Genetic associations for variants identified through genome-wide association studies (GWASs) tend to be overestimated in the original discovery data set as, if the association was underestimated, the variant may not have been detected. This bias, known as winner’s curse, can affect Mendelian randomization estimates, but its severity and potential impact are unclear.We performed an empirical investigation to assess the potential bias from winner’s curse in practice. We considered Mendelian randomization estimates for the effect of body mass index (BMI) on coronary artery disease risk. We randomly divided a UK Biobank data set 100 times into three equally sized subsets. The first subset was treated as the ‘discovery GWAS’. We compared genetic associations estimated in the discovery GWAS to those estimated in the other subsets for each of the 100 iterations.P < 5 × 10–8 in at least one iteration, genetic associations with BMI were up to 5-fold greater in iterations in which the variant was associated with BMI at P < 5 × 10–8 compared with its mean association across all iterations. If the minimum P-value for association with BMI was P = 10–13 or lower, then this inflation was <25%. Mendelian randomization estimates were affected by winner’s curse bias. However, bias did not materially affect results; all analyses indicated a deleterious effect of BMI on coronary artery disease risk.For variants associated with BMI at Winner’s curse can bias Mendelian randomization estimates, although its practical impact may not be substantial. If avoiding sample overlap is infeasible, analysts should consider performing a sensitivity analysis based on variants strongly associated with the exposure. Key MessagesWinner’s curse bias occurs in Mendelian randomization investigations when there is overlap between the data set in which the genetic variants were chosen and the data set in which genetic associations were estimated.P-value close to the typical selection threshold of P <5×10–8.Our empirical investigation indicates that winner’s curse can substantially affect genetic associations, with associations inflated by 1.5–5 times for variants at a However, in our analysis of the effect of body mass index on coronary artery disease risk, overall Mendelian randomization estimates were broadly similar whether there was overlap between the data sets or not.Winner’s curse bias can be mitigated by selecting variants based on a more stringent significance threshold but this concern should be balanced against potential low power due to selecting fewer variants.,,,Mendelian randomization is an epidemiological approach in which genetic variants are used as instrumental variables to assess the existence and potential magnitude of the causal effect of an exposure on an outcome.The concept of winner’s curse originates from auctions in which multiple bidders each have different private estimates on the value of the item for sale.This bias can have a direct impact on Mendelian randomization estimates calculated using association estimates derived from the discovery GWAS. With a single genetic variant, the Mendelian randomization estimate can be expressed as the ratio of the genetic association with the outcome divided by the genetic association with the exposure.Winner’s curse can be alleviated by selecting genetic variants and estimating genetic associations in non-overlapping data sets. However, it may not be possible to find distinct sets of summary statistics from independent data sets for the same trait. The degree of bias from winner’s curse depends on various factors and is typically worse for genetic variants with associations close to the statistical significance threshold. Several methods have been proposed to correct for winner’s curse bias.,,A related phenomenon in Mendelian randomization is weak instrument bias, which arises due to chance associations of the genetic variants with confounders.P < 5 × 10–8), the expected magnitude of bias due to winner’s curse in a Mendelian randomization investigation is typically unclear. This is important to study, as any analysis must balance concerns about winner’s curse and weak instrument bias against other biases, such as bias from instrument invalidity due to pleiotropy.Both winner’s curse and weak instrument bias are finite-sample biases, in that they reduce towards zero when the sample size gets large. However, whereas the expected magnitude of bias due to weak instruments can be approximated  on coronary artery disease (CAD) risk. We picked this example because there is previous evidence supporting a causal relationshipAll analyses were performed in the UK Biobank data set—a prospective cohort study of ∼500 000 UK residents aged 40–69 years. We took data on 367 644 unrelated participants of European ancestries, as ascertained by a mixture of self-report and genetic information following quality-control procedures previously described.P < 5 × 10–8 were selected as instruments. In order to ensure that selected variants are mutually independent, we clumped the selected variants into loci using a distance threshold of 1 megabase and a correlation threshold of r2<0.01 estimated within Group A, to ensure no two selected variants are too close or too highly correlated with each other. We selected the variant with the smallest P-value in each locus to create our discovery set.We split UK Biobank participants into three equally sized groups at random, which we refer to as Group A (the discovery GWAS group), Group B and Group C. Using data from Group A, we performed a genome-wide association analysis for BMI adjusting for age, sex and the first 10 genomic principal components. Variants were filtered for a minor allele frequency of >0.01% and an INFO score  of >0.4.2 higher genetically predicted BMI. This entire process was repeated for 100 iterations taking different random splits of the original data set in each iteration.For each variant in our discovery set, we estimated its genetic association with BMI in each of Groups A, B and C by using linear regression adjusting for age, sex and the first 10 genomic principal components. We also calculated genetic associations with CAD risk in each group by using logistic regression with the same covariate adjustment. We note that the associations with BMI in Group A are the same as those obtained in the discovery GWAS. We combined these summarized genetic association estimates across variants to obtain Mendelian randomization estimates using the random-effects inverse-variance weighted method.We considered five scenarios, corresponding to different situations in which winner’s curse and weak instrument bias should or should not occur . We use No overlap, two-sample: genetic associations with BMI are estimated in Group B and genetic associations with CAD in Group C, i.e. we have three distinct data sets for discovery, exposure associations and outcome associations. This represents a scenario in which winner’s curse will not occur and weak instrument bias will be towards the null. We treat this as the reference scenario for comparison purposes. This scenario is sometimes called ‘three-sample Mendelian randomization’.Overlap with exposure, two-sample: genetic associations with BMI are estimated in Group A and genetic associations with CAD in Group B, i.e. we have overlapping discovery and exposure data sets. Winner’s curse will affect genetic associations with BMI, but not with CAD risk, so Mendelian randomization estimates should be deflated. Weak instrument bias will be towards the null.Overlap with outcome, two-sample: genetic associations with BMI are estimated in Group B and genetic associations with CAD in Group A, i.e. we have overlapping discovery and outcome data sets. Winner’s curse will affect genetic associations with CAD risk, but not with BMI, so Mendelian randomization estimates should be inflated. Weak instrument bias will be towards the null.No overlap, one-sample: genetic associations with BMI and CAD were both estimated in Group B, i.e. we have no overlap between the discovery and estimation data sets, but genetic associations with the exposure and outcome were obtained in the same data set. Winner’s curse will not occur and weak instrument bias will be towards the observational association between BMI and CAD risk (which is positive).Overlap with exposure and outcome, one-sample: genetic associations with BMI and CAD were both estimated in Group A, i.e. all analyses were performed on the same data set. This represents a scenario in which winner’s curse will occur for genetic associations with both BMI and CAD risk, so Mendelian randomization estimates will be subject to both inflation and deflation; it is not clear what the net effect will be. Weak instrument bias will be towards the observational association between BMI and CAD risk.P < 5 × 10–8) divided by the mean beta-coefficient across all 100 iterations:In addition to assessing winner’s curse in the Mendelian randomization estimates, we also considered the amount of winner’s curse in the genetic associations for individual variants, defined as the percentage difference in the mean beta-coefficient across iterations for which the association was significantly associated with BMI : Scenarios 2 , 3  and 5  for the primary choice of variants. We considered the false-discovery-rate inverse-quantile-transformation (FIQT) methodP-values, not the FIQT-corrected P-values. This was done to maintain comparability of estimates and to ensure that any reduction in bias was due to the FIQT method and not simply because fewer variants were selected. However, in practice, the FIQT method could be used to facilitate a more stringent selection of variants.To implement the FIQT method, we input Z-scores for genetic associations with the exposure in Group A into the FIQT function and used the FIQT-corrected Z-scores to scale the beta-coefficients for the outcome (Scenario 2), for the exposure (Scenario 3) or for both the exposure and outcome (Scenario 5). As an example, if the original Z-score is +6 and the FIQT-corrected Z-score is +5.7, then we multiplied the relevant beta-coefficients for that variant by a factor of 5.7/6 = 0.95 (so a 5% reduction). We still selected variants using the original Genetic association analyses were performed using SNPTEST v22 and there were 29 330 CAD events. Overall, variants in 359 loci were associated with BMI at a genome-wide significance threshold (P < 5 × 10–8) in Group A for at least one iteration of the