MEG3/DLK1 locus on platelet phenotypes. We also report a novel maternal effect at the TERT gene on telomere length, thereby providing new insights on the heritability of this phenotype. All our summary statistics are publicly available to help the community to better characterize the molecular mechanisms leading to parent-of-origin effects and their implications for human health.Identical genetic variations can have different phenotypic effects depending on their parent of origin. Yet, studies focusing on parent-of-origin effects have been limited in terms of sample size due to the lack of parental genomes or known genealogies. We propose a probabilistic approach to infer the parent-of-origin of individual alleles that does not require parental genomes nor prior knowledge of genealogy. Our model uses Identity-By-Descent sharing with second- and third-degree relatives to assign alleles to parental groups and leverages chromosome X data in males to distinguish maternal from paternal groups. We combine this with robust haplotype inference and haploid imputation to infer the parent-of-origin for 26,393 UK Biobank individuals. We screen 99 phenotypes for parent-of-origin effects and replicate the discoveries of 6 GWAS studies, confirming signals on body mass index, type 2 diabetes, standing height and multiple blood biomarkers, including the known maternal effect at the  Studies on parent-of-origin effects have been limited in terms of sample size due to lack of parental genomes or known genealogies. Here, the authors develop a method to infer the parent-of-origin of an individual alleles in biobank-scale datasets, without requiring parental genomes or prior knowledge of genealogy, allowing discovery of parent-of-origin effects with an unprecedented sample size. PofO effects are thought to mainly result from genomic imprinting, a mechanism relying on parent-specific DNA methylation, named imprints, that silence one of the parental copies of a gene. Such parent-specific imprints are established during spermatogenesis and oogenesis and are maintained in all somatic cells of the offspring3. This leads to some genes, called imprinted genes, to exhibit an allele-specific expression pattern that depends on the PofO of the underlying genetic sequence. This allele-specific expression can be maintained throughout life or specific to some development states4. One of most studied imprinted loci in the human genome is probably the H19 loci at 11p15.5 that is involved in growth and development disorders such as the Beckwith–Wiedemann or Silver–Russel syndromes5. Multiple studies have investigated PofO effects on complex traits, notably for the KCNQ1 and KLF14 genes whose associations with type 2 diabetes risk depends only on the maternal copies6, as well as for the MEG3/DLK1 imprinted locus associated with age at menarche7 and platelet count8.Parent-of-Origin (PofO) effects refer to genetic variations having an effect on a phenotype that depends on the parent from which alleles are inherited9, representing one of the largest PofO study done to date, with hundreds of phenotypes assessed for more than 22,000 samples with at least one genotyped parent. Alternatively, this can also be achieved by meta-analysis across multiple cohorts regrouping duos and trios, with the caveat of restricting the analysis to the subset of phenotypes in common across datasets10. Second, statistical approaches have been proposed to test for PofO effects in large collections of unrelated samples by exploiting the differences in phenotypic variance between heterozygous and homozygous individuals, with the caveat of also detecting effects unrelated to PofO such as gene-environment interactions11. Third, it has been shown that the PofO of an individual’s alleles can also be determined by the use of cousins as surrogate parents when parental genomes are not available6. This latter approach is particularly well suited for datasets comprising many samples from the same generation but also requires the genealogy of most individuals in the study cohort, which is not the case in large datasets such as the UK Biobank12.Searching for PofO effects on a genome-wide scale requires knowing the PofO of each individual allele. The most direct approach to obtain this information relies on the availability of parental genomes, which allows using the Mendelian principles of inheritance to determine the parent from which a specific allele is inherited. Study cohorts usually include a small number of genotyped parent-offspring duos and trios, resulting in a low discovery power and a challenging detection of PofO effects. To alleviate this problem, multiple approaches have been explored so far. First, by deploying large efforts in data collection, such as the study performed on the DiscovEHR cohorthttp://poedb.dcsr.unil.ch/) and allow the exploration of the PofO effects for variants of interest across phenotypes.In this work, we present a probabilistic method to infer the PofO of alleles in biobank scale datasets from second- and third-degree relatives without requiring any parental genomes nor explicit genealogy to be known. To do so, our approach combines multiple estimation steps, involving surrogate parent groups formation, parental status assignment based on chromosome X, haplotype inference, Identity-By-Descent (IBD) detection, and haploid imputation. When applied to the UK Biobank dataset, this allows us to infer the PofO for 21,484 samples with high confidence in addition to the 4909 duos/trios for which we perform direct inference from parental genomes, resulting in a dataset comprising a total of 26,393 samples and 7.6 million variants. Considering duos/trios as the ground truth, we show that our PofO estimations from second- and third-degree relatives have a high call rate (~75%) and low error rate (<1%) at heterozygous genotypes. Taking advantage of the vast phenotypic diversity of the UK Biobank, we carrie out genome-wide association scans for PofO effects for a total of 99 phenotypes, replicating well-known imprinted loci as well as discovering novel putative PofO associations, thereby demonstrating that our method has the potential to further reveal the contribution of PofO effects to complex traits. All the summary statistics for the conducted association scans are publicly available  to parental groups for male targets only by exploiting the fact that their single chromosome X copy is maternally inherited. Therefore, we search for relatives sharing portions of their chromosome X Identical-By-Descent (IBD) with the target sample and we label them as surrogate mothers. We also propagate the information to other relatives: those from the same parental group are also labeled as surrogate mothers and those from the other parental group as surrogate fathers. In case no IBD is found, we cannot annotate parental groups as maternal or paternal and we exclude the target sample from the dataset. Hereafter, we call surrogate parents the close relatives we identified using this approach.nts Fig. . For eacAssignment of PofO to alleles . We explored a range of different parameter settings for the IBD detection and PofO confidence score  and found that using haplotype segments longer than 3 cM as scaffold and a phasing certainty above 0.7 lead to a good trade-off between call rate and error rate  the error rate, which is the percentage of heterozygous genotypes with incorrect PofO assignment and (ii) the call rate, which is the percentage of heterozygous genotypes for which a PofO call could be made , we inferred the PofO using the method based on surrogate parents, as described above. In total, we found 105,826 samples with second-to-third degree relatives forming groups of surrogate parents. Amongst those, we could assign parental status to surrogate parent groups to a subset of 21,484 samples using IBD matching on chromosome X. Comparing the distribution of surrogate parents per target sample, we found a remarkable match between the full  and the validation (N = 1399) datasets .Distinguishing paternally from maternally inherited alleles allows us to design different association scans to test the PofO specificity of associations: (i) maternal, to test only the maternally inherited alleles, (ii) paternal, to test only the paternally inherited alleles, (iii) differential, to compare paternally and maternally inherited alleles at heterozygous genotypes only and (iv) additive, as a control to test minor alleles regardless of PofO. Using these models we scanned for association 99 quantitative phenotypes of the UK biobank using BOLT-LMMp < 5 × 10−08) and used the paternal and maternal scans to determine the parental origin and the direction of the effect. We found two signals fulfilling these criteria. The first is a PofO association with platelet phenotypes at the MEG3/DLK1 imprinted locus19 . We also replicated the association at rs1555405 , previously reported in different GWAS for multiple cancers23, blood cell counts24, aging25 and telomere length26. When directly testing rs2853677 in our data, we find a strong maternal effect similarly to the lead SNP , all associations have p-values <0.007  to strongly significant on the full sample size  PofO studies using trios or known genealogies, (ii) PofO studies across unrelated individuals using an increased-variance method, and (iii) studies investigating genotype-environment (GxE) interactions.32, 9 of which could be assessed in our data . Seven of these associations are located in two well-known imprinted regions, 11p15.5 and 14q32, and the remaining two are located in the HLA region. Only one association has been replicated across the two of the three studies at rs143840904. In contrast, we replicated 8 associations out of the 9 we could test, with the same parent and direction of effects  using genealogy-based PofO inference. They fall within two distinct regions that harbor well-documented imprinted gene clusters, 11p15.533 and 7q3235. As we could not directly test T2D status due to the small number of cases in our dataset, we tested the biomarker most correlated with T2D: glycated hemoglobin . By doing so, we replicated the three strongest associations with the same parental effect . Interestingly, when performing a phenome-wide scan of the 1q25.2 locus harboring rs539515, we found paternal associations between four SNPs in high LD  with weight, waist circumference, hip circumference, basal metabolic rate and arm/leg mass  and have already been associated with weight- or obesity-related phenotypes under the additive model40. Altogether, this suggests that the GxE effect of SEC16B on BMI is likely due to a paternal effect.We hypothesized that some GxE signals detected for BMI could be due to PofO effects. Using the UK Biobank, Kerin et al.41 and were not able to replicate any of these associations in our data, nor any of the additive ones  or variance QTL  as PofO. We believe that an increase of power is still necessary to detect additional PofO effects with strong confidence but that the current approach already provides a useful resource that can reveal many other associations by meta-analysis. Besides, we also believe that our dataset can be used for more targeted GWAS scans and reveal new putative PofO effects by focusing only on known imprinted loci, only on additive associations or on both criteria together9, thereby decreasing the cost of multiple testing corrections.We looked at PofO associations at three different levels. First, we reported two clear PofO associations supported by additive signals: a maternal effect on platelet phenotypes located in the 49 so that the PofO status inferred within IBD tracks could be confidently propagated to entire chromosomes. Further improvements in phasing algorithms could be made by explicitly modeling IBD sharing between close relatives, eventually