Cortical folds help drive the parcellation of the human cortex into functionally specific regions. Variations in the length, depth, width, and surface area of these sulcal landmarks have been associated with disease, and may be genetically mediated. Before estimating the heritability of sulcal variation, the extent to which these metrics can be reliably extracted from in-vivo MRI must be established. Using four independent test-retest datasets, we found high reliability across the brain (intraclass correlation interquartile range: 0.65–0.85). Heritability estimates were derived for three family-based cohorts using variance components analysis and pooled ; the overall sulcal heritability pattern was correlated to that derived for a large population cohort (N > 9000) calculated using genomic complex trait analysis. Overall, sulcal width was the most heritable metric, and earlier forming sulci showed higher heritability. The inter-hemispheric genetic correlations were high, yet select sulci showed incomplete pleiotropy, suggesting hemisphere-specific genetic influences. Fabrizio Pizzagalli et al. study the genetics factors that regulate cortical folding patterning in the brain. They use in vivo brain MRI datasets from around the world and report the reliability and heritability of sulcal morphometric characteristics for 61 sulci per hemisphere of the human brain. They uncover hemisphere-specific genetic influences on the cerebral cortex. Studies have even identified genetic correlations between human brain structure and risk for disease4.Genetic drivers of brain structural and functional differences are important to identify as potential risk factors for heritable brain diseases, and targets for their treatment. Large-scale neuroimaging consortia, including the ENIGMA5. Macroscale anatomical features of the human cortex can be reliably extracted from structural magnetic resonance imaging (MRI) scans, and among the most common are regional thickness and surface area measures. These MRI-based features show robust alterations in several neurological, neurodevelopmental, and psychiatric disorders6, and are influenced by both environmental and genetic variation7.Enriched in neuronal cell bodies, the cortical gray matter plays an important role in human cognitive functions and behavior, including sensory perception and motor control8 during fetal life and into adolescence9, forming sulci (fissures) and gyri (ridges) in the cortical gray matter. The mechanisms of brain folding are not fully understood11, but the process is largely preserved among humans and nonhuman primates. The brain sulci delimit cortical areas with specific functionalities and are generally consistent across subjects15. The complexity and intersubject variability of brain gyrification are influenced by developmental, aging, and pathological processes, all of which are genetically and environmentally influenced17.Gyrification of the cortical surface occurs in an orchestrated pattern18; genome-wide association studies (GWAS) find that, as with other complex traits, individual common variants typically explain <1% of the population variance in MRI derived measures; still, common genetic factors account for a large fraction of the variance in aggregate21. Successful efforts to discover common variants that affect cortical structure require tens of thousands of scans, as well as independent samples for replication and generalization. Large-scale biobanks have amassed tens of thousands of MRI scans22. Even so, to replicate effects and ensure the generalizability of findings to other scanned populations, we must first assess that the brain measures are reliably extracted across a variety of possible MRI scanning paradigms. This reliability is the basis for pooling statistical effects across individual studies in multisite consortia such as ENIGMA1 and CHARGE23.Large-scale neuroimaging studies have begun to discover common and rare genetic variants that contribute to brain variability as estimated using in vivo brain scans, such as MRI24, neurodegenerative changes in the elderly25, and neuropsychiatric disorders such as schizophrenia27, bipolar disorder28, and autism spectrum disorder29; altered fissuration is also found in several genetic disorders, such as Williams syndrome31. Effects on sulcal patterns have been reported as being partially independent of those on cortical thickness or surface area32.Sulcal-based morphometry provides in-depth analyses of the cortical fissures, or folds, as seen on MRI. Measures of sulcal morphometry—including length, depth, width, and surface area—among others—have been associated with brain maturation in adolescents32. Previous studies have investigated the genetics of cortical folds, but not across the full brain, and without ensuring the reliability of the measures themselves, or the heritability estimates. Kochunov et al.33 analyzed the effects of age on sulcal shape descriptors in a subset of 14 sulci finding wider sulci with older age  in the adult human brain. The central sulcus has been the focus of many earlier publications37. Its depth has been reported to be highly heritable, with the degree of heritability varying along its profile37. In recent works, La Guen et al.38 studied the heritability of sulcal pits in the Human Connectome Project (HCP) and the genetic correlation of sulcal width across ten sulci in the UK Biobank39. The heritability of the depth, length, and surface area of primary sulci has been studied in baboons40. It has been suggested that deeper, earlier forming, sulci have higher heritability41, although this hypothesis has not been confirmed. The reliability of the findings across populations, and the extent to which heritability depends on the reliability of the measures, has not been investigated.Effects on sulcal patterns have been reported being partially independent of those on cortical thickness or surface areaHere we: (a) estimate the reliability of four shape descriptors extracted from sulci across the whole brain; (b) evaluate heritability of these measures across four independent cohorts (three family-based cohorts and one cohort of unrelated participants); (c) determine the extent to which the heritability estimates depend on reliability; and (d) provide insights into the relationship between early forming sulci and higher heritability as well as cortical lateralization.N = 110) and heritability  analysis. Reliability was estimated from four cohorts, totaling 110 participants  who underwent two T1-weighted brain MRI scans across different brain imaging sessions. We included data sets for which we would expect minimal or no structural changes between scans, so we limited the analysis to healthy individuals aged 18–65, with an inter-scan interval < 90 days. See Table We performed an extensive reliability N = 10 and he43.We analyzed heritability in four independent cohorts, three with a family-based design and one using single-nucleotide polymorphism (SNP)-based heritability estimates. The cohorts included two twin-based samples (Queensland Twin Imaging study (QTIM) and HCP), one cohort of extended pedigrees , and another of over 9000 largely unrelated individuals (the UK Biobank) Table . Heritab46, we also evaluated the heritability estimates of the measures for each sulcus averaged across the two hemispheres. This may lead to more stable measurements and, if the bilateral measures are influenced by similar genetic factors, then more stable measures could lead to better powered genetic studies. We also assessed the genetic correlation between the measures across hemispheres. Sulci with limited genetic correlations between hemispheres may reveal novel insight into the brain’s lateralization and identify key biomarkers for relating lateralized traits, such as language and handedness, to brain structure47.We estimated reliability and heritability for measures of each sulcus in the left and right hemispheres, separately. As there is limited evidence for genetic lateralization across most of the human brainSupplementary Data 48, after averaging metrics across corresponding left and right hemispheres, for all the descriptors , but “poor or moderate” (ICC < 0.75) in OASIS for the same trait. Figure The meta-analysis of ICC captures the consensus  in the reliability across cohorts for each sulcus. Reliability measures depend to some extent on the cohort examined, or the scanning acquisition parameters. For example, for QTIM, which was collected at 4 T, the ICC is classified as “good” (ICC > 0.75) for the left sulcal surface area of the For a detailed breakdown of the ICC for measures of sulci morphometry per cohort, please see Supplementary Fig. For the complete meta-analyzed ICC results, please see Supplementary Figs. central sulcus (S.C.) gave the most reliable sulcal measures, followed by the median frontal sulcus (S.F.median), the intraparietal sulcus (F.I.P.), the occipito-temporal lateral sulcus (S.O.T.lat.ant.), the Sylvian sulcus (S.C.Sylvian), the sub-parietal sulcus (S.s.P.), the occipital lobe, and the superior temporal sulcus (S.T.s.) , and the length of the left and right insula (See Supplementary Data We explored test–retest (TRT) consistency in terms of the “bias” , with Bb) of bilaterally averaged sulcal metrics were significantly negatively correlated for all metrics except for length, in particular rlength = −0.11 [pval = 0.07], rmean-depth = −0.14 [pval = 0.02], rwidth = −0.25 [pval = 4.6 × 10−5], rsurface-area = −0.25 [pval = 1.2 × 10−5], suggesting, as expected, that a lower bias between test and retest measurements relates to higher reproducibility as estimated by ICC.ICC and bias (h2) showed a similar pattern across the three family-based cohorts, QTIM, HCP, GOBS , compared to QTIM (h2 = 0.4 ± 0.1) and HCP (h2 = 0.4 ± 0.1); GOBS is a cohort with an extended pedigree design and a wide age range (18–85 years of age), while both HCP and QTIM are twin-based cohorts of young adults aged 25–35 years and 20–30 years, respectively.Across descriptors and sulci, heritability estimates  for sulcal length, r = 0.31  for mean depth, r = 0.26  for sulcal width, and r = 0.25  for surface area  and sulcal width   , the mean depth of the ascending ramus of the lateral fissure (F.C.L.r.asc.) , the surface area of the anterior inferior frontal sulcus (S.F.inf.ant.) , and the width of the calloso-marginal ramus of the lateral fissure (F.C.M.ant.)  ], the width and the surface area for the insula  and , respectively  showed significant heritability estimates. These included the length of the t-test: pval = 1.5 × 10−10).The heritability estimates for the global measures  of sulcal length, mean depth, width, and surface area  are also reported in Supplementary Fig. h2 for sulcal length, 57%  for mean depth, 67%  for width, and 62%  for the surface area. Six sulci were significantly heritable for only one of the four descriptors . No sulcus show significant heritability for length only. Sulci that were significantly heritable across descriptors included the intraparietal sulcus, occipital lobe, subcallosal sulcus, internal frontal sulcus, orbital sulcus, anterior inferior temporal sulcus, and the polar temporal sulcus, among others; in total 15 sulci were significantly heritable across all four descriptors in the meta-analysis, and 19 for the mega-analysis  of the total number of bilaterally averaged sulci showed significant 50 .A significant Pearson’s correlation was identified between heritability estimates averaged across sulcal descriptors and the approximate appearance of sulci (in weeks) during developmentGenetic correlation across the hemispheres: Averaging brain-imaging derived traits across the left and right hemispheres, as above, has been shown to reduce noise due to measurement error in large scale, multi-cohort efforts52. Improvements in the signal-to-noise ratio may be essential for discovering single common genetic variants that explain < 1% of the overall population variability in a trait. However, by assessing left and right separately, we may be able to discover lateralized genetic effects, if they exist.ρG ∼ 0.92 ± 0.10)  between left and right homologous regions was computed for sulcal metrics that showed both “good” reliability estimates (ICC > 0.75) and significant univariate heritability estimates for both the left and right metrics; , and another p value comparing the genetic correlation to the hypothesis of a perfect overlap (ρG = 1), assessing the difference between the genetic correlation obtained and a correlation of 1. Bonferroni correction for the more traditional p values was conducted by correcting for the number of traits tested  so p < 0.05/[N], where N = 11 heritable sulci with ICC > 0.75 for length, +31 for mean depth +36 for width +37 for surface area, for a total of 115 traits. The second set of p values comparing the genetic correlation (ρG) between hemisphere homologs to the indistinguishable value of 1 are listed in Supplementary Data 24, and the −log10 of those significantly different than 1 are mapped in Fig. Two sets of ρP) between the left and right indices were on average less than 0.5 in each cohort  meta-analyzed correlation between left and right homologs compared to the other sulcal descriptors .Phenotypic correlations (Our study has four main findings: (1) many of the sulci common across individuals were reliably extracted across a variety of MRI acquisition parameters, with some sulcal shape descriptors being more reliable than others; (2) cortical folding patterns were highly heritable and sulcal shape descriptors such as sulcal width may be promising phenotypes for genetic analysis of cortical gyrification; (3) the proportion of variance attributed to additive genetic factors  varied regionally, with the earlier forming sulci having higher heritability estimates than later forming sulci; (4) incomplete pleotropy was identified between select left and right sulcal descriptors, suggesting sulcal analyses may provide insights into genetic factors underlying the lateralization of brain structure.36. BrainVISA offers the ability to automatically extract and characterize the sulci at high spatial resolutions, by segmenting and labeling 123 sulci across the cerebral cortex. Here, we analyzed four sulcal shape descriptors: length, mean depth, width, and surface area. Sulcal length has been associated with neurodevelopmental processes54, while sulcal depth and width have been correlated with aging and neurodegenerative processes57. Sulcal surface area represents a combination of depth, width and length features.Cortical sulci may serve as prominent landmarks for identifying homologous functional regions across individuals58 and low reliability could lead to an underestimation of the true heritability59. While heritability is a population-specific estimate, one main goal of imaging genetics consortia such as ENIGMA1 and CHARGE23 is to identify genetic variants that affect brain structure and function in populations around the world. Therefore, it is of utmost importance to ensure that measures are reliably extracted across different data sets, and furthermore, are heritable  across different populations. Even beyond imaging genetics, the reliability of the measurements and the reproducibility of any set of results are essential for reproducible science at large.A primary goal of this work was to identify the sulci and the corresponding shape metrics that may be reliably extracted irrespective of the specific MRI scanner or  scan acquisition protocol, to ensure a globally viable trait for disease related biomarker and genetic association analyses. Poor reliability may be attributable to measurement errors, which could lead to a ceiling effect on heritability estimates. This is because highly heritable traits can only be detected if the traits are robustly measured49 as well as ICC, which compares the within-subject variance to the between-subjects variance. ICC may be affected by the homogeneity of the population under study; when variability in the population is low, for example, if age range is limited, then lower ICC values may be expected, while bias would be unaffected. Our results show high consistency between test and retest (“bias” < 0.149 on average). Furthermore, in considering the number of sulci that had ICC estimates  greater than 0.9, sulcal width was the most reliable metric among the descriptors analyzed. Although some visual quality control was conducted on individual sulcal extractions, we did not ensure the anatomical validity of the entire set of sulcal labels for each of the individual MRI scans used in this study.  Our reliability results are therefore more a reflection of methodological consistency, rather than anatomical accuracy.Here we identified the most reliable sulcal regions using test-retest (TRT) data from four cohorts with independent samples and different scanning protocols to ensure the robustness of results. We assessed bias, a subject-based index of consistency60 found a correlation between ICC and heritability, with a large percentage of traits showing low reliability (ICC < 0.75)60. Here we showed that most of the reliable sulcal shape descriptors were also highly heritable. This trend might be due to the lower variance across subjects for more robust anatomical regions, such as the central sulcus, which are easier to identify with automated image processing pipelines and less prone to segmentation errors. However, even in regions with “excellent” reliability (ICC > 0.9), we identified a range of heritability estimates, suggesting  that not all reliable traits are necessarily highly heritable59.A study examining the relationship between reproducibility and heritability of different brain structures in the QTIM cohort61. We have replicated findings of significant heritability in the central sulcus and further, showed that it is indeed the sulcus  with the highest heritability estimate across the entire cortex. However, out of 61 total bilateral cortical sulci, it is only one of 34 that showed significant heritability estimates across all four shape descriptors.Many earlier works have focused exclusively on the central sulcus40. Our results also confirmed prior findings of sulcal heritability in the temporal lobe62 and the corpus callosum area63 and are also in line with studies showing high estimated heritability in prefrontal and temporal lobes for cortical thickness and surface area70, especially for sulcal mean depth and width.Our results indicated significant heritability estimates for sulcal surface area and width in several medial frontal regions, partially confirming findings in ref. 50 implies that sulci appearing early in brain development72, including the central sulcus, Sylvian fissure, parieto-occipital lobes, and superior temporal sulcus50 may be under stronger genetic control. However, some regions including the frontal lobe and the temporal sulcus also had high heritability, even though these regions are reported to develop later72, suggesting more work is needed to identify the developmental role in the regional genetic architecture.The sulcal descriptors identified as being heritable in this work may serve as phenotypes for large-scale genome-wide association studies, or GWAS, enhancing  our ability to identify specific genomic variants that influence brain structure and disease risk. These reliable and heritable sulcal measures may also serve as biomarkers for understanding genetically mediated brain disorders. The significant correlation identified between heritability estimates averaged across sulcal descriptors and the appearance of sulci (in weeks) during developmentPapio baboons40. Heritability estimates from GOBS were lower than for QTIM or HCP, as may be expected for an extended pedigree design when compared to twin designs73. It has also been proposed that higher image quality, and therefore lower measurement error, could lead to higher heritability estimates74. GOBS and HCP MRI volumes were acquired with a 3T scanner and HCP has higher spatial resolution compared to GOBS. QTIM was acquired with slightly lower spatial resolution but at higher magnetic field strength (4T). Further analyses will be needed to investigate how the signal-to-noise estimates (SNR) vary across cohorts and how this affects heritability estimation74. SNP-based heritability estimated in the UK Biobank showed a similar h2 pattern . This may relate to disorder-specific abnormalities seen in brain folding patterns, for example, as reported in a postmortem study on schizophrenia62 and future investigations focusing on the genetics of brain gyrification and lateralization across handedness are needed to confirm these findings.Some regions that showed this suggested lateralization of genetic effects for sulcal descriptors, showed the same effect for other measures extracted from the cortex; for example, the effect seen with the sulcal surface area of the collateral fissure was also detected with the corresponding gyral  surface area  across the two hemispheres, our results suggest that conducting a separate GWAS of sulcal measures in select frontal, temporal, and occipital regions may provide added insight into the biological mechanisms that drive hemispheric specialization. The discovery and replication of specific genetic influences on brain structure require very highly powered analyses, achievable through large-scale studies and collaboration. Harmonized imaging and genetic analysis protocols, rigorous quality assurance, reproducibility assessments, along with statistical rigor are vital in the collaborative endeavors such as those proposed by the ENIGMA consortium.  To allow for a variety of such international collaborations, the customized MRI image processing protocol using and extending the BrainVISA toolkit as in this work, has been made freely available at: http://enigma.ini.usc.edu/protocols/imaging-protocols/.The genetic influences on brain cortical structure are regionally dependent, and differ according to the metric, or descriptor, being evaluated. For example, the genetic correlation between average cortical thickness and total surface area has been shown to be weak and negative, with largely different genetic compositionsQueensland Twin Imaging study (QTIM): Brain MRI from 1008 right-handed participants86, 370 females and 638 males, were used in this study. This included 376 dizygotic (DZ) and 528 monozygotic (MZ) twins (one set of DZ triplets) and 104 siblings, with an average age of 22.7 ± 2.7 years [range: 18–30]. T1-weighted images were acquired on a 4T Bruker Medspec scanner with an inversion recovery rapid gradient echo sequence. Acquisition parameters were inversion/repetition/echo time (TI/TR/TE) = 700/1500/3.35 ms; flip angle = 8°; with an acquisition matrix of 256 × 256; voxel size = 0.94 × 0.90 × 0.94 mm3.Human Connectome Project (HCP): 816 participants87, 362 females and 454 males, average age 29.1 ± 3.5 years [range: 22–36]. These included 412 siblings, 205 DZ and 199 MZ twins, including triplets. T1-weighted images were acquired using a 3T Siemens scanner. MRI parameters: (TI/TR/TE) = 1000/2400/2.14 ms; flip angle = 8°; voxel size = 0.7 mm isotropic; acquisition matrix = 224 × 224. The subset of TRT scans includes all right-handed subjects.Genetics of Brain Structure and Function (GOBS): A total of 1205 individuals of Mexican-American ancestry from extended pedigrees  were included in the analysis. Sixty-four percent of the participants were female and ranged in age from 18 to 97 (mean ± SD: 47.1 ± 14.2) years. Individuals in this cohort have actively participated in research for over 18 years and were randomly selected from the community with the constraints that they are of Mexican-American ancestry, part of a large family, and live within the San Antonio, Texas region. Imaging data were acquired at the UTHSCSA Research Imaging Center on a Siemens 3T Trio scanner . Isotropic (800 µm) 3D Turbo-flash T1-weighted images were acquired with the following parameters: TE/TR/TI = 3.04/2100/785 ms, flip angle = 13°. Seven images were acquired consecutively using this protocol for each subject and the images were then co-registered and averaged to increase the signal-to-noise ratio and reduce motion artifacts88.UK Biobank: Analyses were conducted on the 2017 imputed genotypes restricted to variants present in the Haplotype Reference Consortium90. UK Biobank bulk imaging data were made available under application #11559 in July 2017. We analyzed 10,083 participants , mean age = 62.4 ± 7.3 years [range: 45–79]. Voxel matrix: 1.0 × 1.0 × 1.0 mm—acquisition matrix: 208 × 256 × 256. 3D MP-RAGE, TI/TR = 880/2000 ms, sagittal orientation, in-plane acceleration factor = 2. Raw MRI data were processed using the ENIGMA FreeSurfer and sulcal analysis protocols. Following processing, all images were visually inspected for quality control of FreeSurfer gray/white matter classifications. For all subjects, the central sulcus segmented and labeled by BrainVISA was visually assessed for labeling accuracy.KKI : 21 healthy volunteers with no history of neurological conditions  were recruited. All data were acquired using a 3T MRI scanner  with body coil excitation and an eight-channel phased array SENSitivity Encoding (SENSE) head-coil for reception. All scans were completed during a 2-week interval. The resulting data set consisted of 42 “1-h” sessions of 21 individuals. MP-RAGE T1-weighted scans were acquired with a 3D inversion recovery sequence: (TR/TE/TI = 6.7/3.1/842 ms) with a 1.0 × 1.0 × 1.2 mm3 resolution over a field of view of 240 × 204 × 256 mm acquired in the sagittal plane. The SENSE acceleration factor was 2 in the right–left direction. Multi-shot fast gradient echo (TFE factor = 240) was used with a 3-s shot interval and the turbo direction being in the slice direction (right–left). The flip angle was 8°. No fat saturation was employed91, https://www.nitrc.org/projects/multimodal/.OASIS: This TRT reliability data set contains 20 right-handed subjects (19–34 years old) without dementia imaged on a subsequent visit within 90 days of their initial session. MP-RAGE T1-weighted scans were acquired on a 1.5-T Vision scanner : (TR/TE/TI = 9.7/4.0/20 ms) with an in-plane resolution of 1.0 × 1.0 × mm2 resolution over a FOV of 256 × 256 mm acquired in the sagittal plane. Thickness/gap = 1.25/0 mm; flip angle = 10° (https://www.oasis-brains.org/)92.MRI image processing and sulcal extraction. Anatomical images (T1-weighted) were corrected for intensity inhomogeneities and segmented into gray and white matter tissues using FreeSurfer (http://surfer.nmr.mgh.harvard.edu/); segmentations and regional labels were quality controlled using ENIGMA protocols for outlier detection and visual inspection (http://enigma.ini.usc.edu/protocols/imaging-protocols/). BrainVISA (http://brainvisa.info) was run for sulcal extraction, identification, and sulcal-based morphometry. Morphologist 2015, an image processing pipeline included in BrainVISA, was used to quantify sulcal parameters. Briefly, the Morphologist 2015 segmentation pipeline computes left and right hemisphere masks, performs gray and white matter classification, reconstructs a gray/white surface and a spherical triangulation of the external cortical surface, independently for both hemispheres. Sulcal labeling has been performed using BrainVISA suite which implements the algorithm fully described in the cited paper by Perrot et al.93. It relies on a probabilistic atlas of sulci. The sulcal recognition is made by combining localization and shape information. The atlas is described in detail and freely accessible here: http://brainvisa.info/web/morphologist.html and can be visualized online here: http://brainvisa.info/web/webgl_demo/webgl.html.orig.mgz, ribbon.mgz, and talairach.auto) were directly imported into the pipeline to avoid recomputing several steps, including intensity inhomogeneity correction and gray/white matter classification. Sulci were then automatically labeled according to a predefined anatomical nomenclature of 62 sulcal labels for the left hemisphere and 61 sulcal labels for the right hemisphere95. The protocol developed for this work is available at http://enigma.ini.usc.edu/protocols/imaging-protocols/ (ENIGMA-Sulci).To improve sulcal extraction and build on current protocols used by hundreds of collaborators within ENIGMA, quality controlled FreeSurfer outputs  of all extracted and labeled sulci. Cortical thickness and surface area have both been found to be moderately to highly heritable, yet with largely independent and even negatively correlated genetic influences96. Cortical thickness, surface area, and folding tend to exhibit different age-related trajectories98. In particular, cortical thickness represents the laminar organization of the cerebral cortex, which contains about 14 billion neurons99. Each of the layers forming the cortex100 has a different cellular organization, mostly distinguished on the basis of pyramidal cells in the various laminae100. Surface area may reflect the number of radial columns perpendicular to the pial surface98 and sulcal morphometry may additionally relate to the microstructure of the neuronal sheets and to the local axonal connectivity within a cortical region, which may influence the degree of folding84.61 . The surface area is the total area of the sulcal surface. The enclosed cerebrospinal fluid (CSF) volume divided by the sulcal surface area gives the width, a gross approximation of the average width of the CSF in the foldz-score across subjects is computed for each descriptor. The set of subjects showing an absolute z-score > 2.5 for one or more descriptors was discarded from further analysis101. Therefore, if the length of the central sulcus for a given subject was an outlier but width, depth, and surface area were not, that subject’s central sulcus was removed from further evaluation; this ensured that the same set of subjects were used for all analyses across descriptors. This led to discarding ∼3% of subjects for each sulcus.To further quality control the extracted sulcal measures and identify subjects whose sulci were not optimally identified, we consider as outliers those subjects showing abnormal values for at least one of the descriptors for each sulcus. That is, for a given sulcus, the Univariate and bivariate quantitative genetic analyses: The relative influences of genetic and environmental factors on human traits can be estimated by modeling the known genetic relationship between individuals and relating it to observed covariance in measured traits; in twin studies, MZ twin pairs—who typically share all their common genetic variants—are compared to DZ twin pairs, who share, on average, 50%. The same principle can be used for extended pedigrees, in which many individuals have varying degrees of relatedness. Here, we used both twins and extended pedigrees to estimate the heritability of these in-depth cortical sulcal measures. For a given cohort of participants, the narrow-sense heritability (h2) is defined as the proportion of the observed variance in a trait (σ2p) that can be attributed to additive genetic factors (σ2g):102, were used for all genetic analyses. Heritability (h2) is the proportion of total phenotypic variance accounted for by additive genetic factors and is assessed by contrasting the observed phenotypic covariance matrix with the covariance matrix predicted by kinship. High heritability indicates that the covariance of a trait is greater among more closely related  individuals; here, for example, MZ twins as compared to DZ twins and siblings. Using SOLAR-ECLIPSE imaging genetics tools (http://www.nitrc.org/projects/se_linux)102, we investigated the heritability profile of four sulcal descriptors for sulci across the whole brain: 62 on the left and 61 on the right hemisphere.Variance components methods, implemented in the Sequential Oligogenic Linkage Analysis Routines (SOLAR) software package2, age × sex interaction, age2 × sex interaction) to calculate the sulcal trait heritability and its significance  for accounting for a component of each trait’s variance within this population.Prior to testing for the significance of heritability, sulcal descriptor values for each individual are adjusted for a series of covariates. We estimated the influence of specific variables , where m = 61 , and the times 4 corresponding to the number of shape descriptors assessed. We set m = 123 when left and right sulcal heritability were estimated separately. This reduced the probability of Type 1 errors associated with multiple measurements.The significance threshold for heritability analysis of individual sulci was set to be ρP) between the left and the corresponding right sulcal measures into the genetic (ρG), and a unique environmental (ρE) components, for each pair of traits. Just as with the univariate model, the bivariate phenotype of an individual is modeled as a linear function of kinship coefficients that express relatedness among all individuals within the cohort . The significance of ρG and ρE was estimated from the likelihood ratio test when comparing the model to ones where the correlation components are constrained to be 0104. This estimates ρG and ρE and their standard error (SE). The significance of these coefficients is determined by a z-test of their difference from 0. If ρG differs significantly from 0, then a significant proportion of the traits’ covariance is influenced by shared genetic factors.For bivariate genetic correlation estimates, classical quantitative genetic models were used to partition the phenotypic correlation . Measurement error, which is uncorrelated between individuals regardless of their relatedness, falls into the environmental component and environmental correlations. Measurement error therefore influences h2, ρE, ρP, but not ρG.Some considerations should be made regarding the measurement error of the traits analyzed here: ρG harder to estimate, because low heritability means that the underlying genetic effects cannot be estimated with precision. This causes the SE of the ρG estimate to increase, but critically, does not change its maximum-likelihood estimate systematically. So measurement error makes it harder to reject the null hypothesis that ρG = 1.In practice, measurement error does make L–R). Indeed, the genetic variance of L–R is:L–R can be obtained. But if L–R shows significant heritability, it could be because: (1) genetic overlap is incomplete and/or (2) L and R have unequal genetic variances. So studying laterality is not recommended here because (1) and (2) are confounded.Moreover, the bivariate polygenic model used here to estimate the left–right genetic correlation is a linear function of laterality (h2) and SE estimates based on measurements from individual cohorts43. We weighted the heritability estimate from each cohort by the heritability SE, as extracted from the variance component model of SOLAR. The heritability weighted by SE43 is:S = 1 to Ns indexes the sulci and j = 1,2,3 indexes the cohorts.Meta-analysis calculates weighted mean heritability (polyclass), developed for SOLAR105 for mega-analysis of heritability on sulci descriptors106. This function fits the model after combining the pedigrees of QTIM, HCP, and GOBS into a single pedigree (for more details see refs. 43).While meta-analyses compute first the heritability independently for each cohort and then combine the results, mega-analyses combine first different cohorts and then run a single computation for heritability evaluation. We use a program  and methods more sensitive to smallest p values 108:S is the sum of o values and k the number of tests . The corrective additional terms are used if the number subtracted from S in the numerator is <S. All the p values in the meta-analyses estimated were computed using this method.To combine 109 to estimate the heritability from the individual genotypes. Genotypes on the autosomal chromosomes were used to calculate the genetic relationship matrix with GCTA109. Heritability was calculated using a linear mixed model, with age, sex, ICV, and the first four genetic components from multidimensional scaling analysis as fixed covariates. We also covaried for the presence of any diagnosed neurological or psychiatric disorder. In our analysis, we excluded participants with non-European ancestry, missing genotypes, or