CSN1S1, CSN2, CSN1S2, CSN3, LALBA, and LGB, and casein haplotypes in Beninese indigenous cattle. Considering 67 animals, DNA sequencing of the genes’ exons, flanking regions and parts of the 5′-upstream regions identified 1058 genetic variants including 731 previously unknown. In addition, four novel milk protein variants were detected, including KCSN3 , FLALBA (p.Arg58Trp), B1LGB  and KLGB (p.Thr92Asnfs*13). KCSN3 is caused by a novel SNP  whereas FLALBA and B1LGB are due to rs714688595C>T (exon 1) and rs109625649C>T (exon 4), respectively. Regarding KLGB, a frameshift insertion of one adenine residue at BTA11:103257980 (exon 3) induces a premature translation termination resulting in a 46% reduction of the reference protein sequence. The casein polymorphisms formed five main CSN1S1-CSN2-CSN1S2-CSN3 haplotypes including B-A1-A-B, B-A1-A-A and C-A2-A-B which are predominant in the investigated cattle breeds. Moreover, in silico analyses of polymorphisms within the 5′- and 3′- untranslated regions of all six milk proteins revealed effects on microRNA and transcription factor binding sites. This study suggests a large genetic variation of milk protein genes in Beninese cattle, which should be investigated in further studies for their effects on milk production, including quality and yield traits.This study investigated polymorphisms in the milk protein genes  The dairy sector in Benin, as in many African countries, is characterized by low productivity and strong dependence on imports to meet the growing demand for dairy products ,2. To adCSN1S1, CSN2, CSN1S2, CSN3) on Bos taurus autosome (BTA) 6 and two whey protein genes (LALBA/LAA and LGB/PAEP) on BTA5 and BTA11, respectively [s1-casein (CN), β-CN, αs2-CN, κ-CN, α-Lactalbumin (α-LA), and β-Lactoglobulin (β-LG), respectively. The four caseins form a 250-kb cluster resulting in a haplotype on BTA6 [CSN1S1 , 15 protein variants for CSN2 , five protein variants for CSN1S2 , 14 protein variants for CSN3 , five protein variants for LALBA , and 11 protein variants for LGB  [HCSN1S1 and JCSN3, are exclusively described in the West-African taurine breed Baoulé and the crossbreed Kuri, respectively [s1-CNX identified by Moazami-Goudarzi et al. [A2, predominant in zebu cattle presented positive effects on calf growth [A1 and κ-CNB variants, observed in high frequency in the Beninese taurine breeds, have been linked to better cheesemaking properties . These descriptions match the appreciable features of Beninese taurine milk as described by the farmers [Six milk protein genes influence milk production traits. They include four casein genes  . Some prectively . Regardiectively ,19. Bothectively ,19. An ui et al.  in Borgoi et al. ,21,22. Ff growth . This vaf growth ,24. In c farmers . TherefoCSN1S1-CSN2-CSN1S2-CSN3 in Swiss Brown Cattle and associated B-B-A-A with lower casein content and higher whey protein content in daughter milk. Similarly, the maternal composite casein genotype CSN1S1BB-CSN2A2A2-CSN1S2AA-CSN3AB have been recently associated with pre-weaning offspring growth in German cattle [In addition to single milk protein variants, casein clusters significantly affected milk characteristics in ruminants . Researcn cattle .ALGB and BLGB [CSN1S1 are linked with different promoter alleles affecting milk production traits [Furthermore, polymorphisms occurring in the promoter regions of the milk protein genes are known to affect the binding sites of transcription factors (TF) or microRNA (miRNA) . Transcrand LGBB ,41. Liken traits ,43. Hencn traits ,44.Screening milk protein polymorphisms and genotype composites and casein haplotypes is particularly important in Benin given the increasing crossbreeding and environmental pressures ,46. CaroSixty-seven animals consisting of 20 Lagune, 20 Somba, 27 crossbreeds  were selected within a large dataset of Beninese indigenous cattle sampled between 2016 and 2017 . The LagCSN1S1, CSN3, LALBA and LGB genes, respectively , following manufacturer’s instructions. A minimum of 20 μL of DNA with a concentration of ~30 ng/μL were submitted to LGC Genomics GmbH  for the sequencing of all exons and flanking intron regions as well as of parts of the 5′-regions of the casein and whey protein genes. The primer sequences described in Said Ahmed et al.  were usevely see . Sequenchttp://www.ensembl.org/Bos_taurus/, accessed on 13 October 2021, see The raw sequence data consisting of 5 million reads were de-multiplexed with the Illumina bcl2fastq v2.20 . QualityCSN1S1, ENSBTAT00000003409.6 for CSN2, ENSBTAT00000006590.6 for CSN1S2, ENSBTAT00000028685.5 for CSN3, ENSBTAT00000007701.2 for LALBA and ENSBTAT00000019538.6 for LGB). Variants detected in the coding regions were considered to annotate milk protein variants following the existing nomenclature [The positions of the identified sequence variants were inferred considering the gene sequence positions in the ARS-UCD1.2 genome assembly. Functional annotation, performed with the Ensembl Variant Effect Predictor online tool , retrievnclature ,15,52 anCSN1S1-CSN2-CSN1S2-CSN3, and their frequencies were estimated using the haplo.group function in the haplo.stats (version 1.8.7) R package [SNP allele frequencies in the respective breeds were calculated with the vcftools v0.1.16 software . In addi package . In thishttps://platform.genexplain.com/bioumlweb/, accessed on 13 October 2021). For the investigation of miRNA target sites, the seed regions of bovine miRNA were downloaded on the TargetScan website  of newly identified variants . A frameshift deletion and insertion were identified in LALBA and LGB, respectively. In addition, a total of six synonymous SNP were detected in CSN2, CSN3, LALBA and LGB (The variants were mainly detected in the intron regions (90.5%), 5′-UTR (2.3%), 5′-upstream (2%) and 3′-downstream (1.9%) regions . Only 2% and LGB . A total of 11 missense SNP and one frameshift insertion have been identified in the milk protein genes in the investigated population. CSN1S1 gene identified the already known casein variants BCSN1S1 and CCSN1S1. The latter protein variant is due to the SNP rs43703010C>T located in exon 17, inducing the substitution of the glutamic AA by glycine (p.Glu192Gly) in the mature protein. In our animal material, 81% of all animals, especially the taurine, had the reference allele rs43703010A, leading to BCSN1S1. In contrast, the alternative allele rs43703010G, causing CCSN1S1, was mainly present in the crossbreed animals with an intra-breed frequency of 38% , inducing the replacement of histidine by proline at position 67 of the mature protein . The casein variant LCSN2 is characterized by a replacement of valine by alanine , due to the SNP rs715383373T>C in exon 7. The allele rs715383373C was identified in one crossbreed animal. Additionally, we identified the SNP rs468218273C>T affecting the AA 42 (p.Ala42) of the immature protein, located within the signal peptide.We detected the casein variants CSN1S2A and BCSN1S2 was found in the investigated Beninese cattle population. Indeed, p.Ser8Phe is the resulting AA exchange differentiating BCSN1S2 from ACSN1S2. The reference SNP allele rs441966828C encoding for ACSN1S2 was observed in the majority (99%) of the animals, while the allele rs441966828T producing the protein variant BCSN1S2 was detected in one crossbreed animal (Only the SNP rs441966828C>T (exon 3) differing the protein variants d animal .CSN3 detected two known  and one new casein variant (KCSN3). The casein variants ACSN3 and BCSN3 are caused by the SNP rs43703015T>C and rs43703016C>A in exon 4, inducing the AA replacements p.Ile136Thr and p.Ala148Asp, respectively. Most of the animals carried the reference SNP alleles rs43703015T and rs43703016C, coding for BCSN3 . Additionally, we detected the synonymous SNP rs110014544G>A (p.Ala168) also differentiating the casein variants ACSN3 and BCSN3. The novel kappa-casein variant (KCSN3) detected in this study is due to a previously undescribed SNP (BTA6:85656526C>T) in exon 4 leading to an AA exchange from alanine to valine . The new variant KCSN3 was observed in one crossbreed animal.The analysis of the five exons of or CSN3B . In contLALBA, three missense SNP causing four milk protein variants including three known  and one previously undescribed (FLALBA), were identified. The SNP rs722550244G>C in exon 1 is responsible for the change of arginine to glutamine (p.Arg10Gln), differentiating BLALBA from ALALBA. The reference SNP allele rs722550244G causing BLALBA was present in most of the animals (95%), whereas the alternative SNP allele rs722550244A leading to ALALBA was only observed in crossbreed animals (ELALBA is caused by an A to G nucleotide transition in exon 2 (SNP rs465119286A>G). The SNP allele rs465119286G found in 7% of the animals (including Somba and crossbreed) causes the replacement of isoleucine to valine . In addition to the SNP rs722550244G>C that distinguishes BLALBA from ALALBA, the SNP rs7144688595C>T also affects the AA 10 of the mature protein α-LA. Here, the SNP leads to the AA exchange p.Arg10Trp. Therefore, we have introduced a new naming FLALBA for this protein variant. The SNP allele rs7144688595T was observed in 6% of the animals. Moreover, a synonymous SNP rs477959124C>T (p.Asp64) was identified in exon 2.Across the four exons of  animals . The proLGB gene, we identified three milk protein variants  but only BLGB, being the reference allele  in exon 1, rs110641366T>C (p.Asn88) and rs715512468C>T (p.Thr97) in exon 4.Concerning the e allele  was prevn exon 4 . The SNPCSN1S1-CSN2-CSN1S2-CSN3) were built from the identified casein variants . However, only five haplotypes display a frequency of 5% in at least one breed  causing the loss and the gain of TFBS simultaneously. Five SNP abrogated miRNA target sites in the 5′-UTR, while five other SNP introduced new miRNA target sites. In addition, simultaneous loss and gain of miRNA target sites were observed in five SNP located in the 5′-UTR. Within the 3′-UTR of the milk protein genes, five and 10 SNP altered TFBS and miRNA target sites, respectively. In contrast, two and five SNP introduced new TFBS and miRNA target sites, respectively.The et sites . In contIn this study, we sequenced the exons, flanking intron regions, and parts of the 5′-flanking regions of the six milk protein genes from 67 animals, and identified a total of 1058 DNA polymorphisms. This value is higher than those reported in previous studies targeting sequence data of cattle milk protein genes ,60. For Similar to Meier et al. , we obseCSN3 and one frameshift insertion in LGB. The total number of missense variants is appreciable, but less than those  reported in the 1000 Bull Genomes consisting of a large variety of cattle breeds [CSN2, CSN3 and LALBA in the present study are correlated with the high polymorphisms of these milk protein genes compared to the others [CSN1S1 , three for CSN2 , two for CSN1S2 , three for CSN3 , four for LALBA , and three for LGB . KCSN3, FLALBA, LGBB1 and KLGB are undescribed so far.Within the coding regions, a total of 12 DNA polymorphisms caused milk protein variants. They include two previously unknown DNA variants, i.e., one SNP in e breeds . Neverthe others ,60. Sevee others ,15. OverBCSN1S1 and CCSN1S1 substantiates the previous identification of αs1-CNB and αs1-CNC at protein level in Beninese cattle [CCSN1S1 in the taurine breeds (in contrast to the crossbreeds) is consistent with results in other African cattle breeds [CCSN1S1 variant with impaired immunoreactivity in humans (in contrast to BCSN1S1) suggests its conservation through the valorization of milk from zebu and crossbreed animals for human health [s1-CNX identified by Moazami-Goudarzi et al. [s1-CNX and to clarify its DNA sequence