Publicly available genome data provides valuable information on the genetic variation patterns across different modern human populations. Neuropeptide genes are crucial to the nervous, immune, endocrine system, and physiological homeostasis as they play an essential role in communicating information in neuronal functions. It remains unclear how evolutionary forces, such as natural selection and random genetic drift, have affected neuropeptide genes among human populations. To date, there are over 100 known human neuropeptides from the over 1000 predicted peptides encoded in the genome. The purpose of this study is to analyze and explore the genetic variation in continental human populations across all known neuropeptide genes by examining highly differentiated SNPs between African and non-African populations.NPPA (rs5065) with high impact stop lost variant; CHGB  with multiple moderate impact missense variants; IGF2 (rs10770125) and INS (rs3842753) with moderate impact missense variants that are in linkage disequilibrium. Phenotype and disease associations of these differentiated SNPs indicated their association with hypertension and diabetes and highlighted the pleiotropic effects of these neuropeptides and their role in maintaining physiological homeostasis in humans.We identified a total of 644,225 SNPs in 131 neuropeptide genes in 6 worldwide population groups from a public database. Of these, 5163 SNPs that had ΔDAF |(African - non-African)| ≥ 0.20 were identified and fully annotated. A total of 20 outlier SNPs that included 19 missense SNPs with a moderate impact and one stop lost SNP with high impact, were identified in 16 neuropeptide genes. Our results indicate that an overall strong population differentiation was observed in the non-African populations that had a higher derived allele frequency for 15/20 of those SNPs. Highly differentiated SNPs in four genes were particularly striking: We compiled a list of 131 human neuropeptide genes from multiple databases and literature survey. We detect significant population differentiation in the derived allele frequencies of variants in several neuropeptide genes in African and non-African populations. The results highlights SNPs in these genes that may also contribute to population disparities in prevalence of diseases such as hypertension and diabetes. Neuropeptide genes , 2 are nNeuropeptides are also expressed in the endocrine and immune system and play a major role in physiological homeostasis. They intersect the immune, nervous and endocrine systems through autocrine, neurocrine, paracrine and endocrine manners, thus playing a core role in influencing postsynaptic cells in a large target area . In physrs2478523 in the AGT gene shows an increase in the risk of high altitude polycythemia (HAPC) in the Tibetan population while in the Han population, rs699, rs4762 and rs5051 are associated with reduced HAPC susceptibility [rs5065 in NPPA was identified as a marker of increased cardiovascular risk [rs1042571 in POMC was shown to increase the risk of obesity [As humans migrated into new frontiers outside Africa their populations became fragmented and genetically differentiated. This genetic diversity can also be a source of differences in genetic risk for particular ailments between different populations. For example, variant tibility . Also, ilar risk , and in  obesity . Due to  obesity –15 so fa obesity , 16–19.The rapid development in sequencing technology and decreasing costs of genome sequencing now proffer an unbiased examination of human genetic variation and have led to the development of several large scale human whole exome and whole genome databases, such as the 1000 Genomes Project , the TraIn this study, we analyzed the genetic variation in continental human populations across known neuropeptide genes. In particular, we examined single nucleotide polymorphisms (SNP) that were highly differentiated between African and non-African populations in publicly available datasets, to gain insights about the patterns of genetic variations in genes that code for neuropeptides and examine whether any are undergoing any adaptive selection in these populations.Homo sapiens”. An additional 26 neuropeptide genes were identified by Ensembl [A total of 105 neuropeptide genes were identified from four neuropeptide databases; StraPep , neurope Ensembl . TherefoUsing the whole genome sequence data, we extracted variants for the 131 neuropeptide genes in 15,164 individuals belonging to 6 different populations, Africans, Latino, Ashkenazi Jewish, East Asian, Finnish and Non-Finnish Europeans. A total of 769,597 variable sites were identified in the 131 neuropeptide genes  between African and non-African populations equal to or more than 0.20, were identified and functionally annotated Figs. . A cutofTwenty SNPs that were highly differentiated (ΔDAF ≥0.20) between Africans and non-Africans occurred in 16 of 131 neuropeptide genes. Their functional consequences were analyzed using available phenotype data in Genome Wide Association Studies (GWAS) catalogue , Online NPPA, CHGB, IGF2 and INS) were especially striking because of the following salient features: NPPA with a high impact stop lost variant (rs5065); CHGB with multiple moderate impact missense variants ; IGF2 (rs10770125) and INS (rs3842753) with moderate impact missense variants that are in linkage disequilibrium. These variants are further examined in the following sections.Variants in four of these genes (rs5065) in NPPA has been associated with cardiovascular disease risk [NPPA . Three of these SNPs  had a high derived allele frequency in African populations and two SNPs (rs6085324 and rs742711) in non-Africans (Table rs6085324 and rs742711) have been associated with schizophrenia in the Korean population [rs236152 has also been associated with schizophrenia in the Japanese population [Five highly differentiated SNPs occurred in the pulation  and SNP pulation .Table 2CHGB 14 kb genomic region , separate the two major haplotypes, whereas the remaining 2 SNPs (rs236150 and rs742710) mainly separate other Africans and minor non-Africans haplotypes from one another.The relationship between these 5 SNPs was further explored by using Africans and non-Africans allele linkage disequilibrium (LD) Table . A mediars10770125) in IGF2 and (rs3842753) in INS are located close together on chromosome 11. GTEx data shows that IGF2 is highly expressed in the Adipose – Visceral (Omentum) and INS is highly expressed in the pancreas. The derived allele frequencies of both SNPs are higher in non-Africans as compared to Africans (Table 2 = 0.336) than in non-Africans (r2 = 0.056). A haplotype network based upon 65 SNPs in a 1 kb genomic region of IGF2  patients in the Uruguayan population of predominantly European ancestry. In a separate study of European American descents in the GoKinD project [IGF2 rs10770125 has been associated with diabetic nephropathy in male patients with T1D, but not in female patients [The SNP  to extract variants for 131 neuropeptide genes. Using differences in derived allele frequencies we identified 20 highly differentiated SNPs between Africans and non-African populations in 16 neuropeptide genes (Table (rs5065) in NPPA has been associated with increased acute coronary syndrome [NPPA encodes a protein implicated in the control of extracellular fluid volume and electrolyte homeostasis and is highly expressed by the heart muscle. Furthermore, the ventricular expression of this gene is strongly increased in the cardiac muscle cells of the mice during stress [The high impact stop lost variant syndrome  and cardsyndrome , 31. NPPg stress .CHGB [CHGB locus, especially in the proximal promoter, influences CHGB expression, catecholamine secretion and the early heritable responses to environmental stress and is associated with changes in blood pressure in the sub-Saharan African and European ancestry groups [ that lie in a single exon have high ΔDAF between Africans and non-Africans and three  of these (Table CHGB expression in the GTEx dataset [(rs236152) has a higher derived allele frequency (63%) in Africans. Another close SNP (rs236150) that also has a higher derived allele frequency (21%) in Africans is also predicted to be differentially O-glycosylated. Another calcium binding protein with a highly differentiated SNP (rs757081) was NUCB2. NUCB2 shares a 60% sequence homology with NUCB1 in the human and mouse genome [AGT, another gene with a highly differentiated SNP, rs699, with a derived allele frequency of 17% in Africans, has also been associated with hypertension in African populations [NUCB2 rs757081 and AGT rs699 decreases their gene expression levels in several tissues and both SNPs have been associated with hypertension in the GWAS catalogue [A number of these highly differentiated SNPs were in genes that help regulate the amount of intracellular calcium that is known to play a crucial role in the regulation of cardiovascular functions. An increase in calcium in vascular smooth muscle cells leads to an augmented muscular tone which further increases vascular resistance that eventually raises the blood pressure . One sucCHGB  that stiCHGB . Common y groups . Five mi dataset . Of these genome  and playe genome . This SNe genome , and in e genome . AGT, anulations . Based oatalogue .INS, GIP and IGF2 with highly differentiated SNPs are involved in regulating glucose homeostasis. Evidence from epidemiological studies suggests that African Americans are also more insulin resistant and have higher insulin responses to glucose than European Americans [INS rs3842753 with a derived allele frequency of more than 75% in non-African populations has been identified as a marker for atypical T2D in the Uruguayan population [IGF2 rs10770125 has been associated with diabetic nephropathy in people with European American ancestry [GIP is secreted from K cells and acts on pancreatic beta cells to stimulate the release of insulin. Using the HGDP-CEPH project and the Human Genome Center at the University of Tokyo datasets, a previous study [rs2291725 is significantly higher (> 60%) in the majority of East Asian populations while varying widely in other populations, ranging between 0.0–9.5% in sub-Saharan Africans and increasing to > 40% in European and Middle Eastern populations. We also noted a low derived allele frequency of 14% for this SNP in the Africans and a significantly higher derived allele frequency of 52% for non-Africans. The highest derived allele frequency was also seen in East Asian populations with frequency of 0.75. NUCB2 rs757081 variant was also associated with the decreased risk of developing T2D in Chinese Han population [CHGB gene is also essential for adequate secretion of islet hormones in mice, where its deficiency led to a phenotype with some hallmarks of human T2D including loss of initial rapid insulin secretion [ have been associated with schizophrenia and increased risk for T2D [Several of the other genes, including mericans . The balmericans . INS rs3pulation , while Iancestry . GIP is us study  showed tpulation . The CHGecretion . Three m for T2D .ST for the highly differentiated SNPs. As expected haplotype networks for the highly differentiated genes show population sub-structure with high frequency population specific haplotypes. However, this could not be considered an unusual feature, because it is dependent upon the underlying linkage disequilibrium between SNPs in these populations and confounded by selection and demography.A major limitation of the study was the non-availability of individual sequences in the gnomAD dataset. Therefore, selected sequences from the 1000 Genomes Project continental populations representing Africans and non-Africans were used to construct the haplotype networks, compute LD and FOur study shows substantial population differentiation between African and non-African, as measured by differences in derived allele frequencies, in variants located in 131 neuropeptide genes. Twenty outlier SNPs with ΔDAF |(African – non-African)| ≥ 0.20 were identified in 16 neuropeptide genes and their functional significance was evaluated. The product of these genes appeared to affect multiple systems and some were associated with ethnic differences in incidence of common human diseases such as high blood pressure and type 2 diabetes. Significantly, our analysis adds to our knowledge of the genetic variation in