phenotypes, and mismatched sex information.We used genome-wide complex trait analysis (GCTA)Sulcal measurement reliability: To evaluate the reliability of the sulcal shape descriptors, we analyzed their variability, or reproducibility error, across the TRT sessions for each of the four TRT cohorts. For each MRI scan there are several sources of variability, including variability from hydration status, variability due to slightly different acquisitions in the two sessions , and finally variability due to the imaging processing methods themselves.b  of a sulcus with respect to its average and (2) the ICC coefficient. b is computed as follows:There could also be variability in the reliability estimates depending on the type of MRI system used , so it is important to address the issue of reliability across a variety of platforms. We used two indices of reliability: (1) the dimensionless measure of absolute percent bias of descriptor, 110. These plots show the spread of data, the bias , and the limits of agreement (±1.96 SD), and were used to confirm that the distributions were approximately symmetric around 0 and to check for possible outliers. While the ICC estimates the relation between within-subject variance and between-subjects variance, b offers a subject-based index that might be used to find outliers. If scan and rescan are perfectly reliable, b should be equal to 0. The cases where b is >0.1, as in ref. 49, are considered unreliable.The estimation of the means is more robust than the estimation of the variance from the signed differences, in particular for smaller sets of subjects. The distributions of sulcal measurement differences plotted the mean across sessions were examined with a Bland–Altman analysisσ2WS) and between-subject (σ2BS) variability113.The ICC coefficient was computed to quantify the reproducibility for sulcal-based measurements. ICC is defined as follows:σ2BS. Values below 0.4 are typically classified as “poor” reproducibility, between 0.4 and 0.75 as “fair to good,” and higher values as “excellent” reproducibility48.The ICC estimates the proportion of total variance that is accounted for by the MA−SE), similar to Eq. (j = 1, 2, 3, 4 indexes the cohorts. The SE was computed like SE = ICC/Z, where Z is obtained from a normal distribution knowing the p value. Equation  was usedr to Eq. , in ordewith Eq.  was estiFurther information on research design is available in the Supplementary InformationSupplementary DataPeer Review FileReporting SummaryDescription of Additional Supplementary Files


Combining the correlation analysis of metabolite‐agronomic traits with the co‐localization of methylation quantitative trait locus (mQTL) and phenotypic QTL (pQTL), genetic relationships between the metabolites and agronomic traits were uncovered. For example, a candidate was identified using correlation and co‐localization analysis that may manage auxin accumulation, thereby affecting number of grains per spike (NGPS). Furthermore, metabolomics data were used to predict the performance of wheat agronomic traits, with metabolites being found that provide strong predictive power for NGPS and plant height. This study used metabolomics and association analysis to better understand the genetic basis of the wheat metabolism which will ultimately assist in wheat breeding.Plants produce numerous metabolites that are important for their development and growth. However, the genetic architecture of the wheat metabolome has not been well studied. Here, utilizing a high‐density genetic map, we conducted a comprehensive metabolome study via widely targeted LC‐MS/MS to analyze the wheat kernel metabolism. We further combined agronomic traits and dissected the genetic relationship between metabolites and agronomic traits. In total, 1260 metabolic features were detected. Using linkage analysis, 1005 metabolic quantitative trait loci (mQTLs) were found distributed unevenly across the genome. Twenty‐four candidate genes were found to modulate the levels of different metabolites, of which two were functionally annotated by  This work performed a comprehensive metabolome analysis of kernels from wheat RILs, which, in conjunction with wheat genome data, provided a valuable resource for mQTL analysis, identification of unknown enzymes, and metabolite‐agronomic trait correlations. It provides c. 20% of the calories and 25% of protein consumed by humans , the subsequent development of high‐throughput genetic analysis techniques will likely prove a considerable basis for the metabolomics study of wheat.Wheat . On the basis of this analysis, we were able to assign 18 candidate genes and verify two of them by in vitro expression studies. Additionally, the relationships between the mQTLs and a range of phenotypic QTL (pQTL) were investigated, revealing the possibility of predicting agronomic traits by using metabolic data. As a result, this study considerably improved both our knowledge of wheat metabolomics and its relationship to agronomic traits, providing a powerful tool for crop improvement.In the current study, we analyzed 1260 metabolite features, of which we were able to structurally annotate 467 using a widely targeted LC‐MS/MS approach. We were subsequently able to localize 1005 high‐resolution mQTLs based on the Wheat660K high‐density SNP map, combining the latest wheat genome annotations  derived from a cross between two elite wheat varieties, Kenong 9204 (KN9204) and Jing 411 (J411), were collected. The parental lines were highly varied in grain traits and spike characteristics with the aim to identify the major genes affecting the agronomic traits  of the metabolic traits demonstrated that over 56% of the metabolites displayed a heritability of above 0.6  than the primary metabolites (with an average of 0.58), and flavonoids exhibited the highest heritability (H2 > 0.70)  of 47.4% Figure . There wne Table . The dis6 Figure . General0) Table . Thus, tet al., et al., et al., Metabolite profiling can elucidate the links between metabolic pathways  across the genome  was mapped on chromosome 2B between 665.2 and 666.4 Mb (LOD = 11.9), explained 31.2% of the phenotypic variance  to a rice flavonoid 3′‐hydroxylase encoding gene OsF3’H  was mapped to the interval 5.6–7.2 Mb on chromosome 2B , and only three genes were found to be located within the interval. One of the three genes, TraesCS2B01G459900, was annotated as a glycosyltransferase, similar to the rice UGT706C1 (52.1% identity at the amino acid level). Therefore, we cloned this gene from CS. Although activity was detected  were determined, with an approximately similar number of positive and negative correlations , harvest index (HI), and kilo‐grain weight (KGW) were mainly positively correlated with the annotated metabolites. However, flag leaf‐related traits  and spike‐related traits  were mostly negatively correlated with the annotated metabolites Figure . InteresTo further investigate the relationship between agronomic traits and metabolites, we used the agronomic trait data for pQTL analysis. As a result, 97 pQTLs were identified for the 17 agronomic traits, which were mainly located on chromosomes 2D and 4B, with the most QTL (11) detected for plant height  and mr1203 (methylluteolin C‐hexoside), which colocalized with six pQTLs covering five agronomic traits  were found to overlap with the mQTL; in total, 369 mQTLs representing 252 metabolic features (including 61 annotated metabolites) colocalized with pQTL Table . The mosts Table . In the C‐hexosyl‐apigenin O‐p‐coumaroylhexoside; n03958, tricin 7‐O‐hexosyl‐O‐xyloside; mr1120, C‐hexosyl‐chrysoeriol O‐p‐coumaroylhexoside) were correlated with the HI and colocalized at chromosome 1B (Table C‐hexosyl‐chrysoeriol 7‐O‐hexoside) and PH, as well as n04711 (pyranose derivative) and the seed length width ratio  to predict 17 agronomic traits. The average predictability of the 17 agronomic traits by BLUP and LASSO were 0.26 and 0.27, respectively, with the highest predictabilities in trait PH  and trait NGPS (0.49) Figure .P < 0.05), as listed in Table P = 0.036) and S19‐0168  were found to have the greatest positive effects on PH and NGPS prediction, respectively (Table P < 0.05) displayed a high predictive effect on both PH and NGPS. These metabolites, which have significant effects on prediction, could contribute to the improvement of wheat breeding.The LASSO method detected 82 and 98 metabolite features, including phytohormone derivatives, sugars, and organic acids, among others, and resulted in significant effects on NGPS and PH . A phylogenetic tree including our verified TaUGTs and the other known UGTs was created for its classification. The result show that the TaUGTs is classified in the UGT88C subgroup, which is not well identified , played a role in the variations of the corresponding metabolite accumulation by the expression levels during grain filling , encoding OsMAT‐2, was verified to be a flavonoid malonyltransferase using a recombinant protein assay , which encodes AAT1, was the first anthocyanin acyltransferase characterized in a monocot species, which analyzed by mutation phenotype . Moreover, the loci corresponding to NGPS in the pQTL analysis was found to be colocalized with the abovementioned mQTL on chromosome 4B  or BrDRM1 (encoding dormancy‐associated protein 1) led to smaller plants and shorter siliques  is more efficient than traditional molecular marker‐assisted selection (MAS) methods in molecular breeding. With the development of high‐throughput sequencing, as well as transcriptome and metabolome technologies, multi‐omics data have been used to predict complex agronomic traits, with great progresses being made in crop studies  was used in this study. In this study, 145 lines from the KJ‐RIL population were used for metabolome analysis. The plants were grown at Yantai in Shandong Province, China . A randomized block design with two replications was used during the 2016–2017 and 2017–2018 cropping seasons, with 40 seeds hand‐planted in each row of a two‐row plot with 2 m long rows spaced 0.25 m apart. All lines were self‐pollinated, and the field experiment was carried out in accordance to the standard agronomic wheat management practices. All material planting conditions have been previously described in detail . The samples were analyzed using an LC‐ESI‐MS/MS system (HPLC) . A stepwise multiple ion monitoring‐enhanced product ion was used to construct the MS2T library, as previously described  using a tissue grinder  for 50 sec at 29 Hz. For each sample, 100 mg of dry powder was weighed, mixed with 1.0 ml of 70% methanol containing 0.1 mg/l acyclovir , vortexed, and extracted for 10 h at 4°C. This was followed by centrifugation at 9500 2‐transformed for statistical analysis to improve normality. Broad‐sense heritability (H2) was calculated using one‐way ANOVA with three biological replicates to determine the environmental effects . Visualization correlation networks were constructed using Cytoscape 3.7.0 , with a scanning step of 1 cM and PIN (probability in stepwise regression) of 0.001  + PB37 (TTATTGGCGTTGCACCTTATC) since the candidate only contained a single exon. The sequencing‐confirmed vector was cloned into pGEX‐6p‐1 . Protein induction, cell disruption, and GST protein purification were performed according to the methods reported in Peng et al. (StrepII‐tag vector (VB5) was modified from the commercial vector pCXSN‐HA (Taxon ID: 643586) by replacing the HA and ccdB with a StrepII coding sequence. Then, the candidate genes were cloned into VB5 using Primer179 (aaattcgtagtggatcccccTTATTGGCGTTGCACCTTATC) and Primer180 (catcctcaatttgaaaaaccaCTGCACATTCCAGAGCAGCA). The resulting constructs were introduced into Agrobacterium strain GV3101. Recombinant protein expression and purification were subsequently carried out, as reported by Schroeder et al. (m Tris‐HCl (pH 7.5), 10 mm MgCl2, 10 mm apigenin, and 0.75 mm UDP‐glucose sampled at 0 and 30 min at 37°C for testing. Different substrate concentrations and time courses were used for the kinetics assay. LC‐MS quantification was used to determine the accumulation of the products. The proteins were then quantified using the Bradford reaction. The values for KM and Vmax  were determined using GraphPad PRISM software  with the Michaelis–Menten model. The reactions were run in duplicate, and each experiment was repeated twice. Electrophoresis, immunoblotting, and Coomassie Brilliant Blue staining were performed as described in Myrach et al. . qRT‐PCR was performed using SYBR Green RT‐PCR Master Mix . Two Actin genes were used as an internal control for the quantification of gene expression, amplified by the primers PB140 (ACCCAGATCATGTTCGAGACC) and PB141 (TTCGACCGCTGGCATACAAA) for Actin‐1D (TraesCS1D01G274400) and PB142 (GCCGTTCTGTCCTTGTATGC) + PB143 (GAGGAAGCGTGTATCCCTCA) for Actin‐1B (TraesCS1B01G283900). TraesCS2B01G459900.1 was amplified using the primers PB136 (GACAGGCGCATTCTTGACG) and PB137 (CAGCTCCTCCACGATGAACA). The relative gene expression was calculated as reported by Schroeder et al. . Briefly, 1 μg of RNA was treated with DNase and reverse‐transcribed according to the manufacturer’s protocol , 2012–2013 , and 2013–2014 (Shijiazhuang). The materials were planted using a randomized block design with two replications, as previously described in the literature  about mQTL.Figure S4. Phylogenetic tree of UGT88C13 and UGT706E7.Figure S5. Gene model of the candidate TraesCS2B01G012000 with the primers used for the amplification.Figure S6. Sequences and alignment of the candidates.Figure S7. Functional annotation of candidate gene in vitro.Figure S8. Box plot for H2 and CVs about 17 agronomic traits.Figure S9. A common wheat flavone‐related and flavonol‐related metabolic network involving the candidate genes mapped in this study.Figure S10. Genomic data used to predict plant height and number of grains per spike based on two models.Click here for additional data file.Table S1. Scheduled MRM transitions for widely targeted metabolite analysis in mature wheat kernels.Table S2. Metabolic profiling in the kernels of the KN9204/J411 RIL population.Table S3. The statistical results of broad‐sense heritability (H2) and coefficient of variation (CV).Table S4. Metabolic quantitative trait loci (mQTLs) results of RIL population.Table S5. Statistical analysis of metabolic quantitative trait loci (mQTLs) on the chromosomes.Table S6. Glucosyltransferase activities and kinetic parameters of UGT88C13 and UGT88C14.Table S7. Pearson’s correlation of 467 annotated metabolite and agronomic traits (P < 0.01).Table S8. Agronomic quantitative trait loci (pQTLs) of the RIL population.Table S9. The co‐localization results of pQTLs and mQTLs.Table S10. Metabolites with significant effects on the prediction of plant height and number of grains per spike (P < 0.05).Click here for additional data file. Click here for additional data file.


The analysis of the effects of autozygosity, measured as the change of the mean value of a trait among offspring of genetic relatives, reveals the existence of directional dominance or overdominance. In this study we detect evidence of the effect of autozygosity in 4 out of 13 cardiometabolic disease-associated traits using data from more than 10,000 sub-Saharan African individuals recruited from Ghana, Burkina Faso, Kenya and South Africa. The effect of autozygosity on these phenotypes is found to be sex-related, with inbreeding having a significant decreasing effect in men but a significant increasing effect in women for several traits . Overall, the effect of inbreeding depression is more intense in men. Differential effects of inbreeding depression are also observed between study sites with different night-light intensity used as proxy for urban development. These results suggest a directional dominant genetic component mediated by environmental interactions and sex-specific differences in genetic architecture for these traits in the Africa Wits-INDEPTH partnership for Genomic Studies (AWI-Gen) cohort. The prevalence of cardiometabolic diseases (CMDs) is increasing rapidly across Africa. Here, the authors investigate autozygosity in CMD-associated traits in over 10,000 sub-Saharan African individuals, showing these traits are influenced by sex-specific inbreeding depression and environmental interactions. However, this health and demographic transition is very heterogeneous across the African continent and varies dependant on the epidemiological transition at the site. The main purpose of the AWI-Gen (Africa Wits-INDEPTH partnership for Genomic studies) H3Africa Consortium study is to examine the prevalence of CMD-associated risk factors, regional burden, and to explore gene-gene and gene-environment interactions that contribute to disease risk2. To achieve its goals, AWI-Gen collected genotype and phenotype data on more than 10,000 individuals from rural and urban sites across four sub-Saharan African (SSA) countries  have been rapidly increasing in sub-Saharan Africa. The continent is in the turmoil of an epidemiological transition characterized by complex patterns of change in health and diseases interacting with demographic, dietary, economic and social determinants3. ID, manifest by an effect of genomic homozygosity on phenotypic values, implies some degree of directional dominance or overdominance in the genetic architecture4. ID is influenced by two main factors: the amount of inbreeding and thus autozygosity; and the degree to which the dominance of causal loci is biased in one direction (which can be mainly caused by selection pressure8). For this study, the inbreeding coefficient is calculated from genomic data through the analysis of runs of homozygosity (ROH: genomic tracts where homozygous markers occur in an uninterrupted sequence)9. African genetic homozygosity is particularly understudied, e.g., 30,000 individuals of African ancestry were included within the largest (~1.4 M) published study, and of those, only 1000 were resident in SSA itself, all of whom were urban dwellers10. Eleven traits in total are shared by this analysis and the one performed by Clark et al. 2019, with our study replicating results for height, weight, total cholesterol, LDL, BMI, and triglycerides levels and presenting a statistically-powered ROH analysis of Africans.Dissecting the genetic architecture of complex traits provides a deeper understanding of disease etiology and insights that could contribute to screening, diagnosis, prognosis and therapyROH, for the entire AWI-Gen cohort is 0.0093, equivalent to their parents being on average about second cousins, once removed, but likely generated in many cases from the sum of multiple more distant relationships. No differences in the FROH were found between men and women or between urban and rural sites  .The mean genomic inbreeding coefficient, FROH was associated with a lower socio-economic-status. When the entire cohort is considered, we detected significant negative ID for weight, BMI and SCAT. However, the intensity of the ID for these traits was not very strong and the offspring of the equivalent of a second cousin mating had on average, a reduction of 0.51 kg, 0.18 kg/m2, and 0.049 cm, respectively. In a previous study using ~1.4 M individuals worldwide, the average reduction in weight and BMI by ID was estimated as 0.85 kg and 0.25 kg/m2, respectively10. As a replication comparison, we can conclude that the effect of the inbreeding depression has the same direction for the 11 traits shared by Clark et al.10 and this study. Furthermore, among the African-descendant individuals analyzed by Clark et al.10 . Moreover, for TG levels that were not significant after multiple test correction in the overall analysis, there is a strongly significant ID effect in the study sites with less luminosity (0.024 mmol/L). As the sample size for the less developed sites is almost double that for more urban sites, we have more power to detect the effects of ID there. However, the effect sizes for ID on SCAT, LDL and TG in more urban sites were significantly different from less developed sites , interestingly showing that the effects in less developed sites were in the opposite direction for these traits  as a proxy for urban development (see Methods) revealed the effect of ID on the traits is more intense in less developed more rural study sites Fig. . The aveits Fig. . We suggFROH) was −4.9 ± 5.9 TraitSD in men and only 1.1 ± 3.8 TraitSD in women (Mann-Whitney U test p = 0.0155). Men born from a second cousin marriage would be, on average, 2.26 kg lighter, have a reduction of 1.01 kg/m2 in BMI and 0.18 cm less SCAT. However, women would be, on average, 2.1 kg heavier, have an increase of 0.84 kg/m2 in BMI and 0.15 cm more SCAT . Sex-specific ID effects for all the traits shared with Clark et al.10  had the same directions, thus replicating their outcomes. Our results further suggest different autosomal genetic architectures, interactions or unmeasured biases for the CMD-associated traits considered in this study for men and women.When the sexes were considered separately, we observed a sex-specific effect of inbreeding. For BMI, SCAT, LDL and TC, inbreeding had a decreasing effect in men but an increasing effect in women Fig. . OverallROH, genomic inbreeding coefficient using the genomic relationship matrix (FGRM) (see methods section) and FoutsideROH. We first fitted bivariate models with FROH and FGRM as explanatory variables and then we explored the effect of FoutsideROH, which is not an estimator of the inbreeding coefficient, but describes the homozygosity of common SNPs outside ROH. As can be seen in Supplementary Table GRM is not associated with any change in trait values. Like FGRM, FoutsideROH which captures common SNPs in strong linkage disequilibrium (LD), was not significant for any trait or any model , which are known to be pleiotropic. KCNH5 (potassium voltage-gated channel subfamily H member 5) has been found to be associated not just with BMI but also with SCAT17, intelligence18, and sleep duration19. FUT8 (fucosyltransferase 8) has also been associated with IgG glycosylation patterns20, age at menarche21, schizophrenia22, plasma N-glycans23, head circumference24, the plasma proteome25, multiple sclerosis26 and systolic blood pressure16. Thus, the strongest directionally dominant effects on BMI are shared with loci also imparting additive effects discovered by GWAS. Two windows on chromosomes 2 and 4 were found to be associated with VAT in women . Only two traits were suggestively significantly influenced by individual regions: BMI in men Fig.  and VAT In this study we analysed the effect of inbreeding depression on 13 cardiometabolic disease-associated traits in the AWI-Gen cohort with the intention of better understanding the genetic architecture of these traits in sub-Saharan Africa. We found significant evidence for inbreeding depression for four phenotypes, which was strongly enhanced in less developed rural regions and also stronger in men. Our results suggest a complex genetic architecture for these traits with interactions by rurality. Larger studies of African populations will further illuminate the factors contributing to complex disease risk in sub-Saharan African populations.27. Currently this definition has been generalized for any complex trait as the change in the mean phenotypic value in a population because of inbreeding. Considering the combined effect of all the loci that affect a character, as far as the genotypic value of the loci combined additively is concerned, the mean character value of a population with inbreeding coefficient (F) is given by:0 is the population mean before inbreeding and p and q are allele frequencies. The change of the mean on inbreeding is therefore5Our objective was to explore the directionally dominant component of the genetic architecture of 13 cardiometabolic disease (CMD) associated traits measured in 10,776 sub-Saharan African (SSA) individuals recruited by the AWI-Gen study. In order to achieve this, we measured the effect of inbreeding depression on these risk factors. In classical population genetics inbreeding depression (ID) is defined as the reduction of the mean fitness in a population because of inbreedingd) is different from 0  genetic