DMPs CpG sites were enriched in variably methylated regions (VMRs), most showing environmental and genetic influences. Hypermethylated probes presented higher rates in different regulatory regions associated with functional SNPs, indicating that the subgroups may have different affected regulatory regions and their liability to disease explained by common variations. Vulnerability components score moderated by epigenetic clock AA was associated with Vineland Total score , suggesting risk factors with stress burden can influence ASD phenotype.Although Autism Spectrum Disorders (ASD) is recognized as being heavily influenced by genetic factors, the role of epigenetic and environmental factors is still being established. This study aimed to identify ASD vulnerability components based on familial history and intrauterine environmental stress exposure, explore possible vulnerability subgroups, access DNA methylation age acceleration (AA) as a proxy of stress exposure during life, and evaluate the association of ASD vulnerability components and AA to phenotypic severity measures. Principal Component Analysis (PCA) was used to search the vulnerability components from 67 mothers of autistic children. We found that PC1 had a higher correlation with psychosocial stress , and PC2 had a higher correlation with biological factors . Comparing the methylome between above and below PC1 average subgroups we found 11,879 statistically significant differentially methylated probes (DMPs,  Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by alterations in social communication and the presence of stereotyped and restrictive behaviors. It is a polygenic multifactorial disorder with a complex genetic architecture and a heterogeneous clinical presentation ,2.The genetic component seems to have a major role in ASD vulnerability. The genetic risk derives mostly from common genetic variants with a smaller contribution from de novo and rare inherited variation (2.6% of the variance in liability) . In someThe heritability of ASD is estimated to be around 50%, although varying between different studies ,10, whicBesides the heterogeneous etiopathology, the ASD clinical presentation is also highly heterogeneous and its dimensional diagnosis represents the idea that its features fall along a continuum of severity ,27,28. IWe hypothesize that a model including environmental exposure factors in addition to genetic ones could better explain the severity spectrum. The prenatal period is probably the stage in which ASD environmental risk factors interacting with genetic play their major role, however epigenetic programming, altered by environmental factors during infancy, has an important impact on neurodevelopmental trajectories  and coulDifferences between epigenetic and chronological age  have been associated with stress exposure ,36. ThisOur results showed two components of ASD vulnerability: one is more associated with psychosocial stress exposure and the other with a biological vulnerability. A gradient of psychosocial stress exposure was detected and split into two groups, above and below average. A biological difference along this gradient was supported by a different pattern of VMRs between formed subgroups. The principal components and age acceleration were associated with Vineland scores measured at 7 years of age. The current study suggests that environmental factors can contribute to ASD phenotypic severity spectrum.The sample of the present study was obtained from a Brazilian population that lives in São Paulo city and contained 68 ASD children with blood samples available from a previously published randomized clinical trial of a video modeling parenting training . One patPatients were diagnosed using Autism Diagnostic Interview-Revised (ADI-R) , a 93-itIQ assessed by the Snijders–Oomen Nonverbal Intelligent Test—Revised (SON-R 2½–7)  used in ASD severity was accessed with the Childhood Autism Rating Scale (CARS) ,47, an iThe patients’ internalizing and externalizing symptoms with the Child Behavior Checklist CBCL . The CBCThe final sample used contains n = 67 children and mother pairs: 55 males (82.1%), ages between 3 and 7 years old . IQ between 49–75 , CARS between 24–52 , CBCL Internalizing Symptoms between 3–21 , CBCL Externalizing Symptoms 3–34  andADI-R scores between 25–60 . The clinical genetic examination was evaluated by a team of geneticists and no genetic syndromes were described.Blood and data were collected at baseline for 66 children for methylation analysis and an arbitrary subsample of 33 children, mother and father trios for exome analysis. The present study was approved by the Hospital das Clinicas  Ethical Committee . All experiments were performed following the ethical principles for medical research involving human subjects (Declaration of Helsinki), Informed consent was signed by the legal guardians of all patients.The risk factor was scored according to (a) Family social class ,52 measuThe statistical language R (v3.5.2) on RStudio was used for all statistical analyses. For descriptive analyses, mean scores, standard deviations (SD), and frequencies were calculated. To achieve comparability among variables, scores were transformed into standardized z-scores. A principal component analysis (PCA) was performed with the risk factors variables using the “prcomp” function with default parameters, except for the scale set to TRUE. Exposure factors represented in at least 30% of the respondent sample were included in PCA analysis. Only components with an eigenvalue greater than one were retained. To select variables with important contributions to PCs compositions we considered correlations >0.70 between variables and components.For the methylation analysis, DNA from whole blood samples was treated using the EZ DNA Methylation kit . The bisulfite-converted DNA was hybridized in the BeadChip Human Methylation 450 microarrays  following the manufacturer’s protocol. Raw data was extracted by iScan SQ scanner using the GenomeStudio software (v.2011.1), with the methylation module v.1.9.0 IDAT files. Raw data is available at the GEO database (GSE164563).http://www.bioconductor.org, accessed on 15 September 2017). Quantile normalization [p-value > 0.01), 11,214 probes in sex chromosomes, 16,474 probes in SNPs and 26,480 located in cross-reactive sites [Data analysis was performed using the minfi package  and othelization  performelization . Batch elization  from thelization . Cell colization , and no ve sites , resultip-value < 0.05, corrected for multiple comparison effects by the Benjamini and Hochberg method, were considered further. Functional Epigenetic Modules (FEM) package (v.3.10.0) was used to find the differential methylation interactive hotspots [Identification of differentially methylated probes (DMPs) was performed with M-values  employing an empirical Bayesian framework linear model from the limma package ,64, sex hotspots . DNA methotspots .p-value. For VMRs enrichment analysis, the Multivariate State Estimation Technique (MSET) algorithm [A set of 1000 random groups was created based on CpG coordinates from the Illumina Infinium Human Methylation 450K BeadChip platform. The specific number of probes selected for each group was based on the number of CpGs analyzed for each cluster. Coordinated comparison of CpGs/random groups and rVarBase  databaselgorithm  with 10,lgorithm , Teh et lgorithm , Garg etlgorithm , and Haclgorithm   and sequenced using HiSeq 2500 . Fragments were aligned to hg19 reference genome using Burrows-Wheeler Alignment (BWA) (v.0.6.1-r104) , samtoolPLINK (v. 1.9)  and BEDTRare (MAF ≤ 0.01) and novel variants were selected according to the databases:http://www.1000genomes.org/, accessed on 2 February 2017), NHLBI GO ESP 6500 exomes  and ExAC project  [1000 Genomes Project  (http://ry 2017) . Only nory 2017) . Exome dAll linear regression analyses were done using the normal distribution of errors in the R core base package. Dependent variables used were represented by phenotype scores measured at the baseline of the clinical trial study and the independent variables were sex, age, and vulnerability scores.The presented psychosocial, genetic, and epigenetic data was obtained for the current study from a clinical sample participating in a recently published randomized clinical trial . There aThe range for maternal schooling was 16% with incomplete primary education to incomplete high school, 52% with complete high school, and 32% with higher education. Most of the families were from B, C, and D social classes, with only one family from social class A (higher); 56% from class B; 29% class C, 15% class D (lower) . During Considering as important exposure factors the ones represented in more than 30% of the respondent mothers’ sample, we performed a PCA analysis. Two principal components (PCs) had eigenvalues >1, explaining up to 60% of the variance .Psychosocial stress Vulnerability component (PV), while the second PC was interpreted as a Biological Vulnerability component (BV).PC1 has a higher correlation with Maternal Stress, Maternal Education, and Social Class, while PC2 has a higher correlation with Psychiatric Family History and Gestational Complications bold in . In thisWe defined two groups: above  and below  average PC1 values . Although there was no difference regarding gestational complications, the group with higher psychosocial stress scores presented lower family history scores .Statistical analysis did not show any significant differences between A and B groups regarding the