Genetic risk scores (GRS) and polygenic risk scores (PRS) are weighted sums of, respectively, several or many genetic variant indicator variables. Although they are being increasingly proposed for clinical use, the best ways to construct them are still actively debated. In this commentary, we present several case studies illustrating practical challenges associated with building or attempting to improve score performance when there is expected to be heterogeneity of disease risk between cohorts or between subgroups of individuals. Specifically, we contrast performance associated with several ways of selecting single nucleotide polymorphisms (SNPs) for inclusion in these scores. By considering GRS and PRS as predictors that are measured with error, insights into their strengths and weaknesses may be obtained, and SNP selection approaches play an important role in defining such errors. Genetic risk scores (GRS) and polygenic risk scores (PRS) are increasingly used as predictors of disease risk , and actA genetic score or polygenic score is a weighted sum of allele indicators at each element of a set of single nucleotide polymorphisms (SNPs). The distinction between GRS and PRS is loosely a function of how many SNPs are in the set, with ‘poly’genic risk scores often containing hundreds of variants, and GRS including only a few. The theoretical performance of PRS as a function of sample size, heritability, and the distribution of the true effect sizes has been expertly discussed by p-value filter, and then refined through one or more filtering strategies to include only variants that contribute non-redundant information. Selection of SNPs for inclusion in a GRS or PRS can be considered to be the most challenging aspect of their construction. One commonly-used approach for SNP selection is clumping and thresholding (C + T) , where SNPs are selected on the basis of their individual statistical significance and low linkage disequilibrium patterns with other nearby SNPs. However, many more sophisticated methods have been developed for SNP selection after GWAS, to improve accuracy of disease risks estimates. Among the methods proposed, some attempt to increase the likelihood that the selected SNPs are truly causal. For example, fine mapping after a Genome-Wide Association Study (GWAS) predicts the most likely causal SNP(s) at a locus . Even is  studies . In an oOne elemental example of variation in GRS and PRS performance is the differential performance across ancestral populations . These dInstead, our goal here is, through the use of a few case studies, to examine variability in genetic score performance as a function of SNP selection and context. First we briefly describe three sets of data. In the Methods section, we briefly introduce a few different approaches for SNP selection. Then in Results, we illustrate the performance of these method choices through illustrative analyses of three datasets. Finally we discuss how SNP selection influences performance in different datasets and contexts.http://links.lww.com/PAIN/A688).Temporomandibular disorder (TMD) is a painful disease of the jaw. We used data on independent subjects from four cohorts containing TMD information: the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study; the Sao Paulo, Brazil, TMD case-control (SPB) study; the OPPERA II Chronic TMD Replication case-control study, and the Complex Persistent Pain Conditions (CPPC): Unique and Shared Pathways of Vulnerability study. Significant associations between TMD and three distinct loci have been previously reported in combined or sex-segregated analyses on the OPPERA cohort . Sample st degree, defined such that the kinship coefficient >0.177, we removed one individual with the lowest call rate, resulting in 71 individuals being dropped. Also, we removed 199 subjects from the SPB study who were classified as having TMD without any pain.Before combining data across the four cohorts, we estimated the relationships between all pairs of individuals. For each pair of related subjects up to 1r2 < 0.2. As recommended by Before merging the raw genotyped data (before imputation) for the four cohorts, we filtered for minor allele frequency greater than 1% and SNP call rate greater than 95%, using PLINK 1.9 . Then, wWe used the imputed data described in M imputed SNPs.After merging all four cohorts, we filtered for HWE separately in cases and controls, using a more strict threshold among cases to avoid discarding disease-associated SNPs that are possibly under selection ukbiobank.org . We extorted in .https://www.pgscatalog.org/), and the numbers of SNPs in the PRS for different phenotypes are shown in https://jmarchini.org/software/, using the merged UK10K and 1000 Genomes phase 3 reference panels). The individual level phenotype data for each one of the 11 traits was collected from the UK Biobank browser (https://biobank.ndph.ox.ac.uk/).For calculating PRS scores, GWAS results for the 11 traits were taken from the Polygenic Score (PGS) catalog (https://www.ssc.wisc.edu/wlsresearch/)   is a popp-value via BEAGLE allelic r2 (r2 no less than 0.3) used in our analysis. Basic descriptive statistics regarding the genotype and phenotype data can be found in We focused on the imputed genetic data. Imputation was implemented using the software IMPUTE2 , which welic r2  and gavegij are the genotypes for a set of individuals i = 1, … N, and for SNPs indexed by j ∈ S, where S is a selected set of SNPs. The same formula applies for PRS although set S will be larger. Often the genotypes will be coded as 0, 1, 2, counting the number of minor alleles at the SNP. In another common approach, the genotype data are centered and scaled to have mean zero and unit variance prior to score construction. How to choose the set S is a crucial question involving considerations of ancestry and linkage disequilibrium, as well as statistical significance thresholds and the analysis methodology used when associating the SNPs with a phenotype. The notation βj represents the weight attributed to each copy of the minor allele; if genotypes are scaled then βj must also be scaled correspondingly. The estimated coefficients j as well as methodology choices. Therefore, any inaccuracy or error in a genetic score could be due to having the wrong set of SNPs (S), the wrong coefficient estimates βj, or incorrect genotype measurements. R2 for a continuous phenotype or by area under the curve (AUC) for a binary phenotype) depends on the heritability, sample size, and distributions of true effect sizes. AUC can be interpreted as the probability that a randomly selected case will have a higher score than a randomly selected control  of the genotype data. The PCs were calculated after merging the raw genotypes from all cohorts, to account for population stratification. The PRS was then calculated on a subset of genetic markers obtained after LD-clumping, which removes highly correlated SNPs, followed by P-value thresholding (see Results). We then used the validation set to re-estimate the coefficients of the covariates , as well as the coefficient of the calculated PRS, and then predicted TMD status on the test set.• First, using the four-cohort combined training datasets (the C + T method), we tested association with TMD genome-wide using PLINK, employing a logistic regression for additive SNP effects, with age, sex, enrollment site and cohort as covariates and the first 10 principal components  below PT, for a range of values for PT. The LASSO penalty was used to force some coefficients to be exactly zero, thereby performing simultaneous variable selection.• A third analysis of the TMD data used multivariable penalized logistic regression. When it is computationally possible, multivariable penalized models may more accurately clump or prune SNPs than any C + T method , while aage in R , we fit For this meta-analysis implementation, cohort-specific training datasets were used to obtain C + T depends on linkage disequilibrium patterns at each GWAS locus. However, algorithms based only on this population-level correlation structure do not take into account whether a SNP is likely to be functional or causal for the disease, or is merely correlated with such SNPs. Numerous methods have been developed to improve on C + T, such as PRS-CS , LDpred Ei) for predicting which is the most likely causal gene at a locus showing multiple GWAS signals  such as eCAVIAR  restrict signals . For Ei,alidated . For eaclgorithm  to prediEi algorithm, we implemented a form of leave-one-out sensitivity analysis to investigate identify which SNPs had the largest influence on the causal gene predictions at each locus. In this exploration, we used the same dataset that was assembled to build the Ei algorithm  is the original Ei score for gene g, Ei(g)j− is the updated Ei score for gene g obtained by re-estimation of the algorithm after removing SNP j. Therefore, δj,g captures the change in the predicted probability for gene g by removing SNP j. We use the notation Gj to denote the set of all genes related to SNP j, i.e. at the same locus. Then δj denotes the average difference of δj,g across the set of genes Gj for SNP j. Finally we defined wj to be the weight assigned to SNP j in PRS construction, by summarizing the mean differences across all SNPs at the same locus (J) as SNP j.We wished to ask whether gene prioritization methods could also be useful in SNP selection for PRS or GRS construction. This idea can be considered as similar to methods for improving SNP annotation. However, here we propose to estimate SNP contributions indirectly through their role in causal gene identification, rather than directly through annotation of the SNP genomic position. Using data from UK Biobank and by reverse engineering the lgorithm . For eacIn Mendelian randomization (MR), genetic data are used as instruments to infer whether a modifiable risk factor has a causal effect on a disease phenotype or trait of interest. A genetic variant must satisfy three core assumptions in order to be a valid instrument: (i) it is informative of the modifiable risk factor; 2) there is no association between the genetic variant and unmeasured confounders; 3) each genetic variant has no effect on the outcome of interest except through the modifiable risk factor of interest e.g. .Combining several genetic variants into GRS can enhance causal inference in observational studies . PreviouOne challenge is that the SNPs used to construct the GRS may violate assumption 3) due to pleiotropy, a phenomenon where a genetic variant may influence a disease or trait through independent pathways . Inclusip-value threshold of 5 × 10–8 controls the family-wise error rate at 5%, but may exclude many SNPs with small effects. In other words, a genome-wide significance threshold applied to a small or moderate sample size study may result in few or even no SNPs for construction of a GRS. Therefore, it may be tempting to use a less stringent significance threshold to obtain a larger number of SNPs and hence a potentially-stronger GRS instrument. However, a less conservative threshold is likely to include more false positive associations, i.e. SNPs having spurious correlations with the risk factor of interest. We refer to such SNPs that are falsely selected as “spurious instruments”.A second challenge is that when the sample size is small to moderate (e.g. in the hundreds or thousands), GWAS will have limited power to detect small SNP effects. The industry-standard GWAS In S. Both scores are then used to assess whether BMI is causally related to quality of life (HRQL) in the Wisconsin data. Unlike a standard GRS, this resampling method also uses information from the disease or outcome variable when performing SNP selection. Detailed methods and discussions can be found in Using data from the Wisconsin Longitudinal Study, we compared performance of a standard GRS  for BMI,AUC). For continuous traits, we used the R2 between predicted and observed values to assess performance. We also evaluated the number of SNPs retained in the GRS or PRS scores by different methods.We evaluated each GRS’s or PRS’s ability to discriminate between cases and controls by determining the area under the receiver-operator characteristics curve . For a subset of the SNPs, weights were exactly 1.0 , and these are SNPs where there was only one potentially-causal gene at a GWAS locus. Most weights were smaller than 1.0, and many were zero. In fact, for DBP, 69 out of 139 (50%) GWAS significant SNPs in the Ei set were assigned locus-specific weights of 0; that is for DBP, the approach we proposed estimates 50% of these SNPs to have no influence on whether a nearby gene is causal.Only a subset of the SNPs included in the PGS catalog for any particular phenotype were used in the construction of the SNPs see . After rEi-derived weights. On this subset, we calculated the PRS with and without using the Ei weights. The numbers of SNPs going into the published PRS, used in constructing the published Ei scores, and the numbers with non-zero weights following R2 values (R2 (Ei_snp) and R2 (Ei_weighted) columns of Ei weights were not included in the calculations.We repeated a similar analysis for 10 additional UK Biobank phenotypes and then built PRS scores in three ways. Firstly, we calculated PRS scores using the published PGS catalog entries for these phenotypes  used scores built from larger numbers of SNPs than went into the Ei algorithm. For example, for height, there are 33,937 SNPs in the PRS, and only 650 of them contribute to the Ei algorithm for this trait.We used the recommended SNP selections from the PGS catalog when building the basic PRS, so the GWAS Ei subset of SNPs are difficult to compare since SNP selection is so different. However, when comparing the R2 columns that start from the Ei SNPs, incorporation of Ei-derived weights leads to very similar or slightly worse R2 values for most phenotypes with the exception of height. Therefore, our Ei-weighting strategy, at least as currently implemented, does not reliably lead to improved PRS performance for these phenotypes. Of the 650 SNPs used in building the Ei predictor for height, only 275 had non-zero weights in our adaptation, and a similar ratio applies to many phenotypes. Despite this, the R2 values changed very little, suggesting that the most important SNPs may be accurately highlighted.The performance of scores from the PGS catalog and scores from the In our recent work , we simuWe found that the valid instruments had effect estimates that were similar to each other, and also that the spurious instruments displayed similar estimates, although this latter group of estimates were very different from the true causal effect. As a result, the largest group of candidate instruments with similar causal effect estimates corresponded to the noise variables that have spurious correlations with the exposure! In fact, these spurious instruments had estimates that were close to the ordinary least squares (OLS) estimate, an estimate that one would expect to obtain without accounting for unmeasured confounding. In other words, the causal effect estimate from MR studies would be subject to a similar amount of confounding as OLS, if one blindly applies existing methods for causal inference with invalid instruments to construct the GRS.Using the Wisconsin Longitudinal Study data, together with the resampling method developed in , we estiFor a simple comparison of this approach to robust methods, we ran an Egger regression on the 29 SNPs selected for the standard GRS approach. This led to a causal estimate of −0.010 with 95% CI , values that are similar to the OLS estimate, and still quite different from the one that eliminated spurious instruments. Hence, more accurately identifying the truly associated SNPs makes an important difference in the causal effect estimates.p-value based filters can be beneficial. The SNP selection strategy had a strong impact on PRS performance in new datasets. These results add to ongoing community discussion on marginal versus joint estimation, since penalized models only performed well when given a large set of SNPs to start. Initial pre-filtering can negatively impact their performance.We have presented several case studies that show challenges and opportunities associated with with PRS or GRS construction and interpretation. The accuracy of SNP selection with C + T versus multivariable penalized models is examined through analysis of several datasets containing patients with TMD; and we show that in these data, SNP selection through multivariate penalized models rather than S should lead to improved predictive accuracies. However, including estimated weights into the score construction may also increase the weighted score’s measurement error. For now, only a small subset of the SNPs in large published PRS scores were annotated by our algorithm, and this may explain why only small effects were seen.Also, using some data from UK Biobank, we touched on the potential benefits and risks associated with basing SNP selection through a new SNP annotation derived from an algorithm originally designed to predict causal genes at GWAS loci. One might expect that improved SNP selection for the GRS or PRS set We also illustrated, with an example, that improved identification of SNPs that are invalid instruments for Mendelian randomization substantially altered inference of the causal effect of BMI on HRQL. The potential confounding of PRS–phenotype (or GRS–phenotype) relationships is always a concern when the predictor (the PRS) is not easy to interpret or decode. When selecting valid instruments for MR, possibly due to certain study design biases, invalid SNPs may all be associated with the same unknown confounders, and this potential situation leads to a bias that is consistent across invalid SNPs. Unlike median-based or robust regression methods for MR, the resampling-based method used here identifies the invalid instruments directly so that they can easily be removed prior to score construction.Differences in coefficients in subgroups of a population–sex-specific effects for example–can certainly be expected to decrease the predictive performance of PRS if these differences are not captured. However, the benefits associated with obtaining less biased estimates of etes see . Perhapsp-value filters influences the joint fitting performance. That is, filtering can lead to winner’s curse bias even after penalized model fitting.We have discussed SNP selection strategies that consider the SNPs one at a time , or jointly . When analyzing the TMD data, we found that pre-selection of SNPs through It may be interesting to consider methods for constructing PRS that are designed to optimize for more than one criterion at the same time. That is, methods for pre-filtering or joint modelling could be combined with identification of valid, possibly causal variants . Combini


Glycine max (L.) Merr.] seed quality. However, research on soybean sugar composition is limited. To better understand the genetic architecture underlying the sugar composition in soybean seeds, we conducted a genome-wide association study (GWAS) using a population of 323 soybean germplasm accessions which were grown and evaluated under three different environments. A total of 31,245 single-nucleotide polymorphisms (SNPs) with minor allele frequencies (MAFs) ≥ 5% and missing data ≤ 10% were selected and used in the GWAS. The analysis identified 72 quantitative trait loci (QTLs) associated with individual sugars and 14 with total sugar. Ten candidate genes within the 100 Kb flanking regions of the lead SNPs across six chromosomes were significantly associated with sugar contents. According to GO and KEGG classification, eight genes were involved in the sugar metabolism in soybean and showed similar functions in Arabidopsis. The other two, located in known QTL regions associated with sugar composition, may play a role in sugar metabolism in soybean. This study advances our understanding of the genetic basis of soybean sugar composition and facilitates the identification of genes controlling this trait. The identified candidate genes will help improve seed sugar composition in soybean.Seed sugar composition, mainly including fructose, glucose, sucrose, raffinose, and stachyose, is an important indicator of soybean [ G. max (L) Merr.] is one of the major sources of human food and livestock feed in the world. Soybean seed consists of approximately 40% protein, 20% oil, and 33% carbohydrates of the dry seed weight [Soybean [d weight ,2,3,4. Sd weight . As the d weight . The prod weight ,8.2 individuals, Maughan et al. [10 lines and 117 F2:10 recombinant inbred lines (RILs) to conduct molecular mapping with simple sequence repeat (SSR) markers and detected six QTLs for oligosaccharides and sucrose on six chromosomes . A major QTL on chromosome 19 accounted for 21.4% phenotypic variation, two QTLs on chromosome 12 and 16 explained 10% phenotypic variation, and remaining QTLs exhibited minor effects [2 populations derived from the crosses of PI870139 × PI200508 and PI2435459 × PI200508, Skoneczka et al. [2:3 line derived from MFS-553 × PI243545, three sucrose QTLs located on chromosomes 