Rabbit wool traits are important in fiber production and for model organism research on hair growth, but their genetic architecture remains obscure. In this study, we focused on wool characteristics in Angora rabbits, a breed well-known for the quality of its wool. Considering the cost to generate population-scale sequence data and the biased detection of variants using chip data, developing an effective genotyping strategy using low-coverage whole-genome sequencing (LCS) data is necessary to conduct genetic analyses.fibroblast growth factor 10 (FGF10) gene as associated with fiber growth and diameter, which agrees with previous results from functional data analyses on the FGF gene family in other species, and is relevant for wool rabbit breeding.Different genotype imputation strategies , sequencing coverages , and sample sizes  were compared. Our results showed that using BaseVar + STITCH at a sequencing depth of 1.0X with a sample size larger than 300 resulted in the highest genotyping accuracy, with a genotype concordance higher than 98.8% and genotype accuracy higher than 0.97. We performed multivariate genome-wide association studies (GWAS), followed by conditional GWAS and estimation of the confidence intervals of quantitative trait loci (QTL) to investigate the genetic architecture of wool traits. Six QTL were detected, which explained 0.4 to 7.5% of the phenotypic variation. Gene-level mapping identified the We suggest that LCS followed by imputation can be a cost-effective alternative to array and high-depth sequencing for assessing common variants. GWAS combined with LCS can identify new QTL and candidate genes that are associated with quantitative traits. This study provides a cost-effective and powerful method for investigating the genetic architecture of complex traits, which will be useful for genomic breeding applications.The online version contains supplementary material available at 10.1186/s12711-022-00766-y. Genome-wide association studies (GWAS) have delivered new insights into the biology and genetic architecture of complex traits. In the past decades, GWAS has accelerated the rate of gene discovery to an unprecedented scale, identifying many replicated genetic variants associated with complex diseases and quantitative traits in livestock, plants, humans, and model organisms –4. PhenoThe approach of LCS followed by imputation exploits the fact that individuals in the same cohort are sufficiently related to share large genome segments . MissingP values method in a multivariate linear mixed model, which were used to identify candidate genes.Angora rabbits are particularly well known for their wool production. The economic value of Angora wool depends on the texture of the hair, which is mainly characterized by fiber diameter and length. In this study, we generated accurate and dense genotype data on Angora rabbits with a cost-efficient LCS approach and evaluated imputation performance across five levels of sequencing coverage and six levels of sample size, using three imputation strategies . To decipher the genetic architecture of complex wool traits in Angora rabbits, we performed GWAS of six important economic traits at various time points with high resolution. We also developed a conditional GWAS and estimated the confidence intervals of identified quantitative trait loci (QTL) by the drop (∆) in log-transformed The experimental procedures used in this study were approved by the Animal Care and Use Committee of Shandong Agricultural University.In total, 629 Angora rabbits  from the same batch were used in this study. The rabbits were raised in five houses on one farm under the same conditions, including diet, water and temperature. Since rabbits are artificially inseminated with mixed semen in standard production conditions, no precise pedigree information was available for the studied population. Wool traits were measured at 70, 140 and 210 days of age and included length of fine wool (LFW), diameter of fine wool (DFW), coefficient of variation of diameter of fine wool (CVDFW), length of coarse wool (LCW), and rate of coarse wool (RCW). Wool samples were obtained from the center of the lateral body by shaving with clippers. Body weight (BW) was measured at weaning weight at 35 days and at 70, 140, and 210 days of age.Ear samples were collected from each individual. Genomic DNA was isolated using the Qiagen MinElute Kit. Genomic DNA from each sample was used to construct a paired-end library (PE150) with ~ 350-bp inserts. All libraries were sequenced on the DNBSEQ-T7 platform. An average of 3.84X genomic coverage for 627 samples was sequenced, with the read depth ranging from 1.51X to 8.03X. In addition, 15 samples were deep-sequenced at 10X coverage for genotype validation. In total, 7305 gigabases of genomic sequence data were generated.https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) with a focus on base quality scores, GC content, N content, and sequence duplication levels. Adapters and low-quality bases were removed using Trimmomatic as “java -jar trimmomatic-0.38.jar PE sample_1.fq.gz sample_2.fq.gz 1_paired.fq.gz 1_unpaired.fq.gz 2_paired.fq.gz 2_unpaired.fq.gz ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 SLIDINGWINDOW:5:20 LEADING:5 TRAILING:5 MINLEN:50” [Oryctolagus cuniculus) using the BWA-mem algorithm [https://broadinstitute.github.io/picard/).Read quality was assessed using the FastQC tool  was developed to call variants for large-scale low-pass whole-genome sequencing (WGS) data and was suitable for our study. Useful discussions on the development of the BaseVar to call SNPs are reported in [Due to the lack of a reference panel, we used imputation tools that do not require reference information, i.e. STITCH and Beagle, to impute genotypes using the low-coverage sequencing data. Error and bias towards the reference allele occur in SNP calling using low-coverage data because of its limited information and when using standard tools that are designed for high-coverage data, such as SAMtools followed by Bcftools and GATK . BaseVarorted in . In addiorted in , 23) havorted in  to imputorted in  and thenorted in  and thenorted in  was condorted in .Genotypes that are directly called from high-coverage sequencing are the de facto standard for validating the imputation of untyped SNPs. Here, imputation accuracy was assessed for the three cost-effective genotype imputation strategies by comparing imputed genotypes to high-coverage genotypes and was measured by two criteria, i.e. genotype concordance (GC) and genotype accuracy (GA), by identifying sites that are shared across the two datasets. Sites were considered shared if their position, reference allele, and alternate allele were identical. Genotype concordance was defined as the proportion of imputed genotypes that were identical to the genotype determined using high-coverage sequencing. For each site, GC was set to 0 if the imputed genotype did not match the true genotype and to 1 if the imputed genotype did match the true genotype. Thus, GC of a site was calculated as the average over all the samples at that site. Genotype accuracy (GA) was defined as the Pearson correlation coefficient between imputed genotypes and genotypes obtained by high-coverage sequencing. Compared to GC, GA was calculated based on imputed genotype probabilities . Furtherp-value > 1 × 10–6 using the PLINK software [According to Teng et al. , when imsoftware . The sitsoftware  based on2 values with the PopLDdecay software [We performed principal component analyses (PCA) on the population of 629 rabbits using the GCTA software [software . In ordesoftware . Nucleotsoftware  simultanK value) ranging from 1 to 5. The K-value of 2 had the lowest cross validation error (CV-error). Pi analysis was applied to estimate the degree of variability within each group and the fixation statistic FST was applied to explain population differentiation on the basis of the variance of allele frequencies between the two groups. Both Pi and FST were calculated using a sliding window approach using Vcftools, with a window size of 50 kb and a step size of 10 kb. The candidate signatures of selection that were discovered with the top 5% of Pi and FST were treated as highly divergent windows. Adjacent windows were merged into a single divergent region and annotated.Domestication and centuries of selective breeding have changed the genomes of rabbit breeds to adapt to environmental challenges and human needs. In order to explore and further detect signatures of selection, 14 domesticated rabbits were sampled from the population to analyze their genetic diversity and population structure compared to 14 wild rabbits, i.e. their wild progenitor. A maximum-likelihood tree was constructed using the phylogeny program IQ-TREE2. PCA of the first two principal components was visualized and LD decay was compared between the two groups of rabbits. The genetic structure of the two populations was analyzed with ADMIXTURE , with thFunctional enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery (DAVID) software to analyze gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathways . The P-vWe used a three-trait model to analyze wool traits at three time points and a four-trait model for body weight. The three-trait model for the estimation of heritabilities of the studied traits was:VanRaden  as:4\docThe four-trait model was accordingly defined as the three-trait model.To perform multivariate GWAS, the genotype at a single SNP was added as a fixed effect to Eq. , resulti command .P value) set as a fixed effect in the following model:To confirm whether the significant SNPs within clusters of loci are independent or are in high LD, we also performed conditional GWAS with the significant lead SNP  method, following [P) value between the lead SNP for that data set and the simulated causal SNP was recorded. The distribution of these drops in log(P) was then estimated across the 1000 simulations, and the 95th percentile was used to determine confidence intervals of the QTL region identified in the original data.Confidence intervals of identified QTL regions were estimated by the drop log as the gold standard for accuracy evaluation  were obtained for common variants with a MAF ranging from 0.05 to 0.5. However, for this MAF range, the Bcftools + Beagle4 pipeline resulted in a poorer imputation accuracy and its accuracy was greatly affected by MAF (GC ranging from 84.66 to 93.10% and GA from 0.75 to 0.80), and using the GATK + Beagle5 pipeline, imputation accuracy was even worse and fluctuated more (GC ranging from 64.02 to 80.60% and GA from 0.42 to 0.63). For SNPs with a MAF lower than 0.05, both GA and GC tended to decrease compared to those with a higher MAF, and were greatly affected by MAF , Fig. 2 decreased to 0.16 and 0.11 for an average distance between SNPs of 500 kb and 1 Mb, respectively. The distribution of r2 with respect to physical distance differed by chromosome. The slowest and fastest LD decays were observed for Chr15 and Chr21, respectively. Chr11 showed a level of LD extent that was similar to that of the whole genome. Combining CLR and Pi analyses, we identified 151 potential signatures of selection, which overlapped with 309 candidate genes  and vitamin B6 metabolism (P = 1.30 × 10–4)  ≥ 4.09)), we identified 464 candidate domestication regions that overlapped with 775 genes under selection in the domestic rabbit . After LD-based pruning with PLINK, 391,976 independent SNPs were in approximate linkage equilibrium with each other and, thus, the genome-wide significance level was 1.28 × 10–9 after Bonferroni correction. Significant SNPs that were found to be associated with DFW, CVDFW, LFW, and BW are in Tables S13–S16  and genotypes on up to 18,577,154 autosomal SNPs after imputation were available for the 629 rabbits. For association testing, we used a multivariate linear mixed model, as implemented in our software