However, the impacts of common full-sib families on accuracy of genomic prediction for commercial traits of economic importance are not well known in many species, including aquatic animals. This research explored the impacts of common full-sib families on accuracy of genomic prediction for tagging weight in a population of striped catfish comprising 11,918 fish traced back to the base population (four generations), in which 560 individuals had genotype records of 14,154 SNPs. Our single step genomic best linear unbiased prediction (ssGLBUP) showed that the accuracy of genomic prediction for tagging weight was reduced by 96.5%–130.3% when the common full-sib families were included in statistical models. The reduction in the prediction accuracy was to a smaller extent in multivariate analysis than in univariate models. Imputation of missing genotypes somewhat reduced the upward biases in the prediction accuracy for tagging weight. It is therefore suggested that genomic evaluation models for traits recorded during the early phase of growth development should account for the common full-sib families to minimise possible biases in the accuracy of genomic prediction and hence, selection response.Common full-sib families ( The c2, also known as non-additive genetic components, include both common environmental and maternal effects or possibly dominance, accounting for a significant proportion of total phenotypic variations, ranging from 5% to 55% for growth-related traits in fish . Under these models, the prediction accuracies for body traits  at harvest were moderate to high, ranging from .38 to .89 . The pres carpio  and .67 na gigas . The preguiensis  and .19–s mykiss . To dateTriticum aestivum L. , single step- GBLUP (ssGBLUP) or Bayesian methods . The Baytivum L.  or cassaa Crantz , althouga Crantz  or charaa Crantz . Recent . mykiss . In thisc2) from statistical models can result in upward biases in genomic prediction accuracy for tagging weight, 2) imputation of missing genotypes can improve the predictive performance of ssGBLUP, and 3) multi-trait genomic evaluation can lessen the overestimation of the prediction accuracy when the common full-sib families were omitted. Ultimately, the study attempted to explore possibilities for the application of genomic selection for early growth traits in striped catfish.Therefore, this study was set out to test three major hypotheses: 1) omission of the common full-sib families  for about 3 weeks. Then a random sample of about 500 fry per family were transferred to a net hapa installed in earthen ponds to raise to fingerling size of about 20 g for physical tagging, using Passive Integrated Transponder (PIT). One family was kept in a single hapa net. Three feeding strategies were applied for different rearing periods: no feeding before hatching (0–24 h), Artemia (day 1 to day 3) and Moina (day 4 to day 7) together with fish flake  were used for tank rearing period before 15 days  was sequenced to obtain genotype data for our analysis in this study.DNA samples of 560 fish  were sent to a commercial service provider in Canberra, Australia for genotyping by sequencing, using Diversity Arrays Technology (DArTseq™). DArTseq™ represents a combination of genome complexity reduction methods and high throughput sequencing platforms . A detaiThe missing genotypes (about 10.0%) were imputed using AlphaFamImpute  which takes into account of the pedigree relationships . Single-y is the observations related to individual records of each fish.X is the design matrix related to fixed estimates (b) that included generation (1–3) and spawning batch. Age from birth to tagging was also fitted as a linear covariate.Z and W are the design matrices related to the additive genetic effects u ∼  and common full-sib groups c ∼ . The random terms fitted in the model were the additive genetics of individual fish and the common full-sib families. LogLikelihood Ratio Test (LRT) showed that the common full-sib effects were statistically significant for tag weight . e ∼  is the error term in the model. Where I is the indentity matrix, H is described as below. σ2g, σ2c, σ2e are corresponding genetic, common environmental and residual variances.A) and kinship (G) matrices into a realised H matrix , ai is the effect of the ith SNP, u is the vector of breeding values obtained from single step analysis from Eq. pi is the frequency of ith SNP, y, X, b, Z, G, and e are described as above. Analysis of ssGWAS was accomplished by three sub-programs, including blupf90 , pregsf90 (derivation of the H matrix) and postgsf90 . The pre-selected SNP panels after GWAS analysis were based on a significant probability of less than .00001 for each of the 25 running sets. Finally, analysis of ssGBLUP genomic prediction were performed using only the highly significant SNPs. The model that omitted the common full-sib effects (c2) was the same as Model 1, except that the “Wc” term or “full-sibs” effect was not included.The model for single step GWAS expressed as below also see :y=Xb+Zii.e., 0,  or 2, ahttps://masuday.github.io/blupf90_tutorial/vc_gs.html) to define the stability of variances, and only samples displaying stabilised variances were used to calculate heritability and/or genetic parameters. The prediction accuracies obtained from AIREMLf90 were almost identical to those obtained from THRGIBBF1f90. Thus, only the estimates from the latter analysis were presented in this study. Finally, we performed pedigree-based PBLUP analysis and single-step genome-wide association study (GWAS) in combination of ssGBLUP to better understand the predictive capacity of our statistical models used to analyse tag weight. These analyses used AIREMLf90 and THRGIBBF1f90 packages (The predictive performance (or prediction accuracy) of ssGBLUP was evaluated using 5-fold cross validation over five replications. This involved the random division of the phenotypic data into 5 subsets (each with 2383–2384 observations). Then the breeding value of one set was predicted based on the data from the other four subsets. In the five fold cross-validation, the process was repeated 5 times and thus, there were 25 runs in total. The genomic prediction accuracy was defined as the correlations between the predicted breeding values and actual phenotypes  to examine re-ranking effects, i.e., re-ranking of breeding candidates based on their EBVs for tagging weight in the selection program for striped catfish.The average tag weight of the population was 22.7 ± 12.4 g . The tagh2) for tag weight was high (.72–.74) when the common full-sib estimate (c2) was omitted from our models: PBLUP, ssGBLUP and ssGWAS .The genomic prediction accuracy for tag weight was high (.636) when the stimates . In othec2 estimates were fitted in statistical models of our analysis  . Regardless of the inclusion or exclusion of the c2, the prediction accuracies were similar between AI-REML and Gibb sampling methods either when original genotypes  improved the prediction accuracy by .031 (or 11.2%) relative to the univariate ssGBLUP model (.307 vs. .276). The two-trait analysis also reduced biases in the prediction accuracy for tag weight when the enotypes  or imputenotypes  were anaThe inclusion of highly significant markers (471 SNPs) slightly increased the prediction accuracy for tag weight relative to ssGBLUP . However2 effects were not included in genomic evaluation models for tagging weight of striped catfish.To examine the impact of the common full-sib families on re-ranking effects, we calculated correlation of EBV for tagging weight between the two models (with the presence and