random splitting procedure. Of these, 7 loci contained a variant significantly associated with BMI in all 100 iterations, whereas variants in the remaining 352 loci were significantly associated with BMI in some iterations but not others. The median number of variants selected in each iteration was 39.The mean age of the 367 644 participants at baseline was 57.2 years and 54.1% of participants were female. The mean BMI was 27.4 kg/mP-value for association with the exposure across iterations of ∼5 × 10–8, the percentage difference for the association with BMI in iterations when it was statistically significant (P < 5 × 10–8) in its association with the exposure compared with its mean value across all iterations varied from 50% to 400%, suggesting that beta-coefficients for associations with the exposure were on average between 1.5 and 5 times too large in these data sets. For genetic variants that had a minimum P-value of ∼1 × 10–13, the percentage difference varied from 10% to 25% (i.e. 1.1- to 1.25-fold inflation due to winner’s curse). A similar pattern was observed in the absolute difference in associations, which reduced in magnitude considerably as the minimum P-value decreased. Inflation was also observed in the genetic associations with CAD risk. On the percentage difference scale, several variants had inflated associations with CAD risk up to and beyond 400% (IJE online). However, some of these percentage differences were large because the mean association with CAD risk across all iterations was close to zero. On the absolute difference scale, associations with CAD risk were less inflated than those with BMI.Inflation of genetic associations due to winner’s curse for individual variants is illustrated in P < 0.001; for CAD risk = –0.299, P < 0.001), with large values of bias being associated with rarer variants. There are several reasons for this, including the greater magnitude of genetic associations with rarer variants and the greater variability of genetic associations with rarer variants. There was a weaker association between relative bias and minor allele frequency , suggesting that rarer variants are also more likely to have larger biases on the relative scale.When assessing the relationship between winner’s curse bias and minor allele frequency  was 0.0829, corresponding to an odds ratio of 1.086 per 1-kg/m2 increase in genetically predicted BMI, which is similar to what has been observed previously.Mendelian randomization estimates are summarized in Comparing Scenario 1  and Scenario 4  shows the impact of weak instrument bias separate from winner’s curse. The median estimate from Scenario 5 was 0.0913 (corresponding to an odds ratio of 1.096), suggesting that the impact of weak instrument bias is less than the impact of winner’s curse in this example. Scenario 5  is the most complex scenario to predict, as here we have winner’s curse in the genetic associations with both exposure and outcome, plus weak instrument bias in the direction of the observational association. Winner’s curse with the exposure would be expected to deflate estimates, whereas winner’s curse with the outcome and weak instrument bias would be expected to inflate estimates. The median estimate from Scenario 5 was 0.0863 (corresponding to an odds ratio of 1.090), indicating that the competing biases approximately cancelled out.Results from the secondary analyses considering different strategies for selecting variants are presented in Compared with Scenario 1, the pattern of median estimates in Scenarios 2–4 is broadly the same for each variant selection strategy. Median estimates are deflated in Scenario 2 and inflated in Scenarios 3 and 4. The degree of variation due to bias was around two to three times larger for the ‘no full replication’ strategy and much lower for the strict strategy. In the primary and ‘no full replication’ strategies, inflation was greater in Scenario 3 (due to winner’s curse) than in Scenario 4 (due to weak instrument bias). In contrast, in the strict strategy, inflation was slightly greater in Scenario 4 than Scenario 3, although the difference was minimal. Whereas there was some difference of median estimates in Scenario 5, this was not substantial for any of the strategies.P < 5 × 10–8), winner’s curse correction methods may have more utility in helping select variants that are robustly associated with the exposure, rather than correcting estimates based on a given set of variants.Results from the FIQT and DIVW methods, which correct for winner’s curse bias, are shown in In this paper, we have explored the impact of winner’s curse bias on Mendelian randomization estimates for the effect of BMI on CAD risk. By dividing the UK Biobank study into three groups at random a large number of times, we were able to compare the distribution of Mendelian randomization estimates in scenarios in which the discovery and estimation data sets were distinct and scenarios in which they overlapped. When the discovery data set overlapped with the data set used for estimating genetic associations with the exposure, Mendelian randomization estimates were typically deflated, whereas when the discovery data set overlapped with the data set for estimating genetic associations with the outcome, Mendelian randomization estimates were typically inflated.P < 5 × 10–8), bias from winner’s curse was greater in magnitude. Bias from both weak instruments and winner’s curse was substantially lower when only including variants associated with BMI at a stricter significance threshold (P < 5 × 10–11) and bias was substantially greater when excluding variants consistently strongly associated with BMI. In the latter case, winner’s curse bias was again greater in magnitude than weak instrument bias. In the former case, both biases were slight, although weak instrument bias was slightly larger than winner’s curse bias. Additionally, we showed that winner’s curse for individual variant associations can be very substantial in practice, with inflation in beta-coefficients for the exposure of 50–400% for variants that only just achieved the GWAS significance threshold (P ≈ 5 × 10–8). However, the degree of bias dropped off sharply for variants associated with the exposure at a stricter significance threshold.We were able to compare the magnitude of bias from winner’s curse to bias from weak instrument bias. In the primary analysis including all genome-wide significant variants , then there is little uncertainty as to whether they will be selected or not from the discovery GWAS and so winner’s curse bias is minimal. In contrast, if genetic associations with the exposure are close to the GWAS significance threshold then bias will be more substantial. Second, bias from winner’s curse was generally greater in magnitude than weak instrument bias but the two biases had a roughly similar order of magnitude in our empirical example.Although winner’s curse bias in genetic associations with the exposure is unwelcome, theoretically in isolation it will not lead to inflated Type 1 error rates for Mendelian randomization estimates. Although the magnitude of a Mendelian randomization estimate depends on associations with the exposure and outcome, its significance only depends on the genetic associations with the outcome. This is because the null hypothesis that the Mendelian randomization estimate is zero is achieved exactly when the genetic association with the outcome is zero; if the genetic association with the outcome is zero, then the Mendelian randomization estimate will be zero regardless of the genetic association with the exposure. Additionally, winner’s curse in outcome association estimates typically inflates Mendelian randomization estimates, which is more worrying as it can lead to false-positive findings, whereas deflation in estimates is typically conservative. Hence, winner’s curse bias is a more serious problem in practice when it affects genetic associations with the outcome. Fortunately, as shown in our example, the absolute bias in genetic association estimates due to winner’s curse is lower for associations with the outcome compared with associations with the exposure, as genetic variants are selected based on their associations with the exposure. However, the proportional bias can still be large.For simplicity, we have only considered scenarios in which there is either complete or no overlap between the discovery and estimation data sets. If there is partial overlap between data sets, winner’s curse bias will be less substantial. Given their international collaborative nature, many GWAS consortia have some degree of overlap. Additionally, the same participants may have been recruited into multiple studies. Hence, even if genetic associations appear to come from non-overlapping data sets, participant overlap may be non-zero in practice.P < 10–11). However, this may again lead to an unwelcome loss of power. Alternatively, a method that attempts to correct for winner’s curse bias can be employed,The ideal situation is that Mendelian randomization analyses operate using a ‘three-sample’ design, in which discovery GWASs, exposure associations and outcome associations are assessed in non-overlapping data sets.Avoiding participant overlap between the discovery GWAS and the estimation data set for associations with the outcome is an important factor in determining how to perform a Mendelian randomization analysis.In conclusion, bias due to winner’s curse affects Mendelian randomization estimates and the magnitude of bias in our empirical investigation was similar but generally larger than that from weak instruments. Analysts should carefully consider the possibility of avoiding sample overlap between the discovery GWAS and the estimation data set for associations with the outcome, particularly if most genetic variants are close to the statistical significance threshold, as well as performing sensitivity analyses that reduce winner’s curse bias.This research was conducted according to the principles expressed in the Declaration of Helsinki. The UK Biobank cohort has been approved by the North West Multicentre Research Ethics Committee, UK (Ref: 16/NW/0274). Written informed consent has been obtained from all study participants. The current study was approved by the UK Biobank Access Management Board under application 7439. Participants who had withdrawn consent by the time of the analysis were excluded from the data set.dyac233_Supplementary_DataClick here for additional data file.


