N = 118,780) and 11 for the frontal QRS-T angle . Seven out of the 61 spatial QRS-T angle loci have not been reported for other electrocardiographic measures. Enrichments are observed in pathways related to cardiac and vascular development, muscle contraction, and hypertrophy. Pairwise genome-wide association studies with classical ECG traits identify shared genetic influences with PR interval and QRS duration. Phenome-wide scanning indicate associations with atrial fibrillation, atrioventricular block and arterial embolism and genetically determined QRS-T angle measures are associated with fascicular and bundle branch block . We identify potential biology involved in the QRS-T angle and their genetic relationships with cardiovascular traits and diseases, may inform future research and risk prediction.The 3-dimensional spatial and 2-dimensional frontal QRS-T angles are measures derived from the vectorcardiogram. They are independent risk predictors for arrhythmia, but the underlying biology is unknown. Using multi-ancestry genome-wide association studies we identify 61 (58 previously unreported) loci for the spatial QRS-T angle  predictors for arrhythmia. This work used genetic analyses to identify associated loci and pathways, and explore their relationships with other ECG traits and cardiovascular disease. Mechanisms underlying the relationship of conventional electrocardiographic (ECG) measures with arrhythmogenesis  have previously been explored and highlight the role of cardiac ion channels. However, the biology reflected by markers derived from the vectorcardiogram is largely unknown2. These markers include the spatial (spQRSTa) and frontal (fQRSTa) QRS-T angles, which are the angles between the directions of ventricular depolarization and repolarization in 3- and 2-dimensional space, respectively  allow investigation of intermediate phenotypes and complex diseases to identify candidate genes and pathways that contribute to the underlying biology without a predefined hypothesis12. A previous GWAS meta-analysis for the spQRSTa  identified 3 independent loci, with candidate genes involved in cardiac conduction and development13. However, this study was limited by a small sample size, and no GWAS has investigated the fQRSTa.While vectorcardiographic measures are not currently used in routine clinical practice, there has been a resurgence of interest in their potential clinical utility, which has coincided with computational advances for efficient calculation of these markers. Recent studies have reported associations of the spQRSTa and fQRSTa with risk for arrhythmogenesis, sudden cardiac death and cardiac-related mortalityN = 118,780) and fQRSTa  to identify additional candidate genes and pathways enriched for these markers, to advance our understanding of their genetic relationship with other ECG traits and cardiovascular disease, and to enhance the interpretation of existing and future clinical studies.We performed the largest multi-ancestry studies to date for the spQRSTa , Hispanic/Latino (10.7%) and African (7%) ancestries from 14 studies. The multi-ancestry GWAS meta-analysis for fQRSTa included 159,715 individuals from 23 studies and a similar ancestral composition  variants at independent loci associated with spQRSTa and fQRSTa, respectively  were GWS and were the most significant loci. A total of 51 and 9 GWS independent loci were identified in European ancestry meta-analyses for spQRSTa and fQRSTa, respectively. All loci were also GWS in the corresponding multi-ancestry analysis, except one fQRSTa locus  and 11 lead genome-wide significant  participant meta-analysis summary statistics from 33,960 individuals. The analyses identified conditionally independent variants at 4 loci for spQRSTa and at 2 loci for fQRSTa 15. Heritabilities of spQRSTa and fQRSTa were 22.3% and 6.8%, respectively (standard error [SE] 1.0%). European ancestry lead and conditionally independent variants explained 4.0% and 0.5% of the variance of spQRSTa and fQRSTa, respectively. Therefore, these variants explain approximately 17.8% and 7.4% of the SNP-based heritability of spQRSTa and fQRSTa, respectively.Common SNP-based heritability was estimated in the same set of UKB participants with BOLT-Restricted Maximum Likelihood  softwareMYH7 . At each locus, Variant Effect Predictor  was used to identify potential functional consequences of lead variants and their proxies (r2 > 0.8)16. Missense variants were identified at 6 (9.8%) loci (Supplementary Data ADPRHL1 and KANSL1). The KANSL1 locus contained missense variants in strong LD with the lead SNP (r2 > 0.94) in multiple genes . The lead variant (or a proxy) of five loci had a Combined Annotation Dependent Depletion (CADD) score ADPRHL1 locus had the highest CADD score (26.7).Over 96% (59/61) of the spQRSTa lead multi-ancestry variants were common . Across all loci, the lead variant with the largest effect size was rs117526881, located upstream of 17. At 11 loci, the lead variant or a proxy was a significant cis- expression quantitative trait locus (eQTL) variant in cardiac  or vascular (coronary or aorta artery) tissue .To identify variants associated with tissue-specific gene expression in cardiovascular tissues, data were extracted from the Genotype-Tissue Expression  projectP < 3.1 × 10−6), 26 of which mapped within GWS loci, and 10 were significant in multiple tissues  was performed with S-PrediXcan software. The expression of 33 genes was significantly associated with the spQRSTa  multi-ancestry loci in LV or RV tissues (Supplementary Data P < 7.5 × 10−36); however, additional tissues were identified, including fetal renal pelvis, adult heart and brain  datasets to identify potential target genes of regulatory variants21. Significant enrichment was observed across all variants in atrial and ventricular cardiomyocytes  data, we tested for enrichment of non-coding variants at open chromatin regions, to identify cell-type specific functional effects in adult heart, by utilizing Chromatin Element Enrichment Ranking by Specificity (CHEERS)22. Significant gene-set enrichment  was observed in cardiac tissues  ; muscle cell differentiation and regulation of organ growth; actin filament-based movement; and cardiac contraction and hypertrophy  software were used to prioritize potential candidate genes based on overlapping functional pathwaysphy Fig. . Signifiphy Fig. .Fig. 4Si24; the International Mouse Phenotyping Consortium25 (IMP); the Human Protein Atlas26; and PubMed literature reviews for each candidate gene. We also performed lookups of each lead variant in the Open Targets Genetics ‘Locus to Gene’ machine learning gene-prioritization pipeline for further annotations . rs112628278 (nearest gene VAV2) is a low frequency Hispanic/Latino variant (MAF = 0.011) and rare among European ancestry individuals .We identified two independent loci in the Hispanic/Latino spQRSTa meta-analysis, including one locus that was not GWS in the multi-ancestry meta-analysis  identified in the African ancestry spQRSTa meta-analysis showed no evidence for association in the multi-ancestry meta-analysis (P > 0.05). The lead variant (rs28377209) has a higher MAF in African ancestry populations, compared with Europeans (0.19 vs 0.10).One unreported locus , but there was no support for colocalization (Supplementary Data CEP85L and MMP11)  (Supplementary Data CCDC60 (Coiled-Coil Domain Containing 60).Three variants at two loci were significant eQTL variants (LV ): 5.4° (5.1–5.7) for spQRSTa; and 2.03° (1.8–2.3) for fQRSTa (per standard deviation [SD] increase in the PRS).P < 6.3 × 10−3). Genetically determined spQRSTa was associated with increased odds for fascicular or bundle branch block (odds ratio [OR] (95% CI) per SD: 1.10 [1.07–1.13]) , supporting an interpretation that the spQRSTa PRS contains independent risk information. There was suggestive evidence for an association with AV block but not at the Bonferroni corrected significance threshold . Genetically determined fQRSTa was significantly associated with fascicular block or bundle branch block (OR 1.05 [1.02–1.08]), and AV block (OR 1.04 [1.01–1.07]).Subsequently, each PRS was tested for association with prevalent cases of cardiovascular disease in 395,758 unrelated European ancestry UKB participants who were not in the GWAS meta-analysis . We used a Bonferroni corrected threshold to identify significant findings  and idiopathic dilated cardiomyopathy33 (DCM) (as outcomes). Lead variants from multi-ancestry and European spQRSTa meta-analyses were used as instrumental variables (IV). A relationship was suggested with HCM , using a fixed-effect inverse variance-weighted (IVW) model. However, the association was not supported in sensitivity analyses, including MR-Egger, weighted median and MR-PRESSO analyses  studies to test for a causal relationship of genetically determined spQRSTa (as the exposure) with hypertrophic cardiomyopathyOur large-scale analyses of spQRSTa and fQRSTa -- two emerging markers for arrhythmogenesis and cardiovascular disease -- significantly advance our understanding of their basic biology and relationships with classical ECG markers. We identify candidate genes involved in cardiac development, muscle cell differentiation, cardiac contraction and actin-filament based movement. The genes also haverelationships with cardiomyopathies and central arterial vascular development. spQRSTa and fQRSTa shared loci with other ECG measures. But there are also 7 unshared loci, suggesting distinct genetic influences. Among spQRSTa and fQRSTa loci, there are fewer genes for cardiac ion channels, in contrast to findings for other ECG traits. Based on a phenome-wide scan, we report associations with atrial fibrillation, conduction disease and arterial embolism. Moreover, PRSs are associated with fascicular and bundle branch block, and AV block, indicating potential downstream effects of the loci.MYH7, TTN, TNNT2, MYBPC3, DSP, RBM20; Fig. 34. There was also support for genes with non-Mendelian roles in cardiac myogenesis, including ADPRHL1, NACA and NFIA. The function of ADPRHL1 in humans has yet to be established, however, knockout of ADPRHL1 in Xenopus laevis causes loss of the myofibril assembly in ventricular cardiomyocytes and prevents ventricular outgrowth35.A substantial proportion of lead candidate genes at spQRSTa loci are associated with development of inherited cardiomyopathies in humans , rather than causal mechanisms for the structural phenotype. The spQRSTa may also reflect mechanisms and conditions predisposing to intermittent changes in ventricular conduction  indicating the development of cardiac memory38. This is supported by our PheWAS and PRS analyses, where we observed associations with fascicular or bundle branch block and AV block. Therefore, although we did not find a causal relationship with structural HCM or DCM phenotypes, the spQRSTa may reflect the burden of intermittent ventricular arrhythmia or conduction abnormalities occurring over time in these conditions39.Small clinical