for hypoglycemia . NocturnSince HAAF increases the risk for severe hypoglycemia by 25-fold , it is iversus or “electronic awareness” by noting the glucose trace falling or hearing the alarms with CGM (These questionnaires have been criticized for 1) having a high degree of inter-questionnaire variability in identifying subjects with IAH and subjects with impaired counterregulation, 2) susceptibility to recall bias by the subject, 3) lacking sensitivity to detect changes in hypoglycemia awareness over a short period, and 4) were developed in the pre-continuous glucose monitor (CGM) era (excluding HypoA-Q). Also, hypoglycemia questionnaires do not distinguish whether awareness reflects true restoration of hypoglycemia awareness  with CGM .Hypoglycemic questionnaires do have many meritorious qualities in that they are 1) inexpensive, 2) non-invasive, and 3) amenable to out-patient settings. In addition, these questionnaires have been validated and adapted to populations beyond their original demographic . Added bMistimed or imprecise dosing of insulin increases the likelihood of hypoglycemic events and recurrent episodes of hypoglycemia lead to the development of IAH . In addiIn spite of their limitations (see above), the most practical method to assess for IAH in a clinical setting is hypoglycemia questionnaires. However, if patients are not asked about hypoglycemia or fail to report asymptomatic hypoglycemia, the diagnosis of IAH can be missed . TherefoPrior to advanced diabetes technology such as CGMs and the automated insulin delivery systems, several of these earlier studies demonstrated that the scrupulous avoidance of recurrent episodes of hypoglycemia could restore  awareness of hypoglycemia . To the Given the complexity of IAH, a variety of clinical treatment considerations have been investigated to decrease hypoglycemia and the cycle of IAH . In the Strategies to avoid hypoglycemia include transplantation (pancreas or islet cells), technology , pharmaceuticals, and patient education. The overarching goal is to decrease incidences of hypoglycemia and thereby restore both awareness of hypoglycemia and improve the counterregulatory response to hypoglycemia.Fundamentally, the most pressing issue with IAH is the inability to sense when blood glucose concentrations fall to severe levels . Diabetes education programs have been successfully employed to improve glycemic control and the overall health of people with T1D and T2D . AlthougGiven the increased risk of hypoglycemia with intensive glycemic control, educational programs began to focus on improving awareness of hypoglycemia. More specific psychological training and bio-psycho-behavioral techniques have been shown to help people with diabetes improve their awareness. The Blood Glucose Awareness Training Program (BGAT) is an IAH focused psychoeducational program . BGAT eqAfter the success of BGAT, educational programs entered an era of utilizing hypoglycemia symptom detection training for improving/treating hypoglycemia awareness/IAH. Adapted from BGAT, the HypoAware training program focused on training and empowering people with T1D and advanced T2D to reduce episodes of hypoglycemia, improve awareness, and reduce fear of hypoglycemia . AnotherBuilding on the DAFNE-HART program, the Hypoglycemia Awareness Restoration Programme for People with Type 1 Diabetes and Problematic hypoglycemia Persisting despite optimized self-care (HARPdoc) was developed as a multidisciplinary strategy targeting cognitive in subjects with IAH. The HARPdoc program was recently evaluated and compared its effectiveness with BGAT in a population who continued to have IAH and developed recurrent severe hypoglycemia despite prior structured diabetes education and offered advanced diabetes technologies . HARPdocTreatment of IAH in people with T2D has been studied to a much lesser extent compared to studies in people with T1D. The Common Sense Model (CSM) assessed illness perceptions in subjects with T2D and IAH on insulin therapy . While tvide infra). For example, the HypoCOMPaSS trial (Comparison of Optimized MDI versus Pumps with or without sensors in severe hypoglycemia)   demonstrlycemia) . The poslycemia) .For people with IAH, hypoglycemia is often detected not by symptoms, but with glucose monitoring technology . Unquestionably, diabetes technologies have markedly improved treatment for people with diabetes . It is iCGMs have revolutionized diabetes management. Since CGMs can measure glucose every 5 min and alert patients of impending low (as well as high) glucose levels, they represent a major leap forward in glycemic management over handheld glucometers. Recent data has shown that CGM users  had better glycemic control  and lower rates of severe hypoglycemia compared to non-CGM users . While HThe Advanced Technologies and Treatments for Diabetes Congress formed a panel of expert individuals to compose CGM guidelines for clinician use . These g©  CGM to determine if it decreased the time spent in hypoglycemia. Researchers found that after 3–4 months subjects decreased their TBR, which was associated with increased TIR and was sustained after 5–6 months  failure to adequately/scrupulously avoid recurrent hypoglycemia for a sufficiently long duration, 2) a methodology issue wherein self-reported hypoglycemia awareness questionnaires may lack sufficient sensitivity to note an improvement in hypoglycemia awareness in follow up testing, and 3) heterogeneous factors distinct from recurrent hypoglycemia  that may play a pathophysiological role the development/perpetuation of IAH, and 4) the mistrust of CGM glucose information during asymptomatic episodes and other barriers to hypoglycemia self-management  which fuIn addition to CGMs, people with diabetes also use insulin pump delivery systems (thus replacing multiple daily injections of insulin). The combination of CGM and insulin pump technologies have been described as the holy grail of diabetes management . The CLSWith low (or predicted low) glucose values detected by CGM, sensor augmented pumps (SAP) allow for automated insulin suspension. By temporarily suspending