GC) gene, is a component of the vitamin D system. In a genome-wide association study of DBP concentration in 65,589 neonates we identify 26 independent loci, 17 of which are in or close to the GC gene, with fine-mapping identifying 2 missense variants on chromosomes 12 and 17 . When adjusted for GC haplotypes, we find 15 independent loci distributed over 10 chromosomes. Mendelian randomization analyses identify a unidirectional effect of higher DBP concentration and (a) higher 25-hydroxyvitamin D concentration, and (b) a reduced risk of multiple sclerosis and rheumatoid arthritis. A phenome-wide association study confirms that higher DBP concentration is associated with a reduced risk of vitamin D deficiency. Our findings provide valuable insights into the influence of DBP on vitamin D status and a range of health outcomes.The vitamin D binding protein (DBP), encoded by the group-specific component ( In this study the authors measure the concentration of 25-hydroxyvitamin D and vitamin D binding protein (DBP) in 65,589 neonatal dried blood samples. Findings from further analyses include that the genetic correlates of DBP concentration predict the risk of vitamin D deficiency. DBP, which is an abundant circulating (plasma) protein structurally related to albumin, is encoded by the group-specific component (GC) gene. Haplotypes determined by two missense variants in the GC gene (rs7041 and rs4588) determine key isoforms of the DBP protein, which are labeled according to their electrophoretic properties . Apart from these haplotypes, there are many additional variants in humans1.The vitamin D binding protein (DBP) is a highly polymorphic protein best known for its role related to the transport of 25-hydroxyvitamin D (25OHD) and 1,25-dihydroxvitamin D 2), most research has focused on the contribution of DBP to overall vitamin D status. It is known from related steroid hormones and their binding proteins, that the concentration of the binding protein can influence the bioavailability of the target hormone. Much of this research has been informed by the free hormone hypothesis3, which proposes that the biological activity of a hormone is related to the unbound  rather than the protein-bound concentration in the plasma1. With respect to the total 25OHD, on average only 0.03% is free, 85% is bound to DBP, and the remainder is bound (less strongly) to albumin4. Apart from some tissues which can retrieve protein-bound 25OHD via endocytosis , free/unbound 25OHD is thought to be the biologically active fraction. Recently, an individual with a homozygous deletion of GC was identified, and shown to have DBP and 25OHD concentrations below the limit of detection 5. Interestingly, the affected individual did not display adverse bone outcomes traditionally associated with vitamin D deficiency. Combined with evidence from transgenic animal models of GC knock-outs6, this indicates that DBP is not necessary for the transport of 25OHD throughout the body, nor is DBP necessary for general bone health. It is now appreciated that the concentration of DBP can influence the half-life of 25OHD7. When the concentration of DBP is lower, then more 25OHD is free/unbound, and this fraction of the total 25OHD is more rapidly transferred to target cells and subsequently catabolized, thus shortening the functional half-life of 25OHD.While DBP also has a range of additional roles , it is clear that there is appreciable variation in DBP concentration within groups sorted by DBP isoform type8. Some of this variation may be related to genetic factors. To date, we are aware of only one genome-wide association study (GWAS) of DBP concentration, based on 1380 men9. This study, which used a monoclonal antibody, identified two genome-wide significant loci, both of which were within the GC gene (rs7041 and rs705117). Of particular interest, variants in GC are also strongly and consistently associated with the concentration of 25OHD13, indicating a critical role for the binding protein in predicting the total 25OHD concentration. In light of the importance of DBP concentration for influencing vitamin D status, there is a need to undertake GWAS analyses of DBP concentration in larger samples, and to explore analytic methods that can take into account potential isoform-specific assay biases. The summary statistics from this type of study could then be used in a range of Mendelian randomization studies  and phenome-wide association studies (PheWAS). While many disorders have been linked to vitamin D-related pathways16, in the Mendelian randomization studies we will focus on (a) a set of neurological, psychiatric and cognitive phenotypes that have been linked to vitamin D pathways  and (b) selected autoimmune disorders also linked to vitamin D pathways . We will explore a much wider range of outcomes in the PheWAS (1027 disease phenotypes).Despite concerns about the accuracy of some DBP assays   for the European ancestry subsample is shown in Fig. Three main haplotypes were inferred from the two well-characterized loci within the GC diplotypes were significantly associated with both DBP and 25OHD levels in the entire sample and European ancestry subsample  >0.2 to at least one other person in the set . For 25OHD, the family-based heritability (and standard error) was 0.36 (0.03) and 0.35 (0.03) after adjustment for GC haplotypes  had been previously identified11. For DBP, we further fine-mapped the genome-wide significant regions in chromosomes 4, 12, and 17 using a combination of PolyFun and SuSiE42. For chromosome 4, the key GC haplotype-determining rs7041 had a posterior causal probability (PIP) of 1  was also identified (PIP = 0.87). From the 26 COJO-independent hits for DBP, 17 loci were in or close to GC . For chromosome 12, there was a credible set of four SNPs with cumulative PIP >0.95, where the leading SNP rs3184504 (PIP = 0.5) is a missense variant in SH2B3. When adjusting DBP for the GC haplotypes, the fine-mapped results decreased the credible set to 3 SNPs, and the PIP of the missense variant increased to 0.78. This shows how the adjusted GWAS increased the power to fine-map potentially causal variants. For chromosome 17 we observed a similar effect. The fine-mapping algorithm did not output a credible set for this region, and the leading SNP rs56030650 (a missense variant in GSDMA) had a low PIP of 0.2. Nevertheless, after adjusting for the GC haplotypes, the cumulative PIP of the credible set of nine SNPs was 0.95, with the missense variant increasing its PIP to 0.26. FUMA gene-based analysis also showed that the SNPs in GC, SH2B3, and GSDMA were over-represented , and 15 independent SNPs (distributed over 10 chromosomes) associated with DBP levels after adjusting for the ome 4 rs152846 hadsome 4 rs52846 hadn = 417,580) UKB adult sample11. Of the 143 genome-wide significant COJO SNPs in the UKB 25OHD GWAS, only the most highly significant one was replicated in our 25OHD sample. However, the Pearson’s correlation coefficient between the allele effect sizes for the union of both GWASs genome-wide significant SNPs  was 0.66 , the proportion of DBP variance explained by the effect of the main SNP rs7048 alone from the DBP GWAS was 54%, while adding more SNPs to the polygenic score  decreased the P value <2.2 × 10−16, 3839 SNPs) for DBP GWAS and of 0.97  for the DBP_GC GWAS. This result supports the hypothesis that the findings based on the overall case-cohort sample are comparable to that found in the nested  general population sample. GWAS findings from the subcohort are shown in Supplementary Data With respect to the planned sensitivity analyses where we compared GWAS findings based on the entire case-cohort versus the subcohort only, we found that the SNP effect sizes had a Pearson’s correlation coefficient of 0.99 4.We performed gene-based and gene-set analyses, for which results can be found in Supplementary Data GC-adjusted and unadjusted summary statistics, loci on chromosome 17 in close proximity to the adjacent genes MED24 and GSDMA were identified . When estimated at the GWAS summary statistics level with bivariate LDSC regression, it was 0.34 (SE = 0.09) and 0.24 (SE = 0.11) when using the unadjusted and GC-adjusted DBP summary statistics respectively, confirming the substantial contribution of DBP to 25OHD concentration . Concerning the reciprocal (reverse) relationship (UKB 25OHD predicting DBP), there was no significant association . After adjusting the GC haplotypes, the general pattern of finding persisted . Figure We used bi-directional GSMR to investigate the relationships between DBP and 25OHD. In the following text, we will focus on the findings with HEIDI filtering . Second, there was a negative association between DBP_GC and rheumatoid arthritis . No pleiotropic SNPs were identified by HEIDI-outlier in either multiple sclerosis or rheumatoid arthritis GSMR analyses.With respect to forward GSMR based on GWAS summary statistics for DBP_GC, we found evidence to support causal associations between this phenotype and two autoimmune disorders Fig. . First, P value = 7.6 × 10−4, NSNPs = 11). This relationship was not apparent in analyses that included two potentially pleiotropic SNPs . In general, the large positive GSMR estimate (when two pleiotropic SNPs were excluded) and the change in sign of the beta estimate when the 2 pleiotropic SNPs were included, suggests that we should be cautious in our interpretation of any potential association between DBP concentration and Crohn’s disease.In addition, we found evidence to support the hypothesis that pleiotropic variants may influence the association between DBP_GC and two additional autoimmune disorders . When one pleiotropic SNP was removed from the analysis, the association became non-significant . The identified pleiotropic SNP was rs3184504, a missense variant in SH2B3. For both the findings related to Crohn’s disease and Type 1 diabetes, the opposite direction of beta coefficients in the presence or absence of potentially pleiotropic variants weakens the hypothesis that there is a direct influence of DBP_GC on these two disorders. Full details of the GSMR analyses are shown in Supplementary Data Furthermore, we identified a negative association between DBP_GC and risk of Type 1 diabetes when assessed without the HEIDI-outlier test . Note that the positive effect size  indicates that variants associated with higher DBP concentrations were associated with higher observed 25OHD concentrations in UKB. Reassuringly, the association with a clinical diagnosis of “vitamin D deficiency”  was negative and nominally significant .Finally, we examined the association between the two DBP-related summary statistics and phenotypes in the UKB. For the GWAS summary statistics based on DBP, only one finding was significant (after Bonferroni adjustment for multiple comparisons) (Supplementary Data 43. We then divided the PRS for DBP into 11 bins (quantiles). The sixth  bin was set as the reference category. We tested the odds ratio of each binned PRS to the reference bin with logistic regression adjusted for sex, age and 20 PCs. Compared to the reference category, those in each of the five upper quantiles had significantly reduced odds of having vitamin D deficiency, while those in the two lowest quantiles had significantly increased odds of having vitamin D deficiency ncy Fig. . CompareP value = 2.52 × 10−11; Ncases = 104,892, Nnon-cases = 242,876). Consistent with this finding, DBP_GC was also negatively associated with both observed diastolic and systolic blood pressure in a large UKB subsample . In addition, DBP_GC was associated with: (a) reduced pulse rate , (b) gastritis and duodenitis ; and associated with an increased risk of (c) vasomotor and allergic rhinitis , and (d) agranulocytosis . There was no association between higher DBP_GC and observed 25OHD concentration , but again we found a nominally significant association with a prior clinical diagnosis of vitamin D deficiency . Finally, DBP_GC was associated with (a) an education-related measure (higher concentration of DBP associated with more years in education), (b) birthweight (higher concentration of DBP associated with higher birthweight), and (c) two adult anthropometric measures (higher concentration of DBP associated with reduced body fat percentage). PheWAS plots for both the DBP and DBP_GC-based analyses can be found in Supplementary Figs. For the DBP_GC DBP summary statistics, we found a range of associated phenotypes  of the variance in DBP concentration is explained by two canonical missense variants (rs7041 and rs4588). Consistent with previous literature, we found that the proportions of the different haplotypes varied by genetically-defined ancestry .First, we confirm that the genetic architecture of DBP concentration is characterized by highly influential loci within or near the 9. When we examined how much of the variance in DBP concentration could be attributed to common single-nucleotide variants included in the GWAS, the proportions remained appreciable, 53% for the GC gene and 5% for the remaining genotypes. The results were consistent across the three methods with different assumptions of genetic architecture, GCTA-GREML, SBayesS, and LDpred2-auto. Moreover, we identified 15 independent COJO loci after the adjustment for the GC haplotypes. These 15 COJO SNPs explained a 0.49% variance of DBP in total. The remaining ~4.5% of the variance is likely to be captured by SNPs which were not significant in the current GWAS. These findings suggest DBP has polygenic features, in addition to the very large genetic variance encoded by the GC gene. These findings reinforce the value of the GC-adjusted GWAS and related post-GWAS analyses.Second, we show that DBP is highly heritable. Using related individuals, the narrow-sense heritability was 68%. The estimate is similar to the heritability (60%) reported by ref. 12. We found one quasi-independent locus in the GC gene. Furthermore, based on the correlation between the effect sizes for the SNPs identified in the UKB-based GWAS 11 and the subset of these SNPs available in our neonatal sample, a significant positive association was found . The family-based heritability for 25OHD was comparable to that reported by Revez et al. in the UKB sample11 . It is important to note that neonatal 25OHD concentration is entirely reliant on maternal 25OHD concentrations44 and while the correlation between the maternal and offspring genotypes would be 0.5, the genetic correlates of neonatal 25OHD may be more strongly correlated with the (unobserved) maternal genotype, rather than the (observed) neonatal genotype. Because maternal DBP does not cross the placenta1, this is not an issue when examining the genetic correlates of DBP in neonates.Third, based on our sample of ~65,000 European-ancestry individuals, we found that the genetic architecture of 25OHD in neonates was consistent with that reported by similar-sized GWAS studies based on adultsGC gene . Fine-mapping identified: (a) a missense variant in SH2B3 (rs3184504), which encodes a widely-expressed protein involved in the activation of kinase signaling activities, and which has been linked to a range of disorders, including diabetes45, and (b) a missense variant in GSDMA (rs56030650) which encodes a precursor of a pore-forming protein that can influence membrane permeabilization. Variants in this gene have been linked to pyroptosis (inflammatory cell death) and inflammatory bowel disease46, however, it is currently unclear how variants in this gene may influence DBP concentration.Fourth, we identify new candidate loci that influence DBP concentration. As expected with a highly polymorphic protein like DBP, many of the quasi-independent loci  were in or very close to the 47, our findings lend additional support to the unidirectional nature of the relationship between DBP and 25OHD.Fifth, our findings provide convergent evidence that variants that influence DBP concentration influence 25OHD concentration, but not vice versa. Apart from the findings from Mendelian randomization, we found no significant variation in DBP concentration by month of testing . The PheWAS results confirm that variants related to higher DBP concentration are associated with (a) a higher concentration of 25OHD and (b) a reduced risk of receiving the clinical diagnosis of vitamin D deficiency. In light of evidence from clinical trials indicating that vitamin D supplementation does not impact DBP concentrationGC gene influence the concentration of DBP and (b) that variants within the GC gene are robustly and consistently associated with 25OHD concentration. Over the last few decades, there has been a focus on the relationship between (a) total 25OHD  and (b) free 25OHD 48. It is clear that free 25OHD concentration is strongly correlated with the total 25OHD concentration7. Our Mendelian randomization findings lend weight to the hypothesis that the higher concentration of DBP is associated with a higher concentration of  25OHD. In light of (a) recent clinical evidence from individuals with homozygous deletions or pathogenic variants of the GC gene49 and (b) findings from GC knock-out animal models6, the concentration of DBP has been proposed to be a key factor in determining the half-life of vitamin D metabolites, as unbound 25OHD is more rapidly transferred to target cells and catabolised7. By extension, those with a lower concentration of DBP would be more likely to experience vitamin D deficiency, because this would shorten the functional half-life of 25OHD. A study tracking the excretion of deuterium-labeled 25OHD supports this hypothesis50. If two individuals have an identical concentration of total 25OHD (free and bound) at baseline, then in the absence of new vitamin D production, over a given period the individual with a higher concentration of DBP would be less likely to subsequently develop vitamin D deficiency because of the longer half-life of 25OHD. While DBP is not directly involved in pathways leading to the synthesis or catabolism of 25OHD, higher DBP concentration acts as a larger reservoir for 25OHD, extending the effective half-life of 25OHD, and thus provides a more effective ‘buffer’ against future vitamin D deficiency. We speculate that (a) observed lower DBP concentration and/or (b) lower polygene risk scores based on summary scores from DBS-related GWASs, may provide an informative proxy measure related to an increased future risk of vitamin D deficiency. In addition, our findings may cast light on the observation that the concentration of DBP increases substantially during pregnancy52. Increased concentration of DBP may reduce the risk of both maternal vitamin D deficiency and prenatal exposure to developmental vitamin D deficiency.It has long been appreciated that: (a) variants in the 57. Based on Mendelian randomization, we also found that increased DBP concentration was associated with a reduced risk of rheumatoid arthritis. In keeping with our findings related to multiple sclerosis, the effect size of this association was substantial . There is evidence linking vitamin D deficiency and an increased risk of rheumatoid arthritis59. The active form of vitamin D  is an immuno-modulator and has anti-inflammatory effects60. Because we identified these autoimmune-related findings only in the summary statistics generated from the DBP_GC analysis (which is a weaker instrument for 25OHD concentration compared to the unadjusted DBP), this raises the possibility that these findings may reflect non-vitamin D-associated properties of DBP . It could also be argued that the established links between 25OHD concentration and the risk of several autoimmune disorders provide a more parsimonious explanation for these particular findings. We hope that our findings can stimulate hypothesis-driven research focused on the role of DBP for multiple sclerosis and rheumatoid arthritis.Sixth, we provide evidence from Mendelian randomization that links DBP concentration and the risk of several autoimmune disorders. There is already a strong body of literature based on observational epidemiology and Mendelian randomization linking low vitamin D status and an increased risk of multiple sclerosis9. We were able to assess 25OHD in the same large sample, and also confirm our findings within a subcohort representative of the general population. Our findings also have several important limitations. The sample were neonates at the time of testing, and it remains to be seen if the genetic architecture of DBP identified in our study will generalize to adult populations. We know that certain factors  and several disorders that lead to proteinuria can impact DBP concentration in adults62. For the assessment of DBP, we used a monoclonal antibody pair, which resulted in a similar bias towards the 1S-isoform of DBP as previously reported in studies comparing Americans of African and Caucasian origin 66. However, we restricted the sample to those with European ancestries and conducted the GWAS with and without adjustment for GC haplotypes. Our sample is enriched with people with mental disorders; however, we found no forward or reverse GSMR association between mental disorders and DBP. In addition, we did a planned sensitivity analyses where we ran the GWAS again only in the population-based subcohort (a rare resource that is free of ascertainment bias). The GWAS results in the entire case cohort versus the subcohort sample were comparable. In addition, because our main analyses were restricted to Europeans, there is a need to examine the genetic correlates of DBP concentration in more diverse ancestry groups. Finally, the DBP_GC analysis also identified an SNP (rs635634) that is ~4 kb upstream from the ABO gene. We noticed that recent studies of the genetic correlates of the human plasma proteome have identified genes that encode for “master regulator” proteins—variants in these genes have widespread correlations with the concentration of other circulating proteins68. SH2B3 and ABO were both identified as having associations with over 50 other protein concentrations, thus variants in these genes could directly or indirectly influence generic protein metabolic pathways . However, variants in these same genes have also been associated with hematocrit69, which may have downstream consequences on the accuracy of a range of sera-based70 and dried blood spot-based assays71. The influence of genetic variants related to hematocrit on (a) the accurate quantification of blood-based biomarkers, and (b) subsequent GWASs based on these measurements, warrants additional investigation.Our study has several strengths. Our sample was over 30 times larger than the only other published GWAS of DBP33. If previously completed randomized