P. trichocarpa mapping population derived from crossings between clones collected across the native range and introduced into Sweden. GWAS analyses were performed using phenotypic data collected across two field seasons and in a controlled phytotron experiment.Entering and exiting winter dormancy present important trade-offs between growth and survival at northern latitudes. Many forest trees display local adaptation across latitude in traits associated with these phenology transitions. Transfers of a species outside its native range introduce the species to novel combinations of environmental conditions potentially requiring different combinations of alleles to optimize growth and survival. In this study, we performed genome wide association analyses and a selection scan in a P. trichocarpa. A comparison between the candidate genes identified with results from earlier GWAS analyses performed in the native environment found a smaller overlap for spring phenology traits than for autumn phenology traits, aligning well with earlier observations that spring phenology transitions have a more complex genetic basis than autumn phenology transitions.We uncovered 584 putative candidate genes associated with spring and autumn phenology traits as well as with growth. Many regions harboring variation significantly associated with the initiation of leaf shed and leaf autumn coloring appeared to have been evolving under positive selection in the native environments of P. trichocarpa, we find complex genetic architectures underlying all phenology and growth traits, and identify multiple putative candidate genes despite the limitations of the study population.In a small and structured introduced population of The online version contains supplementary material available at 10.1186/s12870-021-03103-5. At northern latitudes winter conditions are unfavorable to active plant growth through a combination of low temperatures, frosts, and light conditions leading perennial plants to avoid these conditions by entering winter dormancy (hereafter dormancy) . TransitArabidopsis thaliana [Picea abies [Populus tremula [Populations of widespread species often display signatures of phenotypic and genetic adaptation to their native environments, even in species with considerable gene flow between the populations . This phthaliana , Picea aea abies  and Popu tremula . Empiric tremula  although tremula ). Due to tremula , 16).Populus are deciduous, early succession trees with wide distributions across the northern hemisphere, spanning from the equator to the northern limits of tree growth. The rapid growth rate and ability to generate natural clones [Populus species are frequently utilized in biomass production for forest industry, even outside of their natural distribution ranges [Populus species is an underutilized option due to the phenological maladaptation of commercially bred varieties [Populus species to growth under northern European conditions, but the required genetic resources and understanding of relevant traits for such an undertaking are lacking [Populus trichocarpa) is a deciduous tree native to North America with continuous distribution in western and northwest North America from California to Alaska. The species has been thoroughly studied in its natural range and has been found to display signatures of local adaptation to climate and photoperiod across its natural range [Species within the genus l clones , 18 has l clones , while ml clones . Populusn ranges . In nortarieties . Commerc lacking . Black cal range , 23. Intal range  and its P. trichocarpa. We use the data to dissect the genetic basis of these traits using both population genomic approaches and genome-wide association studies. We compare candidate genes identified across two successive years and between field and phytotron grown plants, in order to identify genes that appear to have reliable effects on different phenology traits. Finally, we compare candidate genes we identified with results from earlier association studies performed in the natural range of P. trichocarpa to contrast the genetic control of phenology traits under native and novel environments.In this study, we perform a field trial and a controlled environment phytotron trial to collect data on spring and autumn phenology and growth traits in p < 0.001) differences between years 2017 and 2018. All chosen traits with the exception of BB2-top and BS2 had an appreciable level of both broad and narrow sense heritability  with only BB2-top having narrow sense heritability less than 0.05 and BS2 having broad sense heritability below 0.25. There were signs of overfitting for CO8-18 and LS5-18 of the field traits and BS2, BS7, BB2-top, BB4-top, BB2-brn, BB4-brn and BB2-stt, which all were potentially overfitted in the model for BLUP -estimation for calculating the broad sense heritability estimations, as the values approached a singularity fit (Table 2 > 0.8)  display variation both within and between years, though BB2-top Fig. E and BS2rd stage for brn, stb and top, while stt remained different until the 4th stage. At stage 5, the different parts of the plants were all behaving in a similar fashion . However, these differences largely disappeared by the 3ion Fig. A. On 1st°C Table  to shiftThe Lindley score method located between 0 and 80 significant slopes for the different traits, yielding overall 250 significant slopes Table A, LS2-17Between 0 and 101 genes were located within 10 kbp of the significant slopes across all traits with 584 unique genes included overall Table .p < 0.05) enriched in the top 0.1 percentile in both selection scans  in temperatures in April, May and July. In 2017 the average temperature was above 5 °C from May to October with an average temperature of 12.8 °C whereas the average temperature in 2018 was above 5 °C already in April and lasted until October with an average temperature of 13.9 °C. The difference in mean temperature is largely caused by an early and very warm spring and an exceptional heatwave during July in 2018. Furthermore, year 2018 had lower levels of precipitation, and the end of the growing season may have been affected by lowered water availability as well Table , though Populus [Populus [Temperature is the main driver of the initiation of spring phenology transitions in many annual and perennial species, including Populus . FurtherPopulus , 31. As [Populus , there i[Populus –34. Ther[Populus . Similar[Populus . Our finP. trichocarpa population, which in similar to earlier studies in P. trichocarpa in its native range [P. trichocarpa [Linkage disequilibrium (LD) decays over, on average, 10 kbp Fig.  in our Pve range . Based ochocarpa , 23. Forchocarpa , 37.Populus and other tree species [Changes in lipid and protein metabolism have been previously observed during phenology transitions in  species , 39. Lip species . Our GO-P. trichocarpa study population. We observed hundreds of markers within 10 kbp of significant slopes in the top 0.1 percentile of estimated selection values for both of the selection scans  for both statistics, chloroplast envelope  for iHS and membrane  for H12, lending further support to the importance of membrane structures for adaptation to northern climates , 40. AmoInitiation of both bud set and leaf senescence has been previously observed to be consistent between years and conditions for the same trees, suggesting a more stringent genetic control of the initiation of autumn phenology traits , 22, 43.https://uniprot.org), they may have roles in cell wall degradation or production of storage carbohydrates. An additional 34 candidate genes that we identify in this study are also shared with Evans [We identified two candidate genes for autumn phenology traits that were also identified in the earlier studies of Evans  and McKoth Evans  but onlyth Evans   and 2018 (18), for bud burst (BB) (spring phenology) and for leaf shed (LS) and autumn coloring (CO) (autumn phenology). Bud set (BS) is the most relevant measurement of season-ending growth [Bud burst was scored using a scale with six steps, ranging from fully dormant buds  to fullyIn February of 2018, cuttings were taken from 99 clones in the Krusenberg field trial. These cuttings were taken from stems of root suckers growing from stumps of thinned individuals or from branches of mature trees and stored at—4 °C until planted in pots in March of 2018. Two cuttings of similar length (~ 10 cm) from the same clone were planted in each pot. This was done in three replicates to produce a 3-block randomized design. After sprouting, the less vigorous cutting was removed leaving only one cutting per pot. Cuttings were then put through two simulated seasons described in Table Bud burst and bud set were measured during the second simulated season Table . Bud burLeaf samples were collected from trees in the Krusenberg trial in the autumn of 2016 and stored dried with silica gel until DNA extractions. DNA extractions were repeated for a few clones using leaves taken from the cuttings used for the phytotron experiment. Genomic DNA was extracted using a Macherey–Nagel NucleoSpin® Plant II kit according to manufacturer’s instructions. Quality and concentrations of the DNA was assessed using a NanoDrop spectrophotometer. Paired-end sequencing libraries with insert sizes of 350 bp were constructed for all samples at the National Genomics Infrastructure at the Science for Life Laboratory in Stockholm, Sweden. Whole-genome sequencing with a target depth of 20 × was performed using an Illumina HiSeq X platform with 2 × 150-bp paired-end reads, generating on average 47.6 M reads per sample with median depth of 21.6.P. trichocarpa v3.0, using BWA-MEM (v0.7.17) [http://broadinstitute.github.io/picard/), which did not exceed 14% of the libraries (ranging from 3 to 13.8%).Sequencing reads for all accessions were mapped against the reference genome of v0.7.17)  using dev0.7.17)  and tagg–6. After filtering, 7,076,549 SNPs were retained and used in all downstream analyses.We used GATK v3.8  to call During the field experiment individual trees sometimes passed through more than one phenology stage between two successive phenotypings. To account for these missing phenotypes, we first converted each ordinal stage into the number of Julian days (measured from January 1) when a stage was first observed for a given individual tree. This was done to remove accidental reversals of phenology stages which occurred at low frequencies in the data set due differences in subjective scoring by different observers or through shedding of yellowed leaves which sometimes case an apparent ‘greening’ of some trees. Once an individual tree had transitioned to the next phenology stage the Julian date was recorded for that stage and any subsequent reversals were discarded. For the first (1) stage for both spring and autumn phenology we used the last observed date as the observation point, as the first stage denotes ‘no change’ making earlier observations of the stage uninformative about the progress of phenology.A local regression model (LOESS) was fitted through the transition days to estimate the missing stage transition days for each individual separately. The method fits a non-linear curve that is not constrained to fit any a-priori distribution for each individual separately. This allows estimation of the day in which these individuals entered each ordinal developmental stage allowing us to include individuals not observed at transitions between stages in the downstream analyses (For more information see Richards ). The meThe phenology traits were phenotyped using multiple stages that are highly correlated across individuals Fig. . Most in2) and narrow sense heritabilities (h2) were also calculated for each of our chosen traits using the R packages  “inti”, utilizing the Cullis -method [For the field phenology traits, a Welch two sample t-test was performed to confirm whether the differences observed in phenology timing between the two years was statistically significant. Broad  will contribute positively to a local Lindley score and vice versa for SNPs that fall below the threshold. Lindley scores can not to go below zero regardless of how many p-values that fall below a given threshold. If enough adjacent tests are significant or a single test is highly