Mate allocation strategies that account for non-additive genetic effects can be used to maximize the overall genetic merit of future offspring. Accounting for dominance effects in genetic evaluations is easier in a genomic context, than in a classical pedigree-based context because the combinations of alleles at loci are known. The objective of our study was two-fold. First, dominance variance components were estimated for age at 100 kg (AGE), backfat depth (BD) at 140 days, and for average piglet weight at birth within litter (APWL). Second, the efficiency of mate allocation strategies that account for dominance and inbreeding depression to maximize the overall genetic merit of future offspring was explored.Genetic variance components were estimated using genomic models that included inbreeding depression with and without non-additive genetic effects (dominance). Models that included dominance effects did not fit the data better than the genomic additive model. Estimates of dominance variances, expressed as a percentage of additive genetic variance, were 20, 11, and 12% for AGE, BD, and APWL, respectively. Estimates of additive and dominance single nucleotide polymorphism effects were retrieved from the genetic variance component estimates and used to predict the outcome of matings in terms of total genetic and breeding values. Maximizing total genetic values instead of breeding values in matings gave the progeny an average advantage of − 0.79 days, − 0.04 mm, and 11.3 g for AGE, BD and APWL, respectively, but slightly reduced the expected additive genetic gain, e.g. by 1.8% for AGE.Genomic mate allocation accounting for non-additive genetic effects is a feasible and potential strategy to improve the performance of the offspring without dramatically compromising additive genetic gain. Mate allocation has been used in animal breeding schemes mainly to control inbreeding but also to increase connectedness among herds, to preserve genetic diversity, and to exploit dominance –4. The tIn the classical pedigree context, obtaining accurate estimates of dominance effects is difficult because it requires large groups of individuals with dominance relationships , such as full-sibs, and also because estimations are computationally demanding , 8. WithAccounting for dominance effects in a GBLUP (genomic best linear unbiased prediction) framework is easier than in a classical pedigree-based context because, instead of probabilities of identical genotypes, we have observations of heterozygous states at SNPs across the genome . Thus, tMate allocation strategies are based on the idea that, although selection should be based on estimated breeding values (heritable effects), the animals used for commercial purposes can be the product of planned matings that maximize the total genetic merit (additive plus dominance effects) of the offspring . Mate alIn pig breeding schemes, although the goal is to improve crossbreed performance , selection is traditionally performed within purebreds , 21. PigThe aim of this study was twofold. First, the dominance variance for age at 100 kg, backfat depth at 140 days, and average piglet weight at birth within litter was estimated in Landrace pigs. Second, mate allocation strategies that focus on maximizing either the average breeding value or the average total genetic value for prospective progeny were compared.Data were provided by a collective French Landrace pig program led by the breeding companies NUCLEUS  and AXIOM . Animal Care and Use Committee approval was not requested for this study because the data used were routinely collected data according to standard practices in breeding herds. Performances recorded in their selection herds and test stations are centralized to perform common genetic evaluations. The main selection objective of the collective program is the improvement of reproductive criteria. However, improving growth and feed efficiency traits without impairing carcass composition and meat quality is also considered in the breeding goal . The Lanmentclass2pt{minimP value < 0.05) were removed. Six animals with a call rate lower than 0.98 were discarded. Three offspring that displayed Mendelian inconsistencies with their parents were removed. After quality control, 39,353 SNPs that were in common between the two panels were retained for analyses and used to build the genomic relationship matrices.Pedigrees were extracted from the collective French Landrace pig program and included all available ancestors of the genotyped animals in the datasets. Three generations were traced back from the pedigree for each trait, resulting in 5534, 6541, and 3331 animals for AGE, BD and APWL, respectively. Two medium-density panels were used for genotyping: the porcine SNP60 Illumina BeadChip  and the GeneSeek Genomic Profiler HD 80 k , on 98% and 2% of the genotyped animals, respectively. SNPs with a call rate lower than 0.98, a minor allele frequency lower than 0.05, and deviating from Hardy–Weinberg equilibrium (Three linear mixed models (one pedigree-based and two genomic-based models) were used to estimate variance components for each trait. The first model (model A) was a classical additive genetic model in which the genetic relationship matrix was computed based on pedigree information from genotyped animals (a sample of the French Landrace population). The genomic models included either additive genetic effects (model G) or additive and dominance genetic effects (model GD). The two genomic models included a genomic inbreeding depression parameter and were implemented using a GBLUP framework. Fixed effects for AGE were the combination of farm-year-sex (68 levels) and the covariate of birth weight. Fixed effects for BD included the combination of farm-year-sex (90 levels), parity of the dam (6 levels), and litter size as a covariate. The APWL trait included farrowing batch (283 levels) and parity of the dam (6 levels) as fixed effects and an animal permanent environmental random effect (1226 sows).In matrix notation, model G can be represented as follows:th SNP  . The modGS3 software available at http://snp.toulouse.inra.fr/~alegarra/, Legarra et al. [Marker effects were estimated by using a BLUP-SNP model that assumes that the variance components estimated above are known parameters . This mg et al.  proved amponents  that werAfter estimating the SNP effects  of SNPs .From the estimates of additive and dominance SNP effects, the total genetic value entclass1pt{minimaAnalogously, the breeding value d^kqk-pk . Missinglpsolve package [The total genetic and the breeding values of the progeny were computed for all potential matings between 120 boars, representing the number of boars that are selected per year, and all available genotyped sows for each trait. In total, 789, 1007 and 1446 males were available for AGE, BD, and APWL , while this ratio was similar for BD and APWL . When dominance was included in model GD for analyses of AGE and BD, a small reduction in residual variance was observed for both traits compared to model G. This means that most of the dominance variance was absorbed in the residual variance for these traits in model G. For APWL with model G, where a repeatability model was used, most of the dominance variance was included in the permanent environmental variance.The average proportion of homozygosity or genomic inbreeding per animal was 0.62 (SD = 0.015) in this Landrace population. Estimates of inbreeding depression (b), expressed as the change in phenotypic mean per 10% increase in genomic inbreeding (standard error), were equal to 4.29 (1.17) days, − 0.01 (0.18) mm, and 42.38 (32.66) g for AGE, BD, and APWL, respectively. The impact of inbreeding was negative for AGE , negligible for BD, and positive for APWL, but not different from zero for BD and APWL.Table Selection in pigs aims at reducing age at 100 kg (AGE) and backfat depth (BD), and increasing the average piglet weight at birth within litter (APWL). As expected, when matings were selected on the mean breeding value The mean ly Table . The expThe consistency of the estimates of additive genetic variance across genomic models (G and GD) is attributed to the orthogonality of the models by using the classical parameterization in terms of breeding values and allele substitution effects proposed by Vitezica et al. . The genFor AGE, reported pedigree-based heritabilities for the French Landrace breed ranged from 0.23 to 0.36 –40, whicOur estimates of the proportion of phenotypic variance explained by dominance effects Table  show thaIn the literature, estimates of dominance variance are reported based on models with or without inclusion of inbreeding as a covariate. When genomic inbreeding is not included in the model, the dominance variance estimate is inflated, as explained by Xiang et al.  and suppIn our analyses, estimates of dominance variance were less accurate than estimates of additive variance, which is the case in most studies that estimate dominance effects e.g. , 17, 19), 1917, 1The observed reduction of the permanent environmental variance when dominance was included in the genomic model GD, suggests that a large part of the dominance variance is confounded with the permanent environmental effects when dominance genetic effects are not accounted for. Similar results have also been observed in other studies in cattle , 19. TheThe average, across animals, of the proportion of homozygosity or genomic inbreeding in our Landrace population was similar to that  reported by Xiang et al.  for a DaGenomic models fitted the data better than the pedigree-based model, probably because realized relationships among individuals are better captured by marker information. This is in agreement with previous findings . DifferOur results confirm that within-breed selection on entclass1pt{minimaOther studies on mate allocation accounting for non-additive genetic effects have also reported increases in total genetic merit compared to selecting on estimated breeding values only. Using simulation, Toro and Varona  showed to et al.  reportedentclass1pt{minimao et al. , except o et al. .Different mating design scenarios can be found in the literature. Ertl et al.  and AlilTo rule out the possibility that preselection of males based on the GEBV influenced our results, 120 boars were chosen at random and included in the optimization process to select the best set of 600 matings for each mating strategy. Optimization based on Our results show that mate allocation strategies for boosting total genetic gain could benefit production levels in the sow herds without placing a cost on additive genetic progress. However, its implementation on a large scale would require some organizational changes, for instance, additional genotyping when not all females are genotyped and high computational costs. An alternative to additional genotyping is to take the maternal grand-sire SNP information into account, as was proposed by DeStefano and Hoeschele .As stated above, genetic gain that is attributable to non-additive genetic effects is expressed in the immediate offspring and is not inherited in subsequent generations. Thus, selection and mating of parents that produce crossbred commercial animals are expected to benefit most from mating allocation strategies for improving total genetic gain in pig production schemes. We focused on single-trait selection but the use of mate allocation strategies can be extended to multiple traits by using an index as objective function to improve total genetic gain as in . The feaIn this study, we estimated non-additive genetic variance components and addressed their use to improve the performance of offspring of selected parents by means of mate allocation strategies. Inclusion of non-additive genetic effects is straightforward in a genomic prediction context. For the traits analyzed here, estimates of dominance genetic variance were small, ranging from 11 to 20% of the additive genetic variance. Addition of dominance genetic effects to a model that included additive genetic effects and genomic inbreeding did not improve the goodness of fit of the model. Inbreeding depression was estimated to have an undesirable effect on AGE and no effect on other traits. Genomic mate allocation improved the performance of future offspring of − 0.79 days, − 0.04 mm, and 11.3 g for AGE, BD and APWL, respectively, but slightly reduced the expected additive genetic gain, e.g. by 1.8% for AGE. Our results show that genomic mate allocation, accounting for non-additive genetic effects, is a feasible and a potential strategy to improve the productive performance  of future offspring without compromising the additive genetic gain. Our conclusions are limited to a single population, to a few tested scenarios of mate allocation strategies and under a single-trait approach. Further research is very much needed to explore the benefits of mate allocation strategies in a broader context.


