Trifolium pratense L.) is an outcrossing forage legume that has adapted to a wide range of climatic and growing conditions across Europe. Red clover is valued for its high yield potential and its forage quality. The high amount of genetic diversity present in red clover provides an invaluable, but often poorly characterized resource to improve key traits such as yield, quality, and resistance to biotic and abiotic stresses. In this study, we examined the genetic and phenotypic diversity within a diverse set of 395 diploid red clover accessions via genome wide allele frequency fingerprinting and multi-location field trials across Europe. We found that the genetic structure of accessions mostly reflected their geographic origin and only few cases were detected, where breeders integrated foreign genetic resources into their local breeding pools. The mean dry matter yield of the first main harvesting season ranged from 0.74 kg m-2 in Serbia and Norway to 1.34 kg m-2 in Switzerland. Phenotypic performance of accessions in the multi-location field trials revealed a very strong accession x location interaction. Local adaptation was especially prominent in Nordic red clover accessions that showed a distinct adaptation to the growing conditions and cutting regime of the North. The traits vigor, dry matter yield and plant density were negatively correlated between the trial location in Norway and the locations Great Britain, Switzerland, Czech Republic and Serbia. Notably, breeding material and cultivars generally performed well at the location where they were developed. Our results confirmed that red clover cultivars were bred from regional ecotypes and show a narrow adaptation to regional conditions. Our study can serve as a valuable basis for identifying interesting materials that express the desired characteristics and contribute to the adaptation of red clover to future climatic conditions.Red clover ( It is either grown in pure stands or in mixtures with tall grasses. Due to its ability to fix atmospheric nitrogen, red clover produces protein-rich forage and, when grown in mixtures, can provide additional nitrogen to companion grasses , insect pollinated, outbreeding species with a high degree of self-incompatibility. Tetraploid red clover accessions have been produced through colchicine treatment, but despite their higher yield, their market success is hampered by low seed yield . In ordeg damage . Freezing damage . In the g damage . In the f Europe . The disf Europe . North Af Europe . To accof Europe . Other tf Europe . Thus, ti.e., the genetic material contained in wild relatives, ecotypes, landraces, and cultivars, are crucial for the continued improvement of modern forage crop cultivars (https://eurisco.ipk-gatersleben.de/apex/eurisco_ws/r/eurisco/home) currently lists more than 10,600 red clover accessions and many more are likely to be found in non-European or local germplasm collections. For targeted utilization in breeding programs, detailed phenotypic and genotypic characterization is indispensable, but is often not available for many of the current genetic resources. Molecular genetics or DNA-based markers allow a rapid characterization of genetic diversity in a large number of samples, independent of environmental effects  markers; e.g., simple sequence repeat (SSR) markers; Plant genetic resources, ultivars . For red effects . PCR-ampAlthough studies on genetic diversity can substantially assist breeding decisions, knowledge about phenotypic diversity is key to efficiently exploit red clover germplasm for cultivar development. While some studies have investigated the variability and inheritance of specific traits such as disease resistance , seed yiIn order to provide a solid foundation for the utilization of red clover genetic resources in breeding programs, we aimed at (i) establishing a representative, diverse collection of red clover accessions, (ii) characterizing the genetic diversity and structure present in this collection, (iii) assessing the phenotypic diversity of agriculturally relevant traits at multiple locations and (iv) getting an insight into the extent of accession x location interaction for future adaptive breeding efforts.22.1www.eucleg.eu) was compiled to cover as much of the available genetic diversity present in diploid red clover in Europe as possible. Members of the project consortium, including breeders, research institutes and genebanks, were asked to contribute with their available material. In addition, other institutes outside the consortium were contacted for possible contributions to compile as many accessions as possible. Accessions were delivered with specific material transfer agreements from each supplier to a central coordinator, who further distributed seeds under the same agreements to the researchers performing the experiments. Thousand seed weight of each accession was measured by the central coordinator to confirm diploidy. The final panel consists of 395 accessions representing available diversity of diploid red clover in Europe, complemented with a few accessions from the Americas and Oceania  for pooled-GBS and PE-150 Illumina sequencing. SNP calling and calculations of allele frequencies were done as described in Genotyping of the 395 accessions and SNP calling was conducted as described in k-means clustering. Partition of variance was investigated using permutational analysis of variance implemented in the function ‘adonis2’ of the ‘vegan’ package  and at DLF Seeds AS in Hladké Životice, Czech Republic . A reduced set of 110 accessions was grown at Graminor in Bjørke, Norway  and reduced sets of 100 accessions each were grown at Aberystwyth University in Aberystwyth, Wales  and at the Institute for Forage Crops Kruševac Ltd. in Kruševac, Serbia . Temperature and precipitation at the trial sites are given in i.e. complete replicates) and incomplete blocks in only one direction (IB1) of dimension 1 row by 10 columns (10 plots) in NOR and dimension 1 row by 5 columns (5 plots) in SRB. Observation rows for assessing flowering time were designed with two CB in all locations. Due to their larger size, the observation rows in CHE and CZE further included rows as first dimension of incomplete blocks (IB1) and columns as second dimension of incomplete blocks (IB2).For the main plot trials in CZE, CHE and GBR, partially replicated (Prep) designs were employed, with accessions being grown unreplicated, in two or in six replicates Table 1.2 and seeds were sown at a depth of 1 cm, whereby the amount of seeds per plot was adjusted for thousand seed weight and germination rate in order to reach a sowing density of 600 or 800 germinating seeds m-2  U. Braun, southern anthracnose caused by Colletotrichum trifolii Bain & Essary, clover rot caused by Sclerotinia trifoliorum Eriks., plant rot caused by different Fusarium ssp. and brown spot caused by Stemphylium sarciniforme (Cav.) Wiltsh.All traits were assessed on a plot basis during the year of sowing (Y0 [2018]), and the first and second main harvesting years . The cutting regime followed common practice for the respective locations, ranging from 3 (NOR), over 4 , to 5 (CHE) cuts per main harvesting year. Before each cut, different traits were assessed visually: plant vigor (VIG) was rated on a scale from 1 (very weak) to 9 (very vigorous), weed occurrence was rated as the percentage of plot biomass derived from weeds (Weed%) and the resistance to spontaneously occurring diseases was rated on a scale from 1 (very high disease infestation) to 9 (no disease infestation). Diseases observed in the trials were powdery mildew caused by For each cut, plots were harvested with a plot harvester and weight of fresh matter was determined using a machine mounted balance. Fresh matter yield (FMY) was determined as fresh matter per plot divided by the plot area. In all trials but CHE, a subsample per plot was dried in an oven at 60°C for 72 hours to constant weight and percentage of dry matter concentration (DMC) was determined as weight of dry subsample divided by weight of fresh subsample multiplied by 100. For the trial in CHE, DMC was determined indirectly using near infrared (NIR) spectroscopy measurements taken on the plot harvester using a PSS 1721 spectrometer equipped with a PSS-H-A03 distance sensor head from Polytec GmbH . Dry matter yield (DMY) corrected for weed occurrence was then calculated for each plot as:-1) was performed using the in vitro assay following For the first and second cut of Y1, samples for forage quality analysis were taken from each plot at all locations and were dried for 72 hours to constant weight at 50 to 60°C. Dried samples were then shipped to Agroscope for central processing. Grinding of samples was done using a cutting mill  with a mesh size of 0.75 mm. NIR spectra on the ground samples were measured in the wavelength range from 800 to 2500 nm using a laboratory spectrometer . The two parameters crude protein content (CP) and digestible organic matter (DOM) were then determined for each sample using NIR calibrations developed in-house based on reference values determined in red clover with the respective reference methods. Reference measurements for calibration development of CP (in % of dry matter) were established by multiplying total nitrogen (% of dry matter) determined following st of January when at least five plants per plot started flowering . Because the plots were cut before all accessions started flowering, DOF was determined on separate observation rows. In CHE, DOF was assessed in Y2, whereas it was assessed in Y1 for all other locations. For accessions that did not start flowering at all, a value of 222 was assigned.In addition to the traits assessed before each cut, plant density was determined visually at the end of Y0 and at the beginning and end of Y1 and Y2. At each date, plant density was rated in percentage of the densest plot observed at the end of Y0. Date of flowering (DOF) was determined in Y1 and Y2 as the number of days after 12.3.3Traits were analyzed for each cut separately. For comparison among locations, additional secondary traits were calculated. Mean vigor rating for Y1 and Y2  was calculated as simple mean over all cuts and a missing value was assigned if data from one cut was not available. The sum of DMY for Y1 and Y2  was calculated as sum of DMY over all cuts of the respective year.Locations were analyzed separately using the following model:imnoy represents the observation for trait y on a single plot basis, µ denotes the overall mean, ig is the effect of accession i, mb the effect of block m, ib1n the effect of incomplete block 1 (i.e. row) n, and ib2o the effect of incomplete block 2 (i.e. column) o, and imnoϵ is the residual error. If not present in the design of a given location, the respective factor was