significant, the local Lindley score will rise above the chromosome specific significance level, signifying an area of interest, which is especially useful for highlighting areas containing multiple weakly significant markers. This threshold is determined for each linked, autocorrelating set of SNPs separately and is essentially the null distribution of said set of markers [Though small and heavily structured, the study population has been previously found to harbor enough genetic variability in phenology traits for adaptation . Thus, w markers . The Lin2) between all SNP pairs in non-overlapping 50 kbp windows using PLINK 1.9. The decay of LD across physical distance was then estimated using nonlinear regression of pairwise r2 against physical distance between sites in base pairs [P. trichocarpa population, and we used this information to determine putative candidate genes for the regions that showed significant association in the GWAS. Boundaries of significant slopes were extended by ± 10 kbp to search for genes using the P. trichocarpa v3.1 annotation [https://phytozome.jgi.doe.gov/pz/portal.html).To identify candidate genes around the significant slopes revealed by the local Lindley score analyses, the rate of decay of linkage disequilibrium (LD) was estimated following method of Wang . Brieflyse pairs . LD decanotation  availablP. trichocarpa, Evans  [The found candidate genes were compared between the years for the field traits and between the different bud set traits for the phytotron experiment. The results of these comparisons were illustrated using Venn diagrams. We compared candidate genes identified for spring and autumn phenology with candidate genes identified in two earlier studies in ompiled)  and McKoompiled) , to revehttps://popgenie.org) and was performed on the Arabidopsis thaliana synonyms of the genes using the default settings of the tool. PopGenIE uses Fisher’s exact test with False Discovery Rate (FDR) correction with a corrected p-value threshold of 0.05 and minimum of two genes by default.GO-term enrichment analysis was performed on each of the study traits by merging the candidate genes from each stage of the trait. The analysis was performed online at PopGenIE  and fathers on top (blue). The parents in bold and all offspring are present in the Krusenberg field trial. Table S2: Monthly mean temperature and rainfall information for years 2017 and 2018. Table S3: Drawings, pictures and descriptions of the 6 stages of bud burst. The table is reproduced as given to the field scorers with drawing of stage 3 and pictures of stages 5 and 6 missing. Drawings reproduced on permission of the owner Alfas Pliura of Lithuanian Research Centre for Agriculture and Forestry. Table S4: Autumn coloration scoring scheme for 2017-2018. Original 2017 and 2018 describe the original scoring systems used in the corresponding years. converted 2017 shows, which stages the original 2017 scores correspond in the 2018 scoring system. Table S5: Phytotron simulated seasons temperature, light conditions and humidity information. The simulated winter shaded in gray. During late July and early August of 2018, a breakdown of the climate chambers occurred causing the simulated winter (marked with *) to be interrupted by high temperatures before resumption of simulated winter temperatures. Table S6: Sequencing breadth and depth coverage of the 121 sequenced individuals. Values have been rounded to three decimals. Genotyped parent and non-parent non-offspring individuals shaded with gray. Table S7: Heritabilites of chosen traits. Broad sense heritability is calculated using the Cullis method. * evident overfitting. Table S8: GO -term enrichment analysis results. Table S9: Hypergeometric test results. Results are rounded to 3 decimals. Table S10: All candidate genes identified within 10 kbp of significant slopes. Table S11: Shared candidate genes between our results and Evans et al. (2014) and McKown et al. (2014). Figure S1: Pearson’s correlation between BLUP and mean values of A) bud burst initation in year 2017 (BB2-17), B) autumn coloring initiation in year 2018 (CO3-18), C) bud set completion (BS7) and D) diameter at breast height (DBH-17). * p < 2.2-16. Figure S2: Spearman’s correlation matrix and heatmap of all our chosen 23 traits. Figure S3: Logarithmic p-value Manhattan plot. Figure S4: Logarithmic p-value QQ-plot. Figure S5: Level of LD as function of distance. Figure S6: Venn diagram of candidate genes for both year for A) bud burst initiation (BB2), B) bud burs completion (BB4) C) autumn coloring initiation (CO3), D) autumn coloring completion (CO8) E) leaf shed initiation (LS2) and F) leaf shed completion (LS5).Below is the link to the electronic supplementary material.


MSTN, and its mutations have been studied in cattle because of the association with increased muscle masses. In horses, some polymorphisms at MSTN have favorable effects on sport performance, as evidenced for the Thoroughbred breed. The Anglo-Arabian horse is a very common breed in many Mediterranean countries because of its extreme adaptability, but genetic investigations are rare in the literature. The present study evidenced the relatively high variability of this breed at MSTN. Many phenotypic effects, such as sex and genealogy, affected the results but, overall, the same polymorphic sites already reported for Thoroughbred horses affected race performances also in Anglo-Arabians.Myostatin is a protein which plays a key role in the regulation and differentiation of cells. The encoding gene, MSTN, was genotyped. Moreover, prizes won and places were achieved for the 1239 races to perform association analyses between the different genotypes and sport traits. Two SNPs already reported in previous studies regarding the Thoroughbred breed, rs69472472 and rs397152648, were revealed as polymorphic. The linkage disequilibrium analysis investigating the haplotype structure of MSTN did not evidence any association block. Polymorphism at SNP rs397152648, previously known as g.66493737 T>C, significantly influenced sport traits, with heterozygous horses TC showing better results than homozygotes TT. The portion of variance due to the random effect of the individual animal, and the other phenotypic effects of sex, percentage of Arabian blood and race distance, computed together with the genotype at MSTN in the statistical models, exerted a significant influence. Hence, this information is useful to improve knowledge of the genetic profile of Anglo-Arabian horses and a possible selection for better sport performance.One hundred and eighty Anglo-Arabian horses running 1239 races were sampled for the present study. DNA was extracted from the blood and myostatin gene,  Myostatin, also known as growth/differentiation factor-8 (GDF-8), is a protein belonging to the superfamily of signal molecules called transforming growth factors beta (TGF-β). These are largely expressed during embryonic development, play a role in regulation and homeostasis of various tissues in adults  and affeMSTN, as the other members of the TGF-β family, is highly conserved among species and homologous sequences are found in mammals as well as in chickens [MSTN, since the discovery of its key roles in the abnormal increase of muscle masses are found in some beef cattle breeds. Genetic studies have revealed that nucleotide variations at the MSTN gene are responsible for the phenotype known as double muscle in Belgian White and Blue cattle [The encoding gene chickens . In livechickens  and the e cattle ,7 and ine cattle .MSTN gene is located on Equus caballus (horse) chromosome 18, Accession number NC_009161.3 , on assembly EquCab3.0 (GCF_002863925.1). It is about 4972 bp long, spans three exons and two introns, and encodes a 375 aminoacids precursor protein, containing a signal sequence, a propeptide amino-terminal domain and a carboxy-terminal domain, which is the active form of the molecule [MSTN, rs397152648 (previously identified as g.66493737 T/C), as predictive of genetic potential, because of its association with the best race distance in Thoroughbreds [MSTN [MSTN has been significantly associated also with precocity traits [In the horse species, some breeds as like as the Thoroughbred breeds has been selected for centuries to improve the race performance and became athletic horses . Althougds [MSTN ,18. MSTNy traits  and the y traits . On the y traits .MSTN variants and race performance traits in Anglo-Arabian horses.The Anglo-Arabian horse is a very common breed in many Mediterranean countries. Many breeders and their stables are located in France and Italy, with a particular and appreciated nucleus in the island of Sardinia  where, over the last two centuries, Thoroughbred and Arabian stallions were crossbred with local mares ,23,24. NMSTN gene sequence and evaluate its variability and association with phenotypic sport performance traits in the Anglo-Arabian horse breed.The objective of this study was to analyse the Ethical review and approval were waived for this study. No specific authorization from an animal ethics committee was required. Blood samples were collected by private and official veterinarians of the local health authorities (ASSL) during clinical samples, control or eradication sanitary programs not linked with the present study. Sampled horses belonged to private breeders, who joined the present study on a voluntary basis.Data and samples were collected from 180 horses bred by 119 different breeders with stables located in the regional territories of Sardinia and Tuscany, Italy. All the horses were officially registered in the stud book of the Anglo-Arabian breed.http://www.unire.gov.it/index.php/ita/Operatori/Banca-dati accessed on 10 January 2017): name, microchip number, sex, year of birth, breeder, the complete genealogy up to the fourth generation and the percentage of Arabian blood. Data regarding the sporting career of each horse were downloaded via the public online databank of the Italian horse races (http://www.hippoweb.it/prestazioni.php accessed on 10 January 2017): races ran at the age of three years, which is the debut age for Anglo-Arabian racehorses; for each of these races: date, racecourse, distance of the race, number of horses at the starting line, order of arrival at the finish line, purse, price won, name of the jockey, trainer. As regards prizes won before 2002, the online databank automatically provides the exchange rate between the currencies in Italian lira and euro. Data from a total of 1239 races were collected.General data of each horse were downloaded via the public online databank of the Italian horse breeds register  and the number of races ran by that horse; place indexes ranged between 0 and 1, and the higher the indexes were, the more prominent the results. As regards the individual race , three new parameters were considered. The arrival index, ArIndex, aimed to differentiate the places on the basis of the competitors; it was calculated as the complement to one of the ratios between the number of horses at the starting line and the place of the horse at the finish line; it ranged between 0 and ~1, and the higher the ArIndex was, the more prominent the result. The purse index, PuIndex, aimed to compute the prestige of the race and differentiate the prizes won on the basis of the total purse; it was calculated as the ratio between the prize won and the purse; it ranged between 0 and 1, and the higher the PrIndex was, the more prominent the result. The success index, SuIndex, aimed to compute both the competitors and the prize won; it was calculated as the multiplication between the ArI and the prize won; it ranged from 0 and, theoretically, to infinite, and the higher the SuIndex was, the more prominent the result.MSTN gene sequence (NC_009161.3) 10 primer pairs were designed using Primer3Plus software (http://www.bioinformatics.nl/cgibin/primer3plus/primer3plus.cgi/ accessed on 10 January 2017) to amplify by PCR 10 overlapping DNA fragments. After sequencing, the 10 amplified DNA fragments gave information about the entire MSTN sequence, including 600 bp of the promoter and 600 bp of the 3′UTR region  according to the following scheme: an initial denaturation at 94 °C for 3 min, followed by 35 cycles at 95 °C for 20 s, 56 °C for 30 s (55 °C only for