absence of the common full-sibs effect). The Pearson correlation coefficient ranged from .30 to .62 , suggestIn the present study we attempted to address five major questions which are worth considering before initiating genomic selection program for early growth in striped catfish as well as other aquaculture species of economic importance.P. hypophthalmus which often takes 3–4 years to maintain a breeding cycle in genetic improvement programs. However, selection for tag weight may not capture all genetic variation in body traits at harvest as the genetic correlation (rg) between these two traits is reported to be .5 in this population  for body weights between successive rearing periods from 180 to 556 days post-hatch. Based on the genetic correlation estimates between tag and harvest weights, it is necessary to examine genomic prediction accuracy for harvest weight in this population of striped catfish. Furthermore, there are also no clear advantages regarding the prediction accuracy of ssGBLUP and ssGWAS as compared with PBLUP in our study. Future work should consider enlarging the sample size  and number of SNPs to take the advantages of ssGBLUP and ssGWAS models that can capture some measures of Mendelian sampling to improve the estimation of genetic (kinship) matrices for all individuals in the pedigree and hence, improving accuracy of estimated breeding values for tagging weight in this population of striped catfish.The prediction accuracy for tagging weight was high due to the high heritability (.72–.74) for this trait, which is opening new opportunities for improving early growth through genomic selection. Selection for early growth could shorten generation time of striped catfish pulation . In Asiac2) were excluded from our statistical methods, this resulted in upward biases in the prediction accuracy by 96.5%–130.3% for tagging weight. The overestimation of the prediction accuracy was to a greater extent when PBLUP was used as compared with other methods . To date, no published information is available in aquaculture species to compare with our studies. However, studies in farmed animals suggested that effects of non-additive genetics should be included in mating structures to improve accuracy of genomic prediction and hence, maximizing productivity for dairy farms  for physical tagging. The c2 estimates were generally not significant if early communal rearing of all families is practised and DNA markers are used for parentage assignment, as demonstrated in common carp C. carpio  weight, genomic evaluation models for these traits should account for the common full-sib families and they should be considered as separate traits in genetic improvement programs for striped catfish as well as other aquaculture species.When the common full-sib families  by 0%–28.5%  aimed to utilise genetic covariation between the traits and hence improved the predictive power of statistical models used. In addition, when the 0%–28.5% . However0%–28.5% . To dateiloticus  or for shthalmus , likely c2 estimates were omitted from our statistical models, mainly because the complete genotypes improved accuracy of estimated breeding values for tagging weight. The benefit of imputation on genomic prediction in aquaculture breeding has been reported in recent studies, such as for disease resistance to photobacteriosis in gilthead sea bream S. aurata  data were used. Second, the imputation reduced the upward biases in the prediction accuracy for tagging weight when the . aurata , resistaS. salar , growth- lalandi  or with . mykiss . In sele. mykiss  for a la. mykiss . Then im. mykiss . These fc2 did not have noticeable impacts on the prediction accuracy for tagging weight. This is likely due to the limited size of the significant SNPs obtained from genotyping by sequencing (GBS) platform but our observation here is consistent with previous findings for disease resistance traits in the same population of striped catfish P. hypophthalmus . However, other studies, which used prioritised variants from GWAS, reported there was an improvement in the prediction accuracy by 1.2%–13.3% for growth-related traits under chronic thermal stress in rainbow trout O. mykiss  as well as use different mating structures  to enable the separation of the dominance from common full-sib effects in order to improve accuracy of genomic prediction for tagging weight and commercial traits of economic importance in this striped catfish population.The prediction accuracy for tagging weight using BLUP-family methods was moderate to high. The omission of the common full-sib families resulted in upward biases in the predictive performance across statistical models used. Imputation of missing values alleviated the impacts of the common full-sib families on the prediction accuracy. As compared with single trait analysis, multivariate model slightly improved the prediction accuracy when the


Reducing harmful aggressive behaviour remains a major challenge in pig production. Social network analysis (SNA) showed the potential in providing novel behavioural traits that describe the direct and indirect role of individual pigs in pen-level aggression. Our objectives were to (1) estimate the genetic parameters of these SNA traits, and (2) quantify the genetic associations between the SNA traits and commonly used performance measures: growth, feed intake, feed efficiency, and carcass traits. The animals were video recorded for 24 h post-mixing. The observed fighting behaviour of each animal was used as input for the SNA. A Bayesian approach was performed to estimate the genetic parameters of SNA traits and their association with the performance traits. The heritability estimates for all SNA traits ranged from 0.01 to 0.35. The genetic correlations between SNA and performance traits were non-significant, except for weighted degree with hot carcass weight, and for both betweenness and closeness centrality with test daily gain, final body weight, and hot carcass weight. Our results suggest that SNA traits are amenable for selective breeding. Integrating these traits with other behaviour and performance traits may potentially help in building up future strategies for simultaneously improving welfare and performance in commercial pig farms. Mixing unfamiliar pigs is a common procedure in commercial farms, although it usually results in unstable social structures and increased aggressive behaviour in the pen. The negative consequences of this procedure on the welfare, performance, and health of pigs have been reported in several studies ,2,3. Exi2 = 0.09–0.26) , woControlling the aggressive behaviour in commercial pig farms requires suitable traits that describe the role of each animal in the aggression. In this study, social networks were performed and weighted using the duration of reciprocal fights between animals, and novel behaviour traits were extracted, i.e., SNA traits. Our results suggest that these SNA traits are heritable and genetically correlated. The genetic correlations of the SNA with performance traits were not significantly different from zero, except for weighted degree and HCW, and between both betweenness and closeness centrality and DFI, FBW, and HCW. Considering SNA traits alongside performance traits may help to build up future strategies for simultaneously improving the performance and welfare in modern commercial pig farms. Finally, the current study can be considered a case study for a novel approach that could be applied to other animal species and different forms of social interaction to better understand the social structure of different animal groups.


