P < 5 × 10−8) and single-nucleotide-polymorphism-based heritability  of 13%–16%. Heritability was enriched for genes expressed in brain tissues and for fetal and adult brain-specific gene regulatory elements, underscoring the role of central-nervous-system-expressed genes linked to the genetic basis of the trait. We performed validations of the self-report phenotype (through separate experiments) and of the genome-wide association study . Genetic correlations with breathing function, motor function, processing speed and chronotype suggest shared genetic architecture with beat synchronization and provide avenues for new phenotypic and genetic explorations.Moving in synchrony to the beat is a fundamental component of musicality. Here we conducted a genome-wide association study to identify common genetic variants associated with beat synchronization in 606,825 individuals. Beat synchronization exhibited a highly polygenic architecture, with 69 loci reaching genome-wide significance ( Niarchou et al. identify 69 genomic loci associated with people’s synchronization to a musical beat. The genetic architecture of beat synchronization was enriched for genes involved in early brain development and lifelong brain function. Rhythmic patterns provide predictable and robust sensorimotor structure to everyday interactions5, helping guide our attention to communicatively important moments in time7. Even young children are sensitive to the social and linguistic signals carried by rhythm10, and parents use rhythmic vocalizations and synchronous movement  to interact with their infants from birth12. Rhythmic musical interactions are structured around the percept of a stable periodic pulse . While music in general and rhythmic structures in particular vary globally17, there is evidence that the hierarchical beat structure of most music is robust to cultural transmission2 and indeed common in many types of music1.Our tendency to perceive, create and appreciate rhythms in a variety of contexts  is a key feature of the human experience18) are important features of musical experiences across many human cultures and musical genres19. The predictive temporal mechanisms afforded by beat structure enhance general perceptual and learning processes in music, including melody perception and production, singing, and joint music-making6. While some features of rhythm perception and production vary across listeners from different cultures21, the same studies showed considerable consistencies across cultures in other features . Musicality  impacts society by supporting pro-social behaviour23 and well-being24. Many have proposed that beat perception and synchronization evolved in humans to support communication and group cohesion26. In modern humans, beat perception and synchronization are predictive of language and literacy skills28 and are related to cognition, motor function and social coordination29. The biology of beat synchronization thus has general importance for understanding the human ability to perceive and predict natural rhythms; may have relevance for characterizing phenotypes such as developmental speech–language disorders, which demonstrate associations with atypical rhythm30; and may further elucidate mechanisms of rhythm-based rehabilitation .Beat perception and synchronization . Large-scale genome-wide association studies (GWASs) of rhythm-based traits  are thus needed to advance this field. Our understanding of the biological underpinnings of beat synchronization, from its genetic architecture to its neural instantiation, behavioural manifestation and relationship to health, requires mechanistic multi-methodological approaches. Post-GWAS approaches  can be deployed to illuminate the relationship between the genetic and neural architecture of music-related traits as well as shared underlying biology with other health traits.Indeed, twin-modelling and other family-based studies point to moderate heritability of rhythm-related traits such as duration discriminationN = 606,825 people to identify genomic loci associated with beat synchronization. (3) We further investigated the genetic architecture of beat synchronization by estimating single nucleotide polymorphism (SNP)-based heritability and partitioned heritability, and conducting gene property and gene-set enrichment analyses. We also evaluated the contribution of genomic regions that have experienced notable human-specific evolutionary shifts. (4) We validated the GWAS results by testing whether a cumulative sum of the genetic effects for beat synchronization detected in our GWAS ) was significantly associated with algorithmically identified musical engagement in a separate sample. (5) We explored shared genetic effects (pleiotropy) on beat synchronization and other traits through genetic correlation and genomic structural equation analyses.Here we report a large-scale genome-wide interrogation of beat synchronization. Our approach was as follows , in relation to rhythm perception and beat production tasks. Both studies were administered in English for consistency. We also explored the relationship between task-based beat synchronization ability, a self-reported rhythm scale and musicality. Study overviews and key results are summarized in Fig. In light of prior work suggesting that musicality and rhythm skills are complex traits that can be quantified with both objective (experiment-derived) assessment and subjective self-reported dataN = 724 people  and complex (syncopated) rhythms46. The rhythm perception task yielded quantitative scores (d′). Individuals with better performance in the rhythm perception test  were more likely to answer Yes (versus No) to the target question (odds ratio (OR), 1.92; P = 0.002; McFadden’s R2 = 0.06; 95% confidence interval (CI), ; Fig. In this experiment, N = 1,412) as a proxy for directly measured beat synchronization ability. The participants . For additional details and preregistered hypotheses (H1–H6), see the We then validated self-reported beat synchronization phenotype N = ,412 as ats Table . Beat syP < 0.001; McFadden’s R2 = 0.24; 95% CI, ; Fig. r = −0.40; P < 0.001; 95% CI, ) ; Fig. r = −0.41; P < 0.001; 95% CI, ; Fig. 43 was positively associated with the target question ; Fig. r = −0.36; P < 0.001; 95% CI, ; Fig. As predicted (Open Science Framework preregistered H1), individuals who responded Yes to the target question  had lower tapping asynchrony  at 69 genomic loci reached genome-wide significance  and Supplementary Table https://bitbucket.org/marianiarchou/beat-synchronization-gwas). The LD score regression (LDSC) intercept was 1.02 (s.e. = 0.01), and the ratio was 0.03, indicating that the majority of inflation in test statistics was due to true polygenicity instead of population stratification.GWAS was conducted using logistic regression under an additive genetic model, while adjusting for age, sex, the first five principal components from genetic data and genotype platforms . Seventyrs848293) was mapped at chromosome 2 close to VRK2  and FANCL, within a region previously linked to multiple neurological phenotypes49. Another strongly associated locus at chromosome 17 (rs4792891) included the Microtubule Associated Protein Tau (MAPT) gene, a locus associated with Parkinson’s disease50. The Mitogen-Activated Protein Kinase 3 (MAPK3) gene at 16p11.2, a region known to harbour rare variants that influence neurodevelopmental disorders51 and language-related phenotypes52, was also strongly implicated. We also identified a locus at Glycoprotein M6A (GPM6A), whose gene promoter contains a transcription factor binding site for GATA2, a gene previously related to music phenotypes37.The top-associated locus . Within these associations, we examined potential replication of 29 genetic associations with musicality in humans from prior reports55; none reached significance after genome-wide correction (Supplementary Table P = 0.297).Gene-based genome-wide association analyses performed with MAGMA yielded 129 genes surpassing the threshold of 56 on the gene-based P values, using MAGMA57 implemented in FUMA58. The results of conditional gene property analysis (based on Genotype-Tissue Expression (GTEx) data tissue types59 and controlling for average expression) demonstrated that the genetic architecture of beat synchronization was significantly enriched in genes expressed in brain tissues  and gene property enrichment analysesues Fig. , includi57 (Supplementary Table P = 1.01 × 10−7) and synaptic adhesion-like molecules (P = 8.35 × 10−7).To further examine potential biological functions associated with beat synchronization, we performed exploratory GSA60 on the GWAS results to partition heritability according to genomic properties, using specific functional categories to gain insight into the types of variation that contribute most to beat synchronization. Among broad SNP annotation categories61 (Supplementary Table P < 9.6 × 10−4) of regions conserved in mammals (considered under purifying selection62) and regulatory regions marked by the acetylation of histone H3 at lysine 9  and the monomethylation of histone