P-value threshold to derive genetic instruments for MR studies (P < 5 × 10−8) and a 10% variance in the exposure explained by all SNPs, block-jackknifing PRS did not suffer from overfitting bias (mean R2 = 0.034) compared with the externally weighted PRS (mean R2 = 0.040). In contrast, genetic instruments derived from overlapping samples explained a higher variance (mean R2 = 0.048) compared with the externally derived score. Overfitting became considerably more severe when using a more liberal P-value threshold to construct PRS , whereas estimates using jackknife score remained robust to overfitting (mean R2 = 0.084). Using block jackknife resampling MR in an applied analysis, we examined the effects of body mass index on circulating biomarkers which provided comparable estimates to an externally weighted instrument, whereas the overfitted scores typically provided narrower confidence intervals. Furthermore, we extended this framework into sex-stratified, multivariate and bidirectional settings to investigate the effect of childhood body size on adult testosterone levels.Participant overlap can induce overfitting bias into Mendelian randomization (MR) and polygenic risk score (PRS) studies. Here, we evaluated a block jackknife resampling framework for genome-wide association studies (GWAS) and PRS construction to mitigate overfitting bias in MR analyses and implemented this study design in a causal inference setting using data from the UK Biobank. We simulated PRS and MR under three scenarios: (1) using weighted SNP estimates from an external GWAS, (2) using weighted SNP estimates from an overlapping GWAS sample and (3) using a block jackknife resampling framework. Based on a  Genome-wide association studies (GWAS) studies have discovered thousands of genetic variants that robustly associate with many different complex traits and disease endpoints in the past two decades. These findings not only lead to potential translatable opportunities for pharmaceutical target development and highlight biological mechanisms and pathways, but also facilitate endeavours in disease prediction and risk stratification using polygenic risk scores (PRS) . Trait-aMR studies rely on the selection of valid genetic IVs, which are robustly associated with the exposure of interest, affect the outcome only through the exposure being analyzed and which do not share a common cause with the outcome . Geneticet al.’s jackknife approach, block jackknife resampling MR applies the jackknife resampling design in blocks to identify genetic instruments robustly associated with the exposure before performing 2SLS to ensure independence between the discovery and applied dataset  regression . This me dataset ,11. As s dataset . Moreove dataset .In this study, we have evaluated the use of block jackknife resampling to maximize sample sizes for both IV identification and MR analysis using simulated datasets . We compP-value thresholds. Externally weighted PRS was constructed based on an external GWAS (discovery dataset) with no sample overlap with the applied dataset. Overlapping sample PRS was constructed with 100% sample overlap between the discovery dataset and applied dataset. In block jackknife resampling, samples in the applied dataset were split into Nblock blocks for SNPs selection as well as PRS construction. After generating the PRS for every block based on the GWAS performed on Nblock-1 blocks, we then combine samples from every block and use their PRS as the instrumental variable for the exposure in a 2SLS regression analysis using the full sample. To investigate the effect of sample overlap and the advantages of using a block jackknife resampling framework to mitigate overfitting bias, we performed extensive simulations to construct PRS under the combination of three GWAS frameworks  and 13 different P-value threshold to derive genetic instruments for MR studies (i.e. P < 5 × 10−8), our block jackknife resampling PRS did not appear to suffer from overfitting bias  . Moreovecument}) . AdditioFor the full summary statistics of this simulated analysis, see <fig id="f4">Supplementary Table S1<graphic mimetype="image" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="ddac186f4.tif"/></fig><fig id="f5">Supplementary Table S2<graphic mimetype="image" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="ddac186f5.tif"/></fig>Many published trait and disease GWAS do not include UKB, but these typically have smaller sample sizes, which is a key determinant of the power of GWAS and the number of trait-associated SNPs detectable from such studies . Thus, tP-value threshold (i.e. P < 5 × 10−8), block jackknife resampling PRS on an applied dataset of 50 000 individuals explained higher variance in the exposure  for PRS construction, where each analysis was repeated 300 times. Results show that overfitting, measured by the divergence between the estimates generated using overlapping sample PRS and externally weighted PRS, becomes a larger issue with the increase of genetic variants simulated , whereas the results from block jackknife resampled PRS showed a high consistency with the externally weighted scores in all scenarios. In addition, a higher number of SNPs involved in GWAS also resulted in higher variations in the MR estimates derived using the PRS generated under a MR level P-value threshold (i.e. P < 5 × 10−8), regardless of the method used for PRS construction. It is also noteworthy that the coverage of 95% CIs of 2SLS estimates generated with the overlapping sample PRS decreases dramatically with the increase of simulated SNP number, from 0.94 (MCse = 0.014) when Nsnp = 100 to 0.58 (MCse = 0.028) when Nsnp = 2000. The decrease in coverage, together with the consistency in bias-eliminated coverage (minimum 0.93) for overlapping sample estimates across all simulated scenarios, suggests that MR using overlapping sample PRS as the IV is severely biased, especially with the increase in SNP number involved in GWAS.To evaluate the impact of block number on estimates, we performed the simulation analysis again under 30 different scenarios each using a different number of genetic variants in simulated genotype data  and a different imulated . Overfit<fig id="f8">Supplementary Figure S1<graphic mimetype="image" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="ddac186f8.tif"/></fig>For the full simulation metrics, see <fig id="f9">Supplementary Table S5<graphic mimetype="image" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="ddac186f9.tif"/></fig><fig id="f10">Supplementary Table S6<graphic mimetype="image" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="ddac186f10.tif"/></fig>block = 10) to generate the block jackknife resampled PRS. We conducted simulation to justify this choice as well as to provide insight for readers to appropriately select this parameter for their future analysis. To evaluate the impact of block number on estimates of interest, we performed the simulation analysis again under 21 different scenarios each using a different number of blocks  and a different P-value threshold  for PRS construction, with each analysis being repeated 100 times. Results show that the correlation coefficient P-value thresholds used for PRS construction. In addition, we observed no clear difference in MR effect estimates  performed in this study, we applied a fixed number of jackknife blocks . The effect estimates for BMI on each of the 12 biomarkers were generally consistent between three PRSs (−3), resulting in narrower CIs compared with the other two sets of PRS .BMI provided evidence of a genetically predicted effect on all 12 biomarkers , with C-reactive protein (CRP) having the strongest evidence of an effect in all scenarios , whereas there was little evidence of an effect in females  (P = 4.01 × 10−29), whereas the opposite direction of effect on testosterone was found in females   . We also= 0.006) .P = 0.728). However, there was evidence for an indirect negative effect putatively mediated along the causal pathway involving adult body size   .−4 per 1 SD increase in the levels of testosterone, 95% CI = –0.02 to 0.02, P = 0.987), while they supported a marginal effect on adult body size . Despite the slight differences in effect estimates, MR using PRS constructed using GWAS on overlapping samples provided results consistent with those from our method  are 629 and 872 for childhood and adult body size, respectively. In reverse MR analysis, the F-statistics for the levels of testosterone in males is 4342. PRS generated using GWAS on overlapping samples provided a F-statistics of 11 313 for the levels of testosterone in males. Detailed results from the applied example 2 are in Sample overlap between multiple GWAS studies is becoming increasingly prevalent owing to the growth of GWAS meta-analyses in recent years. When estimating the causal effect of exposures on continuous outcomes using MR approaches such as 2SLS or the inverse-variance weighted methods, bias owing to sample overlap was shown to be linearly correlated with the percentage of overlap between samples in previous literature . HoweverHaving an elevated BMI has been linked to multiple health conditions and disease endpoints in multiple observational and MR studies . Using tDespite the increase in GWAS meta-analysis for common traits and diseases such as BMI and type 2 diabetes, numerous phenotypes are underinvestigated in GWAS and MR owing to limited data available in non-overlapping samples. In the second applied example undertaken by our study, we examined the sex-specific causal effect from childhood adiposity on adult serum testosterone levels, as an illustration of how block jackknife resampling MR could be applied to study causal relationships involving phenotypes with limited data sources. Testosterone is a sex hormone produced predominately in males and plays important roles in the development of masculine characteristics. Low levels of testosterone produced in the body, namely testosterone deficiency or hypogonadism, is a condition that primarily affects older men . This coP < 5 × 10−8) in GWAS are often selected as candidate IVs for MR. GWAS with a larger sample size usually have more power to identify SNPs associated with traits or diseases, providing stronger instruments for MR. Meanwhile, a dataset for one-sample MR analysis also needs to be large enough to provide statistical power for identifying any causal relationship between the exposure and the outcome. To date, the largest datasets on childhood adiposity  and serum levels of testosterone were both available in UKB. With the advantage of a large sample size, GWAS on UKB traits can provide higher statistical power for subsequent MR analysis by enabling use of a larger number of instruments compared with those available externally, but they have the issue of participant overlap between the samples for IV discovery and MR. Instead, using block jackknife resampling MR, we were able to identify sex-specific genetic IVs for childhood body size and examine the sex-stratified causal effect from childhood adiposity on adult serum testosterone using the largest dataset available without violating the independence assumption of MR or losing much power. Univariable and multivariable MR support an indirect causal effect of higher childhood body size on lower testosterone in men mediated by higher adult body size, consistent with a previous finding that higher adult BMI is causally associated with lower testosterone levels in men  and diseases or traits (outcomes) in a single dataset. It provides maximum statistical power for causal inference while avoiding biases owing to participant overlap between the datasets for IV discovery and for causal inference.Performing a block jackknife resampling MR study involves the following steps:block groups.Split the full dataset randomly into Nblock GWAS on the exposure of interest using all samples from each permutation set of Nblock-1 groups to obtain genome-wide corrected SNPs (i.e. P < 5 × 10−8) and weights for PRS construction across each set.Perform Nblock groups based on the SNPs and weights identified in the GWAS which they were not analyzed as part of .Construct PRS for the exposure for individuals with each of the NCombine all groups together and use the PRS as a genetic IV for the exposure in one-sample MR using 2SLS regressions.With the availability of additional phenotype data, the block jackknife resampling framework can be extended to evaluate direct and indirect effects for the exposure of interest in a multivariate setting. This approach can also be applied to investigate reverse causation when GWAS and PRS constructed are performed on the outcome being assessed.P-value thresholds. To investigate the effect of sample overlap and the advantages of using a block jackknife resampling framework to mitigate overfitting bias, we performed extensive simulations to construct PRS under the combination of three GWAS frameworks and 13 different 1, outcome Y and confounder U1) and genetic data consisting of 500 independent SNPs were simulated for every individual. In the external datasets, two continuous phenotypes (i.e. exposure X2 and confounder U1) and genetic data consisting of 500 SNPs were simulated for every individual. Data simulation was achieved using the ‘stats’ and ‘simulateGP’ R package (https://github.com/explodecomputer/simulateGP/) exp = 0.1), effect allele frequency (AF) of all SNPs (AF = 0.2), the effect from the exposure to the outcome (We generated 1000 pairs of simulated datasets consisting of n = 50 000 (referred to as our ‘applied dataset’) and n = 50 000  individuals. In the applied datasets, three continuous phenotypes  using the external dataset , (B) using all samples in the applied dataset (referred to as ‘overfitted GWAS’) and (C) following a block jackknife resampling framework in the applied dataset (referred to as ‘jackknife GWAS’) . GWAS onP-value) using one of the 13 different P-value thresholds . For individuals in the applied datasets, three PRSs were constructed as the weighted sum of the number of effect alleles of SNPs that reached the P-value threshold. LD clumping