Transcriptomics data also strengthen the relevancy of the findings to metabolic disorders, thus contributing to better understanding of pathogenesis. In addition, genetic risk scores are constructed and validated for absolute risks prediction of T2D in Taiwanese population. In conclusion, our data-driven approach without a priori hypothesis is useful for novel gene discovery and validation on top of disease risk prediction for unique non-European population.To explore the complex genetic architecture of common diseases and traits, we conducted comprehensive PheWAS of ten diseases and 34 quantitative traits in the community-based Taiwan Biobank (TWB). We identified 995 significantly associated loci with 135 novel loci specific to Taiwanese population. Further analyses highlighted the genetic pleiotropy of loci related to complex disease and associated quantitative traits. Extensive analysis on glycaemic phenotypes (T2D, fasting glucose and HbA A phenome-wide association study and polygenic risk score analysis identifies several loci linked to metabolic disease and type 2 diabetes in the Taiwanese population. Conventionally, genetic epidemiological studies focused on specific phenotypic or disease trait, which, to date have reported more than 370,000 associations over 1800 traits3. However, complex diseases require more in-depth parallel analysis due to the heterogeneity and genetic pleiotropy of complex diseases. The abundance of both genotype and phenotype data from biobanks such as the UK Biobank (UKBB)4 and the Biobank Japan (BBJ)5 has allowed extensive phenome-wide genome-wide association studies of quantitative traits and diseases. However, most of the previous discovery are based on subjects with European ancestry and gradually, the establishment and maturation of biobanks with non-European ancestry have revealed the importance of diversity in genetic epidemiology6.Genetic epidemiological methodologies such as genome-wide association studies (GWAS), phenome-wide association study (PheWAS), conditional GWAS, genetic correlation8. Analyses based on phenotypes in TWB may reveal significant genetic effects of critical health-related traits in Taiwanese and in extension, other populations with East Asian ancestry.The population-based biobank of Taiwan  was launched in 2012 and encompasses 0.61% of the total population of Taiwan with more than 144,000 participants, which is comparable to the population coverage of BBJ (0.16%) and UKBB (0.74%). The majority of Taiwanese (over 99%) are of Han Chinese ancestry who migrated from mainland China9. The T2D prevalence in Taiwanese population was estimated to be 11.6% in 20169 with the average age of onset around 59.5 years old10. In addition, the mean annual cost for a patient with T2D-related major complications was estimated to be USD $418911. T2D is a heritable trait compounded by various degrees of gene–gene and gene–environment interactions with heritability estimates ranging from 20 to 80%12. The polygenic risk score (PRS), which aggregates the effects of multiple disease-associated genetic variants, has previously been used for T2D risk prediction in various populations. To further improve the applicability of PRS on identifying high-risk individuals for early intervention specifically for Taiwanese, we constructed absolute risk models which incorporated various risk factors for estimation on the probability that an individual free of T2D at a given age will develop T2D in an upcoming time interval.In this paper, we reported a comprehensive PheWAS of 10 diseases and 34 quantitative traits from TWB with complementary conditional analysis, genetic correlation and MR. As most of the available traits are cardio-metabolically related, we focused our analysis on type 2 diabetes (T2D). T2D has been one of the leading causes of mortality and morbidity in Taiwan with high socioeconomic burdenHACL1, RAD21, ASH1L, and GAK) related to T2D in Asian population provided insights into the pathophysiology of T2D, and could be potential targets for clinical diagnosis and therapeutic interventions.Our findings of novel candidate genes (n = 5), metabolic (n = 8), cardiovascular (n = 6), hematological (n = 5), kidney-related (n = 6), liver-related (n = 6), stress-related (n = 5), pulmonary-immunological (n = 2), and articular-skeletal (n = 1).Supplementary Data P ≤ 5 × 10−8) with more than 100 loci were found to be specific to the TWB population . Another highly pleiotropic region on chromosome 12  was associated with eight traits in five different categories . This pleiotropic region on chromosome 12 contains two genes ; genetic variants of ALDH2 have been shown to be associated with risk to T2D, micro-vascular and macro-vascular complications14. Further details of the PheWAS results are available in Supplementary Data In total, 995 significantly associated loci P ≤ 5 × 0−8 with a 5 Fig. . For insr2 ≤ 0.1; P ≤ 5 × 10−8). Of these signals, 38 were mapped to genes different from their corresponding lead SNPs . For the vascular-metabolic traits, T2D was found to be significantly associated with 27 quantitative traits, such as HbA1c, BMI, WC  with T2D. Moreover, we identified several association signals previously reported by BBJ5 as well as other studies. For example, we observed significant correlations between T2D and several kidney-related traits, such as microalbumin15 , uric acid16 , and BUN17 .To elucidate the underlying mechanisms of identified associations, we estimated genetic correlations between each pair of quantitative traits and binary diseases using bivariate LD score regression1c, and FG)  through MR-PRESSO.MR analyses were carried out for BMI, Waist circumference, Body fat percentage, Waist-hip ratio, Hip circumference, triglyceride, HDL-C, and VLDL-C against glycemia-related traits , for the effect of a 1-unit increase in HDL-C on the risk of T2D was 0.9762 (P = 0.0087), and for the effect of a 1-unit increase in VLDL-C on the risk of T2D was 0.9832 (P = 0.01)  (Supplementary Data P = 0.0229) ), the causal relationship between BMI and T2D remained from our MR analyses. In our study, lipid profiles such as HDL-C and VLDL-C were found to be superior to anthropometric measurements in predicting the risk of glycemia-related traits.In the two-sample MR analysis on T2D, the overall causal estimate (IVW odds ratio (OR) estimate) for T2D per unit increase in BMI was 1.2566 P = 0.00, for theN = 75,627)    and 3844 T2D subjects (5.7%)    after adjustment.  on different loci remained loci-wide significant . NOSTRIN, POLD2/MYL7, CAMKMT/LINC01833, and SND1 were previously reported to associate with glycemia-related phenotypes21. STARD13 and LUC7L were associated with hemoglobin22 and thus not considered to be associated with glycemic traits.The conditional association analyses revealed another 26 additional independent loci , regulating the subcellular localization and allosteric switch of GCK, the rate-limiting enzyme for cellular glucose uptake28. These results clearly demonstrate the strength of PheWAS in identifying genes with pleiotropism on T2D and its comorbid traits. The lead SNP rs6547692 for GCKR gene has a similar prevalence across TWB (MAF = 0.485), BBJ (MAF = 0.4387) and UKBB (MAF = 0.44). The regional association plot for rs6547692 (GCKR) is shown in Fig. As demonstrated in the Fuji plot, we identified several highly pleiotropic loci and discovered putative shared genetic effects for several diseases or traits. For example, HACL1 with HbA1C  in 77,072 individuals. The recent largest trans-ancestral GWAS of glycemic traits showed marginal significance (P < 1 × 10−3) of several single-nucleotide variations within HACL1  in sub-population of around 10,000 individuals29. Their meta-analysis results consist of 281,416 individuals with 13% East Asian. From Genome Aggregation Database (gnomAD)30, we noticed that rs1481559294 for HACL1 was a relatively rare variant  as compared to the MAF in our population of 0.0013 . The regional association plot for rs1481559294 (HACL1) is shown in Fig. HACL1 encodes for the enzyme 2-hydroxyl-CoA lyase 1 which is involved in catalyzing the conversion of even-chain fatty acids into odd-chain fatty acids