H3 at lysine 4 , supporting the hypothesis that the identified associations may affect gene regulation. We next used LDSC applied to specifically enriched genes (LDSC–SEG63) to determine whether genes expressed in specific cell or tissue types  are enriched for beat-synchronization-associated variants. For tissue-specific annotations of active chromatin and enhancers , heritability was enriched in central-nervous-system- and skeletal-muscle-specific regulatory regions  play roles in human-enriched traits66, including cognition67. Two variants significantly (P < 5 × 10−8) associated with beat synchronization (rs14316 at locus 66 and rs1464791 at locus 20) fall within HARs. This is 11.2 times more overlap than expected by chance . The rs1464791 variant is near GBE1, a gene associated with neuromuscular disease68, reaction time69 and cognitive impairment70. Applying LDSC to consider the full set of association statistics, we find that genetic variants in HARs contribute 2.26 times more to the observed heritability of beat synchronization than would be expected if heritability were distributed uniformly across variants (P = 0.14). Given the small number of common variants within HARs, this stratified heritability analysis is substantially underpowered (0.17% of variants considered are in HARs). The general agreement of these two approaches supports the enrichment of functional variation relevant to beat synchronization in HARs. We also evaluated the contribution of genetic variants detected in the Neanderthal genome to the heritability of beat synchronization  and matched controls  were drawn from a study71 that algorithmically identified musically active patients in a health-care research database ), consistent with beat synchronization capturing a dimension of musicality.We investigated whether the polygenic model of beat synchronization from the GWAS would differentiate self-identified musicians from non-musicians in a separate sample. Musicians (database . PGSs fo72 between beat synchronization GWAS and available GWAS of 64 traits classified into seven domains (see Supplementary Table P < 7.8 × 10−4)  Fig. . The res73 to examine whether specific associations between beat synchronization and a subset of associated traits  that are known to be related among each other in prior research76 represent distinct genetic relationships or share a common set of genetic influences with beat synchronization. The best-fitting model, displayed in Fig. We conducted genomic structural equation modelling (SEM)Using genomic SEM, we conducted a multivariate GWAS , more shortness of breath  and smoking 20 or more (lifetime) cigarettes   or by subtle residual population substructure, and the MAPT association is not confounded with Parkinson’s disease.Our results are robust to several potential biases , paving the way to a better understanding of the biological underpinnings of musicality and its health relevance.Our GWAS revealed highly polygenic architecture of the human capacity to synchronize to a musical beat, representing a substantial advancement of our understanding of the genomic basis of a musicality phenotype. Heritability of beat synchronization is enriched for functions of the central nervous system on a number of dimensions: SNPs involved in neural development and brain-specific regulatory regions of the genome, genes involved in synaptic function, and gene expression in cortical and subcortical brain tissues aligned with auditory–motor regions previously linked beat perception and synchronization46, a multi-item rhythm questionnaire and a well-established assessment of musical sophistication43. These results converge with earlier work showing shared variance among task performance of beat synchronization, rhythm perception and musical engagement/training80. The phenotypic associations were robust to demographic factors and self-confidence and were not driven by the presence of professional musicians in the sample. These phenotype validation studies represent critical groundwork81 enabling brief rhythm self-report questionnaires to be deployed online in large-scale population genetic cohorts.In a series of phenotypic experiments, we also demonstrate that self-reported beat synchronization/rhythm measures can be used in large-scale population-based studies as suitable proxies for measuring individual differences in beat synchronization. Our findings indicate that the GWAS phenotype beat synchronization question was highly related to beat synchronization task performance . Clearly, the self-report is an imperfect correlate of beat synchronization; nevertheless, we demonstrate that it is a suitable proxy for very large-scale studies in which task administration is impractical. Furthermore, the GWAS phenotype is also significantly associated with rhythm perception task performance83. The top-associated locus mapped to VRK2, a gene previously associated with behavioural and psychiatric phenotypes , suggesting a biological connection between beat synchronization and neurodevelopment. The SNP-based heritability of beat synchronization on the liability scale was moderate, ranging from 13% to 16%, similar to heritability estimates of other complex traits  and consistent with moderate heritability estimates of musical rhythm abilities reported in twin studies40. Still, the limitation of the heritability adjustment on the liability scale is that the exact population prevalence of atypical beat synchronization is unknown and had to be estimated on the basis of other indices of rhythm . This pattern of results probably reflects a genetic contribution to subcortical–cortical networks underlying musical rhythm perception and productionrs1464791 is an expression quantitative trait locus that regulates the expression of GBE1 in multiple tissues, including muscle59; GBE1 is also linked to neuromuscular disease68 and reaction time69. HARs are involved in many functions, so it is difficult to explicitly link their accelerated evolution to beat synchronization. It is too early to tell whether the overlap between beat-synchronization-associated loci and those two HARs supports evolutionary theories about music . The contribution of the genetic architecture of motor function to beat synchronization is further suggested by enriched heritability of SNPs that are enhancers located in musculoskeletal-tissue-specific regulatory regions of the genome, as well as our findings of genetic correlations between walking pace, musculoskeletal strength and beat synchronization.Insights about the evolution of rhythm traits are suggested by the occurrence of two of the beat-synchronization-associated loci in HARs of the genome. In particular, 95 or measured aptitude-based variables38), providing a path to examine potential genetic correlations between beat synchronization and other musical traits, such as music training or pitch discrimination, in line with family-based findings41. While the current data show a clear connection between beat synchronization and broader musicality at the phenotypic and genetic levels, further genomic investigation in well-powered samples is needed to disentangle the specificity of genetic influences on beat synchronization from other genetic influences on musical traits, or motor or auditory function. Finally, although our GWAS was based on self-report, the magnitude of the sample size bolstered statistical power. This is important because previous GWASs of other health traits based on self-report have effectively replicated associations from other GWASs of deeper phenotypes83, and it is generally acknowledged that large sample sizes can overcome some of the limitations arising from modest measurement error96.Moreover, our findings are promising for future large-scale genomic interrogations using comprehensive music phenotyping yielding continuous musicality variables ; investigating beat synchronization, musicality and cross-trait correlations in populations of non-European ancestry should be a future priority for capturing the spectra of musicality traits in a wider range of ethnic, cultural and socio-economic contexts100. Guidelines for ethical and socially responsible conduct of research on the genetics of musicality are outlined in Box 101.Moving in synchrony to a musical beat encompasses beat perception and extraction, motor periodicity, metre perception and auditory–motor entrainment55  but did not find support for previous gene associations from a set of genes that was drawn from prior candidate-gene, linkage and GWAS studies with relatively small samples54. This is potentially due to well-known methodological problems with these methods, particularly when applied to complex traits in small samples102. Without a second comparably sized GWAS available within which to conduct replication of the loci discovered in the primary GWAS, we were still able to demonstrate the generalizability of these results by showing that the PGS for beat synchronization predicts a musical trait in a separate biobank sample. The GWAS results of beat synchronization were nearly identical even after conditioning the