The amount of genomic data is increasing exponentially. Using many genotyped and phenotyped individuals for genomic prediction is appealing yet challenging.We present SLEMM (short for Stochastic-Lanczos-Expedited Mixed Models), a new software tool, to address the computational challenge. SLEMM builds on an efficient implementation of the stochastic Lanczos algorithm for REML in a framework of mixed models. We further implement SNP weighting in SLEMM to improve its predictions. Extensive analyses on seven public datasets, covering 19 polygenic traits in three plant and three livestock species, showed that SLEMM with SNP weighting had overall the best predictive ability among a variety of genomic prediction methods including GCTA’s empirical BLUP, BayesR, KAML, and LDAK’s BOLT and BayesR models. We also compared the methods using nine dairy traits of ∼300k genotyped cows. All had overall similar prediction accuracies, except that KAML failed to process the data. Additional simulation analyses on up to 3 million individuals and 1 million SNPs showed that SLEMM was advantageous over counterparts as for computational performance. Overall, SLEMM can do million-scale genomic predictions with an accuracy comparable to BayesR.https://github.com/jiang18/slemm.The software is available at  In modern animal and plant breeding, individuals are selected for breeding based on predictions of genetic merit. Pedigree-based predictions of genetic merits have been used for selection in animals and plants for many decades and achieved steady genetic progress. With the advancement of sequencing technologies, millions of genetic markers (e.g. SNPs) have been discovered in the genome of many species. With the availability of cost-effective SNP chips, genomic predictions that use many genotyped SNPs across the genome  have beeIt is appealing to tackle the computational challenge of Bayesian methods but maintain their advantage over GBLUP. One way is to implement the Bayesian methods with variational Bayes, e.g. LDAK . AlternaThis article presents a new software tool to address the challenge, which we refer to as SLEMM (short for Stochastic-Lanczos-Expedited Mixed Models). SLEMM can efficiently perform Stochastic Lanczos Restricted Maximum Likelihood (REML) analysis  for millSLEMM fits the following linear mixed model:y is a vector of phenotypes for a quantitative trait, β is a vector of fixed effects including mean, X is the design matrix for β, α is a vector of SNP effects with a diagonal covariance matrix Z is a matrix of standardized genotypes, and e is a vector of residuals with diagonal covariance matrix R is often equal to an identity matrix; however, when using deregressed estimated breeding values as y in a farm animal population, we need to precompute R to model their reliability  in animal breeding, which is equivalent to a = b = 2 in our method. We can use grid search to determine the optimal values of a and b based on REML log-likelihood. In practice, we can set a = b and search among a small set of values  to reduce search space.We provide two schemes to optimize SNP weighting for genomic predictions. The first scheme is based on the MAF dependence of SNP effect sizes, which has been well studied for complex traits in humans . It can ion test , settingW equal to the identity. A QTL’s effect can be captured by its nearby SNPs because of LD, and SNPs in proximity to each other tend to show similar effects in model fitting. This leads us to develop the following window-based weighting scheme:where C is defined the same as in S is the number of SNPs on each side of SNP j, and kth SNP’s effect in an existing BLUP with W equal to the identity. This specification of jth SNP’s weight borrows information from a window of 2S + 1 SNPs. It is similar to the window-based weighting used in weighted ssGBLUP  and uses n individuals and m SNPs.SLEMM is written in C++ and powered by the Eigen library. It uses bool vectors to store genotypes in memory, occupying two bits per SNP per individual. SLEMM has a time complexity of https://github.com/syntheke/bayesR)  , and LDA/bayesR) . GCTA’s /bayesR) , which iWe applied the following key settings to all our analyses: optimized MAF-based SNP weighting for SLEMM-MAFopt; a fixed window size of 20 and optimized MAF-based SNP weighting for SLEMM-WW-MAFopt; defaults for KAML; blocksize = 4, msize = 500, number of MCMC iterations = 50 000, and burn-in = 20 000 iterations for BayesR; and defaults for LDAK with MAF-based SNP weighting the same as SLEMM-MAFopt.We used repeated random sub-sampling validation to evaluate the predictive ability of the aforementioned methods with seven public datasets. Each dataset was randomly split into a training population  and a validation population  for 20 replicates. Five-fold cross-validation was used for the large dairy cow data to reduce computational time. Predictive ability was calculated as the Pearson correlation in the validation population between genomic estimated breeding values (GEBVs) and pre-adjusted phenotypes or (de-regressed) EBVs.Computational speed and memory usage were evaluated using simulated data for SLEMM, KAML, BayesR, and LDAK. All evaluations were conducted on a Linux server with 2x Intel(R) Xeon(R) Gold 6258R CPU and 512 GB of memory. Ten threads were used for SLEMM, KAML, and LDAK, whereas four threads (=block size) were used for BayesR. Ten threads were tried for BayesR, but it slowed down the computation and sometimes led to failure.We analyzed 19 livestock and plant traits in seven public datasets and nine dairy traits in a large cow dataset from the U.S. CDCB. A summary of the datasets is shown in We simulated two datasets, one with 3M unrelated animals and 50k SNPs and the other with 1M SNPs and 100k unrelated animals. The former dataset was used to evaluate the computational performance with respect to sample size , while the latter dataset was used to evaluate the computational performance with respect to the number of SNPs . Genotypes were simulated using the software genosim  with defConsidering the involvedness of the genetic architecture of complex traits, we used only real data to evaluate prediction accuracy, including 19 traits from publicly available plant and livestock data and 9 traits of 300k dairy cows from the U.S. CDCB. We used simulation to benchmark computing time and memory usage. We considered three types of empirical GBLUP analyses in SLEMM: (i) non-weighted GBLUP (SLEMM-NW) that assumes all SNPs contribute equally to SNP heritability without SNP weighting, (ii) SLEMM-MAFopt that optimizes SNP weighting based on MAF, and (iii) SLEMM-WW-MAFopt that weights SNPs based on both MAF and proximity. We compared SLEMM to non-weighted empirical GBLUP implemented in GCTA (GCTA-NW), KAML, BayesR, and two LDAK models (LDAK-BayesR and LDAK-BOLT).We evaluated the impact of MAF-based SNP weighting, window size, and the number of window-based SNP weighting rounds on genomic prediction accuracy in SLEMM. To find the optimal MAF weighting parameter in Besides a window size of 20, we further tried a much larger window size (500 for livestock traits and 1000 for plant traits) in SLEMM-WW-MAFopt for all seven public datasets. The large window size resulted in similar predictive abilities to the window size of 20 for most traits . In partThe iterative weighting in SLEMM is set to one round by default. We further tried two rounds of window-based SNP weighting for the dairy bull traits. Increasing the number of weighting rounds marginally improved the predictive ability for MFP (0.864 versus 0.866) but decreased that for MY and SCS .h2 estimation. Both SLEMM and LDAK are much more memory-efficient than BayesR  . SLEMM-Wn BayesR . LDAK-BOn BayesR . As the n BayesR . In partIt is challenging to use many genotyped and phenotyped individuals for genomic prediction. In this work, we present a new software tool, SLEMM, to address the challenge. SLEMM uses the Stochastic Lanczos REML and SNP weighting to effectively and accurately estimate SNP effects for large datasets. SLEMM is fast enough for million-scale genomic predictions . In addiThe choice of priors on SNP effects usually has a considerable impact on genomic prediction accuracy for traits underlain by a small number of large-effect QTLs . This waThe prediction accuracy of SNP weighting in SLEMM can be affected by the following settings: MAF weighting parameter, SNP window size, and the number of SNP weighting rounds. Our analysis on the two dairy datasets showed that the optimal MAF weighting parameter was consistent across traits and robust for each trait. We also found that genomic prediction benefited little from MAF-optimized SNP weighting compared to non-weighted SLEMM  and 3. SThere have been several methods for leveraging functional annotations to improve genomic predictions, e.g. LDpred-funct , LDAK Z, and GMRbtad127_Supplementary_DataClick here for additional data file.