The effects of diabetes on the cardiac and aortic structure and function remain unclear. Detecting and intervening these variations early is crucial for the prevention and management of complications. Cardiovascular magnetic resonance imaging-derived traits are established endophenotypes and serve as precise, early-detection, noninvasive clinical risk biomarkers. We conducted a Mendelian randomization (MR) study to examine the association between two types of diabetes, four glycemic traits, and preclinical endophenotypes of cardiac and aortic structure and function.Independent genetic variants significantly associated with type 1 diabetes, type 2 diabetes, fasting insulin (FIns), fasting glucose (FGlu), 2 h-glucose post-challenge (2hGlu), and glycated hemoglobin (HbA1c) were selected as instrumental variables. The 96 cardiovascular magnetic resonance imaging traits came from six independent genome-wide association studies. These traits serve as preclinical endophenotypes and offer an early indication of the structure and function of the four cardiac chambers and two aortic sections. The primary analysis was performed using MR with the inverse-variance weighted method. Confirmation was achieved through Steiger filtering and testing to determine the causal direction. Sensitivity analyses were conducted using the weighted median, MR-Egger, and MR-PRESSO methods. Additionally, multivariable MR was used to adjust for potential effects associated with body mass index.Genetic susceptibility to type 1 diabetes was associated with increased ascending aortic distensibility. Conversely, type 2 diabetes showed a correlation with a reduced diameter and areas of the ascending aorta, as well as decreased distensibility of the descending aorta. Genetically predicted higher levels of FGlu and HbA1c were correlated with a decrease in diameter and areas of the ascending aorta. Furthermore, higher 2hGlu levels predominantly showed association with a reduced diameter of both the ascending and descending aorta. Higher FIns levels corresponded to increased regional myocardial-wall thicknesses at end-diastole, global myocardial-wall thickness at end-diastole, and regional peak circumferential strain of the left ventricle.This study provides evidence that diabetes and glycemic traits have a causal relationship with cardiac and aortic structural and functional remodeling, highlighting the importance of intensive glucose-lowering for primary prevention of cardiovascular diseases.The online version contains supplementary material available at 10.1186/s12933-023-02079-w. Diabetes, a widespread metabolic disease, significantly contributes to cardiovascular-related disability and death, leading to an estimated annual cost of $37.3 billion . DiabeteCardiac and aortic structures, which underpin physiological functions, can exhibit abnormalities even before overt disease symptoms manifest. Cardiovascular magnetic resonance imaging (CMR) is considered the gold standard for non-invasive evaluation of cardiovascular structure and function . Its derMendelian randomization (MR), leveraging genetic variations from GWAS analyses as instrumental variables, helps overcome these biases inherent to observational research, thus reinforcing causal connections between exposures and outcomes . Here, wThis study operated under the three major assumptions of MR analyses: 1) the association assumption, (2) the independence assumption, and (3) the exclusion-restriction assumption. It aimed to explore the individual causal relationships between two types of diabetes and four glycemic traits with 96 CMR traits as endophenotypes, as illustrated in Fig.  the assoP < 5 × 10− 8) were selected. Second, LD clumping was performed with a threshold of r2 > 0.001 and clump distance < 10,000 kb (1000 genome reference panel). SNPs that displayed linkage disequilibrium were excluded. Subsequently, F-statistic were used to estimate the strength of the genetic instruments. Only genetic instruments with an F-statistic greater than 10 were kept for further analysis to avoid the inclusion of weak instruments. Additional file 1: Tables The genetic instruments associated with type 1 diabetes and type 2 diabetes were derived from studies by Forgetta et al. and the DIAbetes Genetics Replication And Meta-analysis Consortium, respectively , 10. InsThis study included 96 CMR features, comprising four left atrial traits, 66 left ventricular traits, four right atrial traits, four right ventricular traits, 11 ascending aortic traits, and seven descending aortic traits. The GWAS summary-level data for these CMR features were primarily derived from six studies based on the UK Biobank, with participant numbers ranging from 29,684 to 43,230 –16. No oP < 5 × 10− 8) for the outcome were first removed, followed by the use of Steiger filtering to test the directionality of the association of the remaining instrumental variables with the outcome. Any instrumental variable labeled as FALSE by Steiger filtering, indicating that the SNP explained more variance in the outcome than in the exposure, was excluded from the MR analysis. For the MR analysis, IVW was chosen as the primary statistical method. This method assumes that all SNPs are valid instrumental variables, offering the most precise estimates. Notably, considering heterogeneity, its presence was first assessed through Cochran Q analysis. If the P value was above 0.05, we used the fixed-effects IVW method, assuming no heterogeneity. Otherwise, the random-effects IVW method was applied. Owing to the susceptibility of the IVW method to horizontal pleiotropy, further sensitivity analyses were conducted using the weighted median method, MR-Egger, and MR-PRESSO. Specifically, the weighted median method assumes that if 50% of the weight from the instrumental variables is valid, a valid causal relationship assessment can still be provided [P < 5 × 10− 8) were identified using the PhenoScanner tool (http://www.phenoscanner.medschl.cam.ac.uk/) among the genetic instruments for diabetes and glycemic traits [Before conducting the MR analysis, the exposure and outcome data were harmonized to remove palindromic SNPs. To avoid potential horizontal pleiotropy, SNPs directly related to the outcome data were further removed, and the directionality of the SNPs was tested. Specifically, SNPs that reached the genome-wide significance . Associations with an original P value < 0.05 but not reaching this threshold were considered suggestive. For the MVMR analysis, the P value < 0.05 was considered statistically significant.All analyses were performed using the TwoSampleMR (version 0.5.7) and MendelianRandomization (version 0.9.0) packages in R (version 4.2.2) , 22. TheFigure P < 0.05) with 23 CMR traits  passed the stringent criteria of multiple hypothesis testing  with 36 CMR traits , maximum area  and minimum area . Additionally, type 2 diabetes was also correlated to decreased descending aortic distensibility . To avoid the confounding effects of obesity on type 2 diabetes, a MVMR was used to adjust for BMI. The findings suggested that the relationship between type 2 diabetes and above six CMR traits remains strong following this adjustment . Additionally, aside from the diameter of the ascending aorta in the context of type 2 diabetes across both studies, no evidence of horizontal pleiotropy was found in the MR-Egger intercept evaluations. The MR-PRESSO assessment identified some outlier SNPs, but their exclusion did not affect the stability of the main findings, thus reinforcing the analytical robustness. Steiger directionality testing was also used to confirm the directional accuracy of the associations between diabetes and CMR traits. All results were considered statistically significant at a P value threshold of less than 0.05  , WT_AHA_9 , WT_AHA_10 , WT_AHA_11 , WT_AHA_12 , WT_AHA_14 , WT_AHA_15  and WT_global .Genetically predicted FIns levels were associated with 37 CMR traits, but only nine passed the multiple hypothesis testing , maximum area , and minimum area . In contrast, higher 2hGlu levels were negatively associated with both the ascending aortic diameter  and descending aortic diameter .FGlu correlated with 25 CMR traits, and 2hGlu with 20 CMR traits , the maximum area of the ascending aorta , and the minimum area of the ascending aorta .Genetically predicted HbA1c levels were associated with 24 CMR traits  Through MR analysis, we have shed new light on the causal links between two diabetes types, four glucose traits, and 96 CMR traits spanning six anatomical areas, including the left atrium, right atrium, left ventricle, right ventricle, ascending aorta, and descending aorta. (2) MR study design reduced residual confounding, yielding more robust causal evidence. (3) While reverse causation might skew MR findings, we addressed this by filtering SNPs closely tied to outcomes with the Steiger method. The Steiger directionality test further confirmed our causal assumptions. (4) We compiled GWAS data on multiple CMR traits and validated the relationships between exposures and outcomes in diverse independent datasets. This approach not only highlighted neglected associations but also lend more weight to our causal conclusions given consistent results. (5) Since our findings mainly draw from summary-level data of European individuals, the potential for bias due to population structure is minimal.This study has several limitations. Firstly, while MR studies ideally should fully avoid horizontal pleiotropy, SNPs related to type 2 diabetes and glycemic traits might also be linked to metabolic factors like obesity. Given that the GWAS data for glycemic traits was adjusted for BMI, making the instrumental variables used likely independent of this factor, thus the impact of obesity-induced pleiotropy was expected to be minimal . For typIn summary, we delved into the impact of two diabetes types and four glucose traits on heart and aortic remodeling. Our research shows that both diabetes and hyperglycemia are risk factors for aortic remodeling, whereas insulin resistance affects the left ventricle. These insights, by providing causal evidence of direct effects on cardiac and aortic morphology and function, advance our understanding of how diabetes and impaired glycemic homeostasis contribute to the onset and progression of cardiovascular diseases. It offers new insights for early detection, risk stratification, non-pharmacological and pharmacological treatments, and overall advancement in intervention strategies.Below is the link to the electronic supplementary material.Supplementary Material 1


