ATXN10, ADAM10, VAX2, ATP6V1B1, CRISPLD1, CAPRIN1, FA2H, SPEF2, PLXNA1, and CACNA2D3, are involved in important biological processes and metabolic pathways related to behavioral traits, social interactions, and aggressiveness in cattle. Future studies should further investigate the role of these candidate genes.Behavior is a complex trait and, therefore, understanding its genetic architecture is paramount for the development of effective breeding strategies. The objective of this study was to perform traditional and weighted single-step genome-wide association studies  for yearling temperament (YT) in North American Angus cattle using haplotypes. Approximately 266 K YT records and 70 K animals genotyped using a 50 K single nucleotide polymorphisms (SNP) panel were used. Linkage disequilibrium thresholds (LD) of 0.15, 0.50, and 0.80 were used to create the haploblocks, and the inclusion of non-LD-clustered SNPs (NCSNP) with the haplotypes in the genomic models was also evaluated. WssGWAS did not perform better than ssGWAS. Cattle YT was found to be a highly polygenic trait, with genes and quantitative trait loci (QTL) broadly distributed across the whole genome. Association studies using LD-based haplotypes should include NCSNPs and different LD thresholds to increase the likelihood of finding the relevant genomic regions affecting the trait of interest. The main candidate genes identified, i.e.,  GRM5, MAML3, C8B, RUSC2, POMC, MIPOL1, and SLC18A2) were in overlap among trait definitions and populations [Behavior is a complex trait influenced by multiple factors  and the interaction among group-housed individuals and the environment . EmotionUsing alternative approaches, such as haplotype-based methods, for detecting genomic regions influencing YT is of great interest to the beef cattle industry. Haplotypes are usually defined as a set of adjacent loci expected to be inherited together with a small probability of recombination . HaplotyBoth phenotypes and genotypes are required when performing GWAS; however, not all phenotyped individuals in a population are genotyped and vice-versa ,20. In tDespite its relevance in animal breeding, to the best of our knowledge, no haplotype-based GWAS has been used to investigate the genetic background of temperament in cattle. Additionally, there is a lack of studies implementing haplotypes under ssGWAS and WssGWAS approaches. Therefore, the objective of this study was to perform a haplotype-based ssGWAS for YT in American Angus cattle. Different haplotype-based GWAS approaches were implemented and tested to uncover the potential genomic regions associated with YT, including: (1) different LD thresholds to create the LD-based haplotypes, (2) including or not including the non-LD-clustered SNPs in the association analyses, and (3) ssGWAS and WssGWAS. Understanding the genetic background of behavioral traits is of great interest for the beef cattle industry because it could enable the optimization of genetic selection for more docile animals in which the genetic progress would be permanent and cumulative over generations. Genetic or genomic selection for any trait impacts several biological mechanisms involved in the phenotypic expression of the trait under selection as well as genetically correlated traits. Therefore, it is paramount to understand these underlying biological mechanisms. A), tracing back ancestors up to four generations. The American Angus Association  provided the phenotypic, genotypic, and pedigree datasets. In total, 266,029 animals recorded for YT, born between 2001 and 2018, were available for the analyses. The phenotypic dataset has previously been processed for quality measurements  of an imputed SNP set similar to the Illumina BovineSNP50V2 and Illumina BovineSNP50V3 , designed for commercial purposes. Markers with minor allele frequency (MAF) < 0.01, call rate < 0.90, difference between observed and expected heterozygosity > 0.15 , and not present in the pseudo-autosomal region (PAR) in the X chromosome were removed from the genotypic data as part of the quality control (QC). PAR was considered the region above BTAX:133,300,518 bp . AdditioThe haplotype block (haploblock) construction process started with phasing the SNP genotypes in the FImpute software v.3.0 . After p2 metric . The Big2 metric  was used2 metric . The “gp2 metric  implemen2 metric  was used2 metric , being sThe unique haplotype alleles within the haploblocks were coded as pseudo-SNPs, which were submitted to the same QC as the SNPs  matrix of genotype calls , m × m diagonal matrix that corresponds to an identity matrix (The contemporary groups (CG) and the animal model used to predict the YT GEBV were previously defined by Alvarenga et al. , i.e.:H−1=A package . G was cVanRaden , which hith pseudo-SNP, ith pseudo-SNP, jth haploblock, jth haploblock, and ith pseudo-SNPs present within the jth haploblock. The variance components g = software  was alsoH) in the next steps in an iterative process [H matrix construction, and percentage of the variances explained by each pseudo-SNP and haploblock loci presented in the topic 2.4 were also used for the WssGWAS.The WssGWAS uses, for simplicity, an interactive process to estimate the weights for the markers. The first step in the WssGWAS is to perform the ssGWAS . The procedure consists of three steps, starting with the prediction of GEBV from ssGBLUP, deriving the weights for the markers by back-solving SNP effects, and including the weights into the  process . For det process . The POS process  was used process  was used process , as sugg process . In addi process ,34, and In addition to the three initial scenarios regarding the ssGWAS method , alternative approaches were used when constructing the i et al. , and thewer bias . TherefoThe percentage of the total additive genetic variance explained by the markers was evaluated to determine the regions to be further investigated. In this context, the moments of the distribution from the percentage of the variance explained by the markers were estimated first, and then the skewness-kurtosis plot proposed by Cullen and Frey  was utilwww.ensembl.org/biomart/martview/ad1112a783c0e0ae22e6572189d5bead, accessed on 14 August 2021) and the Animal QTLdb [www.animalgenome.org/cgi-bin/QTLdb/BT/index, accessed on 14 August 2021), respectively. These analyses were done based on the latest ARS-UCD1.2 bovine genome assembly [https://david.ncifcrf.gov/home.jsp, accessed on 15 August 2021) with focus on the Gene Ontology biological processes (GO_BP) and metabolic pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) using the default options.The top 0.001% genomic regions for YT were used to find genes and overlapping QTL using the Biomart tool from Ensembl  ranged from 20,849 to 36,881 when using the LD thresholds of 0.15 and 0.80, respectively . The numThe number of top 0.001% genomic regions ranged from 5 (WssGWAS_2_H0.80 and WssGWAS_3_H0.80) to 17 (ssGWAS_H0.15) when only haplotypes were used in the ssGWAS . DespiteIncluding the NCSNP in the association analyses resulted in more top regions being captured by the haploblocks from all LD thresholds and under both ssGWAS or WssGWAS approaches. The number of top 0.001% genomic regions ranged between 36 to 64 in the WssGWAS_3_NCSNP_H0.15 and ssGWAS_NCSNP_H0.50 scenarios, respectively . SimilarConsiderable overlap among many of the top markers was found by the different ssGWAS methods. The majority of the top markers identified by ssGWAS using only haplotypes were also present in the scenarios using NCSNP and haplotypes together . Only onCOMMD10) and three  annotated genes were identified exclusively by the ssGWAS_H0.50 and ssGWAS_H0.80, respectively, compared to the scenarios including NCSNP. Considering all ssGWAS scenarios using only haplotypes, a unique gene (SPEF2) was found by the three scenarios. Seven genes  were identified by all three ssGWAS scenarios, including NCSNP, and the unique gene overlapped by all three scenarios, including haplotype-only methods, was also present among them.The overlapping markers among scenarios were also present in genes shared between ssGWAS strategies using only haplotypes built with different LD thresholds and including the NCSNP. All annotated genes identified based on the ssGWAS_H0.15 scenario were also identified by ssGWAS_NCSNP_H0.15 . Only onThe results from the functional analyses are presented in ATXN10 (position 116 Mb on BTA5) was annotated in the GO:0031175 term, which is associated with neuron projection development. The bta05010 KEGG metabolic pathway was annotated for the gene ADAM10 (position 51 Mb on the BTA10) and is related to the Alzheimer disease. The gene VAX2 (position 13 Mb on BTA 11) was annotated in the GO:0007409, GO:0007601, GO:0030900, GO:0048048, and GO:0060041 biological processes, which are axonogenesis, visual perception, forebrain development, embryonic eye morphogenesis, and retina development in camera-type eye, respectively. The biological processes in GO:00076605 and GO:0042472 included the gene ATP6V1B1 (position 13 Mb on BTA11) and are related to sensory perception of sound and inner ear morphogenesis, respectively. GO:0060325 is related to face morphogenesis and includes the CRISPLD1 gene (position 38 Mb on the BTA14). The CAPRIN1 gene (position 64 Mb on the BTA15) was annotated in GO:0050775 and GO0061003, which are related to the positive regulation of dendrite and dendrite spine morphogenesis, respectively. Two biological processes included the FA2H gene (position 2 Mb on BTA18), which are GO:0032286 and GO:0032287, known to be related to central and peripheral nervous system myelin maintenance, respectively. The gene SPEF2 (position 38 Mb on BTA20) was annotated in GO:0048702, GO:0048854, and GO:0069541, which are related to the embryonic neurocranium, brain morphogenesis, and respiratory system development. Four biological processes related to the nervous system included the PLXNA1 gene, which are GO:0021785, GO: 0048841, GO:1902287, and GO:1990138, known to be related to branchiomotor neuron axon guidance, regulation of axon extensions involved in guidance, the semaphorin-plexin signaling pathway involved in axon guidance, and neuron projection extension, respectively. The PLXNA1 gene was also annotated in the bta04360 KEGG pathway, which is related to axon guidance. Finally, the gene CACNA2D3 was annotated in the bta04921 KEGG pathway and is related to the oxytocin signaling pathway.The gene Similar to what was observed with the genes, the overlapping markers across scenarios also implied in QTL found in more than one scenario. All QTL identified using only haplotypes with LD thresholds of 0.15 and 0.50 were captured when the NCSNP were used in the ssGWAS, while only two QTL were found by ssGWAS_H0.80 and not by ssGWAS_NCSNP_H0.80 . Using dThe QTL identified by the scenarios evaluated in this research belong to the classes “Milk”, “Health”, “Exterior”, “Production”, and “Reproduction” . The majWe have investigated the genomic architecture of YT using large phenotypic and genomic datasets from American Angus cattle. Different haploblock structures obtained by three LD thresholds to create blocks were used while including or excluding the NCSNP in order to capture different genes and QTL structures that could affect YT in American Angus. p-values for the markers in the ssGWAS approach was proposed under normality assumptions [All the steps to obtain the top 0.001% genomic regions for YT presented in the umptions  and are p-values for markers that explained ~0.10% (similar to some scenarios in this research) of the additive variance for the birth weights in American Angus using more than one million phenotypes and approximately 1.4 K genotyped sires with phenotyped progeny. Only defining a fixed value to select the candidate regions, e.g., 0.50 or 1.00% of the additive variance, as most of the GWAS studies employ, would not be an equivalent comparison with scenarios using different marker densities , and the accuracy of the phenotypic information ,41. The It was not surprising to not find regions explaining more than 1% of the total additive genetic variation for YT, since behavioral traits are highly polygenic ,5,42. OvThe fact that WssGWAS did not increase the variance explained by major genomic regions gives further evidence of the polygenic nature of YT . FurtherIn early stages in this study, the weighting method proposed by Wang et al.  in the WVanRaden  is conseVanRaden . HoweverVanRaden ’s assumpVanRaden  in this This problem using the Wang et al.  method c7SK, U6, and 5S_rRNA were associated with behavior in cattle by Alvarenga et al. [7SK gene is a small nuclear RNA gene that belongs to a class of subunits spread in the bovine genome and that already had a unit previously associated with fertility traits [U6 gene is a gene found in more than one BTA and also belongs to a small nuclear RNA class which was previously related to temperament [SNORD25, SNORD26, and SNORD27) and long non-coding RNA genes (lncRNA) were found, but no previous functions related to behavior, production, reproduction, or health were found. Beyond the presence in a top region (position 41 Mb on BTA29) for YT, QTL related to milk production and reproduction  were also annotated in the same top region , annotated in the neuron projection development GO_BP, was previously associated with longevity traits in Chinese Holstein cattle [ADAM10) gene was already identified as a biomarker for Alzheimer’s disease in humans, with functions related to the cleavage amyloid precursors that act during the inflammation process of senile plaques [ADAM10 was associated with tick resistance, with its importance in the inflammation process being the most likely reason [The ataxin 10 gene (n cattle . Longevin cattle . The ADA plaques . In catty reason . VAX2) gene was annotated for biological processes related to visual perception, axonogenesis, and forebrain development, which are very important processes in behavioral responses to environmental stimuli [VAX2 gene in cattle [+ transporting V1 subunit B1 (ATP6V1B1), indicating that vision and auditory senses are among the main functions influencing YT. The animal’s perception of the area around it is involved in behavioral responses [ATP6V1B1 gene was also previously associated with carcass composition traits [The ventral anterior homeobox 2  gene, was an interesting result. Neural crest cells are involved in face morphogenesis by generating the craniofacial skeleton, particularly the sensory organs and subsets of cranial sensory receptor neurons, and there are common mechanisms for building faces, brains, peripheral neurons, and central neural circuits that regulate behavioral functions [CRISPLD1 gene found in cattle was for milk fatty acid traits [The face morphogenesis biological process, annotated for the cysteine-rich secretory protein LCCL domain-containing 1 (unctions . In addiunctions . Howeverd traits . Previous studies have reported face hair whorls (FHW) in cattle to be a predictor of cattle temperament ,62,63. ICAPRIN1) gene. The CAPRIN1 gene was previously enriched for bovine respiratory disease [CAPRIN1 gene (block_135 position 64 Mb on BTA15) also overlapped with 2 QTL for milk fat yield. The fatty acid 2-hydroxylase (FA2H) gene was previously associated with carcass traits [Different dendritic spine densities were previously associated with aggressiveness in rats , and the disease ; howevers traits  and was s traits . Fat is s traits , and theSPEF2) gene is a well-known gene in cattle because of its association with important traits, such as adaptation to heat stress [SPEF2 gene was found for all scenarios investigated, and beyond the GO_BP related to immune system and fertility, the embryonic neurocranium, brain morphogenesis, and respiratory system development GO_BP were also functionally annotated. Respiration is changed during flight or fight response in animals [The sperm flagellar 2  gene are involved were mainly related to axon guidance and neuron projection. Specific neuron projections during aggression were previously described in mice, with some periaqueductal gray (PAG) neurons being selective for attack action [PLXNA1 gene were found in cattle; however, this gene was associated to fertility [The GO_BP and metabolic pathway that the plexin A1  gene. Reports related to OSP and social interactions in cattle are scarce. In cattle, the CACNA2D3 gene was previously associated with intramuscular fat [The social behavior response is affected by the oxytocin signaling pathway (OSP) , and thiular fat , which iular fat , but no The vast diversity of functions found by the genes present in the top regions for YT and the previous associations with different sorts of traits may suggest pleiotropic effects. This can also be supported by the range of trait classes presented by QTL overlapped by the top regions , which wMany of the genes and QTL would not have been identified without using different LD thresholds to create the haplotype blocks or the inclusion of the NCSNP. Haplotype-based GWAS using overlapping sliding windows was suggested as more powerful than SNP-based or LD-based haplotype GWAS , because these would be more efficient for regions with low LD and high recombination . ExclusiMXRA5 and CD99 in the position 138 Mb; The LD-block approach considering high LD levels was previously suggested to be inefficient for association studies because many individual SNPs would be placed out of the haploblocks , and thuwww.illumina.com/Documents/products/datasheets/datasheet_bovine_snp5O.pdf, accessed on 11 December 2021), presenting SNPs as further apart compared to the high-density panel available for cattle (~777 K SNPs). However, as the haplotype blocks in cattle are ~70 kb [It is important to highlight that the density of the panel used to make the haplotype analyses can affect the results, as the amount of QTL variance explained tend to be higher with denser haploblocks ,86. The e ~70 kb , the 50 Additional research should be conducted next to further explore the results obtained in this current study. For instance, it would be valuable to repeat these analyses based on whole-genome sequence data in North American Angus cattle as well as in other worldwide Angus cattle populations. The key candidate genes should also be validated in vitro or based on gene editing and gene knock-out experiments. Furthermore, additional polymorphisms located in the candidate genes identified in this study could be added to existing SNP panels to increase the accuracy of genomic predictions for docility. From a practical point of view, these results obtained could be incorporated in current genomic prediction models for YT in North American Angus. We are also evaluating the genetic trends of docility using the traditional and genomic estimated breeding values for YT and correlated responses in other important traits. Another area of research in our research group is the definition of novel indicators of cattle temperament, especially based on data derived from sensors and other precision technologies.ATXN10, ADAM10, VAX2, ATP6V1B1, CRISPLD1, CAPRIN1, FA2H, SPEF2, PLXNA1, and CACNA2D3 are involved in important biological processes and metabolic pathways related to behavioral traits, social interactions, and aggressiveness. Further studies investigating the role of these genes in behavioral traits are recommended. Yearling temperament in cattle is a highly polygenic trait, with genes and QTL broadly distributed across the whole genome. Association studies using LD-based haplotypes should include the non-LD-clustered SNPs, as well as different thresholds to increase the likelihood of finding the genomic regions affecting the phenotype of interest. The key candidate genes


RAR1 encodes an innate immune responsive protein that links to cellular redox homeostasis via interacting with abiotic stress-responsive Hsp90. Future research may apply the knowledge obtained from this study in the molecular breeding program to develop FT-tolerant rice varieties.Excess soluble iron in acidic soil is an unfavorable environment that can reduce rice production. To better understand the tolerance mechanism and identify genetic loci associated with iron toxicity (FT) tolerance in a highly diverse indica Thai rice population, a genome-wide association study (GWAS) was performed using genotyping by sequencing and six phenotypic data (leaf bronzing score (LBS), chlorophyll content, shoot height, root length, shoot biomass, and root dry weight) under both normal and FT conditions. LBS showed a high negative correlation with the ratio of chlorophyll content and shoot biomass, indicating the FT-tolerant accessions can regulate cellular homeostasis when encountering stress. Sixteen significant single nucleotide polymorphisms (SNPs) were identified by association mapping. Validation of candidate SNP using other FT-tolerant accessions revealed that SNP:2_21262165 might be associated with tolerance to FT; therefore, it could be used for SNP marker development. Among the candidate genes controlling FT tolerance,  Oryza sativa L.) is one of the most important cereal grains as more than half of the world’s population consumes it as a staple food. In recent years, rice productivity has declined due to global climate changes. This creates a global need for new stress-tolerant and highly productive rice varieties.Rice (Iron (Fe) is an essential micronutrient controlling multiple biological processes, including chlorophyll biosynthesis, chloroplast development, cellular respiration, nitrogen metabolism, and redox enzyme functions in plants ,3,4,5,6.3+) to ferrous ion (Fe2+), which can be quickly taken up by the root and translocated to the shoot via transpiration [2+ accumulation can enhance the Fenton reaction to produce reactive oxygen species (ROS), such as hydroxide (OH-) and hydroxyl (OH·) radical [Flooding and waterlogging create low oxygen and reductive environments that reduce ferric ion  studies for FT tolerance in rice were performed using DNA molecular markers with a bi-parental population ,33,34,35solution . A genomsolution ,38,40. Tsolution . Second,solution . Lastly,Thailand is among the top three of the world’s largest rice exporters, with an extensive collection of diverse rice germplasm from irrigated, rainfed lowland, deep water, and upland ecosystems ,42. ChakThis study aims to identify candidate loci for FT tolerance in seedlings of a highly diverse Thai rice population. Phenotyping data were applied to GWAS using 41,178 indica SNPs to identify candidate SNP linked to FT tolerance. Finally, selected candidate SNPs were further verified for the SNPs’ presence in other Thai rice accessions with FT tolerance phenotype.2+ was applied to rice seedlings. The accessions used in this screen comprise 240 Thai rice accessions and 30 representative accessions, including 22 selected RPD1 accessions  . LBS demIndica rice is the major type of rice grown in tropics and subtropics, including Thailand ,44,45. Tq-value lower than 0.05 were considered significant. Our results demonstrated that three significant SNPs found on chromosomes 1, 2 and 11 (SNP:1_31789648 (T/A), SNP:2_21262165 (A/G), and SNP:11_3412238 (C/T)) were associated with LBS  was associated with the SDW ratio  associated with SDW ratio , and group 2 consisted of 41 accessions (japonica type) . To miniwith LBS . There wwith LBS . These tDW ratio . We alsoDW ratio . The Q–QWe also compared the GWAS results between indica and japonica SNPs data. Our data revealed that the association of LBS and SNP:2_21262165 was lost with using japonica SNPs . Insteadr2) was averaged in every 20 kb, the highest r2 was 0.47. Our results showed that when the distance at which the average r2 is halved the maximum value, the LD decay of group 1 is 100 kb (https://plants.ensembl.org/Oryza_indica (accessed on 18 December 2020)) [BGIOSGA006309 and BGIOSGA006308, found in the LD block of SNP:2_21262165, function in carotenoid biosynthesis and defense response, respectively. SNP:1_31789648, which is located in the intron of BGIOSGA000995, encodes kinase domain-containing protein. Other candidates in this region are BGIOSGA000987 and BGIOSGA0004247, which are involved in the glutathione process and chloroplast accumulation/avoidance movement, respectively. Interestingly, BGIOSGA000987 is LOC_Os01g49720, which is a candidate FT-tolerant gene in japonica identified by Matthus et al. (2015) [BGIOSGA034416 found in the LD block of SNP:11_3412238 encodes coatomer subunit beta (COPB) protein involved in intracellular protein transport or vesicle-mediated transport. The SNP:5_11219514 and SNP:5_11219586, associated with the SPAD ratio, have one candidate gene, BGIOSGA019494 , which functions in the lipid biosynthetic process (Linkage disequilibrium (LD) decay was analyzed within 2000 kb by PopLDdecay . When ths 100 kb . This LDr 2020)) . We foun. (2015) . In the GA019494 . This ge process . These rTo validate whether the candidate SNP is involved in FT tolerance, we evaluated the candidate SNPs’ presence in other FT-tolerant rice accessions. We searched another set of the Thai rice collection’s genotype by sequencing (GBS) data (200 accessions) and selected 30 accessions with candidate SNP combinations as AA, AT, and GT. Based on the GBS data, 12 and seven accessions were found for the AA and GT groups, respectively. We then randomly picked 11 accessions with the AT genotype to make a complete set of 30 samples. Population structure analysis revealed that the selected 30 accessions were grouped with indica type (group 1), suggesting they could be used as representative accessions . We focuAdditionally, the FT-tolerant accessions with AA combination, RD69 and Pagah Ampuen, and the FT sensitive accessions with GT combination, Khiaw Yai and Kasalath, were included in this experiment. IR64-21 and Niaw look Gah (AT group) represented moderately FT-tolerant accessions. Our results revealed that, based on LBS, AA and AT groups are more FT-tolerant than the GT group . It shouThis study focuses on identifying candidate loci for FT tolerance from seedlings of a highly diverse Thai rice population. Although FT can affect rice growth at any stage, its most harmful effects are found on young seedlings, resulting in complete crop failure . This stFinding the genetic loci regulating the FT-tolerant trait is crucial for developing FT-tolerant rice accessions. Association mapping utilized historical recombination and mutation events within a population to accurately detect marker–trait association . Neverth2+ had already been described as a shoot-base tolerant mechanism [BGIOSGA006309) and innate immune response (BGIOSGA006308). Carotenoids can prevent lipid peroxidation through reactions with lipid peroxyl radicals [1O2) that is not generated by excess Fe [BGIOSGA006308, encodes for RAR1 protein (REQUIRED FOR MLA12 RESISTANCE 1). Previous studies have reported that RAR1 functions in plant innate immune response to multiple pathogen attacks via interacting with HSP90 (HEAT SHOCK PROTEIN 90) and SGT1 (SUPPRESSOR OF THE G2 ALLELE OF SKP1) [osrar1 loss of function mutant displayed a loss of pathogen immunity [OsRar1 increased basal disease resistance [The candidate genes in LD decay (±100 kb) of three significant SNPs  associated with LBS are related to cellular redox homeostasis . Enhanceechanism . The higradicals  and protradicals . Howeverxcess Fe . Overloaxcess Fe , which aOF SKP1) ,67,68,69immunity , while osistance ,75,76,77sistance ; they idIn summary, our study identified 16 candidate loci for FT tolerance from highly diverse indica Thai rice accessions. The presence of the candidate SNPs was validated in other FT-tolerant indica Thai rice accessions. SNP:2_21262165 may play a significant role in the shoot-based FT tolerance mechanism in Thai indica rice. Future research may focus on the functional characterization of candidate genes in this region and developing SNP markers for molecular breeding programs.The population was composed of 240 Thai rice accessions, landrace and inbred accessions covering all ecosystems, such as upland, rainfed lowland, irrigated, and deepwater rice. Thirty representative accessions, including 22 accessions from the rice diversity panel 1 (RPD1) and eight accessions as international parents from the Thailand Rice Department’s breeding program, were included. The 22 RPD1 accessions were classified into five subpopulation groups, including eight indica, five tropical japonica, three temperate japonica, two aromatic accessions, and three Aus-type accessions, including one admixture accession . The lis3, 0.6 mM NaH2PO4·2H2O, 0.5 mM K2SO4, 0.8 mM MgSO4, 0.2 mM CaCl2·6H2O, 0.07 mM Fe-EDTA, 0.009 mM MnCl2·4H2O, 0.0001 mM (NH4)6Mo7O24·4H2O, 0.037 mM H3BO3, 0.0003 mM CuSO4·5H2O, 0.00075 mM ZnSO4·7H2O, pH 5.5), which was modified from Hubbart et al. [2+ as FeSO4 × 7H2O for the high Fe treatment. After three days, the phenotypic data, collated as an LBS, was scored using the standard evaluation system for rice (SES), which ranged from 0  to 9 (dead or dying leaf) [Screening experiments were conducted in a hydroponic system with three replications. Each replication was conducted 1–2 weeks interval in the greenhouse of the Thailand Rice Science Institute (TRSI) from January to March 2019. The average temperature in the greenhouse ranged from 29–40 °C (6.00 a.m.–6.00 p.m.) and 23–29 °C (6.00 p.m.–6.00 a.m.). Seeds of each sample in each replication were germinated on tissue paper in a plastic box for ten days. Twelve uniform plants were transferred to hydroponic solution  . The SPAThe effects of treatment, genotype, and the interaction of both were analyzed by two-way analysis of variance (ANOVA) using RStudio v1.2.1335 . The ratApeKI enzyme for genomic DNA digestion. DNA fragments were ligated with adaptors and then selected for 250–300 bp using E-Gel™ SizeSelect™ agarose gels (Invitrogen) for sequencing. The sequencing data were aligned against the Nipponbare genome as the japonica reference genome by Ion Torrent™ Suite Software Alignment Plugin v5.2.2. The BAM files were converted to fastq files using Samtools v1.9 [The genotypic data as BAM files from Ion S5™ XL Sequencer (Thermo Fisher Scientific) were generated by the Ubon Ratchathani rice research center, Rice Department, Thailand. The DNA library was prepared using the ols v1.9  and realols v1.9  and SAMtols v1.9  and remools v1.9 . These fThe subpopulation was confirmed by comparison with rice accessions in the 3000 rice genomes project (3KRGP). The japonica SNPs that had missing data of less than 0.05 were selected to generate “core SNPs”. Subsequently, the core SNPs of 3KRGP were selected and then removed from the missing data accessions. The core SNPs of the two populations were merged using VCFtools and continued analysis by principal component analysis (PCA) using PLINK v1.9 .The fastq files of 270 accessions were aligned against the 93-11 indica reference genome using BWA. SAM files were obtained using SAMtools. SNPs were called based on previous criteria for GWAS. Population structure was analyzed by PCA using PLINK. Population stratification was visualized by plotting the first two PCs using RStudio. The population was divided into two groups as group 1 (G1: genetic group close to indica type) and group 2 (G2: genetic group close to japonica type) by comparing with RPD1 accessions. To reduce population structure, accessions that fell out of the main group were removed to generate accurately significant SNPs by VCFtools.p-value of the SNP marker was corrected for multiple tests by the q-value  of each trait. SNPs with a q-value lower than 0.05 were selected as significant markers. The association mapping results were presented in Manhattan plots constructed from SNPs positions and −log10  of each SNP and quantile–quantile (Q–Q), which were generated from observed and expected p-values using R “CMplot” package [Genome-wide association (GWA) mapping was performed in the indica subpopulation (G1) using factored spectrally transformed linear mixed models (FaST-LMM-v1.08) . Indica  package  in RStudr2) between a pair of SNPs loci within 2000 kb using PopLDdecay [r2 value within 20 kb was averaged to estimate LD decay and was plotted against the physical distance using the ‘ggplot2′ package [r2 dropped to half of the maximum value was described as LD decay. Candidate genes were considered from the list of genes in the LD decay of significant SNPs.LD in indica subpopulation was calculated using the correlation  under greenhouse conditions of TRSI were from 32 to 40 and 28 to 35 °C. The average phenotype was compared between candidate SNP groups using Tukey’s HSD test and plotted using the ‘ggplot2′ package in RStudio.