by cleaving C1 in peroxisome fatty acid α-oxidation31. Jenkins et al. showed that Hacl1 knockout mice had lower plasma and liver C17:0 fatty acid, but did not observe significant difference in adipose tissue32. Gene expression of HACL1 has low tissue specificity, however, transcriptomics data showed tissue expression of HACL1 clustered in the intestine and liver, associated with lipid metabolism   with fasting glucose. GAD2  was recently reported in the same trans-ancestral GWAS of glycaemic traits by Chen et al.29. Despite having a smaller sample size, we managed to identify the association of GAD2 with fasting glucose, highlighting the importance of genotype data from diverse ancestries such as Taiwan Han Chinese. The lead SNP rs61839365 for GAD2 is more prevalent in East Asian with MAF of 0.293 in TWB and 0.416 in BBJ as compared to 0.18 in UKBB. The regional association plot for rs61839365 (GAD2) is shown in Fig. GAD2 is a major autoantigen in autoimmune-associated type 1 diabetes and in a subset of T2D, latent autoimmune diabetes in adults35. Glutamate decarboxylase 2 catalyzes the formation of gamma-aminobutyric acid (GABA). GABA is an inhibitory neurotransmitter that is a critical component of neurophysiologic function. Upon the stimulation of glucose, GABA, co-secreted with insulin, has been shown to inhibit glucagon secretion via the activation of GABAA-receptor chloride channels of α cells36. It has also been documented that beta cells secrete GABA in a pulsatile manner in synchrony with insulin secretion37. The storage and secretion of GABA in beta cells are defective in islets of type 1 and type 2 diabetic patients37. Taken together, it is plausible that GAD2 may modulate the blood glucose level by regulating glucagon secretion. GAD2 is highly expressed in brain tissues  . The regional association plots  for rs11994747 (RAD21), rs371382391 (ASHIL) and rs1326821916 (GAK) are shown in Supplementary Fig. ASH1L and GAK were previously reported to be associated with obesity traits such as BMI and waist-hip ratio in GWAS of UK Biobank39. Obesity traits were shown to have significant genetic correlation and are causally associated with glycaemic traits in our pairwise genetic correlation analysis and MR. Furthermore, Klarins et al. reported that GAK was associated with lipid traits (HDL and TG) in their genome-wide meta-analysis. Lipid traits were also shown to have significant genetic correlation and causally associated with glycaemic traits in our pairwise genetic correlation analysis and MR40. All these implied that ASH1L and GAK are crucial cardiometabolic genes in T2D. Inhibition of RAD21 has been demonstrated to increase insulin secretion in a MIN6 mouse beta cell line17 and RAD21 was associated with reduced hematopoietic stem cell self-renewal in aging and inflammation41. Rare mutation of RAD21 has been reported in Cornelia de Lange syndrome with premature physiological aging and gastrointestinal tract difficulties42. However, RAD21 has never been reported to be linked to any cardiometabolic traits in any GWAS. Further functional work in both cellular and animal model will be required to confirm the role of RAD21 in T2D and the link between insulin secretion, physiological aging and T2D.The conditional association analyses revealed three novel genetic association with glycaemic traits -HbA1c trait with P value = 4.24 × 10−8 larger than the most stringent Bonferroni-corrected significance threshold in our study. However, as mentioned above that the study of Hacl1 knockout mice supports the potential involvement of HACL1 for fatty acid, we believe that HACL1 is still a promising candidate gene for metabolic traits.As discussed in ref. Our absolute risk model for T2D using PRS and risk factors should be applicable for any Asian population. The strengths of absolute risk model for T2D are the combination polygenic risk of SNPs and risk factor measurements for all subjects allowed joint evaluation on the effects of PRS and family history of T2D. Our findings could be useful in global efforts to generate trans-ancestry PRS. The good model calibration of relative risk demonstrated the validity of the model. The main limitation of our PRS model is that self-report disease status often underestimates the true disease prevalence/incidence. As TWB is an on-going project, we expect to have a sufficiently large follow-up dataset on T2D in the near future, which will allow us to validate our prediction model and evaluate its applicability for population-wide screening on T2D to identify high-risk individuals for early intervention.https://www.twbiobank.org.tw/) The population in Taiwan consists of mostly East Asian ancestry, specifically Han Chinese, therefore, they are suitable to serve as a representative study sample for Asian population. Detailed information of TWB dataset is available in the previous publication23. This study has been approved by the internal review board of the Academia Sinica (Num: AS-IRB02-109063) and the research ethics committee of Taiwan University Hospital (No. 201507020RINB), and Taiwan Biobank. All participants gave informed consent when joining TWB, which allows for sharing of all anonymized data with authorized researchers. Participants can withdraw consent to sharing their data at any stage of their participation in TWB.We used individual genotype and phenotype data of subjects recruited from 2012 to 2019 of Taiwan Biobank (TWB) for subsequent data analysis. (We conducted GWAS through logistic regression model (for binary traits) and linear regression model (for continuous traits) under the assumption of additive allelic effects of the SNP dosages via PLINK v2.0. The regression models were adjusted for age, gender and the first ten genetic principal components.23. For this study 95,252 subjects were genotyped with either the customized TWB1 array  or TWB2 array  or both (Nboth = 1496) and the last group was also typed by whole genome sequencing (WGS).Detailed genotyping and imputation procedures have been described earlier44 algorithm in the cluster package of R (version 3.6) and φ-correlation as our distance matrices. Best-fit group numbers were selected by maximizing the silhouette score45. The final sample sizes for each trait are shown in Supplementary Data We first homogenized control individuals by removing comorbid individuals for each trait. Comorbid diseases are defined by a data-driven method using the Partitioning Around Medoids (PAM)For each trait, outliers beyond three standard deviations (two-tailed) were excluded. Individuals with missing values in any trait were dropped from the analysis. A total of 34 quantitative traits were used in this study  no INDELs. For sample filtering, arrays with generated genotypes for <95% of the loci were excluded. PLINK v1.9 software was used to identify samples with genetic relatedness indicating that they were from the same individual or from first-, second- or third-degree relatives. These determinations were based on evidence for cryptic relatedness from identity-by-descent status (pi-hat cutoff of 0.125). After removing first-, second- and third- degree relatives, 77,072 independent samples and 59,521 SNPs remained.The genotype data from the TWB1 and TWB2 arrays were merged using the GRCh38 assembly and annotation provided by TWB. After filtering out samples with a call rate <0.99 and sex mismatch in either of the TWB1 and TWB2 datasets, 95,215 samples and 95,673 variants remained. For kinship estimation, computation of principal components (PCs), and genomic relation matrix, SNPs were extracted by the following criteria: (1) SNP IDs, chromosomes, physical positions, minor alleles and major alleles had to be all identical in both datasets; (2) call rate >0.95; (3) MAF > 0.01; (4) deviation from Hardy–Weinberg equilibrium (HWE) P ≤ 10−10). Supplementary Table For our analysis, we merged the imputed TWB1 and TWB2 genotype data and selected SNPs according to the merged imputation data released by TWB. Low-quality variants were filtered out using PLINK if an SNP met any of the following criteria: (1) MAF ≤ 0.001; (2) imputation INFO score ≤ 0.8; (3) call rate ≤ 0.95; and (4) deviation from HWE (P < 