Using family data, we were able to further explore potential pathways of direct genetic, indirect genetic (through passive gene–environment correlation) and confounding effects (such as population structure and assortative mating). In 5648 Swedish twins, we found PGSrhythm to predict not only rhythm discrimination, but also melody and pitch discrimination , as well as other music-related outcomes (p < 0.05). In contrast, PGSrhythm was not associated with control phenotypes not directly related to music. Associations did not deteriorate within families (N = 243), implying that indirect genetic or confounding effects did not inflate PGSrhythm effects. A correlation  between musical enrichment of the family childhood environment and individuals' PGSrhythm, suggests gene–environment correlation. We conclude that the PGSrhythm captures individuals' general genetic musical propensity, affecting musical behavior more likely direct than through indirect or confounding effects.To further our understanding of the genetics of musicality, we explored associations between a polygenic score for self-reported beat synchronization ability (PGS Twin modelling studies have also shown that there is considerable overlap between the genetic variants underlying such music-related phenotypes , not only between different specific music skills, but also between music skills and other music-related traits, like music practice or listening to music9. This suggests that there are generalist genes as well as specialist genes underlying variation in music-related traits.Listening to music or engaging actively in music by singing or playing an instrument, is an important part of life for many. Twin research on music-related behavior shows that there is a substantial genetic component to variation in musical engagement, as well as to musical ear, i.e. pitch, melody and rhythm discrimination skills, with heritability estimates ranging between 13 and 86% with an average around 40–50%10 for review), which are known to suffer from replication problems, and are hampered by small sample size11. Recently, a genome-wide association study (GWAS) in a well-powered sample of 606,825 individuals identified sixty-nine genetic variants that influence variation in self-reported beat synchronization ability, assessed with the question: 'Can you clap in time with a musical beat?' Yes/No12. The variance explained by the aggregate of these genetic effects  was ~ 13–16%. It is not surprising that this estimate is lower than heritability estimates of musical rhythm abilities reported in twin studies (~ 50%), as many common genetic variants with very small effects influence human behavior, requiring extremely large GWAS samples to detect all. In addition, potentially influential rare genetic variants are not yet well captured by current microarray chips13. Therefore, up-to-date GWASs capture only a fraction of the expected genetic variation underlying behavioural traits (a phenomenon referred to as 'missing heritability'). Furthermore, although the single item in this GWAS is obviously limited as a measure of musical aptitude, the authors performed extensive validations of this item, showing that it was well-correlated with accuracy in tapping to the beat of musical excerpts and with other related phenotypes such as rhythm perception, a multi-item rhythm questionnaire, and the Goldsmiths test of musical sophistication . Most importantly, their GWAS was the first well-powered gene-finding investigation of a music-related trait, and thus allows us to estimate and investigate individuals' genetic rhythmic propensity using a polygenic score (PGS) approach. This approach can provide additional insight into the genetic architecture underlying musical ability, directing future research when disentangling gene–environment interplay underlying musicality and music acquisition.Which particular genes are relevant for music skills is not yet well understood, with the limited existing research largely being based on candidate gene and linkage studies , and objectively measured rhythm discrimination ability, as measured by a well-validated music discrimination test1. We will further compare this relationship to the effect the PGSrhythm has on other music skills and phenotypes such as pitch and melody discrimination, motor timing, music practice, music listening, the experience of flow during musical activities and achievement in music. As a control, we investigate associations between PGSrhythm and phenotypes not directly related to music, including general intelligence, sport practice, flow in non-musical activities, and achievement in non-musical domains.Over the past decade, post-GWAS analyses were developed to further study genetic variants discovered by GWASs and their associations. In the PGS approach, the estimated effect sizes of genetic variants identified in the discovery sample used in the GWAS, are used as weights in an independent sample, to calculate a score for each individual, representing his or her genetic propensity for the trait under studyrhythm, we aim to identify the pathways through which the PGSrhythm affects music skills. Genetic variants can relate to a trait through direct, indirect or confounding effects15. A direct genetic effect is when genetic variants directly affect the trait16. Importantly, this does not mean that the pathway necessarily needs to be simple or 'straight' in mechanistic terms15. A different scenario, however, is when a genetic variant affects a trait in an environmentally mediated way, for example, through its effect on parental behavior. This would be an example of an indirect genetic effect, i.e., the effect of a genetic variant in one individual on the trait of another individual through the environment15. In this case, genes that are shared between parents and children, tend to cause the parents to provide their children with a family environment of a particular type. This chain of events will result in a correlation between a genotype and the family environment, a phenomenon called passive gene–environment correlation (prGE)17. For example, parents might have provided a musically enriched family environment, due to their genetic predisposition for music; this environment may facilitate music skill acquisition in their offspring. Individuals with certain genotypes will therefore more often also be exposed to facilitating environments, which can lead to an overestimation of the direct effects of an individual's genetic variants in GWASs and therefore of the PGS. Evidence for this overestimation of effects has been found in previous within-family GWASs that found effects of genetic variants to be much smaller within families19. There are other confounding effects that can inflate the association between a PGS and the trait of interest, such as population stratification and assortative mating. Population stratification occurs when there are differences in allele frequencies between (sub)populations, due to systematic differences in ancestry. This can lead to spurious effects of genetic variants causing inflated estimates of the PGS prediction20. Assortative mating refers to a type of non-random mating, namely when partner bonds are established based on similarity on particular phenotypes, like for example, playing a musical instrument. This can also cause an overestimation of the direct effects of genetic variants21. In sum, over and above direct genetic effects, indirect genetic effects as well as population stratification and assortative mating can inflate the effect of individual's genetic variants in GWASs and therefore of the PGS on traits of interest21.Besides investigating the predictive value and specificity of the PGS23. Dizygotic twins, just as regular siblings, differ at random in the genetic variants they receive from their parents during meiosis, while sharing on average 50% of their genes. Importantly, as the twins of a pair have the same parents, within-pair effects are not biased by population stratification or assortative mating24. Furthermore, dizygotic twins share their family environment and have the same age. Therefore, comparing the effect of a PGS on an outcome, between- and within- dizygotic twin pairs, is a way to control for both prGE through the shared family environment, and confounding effects of assortative mating and population stratification. Specifically, if the effect of the PGS on an outcome is larger in a sample of unrelated individuals than within dizygotic twin pairs, this indicates that the effects of the PGS might be inflated by indirect genetic or confounding effects20. This validated 'within-family PGS' method has recently gained popularity in the field of behavioural genetics. Even so, the idea of direct and indirect genetic effects is not new and has been described and studied with various different designs long before the development of PGSs, for example, in the field of evolutionary quantitative genetics (see for example26). Lastly, additional analyses can shed light on whether it is more likely that indirect genetic effects or confounding effects are at play. For example, a relationship between a measure of musical enrichment of the family childhood environment and the PGSrhythm would suggest gene–environment correlation (while the absence of a relationship would not exclude its presence). A significant difference from the expected correlation of 0.50 for the PGSrhythm between dizygotic twins could suggest confounding effects such as population structure and assortative mating27.With the use of genetically informative family data, it is possible to disentangle the PGS' direct and combined indirect genetic (referring to prGE) and confounding effects on a traitrhythm), and a well-validated measure of rhythm ability, other music skills (pitch and melody discrimination), motor timing and music-related phenotypes such as music practice and listening behaviour, musical flow experiences and lifetime achievement in music. As control phenotypes, we included general intelligence, sports practice, global and work-related flow experiences as well as dance-, science- and writing-related achievements. We further explore the potential pathways of direct, indirect and confounding effects of the PGSrhythm on music skills using within-family analyses, and examine the possibility of gene–environment correlation and assortative mating.In a large genetically informative sample of 5648 Swedish twins, we investigated the association between the PGS of self-reported beat synchronization ability , flow proneness and achievements. More details on these phenotypes, their distributions and heritability estimates can be found in earlier published studies using this sample see32. In 2019 and 2020, individuals from the STR, who provided saliva samples between 2006 and 2008, were genotyped. After quality control, genotype data were available for 8,343 individuals from the STAGE cohort, of which 5648 had also completed the web survey in 2012/2013. Informed consent was obtained from all participants. The study and analyses of biomarkers were approved by the Regional Ethics Review Board in Stockholm . The computations and data handling were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Uppsala partially, funded by the Swedish Research Council through grant agreement no. 2018-05973. All research methods were performed in accordance with relevant guidelines and regulations.The Study of Twin Adults: Genes and Environment (STAGE) is a cohort of the Swedish Twin Registry (STR) which includes approximately 32,000 adult twins born between 1959 and 1985Participants answered whether they ever played a musical instrument (including singing) and, if so, at what age they started.Total lifetime music practice of the participants was estimated based on questions about start and ending age of playing music and on the participant's indication of the number of hours per week they practiced  during four age intervals . From these answers and information on the numbers of years they played music, we calculated an estimate of total lifetime amount of music practice.Individuals were asked how many hours per week they listened to music on average since they were 18 years old until the measurement.1. The SMDT was administered online and includes three subtests: a pitch, melody and rhythm discrimination test. The pitch (27 items), melody (18 items) and rhythm (18 items) discrimination scores were standardized before calculating a mean overall musical aptitude score. The SMDT has been shown to have good reliability and internal consistency  and inter-correlations between the discrimination scales ranged between 0.27 and 0.411.Musical aptitude was measured using the Swedish Musical Discrimination Test (SMDT)33. In the ISIP task, participants tap in synchrony with a regular, auditory metronome, followed by a phase where the participant has to continue to tap self-paced without a metronome. After an initial training trial, the participants completed six experimental trials with varying inter-onset intervals . The motor timing score was calculated as the mean coefficient of variation during the self-paced phase across the six trials. We reversed the score so that a higher score indicated a more accurate performance. For detailed information on the task and scoring see Mosing, et al.30.Motor timing (or finger tapping) was measured using the isochronous serial interval production (ISIP) paradigm36. This instrument is a self-report measure of achievement in the visual, music, dance, writing, invention, science, and theater domains using a seven-point scale. As an example, items to rate achievement in music range from 1 ‘I am not engaged in music at all’ via 4 ‘I have played or sung, or my music has been played in public concerts in my home town, but I have not been paid for this’ to 7 ‘I am professionally active as a musician and have been reviewed/featured in national or international media and/or have received an award for my musical activities’. Apart from music, we included dance, which also might be associated with rhythm ability, as well as writing and science as control domains.Achievement was measured with an adapted and translated version of the Creative Achievement Questionnaire (CAQ)2, i.e. using the first principal component derived from the following family environment measures: (1) the number of records available in the family home, (2) how many individuals in the twin’s environment played an instrument, (3) how often they visited concerts and (4) whether or not they were offered music education before the age of 12.The level of musical enrichment of childhood environment was estimated as previously by Wesseldijk et al.37. The test is 25-min long and consists of 24 multiple-choice items, which are scored either one (correct) or zero (incorrect or missing response). A sum of the 24 item scores was used as a measure of the participant's psychometric intelligence.Intelligence was measured with the Wiener Matrizen Test (WMT), a visual matrix test similar to Raven’s standard progressive matrices38. The SFPQ is a self-report measure of the frequency of psychological flow experiences in the domains work, leisure, maintenance and music. All four sub-scales consisted of seven items each rated on a 5-point Likert scale . A global flow proneness was calculated as the mean score of flow proneness in work, leisure and maintenance. We included besides flow proneness in the musical domain, global flow proneness and flow proneness in the domains of leisure and work for comparison. See Ullén et al.38 for a more detailed description of the scale.Flow proneness was measured with the Swedish Flow Proneness Questionnaire (SFPQ)N = 86). The remaining samples were processed using the Ricopoli pipeline for quality control39, which led to the exclusion of 13 more samples. Quality control of the