FIG4 and FYN) were identified. FIG4 is ubiquitously expressed in skeletal muscle and FYN is involved with processes such as forebrain development, neurogenesis, locomotion, neurogenesis, synapse development, neuron migration, and the positive regulation of neuron projection development. The CACNA1A gene, which codes for a calcium channel subunit protein in the calcium signaling pathway, seems the most compelling candidate gene, as many calcium ion channel disorders are non-degenerative, and produce spastic phenotypes. These results suggest that spastic syndrome is of polygenic inheritance, with important genomic areas of interest on BTA7 and BTA9.Spastic syndrome is a chronic, progressive disorder of adult cattle characterized by episodes of sudden involuntary muscle contractions or spasms of the extensor and abductor muscles of one or both hind limbs. In this study, a case-control genome-wide association study (GWAS) was performed on an adult Holstein cattle cohort. Based on the 50 K and high-density (HD) SNP panel GWAS, we identified 98 and 522 SNPs, respectively. The most significant genomic regions identified are located on BTA9 at approximately 87 megabase pairs (Mb) and BTA7 between 1 and 20 Mb. Functional analyses of significant SNPs identified genes associated with muscle contraction, neuron growth or regulation, and calcium or sodium ion movement. Two candidate genes ( Genomic selection has been widely adopted worldwide by the dairy cattle industry and has contributed to great genetic progress in many economically important traits . IntensiSpastic syndrome is a chronic, progressive neuromuscular disorder of adult cattle characterized by episodes of sudden involuntary muscle contractions or spasms of the extensor and abductor muscles of one or both hind limbs. These episodes are induced by movement and are particularly noticed when the animal stands up and is adapting to bear weight. No signs of spastic syndrome are exhibited when the animal is lying down. In the later stages, the spasms can involve the epaxial thoracolumbar and neck muscles ,11,12,13Some of the names applied to this syndrome include crampiness, stretches, crampy, barn cramps, periodic spasticity, inherited periodic spasticity, neuromuscular spasticity, bovine biceps femoris neuropathy, and spasmodic extensor hypertonus ,13,14,15Spastic syndrome occurs in adult cattle of both sexes ,13,16,18The condition has been reported in the following breeds: Holstein-Friesian ,10,19,21Heritability estimates for spastic syndrome were previously reported by Boettcher and Wang  for HolsIdentifying genomic markers and deriving candidate genes for disorders of unknown inheritance could allow for future genetic testing panels to target known detrimental alleles of specific at-risk populations, such as sires in artificial insemination (AI) stations. Also, creating a candidate gene list in an initial affected population will guide future research to genomic areas of potential interest, and mapping significant SNPs to genes may assist in creating a test panel for spastic syndrome disease risk. A test panel could allow producers to identify which young bulls may be at risk of developing and disseminating spastic syndrome before they are widely used in the population. In this context, a genome-wide association study (GWAS) to screen for areas of interest associated with bovine adult-onset spasticity is an appropriate strategy to uncover genomic regions of interest related to spastic syndrome. Therefore, the main objectives of this study were to: (1) measure the degree of inheritance of spastic syndrome via heritability estimation using all available pedigree information; (2) interrogate both moderate and high-density (HD) single nucleotide polymorphism (SNP) genotypes for the discovery of SNPs associated with spastic syndrome through the GWAS; and (3) perform in silico and functional analyses of significant SNPs obtained from the GWAS of a cohort of affected animals, using HD SNP genotypes to obtain a list of candidate genes for spastic syndrome.Spastic syndrome disease status was previously determined by two veterinarians (Dr. Hanna and Dr. Baird) via differential diagnosis, where other diseases/disorders presenting with similar clinical signs were evaluated and systematically eliminated. Animals that were at least three years of age and displaying episodes of spasms of the extensor and abductor muscles of the pelvic limb(s) were considered to have spastic syndrome, once other causes for hind-limb spasm had been ruled out, such as evident injury, tetanus, or epilepsy . AdditioAll animals were genotyped with the Illumina BovineSNP50 BeadChip . This 50 K SNP panel contained 41,847 SNPs after the quality control following , with anThe pedigree data contained 79,613 Holstein cattle from Canada, the USA, Germany, Denmark, Spain, Finland, France, Great Britain, Italy, and the Netherlands. There were 11,227 sires, 51,350 dams, and 9363 founder animals. Within the pedigree, there were 41,264 inbred animals with an average inbreeding coefficient of 0.0441, obtained using the software CFC .An initial study population consisted of 24 Canadian Holstein bulls  and 16 American Holstein bulls . In total, there were 26 affected and 14 control animals, as summarized in www.lactanet.ca; Guelph, ON, Canada). The study animals were genotyped with the Illumina 50 K Beadchip SNP chip , and imputed to the HD (777 K) SNP panel. The genome-wide accuracy of imputation was over 99%, when using a family- and population-based imputation method .α is the proportion of affected individuals within the population of interest, e is the exponential constant, z is the standard normal deviate corresponding to the proportion of affected animals (α), and π is the mathematical constant pi. To estimate the heritability of spastic syndrome, a binomial model was fitted using the ASReml software , includiUsing this transformation, heritability estimates for different reported prevalence rates within either a general population of classified Holstein cows of 0.55%  or a HoliX = ′, with iX defined as the phenotypic (disease status) observation of the ith animal as 0 or 1, and where iY = ′ represents the genotype of the animal, where iY = ½ × . With genotypes coded as “0”, “1”, and “2”, the allelic proportions were 0, ½, or 1. The expected SNP allele frequency is represented by μ, where μ =  = E(Y|X), such that 0 < iμ < 1. To associate the SNP allele frequency, iμ, with the phenotype, iX, the following logistic regression model was defined:β0 is a constant and β1 is the angular coefficient. To verify the association between the marker and the trait, the null hypothesis, H0: β1= 0, assumes the marker is not associated with the trait (spastic syndrome), against the alternative hypothesis, H1: β1≠ 0, where the marker is associated with the trait. Single nucleotide polymorphism (SNP) association analysis was carried out using the Sleuth software , which uses a generalized quasi-likelihood score (GQLS) method to analyze genetic associations between SNPs and the trait considered . The GQLThe 50 K SNP genotypes and the imputed HD genotypes were analyzed with a minor allele frequency (MAF) threshold of less than or equal to 0.00001. This threshold will screen for virtually all possible SNPs of interest, including rare variants. The call rates for individuals and SNPs were both set to 0.90, and a heterozygosity excess greater than or equal to 0.499 was excluded. Significance thresholds were set to 1%, 5%, and 10% chromosome-wise positive false discovery rates (pFDRs). The pFDR was used to increase the sensitivity of correctly detecting SNPs with potentially small effects on the disease, as the pFDR allows for more statistical power than traditional control of family-wise error rates . To narrow down a potentially large number of significant SNPs identified in the GWAS for the HD data, a post-GWAS MAF threshold of 0.2 was applied to obtain SNPs in relative abundance, as spastic syndrome prevalence is approximately 60%. Additionally, a chi-square test was performed to determine which significant SNPs were significantly different when compared with disease status with a threshold of 0.05. The population proportionate distributions of homozygotic SNPs were calculated in the affected and unaffected population, and the differences of the homozygous states were taken. Then, the absolute difference of the differences in homozygotic status was retrieved to identify the potential polarity of homozygous SNP genotypes and disease status using a threshold of 0.3. This analysis highlights SNPs with one homozygotic variant, which is observed more frequently within affected populations, while the alternative homozygotic variant is more frequent in the unaffected population.An alternative GWAS approach based on random forest (RF) regression was used to validate significant SNPs found through the GQLS logistic regression method. Liaw and Wiener  developeTo determine the prediction error of the test set, a process called ‘bagging’ is used, where successive trees do not depend on earlier trees and where all trees are independently constructed using a bootstrap sample of the data . ApproxiThe RF regression was performed using the initial set of animals to estimate the VIM% of SNPs in the HD SNP genotype panel  as the training dataset. The results were used for prediction purposes for the additional 225 phenotyped animals as a testing dataset. The forest was grown to 75 trees, as preliminary research suggests cumulative estimation and prediction error rates do not decrease with an increase in forest size, up to 1500 trees. The forest was replicated 100 times to limit spurious associations. Identified SNPs with occurrence frequencies in the top 50% were classified as genomic areas of interest. All SNPs with a non-zero VIM% were saved and compared with other forest results. Common SNPs from at least two forests were obtained, and average VIM% values were calculated. These ‘significant’ SNPs were then compared with results obtained from the HD SNP genotype panel GQLS regression for possible common areas of significance. The analysis was performed genome-wide to ascertain which SNPs were important on a genomic scale.Several post-GWAS significance threshold criteria were applied to decrease the number of SNPs for in silico and enrichment analyses. The first post-GWAS criterion applied to the SNPs identified was a MAF equal to or greater than 20% since, as disease prevalence within the study population is greater than 60%, the alleles that contribute to the phenotype observed in spastic syndrome are assumed to be relatively abundant. A chi-square test was performed to determine which SNPs were significantly different from expected frequencies when compared with disease status with a threshold of 0.05. The final threshold placed on the remaining significant SNPs was to obtain the population proportionate distributions of homozygotic SNPs in the affected and control populations. Differences of the homozygotic states were taken per phenotype, and then the absolute difference of these differences was obtained to identify potential polarity of homozygous SNP genotypes and disease status. The homozygous polarity threshold was 0.3.The SNP lists generated by the GWAS were mapped using the ‘next-generation sequencing SNP’ tool . The siA candidate gene list was created for the HD GWAS. Mapped genes were submitted to different bioinformatic web tools; the primary tool was the Database for Annotation, Visualization, and Integrated Discovery DAVID; ,41). The. The41])www.ncbi.nlm.nih.gov (accessed on 15 February 2015)] meta-database containing a broad collection of genomic and biomedical databases. The NCBI was utilized for human/mouse ortholog location and verification, gene aliases, and relevant literature regarding genes and gene function. Tissue types such as muscle  and nervous (nerve axons and brain) were emphasized in the search ,6,8,10, Subsequently, Nextbio, a data mining bioinformatics tool, was used to obtain information on the human ortholog of a gene of interest (if applicable), and if it was up- or down-regulated in related human neuromuscular disorders . General2 values) were obtained to quantify linkage between SNPs and candidate genes. For a well-established and studied trait, such as mastitis, significant SNPs were mapped to genes of up to 100 kilobase pairs (kb) [www.ensembl.org). Haplotype blocks of the candidate genes were created with the HaploView software [2 thresholds to create haplotype blocks within candidate genes. A solid spine was developed to search for a “spine” of strong LD from one marker to another, creating an ‘LD block’, where the first and last markers in the haplotype are in relatively strong LD. HaploView was also used to visualize gene length and intra-gene haplotype blocks.Large distances may exist between GWAS-derived significant SNPs and SNPs within the candidate genes, and thus up to two Mb to the left and right of each SNP of interest were considered. Linkage disequilibrium  . Howeversoftware , using t2 metric was used to find syntenic linkage with a threshold of 0.2 [Subsequently, we obtained syntenic LD of GWAS-derived SNPs and SNPs within candidate genes of interest. As the number of study animals was limited, the animals used to obtain pairwise LD were the 1659 HD genotyped Holsteins, also used as the reference population for the imputation analyses. The rd of 0.2 ,47.All SNPs within candidate genes analyses were investigated through NCBI. Variant information, including mutant types and protein consequences of mutants, was derived from the Cattle Genome Analysis Data Repository , as wellThe underlying heritability estimate for spastic syndrome was 0.41 with a standard error of 0.07. On the observed scale and considering a prevalence of 0.59, the estimated heritability was 0.26. Using a prevalence of 0.30, as in older proven AI bulls reported by Tenszen , a heritp-value”. The Manhattan plots given from y-axis at the –log(q) value of 6 to allow for viewing consistency across all chromosomes. SNPs that exceed the significance threshold of 6 are presented as triangles within Manhattan plots. The distribution of significant SNPs per chromosome is summarized in The chromosome-wise pFDR was used. The q-value obtained from the pFDR is the “posterior Bayesian p-value for the 50 K GWAS, highlighting the SNP location and at what pFDR threshold the SNP was found to be significant.The HD GWAS resulted in 522 significant SNPs across all chromosomes except BTA25 when using the significance thresholds described above. A genome-wide Manhattan plot of this GWAS is shown in p-value obtained for the HD GWAS and various post-GWAS threshold criteria values. A total of 73 SNPs were in common when comparing the 50 K GWAS with the HD GWAS. The common significant SNP frequency per chromosome is summarized in The number of SNPs with a non-zero VIM% ranged from 67 to 105 per forest, when 100 forests were grown with 75 trees each, and total OOB estimation and prediction error rates ranged from 25.0% to 47.6%, and 28.4% to 39.1%, respectively. In total, 913 SNPs occurred in at least two forests, up to 19 forests; see −19% to a maximum of 0.00614%, and common SNPs to the GQLS regression had an average VIM% ranging from 0.00067% to 0.00177%. In general, error rates were lower for SNPs regressed to the affected phenotype, ranging from 0.00% to 23.08% for estimation error rates and 1.53% to 14.50% for prediction error rates. The estimation and prediction error values for SNPs regressed on the unaffected phenotype ranged from 50.00% to 100.00% and 56.38% and 89.36%, respectively. This suggests that RF was able to identify variants implicated with the disease phenotype more accurately.We identified 16 common SNPs when comparing significant SNPs of the RF regression with the 522 significant SNPs of the GQLS regression, located on BTA7, BTA8, BTA14, and BTA20. These SNPs have a relatively high MAF, ranging from 0.26 to 0.49, and homozygotic polarity scores ranging from 0.20 to 0.47. One common SNP does not have a homozygotic polarity score as there is only one homozygotic state for all tested animals. FIG4 and FYN, were classified as important candidate genes.The 67 retained significant SNPs from the GWAS resulted in 1048 genes. The 67 retained significant SNPs from the GWAS were located on the following chromosomes: BTA1, BTA2, BTA3, BTA5, BTA7, BTA8, BTA9, BTA10, BTA12, BTA14, BTA18, BTA20, BTA21, and BTA22. In total, the SNPs were mapped to 1048 genes; CACNA1A) on BTA7 at approximately 13 Mb . The CACNA1A gene codes for a calcium channel subunit protein in the calcium signaling pathway. This calcium channel is present in the dendrite, neuronal cell body, cytoplasm, and voltage-gated calcium channel complex, and is part of various pathways such as the calcium signaling pathway, the MAPK signaling pathway, and the synaptic vesicle cycle [Below, the candidate genes are described on an individual basis. The within-gene haplotype blocks  are a vile cycle ,41. Biolle cycle ,41. In tle cycle . There ile cycle ,50. Mutale cycle . This gele cycle ,53.RAB3A) on BTA7, located at approximately 5 Mb (bovineHD0700001946 and Hapmap59438-rs29012637). The RAB3A protein is involved in calcium exocytosis in neurons, axonogenesis, neurogenesis, regulation of neurotransmitter secretion and transport, and synaptic vesicle maturation and exocytosis [RAB3A knockout mice have synaptic depression, suggesting that RAB3A has a regulatory role in synaptic vesicle trafficking. Additionally, one SNP is implicated in ALS, and 20 SNPs are implicated in Parkinson’s disease through meta-analyses of the RAB3A gene [Two SNPs were mapped to the Ras-related protein Rab-3A gene  on BTA7, located at approximately 5 Mb (BovineHD0700001946 and Hapmap59438-rs29012637). MAP1S is involved in processes like brain development, neurogenesis, neuron differentiation, neuron projection development, and the execution phase of apoptosis [MAP1S-regulated programmed cellular destruction (autophagy) could impact neurodegenerative diseases [MAP1S gene are also implicated in ALS through meta-analyses [MAP1S gene and Parkinson’s disease through meta-analyses [There were two SNPs mapped to the microtubule-associated protein 1S gene . There poptosis . This gediseases . Additioanalyses . There aanalyses ,53.bta-mir-23a-201 gene located on BTA7 at approximately 13 Mb (ARS-BFGL-NGS-102773). This gene does not code for proteins, but rather codes for miRNA. This gene is involved in post-transcriptional regulation of gene expression and possibly acts as a chaperone, and there is translational inhibition or destabilization of target mRNA [FIG4) gene, located on BTA9 at approximately 41 Mb (BovineHD0900010787). This gene has been described in detail previously. One significant SNP (BovineHD0900010787) mapped to the FIG4 gene is also mapped to the FYN gene, located on BTA9 at approximately 40 Mb.A significant SNP mapped to the get mRNA ,41. Thisget mRNA . One sigget mRNA ,41. One SNP mutant variants within the candidate genes analysis are summarized in To obtain accurate genomic analyses for spastic syndrome, a universal diagnostic criterion is necessary, as the GWAS results in particular can be skewed if misdiagnosis is present. A first set of adult bulls with phenotypes assessed by experienced veterinarians with respect to spastic syndrome was available. Experienced veterinary diagnostics add validity to the results, as the likelihood for misdiagnosis is relatively low. To increase the statistical power of the GWAS, 225 additional phenotyped animals were added based on observed spasticity by field veterinarians. If the veterinarian is unfamiliar with the clinical signs presented in spastic syndrome, this could lead to misdiagnosis, considering that other disorders can result in limb spasticity.The broad set of clinical signs used for the diagnosis of spastic syndrome  could potentially encompass undiscovered disorders with similar phenotypes, resulting in misdiagnosis ,14. FurtIt is important to note parallels between animals and humans when discussing neuromuscular disease diagnostics, especially the susceptibility for misdiagnosis. This is because various human neuromuscular disorders have been more thoroughly investigated in terms of symptomatic progression and possible causality. One study of the retrospective accuracy  of Parkinson’s disease (a human disorder similar to spastic syndrome) was reportedly 82% , highligThe heritability estimate of 0.41 on the underlying scale and 0.26 on the observed scale indicates that spastic syndrome is moderately heritable within this study population. This is similar to the heritability estimate of 0.23 on the observed scale with a herd prevalence of 30%, reported by Tenszen . Thus, wThe GWAS performed for spastic syndrome assumed no sex linkage, as virtually all reports state an even prevalence between male and female cattle ,6,7,8,12The discussion of the GQLS results will focus primarily on the high-density (777 K) genomic results, as SNPs considered significant are more likely to be closely associated with loci near to or within genes that contribute to spastic syndrome. The threshold for significance that was used was a maximum pFDR of 10%. Although the false discovery rate  is adequate in multiple test screening analyses that need higher power, fixing the chromosome-wise rejection threshold to a certain value, as in the pFDR threshold, contributes to a greater power of detection of genomic areas of possible interest . This thGiven that the affected animals were accurately diagnosed with spastic syndrome and the disease prevalence was considerable within the test populations, any significant SNPs obtained from the GWAS that truly associate with spastic syndrome would likely not be rare in the analyses. However, initial MAF thresholds for the GQLS method were set low, at 0.00001, to observe clearly all significant SNPs that contribute to a possible QTL, even rare allele contributions. Keeping a low initial MAF allows for all significant SNPs to be highlighted if spastic syndrome inheritance is governed by many genes and genomic regions. However, if causal variants for spastic syndrome are very rare, the limited sample size within this study may prohibit detection of these variants.The GQLS method for the GWAS was primarily used, as the single SNP regression used is not constrained by the amount of genotypic data, and because the method allows for additive genetic relationships . PlacingSignificant SNPs are located on various chromosomes for both genotypic densities. The increase in genetic density allows for the GWAS to be conducted on a finer scale, revealing significant SNPs closer to a possibly causative variant, which in this case appears to be located at approximately 87 Mb. This common peak also adds power to the evidence that this genomic area may be an important contributor to spastic syndrome. When comparing the two different genotypic densities (50 K vs. HD), there were 73 common significant SNPs with an initial threshold of