Quantitative characteristics of animals such as traits of productivity, reproduction, physical development, longevity, etc. are influenced by many genes, of which each individual gene has a small effect on the overall expression. There are small differences between these genes, and the number of gene combinations in the group is large, which affects the continuous variability of quantitative traits. These traits are also significantly influenced by non-genetic factors that modify their final expression. The evaluation of genetic parameters (heritability and genetic correlations) of quantitative traits is of crucial interest for breeders, and therefore, these parameters are the main imperative in selection. The genetic progress of quantitative traits is achieved by increasing the accuracy of the assessment of genetic variance and selection accuracy, which depends, among other things, on the estimated genetic correlations between the selected traits. The goal of this research was to evaluate genetic variances and covariances of milk yield, longevity, and type traits of the Holstein breed in Serbia in order to assess their role in practical breeding as well as the importance of genetic parameters in setting and revising already existing breeding goals and selection programs.xy = 0.38).In this study, the authors focused on the evaluation of the genetic parameters of longevity, milk yield traits, and type traits in dairy cattle populations in the Republic of Serbia. The total dataset used consisted of production records and pedigree data for 32,512 Holstein cows that calved from 1981 to 2015. The animal model was applied to determine the variance and covariance components and genetic parameters of the analyzed traits by applying the restricted maximum likelihood (REML) approach and using the programs VCE6 and PEST. The heritability of longevity traits was estimated using the Survival Kit V6.0 software package. Variance and covariance were estimated for five production traits: milk yield (MY), fat yield (FY), protein yield (PY), milk fat content (MC), and protein content (PC), and three longevity traits: length of productive life (LPL), lifetime milk yield (LMY), and the number of lactations achieved (NL) as well as for 18 standard type traits. Heritabilities for the milk, fat, and protein yield traits were 0.20 (MY), 0.15 (FY), and 0.19 (PY), respectively. The estimated coefficients of heritability for the longevity traits were higher when using the Weibull proportional hazards model compared to the traditional linear methods and ranged from 0.08 for NL to 0.10 for LPL. Heritability values for the type traits varied from a low of 0.10 (RLSsv—rear legs set–side view) to medium values of 0.32 (ST-stature). Genetic correlations were found between MY and the following longevity traits: LPL, LMY, and NL with values of −0.18, −0.11, and −0.09, respectively. Genetic correlations were found between MY and a number of linear type traits and varied from 0.02 (between MY and RUH-rear udder height) to 0.28 (between MY and FUA-fore udder attachment). Genetic correlations between the 18 investigated type traits ranged from −0.33 between TL (top line) and RTP (rear teats position) to 0.71 between AN (angularity) and RUH (rear udder height). Genetic correlations between most linear type traits and longevity traits  were generally negative and very low. The highest positive genetic correlation was found between UD and LPL (r Bearing in mind the forecasts that predict the world’s human population will reach 9.7 billion by 2050 , the facThe Holstein cattle breed has the highest milk yield of all dairy breeds in Serbia. It is primarily raised in areas with intensive agricultural production in the vicinity of large cities such as Belgrade and Novi Sad. According to the expert report of the Main Breeding Organization for the region of Central Serbia , realizePlanned breeding of Holstein cows in Serbia began in the mid-20th century, mainly on large co-op farms where crossbreeding was often carried out between the already imported Danish Black and White cows and progeny tested Holstein bulls originating from a number of European countries. At that time, breeding heifers and cows were imported mainly from the Netherlands and later also from other European countries . The next significant import of Holstein cattle into Serbia was in 1970, when the import of Holstein heifers from the USA began, so that by 1978, a total of 3100 were imported. In addition to the import of heifers, the greatest contribution to genetic progress, especially of the milk yield traits, was achieved by importing the semen of sires for mating with the best cows in order to obtain high-quality male calves that were progeny tested at Serbian national centers for artificial insemination. This program achieved a significant improvement in Black and White cows and the Holstein breed in our country. Next, larger imports of heifers that mainly originated from the Netherlands occurred in 2005 and 2006. Today, it is estimated that the total share of the Holstein breed in Serbia is around 15%.The selection of sires as well as the cows themselves is traditionally based on the yield of milk, milk fat, and protein. The inclusion of longevity traits and type traits in the selection index as well as the interest of breeders in improving these traits began in the last two decades with the aim to reduce the herd replacement costs, expenses for veterinary interventions, more efficient management, and thus the overall profitability of milk production.Cattle traits related to longevity are closely related to the survival rate and the period during which the cow generates income . AssessmDespite these shortcomings, many authors agree that the greatest contribution of including longevity in breeding programs consists of increasing the proportion of older cows with higher yields while simultaneously reducing the proportion of young cows, which will allow older cows to reach their maximum milk yield depending on their age ,16. In oThe objectives of this study were (1) to estimate the genetic parameters of longevity, milk yield traits, and type traits in dairy cattle populations in the Republic of Serbia, and (2) compare these estimates with the results of authors around the world in order to provide guidelines and emphasize the importance of including these traits in breeding programs to improve the profitability of the milk production of Serbian Holstein cows.The dataset used consisted of data on the production and pedigree for 32,512 Holstein cows, collected on seven large community farms located in the vicinity of Belgrade  as well as on 2329 smaller farms owned by private farmers on the territory of the province of Vojvodina in the Republic of Serbia . Analysis was conducted on cows born from 1981 to 2013 and with first calvings in the period from 1983 to 2015. Cows were descended from 834 sires. Each sire had at last three daughters in the dataset. Genetic links between analyzed animals were established through the sires. A maximum of six generations of ancestors were known in the pedigree file.Data on cows were collected by the service that kept register records within PK Belgrade (now Aldahra) and the Main Breeding Organization for Livestock, located at the Livestock Department of the Faculty of Agriculture, University of Novi Sad. The downloaded dataset contained a larger number of animals than those analyzed in this research, because during the preparation of the database, all cows without a known date of birth as well as no known calving date were excluded. After that, cows that did not have milk yield results, crossbreed cows as well as cows with incomplete or unknown origin were also excluded from the dataset. In addition, data on the linear evaluations of animals that lacked some of the required information, cows younger or older than the required age at calving at the time of evaluation, cows that were lactating for more than 305 days at the time of evaluation as well as evaluated cows that were not first-calvers were also excluded.The pedigree file used in the analysis of survival and the evaluation of genetic parameters of the milk yield trait, longevity trait, and type trait contained data on a total of 43,298 animals.The collected data contained information on milk yield in the first standard lactation–305 days for milk (MY), fat (FY), and protein (PY) as well as fat (FC) and protein (PC) content. Milk yield control was conducted using the AT4 method, which is the referent milk recording method by the International Committee for Animal Recording ; Yl is the fixed effect of year of first calving (l = 5 years); Sk is the fixed effect of season of first calving ; Uc is the fixed effect of age at first calving , II (24–26), III (27–30), IV (31–33), and V (34–44)); Om is the fixed effect of the evaluator (m = 22); Yn is the fixed effect of the year of evaluation (n = 4 years); FLp is the fixed effect of lactation stage at the moment of evaluation , II (31–60), III (61–90), IV (91–120), V (121–150), VI (151–180), and VII (181–210)); ao is the random effect of the animal; eijlkcmnpo is the random error.Genetic variances and covariances for type traits were evaluated using the following model (Equation (3)):iy is the observation for the ith trait ; b is the vector of fixed effects; a is the vector of additive genetic effects for the ith trait; e is the vector of random residual effects; X and Z are matrices relating the observations to the fixed and random effects.The above multi-trait animal model used to estimate the components of variance and covariance can be represented in the form of matrices as follows (Equation (4)):g is the additive genetic variance for the direct genetic effect of ith trait ; i,jg is the additive genetic covariance between ith and jth traits; ir, I is the residual variance for the ith trait; a is the additive genetic relationship matrix; I is the matrix for the residual.The structure of the (co)variances for random effects in the model is the following (Equation (7)):2h) of each trait was computed using Equation (6):2h is the heritability.The heritability (gxyr) estimates were calculated as follows (Equation (7)):g(xy)Cov is the genetic covariance between the x and y traits; 2g(x)σ is the genetic variance of the x trait; 2g(y)σ is the genetic variance of the y trait.Genetic correlation  .0(t) is the Weibull basic hazard function with scalar parameter λ and ρ form parameter; year is the fixed time-dependent effect of year of calving; season is the fixed time-dependent effect of calving season; rpm is the fixed time-dependent influence of relative milk yield within the herd from which the cow originates; farm is the fixed time-independent effect of the farm where the animal was produced; uzrpt is the fixed time-independent influence of age at first calving; lactation is the fixed time-dependent effect of lactation; sire is a random time-independent effect of the sire following a multivariate normal distribution.The analysis was conducted using the proportional hazards method, and the model itself had the following form (Equation (8)):h2 is the heritability of longevity traits on an ordinal scale; s2σ is the genetic variance between sires; p is the share of censored data.Heritability for longevity traits was calculated using the formula proposed by a number of authors ,38,39, wThis research showed that the first standard lactation average yields of MY, FY, and PY were 6644 kg, 231.6 kg, and 200.8 kg, respectively. The average fat content in milk was 3.55%, while the protein content was 3.07%. Longevity trait averages  had the following values: 1292 days, 20,854 kg, and 3.03 lactations, respectively. The lowest average value for type traits was recorded for RTL (4.45), and the highest for AN (6.52).The stated phenotypic values and variability for the milk yield trait, longevity trait as well as values for 18 linear type traits are presented in The estimated variance components and the heritabilities of milk production traits are presented in Values for the variance components and heritability estimates for the longevity traits obtained with the Weibull proportional hazards model and the linear animal model are presented in Values for heritability of the longevity traits estimated by the Weibull proportional hazards model were higher and amounted to 0.10, 0.09 and 0.08 for LPL, LMY, and NL, respectively, compared to the values estimated using the linear model  to a medium value of 0.32 (ST). Additive genetic variance and heritability values are presented in The genetic correlations between the traits of milk yield and longevity traits are presented in Negative correlations were established between MY and the following traits: FC, PC, LPL, LMY, and NL with the