N=9,063) using a slightly modified version of a wavelet-based method by Shim and Stephens (2015) called WaveQTL.Birth weight (BW) is one of the most widely studied anthropometric traits in humans because of its role in various adult-onset diseases. The number of loci associated with BW has increased dramatically since the advent of whole-genome screening approaches such as genome-wide association studies (GWASes) and meta-analyses of GWASes (GWAMAs). To further contribute to elucidating the genetic architecture of BW, we analyzed a genotyped Norwegian dataset with information on child’s BW , even though our sample size was 26 times smaller than that study and 18 times smaller than the second largest GWAMA of BW by Horikoshi et al. (2016). In addition, the modified WaveQTL performed better in regions of high LD between SNPs.This study is the first adaptation of the original WaveQTL method to the analysis of genome-wide genotypic data. Our results highlight the utility of the modified WaveQTL as a complementary tool for identifying loci that might escape detection by conventional genome-wide screening methods due to power issues. An attractive application of the modified WaveQTL would be to select traits from various public GWAS repositories to investigate whether they might benefit from a second analysis.The online version contains supplementary material available at (10.1186/s12864-021-07582-6). Birth weight (BW) is known to influence a wide variety of adult-onset diseases, particularly cardio-metabolic diseases such as cardiovascular disease and type 2 diabetes. Although the findings from genome-wide association studies (GWASes) have contributed substantially to our understanding of the genetic underpinnings of BW, the genetic variants identified thus far still account for only a small fraction of the total variance attributable to additive genetic effects. For example, the largest genome-wide association meta-analysis (GWAMA) of BW to date  showed tBased on a recent report , a largeTo address these limitations, Vsevolozhskaya and colleagues  exploredFor the current analyses, we use the WaveQTL method by Shim and Stephens  because MoBa is an ongoing nationwide pregnancy cohort study . Particihttp://www.haplotype-reference-consortium.org/) and the free genotype imputation and phasing service of the Sanger Imputation Server (https://imputation.sanger.ac.uk/). For fast and accurate phasing, the Sanger server uses Positional Burrows-Wheeler Transform (PBWT) for indexing multiple sequence alignments across different genomes . OuThe original WaveQTL by Shim and Stephens  tests foΦ measured once per function. WaveQTL tests for association between the functions and the trait by testing for association between the wavelet-transformed function and the trait. Wavelets are useful mathematical functions for conducting a Fourier-like transform. There are different types of wavelets [s,l), where the first number, s, corresponds to the scale or the level of resolution in Fig. l, corresponds to the location. We refer the reader to a textbook by Nason [R.WaveQTL aims at identifying associations between a population of functions and a univariate phenotype wavelets , and, foby Nason  for a moIn essence, WaveQTL tests for association between a population of functions and the trait using hierarchical Bayesian modeling that tests (for each wavelet coefficient) whether the trait is associated with the wavelet coefficient, with a prior probability s and location l based on genotype, Φ is the phenotype of interest , and C is a confounder. The coefficient βsl,1 can be interpreted as the effect of the phenotype on the wavelet coefficient (sl). Additionally, π is a vector of length S, with S being the highest level of resolution. Each component of π,πs, represents the proportion of wavelet coefficients at scale s associated with Φ.Where WaveQTL tests for association between the functions and the phenotype by testing the following hypothesis: π is assessed using the following likelihood ratio: The significance of p-value, we refer the reader to our recent paper [For additional details on how to assess the significance of nt paper .p-value of a region is below the Bonferroni threshold.Here, we treat each individual genotype as a “signal” and BW as the univariate continuous phenotype. For every screened region, we transform the individual genotype into wavelet coefficients and test for association with BW using the WaveQTL framework. An association is identified if the likelihood ratio https://github.com/heejungshim/WaveQTL. The modified WaveQTL is distributed as an R package on GitHub under the name mWaveQTL. The R package of the modified WaveQTL includes the zooming strategy (https://github.com/william-denault/mWaveQTL) and a comprehensive example of a typical run.In our adaptation of WaveQTL to enable the current GWAS, we used a sliding-window approach to sequentially screen the entire genome for associations. WaveQTL  is fast and reduces the number of tests to be performed by using overlapping windows of 1 Mb in length. By employing an alternative modeling to the single-SNP linear regression, WaveQTL enhances the detection of associations that are potentially missed by conventional GWAS methodology. The original software implementation of WaveQTL is available at n is the number of samples. Based on these criteria, we defined 5,170 regions spanning the entire genome. In addition, the user needs to choose the depth of analysis. In Fig. The user has to specify four parameters to run a GWAS using the modified WaveQTL: i) the region size, ii) the maximum distance between two consecutive SNPs, iii) the level of resolution, and iv) the prior standard deviation for the wavelet effect size. We recommend using half-overlapping regions of 1 Mb as the “region size” parameter and a maximum distance between two consecutive SNPs of 10 kb as the “maximum distance” parameter. Following the recommendations of Zhou and Guan , we set Detect all the regions that have a Bayes factor above a given threshold (here set to 1).For each selected region, extract the sub-region that contains all the Bayes factors above the set threshold.p-value.Refit the optimization process in Shim and Stephens (2015)  on this One of the main drawbacks of the original WaveQTL is that the sliding-window size is not easily adjustable. If the window is too wide, the signal may be lost in the optimization step due to the large background noise. To overcome this, we developed a “zooming strategy”. As the wavelet coefficients generally remain the same except at the lowest levels, a sub-region can be analyzed using the Bayes factors computed using a larger window size. We implemented the following procedure: p-value was smaller than nregion is the number of regions initially analyzed . This significance criterion corresponds to the multiple-testing correction for a genome-wide screening based on using regions of the size of the considered sub-region.A sub-region was considered statistically significant if the associated p≈1×10−5. For comparison, we performed a separate GWAS using the traditional additive linear modeling routinely applied to most GWASes, which we simply refer to as “single-SNP modeling”. As with the modified WaveQTL analysis, we assumed an additive genetic model and adjusted for the same set of variables  in the single-SNP modeling. The qq-plot of the p-value is displayed in Supplementary Figure 1. Finally, to demonstrate the good calibration of the p-values under the null, we performed a GWAS using the modified WaveQTL after permuting the phenotype. The qq-plot of the p-values based on this analysis is displayed in Supplementary Figure 2.We used the modified WaveQTL to perform a GWAS of approximately eight million SNPs in the imputed MoBa dataset. We assumed an additive genetic model and included sex as a covariate in the analysis. The first ten principal components were also included as covariates to correct for potential population substructure. Overlapping sliding windows of 1 Mb in size were used to analyze a total of 5,170 regions spanning the entire genome. Based on this sliding-window approach, applying a Bonferroni correction for multiple testing led to a significance criterion of Table ADCY5 on chromosome 3 was identified by the first GWAMA of BW from 2010 [n=27,591 individuals were analyzed; LOC339894/CCNL1 on chromosome 3 and SLC45A4 on chromosome 8 were identified by a GWAMA from 2013  [CLDN7/SCL2A4 on chromosome 17 was identified by a GWAMA from 2016  [n=230,069) [All the significantly associated loci detected by the modified WaveQTL have previously been reported in other genetic studies of BW , 16, 21.m 2016 n=3,781 [15AC016696.1 (rs17034876), LCORL (rs4144829), PTCH1, and HMGA2 (rs1351394) located on chromosome 2, 4, 9 and 12, respectively . We found no clear evidence of an association with these loci in the modified WaveQTL analysis.Despite the enhanced statistical power, the modified WaveQTL only detected two of the ten most significant loci in the next largest GWAMA of BW to date, by Horikoshi et al. , that diESR1 (rs10872678) and ADBR1 (rs740746) on chromosome 6 and 10, respectively . It is also important to note that the MoBa dataset is roughly 18 times smaller than the one in Horikoshi et al. [p-values used for comparison were not corrected for the winner’s curse, where initial studies tend to overestimate the true genetic effect size. The relatively small sample size of the MoBa dataset makes it difficult to distinguish whether the lack of detection in the first scenario was purely due to a cohort-specific effect or due to the winner’s curse.The second scenario pertains to loci in which the signals are too weak to overcome the multiple-testing burden in the current dataset but that might attain significance in a larger dataset. This might be the case with the loci neighbouring the genes i et al. . MoreoveYTK6 (rs138715366) on chromosome 7 . Thus, a regional test to detect an association signal from a single SNP that does not show any linkage disequilibrium (LD) with neighboring SNPs might not have sufficient power for detection.The third scenario corresponds to the case where the association is only with a single SNP. This is exemplified by the sparse signal observed at SLC2A1) was only identified by the modified WaveQTL (p=7.3×10−6) and the largest GWAMA of BW to date comprising 230,069 individuals [This study is the first adaptation of the method originally described by Shim and Stephens  to the aThe ability of the modified WaveQTL to detect associations even with a relatively modest sample size suggests that it may be particularly useful for screening rare diseases where it is inherently difficult to generate a sufficiently large sample size suitable for genome-wide screening. Moreover, the gain in power may be particularly advantageous when examining different subgroups of a disease that are likely to have distinct etiologies . AsCHR7 and YKT6-GCK . As most of the loci detected by GWAS or GWAMA exhibit the classical peaks of p-values that are characteristic of local LD, our modified WaveQTL is expected to perform better for these types of regions. An obvious application of the modified WaveQTL is to reappraise previously published GWASes to verify whether some of the loci that might have escaped detection by conventional GWAS methodology attain statistical significance with the modified WaveQTL. The publicly accessible GWAS catalog maintained by the European Bioinformatics Institute  and thegov/gap/;) are excAdditional file 1 Supplementary figures.


Cannabis sativa, cultivated for centuries for multiple purposes, displays extensive variation in phenotypic traits in addition to its wide array of secondary metabolite production. Notably, Cannabis produces two well-known secondary-metabolite cannabinoids: cannabidiolic acid (CBDA) and delta-9-tetrahydrocannabinolic acid (THCA), which are the main products sought by consumers in the medical and recreational market. Cannabis has several suggested subspecies which have been shown to differ in chemistry, branching patterns, leaf morphology and other traits. In this study we obtained measurements related to phytochemistry, reproductive traits, growth architecture, and leaf morphology from 297 hybrid individuals from a cross between two diverse lineages. We explored correlations among these characteristics to inform our understanding of which traits may be causally associated. Many of the traits widely assumed to be strongly correlated did not show any relationship in this hybrid population. The current taxonomy and legal regulation within Cannabis is based on phenotypic and chemical characteristics. However, we find these traits are not associated when lineages are inter-crossed, which is a common breeding practice and forms the basis of most modern marijuana and hemp germplasms. Our results suggest naming conventions based on leaf morphology do not correspond to the chemical properties in plants with hybrid ancestry. Therefore, a new system for identifying variation within Cannabis is warranted that will provide reliable identifiers of the properties important for recreational and, especially, medical use.The flowering plant  Phenotypic variation within and between populations is an important characteristic to consider for classification purposes. In particular, if phenotypic variation exists between different lineages within a species, then characterizing trait correlations can shed light on how they are inherited, whether they are controlled by the same genes, and if they can be used for taxonomic purposes. Wild populations may carry shared ancestral traits despite being independently inherited, and therefore it may appear as if these traits were associated. Additionally, selection may favor certain trait combinations.Cannabis sativa has been cultivated for millennia for a range of purposes  between a female “Carmagnola” hemp and a male marijuana-type plant “Afghan Kush”. Many of the morphological traits we measured are considered important by the research , it is pA cross was performed between a female, narrowleaf “Carmagnola” hemp plant and a male broadleaf “Afghan Kush” plant at Centennial Seeds in Lafayette Colorado . One of Morphological measurements including height, stalk diameter, inner-node length, petiole length, leaf length and width, among other measurements, were obtained at two different time points during the growing cycle . We chost-tests with sex as the explanatory variable.To understand the change in individual phenotypes through the growing season, we calculated the difference between the initial measurements and the final measurements (delta Δ) for some traits. Specifically, we calculated Δ for the four traits that were measured at both timepoints . We thenWe carried out a geometric morphometric analysis to develop a quantifiable measure of leaf shape. Specifically, we placed landmark coordinates on each leaf picture with the program TPS Dig2 . We usedGeomorph  with shape as the response variable for each of the measured traits in both timepoints and Δ. We then performed MANOVA models within each timepoint and Δ to understand whether the main effects of each trait affected leaf shape. We corroborated the results using multivariate multiple regressions.The concentrations for the three cannabinoids—CBG, THC, and CBD—were measured using gas chromatography on an SRI 86106 equipment with an MXT-35 column using 197–209 mg of dried flower as described in Given that the production of these three cannabinoids may be correlated because they are part of the same biochemical pathway  and bothWe examined the associations between the production of each cannabinoid and each of the measured traits at both timepoints and the Δ. We used cannabinoids as the explanatory variables for several MANOVA models to determine whether cannabinoid production explained differences among the measured traits. We corroborated the MANOVA results with multivariate multiple regressions, and correlated leaf shape to cannabinoid content to understand whether any association exists between those traits. Finally, we generated a variance-covariance matrix to establish the association within and between all phenotypic traits.https://doi.org/10.5061/dryad.6t1g1jwxh). Statistical analyses, including leaf geometric morphometrics, were done using R (https://bit.ly/38DpE8D). All figures were generated in the R Studio platform version 1.1.383 . However, the only significant interaction effect was between leaf length and leaf width . The multivariate multiple regressions confirmed the MANOVA results.Furthermore, the MANOVA models with leaf shape as the response variable  compared to the negative loading of THC (−0.02) indicates that when CBD and CBG tend to increase, THC decreases and covaries in a different direction. Therefore, the value of PC1 increases when CBG or CBD increase. However, on PC2, both CBG and THC have a negative loading (−0.31 and −0.91 respectively), indicating a high association, while CBD has a positive loading (0.28). PC2 is primarily determined by THC given its high loading value. In both PC1 and PC2, CBD and THC go in different directions.Cannabinoid content showed no correlation with any of the other measured phenotypic traits at either time point (IT or FT) nor with the Δs . These rWe found no relationship between leaf shape and cannabinoid content using PC1 for leaf shape and PC1 for cannabinoid variation . TherefoCannabis.In this study we examined correlations among various phenotypic traits from a morphologically diverse first-generation backcross (BC1) population to understand whether these multiple traits covaried with each other. We interpret these patterns of correlations as being genetically-based due to our development of a diverse array of progeny of known parentage all grown in a common environment. Our results suggest these traits are not constrained by strong genetic correlations and the initial associations between the various morphological traits in the parent generation can be broken by recombination. The lack of apparent genetic correlation between these traits suggest they can be selected for independently. Therefore, these traits are inherited independently, and would evolve separately unless selection acts to increase or maintain correlations among them. The lack of genetic correlations between the morphological traits was also reflected in a variable pattern of growth across the growing season. The dramatic trait changes and the distribution variation over the course of plant growth and development  may explCannabis have been found in traits not measured here, such as photosynthetic rates and senescence  within Cannabis are not correlated because of shared genetic basis. Therefore, trait correlations observed are due to either shared ancestry, in the case of comparisons among subspecies or other major lineages, or correlated selection, in the case of modern hybrids. In other words, correlations between leaf shape and phytochemistry may not be due to causal relationships, but rather because breeders have intentionally  selected for these trait combinations. If these traits were associated due to shared ancestry or correlated selection, their association can be broken by recombination.Although some correlations among traits are significant and make biological sense, the traits that are associated with purported groups  is greatly magnified by the fact that scientists can only study the Cannabis produced by the federal government despite its inferiority in potency and diversity, and the fact that it does not reflect the products distributed in consumer markets , while serration in the middle leaflet (B) and number of leaflets (C) are marginally and significantly correlated, respectively. Each panel at the top has the deformation grids at the minimum (left) and maximum (right) values for length (A), serration in the middle leaflet (B), and number of leaflets (C). Males are shown in squares, females in open circles.Leaf length is not correlated to PC1 Click here for additional data file.10.7717/peerj.10672/supp-3Supplemental Information 3A and B show a positive correlation between CBG vs CBD, and THC vs CBG, respectively. C displays the negative linear relationship between THC and CBD. Notice the differences in the axis representing the disparity in production of the different cannabinoids. Males are shown in squares, females in open circles.Click here for additional data file.10.7717/peerj.10672/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.10672/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.10672/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.10672/supp-7Supplemental Information 7Click here for additional data file.


Verticillium dahliae. In this study, we analyzed the variability in resistance to Verticillium wilt of olive trees in progenies from five crosses: ‘Picual’ × ‘Frantoio’, ‘Arbosana’ × ‘Koroneiki’, ‘Sikitita’ × ‘Arbosana’, ‘Arbosana’ × ‘Frantoio’ and ‘Arbosana’ × ‘Arbequina’ and their respective reciprocal crosses. Additionally, seedlings of ‘Picual’ and ‘Frantoio’ in open pollination were used as controls. In October 2016 and 2018, the fruits were harvested, and seeds germinated. Six-week-old seedlings were inoculated by dipping their bare roots in a conidial suspension of V. dahliae, and disease progress in terms of symptom severity and mortality was evaluated weekly. Additionally, seedling growth was evaluated every two weeks. At the end of the experiment, no significant differences were found for any of the assessed parameters when reciprocal crosses were compared. These results suggest that there is no maternal or paternal effect in regard to the heritability of resistance. In addition, this study identifies the best crosses for obtaining the highest number of resistant genotypes, highlighting the importance of the selection of specific cultivars to optimize the breeding process.The use of genetic resistance is likely the most efficient, economically convenient and environmentally friendly control method for plant diseases, as well as a fundamental piece in an integrated management strategy. This is particularly important for woody crops affected by diseases in which mainly horizontal resistance mechanisms are operative, such as Verticillium wilt, caused by  Verticillium dahliae, Kleb., is currently considered the most destructive disease in olive orchards in Spain, the largest olive oil producer country, as well as in most olive-growing regions worldwide [Verticillium wilt of olive trees (VWO), caused by the soil pathogen orldwide ,2. The iorldwide . In thisorldwide ,5. Most orldwide ,7,8.Using the most resistant cultivars mentioned above as genitors, several studies have focused on evaluating the level of this character in progenies coming from different crosses. Root-dipping inoculation of young seedlings has proven to be the most reliable method for the identification of resistance . InteresDifferent genetic phenomena can participate in the asymmetrical inheritance of an agronomic trait: maternal effect and cytoplasmic inheritance. The maternal effect has been defined as the causal influence of the maternal genotype or phenotype on the offspring phenotype, while cytoplasmic inheritance is organelle inheritance via the egg . FollowiOn the other hand, three classes of maternal effects have been identified: cytoplasmic genetic, endosperm nuclear and maternal phenotypic effects. Several studies have shown that variation in seed, seedling and adult traits caused by maternal effects can have important consequences on the seedling response to different treatments ,18.There are several mechanisms involved in the asymmetrical heritability of different agronomic traits in sexual reproduction. For example, imprinting (a type of parent-of-origin effect) is an epigenetic phenomenon where one allele is expressed over the other depending on the sex of the parent that contributed the allele . ImprintArabidopsis seeds [Furthermore, only maternal transcript sequences were detected in both progenies resulting from reciprocal crosses, which were correlated with differential allelic methylation . Exampleis seeds . HoweverCochliobolus heterostrophus) and yellow corn blight (Mycosphaerella zeae-maydis) in maize (Zea mays), both associated with maternally inherited T male-sterile cytoplasm [Colletotrichum dematium, was determined largely by a nonnuclear, additive paternal effect in Ipomoea purpurea [Xanthomonas oryzae pv. oryzae) found that maternal contribution was important in controlling the virulence of this disease [Fusarium circinatum traits in the subsequent generation in Pinus pinaster. Conversely, maternal effects or cytoplasmic inheritance were less influential when ten bean parental lines (Vicia faba L.) were evaluated for their resistance to chocolate spot disease caused by Botrytis fabae [Setosphaeria turcica), the most devastating leaf pathogen in maize [To the best of our knowledge, no information regarding maternal or paternal effects has been published specifically in olive crops, and only a few studies in other plant species have been conducted. Among the latter, we can find cases of resistance to southern corn blight (ytoplasm . Additiopurpurea . Further disease . Interes disease  demonstris fabae . Similarin maize .Verticillium dahliae and (b) explore the best genitor crosses with regard to offspring resistance and good germination to optimize the olive-breeding program process.Although studies of genetic resistance or susceptibility are crucial in devising a viable strategy for current breeding programs in plants, the evaluation of possible asymmetrical heritability on diseases has been scarce and inconsistent. The lack of full-diallel mating designs, which include reciprocal crosses, has limited the information regarding this particular field. In this context, the main goal of this study was to generate and evaluate large olive progenies from reciprocal crosses to (a) assess the existence of possible maternal or paternal effects on seed germination capacity and resistance to The genotypes from the crosses ‘Arbosana’ × ‘Picual’, ‘Sikitita’ × ‘Frantoio’ and their respective reciprocal pairs were eliminated from the experiments, as they were classified as incompatible crosses in paternity testing.One thousand nine hundred ninety-two seeds derived from twelve different crosses germinated in two years, 2016 and 2018. The average germination in the two years was 52.2%. The germination rate in 2016 significantly varied between crosses, being 29.8% for the crossing of ‘Arbosana’ × ‘Sikitita’ and ‘Arbosana’ × ‘Arbequina’ and 80.6% for ‘Picual’ in free pollination . In 2018Approximately three months after germination, the seedlings were successfully infested by the pathogen by root dipping in a conidial suspension .Verticillium dahliae by performing isolations in Petri dishes with PDA (potato dextrose agar) and verifying fungal growth in all isolations.Symptom onset was first observed the fourth week after inoculation and consisted of green defoliation, purple discoloration in leaves, yellowing, total or partial necrosis and lack of growth. We confirmed that these symptoms were caused by In 2016, the disease incidence (DI) of the seedlings ranged between 25% in the offspring of ‘Frantoio’ in open pollination and 62.5% in the offspring of ‘Arbosana’ × ‘Arbequina’, with the average DI in all crossings being 43.1% . The valThe evaluation in 2018 was performed to confirm the patterns previously observed in reciprocal crossings involving different cultivars. The highest values in all evaluated parameters were obtained by ‘Arbosana’ × ‘Sikitita’, with the exception of mortality (M), in which the highest value was obtained by ‘Sikitita’ × ‘Arbosana’. In contrast, ‘Frantoio’ in open pollination showed the lowest values for all evaluated parameters. Corroborating the results obtained in 2016, no significant differences in disease parameters were obtained when we performed a pairwise comparison between each reciprocal progeny.We found significant differences in growth increase (GI) when inoculated seedlings with no visible symptoms and noninoculated (control) seedlings were compared . The aveSince we found no significant differences between any of the reciprocal crosses attending to their phytopathological values, we merged both in a single unit . AfterwaThe results confirmed the high variability among olive crossings in all study variables. Regarding germination, the genotypes coming from the crosses with ‘Arbosana’ and ‘Arbequina’ showed the lowest value (36.1%). On the other hand, the crosses with ‘Picual’ and ‘Frantoio’ had the best germination values (69.2%) .Regarding the resistance to Verticillium wilt, we observed two groups: first, the crosses involving the resistant cultivars ‘Koroneiki’ and ‘Frantoio’, which gave rise to the highest percentage of resistant offspring in terms of DI, M and RAUDPC; and second, the crosses that only had ‘Arbosana’, ‘Sikitita’ and ‘Picual’ as genitors, which generated a larger number of susceptible offspring. For example, the cross with the lowest mortality value (4.3%) was ‘Arbosana’ and ‘Koroneiki’, whereas those reciprocal crosses of ‘Arbosana’ and ‘Arbequina’ showed the highest values of DI (62%), final disease severity (50%), M (38%) and RAUDPC (33.1%).V. dahliae; therefore, soils are heavily infested with this pathogen [In recent decades, olive cultivation has undergone dramatic changes, mainly due to the intensification of plantation systems and the incorporation of irrigation. In many cases, new olive orchards have occupied fertile lands in river valleys previously cultivated with other species . Some ofpathogen . This sipathogen .The first step in a breeding program is the selection of genitors that could confer valuable traits to their offspring. In olive trees, out of more than 250 evaluated cultivars ,8, only Pyrus that the inheritance success of some characteristics depends on the cultivar used as a male or female genitor.The maternal effect has been studied in several crops and for diverse agronomic characteristics. It can constitute a valuable tool in a breeding program to select the most favorable parents so that the character to be improved is present in the greatest possible amount in the offspring ,28. One Verticillium wilt. The mentioned ‘Frantoio’ × ‘Picual’ and ‘Picual’ × ‘Frantoio’ reciprocal crosses, as well as others, were inoculated and analyzed. These results ease the breeding process since the availability of pollen from a certain cultivar to perform directed crosses is not always guaranteed. This low availability of pollen to use in crosses can be due to the prevalence of some cultivars, among others, low pollen production [The identification of a possible maternal effect on the inheritance of resistance to VWO was the main goal of this study along with the identification of the most effective crosses generating seedlings resistant to this disease. There is little available information on the maternal effect on agronomic characteristics associated with olive trees, and particularly, information on olive diseases is very scarce. Trapero et al.  presenteoduction  or variaoduction .V. dahliae after inoculation under artificial conditions. For these two traits, we found high variability in the individuals of the progenies of the different crosses, but we did not find significant differences when we compared the results in the pairs of each reciprocal cross. In Arabidopsis, in contrast, reciprocal crosses have shown that imprinting plays a role in regulating germination processes and that preferential maternal allelic expression can implement maternal inheritance of seed dormancy levels [Arabidopsis seed development [In this study, we assessed a large number of progenies to determine the germination rate of seedlings and their response to infections caused by y levels . In addielopment .Verticillium wilt. Interestingly, one of the demonstrated results from our study was the wide variability in the resistance level of the offspring, which is consistent with previous studies [Once we discarded the existence of the maternal effect, this study focused on selecting the crosses that maximize the percentage of seedlings resistant to  studies ,11,32. I studies ,32. In tVerticillium dahliae of their progeny, such as ‘Arbosana’ and ‘Arbequina’ reciprocal crosses. Furthermore, both cultivars have good agronomic characteristics and are suitable for superhigh-density plantation systems [In addition, it has been found that some genitors are not very suitable to be included in a breeding program for VWO resistance due to the low resistance level to  systems . Both chAccording to this study, ‘Frantoio’ in open pollination and the reciprocal crosses coming from ‘Arbosana’ and ‘Koroneiki’ and ‘Arbosana’ and ‘Frantoio’ are some of the best crosses to obtain new resistant cultivars. Some of these cultivars have been previously reported to generate a higher proportion of resistant offspring than other cultivars ,11. To cV. dahliae according to previous evaluations conducted under controlled [Six olive cultivars were selected as genitors for directed crosses due to their positive agronomical traits and commercial importance . For insntrolled ,38,39 anDirected crosses of these olive cultivars were performed in the spring of 2016 and 2018 in trees of the World Olive Germplasm Bank of Cordoba-UCO Collection . In 2016Directed crosses between cultivars and the germination of their offspring were performed according to Rallo et al.  by applyAfter 30 days, we changed the parameters of the climatic chamber to 24 °C, 70% RH and continuous light for 5 weeks. The percentage of germinated plants of each cross was calculated by counting the plants with fully expanded cotyledons five weeks after sowing . When geTo verify that the crosses were not contaminated with alien pollen, microsatellite (SSR)-based paternity tests were performed to confirm the genitors of each progeny. To do so, we extracted DNA from the leaves of 10 plants per cross, and their SSR profiles were amplified and compared with those of their putative genitors according to the protocol established by Diaz et al. .Verticillium dahliae from cotton, was used as a fungal material to inoculate the seedlings. This isolate belongs to the mycology library of the Agroforestry Pathology Unit of the Department of Agronomy of the University of Córdoba [The V117 isolate, a defoliating pathotype of  Córdoba . The V11 Córdoba ,39V. dahliae were conserved in middle Plum Extract Agar (AEC) at 4 °C and in total darkness. To obtain isolate V117, a small portion of mycelium was taken from the tubes of AEC, planted in PDA medium and incubated at 24 °C for 1–2 weeks in darkness. The margins of the resulting colony were transferred back to PDA. This Petri dish culture was used to obtain the inoculum, remaining active through transfers in PDA during the execution of all experiments. To obtain the inoculum of the V117 isolate, it was sown in portions of PDA with mycelium on Petri plates and incubated for 7 days at 24 °C.The original monoconidic cultures of 7 conidia/mL according to Trapero et al. [Plants with at least two pairs of true leaves were inoculated by dipping their bare root systems for 30 min in a conidial suspension of the pathogen adjusted to 10o et al. . All recPlant infection was confirmed by isolating the fungus from the affected shoots of diseased plants. Affected woody tissue samples collected from infected seedlings were washed in running tap water. The tissue surface was then disinfected in 0.5% sodium hypochlorite for 45 s. Small pieces of nonbark stem were placed on PDA plates and incubated at 24 °C in the dark for 6 days.The experiments were independently carried out in 2016 with seven reciprocal crosses and two reciprocal crosses in 2018 applying the same methodology. Germinated plants were grown in a controlled environment chamber for 3 months after inoculation with continuous light at 24 °C and 60–80% RH. A completely randomized block design was applied in both years. In 2016, we included 8 blocks of 44 inoculated plants each (44 × 8 = 352 plants) and 4 blocks with 31 control plants (not inoculated) each (31 × 4 = 124) . In totaThe symptoms were evaluated weekly for 13 weeks after inoculation. Disease severity was evaluated using a 0 to 100 rating scale. This scale estimated the percentage of affected aerial plant tissue in four main categories or quarters  with four values per category. Thus, each scale value represented the number of sixteenths of affected plant areas. The scale values (X) were linearly related to the percentage of affected tissue (Y) by the equation Y = 6.25X − 3.125 .These values were used to build progress curves for the DI of the affected plants and the mean severity of the symptoms over time and to obtain the final mean severity value. The RAUDPC was estimated as the percentage of the maximum possible value in the considered period according to the formula based on Campbell and Madden : AUDPC =Plant growth after inoculation was assessed in all genotypes by measuring the height of the plants at inoculation time and then every two weeks using a ruler. With these data, the average increase in height over time was estimated in noninoculated plants and inoculated plants without symptoms at the end of the experiment (GI). The GI was calculated by subtracting the final measurement from the height on the day of inoculation or the initial height.p = 0.05 was used to evaluate germination (%), DI (%) and M (%). Once the homogeneity values of variance and normality were verified, an analysis of variance (ANOVA) was performed with disease severity, RAUDPC and GI. The mean values of the analyzed parameters were compared using Fisher’s protected least significant differences test at p = 0.05. The program used in all statistical analyses was the Statistix 10.0 software program .An association chi-square test using multiple comparisons for proportions with


Evolutionary adaptation after sudden environmental changes can occur very rapidly. The mechanisms facilitating rapid adaptation range from strong positive directional selection leading to large shifts in the allele frequencies at a few loci (selective sweeps) to polygenic selection causing small changes in allele frequencies at many loci. In addition, combinations of these two extreme mechanisms may also result in fast evolution. In recent years, following reports of new case studies of rapid adaptation, population genetic models have been proposed to explain these observations. In these models, the role of the major selective forces  is highlighted as well as the genetic architecture of quantitative traits. Furthermore, the factors limiting the speed of adaptation are analyzed, in particular, the effects of random genetic drift and demography due to finite population size. Evolutionary adaptation can occur very rapidly. Well-known new case studies of rapid adaptation in response to environmental changes include those of the color variation in guppies , field mThe genetic basis of these phenotypic traits spans a wide range, from only a few genes of major effect, such as in the peppered moth , to a laIn more recent years, positive selection was combined with other factors characterizing natural populations, in particular, genetic drift, to investigate the hitchhiking effect in natural populations . PurifyiOn the other hand, polygenic adaptation caused by many weakly selected loci of small effects is not nearly as well studied as strong positive selection, leading to selective sweeps . The intThe quantitative genetic analysis of models of polygenic adaptation has a relatively long history ,25,26. HThe studies of the past few years ,35,36,37In this paper, we review the theoretical work on rapid adaptation based on a model of a single quantitative trait that was analyzed by de Vladar and Barton , Jain ann unlinked, diallelic loci in a very large population of diploids. If the phenotypic effect of the trait-increasing allele, also called i is i and z has a Gaussian shape centered about the fitness optimum, which in equilibrium is called s quantifies the strength of selection on the trait, such that 1/s is much larger than the phenotypic variance [ith locus due to selection and mutation is then given by the following:We assume that a trait is perfectly heritable and controlled additively (no dominance or epistasis) by ollowing :(1)c1=∑ivariance . We also optimum . In a ra optimum , and the alleles ,40. De Vladar and Barton  analyzedre 2A of ).This model is different from the so-called infinitesimal model, which is also widely used ,41. In tTo describe the dynamics of rapid adaptation based on our deterministic model, we assume that the population is in equilibrium with no deviation from the phenotypic optimum located at z0 . This asFisher’s  geometriIn the short-term phase, i.e., the time until the phenotypic mean reaches a value close to the new optimum, ODEs (5) can be approximated by the following ,30:(6)p˙Equation (6) is formally identical to the classical model of directional selection. However, the selection term depends on the distance from the new phenotypic optimum. Furthermore, because the mean deviation  Stephan  found ap^ =22μs  , and in First, we consider how the trait mean converges toward the new optimum in these two cases. In case (i), when most effects are small, the new optimum is approached exponentially at rate  by nγ¯2 ,29. In c by nγ¯2 ).Second, our analysis also revealed large differences in the dynamics of allele frequencies for large- versus small-effect loci ,30. In cEquation (6) also allows us to predict the minimum effect size required for rapid fixation (selective sweep) to occur at a major locus. When most effects are large, we find that the allele frequency at a locus with an effect size larger than the mean effect may undergo a large shift (see Equation (41) in ). TherefFinally, when most effects are large such that rapid fixations occur, we often find large allele frequency shifts that resemble fixation processes. However, they are very slow and thus not observed within the short-term phase in which classical sweeps occur. Such an example can be found in Figure 3 in . In thisThe aforementioned scenarios leading to fast adaptation are extremes on a scale, which—in reality—comprises combinations of processes that range from fixations due to strong selection (causing selective sweeps) at a few loci to weak selection at many loci . In contrast to case (ii), in the case of polygenic adaptation due to weak selection at many loci, genetic drift is expected to play an important role. This subject is treated in the following section.In this section, we consider biologically more realistic conditions for adaptation than above by integrating genetic drift into our deterministic model. We show that in the stochastic equilibrium between genetic drift, mutation and selection, the population mean is fluctuating around a state close to the fitness optimum  Stephan  and Step Stephan , and ass^ =22μs  . The  is derived by summing over Equation (5) and using the definitions of the cumulants (Equation (1) to Equation (3)). Since in the case of rapid evolution  to zero  and obtaconstant , the skeconstant ).Using Equation (9), the expected change of the allele frequency i as follows:B denotes the beta function.Using diffusion theory , we obtaollowing :(13)Ci−s = 0.1, N = n −5 and We compare Equations (12) and (13) with simulations, using the following set of parameter values: n humans ,34; the  results . This fiAn important difference to the deterministic model is that in our stochastic analysis, the equilibrium is given as a probability distribution of allele frequencies (Equation (12)), not as a single value (as in the deterministic model). For very large populations, this distribution is bell-shaped if ribution . The reahan nγ¯2 .To model the adaptation to the new fitness optimum, we consider a population that is in equilibrium when the fitness optimum Next, we investigate the effect of drift on the genetic variance in the adaptive phase. We describe the stochastic changes of the frequencies of the trait-increasing alleles roach of ). The diollowing  :(14)ℒi=As in Equation (6), the selection term of this operator includes only the effect of directional selection. Using Equation (14), we obtain ODEs for the lowest-order moments of the allele frequencies as follows :(15)ddtEEquation (17), in combination with Equation (2), suggests that the contribution of individual loci to genetic variance decreases with increasing genetic drift. As the last term on the right-hand side of Equation (17) is negative and proportional to Finally, we use simulations to explore the effect of genetic drift on the dynamics of allele frequencies in the adaptive phase . Of partollowing :(18)δpi≈Equation (18) suggests that the allele frequency shift at a locus depends strongly on the compound parameter Including genetic drift, however, leads to a more complex scenario of polygenic adaptation. We find a good agreement between the theory and simulation for the deviation rm phase . For theWe analyzed a deterministic model to describe the short-term response of a quantitative trait after an environmental change, leading to a sudden shift of the fitness optimum. We provided approximate formulas for the timescales over which the trait mean approaches the new optimum. When the effect sizes are small, subtle to moderate allele frequency shifts occur within the phase of fast adaptation. In contrast, dramatic frequency changes (including fixations leading to selective sweeps) may be observed for large effect sizes. We also integrated random genetic drift into our model to analyze polygenic adaptation, i.e., the case of small effect sizes and weak selection at many loci. We investigated the equilibrium distribution of allele frequencies , based on diffusion theory. For realistic values of population sizes and mutation rates, this distribution is U-shaped. We also studied the adaptive phase. We found that—as in the deterministic model—the trait mean approaches the new optimum exponentially at a rate proportional to the equilibrium genetic variance. This result agrees with Lande’s  predictiThe model we analyzed is simple in that the allelic effects on traits are assumed to be additive (no dominance or epistasis). Our basic assumption, however, fits well with the notion that many quantitative traits are polygenic and that mutations of small effect at a locus tend to act additively. These findings are consistent with observations of GWAS in humans in which the variants identified rarely have large effects (reviewed in ).Our findings have some important implications for the detection of positive selection associated with quantitative traits. Our results suggest that the identification of polygenic selection in the genome may be hindered by the effects of genetic drift. All methods that were proposed for identifying polygenic selection are based on combining signals of changes in allele frequency across many loci that control a given trait and testing whether these changes tend to affect the trait in a certain direction . The detDespite these difficulties, there are several reports on the detection of rapid polygenic adaptation in the literature. The best-studied trait is human height ,48. Turcand Coop ; it alsoAnother approach to detect signatures of polygenic adaptation was introduced by Field et al. . It is bClassical sweeps, on the other hand, that occur when effect sizes are large (see above) can be identified by several methods developed in the past 20 years (reviewed in ). In natFinally, we discussed the effect of population size bottlenecks on polygenic adaptation. In , we descnal size . During This example suggests that genetic drift associated with severe bottlenecks may prevent quantitative traits from appproaching their fitness optimum very closely. Therefore, observing rapid polygenic adaptation, e.g., in experimental populations, is expected to be very difficult, unless their source populations are large. In summary, our analysis of genetic drift in rapid adaptation may indicate that in some of the well-known examples of rapid adaptation , polyge


