Hibiscus cannabinus L.) is a natural fibre crop that can be used for a variety of purposes and has various applications in industry. Despite this, its potential has not been fully exploited because of low yields and a narrow genetic base, limiting hybrids' development. Based on this background, eight kenaf mutants and one commercial cultivar were selected and crossed in a half-diallel for general and specific combining abilities (GCA and SCA) to get the desired results done in this investigation. The 36 hybrid offspring and their parental lines were tested in the field over two environments. Diallel results based on Griffing B method 2 indicated significant differences for all characters studied except for GCA in top diameter and plant height and top diameter SCA, indicating the existence of both additive and nonadditive gene actions for the inheritance of the traits. The amplitude of GCA variation was much higher than that of SCA variation for all parameters except top diameter and node number, showing the additive gene's prevalence and the likelihood of genetic advancement through selection. In both conditions, Hayman and Jinks graphical studies demonstrated that partial dominance controlled various fibre yield component parameters such as plant height, middle diameter, stick weight, and fibre weight. On the other hand, fibre yield and the majority of physical features indicated either dominance or overdominance gene action. Plant height, base diameter, core diameter, middle diameter, fresh stem weight, and stick weight all strongly positively correlated with fibre yield. These traits also had a higher proportion of additive effects, a moderate narrow-sense heritability, and a higher baker ratio, indicating successful indirect selection for fibre yield. The parents P1, P3, and P4 had the most dominant alleles for most of the features, while the parents P2, P7, and P9 had the most recessive alleles. The hybrids P1 × P4, P1 × P9, P2 × P3, P2 × P5, P4 × P6, P4 × P7, P4 × P9, P5 × P8, and P7 × P9 outperformed the parents in terms of heterotic responses and showed that they have a lot of genetic potential for kenaf enhancement in tropical climates.Kenaf ( Hibiscus cannabinus L.) has garnered significant interest as an intriguing multipurpose crop for producing energy, pulp, thermal insulation boards, and fibre-reinforced thermoplastic composites in Mediterranean nations. It has been employed as an alternate raw material for wood, pulp, paper, and textile manufacturing industries. The economic product of kenaf is the stem material, which consists of the outer bark (bast). The bark of the fibre-producing plant's stem is removed as kenaf through a washing and drying procedure [1 generation is 14 to 43 percent more than the parents [In recent years, kenaf  with three replications using a table of random numbers [Mechanical ploughing and laddering were used to prepare the soil for the plant's cultivation. Kenaf seeds from 45 entries comprising nine parents and 36 F numbers .Standard kenaf production provided a good crop with the best management. NPK Green (15 : 15 : 15) and NPK Blue (12 : 12 : 17) were applied at 450 kg per hectare shortly after seeding and 40 days after planting. Intercultural activities like weeding, thinning, additional watering, and plant protection measure were carried out as needed throughout the cropping environment. The recommended cultural guidelines for growing a healthy crop were followed as described by Wong et al. .A total of 10 fibre yield components were discovered. For each feature, observations were obtained from 10 randomly selected plants for each genotype for each replication. Plant height, base diameter, core diameter, middle diameter, top diameter, node number, days to 50 percent flowering, fresh stem weight, stick weight, and fibre weight were among the quantitative data collected in the same direction as the procedure of Al-Mamun et al. . Days to1 and parents) was performed for each growth environment, eliminating reciprocals as indicated by Steel et al. [An analysis of variance (ANOVA) was used with the PROC GLM function of the Statistical Analysis System (SAS) computer software version 9.4 to determine the significance of variation among genotypes and blocks. A combined ANOVA from 9 × 9 diallel  Diallel Analysis by Griffing's Method. Griffing's method 2  was used to analyze the diallel [μ is the mean of the population, αl is the environmental effects of lth environment, rkl is the replication effect in each environment, vij is the F1 hybrid effect, (αv) ijl is the interaction effect between F1 hybrids and environments, and eijklc is the residual effects. αg) il denotes the interaction effect between GCA and environments for the ith parent, (αg) jl denotes the interaction effect between GCA and environments for the jth parent, and (αs) ijl denotes the interaction effect between SCA and environments for the ijth F1 hybrid. diallel . The GCA diallel , the linGCA and MSSCA are the mean squares for GCA and SCA, respectively.The combining ability ratio was calculated according to Baker  as below(2) Diallel Analysis by Hayman's Approach. Most plant breeding programs that attempt to improve yield and other related parameters start with parallel analysis. Breeders utilize it the most to determine the utility of varieties as parents and to analyze gene action in diverse qualities [ualities , 36. The(3) Hayman's ANOVA and Morley Jones Modification. Hayman [1), and additional dominance effects that genes can explain with one allele present in only one line (b2) , and remaining dominance effects (b3) are essentially a straightforward application of least squares suitable constants.. Hayman  analyzed. Hayman  in one d. Hayman  modified(4) Vr-Wr Regression Analysis. The regression coefficient was calculated by using the following formula:b” from zero and unity was tested as follows:Significant difference of “t” values for (n − 2) degrees of freedom.These results were compared to the “b) from unity. The absence of nonallelic interaction is demonstrated if the regression coefficient does not significantly depart from unity. The “t2” test for the uniformity of Vr-Wr values was used to verify the validity of diallel theory assumptions. The Vr-Wr analysis shows the significant variation of the regression coefficient ((5) Components of Variation and Genetic Parameters. Hayman [1. The various components estimated were as follows:U and V represent the percentage of positive and negative genes in the parents, respectively. . Hayman  and Jink. Hayman  were useV0L0 is the variance of parents, V1L1 is the mean variance of the arrays, W0L01 is the mean covariance between parents and arrays, V0L1 is the variance of the means of arrays, (ML1–ML0)2 is the difference between the parents' mean, and the n2 progenies' mean is known as the dominance relationship.1/D)1/2 = the average level of dominance across all loci. The ratio of dominat to recessive allele were categorized as 0 = no dominance, >0<1 = partial dominance, 1 = complete dominance, and >1 = over dominance is indicated. The estimation of genetic ratios was as follows: 1. Vr-Wr Graph. The Vr-Wr graph can be used to interpret a diallel cross [el cross , 39. By rth array and V0L0 is the variance of parents.The Wri (covariance) values for each array were calculated by using the following formula:b is the regression coefficient.By plotting Wr values against Vr values, the external limits of the parabola were found. The limiting parabola is drawn using the Wri values. The expected values of Wrei are needed to create a regression line. The following formula is used to acquire these Wrei values for each array separately:(5)2. Inference from the Vr-Wr Graph. The average degree of dominance is shown by the regression line's position on the Vr-Wr graph. When the regression line crosses across the origin, it indicates complete dominance (D = H1). Partial dominance (D > H1) exists when the regression line crosses the Wr axis above the origin. It represents the absence of dominance when it crosses the origin, cuts the Wr axis, and contacts the limiting parabola. Overdominance is shown when the regression line crosses the Vr axis below the origin.The order of dominance for each parent is determined by the position of parental points on the regression line. Parents with more dominant genes are closer to their ancestors than parents with more recessive genes. Parents with the equal amount of dominant and recessive genes fall in the middle of the range.p ≤ 0.01) variations in environment and genotypes (parents and offspring) except for the middle and top diameters (G × E) were significant (p ≤ 0.01 or 0.05) except for plant height. The CV percent for fibre yield components varies from 12.02 to 69.05, indicating a wide range of variability in the features studied.All traits tested had highly