Punica granatum, L.) is a fruit tree that is increasingly popular worldwide due to the health-related properties of the fruit juice. While several studies highlighted the rich phytochemical diversity, few efforts have been devoted to an integrative understanding of the level of diversity of this species. This study investigated the diversity of 40 pomegranate accessions in an Indian ex situ collection by using twenty-nine morphological traits, six biochemical parameters, and twenty-nine Simple Sequence Repeats (SSR) markers. Among the evaluated traits, fruit volume (23.34% CV), fruit weight (21.12% CV), and fruit color (*a) (22.69 % CV) largely contributed to the morphological classification. Based on Mahalanobis D2 distance and Tocher’s clustering, the 40 pomegranate accessions were grouped into eight clusters, partly consistent with their origin. Specifically, cultivars introduced from foreign countries were present in distinct clusters. The SSR marker analysis generated 66 alleles. The observed heterozygosity values ranged from 0.05 to 0.63, with a mean value of 0.30. Maximum molecular genetic dissimilarity was observed between ‘IC-318720′ and ‘Gul-e-Shah Red’ (0.30). The neighbor-joining dendrogram separated wild accessions from cultivated varieties. The combination of morphological, biochemical, and molecular characterization allowed for comprehensively characterizing the pomegranate diversity and provided information on the relationships between the different aspects of the diversity. This work also suggests that the origin of the accessions is an important factor of discrimination and that the level of admixture between local and foreign material is currently limited.Pomegranate ( Punica granatum L.) is a deciduous fruit tree typically grown in sub-tropical and tropical regions of the world [Pomegranate  and evergreen cultivars ,11. The p < 0.05) variability for all the characters analyzed. Descriptive values of the traits in the germplasm collection are presented in The morphological characterization of the pomegranate germplasm was carried out using 35 quantitatively scored traits. Mean values for each accession and their statistical analysis are presented in www.fao.org, accessed on 1 October 2022) [The coefficient of variation (CV) ranged from 3.07% for Total Soluble Solids to 23.34% (fruit volume), with three traits with a CV value higher than 20% and ten traits with a CV between 10 and 20%. The highest CVs were recorded in traits related to fruits, while usually, the lowest values were noted for leaves (except petiole length and width), flowers and seeds. Overall, it is worth paying attention to the high level of variation for traits that have clear commercial importance, such as fruit weight 21.12%), volume (23.34%), and acidity (18.66%), as well as the number of arils per fruits (19.41%) . Specifi.12%, voler 2022)  Among ther 2022) ,25. All er 2022) , and furer 2022) . Among tThe analysis of the quantitative traits was complemented by scoring three qualitative traits, plant habit and the color of the corolla and calyx . All trap < 0.05) are reported in The correlations between the quantitative variables and their significance  and cluster II (11) . By exclPlinia cauliflora) were divided into four clusters according to 16 fruit traits, with some degree of consistency between clustering and collection sites [Hevea brasiliensis) indicated a six-cluster division [The number of clusters and their subdivision imply an important divergence among the accession taking as reference studies based on Tocher’s optimization method in other fruit trees. For instance, 42 mango cultivars were divided into three clusters based on 14 morphological traits . Thirty-on sites . The anadivision .p < 0.05) differences among the accession for the four biochemical traits under investigation . This implies that the antioxidant properties of fresh juice are strongly affected by this water-soluble vitamin. Finally, the pomegranate juice had a notable quantity of total polyphenols, reaching a maximum value of 2919.00 mg/mL GA eq for the IC-318793 accession.It is long known that pomegranate fruits and juices can contain a high level of anthocyanins , whose pIn order to classify the accessions considering the whole phenotypic diversity , we used a hierarchical clustering procedure based on the Gower distance. A sensible biological classification could be depicted at k = 7 . SpecifiThe SSR technique was employed to evaluate the molecular diversity within the pomegranate accession. These markers have been successfully employed by several researchers to characterize the pomegranate germplasm to study genetic diversity and to understand population structure and association analysis ,35,36,37In total, 29 PGKVR SSR markers were used for genotyping of the selected pomegranate germplasm . They weThe maximum genetic dissimilarity  was observed between ‘IC-318720′ and ‘Gul-e-Shah Red’ (0.30); ‘IC-318724’ and ‘KRS’ (0.29); ‘1182’ and ‘Yercaud-I (0.27); ‘1182’ and ‘Dholka (0.27); ‘1182’ and ‘IC-318712 (0.27); ‘IC-318724’ and ‘Bhagawa (0.27); and IC-24685 and IC-318706 (0.27). Unweighted neighbor-joining cluster analysis divided the accessions into different clusters. All the wild accessions were grouped into clusters I and II, while cultivated varieties were in cluster III. Indian varieties are grouped as sub-cluster-I, foreign varieties (Gul-e-Shah Red and Gul-e-Shah Rose Pink) as sub-cluster-II, and Afghanistan cultivar ‘Kabul yellow’ formed a unique sub-cluster-III under cluster III . p < 0.001) correlation between the ordered distance matrices. When taking the result altogether, this implies that the genetically distant accessions have a higher phenotypic diversity than more closely related accessions. In cereals, the association of low marker distances with low phenotypic distances holds true especially for pairs of related lines, and has been related to breeding activities [The resemblance among the 40 accessions depicted by the morpho-biochemical data  and by ttivities ,40. Althtivities , it is ativities . In walntivities . In apritivities . Finallymrig bahar . The open-field collection includes cutting-propagated accessions of similar age obtained from various sources . The experiment was carried out for two consecutive years during  sources . Plants A total of 40 pomegranate accessions were selected for this study . They inFor the morphological characterization, plant material was randomly sampled from around the canopy of three plants per accession, for a total of 15 hermaphrodite flowers at full bloom, 15 adult leaves (from the middle part of the one-year-old shoot), and 30 fruits at maturity. All seeds were extracted from each fruit, counted, and weighed. Arils were hand removed from a total of fifteen randomly selected fruits per accession. Tree measurements (height and canopy growth) were measured with a measuring tape. Linear dimensions of leaves, fruits, seeds, and arils were determined with a digital vernier caliper. Weight was measured using a precision electronic balance. Volumes were determined by the liquid displacement method. The total soluble solids (°Brix) of the juice were determined using a digital refractometer  and expressed in °Brix. The titratable acidity of arils juice was measured by titration using a 0.1 N NaOH solution. Fruit and aril color were examined by using a colorimeter . The color was expressed using the CIELAB coordinates. L*, a*, and b* . The texture of the seeds was measured using a texture analyzer . Firmness was expressed as the maximum compression force (N) required to rupture the arils . Aril frThe qualitatively scored traits were the tree growth habit, the calyx color, and the corolla color, and they were measured on three plants per variety. Plant growth habit was classified as upright, spreading, or weeping. The calyx color was observed when the sepals were closed, considering the following classes: orange, red, and dark red. The corolla color was observed when the flowers were fully open, considering the following classes: white, pink, orange, and red.Measurements were performed at the Post-Harvest Technology Laboratory, ICAR-NRC. The juice was extracted from arils with a metal aluminum hand-press juicer (Jasper). All chemicals were purchased from HiMedia . The spectrophotometric analysis was carried out with a GENESYS 50 dual-beam UV-Vis spectrophotometer .Total phenolic compounds were determined by the Folin–Ciocalteu colorimetric method, which is based on the chemical reduction in a mixture of tungsten and molybdenum oxides , reading510 − A700)pH1 − (A510 − A700)pH4.5. The results were expressed as cyanidin-3-glucoside (c3g) equivalents using a molar absorptive coefficient (ε) of 26,900 L/mol·cm, the molecular weight (MW) of 449.2 g/mol, the dilution factor (DF), and the cuvette path length , according to the following equation, ANT = (A × MW × DF × 100)/(ε × 1), and it is expressed in mg/100 mL c3g.The content of monomeric anthocyanins (ANT) was estimated with a differential method using two buffer systems: a 25 mM potassium chloride buffer (pH 1.0) and a 0.4 M sodium acetate buffer (pH 4.5) . The absThe total antioxidant activity (TAA) was determined using a ferric-reducing ability of plasma (FRAP) assay, as previously described . BrieflyAscorbic acid in the fruit juice was determined using a previously described method based on the reduction in 2,6 di-chlorophenol indophenol by L-ascorbic acid, and it was expressed in mg of ascorbic acid in 100 mL of material ,51.Genomic DNA was isolated from young leaves following a modified CTAB (Cetyl trimethylammonium bromide) method . The purpost hoc for mean separation (p < 0.05). Bivariate correlation analysis was performed using the Pearson coefficient. For the non-hierarchical grouping of the accession based on the morphological traits, we used the Mahalanobis generalized distance (D2) and the Tocher’s method [p < 0.05). All these analyses were performed in R version 4.2.Quantitative data were analyzed with a one-way analysis of variance (ANOVA) followed by the Tukey s method . For hies method . A dendrFor the molecular analysis, each SSR amplification was analyzed by scoring the presence (1) or absence (0) of polymorphic bands in individual lanes. The DARwin 6.0 software  was usedThe study highlighted the significant differences among the accessions and made evident the pomological characters influencing the classification of the analyzed Indian collection. Moreover, the morphological and molecular marker systems detected a variability that was adequate for the discrimination of all accession. An implication of our work is that the health-promoting activity and the nutraceutical benefits of fruits and juice should always be framed in a specific genotypic context, while pre-clinical studies do not always provide clear indications of the variety employed . Moreove


