Holstein is the most popular dairy cattle breed worldwide due to its milk yield. When these cows are exposed to heat stress, they reduce feed intake and milk production in order to minimize body heat production. The large variability associated with this response appears to be genetically regulated. Therefore, we combined genomic and marker-assisted technologies with the objective to validate genetic markers associated with milk production and thermotolerance. A genome-wide association study detected six candidate single nucleotide polymorphisms (SNPs) as predictors for milk production in heat-stressed Holstein cows. Only three of these SNPs were further validated as markers for milk production and thermotolerance traits  in two independent Holstein cow populations. Such markers belong to genes that regulate metabolic functions needed to accomplish energy demands and minimal heat production. The results of this study revealed that heat-stressed Holstein cows with favorable markers were able to reduce rectal temperature and respiratory rate, which allowed them to maintain adequate milk production levels. In conclusion, we validated three genetic markers in heat-stressed Holstein dairy cows, which are useful to be included in selection programs to improve milk yield and tolerance to heat stress.n = 300) exposed to a heat stress environment were genotyped using a medium-density array including 53,218 SNPs. A genome-wide association study (GWAS) detected six SNPs associated with total milk yield (MY305) that surpassed multiple testing (p < 1.14 × 10−6). These SNPs were further validated in 216 Holstein cows from two independent populations that were genotyped using the TaqMan bi-allelic discrimination method and qPCR. In these cows, only the SNPs rs8193046, rs43410971, and rs382039214, within the genes TLR4, GRM8, and SMAD3, respectively, were associated (p < 0.05) with MY305, rectal temperature (RT), and respiratory rate. Interestingly, these variables improved as the number of favorable genotypes of the SNPs increased from 0 to 3. In addition, a regression analysis detected RT as a significant predictor (R2 = 0.362) for MY305 in cows with >1 favorable genotype, suggesting this close relationship was influenced by genetic markers. In conclusion, SNPs in the genes TLR4, GRM8, and SMAD3 appear to be involved in the molecular mechanism that regulates milk production in cows under heat-stressed conditions. These SNPs are proposed as thermotolerance genetic markers for a selection program to improve the milk performance of lactating Holstein cows managed in a semiarid environment.Dairy production in Holstein cows in a semiarid environment is challenging due to heat stress. Under such conditions, genetic selection for heat tolerance appears to be a useful strategy. The objective was to validate molecular markers associated with milk production and thermotolerance traits in Holstein cows managed in a hot and humid environment. Lactating cows ( Milk production is a very important livestock activity because it generates a basic food for humans. Cattle account for 81% of the total milk produced worldwide .Heat stress is an imbalance between the ratio of heat acquired from different sources such as body metabolism and environmental conditions versus the heat dissipation system, which causes an increase in the animal’s body temperature . The HolFermentation of forage consumed in the rumen produces considerable metabolic heat, which also generates additional heat load for the cow’s body . Additiohttp://www.animalgenome.org/QTLdb. Date accessed: 23 April 2023).With the progress of bovine genome sequencing and high-density SNP genotyping technologies, genome-wide association studies (GWASs) have become an important tool for locating QTL or chromosomal regions associated with traits of economic importance . In variGWASs examine thousand of small variants across the genome called single-nucleotide polymorphisms (SNPs), which are very helpful to identify functional genes likely involved in phenotypic traits . For insRecently, GWASs have been performed in Holstein cows subjected to heat stress conditions. Potential candidate genes involving the heat stress response have been discovered as associated with milk production traits ; physiolTherefore, our objective was to validate candidate genomic SNPs as molecular markers associated with milk production and physiological traits indicative of thermotolerance in Holstein cows managed in a hot and humid environment.The Institutional Animal Care and Use Committee of the Instituto Tecnologico de Sonora approved all procedures performed on animals .2). Annual precipitation was ~341 mm/year ranging from 3 to 90 mm on rainy days.The study was conducted at three neighboring dairy farms located in the Yaqui Valley, Sonora, Mexico (27°21′ N 109°54′ W). These farms were managed under an intensive system for milk production. Climatic conditions throughout the year were characterized by significant variations in temperature (−2.5 to 45.9 °C), relative humidity (25 to 90%), and solar radiation  and milk production  was recorded daily using an electronic system . A total of 183,000 milk records were collected from 300 complete lactations. Data were filtered by the exclusion of wrong records  and then used to calculate the adjusted 305-day milk yield (MY305) per cow in kg. The MY305 was calculated by multiplying milk production levels by an adjustment factor provided by the Dairy Herd Improvement Association, which included days in milk and the age of the cow . These Hhttp://www.siafeson.com/remas; accessed on 8 April 2022). Daily records of AT and RH were used to calculate the temperature–humidity index (THI) using the formula THI = (0.8 × AT) + [(RH/100) × (AT − 14.4)] + 46.4 [Ambient temperature  and relative humidity  data were collected by a nearby meteorological station  and extracted from the REMAS website (] + 46.4 .®), which were sent to Neogen AgriGenomics  for DNA extraction. DNA was genotyped using the SNP panel BovineSNP50 containing 53,218 highly informative SNPs uniformly distributed across the entire bovine genome  [Disposable sterile syringes were used to collect from each cow a blood sample (3 mL) through venipuncture of the coccygeal vein. Five drops of whole blood were spotted on Fast Technology for Analysis of Nucleic Acids cards  .p-value of chi-square goodness-of-fit test greater than 0.05, X2 > 0.05), (3) call rate of an individual genotype higher than 95%, and (4) call rate of a single SNP genotype higher than 90%. Additionally, the SNPs located on the sex chromosome or those with unknown positions were removed from the current study. After quality control measures, 43,691 SNP markers were retained for further analyses.Quality control was performed using PLINK software to remove all SNPs that did not comply with the following criteria: (1) minor allele frequency of an SNP greater than 10% (MAF > 0.10), (2) no deviation from Hardy–Weinberg equilibrium (y was the vector of phenotypic observations (MY305), X was the design matrix of fixed effects, Z was the design matrix of random additive genetic effects, β was the vector of fixed effects, a was the vector of random additive genetic effects, and e was the vector of residual effects. It was assumed that a~N  and e~N , where σ2a is the additive genetic variance, σ2e is the residual variance component, G is the genomic relationship matrix, and I is the identity matrix.The principal component analysis (PCA) option was used to correct batch effects/stratification of the test input data. The GWAS was performed using a single-locus mixed model to study associations between genotypes of each SNP marker with the trait MY305 , by fitting all SNPs simultaneously. The additive, single-locus, mixed model used was: y = Xβ + Za + e, where www.goldenhelix.com; accessed on 12 June 2022) was used to perform single-marker GWAS analysis. The statistical model included genotype, herd, and lactation number as fixed effects, days in milk as the covariate, and sire as the random term to account for family effects. A total of 93 Holstein bulls were included as sires in the current study.The software SNP & Variation Suite v8 . Therefore, the corrected p-value threshold for the genome-wide significant level was 1.18 × 10−6 , which corresponded to 5.93 on a −log10 scale.The Bonferroni correction test (b = α/n), which assumed independence between SNPs, was used to adjust and set for multiple comparisons the analyses . The expBos_taurus_UMD_3.1.1. was used as a reference genome to search for candidate genes according to the chromosomal locations of each significant SNP.The functional genes that contained or were located less than 0.1 MB of distance from the significant SNPs associated with MY305 were selected as candidate genes. The genome assembly P less than 0.01 (n = 36) and the bioinformatics online database KOBAS . Ensembl  and GeneCards  were used for gene descriptions .Functional pathway enrichment analysis was performed using candidate genes that were significant at a n = 112, n = 104) from neighboring dairy farms were used to validate, as molecular markers for milk yield and thermotolerance, three SNPs previously detected as associated (p < 0.05) with milk yield in Holstein cows exposed to heat stress. These SNPs were rs8193046, rs43410971, and rs382039214, which were within the genes TLR4 (toll-like receptor 4), GRM8 (glutamate metabotropic receptor 8), and SMAD3 (SMAD family member 3), respectively.Two independent cattle populations including 216 Holstein cows  and respiratory rate (RR) were measured biweekly (06:00 and 16:00 h) as indicators of thermotolerance. A digital thermometer (TES-1310R) with a contact sounding line  that touched the rectal mucus was used to record RT, whereas RR was evaluated by visual counting of the flank intercostal movements for 60 s (breaths/min). Both RT and RR were evaluated in the waiting parlor before milking, once the cow remained quiet to facilitate collection of physiological data by the same person all the time.