N = 31,688), the genomic latent factor analysis was first conducted in a discovery sample , and then tested in an independent sample from the same cohort . In the discovery sample, we show that the genetic organization of RSNs can be best explained by two distinct but correlated genetic factors that divide multimodal association networks and sensory networks. Eleven of the 17 factor loadings were replicated in the independent sample. With the multivariate GWAS, we found and replicated nine independent SNPs associated with the joint architecture of RSNs. Further, by combining the discovery and replication samples, we discovered additional SNP and gene associations with the two factors of RSN amplitude. We conclude that modeling the genetic effects on brain function in a multivariate way is a powerful approach to learn more about the biological mechanisms involved in brain function.The amplitude of activation in brain resting state networks (RSNs), measured with resting‐state functional magnetic resonance imaging, is heritable and genetically correlated across RSNs, indicating pleiotropy. Recent univariate genome‐wide association studies (GWASs) explored the genetic underpinnings of individual variation in RSN activity. Yet univariate genomic analyses do not describe the pleiotropic nature of RSNs. In this study, we used a novel multivariate method called genomic structural equation modeling to model latent factors that capture the shared genomic influence on RSNs and to identify single nucleotide polymorphisms (SNPs) and genes driving this pleiotropy. Using summary statistics from GWAS of 21 RSNs reported in UK Biobank ( We modeled latent factors that capture the shared genomic influence on functional brain networks. We show that the genetic organization of brain function is represented by two distinct but correlated genetic factors, and identify SNPs and genes driving both factors. We demonstrate that multivariate methods can help in discovering the biological mechanisms shared in brain function. The genome‐wide association study (GWAS) of BOLD amplitude conducted by Elliott et al., PLCE1; four RSNs mainly covering prefrontal regions were associated with the same three intergenic variants  in chromosome 10; one genome‐wide association with a single sensorimotor RSN involved the intronic variant rs60873293.RSN activation is heritable ‐corrected significant .We obtained GWAS summary statistics of BOLD amplitudes of 10 multimodal association and 11 sensory RSNs Figures  measuredhSNP2 estimates  had nonsignificant 2.2p[Bonferroni] ≤ .05/210 = 2E−4), and 67 reached “nominal” significance not accounting for multiple comparisons (p < .05). The Bonferroni and the “nominally” significant genetic correlations were predominantly positive . For more details on the genetic correlation values and respective standard errors and p values, see Table STo test the existence of shared genetic etiologies between the heritable RSN amplitudes, we calculated genetic correlations using Linkage Disequilibrium Regression Analysis  reported with the replication sample .We used confirmatory factor analysis (CFA) to test the model fit of the two‐factor model in the discovery sample, and retested the same model in the replication sample (p[Bonferroni] ≤ .05/22 = .0022) leading also to improved model fit estimates. In Figure r = .47, p = 1.14E−8), where the first factor (F1) comprises all 10 multimodal association networks (MA1–10) and two sensory networks (SN4 and SN10); whereas the second factor (F2) consists of six sensory networks . These results resemble highly the genomic SEM outcome obtained with the 17 RSN amplitudes included in the discovery sample , the EFA on the BIG40 sample led to the same optimal number of factors  and the BIG40 sample . For the discovery sample, 142 SNPs, encompassing three genomic loci, showed genome‐wide significant associations with F1 (p < 5E−8). Table p < .05 in the multivariate GWAS on the replication sample (see Table p[Bonferroni] = .05/9 = .0056). For F2, no SNP reached genome‐wide significance in the discovery sample , indicating that some SNP effects in these loci are driven by specific RSN, rather than by the multiple RSNs associated with F1. The analysis on the BIG40 sample also revealed that F2 is associated with 21 SNPs with genome‐wide significance in a loci with the lead SNP being rs6737318 in chromosome 2 , and appeared specifically driven by the SNP effects on four sensorimotor networks (SN5–8).The multivariate GWAS of F1 in the BIG40 sample reported, in addition to the SNPs found with the discovery sample, an additional amount of 357 SNPs, making a total of 498 SNPs circumscribing seven genomic loci (see Table p < 5E−8) in at least one of the 18 previous original univariate GWASs (open.win.ox.ac.uk/ukbiobank/big40/pheweb33k/). However, the first and fifth loci in F1 encompass SNPs with corrected genome‐wide significance accounting for the number of RSNs (p[Bonferroni] ≤ 5E−8/18 = 2.77E−9), while the p values reached by these SNPs in the univariate GWAS do not reach this threshold.All the genomic loci reported for both factors were also found to be associated (2.5p [FDR] ≤ .05), and chromatin interaction mapping (adjusted p [FDR] ≤ 1E−6), as reported in Tables SWe interpreted our multivariate GWAS results by conducting functional annotation and gene‐mapping of genomic loci using FUMA . Yet, we found six genetic correlations showing nominal significance (p ≤ .05): F1 with major depressive disorder and Alzheimer's disease; whereas F2 was nominally significantly correlated with genetic factors driving autism spectrum disorder, Alzheimer's disease, body‐mass index (BMI), and bone density. For more details on the genetic correlation values and respective standard errors and p values, see Table STo examine shared genetic effects between the two multivariate RSN factors, estimated using the BIG40 sample, and 10 preselected neuropsychiatric and physical traits, we performed genetic correlation analyses with GWAS summary statistics. The genetic correlation results are reported in Figure 3We investigated the genomic basis of pleiotropy of brain function in 21 RSNs across the brain. We discovered that two latent genetic factors best captured the genomic influence on the amplitude of RSNs throughout the brain, with 11 RSN amplitude associations replicated in an independent sample. The first factor was associated with multimodal association networks and two sensory networks; the second factor represented only sensory networks. Further, we found that the first factor was associated with SNPs and genes with implications for our understanding of the molecular basis of brain function.Our genomic factor analyses point to a genetic divergence of multimodal association and sensory functions. This distinction is in line with previous studies using functional connectivity measures , cardiovascular , and more general metabolic traits . The association of PLCE1 was previously reported with seven individual RSNs in Elliott et al., Interesting significant gene‐wide associations also included Despite known associations of the above rfMRI‐associated genes with neuropsychiatric and physical traits and previously reported phenotypic associations between these traits and rfMRI‐derived imaging phenotypes . They contain GWAS results for 3,919 imaging phenotypes of brain structure and function, based on a discovery sample consisting of 22,138 unrelated individuals of UK ancestry, of which 11,624 female . In the discovery and replication samples, 21,081 and 10,607 individuals with available rfMRI data were respectively included in the GWAS on the amplitude of 21 RSNs , so as the 31,688 individuals comprised in the GWAS of the BIG40 sample. The MRI acquisition and analysis procedures of the brain imaging phenotypes have been described previously , while controlling for population structure represented by the first 40 genetic principal components. A total of 20,381,043 SNP associations were reported in the selected GWAS summary statistics of the discovery sample, whereas GWASs of the replication and BIG40 samples contained results for 17,103,079 SNPs. The SNP associations were estimated via linear association testing in BGENIE software  selection of SNPs reported in the HapMap3 reference panel  SNP‐based heritability were taken forward to the next genomic SEM steps.Second, we calculated the SNP‐based heritability of the 21 RSN amplitudes with LD‐Score regression (LDSC v1.0.0)  a genetic covariance matrix quantifying the genetic overlap among the RSNs; (b) the respective matrix containing the standardized genetic covariance values ; (c) a sampling covariance matrix informative of the standard errors associated with the genetic covariance measures.r2) of equal or more than 10% . Further, with the model retaining Bonferroni‐significant factor loadings in the discovery sample, we conducted a CFA on the independent replication sample, in which the replication of the factor loadings was determined by “nominal” significance (p ≤ .05).For the most optimal model, we ran CFA using the genomic SEM package, in order to estimate the factor loadings of the variables included in the model and evaluate the respective model fit. Both the genetic and sampling covariance matrices were analyzed using weighted least squares estimation, providing fit statistics and inferred factor loadings. We retained factor loadings at a Bonferroni significance level across the factor loadings within the model , whereas the replication of SNP associations in the discovery sample was confirmed in case of “nominal” significance (P ≤ .05). Additionally, for each SNP in the BIG40 sample, the results included a heterogeneity statistic (QSNP) and respective p value addressing whether the SNP effect was mediated by the common factor(s) (null hypothesis), or is specific to one of the traits (p ≤ 5E−8). The SNP‐based heritability of genetic factors represented in each model was also estimated, using LDSC . For each multivariate GWAS, FUMA annotates SNPs that reach independent genome‐wide significance (p < 5E−8), or that reach nominal significance (p < .05) and are in LD (r2 ≥ .6) with any of the independent genome‐wide significant SNPs within a 250 kb window. After determining the independent significant SNPs, the lead SNP of each genomic locus is chosen according to a more stringent LD squared coefficient r2 ≤ .1  and chromatin interaction mapping (adjusted p [FDR] ≤ 1E−6), as recommended in FUMA . For detailed information about these GWAS summary statistics, consult Table STo examine shared genetic effects between the RSN genomic factors estimated with the BIG40 sample and other traits, we performed genetic correlation analyses with GWAS summary statistics from 10 selected traits. We followed the same QC and bivariate genetic analysis procedures used for RSN amplitudes (see Section B. Franke has received educational speaking fees from Medice. C. F. Beckmann is director and shareholder of SBGneuro Ltd.The conception of the idea motivating this study was elaborated by João Guimaraes, E. Sprooten, and J. Bralten. João Guimaraes performed the analyses, which were supervised by J. Bralten and E. Sprooten. João Guimaraes, E. Sprooten, and J. Bralten wrote the paper with contributions from the remaining coauthors.AppendixS1. Supporting Information.Click here for additional data file.


