Sleep spindles are characteristic electroencephalogram (EEG) signatures of stage 2 non-rapid eye movement sleep. Implicated in sleep regulation and cognitive functioning, spindles may represent heritable biomarkers of neuropsychiatric disease. Here we characterize spindles in 11,630 individuals aged 4 to 97 years, as a prelude to future genetic studies. Spindle properties are highly reliable but exhibit distinct developmental trajectories. Across the night, we observe complex patterns of age- and frequency-dependent dynamics, including signatures of circadian modulation. We identify previously unappreciated correlates of spindle activity, including confounding by body mass index mediated by cardiac interference in the EEG. After taking account of these confounds, genetic factors significantly contribute to spindle and spectral sleep traits. Finally, we consider topographical differences and critical measurement issues. Taken together, our findings will lead to an increased understanding of the genetic architecture of sleep spindles and their relation to behavioural and health outcomes, including neuropsychiatric disorders. et al characterize sleep spindle activity in 11,630 individuals and describe age-related changes, genetic influences, and possible confounding effects, serving as a resource for further understanding the physiology of sleep.Sleep patterns vary and are associated with health and disease. Here Purcell  Sleep spindles—bursts of 11–15 Hz (sigma frequency band) activity, typically between 0.5 and 2 s in duration—are characteristic transient features of the sleep electroencephalogram (EEG). Spindles are most prominent during N2 sleep and are in fact a defining feature of this stage. Although their underlying neural circuitry has been relatively well characterized—being generated in the thalamic reticular nucleus and synchronized by thalamocortical interactions36Within individuals, spectral properties of the sleep EEG including sigma power are highly stable (fingerprint-like) across nights89121416Genetic studies are further motivated by suggestions that spindle deficits are biomarkers for neuropsychiatric diseases, including schizophrenia1920212224252628Well-powered genetic studies of sleep are emerging, in particular for self-reported traits including chronotype (morning versus evening preference) and sleep duration3132An immediate challenge is that spindle activity cannot unambiguously be reduced to a single quantitative trait, as it likely represents a complex and possibly genetically heterogeneous set of processes. What are the optimal and independent aspects of spindle activity: density, duration, amplitude, frequency or other features? Does it matter when spindles occur: earlier versus later in the night, or in the life course? Where should spindle activity be measured, given clear topographical differences? Do inferences in healthy individuals generalize to patients, or are there other latent group differences that may impact genetic studies?Here we investigate some of these questions as a prelude to future molecular genetic studies, in an analysis of over 10,000 individuals to characterize normative distributions and epidemiological associations of spindle activity. In particular, we demonstrate the considerable heterogeneity that exists—both between and within individuals—as well as establishing a heritable basis for individual differences.We combined polysomnography and demographic, anthropometric and medical history data on 11,630 individuals aged 4 to 97 years  from the35r=0.52, P<10−15, N=11,148; r=0.95, P<10−15, N=11,146).In an initial analysis , we broadly targeted spindle activity in the sigma range, centred on 13.5 Hz, and detected a total of 3,846,408 spindles across C3 and C4 EEG channels. The mean spindle density, averaged over both channels, was 1.88 spindles per minute of artefact-free N2 sleep, comparable to other reports in healthy adults3738r=0.74 (N=245), 0.81  and 0.76 (N=958) for CHAT, SHHS and MrOS, respectively  study were retested ∼6 months after their initial polysomnogram; adults in Sleep Heart Health Study (SHHS) and Osteoporotic Fractures in Men ancillary sleep study (MrOS) were retested after ∼6 years. For spindle density, test–retest correlations were P<10−15, N=10,387, linear regression of spindle density on sex along with other covariates, see Methods section). Although menstrual cycles may impact spindle activityF-test to compare variances, P=0.016) in spindle density estimates for females  than males , consistent with a sex-specific effect on spindle activity that introduces more variability in females.Females had 0.16 more spindles per minute than males , in a linear regression with N2 duration as the dependent variable, controlling for age, sex, race, arousal index, apnoea–hypopnoea index (AHI) and study membership  and a greater duration of REM sleep . The latter effects were stronger in older individuals, with interactions in predicting N3 (P=1 × 10−6) and REM (P=2 × 10−10) sleep obtained by adding an age-by-spindle density term to each of the above regression models. , there was, as others have foundWithin-study statistical modelling produced consistent results: spindle density significantly increased with age during childhood (CHAT study) but declined in all the adult studies . Other sN=719 and 5,572, respectively, both P<0.05 from a linear regression on spindle density, controlling for standard covariates).As previous reports have suggested that the decline in spindle density from middle to late adulthood is attenuated in femalest-test P=0.023, N=245). In contrast, both adult studies showed highly significant  reductions in spindle density , amplitude (5 and 12%) and duration (2 and 2%) across the ∼5-year period, consistent with the cross-sectional analyses.We observed similar patterns of results in the longitudinal, within-individual analyses , which aUp to this point, we have only considered spindles from the canonical wavelet analyses that targeted activity broadly centred around 13.5 Hz. There is neither consensus nor objective data on what constitutes the true range of frequencies for spindles; however, human studies have used different criteria, with lower bounds as low as 9 Hz373839404142434445464748495053105556FC=8 to 18 Hz in 0.25 Hz increments, yielding a vector of spindle density estimates per individual in a frequency-dependent analysis. Across this entire range of frequencies, the extent to which the transient features detected in the EEG as spindles in fact represent the same underlying processes is an open question. Similarly, signal-to-noise ratios may vary when targeting spindles across this broad range of frequencies. To address these problems empirically, we therefore explicitly detected spindles at different frequencies and analysed them separately.To target a greater range of spindle frequencies, we set the wavelet’s FC=11 Hz) peak earlier in development than faster spindles , consistent with earlier studies documenting an upward shift in the sigma peak during childhood and adolescence12P=0.63, N=10,182, linear regression of spindle density on standard covariates; P<10−15, N=10,179), consistently observed across studies , especially so in younger individuals . Spindles during N2 and N3 sleep generally showed similar demographic associations  and within-cycle position (as a six-level factor), marked systematic within-cycle variation was observed for both fast and slow spindles, although the two types showed different profiles .Specific features of the hypnogram might account for part of this within-cycle variability. Neural activity during the so-called descending  versus ascending  N2 epochs , a non-benzodiazepine sedative hypnotic that binds to GABA (γ-aminobutyric acid) receptors at the same location as benzodiazepines.Medications that affect sleep can also affect spindles, most notably benzodiazepines and certain non-benzodiazepine sedative hypnoticsP=3 × 10−5, N=10,023, with 0.12 fewer spindles per minute compared to individuals without sleep apnoea/with low AHI). Treated as continuous measures, AHI and also the arousal index were both moderately associated with reduced spindle density  identified as black .Within N2 sleep, blacks tended to show lower absolute and relative measures of spectral power, particularly in the sigma range , with orP=2 × 10−7, N=9,820, linear regression on spindle density). Blacks also showed lower spindle amplitudes and shorter spindle durations, but no differences in spindle frequency (Blacks had significantly lower estimates of spindle density (0.3 per minute), with the effect also concentrated in younger individuals  and a sirequency . In the Although spindle activity is influenced by genetic factors, there are clearly major non-heritable sources of variation including age, sex and medication use. Similarly, although group differences between races could in part be driven by genetic factors, they could equally arise due to unmeasured environmental confounding factors. Understanding the causes (and consequences) of any racial differences remains an open empirical question. Many factors that we were unable to capture comprehensively, including socioeconomic status (SES) at both family and neighbourhood levels, environmental noise and pollution, diet and normal sleep habits, could be causal factors that mediate the statistical association with race. For four studies, indices of family SES were available, including a self-reported ladder measure, household income, and parental education and employment. All SES measures were significantly lower in blacks compared to whites, and some were modestly associated with spindle density . HoweverWe leveraged the family-based CFS to test for genetic influences on spindle measures1617h2) and genetic correlations (rG), using variance components modelsh2=0.45, P=8 × 10−6 for N=186 whites; h2=0.43, P=3 × 10−6 for N=229 blacks) and other sleep architecture, spectral and spindle traits including sigma power (We used available genome-wide microarray single-nucleotide polymorphism (SNP) data to estimate additive trait heritabilities , and negative genetic correlations (rG=−0.45 and −0.48) with duration of N3, suggesting that the genes that affect spindle density also influence NREM duration. Despite the modest genetic correlations with absolute sigma power shown in rG=0.69 and 0.68 in whites and blacks, respectively, both P=0.0001, not shown in the table). In contrast, we observed negative phenotypic and genetic correlations between spindle density and delta power. There were very high genetic correlations for estimates of spindle density in N2 and N3 , suggesting that similar genetic variation influences density across both stages.A genetic correlation reflects the extent to which two traits are influenced by the same sets of genetic variants. Canonical spindle density and amplitude were positively genetically correlated  despite both showing significant heritability (FC=8 to 18 Hz). In general, faster spindles (FC ∼15 Hz) appeared to have greater heritability, with a second peak around slower spindles (FC ∼11 Hz), but with no evidence for genetic overlap between these two types.Despite significant univariate heritability for spindle frequency, there was no evidence for genetic overlap with spindle density, suggesting that genes that influence how often spindles occur tend not to influence their frequency, and tability . Figure t-test, P<10−15, N=11,035) mean differences between left (C3) and right (C4): spindles at C3 tended to occur at a 1.5% higher rate and be 3.2% stronger. Approximately half of all spindles were bilateral (detected at both C3 and C4 within 0.5 s of each other), although this rate decreased with increasing age  differences in spindle activity . We obsesing age . These eN=53 children) had EEG data on 18 scalp electrodes. Consistent with previous reportsFC=11 Hz) spindles were significantly enriched at frontal channels (F3 and F4) compared to C3, whereas faster (FC=15 Hz) spindles exhibited a more uniform topography  between estimates of spindle density across the two methods , although the band-pass method detected more spindles, with a mean of 2.97 spindles per minute, compared to 1.88  detected at 1<x<2 and all spindles (TA) detected at x>1, compared to the standard (x>4.5) threshold, where x is the wavelet coefficient divided by that individual’s mean for all N2.To extract discrete events from the continuously varying EEG, spindle detection methods typically set thresholds for duration, frequency or amplitude, although these can be arbitrary and not based on any biological ground truth, whether they are fixed  or adaptive . Our primary analyses used the default, previously published value  between standard and TL estimates of spindle density: individuals with more high-amplitude spindles (satisfying the default threshold) tended to have fewer low-amplitude ones, and vice versa. Consistent with this, TL spindles showed highly significant associations that were in opposite directions compared to the standard analysis, for variables including benzodiazepine use  is also an open empirical question. Furthermore, learning-induced state changes in spindle activity may reflect true changes in density rather than amplitude. Moving forward, analyses focused on the density of high-amplitude spindles  should be supplanted by more exact methods for parsing spindle density and spindle amplitude as separable components.TL and stage N3 analyses point to spindle activity that would be unlikely to be detected visually, but nonetheless may be valid. Perhaps, the most pernicious measurement issue (for both human and automated scores) is the dependency between the true properties of a spindle and its detectability.A number of other caveats are warranted. Automated detectors , including EEG and electrocardiogram (ECG) signals on 11,630 individuals across six studies  had a second polysomnogram, typically administered 5 or 6 years after the first. European Data Format (EDF) files and annotation files  were accessed on January 2016. Electrode labels are from the International 10–20 system. All studies used American Academy of Sleep Medicine staging, except the SHHS, which used R&K: here NREM3 and NREM4 were collapsed to a single N3 stage, for consistency with the other studies.All data were obtained from the NSRR , the Cleveland Children's Sleep and Health Study , the CFS (predominantly adolescents and middle-aged adults), the SHHS (middle-aged adults), the MrOS  and the SOF . We stratified analyses by study to control for possible technical and measurement differences as well as the effects of ageing.Key covariates for all individuals included age (years), sex and racial/ethnic status . Unless explicitly noted otherwise, the three levels of the race factor are modelled as two dummy-coded binary variables . Comparisons involving race but that indicate only the levels ‘black’ or ‘white’  imply a comparison of that group against the other, that is, excluding individuals coded as ‘other’ due to relatively low numbers. Individuals’ sleep was scored for an arousal index  and AHI .N=11,630). N’s for individual studies; some specific tests will have slightly fewer observations because of small amounts of missing data. All P values are two sided; those smaller than 10−15 are reported as P<10−15. The total sample size means we are well powered to replicate existing findings from studies two or three orders of magnitude smaller, as well as considering heterogeneity between and within studies.Unless explicitly noted otherwise, all primary individual-level analyses control for age, sex, race, study, arousal index and AHI (entered as covariates in linear regression models). Unless noted otherwise, analyses are based only on the set of independent observations from the first polysomnogram  and, where available, a single ECG channel . For theet al.The above Buckelmüeller The primary method was based on the Morlet wavelet transformationFB=2s2 and s=n/2πFC, where n is the number of cycles of the complex Morlet wavelet. FC is the centre frequency, the frequency targeted most strongly, set to FC=13.5 Hz in the canonical analyses. We secondarily varied FC from 8 to 18 Hz in 0.25 Hz increments , to target spindles across a broader range of frequencies. FB, the wavelet bandwidth, is determined by the number of cycles (n) for a given FC. Varying n represents a tradeoff between resolution in the frequency versus the time domain; for the primary analysis, we set n=7, a common default that provides a reasonable balance of time and frequency domain resolutionn=12 to give better frequency resolution.where t=4.5 times greater than the baseline, which is the average value across all artefact-free N2 sleep for that individual/channel) were flagged. Intervals of consecutively flagged points with durations between 0.3 and 3.0 s were labelled as putative spindle cores. As a modification to the previously published approach, to target the waxing and waning profile of a typical spindle, we further required that all points extending out from each core were greater than a lower threshold of t=2 times the baseline, such that the extended core had a total duration of at least 0.5 s. That is, all detected spindles were at least 0.5 s with the signal above t=2 times baseline, and contained a central core of at least 0.3 s with the signal above t=4.5 times baseline. Spindles within 1 s of each other were merged into a single spindle (but only if the resultant merged spindle was <3.0 s).Wavelet coefficients were smoothed using a moving average (window duration 0.1 s). In the default analysis, sample points with coefficients greater than a multiplicative threshold , for each spindle we calculated the frequency, amplitude, number of oscillations and an index of symmetry. Based on a band-pass-filtered signal (11–15 Hz), spindle amplitude was calculated as the largest peak-to-peak amplitude; spindle frequency was calculated as the modal frequency from a fast Fourier transformation; number of oscillations was based on counting peaks and troughs; symmetry was based on the location of the maximum peak-to-peak change relative to the start (0.0) and end (1.0) of the spindle interval.χ2 goodness of fit. True spindle density fluctuates over the night, so mild overdispersion is expected . The extreme tail  more likely reflects artefactual, non-biological sources of variation, however. A threshold of 2.0 was set, which excluded ∼1% of signals. Clearly spurious spindles were often temporally clustered , reflectr=−0.15, P<10−15, N=11,142) between spindle density and body mass index (BMI). Both height (positively) and weight (negatively) were independently associated with spindle density; neck and hip circumference were also negatively associated. Although AHI was also negatively associated with spindle density, the BMI association remained after statistically controlling for age, sex, race, study, arousal index and AHI in a multiple linear regression of spindle density on BMI and these covariates . The effect, observed for both wavelet and band-pass detectors, in fact represented greater cardiac interference in the EEG signal in higher BMI individuals, with greater levels of ECG artefact leading to higher thresholds for detecting spindles, and so correspondingly lower rates of spindles.In preliminary analyses, we noted an unexpected negative correlation (Pearson’s CS=0.13), although individual CS correlated with BMI in five of the six NSRR studies examined here (r=0.3–0.4). Importantly, this effect was not driven by only a handful of individuals with obvious contamination effects in the EEG.Cardiac activity can differentially interfere with the EEG as a function of body type, and is often exacerbated in infants and, as we presumed here, more obese individuals. We hypothesized that the spindle–BMI association reflected differential interference of cardiac activity in the EEG. http://www.robots.ox.ac.uk/~gari/CODE/ECGtools/ecgBag/rpeakdetect.m). Based on filtered N2 sleep, we averaged over intervals of the EEG synchronized by R peaks, up to 2 s in duration, to create a characteristic signature per individual per EEG channel. The signature shown at the top of the Using available ECG signals, we effectively removed this cardiac interference , eliminaP<10−15) in all studies . On average, spindle density was significantly higher postcorrection . Importantly, however, after ECG correction the association between spindle density and BMI was eliminated in the entire sample . A similar pattern was observed for the other anthropometric correlations noted above. Unless otherwise noted, all subsequent analyses were based on the ECG-corrected signals.After the correction, estimates of spindle density were significantly higher  data set  of cleaned N2 sleep for an individual to be included in downstream analyses, which removed only a handful of individuals. For EEG measures and key quantitative measures such as BMI, we set to missing outlier values defined as ±3 s.d. units from the mean. For EEG measures, we merged the two channels by averaging the individual mean values estimated for C3 and C4. Across all measures considered here, the correlations between estimates based on C3 versus C4 were typically very high. Therefore, we elected to set merged values to missing if C3 and C4 estimates were highly discrepant for a given individual. Specifically, if the difference between an individual’s value for C3 and C4 was more than three times the average of the s.d.’s for C3 and C4, the merged value was set to missing. If only a single channel was available, we used the single-channel estimate as the final, merged value.et al.After removing outlying individuals (as described above), we were left with 3,851,924 spindles  in 11,293 individuals . The vast majority of individuals had > 1 h of filtered N2 sleep available for spindle and spectral analysis. Individuals had on average ∼420 N2 epochs before any filtering. Filtering epochs based on any overlapping arousals or movements removed 113.3 epochs per individual on average. Of the remaining epochs, Buckelmüeller t-test P<10−15, N=11,165) but highly correlated  with results from the filtered data set, and with broadly similar demographic associations . The between-channel (C3/C4) correlation in spindle density was also slightly lower without filtering . This suggests that filtering enhanced spindle detection; at the same time, estimates of individual mean spindle density appeared to be relatively robust with respect to artefact, and rates of spindle activity for filtered and retained epochs were broadly similar.Our conservative approach does not imply that almost half of the epochs exhibited gross artefact. To underscore this, we repeated the canonical analysis without any epoch-level filtering or correction for cardiac interference. Based on C3 (similar results obtained for C4), average spindle density was lower without filtering . All other statistical analyses were performed using R ( https://www.r-project.org).All signal-processing and spindle detection steps were performed using software developed by the author (S.M.P.), as part of a C/C++ package for the large-scale analysis of sleep data , we estimated spectral band power for the sigma range (defined as 12 to 15 Hz), taking the natural log of the absolute power estimate. Relative spectral power was calculated as the log transform of the absolute power per band divided by the sum of the six bands considered here: slow (0.5–1 Hz), delta (1–4 Hz), theta (4–8 Hz), alpha (8 12 Hz), sigma (12 15 Hz) and beta (15 30 Hz).The second spindle detection method, following ref. For individuals with repeated polysomnography, we calculated test–retest correlations as indices of reliability, based both on raw scores and on scores adjusted for age, sex and race, using the residuals from a within-study linear regression of the measure on these predictors . For the CHAT study, treatment arm (early adenotonsillectomy versus watchful waiting plus supportive care) did not correlate with baseline, follow-up or change in spindle density (data not shown). We additionally removed 145 individuals from the CHAT follow-up study due to inconsistent scaling of their EEG signals in the EDFs we obtained.Based on manual staging from the NSRR, we calculated the duration and percentage of sleep spent in N1, N2, N3 and REM, based on all epochs . When estimating spindle density during N3, we applied the same pipeline as described above. Because the duration of N3 was typically shorter than N2, especially for older individuals, a greater proportion of individuals did not meet the criterion for a minimum duration of artefact-free N3 epochs. Therefore, for comparability, some analyses  showed statistical evidence for a nonlinear effect of age within that individual study . To simpFC=11, 12, 13, 14 and 15 Hz) on age (including higher order terms up to the fifth), sex, study, race, BMI, arousal index and AHI). The expectation as a function of age (from 5 to 95 years) was calculated based on the estimated intercept and five age coefficients from each regression model. Each line was scaled to have a minimum of 0 and a maximum of 1, to highlight when each trajectory peaks with respect to age.For the life-course trajectories . We selected the bounds of 8 and 18 Hz to encompass the broadest range of spindle frequencies reported in the human literature. For the majority of frequency-dependent analyses, however, we focused on a more central range of frequencies, in which 11 Hz was chosen to target slow spindles and 15 Hz to target fast spindles. Importantly, our broad conclusions remained unaltered if slightly different spindle frequency values were selected from within the core range.We repeated the wavelet analysis with different values of FC value. We calculated the total number of unique and discrete spindle events for each channel, by merging temporally overlapping spindles within a core frequency range (FC from 10 to 16 Hz in 0.25 Hz increments) (r=0.69) and showed similar associations with age and sex, but had a mean of 7.4 spindles per minute, compared to 1.88. This ‘total’ spindle density estimate was naturally higher than the canonical estimate, which only targeted the smaller, core range of spindle frequencies, because some slower spindles were not detected with higher values of FC, and vice versa.In these analyses, the same true spindle will typically be detected at more than one rements) . SpecifiFC=13.5 and 14 Hz) were obligatorily highly correlated because the wavelets target overlapping ranges of frequencies, there was only very modest correlation between individuals’ spindle density estimates for slow  versus fast  spindles (r∼0.1).Aggregating spindles across frequencies may not be optimal; however, it may mask important heterogeneity that is only evident when different types of spindles are analysed separately. Indeed, although estimates at nearby frequencies  and slow (FC=11 Hz) spindles. Individual was included as a random effect, along with fixed effects including age (including higher-order terms up to the fifth), sex, study and race. All epoch-level analyses of within-night dynamics were based on the C3 channel only. Fixed-effect significance values were estimated from a normal approximation of the corresponding t-statistic. Note that epoch-level means in spindle density may differ from individual-level means, which do not weight by the number of epochs for that individual .Epoch-level linear mixed models were performed using the lme4 R packageWithin-cycle variation was characterized in terms of the N2 sleep within the first six 10-min intervals of the NREM cycle, which was entered into epoch-level models as a fixed six-level factor. Descending N2 epochs transition from wake, N1 or REM sleep into N3 sleep; ascending N2 epochs are those transitioning from N3 to N1/REM/wake. For each N2 epoch, we calculated a score (ranging from −1 to +1), considering up to 5 min of preceding non-N2 epochs, coding N3 epochs as +1 and N1/W/R epochs as −1, and taking the average. A similar score was generated from up to 5 min of subsequent non-N2 sleep, but with reversed coding . Based on the average of these two scores, N2 epochs scored above +0.5 were labelled as ascending, those below −0.5 as descending. Of 1,888,872 N2 epochs, 167,094 were labelled ascending, and 538,760 were descending.Analyses of chronotype used linear regression to relate individual whole-night spindle density to sleep-midpoint controlling for the standard covariates, allowing for both linear and nonlinear (up to fifth order) effects for sleep midpoint.After removing individuals with an unusually short (<30 min) duration of N2 sleep, we fit a series of epoch-level linear mixed models, using full maximum likelihood, with per-epoch spindle density as the dependent variable. We considered only N2 epochs that occurred during persistent sleep and within a defined NREM cycle. All models had a random effect of individual, and fixed effects for age (up to fifth order), sex, study and race. Epoch-level fixed effects in all models were cycle number (1–5) and relative position within the cycle (as a six-level factor). Compared to a null model with only these terms, we fit a series of models that included one additional temporal predictor of spindle dynamics: (a) local clock time, (b) elapsed time since lights out, (c) elapsed sleep time, (d) elapsed N2 sleep, (e) elapsed N3 sleep, (f) elapsed REM sleep and (g) local clock time and elapsed sleep time. Each temporal predictor was a quantitative variable (in min) including higher order terms (up to the fifth). Clock time was coded in terms of minutes past 20:00 . Similar results were obtained if sleep cycle number and relative position within the cycle were not included in the models. We used the Bayesian information criterion to select the best fitting model, separately for fast and slow spindles.t-test. The final elapsed-sleep model used in these analyses included additional covariates to account for ascending/descending status; additionally, epoch-level fixed effects were allowed to vary as a linear function of age .To infer latent circadian components, we analysed the residuals from a model predicting spindle density as a function of elapsed sleep time and the above-mentioned covariates. Specifically, we calculated their mean values in bins of epochs defined by local clock time, separately for old and young individuals. We tested whether the mean residual for each bin was significantly different from the expected value of 0 using a single-sample In testing for possible mediating effects of SES, we used the following indicators . In CHATP>10−3), and imputation quality (R2>0.8). We retained only autosomal SNPs, further pruning these to be in approximate linkage equilibrium using PLINKThe CFS comprised a number of related individuals, including 369 individuals from 160 full sibships. Intraclass correlations were estimated using the ICCest function from the ICC R package, based on the ratio of the variance of the family means to the variance of all observations from a one-way analysis of variance. Correlations are based on age- and sex-adjusted values . For the CFS, genome-wide SNP array data were available. After imputation against the 1,000 Genomes reference panelN=53) of the CHAT study had 18 EEG channels including frontal, central, temporal, parietal and occipital electrodes. Because topographic analyses are particularly sensitive to artefact, we took extra steps to remove spurious correlations between signals: (a) removing two individuals exhibiting uniformly low spindle density estimates across all channels, (b) removing seven individuals flagged as multivariate outliers with respect to the 2 × 18=36 (FC=11/15 Hz × 18 channels) estimates of spindle density based on the Mahalanobis distance (MVN R package), (c) removing three individuals with excessive leverage (DFBETAS>1) in any of the regressions of C3/C4 spindle density on spindle density at other sites and (d) regressing out the effects of age, sex and race on spindle density measures before the correlational analyses. We used the cluster (the agnes function) and phylo packages in R to perform average-linkage agglomerative clustering using default settings, based on a matrix of distance measure calculated as 1−|r|, where r is the correlation between spindle densities. Finally, we noted a trend whereby slow C3 spindles showed slightly greater correlations with fast spindle densities across all other channels, compared to slow C4 spindles. In contrast, fast C4 spindles showed greater correlations with fast spindles across other channels, compared to fast C3 spindles .Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Bos taurus lineage was reduced by a factor of ~1.5 from the Neolithic to the Middle Ages, and increased again only during the Early Modern Ages. Using haplotype analysis, we found evidence that the bovine PLAG1 mutation (Q) with major effects on body size, weight and reproduction is a >1,000 years old derived allele that increased rapidly in frequency in Northwestern European B. taurus between the 16th and 18th centuries. Towards the 19th and 20th centuries, Q was introgressed into non-European B. taurus and Bos indicus breeds. These data implicate a major role of Q in recent changes in body size in modern cattle, and represent one of the first examples of a genomic sweep in livestock that was driven by selection on a complex trait.The recent evolution of cattle is marked by fluctuations in body size. Height in the  Bos primigenius) lost stature during late Pleistocene, decreasing from a withers height range of 165–185 cm to 145–160 cm1. Between 280,0002 and 330,0003 years before present (yBP), the ancestral auroch population diverged into two distinct lineages that would later originate the humpless Bos taurus and the humped Bos indicus cattle. Towards the beginning of the Holocene, B. taurus and B. indicus were independently domesticated in the Fertile Crescent  and in the Indus Valley , respectively5. Later, B. taurus suffered a further decline in stature between the Neolithic and the Early Middle Ages6, approaching wither sizes of 95–123 cm1. Archaeological data further suggested that stature loss followed a gradient from Southwestern Asia towards Northwestern Europe that coincides with the post-domestication route of Europe colonization7. A counterpoint to this process was also suggested, namely introduction of cattle in Northern and Western Europe by the Roman Empire around the 1st century that were much larger than Celtic or Germanic cattle, ranging from 105 to 142 cm1. However, stature of Northwestern cattle decreased again shortly after the fall of the Roman Empire. Along with the intensification of artificial selection in the past few centuries, B. taurus entered a process of stature recovery that started between the 15th and 18th centuries and that lasted until recently6. Consequently, modern European breeds typically range from 105 to 155 cm in average withers height8, which represents a 1.10- to 1.26-fold increase in stature compared to the Early Middle Ages  data of European B. taurus breeds with high (145–155 cm) and low (105–133 cm) median withers height and found that, relative to the UMD v3.1 genome assembly10, the most significant signature related to stature mapped to chromosome 14 (CHR14) positions 24.79–28.25 million base-pairs (Mbp). This signature has been recently confirmed with whole genome sequence data of four B. taurus breeds and constrained to a smaller region spanning positions 24.80–25.08 Mbp11, where the pleiomorphic adenoma gene 1 (PLAG1) is located. Nevertheless, it is still unclear whether this selective sweep is traceable back to the period between the 15th and 18th centuries.If major genetic variants contributed to stature recovery since the Early Modern Ages, their selection signatures should be detectable from genomic data of modern cattle breeds. Randhawa et al.12, we and others22 have previously reported that this CHR14 region is also a major pleiotropic quantitative trait locus (QTL) affecting cattle body size and reproduction, supporting an important role of PLAG1 in recent changes in stature in cattle. The candidacy of PLAG1 is further supported by functional evidence, since the transcription factor encoded by this gene regulates the expression of insulin-like growth factors26. Moreover, mice carrying a null PLAG1 allele exhibit growth retardation and reduced fertility27. An intriguing observation however is that the QTL is detectable in modern breeds of both B. taurus and B. indicus cattle, which is unexpected given the long divergence time between these two subspecies.Following the milestone publication by Karim B. taurus and B. indicus: (i) variants that were present in the ancestral B. primigenius population still segregate in both subspecies; (ii) separate derived alleles account for the QTL in the two subspecies; or (iii) lineage-specific mutations have been recently introgressed from one subspecies to the other. Together with the evidence of selection, hypothesis (i) would imply that very ancient  standing neutral variants at the PLAG1 locus became differentially advantageous under certain circumstances. This hypothesis could be confirmed by showing that ancestral haplotypes have positive effects on body size in both subspecies. On the other hand, hypotheses (ii) and (iii) would require derived haplotypes, and could be tested by showing that B. taurus and B. indicus either carry different (ii) or identical (iii) derived haplotypes positively increasing body size.Three main hypotheses are consistent with a major stature QTL that is detectable in both PLAG1 haplotypes by gathering evidence from the B. indicus lineage and contrasting against data of worldwide B. taurus breeds. It was our objective to investigate whether the selective sweep surrounding the haplotype was traceable to the recent event of stature recovery in Northwestern Europe.Here, we aimed at elucidating the patterns of gene flow of et al.14 presented evidence that the QTL haplotype associated with increased body size has an exclusive B. taurus origin in Brahman, a breed with average 91% B. indicus and 9% B. taurus ancestry28. Although Brahman could be an exception given its higher levels of B. taurus ancestry in comparison to breeds that are generally considered purebred B. indicus, similar heterogeneity of haplotype origin at this QTL in other breeds is plausible since most of the modern B. indicus populations were recently subjected to B. taurus introgression31.In support to the hypothesis of introgression, Fortes B. indicus populations, we decided to re-map the QTL in Brazilian Nellore cattle. Our past investigation in this breed22 pointed to a haplotype with positive effects on weight and conformation traits, in agreement with the findings by Fortes et al.14. However, as for other B. indicus breeds, a B. taurus origin for this haplotype in Nellore is less clear since we found that the current population has on average only 0.9% B. taurus ancestry31. Nevertheless, the introgression hypothesis remains plausible given the documented crossbreeding in Brazil between B. taurus brought by colonizers and B. indicus imported from India in the late 19th century and early 20th century6. Moreover, considering a genome size of ~2.87 billion base-pairs (Gbp) with ~22,000 protein coding genes32, 0.9% would correspond to ~25.8 Mbp of sequence, which is approximately one fourth of an average-sized chromosome (~100 Mbp) or ~200 protein-coding genes.In order to test whether the introgression hypothesis holds in other PLAG1 in B. taurus12; and (iii) birth weight is moderately heritable (h2 = 0.37) and affected by fewer environmental effects in comparison to other massively recorded traits in Nellore cattle. Whole chromosome haplotypes were constructed from 15,132 SNP markers on CHR14 with a Hidden Markov Model33. Haplotypes at shorter segments were then determined in order to map the QTL via regression analysis.We started by analyzing Illumina® BovineHD (HD) genotypes of a sample of 779 Nellore bulls with deregressed estimated breeding values (dEBVs) for birth weight obtained from records of 846,782 calves. These bulls sired over ten Nellore generations and comprised animals born between 1965 and 2008, with 90% born after 1990 and 50% after 2000. We selected birth weight as a proxy for body size because: (i) withers height measurements were not available for these animals; (ii) the QTL was associated with differences in fetal expression of p = 4.83 × 10−17) spanning positions CHR14:24973324–25012733  and a portion of the 3′ end of PLAG1. The significant haplotype included nucleotides G – rs110243083, G – rs136888475, G – rs109636480, T – rs135404594, T – rs134286310 and C – rs135538206, and will be hereafter denoted GGGTTC. Marker rs135404594 was located in the 3′-UTR of PLAG1, whereas markers rs134286310 and rs135538206 were intronic to PLAG1. All remaining markers in the haplotype were intergenic. Frequency of GGGTTC was 17.8%, with an estimated effect of 0.311 ± 0.037 kg on birth weight dEBVs. The distribution of dEBVs according to number of copies of GGGTTC followed an additive pattern  haplotype , 241 (30.9%) and 520 (66.8%) bulls had predicted genotypes QQ, Qq and qq, respectively, which did not significantly deviate from Hardy-Weinberg Equilibrium . Among the six alternative haplotypes, the most common was ATACCT (70.4%). An orthology analysis revealed ATACCT to be the ancestral haplotype  but all 15 were predicted to be qq. In fact, the earliest predicted carrier of Q was a polled animal born in 1975. Coincidently, the polled allele in Brazilian Nellore34 is deemed to be of B. taurus origin. We further performed a model-based clustering analysis35 to estimate B. taurus ancestry on CHR14 using a panel of reference breeds37. Estimated percentages ranged from 0.0% to 46.9% (mean = 10.0 ± 9.55%) and were positively associated with birth weight dEBVs (p = 3.31 × 10−9) and number of copies of GGGTTC , indicating a B. taurus origin for Q in the B. indicus lineage , higher frequency of Q , higher incidence of polledness , and larger mean birth weight dEBV   Fig. . These rQq and fifteen were predicted to be qq based on number of copies of GGGTTC. These data were then compared against sequence variants underlying the stature QTL in B. taurus: by analyzing crossbred steers descended from two B. taurus dairy breeds diverging in height , Karim et al.12 narrowed the QTL down to eight positional candidate variants (Supplementary File 1). Nishimura et al.17 also found these variants in Japanese Black cattle, and reported that the direction of the haplotype effect was consistent between studies. These findings implied Q to be identical-by-descent across B. taurus breeds. An additional implication from the introgression hypothesis was that a single derived haplotype should account for QTL effects in both subspecies of cattle. Here, the coordinates of the associated haplotype region spanned five out of the eight B. taurus positional candidates, namely rs110092040 , ss319607399 , ss319607400 , rs109231213  and ss319607401 . The previously reported B. taurus haplotype at these variants was Q = TAACT. We found that the HD haplotype GGGTTC was in perfect correspondence with the sequence haplotype TAACT in Nellore, suggesting near perfect LD between GGGTTC and Q in this breed. Moreover, given that the reference genome animal was most likely QQ based on its GGGTTC haplotype, predictions of qq genotypes were corroborated by deficit of reference alleles  and two B. indicus (n = 65) breeds. Additionally, samples of five B. taurus x B. indicus crossbred populations (n = 139) and three outgroup species (n = 11) were also available.Given the B. taurus breeds , is positioned immediately after GGGTTC in the HD panel and is located only ~2.9 kbp downstream of the tag haplotype region. Therefore, we attempted to characterize LD between GGGTTC and Q by computing the correlation between the tag haplotype and the nucleotide G at rs109815800 in the two mentioned breeds. Our analysis indicated a correlation of r = 0.884 (p = 1.02 × 10−39), reinforcing the hypothesis that GGGTTC-Q occurs at a higher frequency than GGGTTC-q in B. taurus. Extrapolating from Holstein and Jersey and assuming that the gametic phase of Q and G-rs109815800 is at least partially preserved across cattle breeds, we found r = 0.889 (p = 3.98 × 10−245) when all breeds were analyzed simultaneously. We further inspected how often G and T at rs109815800 segregated with GGGTTC in the overall data and found that G-rs109815800 happened almost exclusively with GGGTTC while accounting for 86.9% of all instances of the tag haplotype. Therefore, LD between GGGTTC and Q was indeed imperfect but yet substantially high across B. taurus populations, which validates this tag haplotype as a proxy for the causal mutation in the present study. However, in order to maximize power, we decided to extend GGGTTC to include G-rs109815800 (GGGTTCG hereafter) for a more robust assessment of the distribution of Q in worldwide cattle.In order to address the issue of imperfect LD between GGGTTC and B. indicus (0.0% – 4.0%). Also, GGGTTCG was absent in African B. taurus. These data indicated a Northwestern European origin for Q or a higher intensity of selection for body weight and size in Northwestern Europe. Haplotype ATACCTT was the most common among B. indicus and outgroup populations, confirming its status as ancestral haplotype and supporting further the B. taurus origin of Q. Additionally, frequency of GGGTTCG in B. taurus was positively correlated with average body weight  and average withers height , and GGGTTCG was nearly fixed in admixed and crossbred populations selected for body size and weight  and 100 (16th century) generations , consistent with the period of formation and grading up of this breed. These results provided further evidence of B. taurus introgression as a source for Q in B. indicus populations. We also noticed that coalescence for haplotypes underlying the dominant mutations causing the polled phenotype had intervals matching with those found for Q in both Northwestern European and Nellore cattle, which suggested a parallel spread of the Q and the polled mutations in worldwide cattle , consistent with introgression after imports to Brazil from India in the 2000), consQ could be refuted by the high frequency of GGGTTCG (53.9%) in the Japanese breed Wagyu. Nishimura et al.17 also reported segregation of the Q allele in Japanese Black cattle, and declared an unknown origin of Q in Japan. However, Japanese breeds were admixed with Northwestern European cattle between 1868 and 191830. Indeed, analysis of Y chromosome haplotypes in Wagyu revealed multiple admixture events, including introgression from Northwestern cattle , which falls in the introgression period and supports a Northwestern European origin of Q also in Japanese cattle.The hypothesis of a Northwestern European selective sweep for Q facilitated stature recovery in Northwestern Europe, a sharp increase in body size should be observed in cattle from the 16th – 18th centuries in comparison to previous centuries. To gain insights on the extent of increase in body size in this period, we analyzed size measurements of bone fragments recovered from two archaeological sites in Iceland44: (i) Gásir (65° 46′ 58″ N – 18° 9′ 58″ W), containing specimens from the 14th – 15th century (n = 33); and (ii) Skalholt (64° 7′ 38″ N – 20° 31′ 35″ W), containing specimens from the 17th – 18th century (n = 89). In order to make the data comparable across different bone fragments, we computed the log-ratio between measurements taken from each specimen and equivalent measurements from a reference skeleton of a 19th century cow from upstate New York kept at the American Museum of Natural History . The difference in the mean log-ratio between the 17th – 18th century and 14th – 15th century data was 0.076 ± 0.019 (p = 7.04 × 10−5), suggesting a ~1.2-fold increase in body size within the time frame when the frequency of Q arose rapidly  from Northwestern Europe. In particular, allele C at rs109231213 can be used as a tag for Q since this variant affects a highly constrained position (PhastCons score = 1 in 100 vertebrates)14 and because it is a functional candidate given its location on the 3′-UTR of PLAG112. We first attempted to find the C allele in short read alignments reported by Park et al.45 of a well-preserved Bos primigenius humerus bone recovered from Carsington Pasture Cave in Derbyshire, England (53° 4′ 47″ N – 1° 38′ 7″ W). This specimen was radiocarbon dated to 6,738 ± 68 yBP and therefore precedes the introduction of domesticated cattle in Britain. Inspection of rs109231213 in their alignment data revealed a clear GG genotype supported by ten single reads, indicating a qq genotype  the specimen was most likely a representative of the non-domesticated Neolithic relative of European B. taurus rather than a proxy for B. primigenius ancestral to B. taurus and B. indicus; and (ii) future studies may need to differentiate between introgression from the B. indicus lineage and introgression from non-domesticated B. taurus. The later is especially important since model-based clustering algorithms tend to cluster B. indicus and outgroups together when only two ancestral populations are assumed, in which case hybridization with outgroups becomes indistinguishable from B. indicus introgression.Insights into the actual age of the ype Fig. . In spitQ in assemblages of cattle remains predating the selective sweep might be challenging if Q was rare prior to selection. However, if our hypothesis of parallel spread of Q and polledness holds, DNA extraction from polled specimens may increase the probability of obtaining a CC or CG genotype at rs109231213. Therefore, we extracted aDNA from two naturally polled crania from Iceland. The first specimen dating over 1,000 yBP . The second specimen  and a long bone (MU18) obtained from a late Roman site in Northeastern Italy (46° 7′ 11″ N – 13° 7′ 24″ E) dating over 1,700 yBP. Additionally, two molar teeth (US123 and US124) from different stratigraphic units and thus likely belonging to different individuals from a 13th – 14th century Medieval site in Northern Italy were also analyzed (46° 15′ 54″ N – 10° 26′ 42″ E). All 45 clones obtained from these samples exhibited GG genotypes 1. Also, geographical distribution of Q followed a pattern opposite to that of stature loss7, reinforcing a role for this mutation in stature recovery. The centre of the selection event for Q seems to lie in Atlantic Europe. The actual Q mutation is at least one millennium old, as suggested by sequencing of an Icelandic B. taurus cranium from ~1,000 yBP.In the present study we provided evidence that the selective sweep encompassing Q allele spread towards Central, Southern and Eastern Europe, but the intensive selection for pure lines in the past few centuries may have constrained its prevalence in these areas. The Q allele was most likely introduced into the New World by colonizers via imports from Northwestern Europe6. Cattle trades between Northwestern Europe and Japan in the late 19th and early 20th century30 probably favored an introgression of Q to Japanese breeds. More recently, Q was introgressed into B. indicus breeds. For instance, Q is speculated to have favored the ‘grading up’ of Brahman cattle, which consisted in introgressing major B. taurus alleles into the breed by crossbreeding and back-crossing14. Another example provided by our study is Nellore, which gained the Q allele after imports to Brazil from crossbreeding with cattle descended from European breeds. This introgression most likely occurred during the population expansion in the late 19th and early 20th century.From its putative centre of origin, our analysis suggested that the Q remained unknown. Karim et al.12 reported that none of the eight B. taurus positional candidates resided in coding regions or were associated with genotype-specific gene products. However, the authors found haplotype-dependent differences in fetal expression of seven genes, notably PLAG1, suggesting a regulon affected by a cis-acting causal nucleotide. They further used allelic imbalance and luciferase reporter assays to suggest causality of one of the following three regulatory variants  and ss319607406 (PhastCons score = 0.992) are in the bi-directional promoter of coiled-coil-helix-coiled-coil-helix domain containing 7 gene (CHCHD7) and PLAG1. Still, the individual contributions of these and other variants to the haplotype effect remained unclear.In spite of our exhaustive analysis, the identity of nts Fig. : (i) rs1et al.14 have successfully used alleles C and G at rs109231213 as tags for the Q and q alleles in Brahman, respectively. This is convenient because the other two functional candidates are difficult to type, since they are located in a repetitive GC-rich region. For instance, Nishimura et al.17 reported difficulties in amplifying the PLAG1-CHCHD7 promoter for sequencing. In the recent selection signatures study by Boitard et al.11, variants ss319607405 and ss319607406 were not included due to low quality scores and alignment issues. Altogether, these results indicate that in the absence of knowledge about the causal variant, the GGGTTCG haplotype from the HD array or the C allele at SNP rs109231213 could be used to analyze Q segregation, trait-association, gene flow, selection and drift in worldwide cattle.Interestingly, rs109231213 was the only previously reported functional candidate included in our haplotype region. If not the causal variant itself, our analysis at least suggested that rs109231213 is a reliable proxy. In fact, Fortes Q continues. Variants ss319607405 and ss319607406 are appealing candidates since they were shown to change transcriptional activity of the bi-directional promoter of PLAG1 and CHCHD712. However, rs109231213 is also a strong candidate since it affects a highly conserved nucleotide at the 3′-UTR of PLAG1. Variation at the 3′-UTR of a transcript may affect its interaction with regulatory molecules, such as MicroRNAs (miRNA)47, and therefore impact levels of translation. Expression levels of PLAG1 were previously shown to be regulated by miRNAs binding to this 3′-UTR region49, which also supports candidacy of rs109231213. Indeed, the G to C substitution at rs109231213 is predicted to cause the loss of a highly conserved miRNA binding-site. Another hypothesis involves shared causality of the entire haplotype, meaning Q = C(CCG)11G and q = G(CCG)9A, with potential epistatic effects. Separating these effects and solving the causality at this locus will demand the analysis of recombinants at these three loci, which are difficult to find naturally. A recombinant construct between ss319607405 and ss319607406 showed that the (CCG)11G haplotype is necessary to change transcription activity12, which suggests interaction between the two loci. In order to disentangle interactions between rs109231213 and the other two variants, gene-edited cell lines of G(CCG)11G or C(CCG)9A recombinants are required and should be the focus of future studies. Moreover, a complex mutation comprising cooperation of multiple regulatory variants at the haplotype region is not yet to be discarded, since other six genes besides PLAG1 have their expression levels affected to some degree by Q. Yet, PLAG1 seems to be the leading gene behind most of the pleiotropic effects observed at this chromosomal domain, as suggested by the recent expression QTL study of Fink et al.50. Finally, previously unavailable functional annotations such as expression QTLs, promoters, enhancers, insulators, transcription factor binding sites, CTCF loops and 3D chromatin structure, among others, will be soon available from the upcoming B. indicus assembly, the new long-read B. taurus assembly and the Functional Annotation of Animal Genomes (FAANG) projects. As our understanding of regulatory elements in the bovine genome progresses, other candidate variants may also emerge and reveal additional trait-specific effects.The quest for the identity of PLAG1 are most likely explained by a single mutation (or haplotype) across modern worldwide cattle breeds. We also found that this mutation has been selected during the period of stature recovery in Northwestern Europe and was later spread around the globe via importation of cattle from Atlantic Europe. Altogether, this study presents one of the first examples of a selective sweep in livestock that was driven by strong  selection on a complex trait.In conclusion, we were able to demonstrate that the pleiotropic QTL and signature of selection spanning 52 for a minium call rate of 95% and minor allele frequency of at least 5%. A total of 447,617 markers  were retained for analysis. Genotypes were phased with the Segmented HAPlotype Estimation & Imputation Tool v2.r837 (SHAPEIT2)33. The following parameter values were used: effective population size (NeLD) of 113, burn in of ten iterations, prune of ten iterations, 50 main iterations, 200 states, and windows of 500 kbp. Effective population size was estimated from the data with SNeP v1.153. Phasing with default parameters and a previously published value for NeLD of 36254 based on a larger sample of Nellore animals yielded very similar results (data not shown). We used GHap v1.2.255 to determine haplotype alleles within chromosomal segments and score haplotype genotypes for each animal. The segment size was chosen based on the extent of LD in the Nellore genome and the intermarker spacing of the HD panel. Given that markers were placed on average every ~5 kbp on CHR14, and that LD extends up to 30 kbp in the Nellore genome56, we determined haplotypes within overlapping segments of six consecutive markers. A total of 15,127 sliding windows of six consecutive markers were screened throughout autosome 14 for short haplotype calling. The same phasing and short haplotype calling procedures were later applied to CHR1 and HapMap genotypes, with NeLD reset to the default value of 10,000 in the later case.Nellore data were filtered in PLINK v1.9018. The heritability was estimated at 0.37. Prior to the association analysis, estimated breeding values were deregressed following Garrick et al.57. The minimum, mean, standard deviation and maximum of the accuracy of dEBV (based on prediction error variance) was 0.58, 0.95, 0.30 and 0.98, respectively. Presence or absence of horns was scored by five independent evaluators from pictures of the genotyped Nellore bulls. Majority voting was used to assign phenotypes to animals, and at least three voters were required to declare an animal to be horned or polled. Furthermore, pictures from animals suspected to be surgically de-horned were excluded. The resulting data included 328 horned and 51 polled bulls.Birth weight estimated breeding values were obtained from a single-trait animal model fitted to records from 846,782 calves born between 1985 and 2012 in 315 grazing-based Brazilian herds. The model included the fixed effects of contemporary group  and age of dam at calving, as well as random maternal effects 59. For a given window, let haplotypes 1, 2, …, K be sorted by frequencies p1, p2, …, pK. We start by defining αk as the average effect of substituting the minor haplotype 1 by k = 2, 3, …, K. From these definitions, the partial breeding value associated with haplotype k is:zk is a scalar taking values:The parameterization presented here builds on previously reported modelspk, for 0 copies of haplotype k0 – 2pk, for 1 copy of haplotype k1 – 2pk, for 2 copies of haplotype k2 – 2y is the vector of phenotypes, b = [μ αk]′, μ is an intercept, X = [1 zk], 1 is a vector of ones, zk is a vector relating phenotypes to substitution effect αk, and e is a vector of residuals distributed as N, where W = Diag(w), w is a vector of weights, and σ2e is the residual variance. In the case of birth weight dEBVs, the vector of weights was defined as w = λ−1(d + c)57, where λ = (1 − h2)/h2, h2 is the heritability of birth weight, d = (1 − r2)/r2, r2 is the vector of dEBV reliabilities, and c is the assumed proportion of genetic variance for which haplotypes cannot account. We have previously estimated18 a 95% confidence interval of 2.12-8.09% for the genetic variance due to the targeted QTL. Therefore, we adopted a value of c = 0.9191 . For the polledness analysis, horned and polled animals had phenotypes coded as −1 and 1, respectively, and W was replaced by an identity matrix. Regression was carried out by solving the generalized least squares equations b = (X′W−1X)−1X′W−1y. Association mapping was then based on the two-tailed t-test αk / SE(αk), and the most significant haplotype was selected as a tag for the underlying causal mutation. Similar results were found when association tests were performed using haplotype windows of 1 , 5, 10 and 20 markers with correction for polygenic effects . Admixed and outgroup populations were excluded from this analysis. Principal components were computed with PLINK v1.9052. Statistical tests for differences in B. taurus ancestry on CHR14 and mean birth weight dEBV between breeding subgroups of Nellore cattle were based on a Mann-Whitney U test and a t-test, respectively. Differences in frequency of Q and polledness were assessed with Fisher’s exact test. Local ancestry was estimated using the same reference data with elai v1.0060. Ten independent runs with different random seeds were performed in parallel, from which eight were based on the Expectation-Maximization (EM) algorithm and two were obtained using a fast linear approximation. The EM algorithm was run with 30 steps. All analyses assumed ten generations since admixture and numbers of upper and lower clusters of two and ten, respectively. The final output was built from the average across the ten replicates.The unsupervised model-based clustering algorithm in Admixture 1.3et al.41. Briefly, EHH between a core marker and another marker upstream or downstream was computed as:nh is the count of haplotype h carrying the allele of interest and N is the total number of chromosomes carrying that allele. Calculation of EHH was repeated for all markers upstream and downstream of a core marker until EHH decayed to 0.05, corresponding to a 5% probability of obtaining a pair of haplotypes identical by descent when two chromosomes were sampled at random. All EHH calculations were performed using the rehh v2.0.0 R package61. The expected number of generations until coalescence was obtained as40:p is the probability of identity-by-descent and r is the segment size in Morgans. The latter was computed from the entire length span of the EHH decay assuming 1 cM ~ 1.23 Mbp62. The use of a genetic map to account for local recombination rates yielded very similar results .For each marker pertaining to the haplotype of interest, and considering only chromosomes carrying that haplotype, the decay in the probability of identity-by-descent was modeled using extended haplotype homozygosity (EHH), following Sabeti Q mutation in the course of 60 or 100 generations was computed considering the equation relating average fitness in a population (w) to allele frequencies under constant selection40:The magnitude of selection required to fix the p is the frequency of Q, wQQ = 1, wQq = 1 − hs and wqq = 1 – s are the relative fitness of genotypes QQ, Qq and qq, respectively, h is a scalar taking values 0, 0.5 or 1 if the effect of Q was assumed dominant, additive or recessive, respectively, and s is the coefficient of selection. Based on the additive distribution of phenotypes according to genotypes, we adopted h = 0.5. Change in p from one generation to the next was then computed as:where Q at t = 0, starting at 1% and going up to 20%. For each scenario, values of s ranging from 0.01 to 0.50 were tested in order to identify a coefficient of selection sufficient to increase the frequency of Q up to 99% in 60 or 100 iterations.We simulated a range of scenarios with different frequencies for th century reference cow skeleton  and the ratio between specimen and reference was calculated. Ratios were then transformed to a log10 scale. A t-test was used to compare the log-ratio means between specimens from the 17th – 18th century and the 14th – 15th century.The archaeological data used for size analysis comprised fragments of humerus (n = 38), tibia (n = 27), metatarsal (n = 24), metacarpal (n = 11), calcaneus (n = 2), femur (n = 15) and radius-ulna (n = 5) bones. According to bone type and integrity, measurements included breadth of the distal end , breadth of the proximal end , breadth of trochlea , depth across the processus anconaeus , greatest breadth , greatest length , smallest breadth of diaphysis  and smallest depth of olecranon . Equivalent measurements were taken from a 19th century bovine cranium (specimen #159) from the site of Hofstadir and one petrous bone from a 15th–17th century bovine cranium (specimen #2439) from the site of Skalholt; one molar tooth (MU15) and one long bone (MU18) from the 3rd – 4th century from the late Roman site of Muris di Moruzzo (UD) in Northeastern Italy (46° 7′ 11″ N – 13° 7′ 24″ E); and two molar teeth (US123 and US124) from different stratigraphic units and thus likely belonging to different individuals from the 13th – 14th century Medieval site of Tor dei Pagà  in Northern Italy (46° 15′ 54″ N – 10° 26′ 42″ E). Extraction, PCR amplification and cloning of aDNA were performed in a dedicated lab facility of the BioDNA Research Centre . To exclude contamination from exogenous sources, stringent criteria for aDNA analysis were followed: extraction and PCR set up were carried out in physically separated clean rooms, irradiated with UV light (254 nm wavelength) each night for 2 h and additionally after every work session, and equipped with a positive air pressure system to prevent external contaminants from entering. All post-PCR analyses were performed in a separate modern-DNA lab. Disposable Tyvek® coveralls and double gloves were worn during the experiments and were changed frequently. All benches and rooms were routinely treated with bleach and UV-light. Extraction and amplification blanks were used as negative controls in each experiment. In the “extraction” clean room, after a 2 h UV light preliminary irradiation of the samples, the external layer of the surface was removed with disposable burs on a Marathon Multi 600 dental device. The samples were further UV-irradiated for 45 minutes and ground into powder with the same tool. Starting from about 250 mg of teeth/bone powder per sample, DNA was extracted according to the method of Rohland and Hofreiter63. Amplification reactions were assembled in the “PCR set up” clean room under a Biosan UVC/T-AR cabinet. PCR amplification of a 104 bp fragment flanking the target polymorphism (rs109231213) was obtained with the primer pair Bt_PLAG1_F 5′ CTCAAAACACACTGTCTTCCCA 3′ and Bt_PLAG1_R 5′ GATCTCCTCCAATGTCGCCT 3′. Two ml of extracted DNA were added to 50 ml PCR reaction mix including 2U of AmpliTaq Gold® DNA Polymerase (Applied Biosystems), 2 mM MgCl2, 160 μM of each dNTP and 1 μM of each primer. The amplification thermal profile was as follows: 95 °C for 10 min (polymerase activation), 45 cycles of denaturation, 95 °C for 20 sec, annealing, 50 °C for 20 sec and extension, 72 °C for 30 sec, and final extension at 72 °C for 10 min. PCR products were checked on 1.5% agarose gel. Bands of expected size were excised from agarose gel, purified with Sepharose® Cl-6B resin  on Micro Biospin Columns 732-6204 (BIORAD) and cloned with the TOPO® TA Cloning® Kit (InvitrogenTM) following the manufacturer’s instructions. A total of 10 to 15 clones per sample were amplified from white recombinant colonies with universal M13 primers. After purification with Sepharose® resin, PCR products were sequenced with M13 forward primer in outsourcing at Macrogen company (http://www.macrogen.com). The resulting chromatograms were visually inspected with SeqTrace v0.9.064 and aligned to the PLAG1 sequence of the cattle reference genome with BioEdit v7.2.565.A total of seven samples were used for aDNA analysis: two petrous bones from a 1066. Reads were aligned against the UMD v3.1 B. taurus assembly10 using the Burrows-Wheeler Alignment (BWA) algorithm v0.7.10-r78967. After alignment, optical and PCR duplicates were marked with PicardTools v1.11968. The total sequencing yield was 2 × 3,205,981,178 paired-end reads of 100 bases (~641 billion bases). Considering only properly paired reads, 92.2% were successfully mapped, resulting in an average fold coverage per sample of 9.25 ± 1.48x, with a minimum of 5.87x and a maximum of 13.75x. The overall percentage of optical/PCR duplicates was 7.0%. Variants on CHR14 were extracted from aligned reads using the mpileup algorithm from SAMtools v1.3.1 and BCFtools v1.3.169, following guidelines for cattle data reported by Baes et al.70. Variant effects were predicted and annotated with Ensembl Variant Effect Predictor (VEP)71. Sequence alignments were visually inspected to confirm variant positions with both Integrative Genomics Viewer (IGV) v2.3 tool73 and the SAMtools v1.3.1 tview applicationPaired-end libraries of 24 Nellore bulls were sequenced in the Illumina® HiSeq. 2000 platform, following the manufacturer’s protocolThe data that support the findings of this study were obtained under license and so are not publicly available. Data are however available for academic use from the authors upon reasonable request.Supplementary File 1


Producing sufficient food for nine billion people by 2050 will be constrained by soil salinity, especially in irrigated systems. To improve crop yield, greater understanding of the genetic control of traits contributing to salinity tolerance in the field is needed. Here, we exploit natural variation in exotic germplasm by taking a genome-wide association approach to a new nested association mapping population of barley called HEB-25. The large population  allowed cross-validation of loci, which, along with two years of phenotypic data collected from plants irrigated with fresh and saline water, improved statistical power. We dissect the genetic architecture of flowering time under high salinity and we present genes putatively affecting this trait and salinity tolerance. In addition, we identify a locus on chromosome 2H where, under saline conditions, lines homozygous for the wild allele yielded 30% more than did lines homozygous for the Barke allele. Introgressing this wild allele into elite cultivars could markedly improve yield under saline conditions. Over one billion hectares of the world’s land are affected by soil salinity, a major constraint on agricultural productionHordeum vulgare ssp. spontaneum) donors from the Fertile CrescentIn addition to its economic importance, barley is a suitable model for studying salinity tolerance in plants because it is the most salt tolerant of the cereal cropsHere, we present a genome-wide association study (GWAS) in the wild barley NAM population HEB-25. This population, comprising 1,336 individuals belonging to 25 families , was evaper se are desirable, lines that are simultaneously capable of high yields under non-stress conditions are of practical agricultural importance. To differentiate lines in terms of stress tolerance and performance, stress-tolerance indices have previously been proposed5The wild barley NAM population HEB-25  was evalWe propose a new index, stress-weighted performance SWP, , which iSalinity significantly reduces all traits measured in our field trials relative to control conditions . Only ripening period, plant height, and thousand grain mass do not exhibit this correlation (There is a strong correlation (|r| > 0.8) between the control and saline conditions in seven of the ten traits  and earlier maturity (by approx. 4 days) under both control and saline conditions. It also reduces plant height and dry mass per m2. This indicates that the reduced resources invested in vegetative tissue may enable the increased number of ears and increased yield (by 21 g.m−2). The reduction in grain number per ear could be related to the production of bigger grains per ear or more tillers per plant. The effect of the wild allele on reducing dry mass per m2 and increasing yield results in an overall increasing effect on the harvest index. We observe that the salinity tolerance index, SWP, but not S/C, is significantly increased at the HvCEN locus, indicating that this locus is associated more with better yield performance than with increased salinity tolerance. We conclude that HvCEN may be of great interest to breeders because of its yield improvement under both control and saline conditions.Similarly, the wild allele at the −2, 21% under control conditions and 37% under saline conditions) that is conferred by the wild barley allele. This QTL appears in cross-validations 56 times under salinity and eight times under control conditions. The SNP showing the significant yield effect is BOPA2_12_30822 in the alpha-glucosidase gene (AK375658). This SNP segregates only in HEB family 1, explaining 19.8% of the phenotypic variation in yield under high salinity in this family. In HEB family 1, lines carrying the wild barley allele at this SNP have a significantly higher yield under both control and saline conditions than do lines carrying the Barke allele at this SNP  and calreticulin 2 , which are calcium-binding proteins, align to position 141.78 cM and may instead be the genes underlying this QTL . Xiang et al.Triticum aestivum calreticulin gene (TaCRT1) showed higher salinity tolerance compared to their wild-type counterparts. Another candidate in the region of interest is a choline-transporter-like gene (MLOC_63287.1). Enzymes mediating choline oxidoreduction have also been shown to be involved in salinity tolerance13Genes related to the barley In several studies, analyses of transcriptomes of wild and cultivated barley grown under control and saline conditions have been conducted to identify differential gene expression in response to salinity1516171819et al.In addition, Wu Using our new stress-tolerance index, SWP, on HEB-25, we identified genes putatively affecting flowering time and salinity tolerance in barley. We also located a QTL responsible for higher yield. Further studies are necessary to validate the candidate genes and to study in more depth the mechanisms leading to increased yield and salinity tolerance with the ultimate goal of introgressing salinity tolerance traits (loci) into commercial barley lines. Studying the causal genes in other crops, notably wheat, also provides significant opportunities to improve performance of major crops in saline conditions, guided by this work in the relatively salt-tolerant crop, barley.H. vulgare ssp. spontaneum (Hsp) from the Fertile Crescent and one Tibetan H. vulgare ssp. agriocrithon (Hag) accession. The resulting F1 plants were backcrossed with Barke as a female parent and then selfed three times through the application of a single seed descent procedure. Subsequently, BC1S3 lines were bulk propagated to BC1S3:6. Further details on HEB-25 are provided in Maurer et al.1S3:7 lines of HEB-25 , Dubai, United Arab Emirates , from December to May (2013–2014 and 2014–2015).3 equivalents), porous (45% porosity), and moderately alkaline (pH 8.22). The saturation percentage of the soil is 26; it has a high drainage capacity; and the electrical conductivity of its saturated extract (ECe) is 1.2 dS.m−1. ICBA soil is classified as Typic Torripsamments, carbonatic, and hyperthermic−1 (at 85% moisture) to increase the soil water-holding capacity and to provide some of the required nitrogen (N), potassium (K), sulfur (S), and micro-nutrients. Two weeks before sowing, fertilization with phosphorus (P) was conducted using a single supply of 45 kg P2O5 ha−1 (100 kg of Triple Super Phosphate from Benedict (Indonesia) with a total of 45% phosphoric anhydride). Granular urea nitrogen (N) fertilizer from Fertil  was applied once at a rate of 30 kg N ha−1 , three weeks after planting. An application of NPK fertilizer (20-20-20) from ADFERT  at a rate of 30 kg.ha−1 was also made six weeks after planting by fertigation.The soil at ICBA is a fine sand , calcareous  and low-saline water (referred to as the control condition throughout the paper). Plots were randomized in an augmented design, and salt-tolerant check lines  were sown every seven plots on average. Four rows of a local barley cultivar (58/1A) were sown around the experimental area to reduce edge effects. The plot size was 1.5 m × 0.5 m, containing five rows, each 25 cm apart. Approximately 25 seeds (at 2 cm spacing and 1 cm depth) were hand-sown per row .−1; and saline plots were irrigated with 1 dS.m−1 water during the first week followed by irrigation with saline water (17 dS.m−1) for the remainder of the growing period. The ionic composition of the irrigation water was monitored throughout the season using salinity sensors. The distribution of drippers was homogeneous and the distance between drippers allowed the overlapping of wetting fronts. Weather data was recorded at the field site at ICBA during the two years of field trials  analysis was conducted for two years on each trait and predicted line means adjusted for environmental variation were obtained . In the v lines and s trials, the latter of which correspond to the two-treatment-by-two-year combinations with y the vector of response, an appropriate linear mixed model for a MET analysis is as follows:Consider a data set consisting of t and nt is the number of observations (plots) in trial t, τ is a vector of fixed terms consisting of an overall mean performance for each trial as well as trial specific global or extraneous trial terms such as linear row or linear column trends, X is the associated design matrix, v lines in each of the s trials with design matrix Zg and associated variance var(g)Iv is the (v × v) identity matrix, G is the (s × s) genetic variance matrix. Here we consider a factor analytic structure for GG = ΛΛT + Ψ, with Λ being a matrix of factor loadings at each of the s trials, and Ψ a diagonal matrix with elements relating to specific variances for trial s. This formulation for G allows a separate variance for each trial and a difference covariance between pairs of trials. u is a vector of random effects and includes extraneous environmental variation specific to each trial such as random row or column effects or a random smoothing splineZu is the associated design matrix. The residual vector, tth trial with Σt represents the Kronecker product between auto-regressive processes of order one (AR1) in the column and row directions for the tth trial.where G = Ψ) as a is the average pairwise prediction error variance of line effects and tThe generalized heritability was calculated for each trial separately  across years of the adjusted values per plot to take into account the year and line-by-year interactions. The adjusted HEB means across years were used in the GWAS.Traits and indices were correlated using Pearson correlation, and analysis of variance (ANOVA) was used to study the effect of treatment on each trait.et al.et al.To conduct GWAS, we used the multiple linear regression Model-B of Liu Proc GLMSELECT. Significant SNPs were determined by stepwise forward-backward regression. SNPs were allowed to enter or leave the model at each step if p < 0.001. To estimate the proportion of phenotypic variance explained by each significant SNP, R2 was calculated after modelling the SNP solely in a linear model. Additive effects of the wild allele relative to the Barke allele were estimated across families and were taken as the regression coefficient of the SNP from the GWAS model. Multiplication of those additive effects by a factor of two represents the absolute difference between the wild allele effect and the Barke allele, which is referred to throughout the paper.The analysis was conducted with SAS 9.4 Software  using 2val’ was then calculated as the squared Pearson product, the moment correlation between predicted and observed phenotypes in the test set, whereas ‘R2train’ represents the model fit of the training set. The detection rate was calculated as the number of times, out of 100, that a SNP showed significance  were averaged across all 100 cross-validation runs and the mean was weighed through multiplying by the sum of occurrences (out of 100) and dividing the overall result by 100. The resulting numbers were referred to as cross-validated −log10(p) in A five-fold cross-validation was run 20 times to increase the robustness of the GWAS. The 100 subsets were taken from the total phenotypic dataset. Each subset consisted of 80% randomly chosen HEB lines used as a training set to define significant SNPs and the remaining 20% of lines were used as a test set. The phenotype of these latter lines was predicted based on the SNP effects estimated in the training set. ‘RHow to cite this article: Saade, S. et al. Yield-related salinity tolerance traits identified in a nested association mapping (NAM) population of wild barley. Sci. Rep.6, 32586; doi: 10.1038/srep32586 (2016).