Picea abies (L.) Karst.), using exome capture as genotyping platform. We used 116,765 high-quality SNPs to develop genomic prediction models for tree height and wood quality traits. We assessed the impact of different genomic prediction methods, genotype-by-environment interaction (G × E), genetic composition, size of the training and validation set, relatedness, and number of SNPs on accuracy and predictive ability (PA) of GS.Genomic selection (GS) can increase genetic gain by reducing the length of breeding cycle in forest trees. Here we genotyped 1370 control-pollinated progeny trees from 128 full-sib families in Norway spruce  and number of SNPs (8000) are required for GS with full-sib family relationship. GS methods had little impact on GS efficiency for growth and wood quality traits. GS model should incorporate G × E effect when a strong G × E is detected. Picea abies (L.) Karst.) is one of the most important conifer species for commercial wood production and ecological integrity in Europe [Eucalyptus) [Pinus pinaster Aiton) [Pinus taeda L.) [Picea glauca [Moench] Voss) [Picea Mariana [Mill] B.S.P.)  Voss) –12, and  B.S.P.) .Ne), and increasing training population size  Voss × Picea engelmannii Parry ex Engelm.). In this study, the heritability estimates for tree height obtained from GBLUP were slightly higher than those from ABLUP, but there is no significant difference from ABLUP considering the estimated standard errors. The heritability estimates for wood quality traits obtained from GBLUP were slightly lower than those from ABLUP. The above two opposite situations indicate that pedigree-based ABLUP model may inflate heritability estimates for tree height and deflate heritability estimates for wood quality traits if estimates from GBLUP reflect true genetic relationships among families and account for Mendelian segregation within families.Heritability is an essential genetic parameter in selective breeding and its values are dependent on the relative contributions of genetic and environmental variations, and vary among traits . In our z et al.  and Gamaz et al.  reportedNe = 55) likely negate the effect of low trait heritability on prediction accuracy [The impact of heritability on the accuracy seems to be low in this study, in line with the report in Douglas-fir  and inteaccuracy . Märtensaccuracy  demonstrIn the present study, tree height accuracy  from ABLUP and GBLUP, respectively . Accuracy and PA decreased from full-sib to half-sib family structure and from half-sib to unrelated family structure, indicating that GS model is more efficient in strongly structured populations where relatedness and LD are higher, and full-sib families also needed less number of markers than half-sib families for obtaining similar accuracy  Voss) [To our knowledge, this study has used the largest number of SNPs  for GS in tree species , 7, 18. h) Voss) . There wAccuracy and PA obtained from the subset of markers with the largest positive effects were slightly higher than those from a subset of markers based on random selection, implying that using the subset of markers with the largest effects in genomic regions with small LD decay could track relationship more effectively than random markers. It also implies that using the subset of markers with the largest positive effect could also obtain some effects based on part of the short-range LD , 45. ThuIn the present study, we observed that the efficiency of GS per year is greater than that based on traditional progeny test selection if GS is used to half generation time. In traditional pedigree-based progeny selection, the generation time for Norway spruce in Northern Sweden is at least 25 years, which is based on clonal replicated progeny trial of selected breeding trees. The clonal based progeny test procedure includes: seed sowing and growing to a sufficient size (2 yrs), vegetative propagation of seed plants by rooted cuttings (2 yrs), testing in field trials (15 yrs), and assessment of trials (1 yr). The final stage, the completion of a crossing scheme to create the next generation, is about 5 yrs. If we could omit the progeny testing of the first three stages  and complete flowering induction and mating within 15 years of GS selection, the time for a breeding cycle could be halved for Norway spruce. In this study, we only considered the efficiency of GS based on the timing of the breeding cycle. Considering the cost for field testing, the benefit of GS might be higher if the cost for establishing and maintaining 3–4 progeny trials in each breeding population is more than genotyping.Using G matrix slightly altered heritability relative to A matrix with a slight increase for tree height and a decrease for wood quality traits.ABLUP is about 11–14% more efficient than GBLUP for tree height and wood quality traits, while the four GS methods  had a similar accuracy.Efficiency of GS increased from 49 to 97% among four growth and wood quality traits if GS could reduce the generation time for a breeding cycle to half.The GS accuracy improved from the TS/VS ratios of 1:1 to 3:1, but accuracy only improved marginally after a TS/VS ratio of 3:1.The number of families and the number of trees per family also had an effect on GS efficiency. Wood quality traits need fewer number of trees within-family than tree height for a similar GS efficiency.GS accuracy decreased from full-sib to half-sib family structure and from half-sib to unrelated family structure. The number of markers need to be increased greatly for half-sib family structure to have a similar efficiency to the full-sib family structure.GS accuracy increased as the number of markers increased. The accuracy almost reached a plateau when the number of markers increased to 4 K–8 K for all the traits.As expected, the main advantage of genomic selection is the potential to shorten the breeding cycle. We observed that:


Replicable genetic association signals have consistently been found through genome-wide association studies in recent years. The recent dramatic expansion of study sizes improves power of estimation of effect sizes, genomic prediction, causal inference, and polygenic selection, but it simultaneously increases susceptibility of these methods to bias due to subtle population structure. Standard methods using genetic principal components to correct for structure might not always be appropriate and we use a simulation study to illustrate when correction might be ineffective for avoiding biases. New methods such as trans-ethnic modeling and chromosome painting allow for a richer understanding of the relationship between traits and population structure. We illustrate the arguments using real examples  and provide a more nuanced understanding of population structure, which is set to be revisited as a critical aspect of future analyses in genetic epidemiology. We also make simple recommendations for how problems can be avoided in the future. Our results have particular importance for the implementation of GWAS meta-analysis, for prediction of traits, and for causal inference. It could be taken for granted that the problem of population structure see “Box ”, in genIndeed, any residual relatedness or familial structure in its broadest sense can now be recruited to help analyses and potentially gain information. The restricted maximum likelihood (REML) method underlying inference in LMMs can be exploited to estimate the “heritability”, or proportion of variation in a phenotype explained by genotyped single nucleotide polymorphisms (SNPs)  on a trait (T). The “true” effect is defined as the effect of G on T when all other traits that are not in the pathway between G and T  are accounted for  is designed to remove non-causal associations when observable ancestry  not in the causal pathway  may associate with ancestry PCs due to having been common in only one ancestral population.A central goal of genetic association studies is to estimate the “true” causal effect of a SNP  and hence also environmental confounders (CE)  do not completely capture the true ancestry.Ancestry can also associate with the environment (CE) Fig. c. There A SNP–trait association estimate may be biased after ancestry correction when there is a correlation between the (true) SNP–trait effect and the contribution to an ancestry observable from the SNP . There are many SNPs contributing to ancestry measures, so the bias for each SNP–trait estimate is likely to be small, but genome-wide estimates sum this bias. For example, heritability estimates can in theory be biased by population structure through the prediction of non-genetic covariates  direct A–O effect. One important mechanism by which G–O (gene–outcome) associations might go via A is linkage disequilibrium (LD). If the instrument SNP G is only a proxy for the true causal SNP, and LD differs between populations, then in theory G can be a strong genetic instrument in one population but weak in another. There is an absence of evidence for this phenomenon, likely due to a lack of large African datasets for whom LD is very different than Europeans.Causal inference via Mendelian randomization exploits the effect of In recent years, Mendelian randomization studies have increasingly used a two-sample design populations, meaning that SNPs on different chromosomes are predictive of each other. This can lead to phenotypic population stratification or “allele frequency differences < associated with phenotype > due to systematic ancestry differences”  relating all individuals that left descendants in the sampled individuals. This is often measured through a kinship matrix or genetic similarity between all individuals in a dataset. Principal component analysis  is a dimension-reducing method to focus on ancestry by measuring the largest-scale variations in kinship, but the full kinship matrix also measures recent relatedness.Individuals are not randomly sampled from a large homogeneous population. Correctly accounting for structure is important. If phenotypic stratification is insufficiently accounted for, then variants associated with population structure become associated with stratified phenotypes: for example, LCT variation is correlated not just with Lactase persistence but also with height  that is not explicitly modeled. It treats environment as separate from ancestry, so that a “test population” can be constructed in a different environment to the training population.j = 1, 2). We then increase the frequency of the SNPs that are associated with the trait within Population 1. Finally, we add an environmental confounder associated with the admixture proportion from Population 1. This leads to a situation in which genes are associated with phenotype via two pathways, a “causal” genetic pathway and a “non-causal” environmental confounded pathway.We simulated a sample of N individuals at L SNPs. To construct a model including correlations between PCs and traits, we allow individuals to be admixed between two populations . However, the raw predictor is fitting the environmental component as well as the genetic. The environmental effect is removed by correcting for PCs. This lowers the prediction accuracy.Specifically, the admixture fraction for individual We then simulate ancestral the allele frequency for SNP The effect sizes are Finally, we construct a final phenotype To generate the plots, we simulate data under this model, and then examine new phenotypes in new data generated under the same model with estimated Simulations use We created a combined dataset of PoBI and ALSPAC mothers, which is described fully in Haworth et al. . BrieflyN = 2039 by K = 35 matrix of the results of chromosome painting for the PoBI participants (after centering and standardizing variance). Each of the M = 7739 mothers for whom genetic and education data were available were mapped into the SVD. Variance explained  was then applied to the The heatmap Fig. c is a 2D