difference. The isoelectric focusing method described by Giambra et al. [The detection of e cattle . The lowe breeds ,17,19. Tn health . We are i et al.  by protea et al.  with adjCSN1S1 represent the causal polymorphisms of the CSN1S1 promoter alleles 1, 2, 3 and 5, as previously described by Prinzenberg et al. [CSN1S1Prom1 and CSN1S1Prom2 were fully identified in our samples. The CSN1S1Prom3 and CSN1S1Prom5 diverge from our sequences with the absence of some characteristic polymorphisms.Seven SNP within the 5′-UTR of g et al.  and Ibeag et al. . HoweverCSN1S1 gene on TFBS and miRNA targets. Similar impacts of SNP in the CSN1S1 flanking regions on TFBS and milk protein content have been previously reported in cattle [Moreover, we observed various effects of the SNP located in the 5′- and 3′-UTR of the n cattle ,43 and in cattle .CSN2A1 and CSN2A2 in the Somba and crossbreed populations are in line with previous findings [ICSN2 reported by Ceriotti et al. [LCSN2 in a crossbreed animal. The protein variant has recently been described at low frequency (0.02) in Gyr cattle by Gallinat et al. [LCSN2. In consequence, our findings reinforce the specificity of LCSN2 to indicine cattle. Further rare variants BCSN2 and DCSN2 previously reported in other African taurine  and indicine breeds  [The distributions of findings ,19. Somefindings . In contt et al. . Said Ahas Arab)  were notCSN2. The sequence analyses suggested that the latter SNP (rs440770944C>A) induces the binding of the p63 TF. However, no influence of the p63 TF on milk protein gene transcription has been previously reported. In addition, the impact of SNP positioned in non-coding regions of the CSN2 gene has been rarely reported in cattle. Only one SNP located in the 5′-upstream region (g.1311T>C) of the caprine CSN2 was associated with a lack of β-CN in milk [In contrast to the other investigated milk protein genes, we detected no SNP in the 5′-UTR and only one SNP in the 3′-UTR of NP rs4407944C>A inCSN1S2 in the Beninese cattle breeds. The gene has a low polymorphic level in cattle, and consequently, it was only marginally addressed [ACSN1S2 in 99% of the investigated animals confirm its prevalence in cattle [BCSN1S2 is detected in only one crossbreed animal, in agreement with its specificity to indicine cattle [DCSN1S2 and ECSN1S2 reported in low frequencies in some European and Iranian cattle breeds, respectively, were not found in this study [In contrast to the other milk protein genes, no previous study reported the distribution of the protein variants of ddressed . The detn cattle . In addie cattle . Ibeaghae cattle  observedis study ,15,71. CSN1S2, whereas Said Ahmed et al. [CSN1S2 in Indian zebu cattle. Furthermore, two SNP in the 3′-UTR induce the deletion and/or the substitution of several miRNA binding sites. Some of these miRNA were associated with milk production traits including milk fat metabolism [CSN1S2 would be interesting for investigations in an experimental study.Only one SNP rs109900747T>C) was observed in the 5′-UTR of 900747T>Cd et al.  in the 5tabolism ,73. TherACSN3 and BCSN3 in Beninese cattle breeds is in accordance with previous results [BCSN3 causative SNP supports the known prevalence of this variant in African taurine [ACSN31, HCSN3 and JCSN3 as previously reported in African indicine and taurine breeds, respectively, were not detected in our samples. However, the SNP (BTA6:85656526C>T) causing p.Ala66Val is interesting, as it has never been reported before. The CSN3 is highly polymorphic with several variants  due to only one SNP, respectively [KCSN3 following the previous nomenclature in cattle [The identification of  results ,19. In a taurine ,17. Furtectively . We namen cattle ,14.CSN3, which has not been described before. This SNP abrogates the target site of the bta-miR-193a-3p. Additionally, the 3′-UTR of CSN3 displays two other SNP including the rs109787476T>A, which affect miRNA target sites. Said Ahmed et al. [CSN3 gene was associated with milk production traits [CSN3 are of interest as they may affect the expression of the gene, and consequently, they influence the technological quality of milk in Beninese cattle. Indeed, the importance of the CSN3 gene in cheesemaking properties of milk is well established [We identified a SNP (BTA6:85658779C>A) in the 3′-UTR of n traits ,74. In cablished .LALBA, we observed the predominance of the variant BLALBA, while ALALBA was only found in some crossbreed animals. These findings are in agreement with reports addressing Beninese cattle as well as other African cattle breeds [ALALBA variant in our samples. This variant is more prevalent in indicine cattle, but has been previously reported in low frequency in African taurine [LALBAE in the Beninese cattle supports its recent description in the African zebu Butana as well as in some European and Asian breeds [ELALBA distribution in other breeds is recommended. Additionally, we identified the missense SNP rs714688595C>T in Somba and crossbreed cattle. This polymorphism is known and annotated in the Ensembl variant table for the bovine LALBA gene. However, we found no study describing the p.Arg10Trp substitution in α-LA caused by this SNP. Indeed, α-LAA diverges from α-LAB by the AA replacement p.Arg10Gln. Further known AA exchanges in α-LA include the variants C , D (p.Gln65His) and E  [FLALBA. In e breeds ,14,17,18n breeds ,52. Teten breeds . Neverthle41Val) ,76, wherLALBA gene, but they were not addressed in previous studies  and rs109625649C>T , and the two synonymous SNP rs110180463C>T (p.Asn63) and rs110641366T>C (p.Asn88) [LGB form, not described until now. Moazami-Goudarzi et al. [A from β-LGB, whereas it is not mentioned if the differences in the electrophoretic separation is caused by changes in the isoelectric point due to both amino acid exchanges p.Gly64Asp and p.Ala118Val, or only due to one of the amino acid exchanges. In the study by Ceriotti et al. [ALGB was based on the visualization of the SNP rs109625649C>T using the PCR-restriction fragment length polymorphism (RFLP) method. The second SNP rs110066229G>A was not analyzed using this method [ALGB but, as in our case, the allele LGBB1. Ganai et al. [ALGB and BLGB in Dutch Holstein Friesian cows. Therefore, the omission of the SNP rs110066229G>A in the Beninese cattle breeds is unprecedented and may suggest the breakage of LD between the two major causal SNP of ALGB and BLGB in the Beninese cattle. Considering the existing information, we annotate the intermediate form of the LGB gene sequence caused by the missense SNP rs109625649C>T  and the synonymous SNP rs110641366T>C (p.Asn88) as LGBB1. The high frequency of p.Asn88) ,77. As w>T p.Ala1Val, and i et al. , the typs method . Therefos method  did not i et al.  observedK. This variant is due to an insertion at BTA11:103257980 in exon 3 altering the reading frame, and inducing a premature stop codon (p.Thr92Asnfs*13). The mutated protein is shortened by 74 AA (46%) and probably lacks domains that are essential for normal protein function. Although peptide deletions or insertions characterize some milk protein variants in ruminants, the loss of a large peptide sequence as observed in this study is uncommon [s1-CNA with the loss of 13 AA caused by a single T>A SNP [s1-CNH (peptide 51 to 58) in Kuri (an African crossbreed) and αs2-CND (peptide 51 to 59) in European breeds due to exon skipping events [s1-CNF (37 AA deleted) as well as several null alleles  were associated with low or null expression of casein genes, respectively [LGB exon 3. With regard to the indirect effect of the LGB gene on the total casein content [KLGB could affect animal milk composition and yield. The low frequency of the variant in the investigated population suggest a relative recent occurrence of the variant or indirect negative selection against animals carrying this variant. Further investigations should evaluate the functional impact of the LGBK variant. Moreover, this protein variant is interesting given the high allergenic potential of β-LG, a milk protein missing in human milk [CSN1S1G and several null casein alleles in goats [Another new protein variant detected in this study is the β-LGuncommon ,79,80. F T>A SNP . Furtherg events ,79. In gectively . Therefo content ,84, the man milk . For insman milk ,86,87. Sin goats ,88. LGB gene. Indeed, Cartegni et al. [CSN2A2, CSN2A3, CSN3A’) [In addition to the missense and frameshift variants, we detected two previously not described synonymous SNP. Further investigations are imperative to clarify the impact of the new synonymous variants in the i et al.  revealed CSN3A’) . LGB induces the loss of a bta-miR-7860 target site  to SNP g.-215C>A (BTA5:103255964) causing a low expression level of LGB in cattle [LGB. This polymorphism was reported within the TF activator protein-2 (AP-2), and associated with differential expression of the LGBA and LGBB protein variants [The unique polymorphism observed in the 3′-UTR of get site . Regardin cattle . We weren cattle , and is variants ,41. ThesCSN1S1-CSN2-CSN1S2-CSN3) in about 50% of the Lagune and Somba cattle  [CSN1S2 gene) with higher milk protein and fat contents, and with lower milk yield in Italian Holstein and Brown Swiss Cattle. Considering that the ACSN1S2 protein variant is the most prevalent in European taurine breeds, we assume that the haplotypes described by the latter authors correspond to B-A1-A-A and C-A2-A-B, and in consequence, we expect the same effects [The presence of the haplotype B-A1-A-B  . These r-A2-A-A)  related  effects . Finally effects ,42,44,60CSN3, LALBA and LGB genes. Hence, the Beninese cattle carry favorable alleles as well as haplotypes, which can be used in genetic improvement programs for milk yield and milk quality of various breeds.The current study confirmed milk protein variants in Beninese cattle breeds as previously identified in commercial or outbred populations. As a novelty based on the analyses of sequence data, unknown polymorphisms including SNP and InDel, were detected. In this regard, the identified new milk protein variants are located in the