omitted for analysis . Because data was only available from one complete block (i.e. replicate) in SRB, no separate analysis could be performed for this location. In a first model, to estimate the best linear unbiased estimates of accession means (BLUEs), ib1n and ib2o were considered as random, while all other effects were considered as fixed. In a second model, to estimate the best linear unbiased predictors of accession means (BLUPs) and the respective variance components, ig was additionally considered as random. For all subsequent phenotypic analyses, BLUE values were used. Multi-location analyses using data from all five locations were done following the modelwhere ijmnoy represents the observation for trait y on a single plot basis, µ denotes the overall mean, ig is the effect of accession i, je the effect of the location j, ijge the interaction between accession i and location j, mb the effect of complete block m (if available), ib1n the effect of incomplete block 1 n, ib2o the effect of incomplete block 2 o, and ijmnoϵ is the residual error. In a first model, effect ib1n and ib2o were considered as random, while all other effects were considered as fixed to estimate BLUEs. To estimate respective variance components, ig and ijge were additionally considered as random in a second model. For single and across location analyses, heritability (h2) values were calculated according to the method of where For the principal component analysis (PCA) on phenotypic data, BLUEs of the traits DOF_Y1, CP_Y1.C1, CP_Y1.C2, DMY_Y1.C1, DMY_Y1.C2, DMY_Y1, DMY_Y2, VIG_Y1, VIG_Y2, PD_Y1 were used. Diseases were additionally included if they occurred at a given location. Occurring diseases were southern anthracnose and powdery mildew in CHE, brown spot in SRB, fusarium in CZE and clover rot in NOR. Only accessions with data in at least 50% of the selected traits were included and only traits that have data in at least 80% of the accessions were kept. Hence, DOF, DMY_Y2 and VIG_Y2 were not included for NOR and DMY_Y2 and VIG_Y2 were not included for CZE in the analysis. The remaining missing values were imputed with the mean of the trait.For estimation of variance components and calculation of mean values, heritability, correlations coefficients, and PCA, DOF values of accessions that did not flower were set to ‘not available’. Thereby, an artificial inflation of variance could be omitted. For all graphical representations, the pre-set value of 222 was kept for these accessions.All statistical analyses of phenotypic data were conducted with R version 4.1.2 within RStudio v4.0.5.  using th33.1Genotyping-by-sequencing on pooled samples of the 395 accessions resulted in a set of 20,137 quality controlled and filtered SNPs evenly distributed across the red clover genome . Three sThe first two axes of the PCA together explained 14.5% of the genetic variation and allowed to distinguish some of the accessions based on their region of origin and their type Figure 2k-means clustering of the PCs and comparing the models using the Bayesian Information Criteria (BIC) indicated the existence of six to 11 clusters (k = 6 (the number of regions in the dataset) revealed three minor clusters that together contained 21 accessions, while the remaining three clusters contained the remaining 371 accessions  and could be used for assessment of yield, quality and disease occurrence data in the subsequent main harvesting years Figure 4i.e., southern anthracnose susceptible) accessions coming along with a high weed occurrence, subsequent analyses mostly focused on Y1. Given the vast amount of data obtained, a main focus was on the traits date of flowering in Y1 (DOF_Y1), total dry matter yield in Y1 and Y2 , average vigor of Y1 (VIG_Y1) and plant density measured in autumn of Y1 (PD_Y1). In addition, we took a closer look at the crude protein data of the first and second cut in Y1 . Furthermore, the natural occurrence of diseases in the different trials is reported and discussed.Due to the lack of data from location CZE after cut 2 in Y1 and the heavily reduced plant stands at location CHE after cut 4 in Y1 due to non-adapted  some variance attributed to accessions was still observed in the multi-location analysis. The absolute amount of variation varied considerably between trials and traits. For the trial in SRB, no variance components could be estimated, because data could only be obtained from one replicate per accession.Quality parameters of the red clover herbage were measured for the first and second cut of Y1. From single location analysis, most of the variation in CP was explained by the residual variance and only minor effects of accession or field heterogeneity could be observed Figure 52 < 0.25) in all trials, except for the second cut in NOR (2 = 0.72).Traits that showed low accession variance were accompanied by low heritability. For CP in both cuts, observed heritability values were very low  and Y1.C4 (h2 = 0.40), while powdery mildew was observed at Y0.C1 and Y0.C2 with heritability of 0.75 and 0.80, respectively. In location SRB, brown spot was observed in Y2.C2 (h2 = 0.67). In location NOR, clover rot was observed in spring Y1.C1 (h2 = 0.59). In location CZE, fusarium plant rot was observed in Y1.C3 (h2 = 0.30). Correlations among traits are given in PCA was performed for each trial location using phenotypic data including disease occurrence Figure 8PCA of phenotypic values showed clustering of accessions according to their origin at each trial location. In the NOR trial, the local accessions (accession origin = Northern Europe) clustered in the upper left side and are distinguished from other accessions by an increased resistance to clover rot and an increased CP_Y1.C2. In addition, the local accessions flowered later and expressed high yields mainly in the first, but not in the second cut (high DMY_Y1.C1 and low DMY_Y1.C2). In GBR, PC1 was strongly determined by yield and PC2 by DOF and CP_Y1.C2. Notably, Northern European accessions and ecotypes showed low yield, while Central and Eastern European as well as Swiss accessions were high yielding. In CHE, PC1 mainly distinguished accessions by their sum of DMY per year (DMY_Y1 and DMY_Y2) and yield of the first cut (DMY_Y1.C1), while PC2 represents variation in disease resistance (powdery mildew and southern anthracnose) as well as DOF_Y1. Thereby, Northern European accessions clustered separately from other materials with lower total yields per season, but higher yield in the second cut and later flowering. In CZE, no clear clustering of accessions by origin was observed. Variation in fusarium resistance Y1.C3 and plant density PD_Y1 both correlated with PC2 and are indicative for reduced plant stands after disease infestation. In SRB, the Eastern European accessions are distinguished from the Central European and Swiss accessions mainly by their increased yield and vigor in Y1. A weak trend of Northern European accessions for later flowering and lower vigor in Y1 was observed Figure 83.2.4To find signs of local adaptation in red clover breeding materials, the performance of accessions belonging to the type ‘breeding material’ or ‘cultivar’ and grouped by their geographic origin, were compared in the different trial locations across Europe Figure 93.2.5Twenty accessions, which are representing all major regions of Europe, were tested in all five trials and their accession x location performance was analyzed  from Belgium clustered with the Swiss landraces, indicating the latter being the initial source of breeding germplasm used Figure 3andraces . ‘AberClandraces , a typicandraces , the preandraces . These edespread .Fusarium spp. was too severe to get reliable data in subsequent years. Several other trials experienced disease pressures that resulted in the loss of several accessions and missing data in the following cuts. Despite the difficult conditions encountered at some trial locations, heritability values of field trials were, with a few exceptions, at a high level  = 0.07-0.18, 2 = 0.72. It was highly correlated with DOF . In comparison, in SRB the plant density was on average 83% for Nordic accessions compared to over 90% for accessions from other regions. The overall high plant density in SRB after Y1 Figure 9Almost 80 years ago, i.e. that ranking of accessions is reversed in NOR compared to other locations. This observation clearly highlights the need for locally adapted breeding programs. We observed a strong adaptation of Northern European accessions to the prevailing conditions and/or management. According to The five trial locations cover a wide range of latitudes and climate zones, and large accession x location interactions were observed. From correlation analysis among locations Figure 7Due to the natural expansion across Eurasia and Northern Africa and the recent domestication at several locations , red cloMedicago ssp., where candidate genes associated with adaptation to environmental gradients have been identified (A peculiarity of forage crops is the co-existence of wild relatives and cultivated forms in many landscapes which carries the potential for intercrossing. Diversity of wild red clover accessions has been shown not only to be influenced by the geographic location, but also by the proximity to sites where landraces have been grown for decades . This inentified . Thus, vAs indicated by the large accession x location interactions observed in our study, breeding a ‘one-fits-all’ cultivar seems to be very unrealistic, due to the very variable environmental conditions demanding different adaptive traits from the genetic material as discussed above. However, there are trial locations that were more similar to each other based on assessed trait values and cultivars being able to perform well in such regions are more likely to be found. Growing a reduced set of diverse accessions at more locations throughout Europe might help to define such macro-environments representing the different red clover growing conditions. Knowledge of available genetic resources and their characteristics is key for breeding red clover well adapted to environmental conditions. This will become even more important in view of changing climatic conditions. For example, with increasing temperatures diseases adapted to warmer climates like southern anthracnose, which has already extended its local distribution , will mohttps://zenodo.org/deposit/7034131.The datasets presented in this study can be found at Study design and curation of the accession collection: CG, RK, and AP. Generation of phenotypic data: CG, HA, JR, LJ, and LS. Generation of genotypic data: LS, TR, and LF. Data analysis and interpretation: MN, RK, CG, and LF. Drafting of the manuscript: MN, RK, and CG. All authors contributed to the article and approved the submitted version.