5, 9, and 16 were mapped and explained 46, 10, and 8% of the phenotypic variation, respectively. Wang et al. [2:3 RILs derived from V97-3000 × V99-5089 as the mapping population and identified eleven QTLs for sugar compositions, including one for glucose, three for fructose and sucrose, and two for raffinose and stachyose. In a study with 92 F5:7 RILs derived from MD965722 × Spencer, Akond et al. [Major quality traits of soybean seed such as protein and oil contents have been extensively investigated. Research spans from quantitative genetics to molecular mapping and candidate gene identification, especially in plant breeding and genetics ,12,13,14n et al. ,16 used  effects ,16. In aa et al.  using 22GWAS is regarded as a valid alternative of linkage-association analysis for its high resolution and accuracy, and it has a wide use in analysis of complex traits in soybean . To dateTo obtain a more comprehensive understanding of the genetic architecture of sugar composition in soybean, we conducted a GWAS of fructose, glucose, sucrose, raffinose, stachyose, and total sugar contents in a population of 323 germplasm accessions, which was phenotyped under three environments and genotyped with the SoySNP50K BeadChips . The obj−1 for total sugar (70.0–140.3 mg g−1), 50.7 mg g−1 for sucrose (33.6–72.8 mg g−1), 40.7 mg g−1 for stachyose (14.7–58.3 mg g−1), 9.7 mg g−1 for raffinose (0.6–24.7 mg g−1), 1.7 mg g−1 for glucose (0.1–5.2 mg g−1), and 0.9 mg g−1 for fructose (0.0–2.9 mg g−1). Heritability estimates were greater than 94% for all traits, indicating that genetic effects predominantly influenced seed sugar. More detailed information of phenotypic and quantitative analysis refers to Jiang et al. [Analysis of variance showed highly significant differences among 323 germplasm accessions for all the traits investigated . The cong et al. . 2 dropped to half  ≥ 0.05 in the population and were included for a GWAS of fructose, glucose, sucrose, raffinose, stachyose, and total sugar. The mean linkage disequilibrium (LD) decay distance of the panel was ∼330 Kb when r to half a. All th to half b, and th to half c. General linear model (GLM) and mixed linear model (MLM) were employed to conduct association analysis. Principal component analysis was performed with the whole set of SNPs to capture the overall population stratification of the association panel. False discovery rate (FDR) and Type 1 error were calculated for both GLM and MLM, and the results are presented in 2 > 0.70 as described previously [In total, 255 SNPs were identified to be significantly associated with the contents of individual sugars and total sugar in soybean seed. Of them, 109 SNPs for fructose were on 13 chromosomes a, 18 SNPeviously , the intAmong all the identified SNPs, some SNPs were associated with more than one trait. For example, the SNPs Gm02_8369052 for total sugar and Gm02_8379231 for sucrose were in the same LD block. The SNP Gm06_14414191 was associated with sucrose, raffinose, and total sugar. Two SNPs Gm09_3173391 and Gm15_49568824 were associated with both sucrose and total sugar, and two SNPs Gm12_35531777 and Gm18_7423285 were associated with stachyose and total sugar. Arabidopsis thaliana) related to the trait under study or genes linked or closely associated with a lead SNP located on the known QTLs were selected. Ten candidate genes on six chromosomes were identified, including four for fructose, two for sucrose, two for raffinose, one for stachyose, and one gene for total sugar , and it belongs to the DNAJ heat shock family. The soybean lines carrying different alleles of this locus showed a significant difference as well , which is involved in the sucrose metabolic process. Four candidate genes for seed fructose were identified. The known QTL Seed oligosaccharide 1-1  was targis locus a. The kn as well b. Two caGlyma.02g218600 for this locus encodes a sucrose transporter and related protein, which shares 84.6% similarity with sucrose transporter 4 in Arabidopsis. The other putative gene for sucrose was Glyma.03g149300, which was led by the SNP Gm03_36427644. This gene shares 83.3% similarity with sugar transporter ERD6-like 7 in Arabidopsis and is involved in sugar–proton symport activity and carbohydrate transmembrane transporter activity pathways. Two candidate genes were identified for seed sucrose. The QTL Seed oligosaccharide 1-1  was led Glyma.05g003900 led by the SNP Gm05_317349 encodes raffinose synthase family protein, which shares 81.7% similarity with the ortholog in Arabidopsis. The other putative gene Glyma.15g142400 encodes glycosyl hydrolase superfamily protein sharing 78.5% similarity with the ortholog in Arabidopsis, which is involved in glucan endo-1,3-beta-D-glucosidase activity.Two candidate genes were identified for seed raffinose. The putative gene Glyma.03g002100 led by the SNP Gm03_212961 encodes glycosyl hydrolase superfamily protein and major facilitator superfamily protein, which shares 83.6% similarity with the ortholog in Arabidopsis and is involved in the carbohydrate metabolic process. One gene for stachyose was detected. The putative gene Glyma.05g168600 led by the SNP Gm05_35918853 was identified. This putative gene encodes protein, sharing 81% similarity with the ortholog UDP-glucose:glycoprotein glucosyltransferase (HUGT) in Arabidopsis.For total sugar, one gene Relative kinship and population structure are considered as two major confounding factors that may lead to spurious results in an association study . Comparehttp://www.soybase.org, accessed on 27 December 2022). In addition to linkage analysis, GWAS has been proven to be powerful in the identification of loci that associated with numerous traits in crop plants such as soybean [As reported, a total of thirty-seven QTLs for sucrose concentration and fifteen QTLs for oligosaccharide concentration have been identified so far . GWAS is the primary choice of methods that can be used for excavating and evaluating major genes due to its high resolution [Glyma.02g218600, Glyma.03g002100, Glyma.03g149300, Glyma.05g003900, Glyma.05g168600, Glyma.15g142400, Glyma.16g156800, and Glyma.16g156900. The putative genes Glyma.02g218600 and Glyma.03g149300 for sucrose encode sucrose transporter and related proteins and sugar transporter ERD6-like 7, respectively, which is directly involved in sucrose transport and sugar transport. The putative gene Glyma.05g003900 for raffinose encodes raffinose synthase family protein. The putative gene Glyma.05g168600 for total sugar encodes UDP-glucose:glycoprotein glucosyltransferase. The putative genes Glyma.03g002100 and Glyma.15g142400 for stachyose and raffinose encode glycosyl hydrolase superfamily protein, and this protein has been proven to be involved in the metabolism of cell wall polysaccharides in plants [Glyma.16g156800 and Glyma.16g156900 for fructose encode sucrose transport protein SUC1-related, which is directly involved in sucrose transport. For the present, hundreds of candidate genes have been known to be involved in the metabolism of sugars in soybean  and GO:0009744, which shows that this gene responds to sucrose stimulus in the biological process of Arabidopsis. Previous research also showed that sucrose seemed to differentially regulate D-type cyclin expression in Arabidopsis regarding both kinetics and the rate of induction [Glyma.06g228000 encodes DNAJ heat shock family protein, which has been proven to be involved in a variety of essential cellular processes, including de novo protein folding, translocation of polypeptides across cellular membranes, and degradation of misfolded proteins [Glyma.06g228000 plays a role in the sugar metabolism of soybean seeds. The identified candidate genes associated with sugar contents are bases for further gene function verification and the improvement of seed sugar composition in soybean. As well known, sugar metabolism is the most basic metabolism in all creatures, and the components or constituents of sugar are interchangeable. Fructose and glucose compose sucrose, while raffinose and stachyose are derived from sucrose, and so on. Therefore, it is assumable that the identified genes could be associated with different sugar compositions. Other two genes nduction . It suggproteins . TherefoA total of 323 plant introductions (PIs) of early maturity groups 0 and 00, obtained from the United States Department of Agriculture (USDA) Soybean Germplasm Collection, were used in this study. All the PIs were planted in single-row plots in a randomized complete block design with three replications at three environments: Aurora , Brookings , and Watertown  in South Dakota, USA. The detailed information of the 323 PIs, field experiments, and seed sampling were given in our previous publication .As described previously , sugar sThe Illumina Infinium SoySNP50K BeadChips were used to genotype the population as described by Song et al. ,47, and 2) of alleles using PopLDdecay [2 dropped to half of its maximum value [Pairwise LD between markers was calculated as the squared correlation coefficient  of genetic effect for each line were calculated using the R package lme4  in the shttp://www.soybase.org, accessed on 27 December 2022) and Arabidopsis genome assembly . Candidate genes were predicted with reference to the following preferences: (i) genes of known function in soybean related to the trait under study, (ii) genes with function-known orthologs in Arabidopsis related to the trait under study, and (iii) genes linked or closely associated with a lead SNP located within the known QTL regions.Genes within the 100 Kb flanking region of the lead SNPs were selected as the source of candidate genes, and the functions of these genes were annotated according to the soybean reference genome  ≥ 5% and missing data ≤ 10% and were finally used for the GWAS. In total, 86 QTLs were identified to be associated with individual sugars and total sugar. Within the 100 Kb flanking regions of the lead SNPs across six chromosomes, ten candidate genes were identified to be significantly associated with sugar composition. Eight of these genes were involved in sugar metabolism in soybean and showed similar functions in Arabidopsis, and the other two located in the known QTL regions may play a role in sugar metabolism in soybean. These identified candidate genes will lay a foundation for further gene function verification and the improvement of seed sugar composition in soybean. This study will not only advance our understanding of the genetic basis of soybean sugar composition but will also facilitate the identification of genes controlling this trait.