single nucleotide polymorphisms (SNPs) led to the exclusion of 581 SNPs, resulting in 491,839 included genotyped SNPs. These SNPs were imputed using the Haplotype Reference Consortium panel (HRC1.1)40. PLINK 2.0 sets were generated, and a light post-imputation quality filter was applied, leaving 9,141,508 markers. This resulted in a total sample size of 8343 twin individuals (2554 complete twin pairs including imputed monozygotic twin pairs). Of these, 5648 individuals  provided information on music variables. Of which 1184 same-sex and opposite-sex dizygotic complete twin pairs provided genetic information, and 243 dizygotic and opposite-sex dizygotic complete twin pairs provided both genetic and music information.A total of 8442 twins from the STAGE cohort were genotyped using the Illumina Infinium assay (chip GSAMD-24v1-0_20011747_A1). Before standardized quality control, samples were excluded in case of discrepancies in observed sex and relatedness (2 < 0.1). The first 10 PCs were included as covariates in further PGS analyses, to control for potential confounding by population stratification, i.e., when there are systematic differences in allele frequencies between subpopulations41.A principal component analysis (PCA) was performed in the full study sample  to generate ancestry covariates. PLINK 1.9 and 2.0 were used to extract the first 20 principal components (PCs) based on common independent genotyped markers  > 0.05, pairwise R43. We generated PGSs based on summary statistics from the large GWAS on self-reported beat synchronization ability in N = 606,825 individuals of European ancestry . Polygenic scores are a weighted sum of each individual’s trait-associated alleles at each SNP multiplied by that SNP’s estimated effect size as detected by a GWAS14. The effect sizes were first re-estimated using the summary-data based on the best linear unbiased prediction (SBLUP) approach45. This approach computes effect sizes with best linear predictor properties that account for linkage disequilibrium between SNPs. As a reference sample for the linkage disequilibrium, a random sample of 11,064 unrelated individuals was extracted from a set of 1,246,531 HapMap 3 SNPs that passed quality control in the UK Biobank sample46. PGSs were generated, based on these re-estimated effect sizes, for the 8,343 samples that passed quality control, using PLINK 1.9. Scores were then imputed for monozygotic co-twins (N = 1350) and merged with the music data and the top 10 of the 20 obtained PCs from the PCA.To create polygenic scores (PGSs) for the 8343 individuals, we extracted a restricted set of common, well-imputed 1,265,094 HapMap 3 SNPs from their genotype dataAll analyses were performed in STATA. All variables, with the exception of sex and age and the PCs, were standardized.rhythm) and objectively measured rhythmic ability, we performed a linear regression analysis, with the PGSrhythm, the top 10 PCs to control for ancestry structures, sex and age as independent variables and the rhythm discrimination score as the dependent variable. Further linear regression analyses were performed to explore the effect of the PGSrhythm on other music-related outcomes, namely pitch and melody discrimination, general musical aptitude, motor timing, amount of music practice, achievement in music, flow proneness in the musical domain, and start age of playing music. In addition, to investigate how specific PGSrhythm predictions were to the music domain, we investigated associations between the genetic variants underlying rhythm discrimination and control phenotypes, i.e. general intelligence, amount of sport practice, flow proneness in non-musical domains and achievements in dance, writing, and science. To estimate an effect size of the PGSrhythm (variance explained R2) for each dependent variable, we subtracted the total R2 of the model without the PGSrhythm from the total R2 of the full model (with PGSrhythm). All regression analyses included sex, age and the top 10 PCs as covariates. We always corrected for relatedness in the twin sample by using the robust standard error estimator for clustered observations48. All analyses were first performed including imputed genetic data for monozygotic twins (N = 5648) and then repeated with only one of the monozygotic twin members included (N = 4787).To investigate the relationship between the genetic variants underlying self-reported beat synchronization ability  of the sample. We applied a recently developed and validated random intercept mixed-effects model on the dizygotic twin data16. In this model, two fixed effects are fitted to partition the total effect of the PGSrhythm on the outcome into a between-family (16)To explore the potential pathways of direct, indirect and confounding effects of the PGSY denotes the outcome and PGS the polygenic score, i = {1,2} corresponds to the individual twins that are clustered within family j, and j. Therefore, the between-family effect represents the expected change in outcome Y given a one unit change in the family PGS average, while the within-effect represents the expected change in outcome given a one unit change in the difference between the individual PGS and the family average PGS. If the between-effect of the PGS on the outcome is significantly larger than the within-effect, indirect genetic or confounding effects may be inflating the effect of the PGS20. If the within- and between-effect are similar in size this provides evidence for direct effects of the measured genetic variants. Including both the between- and within-family effect in the same model, causes adjustment and independence of the two individual estimates, and offers the possibility to compare the confidence intervals for significance. See Selzam et al.16 for more details about the model. Only complete dizygotic twin pairs were included in these analyses (N = 243). The analysis included sex and 10 PCs as covariates. We repeated this analyses with musical aptitude as an outcome and we repeated the analysis only in same-sex dizygotic twin pairs (N = 128).rhythm on outcomes, namely gene–environment correlation and population structure confounding or assortative mating, we performed two additional analyses. We calculated the correlation between an index of the level of musical enrichment of the family childhood environment and the PGSrhythm. A significant correlation would indicate that an individual’s genetic make-up (in terms of rhythmic ability) is related to their (perceived) childhood environment, which could be interpreted as further support for gene–environment correlation. We also tested for the potential presence of confounding, such as assortative mating or other types of population structure confounding, by calculating a correlation between the dizygotic twins for the PGSrhythm. If 0.50 is not included in the confidence interval of the correlation, this would indicate a significant difference from the expected genetic correlation of 0.50 for the PGSrhythm between siblings.To further investigate possible indirect or confounding effects of the PGSβ = 0.11, p < 0.001, R2 = 0.01). Notably, effects for melody discrimination , pitch discrimination , and the overall musical aptitude score  were also significant, and nominally higher than for rhythm discrimination. The PGSrhythm also significantly predicted motor timing  and all other music related outcomes . Analyses in the sample excluding imputed monozygotic twin data were nearly identical , supporting the presence of gene–environment correlation. In addition, the PGSrhythm correlation between dizygotic twin members was slightly higher than expected at 0.54 . Note that this trend was only borderline significant, with the confidence interval including 0.50.There was a small but significant correlation between the level of musical enrichment of family childhood environment and individuals' PGSrhythm) for self-rated beat synchronization ability on objective