clinical variables . Evidentp-value < 0.05) were identified when comparing A and B groups. To verify if these DMPs could be altered in the brain, we searched on the BECon datasets [Methylation analysis was performed with two different aims, the first was to search biological differences between individuals assign to different gradients of vulnerability according to our PC analysis, and the second was to assess AA as a proxy of postnatal stress exposure. To investigate if the differences observed in PV were also reflected in the children methylomes, we searched for DMPs between above (group A) and below (Group B) average PV values on PC1 b. After datasets , which pDifferences in children’s methylomes suggest the presence of CpG regions that vary according to differences measured in mother-based PV. To support these findings, we compared our results with public datasets, one regarding VMRs, which represent methylated regions that vary according to genetic and environmental factors. Comparison of DMPs with all VMR datasets currently openly available showed that our set is enriched for VMRs, and the majority was best explained by environmental and genetic influences . Using tp = 1). Using these 2285 overlapping CpGs, we observed that in group B (1326 CpGs), hypermethylated CpGs were more localized in active promoters and gene enhancers, whereas in group A (959 CpGs) they were more localized in bivalent promoters and enhancers  (p = 0.342)  a–c. The = 0.342) d. Althoude novo: 18) and 2,578 in patients in group A . Χ2-test found no difference between groups .To investigate if individuals assign to different gradients of vulnerability according to our PC analysis are not a consequence of the presence of rare variants of higher impact, we performed a whole-exome analysis on a subset of children and their both parents (8 from A and 25 from B) that were already available in our laboratory. Of the 6625 selected deleterious variants (of which 1966 are on the 25% most intolerant RVIS list), 5512 are in patients in group B , with an R2 = 0.12. In this model, only grouping was a significant predictor  = 2.889, redictor . A smallp-value = 0.047), with an adjusted R2 = 0.13 only considering the Vineland score as the outcome. The significant variables were AA:PC1 and AA:PC2  mostly by PC3, Sex, AA:PC2, AA:PC3 and AA:PC1:PC3 variables .Considering a dimensional approach, we performed moderation analysis with the PC scores instead of groups. Using the vulnerability scores, we built two models, one with only PC1 and PC2 and the other using the 3 PCs  to have more than 70% of variability explained. The moderation analysis using the first model, sex, PC1, PC2, and age acceleration, resulted in a significant regression equation  = 2.149, d AA:PC2 . In the ariables . In regaIn the present work, we showed how adverse environmental exposure during gestation and genetics could contribute to ASD vulnerability and including a biological measure as a proxy of adverse environmental exposure after birth we also presented their contribution to ASD clinical severity.de novo and very rare variants with the higher impact between groups.We searched among a clinically homogeneous group of ASD patients, principal components of ASD vulnerability, considering psychiatric family history representing a polygenic contribution and different gestation environmental exposure as entered features. Two final components explained 60% of the variability: one psychosocial  and the other biological . We arbitrarily divided the samples into two groups , but we cannot see a clear separation between groups, and the formed subgroups were mainly a consequence of the psychosocial exposure component reinforced by statistically differences when comparing Maternal Stress, Maternal Schooling and Social Class between groups. So, even individuals in group B having more familial history than in group A, which implies a larger polygenic genetic background ,81, the Genetic and environmental exposures were entered features used to achieve a dimension reduction so we expected that VMRs (variable methylated regions), polymorphic methylation sites that have been aggregated in genomic regions associated with Genetic, Environmental and GxE effects and independent of age would be enriched in any methylome comparison. Accordingly, differentially methylated positions between groups divided by PC1 were enriched by genetic VMRs or those representing the GxE interaction, that is, associated with SNPs  ,23. As wIn addition to our DMPs being enriched in genomic regions related to genetic and environmental effects, gene enrichment from immune system pathways functions and stress response, already associated with environmental exposition and ASD, were found when analyzing differently methylated gene promoters from epigenetic functional modules. During pregnancy complications as metabolic disorders like insulin resistance, obesity, and chronic hypertension are associated with systemic inflammation . Obesity2, meaning that the variation of these variables can better explain the variation of Vineland Total Score.To achieve our second goal, we used regression analysis considering CARS and Vineland measured at seven years old as outcomes and formed groups, vulnerability components, and AA as a proxy of adverse environmental factors after birth as predictors. In a categorical view, groups were associated with CARS at seven years of age, whereas AA and sex were not. In a dimensional view, PCs, AA, and sex showed interactions associated with Vineland. Evidence for this is clearer when we apply the regression analyses using vulnerability score as a numeric variable represented by PC1, PC2, and later PC3 loads. Using only the first two PCs as independent variables, Vineland Total Score could be associated with the vulnerability score moderated by AA, suggesting that vulnerability scores calculated through risk factors could explain in part the variation in Vineland Total Score. The property of vulnerability score as a continuum is more pronounced using the model with the three PCs loads. This model resulted in an association with Vineland Total Score with a larger adjusted RThis article has a relatively small sample of only Brazilian young patients with severe ASD and it may not reflect the total ASD population. A previous study showed that ASD with and without ID share underlying mechanisms, although they have unique etiologic components . It is aThis paper suggests that individuals with similar symptoms and diagnoses may present differences in their pathophysiological basis and, the variability in phenotypes in the autism spectrum could be explained considering the vulnerability components and postnatal environmental exposure.


Thanks to the recent advances in genotyping technologies, genome-wide association studies (GWAS) have been an established approach to identifying genetic variants that influence certain characteristics of economic or scientific interest in plants, animals and humans. Applications of GWAS cover a wide range of areas in genetics and have enhanced our understanding of the genetic mechanisms in diseases, physiological or behavioral traits and have generated promises in agriculture, medicine and wildlife conservation. Despite great success, GWAS remains challenged by statistical modeling and computing. This collection of twelve articles presents a variety of interesting scientific problems and novel approaches in GWAS.Bu et al. developed a multi-locus association mapping model for the analysis of data from multiple families in the NAM population. A notable feature of their method lies in its ability to deal with genetic heterogeneity due to subpopulations, and therefore their approach improves statistical power for quantitative trait locus (QTL) detection and accuracy of QTL effect estimation. In real data analyses, they found that their method identified most QTLs that were detected by linkage analyses of single-family datasets and was also able to disclose some new QTLs with small effects. Three of the 12 articles in this collection are concerned with multi-trait analysis. Multi-trait analysis has been of interest in GWAS due to its potential gain in statistical power and its ability for formal hypothesis testing of biological importance such as pleiotropy vs. linkage. As a common practice, multiple traits are analyzed separately and markers are scanned one at a time. Deng et al. argued that the former does not take advantage of correlations between traits and thus can be a limitation on statistical power, and the latter ignores complex interactions between genomic variants. Therefore, they proposed a genome-wide gene-based multi-trait method to overcome these limitations. Technically, they adopted kernel-based testing to evaluate the joint effect of multiple variants in a gene and proposed an omnibus test strategy to integrate the test results. They demonstrated that their method achieved excellent power with reasonable control of type I error rates. Lin et al. discussed the interpretation of the results from multi-trait analyses. They introduced a bioinformatic tool, MetaPha, which implements a meta-analysis approach by constructing multivariate analysis from univariate GWAS results and then decomposing multivariate associations into multiple ones that facilitate interpretation. They validated their method using lipid data from the Global Lipids Genetics Consortium and found that only three to five central traits of the twenty-one traits they studied were needed at the majority of