studies have identified an association between a widened spQRSTa and HCM in paediatric and adult populationsALDH1A2, ANGPT1, and VAV2), significant enrichment of GO-terms (coronary vascular development and vasculogenesis), and associations identified in PheWAS . VAV2, a candidate gene identified in Hispanic/Latino ancestry-specific analyses, is a guanine nucleotide exchange factor for Ras-related GTPases and modulates receptor-mediated angiogenic responses41. Knockout mice for this gene show signs of left ventricular hypertrophy, cardiac fibrosis and hypertension42. Abnormal angiogenesis influences cardiac structure and function through physiological and pathological cardiac hypertrophy, effects on tissue recovery following ischaemia, and regenerative capacity44. These processes may potentially lead to an arrhythmogenic substrate. A recent study identified an association between a widened spQRSTa and increased risk for cardioembolic and haemorrhagic stroke45. Our findings provide potential biological explanations for stroke associations.Multiple findings support a role for angiogenesis and arterial development in modulating the spQRSTa, including candidate genes . Knockdown of FAM135B in iPSC lines reduces spinal motor neuron survival and contributes to neurite defects as seen in spinal and bulbar muscular atrophy. These disorders are associated with cardiac arrhythmia and structural abnormalties56.Although sample sizes were significantly larger for fQRSTa than for spQRSTa (134% larger), we found fewer loci and lower heritability estimates for fQRSTa. All multi-ancestry fQRSTa loci overlapped with spQRSTa loci. There were candidate genes involved in cardiac development and cardiomyopathies including 59.Although our study includes individuals from multiple ancestries, ancestry-specific analyses were limited by sample sizes. Larger studies are needed to yield additional signals. The precise algorithms used to calculate the spQRSTa will marginally differ despite efforts to harmonise approaches; however, such differences are unlikely to affect our positive findings , and summary statistics for spQRSTa across all studies are broadly similar , are provided in Supplementary Data 62. All GWAS summary data included in the meta-analyses utilized NCBI build 37 (summary statistics for ARIC sub-studies were converted from build 38 to 37 using a liftover tool [https://genome.sph.umich.edu/wiki/LiftOver]).Fourteen studies (32 ancestry-specific sub-studies) and 23 studies (40 ancestry-sub-studies) contributed GWAS summary statistics for spQRSTa and fQRSTa meta-analyses, respectively. These included members of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium57. In brief, after applying a bandpass butterworth filter and signal averaging to reduce noise, orthogonal X, Y and Z vector beats were estimated using Kors’ regression matrix63. The spQRSTa was subsequently calculated as the angle between mean QRS and T-wave spatial vector loops58. The fQRSTa was defined as the absolute difference between QRS and T-wave frontal plane axes (fQRSTa = abs[QRS-axis − T-axis])3. Values for both phenotypes are between 0 and 180°.A GWAS was performed by each participating cohort for the spQRSTa (mean) and fQRSTa. If the spQRSTa was not already calculated and digitized ECGs were available, it was derived by transformation of the 12-lead ECG using previously published algorithms15 or by including a kinship matrix or pedigree66. Poorly imputed genotypes were excluded (Rsq > 0.3 or similar for IMPUTE) and a MAF > 0.01 was applied, so that only high-quality variants were included in the study.The primary analysis for this study to declare GWS and previously unreported associations, was the rank-based inverse normal transformed phenotype . The raw phenotype was also analysed to calculate clinically meaningful effect sizes . Study level GWASs were performed using an additive genetic model, accounting for relatedness with appropriate software (e.g. BOLT linear mixed model [BOLT-LMM])Summary statistics for cohort level distributions of each ECG trait and covariates included in the GWAS model, are provided in Supplementary Data Individuals were not included in the study-level GWAS if they had a prior diagnosis of heart failure, myocardial infarction, pacemaker or implantable cardiac defibrillator; were prescribed class I or III anti-arrhythmics, QT-prolonging or digitalis medication; or were pregnant at the time of ECG acquisition. In addition, individuals were excluded if atrial fibrillation, BBB or a QRS duration greater than 120 ms, was present on their ECG.67. Allele frequencies of all variants were compared to those reported in the reference panel used by the study for imputation. To identify analytical errors, QQ and P–Z-score plots were inspected, and summary statistics for β estimates and SE were compared across all studies. To identify potential uncorrected population stratification, the genomic-control inflation factor was calculated to identify test statistic inflation.After submissions of results in a standardized format, quality control was performed using EasyQC (R package v9.2)68. If a study’s λ was >1.0, genomic control during the meta-analysis. Summary statistics and plots were produced for the entire meta-analysis. Subsequently in downstream analyses, variants were only included if present in >60% of the total meta-analysis sample size. The GWS threshold was set as P < 5 × 10−08. To calculate the correlation between variants, relevant individuals from the 1000 G reference panel were used; all individuals for the multi-ancestry summary statistics, ancestry-specific for European, African and Hispanic/Latino analyses. Some in-silico analyses relied upon correlations calculated by the software developers and did not permit modification. In these instances (and explicitly stated in the manuscript text), only European-ancestry summary statistics were used in recognition that the multi-ancestry meta-analysis contained a substantial proportion of individuals of European descent.The primary GWAS meta-analysis for spQRSTa and fQRSTa was the multi-ancestry rank-based inverse normal transformed meta-analysis; however, to estimate clinically relevant effect sizes, a GWAS meta-analysis was also performed using the untransformed phenotype . European, African, and Hispanic/Latino ancestry-specific meta-analyses were also performed as secondary analyses. Meta-analyses were performed with METAL (v2011-03-25) using an IVW model13. Using PLINK (v1.9)69, lead variants from the study were extracted to calculate locus boundaries, defined as ±500 kb or r2 < 0.1 within a 4 mb region (whichever was larger), centered on the lead variant. The 1000 G reference panel was used to calculate correlations between variants61. The variants furthest upstream or downstream were declared the locus start and end respectively. We used the same list to define known loci for fQRSTa as no previous GWAS has been reported for this trait and as the phenotypic correlation with spQRSTa is high. The same method was used to identify GWS loci in our study. Loci that did not overlap with the list of known loci, were declared as previously unreported.One previous GWAS has been reported for spQRSTa, with 3 loci reaching GWS2 statistics and forest plots were produced for each lead variant  at each locus, to identify evidence for heterogeneity. Locus-Zoom plots were also produced to visually inspect patterns of association and variant correlations70.Heterogeneity I14. As recommended by GCTA, the largest cohort in the meta-analysis was used as the reference panel . For this analysis, related individuals in the UKB sample were excluded (up to the 2nd-degree [kinship coefficient <0.0884]). A strict threshold (r2 < 0.1 with the lead variant and PJoint < 5 × 10−08) was used to declare “conditionally independent” signals within loci.To identify independent secondary signals within GWS loci, conditional analyses using European-ancestry statistics were performed using Genome-wide Complex Trait Analysis 15. BOLT-REML models directly genotyped SNPs to estimate relatedness within a sample and obtains SNP-based heritability estimates. The percentage of variance explained (PVE) by lead and conditionally independent variants was subsequently calculated 2 > 0.8]) were annotated using Variant Effect Predictor  to identify potential functional consequences16. VEP also contains data from prediction tools Sorting Intolerant From Tolerant algorithm 72 and PolyPhen-2 (v2.2.273), which supplied deleteriousness scores. In addition, CADD74 and RegulomeDB75 scores for each of these variants were extracted. CADD scores annotate coding and non-coding variants, and enable ranking of their potential deleteriousness compared with other variants in the genome74.Lead and conditionally independent variants  and vascular (coronary and aorta artery), for their known influence on cardiovascular disease. If a variant was also a lead cis-eQTL variant, colocalization analysis were performed at the locus using the R package COLOC, to determine whether the variant was causal in both the GWAS meta-analysis and the eQTL study78. These colocalization analyses use Bayesian statistical methods to calculate a posterior probability (PP) for the variant being causal in both analyses (PP > 75%).To identify relationships between lead and conditionally independent variants (and their proxies), with tissue-specific gene expression, cis-eQTL data was extracted from the GTEx portal (v8)79. S-PrediXcan provides a precalculated transcriptome model database from GTEX-based tissues and covariance matrices of SNPs within each gene model (https://github.com/hakyimlab/MetaXcan). We used European meta-analysis summary statistics for these analyses and tested for association in a total of 16,097 genes across LV, RAA and vascular tissues. A Bonferroni corrected threshold  was used to declare significance and results are only reported when more than one SNP was included in the model.To predict the effects of gene expression levels on spQRSTa and fQRSTa, we performed a TWAS using S-PrediXcan. S-PrediXcan is an extension of the original software PrediXcan and infers results using GWAS summary statistics, thus removing the need for individual-level genotype and phenotype data80. GARFIELD annotates variants with data from the ENCODE, GENCODE and Roadmap Epigenomics projects and calculated odd ratio using a generalised linear model framework80.GARFIELD (v2) was used to identify tissue-specific enrichment of variants at DNase I hypersensitivity sites81. An FDR threshold <0.05 was used to report results. In addition, we performed the same analysis using loops called from recently published Knight-Ruiz normalised 5 kb, 10 kb and 25 kb resolution promotor capture Hi-C data19.Chromatin interaction data was used to identify target genes of regulatory variants (RegulomeDB score ≤3b) in LV and RV tissues. First, using FUMA GWAS  software (v1.3.6), overlap was identified between lead and conditionally independent variant, and