®, Terumo, Lakewood, CA, USA) were used to collect a blood sample from each cow by puncturing the jugular vein. After placing in a thermal cooler, the samples were transported to the Instituto Tecnologico de Sonora and centrifuged at 3500 RMP for 15 min. A pipet was used to collect 200 μL of leukoplatelet layer from each sample, which was refrigerated at −20 °C before proceeding with DNA extraction. A commercial kit was used for DNA extraction  according to the manufacturer’s instructions. A NanoDrop automatic spectrophotometer  was used to measure the concentration (260 nm absorbance) and purity (260/280 ratio) of DNA obtained. Then, electrophoresis in 1% agarose gel stained with 1.5 μL ethidium bromide was used to confirm the integrity of the DNA.Vacutainer tubes that contained the anticoagulant disodium ethylenediamine tetraacetic acid  were used to genotype the four significant SNPs reported in our study, according to the procedures described by Castillo-Salas et al. . The Ste2) test was performed in PROC FREQ to analyze Hardy–Weinberg equilibrium (HWE). The SAS software  was used to perform all statistical procedures.The procedure MEANS was used to calculate descriptive statistical measures for the traits MY305, RT, and RR. Similarly, the procedures UNIVARIATE and GLM (Levene’s test) were performed to evaluate the assumption of normality of data distribution and equality of variances, respectively. Then, PROC ALLELE was used to calculate both allele and genotype frequencies. A chi-square . The option LSMEANS with Bonferroni adjustment was used to process these means separation tests [Preplanned pairwise comparisons of least-squares means were generated using the PDIFF option when the associative analyses detected the genotype term as a significant source of variation , whereas the SNPs rs29015299 and rs108988401 were intergenic but more than 0.1 MB from the nearest gene (The single-marker GWAS identified 18 SNPs associated with the trait MY305 at a significant est gene .p < 0.01 are presented in p < 0.05) after Benjamini–Hochberg correction were retained for the validation study. Genes involved in such pathways were TLR4, SMAD3, GRM8, CARD11, TCL1A, NFKB1, SMAD6, and TRPC1.Functional pathways for the candidate genes associated with MY305 at 2 > 0.05). These 3 SNPs were rs8193046, rs43410971, and rs382039214 within the genes TLR4, GRM8, and SMAD3, respectively  and no deviation from the Hardy–Weinberg equilibrium , whereas the SNP rs382039214 was associated with MY305 and RT (p < 0.001).The least-square means for milk yield at 305 d and physiological traits are reported in TLR4, GRM8, and SMAD3 on milk production and physiological traits indicative of thermotolerance in Holstein cows exposed to a heat-stressed environment.The most favorable genotypes for the SNPs rs8193046, rs43410971, and rs382039214 were AA, GG, and TT, respectively, because they were associated with higher milk production and more beneficial measurements of RT and RR in cows. Combined with genotype average values, these results appeared to confirm the favorable effect of the genes p < 0.01) for MY305 and RR, whereas the SNP rs382039214 was the most beneficial contributor for RT. In addition, an additive fixed effect was confirmed (p < 0.01) for these SNP markers, suggesting that the sum of their individual effects was equal to their combined allele effects.Effects of allele substitution and fixed estimates are presented in p < 0.05) in MY305 (p < 0.05) as the number of favorable genotypes increased . Interestingly, this relationship increased in cows carrying more than one favorable genotype . However, a low relationship was detected between MY305 and RR in cows from all favorable genotype groups . The regression coefficient (β1) improved as the number of SNPs with favorable genotypes increased, as well as the coefficient of determination (R2). These results suggested a change of −140.35, −277.51, and −470.39 L in MY305 per unit of change in RT in cows carrying 0, 1, and >1 favorable SNP genotypes, respectively . This low number of significant SNPs may have been influenced by the small sample size. As GWASs involve thousands of SNP markers, a large sample size is usually required to reach appropriate statistical power. However, when a small sample is available, GWASs may be limited to detecting true associations between SNPs and phenotypes leading to increase false negative rates .Genome-wide association studies (GWASs) have become a widely used approach to identify genomic regions and genetic variants associated with phenotypes of interest . A GWAS A QTL with major effects on milk production traits and milk fatty acid profiles has been detected in the centromeric region of cattle chromosome 14 (BTA14) . AlthougResearch involving DGAT1 mutation K232A (Lys232 → Ala) has focused on the study of milk fat production because it encodes the enzyme diglyceride O-acyltransferase 1 (DGAT1), which catalyzes the synthesis of triglycerides in the mammary gland. However, this SNP is also involved in the structure of mammary gland epithelial cells suggesting its influence on milk synthesis . The DGAFrom the six genomic SNPs detected in our study, only three were within a candidate gene and surpassed selection criteria , which were tested in a marker SNP association study. These SNPs were associated with MY305, RT, and RR, and they were proposed as molecular markers for milk production and heat stress tolerance. The results supported prior reports from GWAS about the existence of a genetic component underlying heat stress tolerance in Holstein dairy cows. However, to our knowledge, this is the first report of genomic SNPs from candidate genes associated with milk production in heat-stressed Holstein cows, which were further validated as molecular markers for milk performance and thermotolerance in independent Holstein cattle populations managed under extremely warm environmental conditions.Genomic analyses using GWASs are able to detect markers distributed across the whole genome; it is likely to be more successful compared to the candidate-marker approach, which is generally based on testing a small number of SNP markers . HoweverStatistical models of GWAS are able to detect individual significant associations between thousands of SNPs and a specific phenotype. In order to avoid false positives due to multiple SNP testing, a correction procedure must be applied and it results in a very high significance threshold lacking to detect polygenic effects . ControlPryce et al.  identifiTLR4, GRM8, and SMAD3. Although these SNPs were located within intronic regions, they may be able to influence the expression of their corresponding genes [In the current study, three genomic SNPs were validated as marker predictors for milk production and thermotolerance. These SNPs were in the genes of ng genes . Such abng genes .TLR4) is expressed by cells of the innate immune system in mammals, and it appears to be involved in lipid release pathways [Toll-like receptor 4  gene encodes a presynaptic receptor that modulates glutamate release at the axon neuro-terminals. Glutamate is involved as a chemical messenger in most of the excitatory synapses and participates in the modulation of the adenylate cyclase and stimulation of phospholipase C in the cell membrane [The glutamate metabotropic receptor 8  gene encodes for an intracellular protein that is a key mediator of the TGFβ (Transforming growth factor β) signaling pathway, regulating signal transmission and transcription of their target genes [SMAD3 mediates the inhibition of adipogenesis caused by TGFBβ through the transcriptional suppression of the PPARγ promoter [SMAD3 acts as a transcriptional regulator on the central promoter region of the PLIN1 gene. Phosphorylation of PLIN1 is essential for fat mobilization in adipose tissue and plays an important role in the regulation of lipid storage in adipocytes [The SMAD family member 3  is a useful phenotype indicative of thermal equilibrium in animals. Cattle are able to maintain their physiological and productive functions when RT is within a thermoneutral zone. Once the RT increases above this threshold due to heat stress exposure, normal physiology is altered, leading to an increase in respiration rate (RR) and a decrease in feed intake . SimilarTLR4, GRM8, and SMAD3 increased. These results suggested that the greater the number of favorable genotypes present in the cow, the more dramatic the change in milk production will be due to variations in the RT.In the current study, a negative correlation was observed between MY305 and the physiological traits RT and RR, which increased as more favorable SNP genotypes were inherited. This inverse relationship between milk yield and thermotolerance was confirmed when RT resulted as a significant predictor of MY305. Interestingly, the predictive model supporting this negative association became more powerful as the number of favorable genotypes of the genes Nguyen et al.  also repTLR4, GRM8, and SMAD3, which appeared to be implicated in several physiological mechanisms required to maintain milk performance in dairy cows exposed to heat stress. Together, these results provided strong evidence to propose the genes TLR4, GRM8, and SMAD as potential molecular markers for milk production and thermotolerance in heat-stressed Holstein cows.Results reported in the current study demonstrated a close association of the tested SNPs with milk yield at 305 days and thermotolerance indicators such as RT and RR. In addition, these SNPs showed a favorable contribution to improving MY305, RT, and RR. Moreover, these SNPs were in the genes TLR4, GRM8, and SMAD3 as molecular markers for milk production and thermotolerance in heat-stressed Holstein cows. These three genes appeared to regulate metabolic processes needed to comply with energy demands and minimal heat production, which will favor the productive performance in cattle managed under extremely warm environmental conditions. Therefore, our findings may assist marker selection programs focused on the genetic improvement of milk production and thermotolerance traits in Holstein dairy cows.Validation of genomic markers is considered a beneficial strategy to improve our understanding of adaptive mechanisms in Holstein dairy cows, which are exposed to a heat stress environment common in semiarid or subtropical regions. In the current study, we validated three genomic SNPs in the genes


Hypoglycemia is common in patients with type 1 and type 2 diabetes , treated with insulin or sulfonylureas, and has multiple short- and long-term clinical implications. Whether acute or recurrent, hypoglycemia significantly affects the cardiovascular system with the potential to cause cardiovascular dysfunction. Several pathophysiological mechanisms have been proposed linking hypoglycemia to increased cardiovascular risk, including hemodynamic changes, myocardial ischemia, abnormal cardiac repolarization, cardiac arrhythmias, prothrombotic and proinflammatory effects, and induction of oxidative stress. Hypoglycemia-induced changes can promote the development of endothelial dysfunction, which is an early marker of atherosclerosis. Although data from clinical trials and real-world studies suggest an association between hypoglycemia and cardiovascular events in patients with diabetes, it remains uncertain whether this association is causal. New therapeutic agents for patients with T2D do not cause hypoglycemia and have cardioprotective benefits, whereas increasing the use of new technologies, such as continuous glucose monitoring devices and insulin pumps, has the potential to reduce hypoglycemia and its adverse cardiovascular outcomes in patients with T1D. Hypoglycemia is a major barrier to maintaining satisfactory glycaemic control in patients with diabetes. Level 1 hypoglycemia is defined as blood glucose concentration <70 mg/dL but ≥54 mg/dL, level 2 hypoglycemia as glucose <54 mg/dL in the presence of neuroglycopenic symptoms, and level 3 hypoglycemia as a severe event characterized by altered mental and/or physical functioning that requires assistance from another person [It is difficult to accurately estimate the true frequency of hypoglycemia in patients with diabetes due to differences in the definitions used and in the characteristics of the studied populations. A patient with type 1 diabetes (T1D) experiences around two episodes of symptomatic hypoglycemia per week and