A key abiotic stress that negatively affects seed germination, plant development, and crop yield is moisture deficit stress. Achieving higher vigour and uniform germination under stress conditions is essential for crop establishment and productivity and to enhance the yield. Hence, revealing wheat’s capacity to withstand moisture deficit stress during seed germination and early growth stages is fundamental in improving its overall performance. However, the genetic regulation of moisture deficit stress tolerance during the seed germination phase remains largely unexplored. In this study, a total of 193 wheat genotypes were subjected to simulated moisture deficit stress using PEG-6000 (−0.4 MPa) during the seed germination stage. The induced moisture deficit stress significantly reduced various seedling-vigour-related traits. The genetic regions linked to these traits were found using a genome-wide association study (GWAS). The analysis identified 235 MTAs with a significance −log10(p) value of >4. After applying the Bonferroni correction, the study identified 47 unique single nucleotide polymorphisms (SNPs) that are linked to candidate genes important for the trait of interest. The current study emphasises the effectiveness of genome-wide association studies (GWAS) in identifying promising candidate genes, improving wheat seedling vigour and root traits, and offering essential information for the development of wheat cultivars tolerant to moisture deficit stress. Triticum aestivum L.) is a cereal crop of great significance that is cultivated in almost every part of the world. It is the staple food for around 2.5 billion people in nearly 90 countries worldwide [Wheat . Wheat, a self-pollinated crop, has a higher level of LD across its genome, resulting in high resolution and power of association . UtilisiDue to the substantial diversity, significant differences were observed among various seedling vigour and root traits in the wheat association panel . As showA correlation analysis revealed the presence of relatedness among the studied traits . A highlIn the PCA analysis using phenotypic data under control conditions, the first principal component accounted for 18.9% of the total variation. The primary factors driving this component were the root traits: RSA, RT, RF, and RL . On the Regarding the root traits RL, RSA, RT, and RF, they were also grouped, except for RD. This grouping indicates a consistent relationship among these root traits, while RD appears to behave differently and may have a distinct relationship with the other root traits .Rabi seasons: 2020–21 and 2021–22) with different treatment conditions, viz., control and PEG treatment, 235 MTAs were identified with a significance −log10(p) value of >4. The highest number of SNPs was obtained for the trait SDW (31 MTAs) and the lowest for the RE (2 MTAs). MTAs were filtered with a Bonferroni correction value (−log10(p) > 5.45) to increase the stringency of selection, and 47 SNPs were obtained that were located on 18 different chromosomes  > 5.45) are displayed on their respective chromosomes  was devised to assist in the selection of superior genotypes. This index considers both seedling vigour and root traits, allowing for the selection of the best genotypes under both control and moisture deficit stress conditions. The genotypes are displayed in ascending order using the MGIDI index. The genotypes chosen using this index are marked in red. The central circle, in red, denotes the threshold point determined by the selection pressure (SI = 15%). A total of 58 superior genotypes were selected under both the control and moisture deficit conditions, with 29 genotypes in each condition ; Table 3In the current study, the genotypes used exhibited a wide range of phenotypic variation at the early developmental stage in response to moisture-deficit-induced stress. The population used is known for its high diversity and has been used in GWAS to detect QTL for quality and developmental traits such as grain micronutrients. Moisture deficit stress, induced by PEG, had a significant impact on most traits, likely due to decreased water uptake and delayed or reduced germination ,46. HighAccording to the results of the current study, drought reduces seedling- and root-related development, which has an adverse effect on seedling performance. It has been observed that the traits investigated under the stress conditions of induced moisture deficit have a wide range of genetic variability . In cereThe presence of genetic diversity is an essential characteristic for the successful establishment and early vigour of seedlings, particularly in challenging environments with limited water and resources. The enhancement of root traits during the critical stages of seed germination and seedling development has a significant influence on the emergence of crops and the establishment of seedlings. This, in turn, leads to the development of a healthy plant population, which is a crucial factor for achieving high yields in both favourable and stressful conditions. Thus, emphasising these traits could prove to be an effective approach to quickly assess a large number of genotypes in a drought-like scenario. It could potentially be possible to uncover genotypes that perform better under moisture deficit stress conditions. In order to develop drought-tolerant cultivars through marker-assisted backcrossing, it is necessary to discover markers associated with the target traits. In this study, a diverse mapping panel was used to uncover MTAs associated with seedling vigour and root traits in wheat germinated under moisture deficit stress and control conditions.Discovering new genes and genomic regions related to seedling vigour and root traits during the seedling stage can help develop moisture deficit stress-tolerant, high-yielding crop varieties. In this regard, historical wheat germplasm collections stored in gene banks can be a valuable resource for identifying these genes and regions using an association mapping approach. In this study, a diverse panel of 193 wheat genotypes, including advanced breeding lines, commercial cultivars, elite varieties, germplasm core set, and synthetic derivatives was used to map seedling vigour and root traits. Analysis of variance for all the studied traits in both seasons showed significant variation. The trait variations are a prerequisite for genetic studies and a breeding program of the seedling vigour traits.The discovery of MTAs by GWAS is influenced by the usage of structured populations . PopulatAll the core set lines are clustered together as one group and the varieties of Indian origin were grouped in another cluster in the GWAS panel . Linkagep-value of <0.0001, a total of 235 MTAs were found to be associated with the investigated traits. However, a Bonferroni adjustment was used for stringent selection in order to prevent false positives. A total of 47 stringent markers were identified, including 10 SNPs linked to MGT, AUC, CL, and RD. Under PEG treatment, 27 significant SNPs were found for the variables AUC, SDW, SVI.I, RD, and RV. The multiple comparisons problem is combated by the Bonferroni correction, which minimises type 1 error, or false positives [The significant LD block size for the complete genome in the current investigation is 3.49 Mb. The LDs for the subgenomes for the A, B, and D genomes were determined to be 2.48 Mb, 4.29 Mb, and 3.82 Mb, respectively . In agreositives .TraesCS6D02G039600 gene which codes for Papain-like cysteine peptidase superfamily. This gene is involved in seed germination, plant growth and development, organ senescence, immunity, and stress response [TraesCS2D02G372500) on the 2D chromosome is responsible for encoding the Serine carboxypeptidase enzyme and DNA-binding pseudobarrel domain superfamily. These putative genes play a crucial role in enhancing the ability of coleoptiles and roots as well as in the development of different parts of the plant in wheat to tolerate drought conditions [Only one SNP marker associated with MGT, AX-94471577, was identified to be present near the candidate regions, identified through in silico analysis coding for WD40-repeat-containing domain superfamily, Aspartic peptidase domain superfamily, Protein DCL-like, and UDP-glucuronosyl/UDP-glucosyltransferase. The aspartic peptidase domainsuperfamily plays an important role in the root and stem development of abiotic and biotic stresses . UDP-gluresponse . Among tnditions ,62. The nditions . The AX-nditions ,65. The nditions , was idenditions  and its TraesCS5A02G387800 stands out, encoding the RNA-binding domain superfamily. A wealth of research has highlighted its pivotal role in governing essential processes such as shoot stem cell fates, root growth, gravitropic responses, and embryo development [TraesCS5A02G387800 emerges as a compelling target for further investigation concerning seedling vigour. The second candidate gene, TraesCS5A02G387700, encodes the Helicase superfamily 1/2, ATP-binding domain, and plays a crucial role in regulating plant growth, development, and abiotic stress responses. It achieves this by modulating the degree of membrane lipid peroxidation [TraesCS2D02G474800, encodes the F-box-like domain superfamily and participates in diverse biological processes, including seed germination and seedling development. Moreover, it plays a specific role in protein degradation through post-translational modification [TraesCS2D02G474900, encodes the APO domain and exhibits higher expression levels in early vigorous wheat cultivars [Some of the markers associated with SVI.II have revealed a compelling array of crucial genes, emerging as promising candidates for further exploration. Among them, elopment . Given ixidation . Its invfication . The sigTraesCS3A02G087200 gene, which codes for the AP2/ERF domain. This domain includes dehydration-responsive element-binding factors (DBFs) that play essential regulatory functions in the plant’s response to abiotic stresses [SNP marker AX-94446435 was linked with the candidate genes coding for the Pyridoxal phosphate-dependent transferase domain and domain of unknown function DUF1664. The first gene encodes Pyridoxal phosphate-dependent transferase, which is an enzyme that is involved in the biosynthesis of vitamin B6. Vitamin B6 plays a crucial role in plant growth and development. It is involved in various metabolic processes such as amino acid metabolism, hormone biosynthesis, and stress responses . The secstresses  and enhastresses . Additiostresses .Olivoto and Nardino  proposedThese remarkable findings offer a deeper understanding of the genetic mechanisms governing seedling vigour, root development, plant growth, and stress responses. As researchers continue to explore these candidate genes, their insights hold great potential for future advancements in agriculture and crop breeding strategies, paving the way for more resilient and productive crops in the face of challenging environmental conditions. This work has brought to light the various strategies that wheat employs to cope with drought stress during seed germination and the early stages of growth. The uncovered gene sets have the potential to improve the ability of wheat to withstand drought, which could have positive effects on seedling vigour and root