fragment 1) and 72 °C for 50 s, and a final extension at 72 °C for 5 min. The PCR products were purified using the ChargeSwitch® PCR Clean-Up Kit  and were sequenced using an Applied Biosystems 3730 DNA Analyzer . The entire gene sequence was obtained from two individual horses and the two complete MSTN sequences were submitted to GenBank (https://www.ncbi.nlm.nih.gov/genbank accessed on 10 January 2017) and given accession numbers KY746356  and KY746357 . The two primer pairs SHOR3737F-SHOR3737R  and SHOR_6F-SHOR_6R  were used for genotyping by sequencing the sampled population  and DNA extracted using the Gentra Puregene Blood Kit  according to the manufacture instructions. Based on the R region . Primerspulation . All theThe softwares Phred, Phrap and Crossmatch were used to achieve data quality of sequence plots ,27, Polyhijk is the observed trait; μ is the overall intercept of the model; Gh is the fixed effect of the hth SNP genotype (h = 2 to 3 levels: the two homozygotes and the heterozygote); Si is the fixed effect of the ith class of sex ; ArBj is the fixed effect of the jth Arabian blood percentage ; and ehijk is the random residual ~ N  were analysed only for the debut year (three years of age) using the general linear model procedure and the following model (M1):hijklmn is the observed trait; μ is the overall intercept of the model; Gh is the fixed effect of the hth SNP genotype (h = 2 to 3 levels: the two homozygotes and the heterozygote); Si is the fixed effect of the ith class of sex ; ArBj is the fixed effect of the jth Arabian blood percent ; Dk is the fixed effect of the kth class of race distance ; Hl is the random effect of the lth individual animal (l = 180 horses), pooling all the possible influence of the individual animal, e.g., genealogy, breeder and trainer; Jm is the random effect of the mth jockey (m = 102 jockeys); ehijklmn is the random residual ~ N  were analysed only for races ran during the debut year (three years of age) using the mixed model procedure and the following model (M2):p < 0.05 and multiple comparison of least square means (LSM), for the factors with more than two levels, was performed by using the Bonferroni method at α = 0.05. Statistical analyses were performed using SAS software .Both for the individual horse and race, we analysed one sport performance trait for each SNP at a time, only SNPs with minimum allele frequency, MAF, higher than 0.05  and genoA total of 180 horses were sampled. These horses were born from 1987 to 2013, from 72 different stud farms (from a minimum of 1 to a maximum of 30 horses per stud farm) and 156 mares (from 1 to 3 horses per mare), 119 breeders (from 1 to 8 horses per breeder), trained by 47 trainers (from 1 to 32 horses per trainer). These horses, at the age of three years, which is their debut year, participated in 1239 races (from a minimum of 1 to a maximum of 17 races per horse), ridden by 102 jockeys (from 1 to 143 races per jockey) and ran in ten racecourses (from a minimum of 2 to a maximum of 649 races per racecourse). MSTN gene, including about 600 bp downstream and upstream, was performed in two subjects of Anglo-Arabian breed (KY746356 and KY746357). Sequence analysis evidenced 14 different polymorphic sites  from models (M1) for the individual horse and (M2) for the race, performed to investigate the influence of SNP genotypes at MSTN on sporting performance traits, are reported respectively in Results of the statistical analysis  on all the indexes computed for the races, with the best results regarding the arrival and purse indexes recorded for the class of races from 1000 to 1400 m, while regarding the success index for races between 1850–2400 m (data not shown in tables). Finally, the random effect of the individual animal represented a portion of variance from a minimum of 10.90 to a maximum 26.53%, while the one due to the jockey was from 1.08 to 3.78%  insertion evidenced in the MSTN promoter region of Thoroughbred horses, in linkage with SNP rs397152648C genotype [MSTN gene of this study, with TT, TT, CC and AA genotypes, respectively. The variability observed in the Anglo-Arabian breed of this study may be derived from the fact that Anglo-Arabian horses, in particular the subjects reared in Sardinia, originate from a pool of about three hundred native Sardinian mares, which were initially crossed with Arabian stallions, and later with Thoroughbred and Anglo-Arabian stallions [Pur-sang Anglais and Purosangue Inglese, which stands for English pure blood. Indeed, some authors hypothesize that the low variability in Thoroughbred horses is derived from the high degree of inbreeding [MSTN exon 2, some of those breed-specific, e.g., ECA18:66,492,906 (RefSeq NC_009161.2) 2279A> C for the Arabian breed.The genotype  was not genotype ,33,34, wtallions ,24. Thatbreeding ,36, as tDMRT3 gene have better sports performance than homozygotes [As regards the association analysis performed for the present study, we used only races of the debut year of horses to have the highest level of homogeneity. Different to Thoroughbreds, which begin their racing careers at the age of two, Anglo-Arabians’ debuts take place at the age of three years. During the debut year, horses participate in races which are exclusively limited for three year entrants. Later, several horses are retired from the tracks and there are no more possibilities to compare horses of the same age. The significant influence of the SNP rs397152648 T>C on sport performance evidenced in the Thoroughbred breed has been studied in depth to perform the creation of the “speed gene test” ,38 and pozygotes . FinallyMSTN SNPs in the statistical models, exerted a significant influence. The effect of sex was significant for two out of four traits regarding the individual horse and two out of three traits regarding the race. The best results, in accordance with the literature, were recorded for males. Two studies about Thoroughbred horses report that males are normally more competitive than females [It is worth noting that the portion of variance due to the random effects of the individual animal was relatively high, while the one due to the jockey was lower. Almost all the phenotypic effects, the so-called “non-genetic influence” , compute females  and are  females . This is females ,45. This females .Many hypotheses could be advanced regarding the results about the supremacy in all the traits for horses with the prevalence of Arabian blood. Based on the findings by Giontella et al. , which rMSTN gene has a significant impact on biometric traits [MSTN genotype, the breed and race distance are well described by Rooney et al. [MSTN gene in horse breeds used for sprint races, like the Quarter Horse. Nevertheless, similarly to the study by Hill et al. [p-values were non-significant .As regard the effect of the race distance, noteworthy influences of sex and the genotype on performance in different race distances is reported in the literature. Harkins et al.  have repc traits ,48 and mc traits , and they et al.  Those lay et al.  and Petey et al.  who havel et al. , we trieMSTN locus in the analysed population has a relatively high variability and some SNPs were reported for the first time. With regard to the association analysis of genetic polymorphism with sport indexes, it was confirmed that SNP at ECA18 rs397152648 T>C, already studied in Thoroughbred horses, affected race performances also in Anglo-Arabians. The genetic selection to obtain better performance of horses could be a possibility to be explored, but it is clear that this is not the only way, because the other phenotypic effects studied showed high values of significance.The Anglo-Arabian is a versatile horse breed. The present study aimed to improve the knowledge on this breed and revealed that the


Recently, there is a growing interest in the genetic improvement of carcass merit in the Korean beef industry. Primal cut yields have been proposed to characterize the meat quality and quantity in beef cattle and are known to be genetically correlated with the carcass merit and premium prices. Hence, knowledge of genetic parameters is required to select the weight of primal cuts that may be used as the selection criteria for designing future breeding programs. This study aimed to estimate the heritability and genetic and phenotypic correlations of primal cut yields and carcass traits in Hanwoo cattle. All traits presented a medium to high heritability, which indicates a probable increase in their response to selection. In addition, moderate to highly favorable genetic correlations were found between the primal cut yields and carcass traits, such as the carcass weight and eye muscle area. Therefore, results suggested inclusion of primal cut traits as a selection objective in Hanwoo breeding programs to meet the growing demand for high quality products.This study was carried out to estimate the variance components, heritability, and genetic correlations between the carcass traits and primal cut yields in Hanwoo cattle. Carcass traits comprising 5622 records included back fat thickness (BFT), carcass weight (CW), eye muscle area (EMA), and marbling score (MS). The 10 primal cut yields from 3467 Hanwoo steers included the tenderloin (TLN), sirloin (SLN), striploin (STLN), chuck (CHK), brisket (BSK), top round (TRD), bottom round (BRD), rib (RB), shank (SK), and flank (FK). In addition, three composite traits were formed by combining primal cut yields as novel traits according to consumer preferences and market price: high-value cuts (HVC), medium-value cuts (MVC), and low-value cuts (LVC). Heritability estimates for the interest of traits were moderate to high, ranging from 0.21 ± 0.04 for CHK to 0.59 ± 0.05 for MS. Except genetic correlations between RB and other primal cut traits, favorable and moderate to high correlations were observed among the yields of primal cut that ranged from 0.38 ± 0.14 (CHK and FK) to 0.93 ± 0.01 (TRD and BRD). Moreover, the estimated genetic correlations of CW and EMA with primal cut yields and three composite traits were positive and moderate to strong, except for BFT, which was negative. These results indicate that genetic progress can be achieved for all traits, and selection to increase the yields of primal cuts can lead to considerable profitability in the Hanwoo beef industry. In recent years, carcass and meat quality traits in beef cattle have received great attention, as they determine the profitability of the beef production systems. Meat quality is a comprehensive term used to describe the consumer acceptance of beef, as they are willing to pay more for higher-quality products . In thisOne of the famous breeds for carcass and meat quality traits is Hanwoo, an indigenous beef cattle in Korea . Hanwoo In addition to the assessment of primal cut yields in this study, three composite traits were obtained from the primal cut yields according to the consumer preference and price. To this end, we estimated the genetic parameters of primal cut yields and three composite traits as well as the genetic and phenotypic correlations between these traits and carcass traits in Hanwoo cattle.Data used in this study were collected from the Hanwoo Improvement Center (HIC) of the National Agricultural Cooperative Federation, Korea. Carcass traits were available on 5622 Hanwoo steers (born between 2003 and 2017), of which 3467 steers had records for primal cut traits (born between 2008 and 2017). All animals participate in the progeny test program for selecting proven bulls under the Korean National Hanwoo Cattle Improvement System. The steers were castrated around 4 to 5 months of age. The batch, which represented the contemporary group of the animal, was two batches in each production year. Further details have been previously described by Kim et al. [y is the vector of phenotypic observations; b is the vector representing the fixed effects, comprising the slaughter date (180 levels) and slaughter age (days from birth to slaughter) was considered as covariates for carcass traits; u is the vector of random genetic additive effects, and e is the vector of random residual effects. The X and Z are incidence matrices that relate the records to the fixed and random genetic additive effects, respectively.The genetic and phenotypic correlations among the traits of interest were estimated using a series of bivariate pedigree-based animal models with the average-information algorithm implemented in the AIREMLF90 software  as follou and e were assumed to follow the multivariate normal distribution with the mean of zero and Var (u) = G⊗A and Var(e) = R⊗I, in which A is the numerator relationship matrix, I is the identity matrix, and G and R are additive genetic and residual covariance, respectively; and ⊗ is the Kronecker product.The random effects of The variance components and heritabilities were estimated using a single-trait pedigree-based animal model with the same fixed effects as in Equation (1).−12 for all evaluations as a default of program.The convergence criterion for estimation of variance components was computed according to the formula :C=∑i(θi−g) and phenotypic (rp) correlations were obtained as:In addition, the genetic (rg) was defined as the square root of the additive genetic variance divided by the mean of the trait variance components, correlations and heritabilities was estimated by 10,000 times repetitive sampling with the option “se_covar_function” in AIREMLF90 software .In order to determine significant of genetic and phenotypic correlations different from zero, the full and reduced models were compared by the chi-squared distribution of -2log-likelihood estimates as follows :Λ=likeliG was diagonal in reduced model. The genetic correlation was significant when G and R matrices (in the reduced model) were diagonal and the chi-squared statistic with two degree of freedom and α equal 0.05 was 5.99  for BFT (27.05%) and MS (33.99%), while the other traits had relatively low variations (4.68 to 9.23%). The magnitude of genetic variability in a trait determines its evolvability [The favorable and moderate to high heritability of primal cut yields demonstrate that direct selection may have a considerable influence on these traits. Overall, the discrepancy in the estimates of present study with those of previous studies may be due to differences in slaughter age of animals considered, the number of records, breed, completeness of the pedigree, and statistical models used for analyses. The results also showed considerable additive genetic variation (based on CVvability  and, conIn addition, our results showed that the genetic parameters obtained using bivariate models were close to the corresponding univariate models .Results of the genetic correlations among the primal cuts and carcass traits showed positive, and relatively moderate to high correlations for all the primal cut yields with both CW and EMA and varied from 0.34 ± 0.10 to 0.85 ± 0.04, respectively . The genOur results indicate that the carcass and primal cut yields are heritable, and the existence of sufficient genetic variation can lead to improvements in these traits through genetic selection. Genetic correlations of primal cut yields with CW and EMA were moderate to highly favorable, while low to moderate and negative correlations between the primal cut traits with MS and BFT were observed. In addition, among the composite primal cut and carcass traits, such as CW and EMA, there were relatively strong and favorable correlations, representing that the genetic evaluation of carcass traits could be improved by utilization of primal cut yields as potential indicators for carcass merit. Consequently, the inclusion of these traits by breeders as selection criteria can provide a good opportunity for developing a selection index combining the primal cut yields and carcass traits in order to increase the response to selection and identification of candidate animals and it can result in substantial profitability of production systems.


Fungi are mediators of the nitrogen and carbon cycles in terrestrial ecosystems. Examining how nitrogen uptake and organic matter decomposition potential differs in fungi can provide insight into the underlying mechanisms driving fungal ecological processes and ecosystem functioning. In this study, we assessed the frequency of genes encoding for specific enzymes that facilitate nitrogen uptake and organic matter decomposition in 879 fungal genomes with fungal taxa grouped into trait-based categories. Our linked gene-trait data approach revealed that gene frequencies vary across and within trait-based groups and that trait-based categories differ in trait space. We present two examples of how this linked gene-trait approach can be used to address ecological questions. First, we show that this type of approach can help us better understand, and potentially predict, how fungi will respond to environmental stress. Specifically, we found that trait-based categories with high nitrogen uptake gene frequency increased in relative abundance when exposed to high soil nitrogen enrichment. Second, by comparing frequencies of nitrogen uptake and organic matter decomposition genes, we found that most ectomycorrhizal fungi in our dataset have similar gene frequencies to brown rot fungi. This demonstrates that gene-trait data approaches can shed light on potential evolutionary trajectories of life history traits in fungi. We present a framework for exploring nitrogen uptake and organic matter decomposition gene frequencies in fungal trait-based groups and provide two concise examples on how to use our framework to address ecological questions from a mechanistic perspective.The online version contains supplementary material available at 10.1007/s00248-021-01687-x. Trait-based ecology merges evolution and classic population and community ecology, with a focus on traits rather than species identity. The goal is to better understand the strategies species use to exploit resources by studying their most critical attributes . A trait, saprotrophs, or pathotrophs; (2) functional guild based on the class of environmental resources exploited: animal pathogens, ectomycorrhizal (ECM) fungi, endophytes, plant pathogens, undefined saprotrophs , and wood saprotrophs; (3) growth morphology, where fungi are classified according to their morphological phenotype, such as agaricoids, boletoids, corticioids, gasteroids, microfungi, polyporoids, and yeasts (including facultative yeasts); and (4) rot type based on the ability to decompose certain types of organic compounds, namely, brown rot, soft rot, and white rot fungi [FUNGuild is a tool that links fungal taxonomical molecular identity  to ecological function by grouping fungal taxa into four trait-based categories: (1) trophic mode, where fungi are grouped as symbiotrophsot fungi . This toot fungi . Growth ot fungi .While FUNGuild groups fungal taxa by their functional attributes, MycoCosm is a fungal genomic database that provides access to the complete genomes of over 1000 fungal taxa . TresedeIn this study, we aimed to showcase how linking genes to traits in fungi can be used to forward ecological research. We assessed the frequency of genes encoding for specific enzymes that facilitate N uptake and OM decomposition in nearly 1000 fungal genomes obtained from MycoCosm, with fungal taxa grouped into FUNGuild trait-based categories. We then examined how these gene frequencies compared and contrasted among fungal trait-based categories. We present two examples showing how this linked gene-trait data can be used to address questions in ecological research. In the first example, we used previously published fungal RNA data collected from the Chronic Nitrogen Amendment Study (CNAS) at the Harvard Forest Long-term Ecological Research (LTER) site, in conjunction with the gene frequency data obtained from MycoCosm, to address the question: does knowing something about the frequencies of N uptake and OM decomposition genes of fungal functional groups provide insight into expected fungal responses under simulated N fertilization? This question was motivated by decades of research showing that in temperate forest ecosystems, simulated N deposition often results in OM accumulation –18 due tIn the second example, we examined the frequencies of N uptake and OM decomposition genes in 62 different ectomycorrhizal (ECM) fungal taxa to examine how gene frequencies vary within a single functional guild and to ask the question: what can we learn about ECM life history traits by comparing the frequencies of N uptake and OM decomposition genes to those of fungi categorized by rot type ? This question derived from evidence showing that the evolutionary history of ECM fungi is polyphyletic . That isAcremonium spp.). At the functional guild level, if a species was classified as undefined saprotroph-wood saprotroph or some other combination of functional guilds and belonged to a type of rot, we classified it as wood saprotroph . Moreover, we examined the description for each genome as given in MycoCosm; if the description provided insight into a species’ function , we used that information to designate the final trophic mode. If a species was classified into all three possible trophic mode categories , and we were unable to convincingly designate one final category using the approaches described above , we did a literature search using Google Scholar and designated the final trophic mode based on the top ten most highly cited papers. In general, trait-based saprotrophs are categorized as undefined  or wood saprotrophs, symbiotrophs as endophytes or ECM, and pathotrophs as plant or animal pathogens. We excluded genomes from underrepresented groups , such as arbuscular mycorrhizal fungi, dung saprotrophs, litter saprotrophs, leaf saprotrophs, etc. to avoid drawing conclusions from insufficient data. A complete list of all genomes is available in Supplementary Table We collected public genomic data from the US Department of Energy 1000 Fungal Genomes Project  and created a database comprising a total of 983 genomes, representing nine fungal phyla or subphyla, 118 orders, 296 families, and 571 genera , 29. We P ≤ 0.05. We used R for all tests [We determined, for each genome, the number of gene copies for N uptake and OM decomposition gene families Table  because ll tests .Table 1G−1 yr−1 at this site [4NO3 fertilizer on a monthly basis during the growing season: N50 (50 kg N ha−1 yr−1) or N150 (150 kg N ha−1 yr−1). The N50 plot represents an N deposition scenario predicted for 2050 [The CNAS at the Harvard Forest LTER, initiated in 1988 to examine the long-term consequences of high cumulative rates of N additions to forest ecosystems , 39, conhis site ) and thefor 2050 , while tfor 2050 , 42, 43.We used previously published soil fungal ribosomal RNA (rRNA) data collected from the CNAS  to invest test for each trait-based category and compared the mean N response to μ = 0. Trait-based categories with a significant positive N response had a greater abundance of RNA reads under N addition compared to control, while those with a significant negative N response had a lower abundance of RNA reads under N addition compared to control.When RNA reads were present in control but absent in N50 or N150, we assigned a value of − 1 to indicate a decrease under N. When RNA reads were present in N50 or N150, but absent in N0, we assigned a value of 1 to indicate an increase under N. Any taxon that increased or decreased by a magnitude of 3 or more were eliminated from our analysis; the effect of these outliers was analyzed in Morrison and collaborators . DespiteWe extracted gene frequency data for all genomes listed as ECM fungi at the functional guild level  as most N responses were significant at N150 Fig. . RegardlThe evolutionary histories of ECM fungi are challenging to study because of the polyphyletic nature of the ECM niche and the diversity of saprotrophic lineages from which ECM fungi evolved , 25. A cOn the whole, sequenced ECM genomes appear to have divergent functional potentials for both OM depolymerization and N uptake. Genomes with similar gene frequency distributions to white rot fungi, for example, the Cantharellales and Gomphales, are likely to have vastly different effects on ecosystem C and nutrient cycling compared to ECM more similar to soft rot fungi, for example, the Helotiales. A recently published comprehensive study of 62 mycorrhizal species also suggests a broad variation in the capacity of mycorrhizal fungi to acquire C from soil OM . NonetheThe variety of traits in fungi is vast , 53, 71.Since many ecological modelling trait-based efforts often focus on tradeoffs –74, it iESM 1(DOCX 1.01 mb)ESM 2(XLSX 369 kb)ESM 3(XLSX 22.6 kb)ESM 4(XLSX 18.8 kb)