at least one severe episode per year . It has Hypoglycemia has multiple clinical implications . It can Several physiological responses are activated to counterbalance decreasing glucose levels and maintain glucose homeostasis. This is important to ensure that a sufficient amount of glucose is available to all tissues and primarily to the brain, which depends on glucose as its main source of energy ,15,16. TImpaired awareness of hypoglycemia (IAH) is common in insulin-treated patients with diabetes and correlates with the duration of insulin treatment . IAH is Several mechanisms are implicated in the development of cardiovascular dysfunction related to hypoglycemia in patients with diabetes. These include hemodynamic changes, myocardial ischemia, abnormal cardiac repolarization, cardiac arrhythmias, thrombotic tendency, oxidative stress, and inflammation . RelevanHypoglycemia activates the sympathoadrenal system causing the secretion of catecholamines that exert significant hemodynamic effects. Specifically, sympathetic system activation leads to increased heart rate, systolic blood pressure, cardiac output and ejection fraction, and decreased diastolic blood pressure ,20. ImpoExperimental studies in rats suggest a central role for catecholamines and the sympathetic nervous system in the occurrence of cardiac arrhythmias during severe hypoglycemia. Specifically, severe lethal hypoglycemia can initially cause sinus tachycardia and QT prolongation, followed by premature atrial and ventricular contractions, first-degree atrioventricular block and ventricular tachycardia, evolving to second-degree and third-degree atrioventricular block and finally bradycardia and cardiorespiratory arrest . HypoglyIn humans, hypoglycemia can affect cardiac repolarization and electrophysiology, producing electrocardiographic changes that include ST segment depression, a decrease in height and width of the T wave, and QT interval prolongation ,26,27. IThe observed differences in the occurrence of cardiac arrhythmias between daytime and nocturnal hypoglycemia might be associated with insufficient counter-regulation in response to hypoglycemia during sleep ,38. ThisHypoglycemia induces a procoagulant and prothrombotic state, including promoted platelet aggregation and activation . The mecActivated partial thromboplastin time (aPTT) is reduced, fibrinogen and factor VIII increase and platelet count is reduced following hypoglycemia in patients with diabetes . HypoglyHypoglycemia can induce an inflammatory response, including mobilization of leukocytes, an increase in cluster of differentiation 40 (CD40) expression on mononuclear cells, plasma soluble CD40 ligand (sCD40L) levels and platelet-monocyte aggregation. It can also increase the levels of different adhesion markers, such as intercellular adhesion molecule (ICAM), vascular cell adhesion molecule (VCAM), P-selectin, E-selectin, as well as tumor necrosis factor alpha (TNFα), interleukin 1β (IL1β), IL6, IL8, and vascular endothelial growth factor (VEGF) levels ,46,47. HIn response to hypoglycemia, the numbers of lymphocytes and monocytes, as well as blood levels of C-reactive protein and other inflammatory proteins, significantly increase and may remain elevated for seven days in patients with T2D . It has Hypoglycemia can impair the release of vasoactive substances. The endothelium is responsible for the release of several substances, the most important being nitric oxide and endothelin-1 . FollowiOxidative stress plays an important role in tissue damage caused by hypoglycemia. Several studies have shown an association between hypoglycemia and reactive oxygen species (ROS) production. In vitro studies in human umbilical vein endothelial cells showed that glucose deprivation stimulates mitochondrial ROS generation . InsulinGlucose is a critical energy substrate required for brain functioning. Various functional and metabolic neuroimaging techniques have been employed and have shown that hypoglycemia modulates multiple metabolic pathways in the brain . ModeratOverall, the hypoglycemia-induced changes in blood coagulability, inflammatory response, cell adhesion, release of vasoactive substances, and oxidative stress promote the development of endothelial dysfunction, which is an early marker for atherosclerosis ,61. FurtIn addition to the evidence from experimental studies, several clinical trials and real-world studies suggest an association between hypoglycemia and cardiovascular events in patients with diabetes.Several large-scale clinical trials examined the effects of intensive versus conventional glucose-lowering therapy on cardiovascular outcomes in patients with diabetes . The DCCSeveral studies have been performed in patients with T1D and T2D using concomitantly CGM and electrocardiogram (ECG) monitoring in an ambulatory setting. In patients with insulin-treated T2D and established cardiovascular disease or cardiovascular risk factors undergoing 5 days of concomitant CGM and ECG monitoring, the incidence of bradycardia was eightfold higher, and the incidence of atrial and ventricular ectopic beats was fourfold higher during nocturnal hypoglycemia compared with nocturnal normoglycaemia . During An association has been suggested between hypoglycemia and the unexpected nocturnal death of patients with T1D without cardiovascular disease, described as ‘dead in bed’ syndrome. A retrospective analysis of 50 sudden overnight deaths in patients with T1D was carried out, and in 22 cases, no cause was found . All patp-value < 0.0001) and infarct growth was greater . However, after 3 months, functional outcomes, serious adverse events and death rates were similar between the two groups. Within 24 h of ischemic stroke, glucose potassium insulin (GKI) infusion compared to saline infusion was associated with a higher incidence of asymptomatic hypoglycemia [Hypoglycemia can cause localized cerebral neuroglycopenia and transient focal neurological deficits, usually resolving after correction of the low glucose levels. Case-report neuroimaging studies have shown areas consistent with ischemia in the case of a neurological deficit following severe hypoglycemia ,81. The glycemia . In a seglycemia . Anotherglycemia .p-value < 0.05 [Limited data are available with regard to the impact of hypoglycemia on peripheral artery disease (PAD). Among 6713 patients with T2D admitted to the hospital, 80 had severe hypoglycemia in the preceding three months, and 304 had symptomatic hypoglycemia in the last month . It was e < 0.05 . Importae < 0.05 .A systematic review and meta-analysis were conducted to explore the association between hypoglycemia and increased cardiac arrhythmia risk in adults with T1D or T2D, comparing ECG changes during episodes of hypoglycemia and normoglycaemia ,91,92. HThere is extensive evidence suggesting that hypoglycemia is associated with significant cardiovascular effects in patients with diabetes. Based on data from experimental studies, a number of pathophysiological mechanisms have been proposed, linking hypoglycemia to increased cardiovascular risk. These mechanisms include hemodynamic changes, myocardial ischemia, abnormal cardiac repolarization, cardiac arrhythmias, procoagulant and prothrombotic effects, proinflammatory effects, and induction of oxidative stress, promoting the development of endothelial dysfunction and atherogenesis. Data from large-scale clinical trials and real-world studies, as well as meta-research evidence, confirm the proposed association between hypoglycemia and adverse cardiovascular events in patients with diabetes. Limited data are available with regard to the impact of hypoglycemia on stroke risk and peripheral artery disease.Whether the association between hypoglycemia and increased cardiovascular risk is causal or due to confounding factors remains uncertain. Hypoglycemia can probably trigger cardiovascular events in vulnerable patients who are at high cardiovascular risk and may also interact with other risk factors. It is difficult to confirm causality due to the observational nature of many studies and the inability to capture all hypoglycemic episodes, which may be mild or asymptomatic. New drugs, such as sodium-glucose co-transporter inhibitors and glucagon-like peptide-1 receptor agonists that do not cause hypoglycemia and are associated with cardiovascular benefits, are available for the management of T2D. The use of new technologies, including CGM devices and insulin pumps, is beneficial in reducing hypoglycemia in T1D. It is also important for clinicians to recognize patients at high risk of hypoglycemia, IAH and those at increased cardiovascular risk during hypoglycemia. In any case, appropriate and individualized glycemic goals and continuous education of patients with diabetes on dietary modification, exercise management, glucose monitoring and medication adjustment are needed to effectively prevent, recognize and treat hypoglycemia.


The cranial vault in humans is highly variable, clinically relevant, and heritable, yet its genetic architecture remains poorly understood. Here, we conduct a joint multi-ancestry and admixed multivariate genome-wide association study on 3D cranial vault shape extracted from magnetic resonance images of 6772 children from the ABCD study cohort yielding 30 genome-wide significant loci. Follow-up analyses indicate that these loci overlap with genomic risk loci for sagittal craniosynostosis, show elevated activity cranial neural crest cells, are enriched for processes related to skeletal development, and are shared with the face and brain. We present supporting evidence of regional localization for several of the identified genes based on expression patterns in the cranial vault bones of E15.5 mice. Overall, our study provides a comprehensive overview of the genetics underlying normal-range cranial vault shape and its relevance for understanding modern human craniofacial diversity and the etiology of congenital malformations. The genetic architecture of normal-range cranial vault shape in humans is poorly understood. Here, the authors extract cranial vault shape from MRI and conduct a multi-ancestry GWAS, identifying 30 independently associated genomic loci of which 29 are novel. Because cranial vault morphology has implications for paleoanthropology4, forensics6, and human health9, it is crucial to understand the factors that drive its phenotypic variation. The debate surrounding the relative contribution of genetic and environmental influences on the cranial vault has a long history, starting with the observation by Boas in the early 20th century10 that head dimensions can change in a single generation in response to environmental conditions. Formal heritability studies, including those reanalyzing Boas’s data11, indicate that genetic effects account for a sizable portion (>50%) of the phenotypic variation in vault size. Of course, these positions are not in conflict, as continuous morphological traits are generally considered polygenic, with epistatic and gene-environment interactions playing an important role.The cranial vault—the globular portion of the head, shaped by flat, plate-like bones that surround and protect the brain—shows considerable size and shape variation within and