GMAT  of each QTL was estimated, which showed an average width of 0.71 Mb (7.43 kb to 1.46 Mb), with 67% of the QTL having an average CI width less than 1 Mb  gene was most relevant. The most significant locus  associated with fine fiber was detected closest to the FGF10 gene , which was also the nearest gene to the QTL for DFW . The FGF10 gene is a member of the fibroblast growth factor (FGF) gene family that possesses broad mitogenic and cell survival activities and is well-known for its role in the regulation of hair morphogenesis and hair growth cycle in humans and mice [The number of annotated genes covered by the QTL regions (based on 95% CI) ranged from 0 to 61, with a mean of 12  telogen anagen transition by providing the stimulatory signals to the HF stem cells and/or their progenies that reside in the HF bulge and secondary hair germ [FGF2 [FGF7 [FGF9 [FGF10 [FGFR2 gene. Transgenic mice that are deficient for FGFR2-IIIb suffer from abnormally thin hairs that are characterized by single columns of medulla cells [Using a large sample size and a high-resolution genome screen, recombination events can be detected to accurately identify causal variants that underlie a quantitative trait , 7. In oair germ , 59, 60.rm [FGF2 , FGF7 [6F2 [FGF7 , FGF9 [4F7 [FGF9 , and FGF9 [FGF10  have beela cells .2 higher than 0.2. The quantile–quantile plots showed the deviation of the observed P values from the expected values. In addition, the fact that few QTL were detected for the other fiber traits might be due to their polygenic genetic architecture, i.e., many individual mutations each with a small effect contributing to the total genetic variation and not large enough to reach the significance level when testing for a typical complex trait.Response to strong artificial selection results in standing genetic variation and even in completely fixed mutations across many genomic regions, which reflects the long-term directional selection history for wool traits of the Angora rabbit population and probably explains that few QTL are detected for fiber traits. The LD decay and quantile–quantile plots reflect the strong artificial selection in the Angora rabbit population. LD extends over a long distance in the Angora rabbit genome, in which markers separated by 300 kb have average rLow coverage sequencing combined with genotype imputation allows accurate high-density genotypes, even without a good reference panel. GWAS based on LCS data enables QTL detection and fine-mapping of genes associated with quantitative traits. This study provides a cost-effective analysis pipeline that can contribute to unravel the genetic architecture of complex traits and to increase genetic progress in livestock.Additional file 1:Figure S1. Venn diagram illustrating the number of the variants called by BaseVar, GATK and Bcftools. Figure S2. CLR and Pi analyses in the Angora rabbit population. Figure S3. Q-Q plots for the Angora rabbits. Figure S4. Manhattan plots for the Angora rabbits.Additional file 2: Table S1. Genotype accuracy and concordance of imputation obtained with the three pipelines on 15 individuals. Table S2. Genotype accuracy and concordance of imputation by the three pipelines with the MAF ranges. Table S3. Genotype accuracy and concordance of imputation with different sample size and depth by BaseVar + STITCH. Table S4. Variant analysis by regions and functions. Table S5. LD decay in the Angora rabbit population. Table S6. Genes located in the selected regions in the Angora rabbit population. Table S7. Enrichment of genes located in the selected regions in the Angora rabbit population. Table S8. LD decay in the Angora and wild rabbit populations. Table S9. Genes located in the selected regions between Angora and wild rabbits. Table S10. Enrichment of genes located in the selected regions between Angora and wild rabbits. Table S11. Genes located in the common regions between within-breed and cross-breed signatures of selection. Table S12. Enrichment of genes located in the common regions between within-breed and cross-breed signatures of selection. Table S13. Significant SNPs for DFW. Table S14. Significant SNPs for CVDFW. Table S15. Significant SNPs for LFW. Table S16. Significant SNPs for BW. Table S17. SNP-based heritability. Table S18. QTL contribution to phenotypic variance. Table S19. Genes overlapping with by QTL.


At the beginning of genomic selection, some Chinese companies genotyped pigs with different single nucleotide polymorphism (SNP) arrays. The obtained genomic data are then combined and to do this, several imputation strategies have been developed. Usually, only additive genetic effects are considered in genetic evaluations. However, dominance effects that may be important for some traits can be fitted in a mixed linear model as either ‘classical’ or ‘genotypic’ dominance effects. Their influence on genomic evaluation has rarely been studied. Thus, the objectives of this study were to use a dataset from Canadian Yorkshire pigs to (1) compare different strategies to combine data from two SNP arrays (Affymetrix 55K and Illumina 42K) and identify the most appropriate strategy for genomic evaluation and (2) evaluate the impact of dominance effects  and inbreeding depression effects on genomic predictive abilities for average daily gain (ADG), backfat thickness (BF), loin muscle depth (LMD), days to 100 kg (AGE100), and the total number of piglets born (TNB) at first parity.The reliabilities obtained with the additive genomic models showed that the strategy used to combine data from two SNP arrays had little impact on genomic evaluations. Models with classical or genotypic dominance effect showed similar predictive abilities for all traits. For ADG, BF, LMD, and AGE100, dominance effects accounted for a small proportion (2 to 11%) of the total genetic variance, whereas for TNB, dominance effects accounted for 11 to 20%. For all traits, the predictive abilities of the models increased significantly when genomic inbreeding depression effects were included in the model. However, the inclusion of dominance effects did not change the predictive ability for any trait except for TNB.Our study shows that it is feasible to combine data from different SNP arrays for genomic evaluation, and that all combination methods result in similar accuracies. Regardless of how dominance effects are fitted in the genomic model, there is no impact on genetic evaluation. Models including inbreeding depression effects outperform a model with only additive effects, even if the trait is not strongly affected by dominant genes.The online version contains supplementary material available at 10.1186/s12711-022-00760-4. Genomic selection (GS) , 2 has bbiological) effect (d) in linear mixed models. For example, SNPs are coded as 0, 1, and 2 for genotypes AA, Aa, and aa, respectively, and the coding of dominance effects is equal to 0, 1, and 0 for genotypes AA, Aa, and aa, respectively [AA, Aa, and aa, respectively [statistical). In our study, we used the terms ‘genotypic dominance effect’ and ‘classical dominance effect’ to refer to the dominance effects coded in either a genotypic manner or a dominant deviation manner, respectively, to avoid potential confusion.Although previous studies have demonstrated that dominance effects are not negligible , they arectively , 9. In cectively . Vitezicectively  referredAn increasing number of studies have investigated the influence of including dominance effects in prediction models on genomic evaluations of livestock , 11–18. Thus, the objectives of our study were: (1) to explore an appropriate strategy and procedure for integrating genomic information from different SNP arrays (Affymetrix 55K and Illumina 42K) for further genomic evaluation; (2), to evaluate the impact of dominance effects and inbreeding depression effects on the estimates of the genetic variance and the genomic prediction of four production traits and one reproduction trait in Canadian Yorkshire pigs raised in China; and (3) to compare the models including different dominance effects  in terms of the accuracy of the prediction of breeding values. The work was performed in two stages: first, we determined the optimal imputation strategy and chip, and then, after collecting more data, we addressed the models including dominance effects.Genesus)): 38,785, 38,667, 38,644, 38,785, and 10,504 records were available for ADG, BF, LMD, AGE100, and TNB, respectively. All the phenotypic records for the four production traits were obtained at the same time point, allowing a 10-kg deviation from the final bodyweight (–7) were removed. After quality control, 31,654 and 35,710 SNPs were retained from the Affymetrix 55K and Illumina 42K arrays, respectively. To avoid possible confusion in the subsequent analyses, we refer to the remaining genotypic data as Affymetrix 31K SNPs and Illumina 35K SNPs. Among these remaining SNPs, 23,430 (23K common SNPs) were shared by the two SNP arrays. In other words, after quality control, for the 1208 pigs genotyped with the Affymetrix array there were 31K SNPs, i.e. 23,430 (23K) common SNPs and 7724 (8K) Affymetrix array-specific SNPs; and for the 1106 pigs genotyped with the Illumina array there were 35K SNPs, i.e. 23,430 (23K) common SNPs and 12,280 (12K) Illumina array-specific SNPs.All the data were obtained from a national pig nucleus herd in North China. The herd’s purebred Yorkshire pigs were originally imported from Canada in 2014, and since then, the Yorkshire population within this herd has been continuously selected based on selection indices for five traits: average daily gain (ADG) in the range of 30 to 100 kg, backfat thickness (BF) at 100 kg body weight, loin muscle depth (LMD) at 100 kg body weight, days to 100 kg (AGE100), and the total number of piglets born (TNB) at first parity; in this study, we used phenotypic records for these five traits collected from 2012 to 2019  were used as the reference set, and the remaining 1208 pigs genotyped with the Affymetrix array  were used as the imputation set  between the genotyped and imputed SNPs. To reduce the systematic bias of evaluating imputation accuracy, we repeated the above procedure three times with different random seeds, leading to different subsets of the 10% masked SNPs. We obtained similar results across these repeats, and the average accuracies are presented in the “As mentioned above, 23K common SNPs were obtained in this study. To evaluate the imputation accuracy in Scenarios 1 and 2, 10% of the 23K common SNPs were masked (setting them to an ungenotyped state), and the masked SNPs were then imputed. After imputation, imputation accuracy was estimated by the concordance rate (CR) and the squared Pearson correlation coefficient (rcombinations to univariate single-step genomic best linear unbiased prediction (SSGBLUP) models for the five traits recorded in Canadian Yorkshire pigs [To evaluate the effects of each strategy to combine the genomic information from two arrays on genomic evaluation, we applied the abovementioned four ire pigs , 22 and For the four production traits, the model included sex, herd-year-season, and final body weight as fixed effects and an additive genetic effect, a common litter effect and a residual as random effects. For the reproduction trait, the model included herd-year-season as a fixed effect and an additive genetic effect and a residual effect as random effects. These analyses were carried out using the restricted maximum likelihood (REML) algorithm in the software DMU .Individual model-based reliabilities were computed as follows :\documendel used . Note thp matrix , 22; in p matrix . Our appCombination 3 was the optimal strategy for combining the information from the two arrays; thus, we advised the pig company to use the Illumina 42K SNP array for genotyping additional pigs so that we could estimate the dominance variance more accurately, and 4300 additional pigs with