pre-processed loops determined by Fit-Hi-C pipelines21. Using PLINK, our GWAS meta-analysis summary statistics were partitioned into haplotype blocks centered on each lead variant (r2 > 0.1 within a 2 Mb radius). Peaks within the lowest decile of total read counts from the snATAC-seq data were removed using a SNP enrichment method CHEERS (version accessed 2020)20, followed by quantile normalization of the remaining peak counts20. Enrichment of variants (one-sided P) within the ATAC-seq peaks was estimated and a Bonferroni-corrected threshold (0.05/number of cell-types) used to report significant findings.To identify cardiac cell-type specific enrichment of non-coding variants, we utilized accessible chromatin information from snATAC-seq data, for atrial and ventricular cardiomyocyte, smooth muscle, endothelial, adipocyte, macrophage, fibroblast, lymphocyte and nervous cells22. Additional analyses were performed using DEPICT to identify pathway enrichment of these genes using Gene-Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG), REACTOME and the Mouse genetics initiative (MGI) data. DEPICT also performs gene-set tissue enrichment analyses using annotations from human Affymetrix microarray probes. For all analyses, an FDR < 0.01 was used to identify significant results. To visualise GO biological processes from the DEPICT spQRSTa multi-ancestry meta-analysis output were analysed using the REVIGO web application to remove redundant terms and cluster related nodes23. They were subsequently visualised using Cytoscape (v3.8.2)82.To identify additional candidate genes at each locus, DEPICT (v3) software was used, that prioritizes genes according to common functional pathways. DEPCT calculates a membership probability for each gene within 14,461 reconstituted gene-sets24 and International Mouse Phenotyping Consortium25  databases, the Human Protein Atlas26 (www.proteinatlas.org) and a PubMed literature review 28. LDSC (v1.0.1) uses pre-computed LD scores and therefore these analyses were performed with European ancestry summary statistics only. These LD scores are used as weights in the regression model29.LD score regression with LDSC (v1.0.1), was performed to calculate the genetic correlation of the spQRSTa and fQRSTa with other ECG traits including PR, QRS, JT and QT intervals12), ventricular depolarization (QRS duration83 and QRS voltage84), ventricular repolarization  and HR86 were tested for overlap with spQRSTa loci . Summary statistics for each ECG trait were also extracted and pairwise-GWASs performed using Bayesian bivariate analyses as implemented in GWAS-PW30. GWAS-PW combines GWAS summary statistics using the variance of effect sizes at each SNP to estimate the probability that a given genomic region contains a variant that influences both traits or distinct associations, and learns reasonable thresholds from the data to declare significance. A pairwise GWAS was performed with the summary statistics of the multi-ancestry spQRSTa meta-analysis and each ECG trait of interest. To account for sample overlap between summary statistics, an expected correlation (-cor in GWAS-PW) between two traits was specified for each analysis87. The values used after adjusted for estimated sample overlap were; −0.0045, −0.0258, 0.0539, 0.0107, 0.0045 and 0.0135 for QT, JT, QRS, PR, HR and TpTe respectively. A posterior probability >0.9 was used as evidence supporting the presence of a causal SNP within the genomic region that influences both traits.Lead variants previously reported for other ECG markers including P-wave duration, atrioventricular conduction . A Bonferroni corrected threshold for the number of phecodes tested  was used to declare significance. To identify evidence for pleiotropy with non-cardiac phenotypes and diseases from previously reported GWAS, a look-up was performed of lead and conditionally-independent spQRSTa variants  using Phenoscanner v290. Associations reaching GWS with other traits and diseases were extracted.To identify evidence of pleiotropy with clinical conditions, a PheWAS was performed using the R package PheWAS (v0.99.5-5)N = 34,361). Analyses were repeated without the QT interval as a covariate. Spearman rank correlations (rho [ρ]) for beta estimates and -log10 P-values, were calculated across all variants between the original model and the sensitivity analysis.To determine whether the QT interval significantly influences the findings from our spQRSTa meta-analyses, sensitivity analyses were performed in UKB . Participants included in the GWAS meta-analysis were excluded, along with related pairs up to the 2nd-degree (kinship coefficient <0.0884). To take advantage of genotype probability data in BGEN format, PRSice-291 was used. The PRSs were calculated by summing the dosage of lead variants weighted by the effect size from the corresponding raw-phenotype meta-analysis. Disease status for each cardiovascular outcome of interest was extracted using ICD-9/ICD-10 codes from hospital admission episodes, self-reported data, operation codes and death certification (Supplementary Note −3) was used to declare significant associations.A PRS was calculated for each trait using lead variants from the European meta-analysis, to test for association with atrial fibrillation, stroke, coronary artery disease, conduction disease, heart failure, non-ischaemic cardiomyopathy and ventricular arrhythmia. Analyses were performed in individuals of European ancestry in UKB  and European  HCM GWAS were retrieved32. Summary statistics for multi-ancestry sarcomere positive  and sarcomere negative  HCM GWAS were also extracted. The HCM GWAS included UKB participants as controls; however, there was no overlap of individuals included in the spQRSTa meta-analyses. Multi-ancestry (61 variants) and European (51 variants) IVs were constructed from GWS variants in the rank-based inverse normal transformed spQRSTa meta-analysis, with the corresponding β, SE and P retrieved from the untransformed meta-analysis to facilitate clinical interpretation. Effect alleles were harmonised between IVs and HCM summary statistics. Two variants, rs398110577 and rs35185344, from the multi-ancestry IV were unavailable within the HCM summary statistics and proxies were selected, rs4946230 (r2 = 0.70) and rs12928779 (r2 = 0.98), respectively. Four different methods were performed, specifically IVW, MR-Egger, weighted median and MR-PRESSO , using MR-Base93. Results are reported as OR (95% CI) for risk of HCM per 1° increase in genetically determined spQRSTa. The same process was followed to test for association with DCM, but with the following differences. Summary statistics from a European ancestry “sporadic” DCM GWAS  were used33. Sporadic DCM was defined as a reduced LV ejection fraction and enlarged LV end-diastolic volume/diameter in the absence of any obvious pathology33. For these analyses, one variant from the European IV was not available (rs2668692), therefore a suitable proxy was selected .The TwoSampleMR R package (v0.5.6), was performed to test for association of spQRSTa with cardiomyopathies, using data from cohorts with HCM and DCMFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-19Reporting Summary


Genome-wide association studies (GWAS) have been successful in identifying genomic loci associated with complex traits. Genetic fine-mapping aims to detect independent causal variants from the GWAS-identified loci, adjusting for linkage disequilibrium patterns.We present “FiniMOM” (fine-mapping using a product inverse-moment prior), a novel Bayesian fine-mapping method for summarized genetic associations. For causal effects, the method uses a nonlocal inverse-moment prior, which is a natural prior distribution to model non-null effects in finite samples. A beta-binomial prior is set for the number of causal variants, with a parameterization that can be used to control for potential misspecifications in the linkage disequilibrium reference. The results of simulations studies aimed to mimic a typical GWAS on circulating protein levels show improved credible set coverage and power of the proposed method over current state-of-the-art fine-mapping method SuSiE, especially in the case of multiple causal variants within a locus.https://vkarhune.github.io/finimom/. Leveraging genetic associations from up to millions of individuals, genome-wide association studies (GWAS) have been widely used to find genomic loci associated with complex traits and disease liability .Each genomic loci highlighted in a GWAS may harbor several causal variants [such as single-nucleotide polymorphisms (SNPs)], and the identification of these variants is important for a better understanding of the biological mechanisms underlying the trait of interest. The difficulty in dissecting the causal variants within a specific locus is compounded by the linkage disequilibrium (LD) patterns, as noncausal variants in LD with a true causal variant will also show associations with the trait of interest.Fine-mapping methods aim to distinguish independent causal variants for a given trait within a specific genomic locus .As the effect sizes of individual genetic variants are typically minuscule, large sample sizes are required in both GWAS and fine-mapping to obtain adequate statistical power to detect robust genetic associations. In addition to practical difficulties and privacy concerns in providing access to large-scale individual-level genetic data, summarized genetic associations from GWASs are increasingly publicly available. Therefore, the use of summary-level genomic data has become a standard in carrying out post-GWAS analyses . AccordiA key element in fine-mapping using summarized data is the correct specification of the LD structure underlying the locus-specific genetic associations. The most simple method of summary data fine-mapping via Bayes factors  makes a Here, we propose a Bayesian fine-mapping method for quantitative traits based on nonlocal product inverse-moment (piMOM) priors using summary-level genetic associations. Originally proposed by The proposed method allows a flexible way to take the external LD information into account. We treat the model dimension as a parameter for which we assign a beta-binomial prior distribution. Such prior has been shown to provide optimal model selection in high-dimensional settings  genotype matrix of size N × P for genetic variants within a specific genomic locus. Given a linear modelwhere jx for which m, which only includes the d causal variants, from a set of candidate models K. Typically, the magnitude of variants included in a fine-mapping application vary from Let In the absence of individual-level genetic data, we resort to the use of GWAS summary statistics, where the outcome is regressed on each genetic variant separately, with population stratification adequately taken into account, and with further adjustments for additional covariates, such as sex, age or technical covariates, carried out where appropriate , we follow the proposal by R. If the summary statistics