rhythm ability, other musical skills and music-related outcomes, as well as on a set of control outcomes, not directly related to music. Our results support the validity of PGSrhythm, in that we indeed found an association between the genetic variants underlying self-reported beat synchronization ability and objectively measured rhythm discrimination, aligning with phenotypic associations reported in Niarchou et al.12. Moreover, PGSrhythm did not only predict rhythm skill but tapped broadly into music-related phenotypes. Interestingly, PGSrhythm thus predicted melody discrimination, pitch discrimination and the overall musical aptitude score as well as it predicted rhythm discrimination, if not better. Further, as expected, the PGSrhythm most strongly predicted individual's motor timing skills, converging with neuroscience findings on the role of the motor system in beat synchronization49. In addition, associations with PGSrhythm were also significant for other music-related traits, such as total amount of music practice, music listening behavior, achievement in music, flow proneness in the music domain, as well as achievement in dance, a domain in which one would expect enhanced sensitivity to rhythm, and to music more generally.We investigated the predictive value of a polygenic risk score  and creativity (in writing and science); nor with engagement in another control domain (sports). Overall, the data thus indicate that the PGSrhythm to a large part captures generalist genes underlying the music domain, rather than specialist genes only relevant to rhythmic ability. In other words, the underlying genetic variants essentially appear to tap into a complex of music-related traits that include overall musicality as well as a tendency to enjoy and engage with music, rather than only specifically reflecting a genetic predisposition to rhythmic propensity.Furthermore, discriminative validity of the PGSrhythm was based, identified genetic variants that are associated with whether individuals think they can clap in time with a musical beat or not. Although carefully validated, this binary self-report of beat synchronization ability is a very simplistic and minimal phenotype, as it is a broad 1-item yes/no question that is also likely to capture variation in general musical ability  and we cannot rule out some degree of rater bias in the GWAS sample. Niarchou and co-authors (2021) demonstrated that their derived polygenic score for beat synchronization was related to broad musician status in a health care context (OR = 1.33); however, no other musical phenotypes were available in their cohort to compare and determine specificity of the PRS12. It would be expected, and has been shown for other traits like depression50, that a risk score derived from a GWAS on a more specific, reliable, and objective measure of music skill would show higher discriminant validity for different music abilities . Indeed, as mentioned in the introduction, using twin modelling in the same sample, we have previously found evidence for both specific genetic influences  on rhythm, pitch, and melody discrimination51, and on perceptual and motor aspects of rhythmic skill31, as well as factors shared between the different musical traits . This lends further support for the notion that the PGSrhythm captures more music generalist genes, rather than the genes specific to rhythmic ability.As already touched upon in the introduction, the discovery GWAS on which the PGS52. Conceivably, the PGSrhythm thus taps into genes that pleiotropically influence a whole complex of music-related traits, which facilitate expertise acquisition within music and music-related domains, and include abilities as well motivational factors relevant to musical expertise .The present findings are in line with recent empirical findings and models of expertise, which propose that a high level of domain-specific skill is associated with clusters of relevant traits across different modalities of individual differences, including abilities, as well as e.g. personality, physical trait, and interestsrhythm affects music skills—direct, indirect and confounding effects—using a within-family design. If a PGS between unrelated individuals has a larger impact on a trait than the PGS has within dizygotic twin pairs, indirect genetic or confounding effects may be inflating the PGS effect20. As mentioned in the introduction, among indirect genetic effects are PGSrhythm effects that are mediated by the family environment due to passive GE correlation (prGE). However, we did not find evidence for that indirect (that is prGE) or confounding effects  inflated of the association between PGSrhythm and musical aptitude and rhythm discrimination; specifically, the effects of the PGSrhythm on music skills were not significantly lower within dizygotic twin pairs. Previous studies have found that for several traits related to socio-economic status , indirect genetic effects and passive gene–environment correlations do inflate the effects of PGSs, whereas this appears not to be the case for traits unrelated to educational attainment19. It could be that rhythm discrimination skills are less associated with educational attainment than other music-related behavior, like musical engagement.We further explored potential pathways through which the PGSrhythm. This provides evidence for a role of gene–environment correlation for music skills, as individuals' genotypes correlate with their family environment. Although our within-family analyses suggest that prGE may not be relevant for the effect of the PGSrhythm on music skill, this correlation could reflect reactive or active gene–environment correlations. Reactive gene–environment correlation is when different conditions are evoked from the environment depending on genetically influenced phenotypes of the individual , and active gene–environment correlation will occur when genetic factors influence an individual’s tendency to actively search out and create specific environmental conditions. Neither of these forms of gene–environment correlation can be disentangled with a within-family design.We found the level of musical enrichment of the family childhood environment to be associated with individuals' PGSrhythm correlation of exactly 0.50 between dizygotic twin members being the lower limit of the confidence interval . Assortative mating has been established for traits like height, body mass index53, educational attainment54 and psychiatric problems56, but has also been indicated for personality traits and values57. Preferences for similarities with a mate, including music and art preferences has also been reported58. Although it seems plausible that assortative mating could occur based on levels of musicality or a tendency to enjoy and engage with music, Okbay et al.27 recently showed for educational attainment that an inflated mate-pair PGS correlation most likely reflects other residual population structure effects or possibly more complex mating patterns rather than assortative mating. As the within-family analyses did not indicate an inflation due to prGE or confounding effects (which would include assortative mating) of PGSrhythm on music skills and the PGSrhythm has a relatively low prediction accuracy compared to the PGS of education attainment (which is based on a much larger discovery GWAS), the observed inflation of PGSrhythm correlation between dizygotic twins does not lend strong support for the presence of assortative mating effects.We found a borderline significant trend for potential confounding effects, such as assortative mating or residual population structure effects, with a PGSrhythm for music outcomes is small. This is in line with PGS studies on other phenotypes. When the sample size of the GWASs increase, prediction accuracy of PGSs is expected to also increase with an expected r2 of h2snp/(1 + (M/N × h2snp)), where h2snp is the heritability captured by