FTO) and perilipin1 (PLIN1) genes have been associated with fat mass deposition. These genes have also been found to regulate economic traits  in animals. In this study, the researchers analyzed insertion–deletion (InDel) variations in the FTO and PLIN1 genes and their association with morphometric traits in three sheep breeds . A total of six InDels  were identified in the FTO and PLIN1 genes in the three breeds of sheep. Genetic variations of these InDels were evaluated on the basis of their polymorphism information content (PIC). The FTO-6 and PLIN1 genes showed low levels of polymorphism (0 < PIC < 0.25), while the other four InDels were moderately polymorphic (0.25 < PIC < 0.50) in the three breeds of sheep. The results of the association analysis revealed that four InDels from the FTO and PLIN1 genes were significantly associated with the morphometric traits in the three sheep breeds, such as body weight, body height, chest width, chest depth, cannon circumference, head length, coccyx length, forehead width, and back height. Based on these findings, the FTO and PLIN1 genes can serve as genetic markers to select sheep with desirable morphometric traits.Fat mass and obesity-associated , Dupor sheep (DS), and Small Tail Han sheep (STHS). The FTO and PLIN1 genes were genotyped using the insertion/deletion (InDel) method. A one-way ANOVA with SPSS 26.0 software  was used to assess the effect of the InDel FTO and PLIN1 genes on morphometric traits. The results revealed significant associations between certain InDels and the morphometric traits in different breeds of sheep. Specifically, FTO-2 was significantly associated with cannon circumference (CaC) in HS rams and body height (BoH) in HS ewes (p < 0.05). FTO-2 was also significantly associated with chest width (ChW), CaC, head length (HeL), and coccyx length (CoL) in the STHS breed (p < 0.05). FTO-3 showed significant associations with BoH in HS rams and BoH, back height (BaH), ChW, and chest depth (ChD) in HS ewes (p < 0.05). FTO-3 was also significantly associated with ChW in the DS and STHS breeds (p < 0.05). FTO-5 was significantly associated with body weight (BoW) in the DS breed and BoH in the STHS breed (p < 0.05). Furthermore, PLIN1 was significantly related to BoW in the DS breed and was significantly associated with CoL and forehead width (FoW) in the STHS breed (p < 0.05). In conclusion, the study suggested that InDels in the FTO and PLIN1 genes could provide practical information to improve morphometric traits in sheep breeding.This study aimed to identify InDels from the  Hu sheep (HS), Dupor sheep (DS), and Small Tail Han sheep (STHS) are common types in the sheep farming industry in China. HS sheep have excellent reproductive performance, are adapted to factory feeding under housed conditions, and are resistant to heat and humidity. DS are precocious; they have early growth, high carcass leanness, and tender meat; and they are resistant to rough feeding and suitable for the arid and barren ecological environment. STHS are ideal for fat lamb production because of their fast growth and early sexual maturity. To better meet the needs of consumers and improve the selection of sheep, researchers need to effectively produce larger and heavier breeds of sheep with the lowest feed consumption in the shortest possible time. Marker-assisted selection has become an efficient, rapid, and reliable breeding method, which is widely used in livestock breeding research . InDel iFTO (fat mass and obesity-associated) and PLIN1 (perilipin 1) genes have gained attention due to their involvement in adipogenesis and lipid metabolism in the PPAR signaling pathway [FTO gene in sheep is located on chromosome 14 and has nine exons and eight introns. Previous studies have indicated that FTO not only regulated obesity but was also significantly associated (p < 0.05) with economic traits in pigs, sheep, and cattle [FTO were identified in Tong sheep; eight InDels showed a significant correlation with growth traits; and four InDels were significantly associated with fat-tailed traits (p < 0.05). The PLIN1 gene in sheep is located on chromosome 18 and has nine exons and eight introns. The SNP of PLIN was also associated with body fat weight, low body mass index, and the total cholesterol level [PLIN can also affect growth traits and carcass traits in animals. Zhang et al. [PLIN had a significant effect on carcass traits in Chinese ducks and could be used as a candidate gene to select Chinese ducks for meat quality traits. Gol et al. [PLIN significantly affected the lean percentage in pigs, suggesting that PLIN may be used as a genetic marker for lean pork growth. Zappaterra et al. [PLIN gene rs327694326 was associated with the contents of oleic acid and cis-cowpox fatty acids in the back fat tissue of large white pigs, suggesting that PLIN may be used as a genetic marker for the quality of pork meat. Raza et al. [PLIN1 gene were identified in 510 Qinchuan cattle, including g.3580T > C, g.3898G > A, g.8333G > A, g.10517T > C, and g.10538G > T, and that these five SNPs were significantly associated with the depth of back fat, intramuscular fat, and chest depth in Qinchuan cattle (p < 0.05).Among the many genes known to influence morphometric traits, the  pathway . Both ged cattle ,8,9. Wanol level . Furtherg et al.  reportedl et al.  showed ta et al.  found thFTO and PLIN1 genes and their association with the morphometric traits of the HS, DS, and STHS breeds of sheep using InDel technology based on previous studies of polymorphisms for these genes in other breeds of sheep. The results may provide useful markers for the molecular breeding of sheep.In the present study, we analyzed the polymorphisms of the All the experimental procedures in this study were approved by the Institutional Animal Care and Use Committee of the College of Animal Science, Henan University of Science and Technology (HAUST#2021-9-22), and this study was carried out in strict compliance with the guidelines of the Guide to Animal Welfare in China.This study took three breeds of sheep  as the research objects. All the individuals within the same sheep breed were healthy, unrelated, and managed under the same conditions. One hundred and ninety-two HS , 86 DS , and 96 STHS  blood samples of three sheep breeds and dates of morphometric traits were collected at the age of six months (they were born at the same time) from native sheep meat breeding farms in Henan province. FTO and PLIN1 published on the NCBI website (https://www.ncbi.nlm.nih.gov/ (accessed on 2 May 2022)), 11 specific primers were designed using Primer Premier Software  according to the sequence of 300–500 bp before and after the InDel loci with NC_056067.1 and NC_056071.1 as reference genomes, respectively. This study aimed to identify the InDels using PCR and agarose gel electrophoresis methods. Sites with variant base numbers less than 6 bp were not detected by agarose gel electrophoresis, while sites with variant base numbers greater than 6 bp were selected. The primers were verified by the BLAST program on the NCBI website (https://blast.ncbi.nlm.nih.gov/Blast.cgi (accessed on 2 May 2022)). Primer information, product size, and InDel sizes are shown in Based on the potential InDel sites of 2O) to make up the volume. The PCR amplification procedure was as follows: pre-denaturation at 95 °C for 5 min, followed by 10 cycles of denaturation at 95 °C for 40 s; annealing at 60 °C for 30 s (started at 60 °C and decreased by 1 °C per cycle); extension at 72 °C for 45 s; 25 cycles of denaturation at 95 °C for 40 s; annealing temperature for 30 s, extension at 72 °C for 45 s; and final extension at 72 °C for 5 min. All available individuals were genotyped using 3.0% agarose gel electrophoresis. Genotyping was performed by observing the bands in which one band was the II (insertion/insertion) type or DD (deletion/deletion) and the ID (insertion/deletion) type. The amplified samples were sent to Tsingke Biotechnology Co., Ltd.  for sequencing. Sequence alignment and InDel identification were performed using the MegAlign program . Chromas software  was used to perform the sequence analyses.Polymerase chain reaction (PCR) was performed in a 10 μL reaction volume consisting of 1.0 μL of DNA, 5 μL of 2 × Taq PCR Master Mix , 0.8 μL of each primer, and 2.4 μL of double-distilled water ) [All individual genotypes at each InDel were statistically analyzed using Microsoft Excel . The population genetic parameters of the InDel variants, such as gene frequencies, allele frequencies, homozygosity (Ho), heterozygosity (He), effective allele numbers (Ne), and polymorphism information content (PIC), were calculated by POPGENE (version 1.32) . The exiy 2023)) . Linkagey 2023)) .FTO and PLIN1 InDel genotypes and morphometric traits were evaluated with a one-way analysis of variance (=three genotypes and sample size greater than 3) and independent sample t-test (=two genotypes) in SPSS Statistics . When the analysis of variance performed on each group of genotypes indicated a significant (p < 0.05) difference, statistical differences between the two genotypes were subsequently evaluated with the Bonferroni correction test. All results were expressed as mean ± standard error (SE). The model was used as follows:All the data were in a normal distribution, which indicated suitability for subsequent analysis. Associations between the ij: phenotypic values of morphometric traits; µ: the overall population mean of each trait; Gi: the effect of the genotype; eij: the random residual error. The age, sampling season, and rearing environment were consistent, and their effects were not considered in this model. Differences were considered significant at p ≤ 0.05.YFTO and PLIN1 genes  were identified in three breeds of sheep. Each InDel locus was present in two or three genotypes: the insertion/insertion genotype (II), insertion/deletion (ID) genotype, and deletion/deletion (DD) genotype  was identified in the FTO-3 gene, located at NC_056067.1 g.21606666-g.21606667. A 24 bp insertion (TATTGAGTATTGAGACGAACAGAG) was identified in the FTO-4 gene, located at NC_056067.1 g.21837228-g.21837229. A 20 bp deletion (AGGATGTCTGTCGCCTTCCT) was detected in the FTO-5 gene, located at NC_056067.1 g.21716705-g.21716724. A 21 bp insertion (ACTCAGCCGAGTGCTCTGTGA) was identified in the FTO-6 gene, located at NC_056067.1 g.21845609-g.21845629. Furthermore, a 26 bp segment was missing in the PLIN1 gene, which was located at NC_056071.1 g.20405892-g.20405893, and the missing sequence was CGATCCTCGGTGCCCCAGAAACATTC (FTO gene (NC_056067.1) and the InDel locus (on chromosome 18) of the PLIN1 gene (NC_056071.1) in the NCBI database for sheep. Three genotypes  were identified in the FTO-2, FTO-3, FTO-4, FTO-5, and FTO-6 genes in the HS, DS, and STHS breeds, except that only the ID and DD genotypes were identified in the FTO-2 gene in the DS breed. Additionally, three genotypes were identified in the PLIN1 gene in the STHS breed, and the ID and DD genotypes were identified in the PLIN1 gene in the HS and DS breeds . The other three InDels were moderately polymorphic (0.25 < PIC < 0.50). A related study of the Tong sheep breed showed results similar to those of the present study. All 10 InDels detected were moderately polymorphic (0.25 < PIC < 0.50), which may be related to environmental factors and genetic differences between the breeds. Except for FTO-2 in the three populations, FTO-4 in the HS population, FTO-5 in the STHS population, FTO-6 in the HS and DS populations, which deviated from HWE (p < 0.05), the remaining groups were in HWE (p > 0.05). The homozygosity of the six InDels  was higher than the heterozygosity, indicating a small degree of genetic variation. The maximum number of effective alleles in FTO-5 was 1.999 in the HS breed, which suggested that the genetic drift was minimal, and the alleles were evenly distributed in the population.The population genetic parameters were estimated for the N1 genes . In the nd PLIN1 . Based oFTO InDels using the SHEsis online website showed that the five InDels are not strongly linked in the HS, DS, and STHS breeds (D′ > 0.88 or r2 > 0.33 indicates strong linkage disequilibrium), as shown in The linkage disequilibrium analysis of the five FTO and PLIN1 polymorphisms and morphometric traits in the three sheep breeds are shown in FTO-2 was significantly associated with CaC in HS rams (p < 0.05). Individuals