10−6) were also excluded.Additional typical QC procedure for SNPs to build PRS models were applied. Multiallelic SNPs and SNPs with ambiguous strands were removed from the analysis. SNPs with MAF ≤ 0.01, low imputation quality (info <0.3) or deviation from HWE (P ≤ 5.0 × 10−8 with PLINK2 (https://www.cog-genomics.org/plink/2.0). Linear regression models were used to evaluate the association of all SNPs with each of the 34 quantitative traits under the assumption of additive allelic effects by PLINK. Unless described otherwise, both binary and quantitative traits were adjusted for age and sex with the first ten principal components (PCs) estimated by EIGENSOFT (version 6). Since the standard threshold of 5 × 10−8 had been used in many PheWASs such as in homogeneous population20 and also in trans-ancestral analysis29, therefore, we set the standard threshold of 5 × 10−8 for our GWAS.Significant signals for all binary traits were first screened using a genome-wide significance threshold of r2) within each locus with a window size of 4000 SNPs using 1496 TWB WGS data. SNP was considered to be independent of the lead SNP if r2 ≤ 0.1.Conditional analyses were performed for each aforementioned defined locus using the PLINK2 “--condition” flag. Association tests were conducted based on the generalized linear model with adjustment for all covariates listed in the PheWAS section and an additional lead SNP genotype. Linkage disequilibrium (LD) was estimated with the pairwise squared correlation  calculated by the Benjamini–Hochberg method50 was used to adjust for multiple testing of 946 combinations of pairs of traits using the Python module statsmodels (www.statsmodels.org). A significant genetic correlation between phenotypes was considered if FDR ≤ 0.05. Data visualization was performed through the ldsc-corrplot-rg script (https://github.com/mkanai/ldsc-corrplot-rg) and R.We used bivariate LD score regression51. In our MR analysis, we first filtered horizontal pleiotropic instrumental variables (IVs)52 using MR-Egger53 and identified outliers through MR-PRESSO54. Finally, the valid IVs were applied through IVW55 to analyze causal relationships between the following eleven clinical measurements as exposures , and outcomes .Mendelian randomization (MR) methods utilize common genetic variants to estimate the causal relationship of risk factors with disease outcomesr2 threshold of 0.01 and physical distance threshold of 10 Mb through clump command of the TwoSampleMR package56 in R.TWB samples was randomly split into two sub-groups: 3/4 (G group) for GWAS and the remaining 1/4 (MR group) was tested using the IVW method for MR analysis. For the IV selection, GWAS were carried out in the same manner described above using the G group, and significant SNPs for the eight exposures were selected. The causal associations of the eight exposures with T2D were investigated using the MR group with logistic regression models. The resulting associated SNPs (IVs), their regression coefficients and the effect estimates of the exposures on the outcome were obtained by pooling all MR estimates using the random-effects IVW method. To ensure independence of IVs, strict clumping was performed with a To build absolute model for T2D risk estimation based on PRS and risk factors in T2D-free individuals, we divided the process into three stages: construction of PRS, absolute risk modeling, and validation analysis. For the data preprocessing, we first identified individuals who were free of T2D at baseline and with more than one visit records, which was 15,664 individuals, to be utilized subsequently in the validation analysis . For the PRS model construction, we randomly selected one third of the remaining TWB samples, excluding the 15,664 individuals for validation analysis (TWB-for-PRS). The remaining 2/3 samples (TWB-for-AR) was used to build the absolute risk model.P values  method. The hyperparameters for this method were the thresholds for the correlation 58 to project the individual risk. We used the best PRS model described above and transformed it into 4 quintile variables to facilitate interpretation, where the first quintile represents the lowest 20% of the PRS sample in our sample population and so forth for the other quintiles. Similarly, the continuous BMI values were categorized into four classes . To apply iCARE for absolute risk estimation, relative risk (RR) estimates is required for all variables in the model, including PRS quintiles, categorical BMI, and family history of T2D. We used TWB-for-AR samples to estimate odds ratio (OR) associated with the aforementioned variables by logistic regression. As the prevalence of T2D in the Taiwanese population is about 10%, the OR gives a reasonable approximation to the RR. Absolute risk estimation was also based on the age and sex-specific T2D incidence rate and mortality rate from causes other than T2D in the Taiwanese population as recommended in ref. 9 and data from the Taiwan Ministry of Health and Welfare (https://www.mohw.gov.tw/mp-2.html). Note that the data on the incidence and mortality rates from these references are based on diabetes mellitus cases; however, they should provide a reasonably good approximation to T2D as T2D represents the majority (>95%) of diabetes mellitus cases in our data.To build the absolute risk model, we utilized the R package iCARELastly, we used the R package iCARE to conduct validation analysis on TWB-for-val sample. Model calibration was assessed by comparing the model projected absolute and relative risk estimates to the observed values in the TWB-for-val sample. The Hosmer-Lemeshow and Chi-square tests were used to judge goodness of model fit, respectively. In addition, the model discrimination was assessed by area under the curve (AUC).Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1-14Supplementary Data 15Reporting Summary


Our unique capacities for spoken and written language are fundamental features of what makes us human, yet the biological bases remain largely mysterious. We present a large-scale well-powered genome-wide association study meta-analysis of individual differences in reading- and language-related skills  in tens of thousands of participants. The findings prompt a major reevaluation of prior literature claiming candidate gene associations in much smaller samples. Moreover, we use the novel genetic data as windows into multiple aspects of the biology of these important abilities, revealing molecular links to individual differences in neuroanatomy of language-related brain areas and enriched heritability in archaic deserts of the human genome as well as in fetal brain enhancer regions. P = 1.098 × 10−8) at a locus that has not been associated with intelligence or educational attainment. All five reading-/language-related traits showed robust SNP heritability, accounting for 13 to 26% of trait variability. Genomic structural equation modeling revealed a shared genetic factor explaining most of the variation in word/nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence, and educational attainment. A multivariate GWAS of word/nonword reading, spelling, and phoneme awareness maximized power for follow-up investigation. Genetic correlation analysis with neuroimaging traits identified an association with the surface area of the banks of the left superior temporal sulcus, a brain region linked to the processing of spoken and written language. Heritability was enriched for genomic elements regulating gene expression in the fetal brain and in chromosomal regions that are depleted of Neanderthal variants. Together, these results provide avenues for deciphering the biological underpinnings of uniquely human traits.The use of spoken and written language is a fundamental human capacity. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30 to 80% depending on the trait. The genetic architecture is complex, heterogeneous, and multifactorial, but investigations of contributions of single-nucleotide polymorphisms (SNPs) were thus far underpowered. We present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures  in samples of 13,633 to 33,959 participants aged 5 to 26 y. We identified genome-wide significant association with word reading (rs11208009,  The processing and production of complex spoken and written language are capacities that appear to be distinct to our species . Such skROBO2 associated with expressive vocabulary in infancy , which encompasses the older definition of specific language impairment (SLI) . However infancy ; rs17663dyslexia .https://www.genlang.org/) together with several public datasets that have data available for the relevant phenotypes matched to genome-wide genotype information. Five quantitative traits were identified for which phenotype data could be aligned across different cohorts to yield sufficiently large sample sizes for GWAS: word reading, nonword reading, spelling, phoneme awareness, and nonword repetition. Univariate GWAS meta-analyses were performed for each of the phenotypes to identify genetic variation influencing these traits and to model genetic overlaps between them. For comparative purposes, a GWAS meta-analysis for performance intelligence quotient (IQ) was also performed in the same dataset. Together with publicly available GWAS summary statistics from prior studies of cognitive performance and educational attainment, these data were used to study genetic relationships between reading- and language-related traits, IQ, and educational attainment. A multivariate approach allowed us to optimize the power of GWAS meta-analysis for functional follow-ups, giving insights into the tissues, cell types, brain regions, and evolutionary signatures involved.Here, we present large-scale GWAS meta-analyses of a set of reading- and language-related traits, measured with psychometric tools. We captured variation across the phenotypic spectrum, extending beyond disorder. Our study focused on traits assessed using continuous measures in multiple cohorts from the international GenLang network . The cohorts connected in the GenLang network either were originally ascertained through a proband with a language/reading disorder (DLD/SLI or dyslexia) or were sampled from the general population; all cohorts include quantitative phenotypic data gathered via validated psychometric tests as well as genome-wide genotype data from the tested individuals. Some of the samples are birth cohorts, and some involve family or twin designs. The phenotype data were collected across an array of different ages, test instruments, and languages . We reduced heterogeneity of assessment age by excluding individuals over 18 y of age (except for three cohorts)  and where phenotype data were available from the same participant at multiple ages, by choosing the age that matched best with the assessment ages of the largest cohort(s). We limited the heterogeneity introduced by different test instruments by only including those that measured the phenotypes described in our analysis plan  and in cases where data from more than one test instrument were available, by selecting the test instrument that was used by the largest number of cohorts.Our study focused on five quantitative reading- and language-related traits: word reading accuracy, nonword reading accuracy, spelling accuracy, phoneme awareness, and nonword repetition accuracy . These tDataset S4). We tested those data for genetic correlations using linkage disequilibrium score regression (LDSC) . There was limited heterogeneity of GWAS meta-analysis results, as also evident in the Cochran Q statistics  and LDSC ratios (Dataset S4) for all traits except nonword repetition.We evaluated whether, despite efforts to minimize this, heterogeneity related to age and/or use of different test instruments remained in our data. To do so, we generated GWAS meta-analysis results for word and nonword reading stratified by age or test instrument (n (LDSC) , determiSI AppendixSupplemental Notes, Dataset S4). Given the lack of genetic heterogeneity, the remainder of the study involved analyses of the full non-stratified dataset, to ensure largest available sample size and maximal power.We also assessed effects of sex, observing high genetic correlations for female- and male-only subsets (P = 5 × 10−8), but also for multiple testing based on the correlation structure of our five reading-/language-related traits, as estimated using phenotypic Spectral Decomposition . The significance threshold for assessing the GWAS meta-analysis results was thus set to P = 5 × 10−8/2.15 independent traits = 2.33 × 10−8. We identified a genome-wide significant locus associated with word reading  . Notably, rs11208009 has not shown association with general cognitive performance or educational attainment, while other SNPs in linkage disequilibrium (LD) with rs11208009 (r2 > 0.6) have been associated with triglyceride and total cholesterol levels in blood in previous GWAS (Dataset S5). Three genes are located in the vicinity of rs11208009 and SNPs in LD (r2 > 0.6): DOCK7, encoding a guanine nucleotide exchange factor important for neurogenesis . Genome-wide significant loci were not identified for the other traits. Dataset S7 lists all results with P < 1 × 10−6.We performed univariate GWAS meta-analyses for the reading-/language-related traits in the full GenLang dataset as follows. For each phenotype, associations between SNPs and the quantitative trait were calculated in every cohort separately, then combined into a meta-analysis for that phenotype. This yielded five separate univariate GWAS datasets, one for each trait. For evaluating statistical significance of SNP associations, we determined an appropriate threshold that was adjusted not only for genome-wide screening , indicating that the captured common genetic variation accounts for a substantive proportion of the phenotypic variance. These observations allowed for follow-up analyses that are dependent on significant SNP heritability, including estimates of genetic correlations: a measure that quantifies the overall genetic similarity between two complex traits. Pairwise genetic correlation analyses showed significant overlap among the reading- and language-related traits (Dataset S4). Genetic correlation estimates were especially high for word reading, nonword reading, spelling, and phoneme awareness, ranging from 0.96 (SE = 0.07) to 1.06 (SE = 0.07).The individual effect size of our genome-wide significant hit is small, as is typical for genetically complex traits. We went on to make use of the complete GWAS signal considered in aggregate across the genome to gain insights into the genetic architecture of the reading-/language-related traits as well as relationships with other aspects of human biology. First, for each phenotype, we estimated SNP-based heritability: the proportion of trait variability explained by the SNPs included in the GWAS. All five traits showed significant SNP-based heritability, with LDSC-based estimates ranging from 0.13 for nonword repetition to 0.26 for nonword reading  . Only nonverbal subtests of general intelligence tests were used in this analysis (Dataset S1). Summary statistics were also obtained from another three sources: 1) genome-wide studies of full-scale IQ  and educational attainment  by the Social Science Genetic Association Consortium   that used a decomposition analysis to identify genetic associations with distinct domains of performance  ; and 3) formance .Dataset S8). Interestingly, genetic correlations with full-scale IQ were substantially higher for word reading (95% CI = 0.70 to 0.85) than nonword reading (95% CI = 0.50 to 0.68), likely reflecting the importance of reading skills for verbal tests of cognition. Genetic correlations of reading-/language-related traits with performance IQ (range = 0.20 to 0.35) were much lower than those for full-scale IQ, and the 95% CIs did not overlap. Indeed, only word reading showed a significant genetic correlation with performance IQ. These results indicate that reading-/language-related traits and IQ are at least partly based on distinct genetic factors. Significant trait-specific genetic correlations were observed for components 2 to 4 of the Danish school grade decomposition analysis , educational attainment (range = 0.54 to 0.68), and school performance (range = 0.54 to 0.81) . Exploratory factor models