following values of −0.15, −0.18, −0.18, −0.11, and −0.09, respectively. All other correlations were positive with standard correlation coefficient errors varying from 0.01 to 0.08. Genetic correlations between the milk yield traits and linear type traits are presented in Additionally, several genetic correlations close to zero were established between the analyzed linear type traits, indicating that there is no link between these traits. Genetic correlations between the 18 examined type traits varied from −0.33 between TL and RTP to 0.71 between AN and RUH. Strong positive genetic correlations were found between ST and RW (0.60) as well as between ST and AN (0.69).Leg and udder traits showed relatively small to moderate genetic correlations within and between groups with one stronger correlation between RUH and SL (0.63). The standard errors for the tested parameters are presented in xy = 0.38 was found. Values for genetic correlations between LMY and type traits varied from −0.14 (between BD and LMY) to 0.19 (between SL and LMY). Furthermore, the correlations between the type and NL traits were very low, a little below or above zero  were in accordance with the values published in studies for first calving in the Holstein and Black and White breeds reared in Serbia at the beginning and during the 2000s ,40,41,42The average phenotypic values for milk yield traits in this conducted research  were lowThe average phenotypic values of the longevity traits in this research indicate that the value for LPL in our populations of Holstein cows was higher compared to the results obtained in Croatia, as presented by Raguž . A studyMilk production is one of the most important traits in dairy cows, and has a very relevant influence on the decision to cull cows from the herd. Absolute production is not the most adequate selection indicator for making a decision on culling animals from production. A much more reliable indicator is the relative milk production of a cow in relation to the average of the herd in a given year, which is an indicator of a cow’s position in the herd. High-producing cows are significantly less likely to be culled than low-producing cows from the same herd ,52. When it comes to the type traits of first calvers in this research, the average scores were obtained for the following traits: FTP, FTL, UD as well as for RTP and RTL. The listed traits were close to the average scores for first calvers of the Holstein breed (5), which are also the ideal scores for these traits, according to the ICAR and WHFF nomenclature ,33. RegaAs one of the most important genetic parameters, heritability helps breeders to determine to what extent genes control the expression of a certain trait, enables the evaluation of the effect of selection as well as helps make a decision as to whether a given trait is more effectively improved through selection or through improving the conditions of housing, care, and health protection of animals.The estimated heritability of 0.2 for MY in this study  showed aCompared to estimates from other studies, the heritability for PY in this paper (0.19) was slightly lower than the values found by Kudinov et al. and DeGroot et al. ,58, who The estimated heritabilities for FC and PC  were in accordance with the results of Boujenane et al. , while tHeritability for traits of longevity in the investigated population was estimated based on formulas presented in Materials and Methods. To assess the heritability of milk yield traits, parameters calculated using the Weibull proportional hazards model were first used.The estimated variance between sires for LPL was 0.034, resulting in a total additive genetic variance of 0.136. Based on the estimated additive genetic variance and the share of censored records, the value of effective heritability for LPL was calculated as having a value of 0.10.Significantly higher values for variance between sires in their research were established by Potočnik et al.  in the pThe ρ parameter (basic risk function) for LMY was 2.17, having a lower value compared to the same parameter established when longevity was observed through LPL (ρ = 2.35), which indicates a slightly lower intensity in the increase of the risk of culling from the first calving to culling, if we look at longevity through LMY. A lower value of variance between sires was also established, so heritability also had a slightly lower value (0.09) compared to LPL.The established value of the ρ parameter indicates that even in the case of NL, the risk increases from the moment of first calving to culling. The ρ parameter established for NL had a lower value compared to the value of the ρ parameter established for LPL and LMY, which means that the risk of culling grew less when longevity was observed through NL. A lower variance value between sires (0.030) was also established in relation to LPL, which resulted in a lower heritability value for NL (0.08).Heritability values for longevity traits, calculated using the linear animal model (AM), were from 0.06 for LPL and LMY to 0.07 for NL. Higher values by using AM compared to the use of the sire model were established in the research by Raguž . Close vIn this study, the heritabilities of the longevity traits were estimated using two different methodologies. Heritability for traits of longevity estimated by the Weibull model of proportional hazards was higher compared to the heritability estimated by standard linear models. Although the application of the Weibull model of proportional hazards resulted in higher values of the heritability of longevity traits, survival analysis was based on the sire model, and thus it is not possible to estimate the breeding values for all animals. Even if the linear model may not be most appropriate for all traits, many researchers prefer linear models due to their lower computer requirements. Depending on the amount of data available in the future and breeding goals, either model can be recommended for use in the Serbian Holstein population.The heritability values of type traits measured on first calvers of the Holstein breed in the Republic of Serbia varied from 0.10 for RLSsv to 0.32 for ST, which generally showed higher genetic variability. The estimated heritability value of 0.32 for ST was lower than the value obtained in the study by Campos et al. . A similTL is not a standard type trait that is evaluated in all countries, and it is included as an optional trait in the linear evaluation program for first calvers of the Holstein breed in Serbia. A lower value of 0.09 was obtained by Dadpasand et al.  for the The heritability value of 0.15 for CW, which was estimated in this study, was close to the heritability value for the HF populations in Slovenia and Spain as well as the value of 0.18 for the HF population in the Czech Republic, Estonia, Germany, and Canada .For the BD trait in the investigated first calvers, the heritability value was 0.17. An identical value was obtained by Cassandro et al. , while aThe estimated heritability value for RP was close to the heritability value for the HF population in Norway, while in other Interbull member countries, the heritability was higher, ranging from 0.23 in the South Africa to 0.48 in Scandinavian countries .For the RW trait, the estimated heritability value of 0.18 was the closest to the value obtained by Janković et al. and Bohlouli et al. ,27. In the linear evaluation system, the trait angularity was evaluated as an indicator of the dairy character of cows. The obtained heritability value for the AN trait in first calvers of the HF breed population in Serbia was 0.22, which is close to the value of 0.23 obtained by Toghiani  and CampThe heritability for RLSsv is in agreement with the values obtained by Toghiani and Dadpasand et al. ,71. The FTL is also not a standard type trait and was included as an optional trait in the linear evaluation program in Serbia for first calvers of the Holstein breed. The obtained heritability value for FTL was 0.16, and is in agreement with the value obtained by Janković et al.  and DadpFor the UD trait, the estimated heritability was 0.15, which is closest to the value of 0.14 obtained by Janković et al. . Lower vThe heritability obtained in this paper for the RUH trait in first calvers of the HF breed was close to the values of 0.10 and 0.12 obtained by Janković et al.  and CassThe heritability value of 0.12 for the SL trait in first calvers of the Holstein breed in Serbia was identical to the heritability for the HF population in Hungary, and approximate to the value of 0.10 for HF populations in Ireland and South Korea .Heritability, the RTP trait, were obtained for HF populations in Italy, Portugal, and the USA .For the RTL trait, the estimated heritability in this paper was 0.23, which was close to the values obtained by Janković et al. and Zink et al. ,69. LoweThe heritability value of 0.23 for the RTL trait in the population of first calvers of the HF breed in Serbia was identical to the heritability value for the HF population in Hungary, and approximates the value of 0.24 for the HF population in Australia .Heritabilities estimated for all type traits in this study were generally in accordance with those currently used to estimate EBV in the population of Holstein cattle in the Republic of Serbia as well as with the results for most members of Interbull. Standard errors for heritability were low and varied from 0.015 to 0.027. The estimated values of heritabilities for all type traits show that substantial genetic variability exists within the population of Serbian Holsteins, which will enable accurate genetic evaluation, selection, and mating plans to improve type traits in the next generations.Lifetime milk yield (LMY) is one of the most important traits for dairy cattle breeders. The short life span and high percentage of cows that are culled result in a significant increase in the production costs. To reduce costs, it is desirable for cows to remain in the herd longer and that they have a high milk yield .On 36,663 Slovenian brown cattle, Jenko et al.  establisStudying the relationship between the traits of milk yield and longevity of Holstein cows, Kaupe et al.  found thA recommendation to people working on the selection and breeders of dairy cattle is that when including longevity in the selection index, the effects of all of these traits on the traits of milk yield should be comprehensively analyzed. The short lifespan of dairy cows not only seriously affects productivity, but also hinders the possibility of selection for other traits . In ordThe estimated values of genetic correlations between the MY and type traits in this study were negative for FA, UD, and RTP . These results do not agree with the results established by Bohlouli et al.  and BerrIn studies by Van Niekerk et al.  and DeGrIn the conducted research, positive genetic correlations between MY and type traits ranged from 0.02 for RUH to 0.28 for FUA. In the Jersey breed of cattle in South Africa, Van Niekerk et al.  saw thatGenetic correlations between the analyzed type traits ranged from −0.33 (between TL and RTP) to 0.71 (between AN and RUH). A weak correlation (0.01) existed between TL and FTL as well as between RP and RW, while significantly stronger correlations were recorded between RUH and SL (0.63). Additionally, there was a strong genetic correlation between ST and AN (0.69). Comparing the obtained values for genetic correlations between type traits with other results showed that genetic correlations between FUA and FTP were lower than the values established by Duru et al.  in TurkeFTP has a weak genetic correlation with other udder traits, which was also confirmed in research by Duru et al. . GeneticThe importance of type traits for assessing longevity traits lies in the fact that type traits are recorded as a mandatory selection measure at a relatively early age, already during the first lactation. At the same time, type traits generally have a medium to moderately high heritability, as opposed to longevity traits, which have a low heritability, making it difficult to increase the genetic progress for these traits . Of all the assessed genetic correlations, the genetic correlations between type traits and longevity traits varied the least. The research results in this study were very close to the results published by Vukasinovic et al. . When exUnlike our research, Strapak et al.  obtainedThe association between LPL and type traits was also investigated by Wasana et al. , who obtIn a study conducted by Imbayarwo-Chikosi et al. , all of Schneider et al. , when exGenetic correlations between the level of physical development and longevity traits in the presented studies showed the importance of type traits in the selection process. It is obvious that the selection for LMY has a positive effect on all udder traits, except for udder depth. LPL is an important and complex longevity trait because it includes many factors: production technology, herd management, reproduction, health status, economic justification of milk production, and also indicates the genetic potential of the analyzed cows.The results obtained in this study were generally close to the results from studies of other Holstein populations in other countries. Certain desirable correlations were confirmed, the connection between the udder characteristics and longevity characteristics being especially significant.The results confirm that certain type traits can be included in a selection index, along or instead of the longevity traits to provide additional information on the animals’ genetic merit for longevity, when the longevity phenotype is missing or censored. Using indicator traits can improve the accuracy of genetic evaluation and selection for correlated traits that are expensive and difficult to measure or are recorded relatively late in life.The estimated heritability and genetic correlations for the traits of milk yield, longevity, and type of Serbian Holstein suggest their sufficient manifestation and are therefore acceptable for inclusion in the selection index. Given that a large number of traits were investigated, for a future breeding program, and based on the estimated values of genetic variances and covariances, traits that will ensure that cows have a high milk yield with a satisfactory protein content and possess desirable longevity and type traits should be selected. It is also necessary to take into account the desirable and undesirable correlations between the investigated traits in order to ensure economical milk production in the long term.The estimated genetic parameters of the analyzed traits were compared with a large number of references in order to more objectively evaluate the place of the Serbian Holstein in relation to other countries worldwide.The values of the genetic parameters of the investigated traits of milk yield, longevity, and type traits in the population of Holstein cows in Serbia were mostly lower or similar compared to the data of other researchers. Given that genetic parameters of quantitative traits depend on the variability of the observed traits and previous selection, lower values of these parameters may be the result of lower or higher genetic variability, caused, among other things, by the influence of systematic factors. The estimated genetic parameters of the analyzed traits are significant for the evaluation of their breeding values, which will be the subject of our future scientific work.


An evolutionary model for sex differences in disease risk posits that alleles conferring higher risk in one sex may be protective in the other. These sexually antagonistic (SA) alleles are predicted to be maintained at frequencies higher than expected under purifying selection against unconditionally deleterious alleles, but there are apparently no examples in humans. Discipline‐specific terminology, rather than a genuine lack of such alleles, could explain this disparity. We undertook a two‐stage review of evidence for SA polymorphisms in humans using search terms from (i) evolutionary biology and (ii) biomedicine. Although the first stage returned no eligible studies, the second revealed 51 genes with sex‐opposite effects; 22 increased disease risk or severity in one sex but protected the other. Those with net positive effects occurred at higher frequencies. None were referred to as SA. Our review reveals significant communication barriers to fields as a result of discipline‐specific terminology. In species with separate sexes, an evolutionary conflict at the level of individual genetic loci can occur, where alleles that are favored by selection in one sex are selected against in the other  the VGLL3 locus  was searched for articles on 2 December 2020 with no time limit. The searches were carried out in two stages, with the organism filter set to human in both stages. In Stage 1, eligible studies were required to report specific genetic variants or haplotypes that were referred to as SA or were an example of intralocus sexual conflict. To achieve this, we conducted a Boolean search for articles that used the terms “sexual antagonism” OR “sexually antagonistic” OR “intralocus sexual conflict” AND “locus” OR “loci” OR “gene” OR “snp” OR “polymorphism” OR “variant” OR “allele” in their abstract or title. The Stage 1 search returned 34 articles in total .For this systematic review, we followed PRISMA guidance where possible .In Stage 2, studies were required to report specific genetic variants or haplotypes in humans with opposite effects in the two sexes on either complex traits, the outcome of a medical intervention, or disease risk/severity. We define complex traits as likely with a polygenic genetic architecture but are not directly related to a disease phenotype. In this second stage, search terms were specifically designed to include articles from the biomedical literature that may have been missed in the first stage because they do not report their findings with terms normally found within the evolutionary biology literature. Again, we conducted a Boolean search for articles that used terms in their title or abstract to describe an opposite or different effect in the two sexes , or that capture this concept with alternative words for sex  AND ). Full details of the search terms used and the numbers of articles returned are provided in the Supporting Information. The Stage 2 search returned 881 articles . Full texts of these candidate articles were then reviewed in detail. Articles were included in the final list if they described a sex‐opposite or SA effect linked to a specific genetic locus or loci, and reported the effect to be statistically significant . One additional article was considered from an outside source. Studies that only reported significant sex‐by‐variant effects were not automatically included unless they also satisfied the criteria above.The abstracts of the articles from Stage 1 and Stage 2 were then examined, and any articles that had the possibility of reporting an opposite effect of a specific genetic locus on a complex trait, medical intervention, or on disease risk/severity were considered for further screening. From Stage 1, no articles passed the screening. From Stage 2, this screening produced a shortlist of 70 candidate articles (d) quantifying the magnitude of how a given variant affects the studied trait expressed in the given sex. Specifically, Cohen's d was computed based on the reported descriptive statistics  or by conversion from other effect sizes (Odds ratio) and test statistics  using formulas reported elsewhere  to find the Reference SNP cluster ID for the variants described. Where effect allele frequencies were not reported, we used genotype frequencies to calculate effect allele frequency. Not all studies reported allele or genotype frequencies and so we attempted to supplement these data with allele frequencies from the 1000 genomes database . An evolutionary model of evolution at SA loci predicts that as the magnitude of the positive effect that an SA allele has outweighs the negative effect, the higher the frequency that allele can achieve  and observed allele frequency. To investigate this, we calculated the effect size ratio by dividing the positive standardized effect size by the negative standardized effect size; when bf < 0 < bm, effect size ratio was calculated as bm/bf, and when bm < 0 < bf, effect size ratio was calculated as bf/bm. Thus, SA alleles with a greater beneficial effect will have a smaller, more negative effect size ratio (←1), whereas SA alleles that have a greater deleterious effect will have a larger effect size ratio (>−1), up to a maximum value of 0 .If We modeled how effect allele frequency changes with effect size ratio using a generalized linear model (GLM), weighted by the inverse of the variance of the effect sizes such that data points with smaller variance have a higher weight, because smaller sample sizes were associated with larger and more variable effect sizes Fig. . We inithttps://pubmed.ncbi.nlm.nih.gov/collections/60278165/?sort=pubdate). From the studies examined, 49 SA variants were identified  and were therefore excluded from the subsequent model in which effect size ratio was found to relate negatively to effect allele frequency, as predicted .We found 22 studies provided sufficient data to allow the relationship between effect size ratio and effect allele frequency to be explored by statistical modeling. Trait class and its interaction with effect size ratio were initially included in the model but neither were found to have a significant effect . Although not yet validated, these reports nonetheless represent a substantial body of evidence that humans, like other organisms with separate sexes (Bonduriansky and Chenoweth n = 8) , or that some specific variants have pleiotropic effects on more than one trait or disease in a sex‐opposite or SA way  or sex‐limited effects. For instance, Winkler et al.  report 4A key gap in our knowledge is whether the putative examples of sex‐opposite or SA alleles presented here can be validated using independently derived datasets in the same or different subpopulations. For this, focusing on those variants associated with sex‐differential disease risk would naturally be the most fruitful for advancing our knowledge of sexual antagonism in human disease. We therefore encourage specialists for the particular disease groups presented in Table There is a possibility that some bias exists in the PubMed database against articles from the field of evolutionary biology, and if we had used another database then both stages would have returned valid articles. We subsequently (14 December 2020) sought to investigate this possibility with additional searches of Scopus and Web of Science. Repeating the Stage 1 search initially returned 85 articles in Web of Science  and 71 articles in Scopus , but again no articles made it through the screening process. Repeating the Stage 2 search returned a much larger number of studies in both Web of Science  and Scopus , with 8604 unique records. This large number of records is too many to currently screen and may harbor further examples of SA loci in humans, but it is noteworthy that the majority of the initial list of studies found during Stage 2 using PubMed were also found in the other two databases (712 out of 881) as well as 28 out of the screened list of 32 studies identified in the current systematic review.The disparity between the two stages of the review process provides a remarkable example of how discipline‐specific terminology and concepts can hinder scientific communication between fields for substantial periods of time. For many of the studies identified in this review, the focus of the research was not on quantifying or even identifying sex‐specific genetic effects in traits or disease, although they were tested for. The responses to such findings varied. Some framed these results as a major finding, whereas others merely made note of them, with one suggesting that because the genetic associations were in opposite directions in the two sexes then it should be regarded as a false positive (Wong et al. The authors declare no conflict of interest.JAH and EHM conceived the study. JAH performed the searches and created the shortlists. JAH, EHM, and TJ screened the shortlisted articles. JAH and TJ collected and curated data. JAH, EHM, and TJ performed the analysis. JAH and EHM drafted this article, with all authors commenting on revisions.https://pubmed.ncbi.nlm.nih.gov/collections/60255050/?sort=pubdate.Stage 1 search output can be found at: https://pubmed.ncbi.nlm.nih.gov/collections/60254985/?sort=pubdate.Stage 2 search output can be found at: https://pubmed.ncbi.nlm.nih.gov/collections/57906298/?sort=pubdate.Stage 2 shortlist can be found at: https://pubmed.ncbi.nlm.nih.gov/collections/60278165/?sort=pubdate.Stage 2 selected articles can be found at: Data and scripts are provided as part of the Supporting Information.https://doi.org/10.5061/dryad.rv15dv48k.Data and code have been uploaded to Dryad: Associate Editor: N. G. PrasadHandling Editor: T. ChapmanFigure S1. Study reported effect allele frequencies and database allele frequencies are strongly correlated.Figure S2. Relationship between study sample size and average extracted effect size across all studies.Figure S3. Female effect size and male effect size are negatively correlated in SA alleles.Figure S4. The relationship between effect allele frequency and effect size ratio, grouped by trait class.Click here for additional data file.


Genome-wide association studies (GWAS) with non-targeted metabolomics have identified many genetic loci of biomedical interest. However, metabolites with a high degree of missingness, such as drug metabolites and xenobiotics, are often excluded from such studies due to a lack of statistical power and higher uncertainty in their quantification. Here we propose ratios between related drug metabolites as GWAS phenotypes that can drastically increase power to detect genetic associations between pairs of biochemically related molecules. As a proof-of-concept we conducted a GWAS with 520 individuals from the Qatar Biobank for who at least five of the nine available acetaminophen metabolites have been detected. We identified compelling evidence for genetic variance in acetaminophen glucuronidation and methylation by UGT2A15 and COMT, respectively. Based on the metabolite ratio association profiles of these two loci we hypothesized the chemical structure of one of their products or substrates as being 3-methoxyacetaminophen, which we then confirmed experimentally. Taken together, our study suggests a novel approach to analyze metabolites with a high degree of missingness in a GWAS setting with ratios, and it also demonstrates how pharmacological pathways can be mapped out using non-targeted metabolomics measurements in large population-based studies. It also has antipyretic effects. Known under the brand names Paracetamol and Tylenol, acetaminophen is the most frequently used painkiller world-wide. Acetaminophen is considered a nonsteroidal anti-inflammatory drug and thought to act via the inhibition of the cyclooxygenase pathway and prostaglandin biosynthesis . AcetamiMany genetic variants in genes involved in endogenous metabolism have been identified by genome-wide association studies (GWAS) . In manyHowever, genetic variation in genes involved in exogenous metabolism and detoxification processes has been less studied so far, mainly because the relevant xenobiotics and drug metabolites are only present in small subsets of the study participants. This generally leads to their exclusion from GWAS due to their high degree of missingness, which typically requires a metabolite to be detected in at least 50% of the samples. A second challenge resides in the relative quantification of metabolites with high missingness: due to the large sample numbers required for GWAS, metabolomics measurements are required to be run in batches over a significant period of time, which can lead to differences between batches of several orders of magnitude, especially when changes in liquid chromatography columns are required or multiple instruments are used. Non-targeted approaches have several potential methods to estimate appropriate correction factors ,12, whicWe previously used ratios between metabolite pairs in GWAS ,10,15 anWe therefore hypothesized that genetic variance in acetaminophen metabolism could be identified by a GWAS on all possible ratios between these nine metabolites, and that the characteristics of the associated metabolite pairs would match the enzymatic function of the respective genes. In the following we describe what could be a prototype for the GWAS analysis of drug metabolites from non-targeted metabolomics and show that associations with ratios between related drug metabolites can reveal new information, both about the implicated metabolites as well as about the associated genes.For this study we used all samples of our previous study on medication use in QBB  for whicp-values were smaller than the multiple-testing corrected genome-wide significance threshold of p-valueBonf = 1.1 × 10−9 (=5 × 10−8/45). In addition, we required a p-gain above p-gainBonf = 450 (=10 × 45). The p-gain of an association of a metabolite ratio is defined as the smaller value of the two p-values for a pair of metabolite associations divided by the p-value of the association for their ratio. A p-gain of 10 has been shown to be the equivalent of an alpha level of significance of 0.05 for a single test [Associations were considered as significant at a genome-wide and trait-wide level if their gle test . Large pp-value of 3.7 × 10−10 and a p-gain of 4.6 × 107 for the association of SNP rs1531022 at the UGT2B15 gene locus with the ratio of 2-methoxyacetaminophen glucuronide* to 2-methoxyacetaminophen sulfate* and was therefore considered both significant at a genome- and trait-wide level and significant for the ratio. No other association reached a Bonferroni level of significance.The strongest association in our study had a p-value of p < 10−5, we identified 229 associations at 217 independent loci (LD r2 < 0.1) and annotated these with the nearest genes and overlapping eQTL, pQTL, mQTL, and methQTL information using a PhenoScanner [We therefore continued our investigation using a target gene approach. At a more liberal oScanner  and SNiPVariant rs1531022 is an intronic SNP of the UDP Glucuronosyltransferase Family 2 Member B15 (UGT2B15) gene. UGT2B15 encodes a glycosyltransferase that is involved in the metabolism and elimination of toxic compounds from the body, including acetaminophen. The present association of a polymorphism in UGT2B15 with the ratio of a glucuronidated and a sulfated metabolite of acetaminophen, that is, methoxyacetaminophen glucuronide/methoxyacetaminophen sulfate, reflects a genetically determined shift in the contribution of the two detoxification pathways in the clearance of acetaminophen metabolites and matches the biological function of UGT2B15.2 > 0.99; therefore, rs1531022 can be considered as tagging this likely causal variant. The current association is therefore extending the experimentally established list of UGT2B15 substrates to methoxyacetaminophen.Polymorphisms in the drug metabolizing capacity of UGT2B15 have been reported as long as 25 years ago  and have2 < 0.1) at the UGT2B15 locus that did not reach genome- and trait-wide significance  catalyzes the transfer of the methyl group of S-adenosyl-L-methionine to one of the hydroxyl groups of a catechol substrate . It is p2 = 0.98 with rs4680) with the ratio of 2-methoxyacetaminophen sulfate and 2-hydroxyacetaminophen sulfate and the biochemical function of COMT as a catechol-O-methyltransferase suggests that the hydroxy- and methoxy-groups are therefore on the three-position, and not in the two-position. Interestingly, there is literature precedence for both structures [Our association of rs165722  the conversion of acetaminophen to 3-hydroxyacetaminophen by a yet to be identified reaction step, (2) the conversion of 3-hydroxyacetaminophen to 3-methoxyacetaminophen by COMT, and (3) the sulfonation to 3-methoxyacetaminophen sulfate or glucuronidation to 3-methoxyacetaminophen glucuronide, with the branching of the final step being determining and under genetic control (at least in part) by the efficacy of the glucuronidation reaction catalyzed by UGT2B15.We screened the list of associations for additional biochemically plausible cases and searched for loci that involve UGT, SULT, and CYP genes, and other genes that could be enzymatically relevant to acetaminophen metabolism. We found the following two potentially relevant loci:p = 6.3 × 10−6) with CYP2C8 [p-value = 6.8 × 10−6, p-gain = 2911);SNP rs57945749 is located in an intergenic region at chr19:32616014 and was previously reported as a trans-eQTL .SNP rs2297813 near CYP4A11|CYP4A26P|CYP4B1|CYP4Z2P are associated with the ratio of 3-(N-acetyl-L-cystein-S-yl) acetaminophen and 4-acetamidophenol. While not explicitly annotated to the acetaminophen pathway, CYP4B1 participates in the metabolism of a number of aromatic amines , so it mWe conducted what is arguably the first GWAS with acetaminophen metabolites measured using the non-targeted HD4 metabolomics platform Metabolon Inc.  and samples from the Qatar Biobank study. We present a novel approach to analyze metabolites with a high degree of missingness in a GWAS setting using ratios and show that pharmacological pathways may be gleaned by using data from large population-based studies. Our approach provides further insight into metabolomics-driven pharmacogenomics analyses of acetaminophen .Specifically, the multiple associations that we reported at the UGT2B15 locus may now be used to support fine mapping of pharmacogenetic haplotypes at this important locus. Our results further suggest that 3-hydroxyacetaminophen may play a pivotal role in acetaminophen detoxification and add this molecule to the growing list of known COMT substrates.As with all GWAS, our study has a number of limitations, most importantly its relatively low statistical power, and, as for now, a lack of an independent replication cohort. However, since the lead variants at the UGT2B15 and COMT loci have been previously associated with relevant biochemically related molecules, and since the top association reaches genome-wide significance even at the current number of samples, we do not feel that a lack of replication is an issue for these two loci. Regarding the discovery of further loci of interest, we consider this study as a proof-of-concept that may initiate the conduct of larger-scale GWAS with drug metabolites using a ratio-metric approach.Qatar Biobank (QBB) is a population study that includes adult Qatari nationals and long-term residents (≥15 years living in Qatar) ,35. QBB A total of 3000 EDTA blood plasma samples were analyzed on the non-targeted metabolomics platform of Metabolon Inc.  at the Anti-Doping Laboratory–Qatar (ADLQ) as previously described ,14. Brie2 gas and lyophilized to yield a sticky solid residue [Sulfation procedure: One equivalent of 3-methoxyacetaminophen (0.030 mmol) was added to 0.3 mL of pyridine in an 8 mL vial. Sulfur trioxide pyridine complex  was added to the vial and the mixture was stirred at 40 °C for 3 h. The reaction mixture was cooled to room temperature and 1N KOH (3.5 mL) was added to the vial dropwise. The resulting solution was transferred to a 20 mL vial and 14 mL of iPrOH was added and the solution was stored in a 5 °C chamber overnight. The liquid was decanted off the residue that formed overnight, and the resulting residue was dried with a stream of N residue .2CO3  were added to the vial and the mixture was then stirred at 80 °C. After 2 h the reaction mixture was cooled to room temperature and filtered using a 0.2 µm syringe filter and the filter was washed with 2 mL benzene. The resulting solution was concentrated with a stream of N2 gas. A ratio of 9:1 MeOH:1 M NaOH (1 mL) was added to the dried residue, and the resulting solution was stirred at room temperature. After 30 min the reaction mixture was concentrated with a stream of N2 gas, and the residue was lyophilized to yield a solid.Glucuronidation Procedure: One equivalent of 3-methoxyacetaminophen (0.030 mmol) was added to 0.5 mL of benzene in an 8 mL vial. Acetobromo-α-D-glucuronic acid methyl ester  and Agp-value <10−6 were filtered out leaving 6,375,079 variants for final analysis. All sample and variant filtering QC steps were performed using PLINK version 1.90b6.10 [2 = 0.5 to include independent variants for the computations of principal components (PCs) and the relationship matrix. Ten PCs were used as covariates and estimated using PLINK, the relationship matrix was estimated using GCTA version 1.92.3 [Participant genotypes were determined using whole genome sequencing as described in . The com.90b6.10 . The indn 1.92.3 .https://pathcards.genecards.org/card/acetaminophen_metabolism, accessed on 12 April 2022) and Drugbank  and were then cross-referenced with the PhenoScanner annotation to identify candidate genes  and Rstudio (version 1.4.1717 and above). A total of 45 ratios between metabolites were analyzed for association with all genetic variants and the p-gain was computed as described in . The GWA


There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure needs to be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared. Genome-wide association study (GWAS) has successfully identified a large number of genetic variants that are associated with human complex diseases or phenotypes –4. AmongAt present, a variety of approaches that focus on jointly analyzing multiple phenotypes have been proposed. These statistical methods can be roughly divided into three categories, including approaches based on regression models –19, combL clusters are transformed to follow a standard Cauchy distribution, then the transformed p-values are combined linearly with equal treatment to obtain the ceCLC test statistic. This test statistic of ceCLC has an approximately standard Cauchy distribution even though there is a correlated structure between combined p-values [In practice, multiple phenotypes considered may be in different clusters, but most methods for detecting the association between multiple phenotypes and genetic variants either treat all phenotypes as a group or treat each phenotype as one group and combine the results of univariate analysis. Unlike these methods, the clustering linear combination (CLC) method  works pap-values , so the N unrelated individuals with K correlated phenotypes, which can be quantitative or qualitative (binary), and each individual has been genotyped at a genetic variant of interest. Let Yi = T represent K correlated phenotypes for the ith individual  with i = 1,2,⋯,N. Let Gi denote the genotype for the ith individual at the variant of interest, where Gi∈{0, 1, 2} corresponds to the number of minor alleles. We suppose that there are no covariates. If there are p covariates zi1,…,zip, we adjust both genotypes and phenotypes for the covariates [Assume we consider variates , 37 usinβk1 is the genetic effect of the variant on the kth phenotype and g(∙) is a monotone “link” function. Two types of generalized linear model are commonly used: 1) linear model with an identity link for quantitative phenotypes and 2) logistic regression model with a logit link for qualitative phenotypes. We first conduct a univariate test to test H0: βk1 = 0 for each phenotype, k = 1,2,⋯,K, using the score test statistic [Tk has an approximate normal distribution with mean μk = E(Tk) and variance 1, we can assume that T = T approximately follows a multivariate normal distribution with mean vector μ = T and covariance matrix Σ. Our objective is to test the association between multiple phenotypes and a genetic variant, so the null hypothesis is H0: β11 = ⋯ = βK1 = 0. Sha et al. [P(Y) almost surely, where P(Y) is the correlation matrix of Y = T. Therefore, we can use the sample correlation matrix of Y, Ps(Y), to estimate Σ.For each phenotype, we consider the following generalized linear model :g to cluster K phenotypes. Suppose that the phenotypes are clustered into L clusters, considering L = 1,⋯,K, and B is a K×L matrix with the th element equals 1 if the kth phenotype belongs to the lth cluster, otherwise it equals 0. The CLC test statistic [L clusters is given bypL, for L = 1,⋯,K. Since for a given data set, the number of clusters of the phenotypes is unknown, in the last step of the CLC method [TCLC = minL≤K1≤pL is used as the final test statistic. Because TCLC does not have an asymptotic distribution, a simulation procedure is needed to evaluate the p-value of TCLC. This makes the CLC method computationally demanding. In this paper, instead of using the minimum p-value as the test statistic in the CLC method, we use the Cauchy combination test [K clusters, which is given byBased on the CLC  and ACATtatistic  with L cC method , TCLC = ion test  to combipL is uniformly distributed between 0 and 1, therefore tan {(0.5−pL)π} follows a standard Cauchy distribution. If p1,⋯,pK are independent, the test statistic TceCLC follows a standard Cauchy distribution under the null hypothesis. However, most likely there exists a correlated structure between p1,⋯,pK. Liu. et. al [TceCLC can be well approximated by a standard Cauchy distribution. According to the cumulative density distribution of standard Cauchy distribution, the p-value of TceCLC can be approximated by 0.5−{arctan(TceCLC)/π}. The R code for the implementation of ceCLC is available at github https://github.com/MeidaWang/ceCLC.Under the null hypothesis, we know that . et. al  has provK = 20 and 40 correlated phenotypes for N individuals. Firstly, we generate the genotypes of the genetic variant according to the minor allele frequency (MAF = 0.3) under Hardy Weinberg equilibrium. Secondly, the K quantitative phenotypes are generated by the following factor model [Y = T, G is the genotype at the variant of interest, λ = T is the vector of genetic effect sizes on K phenotypes, c is a constant number, f is a vector of factors, and R is the number of factors, Σ = (1−ρ)I+ρA, all elements of matrix A equals 1, I is an identity matrix, ρ is the correlation between factors; γ is a K×R matrix, ε = T is a vector of residuals, and ε1,⋯,εK~i.i.d. N.In our simulation studies, we generate one common variant and or model , 28, 33c2 and the between-factor correlation is ρc2. We set c = 0.5 and ρ = 0.6.According to different number of factors affected by the genotypes and different effect sizes, we consider the following four models. In each model, the within-factor correlation is R = 1, λ = βT and γ = T.Model 1: There is only one factor and genotypes influence all phenotypes. That is, R = 2, γ = Bdiag, where Di = 1K/2 for i = 1, 2.Model 2: There are two factors and genotypes influence one factor. That is, R = 5, γ = Bdiag, where Di = 1K/5 for i = 1,⋯,5, k = K/5, β41 = ⋯ = βk4 = −β and Model 3: There are five factors and genotypes influence two factors. That is, R = 5, γ = Bdiag, where Di = 1K/5 for i = 1,⋯,5, k = K/5. β31 = ⋯ = βk3 = −β, Model 4: There are five factors and genotypes influence four factors. That is, K phenotypes are quantitative and the second one is that half phenotypes are quantitative and the other half are qualitative (binary). To generate a qualitative phenotype, we use a liability threshold model based on a quantitative phenotype. A qualitative phenotype is defined to be affected if the corresponding quantitative phenotype is at least one standard deviation larger  than the phenotypic mean.We consider two types of multiple phenotypes. The first one is that all λ = T, and consider three different sample sizes, N = 1000, 2000, and 3000, under four different models. The type I error rates are evaluated by 106 replications and at the nominal significance levels of 0.001 and 0.0001, respectively. To evaluate power, we simulate data under the alternative hypothesis and consider two different sample sizes, N = 3000 and 5000. The powers are evaluated by 1000 replications at the nominal significance levels of 0.05. To better demonstrate the advantages of the ceCLC method, we compare ceCLC with other multiple-traits analysis methods: CLC [5 replications to evaluate Type I error rates for both of the methods.In order to ensure the validity of the ceCLC method, we first evaluate the type I error rates of this method. We simulate data under the null hypothesis, that is, ods: CLC , MANOVA ods: CLC , MultiPhods: CLC , TATES [ods: CLC , O’Brienods: CLC , which iods: CLC . Since CK = 20 quantitative phenotypes, and the type I error rates of the other eight methods  are summarized in K = 40 are listed in K = 40 are summarized in 6 replications, the 95% confidence intervals of Type I error rates divided by nominal significance levels of 0.001 and 0.0001 are  and , respectively; for 105 replications, the corresponding confidence intervals are  and , respectively.From Tables Chronic obstructive pulmonary disease (COPD) is a common disease characterized by the presence of expiratory dyspnea due to the excessive inflammatory reaction of harmful gases and particles –43. COPDWe consider seven quantitative COPD-related phenotypes, containing FEV1, Emphysema, Emphysema Distribution, Gas Trapping, Airway Wall Area, Exacerbation frequency, and Six-minute walk distance. We also consider four covariates which include BMI, Age, Pack-Years and Sex. After removing the missing data, there are 5,430 subjects across 630,860 SNPs left for the analysis. Same with the analysis in , 33, the−8 to identify SNPs significantly associated with the 7 COPDrelated phenotypes, In our analysis, we choose the commonly used genome-wide significant level α = 5×10In the medical field, many human complex diseases are often accompanied by multiple correlated phenotypes which are usually measured simultaneously, so jointly analyzing multiple phenotypes in genetic association studies will very likely increase the statistical power to identify genetic variants that are associated with complex diseases. In this paper, based on the existing CLC method  and ACATIn this paper, we apply ceCLC to the COPDGene with seven quantitative COPD-related phenotypes. Recent studies indicate that the pleiotropic effects and genetic heterogeneity are common in the COPD comorbid traits and other immune diseases. For example, Zhu et al.  showed eRecent phenome-wide association studies (PheWAS) require more powerful and efficient methods to identify significantly associated SNPs as a large number of phenotypes are collected, the ceCLC method developed in this paper can be applied to PheWAS. However, one limitation of the ceCLC method is that it requires individual-level phenotype data and GWAS summary statistics, where the individual-level phenotypes are used to estimate the trait correlation matrix. Because the individual-level data is often not easily accessible as a result of privacy concerns, we are currently considering a new strategy to extend the ceCLC method applicable to GWAS summary statistics without the requirement for individual-level phenotype data.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.