maximum pFDR chromosome-wise at 10%. This indicates that approximately 75% of the significant SNPs found in the 50 K analysis remained significant after imputation (73 out of 98). After narrowing the number of HD significant SNPs using the various post-GWAS threshold criteria described previously, 10 SNPs remain in common when comparing the 50 K and HD GWAS, all of which are within observable SNP peaks within the HD GWAS. This suggests that, after all the post-GWAS threshold criteria imposed on the significant SNPs for the HD analysis, there is still sufficient power of the 50 K panel to detect associative SNPs. In conjunction, comparing the GQLS logistic regression with the RF regression of the HD SNP genotype panel resulted in common genomic areas on BTA7, BTA8, BTA14, and BTA20. These areas reflect significant SNPs that remained with the 50 K analysis, as well as HD analysis after the post-GWAS threshold criteria were imposed .Again, the small initial MAF threshold for all GWAS allows for relatively rare alleles to be identified. The post-GWAS thresholds placed on the HD SNPs decreased the number of significant SNPs per chromosome, and in turn this may decrease possible spurious associations with the spastic syndrome phenotype. In contrast, it may also eliminate rare SNPs that are valid associations in the GWAS. This is one reason why the GWAS findings should be validated with a larger sample size, to possibly discern which SNPs are truly associative with the phenotype observed in spastic syndrome.If spastic syndrome is single allelic and the causative allele is in the SNP panel, or in complete or very high linkage with the causative variant in either SNP panels, common significant SNPs should arise. The reduced overlap observed across both genomic densities may be a function of a low power-of-detection due to the small sample size or it could be that more than one area in the genome is responsible for the phenotype observed with spastic syndrome.In total, 75 trees per forest were grown, as similar error rates were obtained for forests of up to 1500 trees. Decreased computing time and computational load for the RF regression were obtained through minimizing the number of trees per forest while maintaining minimal error rates. Additionally, 100 replicates of the RF regression were obtained to avoid spurious associations due to sampling within the forest, as the RF regression can be prone to upward bias due to variable selection being random ,61. The As the average VIM% for all SNPs obtained with a non-zero VIM% was small, the frequency of forest occurrence was an alternative measure of the importance of the SNP. SNPs that occurred within approximately the top 50% regarding forest frequency were highlighted. The SNP that occurs most frequently, BovineHD0700003818, is located on BTA7 at approximately 14 Mb, and is within the bounds of the QTL-like peak at 1–20 Mb obtained from the GQLS regression method. This is validation of the QTL-like peak within BTA7. Additionally, the SNP BovineHD0900015016 occurs in nine forests and is located on BTA9 at approximately 54 Mb. Although the QTL-like pattern located at approximately 87 Mb is relatively far from this significant SNP, there are significant SNPs within the chromosome for all GQLS analyses at approximately 40–50 Mb.The SNPs with a non-zero VIM% from at least two forests were then compared with the 522 significant SNPs obtained from the GQLS regression with the HD SNP genotype panel. In total, there were 16 common SNPs, and those that were common tended to have higher MAF and homozygous polarity scores . The RF The generally large error rates for both the prediction and estimation of the RF regression suggest that this method for regression is not sufficient for association analyses, but appears to be sufficient to validate the GQLS regression results with this study population . The wei2 ≥ 0.2) exists, genes of interest will remain relevant. Direct syntenic linkage was obtained between significant SNPs and SNPs within genes of interest  and have been discussed previously.The population prevalence of spastic syndrome was approximately 60%, and the GWAS results indicate that there are significant SNPs with a moderate-to-high MAF, along with SNPs that have polarized homozygotic distributions when looking at disease status . SpasticMAP1S gene reported increases in mitochondrial size and frequent rupture of the outer membrane, followed by a progressive, lethal aggregation of mitochondria [MAP1S is likely not causal for this disorder [MAP1S that could contribute to the progressive, non-degenerative phenotype observed in spastic syndrome through currently unknown mechanisms.Mitochondrial Function. It is proposed that a MAP1S deficiency contributes to a reduced ability to recognize defective mitochondria and commence cellular destruction . A mousechondria . These kdisorder . HoweverCACNA1A gene are implicated as causal for spinocerebellar ataxia type 6 in humans [CACNA1A knockout results in ataxia and death through the removal of channel-mediated neurotransmission. A deleterious mutation within this gene could result in altered calcium channels, which may alter presynaptic acetylcholine release, resulting in enhanced transmission in motor pathways and spasticity in the affected limb(s). Defects in many ion channels do not cause histopathological changes, thus the CACNA1A gene is a compelling candidate gene with respect not only to function, but also chromosomal location, as this gene is within a QTL in BTA7.Calcium Channel Function. Calcium channels play an important role in the regulation of a variety of cellular functions such as membrane depolarization, muscle contraction, and synaptic transmission, as they are involved in the control of neurotransmitter release from neurons . Mutation humans . Spinocen humans ,62. Addin humans  reportedRab3A constitutive knockout mice (Rab3A(-/-)) are characterized by a deficiency in short and long-term synaptic plasticity. Synapses have the ability to strengthen or weaken over time in response to increases or decreases in activity, and it has been reported that RAB3A regulates the fusion probability of synaptic vesicles [RAB3A gene stopped activity-dependent vesicle accumulation close to and in the active zone of the presynaptic membrane when there was a depolarizing pulse in the presence of calcium [RAB3A [Rab3A knockout mice [RAB3A gene can result in decreased synapse strength over time due to loss of vesicle recruitment upon repeated stimulation. This could possibly lead to ataxia-related phenotypes, where ataxia is defined as an impairment of control of voluntary muscle movement [RAB3A was listed as a candidate gene, though not confirmed at this time, for a familial form of spastic paraplegia. Symptoms of the familial spastic paraplegia include lower-limb spasticity and weakness. Although hind-limb weakness is generally only observed in cattle that are severely affected with spastic syndrome due to disuse atrophy of painfully affected limb(s), it is possible that mutations in the RAB3A gene could contribute to spasticity via a mechanism similar to that proposed for the familial spastic paraplegia reported by Meilleur et al. [Axon and Neuron Function. vesicles ,55. In m calcium . These km [RAB3A . Impaireout mice . A mutatmovement . Such a movement , where Rr et al. .FYN and FIG4, discussed previously, have functions related to either motor neuron degeneration or decreases in dendritic spine density and have been implicated in human spastic phenotypes. At this time, mutations within these genes are not thought to be causal for spastic syndrome; however, they may be indirectly implicated in spastic syndrome through defective protein–protein interactions with other proteins that can produce non-degenerative pelvic-limb spasticity.Additionally, bta-mir-23a-201 helps to fold proteins involved in muscle contraction. A mutated chaperone may not be able to assist with the product of a gene like CACNA1A, for example. In such a case, a deleterious phenotype may arise from aberrant calcium-channel folding even though the channel gene is normal. Additionally, the mutated bta-mir-23a-201 protein could produce a mild functional calcium channel defect that is compatible with life but causes late-onset spasticity, rather than the complete removal of channel-mediated neurotransmission, which causes death in CACNA1A knockout mice [ENSBTAG00000039130 could contribute to the spastic syndrome phenotype in unknown ways. Until more is known about the physiological basis of spastic syndrome, postulation into this gene function may be fruitless, as disruption in many genes can create a spastic phenotype. There is no functional evidence either for or against the involvement of this gene in spastic syndrome at this time. Based on our findings, the most promising candidate genes are located on BTA7 (CACNA1 and RAB3A) and BTA9 (FIG4). The two genes of interest located on BTA7 are within a QTL at approximately 1–20 Mb. Additionally, the FIG4 gene is a candidate gene and is located at approximately 41 Mb on BTA9. Mutations in CACNA1A could disrupt neuromuscular signaling without causing degenerative changes. Mutations in RAB3A could act to decrease fusion probability of synaptic vesicles in axons supplying skeletal muscles [FIG4 gene is wholly expressed in skeletal muscle and mutations are implicated with severe neurodegenerative disorders in mice [FIG4 mutations impair synaptic transmission through mechanisms that are non-degenerative. An investigation into FIG4 functional rescue reports that expression of this gene in neurons is sufficient to correct the development of significant muscle atrophy [Uncertain Functions. It is possible that the chaperone gene out mice . Additio muscles . The FIG in mice . Spastic atrophy .CACNA1A gene and the FIG4 gene are both implicated as causal for progressive neurodegenerative skeletal muscular diseases in humans, and spinocerebellar ataxia type 6 and Charcot-Marie-Tooth Neuropathy Type 4J, respectively. RAB3A is listed as a candidate gene for a familial form of spastic paraplegia [Furthermore, the raplegia . Though To begin with, it would be helpful to identify a single name for this disorder that could be used consistently within academia and industry. At this time, terminology such as “crampy” and “stretches” are commonly used colloquial terms, while “progressive posterior paralysis” and “periodic spasticity” have been used within academia to describe spastic syndrome. The many names for spastic syndrome have been a source of confusion for producers and scientists, which is a problem that should be minimized if a single term could be identified by consensus. Additionally, establishing a universal protocol for the diagnosis of spastic syndrome would enable facilities to make informed decisions regarding affected animals, whether it is culling or management practices while the affected bull is still serviceable. In order for consumers of bull semen to make informed decisions about whether to use semen from affected animals for their own herd, information on spastic syndrome status is necessary and should be made available. Universal diagnostic criteria would also facilitate research, for example by minimizing phenotyping errors in a future GWAS. There could be risk factors for spastic syndrome that are not yet known, such as environmental or nutritional factors that could promote the development of the disease. A follow-up study on the SNPs found significant in the GWAS needs to be performed to ascertain the