architecture. A significant proportion of the genes discovered in the targeted region are crucial for plant growth and development. Numerous studies of comparable genes in other species have offered insights into the functions linked to these genes. The genes encoding the potential candidate regions have been characterised through extensive annotation, shedding light on their functions within the cell organelles.A greater comprehension of the genomic landscape and its significance to breeding efforts aimed at developing drought-tolerant varieties of wheat could come through a further investigation that emphasises each of the regions that have been uncovered. Overall, this study opens novel possibilities to strengthen wheat’s resilience to drought stress, which is of the utmost importance for guaranteeing food security and sustainable agriculture on the backdrop of changing climatic circumstances. It also makes significant contributions to the field of plant genetics.Rabi seasons at the Indian Council of Agricultural Research (ICAR) Institute in New Delhi  were utilised in our laboratory studies, representing the two distinct seasons. More information about the origins (A collection of 193 bread wheat genotypes was used in the current study . The col origins  and popuThe germination test was conducted by following the ISTA rules . In a lathe test . −1) was estimated after taking the final count of the germination test. Ten normal seedlings from each replication were taken out, washed, and dried overnight at 80 + 1 °C. Seedling vigour indices were determined following Abdul-Baki and Anderson [The germination curve was produced by fitting the daily radicle/coleoptile emergence (≥2 mm) data using the four-parameter Hill function, as described by El-Kassaby et al. , using GAnderson , by usinSeedling vigour index-I calculates seedling vigour by multiplying the germination percentage with seedling length, while seedling vigour index-II incorporates the seedling dry weight, offering a more comprehensive measure that accounts for the seedling’s actual biomass, encompassing both structural and physiological aspects of early seedling growth, thus enhancing the accuracy of the seedling health and vigour assessment.® flatbed scanner at a resolution of 400 dpi modified for this purpose  as per the manufacturer’s guidelines. The root images from the scanner were analysed with customised software WinRHIZO™  [The roots were gently removed on day 8 by cutting them at the collar area with a sharp blade. The extracted roots were then placed in a tray filled with distilled water for the phenotyping of root traits. The roots of each genotype were then individually scanned in an Epson Perfection V 700 Photo Canada) . VariousThe multi-trait genotype–ideotype distance index (MGIDI) was used to select the superior genotypes under both the control and moisture deficit conditions based on seedling vigour and root traits by following Olivoto and Nardino .® Best Practices genotyping workflow ). The SNPs were filtered, and monomorphic markers and markers with a minor allele frequency (MAF) of 5%, missing data of more than 10%, and heterozygote frequency greater than 50% were eliminated from the study. The remaining 13,947 SNPs were analysed further, as used by Devate et al. [Genomic DNA from leaf samples of all 193 genotypes was isolated using the cetyltrimethylammonium bromide (CTAB) extraction method  followede et al. .The descriptive statistics and frequency distribution were analysed to check the range of variability among the traits. The mean, standard deviation, and range for each trait were calculated and are given in Pearson correlation coefficients and a PCA biplot were also calculated to determine the relationships among the traits between treatments. The Pearson’s correlation coefficient among the studied traits was calculated and graphically represented using an R package ‘corrplot’ . Phenoty2. Using the r2-value versus genetic distance in base pairs (bps), the LD decay plot was produced. The whole genome’s 3.49 Mb big LD block size signifies that SNPs in this region function as inheritance blocks. For subgenomes A, B, and D, the LD block sizes were 2.48 Mb, 4.29 Mb, and 3.82 Mb, respectively.The diversity in the GWAS panel was assessed using marker-based principal component analysis (PCA) and neighbour-joining (NJ) dendrogram analysis, and intrachromosomal linkage disequilibrium (LD) was previously published in Devate et al. . The stulog10(p) values. Marker trait associations (MTAs) across seasons and combined for RE, GP, MGT, AUC, CL, SL, SDW, SVI.I, SVI.II, RL, RSA, RD, RV, RT, and RF were found to have significant p-values using Bonferroni correction  = 5.45) to ensure stringent selection of MTAs. A total of 13,947 filtered SNPs and the mean value for each treatment was calculated from the two seasons’ data for control (well watered) and moisture deficit conditions (PEG-6000 treatment). An overall mean across the treatment was also calculated. Phenotypic data from all the traits were used to identify associated markers with traits using the ‘BLINK’ (Bayesian-information and linkage-disequilibrium iteratively nested keyway) model  under GAhttps://plants.ensembl.org/Triticum_aestivum/Tools/Blast (Accessed on 25 may 2023)) against the bread wheat reference genome IWGSC (RefSeq v1.0). To identify the candidate genes associated with significant SNPs, gene coding regions located within the 100 kb flanking region of the MTAs were considered. Further gene annotation of identified genes was carried out to know their biological process, cellular components, and molecular functions  search using the sequence information of the markers. The BLAST search was carried out using the data web service Ensembl Plants   in wheat and to validate the associations and genes identified in this study. Furthermore, testing in the field may prove necessary to assess the agronomic significance of our findings.


TNC), TNF superfamily member 8 (TNFSF8) and Collagen type XXVIII alpha 1 chain (COL28A1) are involved in extracellular matrix organization and regulation of cell fate in the immune system. TNC may be involved in ewe lamb growth, and therefore, could be of interest for selection of ewe lamb replacements. The association between ewe lamb live weight and TNFSF8 and COL28A1 is unclear. Further research is needed using a larger population to determine whether the genes identified can be used for genomic selection of replacement ewe lambs.This study estimated genetic parameters and identified candidate genes associated with live weight, and the occurrence of pregnancy in 1327 Romney ewe lambs using genome-wide association studies. Phenotypic traits considered were the occurrence of pregnancy in ewe lambs and live weight at eight months of age. Genetic parameters were estimated, and genomic variation was assessed using 13,500 single-nucleotide polymorphic markers (SNPs). Ewe lamb live weight had medium genomic heritability and was positively genetically correlated with occurrence of pregnancy. This suggests that selection for heavier ewe lambs is possible and would likely improve the occurrence of pregnancy in ewe lambs. No SNPs were associated with the occurrence of pregnancy; however, three candidate genes were associated with ewe lamb live weight. Tenascin C ( Farmers can increase farm profitability by breeding their ewe lambs at approximately eight months of age as it can increase the number of lambs born per ewe in her lifetime and per farm per year, thus increasing income ,2. ThereA potential selection tool is the use of single-nucleotide polymorphisms (SNPs). With genomic technologies becoming cost-effective, selection of replacements via genomic information is a realistic tool for farmers to use and can be used to select replacement animals at an earlier age ,11. The The aim of this study was to perform genome-wide association analyses to estimate the genetic parameters and identify candidate genes associated with live weight at eight months of age, and the occurrence of pregnancy in Romney ewe lambs, when bred for the first time, at eight months of age. It was hypothesized that the genetic correlation between live weight at eight months and fertility in ewe lambs would be positive.The experiment was conducted at Massey University’s Riverside farm , 11 km north of Masterton, New Zealand, from January 2017 to July 2018, with the approval of the Massey University Animal Ethics Committee (MUAEC 17/16 and MUAEC 17/12).n = 513) or B (n = 513), making up four contemporary groups: 16GenA, 16ComA, 16GenB and 16ComB. The 2017-born ewe lambs (n = 493) were born to either mature ewes (Mat) or ewe lambs (Lamb). Those born to mature ewes were either fed concentrates and pasture between weaning and their first breeding (Pref), or fed only pasture (Norm), whilst those born to ewe lambs were all fed concentrates and pasture and were either single- (Sing) or twin-born (Twin). This resulted in four additional contemporary groups: 17MatNorm, 17MatPref, 17LambSing and 17LambTwin.Eight groups of Romney ewe lambs born in either 2016 or 2017 and introduced to rams for the first time at eight months of age were included in this study. Ewe lambs born in 2016 and 2017 were also included in separate studies from which phenotypic information were collected;  for 2016Ewe lambs were bred with mature rams for 34 days. The 2016-born ewe lambs were joined with 18 rams in each group A and B. Group A and B weighed on average 46.2 kg ± 0.18 and 48.3 kg ± 0.19 at the start of breeding, respectively. The 2017-born lambs were bred with 12 rams with all ewe lambs in a single cohort and weighed on average 43.7 kg ± 0.25 at the start of breeding. The minimum live weight to be presented for breeding was 38 kg for the 2016-born ewe lambs [n = 9 in 2016-born and n = 98 in 2017-born). Therefore, there were three possible pregnancy outcomes: pregnant, non-pregnant after exposure to the ram, and not exposed to the ram. Ewes not exposed to the ram were not included in the analysis of occurrence of pregnancy.The first phenotypic trait considered was live weight at the start of the breeding period at eight months of age. All ewe lambs with a live weight recorded at the start of breeding were included in the analysis. The second phenotypic trait considered in the analysis was occurrence of pregnancy during the 34-day breeding period. Occurrence of pregnancy (Pregnant or Not pregnant) was determined and collected at 93 and 84 days after the introduction to the rams via transabdominal ultrasound for the 2016- and 2017-born ewe lambs, respectively. To enable consistent analysis, all ewe lambs that were lighter than 39 kg at the start of breeding were considered as not exposed to a ram  and 121 had both parents unknown based on DNA parentage and data collected during the lambing periods.Ear tissue samples were collected from each of the 1519 ewe lambs for DNA isolation. Ewe lambs were genotyped using a 15,000 SNP chip  and using a custom Infinium Array  designed by Equine Parentage and Animal Genetics Service Centre . Ewe laThe map positions of the SNPs were based on ovine genome assembly (Rambouillet_v1.0) produced by the International Sheep Genome Consortium (ISGC). The average distance between SNPs was 304,834 bp.y is the vector of observations for