through inter- and intra-chromosomal scaffolding as we performed in this work.One of the strengths of our PofO inference method resides in its ability to make PofO calls with a low error rate. Regardless, the presence of errors in the inference is unlikely to produce false positive PofO associations, but only decrease the statistical power of the study, since inference errors are expected to be drawn independently from the phenotypes. Instead, errors are expected to lead to false negatives as PofO signals get diluted onto the two parental origins and thus decrease association power. In this work, we controlled for this by focusing exclusively on high-confidence PofO calls, which corresponds to a call for 74.5% of heterozygous genotypes with an estimated error rate below 1%. The overall high accuracy in our estimates could be achieved thanks to recent progress in the statistical estimation of haplotypes for very large sample sizes50, notably for anthropomorphic traits, are likely missed by this approach. Potential improvements should come with whole genome sequencing (WGS) data: parental status assignment based on rare variant matching on chromosome Y and mitochondrial DNA would likely become possible. In the UK Biobank, this has the potential to substantially increase the sample size above the ~26,000 samples we have so far to a theoretical upper bound of 105,826 samples, which corresponds to the number of samples for which we found groups of close relatives in the dataset. This could further boost the discovery power of downstream PofO association scans. Second, this approach can be confounded by high levels of inbreeding which could lead a sample to share portions of the chromosome X IBD with close relatives on both sides of the family, therefore greatly complexifying sex assignment. However, we consider this issue to be almost negligible in this study as the UK biobank mostly comprises outbred individuals. Conversely, admixture affects kinship estimation and therefore our ability to find surrogate parents, although this can be compensated by using a robust method for kinship estimation in admixed populations51.Our ability to infer PofO depends on the availability of close relatives. Surprisingly, even when only a single third-degree relative is available for IBD mapping, we achieve a high call rate and a low error rate. We believe this could be further improved by using more distant relatives, even if they will contribute less to the inference than second- and third-degree relatives. In addition, our PofO inference depends on the ability to assign parental status to relatives based on IBD sharing on chromosome X, which comes with some flaws. First, our current inference is only possible for males as it leverages chromosome X haploidy, which means that only non-sex specific and male specific PofO effects can be investigated. As a results, female specific PofO effects, which could be of great interest given the recent findings on sexual dimorphismshttps://finngen.gitbook.io/documentation/), the Million Veteran Program52 or The Estonian Biobank53. Collective efforts would allow the detection of PofO effects with an unprecedented sample size by meta-analyzing PofO effects across multiple biobanks and therefore greatly help future research on the molecular mechanisms leading to PofO effects and their implication for human health.Overall, this study is a valuable resource to further characterize PofO effects and investigate the impact of imprinting genes on complex traits. Although the multiple successive steps of this approach  are difficult to fully automatize, we expect it to be applicable to other biobanks, such as those collected by the FinnGen research project , which resulted in 1037 trios and 3872 duos.To identify trios and duos we used pairwise kinship and IBS0 estimates up to third degree relative computed using KINGN = 443,993) , we excluded samples with more than two clusters of relatives from the analysis as it indicates ambiguous cases. Similarly, if a second-to-third degree relative is related to the two clusters, we also excluded the sample to avoid ambiguous cases. Importantly, this is often a symmetric assignment: when A is part of the group of relatives of B, this usually involves B is part of the relatives of A. We identified a total of 105,826 individuals with groups of relatives, ranging from one group of one relative to two groups of more than two relatives. This includes 309 individuals having also both parents genotyped  and 1090 having a single parent genotyped in the data . These 1399 individuals with at least one genotyped parent and groups of close relatives constitute our validation data set on which we applied our PofO inference method using the close relatives as surrogate parents, ignoring the parental genomes. We then used parental genomes to compute the accuracy of our inference.We used pairwise kinship and IBS0 estimates up to the third degree relative to identify sibling pairs (kinship between 0.1767 and 0.3535 and IBS0 above 0.0012), and second- and third-degree relatives’ pairs (kinship below 0.1767) for all genotyped individuals of British and Irish ancestry  to groups of close relatives by examining shared IBD segments on chromosome X using XIBDe X Fig. . This ase X Fig. , a value55, which resulted in 784,256 variant sites across the autosomes for 488,377 individuals. We then used the UK biobank SNPs QC file (UK biobank resource 1955) to keep only variants used for the phasing of the original UK biobank release, resulting in 670,741 variant sites.We used the UK biobank SNP array data in PLINK format. We converted the UK biobank PLINK files into VCF format using PLINK v1.90b5N = 1399 validation samples for which we have both parental genomes and surrogate parents. We ran our inference on N = 1399 validation samples and we assessed its performance by comparing our estimates to the truth given by parental genomes. It is important to note that parental genomes have been used only at the validation stage and not during any phasing runs nor PofO inference. The second dataset includes this time all available UK Biobank samples and has been used to produce the final set of individuals with PofO inference that has been used for association testing. This includes N = 21,484 samples for which PofO could be inferred from surrogate parents and N = 4909 samples for which PofO could be directly inferred from the trios/duos. The final dataset includes 22,652 males (85.8%) and 3741 females (14.2%).We assembled two distinct datasets comprising different collections of samples of British or Irish ancestries by subsampling the original dataset with BCFtools v1.8. The first one includes all UK Biobank samples excluding the parental genomes for the 15 with default parameters so that all data consists of haplotypes. Then, we designed a Hidden Markov Model (HMM)56 to identify IBD sharing between the target haplotypes and a reference panel mixing haplotypes from two different sources: from the surrogate parents of the target (labeled as mother or father) and from unrelated samples. We aimed for such a probabilistic model for its robustness to phasing and genotyping errors compared to approaches based on exact matching such as the positional Burrows–Wheeler transform (PBWT). The model then uses a forward-backward procedure to compute, for each allele of a target haplotype, the probability of copying the allele from (i) the surrogate mother haplotypes, (ii) the surrogate father haplotypes or (iii) unrelated haplotypes. Here, we used 100 unrelated haplotypes as decoys so that the model is not forced to systematically copy from surrogate parents. When the model copies the target haplotype from a specific surrogate parent at a given locus with high probability, we can therefore infer the PofO at this locus from the parental group the surrogate parent belongs to. When the model copies from unrelated haplotypes, no inference can be made at the locus . In addition, we considered in the haplotype scaffold only alleles having a PofO probability greater or equal to 95%. As a result, we could build paternal and maternal haplotype scaffolds that we used in a second step to rephase the entire dataset using SHAPEIT4 v4.2.115. The goal of this second round of phasing is three-folds: (i) to ensure that the pool of alleles coming from the same parent land onto the same haplotype, (ii) to propagate the PofO assignment from IBD tracks to all alleles along the chromosomes and (ii) to correct long range switch errors. Point (ii) is made possible as all alleles with PofO unknown  are phased relatively to the haplotype scaffold so that we can extrapolate their PofO from the scaffold they co-localize with . In practice, we ran SHAPEIT4 with two main options: -scaffold to specify the scaffolds of haplotypes to be used in the estimation and -bingraph to output the haplotype reconstructions together with phasing uncertainties. The latter provides the haplotype reconstructions as parsimonious graphs encapsulating phasing uncertainty so that likely haplotype pairs can be rapidly sampled without being forced to rerun the complete phasing run. As a consequence, we sampled for each target sample a 1000 haplotype pairs using different seeds and computed the probability for a given allele to be paternal or maternal from its frequency of co-localization across the 1000 pairs onto the paternal and maternal haplotype scaffolds, respectively .In this second stage, we inferred PofO for all remaining genotyped alleles. First, we built a haplotype scaffold comprising all alleles for which we know PofO from IBD sharing with surrogate parents16 with the Haplotype Reference Consortium17 as a reference panel. As our data is phased with each haplotype assigned to a specific parent, we used the parameter -out-ap-field to run a haploid imputation of the data and separately imputed the paternal haplotype and the maternal haplotype. Of note, we filtered out all heterozygous genotypes with a phasing certainty below 0.7 prior to imputation (see previous section). As a result of haploid imputation, the PofO of imputed alleles can be probabilistically deduced from the imputation dosages: an allele imputed with a dosage of 0.85 on the paternal haplotype has 85% probability of being inherited from the father . Finally, we filtered out variants with an INFO score below 0.8 and obtained a dataset comprising 22,156,064 variants.In this third stage, we inferred PofO for untyped alleles, i.e., not included on the SNP array. To do so, we imputed the data using IMPUTE5 v1.1.4N = 1399) to compute the errors in the PofO inference and to optimize the parameters of our inference method. For the trios (N = 309) and the duos (N = 1090), we determined the correct parental origin of offspring heterozygous genotypes at sites where a parent is homozygous, excluding sites with Mendel inconsistencies. We assessed the impact of two parameters on the call rate (percentage of heterozygous genotypes with PofO assignment) and the error rate (percentage of heterozygous genotypes with incorrect PofO assignment) of the PofO inference: (i) the length in centimorgan (cM) of the haplotype segments that we included in the scaffold for the second phasing run and (ii) the phasing certainty threshold we used to assume PofO to be known at heterozygous genotypes. To do so, we compute the call rate and the error rate for all combinations of the following parameters . We used BOLT-LMM v2.3.418 to run all association tests. As recommended by the authors, we performed the model fitting only on the genotyped variants. For the additive GWAS scans, we used the -dosageFile parameter to test imputed alleles dosages, as recommended in the documentation. For the PofO GWAS scans , we used the -dosageFile parameter to test the PofO dosages of alleles. In practice, we only used