Which mechanisms underlie the negative association of posttraumatic stress disorder (PTSD) with traits related to educational attainment (EA)?In this mendelian randomization study based on large-scale genomic data sets, including data from more than 1 million individuals, EA was negatively associated with PTSD, also supporting the role of economic status as a mediator in this association.This study suggests that economic status may mediate the association of EA with PTSD independent of the brain mechanisms associated with EA. This mendelian randomization study investigates the association of posttraumatic stress disorder and traits associated with educational attainment. There is a well-established negative association of educational attainment (EA) and other traits related to cognitive ability with posttraumatic stress disorder (PTSD), but the underlying mechanisms are poorly understood.To investigate the association of PTSD with traits related to EA.Genetic correlation, polygenic risk scoring, and mendelian randomization (MR) were conducted including 23 185 individuals with PTSD and 151 309 control participants from the Psychiatric Genomics Consortium for PTSD and up to 1 131 881 individuals assessed for EA and related traits from UK Biobank, 23andMe, and the Social Science Genetic Association Consortium. Data were analyzed from July 3 through November 19, 2018.Genetic correlation obtained from linkage disequilibrium score regression, phenotypic variance explained by polygenic risk scores, and association estimates from MR.rg = −0.26; SE = 0.05; P = 4.60 × 10−8). Mendelian randomization analysis, conducting considering a random-effects inverse-variance weighted method, indicated that EA has a negative association with PTSD . Investigating potential mediators of the EA-PTSD association, propensity for trauma exposure and risk-taking behaviors were observed as risk factors for PTSD independent of EA , while income may mediate the association of EA with PSTD .Summary association data from multiple genome-wide association studies were available for a total of 1 180 352 participants (634 391 [53.7%] women). Posttraumatic stress disorder showed negative genetic correlations with EA (Large-scale genomic data sets add further evidence to the negative association of EA with PTSD, also supporting the role of economic status as a mediator in the association observed. Prospective studies have suggested that many variables previously considered outcomes of trauma are likely to be pretrauma risk factors.5 the underlying mechanisms remain unclear. Reverse causation 8 is a key obstacle to disentangling the direction of the mechanisms that associate these 2 phenotypes.While there is a robust epidemiologic literature on the negative association of PTSD with cognitive ability and EA,9 By combining such small-effect loci, it is possible to build genetic instruments that can be used to investigate complex epidemiological associations, such as the underlying mechanisms connecting PTSD, traits related to EA, and the potential mediation of other pretrauma risk factors. In particular, genetic information can remove the bias of reverse causation from analysis of the association of PTSD with cognition and education. Genetic variants are allocated at conception and do not change throughout life, and they can be used to define reliable genetic instruments that can be applied in a mendelian randomization (MR) analysis.14 The basic principle in MR is that an instrumental variable based on genetic variants associated with a phenotype can be used to represent, or mirror, the disease risk associated with that phenotype without the presence of possible environmental confounders.15Adequately powered genome-wide association studies (GWASs) are able to dissect the predisposition to complex traits. This requires extremely large sample sizes to detect the polygenic architecture of complex traits, overcoming their heterogeneity to have sufficient power to find risk loci of very small effect.19 investigated traits related to EA, identifying a number of loci and biological pathways regulating brain mechanisms at the basis of human cognitive ability. A 2018 genome-wide analysis of multiple brain disorders and phenotypes20 showed pervasive shared heritability of these traits with EA and related traits. Based on the GWAS regarding PTSD newly generated by the Psychiatric Genomics Consortium (PGC),21 we applied multiple statistical methods to large-scale genomic data sets to investigate the mechanisms involved in the association of PTSD with traits related to EA and potential pretrauma risk factors.Previous large-scale GWASs conducted by the Social Science Genetic Association Consortium (SSGAC)21 Summary data were available for a total of 1 180 352 participants. Multiple statistical methods were applied to these data sets to investigate the association of PTSD with EA and related traits. A schematic workflow summarizing the analyses conducted is reported in eFigure 1 in the STREGA) reporting guideline.22This study was conducted using summary association data generated by previous studies. Owing to the use of previously collected, deidentified, aggregated data, this study did not require institutional review board approval. Ethical approval had been obtained in all original studies.21 In this analysis, lifetime and/or current PTSD status was assessed using various instruments and different versions of the Diagnostic and Statistical Manual of Mental Disorders . We focused on the data generated from the analysis of individuals of European descent  because the genome-wide analyses of traits associated with EA were conducted only on this ancestry group.Genome-wide information regarding PTSD was derived from the freeze-2 analysis conducted by the Psychiatric Genomics Consortium Posttraumatic Stress Disorder (PGC-PTSD) Working Group.16 which investigated EA as the primary phenotype in a total of 1 131 881 individuals (EA1M). Additional information about the definition of the EA phenotype is available in the eAppendix in the 16 3 additional phenotypes were investigated. Of these, 2 were analyzed exclusively among research participants of the personal genomics company 23andMe. Participants were asked to rate their mathematical ability  and to list the most advanced math course they had successfully completed . The third phenotype investigated, cognitive performance, was assessed in 257 828 participants from the Cognitive Genomics Consortium study and the UK Biobank.16 For the data sets including participants from 23andMe, we had access only to summary association data of the top 10 000 variants. Accordingly, the data sets derived from 23andMe were not used for the reverse analysis .Genome-wide information regarding traits related to EA were derived from the GWAS meta-analysis by the SSGAC,A summary of the data sets tested is reported in the 23We also used data from the UK Biobank to investigate the possible mediation of PTSD risk factors in their associations with traits related to EA. Self-reported risk-taking behaviors were assessed with the question, “Would you describe yourself as someone who takes risks?” (UK Biobank data field 2040). Income was assessed with, “Average total household income before tax” (UK Biobank data field 738). We also considered traumatic events assessed in the UK Biobank  score regression was used to estimate the genetic correlation among the traits investigated.P value–informed clumping with an LD cutoff of R2 = 0.001 within a 10 000-kilobase window, excluding the major histocompatibility complex region of the genome because of its complex LD structure and including only variants with a minor allele frequency less than 1%. The European samples from the 1000 Genomes Project were used as the LD reference panel.25 The PRS analysis was conducted on the basis of the GWAS summary association data using the gtx R package incorporated in PRSice software.26 For each PRS analysis, we calculated an approximate estimate of the explained variance from a multivariate regression model.27 For the traits related to EA, we considered a genome-wide significance threshold (P < 5.00 × 10−8). For other traits , owing to the limited power to detect a large number of genome-wide significant loci, we evaluated multiple P value thresholds  to increase the variance explained by the genetic instruments. To account for the multiple PTs tested, we considered P < .005 as the significance threshold in the PRS analysis. Tests of statistical significance were 2-tailed. The results of the PRS analyses were used to define the genetic instruments for each pairwise comparison to be investigated further via the MR approach.The polygenic risk scores (PRSs) were calculated after using 28 As mentioned earlier, genetic instruments were based on the results obtained in the PRS analysis. Since different MR methods have different sensitivities to different potential issues, accommodate different scenarios, and vary in their statistical efficiency,11 we considered a range of MR methods. The primary analysis was conducted considering a random-effects inverse-variance