Lettuce is one of the economically important leaf vegetables and is cultivated mainly in temperate climate areas. Cultivar identification based on the distinctness, uniformity, and stability (DUS) test is a prerequisite for new cultivar registration. However, DUS testing based on morphological features is time-consuming, labor-intensive, and costly, and can also be influenced by environmental factors. Thus, molecular markers have also been used for the identification of genetic diversity as an effective, accurate, and stable method. Currently, genome-wide single nucleotide polymorphisms (SNPs) using next-generation sequencing technology are commonly applied in genetic research on diverse plant species. This study aimed to establish an effective and high-throughput cultivar identification system for lettuce using core sets of SNP markers developed by genotyping by sequencing (GBS). GBS identified 17 877 high-quality SNPs for 90 commercial lettuce cultivars. Genetic differentiation analyses based on the selected SNPs classified the lettuce cultivars into three main groups. Core sets of 192, 96, 48, and 24 markers were further selected and validated using the Fluidigm platform. Phylogenetic analyses based on all core sets of SNPs successfully discriminated individual cultivars that have been currently recognized. These core sets of SNP markers will support the construction of a DNA database of lettuce that can be useful for cultivar identification and purity testing, as well as DUS testing in the plant variety protection system. Additionally, this work will facilitate genetic research to improve breeding in lettuce. L. sativa L., 2n = 2x = 18) is one of the agriculturally important leaf vegetables belonging to the Asteraceae family and is cultivated mainly in temperate climate areas of the world. In Korea, the production of lettuce was estimated to be over 93 543 tons from 3773 ha in 2018 [Lettuce  testing in a UPOV system of plant variety protection (PVP) is required. However, DUS testing, which is based on morphological features, is costly, time-consuming, and labor-intensive. Furthermore, it can be influenced by various environmental conditions that can impose limitations on the identification of cultivars . TherefoBefore registration of a new cultivar, DUS tests are carried out to determine whether a new cultivar is distinct, uniform and stable. As a part of DUS testing, the distinctness of the new cultivar is examined by comparison with a similar existing cultivar, which is called “reference varietiy”. SNP markers have been routinely utilized as a tool for the management of reference varieties for DUS examinations. Among the grouped cultivars, the one with the highest genetic similarity to the unknown cultivar can be selected as a “similar variety” and used for the DUS test. In other words, more relevant reference varieties can be selected for DUS testing based on their DNA profiles, and the duration and cost of DUS testing can be reduced.Lactuca L. sativa capitate L.). SSR markers were also used to characterize the genetic diversity of the germplasm of chicory (Cichorium intybus), which also belongs to the same family, Asteraceae, as lettuce [SSR markers have been widely used for the assessment of phylogenetic relationships and DUS testing in commercial lettuce cultivars , 17–23 b lettuce . However lettuce .Compared to SSR markers, SNP markers are bi-allelic, making it simple to merge data between groups, and it is possible to generate large databases of marker information coupled with high-throughput genotyping. Currently, SNPs are the most preferred in cultivar identification and genomic studies due to their high abundance, stability, and efficiency , 30. ForRecent advancement of next-generation sequencing (NGS) technologies has enabled researchers to analyze and utilize genetic resources efficiently , 37,60.Currently, diverse automated platforms for high-throughput analysis have enabled the analysis of large amounts of data within a short period , 49. ForIn this study, we developed core sets of genome-wide SNP markers to identify cultivars of lettuce using the GBS and SNP-genotyping approach. We validated these core sets of SNPs to develop molecular markers for high-throughput analysis using the Fluidigm platform. We also evaluated the utility of core SNPs using genetic differentiation analysis. These developed SNP markers will be useful for database construction and will facilitate cultivar identification, purity testing, and breeding of lettuce.L. sativa cv. Salinas (v8) reference genome, with an average read depth of 19X [Using the GBS approach, a total of 549 123 132 reads were generated with an average of 6 034 320 reads per individual cultivar from the 90 Korean commercial lettuce cultivars analyzed. After barcode and adapter sequences were trimmed and low-quality reads were filtered, 443 005 356 clean reads were obtained . About 8h of 19X .A total of 276 462 SNPs were identified through genome-wide SNP identification. Among the SNPs identified from 90 cultivars used in this study, transition (A/G or C/T) and transversion  SNPs accounted for 62.8% and 37.2%, respectively, with a transitions-to-transversions ratio of 1.69. Both types of transition SNPs (C/T and A/G) were detected in similar numbers. Among transversion SNPs, the A/T type showed higher numbers than other types .Low-quality SNPs and with significant polymorphism among the 90 cultivars were filtered out. After filtering, 17 877 high-quality SNPs  > 0.05; missing data < 30%) were identified. The chromosomal distribution of these 17 877 SNP loci and genes in the lettuce genome is depicted in Out of the 17 877 SNPs identified, 12 959 SNPs (72.5%) were located in intergenic regions and 4928 (27.6%) in genic regions, of which 3502 (19.6%) were located in exons and 1416 (7.9%) were in introns . From thFurthermore, transition (A/G or C/T) and transversion  SNPs accounted for 68.5% and 31.5%, respectively, with a transitions-to-transversions ratio of 2.18 . Both tyThe level of genetic diversity, the polymorphic information content (PIC) values, MAF, and the heterozygosity of the 17 877 SNPs filtered from 90 lettuce cultivars were calculated . As a reA phylogenetic tree was constructed using the neighbor-joining method. The result showed that the 90 cultivars were classified into three divergent groups : 18 cultAfter conducting PCA using the 17 877 SNPs to investigate the genomic differences of lettuce cultivars, the 90 lettuce cultivars were classified into three groups . The topThe population structure analysis for the 90 lettuce cultivars using the 17 877 filtered SNPs determined the optimal number of populations (K = 4) corresponding to the highest peak in the Delta-K graph . The resThe second-highest peak in the Delta-K graph was found when K = 6, assuming six subgroups among the 90 cultivars . No dramGenerally, cultivars with identical morphological types were grouped in the same cluster and a substantially close association was observed between SNP genotypes and horticultural types. In addition, the clusters obtained from the phylogenetic tree using the NJ method were in an agreement with those from STRUCTURE and PCA.To develop SNP markers that are effective for cultivar identification, we selected 294 SNP markers with a PIC value higher than 0.1 and with significant polymorphism between the 90 lettuce cultivars. Of these selected SNPs, 226 SNPs were in genic regions of the lettuce genome. To validate the selected SNPs, the sequence differences between cultivars were confirmed by Sanger sequencing (Data not shown). Subsequently, primer sets for the Fluidigm assay were designed for each confirmed SNP. Fluidigm-based genotyping was conducted for the 90 lettuce cultivars used for GBS and the additional five commercial cultivars from the Netherlands. Genotype calls from the SNP assay for 95 lettuce cultivars are shown in the scatter plot . HomozygThe 294 filtered markers were effective in distinguishing 84 (88.4%) of the 95 lettuce cultivars. Besides, the samples which were not separated by the 294 markers could be used as a “reference variety” for each matching sample in DUS testing. To develop the core markers for cultivar identification via the Fluidigm system, sets of core markers were selected based on polymorphisms from the lettuce SNP data mined from GBS considering high polymorphism based on PIC value . The aveTo evaluate the performance for cultivar identification, a phylogenetic tree was constructed using the selected 192 SNP markers based on the genotyping results . The 192Among the 192 SNP markers, the 96 core SNPs that showed significant polymorphism among the 95 cultivars were selected for further lettuce cultivar identification . GeneticFrom the 96 core markers, 48 and 24 markers were selected and phylogenetic analysis was performed using these markers. The 48 and 24 markers also identified 84 (88.4%) of the 95 lettuce cultivars, like the 294 markers . The 95 Additionally, a smaller number of markers were selected and analyzed to calculate the minimal number of SNP markers that can distinguish all tested cultivars. Eighteen markers were able to differentiate 84 (88.4%) of the 95 lettuce cultivars, similar to the 294 markers, while 12 markers were able to identify 67 (70.5%) of the 95 cultivars . Nine anTo validate the developed 24 core markers, Fluidigm-based genotyping was performed for the sets of DNA mixtures and original samples and their genotyping results were compared. The main goal of this study was to develop a core set of SNP markers suitable for the Fluidigm assay to create a fast and high-throughput screening system for the identification of lettuce cultivars. In this study, the selected 17 877 SNPs using the GBS approach could successfully differentiate 90 commercial lettuce cultivars in Korea. A similar classification pattern was observed with phylogenetic analysis and PCA where both analyses classified the 90 lettuce cultivars into three distinct groups . In bothCore markers for Fluidigm SNP genotyping were selected based on the PIC values, which provide information on the extent of polymorphism revealed by the DNA marker and supports estimating relationships between cultivars , 65. In If a minimal amount of markers can be used for cultivar identification without decreasing resolution, the cost, time, and labor required for cultivar identification will be significantly decreased, thus increasing the overall efficiency of the method. The core sets of markers  selected from 294 filtered SNPs, and the subsets of core markers also successfully distinguished lettuce cultivars by identifying 84 (88.4%) of the 95 studied cultivars similar to the 294 markers . TherefoIn this study, the grouping results based on Fluidigm genotyping data showed a similar pattern to those obtained from 17 877 SNP markers from GBS data. The 24 core SNPs classified 95 lettuce cultivars into three distinct clusters and each cluster showed a tendency to be grouped with the same horticultural type . This reSince lettuce is a principally self-pollinated crop, homozygous genotypes are predicted to be predominant. Here, at first, the results obtained from automated calling data showed an unusually high portion of heterozygous SNPs. Therefore, all 192 SNPs were reanalyzed and the automatically-called heterozygous genotypes by the software were changed to homozygote genotypes. However, heterozygous genotypes at the polymorphic loci among 192 SNPs were still observed at a low level, accounting for 0.25% of the total genotyping results. The appearance of heterozygous genotypes in lettuce cultivars could have been caused by technical limitations, incomplete fixation of cultivars, seed purity problems. Therefore, the core 24 SNPs, which showed a clear separation between two homozygous genotypes were selected for lettuce cultivar identification , 4.1 cultivars were not available, we generated artificial heterozygous lines by mixing equal amounts of DNAs from two homozygous lines with different alleles. Of the genotyping results, 93.3% were heterozygous. This demonstrated that the developed markers could identify not only homozygous lines but also heterozygous lines.To validate that the 24 core markers accurately differentiated between homozygous and heterozygous genotypes, additional genotyping was performed . As F1 cDUS testing is required for plant cultivar registration and protection, and SNP markers have successfully supported the management of reference collections, including the selection of a “similar variety” as the test cultivar with the highest genetic similarity . As mentL. sativa) and validate the selected core sets of markers for cultivar identification as a part of the DUS test of lettuce. Based on these results, we constructed SNP database for lettuce cultivar identification using the genotyping results of Korean commercial lettuce cultivars. The constructed SNP database will support cultivar identification, population structure analysis, lettuce breeding, and DUS test for plant cultivar protection and enforcement of the right of breeders. Further research of SNP genotyping using the core marker sets developed in this study for additional lettuce cultivars will facilitate the utility of the markers to identify diverse cultivars worldwide. Additionally, a genome-wide association study and quantitative trait locus mapping are needed to understand the association between marker and trait.In conclusion, genome-wide SNP marker discovery via GBS and SNP genotyping using the Fluidigm system was successfully applied to assess the genetic diversity of lettuce  and ligated to a specific barcode adapter. Digested DNAs were pooled and purified with the QIAquick PCR Purification Kit .GBS libraries were constructed for the 90 lettuce cultivars following the protocols described by Elshire et al. . In brieGBS libraries were sequenced using the HiSeq2000  by SEEDERS Inc.  and demultiplexing was conducted based on barcode sequence information. Adapter trimming was performed using the Cutadapt (v.1.8.3) program  and sequSNP calling was conducted by SEEDERS Inc.  with an in-house script  and SAMtK) was set from 1 to 10. The optimal K value was calculated using the Delta-K method (ΔK) described by Evanno et al. [To estimate genetic differentiation and the number of subpopulations among lettuce cultivars using the filtered SNPs, the 95 lettuce accessions were analyzed and clustered via hierarchical clustering, PCA, and population structure analysis. Phylogenetic analysis was conducted using the NJ method implemented in Molecular Evolutionary Genetics Analysis 6 (MEGA 6) , and a po et al. .n is the number of alleles and ip and jp are the frequency of the i  th and j  th allele, respectively.To develop markers suitable for lettuce cultivar identification, we selected SNPs based on the PIC value and chromosomal position. The PIC value for SNP markers was calculated according to the following formula:https://d3.fluidigm.com/; Fluidigm, South San Francisco, CA, USA) [To validate SNPs discovered from GBS data, Sanger sequencing was conducted using the flanking sequences of the selected SNPs. For high-throughput analysis using the Fluidigm system, we converted SNPs from GBS into SNP type assays to be used in Fluidigm 192.24, 96.96, 48.48, or 24.192 dynamic arrays, which yielded data points with 192, 96, 48, or 24 markers, respectively. The primer sequences of the selected SNP markers are listed in CA, USA) . TemplatCA, USA) . The preFurther, fluorescence intensity was quantified using the Fluidigm EP1 , and the SNPs were called using Fluidigm SNP Genotyping Analysis software v4.5.1 according to the manufacturer’s protocol. The allelic data matrix of “1” or “0” was used for the population genetic analysis for the genotyping results. A phylogenetic tree was generated based on SNP markers via NTSYS-pc 2.2 program   using seTo demonstrate if the developed markers worked accurately, we conducted SNP genotyping with 24 core markers, as well as 20 sample sets with two cultivars and their DNA mixture, using the Fluidigm system. The genotype results of the DNA mixtures were compared with those of the original cultivars.Web_Material_uhac119Click here for additional data file.