controlled trials of vitamin D supplementation have access to the genotype of their participants, we speculate the use of supplements may be associated with superior outcomes in those with lower genetically-predicted DBP concentration.Our findings may have clinical consequences—those with genetic variants associated with lower DBP concentrations may have a particular requirement for vitamin D supplementation over the course of winter (compared to those with genetic variants associated with higher DBP concentrations). If our Mendelian randomization findings related to multiple sclerosis and rheumatoid arthritis are replicated in future studies, there may be a case to ensure that those with a genetic predisposition to lower DBP concentrations are encouraged to take regular vitamin D supplements. We hope that the research community will use our findings to examine the relationship between the genetic correlates of DBP concentration and a wider range of disorders. We note with interest that a recent large randomized controlled trial found that vitamin D supplementation reduced the incidence of several autoimmune disorders37, a population-based case-cohort design to study the genetic and environmental factors associated with severe mental disorders. The iPSYCH2012 sample is nested within the entire Danish population born between 1981 and 2005 . In total, 86,189 individuals were selected; with 57,377 individuals diagnosed with at least one major mental disorder , attention deficit hyperactivity disorder (ADHD)) and a random population cohort of 30,000 individuals sampled from the same birth cohort. By design, there were individuals overlapping between the case sub-cohorts and the random population subcohort. We also included 4791 anorexia nervosa cases  from the Anorexia Nervosa Genetics Initiative (ANGI)72, which has the same design as iPSYCH2012. Henceforth, we refer to iPSYCH2012 as the combined dataset with the ANGI samples. Blood spots for the individuals included in iPSYCH2012 were obtained from the Danish Neonatal Screening Biobank73 and subsequently genotyped and assayed for the concentrations of 25OHD and DBP. Dried blood spot samples have been collected from practically all neonates born in Denmark since 1 May 1981 and stored at −20 °C. Samples are collected 4 to 7 days after birth. Material from these samples has been primarily used for screening for congenital disorders, but are also stored for follow-up diagnostics, screening, quality control, and research. According to Danish legislation, material from The Danish Neonatal Screening Biobank can be used for research after approval from the Biobank, and the relevant Scientific Ethical Committee. There is also a mechanism in place ensuring that one can opt out of having the stored material used for research. Additional details of the Danish Neonatal Screening Biobank are available in the iPSYCH methods paper37.This study was based on the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) sample74. After storage, the protein extracts were aliquoted and were subjected to DBP and 25OHD analysis. Thus, all experimental data originates from a single DBS extraction. Additional details related to blood spot extraction and storage are provided in Supplementary Methods Two 3.2 mm disks from neonatal dried blood spot (DBS) samples were punched into each well of polymerase chain reaction plates . About 130 µL extraction buffer (PBS containing 1% BSA (Sigma Aldrich #A4503), 0.5% Tween-20 , and complete protease inhibitor cocktail ) was added to each well, and the samples were incubated for 1 h at room temperature on a microwell shaker set at 900 rpm. After separating the extract from the filter paper into sterile Matrix 2D tubes , the extracts were stored at −80 °C for 6–7 years before analysis. DNA was extracted according to previously published methodsThe extracts were analyzed with a multiplex immunoassay using U-plex plates , Maryland, US) employing antibodies specific for DBP (HYB249-05 and HYB249-01), as well as measuring complement C3 and C4 (results will be reported in a separate manuscript). The antibodies were purchased from SSI Antibodies . Extracts were analyzed diluted 1:70 in diluent 101 . Capture antibodies (used at 10 ug/mL as input concentration) were biotinylated in-house using EZ-Link Sulfo-NHS-LC-Biotin  and detection antibodies were SULFO-tagged , both at a challenging ratio of 20:1. As a calibrator, we used recombinant human DBP #C953 . Calibrators were diluted in diluent 101 and detection antibodies (used at 1 ug/mL) were diluted in diluent 3 . Controls were made in-house from part of the calibrator solution in one batch, aliquoted in portions for each plate, and stored at −20 °C until use. The samples were prepared on the plates as recommended by the manufacturer, and were read on the QuickPlex SQ 120 (MSD) 4 min after adding 2x Read buffer T . Analyte concentrations were calculated from the calibrator curves on each plate using 4PL logistic regression using the MSD Workbench software.Intra-assay variations were calculated from 38 measurements analyzed on the same plate of a pool of extracts made from 304 samples. Inter-assay variations were calculated from controls analyzed in duplicate on each plate during the sample analysis . The lower limit of detection was calculated as 2.5 standard deviations from 40 replicate measurements of the zero calibrator. The higher detection limit was defined as the highest calibrator concentration. The lower and upper detection limits for DBP were 2.07 and 79.8 mg/L respectively, and the intra-assay and inter-assay coefficient of variance was 7.6 and 22.4% respectively. To validate the stability of the samples during storage, we randomly selected 15–16 samples from five years . After extracting the samples and adding them to an MSD plate, the rest of the extracts were frozen for 2 months, thawed and measured as described above to imitate the freeze-thaw cycle of the samples in the study. The oldest samples (from 1984) recorded higher concentrations 80 in order to assay 25OHD based on protein pellets previously extracted from dried blood spots.Detailed methods for the main assay of 25OHDTM WebSeal Plate+ 96-Well Glass-Coated Microplate. The samples were dried down in an Eppendorf Bench Top Concentrator PlusTM (60 °C) before the vitamin D metabolites were derivatized with 20 µL of the commercial PTAD reagent (reconstituted in ethyl acetate and diluted to a working solution of 1:12). After incubation and quenching (by the addition of 50 µL ethanol), samples were dried down in a concentrator before being reconstituted in 80 µL 1:1 acetonitrile/deionized water solution. After reconstitution, 40 µL was injected into the LC-MS/MS system. The LC system is a Thermo TLX2 Turboflow system, comprised of a CTC Analytics HTS PAL autosampler, a dual LC system (one Agilent 1200 quaternary and one Agilent 1200 binary pump) and two Thermo Scientific hot pocket column heaters. The LC systems are interfaced with a triple quadrupole mass spectrometer (Thermo Scientific TSQ Quantiva) equipped with a heated electrospray ionization probe. The LC system is controlled by Aria MX Direct Control software, whereas the mass spectrometer is controlled by the TSQ Quantiva Tune Application software (version 2.0.1292.15). Thermo TraceFinderTM 3.2 application software is used to acquire and process data.For the assay of 25OHD, 30 µL of each sample was transferred to a Thermo Scientific 96-well polypropylene storage microplates before 120 µL internal standard (reconstituted in acetonitrile and diluted to a working solution of 1:100 compared to the kit insert) was added. After centrifugation, the samples were prepared for a liquid-liquid extraction procedure. About 200 µL of the upper organic phase (containing the purified vitamin D metabolites) was transferred to a Thermo Scientific81. During the period when the iPSYCH samples were analyzed (November 2018 to February 2021), our laboratory assessed 9 panels of 5 DEQAS standard reference samples . Based on these samples, the mean (and range) bias from the target values was 3.8% .The development of the new assay was validated following the Clinical and Laboratory Standards Institute´s approved guideline for liquid chromatography-mass spectrometry methods (C62-A) ((CLSI), 2014). Intermediate precision was obtained by quantifying the concentration of three stable isotope labeled external quality controls (PerkinElmer) with a low, medium and high concentration of each vitamin D metabolite. To examine intra- and inter-assay precision we used control samples from adult volunteers and examined triplicate samples within one assay run, and also examined these samples on three consecutive days, respectively. In keeping with best practice, we used Standard Reference Material (Vitamin D Metabolites in Frozen Human Serum - SRM® 972 - from NIST). This material was mixed with purified erythrocytes and then transferred onto filter paper. Based on these samples, the accuracy of the assay was between 92 to 105% and the coefficient of variance ranged from 4.7 to 13.2%. The relative errors ranged from −7.9 to 5.7%. In order to determine the lowest level of quantification, dilutions of the lowest stable isotope-labeled calibrator standards for both vitamin D metabolites (2H6-25OHD2 and 2H6-25OHD3) were prepared and quantified. The method was able to reliably detect a concentration of both 25OHD2 and 25OHD3 down to approximately 5 nmol/L in full blood. All analyses were based on a total of 25OHD (the sum of 25OHD2 and 25OHD3). In addition, our laboratory participates in the Vitamin D External Quality Assessment Scheme (DEQAS)37. We excluded SNPs with minor allele frequency (MAF) <0.01, Hardy–Weinberg equilibrium (HWE) p value <1 × 10−5 or non-SNP alleles . About 245,328 autosomal SNPs were retained in the backbone set. The backbone set was used to impute the genotypes with the Haplotype Reference Consortium reference panel82 following the RICOPILI pipeline83. Imputed best guess genotypes were further filtered for imputation quality (INFO score >0.8), genotype call probability (P > 0.8), missing variant call rates <0.05, Hardy–Weinberg equilibrium (HWE) P value ≥1 × 10−5 and minor allele frequency (MAF) >0.01, resulting in 6,091,695 variants remaining.Individuals included in iPSYCH2012 were genotyped using the Infinium PsychChip v1.0 array . In total, 80,873 individuals were successfully genotyped across 26 waves for ~550,000 variants84. The genetic ancestry of the samples was inferred using R packages bigsnpr and bigutilse following ref. 85, where 73,645 individuals were classified as having European ancestry. The genetic relationship matrix (GRM) of the individuals was estimated by GCTA v1.9386. There were 57,747 unrelated individuals with a pairwise coefficient of genetic relationships <0.05.Darker skin color can reduce actinic production of vitamin D, and because non-European ancestry is associated with variants in DBP (which can influence protein concentration), our primary analyses were in those with European ancestry. We performed principal component analysis (PCA) following ref. From the 77,482 individuals with genetic data, 71,944 and 71,212 had DBP and 25OHD measurements respectively. The DBP and 25OHD metabolites were quantified in 1030 and 1010 plates, respectively. The quantification plates for DBP and 25OHD explained 11.8 and 55.6% of the phenotypic variance respectively. Note that the sequence of testing followed the date of birth, so the marked seasonal variation in 25OHD concentration would be captured in the between-plate variance. We used linear mixed models to pre-regress the effect of the quantification plates from DBP and 25OHD and applied a rank-based inverse-normal transformation (RINT) to the model residuals. The raw distributions of the neonatal DBP and 25OHD can be seen in Supplementary Fig. 87 on the subset of European ancestry individuals . After pre-adjusting for the quantification plates, we fitted sex, year of birth, genotyping wave and the first 20 PCs as covariates in the model in the DBP genetic analyses, and additionally month of birth, gestational age and maternal age in the 25OHD genetic analyses. In light of the strong influence of the GC haplotypes of DBP concentration9, and the potential haplotype-related bias in our monoclonal assay8, we also performed a GWAS adjusted for the 6 GC diplotypes, which were fitted as a covariate in the fastGWA model. Henceforth, we will label the two DBP GWASs and related post-GWAS analyses as (a) DBP (unadjusted GWAS) and (b) DBP_GC (GWAS for DBP adjusted for GC haplotypes).To identify genetic variants associated with neonatal DBP and 25OHD blood concentrations, we performed a linear mixed model GWAS implemented in fastGWA88 using default settings and the European ancestry subset of individuals as LD reference. In addition, we conducted a multi-trait conditional and joint (mtCOJO) analysis89 to condition results from the UK Biobank (UKB) 25OHD GWAS11 on (a) DBP and (b) DBP_GC with fastGWA.To identify independent associations, we conducted a conditional and joint  analysis90.The iPSYCH case-cohort study is enriched with individuals with psychiatric disorders  but also contains a uniform randomly-selected population-based subcohort. To explore if case-enrichment in the sample may have biased the findings from the GWAS, as a planned sensitivity analysis, we ran the GWAS again only within the population-based subcohort. Based on the union of the genome-wide significant loci from the entire case-cohort and the subcohort samples, we examined the correlation between the effect sizes  using Pearson’s correlation coefficients41, within the subset with European ancestry. This method estimates pedigree-based and SNP-based heritability simultaneously in one model using family data and is implemented in GCTA86.Our sample had 23,126 individuals that shared at least one off-diagonal GRM value >0.05, of which 6313 had a  GRM value >0.2 with at least one other individual in the sample. We estimated the heritability of both 25OHD and DBP using methods described by ref. 91, SBayesS92, and LDpred2-auto93 from the GWAS summary statistics. We also estimated the polygenicity (p) parameter with SBayesS and LDpred2-auto. In order to derive these estimates, we used linear regression GWAS summary statistics from unrelated European individuals  and filtered down to the intersection with the HapMap3 set of variants (https://www.sanger.ac.uk/resources/downloads/human/hapmap3.html).Finally, we estimated the SNP-based heritability using LD-score regression42 for computing prior causal probabilities based on functional annotations and (b) SuSiE94 which fine-maps the variants and provides posterior inclusion probabilities (PIPs) and credible sets of variants. First, we estimated truncated per-SNP heritabilities for both our GWAS summary statistics (DBP and DBP_GC) using the L2-regularized S-LDSC method described in PolyFun for the set of coding, conserved, regulatory and LD-related annotations described in ref. 95 The LD-scores for these annotations were computed using our subset of European ancestry individuals belonging to the subcohort . We then used the truncated per-SNP heritabilities as prior causal probabilities in SuSiE for fine-mapping. We only performed fine-mapping on the genome-wide significant loci on the DBP GWAS summary statistics. The credible sets obtained in SuSiE were functionally annotated using the Ensembl Variant Effect Predictor (VEP) v8596.Fine-mapping of the GWAS summary statistic results was performed using a combination of (a) PolyFunBy design, the iPSYCH case-cohort samples are born in Denmark. To infer their genetic ancestry we used the sample’s parental country of birth as a proxy, as determined by the Danish Registers. First, we identified the subset of individuals in which both parents were born in the same region . The regions “Denmark”, “Europe”, “N.America”, “S.America”, “Scandinavia”, and “Australia” were all re-defined as “Europe”. We then looked at the country of birth of the father and kept only countries where there were >10 individuals born in that country.Using the father’s country of birth as the grouping variable, we calculated the geometric median of the first 20 principle components (PCs) per country. Then we calculated the distance to all country centers and applied a hierarchical clustering algorithm (base r hclust function with method = “single”). The population centers were then chosen based on a visual inspection of the clusters as the country with the largest sample size. The following countries were chosen as population centers: “Turkey”, “Kingdom of Morocco”, “Islamic Republic of Pakistan”, “Denmark”, “The Somali Republic”, “The Socialist Republic of Vietnam”, and “The Gambia”. After choosing the cluster centers, all other samples were assigned to the nearest cluster inside a threshold defined as thr_sq_dist = 0.002 × ^2)/0.40) ^2)/0.10) 98. The PRSs for the four phenotypes  were constructed using SBayesS92 and LDpred2-auto93 from our set of GWAS summary statistics. We used linear regression GWAS summary statistics (with the sample filtered for relatedness) for the PRS methods. For SBayesS, we used the provided UKB HapMap3 shrunk sparse LD matrix as an LD reference. For LDpred2-auto, we used the LD blocks based on the subset of HapMap3 variants provided in the paper as LD reference.These individuals were used as a pseudo-replication sample to examine the out-of-sample prediction accuracy of polygenic risk scores (PRSs). The PRS for 25OHD was computed with SBayesR88 and the clumping threshold (C + T) method with window size 250 kb and r2 < 0.1 ) and P value thresholds . The prediction models examined the phenotypic variance explained (r2) after adjusting for sex, age, and the first 20 PCs.We also calculated PRSs using the independent SNP weights estimated by COJO99.The genetic correlation between 25OHD and DBP was estimated in a bivariate GREML analysis (GCTA–reml-bivar) and from GWAS summary statistics with bivariate LD-score regression100 was used to examine gene-based and gene-set analyses. We conducted generalized summary-based Mendelian randomization (GSMR)89 to explore the causal relationship between (a) DBP and 25OHD blood concentrations and (b) between DBP concentration and a range of psychiatric and cognitive phenotypes , and with selected autoimmune disorders . All the relevant GWAS summary statistics are publicly available . As the effect of DBP and 25OHD on these phenotypes may be driven by pleiotropy, the analyses were conducted with and without applying the heterogeneity in dependent instrument (HEIDI) outlier method, which removes loci with strong putative pleiotropic effects89. We randomly sampled 10,000 unrelated European individuals from iPSYCH2012 as the LD reference cohort. We used a Bonferroni-corrected threshold of 1.9 × 10−3 (0.05/(13 × 2)) in the GSMR analysis.Functional mapping and annotation of genome-wide association studies (FUMA)113. For this analysis, we used the same LD reference cohort as used in the GSMR analysis. In total, there were 195,904 probes from 49 tissues. We accounted for multiple testing by using a Bonferroni-corrected threshold of 2.6 × 10−7 .We performed summary-data-based MR (SMR) to identify genes with causal/pleiotropic effects on DBP, using the eQTL data from GTEx v8yj = xj + cj + ej for quantitative traits or (2) logistic model, logit(yj) = xj + cj + ej for dichotomous traits, where yj represents phenotype in UKB, xj represents the polygenic score of DBP or DBP adjusted for GC genotypes, and cj represents the covariates. There were 1149 phenotypes included in the PheWAS analysis, 1027 diseases, 52 anthropometric and brain imaging measures, and 70 infectious disease antigens. The diseases were classified by using the International Classification of Diseases, 10th version (ICD-10) code. The quantitative traits were normalized using RINT with mean 0 and variance 1. The PRSs were generated using SBayesR97 with the reference LD matrix estimated from 1,145,953 HapMap3 SNPs in the UKB. PRSs were computed for 348,501 individuals of European ancestry. The individuals were genetically unrelated (relationship <0.05). The covariates included in the model were sex, age and 20 PCs. The significance threshold used was 4.4 × 10−5 (0.05/1149).The PheWAS analysis was conducted in the UKB using; (1) linear model, The study was approved by the Danish Data Protection Agency, and data access was approved by Statistics Denmark and the Danish Health Data Authority. Approval by the Ethics Committee and written informed consent were not required for register-based projects . All data were de-identified and not recognizable at an individual level.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1-17Reporting Summary


Malus domestica) core‐collection.Tree architecture shows large genotypic variability, but how this affects water‐deficit responses is poorly understood. To assess the possibility of reaching ideotypes with adequate combinations of architectural and functional traits in the face of climate change, we combined high‐throughput field phenotyping and genome‐wide association studies (GWAS) on an apple tree  scanning and airborne multispectral and thermal imagery to monitor tree architecture, canopy shape, light interception, vegetation indices and transpiration on 241 apple cultivars submitted to progressive field soil drying. GWAS was performed with single nucleotide polymorphism (SNP)‐by‐SNP and multi‐SNP methods.Large phenotypic and genetic variability was observed for all traits examined within the collection, especially canopy surface temperature in both well‐watered and water deficit conditions, suggesting control of water loss was largely genotype‐dependent. Robust genomic associations revealed independent genetic control for the architectural and functional traits. Screening associated genomic regions revealed candidate genes involved in relevant pathways for each trait.We show that multiple allelic combinations exist for all studied traits within this collection. This opens promising avenues to jointly optimize tree architecture, light interception and water use in breeding strategies. Genotypes carrying favourable alleles depending on environmental scenarios and production objectives could thus be targeted. Throughout the experiment, ψsoil remained constant for WW trees (c. −8 kPa) while it gradually decreased for WD trees and reached on average −72 kPa on 27 July ; 12 July, light WD ; 17 July, moderate WD ; 27 July, severe WD . Predawn leaf water potential  and midday leaf  and stem  water potentials were also measured on 45 trees across the orchard, with two Schölander pressure chambers previously cross‐calibrated