was not undertaken on simulated GWAS statistics because all SNPs were simulated to be uncorrelated.After generating GWAS summary statistics of the exposure, SNPs were filtered based on their genome wide significance level  as the genetic instruments for the exposure.P-value threshold for MR (P < 5 × 10−8). Linear regression and 2SLS were performed to generate the correlation coefficients and estimate of the effects from the exposure to the outcome. The R package ‘rsimsum’  and external datasets with each of the different sample sizes . GWAS and PRS construction were undertaken using the three frameworks performed in the first analysis, except all PRS were calculated using a stringent rsimsum’  was usedsnp = 100, 200, 300, 400, 500, 800, 1000, 1200, 1500 and 2000) together explaining 10% of the variances in the exposure  and external datasets (n = 50 000) with varying numbers of SNPs  and three different P-value threshold . The resulting correlation coefficients and effect estimates were compared with those generated for the sample overlapping PRS and externally weighted PRS, and they were evaluated using the simulation metrics as described previously.In addition, we applied the number of 10 blocks across all analyses in this study. To investigate whether the choice of block number would lead to differences in estimates, we performed additional simulation using the same parameters as the primary analysis and calculated the block jackknife resampled PRS using seven different number of blocks  divided by standing height (m) squared, both measured at the initial assessment. We focused on a subset of circulating biomarkers which were all measured from serum samples also obtained at baseline .In this applied analysis, we constructed a BMI PRS using data from UKB participants (n = 333 894) with BMI associated genetic variants identified and their weights estimated using three GWAS frameworks :et al. BMI GWAS meta-analysis  genetic variants from the summary statistics of the Locke et al. BMI GWAS meta-analysis on up to 322 154 individuals of European descent. In scenario 2, a GWAS for BMI was conducted on 461 377 UKB participants using a linear mixed model (LMM) association method as implemented in BOLT-LMM (v2.3) (r2 < 0.001 within a 1000 kb region) which reached the genome-wide significance (P < 5 × 10−8). Those independent variants were used to construct weighted PRS for BMI. Clumping and PRS construction were achieved using PLINK (v2.0)   to accouM (v2.3)  to identUsing a BMI PRS generated through three frameworks as described before, we performed one-sample MR to examine the relationship between BMI and 12 circulating biomarkers  in the UKB. Biomarker levels underwent rank-based inverse normal transformation before MR analysis to ensure their normality. We applied 2SLS regressions using the BMI PRS as a genetic instrument, with sex, age and the first 10 principal components fitted as covariates. This allowed us to compare estimates from the three different PRS generated using each of the scenarios described before.We applied block jackknife resampling MR to investigate sex-specific effects of childhood adiposity on serum testosterone levels measured in adults in the UKB through (1) univariable MR, (2) multivariable MR and (3) bidirectional MR, all in a one-sample setting.Childhood body size was derived using questionnaire data asking participants to recall their body size at 10 years old as ‘thinner’ or ‘plumper’ than average, or ‘about average’, as described previously . The robBlock jackknife resampling was conducted in female-only and male-only UKB participants separately to construct sex-specific PRS for childhood and adult body size as well as the levels of circulating testosterone. UKB samples were randomly assigned into 10 groups for both males and females. GWAS on the three phenotypes were undertaken on participants from each of the nine groups adjusted for age and genotyping chip, and the results were used to construct PRS for individuals in the remaining group.Next, we performed univariable one-sample MR using sex-specific PRS for childhood body size as genetic instruments. 2SLS regressions were undertaken for males and females separately, where age and the first 10 principal components were fitted as covariates. To assist with comparisons between adult and childhood body size, we also estimated the effects from the comparable three-tier adult body size variable on testosterone levels using the univariable model.Moreover, we used multivariable one-sample MR to estimate the direct and indirect role of childhood body size on testosterone levels where evidence of a total effect was identified in univariable analyses. This was achieved by accounting for adult body size as an additional exposure in the 2SLS analysis.Finally, we performed one-sample MR in the reverse direction, i.e. using the levels of testosterone as the exposure and childhood/adult body size as the outcome, to examine whether genetically predicted levels of testosterone in adulthood influences either childhood body size (i.e. as a negative control) or adult body size. One-sample MR using 2SLS was performed, where age and the first 10 principal components were fitted as covariates. Results were obtained from MR using block jackknife resampling PRS and using overlapping sample PRS  as the genetic instruments for the levels of circulating testosterone.All statistical analyses were undertaken using R (v4.0.2) .Fang_JackknifeResamplingMR_SupplementaryInformation_R2_ddac186Click here for additional data file.Fang_JackknifeResamplingMR_SupplementaryInformation_R2_withTrackedChanges_ddac186Click here for additional data file.Fang_JackknifeResamplingMR_SupplementaryTables_R1_ddac186Click here for additional data file.


To evaluate three of the main verbal models that have been proposed to explain the relationship between fluctuating asymmetry and fitness in humans: the “good genes,” the “good development,” and the “growth” hypotheses.A formal model was generated for each verbal model following three steps. First, based on the literature, a theoretical causal model and the theoretical object of inquiry were outlined. Second, an empirical causal model and the targets of inference were defined using observational data of facial asymmetries and life‐history traits related to fitness. Third, generalized linear models and causal inference were used as the estimation strategy.The results suggest that the theoretical and empirical assumptions of the “good genes” hypothesis should be reformulated. The results were compatible with most of the empirical assumptions of “the good development” hypothesis but suggest that further discussion of its theoretical assumptions is needed. The results were less informative about the “growth” hypothesis, both theoretically and empirically. There was a positive association between facial fluctuating asymmetry and the number of offspring that was not compatible with any of the empirical causal models evaluated.Although the three hypotheses focus on different aspects of the link between asymmetry and fitness, their overlap opens the possibility of a unified theory on the subject. The results of this study make explicit which assumptions need to be updated and discussed, facilitating the advancement of this area of research. Overall, this study elucidates the potential benefit of using formal models for theory revision and development. An empirical estimand, on the other hand, defines the quantity that can be recovered from observed data only, and thus informs us about the theoretical estimand under specific assumptions . The last component of this framework is the estimation strategy or the process that will be used to learn about the empirical estimand, which includes the estimator and estimate. Among other things, it is sought . The difference between DAGs and other ways of encoding the causal relationship, such as structural equation models (SEMs), is that while the former encodes the qualitative relationship between them, the latter specifically encodes the form  of said relationship , a theoretical causal model of this relationship and establish what the theoretical object of inquiry  is. Then, from these general models, I outline the empirical causal model and the target of inference  that is the focus of the present study, and the estimation strategy used.1.2The definition of FA entails two different interpretations of what an increase in FA means , distributed in 78 extended and nuclear families, with an average of three members per family (range = 0–11). This dataset was collected in Chiapas province in Mexico. Volunteers who lived in the same geographic area throughout their lives, without previous facial surgery, craniofacial trauma, congenital anomalies, or orthodontic treatment were included in this study. Informed consent was signed by each participant before personal and phenotypic data were collected , following the standard recommendations for quality and accuracy of the software. The distance chelion‐chelion, measured directly on the individuals using a standard anthropometric caliper, was used as a scale factor.The 3D facial shape was captured using photogrammetric methods applied to a series consisting of five separate digital photographs from different angles . All photos were taken at a constant distance of 1.5 m with a standardized photographic protocol described in detail previously  were placed on the photographs trying to avoid data redundancy and following standard terminology  were used in subsequent analyses.The 3D coordinates of all landmarks were superimposed using the generalized Procrustes analysis (GPA) in the MorphoJ software . The number of offspring  was considered as a measure of fitness, and the adult height  of the individual as a proxy of their health status .Data on life history traits were only available on a subsample of female individuals  of FA, expecting values greater than 0 . Given that the estimation of this effect involves life‐history traits, all three hypotheses were evaluated using the subsample of females.Dagitty, Textor et al., The strategy to estimate the causal effect of facial FA on the number of offspring given the corresponding empirical causal model was, first, to use an automated causal modeling tool  of facial FA on the number of offspring, with possible values for this parameter that were most compatible with the data ranging from 0.244 to 0.402 . In other words, conditional on the DAG, individuals with higher asymmetries have more children. Additionally, the generalized linear mixed model provided a mean heritability of facial FA close to zero (h2 = 0.046) with possible values ranging from 0.0002 to 0.174 , which means that almost none or little of the phenotypic variation in FA is explained by genetic variation.The results for H1 show that there was a positive effect (β = 0.294) of facial FA on the number of offspring conditional on the individual's adult height, with possible values for this parameter ranging from 0.213 to 0.374 . This means that individuals with higher FA values for their height have more offspring. In addition, there was a negative effect (β = −0.275) of the adult height on the number of offspring, with possible values ranging from −0.359 to −0.191 . In other words, those individuals who are taller have fewer offspring. Finally, there was a negative effect (β = −0.13) of height on facial FA, with possible values ranging from −0.266 to 0.006 . This means that taller individuals exhibit lower facial FA.The results for H2 show that there was a positive effect (β = 0.01) with possible values for this parameter that were most compatible with the data in a relatively wide range from −0.127 to 0.148 . Finally, the results show a very low negative effect (β = −0.04) of face size on facial FA with possible values in a relatively wide range from −0.179 to 0.093 .The results for H3 show that the direct and indirect effects of facial FA on the number of offspring calculated under this model are the same as those effects obtained in the first and second hypotheses, respectively. Moreover, there was a very low positive effect of height on face size  for each hypothesis and tested them in the particular case of facial asymmetries and reproductive success.4.1The present study shows that even if the approaches are different, some of the theoretical assumptions overlap across hypotheses Figure , openingFrom the framework of signaling theory, attributes other than physiological information are recognized as signals  on the expression of phenotypic variation and, particularly, on the production of asymmetric traits. Specifically, this assumption differentiates between research on FA variation that focuses on its genetic basis (H1: symmetry reflects good genes) and research that focuses on the development pathways that lead to such within‐individual variation (H2 and H3: symmetry reflects the interplay between the organism and its circumstances). Although the former ignores the idea that has been present since the 1980s in the field of evolutionary developmental biology Müller,  that the4.2This study also shows some similarities and differences between hypotheses when the results are compared with the expectations derived from the empirical assumptions. In the case of the “good genes” hypothesis, the corresponding empirical causal model expects that individuals with greater facial FA values have less reproductive success than individuals with less asymmetry. Moreover, it expects that part of the phenotypic variance of facial FA is explained by genetic variation. In contrast, I found that individuals with greater facial FA values have more offspring and a heritability close to zero . The latter result is the first report of heritability of FA in the human face and is consistent with previous research showing very low or no heritability of other traits in humans and other species ; data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); writing – original draft (lead).The author was supported by the Postdoctoral Fellowship program DGAPA‐UNAM.The author declares no conflict of interest.