Setaria italica (L.) P. Beauv.] is the second most important millet species globally and is adapted to cultivation in diverse environments. Like its wild progenitor, green foxtail [S. viridis (L.) P. Beauv.], it is a model species for C4 photosynthetic pathways and stress tolerance genes in related bioenergy crops. We addressed questions regarding the evolution and spread of foxtail millet through a population genomic study of landraces from across its cultivated range in Europe, Asia, and Africa. We sought to determine population genomic structure and the relationship of domesticated lineages relative to green foxtail. Further, we aimed to identify genes involved in environmental stress tolerance that have undergone differential selection between geographical and genetic groups. Foxtail millet landrace accessions (n = 328) and green foxtail accessions (n = 12) were sequenced by genotyping‐by‐sequencing (GBS). After filtering, 5,677 single nucleotide polymorphisms (SNPs) were retained for the combined foxtail millet−green foxtail dataset and 5,020 for the foxtail millet dataset. We extended geographic coverage of green foxtail by including previously published GBS sequence tags, yielding a 4,515‐SNP dataset for phylogenetic reconstruction. All foxtail millet samples were monophyletic relative to green foxtail, suggesting a single origin of foxtail millet, although no group of foxtail millet was clearly the most ancestral. Four genetic clusters were found within foxtail millet, each with a distinctive geographical distribution. These results, together with archaeobotanical evidence, suggest plausible routes of spread of foxtail millet. Selection scans identified nine candidate genes potentially involved in environmental adaptations, particularly to novel climates encountered, as domesticated foxtail millet spread to new altitudes and latitudes.Foxtail millet [ Foxtail millet is monophyletic with respect to its wild ancestor, green foxtail.There are four major genetic clusters in domestic foxtail.Domesticated foxtail likely adapted to novel environments as it spread from China.Genomic resource base for foxtail enhanced by wide geographic sample coverage. FSTfixation indexGBSgenotyping‐by‐sequencingHKSVgreen foxtail dataset in Huang et al., 2014LDlinkage disequilibriumPCAprincipal components analysisSNPsingle nucleotide polymorphismVIRN.I. Vavilov Institute of Plant Genetic Resources.1Setaria italica (L.) P. Beauv.] is one of the oldest domesticated cereals, cultivated from as early as 10,500 cal BP in northern China , short generation time, and inbreeding nature make foxtail millet and its wild progenitor green foxtail valuable genetic models for C4 photosynthesis and abiotic and biotic stress tolerance in related crops within the Paniceae subfamily. Setaria is also a genomic model for related bioenergy crops with larger genomes, including switchgrass (Panicum virgatum L.), Miscanthus spp., Napiergrass [Pennisetum purpureum (Schumach.) Morrone], and sugarcane (Saccharum officinarum L.)  Morrone]; Upadhyaya et al., Panicum miliaceum L. subsp. miliaceum) in importance  can contribute to understanding its globalization process by identifying the geographical distribution of genetic groups and relating these to ecophysiology and archaeobotanical data. The aim of the current study is to provide a geographically comprehensive survey of genome‐wide single nucleotide polymorphism (SNP) variation in foxtail millet to infer the history of its domestication from the wild ancestor, green foxtail, spread, and adaptation to different environments. Two previous studies have analyzed SNP diversity in foxtail millet using low‐coverage whole‐genome resequencing , meaning that they have been largely overlooked in published studies.Based on 328 landrace accessions, our study presents the most geographically balanced picture of genomic variation in Old World foxtail millet to date. It includes much denser coverage of accessions in Europe and Central Asia, which have been poorly represented in previous work. Of special significance was the inclusion of 119 accessions from Vavilov's collections held by the N.I. Vavilov Institute of Plant Genetic Resources (VIR), enabling coverage of Russia, the Caucasus, and central Asia. The VIR holds ∼4,750 foxtail millet accessions, making it the world's third largest collection  The Foxtail millet is monophyletic with respect to its wild ancestor, green foxtail.There are four major genetic clusters in domestic foxtail.Domesticated foxtail likely adapted to novel environments as it spread from China.Genomic resource base for foxtail enhanced by wide geographic sample coverage.2.2Setaria samples and one negative water control, were prepared for GBS using a modified protocol of Elshire et al.  was used to process the read data to call genotypes for each sample , following the manufacturer's protocol. DNA was checked for high molecular weight quality by electrophoresis on TAE‐agarose gels and quantified using a Qubit 2.0 fluorometer (ThermoFisher Scientific). Four 96‐plex libraries, each of 95 e et al.  using PsThe ‘populations’ module was rerun on the 328 foxtail millet samples without any filtering parameters applied. We subsequently refer to this as the ‘unfiltered SNP dataset.’ The resulting SNP set was filtered in PLINK using the settings ‐‐geno 0.2  and ‐‐maf 0.01 . The data set was then pruned for linkage disequilibrium (LD) between markers using the ‐‐indep option, which prunes based on the variance inflation factor by recursively removing SNPs within a sliding window. We used windows of 50 SNPs, with a stepwise shift of five SNPs in length and a variance inflation factor threshold of 2. We subsequently refer to the output from this as the ‘pruned SNP dataset.’ The unfiltered and pruned SNP datasets were used for downstream analyses.2.3Species‐ and population‐level diversity statistics were estimated using the Stacks ‘populations’ module on the unfiltered SNP datasets. The ADMIXTURE allocations below were used to define populations within foxtail millet for this analysis.K) of 1 to 10 with 10 replicate runs per K. We explored the most informative value of K using the cross‐validation method of Alexander et al. (https://github.com/campanam/ADMIXTURE-stats) to calculate the ΔK statistic  were carried out in PLINK using the pruned SNP dataset.Following selection of the most meaningful value of 2.4Maximum likelihood trees were constructed for the pruned SNP datasets for both the foxtail millet‐only and foxtail millet and green foxtail combined data sets. Trees were constructed in RAxML  were excluded because they had >30% missing data, and a further five samples were excluded because the original germplasm accession codes showed they matched green foxtail accessions sequenced in our own GBS libraries. The ‘populations’ module was then run on the remaining 361 samples and the output was filtered in PLINK using three settings: ‐‐geno 0.1, ‐‐geno 0.05, and geno ‐‐0.02, with ‐‐maf 0.01 in each case. Lower‐stringency filtering among these three resulted in a matrix with higher amounts of missing data because of the different protocols used to generate the two initial data sets. This data set was pruned for LD as above, and the output was used as input in a PCA implemented using the ade4 package  percentage cutoff threshold for outliers rather than using the default posterior odds threshold with a false discovery rate of 0.05 .To look for evidence of adaptive selection within the domesticated gene pool of foxtail millet, we reviewed the literature to generate a list of genes involved in traits associated with four key environmental adaptations to altitude and latitude as well as the functional domains that characterize these genes (listed in Table 33.1n = 328) and green foxtail (n = 12) dataset. Overall differentiation between the two species, measured by the FST, was 0.083, indicating low overall differentiation. The distribution of FST for individual SNPs was strongly bimodal , with 1,955 SNPs having an FST of 1, indicating fixed between‐species differences.Following removal of samples with high levels (>30%) of missing data, a total of 124,785 loci containing 118,324 SNPs was retained from the ‘ref_map’ module for the combined foxtail millet . Their distribution on the nine major scaffolds, corresponding to the nine chromosomes, of the foxtail millet genome is shown in Supplemental Figure S3. The SNP density is lowest around the presumed centromere region of each chromosome.HE)  = 0.0021 for foxtail millet and π = 0.0031 and HE = 0.0028 for green foxtail, indicating that green foxtail harbors somewhat more within‐species variability than foxtail millet.Species‐level diversity estimates taking into account both variant and invariant sites were π = 0.0024 and expected heterozygosity .3.2K = 4 for further analysis, as this model showed highest stability between replicate runs under the Q‐matrix correlation criterion in CorrSieve and a clear peak for ΔK . For the comparison of both Groups 1 and 4 as well as Groups 2 and 3, a locus scaffold_ 9_43137022 was in close proximity to both Seita.9G371300.1 (a heat shock protein gene) and Seita.9G371000.1 (a jasmonate ZIM domain protein gene). This outlier is under apparent balancing selection in the former and positive selection in the latter case. The second locus of potential interest is situated at position scaffold_8_688223 and was discovered in the comparisons of Groups 1 vs. 2 as well as Groups 3 vs. 4. Two genes (Seita.8G012100.1 and Seita.8G012000.1) encoding Phototropic‐responsive NPH3 family proteins are found in the search window. These are predicted to be involved in the blue light signaling pathway. For outlier loci at positions scaffold_4_532966, scaffold_7_2816053, and scaffold_1_28247088, three genes encoding thioredoxin superfamily proteins were discovered in close proximity. These were: Seita.4G008600.1 and Seita.7G279700.1 (comparison of Groups 1 vs. 2) and Seita.1G203000.1 (comparison of Groups 2 vs. 4). Finally, a gene (Seita.5G124300) encoding a brassinosteroid was found near locus scaffold_5_10426326 in the comparison of Groups 1 vs. 2, and an oxygenase superfamily protein gene (Seita.4G045400) near locus scaffold_4_3360999 in the comparison of Groups 2 vs. 4.Because of the wide geographical range across which foxtail millet has been cultivated, it has been selected for local or regional adaptation to a wide range of agricultural environments. To explore possible genomic regions underlying this adaptation, plots of the BayeScan results, which aim to detect the most divergent loci between populations, reveal potential outlier loci for all six pairwise comparisons of the four populations identified. Positive selection is more likely in four of the comparisons and balancing selection in the remaining two comparisons . The top 0.05% of SNPs with the highest (or lowest) 44.1ApeKI, which is expected to produce many more tags than PstI. This study retained a similar number  of high‐quality SNPs for downstream analysis after filtering.The raw number of SNPs in foxtail millet  called here across 328 samples was approximately one‐third that in the one previous GBS analysis of a global foxtail millet set of 181 samples  than the domesticated foxtail millet, as is typically the case for crops that have experienced a gradual reduction in genetic diversity as a result of a series of founder effects and selection after domestication  and four clusters containing accessions belonging to race indica, but did not directly discuss the geographical distribution of each cluster. We did not distinguish samples in our study based on subspecific taxonomy, as information on races was available only for some of the samples. From its distribution, our Cluster 3 (green) probably corresponds to race indica as identified morphologically by Prasada Rao et al.  than to Clusters 2 or 4.Upadhyaya et al.  found sio et al.  and geneo et al. . The geoo et al.  do not ao et al.  that the4.3FST of 0.083 indicates low overall differentiation, although FST is sensitive to the approach for calling SNPs , the incipient stages of speciation between ecotypes that diverged ∼10,000 years ago, that is, a similar likely timeframe to the divergence between foxtail millet and green foxtail, involved divergence of only a few large genomic regions with relatively low overall genome‐wide FST  of foxtail millet accessions successfully sequenced in that study consistent with a single domestication center of foxtail millet in northern China. The monophyly of foxtail millet relative to the HKSV and green foxtail clade in our tree also accords with a single origin, but because of the small number of green foxtail relative to foxtail millet samples in our analyses, we were not able to identify genetic groups within the wild species. However, in the PCA  clusters and pan‐northern‐Eurasian, including Chinese (yellow and cyan clusters). In a simple scenario of single domestication followed by expansion from northern China, it would be expected that the red and green clusters occupy a derived position relative to the yellow and cyan clusters in the tree. Today, the red cluster is entirely absent from China, instead characterizing populations from Nepal, Pakistan, and Afghanistan. The earliest known appearance of foxtail millet in this broad region was at the start of the second millennium BC from northern India  suggests a route of expansion from east to west along this routeway, eventually reaching Europe. Archaeobotanical data trace foxtail millet to the early second millennium BC in the Caucasus . This observation warrants further investigation into flowering time response in different regions.In Europe, the red cluster likely represents one of two introduction episodes of foxtail millet, with the yellow and blue clusters reaching Europe via a more northerly route. Currently there is no coherent chronology for this expansion, as a number of records in Europe appear to predate those in central Asia by two to three millennia, but questions remain over both the dating and identification of some of these early finds in Europe ‐dependent dioxygenase, a relatively uncommon enzyme (only 11 genes in foxtail millet), which is one of the most versatile oxidative enzymes in plant physiology, including biosynthesis of metabolites important in abiotic stress such as UV tolerance  lies within the putative gene itself; the others are adjacent to the genes and could lie in promoter regions for these. Regarding gene functions, the jasmonate ZIM domain protein (Seita.9G371000.1) may aid in abiotic stress resistance; homologous proteins play a role in resistance against pathogens in sugarcane ; Natalia A.S. Przelomska: Methodology, Formal Analysis, Investigation, Data Curation, Writing ; Michael G. Campana: Software, Formal Analysis, Writing (Review & Editing), James Cockram: Writing (Review & Editing), Supervision; H. Frances J. Bligh: Writing (Review & Editing), Supervision; Catherine J. Kneale: Investigation; Olga I. Romanova: Resources, Writing (Review & Editing); Elena V. Malinovskaya: Resources; Martin K. Jones: Writing (Review & Editing), Supervision, Project Administration.The authors declare that they have no conflict of interest.Supplemental MaterialClick here for additional data file.Supplemental MaterialClick here for additional data file.Supplemental MaterialClick here for additional data file.Supplemental MaterialClick here for additional data file.Supplemental MaterialClick here for additional data file.Supplemental MaterialClick here for additional data file.