phenotypic traits; b is the vector of fixed effect of contemporary group, a is the vector of additive genetic effects, e is the vector of random residual effects. X and Z are incidence matrices relating to the records of fixed and animal additive genetic effects, respectively. The expected values (E) of the variables were assumed as E(y) = Xb, E(a) = 0 and E(e) = 0. The residuals were assumed to be independently distributed and var(y) =a) = Ae) = IVariance components, heritability and correlations were estimated using ASReml (v 3.0) . VariancThe heritability lated as ,h2=σg2σE(E(E(E(e) = 0. The residuals were assumed to be normally distributed with zero mean and the following co(variance) structure:(Co)variance components and genetic and phenotypic correlations were also estimated using a bivariate animal model. In matrix notation, the bivariate model was represented as follows:Genetic correlations  was performed using a mixed linear model in the software package GCTA . The stap-value threshold for genome-wide significance level was 0.05/number of SNPs = 3.70 × 10  scale. However, this correction has been considered too conservative, therefore, a p-value threshold for suggestive linkage was also computed. This suggestive linkage procedure corresponded one false positive result across the genome and less conservatively, accordingly, Lander and Kruglyak [p-value one, which was  scale.The level of a significant association between a marker and the phenotype was adjusted using a Bonferroni correction to account for multiple SNP comparisons. The Bonferroni corrected Kruglyak  proposed10  were plotted against the genomic locations of the markers using the qqman package in R software 4.2.1 [The estimated associations were represented in Manhattan plots in which −logre 4.2.1 .Ovis Aries reference genome in the Ensembl database . If no annotated genes were present in the 300 kb examined area, a blastn was performed in NCBI  on a nucleotide sequence including 500 bp around the significant SNP to identify a similar sequence in other species which has been annotated. The biological function of the genes was identified using PANTHER [Each SNP that was significant or nearly significant was examined to locate the genes within 150 kb upstream and downstream from the SNP. Candidate genes were identified for significant SNPs using the  PANTHER ,28, and  PANTHER  was usedEwe lambs that were pregnant after a 34-day breeding period represented 48.8% of the population and weighed 47.4 ± 0.15 kg at breeding, ewe lambs that were non-pregnant after exposure to the rams (43.2%) weighed 46.3 ± 0.17 kg at breeding, and ewe lambs that were not exposed to the rams (8.0%) weighed 35.5 ± 0.26 kg at breeding of their counterparts.Heritability estimates for ewe lamb live weight and the occurrence of pregnancy were 0.57 and 0.31, respectively . The pedp > 0.05) regions associated with ewe lamb live weight at eight months of age, but two SNPs on chromosomes 2 and 4 were significant at a suggestive significance level , TNF superfamily member 8 (TNFSF8) and collagen type XXVIII α 1 chain (COL28A1).There were no significant (p > 0.05) regions associated with the occurrence of pregnancy in ewe lambs. Four peaks, indicating chromosomes 1, 2, 3, 4 and 11, were nearly significant at a suggestive significance level  is expressed in neural, vascular and skeletal tissues during embryonic development [TNC is detected in the extracellular matrix in musculoskeletal tissues [TNC is involved in the adaptative response of musculoskeletal tissues to mechanical stress. At eight months of age, ewe lambs are still physically growing, which can be linked to mechanical stress for musculoskeletal cells and tissues [TNC would be greater in ewe lambs that attained puberty at eight months of age, as they were more advanced in their growth and development.Three candidate genes were associated with live weight of ewe lambs at the start of breeding at eight months of age: elopment , whereas tissues . Gruber, tissues  showed t tissues ,44,45. T tissues ,47. At t tissues . It coulTNFSF8) is expressed in T cells and has a role in the regulation of cell survival and apoptosis [TNFSF8 was mentioned by Lin, et al. [TNFSF8 was also upregulated by IGF1 in cow embryos and was reported to improve the ability of cow embryos to develop pre-implantation [TNFSF8 to IGF1 in cows could be of great interest for ewe lamb replacement selection. IGF1 is a well-accepted candidate gene impacting growth and development in sheep [TNFSF8 function and live weight of ewe lambs at eight months of age is not clear and requires further research.The gene TNF superfamily member 8  was reported to be expressed in the peripheral nervous system and myelin-free areas in mammals [COL28A1 was found in sheep fetal heart exposed to hypoxia [COL28A1 is sparse across species, and its relationship with live weight or growth of ewe lambs is unknown. More research is required to determine its function in mammals and its relationship with ewe lamb live weight.Collagen type XXVIII α 1 chain  and tyrosine 3-monooxygenase/tryptophan-5-monooxygenase activation protein epsilon (YWHAE). SLC43A2 is expressed in the endometrium, uterine epithelia and conceptus tissues in ewes [SLC43A2 increases during conceptus development in beef heifers [YWHAE is expressed in the ovaries, immature oocytes and mature eggs of mice [No SNPs met the statistical threshold for association with occurrence of pregnancy in ewe lambs, but trends were observed on four chromosomes, and 15 potential genes were identified at these locations. Among these 15 potential genes, two were reported to have a link with fertility: solute carrier family 43 member 2 ( in ewes , beef he in ewes  and mare in ewes . The exp heifers  and, the of mice . Althoug of mice . Low expThe threshold in live weight below which an ewe lamb was considered too light to be bred differed between years with 38 kg for the 2016-born ewe lambs and 39 kg for those born in 2017. For consistency in the analysis, ewe lambs weighing less than 39 kg were considered “not exposed to the rams” and therefore were not included in the estimation of the genetic parameters for the occurrence of pregnancy. Whilst ethically appropriate, not exposing lighter ewe lambs to the ram truncated the data and reduced the opportunity to identify highly fertile ewe lambs that became pregnant at a light weight. This limited the size of the ewe lamb population for the estimation of genetic parameters for the occurrence of pregnancy, and the identification of SNPs and candidate genes associated with the occurrence of pregnancy in ewe lambs.None of the SNPs reached the genome-wide significant threshold for both ewe lamb live weight at eight months of age and occurrence of pregnancy. This could suggest that both traits are controlled by many genes, each having small effects. The limited size of the ewe lamb population and the low-density array used in this study challenged the detection of these potential small effects.TNC, TNFSF8 and COL28A1 were associated with ewe lamb live weight at eight months of age. Their functions are known to include extracellular matrix organization and regulation of cell survival and apoptosis in the immune system. TNC’s role suggests they may also be involved in the growth of ewe lambs and therefore could be of interest for replacement selection. The association between TNFSF8 and COL28A1 and ewe lamb live weight remains unclear. Further research is needed to consolidate these results using a larger population of ewe lambs. Investigations are also warranted to determine the specific roles of the genes identified in this study in ewe lamb development and whether they could be used for genomic selection to improve live weight of replacement ewe lambs, resulting in a corresponding increase in ewe lamb fertility through the positive genetic correlation.Ewe lamb live weight at eight months of age had medium genomic heritability, showing that selection for heavier ewe lambs could be integrated into the selection criteria for replacement animals. This selection on live weight would likely improve the occurrence of pregnancy in ewe lambs, resulting in an increase in ewe productivity. No genes were associated with the occurrence of pregnancy; however,


TraesCS3D02G197700 and TraesCS5A02G292900 were identified to potentially regulate CD. To the best of our knowledge, this is the first study to provide the genetic basis of CD, and the genetic loci identified in this study may ultimately assist in wheat breeding programs.Wheat grain has a complex structure that includes a crease on one side, and tissues within the crease region play an important role in nutrient transportation during wheat grain development. However, the genetic architecture of the crease region is still unclear. In this study, 413 global wheat accessions were resequenced and a method was developed for evaluating the phenotypic data of crease depth (CD). The CD values exhibited continuous and considerable large variation in the population, and the broad-sense heritability was 84.09%. CD was found to be positively correlated with grain-related traits and negatively with quality-related traits. Analysis of differentiation of traits between landraces and cultivars revealed that grain-related traits and CD were simultaneously improved during breeding improvement. Moreover, 2,150.8-Mb genetic segments were identified to fall within the selective sweeps between the landraces and cultivars; they contained some known functional genes for quality- and grain-related traits. Genome-wide association study (GWAS) was performed using around 10 million SNPs generated by genome resequencing and 551 significant SNPs and 18 QTLs were detected significantly associated with CD. Combined with cluster analysis of gene expression, haplotype analysis, and annotated information of candidate genes, two promising genes  With increasing world population and food consumption per capita, food production needs to increase by 60% to be able to meet the food demands by 2050 , spike number per area (SN), and grain number per spike (GNS) are the three main important components of grain yield . To datene Rht-1 . In addine Rht-1 . The stuWheat grain has a complex structure. The pericarp, seed coat, nucellar epidermis, aleurone layer, and starchy endosperm are present from the outside to inside. The embryo is embedded in the seed coat on the dorsal side of the wheat grain . CompareIn this study, resequencing and population genomic analysis was performed using 413 accessions, which included 86 landraces and 325 cultivars. The relationship between CD and agronomic traits was identified. Some potential selective footprints were identified by comparing genome-wide genetic diversity between landraces and cultivars. Furthermore, GWAS was conducted to identify the genetic loci and candidate genes associated with CD. To the best of our knowledge, this is the first study to provide the genetic basis of use of CD in breeding improvement, and the genetic loci identified in this study may ultimately assist in improving grain yield in wheat.22.1In this study, 413 wheat accessions, which included 86 landraces and 325 cultivars, from various regions in the world were selected as the experimental materials. This comprised of 275 accessions