Foeniculum vulgare Mill.). A research was started by creating selfed (S1) and half-sib (HS) families on a fennel germplasm in 2018. Populations were studied in the field, applying a normal and a water deficit condition during two years (2019–2020). Considerable genotypic variation was observed within S1 and HS families for all of the evaluated traits, demonstrating that selection for these traits would be successful. Consequences of water deficit were manifested as declined most of the traits; and significantly increased essential oil content, harvest index, and proline content, in both populations. Mandatory selfing reduced the performance of genotypes for most of the traits confirming the existence of inbreeding depression (ID) with higher values for plant dry weight, seed yield, essential oil content, and number of umbelets per umbel. In S1 population, some of the studied traits had higher heritability estimates under normal condition and some of them showed higher heritability under water deficit. Positive relationship between GCA and STI in OP population indicated that it is possible to identify genotypes having high values of combining ability and drought tolerance. Results of the present study suggest that physiological traits cannot be used as an indicator to distinguish drought-tolerant genotypes in S1 progenies, whereas in OP progenies Chl a, Chl b, TChl, CAR, PRO, and RWC, which had significant correlations with drought tolerance, may be used for this purpose. Based on the results contrasting genotypes were identified, which can be used to develop mapping populations for genome studies of drought tolerance and physiological traits of this species in future studies.The consequences of water deficit and its interaction with pollination system (deliberate selfing compared with open-pollination) on physiological, agronomic and phytochemical traits are not understood in fennel ( Foeniculum vulgare Mill.), is a well-known medicinal and aromatic plant belonging to the Apiaceae family [Fennel  matings, including polycross and open-pollination, are widely used as conventional selection methods of population improvement for estimation of combining ability, genetic variability, heritability, and other genetic parameters in quantitative genetic studies . EstimatExtremely limited information is available about the physiological response of fennel germplasm to drought stress and genetic analysis of different traits using both half-sib and selfed populations. Moreover, information is lacking on the impact of inbreeding depression (compared with open pollination), recurrent drought stresses (compared with well-watering), and their interaction with the morphological, physiological, and seed-related traits along with essential oil content of fennel genotypes. Hence, the present study aimed to (i) evaluate genetic variation of morphological, physiological traits, and essential oil content in two separate groups of fennel progeny resulting from self (S1) and open-pollination  under two water environments, (ii) estimate the level of inbreeding depression for different traits under normal and water-deficit conditions and find genotype-specific responses in terms of inbreeding effects; (iii) assess the genetic basis of the mentioned traits through the estimating of general combining ability, heritabilities and the genetic variation of different traits using S1 and OP populations; as well as to find the changes in the genetic parameters in response to mating system and water conditions.The genetic materials of this study were consisted of 49 OP and 30 S1 families derived from open- and self-pollination of parental genotypes of fennel, respectively, during 2016 and 2017 growing seasons. The parental genotypes were mainly consisted of natural ecotypes of fennel from wide geographical areas of Iran along with foreign ecotypes kindly provided by the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) . ParentaIn 2016 and 2017, each plant of each parental genotype had half of its umbels bagged from the start of inflorescence emergence until seed harvest for obligate selfing, whereas the other half were left uncovered to enable open-pollination. At the end of the summer, seeds were separately harvested from selfed and open-pollinated umbels at full maturity stage, and then seeds of each genotype for the two harvest years were bulked to have enough seed for the field experiments. From the 50 parental ecotypes, 49 ecotypes produced enough open-pollinated (OP) seeds and 30 ecotypes produced enough selfed (S1) seeds. Therefore, two populations that are 30 S1 and 49 OP  families were developed as the genetic material for this study . The S1 The field experiment was performed on a Typic Haplargid, silty clay loam soil at the Research Farm of Isfahan University of Technology, situated in Lavark, Najaf-Abad, Isfahan, Iran . The soil of this area is calcareous, non-saline and non-sodic, with pH 8.3. According to Koppen classification, this region has a semi-arid and cold climate generally without rain from late May to mid-October, making irrigation necessary for growing crops. Based on 40-year meteorological data, the region’s mean annual precipitation and temperature were 140 mm and 14.5°C, respectively.st to October 1st. In this period, depending on the weather conditions, the irrigation intervals were variable during the growing season and between the two moisture environments. But the amount of water that was applied for each moisture environment was identical and calculated as follows. To determine the average gravimetric soil water content and detect the irrigation times, soil moisture content was daily measured at different sites of each normal and water deficit treatment, based on standard gravimetric methods [irr is the soil gravimetric moisture percentage at the time of irrigation, D is the root-zone depth (60 cm), and B is the soil bulk density at root-zone (1.4 g cm-3). Water volumes that should be applied in each moisture environment were calculated by multiplication of the irrigation depth by surface area of plots. Water was delivered to the field using a drip irrigation system through a pumping station, polyethylene pipes, and drip tapes. The water volume that was applied for each treatment was measured by using a volumetric counter. Cultural practices including irrigation, fertilization, and weed control  were done each year regularly.After establishment of plants during 2018, genotypes were evaluated under normal and water deficit environments during growing seasons of 2019 and 2020, in which irrigation was occurred when 50% and 85% of the total available soil water was depleted from the root zone, respectively, following accepted methods of determination of evapotranspiration . Water d methods  at the d-1), chlorophyll a content ; chlorophyll b content , carotenoid content , total chlorophyll , ratio of chlorophyll a to chlorophyll b (Chl a/Chl b), and ratio of total chlorophyll to carotenoid (TChl/CAR) were evaluated under two levels of irrigation  during 2019 and 2020. Among them, phenological traits of DG, DF, and DM were measured based on the mean of each plot. Other traits were measured based on 10 randomly selected plants from the center rows of each genotype (plot) and the average values were used for analyses. HI was calculated after harvest based on the ratio of seed yield to plant dry weight multiplied by 100.Plants were allowed to establish in the field for the first year (2018) and no data were measured during this year. Twenty-three agro-morphological, physiological, and seed-related characteristics including days to germination , days to 50% flowering , days to 90% maturity , plant height , plant fresh weight , plant dry weight , harvest index , number of umbels per plant (UP), number of umbelets per umbel (UU), number of seeds per umbelet (SU), seed yield per plant , thousand seeds weight , seed length , seed width , essential oil content , relative water content , proline , chlorophyll b (Chl b) and carotenoids (CAR) was undertaken by means of spectrophotometry, using 80% acetone as a solvent [a + Chl b. Then the ratios of Chl a to Chl b (Chl a /Chl b) and of TChl to carotenoid (TChl/CAR) were calculated. The method which is described by the Bates et al. [Leaf water status was determined by estimating the RWC according to Ritchie’s et al.  method.  solvent . Total cs et al.  was follop represents the mean value of cross-pollinated population and Ms represents the mean value of self-pollinated population. To differentiate the drought-tolerant and susceptible genotypes, three drought tolerance and susceptibility indices, including stress tolerance index (STI) [Inbreeding depression (ID) for each trait was calculated as follows :ID(%)=ex (STI) , toleranex (STI) , and yieex (STI) , were caTo extract the essential oil, air-dried ripe seeds of fennel were ground to a fine powder using an electric grinder and subjected to hydrodistillation for 3 h  using a Essential oil content (EOC) (%) = Weight of EO recovered (g) / Weight of seed (g) × 100.Essential oil yield (EOY) was calculated from multiplication of seed yield per plant with essential oil content for each genotype .F-value was significant, mean comparisons of traits were undertaken using the least significant difference (LSD) test at p < 05 [h2 represents the heritability, Before analysis of variance (ANOVA), the Kolmogorov–Smirnov test was conducted to examine the normality distribution of data. The Bartlett test was used to test the homogeneity of residual variance of moisture environments. Then, to examine the differences between the genotypes, years, moisture environments, and their interactions and also to estimate the variance components, combined analysis of variance, proposed by Steel and Torrie , was pert p < 05 . The pheectively .h2 is the heritability, h2 represents an estimation of narrow-sense heritability  using proc MIXED of SAS. The estimation of heritability was calculated on the basis of genotype means for normal and water deficit conditions, according to Nguyen and Sleper :h2=σg2rogenies .σg is the standard deviation of the genotypic variance, and μ is the phenotypic mean [The linear regression coefficient of OP and S1 progeny values on their parental values was calculated. In the open-pollinated populations, the linear regression coefficient is based on offspring and one parent; then, the linear regression coefficient of OP values on their parental values was multiplied by two to obtain an estimate of narrow-sense heritability . In selfpic mean .General combining ability (GCA) was calculated for important traits on the combined data of two moisture environments as the deviation of each HS family from the population mean as defined by Wricke and Weber . Moreovea/Chl b and TChl/CAR in S1 population, and DG, PRO, and Chl a/Chl b in OP one. The effect of genotype was significant for all traits, indicating significant variation among the selected genotypes with a broad range for each attribute. The interactions between genotype and moisture environment were also significant for all the measured traits except for DF and UU in S1 population, and DG and DF in OP population  between the normal and water deficit conditions for all traits except for Chl pulation  Tables.Mean comparisons of S1 and OP populations for important traits based on the average of two years are given in In OP population, under normal condition, DF ranged from 51.00 to 75.00 days with an average of 65.03 days. Among the studied families, OP-14, OP-15, and OP-6 were the early flowering families, while OP-49, OP-33, OP-3, OP-34, OP-35, OP-41, OP-40, OP-43, and OP-23 were late flowering ones. Under water deficit condition, it ranged from 48.75 to 72.50 days with an average of 61.18 days. Families, OP-14, OP-15, OP-4, and OP-6 were the early flowering families, while OP-33, OP-49, OP-3, OP-41, and OP-35 were late flowering . Seed yib (Chl b), and the ratio of chlorophyll a to chlorophyll b (Chl a/Chl b) under normal condition, and for plant fresh weight (FW), DW, HI, Chl b, CAR, Chl a/Chl b, and the ratio of total chlorophyll to carotenoid (TChl/CAR) under water deficit. However, for the other evaluated traits moderate to low variations were observed. In open-pollinated progenies, GCV was relatively high for FW, DW, HI, and Chl a/Chl b under normal condition, and for days to germination (DG), FW, DW, HI, and Chl a/Chl b under water deficit condition. In contrast, variation for the remaining studied traits was moderate to low (Estimates of genetic coefficient of variation (GCV) for S1 and OP populations under normal and water deficit conditions are given in e to low .a, CAR, Chl a/Chl b, and TChl/CAR compared with open-pollination,  within the germplasm for different traits. Under normal condition, the highest and lowest ID values for SYP were observed in genotypes 42 and 31 and under water deficit were detected in genotypes 40 and 9, respectively. For EOC, the highest value of ID was observed for genotypes 8 and 43 under normal and water deficit, respectively; while the lowest value of this parameter was detected for genotypes 30 and 36 under normal and water deficit conditions, respectively. The highest and lowest value of ID for TSW was observed in genotypes 33 and 8 under normal condition, and in genotypes 33 and 48 under water deficit, respectively. For RWC, the highest and lowest values of ID were detected in genotypes 48 and 40 under normal condition, respectively; while under water deficit these values were obtained in genotypes 49 and 45 .Figs a/Chl b was not affected under water deficit condition. As expected, SYP drastically reduced by water deficit in both populations, so that this reduction was on average 61 and 57% in S1 and OP populations, respectively. Similarly, under water deficit, EOC was decreased compared with normal condition. These reductions were approximately 28 and 35%, in S1 and OP populations, respectively  was used to reduce the dimension of data and view the relationship between traits and genotypes. In S1 population, the first two components justified approximately 54% and 51% of the total variation under the normal and water deficit conditions, respectively . Under nive ones . Under wive ones , PC1 hadior ones .a, Chl b, TChl, TChl/CAR, and TOL. As higher values of these traits lead to higher photosynthetic capacity and drought tolerance, hence PC2 was named ‘photosynthetic pigments component’. In total, improvements in productivity, photosynthetic capacity, yield stability, and drought tolerance can be made by selecting genotypes high in PC1 and PC2. Therefore, genotypes 9, 15, 24, 35, and 40 can be recommended as suitable genotypes. In contrast, genotypes 8, 12, 19, 23, 37, and 46 have low productivity, photosynthetic capacity, and yield stability, and can be considered as sensitive to water deficit  [Coriandrum sativum L.) [Cuminum cyminum L.) [Study of traits related to drought-adaptation and drought-tolerance mechanisms and their responses under stress are important objectives in plant breeding programs. In the current study, water deficit caused a significant reduction in most of the evaluated traits, in both populations. These negative effects could be ascribed to the stomatal closure in response to the low water potential of the soil, decreased rate of photosynthesis, disturbed assimilate partitioning, and disturbance in the grain filling period ,10. The ammi L.) , coriandivum L.) , and cuminum L.) . FloweriThe physiological changes have been considered as an important adaptation mechanism for plants to resist drought and could be the result of harmful effects on metabolic processes . In thisIn breeding for drought tolerance, development of genotypes with high yield under normal condition and less reduction of yield under water deficit is ideal. Selection indices such as STI and TOL can distinguish these tolerant genotypes from others. In this study, several genotypes were identified with higher values of STI from both populations. These genotypes seem to have a higher yield potential under water deficit and may be further improved by crossing to genotypes having higher values of YSI that were stable under both water conditions.b, and Chl a/Chl b under normal condition, and DG, PHT, UU, SU, PRO, CAR, Chl a/Chl b, and TChl/CAR under water deficit, the S1 population had lower means for all measured traits than the OP population under both moisture conditions, which may be due to the effect of inbreeding depression on these traits. Similar results were reported for fennel by other researchers [In the present study, except for UU, SU, PRO, Chl earchers ,57. Inbrearchers . The researchers  in mint.earchers . This faa/Chl b, and TChl/CAR suggesting the presence of some major genes or QTLs affecting them, and these traits could be improved by recurrent or mass selection [h2 were higher under water deficit than at normal condition for all traits with the exception of DG, DF, PHT, HI, and RWC, which were advantageous for successful selection in achieving genetic progress and indicate that phenotypic selection under water deficit would be more effective than normal condition. As different genes may contribute to the same trait in different environments therefore, changes in heritability would seem likely to occur with increased or decreased stress [Estimation of heritability is necessary to design and implement an effective breeding program to maximize genetic improvement; since it provides an indication of the genetic potential available to plant breeders and enables calculation of expected genetic gain for selection in cross-pollinated populations ,60. In telection . In OP pelection ,43,61. Ielection . Howeverd stress . In bothd stress . In the Regression of offspring on parents or one parent is a method of estimating heritability that is commonly used by plant breeders. In this study, the heritability estimates based on parent–offspring regression were higher than those based on genetic variance components for most of the traits. The difference between the two methods is due to the inflation of estimations from simple parent–progeny regressions ; this isa/Chl b under normal condition, and UU, Chl a, Chl b, TChl, CAR, and RWC under water deficit condition were positively and significantly associated with EOC. In OP population, DM, PHT, FW, DW, UP, UU, SYP, STI, Chl a, TChl, Chl a/Chl b, CAR, TChl/CAR, and RWC under normal condition, and DG, PHT, FW, DW, UP, UU, SU, SL, TSW, STI, YSI, TOL, Chl a/Chl b, CAR, and TChl under water deficit were positively associated with EOC. Similar results were reported by Lal [Correlation analysis is a valuable and conclusive analysis for identifying selection criteria for indirectly improving yield potential and economic traits. Highly heritable traits, with easy measurements correlated with complex traits such as essential oil and grain yield, make genotype selection more impressive . Resultsd by Lal  and Safad by Lal  regardind by Lal  reporteda, Chl b, TChl, CAR, PRO, and RWC, which had significant correlations with drought tolerance, may be used for this purpose. Since relatively low heritability was obtained for SYP; both genetic and non-genetic effects play a role in the control of this trait. Therefore, selection based on an index, which is a weighted linear combination of several traits, would be more effective to achieve genetic progress in recurrent selection programs. Moreover, the moderate to high heritability for some of the yield components such as PHT, UP, SW, SL, and TSW suggested that these traits are mainly under additive genetic control, and recurrent selection may be effective to improve the stated traits. These traits were associated with drought tolerance and yield sustainability and could be used in an appropriate selection index to enhance seed yield and identify preferable genotypes for arid and semi-arid regions. Based on the association of STI with GCA, and applying the PCA method, genotypes 9, 13, 29, 38, and 50 from S1 population, and 24, 26, 35, and 38 from OP one were identified as the superior genotypes. They combined higher seed production, yield stability, and drought tolerance, and therefore can be recommended for using in future breeding programs. Further experiments should focus on developing mapping populations for genome studies of agronomic and physiological traits to enhance our knowledge to improve drought tolerance of fennel.In conclusion, the substantial genetic variation observed for all evaluated traits between and within the selfed and open-pollinated populations revealed that any changes in plant natural mating systems could clearly change the genetic structure of germplasm. Different levels of inbreeding depression were observed in the present study for the measured traits, which higher values for FW, DW, SYP, EOC, and UP sugges higher heterosis for these traits. Large genetic variations were also observed for inbreeding depression among the progenies, indicating that selection for low inbreeding depression rates is possible in this species while also facilitating the development of inbred lines for future studies. Water deficit could greatly influence agronomic and physiological traits and thus reduced genotypic variation of measured traits. Results of the present study suggest that physiological traits cannot be used as an indicator to distinguish drought-tolerant genotypes in S1 progenies, whereas in OP progenies Chl S1 Table(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(DOC)Click here for additional data file.S4 Table(DOC)Click here for additional data file.S5 Table(DOC)Click here for additional data file.S6 Table(DOC)Click here for additional data file.S1 Raw data(XLS)Click here for additional data file.