results on GWASs of educational attainment and general cognition (g-factor); these results align with twin findings of specific genetic effects of rhythmic aptitude over and above any common genetic influences between rhythm and non-verbal cognitive variables103. Moreover, given the likely capturing of genetic variation related to socio-economic status104 by the educational attainment GWAS summary stats, as well as the observation that our beat synchronization GWAS loci are robust to educational attainment, socio-economic status is unlikely to play a major role in our findings.We replicated previous findings implicating location 4q22.1 in musicality-related traits75, we used genomic SEM to uncover shared genetic variance among beat synchronization and enhanced breathing function, greater grip strength, faster walking pace and faster processing speed. Poor beat synchronization could be tied to certain health risks during ageing, in light of other genetic and epidemiological work showing that lung function decline predicts later declines in motor function and psychomotor speed in older adults108.Our cross-trait explorations revealed pleiotropic effects between beat synchronization and several breathing-related phenotypes . We demonstrated phenotypically that more accurate beat synchronization task performance was related to a lower likelihood of shortness of breath, mirroring the genetic correlations between beat synchronization and breathing function. In light of our genetic correlation between beat synchronization and three categories of traits  previously shown to be genetically interrelated during the ageing process110. The cerebellum in particular plays important roles in the control of coordinated movement, balance, respiration, dance and even rhythm perception during passive listening to music33. Indeed, our multivariate GWAS of rhythm-related traits demonstrated enriched heritability of genes expressed in cerebellar tissue, potentially of note in relation to experimental findings of functional synchronization of respiratory and upper limb movements during vocalization5. Moreover, ‘beat gestures’ in speech involve the cerebellum111 and are inextricably linked to respiration, upper limb movement and postural control, all of which may be biomechanically related to tapping or clapping to music.The genetic correlation results suggest that beat synchronization shares common biology with a constellation of health traits, converging with the growing literature on the overlapping biomechanical and perceptual mechanisms of rhythms harnessed during synchronization, communication, muscle tensioning and breathing; these relationships start very early in development112, indicating that the relationship between chronotype and musicianship cannot be explained solely by environment  and that other shared biological factors may play a role. Given the genetic correlation between beat synchronization and lowered incidence of insomnia, the relationship between regulation of sleep, musicality and rhythm represents an area for further exploration.Another dimension of biological chronometry was captured in the genetic correlation between chronotype and beat synchronization, which we replicated phenotypically . These results complement recent evidence of the increased prevalence of eveningness in musicians98. Future genetic studies could study beat synchronization as a continuous trait, through either self-report or internet-based task paradigms . Prior literature on liability threshold models has shown that case–control GWASs of complex traits yield similar results to those obtained through continuous phenotypic measures . Moreover, the use here of a population-based control group that are not ‘super-controls’  increases the likelihood that the genetic correlations that we uncovered are reliable and not biased upwards115.Our GWAS effectively identified alleles at 69 separate loci differentially associated with typical versus atypical beat synchronization, complementing existing evidence of underlying neural mechanisms30 and with importance over the lifespan116. We have elucidated the genetic architecture of beat synchronization and revealed its health relevance through cross-trait analyses. This study also provides a solid foundation for future exploration of how specific genetic variants contribute to neural mechanisms of entrainment, prediction and reward harnessed during musical interactions117.Taken together, our results advance knowledge of the biological basis of beat synchronization by identifying genomic regions associated with individual differences in beat synchronization, estimating its cumulative SNP-based heritability, successfully applying a PGS model in a separate genetic sample and exploring the enrichment of heritability in genes tied to central nervous system function. Movement in synchrony with a musical beat is a fundamental feature of music, and sensitivity to the beat emerges early in development, supporting childhood development in numerous waysBox 1 Initial ethical and social considerations related to the genetics of rhythmGenome-wide associations with beat synchronization are not deterministic. The present results show that genetic influences accounted for only a small portion of human variation in beat synchronization. Environmental influences are the primary drivers of rhythmic accuracy. The value of this work arises not from the hypothetical ranking of interindividual differences in beat synchronization but from the discovery that variation in the human genome partly gives rise to the shared experience of musical rhythms. Genomic associations with beat synchronization cannot be used to make deterministic predictions about individual abilities or aptitudes140. The nuances of these results should be kept at the forefront of the interpretation of GWAS findings and are especially important during public dissemination.Historical context matters and lays the foundation for vigilant ethical and social conduct of research on musicality traits. Early research on individual differences in musicality in the early 1900s was pursued not only using what we now recognize as highly culturally biased assessments but also explicitly through the lens of eugenics141, similar to early research on individual differences in cognition. We strongly condemn the intent and design of eugenic-focused studies. The historical context of cognitive genomics and how it intersected with racism and eugenics-motivated early music cognition studies heightens the present need for an ethical framework guiding responsible conduct and use of this research.Ethically and socially responsible conduct of research on the genetics of musicality includes conscious decision-making throughout the life cycle of research. Decisions about what to study, how to study it, how to communicate results and how to apply results  should take place with consideration of historical context and responsible future applications.Inclusive and diverse studies of rhythm and other musicality traits should include populations of diverse ancestries. A limitation of the current study is the restriction of the sample to only European ancestry for technical reasons . While genes carry out essentially the same functions in all humans, the specific variation that tags each gene differs as a consequence of population demography over the course of human evolution. Therefore, to fully understand the genetic architecture of a complex trait like beat synchronization, GWASs within other genetic ancestries should be pursued in the immediate future.PGSs and similar analyses applied to individual genetic data should not be used to make deterministic individual inferences or rankings about musicality. For example, polygenic embryo selection142 for beat synchronization  is not scientifically justifiable or ethically sound. Likewise, it would be inadvisable and harmful to make decisions about cultural enrichment, such as access to music lessons, on the basis of PGSs.101. In addition, a live FAQ for the current study is hosted at https://www.vumc.org/music-cognition-lab/FAQbeatGWAS.Ethical and social issues related to the conduct of research on the genetics of musicality are discussed more extensively in ref. 118 and prior history investigating individual differences in rhythm perception in a variety of brain and behavioural studies in adults and children with typical and atypical development121 as well as feasibility for internet-based adaptation. A questionnaire (self-report questions) was administered before the perception task to avoid biasing participant self-report responses by how they perceived their own task performance. The participants were compensated about US$1.60–US$2.00 for participation; see Supplementary Methods and Results: section Phenotype Experiment 1 was designed to determine whether self-reported rhythm abilities measured with the question used in the GWAS  would be associated with task-based rhythm perception performance. The study was conducted in Amazon’s Mechanical Turk and received ethical approval from the Columbia University Institutional