architecture4). Further, when loci are combined additively, the change of the mean on inbreeding should be directly proportional to the coefficient of inbreeding4. This allows us to detect inbreeding depression in complex characteristics exhibiting directional dominance using regression analysis as long as the population under study practices some inbreeding. It is important to realize that the genetic architecture of a characteristic, including the effects of ID, is not necessarily constant among different populations. The intensity of the ID and the genetic architecture of a trait depend on the selection pressure, environmental factors and population structure inasmuch as genetic frequencies change between populations. The considerable burden of long ROH (>1.5 Mb) present in some sub-Saharan populations28 provides the opportunity to test whether CMD-associated traits gathered by the AWI-Gen initiative exhibit inbreeding depression.This shows that inbreeding will change the mean value of a character in a population when the sum of the genotypic value of the heterozygotes  that includes Health and Demographic Surveillance System (HDSS) Centres and the Developmental Pathways for Health Research Unit (DPHRU), that have longitudinal cohorts in Navrongo, Ghana; Nanoro, Burkina Faso; Nairobi, Kenya; Agincourt, South Africa; Dikgale, South Africa and Soweto, South Africa. In total, over 12,500 participants were recruited between August 2013 and August 2016, of which 10,776 were aged 40–60 years1. Pregnant women, first-degree relatives, recent immigrants and individuals with physical impairments preventing the measurement of different characteristics were excluded. The objectives of the AWI-Gen study can be summarized as follows: (1) To build capability for genomic research in these centers and countries by providing opportunities to develop skills including biostatistics, genomics, and bioinformatics. (2) To understand population structure and genetic architecture among recruiting sites in order to inform analysis strategies and evaluate impact across different ethnicities. (3) To understand the prevalence and genetic basis of cardiometabolic diseases (CMD)2. The study was approved by the Human Research Ethics Committee  of the University of the Witwatersrand (Wits) (protocol numbers M121029 and M170880), and each contributing Centre obtained additional local ethics approval as required. Data and samples were collected following community engagement and individual informed consent1.The Africa Wits-INDEPTH Partnership for Genomic Studies (AWI-Gen) is an NIH-funded Collaborative Centre of the Human Heredity and Health in Africa (H3Africa) Consortium29. See Trait Definition section.Biomarkers of CMD gathered by the AWI-Gen study include anthropometric variables including height, weight, body mass index (BMI), waist-to-hip ratio (WHR) and fat distribution  and subcutaneous adipose tissue (SCAT)); lipid composition variables including low density lipoprotein cholesterol (LDL), high density lipoprotein cholesterol (HDL), total cholesterol (TC) and triglycerides (TG); circulatory traits including pulse rate and blood pressure (systolic and diastolic). Besides the above biomarkers a socio-economic-status variable was also calculated for each individual. This variable (SES) was obtained by adding the items a person has in their household from among a list defined items, determined as appropriate for their country. The SES quintiles for each study site were calculated and individuals assigned to a quintile. This was used in the analysis to remove a potential cultural component related to socio-economic-status as is shown inhttps://www.h3abionet.org/h3africa-chip.AWI-Gen individuals were genotyped using the H3Africa Custom Genotyping Array, which is a high-density genotyping array (2.267 million SNVs) designed by an H3Africa project team to maximize capturing common variation in African population. Manufactured by Illumina, the array has been used by several H3Africa projects. Details of the array and the SNVs captured can be found at p-value < 5.0 × 10−4). Similarly, at the sample level, individuals with genotype missing rate > 0.02, that failed the sex check and one of the individuals from each related pair  were removed. Also, 159 first degree relatives were removed from the Agincourt site. These QC steps were performed using the H3Africa GWAS pipeline30 and resulted in the final dataset containing 1,733,121 SNVs and 10,617 individuals.A total of 11,076 samples were genotyped. We removed duplicate and non-autosomal SNVs; SNVs with genotype missing rate > 0.01; minor allele frequency < 0.01 and SNVs that deviated from Hardy-Weinberg equilibrium test  full name—units. Further details can be found in Ali et al.Socio-economic-demographic variablesage—years. Age at data collection .sex. Sex was self-reported and validated by genomic data.site—Site of sampling. Recruitment site where the sample was collected: Nanoro (Burkina Faso), Navrongo (Ghana), Nairobi (Kenya), Agincourt (South Africa), Dikgale (South Africa), Soweto (South Africa).night-light—Night-time luminosity. The average values of light pixels were obtained using night-light geo-tiffs from NOAA’s NGDC Earth Observation Group (EOG): https://ngdc.noaa.gov/eog/ and spatial boundaries are taken from the Global Administrative Boundaries Database: http://gadm.org/.edu—Education. The highest level of education was self-reported according to four categories: (1) No formal education, (2) Primary, (3) Secondary, (4) Tertiary. Tertiary education includes qualifications such as certificates, diplomas, or degrees.occu—Occupation. Self-declared employment according to five categories. (1) Self-employed. (2) Formal full-time employment by someone else. (3) Part-time employment by someone else. (4) Informal employment (dependent on the availability of work). (5) Unemployed.SES.Q—Socio-economic-status quintiles. The socio-economic status of the individual was calculated using a list of household goods appropriate for each site : electricity, solar energy, power generator, alternative power source, television, radio, motor vehicle, bicycle, refrigerator, washing machine, sewing machine, telephone, mobile phone, microwave, DVD player, satellite TV or DSTV, computer or laptop, internet by computer, internet by mobile phone, electric iron, fan, electric or gas stove, kerosene stove, plate gas, electric plate, torch, gas lamp, kerosene lamp with a glass, toilet facilities, potable water, grinding mill, table, sofa set, wall clock, bed, mattress, blankets, cattle, other livestock, poultry, tractor, plough. In order to establish a meaningful comparison between individuals at different sites, the SES quintiles were calculated, and individuals assigned to a quintile. This approach was used to remove a potential cultural component related to socio-economic-status2.Anthropometry variablesHeight—meters. Standing height measured in meters.Weight—kg. Weight measured in kilograms.BMI—Body mass index—kg/m2. Weight in kilograms divided by height in meters squared.WHR– Waist: Hip ratio—no units. Calculated by dividing the individual waist circumference in centimeters by the hip circumference also in centimeters.VAT—Visceral adiposity tissue—cm. Visceral  fat measured using ultrasound.SCAT—Subcutaneous adiposity tissue—cm. Subcutaneous (transverse) fat measured using ultrasound.Lipid composition (in fasting serum)LDL—Low-density lipoprotein cholesterol—mmol/L.HDL—High-density lipoprotein cholesterol—mmol/L.TC—Total cholesterol—mmol/L.TG—Total triglycerides—mmol/L.CirculatorySystolic BP—Systolic blood pressure—mmHg. Three readings were taken during a single session, two minutes apart, the first was discarded and the second two readings were averaged.Diastolic BP—Diastolic blood pressure—mmHg. Three readings taken during a single session, the first was discarded and the second two readings were averaged.Pulse—Heart rate—beats per minute. Three readings taken during a single session, the first was discarded and the second two readings were averaged.32. It was found to be highly correlated with GDP per capita and other measures of prosperity, like electricity provision33, and can therefore be considered as a valid proxy35. More recently, using Demographic and Health Surveys (DHS) from 29 African countries, night-time light intensity has been found to correlate strongly with indicators of household wealth, education, and health. Also, the variation in night-time light explained a substantial share in the variation of these indicators36.As a proxy for the urban and economic development of the different AWI-Gen sites we used night-light intensity (luminosity). Luminosity has been widely used as a proxy in countries where GDP data are either not available or of poor qualityNight light data are made available by the National Geophysical Data Center (NGDC) of the National Oceanic and Atmospheric Administration of the US, and originate from images taken by satellites of the Defense Meteorological Satellite Program (DMSP) of the U.S. Department of Defense between 1992 and 2013. Night light intensity data are available on pixel (grid cell) level, with each pixel corresponding to 30 × 30 arc s, i.e., one value represents the average night light intensity of an area of 0.86 square kilometers (on the equator). Night light intensity is measured by an integer ranging from 0 (unlit) to 63. We use the latest version (4.0) of the data.homozyg-snp 30. Minimum number of SNPs that a ROH is required to have.homozyg-kb 300. Length in Kb of the sliding window.homozyg-density 30. Required minimum density to consider a ROH (1 SNP in 30 Kb).homozyg-window-snp 30. Number of SNPs that the sliding window must have.homozyg-gap 1000. Length in Kb between two SNPs in order to be considered in two different segments.homozyg-window-het 1. Number of heterozygous SNPs allowed in a window.homozyg-window-missing 5. Number of missing calls allowed in a window.homozyg-window-threshold 0.05. Proportion of overlapping window that must be called homozygous to define a given SNP as in a “homozygous” segment.ROH longer than 300 Kb were called using PLINK software with the following parameters:37.No linkage disequilibrium pruning was performed. These conditions were previously used and validated by different published studies and were shown to call ROH that correspond to autozygous segments in which all SNPs (including those not present on the array) are homozygous-by-descentIT, is defined as the probability that an individual receives two alleles identical-by-descent38. Traditionally, FIT was measured using deep genealogies; currently, we can obtain an estimate of this parameter without having any genealogy by using genomic approaches. Different genomic inbreeding coefficients were calculated.The inbreeding coefficient or FFROH measures the actual proportion of the autosomal genome that is autozygous over and above a specific minimum length ROH threshold39. When analyzing ROH > 1.5 Mb, FROH correlates most strongly (r = 0.86) with the inbreeding coefficient obtained from an accurate six-generation pedigree39. Using extended pedigrees of the royal European dynasties, with complex inbreeding loops, it has been found that above the 10th generation the change in the inbreeding coefficient is less than 1%40. Also, it has been found that individuals with no inbreeding loops in at least 5 generations (and probably 10) carried ROH up to 4 Mb in length but not longer39. FROH using a genomic approach, captures the total inbreeding coefficient (FIT) within the resolution of the data available and the size of ROH that can be called9.FGRM. An alternative genomic inbreeding coefficient was obtained using PLINK’s parameter -ibc (Fhat3). This coefficient described by Yang et al. 2011  in comparison to FGRM (r = 0.682).Clark et al. 2020 showed that FoutsideROH An additional genomic inbreeding measure, FoutsideROH, was calculated as the genomic fraction of homozygous SNPs outside ROH.O(HOM) is the observed number of homozygous SNPs, E(HOM) is the expected number of homozygous SNP according to H-W proportions, N is the total number of