In semi-cold, moderate and warm climates, daughters of French sires had the highest least-square means of LNI index. In cold climate, daughters of German sires had the highest least-square means.We evaluated the impact of imported semen of Holstein bulls from different countries on their daughters’ economic traits using the Lifetime Net Income (LNI) index in different climates of Iran. The first lactation records were collected from 10 big dairy farms in various regions of Iran. Studied traits included milk, fat and protein yields, calving age and calving interval. There were significant differences between sire groups for studied traits in each climate (p < 0.01). The obtained results showed that the highest least-square means of LNI index in semi-cold, moderate and warm climates belonged to the daughters of French sires; however, daughters of German sires were estimated to have the highest least-square means in the cold climate.The aim of the present study was to evaluate the effects of imported semen of Holstein bulls from different countries on the economic traits of their daughters using the Lifetime Net Income (LNI) index in various climates of Iran. The data included the first lactation records of 274,057 Holstein cows collected during 1993 to 2017 by the Animal Breeding Center of Iran from 10 large dairy farms located in various provinces of Iran. The investigated traits included milk, fat and protein yields, calving age and calving interval. Breeding values of progenies were predicted by the Best Linear Unbiased Prediction (BLUP) method under the multi-trait animal model using DMU software. The genetic-economic merit of the progenies was estimated by the LNI index. There were significant differences between the estimated breeding values (EBVs) of sire groups (based on bull semen origin) for milk, fat and protein yields, calving age and calving interval in each climate ( For many years, dairy cattle breeding programs have mainly been directed toward the improvement of production traits. This has been true for most countries, except for Scandinavian countries, whose selection indices have also included health and reproduction, and North American countries, whose selection indices have included conformation traits . On the Selection index is a theory for maximizing genetic progress to achieve maximized profitability . Since 1In the Holstein cattle population, genetic improvement through the selection of males compared to females is of crucial importance . ImportiData used in the present study included the first lactation records of 274,057 Holstein cows from 1993 to 2017 provided by the Animal Breeding Center of Iran (ABCI). The origin of sires was from the United States, Canada, France, the Netherlands, Italy and Germany. The investigated traits included milk, fat and protein yields, calving age and calving interval. Cows with less than 1000 kg and more than 15,000 kg of total milk production, herds with fewer than 30 records and first calving age less than 18 months and more than 42 months were excluded from data.Records were classified under cold, semi-cold, moderate and warm climates based on the climatic data for stations where data collected . Accordiith trait; ith trait ; ith trait; ith trait; and Breeding values of progenies were estimated by the Best Linear Unbiased Prediction (BLUP) methodology under multi-trait animal models using DMU software . The mulData from 10 dairy farms situated in different regions of Iran, where the majority of industrial dairy farms are situated, were utilized to calculate economic weights of traits. The production and reproduction records of cattle in all farms were tracked by ABCI. Performance of the cows was retrieved from a large collection of data given by ABCI that was collected by farmers through a survey, or it was calculated using cost and revenue modeling. Data sources used for deriving economic input parameters were based on the marketing circumstances in 2017 and 2018.Under Iranian dairy farm systems, the main dairy herd population comprises four major types of animals including calves, heifers, cows (both milking and dry), and fattened calves. Because it is related to feedlot and cattle production, the last group was excluded from the profit function. Profit, determined as total income minus total costs, was selected as the economic assessment criterion for the stated production system. Profit was expressed in terms of per cow calving per year as the reference unit. Iran’s currency is the Rial (Rl). Henceforth, the costs are stated in the US dollar, assuming an exchange rate of USD 1 = IRR 42,000. The following is the total profit function (Equation (2)):The impacts of genes expression may be observed as genetic development in diverse characteristics across time in various animal populations. Due to the potential cost of delayed gains, it is usually desired for genetic supremacy to be shown as soon as possible. This research updated the gene flow approach initially described by  for beefAccording to , the ecoThe first derivative of the profit function (Equation (1)) was selected considering milk, fat and protein yields for which the EV was required. Since there are no supply quotas for dairy farms in Iran, this is essentially equal to collecting the difference between the profits and costs of producing 1 kg of each product. Expenses associated with milk production were calculated by taking into account feed and labor, breeding, veterinary, fuel, housing and insurance costs. Only expenses related to metabolizable protein and net energy of lactation requirements were taken into account for protein and fat yields . The estThe EV for age at first calving was estimated according to . In addiThe EV of calving interval was calculated as the first partial derivative of the total profit function (Equation (1)), with respect to calving interval. Since cows express the trait just once throughout calving intervals and as a result have a relative gene expression coefficient of unity, the EW of calving interval is equivalent to the EV.In the present study, the EW was estimated to be USD −0.051 per kilogram of milk yield; USD 4.881 per kilogram of fat yield; USD 3.985 per kilogram of protein yield; USD −0.916 per day of age at first calving; and USD −1.695 per calving interval.The genetic-economic merit of 274,057 cows in Iran and its various climates was estimated using the LNI index as follows (Equation (3)):ijklmy and ijkmy are observation, μ is the overall mean, iH is the fixed effect of ith herd, jYS is the fixed effect of jth year-calving season, kR is the fixed effect of kth climate, lI is the fixed effect of lth selection index, (R × I)kl is the interaction between kth climate and lth selection index, kC is the fixed effect of kth sperm exporting country and ijklme and ijkme are random residual effects.The models used to analyze estimated breeding values (EBVs) and LNI index in Iran (Equation (4)) and each climate (Equation (5)) were as follows:The comparison of EBVs and LNI index of the sire groups was carried out by Tukey test using SAS 9.2 software . FinallyDescriptive statistics of investigated traits is presented in p < 0.01). Daughters of French sires had the highest EBVs for production traits  in Iran and semi-cold, moderate and warm climates. In the cold climate, the highest EBVs for milk and fat yields belonged to daughters of American sires, and for protein yield, the highest EBVs were estimated for daughters of German sires.The comparisons of EBVs for production traits between cows from different sire groups in Iran and cold, semi-cold, moderate and warm climates are shown in The Canadian selection indices have always had a high relative emphasis on the production traits ,8; howevp < 0.01). In Iran and semi-cold, moderate and warm climates, the daughters of French sires had the highest EBVs for the first calving age, and the daughters of German sires had the highest EBVs for the calving interval. Under cold climate, the daughters of German sires and French sires had the highest EBVs for the first calving age and calving interval, respectively. Since 2000, France included reproductive traits in selection indices of dairy cows in addition to the production traits. Thus, from 2001 to 2019, the relative emphasis on reproductive traits increased from 12.5% to 22%. Moreover, from 1997, selection indices in Germany have directly been focused on reproductive traits. However, the extent of this emphasis has always been relatively low so that the relative emphasis on these traits is only 10% today [Comparison of EBVs for reproduction traits between daughters from different sire groups in Iran and cold, semi-cold, moderate and warm climates is shown in 0% today . Applyin0% today .Comparison of least-square means of LNI index for the studied production and reproduction traits between cows from different sire groups in Iran and cold, semi-cold, moderate and warm climates is shown in As a result, it seems necessary to develop a specific genetic-economic selection index for each climate of Iran, and breeding and economic objectives of different regional climates should be considered.There were significant differences between the EBVs for milk, fat and protein yields, the first calving age and calving interval of sire groups (semen origins) in Iran and its various climates. In Iran, the highest and lowest EBVs belonged to French and German sires, respectively. In addition, the daughters of French sires in moderate climate had the highest EBVs regarding milk production, while the best performance of fat and protein production belonged to daughters of German sires in moderate and daughters of French sires in semi-cold climates, respectively. The lowest age at first calving was observed in calves of French sires in moderate climates, and lowest calving interval belonged to daughters of German sires in warm climates. Furthermore, in Iran and semi-cold, moderate and warm climates, the highest least-square means of the LNI index of progenies (genetic-economic merit) were estimated for French sires and in cold climate for German sires. As a result, it seems necessary to develop a specific genetic-economic selection index in each climate of Iran, and we recommended semen be imported based on breeding and economic objectives of different climates.