OsIDEF1 (which explained 12.3% of the phenotypic variance) and OsZIFL7 (8.3–9.1%), OsZIP7 (18.9%), and OsIRT1 (17.9%) were identified by SL-MLM in SEA and SC, respectively, whereas no gene was significantly associated with GZC in DC1. In total, five , three , and two  genes were detected to be significantly associated with GZC in SEA, SC, and DC1 by mrMLM, respectively. Hap-AA indicated that Hap1-OsNRAMP5, Hap5-OsZIP4, Hap1-OsIRT1, Hap3-OsNRAMP6, Hap6-OsMTP1, and Hap6-OsYSL15 had the largest effects for GZC in SEA, whereas Hap3-OsOPT7, Hap4-OsIRT2, Hap4-OsZIP7, Hap5-OsIRT1, and Hap5-OsSAMS1 were the most significant in the SC population. Besides, superior alleles were also identified for the significant genes. The genes significantly associated with GZC and their superior haplotypes identified in different panels could be used in enhancing GZC through molecular breeding, which could further address the problem of Zn malnutrition among rice consumers.Several key genes governing Zn homeostasis and grain zinc content (GZC) have been functionally characterized. However, the effects of these genes in diverse breeding populations have not been evaluated; thus, their availability in breeding is unclear. In this study, the effects of 65 genes related to rice zinc responses on GZC were evaluated using two panels of breeding lines, and the superior haplotypes were identified. One panel consisted of mega varieties from the International Rice Research Institute (IRRI), South Asia, and Southeast Asia (SEA), and the other panel is breeding lines/varieties from South China (SC). In addition, a multiparent advanced generation intercross (MAGIC) population, named as DC1, was also employed. Three analytical methods, single-locus mixed linear model (SL-MLM), multilocus random-SNP-effect mixed linear model (mrMLM), and haplotype-based association analysis (Hap-AA), were applied.  Zinc (Zn) is an important micronutrient for global human nutritional status . Zinc deOsNAS1, OsNAS2, and OsNAS3), oligopeptide transport gene family (OsPT2 and OsYSL15)  (OsVIT1 and OsVIT2), OsFER2, OsIRT1, OsMTP1, OsHMA2, and OsFRDL1  improved GZC by several folds  , ZIP gen OsZIP8) , b, vacu OsFRDL1 . Severalal folds . The ZIPce plant , 2011. Seostasis  and may The generated genomic resources would pave the way for identification of donors, alleles, and haplotypes associated with traits of interest . The GZCArabidopsis, wheat, peas, potato, rye, and perennial ryegrass , South Asia, and Southeast Asia (SEA)  and from1. Reads were aligned to the Nipponbare RefSeq (IRGSP-1.0)2 using BWA-MEM V0.7.103. The duplicated reads were sorted out using Picard tools4. The variants for each accession were called by the GATK V3.2.2 best practices5. A stringent filtering strategy was conducted to choose high-quality SNPs and InDels for subsequent analysis . Next, the DC1 was sequenced with the 55K Affymetrix Axiom Rice Genotyping Array at the Capital Bio-Technology6 , and the SNP data set was obtained according to Total genomic DNA for genotyping was extracted from young leaves using the Cetyl trimethyl ammonium bromide (CTAB) procedure. The accessions from SEA and SC were genotyped using the Illumina HiSeq 2000 (PE150) (50X) by Berry Genomics Corporation, Beijing, Chinar2 = 1.0) for SNPs were excluded from the genotype data set. Besides, the SNPs were filtered according to the following requirements: (1) only two alleles, (2) exclude SNPs of missing data > 0.9, (3) MAF ≥ 0.05, and (4) mean depth values ≥ 5. The haplotype analysis was conducted by CandiHap V2.07 based on R 4.0.1.The nucleotide variations (SNPs and InDels) from SEA and SC were annotated by ANNOVAR . The SNP8 , and neighbor-joining (NJ) tree analysis were used to infer the population structure and kinship for SEA, SC, and DC1. Population structure was analyzed using 10,000 polymorphic SNP markers with Admixture 1.3.0 . PCA and8 . The str3 at 120°C (30 min), and further digested with HClO4 at 180°C until they became transparent. The samples were then diluted with ultrapure water, and the zinc concentrations were evaluated by inductively coupled plasma mass spectrometry (ICP-MS) according to The grain samples from all three populations were dried at 65°C for 3 days. Then, the dried grains were crushed, wet digested in concentrated HNO9. Another R package, mrMLM V2.1, was used to realize the mrMLM algorithm  regions were used to conduct association analysis for SEA and SC populations, whereas the SNPs located in the LD decay interval (150 kb) were selected for association analysis in DC1. Association analysis was carried out using the Tassel V5.1  with a mlgorithm . The crip < 0.05) but not the most frequent one in the subgroup.The resultant significant genes were further used to find superior haplotypes by conducting a Duncan analysis of GZC means (haplotype-wise) for each subgroup across SEA and SC panels. Furthermore, to ensure the accuracy of the results, only the haplotypes validated in at least five lines were considered for statistical analysis. In the present study, superior haplotypes were those with significantly higher average GZC  and the Indica subpopulation (SEA-Indica). The characterization of both subpopulations is consistent with their geographic origins. However, some levels of admixture between the Indica and Japonica subpopulations were detected in this study. The SEA-Indica accessions included the mega varieties from IRRI, South Asia, and SEA, and few released cultivars were from SC. On the other side, most of the accessions from the SEA-Japonica subpopulation were from the SC panel. Structure, PCA, and NJ tree analysis of the SC panel divided the 99 indica accessions into three subgroups, namely, SC-Indica1, SC-Indica2, and SC-Indica3 . The SC-Indica2 accessions mainly included cultivars released from the 1960s to 1990s . Lastly, majority of the accessions from the SEA-Indica3 subgroup were cultivars released from Guangdong and Guangxi provinces of China around the 1990s to 2000s, such as shanyou 836-1, Xiangsimiao 2, and Yuanzhen 397. Because of the multiple hybridizations and selfing that were used in developing the DC1 population, no strong population structure was found in this population, as also previously reported by According to -Indica3 . The SC-OsZIFL10 and OsIRO2. Further, OsZIFL2, OsOPT1, and NRAMP6 genes had only three haplotypes each. The frequency of each haplotypes for the selected genes ranged from 3.25 to 81.2% in all accessions. The range of haplotype frequency was 5.25–72.6% and 6.58–81.6% in SEA and SC, respectively. The highest haplotype frequencies in SEA were recorded in Hap3-ZIP7 (94.5%), Hap3-OsZIFL2 (93.2%), Hap3-VIT1 (93.2%), Hap3-OsNAAT4 (93.7%), Hap44-OsOPT4 (92.5%), and Hap7-OsIDEF2 (85.7%). In the SC panel, Hap1-OsVIT2 (96.1%), Hap1-OsZIP5 (95.1%), Hap1-OsFER1 (94.2%), Hap1-OsYSL1 (93.2%), Hap1-OsZIP9 (92.2%), Hap1-OsZIP4 (91.4%), and Hap1-OsOPT5 (89.3%) showed the highest frequencies. Furthermore, differences in haplotype frequencies were detectable within each subgroup of SEA and SC panels. In the SEA panel, haplotypes with the highest frequencies were Hap1-OsZIP6 (94.3%), Hap1-OsFER1 (96.0%), Hap1-MTA1 (94.3%), Hap1-OsNAS3 (96.0%), and Hap3-OsZIP7 (96.0%), which were found in the SEA-Japonica subpopulation, whereas Hap1-OsVIT2 (93.3%), Hap1-OsYSL1 (93.3%), Hap1-OsMTA1 (91.1), Hap3-OsNAAT4 (91.1%), and Hap3-OsZIP3 (91.1%) were from the SEA-Indica. Haplotypes with the lowest frequencies were Hap1-OsMTP1 (1.3%), Hap1-OsFRO2 (1.3%), Hap2-OsYSL14 (1.3%), Hap5-OsYSL18 (1.3%), and Hap6-OsYSL12 (1.3%) from the SEA-Japonica subpopulation, and Hap1-OsNAAT4 (2.2%), Hap1-OsZIP3 (2.2%), Hap1-ZIP7 (2.2%), Hap4-OsNAAT1 (2.2%), and Hap4-IDEF1 (2.2%) from SEA-Indica. Higher haplotype frequencies for the SC-Indica1 subpopulation of the SC panel were Hap3-OsZIFL2 (94.4%), Hap3-OsZIP8 (94.4%), Hap5-OsNAAT1 (90.5%), Hap5-OsSAMS1 (90.5%), and Hap5-OsYSL4 (88.5%). For the SC-Indica2 of the SC panel, recorded higher haplotype frequencies were Hap1-OsOPT1 (94.2%), Hap1-OsZIP49 (94.2%), Hap3-OsNRAMP4 (92.5%), Hap3-OsZIP8 (92.5%), and Hap4-OsZIP9 (88.5%). The higher haplotype distribution frequencies—Hap1-OsZIP49 (94.5%), Hap3-OsZIP3 (90.5%), Hap3-OsYSL4 (90.5%), Hap5-OsZIP9 (88.2%), and Hap4-OsVIT2 (88.2%)—were from the SC-Indica3 population. On the other side, the lowest haplotype frequencies were recorded for Hap2-OsZIFL2 (5.0%), Hap4-OsZIP49 (5.0%), Hap4-OsZIP8 (5.0%), Hap1-OsZIP3 (5.0%), and Hap2-OsSAMS1 (5.0%) in the SC-Indica1 subpopulation. Hap3-OsOPT1 (3.5%), Hap4-OsZIP49 (3.5%), Hap5-OsZIP8 (5.2%), Hap1-OsZIP3 (5.2%), and Hap4-OsVIT2 (5.2%) were found in the SC-Indica2. Furthermore, Hap2-OsNAAT1 (2.6%), Hap8-OsSAMS1 (2.6%), Hap3-OsZIP6 (4.2%), Hap2-OsOPT3 (4.2%), and Hap4-OsFER1 (4.2%) from the SC-Indica3 subpopulation also showed the lowest frequencies . The largest number of haplotypes was 10 and was recorded for quencies .OsIDEF1 and OsZIFL7 were found to be significantly associated with GZC by SL-MLM, and each explained the phenotypic variation of 12.3 and 8.3–9.1%, respectively  were simultaneously found to be associated with the GZC by mrMLM in the SEA panel. Each of the five QTNs explained phenotypic variation ranging from 7.70 to 15.39% (OsZIP7 (one SNP) and OsIRT1 (four SNPs) were significantly associated with GZC in the SC panel and explained phenotypic variations of 18.9 and 17.9%, respectively  were significantly associated with GZC in the SC panel and explained a phenotypic variation of 16.08% (OsFRDL1), 17.99% (OsIRT1), and 11.87% (OsZIP7), respectively (OsYSL7 and OsZIP7). The amounts of phenotypic variation defined by these two genes were 9.85% (OsYSL7) and 10.87% (OsZIP7), respectively , Hap5-OsZIP4 (11.8%), Hap3-OsNRAMP6 (6.3%), Hap6-OsMTP1 (6.1%), Hap1-OsIRT1 (5.9%), and Hap6-OsYSL15 (3.9%). In the SC panel, the superior haplotypes for GZC were Hap3-OsOPT7 (13.8%) Hap4-OsIRT2 (13.6%), OsSAMS1 (12.6%), Hap5-OsIRT1 (12.1%), and Hap4-OsZIP7 (9.6%) (Hap-AA showed that 7 (9.6%) .SEA-Indica subgroup, including Hap3-OsNRAMP5 , Hap2-OsZIP4 , Hap3-OsNRAMP6 , Hap7-OsMTP1 , and Hap8-OsIRT1 ; four superior haplotypes were identified in SEA-Japonica, including Hap3-OsNRAMP5 , Hap3-OsNRAMP6 , Hap6-OsMTP1 , and Hap1-OsYSL15   for GZC was detected as a superior haplotype in SC-Indica1; four superior haplotypes were detected in SC-Indica2, including Hap2-OsOPT7 , Hap5-IRT1 , Hap4-OsIRT2 , and Hap4-ZIP7 ; another four superior haplotypes were detected in SC-Indica3, including Hap2-OsOPT7 , Hap5-IRT1 , Hap4-OsIRT2 , and Hap4-ZIP7   . Likewislotypes) .Although dozens of Zn response genes have been cloned and validated, their usefulness in breeding remains unclear . Deeper Hap1-OsZIFL2, Hap3-OsZIP49, Hap2-OsNRAMP4, Hap3-OsZIP8, and Hap3-OsNAAT1 were only identified in the SEA panel, whereas Hap4-OsZIFL12 and Hap7-OsNRAMP8 were only detected in the SC panel. The frequencies of haplotype distribution in the SEA and SC panels were also different. The Hap3-OsOPT1, Hap3-OsNAAT1, and Hap2-OsNRAMP5 accounted for 40.5, 23.9, and 48.2% of total GZC content variation in the SEA panel, respectively. Yet, these haplotypes only accounted for 17.2% (Hap3-OsOPT1), 0 (Hap3-OsNAAT1), and 6.0% (Hap2-OsNRAMP5) in the SC panel, respectively. Similar findings were also observed for OsFRDL1, OZIFL12, and OsNRAMP1. We also observed that differences in haplotype frequencies within subgroups of the SEA and SC panels. The Hap1-OsOPT1 with 71.2% of haplotype frequency was the major haplotype in the SEA-Indica subgroup, while it only accounted for 23.5% in the SEA-Japonica subgroup. The frequencies of Hap1-OsNRAMP4 and Hap1-OsYSL14 were 53.0 and 56.0% in the SEA-Indica subgroup, respectively. However, in the SEA-Japonica subgroup, the haplotype frequencies were 4.2% (Hap1-OsNRAMP4) and 18.2% (Hap1-OsYSL14). In the SC panel, the haplotype frequencies of Hap1-OsYSL14 was 70.0% (SC-Indica1 subgroup), 46.7% (SC-Indica3), and 23.5% (SC-Indica2); the Hap1-DMAS1 accounted for 35.0% in SC-Indica1 but was not found in SC-Indica2 and SC-Indica3. Thus, identifying the genes significantly associated with GZC and its superior haplotypes by Hap-AA is crucial for rice breeding because of the various differences of haplotype distribution.The distributions of haplotypes were different in the SEA and SC panels. Previous studies have also reported that haplotype distributions differ across populations . In the OsIRT1, and OsZIP7 were detected in both the SEA and SC panels. This imply that these genes play a stabilizing role in diverse accessions and could be widely used in rice breeding. Besides, OsIDEF1, OsIRT1, and OsZIP7 genes explained the highest phenotypic variations in the SEA, SC, and DC1 panels, respectively. OsIDEF1 involved in Zn synthesis and metabolism could be used for Zn accumulation by upregulating the expression of metal homeostasis genes (OsIRT1 and OsZIP7 are involved in Zn uptake and translocation in plants and can be used for enhancing micronutrient levels in grains (OsNRAMP6 (SEA panel), OsYSL15 (SEA panel), OsZIFL7 (SC panel), OsFRDL1 (SC panel), and OsYSL7 (DC1 panel) are also significantly associated with GZC. Several studies have demonstrated that OsYSL family proteins are involved in the pathway of phloem transport and long-distance transport of metals (OsYSL15 and OsYSL7 are iron-regulated iron (III)-deoxymugineic acid transporters expressed in the root tissues and essential for Zn uptake in rice seedlings (ZINC-INDUCED FACILITATOR-LIKE (ZIFL) family genes are essential for grain Fe and Zn accumulation. It was shown that ZIFL genes interact with OsSPL14 and OsSPL16 to increase grain yield  family gene with Zn transport functions in plants. It localizes at the tonoplast and plays crucial roles in Zn accumulation , some were related with low GZC in several lines. This may be due to the following reasons: firstly, each gene contains multiple haplotypes, ranging from 3 to 12 in our study. Although the superior haplotypes are often with high GZC, there was no significant difference for most of the other haplotypes. Also, the difference of the haplotypes and GZC caused significant PVE. Besides, GZC is a typical complex trait and controlled by numerous genes. Until now, over 60 genes and 100 loci have been reported associated with GZC. Previous studies have reported the interaction among the genes related to Zn homeostasis and GZC .OsYSL15, OsIRT1, OsZIFL3, and OsFRDL1 were not detected by the SL-MLM method , and OsOPT7, OsSAMS1, and OsIRT2 (SC panel). Likewise, OsIDEF1 and OsZIFL7 (SEA panel) and OsFRDL1 (SC panel) loci identified with SL-MLM and mrMLM could not be identified with the Hap-AA. Based on the above analysis, Hap-AA is an effective and reliable approach for genetic analysis of complex traits. The various methods lead to the differences observed in results. We believe that the results should be interpreted according to the purpose of the study. In this study, we want to provide more potential genes for rice breeding. Thus, we analyzed all the genes found in these methods. However, if we want to identify new genes and clone them, the genes shared by all the three methods may be the best. In summary, combining the merits of SL-MLM, mrMLM, and Hap-AA methods is an effective approach to uncover the genetic architecture for complex agronomic traits  and OsZIFL7 (8.3–9.1%), OsZIP7 (18.9%), and OsIRT1 (17.9%) were identified by SL-MLM in SEA and SC, respectively. In total, five , three , and two  genes were detected significantly associated with GZC in SEA, SC, and DC1 by mrMLM, respectively. Hap-AA indicated that OsNRAMP5, OsZIP4, OsIRT1, OsNRAMP6, OsMTP1, and OsYSL15 had the largest effects for GZC in SEA, whereas OsOPT7, OsIRT2, OsZIP7, OsIRT1, and OsSAMS1 were the most significant in the SC population. Of these, OsIRT1 and OsIRT2 significantly associated with GZC in both the SEA and SC panels, whereas OsZIP7 plays crucial roles for GZC in both the SC and DC1 panels. OsNRAMP5, OsZIP4, OsMTP1, OsYSL15, OsNRAMP6, OsIDEF1, and OsZIFL7 significantly associated with GZC in SEA; OsSAMS1, OsOPT7, and OsFRDL1 were only identified in SC; and OsYSL7 was detected in DC1. The above results indicated that OsIRT1 and OsIRT2 could be considered as target genes in MAS breeding in both SEA and SC; OsNRAMP5, OsZIP4, OsMTP1, OsYSL15, OsNRAMP6, OsIDEF1, and OsZIFL7 could be used in GZC MAS breeding in SEA; and OsSAMS1, OsOPT7, and OsFRDL1 played crucial roles in SC.As the distribution of favorable genes and superior haplotypes is different, different genes and haplotypes should be selected from different regions. According to the results from the association analysis, OsYSL15, OsIDEF1, and OsZIFL7 should be applied in the SEA, whereas OsFRDL1, OsIRT1, and OsZIP7 is first choice in SC. OsZIP7 was also identified in the MAGIC-DC1 panel. This indicates high stability of OsZIP7 across different breeding populations. Furthermore, superior haplotypes from different subgroups can be selected to improve the GZC of different accessions. The SEA and South Asia regions are major rice production areas. The accessions from the SEA are important germplasm resources for rice breeding. Among the SEA-Indica accessions, the Hap3-OsNRAMP5, Hap2-OsZIP4, Hap3-OsNRAMP6, Hap7-OsMTP1, and Hap8-OsIRT1 haplotypes are recommended for GZC improvement. Whereas for the cultivars from the SEA-Japonica subgroup, Hap3-OsNRAMP5, Hap3-OsNRAMP6, Hap6-OsMTP1, and Hap1-OsYSL15 are the best candidate genes and haplotypes. Most of the cultivars from SC, particularly Guangdong province, originated from the traditional breeding methods. For the accessions from the SC-Indica1 subgroup, Hap1-OsZIP7 is the first choice; for the SC-Indica2 accessions, Hap2-OsOPT7, Hap5-IRT1, Hap4-OsIRT2, and Hap4-ZIP7 haplotypes are the most valuable for GZC improvement; and for the SC-Indica3 subgroup, priority genes and haplotypes are Hap2-OsOPT7, Hap5-IRT1, Hap4-OsIRT2, and Hap4-ZIP7. In summary, the superior haplotypes identified in this study for each subgroup would provide reliable guidelines for future breeding. Lines carrying multiple superior haplotypes, such as IR64, IR72, and IR36 in SEA, Guangchangai 6, Sanhuangzhan 2, and Shuangai 11 can be used to rapidly combine several superior target haplotypes into one background.OsIRT1 and OsZIP7 were identified in two populations. Also, different superior haplotypes for GZC were identified in the SEA and SC panels. Introgression of these superior haplotypes by the haplotype-based breeding is a promising strategy. This study used robust populations and analytical approaches to identify superior genes and haplotypes, which may pave the way for future breeding efforts for grain Zn content in rice.In the present study, 65 grain Zn-related genes were selected to evaluate their effects and identify superior haplotypes in three different populations. In total, seven unique genes were identified for GZC in various populations. Of these, The original contributions presented in the study are included in the article/JL and JC designed the research, analyzed the physiology data, and drafted the manuscript. JZ, XL, and SZ performed the experiments. GY revised the manuscript. All authors have read, edited, and approved the current version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


P < 0.001), and it is recommended that screening of SNPs in zebu is needed to enable selection for improved thermo-tolerance.Genetic variants at heat shock protein 70 gene and their influence on heat stress (HS) tolerance were studied among selected Nigeria zebu, namely, 25 White Fulani (WF), 21 Sokoto Gudali (SG), 21 Red Bororo (RB), and 23 Ambala (AM). Detection of single nucleotide polymorphism (SNP) followed by determination of genotype and genotypic frequency was made among the selected breeds. The heat tolerance coefficient (HTC) was determined from thermo-related parameters including body temperature, rectal temperature, and respiratory rate. Thermo-Tolerance was evaluated through the SNP–thermo-parameter relationship. Statistical analyses were done using the GLM procedure in SAS. A quantitative real-time/high-resolution melting-based assay detected twelve genetic variants. Five of these were common and shared across all breeds of cattle. Of the remaining seven variants, three were specifically identified in AM, two in SG, and two in RB. Also, SNPs were evaluated and four unique SNPs  were identified. Heterozygous animals had lower HTC suggesting their potential to withstand HS than homozygous counterparts. The WF and RB animals had significantly lower values for all parameters  compared to AM and SG breeds. Thermo-related parameters were significantly different ( Among livestock, cattle are the main contributors to animal proteins and a substantial proportion to the economies of many developing countries . In receHSPs) are a group of proteins conserved in prokaryotic and eukaryotic organisms and play vital roles in cell response to environmental stress  is a post-polymerase chain reaction (PCR) analysis technique, capable of discriminating DNA sequences based on composition, length, GC content, or strand complementarity . It is aPrevious studies in zebu cattle breeds suggested that they are superior in adapting to tropical climatic conditions compared to their European counterparts . Zebu anHSP genes including HSP 70, 90, and 90AB1 have been shown to influence thermo-tolerance capability in various cattle populations  cattle breeds are predominantly zebu, some of which are particularly adapted to the Northern part of the country with the highest heat assault. However, very little effort has been made to understand their HS tolerance status. On the other hand, genetic variants including SNPs in the bovine ulations .SP 90 as strong candidate genes for possible biomarkers for measuring thermal assault. However, the HSP 70 gene is both a constitutive and facultative gene and is more sensitive to thermal signals , rectal temperature (RT), respiratory rate (RR), and panting score (PS) have been evaluated in livestock and associated with the capability of animals to withstand HS . In the HSP 70 polymorphisms and heat tolerability parameters have been detected in various zebu cattle breeds  from four extant breeds of Nigerian zebu: 25 White Fulani (WF), 21 Sokoto Gudali (SG), 21 Red Bororo (RB), and 23 Ambala (AM) were randomly sampled while awaiting slaughter (in government-approved slaughterhouses and abattoirs) in the study area. The animals were brought to the slaughterhouses from 10 states of northern Nigeria, namely, Sokoto, Kebbi, Zamfara, Katsina, Kano, Kaduna, Jigawa, Bauchi, Gombe, and Yobe. The animals are traditionally managed in the communal rangelands by their owners. Care was taken to maintain heterogeneity as much as possible. Animals with similar clear breed characteristics and relatedness as earlier determined by various researchers, e.g., TM Multi-Sample DNA Purification  kit, following the manufacturer’s protocols. Quality and quantity check was done using a Thermo Scientific-NanoDrop 2000 spectrophotometer  and stored at −20°C. The DNA purification was conducted at the Biotechnology Laboratory, Post Graduate Research Institute in Animal Sciences, Tamil Nadu Veterinary and Animal Sciences University, Chennai, India. All incubations were done on an ACCUBLOCKTM digital dry bath , whereas all centrifuge processes were done in a HiElute Miniprep spin column using a Thermo Scientific Nanofuge (MCROCL 21/21R) micro-centrifuge .The DNA extraction was done using the HiPurATM Red Dye PCR Master Mix), and 4.5 μl of nuclease-free water. Amplification was performed in a TaKaRa Thermal Cycler DiceTM version III . The amplification condition involved initial denaturation at 94°C for 5 min, followed by 45 cycles of denaturation at 94°C for 60 s, annealing temperature of 65°C for 45 s, and extension at 72°C for 1 min. Final extension was made with 7 min at 72°C. The PCR products were isolated on 2% agarose gel electrophoresis after staining with 1 μg/ml of ethidium bromide and visualized under Bio-RAD Gel DocTM XR+ Imaging System version 5.1 . The gel bands were then excised carefully and placed into sterilized vials and stored at −20°C. Thereafter, the excised gel bands were digested manually after a series of centrifugations for 5 min at 10,500 rpm. A layer of supernatant (purified DNA) was formed on the surface of the gel, and the purified supernatant (DNA fragment) was treated as DNA template for subsequent qRT-PCR/HRMA-based assays.The polymerase chain reaction was carried out in a total volume of 15 μl comprising of template DNA 1.0 μl, 1.0 μl of each of forward and reverse primers  1.0 μl, 1.0 μl of each of forward and reverse primers, SYBR Green Master Mix (2×) of 10.0, and 7.0 μl of nuclease-free water. The qRT-PCR amplification was performed in a Roche LightCycler® 96 with software version 1.01.01.0050 . The qRT-PCR amplification condition (with HRMA option) comprised of a preincubation step of 5 min, followed by 45 cycles of denaturation at 95°C for 10 s, annealing at 65°C for 10 s, and extension at 72°C for 10 s. Specifically, the HRMA was performed by first heating to 95°C for 1 min, cooling to 37°C for 30 