significant  to 25.74 mm (P1), with P4 having the highest core diameter (21.88 mm) and P6 having the lowest (17.15 mm). The middle diameter ranged from 9.69 mm (P2) to 12.49 mm (P3), with P2 and P9 having the largest and lowest top diameters, respectively. The most nodes were found in P5 (7.38), whereas the fewest were found in P2 (4.15). The period it takes to achieve 50 percent flowering varied between 55.67 and 69.17 days. The hybrid P7 was the first to reach maturity, while the parent P2 was the last (the last . In addi4 (351.42 g) and P6 (222.41 g), respectively. The stick weights varied from 71.35 g (P6) to 123.24 g (P3). Among the parental lines, P1 had the highest mean fibre weight per plant (26.71 g), followed by P4 (24.78 g) and P8 (23.59 g), and P2 had the lowest (17.55 g) (3 had the highest mean stick weight per plant (123.24 g), followed by P7 (96.26 g) and P8 (95.06 g). However, as the node number increased, the fibre production per plant decreased.The highest and lowest fresh stem weight was recorded for P17.55 g) . Accordip ≤ 0.01) for all traits except plant height and top diameter and significant (p ≤ 0.05) for days to 50 percent flowering, showing that additive gene effects predominantly influenced variability between those traits. In contrast, both additive and nonadditive gene effects controlled plant height and top diameter variations. The interaction of GCA with environment effects was highly significant (p ≤ 0.01) for all traits except plant height and fresh stem weight and significant (p ≤ 0.05) for base diameter and days to 50 percent flowering, indicating that the inbred lines' GCA was influenced by the environmental conditions surrounding the hybrids.For all analyzed traits in the combined data  for bothOn the other hand, the SCA by environment interaction effects were highly significant for all traits except plant height and top diameter, while significant for days to 50 percent flowering, indicating that environments altered the effects of specific hybrid combinations on the expression of these traits. As a result, the impacts of nonadditive genes on phenotypes interacted with the environment more. For all traits except top diameter and node number, the mean squares of GCA were bigger than the mean squares of SCA, indicating that additive gene action predominated in our study across both environments . FurtherHibiscus cannabinus, Sobhan [Hibiscus sabdariffa, and Khatun [Corchorus capsularis by combining ability studies.The combining ability analysis revealed significant GCA for all traits except top diameter and significant SCA for all traits except plant height and top diameter from the combined data of the two environments. The results indicate that variations for those traits were controlled mainly by additive gene effects, while both additive and nonadditive gene effects controlled variations for plant height and top diameter. For quantitative traits, the predominance of additive over nonadditive gene effects was rather prevalent. Similar findings were found in kenaf by Jianmin et al.  and Heli, Sobhan  in Hibisd Khatun  in CorchFor all traits except top diameter and node number, the mean squares of GCA were larger than SCA's, showing that additive gene action effects played a significant role in their inheritance. Although additive gene effects contributed the most to the variability in most traits, dominance and overdominance significantly influenced the genetic system that controls yield components. Because of the strong additive gene effects, this conclusion usually favours the breeding selection technique . Mostofa7 had the maximum plant height (12.89) and the lowest negative GCA values for node number (-0.42), suggesting that they were strong general combiners for quality fibre yield and might be exploited in future breeding attempts. Parent P4 showed the most favourably significant GCA effect (p < 0.01) of plant height (9.11), base diameter (2.16), core diameter (1.93), middle diameter (0.70), fresh stem weight (54.54), stick weight (14.14), and fibre weight (3.02). With the exception of the P4, parent P1 (1.49) and P3 (1.38) were also positive and significant general combiners (p < 0.01) for fibre weight, with parent P3 having a positive and very significant GCA effect (p < 0.01) for stick weight (12.66). The parent P2 had the most positively significant GCA effect (p < 0.01) for days to 50 percent flowering (2.99). Parent P7 had the smallest GCA effect for days to 50 percent flowering (-3.89). The parent P2 showed the lowest negative and highly significant GCA effect (p < 0.01) for plant height (-16.83). For base diameter (-1.08), core diameter (-1.01), and fresh stem weight (-21.63), the parent P9 exhibited the lowest negative GCA values.Plant height in a taller stature combination should have a positive GCA effect, while the node number should have a negative GCA effect . In the p < 0.05) were produced by the cross P5 × P8 (23.63), which was evaluated as a good specific combiner for tallness, followed by P7 × P9 (19.91). The SCA effects on the base diameter ranged from -4.24 to 5.61. Of the 23 positive SCA effects, two (P4 × P6 and P5 × P8) were found to be highly significant (p < 0.01) for base diameter, while six  were reported to be significant (p < 0.05). Six of the 36 cross combinations had significantly positive SCA values for kenaf core diameter, showing heterotic performance compared to their parents' mean. The cross P2 × P3 had the highest positive SCA effect (5.97), followed by P4 × P6, P5 × P8, and P7 × P9, indicating that they were found to be highly significant (p < 0.01) for the trait. The SCA effects for middle diameter stem varied from -1.52 to 2.97. The hybrid P5 × P8 had the best SCA effects, followed by P7 × P9 and P8 × P9, considered the best specific combiners for this trait. Another 18 crosses had positive but insignificant SCA values, implying that they could be used as average specific combiners. The SCA effects on top diameter ranged from -0.71 to 1.06. Three crossings exhibited significant positive SCA effects, P4 × P7, P5 × P8, and P6 × P7 were identified as good specific combiners for top diameter.The effects of SCA on hybrids in various contexts in pooled environments are shown in 2 × P9) being highly significant (-1.94) and another (P1 × P9) being significant (-1.31), showing that these hybrids had good SCA for lower branch stems and improved fibre yield. SCA impacts ranged from -5.55 to 4.91 days to 50 percent flowering. The significance of four of the 22 positive SCA effects was determined to be quite high. The cross P2 × P5 of the 15 positive SCA effects, which displayed a good specific combining capacity for this characteristic, had the most significant SCA effects. Five more crossings of the 23 positive SCA effects produced positive and significant SCA effects, making them good specific combiners for fresh stem weight. Three crosses, P5 × P8 (103.70), P4 × P9 (91.15), and P2 × P3 (78.19) had highly significant positive SCA effects, whereas two crosses, P4 × P6 (66.50) and P7 × P9 (58.02), had significant positive SCA effects, indicating that these hybrids had good SCA for fresh stem weight.SCA impacts ranged from -1.94 to 2.52 for a given node number. Negative SCA effects were found in 14 crosses, with one (P1 × P4 had the largest significant positive SCA impact (31.08), followed by P5 × P8 (30.16), P4 × P9 (29.98), and P7 × P9 (29.54); it was an excellent specific combiner for the stick weight per plant attribute, meaning that it was a good specific combiner. Three crosses, P2 × P3 (11.70), P4 × P9 (5.31), and P7 × P9 (4.43), showed highly significant positive SCA effects for fibre weight, while four crosses, P5 × P8 (7.93), P2 × P5 (4.06), P1 × P3 (3.53), and P1 × P7 (3.45), showed significant positive SCA effects. This suggests that hybrids with higher fibre weights than their parents' means are the best specific combiners for increased kenaf fibre weight.In kenaf, 23 cross pairings had positive SCA effects, while the other 13 had negative for stick weight per plant, SCA impacts ranging from -19.01 to 31.08. P4 was among the best parental lines for plant height, base diameter, core diameter, middle diameter, fresh stem weight, stick weight, and fibre weight content, showing the accumulation of favourable additive genes for these traits in the hybrids. The parent P3 had favourable impacts on base diameter, fresh stem weight, stick weight, and fibre weight; on the other hand, the parent P1 was the greatest general combiner for fibre weight and contributed positively to the hybrid for these traits. The main purpose of this breeding effort is to create high-yielding hybrids with high potential fibre production comparable to or equal to existing cultivars.The research demonstrated that parents' GCA effects were linked to their crossings' SCA impacts, which