continental human populations across all known neuropeptide genes. It also highlights the pleiotropic nature of these neuropeptides, their functional significance in extra neuronal tissues and their association with cardiovascular and metabolic diseases.Homo sapiens”. The list was further refined by adding more neuropeptide genes using the search term “Neuropeptide” in Homo sapiens in Ensembl (Ensembl GRCh37.p13) [GO:0005184 neuropeptide hormone activityGO:0051428 peptide hormone receptor bindingGO:0071855 neuropeptide receptor bindingGene ontology - Molecular functionGO:0007218 neuropeptide signaling pathwayGene ontology - Biological processA list of human neuropeptide genes was manually generated by integrating information from neuropeptide databases, Ensembl, and AmiGO Gene Ontology. Four neuropeptide databases were used for obtaining the gene list and included: StraPep , neuropeh37.p13)  and AmiGh37.p13) . In addiThe final list comprised a total of 131 human neuropeptide genes | ≥ 0.20, as this was above the 99VEP tool  to deterVEP tool  databaseVEP tool  and GWASVEP tool  to underVEP tool  was also2 ≤ 0.2 between the most differentiated and other SNPs in the region  package , to inveAdditional file 1 : Table S1. List of 131 neuropeptide genes in human.Additional file 2 : Table S2. General information on the variants data for 131 neuropeptide genes.Additional file 3 : Figure S1. Distribution of ΔDAF values between Africans and non-Africans.Additional file 4 : Table S3. SNPs with absolute differences in derived allele frequencies between African and non-African population equal to or more than 0.20. The data are sorted by differences of derived allele frequency.Additional file 5 : Figure S2. Annotating 5163 SNPs consequences using the Ensembl Variant Effect Predictor (VEP) tool.Additional file 6 : Figure S3. Median-joining haplotype networks for SNPs listed in Table Additional file 7 : Figure S4. Median-joining haplotype networks for selected genomic regions in Africans (YRI) and non-Africans (CEU + CHB). A). Five kb region encompassing INS rs3842753 . B). Another 5 kb region on the same chromosome 11 where ΔDAF = 0.012 between Africans and non-Africans. Both these genomic regions do not have any SNPs with ΔDAF ≥0.20.Additional file 8 : Figure S5. Haplotype network of a 14 kb region encompassing CHGB in Africans (YRI), East Asians (CHB) and Europeans (CEU).Additional file 9 : Figure S6. Haplotype network of a 1 kb region encompassing IGF2 in Africans (YRI), East Asians (CHB) and Europeans (CEU).Additional file 10 : Figure S7. Haplotype network of a 1 kb region encompassing INS in Africans (YRI), East Asians (CHB) and Europeans (CEU).Additional file 11 : Table S4. Analyzed populations and samples. Populations were split into two categories. Sample size indicates the number of individuals for each population.Additional file 12 : Table S5. LD patterns in Africans and non-Africans for highly differentiated SNPs listed in Table


Twenty-five phenotypes were measured as indicators of bull fertility . Measurements included sperm morphology, scrotal circumference, and sperm chromatin phenotypes such as DNA fragmentation and protamine deficiency. We estimated the heritability of these phenotypes and carried out genome-wide association studies (GWAS) within breed, using the bovine high-density chip, to detect quantitative trait loci (QTL).2 from 0.10 to 0.22). To confirm these first estimates of heritability, further studies on sperm chromatin traits, with larger datasets are necessary. Our GWAS identified 12 QTL for bull fertility traits, based on at least five polymorphisms (P < 10−8) for each QTL. Five QTL were identified in Brahman and another seven in Tropical Composite bulls. Most of the significant polymorphisms detected in both breeds and nine of the 12 QTL were on chromosome X. The QTL were breed-specific, but for some traits, a closer inspection of the GWAS results revealed suggestive single nucleotide polymorphism (SNP) associations (P < 10−7) in both breeds. For example, the QTL for inhibin level in Braham could be relevant to Tropical Composites too (many polymorphisms reached P < 10−7 in the same region). The QTL for sperm midpiece morphological abnormalities on chromosome X  is an example of a breed-specific QTL, supported by 143 significant SNPs (P < 10−8) in Brahman, but absent in Tropical Composites. Our GWAS results add evidence to the mammalian specialization of the X chromosome, which during evolution has accumulated genes linked to spermatogenesis. Some of the polymorphisms on chromosome X were associated to more than one genetically correlated trait (correlations ranged from 0.33 to 0.51). Correlations and shared polymorphism associations support the hypothesis that these phenotypes share the same underlying cause, i.e. defective spermatogenesis.Our analyses suggested that both sperm DNA fragmentation and sperm protamine deficiency are heritable (hGenetic improvement for bull fertility is possible through genomic selection, which is likely more accurate if the QTL on chromosome X are considered in the predictions. Polymorphisms associated with male fertility accumulate on this chromosome in cattle, as in humans and mice, suggesting its specialization. Male fertility is of interest to and concerns two distinct fields: clinical medicine, which aims at identifying and treating subfertility, and livestock breeding, which relies on optimal fertility rates for efficient production. Male fertility indicators, such as sperm susceptibility to DNA fragmentation, sperm morphology, or testicular size, are used in a clinical context and, also, to evaluate the breeding capacity of livestock species –3. FertiThe first group of phenotypes represents standard measurements in evaluations of bull-breeding soundness, including sperm morphological abnormalities. Indeed, sperm morphology, concentration, and motility are routinely measured , and visThe second group of phenotypes consists of measurements of inhibin hormone levels and of SC. Levels of inhibin at 4 months of age and SC, which is measured between 12 and 24 months of age, are indicators of pubertal development , 9. PubeThe third and fourth groups of phenotypes correspond to measurements that are associated with the chromatin structure of sperm nuclei. Two flow cytometric methods—the sperm chromatin structure assay (SCSA) , and theSCSA simultaneously determines DFI and the percentage of sperm with high DNA stainability (HDS). Whereas DFI is related to DNA breaks (single-strain DNA), HDS may be related to the retention of nuclear histones, which is consistent with immature spermatozoa , 24. GenAfter performing a GWAS for each phenotype in each breed, we applied a QTL detection method adapted from van den Berg et al. , which iBos indicus origin, while Tropical Composites are cattle that originate from planned crossbreeding between Bos indicus and Bos taurus breeds. The genomic evidence for the breed composition of these Australian cattle was discussed in previous studies [Institutional Animal Care and Use Committee approval was not required for this study because the data and samples used were obtained from existing databases and storage banks. Animals were bred by the Cooperative Research Centre for Beef Genetic Technologies (Beef CRC), and details on these populations were published prior to this study . In brie studies , 36. BreThe number of records were smaller for sperm chromatin phenotypes: at most, 592 for Brahman and 538 for Tropical Composites. Sample size clearly influences the power of any GWAS and we calculated the power for each analysis by using the online tool developed by Visscher and his team . Their mClassic indicators of bull fertility used as phenotypes are traits derived from sperm morphology assessments of 100 sperm cells under light microscopy , SC measured in cm with a standard measuring tape, and the level of the inhibin serum hormone measured at 4 months, as described in the initial Beef CRC project design . We alsoThe percentages of sperm cells with high-, medium-, and low-protamine content  were estimated using the SPDA methodology described in previous studies , 29, 32.Samples thawed for SPDA were also used for SCSA, which measured three related phenotypes: percentage of sperm with intact chromatin (PIC), DFI, and HDS. SCSA was conducted according to the protocol described by Evenson and Jost , which uAB format was used for genotype calling and then genotypes were coded as 0, 1 or 2 with reference to the number of B alleles carried by each animal, at each locus. Genotyping was done with a medium-density chip  for most of the bulls with fertility phenotypes, as reported in the initial GWAS [AA and bulls that were homozygous BB, but no AB heterozygous bull was observed, and so we discarded these SNPs (as these observations point to a possible genotyping error) (except for SNPs on chromosome X). The genotyping results for the “50 K” chip were reported in the initial GWAS, in which 50,353 and 48,821 SNPs passed control quality in Brahman [The Illumina ty chip 5,000 SNPsty chip 5,000 SNPsBos indicus and composite cattle of five breeds . This panel of breeds was part of a larger experiment with 10,181 animals, which allows for accurate imputation of tropical cattle as described before [A panel of 1174 cattle were genotyped with the BovineHD Illumina chip  and used as a reference for imputation of lower density genotypes (“50 K” and “70 K”) up to a higher density. The reference panel included the sires of most of the animals that were genotyped with the lower density panels, some of the dams and additional randomly selected d before . For thid before , before 2 higher than 0.8 were retained, resulting in a final dataset of 722,208 SNPs with genotypes available for the studied bulls, in both breeds.Before imputation, all SNP genotypes were phased using the Eagle software  to complTo perform the GWAS, we built a GRM for each breed using all the SNPs that had a MAF higher than 0.05 (within-breed). Each GRM was built using the first method proposed by VanRaden . We usedAfter the GRM was computed, SNPs with a low MAF (lower than 0.05) were included again in the single-SNP association tests, to accommodate breed differences and to facilitate GWAS comparisons. MAF were reported within-breed together with the association results  was also used to estimate genetic correlations and heritability for the studied phenotypes, within-breed. SNP-derived heritabilities and their standard errors were estimated by fitting the above-mentioned mixed models in the SVS software . Genetic correlations were estimated by fitting bivariate genomic mixed models using the Qxpak5 software . Fixed e−8 and a MAF higher than 0.05 (within breed), which is a conservative Bonferroni correction, because it considered all SNPs as independent tests .The significant SNPs reported for each trait followed the thresholds that we defined: a P-value lower than 10−8 were considered as peak SNPs and used to define QTL boundaries. After selecting the most significant SNP on each chromosome, we tested the other significant SNPs within 0.5 Mb on either side of the peak SNP to determine if they fitted the following criteria: –log10 (P) ≥ -log10 (Ppeak) × 2/3. When a SNP did fit the criteria, it was considered as part of the same QTL. The borders of each QTL were expanded iteratively (one 0.5-Mb window at a time) until no more SNPs satisfied the condition of − log10 (P) ≥ − log10 (Ppeak) × 2/3. When no further SNPs fitted the criteria, the next QTL started in a region 0.5 Mb away from the previous QTL. In the case of a new QTL, a new peak was set until there were no SNPs with a P value lower than 10−8 to call new peaks. As an additional condition, a minimum of five SNPs was required to define a QTL. If there were less than five SNPs, the QTL was not reported, as in van den Berg’s work [Given the large number of significant SNPs present in the large genomic regions, especially on the X chromosome, it was important to identify QTL boundaries by applying a methodology adapted from van den Berg et al. . First, g’s work .In this study, we targeted 25 phenotypes that were measured as indicators of bull fertility. The SNP-derived heritability of phenotypes ranged from very low  to high , their standard errors (SE) and the percentage of the genetic variance explained per SNP are in Table S2 for Brahman and Table S3 for Tropical