with one to four factors were fitted to the data, of which the three-factor model explained the majority of the variance. The three-factor model was followed up using confirmatory factor analysis in GenomicSEM. In the final model   is a metal model B, the fiDataset S10), and these have identified very few associations exceeding genome-wide significance. In those prior studies, a total of 48 independent SNPs met a less stringent threshold of P < 1 × 10−6 in the respective GWAS. We ran lookups of each of those SNPs in our GenLang GWAS meta-analysis results (Dataset S10). Where SNP associations passed a threshold adjusted for multiple testing of 48 SNPs and 2.15 independent GenLang traits (P < 4.84 × 10−4), we then reran the association analyses after exclusion of the original cohort(s) in which the association was first identified to evaluate independent effects beyond those of the respective earlier study. According to these criteria, only one SNP, rs1555839, previously associated with rapid automatized naming in the Genes, Reading, and Dyslexia (GRaD) cohort (P = 3.33 × 10−4). This SNP is one of five SNPs that reached the threshold for genome-wide significance in the original GWAS of the GRaD cohort.The number of previous GWAS on reading-/language-related traits and disorders is small  cohort  yielded Dataset S11 gives gene-based P values from our GenLang GWAS meta-analysis, calculated by Multi-marker Analysis of GenoMic Annotation (MAGMA) . DCDC2 was originally identified in a linkage region for dyslexia susceptibility, and SNPs in and near this gene were subsequently associated with dyslexia in candidate gene studies  (Dataset S11). These null findings highlight the importance of large-scale studies for robust identification of common DNA variation associated with reading-/language-related traits and disorders and warrant a reevaluation of the contributions of candidate genes that are prominent from prior literature.Some 20 genes have been described in the literature as candidate genes for reading-/language-related traits and disorders based on a range of mapping approaches and have been the focus of much of the prior published research in this area . Dataset (MAGMA) , for eac studies , althoug studies . No singSI Appendix, Fig. S4), the approach improved power for follow-up analyses in three ways: 1) by incorporating all available data from the different traits without increasing multiple testing burden; 2) by reducing error variance and thereby, increasing the proportion of phenotypic variability captured by genetic variations, with SNP-based heritability that was higher than any of the univariate estimates with a smaller SE ; and 3) because joint analysis of the different traits maximized the effective sample size of the dataset. Indeed, MTAG estimated the GWAS equivalent sample size of the multivariate results as 41,783 compared with 27,180 for the corresponding univariate results. These multivariate GenLang GWAS results were used for follow-up analyses utilizing not only genetic correlation approaches, but also other genome-based methods, such as gene property analysis and heritability partitioning, further explained below.To improve the power of our GWAS meta-analysis for follow-up analyses, we took advantage of the high genetic correlations between the traits by performing a multivariate GWAS analysis of word reading, nonword reading, spelling, and phoneme awareness with the Multi Trait Analysis of GWAS (MTAG) method . This apMaterials and Methods). This yielded 58 structural neuroimaging traits, including surface-based morphometry (surface area and thickness)  phenotypes and diffusion tensor imaging   results. Second, as many of these brain-based phenotypes were significantly correlated with each other, genetic correlations among their summary statistics were used to calculate the number of independent traits for multiple testing correction. Third, we performed genetic correlation analyses between the multivariate GenLang GWAS results and the 58 neuroimaging traits to identify genetic overlaps. One neuroimaging trait showed significant genetic correlation with the multivariate GenLang results (P < 0.05/24.85 independent traits = 2.01 × 10−3): the surface area of the banks of the superior temporal sulcus (STS) of the left hemisphere  (Dataset S12). This finding suggests the existence of shared genetic factors that contribute both to left STS surface area and to reading-/language-related skills, albeit without identifying which SNPs underlie the relationship. Functional MRI studies have linked this region to different aspects of written and spoken language processing (Dataset S12).The neurobiological circuitry involved in spoken and written language has been extensively investigated in prior literature, from pioneering postmortem studies of brain lesions through to the noninvasive structural and functional neuroimaging research that is now standard for the field , 27. Notocessing –36. To in = 257,828) were obtained as well. A total of 143 traits showed significant genetic correlations with the multivariate GenLang GWAS results after correction for multiple testing [P < 0.05/(535 × 2) = 4.67 × 10−5] (Dataset S13), while 245 traits were genetically correlated with full-scale IQ; 135 traits showed significant correlations with both our multivariate GWAS and full-scale IQ. Traits with strong genetic correlations were related to education, eyesight, chronotype, well-being, lifestyle, physical health and exercise, and socioeconomic status. Representative traits are plotted in Next, we assessed genetic correlations of our multivariate GWAS results with 20 cognitive, education, neurological, psychiatric, and sleeping-related traits and 515 additional UK Biobank traits using LD Hub. To further investigate overlaps with and differences from IQ, genetic correlations between these 535 traits and the published GWAS summary statistics for full-scale IQ  . The tested annotations included human gained enhancers active in fetal and adult brain tissue, ancient selective sweep regions, Neanderthal introgressed variants, and archaic introgression deserts . The latter two annotations relate to admixture events between Homo sapiens and Neanderthal populations that took place when the different hominins encountered each other outside Africa some 50,000 to 60,000 y ago, with the consequent gene flow leaving remnants (introgressed fragments) that can be detected in the genomes of living humans. Archaic deserts are long stretches in the human genome that, despite these admixture events, are significantly depleted for Neanderthal alleles in living humans, possibly due to critical functions of the genetic loci in H. sapiens and intolerance to gene flow (P < 0.01) (Dataset S14). Thus, our results suggest that common DNA variation in archaic deserts makes a larger contribution to individual differences in reading-/language-related traits within present-day populations than expected by chance.Writing and reading are relatively recent cultural innovations, but multiple lines of evidence indicate that the relevant skills, especially those involved in decoding as assessed here, are based on our capacities for spoken language, which emerged through biological evolution along the lineage that led to humans , 38. As ene flow . We obseP < 1.02 × 10−4), three annotations showed significant heritability enrichment: histone-3 lysine-4 monomethylation (H3K4me1) in two fetal brain samples and the adult brain germinal matrix . H3K4me1 is considered a marker for enhancer regions. These results indicate that SNPs associated with reading-/language-related traits are overrepresented in fetal brain enhancers.We next investigated whether regions of the genome with tissue-specific functions are involved in reading-/language-related traits, again using LDSC heritability partitioning. We used annotations reflecting chromatin signatures from a broad range of tissues, as most variants identified in GWAS are located outside coding regions and are often found enriched in tissue-specific functional regions of the genome, such as promoters, enhancers, and regions with open chromatin. After correction for testing of 489 annotations (P < 6.02 × 10−4), no relation was found with tissue-specific gene expression patterns of adult tissues from the Genotype-Tissue Expression (GTEx) project and brain tissues of a specific  time from Brainspan . In the cell type–specific gene expression analysis, three single-cell RNA-sequencing datasets of embryonic, fetal, and adult brain tissue were used. After correction for 142 cell types (P < 3.52 × 10−4), the multivariate GWAS results were significantly associated with one of the mature neurons from the fetal dataset: red nucleus neurons  . This observation could reflect an association with this specific nucleus or with the higher maturity of the neurons compared with the other cell types in the fetal dataset. The red nucleus is a large structure in the ventral midbrain that is part of the olivocerebellar and cerebello–thalamo–cortical systems. It plays roles in locomotion and nonmotor behavior in various animals, and in humans, it might also play a role in higher cortical functions  (45P = 1.10 × 10−8), highlighting DOCK7, ATG4C, ANGPTL3, and USP1 as potential candidates for involvement in this trait. Other SNPs from the locus have been associated with triglyceride and cholesterol levels  in sample sizes  that are substantially larger than previous genetic analyses of reading and/or language skills assessed with neuropsychological tools  –7, 45–47l levels  but may l levels , 50; psyl levels ; and bral levels  and cortl levels . They arl levels  and alcol levels . So, desOur work shows overlapping contributions of common genetic variants to individual differences in reading-/language-related and cognitive traits. Such overlaps are evident both from genetic correlation analyses and from the three-factor structural equation model, in which one factor explains most of the variation in word reading, nonword reading, spelling, and phoneme awareness. This is in line with the widespread pleiotropy found between many aspects of cognitive functioning, including language, reading, mathematics, and general cognitive ability , 55. We Further evidence of differences in trait etiology between the five reading-/language-related traits was observed in our genetic correlation analysis with GWAS results of components  from school grades in the Danish iPSYCH cohort  and withHuman abilities to process spoken and written language depend on an array of distributed brain circuits , 64–66. 6433H. sapiens, which took place through admixture events around 50,000 to 60,000 y ago , only  1 error . Regardi 1 error . OverallReading- and language-related phenotypes pose special challenges for scaling up genetic analysis since psychometric assessments can be labor intensive to administer and score and because of the heterogeneity introduced by differences in assessment tools, ages, populations, and languages, among other factors , 76. OneThis first wave of analysis from GenLang represents the largest GWAS meta-analyses for direct quantitative assessments of reading- and language-related abilities to date, including 22 cohorts with data available for at least one of the phenotypes. Nonetheless, although substantially increased over prior work in this area, sample sizes may still be considered relatively modest compared with the state of the art for genetic association analyses of other complex traits. While they captured a significant proportion of the genetic variation underlying each phenotype, yielding several insights into the associated biology, detection of individual genome-wide significant loci was still limited. In addition, when sufficiently large datasets become available, it would be valuable to validate the genetic correlation and heritability partitioning results in additional independent datasets. A number of phenotypes of interest, including those that tap into syntactic skills or that involve different modalities such as sign languages, could not (yet) be pursued due to inadequate sample sizes, even when combining data available from multiple cohorts. We note that despite our best efforts at harmonizing the included datasets and limited evidence of heterogeneity in the results based on Cochran Q statistics, LDSC intercepts, and genetic correlations between subsets of the data, we cannot fully exclude that heterogeneity is introduced by the inclusion of data from different assessment tools, languages, and ages. The choice of assessment tools for future collection of reading- and language-related phenotypes for genomic studies, to increase the sample sizes of these GWAS meta-analyses and also, to collect additional language-related phenotypes, should, therefore, be based at least partially on optimal matching with existing data. At the same time, we should invest in facilitating and simplifying the collection of language-related phenotypes, in part by developing and optimizing test batteries that could be reliably administered online in web/app-based settings. These are major areas of focus for the GenLang Consortium moving forward.H. sapiens may have been intolerant to gene flow from other archaic hominins. These efforts by GenLang open up avenues for deciphering the biological underpinnings of spoken and written language.In summary, our GWAS meta-analyses of five reading- and language-related phenotypes in sample sizes of up to ∼34,000 participants demonstrated significant SNP heritability for all traits and identified genome-wide significant associations with word reading on chromosome 1. Structural equation models revealed a single factor accounting for much of the genetic architecture underlying word reading, nonword reading, spelling, and phoneme awareness, prompting a multivariate GWAS analysis. The multivariate results were genetically correlated with cortical surface area of the banks of the left STS, a brain region where the processing of spoken and written language comes together. Partitioned heritability analyses showed enrichments in fetal brain enhancers, highlighting links to early brain development, and in archaic deserts depleted of Neanderthal ancestry, suggesting that genomic regions associated with emerging language-related skills in SI Appendix, Extended Methods.A more detailed version of the methods is provided in Dataset S1). Different measures for the reading- and language-related traits had been assessed in each cohort and could be included in the GWAS meta-analyses . Data from children, adolescents, and young adults aged 5 to 26 y were included. The phenotype data were standardized and then, adjusted for covariates ; the genotype data were subjected to stringent quality control according to a detailed analysis plan following standard procedures for GWAS and then imputed using the Haplotype Reference Consortium version 1.1 panel or the 1000 Genomes Project Phase 3 reference panel (Dataset S2). Univariate GWAS analyses were performed using linear regression methods with the imputed additive genotype dosages for the full dataset and for males and females separately.The meta-analyses included GWAS summary statistics from 22 independent cohorts .The summary statistics for each GWAS cohort for each trait were subjected to stringent quality control measures . Meta-analyses of the univariate GWAS results were performed with METAL (version March 2011), with effect size estimates weighted using the inverse of the corresponding SEs. For the follow-up analyses with LDSC, separate meta-analyses without genomic control correction  were perTo investigate the high genetic correlations between the summary statistics of the five reading- and language-related traits and with cognitive performance  and educA multivariate GWAS was performed on the univariate GWAS summary statistics of the four traits with the highest genetic correlations  using MTAG (v1.0.8) .LDSC  (v1.0.0)The FUMA platform (version 1.3.6a)  was usedP values and for gene-property analyses, to study relationships with tissue-specific and cell type–specific gene expression patterns. Several bulk RNA-sequencing and single-cell RNA-sequencing datasets of brain samples were assessed through FUMA (MAGMA  (versionugh FUMA .LDSC heritability partitioning  was usedSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary File