Aquarius remigis. Comparisons among these traits, which have size ratios ranging from 1 to 5 , allow us to ask if sexual dimorphisms are associated with characteristic patterns of quantitative genetic variation. We frame our analysis around three main questions, derived from existing theory and empirical evidence: Are sexual dimorphisms associated with (1) lower additive inter-sex genetic correlations, (2) higher proportions of sex-linked variance, or (3) differences between sexes in autosomal additive and dominance genetic variances? For questions (1) and (2), we find weak and non-significant trends in the expected directions, which preclude definitive conclusions. However, in answer to question (3), we find strong evidence for a positive relationship between sexual dimorphism and differences between sexes in proportions of autosomal dominance variance. We also find strong interactions among the three genetic components indicating that their relative influence differs among traits and between sexes. These results highlight the need to include all three components of genetic (co)variance in both theoretical evolutionary models and empirical estimations of the genetic architecture of dimorphic traits.The evolution of sexual dimorphisms requires divergence between sexes in the evolutionary trajectories of the traits involved. Discerning how genetic architecture could facilitate such divergence has proven challenging because of the difficulty in estimating non-additive and sex-linked genetic variances using traditional quantitative genetic designs. Here we use a three-generation, double-first-cousin pedigree design to estimate additive, sex-linked and dominance (co)variances for 12 traits in the water strider,  The quantitative genetic analysis of non-additive and sex-linked effects is challenging and consequently has been tackled in relatively few cases. However, as described below, theoretical and empirical studies suggest that both types of effects may play important roles in the evolution and maintenance of sexual dimorphisms. In 2006, Fairbairn and Roff proposed a three-generation pedigree breeding design and method of analysis that enables researchers to estimate the genetic (co)variance components due to both autosomal and sex-linked effects  is less than 1.0 for the diverging traits, or if the amount of additive genetic variance for the trait differs between sexes .Question 3 is suggested, in part, by the hypothesis that sexual dimorphism could arise if one sex has more additive genetic variance for the trait than the other, and thus responds more rapidly to selection , whereas the proportions of sex-linked variance had broadly overlapping confidence intervals variation. We use the water strider, and Roff  to estimAquarius remigis are surface-dwelling semiaquatic bugs , commonly called water striders, found on streams and small rivers across much of sub-arctic North America variance structure of morphological traits in The majority of experimental designs or analyses of unmanipulated populations cannot estimate both autosomal dominance and additive X-linked variances and the former are typically assumed to be zero and omitted from the analysis   – 1, which has the conceptual advantage of being zero for monomorphic traits. This is the absolute value of the dimorphism index (SDI) of Lovich and Gibbons , which iFor the analysis of genetic variance structure, we standardized the data for each trait across the entire data set, including both sexes and all generations and replicates, by subtracting the mean and dividing by the standard deviation. This has the effect of removing any possible confounding influence of differences among traits in means and phenotypic variances.We partitioned phenotypic (co)variances for each sex into autosomal additive and autosomal dominance (co)variances and sex-linked additive variance using quantitative genetic linear mixed models, commonly known as ‘animal models’ variances from the model and, in conjunction with the delta method . Our estimates of abs(SDI) for each trait are derived from these subsets and so the estimates for the 12 traits are statistically independent. The reported results are based on these values. VpM, VpF are the phenotypic (p) variances of the males (M) and females (F), respectively, and a) and dominance (d), sex-linked additive (x), and environmental (e) components, where the cross-sex residual covariance is fixed to zero because this parameter is not estimable when traits are only ever expressed in separate female or male environments variances with a preliminary analysis of each sex separately, incorporating generation and replicate as fixed effects and the additive, dominance and sex-linked variance components as random effects. Model convergence was obtained for all 12 traits for both sexes. However, the sex-linked additive genetic variance in males, calculated as a proportion of the total phenotypic variance, averaged only 0.34% compared to 9.91% in females. The very low value in males suggested that the sex-linked additive covariance was probably insignificant or very small. To test this, we ran the full model in which males and females were incorporated into a single model but treated separately by considering them as separate environments, as suggested by Falconer :\documenConvergence was obtained for only three traits and in all of these the sex-linked additive covariance between the sexes was not statistically different from zero. Therefore, all the models presented in this paper assume a sex-linked additive genetic correlation between the sexes of zero, i.e. −8. The results for the variance components as proportions of the total phenotypic variance were identical  and thus we report only one set of values.An important consequence of the very small sex-linked genetic variance in males is that the type of dosage compensation, if any, should not matter. We ran the analyses using a no-dosage compensation model  estimates and reran the regression analysis, storing the F value as F1. This randomized procedure was repeated 10,000 times. The null hypothesis of no covariation was tested by computing the number of times the F value from the randomized data set exceeded Fobs, which we denote as F value. For a one-tailed test the randomization approach was modified in that to be included in Pobs, and the probability from the randomization method, Pr.The usual statistical analysis of linear regression is potentially biased by collinearity, the influence of outliers, excessive leverage and covariance among predictor variables. These factors are hard to evaluate with just 12 data points. To circumvent such problems, we adopted a randomization approach Roff . The genP < 10−6).The full genetic model described above  was compared to the constants-only model  using the log-likelihood ratio test /Vp. They averaged 0.40 (SD 0.19) in males and 0.35 (SD 0.15) in females and were significantly greater than zero in both sexes for all traits except L.Seg8 in males . This negative correlation could arise in part because of the mathematical relationship between these two proportions, both being derived by division by Vp: i.e., Va/Vp = 1 − Vd/Vp − Vx/Vp − Ve/Vp. However, this is likely only if Vp and thus are mathematically constrained to covary. In our data, the sum of these two variances comprised an average of only 0.75 and 0.47 of the phenotypic variances in males and females respectively , and none of the estimates were individually significant in males. In females, only L.Head and L.seg8 were significant. It is of note that these are the traits that had non-significant autosomal heritability but large and significant proportions of dominance variance.As expected from the analyses of each sex separately, the estimated proportions of sex-linked variance , but this was statistically significant only in the standard analysis . As expected, rA tends to be lower than ra , and this difference is proportional to ra – rA = 0.0173 + 0.3172 F1,10 = 17.55, P = 0.0019). These results are consistent with the expectation that sex-linked additive variance lowers rA.We first looked for this relationship using the overall additive inter-sex correlations, rA Fig. . As predra and abs(SDI) might be confounded by the effects of the dominance inter-sex correlation, rd, because the two correlations were significantly correlated with each other . Linear regression of abs(SDI) on rd alone was not significant by either the standard or randomization analyses , and, although multiple regression with both correlations found significant effects of all components (abs(SDI) = 4.81–4.12ra + 4.76rd–5.70rard; Pobs = 0.024; all coefficients with P < 0.02), the randomization was not significant (Pr = 0.158). Thus inclusion of rd did not reveal a more robust relationship between abs(SDI) and ra.We asked if the relationship between t10 = 1.808, P = 0.049, Pr = 0.097, one-tailed because the predicted relationship is positive).To address this question, we first did simple linear regression of abs(SDI) on r = −0.10; r = −0.40; r = −0.53; P = 0.758, 0.194, 0.079, respectively. Thus, collinearity was not likely to bias the regression. The final model consisted of all possible components, F7,4 = 44.9, Pobs = 0.001, Pr = 0.024, P values for all coefficients <0.015). To more fully understand these complex relationships, we did randomization regressions for each of the variance components alone and for the three pairwise combinations. Of these, only the regression with Pobs = 0.003, P values for all coefficients <0.05, Pr = 0.021). Thus, in spite of the complexity of the full model, the pairwise analyses suggest that the components likely contributing most to the covariation with abs(SDI) are To determine if this relationship is influenced by the other two variance components, we ran stepwise multiple regression of abs(SDI) on the three variance components. The variances tended to covary negatively but were not significantly correlated with each