non-missing genotyped SNPs and NSNP_ROH is the number of homozygous SNPs found in ROH.y = βFROH * FROH + Xb + ε (9). Where y is a vector of measured trait values, βFROH is the unknown scalar effect of FROH. FROH is a known vector of individual FROH as described above, b is a vector of unknown covariates effects including the mean (μ). X is a known design matrix for the fixed effects, and ε is an unknown vector of residuals. The traits used in this study have been the subject of genome-wide association meta-analysis (GWAMA), phenotype modeling, such that inclusion of covariates were chosen with reference to leading consortia: The Genetic Investigation of Anthropometric Traits (GIANT) for anthropometry, Global Lipids Genetics Consortium (GLGC) for lipids composition International Consortium for Blood Pressure (ICBP) for blood pressures.Inbreeding depression (ID) was modeled by a multiple regression: SES.Q = sex + age + edu + occu + night_light + pc1 + … + pc152 + SES.Q + night_light + pc1 + … + pc15Height = sex + age + age2 + SES.Q + night_light + pc1 + … + pc15Weight = sex + age + age2 + SES.Q + night_light + pc1 + … + pc15BMI = sex + age + age2 + height + SES.Q + night_light + pc1 + … + pc15WHR = sex + age + age2 + height + SES.Q + night_light + pc1 + … + pc15VAT = sex + age + age2 + height + SES.Q + night_light + pc1 + … + pc15SCAT = sex + age + age2 + SES.Q + night_light + pc1 + … + pc15HDL = sex + age + age2 + SES.Q + night_light + pc1 + … + pc15LDL = sex + age + age2 + SES.Q + night_light + pc1 + … + pc15TC = sex + age + age2 + SES.Q + night_light + pc1 + … + pc15TG = sex + age + age2 + SES.Q + BMI + night_light + pc1 + … + pc15Pulse = sex + age + age2 + SES.Q + BMI + night_light + pc1 + … + pc15Systolic BP = sex + age + age2 + SES.Q + BMI + night_light + pc1 + … + pc15Diastolic BP = sex + age + ageROH on SES.Q for both luminosity groups. We expect that, if there are no confounding variables introducing bias, the effect of the FROH over SES.Q should be similar in both groups.In order to account for unmeasured confounding variables that may differ between rural and urban sites, we used three different lines of evidence. First, socio-economic-status was added as a confounding variable to every model we ran. This allows the model to account for differences in the relative socio-economic status within sites and across individuals in all sites. Second, the luminosity was added to prevent further confounding effects due to differential urban development. Third, when the overall dataset was stratified by luminosity we added “site” as a co-variable and we tested the effect of Fy) was regressed on all fixed covariates to obtain the maximum likelihood (ML) of the model: y = Xb + u + ε’ (10), where u is an unknown vector of polygenetic effects with multivariate normal distribution of mean 0 and covariance matrix σg2A, where A is the genomic relationship matrix (GRM). GRM was obtained using PLINK v1.9 and GRM42, residuals (ε’) were estimated using GenABEL43. These residuals were used in subsequent analyses. To estimate βFROH for each trait, trait residuals were regressed on FROH to obtain the ML solution of the model: ε’ = μ + βFROH * FROH + βF_outsideROH * FoutsideROH + ε (11). Sex-specific estimates of βFROH and βF_outsideROH  and correlates well with the homozygosity of common SNPs but less with rare SNP which may be in weak LD. Following previous studies10 and in order to assess if ID is caused by common or rare variants, we performed bivariate models of all traits (Trait ~ FROH + FGRM) to establish whether the observed inbreeding effects associate more strongly with FROH or FGRM.F10 showed that homozygosity of common SNPs is better predicted by FGRM, but rare variant homozygosity is better predicted by FROH.By analyzing the excess homozygosities of SNPs, extracted from the UK Biobank, at seven minor allele frequencies, Clark et al.P < 5 × 10−5. Results are shown in a Manhattan-plot in Fig. In order to learn more about the genetic architecture of each trait, the effect of ROH burden was tested in each of ~1000 3 Mb-wide windows along the genome. For each window, the fraction which is in ROH was calculated, and then the association between ROH in each 3 Mb window and the mean trait residual was tested. A Bonferroni correction for 1000 windows was applied, significance was considered when Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Drought is projected to become more frequent and severe in a changing climate, which requires deep sowing of crop seeds to reach soil moisture. Coleoptile length is a key agronomic trait in cereal crops such as barley, as long coleoptiles are linked to drought tolerance and improved seedling establishment under early water-limited growing conditions.Hordeum vulgare L.) accessions. To understand the overall genetic basis of barley coleoptile length, all accessions were germinated in the dark and phenotyped for coleoptile length after 2 weeks. The investigated barleys had significant variation for coleoptile length. We then conducted genome-wide association studies (GWASs) with more than 30,000 molecular markers and identified 8 genes and 12 intergenic loci significantly associated with coleoptile length in our barley panel. The Squamosa promoter-binding-like protein 3 gene (SPL3) on chromosome 6H was identified as a major candidate gene. The missense variant on the second exon changed serine to alanine in the conserved SBP domain, which likely impacted its DNA-binding activity.In this study, we detected large genetic variation in a panel of 328 diverse barley (This study provides genetic loci for seedling coleoptile length along with candidate genes for future potential incorporation in breeding programmes to enhance early vigour and yield potential in water-limited environments. Germinating seedlings of monocotyledons have a coleoptile, which is a sheath-like tissue covering the primary leaf to protect the emerging shoot as it breaks through the soil to the surface. The coleoptile stops growing after the first true leaf pushes through the pore at the tip. The coleoptile is essential for early crop establishment and its length determines the maximum depth at which the seed can be sown , 46, 57.+-ATPase level in the plasma membrane to increase the H+ extrusion into the apoplast to adjust to an optimum pH for cell wall enlargement [Zea mays K+channel 1 (ZMK1) is upregulated by auxin and essential for coleoptile elongation by maintaining K+ accumulation and turgor [Auxins are a class of plant hormones that can modify plant cell walls, and are essential for coleoptile cell elongation and expansion . In cereargement . Some std turgor . Some otd turgor  and β-exd turgor . Nucleosd turgor . AlthougBarley varieties show significant differences for coleoptile length. Paynter and Clarke (2010)  determinIn this study, we performed genome-wide association mapping with more than 30,000 genetic markers to map the marker-trait associations (MTAs) for coleoptile length. We used a worldwide collection of mainly domesticated barley cultivars , including a large proportion of barley cultivars grown in the driest regions in the world such as Australia. The aims of this study were (i) to investigate the phenotypic variation of coleoptile length in a diverse worldwide collection of barley genotypes, (ii) to determine genomic regions associated with coleoptile length via GWAS, and (iii) to identify and characterise the most likely candidate genes underlying the MTAs.2 = 0.87)  and North and South American varieties (p = 1.26 × 10− 3), African longer than European (p = 2.51 × 10− 2) and North and South American (p = 4.26 × 10− 2), Asian longer than European (p = 1.93 × 10− 4) and North and South American (p = 8.87 × 10− 4)  tree based on genetic distances for the barley population in this study (328 barley accessions) was constructed, incorporating their origins, row types and growth habits respectively Fig. ). The reThe optimal K value (number of subpopulations) of the barley germplasm collection in this study was predicted using ADMIXTURE v.1.3.0 [2). Apart from the common markers identified by both MAF01 and MAF05, MAF01 identified 49 additional markers, representing extra 19 genic loci and 9 intergenic loci . Apart from the common markers identified by MAF01 and MAF05, MAF05 identified 21 additional markers, representing extra 7 genic loci and 9 intergenic loci .The association study was performed using two subsets of markers: subset MAF01 (MAF > 0.01) including 33,146 markers and subset MAF05 (MAF > 0.05) including 23,193 markers. Significant MTAs (qFDR< 0.05) were identified on all 7 chromosomes  > 2.0) identified by either MAF01 or MAF05 are listed in Table HORVU1Hr1G073010 (unknown function), HORVU1Hr1G076430 (Protein FLOWERING LOCUS T (FT)), HORVU1Hr1G077230 (Cellulose-synthase-like C6 (CSLC6)), HORVU5Hr1G058300 ) and HORVU5Hr1G007340 (Leucine-rich repeat receptor-like protein kinase family (LRR-RLK)). One genic locus within gene HORVU6Hr1G019700 (Squamosa promoter-binding-like protein 3 (SPL3)) was only detected in MAF01. Two genes harbouring genic loci, HORVU6Hr1G022770 (Protein VERNALIZATION INSENSITIVE 3 (VIN3)), and HORVU6Hr1G022500 (BTB/POZ domain-containing protein (BTBD)) were only detected in MAF05. The top significantly associated markers (with the lowest qFDR) for each candidate gene are summarized for their effect on the coleoptile length  and 11 markers on SPL3 showed highest significance of all associations (qFDR = 5.4 × 10− 3). In MAF05 there were 2 markers on SPL3 identified to be significant (0.01 < qFDR< 0.05)  on SPL3 showed the most significant effect: the mean length was at 5.37 cm when the allele C presented and 6.79 cm when the allele T presented (p = 2.85 × 10− 6)  and had the strongest effect on the phenotype. Therefore, SPL3 was considered the major candidate gene associated to the coleoptile length in this study.The markers within gene SPL3 is a transcription factor gene located on chromosome 6H:53,909,817 to 53,916,886 (7070 bp), consisting of 5’UTR, 3’UTR, four exons and three introns . Three other missense variants included glutamic acid replaced with lysine at position 53,913,549, alanine replaced with valine at position 53,913,335 and 53,910,588. However, no variant was found on the miR156 binding target  and CI5791 (coleoptile length 7.17 cm)), representing two major haplotypes of SPL3 (C/T at marker C6H53910826) in the population. The comparison of the SPL3 expression between CDC Unity and CI5791, and in dark and under natural daylight is presented in Fig. SPL3 gene expression in coleoptile of CDC Unity in dark) (considered as 1). There was no statistically significant difference between CDC Unity and CI5791, either in dark or under natural daylight. However, in CDC Unity, the expression under light decreased by 43% compared with dark conditions. Similarly, in CI5791, the expression under light decreased by 39% compared with dark conditions. In conclusion, the coleoptile length variation between two SPL3 alleles was not due to the gene expression, but for the both alleles there was a steady decline in gene expression when the coleoptile was exposed under daylight.To further understand the role of More frequently occurring drought conditions as result of changing climate has become a major challenge in many growing regions of the world. Long coleoptile barleys have the potential to help mitigate limitations on emergence and improve the field establishment of barleys in water limited areas. In this study we performed GWAS on coleoptile length using 328 barley accessions with more than 30,000 molecular markers. As a result, the coleoptile length showed significant variation in this barley population and the GWAS identified 8 genes and 12 intergenic loci with high significance, providing a valuable source for selecting long coleoptile barleys in the future .In this study, the seeds were sown in washed river sand, a commonly used method for coleoptile measurement , 56, andAllele frequency is a key factor affecting the power of GWAS and impacting on detecting marker-trait associations . To dateSPL3 is the major candidate gene for