imputed allele dosages (i) of the paternal haplotype for the paternal-specific GWAS and (ii) of the maternal haplotype for the maternal-specific GWAS, so that PofO assignment uncertainty is propagated to association testing. We conducted a third PofO GWAS scan that compares the effect of maternally and paternally inherited minor alleles at heterozygous genotypes . For this, we used only heterozygous genotypes with imputed minor allele dosages greater or equal to 0.95 to keep only genotypes with high confidence in the PofO. We encoded such alleles as 0 when inherited from the father and 1 when inherited from the mother. We again used the -dosageFile parameter to test whether the paternal and maternal alleles have differential effect at heterozygous sites with all homozygous genotypes set to missing. Prior to running association testing, we coded all variants so that we systematically tested the effects of minor alleles. We filtered out all variants with a minor allele frequency (MAF) below 1% which resulted in 7,645,537 variants for association testing.We tested 99 quantitative phenotypes of the UK biobank data set  < 0.05 and being located at least 500 kb apart. If two hits are not independent, we select the one with the lowest p-value. We identify PofO associations as being Bonferroni significant (p < 5 × 10−08) in the differential scan and in the additive scan. We inferred the parent and direction of the effect using the paternal and maternal scans.We identify independent hits as having Linkage Disequilibrium . We restricted the analysis to the subset of 443,993 genotyped samples of British or Irish ancestry. We used as covariates age, sex and the first ten PCs.We used the QUICKTEST softwareFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1–3Reporting Summary


Body conformation traits are closely related to economically important characteristics and should be considered in cattle breeding programs. A variety of body conformation traits recorded by classifiers can complicate the analysis process. Factor analysis can reduce the number of variables by combining two or more variables into a single factor, which has biological significance. The results of this study could be used by breeders to define conformation indexes and implement genetic assessments for conformation traits in dual-purpose breeds.In this study, we estimated the genetic parameters for 6 composite traits and 27 body conformation traits of 1016 dual-purpose Simmental cattle reared in northwestern China from 2010 to 2019 using a linear animal mixed model. To integrate these traits, a variety of methods were used as follows: (1) genetic parameters estimates for composite and individual body conformation traits based on the pedigree relationship matrix (A) and combined genomic-pedigree relationship matrix (H); (2) factor analysis to explore the relationships among body conformation traits; and (3) genetic parameters of factor scores estimated using A and H, and the correlations of EBVs of the factor scores and EBVs of the composite traits. Heritability estimates of the composite traits using A and H were low to medium (0.07–0.47). The 24 common latent factors explained 96.13% of the total variance. Among factors with eigenvalues ≥ 1, F1 was mainly related to body frame, muscularity, and rump; F2 was related to feet and legs; F3, F4, F5, and F6 were related to teat placement, teat size, udder size, and udder conformation; and F7 was related to body frame. Single-trait analysis of factor scores yielded heritability estimates that were low to moderate (0.008–0.43 based on A and 0.04–0.43 based on H). Spearman and Pearson correlations, derived from the best linear unbiased prediction analysis of composite traits and factor scores, showed a similar pattern. Thus, incorporating factor analysis into the morphological evaluation to simplify the assessment of body conformation traits may improve the genetics of dual-purpose Simmental cattle. D−1 .Body conformation traits are closely related to economically important traits, such as milk production , reproduTo date, a linear identification of body conformation traits of dual-purpose Simmental cattle in Northwest China has not been conducted, and breeding programmes do not include linear-type traits. This study aims to estimate genetic parameters for body conformation traits in dual-purpose Simmental cattle using factor analysis. The results could be used to develop a national genetic evaluation framework for the improvement of body conformation traits for dual-purpose Simmental cattle in China.Approval from the local Institutional Animal Care and Use Committee was not required for this study because the data were obtained by field measurements, and no animal experiments were conducted.In 2020, our team applied for a dual-purpose cattle type classification project. After deliberation, we identified 6 composite traits and 27 body conformation traits. There were 17 measured traits and 10 scored traits. The measured traits included stature (ST), body depth (BD), chest width (CW), withers width (WW), hind leg half circumference (HLHC), rear leg height (RLH), rump length (RL), rump width (RW), rump angle (RA), rear udder height (RUH), rear udder width (RUW), median suspensory (MS), udder depth (UD), fore udder length (FUL), front teat length (FTL), front teat diameter (FTD), and heel depth (HD). The scored traits included ribs and bone (RB), rear legs side view (RLSV), bone quality (BQ), foot angle (FA), rear legs rear view (RLRV), fore udder attachment (FUA), rear udder length (RUL), udder balance (UB), fore teat placement (FTP), and rear teat placement (RTP). The individual body conformation traits are specific to certain body regions of the animals, including the body frame, muscularity, rump, feet and legs, and mammary system. The six composite traits summarized body frame, muscularity, rump, feet and legs, mammary system, and final score.Conformation traits of 1200 dual-purpose Simmental cattle born from 2010 to 2019 were measured at Xinjiang Hutubi Farm, Kekedala Chuangjin Farm, and Xinjiang Haozi Animal Husbandry Farm in Northwest China. After quality control was applied using the threshold of the mean ± three times the standard deviation, 1016 conformation records remained and were used for analysis. The pedigree file used for the analysis included data for 1988 animals, with each animal being traced back three generations. In the full datasets, one sire had a maximum of 195 offspring with records, whereas 16 sires had only one offspring. More than 62 dams had 2 or more offspring.The Illumina 100K Bovine BeadChip was used to genotype 516 Simmental cows. For analysis, common SNPs were obtained from 100K bead chips as target files. Quality control of the SNP genotyping was carried out with PLINK 1.07 software  . All genTo estimate genetic parameters, the genetic connectedness of the animals in the dataset must be determined. If the breeding process affects the genetic connectedness of cattle at different farms over time, this change would be reflected in a change in the average relatedness in birth-year cohorts and among the populations at different farms. The indirect method of Sargolzaei , implemeY indicates the vector of 6 composite traits and 27 individual body conformation traits; The single-trait animal model was used to estimate genetic and residual variance for the 6 composite traits and 27 individual body conformation traits with the average information-restricted maximum likelihood (AI-REML) method. The AIREMLF90 procedure of BLUPF90 1.0.1 software  was used . The aniThe genetic effect was modeled using two kinds of genetic variance-covariance matrices: the pedigree relationship matrix (A) , and theThe heritability and standard error were estimated according to Austin Putz et al.  using thn observation variables (p (p < n) latent variables . In this analysis, a set of ; Russel , Mazza e; Russel , Olasegey Kaiser 60, varim; Russel , the claom error .In the single-trait animal model presented above, genetic parameters were estimated by fitting the factor scores as y. The estimated breeding values (EBVs) of the factor scores were then subjected to rank correlation analysis with the EBVs of the composite traits using SPSS.The descriptive statistics for the 6 composite traits and 27 individual body conformation traits, including the mean, standard deviation, minimum, maximum, and coefficient of variation are summarized in All three farms use artificial insemination for breeding. The heritability estimates for the 6 composite traits and 27 individual body conformation traits are presented in The eigenvalues and the proportion of total and cumulative variance explained by each factor are listed in  0.40  were repVariance components for nine different factor scores using the different relationship matrices are shown in Results of the Spearman and Pearson correlation analyses  and feet and legs scores (86.72) were slightly higher than those for Slovenian Simmental dairy cows . In contrast, scores for muscularity (80.72) and the mammary system (78.77) were similar for the two species . For bodIn this study, except for muscularity traits, the composite traits were consistent with those of dual-purpose Rendena cattle , dual-puIn addition, we investigated the heritability for composite traits and individual body conformation traits using the H matrix in dual-purpose Simmental cattle. Comparing the estimates of heritability and their standard errors for each trait using A and H, there was no significant difference for any conformation trait, except for ST. Based on the standard errors, there were no significant differences in any traits. It is possible that the construction of H was primarily determined by the information found in A since there were too few individuals with genotypes. Therefore, they had little influence on the genetic parameter estimates. In addition, the small number of individuals with phenotypic and pedigree records, and the specific environmental effects on farms have influenced the results of the genetic parameter estimation in this study. Some researchers have shown that genomic information can improve the accuracy of genetic parameter estimation for breeding target traits. For example, Veerkamp et al.  and Wei In multivariate statistical analysis, factor analysis is one of the classical tools ,19,42,43Phenotypic factor analysis in this study extracted 24 principal components, which accounted for 96.13% of the total variance among the 27 body conformation traits. Chu and Shi  found thConsistent with the findings of Mantovani et al. , the SpeThe amount of phenotypic and genotypic data collected was small due to the late start of the linear assessment of body conformation and genomic evaluation of dual-purpose Simmental cattle in Northwest China. In the future, we will establish a protocol for type classification in dual-purpose Simmental cattle and conduct annual body conformation linear identification and incorporate it into the breeding programme.In this study, the body conformation traits showed a range of heritability from low to high, with stature yielding the highest estimates. The number of animals with recorded body conformation traits is rather low, especially the number of genotyped animals, which led to little difference in the precision of estimating heritability using the pedigree relationship matrix and the combined genomic-pedigree matrix. The factor scores exhibited low to medium heritability, and the generally high Spearman and Pearson correlations between EBVs of Factor 1, Factor 2, and Factor 4 and the corresponding EBVs for composite traits suggested their utility in selection programmes. These analyses suggest that a few factors can describe a variety of body conformation traits without reducing the accuracy of genetic assessments.