the loci of their interest. Fernandes et al. focused on a biologically interesting question, linkage vs. pleiotropy, which can be tested by either multivariate or univariate approaches. Using simulation studies, they found that neither of these two approaches alone delivered a satisfactory result, and thus suggest that multivariate and univariate GWAS should be complementary rather than competing. For those interested in mapping imprinted QTLs, Zheng et al. proposed a special type of immortalized F2 population and correspondingly two methods for mapping imprinted QTL and demonstrated the merits of their proposed population and methods in mapping precision. If your research is related to Barley, you may be interested in Li et al. who studied a few traits in Qingke Barley. Other interests include heritability estimation , disease studies using machine learning techniques , and Mendelian randomization .Nested association mapping (NAM) is a technique for dissecting the genetic architecture of complex traits in crops. It is designed for NAM-specific populations by taking the advantages of linkage analysis and association mapping while avoiding their disadvantages. Zhu et al. who reviewed statistical methods for identifying trait-relevant tissues and cell types. Genome-wide association studies (GWAS) have reported numerous quantitative trait loci (QTL). However, few reported QTL have been validated while the ultimate goal of GWAS is to help understand the biological mechanisms of the trait-QTL association. Some researchers have developed statistical methods to integrate genomic information with functional annotations, gene expression data, and gene network information into GWAS, and aim to identify relevant tissue and cell types. Zhu et al. extensively reviewed ten of these methods.Lastly, we would like to introduce To conclude, this volume highlights new insights and fascinating perspectives in statistical methods, computing, and resources for GWAS. We hope the collection will stimulate more developments in this important topic as biotechnology continues to evolve.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Eucalyptus globulus breeding populations to provide direct comparisons across the programs and indirect predictions in environments where pedigreed families had not been evaluated. Despite few known pedigree connections between the programs, genomic relationships provided the connectivity required to create a unified relationship matrix, H, which was used to compare pedigree-based and HBLUP models. Stem volume data from 48 sites spread across three regions of southern Australia and wood quality data across 20 sites provided comparisons of model accuracy. Genotyping proved valuable for correcting pedigree errors and HBLUP more precisely defines relationships within and among populations, with relationships among the genotyped individuals used to connect the pedigrees of the two programs. Cryptic relationships among the native range populations provided evidence of population structure and evidence of the origin of landrace populations. HBLUP across programs improved the prediction accuracy of parents and genotyped individuals and enabled breeding value predictions to be directly compared and inferred in regions where little to no testing has been undertaken. The impact of incorporating genetic groups in the estimation of H will further align traditional genetic evaluation pipelines with approaches that incorporate marker-derived relationships into prediction models.Single-step GBLUP (HBLUP) efficiently combines genomic, pedigree, and phenotypic information for holistic genetic analyses of disjunct breeding populations. We combined data from two independent multigenerational  Quantitative genetics has utilized pedigree-derived relationships to improve selection efficiencies of breeding programs for many years . Well-deGenetic markers such as single nucleotide polymorphisms (SNP) provide empirical rather than expected relationships by using estimates of allele similarity between pairs of individuals . GenotypG) are merged into the traditional pedigree-derived matrix of expected relationship coefficients (A) of the entire population (H) is then used in place of A in linear mixed models (LMM) to produce Best Linear Unbiased Predictions (BLUP) of genetic value. This “HBLUP” approach has been combined with efficient computing procedures , in some cases only for genotyped individuals  has on genetic parameter estimates and the accuracy of breeding value predictions. Replacement of A with H as the numerator relationship matrix (NRM) involves two types of changes to the information used in the analysis. Not only do genomic data inform the relationships amongst individuals in H, but the formulation of H applied thus far in tree breeding does not allow for races as genetic groups. Therefore, we aimed to disentangle the impact of including genomic information from that of removing race groups by comparing results from three types of models: ABLUP with race genetic groups , ABLUP without groups (ABLUP−race), and HBLUP.Most commercial tree improvement activities are practiced within the confines of an individual program. However, important exchanges of information and genetic material also occur amongst programs to support breeding and the establishment of planted forests. A major limitation to such exchanges exists when pedigrees have been coded differently and little connectivity is available between programs to provide unbiased comparisons among populations. This situation provided the stimulus for our work to quantify the impact that combining two distinct pedigrees into a joint relationship matrix  and EG2 (HVP Plantations). These commercial tree improvement programs progeny test full-sib families predominantly derived from first- and second-generation selections. Forty-eight field trials established between 1998 and 2015 provided phenotypic data from 126,467 full-sib progeny (1973 families), 7051 half-sib progeny (263 families), and 24,767 unpedigreed trees of commercial entries , the Green Triangle (GT), and Gippsland (GIPPS). EG1’s breeding program was focused in WA  with five trials in GT and none in GIPPS. EG2 trials were located predominantly in GT , with six trials in GIPPS and three in WA (The EG1 population of full-sib families was founded on 112 base-population trees and the EG2 population was founded on 83 base-population trees (Supplementary Table S1C describes populations). These “founders” are trees in native forests or international landraces that provided the seed for testing and domestication. Half the founders of the EG2 program originated from Strzelecki Ranges and Western Otways races, whereas the EG1 program is more strongly influenced by Furneaux, King Island, Portugal (landrace), and NE Tasmania. The classification of races follows the results of Inadequate interprogram relationships were available for joint analyses using pedigree derived relationships as only four founders were common to both programs. Pedigree-derived relationship coefficients amongst the two parent cohorts were rare, with two relationships of 0.25, 152 relationships of 0.125, and one expected relationship coefficient of 0.0625.Trials were primarily established in randomized incomplete-block designs  with four to eight replications of each family established in four- to five-trees in contiguous row-plots. Two EG1 trials were single-tree plot designs. Incomplete blocks were arranged into replicates that were generally contiguous to enable the resolution of spatial trend effects within each trial.Diameter at breast height (DBH) was measured with a tape for each tree at 3 years (7 sites), 4 years (9 sites), 5 years (29 sites), or 8 years  after planting. Tree height (HT) was measured with a hypsometer for each tree in the majority of trials, although a subset of 3–17% (mean 10%) of trees were assessed in 11 of the EG2 trials. In these cases, unmeasured HT data were predicted from the DBH–HT relationship established amongst measured trees for each trial. Stem volume (VOL) was calculated for each tree using DBH, HT (measured or predicted), and the region-specific volume function provided by each program. DBH of all stems forking below breast height was measured in 36 trials and in these cases, VOL was aggregated to the tree level before analysis.Given the expense of wood property assessments, a subset of trees in 20 of the 48 trials were selected for phenotyping (Supplementary Table S1D describes subset of populations assessed for wood properties). Basic density (DENS) data were collected for a subset of trees between 5 and 8 years. EG2 measured DENS directly by the displacement method using the outer 50 mm of wood cores extracted at breast height. EG1 used pilodyn penetration as an indirect measure of wood density . PilodynE. globulus marker panel consisted of 2579 SNP and small biallelic insertion/deletion (INDEL) markers within candidate genes associated with diameter, density, and cellulose yield that were identified using association analyses in previous studies  was formed for 238 genotyped individuals, the EG2 G matrix (EG2G) was formed for 144 genotyped individuals, and the joint G matrix (JOINTG) was formed amongst all 382 genotyped individuals. G was calculated in preGSf90 following the first method of Z is a matrix of centered genotype scores calculated as Z = M − 2 P; M is the n × m matrix of n genotypes and m markers scored 0 for homozygous reference allele, 1 for heterozygous, and 2 homozygous alternative allele, P