N = 129,237) using the derived wellbeing index, followed by polygenic profiling . The wellbeing index, its subcomponents, and negative indicators of mental health were compared via phenotypic and genetic correlations, and relationships with psychiatric disorders examined. Lastly, the impact of childhood maltreatment on wellbeing was investigated. Five independent genome-wide significant loci for wellbeing were identified. The wellbeing index had SNP-heritability of ~8.6%, and stronger phenotypic and genetic correlations with its subcomponents (0.55–0.77) than mental health phenotypes (−0.21 to −0.39). The wellbeing score was lower in participants reporting various psychiatric disorders compared to the total sample. Childhood maltreatment exposure was also associated with reduced wellbeing, and a moderate genetic correlation (rg = ~−0.56) suggests an overlap in heritability of maltreatment with wellbeing. Thus, wellbeing is negatively associated with both psychiatric disorders and childhood maltreatment. Although notable limitations, biases and assumptions are discussed, this within-cohort study aids the delineation of relationships between a quantitative wellbeing index and indices of mental health and early maltreatment.Wellbeing is an important aspect of mental health that is moderately heritable. Specific wellbeing-related variants have been identified via GWAS meta-analysis of individual questionnaire items. However, a multi-item within-subject index score has potential to capture greater heritability, enabling improved delineation of genetic and phenotypic relationships across traits and exposures that are not possible on aggregate-data. This research employed data from the UK Biobank resource, and a wellbeing index score was derived from indices of happiness and satisfaction with family/friendship/finances/health, using principal component analysis. GWAS was performed in Caucasian participants ( Wellbeing is an important aspect of mental health, and is defined by the core concepts of happiness, life satisfaction and optimal psychological functioning . This coWellbeing is defined by two core concepts: (1) subjective wellbeing , consisting of positive affect and satisfaction with life, and 2) psychological wellbeing , consisting of attributes that enable realisation of human potential including autonomy, mastery and life purpose  psycholo, 4, but Wellbeing is moderately heritable, with estimates from twin studies ranging from 17 to 67% . There were 45,723 participants with non-missing childhood maltreatment sum-score and wellbeing index.Five items related to childhood maltreatment were identified, including emotional or physical neglect, and physical, emotional, or sexual abuse [Data-Field:20487-20490], and responses were on a 5-point Likert scale . These exposures are collectively referred to as “maltreatment”, henceforth. First, the impact of each type of childhood maltreatment on the wellbeing index was examined as categorical variables, relating to the frequency of maltreatment experience. Then to evaluate the cumulative impact of maltreatment exposure, each item was dichotomised as “exposed” or “not-exposed” , examined how childhood maltreatment (or exposure sum-score) impacts wellbeing. The regression standardised estimate was considered the magnitude of effect of the dependant variable.The question “Have you been diagnosed with one or more of the following mental health problems by a professional, even if you don’t have it currently?” (Data-Field:20544) was used as the indicator of a lifetime psychiatric diagnosis. “Prefer not to answer” responses were coded as missing. As participants could select ≥1 of 16 listed diagnoses, representation in each mental health category was not mutually exclusive.correlation (v0.6.0) R package was employed to examine phenotypic correlations (rp), employing different types of correlations based on data type: Spearman’s rank for continuous/ordinal variables, Tetrachoric for binary, and Point-biserial for continuous/ordinal versus binary. Analyses were limited to participants with no missing phenotype data to enable head-to-head comparisons using the same sample size across all measures . The significance threshold for rp was set at p < 0.001, using a Bonferroni correction for 45 tests (α = 0.05/45).The p < 1 × 10−6, left ~8,068,119 SNPs for analysis. Results from the standard infinitesimal mixed-model association were employed for downstream analysis.To perform GWAS, we employed BOLT-LMM v2.3.4 . AnalysiFunctional Mapping and Annotation of GWAS (FUMA)  was emplSNP-heritability calculations employed Linkage Disequilibrium Score Regression (LDSC) , utilisirg) between the wellbeing index, its five subcomponents, four negative indicators of mental health, and childhood maltreatment using GWAS summary statistics generated herein.LDSC  was usedrg with the derived wellbeing index phenotype, including: positive affect, life satisfaction, wellbeing spectrum, [Published summary statistics of additional wellbeing-related traits were used to estimate pectrum,  subjectipectrum, , consciepectrum, , extravepectrum, , neurotipectrum, , depresspectrum, , lonelinpectrum,  and bodypectrum, .rg with major psychiatric conditions were examined using published disease-specific summary statistics, including: major depressive disorder (MDD) [rg estimations. The significance threshold for rg after Bonferroni correction for 72 tests (α = 0.05/72) was p < 6.94 × 10−4.In addition, er (MDD) , bipolarer (MDD) , schizoper (MDD) , attentier (MDD) , autism er (MDD) , obsessier (MDD)  and poster (MDD) . We incl2, defined as the difference between the R2 from the full-model and the model without PGS as a predictor, was reported as predictive power. For binary variables  a logistic regression was used instead of linear regression, and Nagelkerke R2 calculated using the R package fmsb (v0.7.0). A 95% confidence interval around the R2 was calculated using the R package psychometric (v2.2).Polygenic scores (PGS) were constructed in the independent confirmatory cohort using PRS-CS software , employiThe discovery cohort comprised 129,237 participants  who were of British-white ancestry and had no missing phenotypes for the five core wellbeing questions. The age range was 40-70 years (mean ± SD = 57.3 ± 8.0), and 31.9% reported having college education. The data-field IDs and description of study variables in the discovery sample is presented in Table The confirmatory sample comprised 23,703 participants , who were genetically unrelated to the discovery sample but with identical inclusion criteria, and were ~7 years older  than discovery cohort participants.p < 2.2 × 10−16; Fig. Principal component analysis in the discovery sample indicated the first component explained 47.48% of the variance of the five questions  Fig. , and wasrp with the subcomponent happiness (rp = 0.77), and the weakest with health satisfaction (rp = 0.55). From the negative mental health indicators, the wellbeing index had the strongest rp with neuroticism (rp = −0.39) and weakest with depressive symptoms (rp = −0.22)  Fig. .Fig. 1Php < 5 × 10–8)  Fig. , which r–8) Fig. .Fig. 2Gen = 44 genes), eQTL (n = 27) and chromatin interaction mapping (n = 62) modules   Table . The GWAellbeing  and bothellbeing , and depellbeing , 29, andophrenia , 38 (TabTissue-specific enrichment of wellbeing index GWAS genes in MAGMA (implemented in FUMA)  showed sh2 = 8.6%, SE = 0.005), although it was not significantly different from health satisfaction . Other subcomponents had similar SNP-heritability   Fig. .rg between traits were stronger than their rp . Amongst the subcomponents . Across the negative mental health indicators .Generally, the  rp Fig. , and rg nts Fig. , wellbeiors Fig. , wellbeirg between wellbeing index and other positively and negatively related phenotypes showed significant correlation for all tested traits after multiple-testing correction. Except for OCD and PTSD, all psychiatric conditions had a significant negative relationship with wellbeing index – the strongest effect was for MDD  , and the lowest for financial satisfaction . Amongst negative mental health indices, the best predictive power of wellbeing PGS was for loneliness , although not significantly different from emotional neglect   Fig. , panel A10) Fig. , panel B10) Fig. .Fig. 5Thp = 1.7 × 10−12), chromosome 15  and chromosome 16   and its components (rg = −0.60 to −0.37), and a positive rg with negative indicators of mental health  and the strongest positive rg for depressive symptoms .Three loci exceeded genome-wide significance for association with childhood maltreatment: chromosome 7  Fig. . There wlth Fig. . The strz-score was negative for 16 self-reported psychiatric conditions (−1.66 to −0.22)  and “Anxiety, nerves or generalised anxiety disorder”  were associated with lower mean wellbeing index z-score of −0.355 and −0.289, respectively. Of mental illness groups with >200 participants (>0.2% population frequency), the largest mean differences were with over-eating/binge-eating, social-anxiety/social-phobia, OCD, and bipolar-mania.The mean wellbeing index 22) Fig. . Some psrg = 0.65–0.91, rp = 0.55–0.77) and moderate to low negative correlation with adverse mental health indicators . The wellbeing GWAS had five genome-wide significant associated loci, with a relatively high SNP-heritability. Despite very small estimated effects on phenotypic variance, the wellbeing index PGS significantly predicted the wellbeing score and correlated items in an independent sample. Moreover, both childhood maltreatment and psychiatric illness were associated with lower wellbeing.A wellbeing index score was constructed and evaluated phenotypically and genetically in the UK Biobank. The wellbeing index had moderate to high correlation with its subcomponents (rp and rg relationships. We demonstrated that SNP-heritability of the wellbeing index score was slightly higher than previously reported wellbeing SNP-heritability (h2 = 8.6% vs. ~4.0–6.4%) [While previous meta-analyses of GWAS summary statistics for correlated phenotypes has significantly advanced our understanding of wellbeing genetics , 10, 11,.0–6.4%)  vs. SNP-.0–6.4%) , 41, whi.0–6.4%) .z-score, indicating that even within a background of mental illness an individual’s wellbeing can be positive – supporting the notion that wellbeing is not simply the absence of mental illness [rg between wellbeing index and major psychiatric illnesses implies that relationships might be partly due to overlapping genes or gene-environmental correlations influencing both outcomes.Although mental illness can influence wellbeing, we defined wellbeing in the entire population – including those with a mental illness – to ensure that findings were closely representative of a general population, within inherent cohort selection biases . While t illness –46. Howeemotional rather than physical and sexual maltreatments – consistent with a recent report [While the wellbeing index was derived from the baseline questionnaire, childhood maltreatment data was collected in the follow-up mental health assessment, 6–10 years later. We did not consider this a methodological concern given that the trauma questionnaire asked retrospectively about childhood events. Notably, we observed discrepancies relating to response endorsement for physical neglect – this potential issue has not, to our knowledge, been reported previously – hence we recommend caution with the use of Data-Field:20491, which was excluded herein. The stepwise decline in mean wellbeing index with increasing frequency of each individual type of maltreatment, as well as maltreatment accumulation, is consistent with existing evidence indicating that frequency and accumulation of different trauma types are key factors that can influence the way childhood maltreatment affects adults , 47, 48.rg and rp was found across all phenotypes (r = 0.97), with rg consistently higher than rp  – consistent with comparisons of genotype/phenotype correlations of other traits [rp and rg are somewhat distinct, with the former comparing individual-level