phenotypic records obtained between 2019 and 2020 were genotyped. These pigs were selected based on the following criteria: (1) they had progeny; (2) they were phenotyped for at least three of the target traits ; and (3) the total number of males or females within a litter was limited to four. Based on these criteria, we obtained 6614 genotyped pigs. The numbers of phenotypic records for ADG, BF, LMD, AGE100, and TNB were 41,367, 41,249, 41,224, 41,367, and 10,811, respectively , each paired group showed similar The mean genomic-based inbreeding coefficient was 0.67 (ranging from 0.55 to 0.763). Estimates of the inbreeding depression parameter . For the production traits, compared to model MA, the predictive ability was increased by ~ 1.5% with model MAI, ~ 1.5% with model MAID, and ~ 1.6% with model MAID*. For TNB, the predictive ability increased by ~ 6.0% with model MAD, ~ 6.6% with model MAD*, ~ 3.0% with model MAI, ~ 7.8% with model MAID, and ~ 8.4% with model MAID*. In terms of the regression coefficients of the corrected phenotypes on estimated genetic values . As shown in Table In this study, first we investigated different strategies to combine the genomic information from two SNP arrays. Subsequently, we examined the impact of the nonadditive effects on genomic predictive ability and further explored two models of fitting dominance effects  in the genomic prediction models. For the five traits studied, fitting inbreeding depression effects yielded the highest predictive abilities, and for one trait (TNB), the inclusion of dominance effects in the genomic model slightly increased the predictive ability.The imputation accuracy in Scenario 1 was slightly higher than in Scenario 2. Previous studies have shown that imputation accuracy is affected by the MAF of the imputed SNPs and by the top relatedness between animals in the reference and imputed populations –35. ThusIn this study, the estimated narrow-sense heritability confirmed that ADG, BF, LMD, and AGE100 were moderately heritable and that TNB was lowly heritable, in line with many other reports , 15, 36.*), which confirmed the equivalence of the estimates of dominant variation generated in this study. The standard error of the estimates of dominance variation was still relatively large, which indicates that the size of our dataset was not sufficient to accurately estimate dominance variation. Therefore, more data are needed to further investigate the dominance effects in the current population.In this study, the proportions of dominance variation to the total genetic variance in production traits were relatively low (ranging from 1.9 to 10.5%) and generally lower than those found in other studies on production traits in pigs , 16. TheIn this study, we used the pre-corrected phenotypes of the genotyped pigs as the response variables to estimate dominance variances. These genotyped pigs were not randomly sampled from the population, and most of them showed high EBV and were selected as parents for producing the next generation. Xiang et al.  reportedAs shown in Table  backfat , and the backfat . NotablyThe ratio of the estimated inbreeding depression effect divided by the phenotypic standard deviation for the trait is an indicator of the importance of the inbreeding depression effect . In modeOur study is the first to report the proportion of additive genetic effects that is contributed by inbreeding depression effects. According to the formula for calculating the additive variance, the proportion contributed by inbreeding depression is mainly affected by allelic frequencies, the magnitude of the estimated inbreeding depression parameter, and the number of SNPs used. As shown in Additional file Overall, the inclusion of the inbreeding depression effect in the genomic model had no significant effect on the estimation of variance components for all traits, although all of the dominance variances were slightly reduced, as also reported by Aliloo et al. .The goodness-of-fit of the six genomic models showed that those with inbreeding depression effects  presented a better goodness-of-fit than the model with only additive effects (MA) for all traits, in line with Aliloo et al. . This reOur results revealed that the inclusion of an inbreeding depression effect in the genomic model increased its predictive ability for the four production traits  and the reproduction trait (TNB) studied and that when the tested trait was strongly affected by dominance genes, the inclusion of the dominance effect in the model further improved its predictive ability. Even when the trait was little affected by dominance, the inclusion of the dominance effect in the model did not decrease its predictive ability.Additional file 1: Table S1. Estimates of variance components, standard error (SE) of parameters, − 2 log likelihood (− 2LogL), AIC (Akaike’s Information Criterion) from models MA, MAD, MAD*, MAI, MAID and MAID*. Table S2. Contributed additive genetic variance from the inbreeding depression effect. Table S3. P-value of likelihood ratio test based on model MA. Table S4. P-value of likelihood ratio test based on models MAI, MAD and MAD*. Table S5. P-value of inbreeding depression effect based on the Wald test. Table S6. Average genomic relationships between animals in the imputed and reference sets. Table S7. Converted classical variance components based on the genotypic variance component in models MAD and MAID.Additional file 2: Figure S1. Distribution of the minor allele frequencies of the Illumina array-specific SNPs in Scenario 1 and the Affymetrix array-specific SNPs in Scenario 2. Figure S2. Effect of allele frequency on


PCSK9 is expressed in systems critical to both SMI and CMD and influences lipid homeostasis and brain function. We systematically investigated relationships between genetic variation within the PCSK9 locus and risk for both CMD and SMI. UK Biobank recruited ~500,000 volunteers and assessed a wide range of SMI and CMD phenotypes. We used genetic data from white British ancestry individuals of UK Biobank. Genetic association analyses were conducted in PLINK, with statistical significance defined by the number of independent SNPs. Conditional analyses and linkage disequilibrium assessed the independence of SNPs and the presence of multiple signals. Two genetic risk scores of lipid-lowering alleles were calculated and used as proxies for putative lipid-lowering effects of PCSK9. PCSK9 variants were associated with central adiposity, venous thrombosis embolism, systolic blood pressure, mood instability, and neuroticism . No secondary signals were identified. Conditional analyses and high linkage disequilibrium (r2 = 0.98) indicated that mood instability and central obesity may share a genetic signal. Genetic risk scores suggested that the lipid-lowering effects of PCSK9 may be causal for greater mood instability and higher neuroticism. This is the first study to implicate the PCSK9 locus in mood-disorder symptoms and related traits, as well as the shared pathology of SMI and CMD. PCSK9 effects on mood may occur via lipid-lowering mechanisms. Further work is needed to understand whether repurposing PCSK9-targeting therapies might improve SMI symptoms and prevent CMD.The association between severe mental illness (SMI) and cardiovascular and metabolic disease (CMD) is poorly understood.  It has been long recognised that severe mental illness (SMI) is accompanied by an increased burden of cardiovascular and metabolic diseases (CMD) , 2. The PCSK9 and its encoded protein , because of its key role in lipid homeostasis. Specifically, PCSK9 binds to the low-density lipoprotein (LDL) receptors (LDLR) and targets them for degradation [For many years CMD research has focused on radation . Lipid hradation . In partradation  and the radation ). In addradation , which hradation  and clinradation .,, which include brain cholesterol trafficking and lipoprotein trafficking [PCSK9 is associated with reduced neuroinflammation. PCSK9 has been investigated in neurodegenerative disorders such as Alzheimer’s disease, where altered levels of the protein levels in cerebrospinal fluid (CSF) [PCSK9’s association with SMI remains less established, however an association seems plausible due to its roles in brain functions , 16,, whfficking  and insufficking . Indeed,fficking . Under sid (CSF)  and braiid (CSF)  have beeid (CSF) , 20. Cliid (CSF) , althougid (CSF) .N = 878) [N = 2487) identified sex-specific effects of two variants in the PCSK9 locus on CSF PCSK9 levels [A number of studies exploring the genetic variants in the PCSK9 locus has been conducted: One explored a single missense variant, rs11591147 (R46L), as an indicator of potential long-term effects of PCSK9 inhibition on a large number of phenotypes , howeverN = 878) . A furth9 levels .PCSK9 locus contributes to the shared genetic predisposition and underlying pathological mechanism of both CMD and SMI. Furthermore, we describe the genetic architecture in the PCSK9 locus and identify mechanisms by which genetic variants in this region may influence biological traits. Finally, we evaluate whether the effects of PCSK9 genetic variation were likely to act through LDLR-lowering pathways. If PCSK9 variation contributes to SMI by the same mechanisms as its effect on CMD, there is the potential to repurpose CMD therapies for the improvement of SMI symptoms and CMD prevention in these high CMD-risk individuals.Given that the expression of PCSK9 is observed in the brain as well as the liver Fig. , and theThe UK Biobank (UKB) is a large dataset of 502,613 participants, which has generic ethical approval granted by the NHS National Research Ethics Service . This work was carried out using UKB application #6553 (PI Rona Strawbridge) and #17689 . Data is provided by participants and collected by the National Health Service as part of their care and support. A more thorough detailed description of the UKB data set can be found elsewhere in open-access reports , 27.UKB volunteers were recruited through 22 assessment centres during 2006–2011. Individuals were largely aged between 40–69 at baseline. Trained clinical staff carried out physical examinations and touchscreen questionnaires to gather baseline measurements along with individual and family medical history. Blood samples allowed biomarker analysis and DNA extraction for genome-wide genotyping. The genotyping, quality control and imputation used standard protocols and took place centrally.2, reflective of total obesity) calculated. Waist and hip measurements allowed for calculation of waist:hip ratio  which was adjusted for BMI  prior to analysis as per Shungin et al. [Average systolic and diastolic blood pressure reading was calculated, each from two measurements, and adjusted for the use of antihypertensive medications as per Ehret et al. , prior tn et al. . Type 2 n et al.  . Individuals responding “not at all” were considered controls, whilst those responding “several days”, “more than half of the days” or “nearly every day” were considered cases. Risk-taking was assessed by a self-reported question asking, “Would you describe yourself as someone who takes risks?” (data field 2040) and mood instability by the question “Does your mood often go up and down?” (data field 1920). Individuals responding “yes” were considered cases, and those responding “no” were considered as controls. Any individual responding “prefer not to answer”, or “don’t know” was excluded from the analysis. A follow-up online mental health questionnaire (collected 2016–2017) was used to clarify the lifetime history of SMI , 32 and PCSK9 locus is defined as the PCSK9 gene ±250 kb . Initially all single nucleotide polymorphisms (SNPs) within the locus were identified. Discovery analyses included only unrelated individuals of white British ancestry . Secondary analyses were conducted in unrelated individuals of white European ancestry , south Asian ancestry , African-Caribbean ancestry  and mixed ancestry . Genetic variants with minor allele frequencies of >1% by ancestry group were analysed.The p value <1.16 × 10−4 and in secondary analyses, p value <1.28 × 10−4 for South Asian, <4.69 × 10−5 for African-Caribbean, <1.20 × 10−5 for white European and <8.85 × 10−5 for mixed ancestry individuals. LocusZoom [Minitab 19.2 was to calculate descriptive statistics. For genetic analyses, an additive model was assumed and Plink 1.07  was usedocusZoom  was usedConditional analyses were used to identify multiple independent signals. Analyses of traits with significant associations were repeated including the lead SNP as a covariate . An additional independent signal was defined as at least one SNP meeting the threshold for significance in the conditional analysis . Where an additional signal was identified, a further round of conditional analysis was conducted, including the lead SNPs from both signals as covariates. This process was repeated until no further significant signals remained.Conditional analyses were also used to determine whether the signals for each significant trait were independent. Here the analyses of traits with significant associations were repeated, with the addition of the lead SNP for a different trait as a covariate . Lead SNPs were considered independent if the effect size (Beta or OR) changed by <0.05 . No distance measure was used to assess independence. Linkage disequilibrium (LD) between lead SNPs was assessed using a randomly selected 10,000 unrelated white British ancestry individuals from UKB. Haploview  allowed PCSK9 levels was investigated using publicly available genome-wide meta-analysis summary statistics [The impact of lead SNPs on circulating atistics .Three studies have highlighted genetic variants with functional effects that can be used to proxy lifelong reduction in LDL levels {Schmidt, 2019 #275;Ference, 2016 #274;Lyall, 2021 #273}. Schmidt et al. used four SNPs in a polygenic score (PGS) {Schmidt, 2019 #275}, whilst Ference et al. selected seven SNPs {Ference, 2016 #274}. Lyall et al. used six of the seven Ference SNPs and noted that individual SNPs had little impact when removed {Lyall, 2021 #273}. We constructed two PGS, using four variants  or seven variants , by summing the lipid-lowering alleles for each individual. Only individuals with complete genotyping for the four of seven SNPs were included.https://www.gtexportal.org/home/ [PCSK9 gene expression and genotype-specific gene expression patterns. LDExpress was used to explore genotype-specific gene expression patterns of proxies of lead SNPs , was used to identify previous associations in the PCSK9 locus. Ensembl VEP  was useas.org/, ) was useTable p = 4.20 × 10−5, Fig. p = 9.21 × 10−6, Fig. p = 3.74 × 10−5). As the lead SNP for VTE is not available in LocusZoom, the second significant variant was used for plotting and also considered in conditional analyses , p = 8.89 × 10−5, Fig. p = 9.56 × 10−5, Fig. Significant associations are summarised in Table p = 2.68 × 10−5, Fig. p = 4.53 × 10−6, Fig. When considering SMI-related traits Table , 30 signConditional analyses was used to assess whether signals for SMI and CMD traits were independent (ie. represent distinct genetic effects). If there is little or no change in the effect of a trait when adjusting for the lead of a different trait, then signals can be considered independent.r2 = 0.21, Fig. r2 = 0.4, Fig. r2 = 0.98, Fig. p = 1.50 × 10−4). The VTE signal appears to be distinct from other signals, as conditioning on other lead SNPs had little or no impact .The lead SNP for SBPadj rs2647282) demonstrated little change when conditioning on the other lead SNPs STable , consist647282 det STable , consistr2 = 0.04, Fig. p = 1.25 × 10−5).Visual inspection suggests that the signals for mood instability and neuroticism partially overlap STable –6, consip < 0.05) associations were observed for the SNPs associated with SBP , WHRadjBMI  and mood instability .Of the seven lead/proxy SNPs, five were available in the meta-analysis of PCSK9 levels  Table 33. Nominap = 3.75 × 10−5), and a non-significant effect on mood instability  p = 0.0003), consistent with high LD between rs10888897 and the lead SNPs for these traits  effects on mood instability and both PGS4 and PGS7 had nominally significant (p < 0.05) effects on neuroticism, where the direction of effect suggests that lipid-lowering effects of the PCSK9 locus might contribute to increasing these psychological traits. Whilst rs10888897 is in high LD with the lead SNPs for mood instability .To explore whether lipid-lowering functions of PCSK9 might explain the associations observed, we tested two lipid-lowering PGS for impact on our traits of interest (PGS4  and PGS7l models . PGS7 bup < 1 × 10−3) are presented in STable Analyses of additional ancestry groups were restricted to phenotypes significant in discovery analyses with sufficient data. No SNPs met the thresholds for significance in any analyses . Lead SNPs for secondary analyses  tissue mRNA expression levels and circulating levels of PCSK9 protein, has previously been reported [PCSK9 and therefore lipids, between the brain and adipose tissues contributes to mood instability and central fat accumulation (as indicated by WHRadjBMI).PCSK9 protein expression was detected in both the liver and brain. mRNA expression of ing Fig. . ExpressR24 Fig.  is predoP24 Fig.  is ubiqureported . The oppUSP24 in many tissues, where the mood instability/neuroticism-increasing allele was associated with reduced expression. No other trait-associated SNPs were predicted to have functional effects.VEP highlighted rs487230 (significant for mood instability and neuroticism) as a tolerated/benign missense variant. This SNP had effects on Our results do not contradict the observation that PCSK9-associated genetic variants reported so far have loss-of function effects (reduced expression or function), with the only positive effect being attributed to compensation for reduced protein function .PCSK9 locus, of which many are for CMD traits. It is noteworthy that there were no previous associations of PCSK9 SNPs with VTE or psychiatric traits in GWAS.  and two psychological traits (mood instability and neuroticism). We demonstrated that the WHRadjBMI and mood instability signals overlap, that the effects of PCSK9 on the SMI (but not CMD) likely act through lipid-lowering mechanisms. Finally, we present evidence that these SNP regulate mRNA expression levels of PCSK9, DHCR24 and USP24.This systematic analysis of the PCSK9 locus, specifically through lipid-lowering effects, on psychological phenotypes (mood instability and neuroticism) of relevance to psychiatric traits. The associations with SBPadj and WHRadjBMI were unsurprising, however, it is unusual that SBPadj, but not DBPadj, was significantly associated with this locus. The association of the PCSK9 locus with VTE was unexpected, given that lipid accumulation and inflammation are generally considered less relevant to VTE than arterial disease. However, a non-lipid-lowering mechanisms is plausible, as pcsk9-knockout in experimental animals reduced platelet activation [To our knowledge this is the first study to report an effect of the tivation , likely tivation , with retivation .PCSK9 levels in adipose tissue and decreasing PCSK9 levels in brain tissue suggests that looking at one system in isolation might not be enough.The almost complete LD reported here between the lead variants for mood instability and WHRadjBMI is intriguing: does mood instability subsequently influence central fat accumulation (through diet and exercise preferences for example), or does this genetic signal have multiple effects on different genes and in different tissues (pleiotropy), or is there one mechanism common to these two very different traits? The gene expression evidence of the minor allele (associated with increased WHRadjBMI and decreased PCSK9 protein levels) both increasing The non-white British ancestry results were largely uninformative (due to limited power), however the consistent LD patterns across ancestry groups supports the overlap of signals for mood instability and central fat accumulation SFig. .PCSK9, expression data highlights the roles of DHCR24 and USP24 in SMI and CMD: DCHR24 has been shown to have a neuroprotective role during inflammation [DCHR24 expression being observed in Alzheimer’s disease [DHCR24 levels in the brain could suggest the same mechanisms underly central adiposity and lack of neuroprotection. USP24 is implicated in Parkinson’s disease and increased autophagy [USP24 in heart, nerve, and muscle tissue, but not adipose or brain tissues.In addition to ammation  with los disease , 46. WHRutophagy , but expFig. SMI and CMD and their comorbidity are increasingly recognised as an important individual, societal and economic burden, therefore this study is very relevant. The large sample size and consistent phenotyping in the UKB is a strength, as is the conservative Bonferroni correction, both of which contribute to the reliability of these findings. The PGS provided additional depth to mechanistic insights of this study. The biases of UKB are well established {Swanson, 2012 #268}, and self-report to assess psychiatric traits could provide additional biases . Therefore, UKB likely underestimates effects of SNPs on disease. Further exploration of these findings in additional ancestry groups would be of great value.PCSK9 locus, we identified genetic associations with SBPsdj, WHRadjBMI, VTE, stroke, mood instability and neuroticism score, with the same signal (indicated by LD r2 = 0.98) associated with WHRadjBMI and mood instability. This is the first study to implicate the PCSK9 locus in the shared pathology of CMD and SMI. The lipid-lowering PGS demonstrating a nominal association with neuroticism score and mood instability highlights important considerations for lipid-lowering drugs. Subsequent analyses should consider causal assessments and temporal patterns of PCSK9, DCHR24 and USP24 expression, to elucidate the mechanisms linking CMD and SMI. Further exploration of this locus in a more diverse cohort would improve understanding of the locus and health equality in the future.In a systematic analysis of the Supplemental FiguresSupplemental Table 1Supplemental Table 2Supplemental Table 3Supplemental Table 4Supplemental Table 5Supplemental Table 6Supplemental Table 7Supplemental Table 8Supplemental Table 9Supplemental Table 10Supplemental Table 11