Review Board; the participants gave their written informed consent, and the research complied with all relevant ethical regulations. We selected the Beat-Based Advantage paradigm as a rhythm discrimination (perception) test due to its design of stimuli with simple and complex metresN = 724 participants recruited anonymously in Amazon’s Mechanical Turk. All consented and passed a common headphone check122 that guarantees good listening conditions and the ability to follow basic instructions; this test also effectively filters out bots. The participants  were 18–73 years old  with 0–45 years of self-reported musical experience , representing an average degree of musical experience (see the norms in ref. 43); the demographics are reported in Table N = 3 did not report their age). No exact sample size was predetermined, but the final N was in line with expectations for participation across two waves of recruitment via Mechanical Turk.The study sample was 118. For each participant, we randomized with probability of 0.5 the occurrence of ‘simple’ rhythms  and ‘complex’ rhythms . Each rhythm was presented using pure tone stimuli in one of six frequencies  and one of four durations . Each trial consisted of three rhythms separated by 1,500 ms of silence; there were 32 trials of the task. The two first presentations were always identical, and in half of the trials , the third rhythm was also identical (standard condition); in the other half of the trials, the rhythm differed by having one interval swapped (deviant condition). The pairings and structure of standard and deviant trials were taken from ref. 46. The participants were instructed that in each trial, they would listen to the series of three rhythms , and they had to indicate whether the third rhythm was the same or different  participants answered ‘Yes’, N = 25 (3.5%) answered ‘No’ and N = 45 (6.2%) answered ‘I’m not sure.’ Regarding an additional self-report question, ‘Do you have a good sense of rhythm?’, N = 503 (67%) answered ‘Yes’, 102 (14%) answered ‘No’ and N = 117 (16%) answered ‘I don’t know’. N = 488 answered ‘Yes’ to both questions; the tetrachoric correlation between these two self-report questions was r = 0.73.Responses to the target question were as follows: 123; this method is appropriate for discrimination tasks where the participant has to categorize stimuli along some dimension, with the resulting d′ values representing the strength of detection of the signal relative to noise. We calculated d′ values on the 32 test trials. As expected from prior work124, individuals performed better at discriminating simple rhythms  than complex rhythms  (t(724) = 11.11, P < 0.001, Cohen’s d = 0.58).Responses to the rhythm perception test were analysed using signal detection theoryd′ scores) were the predictor. Covariates included age, education and sex. McFadden’s R2 was also computed. We did not include ‘I’m not sure’ in the regressions, because this response was not available for data analysis in the GWAS. Given that the simple rhythms have a strong metrical structure that is known to facilitate detection and synchronization of the beat46, we also tested whether performance on the simple rhythm trials predicted self-reported beat synchronization . All continuous measures met the assumption of normality . See Supplementary Methods and Results: section To examine whether the target question was related to the objective (experimenter-measured) performance on the rhythm perception test, we performed a logistic regression analysis in which the clap-beat target question (Yes versus No) was the outcome and quantitative scores on the rhythm perception test (https://osf.io/exr2t) on 8 July 2020, prior to data collection. This internet-based study consisted of a beat synchronization task to assess the accuracy of participants’ tapping in time with musical excerpts, and a series of questionnaires assessing self-reported rhythm, musicality/music engagement, selected health traits, confidence as a personality trait and demographics. We used REPP47 to measure the participants’ tapping responses online with high temporal fidelity. The item from the GWAS study, ‘Can you clap in time with a musical beat?’ with possible responses Yes/No/I’m not sure, is referred to as the ‘target question.’The aims of Phenotype Experiment 2 were twofold: (1) to validate self-reported beat synchronization phenotype as a proxy for objectively measured beat synchronization ability and (2) to explore phenotypic associations between rhythm/beat synchronization and assorted traits found to be genetically correlated with beat synchronization. Phenotype Experiment 2 was preregistered with the Open Science Framework , such that individuals who respond Yes to the target question are predicted to tap more accurately to the beat of musical excerpts  (H1). Self-report on a highly similar self-report question  with responses on a seven-point agreement Likert scale is also predicted to be correlated with tapping accuracy (H1a). Second, the target question will be associated with broader rhythm ability/engagement  (H2a), and beat synchronization task performance will reflect broader self-reported rhythm ability/engagement (H2b). Third, confidence  affects the reliability of self-reported beat synchronization (H3). Fourth, selected traits found to be genetically correlated with beat synchronization in the GWAS will be phenotypically correlated with beat synchronization task performance and the rhythm scale (H4). Specifically, better beat/rhythm is correlated with evening chronotype (H4a), less shortness of breath (H4b), more tinnitus and loud music exposure (H4c), and more smoking (H4d); and these associations will survive controlling for age, sex and education (H4e). Fifth, the responses to the target question will be positively correlated with musical engagement measured with the Gold-MSI (H5). Sixth, the associations in H4 will interact with being a musician, or more generally, with musical engagement (H6).N = 1,412 individuals met the participation criteria outlined in the preregistration (including passing the attention check item and not abandoning the study before completion). The study took place in Amazon Mechanical Turk, and all participants provided informed consent in accordance with the Max Planck Society Ethics Council’s approved protocol; the research complied with all relevant ethical regulations. The participants  were 18–77 years old  and had 1–2 years of self-reported musical experience (Table 47). Thus, several precautions, including calibration tests and practice trials, were taken to make sure the tapping technology would work effectively, excluding cases that did not meet the requirements , in line with power estimates for predetermined sample size (minimum N = 500 with usable tapping data) as detailed in the preregistration. Questionnaires were administered in the full sample of participants. The sample demographics are reported in Table A total of 125 between items 10 and 11 to exclude fraudulent responders, such as computer bots or disengaged participants responding randomly to the experiments. The end questionnaire consisted of items covering the following additional self-report topics: another question about being a musician, a task confidence rating question, a confidence scale, a 16-item short version of the Gold-MSI43  and a demographic questionnaire. The questionnaire items for Phenotype Experiment 2 are listed in Supplementary Methods and Results: section The first questionnaire included self-report items, including the target question and also covering a variety of musical, health and interest phenotypes. The health phenotype questions were chosen from phenotypes  found to be genetically correlated with beat synchronization in our genetic analyses. The rhythm questions were selected for their particular relevance to various aspects of interacting/engaging with musical rhythm. The order of the questions was fixed for all participants. In addition, we used an attention check item127. Here we used REPP , a robust cross-platform solution for measuring sensorimotor synchronization in online experiments that has high temporal fidelity and can work efficiently using hardware and software available to most participants online. To address core issues related to latency and jitter, REPP uses a free-field recording approach: specifically, the audio stimulus is played through the laptop speakers, and the original signal is simultaneously recorded with the participants’ tapping responses using the built-in microphone. The resulting recording is then analysed using signal processing techniques to extract and align timing cues with high temporal accuracy.Beat synchronization is particularly challenging to study with online research, where variability in participants’ hardware and software can introduce delay in latency and jitter into the recorded time stampsThe beat synchronization task procedure consisted of three parts: calibration tests, a practice phase and the main tapping phase. The participants started with the calibration tests, including a volume test to calibrate the volume of the laptop speakers to a level sufficient for detection by the microphone, a background noise test to make sure the participants were in a quiet environment and a tapping test to help the participants practise how to tap on the surface of their laptop in the right level and location to be detected by the microphone. The participants were then presented with the practice phase, which consisted of four 15 s trials of isochronous tapping to a metronome beat . Following the practice phase, the participants were presented with the main tapping task consisting of eight trials , with each trial 30 s long. The order of presentation of the practice trials and test trials was randomized for each participant.128. We chose four MIREX clips that represent different music genres with different tempos and tapping difficulty: track 1 , track 3 (‘El Contrapunto’ by Los Mensajeros de La Libertad), track 7 (‘Le Sacre du Printemps’ by Stravinsky) and track 19  of the MIREX training set. On the basis of the annotations in ref. 128, we identified the target beat locations from those consistently produced by the annotators. Additional technical details are provided in the Supplementary Methods and Results: section The musical excerpts were drawn from the MIREX 2006 Audio Beat Tracking database, in which musical excerpts had been annotated for beat locations by 30 listeners who tapped along to the musict and Rt be the stimulus and response onsets, respectively. In case of the metronome, St is the metronome onset (practice phase), and for the music clips, St is the target beat location based on the annotations. We define the asynchrony as at = Rt − St. On the basis of prior work129, we chose the standard deviation of the asynchrony (s.d.(at)) as our main target interest variable, as this appears to be a robust measure of individual performance and tightly linked to musical abilities130. We used metronome onsets to mark the beat metric level in an unambiguous way131. We emphasize that the metronome onsets were physically present only during the beginning and end of each clip. We used only the participant-produced asynchronies during the epoch at beats in which the guiding metronome was not present, in order to test the ability of the participants to synchronize to music without the metronome sounds . For the main test scores, we used the asynchronies computed relative to the virtual beat locations computed from prior human annotators in MIREX. We also computed vector length to confirm key associations of interest between the target question and beat synchronization accuracy  and linear regression (for H1a and H2b). Likewise, we used linear regression to examine potential replication of cross-trait associations uncovered by genetic analyses (H4a–d) and to examine whether musical background interacted with the above associations (H6). The analyses were conducted in R v.3.5.1 (ref. 132). As described in our preregistration, individuals were recruited using Amazon Mechanical Turk and were included unless they failed an attention check item or abandoned the experiment before completing the study . Usable tapping data were available for N = 542 individuals. The majority of exclusions were due to technical reasons detected by REPP’s signal processing pipeline during the practice trials , but some additional participants (N = 19) were excluded for not having enough usable trials during data analysis. Missing covariates were handled using pairwise deletion. All continuous measures met the assumption of normality . The exclusion criteria are detailed in the Supplementary Methods and Results: section In accordance with the Open Science Framework preregistration, we examined whether responses to self-reported beat synchronization phenotype were associated with objectively measured tapping accuracy and other self-reported measures of rhythm ability, confidence and musical sophistication using logistic regression and McFadden’s N = 606,825; mean age (s.d.), 52.09 (18.5); prevalence, 92%) who were unrelated individuals of European ancestry; the age range breakdown is provided in Table 133) have indicated that very large sample sizes are needed to detect the genetic architecture of such traits. All individuals provided informed consent according to 23andMe’s human subject protocol, which is reviewed and approved by Ethical & Independent Review Services, a private institutional review board (http://www.eandireview.com); the study complied with all relevant ethical regulations.The GWAS summary statistics were generated from data acquired by personal genetics company 23andMe, Inc. Phenotypic status was based on responses to an English-language online questionnaire in which individuals self-reported ‘Yes’ (cases) or ‘No’ (controls) to the question ‘Can you clap in time with a musical beat?’. Individuals who responded ‘I’m not sure’ were excluded from the genomic dataset, as their data were not available. The GWAS included a total of 555,660 cases and 51,165 controls  and indels, resulting in 8,288,850 SNPs in the GWAS summary statistics. Genotyping and quality control details are provided in the Supplementary Methods and Results: section The GWAS was conducted using logistic regression under an additive genetic model, while adjusting for age, sex, the top five principal components estimated from genetic data to control for population stratification, and indicators for genotype platforms to account for batch effects. We excluded SNPs with minor allele frequency < 0.01, low imputation quality  that are in approximate LD with each other at r2 < 0.1 and to generate Manhattan plots and quantile–quantile plots. GWAS Catalogue associations for the top loci were performed in FUMA  per tissue type were adopted after winsorization at 50 based on GTEx RNA-seq data; this analysis was performed for 54 tissue types, where the result of gene analysis was tested for one side while conditioning on average expression across all tissue types. We also performed exploratory GSA57 in FUMA using 15,556 Gene Ontology gene sets from the MsigDB database135; a Bonferroni threshold of 3.2 × 10−6 was used.Next, using the GWAS summary statistics as input for MAGMA (v.1.08), we conducted a gene-based test of association, a gene property enrichment test and a gene-set enrichment analysis. Gene property analysis60, and heritability estimates were adjusted to the liability scale on the basis of population prevalence of atypical beat synchronization of 3.0%–6.5% (Supplementary Table 63 (Bonferroni-corrected significance level of P = 9.6 × 10−4). We hypothesized that SNPs falling into open chromatin regulatory regions  and regions with human-specific variation would be enriched for beat-synchronization-associated variation.SNP-based heritability was computed with LDSC software67, using a total of 697 gene sets ; the Bonferroni-corrected significance level for this analysis was 7.1 × 10−5 (Supplementary Table We further investigated (SNP-based) cell-type-specific and tissue-specific enrichments with LDSC–SEG65. All variants in perfect LD (r2 = 1.0 in 1000 Genomes European participants) with variants in HARs were considered in the analysis. Similarly, variants tagging Neanderthal introgressed haplotypes were defined as in ref. 136. All variants in perfect LD with a Neanderthal tag SNP were considered Neanderthal variants. For each set, we performed stratified LDSC (v.1.0.0) with European LD scores and the baseline LD-score annotations v.2.1. The heritability enrichment is defined as the proportion of heritability explained by SNPs in the annotation divided by the proportion of SNPs in the annotation. Standard effect size (τ*), which quantifies the effects unique to the annotation, is the proportionate change in per-SNP heritability associated with a one-standard-deviation increase in the value of the annotation, conditional on other annotations in the baseline v.2.1 model62. To determine the expected number of overlaps between the N loci significantly associated with beat synchronization and HARs, we computed all overlaps between these sets of genomic regions (in hg19 coordinates) using bedtools2 (ref. 137). We then randomly shuffled the locations of HARs around the genome, choosing segments of equal lengths and avoiding gaps in the genome assembly. We repeated this process 10,000 times and for each iteration computed the number of overlaps observed with the significantly associated loci. On the basis of this empirical distribution created with no association between the region sets, we computed the enrichment and P value for the observed number of overlaps.The set of HARs was taken from ref. 72, with a Bonferroni threshold of 7.5 × 10−4  and traits measured in other GWAS studies. Given that beat synchronization is a brain-related trait with a motor periodicity component, the guiding principle of our selection criteria was foremost to explore its relationship with other brain-related traits as well as traits encompassing biological rhythms, and also leaving open the possibility of hitherto-unstudied relationships with general health. We thus