from the major agro-ecological zones in China and 137 accessions from other countries. The origin information of remaining accession was not available Table S1All populations were planted in the field nursery at Xiangyang  in 2019–2020 (XY19) and Luoyang  in 2020–2021 (LY20). Each accession was space-planted in a 3-m single-row plot with 5-cm distance between plants and 20-cm distance between rows. The field experiments used a randomized complete block design with two replicates and standard agronomic wheat management practices were conducted.2.2To evaluate the crease depth trait, the length of real crease depth (L1) and thickness of the grain (L2) were measured . The seePhenotyping of grain-related traits, including TGW, grain length (GL), and grain width (GW), were analyzed using an automatic seed size analyzing system  from the seeds dried after harvesting.The quality-related traits, such as grain protein content (GPC), wet gluten content (WGC), water absorption (WA), and stability time (ST), were measured using near infrared transmittance spectroscopy . For each variety, 100 g seeds were weighed and ground using a flour mill. The extraction rate of flour (ER) was calculated as follows: ER = (flour weight/grain weight) × 100% .2.3https://github.com/braverock/PerformanceAnalytics). The value of the best linear unbiased prediction (BLUP) for trait across multiple environments and broad-sense heritability (2H) were calculated using lme4 package in R 3.6.1 (http://www.r-project.org/).Basic descriptive statistical analysis and two-tailed t-test were performed using IBM SPSS statistics version 20.0. Pearson’s correlation coefficients between different environments or different traits were calculated using the R base function cor , and the results with the histogram plot and correlation coefficients were displayed using the PerformanceAnalytics package (2.4https://db.cngb.org/cnsa) under the accession number of CNP0004251.Total genomic DNA of all samples was extracted from leaf tissues using the CTAB method, and paired-end sequencing library of each accession was constructed as per the manufacturer’s instructions . All accessions were sequenced using the MGISEQ-2000 platform. The paired-end reads were mapped to the bread wheat reference genome , and SNP2.5landraces/πcultivars) and fixation index (Fst) of different populations with the same sliding-window approach (200 kb windows and 100 kb steps) across the genome were calculated using VCFtools software  was selected to detect the significant loci of interest. According to previous studies, the regions 3-Mb upstream and downstream of the significant SNPs could be defined as QTLs and overlapping QTLs were treated as the same QTL  detected in at least two environments and the GWAS P-value of peak SNPs < 6.20 × 10−7  were retained and subjected to subsequent analysis.Association analysis was performed using the Fast-LMM program with a mixed linear model . BLUP vasame QTL . SNPs wi2.7The expression data of all candidate genes in the root, stem, leaf, spike, and grain, were downloaded from WheatOmics 1.0 . Cluster2.8Arabidopsis and rice were acquired using the protein sequence of these putative genes via the BLASTP program. Genes with annotations related to spike or grain development were regarded as the promising genes. Furthermore, gene expression profiles in the transcriptome data of these genes were examined, and the genes that were predominantly expressed during the spike or grain development stage of wheat were selected as the candidate genes.To narrow down the range of candidate genes associated with CD, the genes located within QTL regions were extracted from the gene annotations of the reference genome of Chinese Spring (IWGSC Refseq1.1). Meanwhile, homologous genes in 33.12H of CD was 84.09% , GL (r = 0.26), and GW (r = 0.25) and a significant negative correlation with GPC (r = −0.21), WGC (r = −0.23), WA (r = −0.16), and ST (r = −0.16) S3. MoreWhen comparing the differences in different groups of these tested traits, it was observed that cultivars had a substantially higher TGW, GW, and GL than landraces , Fst, and XP-CLR scores were calculated between landraces and cultivars across 21 chromosomes using SNP data  was detected between the phenotypic values of accessions with two alleles at each SNP loci in all tested environments  on chromosome 3D from 202.81 to 241.52 Mb was associated with CD, including 10 significant SNPs  transcriptional activator, which has been proven to control cell elongation and positively affect leaf angles in rice  from 501.45 to 515.79 Mb in chromosome 5A , QTkw.sdau-6A  were identified (TraesCS3D02G197700 encodes a kinesin-like protein. In rice, its orthologous gene OsKinesin-13A plays an important role in promoting microtubule turnover, affecting cellulose microfibril orientation and cell elongation (TraesCS5A02G292900 in rice, namely, OsBC1, affects grain size (TraesCS3D02G197700 and TraesCS5A02G292900 may be the candidate genes of QTL9 and QTL13, respectively, contributing to CD phenotype in wheat.To identify the genes controlling CD in wheat germplasm, a combined analysis of GWAS and gene expression profile was conducted, which has been proven to be a useful approach to explore potential causal genes for complex traits . In thisGS5 , TaBT1 (5 (TaBT1  and TaSP(TaSPL14 . Clusterentified 6. Traesongation . Similarain size . From th5TraesCS3D02G197700 and TraesCS5A02G292900 were identified as strong candidate genes for CD regulation. To the best of our knowledge, this is the first study to perform the genetic basis of CD, uncovering the potential CD artificial selection together with grain yield during wheat breeding processes. The genetic loci identified in this study may ultimately assist in improving grain yield in wheat.In summary, a method was developed for evaluating the crease depth of wheat and the genetic basis of CD was unraveled for the first time. Around 10 million SNPs generated by whole genome resequencing of 413 wheat accessions were used for GWAS. In total, 551 significant SNPs enriched in 18 QTLs were detected significantly associated with CD. Two promising genes The data presented in the study are deposited in the China National Genebank repository, accession number CNP0004251.HM and LG conceived and designed the research. CS, KX, XH, ZZ, AL, YZ and JD performed the experiments. JJ and LG prepared the materials. CS and KX analyzed the data and wrote the manuscript. HM, LG, and JJ revised the manuscript. All authors contributed to the article and approved the submitted version.


Associations between the STin2 and 5-HTTLPR polymorphisms within the serotonin transporter gene, SLC6A4, and culture across societies were examined. Based on an analysis of 75 primary studies , STin2 allelic frequencies were found to vary widely across countries, ranging from 26% in Germany to 85% in Singapore. Across 53 countries, and after controlling for all major environmental influences of culture, STin2 and 5-HTTLPR were found to explain 23.6% unique variance in monumentalism but none in individualism. Our findings evidence a significant role of genetics in predicting cross-societal cultural values variation, and potentially speak to the need for and importance of incorporating both nature and nurture in theories of cultural values variation across societies. Via this paradigm, evidence indicates that there are unique and meaningful cross-societal effects of genes on complex traits such as social stratification, social trust, intelligence test performance, educational attainment, voter turnout, happiness, and cognitive achievement6. Scholars have suggested that a deeper understanding of the interplay between genes and culture is essential for a holistic understanding of psychological processes7. Yet, very little is known about the role of genetics in cross-cultural differences, defined as differences in cultural values orientations between societies10. To the extent that prevailing theories of cultural values focus only on environmental factors e.g.,10, a full accounting that considers the potential role of genetic differences between individuals from divergent populations is lacking. Indeed, the promise of finding genetic explanations for cultural values variation across societies is important. Insight into the genetic bases of said variation may help explain persistent differences in cultural values orientations between equally modernized and prosperous countries such as the US and Japan10. A holistic understanding involving both environmental and genetic influences of cross-cultural values variation between societies will enable scientists, students, and business and governmental decision makers to build more accurate templates for understanding social behavior and tackling social problems that arise resultant of differences between nations.Perhaps resulting from a revolution in contemporary understanding of genetic variability, interest in research linking genes to social and psychological phenomena, termed “sociogenetics”, has burgeoned in the last decade11. Comparing average population differences in genetic variability at the country or societal level has been advocated as an approach that helps address these small effects of genes at the individual level12. Accordingly, an emerging stream of research studying culture and genetics, via the paradigm of culture-gene co-evolution theory, has focused on associations between cultural values  and country-level variability in 5-HTTLPR15, a polymorphism in SLC6A4 (“Solute carrier family 6 member 4”), the gene containing information for production of the SERT protein that is responsible for transportation of serotonin from the synaptic cleft to its neuron1. However, results have been conflicting, possibly as a result of two important problems in this literature on the sociogenetics of culture.Consistent heritability estimates from twin and whole-genome studies suggest there is a reliable, albeit small, effect of genes on complex psychological traits such as personality and cultural values differences between individualstwo polymorphisms in SLC6A4 that play complementary roles in regulating serotonin receptivity, combining to either reduce or enhance serotonin uptake: 5-HTTLPR and STin217. Second, extant studies lack model completeness and sample representativeness. Former studies on the sociogenetics of culture have only sporadically included environmental control variables, without consideration of the full range of environmental influences18. This is problematic because failing to account for environmental covariates leaves open the question whether documented associations between 5-HTTLPR and cultural values15 are robust. Additionally, said associations have also mostly not included populations from the “global South”, most notably sub-Saharan Africa.First, a singular focus on only one polymorphism, 5-HTTLPR, has been a conceptual limitation restricting the accuracy of potential conclusions. The SLC6A4 gene is a complex system consisting of various polymorphic regions that each serve specific functions. Focusing on only one aspect of this system may lead to incorrect conclusions regarding associations between genes and culture. Crucially, there are all environmental factors that have been identified as important antecedents of cultural values; (4) including good representation of countries in the global South via reference to genetic maps of Eurasian, African, and Amerindian populations. We do so by examining population level associations between the SLC6A4 polymorphisms and two cultural values for which measurement-valid and scientifically rich data are available for most countries—individualism  and monumentalism .Hence, and as detailed below, we address the just-noted issues by (1) synthesizing culture-gene co-evolution theory with advances in medical science involving allelic variation in serotonin receptivity to better clarify underlying associations between genes and cultural values; (2) conducting the first study on the effects of both polymorphisms in SLC6A4, 5-HTTLPR and STin2, on cultural values; (3) including 21. Natural selection works to enhance the survival and reproductive fitness of individuals possessing genes linked to behavior patterns that yield success in a given social and physical environment22. Notably, genes affecting brain function are likely to influence the adoption and formation of cultural norms; conversely, culture may also shape the expression and selection of genes21. Consequently, cross-cultural variation between societies may be a function of genetic variation, with different patterns of gene-brain interactions giving rise to cultural differences between populations23. These genetic variation patterns between populations are also unlikely to be a result of chance  because similar patterns of gene-brain interactions have been found worldwide in ecologically threatening regions that are geographically isolated, involving SLC6A415.In more detail, culture-gene coevolution theory suggests that environmental pressures giving rise to genetic adaptations also give rise to corresponding sociocultural adaptations26. 5-HTTLPR has two allelic variants including the S (“short”) and L (“long”) alleles. Individuals with a homozygotic S-allele have lower serotonin receptivity than individuals with a homozygotic L-allele. The L-allele causes more efficient SLC6A4 transcription than the S-allele. SERT translation is decreased among homozygotic S-allele carriers, vice versa for homozygotic L-allele carriers29. STin2, responsible for SERT protein translation also30, is in intron 2 of the SLC6A4 gene and consists of 9, 10, or 12 repeat units, . STin2.9 is quite rare, but almost everyone has either STin2.10  or STin2.12 . Like the S-allele, 12/12 increases SLC6A4 transcription and decreases SERT translation; like the L-allele, 10/10 leads to more efficient SLC6A4 transcription and increased SERT translation31. Overall, both polymorphisms have been linked to similar outcomes including mental health problems, depression, suicide, and nicotine dependence34. Although underlying biological mechanisms for these findings are not fully understood yet36, because 12/12 and the S-allele both decrease SERT protein translation, it may generally be surmised that they should have similar effects on social outcomes and vice versa for 10/10 and the L-allele, which increase SERT translation31. Hence, potential associations between cultural values and STin2 may be informed by the extant research on 5-HTTLPR.Two Variable Number of Tandem Repeat (VNTR) polymorphisms in SLC6A4, 5-HTTLPR and STin2, work together to increase SERT protein transportation from the SLC6A4 gene, combining to reduce or enhance serotonin uptake37. It promotes hypervigilance, leading to more anxiety, heightened responses to emotional stimuli, and more attention to negative information but also more environmental monitoring39. Consequently, individuals with homozygotic S-alleles are more risk-averse and more loss-averse than those with homozygotic L-alleles41. Thus, because national culture is closely related to aggregated personality traits5, it is reasonable to expect that societies typified by a greater prevalence of S-allele  carriers will also be more risk-averse, favoring cultural norms that embody more social harmony and social control13 . Indeed, environmental threats that select for less serotonin uptake in the population also have been evidenced to covary with “survivalist” cultural tendencies dictating greater social cohesion such as the creation of strong hierarchies, tightly knit societies, and the prevalence of strong norms42. Therefore, we predict that SLC6A4 polymorphisms associated with reduced serotonin uptake  will be positively associated with monumentalism  but negatively associated with individualism ; vice versa for 10/10, which increases serotonin uptake17. Going further, because culture-gene co-evolution theory maintains that associations between allelic and cultural variability are independent of environmental factors23, we predict that 5-HTTLPR and STin2 will explain unique variance in individualism and monumentalism beyond the effects of all essential environmental factors.The S-allele is associated with enhancement of a wide range of cognitive functions, notably improved decision-making through better probabilistic and temporal discountingand allelic variation46, we draw upon conceptual advancements in ecological psychology18 to statistically model 14 environmental factors including: geographical latitude, axial orientation, climatic demands, rainfall steadiness, historical/contemporary prevalence of disease, social diversity including ethnic/racial, linguistic, and religious diversity, population density, urbanization, wealth differences both between and within countries, and social conflict. This set subsumes all environmental covariates included in former studies of 5-HTTLPR15 and no other factors beyond these have been identified as important antecedents of cultural values8. Thus, we control for most confounds to statistically test the supposition that there is a genetic explanation for cross-cultural values variation between societies. To be clear, we do not make any inferences about a causal link between genes and culture among individuals. Rather, we model associations using a correlation and regression approach, based on overall population-wide data, focused only on predicting statistical variation in cultural values orientations.Summarily, we conduct the most comprehensive study to date on the confluence of cultural values and genetics, providing insight into specific patterns of genetically driven cross-cultural differences for a set of 28 countries representing 57% of the world population. First, we review the medical literature and aggregate data from 75 studies representing 28,726 individuals to determine the prevalence of the STin2 polymorphism across said countries. Ours is the first tabulation of STin2 across countries, providing a wealth of information on serotonin receptivity across populations that can be used by scholars of sociogenetics. Further, we use genetic mapping of the populations spanning the various continents to provide estimates of the STin2 polymorphism in another 27 countries that also have data available for S-allele prevalence. We thereby greatly expand the country-level database available for scholars to study SLC6A4, the serotonin transporter gene, while also ensuring that any estimates we provide are based on genetically comparable populations. Most importantly, because environmental factors exert powerful influences on cultural values and may in fact be responsible for both cultural 51 . As expected also, given that they are in different regions of SLC6A4, neither of the STin2 alleles are strongly correlated with S-allele prevalence . To the extent that all correlations between the genetic variables are in logically expected directions and are consistent with prior research, we may have some confidence that the currently presented genetic data have measurement validity.Correlation analyses were conducted in SPSS (Ver. 28). A detailed tabulation of the means, standard deviations, and intercorrelations between all study variables is provided in Table r = 0.07, n. s.; for S-allele: r = 0.58, p < 0.01). However, only 12/12 is, as expected, negatively correlated with individualism ; S-allele prevalence is positively correlated with individualism, although this latter correlation is not statistically significant . On the other hand, the 10/10 allele is, as expected, positively correlated with individualism and negatively correlated with monumentalism; the correlation is significant for individualism but not monumentalism . Overall, findings provide some support for the notion that 12/12 and the S-allele are positively associated with cultural values that promote social harmony or social control  and negatively with those that do not , vice versa for 10/10.As shown in Table 15. The regression analysis was conducted in three steps. All control variables were included in Step 1—this step indicates the amount of cultural values variance explained by factors other than the SLC6A4 polymorphisms and serves as a baseline to compare the unique contributions of the polymorphisms in explaining values variation between societies. The S-allele percentage was included in Step 2—this step indicates the unique contribution of the S-allele to values variation between societies after accounting for the control variables. Finally, 12/12 and 10/10 allelic percentages were included in Step 3—this step indicates the unique contribution of STin2 to cultural values variation between societies after accounting for the control variables and S-allele prevalence. Japan and New Zealand were excluded from the regression analyses because they lacked data on axial orientation. Findings are thus based on the remaining 53 countries.Hierarchical regression analyses were conducted in SPSS (Ver. 28) to test the effects of the SLC6A4 polymorphisms on cultural values after accounting for the effects of environmental factors, with country as the unit of analysis. This analysis helps shed light on concerns surrounding the validity of previously evidenced correlations between SLC6A4 polymorphisms and cultural values orientationsAdjusted R2), the incremental variance explained by the SLC6A4 polymorphisms on individualism and monumentalism in each of Steps 2 and 3 (Δ R2), and a test statistic (F) indicating whether said variance explained for individualism or monumentalism is statistically significant. Statistically significant effects are bolded.A summary snapshot displaying the main findings of interest is displayed in Fig. As shown in Table There are several limitations with our statistical model. Four countries including France, Italy, Singapore, and Sweden, and one region, Taiwan, had only small sample sizes for STin2 frequency estimates in the population, raising the possibility that small sample sizes for population genetic estimates may have confounded study results. Four countries including Burkina Faso, El Salvador, Tanzania, and Zambia relied on estimated data on both genetic variables and cultural dimensions, thereby raising the possibility that data dependencies may have confounded study results. Twenty-seven countries . On that note, we caution the reader to not derive a deterministic understanding of culture based upon a reading of our paper. We take the position that both nature and nurture are important antecedents of culture, and we hope that more research on this issue will spur