had the highest significant positive intensity. Additive genetic variance is a major contributor to the GCA component. Because additive variance may be fixed, selecting qualities regulated by additive variance is a very effective strategy . As a reCrosses with high vs. low general combiners outperform others in terms of yield components in general. According to an investigation of combining ability impacts , high-sp2 × P3, P2 × P5, P4 × P9, and P7 × P9 were shown to be the most effective specific combiners. The cross P5 × P8 is good for plant height, base diameter, middle diameter, top diameter, stick weight, and fibre weight. As the finest specific combiners for stick weight, the hybrids P1 × P4, P5 × P8, and P7 × P9 were selected. Days to 50 percent flowering for hybrids P2 × P5 were also picked as unique combiners.SCA effects were found to be significant for most yield traits, as mentioned in 1 × P4, P1 × P9, P2 × P3, P2 × P5, P4 × P6, P4 × P7, P4 × P9, P5 × P8, and P7 × P9 showed promising heterotic responses and could be beneficial in future breeding program. Hybrid vigour can be induced by dominant, overdominant, or epistatic gene action in any combination of parents, according to Moll and Stuber [Complementing gene effects could explain the strong SCA effects of these crosses. Crossings of Pd Stuber . In this(1) Hayman's Analysis of Variance. After Morley Jones' modification [l with the environment, and a significant mean square was detected for fibre weight and fresh stem weight, revealing that changes in soil types and climatic factors at each environment influenced the mean departure of the F1s from their mid-parental values for the two traits [2 with environment interaction, indicating that b2 and variety heterosis components were consistent across the two environments [3-environment interaction, demonstrating that b3 and specific heterosis components were stable across the two environments [fication , Hayman'o traits . The remronments . All traronments .Hibiscus cannabinus flowering days. Significant mean square due to the interaction of bl with the environment was detected for top diameter, node number, fibre weight, and fresh stem weight, indicating that the mean deviation of the F1s from their midparental values for the two traits was most likely influenced by differences in soil types and climate conditions at each environment [In combined environments, additive genetic effects (a) were significantly larger than dominance genetic effects (b), suggesting the presence of both additive and dominant gene actions in the inheritance of these traits . The finironment .(2) Components of Variation and Genetic Parameters. The genetic component analysis of Hayman [1), and environmental error in most parameters (1 and H2) values were found, demonstrating the relevance of nonadditive components in the inheritance of these traits [1 and H2) was greater than the additive component (D), indicating overdominance [1 was greater than H2 for six of 10 traits, showing that the frequency of gene distribution in the parents was asymmetrical, implying dominance for all of these traits [F value was negative for all traits suggesting the presence of dominant recessive genes in the parents influencing these traits [f Hayman  differedrameters . The date traits . For theominance . H1 was e traits . These fe traits , who obse traits .2 component is significant for a few, the frequency of dominant and recessive alleles cannot be assumed equal. Furthermore, the importance of nonadditive gene action in influencing the traits was demonstrated because h2B and h2N for the examined traits varied from 0 to 37.86 percent and 0 to 23.05 percent, respectively. Fibre weight shows a moderate heritability of 37.86 percent. Plant height had the highest narrow-sense heritability of 23.05 percent in the combined environment, followed by fresh stem weight (20.06 percent). On the other hand, poor narrow-sense heritability indicated that nonadditive gene effects predominated in trait inheritance [1/D)0.5 ratio, was found to be greater than one for all traits except base diameter, core diameter, node number, and fibre weight, indicated by the Vr-Wr graph; overdominance gene effects have a role in the inheritance of most examined traits. On the other hand, the remaining attributes had a zero (H1/D)0.5 ratio, indicating no dominance in the control of the traits. Patil and Thombre [2/H1)  is less than 0.25 for 6 out of the 10 traits and the beritance . The ave Thombre  found a e [2/H1) .1) of sqr (H1/D) were zero, which indicated the presence of no dominance effects for the base diameter, core diameter, node number, and fibre weight, while there were dominance effects for the other traits showing 1.34 to 2.06. Heritability in the narrow-sense assessed from genetic components appeared to be moderate to low for most traits. The moderate narrow-sense heritability values of plant height and fresh stem weight were indicative of their early generation selection success for breeding improvement [According to Hayman's analysis of variance and components of variation in gene action, all traits were influenced by both additive and dominant (nonadditive) gene actions. It also revealed that positive and negative alleles were not equally common in the parents and asymmetrical gene distribution at the loci, indicating that all these traits were dominant. However, the value of additive (D) and dominance  involving the nine parents studied. This is considerably different from zero but not from unity, showing no nonallelic interaction for these traits' inheritance. However, plant height and fresh stem weight showed highly significant “b” values in F1. It therefore did not follow the models clearly, which were significantly different both from zero and unity, indicating the presence of an interallelic interaction in the inheritance of these traits. The remaining of the nine traits in combined environments were considered to follow epistatic or nonallelic gene interaction as the regression coefficient significantly differed from unity but not from zero involving all nine parents studied.For the traits and their related statistics, Hibiscus sabdariffa, a close relative of H. cannabinus, the days to flower suited the model well and the regression coefficient was substantially different from zero but not from unity. Plant height and fresh stem weight differed significantly both from zero and unity, indicating the presence of an inter-allelic interaction in the inheritance of these traits. The remaining seven traits in combined environments were considered to follow epistatic or nonallelic gene interaction as the regression coefficient significantly differed from unity but not from zero involving all nine parents studied. The regression coefficient b was not statistically significant when compared to zero. The deviation from unity for all 10 characters except for the top diameter indicated the validity of the hypothesis proposed by Hayman [t2 values satisfied the uniformity of covariance and variance (Wr-Vr) and thus supported the validation of the assumptions of Hayman [The regression analysis revealed that days to 50 percent flowering differ considerably from zero but not from unity, indicating that nonallelic interactions do not inherit these traits. The findings were comparable to those of Sobhan [y Hayman . The nonf Hayman  for thesCorchorus olitorius and Khatun [Corchorus capsularis.The deviation from the origin of the point where the regression line cuts the Wr axis, one of the information points acquired from the graph, measures the average level of dominance. In view of this, the intercept of the regression line on the covariance axis Figures  is belowd Khatun  for Corc4 falls near the point of origin, in plant height, base diameter, core diameter, middle diameter, and fibre weight, as well as in Figures 3 also falls near the point of origin, in middle diameter, top diameter, and stick weight. In Figures 1 also falls near the point of origin, in base diameter, core diameter, days to 50 percent flowering, and fibre weight. Results demonstrated the presence of dominant genes for the traits as mentioned earlier in the parents P1, P3, and P4. Those parents that are located closest to the regression line's start had more dominant genes for that trait, whereas those located farthest away had more recessive genes [2, P8, and P9 are the furthest from the origin in most features, implying that they have the most recessive genes for these traits.The array points along the regression line reflect the distribution of dominant and recessive genes between the parents. The parents with the most dominant genes are closest to the origin, whereas the parents with the most recessive genes are the furthest away. It is noticed that in Figures ve genes . In contr-value of a correlation coefficient conveys