genes holding or surrounding the significant SNPs found in this study. Functional and in silico analyses of these genes, with an emphasis on the location at approximately 87 Mb on BTA9 and 1–20 Mb on BTA7, will enrich knowledge of gene function related to spastic syndrome.A GWAS using imputed whole-genome sequence data  may higGPR126 gene has functions related to the myelination of axons, the CACNA1A gene is expressed at neuromuscular junctions, the RAB3A gene associates with synaptic vesicles until depolarization of the nerve terminal occurs, and the FIG4 gene is only expressed in skeletal muscle. The CACNA1A gene is the most compelling candidate gene, as many calcium ion channel disorders are non-degenerative and produce spastic phenotypes. The candidate genes identified within this study will contribute to a further understanding of the pathophysiology of spastic syndrome. Additionally, mutant variants predicted to be deleterious within candidate genes were quantified. This study has identified regions of the genome and candidate genes that may facilitate further research into the genetic nature of spastic syndrome in North American Holsteins when larger datasets become available.Spastic syndrome is a heritable disease, based on the heritability estimate derived from this study population. The genome-wide association studies using both the 50 K and the imputed high-density SNP panel indicated that spastic syndrome is likely polygenic. The most notable findings were significant SNP peaks located on BTA9 at approximately 87 Mb for all analyses, and a peak within BTA7 located at 1–20 Mb. Findings within the areas of interest in BTA7 and BTA9 were validated with random forest regression. In total, 1048 genes were mapped to significant SNPs. Functional and in silico analyses focused on genes involved in skeletal muscle and nervous system function. In particular, the


Teira dugesii exhibits morphological divergence between beach and inland habitats in the face of gene flow, within the volcanic island of Madeira, Portugal. Here, we analyzed genomic data obtained by genotyping-by-sequencing, which provided 16,378 single nucleotide polymorphisms (SNPs) from 94 individuals sampled from 15 sites across Madeira. Ancient within-island divergence in allopatry appears to have mediated divergence in similar species within other Atlantic islands, but this hypothesis was not supported for T. dugesii. Across all samples, a total of 168 SNPs were classified as statistical outliers using pcadapt and OutFLANK. Redundancy analysis (RDA) revealed that 17 of these outliers were associated with beach/inland habitats. The SNPs were located within 16 sequence tags and 15 of these were homologous with sequences in a 31 Mb region on chromosome 3 of a reference wall lizard genome (the remaining tag could not be associated with any chromosome). We further investigated outliers through contingency analyses of allele frequencies at each of four pairs of adjacent beach–inland sites. The majority of the outliers detected by the RDA were confirmed at two pairs of these matched sites. These analyses also suggested some parallel divergence at different localities. Six other outliers were associated with site elevation, four of which were located on chromosome 5 of the reference genome. Our study lends support to a previous hypothesis that divergent selection between gray shingle beaches and inland regions overcomes gene flow and leads to the observed morphological divergence between populations in these adjacent habitats.The lizard  Here, we find allelic differences between pairs of beach and inland sites, largely at different locations on a single chromosome of an island lizard. Some of these differences are replicated at different pairs of beach and inland sites. We also found some evidence of elevation effects. Our findings are consistent with the hypothesis of divergent natural selection between habitats leading to genetic divergence in the face of gene flow.Divergence of lizard populations can occur within some relatively small islands. This led to a large number of within-island analyses, largely from the late 1980s until the 2000s, many of which focused on island lizards because of the pronounced patterns they often show  patterns previously described on the neighboring Canary Islands  and used redundancy analysis  to help identify/locate these SNPs. Despite arguments against using reduced representation genomic analyses (such as GBS) to detect selection (Genotyping-by-sequencing (GBS) data were used to test the hypotheses about divergence within sis RDA;  to test election , we showelection .We retained 94 individuals for analysis, covering all 15 sample sites  online. K) that best-described structuring, using all 93 principal components (PCs), was K = 1  did not reveal distinct genomic clusters : the number of clusters  or for the chord distances .Tests of isolation-by-distance using the Mantel test (thinned-SNP data set) were not significant for either the Weir and Cochran k = 4 axes represented most variation among individuals  for individuals from the four beach sites tended to be mainly negative, while those for the inland sites tended to be mainly positive  screeplot indicated that r2 (0.060): this shows that only a relatively small proportion of outliers were associated with the two environmental variables. The eigenvalue for the first constrained axis was much larger (10.63) than that for the second axis (2.81). Both of these axes were significant . The permutation test on the marginal effects of each independent variable revealed highly significant effects for habitat  and elevation . These effects are presented as a triplot of individuals, SNPs, and the two environmental variables  against the  20 hits . FifteenSNPs that showed beach/inland divergence in matched pair comparisons of allele frequencies showed overlap with those detected by the RDA  and provPodarcis reference genome. Hence, we found support for our previous suggestion that divergent selection could explain phenotypic divergence of beach populations in the face of gene flow  and subsequently found that 23  of these were correlated with island environmental variation. Most SNPs (17) were associated with beach/inland habitat variation and located on chromosome 3 of the ene flow  and alsoT. dugesii genome within Madeira. However, multivariate analyses revealed some divergence of samples from an area of the north coast, represented by two pairs of adjacent inland and beach sites. The same analyses revealed similar levels of (overlapping) divergence between beach and nonbeach sites, which is likely to be mediated by a relatively small number of SNPs associated with regions under selection. These findings support rejection of major historical divergence in allopatry. Several lizards from other nearby volcanic Atlantic islands  show patterns of divergence expected under selection, and 2) show allelic variation that is correlated with beach versus inland sites. This further supports the hypothesis that within-island genetic divergence can occur in the absence of isolation. Our study also highlights the utility of reduced representation genome methods for studies of nonmodel organisms. A major argument against use of these methods in studies of selection is that they will have low power due to the small proportion of the genome that is sequenced relative to the lengths of haplotype blocks  were collected from uly 2019  online, Total genomic DNA was extracted from the lizard tail tips and GBS carried out by the Hangzhou Lianchuan Biotechnology Company Ltd (paired-end Illumina reads). Extracted DNA was incubated with the restriction enzymes ApeKI and PstI at 37 °C and the digested DNA recovered using magnet beads. The GBS library was prepared using the NGS Fast DNA Library Prep Set . The library was purified and electrophoresed on a 2.5% agarose gel and DNA fragments of 350–450 bp were excised and diluted before paired-end sequencing on a NovaSeq 6000 platform (Illumina). Quality filtration was carried out: adapters were removed using AdaptorRemoval v2 , and lowP-value of 0.01, a PEAR parameter of 100, and minimum acceptable length of a mock reference cluster of 100. At step 5 , all default settings were used except for optional setting for SAMtools view (-opt “-m1 -s156”). Defaults were used at alignment step 6 (identification of variants) and step 7 (filtering of variants) except that a maximum average depth of an acceptable variant was specified as 50. Resultant SNPs and their positions/contigs were output as a VCF file. SNPs were examined using VCFtools and any that showed major heterozygote excess were removed (under the criterion of sufficient heterozygote excess to cause deviation from the Hardy–Weinberg equilibrium at the 1% significance level). The data set containing all SNPs is referred to as “all-SNP.” A thinned data set (“thinned-SNP”) was also created by sampling one SNP per sequence tag from the main data set.SNPs were called using the GBS-SNP-CROP v. 4.1 pipeline  which isWe used adegenet v.2.1.8 , a packaGenetic structuring was also explored using the R package LEA v.3.10.2  on the tDCSE . The all-sites analyses should provide greater power plus the potential to detect elevation effects. Beach–inland pairwise analyses may have lower statistical power but could provide additional insights, potentially revealing parallel changes between different beach/inland locations.k PC axes, where k is the number of axes that have been selected to adequately reflect genomic variation among individuals. The optimal value for the number of PCs was assessed from the screeplot of eigenvalues (and examination of PC scores). We applied a false discovery rate of 0.05 and determined outliers from their q-values  and subsequently identifies SNPs that are outliers to this distribution. Individuals were grouped by capture site. The mean FST and degrees of freedom of the expected χ2 distribution of FSTs were determined using the thinned data set which should provide greater independence of individual SNPs. Deviation from this distribution was used to define SNPs under selection. The q threshold in the analysis was 0.05.For the all-sites comparison, we first used the R procedure pcadapt v.4.3.3  to identT. dugesii from Madeira: 1) shingle beach or inland site (two-state factor), which was previously proposed to be the basis for divergent selection on morphology  elevrphology .e-transformations of the P-values from the tests were negated and their goodness of fit to expected values determined from an exponential distribution  was examined using a chi-squared goodness of fit test. Next, the exponential probability distribution function was used (with rate specified by the sample mean) to determine the new probability, P(x > ix), where ix = −loge P for the ith SNP. Finally, the new probabilities were adjusted for multiple testing using the Benjamini–Hochberg procedure : sequence tags containing the outlying SNPs were compared with the P. raffonei (Genbank master accession JAPYJY000000000.1) genome using nblast. This species has the same conserved wall lizard karyotype as T. dugesii, that is 2n = 38, with 36 single-armed macrochromosomes and 2 microchromosomes, and likely shares a most recent common ancestor with T. dugesii around the mid-Miocene (Attempts to assign any SNPs that were correlated to environmental variables with genomic regions were performed using an NCBI Nucleotide Blast search (-Miocene . R1 and evad193_Supplementary_DataClick here for additional data file.