forward.Because we used population-wide metrics for environmental antecedents, polymorphisms, and cultural values, our study is strictly correlational only—a causal link between serotonin receptivity and values cannot be presently inferred. Ideally, individual-level data on the studied polymorphisms, coupled with population-wide metrics on environmental antecedents and cultural values, ought to be paired in a hierarchical linear model to make inferences regarding said causal link. We are unable to do this because the polymorphic data do not exist at the individual- but only at the sample-level , thereby somewhat alleviating this concern. Moreover, it is unlikely that estimation sensitivity could rule out the effects we found considering the sheer size of said effects, whereby the SLC6A4 polymorphisms accounted for almost a quarter of the variation in monumentalism but close to zero in individualism  the number of participants with STin2.12/STin2.12 (“12/12”), (b) the number of participants with STin2.10/STin2.10 (“10/10”), and (c) the total number of participants. Seventy-five studies met our inclusion criteria. The 75 studies are listed in Table t (115) = − 1.146, p = 0.254) or 10/10 alleles (t (115) = 0.367, p = 0.715). These non-significant results are consistent with the extant medical literature indicating similar frequencies of the STin2 alleles between clinical and non-clinical samples55. Therefore, both clinical and non-clinical samples were summed to calculate STin2 percentages across specific countries.Across all countries, some samples were clinical (defined as samples whose members have specific ailments or comorbidities such as depression or cancer), whereas others included only healthy adults. To confirm genetic equivalencies between clinical and non-clinical samples, we compared the number of individuals with 12/12 and 10/10 alleles present in each type of sample. Independent-samples t-tests indicated no statistically significant differences between clinical and non-clinical samples on either the 12/12  prevalence estimation procedures for these 27 countries are detailed below, by continent. Estimates are provided in the bottom half of Table Following prior research47. Using these data, estimates for Burkina Faso, Ghana, Nigeria, Zambia, and Zimbabwe are made based on South Africa; estimates for Rwanda and Tanzania are made based on Uganda.A genetic map of Africa is provided by Tishkoff et al.48. Using these data, estimates for Chile, Argentina, and Uruguay are based on Brazil; El Salvador and Guatemala are based on Colombia; Peru is estimated based on the average of Brazil and Colombia.A genetic map of the Americas is provided by Salas et al.49. Using these data, Azerbaijan, Israel, and Turkey are estimated based on the average of Iran and Russia.Genetic clusters for the various Eurasian populations are provided by the GenomeAsia100K Consortiumhttps://www.eupedia.com/europe/european_y-dna_haplogroups_by_region.shtml. The map is based on converging genetic data on the Y-chromosome prevalence from fourteen peer-reviewed, scientific sources e.g.,51. Based on these data, and coupled also with genetic cluster data from the GenomeAsia100K Consortium49, STin2 estimates for Europe are as follows: Austria is estimated based on the average of Hungary and the Czech Republic; Estonia, Finland, and Ukraine are estimated based on Russia; Denmark and Norway are estimated based on Sweden; Poland is estimated based on the average of Czech Republic, Hungary, and Russia; Slovenia is estimated based on Croatia; Switzerland is estimated based on the average of Germany and France; Ireland is estimated based on the UK.A genetic map of Europe is available at: 49 detailed the DNA analysis of the local Maori population. However, the native Maori are a minority in New Zealand; 70% of the country’s population is of European (mostly British) descent. Therefore, we estimated New Zealand based on the UK.One exception to the use of these genetic maps is New Zealand. The related genetic map12, excepting Croatia and Portugal. The S-allele percentage for Croatia was calculated by averaging data from available studies58. Data for Portugal are provided by Ferreira et al.59.S-allele percentages for most countries are provided by Minkov et al.18 have introduced a typology of four spatial and temporal explanations that are theoretically linked to variation in human behavior across countries. First, geographical explanations based on human biogeographical theory suggest that peoples’ habits are dependent on their habitat locations60. Second, ecological explanations based on climatic theories of human behavior e.g.,61 suggest that climatic harshness shapes peoples’ behaviors as a function of the number of resources they may have to use in coping with these threats coupled with threat severity. Third, evolutionary explanations drawing upon life-history theory e.g.,62 suggest that dangerous and unpredictable environments promote fast life strategies , whereas stable and safe environments promote more self-control. Fourth, contemporary explanations draw upon compensatory-control theories of human behavior e.g.,63, suggesting that threat adaptation is a function of the amount of material resources that people have. Thus, we control for variables subsuming all four sets of environmental factors.Conceptual advancements in ecological psychology18. Latitude has been found to robustly predict cultural values such as societal individualism, dwarfing associations between values and the concomitant longitudinal position18. Axial orientation, defined as the extent to which countries spread out in an east–west vs. a north–south fashion, drives cultural diversity because flora and fauna are more easily domesticated across longitudes than latitudes, allowing for better cultural spread64. It has been found to be strongly associated with cultural diversity, even after considering other environmental factors; scores for all non-archipelago countries are provided by Laitin et al.44. Data were unavailable on this index for two countries in our study, Japan and New Zealand, because they are archipelagos.First, we control for two geographical factors including latitude and axial orientation. Latitudinal position is defined as the geographical position of a country on a bipolar axis ranging from the South Pole through the equator to the North Pole; scores are available for all countries from van de Vliert and van Lange18. Climatic demands have been found to robustly predict cultural values such as societal collectivism61. Rainfall steadiness is defined as the minimum monthly precipitation divided by the maximum monthly precipitation18. Scores for all countries are available from van de Vliert and van Lange18. It represents year-round water accessibility, a vital resource for human populations, with more steady rainfall leading to reduced threats of both deluge and drought and is theorized to be an effective proxy for climate-induced calamities, such as food scarcity, flooding, soil erosion, and crop ruination18.Second, we control for two ecological factors including climatic demands and rainfall steadiness. Climatic demands are defined as a country’s absolute deviation from ambient temperature (22 °C) in both heat and cold in both the hottest and coldest months. It represents the bipolarity of a climate, with larger seasonal variations having greater impacts on human behaviorThird, we control for four evolutionary factors including pathogen prevalence , social diversity , modernization (including urbanization and population density), and social conflict.42. Scores for all countries are available from Fincher et al.42. Data on pathogen prevalence were not available for a few countries in our analysis and were thus estimated based on neighboring countries: Azerbaijan was estimated based on Iran; Rwanda and Uganda were estimated based on Tanzania; Ukraine was estimated based on Russia. Because both historical and contemporary pathogen prevalence were very strongly and positively correlated in our data (r = 0.81), an “average pathogen prevalence” measure was calculated for inclusion in analyses.Pathogen prevalence is calculated as the average rate of nine pathogens that capture all biological detriments to human fitness resulting in disease including leishmanias, trypanosomes, malaria, schistosomes, filariae, leprosy, dengue, typhus, and tuberculosis. Both historical and contemporary epidemiological indicators of these variables were calculated because they have both been found to be strongly associated with cultural values such as societal individualisme.g.,67. We thus control for social diversity, defined as the extent to which a country is fractionalized along ethnic/linguistic/religious lines, and using country scores provided for all countries from Luiz68. Because these three types of diversity tend to coincide , we calculated an averaged “social diversity” score based on the three indices for inclusion in all analyses (α = 0.70).Social differences in language, race, and religion have a long history of being associated with culture 70. We thus control for this factor using World Bank data on the urbanization rate within countries, defined as the percentage of people living in cities71, and population density, defined as the logged number of inhabitants per square kilometer in each country72. Both these variables have been found to predict cultural values such as collectivism and tightness73.Likewise, modernization theory has long attributed cultural differences to the shift from agricultural and relatively isolated forms of living to urbanized and crowded forms of living74. More specifically, a country’s history of territorial threat is defined as the number of conflict incidences that have occurred within its territorial borders8. Illustratively, the bombing of Pearl Harbor and the Russian Revolution were conflict incidences occurring within US and Russian territorial borders, respectively, whereas the Iraq wars and the invasion of Afghanistan were extra-territorial conflict incidences involving these countries. Data on historical conflict incidences from 1918 to 2016 are available for all countries from the International Crisis Behavior Database76.Finally, social conflict has long been identified as a source of threat mandating the formation of socially cohesive societies61. Wealth differences between countries is defined as the gross domestic product per capita (GDP PPP); data were available for all countries in our study from the World Bank77. Wealth differences within countries is defined as the Gini coefficient; data were available for all countries in our study from the World Bank78.Fourth, we control for two contemporary factors, including material resources available for threat adaptation both between- and within-countries . Wealth differences have been found to strongly predict cultural values such as individualism-collectivism20. The remaining five countries were estimated based on cultural values clusters across world regions10, as follows: Burkina Faso and Zambia were based on South Africa; El Salvador was based on Colombia; Tanzania and Uganda were based on Rwanda.Individualism and monumentalism country scores were available for 50 out of 55 countries in our studySupplementary Information.