with the ID genotype had higher CaC. FTO-2 was significantly associated with BoH in HS ewes (p < 0.05). Individuals with the DD genotype had higher BoH. FTO-2 was significantly associated with ChW, CaC, HeL, and CoL in the STHS breed (p < 0.05). Individuals with the II genotype had higher ChW, CaC, HeL, and CoL. FTO-3 showed a significant association with BoH in HS rams (p < 0.05). Individuals with the ID genotype had higher BoH. FTO-3 was also significantly associated with BoH, BaH, ChW, and ChD in HS ewes (p < 0.05). Individuals with the II genotype had higher BoH, BaH, ChW, and ChD. FTO-3 showed a significant association with ChW in the DS and STHS breeds (p < 0.05). Individuals with the ID genotype had higher ChW in the DS breed, while individuals with the DD genotype had higher ChW in the STHS breed. FTO-5 was significantly associated with BoW in the DS breed (p < 0.05). Individuals with the II genotype had higher BoW. FTO-5 was also significantly associated with BoH in the STHS breed (p < 0.05). Individuals with the ID genotype had higher BoH. PLIN1 was significantly related to BoW in the DS breed (p < 0.05). Individuals with the ID genotype had higher BoW. PLIN1 was also significantly associated with CoL and FoW in the STHS breed (p < 0.05). Individuals with the ID genotype had higher CoL and FoW. No significant differences were observed between the rest of the InDels for the other morphometric traits  with economically important traits in pigs, sheep, and cattle [GHR-InDel and growth traits (p < 0.05). Yang et al. [G0S2 and carcass traits in chickens. Specifically, individuals with the AG genotype at g.444G > A exhibited the highest breast muscle weight, while individuals with the AA genotype at g.556G > A showed the lowest fat weight (p < 0.05). Sun et al. [AGPAT3 and various milk production traits in Chinese Holstein cows, including test-day milk yield, protein percentage, fat percentage, 305-day milk yield, milk urea nitrogen, and somatic cell score. Huang et al. [Cry2 and litter size in Australian White sheep. Specifically, individuals with the II genotype had larger litter sizes than those with the ID genotype in P2-Del-6-bp at first parity, while individuals with the ID genotype had larger litter sizes than those with the II genotype in P1-Del-6-bp at third parity (p < 0.05). Liu et al. [GDF9 gene in Dongxiang blue-shelled chickens and Luhua chickens. Among these, three SNPs  exhibited significant associations with age at the first egg, the weight of the first egg, egg weight at 300 days of age, and the total number of eggs at 300 days of age in chickens. These studies provide evidence that mutations can induce alterations in domestic animal economic traits. animals ,21,22. P obesity . Previoud cattle ,8,9. Wu d cattle  conducteg et al.  found sin et al.  discoverg et al.  demonstru et al.  identifiFTO in Tong sheep, which were similar to those of this study. These results indicate that the frequency of InDels varies across different species and is associated with differentiation, gene penetration, genomic segment size, and effective population size. In our study, there was a significant association (p < 0.05) between the four InDels  we detected in FTO and PLIN1, with morphometric traits in the HS, DS, and STHS breeds. This result was similar to those of previous findings in other species, where mutations in these genes were significantly associated with the animals’ growth traits [Identifying the causal variant of a gene can enhance the accuracy of genomic selection and is considered to be an efficient approach for analyzing the associations between genetic polymorphisms and economically important traits . This sth traits ,28,29. TFTO and one InDel in PLIN1. Three InDels in FTO were associated with HS, DS, and STHS growth traits, and an InDel in PLIN1 was associated with DS and STHS morphometric traits. Our findings implied that the InDels of the FTO and PLIN1 genes could be used as molecular markers for sheep breeding.In summary, we identified six InDels from three sheep populations, including five InDels in


Actinidia eriantha is a shade-tolerant species, widely distributed in the southern tropical region of China, occurring in spatially heterogeneous environments. In the present study we combined population genomic, epigenomic, and environmental association analyses to infer population genetic structure and positive selection across a climatic gradient, and to assess genomic offset to climatic change for A. eriantha. The population structure is strongly shaped by geography and influenced by restricted gene flow resulting from isolation by distance due to habitat fragmentation. In total, we identified 102 outlier loci and annotated 455 candidate genes associated with the genomic basis of climate adaptation, which were enriched in functional categories related to development processes and stress response; both temperature and precipitation are important factors driving adaptive variation. In addition to single-nucleotide polymorphisms (SNPs), a total of 27 single-methylation variants (SMVs) had significant correlation with at least one of four climatic variables and 16 SMVs were located in or adjacent to genes, several of which were predicted to be involved in plant response to abiotic or biotic stress. Gradient forest analysis indicated that the central/east populations were predicted to be at higher risk of future population maladaptation under climate change. Our results demonstrate that local climate factors impose strong selection pressures and lead to local adaptation. Such information adds to our understanding of adaptive mechanisms to variable climates revealed by both population genome and epigenome analysis.A full understanding of adaptive genetic variation at the genomic level will help address questions of how organisms adapt to diverse climates.  Global climate change is accelerating the melting of glaciers and sea level rise, which will have far-reaching and extensive impacts on the earth’s ecosystem. In response to climate change, organisms may migrate to higher latitude and altitude areas due to the gradual loss of their original conditions, which may eventually lead to species extinction –3. The eThe process of local adaptation results from interactions of different evolutionary forces, including selection, genetic drift, mutation, and migration . KnowledRecently, improvements in high-throughput sequencing technologies and bioinformatic approaches make it feasible to detect genome-wide genetic variation and reveal the genomic properties associated with the process of local adaptation. Restriction-site associated DNA sequencing (RADseq), one of the high-throughput sequencing methods with restriction enzyme-based reduced representation of individual genomes, can dramatically reduce sequencing costs and produce tens of thousands of genetic markers in non-model species, making this approach widely used in ecological genomics studies . Furtheret al.  with log-transformed geographic distance using a Mantel test with 10 000 permutations in the R package Vegan v2.6-2 [http://www.diva-gis.org/climate) using DIVA-GIS v7.5 [r| < .8) using ENMTools v.1.3 [IBD was evaluated by comparing linearized n v2.6-2 . Pairwisn v2.6-2 . EnvironGIS v7.5 . To avoils v.1.3 . The fivD statistic (ABBA-BABA test) was further used to verify the gene flow detected by Treemix using Dsuite v0.5 based on the minimum D (Dmin) score regardless of the tree topology [Z-scores calculated as Z = D/std_err (D) and the associated P-values were used to assess the significance of ABBA-BABA tests where D = (nABBA − nBABA)/(nABBA + nBABA).Treemix v1.171  was usedtopology . In the q-value cutoff of 1% significant false discovery rate (FDR). Second, we used an FDIST approach implemented in Arlequin v3.5 [FST value conditioned on heterozygosity. We ran FDIST with selective neutral SNPs detected by BayeScan first to establish a selectively neutral threshold (null distribution), and then reanalyzed with all SNPs. Loci outside of the null distribution were considered candidates for selection. Loci under selection show a higher value of FST than loci subjected only to gene flow and genetic drift.Two alternative genome-scan methods were used to identify loci potentially targeted by selection. First, we used BayeScan v2.1, which implements a hierarchical Bayesian approach, to infer the posterior probability of each locus being under selection . Outlieruin v3.5 . This meFST increased with climatic differentiation, as implemented in BayeScEnv v1.1 with default settings [q-value for the model including the environmental factor .Both BayeScan and FDIST cannot determine which climatic variables may be driving directional selection on loci. Associations between SNP allele frequencies and climate variables were calculated based on a Bayesian linear model method  to test settings . One mai5. Only SNPs that were closely linked to a mapped gene in the A. eriantha reference genome (overlapping or within 10 kb of an exon) were used. GO term enrichment for candidate genes was performed using the AgriGO toolkit v2.0 [P-value cut-off of .05).LD decay analysis was performed by PopLDdecay v3.41 . The deckit v2.0  based ongetData function). The GF analysis was conducted using the R package gradientForest v0.1–32 [et al. [et al. [A. eriantha. We also used outlier SNPs to estimate allele frequencies to predict genomic offset. The fitted model was then applied to calculate the Euclidian distance between genomic compositions under the contemporary and future projected climates. The distribution areas with higher values of Euclidian distances are predicted to have a larger genetic offset.Genomic offset under climate change is a predictive measure of the mismatch between contemporary and future spatial genomic composition . GF is a v0.1–32  followin [et al. . Briefly [et al. , we analet al. [https://github.com/nickFurlotte/pylmm), which offers the advantage of accounting for genetic structure and relatedness among individuals. The methylation levels were set as the explanatory variables and the environmental variables to the response variables. The methylation data were used to estimate the kinship matrix by referring to the method of Gugger et al. [y = μ + xꞵ + g + e was applied, where μ is a common intercept term, x is the corresponding design matrix based on methylation levels, ꞵ is the fixed effect, g is the polygenic background effect and e is a residual term, with Var(g) = gKσ2 and Var(e) = eIσ2, where K is the kinship matrix estimated according to the method of Gugger et al. [I is the identity matrix. The significance thresholds were estimated by permutation testing. We performed permutation tests (with 200 repeats) by randomly selecting 10 000 SMVs for each iteration. Here, the permutated significance threshold for BIO2, BIO4, BIO8, BIO16, and BIO19 corresponded to P-values (corresponding to the level of .05) of 5.6E−6, 1.5E−6, 1.6E−5, 1.7E−6, and 9.0E−6, respectively. These values are approximately two to three orders of magnitude larger than the significance threshold obtained from Bonferroni correction (4.25E−8), which would give an overly stringent estimate of significance due to small sample size. Candidate loci identified in EWAS were searched for in the non-redundant Swiss-Prot protein database using a BLASTp analysis. All parameters were kept at their default values with a cut-off E-value <1.0E−5.Reads were filtered using the same method as used for RADseq. Cytosine methylation in CG, CHG, and CHH contexts for each sample were measured by BS-SEEKER2 . The deget al. . To rever et al. . The modr et al.  and I isWe thank Na Wei for her assistance in data analysis. This study was funded by the National Natural Science Foundation of China (grants 31770374 and 32070377) and the Key Projects of the Joint Fund of the National Natural Science Foundation of China (grant U1802232).X.Y. and H.W. designed the project. X.Z. and F.L. performed data analysis. R.G., R.S., and Q.J. sampled the plant materials. X.Z., R.G., and X.Y. wrote the manuscript. J.B.L. revised the manuscript. All authors read and approved the final manuscript.bsRADseq clean reads were deposited at NCBI under the bio-project accession numbers PRJNA788157 and PRJNA788205, respectively.All RADseq clean reads and The authors declare no competing interests.Horticulture Research online.Web_Material_uhad031Click here for additional data file.