weighted (IVW) method.29 The secondary MR methods included MR Egger,30 simple mode,31 weighted median,32 and weighted mode.31 These MR analyses were conducted using the TwoSampleMR R package.29 Additionally, owing to the fact that some traits showed a limited number of associated genome-wide significant variants, genetic instruments associated with PTSD, income, risk-taking behaviors, and trauma exposure were based on suggestive PTs similar to previous MR studies.35 We verified these IVW estimates using the MR–Robust Adjusted Profile Score (MR-RAPS) approach, which is a method designed to identify and estimate confounded associations using weak genetic instrument variables.37 We conducted multiple sensitivity analyses with respect to the MR tests conducted to exclude possible biases  under different scenarios in the MR estimates. These included the IVW heterogeneity test,29 the MR-Egger intercept,30 the MR-RAPS overdispersion test,36 and the MR–Pleiotropy Residual Sum and Outlier (MR-PRESSO) global test.40 Finally, a leave-1-out analysis was conducted to identify potential outliers among the variants included in the genetic instruments tested. The MR results without evidence of horizontal pleiotropy and heterogeneity were entered in the multivariable MR (MVMR) analysis41 conducted using the IVW approach. This method permits evaluation of the independent association of each risk factor with the outcome, similar to the simultaneous assessment of several treatments in a factorial randomized trial.41To assess the association among the traits tested, we used GWAS summary association data to conduct 2-sample MR analyses.42 In this scenario, MVMR is not a form of mediation analysis but instead estimates the direct association of the exposure with the outcome that does not act via the mediator.42 The MVMR analysis was conducted using the MendelianRandomization R package.43Comparing the utility of MR with MVMR methods, MR estimates the total association of the exposure with the outcome, whereas MVMR estimates the direct association of each exposure with the outcome.48 These analyses were conducted using the MAGMA tool49 implemented in FUMA.50We tested for functional differences between the traits of interest by means of enrichment analyses based on tissue-specific and cell type–specific gene expression reference panels.Data were available for a total of 1 180 352 participants (634 391 [53.7%] women). Information regarding PTSD was available for 23 185 individuals with PTSD and 151 309 control participants  from the PGC-PSTD Working Group and regarding EA for 1 131 881 individuals (96%) from the total sample.rg = −0.26; SE = 0.05; P = 4.60 × 10−8) and cognitive performance . The PRS analyses were conducted using PTSD as the target and traits related to EA as the training data set, considering only genome-wide significant variants . The same PTSD data set showed a weaker association with MA PRS . Significant associations were also observed with respect to PGC-PTSD freeze-1.5 outcome for EA , MC PRS , and EA1M PRS . The phenotypic variance explained by the significant PRS is in line with the cross-phenotype association expected between 2 complex traits with a moderate genetic correlation. No association of the PRS of cognitive performance and MA was observed with respect to PGC-PTSD freeze-1.5 data set , and accordingly, these phenotypes were not investigated further. We tested the reverse direction . Owing to the limited number of genome-wide significant loci in the PGC-PTSD analysis, the PRS analysis was conducted considering multiple-association PTs to include at least 10 LD-independent variants in each PRS tested  and EA  that would not survive a Bonferroni correction for the number of PTs tested.Our study investigated multiple data sets generated from different cohorts and with different data availability . Using L−5) with respect to the EA data set. It was not possible to conduct reverse analyses for the MC and EA1M data sets because we did not have access to the full GWAS summary association data for these studies. A significant association was found between MC and PGC-PTSD freeze-2 data . Concordant results were observed when considering other MR methods (P = 3.70 × 10−6), and no outliers were identified by the leave-1-out analysis . Thus, we identified the outliers on the basis of the MR-RAPS standardized residuals (−1.96 > z > 1.96) and verified their contributions on the results of the IVW heterogeneity test  and the lack of evidence of possible confounders from the sensitivity analyses . We verified that no bias in the MR results was present owing to palindromic variants with an ambiguous allele frequency51 and to the presence of assortative mating in EA52 . The reverse MR test was conducted on the basis of PGC-PTSD freeze-1.5 data . To further confirm the absence of reverse association, we conducted an MR analysis including all LD-independent variants in the genetic instrument and applied the MR-RAPS method only. No directional association of PTSD with EA was observed , but we confirmed a directional association of EA with PSTD . These outcomes were stable across different adjustments of the MR-RAPS method  and EA as the outcome of the MR analysis. We included PTSD genetic instrument variants with a PTSD GWAS −4 with risk-taking behaviors  and PT = .001 for income . The MR analysis based on these genetic instruments confirmed that PTSD is associated with the genetic instruments related to risk-taking behaviors , and income . No evidence of heterogeneity or pleiotropy was observed in either analysis  in an MVMR analysis. Before entering these potential mediators in the MVMR analysis, we verified the reliability of each genetic instrument by conducting a standard 2-sample MR and verifying the evidence of bias owing to heterogeneity and horizontal pleiotropy. Because of the limited number of genome-wide significant variants with respect to these traits, we conducted a PRS considering multiple PTs as described earlier, to determine the best genetic instrument for each trait with respect to the PGC-PTSD freeze-1.5 data set  , but when adjusted by income, this result is null . Conversely, the association of income with PTSD was still significant when adjusted by EA .In the MVMR analysis, we observed that trauma exposure and risk-taking behaviors were independent risk factors for PTSD , income is significantly more correlated with PTSD than EA . Both traits are enriched for the transcriptomic profile of multiple brain tissues  and neuronal cell types , but the enrichment signals of the EA GWAS are more significant than the ones observed in the income GWAS , cognitive performance and MA showed a lower genetic association with PTSD. A 2018 genome-wide investigation of social stratification56 showed that cognition and socioeconomic status are correlated with a wide range of factors, including personality, psychological traits, mental health, substance use, physical health, reproductive behaviors, and anthropometric traits. Additionally, income may reflect indirect effects, such as those induced by genetic nurture on EA.58 Socioeconomic factors might also be associated with the outcome of PTSD, given that individuals with poorer outcomes are more likely to be included in studies of prevalent cases.Although our findings are consistent with a specific direction, we had to evaluate whether other factors could be responsible for this association. Accordingly, we tested 3 phenotypes of known relevance for PTSD: propensity to trauma exposure,1 and the association of socioeconomic status with PTSD is also a well-established risk factor reported in several observational studies.59 Compared with these previous investigations, the current analyses are based on a much larger population  than would ever be feasible for a traditional experimental design (which would include randomized interventions and measurements of outcomes) and without the ethical quandaries that would accompany such randomizations. Also, these results are not expected to be affected by reverse association because of the genetic information used. The findings show that income may explain the EA-PTSD association, suggesting that brain mechanisms related to cognitive ability are not directly responsible for the association observed.To our knowledge, this study represents the first MR analysis to investigate the underlying mechanisms linking cognitive ability to PTSD. It is based on the largest genome-wide data sets for these traits available at this time. The EA-PTSD association observed is in line with several prospective studies,60 The results of our current analysis are also limited by the statistical power of the PTSD GWAS data sets, which may have limited our ability to observe the reverse association of PTSD with EA. However, we used multiple methods that showed significant associations when applied to similarly powered GWAS data sets. Another potential limitation is owing to the pervasive presence of horizontal pleiotropy among complex traits.39 We applied multiple sensitivity analyses that accounted for different scenarios related to the potential confounding effect of horizontal pleiotropy and heterogeneity in the genetic instruments applied in our MR analyses. Although no evidence of bias was observed by the methods used, our current findings could be affected by an unaccounted confounder.Our study has limitations. The present study is based on genetic information generated from the investigation of heterogeneous, clinically defined phenotypes such as PTSD. We tested this complex trait with respect to a series of complex, socially contextualized phenotypes, including EA, income, risk-taking behaviors, and predisposition to traumatic events. Although we used appropriate statistical methods and conducted the analyses across multiple independent cohorts, findings related to genetic data associated with these phenotypes need to be interpreted cautiously.This study provides new evidence to elucidate the association of EA with PTSD, pointing toward risk factors associated with economic status rather than brain pathways. These findings have relevant implications with respect to our understanding of the pretrauma risk factors associated with increased vulnerability to PTSD. Additionally, MR analysis should consider testing the independence of multiple correlated risk factors with respect to the outcome of interest. This is particularly relevant when investigating the potential role of EA in human phenotypes and disorders.