insulin delivery, SAP can avoid (or limit) the severity of hypoglycemia . SAPs haet al. (2021) examined both arms of HAAF that perpetuate IAH, counterregulation and awareness (Automated insulin delivery (AID) systems use an algorithm to automate insulin delivery to manage sugar levels; however, it requires the user to manually enter meal insulin boluses and thus is often termed a “hybrid closed loop” rather than a fully “closed loop” . AID syswareness . While cwareness . In contwareness . Anotherwareness . Out of wareness . Additiowareness . The benIt is worthwhile to note that while some intervention studies do demonstrate an improvement in hypoglycemia questionnaire scores, it is unclear if a statistical improvement is clinically relevant as study subjects often demonstrate a persistent impaired awareness of hypoglycemia .It should be noted that the study design is another factor contributing to these seemingly discordant results viz-a-viz the ability of technology to restore awareness of hypoglycemia. The putative factors that contribute to the short-term blunting of the sympathoadrenal response to hypoglycemia induced by a few bouts of antecedent hypoglycemia in non-diabetic subjects are almost certainly different from the factors that contribute to HAAF (having developed over years in people with T1D). Disparate patient inclusion criteria are also confounding factors when comparing results from different studies. For example, early studies that established the efficacy of hypoglycemia avoidance to improve autonomic symptom responses were conducted in subjects with relatively short disease duration (∼7 years) . Some ofvide supra) is not necessarily due to a failure to scrupulously avoid recurrent hypoglycemia. Consequently, it is possible that multiple interventions addressing these many potential confounding variables may be necessary to completely restore normal awareness and counterregulation in all subjects.An alternative notion to the exclusively glucocentric etiology of HAAF, is the possibility that HAAF is a heterogeneous clinical entity that develops, in part due to recurrent hypoglycemia, but also develops due to other factors  . If thesFor people with intractable episodes of severe hypoglycemia, whole pancreas or islet cell transplantation remains an important treatment option recommended by the American Diabetes Association . PreviouGiven that CGMs and questionnaires make it relatively easy to identify subjects at high risk for severe hypoglycemia, and some of the neuronal transmitters/circuits that contribute to HAAF have been identified, an unanswered call to action is the identification of potential pharmacological therapies that could improve awareness of hypoglycemia. The effects of various drugs on hypoglycemia awareness and counterregulatory responses have been assessed in preclinical models of HAAF, clinical models of inducible HAAF, and subjects with long-standing T1D and HAAF  a SGLT3 agonist, was shown to restore the counterregulatory response to hypoglycemia in rats  as a key glucose-sensing region , but sevIn terms of testing responses to drug therapy, one study examined the effects of systemic and central (VMH) administration of a beta 2-adrenergic receptor agonist, formoterol, on the counterregulatory responses following hypoglycemia . SystemiIn rodent models of HAAF, recurrent hypoglycemia consistently blunts the sympathoadrenal response (noted by a blunted plasma catecholamine response) . Unfortui.e., did not exhibit a blunted food intake response to hypoglycemia)  .Another area of the brain that has been implicated in glucose sensing is the perifornical hypothalamus (PFH). Researchers focused on the orexin-glucose-inhibited neurons in the PFH  as a target for IAH and explored treatment with the anti-narcolepsy drug, modafinil  . Mice unDrugs that work within the adrenergic system seem like an obvious target that might improve both the counterregulatory response and awareness of hypoglycemia . ConsistAnother, similar pharmacological approach to treating IAH is targeting adenosine receptors to increase alertness and enhanced secretion of the counterregulatory hormones . One stuThe glucagon-like peptide-1 receptor agonist, exenatide, was used in a crossover trial in subjects with T1D and IAH . SubjectA sodium-glucose cotransporter-2 inhibitor, dapagliflozin, has shown effectiveness . DapagliTreatment with the CNS stimulant, modafinil, resulted in improved autonomic symptom scores, higher heart rates, higher glucagon concentrations during hypoglycemia, and improved scores on cognitive tests; however, the epinephrine response was not altered . Since mModulating GABA signaling as a means to restore counterregulation and hypoglycemia awareness is supported by pre-clinical models . ClinicaThe role of opioid receptors in the development and/or treatment of HAAF is an area of active investigation. Pre-treatment with opioid receptor agonists can impair the counterregulatory response to hypoglycemia . ConversBased on animal studies that indicate a possible role for selective serotonin reuptake inhibitors (SSRIs) to augment the counterregulatory response to glucoprivation , clinicaWith a goal of improving both the counterregulatory response to hypoglycemia and awareness of hypoglycemia, the development of novel drugs and/or the repurposing of existing FDA approved drugs remains an important area of research.IAH continues to be a complication in people with both T1D and T2D who seek optimal glycemic control with insulin therapy. Providers who care for patients with diabetes should inquire about hypoglycemia and IAH with a view towards considering treatment options. This review shows that there are several advances in technology and educational approaches that can improve hypoglycemia awareness. With regards to pharmacological treatments, basic science research in animal models is continuing to elucidate the mechanism(s) responsible and these novel treatments for IAH are being advanced into clinical trials. Future studies should focus on these possible mechanisms to develop more targeted therapies for patients who suffer from IAH.