P ≤ 0.01) differences. Additionally, throughout the course of two growing seasons, the mean squares for genotypes (parents and crosses) showed strong significance for all the variables under study. The greatest and most desired means for all the examined qualities were in the parent G.94, Pima S6, and tester G.86. The best crossings for the qualities examined were G.86 (G.89 × G.86), G.93 × Suvin, and G.86 × Suvin. The parents' Suvin, G89x G86 and TNB were shown to have the most desired general combining ability effects for seed cotton yield/plant, lint yield/plant, boll weight, number of bolls/plants, and lint index, while Suvin, G.96 and pima S6 were preferred for favored lint percentage. For seed cotton yield, lint percentage, boll weight, and number of bolls per plant per year, the cross-G.86 x (G.89 × G.86) displayed highly significant specific combining ability impacts. The crosses G.86 × Suvin, Kar x TNB, G.93 × Suvin, and G.93 × TNB for all the studied traits for each year and their combined were found to have highly significant positive heterotic effects relative to better parent, and they could be used in future cotton breeding programs for improving the studied traits.To generate high-yielding cultivars with favorable fiber quality traits, cotton breeders can use information about combining ability and gene activity within a population to locate elite parents and potential F1 crosses. To this end, in the current study, twelve cotton parents  and their F1 crosses obtained utilizing the linex tester mating design were evaluated for their general and specialized combining abilities  of yield traits. The findings showed that for all the investigated variables, variances owing to genotypes, parents, crosses, and parent vs cross showed extremely significant ( Cotton is one of the most essential multi-purposes crop due to a wide range of its benefits whether for human or animal such as feed, fiber, protein and oil. Despite the importance of cotton crop for many countries, its yield and productivity as any crop facing and affected by many environmental factors such as biotic and abiotic stresses. Therefore, the plant breeders meet a big challenge to improve cotton crop in terms of yield, yield components and fiber quality against theses harmful stresses . Cotton The main focus of the cotton improvement program was on developing hybrids, which has helped increase the productivity of cotton –17. The The selection of parents or inbreeds based on their phenotypic diversity with strong combining ability is critical in developing better hybrids in a heterosis breeding program. The investigation of general and specific combining ability aids in the identification of parents or inbreeds for the generation of superior hybrids. The Line x Tester analysis is one of the easiest and most efficient methods of assessing the combining ability of many inbred/parents. Production of commercially viable hybrids is achievable based on the results of the Line x Tester analysis. Yield is a complicated polygenically inherited character that is the outcome of the multiplicative interaction of its constituent characteristics. Because it is heavily influenced by the environment, selecting solely on yield may limit future improvements. The yield component traits, on the other hand, are less complex in inheritance and are influenced by the environment to a smaller extent. Thus, selection on yield component quality can result in effective yield improvement. To differentiate between the high- and low-performing parents in a hybrid combination, data on the nature of gene activity must be evaluated. As a result, the line by tester approach aids in identifying the gene activity responsible for the manifestation of features of interest in both small and large sample sizes , 22. A sIn this respect and with the above-mentioned various aspects of cotton background. The main objectives of the current research are to assess the combining ability, heterosis and performance of cotton for yield, and yield components using a line × tester mating design strategy, which helping the cotton breeder to determine best superior parents and progenies for simultaneous improvement of cotton crop for its yield, yield components and quality.1 top crosses as out lined by [1 hybrids for two years 2016 and 2017, respectively. The experimental design was randomized complete blocks design with three replications. Each plot was represented by one row 4 m long and 0.7 m width and 40 cm between hills and one plant were left per hill. The recommended agricultural agronomic and cultural management practices  by agriculture research center (ARC) were applied at the proper time as and when required.The present investigation was carried out at Sakha Agricultural Research Station during the three growing seasons 2015, 2016 and 2017. The genetic materials used in the current study were twelve genotypes, four of them as tester male parents and eight genotypes as female parents. The names, origin of these cotton genotypes is furnished in , by weighting of seed cotton (Sc) for one hundred seeds. Number of bolls/ plant (No. B./P) of random plants, Lint index (L.I), by weighting of lint from one hundred seeds.corrplot package cluster dendrogram and PCA biplot were conducted using factoextra package, and path analysis was performed with lavaan package.Statistical analysis was performed for each year. Combined analysis between the two years was done whenever homogeneity of error mean squares was detected for the studied characters according to . The comThe Shapiro–Wilk and Brown–Forsythe tests were used to determine whether the data had a normal distribution and whether the variances were homogeneous. Analyses of variance (ANOVA) and mean discrimination analysis were conducted on variables that met both assumptions.The cluster analysis of six traits was conducted based on Euclidean distances using by unweighted paired group method using the arithmetic average (UPGMA). Based on the matrix data of dissimilarity coefficients, a dendrogram was performed as shown in Fig. Analysis of variance (ANOVA) shown in Table The mean performance of lines, testers, and their interaction for all studied traits in each year are showed in Table . The perThe mean performance of the thirty-two top crosses in each year for all the studied traits are shown in Table . The resThe average of lines x testers crosses performances were used to estimate general combining ability effects (GCA). Estimates of general combining ability effects of lines and testers for all the studied traits in each year are presented in Table , Data shThe SCA mean squares were significant for all studied traits. Thus, the significance of SCA (variances due to lines x testers) implied that both additive and non-additive types of variation was available for all the characters, yet additive genes were more important than the dominant genes because variance due to GCA was higher than that of SCA. Estimates of specific combining ability effects of 32 crosses for all the studied traits for each year are presented in Tables  and 6. D1 mean performance from the corresponding better parent for all the studied traits are presented in , Kar. X TNB, G.93 X (G.89 X G.86) and G.93 × G.70 had highly significant positive heterosis relative to better parent in each year. The other crosses remaining exhibited significant negative or non-significant positive or negative values for these traits. It could be concluded that most of the crosses which exhibited highly significant positive heterosis relative to better parent could be utilized in perspective cotton breeding programs for improving these traits.For boll weight seven crosses gave significant positive heterotic effects relative to better parent in each year. The 32 FThe result of direct and indirect correlation coefficients regressed with seed cotton yield was presented in Fig. Figure 1 hybrids from a cross between eight different cotton genotypes, or "female parental lines," and four high-yielding testers in this study. Using cluster analysis, we were able to see how the different lines and testers used to build heterotic pools related in terms of the six yield attributes we were looking at. Then, we were able to cross these lines and testers. Based on this study's findings on cluster analysis, it was decided to cross twelve different female paternal genotypes and testers, resulting in the development of super heterotic hybrid cottons. As a result of this research, we can confidently say that certain cotton types can be further altered for crossbreeding utilizing cluster analysis. Previously, similar findings were found in another experiment. [A "line x tester" analytical approach was used to develop 32 Feriment. , 46–48. eriment. –53.P < 0.01) for all traits, further subsequent analysis were performed to assess combining ability [The results of the F1 crosses, together with the twelve parent genotypes, were examined for six yield and yield components traits. Heterosis breeding can only succeed if the genetic diversity between female parent genotypes and tester lines can be accurately measured. There were substantial genetic differences among the 44 genotypes found by using ANOVA in this investigation . These promising hybrids could be selected for further recombination breeding programs based on their performance and significant specific combining ability. Nevertheless, not all of F1 hybrid combinations showed positive SCA values for all the evaluated traits concurrently, stating that specific hybrid combinations having high significant SCA for several traits had both parents with a good GCA , 84, 85.In general, variances of GCA and SCA point out the magnitude of gene action, and this further helps in developing an appropriate breeding strategy for future breeding programs , 86, 87.The regression analysis was conducted to investigate the dependence between several variables. All the yield-related traits are correlated with each other in a way that increases or decreases in one trait directly affects others. Thus, estimation of association among yield,, and yield components are helpful to initiate and select the most appropriate breeding methods . EstimatDespite being essential, the correlation coefficient can lead to misunderstandings regarding the relationship between two traits and does not have to be an accurate measure of cause and effect. Thus, the strength and efficiency of the correlation coefficient between two characters may be attributed to the influence possessed by a third trait or collection of traits on the traits, which does not provide the precise relative importance of the direct and indirect effects of the elements being studied . These jThe parent G.94 and Pima S6, as well as the tester G.86, had the best means for all the traits. The crosses G.86 (G.89 × G.86), G.93 × Suvin, and G.86 × Suvin were the elite genotype for all studied traits. The parents Suvin, G89x G86, and TNB had the most desirable GCA effects for SCY, LCY, BW, NOBP, and LI. Suvin, G.96, and pima S6 had the most desirable L%. The cross-G.86 x (G.89 × G.86) showed high significant SCA effects for SCY, L%, BW, and NOBP. whereas the crosses G.86 × Suvin, Kar x TNB, G.93 × Suvin, and G.93 × TNB had a highly significant positive heterotic effects for all the studied traits. This could be recommended this cross for use in future breeding programmes to improve both lint yield and fibre length.