We obtained a similar estimate between the former and the PGC ASD phenotype (rg = 0.3997). Both estimates are lower compared to the genetic correlation between ASD without a history of maternal infections and the PGC ASD phenotype (rg = 0.6735), and between ASD with a history of maternal infections occurring only more than 2 months following childbirth and the PGC ASD phenotype (rg = 0.6293). Additionally, we observed genetic variance between the two main ASD phenotypes using summary statistics from the case-case GWAS in iPSYCH (h2cc = 0.1059), indicating genome-wide differences between the phenotypes. Our results suggest potentially different etiologies of ASD based on a history of maternal pregnancy-related infections, which may, in part, be genetic. This highlights the relevance of maternal pregnancy-related infections to genetic studies of ASD and provides new insights into the molecular underpinnings of ASD.Autism spectrum disorder (ASD) refers to a group of neurodevelopmental disorders which include deficits in behavior, social interaction and communication. ASD has a complex genetic architecture, and it is also influenced by certain environmental exposures. Both types of predisposing factors may be related to immunological mechanisms, involving, for example, immune system genes and infections. Past studies have shown an association between infections occurring during the pregnancy in the mother and increased risk of ASD in the child, an observation which has received recent support from experimental animal studies of ASD-like behavior. The aim of this study was to study the genetic contribution to this effect. We employed genetic correlation analyses across potential ASD subtypes stratified on the basis of maternal pregnancy-related infections within the iPSYCH ASD case-cohort sample, as well as a case-case GWAS. We validated the trends of the genetic correlation analyses observed in our sample using GWAS summary statistics from the PGC ASD study (excluding iPSYCH). The genetic correlation between ASD with a history of maternal pregnancy-related infections and ASD without a history of maternal infections in iPSYCH was  Autism spectrum disorder (ASD) includes a group of neurodevelopmental disorders which involve deficits in the domains of behavior, social interaction and communication . The etiIn this study we examined the relevance to the study of the genetics of ASD of stratifying ASD cases based on exposure to maternal infections requiring hospitalization/hospital contact (hereafter: infections) during or shortly after the pregnancy. We defined the relevant period for maternal infections as the period of 9 months prior to the date of birth of the offspring and up to 2 months after it. This afforded us a “safety margin” that covered maternal infections which started before childbirth but were diagnosed after it, as well as cases in which the infection and/or inflammation cytokines might have passed from the mother to the child during the puerperium, for example through breastfeeding , 23. FurN = 1,472,762), and which included individuals diagnosed with at least one of: schizophrenia, bipolar disorder or depression (affective disorder), autism spectrum disorder, attention deficit/hyperactivity disorder and anorexia, or included as part of a random population sample. Data pertaining to hospitalization for infections for iPSYCH individuals and their parents were obtained from the National Hospital Register. The iPSYCH sample has undergone extensive quality control (QC), as described in our previous studies  can be obtained, possibly due to sampling variation. However, if the estimate is not extreme (by default LDSC issues a warning only if the estimate is above 1.25 or below −1.25), it can still be interpretable. Therefore, we interpret this result to mean that there is no significant evidence to the effect that the two ASD phenotypes in question differ genetically. Similar to our previous report, the genetic correlation between (susceptibility to) overall infection and psychiatric diagnosis was high: rg = 0.4061 . However, when examining ASD specifically, there was no significant genetic correlation with overall infection: rg = −0.0126 . The former result in particular should be evaluated with caution, since it was measured between a primary phenotype and a secondary phenotype that show comorbidity. The heritability estimates from GCTA showed similar trends: for ASD with a history of maternal pregnancy-related infections and ASD with no history of maternal infections, these were h2 = 0.0454  and h2 = 0.1290 , respectively.In the genetic analyses, both ASD phenotypes  showed heritabilities significantly different from zero using LDSC: observed scale rg = 0.3997 . The genetic correlation between the ASD phenotype with no history of maternal infections and the PGC ASD phenotype was rg = 0.6735 . When testing the genetic correlation between overall ASD in the iPSYCH sample  and the PGC ASD phenotype, the correlation was rg = 0.6344 . When using ASD cases whose mothers had infections occurring only more than 2 months after childbirth, the genetic correlation with the PGC ASD phenotype was rg = 0.6293 . This result indicates that the infection’s temporal relation to the pregnancy may have reduced the genetic correlation to < 0.6, but it should be noted that, while the PGC sample allowed us to check genetic correlation trends with our phenotypes, it was not a full replication sample, in the sense that we did not have raw genotype data and maternal infection data for that sample, so we could not do exactly the same analyses as in iPSYCH. Table rg across all instances with P ≤ 0.05 was 0.6742 with a standard deviation (SD) of 0.1184. Across all rgs obtained in this test, irrespective of the p value, the average rg was 0.6561 (SD = 0.1747). One rg was excluded in this analysis because one of the h2s for it was out of bounds.The summary statistics from the PGC replication sample were for a general ASD outcome , we assume that most ASD cases in the PGC did not have a history of maternal pregnancy-related infections). The genetic correlation between ASD with a history of maternal pregnancy-related infections and the PGC ASD phenotype was h2cc = 0.1059 . It should be noted that the estimate from this analysis is not to be interpreted as a heritability estimate for a disease in the traditional sense, i.e., as when looking at a binary trait with case–control data; rather, we used this analysis to estimate the genetic differences between the two ASD groups directly; if cases of one phenotype are more similar  within themselves, and cases of the other phenotype are also more similar  within themselves, then this would result in increased genetic variance across the groups and hence a non-zero heritability estimate [h2cc) to distinguish it from the case-control heritability estimates elsewhere in the paper. The top association in the GWAS was with marker rs115840688, which nearly achieved genome-wide significance . The second most significant association was with rs116870656 . Supplementary Table P ≤ 1 × 10−5) from this GWAS. Figure h2cc = −0.0203, SE = 0.0323, P = 0.2648). Note that a negative h2 usually means that the true heritability is close to zero. According to the LDSC website, if this happens and the mean χ2 is not above ~1.02 , it means there is very little polygenic signal, as would be expected if the two groups did not differ genetically. The GCTA estimates showed similar trends: h2cc = 0.0806  and h2cc = 0.0239  for the first and second case-case GWAS phenotypes, respectively.We performed a case-case GWAS with the two main ASD case groups and estimated the “heritability” directly from that with LDSC, obtaining estimate . BarringIn this study we examined genetic correlations between potential ASD etiological subtypes stratified on the basis of a history of maternal pregnancy-related infections. We replicated the previously reported epidemiological observation of increased risk of ASD in the child conferred by maternal pregnancy-related infections. This effect was observed even when adjusting the model for infections diagnosed in the child. In addition to confirming the presence of this effect in our sample, this also supported the adequacy of the time range selected for the genetic analyses. When comparing this result to the direct association between infection in the child and ASD, the OR is higher for the latter. Thus, the effect of having an infection on the odds of having ASD is higher than the effect of the mother’s having had a pregnancy-related infection on the odds of having ASD, but the latter remains significant even when adjusting for the infection in the child . Interestingly, while the epidemiological association exists on both of these generational levels, we observed no significant genetic correlation between ASD and overall susceptibility to infection.h2cc estimate derived from the first case-case GWAS. In this respect, one needs to consider the interpretation of this result; the difference between the case groups based on which we stratified the sample was the presence or absence of maternal infections . Clearly, however, we are not estimating the heritability of this “trait” . Rather, we hypothesize that there are different routes to ASD, which may differ genetically. With this in mind, the second case-case GWAS , which did not result in a non-zero heritability, adds further support to this hypothesis. Thus, genetic risk factors unique to ASD with a history of maternal pregnancy-related infections could also include genetic modulation of immune-related risk. Moreover, the lack of significant genetic correlation between ASD and overall susceptibility to infection  implies that it is not the case that pleiotropy between these last two traits is driving the main observations (through the inheritance of common ASD-infection susceptibility risk variants from the mother). It is worth mentioning that a similar approach was developed to study single-variant differences between genetically correlated diseases; this approach uses summary statistics from case-control GWASs for individual diseases, but the results were concordant with using individual-level data from a direct case-case GWAS [In the genetic analyses, we observed that stratifying individuals with ASD based on exposure to maternal pregnancy-related infections highlighted genetic differences between them. First, both the heritability of ASD with a history of maternal pregnancy-related infections and that of ASD with no history of maternal infections were significantly different from zero, suggesting that genetic variants predispose individuals to their respective ASD phenotypes in both groups. That said, as the heritability estimates are on the observed scale , we do not interpret these results in a direct quantitative comparison between the two phenotypes. With regards to the former phenotype, this suggests that, while environmental factors  may play a role in the development of ASD in children whose mothers had a pregnancy-related infection, genetic predisposition still exists. Moreover, the positive and significant genetic correlation between the two ASD phenotypes suggests that some of this genetic predisposition is related to genetic risk of ASD in general, as the estimate is significantly different from zero. However, our results also indicate that their genetic correlation is significantly different from one, suggesting that they may have different, albeit overlapping etiologies, from the genetic perspective. These observations could result from several scenarios or a combination thereof; for example, there could be genetic differences between both ASD case groups on the one hand, and ASD controls on the other hand, and there could be phenotype-specific genetic risk factors, as illustrated by the ase GWAS . Similarase GWAS .P = 1.33 × 10−8). The same marker almost achieved genome-wide significance and was the top marker in the first case-case GWAS , illustrating the fact that, for some variants, cases of this ASD phenotype are almost as different from ASD controls as they are different from ASD cases with no history of maternal infections. On the Ensembl genome browser, this variant falls within a transcript of the SRRM1 gene , which has been implicated in Parkinson dementia, a disease associated with neuroinflammation [SUB1 gene) from B cells, which, in turn, is important for B cell differentiation [P = 8.31 × 10−7). This marker falls within NTRK2, a gene whose mouse ortholog has been implicated in splenomegaly in