the idea of a relationship between two unique traits by making a connection. Traits' correlation coefficients ranged from 0.05 to 0.99, indicating that the phenotypic variation is greater. Negative linear relationship, no linear relationship, and perfect positive linear relationship are shown by r-values of 1, 0, and +1, respectively. According to Field [As demonstrated in to Field , values Plant height, base diameter, core diameter, middle diameter, fresh stem weight, and stick/fibre weight all demonstrated a positive and highly significant correlation with dry fibre and stick weight. This was the most significant correlation yet observed for a single trait. For at least one of the types, in combined environments, a negative phenotypic correlation was seen in days to 50 percent flowering with fibre weight and stick weight. The fact that these attributes had a negative correlation with fibre weight suggested that they would not improve the trait. Plant height, base diameter, core diameter, middle diameter, node number, fresh stem weight, and stick weight all showed significant positive correlations with fibre weight, while top diameter and days to 50 percent flowering showed significant negative correlations. In contrast, there were significant correlations with all traits in the case of combined environment. Between the types, there was no discernible pattern or similarity. This suggested that progenies within each type may have a variety of strategies for expressing a certain phenotype. Significant and strong correlations will aid in selecting traits and qualities that influence commercial yield traits, making future selection easier and more reliable.The negative relationship between fibre weight, stick weight, plant height, base diameter, core diameter, node number, and fresh stem weight suggests that early maturity reduces fibre yield (bast and core fibre) per plant . Further1 (ML5), P3 (ML36-10), and P4 (ML36-24) were outstanding general combiners for fibre yield components. In contrast, the hybrids P1 (ML5) × P4 (ML36-24), P1 (ML5) × P9 (ML36-21(2)), P2 (ML9) × P3 (ML36-10), P2 (ML9) × P5 (ML36-25), P4 (ML36-24) × P6 (ML36-27), P4 (ML36-24) × P7 (BJRI Kenaf4), P4 (ML36-24) × P9 (ML36-21(2)), P5 (ML36-25) × P8 (MLRing4P2), and P7 (BJRI Kenaf4) × P9 (ML36-21(2)) showed promising heterotic responses and could be beneficial in future breeding program. Plant height, middle diameter, days to first flowering, stick weight, and fibre weight indicated a clear partial dominance in the variance and covariance graphs. For most of the phenotypes evaluated, parents P2 (ML9), P7 (BJRI Kenaf4), and P9 (ML36-21(2)) had the most recessive genes, while parents P1 (ML5), P3 (ML36-10), and P4 (ML36-24) had the most dominant alleles. The Baker ratio showed that selection-based approaches highly responsive to additive effects enhance genetic improvement in the fibre output of hybridization-based breeding programmes. Because the fibre yield components studied have a high to moderate broad-sense heritability, a high Baker ratio, and moderate to low narrow-sense heritability, it was determined that selection should be done in advanced generations after homozygosity and genetically fixed.The fibre yield components evaluated in this study were genetically controlled by additive and nonadditive variants. From the combined data of the two environments, the combining ability analysis revealed strong GCA for all traits except top diameter and considerable SCA for all traits except plant height and top diameter. Additive gene effects mostly governed variations in those parameters. In contrast, according to the findings, plant height and top diameter were influenced by both additive and nonadditive gene effects. Except for top diameter and node number, GCA effects were higher than SCA effects, as shown by mean squares, indicating that additive gene action predominates for these traits. In conclusion, the parental lines P


Brassica yield has steadily risen over the last 50 years, with the major crop Brassica napus  production increasing to ~72 Gt in 2020. In contrast, the production of Brassica mustard crops has fluctuated, rarely improving in farming efficiency. The drastic increase in global yield of B. napus is largely due to the demand for a stable source of cooking oil. Furthermore, with the adoption of highly efficient farming techniques, yield enhancement programs, breeding programs, the integration of high-throughput phenotyping technology and establishing the underlying genetics, B. napus yields have increased by >450 fold since 1978. Yield stability has been improved with new management strategies targeting diseases and pests, as well as by understanding the complex interaction of environment, phenotype and genotype. This review assesses the global yield and yield stability of agriculturally important oilseed Brassica species and discusses how contemporary farming and genetic techniques have driven improvements.The global demand for oilseeds is increasing along with the human population. The family of Brassicaceae crops are no exception, typically harvested as a valuable source of oil, rich in beneficial molecules important for human health. The global capacity for improving  Brassica is considered the most important as it contains many of the economically important crops such as oilseeds and condiment varieties, or cruciferous vegetables . Th. ThB. naBrassica oilseed crops is yet to be established. The second major period is the seed-filling period, also known as grain-fill. The environmental conditions during this time have been found to influence seed size and oil content [B. napus. As such, not only does high density improve total crop yield, it also improves the actual yield captured through mechanical harvesting [B. carinata, row spacing affected seed and oil yield, branch production and the number of pods per plant [Environmental conditions are the greatest influencers of biomass accumulation, yield during the growth period  and may  content . Further content . Along w content ,46,47,48rvesting . In contB. napus is 58.5 × 104 plants/ha [4 plants/ha [4 plants/ha [4 plants/ha [Plant density differs by climate. In China, the optimum density for lants/ha ,49; in Elants/ha ,51; in Clants/ha . FurtherBrassica oilseed crops is a major concern. Andert et al. 2021., performed a case study of East-German canola farmers and found that on-field data showed yield instability for winter hybrid varieties, which in turn, may cause farmers to begin growing less canola overall [Brassica oilseed crops is dictated by (1) environmental conditions during the critical period, (2) plant density and (3) disease management strategies; grain quality is dictated by: (1) the environmental conditions during the pod-fill stage and (2) genetic diversity. As such, the complex interaction between genotype, phenotype and the environment together, drive grain yield capacity, quality and stability.Lastly, yield security of  overall . The con overall . Integra overall ,25,51. IB. napus populations showed that 81.5% of the QTL linked to yield were pleiotropic [B. napus, a multi-environment analysis of seed composition traits observed that most QTL suffered from GxE effects, with a major QTL qWIE_N9 associated with seed pigment in over five environments [Determining the underlying genetic mechanisms driving crop yield is a complex task, considering the interactions between genetic (G), environmental (E) and GxE forces that may affect crop performance ,55,56. Miotropic . In B. nronments . HoweverHigh-throughput phenotyping (HTP) has emerged as an alternative to manual phenotypic trait measurement, accelerating the process of collecting phenotype information through remote and proximal sensors to measure a variety of plant traits ,60,61. HSetaria [https://sciwheel.com/work/citation?ids=13155099&pre=&suf=&sa=0, accessed on 4 September 2022. Multispectral and hyperspectral cameras have been widely employed to obtain quantitative measurements of the canopy reflectance throughout plant development, supporting the identification of potassium deficiency and green peach aphid susceptibility [B. napus seeds [B. napus genotypes revealed multiple medium and small effect QTL were associated with early plant growth, most of which were active during short phases of the development [B. napus trial that monitored 43 traits across twelve time points, reporting that only 35% of the QTL identified were present on all time points [A wide range of sensors is available for monitoring specific plant traits under field or controlled conditions such as RGB, multispectral and hyperspectral cameras, infrared thermal or LiDAR sensors. Infrared thermography images of the plant canopy contribute to determining crop water stress  and assiSetaria  https://tibility , classifus seeds  and seedus seeds . Besidesus