curated GWAS summary statistics for 64 complex traits representing a broad range of phenotypic categories: cognitive, psychiatric, neuroimaging/other neurological, motor, other biological rhythms , overall health/well-being and hearing . The phenotyping method was based on mining of clinical notes, using four keywords and 449 regular expressions ; see Supplementary Methods and Results: section 71 for the details. The method was then validated with manually conducted chart review, with a positive predictive value of 93%. Here we accessed the subset of N = 1,259 musicians and 4,893 controls , ethnicity, race and length of record) that were also part of the BioVU database and had genotyped data on file, to test the hypothesis that higher PGSs for beat synchronization would be associated with musical engagement operationalized as a having musician-related keywords or regular expressions recorded in an individual’s electronic health record.We examined whether beat synchronization PGSs would be associated with music engagement reported in health records. Individuals who disclosed music engagement to their care providers (which was subsequently recorded by their providers) were drawn from a recent phenome-wide study of 9,803 musiciansN = 1,259 individuals (553 (44%) females; mean median age of record (s.d.), 53.1 (16.5)) as musician ‘cases’ and 4,893 controls  females; mean median age of record (s.d.), 53.2 (16.3)). See Supplementary Methods and Results: section We selected only individuals of European ancestry with genetic data that met standard quality control thresholds due to the poor performance of PGSs trained in individuals of one ancestry and applied to individuals of another. This resulted in 138. Briefly, this method uses a Bayesian regression framework and places a continuous shrinkage prior on SNP effect sizes; this method outperforms previous methods in terms of prediction accuracy, especially when the training sample size is large138, as is the case with the beat synchronization GWAS. The 1000 Genomes European reference set was used. The PGS was standardized to have a mean of 0 and s.d. of 1. Chromosome X was not included in the BioVU sample.We used an identity by descent (IBD) filter of 0.2 to include only unrelated European samples of BioVU. PGSs were generated using the beat synchronization GWAS summary statistics, using software PRS_CSWe conducted a logistic multivariable regression where the outcome variable was musician versus control, the predictor variable was PGS for beat synchronization and covariates included median age across the patient’s medical record, sex and the top ten principal components estimated from the BioVU genetic data.Further information on research design is available in the Supplementary InformationGlossary and Supplementary Methods and Results, figures and references.Reporting SummarySupplementary TablesSupplementary Tables 1–19.Supplementary Data 1Results of gene property analysis on rhythm-related common factor GWAS (from Magma implemented in FUMA with GTex-v.8 54 tissue types) for Supplementary Fig. 9.Supplementary Data 2Source data including vector variables for Supplementary Fig. 3.Supplementary Data 3Source data LDSC–SEG results for Supplementary Figs. 5, 6 and 7.


Hyperuricemia (serum urate >6.8 mg/dl) is associated with several cardiometabolic and renal diseases, such as gout and chronic kidney disease. Previous studies have examined the shared genetic basis of chronic kidney disease and hyperuricemia in humans either using single-variant tests or estimating whole-genome genetic correlations between the traits. Individual variants typically explain a small fraction of the genetic correlation between traits, thus the ability to map pleiotropic loci is lacking power for available sample sizes. Alternatively, whole-genome estimates of genetic correlation indicate a moderate correlation between these traits. While useful to explain the comorbidity of these traits, whole-genome genetic correlation estimates do not shed light on what regions may be implicated in the shared genetic basis of traits. Therefore, to fill the gap between these two approaches, we used local Bayesian multitrait models to estimate the genetic covariance between a marker for chronic kidney disease (estimated glomerular filtration rate) and serum urate in specific genomic regions. We identified 134 overlapping linkage disequilibrium windows with statistically significant covariance estimates, 49 of which had positive directionalities, and 85 negative directionalities, the latter being consistent with that of the overall genetic covariance. The 134 significant windows condensed to 64 genetically distinct shared loci which validate 17 previously identified shared loci with consistent directionality and revealed 22 novel pleiotropic genes. Finally, to examine potential biological mechanisms for these shared loci, we have identified a subset of the genomic windows that are associated with gene expression using colocalization analyses. The regions identified by our local Bayesian multitrait model approach may help explain the association between chronic kidney disease and hyperuricemia. Hyperuricemia is defined by serum urate (sU) concentration >6.8 mg/dl and is contributed to by deteriorating renal function (Chronic kidney disease (CKD) carries significant global health and economic burden  and sU of 0.20  in one dataset and 0.25  in the other  and hyperuricemia (sU levels) by identifying pleiotropic genomic regions. To achieve this goal, we identified the local regions contributing to genetic variances and covariances across the whole genome  that did not have phenotype data of interest collected at the first visit, we retrieved data from the second visit. sCr was used to define eGFR and details on this can be found in the Yn×2 is a matrix containing the preadjusted phenotypes, 2×1 is a vector of trait-specific intercepts, 1,500×2 is a matrix of SNP effects, and 2×2, where ith row of 2×2. The extent of shrinkage and variable selection was influenced by 3 groups of parameters: 1, 2} and represents the prior probability of nonzero effects for traits 1 and 2 (sU and eGFR), respectively. We treated the {We estimated local (co)variances by fitting Bayesian models to chromosomal segments with a nonoverlapping core of 1,000 contiguous SNPs (between 3 and 4 Mbp depending on the region). We included 2 overlapping flanking regions each consisting of 250 SNPs to each side of the core. The SNPs in the flanking regions were included to account for the effects of SNPs that were outside of the core region but possibly in LD with SNPs in the core segment. Whole-genome regressions have been used to fit several markers concomitantly  was at least 0.5 for both traits and at least 0.8 for one of the 2 traits, according to A colocalization analysis was performed between GWAS significant markers for sU and sCr and the publicly available eQTL data from Genotype Tissue Expression (GTEx) V8  percent of the individuals had an ICD10 diagnosis or self-diagnosis of gout, 12.4% had hyperuricemia, 0.5% had CKD, and 0.3% had hyperuricemia and CKD.This study was based on 333,542 distantly related white participants, of whom 53.7% were female with an average age of 56.9 ± 8.0 years old. The average sCr level was 0.8 ± 0.2 mg/dl (the average ± SE), average eGFR was 144.2 ± 56.0 ml/min/1.73 mWe analyzed the markers (sU and eGFR) using a sequence of Bayesian multitrait models where the markers were regressed on contiguous SNPs in a large chromosomal segment (core) plus overlapping flanking buffers. We collected the samples from the posterior distribution of effects for each core segment and used these samples to estimate the local variances for each marker  and the We found 134 LD windows with covariance estimates that had a 95% CR excluding zero . The numThe 134 significant LD windows often included the same variants and mapped to identical GWAS loci, so we collapsed the 134 windows to 64 unique loci that possessed genetic covariance signal between eGFR and sU . The topTRIM6 and L3MBTL3 in cis, which are genes that have a significant covariance signal and a colocalized eQTL that is expressed in the kidney.We used COLOC  to identn = 57,370) than the unrelated cohort used in the discovery set , so our validation analysis was comparatively underpowered to the main analysis.In the related white UK Biobank validation cohort 12 LD windows were significant for genetic covariance between sU and eGFR . All of MMP11/SMARCB1, ADH1B, MIP/GLS2, ENG/AK1, EPB41L5, KIAA1199, CELSR2, SOS2, KCNS3, TET2, SMLR1/EPB41L2, GLIS1, KIAA1683/JUND, and METTL10/FAM175B. Furthermore, 14 distinct loci identified were previously only known to be associated with only one of the 2 traits, demonstrating that the set of loci contributing to both traits is substantially larger than previously thought. These loci are partially responsible for the comorbidity between hyperuricemia/gout and CKD.The goal of this study was to infer the shared genetic architecture of sU , and eGFR (a marker for CKD). Our results highlight genes that may be involved in the