Our DC estimates for complex traits and gene expression are consistent with a small proportion of genes escaping X-inactivation in a trait- and tissue-dependent manner. Finally, we highlight examples of biologically relevant X-linked heterogeneity between the sexes that bias DC estimates if unaccounted for.Quantitative genetics theory predicts that X-chromosome dosage compensation (DC) will have a detectable effect on the amount of genetic and therefore phenotypic trait variances at associated loci in males and females. Here, we systematically examine the role of DC in humans in 20 complex traits in a sample of more than 450,000 individuals from the UK Biobank and 1600 gene expression traits from a sample of 2000 individuals as well as across-tissue gene expression from the GTEx resource. We find approximately twice as much X-linked genetic variation across the UK Biobank traits in males (mean  Dosage compensation (DC) on the X chromosome has predictable effects on genetic and phenotypic trait variance. Here, the authors use information for 20 quantitative traits in the UK Biobank and across-tissue gene expression to compare X-linked heritability and the effects of trait-associated SNPs between the sexes. Due to random choice and the stability of the X chromosome inactivation (XCI) in mitotically derived cell lineages, the paternal or maternal alleles are expected to be mono-allelically expressed in different cell populations approximately 50% of the time, making females functionally haploid mosaics with respect to the X-linked genes. Notably, visible examples of diverse patterns of mosaicism in females heterozygous for easily distinguishable, monogenic, X-linked traits, such as X-linked fur colour in mice, were among the first lines of evidence for the hypothesis of random XCI2.In eutherian mammals, including humans, females inherit two copies of the X chromosome and males only one. To balance allele dosage differences in X-linked genes between the sexes, dosage compensation (DC) mechanisms have evolved to randomly inactivate one of the X chromosomes in female cells5, and a combination of epigenetic modifications including histone modifications and DNA methylation to achieve transcriptional silencing of the X-linked genes7. The inactive X chromosome can be observed as a tightly condensed heterochromatin body, also known as a Barr body8. However, an increasing body of evidence suggests that XCI is incomplete with significant variability across tissues and even between the single cells10. Studies focused on identifying X-chromosome inactivation profiles14 agree that only 60–75% of the assessed X-linked genes are subject to complete silencing on the inactive copy of the X chromosome, posing the question of how escape from XCI affects complex trait variation and sex differences.The process of XCI is initiated during early embryogenesis and is mediated via expression of the non-coding RNA X inactivation-specific transcript (XIST) from the X inactivation centre of the future inactive X chromosome17. The difference in ploidy between males and females implies that a segregating mutation with alleles B and b (with frequency p and (1-p), respectively) occurs as haplotypes B- and b- in males and as genotypes BB, Bb and bb in females. If the additive value of allele B on a trait is α then the haplotype values of B- and b- are (apart from a constant) α and 0, respectively, and the variance due to this mutation in males is p(1-p)α2. In females, the genotype values are 2α, α and 0 for BB, Bb and bb, respectively, with a variance of 2p(1-p)α2, twice of that in males. In contrast, due to random XCI in females and the resulting mosaicism, the three genotypes  have genotypic values of B-, ½(B-) + ½(b-), and b-, respectively, so values of α, ½α, 0, leading to a variance of 2p(1-p)(½ α)2 = ½p(1-p)α2, half of that in males. Therefore, ploidy and XCI have opposite effects on the variance difference between males and females at trait loci on the X chromosome. For complex traits, where genetic variation is contributed by a large number of variants with small effects, the overall heritability attributable to the X chromosome will depend on the inactivation state of the X-linked loci affecting a trait. The limiting case of DC at all associated loci, which we refer to as full DC (FDC), is therefore predicted to lead to twice as much genetic variation in males compared to females, whereas the complete lack of inactivation, where only the ploidy difference matters, would lead to twice the variation in diploid females compared to haploid males16. Moreover, the double dosage of the genes that escape from DC would affect the mean value of the trait of interest in females compared to males and may contribute to sex differences in complex traits.From the perspective of quantitative genetics, DC at loci affecting a trait has a predictable effect on differences in genetic and therefore phenotypic trait variances in males and females and on the resemblance between male-male, male–female and female-female relativesN = 208,419 males and N = 247,186 females), 1649 gene expression traits in whole-blood , and a mean of 808 gene expression traits across 22 tissue-types in GTEx  to compare the predicted effect of random X-inactivation to the empirical data. The expected ratio of male–female (M/F) heritability attributable to the X chromosome  is equal to 2 in the case of FDC and 0.5 in the case of no DC. We perform a sex-stratified X-chromosome-wide association analysis (XWAS) for all traits to estimate DCR in high-order UK Biobank traits and compare M/F effect estimates of associated SNPs for both phenotypic and gene expression traits. Our results confirm the predicted effect of DC on X-linked trait variation and are consistent with a small proportion of genes partially or fully escaping from X-inactivation.In this study, we leverage information on 20 complex phenotypes in the UK Biobank ((for trait information see Supplementary Table h2SNP) from the summary statistics. Depending on the amount of DC on the X chromosome in females, this ratio is expected to take a value between 0.5 (no DC) and 2 (FDC). We refer to this as the DC ratio (DCR). For 19 out of 20 traits, the DCR estimates on the X chromosome  were significantly different from the expectation for no DC (i.e. DCR = 0.5), and consistent with evidence for DC between sexes on the X chromosome and its detectable effect on phenotypic trait variation  and 0.30% (SD = 0.20%) across the UKB traits in males and females, respectively. Notably, using height and BMI in males as an example, we established that the observed lower variation contributed by the X chromosome compared to autosomes of similar length is a result of haploidy in males and not of smaller per-allele effects at segregating trait loci   h2SNP between the sexes in autosomal loci , consistent with the predicted effect of DC. In contrast, the estimates of the ratios for autosomal SNP-heritability varied from 0.66 to 1.17 with mean of 0.95, in agreement with a limited difference in rg) between males and females is equal to one, and that any difference in the genetic variance is due to differences in dosage (i.e. number of active copies) of the X-linked genes. We estimated autosomal (rgA) and X-linked (rgX) genetic correlations in our sample using the GWAS summary statistics (see Methods). The evidence for autosomal genetic heterogeneity in complex trait is limited20 and our estimates of rgA between sexes are similar to published results  , Fat% , red blood cell count (RBC)  and Hgb . These relatively low rgX estimates may indicate local differences in genetic variance between males and females on the X chromosome that is independent of DC, which may explain the observed extreme DCR estimates for these traits. We thus explored biological heterogeneity as an explanation for these observations.When estimating the DCR, we assumed that the genetic correlation ) showed evidence for heterogeneity. We identified four regions of heterogeneity, two of which overlap due to the complex linkage disequilibrium (LD) structure in the centromere region Fig. .Fig. 2FoSex-related differences between males and females are most likely to arise due to naturally differing sex hormone levels, thus we examined the evidence for hormonal regulation in these regions  for each of the 20 UKB traits. This identified 153  and 61  lead SNPs on the non-PAR X chromosome at a genome-wide significance (GWS) level of PCOJO < 5.0 × 10–8 across the tested phenotypic traits ). DCC for the markers identified in the combined male–female analysis was 1.85 (SE = 0.04), consistent with a small proportion of genes variably escaping from X-inactivation among the studied traits  and 1.47 (SE = 0.07) in the male and female non-PAR discovery analyses, respectively, using the lead SNPs across the analysed complex traits , while being only nominally significant in the male-specific analysis  and the per-allele effect sizes on WHR are similar in both sexes . Similarly, the effect size ratio of SNP rs35318931 , a possible missense variant in the SRPX gene, is 0.63 (SE = 0.20) consistent with escape from XCI for WHR. Assuming that these SNPs are the causal variants, the observed effect size estimates may indicate potential escape from XCI for FHL1 and SRPX. Interestingly, for height  and BMR  the results for the SNP rs35318931 in the SRPX gene were indicative of DC. Consistent with these observations, SRPX is annotated with Variable XCI status in13. For FHL1, although, annotated as Inactive in10, findings from two earlier studies14 show that XCI is incomplete. Moreover, heterogeneous XCI of FHL1 is detected in single cells and across tissues10.The ratio of the M/F per-allele effect sizes for individual SNPs, which approximates the DC parameter, indicated the evidence for escape from XCI only for a few candidate variants. For instance, SNP rs113303918 in the intron of the ITM2A gene  was proposed as a potential XCI-escaping locus associated with height22. In our sex-stratified and combined analyses with a sample size an order of magnitude larger, the lead marker for height was a nearby SNP rs1736534 located approximately 100 bp upstream of the previously reported SNP rs1751138. The estimated M/F effect size ratio for the both variants was 1.75 (SE = 0.11) , providing evidence against extensive escape of ITM2A from XCI.Previously, a locus near the 10 and compared the effect size ratios between Escape/Variable and Inactive genes. The results remained similar between the two groups of genes  measured in whole blood from the Consortium for the Architecture of Gene Expression (CAGE)23. For each gene expression probe, we identified the top associated X-chromosome SNP with MAF > 0.01 that satisfied the Bonferroni significance threshold of PeQTL < 1.6 × 10–10 ) in the discovery sex , and extracted the same eQTL in the other sex and calculated DCC for M/F eQTL effect size estimates. We observed DCC of 1.95 (SE = 0.04) for 51 eQTLs (48 unique SNPs) in the female discovery analysis, and DCC of 2.07 (SE = 0.04) for 74 eQTLs (68 unique SNPs) in the male discovery analysis , we observed higher expression in females compared to males , as expected for genes that escape from XCI. However, we found significant differences between the eQTL effect estimates of the top associated SNP on gene expression after correction for mean differences in expression between the sexes (genotype-by-sex interaction PGxS < 3.1 × 10–3), which is consistent with DC. This suggests that