seeds ,75, HTP us seeds . Similarelopment . Dynamice points . These se points .B. napus varieties. Observing the impact of genomic variation on crop yield can be facilitated by the use of HTP, but a comprehensive database on B. napus genetic resources is required to design future resilient crops.HTP platforms have the potential to provide ample information regarding the plant phenotype; however, efficient data collection and processing are considered a key constraint for breeding ,80. ImagB. napus linking PA with yield. For example, a gene knockout experiment showed that a stop codon mutation in the meristem identity gene APETALA1 affected flower morphology, PA and yield [B. rapa showed plant yield was significantly correlated with PA-related traits such as main inflorescence length, branch height and branch segment [In order to improve yield in plants, one must study the underlying genetic mechanisms and related mechanisms, such as plant architecture. Plant architecture (PA) refers to the three-dimensional organisation of the plant, including its morphological characteristics ,91. PA mnd yield . Additio segment . B. napus. These studies primarily focus on identifying QTL and single nucleotide polymorphisms (SNPs) related to yield and PA. For example, genetic mapping of different genotypes of B. napus and genetic mapping identified 190 PA-related genes for 91 unique PA QTL and 2350 yield loci-pairs [B. napus onto one genetic map and identified 92 regions where 198 QTL overlapped. The study showed that the regions identified could be used to select for desired traits. Additionally, 147 candidate genes potentially influencing PA and yield were identified [B. juncea. It also showed that epistasis among loci plays an important role in controlling heterosis in yield of B. juncea [Identifying yield-related traits has been the focus of many genetic studies in ci-pairs . In a seci-pairs . Raboanaentified . A SNP aB. napus have used GWAS to identify yield-related genetic variants. Using data from 520 B. napus accessions, GWAS for seven yield-determining traits; main inflorescence pod number, branch pod number, pod number per plant, seed number per pod, thousand seed weight, main inflorescence yield, and branch yield, identified 128 SNPs and 14 candidate genes for yield improvement [B. napus [B. napus and identified 85,126 SNPs for GWAS, directly associating 18 SNPs with seed yield and another 61 SNPs with yield-related traits [Many studies in rovement . GWAS waB. napus . Anotherd traits . By unded traits . Streptococcus agalactiae [B. oleracea [B. rapa [B. napus [A pangenome is the collection of all genes within a species, first coined by Tettlin et al. in 2005 to describe the gene diversity in alactiae . Pangenooleracea , B. rapa[B. rapa  and B. nB. napus . PangenoB. napus ,108.B. oleracea, use of the pangenome has identified multiple genes coding for resistance against other abiotic factors such as drought [Brassica genus. In B. oleracea and Brassica macrocarpa, a pangenome was used to identify PAVs associated with disease resistance, secondary metabolites and flowering time [B. napus, the pangenome identified PAVs associated with flowering time, silique length, seed weight and flowering time [B. rapa, a pangenome was used to identify PAVs associated with flowering time, stress resistance and lignin formation [Unlike single reference genomes, pangenomes allow the capture of sequences affected by structural variation such as presence/absence variation (PAVs) or copy number variations (CNVs), that may affect agronomically important traits such as disease resistance and yield ,110. In  drought . Similaring time . In B. ning time  as well ing time ,113. In ormation . PangenoBrassicas is to construct pangenomes based on specific functional traits. Trait pangenomes aim to describe the landscape of genetic variation related to a trait and investigate the impact of genetic variation. Trait-specific pangenomes have been used to study resistance gene analogs in B. oleracea and B. napus [B. napus [Brassica research recently and use of pangenomics in Brassica breeding is still in its infancy. However, further understanding of the genetics underlying variation can lead to the development of molecular markers that can be used to predict the location of desirable crop traits and marker-assisted breeding for improvement of crop yield in Brassicas.Another way to study candidate genes in B. napus ,113,115 B. napus . Trait pB. napus . PangenoBrassicas, which greatly benefit from sharing homologs of well characterised genes associated with yield in Arabidopsis. However, the high gene homology and copy number of the allotetraploid Brassicas, such as B. napus and B. juncea, present a challenge in terms of editing accuracy, in particular single base editing [Brassica. In Brassica oilseeds, yield remains poorly characterised on the molecular level, and knowledge of the genetic mechanisms controlling seed size and oil content is scarce [Genome editing presents an opportunity to rapidly introduce or manipulate specific traits of interest that may not be present in the existing gene pool of elite crop varieties, or that may be difficult and time consuming to introduce through traditional introgression breeding approaches. The advent of clustered regularly interspaced short palindromic repeats systems associated with Cas enzymes (CRISPR/Cas) has opened the doors for widespread editing in oilseed  editing , and in  editing . Polygen editing . Althoug editing ,122, thes scarce . Hence, s scarce . In addis scarce ,124 and s scarce , which sB. napus has received the most attention of all the oilseed Brassicas when it comes to the use of editing for yield improvement. Yang et al. were the first to examine multilocular siliques in B. napus using CRISPR/Cas9 and introduced mutations to CLV pathway genes, resulting in an increased number of multilocular siliques. Multilocular siliques were found to contain more seeds than regular bilocular siliques, and seed weight per silique was increased by 74% in the mutants [BnD14, a strigolactone receptor, to change architectural traits in B. napus relating to yield [BnD14 displayed dwarfed phenotypes, which are easier to harvest, with prolific branching and nearly 40% more flowers than their WT counterparts. To combat pod shattering, Zaman et al., knocked out homeologs of SHATTERPROOF1/2, a class of genes involved in the pod dehiscence regulatory pathway, creating a transgene-free line that was approximately 10 times more resistant to pod shattering when measured on the pod-shattering resistance index [B. napus. Although examples of editing for yield improvement in B. juncea are yet to be seen, several potential targets have been identified and characterised, such as genes involved with multilocular silique development [Not surprisingly,  mutants . A more to yield . Knockouce index . These eelopment ,129, andBrassicas, exponentially improving their yield through genome editing is only just out of reach. Advances in CRISPR systems in terms of simplicity and accuracy is a driving force propelling CRISPR into the mainstream. Novel CRISPR techniques, such as the recently proposed and highly sophisticated CRISPR-Combo system, which simultaneously enables gene editing and gene activation at multiple loci, will likely spearhead the race to high-yielding genome edited crops [As we start to understand the complex mechanisms underpinning yield and identify yield-related genes in oilseed ed crops , as welled crops ,131,132.B. napus production has increased from 154.2 Mt in 1979 to 72.37 Gt in 2020 and is now only second to soybean as a source of oilseed. Although primarily grown as a source of vegetable oil or mustard oil, B. napus (and to a lesser extent B. carinata), B. juncea and B. rapa have also been identified as excellent sources of biofuel, applying more pressure for studies to maximise yields. The majority of  studies that are associated with maximising yield involve genetics and/or gene-editing technology as it is a proven and rapid method for yield improvement. The global FE (t/ha) of B. napus pre-genetics age  was 1.48 t/ha, compared to now (2004–2020) which is 1.94 t/ha. Despite the adoption of genetic resources, the global yield of Brassica crops is insufficient for current and future demands, in particular mustard crops, however, this is more likely due to yield instability. An investigation of global and national FE clearly shows fluctuations for both B. napus and Brassica mustard crops in recent years, which can cause repercussions towards the supply chain of these crops, for example, farmers losing confidence in their crops. As such, research studies investigating the influence of environment, phenotype and genotype on crop yield and yield stability are required to produce reliable crops for farmers to meet demands.Crop yield and