L. donovani infection [One way to interpret these findings is in the context of polygenicity and liability , 48: it ammation , althougntiation . The secnfection .The main limitation of our study is that the number of ASD cases with a history of maternal pregnancy-related infections was small. This can affect downstream analyses such as genetic correlation analysis and GWAS. In order to tackle the issue, we employed three strategies: (i) we used an internal control  to confirm that the timing of the infection in the mother is important; (ii) we used the PGC ASD sample as replication sample for the genetic correlations, under the assumption that most of its ASD cases do not have a history of maternal pregnancy-related infections  by design); (iii) we used a repeated random sampling procedure with the larger of the two main case groups, selecting at random the same number of cases as in the smaller case group, to assess the effect of the smaller sample size on the genetic correlation. The small sample size, however, could also impact the power of the case-case GWAS, for which we did not have a suitable replication sample. Therefore, the results of this GWAS, directly assessing the genetic differences between the case groups, should be considered at most suggestive.rg = 0.779  [α = 0.05, but the p value was not very much greater than that. Thus, a study with larger sample sizes of clinical ASD subtypes is needed to investigate this further.Some caution regarding the interpretation of the results of the genetic analyses is also important. In these analyses, our goal was to assess the genetic differences and similarities between two potential ASD subtypes. Therefore, unlike in the epidemiological analyses, which tested the direct effect of maternal pregnancy-related infections on ASD risk, we were agnostic about maternal infection in ASD controls, and the control subsets in the iPSYCH ASD genetic analyses overlapped, an approach which has been used to study subtypes of major depression . Any genatasets) . This suIn conclusion, the results of our study suggest that there may be genetically different etiological subtypes of ASD based on a history of maternal pregnancy-related infections, which share some genetic predisposing factors, but may differ in others, some of which could be related to immune-related genes. In other words, there could be different routes to ASD in the child based on their exposure to infections during pregnancy or in early development. Larger studies, where both maternal infection data and ASD phenotype data, and potentially more immune-related data, are available would be needed in order to identify specific underlying genetic factors that may distinguish the main two ASD phenotypes investigated in this study. While it is possible that some of the estimates reported here were influenced by the small number of cases of ASD with a history of maternal pregnancy-related infections, our results nonetheless suggest that future studies, in particular genetic studies, may benefit from taking into account this potential difference.Supplementary Figure S1Supplementary Table S1Supplementary Table S2


The release of new wheat varieties is based on two main characteristics, grain yield and quality, to meet the consumer’s demand. Identifying the genetic architecture for yield and key quality traits has wide attention for genetic improvement to meet the global requirement. In this sense, the use of landraces represents an impressive source of natural allelic variation. In this study, a genome-wide association analysis (GWAS) with PCA and kinship matrix was performed to detect QTLs in bread wheat for fifteen quality and agronomic traits using 170 diverse landraces from 24 Mediterranean countries in two years of field trials. A total of 53 QTL hotspots containing 165 significant marker-trait associations (MTAs) were located across the genome for quality and agronomical traits except for chromosome 2D. The major specific QTL hotspots for quality traits were QTL_3B.3 (13 MTAs with a mean PVE of 8.2%) and QTL_4A.3 , and for yield-related traits were QTL_2B.1  and QTL_4B.2 . A search for candidate genes (CG) identified 807 gene models within the QTL hotspots. Ten of these CGs were expressed specifically in grain supporting the role of identified QTLs in Landraces, associated to bread wheat quality traits and grain formation. A cross-validation approach within the collection was performed to calculate the accuracies of genomic prediction for quality and agronomical traits, ranging from -0.03 to 0.64 for quality and 0.46 to 0.65 for agronomic traits. In addition, five prediction equations using the phenotypic data were developed to predict bread loaf volume in landraces. The prediction ability varied from 0.67 to 0.82 depending on the complexity of the traits considered to predict loaf volume. Triticum aestivum L.) is one of the globally essential cereals cultivated in a wide range of latitudes and is usually consumed in baked products. Moreover, wheat contributes 18% of the daily intake of calories and 20% of protein. To ensure global food security in the future, overall wheat production needs to increase but producing high nutritional value food as society demands  which are related to grain yield and quality with varying heritability . Grain hDuring its domestication, wheat was selected mainly for adaptation to the local environment by considering traits like flowering time, biotic and abiotic stress resistance, yield parameters, and plant height . SelectiThe fundamental focus of this study is to study the genetic architecture and candidate genes associated with the quality and grain yield traits in a collection of 170 Mediterranean bread wheat landraces through a GWAS approach. In addition to detecting the QTLs associated with the targeted characteristics, we also evaluate the genomic selection prediction accuracies and prediction of loaf volume with different quality parameters, which serve as the pipeline for breeders to select the individuals in the early stages and allow the breeders to evaluate a large population.Triticum aestivum L.) landraces from 24 Mediterranean countries derived from the MED6WHEAT IRTA panel  in rainfed conditions at Gimenells, Lleida (Northeast Spain). The experimental design followed a non-replicated augmented design using six genotypes, and two replicated checks per line, the cultivars ‘Anza’ and ‘Soissons’. Each experimental plot was 3m x 1.2m with 250 germinable seeds per m-1) was determined by mechanically harvesting the plots at ripening and is expressed at a 12% moisture level. Thousand kernel weight  was determined by counting the grains in 10 g drawn randomly from harvested grains of each plot. Duration of grain filling  was calculated as the number of days from flowering to physiological maturity. The grain filling rate  was estimated as the ratio between grain weight and GFD.Grain yield  was estimated by the AACC method 55-10. The single kernel character system (SKCS) 4100 equipment  was used to quantify the grain hardness index (GH). Grain protein content  was determined using a NIR Systems 6500 machine  with a calibration validated using the Kjeldahl method AACC 46–11A (AACC 2010) and the Dumas method . Before milling, the grain samples were tempered with water according to their hardness and the official AACC method 26-95. The samples were milled into refined flour using a Brabender Quadrumat Senior mill . Flour yield  was measured. In the flour samples, protein  was determined by a DA7200 NIR machine . Overall gluten quality  was determined with the SDS-sedimentation test performed according to (-4) according to the manufacturer’s instructions and the AACC method 54-30A. Finally, bread-making quality was assessed using a direct dough method with 100 g of flour (AACC method 10-09). A volume meter measured the bread loaf volume  by rapeseed displacement. The dough water absorption levels used to run the mixograph, alveograph, and baking tests were calculated according to . Test weight  was used to estimate the variance components and to produce the best linear unbiased predictors (BLUPs) for the agronomic and grain quality phenotypic data. The MIXED procedure of the SAS-STAT statistical package  was used with year and genotype.The analysis of variance, box plot, and normal distribution plots was calculated using the mean phenotypic values of three years with PROC MIXED and PROC UNIVARIATE PLOTS procedures with the SAS-STAT statistical package  ). The phenotypic and genotypic correlations analysis were performed with two years of phenotypic data in Meta-R software . Heritabσe is the residual variance, and r is the number of replicates per year (geometric mean r=1).Where To develop an effective predictive model for a complex phenotyping trait (LV) by simple traits, a multiple linear regression model procedure was applied with the BLUPs for two years. This procedure was performed in R software with the lm package using the basic structure “response variable ~ explanatory variable(s)”. The predicted models were validated with 50% of individuals randomly selected as training and 50% as a testing population with 150 iterations.The landraces were genotyped with 13177 SNPs using the Illumina Infinium 15K Wheat SNP Array at Trait Genetics GmbH . After excluding SNPs with more than 20% missing data and minor allele frequency (MAF) less than 0.05, a total of 10090 SNPs distributed throughout the genome remained. Genome-wide association analyses (GWAS) were performed using the TASSEL V5.2.25 software  followinβ is a vector of SNP marker fixed-effects parameters, u is a vector of random additive effects of inbred lines, X and Z represent matrix, and e is a vector of random residuals. The random genotype effect was estimated as Var(u)=PCA+K Where y is the vector of BLUPs, K model) . Restric10 P > 3, as previously reported in the literature , available at inra.fr/ . QTL oveinra.fr/ . Circulainra.fr/ .https://wheat-urgi.versailles.inra.fr/Seq-Repository/Annotations, and positions of CGs in version 2.1  by integrating the different versions of genome sequence following (Identification of candidate genes (CGs) was carried out within the window of ±500 kb of the MTAs within the QTL hotspot peaks. The annotation and function of the candidate genes were performed using the wheat genome browser version 1.0 at ollowing . MTAs wiA cross-validation strategy was used to evaluate the genomic prediction within the landrace collection. Ridge regression best linear unbiased prediction (RR BLUP)  was usedThe coefficient of variation (cv) ranged from 33.5% for P/L to 2.8% for FY, with an average of 16.8%. Most traits showed moderate to high heritability (0.66-0.90) except for TW (0.24). The traits with the highest heritability were W (0.90), FS (0.85), and FY (0.81). In contrast, GY, TKW, GFR, GH and TW showed the lowest values below 0.60.To identify Mediterranean landraces as a source for improving grain quality in breeding programs, the genotypes were grouped based on the three quality parameters used nowadays   , being Soissons one of the genotypes with higher gluten strength (W=346), only below the landrace ‘Bistra’ (W=451).The genetic correlations among traits were calculated Figure 2When correlations were calculated independently for the different genetic subpopulations (2 of 0.67 (Equation 1). In equation 2, the prediction ability was increased from 0.67 to 0.73 by replacing GH with MT  (2 = 0.72), was also significant. The combination of simple traits (FS) and complex traits (W+P/L) in the model resulted in the best predictive model with an R2 of 0.82. Moreover, including complex traits in the model (Equation 5) did not result in the highest predicting ability compared to Equations 2 and 3. The five models were tested for cross-validation to check the correlation between predicted and observed, and the correlations ranged from 0.74 to 90%  ranged from 6.66% to 13.02%, with a mean of 8.44% across all the traits (2 was identified for P (10.17%) and the least for GFR (7.24%). The number of MTAs detected was 71, 78, and 16 for A, B, and D genomes. The highest number of associations were detected on chromosomes 4A (27), 3B (24), 5B (15), and 1A (14). In contrast, chromosomes 1D, 5D, and 6A reported only one MTA. Among 165 MTAs, 25 MTAs were significant for more than one trait, referred to as multi-trait MTAs, and the remaining 140 were trait-specific MTAs. The trait with the highest number of MTAs detected was dough strength (W), with 20 MTAs, showing an allelic effect ranging from 