the GWAS, M is the number of independent genetic variants and N is the discovery sample size13. Therefore, even larger sample sizes and better measures of (objective) music skills will surely be needed to increase the predictive value and accuracy of a musicality genotype in the future. A methodological challenge is that this requires harmonization of music related phenotypes across multiple genetically informative samples. Further, the availability of family data allowed us to explore potential pathways of direct genetic, indirect genetic and confounding effects of the genetic variants associated with self-reported rhythm. It is important to note that one should be careful when trying to isolate and interpret 'pure' direct genetic effects with sibling analyses, as measurement error due to genetic nurture effects can attenuate the associations59. Additionally, it is yet unknown whether our results apply to rare genetic variants, since the GWAS and our polygenic scores are based on common genetic variants only. Lastly, findings from the within-family analyses are based on only complete dizygotic twin pairs, which resulted in a much smaller sample of 243 twin pairs. Lack of power could therefore cause a failure to detect a difference between within- and between-family effects and therefore we may have missed possible indirect and confounding effects of the PGSrhythm.This is the first study to investigate the validity and specificity of the self-reported rhythm genotype in a large target sample of individuals that had completed well-validated music discrimination tests and a rich array of questionnaires about musical engagement. Moreover, it is the first study that investigates the genetic basis of music skills using a polygenic score approach, which is a substantial improvement to candidate gene approaches. However, despite using a powerful discovery GWAS and a large genetically informative sample, variance explained by the PGSrhythm predicts overall musicality as well as a tendency to enjoy and engage in music including achievement in dance. The PGSrhythm affects music skills and music-related behavior more likely through direct genetic effects rather than through genetic effects mediated by the family environment or confounding effects. Further, we found evidence for gene–environment correlation for music-related traits and the potential presence of assortative mating for self-reported beat synchronization ability. In this study, we showed that a polygenic score based on a GWAS that had been conducted on a minimalistic and broad music-related item can serve as a valid proxy for individuals' general genetic musical propensity, tapping into the genetic architecture underlying the broader music domain. Overall, our findings show that this PGSrhtyhm can reliably be used to further disentangle gene–environment interplay underlying the broader musical trait complex, but it also lends hope that, as more genetically informative samples with minimal musical phenotyping become available, researchers in this emerging field may be able to join forces for a GWA-meta-analysis to identify additional generalist music genes and enhance our understanding of the genetic architecture of musicality.To conclude, we investigated the validity and specificity of a self-reported rhythm genotype using the PGS approach. Our findings showed that the PGSSupplementary Table 1.


We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect.Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS. Genome-wide association studies have identified tens of thousands of genetic variants associated with complex traits. An ever increasing number of associated variants are shown to affect multiple traits, a phenomenon known as pleiotropy. We propose a method that leverages this genetic architecture and uses summary statistics to perform an omnibus association test between one genetic variant and a set of traits. Simulations show that the method properly controls for type-I errors and increases statistical power. In addition to a powerful omnibus test, we also enable a per trait interpretation of the associations by extending the m-value framework to account for the correlation structure between traits. This framework enables a significant increase in the identification of per trait effects. Genome-wide association studies (GWAS) have been instrumental in identifying genetic variants associated with complex traits –3. As a We hypothesize that because variants often affect more than one trait, we can leverage this pleiotropy to jointly analyze multiple traits. This would potentially increase statistical power and identify variants with even weaker effect sizes. Following this intuition, there have been many approaches for performing association tests using summary statistics across multiple traits –26. WhilIn this paper, we propose an alternative framework with a two step procedure. First, all traits are jointly analyzed to produce one p-value for each variant. If this p-value is significant, it suggests that the variant is associated with one or more of the traits. To accomplish this first step, we develop an efficient method called pleiotropic association test (PAT) which leverages the estimated genome-wide genetic correlation between the traits to improve power and uses null simulations to accurately calibrate p-values. PAT also utilizes importance sampling to allow for estimation of significant p-values efficiently. The second step builds upon an interpretation framework first developed in the context of meta-analysis, m-values, to compute the posterior probability that a variant is associated with each trait . We exteIn simulated data reflecting estimates of genetic and environmental covariance between real UK Biobank traits, we find that PAT is able to correctly control for false positives and increase power to identify novel associations. In comparisons to three multi-trait methods, MTAG, HIPO and ASSET, PAT has a 15.3% increase in the number of associations over the next best method , 25, 28.g is associated with a quantitative trait y. Let y and g be measured for N individuals where gj ∈ {0, 1, 2} is the minor allele count for each individual j. The column vector g is then standardize according to the population proportion of the minor allele p where 2p is the mean and 2p(1 − p) is the variance of g. This standardized column vector x is defined as follows y is normally distributed such that μ is the mean and β is the effect size of the variant x on the trait y and the error e follow the standard normal  and with genome-wide association studies using millions of variants, Σg + Σe ≈ Σe under the polygenic model. While a valid model for association testing, distinguishing between different configurations of Σg(c) to calculate the m-value is very difficult. Therefore, we scale Σg and the resulting Σg(c) by randomly selecting one associated variant per 100KB region for a total of k variants. We then perform a grid search for Q ∈  and retain the value of Q which maximizes the likelihood function as shown below:This is necessary because e + Σg). With this assumption in mind, PAT performs a likelihood ratio test (LRT) between two proposed MVN distributions. The null hypothesis is Σg = 0; therefore, the summary statistics for one variant, S = {s1, …, sT} has the following distribution: Here we introduce PAT (pleiotropic association test) which takes in GWAS summary statistics measured for T traits and assumes each variant is drawn according to the multivariate normal (MVN) distribution: g ≠ 0), PAT models the genetic effect size according to the polygenic model and assumes the standard genetic correlation structure between traits We are sorry that we cannot be more positive about your manuscript at this stage. Please do not hesitate to contact us if you have any concerns or questions.Yours sincerely,Michael P. EpsteinAssociate EditorPLOS GeneticsDavid BaldingSection Editor: MethodsPLOS GeneticsComments to the Authors:Reviewer's Reviewer #1: ## OverviewThis work introduces a new likelihood-based approach, PAT, for GWAS summary statistic data to test the global null hypothesis of no association across multiple traits. PAT uses estimates of the genetic covariance from cross-trait LD-score regression to calculate a likelihood ratio test statistic where the p-values are estimated using a computationally efficient approach. The authors find that PAT makes overall more discoveries compared to MTAG/HIPO on simulated and real data. In addition to PAT, the authors calculate posterior probabilities for each trait to help determine which traits may be driving statistical significance by extending a previous quantity called m-value.