Vitis vinifera ssp. vinifera) is the fruit crop with the largest economic value worldwide, considering its derived products. However, emerging climate change-derived threats along with established pathogens compromise the sustainability of traditional viticultural systems. Grapevine genetic improvement is critical to face this situation, as well as to adapt to novel market needs and regulatory frameworks. Current breeding and selection programs focus on the obtention and exploitation of cultivars combining high quality fruit traits, adequate yield, and some level of resistance to major biotic and abiotic stressors. To this aim, two main activities are conducted: (i) the screening of today’s standing Vitis diversity , as well as at shedding light on the genetic mechanisms involved in grape quality and adaptation to biotic and abiotic stress traits. It comprises five original research articles, one review article and one perspective article, which can be grouped into the following three major topics:V. vinifera parental genotype and a resistant (or tolerant) non-vinifera parental genotype (via Marker-Assisted Selection (MAS). Combining disease severity ratings and information on 2,000 genetic markers, Karn et al. (2021) identified a new QTL derived from Vitis aestivalis responsible for the resistance to Erysiphe necator, causal agent of powdery mildew. The new locus is named REN11 and it is located on chromosome 15. The authors suggested that the markers flanking REN11 can be directly used for MAS. On the other hand, modern breeding is the process through which new varieties are developed applying genetic modification and genome editing techniques. Genome editing via CRISPR/Cas9 system is a powerful technique making possible different types of genetic modifications, such as insertion, deletion, or mutation. Olivares et al. (2021) proved the use of the CRISPR/Cas9 technology to knock-out four putative grapevine susceptibly genes to fungal diseases (E. necator and Botrytis cinerea) in cv. ‘Thompson Seedless’: VviAIR12, VviSWEET4, VviLIN2, and VviDEL1. Authors found that a VviDEL1-edited line showed a reduced susceptibility to E. necator infection, supporting the role of VviDEL1 on grapevine resistance mechanisms against this fungal agent.Grapevine improvement to increase grape resistance to pests and diseases can rely on both conventional and modern breeding activities. Conventional breeding  starts from the selection of beneficial individuals from a crossing population, commonly generated through the cross between a susceptible genotype . This apPossamai and Wiedemann-Merdinoglu (2022), phenotyping remains a major bottleneck for research activities. In their work, the authors reviewed the literature concerning the disease evaluation methods available for the discovery of the loci involved in resistance to downy (Rpv) and powdery (Ren/Run) mildew. The great variability recorded for environment , organs (from whole plant to leaves), rating systems , and inocula  used in different assays highlights the need for a standardization of the methods used for phenotyping. Likewise, the development of affordable and high throughput phenotyping systems is one of the aims for grapevine genetic improvement. Herzog et al. (2022) applied fast sensor technologies for investigating multiple traits  involved in the interaction between Botrytis cinerea (the grey mould agent) and grapevine berries. Impedance of berries, an indirect method for the assessment of cuticle thickness and permeability, was identified as a reliable indicator for disease infection that could be used as a proxy to identify grape varieties resilient to B. cinerea.Phenotyping is fundamental for the discovery and exploitation of QTLs that are associated with disease resistance. However, as highlighted by Chang et al. (2022) conducted a transcriptomics comparison of two muscadinia (Muscadinia rotundifolia) accessions that released candidate genes to account for ripening control and determination of breeding desirable and unfavorable fruit traits present in this species. Besides, Awale et al. (2022) proved the efficiency of a metabolomics-driven approach to explore the genetic basis of wine quality traits in inter-specific hybrids. They identified a series of volatile compounds and volatile precursors characterizing the aroma profile of ‘Cabernet Sauvignon’ and ‘Norton’ grapes and wines, which enabled the full characterization of a breeding hybrid population .North American wild grape species show desirable features to counteract biotic and abiotic stresses, but they produce uneven yields and certain off-flavors and aromas that are generally perceived as negative notes by consumers . Chang eNavarro-Payá et al. (2022) present a grape gene reference catalog that is in development by the grapevine research community. Genes Card, a visualization tool gathering gene functions collected in the catalog linked to expression data derived from public transcriptomic datasets, is presented as well and potential breeding-oriented applications of the catalog are discussed.While there is an increasing generation of knowledge on grapevine genomic resources and gene functions that can aid the design of efficient breeding strategies , effortsGL, PC-B, SLT, and JT co-edited the Research Topic and wrote, edited, and approved this Editorial.JT was funded by a Juan de la Cierva-Incorporación grant (IJC2018-035036-I).We thank the Frontiers Editorial Office, authors and reviewers for their work in this Research Topic.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.


The single nucleotide polymorphism heritability of a trait is the proportion of its variance explained by the additive effects of the genome-wide single nucleotide polymorphisms. The existing approaches to estimate single nucleotide polymorphism heritability can be broadly classified into 2 categories. One set of approaches models the single nucleotide polymorphism effects as fixed effects and the other treats the single nucleotide polymorphism effects as random effects. These methods make certain assumptions about the dependency among individuals  as well as the dependency among markers (linkage disequilibrium) to provide consistent estimates of single nucleotide polymorphism heritability as the number of individuals increases. While various approaches have been proposed to account for such dependencies, it remains unclear which estimates reported in the literature are more robust against various model misspecifications. Here, we investigate the impact of different structures of linkage disequilibrium and familial relatedness on heritability estimation. We show that the performance of different methods for heritability estimation depends heavily on the structure of the underlying pattern of linkage disequilibrium and the degree of relatedness among sampled individuals. Moreover, we establish the equivalence between the 2 method-of-moments estimators, one using a fixed-single nucleotide polymorphism-effects approach, and another using a random-single nucleotide polymorphism-effects approach. Fundamental to the study of inheritance is the partitioning of the total phenotypic variation into genetic and environmental components . SpecifiGWASs seek to understand the relationship between these traits and millions of single nucleotide polymorphisms (SNPs), a type of genetic variant. Linear models are widely used in the field of statistical genetics to assess both individual and cumulative contribution of genetic variants on a trait. The individual contribution is assessed by treating each variant as a fixed effect (fixed-SNP-effect model) while adjusting for relevant covariates in a linear regression  is different, and m causal markers, so d of these markers are replicated r additional times, but these replicates are noncausal. So In other cases, there can be bias. For example, if causal markers are in regions of high LD, then (per marker) d = 0) or all markers are replicated (d = m) then there is no bias. Note also that the result only depends on the proportion of markers replicated. If d = gm, then Note that if no markers are replicated (Simulation Study 1: Impact of Marker LD). This is because the Dicker-1 estimator is derived from a linear expression of quadratic forms which is inflated due to LD. On the other hand, the HE estimator is a ratio of quadratic forms, and LD inflates both the numerator and the denominator, which potentially reduces the overall effect of LD. We recall from Through our simulations, we found that the Dicker-1 estimator had a generally greater bias than the HE estimator , for Under relatedness and inbreeding, it remains the case that Hence as We can also calculateThus, contrary to the results of Relatedness poses greater problems for the Dicker-1 estimator , which imn but Now, by expanding and simplifying, even the coefficient of This expectation now involves not only n independent genomes, there is nothing in the derivation of Equivalence of HE and Dicker-2 that assumes Simulation Study 3: Impact of Relatedness in Individuals, the close equivalence of the Dicker-2 and HE estimators should hold under relatedness, and, as seen from Although the derivation of distributional properties of the Dicker method-of-moments estimators depends critically on the assumption of 2G at M markers  for n unrelated individuals. The marker allele frequencies were those of a randomly chosen subset of markers from the 1000 Genomes Project from Chromosome 1 in the African (AFR) population .For the chosen value of We implemented the Dicker and HE estimators in R Version 4.0.2 as described in simulation study 1, we assessed the impact of different LD structures on heritability estimation. We generated genotypes assuming three kinds of LD structure: autocorrelated, block, and repeat. More details of the LD structures are given in n and the number of causal markers m on heritability estimation, and simulated data sets at 5 levels of LD. For each LD structure and level, we generated 500 simulated data sets.In n and m: (1) h2 depending on if n > m or m > n, whereas (2) and (3) compares estimates with different number of causal markers, and (2) and (4) compares estimates with different numbers of individuals. We first generated genotypes at ρ = 0 is the no-LD case.) The markers were then assigned to be alternating causal and noncausal  The repeat copies of the markers are noncausal, so the number of noncausal markers is m markers are causal, and the last For