Composites  that mapped to chromosome 2 were associated with inhibin levels. Suggestive SNP associations clustered on chromosomes 2, 3 and 5 for inhibin levels in Tropical Composites (P < 10−7)  and the nearest gene is TORA2. We used non-additive models to verify if any recessive or dominant effects were present for SNPs in all QTL identified. The only instance when the non-additive models could better capture SNP associations was for chromosome 11 and the DFI3 phenotype in Brahman. The recessive model for DFI3, resulted in extreme SNP associations (P = 3.38 × 10−30), compared to the result with the dominant (P = 2.74 × 10−10) and the additive models (P = 2.46 × 10−10). Thus, recessive inheritance may be considered when searching for the causal variant that underlies DFI3 variance in Brahman.In Brahman, the associations on chromosome 11 suggest a QTL that is relevant for sperm chromatin fragmentation at 98.4 Mb. In this region, BovineHD1100028635 is the most significant SNP associated with DFI3 in Brahman  was previously genotyped as part of a study on the candidate gene TEX11, and was found to be associated with scrotal circumference traits in a sub-set of our bull populations [rs452801659 was not associated with sperm chromatin fragmentation, but suggestive associations were detected with sperm protamine deficiency traits: LCB (P = 1.29 × 10−5), MCB (P = 3.26 × 10−6), and HCB (P = 1.58 × 10−4). In Tropical Composites, no associations were observed between rs452801659 and the new sperm chromatin or protamine traits. We confirmed the already known association between this SNP and scrotal circumference with the following results: P = 2.00 × 10−6 (SC12), P = 8.65 × 10−7 (SC18), and P = 1.70 × 10−6 (SC24) for Tropical Composites, and P = 1.20 × 10−3 (SC12), P = 6.64 × 10−6 (SC18), and P = 2.30 × 10−5 (SC24) for Brahman. These SNP associations indicate that the stringent conditions used here to define the 12 detected QTL ensure reliable associations, but probably fail to capture all the genomic regions that are relevant to bull fertility.SNP ulations . Thus, a2 ~ 0.23–0.33), which means that bulls with a higher PNS will have less DNA fragmentation, and will thus be more fertile according to both indicators.Estimates of heritability and genetic correlations guide the choice of phenotypes for selective breeding. Genetically-correlated phenotypes with a high heritability can increase the rate of genetic gain and lift bull fertility. Our results confirm that PNS and SC are useful indicators of bull fertility and suggest that there is value in adding sperm chromatin and sperm protamine measurements to evaluate bulls, especially for the high-value animals used for artificial insemination. Sperm chromatin and sperm protamine phenotypes are moderately heritable, approximately 20% for DFI in Tropical Composites and for LCB in Brahman. Studies on twins in humans  and in HGiven the genetic correlations between phenotypes of different groups, it is possible to consider that sperm chromatin phenotypes can be combined with PNS and SC, to form a comprehensive male fertility index. Selection indices that include fertility traits are currently implemented in the dairy industry  and thesThe genetic correlations estimated in our study suggest that sperm chromatin fragmentation is linked to protamine deficiency and support the hypothesis that the lack of protamines contributes to sperm chromatin instability and therefore reduced fertility. Since the cause for increased DFI is largely unknown , 17, thehttps://www.ncbi.nlm.nih.gov/ assembly/GCA_002263795.2) [First, we discuss the significant QTL on the X chromosome, because they stand out in these GWAS. Using the new reference assembly was important for the X chromosome   because 63795.2) . The knoBos indicus and Bos taurus bulls. However, the causative mutations may not be the same in all breeds. It is important to mention the breed-specific QTL in this discussion. The specialization of the X chromosome may continue after species divergence. An example is the QTL for MA, which appears to be unique to Bos indicus cattle and might be underpinned by a mutation that is absent in Bos taurus. The X-linked causative mutations or candidate genes might be breed-specific and difficult to pinpoint in the reported QTL, some of which span a few Mb.The X chromosome is conserved among mammals . A recenPrevious studies on mice identified a higher proportion of X-linked genes that are specifically expressed in spermatogonia . Two currs452801659 on the X chromosome could be an important mutation for scrotal circumference and protamine deficiency, especially in Tropical Composites [TEX11 gene (testis-specific gene), according to the previous reference genome (UMD3 assembly). In the current assembly (ARS-UCD1.2), this SNP is now an intergenic variant, outside of TEX11 and may affect other nearby genes.Previous work and the current GWAS suggest that SNP mposites . HoweverConsidering the QTL that were identified for both breeds, there are two broad regions on chromosome X that harbour SNPs associated with one or another type of sperm morphological abnormality. These abnormalities are genetically and phenotypically correlated traits that may arise from the same problem: defective spermatogenesis , 32. It INHA gene that encodes inhibin alpha. Two sub-units, alpha and beta, form the inhibin hormone, which is encoded by four genes: INHA (chromosome 2), INHBB (chromosome 2), INHBC (chromosome 5), INHBE (chromosome 5), INHBA (chromosome 4). In short, significant and suggestive SNP associations could be pointing to genes involved in inhibin level and explain the variance in inhibin hormone level.On BTA2, it is interesting to note that the QTL peak for inhibin level is 54 kb away from the HELB and HMGA2 are known candidate genes in Tropical Composites. A missense mutation in HELB  was associated with SC18 and SC24 in a mixed-breed analysis of bulls that are a sub-set of the same population explored here [HELB mutation was fitted as a fixed effect, the SNPs in the same region continued to be significant [HMGA2 distinguishes between different types of post-pubertal testicular germ cell tumours [HMGA2 has a role in germ cell proliferation, which could affect testicular development in bulls. HMGA2 is located within the QTL described for SC24 in our GWAS . Future work should test mutations in HMGA2 for their associations with SC.On BTA5, the genes red here . This gered here , 64. Hownificant , which s tumours , thus, iBrahman SNP associations on BTA11 serve as evidence to propose a QTL involved in sperm chromatin phenotypes, which might be recessive and requires further investigation. Sperm chromatin phenotypes are not routinely measured and the sample size available for this GWAS was small (~ 500 bulls), which limited the power of our analyses. Still, it is the first time that genetic parameters are estimated and that QTL are proposed for sperm DNA fragmentation in tropical bulls.Visual scores and assessment of semen quality, such as motility and density, are perhaps more prone to human errors because these measures are more subjective than all the other measured phenotypes, and this could explain the lower heritability observed for these phenotypes and the extremely high correlations between all visual scores. In future studies, ideally, progressive motility should be measured with a purposed-designed software such as computer-assisted sperm analyses . Future Bos species, leading to breed-specific QTL. Low-frequency alleles beneficial to the heterozygous sex (XY) could be a consequence of their deleterious impact on female fertility (when inherited in the homozygous state). The causative mutations that underlie the identified QTL remain unresolved and the power of the current GWAS is limited for some traits . Nonetheless, the traits studied here (including sperm chromatin phenotypes) have a heritable component and could be targeted in selective breeding. Selective breeding for bull fertility could benefit from genomic models that include the X chromosome QTL.Our investigation of 25 phenotypes in two bovine breeds suggests that X-linked genes are relevant to bull fertility. The X chromosome likely evolved from an autosome and became specialized by accumulating genes and alleles that are linked to spermatogenesis. The results from this study, and previous work, support the idea that X-linked genes have orthologues across mammalian species that affect spermatogenesis and, therefore, male fertility. Furthermore, specialization of the X chromosome may have continued after divergence of Additional file 1: Table S1. Statistical power to detect the genetic variance of bull fertility phenotypes. Statistical power analyses for detecting the genetic variance with the genomic relationship matrices models used are provided for each measured phenotype, within each breed.Additional file 2. Additional methods and results. This additional file includes details on the SPDA and SCSA methods, on the studied populations and their structure, and results for the GWAS performed. For further analyses on the structure of the Brahman population, we refer to the principal component analyses previously published [ublished . The varublished .Additional file 3: Table S2. Estimates for SNP associations in Brahman bulls. Link to download all GWAS results (Brahman) https://doi.org/10.25919/5e670b1621b21.Additional file 4: Table S3. Estimates for SNP associations in Tropical Composite bulls. Link to download all GWAS results  https://doi.org/10.25919/5e670b1621b21.Additional file 5: Figure S1. Genetic correlations for 25 bull-fertility phenotypes in Brahman  and Tropical Composites . Genetic correlations were estimated using genomics and pairwise analyses for all traits, within breed. Figure S2. Manhattan plot for the analyses in each of the two breeds: SNP associations for the percentage of normal sperm (PNS). This figure provides a visual for the results of the genome-wide association studies in each breed, for PNS. Figure S3. Manhattan plot for the analyses in each of the two breeds: SNP associations for the percentage of sperm with head abnormalities (HA). This figure provides a visual for the results of the genome-wide association studies in each breed, for HA. Figure S4. Manhattan plot for the analyses in each of the two breeds: SNP associations for the percentage of sperm with mid-piece abnormalities (MA). This figure provides a visual for the results of the genome-wide association studies in each breed, for MA. Figure S5. Manhattan plot for the analyses in each of the two breeds: SNP associations for inhibin hormone levels (INH). This figure provides a visual for the results of the genome-wide association studies in each breed, for INH. Figure S6. Manhattan plot for the analyses in each of the two breeds: SNP associations for the DNA fragmentation index (DFI3). This figure provides a visual for the results of the genome-wide association studies in each breed, for DFI3. Figure S7. Manhattan plot for the analyses in each of the two breeds: SNP associations for the DNA fragmentation index measured with an alternative cytometry method . This figure provides a visual for the results of the genome-wide association studies in each breed, for DFI4. Figure S8. Manhattan plot for the analyses in each of the two breeds: SNP associations for the percentage of sperm with proximal droplets (PD). This figure provides a visual for the results of the genome-wide association studies in each breed, for PD. Figure S9. Manhattan plot for the analyses in each of the two breeds: SNP associations for the percentage of sperm with total droplets (TD), which seem specific to Tropical Composites. This figure provides a visual for the results of the genome-wide association studies in each breed, for TD. Figure S10. Manhattan plot for the analyses in each of the two breeds: SNP associations for scrotal circumference measured at about 24 months of age (SC24). This figure provides a visual for the results of the genome-wide association studies in each breed, for SC24. Figure S11. Manhattan plot for the additive (top) model and the recessive (bottom) model: SNP associations on chromosome 11 for DNA fragmentation index (DFI3) in Brahman. This figure provides a visual for the results in chromosome 11 of the SNP associations in Brahman for DFI3 using alternative models to compare the additive model with a recessive model.