Euterpe edulis has been gaining prominence in the fruit growing sector and has demanded the development of superior genetic materials. Since it is a native species and still little studied, the application of more sophisticated techniques can result in higher gains with less time. Until now, there are no studies that apply genomic prediction for this crop, especially in multi-trait analysis. In this sense, this study aimed to apply new methods and breeding techniques for the juçaizeiro, to optimize this breeding program through the application of genomic prediction. This data consisted of 275 juçaizeiro genotypes from a population of Rio Novo do Sul-ES, Brazil. The genomic prediction was performed using the multi-trait (G-BLUP MT) and single-trait (G-BLUP ST) models and the selection of superior genotypes was based on a selection index. Similar results for predictive ability were observed for both models. However, the G-BLUP ST model provided greater selection gains when compared to the G-BLUP MT. For this reason, the genomic estimated breeding values (GEBVs) from the G-BLUP ST, were used to select the six superior genotypes . This was intended to provide superior genetic materials for the development of seedlings and implantation of productive orchards, which will meet the demands of the productive, industrial and consumer market.Popularly known as juçaizeiro,  Euterpe edulis, a palm tree native to the Atlantic Forest, is distributed along the entire length of this biome and in the gallery forests of the Brazilian cerrado [Euterpe, which has become popular in recent years through açaí, processed pulp from the fruits of species of this genus [Euterpe oleracea, açaí, which was recognized as a fruit tree of economic expression in 2008 [ cerrado –3. It beis genus . The mos in 2008 .Euterpe edulis has become popular under the name of juçaizeiro. It is a perennial species that can exceed 15 meters in height, with a single stem [As a crop, gle stem  that doegle stem —with extgle stem . It is agle stem , 9.Juçaizeiro has shown great economic potential for use in the pulp processing industry –12. The Within breeding programs, the genome-wide selection (GWS) proposed by Meuwissen et al. 2001) , uses te01 , usesGenomic prediction, based on single traits, has become a widely applied procedure among breeders after reducing genotyping costs –24—mainlCurrently, for the juçaizeiro, the techniques and approaches applied to its scarce existing breeding programs are simple. In general, the selection of the best genotypes is carried out in natural populations, based on data from a single year, and with a mass phenotypic selection technique. This is due to the absence of experimental fields with adult representatives of the species, without pre-established delineation and lack of knowledge of relationship between the individuals. In this sense, the selective bias can compromise the success of the species as a crop, by indicating low productivity genetic materials. Therefore, the present work aims to apply new methods and breeding techniques for juçaizeiro, to optimize the breeding programs through the application of genomic prediction. This study was carried out in a natural open-pollinated population containing 275 genotypes, with no knowledge of relationship between individuals; in order to select the genotypes with the greatest potential to serve the productive and industrial sector; and to compare the efficiency between multi-trait (G-BLUP MT) and single-trait (G-BLUP ST) models.Experimental evaluations were carried out from 2018 to 2021 in the municipality of Rio Novo do Sul in the state of Espírito Santo, Brazil . The 275Between 2018 and 2021, the number of bunches per plant (NB) was evaluated by visually counting the number of bunches with fruits of each matrix plant. In the years 2018, 2019 and 2021, the mass of fruits per bunch (MFB) (kg), rachis length (RL) (cm), equatorial diameter of fruits (EDF) (mm) and pulp yield (PY) (%) were evaluated. For the evaluation of MFB, in all evaluations, the same trained professional performed the determination of the harvest point of the bunches. The harvest of each genotype was carried out when the fruit maturation point reached the stage used for fruit processing in the industry. Each year, one bunch per plant was harvested, the fruits were separated from the rachillas, and they were weighed on a scale with an accuracy of 0.1 g. For RL, after separating the fruits, the length of the rachis of the inflorescence was measured with a tape measure.A sample of fruits was taken from each genotype, packed in properly identified plastic bags and transported to the Plant Biometry laboratory at the Federal University of Espírito Santo, where morphometric evaluations of fruits and seeds were carried out in a completely randomized design. For EDF, data were measured in millimeters (mm) obtained using a 6” digital caliper (Zaasprecision®), performed on five fruits individually, as recommended by Marçal et al. (2015) , who obsThe PY was estimated by the following relationship:In the equation, FFM is fruit fresh mass and SFM is seed fresh mass, measured by weighing four replicates of 25 fruits and seeds, using an analytical balance (0.0001g).Genomic DNA was obtained from leaf samples of the genotypes under study. Extraction was performed using the cetyltrimethylammonium bromide or CTAB method by Doyle (1990)  with modDNA concentrations and integrity were estimated using a NanodropTM 2000 spectrophotometer (Thermo Scientific). DNA quality was verified on 0.8% agarose gel. DNA genotypes prepared for genotyping using the DArTseqTM methodology were sent to the Service of Genetic Analysis for Agriculture (SAGA) in Mexico for high-throughput genotyping using the DArTseqTM technologyThe genome representation of the 275 genotypes was obtained from the reduction of DNA complexity using two restriction enzymes, HpaII and Msel. The ends of the cleaved fragments were linked to a code adapter and a common adapter to identify each sample. The fragments were amplified using PCR reaction; subsequently, equimolar amounts of amplification products from each sample of the 96-well microtiter plate were pooled, purified and quantified, then sequenced on the Illumina Novaseq 6000 System platform. The sequences were analyzed using Dartsoft14, an automated genomics data analysis program and DArTdb, a laboratory management system, developed and patented by DArT Pvt. Ltd. , generating SNP marker data as described by Killian et al. (2012)  and SansCall Rate (CR) ≤ 90% and Minor Allele Frequency (MAF) ≤ 5%. After quality control, the marker dataset reduced by 81.75% from 44,457 markers to 8,112.The dataset of codominant markers of the SNP type was submitted to quality control analysis in the R . We remoyik is the phenotypic value for the genotype i and the year k; μ is the population mean; ak is the fixed effect of the kth year; gi is the fixed effect of the ith genotype; and eik is the random effect of residual, with The phenotypic values were corrected for the effect of years in the R software, according to the correction proposed by Carvalho et al. (2020) . The linGenomic prediction was performed using the multi-trait (G-BLUP MT) and single-trait (G-BLUP ST) models. For this the SOMMER package version 3.4  was implb is the vector of means of each characteristic structured as u is vector of individual additive genomic genetic values of each trait structured as u~N, where G is the genomic relationship matrix between individuals for additive effects, Σu is the additive genetic covariance matrix and ⊗ denotes the Kronecker product; e is the random error vector with e~N where Σe is the residual covariance matrix.Where y is the vector of previously estimated eBLUES and structured as ith trait with i = 1,…,n = 5; ith and jth traits with j = 1,…,n = 5 and i ≠ j. The variance and covariance components were obtained via the restricted maximum likelihood method (REML). The additive genomic relationship matrix (G) was obtained as described by VanRaden (2008) [Covariance matrices can be written as:n (2008)  by the cpi and qi are allele frequencies of M and m, respectively.The parameterization for the incidence matrix W is presented below and is in accordance with the classical theory of quantitative genetics :W={IfMWith the genetic values of the individuals for the NB and MFB traits, the fruit production per plant (FPP) for each genotype was estimated.ith trait was estimated following the equation below:ith trait, respectively. The genetic correlation between the ith and jth traits was obtained through the following equation:ith and jth trait, respectively. ith and jth traits.The narrow-sense heritability of the y).The predictive ability  by multiplying the GEBVs between NB and MFB. The selection of the best genotypes was based on multiple variables. For this, the method developed by Mulamba and Mock (1978)  was usedSG is the expected selection gain for characteristic i by model j; i estimated by model j and SDi is the selection differential for characteristic i in model j estimated based on corrected phenotypic values.In order to compare the selective efficiency between the G-BLUP ST and G-BLUP MT models, it was estimated the expected selection gain for the population of the next selection cycle and for the commercial seed donor population. For both methods and all characteristics, with the exception of PFP, estimates were obtained by:The SG for PFP was estimated based on the product of the SG estimates of NB and MFB.NAO are the number of observed agreements, NAEC is the number of expected agreements by chance, and NOA is the number of analyzed observations [Cohen’s Kappa coefficient  was usedrvations .A summary of the descriptive statistics including mean, standard deviation, and maximum and minimum values for the five agronomic traits evaluated in this work is shown in The heritability and the residual and additive genetics variance components were estimated for G-BLUP ST and G-BLUP MT models . ConsideThe genetic and phenotypic correlations between the traits are shown in Comparing the phenotypic and genotypic correlations between the traits in The predictive ability  with the highest rank positions based on the Mulamba and Mock index , aimed tEven though the agreements of the Cohen’s Kappa coefficient were classified from moderate (0.41–60) to substantial (0.61–80) , is obseThe population mean, the phenotypic and genotypic behavior of the selected individuals, based on the information from the G-BLUP ST model and the