the repeat structure, we considered the cases: (1) simulation study 2, we investigated the behavior of likelihood models by plotting log-likelihood values  and (iv) of In d values  as a funThe log-likelihoods minus the maximum log-likelihood were plotted. Likelihoods were truncated at the 60% quantile of b(i) for rows (i) and (iv), and at the 60% quantile of a(ii) for rows (ii) and (iii). These cutoffs were chosen because they were the plots that had the lowest 60% quantile. Plots were generated for (i) simulation study 3, we assessed the impact of related individuals on heritability estimation. We simulated first, second, and third cousins using the rres package in R  and (iii)], which may suggest that the number of individuals affects the likelihood-based estimators more than the moments-based estimators. The lower variance resulted in lower MSE for GCTA for all conditions with ρ = 0, but the bias in LDAK caused it to have comparable MSE to the moments estimators when n = 200 –(iv). Consequently, the MSE of the Dicker-1 estimator increases rapidly compared to all of the other estimators as we increase ρ .r increases, agreeing with analytical calculations from Impact of LD on the Dicker-1 Estimator. In contrast to r increases, corresponding to r increases , we can also explain the unbiasedness of Dicker-2. LDAK was also largely unbiased in the case of unrelated individuals, but in the case of first cousins, as the number of markers increased, we observed that LDAK started showing downward bias.The Dicker-2, HE, and GCTA estimators appeared unbiased for each of the relatedness structures . For GCTImpact of Relatedness of Individuals on Moments Estimators.For the different relatedness structures  we considered, we observed similar pattern in the change of MSE as we increased the number of markers. MSE was generally the lowest when the number of markers was closer to the sample size. However, as we increased the number of markers, MSE for each estimator increased . For HE For each of our estimators, unrelated individuals had the highest MSE and first cousins had the lowest MSE. Furthermore, comparing the case of related to unrelated individuals, the MSE increased more slowly with the increase in the number of causal markers in related individuals.The methods for SNP heritability estimation can be broadly classified into 2 groups; fixed-SNP-effects models and the random-SNP-effects models. The fixed-SNP-effect models , n and M  and HE estimator (random-SNP-effect model-based estimator), are essentially the same. Hence, in the situation In the case where n > M), if no binning is used, the M remains constant  of individuals. In any real sample, regardless of the extent of correction for population structure, there will always be variation in the degree of relatedness of individuals, and the extent of variation would depend on the nature of relatedness present in the sample. As shown in Simulation 3, the precision of the heritability estimators improves if we include relatives in the sample. The MSE of the estimators was generally lower when we had certain relatedness present in the sample. Moreover, the impact of increasing the number of markers on MSE was significantly less pronounced if we had relatedness in the sample. Hence, we highly recommend to at least include second cousins, if present in the study sample, in the SNP heritability estimation. If the study sample has substantial number of first cousins, it may be beneficial to assess the sensitivity of the heritability estimate after inclusion of first cousins.n and M and often outweighs limitation of large standard error (In general, MOM estimators had much larger standard errors compared to the likelihood-based estimators. However, the computational gain of these MOM estimators over the likelihood estimators is significant for large https://github.com/alantmin/heritability.The code used to generate the data is available at G3 online.jkac134_Supplementary_DataClick here for additional data file.


Chickpea is the most important nutrient-rich grain legume crop in the world. A diverse core set of 147 chickpea genotypes was genotyped with a Axiom(®)50K CicerSNP array and trait phenotyped in two different environments for four seed micronutrients . The trait data and high-throughput 50K SNP genotypic data were used for the genome-wide association study (GWAS). The study led to the discovery of genes/QTLs for seed Zn, Cu, Fe and Mn, concentrations in chickpea. The analysis of seed micronutrient data revealed significant differences for all four micronutrient concentrations (P ≤ 0.05). The mean concentrations of seed Zn, Cu, Fe and Mn pooled over the 2 years were 45.9 ppm, 63.8 ppm 146.1 ppm, and 27.0 ppm, respectively. The analysis of results led to the identification of 35 SNPs significantly associated with seed Zn, Cu, Fe and Mn concentrations. Among these 35 marker-trait associations (MTAs), 5 were stable (consistently identified in different environments), 6 were major  and 3 were both major and stable MTAs. A set of 6 MTAs, MTAs  reported by us during the present study have been also reported in the same/almost same genomic regions in earlier studies and therefore declared as validated MTAs. The stable, major and validated MTAs identified during the present study will prove useful in future chickpea molecular breeding programs aimed at enhancing the seed nutrient density of chickpea. Cicer arietinum L.; 2n = 2x = 16) is one of the most important grain legume crops in the world and is well known for its health and nutritional benefits. As far as importance is concerned, the crop holds second place with respect to its importance and third place in production among the pulses grown worldwide2. The world population is growing at a very fast rate and is believed to escalate the mark of 9 billion by 20504. To meet the dietary demands of this expanding population, it is important to develop nutrient-dense crops for the poorer sections of society. Micronutrient elements such as zinc (Zn), iron (Fe), copper (Cu), magnesium (Mg), phosphorous (P), potassium (K) and manganese (Mn) play vital roles in the metabolism and physiological processes of both plants and humans7. The deficiency of these nutrient elements is caused mainly by non-availability or their low concentrations in the staple diets, resulting in serious health issues for billions of people world-wide9. The dietary importance of chickpea is well known across the global human population10. As far as bio-fortification through marker-assisted breeding (MAB) is concerned, the complex genetic inheritance pattern and the gene regulatory functions of micronutrients such as Zn and Fe concentrations are of paramount importance. These micronutrients in chickpea are complex quantitative in nature, showing minor genotypic effects but a higher magnitude of environmental effects11. The same was also validated in another study where it was demonstrated that genotypic contribution to total variability is limited compared to year and location effects12. The concentration of these minerals is also influenced by the seed size, seed type (Desi vs Kabuli) and some time by seed coat color14.Chickpea  for Fe and Zn were reported using a genotyping by sequencing (GBS) approach19. GWAS serves as an alternative method of gene discovery to QTL mapping that involves the use of biparental mapping and eradicates several drawbacks of the latter in the identification of QTLs/genes23. GWASs have been successfully used in the discovery of genes/QTLs for a variety of traits, including seed micronutrients, in cereals24 as well as legumes26. Association-mapping studies have brought insights into how to improve the concentration of mineral nutrients in crops such as rice27, barley18, field pea28, common bean29 and chickpea30. Recently, in the context of climate change, efforts were also made to identify the effect of drought and heat on the accumulation of micronutrients, and MTAs were also reported31. For GWAS, several genotyping platforms based on SNPs have been developed over the years and have been used successfully for the identification of genes for different traits in crop plants32. The availability of the draft genome sequence of chickpea33 and large-scale resequencing efforts36 have led to the discovery of large-scale SNP markers for the development of SNP genotyping platforms such asAxiom®CicerSNP Array37.Genome-wide association studies (GWASs) are considered one of the paramount and emerging approaches of gene/QTL discovery for the genetic dissection of quantitative traits, including seed micronutrients, in crop plants®CicerSNP Array. The results of the present study will encourage the effective utilization of chickpea genetic resources, opening possibilities for biofortification programs in chickpea in the near future.In the present study, we report the marker trait associations (MTAs) for micronutrients using the core set of germplasm and the AxiomWe estimated the concentrations of micronutrients such as Zn, Cu, Fe and Mn in all 147 chickpea seed samples based on 2017–2018 and 2018–2019 as well as pooled samples from both years. We observed a large variation in the concentration of the estimated micronutrients between the years as well as the pooled samples. Based on the samples obtained during 2017–2018, the Zn concentration ranged from 14.56 to 204.39 ppm with a mean of 45.31 ppm, the Cu concentration ranged from 4.23 to 185.47 ppm (mean 60.99 ppm), the Fe concentration ranged from 79.36 to 198.26 ppm (mean 143.24 ppm) and the Mn concentration ranged from 9.07 to 54.29 ppm (mean 25.62 ppm)  Fig. . Analysi–3) on all eight pseudomolecules. The highest number of significant MTAs for micronutrients was identified on Ca6, while Ca8 contained the lowest number of MTAs. We identified 4, 3, 4, 6, 3, and 6 significant MTAs on Ca1, Ca2, Ca3, Ca4, Ca5 and Ca7 pseudomolecule SNPs, respectively  for Zn  was identified with 10.1% of the average phenotypic variation, while no MTAs were found for Fe and Mn. A set of 6 major MTAs (explaining more than 15% of phenotypic variation) were identified, of which 5 MTAs were for Cu . The remaining major MTA (Affx_123275255) was identified for Fe, with an average phenotypic variation of 19.9%. Out of 5 major MTAs identified for seed Cu concentration, 3 MTAs  were both stable and major. Affx_123280871 explained the maximum phenotypic variation (18.25%). The list of these markers and their chromosomal location and association with the trait