Glycine max) serves as a major source of protein and edible oils worldwide. The genetic and genomic bases of the adaptation of soybean to tropical regions remain largely unclear. Here, we identify the novel locus Time of Flowering 16 (Tof16), which confers delay flowering and improve yield at low latitudes and determines that it harbors the soybean homolog of LATE ELONGATED HYPOCOTYL (LHY). Tof16 and the previously identified J locus genetically additively but independently control yield under short-day conditions. More than 80% accessions in low latitude harbor the mutations of tof16 and j, which suggests that loss of functions of Tof16 and J are the major genetic basis of soybean adaptation into tropics. We suggest that maturity and yield traits can be quantitatively improved by modulating the genetic complexity of various alleles of the LHY homologs, J and E1. Our findings uncover the adaptation trajectory of soybean from its temperate origin to the tropics.Soybean ( Tof16, an ortholog of LHY, and the previously identified J locus, control soybean yield under short-day condition and loss of function of these two genes contributes to the adaptation to tropics.How soybean, a temperate origin crop, adapted to a tropical environment remains unclear. Here, the authors report  Glycine max [L.] Merr.) is one of the most economically important leguminous crops, as it provides more than one-quarter of the world’s protein for human and animal consumption1. In 2020/2021, global soybean production was 362 million tons; soybean production in low latitude regions  comprised 53.45 percent of the total worldwide production . Therefore, improving soybean productivity in the tropics will have a great effect on fulfilling the demands of the growing worldwide population.Soybean (Glycine soja Sieb. & Zucc.) in temperate regions of China ~5000 years ago4. This facultative short-day (SD) plant is extremely sensitive to photoperiod5. This high sensitivity to photoperiod dramatically impedes the improvement of soybean productivity in the tropics. For instance, cultivars that are adapted to temperate regions flower early and produce extremely low grain yields when grown in low-latitude regions8. The long-juvenile (LJ) trait, which delays flowering and enhances grain yields under SD conditions, was first introduced into soybean cultivars in Brazil in the 1970s, which expanded soybean cultivation into the tropics10. Only two loci, J and E6, have been reported to control the LJ trait; both contain the same ortholog of Arabidopsis thaliana EARLY FLOWERING 313. However, the genetic basis and trajectory of adaptation of soybean to low latitudes remain largely unknown.Cultivated soybean was domesticated from its wild progenitor  gene, as reveals by whole-genome resequencing and genome-wide association studies (GWAS) and by positional cloning of quantitative trait loci (QTL). We confirm that four LHY homologs redundantly control flowering time and yield. We also demonstrate that Tof16 and J additively but independently control soybean flowering. Both Tof16 and J directly bind to the promoter of E1, which encodes a core flowering suppressor, to repress its transcription. Interestingly, weak mutant alleles for both tof16 and j are initially selected, and additional null alleles are subsequently acquired in addition to these weak alleles and further selected, suggesting that stepwise selection of natural mutations of both genes occur during the adaptation of soybean from temperate regions to the tropics. By modulating the combinations of mutations of LHY homologs or combinations of different alleles of Tof16, J, and E1, maturity and yield are altered in a quantitative manner. Our findings uncover the genetic and genomic basis for the adaptation of soybean to low-latitude regions and provide a new approach for precise breeding for improve soybean productivity in the tropics.In the current study, we identify a novel locus that controls flowering time and yield in soybean under SD conditions. This locus, 15, the decay of linkage disequilibrium (r2) with the physical distance between SNPs occurred in all three groups  controlling flowering time under natural SD conditions in both 2018 and 2019 in Guangzhou.  is widespread and substantially contributes to the control of flowering time under SD conditions.Using a linear mixed model for GWAS of the panel of 329 accessions, we identified one consistent significantly associated locus on chromosome 16 (n = 2418) inbred F6 population from the PI591429 × PI628930 cross for fine-mapping of the Tof16 locus by recurrent selection for heterozygosity at Tof16 from the F2 to F5 generations. Analysis of this population located Tof16 within a 120-kilobase (kb) region harboring 12 genes based on the Williams 82 reference genome17  differed between the two parents (PI591429 and PI628930)  in the Harosoy background using CRISPR/Cas9-mediated gene editing and evaluated the phenotypes of the mutants vs. wild-type Harosoy .To validate whether soy Fig. . These rTof16 locus, we also compared the phenotypes of two F7 near-isogenic lines (NILs) carrying either the functional Tof16 allele (NIL-Tof16) or the non-functional tof16-1 alleles (NIL-tof16-1 or NIL-tof16-2) in different genetic backgrounds. NIL-tof16-1 and NIL-tof16-2 showed significantly delayed flowering time and maturity  conditions using tof16CR and Harosoy or independent NIL pairs for each locus. As expected, E1 was expressed at higher levels in tof16CR -qPCR assays 19 and its amino acid sequences were high homology  mutation had higher grain yields than those without this mutation, indicating that lhy1a plays a crucial role in controlling flowering time, maturity, and grain yield under SD conditions  conditions, we also evaluated the mutants in the field in Changchun under natural LD conditions. All mutants except lhy2a, lhy2b, and lhy2a/2b showed delayed flowering time and maturity compared to the wild type  Duents Fig. . (2) Dueced Fig. . How theTof16 and J play essential roles in the adaptation of soybean to SDs and yield development. To gain insight into the evolutionary trajectory of soybean adaptation from its temperate origin to the tropics, we examined the genomic variations in the Tof16 and J coding sequences in 1624 resequenced soybean accessions, including 1295 previously described accessions20 and the 329-accession panel used in the current study. We identified 15 unique high-confidence haplotypes in Tof16. In addition to haplotypes H10 (tof16-1) and H11 (tof16-2), we identified two novel loss-of-function haplotypes: H1 (named tof16-3) and H8 (named tof16-4)  first occurred in wild soybeans originating in the Huanghui region and were subsequently domesticated into landraces in areas where soybean domestication occurred  was intensively selected in the accessions that adapted to low-latitude regions, suggesting that this haplotype plays critical roles in soybean adaptation to the tropics  and H8 (tof16-4) originated from H11 (tof16-2), suggesting that loss-of-function alleles of tof16-2, tof16-1, and tof16-4 were under stepwise selection during adaptation to low latitudes. However, H8 (tof16-3) originated from H1 and only occurred in India and Nepal, while tof16-1 and tof16-4 mainly occurred in Brazil, indicating that unique loss-of-function alleles of tof16 were selected in different regions to enhance the adaptation of soybean  was first selected during soybean adaptation to the tropics but was not sufficient for full adaptation. In this genetic background, null alleles of tof16-1 and tof16-4 occurred and were further selected for better adaptation to the tropics. Population genetic association analysis of flowering time in the 329-accession panel showed that tof16-2 flowered the latest, followed by tof16-3, tof16-1,4, and Tof16 in both 2018 and 2019  in the tropics.To further validate the functional significance of 019 Fig. . The unef j Fig. . Taken tJ coding sequence in the same 1624-accession panel. In total, 28 haplotypes were identified, including seven distinct loss-of-function alleles and two weak loss-of-function alleles. These haplotypes included the previously reported alleles j-1, j-2, j-3, j-4, j-6, j-8 (including j-8-1 and j-8-2), and j-9 (e6)13 and the newly discovered alleles haplotype H18 (named j-10) and haplotype H21 (named j-11)  also first occurred in wild soybean originating from Huanghuai and was later was domesticated into landraces, but this allele was substantially selected in accessions grown in low-latitude regions , indicating that, similar to tof16, stepwise selection of the weak allele of j-11 and other loss-of-function alleles of j occurred during the adaptation of soybean to the tropics  is a weak loss-of-function allele that might contribute to adaptation to low latitudes. We further classified all haplotypes of J into four groups, J, j920, j920bm , and jother (j mutations other than j920bm) and evaluated their associations with flowering in the 329-accession panel. j920bm flowered the latest, followed by jother, j920, and J in both 2018 and 2019  and j-11 allele (47.42%) were highly variable in tropical accessions  and examined the geographic distributions of the eight Tof16/J allelic combinations in the 1624 accessions covering all latitudes. Accessions carrying loss-function-of Tof16 alleles were enriched in Brazil, but accessions carrying loss-function-of J alleles were enriched in Southern China, Southeast Asia, and Brazil . Accessions carrying two recessive alleles flowered significantly later than accessions carrying single recessive alleles tof16 or j, and accessions carrying both functional alleles (Tof16/J) flowered earlier than the other three genotypic groups in all environments  are relatively weak alleles encoding proteins with amino acid substitutions that occurred in wild soybean from the Huanghuai region in China, the center of origin of soybean /PYR1-like (PYL) family genes were edited by CRISPR/Cas9. Characterization of the combinatorial mutants suggested that pyl1/4/6 exhibited the best growth and improved grain productivity in natural paddy field conditions30. Here, we generated all combinatorial null mutants of four LHY homologs by CRISPR/Cas9 and found that lhy1a/1b/2b exhibited the most improved grain yield under natural SD conditions  and lhy1b/2a/2b had the highest grain production under natural LD conditions 17 with BWA 0.6.1-r104 software with default parameters31. The SNPs and indels were called with GATK (ver. 3.1.1)33 and SAMtools (ver. 0.1.19) software34, independently; then, the common sites identified by both methods were retained for further analysis. SNPs with missing data minor allele frequency (MAF) <1% were filtered, and indels with a maximum length of 10 bp were included. Annotations of SNP and INDEL were performed based on the Williams 82 genome using snpEff (ver. 3.1) software35. SNPs in coding sequences were classified as synonymous SNPs or nonsynonymous SNPs. Indels in exons were further categorized according to whether they led to a frameshift.The libraries for each accession of 329 panel were constructed following the manufacturer’s instructions . The DNA-seq libraries were sequenced on the HiSeq X Ten system (150 bp Paired-end reads). Resequencing reads of the 329 accessions sequenced in this study and the 1295 previously sequenced accessions mapping and SNP calling were