field phenotypic, is shown in The selection of the six best genotypes provides a change in phenotypic responses equivalent to 0.99 bunches, 1.70 kg, 10.43 kg, 7.08 cm, 5.09%, and -1.00 mm for the traits NB, MFP, PFP, RL, PY and EDF, respectively . With SGEuterpe edulis, with the objective of increasing selective accuracy and selection gains within the breeding cycle.As there are few programs that are based on the improvement of juçaizeiro, there are also a few scientific works that are aimed at the application of selective techniques for individuals of this species. In this sense, the existing breeding programs are in the early stages of development, and thus, their techniques are basically based on classical methodologies with phenotypic information. In contrast to these processes, the present work innovates in being the first scientific study focused on genomic prediction of Euterpe oleracea (açaizeiro), a species of the same genus as juçaizeiro [The çaizeiro , 46.It is expected that traits with higher  ability . The sam ability , where t ability . The higEuterpe edulis traits are scarce in the literature [Published scientific studies evaluating associations between terature , 30, 48.The knowledge of correlations in the genetic scope is important in breeding programs because they are heritable and can be used in indirect selection . HoweverThe (co)variances estimated by the G-BLUP MT, made it possible to estimate the Pearson correlation for genetic and phenotypic effects between the traits RL, EDF, MFB, NB and PY , allowinEvaluating biometric characteristics of juçaizeiro fruits, Marçal et al. (2015)  reportedEuterpe edulis, are close to those observed in the present work between the characteristics NB, PY and RL. In both situations, the magnitudes of the associations can be classified from very weak to weak [Euterpe oleracea.The results observed by Oliveira et al. (2015)  for the  to weak . Oliveir to weak  reported to weak  also fouRegarding the correlation results observed in In order to increase selection efficiency, choosing the best methodology for analyzing and predicting the genetic values of individuals is one of the fundamental aspects to be taken into account in a breeding program. In this study, we compare G-BLUP ST e G-BLUP MT models for the evaluation of genomic prediction in a base population of juçaizeiro. The existence of genetic correlations between the selection traits is the basis for the advantages presented by G-BLUP MT  and the However, the superiority of G-BLUP MT over G-BLUP ST for (≤ 0.42)  and, hig(≤ 0.42) . As repoThe slightly lower performance of G-BLUP MT for predictive abilities can be explained by the low genetic and residual correlations between traits, as reported by Runcie and Cheng (2019) , who attThe  studies , 55–59,  studies , 53. DueTo build the next selective cycle breeding population, and in order to maintain genetic variability, a selection intensity of approximately 20% was defined. In this condition, the Kappa agreement observed among the approaches evaluated was classified as substantial (0.68), considering that its value was between 0.60 and 0.80 . This siWe should add that the high Kappa agreement between the methods is also associated with the conditions of the phenotypic data set used in the study, which has a low degree of imbalance. Thus, the predictive abilities of the approaches did not have a great impact on the selection of different genotypes between models. However, the influence on the ranking of the best individuals was strongly impacted, as shown in When considering the six best genotypes, the selection of the best genotypes had greater divergence between the models, in which the Kappa value was 0.50, evidencing the need to determine the best model to carry out the selection process, aiming at greater future gains. In this way, the models are chosen according to their ability to predict more accurately the GEBVs of the analyzed individuals. However, in the present study, Due to the similarities of the The production of fruits per plant (PFP) is a feature that serves both the productive sector and the industrial sector, as it provides greater economic return to producers and increases the amount of raw material for industries that currently suffer from a shortage of this base product for their operations. In this sense, the selection differential amounts to an increase of 98.40%, which indicates a great advance for the culture due to the possibility of achieving great gains in productivity, resulting in an incentive to install of culture.Euterpe edulis, a wild open-pollinated species, aiming to increase gains per unit of time.As the juçaizeiro is a wild and perennial species, which presents a series of characteristics intrinsic to its development, the practical activities of conventional breeding techniques become more difficult. In general, when evaluating the structure of a breeding program aimed at juçaizeiro, it is expected that the gains obtained per unit of time will be reduced. In this sense, the present study stands out for applying genomic prediction techniques in Euterpe edulis, is the use of the genomic matrix to bring knowledge of the genetic relationship between individuals for statistical analysis, which was previously unknown. Thus, the establishment of this information provides more accurate estimates and, consequently, greater reliability to the program. Furthermore, the use of genomic prediction may allow a reduction in the selection cycle by being able to predict the behavior of non-evaluated genotypes.Therefore, perhaps the biggest advantage brought by this methodology for a wild breeding population, as is the case of All the traits evaluated in the study influence the economic potential of the species, covering the interests of the productive sector and the industrial sector. Additionally, in function of the alterations observed by the selection of the six superior genotypes, we can conclude that the genomic prediction using the G-BLUP ST was efficient to provide alterations of the means in the desired directions. That is, obtaining an increase in the average phenotypic response of the selected genotypes for the characters of NB, MFB, PFP and PY, and a reduction for EDF.Our results showed that the GBLUP-ST genomic prediction was more efficient in selecting the best genotypes, the selection provided substantial gains in the desired direction for multiple traits. Thus, the six selected genotypes  can be used as commercial seed donor genotypes for the development of seedlings for the implantation of productive orchards, which will meet the demands of the productive sector and the consumer market.S1 Table(DOCX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.S2 Data(XLSX)Click here for additional data file.S1 File(TXT)Click here for additional data file.


Founder populations with deep genealogical data are well suited for investigating genetic variants contributing to diseases. Here, we present a major update of the genealogical analysis R package GENLIB, centered around a new function which can simulate the transmission of haplotypes from founders to probands along very large and complex user-specified genealogies.https://github.com/R-GENLIB/GENLIB. Examples can be accessed at https://github.com/R-GENLIB/simuhaplo_functions.The latest update of the GENLIB package (v1.1.9) contains the new gen.simuHaplo function and is available on the CRAN repository and from  Founder populations have been utilized extensively in the study of Mendelian diseases because they can have higher incidence rates of rare autosomal-recessive genetic diseases due to drift effects, e.g.: Gaucher disease, Tay–Sachs disease, and cystic fibrosis in the Ashkenazi Jewish population , or one http://www.R-project.org/) package  of the segments bounded by the crossovers . We have package . This neGENLIB is an R package designed to analyze large genealogical datasets. The basic argument for all GENLIB functions is a “genealogy” object created by the gen.genealogy function from a matrix or data frame where each line describes an individual with the following information: identification number (ID), father ID, mother ID, and sex (see The new gen.simuHaplo function simulates genomic segments (hereafter interchangeably referred to as haplotypes) of user-specified lengths in specified or all probands of a genealogy. Meiosis in the parents of each individual is simulated using one of three possible models see , (ii) a After creating a “genealogy” object, the gen.simuHaplo function can be called by specifying the following arguments: the “genealogy” object, a vector of proband IDs for whom to simulate haplotypes, a vector of founder IDs to include, the number of simulations, the meiosis model, the meiosis model parameters, the length of the chromosomal segment to be simulated, and other optional arguments (see The output of the gen.simuHaplo function is a text file (Proband_Haplotypes.txt) containing the description of each proband’s simulated haplotypes. An example of the output format is shown in Many post-simulation analyses are possible and their types will vary depending on the field of application. The format of the simulation output saved as text files provides maximum flexibility for the different user types. Within GENLIB, we provide two new functions to analyze the simulation output , tracking a large number of loci , or having additional functionalities (e.g. handling phenotypes). A detailed comparison to other software is provided in Gene-dropping simulations can be used for many purposes, including estimating the distribution of haplotype lengths in a founder population, the distribution of the length of IBD segments shared by a pair of individuals, or the likelihood of IBD segments being transmitted from ancestors. Simulation results can also be used to compare different statistical methods, as illustrated in The theoretical distribution of the lengths of inherited segments for a given proband–founder relationship is difficult to obtain in consanguineous populations . We usedThe gen.simuHaplo function combines the GENLIB R package’s existing support for handling large genealogies to allow users to simulate inheritance of large genomic regions with a high density of markers even in genealogies with consanguinity and hundreds of thousands of individuals. To our knowledge, no other simulators with similar functionalities support such large and complex genealogies and marker densities.btad136_Supplementary_DataClick here for additional data file.