Macrobrachium nipponense is an important economic aquaculture animal, and due to its delicious taste, it is a popular food in South China. The growth of male and female Macrobrachium nipponense is significantly different. The female grows slowly due to precocious puberty, whereas males have a clear growth advantage. Therefore, due to their fast growth, males are often the subject of genetic breeding by scientists. For this study, we obtained single-nucleotide polymorphisms (SNPs) that could affect growth and sex differentiation to assist artificial selection in genetic breeding. This study entailed a genetic evaluation of 10 growth traits and one sexual trait using genome-wide association analysis (GWAS), while some significant SNPs associated with growth and sexual traits were detected. Moreover, genetic correlations were also found between traits, especially between growth and sexual traits, which greatly simplified genetic selection or made multiple joint selection convenient for more than one trait. At the same time, multiple SNPs were found to be located in two chromosomes and greatly contributed to a high heritability. These results illustrate the genetic nature of growth and sex traits in Macrobrachium nipponense.Macrobrachium nipponense were recorded, and each individual genotype was evaluated using the 2b-RAD sequencing method. The genetic parameters for growth and sex traits were estimated. A genome-wide association analysis (GWAS) of these traits was performed. In total, 18 growth-related SNPs were detected from 12 chromosomes using a mixed linear model. The most significant loci of weight are located on the position of the SNP , which can explain 11.87% of the phenotypic variation. A total of 11 significant SNPs were detected on four chromosomes associated with sex trait . The heritability of this trait is 0.8998 and belongs to the range of ultra-high heritability. Genetic correlations were prevalent among the 11 traits examined, the genetic coefficient between sex and body weight reached a significant level of −0.23. This study is the first GWAS for sex of binary and growth traits in oriental river prawn. Our results provide a set of markers for the genetic selection of growth traits and help us to further understand the genetic mechanisms of growth in Macrobrachium nipponense.Male hybrid oriental river prawns grow significantly faster than hybrid females. In this study, the growth and sex traits of 181 individuals of  Macrobrachium nipponense is a commercial freshwater prawn species, generally known as oriental river prawn and widely distributed worldwide [Macrobrachium nipponense, females and males are greatly reduced due to the growth inhibition of females. Therefore, intensive research should be directed towards the controlled establishment of prawn mono-sex populations [orldwide . In aquaulations . The conulations ,4. HowevMacrobrachium nipponense [Macrobrachium nipponense was recently achieved, providing rich genomic data for genetic analysis. Research on oriental river prawn mostly focused on the optimization of previous nutrition formulae  or the ipponense . An impoA genome-wide association study  based onOriental river prawn is an important aquaculture species native to South China with a huge market and economic benefits. However, few SNPs or quantitative trait loci (QTLs) of growth and sex traits were reported due to the genomic complexity of this species. We first applied genotyping by sequencing in oriental river prawn and used the methods of GWAS, SAIGE and EMMAX to obtain associated SNPs involved in sex and growth traits. The correlation coefficient between traits was evaluated, providing guidance for subsequent breeding programs.Macrobrachium nipponense individuals with complete phenotype records were randomly selected. Growth traits, including body weight (BW), ration of meat yield (RMY), length of beard (LB), length of hepatopancreas (LH), chest circumference (CC), length of fifth leg (LFL), body length (BL), head width (HW), tail width (TW) and length of pliers (LP) were analyzed. In addition, tissue of each sample from the test population were kept in a cryopreservation tube and placed in a refrigerator at −80 °C for DNA isolation and genotyping by resequencing. The populations of the tested river oriental prawn derived from the same family that underwent four generations of successive genetic selection in an aquaculture farm in Xinghua, Jiangsu, China. A total of 81 male and 100 female DNA was extracted from the tissues of samples according to a standard phenol–chloroform protocol . A 1% aghttps://github.com/broadinstitute/picard accessed on 12 March 2022). The filtering of mutation sites and screening of SNP were carried out. Next, the results were converted to VCF files using GATK software (https://github.com/broadinstitute/gatk accessed on 9 May 2022). Finally, the files of binary traits and other quantitative traits comprising the genotyping dataset were produced using PLINK1.9 software (https://www.cog-genomics.org/plink2/ accessed on 18 July 2022).The quality control for each sample was conducted using an NGS QC Toolkit . SequencClassic linear mixed-model association analyses were performed using GEMMA for genome-wide association analyses , which tMacrobrachium nipponense. SAIGE is usually used in two steps. The first step is to fit the null logistic mixing model through genomic markers without considering any covariates. In the second step, a univariate association test was carried out for variation data according to the method of leave-one-chromosome-out. A sex trait is a typical binomial trait that does not follow a normal distribution; therefore, a linear mixed model was not suitable for the gene mapping of this trait. For the above reasons, the state-of-the-art method SAIGE , based oPrincipal component analysis (PCA) using EIGENSOFT was performed to evaluate population structure . The indp ≤ 0.05) was used to identify the candidate SNPs associated with each trait. The tested SNP was considered as a candidate for a significant SNP when its p-value was over the threshold. Significant SNP markers were visualized in a Manhattan plot using Haploview4.2 software [p-value distributions  are shown in a quantile–quantile plot (Q–Q plot).The Bonferroni-corrected significance threshold , and the significance threshold at the 0.01 was 1.28 × 10−4 (0.01/7793). A principal components analysis is shown in lambda > 0.5) were used for stratified population correction.The descriptive statistics of all the traits are shown in A GWAS was performed for these 11 traits (1 binary and 10 quantitative) and evaluated their heritability through methods of GEMMA, respectively . The herFor the genome-wide association analysis of binary sex trait, three SNPs on chromosome 4, one SNP on chromosome 7, and seven SNPs on chromosome 17 were found. Detailed information on these candidate SNPs is shown in For quantitative growth traits, significant SNPs were detected for each trait in the quantile–quantile plot (Q–Q plot) and Manhattan plot . At the A genetic correlation analysis showed that there was a general genetic correlation among these traits , in whicMacrobrachium nipponense focused on the cloning and functional analysis of candidate genes [Sex is one of the many limitations of efficiency in aquaculture farming , and sexte genes ,34, whicte genes . HoweverMacrobrachium nipponense. A GWAS of the growth-related traits was carried out using GEMMA, and eighteen SNPs associated with the growth traits were identified across the whole genome. Of these significant SNPs, one is associated with body weight. The estimated heritability of the sex trait reaches 0.99, indicating a super-high heritability. Thirteen SNPs were identified associated with BL and four SNPs associated with LH trait, the estimated heritability of them is 0.45 and 0.16, respectively, they exhibited high and moderate heritability, respectively. The estimated heritabilities of other growth traits were generally high, indicating that the effect of genetic factors for each trait was much greater than that of environmental factors, which is conducive to genetic selection by genome selection and provides theoretical support for the genetic breeding of