## Major comments- Can the authors make the software/reproducible code for the analysis/simulations available?- There should be a set of simulations exploring the impact of the environmental correlation among traits since PAT’s improvements seem to stem from low environmental correlation (or is it because height has a negative genetic correlation with the other traits?). In either case, I think this should be explored a bit more in the paper. For example, varying the proportion of traits with no environmental correlation from 0, 1/4, 1/2, 3/4, 1 and the magnitude of the correlation . I think this is important to include as a motivator for PAT and the prevalence of such analysis in practice. It could also be worth exploring the direction of the genetic correlations as that may be important in the method comparisons.- If I interpreted it correctly then the authors are determining statistical significance in HIPO if any component passes the threshold. However, with eigenvectors, the signal can get spread out across multiple components. I suspect this is happening in the simulations/real data because height is negatively correlated with the other traits. Can the authors combine the p-values across these components and then generate an empirical null similar to PAT to estimate empirical p-values. An initial thought could be to combine via Fisher's method and then probit-transform this p-value into a z-value. You can then estimate the empirical null for this statistic in a similar way as PAT. This is probably a better comparison to test the global null that leverages all the information across the components.- Shouldn't the estimated number of causal SNPs be different for each entry in the genetic covariance for the m-value optimization?- Does the calculation of m-values have to take into account the selection process ? So the actual distribution in the equation is not Gaussian but instead a truncated Gaussian?## Minor comments- Can the authors also include the computational time for each method in Table 1?- m-values are a simple and effective method to help determine important traits. The discussion in the main text about m-values/z-values and the corresponding threshold of 0.9 was a bit confusing. m-values are a Bayesian quantity and the threshold is somewhat arbitrary in practice? The quantity itself is just a measure of the strength of evidence? If so, I think it should be made a bit clearer  in the main text.- It would be useful to add a couple sentences in the methods section about choosing r for the importance sampling algorithm (and then reference the section in the supplement).## Typos- Double check the equations: there are small math mode typos across document - Page 16: In the regression model, e is the random error not the residual.- Figure 2: Might want “s_1”, “s_2” to be in math mode. Also, might want to reorder the labels on figure legend so “No method” is on the bottom/top.- Page 7/33: Supplementary Fig 3. -> Fig in main text- Page 2: “Fiallly,” -> “Finally”Reviewer #2: The authors presented a powerful new method for multi-trait genetic association analysis, and a modified m-value for per-trait interpretation. In general, I found the method interesting, and the paper clearly written. However, I have the following comments:Table 2 compared the number of per-trait associations discovered by PAT and HIPO using m-values and MTAG using p-values. What is the p-value threshold for MTAG? Since p-values are not directly comparable to m-values, the authors need to justify the choice of p-value that is comparable to m-value>0.9. I would also suggest using precision-recall curve to compare the three methods, which is free of the effects of threshold choice.I found the third column of Figure 2 a bit confusing for comparing MI GWAS and PAT, mainly because the density of dots is not well reflected and hence performance is not proportional by the area of each color. I suggest adding the proportion of dots falling into each color. It may also be helpful to use a smoothed density plot with color gradient proportional to density.Table 4 and page 14 shows low replication rate in the GIANT consortium. I would suggest including the replication rate of HIPO and MTAG to get a sense of the relative performance.The paper mainly uses the number of significantly associated variants as a metric of performance. This is okay in simulations, but in real data analysis (e.g. Table 3) it would be more interpretable to use the number of independent genetic loci.In Tables 1 and 2, the definition of \\sigma_g/causal is a bit confusing. If they refer to genetic effect size, why are they such large numbers? More description would be appreciated.Minor comments:Page 2 fifth line from bottom: “finally” instead of “fially”.Page 16 line 7 of section Generalizing GWAS testing to multiple traits (MI GWAS): “instead of” duplicated.Reviewer #3: The authors describe and address a pressing and common problem in genetic epidemiology: how to identify variants that (a) show evidence for pleiotropy (i.e. are associated with two or more traits) and (b) how to identify which traits a potentially pleiotropic variant is associated with. As the authors nicely review, there are many methods that leverage data on multiple traits to increase power to test the global null hypothesis that a variant is not associated with any of the studied traits--but these are not tests of pleiotropy per se (where the null is that the variant is associated with at most one trait), making it hard to assess whether more than one trait is contributing evidence for association or which traits are associated with the variant.The authors propose a summary-statistic-based likelihood ratio test of the omnibus null. This test aims to use a decomposition of the cross-trait correlation into "environmental" and genetic components to improve power. They then propose "m-values"  to identify which traits contribute to a significant omnibus test (and in particular whether more than one trait contributes). The use of summary statistics  is an important practical advantage. The proposed method appears to outperform the approaches they've chosen for comparison in many situations .The paper could use one more close read for grammar and typos, but I commend the authors on their clarity and organization. My main concerns/suggestions are:1) Expanding (slightly!) the set of comparison methods2) Being more clear in the main text of the impact of sample overlap across traits on the relative performance of methodsAs for #1: First, there are relatively simple multi-trait test statistics that both account for the correlation in test statistics due to sample overlap and define the rejection region differently (arguably more cleverly) than MIPAT (see PMIDs 28653391 and 31564761). It might be interesting to include some these in the power comparisons and in the visual illustration of the test rejection region , especially the "SUM" and "variance component" tests (PMID 28653391).Also, the ASSET method (PMID 22560090) is widely used as a multivariate test of association and as an ad hoc method for detecting pleiotropy/identifying which traits are associated with a variant . It would be quite interesting to compare the power/sensitivity/specificity of ASSET to the other methods in Tables 1 and 2.As for #2: the potential gain for the authors’ proposed likelihood ratio test—which decomposes the correlation in test statistics into an “environmental” and genetic component—relative to other methods—which do not decompose the test statistic correlation (like MI GWAS or MPATs 28653391 or ASSET)—seems to depend on the magnitude of sample overlap. No or very little sample overlap=no or very little “environmental”contributions, and PAT collapses to something like a Wald test (28653391). This needs to be emphasized more strongly and earlier. The authors’ simulations and applications focus on scenarios where there is substantial overlap. Worth presenting simulations or theoretical results for relevant scenarios where there is less overlap ?