among human populationsFibroblast Growth Factors and their receptors, as being important. Genes in several signaling pathways  have been implicated in congenital malformations characterized by vault dysmorphology, such as syndromic and non-syndromic forms of craniosynostosis13. Genome-wide association studies (GWASs) of non-syndromic craniosynostosis have yielded candidates such as BMP2, BBS9, and BMP715. Moreover, gene expression and experimental studies of the developing vault have also implicated many of these genes (and others in their pathways) in suture morphogenesis19. When considering normal-range variation in cranial vault morphology, genome-wide QTL studies of skull shape in mice have implicated a few dozen genes, including some with effects on the vault22. In humans, two candidate gene studies have reported associations between common variants in FGFR1 and cranial vault dimensions24, and several large GWAS of global vault size (head circumference) have identified a handful of loci27. In addition, a GWAS of vault length, width, and cephalic index in over 4000 individuals reported associations at several loci near relevant genes like SOX9 and SOX1128; notably, no association with FGFR1 was observed.Revealing the genetic architecture of the cranial vault is a necessary step toward elucidating the various molecular pathways involved in both normal and abnormal cranial development and growth. However, despite evidence of a genetic contribution, we know little about the specific genes that impact variation in human vault morphology. Several lines of evidence point to signaling molecules, like 29. In the present study, we advance the pace of genetic discovery by applying a similar phenotyping strategy to the cranial vault. We accomplish this by extracting 3D vault surfaces from magnetic resonance images (MRIs) collected on a multi-ancestry adolescent cohort, partitioning the surfaces into anatomical regions in a global-to-local pattern, quantifying the shape variation present in each region, and then performing multivariate GWASs. In addition, we test whether our discovered variants impact risk for single suture craniosynostosis, and, given the close relationship between brain, facial, and vault morphology30, we investigate the degree of overlap between their genetic architectures.One of the limitations of prior genetic studies of human vault morphology is a reliance on relatively simple phenotyping approaches . Such measures are often straightforward to acquire but suffer from an inability to adequately describe complex 3D shapes and may not capture the most salient aspects of variation for genetic investigations. As a result, it is likely that the genes identified to date account for a small fraction of the heritable variation in human vault morphology. In genetic studies of human facial morphology, we have previously shown that data-driven multivariate approaches capable of more fully exploiting the information contained in 3D biological shapes outperform more traditional morphometric approaches31, shape variation associated with the neurocranial bones is well captured by our phenotypic definition. To study shape variation at both a global and local resolution, the cranial vault surface, represented by a mesh of 11,410 vertices, was partitioned into a set of smaller segments through hierarchical spectral clustering following the data-driven approach introduced by Claes et al.29  to extract a smaller number of morphological dimensions, followed by parallel analysis to identify the optimal number of PCs to retain. We then applied canonical correlation analysis (CCA), which extracts the linear combination of those dimensions that maximally correlates with the state of a given single nucleotide polymorphism (SNP). In total, 10,647,531 SNPs across the genome were tested in a US cohort comprising of rich ancestral diversity. After applying quality control procedures on images, genotype, and covariate data, the final GWAS cohort consisted of 6772 unrelated individuals.Given the high level of admixture in our dataset and the lack of more than a single substantial homogenous group, any stratification-based approach, such as a GWAS meta-analysis, was deemed unfruitful and we instead opted to run a single joint multi-ancestry and admixed GWAS. As a compromise between sample inclusion and model complexity, our GWAS was limited to three ancestral groups , and any admixture thereof. Prior to GWAS, cranial vault shape was adjusted for the effects of age, sex, height, weight, cranial size, MRI machine/scanning site, and ancestral heterogeneity using partial least squares regression (PLSR). The latter was done by including global genomic ancestry, expressed by the first 10 genomic PCs, as well as local genomic ancestry, expressed by ancestral dosages at 0.2 cM windows throughout the genome (Methods).P < 5e−8) in at least one of the segments. Of these SNPs 1138 were also study-wide significant (P < 4.37e−9), correcting for the effective number of GWAS runs as estimated by permutation testing (Methods). Based on genomic distance and linkage disequilibrium (LD), SNPs were clumped into 30 independent genome-wide and 24 study-wide significant genomic loci , segments involving the frontal portion of the vault showed the largest number of significant associations  effects on global cranial vault shape  that had most significant effects at the finest level of segmentation , only 3  were also significant (P < 5e−8) in the global cranial vault. For most loci with their lowest P-values in the global cranial vault, significance gradually decreased as the vault was partitioned into smaller segments. In contrast, most of the loci with their lowest P-values in one of the more local level-4 segments exhibited the opposite effect. This suggests a distinction between globally and locally acting loci.In total, 15 GWASs were conducted (one per vault segment), yielding 1658 SNPs that reached genome-wide significance  associated with global vault shape, we calculated deformation hotspots . No inflation of test statistics was observed  and power in our GWAS, we calculated the genomic control factor lambda (λGC) for different subsets of SNPs, defined by varying upper limits for the FST. The highest λGC of 1.002 was found for the set of SNPs with FST smaller than 0.001  during the discovery phase, totaling 108 tests (Methods). In total, 55 out of 108 (50.9%) lead-SNP/segment pairs and 20 out of 30 (66.7%) individual lead SNPs were replicated in at least one segment at a 5% false discovery rate (FDR) 36. Significantly lower inter-African/European, but not inter-Indigenous-American/European FST was found for our set of lead SNPs compared to the expected values for a set of similar SNPs in terms of minor allele frequency (MAF) and LD score  was rerun, this time without adjusting for the effects of local genomic ancestry. Comparing the outcome with the main GWAS revealed that the adjustment for local genomic ancestry induces a stronger selection for shared loci between ancestral groups compared to the adjustment for global genomic ancestry alone  (Methods). In this more ancestrally homogeneous group, adjustment for the first 10 genomic PCs was still performed to adjust for the within-Europe population structure. In contrast to the main GWAS, no significant over- or underrepresentation of SNPs shared between ancestral groups was observed  the allelic dosage would correlate perfectly with the local genomic ancestry. Therefore, those SNPs would attain no power after removing the effects of local genomic ancestry . Briefly, each test is based on the likelihood ratio of nested models, where the full model includes differential effect sizes between ancestral groups, and a constraint model includes a single joint effect size. All testing scenarios yielded mostly consistent results  was unique to the European-only GWAS. Nonetheless, all those 9 loci stilled showed some degree of association in the other GWAS. Overall, the multi-ancestry GWAS thus achieved a higher discovery rate, identifying 7 more loci, and reaching lower P-values in 25 of the 31 loci (80.6%). Furthermore, shape effects and segment-wise association profiles were highly concordant between both GWASs at each of the 31 loci  the lowest P-values were generated in segments that were directly related. Strong co-localization was observed for the genomic signals in both GWASs as shown by LocusZoom40 and LocusCompare41 plots in Supplementary Data We next performed a sensitivity analysis to evaluate the discovery of genome-wide loci when including or excluding the 2504 non-European-ancestry GWAS cohort subjects. To this end, a locus was considered to be overlapping between both GWASs if the corresponding lead SNPs were within 250 kb, resulting in a joint set of 31 unique loci , genes near the 30 lead SNPs were tested for enrichment of biological processes and pathways, as well as associated mouse phenotypes  and cartilage development . Furthermore, the analysis yielded enrichments for mesenchyme-related terms , as well as more broad terms related to embryonic development . In agreement with these terms, enrichment of phenotypes in GWAS Catalog using FUMA yielded bone-related traits, including, bone mineral density  and height  out of approximately 30,000 analyzed genes from the sequencing library. Of the DEGs, 410 were upregulated in the parietal bone tissues and 238 were upregulated in the frontal bone tissues . As GWAS lead SNPs were identified using vault segments from several overlapping hierarchical layers, we performed our parietal/frontal/both classifications twice using the following two schemes—‘most significant hit’ and ‘most specific hit’. Both classifications schemes yielded concordant results, demonstrating robustness of the assigned labels.ALX1 via GREAT, was identified in cranial vault segment 5 in the GWAS analysis, which corresponds to the frontal region. Since segment 5 contains the most significant signal for the lead SNP and since segment 5 is also phenotypically specific, the classification of ALX1 for the ‘most significant hit’ and ‘most specific hit’ method is the same—frontal. For several other loci, the most significant signal was found in a segment with both parietal and frontal bone content, such as the full cranial vault (segment 1). Nonetheless, their association may still be driven by a predominant frontal or parietal activity. For those loci, we identified the most specific segment by tracing the association signal into hierarchical levels three and four.For example, GWAS lead SNP rs11609649, associated with Alx1, Eya4, Hmga2, Shox2, Cped1, and En1, which all showed higher transcriptional activity in the frontal bone and Rspo3 and Bmper, which showed higher transcriptional activity in the parietal bone. Conversely, while Fgf10, Zeb2, and Adamtsl3 were downregulated in the murine parietal bone, the GWAS analysis identified a stronger effect in the parietal region. Specifically, the association with rs3822730 near FGF10 was most significant in a parietal segment  near BBS9 and rs1884302  near BMP2 reached P-values of 3.02e−14 and 2.40e−10 respectively in our normal-range cranial vault shape GWAS, thus both reaching study-wide significance , is etiologically