Growth traits are economically important characteristics for the genetic improvement of local cattle breeds. Genome-wide association studies (GWAS) provide valuable information to enhance the understanding on the genetics of complex traits. The aim of this study was to perform a GWAS to identify genomic regions and genes associated to birth weight, weaning weight adjusted for 240 days, 16 months, and 24 months weight in Romosinuano (ROMO) and Blanco Orejinegro (BON) cattle. A single-step genomic-BLUP was implemented using 596 BON and 569 ROMO individuals that were genotyped with an Illumina BovineSNP50 BeadChip. There were 25 regions of interest identified on different chromosomes, with few of them simultaneously associated with two or more growth traits and some were common to both breeds. The gene mapping allowed to find 173 annotations on these regions, from which 49 represent potential candidate genes with known growth-related functions in cattle and other species. Among the regions that were associated with several growth traits, that at 24 – 27 MB of BTA14, has important candidate genes such as LYPLA1, XKR4, TMEM68 and PLAG1. Another region of interest at 0.40–0.77 Mb of BTA23 was identified in both breeds, containing KHDRBS2 as a potential candidate gene influencing body weight. Future studies targeting these regions could provide more knowledge to uncover the genetic architecture underlying growth traits in BON and ROMO cattle. The genomic regions and genes identified in this study could be used to improve the prediction of genetic merit for growth traits in these creole cattle breeds. Growth traits are commonly used as a selection criterion for the genetic improvement of beef cattle, due to its association with beef production and slaughter age, which have great economic importance for farmers and the food industry   and Blanco Orejinegro (BON).Dichanthium aristatum (angleton grass), Megathyrsus maximus (guinea grass) and Cynodon spp. (stargrass). BON individuals were kept in the El Nus research center located in San Roque, Antioquia, Colombia (Andean region), with an annual average temperatures between 18 and 24 °C and a bimodal rainfall regime. BON cattle were fed with a mixture of Brachiaria decumbens , Brachiaria brizantha , and Cynodon plectostachyus (giant star grass).A genealogical database of 7078 ROMO and 8255 BON individuals was used, including 4063 ROMO and 3922 BON with productive records for one or more of the following traits: birth weight (BW), weaning weight adjusted for 240 days of age (WW), weight at 16 months (16mW), and weight at 24 months (24mW). Both genealogy and phenotypes were retrieved from the databases of the Colombian national animal germplasm bank. ROMO individuals were kept in the Turipaná research center located in the valley of the Sinú river in Cereté, Córdoba, Colombia (Caribbean region), with an average temperature of 27.5 °C, relative humidity of 83%, and an annual precipitation of 1200 mm, distributed in a low precipitation season from December to March and another period of high precipitation from April to November. ROMO cattle were fed with a mixture of in vivo) and semen (germplasm bank in vitro) samples were collected, and DNA extraction was carried out in the molecular genetics’ laboratory of AGROSAVIA, employing a commercial kit . The concentration (≥ 50 ng/ml) and integrity (ratio = 1.8) of the DNA was evaluated in a NanoDrop 2000®. A total of 596 BON and 569 ROMO cattle were genotyped for 52784 polymorphisms. This information was obtained from two different SNP panels of high density, the BovineSNP50K_v2  after combining both data sets. Therefore, genotypes of SNPs included in the high density chip (BovineSNP50_v2) were imputed for all animals genotyped with the low-density panel (IlluminaSNP7K), using the FImpute software  was used to examine the genetic structure of the two populations. The PCA was performed using a genomic identity-by-state (IBS) relationship matrix.−122 is the inverse of the numerator relationship matrix for the genotyped individuals and G is a matrix of genomic relationships. The matrix G was constructed weighting each marker contribution by its expected variance, according to the methodology described by VanRaden  corrected by allele frequency  was performed, which is an alternative approach for association analyses proposed by Wang et al. , based oVanRaden , with G −1 matrix was replaced within the mixed model equation, and an animal model was implemented for each breed and trait including the fixed effects of sex, year, and season birth (rainy and dry) and the random effects of animal direct additive genetic, maternal additive genetic, maternal permanent environment (only for BW and WW), and residual variance, which were all set in the following mixed linear model:Y is the vector of observations for the respective trait evaluated, 1 is all-ones vector, µ is the overall mean for the phenotypic records, β is a vector of fixed effects, X is the corresponding incidence matrix for the fixed effects, N, where H represents the additive relationship matrix that combines pedigree and genomic information, e is a vector of residual random effects with ~ N. The variance–covariance structure of the additive genetic effects was Var =  H is a matrix of additive genomic relationships among individuals built from SNPs data combined with pedigree data and The Hû is the vector of SNP marker effects, D is a diagonal matrix of weights of SNPs, Z is a matrix related to the genotypes of each locus for each individual, and ai is the genetic value of the i-th genomic region that consists of 4 adjacent SNPs,  j-th SNP for all individuals, and j-th SNP within the i-th region. GWAS results were plotted with qqman package included in R to build Manhattan plots  to find the surrounding genes within 500 kb. Based on the starting and ending coordinates of the windows, gene annotations were obtained using the genome databanks National Center for Biotechnology Information NCBI,  and EnsemalQTLdb  was acceThe results of the PCA show that the first principal component (PC) explains 8.44% of the total variation and clearly separates the BON breed (black) from the ROMO breed (blue), while the second PC explains only the 1.25% of the variance , which are represented in Table Y-axis) and the position that SNP occupies in pairs of bases (bp) within each chromosome (X-axis).The results of the ssGWAS are shown in Fig. The details of the regions of interest identified in the ssGWAS are presented in Table Some genomic regions were simultaneously associated with two or more growth traits and in some cases were common to both breeds. Chromosomal regions with simultaneous effect for several traits were more frequent in ROMO Table , for exaMultiple regions associated with several growth traits were found in chromosome 14. Interestingly, eight regions associated with BW in BON were very close to each other at 19.3–27.3 Mb of BTA14. Another region at 27.23–27.33 Mb of BTA14 in BON showed an important effect on BW (0.37% gVar), WW (0.22% gVar), 16mW (0.15% gVar), and 24mW (0.1% gVar). Close to this location on BTA14 (24.52–24.65 Mb), but in ROMO, another region was highly associated with WW (0.57% gVar), 16mW (0.52% gVar), and 24mW (0.25% GVar) , 16mW (0.24 gVar), and 24mW (0.21% gVar)  and composite tropical breeds  , weight at different ages, hip height, and deposition fat in Brahman cattle , SNAI2 (14:21.57–21.58 Mb), SOX17 (14:23.88–23.89 Mb), and LYPLA1 (14:23.65–23.67) are involved in replication processes, differentiation, and cell function. Some polymorphisms in PLAG1 have been linked to growth, development, and carcass traits in different cattle breeds, suggesting its possible pleiotropic effect  and TMEM68 (14:24.71–24.75 MB) have been mapped. Previous studies identified several SNPs in XKR4 associated with rump fat thickness in Australian and composite cattle  associated with 16mW and 24mW in ROMO, is involved in the negative control of osteogenic differentiation of osteochondrogenic precursor cells in peripheral zones of fetal cartilage and at the cartilage-bone interface  and contain 50 potential candidate genes with known functions related to biological processes of growth in cattle and other animal species. Some of these genomic regions were simultaneously associated with two or more growth traits and in some cases were common to both breeds (on BTA14 and BTA23). Future studies targeting these areas could provide more knowledge to uncover the genetic architecture underlying growth traits in BON an ROMO creole cattle. The genomic regions and genes identified in this study could be used to improve the prediction of genetic merit for growth traits in local cattle breeds, where there is a limitation of performance records and genealogical data.