s, heating to 65°C for 1 s, and then melting with continuous acquisition (15 readings/°C) of the florescence signal up to 97°C. Genetic variants of the HSP 70 gene generated across breeds of Nigerian Bos (B.) indicus were analyzed using Roche HRM Software version 1.1.0.1320. Individual samples (PCR products) were differentiated into distinct genetic variants via high-resolution melting curve profiles  as depicted with distinct SYBR green (dye) fluorescence after the methods of The quantitative real-time PCR and HRMAs were carried out to identify and detect possible genetic variants of ®, Inc., Madison, WI, United States). We aligned our sequences with that of unrelated bovine HSP 70 nucleotide sequence (NCBI Ascension No: U09861) obtained from the NCBI GenBank database to detect SNP variabilities. A sequence map showing a unimodal peak indicated the homozygous genotype, and the overlapping peaks indicated heterozygous genotype and the total genotypes were obtained by direct counting. The SNP genotypes generated were associated with thermo-related measurements to unravel the potential ability of the animals to withstand the negative effects of high heat.The polymerase chain reaction products were purified and subsequently sequenced using an automated ABI DNA Sequencer . The nucleotide sequences were visualized and edited by chromatogram analyses on a SeqMan Ngen Tool  using a Linear Equation (1) according to Benezra’s coefficient of adaptability (BCA) in where RR, respiratory rate (bpm); RT, rectal temperature (°C), normal rectal temperature (38.33°C), and normal respiratory rate (23 bpm). The essence of rectal temperature as used in this formula tends to be a more accurate measurement of body temperature than oral and axillary temperature readings; therefore, it is taken as a measure of internal temperature that reflects and gives a more accurate body temperature .P-values <0.05 were considered significant. Finally, the Hardy–Weinberg equilibrium (HWE) for genotypic frequency was analyzed with goodness of fit and tested using the Chi-square (χ2) test.Thereafter, statistical analyses were performed using the generalized linear model (GLM) procedure of the statistical analysis system (SAS) PROPRIETARY Software Version 9.2  followed by Duncan’s multiple-range test to separate the means. The model (2) involved parameters BT, RT RR, and HTC as dependent variables whereas SNP genotypes and breeds were the fixed variables. whereYijk = observation of the thermo-related measurements;μ = overall mean;Si = effect of the ith genotypes on thermo-tolerance measurements;Bj = effect of the jth breed on thermo-tolerance measurements traits; andEij = random error associated with Yij observations for thermal-related measurements of the zebu cattle breeds.HSP 70 among our DNA samples has been made by looking at the different melting temperatures shown by 12 distinct SYBR Green fluorescence (dye), viz., Purple (PRP), Red (RED), Orange (ORG), Green (GRN), Lemon (LMN), Brown (BRN), Chocolate (CHO), Yellow (YLO), Magenta (MGT), Blue (BLU), Army green (AGN), and Navy blue (NBL), and the respective melting temperatures are presented in In this study, detection of variations at HSP 70 and rhythm of co-mingled/intercalating dye (SYBR Green) fluorescence (The fluorescence (difference in melting temperature) was considered to be a genetic variant among the samples and was confirmed by various HRM curve profiles, and the same was summarized in rescence , and therescence .The distribution of variants in the samples  including percentages of each is presented in We arranged the variants to obtain the distribution among the four breeds and observed that there were more variants (7) in AM samples followed by RB (6) and SG (5) and least (4) in WF .B. indicus) with sequences of a similar region in B. taurus (obtained from NCBI), and SNPs were revealed at four locations, viz., locations 90 (from G to A), at site l46 and 151 change (from C to T), as well as at site of sequences 219 with change from C to A. The sequencing map showing a unimodal peak indicated the homozygous genotype, and the overlapping peaks indicated heterozygous genotype  were transitions causing base change from purine to purine, whereas one (C219A) was transversion, i.e., causing change of a pyrimidine base to purine base , 6. FurtP > 0.05), and one (C146T) was not (P < 0.05).Furthermore, the genotypic and allelic frequencies of SNPs were computed. Frequencies were variable in each group and no clear pattern of the genotypes; the three SNPs  were in agreement with the HWE  among the breeds.These parameters  were evaluated with respect to the breeds of cattle used in the present study, and it was found that the WF and RB animals had significantly lower values for all parameters  compared to AM and SG breeds. On the other hand, the SG animals showed the highest values for all parameters and the values were statistically significant (HSP 70 gene were associated with the thermo-tolerance traits in the WF breed and the results showed a significant effect (P < 0.001) on the considered thermo-tolerance traits.The association between SNP genotypes and heat-related parameters revealed that the SNP genotypes at polymorphic locus C151T within the exon 1 region of the Pseudomonas savastanoi and Pathovars  with a new generation of light cyclers which is a high-throughput technology with an established average temperature range of 66–96°C, which enables analyses of mutation and evaluation of polymorphisms in DNA samples, making it a technique of choice for such studies . Using tathovars , and in in types . In addiin types , genotypin types , and humin types .HSP 70 gene in Nigerian Zebu through which 12 genetic variants were detected. Polymorphisms at the HSP 70 gene in DNA samples of our Zebu suggested evidence of genetic diversity among them at this locus, and our results corroborate the findings of previous researchers , whereas one was transversion (C219A in RB). The detection of these SNPs is an indicative of polymorphism at the gene among the Nigerian native cattle breeds. The results of this work agreed with those of previous reports of HSP B8 gene in the Indian Sahiwal breed and two SNP variants within the nucleotide sequence of HSP 70 in Boer goats and their crosses. Furthermore, SNPs within the coding region of HSP 70 have been reported in Chinese Holstein cattle (five novel SNPs) by HSP 70 (54 SNPs) in three breeds of Indian Zebu and riverine Buffalo. In boars, HSP 70. The implication of the variability at HSP 70 in Nigerian local cattle is the differences among them in relation to thermo-tolerance capability , 90G > A (SG breed), and 219C > A (RB breed) within the coding region of exon 1 of HSP 70 had influence on the animals (lower thermo-tolerance). Different genotypes of the gene have been shown to reduce the capability of thermo-tolerance in Holstein cattle (Regarding SNP genotypes of the n cattle . In the n cattle . The lown cattle .HSP 70 had evident influence on thermo-tolerance traits of Frieswal crossbreeds and HSP 70 to confer better thermo-tolerance and survivability advantage on Indian Tharparkar cattle. Furthermore, lower values of HTC in our animals are in consonance with the reports of earlier works in Thai native cattle (Lower thermo-tolerance values reported in this study confirmed the earlier findings of e cattle  and Sahie cattle  and Friee cattle . In thesIn the present study, the WF and RB breeds had lower BT and RR than SG and AM breeds. The differences could be possibly due to inherent breed differences among these animals as suggested by HSP 70 to have association with other traits in animals including for instance semen quality traits in boars (HSP 70 gene led to a significant increase in apoptosis (HSP 70 and spermatid DNA-packaging proteins during spermatogenesis.Elsewhere, several reports have found SNPs at in boars , sperm cin boars , and calin boars . The lacpoptosis . Govin eHSP 70. The positional base changes that resulted in changes in amino acid subsequently brought about changes in the protein product and functions of the favorable SNPs. These observations followed a similar trend with the findings of HSP 70 resulted in amino acid changes from glycine to alanine in Chinese Holstein which leads to changes in functional capacity of favorable SNPs. Furthermore, our work confirmed the earlier works of HSP 70 resulted in functional changes in the corresponding protein product in crossbred Brahman cows and they were implicated for better thermo-tolerance.Finally, this study identified amino-acid changes in the translated protein due to single-nucleotide changes at the detected polymorphic loci of HSP 70 in Nigerian zebu detected through multiplex qRT-PCR-HRMA-based assay. Also, four SNP variants were detected at HSP 70 in our samples. The present investigation revealed that the animals with heterozygous SNP genotypes of HSP 70 remarkably had lower values of BT, RT, RR, and HTC, and this is indicative of better thermo-tolerance, survivability, and ability to cope under stressful thermal conditions. The elevated HTC index indicates poor adaptability/ability to cope under thermal environmental stress. Favorable SNPs of HSP 70 in Nigerian B. indicus can be used as a molecular biomarker for screening and selection of a large population of Nigerian native cattle for better thermo-tolerance and adaptability under thermal assault.Heat stress is one of the critical factors among various environmental stressors that impede profitable livestock rearing. Our results elucidated 12 genetic variants of The Northern part of Nigeria, where the study was conducted, is frost with logistic constraints such as Boko Haram terrorism, Islamic jihad/insurgency, banditry, and kidnapping which limited sampling. The present study was conducted with only 90 animals; future study using larger sample size to validate current results is therefore recommended.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The animal study was reviewed and approved by Federal University of Agriculture Research Ethics Committee.