Genetic studies of human traits have revolutionized our understanding of the variation between individuals, and yet, the genetics of most traits is still poorly understood. In this review, we highlight the major open problems that need to be solved, and by discussing these challenges provide a primer to the field. We cover general issues such as population structure, epistasis and gene-environment interactions, data-related issues such as ancestry diversity and rare genetic variants, and specific challenges related to heritability estimates, genetic association studies, and polygenic risk scores. We emphasize the interconnectedness of these problems and suggest promising avenues to address them.The online version contains supplementary material available at 10.1186/s13059-022-02697-9. Since the days of Gregor Mendel and his series of pea plant experiments in the middle of the 19th century, a key motivation of genetic research, perhaps the leading motivation, has been to understand the link between genotype and phenotype. When the human genome project was declared complete in 2003, hopes were high for a full understanding of human genetics and its effects on human traits. The idea to genotype large cohorts of individuals and, through simple statistical tests, compiling an atlas mapping between genes and human traits—be it diabetes, schizophrenia, or height—had been around for quite some time . I. I72]. Iectively . NotablyA second class of methods for estimating heritability, which has become popular since the era of GWAS, leverages genetic cohorts of unrelated individuals and includes methods such as GREML (Genomic Relatedness Restricted Maximum Likelihood) and LDSC (Linkage Disequilibrium Score Regression) . GREML eA third class of methods utilizes family-based data on genotyped relatives . The keyH2, known as the broad-sense heritability. This is the type of heritability that twin studies presume to measure . Another type of heritability, known as the narrow-sense heritability (denoted h2), reflects only the additive part of heritability, namely the part that can be expressed as a linear combination of individual variant effects. Since the genetic component of a trait could also include non-additive genetic effects, it generally holds that h2 < H2.A crucial difference between heritability estimation methods is the type of heritability they measure. Earlier we gave the definition of H2), as in the case of twin studies, or only the additive component reflected by genotyped variants . The problem is aggravated by the sensitivity of the methods to modeling assumptions and the fact that different methods often provide different estimates , 76, eveH2. Rather than a strict upper bound, it is probably more sensible to see heritability estimates as a crude assessment for the theoretical limit of PRS, given our current knowledge.As mentioned, one of the motivations to estimate heritability is getting a sense of how much predictive power we can expect from PRS. But does it mean that a trait’s heritability is an absolute upper bound on the performance of a PRS? Since heritability estimation methods are never completely assumption-free, it would probably be a mistake to interpret these numbers as strict upper bounds that could never be exceeded. Furthermore, population structure and indirect genetic effects may allow genetics to capture phenotypic variance above H2.It was noticed early on that discovered genetic associations tended to explain only a small fraction of the heritability of most complex traits. The phenomenon was named “the missing heritability problem,” expressing confusion about where most of the heritability was hiding . Later, Some explanations for the  effects , 76. Oth effects , 17, env effects , indirec effects , and genSome go as far as arguing that the whole notions of heritability and missing heritability are ill-posed and that these statistical models are based on too many assumptions to be taken at face value . Under tPerhaps the most discussed potential source of missing heritability is rare variants. Variants exerting strong phenotypic effects are expected to be under intense selective pressure, and therefore remain at low frequency. As a result, genetic effects on complex traits are usually constrained to either common variants exerting weak effects or rare variants with strong effects; both are statistically hard to detect and quantify.After analyzing very large cohorts of whole-genome sequencing (which have only become available in recent years), some argue that most of the heritability can now be explained and that the missing heritability problem should be considered resolved , but thiA key problem in dealing with rare variants is that sequencing is still considerably more expensive than SNP-array genotyping, and reduction in sequencing costs has stagnated in recent years . As a rey = β1g1 + … + βkgk + ϵ, where y is an individual’s phenotype value, g1, …, gk are the individual’s genotype values over k variants, β1, …, βk are constant per-variant weights indicating the effect size of each of the k variants, and ϵ is Gaussian noise  capturing the remainder of the phenotype . When dealing with a binary or categorical trait , the model is typically altered into P(y = 1) = σ(β1g1 + … + βkgk), where σ(x) =  exp (x)/(exp(x) + 1) is the standard logistic function, and P(y = 1) is the probability of the individual to have the trait. It is also common to include in the model other covariates in addition to the genotype values, such as sex, age, and the principal components of genetic variation (to account for population structure). Consistent with the overall assumption of additivity, the genotype values g1, …, gk are typically also encoded in an additive way, namely each gi is set as 0, 1, or 2, depending on the number of copies of the alternative allele of variant i that the individual has. Following the 0/1/2 encoding, genotype values are commonly normalized such that each gi would have a mean of 0 and standard deviation of 1 over the study cohort.Nearly all methods for studying the genetics of complex human traits, from GWAS to PRS to heritability estimates, assume additivity in the effects of variants. The typical additive model for a continuous trait is y and g1, …, gk are observed, and the study’s goal is to make inference about the coefficients β1, …, βk. In GWAS, one is typically interested in finding out whether βi ≠ 0 for each of the tested variants by calculating a p-value for the null hypothesis H0 : βi = 0. In PRS, the goal is to simultaneously estimate all of the coefficients β1, …, βk and to use the expression β1g1 + … + βkgk as a predictor for the phenotype y. In heritability estimation, the objective is to estimate the overall genetic variance Var(β1g1 + … + βkgk).Within this framework, It is obvious to anyone familiar with complex biological systems that genetic effects are not truly linear. Nonetheless, many argue that linear genetic models are a good-enough approximation of the real biological complexity . AccordiBy now, the additive genetic model has become so mainstream that it is commonly just taken for granted without any explicit justification. Despite its popularity and despite being very convenient for computational and statistical analysis, it is important to understand the empirical and theoretical support in favor of the additive genetic model, and consider the possibility that it might nonetheless be wrong or incomplete.rMZ = 2rDZ. However, results in this meta-analysis were shown only for groups of traits (such as “cognitive traits”), but not for individual phenotypes. Furthermore, the heritability of some traits (such as height) had been studied much more extensively than others, and most of the analyzed studies had investigated continuous  traits. Another limitation acknowledged by the authors was the presence of substantial overlap in the twins recruited across the different studies.A strong empirical result in favor of the additive genetic model was presented in a 2015 meta-analysis covering virtually all twin studies published between 1958 and 2012 . It was Also supporting the additive model is the poor track record of nonlinear phenotype prediction models, which generally do not substantially outperform linear PRS. On the other hand, there have not been that many attempts to develop such models, and it might be too early to give up on outperforming linear PRS (see next section).Another argument in favor of additivity appeals to theoretical considerations that, under certain assumptions and in the presence of a sufficiently large number of causal variants , the overall phenotypic variance attributed to additive genetic effects will almost certainly dominate the epistatic  genetic effects . On the Empirical evidence against the additive model includes the fact that epistatic genetic effects are often detected when sought, and possibly contribute to phenotypic variance quite substantially in some non-human organisms (see later section). Another argument opposing the additive genetic model is that we still have fundamental gaps in our understanding of the genetic architectures of complex human traits, and the assumption of additivity  should be considered an immediate suspect for the missing heritability problem , 79. GivIt is also important to note that a lot of the research and discussion on the genetic architecture of complex human traits, arguably too much of it, has focused on a rather small set of human traits, and especially on human height. It is possible that we have been somewhat led astray by focusing too much on a non-representative example. Height is actually rather unusual within the human phenotypic landscape in how heritable it is, and it is also very polygenic . PerhapDespite many years of debate on the question, we argue that the question of additivity is still not fully settled and that more work is needed to determine whether non-additive genetic effects underlie substantial phenotypic variance in complex human traits (open problem #2: non-additive and epistatic genetic effects). It would also benefit the discussion on additivity if the claims being defended or argued against were more precisely defined. For example, there is a huge difference between arguing that over 50% of the phenotypic variance attributed to genetics could be approximated by a linear model, which is a rather modest claim, and arguing for near 100%, which is a much stronger claim. We would also like to see evidence in favor or against genetic additivity being presented over more diverse human phenotypes and with more clarity and rigor, in