We map the genetic relationship between thalamic nuclei and 180 cerebral cortical areas and find overlapping genetic architectures consistent with thalamocortical connectivity. Pleiotropy analyses between thalamic volumes and ten psychiatric and neurological disorders reveal shared variants for all disorders. Together, these analyses identify genetic loci linked to thalamic nuclei and substantiate the emerging view of the thalamus having central roles in cortical functioning and common brain disorders.The thalamus is a vital communication hub in the center of the brain and consists of distinct nuclei critical for consciousness and higher-order cortical functions. Structural and functional thalamic alterations are involved in the pathogenesis of common brain disorders, yet the genetic architecture of the thalamus remains largely unknown. Here, using brain scans and genotype data from 30,114 individuals, we identify 55 lead single nucleotide polymorphisms (SNPs) within 42 genetic loci and 391 genes associated with volumes of the thalamus and its nuclei. In an independent validation sample ( Differences in thalamic structure have been observed in several psychiatric disorders, but the genetic overlap has not been explored. Here, the authors perform a genome-wide association study on thalamic nuclei volume and find genetic loci in common between thalamic volumes and brain disorders. Cognitive neuroscience is therefore shifting focus to how the thalamus regulates cortical activity and supports higher-order functions such as working memory6, attentional control7, and visual processing8. There is also a growing appreciation of thalamic contributions to the pathogenesis of neurological and psychiatric disorders, including dementias9, Parkinson’s disease (PD)10, schizophrenia (SCZ)11, and bipolar disorder (BD)12. Despite the importance for human cognition and disease, the genetic architecture of the thalamus and its relationship with cortical structure and common brain disorders remain largely unknown.Recent studies indicated that the thalamus has a broader role in cognition than previously assumed4. Two recent studies found one13 and two14 genetic loci associated with whole thalamus volume, yet there is no genome-wide association study (GWAS) of thalamic nuclei. In this work, we further identify genetic loci and candidate genes for volumes of the whole thalamus and its nuclei. We detect overlap between genetic architectures of the thalamic nuclei and 180 cortical areas consistent with thalamocortical structural connectivity. Moreover, pleiotropy analyses between thalamic volumes and ten neurological and psychiatric disorders reveal shared variants for all disorders. Together, these analyses provide insights into the genetic underpinnings of human thalamic nuclei and substantiate the emerging view of the thalamus having central roles in common brain disorders.The thalamus can be divided into nuclei that mainly relay peripheral information to the cerebral cortex and higher-order nuclei that modulate cortical functionsn = 30,432 genotyped White British from the UK Biobank15. The MRI data were segmented into the whole thalamus and six thalamic nuclei groups—anterior, lateral, ventral, intralaminar, medial, and posterior—using Bayesian thalamus segmentation17  after visually inspecting the segmentations for each of the 30,432 participants. The resulting 30,114 data sets comprised the discovery sample .We accessed brain magnetic resonance imaging (MRI) data from n17 Fig. . We remo18 on whole thalamus and the six nuclei volumes accounting for age, age-orthogonalized age-squared, sex, scanning site, intracranial volume, and the first 20 genetic principal components. The thalamic nuclei GWAS also accounted for whole thalamus volume, thus revealing genetic signals beyond commonality in volume20. The thalamic nuclei GWAS were also run without covarying for whole thalamus volume and are presented in the Supplementary Information.We conducted GWAS with PLINK21 was 25% for whole thalamus and 18–32% for the six nuclei groups  and identified a total of 55 lead SNPs in 50 genomic loci , whereas the anterior nuclei showed the weakest inflation (λ = 1.105).Single-nucleotide polymorphism (SNP)-based heritability estimated using linkage disequilibrium (LD) score regressionups Fig. . We foun22. rs13107325 (score = 23.1) was associated with anterior and lateral nuclei volumes, is located within the metal ions transporter gene SLC39A8 on chromosome 4, and is linked to cognitive performance, PD, and SCZ24. The gene nearest to rs12146713 on chromosome 12  is NUAK1, which regulates the Tau protein level25. Cerebral Tau accumulation is a defining characteristic of Alzheimer’s disease (AD) and other neurodegenerative disorders26. rs951366 on chromosome 1 was significant for posterior nuclei volume (score = 15.7) and is associated with PD27. Further GWAS results are provided in Supplementary Figs. Three lead SNPs had a combined annotation-dependent depletion (CADD) score > 15, which indicates deleterious protein effects15. We found that 53 out of the 55 lead SNPs from the discovery GWAS had the same effect direction in the replication . Moreover, 32 of the discovery lead SNPs had uncorrected P < 0.05, whereas 23 had uncorrected P > 0.05, in the replication  from the UK Biobank28. We found that volumes of the whole thalamus, and intralaminar and medial nuclei groups were enriched for central nervous system (CNS) tissues and cell types, yet none of these associations remained significant after Bonferroni correction 29 to map candidate SNPs to genes. This identified 336 unique genes across the 7 volumes , and chromatin interaction mapping in Functional Mapping and Annotation of GWAS (FUMA)30 and found significant Gene Ontology sets for the intralaminar nuclei, implicating cell–cell adhesion, and for the lateral nuclei, involving cell growth and synapse organization 41, autism spectrum disorder (ASD)42, BD43, major depression (MD)45, SCZ46, AD47, multiple sclerosis (MS)48, PD49, and generalized and focal epilepsy (GEP/FEP)50, and detected significant genetic correlations between volumes and PD, BD, and MS  analyses, which enable detection of genetic loci shared between traitsers Fig. , and fouers Fig. .Fig. 5GeBCL11B  and encodes a transcription factor expressed in the fetal and adult brain55. BCL11B is thought to regulate neuron development and its mutations are associated with intellectual disability and neuropsychiatric disorders56. rs5011432 was associated with medial nuclei volume in MD and AD, and the nearest gene is TMEM106B, which regulates lysosome functions57. rs13107325, within SLC39A8 as discussed above, was significant for both SCZ and PD, and associated with anterior, lateral, and intralaminar nuclei volumes.Further studies of how the overlapping genetic regions influence thalamus structure and disorder risk are warranted, yet several of the shared loci are noteworthy. rs2693698 was significant for SCZ and BD, and jointly associated with medial and posterior nuclei volumes. The gene nearest to rs2693698 is 20. In addition, we found genetic correlations between the thalamic nuclei and distinct cortical regions. Notably, the positive associations were mainly found for higher-order thalamic nuclei and the cortical distributions are consistent with thalamocortical connectivity. These results are in line with emerging findings, suggesting that functionally connected brain regions exhibit stronger genetic correlations than less-connected regions36. Finally, we identified genetic loci shared between thalamus volumes and ten neurological and psychiatric disorders. Further mechanistic studies are required to clarify how the thalamus contributes to the pathogenesis of brain disorders and the pleiotropic loci identified by our analyses could inform such experiments.In summary, our study provides insights into the genetic underpinnings of the human thalamus and identifies genetic loci linked to thalamic nuclei. We found that the majority of thalamus-linked loci and genes were associated with only one of the seven volumes, which may suggest at least partly independent genetic architectures. This demonstrates the importance of targeting individual nuclei rather than studying the thalamus as a whole, in line with similar benefits observed for other brain structuresn = 30,432 genotyped White British from the UK Biobank15 in the GWAS discovery sample. The results from the discovery GWAS were also used in the analyses of genetic overlap with brain disorders and the discovery sample therefore excluded individuals with CNS diagnoses. Additional data became available to us after we had performed the main discovery analysis and we combined this data with the previously excluded data of individuals with CNS diagnoses, to maximize the sample size for a replication sample. The replication sample therefore comprised MRI and SNP data from an additional 5266 White British of the general population in the UK Biobank15, irrespective of diagnoses. The National Health Service National Research Ethics Service (ref. 11/112 NW/0382) has approved the UK Biobank and all participants gave signed informed consent before study inclusion. The projects from which summary statistics of GWAS were used for genetic overlap analysis were each approved by the local ethics committees and informed consent was obtained from all participants50. The Norwegian Regional Committees for Medical and Health Research Ethics (REC South East) evaluated our pipelines that use summary statistics from published works for genetic analysis as performed in the current study and found that no additional institutional approval is needed.We included raw T1-weighted three-dimensional brain magnetic MRI data from 17, we segmented the MRI data into the whole thalamus and six thalamic nuclei groups, i.e., anterior, lateral, ventral, intralaminar, medial, and posterior nuclei groups. These groups, as defined by Iglesias et al.17, include the following thalamic nuclei: anterior group—the anteroventral nucleus; lateral group—the laterodorsal and lateral posterior nucleus; ventral group—the ventral anterior, ventral anterior magnocellular, ventral lateral anterior, ventral lateral posterior, ventral posterolateral, and ventromedial nucleus; intralaminar group—the central medial, central lateral, paracentral, centromedian, and parafascicular nucleus; medial group—the paratenial, reuniens, mediodorsal medial magnocellular, and mediodorsal lateral parvocellular nucleus; and the posterior group—the lateral geniculate, medial geniculate, limitans, pulvinar anterior, pulvinar medial, pulvinar lateral, and pulvinar inferior. The segmentations of these thalamic nuclei groups have high test–retest reliability, show agreement with histological studies of thalamic nuclei, and are robust to differences in MRI contrast17. For each nuclei group, we summed the volumes in the left and right thalamus, and these were used in all analyses.The MRI data for all individuals was stored and analyzed locally at the University of Oslo. Using Bayesian thalamus segmentation based on ex-vivo MRI and histology in Freesurfer 6.0n = 30,114 and n = 5173, respectively.We then manually assessed the quality and delineations of the discovery and replication sample MRI data sets by visually inspecting axial view figures of the segmentations for each participant. This procedure excluded 318 data sets from the discovery sample (due to tumors and other lesions (6%), cysts (12%), ventricle abnormalities (18%), segmentation errors (14%), and insufficient data quality (50%)) and 93 data sets from the replication sample (due to tumors and other lesions (4%), cysts (13%), ventricle abnormalities (13%), segmentation errors (9%), and insufficient data quality (61%)). Thus, the final sizes of the discovery and replication GWAS samples were P < 1e − 6.We performed GWAS on the thalamus volumes and genotype data from the participants in the GWAS discovery and replication samples, and due to data availability restricted the analyses to White British individuals. We used standard quality control procedures to the UK Biobank v3 imputed genetic data and removed SNPs with an imputation quality score < 0.5, a minor allele frequency < 0.05, missing in more than 5% of individuals, and failing the Hardy–Weinberg equilibrium tests at a 18. All GWAS accounted for age, age-orthogonalized age-squared, sex, scanning site, intracranial volume, and the first 20 genetic principal components to control for population stratification. The resulting P-values were Bonferroni-corrected for analyses of seven volumes. In addition, the GWAS for the six thalamic nuclei groups was run, both with and without whole thalamus as an additional covariate. The main text presents results for thalamic nuclei groups when accounting for whole thalamus volume, whereas GWAS results for the nuclei volumes are provided in the Supplementary Information.GWAS was conducted for the seven thalamic volumes, i.e., volumes of the whole thalamus and the anterior, lateral, ventral, intralaminar, medial, and posterior nuclei groups, using PLINK v2.029. The settings and results of the FUMA analyses can be found at https://fuma.ctglab.nl/browse (FUMA ID 135-141). For these analyses, we used the UKB release2b White British as the reference panel. Independent significant SNPs were identified by the genome-wide significant threshold (P < 7e − 9) and by their independency (r2 ≤ 0.6 within a 1 mb window). We defined independent significant SNPs with r2 < 0.1 within a 1 mb window as lead SNPs and genomic loci were identified by merging lead SNPs closer than 250 kb. All SNPs in LD (r2 ≥ 0.6) with one of the independent significant SNPs in the genetic loci were defined as candidate SNPs. We used the minimum r2 to determine the borders of the genomic risk loci.We identified genetic loci related to thalamic volumes using the FUMA platform v1.3.529, which is based on information from 18 biological repositories and tools. FUMA