of interest is presented in Table The MTAs identified in more than one environment were grouped as stable/promising MTAs, while MTAs with greater than 15% phenotypic variation were grouped as major MTAs. Out of the 35 identified associated SNPs, 5 SNPs were stable/promising, 6 MTAs were found to be major MTAs, and 3 MTAs were both stable and major. Among the stable/promising MTAs, 4 MTAs were for Cu , explaining 15.6%, 17.3%, 14.05% and 18.25% of the average phenotypic variation with a P value range of 4.8 × 1038. Iron and zinc are indispensable micronutrients in the human diet. By acting as cofactors for proteins, including hemoglobin, transcription factors and cytochrome, they play pivotal roles in growth and development. Their deficiency leads to physiological disorders such as tissue hypoxia, anemia, acrimal dermatitis, and hypogonadism. Copper plays an important role in metabolism in both plants and animals39. Its deficiency in the human diet causes several abnormalities in important life processes, such as collagen and elastin biosynthesis, iron mobilization, and wound healing40. To overcome or mitigate this problem of micronutrient malnutrition, strategies such as mineral supplementation, dietary diversification and biofortification need to be implemented41. Chickpea is the most important nutrient-rich grain legume crop, and incorporation of this crop in human diets is considered a very good strategy for diet diversification and alleviating micronutrient malnutrition13.One of the major global health issues that need immediate attention is micronutrient malnutrition42. The availability of reliable markers will help in tailoring climate-smart and nutrient-rich varieties that cater to both the demand and health aspects of humans through genomics-assisted breeding programs. The availability of genetic and genomic resources will pave the way for the discovery of useful/stable/major genes/QTLs/markers for seed micronutrients in chickpea through a variety of approaches. A variety of molecular markers have been developed in almost all crop plants and these markers have been used for variety of purposes including the study of genetic diversity, study of population structure and also in gene discovery programs51). However, despite the abundance of different types of markers available, SNPs have witnessed exponential utilization for a variety of diverse applications since their discovery52.Recent advances in genomics and next-generation sequencing have enabled the availability of ample genomics resources, making crop genomics resources rich. Conventional QTL mapping approaches to identify genomic regions have been restricted to biparental mapping populations at less resolution53, field pea, chickpea, common bean and lentil12. In the present study, the concentrations of different micronutrients obtained were similar to the results observed in earlier studies55. The candidate lines identified for different micronutrients should be used in chickpea breeding programs and would also serve as useful genetic resources for gene discovery and transcriptomic studies. Diversity analysis was conducted to identify molecular genetic variations present in chickpea germplasm, revealing that indigenous chickpea genotypes are more diverse in comparison to exotic ones, which may be due to the genetic bottleneck in exotic lines or a lower number of lines included in the present study. The availability of lines carrying contrasting genetic variations with different genetic parameters will benefit breeders in chickpea breeding programs. The PCA clustered the germplasm set into two groups, indicating the existence of two subpopulations for the set of genotypes used. Similar results have been reported in some other earlier studies in chickpea making use of SSR markers57.Our trait variability results indicated that the genotype × environment interactions were significant, which indicates that the significant proportion of seed micronutrient concentrations in chickpea is dependent on the soil conditions, temperature, precipitation, and various crop management practices and that significant variation also exists between the genotypes. A similar effect of location and year on seed micronutrient concentrations has been reported in rice®CicerSNP Array and 2 years of phenotyping of core collections of chickpea. To optimize the results, we utilized 7% high-quality SNPs (PIC > 0.349%) after deep filtration to precisely map and identified 35 highly associated SNPs for four seed micronutrients  in chickpea with an average of ~ 9 SNPs/trait. Quantitative trait loci for seed micronutrients have also been identified in different earlier studies in chickpea (2 QTLs for Cu54 and 24 QTLs for Zn and Fe55). In addition, several important genes/QTLs for different micronutrients  have also been reported in some other earlier studies in chickpea7. For seed Cu concentration, we identified significant MTAs on LG1, 2, 3, 4, 5, 6 and 8, while as MTAs were identified on LG5 and LG7 only in an earlier study5. For seed Zn concentrations, we identified significant MTAs on LG1, 4 and 7, while as in an earlier study, MTAs have been reported on LG4 and LG57. For seed Fe concentration, we identified MTAs on LG1, 3, 4, 5, 6, 7 and 8, while as MTAs have also been reported for seed Fe concentrations on LG1,4 and LG5 in an earlier study7. For Mn concentration, we identified significant MTAs on LG1, 2, 3, 4 and 7, while MTAs have been reported on LG1, 2, 3, 4, 6 and 7 in an earlier study6. The comparison of the results revealed that several unique and new MTAs on different chromosomes not reported on these chromosomes in earlier studies have been reported during the present study , and Fe 58; pea for Zn , Fe , and Mn 59; and Medicago truncatula for Zn , Fe (on chromosomes 7 and 8), Cu , and Mn 60. The markers found to have a significant association with the trait should be used in marker-assisted selection for biofortified chickpea to mitigate the problem of micronutrient malnutrition or hidden hunger.We report significant MTAs associated with key micronutrients through SNP data based on AxiomOur study is the first of its kind that presents the features of a large population of chickpea genotypes in different environments grown in the region of Kashmir, providing an understanding of the influence of environmental and growing conditions on the seed micronutrient concentrations of a crop. This study provides insight into the genetic basis of variability for seed micronutrient concentrations in chickpea and the potential of GWAS in unravelling marker trait associations in economically important crops. Because of their importance, stable MTAs should be recommended for chickpea molecular breeding programs aimed at enhancing the seed micronutrient concentrations of local chickpea landraces preferred by consumers. The stable and major MTAs identified during the present study will prove useful in breeding programs aimed at enhancing seed micronutrient concentrations in chickpea.The plant material used, trait data recorded, genotyping done and data analysis conducted in this study has been presented under separate headings below. All the methods in this manuscript were carried out in accordance with relevant guidelines and regulations.56  based on SSR marker data generated on 384 composite sets61. Healthy mature harvested seeds from each genotype, free from any impurity, were surface cleaned with double-distilled water and dried in an oven at 55–60 °C for 48 h. The dry seeds were ground into powder using a ball mill (MM400). For each genotype, one gram of powder was weighed in an Erlenmeyer flask, and acid digestion was conducted on a hot plate (250 °C) by adding 10 ml of HNO3-HClO4 (9:4) solution to each flask61. The digested mixture was filtered through Whatman No. 40 filter paper, and using double distilled water, the total volume was made up to 50 ml. The chemicals used in the digestion of the samples were of analytical grade. The samples were analyzed against standards of known concentration. The concentrations of nutrients were determined using an atomic absorption spectrophotometer (AAS) . The concentrations of micronutrients present were estimated and expressed as mg/kg (ppm) of seed. Three independent replications were noted for each accession, and their mean was used for the statistical analysis.Trait phenotyping to quantify the seed micronutrient concentration for four micronutrients Zn, Fe, Cu, and Mn was performed using the core set, grown in two replicated field trials arranged in an augmented block design with a planting density of 10 seeds per row per genotype in rows of 4 m long with a row-to-row spacing of 30 cm. The trial was conducted for two consecutive years, 2017 and 2018, in the research fields of the Faculty of Agriculture (FoA), SKUAST-K, Wadura Campus, Sopore, Kashmir, India. The concentrations of four micronutrients, Zn, Fe, Cu, and Mn, were determined following the procedure described by Ref.www.qiagen.com/handbooks) following the manufacturer’s instructions. For DNA isolation, leaf samples from three random healthy plants for each genotype were taken. After purification, the DNA was dissolved in 100 μl Tris–EDTA (TE) buffer. A NanoDrop (ND 1000) was used for the quantification of the isolated DNA samples, and the purity of the samples was analyzed by agarose gel electrophoresis using 0.8% agarose gel in the presence of Tris–acetate-EDTA (1× TAE) buffer.The genomic DNA of 147 chickpea genotypes was isolated and purified using a high-throughput DNeasy Plant Mini Kit by Qiagen (−1) from a set of 147 chickpea genotypes was further used for genotyping using 50K SNP array37 at the Centre of Excellence in Genomics and Systems Biology (CEGSB) Lab Facilities, ICRISAT, Patancheru, Hyderabad. The total genomic DNA before hybridization was fragmented (25–125 bp) followed by purification and hybridized with the 50K SNP Axiom CicerSNP Array to generate the genotyping data. The nonspecific random ligations bound to the targets were washed off under stringent conditions. The Gene Titan-Multi-Channel instrument was used to stain, image and process the array to track to the multicolor ligation events at the array surface, which points toward the polymorphic nucleotides and ultimately generate the data.The purified high-quality DNA (100 ng µlhttps://www.youtube.com/watch?v=HoYos9IwZNY). The test is used to test whether the trait data collected in two different experiments/environments are significantly