is a matrix of frequency for the alternative allele, and jp is the reference allele frequency of the jth marker.Relationship matrices were produced using A was divided into four submatrices: A11 representing relationships amongst nongenotyped individuals, A22 representing relationships amongst genotyped individuals, and A12 and A21 representing relationships between genotyped nongenotyped individuals:The pedigree relationship matrix G and 22A H=AH was then resorted to align with the pedigree relationship matrix for comparisons with A. The section of H relating to genotyped individuals, their parents, and ancestors were compared with A in a preliminary step to highlight identity errors and mistakes in the documented pedigree. These errors were rectified in the pedigree and the process of creating the H−1 matrix was then repeated with the correct pedigree information.y is the vector of phenotypic values, X is the incidence matrix for fixed effects, b is the vector of fixed effects, Z is the incidence matrix for random effect, u is the vector of random effects with E(u) = 0, and e is the vector of residual effects expected to be independently normally distributed with E(e) = 0. The relationship matrix that differentiates ABLUP and HBLUP connects each individual in u, and this change in connectivity impacts genetic parameter estimates and the accuracy of breeding value predictions.All genetic analyses were conducted using ASReml v. 4.1 (adj) data that were used for preliminary cross-site analyses  in H matrices were greater than zero and provide further support for differentiation of races and population structure . The As expected, removing the race/landrace genetic groups from the EG2 pedigree caused significant increases in affected . The remded race  providin−race  for the ABLUP model when race effects were removed from the pedigree, which was slightly improved by using HBLUP instead of ABLUP+race models  based on core samples than the EG1 program (0.28 ± 0.03) based on penetrometer data, with an intermediate analysis . The pro9 ± 0.03 . Dominan+race models for the same parents (see Supplementary Table S2), resulting in generally higher estimates of +race due to the substantially smaller Overall, the greatest breeding value accuracy estimates  when parents were genotyped and accuracy increased by 44% (0.218 ± 0.029) when they were not genotyped. For EG2 parents that were unrepresented by progeny in WA, Prediction accuracy for parents of progeny tested in other regions improved markedly in the joint HBLUP analyses , although average a units) for EG2 in GIPPS, WA, and GT, respectively, when estimated with the joint HBLUP model (Supplementary Table S2) . However, average accuracy from joint HBLUP was 3–5% lower for EG2 progeny across regions due to the lower values of Accuracy of progeny EBVs was the least impacted by choice of relationship matrix. Average PEVs for progeny were marginally higher for EG1 (by 0.050 and 0.008 SDThe single-step GBLUP (HBLUP) approach was used to integrate genomic, pedigree, and phenotypic information and provide unbiased predictions of genetic merit for breeding programs with few pedigree connections. Genotyping the parents that were evaluated in separate progeny trial networks created sufficient connectivity between the programs to provide predictions of genetic merit for parents in regions where they were not evaluated and also improved the accuracy of predictions. Although the benefits of HBLUP to tree improvement have been demonstrated in a number of studies , this isA clear advantage of HBLUP is that relationships amongst genotyped individuals are quantified precisely, rather than represented by the expected average relationship derived from the pedigree. This advance has been well documented in tree breeding contexts , which generally resulted in larger values of H compared with A, and this is a subject of ongoing exploration and validation.Our results illustrate that HBLUP conferred little benefit to the prediction accuracy of parents and progeny within each program, possibly due to the very low proportion of genotyped individuals. PEV and HBLUP offers a clear advantage over ABLUP for predicting the value of genotyped individuals with no phenotype data and no pedigree linkages with individuals evaluated in trial networks. Breeding values were estimated for 52 EG1 individuals and 5 EG2 individuals that were genotyped but had no connections via pedigree. While accuracy estimates were lower than parental estimates, at around 0.4 and 0.5 for within-program and joint HBLUP, respectively, indirect estimates of breeding values were produced. Joint HBLUP enabled the prediction of a complete set of EBVs for EG1 parents, genotyped individuals with no phenotype data, and progeny in the GIPPS region where no progeny trials were established. Extending genomic prediction models to connect distinct pedigrees allows breeding programs to leverage information from one another and infer performance in environments where populations have not been evaluated. This study provides empirical evidence that may be used to promote collaboration among tree improvement programs to better characterize the genetic merit of individuals in environments where they have not been evaluated. The need for breeding value predictions in untested environments is expected to increase as forestry organizations examine options for adapting to climate change (Using genotype data to blend disconnected pedigrees and phenotype data from separate breeding programs into a unified analysis produces unbiased breeding values for direct comparisons between programs and indirect predictions of merit in environments where individuals may not have been evaluated. The joint HBLUP analysis significantly improved prediction error variance of parents and genotyped individuals, provided similar estimates of genetic parameters, more accurate EBV predictions, and offered the profound advantage of EBV prediction for genotyped individuals in regions they had not been evaluated.H will further align the traditional genetic evaluation pipelines that underpin tree breeding programs with approaches that incorporate marker-derived relationships into prediction models.Overall, the genotyping proved useful for correcting pedigree errors, more precisely defining relationships within and among populations, identifying the source of landrace populations, and integrating genotyped individuals with no phenotype or pedigree connections into the prediction framework. Understanding the impacts of incorporating genetic groups in the estimation of


The social organization of the first fully sedentary societies that emerged during the Neolithic period in Southwest Asia remains enigmatic, •Genetic kinship estimated from co-buried individuals’ genomes in Neolithic Anatolia•Close relatives are common among co-burials in Aşıklı and Boncuklu•Many unrelated infants found buried in the same building in Çatalhöyük and Barcın•Neolithic societies in Southwest Asia may have held diverse concepts of kinship Yaka et al. use ancient genomes from Neolithic Anatolia and present evidence for diverse concepts of social kinship in Neolithic societies. In some communities, like Çatalhöyük, many genetically unrelated infants were buried together inside the same buildings, whereas in other sites, people buried together were frequently close biological kin. After confirming the authenticity of the data the data  and Z2. ADMIXTURE analysis,FST, f3- and D-statisticsD-tests per site, 84%–93% were nominally significant in this direction; FST, f3- and D-statistics also showed that residents of the two Aceramic Neolithic settlements, Aşıklı Höyük and Boncuklu Höyük, were genetically highly similar to each other  than did Ceramic Neolithic groups , where X represents an early Holocene Zagros or Levantine population, we found results compatible with southern and eastern gene flow into Central and West Anatolia between roughly 7,500 and 6,500 cal BCE  and c.10% Levantine ancestry (8%–11% ± 2%–4%) (models that included Zagros or Caucasus populations were not supported) (Table Z10). Notably, the timing of increased population mobility is contemporaneous with a stronger reliance on agriculture and animal husbandry as food sources, a shift to larger buildings, likely population growth, and possible shifts in patterns of social organization, as we describe below.Aceramic Neolithic-period populations had lower within-group genetic diversity , although the low coverage of our genome data constrained the utility of this latter approach (θ) estimated from autosomal and from X chromosomal loci, (b) mitochondrial haplotype sharing, (c) osteological age-at-death estimates, and (d) radiocarbon dates  = 9.5, n = 4; Table Z2) and could have lived at the same time. These individuals, all females, were interred in two buildings in close proximity and that shared a workspace, likely used by a single householdThe data from Aşıklı Höyük included genomes of five individuals from the same stratigraphic layer who produced statistically consistent radiocarbon ages  = 3.8, n = 1; Table Z2), and suggested that the woman (ZHJ) died first with 90% probability. The second included a possible pair of adult male and female siblings . These individuals were buried in the proximate consecutive buildings (B12 and B14). Thus, as at Aşıklı Höyük, we could identify close relatives across the majority of individuals (4 out of 5) associated with neighboring building pairs  = 3.8, n = 1). None of the other pairs of individuals tested were closely related. Hence, among Çatalhöyük individuals co-buried in these three buildings and tested genetically, only 2 out of 10 had genetic kin identified  = 3.8, n = 1 for both; Table Z2). None of the other individuals had close relatives identified, including four infants buried in Building 4. Hence, among co-buried individuals we could identify relatives for only 4 out of 10  D. Ten ofut of 10 .2 = 8.0, χ2 (5%) = 3.8, n = 1), and which suggest that it is 96% probable that the woman  died first (Table Z2).The Tepecik-Çiftlik data included genomes of a total of five individuals from two strata. We identified a probable pair of a mother and her adult son  buried in different parts of the same building (Building AY/AK)  and Z11.The identification of multiple instances of close genetic relatedness among co-burials across all Neolithic Anatolian settlements studied suggests that early Neolithic social arrangements and possibly household composition were to some extent linked to genetic ties. Although long assumed,Nevertheless, a notable fraction of our sample also contained individuals  buried in buildings together with genetically unrelated individuals . This effect appears to be caused by better DNA preservation in subadult bones, at least at Çatalhöyük , possiblf3-statistic  and Reyhan Yaka (yakaryhn@gmail.com).Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contacts, Mehmet Somel (This study did not generate new unique reagents.https://doi.org/10.5281/zenodo.4587657.Ancient genome data produced for this study is deposited at the European Nucleotide Archive (ENA) under the accession number PRJEB39316 as BAM files. The computer simulation code used in the study is available at github.com/CompEvoMetu/kinshipsim. Bash scripts and R code used in regular population genetic and statistical analyses are available upon request. The supplemental data tables (identified throughout the text by the prefix Z) are available at Zenodo Data: The concept of “house” refers to a social institution through which societies define a particular type of membership group, i.e., the “household.” What defines a household is based upon the cooperating individuals’ criteria for relatedness, task-orientation, and co-residence.55,,65Some of the earliest long-lasting residential architecture, considered to be the primary context for the socialization of household members, is found in early Neolithic SW Asia, c. 10th-7th millennia cal BCE. The criteria that define relatedness among the household members of these societies, however, have long been debated: were the co-residents genetic kin, or did other factors determine household membership? Based on the size and form of the buildings, it has been suggested that the earlier curvilinear structures of the c.10th-9th millennium cal BCE were used by extended families, perhaps related to polygamous social structures, whereas the adoption of larger rectilinear and compartmentalized buildings of 8th-7th millennium cal BCE reflects a shift to close genetic kin-based organization.,,,Research on mortuary practices also underlines the broad regional changes through time, including suggested shifts from community membership to increasingly separate and autonomous household organizations in the PPNB.,,,,One potential source of information that could help resolve the nature of Neolithic household composition and social organization comes from burials within buildings during their occupancy. Neolithic SW Asian societies frequently practiced the burial of individuals beneath the floors of domestic buildings, usually during the time these structures were inhabited.If co-burials were indeed household members, we may expect them to share specific attributes more with each other than with other co-burial groups; most notably, elements of their diet. Evidence on dietary similarity among Neolithic Anatolian households is currently equivocal. A 2015 study reported no dietary differentiation among Çatalhöyük co-burials in different buildings.There exist additional arguments against the hypothesis that co-burials represent households. It appears that the average number of burials per residential structure is generally too small to represent full households.If co-burials do not represent household members, their interment in the same buildings could be driven by at least two distinct traditions. First, individuals may be buried together because they died at the same time . Excavations at the site started in 1989 as salvage excavations under the direction of Prof. Ufuk Esin (İstanbul University). Since 2010, the research and excavation project has been led by Prof. Mihriban Özbaşaran (İstanbul University) and Güneş Duru (Mimar Sinan Fine Arts University) in collaboration with an international team from various universities and institutions.,The first inhabitants of Aşıklı settled near the western bank of the Melendiz River. The river, flowing from the Ihlara Valley, and the volcanic landscape provided a rich habitat for various animal and plant species. A warm climate and park-woodland vegetation was dominant in the region during the beginning of the Holocene.,,Archaeozoological data attest to broad spectrum hunting during the 9th millennium BCE, including a variety of small prey animals, birds and fish, although the main focus was always on sheep/goat.With the start of the 8th millennium BCE, changes took place in architecture and settlement patterns. Rectangular structures replaced the oval and semi-subterranean buildings. These rectangular buildings were mostly single-roomed. Although few in number, buildings with two or three-room buildings are also present. Toward the end of the settlement occupation, buildings started to cluster. Building clusters, generating neighborhoods, were separated by narrow spaces or passages with access to communal middens.Two concepts, a communal way of life and continuity, characterized the social organization of the Aşıklı community. Interaction with other regions and communities had a certain tempo during the mid-9th millennium BC, as evidenced by the material culture. However, simultaneous with the increasing focus on the full establishment of sedentism and caprine management, the pace of interaction decreased, only to increase again during the last 200-300 years of site occupation, corresponding roughly to the second half of the 8th millennium BC. This is well illustrated by the sudden appearance of non-local materials and technologies during this period. In contrast with this pattern of temporal change, continuity of certain elements, such as the location of the buildings and interior architectural features, constant renewal and maintenance of the floors and walls of buildings, and the transferring of objects and know-how was another factor that characterized the social fabric of the community. The inhabitants managed to live in cohesion throughout the occupation sequence and the communal way of life was maintained with new solutions, but also continuity through temporal changes and transformations was the main characteristic of the newly established Neolithic way of life at Aşıklı Höyük.The burial customs consist of intramural, single sub-floor inhumations. The deceased were buried in pits under the floors of the buildings in a flexed position. To date, 90 burials have been found in 400 rooms. Although this tradition was not subject to change for hundreds of years, new practices arose during the latest levels of the occupation at the site. The dead were not buried with any items of personal adornment during the mid-9th millennium BCE. However, changes can be observed toward the mid-8th millennium when some individuals are found buried with ornaments. Of the 82 individuals subjected to bioarchaeological analysis, adults constitute 60% while children make up 40%. Of the 46 adults for whom sex can be determined, females constitute 65% while males constitute 35%, a marginally significant difference . In terms of the daily activities conducted by the Aşıklı Höyük individuals, task-related pathologies of adults show that the shoulders, hips, ankles, elbows and knees were affected by osteoarthritis, possibly stemming from habitual stress. Males exhibit significant degrees of shoulder osteoarthritis, followed by their elbows and hips; for females the ankles were most affected by this disease, followed by the shoulders and hips. This may suggest that males were routinely engaged in activities such as carrying heavy loads, throwing, walking and kneeling, and females were probably engaged in activities that involved walking and squatting.Five of these burials genetically studied here were interred in Building 1 and Building 3 of Aşıklı Höyük Layer 4 A. These ,,Boncuklu is situated in the middle of the SW Konya basin (37°45’N 32°52’E) and lies 33.4 km northwest of the site of Pınarbaşı and 9.5 km northeast from Çatalhöyük. The site was discovered during the archaeological survey under the direction of Prof. Douglas Baird from the University of Liverpool, UK. Excavations directed by Baird began in 2006 and continue at the present time.,,The exploitation of wild resources seems to have predominated, especially wild cattle and boar, fish and wetland birds, along with nuts and fruits from surrounding hill areas.,The site possessed a number of sunken-floored sub-oval domestic buildings with mudbrick walls. The households display highly structured use of internal house space, divided into a ‘clean’ area presumably for sleeping, socializing and food consumption and a ‘dirty’ kitchen area.Primary