data (and different variable types), and the latter comparing summary statistics across a population. Therefore, these findings should be interpreted with caution. A stronger rg for wellbeing-related phenotypes has previously been reported [rg between two traits may indicate that although shared genetic variants influence both phenotypes, environmental factors may impact them differently [as discussed [rg with the wellbeing index, as well as its subcomponents. This further highlights the importance of loneliness in wellbeing [rp, shows strong rg [rg with the financial satisfaction subcomponent, despite its stronger rp with happiness, family- and friendship-satisfaction. The rg between wellbeing index and depressive symptoms or neuroticism  were weaker than previously reported relationships with subjective wellbeing  [rg estimates). Furthermore, the genetic correlation between the wellbeing index and Okbay’s subjective wellbeing is 0.77, indicating the two scores are not identical; possibly due to inclusion of more elements of life satisfaction than affect in our score, which would diminish apparent relationships with neuroticism and depressive symptoms. One of the strengths of our study is using the same cohort and measures for different phenotypes, which makes genetic and phenotypic correlations more comparable – though we acknowledge that consistent reporting biases  may influence associations derived from a single cohort.A strong correlation of reported , and a rreported . The strellbeing , which dtrong rg . Interesp = 2.7 × 10−9) lies in an intron of FSHR (Follicle Stimulating Hormone Receptor), and influences its expression  display enhanced anxiety- and depression-like behaviours, and modulate gene expression in mood-mediating brain regions [The wellbeing index GWAS revealed five independent genome-wide significant loci; three consistent with previous reports , 10. The × 10−11 ). Knocko regions . Two add regions , 55. One regions , are conOne design limitation of our index score is that only subjective wellbeing indicators were included. The single item that related to psychological wellbeing was excluded due to the time-lag between baseline and follow-up assessments. Additionally, job satisfaction was excluded due to significant data missingness – representing individuals who were unemployed or retired at the time of assessment – which may have skewed the index score and limited generalisability, despite job satisfaction being an important component of wellbeing. Another caveat was the weak correlation (~0.25) of financial- and health-satisfaction to other items in the factor score. However, given their importance on subjective wellbeing , 56 and Our study inherits the limitations of UK Biobank data. All the measures used in this study were self-report, with limited evidence of their validity, and reliability over time. The large sample sizes available through the UK Biobank enable highly significant associations to be derived from weak effect sizes, and caution should be taken in their interpretation. One should consider participant age (40–70 years), recall and reporting bias, and under-representation of psychiatric illnesses when interpreting results . We cannFinally, genetic correlations were estimated from common SNP variants identified via GWAS, and thus may underestimate pleiotropic contributions of other variant classes (e.g. rare SNVs or CNVs), and non-additive effects. Furthermore, as the SNP-heritability of the traits examined were small, only a small fraction of the causal SNPs for each phenotype contributed to the genetic correlations reported herein. While the LDSR method assumes that this small fraction is a random selection of all causal variants , recent Using a quantitative index of subjective wellbeing – which encompasses elements of general happiness and satisfaction with family, friendship, health, and financial situation – we explored the genetic and phenotypic relationships between positive and negative aspects of mental health and their correlation with mental illness and childhood maltreatment. The wellbeing index GWAS suggested that improving the measure by using multiple indicators of wellbeing could be a valid method to increase the explained genetic variance (SNP heritability) of wellbeing; although we note that unrealistic underlying models that are fundamental to PGS and LDSR that assume independent additive effects on traits in isolation from environmental exposures may contribute more substantially to ‘missing heritability’ and limit clinical utility. However, future studies will benefit from a combination of denser phenotyping, improved statistical genetic methods that are based on realistic assumptions (i.e. that reflect networks of genotype-phenotype effects across complex interrelated traits), and probably integration of technologies such as artificial intelligence and machine learning to advance the field. The negative association between childhood maltreatment and wellbeing, especially relating to emotional domains, highlights the importance of early life environment on wellbeing later in life. Most studies focus on the association of childhood maltreatment and psychopathologies; however, our study suggests that we might also need to measure the positive spectrum of mental health in relation to the childhood environment. Furthermore, our work may help to delineate the possible negative effects of maltreatment (i.e. on reduced wellbeing) before they become clinically significant. This is useful to know because indices of wellbeing can enable targeted provision of early support for people with an increased risk of developing psychiatric illnesses, which will segue into mental illness prevention. Finally, reduced wellbeing in the context of psychiatric illness, alongside a negative genetic correlation between wellbeing and psychiatric outcomes, demonstrates an interdependent relationship that can be influenced both by genetic and environmental factors, although directionality and causality remain to be determined in future studies. Determining the direction of effects and causality is possible using longitudinal studies with appropriate experimental design or genetic methods such as Mendelian randomisation. A better understanding of causality can then directly inform population-based interventions at societal or policy levels, to reduce the incidence of mental disorders and improve health and quality of life.Supplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Information


Kukri_rep_c103359_233 and wsnp_Ex_rep_c107911_91350930 deserve particular attention. The consistently significant markers with large effect could be useful for marker-assisted selection. Genomic selection revealed medium to high prediction accuracy ranging between 52% and 92% for the assessed traits with the least and maximum value observed for stigma length and visual anther extrusion, respectively. This indicates the feasibility to implement genomic selection to predict the performance of hybrid floral traits with high reliability.Hybrid wheat breeding is one of the most promising technologies for further sustainable yield increases. However, the cleistogamous nature of wheat displays a major bottleneck for a successful hybrid breeding program. Thus, an optimized breeding strategy by developing appropriate parental lines with favorable floral trait combinations is the best way to enhance the outcrossing ability. This study, therefore, aimed to dissect the genetic basis of various floral traits using genome-wide association study (GWAS) and to assess the potential of genome-wide prediction (GP) for anther extrusion (AE), visual anther extrusion (VAE), pollen mass (PM), pollen shedding (PSH), pollen viability (PV), anther length (AL), openness of the flower (OPF), duration of floret opening (DFO) and stigma length. To this end, we employed 196 ICARDA spring bread wheat lines evaluated for three years and genotyped with 10,477 polymorphic SNP. In total, 70 significant markers were identified associated to the various assessed traits at FDR ≤ 0.05 contributing a minor to large proportion of the phenotypic variance (8–26.9%), affecting the traits either positively or negatively. GWAS revealed multi-marker-based associations among AE, VAE, PM, OPF and DFO, most likely linked markers, suggesting a potential genomic region controlling the genetic association of these complex traits. Of these markers,  Triticum aestivum L.) is the second most important cereal cultivated crop worldwide with 766 million tons produced in 2019 [Bread wheat  investigating the potential of genome-wide prediction (GP) for the evaluated floral traits. Hybrid wheat is a promising technology to increase yields worldwide. However, less than 1% of the global wheat area is planted with hybrids, mainly due to the lack of cost-effective hybrid seed process and low heterosis , althougl traits ,8. Therel traits . These Gl traits ,10. To dl traits ,11,12. Il traits ,14,15,16p < 0.001) with the highest correlation recorded between anther extrusion and pollen mass  was investigated by Bayesian-based structure analysis using 102 non-redundant markers across the whole genomes. The population structure of the 196 genotypes showed three subpopulations. A similar clustering result was obtained using discriminant analysis of principal components (DAPC) and principal component analysis (PCA). A detailed analysis was described by Tadesse et al. .2) as a function of the intermarker distance (Mbp) for all genotypes were described by El Hanafi et al. [2) as a function of the intermarker genetic distance (Mbp) within the same chromosome for all genotypes indicated a clear LD decay with genetic distance. The baseline intersected with the spline curve at 6 Mbp based in the average R2 = 0.23, as shown by El Hanafi et al. [In total, 10,477 polymorphic SNP markers out of 12,725, retained after removing SNPs with >10% missing data and MAF < 5%, were used to determine LD between all pairs of markers. The LDs for locus pairs and the scatter plot of LD  were significantly associated with AE, VAE, PM and OPF. MTA analysis based on semi-dwarfing genes Rht-1 (Rht-B1 and Rht-D1) detected an association of Rht-D1b with both AE and VAE, which explained 20.57% and 17.34% of the phenotypic variation, respectively. There was no significant association with Rht-B1. A small negative effect was observed for the dwarfing allele Rht-D1b on AE and VAE of −3.12 and −0.59, respectively , 18 with visual anther extrusion (VAE), 14 with pollen mass (PM), 3 with anther length (AL), 1 with pollen shedding (PSH) and 1 with pollen viability (PV) . For thet traits . Kukri_rectively .2 = 0.16 and R2 = 0.18 (p < 0.001), respectively. We investigated the relationship between the number of favorable alleles and the performance of the genotypes. Of the 17 significant markers associated with AE, 11 have a favorable effect (an increasing effect on AE) while 6 have an unfavorable effect (decreasing effect on AE). Sixty-six genotypes harbored the maximum favorable allele and five the maximum unfavorable one . The besRht-D1b was the marker with the highest number of observed alleles. SERI.1B//KAUZ/HEVO/3/AMAD/4/ATTILA//PSN/BOW/3/ATTILA/5/KAUZ’S’/SHUHA-15 and SOMAMA-9/ICARDA-SRRL-2, with the best VAE performances of 9.45 and 9.3, respectively, were the genotypes having the maximum favorable alleles (12). The linear regressions of the number of favorable and unfavorable alleles per genotypes versus the VAE-BLUEs values showed significant association with R2 = 1.6 and R2 = 1.5 (p < 0.001), respectively (Alleles per marker for VAE varied between 24 and 139. ectively .2) ranging from 21.29% to 25%. Again, SOMAMA-9/ICARDA-SRRL-2 appears among the best genotypes having the highest number of favorable alleles (six) and showing good pollen mass performance (41.11 mg). The linear regressions of the number of favorable and unfavorable alleles per genotypes versus the PM-BLUEs values showed significant associations with R2 = 1.7 and R2 = 1.6 (p < 0.001), respectively.GWAS identified 14 significant markers associated with PM at FDR < 0.05, which were all commonly present with those associated with AE . Ten mar2) ranging from 23.48% to 24.53%. Eighty-six genotypes had the maximum cumulative number of favorable allele effect (six) (2 = 1.13 and R2 = 1.19 (p < 0.001), respectively.GWAS identified 11 significant markers associated with OPF on chromosomes 1B (10) and 2A (1), of which 12, 11 and 12 were common with AE, VAE and PM, respectively . The sigct (six) . SOMAMA-Besides the female floral traits, GWAS detected three significant markers associated to duration of floret opening (DFO) with phenotypic variance of 18.7% each located in 1B (two) and 2A (one) . The sam2) was observed for the evaluated floral traits with the highest value (0.30) recorded for anther length while stigma length showed the lowest heritability (0.08)  . The preHybrid wheat breeding technology has great potential to raise wheat production, particularly in the context of global climate change and future food security. The maximum heterosis achieved can assess the usefulness and efficiency of a hybrid wheat program. However, the stringent autogamous nature of wheat presents a bottleneck for suitable seed production system. The ability of enhanced outcrossing might be achieved by choosing specific parental lines for best hybrid combination. Selection for potential hybrid wheat traits such as anther extrusion, pollen mass, degree and duration of floret opening is challenging and time consuming. Molecular tools are widely considered to hold promise for the dissection of genomic regions harboring genes/markers for hybrid traits. In this study, we evaluated the potential of genomic approaches in identifying genes/QTL that could be effectively used to fine-tune the floral traits and greatly simply redesigning wheat flower.While hybrid wheat programs have operated for several decades and phenotyping floral characteristics traits have been fairly studied, little is known of the genetic mechanisms controlling male and female flower development. Marker–trait associations (MTAs) were identified for the potential floral hybrid wheat traits by using a mixed linear model considering both kinship (K) and population structure (Q) matrices as covariates. In total, 70 markers were associated with various evaluated traits. For the floral associated markers, 17 and 18 MTAs were linked to AE and VAE, respectively, of which 14 markers were in common. The phenotypic variance explained by these markers ranged 17.33–26.92%, which is greater than what was reported by Muqaddasi et al.  and Boevwsnp_Ex_c5101_9053178 on chromosome 1B found associated with AE and VAE was reported previously by Boeven et al. [Excalibur_c20309_539 and Kukri_rep_c110544_248 (3B) associated with VAE were previously reported by Muqaddasi et al. [The comparison of marker intervals with previously reported studies is challenging because most used different marker systems  and genetic linkage maps  ,19,20,21i et al. . Other tThe complex nature of anther extrusion indicates the trait is governed by many loci with small to large effects and explains the considerable level of phenotypic variance. A similar conclusion has been drawn in many studies ,19,20,23Rht-D1b showed an association with both AE and VAE, which explained, respectively, 20.57% and 17.33% of the phenotypic variance. The candidate gene Rht-B1 was not significantly associated with any of the evaluated floral traits, probably due to the very low number of lines carrying the mutant type of Rht-B1, or it might also be attributed to the different mechanism of action of the semi-dwarfing gene under the influence of the surrounding genetic backgrounds [Rht-D1b had a reduced AE by 1.45% and showed reduced VAE by almost two scoring points . Semi dwarfing alleles have gibberellin-insensitivity resulting in reduced plant height and low anther extrusion at the same time. This shows that Rht genes are involved in multiple floral traits and, therefore, can be used as candidate genes for improved anther extrusion. However, it would be interesting to test additional plant height genes for better understanding of their effect on anther extrusion and other floral traits, and it might subsequently offer choices to take full advantage of the existing dwarf genetic resources and to use them in designing superior hybrids in breeding programs. It has been demonstrated that dwarfing genes affect not only the height but also some other floral associated traits. We showed weak correlation between plant height and anther length using the same panel in El Hanafi et al. , but we a et al.  found thg et al.  reportedRht-D1 locus on 4DS, which is in concordance with our findings. Similarly, the two markers Excalibur_c20309_539 and Kukri_rep_c110544_248 on chromosome 3B at very close distances were associated with VAE and located in the QFhs.ndsu-3B region, which is a major QTL for FHB resistance [More recently, anther extrusion has been the focus of several linkage mapping for fusarium head blight (FHB) resistance since high extruded anthers showed tight association with lower FHB infection ,22,31,32sistance . ConsideRht-D1 was the most strongly associated locus [Rht alleles. Therefore, the absence of a major marker and small effect markers suggests the complex genetic architecture of pollen mass.Pollen mass, another important male floral trait, was shown to be taken as proxy for an initial selection of superior genotypes in early generations . Howevered locus . ConversExcalibur_c10496_651 on 1B associated to PSH commonly identified for PM, AE and VAE. The small number of markers with no major gene associated to PSH more likely suggests a quantitative inheritance and thus a complex genetic architecture underlying the trait pollen shedding. Pollen shedding, taken as a proxy, appears to be a promising trait, allowing indirect selection of the best performing male and female associated traits . Being aExcalibur_rep_c109881_701 (7A) was the only marker showing an association with pollen viability and contributing 19.35% of the observed phenotypic variation. The small number of associated markers might be in part due to gaps in the genome with poor marker coverage or the complex genetic architecture underlying this trait.The importance of pollen viability as a factor in enhancing the out-crossing ability in wheat has long been recognized. However, to our knowledge, assessment of the viability of wheat pollen has been done using staining methods, in vitro germination or impedance flow cytometry . HoweverAdequate cross pollination attributes are essential for hybrid development. Many reports have emphasized the importance of wheat anthers and stigma size as favorable pollinator traits in grain setting, a crucial factor determining the success of hybrid wheat programs ,35,36. Wwsnp_Ex_rep_c103167_88182254 (2A) was reported to be associated with OPF DFO, PM and AE. Kukri_rep_c103359_233 and wsnp_Ex_rep_c107911_91350930 on chromosome 1B were potentially linked to OPF, DFO, PM, AE and VAE, suggesting a potential genetic association that controls these complex traits.The genetic architecture of female traits is far less dissected. Previous studies have hinted at wide variability among female hybrid traits such as the degree and the duration of floret opening. However, it is often challenging to address these traits due to the intensive work and time requirements. Therefore, we opted to detect the genomic regions harboring markers associated to openness of the florets and duration of floret opening to substantially select the superior female parents. GWAS revealed 11 and 3 MTAs associated with OPF and DFO, respectively. Among these markers, Moreover, it has been demonstrated that wheat plants with narrow flower opening (cleistogamous) and/or short duration of floret opening will have a lower incidence of FHB by reducing the area and period in which Fusarium spores can enter the floret and initiate infection ,38,39. TRht loci via based on weighted ridged regression BLUP (wrrBLUP) increased the prediction abilities to 0.5, 0.7 and 0.57 for VAE, counted AE and PM, respectively. In [The potential of genomic selection for hybrid floral traits have previously only been conducted for anther extrusion and pollen mass ,11,12. Bvely. In .2 plots in an augmented design for two more years during the 2017 and 2018 cropping seasons at Marchouch Station, Morocco .This study was based on a diverse set of 196 elite genotypes from ICARDA bread wheat breeding program . The panDuring the growth period, various floral traits were assessed in all environments, as described by El Hanafi et al. : (1) Vis−1 DNA of each sample were sent to Trait Genetics, Germany [Genomic DNA from two-week-old bulked leaf samples were frozen in liquid nitrogen and stored at −80 °C before DNA extraction. The DNA was carried out according to Ogbonnaya et al. , after w Germany , as a coThe genetic structure of the 196 genotypes was assessed with 102 unlinked SNP markers distributed across the wheat genome with at least two loci on each wheat chromosome using the STRUCTURE software . A detai2) and measure the significance of R2 at p-values for each pair of loci on different chromosomes  and within the same chromosome .In total, 10,477 out of 12,725 SNPs, retained after removal of those with MAF < 5% and missing values over 10%, were used to study linkage disequilibrium (LD). TASSEL 5.0  was usedGenome-wide association studies (GWAS) were utilized to find target loci associated with higher anther extrusion, pollen mass, maximum pollen shed outside the anthers, long and viable pollen grains, wide florets opening and hairy stigmas receptive for extended periods.p-values of all the SNPs at −log10 scale [Marker trait association analysis was performed using mixed linear model (MLM) with TASSEL 5.0 software , applyin10 scale . p) values were displayed in the form of Manhattan plots using R library “CMplot” package in R 3.3.1 [For the detection of significant marker trait associations (MTAs), we applied a significance threshold of false discovery rate (FDR) ≤ 0.05. The −log10 ( R 3.3.1 .2) was computed as the ratio between the genetic variance due to markers over the sum of the genetic variance plus the error variance. This was done using the complete dataset to estimate variance components  and hence the genomic heritability. Genomic best linear unbiased prediction (G-BLUP) was used to perform genome wide predictions. Models were performed using ASReml-R version 3.0 [The genomic prediction (GP) was performed based on a two-stage analysis with weights. A genomic relationship matrix G was generated using 10,478 markers resulting from filtering and following the methodology described by Endelman et al.  package sion 3.0 .The evaluation of the different GP models for each trait was done by calculating the predicted ability (PA) and the prediction accuracy (Pacc) of the genomic estimated breeding values (GEBV) by using 10-fold cross-validation with 20% of the genotypes as validation set and the remaining 80% as training set for the models. PA was estimated as the correlation between the adjusted phenotypic value and the GEBV from the GP model. In addition, Pacc of GEBV is the correlation between the true genomic breeding value and the predicted breeding value, which was estimated according to Equation (1): Considering the high heritabilities coupled with high genetic advance, conventional methods based on direct or visual selection of phenotypes have contributed significantly to improving floral traits with the relevance for hybrid breeding. However, the assessment of these traits is challenging and time-consuming. To cope with this constraint, we employed genome-wide association study to reveal the genetic basis of various male and female hybrid potential traits. Many markers with small to large effects were detected, indicating the genetic complexity of the evaluated traits. Within this context, the significant markers with medium to large effects can be applied in hybrid breeding via marker-assisted selection. However, in the case of minor genes/QTL associated to the trait of interest, phenotypic selection remains most cost-effective and promises a high selection response. Nevertheless, these minor effect genes might be included with the aim of improving the related trait. In parallel, genome-wide prediction appears promising to predict most of the hybrid floral traits with high accuracy for the highly quantitative traits that are controlled by small- to large-effect genes/QTLs.