different. A P value < 0.05 indicates significant differences between the variances (heterogeneous variances), while P > 0.05 indicates nonsignificant differences between the trait values of two experiments (homogenous variances).Based on the mixed-effect model for three different replications and 2 years using the statistical functions package of R software according to 5% significance levels, analysis of variance (ANOVA) was conducted to analyze differences in the seed nutrient concentrations. The diverse statistical measures, including minimum, maximum, average value, standard deviation and coefficient of variation (CV), among the diverse accessions were measured. The effect of genotype, environment (year) and their combined effect (G × E) was also analysed. Levene’s test was also conducted to test the homogeneity of variance of genotypes in two different environments/experiments using an established procedure in Microsoft Excel , the number of effective alleles (Ne) and Shannon’s information index were calculated. To conduct principal coordinate analysis (PCA), the genotypic data (multiple loci and multiple genotypes) were also analyzed by GenAlEx version 6.5 software. For the construction of the PCA plots, a pairwise genetic distance matrix was calculated. The same software program GenAlEx version 6.5 was used to compute the other genetic diversity parameters, such as genetic identity, gene flow (Nm), and pairwise Nei’s unbiased genetic distance. Using the software package Power-Marker version 3.2563, we calculated the polymorphic information content (PIC) for each SNP marker locus.Diversity analysis was performed using genotypic data generated by a set of 7277 SNPs on 147 chickpea genotypes. The software program GenAlEx version 6.5To calculate the level of genetic variation not only among the genotypes but also within the populations, analysis of molecular variance (AMOVA) was performed. The genetic distance matrix (same as used for PCA) was utilized for conducting AMOVA. The software program GenAlEx6.5 was utilized to perform the AMOVA.http://zzlab.net/GAPIT) was used for analyzing the trait phenotyping data  and genotyping data (obtained through the use of Axiom Cicer SNP Array). The model used in GWAS was MLM (mixed linear model), as it has proven quite useful for controlling the relatedness and population structure within GWAS. The population structure in MLM is considered a fixed effect, whereas kinship among individuals is incorporated as the variance–covariance structure of the random effect for the individuals64. SNPs showing minor allele frequencies below 0.05 were eliminated from the dataset. In total, 41,489 polymorphic markers were retained for the 147 genotyped accessions. To show the distribution of the P-values for the SNP markers, Manhattan plots were generated .To identify markers associated with seed Zn, Fe, Cu, and Mn concentrations, agenome-wide association study (GWAS) was conducted. GAPIT (The Genome Association and Prediction Integrated Tool) version 3.0, R package (All the methods in this manuscript were carried out in accordance with relevant guidelines and regulations.Supplementary Figures.


Marker-assisted selection (MAS) has been widely used in the last few decades in plant breeding programs for the mapping and introgression of genes for economically important traits, which has enabled the development of a number of superior cultivars in different crops. In sugarcane, which is the most important source for sugar and bioethanol, marker development work was initiated long ago; however, marker-assisted breeding in sugarcane has been lagging, mainly due to its large complex genome, high levels of polyploidy and heterozygosity, varied number of chromosomes, and use of low/medium-density markers. Genomic selection (GS) is a proven technology in animal breeding and has recently been incorporated in plant breeding programs. GS is a potential tool for the rapid selection of superior genotypes and accelerating breeding cycle. However, its full potential could be realized by an integrated approach combining high-throughput phenotyping, genotyping, machine learning, and speed breeding with genomic selection. For better understanding of GS integration, we comprehensively discuss the concept of genetic gain through the breeder’s equation, GS methodology, prediction models, current status of GS in sugarcane, challenges of prediction accuracy, challenges of GS in sugarcane, integrated GS, high-throughput phenotyping (HTP), high-throughput genotyping (HTG), machine learning, and speed breeding followed by its prospective applications in sugarcane improvement. Recent advancements in the field of plant breeding and crop-raising practices have profoundly contributed to the tune of 0.8–1.2% annual crop yield rate increase for major crop plants. However, the current rate of productivity gain is not sufficient, and ~2.4% yield gain is required to meet the expected global food demand by 2050 [Saccharum species hybrid) are the outcome of inter-specific hybridization between domesticated octaploid S. officinarum  and wild S. spontaneum  with minor contributions from S. robustum, S. sinense, and S. barberi and related genes such as Miscanthus, Narenga, and Erianthus. Due to this, the cultivated varieties have a highly complex polyploid genome and exhibit a high level of heterozygosity coupled with varied number (100–130) of chromosomes. Sugarcane has one of the most challenging genomes of all crops; it is an auto- and allo-polyploid species with a high level of aneuploidy [Approximately 80% of global sugar production comes from sugarcane crop, and millions of farm workers, in addition to sugar factory/mill workers, directly or indirectly depend upon sugarcane agriculture. The modern cultivars of sugarcane , which have great potential in enhancing selection efficiency. GS has a clear edge over pedigree selection and MAS, particularly in the improvement of complex traits such as yield in a short span of time by the reduction in number of breeding cycles . Speed bi is the selection intensity, h is the narrow sense heritability, σa is the additive genetic variance, and L is the length of the breeding cycle interval or generation.Genetic gain in a crop breeding program is determined by the following popular breeder’s equation:Genetic gain per unit time can be accelerated by reducing the length of the breeding cycle interval. Increasing the breeding cycles per unit time and reducing the cost of phenotyping would greatly increase genetic gain in GS . A higheIn 2001, Meuwissen and associates were the first to demonstrate GS as an approach to capture total additive genetic variance using genome-wide molecular markers . AlthougAlthough, many GS models have been designed to predict genotypic performance in crops, the selection of a suitable statistical model is essential to obtain a relatively higher predictive ability and thereby the success of GS. The assumptions and treatments of marker effects decide the level of prediction accuracy in different GS models ,40. The Gouy and associates opined that in a highly polyploid crop like sugarcane, larger panel and high-throughput genotyping may bring out interesting new revelations if GS is applied to the populations that are otherwise rejected in the first stage . In addiMiscanthus × sugarcane hybrids, Miscanthus × Miscanthus and sugarcane × sugarcane F1s and BC1 populations were analyzed. GS yielded a higher prediction accuracy, identified more genotypes with the best-performing simulated trait values, and more accurately predicted the traits. Although, none of the GS models decisively outperformed the others, MAS was suggested to be a reasonable option for advancing vertical disease resistance. Deomano and co-workers assessed genomic prediction accuracy for cane yield and sugar content using three different commercial sugarcane populations consisting of 467, 1146, and 738 clones in different stages of selection trials [Breeding processes in sugarcane are challenging due to the factors discussed above, therefore, sugarcane breeding is heavily dependent on phenotypic selection. This necessitates large-scale field trials and several cycles of selections spanning the whole process over 8–10 years. Olatoye and co-workers studied the abilities of GS and MAS to predict traits under different genetic architectures and marker densities . Due to n trials . It was n trials . Aono ann trials . Similarn trials . Islam an trials . The modn trials .Most of the genetic gains of GS come from obtaining accurate predictions in early stages of the breeding cycle and shorter breeding cycles. The genomic prediction accuracy is affected by several factors: genetic relatedness , marker Compared to other crops, sugarcane undergoes various growth phases—germination (0–60 days), formative phase (60–150 days), grand growth phase (150–240 days), and maturity phase (240–360 days)—thus facing weather variations for a longer period. Hence, phenotyping of a large number of genotypes becomes a challenge, especially considering different growth phases. Sugarcane possess one of the most challenging genomes of all crops  posing a formidable challenge to genotyping techniques. In addition, the population and unique genetic parameters also pose challenges to the adoption of GS models, which have been primarily developed for seed-propagated crops. For example, a majority of the GS models consider the additive effects and assume dominance and epistatic effects as residual. For clonally propagated crops like sugarcane, however, dominance and epistatic effects play an important role in addition to additive effects. This hold true as the whole set of alleles, together with their interactions, are passed to the next generation through clonal propagation. Another possible source of variation that is not accounted for could be additive effects due to varying allele dosages. Since SNPs are only defined as being present or absent, in sugarcane, variation in the numbers of copies of each allele may be important. In addition, data from different stages of the selection may have implications on the accuracy of genomic prediction.Since a large population needs to be phenotyped as well as genotyped, GS may need to be integrated with high-throughput technologies and statistical robustness. This necessitates a thorough understanding of sugarcane phenotype at various growth phases so as to integrate the complex pathways and the physiological and biochemical