particular with respect to the modeling assumptions underlying such works. When reporting or interpreting the results of genetic studies, it is important to be mindful of the assumption of additivity.The question of additivity is tightly related to the question of missing heritability (open problem #11: missing heritability) and is therefore also relevant to the performance of PRS (open problem #14: genotype-to-phenotype prediction performance). As virtually all contemporary heritability estimation methods assume additivity, non-additive genetic effects could be part of the explanation for the missing heritability problem. As mentioned, SNP heritability does not include non-additive variance, so it is expected that methods such as GREML and LDSC would underestimate the full (broad-sense) heritability. It was demonstrated through simulation analysis that methods for SNP heritability estimation may dramatically underestimate the heritability of phenotypes with nonlinear genetic effects  . Twin stIf non-additive effects indeed make up a big part of the heritability of complex traits, then this has important ramifications for PRS: we would expect linear models to be limited, at least in principle, compared to the full potential of nonlinear models. Some attempts have been made to train PRS with nonlinear models, including support vector machines, random forests, and deep neural networks –91. ThesThe adequate performance of linear models compared to nonlinear alternatives is the primary reason for their dominance in genetic studies, but there are also other motivations swaying researchers to favor them. Other advantages of linear models include high robustness and interpretability, both are crucial for clinical applicability. Also of great importance to researchers is the capacity to work with summary statistics of published results without being dependent on individual-level data. This is easily done with linear models, but usually impossible with nonlinear algorithms. Since human genetic datasets with individual-level information tend to be highly restricted (due to privacy concerns), this is often a critical consideration. If large-scale, highly accessible biobanks (such as the UK Biobank) become more prevailing in the coming years, we might see nonlinear methods becoming more popular.Related to the principal question of non-additivity is the more practical problem of finding epistatic genetic interactions (known as GxG), namely finding combinations  of variants or genes that interact together in a nonlinear way  . From a A lot of epistasis research has focused on non-human model organisms, where the phenomenon could be studied with experiments (including genome screening projects with libraries of double gene deletions) . Specifigi. Specifically, assigning the values 0/0/1 or 0/1/1 instead of 0/1/2 would capture recessive or dominant effects at the variant level, respectively. However, it is anticipated that many recessive effects would occur at the gene level and not at the variant level. If the two copies of a gene are affected by different variants, a case known as compound heterozygosity, then a variant-level recessive model would be completely blind to it. Due to the inherent blindness of GWAS to compound heterozygosity, the study of recessive genetic effects in complex human traits is highly neglected, but some recent works show that they are in fact common in complex traits [Recessive inheritance is an interesting special case of non-additive genetic interactions. While dominant and recessive inheritance play an important role in the study of Mendelian traits, these inheritance modes are hardly studied in the context of complex traits. In principle, dominant and recessive genetic effects are perfectly applicable to complex traits as well as Mendelian. It should be noted that the textbook definitions of dominant and recessive effects are often just approximations of the genetic effects found in the real world. For example, there are Mendelian diseases exhibiting imperfect recessive inheritance, as in the case of thalassemia (a type of anemia caused by mutations in the hemoglobin genes), which is considered a recessive disorder, but individuals who carry one copy of a deleterious mutation may develop mild symptoms of anemia. Since strong genetic effects are mostly confined to rare variants, an additive model is usually a good approximation for dominant effects but a very poor approximation for recessive effects. The additive model mentioned earlier can in principle accommodate dominant and recessive effects at the variant level through changing of the encoding of the genotype values x traits , 96. Genx traits .Similar to epistatic effects (GxG), interactions between genetic and environmental factors, known as GxE, also play a role in complex human traits , 97. GxEThere are many pragmatic reasons to be interested in GxE. By taking into account the modifying effects of environmental factors, more genetic associations could be found . FurtherWhile generally recognized an important piece in the genetic puzzle of complex human traits, GxE is a notoriously difficult subject of study, and there has been limited progress in addressing it (open problem #3: gene-environment interactions) . It is vVarious statistical methods and computational approaches have been developed to detect GxE interactions . In someP = G + E, where P is the phenotype value, G is the overall contribution of genetic factors to the phenotype, and E is the contribution of environmental factors . It then holds that Var(P) = Var(G) + Var(E) + 2Cov. This means that when Cov ≠ 0 (in the presence of GxE interactions), Var(G) will not capture all the variance of the trait that is due to genetics, as implicitly assumed by defining heritability as H2 = Var(G)/Var(P)  is negative, it may even capture too much). It has been argued that environmental effects, when not properly accounted for, may indeed inflate heritability estimates. For example, it has been asserted that heritability estimates for educational attainment might be inflated by ~70% [The study of GxE has important ramifications for the concept of heritability and the question of missing heritability. There have been works trying to quantify the overall contribution of GxE to phenotypic variance . These attempts have often ended up with the conclusion that GxE interactions contribute quite substantially to the phenotypic variance of some traits (such as BMI and pulse pressure) , 102, ye by ~70% . Some ha by ~70% .Selection bias describes a situation where a study cohort does not perfectly represent the population that it is presumed to represent, leading to unjustified conclusions about the general population. Since participation in human studies is generally voluntary, this is a difficult challenge to overcome. While the problem and the challenges it presents are by no means unique to genetic studies, human genetics researchers appear to have been surprisingly unconcerned about the implications of selection bias. Only very recently did the problem gain some attention, following a few demonstrations of its potential to lead to false discoveries. For example, a recent study detected statistically significant associations between autosomal variants and sex . Since sWe have deliberately focused this review on problems that are specific to the link between genotype and phenotype, putting aside most of the upstream technical aspects of collecting genetic data . Nonetheless, it is crucial to acknowledge that, like in any field, the results of our research will only be as good as the quality of the data we use. In particular, there are certain types of genetic variation that are systematically underrepresented, or altogether absent, from contemporary genetic studies (open problem #5: non-standard genetic variation).We have already touched on the challenges of studying the effects of rare variants due to data scarcity (open problem #4: rare variants). Other neglected variants are those in sex chromosomes (X and Y) , which aAnother aspect of genetic variation that tends to be overlooked is phasing, namely whether two heterozygous variants occur on the same or different copies of the chromosome  . This coA related question is how to represent genetic variation. The accepted norm today is to represent an individual’s genome as a set of unrelated variants indicating deviation from the human reference genome . Is it In a sense, we have only been dealing with the easiest parts of human genetics, focusing on common, simple autosomal variants, without genetic and environmental interactions. The overall contribution of unaccounted genetic variation to heritability, and whether this is a major source of missing heritability, is yet to be determined.Just as the quality and representation of genotypes is expected to have a major influence on the results of genetic studies, so can the exact definitions and measurements of human phenotypes be important. While some phenotypes are straightforward to measure accurately , other traits are a lot more nuanced and could be defined in different ways. For example, there has been a long discussion on whether schizophrenia and bipolar disorder, two distinct but highly related psychiatric diagnoses with strongly overlapping genetic determinants, should be seen as residing on a single psychosis continuum . MoreoveIt has been postulated that some clinical diagnoses are in fact a combination of distinct biological phenotypes, each involving separate genetic and cellular pathways, and that the similar clinical manifestations of these different phenotypes are only superficial. In such cases, it could be beneficial to study each of the subphenotypes separately . ConversSex differences, which are prevalent across the human phenome, could be seen as an important special case of subphenotypes . When seWe have attempted to provide a broad view on current important open problems in the field of human trait genetics. Towards this goal, we reached out and spoke with many active researchers in the field covering a diverse set of backgrounds and perspectives. Following these conversations, we converged on a list of 16 open problems that were consistently acknowledged by us and others to be particularly important  would be beneficial. We think that an open dialog is especially important among researchers and practitioners from diverse backgrounds and expertise, for example between researchers and clinicians. Other important discussions should include technology providers  and resource providers (such as biobanks). For example, whether data resources are dominated by publicly available biobanks or restricted databases will greatly affect the kind of challenges we face.Additional file 1.


Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene–gene interaction, gene–environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society. Genome-wide association study (GWAS) is the most popular strategy for dissecting complex traits of agronomical significance and human diseases with the rising of cutting-edge microarray and next-generation sequencing (NGS) tools and the development of linear mixed models ,2. GWAS The major problems in GWASs are confounding factors, including population stratification, familial correlation, and relatedness among individuals ,20,21,22Recently, several papers have been published to highlight the importance of LMMs in GWAS ,30,31. TLMMs can solve different problems, including population stratification, family structure, cryptic relatedness, estimating polygenic effect, and missing heritability . The LMMs were originated to account for multiple levels of relatedness by using a kinship matrix, which greatly enhanced the performance of GWAS by reducing both the false-positive and the false-negative rates . LMMs iny is an Many LMM methods have been proposed and applied in GWAS according to the recent advancement ,23,27,32 defined  as folloStudies confirmed that the methods of controlling the population structure and the confounding factors had better performance than those that did not consider confounding factors ,21. For p-value for significance tests [p-values obtained by linear regression, then constructs a genetic similarity matrix involving SNPs until it detects the first minimum in the GC factor (λGC). The GRAMMAR-Gamma method has been proposed as an analytical estimate within the basis of the score test technique [RMLM (random-SNP-effect MLM) considers the SNP-effect as random and permits using Bonferroni correction to estimate the ce tests . The idece tests . ECMLM  extracted a small subgroup of SNPs and applied them in FaST-LMM . SUPER fEMMAX > EMMA > CMLM/P3D > Fast-LMM > GRAMMAR > GMMAT > RMLM > GRAMMER-Gamma. These orders are based on the most cited to less cited models, and this style is followed for all other cases to suggest the models that may be chosen by the researchers in this manuscript. The single-locus model and their respective software and packages for GWAS using LMM are given in Most of the methods perform a single-dimensional genome scan by testing one single marker at a time, where multiple test adjustments are needed for the cut-off value of the significance test. Several single-locus methods such as EMMAX , P3D 2020, FaST-Multi-locus methods consider all loci info together and do not need multiple test corrections due to the nature of multi-locus . Some muA multi-locus model can be written as the extended version of Equation (1) followed by the previous study, defined ,43 as foing loci . Resultsing loci . LMM-Lasing loci . It perming loci . It is ting loci . It can ing loci .mrMLM (multi-locus RMLM) uses markers selected from the RMLM method with a flexible selection criterion, and is more reliable in QTN discovery and more precise in the QTN effect estimation than the RMLM and EMMA . RecentlStepLMM (stepwise LMM) is a consistent, versatile, and computationally proficient method that can be applied to both GS (genomic selection) and GWAS . It usedBOLT-LMM > MLMM > FarmCPU > mrMLM > FASTmrEMMA > LMM-Lasso. Researchers could use these methods based on their research interests or data types. The multi-locus models and their respective software and packages for GWAS using LMM are given in jth phenotype. According to the single-locus LMM model in Equation (1), each phenotype follows a multivariate normal distribution with mean +jth phenotype. The details about the multi-traits model and variance-covariance matrix calculation can be found elsewhere [Multivariate LMMs are commonly used to assess the association between SNPs and multiple correlated phenotypes in genetics due to their effectiveness in controlling relatedness amongst samples . Many mulsewhere .Korte, Vilhjalmsson  initiallHowever, while various multivariate methods are proposed to discover variants linked to more than one phenotype, these existing approaches do not investigate the population structure . GAMMA , LRT-based VC studies ,19,27 arGEMMA > WOMBAT > BOLT-REML > MTMM > LIMIX. The widely used method’s recommendation could help the users and readers make a quick decision and save time in analyzing their GWAS data using a multivariate/multi-traits LMM model. Multi-trait/multivariate model and their respective software and packages for GWAS using LMM are given in k-th subject and h-th environment can be written by the following LMM [μ is the population mean; h-th ethnic population; i-th locus effect with coefficient i-th locus and the h-th environment with coefficient h-th environment with coefficient k-th individual in the h-th environment. The details about parameter estimation and test statistic for GG and G E interaction models can be found elsewhere [Though many optimistic results have been produced using the different methods in GWAS data analysis, it has been recognized that additive effects can elucidate only a portion of genetic variations . Epistaswing LMM :(4)ykh= lsewhere .p-values for the best model can be estimated after randomly permuting the traits under the assumption of familial correlation-free traits in this step [FAM-MDR, a multifactor dimensionality reduction technique, detects epistasis in minor or extensive pedigrees . It aggrhis step . Simulathis step . Zhang, his step  developehis step . A study showed that the LMMs were unable to control the inflation of test statistics for G × E but were only capable of handling population structure when considering the genetic relatedness in the model . To overx traits .G × E interaction can detect the genetic effects, which are avoided in the linear models, enhance the GWAS power, and give the fractional answer to the missing heritability ,103. AnoVarious phenotypes and environmental variables such as nutrition, physical exertion, or lifestyle covariates can help the G × E interaction study . The stuGWAS has been effectively used for discovering various genetic variants linked with complex traits/diseases . HoweverSMART  is based on the extension of LMM . This mo2 methods proposed using summary statistics GWAS data rather than individual-level data [2 has some benefits over CoMM. For example, CoMM-S2 is computationally more proficient than CoMM when using larger sample sizes. The authors showed that CoMM-S2 performed better when the cellular heritability was small [2 cannot be applied in a cross-tissue study. Additionally, CoMM-S2 cannot differentiate whether the discovered genes are only correlated with the complex traits or if they are genuine causal effects [Real data analysis demonstrated that CoMM could identify more genetically governed genes associated with complex traits deprived of excessive type I errors. However, CoMM is an effective method, but it uses individual-level GWAS data and cannot entirely use extensively existing summary statistics data in GWAS . CoMM-S2vel data . This meas small . However effects . 2 > LSMM. TWAS and longitudinal-related LMM models, software, and packages for GWAS are given in Numerous GWASs have been implemented in population cohorts that have repeated measures at multiple time points for each individual ,118,119,Many LMM-based packages have been developed in GWAS . DMU is Linear and logistic regression models can be fit using this software, which permits accommodating millions of regression models on a desktop by employing an effective execution, concurrent, and out-of-core data processing for big datasets. ASReml, a statistical package, utilizes LMMs by REML for big datasets with complex variance frameworks . Many vaSingle-RunKing, an efficient R software, has been proposed to speed up the GWAS by LMMs . It usesMany software and server-based tools have been developed for multi-omics data analysis in GWAS. Qxpak is a software-based mixed-model, which allows a versatile tool for QTL mapping in various populations, including cross-between inbred lines and within-population analysis . AssociaQTLNetwork is a widely used software for linkage mapping and visualizing the genetic structure for complex traits, where analytical populations are derived from a crossing of different inbred lines . It can MASTOR  has been proposed for genetic association mapping a quantitative attribute . It can PEPIS (Pipeline for estimating EPIStatic) has been proposed to estimate polygenic effects based on the LMM . PEPIS uOSCA was proposed to manage omics data from high-throughput trials in big cohorts and help analyze complex traits utilizing omics data . OSCA usLMMs are attractive because they can control population structure and explain polygenic information for typical single-variant analysis in GWAS ,23,27,65Recently, multi-traits association mapping has received more attention as these methods provide more power and in-depth knowledge for dissecting the genetic architecture of complex traits . Many unThe statistical power of the multi-traits LMMs increases across traits by combining small genetic effects  and consThere are many benefits of using G × G and G × E interaction methods, including the detection of the genetic effects which are missed in the linear models, enhancing the GWAS power, and giving the fractional answer to the missing heritability. However, different G × G and G × E methods have limitations. For example, GxEMM has several limitations, such as it is very computationally intensive, considering Gaussian random effects, which reduce power; and it does not correct for G–E correlation, which is a familiar origin of bias in the fixed effect situation . AdditioGene expression data and GWAS are incorporated by the TWAS to discover gene-traits associations. TWAS methods are needed to overcome the limitations of the other methods. For example, most of the GWAS hits are in non-coding regions, and their biological explanation is unclear. Additionally, all information from GWAS proposes that complex traits are frequently controlled by many variants with minor or moderate effects. In contrast, a prominent part of risk variants with minor effects remain unidentified . A TWAS However, there are some major restrictions on the current LMMs approaches. Firstly, LMMs are computationally costly and require a long time to analyze big datasets compared with simple models. For example, the run time and memory needed by LMM models are the scale as the cube and square of the cohort size, respectively . SecondlThe LMMs have been applied in most aspects of GWAS, including population stratification and relatedness, resulting in computational proficiency and increased statistical power in GWAS studies. However, genomes sequencing has rapidly increased due to the development of NGS, and genomic datasets are growing progressively . This coMoreover, artificial intelligence (AI) is growing quickly due to its robust and stable application for resolving problems in conventional computing methods . FurtherThis review introduced the available LMMs methods on GWAS, including single locus, multi-locus, multi-traits, TWAS, longitudinal GWAS, packages, and software in omics data. It provides a practical explanation and guides the reader to fundamental references that allow for an advanced methodological feature and better comprehension of GWAS. It also assists in finding appropriate LMM methods for dissecting complex traits in GWAS and further help to investigate these methods using diverse NGS and omics datasets. This review could guide both the new scholars and those desiring to update their knowledge in the field of GWAS by applying LMMs using the omics data. However, there is no unique and sophisticated software that users would like, including flexible and easy to use, combining different types of omics data, and which can handle big GWAS data analysis much faster than the existing methods. Necessary software and packages should be developed for analyzing big GWAS data sets and marker derivative kinship matrices. Overall, there is much scope to utilize the LMMs in diverse fields, including biostatistics, bioinformatics, and statistical genetics, which could be helpful for medical scientists, agriculturists, technologists, and data scientists to solve real-world problems.