MMP12 gene promoter and therefore macrophage elastase deficiency, which is a well-known physiological marker of accelerated atherogenesis that could be retarded nutritionally using dairy fermented by lactobacilli. (4) Conclusions: Our results uncovered SNPs near clinical SNP markers as the basis of neutral drift accelerating atherogenesis and SNPs of genes encoding proteins related to mitochondrial genome integrity and microRNA genes associated with instability of the atherosclerotic plaque as a basis of directional natural selection slowing atherogenesis. Their sum may be stabilizing the natural selection that sets the normal level of atherogenesis.(1) Background: The World Health Organization (WHO) regards atherosclerosis-related myocardial infarction and stroke as the main causes of death in humans. Susceptibility to atherogenesis-associated diseases is caused by single-nucleotide polymorphisms (SNPs). (2) Methods: Using our previously developed public web-service SNP_TATA_Comparator, we estimated statistical significance of the SNP-caused alterations in TATA-binding protein (TBP) binding affinity for 70 bp proximal promoter regions of the human genes clinically associated with diseases syntonic or dystonic with atherogenesis. Additionally, we did the same for several genes related to the maintenance of mitochondrial genome integrity, according to present-day active research aimed at retarding atherogenesis. (3) Results: In dbSNP, we found 1186 SNPs altering such affinity to the same extent as clinical SNP markers do (as estimated). Particularly, clinical SNP marker rs2276109 can prevent autoimmune diseases via reduced TBP affinity for the human  Atherosclerosis is an inflammatory disorder of arteries that can lead to myocardial infarction and stroke  ). The cu10 potential SNPs throughout the human genome, as do the researchers behind two databases: ClinVar [This is what predictive-preventive personalized participatory (4P) medicine can already do . Its key ClinVar  and OMIM ClinVar , containAccording to OMIM data , the abs10 human SNPs [The key concept of 4P medicine  is a climan SNPs  affects man SNPs ), and whman SNPs  and Haldman SNPs  predict As a first step, why not try cheap and fast bioinformatic genome-wide calculations to find this vast majority of neutral SNPs in humans? This approach should exclude them from clinical studies, which consequently could become more targeted and less expensive. Although the accuracy of current genome-wide computational predictions remains below the applicability threshold for clinical SNP tests , this acLUC gene under the control of a promoter containing the SNP in question . In thiiew, see  statistiiew, see .Finally, we applied this software to predict candidate SNP markers of resistance to anticancer therapy , obesityOur recent study on this topic is about 34 candidate SNP markers of atherosclerosis  that areHere, we followed the same approach by means of current dbSNP build No. 151 dated 2017  , and we compared the results with those from build No. 147 [To verify the robustness of our Web service predictions in terms of the annual SNP count growth, using current dbSNP build No. 151 , we anal No. 147 , as readIn addition, with the help of our SNP_TATA_Comparator , we analCETP, whose promoter contains the only clinically proven SNP marker of atherosclerosis, so that we next review all our other predictions briefly in a similar way.We will describe in depth only our predictions regarding human gene Finally, we will evaluate them all as a whole regarding their statistical significance with respect to the widely accepted genome-wide patterns of SNP occurrence.CETP (plasma lipid transfer protein) carries the only known clinical SNP marker rs1427119663  and a double-headed red arrow, respectively, in Human gene CETP overexpression as a physiological marker of accelerated atherogenesis [p-value of Fisher’s Z-test in terms of heuristic rank ρ-values, which vary in alphabetical order from “A” (the best) to “E” , there are 22 SNPs a, none oogenesis . AccordiFinally, using a PubMed keyword search , we founMBL2 encodes mannose-binding lectin 2 and has a clinically proven SNP marker (rs72661131) of stroke [Human gene f stroke , preeclaf stroke , and varrker rs721131 of sf stroke , we founf stroke  on MBL2 Around a known SNP marker, rs72661131, we found an unannotated SNP (rs567653539) causing MBL2 overexpression as well as two unannotated SNPs (rs1471733364 and rs562962093) causing its underexpression . Using PF3 (thromboplastin) contains clinically proven SNP marker rs563763767 increasing the expression of this gene thus causing either myocardial infarction or thrombosis [F3 gene within this table, we predicted five candidate SNP markers of atherogenesis types listed there.Human gene rombosis  as complrombosis , which crombosis , accordirombosis . Near rsrombosis , as we lrombosis  (Table 2TPI1 coding for triosephosphate isomerase includes a known SNP marker (rs1800202) reducing this enzyme’s amount [Human gene s amount , and thes amount , which cs amount  and a ris amount , accordis amount . In the s amount ,75,76,77HBB and HBD correspond to β- and δ-subunits of hemoglobin, promoters of which carry the greatest number (seven) of clinical SNP markers  of hemoglobin insufficiency responsible for malaria resistance and thalassemia [HBB: rs281864525 and HBD: rs996092254), which can also cause hemoglobin deficiency, and therefore may be candidate SNP markers of an atherogenesis slowdown  [HBB overexpression that has been clinically associated with the norm of the two above-mentioned biomedical traits [In addition, there is substitution A>T at position −27 of the :−27A>T)  (not cov:−27A>T)  or dbSNP:−27A>T) ), which l traits   contains the known SNP marker (rs2814778) of leukopenia [Human gene ukopenia  and resiukopenia  due to lukopenia , we learukopenia . For thiukopenia  that wasukopenia  (Table 3NOS2 carries a known SNP marker (NOS2:−51T>C) of malaria resistance [The promoter of human gene sistance ,87, whicsistance , we knowsistance  and nutrsistance  originalsistance ,87 couldAdditionally, looking through the same promoter, we encountered an unannotated SNP, rs1339255364, which can reduce NOS2 expression, whereas any inhibition of NOS2 yields an atherosclerosis slowdown in line with a mouse model of human atherosclerosis , as reveF7  contains a clinically proven SNP marker (F7:−33A>C) of moderate bleeding [F7 promoter under study, namely, the above-mentioned substitution F7:−33A>C, two unannotated SNPs reducing the F7 level in the blood , and eight unannotated SNPs elevating it  of this gene’s underexpression  contains a known SNP marker (rs5505) of neonatal diabetes mellitus mediated by hyperinsulinemia [Human gene ulinemia , which iulinemia ,104. Aroulinemia . As readulinemia , we evenulinemia .MMP12 contains a clinical SNP marker (rs2276109) of lower risk of many immune diseases, such as psoriasis [Human gene soriasis , systemisoriasis , and aster rs2276 of lowersoriasis ,111, itssoriasis   as candAPOA1 for apolipoprotein A1 carries a known SNP marker (APOA1:−35A>C) of hematuria, fatty liver, and obesity [Human gene  obesity , and is  obesity . Indeed, obesity  as well  obesity . Near thHTR2C  contains a known SNP marker (rs3813929) of overexpression of this gene causing obesity as a complication of antipsychotic treatment with olanzapine [Human gene anzapine . This SNanzapine  and indeanzapine ,126  of IL1B overexpression causing the greatest number and variety of human diseases, including obesity [Human gene  obesity , Graves’ obesity , major r obesity , non-sma obesity , hepatoc obesity , gastric obesity , gastric obesity  , which can reduce the expression of this gene  contains a clinically proven SNP marker (rs17537595) of ADH7 underexpression responsible for esophageal cancer [Human gene l cancer , as showl cancer , post-esl cancer . This reADH7 gene promoter, we found three unannotated SNPs, which can also reduce its expression and thus speed up atherogenesis too  dehydrogenase 1, whose deficit in the case of the well-known SNP marker rs201739205 is clinically detectable in patients with hereditary breast cancer [Human gene t cancer , as readt cancer . That isHSD17B1 promoter region in question, we detected three unannotated SNPs able to raise the HSD17B1 level , which is a well-known physiological marker of accelerated atherogenesis [Finally, within the ogenesis  accordinogenesis   of nonpolyposis colon cancer [MLH1 expression and, on the contrary, accelerate atherogenesis , which can be tested as candidate SNP markers for atherogenesis acceleration ) contains a clinical SNP marker (rs35036378) of an ESR2-deficient primary pT1 tumor of the mammary gland [Human gene ry gland , whereasry gland   of methotrexate resistance during anticancer chemotherapy because of this enzyme’s overexpression [Human gene pression , which ipression ,151. ForCOMT carries two known SNP markers (rs370819229 and rs777650793) corresponding to dilated cardiomyopathy and cardiovascular disease [Human gene  disease  as a con disease , we lear disease . That isTGFBR2 (transforming growth factor beta receptor 2) carries a clinical SNP marker (rs138010137) of underexpression of this gene resulting in aortic thoracic aneurysm and dissection [Human gene ssection . Our keyarker rs1010137 ofssection  about a Finally, there we found another unannotated SNP, rs1310294304, which, on the contrary, can cause overexpression of this gene, as presented in FGFR2 encodes fibroblast growth factor receptor 2  and carries two clinically proven SNP markers, namely, rs886046768 for FGFR2 overexpression associated with craniosynostosis and rs777650793 for its downregulation linked to bent bone dysplasia [Human gene ysplasia   and an atherogenesis slowdown , which are listed in GCG: rs183433761, LEP: rs201381696, HBB: rs34500389, HBD: rs35518301, and F3: rs563763767—are exemplified by GCG: rs183433761 in After our bioinformatics prediction of 145 candidate SNP markers of atherogenesis near the clinically proven ones , we firsD of TBP-promoter complexes, which are expressed in natural logarithm units. Independently, for the same five statistical tests, As readers can see in Considering all these robust correlations together, we can say that in this work, this application of our Web service SNP_TATA_Comparator  to studyAfter experimental verification of our predictions, next we compared them (using current dbSNP build No. 151 dated 2017 ) with thn ≤ 800) than those improving these sites (n ≥ 200). This genome-wide pattern was first noted empirically [p > 0.99, binomial distribution) using