Macrobrachium nipponense to identify SNPs associated with sex and growth traits at a genome-wide level. Our results showed that sex and body weight traits are highly genetically correlated; the genetic correlation between sex and BW trait was 0.23, which indicates a significantly negative correlation, meaning that the detected QTNs can participate in the processes of both sex and growth development. Furthermore, the location of these candidate QTNs can be used for functional gene mapping for target trait control, which is one of the main functions of GWAS. This high correlation between growth traits was similar to the correlations found for Pacific abalone  [Macrobrachium nipponense can provide a more measurable index and greatly simplify the complexity of genetic selection, which is also used in the selection of the feed conversion of abalone [This is the first study concerning hannai.) . Interes abalone . Macrobrachium nipponense showed that, in the online GWAS using GEMMA [The significant correlation between various growth traits of ng GEMMA , multiplh2) is the ratio of genetic variance to total variance. The total variance is also known as the phenotypic variance . Heritability can also be understood as the influence of genetic factors on traits. The values of heritability calculated using the ratio of genetic to phenotypic variance are not equal to the total variance of all SNPs, which may be due to a number of reasons: firstly, the genetic variance was calculated from all genetic markers, some markers are not significant when tested individually. Secondly, the missing heritability perhaps suggests that the linkage disequilibrium (LD) heterogeneity among regions has an adverse effect on genomic prediction and heritability estimation. As illustrated in our study, the estimated heritability of BW reached 0.99, which was higher than that of the total SNPs (0.91), and such similar cases of “missing” or “hidden” heritability are mentioned in several studies [The heritability ,39 of th studies ,41,42.On chromosome 17, seven SNPs were found to have significant genetic correlation with sex traits at the whole genome level, indicating that some SNPs had relatively significant effects on chromosome 17. These SNPs enhanced the effects of other detected SNPs that were not significantly correlated and caused them to have a significant genetic correlation. Such a linkage disequilibrium affects the interpretation of GWAS . In addiIn conclusion, this study helps us to further understand the genetic structure of growth and sex traits of oriental river prawn by GWAS. In this study, a total of 11 SNPs related to sex traits were found, suggesting that some genes near these loci may have a significant sex differentiation potential. These candidate genetic markers of SNP may provide valuable resources for further understanding sex determination in oriental river prawn. In addition, 18 SNPs related to growth traits, such as BW, LH and BL, were detected in this study. Interestingly, these SNPs may also affect sex differentiation, based on the results of the correlation relationship between sex and other growth traits. This discovery provides a convenient tool for genetic selection and makes the breeding process more efficient.Macrobrachium nipponense. With the publication of detailed genomic data in the future, the genetic markers associated with these traits will be developed into genetic units that can be used for different purposes: genetic selection, identifying genes and their traits, exploring gene networks, and studying their biological functions. Therefore, we can facilitate the genetic selection and improvement of production efficiency for Macrobrachium nipponense.In summary, for the first time, we found some SNPs that were significantly correlated with growth traits and sex traits of oriental river prawn at a genome-wide level. We also found that there was a wide range of high genetic correlations between growth traits and between growth and sex traits. Some growth traits and sex traits of oriental river shrimp not only have a medium and super high heritability, they also demonstrate a high genetic correlation among traits, which provides a more convenient and efficient scheme for genetic breeding, such as multi-trait joint selection. This study provided a basis for understanding the genetic mechanism of the disparity in growth performance between male and female in


Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand the functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Our simulation studies showed that incorporating functional annotation data using GGPA 2.0 not only improves the detection of disease-associated variants, but also provides a more accurate estimation of relationships among diseases. Next, we analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus, along with the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood-related epigenetic marks, especially B cells and regulatory T cells, across multiple diseases. Psychiatric disorders were enriched for brain-related epigenetic marks, especially the prefrontal cortex and the inferior temporal lobe for bipolar disorder and schizophrenia, respectively. In addition, the pleiotropy between bipolar disorder and schizophrenia was also detected. Finally, we found that GGPA 2.0 is robust to the use of irrelevant and/or incorrect functional annotations. These results demonstrate that GGPA 2.0 can be a powerful tool to identify genetic variants associated with each phenotype or those shared across multiple phenotypes, while also promoting an understanding of functional mechanisms underlying the associated variants. Genome-wide association studies (GWAS) have identified hundreds of thousands of genetic variants significantly associated with human traits and diseases . Despitep-values of each single-trait analysis to generate one comprehensive p-value by applying eigen-decomposition to the correlation matrix of p-values. In recent years, MTAG has been a popular method for conducting meta-analysis of GWAS summary statistics for different traits, and it has been reported that it is robust to sample overlap  approaches. An early example is the stratified FDR (sFDR) method , which eThe third group of approaches aims to achieve the best of both worlds by integrating multiple GWAS datasets along with functional annotations. GPA  is a pioFor the purpose of multi-disease analysis, we previously proposed graph-GPA (GGPA), a Bayesian approach that models a pleiotropic architecture using a latent Markov random field (MRF) approach indicating phenotype-genotype associations . First, In spite of such strengths and flexibility, unfortunately, the previous version of GGPA did not allow incorporating functional annotations. This was an important limitation given the potential of functional annotations to further improve genetic analysis. Incorporating functional annotations can not only potentially improve understanding of functional mechanisms underlying identified genetic variants, but also lead to more reliable and meaningful findings of genetic variants themselves . In ordep-values) for SNP t and phenotype i, denoted as pit, as input, where i = 1, … , n and t = 1, … , T. For convenience, in modeling and visualization, we transform pit as yit = Φ−1 (1 − pit), where Φ is the cumulative distribution of the standard normal variable. We model the density of yit with the latent association indicator eit using a lognormal-normal mixture:eit = 1 if SNP t is associated with phenotype i and eit = 0 otherwise, and LN and N denote the lognormal density and the normal density, respectively. For yit corresponding to the associated SNPs (eit = 1), we assume the lognormal distribution because the p-values of those SNPs are very likely to be less than 0.5 leading to yit greater than zero  denote an MRF graph with nodes V =  and edges E = {E : i, j = 1, … , n}. We can interpret vi as phenotype i and E  = 1 means that phenotypes i and j are conditionally dependent . Specifically, we model the latent association indicators of SNP t, et = , and the graph structure with an auto-logistic scheme. The probability mass function for et is given byαi is the MRF coefficient for the phenotype i such that larger values represent stronger SNP-phenotype associations, βij is the MRF coefficient for the pair of phenotypes i and j such that larger values represent stronger associations between the phenotypes, the symbol i ∼ j denotes that vi is adjacent to vj, i.e., E = 1, and To model genetic relationships among G is one of our key inferential targets in this framework. In our previous work, we found that MRF coefficient estimation can be biased when signals are weak in GWAS data and we showed that incorporating prior information for G can help address this issue and improve stability of the phenotype graph estimation  denotes the inverse gamma distribution with the shape parameter a and the rate parameter b. For the MRF coefficients in Eq. a, b) denotes the gamma distribution with the shape parameter a and the rate parameter b, and δ0 denotes the Dirac delta function. Weakly informative priors are used for the top level of the Bayesian hierarchical model with the hyperparameters: θμ = 0, θα = 0, aσ = bσ = 0.5. We use aβ = 4 and bβ = 2 so that most of βij’s with E = 1 are a priori distinct from zero.For the log-normal density in Eq. G represents genetic relationship among phenotypes, where the posterior probability for each edge p|Y) indicates the probability that two phenotypes i and j are genetically correlated with each other, where Y indicates the GWAS dataset, i.e., the set of yit, i = 1, … , n, t = 1, … , T. In addition, the posterior samples of βij can be interpreted as a relative metric to gauge the degree of correlation between phenotypes i and j. Based on this rationale, we conclude that phenotype i and j are correlated if p|Y) > 0.5 and p(βij > 0|Y) > 0.95. Second, association mapping of a single SNP with a specific phenotype is implemented based on p(eit = 1|Y), i.e., the posterior probability that SNP t is associated with phenotype i. Likewise, pleiotropic variants can be detected using p representing the posterior probability that SNP t is associated with both phenotypes i and j. Identification of pleiotropic variants for more than two phenotypes can be implemented in similar ways. Global FDR were controlled using the direct posterior probability approach (The posterior inference is made using the Markov chain Monte Carlo (MCMC). First, we can make an inference about the genetic correlation among phenotypes by using both the estimated phenotype graph structure and the MRF coefficient estimates. Specifically, the phenotype graph approach .at = , a vector of length M, for SNP t. Here we mainly focus on the binary annotations, i.e., amt = 1 if tth SNP is annotated in the mth (1 ≤ m ≤ M) functional annotation data. In GGPA 2.0, we incorporate the functional annotation as a modifier for the MRF intercept so that when the tth SNP is annotated in more functional annotation data, it can have a higher probability to be associated with phenotypes. Specifically, we modify Eq. γim (>0) is the MRF coefficient for importance of annotation m for phenotype i such that larger values represent richer enrichment of tissues or cells in phenotypes. Note that here we assume γim > 0 so that associations of genetic variants with phenotypes are supported, rather than penalized, by being annotated.In GGPA 2.0, in addition to the GWAS summary statistics, we also consider functional annotations γim has the following hyperpriors:p) denotes the Bernoulli distribution with success probability p, Unif denotes the uniform distribution with lower and upper limits l and u, and Beta denotes the beta distribution with two shape parameters, a and b. We use aγ = 4 and bγ = 2. Given this model, the posterior inference is made using MCMC. Specifically, we implement a Metropolis-within-Gibbs algorithm whose full details are provided in Supplementary Section 1. The genetic correlation among phenotypes can be inferred and the association mapping can be implemented as described in the previous section. We note that although we use the same set of parameters for these purposes, their inference results will be different from GGPA 1.0 because incorporation of functional annotation data affects estimation of these parameters. Moreover, relevance of functional annotations with disease-risk-associated variants can be inferred using γim representing the importance of functional annotation m for phenotype i. Specifically, we declare that annotation m is associated with phenotype i if γim is significantly different from zero, e.g., p(γim > 0|Y) > 0.95. Based on significantly nonzero γs, we can identify cells or tissues that are enriched in the corresponding phenotypes. Again the direct posterior probability approach  =  and  = , while all the remaining βij were set to zeros. Second, assuming T = 200, 000 SNPs and M = 5 annotations, we generated each binary vector am, of which elements are set to one for 10% SNPs. We assumed γ11 = γ21 = γ31 = 1 and γ42 = γ52 = γ62 = 2, while all the remaining γim were set to zeros. We also considered two other settings for γs whose results are provided in Supplementary Section 2. Third, we generated et by running the Gibbs sampler for 1,000 iterations based on Eq. yit using Eq. μ =  and σ = .For the simulation study, we generated the simulated data using the following steps. First, we assumed the true phenotype graph depicted in amt = 1 if the corresponding GenoSkyline score is above 0.5. GenoSkyline-Plus is a comprehensive update of GenoSkyline by incorporating RNA-seq and DNA methylation data into the framework and extending to 127 integrated annotation tracks, covering a spectrum of human tissue and cell types. Similarly, we generated the binary annotations using the same cutoff at 0.5. We considered 1,919,526 SNPs that are shared among these GWAS datasets. We further removed SNPs with missing values and kept one SNP in every 10 SNPs to reduce dependent SNPs, leading to 187,335 SNPs. As a reference, after subsampling every tenth SNP, the average r2 of the nearest pair drops notably from 0.48 to 0.36, as calculated using the R package “LDlinkR” and 10,000 randomly chosen pairs. Our approach involves conducting statistical inference by incorporating functional annotations, accounting for the correlation among p-values, as covariates. In general, identifying the source of variability can lessen conditional correlations between responses. In other words, including the variables responsible for the correlation in the model can lead to a lower correlation. For example, if X contains all factors that explain the correlation of the Y vector, the elements of Y are  independent given X, which is the foundation of the regression model. Additionally, recognizing the source of dependence can improve statistical inference. This viewpoint suggests that our approach can be effective without negatively impacting FDR control. Consequently, both the marginal correlation reduction between SNPs resulting from our sampling strategy and the conditional correlation reduction achieved by incorporating functional annotation as covariates are simultaneously implemented, expected to significantly reduce the violation of model assumptions and substantially decrease the infringement on FDR control.Here we analyzed GWAS data for two sets of diseases to demonstrate the usefulness of GGPA 2.0. The first set consists of five autoimmune diseases, including systemic lupus erythematosus (SLE), ulcerative colitis (UC), Crohn’s disease (CD), rheumatoid arthritis (RA), and type I diabetes (T1D). The second set consists of five psychiatric disorders, including attention deficit-hyperactivity disorder (ADHD), autism spectrum disorder (ASD), major depressive disorder (MDD), bipolar disorder (BIP), and schizophrenia (SCZ). Summary statistics for ten different disease types were downloaded from the GWAS Catalog: SLE , RA Oka, UC De , CD De , T1D Br, ADHD L, ASD Le, BIP Le, SC) Le, and MDDyit as described in Section 2.1, we decorrelated them by Ydecorr = C−1/2Y, where C is the sample correlation matrix of Y, and Y is the observed matrix of which element is yit. It has been reported that the resultant Ydecorr is less biased by the sample overlap for the genetic correlation inference, compared to the case of using the original Y  and it indicates that 7 pairs out of 10 have edges, suggesting extensive pleiotropy among these diseases. Compared with the prior phenotype graph, GGPA 2.0 additionally detected the pleiotropies between RA and T1D, and between SLE and CD. These two pleiotropies have been reported in previous studies , heart, lung, and muscle.  studies . We furt studies  and LPM  studies  to the s studies . We coulγ coefficient estimates indicating importance of functional annotations for each disease. Blood was determined to be the key tissue for most of the autoimmune diseases, which is well supported by existing literature indicating the established relationships between blood and autoimmune diseases  B cells were enriched for CD, RA, SLE, and UC; ii) regulatory T cells were enriched for CD and T1D; and iii) natural killer cells were enriched for T1D. These results are consistent with previous literature indicating connections between autoimmune disease and these immune cell types  vs. those without (denoted as -SNPs). γ coefficient estimates and indicates that blood and brain tissues are significantly enriched for BIP and SCZ, respectively. Along with the natural connection between psychiatric disorders and brain  are not considered in the current framework. However, it is important to consider the directionalities of effects to further elucidate biological mechanisms of phenotype-genotype association. Hence, extension of GGPA 2.0 by considering directionalities of effects will be an important and interesting future research direction. Third, in the current framework, functional annotations are considered at the SNP level. Using the gene- or pathway-level information will be an interesting direction and left as a future work. Fourth, GGPA 2.0 still relies on the assumption that SNPs are independent. While GWAS data preprocessing  can help better satisfy this assumption, relaxation of this assumption will be an interesting work. Finally, as we discussed in In spite of such exciting improvements, there are still some limitations to be addressed. First, the computational efficiency needs to be further improved. Specifically, the computation time increases as the number of phenotypes and functional annotations increases (With the aforementioned strengths and the planned improvement, we believe that GGPA 2.0 will be a powerful tool for the integrative analysis of GWAS and functional annotation data.