The caffeine content varied from 0.7 to 11.7 mg g−1, and the L-theanine content varied from 0.2 to 5.8 mg g−1 dry leaf mass. Significant positive correlations were detected between the nitrogen content and biochemical parameters such as theanine, caffeine, and most of the catechins. However, significant negative correlations were observed between the photosynthetic parameters  and several biochemical compounds, including rutin, Quercetin-3-O-glucoside, Kaempferol-3-O-rutinoside, Kaempferol-3-O-glucoside, Theaflavin-3′-gallate, gallic acid. From our SNP-analysis, three SNPs in WRKY57 were detected in all genotypes with a low N content. Moreover, 29 SNPs with a high or moderate effect were specific for #316  or #507 . The use of a linear regression model revealed 16 significant associations; theaflavin, L-theanine, and ECG were associated with several SNPs of the following genes: ANSa, DFRa, GDH2, 4CL, AlaAT1, MYB4, LHT1, F3′5′Hb, UFGTa. Among them, seven SNPs of moderate effect led to changes in the amino acid contents in the final proteins of the following genes: ANSa, GDH2, 4Cl, F3′5′Hb, UFGTa. These results will be useful for further evaluations of the important SNPs and will help to provide a better understanding of the mechanisms of nitrogen uptake efficiency in tree crops.This study discusses the genetic mutations that have a significant association with economically important traits that would benefit tea breeders. The purpose of this study was to analyze the leaf quality and SNPs in quality-related genes in the tea plant collection of 20 mutant genotypes growing without nitrogen fertilizers. Leaf N-content, catechins, L-theanine, and caffeine contents were analyzed in dry leaves via HPLC. Additionally, the photochemical yield, electron transport efficiency, and non-photochemical quenching were analyzed using PAM-fluorimetry. The next generation pooled amplicon–sequencing approach was used for SNPs-calling in 30 key genes related to N metabolism and leaf quality. The leaf N content varied significantly among genotypes ( Nitrogen (N) is an essential element that is a part of proteins, nucleic acids, enzymes, hormones, and bioactive compounds. Under insufficient N conditions, the synthesis of enzymes slows down, which leads to the disruption of biosynthesis, metabolism, and, as a result, decrease in yield ,2. It isANR1, AP2/ERF, TGA1/TGA4, TCP20, NLP7,MYB, LBD37/38/39, bHLH35, bHLH36, WRKY [Efficient nitrogen-utilizing plants have evolved adaptive mechanisms to detect nitrogen concentrations in soils ,9,10 and36, WRKY ,19. In a36, WRKY ,22,23,24Camellia sinensis (L.) Kuntze) is an important agricultural tree crop that is widely grown in more than 52 countries and used to produce the world’s second most popular non-alcoholic beverage [Tea plant  and the regulators of N assimilation into amino acids in leaves and young shoots  and root-amino-acid transporters (LHT1) have been identified [AAPs, LHTs, CATs, ProTs, and UMAMITs play important roles in the N assimilation [The current knowledge of the relationships between the elements and metabolites in tree crops, particularly in tea, is still fragmentary. Recently, a set of genes involved in nitrogen metabolism in plants, including tea, were identified. Specifically, the co-expression of key genes such as milation . Moreovecontents . Throughcontents  revealedentified ,57,58,59milation . However2 (tetra-ploidy genotype #619), (b) those with a mean leaf area of 83–112 cm2 , and (c) those with a mean leaf area of 65–75 cm2 . Most of the genotypes in the (b) and (c) groups were characterized by an increased genome size and related to are tri- and aneuploidy  in leaf N content were observed among the studied genotypes, with leaf N content values varying from 2.3 to 3.7%, and the mean standard deviation was 0.05–0.20% (−1 dry leaf mass) varied significantly among the genotypes, with values ranging from 0.7 to 11.7 mg g−1. The highest caffeine content (8.6–11.7 mg g−1) was observed in several genotypes with a high leaf nitrogen content, namely #316, #582, #1405, #212, #551, and cv Karatum. The content of L-theanine (mg g−1 dry leaf mass) varied significantly among the genotypes, with values ranging from 0.2 (in #507) to 5.8 (in #316) mg g−1. Additionally, most of the genotypes with a low N content  were also characterized by low caffeine and low theanine contents compared to the genotypes with greater N contents. Thus, 5.8 mg g−1 of L-theanine and 11.0 mg g−1 of caffeine were observed in #316. The lowest nitrogen (2.3%), theanine (0.19 mg g−1), and caffeine (0.84 mg g−1) contents were observed in the mutant form #507. However, the correspondence between the N content in the leaves and catechins were not clear. The highest contents of simple catechins (13–20 mg g−1) were observed in the following genotypes: #582, #619, #2697, #1405, and #855. The lowest content (about 5 mg g−1) was observed in #316, #551, and #157 (−1) were observed in #2697, #1405, #1385, #212, and #1877, and the lowest (25–35 mg g−1) were observed in #536, #1467, and #507  was detected in the exons of bG, WD40, GDH2, LAR, AlaAT1, bHLH35, MYB7, and bHLH36. Additionally, the highest rate of SNPS/exon (more than 30%) was detected in F3′5′Hb, AMT1.2, DFRa, PIP, and bG.A high level of variability in SNP number was observed in target genes . The higFLS, CHS, and ANRa. Interestingly, no polymorphisms were detected in The SnpEff tool variation annotation was used to classify the SNPs according to their effect impact, and the SNPs were classified as either high-effect, moderate-effect, or low-effect or considered to have a modifier effect. In total, 0.2–0.3% of the SNPs were classified as a high-effect SNPs, and these were detected in the following genotypes: #619, #2697, #536, #1385, and #3986 A. AdditiAMT1.2, UFGTa, UFGTb, and 4Cl in most of the genotypes, while the lowest frequencies were observed in GDHa, GDH2, WD40, bHLH35, AlaAT1, LAR, and bG. The application of the neighbor joining method to the studied mutant forms indicated two distinct branches, and one of them was divided into two sub-branches. The first branch consisted of six genotypes with the highest SNP frequencies, namely #507, #855, #1385, #3986, #1467, and #619. Most of them were characterized by a low N content and low caffeine and L-theanine contents. The second branch consisted of 14 genotypes and was divided into two sub-branches. Most of the genotypes with high leaf quality shared the same sub-branch. We generated heatmaps to compare the SNP frequencies in the exon regions of the studied genes . The heaWRKY57 were most frequent among the most genotypes with a low nitrogen content and low tea quality. Moreover, 29 SNPs with a high or moderate effect were associated with #316 or #507 . Specifically, for #507, these specific SNPs were observed in the following genes: GDHa (1 SNP), GDH2 (1), WD40 (2), 4CL (7), F3′5′Hb (3), WRKY57 (2), and UFGTa (2). Additionally, a few specific SNPs were observed in #316, namely 4Cl (1), NRT2.4 (1), F3′5′Ha (1), F3′5′Hb (2), AlaDC (3), and UFGTa (1). Most of these SNPs may be responsible for changes to the biochemical properties of proteins. The sets of SNPs that were classified as high- or moderate-effect SNPs were finally identified . Among tp value < 0.05 , operating efficiency of the PSII (Y), and Electron transport rate (ETR); and the highest non-photochemical quenching compared to the other genotypes. These results indicate this genotype’s low level of photochemical utilization of light energy, leading to the low photosynthetic capacity of this genotype ,63,64. TSignificant positive correlations were detected between the nitrogen content and biochemical parameters such as theanine, caffeine, and catechin contents. L-theanine and caffeine have been shown to be positively correlated with soil N content, which is consistent with our results . HoweverUFGTa, UFGTb, 4Cl, and AMT1.2, indicating their high variability among tea accessions. Among them, the first three genes are related to tea quality; UFGT encodes UDP-flavonoid 3-O-glucosyl transferase and genes related to the anthocyanin biosynthesis pathway, while 4Cl encodes 4-coumaric acid, CoA ligase, which participates in the biosynthesis of flavonoids [AMT1.2 is one of the key genes that encodes the ammonium transporter that regulates NH4+ uptake [The allele frequency data are useful for identifying the loci underlying phenotypic responses to selection or natural variation in phenotypes. The highest SNP frequencies were detected in the following genes: avonoids . AMT1.2 + uptake . Tea pla+ uptake ,70. Howe+ uptake .GDHa (1 SNP), GDH2 (1), WD40 (2), 4CL (7), F3′5′Hb (3), WRKY57 (2), and UFGTa (2). Among them, only two SNPs in GDH2 and 4CL are significantly associated with theaflavin and ECG and promote changes in protein structures. As mentioned above, 4Cl has been shown to participate in the flavonoid biosynthesis pathway, and this finding is consistent with our results. GDH encodes glutamate dehydrogenases, central enzymes in nitrogen metabolism, assimilating ammonia into glutamine or deaminating glutamate into α-oxoglutarate. Tea plant has two GDH genes: CsGDH1 encodes the β-GDH subunit, and CsGDH2/3 encode the α-GDH subunit, and their proteins all feature an NADH-specific motif [SNPeff can determine the different impact of SNPs . A high-ic motif .O-glucoside, Kaempferol-3-O-rutinoside, Kaempferol-3-O-glucoside, Theaflavin-3’-gallate, and gallic acid, were observed. The application of a linear regression model revealed 16 significant associations; theaflavin, L-theanine, and ECG were associated with several SNPs of the following genes: ANSa, DFRa, GDH2, 4CL, AlaAT1, MYB4, LHT1, F3′5′Hb, and UFGTa. Among them, seven SNPs of moderate effect led to changes in the amino acid contents of the final proteins of the following genes: ANSa, GDH2, 4Cl, F3′5′Hb, and UFGTa. Among the 18 SNPs that were found to be unique to the low-quality genotype #507, only two SNPs (in GDH2 and in 4CL) were observed to have significant associations with theaflavin and ECG and promote changes in protein structure. Our results will be useful for further analyses of the associations of these SNPs in broad germplasm diversity with respect to tea collection and for the development of molecular markers for trait-oriented tea breeding.To summarize, in this study, we revealed significant positive correlations between nitrogen content and biochemical parameters such as theanine, caffeine, and catechin contents. However, significant negative correlations between photosynthetic parameters  and several biochemical compounds, such as rutin, Quercetin-3-−1 . No fertilizers have been applied in the experimental plot for the last 27 years.The plant materials were obtained from the field gene bank of the Federal Research Centre the Subtropical Scientific Centre of the Russian Academy of Sciences (FRC SSC RAS). Mutant forms derived in USSR between 1970 and 1980 via the γ-irradiation of seeds  were included in this study . All pla2); (2) middle-leaf (leaf area 20–40 cm2); (3) large leaf (leaf area 40–60 cm2); and (4) extra-large leaf (leaf area ≥ 60 cm2).The leaf-related traits were characterized using the ten most fully expanded mature leaves collected from each cultivar and each replicate. The leaf area size was classified for all 106 genotypes of the entire tea collection according to Wang and Tang : (1) smaPhotosynthetic efficiency was analyzed in the dark-acclimated leaves using the JuniorPAM chlorophyll-fluorometer with default settings. Ten mature leaves from each plant were included in the analysis. After applying actinic light, the following parameters were analyzed: Fv/Fm—maximum photochemical quantum yield of PS II; Y(II)—Effective photochemical quantum yield of PS II; NPQ—Stern-Volmer type non-photochemical fluorescence quenching; and ETR—electron transport rate ,63,64.The leaf nitrogen content in the mature leaves was analyzed spectrophotometrically using the Kjeldahl method, which includes the digestion (samples were heated in the presence of sulfuric acid) and distillation of the solution and the conversion of ammonium salt to ammonia via the addition of sodium hydroxide, followed by trapping the distilled vapors in HCl–water solution. Finally, the amount of ammonia or the amount of nitrogen present in the sample was then determined via back titration via the neutralization of HCl using NaOH solution .