60.52 j×10-4 to 68.3 j×10-4. In contrast, only 3 trait-specific MTA were detected for P/L and TW. Eleven and fifteen markers were significantly associated with grain and flour protein concentration, respectively, with allelic effects from -0.76% to 0.84%. Among them, nine SNPs were significant for both traits. Thirteen MTAs for MT  and 14 MTAs for PEAK  were identified; among them, 11 were in common for both traits, and only 2 were trait-specific for MT, and 3 for P. Flour sedimentation test reported 10 MTAs (6 trait-specific MTAs) and for loaf volume 4 MTAs (3 trait-specific MTAs). Ten and thirteen trait-specific MTAs were identified for FY  and GH  respectively. The allelic effect for all multi-trait MTAs was in the same direction except 3 SNPs  on chromosome 2A, which were significant for protein concentration and test weight with an opposite allelic effect between protein concentration and test weight.A summary of marker traits associations (MTA) for the mean values of the traits across two years is shown in in wheat  at -log For agronomical traits, we identified 14, 12, 10, and 8 MTAs for yield, GFD, TKW, and GFR, respectively. Among them, two markers associated with yield were also associated with MT and GFD. The rest of the MTAs were trait specific. The mean of phenotypic variation explained by marker (PVE) of GY MTAs (8.33%) was similar to the reported by the quality traits, which ranged from 10.17% for P and 7.41% for TW . Whereas, GFD and GFR showed lower PVE of 7.74 and 7.24, respectively.To identify the genomic regions most involved in trait variation, excluding spurious associations, QTL hotspots were considered, and MTAs were grouped following the QTL overview index developed by  as previThe two major QTL hotspots for yield-related traits were QTL_2B.1 with 8 trait-specific MTAs, 5 for TKW and 3 for GFD, QTL_4B.2 with five MTAs for TKW and QTL_5B.3 with 3 MTAs for GY. QTL hotspots, including quality and agronomical traits, were QTL_1A.2, QTL_2A.1, QTL_4A.1, QTL_4A.2, QTL_5A.1, and QTL_7A.2.The significant MTAs within the hotspots were considered for candidate genes (CG) screening. In order to delimitate a short region for searching candidate genes, a window of ±500kb was defined for the significant marker in the central point of the QTL hotspot. A total of 807 candidate genes were found across the 52 QTL hotspots  was performed using the public expression database at ion.com/ . DEGs weion.com/ . Seven Cion.com/ Table 4.Prediction performance was evaluated following a cross-validation approach using a random selection of 80% of the landraces as training population and 20% as testing population in 150 iterations. The genomic prediction accuracies corresponding to the mean of the 150 iterations are shown in The chemical composition of grains makes wheat the preferable crop for baked end products. However, to meet the industry requirements, improving the quality traits without reducing grain yield is necessary. Due to the green revolution at the end of the 1960s, breeding was focused on increasing grain yield, which originated in a reduction in the quality parameters of the new cultivars, particularly protein content, which is considered a significant limitation for the baking industry . To enhaBread wheat varieties have different and specific quality properties for adaptation to various baking processes, dough handling properties, and many more . The claMoreover, the ratio of the variance components of genotype and genotype × environment of protein concentrations and grain hardness compared to milling, mixing time, baking, and rheological traits were low. Therefore, the influence of the genotype by the environment interaction was strong in grain hardness and protein concentration compared to other quality traits. This agrees with The landrace population has shown a wide range of variations in kernel hardness. Hard kernels require more energy to crush, and a high amount of water is absorbed due to their coarse texture and high amount of starch damage that is useful for bread making. In contrast, soft kernels require less energy during milling, resulting in a fine flour that is complicated to handle because of less moisture absorption while baking and is used for cakes, cookies, and pastries . SimilarAs expected, the protein concentrations in grain and flour are highly correlated because of the large proportion of protein stored in wheat endosperm. The protein concentrations are negatively correlated with LV and MT, whereas FS is highly correlated due to the gluten strength influence on the bread loaf volume . LV showDifferences in the number of significant correlations between the whole panel of landraces when compared with single subpopulations could be due to the sample size and the statistical power associated to a higher number of genotypes in the analysis that makes it more likely to detect significant differences. However, differences between subpopulations may be due to the genetic differences among them that increase the performance of different traits depending on the environmental conditions. To increase the statistical power of the analysis and make it more likely to detect significant differences between the different genetic backgrounds, to increase the sample size for each subpopulation should be necessary. This could help to reduce the influence of random variation and make it easier to detect any true differences in the relationships between the traits.Phenotype-based selection and prediction of complex traitsGiven the ongoing demand of the bakery industry, it is necessary to widen the selection for different traits to cope with the different bakery products . Hence t-4), that may be due to the preference in this region for flat breads, which require less gluten strength, as previously suggested by -4) and could be interesting sources for this trait, although at the same time, their gluten was not extensible . On the other hand, accessions Florence 193, TRI 2100, Isla de Fuerteventura, T-317, Mokhtar, and Candeal were identified as excellent sources of gluten extensibility (P/L> 0.7). However, in this case, all of them, except accession T-317, showed inferior gluten strength. Accession T-317 showed medium gluten strength (W=208 J×10-4) having medium-low GP, which indicates this accession’s high intrinsic gluten quality. In terms of end-use quality (bread-making in the case of the current study), accessions Moriborska, Mars Rouge Sans Barbe, and TRI 7821 were the best performers (LV> 770 ml). The three of them exhibited medium gluten strength (W= 216-208 J×10-4) combined with balanced gluten (P/L ≤ 1). Of these three accessions, TRI 7821 is probably the most interesting from the breeding point of view, as it performed similarly to the other two accessions but showed almost 1% less protein content, indicating its superior intrinsic gluten quality.According to the biplot showing the three traits used for quality assessment Figure 1It is also of special interest the prediction of complex traits by the most straightforward traits to reduce phenotyping efforts . Loaf voThe results of multiple linear regression to predict loaf volume by multiple traits depending on the complexity of phenotyping concludes that the prediction ability is increased by using simple traits to complex traits. Model equation 2 with manageable traits was intermediate compared to the complexity of traits in equations 3 and 5; equation 4 stood outstanding by using complex traits. Similar models were previously performed using modern cultivars , and thiDissecting the genetic bases of complex traits in crop breeding is essential to carry out molecular-based approaches to improve cultivars. Numerous attempts have been previously done to identify QTLs and marker–trait associations linked to traits of interest to achieve marker-assisted selection (MAS) approaches and the introgression of alleles of commercial interest in improved genotypes .The distribution of the 165 MTAs across genomes is similar to the reported by  using agTo reduce the complexity of the high number of MTAs found by the GWAS analysis, QTL hotspots were defined using the QTL overview index developed to identify the regions of the genome with a higher density of QTL peaks . The suihttp://www.wheat-expression.com.Using the gene annotation from the ‘Chinese spring’ reference genome sequence  led to tOur results indicate that 10 candidate genes were specifically expressed in grain. Among them, those found in the GFR and GFD hotspots were mainly expressed in starchy endosperm. The CG TraesCS3B03G0177500 located in the hotspot 3B CI of MTA significant for GH is also mainly expressed in the starchy endosperm. This CG encodes a cysteine proteinase inhibitor protein. This dominant protein plays a crucial role in the transportation of storage proteins and is also involved in the hydrolysis of gluten protein responsible for the rheological properties of dough in wheat . As repovia the Golgi complex and vesicles in ER  and phospoglucan water dikinase (PWD1/GWD3) . TransgeCGs within the QTL hotspots involved in agronomical traits are mostly related to biotic and abiotic stress resilience, growth regulators, photosynthesis process, and other metabolic processes. For instance, eighteen Peroxidase (POD) and two flowering-promoting genes are detected in the region of QTL_2B.1 responsible for grain filling and grain weight duration. UDP-Glycosyltransferases have been correlated in wheat with the increase of grain wheat in chromosome groups 2 and 7 . In our Genomic prediction or selection (GS) refers to a marker-based selection aimed to enhance the genetic gain of quantitative traits in breeding programs. We used a cross-validation design to evaluate the accuracy of GS in Mediterranean bread wheat landraces to improve quality traits.5-derived F7 lines from the CIMMYT wheat breeding program  except for TW, GH, and P/L, which reported low accuracies. The low accuracy in TW and GH was expected of its low heritability. The magnitude of the accuracies of quality traits is similar to a previous study in wheat using F program . These rWhen data of both years of experiments were included in the prediction equation and including the year as environmental factor, the cross validation accuracy for the majority of the traits were relatively high, especially for GY, W, GFD and GFR, which have accuracy values greater than 0.55. Most of the quality traits with accuracies from 0.4 were not influenced by the environment and the mean values showed the highest accuracies. Differences among years were observed specially for traits as GY, TKW, GH and LV, indicating the high influence of environmental conditions in the prediction of these traits. However new experiments under different conditions would be necessary to improve the accuracy of predictions.Traditionally, grain yield in wheat has been negatively linked to bread quality properties in diverse sets of wheat germplasm, particularly in modern wheat varieties. However, ideally, farmers and the industry demand quantity and quality of wheat products, and selection methods to build allelic preference for high yield together with high quality are necessary. Herein, genetic variation for wheat quality traits is explored in a population of landraces and significant marker-trait associations were identified in most of the chromosomes.The baking industries prefer rapid and straightforward procedures for preparing the dough on a large scale. Knowing the genetic control of grain components compared to agronomical characteristics can be an elective way to develop a new variety for commercial purposes. Although the bread wheat quality parameters are highly polygenic, their high heritability indicates that these traits can be easily manipulated in the selection process but considering that their negative correlation with yield can indirectly affect agronomical traits. The genomic regions proposed are potential targets for developing a new allelic variation through genome editing. The landraces used in our study can be useful as a resource for a new natural variation and can be introduced in the breeding programme as a parent.The original contributions presented in the study are included in the article/Conceptualization: JS. Methodology: VY, ML, CG, JS. Formal analysis: VY, JS. Data curation: VY, ML, JS. Writing—original draft preparation: VY. Writing—review and editing: ML, CG, JS. Supervision: ML, JS. Project administration: ML, JS. Funding acquisition: ML, JS. All authors contributed to the article and approved the submitted version.