using a distributed, pressurized nitrogen source .At the flight dates, soil dryness increased on WD rows Fig. : 5 July,et al., et al., et al., penAlea  assuming a cylinder trunk shape. TCSA is reported to be a good indicator of tree size  on the variables related to tree architecture using individual tree values. Tree coordinates on the two first PCA dimensions (respectively representative of vegetative development and light interception capacity) were further used as additional traits.All data analyses were performed using R  with a random effect of the genotype (G), fixed effects of the watering scenario (S), spatial design (row), date of measurements (D), and flight within each date (F). For T‐LiDAR variables, a fixed‐effect of the tree position relative to the LiDAR (R) was included. The best model was selected (Table H2) as:G the genotypic variance, varRes the residual variance, and n the number of replicates per genotype.Different mixed‐effect models were tested  with the function BootMer (R/lme4).A 95% confidence interval (CI) for Best linear unbiased predictors (BLUPs) of genotypic values were extracted for each trait. To quantify the genotypic response to soil water deficit, we calculated at each date another variable named resp.TSTA computed as the difference between BLUPs of TSTA under WD condition (TSTA.WD) and BLUPs of TSTA under WW condition (TSTA.WW). The ratios resp.TSTA_12.07/resp.TSTA_27.07 (named contrib_12.07) and resp.TSTA_17.07/resp.TSTA_27.07 (contrib_17.07) were further calculated to quantify the proportion of variation in resp.TSTA observed at severe soil dryness intensity (27 July) that was achieved at light (12 July) and moderate (17 July) intensities of soil dryness.Multivariate analyses of correlations between all traits were performed on BLUPs.et al., et al., et al., GWAS was performed to look for associations between BLUPs and genomic markers. We used the Axiom®Apple 480 K array that resulted in 275 223 robust SNPs for GWAS upon stringent filters  matrix estimated based on SNPs with MAF > 0.05, using VanRaden kinship estimator  were computed as the ratio of variance explained by the polygenic effects (G) to the total BLUP variance, in the null GWAS model, i.e. without considering the effect of individual SNPs (β). This model, accounting only for relatedness, was found  to be the best to control confounding factors as compared to models accounting for structure  from the Wald test statistic was smaller than the Bonferroni threshold, controlling the family‐wise error rate at 5% to account for multiple testing. This threshold was calculated on a subset of nearly independent SNPs using a LD pruning method implemented in Plink/indep  = 5.63.Two types of GWAS models were used. A SNP‐by‐SNP model implemented in et al.  proposed by Segura et al. . The modgemma and MLMM were compared. SNPs selected by both methods were considered ‘highly reliable’ and further used for analysing interaction effects and candidate genes. R/rutilstimflutre . For the particular situation of chr13 where significant SNPs were detected for several traits, a haplotype‐based analysis was attempted to decipher the genetic control of these traits . Annotations for gene biochemical functions were enriched by the biological functions inferred from the putative orthologues identified in Arabidopsis thaliana retrieved from (www.rosaceae.org). We searched UniProt (www.uniprot.org/) and TAIR (www.arabidopsis.org/) databases to get a complete description of the gene functions based on gene ontology and annotation.In order to investigate candidate genes in the associated regions, QTLs were defined as intervals of ±100 kb around the most significant SNPs (in line with Urrestarazu r > 0.7) were observed between dates. We thus estimated a single BLUP for subsequent analyses for each of these three indices, integrating the four dates and both scenarios. Corresponding H2 values were higher than 0.9  and light interception (e.g. STAR varied from 0.55 to 1.06). High values of H² were observed .Canopy development, tree architecture and light interception related traits were computed at a single date, in either summer or winter. The models selected sometimes accounted for the watering scenario but its effect remained low Table , revealir = 0.85 and 0.95, respectively) while the correlation between GNDVI and MCARI2 was lower (r = 0.67). BLUPs of these three variables were significantly, positively correlated with the ones related to canopy development, particularly a_volume and Dim.1 . To estimate BLUPs of TSTA.WW independent of atmospheric variations, we used data collected on the 4 d with a mixed‐effect model including a fixed effect of the measurement date. These TSTA.WW BLUPs displayed a substantial range of variability among the cultivars (0.4–2.3°C) with H2 = 0.67 (CI 0.65–0.68). This suggests a genotype‐dependent control of water loss in WW conditions.TSTA, the difference between canopy surface and air temperature computed from thermal imaging, was first evaluated for the WW trees as a proxy for water loss in nonlimiting watering conditions (TSTA.WW). Despite the stability of tes Fig. , mean vates Fig. , likely tes Fig. . HoweverH² values were similar between dates . TSTA.WD displayed a marked and progressive increase along with intensification of soil drying , and contrib_12.07 and contrib_17.07 did not correlate with the absolute value of maximal resp.TSTA observed on 27 July  and canopy development, suggesting a tendency for more vigorously growing cultivars to maintain stronger canopy temperature regulation than small cultivars. However, the weakness of these correlations (r < 0.43) indicated that multiple combinations of these traits exist depending on the cultivar. Regarding the relationship between canopy surface temperature (either TSTA.WW or resp.TSTA_27.07) and light interception efficiency, correlations appeared even weaker (0.04 < r < 0.26), although significantly different from zero.We further investigated correlations between BLUPs for traits related to canopy development or light interception on the one hand, and canopy surface temperature under WW (TSTA.WW) or in response to severe WD conditions (resp.TSTA_27.07) on the other hand Figs , S6. Corgemma  = 5.73), had a −log10 of 4.68 for STAR and of 3.84 for ci  = 5.77), had a −log10 of 4.35 for Dim.2 and 3.12 for STAR  7 (Dim.2) and one at the top of chr15 (ci). The results obtained on the corresponding SNPs for the other traits related to light interception confirmed their substantial, although nonsignificant, effects. For instance, the SNP on chr7 found significant for Dim.2  up to 7.7  ABNORMAL SHOOT 5, that was shown to have a crucial role for leaf development and morphogenesis  distinct from those associated with vegetative development, highlighting the complementary nature of the information they carry. These associations were consistent with the chlorophyll‐related characteristics often attributed to vegetation indices. For instance, on chr13, the gene homologous to STN7, a protein kinase involved in photosynthesis regulation was found in the region specifically associated to MCARI2 and NDVI. This kinase plays a well‐described role in the redistribution of light excitation energy between photosystem II and photosystem I  considered as a proxy of canopy transpiration  increased with its severity, and a strong and increasing genotypic variability was observed along the month of measurements. It should be noted that in such an orchard experiment, the evolution of the soil WD was only partly controlled, resulting in variability for oil Fig.  as well wn Table  at any dA major outcome of this study is that phenotypic and genetic analyses indicate independent controls of traits respectively related to (1) vegetative architecture; (2) light interception; and (3) water loss estimated by canopy surface temperature. This represents an important step in our understanding of the physiological and genetic basis of these three groups of traits, which are all relevant breeding targets in the context of climate change. As a consequence, multiple allelic combinations exist within the apple tree collection for the 16 highly reliable SNPs Table . This alAC‐L, BP, MD, J‐LR, and EC designed research. AC‐L, BP, MD, and J‐LR performed the experiments. MD performed the MS and TIR images post‐processing. BP performed the T‐LiDAR scans post‐processing. AC‐L and BP analysed data and carried out GWAS analyses with contribution from VS and BG. HM and C‐ED generated the genotyping data. AC‐L and BP wrote the article with the contribution of EC, J‐LR, C‐ED, HM, VS and MD. All authors approved the manuscript.Fig. S1 Evolution of soil water potential during the experiment.Fig. S2 Tree architecture and light‐interception related traits retrieved from T‐LiDAR measurements.Fig. S3 Vegetation indices  are stable and correlated across dates and watering scenarios.Fig. S4 Canopy surface temperature (TSTA) under the well‐watered scenario across dates.Fig. S5 Correlations between the proportion of maximal canopy temperature increase observed at the light and moderate intensities of soil dryness, and the maximal canopy temperature increase.Fig. S6 Weak correlations between tree canopy temperature and traits related to vegetative architecture and light interception.Fig. S7 Two regions associated with traits related to light interception capacity on chromosomes 7 and 15.Fig. S8 Three regions associated with traits related to tree architecture on chromosomes 2, 4, and 6.Fig. S9 Boxplots of allelic effects for a large region associated with several architecture‐related traits on chromosome 13.Fig. S10 Heatmap of linkage disequilibrium in a large region associated with several architecture‐related traits on chromosome 13.Fig. S11 A large genomic region controls architecture‐related traits on chromosome 13.Fig. S12 Haplotype analysis of a genomic region controlling architecture‐related traits on chromosome 13.Fig. S13 Different regions controlling respectively the canopy temperature in well‐watered conditions, or its response to water deficit.Fig. S14 Heatmap of allelic effects for the 16 highly reliable associations and the 241 cultivars of the apple tree core‐collection.Methods S1 Haplotype analysis of a genomic region controlling architecture‐related traits on chromosome 13.Table S1 Summary of the meteorological variables during the drones’ flights at the four dates of measurements in July 2017.Table S2 Leaf and stem water potentials measured on 45 trees among the apple tree core‐collection.Click here for additional data file.Table S3 Complete set of associations detected for the traits related to vigour, light interception and canopy temperature response to water deficit computed from T‐LiDAR, multispectral and thermal imaging on a collection of 241 apple tree varieties.Table S4 Summary of the GWAS results per trait.Table S5 List of genes underlying the most highly reliable SNPs.Table S6 Co‐localizations between associated SNPs on the apple core collection and SSR associated on bi‐parental populations for similar or related traits.Table S7 List of allelic effects for the 16 highly reliable associations and the 241 cultivars of the apple tree core‐collection.New Phytologist Central Office.Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries  should be directed to the Click here for additional data file.


Spikelet abortion is a phenomenon where apical spikelet primordia on an immature spike abort. Regardless of the row-type, both apical and basal spikelet abortion occurs, and their extent decides the number of grain-bearing spikelets retained on the spike—thus, affecting the yield potential of barley. Reducing spikelet abortion, therefore, represents an opportunity to increase barley yields. Here, we investigated the variation for apical spikelet abortion along with 16 major spike, shoot, and grain traits in a panel of 417 six-rowed spring barleys. Our analyses showed a significantly large genotypic variation resulting in high heritability estimates for all the traits. Spikelet abortion (SA) varies from 13 to 51% depending on the genotype and its geographical origin. Among the seven spike traits, SA was negatively correlated with final spikelet number, spike length and density, while positively with awn length. This positive correlation suggests a plausible role of the rapidly growing awns during the spikelet abortion process, especially after Waddington stage 5. In addition, SA also showed a moderate positive correlation with grain length, grain area and thousand-grain weight. Our hierarchical clustering revealed distinct genetic underpinning of grain traits from the spike and shoot traits. Trait associations showed a geographical bias whereby European accessions displayed higher SA and grain and shoot trait values, whereas the trend was opposite for the Asian accessions. To study the observed phenotypic variation of SA explained by 16 other individual traits, we applied linear, quadratic, and generalized additive regression models (GAM). Our analyses of SA revealed that the GAM generally performed superior in comparison to the other models. The genetic interactions among traits suggest novel breeding targets and easy-to-phenotype “proxy-traits” for high throughput on-field selection for grain yield, especially in early generations of barley breeding programs. Hordeum vulgare L.) breeders and geneticists. GY is a complex trait mainly influenced by the concerted action of several agronomic/morphological traits such as plants per m2, spikes per m2, spikelets per spike, spike length, grain number per spike, grain weight, thousand-grain weight, and plant height. Therefore, it is of utmost importance to study the variation and relationship among these traits in order to better understand the contribution of these traits towards GY. One of the promising areas to improve barley GY is to increase the spikelet number per spike, which could be achieved by understanding the spikelet abortion process and its association with other spike, grain, and shoot traits.Increasing grain yield (GY) remains one of the major goals of barley  stage is reached; after this stage, the number of spikelet primordia plateaus . The totVrs1 gene  their genotypic variation, (2) their interaction, and (3) their trait associations with respect to the geographical origin—all in relation to SA. We report that among the spike traits, only awn length positively correlates with SA and that SA, in turn, influences grain morphometric traits—thus, representing a possible sink competition for grain yield. Our hierarchical clustering points to a distinct genetical underpinning of grain traits from spike traits. We have also analyzed the data with different regression methods to check other non-linear associations among the traits. Our results provide a deeper understanding of the genetic interaction between SA and the major spike, grain, and shoot traits.Ppd-H1 sensitive allele and accessions imitating the genetic diversity harbored in the Federal ex-situ German genebank. The comprehensive details for the panel selection criteria and field trails are described in n = 350; 84%) along with a decent proportion of recent cultivars . Standard agronomic practices were applied except for the plant growth regulators.A panel of 417 six-rowed spring barleys was evaluated at the fields of Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany  for three consecutive years (2018–2020). Each year, three replications were planted, and three main culms were selected and tagged in the center of each plot (replication) for data collection. All the accessions were selected based on their spring growth habit, n = 7), grain (n = 7) and shoot (n = 3) traits , grain weight per spike  and thousand-grain weight  were measured using a digital seed analyzer “Marvin” . Marvin analyzer takes into account GNS to calculate TGW as [(GWe/GNS) × 1000]. Lastly, grain-set  was calculated from the ratio of GNS to FSN. Among the shoot traits, heading date (HD) was calculated as the number of days from January 1st until 50% of the spikes were out from the flag leaf sheath. Plant height  was measured on the same three main culm selected earlier for FSN calculation as a distance from the soil surface to the base of the spike and culm dry weight  was measured after harvest.In the text, the abbreviation “SA” represent the trait calculated from PSN and FSN using equation 1 whereas spikelet abortion represents the “in-between ongoing” abortion process during the early reproductive stages. The grain traits namely grain number spike (GNS), grain length , grain width , grain area  is the block effect of the thk block nested in the jth replication and ijkε is the corresponding residual term. Within-year repeatability (Ĥ2) was calculated as:where, σ2g and σ2ε represent the genotypic and residual variances, respectively; Rn denotes the within-year number of replications.where, Except for SA, SD, and GS, the across-years variance component analyses were performed by assuming all effects except the intercept as random in eq. 5 as:ijky is the phenotypic record of the thi genotype in the thj year and thk replication nested in thl block, µ is the common intercept term, ig is the effect of ith genotype, jy is the effect of the thj year, (g×y)(ij) is the genotype-by-year interaction effect of the thi genotype and thj year, (y×r×β)(j|k|l) is the thl block nested in kth replication in thj year, and ijklε is the corresponding residual term. The across-years broad-sense heritability (H2) was calculated as:where, where σ2g, σ2g×y, and σ2ε denote the genotypic, genotype-by-year, and the residual variance, respectively; yn and Rn represent the average number of years and number of replications, respectively.Since SA, SD and GS were derived traits, we used the following model to compute the variance components of genotype and years:ijy is the phenotypic record of the thi genotype in the thj year, µ is the common intercept term, ig is the effect of the thi genotype, jy is the effect of the thj year and ijε is the corresponding residual term. Accordingly, the H2 was calculated based on eq.4, except that Rn is replaced with yn.where, We drew a scree-plot to describe the percentage of variation accounted for by each principal component (PC) and the principal component analysis (PCA) plot to explain the relationships among the traits. A projection from the origin represented each trait. The length of the projection of a given trait from its origin measured the quality of the trait on the plot. In PCA plot, traits that are away from the origin are the major contributors of the corresponding PC. We also calculated the major trait contributors for the first five PCs.r) was computed to examine the relationship among the traits. Moreover, to check the across-years general performance of a given trait, we computed average correlation z transformation, as described in (t-test to check if significant (P<0.05) differences exist for a trait with respect to geographical origin of the accessions.Pearson’s product-moment correlation  functions that separate the data into “smooth + rough” parts to maximize the smooth and minimize the rough part. In addition to implementing different models, we performed ANOVA to examine the model fit, i.e., to check which model significantly better capture the variation in SA with respect to a given predictor trait. Unless stated otherwise, all calculations were performed in R software  greater than zero and was the principal contributor for variance in these traits , seven grain traits  and three shoot  traits to study the relationship between three classes of morphological traits. Restricted maximum likelihood based variance component analyses showed that both within- and across-years genotypic variance was significantly (e traits S4.H2) estimates with highest value for SD (H2 = 0.96) and lowest for SW (H2 = 0.81) (H2 = 0.95), while the least heritability was observed for GNS (H2 = 0.75) —calculated within- and across-years—exhibited large genotypic variation S3–5. Am1, explaining 31.6% variance, showed that all the grain traits and SW vary together  on all 17 traits to check the variation and major contributing traits for first five PCs. The scree-plot showed that first five PCs together explained 86.7% of the total variation in the data set  on within- and across-years BLUEs and observed a strongest positive association between PSN and FSN ; SA was, however, positively correlated with AL . Usually, with an increase in GNS, TGW tends to decreases; however, a positive correlation was observed between GNS and TGW in both within- and across-year data analyses. GA showed higher correlation values with GL (r = 0.96) than GWi (r = 0.76). But, this trend was reversed for TGW, where GWi (r = 0.84) showed a higher correlation with TGW than GL (r = 0.78). Except for GA, GWi showed higher correlation coefficients for GNS, GWe, GS and TGW—as compared to GL—indicating that GWi could be an important factor deciding the grain parameters in barley. For shoot traits, HD was more significantly correlated with CDW than PH. PH and CDW also show a positive and significant correlation  and two spikes (PSN and FSN) traits. Both HD and CDW were positively correlated with PSN and FSN suggesting the positive influence of HD and CDW on the spikelet primordia initiation. Across sub-clusters, CDW was positively correlated with all the spike traits except SA. In contrast, HD showed an insignificant correlation with SA, AL and SW, suggesting that spike characteristics and eventually grain number varies with both variation in days to heading and culm biomass.r = 0.34) suggesting a role for awn growth during spikelet abortion process (further explained in the discussion section). Across clusters, all grain traits except GNS and GS also showed a positive correlation with AL. Hence, it could be speculated that simultaneous awn development and spikelet primordia growth during juvenile spike growth increases the number of aborted spikelets leading to lesser but bigger grains on the spike. Two spike traits, AL and SA were in cluster 2. Interestingly, AL was the only trait that showed a positive correlation with SA (r = 0.92). Furthermore, GNS and GS showed a positive correlation with SW, suggesting spikes with more fertile grains are heavier and wider—an important indirect selection criterion in high-throughput on-field phenotyping, especially early generations of breeding programs. We observed an insignificant correlation for GS with most of the spike traits except FSN and SW. Cluster-5 contained only SD which was closely placed with other spike and shoot traits. SD was positively related to GNS and HD but negatively with SA, GL, and GA.Cluster-3 also harbored two traits, namely, SL and PH. The hierarchical clustering highlighted a higher association of these traits with other shoot and spike traits than grain traits. Cluster-4 composed of GNS, GWe, GS and one spike trait, SW. The highest correlation was observed between GWe and SW  and TGW were in cluster 6 Figure 4n = 73), Asian (n = 183), European (n = 80), North American (n = 28), South American (n = 12) and accessions with unknown origin (n = 41). For comparison, we excluded the accessions with an unknown origin. For most of the traits, North- and South-American accessions did not show any significant differences from other continents  lower values for PSN, FSN, SL, SD, GNS and HD. In nutshell, African accessions, took fewer days to head; therefore, immature spikes stay in spikelet initiation phase for a shorter period leading to lower PSN, consequently lower FSN, and ultimately lower GNS at harvest. Contradictorily, European accessions displayed higher values for all the shoot traits  and cultivars (n = 67), we performed an analysis to check whether there exist any differences between landraces and cultivars for all the investigated traits. The significant differences were observed only for FSN, SA, SW and GNS. SA was higher for landraces than cultivars resulting in lower FSN and consequently lower GNS and SW , breeders select the genotypes or plots based on the plant and spike ideotypes to further test in yield plots. Since GY is an integral parameter of many component traits, such as number of spikes per unit area, number of spikelets per spike, number of grain per spike, spikelet fertility, spike density, spike length, grain weight per spike and many more, the extent of SA directly or indirectly affect these traits. It is thus vital to study spikelet abortion from early to late reproductive stages and its association with other agronomically important traits.Early and late reproductive phases govern important agronomic traits in cereals. From a practical standpoint, early vigor and later stand strength correspond to better GY in barley and wheat. In early generations  and late reproductive (for FSN) stages. Previously, in barley, the relationship of SA with other traits was elucidated but in only a handful of genotypes. This is the first study where we report the genotypic association of SA with other spike, shoot, and grain traits at a large scale. As mentioned in the result section, AL was the only spike trait positively (moderately) correlated with SA. Depending on the growing conditions and genetic background of an accession, awns act as a boon and bane for a plant. . The posIn the present study, within- and across-year analyses revealed—except for 2018, where the correlation was non-significant but positive—a positive association between AL and SA. One possible reason for the insignificant correlation in 2018 could be the extreme weather conditions. Interestingly, while performing multiple linear regression, AL was not one of the predictor variables for SA. Both PSN and FSN were the major predictor of SA  but whens) of the expected value of the response where the response has a known mean-variance relationship , the linear predictors predict some unknown smooth monotonic function  than FSN , suggesting that with an increase in HD, the length of the spikelet initiation phase might also increase, resulting in more spikelet primordia on the spike. With an increase in PSN, the chances of retaining more spikelets (FSN) also increases. Our results showed that PSN, FSN, and HD negatively impact grain morphometric traits. Also, variation in the PC1 was mainly due to grain traits, and variation for PC2 (25.1%) could be ascribed to spike and shoot traits , grant agreement 681686 ‘LUSH SPIKE,’ ERC-2015-CoG; the HEISENBERG Program of the German Research Foundation (DFG), grant no SCHN 768/8-1 and SCHN 768/15-1; and the IPK core budget. Costs for open access publishing were partially funded by the Deutsche Forschungsgemeinschaft .We thank all the previous and present scientific members of the Plant Architecture (PBP) group for their support in phenotypic data collection. We thank Corinna Trautewig, Angelika Püschel, Franziska Backhaus, Kerstin Wolf, Mechthild Pürschel, Mattis Schnurbusch and Ellen Weiss for their outstanding technical support during all the growing seasons. We thank Dr. Guy Golan and Dr. Yongyu Huang for their valuable comments and suggestions on previous versions of the manuscript. We gratefully acknowledge the efforts of Peter Schreiber and his team for their help in managing the field experiments. We thank Martin Mascher and Sara Milner for their assistance in the barley panel selection.Author QHM is employed by BASF Agricultural Solutions GmbH.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Si3g11200 (OsDER1), Si1g27910 (OsMADS6), Si7g27560 (GS5), etc., affected panicle-related traits by involving multiple plant growth regulator pathways, a photoperiod response, as well as panicle and grain development. Most of these genes showed multiple effects on different panicle-related traits, such as Si3g11200 affecting all six traits. In summary, this study clarified a strategy based on the integration of GWAS, a homologous comparison, and haplotype analysis to discover the genomic regions and candidate genes for important traits in foxtail millet. The detected QTL regions and candidate genes could be further used for gene clone and marker-assisted selection in foxtail millet breeding.Panicle traits are important factors affecting yield, and their improvement has long been a critical goal in foxtail millet breeding. In order to understand the genetic basis of panicle formation, a large-scale genome-wide association study (GWAS) was performed in this study for six panicle-related traits based on 706,646 high-polymorphism SNP loci in 407 accessions. As a result, 87 quantitative trait loci (QTL) regions with a physical distance of less than 100 kb were detected to be associated with these traits in three environments. Among them, 27 core regions were stably detected in at least two environments. Based on rice–foxtail millet homologous comparison, expression, and haplotype analysis, 27 high-confidence candidate genes in the QTL regions, such as  Setaria italica L.) is one of the oldest cereal crops and has been domesticated from green foxtail (Setaria viridis L.) about 11,000 years ago in northern China .After harvest, 10 panicles of each foxtail millet genotype were separated for the measurement of five panicle-related traits, including panicle length , main panicle diameter , panicle weight per panicle , grain weight per panicle , and bristle length . Thousand-grain weight  was measured after seed threshing and drying. All measurement of these traits followed the Descriptors and Data Standard for Foxtail Millet [p < 0.05 and p < 0.01, respectively. The coefficient of variation (CV) of these traits among different accessions was obtained by dividing the standard deviation by the mean value. The broad-sense heritability (h2) of these traits in different environments was calculated by the following formula:l and r represent the single environment repetition and environment number, respectively. Moreover, the basic correlation coefficients among these traits in two environments were calculated with Pearson approaches using the software of SPSS 19.0 , and visualization was completed by the performance analytics R package.The best linear unbiased prediction (BLUP) values of six panicle-related traits in two environments (2015 and 2016) were calculated by R package Lme4, and ANOVA analysis was performed by SAS 8.0  . The difXiaomi Refseq) and annotated by Annovar software (v1.1) to obtain the location and relationship with gene models.The high-quality SNP variation datasets from deep resequencing were used to genotype 407 foxtail millet accessions, which were provided by Xianmin Diao, a researcher from the Institute of Crop Science, the Chinese Academy of Agricultural Sciences. After removing the low-polymorphism SNP loci with minor allele frequency (MAF) < 0.05 and missing data > 20%, high-polymorphism SNP loci were retained from 3.8 million polymorphic SNP loci for genotyping, and downstream GWAS analysis. All high-quality SNP loci were re-mapped onto the recent foxtail millet genome  attenuation distance of thr foxtail millet was determined.The high-polymorphism SNP loci filtered by the above step were used as genotypic data. The measured values of six panicle-related traits in three environments  were used as phenotypic data and the GWAS analysis was performed using Tassel 5.0 with a mixed linear model (MLM). The Q-matrix obtained from the population structure and the Kinship matrix (K-matrix) calculated by Tassel 5.0 were used as covariates to correct the association analysis. The significance threshold level of each phenotype was determined by the replacement test (the number of replacement tests was 1000).Oryza sativa L.) is an important model crop for the study of gene function in cereal crops. A large number of genes affecting yield and yield-related traits have been cloned in rice [https://www.ricedata.cn/ (accessed on 15 June 2022)) and previous studies, the functions of rice genes related to yield and panicle were identified and these protein sequences were extracted from the rice protein database by TBtools. The orthologous genes of foxtail millet that are homologous to rice functional genes were identified using BLASTP and MCscanX. The orthologous genes of foxtail millet, which had similar expression patterns to orthologs in rice and located in the QTL regions of the GWAS, were considered important candidate genes for regulating panicle-related traits in foxtail millet. The expression levels of genes in foxtail millet  and rice  were downloaded from the multi-omics database for Setaria italica ) and the rice expression database ). The transcripts per million (TPM) values were used to assess the expression profiles of these genes, and heatmaps were drawn by log2-normalized (TPM + 1) expression levels.Rice ( in rice . Followi in rice , we usedIn this study, GWAS analysis was performed for six panicle-related traits based on 706,646 high-polymorphism SNP loci in 407 foxtail millet accessions. Eighty-seven QTL regions were identified to be significantly associated with these panicle-related traits in three environments. Among these, 27 core QTL regions were detected in at least two environments. Based on homologous analysis, twenty-seven orthologous foxtail millet genes of rice genes that affected yield-related traits were identified in the QTL regions of panicle-related traits in foxtail millet. In light of this, haplotype analysis showed that 24 genes were significantly associated with panicle-related traits in foxtail millet under at least two environments. These genes regulated panicle-related traits through photoperiod response, grain development, multiple plant growth regulatory pathways, and panicle and floral organ development. In summary, this study clarified a method based on the integration of GWASs, homologous comparison, and haplotype analysis to discover the genomic regions and candidate genes for panicle-related traits in foxtail millet. This work will speed up the functional gene mining and molecular breeding efficiency of foxtail millet and other important minor cereal crops.