−1 dry leaf mass) were evaluated via HPLC using the following extraction protocol: adult tea leaves (3–4 leaves from the top of the branch) were fixed via steam treatment at 100 °C for 20 min in a water bath and subsequently dried. Approximately 200 mg of dried tea leaves were placed into a hermetically sealed container containing 4.0 mL 80% methanol-water solution, which was subsequently incubated for one week at +4 °C in the dark. After that, the vessels with the methanol leaf extracts were placed in a UV bath for 30 min and then centrifuged at 13,000× g rpm 10 min. A total of 1 mL of supernatants were injected into a HPLC column. The Agilent Technologies 1100 HPLC chromatographer, equipped with a flow-through vacuum degasser G1379A, 4th channel low-pressure gradient channel pump G13111A, automatic injector G1313A, column thermostat G13116A, and diode array detector G1316A, was used. The 2.1 × 150 mm column filled with octadecyl silyl sorbent, grain size of 3.5 µm, “ZORBAX-XDB C18” was applied. The acetonitrile solution was used for the gradient; the initial composition of the mobile phase, consisting of 90% (v/v) of solvent A (0.1% H3PO4) and 10% of solvent B (90.0% acetonitrile), was maintained for 8 min. Solvent A was then decreased linearly to 40% at 25 min and 0% at 90 min before being increased to 100% at 29.1 min to 34 min. Programming was then continued in the isocratic mode as follows: 40% A at 70.1 to 75.0 min and 7% A at 75.1 to 90.1 min . The detection wavelengths were 195 nm for L-theanine and 273 nm for caffeine and catechins. The identification of the substances was performed based on the holding time of the standards of the respective compounds.The contents of caffeine, L-theanine and catechins , epigallocatechin (EGC), epicatechin (EC), epicatechin gallate (ECG), gallocatechin gallate (GCG), and epigallocatechin gallate (EGCG)) (mg gCamellia sinensis var. sinensis cv. Shuchazao [https://primer3.ut.ee/ (accessed on 21 September 2023), GeneAlign https://pubmed.ncbi.nlm.nih.gov/16845010/ (accessed on 21 September 2023), OligoCalc https://pubmed.ncbi.nlm.nih.gov/17452344/ (accessed on 21 September 2023), https://www.ncbi.nlm.nih.gov/guide/sequence-analysis/ (accessed on 21 September 2023), https://molbiol-tools.ca/Alignments.htm (accessed on 21 September 2023), OligoAnalyzer https://eu.idtdna.com/pages/tools/oligoanalyzer (accessed on 21 September 2023), https://www.bioinformatics.org/sms/rev_comp.html (accessed on 21 September 2023)  .https://biolabmix.ru/catalog/pcr/long-range/ (accessed on 21 September 2023), 0.3 µL (10 µM) of each primer (forward and reverse), sterile PCR water, and 1 µL of the DNA sample (50 ng µL−1). Amplification was performed in a MiniAmp thermal cycler  according to the following protocol: one cycle of preheating at 94 °C–4 min, 35 cycles of amplification , and final elongation at 68 °C—10 min. The PCR products were separated in 2% agarose gel for 2.5 h at 90 V. After that, the fragments were cut out from the agarose gel, filtered through absorbent cotton [g for 15 min; 1/5 volume of acetate Na 3M and 80% volume of isopropanol were added, mixed, incubated vertically at −80 °C at 15 min, and centrifuged at 13,000× g for 20 min at +4 °C. Finally, the pellets were washed twice with 500 µL of 80% ethanol and dissolved in 10 µL of PCR water.The LR-PCR mixture of 20 µL consisted of 10 µL 2 × LR-PCR buffer containing a mix of HS-Taq and Pfu DNA-polymerases  in 50 µL of sterile water. Fragmented DNA was used for the further 3′ adenylation and ligation of the NEBNext Adapter for Illumina and following 3 cycles of amplification. A qualitative evaluation of the resulting libraries was carried out on an Agilent bioanalyzer TapeStation 4150 using High Sensitivity D5000 ScreenTape and High kits Sensitivity D5000 Reagents . A quantitative evaluation of the products was performed via real-time PCR using the KAPA Library Quantification Kit .The obtained fragments of the DNA library were mixed equimolarly into a pool and sequenced on the Illumina MiSeq via pair-end reads 76 + 76 bp. Sequencing data were demultiplexed via index sequences using the bcl2fastq v2.20.0.422 program with default parameters. In total, 184,000–392,000 pairs of reads were obtained for each DNA library. The initial quality assessment of the deep sequencing data was performed using the FastQC v0.11.2 software . Adapterhttps://broadinstitute.github.io/picard/ accessed on 21 September 2023) . On average, 96.44% of reads were mapped to the tea genome. For each sample, on average, we obtained 229-fold coverage of the target genome regions of tea.Filtered data were mapped against the reference genome of tea plant (GCF_004153795.1). Mapping was performed using the bwa mem function from the package BWA programs . The MarTo control the raw read quality, FastQC (version 0.11.9) was used, and Trimmomatic (version 0.39) was employed with the parameters ‘ILLUMINACLIP:TruSeq3-PE-2.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:50’ to remove adapter sequences and low-quality sequences. The clean reads were aligned to the reference genome ‘Shuchazao’ with BWA-MEM (version 0.7.12) and sorted using SAMtools (version 1.16.1). To add read groups, GATK software (version 4.2) was used, and the GATK-HaplotypeCaller method was applied for variation calling. GATK software was used to select and filter SNPs/InDels according to the following parameters: ‘QD < 2.0||FS > 60.0||MQ < 40.0||SOR_filter||SOR > 4.0||DP < 10’ and ‘QD < 2.0||FS > 200.0||SOR > 10.0||DP < 10’, respectively. The database for the reference genome ‘Shuchazao’ was built using snpEFF (version 5.0) , which w3 was applied to the denominator to leverage the SNP_Freq values in order to facilitate a fair and easy comparison.SNP density was calculated as mean SNP per gene/fragment length of gene in kb. To obtain an overview of the SNP distribution and the possible enrichments of the SNPs for the genes, we normalized the SNP frequency in each gene. The SNP frequency in each gene was calculated using the following formula:https://www.xlstat.com/ Accessed on 21 September 2023). A one-way ANOVA, Student’s t-test, and Tukey’s test were applied to determine the significant differences between the variants. Additionally, hierarchical clustering was performed, and dissimilarities were calculated using the DICE coefficient (with agglomeration using Ward’s method). Additionally, a principal component analysis was conducted based on Pearson (n) correlations. To find the associations between SNPs and the phenotypes, a linear regression model was applied in conjunction with a statistical test adjusted for multiple comparisons (Bonferroni), with significant associations being noted at p values < 0.05.Statistical analyses of the data were carried out using XLSTAT software  (


GRM4 gene emerged as a pleiotropic gene that correlated with carcass length and backfat thickness. The ADGRF1, FKBP5, and PRIM2 genes were associated with carcass length, while the NIPBL gene was linked to backfat thickness. These genes hold the potential for use in selective breeding programs targeting carcass traits in Ningxiang pigs.Ningxiang pig is a breed renowned for its exceptional meat quality, but it possesses suboptimal carcass traits. To elucidate the genetic architecture of meat quality and carcass traits in Ningxiang pigs, we assessed heritability and executed a genome-wide association study (GWAS) concerning carcass length, backfat thickness, meat color parameters , and pH at two postmortem intervals (45 min and 24 h) within a Ningxiang pig population. Heritability estimates ranged from moderate to high (0.30~0.80) for carcass traits and from low to high (0.11~0.48) for meat quality traits. We identified 21 significant SNPs, the majority of which were situated within previously documented QTL regions. Furthermore, the  Carcass traits encompass backfat thickness (BFT), carcass length (CL), and other traits. Generally speaking, elevated body size in length and height is associated with heightened meat production. Compared to imported commercial breeds, most indigenous Chinese breeds exhibit smaller body sizes and lower meat production . Howevercid meat ,11.The genetic architecture characterizes the phenotype alterations resulting from genetic variation, with specific research areas encompassing the number of variations impacting traits, population occurrence frequency, the genetic effect’s scope, and relationships with other genes (additive and interactive effects) or environmental factors ,13. Expln = 508, including 21 females and 487 males) that were slaughtered at a predetermined age (180 ± 5 days) from the Ningxiang Chu Weixiang Slaughterhouse and Meat Processing, LLC . Carcass traits included left half carcass weight (LW), carcass oblique length (COL), carcass length (CL), and backfat thickness (BFT), measured by the national technical regulation for testing of carcass traits in lean-type pig (NY/T 825-2004). Meat quality traits, such as three meat color parameters  of longissimus dorsi  at 45 min after slaughter and pH of longissimus dorsi at two postmortem time points (45 min and 24 h), were assessed following the national technical regulation for determination of pork quality (NY/T 821-2019). Detailed measurement results and methods for carcass and meat quality traits are presented in Phenotypic data were collected for Ningxiang pigs (260/280 ratio between 1.7~2.0 were genotyped using the GeneSeek Genomic Profiling (GGP) version 2 Porcine 50K SNP chip , which comprises 50,697 SNP loci.Genomic DNA was extracted from muscle tissue using standard phenol chloroform method, and the DNA was dissolved in TE buffer. The Nanodrop 2000 spectrophotometer was used to measure the concentration and purity of DNA samples. The samples with Ap-value ≤ 10−6; (4) on autosomes with known positions. After quality control, 537 and 14,812 SNPs were removed due to HWE and MAF thresholds, respectively. Additionally, 4197 SNPs located on the sex chromosome or with unknown chromosome positions were excluded. Ultimately, 31,106 SNPs distributed across 18 autosomes remained for association analysis  and environmental random effects, respectively; b and r are the vector of corresponding and estimated effects. Zi is the design matrix for the i-th genetic random effect and ui is the vector of its responding genetic effects. Ki is the additive genetic relationship matrix, I is an identity matrix, and e is the vector of residual errors. The heritability (h2), genetic correlation (Ar), and phenotypic correlation (Pr) are calculated by Cov is the additive effect covariance between a1 and a2 traits, and Cov is the phenotypic covariance between p1 and p2 traits.The heritabilities and genetic correlations for the studied traits were estimated using the multiple-traits model of the HIBLUP software . The modTo avoid hidden population stratification causing false positives in GWAS, we used imputed genotypes to perform principal component analysis (PCA) with PLINK v1.9 (command: --pca). The results depicted in y is a vector of phenotypic observations, b is a vector of fixed effects , a is a vector of SNP effects; u is a vector of random polygenic effects with a covariance structure; e is a vector of residual errors. X, Z are the design matrix of fixed and SNP effects, respectively. I is an identity matrix, and G is the genomic relationship matrix calculated by the following [Z is the matrix related to genotypes of each SNP ; D is a diagonal matrix of weights for SNP variance; k is the number of SNPs; jp is the minor allele frequency at j-th loci. The genome-wide and suggestive significant thresholds were 0.05/NSNP and 1/NSNP, respectively. The proportion of variance explained (PVE) by a SNP was defined as follows [MAF is the minor allele frequency for SNP marker, N is the sample size.GWAS was conducted using the rMVP package . Sex wasollowing :(3)G=ZDZ follows :(4)PVE=2To detect the linkage disequilibrium (LD) between significant SNPs, SNPs centering on each significant SNP was utilized to conduct LD analysis using the LDblockShow software (v 1.40) .https://bioconductor.org/packages/3.15/bioc/html/biomaRt.html accessed on 5 July 2022). To annotate significant SNPs located in previously mapped QTLs in pigs, all QTL data in pigs were downloaded from the animal QTLdatabase. . Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were employed to identify related pathways. KEGG and GO analyses were performed using KOBAS [http://amigo.geneontology.org/amigo/ accessed on 5 July 2022). To obtain more comprehensive gene enrichment results, we used the Homo Sapiens database for GO and KEGG pathway enrichment. The Benjamini–Hochberg procedure was used to correct the significance of the enriched terms, with p-adj < 0.05 as the significant threshold.To identify candidate genes near the significant SNPs, we examined the annotated genes within a 500 kb radius round each SNP in the Sus scrofa 11.1 genome, using the biomaRt package , and except L.LD (lightness), carcass and meat color traits exhibited an extremely significant negative or positive correlation. L.LD only demonstrated a significant negative correlation with two pH traits, and positive correlations with a.LD and b.LD. BFT also exhibited a negative correlation with CL and COL in genetic correlations. CL showed a negative correlation with pH traits and a positive correlation with L.LD. In this study, the heritabilities (±SE) of carcass traits were moderate to high and ranged from 0.47 (±0.07) to 0.80 (±0.07), and meat quality traits demonstrated low-to-high heritability, ranging from 0.11 (±0.07) to 0.44 (±0.08).The estimates of the heritabilities of these traits and the phenotypic and genetic correlations between them are shown in p-values and the computed genomic inflation factors (λ) indicated no evidence of population stratification.After quality control, 31,106 SNPs were available for subsequent GWAS. The average physical distance between two neighboring SNPs was approximately 71 kb and ranged from 55 kb (SSC7) to 82 kb (SSC1) . Single-GRM4, MLIP, FKBP5, PRIM2, TINAG, and ZNF76, respectively). Additionally, the most significant SNPs were intron variants; a few belonged to unknown variants . For BFT, there were five genome-wide significant SNPs identified and distributed on SSC2, SSC7, SSC8, SSC16, and SSC18, respectively . LD block analysis revealed that the multiple significant SNPs on SSC7 associated with CL spanned 146.72 kb (R2 = 0.3)  .To annotate the potential SNPs, candidate genes overlapping with the extended genomic regions were selected for GO term enrichment analysis. A total of 135 genes were identified in carcass traits  and 32 genes in meat color a.LD (redness). However, only 10 SNPs were located within 8 genes. , followed by cellular components (CC). There were three significant KEGG pathways: amyotropic lateral sclerosis (ALS), spliceosome, and cellular sensitivity.For COL, there were 37 potential genes within these genomic regions. For BFT, there were 43 genes within 1Mb genomic regions, and these genes were significantly enriched in only one pathway (Glutamatergic synapse). Protein binding (GO:0005515) was the most enriched GO term among the three carcass traits a–c.Thirty-two genes were used for enrichment analysis for a.LD, and biological process (BP) was the most enriched category in the top 10 GO terms d. Protea45min and pH24h were negatively genetically correlated with redness, and yellowness, respectively. The study revealed that low acidity could affect meat color, structure, and tenderness [DGAT1 gene affected meat color, known for its role in fat deposition [In this study, the heritability of carcass traits ranged from 0.47 to 0.80, while meat quality traits ranged from 0.11 to 0.44. The genetic parameters obtained in this study for carcass and meat quality traits demonstrated congruence with previous studies ,30,31. Cnderness , consistnderness . Additioposition .GRM4 (Glutamate metabotropic receptor 4) gene. Metabolic glutamate (mGlu) receptors are a family of G protein-coupled receptors that regulate cell physiology throughout the nervous system [GRM4 gene may play an essential role in adipogenesis by activating MAPK activity.In this study, we performed a GWAS in a Ningxiang population to explore the genetic architecture of carcass and meat quality traits. We identified 21 genome-wide significant SNPs and several candidate genes for carcass traits  and one meat quality trait (a.LD). We identified some novel SNPs and genes potentially associated with these traits, which had no research previously. Therefore, it is essential to conduct GWAS in different pig breeds to identify more genes underlying the complex traits, which would benefit Ningxiang pig breeding programs. Previous studies concluded that some SNP-containing annotated genes were highly associated with carcass and meat quality traits. Notably, we found that some SNPs exhibited pleiotropy in multiple traits. Watanabe et al.  indicates system . GRM4 bes system . This ges system , breast s system , and osts system . Osteosas system . Maya ets system  found ths system  indicateHMGA1, MLIP, UNC5CL, ADGRF1, FKBP5, PRIM2, TINAG, TMC3, SNCA, SRSF3, ZNF76, and ERMARD). Some of these genes have been reported to be associated with interesting phenotypes. For example, The HMGA1 (high-mobility group AT-hook 1) gene is a nonhistone chromatin structural protein characterized by the absence of transcriptional activity, and belongs to the high-mobility family A, which comprises three members: HMGA1, HMGA2, and HMGA3. This gene plays a vital role in osteoblast commitment and mediates the function of NFIX by transcriptionally activating canonical Wnt signaling [HMGA1 gene is a vital regulator of the insulin receptor (INSR) gene [HMGA1 was associated with fat deposition traits in pigs. Additionally, this gene has been reported to be associated with obesity [HMGA1 gene affected the measurement of meat color. In this study, BFT and carcass length traits also exhibited strong phenotypic and genetic correlations. The ADGRF1 gene, also known as the GPR110 gene, is a member of the adhesion GPCR family, and functions as a receptor of N-docosahexaenoyl ethanolamine [PRIM2  encodes 58 kDa protein containing a 4Fe-4S cofactor that forms a heterodimeric DNA primase with PRIM1, a small subunit of DNA primase [PRIM2 gene as associated with body length. The FKBP5 gene  encodes the FKBP5 protein, an immunoaffinity protein with multiple biological functions. Lu et al. [FKBP5 gene is involved in NF-kB and Akt signaling pathways, which regulate and control osteoclasts differentiation and development. They also pointed out that the FKBP5V55L mutation is related to osteoclastogenesis and function, which affects the development of Paget’s disease. This gene is a potential candidate for skeletal muscle development. The MLIP  gene encodes alternatively spliced variants (23–57 kDa) with several novel structural motifs not found in other proteins, and is highly expressed in heart, skeletal, and smooth muscle [In this study, all significant SNPs were located within or near several genes  gene . This geSR) gene , Wang etSR) gene , and KimSR) gene  reported obesity , diabete obesity , and met obesity  in human obesity  and Liu  obesity  reported obesity  identifinolamine . Hidaka nolamine  suggestenolamine . PRIM2 , SNCA (α-synuclein), TINAG  and ZNF76 (Zinc finger protein 76) genes. The TINag gene encodes an extracellular matrix protein, TINag, which is expressed in tubular basement membranes [TINAG as a prognostic biology marker for pectus excavatum (PE). Jakowlev et al. [TINAG might be a potential susceptibility gene for hand osteoarthritis. The UNC5CL gene  is a member of the UNC5 family, and has a unique death and ZU5 domain in its molecular structure. It is also involved in immunity and inflammation [Furthermore, few studies have investigated these genes in livestock or their association with interesting phenotypes, such as embranes . Most stembranes  identifiv et al.  suggesteammation . This geammation ,74.HECW1, NIPBL, SNCA, TMEM174, GRM4), of which four were also found in a.LD, including HECW1, NIPBL, SNCA, TMEM174 genes. Additionally, two significant SNPs were located within NIPBL (nipped-B-like protein cohesin loading factor) and HECW1 , respectively. The NIPBL encodes the homolog of Nipped-B-like protein and colon tumor susceptibility 2-type sister chromatid cohesion proteins, facilitating enhancer–promoter interaction of remote enhancers. It is highly expressed in the lung, spleen, and subcutaneous adipose tissue. This study discovered that the NIPBL gene was enriched in embryo development, such as embryonic viscerocranium morphogenesis (GO:0048703) and embryonic digestive tract morphogenesis (GO:0048557). Alonso-Gil et al. [HECW1 was highly expressed in the kidney and ovary and is one of nine HECT ubiquitin-like ligase NEDD4 family members. No studies have shown that this gene is related to traits of interest. The other two genes, SNCA and TMEM174 (Transmembrane protein 174), are also unrelated to fat deposition or meat color formation.For the BFT trait, we identified five candidate genes (l et al.  reportedl et al. , milk trl et al. , and adil et al. . HECW1 wMeat color is a significant factor affecting consumer preferences. Redness, yellowness, and lightness serve as primary indicators of meat color. Factors influencing meat color include pigment sources such as myoglobin, hemoglobin, cytochrome C, and muscle structure . In thisWe searched the pig QTL database based on SNP and QTL locations to assess whether this study’s SNPs associated with carcass and meat quality traits replicated any previously known QTLs. We identified 21 SNPs associated with carcass and meat quality traits within genomic regions. The top 10 traits with the highest enrichment are shown in GRM4 emerged as a potential pleiotropic gene associated with carcass length and BFT. HMGA1, ADGRF1, FKBP5, and PRIM2 genes were identified as associated with carcass length, while the NIPBL gene was associated with BFT. These findings contribute to a better understanding of the genetic architecture of carcass and meat quality traits in Ningxiang pigs and hold the potential for application in inbreeding programs.Through a genome-wide association study on carcass and meat quality traits in a Ningxiang pig population, we detected 21 SNPs associated with the traits of interest and identified several candidate genes related to these SNPs.