traits. In recent times, hyperspectral cameras and mechanical devices have been developed by which one can study complex traits much faster and in a more precise manner. The long-read sequence platforms such as the PacBio Single-Molecule Real-Time (SMRT) system, Illumina TruSeq, and Oxford Nanopore sequencing could solve the problem of genome assembly. Furthermore, integration of NGS techniques with machine learning and deep learning tools would not only facilitate robust SNP mining but also GS model development and their validation in a high-throughput fashion to increase the prediction accuracy. Finally, speed breeding (SB), once standardized for sugarcane, could be effectively utilized to increase the rate of genetic gain by reducing the length of the selection cycle. In a complex crop like sugarcane, the full potential of GS could be realized by an integrated approach combining high-throughput phenotyping, genotyping, machine learning, and speed breeding .4 crop, is considered as a high-biomass crop that is efficient in harvesting sunlight and converting the carbon dioxide to sucrose through complex redox reactions; it also produces various complexes of carbohydrates, lignin and fiber. The looming dangers of climate change coupled with increasing population and food insecurity have led the focus toward sustainable crop production that does not affect the environment. Compared to other crops, sugarcane is a complex one that undergoes various phenophases, as mentioned above [Sugarcane, a Ced above , and it ed above . These aed above . VariousIn sugarcane, the cane yield is observed as a function of the stalk population per unit area  and single cane weight. The foremost component of yield is the number of tillers at harvest, and having a higher correlation coefficient, it is directly associated with yield ; cane le2 = 0.97. A successful (>90% accuracy) forecasting model for pre-harvest sugarcane yield determination using UAV-acquired RGB color images coupled with ground information data has also been reported [Sugarcane is a multifunctional crop primarily used for sugar and renewable bioenergy production, and precise and timely assessment of the sugarcane yield before harvest plays a predominant role in the supervision of agroecosystems . LiDAR mreported . Recentlreported .Based on systematic near-infrared spectroscopy, it has been reported that stalk quality in terms of soluble sugar, insoluble residues, and the corresponding fundamental ratios can be assessed in high-throughput mode . FurtherConsidering cellular water deficits and their physiological significance, relative water content (RWC) is perhaps the most useful indicator of plant water status. The RWC protocol by Barrs and Weatherly in 1962 still has the highest relevance for studying the plant water status for drought research in crop plants, as shown in Equation (1) .RWC = [In sugarcane, the RWC of transpiring leaves ranges from 60 to 85%. A lower value of RWC is observed in plants grown under water-limited conditions and vice versa under irrigated conditions. The RWC and water potential plays a major role in sugarcane under drought stress signifying cellular water status, establishing cellular processes, and eventually, sugarcane productivity. Nowadays, through a plant phenomics facility, the RWC/water content/water potential can be derived easily with an NIR camera by creating indices that could facilitate the screening of a large number of genotypes for drought tolerance and sugarcane productivity. Ripple emphasized the significant correlation between leaf water potential and reflectance . The vibIn well-watered plants, transpiration helps the plants to adapt to stress conditions. Canopy temperature is generally measured with IR thermometers or thermal gun. Generally, the plant canopy emits long-wave infrared radiation as a function of temperature, and the IR thermal gun records the IR radiation, converts the recorded radiation as an electric signal, and displays the signal as a temperature . The canBased on the field experiment, Natarajan and co-workers suggested the use of various sensors  mounted on a UAV in high-throughput phenotyping, especially for indirect traits ) . In earlThe chlorophyll content is reported to have a significant positive correlation with cane yield. Non-destructive measurement of chlorophyll content through SPAD meter  offers a simple way to determine the chlorophyll content in large populations . Due to Roots are the first point of contact of plants with the environment, beginning from the germination of seed/propagules, wherein roots provide anchorage in addition to facilitating the uptake of water and nutrients most essential for plant growth and development. Sugarcane root systems are more complex due to their varied forms and distribution, highly branched superficial roots, positively geotropic buttress roots, and deeply penetrating vertical rope roots, each with distinct functionalities. Roots in sugarcane are unique, as they survive even after harvest of the crop, facilitating ratoon establishment. For sugarcane, being a 12–18 month crop from planting to harvest, root growth is massive for different clones. Plasticity in root morphology, distribution, and physiological aspects are important in determining the crop growth under biotic and abiotic stress situations. The hydraulic properties of sugarcane roots are correlated to stomatal conductance, thereby influencing photosynthetic rate and biomass assimilation . Long-teOperational difficulties in phenotyping sugarcane roots include the sheer size of the root system per se, in addition to the huge cost of establishing advanced facilities for high-throughput underground imaging and data analysis. Phenotyping platforms such as GrowScreen-Rhizo, Phytomorph, GrowScreen-PaGe, RADIX, and RhizoTubes have been demonstrated to be effective for root investigation in several other crops. Imaging techniques including X-ray computed tomography (X-ray CT), magnetic resonance imaging (MRI), positron emission tomography (PET), electrical resistance tomography (ERT), electromagnetic inductance (EMI), and ground-penetrating radar (GPR) aid in non-destructive imaging of roots with minimum damage to the plants . High thHigh-throughput genotyping is one of the most crucial components of genomic selection, where the type of marker system, method of genotyping, and genotyping platform does not only affect the prediction accuracy, but also the speed, accuracy, and cost of genotyping. The most suitable marker system considered for high-throughput genotyping is single-nucleotide polymorphisms (SNPs), mainly due to its abundance, genomic coverage, ease in identification, high reproducibility, etc. . The advHowever, GBS in sugarcane with reference to GS is still in its infancy. Still, it is the method of choice for most of the researchers working in different crops . It is aThe breeding cycles for sugarcane to select superior genotypes can take several years, and genomic selection (GS) is an alternative to reduce time period. A major use of GS is to predict the GEBVs using statistical methods built with markers based on training and a testing population to select a set of promising individuals . HoweverGenomic selection was first used in sugarcane breeding with two sets of 167 sugarcane clones with 1499 DArT markers, and the prediction accuracies ranged between 0.11 and 0.62 for ten traits related to plant morphology, digestibility and composition of the bagasse, sugar and bagasse contents, and disease resistance ,134,135.1 and BC1 populations of Miscanthus × Miscanthus and sugarcane × sugarcane crosses, showing that GS is preferable to MAS for introgression of genetic sources of horizontal disease resistance from Miscanthus to sugarcane. This study used markers comprising 3044 RAD-Seq SNPs and 136 Goldengate SNPs for 85 individuals [Another report showed prediction accuracies from 0.25 to 0.45 for yield and sugar content using three panels with 2351 sugarcane clones and Affymetrix Axiom array-based SNPs . Using pividuals . Severalividuals . BayesA ividuals ,145.Recently, Hayes and associates tested the potential of GS using a reference population of 3984 sugarcane clones and 26K SNPs for genotyping , reportiSugarcane, being a long-duration crop, requires almost 12–14 years to develop a variety through conventional breeding methods ; therefoIncreasing the rate of genetic gain is essential to meet the ever-increasing global food demand. To achieve rapid genetic gain, advanced breeding tools are required. GS is one such proven technology in animal breeding, and it is also incorporated in plant breeding programs, especially in the ever-expanding private sector. GS could be a promising strategy to accelerate genetic gain per unit time and cost, mostly for traits governed by genes with small and cumulative effects. The optimal integration of GS in active breeding programs faces several challenges. Nevertheless, GS has a clear-cut advantage over the other breeding techniques to enhance genetic gains for complex traits. The implementation of GS for low-heritability traits faces challenges due to high environmental effects, genotype–environment interaction, dominant, and epistatic genetic effects, and so on. Breeding programs are always dynamic and required to choose various factors carefully to optimize the genetic gain. Constant improvement and cost reduction of genotyping, as well as genotyping of large breeding populations, may help in the inclusion of distantly related individuals in the TP; GBS could be one such potential genotyping platform. In the last decade, many solutions have been offered to overcome the challenges associated with GS-assisted breeding, but still, there are certain practical difficulties, particularly for complex genome crops (such as sugarcane) and G × E sensitive traits (including yield and component traits). Multi-trait and multi-environment modelling are essential for improving the accuracy to predict the performance of newly developed lines in coming years. Frequently updated prediction models may reduce the prediction accuracy decay, especially when the target population becomes further separated from the TP. Both phenotyping and genotyping have improved progressively over the recent past, and big biological data generation through high-throughput genotyping and phenotyping have contributed to a boom in machine learning and deep learning in commercial agriculture to deliver precision farming strategies.