Our understanding of plant biology has been revolutionized by modern genetics and biochemistry. However, biochemical genetics can be traced back to the foundation of Mendelian genetics; indeed, one of Mendel’s milestone discoveries of seven characteristics of pea plants later came to be ascribed to a mutation in a starch branching enzyme. Here, we review both current and historical strategies for the elucidation of plant metabolic pathways and the genes that encode their component enzymes and regulators. We use this historical review to discuss a range of classical genetic phenomena including epistasis, canalization, and heterosis as viewed through the lens of contemporary high-throughput data obtained via the array of approaches currently adopted in multiomics studies. In this review we describe how the application of modern metabolomics techniques is providing insights into many classical genetic questions by viewing them through a biochemical lens. Neurosporra crassa for which it was developed, the extensive gene duplication that has characterized the plant kingdom  proceeded that of advanced breeding populations, given that it has only been possible to provide mechanistic rather than descriptive results since the recent advent of genome-wide association analysis, we will start this discussion with the use of advanced breeding populations. There are five types of population that are worthy of discussion in the context of biochemical genetics, namely recombinant inbred lines, introgression lines, backcrossed inbred lines, double-haploids, and heterogeneous inbred families , or by rA complementary approach to that offered by breeding populations is that of directly assessing the broad-range natural variation of traits and associating this with differences in gene sequences via genome-wide association studies , and epistatic variance (interlocus interactions) that depend on allele frequencies . At the Multiple genetic factors and their interactions (epistasis) are the key regulators for elucidating the genetic basis of phenotypic variances, and are also implicated in gene–environment interactions. The great influence of epistasis and complicated forms of environmental effects have been neglected in studies detecting phenotypic variance. The elucidation of statistical associations between millions of genetic variants and phenotypes has become possible with the use of single-nucleotide polymorphisms (SNPs), and these have also contributed to the investigation of genotype × environment (G×E) interactions . It has In all studies of quantitative variation, statistical power is a central issue. Theoretical models and experimental results indicate that population bottlenecks and subdivisions expose hidden additive genetic variance selection due to epistasis. Many distinct methods are available to analyse and visualize one-, two-, or three-way epistatic interactions  and the reaction norm of the variance (RxNV) of a trait . While tE. coli identified six capacitor reactions that have a high influence on genetic canalization of metabolism  is a further type of epistasis that we consider separately here given that it has long been studied in its own right. Organellar genomic variation can be linked to dramatic phenotypic alterations in both mammals . SimilarPlant metabolic studies have traditionally focused on the role and regulation of the enzymes that catalyse key reactions within specific pathways . Althoug2 cycles, as well as gluconeogenesis, the sucrose-to-starch transition, and amino acid metabolism  has long been well known and hence been the subject of a large amount of study. Chitinases secreted by host plants can hydrolyse the insoluble polymer to chitin oligosaccharides, which can then be recognised by plasma membrane-localized lysin-motif (LysM) proteins, and this in turn induces the accumulation of antimicrobial metabolites such as reactive oxygen species (ROS) and salicylic acid (SA) -triggered immunity (PTI) and effector-triggered immunity (ETI), which require the function of different metabolites . Among tcid (SA) .N-hydroxypipecolic acid (NHP), catalysed by flavin-dependent-monooxygenase 1 -3-hexenal has been identified as playing a powerful role in non-host resistance against Empoasca leafhoppers  are representative fungal resistance-related metabolites (In addition to functioning in pathogen recognition, immunity signalling, and response regulation, some primary metabolites can participate in the immunity process by acting as precursors of key immunity metabolites, and some secondary metabolites can act directly as antimicrobial metabolites (per se, utilizing broad natural variance as well as advanced breeding lines will probably represent invaluable tools to address this question in the coming decades.In recent decades, climate change has not only caused abnormal rainfall patterns and temperatures that induce drought and heat stress on plants, it has also changed the habits of temperature-dependent pathogens and increased the risk of infection of plants (per se. This fact notwithstanding, the study of epigenetic and epigenomic control of biochemical traits is clearly an area in which we can anticipate considerable breakthroughs in the future. Another area of great promise is the prospect of gaining a far more comprehensive understanding of gene–environment interactions that encompasses both canalization and plasticity, with the genetic populations and analytical tools required to address these topics now becoming increasingly available and well tested (In the sections above, we have documented how the answers to many classical questions in genetic have either influenced, or been influenced by, biochemical factors. One notable omission from our review is the subject of the impact of epigenetics and epigenomics on metabolism and vice versa. There were two reasons for this: firstly, two recent reviews by l tested . The ada


Glycine max) production is greatly affected by persistent and/or intermittent droughts in rainfed soybean-growing regions worldwide. Symbiotic N2 fixation (SNF) in soybean can also be significantly hampered even under moderate drought stress. The objective of this study was to identify genomic regions associated with shoot carbon isotope ratio (δ13C) as a surrogate measure for water use efficiency (WUE), nitrogen isotope ratio (δ15N) to assess relative SNF, N concentration ([N]), and carbon/nitrogen ratio (C/N). Genome-wide association mapping was performed with 105 genotypes and approximately 4 million single-nucleotide polymorphism markers derived from whole-genome resequencing information. A total of 11, 21, 22, and 22 genomic loci associated with δ13C, δ15N, [N], and C/N, respectively, were identified in two environments. Nine of these 76 loci were stable across environments, as they were detected in both environments. In addition to the 62 novel loci identified, 14 loci aligned with previously reported quantitative trait loci for different C and N traits related to drought, WUE, and N2 fixation in soybean. A total of 58 Glyma gene models encoding for different genes related to the four traits were identified in the vicinity of the genomic loci.Soybean ( However, for most large-scale breeding programs, effective screening for WUE using lysimeters or other time-consuming tools is not feasible (13C), is physiologically linked to leaf and whole plant WUE (Triticum aestivum) (Hordeum vulgare) (Vigna unguiculata) (Phaseolus vulgare)  and symbiotic Nolerance . Vadez afeasible . Isotopilant WUE . Isotopiestivum) , barley vulgare) , peanut vulgare) , cowpea iculata) , common vulgare) , and soyvulgare) , and hasvulgare) . Previouy panels  and bipay panels  using 35Symbiotic N fixation provides an important advantage for soybean production compared to other grain crops, as it eliminates the need for N fertilization in most production environments under standard management practices. Up to 94% of the total N requirement by the plant can be acquired through SNF in N-deficient soil . However15N), , , and per15N), .15N is a measure of the relative abundance of 14N and 15N isotopes in plant tissue relative to their relationship in air and is expressed in ‰. Because soils have a higher 15N abundance than air, SNF enriches 14N relative to 15N in tissues of N-fixing plants, which consequently exhibit a lower δ15N compared to plants that rely on soil mineral N uptake for their N supply , δ15N (SNF), [N], and C/N ratio are quantitative in nature and controlled by many genes with minor effects  in crop species  and post-emergence  herbicide applications, which were complemented with manual weeding. Experiments were conducted under rainfed conditions without supplemental irrigation. Rainfall and temperature data were recorded by a weather station located within 500 m of the field sites and downloaded from the Missouri Historical Agricultural Weather Database (http://agebb.missouri.edu/weather/history/).Genotypes included in this study were selected because of available whole-genome resequencing data and the desired focus on maturity group (MG) II and III germplasm. The 105 diverse genotypes (2.2https://stableisotopefacility.ucdavis.edu/carbon-and-nitrogen-solids) for analysis with an isotope ratio mass spectrometer  coupled to an elemental analyzer . The δ13C and δ15N of each sample were expressed relative to the international standards V-PDB (Vienna Pee Dee Belemnite) and air, respectively, according to the following equations  and Rx and RVPDB are the isotope ratios of the sample and the standard, respectively.where