are given in per-allele units, these can be trivially transformed into standardized units by assuming a Hardy–Weinberg equilibrium for the variants and multiplying both Assuming a large m which only includes the causal variant(s), such prior densities may seem counter-intuitive due to unnecessary strong shrinkage of the causal variants toward the null value. Therefore, conditioned on model m, we set a nonlocal product inverse-moment (piMOM) prior for the causal effect size vector d.A Gaussian density is commonly used for an effect size prior of a causal variant. Such zero-centered, i.e. “local,” priors have the maximum density for the causal effect size at the null parameter value. However, when conditioning on model rameters . Of noter controls the tail behavior of the distribution. Selecting r = 1 leads to Cauchy-like tails, which are known to protect against the potential over-shrinkage of large effect sizes for sparse signals , we assign a beta-binomial distribution:where P and q, where q is being assigned a Beta distribution. As fine-mapping is typically applied in situations with at least one causal variant, we set As a prior for model dimension a and b, as discussed by a = 1 and u > 1. The hyperparameter u controls the concentration of the probability mass of the model dimension prior, in that larger u prioritizes smaller models a priori  LD matrix being available, such strong shrinkage toward sparse models is not necessarily needed, and therefore u can be set to a smaller value, thus not compromising power at the expense of the false positives.One choice for the parameters a priori . EarlierAll model hyperparameters and our reasoning for choosing their values are summarized in m, again implicitly conditioning on the model dimension d. To obtain the posterior probability for model m of dimension d, given data j refers to the dimension of model j. When comparing different models, the denominator is the same for all models and can be canceled out in the algebra. To calculate m,and f, respectively, evaluated at Let f needs to be optimized  sampling scheme:Choose initial model For To create a proposal model Add variable with probability Delete variable with uniform probability from all active variables.Swap: select a variable to be swapped to inactive with uniform probability from all active variables in Using the proposed model wherem.a > u, then set Sample In the “add”-step, we give the largest probabilities to variables that have the largest correlations with the residuals of the current model. This bears similarities to the screening method proposed by In the “swap”-step, we prioritize variants with high correlations with a variant already existing in the model. This prevents the model from deviating far from an already good fit, in that the variant added to the model is likely in high LD with a variant to be replaced. This also provides a natural and efficient way to take into account the uncertainty of a true causal variant among a group of variants in very high LD  with the described sampling scheme is implemented in a freely available R package: α.We adopt the proposed approach by l, and equivalently, the number of credible sets. The credible sets are then created at coverage lα, which represent the coverage of α conditioned on l signals. In addition to the credible sets, posterior inclusion probabilities (PIPs) for each variant j is included in the model.Via credible sets, we can present the uncertainty in both (i) the number of causal variants that are supported by the data and (ii) the identification of a true causal variant from a candidate set of variants for a specific set . We can To improve the implementation of our method, we also present options for clumping extremely highly correlated variants, and a consistency check for the out-of-sample LD reference. The details of these procedures are given in N = 5400; http://urn.fi/urn:nbn:fi:att:bc1e5408-980e-4a62-b899-43bec3755243)    (Supplemdf95b8e) . GenotypWe randomly selected five protein-coding genes of varying size from different chromosomes, and selected the region ±100 kb around each gene for fine-mapping. The variants were filtered for availability in both NFBC1966 and NFBC1986 datasets, resulting in the number of variants in the fine-mapped loci varying from 387 to 2996 .We considered scenarios of 1, 2, or 5 causal variants explaining 0.015 or 0.03 of the phenotypic variance . The parTo investigate the influence of the choice of the LD matrix r = 1, and the maximum number of causal variants K = 10. We varied the hyperparameters τ values for the simulations were selected). MCMC was run for 12 500 iterations, of which we excluded the first 2500 as a “burn-in,” and used the last 10 000 for posterior inference.Each scenario was repeated 100 times. For all simulations, The two main performance measures were the 95% credible set coverage  and power . We also evaluated the median size of the credible sets, the sets being ideally as small as possible with adequate coverage and power. The sampling variability of the estimates in the replication results is quantified by 95% confidence intervals, calculated using binomial distribution for coverage and power, and by 1000 bootstrap samples for median model dimension.We compare our method with the current state-of-the-art method of sum-of-single-effects (SuSiE) regression using summary statistics, which has been shown to perform well against many other fine-mapping methods  described by L = 10 and with the effect size prior variance L), and variant-specific PIPs were extracted from SuSiE output.The overall vector of effect sizes To compare the running times of the methods and to assess the scenarios of a larger sample size and a smaller phenotypic variance explained, we conducted additional simulations using 50 000 observations from a synthetic HAPNEST genotype dataset .P-value) were selected for fine-mapping, in which we applied FiniMOM with K = 10, and SuSiE (version 0.12.16) with L = 10 and We illustrate the performance of our method in real data for genetic associations of interleukin-18 (IL18), a proinflammatory cytokine that stimulates several cell types as an inflammatory factor (https://github.com/vkarhune/finimomSimulations.The analyses were conducted using R software. The scripts for simulations are available at: τ and u are presented in u and smaller values of τ. While the credible set power also improved with decreasing τ, it plateaued for u such that the largest power was detected for u = 1.5, with a slow decrease in power for larger values. As expected, using the in-sample LD consistently outperformed the use of an LD matrix from a reference panel.The FiniMOM main simulation results across all scenarios with varying values of hyperparameters τ and u, we carried out the comparison with SuSiE fine-mapping using u = 2 for in-sample LD and u = 2.25 for out-of-sample LD. These values were selected as a compromise of the ideal credible set coverage and power.Based on the investigation of FiniMOM performance using different values of N = 50, 000; The comparison of credible set coverage between FiniMOM and SuSiE in the main simulations is shown in FiniMOM showed better statistical power to detect multiple causal variants than SuSiE . The larThe median credible set sizes were similar or somewhat larger in FiniMOM . The creWhen comparing the computational times, both FiniMOM and SuSiE scaled well with increasing number of variants considered . Both meThe coverage and power estimates across all simulation scenarios using NFBC1966 genotype data are given in NLRC4, RAD17, and BCO2—harboring variants at NLRC4, RAD17, and BCO2 loci were 8.3, 3.4, and 6.8 s, respectively. For SuSiE, the same run times were 5.8, 3.1, and 5.8 s, respectively.We then ran an applied example using GWAS summary statistics on circulating IL18 levels (-mapping , and invThe results for IL18 are summarized in NLRC4, FiniMOM produced a smaller credible set, consisting of only two variants, rs385076 and rs659239. The former SNP was also the top variant in the replication GWAS. The size of the corresponding SuSiE credible set was 45 variants (RAD17 locus. In addition, there were two other credible sets detected by both methods. However, no strong signal was detected for the variants in these credible sets in the replication GWAS (BCO2, and both produced one credible set with the same variants included. This signal was also detected in the replication GWAS (For variants . This isvariants . Both meion GWAS . Both Fiion GWAS .In this article, we have proposed a novel fine-mapping method for summarized genetic associations using product inverse-moment priors. Additionally, incorporating theoretical results of the model dimension prior in very sparse regression settings, we propose an adjustable beta-binomial prior to be used. The proposed approach showed improved detection of causal variants across various scenarios which were aimed to mimic typical settings in pQTL association analyses (a priori. Apart from the use of nonlocal priors (In Bayesian fine-mapping methods, a common way is to set a Gaussian prior for the causal effect sizes. This allows for effects that are arbitrarily close to zero with non-negligible probability . While sThe better performance of FiniMOM over SuSiE seems to come at the expense of slightly larger credible sets. However, based on the simulations, the differences in the set sizes were notable in the cases of low phenotypic variance explained and multiple causal variants, implying that FiniMOM better allows for uncertainty in these situations. In the scenarios of a single causal variant, the sizes were largely similar.τ and u which control the detectable effect sizes and the model dimension prior, respectively. Similarly, FiniMOM is highly adaptable to deal with either in-sample or out-of-sample LD reference. Moreover, unlike in SuSiE, but similarly as in FINEMAP method (We also highlight our method’s flexibility in the tradeoff between credible set coverage and power, easily incorporated via the hyperparameters r and a as constants and did not conduct an exhaustive search over their parameter spaces. We did not consider the situations where the effect sizes depend on the allele frequencies of the variants (τ to vary across variants (Some limitations of our work should be mentioned. In the absence of in-sample LD information, the importance of a good LD reference cannot be overstated and, while our method is shown to be robust for out-of-sample LD references, it naturally cannot recover poor LD reference and data quality. We set the hyperparameters variants . FinallyIn summary, we have proposed a novel genetic fine-mapping method for summarized data that outperforms a current state-of-the-art fine-mapping method. The specific strengths in detecting multiple causal variants and adaptability to deal with out-of-sample LD information make FiniMOM an attractive option for fine-mapping studies of quantitative traits.btad396_Supplementary_DataClick here for additional data file.