**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: NoneReviewer #2: NoneReviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article . 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Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.Similarity Check, powered by iThenticate, into its journal-wide submission system in order to screen submitted content for originality before publication. Each PLOS journal undertakes screening on a proportion of submitted articles. You will be contacted if needed following the screening process.PLOS has incorporated To resubmit, you will need to go to the link below and 'Revise Submission' in the 'Submissions Needing Revision' folder.[LINK]Please let us know if you have any questions while making these revisions.Yours sincerely,Michael P. EpsteinAcademic EditorPLOS GeneticsDavid BaldingSection EditorPLOS GeneticsComments to the Authors:Reviewer's Reviewer #1: I thank the authors for the detailed responses to my comments.- As mentioned in my previous review, it is worth exploring the role of the direction of genetic correlation (positive/negative) on PAT's power: As you mentioned in the text, the results in Table 1 suggest that PAT outperforms other methods when height is included (it has a negative genetic correlation with other traits) but otherwise it underperforms when all traits happen to have positive genetic correlations. Does PAT always underperform compared to the other procedures whenever the genetic correlation structure between traits are in same direction , or is this an artifact of the selected traits? I think this is important point to explore and discuss in the paper.Reviewer #2: The authors have addressed my comments.Reviewer #3: The authors have done a nice job responding to the reviewers' comments. Really comprehensive, interesting stuff.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: NoneReviewer #3: Yes**********what does this mean?). 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Striga resistance is invaluable for accelerating genetic gains in breeding for Striga resistance in maize. We conducted a genome-wide association study to identify genomic regions associated with grain yield and other agronomic traits under artificial Striga field infestation. One hundred and forty-one extra-early quality protein maize inbred lines were phenotyped for key agronomic traits. The inbred lines were also genotyped using 49,185 DArTseq markers from which 8,143 were retained for population structure analysis and genome wide-association study. Cluster analysis and population structure revealed the presence of 3 well-defined genetic groups. Using the mixed linear model, 22 SNP markers were identified to be significantly associated with grain yield, Striga damage at 10 weeks after planting, number of emerged Striga plants at 8 and 10 weeks after planting and ear aspect. The identified SNP markers would be useful for breeders for marker-assisted selection to accelerate the genetic enhancement of maize for Striga resistance in sub-Saharan Africa after validation.Identification of genes associated with  Striga hermonthica parasitism is fast becoming an endemic in West Central Africa because of its increased dispersal mechanisms which include wind, animals, farm implements, and surface water  countries  located on chromosome 9 and 10 were found toThe advancement in molecular breeding through rapid genotyping and next-generation sequencing technologies has enabled the use of genome-wide association study (GWAS) to be used as a tool for revealing genotype–phenotype associations in crop species . GWAS haStriga. In another study involving 380 diverse tropical inbred lines, Striga resistance, none of the QTLs detected have been employed in Striga resistance breeding. Therefore, there is a need to conduct additional studies using different genotypes to detect more QTL so as to facilitate the introgression of novel Striga resistant genes into maize breeding programs in SSA. The objectives of this research were to (1) determine the genetic structure of a panel of 141 diverse extra-early maturing white quality protein maize (QPM) inbred lines with varying levels of resistance to S. hermonthica parasitism and (2) identify significant SNPs and putative genes associated with grain yield and other Striga adaptive traits under Striga-infested conditions.be linked to plant defense mechanism against 8 inbred lines, 4 standard IITA testers , and 2 inbred checks (TZEEQI 11 and TZEEQI 60) with combined resistance to Striga, tolerance to drought and low soil N. The 163 inbred lines were extracted from the F1 maize hybrids of 9 biparental crosses involving crosses among extra-early white QPM inbred testers and early maturing white QPM inbred testers. The testers and the checks were extracted from Striga-resistant populations. The F1 hybrids were taken through a cycle of backcrossing to the extra-early inbred testers to recover the earliness. The BC1F1 with desirable agronomic characteristics were selected using the pedigree selection method from each backcrossed population, and advanced through repeated inbreeding to the S8 generation.One hundred and sixty-nine extra-early QPM inbred lines from the International Institute of Tropical Agriculture (IITA) Maize improvement program (MIP) were used for this study. The inbred lines comprised 163 SStriga-infested conditions at Mokwa  in 2019 and 2020 and Abuja  in 2020. At all locations, the experiments were laid out using a 13 × 13 lattice design with 2 replicates and single row plots each 3-m long, spaced 0.75 m apart with 0.4 m between plants in each row. Three seeds were sown per hill, and later thinned to 2 plants per hill at 2 weeks after planting (WAP) to obtain a plant population density of 66,000-plants ha−1.The experiments were conducted under S. hermonthica seeds/hill. The Striga infestation method developed by IITA-MIP was adopted to ensure uniform Striga infestation with no escapes ] to reduce the heterogeneity of variance for Striga counts. Restricted maximum likelihood estimates of the genetic and phenotypic variances of the inbreds were obtained with SAS PROC Varcomp and used to compute the broad-sense heritability for each trait. Correlation analysis was done using the performance analytics package in R. The phenotypic data across environments were collapsed to a single best linear unbiased estimate (BLUE) value using the linear mixed models in META—R , B(E) = replication effects nested in location, G = genotypic effect, GE = genotype by environment interaction, e = random residual error. Broad sense heritability (H2) estimates were calculated from the phenotypic variance (σ2p) and the genotypic variance (σ2g)  for DArTseq analysis following the protocol described by One leaf per plant was collected from 15 representative plants to form a bulk of each of 141 inbred lines 2 WAP in the IITA maize breeding nursery in Ibadan. The leaf tissues were placed in jute bags and freeze-dried using FreeZone Freeze Dryer  following the recommendations of the manufacturer's manual. Genomic DNA was isolated from