Lameness in dairy cattle is primarily caused by foot lesions including the claw horn lesions (CHL) of sole haemorrhage (SH), sole ulcers (SU), and white line disease (WL). This study investigated the genetic architecture of the three CHL based on detailed animal phenotypes of CHL susceptibility and severity. Estimation of genetic parameters and breeding values, single-step genome-wide association analyses, and functional enrichment analyses were performed.Bos taurus chromosome (BTA) 3 and 18 with potential pleiotropic effects associated with multiple foot lesion traits. A genomic window of 0.65 Mb on BTA3 explained 0.41, 0.50, 0.38, and 0.49% of the genetic variance for SH susceptibility, SH severity, WL susceptibility, and WL severity, respectively. Another window on BTA18 explained 0.66, 0.41, and 0.70% of the genetic variance for SH susceptibility, SU susceptibility, and SU severity, respectively. The candidate genomic regions associated with CHL harbour annotated genes that are linked to immune system function and inflammation responses, lipid metabolism, calcium ion activities, and neuronal excitability.The studied traits were under genetic control with a low to moderate heritability. Heritability estimates of SH and SU susceptibility on the liability scale were 0.29 and 0.35, respectively. Heritability of SH and SU severity were 0.12 and 0.07, respectively. Heritability of WL was relatively lower, indicating stronger environmental influence on the presence and development of WL than the other two CHL. Genetic correlations between SH and SU were high (0.98 for lesion susceptibility and 0.59 for lesion severity), whereas genetic correlations of SH and SU with WL also tended to be positive. Candidate quantitative trait loci (QTL) were identified for all CHL, including some on The studied CHL are complex traits with a polygenic mode of inheritance. Most traits exhibited genetic variation suggesting that animal resistance to CHL can be improved with breeding. The CHL traits were positively correlated, which will facilitate genetic improvement for resistance to CHL as a whole. Candidate genomic regions associated with lesion susceptibility and severity of SH, SU, and WL provide insights into a global profile of the genetic background underlying CHL and inform genetic improvement programmes aiming at enhancing foot health in dairy cattle.The online version contains supplementary material available at 10.1186/s12711-023-00784-4. About one in three dairy cows in the United Kingdom (UK) suffer from lameness at any time point  and reduRecent studies in dairy cattle have revealed genetic variation in resistance to CHL, with reported heritability estimates ranging from 0.01 to 0.35 in Holstein cattle –19. GenoIn the present study, we closely monitored a large cohort of Holstein dairy cows across multiple lactations and collected detailed foot lesion records from individual animals. The objectives of our study were to (i) estimate genetic parameters for different phenotype definitions of SH, SU, and WL using linear and threshold models, and (ii) characterise the genomic architecture that underlies SH, SU, and WL phenotypes with single-step GWA analyses and post-GWA functional enrichment analyses.In total, 2352 Holstein dairy cows were prospectively enrolled in four herds in the northwest of the UK. The four herds were selected for participation based on practicalities of frequent data collection; animals were enrolled between April and December 2019. A few animals that were enrolled prior to calving but did not subsequently calve due to health issues were excluded from further analyses. Consequently, data on a total of 2305 cows were considered in the present study. The number of animals by herd and lactation number are in Table Data collection was performed from February 2019 to July 2020. Individual animals were assessed for foot lesions at four time points (one inspection per time point): (1) prior to calving ; (2) immediately after calving ; (3) near peak yield ; and (4) latter part of lactation . A few records that fell outside of the planned sampling time frame were removed from the final data analyses. Consequently, the number of cows included at each stage was 2277 for pre-calving stage, 2185 for calving stage, 2124 for early lactation stage, and 1931 for late lactation stage. The relatively smaller number of phenotyped cows at the late lactation stage was due to temporary movement restrictions for farm visits during the COVID-19 pandemic.During the visit, animals were restrained in a foot-trimming crush and lesions on each claw were recorded by qualified veterinary surgeons using case definitions described in the International Committee for Animal Recording (ICAR) claw health atlas . All fouTwo phenotypes were defined for each CHL: lesion susceptibility and lesion severity.Lesion susceptibility classified animals as either affected  or unaffected  by each CHL over the whole study period. Animals were considered affected if the lesion was present on any foot at any time point, regardless of the lactation stage or the severity of the lesion. Animals were considered unaffected if the lesion was absent on all feet at all four time points. Based on this trait definition, each animal had one lesion susceptibility record (0 or 1) for each CHL. Repeated measurements of the same CHL at multiple time points were not taken into account.This phenotype was calculated as the average score of each lesion across all assessed feet of an individual cow. The trait definition accounts for both the number and severity of the lesions, providing extra information on the foot health status of each individual. The phenotype was calculated separately at each time-point of foot examination, so each animal had repeated records of lesion severity at multiple lactation stages.Bos taurus genome (ARS-UCD 1.2) [P < 1E − 6), and animals with parent-progeny Mendelian conflicts. After quality control, 65,211 SNPs for 2167 genotyped individuals were retained for further analyses.Genome-wide genotypes were available for 2250 animals among all the phenotyped animals. The genotypes had been imputed to 80K single nucleotide polymorphism (SNP) genotypes using the platform for the UK national dairy cattle genomic evaluation. The 80K SNP panel , used for national dairy cattle genomic evaluation in the UK, was developed based on Illumina BovineSNP50 BeadChip, 777K Illumina BovineHD BeadChip , and other commercial genotyping arrays, extra gene tests, and large-effect sequence variants . The chrUCD 1.2) . Genotypcoda package in R (version 4.2) [As binary phenotypes, lesion susceptibility for SH, SU, and WL was analysed using a threshold model on a latent liability scale . A Markoion 4.2) . A thinnion 4.2) . Geneticormation ; \documeVariance components and genetic parameters for lesion severity of SH, SU, and WL (continuous phenotypes) were analysed with linear models using the average information-restricted maximum likelihood (AIREML) algorithm implemented in the AIREMLF90 software (version 1.148) . An animormation ; \documeThe structure of the studied population was explored with principal component analyses (PCA) using all animals’ genotypes and the GEMMA software . The priP-values of marker effects were calculated for lesion severity of SH, SU, and WL using the POSTGSF90 software (version 1.73) [P-value = 7.67E−07  and a suggestive significance threshold at P-value = 1.53E−05  were applied. Specifically for the binary lesion susceptibility traits, the P-values of marker effects were not applicable to single-step GWA analyses when a threshold model with Gibbs sampling was used. Instead, the standardized SNP effects were calculated using the estimated SNP effects divided by the empirical standard deviation of SNP effects in the POSTGSF90 software (version 1.73) [The single-step genome-wide association method (ssGWAS) was applied to estimate single marker effects on each trait by back-solving the animals’ GEBV , as implon 1.73) . To accoon 1.73) . A genomon 1.73) .In addition to assessing single marker effects, 0.65-Mb sliding genomic windows were constructed to calculate the corresponding proportion of genetic variance explained by each window. The 0.65-Mb window size was determined based on the average distance in the genome where linkage disequilibrium (LD) between markers was halved Fig. b, using Bos taurus genome assembly of ARS-UCD1.2 and NCBI Bos taurus Annotation Release 106. Gene functional annotation was performed using the UniProt database (Release 2022_04) [Candidate genes located in the vicinity of large-effect variants within 0.2 Mb upstream and downstream or within the candidate windows from GWA analyses were identified using the USCS Genome Browser  based on2022_04) . The can2022_04) , 50 to h). The highest SH prevalence (57.5%) and most severe SH lesions appeared at the early lactation stage. For SU, lesion prevalence and severity were similar in early and late lactation stages, both higher than those in pre-calving and calving stages. For WL, the prevalence increased from calving (30.3%) to early lactation (37.8%) and then to late lactation (59.2%). The average severity of WL also tended to increase from calving and early lactation stages to the late lactation stage.The prevalence of SH and WL was generally higher than that of SU in the studied population Table . The higFor lesion susceptibility traits, SH and SU were found to be under genetic control with low to moderate heritability estimates Table . These eFor lesion severity, SH, SU, and WL were found to be under genetic control with heritability estimates of 0.12 (SE = 0.02), 0.07 (SE = 0.02), and 0.04 (SE = 0.01), respectively Table . The genSingle-marker GWA results for each lesion susceptibility and severity trait are illustrated in Additional file Bos taurus chromosome (BTA) 8 at 44,652,431 bp and 44,735,178 bp, and one marker was found associated with WL severity on BTA23: 43,909,068 bp. However, the signal on BTA23 for WL severity appeared to be a single significant marker without a trailing tail of adjacent markers, and this result needs to be viewed with caution. No single marker was significantly associated with any lesion susceptibility trait , complement activation (C8B and C8A), B cell activities (PLCL2), leukocyte migration (TRIO) [PLPP3). In addition, genes within the top windows are also related to calcium ion activities , neuronal excitability (KCNQ3), gene expression  and protein biosynthesis (GATB).Genes located within the top genomic windows or 0.2-Mb upstream/downstream of large-effect variants belong mainly to the group of immune system functions in n (TRIO) , and proBos taurus) (R-BTA-556833) from the Reactome pathways.In the functional enrichment analyses, the identified genes from GWA results are enriched in immune responses of complement activation  and cytolysis (GO:0019835) from biological processes of Gene Ontology (GO) terms, and are also enriched in metabolism of lipids  at any time point and on any foot, regardless of lesion severity, number of feet affected, or lactation stage when the animal was affected. The lesion susceptibility phenotype is relatively close to the trait definition derived from foot-trimming records that is commonly used for genetic studies on foot lesions, except that in our study we carefully recorded mild lesions of SH, SU or WL, which may not always be represented in foot-trimming records. We recognize that it would also be possible to use binary lesion records at four time points as repeated measurements to analyse lesion susceptibility with an animal repeatability model, although this approach would increase the chance of mis-phenotyping because less information is used to classify unaffected  animals. Given this, we explored this approach and found similar heritability estimates for lesion susceptibility considering standard deviation of estimates. Thus, the results of analysing lesion susceptibility using repeated records are not reported in this paper.Genetic parameters were estimated for lesion susceptibility and lesion severity, respectively. Although heritability estimates for lesion severity traits are not directly comparable to those for lesion susceptibility, since the latter are expressed on an underlying liability scale, both suggest the presence of significant genetic variance among animals, at least for SH and SU, to underpin genetic selection for improved foot health.) is in broad agreement with previous studies that reported positive genetic correlations between SH and SU ranging from 0.38 to 0.90 [Lesion susceptibility for SH and SU was found to be under genetic control with low to moderate heritabilities, in agreement with previous studies on foot lesions in dairy cattle , 52, 53. to 0.90 , 52–55.  to 0.90 , with th to 0.90 . The posThe variance components and heritability for binary lesion susceptibility traits were estimated on the liability scale using a threshold model to consider the genetic variation underlying the observed case–control phenotype of disease traits. An alternative method is to estimate heritability on the observed scale  for binary lesion traits and then transform to the liability scale . We testFor lesion severity traits, the lesion severity of SH, SU, and WL was also shown to be under genetic control with low heritabilities. The severity of claw horn lesions has been less studied in previous research mainly due to the nature of foot-trimming records. Similar to lesion susceptibility, the severity of SH and SU was also shown to be positively correlated to each other. The genetic correlation between WL severity and SU severity tended to be positive, inferring genetic association between the severity of the two lesions. In contrast, the genetic correlation between the severity of SH and WL was close to 0 but with uncertainty due to the large standard error, which may be partly affected by the low heritability of WL. Based on previous research, the genetic background of WL-related phenotypes is generally poorly understood with a heritability of WL-related phenotypes ranging from 0.01 to 0.11 with relatively large standard errors , which iAll GWA results indicated complex genetic architecture underlying lesion susceptibility and severity of SH, SU, and WL, which is consistent with findings from previous studies , 21–24. PLPP3 gene) and complement activities (C8B and C8A genes) in the immune responses of Bos taurus. The PLPP3 gene has also been reported to be associated with resistance to clinical mastitis in Holstein cattle [Among all the top genomic windows associated with CHL in our study, two regions located on BTA3: 88.7–91.4 Mb and BTA18: 21.4–22.3 Mb may be of particular interest as they were found to be associated with multiple foot lesions. The region on BTA3: 88.7–91.4 Mb that is associated with both SH and WL in our study overlaps with a reported candidate QTL for digital dermatitis, an infectious foot lesion, in Holstein cattle . The regn cattle .GC gene (GC vitamin D binding protein), within this region on BTA6, is linked with the enhancement of neutrophil activities in inflammation and macrophage activation. The GC gene on BTA6 has been widely reported to be associated with clinical mastitis in dairy cattle based on large-scale genetic studies in Nordic Holsteins [GC gene on the genetic control of multiple conditions.Certain additional genomic regions identified for CHL in our study were also previously reported. The genomic window on BTA6: 86,762,457–87,405,290 bp that is associated with SU susceptibility overlaps with a candidate QTL previously reported for WL in Holstein cattle . The GC olsteins . ResultsIn addition, the large-effect markers with a clear peak around BTA1:137.3–141.7 Mb that are identified here for SH severity are in high LD with a reported candidate QTL for SH around BTA1: 136.9 Mb that was reported in the French Holstein population . The preGenetic parameters, large-effect markers, candidate genomic regions and annotated genes identified for CHL susceptibility and severity phenotypes offer a holistic view of the genetic background of CHL. Based on these results, we found that some of the candidate QTL identified here were shared between susceptibility and severity for a lesion, but mostly they were specific to either its susceptibility or severity. The results might imply potential differences between the genetic basis of lesion susceptibility and lesion severity even if the phenotypes are related to each other. Apart from lesion susceptibility and severity, the phenotype of “lesion recovery”, i.e. the animals’ ability to recover from a foot lesion, was previously studied in Holstein cattle . ResultsClaw horn lesions in dairy cattle are complex traits with a low heritability that are potentially controlled by multiple genes . We applApart from the insights into the genetic basis of CHL, large-effect markers and candidate genomic regions identified for CHL may also inform the development of accurate genomic selection  towards reducing the incidence of CHL and lameness in dairy cattle populations. Improving functional annotation of the cattle genome and inclusion of biological priors such as variants with functional significance will contribute to improved accuracy of genomic predictions for complex traits in dairy cattle breeding . PreciseThe studied claw horn lesions, which are responsible for lameness in dairy cattle, are complex traits that follow a polygenic mode of inheritance. Most traits exhibited genetic variation, which suggests that animal resistance to CHL can be improved with selective breeding. The individual CHL traits were generally positively correlated, indicating the possibility to expedite genetic improvement to CHL resistance as a whole. Candidate genes and QTL associated with CHL are linked to the immune system function and inflammation responses, lipid metabolism, calcium ion activities, and neuronal excitability. The presence of some common QTL that are shared between multiple lesions and between lesion susceptibility and severity further corroborates the genetic associations among foot lesions. Candidate QTL identified for lesion susceptibility and severity provide insights into a global picture of the genetic background that underlies CHL and inform genomic selection for improved foot health in dairy cattle.Additional file 1: Table S1. The top ten markers associated with lesion severity for sole haemorrhage (SH), sole ulcer (SU), and white line disease (WL) from single-marker GWA analyses, including chromosome (BTA), position (Pos), minor allele frequency (MAF), and P-value. The data provided represent the position, allele frequency, and the P value of the top ten markers associated with lesion severity for sole haemorrhage (SH), sole ulcer (SU), and white line disease (WL) from the genome-wide association analysis. Table S2. The top ten markers with the largest standardized SNP effects (Std_SNP_effect) on lesion susceptibility for sole haemorrhage (SH), sole ulcer (SU), and white line disease (WL) from single-marker GWA analyses, including chromosome (BTA), position (Pos), and minor allele frequency. The data provided represent the position, allele frequency, and the effect size of the top ten markers associated with lesion susceptibility for sole haemorrhage (SH), sole ulcer (SU), and white line disease (WL) from the genome-wide association analysis.Additional file 2: Figure S1. Manhattan plots of lesion susceptibility for sole haemorrhage (SH), sole ulcers (SU), and white line disease (WL). The standardized SNP effects were calculated by the estimated SNP effects divided by their empirical standard deviation. The data provided represent the Manhattan plots of the standardized SNP effects for lesion susceptibility of sole haemorrhage (SH), sole ulcers (SU), and white line disease (WL) from the genome-wide association analysis.