Despite its major role in complex human functions across the lifespan, most notably navigation, learning and memory, much of the genetic architecture of the hippocampal formation is currently unexplored. Here, through multivariate genome-wide association analysis in volumetric data from 35,411 white British individuals, we reveal 177 unique genetic loci with distributed associations across the hippocampal formation. We identify genetic overlap with eight brain disorders with typical onset at different stages of life, where common genes suggest partly age- and disorder-independent mechanisms underlying hippocampal pathology. The hippocampus has been associated with memory traits and a variety of neurodegenerative and psychiatric disorders. Here, the authors have done a multivariate GWAS revealing 177 genetic loci, and overlap with various brain disorders may suggest partly age- and disorder-independent mechanisms underlying hippocampal pathology. Consequently, impaired or lesion-induced loss of hippocampal functioning has tremendous and diverse impact on emotions and cognitive functions7. Thus, the hippocampus has been extensively studied across a wide variety of diseases and traits, including trajectories of early development and aging.The hippocampal formation plays critical roles in episodic memory8. Indeed, the hippocampal formation is known to be involved in disorders with typical onset early in life9, such as autism spectrum disorders (ASD), attention-deficit hyperactivity disorder (ADHD), schizophrenia (SCZ) and bipolar disorder (BIP), through its roles in perception, memory processes, modulation of executive function, emotion regulation, among others12. Furthermore, through its involvement in stress response, the hippocampal formation is potentially involved in migraine (MIG)13 and tests of recollection memory indicate hippocampal dysfunction in major depression (MD)14, both of which are disorders that can appear at any stage from adolescence to old age. Finally, the hippocampal formation is implied in diseases that primarily emerge during senescence such as Parkinson’s disease (PD) and Alzheimer’s disease (AD). Emerging data suggests a complex hippocampal crosstalk among the dopaminergic and other transmitter systems in PD, where the hippocampal formation is involved in adaptive memory and motivated behaviour15. Loss of hippocampal functions like navigation and episodic memory are core markers of AD and hippocampal atrophy is an established finding17. Together, these studies highlight the role of the hippocampal formation in a range of psychiatric and neurological disorders across the lifespan.The degree of hippocampal maturation and change throughout the lifespan may capture information relevant to the study of psychiatric and neurological disorders, where the age at which individual neurophysiological trajectories usually diverge from the norm may reflect key characteristics of the underlying pathophysiology18 to brain imaging-based genome-wide-association studies (GWAS) in humans22, initially based on total hippocampus volume reporting one20 and six19 loci, and further increasing to 15 when studying individual hippocampal subfields21. Given the broad functional portfolio of the hippocampal formation, however, it is clear that much of the genetic architecture remains to be explored, calling for further studies and novel analytical approaches23.The past decades have brought significant progress towards a characterization of the genetic architecture of the hippocampal formation, from experimental manual mapping in mice25 and function26 and suggested that capitalizing on this distributed nature in a multivariate GWAS approach can significantly improve the discovery beyond standard GWAS approaches24. Advancements into deriving subdivisions of the hippocampus through adaptive segmentation has allowed for a fine-grained assessment across multiple subregions27. We hypothesized that the genetic architecture within the hippocampal formation is distributed across its subregions and thus aimed to gain novel insights into the genetics of the hippocampal formation by deploying such multivariate GWAS approach to the 19 subregion volumes that can currently be segmented with MRI27. Further, given hippocampal involvement in many severe and highly prevalent brain disorders across the lifespan, we targeted common neurological and psychiatric disorders ranging from developmental disorders to neurodegenerative disorders, aiming to reveal gene variants potentially involving the hippocampus at different stages in life.Recent work revealed a distributed genetic architecture of human brain anatomy28 (permission no. 27412) and segmented the hippocampal formation into 19 subregions in addition to total hippocampus volume  using FreeSurfer 7.127  from the UK Biobank127 Fig. . For eacIn line with our hypothesis, we found strong support of a distributed genetic architecture in the hippocampal formation. Multivariate GWAS revealed 177 unique genetic loci with distributed associations across the hippocampal formation. The upper part of Fig. n = 25704) that were in linkage disequilibrium (r2 ≥ 0.6) with one of the independent significant SNPs using functional mapping and annotation of GWAS (FUMA)29. About 90% of the SNPs had a minimum chromatin state of 1–7, thus suggesting they were in open chromatin regions31, and 6.1% were in regulomeDB category 1 or 2, suggesting potential regulatory function30 , important for immune regulation36. The second strongest GWAS hit, mapped to LEMD3  is also relevant for immune regulation37. FAM53B, mapped from the third, POU3F3, mapped from the fourth, and VCAN, mapped from the fifth strongest locus, all have important roles in neurodevelopment40.We mapped the 177 loci implied for the hippocampal formation to 963 genes based on four different mapping strategies , chromatin interaction mapping and MAGMA analysis). Out of these, 87 genes were identified by all four mapping strategies Fig. , supportP < 2.7 × 10−6) through MAGMA identified 303 unique genes across the hippocampus (Supplementary Data PBonf = 9.2 × 10−8), regulation of anatomical structure morphogenesis (PBonf = 1.3 × 10−8) and neuronal differentiation (PBonf = 1.6 × 10−6), potentially indicating that individual differences in hippocampal volumes later in life may be largely determined early in development. Also, when focusing on the 87 genes implicated from all four mapping strategies (see above), the gene-sets reflect processes related to early development like regulation of cell morphogenesis  overlapping with ADHD, 4 loci with ASD, 77 with BIP, 161 with SCZ, 41 with MD, 80 with MIG, 19 with AD and 10 loci significantly overlapping with PD , an enzyme crucial for the glycine decarboxylase complex (GCS) in mitochondria. Glycine is a primary inhibitory neurotransmitter in the spinal cord and brainstem, but increasing evidence shows important glycine involvement also in the hippocampal formation44. Glycine exerts a tonic inhibitory role through extrasynaptic glycine receptor chloride channels45, in addition to modulation of NMDA receptors in the hippocampal formation as evident from rodent studies47. Rodent studies also suggest that through regulation of both glycine and serine synthesis and cleavage, aminomethyltransferase as part of the glycine decarboxylase complex, may provide a homoeostatic regulation of hippocampal function and plasticity by simultaneous activation of excitatory NMDA receptors and inhibitory glycine receptors49. The glycine site on the NMDA receptors is currently under investigation as a promising drug target for several of the disorders that we here associated with the AMT gene, including ADHD51, PD52, SCZ53 and MD54, either by direct glycine supplementation, other substances working on the same receptor site or by increasing endogenous glycine by inhibiting the glycine transporter. Another example for a potential age- and disorder-independent drug target is the microtubule-associated protein tau (MAPT) gene, which was here implicated for ASD, SCZ, AD and PD. Indeed, tau has for long been a marker of AD and PD55 yet has recently also gained focus for ASD56, with animal models suggesting that tau reduction may prevent behavioural signs of this neurodevelopmental disorder57. Taken together, our results therefore add support for disorder-independent gene targets for hippocampal pathology across the lifespan, including AMT and MAPT, among others, and illustrate how the multivariate GWAS approach can reveal overlapping biochemical mechanisms underlying different disorders and traits.Our results suggest that several genes have a role in hippocampal pathology across multiple brain disorders, with onset times ranging across the lifespan. These findings not only support the notion of pleiotropy across a spectrum of neurological and psychiatric disorders but may also pinpoint to both, age- and disorder-independent drug targets. For example, our discovery of the Some aspects are relevant for interpreting the results of this study. First, there is currently no optimal method for gene mapping. We here chose to apply four different mapping strategies and highlighted genes that were identified in all four strategies. While this cannot fully overcome all limitations of current gene mapping approaches, we consider an identification of a given gene by four strategies as an indicator of robustness. Second, we identified genes associated with the hippocampal formation and several of the brain disorders across the lifespan, which might point to shared molecular pathways. However, it has to be emphasized that they could also have different roles in different pathologies. This study lays the foundation for future detailed studies of potential common pathways and drug targets. Third, generalizability of our findings beyond the study population remains to be investigated. We here performed our main analysis in data from 35,411 white British individuals and replicated our findings in data from 5262 individuals with non-white ethnicity, however, both samples were drawn from the same UK Biobank study population. Forth, it should be noted that the here used GWAS for major depression partly included samples that were not necessarily clinically diagnosed with major depressive disorder but reported symptoms of the disorder. This may factor into the specificity of this GWAS. Finally, future research targeting lateralization effects may yield additional insight into hemispheric similarities and differences in distributed genetic effects across regions of the hippocampal formation.In conclusion, our results suggest a polygenic architecture of the hippocampal formation, distributed across its subregions. The genetic overlap with various brain disorders with typical onset at different stages of life implicated genes that may be relevant targets for future studies into the mechanisms underlying hippocampal functioning and pathology across the lifespan. With several of the findings fitting currently studied treatment targets (e.g. the glycine site on the NMDA receptor), our results also confirm the utility of the approach and suggest that capitalizing on the distributed nature of genetic effects on the brain will be instrumental in our future endeavours to further understand mechanisms underlying the brain and its disorders.28  for the main analysis, and of 5262 individuals with non-white ethnicity  for the replication in independent data.The UK Biobank was approved by the National Health Service National Research Ethics Service (ref. 11/NW/0382). We accessed raw T1-weighted magnetic resonance brain imaging data from 35,411 genotyped white British from the UK Biobank58, and subsequently segmented the hippocampal formation using Freesurfer 7.127. The segmentation method has previously been validated in three independent data sets, attributing robust performance and highly replicable results27. For genetic analyses, we followed the standard quality control procedures to the UK Biobank v3 imputed genetic data and removed SNPs with an imputation quality score < 0.5, a minor allele frequency < 0.005, missing in more than 10% of individuals, and failing the Hardy–Weinberg equilibrium tests at a P < 1e−9.We processed T1-weighted images using the standard recon-all pipeline in Freesurfer 5.359, intracranial volume and the first 20 genetic principal components. The resulting residuals for the 20 regions were jointly fed into the multivariate omnibus statistical test (MOSTest)24 analysis. MOSTest implements permutation testing to identify genetic effects across multiple phenotypes, yielding a multivariate GWAS summary statistic across all 20 features. For mathematical details of the implementation, see van der Meer et al. (2020)24, for details on the software implementation see github.com/precimed/mostest. MOSTest has been extensively validated in the original methods paper, including simulations and comparisons with other methods that have confirmed its solid performance in discovery and an order of magnitude shorter runtime compared to other tools24. For comparison to standard univariate approaches, we also performed univariate GWAS (extracted from the univariate stream of MOSTest24). Supplemental genetic correlation analyses were performed using LD-score regression61. Heritability was estimated using genome-wide complex trait analysis (GCTA)62, and for comparison using LD-score regression61.For each of the 19 regions of the hippocampal formation—parasubiculum, presubiculum head and body, subiculum head and body, CA1/CA3/CA4 head and body, GC-ML-DG head and body, molecular layer head and body, HATA, fimbria, hippocampal tail, hippocampal fissure—as well as for total hippocampus volume we calculated the average volume between the left and right hemisphere and subsequently residualized the volumes for age, age squared, sex, scanning site, Euler number as a proxy of image quality29. Using the 1000GPhase3 EUR as reference panel, we identified independent significant SNPs at the statistical significance threshold P < 5e−8. All SNPs at r2 < 0.6 with each other were considered as independent significant SNPs and a fraction of the independent significant SNPs in approximate linkage equilibrium with each other at r2 < 0.1 were considered as lead SNPs. FUMA annotates associated SNPs based on functional categories, Combined Annotation Dependent Depletion (CADD) scores which predicts the deleteriousness of SNPs on protein structure/function63, RegulomeDB scores which predicts regulatory functions30; and chromatin states that shows the transcription/regulation effects of chromatin states at the SNP locus64. We also conducted Gene Ontology gene-set analyses based on FUMA’s gene ontology classification system29 and pathway analyses65 for all mapped genes and the 87 common mapped genes of hippocampal formation. We conducted genome-wide gene-based association and gene-set analyses using MAGMA v.1.0866 (http://ctg.cncr.nl/software/magma) in FUMA. All variants in the major histocompatibility complex (MHC) region  were excluded before running the MAGMA analyses. MAGMA performs multiple linear regression to map the input SNPs to 18091 protein coding genes and estimates the significance value of that gene. Genes were considered significant if the P value was <0.05 after Bonferroni correction for 18,091 genes. We performed cell type analysis of the 87 genes identified by all four mapping strategies using data available as part of the supplements in Zhang and colleagues 201641.To identify genetic loci we uploaded this summary statistic to the FUMA platform v1.3.767. In brief, for each locus identified in the multivariate analysis in the discovery sample, this procedure derives a composite score from the mass-univariate z-statistics and tests for associations of the composite score with the genotype in the replication sample . Four of the 177 loci could not be tested as the lead SNPs from the discovery sample were not available in the replication sample. For the remaining loci we report the percent of loci replicating at P < 0.05 and the percent of loci showing the same effect direction.To ensure that not only single locus associations replicate but that also the multivariate pattern of these associations are consistent in the discovery and replication sample, we implemented a multivariate replication procedure established in Loughnan et al.68 and for Parkinson’s disease (PD) from the International Parkinson Disease Genomics Consortium70. The latter included 23andMe data. 23andMe participants provided informed consent and participated in the research online, under a protocol approved by the external AAHRPP-accredited IRB, Ethical & Independent Review Services (E&I Review). Furthermore, from the Psychiatric Genomics Consortium we accessed summary statistics for attention deficit hyperactivity disorder (ADHD)71, autism spectrum disorder (ASD)72, bipolar disorder (BIP)73 and major depression (MD)74. Finally, we included data from recent studies of schizophrenia (SCZ)75 and of Alzheimer’s disease (AD)76. The included disorders have typical onset times at different phases in life, thus potentially covering a wide range of biological processes affecting the hippocampal formation.We accessed GWAS summary statistics for migraine (MIG) from International headache genetics Consortium43, we identified shared variants associated with hippocampal formation and each of the above-mentioned brain disorders. In contrast to genetic correlation analysis, conjunctional FDR does not require effect directions and can therefore be applied to summary statistics from multivariate GWAS, which do not contain effect directions. Two genomic regions, the extended major histocompatibility complex genes region (hg19 location Chr 6: 25119106–33854733) and chromosome 8p23.1 (hg19 location Chr 8: 7242715–12483982) for all cases and MAPT region for PD and APOE region for AD and ASD, respectively, were excluded from the FDR-fitting procedures because complex correlations in regions with intricate LD can bias FDR estimation. We submitted the results from conjunctional FDR to FUMA v1.3.729 to annotate the genomic loci with conjFDR value < 0.10 having an r2 ≥ 0.6 with one of the independent significant SNPs.Using conjunctional FDR statistics (FDR < 0.05)Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-19Reporting Summary


Advances in sugarcane breeding have contributed significantly to improvements in agronomic traits and crop yield. However, the growing global demand for sugar and biofuel in the context of climate change requires further improvements in cane and sugar yields. Attempts to achieve the desired rates of genetic gain in sugarcane by conventional breeding means are difficult as many agronomic traits are genetically complex and polygenic, with each gene exerting small effects. Unlike those of many other crops, the sugarcane genome is highly heterozygous due to its autopolyploid nature, which further hinders the development of a comprehensive genetic map. Despite these limitations, many superior agronomic traits/genes for higher cane yield, sugar production, and disease/pest resistance have been identified through the mapping of quantitative trait loci, genome-wide association studies, and transcriptome approaches. Improvements in traits controlled by one or two loci are relatively easy to achieve; however, this is not the case for traits governed by many genes. Many desirable phenotypic traits are controlled by quantitative trait nucleotides (QTNs) with small and variable effects. Assembling these desired QTNs by conventional breeding methods is time consuming and inefficient due to genetic drift. However, recent developments in genomics selection (GS) have allowed sugarcane researchers to select and accumulate desirable alleles imparting superior traits as GS is based on genomic estimated breeding values, which substantially increases the selection efficiency and genetic gain in sugarcane breeding programs. Next-generation sequencing techniques coupled with genome-editing technologies have provided new vistas in harnessing the sugarcane genome to look for desirable agronomic traits such as erect canopy, leaf angle, prolonged greening, high biomass, deep root system, and the non-flowering nature of the crop. Many desirable cane-yielding traits, such as single cane weight, numbers of tillers, numbers of millable canes, as well as cane quality traits, such as sucrose and sugar yield, have been explored using these recent biotechnological tools. This review will focus on the recent advances in sugarcane genomics related to genetic gain and the identification of favorable alleles for superior agronomic traits for further utilization in sugarcane breeding programs. Saccharum spp.) is a major industrial crop grown in tropical and subtropical regions across the world. It is cultivated mainly for sugar production and supplies >70% of the world’s sugar. Additionally, it also provides a sustainable and renewable source of bioenergy. It is cultivated in over 24.9 million hectares in about 80 countries with a production value of 174 million tons . India is the world’s second largest sugar producer (26.6 million tons) after Brazil (40% of world production). China, Thailand, the United States, Pakistan, Mexico, and Russia are the other major sugar-producing countries in the world. In India, substantial sugar production is projected for the coming years as a result of higher cultivation rates of promising sugarcane varieties and good rainfall during the 2020 season .Sugarcane  and S. spontaneum . Around 70–80% of the genome is from S. officinarum, while 10–20% is from S. spontaneum; the remaining 10% of the genome has arisen from interspecific recombination (The modern cultivars of sugarcane originated from an interspecific hybridization between bination . The sugbination  and is hbination .It is hoped that progresses in genome sequencing and the accumulation of genomic resources such as the SUEST (sugarcane expressed sequenced tag) database, genetic maps, transcriptomes, bacterial artificial chromosome (BAC) libraries , etc. wiGenomic selection (GS), which utilizes genome-wide genetic markers to estimate breeding value, when used along with genomic strategies, can substantially increase selection efficiency and genetic gain in sugarcane breeding programs. Furthermore, selection intensities in sugarcane breeding programs can be further enhanced by utilizing large breeding lines and high-throughput phenotyping and genotyping with bioinformatics tools.In the wake of enormous advancements in sugarcane genomics, we outline in this article the recent progress and future possibilities in sugarcane genomics and phenomics in identifying superior alleles for agronomically superior traits in sugarcane crops. These desirable/superior alleles/traits can be incorporated into sugarcane breeding lines either to improve existing cultivars or to create new cultivars with enhanced cane and sugar yield. These approaches can ensure sustainable increases in sugarcane production with available resources to meet growing demands.in-situ hybridization (GISH) have revealed that clones with 80 chromosomes are classic S. officinarum, while those with more than 80 chromosomes are inter-specific hybrids between S. spontaneum and S. officinarum , uniform tillering population, single cane weight (SCW), high biomass, commercial cane sucrose (CCS), etc., which are polygenic with each gene exerting small effects. It is challenging to improve such traits through marker-assisted selection (MAS) approaches . Using GSeveral predictive models have been developed for GS studies in crop plants; however, selecting the correct model is crucial for high prediction accuracy. In general, the prediction accuracy of GS models varies according to their assumptions and the treatment of marker effects . In cropMost Bayesian methods, however, allow different markers to have different effects and variances, unlike G-BLUP and RR-BLUP. As part of the BayesB model, most loci are assumed to have no effect on a trait, and thus most markers are excluded from the prediction model. If the trait expression is governed by large-effect QTLs, which explain much of the genetic variability, BayesB fits well . The parSelective shrinkage models, such as BayesB, BayesCπ, BayesA, and Bayesian LASSO, are sensitive to a number of QTLs; as the numbers of QTLs increase, prediction accuracies decrease . In contSugarcane breeding programs aim to improve genetic gains for major traits such as cane and sugar yield. However, there is negative correlation between commercial cane sugar and cane yield, indicating that many of the genes controlling these traits are independent. Genetic gain for these traits through phenotypic selection is very low. This is because the accumulation of superior alleles for these traits via phenotypic breeding value is very rare . TherefoUnder present sugarcane breeding programs, phenotypic selection takes 12–13 years to develop a new variety for commercial cultivation. The crosses effected once are maintained in a clonal form and evaluated over the breeding cycle. Hence, chances of introgression of new alleles through recombination are possible only at the beginning of the crossing program, which is carried out in once