freeze-dried leaf tissues of each inbred line following the modified Cetyl-trimethyl ammonium bromide (CTAB) protocol as described by High-throughput genotyping was carried out in 96 plex following the DArTseq protocol. The 49,184 DArTseq markers obtained as raw SNPs were filtered to eliminate SNPs with missing rate greater than 10%, heterozygosity greater than 20%, and minor allele frequency (MAF) less than 5%. SNPs with unknown or multiple chromosome locations were also eliminated. After quality filtering, a total of 8,144 DArTseq markers distributed across the 10 maize chromosomes were retained for the population structure and for GWAS analyses.et al. 2000) was used to cluster the 141 inbred lines into populations. Structure simulations were carried out using an admixture model with a burning period of 10,000 iterations, followed by Markov chain Monte Carlo set at 10,000. The assumed number of subpopulations was simulated from k = 1 to k = 10 for an initial assessment of the most likely number of subpopulations, each K was run 10 times. The ideal number of sub-populations (K) was found by examining the optimal ΔK value  other than molecular marker effects and population structure; α is the fixed-effect vector of the molecular markers; ν is the fixed-effect vector from the population structure; u is the random effect vector from the polygenic background effect; X, W, and Z are the incidence matrice from the associated β, α, ν, and u parameters; ε is the residual effect vector. Quantile–quantile (Q–Q) plots were generated by plotting the negative logarithms (−log10) of the P-values against their expected P-values to fit the appropriateness of the GWAS model with the null hypothesis of no association and to determine how well the models accounted for the population structure. The Manhattan plot was generated for visualizing GWAS on the entire genome and zoom mapping was performed on a particular chromosome after identifying a significant SNP marker. The marker effect or SNP contribution was estimated for the significant SNPs using multiple regression analysis using lme4 function implemented in R where the trait was considered as a response variable while the SNP markers above the Bonferroni threshold for the trait was the independent variable. A threshold of –log (p) = 3 was used to declare significant marker–trait associations, which were determined based on the Q–Q plots and distribution of P-values for all the measured traits (The association between SNP genotypes and the phenotypes was determined using a compressed linear model implemented in GAPIT (Genome Association and Prediction Integrated Tool)—R package (Striga-infested environments is presented in P < 0.05) environment (E) and genotype mean squares for measured traits. Genotype × environment interactions were not significant (P < 0.05) for the number of emerged Striga plants at 8WAP and 10 WAP. Broad sense heritability (h2) estimates on plot mean basis ranged from 46% for Striga damage at 10 WAP to 69% for ear aspect. Moderately high broad sense heritability was observed for the measured traits.The combined analysis of variance of the 169 inbred lines (including the 2 checks) across Striga adaptive traits differed under artificial Striga infestation (Striga damage at 8 WAP (r = −0.58**) and 10 WAP (r = − 0.71**), ear aspect (r = −0.85**). Significant and positive correlations were obtained between Striga damage at 8 WAP and Striga damage at 10 WAP (r = 0.56**), number of emerged Striga plants at 8 WAP and number of emerged Striga plants at 10 WAP (r = 0.72**), ear aspect and Striga damage at 8 WAP (r = 0.67**), and Striga damage at 10 WAP (r = 0.78**).The phenotypic correlations among grain yield and other measured estation . Grain yThe results revealed that PIC ranged from 0.09 to 0.37 with an average of 0.24 whereas the heterozygosity averaged 0.08 and varied from 0.00 to 0.50 . The meaK values from the mean log-likelihood probabilities peaked at k = 3. At k = 3, 84% of the inbred lines were assigned to 3 groups, with only 16% of the lines assigned to the mixed group. A total of 98 inbred lines were placed in group 1, 13 in group 2, 7 in group 3, and 23 in the mixed group. The 3 groups comprised inbred lines derived from 2 or more germplasm sources. The phylogeny tree displayed 3 genetic groups and was aligned with the kinship population structure  = 3 (R2) varied from 14% to 22%. Five SNP markers were significantly associated with grain yield  = 3 . The train yield . These m markers . These mat 8 WAP . These mt 10 WAP  and explt 10 WAP  and explThe significant genotypic variation observed among the inbred lines for grain yield and other measured traits in our study indicated the existence of genetic variability among the extra-early maturing QPM inbred lines. The significant environmental effects for measured traits indicated that the research conditions were unique and provided distinct information on the QPM inbred lines. The differential response of the genotypes for grain yield and other measured traits suggested that the inbred lines performed differently under the research condition. This differential performance could be attributed to variation in environmental factors such as temperature, rainfall, and soil types in the different test environments  = 3, a total of 22 markers were identified to be significantly associated with Striga damage, number of emerged Striga plants, ear aspect, and grain yield under Striga infestation. Information on the SNP markers from this study could accelerate the use of genomics-informed selection techniques in breeding Striga resistant maize cultivars.In the GWAS analysis, the population structure information was used to correct possible false discovery. Q–Q plots were generated by comparing the observed and expected Striga resistance indicator traits have been reported in earlier studies. For example, Striga resistance traits using biparental population derived from 2 early maturing maize lines. A similar study by Striga resistance traits. Striga damage, number of emerged Striga plants, number of ears per plant, ear aspect, and grain yield. Also, Striga resistant traits and yield distributed across the maize genome and controlled by a few major and many minor genes. Striga resistance trait. Among these studies, S8_17232945 which was detected for Striga damage syndrome rating in this study was found to overlap with 1 QTL reported by Several QTL associated with various Striga resistance, which can be crucial to breeding Striga resistance cultivars through marker-assisted breeding. However, these QTLs have not been utilized in maize Striga resistance breeding due to factors, such as limited marker–trait association, a low number of markers used in mapping, small phenotypic variance explained, differences in the genetic backgrounds, and environmental effects using a total of 428 individual QTLs for 23 root-related traits identified 53 Meta-QTLs (MQTLs) retrieved over 10 maize chromosomes. Three maize genes  that could play important roles on lateral root and crown root development of maize were also identified. The QTL analysis in this study, and previous studies provided information on the chromosonal regions controlling  effects . A meta-Striga-related traits. The GWASs conducted in this research revealed 22 SNPs that were significantly associated with Striga resistance adaptive traits. The identified SNP markers after validation would be invaluable for molecular breeding for maize Striga resistance in SSA. A meta-QTL analysis should be employed for the identification of stable QTLs from existing genomic regions that have been associated with yield and other Striga resistance traits. This will facilitate the detection of putative genes underlying Striga resistance in maize.The 141 QPM inbred lines used for this study displayed high genetic variability in response to the jkac237_Supplementary_DataClick here for additional data file.