The functional SNPs discovered in this work will give helpful information on the crucial molecular markers that may be employed in breeding efforts to improve the heart development of broiler chickens.Transcription factor 21 (TCF21) gene that affected the economically important traits in broilers. Ten single nucleotide polymorphisms were detected in the TCF21 gene by monoclonal sequencing. The polymorphisms of these 10 SNPs in the TCF21 gene were significantly associated (p < 0.05) with multiple growth and body composition traits. Furthermore, the TT genotype of g.-911T>G was identified to significantly increase the heart weight trait without affecting the negative traits, such as abdominal fat and reproduction by multiple methods. Thus, it was speculated that the g.-911T>G identified in the TCF21 gene might be used in marker-assisted selection in the broiler breeding program.This study aims to identify molecular marker loci that could be applied in broiler breeding programs. In this study, we used public databases to locate the  Gallus gallus) is the most commonly raised poultry by humans. Since the 1950s, the growth rates and meat yield of broilers have significantly improved. However, with the rapid growth of broilers, some problems inducing huge economic losses have emerged, such as obesity, ascites syndrome, leg diseases, broiler immunity decline, and sudden death [Chicken  plays an important role in a variety of economically important traits such as heart development [TCF21 gene that are significantly associated with the growth and body composition traits of broilers. The results of this study can provide useful information for the molecular genetic marker-assisted breeding of the economically important traits of broilers.Transcription factor 21 elopment , testis elopment , and adielopment  in chickGallus gallus) were downloaded from the UCSC Genome Browser . These combined data were genetically annotated using the ChIPseeker software 1.30.2 version [http://software.broadinstitute.org/software/igv/, accessed on 16 June 2021).The ChIP-seq dataset for histone modification marks  and CTCF data in the seven tissues of chickens used in this work were downloaded from the GEO Datasets: GSE158430 . The BedDetails on the Northeast Agricultural University broiler lines divergently selected for abdominal fat content (NEAUHLF) were described by Zhang et al. . In thisFor the G21 populations, the body weight (BW) of all male birds was measured at 1, 3, 5, and 7 weeks of age . At the age of 7 weeks, the above birds were slaughtered, and the body composition traits were recorded. Before slaughter, the chest angle (ChA), keel length (KeL), body oblique length (BoL), chest width (ChW), metatarsus length (MeL), and metatarsus circumference (MeC) of all birds were measured. After slaughter, the carcass weight (CW), abdominal fat weight (AFW), liver weight (LW), muscular stomach weight (MSW), glandular stomach weight (GSW), heart weight (HW), spleen weight (SW), and testicle weight (TeW) were measured. For the reporting of results, we complied with the Animal Research: Reporting In Vivo Experiments (ARRIVE) guidelines .TCF21, including the gene body region, the 5 ‘flanking region (2000 bp), and the 3’ flanking region (2000 bp), was sequenced, which resulted in 10 SNPs, referred to as g.-1243C>T, g.-1171T>C, g.-911T>G, g.-891C>T, g.691C>T, g.897T>C, g.1033G>A, g.1892A>G, g.2091C>T, and g.2155C>T, according to their respective positions in the TCF21 gene.In total, 20 individuals from NEAUHLF were randomly selected (with 10 birds from each line). Then, the whole gene of TCF21 genomic DNA sequence based on the chicken gene sequence (NCBI Reference Sequence: NC_006090.5), and all PCR primer sequences were synthesized and purified by Invitrogen . The primer sequences are shown in Using Primer Premier 5.0 , a series of PCR primers were designed to amplify the various portions of the chicken Furthermore, the total genomic DNA was extracted from 675 male birds of the G21 of NEAUHLF for PCR analysis, according to previous depiction . These Shttps://www.snapgene.com/snapgene-viewer/, accessed on 20 October 2021) was used to select the restriction enzymes, and the endonuclease for each SNP are displayed in The PCR amplification product was detected by the agarose electrophoresis of the target band single bright sample, and carried out by an enzymatic reaction test and enzymatic reaction system . The SnapGene 5.0 Viewer , TFBIND , and Mulan . These three bioinformatics software predicted overlapping transcription factors that were considered to possibly bind to the DNA sequence of SNPs in the TCF21 gene.To explore the potential molecular mechanisms underlying the association of SNPs loci in the p < 0.05 as a significant difference between the lean and fat lines.The difference in allele frequencies between the lean and fat lines was determined and examined using the Chisquare test, with p < 0.05 being adopted as a threshold. In addition, the significant differences between the least-square means of different genotypes were calculated by the contrast test (p < 0.05). Then, the statistical model for analyzing the associations of genotypes with the growth and body composition traits was constructed based on the population characteristics [I was adopted to analyze the associations of SNP polymorphisms with the growth and body composition traits in 675 male birds  from the G21 population of NEAUHLF, in which each line consisted of 40 family lines .In this study, the JMP 7.0 software  was employed for establishing a generalized linear mixed model to analyze the associations of SNP polymorphisms with the growth and body composition traits, with eristics . The folII was applied in estimating the genetic parameters of the growth and body composition traits of the lean and fat lines in the G21 population of NEAUHLF.The statistical analysis model for genetic parameter estimation is shown below:TCF21 gene plays an important role in the adipose, liver, lung, and spleen tissues of broilers  between the lean and fat lines in the NEAUHLF population.The phenotypic information of the growth and body composition traits is displayed in 2) values of the growth and body composition traits were estimated. The results indicated that AFW, BW1, BW5, GSW, MSW, and TeW showed high heritability values (h2 > 0.3), whereas BW3, BW7, CW, and MeC had moderate values (0.2 < h2 < 0.3), and BoL, ChA, ChW, HW, KeL, LW, and MeL had low values  to HW. In addition, the polymorphisms of g.2091C>T and g.2155C>T were significantly correlated (p < 0.05) with BW and TeW , we carried out an in silico analysis of the transcription factor binding site using three bioinformatic tools. The results showed that g.-911T>G was located in multiple potential transcription factor binding regions .p < 0.05) between the lean and fat lines. The above results indicated that when AFW was selected, the other traits associated with AFW were also under selection. Therefore, the genetic correlations between AFW and other growth and body composition traits were estimated. The results indicated that AFW showed high genetic correlations with most of the other growth and body composition traits, including LW, GSW, BW1, 3, 5, 7, MeL, KeL, and BoL. Some studies also analyzed the correlations of AFW with the growth and body composition traits in chickens and reported that AFW exhibits high genetic correlations with BW5, BW7, LW, CW, and skin weight [In this study, two broiler lines were divergently selected for abdominal fat content for over twenty generations. The results revealed significantly different AFW values between the lean and fat lines. In addition to AFW, some other growth and body composition traits (except for HW and BW5) also showed significant differences  with the TeW and BW traits. As revealed by studies on mammals, the TCF21 gene plays an important role in the functions of testicles [TCF21 gene knockout have sex differentiation phenotypes [SRY affects TeW through regulating TCF21 [TT genotype of g.2091C>T and g.2155C>T had higher body weight and lower testicle weight. It also indicated that selection for these two SNPs did not result in neither fast growth rate (BW) nor high reproductive performance (TeW) in broilers.It was discovered in this study that the polymorphisms of g.2091C>T and g.2155C>T in the esticles . In addiesticles ,30,31,32esticles . Furtherenotypes . The malng TCF21 ,36. Regrp < 0.05) to the HW trait. Some literature reports 13 susceptible sites are detected in a GWAS on human coronary heart disease, among which rs12190287 is located at the 3’UTR of TCF21 [TCF21 is expressed in mesoderm cells in the epicardial organ and then in mesenchymal cells that form the pericardium [TCF21 in chickens leads to epicardial blistering, increased smooth muscle differentiation on the heart surface, a paucity of interstitial fibroblasts, along with neonatal lethality [CC genotype of g.-1243C>T, TT genotype of g.-1171T>C, TT genotype of g.-911T>G, and CC genotype of g.-891C>T had higher heart weight . Studies have shown that these transcription factors all play important roles in animal heart development [TCF21 gene g.-911T>G regulates the HW trait probably through binding to transcription factors  to influence the activity of regulatory elements in this region.Heart hypertrophy increases the risk of sudden death in broilers, especially those with higher BW and AFW traits . This reof TCF21 ,39. Geneicardium . The losethality . It is e5; BACH1 , and theelopment ,23,24,25TCF21 gene polymorphisms with the growth and body composition traits in broilers were analyzed. The results indicate that the g.-911T>G in the TCF21 gene may be important molecular markers that affect the HW trait, and could be used in breeding programs to improve the heart development of broilers.In this study, the associations of