Alnus cremastogyne is a rapidly growing broad-leaved tree species that is widely distributed in southwest China. It has a significant economic and ecological value. However, with the expansion of the planting area, the influence of phenotypic variation and differentiation on Alnus cremastogyne has increased, resulting in a continuous decline in its genetic quality. Therefore, it is crucial to investigate the phenotypic variation of Alnus cremastogyne and select excellent breeding materials for genetic improvement. Herein, four growth-related phenotypic traits  and twelve reproductive-related phenotypic traits  of 40 clones from four provenances were measured and analyzed. The phenotypic variation was comprehensively evaluated by correlation analysis, principal component analysis and cluster analysis, and excellent clones were selected as breeding materials. The results revealed that there were abundant phenotypic traits variations among and within provenances. Most of the phenotypic traits were highly significant differences (p < 0.01) among provenances. The phenotypic variation among provenances (26.36%) was greater than that of within provenances clones (24.80%). The average phenotypic differentiation coefficient was accounted for 52.61% among provenances, indicating that the phenotypic variation mainly came from among provenances. The coefficient of variation ranged from 9.41% (fruit shape index) to 97.19% (seed weight per plant), and the repeatability ranged from 0.36 (volume) to 0.77 (cone width). Correlation analysis revealed a significantly positive correlation among most phenotypic traits. In principal component analysis, the cumulative contribution rate of the first three principal components was 79.18%, representing the main information on the measured phenotypic traits. The cluster analysis revealed four groups for the 40 clones. Group I and group II exhibited better performance phenotypic traits as compared with group III and group IV. In addition, the four groups are not clearly clustered following the distance from the provenance. Employing the multi-trait comprehensive evaluation method, 12 excellent clones were selected, and the average genetic gain for each phenotypic trait ranged from 4.78% (diameter at breast height) to 32.05% (dry weight of single cone). These selected excellent clones can serve as candidate materials for the improvement and transformation of Alnus cremastogyne seed orchards. In addition, this study can also provide a theoretical foundation for the genetic improvement, breeding, and clone selection of Alnus cremastogyne. Alnus cremastogyne, a deciduous tree, belonging to the genus Alnus in the family Betulaceae, is an endemic species among the 11 Alnus species found in China [A. cremastogyne is native to the Sichuan Basin and its surrounding areas. It has been widely introduced and cultivated in the middle and lower reaches of the Yangtze River Basin since the 1960s [A. cremastogyne has a well-developed root system with nodules that efficiently fix free nitrogen from the atmosphere, thereby enhancing soil fertility [he 1960s . Due to he 1960s . As a noertility . In addiertility .Phenotypic variation in forest trees is a crucial manifestation of genetic diversity, adaptability, and evolution . PhenotyJuglans regia [Eucalyptus urophylla [Larix kaempferi [Excellent clone selection is an important basis for forest tree genetics and breeding . Due to ns regia , Eucalyprophylla , and Laraempferi , etc.Alnus species. These studies have mainly focused on their gene sequences [A. cremastogyne seeds for afforestation. However, most of the used A. cremastogyne seeds are collected in the wild, lacking a proper selection. Hence, there is an urgent need to improve breeding materials’ quality through genetic improvement. Unfortunately, the research on the genetic improvement of A. cremastogyne is mainly focused on conventional breeding, and the study of phenotypic variation remains somewhat inadequate. In previous studies, Chen et al. [A. cremastogyne from different provenances and found that there were extremely rich phenotypic variations among and within provenances. Moreover, there were highly significant differences among different provenances for the tree height and volume of A. cremastogyne; in addition, the interaction between provenance × location was significant [A. cremastogyne. Subsequently, Chen et al. [A. cremastogyne. The above mentioned studies on the genetic variation of A. cremastogyne were based on the measurement and analysis of phenotypic traits in growth, cone, seed, germination, and other traits, while rarely considering the association of several phenotypic traits. It is difficult to clarify this genetic variation in detail and accurately, which hinders the further exploration and utilization of excellent germplasm resources for A. cremastogyne.As an important economic tree species, extensive research has been conducted on the equences ,26,27, tequences ,29,30, nequences ,32, photequences ,34, etc.n et al.  studied nificant . These fn et al.  found thA. cremastogyne clones from four provenances were measured and analyzed. The main purposes of this study were: (a) to analyze the variation characteristics of phenotypic traits and determine the differentiation of phenotypic traits; (b) to select excellent clones as breeding materials for genetic improvement in A. cremastogyne seed orchard based on phenotypic variation characteristics. This study will provide a theoretical basis for genetic improvement and breeding of A. cremastogyne.In this study, 16 phenotypic traits (growth-related phenotypic traits and reproductive -related phenotypic traits) of 40 A. cremastogyne clones from 4 provenances of Pingchang County (PC), Enyang District (EY), Jintang County (JT), and Xuanhan County (XH) in Sichuan Province in the seed orchard. The results of variance analysis showed that highly significant differences (p < 0.01) were observed in all phenotypic traits among provenances. Except for the volume, other phenotypic traits were highly significant differences (p < 0.01) or significant differences (p < 0.05) within provenances (p < 0.05) were observed in the phenotypic traits across the 4 provenances, indicating extensive variation. As for growth traits, the PC provenance had the largest DBH (9.77 cm) and V (0.032 m³), while the JT provenance had the largest H (8.07 m). Conversely, the XH provenance had the smallest DBH (6.68 cm), H (6.44 m), and V (0.013 m³). Concerning cone and seed traits, the PC provenance had the largest FWSC (1.72 kg), DWSC (1.00 kg), SWPP (0.46 kg), TKW (0.62 g), and SR (49.52%), whereas the XH provenance had the smallest FWSC (0.60 kg), DWSC (0.41 kg), SWPP (0.11 kg), TKW (0.43 g), and SR (31.75%). They were 2.9 times, 2.4 times, 4.1 times, 1.4 times, and 1.6 times, respectively, greater than those of the XH provenance. No significant differences on GR, GP, and GI between the PC and EY provenance were observed regarding germination traits. However, significant differences (p < 0.05) were observed between the PC, JT, and XH provenance. Additionally, there were no significant differences in GR, GP, and GI between JT and XH provenance.The variance analysis considered 16 phenotypic traits of 40 venances . The phevenances . SignifiA. cremastogyne was mainly seen among provenances , maximum values (max), range, and standard deviation (SD) of 16 phenotypic traits were summarized in A. cremastogyne, with abundant variation among the different phenotypic traits. The coefficients of variation for each phenotypic trait ranged from 9.41% to 97.19%. Among them, the highest coefficient of variation was 97.19% for SWPP, and the lowest was 9.41% for FSI. Additionally, the SWPP, DWSC, and FWSC exhibited relatively large CV values of 97.19%, 91.32%, and 89.21%, respectively. On the other hand, the DBH, H, CL, CW, and FSI had relatively smaller CV values of 18.39%, 14.78%, 13.62%, 14.56%, and 9.41%, respectively. The repeatability of the 16 phenotypic traits in A. cremastogyne varied from 0.36 to 0.77. The highest repeatability value was 0.77 for CW, while the lowest was 0.36 for V. Among the tested phenotypic traits, the repeatability values for DBH (0.43), V (0.36), and SR (0.41) were less than 0.5. However, the repeatability values for the remaining phenotypic traits were all greater than 0.5. The high coefficients of variation and repeatability reflected the immense potential for genetic improvement.Coefficient of variation (CV) and repeatability are commonly used as two important genetic parameters, representing the dispersion and genetic stability of phenotypic traits. p < 0.05) correlation among most phenotypic traits. The growth-related phenotypic traits, DBH, H, and V demonstrated highly significant (p < 0.01) positive correlations, indicating a strong association among these three growth traits. Remarkably, the correlation coefficient between DBH and V stood out as the highest among all phenotypic traits, further highlighting the strong relationship between DBH and V. In contrast, HUB exhibited no significant correlation with FSI, and was negatively correlated with various phenotypic traits. In addition, DBH, H, and V were significantly and positively correlated with FWSC, DWSC, SWPP, TKW, CL, CW, CL × CW, and SR, respectively. Regarding reproductive-related phenotypic traits, FWSC, DWSC, SWPP, CL, CW, and CL × CW were also significantly and positively correlated. Additionally, GR, GP, and GI showed significant positive correlations, as well as with TKW. These correlations among traits highlight the potential for evaluating and selecting exceptional genotypes of A. cremastogyne based on their growth characteristics, fruit traits, seed traits, and germination performance.The correlation analysis results of the phenotypic traits are depicted in A. cremastogyne (1), with an eigenvalue of 7.95, was 49.69% of the variance. DBH (0.86), H (0.68), V (0.86), FWSC (0.85), DWSC (0.85), SWPP (0.89), TSW (0.77), CL (0.75), and CW (0.87) exhibited the highest eigenvalues. Furthermore, this had the most significant impact on the first principal component. This indicated that the first principal component primarily represented the information related to growth, cones, and seeds. The second principal component (Y2) had an eigenvalue of 3.26, with 20.36% of the variance. Among its contributing traits, GR (0.95), GP (0.94), and GI (0.94) had the highest eigenvalues, suggesting that the second principal component mainly represented information related to germination characteristics. The third principal component (Y3) had an eigenvalue of 1.46, was 9.13% of the variance. HUB (−0.04) and FSI (0.50) had the highest eigenvalues, suggesting that the third principal component primarily represented information regarding the seed yield in cones. Importantly, the eigenvalue of HUB was negative, indicating an inverse relationship between the HUB value and the score. In the principal component analysis loadings plot  were greater than that on germination traits (PCA2). PC and XH provenances of A. cremastogyne were highly influenced by the geographic factors in terms of their phenotypic traits. However, the phenotypic traits of EY and JT provenances were less affected by geographic factors. EY and JT provenances had higher degrees of consistency in their phenotypic traits, while PC and XH provenances showed a larger variation in phenotypic traits.The PCA scatter plot was constructed with PCA1 on the X-axis and PCA2 on the Y-axis . The geoThe 40 clones from 4 provenances were clustered using the inter-group connection method based on Euclidean distance. The results are displayed in iQ value of each