Nitrogen isotope ratio:R is the ratio of the heavy to the light isotope (15N/14N) and Rx and RairN2 are the isotope ratios of the sample and the standard, respectively.where, −1 dry weight. Sample C to N ratios (C/N) was calculated by dividing the C concentration by the N concentration.Carbon and N concentrations were determined as part of the stable isotope analysis with the elemental analyzer . The C and N concentrations of each sample were expressed as mg C or N g2.3H2 = σ2g/[σ2g + σ2ge/k + σ2e/(rk)], where σ2g is the genotypic variance, σ2ge is the genotype by environment interaction variance, k is the number of environments, r is the number of replications  was performed in SAS 9.4  for each year and across the two years using the PROC MIXED procedure. In individual-environment ANOVA, genotype was treated as a fixed effect, and replications within an environment were treated as random . In acroications . The varThe best linear unbiased predictions (BLUP) were calculated for each trait for each year using PROC MIXED functions in SAS 9.4 , where all factors were treated as random factors . Calcula2.4http://soykb.org/NGS_Resequence/NGS_index.php]. SNPs were identified following the PGen workflow described by (Whole-genome resequencing (15X and 40X coverage depending on genotype) was conducted by others, and the information was deposited in Soykb , and C/N ratio. The environment effect was also significant for δ13C (P < 0.0001), [N] (P < 0.01), and C/N (P < 0.01). For δ15N, genotype and environment effects were highly significant (P < 0.0001 and P < 0.001), but the genotype x environment interaction effect was marginal (P = 0.08). The observed environment and genotype x environment interaction effects were consistent with the considerable differences in environmental conditions between the two years. Although mean daily temperatures from planting to sampling were similar in the two years (21.3°C vs. 22.2°C), temperatures during early growth were higher (18.6°C vs. 16.9°C), and those later in the season were lower  in the first than the second year (P < 0.0001) for all traits in both environments except for δ15N in 2014, where the genotype effect was significant at P < 0.05 level . In contrast, the mean C/N ratio (14.31 vs. 17.44) and the range in C/N ratio (10.36 vs. 17.37) were smaller in 2014 than in 2016.Phenotypic values for all four traits exhibited a broad range in both environments, with δectively Figure 1r = 0.29 (P < 0.01) for δ13C, r = 0.20 (P < 0.06) for δ15N, r = 0.28 (P < 0.01) for [N], and r = 0.32 (P < 0.01) for C/N ratio . In contrast, relationships were not consistent among other traits when analyzed by environment, but, when analyzed across environments, the C/N ratio and δ13C  as well as C/N ratio and δ15N  were negatively correlated, and δ13C and δ15N  were positively correlated.The considerable differences in environmental conditions were also reflected in the relatively weak correlations of phenotypic data between environments, namely, /N ratio Table 2.13C (0.94 and 0.81), lowest for δ15N (0.35 and 0.42), and intermediate for C/N ratio (0.85 and 0.67) and [N] (0.75 and 0.63). When combined across the two years, heritabilities were 0.58, 0.24, 0.39, and 0.44 for δ13C, δ15N, [N], and C/N ratio, respectively  and the lowest marker density on Gm05   value threshold determined by the FarmCPU model , δ15N, and C/N) in both years  for δ13C in diversity panels consisting primarily of MGIV or MGVI-MGVIII soybean from the USDA soybean germplasm collection (15N (0.35 and 0.42). The comparatively low heritabilities are consistent with 15N ranging from 0.07 to 0.40 in a GWAS and biparental mapping study, respectively. Low heritability for N2 fixation was also documented by 15N in both years in the current study indicates considerable environmental influence on this trait, possibly due to spatial within-field variability in soil mineral N concentration  and  between shoot C/N ratio and shoot [N], which were expected  to identify genomic regions associated with δBeadchip . HoweverBeadchip . DespiteBeadchip Figure 3Beadchip . In the  studies . Based o4.2.113C identified a total of 11 loci, 10 of which were marked by single SNPs and one that was marked by three SNPs , when overexpressed in maize (Zea mays), resulted in a much lower reduction in net photosynthesis and stomatal conductance than in wild-type plants under drought conditions  (Glyma.13G040100), another locus-7 candidate gene, can control the stomatal number and density by initiating asymmetric cell divisions to establish stomatal lineage in Arabidopsis and stomatal index compared to the untressed soybean leaves.GWA mapping of δ  and a CBL-interacting protein kinase 11 . These genes are annotated with GO biological processes like stomatal movement and ABA signaling. Overexpression of CIPK2 in tobacco, Arabidopsis, and soybean has been reported to confer drought tolerance in control environment studies by modulating stomatal closure through ABA signaling under drought stress (CIPK11 confers drought sensitivity in transgenic Arabidopsis (CIPK11-mediated plant response to drought tolerance is dependent on Di19-(dehydration-induced19-3), a transcriptional activator known to be involved in modulating plant response to different abiotic stresses, including drought stress. Another candidate gene at locus 9, Glyma.14G202900, encodes CHAL or EPFL-6, which is a homologue of Epidermal Patterning Factor-1 (EPF1)  . Overexpnsic WUE . The ide4.2.215N) of plant tissue is commonly used to determine relative SNF expressed as %Ndfa. Nitrogen derived from the atmosphere is computed by relating the δ15N value of a genotype to the δ15N value of a non-nodulating reference line  homologue, which, in Arabidopsis, is responsive to N deprivation and re-supply   .Importin α isoform 4 homologue identified at locus 21 may play a role in nodulation in soybean. Additional studies aimed at identifying the regulatory regions associated with the identified loci are critical to better understand and improve SNF in soybean. Of particular interest for follow-up, studies are the loci that were consistent across the environments as well as those that overlapped with previously identified regions of the soybean genome  homologue. In several legumes, including soybean, SCR is expressed in root cortical cells, and, in Medicago truncatula, nodule number and density are reduced in SCR mutants (Ammonium transporter 2 (AMT2) homologue (Glyma.02G043700) was identified close to the SNP that anchored the novel [N] locus 2. In Arabidopsis, AMT2.1 contributes to ammonium uptake in roots and functions mainly in root-to-shoot translocation of ammonium under N deficiency conditions  was identified based both on keyword search and on the allelic combination approach. The anchoring marker of locus 10 is located within the coding region of CEP14, which also is within about 600 Kb of the δ15N locus 7 and [N] locus 12. In Arabidopsis, under N-limitation, CEP1 serves as a root-to-shoot signal that is perceived by CEP receptors in the shoot which leads to shoot-to-root signaling that causes an upregulation of nitrate transporter genes in roots (CEPs are also involved in the regulation of nodule numbers in legumes, where CEPs positively regulate nodulation (CEPs, including CEP14, are regulated by environmental cues such as N depletion and increased CO2 levels in shoots and roots of Arabidopsis (CEP1 in Medicago (CEP14 is causal for the association of locus 10 with C/N, but its involvement in the signaling of N conditions and responsiveness to CO2 documented in the literature underscores its potential as a target to develop a better understanding of the regulation of C and N status in soybean.The relationships among C assimilation, N uptake, and SNF are closely regulated. Photosynthetic output can be negatively affected due to N deficiency but can recover with N supplements . On the in roots . CEPs arbidopsis , as was Medicago . Further513C, δ15N, [N], and C/N with 105 genotypes and a high-density marker panel consisting of more than 4.1 million SNPs derived through whole-genome resequencing. A total of 76 genomic loci associated with these traits were detected, namely, 11 for δ13C, 22 for δ15N, 21 for [N], and 22 for C/N. Nine loci associated with different traits were consistent across the two environments. In addition, several loci were associated with more than one trait, including four loci for [N] and C/N, and one locus for δ15N and C/N and, consequently, may serve as markers for multiple traits. Although most identified loci were novel, 14 aligned with previously detected genomic regions for the same or similar traits. Exploration of the gene models in the vicinity of the loci identified 59 candidate genes. Further scrutiny of these candidate genes will facilitate a better understanding of WUE, SNF, and N and C dynamics. The genomic regions and candidate genes identified in this study may serve to breed for soybean with enhanced WUE and improved and sustained SNF and to further dissect the mechanisms controlling WUE and SNF.Genomwide association analysis was conducted for δThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.MA: Data curation, Formal analysis, Writing – original draft, Writing – review & editing. SM: Data curation, Formal analysis, Writing – review & editing. AS-S: Investigation, Writing – review & editing. HZ: Investigation, Writing – review & editing. AS: Conceptualization, Writing – review & editing. FF: Conceptualization, Funding acquisition, Writing – original draft, Writing – review & editing.