other:Pobs = 0.386, Pr = 0.276).We were unable to duplicate these analyses in males because of the lack of variation in entclass1pt{minimahx2 Fig. . Howeverra were ≥0.97 for all traits except L.Seg7marg, SpineDist and L.Seg9.10 . These results suggest that the autosomal additive effects covary between sexes both within and between traits. However, covariance does not mean that the additive variances do not differ between the sexes. To the contrary, t-test, t11 = 4.797, P < 0.001), and this was due to males having slightly larger Va  rather than smaller Vp . In addition, ra was significantly less than 1.0 for L.Seg7marg and SpineDist, and was not significantly greater than zero for L.Seg9.10. Both the overall higher ra for three traits indicate the presence of sex-specific patterns of autosomal additive variance.Looking first at autosomal additive genetic variance, we find evidence of both similarities and differences between the sexes. The estimates of .10 Fig. , and theentclass1pt{minimaPobs = 0.753, Pr = 0.227). Thus, within our suite of traits, the magnitude of the difference between sexes in autosomal additive variation is not correlated with the magnitude of sexual dimorphism.To determine if these differences between the sexes are associated with sexual dimorphism we expressed the magnitude of the difference in variances as the ratio of the larger variance to the smaller variance for each of the 12 traits, and then estimated the correlation between abs(SDI) and the variance ratios. The correlation estimate was positive but not significant  and was significantly larger in males than in females overall . As with t-test: t11 = 1.904, P = 0.083) rather than the phenotypic variance being lower. However, unlike the additive inter-sex correlations, the inter-sex dominance correlations (rd) were quite variable in both magnitude and sign , but in two cases (L.Seg8 and SpineDist), it was strongly and significantly negative. Of the 10 traits with positive estimates, four were not significantly greater than zero . These results indicate that dominance variances differ between sexes for some traits, and that these differences can be extreme (as in the two cases of strong negative rd).We analyzed the autosomal dominance variances following the same protocol. In a pattern similar to the additive variances, ign Fig. . On averr = 0.961, F1,10 = 125.8, Pobs < 0.001, Pr = 0.003). This indicates a very strong relationship between the magnitude of the difference between sexes in dominance variance and the magnitude of sexual dimorphism.In contrast to the additive variances, the correlation between the ratio of dominance variances  and abs(SDI) was significant and strongly positive  to the contribution of strong, sex-specific patterns of dominance variance. Thus, for our data, the answer is affirmative for question 3.A. remigis. Our estimates revealed previously unknown aspects of the quantitative genetic architecture of these traits. In addition, because the magnitude of sexual dimorphism differed greatly among traits, we were able to use comparisons among traits to address three key questions about the quantitative genetic architecture of sexual size dimorphism in this species.In this paper, we applied the method described by Fairbairn and Roff  to estimB matrix; Wyman et al. As predicted, given our choice of traits, the matrix of genetic correlations among traits demonstrates considerable genetic independence: all of the between-trait additive genetic correlations were less than |1|, with averages in both sexes of less than 0.5. Although webs (matrices) of genetic correlations among traits may be more constraining than pairs of within-trait correlations might suggest  characteristic of most populations . We tested for this relationship only in females because the X-linked variance was negligible for most traits in males. The estimated correlation between abs(SDI) and Our second question was whether or not sexual dimorphism is associated with increased proportions of X-linked variance . Nevertheless, we found that the proportion of autosomal additive variance : overall heritabilities are larger in males for all traits except abdomen width  and the length of segments 9 and 10 . Thus, we find no indication of the distinction noted by Wyman and Rowe between sexually selected and non-sexually selected traits, and no indication that any of the traits we used have higher heritabilities in females.Our final question asked if sexual dimorphism is associated with differences between sexes in autosomal additive and dominance variances. We found consistent differences between the sexes in both additive and dominance autosomal variance components, with males having significantly higher proportions of both. For additive variances, we found no indication of the pattern of differences between sexes suggested by the interspecific comparative study of Wyman and Rowe . They foand Rowe , gave thThe key prediction for answering question 3 is that the magnitude of these sex differences in autosomal variances should be positively correlated with the magnitude of sexual dimorphism. This prediction was not supported for autosomal additive variances but was strongly supported for autosomal dominance variances. Thus, for our traits, sexual dimorphism is positively associated with the magnitude of the difference between the sexes in the proportion of autosomal genetic variance, but this is true only for the dominance variance component. This pattern of association between sexual dimorphism and sex-specific patterns of dominance variance aligns with mounting empirical evidence for sex-specific dominance associations in traits under sexually antagonistic selection . Thus, we found a clear positive answer only to the last of the three questions that framed our analyses: sexual dimorphisms for body size traits in A. remigis are associated with sex differences in autosomal genetic variance and this is due to large differences in the proportions of autosomal dominance variance. Unfortunately, our data do not provide clear answers to questions 1 and 2. In both cases, we found weak, non-significant trends in the expected directions, so we cannot say with assurance that the predicted relationships exist, nor can we say that they do not.The clearest conclusion that arises from our comparative analysis of traits in Callosobruchus maculatus, revealed significantly more autosomal additive and dominance variance in females than males, the opposite of the pattern seen in A. remigis and, unlike in A. remigis, the proportions of dominance variance were very low and significant only in females. Even more striking is the difference between species in the pattern of sex-linked variance. Water striders lack a Y chromosome variation, indicating that the genetic pathways by which sexual dimorphism evolves may vary among traits, as well as between sexes. These results contrast sharply with those of Kaufmann et al. (Overall, our findings of genetic differences between the sexes are consistent with the hypothesis that the evolution of sexual differences in n et al. , and takSupplementary Information


Aigeiros and their parents) grown in the same field and conducted full-sib family-based association analyses of two growth and six woody traits using GEMMA as a choice of association model selection. We identified 1342 significantly associated single nucleotide polymorphisms (SNPs), 673 located in the region upstream and downstream of 565 protein-encoding genes. The transcriptional regulation network of secondary cell wall (SCW) biosynthesis was further constructed based on the published data of poplar miRNA, transcriptome, and degradome. These provided a certain scientific basis for the in-depth understanding of the mechanism of poplar timber formation and the molecular-assisted breeding in the future.Xylogenesis is a complex and sequential biosynthetic process controlled by polygenes. Deciphering the genetic architecture of this complex quantitative trait could provide valuable information for increasing wood biomass and improving its properties. Here, we performed genomic resequencing of 64 24-year-old trees (64 hybrids of section  Wood, commonly referred to as secondary xylem, produced by the vascular cambium of woody plants, is an indispensable and renewable feedstock for industrial production and daily life. Wood in the vascular cambium that programs cell death (PCD) through the maturation of cambial derivatives is considered a sequential and complex biosynthetic process driven by the coordinated expression of numerous genes involved in cell division, cell expansion, secondary cell wall (SCW) thickening, and PCD ,2,3. TheDeciphering the genetic architecture of xylogenesis could provide valuable information for increasing wood biomass and improving its properties. With the tremendous research focused on the secondary growth of forest tree growth, the anatomical, physiological, and molecular characteristics of each stage of xylogenesis have been continuously updated from a multi-dimensional perspective . A multiPicea abies [Association mapping, namely linkage disequilibrium (LD) mapping, can identify allelic variations or functional genes significantly associated with complex quantitative traits by inferring the correlation between phenotypic diversity and DNA polymorphisms generated during biological evolution and then analyzing the genetic effect of alleles on phenotypes. Quantitative trait locus (QTL) mapping can be further divided into targeted candidate gene association and genome-wide association studies (GWAS). The latter has been exploited in many plants because of the dramatic reduction in genome sequencing costs. GWAS for complex traits has made significant progress not only in broad-leaved species such as poplar and eucalyptus , but alsea abies . Intriguea abies , chrysanea abies , and eucea abies .Populus in Salicaceae, collectively known as poplars, are widely distributed in the Northern