ChIP-seq data within the “1000 Genomes” project [In row 1 of irically  and then project . This ob project ,50,51,54Row 2 of this table shows the results of biomedical verification of our Web service SNP_TATA_Comparator using the clinically known SNP markers of diseases in TBP-sites that was conducted when the software was just created . As one Row 3 here presents the results of our previous article on the atherosclerosis-related candidate SNP markers identified using one of the previous builds (No. 147) of dbSNP . These dn↑ = 91) and slowing (n↓ = 54) atherogenesis as well as an estimate of the statistical significance of their difference from one another according to binomial distribution (p < 0.01). Consequently, readers can see reliable predominance of candidate SNP markers of atherogenesis acceleration over those of its deceleration, which means that the above-mentioned neutral drift of this kind of human SNPs supports atherogenesis acceleration. This finding seems to be in line with one of the well-known gerontological health care strategies when in utero, in childhood, adolescence, and reproductive adulthood, the human body can successfully respond to deadly stressors  by subthreshold slowly progressing pathologies , with clinically over-threshold manifestations  seen only in the very old [n↑ =19, n↓ =16, p > 0.33). Therefore, the fourfold increase in the SNP count in the current build No. 151 of dbSNP [As readers can see, the three rightmost columns of rows 1–4 of very old . It is ivery old , there wof dbSNP  versus iFollowing both in vitro and in silico verification of our predictions, we finally applied our newly validated Web service to atherosclerosis-related research to investigate several human genes associated with the maintenance of mitochondrial genome integrity , which aPOLG encodes a catalytic subunit of DNA polymerase γ  and carries six and 23 unannotated SNPs corresponding to under- and overexpression of this gene  contains two unannotated SNPs—rs1254748756 and rs1206245736—causing PGC1A downregulation as an atheroprotector [Human gene rotector . Thus, trotector . AccordiTFAM  carries seven unannotated SNPs reducing concentrations of this regulatory protein and as many unannotated SNPs upregulating it  or predominance of candidate SNP markers accelerating atherogenesis (row 5 of n↑ = 27 and n↓ = 50 at p < 0.01). By contrast, these patterns were significant in the case of local surroundings of clinical SNP markers of human diseases , which can elevate the miR-145 level, as readers can see in Human gene n< = 5 and n> = 11 at p < 0.05) and no predominance of candidate SNP markers accelerating atherogenesis (row 6 of n↑ = 2 and n↓ = 14 at p < 0.01). This means that the two independent predictions are in agreement that natural selection against underexpression of the human genes dealing with mitochondrial genome integrity [Row 6 of ntegrity  and athentegrity  supportsWe retrieved SNPs from the dbSNP database  and DNA We analyzed SNPs within DNA sequences using our previously created public Web service SNP_TATA_Comparator , which iFor each candidate SNP marker predicted in this work, we performed a standard keyword search in the PubMed database  as illusGCG: rs183433761, LEP: rs201381696, HBB: rs34500389, HBD: rs35518301, and F3: rs563763767—using an electrophoretic mobility shift assay (EMSA), we experimentally measured absolute values of equilibrium dissociation constant KD of TBP-promoter complexes, as described in detail within For each of the five chosen candidate SNP markers of atherogenesis predicted here—D of TBP-promoter complexes was conducted using two options, “Multiple Regression” and “Nonparametrics,” in a standard toolbox, STATISTICA .A comparison of our predictions with the experimental values of equilibrium dissociation constant KBecause TBP-binding regions are the best-studied regulatory sequences within the human genome , here, uThis result increases targetability fivefold, and thus reduces the costs as compared to the traditional random heuristic selection of candidate SNP markers for clinical testing in patients and conventionally healthy volunteers . Given Those atherosclerosis-related candidate SNP markers that can survive clinical examination—on cohorts of patients and healthy volunteers as whole-genome landmarks of this disorder—may become useful for physicians  as well as for the general population (may help to choose a lifestyle slowing the inevitable atherogenesis).Additionally, when the dbSNP database  includesUnexpectedly, summing up all the above results, here we for the first time detected two statistically significant patterns in the genome: (1) neutral drift that leads to accelerated atherogenesis and (2) the direction of natural selection that impedes atherogenesis. Therefore, their superposition on each other stabilizes the total effect and thus establishes the norm of the trait of atherogenesis development.Finally, it should be noted that the atherogenesis rate is a trait that can be controlled via adjustment of the lifestyle , as unambiguously demonstrated in


Identifying associations between genetic markers and traits of economic importance will provide practical benefits for the dairy goat industry, enabling genomic prediction of the breeding value of individuals, and facilitating discovery of the underlying genes and mutations. Genome-wide association studies were implemented to detect genetic regions that are significantly associated with effects on lactation yields of milk (MY), fat (FY), protein (PY) and somatic cell score (SCS) in New Zealand dairy goats.A total of 4,840 goats were genotyped with the Caprine 50 K SNP chip . After quality filtering, 3,732 animals and 41,989 SNPs were analysed assuming an additive linear model. Four GWAS models were performed, a single-SNP additive linear model and three multi-SNP BayesC models. For the single-SNP GWAS, SNPs were fitted individually as fixed covariates, while the BayesC models fit all SNPs simultaneously as random effects. A cluster of significant SNPs were used to define a haplotype block whose alleles were fitted as covariates in a Bayesian model. The corresponding diplotypes of the haplotype block were then fit as class variables in another Bayesian model.Across all four traits, a total of 43 genome-wide significant SNPs were detected from the SNP GWAS. At a genome-wide significance level, the single-SNP analysis identified a cluster of variants on chromosome 19 associated with MY, FY, PY, and another cluster on chromosome 29 associated with SCS. Significant SNPs mapped in introns of candidate genes (45%), in intergenic regions (36%), were 0–5 kb upstream or downstream of the closest gene (14%) or were synonymous substitutions (5%). The most significant genomic window was located on chromosome 19 explaining up to 9.6% of the phenotypic variation for MY, 8.1% for FY, 9.1% for PY and 1% for SCS.The quantitative trait loci for yield traits on chromosome 19 confirms reported findings in other dairy goat populations. There is benefit to be gained from using these results for genomic selection to improve milk production in New Zealand dairy goats. The majority of dairy goats in New Zealand are housed and their milk is primarily used to manufacture powdered nutritional products for sale in international markets. There are estimated to be 92 farms in New Zealand milking 66,100 dairy goats. Current estimates indicate that 85% of the dairy goats belong to the Saanen breed, while Toggenburg, British Alpine, and Nubian type crosses comprise the remaining 15%. The Dairy Goat Co-operative (DGC) Ltd. is the main processor of goat milk in New Zealand, and accounts for 80% of the dairy goat production. Farms that supply DGC, and undertake herd testing, participate in an annual genetic evaluation for lactation yields of milk (MY), fat (FY) and protein (PY) and for somatic cell score (SCS). Breeding values for these traits were estimated for each animal from a multi-trait repeatability animal model using available pedigree . Results obtained from the ssGWAS model showed that the top SNP  explained 4.4% of the total variance for MY and 3.4% for FY and PY.Figure P-values [expressed as log10(P)] of the ssGWAS for lactation yields of MY, FY, PY and SCS. The dashed line represents the distribution of the SNPs under the null hypothesis that there is no association of SNPs with the trait of interest. The strong deviation of the observed from the expected P-values for all four QQ-plots indicate that there were more SNPs significantly associated with all of the four traits than would be expected by chance.The Quantile-Quantile plot (QQ-plot) in Fig. In this study the sBayesC model partitioned the genome into 2,520 1 Mb SNP windows with an average of 17 SNPs per window. The windows were sorted based on the proportion of genetic variance each window captured. The genomic region with the highest proportion of explained genetic variance for MY, FY and PY was on chromosome 19 . The combined effect of the 15 SNPs within this window was estimated to explain 9.62% of the genomic variance for MY, 8.09% for FY, 9.09% for PY and 0.94% for SCS. The probability that this window explained more than the average genetic variance expected under an infinitesimal model of inheritance was 1.00 for MY, FY and PY and 0.98 for SCS. Other windows of interest included one on chromosome 6  explaining 1% of the genomic variance of MY, chromosome 14  explaining 2% of the genomic variance of FY and a window on chromosome 29  explaining 3% of genomic variance of SCS.Table When the BayesC model was adjusted for the effects of the haplotypes or diplotypes , the SNPs that showed the highest model frequency were located on chromosomes 6 and 8. This suggested that all of the informative SNPs located on chromosome 19 were accurately included in the haplotype block.The population frequency of the haplotype alleles and their diplotypes are presented in Table Diplotype numbers with a h0 refers to the occurrence of any haplotype other than h1 and h2. The estimated effect of haplotypes and diplotypes on milk traits are reported in Table RNASEK gene (ENSCHIG00000009505) and at 26,662,281 bp, located within the intron of the ASGR2 gene (ENSCHIG00000003690). Both SNPs were significantly associated with all four milk traits. Other SNPs included in the haplotype block were SNP 19:26,724,454, located within the intron of the DLG4 gene (ENSCHIG00000009974), and 19:26,780,952, located downstream of the ELP5 gene (ENSCHIG00000010521), that were also significantly associated with MY, FY and PY. The functional annotation of SNP  resulted in a synonymous substitution in MYH10 (ENSCHIG00000018616) and SNP  located within an intergenic region but the closest gene being 166 kb from the MYH10 gene. Both SNPs were significantly associated with SCS.Table PTPN5 (ENSCHIG00000008345). Another significant SNP , is also near the same gene (22 kb). In addition, two SNPs  significantly associated with SCS were located within introns of the LDHC gene (ENSCHIG00000013476) and OR8B4 (ENSCHIG00000012776), respectively. The two remaining SNPs on chromosome 29 , were located within introns are of the ZDHH13 gene (ENSCHIG00000024992).Of the 11 SNPs on chromosome 29 significantly associated with SCS, 36% were mapped to introns and 64% were in intergenic regions. The most significant SNP on chromosome 29  is located in an intergenic region and is 60 kb from the closest gene, Genome wide association studies have been used to identify associations between genetic markers and candidate genes for traits of economic importance. This study evaluated the associations of 41,989 SNPs with MY, FY, PY and SCS from 3,732 New Zealand dairy goats.r2 = 0.94) and it is highly probable that these SNPs were in high LD with a QTL or causal variant that had a very significant effect on MY, FY and PY in this dairy goat population.The ssGWAS identified 43 SNPs significantly associated with MY, FY, PY and SCS in this population. A cluster of highly significant SNPs were identified on chromosome 19 for all four traits and on chromosome 29 for SCS. The two strongest signals were identified at SNP 19:26,610,610 and 19:26,662,281. These two SNPs were in high LD  or all SNPs are jointly considered. For these reasons, an additional analysis was performed fitting all SNPs simultaneously in a BayesC GWAS.The BayesC GWAS that fits all SNPs simultaneously, can improve the accuracy of detecting QTLs  and the In the Bayesian analysis, the percentage of genetic variance explained by 1 Mb genomic windows are used to make inference about the proportion of variance explained by a QTL and whether the QTL bleeds over multiple windows. The genomic window that explained the greatest level of genetic variance (8–9%) for MY, FY and PY included 15 SNPs and ranged from 26,420,506 to 26,780,952 bp on chromosome 19. Two of the SNPs located in this window were also the most frequent SNPs included in the model (suggesting they are informative SNPs that contribute to the model) and were the top SNPs identified in the ssGWAS to be associated with MY, FY and PY.Combing these results provides strong evidence that those SNPs with the highest model frequency within the genomic window on chromosome 19 with the largest effect, are likely to be in LD with the causal variant.To learn more about this potential QTL on chromosome 19 the seven most significant SNPs identified in the ssGWAS were combined into a haplotype block and the Bayesian analysis was re-run but adjusting for the SNPs in the haplotype block. Fitting covariates for haplotype alleles rather than the SNP alleles provides higher LD between causal mutations and haplotype alleles as the multilocus haplotype takes into account not only the LD information from the SNPs within the haplotype but also other important polymorphisms within the QTL cluster region. In addition, the use of haplotypes can provide information regarding the genetic determinants that cannot be captured by the biallelic markers. For example, when a SNP is fit in the model there is no guarantee that its alleles are in high LD with the QTL allele, whereas in the haplotype, provided there are enough SNPs to represent them, at least one haplotype must contain the favourable QTL allele and at least one must include the unfavourable allele. When the haplotypes were fitted into the hBayesC model as a fixed effect, there were no other signals on chromosome 19 of large effect, indicating that the majority of the QTL was indeed captured within the genomic region of that haplotype block. Also, the genetic variance from the hBayesC model was lower than the sBayesC, indicating that the seven SNPs located in the haplotype are capturing the variation that exists in that genomic region.The haplotype effects on milk production in this dairy goat population were estimated for the 10 haplotypes. Haplotypes h1 and h9 had the greatest positive effect on the milk traits. Animals that carry one copy of h1 or h9 are estimated to produce + 73.6 and + 7.8 kg milk, + 2.2 and + 1.5 kg fat and + 1.9 and + 1.0 more protein, respectively, per lactation, compared with the average of the population. Both h1 and h9 are the only haplotypes that contain the T allele at the loci 19:26,610,610, which had the strongest signal on the milk traits as well as the C allele at the loci 19:2,666,281, which had the second strongest signal on the milk traits. This suggests that an animal carrying the T and C alleles at the corresponding loci will have the greatest yields per lactation compared to the average of the population. The positive effect of these loci on milk production traits should be used in combination with performance and pedigree information to generate more accurate breeding values. When selecting animals for breeding replacements, genotyped males carrying the desirable alleles can be used for mating to females to produce replacements that carry the desirable alleles and thus the potential to be high yielding animals.When haplotypes are fitted in the model as dosage covariates we assume the haplotypes have an additive effect, which may not be true. Therefore, to test whether the effect of the haplotype block was truly additive, we fit diplotypes (pairs of haplotypes) into the model. Fitting diplotypes allows the estimation of the effect of the heterozygote without assuming it is intermediate between the opposite homozygotes, which can determine whether that haplotype allele is additive, or dominant or over-dominant etc. Results from the diplotype analysis showed that animals with the h1h2 haplotype indeed had a greater effect than the h2h2 diplotype animals, demonstrating that the h1 haplotype does have an additive effect on milk production traits in New Zealand dairy goats.The diplotypes included in the trend regression were derived from the two most frequent haplotypes in the population, h1 and h2. The predominant diplotype (29%) in the study population had two copies of h1. Animals that do not carry either h1 or h2 had an average effect of − 37.3 kg milk per lactation, relative to the population average. If an animal has only one copy of h2 then they will have − 58.0 kg, which is 20.7 kg less than animals with neither h1 nor h2. If an animal carries one copy of h1, they will have + 53.4 kg milk, producing 90.7 kg more than an animal that carries neither h1 nor h2. If an animal carries two copies of h1 then it will have + 103.6 kg milk than the population, producing an extra 50.2 kg milk more than an animal with one copy of h1. These results follow a similar trend for FY and PY and suggest that h1 has a positive effect on milk traits and can lead to increased productive value of dairy goats in New Zealand.Several studies have identified QTL significantly associated with milk production traits in goats Table . ResultsThe QTL on chromosome 19 that was strongly associated with all four traits, was reported in the French Saanen dairy goat population  and a miRNASEK gene (ENSCHIG00000009505). RNASEK is a transmembrane protein ubiquitously expressed and highly conserved across mammals. RNASEK localizes to the cell surface and endosomal pathway and closely associates with the vacuolar ATPase (V-ATPase) proton pump. RNASEK is required for endocytosis that prevents the replication of multiple pathogenic viruses such as rhinovirus, influenza A and dengue [r2 = 0.94) with SNP 19:26,662,281, which was also strongly associated with all four milk traits. This SNP , is located within the intron of the ASGR2 gene (ENSCHIG00000003690) and is in the same region where Mucha et al. [ASGR2 gene encodes a subunit of the asialoglycoprotein receptor involved with the glycoprotein metabolic process, lipid homeostasis and the regulation of protein stability. Therefore, the possibility of the ASGR2 genes involvement with the milk production traits is supported by its activity in lipid homeostasis and protein stability.In this study, the most significantly associated SNP 19:26,610,610) was located on chromosome 19 introducing a synonymous substitution in the d dengue . This mo,610,610 ALOXE3 gene. A SNP  in the same gene region was reported by Mucha et al. [Another signal strongly associated with all four milk traits is SNP  which is located within the intron of the A SNP 19:,972,244 a et al. . As thisA SNP 19:,972,244 ALOX12 gene (GOAT_ENSP00000251535) and has a significant effect on MY in dairy goats [Another association which was reported by Mucha et al.  was for ry goats . HoweverKIF1C gene (ENSCHIG00000000772). This gene is involved in the movement of molecules from the Golgi back to the endoplasmic reticulum. This SNP was also reported at the genome-wide significance level, to be associated with functional longevity in Saanen dairy goats [In the current study, the SNP  was significantly associated with MY, FY and PY and is located downstream from the ry goats . In the ry goats  also repry goats , 39.MYH10 gene (ENSCHIG00000018616) which is involved in mitotic cytokinesis. The SNP  causes a synonymous substitution and the 19: 28,079,607 SNP is located 166 kb from the gene. Both SNPs were significantly associated with SCS and not with the other milk traits.Two significant SNPs were mapped within and close to the GH1 gene located in the 47 Mb region [STAT5A gene located in the 42 Mb region [Other genes on chromosome 19 associated with milk production traits in dairy goats include the b region , 41 and We identified a peak of significant SNPs on chromosome 29 associated with SCS. It is evident there is a QTL located on this chromosome for SCS as we detected strong signals for 11 SNPs from the ssGWAS. However, further investigation using Bayesian methods would provide more information about the genomic region of the QTL and the level of variance it explains. Previous studies have reported a chromosome-wide significant SNP on chromosome 29 associated with MY  and fattZDHHC13 gene (ENSCHIG00000024992), which is associated with signal transducer activity and palmitoyltransferase activity. Palmitoyltransferase is important for the positive regulation of I-kappaB kinase/NF-kappaB signalling, which is an inflammatory signalling pathway. This gives credibility to the SNP being associated with SCS in this study.Two of the top SNPs associated with