prioritizes the most likely causal SNPs and genes by employing positional, eQTL, and chromatin interaction mapping29. The platform annotates significantly associated SNPs with functional categories, combined CADD scores22, RegulomeDB scores58, and chromatin states29. A CADD score > 12.37 indicates a deleterious protein effect22, whereas the RegulomeDB score suggests the regulatory functionality of SNPs based on eQTLs and chromatin marks. Chromatin states show the genomic region’s accessibility for every 200 bp with 15 categorical states predicted by ChromHMM 1.00 based on five histone modification marks  for 127 epigenomes59. Lower scores reflect higher accessibility in the chromatin state and to a more open state. Roadmap suggests the following 15-core chromatin states: 1 = Active Transcription Start Site (TSS); 2 = Flanking Active TSS; 3 = Transcription at Gene 5′ and 3′; 4 = Strong Transcription; 5 = Weak Transcription; 6 = Genic Enhancers; 7 = Enhancers; 8 = Zinc Finger Genes and Repeats; 9 = Heterochromatic; 10 = Bivalent/Poised TSS; 11 = Flanking Bivalent/Poised TSS/Enh; 12 = Bivalent Enhancer; 13 = Repressed PolyComb; 14 = Weak Repressed PolyComb; and 15 = Quiescent/Low60.We functionally annotated all candidate SNPs of thalamus volumes using FUMA30 in FUMA on the complete GWAS input data. We excluded the major histocompatibility complex region before running the MAGMA-based analyses. MAGMA runs multiple linear regression to obtain gene-based P-values and the Bonferroni-corrected significant threshold was P = 0.05/18158 genes/7 volumes = 3.9e − 7. We ran gene-set analyses with hypergeometric tests for curated gene sets and GO terms obtained from the MsigDB61.We performed GWGAS and gene-set analyses with MAGMA v1.07https://github.com/bulik/ldsc/wiki/Cell-type-specific-analyses). Following the analyses of Finucane et al.28, we classified 205 tissues and cell types into nine categories for visualization, i.e., “Other,” “Musculoskeletal-connective,” “Liver,” “Endocrine,” “Digestive,” “CNS,” “Cardiovascular,” “Blood/Immune,” and “Adipose.” We used a Bonferroni-corrected significance threshold of P = 0.05/205 tissues and cell types/7 thalamic GWAS = 3.5e − 5.We used LDSC-SEGs to assess the tissue and cell-type specificity of the GWAS findings  using FDR correction in R statistics62.We derived volumes of 180 cortical regions in each hemisphere based on a multimodal parcellation of the cerebral cortex41, ASD, SZ, BD, and MD from the Psychiatric Genomics Consortium46, for AD from the International Genomics of Alzheimer’s Project47, for MS from the International Multiple Sclerosis Genetics Consortium48, for PD from the International Parkinson Disease Genomics Consortium and 23andMe49, and for GEP and FEP from The International League Against Epilepsy Consortium on Complex Epilepsies50. We performed similar analysis of genetic correlations between volumes and disorders as described for the thalamocortical correlations. In addition, we employed conjunctional FDR statistics64 to assess polygenic overlap between the seven thalamic volumes and the ten brain disorders. The conjunctional FDR analyses were run using in-house software, available at https://github.com/precimed/pleiofdr/, MATLAB 2017a, and Python 3.7.4.Furthermore, we obtained GWAS summary statistics for ADHD54. In brief, the conjunctional FDR is defined by the maximum of the two conditional FDR values for a specific SNP. The method calculates a posterior probability that an SNP is null for either trait or both at the same time, given that the P-values for both phenotypes are as small, or smaller, than the P-values for each trait individually. The empirical null distribution in GWASs is affected by global variance inflation and all P-values were therefore corrected for inflation using a genomic inflation control procedure. All analyses were performed after excluding SNPs in the major extended histocompatibility complex (hg19 location chromosome 6: 25119106–33854733) and 8p23.1 regions (hg19 location chromosome 8: 7242715–12483982) for all cases, and MAPT and APOE regions for PD and AD, respectively, as complex correlations in regions with intricate LD can bias the FDR estimation.A review and mathematical description of this method when applied to neurological and psychiatric disorders were recently published by Smeland et al.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Reporting Summary


An independent twin cohort from Hong Kong (n = 358) was used to replicate results from structural equation modelling (SEM). Parental left-side preference increased the chance of an individual to be left-sided for the same trait, with stronger maternal than paternal effects for footedness. By regressing out the effects of sex, age, and ancestry, we transformed laterality categories into quantitative measures. The SNP-h2 for quantitative handedness and footedness was 0.21 and 0.23, respectively, which is higher than the SNP-h2 reported in larger genetic studies using binary handedness measures. The heritability of the quantitative measure of handedness increased (0.45) compared to a binary measure for writing hand (0.27) in the Hong Kong twins. Genomic and behavioural SEM identified a shared genetic factor contributing to handedness, footedness, and eyedness, but no independent effects on individual phenotypes. Our analysis demonstrates how quantitative multidimensional laterality phenotypes are better suited to capture the underlying genetics than binary traits.Handedness is the most commonly investigated lateralised phenotype and is usually measured as a binary left/right category. Its links with psychiatric and neurodevelopmental disorders prompted studies aimed at understanding the underlying genetics, while other measures and side preferences have been less studied. We investigated the heritability of hand, as well as foot, and eye preference by assessing parental effects ( For inssymmetry , althougsymmetry . Neurodesymmetry , 6, schisymmetry , or autisymmetry  have beeThe most commonly studied lateralised trait is handedness. Worldwide, around 10% of the general population is left-handed with slight geographical variation , likely Family studies have suggested differential effects of fathers and mothers to their offspring’s handedness. A stronger maternal than paternal effect was repeatedly found in biologically related parent-offspring trios , 21 and n ~ 2 M) estimated that up to 6% of the variance in left-handedness and up to 15% of the variance in ambidexterity are explained by common genetic markers [n = 32,256) revealed that genetic markers associated with structural brain asymmetries overlapped with markers previously associated with writing hand preference. Moreover, genetic factors involved in brain asymmetry overlap with neurodevelopmental and cognitive traits such as ASD, schizophrenia, educational attainment (EA) [A recent large-scale genome-wide association study  , and intent (EA) . These dncases = 2431, ncontrols = 116,938) [ncases = 88, ncontrols = 118 [ncases = 68, ncontrols = 944 [ncases = 37; ncontrols = 20) [Foot and eye preference have received considerably less attention, even though associations with neurodevelopmental disorders have been reported as well. For example, we found an increased prevalence of non-right-footedness in neurodevelopmental and psychiatric disorders  . Smallerls = 118 ; ncases ls = 944 ) and ASDls = 20)  reportedls = 20)  providedls = 20) . These sWe performed the largest heritability study to date for multiple side preferences in the Avon Longitudinal Study of Parents and Children (ALSPAC) and a twin cohort from Hong Kong to investigate the heritability of laterality phenotypes, their associations with one another, and their links to neurodevelopmental and cognitive outcomes.ALSPAC: ALSPAC is a population-based longitudinal cohort. Pregnant women living in Avon, UK, with expected dates of delivery from 1st April 1991 to 31st December 1992 were invited to take part, resulting in 14,062 live births and 13,988 children who were alive at 1 year of age [http://www.bristol.ac.uk/alspac/researchers/our-data/).r of age , 36. InfHong Kong: Study participants were recruited from the Chinese-English Twin Study of Biliteracy, a longitudinal study of primary school twin children starting in 2014 [ in 2014 . ParticiALSPAC: Laterality phenotypes were assessed for children based on maternal reports and for parents as self-report. Hand preference was assessed using eleven items for parents and six items for children. Foot preference and eye preference were assessed using four and two items, respectively, for parents and children. All items were rated on a 3-point scale  with a mean age of 8.67 years (SD = 1.23). This sample included 81 MZ pairs  and 102 DZ pairs . Twin zygosity of same-sex twins was determined by genotyping small tandem repeat (STR) markers on chromosomes 13, 18, 21, X and Y by Quantitative Fluorescence-Polymerase Chain Reaction (QF-PCR).Hand, foot, and eye preference were assessed using a modification of the EHI . The queALSPAC: Children’s genotypes were generated on the Illumina HumanHap550-quad array at the Wellcome Trust Sanger Institute, Cambridge, UK and the Laboratory Corporation of America, Burlington, NC, US. Standard QC was performed as described elsewhere [lsewhere . In totaHong Kong: Genotyping was performed using Illumina Human Infinium OmniZhongHua-8 v1.3 Beadchip at the Prenatal Genetic Diagnosis Centre and the Pre-implantation Genetic Diagnosis laboratory in the Prince of Wales Hospital and The Chinese University of Hong Kong, Hong Kong SAR. Standard quality control measures were carried out. Genetic variants with missing rate >10%, minor allele frequency (MAF) < 0.01 and with significant deviation from Hardy–Weinberg equilibrium (p < 1 × 10−6) were excluded. Individuals with genotyping rates <90% and outlying heterozygosity rates were excluded. In total, 911,178 SNPs passed QC filtering. Among the n = 366 twin children, genotype data were available for n = 358 (81 MZ pairs and 98 DZ pairs).n = 113) and children with physical disabilities (n = 65) or sensory impairments (n = 50), resulting in a sample size (number of trios) of nhand = 5028, nfoot = 4960 and neye = 4762 . This analysis was performed for child sidedness  using both the A) R-M-L classification (excluding mixed-sided children and their parents) and the B) R-L classification.Next, we differentiated maternal and paternal effects by using maternal sidedness, paternal sidedness , and offspring sex, as well as interaction terms between maternal and paternal sidedness with offspring sex as predictors. We used the wald.test function to test for a difference between maternal and paternal effects using the R-M-L and the R-L classification.n = 1719 fathers. We used the R package Sequoia [2 < 0.01 within a 50 kb window). The 500 SNPs were spread across chromosomes 1–22. Sequoia confirmed paternity for n = 1624 fathers. Among this subsample of 1624 trios, complete phenotypic data were available for 1161 trios for handedness, 1150 trios for footedness, and 1105 trios for eyedness .To assess the reliability of maternal reports, we performed a Spearman rank correlation analysis between hand preference for drawing (left/right) assessed by maternal report at age 3.5 and self-reported hand preference for writing at age 7.5 (n = 5956) with genome-wide genetic and phenotypic data were selected for Genome-wide Complex Trait Analysis (GCTA) [n = 4630 (foot used to pick up a pebble) to n = 5931 (summary item for hand preference).Unrelated children  . The sami is the laterality summary item , β0 is the intercept, β1 is the regression weight for offspring sex, X1i is offspring sex, β2 is the regression weight for offspring age in weeks, X2i is offspring age in weeks, β3 is the regression weight for PC1, X3i is PC1, β4 is the regression weight for PC2, X4i is PC2, and εi reflects random error.Summary items in the R-M-L classification for hand, foot, and eye preference and 12 single items were residualised for sex, age, and the two most significant principal components:2 < 0.01 within a 50 kb window) SNPs (excluding high LD regions) using Plink v2. The rationale for including PCs in the phenotype transformation was based on the Genetic-relationship-matrix structural equation modelling (GRM-SEM) method which has been developed using the ALSPAC cohort [Phenotypes were then inverse rank-transformed to achieve