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Epinephelus lanceolatus) taking advantage of Genotyping-by-sequencing (GBS), and compared the power of SNPs and microsatellites for parentage and relatedness analysis, based on a mixed family composed of 4 candidate females, 4 candidate males and 289 offspring. CERVUS, PAPA and COLONY were used for mutually verification. We found that SNPs had a better potential for relatedness estimation, exclusion of non-parentage and individual identification than microsatellites, and > 98% accuracy of parentage assignment could be achieved by 100 polymorphic SNPs (MAF cut-off < 0.4) or 10 polymorphic microsatellites . This study provides a reference for the development of molecular markers for parentage analysis taking advantage of next-generation sequencing, and contributes to the molecular breeding, fishery management and population conservation.Pedigree information is necessary for the maintenance of diversity for wild and captive populations. Accurate pedigree is determined by molecular marker-based parentage analysis, which may be influenced by the polymorphism and number of markers, integrity of samples, relatedness of parents, or different analysis programs. Here, we described the first development of 208 single nucleotide polymorphisms (SNPs) and 11 microsatellites for giant grouper ( The development of molecular markers and sequencing technologies over the past decades has brought great innovations for aquaculture. Since mixed breeding system is often adopted in aquaculture breeding programs, accurate pedigree information is the basis of sustainable genetic selection and hatchery management . AlthougA recent rising molecular tool for parentage analysis is single nucleotide polymorphisms (SNPs), since the availability of genomic resources and multiplexed detecting methods have been greatly enhanced by NGS and bioinformatic techniques . Over thMytilus galloprovincialis) [Micropterus floridanus [Chrysophrys auratus [Salvelinus alpinus [Pinus sylvestris [Pinus radiata [Genotyping-by-sequencing (GBS) is a low-coverage genotyping technique, which involves restriction enzymes to fragment genomic DNA followed by high-throughput sequencing, in order to generate high-quality polymorphism data at a relatively low per sample cost ,31. GBS ncialis) , fish  and plalvestris , radiata radiata ). DespitEpinephelus lanceolatus Bloch, 1790) is the largest bony fish living in coral reefs, with the largest individual recorded reaching up to 2.7 m in length and 455 kg in weight [https://www.iucnredlist.org/, accessed on 1 June 2021). Whether in conservation or aquaculture programs of this species, the application of appropriate molecular tools to trace pedigree information will help to monitor the genetic diversity of wild or cultured population and thus inform researchers of adjusting management plans. However, although 72 microsatellite markers have been developed for giant grouper so far [E. tukula) [E. coioides) [Giant grouper   and oranoioides) , while tIn this study, we described the first GBS-based bioinformatic pipeline for discovery and screening of microsatellite and SNP loci applied to a mixed cultured family of giant grouper, in order to compare and evaluate the power of two molecular markers for testing questions about parentage and relatedness. The results testified the power of application of SNPs and microsatellites for parentage analysis in a mixed family with a certain degree of inbreeding in giant grouper. Our study contributes to providing a valuable reference for grouper conservation, fishery management and molecular breeding process.3 (length × width × depth) concrete-walled ponds. After about 10 months of hatching, 289 individuals of F1 offspring were randomly sampled. Thumbnail-sized fin clips of both parents and offspring were collected and stored in 95% alcohol until they were placed at −80 °C, prior to DNA extraction. Genomic DNA was extracted from fin samples using TIANamp Marine Animals DNA Kit  following the manufacturer’s protocol. The DNA extraction was qualified by 1% agarose gel electrophoresis and quantified by the NanoDrop 2000 spectrophotometers , so as to ensure the DNA concentration met the requirement of library construction (≥20 ng/μL).The giant grouper samples in this study were described in our previous work . BrieflyEcolI and HaeIII in 96-well plates. Barcode adaptors corresponding to each individual were designed and ligated onto the sticky ends. The DNA fragments with unique barcode adapter were pooled into libraries of 24 individuals each, purified with a PCR purification kit , and then amplified for 12 × cycles using Phusion DNA polymerase  to produce sequencing libraries. The PCR products were purified as above and quantified on a Bioanalyzer 2100 . The final pooled libraries were adjusted to 10 nmol, and paired-end sequencing was performed on a lane the Illumina HiSeq 2000 platform  with 150-bp reads.The library construction and sequencing followed the original description of the authors ,31. In bmisa.ini file configured as: definition : 1–10 2–6 3–5 4–5 5–5 6–5; interruptions (max_difference_for_2_SSRs): 100. Modified Primer3 scripts (p3_in.pl and p3_out.pl) were applied to design batch primers, which were subsequently screened by laboratory procedures which referred to our previously published work [o), expected heterozygosity (He), non-exclusion probability (NEP) for parentage as well as for identity across loci, and polymorphic information content (PIC). The Pearson’s correlation between locus polymorphism and non-exclusion probability has also been calculated.Raw sequencing data were filtered and low-quality reads were removed according to the following stringent criteria: i. reads with barcode adapter contamination; ii. reads containing ≥ 10% unidentified nucleotides (N); iii. reads with > 50% of bases with a Q value ≤ 10. After filtering, the clean data of one paternal fish was chosen for microsatellite loci identifying using MIcroSAtellite identification tool (MISA) , with mihed work  with minhed work  were empE. coioides) using BOWTIE2 [view command, and then sorted with sort command. To call SNP, we indexed the reference sequences by SAMtools faidx command, used mileup and call command of bcftools [The filtered clean reads of each sample, as mentioned above, were aligned to our unpublished reference genome of orange-spotted grouper  were removed by Plink 1.9 [r2) decreased sharply until around 10 kb, so for each SNP panel with differed MAF cut-off, we pruned one locus from each pair of loci within a 50 kb sliding window with r2 > 0.2, shifting windows by 1 bp steps, conducted by Plink 1.9. After that, we visualized the LD blocks of each locus set using Haploview 4.2 [o), expected heterozygosity (He), combined non-exclusion probability across loci, and polymorphic information content (PIC) were estimated by CERVUS 3.0, and Pearson’s correlation was calculated.Since the polymorphism of SNP loci, reflected by minimum allele frequency (MAF), can directly influence the efficiency of parentage assignment, we set a series of MAF cut-off values  to figure out the lowest number with the highest polymorphism of loci for parentage assignment. SNPs deviated from Hardy-Weinberg equilibrium (HWE) with link 1.9 . Furtherlink 1.9  indicateview 4.2 , which iview 4.2 . LikewisWe conducted parentage assignment with microsatellite and SNP panels in CERVUS 3.0 and COLONY 2.0 . ParentaFor microsatellites, in order to compare with SNPs and estimate optimal number alongside with polymorphism of loci, we ranked 11 microsatellite loci by PIC value, and conducted parentage assignment stepwise adding locus from the top (the most polymorphic) one to the bottom one accumulatively, which means 11 times of assignment in CERVUS. We simulated 10,000 offspring produced by 4 candidate fathers and 4 candidate mothers, with 100% parents sampled, 98% proportion of loci genotyped, 1% genotyping error rate and confidence levels assessed by LOD distribution . The minimum typed loci were set to ≥ 45% of the whole number of loci. Based on the simulation, the empirical data was tested. In PAPA, we ran loci cumulative sequence parentage allocation with a ±1 offset model (error distribution: 0.000 0.010 0.980 0.010 0.000), and correctness was assessed in simulator producing 10,000 offspring with 100 iterations. We also used COLONY to conduct parentage assignment for each microsatellite panel, assuming polygamy for both parents, using full-likelihood method with no sibship prior, updating allele frequency. The genotyping error rate and allele dropout rate was set to 0.01. Only the inferred parent pair with > 95% probability were accepted.For SNPs, we imported each SNP panel filtered by different MAF cut-off values, as mentioned above, into CERVUS to conduct simulation and parentage assignment, with the same parameter as microsatellites except proportion of loci genotyped, which was set to the corresponding value to the specific panel. We also used COLONY to conduct parentage assignment for each SNP panel, with the same parameter described above as microsatellites.Since true pedigree information was unknown prior to analysis, we defined the ‘standard pedigree’ as the exactly consistent pedigree assigned by 208 SNPs  and 11 microsatellites in CERVUS (confidence > 95%) and COLONY. Individuals obtained different parentage assignment using the two types of molecular markers were excluded for the downstream analysis, because we could not ensure their true parents. We then compared each allocation result obtained from different marker panels with ‘standard pedigree’ to assess the accuracy of parentage assignment, so as to determine the most efficient loci number of SNP and microsatellite panel.r) are 0.5 between full-sib pairs or parent–offspring pairs, 0.25 between half-sib pairs and 0 between unrelated individuals [compareestimators function in this program to generate 100 pairs of individuals for estimating different relatedness, and determined that the wang [familysim function, and then estimated their pairwise relatedness values using coancestry function with the wang estimator. According to the result, we drew density plots of relatedness values for simulated pairs, so as to assess the overlap in relatedness values of individuals of different relatedness, which reflects the power of marker panels in distinguishing different pairwise relatedness. We also calculated the relatedness values between the 8 candidate parents to test their kinship.In order to compare the performance of the two types of molecular markers in distinguishing different relatedness , we also estimated pairwise relatedness between each pair of individuals using RELATED . RELATEDividuals . We firsthe wang  estimatop < 0.05) and the loci in LD (r2 > 0.2), we finally retained 208, 137, 123, 100, 91, 78 and 44 loci for SNP panels with MAF cut-off values 0.1, 0.2, 0.3, 0.4, 0.425, 0.45 and 0.475, separately  with different MAF cut-off values. After removing the loci deviated from HWE (parately .o (ranged from 0.401 to 0.501) as well as mean He (ranged from 0.395 to 0.501) was lower than that of microsatellites   . Meanwhiarately) .−3 to 6.53 × 10−9) was much lower than that of microsatellites (ranged from 0.0289 to 0.559)  than the best value of microsatellite panels   of SNPs (ranged from 2.83 × 10o 0.559) . Combinectively) .p < 0.01) (e) of loci was significantly negatively correlated with non-exclusion probability for parentage as well as for identity in both microsatellites and SNPs (p < 0.01), while the observed heterozygosity (Ho) was only significantly negatively correlated with non-exclusion probability in SNPs (p < 0.01)  . The pol < 0.01) .Due to the inconsistency or low confidence of parentage allocation of SNPs and microsatellites as well as low genotyping rate (<50%), a total of 11 individuals were eliminated (3.81%), and the remaining 278 offspring which were assigned to the same parent pairs using both 208 SNPs and 11 microsatellites (confidence > 95%), were thus retained for the downstream analysis . AccordiOverall, the accuracy of parentage assignment using both microsatellites and SNPs increases as the number of loci used increases, and ultimately reaches saturation at a certain point. For microsatellites, we analyzed differently sized panels using CERVUS, COLONY and PAPA. The accuracy of parentage assignment obtained from CERVUS was slightly better that from PAPA at the same number of loci, and both of the accuracy curves went similarly, which increases rapidly to 85.25% (83.81% in PAPA) when the second polymorphic locus gets involved, grows to 94.96% (93.17% in PAPA) at five loci, and then reaches 98.20% (96.04% in PAPA) at 10 loci . On the The simulated estimation of relatedness value was closer to expected value when using SNP panels than that of microsatellites . DensityThe relatedness values of the eight candidate parents were calculated using 11 microsatellites and 208 SNPs by wang estimator. Although most of the candidate parents seem to be unrelated , a certain proportion of potential kinship was found  .Until recent years, obtaining genetic markers was costly and laborious, and as a result, large numbers of markers were unavailable except for a few well-studied species . With thWe assessed the polymorphism of loci to compare the potential for parentage analysis between microsatellites and SNPs. The 