It is highly correlated with genes already reported for facial growth. This study is the first to classify and characterize facial growth patterns and related genetic polymorphisms.Childhood to adolescence is an accelerated growth period, and genetic features can influence differences of individual growth patterns. In this study, we examined the genetic basis of early age facial growth (EAFG) patterns. Facial shape phenotypes were defined using facial landmark distances, identifying five growth patterns: continued-decrease, decrease-to-increase, constant, increase-to-decrease, and continued-increase. We conducted genome-wide association studies (GWAS) for 10 horizontal and 11 vertical phenotypes. The most significant association for horizontal phenotypes was rs610831 (TRIM29; β = 0.92, p-value = 1.9 × 10 Therefore, skull growth and facial morphology are of interest3 to many scientific disciplines, especially anthropology, genetics, and forensic science4. Our face shapes change continuously from infanthood to adulthood. During the early ages, from 1 to 20 years, our face shapes grow rapidly, and genetic features may be responsible for individual differences in facial phenotypes. The period from childhood to adolescence is characterized by accelerated growth, and developmental modeling of facial morphology is useful for forensic and biomedical practices. Because the number of missing persons is increasing every year and technology is required to estimate the present face from the past. To understand early age facial growth, an important point is considering of the growth direction7.Differences in the relative size, shape, and spatial arrangement 5. Similarly, the development of genotyping technology facilitates the exploration of genetic impacts on human facial morphology phenotypes6. Although various studies have been conducted to examine facial growth, most studies have focused on identifying the causes of craniofacial abnormalities7. In the study of the healthy individuals of Europeans ancestry, some genes such as MAFB, PAX9 associated with craniofacial development or syndromes8. The genetic loci associated with facial phenotypes were reported genes such as PRDM16, PAX310, and TP639. Research to investigate the biological basis underlying the normal range of facial variability has only recently been conducted. Over the past few years, as facial recognition technology has improved, substantial progress has been made in the identification of loci related to facial traits in published genome-wide association studies (GWAS)11. The starting point of GWAS analyses for facial morphology begins with craniofacial development or the identification of genetic loci associated with genetic facial deformities and syndromes. According to recently reported GWAS results2, studies on human phenotypes have identified and reported multiple loci associated with normal facial surface morphology.The past 10 years of facial morphology research has benefited from the development of image recognition technology, which can quickly and accurately capture the details of the face14. However, genetic and GWAS studies are mainly studies of facial morphology in adults. The association between facial phenotype and SNP has been reported in European adolescents16, and facial changes  in 15-year-old British children have been studied17, but mostly adults. Therefore, understanding the relationship between facial shape and genetic loci during rapid facial changes period helps innate understanding of the face. It is expected to affect the overall industry necessary for children's awareness. This study was conducted on Korean subjects, the purpose is to increase the probability of identity inference by predicting the faces of long-term missing children. Therefore, it is important to understand the research in the period when the face shape changes rapidly.While facial variation is influenced such as age and nutritional status, striking facial similarities within families reveal a strong genetic component18 not for facial growth. In this study, we aimed to analyze early age facial growth (EAFG) patterns using facial landmark distances measured from normal facial surface phenotypes to examine the possible genetic basis of individual differences among two Korean populations.A reported study of facial morphology in Koreans identified five GWAS loci associated with facial tratsWe collected current and past photos from participants. For each participant, one current photo was obtained through studio photography, and 5 to 7 past frontal photos were collected. For the age of the past photos, 1 to 2 photos were collected and used for the study, each of which was less than 5 years old, 5 to 10 years old, 10 to 15 years old, and 15 to 20 years old. Supplementary Table We quantified the facial features of two independent populations who were recruited during separate periods: 172 individuals in Population 1 (POP1) were recruited from January 2019 to July 2020, and 100 individuals in Population 2 (POP2) were recruited from July 2020 to September 2020. We collected a total of 172 current photos and 884 past photos for POP1 and 100 current photos and 600 past photos for POP2. The 21 facial phenotypes of each current and past profile photograph were determined by measuring the distances between 19 facial landmarks. Facial landmarks and measurement areas are depicted in Fig. The measurements from each past photograph were compared against those of the current photograph using non-linear regression methods to determine the facial changes over time. The regression patterns determined by the visual inspection of individual’s changes were used for the genetic association study  plots , and 759 SNPs were identified with suggestive association p-values  for 21 facial phenotypes (Supplementary Table Genome-wide single-nucleotide polymorphism (SNP) genotypes were obtained from an 800 K SNP microarray experiment using an Axiom array followed by imputation using 1000 Genomes Phase 3 dataThe SNPs of the existing Facial Measurement GWAS results were checked once more in the results of this study, and the results were included in a separate Supplementary Table A limitation of this study was that there were not enough participants and some factors that could affect facial growth were not measured. The facial measurements were appeared 2D images rather than 3D images. To understand for facial growth patterns, we tried to measure for facial traits, such as take a photograph of early age participants in the studio and recruited past pictures. Through like this study, further study will be more understand for facial morphology and facial growth.Human development is characterized by distinct developmental processes, especially during adolescence, and the speed and direction of craniofacial development differ for each person. Therefore, under the assumption that the speed and direction of development would differ across individuals, we calculated the changes in facial measurements according to age over time for each individual and for each indicator and divided these change patterns into five major categories. An index was calculated based on the positioning of landmarks in the facial profile picture, according to a widely used method for current facial analyses. GWAS analysis of facial development patterns was performed by recoding each of the five growth patterns as individual values.Despite the significant difference from the existing approach such as GWAS analysis of face measurements or facial deformities, we were able to obtain GWAS results that were repeatedly associated with facial features. A total of 97 significant indicators were identified, including indicators related to craniofacial development in 19 areas. Because the probabilistic effects and differences of facial phenotypes must be confirmed by replication analysis of identified loci, we divided the collection into two groups, and the genetic influences in each group were analyzed for stochastic effects through replication analysis and it were confirmed through the replication indicators.In the nose, the width of the nostrils most commonly increased with age, and the length of the nose showed the largest tendency toward increase across all of the vertical axis. Around the lips, many individuals showed patterns of increase or maintenance, whereas the patterns associated with the mouth were distributed differently in each population, indicating the