normally distributed phenotypes. Principal components were calculated based on directly genotyped (MAF < 0.05) and LD pruned  and single items (12) using restricted maximum-likelihood (REML) analysis in GCTA [n = 5956) using GCTA.SNP-h in GCTA , which c2 was calculated for the untransformed categorical items using sex, age, and the first two principal components as covariates. We estimated SNP-h2 separately for left-sidedness  and mixed-sidedness .As a comparison, SNP-hi is the transformed offspring laterality item, β0 is the intercept, β1 is the regression weight (heritability index), Xi is the mean parental laterality, and εi reflects random error.Next, we estimated heritability from parent-offspring data . Among tn = 5956 unrelated children using GCTA). A GRM-SEM was fitted using the grmsem library in R (version 1.1.0) using all children with phenotypic data for at least one phenotype. Multivariate trait variances were modelled using a saturated model (Cholesky decomposition). GRM-SEM was also used to estimate bivariate heritability, i.e. the contribution of genetic factors to the phenotypic covariance.We applied GRM-SEM  to quantThe heritability of laterality phenotypes was additionally estimated using a classical twin design that compares the similarity of MZ to that of DZ twins. Since MZ twins share nearly all their genetic variants, whereas DZ twins share on average 50% of their genetic variants, any excess similarity of MZ twins over DZ twins is the result of genetic influences. This method partitions phenotypic variance into that due to additive genetic (A), shared environmental (C) and non-shared environmental influence (E). The variance attributed to each component can be estimated using the structural equation modelling (SEM) technique and the proportion of variance explained by the genetic influence (A) is termed heritability. Phenotypes were transformed following the same procedure described for ALSPAC above. We fit a multivariate ACE model to the transformed phenotypes  and compared ACE with its constrained models, such as the AE model. Analyses were performed using the OpenMx software package 2.18.1 . The scr2) in the ALSPAC cohort. The summary statistics for hand preference (left vs. right) were calculated after excluding individuals from 23andMe as well as ALSPAC from the original GWAS [https://www.med.unc.edu/pgc/data-index/). Summary statistics for IQ [https://ctg.cncr.nl/software/summary_statistics), and the Social Science Genetic Association Consortium (https://www.thessgac.org/data), respectively.We conducted PRS analyses using summary statistics for handedness assessed as a binary trait, psychiatric and neurodevelopmental conditions , schizophrenia (SCZ)), and cognitive measures (EA and IQ) using PRSice 2.3.3 . PRS ananal GWAS  sample. nal GWAS , ASD [53nal GWAS , and SCZnal GWAS  were accs for IQ  and EA [s for IQ  were acc2 < 0.1 within a 250 kb window) as the weighted sum of risk alleles according to the training GWAS summary statistics. No covariates were included as phenotypes had been corrected for effects of age, sex, and ancestry. Results are presented for the best training GWAS p value threshold (explaining maximum phenotypic variance) as well as GWAS p value thresholds of 0.001, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, and 1. Results were FDR-corrected for 126 comparisons  using the Benjamini–Hochberg method [PRS were derived from LD-clumped SNPs  and left-sided (R-L) offspring as a function of parental sidedness in the whole sample and in trios with confirmed biological paternity. As expected, the percentage of non-right-sidedness and left-sidedness were highest in individuals with two non-right-sided or two left-sided parents, respectively , parental sidedness predicted hand, Χ2(5) = 39.5, p = 1.9 × 10−7, foot, Χ2(5) = 59.9, p = 1.3 × 10−11, and eye preference, Χ2(5) = 27.4, p = 4.8 × 10−5. In the R-L classification , parental sidedness also predicted hand, Χ2(2) = 42.6, p = 5.5 × 10−10, foot, Χ2(2) = 69.1, p = 1.0 × 10−15, and eye preference, Χ2(2) = 14.6, p = 6.9 × 10−4. ORs show that having one or two left-sided parents increased one’s chances to be left-sided for hand, foot, and eye preference in the R-M-L classification  showed similar, although attenuated, parental effects for hand (R-M-L: Χ2(4) = 14.9, p = 0.005; R-L: Χ2(2) = 12.1, p = 0.002) and foot (R-M-L: Χ2(4) = 22.5, p = 0.0002; R-L: Χ2(2) = 19.1, p = 7.1 × 10−5), but not for eye (R-M-L: Χ2(5) = 5.3, p = 0.380; R-L: Χ2(2) = 2.7, p = 0.250) preference  = 38.9, p = 7.4 × 10−8; R-L: Χ2(2) = 31.7, p = 1.3 × 10−7), foot (R-M-L: Χ2(4) = 52.7, p = 9.8 × 10−11; R-L: Χ2(2) = 96.6, p < 2.2 × 10−16), and eye preference (R-M-L: Χ2(4) = 38.3, p = 9.7 × 10−8; R-L: Χ2(2) = 34.1, p = 3.9 × 10−8). Paternal sidedness predicted hand (R-M-L: Χ2(4) = 10.3, p = 0.036; R-L: Χ2(2) = 12.3, p = 0.002) and foot (R-M-L: Χ2(4) = 15.1, p = 0.005; R-L: Χ2(2) = 6.0, p = 0.049), but not eye preference (R-M-L: Χ2(4) = 4.6, p = 0.330; R-L: Χ2(2) = 0.6, p = 0.760). Wald tests contrasting maternal and paternal effects revealed a stronger maternal than paternal effect only for foot preference (R-M-L: Χ2(1) = 4.6, p = 0.033; R-L: Χ2(1) = 23.9, p = 1.0 × 10−6). This effect was confirmed in the subsample with confirmed paternity (R-M-L: Χ2(1) = 8.4, p = 0.004; R-L: Χ2(1) = 10.0, p = 0.002). Although attenuated in the smaller subsample with confirmed paternity, this finding suggests a genuinely stronger maternal than paternal effect on footedness. In the whole sample, interaction terms between maternal/paternal sidedness and offspring sex revealed that in the R-L classification, maternal left-sidedness had a greater effect on left-footedness in girls compared to boys , which was confirmed in the smaller subsample . The full regression model outputs for both the whole sample and the subsample with confirmed paternity can be found in Supplementary Tables Third, we investigated maternal and paternal effects and possible interactions with offspring sex. In the whole sample, Wald tests revealed a significant maternal effect on hand . Among the 2838 children with a right-hand preference at age 3.5, seven reported a left-hand preference for writing at age 7.5. Of the 291 children with left-hand preference at age 3.5, 19 showed a right-hand preference for writing at age 7.5. Overall, 99.2% of individuals showed stable hand preference  were performed in unrelated children from the ALSPAC cohort (n ≤ 5931). Multivariate behavioural SEM analysis was performed in the Hong Kong twin sample (n ≤ 358). The absolute numbers and percentages of children with left, mixed, and right side preference for the three summary items in both cohorts are shown in Table Phenotypic correlation and genomic analyses  = 139.01, p < 2.2 × 10−16), “foot used to stamp” for foot preference  = 95.78, p < 2.2 × 10−16), and “eye used to look through a bottle” for eye preference  = 249.61, p < 2.2 × 10−16). In both cohorts, summary items showed positive correlations with each other  for “eye used to look through a bottle” to 0.42 (p = 8 × 10−13) for “hand used to cut” , followed by handedness . There was no significant SNP-h2 for eyedness .We then tested the heritability of the transformed phenotypes. SNP-hut” Fig. . The hig2 for the untransformed categorical items for left- and mixed-side preference categories. SNP-h2 for left-side preference ranged from 0.00 (p = 0.500) for “foot used to climb a step” to 0.13 (p = 0.031) for “hand used to cut”  for the hand preference summary item to 0.12 (p = 0.031) for “hand used to draw” , 0.09 for footedness , and 0.08 for eyedness .Parent-offspring regression run on the transformed summary items suggested heritability estimates of 0.27 for handedness , 0.08 for footedness , and 0.08 for eyedness . Therefore, the heritability estimates for the quantitative phenotypes were consistently higher than for categorical measures, both for SNP-h11) and genetic variance that is shared with foot (a21) and eye preference (a31). A single genetic factor (A1) explained 20.36% of the phenotypic variance in handedness , 22.12% of the variance in footedness  and 3.84% of the variance in eyedness . All other path coefficients were non-significant, suggesting that one shared genetic factor (A1) contributes to a general left/right directionality across all three phenotypes.Multivariate GRM-SEM analysis was performed on the transformed R-M-L summary items for handedness, footedness, and eyedness in ALSPAC Fig. . The squp = 6.6 × 10−6), 24.9% of the correlation of between footedness and eyedness (p = 0.020), and 26.2% of the correlation between handedness and eyedness (p = 0.020). We replicated these findings with multivariate behavioural SEM in an independent cohort (n = 358). In the Hong Kong cohort, A1 explained 44.30%  of the phenotypic variance in handedness , 5.00%  of the variance in footedness , and 7.00%  of the variance in eyedness   Fig. . All othR2 = 0.13%, p = 0.005). PRS results for all p value thresholds are reported in Supplementary Table None of the PRS associations survived correction for multiple comparisons. The strongest association was found for PRS for IQ, suggesting that genetic predisposition towards higher IQ is associated with a tendency towards right-handedness  and confirmed in the subset with genetically confirmed paternity (n = 1150 trios). Maternal/paternal effects could be explained with sex-linked genetic or parent-of-origin effects. For example, the imprinted LRRTM1 gene was found to be associated with handedness under a parent of origin effect [r = 0.95) between the preferred hand for drawing assessed using maternal report at age 3.5 and self-reported preferred hand for writing in later childhood. The fact that more children switch hand preference from left to right [We investigated the heritability of hand, foot, and eye preference using multiple approaches. To the best of our knowledge, this is the largest study conducted to date for multiple laterality measures in the same individuals. Our analysis of family trios showed that the probability of being left-sided increased for any left-sided parent on the same trait, with stronger effects for hand and foot, rather than eye preference, in line with previous reports , 34. Strn effect . Parent n effect . Few exan effect . Besidesto right  could into right .2 for handedness, footedness, and eyedness to be 0.21, 0.23, and 0.00, respectively. The heritability estimate for handedness is similar to what has been reported in behavioural twin studies (h2 = 0.25) [2 = 0.06) [Using transformed quantitative phenotypes , we esti = 0.25) , 19 but  = 0.06) , 65, 66  = 0.06) . Parent- = 0.06) . The lac = 0.06)  suggests = 0.06)  or fish  = 0.06) , where e2 for “hand used to cut” (with a knife). Previously, this item showed the weakest phenotypic correlation with the other questionnaire items [2 for the summary rather than single footedness items in ALSPAC, suggesting that in contrast to handedness, multiple items might better capture a genetic component for footedness. One possible interpretation is that multiple items will allow identifying mixed-footed rather than ambipedal individuals, who prefer both feet equally. Similar to Suzuki and Ando [Heritability estimates differed substantially between items used to assess handedness, footedness, and eyedness. We found the highest SNP-hre items , 71 and re items . It has re items . This waand Ando , our resand Ando . Overalland Ando  in additAll transformed items showed positive correlations on the phenotypic level. Previous research has shown a tendency towards a higher probability of left-sided lateral preferences in left-handers , 73, sugAn association between laterality and psychiatric disorders, especially schizophrenia , has lonWe assessed the heritability of multiple side preferences using family, genomic, and twin analyses. For footedness, stronger maternal than paternal effects highlight the necessity of examining parent-of-origin effects on the genetic level in future studies. SEM supports a shared genetic factor involved in all three phenotypes without independent genetic factors contributing to footedness and eyedness. The transformed quantitative phenotypes present a heritability that is higher than categorical measures in both molecular and behavioural analyses, suggesting that they might be better suited to identify the underlying genetic factors.Supplementary methods and figures