complex and influenced by genetic and environmental factors. A previous GWASnts Fig. . No suchn = 189). At 5% FDR, significant associations were identified for our lead SNPs rs6054748, rs148673350, and rs1034266 near BMP2, BBS9, and ZIC2 respectively. This ultimately resulted in a bidirectional replication for BMP2 and BBS9, as well as an additional candidate risk gene for sagittal NCS, ZIC2, not previously reported. Furthermore, we identified significant associations at the nominal P-value threshold (P < 0.05) with 6 other genomic loci in 92 coronal NCS trios (n = 276), 62 metopic NCS trios (n = 186), and 17 lambdoid NCS trios (n = 51) 32 who used a similar global-to-local approach to phenotyping facial shape, comparisons could be made for multiple facial and cranial vault segments. At 5% FDR, significant sharing of genetic signals was observed between the frontal cranial vault segment and all the facial segments, though weaker for the upper lip, nose, and philtrum , we found direct genomic overlap between facial GWAS loci and 18 out of our 30 (60.0%) cranial vault loci , HMGA2 , ZEB2 , ALX1 , MEIS1 , EN1 , RUNX2 , BMP2  . As such, the dominance of the frontal cranial vault segment and potentially others among the brain and cranial vault segments was mitigated. Mutual strongest connections were then extracted, revealing evidence for spatially dependent sharing of genetic signals between the brain and cranial vault ent Fig. 44. To fuult Fig. . These r45. Additionally, brain and skull development may rely on coordinated integration of signaling through fibroblast growth factor (FGF), bone morphogenic proteins (BMP), Wnt, and hedgehog30. In the current GWAS, we identified genes encoding members from all four signaling families with established roles in brain development and craniofacial skeletal development, FGF , BMP (BMP251), hedgehog (SHH53), and Wnt (WNT1655). Furthermore, we found that genes near a substantial portion of the 30 cranial vault loci showed evidence of involvement in brain development in mouse knockout studies , neural tube , and neural crest .Sharing of genetic signals between the brain and the cranial vault may be facilitated through mechanical interactions, where intracranial pressure promotes the remodeling of the cranial vault to mirror the shape of the expanding brain2 > 0.2) brain-face-cranial vault loci , also BMP283 and ALX181. Other shared loci include craniofacial skeletal development genes, such as EN184, FGF1849, BMP251, DLX586, RUNX292, PTHLH93, and TBX1594. In part, this sharing of genetic loci is likely due to pleiotropy, with genes like ZEB2, ZIC2, and DLX5 being expressed in both the neural crest and the brain. In other cases, however, genes like ALX1, RUNX2, and TBX15 have roles primarily in the mesenchyme with no expression in the brain. Therefore, sharing of these loci is likely driven by the cranial vault.Among the shared  and downstream of RUNX295. Previous genetic studies on cranial vault dimensions have identified variation near FGFR124, which, together with FGFR2 and FGFR3, is under direct transcriptional control of RUNX292. Though no association was found with FGFR1 here, we did find associations with FGF10 and FGF18, whose proteins are expressed in the calvarial mesenchyme and have high affinity for FGFR2 and FGFR3 respectively96. It has been proposed that increased signaling by FGFs, including FGF10 and FGF18 drives a switch from FGFR2 to FGFR1 expression in osteoprogenitor cells, which is associated with the onset of osteogenic differentiation98. We also identified EN1, which was found to regulate proliferation and differentiation of osteoblasts by influencing responsiveness to FGFs through attenuation of FGFR1 and FGFR2 expression84. Moreover, we identified WNT16, which induces osteogenesis and suppresses osteoclast differentiation, cells involved in bone resorption99.In this study, we used an unbiased multivariate approach to phenotyping normal-range cranial vault shape, expanding on previous work on the face and brain. By doing so, we have accelerated the pace of discovery by comprehensively documenting the genetics underlying normal-range cranial vault shape variation in humans. Overall, the 30 genome-wide significant loci reflect known aspects of cranial vault biology. Many of the genes near these loci are involved in craniofacial skeletal development, with 100. Premature ossification of the cranial sutures results in craniosynostosis16. Interestingly, several genes identified through our GWAS, or their interacting partners have been implicated in syndromic and non-syndromic craniosynostosis. For example, early expression of RUNX2 induces ossification in the suture mesenchyme resulting in craniosynostosis101. Importantly, gain-of function coding variants in RUNX2 were reported in patients with midline craniosynostosis and the RUNX2 p.Ala84-Ala89del variant was reported to be significantly enriched in sagittal NCS, implicating overexpression of this gene in the etiology of craniosynsotosis102. We also identified BCL11B, a transciption factor involved in keeping suture patency by preventing expression of RUNX2 and FGFR2 in the suture mesenchyme103 and for which de novo mutations have been observed in several craniosynostosis cases105. In addition, we identified PTHLH, whose associated receptor gene, PTH2R is implicated in syndromic craniosynostosis106. While no significant associations were found near FGFR1, FGFR2, or FGFR3, all implicated in syndromic and non-syndromic craniosynostosis, we found associations with genes encoding their ligands, FGF10 and FGF1813. In addition, RAB23 mutations cause craniosynostosis through failure to repress FGF10 expression107, and ZIC1, implicated in syndromic craniosynostosis, exerts its pathogenic effect through transcriptional regulation of EN1, which we identified in our GWAS108.During normal craniofacial development, a balance between proliferation and differentiation of the suture mesenchyme keeps the sutures patent to accommodate the growing brainBMP2 and BBS9, which were both implicated as risk genes with large effect sizes (odds ratio > 4 for both loci) in a GWAS on non-syndromic sagittal craniosynostosis14. Using data from that GWAS, we performed a cross-GWAS replication of the lead SNPs near BMP2 and BBS9 and found bi-directional significant associations for both. Consistent with its effect on sagittal suture morphogenesis, we found our lead SNP near BMP2 to be associated with an elongation of the cranial vault. These results suggest that some variants affecting normal-range cranial vault shape variation may do so by attenuating the timing of cranial suture closure. In fact, a recent report has concluded that undiagnosed sagittal craniosynostosis is common in the general population, detected in 41 out of the 870 (4.71%) patients based on a retrospective review of head computed tomography scans, but goes unnoticed if the patient has a cranial index within the normal range (width/length > 0.7)109. The prevalence of this normocephalic sagittal craniosynostosis increased until 36 months of age, then plateaued, suggesting that, indeed, the timing of cranial suture closure is a source of normal-range cranial vault shape variation with milder features at increased age of synostosis109.Furthermore, we identified ERF110, SIX2111, SMAD6111, SMURF1111, MSX2112, ALX4112, TWIST1113, TCF12113, EPHA4114, and FREM1115) implicated in syndromic and non-syndromic forms of craniosynostosis affect ossification of the suture mesenchyme through modulation of BMP, FGF, or HH signaling and/or attenuation of RUNX2 expression, highlighting the shared molecular pathways of craniosynostosis and normal cranial vault development. Because of this spatiotemporal overlap, variation at our 30 GWAS loci may work synergistically with rare variants in other relevant genes, modifying their effect. One such example is the suggested interaction between common variation near BMP2 and rare loss of function mutations in the gene encoding its inhibitor, SMAD6116. Mouse models of craniosynostosis have supported the idea of modifying genes, whereby mice carrying identical Fgfr2 mutations exhibited variable phenotype expression depending on their genetic background117. Taking into account such interactions may guide surgical interventions and treatment planning in craniosynostosis patients and ultimately lead to better outcomes.Though not identified in the current GWAS, several other genes , frontonasal dysplasia 3 (ALX1), cleidocranial dysplasia (RUNX2), Endove syndrome (EN1), cousin syndrome (TBX15), and sagittal craniosynostosis (BMP2). One explanation could be that these genes promote remodeling of the cranial skeleton to accommodate the growing brain, allowing for subtle changes in brain shape. This is especially plausible given that we detected spatially dependent sharing of genetic signals between the brain and cranial vault. Furthermore, mutations in several genes implicated in our GWAS result in abnormal brain morphology or size in mice, such as MEIS156, EN161, KIF662, SHH68, HMGA269, ALX170, ZIC273, and CEP5574. For example, mutations in KIF6 result in increased brain size, and a domed cranium, which likely results from the force exerted by the brain62. This raises the question whether cranium morphology influences brain morphology, or whether it is the other way around. Likely the influence is bidirectional, and starts early in development, as we have shown elevated transcriptional activity near our GWAS loci in CNCCs and embryonic craniofacial tissue. From then, this mutual influence may continue throughout development. Altogether, shared loci between the cranial vault and the face and/or brain comprise genes implicated in diverse molecular pathways across different stages of brain and craniofacial development. The exact mechanisms of action are a relevant topic for future studies.Sharing of loci between the cranial vault and the face and/or the brain was twofold. On the one side, shared loci harbor genes, such as TBX15, which was associated specifically with the frontal region, and previously identified to be associated with forehead morphology32. Another example is rs11609649 near ALX1, which also showed association locally with the frontal region, and not with the posterior regions, matching in situ expression patterns in mouse embryos118. While for those loci, we observed effects in the regions where they were expected, it is interesting to see the lack of any significant association in other regions of the vault, thereby affirming their localized roles.As we have shown previously with facial surface shape and brain shape, the global-to-local segmentation approach used here yielded replicable associations that would otherwise have been missed by limiting to a global definition of the vault. While few additional hits were detected for the 14 segments that were not the global cranial vault, this could not have been known a priori given the poor understanding of normal-range cranial vault shape. Hence, we retained the segmentation approach to show that SNPs affecting vault shape mostly do so on a global level. Still, certain loci showed significant effects on local segments only, whereby the association strength decreased gradually