observed relationship between the traits. In this study, we estimated local genetic (co)variances between sU and eGFR and identified regions with pleiotropy. This study was based on the large-scale UK Biobank and formal statistical inference from local Bayesian multitrait models. Our results demonstrated that genetic covariance between eGFR and sU was widespread across the genome. Our method identified 64 distinct LD windows with shared genetic effects between eGFR and sU, the majority of which had negative genetic covariance estimates. We identified 22 distinct novel shared loci, to our knowledge, with significant local genetic covariance for sU and eGFR, including GCKR and CPS1, are mainly expressed in the liver and one, LRP2, is mainly expressed in the kidney  had covariance directionality that matched the directionality of effects found by Previous research investigating pleiotropic genetic loci between sU and eGFR has implicated loci as shared if signals of association obtained from marginal single-marker regressions (e.g. GWAS) for both traits are colocalized  and rs17050272 (LINC01101) were identified by us as shared, and rs1165151 and rs3741414 were located within one of our significant pleiotropic regions but were not in our genotyping platform.Our covariance approach may have direct implications for assessing causal relationships between exposures using Mendelian randomization (MR). Pleiotropic genetic variants violate assumptions of univariate MR, however, they are useful in multivariable MR that can simultaneously assess the causal effects of multiple risk factors on an outcome . For exaSLC7A9, which encodes a solute transporter largely expressed in the small intestine, A1CF, which encodes a protein involved in apolipoprotein B synthesis in the liver, and TRIM6, which encodes an E3 ubiquitin ligase involved in interferon gamma signaling and innate immune response with high expression levels in the kidney variance estimates from Bayesian multitrait models in very large datasets, we have uncovered 22 novel shared genetic regions for sU and eGFR. The approach presented in this paper was applied in the context of sU and eGFR, but it could be applied to any pair of traits. While our discovery set sample size is excellent, we lack a dataset of a similar size for the validation. Some regions were validated but not all.The local shared genomic regions we have uncovered in this study can provide insight into the relationship between hyperuricemia/gout and CKD, elucidating the biological mechanisms underlying the traits. This will help further understanding of the genetic basis of hyperuricemia/gout and CKD.cis- and trans-eQTL data were downloaded from the GTEx V8 portal (All data used are secondary and are held in public repositories. This study utilized deidentified data from the UK Biobank where genotype and phenotype data are available to researchers upon registration. The protocol and consent were approved by the UK Biobank’s Research Ethics Committee and were conducted under the application number “15326.” For eQTL analysis, 8 portal .G3 online.jkac158_Supplementary_MethodsClick here for additional data file.jkac158_Supplementary_Table_1_2Click here for additional data file.jkac158_Supplementary_Table_3Click here for additional data file.


Pennisetum glaucum L.) genotypes, selected to possess a high concentration of slowly digestible starch (SDS) and resistant starch (RS) in their grains. Entries were grown in a randomized complete block design with three replications at locations in Bawku-Ghana, Sadore-Niger, Bamako-Mali, Konni-Nigeria, and Gampella-Burkina Faso across West Africa. Harvested grains from these locations were metabolomically profiled using flow injection ionization-high-resolution mass spectrometry (FIE-HRMS). A total of 3144 mass features (m/z) (1560 negative ion mode and 1584 positive ion mode) were detected, of which, 475 m/z were linked to metabolites be involved in starch, antioxidant and lipid biosynthesis, and vitamin metabolism. Combined ANOVA revealed that the GEI was significantly evident for 54 health-benefiting metabolites, many associated with sugar, especially galactose, metabolism. Additive main effects and multiplicative interaction (AMMI) analysis examined genotype variation and GEI effects, which, when combined with principal component analysis (PCA), found that m/z 171.14864  accounted for 89% of the GEI variation along PC1. The AMMI-based stability parameter (ASTAB), modified AMMI stability value (MASV), and modified AMMI stability index (MASI) were then applied to identify stable and high-performing genotypes for all the health-benefiting metabolites. Similarly, the best-linear-unbiased-prediction (BLUP)-based stability estimation was also performed using the harmonic mean of genotypic values (HMGV), relative performance of genotypic values (RPGV), and harmonic mean of relative performance of genotypic values (HMRPGV), to identify genotype rankings across multiple environments. The multi-trait stability index (MTSI) was calculated and found that the genotypes G1 (ICMH-177111) and G24 (ICMX-207137) were the most stable and were the best mean performers across 52 health-benefiting metabolic traits. These findings demonstrate the potential of G × E assessments on the delivery of health-benefiting metabolite-rich grains in future varieties and hybrids of pearl millet.Genotype × environment interactions (GEIs) should play an important role in the selection of suitable germplasm in breeding programmes. We here assessed GEI effects on pearl millet ( Pennisetum glaucum L.) is an important staple cereal crop that is grown on more than 26 million hectares to produce more than 29 tons of grains worldwide. It is mostly grown in the arid and semi-arid tropical regions of Africa (17 million ha) and Asia (10 million ha), where drought, heat, and low-fertility soils can lead to losses in crop production +PC×θ22) by o et al. , the sumby Zobel , the sta by Raju , the mody et al. , and SpeE represents the number of environments; ijGv is the genotypic value (BLUP) for the ith genotype in the jth environment. The best-linear-unbiased-prediction (BLUP) based stability estimation method was implemented in the Metan r package for the selection of stable genotypes with the best performance in a mixed effects model structure. Colombari et al.  (2013) dThe multi-trait stability index (MTSI) by Olivoto and Lúcio  was implo et al. .m/z) (1560 negative ion mode and 1584 positive ion mode) were assessed based on the presence in at least three replicates of each genotype of pearl millet. Metabolites were tentatively identified, and pathway enrichment factor analysis using the mummichog module of Metaboanalyst  linked 475 mass features to metabolites linked with human health benefits   were targeted as being involved in “starch and sucrose”, “galactose”, and “fructose and mannose” metabolism through pathway enrichment analysis. Similarly, 115 mass features (63 negative ions and 52 positive ion mode) were associated with antioxidant biosynthesis pathways  and 131 metabolites (73 negative ions and 58 positive ion mode). Other important pathways were linked to vitamin metabolism ), pyridoxine (vitamin B6), folate (vitamin B9), lipid metabolism , nitrogen metabolism, inositol phosphate, and zeatin biosynthesis . Two-way0.02078) B. These p < 0.05 for both factors (genotypes (G) and environments (E)) and GEI (genotype and environment interaction) to define multi-factor responses in these genotypes in the five different environmental conditions. Combined analyses of variance across the environments suggested that genotypic, environmental, and interactions were significant for 52 health-benefiting metabolites  was strongly heritable (>0.88) across five environments, whereas broad-sense heritability for m/z 164.04323 was higher (>0.87) in an individual environment . However, when assessed on the basis of a pooled environment, a partitioning of the GEI component lowered the heritability for both traits across environments  for 40 metabolites, whereas 9 metabolites showed non-significant genotype and environment interactions , which ranged from 39.9 to 91.2% for health-benefiting metabolites. One m/z  showed the maximum variance with the first PC , while the second, third, and fourth PCs contributed 7.2, 1.0, and 0.7%, respectively , and GEI contributed 4.84%. Compared to other mass features, PC1 for m/z 380.1561  explained the least variance at only 39.9% of the total GEI, while the second, third, and fourth PCs contributed 26.9, 18.4, and 14.9%, respectively. Genotypic variance explained 13.70% for m/z 380.1561; however, the environment and GEI contributed about 15.78 and 2.34%, respectively.At the same time as the ANOVA based on an additive model, which illustrates the effect of the source of variation, the AMMI model emphasizes the pattern of genotypes (G) and/or environments (E) and their interactions. AMMI-based statistical analysis for 54 health-benefiting metabolites showed significant variation within the eleven genotypes across the five environments . AMMI-baractions . The