sex differences in the expression of these genes may not be due to escape from XCI  female-bias ; female discovery analysis: 16 eQTLs with DCC of 1.86 (SE = 0.09)) or no mean difference (Psex_diff > 0.05) in expression between the sexes ; female discovery analysis: 29 eQTLs with DCC of 2.03 (SE = 0.06)). Full details of the eQTLs in whole blood can be found in Supplementary data We extended our DCC analysis to gene expression traits and performed a sex-stratified N = 50 in both males and females  for which within tissue sample size was greater than p releasecis-eQTL analysis in 36,267 autosomal gene expression probes in CAGE whole blood. A similar number of eQTLs with PeQTL < 10–10 were identified in both sexes , indicating that an approximately equal proportion of autosomal genetic variance per locus is explained in each of the sexes. As expected, DCC in the male and female discovery was 1.00 (SE = 2.3x10–3) and 0.94 (SE = 2.3 × 10–3), respectively, indicating that the autosomal eQTL effect sizes are approximately equal in males and females  analysis24 identified 18 genes (tagged by 20 probes) that are significantly (PSMR < 3.0 × 10–5 (0.05/1639) and PHEIDI > 0.05) associated with 13 complex traits  in the combined male–female sample . This also included previous signals in escape genes being assigned to inactive genes . The expression of only two genes (MAGEE1 and PRKX) annotated as Variable or Escape from XCI, respectively, showed evidence for pleiotropic association with a phenotypic trait (hand grip strength (Grip) and white blood cells (WBC), respectively) due to a shared genetic determinant  on hand grip strength was not consistent with the escape from X-inactivation (the expected ratio for an escape gene is 1). For the SNP rs6641619 (associated with PRKX expression and WBC), the effect size ratio estimate was 1.33 (SE = 0.44), which is indicative of partial escape from X-inactivation.Our SMR analysis linked many SNPs located in the intergenic regions to the expression of specific genes, however, a number of SNPs physically located within a gene were found to be associated with expression of another gene  had the strongest evidence of heterogeneity, its removal had modest effect on DCR, while the exclusion of the genomic region near the centromere had the strongest effect. In addition to possible androgen-specific influence of this region, the tight LD structure may contribute disproportionately to sex-specific genetic variance. Second, we observe DCR supporting possible escape from XCI rather than FDC in brain related traits, such as educational attainment (EA) and fluid intelligence (FI), and also diastolic blood pressure (DBP). Consistently, brain tissues have the highest X chromosome to autosome expression ratio, followed by heart28, in agreement with an enhanced X-chromosome role in cognitive functions. This indicates that the targets of segregating causal variants may be trait- and tissue-dependent. Overall, if the causal variants are random with respect to XCI then our results are not consistent with large proportion (e.g. 30%) of genes partially or fully escaping XCI for most of the traits. Alternatively, the genes targets of the associated SNPs may be enriched in non-escaping genes for some traits. Distinguishing between these two competing hypotheses would require knowledge of the underlying causal variants, thus limiting our interpretation of the results.The theoretically predicted effect of random X-inactivation in female cells is a two-fold reduced amount of additive genetic variance in females compared to males, whereas escape from XCI would increase genetic variance in females. For example, an escape rate of 15% is expected to reduce the DCR to 1.78 (=2*0.85 + ½*0.15) and the mean effect size ratio at the trait-associated loci to 1.85 (=2*0.85 + 1*0.15). Having analysed phenotypes with varying degree of polygenicity, we confirmed the predicted effect of DC on X-linked trait variation both in moderately and highly polygenic traits . The two strategies that we use to estimate DC are the overall ratio of M/F X-linked heritabilities (i.e. the DC ratio) and the comparison of the individual effects for trait-associated variants between the sexes (i.e. the effect size ratio and DCC). These are parameterisations of the same effect, the former based upon the variance contributed by all X-linked trait loci and the latter based upon per-allele effect sizes of trait-associated loci. Previous studies have demonstrated that ~1% of the phenotypic variance for traits such as height and BMI, is attributable to the X chromosomeITM2A, a gene known to be involved in cartilage development, differ from reported evidence for lack of DC22 and only a few loci associated with WHR were candidates to be putative escapees. It should be noted, however, that the genetic correlation for WHR on both autosomes and the X chromosome is markedly low, which may reflect the sex-specific genetic control for this trait. However, unless we establish the underlying causal variants, we are not able to reliably infer the inactivation state of a single gene, since trait-associated loci can act over long distances29, and cis-acting variants only tag causal variants through LD. Moreover, it is still unclear if the XCI status of a SNP is most relevant to the causal variant or its gene target, which may differ with respect to XCI status.We also found consistent evidence for DC when examining individual trait-associated markers. Interestingly, our results for height associated loci near 31. These results were broadly consistent across multiple tissue-types, where a larger number of eQTLs were identified in males compared to females and, in the male discovery analysis, DCC is ~2.In contrast to the high-order complex traits, gene expression traits have a notably different genetic architecture with as much as 65% of the expression variance for a gene explained by a single SNP, thus potentially violating the (polygenic) modelling assumptions for a DCR analysis, and thus was not included as part of this study. We were, however, able to leverage information from eQTLs to show that DCC estimates in gene expression are consistent with expectations from DC, and in agreement with our observations in high-order complex traits and previous eQTL studiesAcross both the high-order complex traits and gene expression traits, we observed DCC estimates larger than 2 in the male discovery analyses and smaller than 2 in the female discovery analyses. This may be attributed to a combination of partial escape from XCI and “winner's curse” of the XWAS analysis. For example, any loci that partially escape XCI in females would be preferentially selected in the female discovery analysis due to increased statistical power of detection, and thus bias DCC estimates towards 1. Further, winner’s curse would upwardly bias the per-allele effect estimates in the discovery sex compared to the corresponding estimates in the other sex, which may also affect DCC estimates. The combined male–female analyses attempts to avoid the caveats of the sex-stratified analyses and provides unbiased estimates of DC as the effects are assumed to be present  in both males and females.HSD17B10 gene with obesity-related traits (Fat% and BMI) and EA.  Notably, HSD17B10 encodes a mitochondrial enzyme involved in oxidation of neuroactive steroids, fatty acids as well as sex hormones and its deficiency is implicated in neurodegenerative disorders32. Consistently, similar putative causal relationships were recently identified for the autosomal gene HSD17B12, where its increased expression of this gene was associated with decreased BMI across 22 tissues33. Therefore, comprehensive surveys of sex-stratified X chromosome wide association studies for disease and other traits are likely to be rewarding, and may provide insight into new biology and sex differences. Moreover, since our method for estimating the amount of DC only requires summary statistics from association analyses, the availability of sex-stratified results from XWAS studies can further be informative on the effect and dosage of X-linked variation across a range of complex traits.While identification of new associations between X-linked SNPs and complex traits was not the primary aim of this study, our results show these are readily found and that they cumulatively contribute to trait variation. For example, we find pleiotropic association between expression levels of the 34, GCTA 1.9418, and PLINK 1.9035, all of which have default settings for the treatment of X-chromosome SNPs. For analyses performed using PLINK, we used the default parameters which codes males as {0,1}, and thus gives the appropriate per-allele effect estimates. For BOLT-LMM and GCTA, the male genotypes were analysed as diploid using a {0,2} coding. This distinction makes no impact on the strength of association , however, we multiply the effect estimates and the corresponding standard errors from the diploid male-specific analysis by 2, allowing us to report our results as per-allele effect estimates. In all cases, females were coded as {0,1,2}.The summary statistics reported in this study were generated with a combination of BOLT-LMM v2.3Nm = 208,419 males and Nf = 247,186 females of European-ancestry and UKB Version 3 release of imputed genotype data  and 253,842 SNPs in non-pseudoautosomal region (non-PAR) satisfied our quality control criteria and had minor allele frequency, MAF > 0.01). The phenotypes were adjusted for appropriate covariates and converted to sex-specific Z-scores prior to analysis (See Supplementary Table 36. We extracted those medication classified in C02 (Antihypertensives), C03 (Diuretics), C07 (Beta Blockers), C08  and C09 (Agents acting on renin-angiotensin system) category.Sex-stratified association analyses of 20 complex was performed using the phenotype data on N = 2130 individuals of verified European ancestry  from the CAGE23. A total of 36,267 autosomal and 1639 X-chromosome gene expression probes (28 in the PAR) in whole blood were available for analysis following quality control. Gene expression levels were adjusted for PEER factors38 that were not associated with sex (Psex_diff > 0.05) in order to preserve the effect of sex on expression and where available, measured covariates such as age, cell counts, and batch effects. A total of 1,066,905 autosomal HapMap3 SNPs imputed to 1000 Genomes Phase 1 Version 3 reference panel39 and 190,245 non-PAR X-chromosome SNPs (MAF > 0.01) imputed to the Haplotype Reference Consortium 40 were available for analysis.Gene expression and X-chromosome genotype data were available in a subset of N = 50 in both males and females . X-chromosome imputed SNP data was obtained from dbGap (Accession phs000424.v6.p1). We restricted our analyses to 22 tissue samples for which within tissue sample size was greater than p release34 for the X-chromosome SNPs with MAF > 0.01 in both sexes and using 561,572 HapMap3 SNPs  as model SNPs to estimate genetic relationship matrix (GRM) and correct for confounding.Summary statistics were generated for 20 complex traits in the UK Biobank using BOLT-LMM v2.3For complex traits, the results from the sex-stratified association testing were meta-analysed using the inverse-variance weighted method to obtain combined results (performed in R). For combined analysis of gene expression traits, individual data from males and female were pooled together. We assumed FDC for all loci for these analyses.21 was used to identify sets of jointly