Potato tubers contain biochemical compounds with antioxidant properties that benefit human health. However, the genomic basis of the production of antioxidant compounds in potatoes has largely remained unexplored. Therefore, we report the first genome-wide association study (GWAS) based on 4488 single nucleotide polymorphism (SNP) markers and the phenotypic evaluation of Total Phenols Content (TPC), Ascorbic Acid Content (AAC), and Antioxidant Activity (AA) traits in 404 diverse potato genotypes (84 diploids and 320 tetraploids) conserved at the Colombian germplasm bank that administers AGROSAVIA. The concentration of antioxidant compounds correlated to the skin tuber color and ploidy level. Especially, purple-blackish tetraploid tubers had the highest TPC (2062.41 ± 547.37 mg GAE), while diploid pink-red tubers presented the highest AA  and AAC (4.52 mg ± 0.68 AA). The index selection allowed us to choose 20 promising genotypes with the highest values for the antioxidant compounds. Genome Association mapping identified 58 SNP-Trait Associations (STAs) with single-locus models and 28 Quantitative Trait Nucleotide (QTNs) with multi-locus models associated with the evaluated traits. Among models, eight STAs/QTNs related to TPC, AAC, and AA were detected in common, flanking seven candidate genes, from which four were pleiotropic. The combination in one population of diploid and tetraploid genotypes enabled the identification of more genetic associations. However, the GWAS analysis implemented independently in populations detected some regions in common between diploids and tetraploids not detected in the mixed population. Candidate genes have molecular functions involved in phenolic compounds, ascorbic acid biosynthesis, and antioxidant responses concerning plant abiotic stress. All candidate genes identified in this study can be used for further expression analysis validation and future implementation in marker-assisted selection pre-breeding platforms targeting fortified materials. Our study further revealed the importance of potato germplasm conserved in national genebanks, such as AGROSAVIA’s, as a valuable genetic resource to improve existing potato varieties. In humans, polyphenols have an effect in the cell proliferation, metabolism, weight, and chronic disease . Potatoes are the fourth most important food crop worldwide after corn, rice, and wheat, with a consumption per capita of 32.4 kg in 2019  and oranges (Citrus×sinensis); 50% of phenolic compounds accumulate in the potato peel and tissues nearby , C4H (cinnamate 4-hydroxylase), 4CL (4-coumarate-CoA ligase), CHS , HCT (hydroxycinnamoyl-CoA shikimate), HQT (hydroxycinnamoyl-CoA quinate) and transcriptions factors (TFs) as MYB and basic helixloop-helix (bHLH) play a role in the regulation of the synthesis of these compounds (ascorbate peroxidase), GDH , GGP , GME , GMP  among others have a role in the synthesis of this component  to analyze the natural phenotypic variation of Total Phenol Content (TPC), Ascorbic Acid Content (AAC), and Antioxidant Activity (AA) in potato tubers of diploid and tetraploid genotypes of the CCC to select promising genotypes to be used in future breeding programs, and 2) to associate this phenotypic information with SNP through GWAS to identify market-trait association which may collocate candidate genes with possible implication in the production of polyphenols and ascorbic acid and their antioxidant activity in potato. We hypothesize that the ploidy level affects the concentration and the genetic regulation of polyphenols, ascorbic acid biosynthesis, and their antioxidant activities.https://direcciondelaboratorios.medellin.unal.edu.co/index.php/nuestros-laboratorios/facultad-de-ciencias/22).For this study, we used 404 genotypes (accessions) of the potato CCC germplasm bank previously characterized at the molecular level using SNPs by We cut one section of each tuber per accession collected (10 tubers); these sections were mixed and macerated. From this tissue, five grams (g) were mixed with 25mL of 80% methanol and shook at 500 rpm per 60 min. The mixtures were then centrifuged at 3000 rpm for 15 min. Subsequently, the suspensions were filtered and conserved at four (4) °C until further analysis (approximately between one and five days). Three suspensions per potato accession were independently prepared to measure all traits, constituting three technical replicates.2O, and 125μL of Folin-Ciocalteu reagent were mixed. After five min of mixing, 400μL of 7.1% Sodium carbonate (Na2CO3) was added, and the mixtures were conserved for one hour in dark incubation at room temperature; after this time, the absorbance was measured at 760nm using gallic acid as a standard. The TPC was expressed in terms of milligrams of Gallic Acid Equivalent (GAE) per 100g of fresh weight (FW) (mg GAE/100g FW).The TPC was assayed using a modified protocol for the Folin-Ciocalteu method . Fifty m2.2-Diphenyl-1-picrylhydrazyl Radical Scavenging Activity) assay was used to assess the AA (6-hydroxy-2.5.7.8-tetramethylchroman-2-carboxylic acid) reagent as a standard. The DPPH results (AA_DPPH) were expressed in terms of micromoles of Trolox Equivalent (TE) per 100g of FW (μmol TE/100g FW).A modified protocol for DPPH (s the AA . Nine huFerric Reducing Antioxidant Power) assay (2.4.6-tripiridil-s-triazine), 20 mM of Ferric chloride (FeCl36H2O), 0.3 μM acetate buffer at pH 3.6). The mix was incubated for 30 min in the dark at room temperature. After this time, the absorbance was measured at 590nm using L-ascorbic acid as a standard. The FRAP results (AA_FRAP) were expressed as milligrams of Ascorbic Acid Equivalent (AAE) per 100g of FW (mg AAE/100g FW).The second assay to assess the AA was a modified protocol for FRAP (r) assay . Fifty mThe AAC was evaluated through the modified method of p<0.05) were conducted using the shapiro.test, aov, and TukeyHSD functions of R-stats package (R-stats package (p<0.05) was used to check the relationship among variables. A Principal Component Analysis (PCA) was performed, and the two first components of the PCA were used to generate a multivariate clustering analysis using the Ward method and the Euclidean distance. These multivariate analyses were implemented in the R-factoextra package  were calculated. The Shapiro-Wilk test was used to check the normality of the data. One-way analysis of variance (ANOVA) and Tukey’s test ( package  of R sof package  to 1) co package  using th package . The fol package  a Pearso package .fitTetra R-package (SaveMarkersModels function with the parameters of filtering reported by The 404 potato accessions used in this study were previously genotyped in the study of -package  using wiSTRUCTURE software (STRUCTURE Harvester (adegenet R-package (poppr R-package (adegenet package. We determined the linkage disequilibrium (LD) of the diploid, tetraploid, and mixed potato populations following the methodology reported by 2r) were computed. The LD mean and decay per population were calculated using this information. The LD decay was determined by plotting the 2r values against the physical distance and visualizing it using the ggplot2 package in the R program  that was computed from phenotypic and genotypic data using the heritability R-package . A new cluster analysis was developed, and the genetic groups were described at the phenotypic level. Promising genotypes to TPC, AAC, and AA of the AGROSAVIA collection were identified and selected using an Index Selections (IS) calculated through the -package , adjusti-package .GWASpoly  obtained for each genotype. These analyses were implemented in the mixed population and independently in diploid and tetraploid populations because the CCC has a clear population genetic structure in function to the ploidy level . The comGWASpoly  and mrMLy   method  were evaluated  implemented in dom-ref) . The p-v) method  at p<0.0valuated  using thvaluated . Some stvaluated  have insSolanum tuberosum group Phureja DM1-3 PGSC v4.03 in the Spud database - Potato Genomic Resource (The STAs and QTNs detected in common by single and multi-locus models (LOD score > 3.5) were searched against the potato genome of Resource . Those gp=0.989) (p<0.0001) (p=0.3302), but for some tetraploids, TPC was higher. Therefore, the range for tetraploids was wider  than in diploids  genotypes (p<0.0001) and AA measured by FRAP (p<0.0001) and DPPH assay (p<0.0001). Finally, AAC values ranged from 1.94 to 6.61 mg AA in diploid genotypes (median: 4.04) and between 1.65 to 4.26 mg AA in tetraploid genotypes (median: 2.54)  except for AAC in the diploid population (p=0.989) Figure 1p<0.0001) in diploid (2r>0.47 in TPC vs AA_DPPH) and tetraploid (2r>0.62 in TPC vs AA_DPPH) populations, respectively. AA measured by DPPH and FRAP assays presented high significant positive correlations (p<0.0001) in both populations (2r>0.75). In contrast, low or no significant correlations were detected between TPC and AAC (2 =r0.19 in diploids and 2r<-0.11 in tetraploids) and between AAC and AA (2r<0.25 in diploids and 2r<-0.09 in tetraploids) in both populations  depending on the skin tuber color. In the tetraploid population, purple-blackish tubers had higher levels of TPC and AA (p<0.05)  of the total variation for the diploid and tetraploid potato populations in the multivariate analyses (2r) and LD decay. These values were similar in mixed (2 =r0.192 with a LD decay=12500 base pairs - pb) and tetraploid (2 =r0.179 with a LD decay=12000 pb) populations, while the LD was higher in the diploid population (2 =r0.403 with a LD decay=11500 pb)  suggested the division of these genotypes into five genetic groups, three subgroups for tetraploid and two subgroups for diploid genotypes . The tetraploid accessions, And_424, And_186, And_210, And_18, and And_209, had higher SI values (SI>1845) and concentrations for TPC, AA (DPPH and FRAP), and AAC traits with a concentration of phenolic compounds above 2651 mg GAE. We also selected the tetraploid accessions And_210 and And_290, and diploids Cha_49, Cha_46, and Phu_42 genotypes with SI>11886 and antioxidant activities above 18271 μmol TE measured by DPPH. Tetraploids And_186 and And_209 and diploids Cha_49, Cha_15, and Cha_16 genotypes were selected (SI>1453) for presenting antioxidant activities above 3113 mg AAE measured by FRAP. Diploid accessions Cha_2, Cha_16, Phu_7, Phu_75, and Phu_48 were selected (SI>7) based on their ascorbic acid contents above 5.24 mg AA. The tetraploid accessions And_186, And_209, and And_210 and the diploids Cha_49 and Cha_16 genotypes are particularly interesting for presenting high values for more than two traits (The selection index (SI) for each trait was calculated based on heritability values  STAs, 25 STAs in the mixed population, 16 STAs in diploid, and 17 STAs in tetraploid populations (2r) of 0.2-4.4%, 17 STAs (in chromosomes 2-6 and 9) with AA_DPPH (0.1-7.1%), nine STAs  with AA_FRAP (0.1-7.8%) and nine STAs  with AAC (0.1-2.1%) . In mixed and diploid populations, the analyses displayed 11 QTNs and two QTNs , two QTNs in the chromosomes 1 (5.1-32.5%) and 4 (10.4-11.0%) were associated with AA_FRAP. Finally, one QTN in chromosome 9 was associated with AAC (13.6-30.8%)  of the CAZy family with a Glycosyltransferase function was detected by two STAs/QTNs (c1_12945 and c2_43998) associated with TPC (phenotype variation estimated PVE-2r 3.3-22.4%), AA_FRAP (2.1-36.4%), AA_DPPH (0.7-36.4%) and AAC (0.2-5.1%). The PSC0003DMG400002675 gene (Chr 9) that has a fructose-bisphosphate aldolase function was associated with TPC (0.2-1.3%), AA_DPPH (4.8-24.7%) and AA_FRAP (4.2-4.9%). The Soltu.DM.03G022550.1 gene (Chr 4) with a pectin methyl esterase function was associated with AA_DPPH (0.1-63.8%), AA_FRAP (22.7-25.3%) and AAC (0.1%) and the PGSC0003DMG400001451 gen (Chr 2) with folate carrier protein were associated with TPC (13.9-22.6%) and AA_FRAP (7.8%). Additionally, three genes were associated with only one trait. The Soltu.DM.01G033780.1 gen (Chr 1) with a cysteine synthase C1 function was associated with AA_FRAP (6.6-11%), the Soltu.DM.03G015260.1 gene (Chr 3) with a BCL-2-associated athanogene function was associated with AA_DPPH (0.1-9.5%), and the Soltu.DM.09G021110.1 gene (Chr 9) annotated as a basic helix-loop-helix (bHLH) DNA-binding superfamily protein was associated with AAC (0.6-30.8%) , in agreement with previous reports (2 =h0.413-0.657)  allowed the choice of 20 promising genotypes from AGROSAVIA’s collection (13 diploids and seven tetraploids) with mainly dark skin tuber color. Some of the selected materials can be attractive for a breeding program  because they presented good levels of both TPC and AA. The heritability values varied between medium and high (3-0.657) . The h2 and AAC . In thisIn the present work, GWAS could dissect the genetic basis of the natural variation of phenolic compounds, ascorbic acid content, and antioxidant activity in potato tubers. So far, few studies have evaluated the genetic control of secondary metabolites in potatoes , and fewHere, we implemented single and multi-locus GWAS analyses of the complete dataset that included diploid and tetraploid potatoes and also ran the analyses separating the potatoes by their ploidy level. The CCC has a strong population structure based on the ploidy level; this collection is structured into diploid and tetraploid potatoes . Ploidy In the single and multi-locus GWAS analyses, the number of significant associations found for TPC, AAC, and AA (AA_DPPH and AA_FRAP) traits depended on the dataset/populations  and the GWAS models (single and multi-locus) used. At the level of the dataset, the number of detected STAs and QTNs was higher in the mixed (in single locus GWAS) and tetraploid (in multi-locus GWAS) datasets than in the diploid dataset (in both GWAS models: single and multi-locus), probably due to the higher sample number in the mixed and tetraploid populations. The size population affects the obtention of reproducible and robust results in GWAS analyses ; for thi2r) were higher in multi-locus than in the single-locus GWAS. The conventional single-locus GWAS, such as the general linear model (GLM) and mixed linear model (MLM), is considered less robust because it neglects the overall effects of multiple loci and needs corrections for critical values. QTLs with minor effects are sometimes not detected, and the effect estimations (2r) are less accurate with single-locus models. GWAS multi-locus methods would have lower false-positive error and higher statistical power in detecting a higher number of genetic associations  with a Glycosyltransferase function was associated with the three analyzed traits, TPC, AAC, and AA (AA_DPPH and AA_FRAP). This gene belongs to the UDP-glycosyltransferase (UGTs) superfamily that codifies proteins involved in the biosynthesis of phenolic compounds such as anthocyanins and flavonoids (PGSC0003DMG400002675 (Chr 9) gene with a fructose-1, 6-bisphosphate aldolase (FBA) function was associated with TPC and AA (AA_DPPH and AA_FRAP). Recently, a possible association of the FBA with the phenylalanine biosynthesis of a phenolic compound in Cannabis sativa was reported (Soltu.DM.03G022550.1 (Chr3) gene with a pectin methylesterase (PMEs) function was associated with AAC and AA (AA_DPPH and AA_FRAP). This enzyme has been associated with the production of ascorbic acid through the D-galacturonate pathway in tomato and tobacco (PGSC0003DMG400001451 gene (Chr 2) with folate carrier proteins function was associated with TPC and AA_FRAP; these proteins are involved in the downregulation of folate biosynthesis as a response to cold in rice  gene that transcribed for basic helix-loop-helix (bHLH) transcription factor (TF) was associated with AAC. The bHLH TF acts as a co-regulator in the biosynthesis of phenolic compounds such as anthocyanins (Soltu.DM.01G033780.1 (Chr 1) gene with a cysteine synthase C1 (CSase) function was associated with AA_FRAP. This enzyme responds to stress caused by high-salt conditions, heavy metals, and pathogen responses in alfalfa and brassica (Soltu.DM.03G015260.1 (Chr 3) gene with Bcl-2-associated athanogene (BAG) family protein functions are involved in heat stress responses in rice  markers (Many detected STAs/QTNs presented minor effects, suggesting that multiple genes probably control the phenotypic traits. It is recognized that phenolic compounds and ascorbic acid are quantitative traits . However markers . In the  markers  and peac markers . It is aGenotypic and phenotypic results revealed that the potato germplasm conserved in AGROSAVIA holds valuable and promising genotypes with high levels of TPC and AAC with good antioxidant properties, especially the diploid genotypes. We identified higher values for the studied traits than those found in other studies, probably as an effect of the reduced number of potato accessions or breeding lines evaluated in those studies . We wereThe SNP data used in this manuscript were provided in the supplementary data (JB-C carried out phenotype and genotype statistical analyses, made graphics, and wrote the manuscript. MC-L achieved the funding for the biochemical process and contributed to revising the manuscript. RY contributed to interpreting data, editing the graphics, and writing and revising the manuscript. All authors contributed to the article and approved the submitted version.