Extreme longevity (EL) runs in families which supports the hypothesis that this is a genetically regulated trait. However, with the exception of APOE, genome-wide association studies (GWAS) of EL have not identified many genetic variants that replicate in independent studies. The majority of GWAS of EL have used imputed genotype data. Recently, the Long Life Family Study has generated the largest whole-genome sequencing data of centenarians and matched controls. We perform single-variant and gene-based tests of EL in these data using the nf-gwas-pipeline with the saddle point approximation adjustment of the p-values. These analyses suggest that, in addition to the APOE/TOMM40 region, some uncommon variants of GRM7 (chr3), AUTS2 (chr7), KIF13B (chr8), SLC2A14 (chr12), and ADCY9 (chr16) genes, and other intergenic SNPs in chromosomes 5, 10, and 20 may be implicated with EL.


Genome-wide association studies do not always replicate well across populations, limiting the generalizability of polygenic risk scores (PRS). Despite higher incidence and mortality rates of prostate cancer in men of African descent, much of what is known about cancer genetics comes from populations of European descent. To understand how well genetic predictions perform in different populations, we evaluated test characteristics of PRS from three previous studies using data from the UK Biobank and a novel dataset of 1298 prostate cancer cases and 1333 controls from Ghana, Nigeria, Senegal, and South Africa.Allele frequency differences cause predicted risks of prostate cancer to vary across populations. However, natural selection is not the primary driver of these differences. Comparing continental datasets, we find that polygenic predictions of case vs. control status are more effective for European individuals  than for African individuals . Furthermore, PRS that leverage information from African Americans yield modest AUC and odds ratio improvements for sub-Saharan African individuals. These improvements were larger for West Africans than for South Africans. Finally, we find that existing PRS are largely unable to predict whether African individuals develop aggressive forms of prostate cancer, as specified by higher tumor stages or Gleason scores.Genetic predictions of prostate cancer perform poorly if the study sample does not match the ancestry of the original GWAS. PRS built from European GWAS may be inadequate for application in non-European populations and perpetuate existing health disparities.The online version contains supplementary material available at 10.1186/s13059-022-02766-z. Prostate cancer (CaP) has a complex etiology, with substantial contributions from inherited genetic factors –3. AmongGenome-wide association studies (GWAS) have identified hundreds of loci associated with increased risk of CaP –20, but GWAS results can be leveraged to generate polygenic risk scores (PRS), which quantify an individual’s genetic propensity to develop disease , 37. PRSMen of African Descent and Carcinoma of the Prostate (MADCaP) Network [Here, we assess the generalizability of CaP PRS using European data from the UK Biobank (UKBB) and a novel African dataset from the  Network . We inveAfrican cases and controls were sampled from MADCaP study sites in Senegal, Ghana, Nigeria, and South Africa. Summary statistics of MADCaP samples are described in Table p-values ≥ 0.2189, Kolmogorov-Smirnov tests).Using data from the 1000 Genomes Project (1KGP), we compared risk allele frequencies at CaP-associated loci in Europe and SSA. Figure Polygraph [Polygraph includes a p-value for selection on the entire admixture graph as well as selection parameters for each branch .Tests of polygenic adaptation for each set of PRS variants were conducted using olygraph . Note thnch Fig. c. Overalp-value = 4.098 × 10−8, Conti p-value = 1.343 × 10−6, PHS46+African p-value = 6.575 × 10−5, two-sided binomial tests). Note that this novel population genetic approach does not require individual-level phenotype data. Focusing on CaP, PRS variants are more likely to have African allele frequencies that are close to zero or one than European allele frequencies that are close to zero or one . However, continental differences in risk score distributions are smaller for the PHS46+African PRS than the Schumacher and Conti PRS. This suggests that at least some of the rightward PRS shifts observed for Africans may be due to ascertainment bias. An alternative possibility is that differences in PRS shifts are due to the numbers of variants in each risk score.We examined how predicted risks of CaP vary across the world by applying the Schumacher, Conti, and PHS46+African PRS to 1KGP data Fig. . Recall p-value < 2.2 × 10−16, DeLong’s test); AUC statistics for the multi-ancestry Conti PRS were 0.703 for UKBB samples and 0.579 for MADCaP samples ; and AUC statistics for the PHS46+African PRS were 0.614 for UKBB samples and 0.547 for MADCaP samples .Using British samples from the UKBB and SSA samples from the MADCaP Network, we tested how well PRS are able to distinguish between case/control status after correcting for covariates such as age and principal components. Summary statistics of these comparisons can be found in Table Odds ratios (OR) can also be used to quantify the effectiveness of PRS. Note that the OR described here do not refer to the relative risks of CaP in Europe and Africa. Instead, they refer to the ability of each PRS to distinguish between case and control status within each continental dataset, after correcting for age and principal components. We calculated covariate-adjusted ORs using generalized linear models, comparing individuals with high risk scores (population-specific PRS percentiles above 90%) to individuals with moderate risk scores (population-specific PRS percentiles between 40% and 60%). In general, European ORs were larger than African ORs Table . This inp-value = 0.021, DeLong’s test).We assessed the impact of applying ancestry-specific weights from the Conti PRS to case and control data from Europe and Africa. For British individuals from the UKBB, multi-ancestry and European PRS weights performed the best Table . Other aWe also examined the benefits of including ancestry-matched information in polygenic hazard scores Table . The PHSWe also tested how well PRS can distinguish between individuals who have more severe forms of CaP. Here, we focused on two different ways of classifying CaP as aggressive: tumor stages and Gleason scores. Tumor stage data were available for 1002 MADCaP cases and Gleason score data were available for 1068 MADCaP cases. Neither of these clinical phenotypes were available for UKBB samples. We classified CaP as aggressive if tumor stage = T4  or Gleason score ≥ 8 (as opposed to Gleason score ≤7). ROC curves for aggressive CaP are shown in Fig. Distributions of PRS vary across continental populations. Despite appreciable allele frequency differences between continents, PRS variants are not enriched for signatures of selection acting on new mutations . This suggests that allele frequency differences at CaP-associated loci are largely driven by genetic drift and other neutral evolutionary processes . Allele frequency differences also contribute to the relative effectiveness of PRS in different populations.Using British data from the UKBB and SSA data from the MADCaP Network, we examined how well genetic predictions of CaP generalize across populations. PRS were much more effective at predicting case vs. control status in men of European descent than in men of African descent. SNP ascertainment bias incurred by using genetic variants discovered in European populations likely contributes to these differences in PRS , 31, 50.Additional factors may contribute to the observed differences in PRS performance. First, genotype data comes from arrays  . Second,PRS performance was poorer for tumor stage and Gleason score than for case/control status. This finding is not surprising, given the relative paucity of GWAS loci that have been associated with aggressive or early-onset CaP . ImportaHere, we found that genetic predictions of CaP risks perform poorly if the study sample does not match the ancestry of the original GWAS. In a clinical setting, predictions are likely to benefit from the inclusion of additional factors . Going forward, transferability of genetic risk scores can be improved by incorporating evolutionary  as well K = 3, as per [We extracted genotype and phenotype data for 191,941 British males of European descent from the UKBB , 64 . Self-rr2 < 0.4) were excluded. The indel rs11293876 is absent from dbSNP, causing the Schumacher PRS to shrink to a total of 146 markers. As per [PRS were generated using sets of CaP-associated loci as per Schumacher et al. , Conti e. As per , genotypi in individual j  ranges from 0 to 2. Mean counts of risk alleles for each study site were used to fill any missing genotype data. This was done to avoid biases whereby individuals with more missing data have lower polygenic scores. In practice, missing data had little effect, as overall missingness rates of PRS variants were low for each sample (0.67% on average). For each risk score, allele doses were weighted using adjusted effect sizes: Standard approaches were used to generate PRS for each individual . For eac. As per , raw ris. As per .Integrated haplotype scores (iHS) quantify signatures of recent natural selection . PRS varPolyGraph [PolyGraph infers branch-specific selection parameters on admixture graphs using a Markov Chain Monte Carlo (MCMC) algorithm. Data requirements of PolyGraph are summary statistics from GWAS for a trait, a set of neutral SNPs, ancestral state information, and an admixture graph of the populations being studied. SNPSnap [MixMapper [Signals of polygenic adaptation for sets of CaP-associated loci were also tested using olyGraph . PolyGra SNPSnap  was usedixMapper  was usedixMapper  was usedStandardized procedures were used to collect clinical data on CaP and quantify the aggressiveness of CaP in MADCaP samples . ClinicaU tests. Using R, one-sample Kolmogorov-Smirnov goodness of fit tests were used to infer whether iHS percentiles of PRS variants are uniformly distributed. Sets of frequency-matched SNPs were used to infer p-values via PolyGraph [Two-sided binomial tests were used to infer whether European or SSA allele frequencies are closer to 0.5 . This nolyGraph . ROC curolyGraph . For eacAdditional file 1: Table S1. Details about PRS variants and proxies used in this paper.Additional file 2: Table S2. Ability of PRS to distinguish between case and control status using a shared set of variants for both continental datasets.Additional file 3: Table S3. Ability of PRS to distinguish between aggressive and non-aggressive forms of CaP using the optimal set of variants for European and African datasets.Additional file 4. Peer review history.