The presence of β casein haplotypic sequences GAGACCCC, GGAACCCC, GGAACCTC, GGAATCTC, GGGACCCC, GGGATCTC, and GGGGCCCC, linked to differential combinations of increased quantities of higher quality milk in terms of its composition, may also be connected to increased zoometric/linear appraisal predicted breeding values. Selection must be performed carefully, given the fact that the consideration of apparently desirable animals that present the haplotypic sequence GGGATCCC in the β casein gene, due to their positive predicted breeding values for certain zoometric/linear appraisal traits such as rear insertion height, bone quality, anterior insertion, udder depth, rear legs side view, and rear legs rear view, may lead to an indirect selection against the other zoometric/linear appraisal traits and in turn lead to an inefficient selection toward an optimal dairy morphological type in Murciano-Granadina goats. Contrastingly, the consideration of animals presenting the GGAACCCC haplotypic sequence involves also considering animals that increase the genetic potential for all zoometric/linear appraisal traits, thus making them recommendable as breeding animals. The relevance of this study relies on the fact that the information derived from these analyses will enhance the selection of breeding individuals, in which a desirable dairy type is indirectly sought, through the haplotypic sequences in the β casein locus, which is not currently routinely considered in the Murciano-Granadina goat breeding program.Genomic tools have shown promising results in maximizing breeding outcomes, but their impact has not yet been explored. This study aimed to outline the effect of the individual haplotypes of each component of the casein complex  on zoometric/linear appraisal breeding values. A discriminant canonical analysis was performed to study the relationship between the predicted breeding value for 17 zoometric/linear appraisal traits and the aforementioned casein gene haplotypic sequences. The analysis considered a total of 41,323 zoometric/linear appraisal records from 22,727 primiparous does, 17,111 multiparous does, and 1,485 bucks registered in the Murciano-Grandina goat breed herdbook. Results suggest that, although a lack of significant differences ( DevelopThus, the casein cluster is a genomic region of great interest in the goat, and has shown significant genetic diversity and differentiation among small ruminant populations . This geThe consideration of casein haplotypes rather than the use of a single gene or genetic marker has been suggested to maximize the comprehension of heritable mechanisms and how they affect the expression of functional traits related to milk yield, the production of its different components , cumulative milk production, and the greater or lesser presence of somatic cells , 12. AltIn 1993, the American Dairy Goat Association introduced the Linear Appraisal System for dairy goats to improve production yields. However, the implementation of the Combined Goat Index and Morphological Index in the selective nucleus of the rustic Murciano-Granadina goat breeders’ association (CAPRIGRAN) did not occur until 2010 , 14. TheThe phenotypic relationship between zoometrics and dairy production  has been investigated –19 and tIn this context, the present study aims to develop a discriminant canonical analysis (DCA) tool that outlines the effect of the individual haplotypes of each component of the casein complex  on zoometric/linear appraisal breeding values. The information derived from the present analyses will help to plan strategies that support the standardization and improvement of the productive capacity of this native goat breed to seek the consolidation of the breed in the international dairy goat panorama.2.2.1.2.1.1.ad libitum water, forage, and supplemental concentrate were provided.The Murciano-Granadina whole-pedigree data file comprised 279,264 animals  and was used as the pedigree matrix for genetic analyses. A total of 15 maximum generations and six complete generations were evaluated. Animals were born from June 1966 to November 2019. The linear appraisal was performed for 41,418 animals across the year. Animal records were collected from 76 farms in the south of Spain from 09/06/2010 to 18/12/2019. All the farms considered in the study had received official National and International Sanitary Certificates. All farms were controlled and officially declared tuberculosis-free (C3), brucellosis-free (M4) (Order of 22 June 2018 and Directive 91/68/EEC), and scrapie RC free [Regulation (EC) No 999/2001 of the European Parliament and the Council]. Additionally, these farms followed voluntary control plans for caprine contagious agalactia (CCA)  and caprine arthritis encephalitis (CAEV) (Order AYG/287/2019 of 28 February 2019). Goats were clinically examined by an official veterinarian, and individuals presenting signs of illness or disease were officially declared, removed from the herds, and discarded from the analyses. Permanent stabling practices were followed by all farms considered, and Records from 95 individuals were discarded due to missing or incomplete zoometric and linear appraisal observations. A total of 41,323 records, belonging to 22,727 herdbook-registered primipara does, 17,111 multipara does, and 1,485 bucks, were considered in the analysis. The average ages for primipara, multipara does, and bucks in the sample were 1.61 ± 0.35 years, 3.96 ± 1.74 years, and 2.43 ± 1.49 years (μ ± SD), respectively. Descriptive statistics, kurtosis, and skewness for Murciano-Granadina Linear appraisal system (LAS) zoometric traits can be found in Fernández Álvarez et al. .2.1.2.Each observation comprises the rater’s score for each animal in the following four major categories for primipara and multipara does : structure and capacity, dairy structure, mammary system , and legs and aplomb. In primipara and multipara does, each record comprised information on 17 linear traits rated on a 9-point scale. Given that bucks were not scored for the mammary system major category, only 10 traits were scored for them following the aforementioned 9-point scale. Body depth from the structure and capacity major category and the dairy structure and legs and feet major categories followed the same criteria as the independence of sex and sexual status. The same trained rater scored all animals in the study.Once all major categories are scored, the final score represents how close the overall animal comes to the optimal dairy standard. Murciano-Granadina LAS establishes that each major category contributes to the final score  for primipara and multipara does (any doe that has ever produced milk). In bucks and young males, the percentages change to 50% for structure and capacity, 20% for dairy structure, and 30% for legs and fee.The rater’s scores are assigned one of the six category qualifications considered by CAPRIGRAN as follows: insufficient (IN) for animals that display less than 69% of the optimal standard for Murciano-Granadina dairy goats ; mediocre (R), 70–74% of the optimal standard ; good (B), 75–79% of the optimal standard ; quite good (BB), 80–84% of the optimal standard ; very good (MB), 85–89% of the optimal standard ; or excellent (E), when at least 90% of the optimal standard is displayed . The scales used and the translation process from zoometric traits to LAS traits is described in detail by Sánchez Rodríguez et al.  Supplem.p < 0.01), hence redundancies could be presumed for the outputs of linear or type appraisal if both age components were simultaneously considered. Thus, the lactation stage was considered and results for primipara and multipara goats were reported separately.Age elements, for instance, the age of does and/or the lactation order condition the dairy linear or type appraisal-related traits . Hence, 2.1.3.p < 0.0001 or > 0.5, p-values are computed using a well-fitting linear regression function in log P against the test statistic.Common parametric assumptions were tested in the Murciano-Granadina goat breed zoometric and LAS historical records collected up until December 2019. A Kolmogórov–Smirnov test and Levene’s test were used to evaluate normality and homoscedasticity, respectively, using SPSS Statistics for Windows (version 25.0). Given the large sample size used in this study, the non-parametric method proposed by Hoeffding , which u2.2.2.2.1.The estimation of phenotypic and genetic parameters is presented, developed, and discussed in the study by Fernández Álvarez et al. . HoweverThe complete kinship matrix used for genetic analyses comprised all the 279,264 animals  in the Murciano-Granadina goat breed pedigree. As the literature suggests, when bucks start rutting, male goats display behaviors associated with the urge to breed; they go through physical changes that even make specific variables, such as rump angle, decrease by 3 degrees . The breAs a result, a total of 39,838 records, belonging to 22,727 herdbook-registered primipara does and 17,111 multipara does, were considered in the genetic analysis. Animals were only scored once in their lifetime. Therefore, a multitrait animal mixed model with single measures was used to estimate (co) variance components, and the corresponding heritability, repeatability, phenotypic and genetic correlations, and standard errors of such correlations for the traits under examination. Pairs of two zoometric/linear appraisal variables were evaluated once each time until all possible pairwise combinations had been tested. In matrix notation, the following multitrait animal model with single measures was used:ijklmn is the vector of observations for each separate measure of each pair of zoometric or LAS traits (i is the vector for the fixed effect of the ith farm/herd (i = 76 farms); LacStatj is the vector for the fixed effect of the jth lactation stage (j = primipara/multipara does); KMonk is the vector for the fixed effect of the kth kidding month (k = January to December); and IntFarm/KYearl is the vector for the fixed effect of the lth level of interaction between farm/herd and kidding year (l = 400 interaction levels possibilities combining the 76 farms and kidding years from 2005 to 2019); days in milk was considered a linear covariate, hence b1 is the linear regression coefficient on days in milk (DIMm), age in years was considered a linear and quadratic covariate, hence b2 and n), eijklmn is the vector of random residual effects, and Ai, Bj, Ck, and Dl are incidence matrices relating records to their respective fixed effects, while Em and Fn are incidence matrices relating records to their respective random effects. Only the direct genetic effect  was fitted in each model due to zoometric/LAS scores being recorded only once for each animal.where YS traits  for a gi−12. Link functions can be found in Boldman et al.  and their accuracies and reliabilities for zoometric and LAS traits for each animal in the matrix, using MTDFREML software. The standard errors for heritability and genetic and phenotypic correlations were computed by the same software. The fact that bucks were not considered for genetic evaluations and genetic parameter estimation must be considered. As a result, bucks’ breeding values were estimated from the female individuals connected to them through their genealogy. When the average difference in zoometric parameters between males and females is ignored, the estimate of heritability has been reported to reduce  as well,2.3.1.AP), and accuracy (RTi) were calculated. The aforementioned parameters relate to each other through their definition and equation determination [RAP = RTi2 = (1–SEP2/Va)2], from which Va is genetic additive variance.Standard error of prediction (SEP), reliability , while accuracy measures how close a certain estimated value is to the real value. Their interpretation is, therefore, different. For example, for the evaluation of RTi, Scheme  suggestsAP) is considered, the rule of thumb proposed by Horse  measures how large prediction errors  are for the data set measured in the same unit for each of the zoometric or LAS traits measured; therefore, it provides a direct measure of possible change, i.e., the risk of the true breeding value of the animal (TBV) not to be aligned on the PBV.Van Vleck  suggesteThe first half of the TBV would be expected to be higher than the PBV, while the second would be expected to be lower than the PBV. The interval range from PBV – (1) SEP to PBV + (1) SEP corresponds to a 68% probability that the TBV for an animal is centered on the PBV for the animal. Such an interval can be narrowed or widened corresponding to the probability of the TBV in the interval. For instance, one could expect the interval from PBV – (2) SEP to PBV + (2) SEP to contain 95% of the TBV. Units of SEP other than (1) or (2) would correspond to other confidence intervals. With a 68% confidence interval, 32% would be half over and half below the intervals’ ends, while