every 12 years. Furthermore, handling a large number of seedlings in the ground nursery and carrying them forwards in the subsequent stages of first and second clonal trials before entering pre-zonal variety testing is a herculean task . These tTherefore, GS can aid in rapid genetic gains in sugarcane breeding through the selection of superior clonal entries at the early crop stage; this helps in reducing the length of the breeding cycles in such programs.Continuous advances in plant breeding and agronomic practices in the context of systematic crop improvement have contributed significantly to annual productivity gains in major food commercial crops. However, the demands for food, sugar, fiber, energy, and fuel are growing due to the ever-increasing human population, increasing per capita income, and diversified food consumption/industrial use patterns. Although sugarcane has been cultivated primarily as a source of sugar for centuries, the crop has recently gained much attention as a source of ethanol and biodiesel to satisfy global energy demands. Owing to its economic value, the sugarcane genome has received growing interest in recent times in the context of the changing climate. In the current breeding program, ground nursery populations (∼15–25 K), generated through the random crossing of desired parents (∼20–30 parents) in the national hybridization garden facility, can be evaluated for desired agronomically superior traits. Within and between family selection is employed and around 100–125 improved families can be selected from a population of 15–25 K seedlings (training population) based on the desired trait value. Individual selection is employed at the first and second clonal stages based on increasing cane and sugar yielding traits, better stem elongation, shoot tillering, NMC, deep root system, and high biomass production, depending upon the breeding objective. GEBVs for these desired traits can be estimated using SNP markers in the first and second clonal stages (breeding population) . PredictRecent advancements in sugarcane genomics, including the compilation of a draft genome sequence, are opening new windows in the utilization of genomic resources for rapid genetic gain through molecular breeding tools such as genome-wide association studies (GWAS) and GS. In GS, available genome-wide markers are used to estimate the effects of all loci and thereby calculate the GEBV, which has a substantial potential for increasing selection efficiency. GS in livestock breeding has incredible potential in accelerating genetic gains (sometimes as high as two-fold) compared to conventional progeny testing . TherefoIn any sugarcane breeding program, high cane yield and CCS yield are two major traits of commercial importance, and they are governed by many genes and/or QTLs . Cane yiThe development of breeding sugarcane clones with agronomically superior traits is slow and expensive. During recent decades, the rates of genetic improvement in sugarcane have been static compared to other crops . TherefoTherefore, the successful application of GS approaches for these important traits could substantially accelerate genetic gain and increase the accuracy of the selection of desired traits in sugarcane breeding.As GS is an innovative approach in sugarcane crop, few researchers have reported the accuracy of GS approaches for economically important traits in sugarcane. GS in sugarcane was first reported by In another study by Recent progress in GS techniques in sugarcane for key cane traits, such as cane yield, sugar yield, fiber percent, and other traits, has been found to provide good prediction accuracies . GEBV acviz. the production of high sucrose, fiber, biomass, bioenergy, ethanol, etc. The population density is characterized by the numbers of canes, leaf area, biomass, and canopy structure for efficient solar energy harvest for the photosynthetic conversion of light energy into carbohydrates. Cane yield also depends on several plant characteristics, and the two factors collectively enhance yield per unit area.Sugarcane is also valued for other products besides sugar. The population density of a sugarcane crop has a strong influence on the agronomic output Tillering or underground branching is responsible for the crop stands in sugarcane. Tillering, in general, peaks at 120 days after planting (DAP) and declines thereafter due to mortality, and usually stabilizes at 180 DAP. Thereafter, the majority of shoots are converted to cane stacks . Inter- Considerable knowledge has been gained concerning the physiology of yield in crops and it is now possible to quantitatively estimate limits to crop yield based on physiological and environmental constraints .The two Fv/Fm), the soil plant analytical division (SPAD) index, and the leaf rolling index were observed under limited irrigation during the grand growth stage of sugarcane  has been proposed by ugarcane . This stficiency , are relficiency  suggesteviz. early synchronized tillering, conversion efficiency of tillers to canes, optimized NMC for higher CCS%, superior partitioning towards economic traits, and the maintenance of high quality and high biomass for varied end products . The quantifiable and desirable physiological plant behaviors for efficient output are depicted in tier three. The penultimate tier explains both phenotyping and genotyping for the aforementioned physiological and plant processes. Most of these processes have been defined by specific phenotype and genotype markers by sugarcane researchers  can help in studying complex phenotypes in sugarcane crops rapidly. The usefulness of unmanned aerial vehicles (UAVs) has been clearly reviewed by viz. chlorophyll content, relative water content, nutrient status, chemical composition, plant health, and photochemical reflectance index  between measured plant height (PH) and estimated PH, reflecting the usefulness of aerial images from a UAV three months prior to harvesting.Cane height is highly influenced by the soil, total sugar content, leaf nitrogen content, temperature, and light intensity, and it can be an indicator of yield and other parameters . Bunruanviz. visual, multispectral, and thermal mounted on UAVs in six-month-old sugarcane plants, revealed that stalk number and plant height were highly correlated to canopy cover (rg = 0.72) and canopy height (rg = 0.69), respectively , likelihood estimation of mixed models, multiple regression, and artificial neural network (ANN), favors the accelerated selection of better genotypes in various crops against abiotic and biotic stresses under the changing climate. Tests for multicollinearity, autocorrelation, and heterogeneity will be extremely useful in solving the complex data analysis , net isothermal radiation (Rni), vapor pressure deficit (VPD), and wind speed, multiplies the background noise in canopy temperature determination, and the dissection of leaf temperature needs stable weather conditions .Thorough understanding of the sugarcane phenotype according to the phenophase  [i.e. geThe recent assembly of a draft sugarcane genome has accelerated crop improvement. In spite of this, its polyploid nature, the large size of its genome, low transformation efficiency, and lack of high-throughput tools and transgene silencing in sugarcane poses a great challenge for these efforts . The genAmong the highlights of sugarcane genome editing are the generation of events incorporating and expressing TALEN, capillary electrophoresis experiments to demonstrate targeted mutation, analysis of 89–148 nucleotide PCR amplicons encompassing the TALEN target site, and phenotypes that show reduced lignin and altered lignin and cell wall composition under greenhouse conditions . Sun et This genome-editing based on RNA-guided nucleases has emerged as a one of the most simple and versatile methods  during tMgCh) gene, which is a key gene in chlorophyll biosynthesis. The CRISPR-Cas9 system could target 49 copies/alleles of magnesium chelatase, as confirmed via Sanger sequencing. The edited plants produced light green to yellow leaves. This work proved that genome editing could be achievable in polyploid crops, overcoming the limitations of the protospacer adjacent motif site , can be improved in the existing cultivars.The CRISPR-Cas system has been successfully used to produce herbicide tolerance in sugarcane . The co-As far as the regulatory aspects of genome editing are concerned, the process is different for different countries. In the US, gene-editing events without transgenes do not require regulatory approval by the US regulatory agencies. In Brazil, the National Biosafety Technical Commission (CTNBio) states that the classification can be made on a case-by-case analysis by CTNBio for the varieties of sugarcane. Cana Flex I and II are classified as non-transgenic. In India, the Department of Biotechnology, Ministry of Science and Technology, and the Department of Agriculture Research and Education, Ministry of Agriculture and Farmers Welfare have recommended that the SDN1 and SDN2 genome-edited products that are transgene-free should be exempt from biosafety assessment.Data-intensive modes of research, both in basic and applied plant sciences, have gained momentum over the last two decades due to greater advances in the field of big data generation . AdvanceThe simulation of human intelligence in machines that are programmed to think like humans and mimic their actions is generally refereed as the AI. It refers to machines displaying characteristics of the human mind . MachineThe recent advancements in the field of AI have presented ample opportunities; AI applications in crop research and agriculture have so far primarily benefited large-scale industrial farming , with reA novel deep learning (DL) approach with three different extractor models  and seven  different classifiers has been used to classify sugarcane crops as diseased and non-diseased with the highest area under curve (AUC) of 90.2% . In a siThe integration of GS techniques in current breeding programs will be very useful in identifying better-performing sugarcane clones at earlier stages than conventional breeding techniques. GS can not only help in reducing population sizes but can also shorten breeding cycles and help in the selection of parents with superior agronomic traits. Utilizing rapid recurrent selection in superior clones can help in accumulating desired alleles associated with superior agronomic traits, such as cane yield, sucrose percent, NMC, uniform tillering, staying green, erect canopy, non-flowering habit, deep root system, better stem elongation, and tolerance to biotic and abiotic stresses. Identifying clones with high accuracy helps in increasing genetic gains for complex traits in sugarcane breeding programs. Implementing GS techniques with modified breeding schemes can have great potential to increase selection efficiencies and the rate of genetic gain in sugarcane. Real-time monitoring of phenomic changes with respect to the response of plants to different stresses has been simplified by the genomics revolution, which has alleviated the bottlenecks in phenomics. Automated and remote sensors, data integration, and experimental design all contribute to effective phenotyping by increasing the accuracy, precision, and throughput of measurements, as well as reducing costs due to rapidly evolving low-cost phenotyping facilities. The invention of chemicals and physical mutagens has shown a multifold improvement in creating genetic changes that have shaped a new revolution in agriculture production. The introduction of the precise and specific biologically highly efficient tool CRISPR-Cas9 is expected to accelerate the beneficial genetic change in crop plants, assisting in feeding the world’s growing population. Advancement in the application of AI and ML is expected to significantly improve instant planning and decision making and maintain product quality.