performed as described previouslyhttps://itol.embl.de). PCA was performed with this SNP set with the smartpca program in the EIGENSOFT v.5.0.1package37.To conduct phylogenetic analysis, SNPs with MAF < 0.05 were filtered out for all soybean accessions. The remaining SNPs were used to construct a neighbor-joining tree with MEGA v6.06 software and were visualized with the online tool iTOL (r2 between two SNPs and averaged in 1-kb windows with a maximum distance of 1 Mb (ref. 38).Linkage disequilibrium was calculated for each subpopulation with SNPs with MAF > 0.05. To perform the linkage disequilibrium calculation, plink v1.9 software was applied with the parameters (–ld-window-r2 0–ld-window 99999–ld-window-kb 1000). Linkage disequilibrium decay was calculated based on 20. Kinship was derived from all of these SNPs. The threshold for GWAS was determined by Bonferroni correction . The significantly associated regions were manually verified from the aligned resequencing reads against the Williams genome with SAMtools v0.1.18 (ref. 34).We used 4,435,213 high-quality SNPs (MAF > 0.05) to perform GWAS for flowering time in the 329 accessions. Association analyses were performed by MLM implemented in efficient mixed-model association expedited (EMMAX) softwareTof16 locus was subsequently developed. NILs for the Tof16 locus were selected from F7 progeny of this same cross using molecular markers for Tof16. The heterozygous inbred progenies, NILs, and CRISPR–Cas9 knockout mutants used for phenotyping were grown under natural SD conditions in the field (day length 13 h light/11 h dark) from 2018 to 2020 at the Experimental Station of the Guangzhou University or under natural LD conditions (day length 15 h light/9 h dark) in 2020 at the Experimental Station of the Jilin Academy of Agricultural Sciences. For natural SD conditions, plants were sown in the beginning of July, spaced 0.15 m apart in rows 5 m long, with 0.7 m between rows, and harvested in November or December of each year. For natural LD conditions, plants were sown in the beginning of May, spaced 0.15 m apart in rows 5 m long, with 0.7 m between rows, and harvested in September or October. Plants used for expression analysis and ChIP assays were grown under SD conditions (day length 12 h light/12 h dark) in a plant growth cabinet (Conviron Adaptis A1000) with a light intensity of 500 μmol m−2 s−1.The 329-accession panel was grown during the cultivation season (July to December) in 2018 and 2019 at the experimental station of the Guangzhou University in Guangzhou . Flowering time was investigated in 2018 and 2019. For map-based cloning, one heterozygous inbred progeny population of 2418 individuals segregating at the 39 were scored. The days from emergence to pods attained mature color, corresponding to the R8 stage39 were scored. Yield-related traits were recorded at the R8 stage12.For phenotypic investigations, the days from emergence to the first flowering, corresponding to the R1 stageTof16 based on the resequencing data of the two parents, PI591429 and PI628930. Seven recombinants were identified in the fine-mapping population of Tof16 using seven markers. The flowering time of the progeny of these recombinants was evaluated to delimit the genomic interval containing Tof16.Genomic DNA was extracted from fresh trifoliate leaves of 2-week-old seedlings with a SurePlant DNA kit (CWBIO) and used to amplify indel markers. The primer sequences used to amplify the markers for mapping are listed in Supplementary Table Tubulin (Glyma.05G157300) gene was used as the internal reference. All qPCR primers are listed in Supplementary Table Total RNA was extracted using an Ultrapure RNA kit (CWBIO) and the RNA was reversely transcribed using a Super Script First-strand cDNA Synthesis System . Quantitative reverse-transcription PCR (qRT-PCR) was performed using SYBR Green Real-Time PCR Master Mix (Roche). Three independent RNA samples were prepared for biological replicates. The soybean n Glyma.0G157300 gTof16 was amplified with the primer set Tof16-6HA-F/R and inserted into the Xba I and Mlu I sites of the pTF101-6HA vector (containing the Bar gene for glufosinate resistance) to generate the pro35S-Tof16-6HA construct. The recombinant vector was introduced into Agrobacterium tumefaciens strain EHA101 and used to transform Williams 82 via Agrobacterium tumefaciens-mediated transformation41.The coding sequence of proE1-LUC reporter construct, the 3147-bp promoter sequence of E1 was amplified from Williams 82 and introduced into the pGreenII 0800-LUC vector42. The different alleles of Tof16  were introduced into the pTF101-6HA vector to generate the constructs pro35S-Tof16-6HA, pro35S-Tof16-1-6HA, pro35S-Tof16-2-6HA, pro35S-Tof16-3-6HA, and pro35S-Tof16-4-6HA. The different alleles of J  were introduced into the pTF101-6HA vector to generate the constructs pro35S-J-H1-6HA, pro35S-J-H1-6HA, and pro35S-j-11 -6HA. The proE1-LUC construct was used as the reporter and various Tof16 and J constructs were used as the effectors in the Arabidopsis protoplast transient expression system to test whether Tof16 and J suppress the transcription of E1.To generate the p35S-Tof16-6HA transgenic lines in protein extraction buffer  and used for immunoblot analysis. Immunoblot analysis was performed as described previously12. In brief, total proteins were separated by SDS-PAGE. After electrophoresis, the proteins were transferred to polyvinylidene difluoride membranes (Millipore) and probed using antibodies anti-HA antibody . The anti-HA antibody (ab18181) was obtained from Abcam.To analyze protein expression in the transgenic plants, total proteins were extracted from Williams 82 and the p35S-Tof16-6HA transgenic lines. The samples were fixed in 1% formaldehyde on ice for 15 min under a vacuum. Nuclei were isolated from the samples and sonicated to generate DNA fragments with an average size of ~250–500 bp. The solubilized chromatin was immunoprecipitated by Protein G PLUS agarose  with anti-HA antibody (ab18181). The coimmunoprecipitated DNA was recovered and analyzed by qRT-PCR in triplicate. Data normalized with input transcripts are means from three biological repeats. The enrichment of the EF1b genomic fragment was used as a negative control. The primers used for amplification are listed in Supplementary Table Leaf samples were collected from 20-day-old seedlings at Zeitgeber time 0 under SD conditions from Williams 82 and n) of samples or replicates are indicated in figure legends. Data were analyzed with GraphPad Prism 8 (ver. 8.0.1). Significance levels of differences were calculated by one-tailed, two-sample Student’s t-tests or one-way ANOVA with GraphPad Prism 8 (ver. 8.0.1). For 329 accessions phenotypic evaluation, at least 10 individual plants were analyzed.In this study, all values were presented as mean ± s.e.m. and numbers  were identified  generated a total of 21.8 billion paired-end reads of 100 bp in length (6.5 Tb of sequences), with an average coverage depth of more than 10x for each line. After mapping to the reference genome of soybean cultivar Williams 82 (W82)20 based on the SNPs with a MAF > 0.05 and using the flowering time at GuangZhou in 2018 and 2019 for each accession.To identify loci influencing flowering time in the 329-accession panel, we conducted a GWAS through the MLM implemented in Efficient Mixed-Model Association eXpedited (EMMAX) softwareFurther information on research design is available in the Supplementary Information fileSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Description of Additional Supplementary FilesReporting Summary


Fraxinus spp.) is one of the most widely distributed tree genera in North America. Populations of ash in the United States and Canada have been decimated by the introduced pest Agrilus planipennis , having negative impacts on both forest ecosystems and economic interests. The majority of trees succumb to attack by A. planipennis, but some trees have been found to be tolerant to infestation despite years of exposure. Restriction site‐associated DNA (RAD) sequencing was used to sequence ash individuals, both tolerant and susceptible to A. planipennis attack, in order to identify single nucleotide polymorphism (SNP) patterns related to tolerance and health declines. de novo SNPs were called using SAMtools and, after filtering criteria were implemented, a set of 17,807 SNPs were generated. Principal component analysis (PCA) of SNPs aligned individual trees into clusters related to geography; however, five tolerant trees clustered together despite geographic location. A subset of 32 outlier SNPs identified within this group, as well as a subset of 17 SNPs identified based on vigor rating, are potential candidates for the selection of host tolerance. Understanding the mechanisms of host tolerance through genome‐wide association has the potential to restore populations with cultivars that are able to withstand A. planipennis infestation. This study was successful in using RAD‐sequencing in order to identify SNPs that could contribute to tolerance of A. planipennis. This was a first step toward uncovering the genetic basis for host tolerance to A. planipennis. Future studies are needed to identify the functionality of the loci where these SNPs occur and how they may be related to tolerance of A. planipennis attack.Ash  were identified in a distinct cluster of Fraxinus individuals despite different geographic locations. This provides an initial step toward uncovering the genetic basis for host tolerance to A. planipennis.The majority of  This pest was introduced into the Detroit/Windsor area of Michigan, USA/Ontario, Canada, and quickly dispersed via human assistance, including movement of firewood, nursery stock, and wood packing material  and is a secondary pest in this tree species, requiring a primary stressor for successful attack , green (F. pennsylvanica Marsh.), and white (F. americana L.) ash are the most susceptible in the introduced range of A. planipennis, all North American ash are susceptible  has the lowest susceptibility to A. planipennis attack among North American ash species  identify phenotypic and genotypic relationships between trees relative to this tolerance‐susceptibility gradient, and (3) test the hypothesis that tolerance and susceptibility are linked to identifiable genetic markers.Mechanisms of tolerance are more difficult to quantify and therefore have not been as well studied. Identifying the genetic variants that allow these surviving trees in North America to tolerate infestation would greatly aide in the conservation of ash ; Huron‐Clinton Metroparks, Michigan, USA ; and Houghton County, Michigan, USA . Assessments followed those conducted in previous studies , crown dieback (percent of dead branch tips: 5%–100%), and signs of  Hietala . Individ2.3Entire bud samples (two to three buds) were homogenized using sterile ceramic mortars and pestles, which were first cooled with liquid nitrogen. DNeasy Plant Mini Kit (QIAGEN) was used to extract total genomic DNA following the manufacturer's protocol. DNA from each sample was quantified using UV spectrophotometry (NanoDrop 1000) absorbance. All samples were subsequently diluted to a concentration of 25 ng/μl.2.4Genomic DNA was converted into nextRAD genotyping‐by‐sequencing libraries  as described by Russello et al. . Brieflyhttp://sourceforge.net/projects/bbmap/). Command was as follows:The genotyping analysis used custom scripts  that trimmed the reads using bbduk . In order to assess the proportion of sequence reads that originated from Genotype calling was done using SAMtools and BCFtools . The absence of artifacts was checked by counting single nucleotide polymorphisms (SNPs) at each read nucleotide position and determining that SNP number did not increase with reduced base quality at the end of the read. All polymorphic sequences retained were subject to BLASTN analysis in the NCBI database.VCFtools  using prcomp R base function. Pearson's correlation was used to test the hypothesis that the first principal component axis for SNP PCA and the first principal component axis for phenotypic PCA had a linear relationship. Similarly, linear correlations were tested between the first principal component axis for SNP PCA with vigor and dieback values.The packages Jombart,  were use2.6STF coefficients as a fixation index, described by Beaumont and Balding  suggest that the locus has undergone directional selection . Fisher's exact test was used in PLINK v1.07  were present in 37 individuals, 31 of which had more than one sign present. Of the ten trees lacking signs, eight were categorized as tolerant and two were categorized as susceptible. There was a significant difference in dbh between species  with white being larger than green. However, there was no difference in dbh among tolerance groups  and the interaction between species and tolerance groups was not significant .The 47 ash trees selected for this study were sampled from six different geographic locations Figure . Based oy Figure . Howevery Figure . Overall3.2Populus tremula, 0.5% Sesamum spp., and 0.5% Olea europaea. Due to the high mitochondrial hit counts, a reference of just polymorphic loci was created and BLASTN analysis was performed on those sequences. Out of this entire subset, only eight loci mapped to mitochondrial sequences.Restriction site‐associated DNA sequencing was used to sequence the genomes of 47 ash trees in order to identify SNPs that are correlated with tolerant or susceptible phenotypes. BLASTN analysis of the 1,000 random sequences from each individual revealed that 60.7% of the genomic sequences were potentially ash‐specific (no hits discovered). A high percentage of mitochondrial reads (18.0%) was found, potentially diverting reads from nuclear loci. Of sequences mapped, 17.7% aligned to After SNP calling and initial filtering, a set of 23,243 SNPs were produced. Application of the more stringent filtering criteria Table  generate3.3p‐value = .240). This lack of variability between pairs extended into the other pairwise tests, with green pairs not more similar than white pairs . Additionally, the pairs between tolerance groups (tolerant and susceptible) were not less similar than pairs within groups . However, susceptible pairs were more similar than tolerant pairs .Pairwise IBS distance test to compare SNP similarity between species (green and white) failed to reject the hypothesis that pairs between the two species were less similar than pairs within species  were identified in the PCA based on 17,807 SNPs Figure . Divergik Figure . IBS clu3.4STF values were skewed to the right and outliers were relatively high, suggesting directional selection . There was no correlation between the SNP PC1 axis and vigor . Likewise, there was no correlation between the SNP PC1 axis and dieback .PCA based on all phenotypic data resulted in no distinct clustering between geographic locations Figure . Toleran4Fraxinus spp. using RAD‐sequencing genotyping‐by‐sequencing. The filter settings were selected to ensured high‐quality nucleotide data with sufficient coverage across individuals  being the most likely attacked , and green ash  but most often diploid similar to green ash, and hybridization appears to confound genetic results  revealed that the five individuals in the right cluster still displayed the same pattern of divergence, indicating that these 28 loci are not the only source of variation within this group.All outlier SNPs detected between the PCA clusters had high A. planipennis attack , despite being located in areas where A. planipennis is present.Throughout the outliers identified, there were similar genetic trends between trees in the middle and right clusters. The similarities between these two clusters are evident when looking at just the ten polymorphic loci that showed a pattern of almost exclusive presence in the right group. For seven of the ten loci, one to two trees from the middle cluster also had the polymorphic nucleotide. Two of these trees from the middle cluster were classified as susceptible; however, all of the trees from the middle group that had genotypic similarities with the five right group trees had no signs of A. planipennis by amplifying pathways necessary to tolerate infestation.The outlier locus 30133_137 mapped to a PTI1‐like tyrosine‐protein kinase 2. This protein is known to be involved in growth and development, as well as defense responses .From the outliers identified based on the visual PCA cluster, several were significantly associated with the tolerant group. These may be important in identifying host tolerance and susceptibility. Locus 16669_22 is another potentially important gene for host tolerance. Four outlier SNPs at this locus had distinctive patterns in individuals with either all present as the polymorphism or all in their reference form. For the five individuals in the right cluster, the reference nucleotides were retained for all four SNPs at this locus. Unfortunately, BLAST analysis did not map this locus to any known genes. Additionally, two trees that clustered in the middle PCA group also had the reference nucleotides at this locus. These two trees were classified as susceptible, but interestingly, they were the only two susceptible trees with no signs of Outliers detected between trees based on vigor rating resulted in an additional 13 loci being identified as potential candidates for host tolerance. None of these mapped to any known functional genes; however, the five outliers that did show a pattern of either the polymorphic or the reference nucleotide being present exclusively in high vigor trees are of particular interest. With five outliers associated with each of the tolerant and susceptible groups, there may be important SNPs to identify potential host tolerance and susceptibility. One constraint to mapping sequences is the amount of recombination due to divergence and availability of a representative reference genome  as opposed to whole tree values. Boyes et al. ; Data curation (supporting); Formal analysis (lead); Writing‐original draft (lead); Writing‐review & editing . Mark Jordan: Formal analysis (supporting); Methodology (supporting); Writing‐review & editing (supporting). Gloria Iriarte: Formal analysis (supporting); Writing‐review & editing (supporting). Kirk D Broders: Formal analysis (supporting); Writing‐review & editing (supporting). Andrew J Storer: Conceptualization ; Writing‐review & editing (supporting). Vamsi J Nalam: Conceptualization (lead); Data curation (lead); Formal analysis (supporting); Methodology (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Jordan M Marshall: Conceptualization (lead); Formal analysis (supporting); Methodology (supporting); Writing‐original draft (supporting); Writing‐review & editing (lead).


SCD), fatty acid binding protein-4 (FABP4), and fatty acid synthase (FASN) genes identified functional SNP with unique DNA marker signatures for TAW genetics. The SCD g.23881050T>C locus was significantly associated with IMF, C22:6n-3, and C22:5n-3; FASN g.12323864A>G locus with FMP, C18:3n-3, C18:1n-9, C18:0, C16:0, MUFA, and FABP4 g.62829478A>T locus with IMF. These add new knowledge, precision, and reliability in directly making early and informed decisions on live sheep selection and breeding for health-beneficial n-3 LC-PUFA, FMP, IMF and superior meat-eating quality at the farmgate level. The findings provide evidence that significant associations exist between SNP of lipid metabolism genes and n-3 LC-PUFA, IMF, and FMP, thus underpinning potential marker-assisted selection for meat-eating quality traits in TAW lambs.Meat quality data can only be obtained after slaughter when selection decisions about the live animal are already too late. Carcass estimated breeding values present major precision problems due to low accuracy, and by the time an informed decision on the genetic merit for meat quality is made, the animal is already dead. We report for the first time, a targeted next-generation sequencing (NGS) of single nucleotide polymorphisms (SNP) of lipid metabolism genes in Tattykeel Australian White (TAW) sheep of the MARGRA lamb brand, utilizing an innovative and minimally invasive muscle biopsy sampling technique for directly quantifying the genetic worth of live lambs for health-beneficial omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA), intramuscular fat (IMF), and fat melting point (FMP). NGS of stearoyl-CoA desaturase ( Eating quality is the single largest determinant of consumer acceptability and satisfaction with meat products. The eating and nutritional quality of lamb is influenced by intramuscular fat (IMF) content , fat melIn a recent review of the development, calibration, and validation of objective measurement technologies for carcass composition, lean, fat, and meat-eating quality traits in the Australian and New Zealand livestock industries, Gardner et al.  highlighlongissimus dorsi thoracis et lumborum muscle biopsy sampling of Tattykeel Australian White (TAW) sheep exclusive to MARGRA lamb brand, laboratory-based IMF, FMP, and fatty acid analyses, and advanced genomics technique of next-generation sequencing (NGS) of single nucleotide polymorphisms (SNP) of lipid metabolism genes for directly quantifying the genetic worth of live lambs for health-beneficial n-3 LC-PUFA, IMF, and FMP. The primary objective was to conduct a NGS of stearoyl-CoA desaturase (SCD), fatty acid binding protein-4 (FABP4), and fatty acid synthase (FASN) lipogenic genes to identify functional SNP that provide unique DNA marker signatures for TAW genetics, breeding, and selection programs for meat-eating quality. The hypothesis tested was that significant associations exist between SNP of lipid metabolism genes and n-3 LC-PUFA, FMP, and FMP underpinning potential marker-assisted selection for meat-eating quality traits in TAW lambs.However, meat quality data can only be obtained after slaughter when selection decisions about the live animal are already too late. Carcass estimated breeding values ,15, visuThe experimental design for the selection, breeding, and evaluation of n-3 LC-PUFA, IMF, and FMP in Tattykeel Australian White (TAW) sheep is shown in 1), and second (F2) composite generations of lambs were bred, raised, and maintained under the same management at the Tattykeel Australian White Stud in Black Springs, Oberon, New South Wales, Australia. The parental composite generation comprised 47 rams mated to 500 ewes after evaluating their longissimus dorsi thoracis et lumborum muscle biopsy samples for health-beneficial n-3 LC-PUFA, IMF, and FMP with minimum thresholds set at 30 mg/100 g, 3.0%, and 35 °C, respectively. The top 10 rams and 200 ewes were selected and mated to generate 