G ×ectively . Genotypm/z 171.04079 (negative ionisation), tentatively identified as luteone 7-glucoside, involved in the antioxidant pathway, and m/z 539.13831 (negative ionisation), tentatively identified as raffinose and involved in starch metabolism. Similar environmental effects for m/z 285.04037  were observed except with E2 (Sadore-Niger), which had a distinct effect on genotype adaptation  and E4 (Konni-Nigeria) were the furthest from the biplot origin, which indicated strong interaction influences for genotype adaption. E1 (Bawku-Ghana), E3 , and E5 (Gampella-Burkina Faso) were closest to the biplot origin point and had shorter vectors, indicating weak interaction with genotype adaptation. The AMMI2 biplot described the performance of the entries and their adaptation to a specific environmental condition (AMMI1 biplots were generated to explain the interactive correlation between genotypes and environments for the 54 metabolites –e. The aaptation . For m/zondition . m/z 171.04079) showed that the genotype G24 (ICMX-207137) showed the best performance in the E1 (Bawku-Ghana) environment. Genotype G33 (ICMX-207183) had the highest levels, as this showed good adaptation in E5 (Gampella-Burkina Faso). In contrast, genotypes, namely G15 (ICMX-207076), G38 (ICMX-207190), and G1 (ICMH-177111), had higher m/z 285.04037 content, but negative adaptability across the environments such as E2 (Sadore-Niger), E3 , and E4 (Konni-Nigeria). The AMMI2 biplot for the metabolite involved in starch metabolism  and genotypes such as G1 (ICMH-177111), G18 (ICMX-207094), G31 (ICMX-207171), and G32 (ICMX-207183) exhibited good performance in E2 (Sadore-Niger) and E5 (Gampella-Burkina Faso). However, with genotypes, G23 (ICMX-207136) and G3 (ICMX-207207) had higher levels of m/z 539.13831, but had negative adaptabilities across certain environments . Similarly, for m/z 285.04037, genotypes such as G33 (ICMX-207183) had the highest degree of performance with high-ranking adaptability in E2, but G38 (ICMX-207190) was associated with negative adaptability in E3  and E5 (Gampella-Burkina Faso).The AMMI2 biplot for the antioxidant metabolism metabolite  metabolite  and E2 (Sadore-Niger).Genotype-by-environment (GGE) biplot analysis of multiple traits was used to compare multiple genotypes in multiple environments (five environments) for the 54 health-benefiting metabolites . GGE-bastabolite a. The GGm/z 285.04037. The biplot showed four mega environments with E3  and E5 (Gampella-Burkina Faso) sharing a common mega environment and having a similar effect on m/z n285.04037. Similarly, the first two IPCs explained 75.28% (genotype–environment interaction) for the starch metabolism (m/z 539.13831) and 95.27% of the GEI for m/z 285.04037. The convex hull biplot exhibited two mega environments for m/z 539.13831 in which E1 (Bawku-Ghana) and E3  share a common mega environment and E2 (Sadore-Niger), E4 (Konni-Nigeria), and E5 (Gampella-Burkina Faso) are assigned together to have effects on the starch metabolism (m/z 539.13831) metabolite (m/z 285.04037 in which E1 (Bawku-Ghana), E3 , E4 (Konni-Nigeria), and E3  share a common mega environment and were different in E2 (Sadore-Niger). The GGE biplots also indicated the best-performing genotypes in each of those environments by highlighting the convex hull around genotype scores, sectors, and mega environments that share the same sector . For exatabolite b. The com/z 285.04037. For the starch-metabolism-related compound (m/z 539.13831), G15 (ICMX-207076) was ranked 1st and G51 (ICMX-207192) 2nd for their performance across the various environments. Similarly, the genotype G32 (ICMX-207181) ranked 1st and G51 (ICMX-207192) 2nd and showed the highest stability for the vitamin-metabolism-related metabolite m/z 593.15131 , 2nd G31 (ICMX-207171), 3rd G38 (ICMX-207190), and 4th G23 (ICMX-207136) were identified as highly stable genotypes for starch metabolism m/z 539.13831 by HMGV, RPGV, and HMRPGV, but not the WAASB method. For the vitamin-associated metabolite (m/z 593.15131), G32 (ICMX-207181) ranked 1st, G31 (ICMX-207171) 2nd, G38 (ICMX-207171) 3rd, and G23 (ICMX-207136) 4th using HMGV, RPGV, and HMRPGV stability predictions  and G24 (ICMX-207137) had the best stability and performance in the five environments. The stability index score was lowest for G32 (ICMX-207181), showing poor stability and mean performance in multiple environmental conditions .Millets, particularly pearl millet, are important due to their critical role in human nutrition. Substantial progress in nutritionally rich crop production has been achieved through the exploitation of classical genetics and the selection of crop varieties with improved health-benefiting traits. However, the increasing worldwide population and adverse climatic conditions have driven the need to protect and augment health-benefiting traits in micronutrient-rich grains such as pearl millet. Numerous studies have suggested that capturing naturally occurring genetic variation could be important for nutritional values, especially when considering genetic interactions in improving desired attributes . Thus, tp-value ≤ 4.0. The study revealed 738 probable candidate genes, which had significant roles in starch, antioxidant, vitamin, and lipid metabolism. These genes encoded starch branching, α-amylase, β-amylase, vitamin-K reductase, UDP-glucuronosyl, UDP-glucosyl transferase (UGTs), L-ascorbate oxidase, and isoflavone 2’-monooxygenase. However, environmental impacts on the levels of the associated metabolites were not considered in that study and therefore was the aim of the current study. To address G × E and GEI effects, we quantified health-benefiting antioxidant, polyphenol, vitamin, and starch-associated, especially dietary starch, metabolites in eleven pearl millet lines at five growing locations in West Africa using FIE-HRMS profiling. Previously, we used metabolomic-based quantifications of health-benefiting nutritional metabolites as phenotypic traits in conjunction with 76K SNP variants to conduct metabolic genome-wide association analyses . This fom/z 285.04037. This signified that the environmental factor contributed sufficient variations, which was explained by genotypes’ interaction with their respective environment. Further, comparatively less variation in the genotype was found due to other factors such as differences in rainfall and temperature, and this led to different patterns of genotypic performances. The large proportion of total variation linked to the venvironment was also reported by Adugna et al. [The analysis of variance indicated the existence of homogenous error variance for all the health-benefiting metabolites in each of the five environments. Further, the combined ANOVA analysis showed that the mean sum of squares due to genotype, environment, and G × E were significant for 54 health-benefiting metabolites features involved in starch, antioxidant, lipid biosynthesis, and vitamin metabolism . Furthera et al. , Molla ea et al. , Dagnacha et al. , Birhanua et al. , Lakew ea et al. , and Seya et al.  in fingea et al.  in pea aSeveral other statistical models, namely ASI, ASV, ASTAB, AVAMGE, DA, DZ, EV, FA, MASI, MASV, SIPC, and Za, were also applied to understand the stability of genotypes. For all the genotypes, most of the stability parameters showed consistent prediction for genotypic ranking, except ZA and DA. Based on genotypic stability score estimates, G1 (ICMH-177111), G24 (ICMX-207137), G18 (ICMX-207094), and G38 (ICMX-207190) were identified as top-ranking in terms of stability for most of the health-benefiting metabolites. Such AMMI-based stability was also used by Anuradha et al.  in fingeBLUP-based stability methods such as HMGV, RPGV, and HMRPGV further represent robust statistical approaches for predicting stability ,29,39. TThe multi-trait stability index (MTSI) was calculated to identify the best-performing genotypes across various environments for multiple traits. In this study, have found that G1 (ICMH-177111) and G24 (ICMX-207137) are the most stable and best mean performers across the 52 health-benefiting metabolite traits. Our study had similar conclusions to that of Szareski et al. , which aBy combining metabolomics tools, we extensively characterized the metabolite profiles of eleven pearl millet hybrids by growing them in five distinct locations in West Africa. Multivariate statistical models applied to the data dissected the genetic and environmental effects on the accumulation of health-benefiting metabolites in these pearl millet entries. The study identified the correlation of individual environmental diversity with the metabolite diversity. The AMMI-based models used in the study identified better-adapted hybrids for West African regions. This study for the first time characterized G × E interactions on health-benefiting metabolites’ accumulation in pearl millet entries and identified hybrids that exhibit stable preferences across locations. Such information is set to be very important for breeders interested in breeding health-benefiting traits in pearl millet varieties.