inhumations were placed under the ‘clean’ area of the houses during their occupation. It seems the dead ‘ancestors’, whether biologically related or not, were kept close to the living. In the case of Boncuklu the modest numbers of burials under house floors, maximum 5 and more usually 1-3, per house, suggest many of these could easily be members of the household that lived in these buildings, although we certainly cannot assume that to be so. Nevertheless, reflecting the fact they occurred within the houses while still in use and that these were small-sized buildings with very intimate spaces, presumably means the co-burial of the dead expressed some type of relationship to the households of the living, and thus represented a symbolic statement of connection between the dead and the living. Indeed, evidence attests to ongoing attention to burials and knowledge of their location.,There were also primary burials and burials of deliberately disarticulated human remains, including human crania, in open areas between buildings in areas of midden accumulation.Five of these burials , including 2 pairs of individuals with first-degree genetic relationships were all articulated primary inhumations stratified within a sequence of 2 buildings in Area H, Building 12 and Building 14. Building 12 predates Building 14 and, indeed, the foundation cut for Building 14 removed the northern edge of Building 12 B. BuildiZHAJ was a primary inhumation burial of an adult male that predated Building 14 and seems to have been located in an open area. ZHB was the burial of an adult female burial post-dating Building 14. Overlying stratigraphy was eroded so it was unclear whether the grave for ZHB was cut through the floor of a building or was placed in an external area. These two burials do not show any close genetic relationships to the other sampled individuals.The other burials analyzed, genetically unrelated to any of these burials in Area H, was one adult male primary inhumation, ZKO, buried in Building 9 in Area K, broadly contemporary but c. 15 m from the cluster in Area H. ZMOJ was a primary inhumation in an external area in the middens of Area M, located c. 25 m from the cluster in Area H. Although well stratified in Neolithic deposits the chronological relationship with the Area H cluster and ZMOJ is not clear.Located 9 km to the south of Boncuklu Höyük on the Konya Plain in Central Anatolia, the site of Çatalhöyük was discovered and first excavated between 1961-1965 by James Mellaart (British Institute of Archaeology at Ankara), and later between 1995-2015 by Ian Hodder (Stanford University). Çatalhöyük was designated a UNESCO World Heritage Site in 2012.,,The site consists of two separate mounds or “tells.” The larger East Mound covers an area of 13 ha and has been dated to c. 7100-5950 cal BCE,,,,,,,Between 1993 and 2017 the skeletal remains of over 700 individuals had been recovered from stratified Neolithic contexts at Çatalhöyük.in utero). Among the adults and adolescents whose sex could be determined (n = 155), 89 individuals (57%) were assessed as females or possible females, while 66 individuals (43%) were assessed as males or possible males, a marginally significant difference .Of the 471 individuals from primary burial contexts,,,,Located in the Yenişehir Valley in the province of Bursa in northwestern Turkey, the site of Barcın Höyük yielded an uninterrupted stratigraphic sequence from 6600 to 6000 cal BCE.,The initial settlers in the region were accomplished potters even though pottery use was initially limited and indirect methods of heating foods were preferred.Burials associated with the settlement were placed within and near structures. Interestingly, many infants were buried within the house proper beneath floors while adults were typically placed in the courtyard areas. Children often tended to be buried outside but closer to the structures, sometimes beneath the floors of the verandas in front of the houses.,,Although intensively analyzed for DNA,Tepecik-Çiftlik is located in the Volcanic Cappadocia region of Central Anatolia in the Melendiz/Çiftlik Plain. The excavators suggest it was occupied from the end of the Aceramic Neolithic Period until the early Chalcolithic Period, between c.7500-5800 cal BCE.A 2016 report on Tepecik-Çiftlik indicated that over 170 individuals’ remains dating to the Neolithic levels, buried inside buildings and in open areasThis section describes bioarchaeological characteristics of the individuals from Aşıklı Höyük, Çatalhöyük and Boncuklu Höyük. Some of this data are unpublished. Barcın Höyük and Tepecik-Çiftlik individuals included in this study have been described in the supplementary material of Mathieson et al.Sex was estimated using dimorphic markers, and individual ages-at-death were estimated using standard methods such as human growth and epiphyseal fusion, dental calcification and bone maturity/size.SK2 : the burial of a young adult female. Double burial. SK2 was buried in the same burial pit of a male, slightly later. The pit is located in a one-room rectangular building of the mid-8th millennium BCE settlement. Radiocarbon dating places the individual to 7585-7475 cal BCE (Table Z2).SK33 : the burial of a child, buried under the floor of a rectangular planned kerpiç (mudbrick) building. Radiocarbon dating places the individual to 7945–7890 cal BCE (9%) or 7870–7595 cal BCE (86%) (Table Z2). Building C was renewed 10 times at the same location : the burial of an old adult female. Sub-floor inhumation in a rectangular kerpiç building of the 8th millennium BCE settlement. One of the three individuals buried in the same building: a one-month old infant and a middle adult female. Radiocarbon dating places the individual at 7935–7915 cal BCE (1%) or 7825–7590 cal BCE (94%) (Table Z2).SK128 : the burial of a female child. She is one of the two individuals buried in the same building. Radiocarbon dated to 8225–7955 cal BCE (95%) (Table Z2).SK129 : the burial of a young adult female, buried in a semi-subterranean oval building. She is one of the two individuals buried in the same building. Excavated in 2011; primary burial; radiocarbon dated to 8170–8115 cal BCE (6%), 8060–8045 cal BCE (1%), 8010–7985 cal BCE (1%), 7970–7735 cal BCE (86%) (Table Z2).SK131 : the burial of a female child, exposed lying on the pavement of a hearth in a semi-subterranean oval building. This is an exceptional burial, in position and in location. Four more individuals were buried in the same building. The burial was exposed in 2012. She was radiocarbon dated to 8200–8110 cal BCE (16%) or 8095–8035 cal BCE (7%) or 8015–7740 cal BCE (72%) (Table Z2).SK133 : the burial of an old adult female, the oldest member of the community thus far excavated. She was one of the five individuals buried in the same oval, semi-subterranean building, B.1. She was a primary burial and was radiocarbon dated to 8170–8115 cal BCE (8%), 8060–8040 cal BCE (1%), 8010–7980 cal BCE (2%), 7975–7735 cal BCE (84%) (Table Z2). Excavated in 2012.SK136 : the burial of a young adult female, one of the five individuals from Building 1. She was a primary burial, and was radiocarbon dated to 8175–8110 cal BCE (7%) or 8090–8075 cal BCE (1%) or 8065–8040 cal BCE (1%) or 8015–7705 cal BCE (84%) or 7695–7655 cal BCE (2%) (Table Z2). Excavated in 2015.Sk.5357 : primary burial of a male infant. He was 9 months ± 3 months at death based on dental development. It was buried in a flexed position along the east wall of B.17 in association with red pigment and traces of reed basketry. The burial was excavated in 1999. Radiocarbon dating places this individual between 7035–6680 cal BCE (93%) or 6670–6650 cal BCE (2%) (Table Z2).Sk.21855 : the primary burial of a female child. She was 4 years ± 1yr at death based on dental development. It was placed in a flexed position in a burial cut along the west wall of B.17. The burial was excavated in 2016.Sk.1885 : the primary flexed burial of a male child. He was 7 years ± 2yrs at death, excavated in 1995. This individual was interred directly above Sk.2033 (see below) in the southwest corner of B.50. Radiocarbon dating places this individual between 6905–6885 cal BCE (1%) or 6825–6635 cal BCE (92%) or 6625–6600 cal BCE (2%) (Table Z2).Sk.2033 : the primary flexed burial of a male child 3 years ± 1yr at death, excavated in 1995. This individual was interred directly below Sk.1885 (see previous) in the southwest corner of B.50. Radiocarbon dating places this individual between 6690-6590 cal BCE (95%) (Table Z2).Sk.2017 : the primary burial of a female neonate , excavated in 1997. The burial was located near the oven along the southern wall of B.50. The bones of this individual were scorched as a result of the burial’s proximity to the oven. Radiocarbon dating places this individual between 6815–6790 cal BCE (2%) or 6775–6595 cal BCE (93%) (Table Z2).Sk.2728 : an undisturbed primary burial of a female infant aged 9 months (±3 months) at death based on dental development. It was excavated in 1997 from Building 50, located in the South Area of the site. The body was placed in a small pit near the eastern wall of the main room. Radiocarbon dating of the petrous bone places this individual between 6695-6505 cal BCE (95%) (Table Z2).Sk.2779.1 : the primary burial of a male neonate , excavated in 1997. The burial was heavily disturbed by Mellaart’s earlier excavations in this building during the 1960s.Sk.2842 : a disturbed primary burial of a female infant aged 18 months (±6 months) at death based on dental