Genomic selection (GS) is increasingly widely used in animal breeding, owing to its high efficiency in the genetic improvement of economic traits. In China, GS has been implemented for genetic evaluation of young bulls in dairy cattle breeding programs since 2012. GS is commonly based on single nucleotide polymorphism (SNP) chips. The cost of whole genome sequencing (WGS) has decreased tremendously in recent years, allowing increased studies of WGS-based GS. In this study, based on the imputed WGS data of approximately 8000 Chinese Holsteins, we investigated the performance of GS of milk production traits using the feature selection method of regularized regression. The results showed that WGS-based GS using regularized regression models and the commonly used linear mixed models achieved comparable prediction accuracies. For milk and protein yields, GS using a combination of SNPs selected with a regularized regression model and 50K SNP chip data achieved the best prediction performance, and GS using SNPs selected with a linear mixed model combined with 50K SNP chip data performed best for fat yield. The proposed method of GS based on WGS data, i.e., feature selection using regularization regression models, provides a valuable novel strategy for genomic selection.N single nucleotide polymorphisms (SNPs) with the lowest p-values were selected (LMMLASSO and LMMEN), where N was the number of non-zero effect SNPs selected by LASSO or EN. GS was conducted using a genomic best linear unbiased prediction (GBLUP) model and several sets of SNPs: (1) selected WGS SNPs; (2) 50K SNP chip data; (3) WGS data; and (4) a combined set of selected WGS SNPs and 50K SNP chip data. The results showed that the prediction accuracies of GS with features selected using LASSO or EN were comparable to those using features selected with LMMLASSO or LMMEN. For milk and protein yields, GS using a combination of SNPs selected with LASSO and 50K SNP chip data achieved the best prediction performance, and GS using SNPs selected with LMMLASSO combined with 50K SNP chip data performed best for fat yield. The proposed method, feature selection using regularization regression models, provides a valuable novel strategy for WGS-based GS.Genomic selection (GS) is an efficient method to improve genetically economic traits. Feature selection is an important method for GS based on whole-genome sequencing (WGS) data. We investigated the prediction performance of GS of milk production traits using imputed WGS data on 7957 Chinese Holsteins. We used two regularized regression models, least absolute shrinkage and selection operator (LASSO) and elastic net (EN) for feature selection. For comparison, we performed genome-wide association studies based on a linear mixed model (LMM), and the  Genomic selection (GS), an upgraded form of marker-assisted selection (MAS), aims to use the genetic effects of genome-wide molecular markers to determine the genomic estimated breeding value (GEBV) of individuals based on optimized statistical models . In GS, Decreased costs for whole-genome sequencing (WGS) have provided an exceptional opportunity to systematically detect genetic variants throughout the entire genomes of numerous individuals . In theohttp://1000bullgenomes.com/, accessed on 10 August 2022), most relevant research has been undertaken with dairy cattle [Meuwissen and Goddard  publishey cattle ,14,15. SNP chips can typically detect tens or hundreds of thousands of SNPs, whereas WGS data can be used to identify millions of SNPs. The use of such ultra-high-dimensional data causes two key challenges in GS. First, irrelevant and redundant information exists in predictive variables , impacting the prediction performance ,17,18; sp-value), and selection based on the estimated effect or effect variance of a SNP. Brondum, et al. [p-values to select SNPs from WGS data, and found that, compared with SNP chips, using only the selected SNPs could significantly improve the prediction accuracy, whereas the increase in accuracy was small when both the selected SNPs and SNP chips were used. Ye, et al. [p-values were further used to select loci for prediction, and the accuracy was unchanged compared to the model using all SNPs. Starting with a set of SNP chip data, Warburton, et al. [p-values; they found that for the 50K and 800K SNP chips, adding the selected loci resulted in little improvement in accuracy. Among feature selection strategies , VanRaden, et al. [Feature selection has become an important strategy for GS using WGS data. Feature selection of predictive variables is often carried out based on genome annotation information or genome-wide association study (GWAS) results. Studies have shown that feature selection using genomic annotation information (such as gene location) can improve the prediction performance of GS to some extent . There a, et al.  built a , et al.  used dif, et al.  found th, et al.  added si, et al.  found thAlthough the aforementioned feature selection methods have been used to improve the prediction performance of GS to varying degrees, they have several shortcomings. First, the current annotations of livestock and poultry genomes are not sufficiently accurate, so feature selection based on genome annotation information cannot be used to generate models with optimal performance . Second,Regularized regressions are a group of classical methods for variable selection. They identify outcome-associated features, estimate non-zero parameters simultaneously, and are particularly useful for high-dimensional datasets with small sample sizes. Commonly used regularized regression models include least absolute shrinkage and selection operator (LASSO) , ridge rThe cattle used in this experiment came from various farms all over the country. All experimental procedures were approved by the Institutional Animal Care and Use Committee (IACUC)  at the China Agricultural University (permit number DK996). Estimated breeding values (EBV) of milk yield (MY), fat yield (FY), and protein yield (PY) for 7957 Chinese Holstein cattle were provided by the Dairy Data Center of China. They were estimated based on a random regression model using pedigree and milk production records. De-regressed proofs (DRP) and reliability were calculated using pedigrees and EBV; this was conducted using the method and calculation program proposed by Garrick, et al. .https://broadinstitute.github.io/picard, accessed on 15 May 2020) was used to remove PCR duplicates. Base-quality score recalibration (BQSR) was performed in GATK 3.8 [According to the data requirements of 1000 Bull Genomes Project (Run 8), a total of 86 individuals (76 bulls and 10 cows) with EBV reliability values > 0.8 were selected for re-sequencing. Moreover, this ensured that the sequences of that Chinese Holsteins were appropriately represented in the reference population for imputation. DNA was extracted from semen samples from the bulls and blood samples from cows using the routine procedures. Whole genome sequencing was conducted for each individual. Libraries were prepared using a Twist Library Preparation Enzymatic Fragmentation Kit and sequenced on an Illumina HiSeq X Ten system. The average sequencing depth was 9.84×. First, Trimmomatic v0.38  was usedGATK 3.8 . Finallyp-value > 1.0 × 10−6. SNPs on sex chromosomes were excluded from the analysis. After this filtering step, 7957 individuals and 34,384 SNPs remained. Genotyping was performed for 7957 individuals using the Illumina BovineSNP50 v1 or v2 Beadchip. For the 50K SNP chips, SNP positions were converted to ARS-UCD1.2 genome positions using the UCSC liftover command. Quality control was carried out using PLINK . Individp-value > 1.0 × 10−6. After filtering, 7957 individuals and 5,912,383 SNPs remained. SNPs were coded based on the number of copies of the minor allele: 0, 1, and 2 for the minor allele homozygote, the heterozygote, and the major allele homozygote, respectively. Genotypes were imputed using Beagle 4.1 ; 1059 HoTo improve the performance of GS for MY, FY, and PY, SNPs were selected from the WGS data as genomic feature markers. More specifically, they were selected based on GWAS using single-marker mixed linear model and regularized regression models. GBLUP was then used for GS.p-values. Univariate association tests were performed using a linear mixed model (LMM) implemented in GEMMA v0.98.1 [y is a vector of DRPs for milk production traits  of individuals in the reference population; μ is the overall mean; 1 is a vector of ones; b is a fixed effect of the tested SNP; X is a vector of genotypes ; g is a vector of random polygenic effects with the normal distribution G is the genomic relationship matrix and Z is the corresponding design matrix; and e is a vector of residuals, which is assumed to follow a normal distribution of I is the identity matrix and To link SNPs with complex traits, GWAS was performed separately for MY, FY, and PY using the reference population. The imputed SNPs were pre-selected based on  v0.98.1 , as follG matrices were constructed based on all SNPs except those on the same chromosome as the tested SNP [G matrices were calculated as follows [ijx(ikx) is the genotype  for the ith SNP of the j(k)th individual; N is the total number of markers; and ip is the MAF of the ith SNP.To prevent possible bias from fitting the same SNP twice, sted SNP . G matri follows :(2)Gik=1p < 0.05 for each trait were retained for subsequent analyses.The Wald test was used to test the hypothesis for the substitution effect of each SNP. SNPs with For the pre-selected SNPs, feature selection was performed with the regularized regression models LASSO and EN. Both are multi-locus linear regression models, for which the basic linear regression model was:L1  for subsequent analyses, where 1n and 2n were the numbers of SNPs selected by LASSO and EN, respectively.For each trait, the value of the tuning parameters in LASSO and EN were determined based on the one standard error (1SE) rule  via ten-LASSO, or LMMEN; (ii) only 50K SNP chip data; (iii) both selected SNPs and 50K SNP chip data; and (iv) all imputed SNPs. The GBLUP model was as follows:y is a vector of DRPs; 1 is a vector of ones; μ is the overall mean; a is a vector of breeding values; Z is the corresponding incidence matrix; and e is a vector of the residuals. It is assumed that G is an additive genomic relationship matrix constructed using the first of the methods described by VanRaden [D is a diagonal matrix, We compared the performance of GBLUP using four classes of information: (i) only WGS SNPs selected with LASSO, EN, LMMVanRaden ; σa2 is FSa and 50Ka are vectors of additive genetic effects accounted for by the selected SNPs and 50K chip SNPs (with overlapping SNPs removed from the 50K dataset), respectively; and FSZ and 50KZ are the corresponding incidence matrices. It is assumed that FSG and 50KG are G matrices constructed from selected SNPs and 50K SNP