significant SNPs associated with a trait at GWS threshold PCOJO < 5.0 × 10–8. We use genotypes of a random sample of 100,000 unrelated UKB females of European ancestry as a LD reference and increased the distance of assumed complete linkage equilibrium between markers (window size) to 50 Mb due to higher levels of LD on the X chromosome.GCTA-COJO41, we calculated the DC ratio for 20 complex traits from the summary statistics of the sex-stratified X-chromosome analysis using the following equation:h2m and h2f are the M/F SNP-heritabilities, respectively; Nm and Nf are the corresponding sample sizes in males and females, respectively.FollowingMeff)[1 + 2.The corresponding standard error is estimated as:We also we obtained an estimator for the male–female genetic correlation on the X chromosome (non-PAR region) or autosomes using the following equation,B = 1000) and for each block k we calculate an estimate of the genetic correlation We calculate standard errors using a block jackknife method. We assigned SNPs across the X chromosome to blocks  to identify regions of heterogeneity. The coordinates of protein coding genes in these regions were extracted with BioMart tool (see URLs), using the genome assembly GRCh37.p13 from Genome Reference Consortium.which follows a 26, which is implemented in the GCTA software package18. Here, we model the trait as,N; gG is an N × 1 vector of the total genetic effects from the autosome with AG is the GRM between individuals estimated from 548,860 autosomal HapMap3 SNPs; gX is an N × 1 vector of X-linked genetic effects with AX is a GRM calculated from 253,842 X-chromosome SNPs; and σ2X conditional on the autosomal GRM. Thus, we can estimate the proportion of phenotypic variance that is due to the X chromosome while controlling for sample structure captured by genetic variants on the autosome26. We applied this model to the 20 complex traits, limiting our analysis to a maximum of 100,000 unrelated males or females due to computational restrictions.We estimated the proportion of variance explained by X-chromosome SNPs in males and females separately using GREML and a genome partitioning approach as in ref. The standard errors of the M/F ratio of the estimated SNP-heritabilities on the X chromosome was estimated as,18 and PLINK35 to analyse the CAGE and GTEx datasets, respectively. Sample structure was accounted for by adjusting for genotyping principal components and PEER factor in the GTEx analysis, and a random polygenic effect captured by an autosomal genetic relationship matrix in the CAGE analysis. For each gene expression probe/transcript, we identified the top associated SNP that satisfied a Bonferroni corrected significance threshold in the discovery sex (i.e. eQTL), and extracted the same eQTL in the other sex to compare the per-allele eQTL effect estimates between the sexes (see Estimation of dosage compensation coefficient (DCC), below).Gene expression levels were modelled as a linear function of the number of reference alleles for SNPs on the same chromosome in males and females, separately. We used GCTA18. We fit sex as a fixed effects covariate, and sample structure was accounted for with random polygenic effects captured by both an autosomal and X-linked genetic relationship matrix. A Wald statistic was used to assess significance, and a P-value (Psex_diff) was calculated by comparing the test statistic to a χ2-distribution with one degree of freedom.Sex differences in X-linked gene expression in the CAGE dataset was examined with a mixed linear regression model implemented in GCTA24 are implemented in the SMR software package (see URLs). We applied the SMR method to summary-level GWAS data and the sex-stratified X-chromosome eQTL data generated in our analyses  to test for pleiotropic associations between 1639 X-linked gene expression probes and 20 complex trait phenotypes. A total of 113, 66 and 136 probes with at least one cis-eQTL at GWS threshold PeQTL < 5.0 × 10–8 were retained in male-, female- and in a combined male–female cis-SMR analysis, respectively. SMR analysis in the trans regions was performed with combined male–female data, with 78 probes with trans-eQTLs (PeQTL < 5.0 × 10–8) included. A random sample of 100,000 unrelated UKB females of European ancestry was used as a reference for LD estimation. Trait-gene SMR associations were identified using a significance level of PSMR < 3.0 × 10−5 (i.e. 0.05/1639). These associations were then tested for evidence of linkage, rather than pleiotropy/causality, using the HEIDI test, which tests for heterogeneity in the effect estimates of the exposure on the outcome at SNPs in LD with the top associated eSNP under the null hypothesis of no heterogeneity. Gene-trait associations with PHEIDI > 0.05 were selected.The SMR and HEterogeneity In Dependent Instrument (HEIDI) testsWe refer to the effect size ratio as the ratio of M/F per-allele effect estimates for a single trait-SNP association. The corresponding standard errors are estimated as,As before, 21 analysis were used for estimating DCC in the UKB traits. DCC is expected to take on values between 1 and 2, where DCC of 1 indicates that, on average, the effect sizes in males and females are equal (i.e. no DC or escape from XCI), and DCC of two indicates that, on average, the effect sizes in males are twice that of females (i.e. FDC).To compare the per-allele effect estimates across all conditionally independent trait-associated SNPs  and top eQTLs  identified in the discovery datasets, we calculated a DCC by regressing the per-allele effect estimates in males onto females weighted by inverse of the variance of male-specific estimates, and extracting the slope estimate and corresponding standard error. The estimates from sex-stratified XWAS, rather than joint effect estimates from the GCTA-COJO10 and mapped gene expression probes to XCI status using the gene name. A total of 683 X-linked transcripts were available, where transcripts were classified as either Escape (82 transcripts), Variable (89 transcripts), Inactive (392 transcripts) or Unknown (120 transcripts). For each SNP in UKB dataset we determine if it is physically located within a gene to infer the presumable gene and its inactivation status for independent GWS SNPs. The CAGE and GTEx datasets were matched on the “Gene.name” and “Gene.ID” columns, respectively.To determine X-chromosome inactivation status, we downloaded annotation from the “Reported XCI status” column in Supplementary Table https://www.ukbiobank.ac.uk/the-ethics-and-governance-council/); the CAGE dataset;23 and the GTEx dataset42.The research was carried out under the University of Queensland Institutional Human Research Ethics (UQ-HREC) Approval Number 2011001173 (UK biobank and GTEx human data) and UQ-HREC 2013000682 (CAGE human data). Each of the participating cohorts holds an individual ethics approval. For further details see: the UK Biobank (https://www.gtexportal.org/home/. For GCTA, see http://cnsgenomics.com/software/gcta/. For SMR, see http://cnsgenomics.com/software/smr/. For PLINK, see https://www.cog-genomics.org/plink2/. For BOLT-LMM, see https://data.broadinstitute.org/alkesgroup/BOLT-LMM/. For UK Biobank, see http://www.ukbiobank.ac.uk/. For BioMart, see http://grch37.ensembl.org/biomart/martview/.For GTEx, see Further information on research design is available in the Supplementary informationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Reporting Summary


Bactrocera dorsalis (Hendel) is an important pest of fruit-bearing plants in many countries worldwide. In Africa, this pest has spread rapidly and has become widely established since the first invasion report in 2003. Wolbachia is a vertically transmitted endosymbiont that can significantly influence aspects of the biology and, in particular, the reproduction of its host. In this study, we screened B. dorsalis specimens collected from several locations in Africa between 2005 and 2017 for Wolbachia using a PCR-based assay to target the Wolbachia surface protein wsp. Of the 357 individuals tested, 10 were positive for Wolbachia using the wsp assay. We identified four strains of Wolbachia infecting two B. dorsalis mitochondrial haplotypes. We found no strict association between the infecting strain and host haplotype, with one strain being present in two different host haplotypes. All the detected strains belonged to Super Group B Wolbachia and did not match any strains reported previously in B. dorsalis in Asia. These findings indicate that diverse Wolbachia infections are present in invasive populations of B. dorsalis. Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) is amongst the most serious pests of cultivated fruits across Asia and Africa owing to its high adaptation, polyphagy, fecundity and the extent to which it causes yield and revenue losses [B. dorsalis [B. dorsalis has been intercepted on more than 50 occasions since the 1980s in Florida and California, indicating that North America is at constant risk for the establishment of this pest [B. dorsalis invading Europe, where is has been found in Italy [B. dorsalis was first detected in 2003 in Kenya and since then the pest has rapidly spread and established in most African countries often displacing the native Ceratitis cosyra  (Diptera: Tephritidae) as the primary fruit fly pest of mango [e losses . Many otdorsalis ,3. Notabhis pest . In addiof mango ,7. The iWolbachia is an intracellular bacterial parasite known to infect many arthropod species [Wolbachia are maternally-transmitted in the egg cytoplasm, and therefore, have evolved a number of reproductive manipulations to increase the fitness of Wolbachia-infected matrilines. In many cases, Wolbachia cause cytoplasmic incompatibility between uninfected females and infected males. This ability to cause cytoplasmic incompatibility can result in Wolbachia-infected lineages rapidly increasing in frequency in a host population. The release of Wolbachia-infected incompatible males is potentially a very effective mechanism for decreasing pest insect populations  [Wolbachia strain that causes cytoplasmic incompatibility in fruit flies has been demonstrated in the Mediterranean fruit fly, Ceratitis capitata (Wiedemann) (Diptera: Tephritidae) [Bactrocera oleae (Rossi) (Diptera: Tephritidae) [Wolbachia-induced cytoplasmic incompatibility can be used to spread symbionts and transgenes through target insect populations, which could be useful for controlling pests and blocking the capacity of vectors to transmit diseases [Wolbachia strains have also been found to modify their host’s susceptibility to parasitoids [Wolbachia infection status can be of relevance to optimizing integrated pest control strategies employing parasitoid wasps. species ,11,12. Wue, IIT) . IIT mayritidae)  and evalritidae) . In addidiseases ,17,18,19asitoids ; therefoWolbachia are a diverse bacterial clade that has been broadly categorized into several super groups. Currently, there are at least 16 recognized super groups, designated A–F and H–Q [Wolbachia is commonplace [Wolbachia are transmitted vertically and since mitochondria are co-inherited, this can establish a linkage disequilibrium between Wolbachia and the host mitochondrial haplotype [ and H–Q ,28,29,30 and H–Q ,35,36,37monplace . At the aplotype .Wolbachia strains in the genera Rhagoletis [Anastrepha [Ceratitis [Dacus [Bactrocera [B. dorsalis, Wolbachia has been reported at low prevalence in populations from China [Wolbachia strains in B. dorsalis populations in Africa and to