going from a local to a global level, though still with some degree of association with the global cranial vault. This is exactly what one should expect if the true effect of a locus is indeed local, i.e., that considering a larger set of vertices increases the total variance in the phenotype while the variance explained by the genotype does not increase with the same extent, thereby resulting in a statistically weaker association. One example is rs3936018 near Fgf10, Zeb2, and Adamtsl3 showed a predominant parietal association with cranial vault shape while being downregulated in the parietal mouse tissue. It is possible that the parietal tissue is more sensitive to alterations in corresponding protein levels. If so, genetic variation relating to gene regulation would be expected to have a more pronounced effect despite the lower overall transcriptional activity relative to the frontal tissue. Functional assays could test this idea. Other explanations relate to how both methods record their signals, i.e., a GWAS captures accumulated effects over time, whereas RNAseq provides a snapshot in time. As such, RNAseq might fail to capture potentially more dominant effects of genes in different regions at different times. Furthermore, brain-related influences may substantially impact cranial vault shape and could not be picked up by the RNAseq.Furthermore, we specifically examined the parietal versus frontal localization of candidate genes near our GWAS loci based on the pattern of their GWAS signal throughout the cranial vault segments, as well as RNAseq data from murine (E15.5) parietal and frontal bones. We found several of the GWAS signals in cranial segments to be prominently frontal or parietal while also showing expression differences in the same frontal and parietal mouse tissue. These trends add additional support that the human GWAS-based signals are functionally rooted to their role in cranium development in mouse embryos, affirming that some genes may have a predominant frontal or parietal contribution to cranial vault shape. A possible explanation may lie in the different cellular origins of both bones, the frontal being derived from the cranial neural crest and the parietal from the mesoderm. Nonetheless, 122 or admixture mapping127, several works have intensively explored the idea of a joint multi-ancestry GWAS129. In general, those works have agreed that adjustment for global genomic ancestry is necessary to avoid spurious results and that adjustment for local genomic ancestry is useful for obtaining more accurate effect sizes and better signal localization in the presence of LD expansion that comes with admixture130. Because the ancestral make-up of the ABCD study cohort is highly heterogeneous and unbalanced , we opted to adjust for local genomic ancestry to ensure accurate results of downstream analyses and biological interpretations. Atkinson et al. (2021) have made recent efforts toward multi-ancestry GWAS. Their methodology, called Tractor, splits the allelic dosage of a SNP by the inferred donor ancestries and estimates different effect sizes for the different ancestral groups in a mixed ancestral cohort (including admixed). Its implementation can theoretically be extended to support any number of ancestral groups but is currently limited to univariate traits only. As with many statistical methods, multivariate implementations are likely to follow. However, accurate estimation of multivariate phenotypic coefficients  for different ancestral groups based on a single multi-ancestry cohort, is still likely to require many representatives from each ancestral group. This is therefore a limitation of the current study and similar studies to come.Although most multi-ancestry genome-wide mapping efforts to date have been limited to GWAS meta-analysis131. With the high need for transferable results from GWAS and its derived applications, such as polygenic (risk) scores, running a single joint multi-ancestry and admixed GWAS as opposed to a stratified analysis may be a direct way to obtain more widely generalizable results.We acknowledge that although multi-ancestry, our GWAS cohort is still predominantly comprised of individuals with recent European ancestry. While 4198 out of 6772 (62.0%) cohort subjects were assigned European ancestry, the prevalence of European ancestry in the admixed individuals contributed strongly to the overall local genomic European ancestry in the cohort. Hence, our discovery effort may be largely European-driven, and the improved discovery rate in our GWAS over a European-only GWAS may be due to the additional European-derived alleles. Nonetheless, we demonstrate that our GWAS hits are enriched for shared SNPs between the ancestral groups as a direct result of our multi-ancestry approach, and that effect sizes are mostly consistent across those groups, clearly illustrating the contribution of non-European inclusion. This also suggests that our GWAS successfully identified shared genetic components of cranial vault shape variation and implies that the phenotypic effects associated with our 30 lead SNPs likely hold more predictive value across populationsUse of patient data was approved by local ethics committees at U.C. Davis . Data available through controlled access repositories  has been approved for broad sharing and local institutional approval  was granted for access to these datasets. All participants of these studies provided written informed consent to participate and have their data shared. Experiments resulting in RNAseq data were approved by and performed under the oversight of the University of Pittsburgh Institutional Animal Care and Use Committee (protocol: 20057353).132 is a longitudinal study following brain development and health through adolescence. From data release 3.0 (February 2020), high-quality full-head MRIs were available for 11,702 participants. Following the ABCD recommendations for image inclusion, 471 individuals were removed from the dataset. Anthropometric data, including age, sex at birth, weight, and height were available on 11,787 individuals. Individuals with missing data (n = 11) or extreme outlier values  for any of these variables were excluded. All participants were between 9 and 10 years old at the time of data collection and represent diverse ancestral backgrounds.The Adolescent Brain Cognitive Development (ABCD) studyFor this study, Quality Controlled Genotype Data from the ABCD cohort was downloaded as ABCD_release3.0_QCed Data files . It contained 11,099 unique individuals and 516,598 genetic variants aligned to the positive strand of build GRCh37. Subject information such as interview age, sex at birth, height, weight, BMI, and MRI information were extracted from the compiled RDS file (nda3.0.Rds) in the Data Exploration and Analysis Portal (DEAP).133 2.0. Reference SNP cluster IDs (rsIDs) were added using Bcftools135 'annotate' and dbSNP154.hg37. The ABCD data was then lifted and sorted from build GRCh37 to GRCh38 using Bcftools and in-house scripts. SNPs and indels were merged and only SNPs  were kept for downstream phasing and imputation per chromosome 1–22. Phasing was performed using default parameter settings from SHAPEIT v4.2.2136 and the New York Genome Center (NYGC) 30x-1000 genomes-phased-dataset as reference, which can be found at [http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/working/20201028_3202_phased/].The ABCD data files were first converted to a variant call file (VCF) using PLINKIliad genomic data pipeline137. In short, the final reference dataset was compiled with data from the Human Genome Diversity Project (HGDP)138 and the 1000 Genomes Project139. HGDP samples were retrieved as individual sequence alignment files in CRAM format [https://ftp.sra.ebi.ac.uk/1000g/ftp/data_collections/HGDP/data/]. The samples underwent variant calling via Bcftools ‘mpileup’ and ‘call’ commands for each chromosome. The HGDP dataset alone consists of 828 individuals from 54 populations. The data was reduced in variant size using an annotations list provided by the NYGC140 which was input as a regions file. Finally, each HGDP combined chromosome VCF was phased and then merged with its chromosomal representative from the NYGC 30x-1000 genomes-phased-datasets to create a more globally represented reference set (NYGC1KGEN-HGDP) of 61,876,281 variants (including X chromosome) from 4030 Individuals in 80 populations that was used for imputation.The reference data file used for imputation was prepared by the 141 with SHAPEIT4 b38 genetic maps and the combined NYGC1KGEN-HGDP reference dataset using default parameter settings. Imputation regions that would maximize the use of both the reference and target sets were generated using imp5Chunker_v1.1.5141. Setting a threshold imputation INFO score of > 0.3, an ABCD imputed dataset of 11,099 individuals with n = 49,745,078 variants was generated. Following the ABCD recommendations, 82 individuals that were genotyped on plate 461 were excluded for further analysis.Imputation of the ABCD data was performed using IMPUTE5 v1.1.5142 with a cutoff of 0.0442) were merged with 1000 G139 Phase 3 and HGDP138 genotypes of unrelated individuals. Markers in common were pruned iteratively, using PLINK133 2.0, using a window of 1000 markers, a step size of 50 markers and an r2 cutoff of 0.2 until no more markers were being excluded. The resulting dataset was subjected to PCA to build an ancestry space, in which the relatives were then also projected. The first 10 PCs were used to express global genomic ancestry for each ABCD participant.Genotypes of unrelated ABCD participants . This effectively created the desired reference data of anchors to estimate the unknown biogeographical ancestries of the ABCD subjects. Analyses of the anchor reference data are shown in Supplementary Fig. Further analysis of the six co-ancestry proportions for this reference data included K-means clustering and sample filtering based on each designated cluster’s main ancestry component to decrease the effect of noise with the clusters and create ‘anchor’ population reference data. K-means 143 was used to estimate the co-ancestry proportions of the entire ABCD dataset from the anchor populations with a K:6 model. There were n = 251,073 variants after additional, independent QC (--hwe 1e-50 ‘midp’ --maf 0.01) was performed on ABCD subjects prior to merging with the reference anchor samples. The co-ancestry proportions output from the supervised K:6 test was used to filter ABCD participants. Specifically, we retained 10,334  participants with >90% joint European, African, and Indigenous American ancestry proportions for local ancestry inference. To identify European-ancestry participants, the co-ancestry proportions were subjected to K-means clustering, followed by additional filtering, similar to what was done to the anchor references as mentioned above, yielding 5746 participants with a nearly wholly European ancestry component.The supervised technique in ADMIXTUREn = 613), African , and Indigenous American  clusters as ancestry representatives. This was done using RFMIX144 v2 with one expectation-maximization step, a window size of 0.2 cM, and a terminal node size of 5. The expectation-maximization step