chip data (with overlapping SNPs removed from the 50K dataset); FSG and 50KG, respectively. The other terms are defined as detailed above.As stated above, method iii) involved the use of 50K SNP chip data and selected SNPs from WGS data. To take into account the heterogeneity of these two sets of SNPs, we also employed the following GBLUP model for comparison:The G matrix in the GBLUP model was constructed using gmatrix for Methods i-iii and with the gmatrix module in GMAT v1.0.1  for Method iv. The DMUAI module of DMU v6 release5.2  was usedTo investigate the prediction performances of different strategies, the 7957 Chinese Holstein cattle were divided into reference and validation populations based on the year of birth. The 6875 individuals born before 2013 were assigned to the reference set, and the 1082 born between 2013 and 2015 were assigned to the validation set. Similarly to our previous study , individp < 0.05  achieved the highest accuracy for all three milk production traits. Accuracies for models utilizing SNPs selected by EN were 1.65%, 4.21%, and 15.65% higher  compared to the accuracies of the corresponding models utilizing SNPs selected by LASSO. The prediction accuracies of GBLUP using SNPs obtained with different feature selection strategies are presented in EN or with LASSO compared to LMMLASSO. Specifically, GS using LMMEN for feature selection was 1.33% more accurate than when EN was used, whereas GS using LMMLASSO was 1.50% less accurate than when LASSO was used. For FY, prediction accuracy was 3.38% higher for GS using EN compared to LMMEN. However, the prediction accuracy was nearly 20% less for GS when LASSO was used compared to LMMLASSO. For PY, the accuracy of GS using LMMLASSO was 18.95% higher than with LASSO, and the accuracy of GS using LMMEN was 8.37% higher than with EN.For MY, there were very small differences in the accuracy of GS when feature selection was conducted with EN compared to LMMEN and LMMLASSO) showed less bias than direct feature selection with EN or LASSO. For all traits, GS using SNPs selected with LMMLASSO had the least bias (range = 0.566–0.685). Consistent with the higher accuracy, GS using EN feature selection resulted in less biased predictions (0.200–0.267) than when LASSO was used (0.183–0.248). The regression coefficients of DRP on GEBV differed between feature selection methods, reflecting prediction biases . An optiGS was performed using a combination of 50K SNP chip data and selected SNPs from WGS; furthermore, two GBLUP models were used, as shown in equations ,7. GS waLASSO) to 114 (50K + EN) for the three traits. The numbers of SNPs used in different scenarios are shown in EN, about 46,000 SNPs were used in GS of MY and FY; and about 44,800 SNPs in GS of PY. With the datasets of 50K + LASSO and 50K + LMMLASSO, about 37,900 SNPs were used in GS of MY and PY, and about 38,600 SNPs in GS of FY. For the combined datasets of 50K SNP chip data and selected SNPs, the numbers of overlapped SNPs ranged from 22 (50K + LMMEN and 50K+LASSO when model (6) was used compared to model (7). For all three traits, GS using model (6) had higher accuracy with 50K+LASSO or 50K+LMMLASSO compared to 50K+EN or 50K+LMMEN.The prediction accuracy and bias (regression coefficients of DRP on GEBV) of these models are shown in EN or 50K+LMMLASSO had higher accuracy than all other models; GS using 50K+LMMLASSO had the highest accuracy (0.2905).For MY, GS using 50K+LASSO had the highest accuracy, which was close to that only using 50K SNP chip or WGS data. Similarly, accuracy was highest for PY with 50K+LASSO (0.2903), although models utilizing 50K SNP chip or WGS data had <3% higher accuracy. For FY, GS using 50K+LMMEN was the least biased, followed by 50K+LASSO. Similarly, GS using 50K+LMMEN was the least biased for PY, followed by 50K+LASSO. For FY, GS using 50K+LMMLASSO and model (6) achieved not only the highest accuracy but also the lowest bias.Regression coefficients of DRP on GEBV for GS using 50K SNP chip data combined with selected SNPs differed between feature selection methods, showing a range of prediction biases . For allThe objective of this study was to explore the application of regularized regression feature selection strategies to WGS-based GS for milk production traits in Chinese Holstein cattle. It was previously shown that it may be necessary to improve the prediction accuracy of GS based on WGS data by increasing the number of individuals in the reference population  or by seLASSO method performed best for GS of FY. GS using SNPs selected by EN and LASSO did not lead to higher accuracy than GS using SNPs selected based on p-values from single-marker linear mixed model GWAS analysis (LMMLASSO and LMMEN), and the bias was much higher in the former. There are four possible reasons for this result; these are enumerated below.In the present study, GS using 50K SNP chip data and WGS SNPs selected with LASSO (50K+LASSO) had the best prediction performance for MY and PY; the 50K+LMMp) was much larger than the number of observations (n). Theoretically, LASSO and EN analyses are suitable for such instances where p >> n. However, the ‘GLMNET’ package in R could not handle such a large volume of data, preventing optimal feature selection. To solve this problem, single-marker GWAS analysis was used for SNP pre-selection prior to the use of LASSO or EN for feature selection. With the single marker model, adjacent SNPs may be selected for subsequent analyses, although they may have low p-values due to being in LD with the same causal mutation. This would prevent EN and LASSO from fully exploiting their advantages.First, there were a total of 5,912,383 SNPs in the WGS data, meaning it was an ultra-high-dimensional dataset. Therefore, the number of SNPs ) achieved higher accuracy than the model that considered two G matrices (model (7)). Brondum, et al.  reportedLASSO or LMMEN. In addition to 50K SNP chip data, selected WGS SNPs were integrated to construct one or two G matrices in a GBLUP model. The model incorporating one G matrix achieved better prediction performance. For milk yield and protein yield, GS using the combination of SNPs from the 50K SNP chip and WGS SNPs selected with LASSO achieved the best prediction performance; for fat yield, GS using SNPs from the 50K SNP chip and those selected with LMMLASSO had the best performance.In this study, we compared the performance of different feature selection strategies in GS based on WGS data. The results showed that GS accuracy was slightly lower when SNPs selected with LASSO or EN were used compared to SNPs selected with LMM


Macrotyloma uniflorum (Lam.) Verdc.) is an under-utilized legume grown in India. It is a good source of protein, carbohydrates, dietary fiber, minerals, and vitamins. We screened 252 horse gram germplasm accessions for horse gram yellow mosaic virus resistance using the percent disease index and scaling techniques. The percentage values of highly resistant, moderately resistant, moderately susceptible, susceptible, and highly susceptible were 0.34, 13.89, 38.89, 46.43, and 0.34, respectively. Repetitive trials confirmed the host-plant resistance levels, and yield loss was assessed. The present disease index ranged from 1.2 to 72.0 and 1.2 to 73.0 during the kharif and rabi seasons of 2018, respectively. The maximum percent yield loss was noticed in the HS (75.0 –89.4), while HR possessed the minimum (1.2–2.0). The methanolic leaf extracts of highly resistant and highly susceptible genotypes with essential controls were subjected to gas chromatography–mass spectrometry analysis. Differential accumulation of metabolites was noticed, and a total of 81 metabolites representing 26 functional groups were identified. Both highly resistant and susceptible genotypes harbored eight unique classes, while ten biomolecules were common. The hierarchical cluster analysis indicated a distinct metabolite profile. Fold change in the common metabolites revealed an enhanced accumulation of sugars, alkanes, and carboxylic acids in the highly resistant genotype. The principal component analysis plots explained 93.7% of the variation. The metabolite profile showed a significant accumulation of three anti-viral , one insecticidal -triene-3,24,25-triol), one antifeedant (cucurbitacin B), and six metabolites with unknown biological function in the highly resistant genotype.Horse gram ( Macrotyloma uniflorum (Lam.) Verdc.) is a multipurpose diploid legume  grown for seed and fodder purposes in the arid regions of Asia, Africa, and Australia ..27].The observation on disease rating was used to calculate the Percent disease index, i.e., PDI = [sum of all the numerical ratings/(number of observations × maximum disease rating)] × 100 . SubsequThe disease reactions of the genotypes of the extreme classes (highly resistant and highly susceptible) and the top three genotypes from moderately resistant, moderately susceptible, and susceptible classes were reconfirmed during the kharif (June–July) and rabi seasons of 2018. The number of genotypes was restricted in the confirmative studies to select highly promising genotypes for developing the mapping population for further exploitation. The kharif 2018 trial was laid out only in the field conditions (because the kharif season crop does not set pods), while in the rabi season 2018 experiment, the selected genotypes in each category, along with checks, were grown in field and pots in four replications to estimate the percent yield loss (PYL). The procedures of HgYMV scoring and whitefly count were adopted as done in the preliminary screening. The potted genotypes maintained in the net house served as control and were used for calculating yield loss due to HgYMV infection. The present disease index ranged from 1.2 to 72.0 and 1.2 to 73.0 during the kharif and rabi seasons of 2018, respectively. The maximum percent yield loss was noticed in the HS (75.0–89.4), while HR possessed the minimum (1.2–2.0). These results helped in tagging genotypes with extreme levels of resistance and susceptibility.www.ncbi.nlm.nih (accessed on 17 December 2017)) database, which matched with the HgYMV DNA A component. A specific primer for diagnosing HgYMV was synthesized and utilized for confirmation in different experiments. The specific primer sequence (5′–3′) was ATCATACTGAGAACGCTTTG (forward) and TGTCATACTTCGCAGCTTC (reverse). The target region was the complete genome of HgYMV DNA A with an amplicon size of 2.7 kb. The polymerase chain reactions were set for 35 cycles with the temperature profiles as initial denaturation: 94 °C; denaturation: 94 °C; annealing: 55 °C; elongation: 72 °C and final elongation: 72 °C  heptane1-carboxylic acid, pentadecanoic acid, tetradecanoic acid, n-hexadecanoic acid, octadecanoic acid, hexanoic acid (Hx), and cis-11-Eicosenoic acid. The defensive roles of 10, 12-tricosadiynoic acid and propanoic acid were reported  tridecane-3,11-dione, 