SCS  are within introns of the LDHC gene, which is involved in carbohydrate metabolic processes such as the chemical reactions and pathways resulting in the formation of ATP, a universally important coenzyme and enzyme regulator. And the OR8B4 gene, which changes the activity or state of a cell in response to a chemical stimulus by chemoreceptors i.e. smell perception.Other genomic regions that may be involved in SCS include the DGAT1 gene, a gene known to have a major effect on milk fat content in goats [ZNF16 gene (ENSCHIG00000020215). Although not studied in goats, this gene promotes cell proliferation and inhibits cell apoptosis in humans [Only one genome-wide significant signal was detected on chromosome 14 14:81658443) with FY. Although associated with FY, the genome-wide significant SNP was not located in the immediate region of the in goats  and catt3 with FYn humans .LALBA gene (chromosome 5) which is linked to milk yield, lactose content and milk coagulation properties [MTHFR gene (chromosome 6) involved in milk protein synthesis [TLR2 gene (chromosome 17) which is important in the recognition of the innate immune system of mastitis causing bacteria [PRLR gene (chromosome 20) [STAT5A gene (chromosome 19) [Despite only a few papers reporting GWAS studies in dairy goats, candidate genes related to milk traits have been widely studied. Some polymorphisms associated with milk production in goats include the operties , the MTHynthesis , the β-lynthesis , 49 assobacteria  and the some 20)  and the some 19) , both whAlthough numerous studies have provided evidence of polymorphisms within specific genes influencing milk production, there are limited studies using GWAS methodologies to identify QTL for milk production traits in dairy goats. All of the previous GWA studies identified SNPs that were of varying significance levels for different breeds , 24–26. Results from the GWAS strongly show a QTL located on chromosome 19 and the trend regression analysis suggest this is biallelic with h1 containing the desirable allele. This was detected when analysing the effects of the haplotypes and confirmed by the estimated diplotype effects. All diplotypes containing h1 resulted in positive effects on milk traits, while every diplotype that contained h2 had negative effects on the milk traits, compared with the average of the population. The fact that animals carrying one copy of both h1 and h2 still had positive effects on the milk traits shows the greater magnitude of the positive effect of h1 over the negative effect of h2.The results from this study provide evidence that there is a likely QTL strongly associated with milk traits in this population. It is possible that the QTL has an additive effect and is biallelic. In addition, it is concluded that this QTL has a pleiotropic effect as it has been identified in other goat populations and associated with a range of traits besides milk production traits.Although the study population was small, the significant regions identified were also reported in other studies, which gives confidence in the results. Nevertheless, the results require validation. If the new results are consistent with the current results, the identified markers could be used for marker-assisted-selection. This will enable the prediction of genetic and phenotypic value of individuals. For example, to predict the future phenotypes of offspring so that those with the best breeding values can be selected as parents of the next generation . At the The study identified one region strongly associated with milk production traits in New Zealand dairy goats. The highly significant region identified on chromosome 19 was also reported in French dairy goat populations and suggests a major pleiotropic QTL for milk production traits in dairy goats. The significant SNPs will increase the accuracy of predicted genetic and phenotypic values of individuals to allow for genomic selection. The results demonstrated in this study require validation using a larger dataset before implementing genomic selection within the New Zealand dairy goat population.


Ascaris suum infection in a domestic dog in China in 2019 is described for the first time. This pathogen is a common roundworm of pigs with few reported cases in domestic animals. Our findings suggest a wider infection range with a possible transmission of A. suum to domestic animals that interact with humans.An aberrant  Letter to the EditorAscaris suum is a common roundworm of pigs. Although pig husbandry has become more modernized and industrialized, A. suum is still prevalent on a large number of pig farms across the world and the situation has not changed significantly over the last few decades [A. suum is zoonotic and its cross-infection and hybridization with the human roundworm Ascaris lumbricoides has been recently confirmed in areas of human-pig sympatry [A. suum-related human infections have been reported [A. suum infections were also reported in domestic animals, such as cattle [A. suum infection in domestic dogs in China.Roundworms, belonging to the family Ascarididae, are among the most common zooparasitic nematodes and cause ascariasis in all major lineages of vertebrates including domestic animals, wildlife and humans.  decades . It is esympatry . Importareported –5, inclureported . In addis cattle . Here wepost-mortem examination. The puppy died with clinical manifestations of dyspnea, coughing, wheezing and general weakness. This farm kept five German shepherd dogs (a bitch and her four puppies), and all the puppies had shown similar clinical signs for about two weeks. Routine vaccinations were administrated to all of the dogs.In April 2019, a two-month-old female German shepherd dog puppy from a pig farm was brought to Veterinary Medical Teaching Hospital (VMTH), Sichuan Agricultural University , for Necropsy of the puppy revealed bilateral lungs with significant emphysema and diffusely firm red parenchyma, and the trachea and bronchi were filled with massive blood-tinged froth. Interstitial pneumonia was diagnosed according to the gross appearance, and viral or bacterial pneumonia was suspected. However, histopathological examination showed a diffuse, severe, hemorrhagic, fibrinous interstitial pneumonia with multiple nematode larvae present in alveolar sacs and bronchi which were partially surrounded by inflammatory cells  Fig. a, b. TheToxocara canis based on morphology: the presence of a post-oesophageal bulbus, the length and shape of the alae and the length of the spicules. Toxocara canis is a canine roundworm that is highly prevalent in puppies under three months-old and can adopt “hepato-tracheal” migration to develop into adults. Thus, this parasite can also cause histopathological changes of lungs similar to those identified in this study.The three acaridid nematodes were provisionally identified as 18S ribosomal DNA (18S rDNA) gene [12S ribosomal DNA (12S rDNA) gene [18S rDNA  and 12S rDNA  were found to be 99.9–100% identical with those of A. suum , and both grouped together and were clearly distinct from T. canis in phylogenies inferred from either 18S rDNA or 12S rDNA using maximum likelihood and Bayesian inference methods  [A. suum eggs. This suggested that the free-range German shepherd dogs were probably exposed to the eggs and became infected.To accurately identify the nematodes in the infected lung, genomic DNA was extracted from formalin-fixed, paraffin-embedded (FFPE) tissues and subjected to genetic analysis by sequencing partial fragments of the nuclear NA) gene . The resA. suum has been suggested as causing a widely distributed zoonosis with human cases in North America, Europe and Asia [A. suum have been also recently reported [A. suum in nature that are difficult to address by traditional biological, epidemiological and clinical approaches. Moreover, in consideration of the cultural and agricultural tradition of using pig feces as fertilizer and the feature that A. suum eggs remain viable in farm soil for years as well as the location of pig latrines where dogs easily access, the chance of direct or indirect exposure for dogs is likely. Recommended preventive procedures to reduce transmission of A. suum between pigs and dogs, and probably between pigs and humans, include restraining dogs from pig lactrines, periodically examining farm dogs and pigs for infection, prophylactic and therapeutic treatment of pigs to reduce infection, avoiding use of pig manure as fertilizer, and washing hands when there is contact with pigs, pig waste or soil contaminated with pig waste. In addition, the farm owner should have dedicated equipment for handling or depositing animal waste. Because A. suum eggs can persist in the farm surrounding for extended periods, daily foodstuffs, especially produce and vegetables grown using pig manure should be avoided or fed only after thorough cleaning. Overall, an effective prevention of A. suum infections requires an integrated “One-Health” approach that encourages collaboration between farmers, veterinarians and physicians and enhances their awareness to the risk for aberrant infections of A. suum and its transmission to humans and other domestic animals.The pig roundworm and Asia , 3, 6. Sreported . Our fin