reanalyzed admixture in the ancestry representatives to improve the ability to distinguish ancestries. A genomic map, to inform of switch positions was obtained from the Eagle145 software website.Genome-wide local ancestry was then inferred for ancestry-pre-filtered ABCD participants, using the 1000G and HGDP anchor references from the European . In addition, genome-wide local ancestry proportions doubled as a quality control for imputation, which to date is still a non-trivial task for multi-ancestry datasets. We reasoned that any systematic bias in haplotype matching during the imputation process would result in a detectable change in local genomic ancestry. Overall, genome-wide local ancestry proportions were consistent, except for some centromeric  and telomeric  regions, as well as some known regions of long-range LD , some of which have been previously reported to result in local ancestry biases148 —matched in terms of sex at birth, height, weight, and genomic ancestry—were registered to a single target MRI scan using Elastix (SimpleITK library150 in Python) with the Param0000 parameter map (affine and B-spline)151. The use of 300 floating scans per ‘target’ image was chosen based on visual inspection of the results while controlling for computational time and resources. The resulting, denoised consensus ‘target’ image was defined as the voxel-per-voxel median of the resulting warped ‘floating’ images.Minimally processed T1w structural MRI data were downloaded from the ABCD data repository. Image preprocessing steps include distortion correction, movement correction, resampling (1 mm isotropic voxels), alignment to standard space, and initial quality control152 a full head template  was constructed as the average of the expected head shapes of boys and girls at 9.5 years old, i.e., those closest in age to our study cohort. The cranial vault  was manually delineated on this template, encompassing the supraorbital ridge and extending towards the occipital bone 8152 a fuisosurface). Full head surfaces were cleaned by cropping internal head structures, also captured by the volumetric imaging protocol, based on the distance of each vertex to the centroid. The full head template was then registered to the extracted craniofacial surfaces using MeshMonk153 following a scaled rigid and non-rigid registration step. Prior to rigid registration, an initial rough alignment was performed to ensure a similar orientation of both template and target scans. Because all MR scans were already aligned to standard space during image preprocessing, this could be done by manually placing five positioning landmarks on 100 randomly selected target surfaces and transferring the averages of these landmarks to all individual scans.Denoised MR scans were imported as.nii files into Matlab (version 2021a) and the craniofacial surface was extracted as a mesh using the isosurface function , erroneous regions were interpolated using thin-plate splines in Matlab.32, we measured the Mahalanobis distance for each cranial vault to the overall average cranial vault in a shape space spanned by PCs that capture 98% of total shape variation. Based on the distribution of Mahalanobis distances, a z-score was calculated for each image, and only images with an absolute z-score lower than 1.5 were considered for further analysis, yielding a set of 9015 well-QCed images.For all participants, the cranial vault shape was extracted from the full head meshes based on the selected region in Supplementary Fig. 33. First, the meshes were adjusted for age, sex at birth, height, weight, cranial size, the scanner (encoded by dummy variables), and the 10 first genomic PCs.To study genetic effects on cranial vault variation at both a global and local level, we performed a data-driven hierarchical segmentation on symmetrized cranial vault shape, similar to earlier work on facial and brain shape29 was carried out based on the resulting similarity matrix. At each step the vertices were split into two maximally similar clusters, resulting in 1, 2, 4, and 8 non-overlapping segments at hierarchical levels 1, 2, 3, and 4 respectively. For each of the resulting segments, independently of the other segments, the configurations were aligned using generalized Procrustes analysis (GPA) and subjected to PCA. Parallel analysis155 was used to determine the number of PCs required to explain the major phenotypic variance with fewer variables.Next, a vertex-wise similarity matrix was constructed by measuring the correlation of the distance-to-centroid between each pair of vertices. This measure was augmented with pairwise Euclidian distance (proximity in 3D) to make nearby vertices more similar to enforce connectivity and coherence within the segments. Specifically, both measures were normalized separately so that the most alike pair of vertices  got a value of 1, and the least alike pair got a value of 0. These were then combined using a weighted sum of 60% correlational similarity and 40% proximity-based similarity. These percentages were chosen based on connectivity and coherence of segments resulting from different weighted sums of both measures at 5% intervals. Hierarchical spectral clusteringn = 11,017), ancestral pre-filtering , and image quality control (n = 9015) yielded 8217 participants. From this set, we removed 1445 relatives using the King Robust142 software with a cutoff of 0.0442 (3rd degree relatives), resulting in a final GWAS sample size of 6772  of which 4198  were assigned European ancestry and 2504 non-European, predominantly mixed ancestry. Imputed variants of ancestry-selected participants  were filtered for SNPs only, with MAF > 1% and missing genotyping rate > 95%, resulting in a final set of 10,647,531 SNPs.The intersect of ABCD participants which satisfied covariate quality control, genotype quality control . This adjustment for local genomic ancestry aimed to remove phenotypic differences due to ancestry-associated genotyped or ungenotyped variation, such that any phenotypic effect measured is dose-dependent on the SNP, independent of genomic ancestry . In other words, we wanted to ensure that we found genotype-phenotype associations at marker-level resolution to ensure accuracy of downstream analyses.Under the reduced model, the residual phenotypic variance in each of 15 cranial vault segments was tested for association with the presence of the major allele, coded based on the additive genetic model. This was done using CCA, which extracts the linear combination of PCs maximally correlated with the SNP, representing a multidimensional shape deformation in the PC space. Significance was tested using a right-tailed chi square test with degrees of freedom equal to the number of phenotypic PCs 156. Briefly, for a single SNP we randomly permuted the genotypes 10,000 times, essentially creating the distribution of chance associations with cranial vault shape under the null hypothesis. Each permuted genotype was tested for association with the 15 cranial vault segments and the lowest P-value was retained. We then divided 0.05 by the 5th percentile of the resulting 10,000 P-values to estimate the number of effective GWAS runs performed. This was repeated for 500 random SNPs, resulting in an average effective number of phenotypes of 11.44 (SD: 0.56). The effective number of phenotypes was additionally estimated following the same approach using the 30 lead SNP as well as using three eigenvalue-based methods159 on the 15 × 15 segment-segment correlation matrix as estimated using either genome-wide P-values, chi squared statistics, or genomic Pearson correlations, yielding 10 additional estimates in the range of 8.52–11.28 .Genome-wide significance of genotype-phenotype associations was declared at the traditional threshold of No sex stratified GWAS was performed in this work for reasons related to power, however, we did adjust for sex assigned at birth and resulting GWAS thus focused on aspects of cranial vault shape variation independent of sex.2 > 0.01) were clumped into a single locus represented by the most significant SNP (lead SNP). This was repeated until all SNPs were assigned a locus (n = 32). Next, any two loci were merged into a single locus if the lead SNPs were within 10 Mb and in LD with r2 > 0.01. The lead SNP with the lowest P-value was chosen to represent the newly merged locus. Lastly, for robustness any singleton SNP below the study-wide significance threshold was removed (n = 0). This resulted in 30 genome-wide and 24 study-wide significant genomic loci associated with cranial vault shape. LD was calculated using the genotypes of the GWAS cohort. LocusZoom40 plots for all 30 loci are available in the Supplementary Data Peak detection was performed in three steps starting from 1658 SNPs that reached genome-wide significance in the GWASs. Starting from the most significant SNP, all SNPs within 250 kb, as well as those within 1 Mb and in LD  enrichment analysis using GREAT42, FUMA160 v1.3.7, as well as manually annotated genes within 500 kb of the lead SNP based on available literature (Supplementary Tables 161 (R package ‘coloc’) to check if our GWAS hits coincided with an expression quantitative trait locus (eQTL) in any of 22 tissues selected from GTEx8 based on their relevance to the cranial vault  as the median observed test statistic divided by the median expected test statistic under the null hypothesis . As SNPs in the combined GWAS were represented by their lowest P-value across the 15 segments, a null distribution of P-values was obtained empirically by repeating the GWAS with randomly permuted genotypes for each SNP and taking its lowest P-value across the segments. Because P-values corresponding to each segment were derived from chi square distributions with different degrees of freedom, we obtained a normalized test statistic, 33 as Following the original definition of the genomic inflation factorP < 5e−8) effects on global cranial vault shape, vertex-wise normal distances were calculated between the average cranial vault and the cranial vault transformed along the latent shape direction associated with the SNP. The absolute values were then normalized to unit length per SNP and a vertex-wise distribution of deformation magnitudes was subsequently obtained. At each vertex, the 95th percentile of the SNP-associated deformation magnitude was used for visualization in Supplementary Fig. For each of the 21 SNPs with genome-wide significant . These scans were obtained in anonymized form, meaning that the entire face and regions around the ears were removed. The process of anonymization has been described in detail by Alfaro-Almagro et al.163.The UK Biobankisosurface with an iso value of 250) from each image, followed by taking the concave hull (function: boundary with shrink factor set to 1) to remove internal structures. A cranial vault template was aligned to each mesh based on four landmarks located at the most superior, posterior, and lateral (left/right) points of the head. We then performed rigid and non-rigid surface registration, using MeshMonk153 while mitigating the damage from the MRIs. Specifically, we masked all vertices in the damaged regions , and the isosurface was extracted  images were retained  were