Miscanthus, leading to increased response to selection and substantial improvements in genome-wide prediction.Multivariate analysis unveils complex genetic relationships between spring emergence, canopy phenology and biomass yield in  Miscanthus has potential as a bioenergy crop but the rapid development of high-yielding varieties is challenging. Previous studies have suggested that phenology and canopy height are important determinants of biomass yield. Furthermore, while genome-wide prediction was effective for a broad range of traits, the predictive ability for yield was very low. We therefore developed models clarifying the genetic associations between spring emergence, consequent canopy phenology and dry biomass yield. The timing of emergence was a moderately strong predictor of early-season elongation growth (genetic correlation >0.5), but less so for growth later in the season and for the final yield (genetic correlation <0.1). In contrast, early-season canopy height was consistently more informative than emergence for predicting biomass yield across datasets for two species in Miscanthus and two growing seasons. We used the associations uncovered through these models to develop selection indices that are expected to increase the response to selection for yield by as much as 21% and improve the performance of genome-wide prediction by an order of magnitude. This multivariate approach could have an immediate impact in operational breeding programmes, as well as enable the integration of crop growth models and genome-wide prediction. Miscanthus is a perennial C4 grass that has great potential as a bioenergy crop  over time  demonstrated the importance of extended canopy duration for higher yield, with early emergence being slightly more important than extended growth at the end of the season  it receives over the growing season, the proportion of that radiation it intercepts and the efficiency with which it uses the intercepted PAR to create dry matter . A detailed description of the trial was given by  studies . In addi markers . In addiPstI restriction endonuclease. The 53 174 SNPs used in this study were identified by alignment to the Sorghum bicolor v 1.0 genome  of the following form was used:xijkl was the phenotypic value of the lth genotype in the kth column of jth row within the ith block and eijkl was the residual error effect. All terms were treated as random effects, making this approach equivalent to the PRM model of spatial variability evaluated by lmer function of the lme4 package , best linear unbiased predictors (BLUPs) of genotype values from that model were used for all subsequent analyses  were obtained based on 100 model-based parametric bootstrap samples generated using the bootMer function of lme4. If a trait’s data caused a warning message during LMEM analyses and/or had vg that was not significant at α=0.05 (i.e. 95% CI included zero), it was excluded from subsequent analyses.where analyses . Likelihrg) using several different approaches  were excluded if vg was not significant or caused warnings in the R statistical routines used prior to the MLR (see above). For analyses aimed at identifying and comparing predictors among species, flowering time (DOYFS1) was excluded from all MLR analyses because most M. sacchariflorus genotypes did not flower in 2008 and 2009. However, we did include this trait in model selection procedures aimed at developing selection indices for M. sinensis as roughly 90% of the genotypes for that species flowered in both years.Although several traits were moderately correlated with DryMatter , whereas w1, w2, …, wm are calculated so that selection on the SI maximizes the response in the SO. The w values are found by solving:where the a is a vector of the a1, a2, … an economic values in Eqn 3, w is a vector of the w1, w2, …, wm values in Eqn 4, P is the variance–covariance matrix for the phenotypic values of the traits in the SI, and G is the (additive) genetic covariance matrix for the traits in the SI and the SO. Thus, G provides the genetic level link between the SI and SO.where a1, a2, … an terms in the SO (Eqn 3) were all set to 0, except for that for biomass yield, which was set to 1. Thus, the SO was in practice biomass yield alone, while other traits were only included to improve the predictive ability and response to selection for yield through their genetic correlations with this trait.Two caveats need to be acknowledged here. Firstly, in all our analyses, we used the same traits in the SO and SI, and the matrix G in Eqn 5 was therefore a variance–covariance matrix. However, G contained the total genetic variances and covariances because our design  did not allow their decomposition into additive and non-additive components. Secondly, the vg1 and vg2) and residual error variances (ve1 and ve2). Using the properties of linear functions of random variables (g12) was calculated as:For each pair of traits (subscripts 1 and 2) in the P and G matrices, LMEMs were used to estimate their respective genetic . Following Rrel was then calculated as:After solving Eqn 5 for the H2 and vg were the broad-sense heritability and genetic variance for dry matter yield, whereas vSI was the variance of the SI, which was calculated as:where Trait 1 was always dry matter yield, and all covariance terms were therefore between the additional traits in the SI and yield.A schematic diagram of the custom R-scripts used to perform all analyses in this section is shown in H2) for biomass-related morphological traits  increased slightly as the crop matured from 2008 to 2009, but this was not the case for biomass yield (DryMatter). Heritabilities for emergence score were time dependent, with a clear peak that tended to be in the range 0.51–0.54 for both M. sinensis and M. sacchariflorus  in M. sacchariflorus. In contrast, the H2 values for canopy height rapidly increased to a plateau value . In 2009, the reference day for M. sinensis was 82 (23 March) and for M. sacchariflorus 96 (6 April), reflecting the differing weather, growth and measurement days in the two years. Early-season canopy heights were moderately correlated with emergence, but these correlations rapidly declined in both 2008  and stem number (i.e. TransectCount) were by far the most robust predictors. In M. sinensis, senescence and to a lesser extent emergence were also significant predictors, whereas neither was significant in M. sacchariflorus. Overall, our MLR models confirmed indications from the genetic correlation analyses that (i) the timing of emergence was a significant predictor of early-season elongation growth, but less so for growth later in the season and for the final dry biomass yield; (ii) early-season canopy height was consistently more informative than emergence for predicting biomass yield; and (iii) there were substantial species differences with respect to the relative importance of phenological and morphometric traits as determinants of biomass yield.The weak genetic association between emergence and yield , presumaM. sinensis genotypes used here in order to study these traits at the genomic level. Firstly, exploratory genome-wide association study (GWAS) analyses identified a large number of SNPs, with relatively small individual effects, associated with emergence after Bonferroni adjustments (data not shown), but these results will be reported separately, after re-analyses in larger GWAS populations (n=1000 genotypes).Thus far it has been established that canopy phenology and, to a lesser extent, emergence are important determinants of biomass yield and that all of these traits are heritable. Our next step was to build on this by exploiting a genome-wide SNP set for the H2. In 2008 the peak predictive ability for canopy height was on day 148 and in 2009 on day 152, but in both cases the equivalent median canopy height was 65 cm. Thus, we have identified both the optimal time and developmental stage for collecting spring phenology data under the conditions of our field trial, thereby substantially reducing the time and cost of future measurements.Secondly, we performed genome-wide prediction to explore the practical value of our research to plant breeders. As expected from our previous study , reachinH2 and/or predictive abilities  did not result in practically significant improvements in the relative response to selection or genome-wide predictive ability , including all widely distributed species and a set of highly productive inter-specific hybrids. Secondly, early breeding efforts in Miscanthus have focused exclusively on increasing biomass yield, but other traits, such as seed-based propagation to reduce establishment cost (or its absence to reduce invasiveness), abiotic stress resistance and biomass composition are likely to become increasingly important in the future (The findings of this study, and particularly the results of combining SIs and genome-wide prediction, have several important practical implications for breeding sinensis . Thus, te future . Thus, oe future . Recent JXB online.Supplementary data are available at M. sinensis and M. sacchariflorus in 2008 and 2009.Dataset S1. Heat maps of the phenotypic and genetic correlations for the traits in Fig. S1. Flowchart of the data analysis for the selection index calculations and subsequent genome-wide prediction.M. sinensis during 2008.Table S1. Broad sense heritabilities of all traits measured in M. sinensis during 2009.Table S2. Broad sense heritabilities of all traits measured in M. sacchariflorus during 2008.Table S3. Broad sense heritabilities of all traits measured in M. sacchariflorus during 2009.Table S4. Broad sense heritabilities of all traits measured in Table S5. Individual traits selected in the multiple linear regression models shown as generalized summaries in Supplementary DataClick here for additional data file.Supplementary Tables and FigureClick here for additional data file.