Hemisphere, from the tropics to the far northern boreal latitudes. This genus comprises five taxonomically distinct sections: Leuce Duby, Tacamahaca Spach, Leucoides Spach, Aigeiros Duby, and Turanga Bunge. Among them, the section Aigeiros, comprising P. deltoides, P. nigra, and their hybrid P. × euramericana, is the most important member of Populus for plantation culture worldwide. Both pure species and their hybrids are important commercial timber species in the mid-latitude plate of the Northern Hemisphere with fast growth, straight trunks, and broad adaptability. Poplars are an important model tree species for studying wood formation. Recently, extensive research has been conducted on secondary xylem development through association mapping and genetic manipulation experiments, and the regulatory pathways involved in poplar SCW biosynthesis have been explored.Wood properties are complex quantitative traits controlled by polygenes and vary among species and genotypes. As an ecologically and economically important hardwood species, the species of the genus Populus section Aigeiros, 66 24-year-old trees (64 hybrids and their parents) grown in the same field were resequenced and tested for wood properties. Furthermore, a full-sib family-based GWAS was conducted using the general linear model (GLM) and linear mixed model (LMM). The transcriptional regulation network of these significantly associated genes was further constructed based on the published data of poplar miRNA, transcriptome, and degradome. These results provide new insights into the improvement and cultivation of large-diameter poplar timber plantations.In this study, to decipher the genetic architecture of wood traits and the correlation between wood traits and other growth traits in hybrids of P. deltoides cv. I-69 (♀), P. × euramericana cv. I-45 (♂), and 64 F1 hybrids  and height (H)) and six wood property traits (fiber length (FL), fiber width (FW), double wall thickness (DWT), cell lumen diameter (CLD), air-dry density (AD), and basic density (BD)) in  hybrids . FL, FW,p > 0.05 (2 = 0.94), showing a significant correlation level.The distribution of the eight traits exhibited an approximate bell shape . Using tp > 0.05 , indicatp > 0.05 . The corAll approximately 671.6 Gbp raw reads  were generated through the Illumina whole-genome resequencing of all 66 clones. After filtering, 203,819,202 (51.5×) and 210,409,496 (53.4×) clean reads were obtained from parents I-69 and I-45, respectively. A total of 2,608,389,006 clean reads were generated for 64 F1 hybrids, with a mean sequencing depth of 9.9× each hybrid, ranging from 6.0 to 13.8 . We idenIn total, 332,639 high-quality SNPs were selected under stringent quality control metrics for subsequent LD and GWAS analyses. The mean number of high-quality SNPs was 855 per Mbp window, ranging from 731 per Mbp on Chr19 to 963 per Mbp on Chr08 a. All se2) between intrachromosomal SNP pairs within the full-sib population. We plotted LD decay along the entire poplar genome using 332,639 high-quality SNVs in 66 poplar clones from a full-sib family. LD decay is an index of genetic recombination in the biparental population [2) for the whole genome decayed below 0.2 was approximately 3 Kbp, which was like the LD decay distance of 2.6 Kbp (r2 = 0.2) in P. euphratica [LD was estimated by calculating the squared correlation coefficient  to 311 (FL)  and GLM , have been applied to map QTLs associated with complex traits in plants . In this311 (FL) . The num311 (FL) .The QQ (Q-Q) plot and the genomic inflation factor (λ statistic) are two frequently used indicators for evaluating GWAS models/tools ,27. No op < 0.0005 (P. trichocarpa (v4.1) genome. There were more genes associated with growth traits (177 genes for DBH and 129 genes for H) than with the six wood traits   .P. trichocarpa (v4.1) genome. Many genes belong to the same gene family, including four R2R3-type MYB and three LAC (laccase) family genes. A total of 41 genes were significantly associated with two traits , and only one gene was significantly associated with three traits  (2 = 0.94). A total of 12 genes associated with DBH and H were identified, due in part to the high similarity in the morphogenesis of DBH and H. The GO and KEGG annotations of the 565 significant genes are listed in In total, 342 of the 565 significant genes were assigned functional information in the functional annotation file of the  and BD) . There wWe adopted fragments per kilobase of transcript per million mapped reads (FPKM) values, which were used to measure the expression levels of these candidate genes in two poplar clones: CL290 (clone 290 with fast growing) and CL33 (clone 33 with slow growing). Of the 565 candidate genes, 143 (25.31%) and 165 (29.20%) were highly expressed (Expression > 10) in CL290 and CL33 cells, respectively . We idenA total of 119 candidate genes were regulated by 128 miRNAs , which cp-value [P. deltoides with P. × euramericana for GWAS in this study.Population structure is a frequent consideration in GWAS, and the greater the differences between populations, the easier it is for GWAS to identify them. Therefore, most contemporary studies usually aim to sample natural populations with rich genetic diversity, which is more suitable for GWAS . Howeverp-value , also a p-value . TherefoPopulus, an LD decay ranged from 1.4 to 5.1 Kbp (r2 threshold of 0.2) was reported in P. deltoides [P. euphratica at r2 = 0.2 [2) for the whole genome decayed below 0.2 was approximately 3 kb, indicating that at least 134,000 markers were required [P. deltoides and P. simonii based on good genomic control  [p values [LD is related to genetic recombination and is used to estimate the number of markers required for GWAS . The reseltoides , and an r2 = 0.2 . In thisrequired . This shrequired ,27. Yangal to 1) . Furtherp values . We selep values ,40.Arabidopsis thaliana to have the ability to induce plant-to-embryo transition, the formation of somatic embryos from root explants [A. thaliana to be involved in the transcriptional regulation of secondary xylem formation [A. thaliana), PtrMYB55, and PtrMYB121 [A. thaliana), PtoMYB92, and PtoMYB125 [A. thaliana), PtoMYB170 [A. thaliana) were identified to be involved in regulating secondary cell wall formation. Conversely, PtrMYB6 [MYB genes, NAC transcription factors play an important regulatory role in xylem development [Brassica napus [A. thaliana), were identified to affect secondary wall thickness. Peroxidase (Pox), laccase (LAC), and 4-coumarate-CoA ligase (4CL) are key genes involved in lignin synthesis. The laccase gene LAC17, identified in this study, was characterized in A. thaliana for the lignification of vascular cells [4CL1 gene has been identified as being involved in lignin biosynthesis based on molecular and reverse genetic characterization in P. tremula × P. alba ‘INRA-France 717-1B4′ [Arabidopsis [NAC gene, one LAC gene, and two Pox protein-encoding genes were regulated by miRNAs. These gene-miRNA regulatory pairs may play important roles in wood formation.Wood formation is a complex process in which transcription factors play important regulatory roles in various processes of xylem development, including transcription factor families such as HD-ZIP, MYB, and NAC. The MYB family, as one of the largest families of transcription factors in plants, not only plays a role in plant stress, but also plays an important regulatory role in xylem development, and several MYB transcription factors related to wood formation were associated in this study. Among them, MYB118 has been verified in explants , and to explants . MYB59 iormation . Severalormation ;  and two growth traits (H and DBH), and identified several SNP loci associated with these material traits. Additionally, we constructed a transcriptional regulatory network of SCW biosynthesis through transcriptome analysis. These findings provide a solid scientific foundation for gaining a deeper understanding of the mechanism behind poplar timber formation and for future molecular-assisted breeding efforts.In this study, we performed resequencing using GWAS on 64 full-sibling families derived from a cross between Aigeiros from Europe and the United States were introduced into China. They have had strong adaptability and rapid growth in the plains of Huanghuai and Jianghuai, and in the middle and lower reaches of the Yangtze River [Aigeiros. The full-sib population used in this study was derived from a cross between P. deltoides cv. I-69 and P. × euramericana cv. I-45 in the early 1980s. The full-sib progenies, along with I-69 and I-45, were planted and spaced in a 6 × 6 m configuration at the Zhangji Forest Farm  in Jiangsu Province, China. In total, 66 24-year-old trees (64 progenies and 2 parents) were selected for genome resequencing and wood property measurement.In the 1970s, excellent clones of section ze River . Since tFor each clone, discs (50 mm thick) were collected at ground level and at 1.3 m. The wood was sampled in two ways, the first was to take wedge-shaped strips of the discs cut radially through the center of the circle, and then split the wedge-shaped blocks into matchstick size for fiber morphology determination. The second was to take 2 cm × 2 cm strips from the center of the circle, and then quintupling the samples into five pieces of wood, including early wood and late wood, for the determination of BD and AD.To measure fiber morphology using the Franklin method , the woo3). The samples were then placed in a BINDER/ED Series oven  and dried at 105 °C until constant weight (about 48 h), and then quickly weighed with an L-IC precision electronic balance  (accurate to 0.0001 g) after drying and cooling. BD was calculated according to the following formula: ρ = m0/Vmax where ρ is the basic density of the wood block (g/cm3); m0 is the weight of the wood block at full dryness (g); and Vmax is the saturated volume of the wood block (cm3).BD was determined by soaking the wood blocks in deionized water, changing the water every 2 days to prevent rotting and deterioration, for 2 weeks until the samples were saturated with water. The samples were taken out and the volume of the wood blocks was read on a GP-120W Wood Basic Density Tester   and measuring AD using the sealing wax method on a GP-120W Wood Basic Density Tester (accurate to 0.001 g).P. trichocarpa  genome using bwa-mem2 . Sequence alignment format (SAM) files were converted to binary SAM (BAM) files using SAMtools (v.1.9) [https://github.com/broadinstitute/picard/releases/ accessed on 16 July 2022). Raw DNA variants were called using mpileup implemented in BCF tools . Tri- and tetra-allelic SNPs were discarded from the raw variants. The identified variants were filtered using BCFtools under the following parameters: (a) SNPs within 5 bp of an InDel, (b) SNPs with a missing rate of >5%, (c) SNP with a <5% minor allele frequency (MAF), (d) SNPs detected at an average depth of <5×, and (e) Hardy–Weinberg equilibrium (HWE) test p > 10−6 using PLINK  [Genomic DNA was extracted from young leaves using the DNeasy Plant Mini Kit . DNA libraries were constructed and sequenced on an Illumina HiSeq2000 platform to generate raw paired-end (PE) reads. Raw reads were trimmed using Fastp (v.0.23.2)  with the (v.1.9) . PCR duply 2022) .2 value between the SNP markers was assessed for each of the 19 chromosomes and the entire genome within the full-sib population. The LD decay was plotted using the distance against the average r2 value within 50-Kbp windows the R package ggplot2 (v.3.3.6).These high-quality SNPs in the VCF format were used for the LD decay analysis using PopLDdecay (v.3.42) with default parameters . The avep < 0.0005 was considered significant. Manhattan and quantile–quantile plots (QQ plot) were visualized using the R package CMplot .Four software programs—GLM of GAPIT (v.3.0) , GLM of https://github.com/sinarueeger/GWAS.utils accessed on 5 August 2022) [The top 500 SNPs were selected from the eight traits to compare the models. The intersection of the top SNPs was visualized using the UpSetR R package (v.1.4.0) . The mosst 2022) ,72.P. trichocarpa v4.1 assembly and annotation. Enrichment analyses were performed using the ClusterProfiler R package  [https://cn.string-db.org/ accessed on 5 August 2022). Transcriptome, sRNA, and degradome data were obtained from a previous study [The potential function annotation, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) information were obtained from the st 2022) . Gene neus study .Aigioros full-sib family, using high-quality 332,639 SNPs exceeding the LD-based estimate of markers required for GWAS. We identified 1342 SNPs and 565 protein-encoding genes significantly associated with the eight poplar traits. Published poplar multiomics data  have demonstrated that some of the significantly associated genes, such as NAC, R2R3-type MYB, and lignin-related genes, could be involved in wood formation. These results elucidate information for dissecting the molecular mechanism of wood formation and developing new poplar varieties with large-diameter timber.In this study, we conducted an association study of two growth traits and six woody traits of 66 24-year-old clones from an


This study was to estimate heritabilities, additive genetic correlations, and phenotypic correlations between number of piglets born alive (NBA), litter birth weight (LTBW), number of piglets weaned (NPW) and litter weaning weight (LTWW) in different parities of Landrace (L), Yorkshire (Y), Landrace×Yorkshire (LY), and Yorkshire×Landrace (YL) sows in a commercial swine operation in Northern Thailand.Two models were utilized, a single trait repeatability model (RM) and a multiple trait animal model (MTM). The RM assumed reproductive records from different parities to be repeated values of the same trait, whereas the MTM assumed these records to be different traits. The two models accounted for the fixed effects of farrowing year-season, genetic group of the sow, heterosis, and age at first farrowing, and the random effects of sow, boar, and residual.Heritability estimates from RM were 0.02±0.01 for NBA, 0.10±0.01 for LTBW, 0.04±0.01 for NPW, and 0.11±0.01 for LTWW. Heritability estimates from MTM fluctuated across parities, ranging from 0.04±0.01 in parity 2 to 0.09±0.02 in parity 4 for NBA, 0.07± 0.02 in parity 2 to 0.16±0.02 in parity 3 for LTBW, 0.04±0.02 in parity 4 to 0.08±0.01 in parity 1 for NPW, and 0.16±0.02 in parity 1 to 0.20±0.02 in parity 2 for LTWW. Additive genetic correlation estimates from MTM were also variable, ranging from 0.29±0.24 between NBA in parity 1 and NBA in parity 2 to 0.99±0.05 between LTWW in parity 3 and LTWW in parity 4.The findings of this study highlight the advantage of using MTM for the genetic improvement of reproductive traits in swine and contribute to the development of sustainable swine breeding programs in Thailand. Reproduction traits are critical factors for maximizing sow productivity and ensuring the sustainability of commercial swine operations. Key traits such as the number of piglets born alive (NBA), litter birth weight (LTBW), number of pigs weaned (NPW), and litter weaning weight (LTWW) play pivotal roles in swine selection programs across diverse temperate and tropical environmental conditions –3. In ThRepeatability models have conventionally served as a cornerstone in swine genetic evaluations, capitalizing on the availability of repeated records of productive performance and pedigree data –7. ThesePrevious studies ,7,9–12 hTherefore, the primary objective of this research was to conduct a comprehensive comparison of genetic parameters for NBA, LTBW, NPW, and LTWW across different parities within a commercial swine population in Northern Thailand. By employing both the traditional repeatability model and the more sophisticated MTMs, this study aims to elucidate the benefits and suitability of MTM in the context of swine genetic evaluation under Thai environmental conditions. Emphasizing the advantages of the MTM approach, this research seeks to provide compelling evidence of its effectiveness in capturing the complexity and variability associated with swine reproduction traits in Thailand. The outcomes of this study hold significant implications for advancing swine genetic evaluation practices because they highlight the potential advantages offered by MTMs. By demonstrating their suitability and enhanced accuracy in capturing the underlying genetic architecture, this research contributes to the ongoing efforts aimed at refining genetic selection strategies for reproduction traits. Ultimately, these advancements have the potential to drive more precise and reliable breeding programs, leading to improved sow productivity and the establishment of sustainable and efficient swine production systems.The sows utilized in this study were sourced from the reproductive herd of a commercial pig farm, which adhered to the good agricultural practices as prescribed by the National Bureau of Agricultural Commodity and Food Standards. The performance data of the sows were extracted from a comprehensive and regularly updated database maintained by the farm. Ethical clearance for the study was obtained from the Institutional Animal Care and Use Committee of Kasetsart University, with approval number ACKU60-AGR-007.This study was approved by the IACUC of Kasetsart University . The data for this study were obtained from a swine database recording system (PigCHAMP) in a single farm located in Northern Thailand. The dataset comprised sow reproduction records from the first to the last or tenth parity containing identification number, breed group, sire, dam, birth date, farrowing date, weaning date, NBA, LTBW, NPW, and LTWW. To ensure data quality, the original dataset of 14,604 sows underwent thorough editing to remove records with missing, erroneous, or incomplete information. Sows without recorded birth date, farrowing date, or weaning date were excluded, as well as gilts with a first farrowing before 350 days or after 550 days. Only litters with at least first parity records were included in the analysis, and parities greater than 10 were excluded. Approximately 33% of records were excluded from the original dataset due to incomplete information. This rigorous data editing process ensured the reliability and integrity of the dataset and set the foundation for robust and meaningful analyses. The final edited dataset encompassed 49,145 reproduction records from 9,830 sows and 1,359 boars. Reproduction data were gathered from July 1989 to December 2013. Four breed group of sows were included in the analysis: Landrace , Yorkshire , Landrace×Yorkshire , and Yorkshire×Landrace . Purebred L and Y sows were the progeny of 640 sires (395 L and 245 Y) and 1,319 dams (895 L and 424 Y), and crossbred LY and YL sows were the progeny of 969 sires (608 L and 361 Y) and 2,674 dams . A total of 260 Duroc (D) boars were represented in the dataset for the three-breed terminal crosses. Four reproduction traits  were considered for analysis.This study was conducted at a single commercial swine farm located in Northern Thailand, between latitude 18° 47′ 43″ North and longitude 98° 59′ 55rime; East, at an elevation of 310 meters above sea level. The farm experienced three distinct seasons, namely winter (November to February), summer (March to June), and rainy (July to October). Over the course of the 24-year study period, the average outdoor temperature in this region ranged from an average minimum of 17°C to an average maximum of 35°C, with relative humidity ranging from an average minimum of 37% to an average maximum of 99%. The average annual rainfall varied from an average minimum of 880 mm to an average maximum of 1,457 mm over the past thirteen years.Ad-libitum water was provided to the animals through water nipples. Gilts and non-lactating sows were fed twice a day and had an approximate intake of 2.5 kg/d of feed containing 16% crude protein and 3,200 to 3,500 kcal/kg. Nursing sows were fed four times a day and had an approximate intake of 5 to 6 kg/d of feed containing 17% to 18% crude protein and 4,060 kcal/kg.Gilts and sows were reared in an open-house system equipped with water drippers, sprinklers, and fans, while boars were housed in an evaporative cooling system (EVAP) to mitigate the effects of the tropical climate. Females that had their first farrowing in the same year-season were assumed to have received similar feeding and management. Estrus was detected twice daily utilizing the back-pressure test and boar exposure as standard protocols. Gilts and sows exhibiting signs of standing heat in front of the boar, along with clear reddening and swelling of the vulva, were identified as being in estrus. Artificial insemination was the sole method of mating employed in this study. Gilts were mated after their third observed estrus or at 8 to 9 months of age, or when their body weight reached approximately 140 kg. Sows were mated after their second observed estrus. The first insemination was carried out within 12 hours of the onset of estrus, followed by a second insemination 12 hours later. Sows were housed in individual stalls during mating and gestation and kept in individual pens along with their litters during lactation. Pregnant gilts and sows were housed in gestating stalls until 7 days prior to being moved to farrowing pens. Sows were transferred to mating stalls after weaning. Piglets were weaned when they reached a body weight of 5 to 7 kg or were between 26 and 30 days of age.Variance and covariance components were estimated using restricted maximum likelihood (REML) procedures, employing the Average Information Restricted Maximum Likelihood (AI-REML) algorithm implemented in the ASREML program . The prey is a vector of records for NBA, LTBW, NPW, or LTWW, b is a vector of first farrowing year-seasons, covariates for age at first farrowing (mo), parity of sow, and days to weaning (d), ga is a vector of regression additive genetic group effects (difference between Y and L as a function of Y fraction), gn is a vector of heterosis effects of the sow and the litter, aa is a vector of random animal additive genetic effects, X is an incidence matrix of ones and zeroes that relates sow records to elements of vector b, pe is a vector of random permanent environment effects uncorrelated to animal additive genetic effects, Zga is an incidence matrix of expected Y fractions of sows that relates sow records to elements of vector ga, Zgn is an incidence matrix of heterozygosities of the sow and the litter that relates sow records to elements of vector gn, Za is an incidence matrix of ones and zeroes that relates sow records to elements of vector aa, W is an incidence matrix of ones and zeroes that relates sow records to elements of vector pe, and e is a vector of residuals. Expectations and (co)variance matrices of random variables in the mixed repeatability model were:where A is the is the additive relationship matrix among all animals in the pedigree file , I represents identity matrices for permanent environmental and residual effects, where Multiple trait analyses were conducted for two data sets. The first dataset included NBA, LTBW, NPW, and LTWW records from the first to the fourth parity. The NBA, LTBW, NPW, and LTWW records from different parities were considered to be different traits. Thus, a 4-trait mixed animal model analysis was conducted for each reproduction trait . The 4-trait mixed animal model in matrix notation was as follows:y is the vector of records for each reproduction trait  in parities 1 to 4, b is a vector of contemporary group (first farrowing year-season) effects and covariates for age at first farrowing (mo) and days to weaning (d), ga is a vector of regression additive genetic group effects (difference between Y and L as a function of Y fraction), gn is a vector of heterosis effects of the sow and the litter, aa is a vector of random animal additive genetic effects, e is a vector of random residuals, X is an incidence matrix of ones and zeroes that relates sow records to elements of vector b, Zga is an incidence matrix of expected Y fractions of sows that relates sow records to elements of vector ga, Zgn is an incidence matrix of heterozygosities of the sow and the litter that relates sow records to elements of vector gn, and Za is an incidence matrix of ones and zeroes that relates sow records to elements of vector aa. The assumptions of the 4-trait mixed animal model were as follows:where Ga = G0 ⊗ A, G0 is a 4×4 matrix of additive genetic variances and covariances for a single reproduction trait  in parities 1 to 4, ⊗ is direct product, and A is the additive relationship matrix among all animals in the pedigree file . The pedigree file included all sows, all boars mated to L sows , Y sows , and LY and YL sows , and all known relatives. Lastly, matrix R = R0 ⊗ I, where R0 is a 4×4 matrix of residual variances and covariances for a single reproduction trait  in parities 1 to 4, and I is an identity matrix.where 2) is a measure of the proportion of the phenotypic variance attributed to additive genetic factors, calculated as the ratio of the additive genetic variance ), which quantifies the joint variability due to additive genetic factors, is divided by the square root of the product of the additive genetic variances (vara(x) and vara(y)) for each respective trait. This calculation yields the additive genetic correlation (ra) between the two traits. Similarly, this methodology can be applied to determine phenotypic correlations, which capture the overall association between traits, including both genetic and environmental influences.The estimation of heritabilities, additive genetic correlations, and phenotypic correlations involves analyzing the variance and covariance components associated with the four parities for all reproduction traits. Heritability  were classified into three groups in the second dataset. Group 1 included first parity records only, group 2 contained second parity records only, and group 3 contained sums of NBA, LTBW, NPW, and LTWW records from the third to the last parity. Reproduction records in parities 1, 2, and 3+ (third and later parities) were considered to be different traits. Thus, a 3-trait mixed animal model was utilized to analyze each reproduction trait in the second dataset. The expression for the 3-trait mixed animal model is as in equation 2. The assumptions for the 3-trait mixed animal model were as follows:Ga = G0 ⊗ A, G0 is a 3×3 matrix of additive genetic variances and covariances for a single reproduction trait  in parities 1 to 3+, matrix R = R0 ⊗ I, R0 is a 3×3 matrix of residual variances and covariances for a single reproduction trait  in parities 1 to 3+, and all other vectors and matrices are as defined for the 4-trait mixed animal model used for the first dataset. Similarly, the estimated variance and covariance components were used to compute heritabilities, additive genetic correlations, and phenotypic correlations between the three parities for the four reproduction traits.where The estimates of additive genetic, permanent environmental, temporary environmental, and phenotypic variance components for NBA, LTBW, NPW, and LTWW in parities 1 to 4 computed using a single trait mixed animal repeatability model are presented in 2 in parity 2 to 0.51±0.13 piglets2 in parity 4 for NBA, from 0.98±0.26 kg2 in parity 2 to 3.10±0.43 kg2 in parity 3 for LTBW, from 0.17±0.07 piglets2 in parity 4 to 0.42±0.08 piglets2 in parity 1 for NPW, and from 56.85±6.37 kg2 in parity 1 to 73.82±7.67 kg2 in parity 2 for LTWW. Environmental variances ranged from 5.38±0.12 piglets2 in parity 2 to 6.34±0.12 piglets2 in parity 1 for NBA, from 13.77±0.32 kg2 in parity 2 to 17.46±0.50 kg2 in parity 4 for LTBW, from 3.53±0.08 piglets2 in parity 3 to 5.01±0.10 piglets2 in parity 1 for NPW, and from 281.23±8.21 kg2 in parity 4 to 298.11±6.65 kg2 in parity 1 for LTWW. Phenotypic variances ranged from 5.58±0.09 piglets2 in parity 2 to 6.64±0.10 piglets2 in parity 1 for NBA, from 14.74±0.25 kg2 in parity 2 to 19.95±0.38 kg2 in parity 4 for LTBW, from 3.74±0.07 piglets2 in parity 3 to 5.43±0.08 piglets2 in parity 1 for NPW, and from 339.00±6.46 kg2 in parity 4 to 364.60± 6.06 kg2 in parity 1 for LTWW. These estimates of variance components show the variability in additive genetic, environmental, and phenotypic variances across parities, highlighting the importance of considering parity-specific estimates when analyzing reproduction traits in swine populations.The estimates of additive genetic, environmental, and phenotypic variance components for NBA, LTBW, NPW, and LTWW in parities 1 to 4 computed using a 4-trait mixed animal model are presented in Estimates of additive genetic correlations between NBA, LTBW, NPW, and LTWW in the first four parities were positive and moderate to high for all reproduction traits. Values ranged from 0.29±0.24 between parities 1 and 2 to 0.96±0.20 between parities 2 and 4 for NBA, 0.72±0.01 between parities 1 and 4 to 0.93±0.07 between parities 3 and 4 for LTBW, 0.52 ±0.20 between parities 1 and 4 to 0.91±0.17 between parities 2 and 3 for NPW, and 0.68±0.08 between parities 1 and 3 to 0.99±0.05 between parities 3 and 4 for LTWW. Conversely, phenotypic correlations between the parities 1 to 4 were positive and low for the four reproduction traits, with values ranging from 0.05±0.01 between parities 1 and 2, 1 and 3, and 2 and 3 for NPW to 0.27±0.01 between parities 3 and 4 for LTWW. Additive genetic correlations between reproduction traits were positive and moderate to high across parities, indicating a shared genetic influence. In contrast, phenotypic correlations between parities were positive but low, suggesting the presence of additional environmental or management factors influencing the observed correlations.The estimates of additive genetic, environmental, and phenotypic variance components for