evaluate the infection patterns of Wolbachia and associations with mtDNA haplotypes in populations of this host sampled between 2005 and 2017.In the Tephritidae family, several studies have detected agoletis ,40,41, Aastrepha ,44,45,46eratitis ,47,48 Das [Dacus ,50,51 anctrocera ,53,54. Iom China ,54. The Bactrocera dorsalis male flies were collected using attract-and-kill bait stations with methyl eugenol as attractant and malathion as killing agent, placed in Lynfield traps. Trapping was done in mango farms in 2017 from Mwanza (S 2°43′01.3″ E 33°01′20.4″) and Morogoro (S 06°57′38.5″ E 037°31′59.1″) in Tanzania, Bunamwaya (N 0°16′17.8752″ E 32°33′25.6284″) in Uganda and Kassala , Gezira (N 14°36′29.4″ E 33°47′27.5″) and Singa (N 12°47′46.6″ E 33°11′51.5″) in Sudan. Bactrocera dorsalis female flies were retrieved from infested mango collected from mango farms in 2017 from Nguruman (S 01°48′32″ E 36°03′35″), Kitui (S 01°21′ E 38°00′), Muranga (S 0°42′50.0″ E 37°07′03.4″) and Embu (S 0°28′56.6″ E 37°34′55.5″) in Kenya. Infested fruit were dissected for third stage larvae to emerge and pupate in fine sterile sand. Puparia were sieved from the sand and maintained in ventilated perspex cages until eclosion. All samples were stored in absolute ethanol at −20 °C. DNA was extracted from each individual using the ISOLATE II Genomic DNA Kit . Voucher specimens collected between 2005 and 2009 in African sites as well as in Sri Lanka from an earlier study of B. dorsalis were obtained from the molecular biology laboratory at the International Centre of Insect Physiology and Ecology, icipe [Wolbachia infections were initially screened by PCR using the wsp primers 81F and 691R [16S rRNA primers for Wolbachia pipientis [Wolbachia MLST gene primer sets [16S rRNA primers included an initial denaturation for 2 min at 95 °C, followed by 30 cycles of 1 min at 95 °C, 1 min at 52 °C and 1 min at 72 °C, then a final elongation step of 10 min at 72 °C. For the MLST and wsp primers, an initial denaturation for 2 min at 94 °C was used followed by 40 cycles of denaturation of 30 s at 94 °C, 45 s at annealing temperature , 1 min 30 s at 72 °C followed by a final extension step of 10 min at 72 °C. Host mitochondrial DNA was amplified by PCR in similar reaction volumes and cycling conditions as the wsp and MLST genes, using the primer LCO1490 and HCO2198 [Wolbachia positive samples. Sequencing was carried out in both directions (F and R) for Wolbachia and host COI. Wolbachia sequences and representative host haplotype sequences were submitted to the GenBank. Sequence alignments were performed using Clustal W in Geneious 8.1.9 software (www.geneious.com) [http://popart.otago.ac.nz) [wsp and MLST sequences obtained were inferred using the Wolbachia MLST database (https://pubmlst.org) [ipientis  and the  HCO2198  at an anous.com) . Phylogeo.ac.nz) . AllelicWolbachia wsp gene, we found that out of the 357 individuals tested, 10 were positive for Wolbachia  other MLST genes were amplified and sequenced , we amplified the equenced . A full  strains . The resWolbachia, we constructed a phylogenetic tree with 41 sequences available in GenBank. We used wsp sequences for 5 African samples and 1 Sri Lankan sample for which we were able to amplify at least one additional MLST gene. None of these Wolbachia were identical to previously detected Wolbachia strains in B. dorsalis or other Bactrocera species. The wsp sequences clustered with other Wolbachia strains in super group B  are included, while a sequence from Wolbachia endosymbiont of Brugia Malayi is included as an outgroup. Wolbachia super groups are indicated in higher case letters on the right. The detected Wolbachia are denoted by W followed by the host denoted as dor , population  and population sample ID.Sequences from other common coxA, the MLST genes fbpA, hcpA, gatB and ftsZ were amplified in a number of Wolbachia positive B. dorsalis samples. Phylogenetic inferences based on these genes largely supported the inferences based on wsp; H-Mu2 and H-Ng13, which were found to have identical wsp and coxA sequences were also found identical at the fbpA locus , whereas samples collected between 2005 and 2009 clustered into five of the aforementioned haplotypes (with the exception of Hap5 and 7) and an additional 12 smaller haplotypes  or Hap2 (H-Mu2 and H-Tg6). The infected Sri Lankan sample (H-Sl11) did not cluster into any of the 7 major and 12 minor mitochondrial COI haplotypes known from Africa. The two samples that had identical wsp and coxA gene sequences (H-Ng13 and H-Mu2) were found to have different mitochondrial COI haplotypes.The samples collected in 2017 clustered into 7 mitochondrial plotypes . The COIIn sites that were sampled in both the 2005 to 2009 period and 2017, the most dominant haplotype in the 2005 to 2009 population was also dominant in the 2017 populations , inferriWolbachia infections in B. dorsalis in Africa and one location outside of Africa. Wolbachia sequences were detected in 10 samples. Overall, this indicates a low rate of Wolbachia infection across the African populations, although infection rates appear to be marginally higher than found in Asia [Wolbachia infection was observed in Sri Lanka, which is within the native range for this species. Based on their wsp and coxA sequence, four distinct variants of Wolbachia: WdorTg6, WdorTzc13, WdorKi1 and WdorNg3/WdorMu2 were detected in the African populations of B. dorsalis. A fifth variant, WdorSl11, was detected in one of the Sri Lankan samples. All of these variants were distinct from those previously observed in B. dorsalis [Wolbachia.We investigated  in Asia ,53. It idorsalis ,53; thisWolbachia strains only transmitted from an infected female to her offspring  tend to associate strictly with the same host haplotypes while strains that are occasionally transmitted horizontally  do not. Altogether, the non-concordance with mitochondrial DNA, suggests that some of the Wolbachia strains infecting African populations of B. dorsalis may have an appreciable rate of horizontal transmission.The WdorNg3/WdorMu2 variant was detected in two individuals from two different sampling sites, Nguruman and Muranga, which are within geographically-separated agro-ecological zones in Kenya. It was notable that this variant was observed in two different mitochondrial haplotypes (Hap1 and Hap2). wsp positive samples, we were not able to amplify any of the MLST or the 16S rRNA genes. This may be indicative of transient infections that could have spilled over from an unknown source, or less likely they could be related to a partial transfer of genes (more common than the transfer of a full Wolbachia genome) from the symbiont to the host genome. This phenomenon has been observed in previous studies [Wolbachia pseudogenes have been detected in other tephritid fruit flies. The nature of these infections could be examined by monitoring vertical transmission rates and tissue localization patterns. We also cannot rule out the possibility that sample trapping and storage may have caused DNA degradation, which could have led to some false Wolbachia negative samples and an inability to amplify all loci for the Wolbachia positive samples.In four out of the ten  studies , where WWolbachia variants detected among the African populations clustered with super group B Wolbachia, which differs from the majority of super group A strains as found in B. dorsalis species in China [fbpA) segregated with group A Wolbachia, suggesting a possible recombination event between an A and B group strains, a phenomenon that has been reported in previous studies [wsp gene segregated with super group B Wolbachia in contrast to the coxA and hcpA loci which segregated as super group A Wolbachia. However, sequencing results of the 16S rRNA, gatB and ftsZ loci of H-Sl11 (data not shown) revealed an interfering secondary chromatogram in few segments suggesting that the sample is likely to have been infected by two Wolbachia strains (A and B group).At the majority of the loci investigated, the in China  and Thaiin China . For sam studies ,65. For Wolbachia had complete identity of allelic profiles to Wolbachia strains in the Wolbachia MLST database. Full length sequences of each of the MLST and wsp marker could not be retrieved for all the samples, and this limited our ability to confirm their designation as novel strain types through allelic profiles.In addition, none of the detected Wolbachia are known to cause male killing, and in insect populations with these strains, infected male specimens are generally not observed or observed only very rarely. To avoid bias against the detection of male-killing Wolbachia, female specimens are generally screened. Wolbachia are also known to reach high densities in insect ovaries and therefore may be easier to detect in females. Male lures are among the most widely used attractants in trapping of tephritid fruit flies. Therefore, the majority of the samples we screened, in particular from the 2005 to 2009 collections, were male. It is possible that if female flies had been screened, a higher Wolbachia prevalence rate would have been observed. However, it is noteworthy that the female samples from the 2017 collections did not have a higher rate of Wolbachia infection. Similar observations have been reported among B. dorsalis populations in Asia where the sexual differences in this host does not influence the detection rate of Wolbachia [Wolbachia variants that were found in males  are unlikely to have a male-killing phenotype, whereas we cannot rule out the possibility that WdorKi1, which was detected in a female fly, causes male killing.Some strains of olbachia . The WolB. dorsalis in the African region was observed to be largely unchanged between the 2005 to 2009 and 2017 samples, with two major mtCOI haplotypes (Hap1 and Hap2) dominating in both [B. dorsalis invasion in the Eastern Africa region, and that no significant new reintroductions with new mtCOI haplotypes have occurred in this region since the first invasion. To confirm this, however, it would be necessary to use nuclear DNA markers as mitochondrial DNA introgression could have occurred. The two main haplotypes, Hap1 and Hap2, in addition to Hap3, Hap4 and Hap7 were observed to be fairly distributed across the East and West of Africa. Only two haplotypes: Hap5 and Hap6 were observed in Eastern Africa only while the rest of the smaller mtCOI haplotypes (Hap8 to Hap18) were observed in West Africa, particularly in Nigeria, except for Hap8 that was observed exclusively in Benin. Previous microsatellite genotyping data also revealed differences between the Eastern Africa populations with that of Nigeria, which were more closely allied to the diverse Sri Lankan populations of B. dorsalis [The population structure of  in both . This sudorsalis . It has dorsalis .Wolbachia variants in African populations of the oriental fruit fly. Analysis of the host mtCOI haplotypes did not reveal a link between a particular Wolbachia variant and host haplotypes. Only the two dominant haplotypes were found to be infected with Wolbachia in Africa. These Wolbachia should be investigated for their capacity to manipulate host reproduction and to confer hosts with differential susceptibility to parasitoids and pathogens. A comprehensive understanding of the role of Wolbachia in this species could improve the effectiveness of integrated control strategies and eventually play a role in the sustainable pest management of B. dorsalis.We detected four different