lineage within a population of 40 clones was calculated. The results of the evaluation are presented in iQ values were chosen, with a selection rate of 30%. Therefore, PC5, PC2, PC3, PC10, PC1, PC9, PC6, PC4, JT10, JT2, JT9, and EY6 were selected as excellent clones with outstanding comprehensive phenotypic traits, as shown in The comprehensive multi-trait evaluation method was employed to select excellent clones. The interrelationships among phenotypic traits, determined through correlation analysis and principal component analysis, were considered during the selection process. Evaluation indices, including DBH, V, FWSC, DWSC, SWPP, TKW, CW, GR, GP, and GI, were chosen to assess the lineages. The A. cremastogyne clones sourced from four provenances. The results of variance analysis showed that most of the phenotypic traits had highly significant differences (p < 0.01) or significant differences (p < 0.05) among and within provenances, and there were abundant variations in phenotypic traits of A. cremastogyne. This finding is consistent with studies conducted on other tree species, such as Pinus sylvestris [Phoebe bournei [Pinus wallichiana [A. cremastogyne indicates a high potential for genetic improvement. Therefore, selecting excellent trees based on phenotypic traits provides a solid foundation.Genetics and variation are the main components of forest tree breeding research, as well as a prerequisite and guarantee in selecting forest trees . The analvestris , Phoebe  bournei , and Pinlichiana . The conA. cremastogyne was 52.61%, indicating that the phenotypic variation of A. cremastogyne was mainly derived from among provenances. According to the phenotypic variation analysis in A. cremastogyne, we can clearly observe substantial differences in the phenotypic traits among different provenances. This is similar to the phenotypic diversity observed in populations of Idesia polycarpa [A. cremastogyne demonstrate superior comprehensive phenotypic performance compared to the Xuanhan provenance. This difference may be attributed to the variations in temperature across different provenance locations. Previous studies have indicated that temperature can significantly impact plant growth, with lower temperatures to some extent limiting plant development [Pinus yunnanensis, a positive correlation between temperature and growth was observed, highlighting the growth promotion effects in higher temperature environments [A. cremastogyne is a thermophilic and heliophilic tree species that thrives in regions with higher temperatures, which also promote its growth [A. cremastogyne presented in A. cremastogyne from the Xuanhan provenance, growing at higher altitudes in mountainous regions, receives less sunlight and experiences lower temperatures compared to the other three provenances. These factors to some extent restrict its growth. Under prolonged temperature influences, phenotypic traits of A. cremastogyne among different provenances have undergone varying degrees of differentiation.The phenotypic differentiation coefficient reflects the degree of phenotypic differentiation between provenances or production areas. It shows the extensive degree of plant adaptation to different environments. This study found that the average differentiation coefficient of 16 phenotypic traits of olycarpa . Overallelopment . In the ronments . A. crems growth . AccordiA. cremastogyne in Sichuan Province, and they observed higher levels of genetic variation within populations than among populations. The possible reasons for the differences observed at the molecular and macroscopic levels are that SSR markers are not influenced by the environment, but are affected by population migration or gene flow within the species, which can lead to genetic variation within the population [A. cremastogyne. In comparison to other tree species, A. cremastogyne has a lower phenotypic differentiation coefficient than P. bournei (70.83%) [Orchis mascula (20.00%) [Juglans mandshurica (50.31%) [A. cremastogyne is above the middle level, and the phenotypic variation among provenances is higher than that within provenances. This may be due to the geographical isolation among the various source areas, leading to phenotypic differentiation among the provenances of A. cremastogyne. It has been shown that species of the Betulaceae family are pollinated by wind [A. cremastogyne have been geographically isolated for a long time, thereby each population gradually formed its own relatively stable population phenotypic characteristics. These factors contribute to the independent differentiation of populations and reflect the adaptation of different provenances of A. cremastogyne to diverse environments [A. cremastogyne in Sichuan has changed significantly from the mountains around the Sichuan Basin to the hills and plains of the Yangtze River Basin, which has affected the spread of pollen and seeds in space. The gene exchange among populations is blocked, which increases the possibility of differentiation between populations. The complex geographical and climatic conditions have a great influence on the phenotypic variation of A. cremastogyne. It can be seen that the isolation of A. cremastogyne in time and space has caused abundant phenotypic trait variations to adapt to a new environment.In contrary to the results of this study, Guo et al.  conductepulation . Howeverpulation . Consequ(70.83%) , while t(20.00%) . It is a(50.31%) . Provena(50.31%) . This di(50.31%) . Researc(50.31%) . The phe by wind  since, dronments . Variatironments . TherefoXanthoceras sorbifolium and Liang et al. [Pinus koraiensis, they calculated repeatability at medium to high levels. This suggests that the measured phenotypic traits are more strongly controlled by the genotype, and the phenotypic variation is relatively stable. Therefore, utilizing these phenotypic traits with higher repeatability allows for more accurate assessment of trait differences among individual trees and selection of superior individuals with desirable traits, which is of great significance for tree breeding and genetic improvement. We observed a wide range of variation in phenotypic traits, with the CV ranging from 9.41% (FSI) to 97.19% (SWPP). It was noteworthy that the coefficients of variation for CL (13.62%) and CW (14.56%) were relatively small, whereas FWSC (89.21%), DWSC (91.32%), and SWPP (97.19%) exhibited larger coefficients of variation. This indicates that CL and CW traits are more stable, while FWSC, DWSC, and SWPP showed greater variability as compared to other traits. Previous studies had shown that there may be a functional correlation between cone and seed traits in some tree species, so it is estimated that they are genetically related [A. cremastogyne indicate a significant variation in the phenotypic traits, and the high coefficient of variation is crucial for the selection of excellent clones. The abundant phenotypic variation among clones can lead to greater genetic gain. Regarding repeatability, our findings showed repeatability values ranging from 0.36 (FSI) to 0.77 (SWPP) for each phenotypic trait, belonging to the moderate or high level (R > 0.30). The repeatability of growth traits  is similar to that of Betula platyphylla [L. kaempferi [Pinus sylvestis [P. koraiensis [A. cremastogyne were predominantly influenced by moderate to high genetic factors. This ensured stable inheritance of these traits. Such moderate to high genetic control facilitates the selection process in breeding programs, enabling the use of fewer families for achieving a significant genetic gain [CV and repeatability are essential parameters used to assess genetic variation. This is beneficial to selecting excellent clones within a breeding population. These parameters provide valuable insights into the extent of variation present. The CV measures the population’s phenotypic diversity dispersion and reflects the genetic variation potential of phenotypic traits. A higher CV indicates a more extensive distribution among populations and a greater genetic variation . Repeatag et al.  on Pinusor CL 13.% and CW typhylla , while iaempferi . As for ylvestis , althougraiensis . Overalltic gain . The higA. cremastogyne and to evaluate and select high-yielding excellent clones for genetic improvement purposes. The correlation between V and DBH was exceptionally high. This finding is similar to research on Jatropha curcas [A. cremastogyne trees can increase wood production. The HUB showed a significant negative correlation with most of the phenotypic traits. This implied that, when the HUB surpassed a specific height, the longitudinal canopy size decreased, leading to reduced nutrient acquisition through photosynthesis and negative effects on other traits.Identifying the relationship between phenotypic traits is important in forest tree breeding. Correlation analysis provides valuable insights into the relationship between different traits, which could contribute to the selection of excellent clones and the comprehensive evaluation of parents during forest improvement. In this study, the traits H, DBH, and V exhibited a strong positive correlation. There is a significant positive correlation between traits H, DBH, and V, indicating that these traits can be used both to assess the genetic variation in a curcas . It furtCedrus deodara was related to seed/cone size, and higher germination rates. This study showed a significant positive correlation between TKW GR, GP, and GI. However, these findings were differed from a previous work on A. cremastogyne by Li et al. [There was a highly significant positive correlation among growth, cone, and seed traits, likely attributable to growth and reproductive competition during nutrient redistribution processes. Furthermore, most cone and seed traits displayed significant positive correlations, which was consistent with previous research findings . The sigi et al. . It was A. cremastogyne was 79.18%, which is comparable to oak [Carthamus tinctorius [A. cremastogyne was influenced by a combination of growth traits, cone traits, seed traits, and germination traits. This observation is consistent with previous research on Arenga pinnata [The PCA is a classical multivariate value technology . It utile to oak , while ie to oak  and Cartnctorius . The hignctorius . SpecifiP. yunnanensis [A. cremastogyne provenances.A clustering dendrogram of the 40 clones was constructed based on PCA, and the results found that the first and second groups exhibited better comprehensive phenotypic traits than the third and fourth groups, indicating that excellent clones were likely to be selected from the first and second groups after the evaluation and screening of the parents. The performance of phenotypic traits within each classification group further supported the selection of excellent clones. It is essential to highlight that the clustering results do not align perfectly with the geographical distance between provenances, which is similar to the findings of nanensis . This obnanensis . MoreoveiQ from multiple traits is considered more accurate. To avoid reducing the genetic gain of individual traits, selecting appropriate traits as evaluation indicators is necessary [iQ value. These traits represented the growth, cone, seed, and germination characteristics of A. cremastogyne. Twelve excellent clones were selected based on their iQ values. The average genetic gains for growth traits of DBH and V were 4.78% and 9.10%, respectively. They were slightly lower than that of P. koraiensis [L. kaempferi [P. koraiensis [Cunninghamia lanceolata [A. cremastogyne. In general, the selected excellent clones with high genetic gain will