1-(Methoxymethoxy)-3-methyl-3-hydroxybutane, 3-Methoxyhex-1-ene, Pyridine-4-carbohydrazide, Androstan-3-one, and 17α,21β-28,30-Bisnorhopane were observed in the HR, whose biological activities are less understood and need further studies.The metabolites ketones, glycosides, and alcohols were upregulated in HS b.A sufficient quantity of ketones is required to maintain the signal transduction and functionality of mitochondria and protect tissues from free radical damage. Ketones exhibit antimicrobial activity by interfering with pathogens by inhibiting cell wall synthesis, nucleic acid, and protein synthesis, and metabolic pathways . The antPathogen invasion activates glycosides through hydrolysis and releases sugar as a by-product. Broken sugar plays an essential role in signal transduction  and immuThe plant cell wall is assumed to be essential for developmental processes and acts as a physical barrier against pathogen invasion. During infections, pathogens produce several hydrolytic enzymes to collapse the cell wall integrity , activatAlthough the stress-responsive metabolites, such as ketones, alcohols, and glycosides, were upregulated in HS, they failed to resist the pathogen invasion and developed characteristic HgYMV symptoms. The stress-responsive function in HS may not be functional, and/or some of the metabolites might be diverted for the plant developmental process to enable it to survive in the stressed situation, evidenced by a few poorly filled pods and new flowers occurring up until harvest.The in vivo HgYMV screening yielded different host-plant resistance classes of horse gram genotypes, and these classes maintained the PDI across seasons. A relationship between genotype and HgYMV severity was established. A spectrum of biomolecules responsible for HgYMV resistance was identified through GC/MS. The HCA analysis revealed the accumulation of a distinct metabolite profile in HR and HS genotypes. Significant upregulated synthesis of biomolecules of varied pathways, such as secondary metabolite, signal transduction, glucosinolate, heterocycle biosynthesis, oxazole, and fatty acid, enabled HR to resist HgYMV infection with the least PYL. A total of six newer metabolites, 12-Oxatricyclo [4.4.3.0] tridecane-3,11-dione, 1-(Methoxymethoxy)-3-methyl-3-hydroxybutane, 3-Methoxyhex-1-ene, Pyridine-4-carbohydrazide, Androstan-3-one, and 17α,21β-28,30-Bisnorhopane were identified only in HR, which needs to be explored. Although ketones, alcohols, and glycosides were upregulated in the HS, it failed to resist the pathogenicity due to its photosynthate diversification for developmental processes instead of defensive roles.


Salmo trutta), like many other freshwater species, is threated by the release in its natural environment of alien species and the restocking with allochthonous conspecific stocks. Many conservation projects are ongoing and several morphological and genetic tools have been proposed to support activities aimed to restore genetic integrity status of native populations. Nevertheless, due to the complexity of degree of introgression reached up after many generations of crossing, the use of dichotomous key and molecular markers, such as mtDNA, LDH-C1* and microsatellites, are often not sufficient to discriminate native and admixed specimens at individual level. Here we propose a reduced panel of ancestry-informative SNP markers (AIMs) to support on field activities for Mediterranean trout management and conservation purpose. Starting from the genotypes data obtained on specimens sampled in the main two Molise’s rivers , a 47 AIMs panel was identified and validated on simulated and real hybrid population datasets, mainly through a Machine Learning approach based on Random Forest classifier. The AIMs panel proposed may represent an interesting and cost-effective tool for monitoring the level of introgression between native and allochthonous trout population for conservation purpose and this methodology could be also applied in other species.Brown trout ( S. trutta) has traditionally attracted the attention of conservation biologists and public institutions due to its iconic significance for fishery management and aquaculture. Brown trout is considered a complex of incipient species [The introduction of alien species is causing dramatic changes in many fresh water ecological systems worldwide, determining the erosion of local species integrity ,2. Among species ,4, which species . Recentl species ,7,8,9,10 species ) have be species  but the  species .LDH-C1* locus. Such loci have been extensively used in order to provide a rapid genetic characterization in many studies [S. trutta complex is one of the most genetically diverse vertebrate groups, consisting of more than 60 species, including several ecotypes and evolutionary lineages [At a genetic level, the Mediterranean trout population can be easily discriminated from its Atlantic lineage through PCR-RFLP analysis of mtDNA segments in combination with nuclear  studies ,9,13, so studies . Neverth studies  which is studies ,17,18. I studies , and a r studies . Neverthlineages ,21. In tlineages . In a relineages  for the In order to provide a most affordable and cost-effective solution to further support native trout conservation and management activities we decided to exploit the genetic information obtained in such a previous study  to creat16S rRNA and LDH-C1* loci, according to McMeel et al. [16S rRNA and LDH-C1* loci has been used as criterion for the identification of six different classes of introgression , according to Pensierini et al. [In total 288 specimens from Biferno and Volturno rivers, the main two basins of Molise, were enrolled in this study. As previously described , those sl et al.  and Chiel et al. . Within i et al. . In parti et al. ,34,35,36p-value ≤ 10−3). SNPs that met the QC criteria were therefore used to perform a preliminary assessment of the entire dataset in Admixture software (v.1.3.0) [i) ≥ 0.99 to their respective population clusters , were retained to construct a reference dataset for AIMs identification.An initial quality control (QC) of the dataset was performed by first applying a filter based on individual genotyping success and retaining profiles with ≥80% success rate. Next, data was screened with a per-SNP genotyping threshold of ≥95% and pruned for loci deviating Hardy–Weinberg equilibrium   with K =v.1.3.0) . AdmixtuST estimation of Weir and Cockerham [i − pAj|, where pAi and pAj are the frequencies of allele A in the ith and jth subpopulation. The Delta value for each SNP locus was estimated as the mean across all pair-wise comparisons. The third selection method was principal component analysis (PCA). Informative markers were selected, according to Paschou et al. [Four different statistical methods were employed on the dataset for marker information content estimation. Based on obtained results and considering that most common microplates have a 96-well plate format, the top 96 ranked SNPs were used to declare AIMs for each approach. Specifically, the first method, which has been one of the most popular for selecting informative loci, was the pairwise Fockerham  as calcuockerham . The secockerham , which iu et al. , consideu et al. , and theu et al. . RF algou et al. : (1) SNPIn order to test and validate the AIM panels identified through marker selection analyses, a RF classifier was performed using genotype data from simulated hybrid populations. Simulated hybrids were artificially constructed using Hybridiser v0.1 R script developed by Somenzi et al. . More ini admixture score <0.99. Finally, to measure how well the candidate AIMs estimated the admixture level compared to that determined by the full set of markers, we compared the admixture results using the coefficient of determination (r2). To test if the AIM panels performed better than an equally sized set of SNPs chosen at random, 1000 random AIM sets were generated, and for each random set supervised admixture analysis was performed. Finally, coefficients of determination values between the ancestry assignment of the full set and the reduced random panel were computed. The coefficient of determination values obtained using the 1000 random SNP sets were standardized by z-scores.Furthermore, to visually compare the performance of the full set of SNPs and the candidate AIMs shared among the seven panels, PCA was performed considering both simulated and real hybrid populations. Real hybrids were extracted from initial dataset considering a qS. trutta genome assembly  by BLASTN software, considering an e-value cut-off of 1 × 10−6 and a percent identity threshold of the matching sequence ≥85%. Hits were used to infer position on the reference genome and annotate genes spanning a region of ±50 Kbp around each SNP using Ensembl Variant Effect Predictor (VEP) tool (release 107) [To further disentangle the information carried out by common AIMs identified across the seven reduced panels, 35 bp flanking sequence from each side of the SNP, provided by the array manufacturer, was aligned to ase 107) . In ordease 107)  and by gase 107) , considei ≥ 0.99), 49 and 19 samples were classified as non-admixed native (NAT) or alien (ALI), respectively . In total, seven different reduced panels were obtained, considering top 96 ranked SNPs selected by four different approaches applied on reference population. One panel was obtained by FIn order to assess the reliability of the identified panels, a validation step was performed applying a RF classifier. OOB scores and correct prediction proportions are reported in 2 values were high overall across all panels encompassing 96 SNPs  were successfully mapped on 9 and 26 . FocusinAn increasing popularity of SNP analysis tool is widely recognizable in wild conservation projects to discriminate between pure and alien/hybrid specimens. However, the large-scale use of SNP arrays can be challenging for the average financial availability of conservation projects; thus, the development of a small panel of AIMs can be considered an effective alternative ,46,47,48LDH-C1* loci genotypes can be a useful approach to suggest the introgression degree at the population level, its consistency at individual level is far from being accurate, especially after several generations. Our results would corroborate this general consideration, indeed classes of introgression estimated by combination of mtDNA and LDH-C1* loci genotypes [In this work, we evaluated marker selection methods and determine a small number of highly discriminant SNPs from the rainbow trout Axiom array, required to effectively and confidently assign individual genotypes to native and alien populations notably within LIFE17 NAT/IT/000547 Project’s activities. More in detail, the Project had the main goal to restore genetic integrity of native Mediterranean trout populations inhabiting Molise’s rivers. Our final aim is to support the monitoring and conservation