With increasing hypercholesterolemia prevalence in East Asian adolescents, pharmacologic interventions  and PCSK9 inhibitors) may have to be considered although their longer-term safety in the general adolescent population is unclear. This study aims to investigate the longer-term safety of HMGCR inhibitors and PCSK9 inhibitors among East Asian adolescents using genetics.n = 146,492) and individual-level data from Chinese participants in the Biobank clinical follow-up of Hong Kong’s “Children of 1997” birth cohort . Safety outcomes (n = 100) included anthropometric and hematological traits, renal, liver, lung function, and other nuclear magnetic resonance metabolomics. Positive control outcomes were cholesterol markers from the “Children of 1997” birth cohort and coronary artery disease from Biobank Japan.A drug-target Mendelian randomization study leveraging the Global Lipid Genetics Consortium , genetic inhibition of HMGCR was associated with lower linoleic acid − 0.79 [− 1.25 to − 0.35]. Genetic inhibition of PCSK9 was not associated with the safety outcomes assessed.Genetic inhibition of HMGCR and PCSK9 were associated with reduction in cholesterol-related NMR metabolomics, e.g., apolipoprotein B  and had the expected effect on the positive control outcomes. After correcting for multiple comparisons (Statins and PCSK9 inhibitors in East Asian adolescents appeared to be safe based on the outcomes concerned. Larger studies were warranted to verify these findings. This study serves as a proof of principle study to inform the medication safety among adolescents via genetics.The online version contains supplementary material available at 10.1186/s12916-023-03115-y. Three-hydroxy-3-methylglutaryl coenzyme A (HMGCR) reductase inhibitors, collectively referred to as statins, are the first-line pharmacologic treatments for children with familial hypercholesterolemia (FH), which demonstrated a reduction in cardiovascular risk in adulthood . Howevern = 100) in an originally population-representative birth cohort in Hong Kong. For completeness, we also considered genetic inhibition of proprotein convertase subtilisin/kexin type 9 (PCSK9) and Niemann-Pick C1-Like 1 (NPC1L1) as these are emerging classes of medications for the treatment of hypercholesteremia in adolescents [Real-world data, such as pharmaco-epidemiologic studies of medication side effects, are susceptible to confounding by indication or immortal time bias . The useHMGCR, PCSK9, and NPC1L1) and associated with LDL-C were used to proxy the use of statins, PCSK9 inhibitors, and ezetimibe respectively. Second, the use of genetic variants not associated with confounders of exposure-outcome association addresses independence. Third, genetic variants are independent of the outcome given the exposure thereby addressing exclusion restriction [https://www.strobemr.org/).This is a drug-target Mendelian randomization study using individual and summary data, which relies on three instrumental variable assumptions. First to satisfy relevance, genetic variants within the protein-encoding genes (triction . The schn = 8327) in Hong Kong in April and May 1997 [n = 7999). During the Biobank clinical follow-up phase 1 in 2013–2016, at 17.5 years (n = 3460), with a supplementary Biobank clinical follow-up phase 2 in 2017 (n = 158), participants provided biospecimen  and completed comprehensive measurements . Fasting blood samples and their derivatives  were used for biochemical assays such as liver function tests and metabolomic profiling using high-throughput nuclear magnetic resonance (NMR) spectroscopy metabolomics  [The Hong Kong “Children of 1997” birth cohort is a population-representative Chinese birth cohort, which recruited 88% of all ethnic Chinese births (May 1997 . RecruitMay 1997 . In 2005th.com/) , which ath.com/) , 15.https://imputationserver.sph.umich.edu/index.html#!). For quality control, samples with call rate < 0.98, recorded sex mismatched with genetic-inferred sex, second degree of relatedness or above, high heterozygosity (deviation > 3 standard deviation (SD)), or variants with call rate < 0.98, and imputation score < 0.3 were excluded.DNA extraction from either blood, buffy coat, or saliva samples, where appropriate, and genotyping were performed for 3582 participants by the Centre for PanorOmic Sciences, The University of Hong Kong, with Infinium Asian Screening Array BeadChip Kit (v1.0). Phasing was performed using Eagle v2.4, and genotyping imputation was performed with 1000 Genomes phase 3  in the Michigan Imputation Server using Minimac4 software , and those associated with LDL-C at genome-wide significance  in GLGC were considered. None of the genetic variants for NPC1L1 passed through the selection process and hence was not considered further. A recent study found that genetic variants of LDL-C identified in middle-aged adults of EAS ancestry are highly predictive of LDL-C measurement at different stages of the life-course including adolescents [p-value < 0.05) and in low linkage disequilibrium (LD)  using a reference panel of EAS ancestry from 1000 Genomes Project (phase 3). To assess the robustness of the results, we also used a single index variant .We identified genetic instruments to proxy the effects of each lipid-modifying target using the Global Lipids Genetics Consortium (GLGC), the latest genome-wide association study (GWAS), which included 146,492 middle-aged (mean aged of 55.8 years) participants of East Asian (EAS) ancestry . LDL-C wlescents . NeverthHMGCR and PCSK9 gene regions, weighted by the effect of that variant on LDL-C in EAS ancestry reported by GLGC [To maximize statistical power,  an exter by GLGC .To verify the validity of the GRS used to proxy the effect of HMGCR and PCSK9 inhibition, we considered cholesterol-related metrics and coronary artery disease (CAD) as the positive control outcomes, as lowering cholesterol levels and reducing CAD risk are well-established effects of HMGCR and PCSK9 inhibition . We used1), forced vital capacity (FVC), FEV1/FVC, peak expiratory flow (PEF), the mid-forced expiratory flow at 25–75% of the pulmonary volume (FEF 25–75%)). We also included other metrics from NMR metabolomic panel, i.e., amino acids, fatty acids, fluid balance, glycolysis-related metabolites, inflammation, and ketone bodies as safety outcomes given some of these metabolites  may be related to cardiometabolic diseases [To systematically evaluate the safety of genetic inhibition of HMGCR and PCSK9, we considered extensive health outcomes assessed at ~ 17.6 years during the Biobank clinical follow-up. We included 20 hematologic traits, 12 anthropometric traits, 18 body composition traits, renal function (urea), liver function , alanine transaminase (ALT), bilirubin and total protein), and lung function  to facilitate comparison. With individual level “Children of 1997” birth cohort data, the associations of the HMGCR and PCSK9 GRS with these outcomes were obtained using a multivariable linear regression model adjusted for age at sample collection, sex, and the top 6 PCs of ancestry. The variance explained by the HMGCR and PCSK9 GRS were assessed as the proportion of variance remaining after covariates fitted in a linear model [Instrument strength was assessed using approximated ar model . The effttrition . Brieflyttrition . Inversettrition . A complQ statistic [With summarized data from Biobank Japan, the effects of genetic inhibition of HMGCR and PCSK9 on the positive control outcome CAD were assessed using weighted generalized linear regression, i.e., a modified version of inverse-variance weighted (IVW) method that accounts for pairwise correlation between variants . We obtatatistic .p value threshold after correction for multiple testing was set as 6 × 10−4 (α = 0.05/75). Post hoc power calculation using type I error α of 0.05 and power of 0.8 was performed using an online tool (https://shiny.cnsgenomics.com/mRnd/). Data analyses and visualization were obtained using the R packages “ggforestplot” and “EpiViz,” R version 4.0.3 .To correct for the multiple testing of these biologically correlated outcomes, we used PC analysis to obtain the number of PC that explained 99% of the variance in the cholesterol related and safety outcomes. The There were 3443 participants  with valid genotype information and outcomes. Characteristics of these participants by tertile of HMGCR score and PCSK9 score are shown in Table F-statistics were greater than 10 (HMGCR: 38 to 266 and PCSK9: 34 to 42). HMGCR and PCSK9 GRS explained 0.54% and 0.27% of LDL-C, and thus, our study was adequately powered to detect an odds ratio of 0.77 and 0.67 per SD reduction in LDL-C for CAD and a beta coefficient of ± 0.46 and ± 0.60 per SD reduction in LDL-C for the continuous outcomes. Genetic inhibition of each lipid-modifying target was associated with lower CAD risk . Suggestive evidence of heterogeneity was found for the effects of genetic inhibition of HMGCR on CAD, which may be due to the missense variant of rs19183514  were selected for targeting HMGCR and 3 variants  for PCSK9. Their associations with LDL-C in GLGC and “Children of 1997” are summarized in Additional file p-value ≤ 6 × 10−4  , remnant cholesterol, very low lipid density lipoprotein (VLDL-C), clinical LDL-C and LDL-C) and apolipoproteins (ApoB and the ratio of apolipoprotein B to apolipoprotein A1 (ApoB/ApoA1)); p-value = 6 × 10−4). In addition, genetic inhibition of HMGCR was nominally associated with lower omega-6, polyunsaturated, total, saturated, and monounsaturated fatty acids, pyruvate, FEV1, FVC, head circumference, height, and higher glycerol  , such as metabolomics, we did not cover all possible side effects, including muscle symptoms and related biomarkers  [This drug-target Mendelian randomization study has some limitations. First, the study used genetic variants to mimic the on-target effect of life-long  exposure to a relatively modest LDL-C reduction induced by genetic inhibition of HMGCR and PCSK9 among Chinese adolescents. This may not be equivalent to the effects of pharmacological interventions administered at specific time points, higher dosages, for shorter periods, or to individuals with particular indications such as FH . Second, and EAS . The div and EAS . It woul and EAS . Further and EAS . Furtheroglobin)  and testoglobin) . Lastly,oglobin)  and utilThis study did not find evidence that raise substantial concerns about the longer-term safety of statins and PCSK9 inhibitor in this Chinese adolescent population; these findings require verification in larger studies. Our study serves as a proof-of-concept study showing the use of well-characterized birth cohorts with multi-omics data (genetics and metabolomics) and phenotypes to facilitate the assessment of the efficacy and safety of drug targets among adolescents.Additional file 1. Table S1. The primary efficacy outcomes from the “Children of 1997” birth cohort. Table S2. The safety outcomes from the “Children of 1997” birth cohort. Table S3. The genetic variants selected for the HMGCR score and PCSK9 score and their association in the Global Lipids Genetics Consortium and the “Children of 1997” birth cohort. Table S4. The effect of genetic inhibition of HMGCR and PCSK9 on coronary artery disease in East Asian. Table S5. The effect of genetic inhibition of HMGCR and PCSK9 on efficacy outcomes in the “Children of 1997” birth cohort. Table S6. The effect of genetic inhibition of HMGCR and PCSK9 on efficacy outcomes using complete case analysis in the “Children of 1997” birth cohort. Table S7. The effect of genetic inhibition of HMGCR and PCSK9 (using index variant) on efficacy outcomes in the “Children of 1997” birth cohort. Table S8. The effect of genetic inhibition of HMGCR and PCSK9 on safety outcomes in the “Children of 1997” birth cohort. Table S9. The effect of genetic inhibition of HMGCR and PCSK9 on safety outcomes using complete case analysis in the “Children of 1997” birth cohort. Table S10. The effect of genetic inhibition of HMGCR and PCSK9 (using index variant) on safety outcomes in the “Children of 1997” birth cohort. Supplementary Note. R code for Mendelian randomization analysis, principal component analysis, and visualization.Additional file 2.