yield stability have been the focus of agriculture for thousands of years. However, with realization of climate change and the predicted increase in world population by 2050, concerted efforts are necessary to meet growing demands. As an example of this demand, Brassica cultivars of their ability to grow well in a region. As an alternative to manual phenotyping, HTP technology accelerates the process of collecting phenotype information through remote and proximal sensors to measure a variety of plant traits all of which are catalogued and integrated. Coupled to deep/machine learning technology, HTP can quickly establish the optimal phenotype (highest yield) for the region. However, HTP is limited by the availability of genomic resources, such as QTL, GWAS and SLAF-seq data associated with plant architecture. As such, genetic studies are critical. However, the underlying genes that are associated with phenotypic variations exhibited by plants are great and nearly impossible to identify by genomics alone. Hence, pangenomic analysis has been developed as a strategy to capture the genetic diversity to bridge the gap between genotype to phenotype. Many Brassica crops have pangenomes available such as B. napus, B. rapa and B. oleracea, providing unique insights into DNA polymorphisms, structural variation and patterns for genome duplication. The combination of genomics/pangenomics and phenotypic studies has enhanced the prediction of molecular markers associated with specific crop traits for crop yield and yield stability. Furthermore, like HTP technology leading to improved phenotyping capabilities; gene-editing technology has enhanced genotyping by rapidly characterizing yield-associated candidate genes and creating new genotypes. It is predicted that DNA-free gene-editing technology will lead to the development of non-GMO plant genotypes that can rapidly be produced and adopted by farmers.Studies have shown that environmental conditions are the greatest influencers of biomass accumulation, furthermore, climate dictates the particular genotypes and phenotypes that can thrive in any one region. High-throughput phenotyping technology allows for the rapid analysis and characterization of new and potential Brassica oilseed and mustard crops. Only through continued improvement and innovation of these resources can yield increase and be secured.The combination of environment, phenotype and genotype dictate yield and yield stability. As such, developing methodologies to understand these factors and their complex interactions are critical for ensuring future demand is met. This review shows how farming practice and management programs, HTP, genomics, pangenomics and gene editing technology have influenced the production and security of


Genome-wide association studies (GWAS) have successfully revealed genetic risk variants for schizophrenia (SCZ). However, the vast majority of GWAS largely comprise European samples. As a result, the derived polygenic risk scores (PRS) show decreased predictive power when applied to non-European populations.A long-term scientific cooperation between the Charité Universitätsmedizin Berlin and the Hanoi Medical University aims to address this limitation by recruiting a large genetic cohort of comprehensively phenotyped schizophrenia patients and controls in Vietnam.A pilot study was conducted at the Department of Psychiatry of the Medical University Hanoi in 2017. Data collection encompassed i) genome-wide SNP genotyping of 200 schizophrenia patients and 200 control subjects ii) structured interviews to assess symptom severity (PANSS), iii) clinical parameters  and demography.2liability=0.053, p=3.11*10-8, Pd <0.5), followed by PRS based on the East-Asian summary statistics  and the European sample .SCZ-PRS of the pilot sample (N=400) were generated using different training data sets: i) European, ii) East-Asian and iii) mixed GWAS summary statistics from the Psychiatric Genomics Consortium’s latest discovery sample. Most variance explained was observed using a mixed discovery sample (RWith this pilot project we established an efficient recruitment, genotyping and data analysis pipeline. Our results corroborate previous findings indicating that transferability of PRS across populations depends on the ancestral composition of the initial discovery dataset. We therefore aim to expand data collection efforts in the future in order to improve risk prediction across diverse populations.No significant relationships.


PCSK9-expression is evident in non-hepatic tissues, notably the brain, and genetic variation in the PCSK9 locus has recently been shown to be associated with mood disorder-related traits. We investigated whether PCSK9 inhibition, proxied by a genetic reduction in expression of PCSK9 mRNA, might have a causal adverse effect on mood disorder-related traits. We used genetic variants in the PCSK9 locus associated with reduced PCSK9 expression (eQTLs) in the European population from GTEx v8 and examined the effect on PCSK9 protein levels and three mood disorder-related traits , using summary statistics from the largest European ancestry genome-wide association studies. We conducted summary-based Mendelian randomization analyses to estimate the causal effects, and attempted replication using data from eQTLGen, Brain-eMETA, and the CAGE consortium. We found that genetically reduced PCSK9 gene-expression levels were significantly associated with reduced PCSK9 protein levels but not with increased risk of mood disorder-related traits. Further investigation of nearby genes demonstrated that reduced USP24 gene-expression levels was significantly associated with increased risk of mood instability , and neuroticism score , but not with PCSK9 protein levels. Our results suggest that genetic variation in this region acts on mood disorders through a PCSK9-independent pathway, and therefore PCSK9-inhibitors are unlikely to have an adverse impact on mood disorder-related traits.Prescription of PCSK9-inhibitors has increased in recent years but not much is known about its off-target effects.  PCSK9 gene is located on chromosome 1 and encodes the proprotein convertase subtilisin/kexin type 9 (PCSK9) protein whose function is to target LDL-C receptors (LDLR) for degradation. Certain mutations in the PCSK9 gene lead to excess removal of LDL-C receptors which in turn lowers the uptake of LDL-C into the cell and ultimately lead to higher levels of LDL-C in the blood. PCSK9i are usually monoclonal antibodies that block the excess PCSK9, preventing the degradation of LDL-C receptors, thereby lowering circulating LDL-C levels [Cardiovascular disease (CVD) is currently the leading cause of mortality in the world with 32% of global deaths attributed to it in 2019 [C levels . HoweverPCSK9, both protein level [PCSK9 locus [PCSK9 locus, that this gene is associated with an increased risk of major depressive disorder (MDD) but not neuroticism [In vivo experiments have also demonstrated that overexpression of LDLR (which is targeted for degradation by PCSK9), in mice brains led to neuroinflammatory responses, suggesting that inhibition of PCSK9 may also lead to neuroinflammation [Despite the main role of PCSK9 in lipid metabolism being established primarily in the liver, the gene has detectable levels of expression in non-hepatic tissues, including the brain . There iin level  and geneK9 locus , and mooroticism . In vivoammation . MR studammation . In contammation , clinicaammation . In suchammation .PCSK9 gene-expression (expression quantitative trait loci or eQTLs) to proxy the effects of PCSK9i (exposure) on mood disorder-related traits, circulating LDL levels, and PCSK9 protein levels (outcomes). Genetic effects on the outcome traits were identified from the respective largest publicly available genome-wide association study (GWAS) summary statistics for these traits.Two-sample MR is a technique used to estimate the causal association of a risk factor (exposure) on a phenotype (outcome) using genetic variants as instrumental variables (IV), a proxy for the risk factor, using summary statistics for genetic variants associated with the exposure and outcome derived from the GWAS of non-overlapping samples , 15. In eQTLs are genetic variants which have genotype-specific effects on levels of the gene’s expression . The pri-8) in the discovery analyses, and eQTLGen consortium  [-8) [-8) [Summary statistics for significant cis-eQTLs were extracted from GTEx v8  (49 tiss<1x10-8) , CAGE co-8) [-8) , and Bra-8) [-8)  in repli2 threshold of 0.8 was used wherever possible. The cis-eQTL and GWAS summary statistics were combined, harmonized to the PCSK9 gene-expression reducing allele.GWAS summary statistics with the largest sample sizes in European ancestry individuals were extracted using the IEUGWAS R package . OutcomePCSK9 eQTL with the highest  and lowest  sample size in the GTEx cohort. Wherever reported, we used the observed estimate for the effect of PCSK9 gene expression on neuroticism score, MDD, and LDL-C. Where these were not available, we used reported estimates of PCSK9 genetic risk score on the above outcomes instead [PCSK9 expression on mood instability and hence we could not calculate the power for this outcome. For the standard deviation (SD) of gene expression GWAS in the GTEx cohort, we used the reported value of log SD 0.13 as modelled in the initial cohort analyses [We used mRnd web version (accessed 19/10/2022)  to calcu instead . No repoanalyses . We assuPCSK9 levels had a significant causal effect on mood disorder-related traits. The detailed information on the SMR method has been described previously [PCSK9 gene-expression levels were associated with an outcome. The heterogeneity in dependent instruments (HEIDI) test, described previously [HEIDI<0.1). The following settings for SMR were used: PeQTL<5x10-8, MAF<0.1, LD pruning threshold of r2<0.2, and exclusion of eQTLs in very high LD (r2>0.9) or in low or no LD (r2<0.05) with the top associated eQTL for the HEIDI test. For the discovery analysis, we used PCSK9 gene-expression levels in 49 different tissues from GTEx v8 as exposure and mood-related traits and positive controls as outcomes. Limitations of cis-eQTL analyses include the significantly lower sample size of an eQTL study compared with the sample size of the GWAS used and the single time-point of expression data. These concerns, combined with the evidence of PCSK9 gene-expression and cis-eQTLs in multiple tissues, justified our decision to use data from all available tissues in GTEx v8. SMR multi-SNP analysis was also performed to obtain a weighted estimate and p-value for all independent cis-eQTLs per gene. The p-value threshold was adjusted using Bonferroni correction for multiple testing. Furthermore, to explore whether a neighbouring gene, rather than PCSK9, might be the effector gene through which genetic variants in the PCSK9 locus act, we also performed SMR for genes within 1Mb of PCSK9 using the same IVs as that of PCSK9 gene-expression. We also performed the SMR analyses with LDLR gene-expression level as exposure to assess if any association of PCSK9i on mood-disorder traits could be due to lower PCSK9 levels translating into higher LDLR levels.SMR version 1.03 software was used to investigate whether reduced eviously , 29. In eviously , is alsoPCSK9 and USP24 from the GTEx v8 portal . For each gene, the eQTLs were combined irrespective of its tissue. Where a variant was an eQTL in multiple tissues, the summary statistics for the tissue with lowest p-value was taken forward. These cis-eQTLs were used as IVs for subsequent MR analyses. The cis-eQTLs were harmonised with GWAS summary statistics and LD-pruned (r2<0.2 as above) to select only independent instruments. For every gene-phenotype combination, the Wald ratio was calculated for each eQTL followed by meta-analysis, using inverse-variance weighted (IVW), MR-Egger, weighted-median, weighted-mode, and maximum likelihood methods using the R-package TwoSampleMR (version 0.5.6) [We combined the eQTLs in all available GTEx v8 tissues and calculated a weighted estimate, modified and adapted from previously described cross-tissue IV selection methods , 31. Then 0.5.6) . The MR-n 0.5.6) , 33. BonUSP24 within and ±1Mb flanking the gene. We used coloc v5.1.0.1 R package, the method of which has been described previously [0: no genetic association with either trait, H1: association with trait 1 but not trait 2, H2: association with trait 2 but not trait 1, H3: association with trait 1 and trait 2 but with two independent causal variants, H4: association with both trait 1 and trait 2 and they share the same causal variant. Summary statistics for neuroticism score and mood instability from the same GWASs as the MR analyses, and USP24 eQTL summary statistics from GTEx for all tissues were used for the colocalization analyses. The default priors were used: p1 and p2 = 10−4, and p12 = 10−5.We performed a genetic colocalization analysis with mood disorder traits and genetically modulated gene-expression levels of eviously . In brieAs all data used in this study is publicly available summary statistics from anonymised individuals, no ethical approval was required.PCSK9 gene-expression with PCSK9 and LDL-C levels (as positive controls) as well as MDD, mood instability, and neuroticism score. The expected associations with PCSK9-expression and LDL-C levels were observed, and there was a suggestive association between PCSK9-expression and mood instability in testis , and whole blood , but this did not reach significance after multiple testing correction. No significant associations were observed between PCSK9 gene-expression, and MDD, or neuroticism score , and neuroticism score  with the same instruments (shared eQTLs with PCSK9) (h PCSK9) .USP24 , we found a significant association for reduced USP24 gene-expression with an increase in mood instability , and neuroticism score  in all tissues for which data was available  was consistent across all datasets.  was greater than 0.8 for 19 tissues for mood instability , USP24, was significantly associated with an increased risk of mood instability, and neuroticism score.We investigated the potential effect of PCSK9i on mood disorder-related traits using MR, and publicly available GWAS and eQTL data. Genetically reduced USP24. USP24 is located approximately 175Mb downstream of PCSK9 and many genetic variants are eQTLs for both genes. USP24 encodes a ubiquitously expressed ubiquitin carboxyl-terminal hydrolase 24, a 2620-amino acid protein that belongs to ubiquitin-conjugating and deubiquitinating cysteine protease family whose main role is to clear abnormal proteins from cells and regulate cell survival [USP24 gene-expression has low tissue specificity and is detected in all tissues with cytoplasmic protein expression in most cell types (Human Protein Atlas v21.0.proteinatlas.org) [USP24 dysregulation to be associated with LDL-C levels , Parkinson disease (GDA 0.08), and various cancer types (GDA = 0.04–0.01) including lung cancer, multiple myeloma, T-cell lymphoma, and neuroblastoma [USP24 as a prioritised gene for GWAS variants associated with LDL-C (L2G score: 0.7), estimated glomerular filtration rate (L2G score: ~0.6), various CVD (L2G score: ~0.8), metabolic disorders (L2G score: ~0.01) and various cancers (L2G score: ~0.06) [USP24 in mood instability, and neuroticism score, though it should be noted that mood instability is a component of the neuroticism score and could be driving the latter association. Understanding how USP24 impacts mood traits requires further work. Investigating the impact of USP24 on all of the traits/questions that contribute to neuroticism score would be an interesting starting point.The PCSK9 locus has only recently been implicated in GWAS, specifically in gene-based analyses of opioid use disorder . The gensurvival , 37. USPlas.org) . The Disblastoma . The Loc: ~0.06) . This isLDLR gene-expression level as another way to assess if PCSK9i were associated with mood-disorder traits via the main function of the drug, that being to increase LDLR levels. Moreover, it also showed that USP24 gene-expression does not affect PCSK9 protein levels, suggesting that the effect of the PCSK9/USP24 locus on mood-related traits is through a pathway independent of PCSK9. We also used all available tissues in GTEx for the analyses with the rationale that even when the disease specific tissues is known, PCSK9 is expressed in multiple tissues and its non-LDLR specific effect in other tissues has not been proven to be null. A limitation of our analyses is the sample size of eQTL cohorts as they are far smaller than a conventional GWAS, and therefore may have provide limited confidence. Whilst replication in different cohorts and tissues has increased the confidence of our findings, GWAS summary statistic for the PCSK9 eQTLs, or their proxies, that met the eQTL p-value threshold of 5x10-8 were unavailable in eQTLGen, CAGE, and Brain-eMETA consortium datasets. Moreover, as PCSK9’s main function is in the hepatic tissues, it would have been beneficial to analyse the effects in the liver. For example, Inclisarin, an small interfering RNA (siRNA) PCSK9i approved in 2021 by both the FDA and NICE, binds specifically to hepatocytes leading to a targeted reduction of PCSK9 secretion in the liver [PCSK9 were present for the liver tissue in GTEx v8. This might be due to the lower sample size (n = 208) or technical reasons such as sample collection methods. In addition, we will also have to be mindful that cell types and environments of tissues used in both eQTL and GWAS studies may not be the same. For example, LDL-C GWAS conducted using DNA from blood