NBA, LTBW, NPW, and LTWW obtained from dataset 1 using the 4-trait mixed animal model varied across parities. However, there was no consistent pattern regarding which parity exhibited the minimum and maximum values. These fluctuations in variance component estimates among parities present an intriguing challenge when attempting to interpret their broader implications. This challenge stems from the fact that these estimates are influenced by the unique genetic and environmental characteristics inherent to each specific swine population. Understanding the underlying factors driving the observed differences in variance component estimates across parities is crucial for accurate genetic evaluations and informed breeding decisions. The genetic architecture, including the presence of specific genes or gene combinations, may contribute to the variations observed. Additionally, environmental factors such as management practices, nutrition, and housing conditions can significantly impact the expression of reproductive traits. Consequently, the interplay between genetics and the environment complicates the interpretation of variance component estimates.Further investigation is necessary to unravel the specific mechanisms influencing differences in variance components among parities. This may involve examining additional factors, such as maternal effects, gene-by-environment interactions, and potential genotype-environment correlations. By delving deeper into the underlying genetic and environmental influences, we can enhance our understanding of the complex dynamics governing reproductive traits in swine populations. Therefore, caution should be exercised when making broad generalizations based solely on the observed differences in variance component estimates among parities. Instead, a comprehensive analysis considering the unique characteristics of each swine population is warranted to unravel the underlying factors and provide a more nuanced interpretation of the results.The heritability estimates for NBA, LTBW, NPW, and LTWW in our study were compared to previous research conducted in different populations, revealing both similarities and differences –17. The Heritability estimates for NBA in different parities differed from those obtained by Skorput et al  in BlackOur findings demonstrate the complex nature of heritability estimates for NBA, LTBW, NPW, and LTWW, which are influenced by genetic and environmental factors specific to each population. The observed trend of increasing heritability estimates as parity number increases for certain traits highlights the potential for genetic improvement in later parities. However, caution must be exercised when making comparisons across studies due to differences in population characteristics and modeling approaches. Collectively, these findings contribute to our understanding of the genetic basis of reproductive traits in swine populations and provide insights for future breeding and selection strategies.The analysis of additive genetic correlations between NBA, LTBW, NPW, and LTWW across parities from datasets 1 and 2 revealed intriguing patterns and provided insights into the reproductive traits of the swine population under study. Notably, we found a higher level of additive genetic association between reproduction traits in the first parity and subsequent parities at weaning than between reproduction traits at birth. This observation suggests that the genetic influences on these traits become more evident as sows progress through subsequent parities. In contrast, phenotypic correlations between reproduction traits in the first and subsequent parities were consistently low at both birth and weaning, indicating limited phenotypic associations between these traits in this particular swine population. While these phenotypic correlations provide less information, additive genetic correlations offer valuable insights into the genetic associations among the traits.Comparing our estimates to those from previous studies, we found that the additive genetic correlation between NBA in the first parity and the sum from the third to the last parity in this population 0.11±0.19) was lower than those reported 0.84±0.05) for Iberian pigs in Spain  and a La±0.19 was4±0.05 foThe consistency of these estimates across multiple swine populations and different parities suggests that additive genetic correlations across parities tend to have moderate to high positive values. This implies that the information from earlier parities can greatly enhance the accuracy of sow additive genetic predictions in later parities. Therefore, selecting replacement sows based on reproductive information from all parities is expected to improve the lifetime productivity of sows in terms of the number of piglets and litter weights. These findings provide valuable insights for breeding programs and selection strategies aimed at enhancing sow lifetime productivity. By considering the additive genetic associations across parities, breeders can make more informed decisions and improve the efficiency of genetic selection. Furthermore, the observed patterns of genetic correlations shed light on the underlying genetic architecture of reproductive traits in this swine population and contributed to our understanding of the complex interplay between genetics and reproduction in pigs.Our investigation into the heritability values of NBA, LTBW, NPW, and LTWW across different parities using MTM has revealed important insights into the genetic improvement potential of these reproductive traits in this Thai swine population. Importantly, we observed that the heritability estimates obtained with the MTMs were consistently higher than those obtained with the repeatability model for all four traits. This indicates that utilization of a MTM has a greater impact on genetic progress for NBA, LTBW, NPW, and LTWW than the repeatability model in this swine population. The higher heritability values, along with the positive moderate to high additive genetic correlations observed between parities for these traits, further emphasize the need to consider first, second, and later parities as distinct traits in genetic evaluations. This finding is consistent with previous research conducted by Roehe and Kennedy , HermescIn conclusion, this study demonstrates that utilizing a multiple trait model leads to higher estimates of heritabilities for NBA, LTBW, NPW, and LTWW in this swine population compared to a repeatability model. This underscores the significance of considering first, second, and later parities as separate traits within swine genetic improvement programs. The positive additive genetic correlations observed among different parities for all reproduction traits provide a basis for optimizing breeding strategies and enhancing the lifetime productivity of sows. By adopting a multiple trait approach and acknowledging the distinct genetic contributions to each parity, swine breeders in Thailand can make more accurate selection decisions while achieving sustainable improvements in reproductive performance. Further research involving multiple swine operations would contribute to the continued development of effective and sustainable swine breeding programs in the country, thus further advancing the field of swine genetic evaluation and selection.


Results were followed up with functional characterisation using a drosophila model. We identified genome-wide associated SNPs at 8q13.3 (P = 7.5 × 10−9) and 10p11.21 (P = 3.6 × 10−8). The 8q13.3 locus colocalizes with SLCO5A1 expression quantitative trait loci in cerebral cortex (P = 9.5 × 10−3). SLCO5A1 codes for an organic anion transporter and upregulates synapse assembly/organisation genes. Pathway analysis demonstrates 12.7-fold enrichment for presynaptic membrane assembly genes (P = 0.0005) and 14.3-fold enrichment for presynaptic organisation genes (P = 0.0005) including NLGN1 and PTPRD. RNAi knockdown of Oatp30B, the Drosophila polypeptide with the highest homology to SLCO5A1, causes over-reactive startling behaviour (P = 8.7 × 10−3) and increased seizure-like events (P = 6.8 × 10−7). Polygenic risk score for ADHD genetically correlates with impulsivity scores in JME (P = 1.60 × 10−3). SLCO5A1 loss-of-function represents an impulsivity and seizure mechanism. Synaptic assembly genes may inform the aetiology of impulsivity in health and disease.Elevated impulsivity is a key component of attention-deficit hyperactivity disorder (ADHD), bipolar disorder and juvenile myoclonic epilepsy (JME). We performed a genome-wide association, colocalization, polygenic risk score, and pathway analysis of impulsivity in JME ( Estimates of heritability for impulsivity from a study of twins were between 33% and 56% at ages 11–13 years, and between 19% and 44% at ages 14–162. Raised impulsivity is a key endophenotype of attention-deficit hyperactivity disorder (ADHD)3, bipolar disorder4 and juvenile myoclonic epilepsy (JME)7. ADHD is characterised by inattention, hyperactivity and impulsivity. Individuals with ADHD show more signs of impulsivity  compared to controls8. A previous genome-wide association study (GWAS) of impulsive personality traits  in 22,861 healthy individuals of European ancestry demonstrated two significant associated loci at 3p12.1 and 22q13.19. Variants at the 3p12.1 locus correlated with predicted Cell Adhesion Molecule–2 (CADM2) expression, in the putamen10, and the 22q13.1 locus near CACNA1I has been previously implicated in schizophrenia11. CADM2 mediates synaptic signalling and is highly expressed in the human cerebral cortex and cerebellum12. Given impulsivity is elevated in neuropsychiatric disorders, there may be shared genetic mechanisms across disorders and/or with impulsivity in the general population, however to our knowledge there has been no GWAS of impulsivity in any neuropsychiatric disorder.Impulsivity is a heritable behavioural trait leading to actions that are “poorly conceived, prematurely expressed, unduly risky, or inappropriate to the situation and that often result in undesirable consequences”7. JME is a common adolescent-onset syndrome characterised by awakening myoclonic, generalised tonic-clonic and absence seizures, often triggered by sleep deprivation. Trait impulsivity in JME is associated with the frequency of both myoclonic and absence seizures6, but it is not clear if this indicates a causal relationship or a common mechanism regulating both impulsivity and seizures, though convergent lines of evidence suggest the involvement of overlapping prefrontal-striatal networks in both JME and impulsivity20. Finding a shared aetiology would offer new therapeutic approaches for drug-resistant epilepsy.Impulsivity is elevated in different epilepsies, but the evidence across multiple dimensions of impulsivity is strongest in JME22, and other loci with less support24. A major challenge for epilepsies of complex inheritance is to explain the wide variation in phenotypic expression and treatment response between individuals. Forty-percent experience antiseizure medication (ASM) resistance or intolerance25. In addition, no current ASM modifies the lifelong disease course of JME and the pharmacological options are sparse, especially for women25. Hence novel treatments based on genetic disease mechanisms, such as those emerging for monogenic channelopathy and mTOR pathway epilepsies, are urgently needed27. Our methodological approach is to carry out genome-wide analysis of endophenotypes in JME such as impulsivity and clinically relevant outcomes such as ASM resistance, a strategy with predicted advantages for reducing heterogeneity, increasing statistical power29 and improving direct clinical translation for precision medicine.The overall syndrome of JME has complex inheritance with few replicated susceptibility locin = 324) and a mega-analysis including all ancestries (n = 372), who self-rated their trait impulsivity using the Barratt Impulsivity Scale, eight-item BIS-Brief version30. We conducted a GWAS of BIS-Brief score  with stronger evidence of association in the combined ancestry mega-analysis as measured by the p value  for rs73293634 was 10.1% in the European analysis. Although a second JME cohort with impulsivity measured is not available for replication, Watanabe et al. 31 reported a rs73293634 association with risk taking in the UK Biobank, where they asked the question “Would you describe yourself as someone who takes risks?” (OR (95% CI) = 1.032 (1.001–1.065), p = 0.04, minor allele frequency (MAF) = 0.03, N = 371,049). Association results posted on the same data by the Neale Lab32 with ~23 K fewer participants, provides a similar qualitative conclusion (β (SE) = 0.005 (0.003), p = 0.09, MAF = 0.03, N = 348,549). Two individuals with large structural deletions that include SLCO5A1 are reported in the Decipher Genomics database with seizures and neurodevelopmental disorder (www.deciphergenomics.org/gene/SLCO5A1/patient-overlap/cnvs).rs73293634 falls in an intergenic region near PARD3 (NM_001184785.2). The PVE by the SNP is 9.3%, although there are no variants in linkage disequilibrium with this SNP so further interrogation and confirmation of this locus is required. We note, however, that significant linkage  was previously reported to this locus in French-Canadian families with idiopathic generalised epilepsy (IGE)33, of which JME is a common subtype. As well, rs75042057 was also associated with risk-taking in the UK Biobank (OR (95% CI) = 1.067 (1.029–1.106), p = 4.79E-4, MAF = 0.02, N = 371,049)31.The significant genome-wide association on chromosome 10 (rs75042057) falls in intron 22 of 12, PsychENCODE34, and human fetal brains35 and combined them with the GWAS summary statistics from the mega-analysis, for colocalization analysis adjusting for multiple hypothesis testing36. Colocalization analysis with eQTLs from GTEx brain and tibial nerve tissues for genes at the locus  shows significant colocalization with SLCO5A1 in the cerebral cortex, and no colocalization with other genes in the region . The minor allele for the lead SNP rs73293634 (T) decreases expression in GTEx cerebral cortex  in brain tissues. We used eQTLs from the Genotype-Tissue Expression project (GTEx) v8tex Fig. . We founion Fig. . Accordiood Fig. . We did SLCO5A1 is a membrane-bound organic anion transporter with no known substrate39 . SLCO5A1 was the closest human analogue of Oatp30B also in a reverse BLASTp. Indeed, BLASTp of Oatp30B polypeptide sequence (Q9VLB3) across all species for conserved domains reveals this gene has conserved major facilitator superfamily (MFS), OATP, and Kazal domains :0097090) and a 14.3-fold enrichment of associated genes from the presynaptic membrane assembly gene set . These genes were PTEN, NLGN1, PTPRD, IL1RAPL1 and NLGN4X (Table SLCO5A1 locus and rs75042057 from the PARD3 locus).We next sought to assess whether there was additional signal in the GWAS where sub-GWAS significant variants could inform additional contributing genes or pathways and whether there were shared genetic contributions with other psychiatric or epilepsy phenotypes. We selected all variants displaying p values of 0.0005.The permutation tests of presynaptic membrane organisation (GO:0097090) over-enrichment and of presynaptic membrane assembly (GO:0097104) over-enrichment both produced permutation-based 45 that there was a significant overlap with genes reported in the GWAS catalogue that contribute to phenotypes relevant to the predominance of JME seizures on awakening, impulsivity and metabolism: chronotype , obesity-related traits , general risk tolerance , and adventurousness .Investigation of these 810 genes revealed furtherp = 1.60 × 10−3)  = 0.9481(0.0562), p = 0.34)46. The rs75042057 SNP on Chr10 was also not present in the ADHD dataset nor was there a proxy with R2 > 0.6 available. The risk-taking PRS was also nominally associated with a higher BIS-Brief score (p = 0.018). PRSs for bipolar disorder, generalised and focal epilepsy did not reach statistical significance for association with BIS-Brief score at the 5% or Bonferroni-corrected level of 1%   was significantly associated with a higher BIS-Brief score . Genome-wide summary statistics were not available to make additional comparisons. Genome-wide summary statistics were available for the risk-taking phenotype in the UK Biobank31, in which we observed replication of our lead genome-wide significant SLCO5A1 variant, rs73293634.One GWAS of impulsive traits in the general population identified genome-wide significant association with variants in the SLCO5A1 upregulates gene sets implicated in cell adhesion, synapse assembly and organisation, principally belonging to the cadherin superfamily39; and the enrichment for presynaptic membrane assembly and organisation pathways in our dataset includes genes encoding trans-synaptically interacting proteins that are implicated in a wide range of neuropsychiatric disorders50. Genetic correlation between ADHD and the BIS-Brief score suggests converging genetic influences across ADHD and epilepsy. Taken together, these results support an important role for specific cell recognition molecules in the organisation of synaptic connections as a mechanism for variation in impulsivity across health and disease51.Previous expression studies show that 20, a role for these limbic networks has only been hinted at in epilepsy. Striato-nigral circuits, preferentially involving the ventral striatum, have long ago been implicated in the regulation of generalised seizures in rodent models of generalised epilepsy19. Recently, an initiating role for cortico-striatal networks in absence seizures with generalised spike-and-wave discharges has been shown in the mouse model of genetic epilepsy caused by haploinsufficiency of STXBP152, specifically by reduced cortical excitatory transmission onto striatal fast-spiking interneurons. The startling and the seizure-like phenotype of the SLCO5A1/Oatp30B knockdown in Drosophila suggests the genetic co-causality of startling and seizures. While it is not possible to define startling as the Drosophila equivalent of impulsivity, the two traits share some commonality in the lack of moderation in behaviour. This offers some additional support to the idea that excitatory-inhibitory imbalance in the prefrontal-striatal network may predispose simultaneously to epilepsy and impulsivity substrates and invites new approaches to neuromodulation of generalised seizures.While prefrontal-striatal inhibitory control networks are implicated in impulse control, specifically between mPFC and nucleus accumbensn = 864)25. Inclusion criteria have been discussed previously6. BIOJUME is a study across 50 sites in 10 countries (Appendix). Furthermore, all participants’ medical history was reviewed by a phenotyping committee to validate the diagnosis of JME. Written informed consent was obtained from all participants prior to inclusion in the study and ethical approval from the UK Health Research Authority, South Central Oxford C Research Ethics Committee (16/SC/0266) and all other collaborating sites was obtained. The SickKids Research Ethics Board of The Hospital for Sick Children (1000033784) also gave ethical approval for this work.We collected cross-sectional clinical and genetic data from the Biology of Juvenile Myoclonic Epilepsy (BIOJUME) consortium study, which focuses on gathering cases with JME  includes 30-items measuring 3 theoretical subtraits: attentional, motor, and non-planning impulsiveness. BIS-Brief is a unidimensional scale including 8 of the original BIS-11 items generating a total score ranging from 8 to 32. BIS-Brief demonstrated similar indices of construct validity observed for the BIS-11 total score. Using BIS-Brief in large epidemiological studies of psychiatric disorders reduces the burden on respondents without loss of information29.We collected self-rating of trait impulsivity through the BIS-briefn = 702) using the Illumina