11 microsatellites used here represented a relatively high polymorphism and low non-exclusion probability (0.623 for mean PIC and 0.0289 for NE-1P), consistent with that of the 15 microsatellites developed by Kim et al. for giant grouper (0.511 for mean PIC and 0.02464 for NE-1P) . In thisFor captive breeding programs, it is important to maintain the genetic diversity of broodstock, and reliable relatedness parameter is needed to prevent an overrepresentation of inbreeding . Moreoveo = 0.821) could offer an accuracy of about 98% for parentage analysis, and meanwhile for SNPs, a panel of 100 loci  was able to offer about an accuracy of 99%. This result is similar to previous reports: Kaiser et al. [o = 0.19) was as powerful as six multiallelic microsatellites (mean Ho = 0.86) for paternity assignment in black-throated blue warbler; Steele et al. [o = 0.74); Glaubitz et al. [e of 0.75) for relationship discrimination or parentage analysis. It has also been concluded that a relatively small number of SNPs (60 to 200) can have an equivalent or better performance in parentage analysis than available microsatellites [To maximize the efficiency of parentage analysis, it is necessary to consider the trade-offs between number of putative primers, cost of development and panel efficacy . Generalr et al.  found the et al.  empiricaz et al.  suggestetellites . Indeed,E. coioides) [According to the result of pedigree reconstruction, interestingly, we found a severe skew contribution of spawners. Among the eight communally rearing candidate parents, it seems that only two males and one female engaged in breeding, producing a large family consisting of 173 half-siblings and 105 full-siblings, respectively. The higher producing male GM01 contributed to 62.23% of the offspring, and female GF01 contributed to all of the offspring. We also found the similar skew phenomenon in previous parentage research on orange-spotted grouper (oioides) . This reoioides)  have desoioides) . Moreoveoioides) ,74. MoreA total of 11 individuals, which accounted for 3.81% of the total sampled offspring, were eliminated from the parentage analysis because of the inconsistency or low confidence of parentage allocation using SNPs and microsatellites. Among them, two individuals were not tested because of a low genotyping rate of 208 SNPs (< 50%), and two individuals were eliminated due to a low confidence albeit the consistent allocation between microsatellites and SNP panels. The remaining seven individuals were eliminated for inconsistence between the assignment of two molecular marker panels, while five of them showed fairly different genotype from all of the candidate parents in most loci. Besides this, we also compared the results of parentage analysis with our previous GWAS research using the same family —which coIn this study, we found that the accuracy of parentage assignment using CERVUS was similar to that of PAPA, while differs from that of COLONY. Generally, CERVUS and PAPA performed better with a relatively small marker set, while COLONY offered a higher accuracy when the number of loci went to a certain amount. This result was surprising, since COLONY usually has a better performance than CERVUS in previous reports, despite a longer running time ,77. COLOr2 > 0.2) [E. coioides), and considering the DNA sequence divergence between orange-spotted grouper and giant grouper, it might be more ideal to refer to a genome of giant grouper for this study.Another possible factor that might reduce the genotyping rates could come from the bioinformatic pipeline, although we have filtered SNPs following a stringent criterion including eliminating loci deviated from Hardy–Weinberg equilibrium or with high linkage (2 > 0.2) . Since tHere, we described a GBS-based bioinformatic pipeline for the development of microsatellite as well as low-density SNP marker panels for giant grouper for the first time, and testified their performance on parentage and relatedness analysis. The power of SNPs for exclusion and relatedness estimation was better than that of microsatellites, and > 98% accuracy of parentage assignment could be achieved by around 100 polymorphic SNPs or 10 polymorphic microsatellites. Taking advantage of the next-generation sequencing like GBS, pedigree reconstruction could be conducted with a considerably low cost of money and time.


RDX, FDX1, ARHGAP20, ZC3H12C, THBS1, and EPG5. Identified genes and pathways have functions related to pregnancy, such as autophagy in the placenta, progesterone production by the placenta, placental formation, calcium ion transport, and maternal immune response. For composite traits at weaning, genes  and pathways affecting feed intake and food conservation, development of mammary glands cytoskeleton structure, and production of milk components like fatty acids, proteins, and vitamin B-12, were identified. The results show that calcium ion transport during pregnancy and feeding lambs by milk after parturition can have the greatest impact on weight gain as compared to other effects of maternal origin.Ewe productivity is a composite and maternal trait that is considered the most important economic trait in sheep meat production. The objective of this study was the application of alternative genome-wide association study (GWAS) approaches followed by gene set enrichment analysis (GSEA) on the ewes’ genome to identify genes affecting pregnancy outcomes and lamb growth after parturition in Iranian Baluchi sheep. Three maternal composite traits at birth and weaning were considered. The traits were progeny birth weight, litter mean weight at birth, total litter weight at birth, progeny weaning weight, litter mean weight at weaning, and total litter weight at weaning. GWASs were performed on original phenotypes as well as on estimated breeding values. The significant SNPs associated with composite traits at birth were located within or near genes  In sheep breeding, ewe productivity is the most important trait affecting profitability, and genetic progress in this complex trait can lead to more efficient lamb production . In someEwe productivity is often regarded as an overall measure of lamb production capacity by ewes . ComposiDue to a strict threshold used in GWASs to find significant SNPs, several poorly associated SNPs are always ignored. An alternative strategy is to add gene set analysis as a complement approach after GWAS . In thisThe Baluchi sheep is the largest sheep breed in number in Iran and is well-adapted to a wide range of arid subtropical environments from the northeast to the southeast of the country . Due to The data set consisted of 1,509 ewes with 3,916 and 3,635 records of birth weight and weaning weight (sheep at 90 days of age), respectively. Progeny birth weight (PBW), litter mean weight at birth (LMWB), and total litter weight at birth (TLWB) were used as maternal composite traits at birth. Also, progeny weaning weight (PWW), litter mean weight at weaning (LMWW), and total litter weight at weaning (TLWW) were considered maternal composite traits at weaning. The PWW trait was adjusted for birth weight according to the formula:and then used for TLWW and LMWW calculation. The LMWB and LMWW are arithmetic mean values of TLWB and TLWW traits. They were calculated for each lambing per ewe. Phenotypic correlation between traits ranged from 0.005 to 0.16. Correlation between the EBVs of traits ranged from 0.13 to 0.99. Correlation table of traits provided in P-value cut-off of 1 × 10–6) removed from the dataset and 45,342 SNPs were kept for the analysis.Genotype data of 54,241 SNP markers were provided for 91 Baluchi ewes by the animal genetics group of Sari Agriculture Science and Natural Resource University (SANRU), Iran . DetailsHere, we regressed progenies’ weights on mothers’ genotypes. As ewes had several lambing records, we used a repeatability model framework for the association analyses and could consider year and ewes age effects. We also could incorporate multiple records for each ewe in the analyses. Due to the small sample size, we used two different GWAS approaches to understand whether they confirm each other or not. In the first approach, phenotypes were used as the response variable, and in the second approach, EBVs were used as the response variable.For the GWAS using phenotypes, the repeatability model was extended as follows:y is a vector of observations (ewe’s progenies weight); b is a vector of fixed effects, including the lamb’s sex, birth type, birth year, and the dam’s age at lambing; u is the vector of random direct additive genetic effects, pe is the vector of permanent environmental effects, and e is the vector of random residual effects. The lamb’s sex fixed effect was classified for all possible combinations and all traits except the PBW and PWW. The X, Z, and W are design matrices that relate individuals’ records to their fixed effects (b), additive genetic effects (u), and permanent environmental effects (pe), respectively. XSNP is the incidence matrix for the SNP markers and βSNP is the regression coefficient. In this case, the random effects have multivariate Gaussian (co)variance:where n is the number of genotyped individuals with reproductive records (n = 91) and N is the total number of observations for the genotyped individuals . We can write the extended repeatability model as follows:where G is the genomic relationship matrix, I is an identity matrix, This model is the same as the model above if,P-value for SNP effects that occur in the original model can be calculated from the ratio of the βSNP to its standard error . An alternative approach is to use the following score test statistics that can be more computationally efficient, be asymptotically a normal standard, and one that approximates the Wald test,In this approach, the V∘ is computed in the same way as V using a model where the SNP effects (SNPβSNPX) have been excluded and where y=Xb+XSNPβSNP+e, assuming but here peatABEL .The small number of available genotypes in this study can contribute to the low power of the association analysis, but the use of EBVs can increase the power to some extent as we have a better estimate of the actual genetic variance. EBVs can largely compensate for the limited number of genotypes to get reasonable estimates . In thisi is the standard error of EBVi, derived from the diagonal element of the inverted left-hand side in the mixed model equations  for all traits, however, partial inflation was corrected using the genomic control (GC) method, and all p-values were presented without any inflation (λ = 1). The CMplot1 R package was used for drawing Manhattan plots.where  GenABEL  package P-value cutoff 0.05, we determined 6.12∗10–6 and 1.67∗10–4 as genome-wide and chromosome-wide (suggestive) thresholds, respectively.We used the simpleM method  for mult2 UCSC Genome Browser3, and National Center for Biotechnology Information4 were used along with the Ovis aries reference genome assembly (Oar_v3.1) to identify candidate genes within a window of 300 kb up and downstream of the significant SNP.Some well-known databases including BioMart-Ensembl,We performed gene-set enrichment analysis in three steps: (i) the assignment of SNP to the known genes, (ii) the assignment of genes to functional categories, (iii) the association analysis between each functional category and the studied traits.P-value ≤ 0.05 was applied to determine significant SNP (based on the results of the pGWAS) for enrichment analysis. The Bioconductor R package biomaRt  database  was useds is the total number of significant genes associated with a given maternal composite trait at birth or weaning, m is the total number of analyzed genes, and k is the total number of genes in the term under