GPX1 and LGR4 and altogether, are enriched in cancer and oxidative stress pathways. Our findings suggest that placental DNAm could be one of the mechanisms by which maternal obesity is associated with metabolic health outcomes in newborns and children, although further studies will be needed in order to corroborate these findings.Higher maternal pre-pregnancy body mass index (ppBMI) is associated with increased neonatal morbidity, as well as with pregnancy complications and metabolic outcomes in offspring later in life. The placenta is a key organ in fetal development and has been proposed to act as a mediator between the mother and different health outcomes in children. The overall aim of the present work is to investigate the association of ppBMI with epigenome-wide placental DNA methylation (DNAm) in 10 studies from the PACE consortium, amounting to 2631 mother-child pairs. We identify 27 CpG sites at which we observe placental DNAm variations of up to 2.0% per 10 ppBMI-unit. The CpGs that are differentially methylated in placenta do not overlap with CpGs identified in previous studies in cord blood DNAm related to ppBMI. Many of the identified CpGs are located in open sea regions, are often close to obesity-related genes such as  A meta-analysis of pre-pregnancy maternal body mass index (ppBMI) and placental DNA methylation from 2631 mother-child pairs identifies 27 CpG sites associated with ppBMI, providing insight into how maternal obesity could be associated with metabolic health outcomes in offspring. It has been shown that maternal adipokine and insulin signaling in the placenta could contribute to regulate both the vascular development of this organ and the nutrient transport to the fetus, and therefore impact fetal development3. Additionally, it has also been observed that maternal ppBMI is associated with other offspring health outcomes in later life, including increased risk for obesity in children4. Observational studies have suggested links between maternal obesity and long-term risk of coronary heart disease, stroke, type 2 diabetes and asthma in offspring5. Very high maternal ppBMI has also been associated with poorer cognitive performance in children and greater risk of neurodevelopmental disorders6, while there is also preliminary evidence in favor of potential implications in immune and infectious disease-related outcomes3. These associations could be mediated by epigenetic changes, including DNA methylation (DNAm), but the implication of an early epigenetic reprogramming in utero deserves further research7.Higher maternal pre-pregnancy body mass index (ppBMI) is associated with aberrant fetal growth8 has shown that maternal ppBMI is widely associated with differences in cord blood DNAm in the newborn9. However, the authors observed that many of the significant epigenetic effects were modest (<0.2% methylation per BMI unit) and they did not detect enrichment for any particular biological pathway, leaving open questions regarding potential intra-uterine mechanisms that could be affecting the epigenetic profile of the newborn9. In this context, while the epigenetic alterations in cord and peripheral blood have been thoroughly investigated10, the potential impact of maternal ppBMI in placental DNAm remains poorly explored. As far as we know, the most recent studies have performed methylation profiling with methylation arrays or reduced representation bisulfite sequencing in up to 300 term placentas of obese and non-obese mothers12. Although interesting, these studies have yielded a limited number of significant results, probably because of their relatively small sample size.A previous study carried out within the Pregnancy and Childhood Epigenetics (PACE) consortium13 has discovered a placental DNAm signature of maternal smoking during pregnancy that is quite different from what has been observed in cord blood14. Differentially methylated CpGs related to smoking in pregnancy fall within active regions of the placental epigenome, and nearby genes are involved in the response to environmental stressors, regulation of inflammatory activity, and growth factor signaling. The placenta is a transient organ at the maternal-fetal interface, with endocrine and substrate-transport functions, that is sensitive to pregnancy environmental influences – exogenous or endogenous. Maternal pre-pregnancy obesity is often characterized by an adverse metabolic milieu that may alter placental function by increasing oxidative stress, vascular endothelium thickening, and inflammatory lesions in placental tissues11. Altogether, these facts encourage the investigation of the placenta as a putative mediator of maternal obesity and health outcomes in the offspring, specifically through the modification of the placental DNAm landscape.A recently published meta-analysis with 1700 placental samples by the PACE consortiumIn this context, the overall aim of the current analyses was to investigate the association of maternal ppBMI with epigenome-wide placental DNAm in 10 studies from the PACE consortium amounting to 2631 mother-child pairs. We also conducted functional enrichment analyses and comparison of our results with maternal ppBMI-associated cord blood DNAm alterations previously reported by PACE.N = 2631) contributed to the epigenome-wide association study (EWAS) to determine the associations of maternal ppBMI with placental DNAm 16, Study on the pre- and early postnatal determinants of child health and development (EDEN)17, Genetics of Glucose regulation in Gestation and Growth (Gen3G)18, Genetics, Early Life Environmental Exposures and Infant Development in Andalusia (GENEIDA)19, Harvard Epigenetic Birth cohort (HEBC)20, Environment and Childhood Project (INMA)21, The Intrauterine Sampling in Early Pregnancy Study (ITU)22, Markers of Autism Risk in Babies-Learning Early Signs (MARBLES)23, New Hampshire Birth Cohort Study (NHBCS)24, and Rhode Island Child Health Study (RICHS)25. MARBLES was excluded at a later stage as the sample size was too small and results were inconsistent with the other cohorts  was generally self-reported. In those cases where ppBMI was not available, BMI in early pregnancy (1st trimester) was used. For simplicity, we will refer to both of them as maternal ppBMI. In all the analyses performed, we used ppBMI as a continuous variable. The cohort-specific average maternal ppBMI ranged from 22.9 in EDEN (France) to 27.6 in EARLI (USA) (standard deviation-SD = 1.60). In general, 548 (20.8%) and 369 (14.0%) mothers reported overweight (>25 BMI) and obesity (>30 BMI), respectively, while 115 (4.37%) appeared to be underweight (<18.5 BMI). Mean age of the mothers was 30.7 years (SD = 2.9). The distributions of other covariates by cohort are provided in Supplementary Data Maternal ppBMI  , we obtained 27 and 42 CpGs at which maternal ppBMI was significantly associated with placental DNAm in the models adjusted and unadjusted for cell type proportions, respectively. Full results for both models are provided in Supplementary Data vs. 34/42 CpGs presented Cochran’s Q-test p-values > 0.01) and thus, we continued with the results from the fully adjusted model for all downstream analyses.After applying the Bonferroni correction for multiple-testing  and a beta-coefficient of −1.12e-03, meaning that a 10-unit difference in maternal ppBMI is associated with a 1.1% lower DNAm at this specific CpG site. This association was consistent across all cohorts    with 1.8% lower DNAm per 10-unit ppBMI   Fig. . The lar09) Fig. . In turn97) Fig. . The folely Fig. . The rem28 using KEGG, Reactome, Wikipathways and Biocarta reference databases. Two gene-set pathways were significantly enriched , namely small cell lung cancer and oxidative stress-induced signaling pathway .To gain insight into the biological processes that may be captured by placental DNAm associated with maternal ppBMI, we performed gene-set and regulatory enrichment analyses. To this end, first, we annotated CpGs to genes and regulatory elements as explained in the Material and Methods section. Then, we conducted gene-set enrichments for the 26 unique genes annotated to the 27 maternal ppBMI-sensitive CpGs with ConsensusPathDB29, regulatory features from the placenta-specific 15-chromatin state annotations from ROADMAP31, or placenta-specific partially methylated domains32 that contain placenta-specific repressed genes. We did not find any significant enrichment except for CpG island location and features: the maternal ppBMI-associated CpGs were depleted in CpG islands  and highly enriched in open sea regions  , birth length (N = 5), head circumference (N = 3), gestational age  and BW + GA (N = 6)38. Of the total 330 birth outcome SNPs in autosomal chromosomes, 10 BW-associated SNPs were within 0.5 Mb of CpGs that were associated with maternal ppBMI. Therefore, more than a third of the 27 ppBMI-associated CpG sites were within 0.5 Mb of BW SNPs, including cg00423969 and cg14163484 (FER1L5), cg00510149 (IRAG1), cg02286857 (TTC7A), cg15258080 (HK1), cg22673972 (SLC6A6) and cg24893073 (KDM6B) . Thus, we investigated whether ppBMI-associated CpGs were within ± 0.5 Mb (1 Mb window) of single nucleotide polymorphisms (SNPs) that have been associated with birth weight 56. Second, the unavailability of genotype data in some of the participating cohorts did not allow to add genotype principal components to our models and there might be residual confounding by population structure that we did not account for. Third, most of the cohorts were composed by a majority of individuals from European descent, which limits generalizability of our findings to other populations.Regarding limitations, we did not have access to individual data addressing whether each ppBMI measurement was self-reported or taken at the end of the first trimester of pregnancy. Therefore, we cannot use this variable as a covariate nor compare between measurement types. We are very aware that self-reported ppBMI may not be the most accurate measurement