Omni 2.5 array. SNPs were called using the self-clustering method in Genome Studio. We performed quality control (QC) for each genotyped batch using PLINK v1.90b6.1853 and custom in-house scripts. Briefly, we removed individuals and variants with call rates below 90%; samples with sex mismatches and/or high heterozygosity; males with heterozygous calls for X chromosome markers ; and females with non-missing calls for markers on the Y chromosome. We retained heterozygous calls for mitochondrial markers in both sexes . We obtained an unrelated sample by using KING v.2.2.4 software’s54 --unrelated option . We corrected and updated the ped file with all found relationships, and identified markers with Mendelian errors using PEDSTATS 0.6.1255. We flagged 399 markers but did not remove those out of Hardy–Weinberg Equilibrium (P < 10−4). We conducted principal component analysis adjusted using the kinship matrix output by KING using PC-AiR in the GENESIS v2.16.0 package56.DNA was extracted from blood by each consortium site and sent to The Centre for Applied Genomics at The Hospital for Sick Children in Toronto for genotyping. We genotyped participants’ DNA in four batches (www.well.ox.ac.uk/~wrayner/strand/), then merged all batches. We re-analysed and removed cryptic relationships across batches. The final merged set contained 1,489,917 variants, 695 individuals  including 23 related pairs .We performed quality control on each genotyping batch separately, followed by removal of ambiguous A/T, G/C SNPs, chr0 SNPs, indels, monomorphic variants, and duplicate variants; and performed strand alignment using Will Rayner’s alignment files (www.well.ox.ac.uk/~wrayner/tools/HRC-1000G-check-bim-v4.3.0.zip) using TOPMed as the reference panel (r2@1.0.0) on the TOPMed imputation server59. We converted coordinates from hg37 to hg38 coordinates using strand files (www.well.ox.ac.uk/~wrayner/strand/InfiniumOmni2-5-8v1-4_A1-b38-strand.zip). We merged the pseudoautosomal region (PAR) using PLINK’s --merge-x option and checked variants using the HRC checking tool. We removed a total of 282,660 variants due to no matches in the reference , and 1,739,329 variants remained for imputation on the server. We used Eagle v2.4 for phasing, and minimac v4 v1.0.2 for imputation. We kept variants with imputation quality score r2 > 0.4 and MAF > 1% for analysis. A total of 8,950,360 variants remained for association analysis.We used the McCarthy Tools v4.3.0 to prepare the genotype data for imputation (n = 376). The mega-GWAS analysis consisted of a total of 372 unrelated individuals adjusted for sex, genotyping batch, and population stratification . Among these, five patients had missing information on seizure frequency, so we used 324 individuals for the genome-wide association analysis. The current sample size is sufficient to detect genetic variants that explain 12% of the variance in the BIS-Brief score with 80% power after adjusting for multiple hypothesis testing at the genome-wide significance level. We adjusted for sex, genotyping batch, age at consent, population stratification, and the frequency of myoclonus or absence seizures. The relationship of the frequency of myoclonus or absence seizures, and its relationship with ASM and sex with trait impulsivity in JME, has been described previously and was thus adjusted for in current regression analyses25. All analyses were conducted in the European subset unless noted otherwise. Chromosome X  was analysed with males coded as zero for the reference allele and two for the alternate allele, under the assumption of X-inactivation61. Genome-wide significant loci were further investigated for replication of association with risk-taking phenotypes in the general population using publicly available summary statistics32.We included for analysis 381 individuals who passed phenotype QC with complete BIS-Brief rating. From these, four failed genotyping QC, and one individual was removed due to cryptic relatedness (P ≤ 5 × 10−4 were annotated to the gene with the nearest transcription start site using the Ensembl Variant Effect Predictor (v94)62. This gene set was used as input in a GO enrichment analysis64, to test for enrichment in annotated pathways. One-sided hypergeometric tests were completed to identify over-representation of pathways42. To reduce the risk of false positive results, a permutation procedure65 was employed by randomly shuffling GWAS p values 2000 times, each time re-applying the P ≤ 5 × 10−4 threshold and calculating the hypergeometric test statistics. For one pathway, the final permutation-based p value was calculated as the percentage of the 2000 permutations that produced a p value less than or equal to the p value calculated from the non-permuted data. A pseudo count was added during this calculation to prevent calculating p values equal to 0.Variants with https://atlas.ctglab.nl/), Global Biobank Engine66, PheWeb67, and Gene Atlas68.We queried the top associated genome-wide variant and the top associated variant for each of the nine presynaptic assembly enriched genes across PheWAS databases: GWAS Atlas (https://pheweb.org/MGI-freeze2/UK Biobank: http://big.stats.ox.ac.uk/Oxford Brain Imaging Genetics (BIG) Project: https://yanglab.westlake.edu.cn/resources/ukb_fastgwa/imp/fastGWA: https://pheweb.org/UKB-SAIGE/We used PheWeb portals:53. Five PRS were calculated. A Bonferroni-corrected critical value for significance would therefore be p < 0.05/5 = 0.01. The source of summary statistics used, variant filtering, clumping and thresholding details are summarised in Supplementary Table 10(OR)/Beta), and then summing over the variants. The PRS values were then centred to the mean. Association of PRSs with BIS-Brief was tested using linear regression with age, sex, and frequency of absence/myoclonic seizure as covariates in the model.Clumping and thresholding were used to calculate ADHD, risk taking, bipolar disorder, generalised epilepsy, and focal epilepsy PRS in individuals of European ancestry using PLINK v1.936 and COLOC269 colocalization methods as implemented in LocusFocus70 (v1.4.9) to test for colocalization of the genome-wide peaks with eQTL analyses in brain tissues in GTEx v812, PsychENCODE34, and fetal brain35. For the genome-wide associated locus on chromosome 8, we performed colocalization analysis using both the mega-GWAS and Europeans-only GWAS. The required significance threshold, after multiple testing of all colocalization datasets analysed was 0.01.We used the Simple Sum 2E values > 0.002, clearly distinguishing between significant and non-significant hits. 7EEB is a large complex containing several subunits, among which is SLCO6C1, which is the region scoring for SLCO5A1.A BLAST search against the entire Protein Data Base (PDB) identified only one hit with a convincingly high E-value (1e-55) that pointed to the Chain L of the Kazal-like domain containing mice protein (7EEB). The search had a 26% identity and a coverage of 74%. After this hit, the other four identified sequences had r2 as the proportion of the residual sum of squares (RSS) reduced when adding the SNP (or group of SNPs) to the base regression model with all covariates.To assess the PVE by a SNP or a group of SNPs, we calculated the partial Flies were maintained and crossed at 18 °C. All ageing was done in a controlled environment of 29 °C and 60% humidity.ubiGal80ts // UAS-Oatp30BIR (GD12775) obtained from the VDRC // w1118, nSyb-Gal4, TubGal4 and UAS-GFPIR obtained from the BDSC.41. All crosses were maintained at 18 °C during the developmental stages of the progeny. Newly eclosed adult flies were collected within 5 days at 18 °C. Females and males were pooled together and equally distributed within vials.Lifespan analysis was performed as previously reported41. In each of three experiments, up to 12 flies per genotype, aged 15 days (adult stage) at 29 °C to allow RNAi expression and knockdown, were placed into individual round six-wells arenas. The protocol used consisted of 6 stimuli events equally split during a period of 2 h and 15 min, the first one starting after 30 min of recording, and the last one 30 min before the end of the protocol. Each stimuli event was composed of five vibrations of 200 ms spaced by 500 ms. The x/y position of each single fly was tracked and analysed using the DART software in order to evaluate the relative speed and activity before, during and after the stimuli event. The speed analysis was used for the “Stimuli Response Trace” and the general activity used to deduce “Active Speed”, “Mean Bout Length” and “Inter-Bout Interval”, using a custom-made modification of the DART software40.Single-fly tracking was carried out as previously describedFlies aged 15 days at 29 °C to allow RNAi expression and knockdown were isolated into new plastic vials without food for 10–20 min before immersion in a 42 °C water bath for 120 seconds. Each tube was video recorded during and post immersion and seizures were defined as a period of brief leg twitches, convulsions, and failure to maintain standing posture. Flies were, thereafter, allowed to recover at room temperature and the time to recover from seizure was calculated only for flies that had undergone seizures. All experiments were randomised and double-blinded.71 from 15 adult flies of both sexes, aged 15 days at 29 °C to allow RNAi expression and knockdown, using TriZol (Thermo-Fischer). cDNA was generated using SuperScript III Reverse Transcriptase (Thermo-Fischer). Quantitative PCR was performed in combination with qPCRBIO SyGreen Blue mix (PCR Biosystems) on Quantstudio 7 from real-time PCR system (Thermo-Fischer). eIF4a was used as housekeeping control. The following oligos were used: Oatp30B Fw (GAATCCGACCAACCGCCTGA), Oatp30B Rv (ATGGATTCCTGCCGCCTGTG), eIF4a Fw (CGTGAAGCAGGAGAACTGG), eIF4a Rv (CATCTCCTGGGTCAGTTG).RNA was extracted as previously reportedFurther information on research design is available in the Supplementary MaterialReporting Summary