adjusted for age, age squared, weight, height, sex, cranial size, scanner, and the first 10 genomic PCs using PLSR. The residuals were added to the average ABCD cranial vault shape, followed by applying the same global-to-local segmentation as defined in the ABCD cohort. Segments were then independently aligned using GPA and projected into the corresponding ABCD shape space per segment. Subjects without genotype data, or with more than 5% missingness were omitted from further analysis, as well as relatives up to the third degree identified using the King142 robust algorithm with a cutoff of 0.0442. This resulted in a sample size of 16,846  for the final replication cohort. Out of the 30 genome-wide significant discovery lead SNPs, 29 overlapped with the replication dataset directly; rs34898775 was selected as a proxy SNP for rs202055590 .All subjects with missing covariates or covariate outliers (>6 SD) were removed. Ancestral outliers (>3 SD) were removed based on the 4 first genomic PCs (‘elbow’ of Scree plot). The remaining cranial vaults (n = 108) for which significant (P < 5e−8) associations were found. The F-statistic from a linear regression was calculated to determine the significance of association between the projection scores and the SNP genotypes for all 108 SNP-segment combinations. A 5% FDR-adjusted P-value threshold of P < 0.0244 was calculated using the Benjamini-Hochberg165 method, yielding 55/108 significant tests, and 20/30 significant SNP replications in at least one cranial vault segment.To measure the presence of the associated shape trait from the discovery panel (ABCD), the replication panel (UK Biobank) was projected onto the latent shape trait, identified by CCA, for a particular SNP in a particular cranial vault segment. The resulting univariate scores were calculated for each lead SNP/segment pair  and African (AFR), and EUR and Indigenous American (AMR) samples in the 1000 G Phase 3 dataset using vcftools167 v0.1.17. Next, FST enrichment analysis36 was performed on our set of GWAS lead SNPs to see if the GWAS signals were significantly enriched for high or low FST SNPs. The average FST across the lead SNPs was compared to a distribution of 10,000 averaged FST values, each calculated based on the same number of SNPs with matched MAF and LD scores43. To match the LD scores to the GWAS cohort, covariate-adjusted LD scores were calculated based on the 6772 GWAS cohort subjects with a window size of 20 cM (--ld-wind-cm 20), and after adjusting genotypes for the first 10 genomic PCs using scripts of Luo et al. (2021)168. For matching SNPs, the range of MAFs and LD scores was divided into 20 equally spaced quantiles, and a matched SNP was selected at random from the same bins as the original SNP. Significance of the enrichment was declared if the observed average FST was lower than the 2.5th percentile or higher than the 97.5th percentile of the null distribution .The Weir and Cockerham estimatorsP < 5e−8) segments. Under the best segment scenario, heterogeneity of effect size was only tested in the segment most significantly associated with the SNP in the GWAS, while under the significant segments scenario, heterogeneity of effect size was tested in all segments where that SNP was significant (P < 5e−8) in the GWAS, and the lowest 5% FDR-adjusted P-value was kept.tractor by Atkinson et al. (2021)129, where alleles are assigned a donor ancestry, and are then modeled under the additive genetic model per ancestry. Tractor scripts were used to extract ancestry-specific allelic dosages, For each of the 30 lead SNPs, we performed 2 × 2 × 2 tests of effect size heterogeneity between ancestry groups, with each test imposing different restrictions on a generative model. First, we considered univariate versus multivariate SNP-trait associations. Under the univariate , and the parietal  components. Each biological replicate consists of the frontal and parietal components from 3 embryos therefore no sex-stratified analysis was possible. The tissue was lysed using with Trizol (Invitrogen), followed by additional homogenization using the Qiashredder and then the RNA was extracted with the Qiagen RNAeasy kit. Axeq made and sequenced the libraries from 6 biological replicates  as 100 bp paired end reads on an Illumina HiSeq2000 sequencer .Timed-pregnant female CD1 mice were purchased from Charles River Laboratory. The mice were housed under standard conditions in the University of Pittsburgh Division of Laboratory Services vivarium. The E15.5 embryos were collected via C-section after CO169 v0.11.9. Read trimming was performed by Trimmomatic v0.32170 using a sliding window of four nucleotides and a mean Phred score  greater than 20 (SLIDINGWINDOW:4:20) corresponding to an error probability of 0.01 over the four nucleotides. Sequences shorter than 25 nucleotides (MINLEN:25) were removed.The resulting 6 FASTQ formatted paired-read sequence files produced by Axeq Technologies were subsequently downloaded and visually inspected for quality control purposes with FASTQChttp://ftp.ensembl.org/pub/release-106/fasta/mus_musculus/dna/], utilizing the GRCm39 gene annotation file, available at: [http://ftp.ensembl.org/pub/release-106/gtf/mus_musculus/] using the STAR171 sequence aligner version 2.7.10a. Alignment for the 6 trimmed paired-end reads showed an average uniquely mapped read percentage of 94.3% ± 0.2%.The resulting trimmed reads were then aligned to the Ensembl primary assembly mouse reference Genome, GRCm39, available at [172 set with the following options: Mode = Union, singleEnd = FALSE, ignore.strand = TRUE, fragments = FALSE. Normalization of differentially expressed gene counts were based on five stable housekeeping genes  identified in the Ho and Patrizi, 2021 mouse cranium and brain development study173. To improve visualization of gene expression, the normalized gene counts were variance stabilizing transformed (VST)176, which is roughly similar to transforming the data to the log2 scale, allowing for better visualizations. To allow for easier comparisons between genes, especially for the combined expression plots, the mean difference in expression, hereafter referred to as ‘mean difference’, of the sample grouping to all other  sample groupings were calculated and plotted. This allows the plots to report the ~log2 expression change of each tissue-specific group  to the mean of all other samples . The R package DESeq2 1.36177 was used to analyze the expression data using the DESeq function177, which transforms read counts based on the estimation of size factors and dispersion, fits to a negative binomial GLM, performs a Wald significance test, and assesses based on a cutoff false discovery rate of <0.05 using the Benjamini-Hochberg procedure165. All bioinformatic analyses were performed using R-4.2.1 and Bioconductor178.Gene expression quantification was carried out using the GenomicAlignment R packageAs part of the validation of GWAS hits via differential analysis, we compared the phenotypes utilized in the GWAS to those of the two tissues sampled in the expression analysis. Since the expression analysis was based on tissues taken from the parietal and frontal areas of the mouse embryo while the GWAS utilized 15 cranial vault segments, to directly compare the phenotypes we classified the 15 GWAS segments as containing either primarily ‘frontal’ or primarily ‘parietal’ content. As some segments, like segment 1, contain both parietal and frontal content, those segments were labelled as ‘both.’ Subsequently, this allowed candidate genes near the GWAS lead SNPs to be classified as either frontal, parietal, or a combination of both parietal and frontal, depending on the cranial vault segment in which the lead SNPs were initially found. One issue of this classification scheme is that lead SNPs were not always identified in the most phenotypically specific cranial vault segment . To remedy this, we performed several classifications. One classification scheme, labelled as ‘most significant hit’, based the classification of candidate genes on the most significant segment in which it was found . The other classification, labelled as ‘most specific hit’, utilized the more phenotypically specific segments in which the candidate gene was either found in or directly related to. In this classification, a label with equal or higher specificity was obtained recursively by considering the most significantly associated ‘child’ segment until the encountered segment comprised solely of frontal or parietal content or until hierarchical level four was reached. As an example, GWAS lead SNP rs17479393 was identified in segments 1, 2, 3, 7, 8, and 15. The ‘most significant hit’ classification simply takes the most significant segment, segment 1 in this example, and assigns a classification. As segment 1 encompasses both the parietal and frontal regions, it is labelled as ‘both’. The ‘most specific hit’ classification starts at segment 1 and follows the strongest association of its ‘child’ segments to reach segment 8 (through segments 2 and 4), which contains solely parietal bone content.179.Frontal and parietal tissue gene expression from mice embryos were analyzed to generate expression profiles that were ultimately compared to human candidate genes located near lead SNPs identified in the GWAS analysis. To make this comparison possible, gene symbol and human homolog mappings were carried out on mouse Ensembl identification IDs using the Biomart R package32. Information regarding the cell types and tissues used together with a link to where they are available can be found in Supplementary Table Signals of acetylation of histone H3 on lysine K27 (H3K27ac) in the vicinity of the 30 genome-wide significant lead SNPs were calculated as described in White & Indencleef et al. (2021)33. Briefly, SNPs were first selected to overlap with the HapMap3 SNPs180, and SNPs within the major histocompatibility complex were removed. We then organized the remaining SNPs in 1725 LD blocks that are approximately independent in individuals of European ancestry181. The mean SNP -log10 was calculated per block, and a rank-based Spearman correlation was calculated on the averaged association score per block. A standard error of the correlation coefficient was estimated based on 100 bootstrapping cycles.To quantify sharing of genetic signals between a pair of GWAS, we calculated Spearman correlations as described in Naqvi et al. (2021)n = 276), lambdoidal , metopic , and sagittal . These families are trios with sequence data on an affected child and two unaffected parents. Trios were analyzed using the transmission disequilibrium test (TDT) via PLINK133. Trios were only analyzed with trios of the same phenotype  so we had four discrete analyses based on the four types of craniosynostosis. We then checked the results of the 30 genome-wide significant cranial vault SNPs in these four analyses.We tested for genotype-phenotype associations between our 30 genome-wide significant SNPs and craniosynostosis based on a dataset comprising whole genome sequence data  from families with children affected with four different types of craniosynostosis – coronal (72 trios; Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting SummarySource Data