activities proposed by the Project, through the development of a reduced SNP panel, which guaranties a rapid and low-cost genotyping analysis without significantly compromising its informativeness. Indeed, although the combination of mtDNA and enotypes  were notIt is well-known that the accuracy of AIM panels depends on the quality and sample size of the reference populations. Clearly, a high number of genotyped samples helps to take into account the whole within population variability and, in turn, it reduces the possibility that few individuals might be not assigned correctly due to their atypical genotypes. Nevertheless, for many practical reasons it is not always possible to use large reference datasets. In our study, the number of specimens considered in the reference datasets was conditioned by Project objectives, which was focused on native trout conservation in Molise’s rivers. Furthermore, to the best of our knowledge, except for Palombo et al. study  there arST, PCA and RF statistics), according to Schiavo et al. [ST and RF GI 2 methods . Performance outcomes appeared in line with the fact that there was a general high SNPs overlapping between all tested approaches, excluding the Delta and FST panels, and this supported the idea that most informative markers were effectively selected in our study. This consideration has been also supported by the fact that significant low r2 values were estimated between ancestry proportions obtained using the full set of SNP and 1000 random reduced panels (2 across all panels was detected for both rivers when candidate SNP panels were tested (r2 ≥ 0.973; A total of 633 SNPs was retained after filtering steps. PCA plots obtained using quality filtered SNP datasets showed a clear separation of native and alien trout populations in both rivers . Four sto et al. . Severalo et al.  and it io et al. . As explo et al. , the maio et al. . Furthero et al. . This le87 SNPs, . Convers87 SNPs, . More in87 SNPs,  suggeste87 SNPs, . This is87 SNPs, . In the d panels ; whereas2 was quite high (r2 was 0.955 and 0.979 for Biferno and Volturno rivers, respectively; 2 detected in Biferno is consistent with what reported by Palombo et al. [In total, 47 SNPs resulted in common among all seven identified panels and therefore declared as main candidate AIMs. Our results showed that such AIMs can accurately discriminate Mediterranean native trout ancestry from alien as well. In particular, we assessed the performance of such SNPs panel in identifying crosses between native and alien trout using both simulated and real data . As expeo et al. , which do et al.  (Figure Noticeably, the distribution of 47 common AIMs along the genome appeared to be heterogeneous, the higher number of identified AIMs were harbored on chromosome 12, 19, and 26 . In thisThe use of molecular tool to support Brown trout conservation programs and management is of paramount importance; however, conventional molecular markers are often insufficient to classify the specimens at individual level and/or are expensive and time consuming.In this work, a SNP-array technology and ML approach were combined for the first time to select most informative markers for Atlantic and Mediterranean trout identification. A reduced panel of 47 AIMs was identified. The high correlations between ancestry coefficients calculated using the full set of SNPs and the reduced panel, supported the idea that such panel encompassed AIMs with the high discriminant capacity.Further studies with larger samples size and/or new populations are required to corroborate our outcomes outside Molise rivers’ basins and to develop a customized multiplex PCR panel to run massive genotyping based on simple amplicon sequencing for Mediterranean tout populations in Italy. However, the methodology described in this study could be useful for the AIMs identification in other species.


Receiver operating characteristic (ROC) analysis revealed GDF-15 as a predictor of no-reflow . The multivariate logistic regression analysis revealed that the risk factors for no-reflow occurrence were higher age, a concentration of GDF-15 > 1503 pg/mL, lower systolic blood pressure, and higher troponin I concentration on admission. A higher concentration of GDF-15 can be used as an additional marker of ischemia/reoxygenation injury, subsequent no-reflow phenomenon, and worse long-term outcomes in patients with STEMI.Growth differentiation factor 15 (GDF-15) and the no-reflow phenomenon are predictors of mortality after ST-segment elevation myocardial infarction (STEMI). We aimed to assess the relation between GDF-15 concentration on admission and the no-reflow phenomenon. The study was conducted prospectively among 80 consecutive STEMI patients who underwent primary PCI. No-reflow was defined as a corrected TIMI frame count > 27 and myocardial blush grade < 3 after PCI. GDF-15 was measured on admission. We assessed long-term (1.3 years) total mortality and the risk factors of no-reflow. The mean age was 65 (SD 12) years. Mortality rates were 2.5% and 7.5% for in-hospital and long-term observations, respectively. No-reflow occurred in 24% of patients. A negative correlation between TIMI flow after PCI and GDF-15 concentration (R = −0.2540,  Growth differentiation factor 15 (GDF-15) is a member of the transforming growth factor superfamily . DespiteThe study was conducted prospectively among 80 consecutive STEMI patients who underwent primary PCI in our center between 2012 and 2014. GDF-15 concentration was measured on admission to the hospital in the peripherally collected blood samples. Standard commercial ELISA kits were used  for the measurements. Angiographically established no-reflow was defined as a corrected TIMI frame count exceeding 27 and myocardial blush grade less than 3 immediately after the procedure of PCI . All angThe mean follow-up was 461 (SD 248) days. Follow-up data were collected from the Voivodship Office. Information concerning total mortality was obtained in 100% of cases. The Office provides information concerning the death of a person, however, the cause of death remains unknown. p value < 0.05 as statistically significant. Statistical analysis was performed with Statistica 10.0 program .Distributions of all variables were assessed with the Kolmogorov–Smirnov test. The correlations between constant variables were calculated with Spearman or Pearson tests depending on the statistical distribution. Correlations between dichotomous variables were analyzed with the Chi2 test. Multivariate analysis was performed with a logistic regression test. Receiver operating characteristic (ROC) curves analysis was used to establish the predictive value of GDF-15 for the no-reflow phenomenon occurrence. The results are presented as mean values with standard deviation, median with interquartile range (IQR) according to variation distribution, or as percentages presenting relative frequency. We considered a The mean age of the studied population was 64.75 (11.72) years, and 73% of the study population were males. The study population consisted of 5% of patients with previous myocardial infarction and 6% of patients with a history of revascularization . No-reflp = 0.05) and long-term mortality . GDF-15 concentration and no-reflow phenomenon were strongly correlated with the risk of long-term mortality .Mortality rates were 2.5% (N = 2) and 7.5% (N = 6) for in-hospital and long-term observation, respectively. No-reflow was associated with a worse hospital  was found. The analysis of ROC curves revealed GDF-15 as a predictor of no-reflow; the area under the curve was significantly higher than for the random model  vs. 1075 (711.6), respectively, No-reflow, as a clinical manifestation of ischemia/reoxygenation injury, is found in many patients with reperfused STEMI. No-reflow phenomenon affecting patients with myocardial infarction treated invasively diminishes the potential benefits of the treatment . In thisp = 0.049), which is a well-recognized risk factor for poor prognosis. These results support the conception of GDF-15 as a predictor of no-reflow and larger myocardial injury leading to poor outcomes. However, this observation should be interpreted cautiously as we found GDF-15 concentration as an independent from ejection fraction to be a significant risk factor for long-term mortality in multiple regression analysis .The recent individual patient meta-analysis that investigated GDF-15 prognostic performance in atherosclerotic cardiovascular disease revealed that its baseline concentration is associated with the incidence of major adverse cardiovascular events (MACE) and cardiovascular death . HoweverOur research brings GDF-15 into sharp focus as a potential marker of ischemia/reoxygenation injury and subsequent no-reflow phenomenon. Due to the fact that the assessment of GDF-15 and other biomarkers’ concentrations requires highly-specialized laboratories and time-consuming methods, their clinical usefulness is currently low. However, there are already rapid bedside tests for the quantitative determination of protein levels . Further research may contribute to the development of such tests for GDF-15, as opposed to a time-absorbing standard laboratory assay. In the future, this would improve the decision-making process and influence rapid initiation of the treatment that protects against the no-reflow phenomenon, such as the administration of GP IIb/IIIa inhibitors.p = 0.05). However, to improve the clarity of the methodology and the meaning of the finding, we decided to reduce our analysis to an angiographic assessment of no-reflow. Lastly, our study does not report case-specific mortality, which prevented us from assessing the prognostic value of GDF-15 on CV mortality.The basal limitation of the presented analysis is the relatively small number of patients. However, this was a prospective study and the number of patients included in the analysis was enough to find statistically significant correlations which additionally supports the strengths of observed findings. We made only one measurement of GDF-15 on admission. Further measurements in a number of time intervals would enrich our knowledge of dynamic changes of GDF-15 in the studied population. Such assessments would provide additional data about the kinetics of GDF-15 after reperfusion. Nevertheless, the main aim of our study—observation of the correlation between GDF-15 levels on admission and no-reflow occurrence—was possible with only one measurement of GDF-15 concentration. The method used for the no-reflow assessment can be interpreted as another limitation of the presented study. There are many different methods defining the no-reflow phenomenon ,9,13. MoConcluding our prospective study results, a higher concentration of GDF-15 on admission can be used as an additional marker of ischemia/reoxygenation injury and subsequent no-reflow phenomenon in patients with STEMI treated with primary PCI.