SAURs) function in a range of developmental processes, but their role in seed vigor remains unclear. Here, we observed that disruption of OsSAUR33 resulted in reduced germination rates and low seed uniformity in early germination. Expression of OsSAUR33 was higher in mature grains and early germinating seeds. RNA-seq analysis revealed that OsSAUR33 modulated seed vigor by affecting the mobilization of stored reserves during germination. Disruption of OsSAUR33 increased the soluble sugar content in dry mature grains and seeds during early germination. OsSAUR33 interacted with the sucrose non-fermenting-1-related protein kinase OsSnRK1A, a regulator of the sugar signaling pathway, which influences the expression of sugar signaling-related genes during germination. Disruption of OsSAUR33 increased sugar-sensitive phenotypes in early germination, suggesting OsSAUR33 likely affects seed vigor through the sugar pathway. One elite haplotype of OsSAUR33 associated with higher seed vigor was identified mainly in indica accessions. This study provides insight into the effects of OsSAUR33 on seed vigor in rice.Seed vigor affects seed germination and seedling emergence, and therefore is an important agronomic trait in rice. Small auxin-up RNAs ( Oryza sativa L.) is one of the most important food crops in the world. Direct seeding of rice has become popular in China and South Asia over the past three decades due to its low cost and operational simplicity . Tw. TwOsSAUcessions A. The elcessions B–D. Sevehan 15%) . The expmination F,H.OsSAUR genes based on their typical auxin-inducible domain (pfam02519) in rice. This is fewer OsSAUR genes than the 58 OsSAUR members identified in a previous study [OsSAUR33 reduced seed vigor in rice. To the best of our knowledge, this is the first report highlighting the involvement of SAUR regulation in seed vigor in rice.High seed vigor, including rapid, uniform germination and vigorous seedling growth, is essential for direct seeding of rice . Identifus study  because us study ,31,32,33SAUR genes exhibit specific expression patterns throughout plant development in cotton (Gossypium hirsutum) [Zea mays) [Arabidopsis [OsSAUR genes were expressed during seed germination in rice. Among these, only OsSAUR33 exhibited specific expression in both embryo and endosperm tissues during seed germination. Therefore, we focused on the role of OsSAUR33 in seed vigor in this study. Our results showed that OsSAUR33 exhibited relatively higher expression at the late seed maturation and the early germination stages. This suggests that OsSAUR33 might function in regulating seed vigor through influencing seed development and early seed germination in rice. The plasma membrane-localized SAURs have been shown to function in cell elongation in Arabidopsis and rice, while a number of cytosol- or nucleus-localized SAURs probably function in cell division [OsSAUR33 also regulates seed vigor via cell elongation or cell division needs to be investigated in the future.Several studies have revealed that different irsutum) , maize (ea mays)  and Arabbidopsis . Similardivision ,41,42,43OsSAUR33 is involved in carbohydrate metabolism, starch biosynthesis, and seed germination. Seed maturation is the most important stage for establishing seed vigor, as soluble sugars such as glucose and fructose progressively disappear, while storage carbohydrates such as starch increase during seed maturation [Pisum sativum) [OsSAUR33 resulted in higher soluble sugars in the mature grains of rice.The plant SnRK1 subfamily is primarily involved in carbohydrate metabolism, starch biosynthesis, fertility, stress responses, seed germination, and seedling growth ,44. In tturation . We thussativum) . SimilarOsSAUR33 may regulate seed vigor by influencing the accumulation of sugars during seed maturation and seed germination stages. Rice SnRK1A acts upstream of OsMYBS1 and aAmy3 and plays a central role in the sugar signaling pathway by regulating their expression during seed germination [OsMYBS1 promotes αAmy3 expression under sugar starvation, whereas OsMYBS2 represses αAmy3 expression in rice [OsSAUR33 in regulating seed vigor by focusing on the sugar signaling pathway. We observed that the knockout of OsSAUR33 resulted in lower expression of OsMYBS1 due to the higher soluble sugar accumulation in the early germinating seeds, and the reduced levels of OsMYBS1 then resulted in reduced aAmy3 expression and α-amylase activity. Moreover, we confirmed exogenous glucose-induced reduction of rice seed vigor in this study. Our data preliminarily demonstrate a positive role of OsSAUR33 in seed vigor by maintaining the sugar balance during seed maturation to promote OsMYBS1 and aAmy3 expression at the early germination stage for hydrolysis of starch. However, the role of the OsSAUR33–OsSnRK1A interaction and whether OsSnRK1A directly interacts with OsMYBS1 in the regulation of seed vigor needs to be further investigated.SnRK1s function as sensors to monitor cellular carbohydrate status and/or AMP/ATP levels to maintain the equilibrium of sugar production and consumption necessary for proper growth ,44,47. Tmination . A recen in rice . TherefoOsSAUR33 using 180 randomly selected rice accessions, including indica and japonica accessions [OsSAUR33 that positively correlated with seed vigor. Interestingly, the elite Hap 2 haplotype mainly existed in indica accessions but not in japonica accessions. The early induction of OsSAUR33 expression was observed in accessions harboring Hap 2 during seed germination. This suggests that the early induction of OsSAUR33 during seed germination might contribute to seed vigor in rice. However, how the variations of OsSAUR33 affect its expression pattern and contribute to seed vigor needs to be further investigated. The determination of OsSAUR33 allelic diversity with a focus on newly identified elite rice accessions is of interest. Indeed, we identified several elite accessions from China harboring Hap 2, including Ai-Chiao-Hong, Pao-Tou-Hung, TeQing, ZHE 733, Zhenshan 2, Chang Ch’Sang Hsu Tao, and Zhenshan 97B. These elite accessions might be useful for improving seed vigor in rice. We speculate that the seed vigor of japonica rice could be improved by introducing Hap 2 from indica into japonica rice accessions. The confirmation of this hypothesis is now in progress.We also analyzed the allelic diversity of cessions . After aOsSAUR33 mutants (ossaur33-1 and ossaur33-2) in the japonica Nipponbare background (Oryza sativa L.) were generated using the CRISPR/Cas9 system. The two mutants were generated using two target guide sequences in the exon of OsSAUR33 were cloned into the pHUE411 plasmid vector. The mutants were identified by direct sequencing of the PCR products from the editing site using specific primers (Two  primers . All plaOsSAURs were downloaded from the rice genome annotation project (http://rice.plantbiology.msu.edu/). The conserved domains and Pfam searches were performed after removing redundant gene sequences with default parameters . Multiple sequence alignment was conducted with ClustalW, which was integrated in Mega v6.0 [OsSAURs in embryo and endosperm tissues during seed germination was investigated using Genevestigator in rice (https://www.genevestigator.com/).The information and sequences of ega v6.0 . Phylogeega v6.0 ,50. The ossaur33 mutants and WT Nipponbare were germinated in 9 cm-diameter Petri dishes under normal conditions at 25 ± 1 °C for 9 days. Meanwhile, 30 seeds per replicate were sown in 1 cm-deep soils at 25–30 °C for 9 days. Additionally, the influence of glucose (3% and 6%) treatments on seed vigor was also tested. The criteria for seed germination and seedling establishment were as stated in He et al. [The evaluation of seed vigor was conducted according to He et al.  under noOsActin as an internal control. The PCR conditions were as follows: 95 °C for 2 min, followed by 40 cycles of 95 °C for 5 s and 60 °C for 10 s. Primers used for qRT-PCR are listed in T method [Total RNA was extracted from various tissues , the developing grains , and germinating seeds  of the WT using the HP Plant RNA Kit  following the manufacturer’s instructions. The qRT-PCR reactions were performed in a CFX96 Real-Time System  with the rice T method . Three bOsSAUR33 promoter:GUS fusion construct in Nipponbare were used for a GUS staining assay. Briefly, a 2-kb genomic DNA fragment of the 5′ upstream region of OsSAUR33 was amplified by PCR. These fragments and the GUS gene were cloned into the pCAMBIA 1304 plasmid vector. GUS staining of tissues from the positive transgenic plants was performed as previously described [OsSAUR33 (without the stop codon) was amplified and inserted into the pCambia1305-GFP vector driven by the CaMV 35S promoter according to the manufacturer’s instructions . The plasma membrane (PM) marker RFP:SYP122 and nucleus marker RFP:Fib2 were used for co-localization analysis. Then, the construct was introduced into Agrobacterium tumefaciens strain GV3101 and infiltrated into Nicotiana benthamiana leaves [http://www.zeiss.com).Transgenic plants carrying the escribed . The opea leaves . The fluossaur33-1 seeds after 12 h of imbibition using HP Plant RNA Kit  according to the manufacturer’s instructions. Construction of cDNA libraries and BGISEQ-500RS sequencing were performed at BGI-Wuhan Co., Ltd., Wuhan, China. Levels of gene expression were quantified in terms of fragments per kilo base of exon per million (FPKM) using RNA-Seq by Expectation-Maximization (RSEM) version 1.1.11 [P-adj (p-adjusted) <0.001 and fold change ≥2.0 were selected for further KEGG pathway analysis. Three biological replications were performed.Total RNA was extracted from WT and n 1.1.11 . The DEGossaur33 mutants and WT plants and their seeds after 6, 48, and 72 h of imbibition in 9 cm-diameter Petri dishes at 25 ± 1 °C were harvested to detect the levels of total soluble sugar, glucose, and α-amylase activity by using commercial assay kits, according to the manufacturer’s instructions . Three biological replications were performed.The dry mature grains of Agrobacterium strain GV3101. Overnight agrobacteria cultures were resuspended with infiltration buffer . Different experiment and control group agrobacteria suspension were mixed and co-infiltrated into 5- to 6-week-old Nicotiana benthamiana leaves by using a needleless syringe, then weak light growth. YFP fluorescence was observed by confocal microscopy after two days. Luciferin (1 mM) was sprayed onto the leaves, and the plants were kept in the dark for 2 to 5 min. LUC images were captured using a cooled CCD imaging apparatus [The OsSAUR33 and OsSnRK1A were fused with the C-terminus or N-terminus of the split-yellow fluorescent protein (YFP) by homologous recombination, respectively, for the BiFC assay. The vectors of pCAMBIA1300-nLUC and pCAMBIA1300-cLUC were used for LUC assay. Different recombinant plasmid including p2YN-OsSnRK1A, p2YC-OsSAUR33, nLUC-OsSnRK1A and cLUC-OsSAUR33 with the control vector were introduced into pparatus . The priOsSAUR33 and OsSnRK1A into pGEX-6p-1 and pMAL-c2 vector, respectively. Transform constructs into the E. coli strain BL21  to produce the GST-OsSAUR33 and MBP-SnRK1A fusion proteins. For pull-down, 15 μg SnRK1A-MBP or MBP was incubated with MBP-Tag Dextrin Resin  at 4 °C for 2 h then 40 μg OsSAUR33-GST was added and incubated overnight at 4 °C. The beads were washed three times with PBS  + 1% Triton 100  and three times with PBS, then were boiled at 95 °C for 10 min with 5× sodium dodecyl sulfate (SDS) loading buffer . For western blotting, the GST antibody  and MBP antibody  were used to detected the proteins.Clone the full-length cDNA of OsSAUR33 (https://ricediversity.org/) [The 700,000 SNP markers of rice accessions were used to determine the haplotypes of ty.org/) . Haplotyty.org/) . The seet-test or analysis of variance (ANOVA) test.Experimental data were analyzed using the SAS software , and significant differences among samples were compared using Student’s