degree of difference in the growth patterns among individuals.The length of H1 and H2 have the most decreasing patterns with aging. H5 and H6 have the most increasing patterns with aging. The most noticeable changes were observed for the cutis and subcutaneous bone.For both POP1 and POP2, as shown in Table 19. The bone base that supports the nose in youth, a pair of nasal bones, and the ascending process of the maxilla are responsible for many of the soft tissue changes that are observed in the nose during aging20.Among the vertical axis lengths, many indicators showed similar patterns of increase as the horizontal area, and the vertical axis indicators of the face appear to affect each other during growth. The vertical lengths of the eyes, V1 and V2, showed the greatest tendency toward an increasing pattern. The length of the nose, measured by V5–V8, commonly increases until the age of 20 years. The characteristics of the nose are well known and include major changes, such as long, drooping tipsThe pattern frequencies measured for EAFG showed that although we used independent populations, our results were replicated in each population. As shown in Table 22.However, no analysis model exists for facial growth, and the classification of facial growth as a visual expression clustering model is limiting. In this study, we analyzed by applying the –assoc option provided by PLINK software, and the results of this analysis are based on statistical models called likelihood ratio test and Wald test. The reason for applying this analysis is that the phenotype we are targeting is not a general quantitative phenotype, but multinomial variables called facial growth pattern. The currently available method for genome-wide analysis of these variables and multiple SNPs was the statistical model provided by PLINK software. Therefore, the significance between the SNP and the phenotype discovered in this study can be understood as an analysis result of whether the SNP has the explanatory power to explain the phenotype. Some genetic studies based on multinomial variables, and among them, we can check an example of applying the same likelihood ratio test as oursIn addition, the number of samples cannot be considered representative of all Koreans. However, this study represents the first attempt to classify the pattern of facial growth, and when data from two independent groups collected at the same time are analyzed and compared, the common result (the frequency of the pattern is more than 70% coincident) overcomes these limitations.23, which is reflected in the gradual increase in certain facial anthropometric measurements with age, such as anterior nasal cavity and facial width. Certain measurements increase significantly with aging, but some measurements are reduced. The chin length becomes shorter as the mandible of the face grows backward due to aging, resulting in a shorter overall face length.Most facial changes occur before age 18, but growth and facial remodeling have been shown to continue throughout life. The facial skeleton is generally believed to expand continuously throughout life24, body mass25 are known to effect facial morphology. The main influence of gender on facial phenotypes was reported as nasal area and upper facial area, and body mass index (BMI) was reported as a face width characteristic24. Obesity-related sites such as cheeks and neck were excluded from the measurement. So, it is thought that the effect of the degree of obesity in this study is relatively small.Some extrinsic variables like gender26. A total of 97 significant or suggestive SNPs in 19 gene regions and loci that have previously been associated with facial morphology were identified in this study. For 19 loci showing significant and suggestive phenotypic associations, substantial literature was identified associating these loci with facial development, as shown in Fig. FOXK127, IGSF1028, FAM161A29, POU3F230, DYNC11131, SFSWAP32, TRIM2933, RAPGEF134, PCDH735, and CXCR436. We also found 9 suggestive SNPs in the vertical region: ZSWIM637, CSN338, ATXN139, COL18A140, CHST941, CTNNA342, ASTN243, TUSC344, and MTCL145. The gene annotations from the UCSC database (https://genome.ucsc.edu) was used to predict the functional effects of the variants. The genes reported to affect embryonic development from UCSC database included CXCR446 in the horizontal region and CSN338 and TUSC344 in the vertical region. The genes reported to affect cranial growth and brain development included ZSWIM6, ATXN138, ASTN243, and MTCL145 in the vertical region. In addition, genes related to molecular mechanisms in the regulation of skeletal muscle and cartilage included FOXK127, RAPGEF134, and IGSF1028 in the horizontal region, and COL18A140 and CHST941 in the vertical region. The genes associated with retinal circuit components and the growth of sensory organs were FAM161A29, POU3F230, and SFSWAP32 in the horizontal region. Finally, the genes related to frontonasal and dysmorphic facial features were PCDH735, TRIM2933, and DYNC11131 in the horizontal region and CTNNA342 in the vertical region.GWAS results provide a hypothesis-free approach to identifying important genetic variations that underlie craniofacial shape differences within populationsThe authors have generated an interesting report using 2 dimensional images to perform a facial shape GWAS study focused not on fixed time points, but on ontogenetic growth trajectories. To my knowledge, this is a novel analysis. It is also performed in an interesting way, using a mixture of photo types. The argument for this paper is framed around using this for the purpose of being able to improve the accuracy of age progression for missing children, but there is very little discussion of that and this is a very intriguing basic science question.The facial growth patterns that occur in childhood remain poorly understood and estimating facial growth simply through photographic analysis or the use of existing facial indicators can be difficult. In addition, an individual’s unique facial morphologies can be difficult to quantify using simple photographic indicator analysis. The characteristics of facial growth should consider differences in each individual’s innate genetic makeup. This study is meaningful because we have classified and characterized facial growth patterns that occur in childhood and will contribute to research on face growth, face recognition, and potentially contribute to finding missing children in the future. This study can serve as a basis for understanding facial morphology and can be expanded to various research fields exploring facial growth, including forensic sciences for both adults and children.The facial images were obtained from the Human ICT, a company specializing in collecting face data. Related individuals among the participants were not included in the analysis. The facial measurement data were obtained from the National Project of the Missing Child conducted by the Korea Institute of Science and Technology (KIST). Two independent populations (POP1 and POP2) between the ages of 18 and 20 were recruited in different periods: 172 individuals in POP1 were recruited from January 2019 to July 2020, and 100 individuals in POP2 were recruited from July 2020 to September 2020. Relatives were not included, only individuals were included. Two types of facial photos were collected: a current picture and older pictures of the same individual. Each individual’s current photo was taken in a studio using a Canon EOS 1300D camera with 2592 × 1728 resolution at 400 lx illumination. The participants were asked to close their mouths, hold their faces in a neutral expression, and prevent hair from covering their foreheads. The older photos comprised various ages that can constitute individual chronologies, and a minimum of 4 to a maximum of 21 photos were collected from each participant. Among older facial photos, high-quality photos, such as passport photos and school graduation photos, were prioritized. We also asked the participants to supply photos in which the face was in a neutral expression, not family photos, and torn photos or those that were creased over the face were excluded. Age information was collected for each of the past photos. All facial photographs were digitized with a scanner. We collected a total of 172 current photos and 884 past photos for POP1 and 100 current photos and 600 past photos for POP2.46. The Institutional Review