meet the current market demand for high-quality A. cremastogyne seeds. Moreover, it will contribute to the improvement of the structure of A. cremastogyne seed orchards and the optimization of breeding material selection.Phenotypic variation is the fundamental guarantee for excellent clones. Trees have abundant genetic variation, allowing selective breeding to promote the application of superior germplasm. Genetic improvement of trees plays a crucial role in ensuring stable inheritance for the offspring. There are multiple methods for selecting excellent clone lines, such as indicator selection  and PCA ecessary . This straiensis  and L. kaempferi . The averaiensis . Similarnceolata . These vA. cremastogyne, located in Pingchang County (PC), Sichuan Province, Southwest China . The seed orchard is located in the subtropical monsoon climate, with an average altitude of 540 m. The average annual temperature is 16.8 °C, the annual average rainfall 1213 mm, and the soil in the experimental area is slightly acidic and moderately fertile. In the spring of 2017, the cuttings of A. cremastogyne were collected from four provenances of Pingchang County (PC), Enyang District (EY), Jintang County (JT), and Xuanhan County (XH) in Sichuan Province  was measured using a height measuring rod with an accuracy of 0.1 m. The diameter at breast height (DBH) was measured 1.3 m above the ground level using a DBH tape measure with an accuracy of 0.1 cm. The height under the branches (HUB) was determined by measuring the height of the first living branch using a tape measure with an accuracy of 0.1 m. The volume (V) was calculated based on the following formula [V is volume, D is the diameter at breast height, and H is the height of trees.The height of trees ( formula :(1)V=5.2DWSC), and seed weight per plant (SWPP) were measured using an electronic balance with a precision of 0.01 kg. To determine the cone length (CL) and cone width (CW), a vernier caliper with an accuracy of 0.01 mm was employed. For each plant, a random selection of 20 cones was made, resulting in a total of 60 cones measured per clone. The thousand kernel weight (TKW) was determined using the quartering method, with a precision of 0.01 g. The fruit shape index (FSI) and seed rate (SR) were calculated according to the following formulas:FSI is the fruit shape index, CL is the cone length, and CW is the cone width.SR is the seed rate, SWPP is the seed weight per plant, and DWSC is the dry weight of single cone.The fresh weight of cones (FWSC), dry weight of cones (GR), germination potential (GP), and germination index (GI) were calculated the following formula [GR is the germination rate, N is the number of normal germinating seeds, and M is the total number of seeds.K is the number of normal germinating seeds at peak times, and M is the total number of seeds.GI is the germination index, Gt is the germination rate at time (t), and Dt is the duration of the germination test.The seed germination test was conducted in an incubator, and the germination rate ( formula :(4)GR=NMA. cremastogyne, the Duncan multiple comparison method was employed to compare the means and standard deviations of the phenotypic traits, and relevant genetic erosion parameters were calculated. Pearson correlation coefficient was used to analyze the correlation among the various phenotypic traits. Principal component analysis was conducted to reduce the dimensionality of the phenotypic trait variables, and based on the results of principal component analysis the Euclidean distance method was used to cluster the clone. The experimental data were summarized using Microsoft Office Excel 2019, and the nested variance analysis, multiple comparisons, correlation analysis, principal component analysis, and cluster analysis were conducted using IBM SPSS Statistics 27, and the graphs were generated using OriginPro2022b. For the data analysis, nested variance analysis was employed, and the linear model of variance analysis according to the following statistical model [According to the results of nested analysis of variance, which revealed differences in the phenotypic traits among different provenances of al model :(7)Yijk=The phenotypic coefficient of variation was calculated as follows:The phenotypic differentiation coefficient was calculated according to the following formula :(9)Vst=σR) for phenotypic trait was calculated according to the following formula [R is the repeatability, and F is the value in nested analysis of variance.The repeatability ( formula :(10)R=1−x and y; x and y, respectively.The correlation coefficients formulas :(11)rpQi=∑R is the repeatability of the trait; S is the selection bias; and The genetic gain was calculated according to the following formula :(13)∆G=RA. cremastogyne, as one of the main fast-growing timber species in Southwest China, has significant economic and ecological value. Therefore, it is crucial to study and evaluate the genetic variation of its phenotypic traits the growth, cone, seed, and germination traits of 40 A. cremastogyne clones were measured and analyzed in this work. The results demonstrated that the phenotypic traits of A. cremastogyne were predominantly governed by genetic control, exhibiting substantial genetic variation. Moreover, the observed phenotypic traits variation primarily came from among provenances. Using correlation analysis and principal component analysis, and ten phenotypic traits serving as comprehensive evaluation indicators, PC5, PC2, PC3, PC10, PC1, PC9, PC6, PC4, JT10, JT2, JT9, and EY6 were selected as excellent clones. These excellent clones can be used as breeding materials for the upgrading of the A. cremastogyne seed orchard. This selection process aimed to enhance the reliability and genetic gain of improved varieties, and provide a theoretical foundation for the genetic improvement and the breeding of excellent resources of A. cremastogyne.


NIRS of wheat grains as phenomic predictors for grain yield show inflated prediction ability and are biased toward grain protein content.Estimating the breeding value of individuals using genome-wide marker data (genomic prediction) is currently one of the most important drivers of breeding progress in major crops. Recently, phenomic technologies, including remote sensing and aerial hyperspectral imaging of plant canopies, have made it feasible to predict the breeding value of individuals in the absence of genetic marker data. This is commonly referred to as phenomic prediction. Hyperspectral measurements in the form of near-infrared spectroscopy have been used since the 1980 s to predict compositional parameters of harvest products. Moreover, in recent studies NIRS from grains was used to predict grain yield. The same studies showed that phenomic prediction can outperform genomic prediction for grain yield. The genome is static and not environment dependent, thereby limiting genomic prediction ability. Gene expression is tissue specific and differs under environmental influences, leading to a tissue- and environment-specific phenome, potentially explaining the higher predictive ability of phenomic prediction. Here, we compare genomic prediction and phenomic prediction from hyperspectral measurements of wheat grains for the prediction of a variety of traits including grain yield. We show that phenomic predictions outperform genomic prediction for some traits. However, phenomic predictions are biased toward the information present in the predictor. Future studies on this topic should investigate whether population parameters are retained in phenomic prediction as they are in genomic prediction. Furthermore, we find that unbiased phenomic prediction abilities are considerably lower than previously reported and recommend a method to circumvent this issue.The online version contains supplementary material available at 10.1007/s00122-023-04479-8. The advancement of plant and animal breeding relies on the selection of superior offspring, or superior combinations with enhanced performance, especially increased yield. Historically and even now, this selection has been primarily based on phenotypic observations and pedigree information. The advent of high-throughput genotyping technologies has brought unprecedented insight into the genetic makeup of individuals, thus enabling a selection process based on their genome. Genomic selection (GS)  is one of the most important field crops in terms of providing calories and consumable protein to the human diet   trials to train PLSR models on GY. They found Since the introduction of near infrared reflectance spectroscopy/spectra (NIRS) in the 1970 s, NIRS has been used to easily and non-destructively derive grain compositional properties as proxies for end-use quality in wheat breeding Munck  early inZea mays L. ssp. mays) grain with NIRS-based PS by employing the NIRS-BLUP model. Cuevas et al. (Glycine max L.) populations using the various commonly used GS models. They found high predictive abilities for NIRS, which generally outperformed marker-based predictions for GY and PH. Weiß et al.  in the form of genomics-like models to predict GY and other traits unrelated to grain composition, such as heading date (HD). Furthermore, PS can potentially outperform GS in terms of predictive ability and is less expensive than genotyping. The mathematical basis for predictive breeding has been laid out by Henderson  in the awww.saatzucht-donau.at). Locations WEI and PRO were treated with moderate, split application nitrogen fertilization and without fungicides, according to common agricultural practices in this region. Location DOE was managed according to certified organic practices (https://agriculture.ec.europa.eu/farming/organic-farming_en).We collected phenotypic data from 2019 to 2022 in three locations in Northeastern Austria, routinely as part of the commercial winter wheat breeding program of Saatzucht Donau  designs   as the heading date (HD). HD was recorded in all years at location PRO and recorded in 2020 and 2022 at location DOE, no records for HD are available from WEI. We measured plant height (PH)(cm) as the average distance from topsoil to the tip of the heads on multiple plants per plot between flowering and maturity. PH was recorded in 2020 and 2022 at location WEI, in all years at location PRO and in 2020, 2021 and 2022 at location DOE, leading to a partially complete dataset for HD and PH. In addition we measured the grain quality parameters, thousand kernel/grain weight (TKW)(g) using a seed imaging analyzer  and test weight (TW)  FT-NIRS with a spectral range of We acquired NIRS from harvested grains, using a Büchi NIRFlex N-500  H matrix is not to be confused with the concept of a hybrid matrix from markers and pedigrees . This is similar to the method of VanRaden (From column-wise (spectra) and row-wise (genotype) centered  extracted DNA from single kernels and genotyped using a custom 7k Illumina SNP array yielding 6722 polymorphic markers. The genotyping provider called the alleles, which we recoded on a trinary numeric system with “-1” and “1” being used for either of the homozygous states (aa or AA) and “0” for a heterozygous state (Aa and aA) at a given locus. We imputed missing calls chromosome-wise using the missForest algorithm  or pretreated NIRS-derived variance–covariance matrices (H) from various locations, leading to a set of different models and predictions. Note that G only captures genetic differences, whereas H is environment-specific and is expected to also capture environmental effect. This might increase the predictive ability of PS if H is derived from seeds, especially for traits which describe seed characteristics, like