for our variable of interest, as may also be the case of the measurement of BMI at the end of the first trimester. However, self-reporting of ppBMI has been shown to be reliable and highly correlated to measured BMI at 12 weeks of gestation 27. DNAm from the fetal-facing side of the placenta was assessed with the Illumina 450 K or EPIC arrays. See Supplementary Note p-values >0.01 were excluded 60 was applied to correct for probe type bias. Cohorts examined their data for batch effects by depicting box-plots that divided the samples into different groups according to suspicious variables, and applied ComBat when applicable; all but one cohort (GENEIDA) identified batch effects and used ComBat to remove this source of variation. Probes that were exclusive for the EPIC array, hybridized to the X/Y chromosomes, cross-hybridizing probes and probes with SNPs at the CpG site, extension site, or within 10 bp of the extension site with an average minor allele frequency > 0.01 were filtered out61. Overall, 419,460 probes were available in the ten participating cohorts to assess placental DNAm. Methylation beta-values were modeled in robust linear regressions considering maternal ppBMI as a continuous variable. Finally, DNAm extreme outliers  were trimmed.All DNAm data processing and analyses were conducted in R 3.3.226. The number of components varied between cohorts and ranged from 2 to 6, maybe because different sampling protocols result in differential heterogeneity across cohorts, or since the approach is data driven, those components could be capturing other major sources of variation in the array data, such as residual technical artifacts. Models for differential DNAm were corrected for the number of surrogate variables minus one to reduce multi-collinearity.Putative cellular heterogeneity was estimated from DNAm data using a reference-free cell-mixture deconvolution method (RefFreeCellMix)62 in R was run to account for potential heteroskedasticity and to test the associations between normalized placental DNAm beta-values at each CpG and maternal ppBMI. Models were adjusted for maternal age, parity, maternal education and maternal smoking during pregnancy. Cohorts ran models both with and without adjustment for RefFreeCellMix cell type proportions. Covariate data are described in more detail in Supplementary Note Within each cohort, robust linear regression from the MASS package27. The meta-analysis was performed independently by two groups to ensure consistent results and identical results were reproduced. We used the Bonferroni adjustment to correct for multiple testing. Secondary analyses were only performed on CpGs that passed the Bonferroni correction, particularly in the RefFreeCellMix-adjusted model. It is worth mentioning that both the meta-analysis and shadow meta-analysis teams performed a general quality control separately prior to the meta-analysis itself, showing consistent results. In summary, we checked that conflictive probes had been removed and drew the cohort-specific qq-plots, as well as the correlation between sample sizes and significant hits across cohorts. Finally, we checked the genomic inflation of the whole meta-analysis and plotted forest plots of the significant hits after leaving one cohort out at a time, to see whether any of the cohorts was guiding the associations. The MARBLES cohort was excluded due to its small sample size and to the fact that forest plots showed inconsistencies compared to the rest of the cohorts , placental germline differentially methylated regions29 and placental partially methylated domains32.We annotated CpGs to their closest genes and to CpG islands with annotations from the Illumina Human Methylation 450 K annotation file, and with several regulatory features using publicly available data: placental 15-chromatin states28 using KEGG, Reactome, Wikipathways and Biocarta as reference databases. ConsensusPathDB performs a hypergeometric test, with a default background equal to the number of ConsensusPathDB entities that are annotated with an ID of the type the user has provided, and participate in at least one pathway. Finally, the program corrects multiple-testing with FDR. Enrichment for TFs was assessed at the gene level with EnrichR using ENCODE and ChEA consensus TFs from ChIP-X database. EnrichR results were ranked using the combined score 63.Over-representation analyses for gene-sets or pathways were performed at the gene level with ConsensusPathDB35. Briefly, we verified the genomic proximity between SNPs from the largest GWAS performed to date on the above-mentioned birth outcomes and our identified CpGs by using the Genomic Ranges package64 in R, within 1 Mb windows (±0.5 Mb) surrounding each of the 367 autosomal SNPs.We assessed the genomic proximity between CpGs identified by our maternal ppBMI placental DNAm EWAS (Bonferroni significant in the cell-type adjusted model) and SNPs previously associated with BW, birth length, head circumference and GA9. As no overlap was found between the hits that passed the Bonferroni correction in each study, we searched for CpGs from the cord blood study present 0.5 Mb upstream or downstream of each of the maternal ppBMI-associated CpGs in placenta (1 Mb windows), by using the GenomicRanges R package, with the aim of finding genomic regions where DNAm was related to ppBMI in the two different tissues.We examined whether maternal ppBMI-associated CpGs in placenta were the same as those previously reported in cord bloodp-value = 1.2e-07, after Bonferroni correction for multiple-testing. Only CpGs below this threshold were taken into account in the downstream analyses. Generally, the statistical methods used in each of the downstream analyses were the ones suggested by the developers of each of the analytic tool implemented. More details in each of the analytical steps included in this study can be obtained in the specific section in Methods, Supplementary Note DNAm data from up to 419,460 CpG obtained with the Illumina 450 K or EPIC BeadChips in 2631 placental samples of the fetal side were normalized with functional normalization and BMIQ, and corrected for batch effects with ComBat if applicable. Afterwards, DNAm was correlated with maternal ppBMI in each of the participating cohorts using robust linear regressions with the MASS package in R 3.3.2. The code to perform the full analysis and the details are publicly available as described in the Code Availability section. The cohort-specific results were then meta-analyzed using the inverse variance-weighted fixed effects method in the GWAMA software, simultaneously in two independent laboratories. Results were fully consistent. Statistical significance was set at a meta-analysis nominal Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Reporting Summary


Panicum virgatum L.) has immense potential as a bioenergy crop with the aim of producing biofuel as an end goal. Nitrogen (N)-related sustainability traits, such as nitrogen use efficiency (NUE) and nitrogen remobilization efficiency (NRE), are important factors affecting switchgrass quality and productivity. Hence, it is imperative to develop nitrogen use-efficient switchgrass accessions by exploring the genetic basis of NUE in switchgrass. For that, we used 331 diverse field-grown switchgrass accessions planted under low and moderate N fertility treatments. We performed a genome wide association study (GWAS) in a holistic manner where we not only considered NUE as a single trait but also used its related phenotypic traits, such as total dry biomass at low N and moderate N, and nitrogen use index, such as NRE. We have evaluated the phenotypic characterization of the NUE and the related traits, highlighted their relationship using correlation analysis, and identified the top ten nitrogen use-efficient switchgrass accessions. Our GWAS analysis identified 19 unique single nucleotide polymorphisms (SNPs) and 32 candidate genes. Two promising GWAS candidate genes, caffeoyl-CoA O-methyltransferase (CCoAOMT) and alfin-like 6 (AL6), were further supported by linkage disequilibrium (LD) analysis. Finally, we discussed the potential role of nitrogen in modulating the expression of these two genes. Our findings have opened avenues for the development of improved nitrogen use-efficient switchgrass lines.Switchgrass ( In the past 50 years, the application of synthetic N fertilizer to farmland has resulted in a dramatic increase in crop yields but with considerable negative impacts on the environment . A largeectively . Therefoectively .Panicum virgatum L.) is a perennial grass native to North America and developed as a potential bioenergy crop due to its high biomass production, ability to grow in marginal land, low input requirements for maintenance, and high cellulose content , has been a powerful tool to dissect the genetic architecture of complex traits . GWAS focajuncea . Studiescajuncea . GWAS anIt has been reported that the most fundamental approach to enhanced NUE cultivar development necessitates plant evaluation under both low and high N conditions . This he–1), while the other with low (0 kg of N ha–1) supplementation in 2019 and 2020. Each of the 330 accessions has four replicates in the field (2 replicates per N treatment), totaling 1,320 switchgrass plants, plusone control AP13 (“Alamo”) grown with 40 replicates (20 at low and 20 at moderate nitrogen), which were arranged in honeycomb design with ∼2.5 m interplant spacing. AP13 is the reference sequenced lowland cultivar, which is broadly used as a reference genome for switchgrass (A highly diverse panel of 331 switchgrass accessions  was plantchgrass . The fieThe dry biomass for each switchgrass accession was quantified as previously described . BrieflyThe following equations were adopted from f = Biomass yield (BMY) of the fertilized plant.BMYu = Biomass yield of the unfertilized plant.BMYs = Nitrogen fertilizer at the given rate.Nvia near-infrared spectroscopy (NIRS) using a FOSS 6500 NIR system . Nitrogen content was measured at two developmental time points during the field-growing season: one in August 2020 at mid-season (M) and the other in December 2020 at the end of season (S). A total of 150 accessions in two replicates in two N treatments and two time points  were used, i.e., 300 plants at mid-season at low N, 300 plants at mid-season at moderate N, 300 plants at the end of season at low N, and 300 plants at the end of season at moderate N were used. The 150 accessions were chosen based on biomass yield data of the 2019 growing season  and senescent (the end of season) stages, respectively. Since we took N samples at two different time points (M and S) from each of the 150 accessions grown at low N condition and moderate N condition, we calculated NRE for low N and NRE for moderate N condition separately.where We evaluated five NUE-related traits. All traits were treated independently. NUE ratio/indices traits were derived prior to calculation of the best linear unbiased predictions (BLUP) to minimize noise. We removed outliers using the median absolute deviation (MAD) method such that any phenotypic values with MAD > 4 from the population median for a particular trait were removed. Variance components were estimated from a mixed linear model where accession and replication were fitted as random effects and were used to estimate broad sense heritability on a line-mean basis as previously described .2 ≥ 0.95, a total of 11,976,627 SNPs were available for the downstream analysis.Methods for SNP variant calling were described previously . BrieflyGenome-wide association tests for all phenotypic traits were performed using the genome-wide complex trait analysis (GCTA) software using the following linear model . Phenotywhere y is a phenotypic vector of size n x 1, with n representing sample size, β is a q x 1 vector of fixed effect that includes the first three eigenvectors from the PCA analysis of the genomic data with its incidence matrix X (an n x q matrix of covariates), u is the p x 1 vector of additive SNP effects with its incidence matrix W (an n x p genotype matrix), α is the n x 1 vector of random effects that include the genetic relationship matrix (GRM), and ε is the n x 1 vector of residual random effects.–8 . Bonferroni assumes each tested SNP is independent, but the presence of LD between SNPs makes that assumption incorrect and overly stringent. Previously, a similar threshold set was being widely used in other species, such as maize and Arabidopsis .Since the Bonferroni threshold for multiple hypothesis correction is too stringent for significant SNPs, the genome-wide threshold of significance in this study was set to 8. × 10bidopsis . Candida2) using plink version 1.9  of peak GWAS SNPs were calculated using squared allele-frequency correlations  was larger compared to dry biomass at low N (0.08–7.77 Kg) . The her2. We found a broad range of NUE in our diversity panel, including negative values. The negative values indicate that the dry biomass at low N is higher compared to the dry biomass at moderate N, and, as such, those lines could be even more effective to explore further as nitrogen use efficient lines. While the heritability of absolute dry biomass at low and moderate traits was found to be high, the heritability of NUE was found to be low (0.39), indicating derived indices, such as NUE, are much more complex and should be influenced much more by the environment and governed by multiple genes . The mean NUE was found to be 0.67 kgkg/mle genes .Regarding the N content in tillers, we found that the mean N content of the switchgrass accessions at the end of season for both low and moderate N conditions  is lower than the mean N content at the mid-season  , indicatWe used two different perspectives to identify the top 10 NUE lines ; (1) accIn Category 2, Accession J477.B was found to be the most efficient NUE accession with fold change of 4.61, followed by J500.B (4.33), J006.C (3.49), J466.B (2.75), and J516.C (2.65) .2. J500.qFDR-values < 0.05) was only included for further explanation  , while an insignificant negative correlation with dry biomass at low N , inferring strong contribution from moderate N toward the plant biomass  . NUE had biomass .r, −0.83) (It is surprising to not see any significant correlation between any of the absolute dry biomass traits or their derivative traits (NUE) with that of absolute N content at mid-season and at the end of season, as well as their derivative traits (NRE) . This mi, −0.83) To uncover the genetic architecture of NUE, dry biomass at moderate N and dry biomass at low N, we explored the natural variation in the 331-switchgrass diversity panel genotyped with 11,976,627 SNP markers. The SNP numbers are after the minor allele frequency (MAF) filtration at 1%. For NRE at low N and NRE at moderate N, we explored the natural variation in 150 switchgrass accessions. We used the mixed linear model using GCTA  to perfoWe identified 32 unique candidate genes from the 10-kb interval, as well as from the nearest left and right from each unique GWAS peak SNPs . The GWAp-value, 5.43E-10), which led to two candidate genes: Pavir.6NG264600 and Pavir.6NG264700 (We found the GWAS peak SNP Chr06N_45879490 (located in Chromosome 06N at 45879490 bp) being significantly associated with the dry biomass at moderate N (NG264700 . Gene PaNG264700 .2) between the GWAS peak SNP and the SNPs across 20 kb (10 kb up/downstream) of the peak SNPs. Emphasis was given between the GWAS peak SNPs and SNPs, residing within the candidate genes within the 20 kb region to better understand the LD association between them. Our LD analysis strongly supported the Pavir.6NG264700 to be a strong candidate gene for dry biomass at the moderate-N condition , Chr06N _45879172 , Chr06N _45879369 , Chr06N _45879412  , with the GWAS peak SNP Chr06N_45879490, suggesting gene Pavir.6NG264700 is worthwhile to explore for downstream analysis as compared to gene Pavir.6NG264600  . The SNPNG264600 . We compp-value, 4.07E-8) that led to the candidate gene Pavir.8KG136200  between the GWAS peak SNPChr08K_12432787 and the SNPs residing within the candidate genes within the 20 kb region (10 kb up/down stream of GWAS peak SNP) to better understand the LD association between them. Our LD analysis strongly supported Pavir.8KG136200 to be a strong candidate gene for NUE    residing2, 0.78) , 7. We aCCoAOMT and AL6 as important candidate genes to regulate NUE and the potential role of N treatments in modulating the expression of these genes.Understanding the genetic basis of NUE is the key for developing nitrogen use-efficient switchgrass lines. To our knowledge, there is no documented study on the genetic basis of NUE in field-grown switchgrass at two contrasting nitrogen fertility treatments. Here, we studied the genetic basis of NUE in switchgrass. Our results provide interesting insights into the genetic architecture of NUE and its relationship among the various NUE-related traits. We highlighted We found the mean dry biomass for the switchgrass diversity panel grown in low- and moderate-N conditions to be similar . One of We have found a range of both positive and negative values when calculating NUE and NRE in our study , 2. Thescaffeoyl-CoA O-methyltransferase (CCoAOMT) in rice (LOC_Os08g38900) and S-adenosyl-L-methionine-dependent methyltransferases superfamily protein in Arabidopsis (AT4G34050) (We found GWAS peak SNPChr06N_45879490 significantly associated with dry biomass at moderate N that led to candidate gene Pavir.6NG264700 . Pavir.64G34050) .CCoAOMT is one of the key enzymes reported to be involved in the biosynthesis of monolignols  grown at contrasting N treatments could be identified with significant differential expression levels of CCoAOMT between the two N contrasting treatments, supporting the dynamic role of N in regulating the expression of CCoAOMT , Cinnamate-4-hydroxylase (C4H), 4-coumarate CoA ligase (4CL), caffeic acid O-methyltransferase (COMT), and CCoAOMT were downregulated in response to high N fertilization while upregulated in the N-limiting condition  ; all of which are critical to root hair elongation  and 2G02470) . A limitbidopsis . The stusis  and gene encoding root hair elongation (AL6), regulating the natural variation of NUE and related traits in switchgrass. We also highlighted the N fertilizer application potentially plays a role in modulating not only the biomass quantity but, more importantly, the biomass quality and composition. We identified the top ten nitrogen-efficient switchgrass accessions and uncovered the relationship among the various NUE-related traits. Our findings provide exciting possibilities to explore the underlying biological mechanism of NUE and to use marker-assisted selection and GWAS-assisted genomic selection in developing nitrogen use-efficient switchgrass cultivars.We have shown here that, by targeting NUE using a holistic approach, we can dissect its genetic architecture to identify novel SNPs and genes. We found genes related to lignin biosynthesis (The original contributions presented in the study are included in the article/VS performed the experiments, processed and analyzed the data, and wrote the manuscript. HC performed the BLUP and GWAS analysis and assisted in writing the methodology. DK performed GWAS model optimization. YX assisted in writing and revisions to the manuscript. LH, CP, and BW participated in the field experiments and data collection. XW and MS performed nitrogen quantification analysis. DJ performed statistical and computational analysis. RM and MM designed the experiments, participated in result interpretation, supervised the work, and assisted in writing and revisions to the manuscript. CS conceived the study and its coordination, acquired funding, and assisted in interpretation of the results and the revisions to the manuscript. All authors contributed to the text and approved the final manuscript.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.