Board of Theragen Bio Institute approved this study , and all participants provided written informed consent.The research was conducted in accordance with the principles described in the Declaration of Helsinki47. Dlib47 detects the face and automatically identifies facial landmarks after the facial region is detected. Among the numerous facial feature points detected, we selected 19 feature points and utilized two facial landmark detectors to automatically extract those feature points, as shown in Fig. 48 approach, whereas the other detector was an in-house program that combined Dlib47 and Stasm49. The FAN method stacked three hourglass networks, including residual architecture, which is parallel, hierarchical, and multi-scale blocks50, to enhance the performance of the feature localization. Stasm50 is an active shape model (ASM)51 method with feature descriptors that we fused to the Dlib47 program. All detectors were programmed in C++ and python in a Qt environment. Facial detection and feature point extraction were performed as automatic processes but could also be manually modified to obtain more accurate feature locations by a well-trained operator (accuracy: 98.8% on average).To extract features for craniofacial measurements in current and past facial photographs, we first detected the facial region in each photograph using DlibEuclidean distances between two selected points based on the 19 selected features were calculated, as shown in Supplementary Table 52. Obesity-related sites such as cheeks and neck were excluded from the measurement. An ordinal multinomial model was applied to show that the results were not due to bias. In this study, we analyzed by applying the –assoc option provided by PLINK software, and the results of this analysis are based on statistical models called likelihood ratio test and Wald test22.We clustered measurements into groups of horizontal and vertical measurements and selected 10 phenotypes to represent the horizontal index and 11 phenotypes to represent the vertical index. The 21 facial phenotypes were measured in each current and past profile photograph, using the 19 facial landmarks in Supplementary Table 53. We defined a total of 5 EAFG growth patterns that were clustered as follows , a customized array based on the Asian Precision Medicine Research Array , Following hybridization, the bound targets were washed under stringent conditions to remove non-specific background to minimize noise resulting from random ligation events. The SNP set was filtered based on genotype call rates (≥ 0.98) and MAF (≥ 0.10). Hardy–Weinberg equilibrium (HWE) was calculated for individual SNPs using an exact test. All SNPs reported in this manuscript demonstrated HWE p-values > 0.0001. After filtering, 560,795 polymorphic SNPs were analyzed on chromosomes 1–22.Oral swab samples (KIST) were obtained, and DNA was extracted using ExgeneTM Tissue SV . All DNA samples were amplified and randomly portioned into 25–125-bp fragments, which were purified, resuspended, and hybridized to an Axiom 54 at the Michigan Imputation Server (MIS) using the 1000G Phase 3 v4 reference panel20. We uploaded phased GWAS genotypes and received imputed genomes in return. After imputation, 7,375,270 polymorphic SNPs were analyzed on chromosomes 1–22. INFO score is over than 0.8.We conducted an imputation analysis to increase the genome coverage. Imputation of genotypes was performed using minimac4https://www.cog-genomics.org/plink/)55, SPSS 56, and R Statistical Software52. We calculated the beta coefficient and the standard error (SE) values for the association study. To compare the GWAS results for each population, we conducted a replication study using 172 samples from POP1 and 100 samples from POP2. We selected the genetic markers associated with the 5 EAFG patterns in each GWAS, determined by association p-values < 1 × 10−5 in the combined dataset and p-values < 0.05 for the individual population datasets and the replication study. In this study, GWAS analysis was performed based on about 800,000 SNPs. The Bonferroni correction p-value threshold is applied. The results are shown in Tables 52, of the observed p-values showed minimal inflation of the GWAS results from the combined population sample  using the combined population of POP1 and POP2. Population-specific and combined population analyses were performed using PLINK version 1.9  were used to predict the functional effects of the variants. We constructed regional plots of association for regions of interest using the program LocusZoom . The gene annotations from the UCSC database and Genotype-Tissue Expression (GTEx) database (GTEx Analysis Release v.8, Supplementary Information.


Better understanding of genetic structure of economic traits is crucial for identification and selection of superior genotypes in specific breeding programs. Best linear unbiased prediction (BLUP) is the most efficient method in this regard, which is poorly used in forage plant breeding. The present study aimed to assess genetic variation, estimate genetic parameters, and predict breeding values of five essential traits in full sib families (recognized by EST-SSR markers) of tall fescue using REML/BLUP procedure.Forty-two full-sib families of tall fescue , recognized by EST-SSR markers along with twenty-one their corresponding parental genotypes were assessed for biomass production and agro-morphological traits at three harvests  in the field during 4 years (2017–2020).2n) for dry forage yield (DFY) at three harvest indicates that non-additive gene actions may play an important role in the inheritance of this trait. Higher h2n of yield related traits and flowering time and also significant genetic correlation of these traits with forage yield, suggests that selection based on these traits may lead to indirect genetic improvement of DFY.Considerable genotypic variability was observed for all traits. Low narrow-sense heritability (hOur results showed the adequacy of REML/BLUP procedure for identification and selection of preferable parental genotypes and progenies with higher breeding values for future breeding programs such as variety development in tall fescue. Parental genotypes 21 M, 1 M, and 20 L were identified as superior and stable genotypes and could also produce the best hybrid combinations when they were mostly used as maternal parent.The online version contains supplementary material available at 10.1186/s12870-022-03675-w. Climate change and desertification offer several biotic and abiotic stresses, limiting growth and production of plants in many areas of the world , 2. As aFestuca arundinacea Schreb. syn. Lolium arundinaceum) is known as an important cool-season perennial grass, widely used for forage and turf application due to its persistence, notable local adaptation, forage production, and tolerance to biotic and abiotic stresses [Tall fescue  followed by the best linear unbiased prediction (BLUP) is the most efficient method, for estimation of genetic parameters and prediction breeding values and identification of favorable individuals in the selected breeding populations , 19. TheBreeding of cross-pollinated grasses such as tall fescue is very difficult due to their high degree of self-incompatibility and cleistogamous flowers . In thisP < 0.01) between the parental genotypes and full-sib families of tall fescue across three harvests for all the measured traits  was observed in the third year of this experiment compared to the first, second and fourth years Fig. . Mean va2n) for measured traits and relative selection efficiency (RSE) for improvement of dry forage yield (DFY) in a single harvest and in multiple harvest analysis are given in Tables 2n for measured traits were obtained in the spring harvest, whereas the lowest estimates were obtained in the later harvests (summer and autumn). In multiple harvest analysis, low to moderate values of h2n were observed for all of the evaluated traits (Table 2nof yield related traits consisted of plant height (H), the number of stems per plant (NS), and CD was greater than the h2nof dry forage yield  to 0.86 (between DFY and H) Table . The higA broad range of breeding value was observed for all measured traits in both parental genotypes and their clonal progenies Table S and S3. Principal component analysis (PCA) revealed that the first two components explained more than 83, 72, and 69% of the genetic variation in the parental genotypes, full-sib families, and individual progenies of tall fescue, respectively  was found to be lower than spring (SPDFY) and autumn dry forage yield (AUDFY) during the years of experiment. Consistent with our finding, several researchers have reported the reduction of summer forage yield in some grass species such as tall fescue which is likely due to the higher temperature and induction of summer dormancy –33. KallEstimation heritability and genetic correlation of traits is one of the main objectives in the plant breeding programs specially to identify an efficient approach for developing new varieties . General2n) for measured traits were higher in the multiple harvest analysis than the single harvest analysis, which could be due to the increasing the number of harvest. Consistent with our results, Acharya et al. [2n for forage yield, plant height, and blooming through BLUP method in the single harvest analysis than in the multiple harvest analysis in alfalfa (Medicago sativa L.). Generally, estimation of h2nfor seasonal and annual dry forage yield were lower than the yield related traits  and flowering time (FL). Low values of h2n for forage yield indicates that non-additive gene action may have a greater contribution to the expression of this economic trait, which implies lower odds of enhancement this trait through direct selection. Hence, indirect selection of the traits having higher h2n values compared to yield productivity as well as those strongly associated with forage yield would be more efficient and promising [The estimates of narrow-sense heritability  [2n values for plant height (0.15–0.53) and flowering (0.08–0.51) in alfalfa during eleven harvests using BLUP procedure.Although, different range of hf values , 38–40. to 0.37) , orchardto 0.37) , smooth to 0.37) , switchgto 0.37) , 43, andto 0.37) , 44. Achto 0.37)  also repPrediction of breeding values is a prerequisite to successful implementation of long-term breeding programs. Breeding value refers to the property of an individual in a breeding population, which is mainly related to the additive genetic variance of a trait; therefore, is transmittable from parents to progenies and pertinent to the selection response , 46. REMThe principal component analysis (PCA) is one of the most successful multivariate techniques used for screening suitable genotypes . Wide diIn conclusion, wide range of genetic variability for forage yield, yield related traits, and flowering time points to the high potential of the studies germplasm for genetic improvement of these traits through full-sib mating in tall fescue. The narrow-sense heritability of seasonal and annual dry forage yield was lower than the narrow-sense heritability of yield related traits  and FL, indicates that non-additive gene action may play a major role in the genetic control of forage yield (DFY) which led to lower odds of enhancement this trait through direct selection. Positive genetic correlation between H, CD, NS, and FL with dry forage yield, also high relative selection efficiency of yield related traits (more than 1) suggest that these traits could be used for enhancing DFY through indirect selection. The REML/BLUP was an adequate method for estimating the heritability and detecting the superior parental genotypes and progenies with higher breeding values for future breeding program in tall fescue. Parental genotypes 21 M, 1 M, and 20 L were identified as superior and stable genotypes, which can be used in breeding programs for developing synthetic varieties. These genotypes could also produce the best full-sib families or hybrid combinations  when they were mostly used as maternal parent.3 in the top 60 cm layer of the soil profile. The average annual precipitation and temperature were 122 mm and 17 °C, respectively.This research was conducted at the research farm of the College of Agriculture, Isfahan University of Technology, Isfahan, Iran . The soil at the site was clay loam (pH 7.5) with an average bulk density of 1.48 g/cmThe primary plant materials included of 21 genotypes of tall fescue  was harvested manually three times in each year. The first harvest was in late spring after flowering, the second and third one was in late summer and autumn to assess complete growth, respectively. At each harvest, the grass was cut from 5 cm above the ground and the weight of dry forage yield per plant was recorded after drying at 72 °C for 48 h. The annual dry forage yield of each year (ADFY) was calculated by the sum of the spring (SPDFY), summer (SUDFY) and autumn (AUDFY) forage yield. Number of stems per plant (NS), plant height (H), crown diameter (CD), and flowering time (FL) were measured as recommended by Pirnajmedin et al. [P < 0.05). Stability analysis was calculated for forage yield using the stability parameter proposed by Eberhart and Russell [Data  were tested for normality using the Kolmogorov-Smirnov test; subsequently, the analysis of variance (ANOVA) was performed using the PROC Mixed by repeated measures in SAS software (v9.2) . The mea Russell . The staThe pedigree of all the families were known and the recode of genotypes was done using CFC software. The pedigree information in BLUP analysis was used for constructing the relationship matrix and then estimating the genetic parameters and predicting the breeding values, which is done by the DMU software . The firMVN  and e ~ MVN , where MVN  denotes the multivariate normal distribution with mean vector u and variance-covariance matrix V. The G is the genetic variance/covariance matrix and R is the residual variance/covariance matrix.where, Y is the vector of observation, β and u are vectors of fixed and random effects, respectively, X and Z are the associated design matrices, and e is a random residual vector. The random effects are assumed to be distributed as u ~ Individual harvest analysis was performed using the model:MVN , which σ2g is variance of genotype and A is a relationship (kinship) matrix; e is the random vector of error, e ~ MVN , which σ2e is the variance of residual and I is an identity matrix of its proper size.where, the u is the overall mean; X and Z represent the incidence matrices for fixed and random effects, respectively; y is the fixed effect for year; g is the random vector of genotype, g ~ Multiple harvest analysis was performed using the model:MVN , which G is the genetic variance/covariance matrix and A is a relationship (kinship) matrix; p is the random vector of the permanent environment, p ~ MVN ; e is the random vector of error within each cut, e ~ MVN , which σ2e is the variance of residual, R is the residual variance/covariance matrix, and I is an identity matrix of its proper size. X, Z, and W represent the incidence matrices for these effects.where, the u is the overall mean; where, the u is the overall mean; c is the fixed vector of cut; y is the fixed vector of year; gc is the random vector of genotype within each cut, gc ~ Variance component and narrow sense heritability of traits were estimated using the restricted maximum likelihood (REML/BLUP) analysis by DMUAI procedure and breeding values (BVi) were computed by DMU4 procedure in DMU software, respectively . The narσ2a, σ2g, σ2p, σ2e are the additive genetic variance, genotypic variance, permanent environment variance, and residual variance, respectively.where Using the bivariate analysis, the genetic correlations for each pair of traits were estimated from the genetic variance-covariance matrices from the model described above.Relative selection efficiency (RSE) for improvement of dry forage yield (DFY) were estimated as described by Falconer and Mackay  and Seary is correlated response to selection, Ry is response to selection, i is the selection intensity of 10% (1.75), h2x is heritability of trait x, σp(x) is the square root of genotypic variance of trait x, rg is the genotypic correlation coefficient between two traits, hx and hy are the root square of narrow-sense heritability of traits of x and y, respectively. The correlated trait (y) is dry forage yield and RSE was only calculated based on dry forage yield.where CRPrincipal component analysis (PCA) was performed based on correlation matrix to reduce the multiple dimensions of data space using SAS (Proc princomp), and biplots were drawn using Stat Graphics statistical software .We confirm that all methods complied with relevant institutional, national, and international guidelines and legislation.Additional file 1.


Multi-trait genetic parameter estimation is an important topic for target traits with few records and with a low heritability and when the genetic correlation between target and secondary traits is strong. However, estimating correlations between multiple traits is difficult for both Bayesian and non-Bayesian inferences. We extended a Hamiltonian Monte Carlo approach using the No-U-Turn Sampler (NUTS) to a multi-trait animal model and investigated the performance of estimating (co)variance components and breeding values, compared to those for restricted maximum likelihood and Gibbs sampling with a population size of 2314 and 578 in a simulated and real pig dataset, respectively. For real data, we used publicly available data for three traits from the Pig Improvement Company (PIC). For simulation data, we generated two quantitative traits by using the genotypes of the PIC data. For NUTS, two prior distributions were adopted: Lewandowski-Kurowicka-Joe (LKJ) and inverse-Wishart distributions.For the two simulated traits with heritabilities of 0.1 and 0.5, most estimates of the genetic and residual variances for NUTS with the LKJ prior were closer to the true values and had smaller root mean square errors and smaller mean absolute errors, compared to NUTS with inverse-Wishart priors, Gibbs sampling and restricted maximum likelihood. The accuracies of estimated breeding values for lowly heritable traits for NUTS with LKJ and inverse-Wishart priors were 14.8% and 11.1% higher than those for Gibbs sampling and restricted maximum likelihood, respectively, with a population size of 578. For the trivariate animal model with real pig data, the estimates of the genetic correlations for Gibbs sampling and restricted maximum likelihood were strongly affected by population size, compared to NUTS. For both the simulated and pig data, the genetic variances and heritabilities for NUTS with an inverse-Wishart prior were overestimated for low-heritability traits when the population size was 578.The accuracies of variance components and breeding values estimates for a multi-trait animal model using NUTS with the LKJ prior were equal to or higher than those obtained with restricted maximum likelihood or Gibbs sampling. Therefore, when the population size is small, NUTS with an LKJ prior could be an alternative sampling method for multi-trait analysis in animal breeding.The online version contains supplementary material available at 10.1186/s12711-022-00743-5. Selection of livestock is usually based on a combination of several traits of economic importance that may be phenotypically and genetically related. Multi-trait analysis was introduced in quantitative genetics by Henderson and Quaas . It is bRestricted maximum likelihood (REML) and Bayesian analyses have become standard estimation methods in animal breeding. Patterson and Thompson  first deThe Hamiltonian Monte Carlo (HMC) approach has become a popular alternative MCMC method, which is based on Hamiltonian dynamics used in physics and is a Metropolis strategy for all parameters simultaneously . HoffmanWe have introduced two estimation methods, i.e. REML and Bayesian analysis, and two computing algorithms of Bayesian analysis, i.e. GS and NUTS. The goal of this study was to compare the performances of REML, GS and NUTS for the accuracy of the estimation of variance components and breeding values. This comparison was based on multi-trait genomic best linear unbiased prediction (GBLUP) models using simulated and real pig data.https://academic.oup.com/g3journal/article/2/4/429/6026060). This dataset is composed of 3534 animals with phenotypes for five traits and genotypes from the PorcineSNP60 chip (Publicly available data including genotypic and phenotypic information on a single Pig Improvement Company (PIC) nucleus pig line were used (of birth . We usedCriteria to exclude single nucleotide polymorphisms (SNPs) were: a minor allele frequency lower than 0.05, a call rate lower than 0.95 and a Hardy–Weinberg equilibrium cut-off P value lower than 0.001. After quality control, the final data set included 33,860 SNPs. In this study, we used the genomic relationship matrix as an additive genetic relationship matrix that was denoted VanRaden :1\documeTo validate the performances of REML, GS and NUTS for the estimation of variance components, we also generated a simulated dataset from the genotypes of the real PIC pig dataset. Similar to the real data, we used the genomic relationship matrix , respectively [Variance components were estimated using REML and GS with the airemlf90 and gibbs2f90 software . For the NUTS approach, we used RStan, which is the R interface for Stan.In the Stan software, a Bayesian model is implemented by defining its likelihood and priors. Stan is an open-source software, with a publicly available manual online (We used a Lewandowski–Kurowicka–Joe (LKJ) distribution as a prior of the correlation. Following the separation strategy of Barnard et al. , the covcument}η : \documeFor efficient calculation, we used Cholesky factor parameters for variance components. Let Stan provides an implicit parameterization of the multivariate normal density in terms of its Cholesky factor. The conditional distribution of which implies that mentclass2pt{minimBecause for all but the most trivial model cases there is no analytical solution, NUTS uses a process called the leapfrog integration to draw a sketch of the posterior probability surface. The failures in this integrator are identified by “divergent transitions”, which basically means that the sampler is no longer following the surface of the model correctly . To checTo investigate the accuracy of the estimation of variance components using the simulated data, we calculated two indices: the root mean square error (RMSE) and the mean absolute error (MAE). These indices of the estimator The simulated population was divided into a training and a test population to investigate the accuracy of estimated breeding values. The training and test populations consisted of 2000 and 314 animals in Scenario 1 and of 500 and 78 animals in Scenario 2, respectively. The test population was randomly selected from the last generation. The training population had both phenotypic and genotypic values whereas the test population had only genotypic values. The accuracies of estimated breeding values were calculated from the Pearson’s correlations between the true and the estimated breeding values in the test population. In addition, we investigated the RMSE of estimated breeding values in the test population.Establishing convergence of MCMC is one of the most important steps of Bayesian analysis. We used two MCMC diagnostic tools: the Gelman and Rubin’s convergence diagnostic  and the n et al. . In this package .Compared to GS and REML, the average estimates of genetic variances and residual variances for trait2 obtained using NUTS with an LKJ prior were close to the true values in Scenario 1 variance and correlation parameters in multi-trait analysis is an important topic in animal breeding programs. However, Bayesian and non-Bayesian inferences for multi-trait mixed models are complex. In this study, we focused on the NUTS approach and implemented NUTS with LKJ and IW priors for a multi-trait animal model using the recently developed software Stan, and compared the results with the commonly used REML and GS methods. The results obtained with the simulated and real pig data indicate that the estimation of genetic parameters for a multi-trait animal model is improved by using NUTS with an LKJ prior, particularly when the population size is small. Moreover, for real pig data, NUTS can provide the unimodal and bilaterally symmetrical posterior distributions of genetic correlations regardless of the level of the heritability.The NUTS approach has two advantages over GS. First, the NUTS algorithm is extremely effective for the MCMC sampling process because it can generate samples from a wide range of parameter spaces with a high level of acceptance probability and automatic tuning of the hyperparameters of HMC. Nishio and Arakawa  demonstrFor an LKJ distribution, one positive scalar hyperparameter  for estimating variance components.


Access to biologically available nitrogen is a key constraint on plant growth in both natural and agricultural settings. Variation in tolerance to nitrogen deficit stress and productivity in nitrogen limited conditions exists both within and between plant species. However, our understanding of changes in different phenotypes under long term low nitrogen stress and their impact on important agronomic traits, such as yield, is still limited.Here we quantified variation in the metabolic, physiological, and morphological responses of a sorghum association panel assembled to represent global genetic diversity to long term, nitrogen deficit stress and the relationship of these responses to grain yield under both conditions. Grain yield exhibits substantial genotype by environment interaction while many other morphological and physiological traits exhibited consistent responses to nitrogen stress across the population. Large scale nontargeted metabolic profiling for a subset of lines in both conditions identified a range of metabolic responses to long term nitrogen deficit stress. Several metabolites were associated with yield under high and low nitrogen conditions.Our results highlight that grain yield in sorghum, unlike many morpho-physiological traits, exhibits substantial variability of genotype specific responses to long term low severity nitrogen deficit stress. Metabolic response to long term nitrogen stress shown higher proportion of variability explained by genotype specific responses than did morpho-pysiological traits and several metabolites were correlated with yield. This suggest, that it might be possible to build predictive models using metabolite abundance to estimate which sorghum genotypes will exhibit greater or lesser decreases in yield in response to nitrogen deficit, however further research needs to be done to evaluate such model.The online version contains supplementary material available at 10.1186/s12870-022-03823-2. The tissue samples were disrupted and homogenized by adding 2 stainless steel beads (SSB 32) using the TissueLyserII (Qiagen) at 20 Hz for 5 mins. After centrifugation at 16,000 g, the supernatants were collected and the same extraction was repeated on the pellet one more time. The supernatants were pooled and vacuum dried down using a SAVANT speed-vac. The pellets were re-dissolved in 100 http://prime.psc.riken.jp/compms/msdial) for MS/MS positive  and MS/MS negative . Metabolites missing in more than 80% of the total samples were removed. The remaining 3,496 metabolites from all four analytical conditions, that is from separation on HILIC and RP column with negative and positive ion mode, were manually checked for Gaussian chromatographic peak and, peak alignment and MS/MS profile. Identified metabolites were classified either as level I when peak matched to m/z and retention from an in-house library prepared from authentic standards, or as level II based on their spectral similarities with public/commercial spectral libraries in accordance with the Metabolomics Standards Initiative guidelines [Data from LC-MS/MS analysis were processed with MS-Dial software v4.70 for peak detection, deconvolution, alignment, quantification, normalization, and identification . Backgroidelines .tidyverse v.1.3.1 was employed for data processing and visualization [lmer function provided by the lme4 package [p-values for the significance of treatment effects. P-values from metabolite data analysis were corrected for multiple tests using false discovery rate (FDR) [All statistical analyses were conducted in R v.4.1.2 . The metlization . In orde package . The fulte (FDR) , and valA more complex model which, in addition to treatment (nitrogen) as a fixed effect and genotype as a random effect, also included genotype by environment (GxE) interaction as random effects was fit for each metabolite in order to estimate total variance potentially explainable by each of these three factors , 34. Met2g is genetic variance, 2e is residual variance, and n is the number of replicates. Variances were obtain from mixed model fitted separately to values from each experimental conditions with genotypes treated as random effect.Broad-sense heritabilities were estimated from the following equation:PCA function provided by the FactoMineR package [cor.test function in R.Principle component analysis for metabolite values across the 96 samples were calculated using the  package . Pearsonranger package [glmnet package [preProcess function. Repeated 100x times five-fold cross validation were used to determinate optimal parameters for each model based on minimization root mean square error (RMSE). Importance value were calculated with varImp function from caret package based on permutation. The mean squared error is computed on the out-of-bag data for each model, and then the same computed after permuting a single variable. The differences are averaged and normalized by the standard error and scaled to values between 0 and 100.Yield predictions were done based on three metabolite data sets: all identified metabolites (ramework . Random  package  and elas package . Prior tSupplementary file 1:Table S3. and Figures S1-10.Supplementary file 2:Table S1. Plot-level values of morpho-physiological traits.Supplementary file 3:Table S2. Raw ground truth and spectral data used to build PLSR models and predict three traits.Supplementary file 4:Table S4. Summary statistics for 145 annotated metabolites.Supplementary file 5:Table S5. Correlation values between yield and 145 metabolites.