TW or TKW, but possibly also other traits. This can also be undesired if spectra are only available from one environment, but the goal is to predict traits in another environment or predict only the genetic value for a trait.To evaluate predictive performance, we trained genomic (GBLUP) and phenomic (HBLUP) prediction models according to the same model for various traits:To assess the predictive ability of the above-mentioned models, we used location-specific across-year BLUEs in a fivefold cross-validation, where the genotypes are randomly split into five equally sized groups, four of which are used for model training and one is used for model validation. The cross-validation procedure was repeated for the same split until every fold was used for validation once. To furthermore assess the effect of training population size, we randomly sampled 100, 200, 300 or 400 lines out of all lines, to perform cross-validation, which was repeated for every size 20 times to get robust parameter estimates. Predictive ability was measured as the Pearson product-moment correlation coefficient of the predicted versus the observed values in the validation fold. Furthermore, we investigated the Pearson correlation of the validation fold’s predicted values with other phenotypes to assess predictor bias.Single-trial repeatability (mentclass2pt{minimR) between GY and PC in three locations . Spectral repeatability  generally increased with population size  generally performed best, although it was found to be trait dependent. In some location-trait combinations, marker- and NIRS-based predictions yielded very similar average predictive ability while in others, they were clearly separated, showing a better performance of one or the other predictors consistently for a given trait across locations.In two environments (DOE and PRO), NIRS predictive ability was either similar or slightly higher than marker predictive ability and in the third environment (WEI) NIRS clearly performed better than markers with an average predictive ability ranging from Due to its high heritability  and similarly in WEI.To compare predictive performance among the prediction models, we also investigated the use of rrBLUP for markers and NIRS as well as PLSR, GBLUP, and HBLUP for NIRS. The various models yielded similar results, which we therefore only include in Online Resources.To address whether markers or NIRS can serve as unbiased predictors, we compared the phenotypic correlations between each trait and PC with the correlations between each trait prediction and PC Fig. . For thiBecause the negative correlation between GY and PC commonly observed in bread wheat breeding requires independent selection for both traits, this relationship deserves particular attention in phenotypic and genomic selection strategies. In this study, the phenotypic correlations of GY and PC ranged from We found no phenotypic correlation between HD and PC in PRO, the only completely evaluated environment for this trait. In this environment, predictions from markers showed no correlation with PC. Predictions from NIRS showed lower predictive ability than from markers, but increased positive correlations with PC. Selecting for earlier HD using NIRS would therefore lead to weak indirect selection toward lower protein content and vice versa.We scored PH in two environments (DOE and PRO) and found weak positive correlations with PC. In both environments, markers showed higher predictive ability than NIRS, and predictions showed on average the same weak correlations with protein content that we observed in the phenotypic data. However, predictions from NIRS clearly showed increased average correlations with PC independent of pretreatment.The grain characteristics TW and TKW both showed very weak correlations with PC, with the highest values in DOE, which is the location under organic management conditions. Quite surprisingly, they showed a contrasting response in terms of predictive ability and their correlations with PC. Markers and NIRS were both able to predict TW with high accuracy, although markers performed worse. All predictors reflected the phenotypically observed correlations of TW and PC, except markers in WEI.The results for TKW were strongly dependent on the trial location. In DOE markers showed high predictive ability and predictions reflected the phenotypic correlations with PC, while NIRS showed lower predictive ability and stronger positive correlations to PC. In the other two locations PRO and WEI, both predictors were overlapping in terms of predictive ability and correlation between predictions and PC, although on average the ranking was similar to DOE.For selection indices, prediction ability is not the main interest, but rather correlations between the index of interest and the traits the index was derived from Fig. . SelectiGYD is designed to be uncorrelated with PC, while being highly positively correlated with GY. Prediction ability for GYD using markers was low, ranging from 0.150.29 across locations, but in most cases even lower using NIRS as predictors, ranging from 0.020.30 across locations and pretreatments (Online Resource 1). GYD was weakly positively correlated with GY for both markers and NIRS on average; only in location PRO was the average correlation between GYD and GY moderate, exceeding 0.3 for all predictors. Although we expected to observe no correlation between predicted GYD and PC, we found low negative or positive correlations ranging from PY is a type of environmentally dependent multiplicative index with variable correlations with its basis traits. In the three locations, PY showed high positive correlations with GY and weaker positive correlations with PC, therefore putting a much larger weight on GY than PC. The prediction abilities ranged from 0.080.27 across locations using markers and from 0.140.35 using NIRS as predictors across locations and pretreatments. The correlations between PY and its basis traits revealed that marker-based predictions were positively correlated to GY in all environments, ranging on average from 0.080.2. Furthermore, marker-based predictions of PY either had no or weak positive correlation with PC. Predictions derived from various pretreated NIRS showed very low positive or low negative correlations with GY on average, ranging from EWPY is designed to show balanced correlations with its basis traits. Using markers as predictors, we found similar prediction ability as for the other indices, ranging from 0.090.37 across locations. Using NIRS as predictors, we found the highest prediction ability across all indices, ranging from 0.290.50 across locations and pretreatments. The correlations between EWPY and its basis traits were similar to that of the PY index, with low or negative average correlations of predictors with GY for markers and NIRS, respectively, and a bias (over-correlation) toward PC using NIRS as predictors.Estimating breeding values facilitates efficient selection of breeding candidates. Multi-location, multi-year replicated trials can allow very accurate estimation of phenotypic values, sometimes referred to as “true” breeding values, but such trials are expensive and only employed in later stages of a breeding program. However, the breeding values of individuals can be estimated from related individuals with known breedings value much earlier in the breeding cycle, although with some uncertainty. In the well-established concept of GS, molecular markers are used as predictors, establishing covariance (relatedness) between individuals. In this study, we investigated the use of endophenotypes instead of molecular markers for prediction of breeding values in a wheat breeding program, for three environments in Austria, which was also termed genomics-like omics-based (GLOB) selection  within the material we used for this study . For example, the weak positive correlation between PH and PC in this study may reflect breeders’ selection for lines suitable for organic production systems. These require excellent baking quality, and therefore high PC, as well as strong competitiveness against weeds. For the quality traits TW and TKW, we found contrasting results, although they were both measured on the same material used for NIRS. Therefore, we assume that NIRS would be a good predictor for these traits. For TW, both markers and NIRS performed well, but PS consistently outperformed GS. TKW was generally harder to predict than TW and with GS outperforming PS in most cases. Rincent et al.  found thAccording to the central dogma of molecular biology, the expression of genes leads to an observable phenotype, which is modulated by the environment as well as genotype-by-environment interactions. Molecular markers can capture genetic relationships between plants, which can be employed by GBLUP to predict plant phenotypes of unobserved genotypes. If the molecular markers are distributed at sufficient density and uniformity across the genome, capturing most of the genetic diversity that influences trait variation in the population, the relationship between different traits  should also be preserved in the predictions. Therefore, we expect molecular markers to be unbiased predictors. NIRS are highly tissue specific; however, according to the literature, relationships derived from NIRS seem to be useful in predicting plant phenotypes. In wheat breeding, NIRS are commonly used to estimate the protein content of seeds with high accuracy. However, it is necessary to select for multiple traits independently at the same time, especially for GY and PC.The common way to judge the performance of a genomic or phenomic prediction model is prediction ability or accuracy, as derived from k-fold cross-validation within or validation across environments, years or populations. The relationship between predictions and other traits is, however, mostly ignored. In wheat breeding, the relationship between GY and PC is of particular importance. Therefore, we investigated the correlations of GY and other traits predictions to PC and compared these to the phenotypically observed correlations. For GY we observed that GS predictions were largely replicating the phenotypically observed correlations to PC while PS showed severe negative “over-correlation” or bias toward PC. This means that the use of PS for GY would lead to unintentional selection toward low PC. This is for most breeding programs not only undesirable, it is also not sustainable since a biological limit in seed PC could be reached within few generations  or oil content (negatively correlated with yield) might be additional factors which enable indirect prediction ability of NIRS spectra from seeds. For example in soybean, the negative correlation between yield and seed protein content has also been reported  as a covariate, including all possible multi-way interactions.Supplementary Materials: Phenotypic trait correlations. Normalized adjusted spectra, repeatability and variance components within years. Normalized adjusted spectra, heritability and variance components across years. Mantel correlation of kinship and NIR derived similarity matrices and variance explained by principal components from these matrices. Visualized kinship matrix from markers. Visualized kinship matrices derived from various (locations and pretreated) NIR. Prediction ability of GBLUP, HBLUP and PLSR for all traits, indices and locations. Prediction ability vs prediction correlation to PC of GBLUP, HBLUP and PLSR for all traits and locations. Prediction ability vs prediction correlation to PC of GBLUP, HBLUP and PLSR for all indices and locations. Below is the link to the electronic supplementary material.