Medicago sativa) is one of the most important leguminous forages, widely planted in temperate and subtropical regions. As a homozygous tetraploid, its complex genetic background limits genetic improvement of biomass yield attributes through conventional breeding methods. Genomic selection (GS) could improve breeding efficiency by using high-density molecular markers that cover the whole genome to assess genomic breeding values. In this study, two full-sib F1 populations, consisting of 149 and 392 individual plants (P149 and P392), were constructed using parents with differences in yield traits, and the yield traits of the F1 populations were measured for several years in multiple environments. Comparisons of individual yields were greatly affected by environments, and the best linear unbiased prediction (BLUP) could accurately represent the original yield data. The two hybrid F1 populations were genotyped using GBS and RAD-seq techniques, respectively, and 47,367 and 161,170 SNP markers were identified. To develop yield prediction models for a single location and across locations, genotypic and phenotypic data from alfalfa yields in multiple environments were combined with various prediction models. The prediction accuracies of the F1 population, including 149 individuals, were 0.11 to 0.70, and those of the F1 population, consisting of 392 individuals, were 0.14 to 0.67. The BayesC and RF models had the highest average prediction accuracy of 0.60 for two F1 populations. The accuracy of the prediction models for P392 was higher than that of P149. By analyzing multiple prediction models, moderate prediction accuracies are obtained, although accuracies will likely decline across multiple locations. Our study provided evidence that GS can accelerate the improvement of alfalfa yield traits.Alfalfa ( Medicago sativa) is a perennial leguminous forage with a high yield, high forage value, and wide adaptability and is known as the “Queen of Forages”. Yield is one of the essential traits of alfalfa and is the primary goal of breeding. Yield is a quantitative trait controlled by multiple genes and influenced by the environment  is a new approach to select candidate individuals at an early stage or in an off-season nursery or greenhouse based on the prediction of breeding values, which can accelerate genetic gain by capturing genetic information through high-density SNP markers . The GS GS breeding methods have been used for many plants, such as barley, maize, and wheat . As alfa1 populations were then genotyped using GBS and RAD-seq to obtain SNP markers covering the entire genome. The number of SNP markers from the two library construction methods was compared, and eight GS prediction models were established to analyze the effects of population size and SNP markers on the accuracy of genome prediction.The present study evaluated two full-sib populations of 149 and 392 progeny lines segregating for yield-related traits in three field trials over seven years. The two F1 individuals, whereas the second population, which was designated as P392, included 392 F1 individuals r represents the effect of the ith replication nested in the kth location; yh represents the effect of the hth year; jg represents the genetic effect of the jth genotype; jkgl represents the interaction effect of the jth genotype and kth location. The interaction effect of the jth genotype and the hth year is denoted by the symbol jhgy; the interaction effect of the jth genotype, the kth location, and the hth year is denoted by the symbol jkhgly; and the residual is denoted by the symbol eijkh. The grand mean was the only element that was not determined by random effects.where The BLUP estimate was carried out using the lme4 package in R . EstimatDNA extraction and the development of RAD for P149 and GBS for P392 libraries have been described in previous studies . The RADM. sativa ‘Xinjiangdaye’ was used as a reference for GBS sequencing analysis of P392  was utilized to mark duplicate reads, while Genome Analysis ToolKit was utilized to repair indels that can be mistaken for SNPs  with the following dios: https://doi.org/10.6084/m9.figshare.21172051.v1 and https://doi.org/10.6084/m9.figshare.21162283.v2.The SNP data from the P149 and P392 populations were uploaded to figshare  of LF_2017 and LF_2018 was only 0.02 and 0.05, indicating that the yield in these two years was greatly affected by the environment  from Tongzhou and Langfang. The model prediction accuracies ranged from 0.17 to 0.70, with single-location predictions from TZ, LF, and TZ-LF having the highest accuracy of the BayesC model at 0.70, 0.57, and 0.65, respectively. The gBLUP model had the lowest prediction accuracy of 0.19, and the BayesC model had the highest prediction accuracy of 0.60 among the eight models Table 4.For the P392 population, the total biomass yield was calculated by applying BLUP, using harvest yield data for four years of nine harvests at Langfang and four years of 15 harvests at Changping, and the BLUP values were used for the GS prediction model. When using the data set consisting of 47,367 SNPs, each had fewer than 50% of their genotypic values missing.The highest accuracy of the total biomass yield was predicted by the RF model in Langfang, two locations, and the rrBLUP model in Changping, with 0.56, 0.67, and 0.61, respectively. The overall prediction accuracy of the eight models ranged from 0.14 to 0.67 Table 5.In the P149 population, we randomly selected different SNP markers for the GS analysis of the Tongzhou yield data using the rrBLUP model, with five replications for each number of SNP markers. When the number of SNP markers is 1000, the prediction accuracy is at least 0.45, and when the number of SNP markers is more than 5000, the prediction accuracy is 0.47 in all cases Figure 8GBS and RAD-Seq are two of the most common simplified genome sequencing technologies that have been used to detect SNP loci in the genomes of many plants. The GBS and RAD-seq methods employ methylation-sensitive enzymes to trim genomic sequences and decrease redundancy. However, the sensitivity enzymes and sequencing methodologies used by the two systems are distinct . In thisGBS simplifies the library building step and is a cost-effective method for SNP typing, but the number of SNPs obtained is significantly lower than RAD-Seq . In the M. truncatula and M. sativa, many previous studies have used the M. truncatula genome as a reference for genotype data analysis, although M. truncatula is self-pollinated and diploid   markers with 2% missing data in barley (ivum L.) . For incivum L.) , thus imivum L.) . In thisSince simplified sequencing can only cover about 3% of the genomic regions, GBS and RAD-seq can capture a few genomic regions together, and thus the number of common SNPs that can be detected is also small. In this study, after several attempts, we found that the common SNP loci only represented 0.07% of the total SNPs sequenced by RAD-seq, which was close to the theoretical value of 0.09%. Therefore, it was impossible to combine the data from the two sequencing results for the integration validation of the yield model in two populations, P149 and P392, at a later stage. In order to improve the applicability of the yield model, representative loci with comprehensive coverage and uniformity should be selected to design genotype detection chips and establish a yield prediction model based on a uniform genotype data matrix. Then, when genotype analysis is done on the population to be tested, the yield model created in the first step can be used to predict the yield of the population.GS breeding methods have been used for many plants, such as winter barley, with prediction accuracies of 0.80 for malt quality, 0.72 for maize yield, and 0.94 for oil palm fruit yield . In thisDifferent GS models resulted in different accuracies in marker assumptions and treatment effects. This study used the gBLUP model, which predicts on an individual basis, and the rrBLUP, BRR, BayseA, BayesB, and BayesC models, which predict on an SNP benefit value basis. The results showed that gBLUP was less effective than the other models, which indicates that predicting yield using individuals is not ideal for alfalfa. Moreover, prediction based on SNP effect values could lead to better prediction accuracy.The worst prediction accuracy of the gBLUP model may be related to the fact that the gBLUP model is based on individual prediction, with a heritability of yield traits of approximately 19% and approximately 80% of phenotypic variation that cannot be predicted by individual effects, resulting in lower prediction accuracy. Furthermore, the covariance used in this study was replaced by the principal component analysis (PCA) results using TASSEL to calculate genotype data. However, the covariance between individuals for a specific trait is determined by the association at the causal loci rather than the association across the entire genome . This isFor the GS models used in the P149 population, we established four Bayesian models, among which the TZ-LF and TZ-TZ-LF models predicted very low accuracy. However, the prediction accuracy of the TZ single-location was all above 0.67. This is probably because Tongzhou had only two years of data for six harvests. Because of the disparity in the volume of the data, a GS model was developed that failed to capture the majority of biomass yield genes across ages or growth seasons, and as a result, it had an inaccurate forecast. We then used two years of data from Tongzhou and Langfang (2014 and 2015). When the GS model was rebuilt, the accuracy of its predictions increased from 0.11–0.33 to 0.42–0.57, and the accuracy of both the local and cross-location predictions increased. This showed us that the stubble numbers of the modeling and validation populations should not be too different when conducting GS breeding and that the quality of biomass data can significantly impact the accuracy of the GS model prediction.In addition, population size also affects the prediction accuracy of GS models. Generally, the larger the breeding population, the higher the accuracy of GS prediction and the higher the cost. We constructed two populations including different individuals to investigate the effect of the population size on the accuracy of the GS prediction. The seven models showed high prediction accuracy for the P392 population (0.42–0.67). In conclusion, when developing the alfalfa GS model, we should establish as large a breeding population as we can afford.In this study, the prediction accuracy of large breeding populations was higher than that of small breeding populations. The prediction accuracy of total biomass was high, and high accuracy was also observed in cross-location prediction. Between different models the prediction accuracy varied greatly. It is also surprising that when the number of SNP makers increases, the prediction accuracy does not increase significantly, which will be our next research direction. This study shows that GS has a high potential to accelerate alfalfa breeding.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.TZ and JK designed and supervised the project. XH performed bioinformatic analysis and drafted the manuscript. FZ and FH performed experiments and participated in the bioinformatic analysis. YS collected the samples, maintained the growth of the experiment materials, and performed DNA extraction. L-XY revised the manuscript and participated in the interpretation of the data. TZ and JK contributed equally as corresponding authors. All authors contributed to the article and approved the submitted version.This work was supported by the Breeding and Industrialization Demonstration of New High-quality Alfalfa Varieties (No. 2022JBGS0020), National Natural Science Foundation of China (No. 31772656), and China Forage and Grass Research System (CARS-34).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.