Female fertility is a challenging trait to improve genetically because of its low heritability, its unfavorable genetic correlation with milk yield, and its relatively small number of records. The MFERT trait is the probability of conception to first insemination predicted using mid-infrared (MIR) spectroscopy of a milk sample collected during lactation as part of routine milk recording, age at calving, days in milk, and milk production. As such, MFERT could become available on many more cows than traditional fertility traits. Our objectives were (1) to estimate the heritability of MFERT; (2) to estimate genetic correlations between MFERT, traditional fertility traits, and milk production traits; and (3) to assess the potential of MFERT to be used as an indicator trait for fertility in a selection index. The MFERT trait had a heritability of 0.16, which was higher than that (0.05) obtained for traditional fertility traits. Genetic correlations between MFERT and traditional fertility traits were low to moderate. The weakest and strongest correlations (mean ± standard error) were with pregnancy at the end of the mating season (0.13 ± 0.05) and calving to first service (−0.61 ± 0.03), respectively. Based on our estimates, including MFERT in a fertility index will only substantially increase the accuracy of the index when there are many more records available for MFERT than for the traditional fertility traits. This is likely to be the case because the number of milk samples from commercial machines belonging to milk recording companies in Australia that are capable of generating MIR spectra is growing. Hence, the number of records for MFERT is expected to increase substantially in the near future. Female fertility is an important trait in dairy cattle. Many studies have shown that it has been subject to declines due to its negative genetic correlation with production traits . As a reThe accuracy of EBV, whether they are predicted based on progeny records or genomic prediction, increases when the number of records for a trait increase. A larger number of progeny records increases the accuracy of the breeding value of a bull, and a larger number of records in the reference population used for genomic prediction increases the accuracy of genomic prediction . This isMIR) spectroscopy is used to quantify the composition of milk, including fat, protein and lactose concentration. Recently, MFERT), using MIR data, age at calving, DIM, and milk production. MFERT was able to phenotypically classify cows into high or low fertile groups. The inference here is that by training the MFERT model that contrasts the most fertile cows  with the least fertile , clearer biological signals might be observed and thus higher modeling accuracy could be obtained , lactation length (LL), pregnancy at the end of the mating season (PREG), first-service nonreturn rate, and interval from calving to first service (CFS) using a 5-trait model  includes 6-week-in-calf rate derived from calving interval (it model . We hypoMY) and fertility also means that MY could be used as an indicator trait to predict fertility (The negative genetic correlation between milk yield (ertility . Milk yiTFERT) including CI, LL, CFS, and PREG, as well as test-day MY, test-day fat yield (FY), test-day protein yield (PY), test-day fat percentage (F%), and test-day protein percentage (P%) were provided by DataGene , for 618,856, 598,732, 357,110, 302,069, 642,617, 641,340, 641,339, 641,341 and 641,399 Holstein cows with records for CI, LL, CFS, PREG, MY, FY, PY, F%, and P%, respectively, with one record per cow. Depending on the trait, between 3,157 (MFERT) and 39,177 (MY) cows with phenotypes had imputed genotypes for the Illumina Bovine HD BeadChip (HD). More details on the genotyping and imputing pipeline are described by The MFERT trait is the probability of conception to first insemination predicted using milk MIR, age at calving, DIM, and MY, derived using the model described in detail by To independently obtain the value of MFERT for cows in each herd, we applied a procedure that is similar to external validation. To achieve this, we predicted the probability of conception of cows (ranging from 0 to 1) in each herd in turn by applying a model that was trained using data from the other 28 herds. Hence, the model was completely independent from the herd being predicted. In this study, we only used predicted probabilities of Holstein cows. Where a cow had multiple records within a lactation, only the first record was selected. This resulted in 4,124 cows with MFERT records.H) was constructed following H and a vector of random residuals, was used to estimate heritabilities. A bivariate model with the same effects was used to estimate genetic correlations between traits.Genetic parameters were estimated using GIBBS2F90 . A combifn, where fn is the number of progeny records for CI, LL, CFS, and PREG, with fn = 0, 10, 30, or 60 for each of the TFERT traits. The accuracy of the FERT index was compared with indices with fn records for FERT and 60 for MFERT (FERTfn+MFERT60), MY (FERTfn+MY60), or MFERT and MY (FERTfn+MFERT60+MY60). The accuracy of each index was calculated using software developed by J. van der Werf (https://jvanderw.une.edu.au/software.htm ), following To assess the potential advantage of including MFERT in a fertility index, we compared the accuracy of the EBV of a bull for several indices. The basic index contained CI, LL, CFS, and PREG and is referred to as FERTThe heritability of MFERT was substantially larger than that of the fertility traits currently included in the Australian selection indices  and the strongest correlation with CFS −0.61; . Based oSimilar to TFERT, MFERT had a moderate, unfavorable genetic correlation with MY. However, the correlation between MFERT and MY (−0.32) was lower than that between MY and CI, LL, and CFS (0.43 to 0.45). Consequently, the correlated response to selection for MY was −2.1 kg with the FERT0+MY60 index and −0.68 kg with FERT0+MFERT60. Hence, using MFERT as predictor fertility trait has a smaller effect on MY than using MY as predictor. Genetic correlations between MFERT were larger with F% (0.50) and P% (0.46) than with FY (0.24) and PY (−0.14). There has been considerable interest in protein percentage as a predictor of fertility genetically . DouglasThe main appeal of MFERT for use as an indicator trait for fertility is that it requires only a milk sample. Hence, it could be available for all cows that go through routine herd testing. Additionally, MFERT could be available as soon as a cow starts lactating, which is earlier than most TFERT. While having MFERT records obtained routinely through herd testing would result in a much larger number of records for MFERT than for TFERT, the current situation in Australia is the reverse: we have many more records for TFERT than for MFERT. Therefore, the benefit of using MFERT for genetic selection is limited at the moment. However, MFERT is a promising tool for phenotypic prediction and management , and MIR