consideration  were considered as significant categories. The ggplot2 (where deration . The GO deration . GO term ggplot2  R packag2), and repeatability (R) for the traits are shown in Estimates of variance components, heritability  on OAR15 had a suggestive association with the LMWB trait but did not contain any genes in the 300 kb flanking regions. In addition, we found three marginally suggestive SNPs on OAR7  at a distance of 30.2–35.1 Mb. Our BLAST search identified 12 genes in this region, while the SNP rs400684038 was located within the intron 8 of the TTBK2 gene. Moreover, two SNPs (rs430043751 and rs426428997) were located on OAR23 and OAR6 as they had a suggestive association with TLWB trait.Three SNPs including rs422482383, rs423274340, and rs428350449 were identified on OAR15 (19.7–20.3 Mb) which harbors four genes, EPG5, PSTPIP2, ATP5F1A, HAUS1, RNF165, and LOXHD1. This SNP was very close to the threshold line for PBW and LMWB traits in both GWAS approaches. The SNP rs426428997 on the OAR6 did not contain any genes in the searched region.The SNP rs430043751 on OAR23 was identified in both GWAS approaches and was related to nearly six genes in a 300 kb span, including For maternal composite traits at weaning, we looked for maternal genes and pathways that influence the progeny’s weaning weight. The circular Manhattan plot shows associations of SNP markers with traits for both GWAS approaches . After Fp = 4.72 × 10–6) was located on OAR3 (131.2 Mb) and was significant for the three composite traits at weaning in the pGWAS approach. Besides, this SNP had a suggestive association with LMWW in the eGWAS approach and was found to be located within the intron 1 of the VEZT gene. For PWW and TLWW traits, there were no significant or suggestive SNPs using eGWAS. Another common suggestive SNP, rs398620273, and was associated with three composite traits at weaning. This SNP located within the DTWD2 gene (intron 2) on OAR5. SNP rs412011189 on OAR2 had a suggestive association with PWW and LMWW and was close to the EIPR1 gene. In addition, we found five suggestively associated SNPs with PWW, including, rs411656768 and rs403459195 on OAR2, rs430218107 and rs419540936 on OAR7, and rs401393221 on OAR13. The SNPs on OAR7 harbor 24 genes in a 300 Kb span, seven of which were RNase genes. SNP on OAR13 was located in the RSU1 gene (intron 2), while the VIM gene is located downstream of this SNP at a distance of 4.2 kb. Three suggestive SNPs were found to be related to LMWW and were identified on OAR3 (rs404069303), OAR16 (rs409558668), and OAR25 (rs405045517). The SNP on OAR16 was near seven genes in a 300 kb span . This SNP was located in the ovine gene TTC23L (intron 2). Additionally, the Prolactin receptor (PRLR) gene was located close to this SNP. Another suggestive SNP (rs405045517) was located on OAR25 and harbored three genes  in the searched region. This SNP was located within the RHOBTB1 gene (intron 4). Also, the FTH1 gene was found to be located close to this SNP.The most significant SNP  and were defined as significantly associated with maternal composite traits. GO terms with a nominal P-value ≤ 0.01 were reported as significant terms. GWAS results using direct phenotypes (pGWAS) were used for analysis and each trait was analyzed separately.The results of GWAS were complemented with gene set enrichment analysis using the GO database. In total, 23,462 of the 45,342 SNPs being tested in the GWAS, were located within or 15 kb upstream or downstream of 15,815 annotated genes in the Oar.v3.1 ovine genome assembly. On average, 1,310 out of the 15,815 genes  contained at least one significant SNP (P ≤ 0.01) enriched with genes associated with maternal composite traits at birth. Several GO terms related to the neural system, showed an overrepresentation of significant genes, including postsynaptic density (GO:0014069), Schaffer collateral-CA1 synapse (GO:0098685), glutamatergic synapse (GO:0098978), synapse (GO:0045202), neuron projection development (GO:0031175), neurogenesis (GO:0022008), and many other terms that were not included in calcium ion transport (GO:0000045) was associated with all composite traits at birth. Furthermore, calcium channel inhibitor activity (GO:0019855) showed an overrepresentation of significant genes associated with TLWB. Many GO terms related to the immune system also showed significant enrichment of genes associated with composite traits at birth, including cellular response to chemokine (GO:1990869), positive regulation of T-helper 1 type immune response (GO:0002827), positive regulation of interleukin-12 production (GO:0032735), and positive regulation of T cell activation (GO:0050870). Several significant GO terms were related to the signaling process. In particular, SMAD protein signal transduction (GO:0060395), negative regulation of Notch signaling pathway (GO:0045746), and regulation of NIK/NF-kappaB signaling (GO:1901222) showed an overrepresentation of significant genes. In addition, we identified cell adhesion (GO:0007155) and metallopeptidase activity (GO:0008237) GO terms as significant processes that were associated with the composite traits at birth. Several other GO terms were also significant in terms of composite traits at birth. The full list is provided in p ≤ 0.01) enriched by genes associated with weaning traits. The Filopodium (GO:0030175) term was significantly associated with the composite traits at weaning. Moreover, multiple GO terms were linked to protein metabolism, including protein catabolic process (GO:0030163), positive regulation of intracellular protein transport (GO:0090316), protein processing (GO:0016485), and protein localization to plasma membrane (GO:0072659). Several GO terms related to GTPase activity were recognized as significant. Among these, GTPase activator activity (GO:0005096) showed an overrepresentation of significant genes associated with all composite traits at weaning. Many significant GO terms were related to ion transport and homeostasis and channel activity, including cellular calcium ion homeostasis (GO:0006874), ion channel activity (GO:0005216), ion transmembrane transport (GO:0034220), and ion transport (GO:0006811). On the other hand, many GO terms that were related to the metabolism of lipids, cholesterol, and fatty acids showed an overrepresentation of genes associated with the traits at weaning, including phospholipid translocation (GO:0045332), lipid phosphorylation (GO:0046834), cholesterol homeostasis (GO:0042632), and fatty acid beta-oxidation (GO:0006635). In addition, several terms were related to cell proliferation (GO:0008283 and GO:0042127), gene expression (GO:0010628), cell adhesion (GO:0098609), cell junction GO:0005911), Protein kinase activity (GO:0016301), and phosphorylation (GO:0016310). The full list of associated terms with weaning weight traits is provided in TEX12, BCO2, WDR70, INHBE, and INHBC as possible candidate genes affecting composite traits of the Lori–Bakhtiari sheep. In this study, to attain more consistent findings, two different GWAS approaches were used. Both approaches identified similar regions that may explain some part of the genetic variation in the studied traits. We identified four genes on OAR15 (19.7–20.3 Mb), namely, RDX, FDX1, ZC3H12C, and ARHGAP20 as maternal genes affecting composite traits at birth. RDX (Radixin) is part of the ERM (EZR-RDX-MSN) cytoskeleton linker protein family. The expression of ERM proteins in the blastocyst and the uterus has been reported and linked to implantation potential in mice (Ferredoxin (FDX1) is an electron transport intermediate that is functional in mitochondrial cytochromes P450 and is found mainly in steroidogenic tissues like testis, adrenal glands, ovaries, and placenta  in the genome of ewes that affect pregnancy outcome (birth weights) and weaning weights of the lambs. To our knowledge, this is the second GWAS on these traits in sheep. In the first study,  in mice . Ferredoplacenta . In thisogenesis . The Zc3regnancy . Abdoli THBS1 and TTBK2. It has been reported that the expression of THBS1 by placental cells is crucial for the formation of the placental structure  related to calcium ion metabolism. A report suggests that placental calcium transfer increases during pregnancy to match fetal needs and ensure appropriate fetal skeletal mineralization  antigens renders it foreign, and thus, the maternal immune system must tolerate the semi-allogeneic fetus to support the pregnancy, without causing the mother to become susceptible to infection (Helper (TH1)/TH2 cytokine production by maternal peripheral blood leukocytes is regarded as a commonly important feature of successful mammalian pregnancy  was one of these pathways. SMAD proteins transduce signals from the TGF-β superfamily ligands and, as a result, regulate target gene expression. TGF-β superfamily signaling is vital for female reproduction  works as a transcription factor involved in inflammatory and immune responses  pathway as a significant GO term associated with the PWW trait in our gene set analysis. Another gene in this region, NR2C1, is a nuclear steroid hormone receptor. This gene acts as a transcription factor and plays an important role in the differentiation of mammary glands and its development in late pregnancy and during lactation . It seems that this region plays an important role in RNA processing. The RNASE5 is known as a functional gene in milk production, specifically in milk protein percentage  gene. The TTC23L gene is highly expressed during lactation  pathway was identified as a significant term (Phosphorylation (GO:0016310) was identified as a significant pathway associated with PWW and LMWW traits. On average, caseins comprise 80% of proteins in cow and sheep milk, so, phosphorylation by the Casein Kinase enzyme is a crucial step for milk production in the lactating mammary gland (kinase activity (GO:0016301) pathway is another significant term for LMWW that catalyzes the transfer of a phosphate group to a substrate molecule. The cell-cell adhesion pathway was significantly associated with PWW. The effects of cell adhesion molecules on the lactogenesis process have been reviewed thoroughly in the scientific literature  as genes affecting composite traits at weaning. Our gene set analysis on these traits identified several significantly related GO terms, e.g., protein processing and transport, phospholipid translocation, ion transport, and cell-cell adhesion. As expected, most identified genes and GO terms have a role in milk production or in mammary gland development, which means that feeding lambs by milk can have the greatest impact on weight gain as compared to other effects of maternal origin. This suggests that farmers should select ewes with higher milk yields to maximize lamb growth until weaning. Moreover, the results provide a good insight into how maternal genes and pathways influence progeny weight at birth and, subsequently, at weaning.We used GWAS and GSEA together to find genes and pathways affecting maternal composite traits at birth and weaning in sheep. Several genes including https://figshare.com/ and https://doi.org/10.6084/m9.figshare.11859996.v1.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The animal study was reviewed and approved by the Sari Agricultural Sciences and Natural Resources University.SME-F: conceptualization, data curation, methodology, formal analysis, software, visualization, writing—original draft, and writing—review and editing. MG: methodology, supervision, resources, and writing—review and editing. SH: project administration, supervision, and writing—review and editing. RA-A: methodology, supervision, and writing—review and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.