DERL2, SNX4, LY75 and EFCAB6) which were shared by SCZ and BMI. We observed that the novel genetic variant rs13307 and the most proximate gene LY75 exerted potential effects on SCZ and BMI comorbidity. Also, we observed a mixture of concordant and opposite direction associations with shared genetic variants. We demonstrated a moderate to high genetic overlap between SCZ and cardiometabolic traits associated with a pattern of bidirectional associations. Our data suggested a complex interplay between metabolism-related gene pathways in SCZ pathophysiology.Individuals with schizophrenia (SCZ) have, on average, a 10- to 20-year shorter expected life span than the rest of the population, primarily due to cardiovascular disease comorbidity. Genome-wide association studies (GWAS) have previously been used to separately identify common variants in SCZ and cardiometabolic traits. However, genetic variants jointly influencing both traits remain to be fully characterised. To assess overlaps (if any) between the genetic architecture of SCZ and cardiometabolic traits, we used conditional false discovery rate (FDR) and local genetic correlation statistical framework analyses. A conjunctional FDR was used to identify shared genetic traits between SCZ and cardiometabolic risk factors. We identified 144 genetic variants which were shared between SCZ and body mass index (BMI), and 15 variants shared between SCZ and triglycerides (TG). Furthermore, we discovered four novel single nucleotide polymorphisms (SNPs)  and four proximate genes (The online version contains supplementary material available at 10.1186/s12864-022-08766-4. Individuals with schizophrenia (SCZ) have a 10- to 20-year shorter life span when compared with healthy individuals in the same population . For exaRecently, the polygenic nature of SCZ and cardiometabolic traits have become increasingly clear . These tEven though abundant genetic variants are associated with SCZ comorbidity and cardiometabolic traits, understanding the functional consequences of genetic variations and identifying pleiotropic genes and pathways for both phenotypes remains challenging. Powerful statistical approaches, specifically designed to analyse the polygenic architectures of complex traits, could improve gene or loci discovery and replication rates –27. The In this study, we analysed GWAS summary statistics of SCZ and cardiometabolic traits, including WHR, BMI, TG, TC, HDL, LDL, FG, FIN and T2D, using pleiotropic-based conditional and conjunctional FDR statistics to estimate shared genetic characteristics between SCZ and cardiometabolic traits. We hypothesised that these methods could help identify shared genetic variants, shared polygenic architecture, and potential pleiotropic genes and biological pathways shared between SCZ and cardiometabolic traits.P-values for that SNP in both primary and conditional traits were as small as, or smaller than the observed P-value.As shown Fig. , we perfOur fold-enrichment plots demonstrated that SNPs were highly enriched 4.8–25.0 fold) in SCZ, across increasingly stringent significance levels, for a BMI association (SCZ|BMI)  package to estimate and visualise local SNP-heritability and genetic covariance, to examine if a specific genomic region was genetically linked to SCZ and cardiometabolic traits. We estimated local genetic covariance and correlations in 104 regions between SCZ and BMI, and 32 regions between SCZ and TG. In analysis between two trait pairs (SCZ|BMI and SCZ|TG), we identified four genomic regions on different chromosomes: chr11:27020461–28,481,593, chr12:122007651–124,977,980, chr16:29036613–31,382,943 and chr16:63691589–65,938,566, which showed strong local genetic associations between SCZ and BMI . All genes were previously reported as being associated with BMI and SCZ.To identify genetic variants shared between SCZ and BMI and TG, we performed conjunctional FDR analysis. This approach assessed the posterior probability that an SNP was null for either trait or both, given that HCN1 as a novel gene for TG, and was associated with SCZ in a previous study . This novel TG gene was associated with SCZ  shared between SCZ and BMI. Ten candidate SNPs, in strong linkage disequilibrium (LD) (r2 ≥ 0.8) with rs3865350 (DERL2) at 17p13.2, were extracted using the HaploReg v4.2 tool  with rs9860913 (SNX4) at 3q21.2 were identified  with rs13307 (LY75) at 2q24.2  at 22q13.2 were identified  and four most proximate genes  which were not previously associated with TG, but with SCZ. Ninety candidate SNPs, in strong LD (r2 ≥ 0.8) with rs1472584 (HCN1) at 5p12, were extracted  with rs3130544 (C6orf15) at 6p21.33, were extracted  and two most proximate genes  neuron differentiation, brain-derived neurotrophic factor (BDNF) signalling, positive regulation of growth, post synapse and modulation of chemical synaptic transmission  in BMI (H4-Posterior Probability: 81.2%) and SCZ (H4-Posterior Probability: 82.1%)  database was used to perform PPI network analysis. The PPI network of genes shared between SCZ and BMI consisted of 219 nodes (genes) and 535 edges (interactions) Fig. c. After The PPI network for SCZ and TG shared genes consisted of 114 nodes (genes) and 699 edges (interactions) Fig. c. After P = 0.049, Additional file In MR analysis, when cardiometabolic traits were considered the exposure, no causal associations were observed between traits associated with SNPs for SCZ risk . The MR analysis indicated that genetic liability, the heritability of a disease, to SCZ exerted no influence on BMI|TG and vice versa, suggesting no evidence identified for a causal relationship between SCZ and BMI|TG. We identify 144 genetic variants between SCZ and BMI, and 15 genetic variants between SCZ and TG. Genetic variants were mainly enriched for neuronal system functions, including CNS neuron differentiation, BDNF signalling and the positive regulation of growth. Also, shared genetic variants demonstrated a mixture of concordant associations and associations with opposite directions between trait pairs. In total, 50% of shared SCZ SNPs had positive associations with BMI, while 46.7% had positive associations with TG. Additionally, we identified four novel genetic variants  shared by SCZ and BMI, and two novel genetic variants (rs1472584 and rs3130544) shared by SCZ and TG.rg = − 0.081, P < 0.01, Additional file P = 0.049) and indicated shared genetic variants between the exposure (BMI) and outcome (SCZ). While underlying mechanisms remain unclear, one hypothesis suggests that poor nutrition, though subtle, may exert negative effects on neural development, leading to a increased incidence of mental health disorders, such as SCZ )  from thecestry]) . Datasethttp://genome.sph.umich.edu/wiki/LiftOver) to convert all GWAS summary data to the GRCh37/hg19 reference genome. Thirdly, we filtered out variants with a minor allele frequency (MAF) < 1% and removed SNPs with duplicates by keeping the first one. Lastly, we deleted SNPs with any missing values of OR/beta, SE, or P-values. Additionally, we restricted our analysis to autosomal chromosomes and European ancestry. The original number of SNPs and those remaining after QC are shown in Additional file We applied standardised GWAS summary data to minimise potential biases due to different array platforms and QC procedures. Firstly, we compared the md5 code of GWAS summary statistics and reported total SNPs to check data quality. Secondly, we used the LiftOver tool  values for associations with SCZ on the abscissa [Our pleiotropy analysis strategy was based on conditional FDR. Fold-enrichment plots are described elsewhere , 71. BriOur analyses followed two directions; firstly, with SCZ as the primary phenotype A and cardiometabolic traits as the conditional phenotype B, then vice versa for the second direction. We also generated a fold-enrichment plot to assess polygenic overlap between SCZ and cardiometabolic risk traits.To investigate if a local genetic correlation existed between SCZ and cardiometabolic traits, the HESS  package P-values for both phenotypes are as small as, or smaller than the P-values for each trait individually. More details are provided elsewhere [To determine if genetic variants were likely to be shared by two phenotypes, we computed conjunctional FDR statistics. The conjunctional FDR is an extension of the conditional FDR and is defined as the maximum of two conditional FDR statistics for a specific SNP. The conjunctional FDR estimates the posterior probability that an SNP is null for either trait or both, given that the lsewhere –75. In or2 ≥ 0.8) with the index variant based on the 1000 Genomes Phase 1 European individuals from the online HaploReg v4.2 tool [We mapped SNPs identified by conjunctional FDR to promising genes using ANOVAR software  and desc4.2 tool . Using d4.2 tool  and Road4.2 tool  databasehttps://fuma.ctglab.nl/) was used to annotate significantly shared lead SNPs with functional categories using combined annotation dependent depletion scores (CADD) [http://renlab.sdsc.edu/GVATdb/search.html) capabilities and was also used to characterise the allelic binding of common human SNPs (MAF > 1% in European and Asian populations) to distinct TFs. The transcriptional regulation in the context of enhancers (EnhancerDB) resource was used to define tissue-specific enhancers by setting threshold scores for tissue-specific enhancers (http://lcbb.swjtu.edu.cn/EnhancerDB/).Functional Mapping and Annotation of GWAS (FUMA s (CADD) , Reguloms (CADD) . The Genhttp://metascape.org), with default parameters, to assess the overrepresented enrichment of shared gene sets between SCZ and cardiometabolic traits (BMI and TG) in KEGG pathway analyses (www.kegg.jp/kegg/kegg1.html), GO Biological Processes, GO Cellular Components, GO Molecular Functions, WikiPathways, Hallmark and Reactome gene sets.To understand the biological role of genes nearest the shared genetic variants between SCZ and cardiometabolic traits, we performed multiple post-GWAS functional analyses in shared genes identified by conjunctional FDR. We used the Metascape tool   within 200 kb of significant lead SNPs and common to both GWAS and eQTL studies, were inputted into coloc under default parameter settings [4 tested the hypothesis that the same causal variant was shared between GWAS and tissue-specific eQTLs. Genetic variants with 80% or higher probability for H4 were compared to understand the LD structure and the most prominent variant being shared by GWAS and eQTL [Colocalisation analysis was performed to test the probability of genetic variant the same one of GWAS and tissue-specific eQTL using GTEx dataset. Summary statistics for SNPs (regardless of GWAS settings . This apand eQTL .https://string.embl.de/) database, with a confidence score = 0.7 (high confidence) [P < 0.05 was considered a statistically significant difference.PPI networks of shared genes were generated using the STRING 11.0; fidence) . The PPIfidence) . The confidence) . PathwayP threshold of 5 × 10− 8, LD R2 = 0.001 and clumping distance = 10 kb were used [To test for causal relationships between SNPs identified by conditional analysis, we performed MR analysis  using caere used . We condIn the main analysis, we reported IVW estimates, however, if Egger’s method identified horizontal pleiotropy , then MR-Egger results were used.Additional file 1. Supplementary Tables.Additional file 2. Supplementary Figures.Additional file 3. Additional datasets.Additional file 4.Additional Tables.Additional file 5.Additional Figures.Additional file 6.Supplementary Methods.Additional file 7.Supplementary Results.