samples collected from living population and clinical cohorts, while RNA estimates used for eQTL analyses in GTEx were from post-mortem tissues, leading to a potential variation in the regulatory environment of the tissue types.Strengths of our study included the use of cis-eQTLs that passed a strict association threshold in the discovery dataset, positive control outcomes  and replication in 3 independent datasets . We also used he liver , 42, andLastly, we have not focused on the magnitude of effect size (beta) as the reported effect sizes for the GTEx cohort is calculated in a normalised space and the magnitude of it cannot be used for any direct biological interpretation . It is aPCSK9 locus and mood disorder-related traits [USP24 –and not PCSK9 –is responsible for the causal effects on mood instability, and neuroticism scores. PCSK9i are therefore unlikely to have an adverse impact on mood disorder-related traits, a reassuring finding for a drug class that is widening in use in many countries as costs levels start to decline.Although our previous study demonstrated an association between the d traits , here weS1 Fig10 p-value of the MR association, while the direction signifies the direction of effect of gene-expression reduction, which serves as a proxy for PCSK9i. SMR multi-SNP analyses association of the available 25 tissues for PCSK9 and USP24 gene-expression in GTEx v8 for outcomes PCSK9 levels, LDL-C levels, and MDD. The blue and red lines signify p-value of 0.05 and Bonferroni-corrected threshold for 5 traits per gene of 0.005, respectively.The Y-axis represents the -log(TIF)Click here for additional data file.S2 Fig10 p-value of the MR association, while the direction signifies the direction of effect of gene-expression reduction, which serves as a proxy for PCSK9i. SMR multi-SNP analyses association of the available tissues for expression of genes within 1Mb flanking of PCSK9 using only PCSK9 eQTL from GTEx v8 as IV for outcomes PCSK9 levels, LDL-C levels, mood swings, neuroticism score, and MDD. PCSK9 eQTLs having GWAS association with our outcomes are shared with BSND and USP24. The blue and red lines signify p-value of 0.05 and Bonferroni-corrected threshold for 5 traits per gene of 0.005, respectively.The Y-axis represents the -log(TIF)Click here for additional data file.S1 TableIEUGWAS id: study ID in the IEU GWAS database, Outcome trait: name of the GWAS trait, Cohort: name of the GWAS cohort, Sex: sex of the GWAS population, Population: ethnicity of the GWAS population, Unit: measurement unit of the GWAS trait, Sample size: sample size of the GWAS cohort, Ref (see manuscript): reference for the study. Please refer to the manuscript.(XLSX)Click here for additional data file.S2 TableCohort: study used for eQTL data, Tissue: eQTL summary statistics obtained from this tissue, n eQTL: sample size for eQTL specific to tissue, r2 eQTL: proportion of variance in exposure variable explained by SNPs in gene expression GWAS, Outcome: trait used as outcome for MR, Outcome category: trait category, βOLS: regression coefficient for the observational association between the exposure and continuous outcome variables, βyx: unknown true causal association between exposure and continuous outcome, σ2 eQTL: variance of the exposure variable, σ2 GWAS: variance of the continuous outcome variable, n GWAS: sample size of GWAS, n cases GWAS: sample size of cases for binary outcomes, True OR: true odds ratio of the outcome variable per standard deviation of the exposure variable, K: proportion of cases in binary outcome, Power: estimated power for the exposure-outcome combination.(XLSX)Click here for additional data file.S3 TableCohort: cohort of the eQTL study, probeID: ID of the probe used in respective cohorts for gene of interest, ProbeChr: chromosome of the probe used in respective cohorts for gene of interest, Gene: gene analysed, Probe bp: base pair position of the probe bp used in respective cohorts for gene of interest, topSNP: eQTL/SNP with lowest p-value for gene-tissue combination, topSNP chr: chromosome of the topSNP, topSNP bp: base pair position of the topSNP, A1: effect allele of topSNP, A2: other allele of topSNP, Freq: allele frequency for A1, b GWAS: beta for topSNP in the GWAS study, se GWAS: SE for topSNP in the GWAS study, p GWAS: P-value for topSNP in the GWAS study, b eQTL: beta for topSNP in the eQTL cohort, se eQTL: SE for topSNP in the eQTL cohort, p eQTL: P-value for topSNP in the eQTL cohort, b SMR: beta for SMR analysis using the topSNP only, se SMR: SE for SMR analysis using the topSNP only, p SMR: P-value for SMR analysis using the topSNP only, p SMR multi: P-value for SMR analysis using all significant independent SNPs, p HEIDI: P-value for the HEIDI analysis. Blank if test wasn’t conducted, nsnp HEIDI: number of SNPs used for HEIDI analysis. Blank if less than 3 SNPs—analysis was not conducted, Tissue: tissue type used for eQTL summary statistics, Outcome: Traits analysed.(XLSX)Click here for additional data file.S4 TableCohort: cohort of the eQTL study, probeID: ID of the probe used in respective cohorts for gene of interest, ProbeChr: chromosome of the probe used in respective cohorts for gene of interest, Gene: gene analysed, Probe bp: base pair position of the probe bp used in respective cohorts for gene of interest, topSNP: eQTL/SNP with lowest p-value for gene-tissue combination, topSNP chr: chromosome of the topSNP, topSNP bp: base pair position of the topSNP, A1: effect allele of topSNP, A2: other allele of topSNP, Freq: allele frequency for A1, b GWAS: beta for topSNP in the GWAS study, se GWAS: SE for topSNP in the GWAS study, p GWAS: P-value for topSNP in the GWAS study, b eQTL: beta for topSNP in the eQTL cohort, se eQTL: SE for topSNP in the eQTL cohort, p eQTL: P-value for topSNP in the eQTL cohort, b SMR: beta for SMR analysis using the topSNP only, se SMR: SE for SMR analysis using the topSNP only, p SMR: P-value for SMR analysis using the topSNP only, p SMR multi: P-value for SMR analysis using all significant independent SNPs, p HEIDI: P-value for the HEIDI analysis. Blank if test wasn’t conducted, nsnp HEIDI: Number of SNPs used for HEIDI analysis. Blank if less than 3 SNPs—analysis was not conducted, Outcome: Traits analysed.(XLSX)Click here for additional data file.S5 TableCohort: cohort of the eQTL study, probeID: ID of the probe used in respective cohorts for gene of interest, ProbeChr: chromosome of the probe used in respective cohorts for gene of interest, Gene: gene analysed, Probe bp: base pair position of the probe bp used in respective cohorts for gene of interest, topSNP: eQTL/SNP with lowest p-value for gene-tissue combination, topSNP chr: chromosome of the topSNP, topSNP bp: base pair position of the topSNP, A1: effect allele of topSNP, A2: other allele of topSNP, Freq: allele frequency for A1, b GWAS: beta for topSNP in the GWAS study, se GWAS: SE for topSNP in the GWAS study, p GWAS: P-value for topSNP in the GWAS study, b eQTL: beta for topSNP in the eQTL cohort, se eQTL: SE for topSNP in the eQTL cohort, p eQTL: P-value for topSNP in the eQTL cohort, b SMR: beta for SMR analysis using the topSNP only, se SMR: SE for SMR analysis using the topSNP only, p SMR: P-value for SMR analysis using the topSNP only, p SMR multi: P-value for SMR analysis using all significant independent SNPs, p HEIDI: P-value for the HEIDI analysis. Blank if test wasn’t conducted, nsnp HEIDI: number of SNPs used for HEIDI analysis. Blank if less than 3 SNPs—analysis was not conducted, Tissue: Tissue type used for eQTL summary statistics, Outcome: Traits analysed.(XLSX)Click here for additional data file.S6 Tableoutcome: outcome analysed, exposure: exposure names (expression levels of the respective genes), method: MR methods used, nsnp: number of IV used for the MR method used, b: beta/effect size for the MR method used, se: Standard error for the MR method used, pval: P-value for the MR method used, Q: Cochrane Q value, Q df: Cochrane Q degrees of freedom, Q pval: Cochrane Q P-value, egger intercept: MR-Egger intercept value, se: MR-Egger standard error, pval: MR-Egger p-value.(XLSX)Click here for additional data file.S7 Tablensnps: number of SNPs used for coloc analyses, H0: posterior probabilities for colocalization—neither trait has a genetic association in the region, H1: posterior probabilities for colocalization -only gene-expression has a genetic association in the region, H2: posterior probabilities for colocalization -only GWAS trait has a genetic association in the region, H3: posterior probabilities for colocalization—both traits are associated, but with different causal variants, H4: posterior probabilities for colocalization—both traits are associated and share a single causal variant, tissue: GTEx tissue used, gwas_trait: trait in the GWAS used.(XLSX)Click here for additional data file.