150 progeny whose muscle biopsy samples were laboratory tested for n-3 LC-PUFA, IMF, FMP, and genomic DNA sequenced at 10 months of age prior to being finished at a commercial feedlot. The Poll Dorset and Texel were used as positive control and the Rambouillet as the negative control in assessing extracted genomic DNA, polymerase chain reaction products, and next-generation sequencing procedures in the laboratory. Details of the muscle biopsy procedure and laboratory analyses of IMF, FMP, and fatty acid composition had already been published [Three composite generations—parental, first  lambs of the same age and under the same management conditions by jugular venipuncture into vacutainers containing EDTA. Blood samples were stored at −80 °C until ready for genomic DNA (gDNA) extraction. gDNA was extracted from 2 mL of blood using NucleoSpin Blood Kits  according to the manufacturer’s protocol. gDNA yield was quantified with a NanoDrop ND-1000 spectrophotometer .http://www.geneious.com). A targeted candidate gene approach of lipid metabolism genes  was utilized. Single coding sequences of each gene deposited in the National Center for Biotechnology Information (NCBI) database (Genbank) of FASN, FABP4, and SCD of Ovis aries breed were used as reference points. In order to amplify the 18 kb of the FASN gene (Accession Number: NC_040262.1), a long-range PCR approach was used to split the gene sequence into 3 overlapping fragments of 8.5 kb each , comprising approximately 91% of the total gene sequence. For the 4 kb FABP4 (NC_040260.1) and 12 kb SCD (NC_040273.1) gene fragments, a single primer set was designed as shown in Table 1. All primers were synthesized at Integrated DNA Technologies Pte. Ltd., Melbourne, Australia (Itddna.Com (accessed on 12 June 2021)).All primers were designed using Geneious Prime Software Program 2020 v.2.2 . PCR reaction assay was set up in a total volume of 50 µL containing 10 µL of 5× TakaRa PrimeSTAR GXL Buffer, 200 μM of TaKaRa dNTP Mixture, 1.25 units of TaKaRa PrimeSTAR GXL DNA Polymerase, 0.2 μM of each primer , and 100 ng of DNA template. PCR was performed in a SimpliAmp™ Thermal Cycler , in a 2-step protocol using the following conditions: 98 °C initial denaturation for 1 min (1 cycle); 98 °C denaturation for 10 s; 68 °C annealing/extension for 10 min for 30 cycles. PCR success was checked in 0.8% agarose gel electrophoresis as depicted in SCD gene, Hot Start II High-Fidelity PCR Master Mix  was used under the same PCR conditions. The amplification reactions were performed in a total volume of 50 µL containing 25 µL of 2× Platinum™ SuperFi™ II PCR Master Mix or Phusion Hot Start II High-Fidelity PCR Master Mix , 0.5 µM of each primer , and 100 ng of DNA template. PCR was performed in a SimpliAmp™ Thermal Cycler , in a 3-step protocol, using the following conditions: 98 °C initial denaturation in 1 min (1 cycle); 98 °C for denaturation 15 s; 60 °C (FABP4)/and 65 °C (SCD) annealing for 15 s; 72 °C extension for 9 min; 72 °C final extension for 9 min; 4 °C hold for 35 cycles. PCR success was checked in 0.8% agarose gel electrophoresis, as depicted in For the FAPB4 gene, Platinum™ SuperFi™ II PCR Master Mix  was used, while for the XT DNA.Sera-Mag™ SpeedBeads was prepared according to Faircloth et al.  and used® dsDNA System  to give an additional concentration estimate. The resultant size and concentration data from Tape Station and Quantifluor system were used to normalize each library to 4 nM by diluting with 10 mM Tris-HCl (pH 8.5) prior to pooling. An equal volume of 5 µL was pooled and sequenced on an Illumina MiSeq benchtop sequencer, using a 500-cycle MiSeq Reagent Nano Kit v2 with a 10 pM input and 10% PhiX spike-in.Libraries were prepared using Nextera XT DNA Library Prep kit  in accordance with the manufacturer’s protocols using the recommended input of 5 µL of 0.2 ng/µL gDNA per sample. This was followed by Sera-Mag™ SpeedBeads purification using 0.6× beads and 2 washes using 80% ethanol to select fragments > 250 bp and remove unincorporated adapters. Each DNA library fragment size and concentration was determined using Agilent High Sensitivity D5000 reagents and ScreenTape on the Tape Station 4200 Instrument  according to the Agilent assay quick guide. Additionally, all individual libraries were quantified using QuantiFluorhttp://www.geneious.com (accessed on 12 June 2021)) to analyze the sequences. The following reference sequences deposited in the NCBI database were used for comparative analysis: NC_040262.1, NC_040260.1, and NC_040273.1 for FASN, FABP4, and SCD genes, respectively. Next Generation Sequenced data were retrieved from Illumina Dashboard-BaseSpace Sequence Hub (https://basespace.illumina.com/dashboard (accessed on 15 July 2021)) as paired read data in 2 separate forward and reverse read lists in FASTQ format. The retrieved raw reads were subjected to quality control measures. Reads were trimmed and adapters removed using the BBDuk trimmer in Geneious Prime 2020 v.2.2 with the default setting for paired-end reads. The Quality (Q) value of Phred score was set at 20 to improve sequenced data and increase the likelihood of calling true SNPs to 99%. Short reads with a minimum length of 20 bp were discarded, resulting in clean reads. Regions of low coverage were excluded when calling SNPs using the Annotate and Predict → Find Low/High Coverage. The reads were mapped to reference in Geneious. The reference sequences were retrieved from NCBI database (Genbank) of FASN, FABP4, and SCD of Ovis aries breed. The Sensitivity was set on the Medium Sensitivity/Fast and Fine-Tuning (iterate up to 5 times) option selected to improve the results by aligning reads to each other in addition to the reference sequence. Major allele frequencies from the next-generation sequence data based on observed and expected genotypes were computed using the Hardy–Weinberg equilibrium principle as described by Graffelman et al. [Genomic data analysis was performed using commercial bioinformatics program Geneious Prime software program 2020 v.2.24  accounting for composite generation effects. Functional allele mutations at the coding regions of identified FABP4, SCD, and FASN loci were statistically analyzed for association with FMP, IMF, and fatty acids. Least-square means were compared using the Tukey-adjusted multiple comparisons test. The full statistical model was:ij = dependent variable  of jth TAW of ith composite generation, µ = overall mean, αi = effect of the ith composite generation, FA = the genotype FASN , SC = the genotype SCD , SK = the genotype FABP4 , γ = effect of the genotype, and eij = residual error.All statistical analyses of the associations between detected SNP of the 3 genes and meat-eating quality traits were performed using R statistical software version 3.6.3 . Linkagerequency . Major aSCD, FASN, and FABP4 lipogenic genes SNP in TAW lamb muscle biopsy samples bred, selected, and evaluated as per the experimental design shown in This study of SCD gene SNP loci  with major allele frequencies ranging from 0.53 to 0.93 were identified as depicted in Using the Poll Dorset and Texel as positive controls, and Rambouillet as negative controls, eight FASN gene sequence were identified. The genotypes at the nine loci were all the same in TAW, indicating a consistent heredity pattern from the composite TAW parents to the first and second generations, which were all distinguishable from the Rambouillet negative control breed. For the FABP4 gene, three SNP loci were genotyped with major allele frequencies ranging from 0.50 to 0.97 , and g.23888763C>T and g.23881050T>C loci ranging from 0.37 to 0.47 were observed. IMF was moderately to highly correlated with n-3 LC-PUFA (0.38–0.66), while FMP was negatively correlated with IMF (−0.66) and DHA (−0.42). Among the different fatty acids and their summations, very high correlations of up to 0.99 were evident  and DHA (−0.42) were also observed, while the highest positive correlations were between the various fatty acids  and DHA (p < 0.0111), while FABP4 g.62829478A>G SNP was associated with only IMF (p < 0.0539). The FASN g.12323864A>G SNP was associated with FMP (p < 0.0544), ALA (p < 0.0033), MUFA (p < 0.0025), SFA (p < 0.0025), C18:2n-6 (p < 0.0138), C16:0 (p < 0.0039), C18:0 (p < 0.0012) and C18:1n-9 (p < 0.0023) fatty acids (ty acids .SCD g.23881050T>C SNP locus confirmed significant differences where the homozygous TT genotype had the highest DHA (11.00 ± 2.34 mg/100 g), IMF (5.43 ± 0.516%), and DPA (27.1 ± 3.26 mg/100 g) compared to the CC genotype with the lowest DHA (7.00 ± 2.11 mg/100 g), IMF (3.98 ± 0.312%), and DPA (17.9 ± 6.81 mg/100 g). The heterozygous genotype CT had intermediate DPA (7.64 ± 2.09 mg/100 g), IMF (4.39 ± 0.287%), and DPA (19.4 ± 6.74 mg/100 g) that were in-between the highest and lowest values .There were many more significant genotype variations at the genotype . In contIt is well-established that DNA-based inheritance enables the transmission of selected phenotypes across generations either without changes in the DNA sequence through epigenetic inheritance  or throuThe prediction of meat-eating quality traits is highly challenging due to the hurdles associated with low accuracy of estimated breeding values, inconsistency in technical ease of measurement in live animals, non-repeatable reproducibility of carcass data, and high costs of rapid generation of data from large scale consumer sensory panels . While tSCD gene increases the desaturation of stearic acid to oleic acid and a functional variant in the SCD gene promoter affects fattening performance, carcass traits, meat quality, blood metabolites, and gene expression in ovine muscle [SCD SNP loci g.23881050T>C, g.23883280G>A and g.23885910C>A in the parental, first, and second composite generations . Our current study is the first report in TAW, which provides foundational data for the selection and breeding of lambs for marbling and healthy n-3 LC-PUFA using the identified SNP herein.The proteins of the in sheep ,72. Sevein sheep ,88,89,90in sheep ,91,92,93with EPA  provideslongissimus dorsi thoracis et lumborum muscle biopsy sampling technique for directly quantifying the genetic worth of live lambs overcomes the problem of waiting to collect meat quality data after slaughter when selection decisions about the live animal are already too late. As the present data are laboratory-tested, personalized, and customized to actual individual lamb performance and not based on estimated breeding values, precision problems due to low accuracy are minimized. The identified SNP of these lipid metabolism genes can also be used for breed-specific identification and marker-assisted selection of Tattykeel Australian White (TAW) sheep exclusive to MARGRA lamb brand for high-end meat-eating quality. Next-generation sequencing of the FABP4, FASN, and SCD genes also provides foundational data underpinning their roles in fatty acid metabolism unique to the TAW breed.This study has provided novel insights into the shared genetic control of the fat melting point, intramuscular fat content, and health-beneficial omega-3 long-chain fatty acid composition traits that are helpful in designing breeding strategies to genetically improve meat-eating quality traits in TAW lambs while they are still alive. The early decision making utilizing this innovative and minimally invasive