coleoptile length in this study. The SPL3 protein belongs to SBP family and contains the conserved SBP domain. It is an important plant-specific transcription factor regulating plant growth and development, and the two zinc finger structures are located in the core region of the SBP domain [SPL3 was a key target for post-transcriptional regulation by microRNA [SPL3 is expressed in the coleoptile tissue and the expression increased in dark conditions, in accordance with the light sensitivity of coleoptile growth. Although the expression in dark only increased by 60–70% compared with daylight conditions, the effect can be significantly amplified since SPL3 is a transcription factor potentially activating other genes [SPL3 variants affect the coleoptile length between two varieties remains unclear. Although in our initial GWAS we provide statistical evidence that a gene is likely to harbour a causal variant, as in any GWAS study additional statistical methods and follow-up functional studies are needed to discriminate likely functional variants from variants that in LD with the functional variants. Further studies are also needed to determine the function and the downstream target of this gene, as well as how it affects coleoptile growth in barley.P domain . AccordiP domain . AlthougmicroRNA , but no er genes , 60. Hower genes , 15. At CSLC6 on 1H, encoding an enzyme involved in cell wall biosynthesis, was determined a candidate gene due to highly significant associations with coleoptile length. The gene, a member of the cellulose-synthase-like family, was proposed to encode the catalytic subunit of enzymes that synthesize hemicellulose backbones [GA2ox (HORVU1Hr1G076730) on 1H was identified as another candidate gene, and it is also a known semi-dwarf gene [EXPB2 (HORVU1Hr1G054230) on 1H was only identified by MAF01 with significant association. Members in this gene family had been showed to regulate coleoptile growth in other cereal crops [A number of candidate genes identified in this study were shown to regulate cell growth in other plants. ackbones . A previackbones . Therefoarf gene . This gearf gene , 16. Thial crops , 24. Preal crops , 46. FurFor cereal crops grown in drought prone regions, cultivars with longer coleoptile can emerge quicker and have better early vigour . Longer SPL3 was determined as the novel major candidate gene for coleoptile length in this study. It had the strongest effect on the coleoptile length among the candidate genes and was identified by multiple markers and different methods. SPL3 was expressed in the coleoptile but no significant expression difference was found between haplotypes. The substitution of serine for alanine in the second zinc-finger structure of SBP domain in SPL3 likely impacted its DNA-binding activity. This work provides a valuable overview of genetic factors responsible for coleoptile length in barley, detecting genome-wide loci with improved resolution, making it easier to pinpoint the responsible genes in the future research. It provides opportunity to improve seedling early vigour and stand establishment for barley and other crops by better understanding the mechanism regulating coleoptile growth.In this study, a worldwide collection of barleys (328 accessions) showed a large phenotypic variation for coleoptile length. The genome wide association analysis using more than 30,000 markers identified 8 genes and 12 intergenic loci with high significance (qFDR< 0.01), as well as 71 genes and 60 intergenic loci with lower significance (0.01 < qFDR< 0.05). In this study, 328 barley accessions were selected from a barley diversity panel described previously , 22. TheSeeds with normal size and plumpness were selected from each accession of 328 varieties. The commercially available washed river sand was sourced from the Avon river , and was tested for its properties by the CSBP Soil and Plant Analysis Laboratory . It contained no chemical residues that may affect plant growth including coleoptile growth. Seeds of each barley genotype were sown at 2 cm depth in individual 8x8cm cells of germination trays filled with washed river sand (12% v/w moisture) and stored in the dark at 20 °C. After 14 days the etiolated seedlings were pulled out from the river sand and the coleoptile length from the embryo end of grain to the coleoptile tip was measured manually using a ruler. Eight seeds  from each accession were used for coleoptile length measurement, and each seed was measured for three times . The experiment was repeated once. Absolute measurements that deviated from mean values by more than 2 standard deviation units were considered as outliers and set as missing values. The average values of the sixteen biological replicates were used to represent the varieties’ coleoptile lengths.The collection of 328 barley varieties were grown in a naturally lit glasshouse at Murdoch University, Perth, Western Australia  and harvested at the three-leaf stage (three plants per each variety). Genomic DNA extraction was performed using a rapid cetyl-trimethyl- ammonium bromide (CTAB) method .We used a combination of three sequencing methods , and DArTseq) to capture variation in and around the gene-containing regions of the 328 barley genotypes. The target-enrichment sequencing of genomic DNA regions and low-coverage whole genome sequencing were performed as described before , 22. GenGenetic distance based on IBS (identity-by-state similarity) were calculated and a Neighbor-Joining (NJ) dendrogram was computed, all using TASSEL v.5.2.39 software .2 threshold set to 0.5, yielding 19,014 genetic variants. The K value was set from 1 to 20 to do a preliminary analysis with 100 replicate runs and a 10-fold cross validation (CV) procedure was performed as described before [http://royfrancis.github.io/pophelper/) implemented in R software (http://www.R-project.org/) as detailed previously [The model-based clustering algorithm of ADMIXTURE v.1.3.0 was used to investigate subpopulation structure of the barley diversity panel. Prior to population structure analysis in ADMIXTURE, the genotype dataset was LD pruned using Plink v1.939 with the window size set to 50 kb, step size to 5, and the pairwise rd before . ADMIXTUd before . The ploeviously .Principal component analysis (PCA) was also conducted based on all markers data using TASSEL v.5.2.39 to summarize the genetic structure and variation present in the barley germplasm. The first two principal components were plotted against each other using ‘scatter plot’ function in Microsoft Excel 2016. NJ trees were constructed using the Java application Archaeopteryx v.0.9909  based on2) between the intra-chromosomal pairs of loci. To investigate the extent of LD decay, intra-chromosomal r2 values were plotted against the physical distance (kb) between markers. Curves were fitted by second-degree LOESS using R software v.3.5.1 (http://www.R-project.org/).Genome-wide LD analysis was performed by pair wise comparisons among the intra-chromosomal genetic markers using Plink v.1.93 . LD was Genome wide association studies were performed using 33,146 (MAF > 0.01) and 23,193 (MAF > 0.05) genetic markers, respectively in TASSEL .P-values for putative MTAs, population structure (Q) and the kinship matrices (K) were computed by many different statistical models to prevent spurious associations. The following models were tested: i) Naïve model: GLM without any correction for population structure; ii) P-model: GLM with Q  or K matrix  as a correction for population structure; iii) Q + K model: Compressed Mixed Linear Model (MLM) with population structure (Q) matrix  and K matrix  as a correction for population structure. Results were compared, and according to the quantile-quantile (Q-Q) plot, for this study the GLM with population structure (Q) matrix (the first three PCs) as a correction for population structure was most suitable out of all the tested models.For the GLM model and analysis, the qvalue was calculated in different testing using R package R 3.4.2) as described previously , 54, 55,.2 as des2) was calculated in R statistical software using the ‘lme4’ package and the standard ‘anova’ function according to the following equation by treating genotype and environment as random effects:Broad-sense heritability (HSquamosa promoter-binding-like protein 3 (SPL3) in the population  and SPL3_exp_R (5′-GAACATATGGAGCCTGACCGA-3′) . The target gene expression was normalized using Actin as a reference [−△Ct. The size of the PCR product was estimated by 2% agarose gel.Seeds with normal size and plumpness were selected from barley varieties CDC Unity (short coleoptile haplotype) and CI5791 (long coleoptile haplotype), representing the two major haplotypes of the primary candidate gene eference . The theAdditional file 1 Figure S1. Diversity of barley varieties. a) The geographic distribution of barley accessions. Numbers above the bars showed the varieties numbers from each geographical origin. b) Row type and c) growth habit in barley accessions. Numbers on the top of the bars showed the varieties numbers in different row-types and life forms.Additional file 2 Figure S2. Selection of the optimal number of K (genetic subpopulations). The red line indicated the optimal K value (the most likely number of subpopulations) as 7 based on Δ cross-validation error and standard error in barley accessions.Additional file 3 Figure S3. Population structure analysis using ADMIXTURE for 328 worldwide barley genotypes with 19,014 SNPs. Different populations are showed in different colours. The proportional membership in the population is indicated by the colour of the individual haplotypes. CLUMPP was used to merge the membership coefficients for each population with 100 replicate runs. The number of clusters (K) in 328 barley varieties was determined to be 7 based on the CV error.Additional file 4 Figure S4: The LD decay of marker pairs in the worldwide collection of domesticated barley varieties. X-axis indicated the distance (kbp) and Y-axis indicated the mean r2 values of all intra-chromosomal pairs of SNPs. The red lines are the LOESS fitting curves fit by second-degree loess.Additional file 5 Table S1 Barley accessions with long coleoptile.Additional file 6 Table S2 Loci significantly associated with coleoptile length using two MAF (q < 0.05)Additional file 7 Table S3 Intergenic Loci with highly significant association (qFDR < 0.01) and candidate genesAdditional file 8 Figre S5. Manhattan plots and quantile–quantile (QQ) plot of coleoptile length. (A) The Manhattan plot of coleoptile length by GLM model using minor allele frequency (MAF) < 0.05. (B) The Manhattan plot of coleoptile length by GLM model using minor allele frequency (MAF) < 0.01. The significance of marker-trait associations was presented using q values . The blue dashed lines indicated the significant threshold at qFDR< 0.05 and the red dashed lines indicated the significant threshold at qFDR< 0.01. (C) QQ plot for coleoptile length using q values  by GLM model with MAF < 0.05. (D) QQ plot for coleoptile length using q values by GLM model with MAF < 0.01.Additional file 9 Figure S6. The amino acid substitution site and the conserved motifs on SBP domain of gene SPL3. Zn-1: zinc finger 1; Zn-2: zinc finger 2; NLS: nuclear localization signal. Blue colour indicated the most conserved region on zinc finger 1. Red colour indicated the most conserved region on zinc finger 2. Purple colour indicated the most conserved region on nuclear localization signal.Additional file 10 The dataset supporting the conclusions of this article. Sheet 1: The coleoptile length data including experiments conducted across two years (2017 and 2019) with a total of 16 biological replicates. The length unit is centimetre. Missing data due to germination failure is left blank; sheet 2: The single-nucleotide variants between short and long coleoptile haplotypes. 0 represented the reference allele and 1 represented the alternative allele; sheet 3: The raw data for SPL3 gene expression experiment; sheet 4: the coleoptile length variation between different alleles of high significant candidate genes.