with the 95% interval, the percentage placed out of the interval would be 5% . Ranges for many combinations of PBV and SEP may overlap considerably. Then, by observing which PBV centers the interval and comparing intervals, a rather direct measure of risk compared with that of accuracy (RTi) is obtained.2.3.2.AP) were calculated using the Descriptive statistics routine of the Analyze set of SPSS (version 26.0). Afterward, a Kruskal–Wallis H test was used to study the potential existing differences between predicted breeding values for zoometric/linear appraisal traits across haplotypes of the same casein gene, as three or more possibilities were available using the independent samples routine of the Nonparametric tests package within the Analyze set of SPSS (version 26.0).Maximum and minimum for zoometric/linear appraisal traits predicted breeding values (PBVs) and standard error of prediction (SEP). Accuracy (RTi) and reliability  were ranked considering the most recent and updated official breeding values for milk yield and composition reported for all the animals published in 2015. Provided multiple traits were considered, we developed combined selection index (ICO) procedures following the premises in Van Vleck  to summa1 is the weight for milk yield, W2 is the weight for fat, W3 is the weight for protein, and W4 is the weight for lactose in kg and normalized at 210 days; and where PBV is the predicted breeding value for each of the traits and animals included in the matrix; W2.3.4.A modification of the procedure described by Miller et al.  was perfMinor allele frequency (MAF) was calculated to differentiate between common and rare variants (MAF < 0.05) using PLINK v1.90 . The lin2.3.5.Phasing (or haplotyping) describes the process of determining haplotypes from the genotype data . As sugg2.4.If protein levels in milk have been associated to certain zoometric variables at a phenotypic level, it is reasonable to think that a genetic connection may exist between such zoometric traits and specific regions in the genome encoding for the expression of some of those proteins in milk. Haplotypes act as categorical variables, while PBVs act as numeric traits, hence DCA is appropriate for testing differences exhaustively. Canonical discriminant analyses (CDAs) were performed to design a tool that enables the classification of goats while determining whether linear combinations of predicted breeding values for zoometric/linear appraisal traits  describe within-and between-population αS1, αS2, β, and κ casein haplotypes and haplogroups clustering patterns. The explanatory variables used for the present analyses were the predicted breeding values for each of the zoometric/linear appraisal-related traits presented in Canonical relationships with traits were plotted to depict the group differences in an easily interpretable territorial map. Regularized forward stepwise multinomial logistic regression algorithms were used to perform the variable selection. Priors were regularized according to the group sizes calculated using the prior probability of commercial software  instead of considering them the same to avoid groups with different sample sizes affecting the quality of the classification .The same sample size contexts as those used in this study across groups have been reported to be robust. In this regard, some authors have reported a minimum sample size of at least 20 observations for every four or five predictors, and the maximum number of independent variables should be n-2, where n is the sample size, to palliate possible distortion effects , 52. ConThe discriminant routine of the Classify package of SPSS (version 26.0) and the canonical discriminant analysis routine of the Analyzing Data package of XLSTAT  were used to perform canonical discriminant analysis.2.4.1.per se, a multicollinearity analysis was run to discard potentially strong linear relationships across explanatory variables and ensure data independence. In this way, before data manipulation, redundancy problems can be detected, which limits the effects of data noise and reduces the error term of discriminant models. The multicollinearity preliminary test helps to identify unnecessary variables that should be excluded, preventing the overinflation of variance explanatory potential and type II error increase (2) concerns the amount of variability in a certain independent variable that is not explained by the rest of the dependent variables considered (tolerance >0.20)  and tolerance . VIF is increase . The varincrease . On the e >0.20) . The mul2 is the coefficient of determination of the regression equation.where R2.4.2.The maximum number of canonical correlations between two sets of variables is the number of variables in the smaller set. The first canonical correlation usually explains most of the relationships between different sets. In any case, attention should be given to all canonical correlations, despite reporting only the first dimension as common in previous studies . When ca2.4.3.2-tests the Wilks’ lambda significance. If the significance is below 0.05, it can be concluded that the function can explain the group adscription well  and the phylogeny procedure of MEGA X 10.0.5 .2.4.6.Afterward, to determine the probability that a goat presenting an unknown haplotype belongs to a particular haplotypic group , the hitPress’ Q statistic can support these results as it can be used to compare the discriminating power of the cross-validated function, as follows:where n is the number of observations in the sample; n’ is the number of observations correctly classified; and K is the number of groups.2 with a degree of freedom at a significance of 0.01. When Press’ Q exceeds the critical value of χ2 = 6.63, the cross-validated classification can be regarded as significantly better than chance.The value of Press’ Q statistic must be compared with the critical value of 6.63 for χ3.3.1.AP) for zoometrics and LAS traits sorted by sex and lactation stage is shown in AP in bucks (0.000–0.980) when compared to primipara (0.000–0.672) or multipara does (0.000–0.740)]. The lowest RAP was reported for the PBVs for zoometric or LAS traits in either multipara or primipara does, while the highest was again reported for stature .The estimation of genetic parameters was performed as a necessary intermediate stage for the prediction of breeding values. The estimation, presentation of results, and a deep discussion can be accessed in Fernández Álvarez et al. . A summap < 0.001), suggesting that the translation of zoometry to LAS was nearly flawless.The RTi scores confirmed a similar pattern, and the Pearson product–moment correlation analysis revealed a correlation coefficient of approximately 1, indicating a high level of consistency in the genetic parameters between the PBVs for zoometric and LAS traits. Additionally, this correlation was statistically significant (3.2.p < 0.05) were found across the haplotypes of the β casein gene for the predicted breeding values of stature (height to withers), rump width, rump angle, median suspensor ligament, and body depth.The only significant differences revealed after the performance of the Kruskall–Wallis H test (3.3.PBVs for stature (height to withers) and rump width were discarded from the analyses because they presented VIF values over 5 . A signi3.4.Variables were ranked depending on their discriminating properties. For this, a test of equality of group means across β casein haplotypes was used . Lower vQ-value of 210.19  was obtained. Therefore, it can be considered that predictions were significantly better than chance at 95% , pelvic , and udder morphometry have been the basis of application of morphological indices in reproductive and milk production performance assessment of type and function in goat and sheep production. Udder morphological characteristics values are significant indicators of the milking capacity of individual animals in dairy goat and sheep enterprises, and udder size is strongly and positively correlated with milk yield. The review recommends the incorporation of highly correlated linear type traits through the development of genotypic characterization in community-based breeding schemes. This will feed into genetic improvement strategies and productivity schemes. In most cases, the number of conformation traits recorded in conventional breeding schemes is relatively small because it might be expensive. Therefore, there is a need to focus on developing methods to measure morphological traits that are rapid and accurate at quantifying multiple conformation measurements while minimizing costs.Our study moves from the investigation of the mere relationship between zoometry and milk production and composition to the relationship between breeding values for zoometric/linear appraisal breeding values and casein haplotypes. The casein haplotype structure varies greatly across breeds. However, its study is still scarce, particularly in species other than the cow. This translates into a patent lack of documents to compare with. Our results suggest that no differences in zoometric/linear appraisal related traits may be ascribed to the different haplotypic forms of the αS1, αS2, and κ Casein genes .Particularly, some authors, such as Pizarro Inostroza et al. , reporteThe different haplotype combinations can be determined when the β casein locus is considered to exert a strongly favorable effect on milk yield and composition, in which the presence of the alleles A, G, T, and C is related to higher production and composition percentages. Contrastingly, G and T alleles may imply a reduction in somatic cell counts. However, this contrasts the finding by Baltrėnaitė et al. , who didPizarro Inostroza et al.  reportedConsequently, the GGGATCCC haplotype may indeed be linked to reduced PBVs for several zoometric/linear appraisal traits, including those for rear insertion height, bone quality, anterior insertion, udder depth, rear legs side view, and rear legs rear view. This, in turn, means that goats carrying this haplotype may not be ideal for breeding if the goal is to improve these specific traits. However, it is important to note that the relationship between the GGGATCCC haplotype and these traits is not fully understood. Further research is needed to clarify the genetic basis of this association and to determine whether it is a causal relationship or simply a correlation.By contrast, the GGAACCCC haplotype has been associated with increased PBVs for several zoometric/linear appraisal traits, including rear insertion height, bone quality, anterior insertion, udder depth, rear legs side view, and rear legs rear view. This makes goats carrying this haplotype highly desirable for breeding if the goal is to improve these traits.Given the importance of the β casein gene in milk production and the potential impact of these haplotypes on zoometric/linear appraisal traits, the inclusion of genotype or haplotype for β casein in the stud catalogs of the Murciano-Granadina breed is recommended, alongside those of αS1 and κ casein, which are routinely tested for in merit bucks. By routinely testing for these haplotypes, breeders would be able to identify animals that have the potential to genetically transmit desirable traits to their offspring and avoid breeding animals that may have negative effects on certain traits. This can help maintain the overall quality and productivity of the Murciano-Granadina breed and ensure that it remains a valuable contributor to the dairy industry.5.In conclusion, although the relationship between β casein haplotypes and zoometric/linear appraisal traits in Murciano-Granadina goats is not fully understood, inclusion of the genotype or haplotype for β casein in stud catalogs is highly recommended. This will enable breeders to identify animals that have the potential to genetically transmit desirable traits to their offspring and avoid breeding animals that may have negative effects on certain traits. Ultimately, this can help to maintain the overall quality and productivity of Murciano-Granadina goats.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Ethical review and approval was not required for the study on animals in accordance with the local legislation and institutional requirements.JF, FN, and AG: conceptualization and writing—original draft preparation. FN and JL: methodology, data curation, software, and visualization. MM and JD: validation, formal analysis, resources, and funding acquisition. JF, FN, AG, CP, and MP: investigation. JD: project administration. FN and AG: writing—review and editing. FN: supervision. All authors have read and agreed to the published version of the manuscript.The present research was developed under the context of a Ramón y Cajal Post-Doctoral fellowship financially supported by MCIN/AEI/10.13039/501100011033 and European Union Europea “NextGenerationEU”/PRTR” .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.