development. It was excavated in 1998 from Building 50, located in the South Area of the site. The body was placed in a small pit near the center of the main room and was partially disturbed by a later burial. Radiocarbon dating of the petrous bone places this individual between 6690-6505 cal BCE (95%) (Table Z2).Sk.21981 : a disturbed primary burial of a female infant/child aged 3 years (±1 year) at death based on dental development. It was excavated in 2015 from Building 89, located in the South Area of the site. The body was placed in a small pit within the north platform of the main room and was subsequently truncated by the digging of a post retrieval pit.Sk.5747 : a primary burial of a female infant aged 18 months (±6 months) at death based on dental development. It was excavated in 2002 from Building 91, located in the South Area of the site. The body was placed in a small pit located in the northeast corner of B.91. Radiocarbon dating of the petrous bone places this individual between 6640-6490 cal BCE (95%) (Table Z2).Sk.30006 : a primary burial of a female infant aged 9 months (±3 months) at death based on dental development. It was excavated in 2015. The body was interred with a middle adult female in an oval pit along the south wall of the main room. Radiocarbon dating of the petrous bone places this individual between 6645–6495 cal BCE (94%) or 6490–6480 cal BCE (1%) (Table Z2).Sk.8587 , Level North G, Middle period, Building 114): a primary burial of a female neonate (0-2 months at death – based on long bone length) excavated in 2002 and located under the southeast platform. The burial was partially disturbed by subsequent burials in this location, and likely also by rodent burrowing.Sk.11739 : a heavily disturbed set of human remains belonging to a middle adult (35-50 years of age-at-death) based on dental occlusal wear. The individual was assessed as a possible male based on cranial morphology, although aDNA suggested the individual was genetically female. These remains, potentially representing a secondary burial, were excavated in 2005 from Space 411, located in the TP Area of the site. Radiocarbon dating of the petrous bone places this individual between 6235-6075 cal BCE (95%) (Table Z2).Sk.20217 : a female child aged 6 years (±2 years) at death based on dental development. This individual, excavated in 2012, is one of three individuals recovered from burial feature 3931 in the TPC Area. The burial was badly damaged as it was found directly beneath the surface. Hence, it could not be associated with any Neolithic buildings or spaces. Its stratigraphic position indicates that it post-dates B.122 from the Late period, which implies it most likely comes from the Final period. However, this is not corroborated by radiocarbon dating of the petrous bone that places this individual significantly earlier, between 6415-6240 cal BCE (95%) (Table Z2).ZHAJ : this is a primary single inhumation of a middle adult female (as determined by aDNA) buried in a sub-oval cut. The individual was found lying tightly flexed on her left side, positioned east-west with the head toward the west and facing north.ZHAG and ZHAF : grave 18 contained a double inhumation of a middle adult female (ZHAF) and a perinatal infant (ZHAG) found in an oval cut larger than average. The adult (ZHAF) was found lying tightly flexed on her left side and positioned with a northwest-southeast orientation with the head toward the northwest. The perinate was articulated and found with the head on top of the adult pelvis. The female sex of the adult could be confirmed by ancient DNA.ZHB : a single inhumation of an adolescent-young adult female. The sex of the individual has been confirmed by ancient DNA analysis.ZHF : single inhumation of an adult male buried in a sub-oval cut. The age-at-death of the individual was difficult to estimate accurately because both the skull and pelvis were highly fragmented. The sex has been confirmed by ancient DNA.ZHJ : this is a primary single old adult inhumation found in a sub-oval cut. The individual was found in a flexed position lying on its right side and positioned north-south with the head orientated toward the south. The bones were relatively well preserved compared with other graves, although burrowing animals had destroyed parts of the skull and axial skeleton, including the left foot. Morphological sex determination was difficult because the remains were gracile, probably as a result of the aging process. Ancient DNA analyses demonstrated that this individual was female. She has been radiocarbon dated to 8295–8240 cal BCE (95%) (Table Z2).ZHBJ (Area H Grave 30): single inhumation of a middle/old adult male in a suboval cut. Sex has been confirmed by ancient DNA.ZKO : this is a single inhumation of an old adult male in an oval cut. The individual was found lying tightly flexed on his left side and orientated east-west with the head toward the east. The bones were generally well preserved, but rodent burrowing activity caused significant disturbance of the ribs, scapulae and vertebrae. Sex was confirmed through aDNA analysis as male.ZMOJ : a primary but heavily disturbed burial of a young adult male (determined by aDNA) in a sub-circular grave. The individual was orientated east-west with head to the west and facing north. The skull was found at one end of the grave and many of the other bones had been moved by animal action, so their anatomical position was not maintained. Ancient DNA indicates that this individual was male.,13C values measured by AMS. Two samples from Çatalhöyük (30006 and 5747) were dated at both the TÜBİTAK-MAM facility and the Oxford Radiocarbon Accelerator Unit, and in both cases produced measurements that are not statistically different at the 5% significance levelRadiocarbon measurements have been obtained on remains from a total of eighteen individuals, eight from Aşıklı Höyük and ten individuals from Çatalhöyük as part of this study. Fourteen samples were dated at the TÜBİTAK-MAM facility  in 2019, three at the Oxford Radiocarbon Accelerator Unit in 2009, 2016, and 2018, three at the Keck Carbon Cycle AMS Facility, University of California (Irvine) in 2014 and 2018, and three at Uppsala University in 2018. At TÜBİTAK-MAM and Uppsala University samples obtained from petrous or rib bones of each individual were dated. At Oxford and Irvine samples were processed from lower limb bones (femora or tibiae). At TÜBİTAK-MAM the human bone samples were gelatinised and ultrafiltered,IntCal20Radiocarbon ages were calibrated using þC for 24 h and then 37 þC for another 24. The supernatant was then transferred to an extension reservoir (Zymo Research) and fully mixed with the 13 mL custom binding buffer , which is fitted to MinElute silica spin column (QIAGEN). First, extension reservoir -MinElute assembly was placed into a 50 mL falcon tube and centrifuged for 4 min at 1,500 x g, then rotated 90þ and centrifuged for another 2 min at 1,500 x g. Collection tubes with MinElute spin column are centrifuged (dry spin) for 1 min at 3,300 x g followed by two washes with PE buffer . The DNA was then eluted twice in 50 mL of Elution Buffer (EB)  with 0.05% Tween-20 and centrifuged at 16,100 x g for 1 min to collect DNA. Two negative extraction controls accompanied every 8-10 samples.Sample preparation and DNA extraction were performed using two different protocols. In the first protocol, sample preparation, DNA extraction and library preparation from Aşıklı Höyük and Çatalhöyük samples were carried out in a dedicated laboratory for ancient DNA analysis at the Middle East Technical University (METU) . All necessary precautions to avoid contamination were taken during the sample grinding, DNA extraction and library preparation processes: tools and surfaces were regularly cleaned with bleach, RNase AWAY and long exposures with UV light in the aDNA laboratory. Samples were decontaminated and prepared as in:In the second protocol, one sample (21981) from Çatalhöyük was processed in the laboratory dedicated to ancient DNA analyses, at the Institute of Human Biology and Evolution, at the Adam Mickiewicz University in Poznań (AMU) (Poland). Similar precautions to avoid contamination were taken as in the METU laboratory. In addition, prior to DNA extraction, the sample was cleaned with 2% NaOCl and rinsed with sterile water. After cleaning, the sample was UV irradiated for at least one h on each side. Following UV irradiation, the petrous bone was cut in half and drilled with the single use Dremel cutting disc and drill bit. Then DNA was extracted from 150 mg of bone powder following a silica-based method developed by Yang et al.UDG-treatment was not applied on any sample.Double-stranded DNA libraries were prepared using 20 mL of DNA extract using blunt-end ligation method following Meyer et al. (2010)http://www.mycroarray.com/pdf/MYbaits-manual-v4.pdf). PCR was performed for each captured library at 16-19 cycles with primers IS5 (5¢AATGATACGGCGACCACCGA) and IS6 (5¢ AAGCAGAAGACGGCATACGA) using either Herculase II Fusion DNA Polymerase (Agilent Technologies) or KAPA HiFi HotStart Polymerase (Kapa Biosystems). Captured libraries were purified with AMPure XP beads and quantified on the Bioanalyzer 2100 using the High Sensitivity DNA Kit (Agilent Technologies). Purified libraries were pooled in equimolar concentrations and sequenced on Illumina Hiseq 2500 and Hiseq X platforms at SciLife, Stockholm, with 100-150 bp paired-end reads on one or several lanes. The enrichment procedure increased human DNA endogenous proportions by 1.2× −27× (median 8× ) (Table Z1).To increase the depth of coverage, the best libraries of 11 individuals (Table Z1) -in terms of human DNA proportions in the prescreening data- we enriched for human genomic DNA using the MYbaits Human Whole Genome Capture Kit (African baits) from Arbor Biosciences  following the manufacturer’s instructions “-n 0.01 -o 2” and disabled the seed with “-l 16500”,FilterUniqueSAMCons.py.We processed sequencing data from each library as described in Kılınç et al.We estimated the authenticity and level of contamination in the ancient genomes using three different approaches: (a) studying the post-mortem damage (PMD) patterns that are characteristic of ancient DNA, (b) checking for mtDNA contamination based on the presence of heterozygous sites, and (c) ruling out X chromosome contamination in male individuals.PMDtoolsWe assessed the authenticity of all samples from Aşıklı Höyük (n = 8) and Çatalhöyük (n = 14) by estimating aDNA-specific damage patterns represented by high frequency cytosine to thymine (C to T) transitions at 5¢ ends of reads, and guanine to adenine transitions at the 3¢ ends. We used contamMix (version 1.0.10).samtools mpileup (version 1.9)vcfutils modules. Then, the consensus sequences were added to a set of 311 modern human mtDNA sequences and mapped to these 311 modern human mtDNA sequences. Here, we calculated contamination rates with and without transitions using contamMix. Authenticity was estimated at 393% for all 22 libraries (Table Z1).We calculated mitochondrial DNA (mtDNA) contamination of all samples from Aşıklı (n = 8) and Çatalhöyük (n = 14) using ANGSD.BAM file with the following parameters “-r X:5000000-154900000 -doCounts 1 -iCounts 1 -minMapQ 30 -minQ 30” for X chromosome positions. Then, we ran the contamination.R script to estimate chrX contamination using Fisher’s exact test and the jackknife procedure (Table Z1).X chromosome contamination was estimated for the male individuals from Aşıklı (n = 1) and Çatalhöyük (n = 4) using Evaluating the above results together, we confirmed that all n = 22 individuals examined passed at least two of the contamination estimation methods.Ry method as described in ,To assess the molecular sex of all individuals, we calculated the ratio of reads mapping to the Y chromosome to mapping to both X and Y chromosomes using the samtools (version 1.9) mpileup and variant caller toolsHaploGrep2v2.1.1 (https://haplogrep.i-med.ac.at/).We obtained mitochondrial genomes with mean coverages between 0.4- and 258-fold per individual (Table Z1). In order to assess the mitochondrial haplogroups, we called consensus mitochondrial sequences of each individual using ,The results are presented in Table Z2 and yHaplo program (version 1.0.19)http://isogg.org, version 11.04) consortium SNPs, excluding strand-ambiguous SNPs (C/G and A/T) and by randomly choosing one allele for each of 13,508 ISOGG SNPs. We called all single base substitutions using BAM files mapped to hs37d5 and samtools mpileup (version 1.9)We used the We observed haplogroup G2a2b in Aşıklı33 male individual. Two individuals from Çatalhöyük (5357 and 2779) were assigned to haplogroup C1a2 and the remaining two Çatalhöyük individuals (1885 and 2033) belonged to haplogroups, G2a2a1 and H3a1, respectively (Table Z2).We prepared three datasets by merging the ancient genomes generated in this study (n = 22) with published ancient genomes from previous studies  and with different datasets of modern-day populations:,ADMIXTURE analyses. When genotyping using DS1, transitions were included only conditionally (see ‘Genotyping’ below) to avoid the influence of postmortem damage.DS1: The Human Origins SNP Array dataset,f3 and D-statistics, FST estimation, ROH analysis and also for kinship estimation analysis. For qpAdm analyses, we merged DS2 with SGDP v4https://reichdata.hms.harvard.edu/pub/datasets/sgdp/) by overlapping 1,479,034 SNPs.DS2: The 1000 Genomes whole genome sequencing dataset (phase 3)DS3: X chromosome SNPs from the 1000 Genomes whole genome sequencing dataset version (phase 3)For each of the described SNP datasets, we genotyped ancient individuals at each SNP using reads with minimum base quality and mapping quality of 30.,We pseudohaploidized our data, such that when multiple reads overlapped with the same SNP we randomly selected one read and this position was assumed to be homozygous in the ancient individuals.,To avoid the influence of postmortem damage, whenever we encountered a T or an A when the reference genome carried a C or a G, respectively, we coded that position as missing in the ancient genotype, following.NgsRelate we used the ANGSD program .For kinship estimation using In all population genetics analyses we excluded related individuals, by choosing the individual with the highest genome coverage among groups of closely related individuals (> 3rd degree).,smartpca program in the EIGENSOFT package“numoutlieriter:0” and “lsqproject:YES”. We used dataset DS1 for We carried out principal component analysis (PCA) as a first assessment of the genetic affinities of the newly generated genomes from Aşıklı and Çatalhöyük following.f3-statistics using DS2  with the qp3Pop program in the ADMIXTOOLS package.To investigate pairwise genetic affinity between populations or between individuals, we computed outgroup f3-statistics into a pairwise distance matrix by subtracting all values from 1; we then summarized this distance matrix on two dimensions using multidimensional scaling (MDS) with the ‘cmdscale’ function in the R ‘stats’ package (http://www.r-project.org/)  C and S2Af3-statistics were used for 3 different purposes: (a) studying inter-population relationships (Table Z5), (b) comparing inter-individual genetic diversity within populations (see below) (Table Z8), (c) studying correlation between burial location and genetic distance.These outgroup D-statistics for two purposes. First, we tested whether pairs of individuals from a Neolithic site systematically formed clades  relative to individuals from other sites  only using shotgun data for Boncuklu to avoid the influence of technical biases that may arise due to use of capture and shotgun data, (b) comparing only Boncuklu and Aşıklı Höyük data to directly test possible structure within Aceramic Central Anatolia  among all members of a group (Table Z8). To test the statistical significance of differences in diversity between 2 groups, we used a random permutation test. Specifically, to calculate the null distribution for the diversity difference between 2 groups, we permuted group membership randomly using the R “sample” function, we then calculated the mean of 1- f3 values among all members of each pseudogroup, and calculated the absolute difference of these means (Table Z9). This was repeated 10,000 times to create a null distribution. Finally, we compared the observed absolute mean diversity differences to the null expectation, yielding an empirical P-value for each pair of compared groups. Note that permutation tests can be convenient for testing null hypotheses of no difference between groups, but they are also conservative because the null distribution can include the observed difference especially when the sample size is small.We analyzed genetic diversity differences among groups, either comparing (a) populations, represented by all individuals sampled per settlement, or (b) subsets of individuals from specific phases of settlements. Diversity per group was calculated as the mean of 1- ADMIXTURE,PLINK“–indep-pairwise 200 25 0.4” and for missing genotype with “-geno 0.99”, leaving 518,401 SNPs to be analyzed. We conducted ADMIXTURE analysis for each value of K ranging from 2 to 15 and determined the clusters of each ancient individual using the “projection” function of ADMIXTURE. We visualized the results using PONG software.We performed unsupervised genetic clustering using FST to evaluate genetic differentiation among early Holocene populations from Anatolia, Levant and Iran. Population-level FST were calculated for ancient population samples that included at least two ancient individuals - the same criterion as used for f3-statistics-based population comparisons. The smartpca program in the EIGENSOFT package“inbreed:YES” and “fstonly:YES”, and with DS2 . We visualized the results on two dimensions using multidimensional scaling (MDS) with the ‘cmdscale’ function in the R ‘stats’ package (http://www.r-project.org/) (We computed pairwise qpWave (v1200) and qpAdm (v1201)ADMIXTOOLS (v7.0),,,,,,qpWave and qpAdm analysis.We modeled target Anatolian Ceramic period populations  as admixture between two or three source populations using the qpWave showed that neither the Aşıklı versus Boncuklu populations could be distinguished from each other, nor Çatalhöyük versus Barcın populations (p > 0.01), a result expected given their similarities observed in other analyses . We could model the latter Ceramic Neolithic populations  as mixtures of Aceramic Neolithic (Aşıklı or Boncuklu) and Levant Neolithic . However the fit was poor when we tried to model Ceramic Neolithic populations  as mixtures of Aceramic Neolithic (Aşıklı or Boncuklu) and Iran Neolithic . Thus, in a second round of analyses, we tested whether CHG could be a better proxy for a possible eastern source of gene flow. For this we removed CHG from the “right populations” list and used it instead of Iran Neolithic among the “left populations” (Table Z10). This likewise produced poor fits.Results of NgsRelate,lcMLkin,READ,NgsRelate and lcMLkin infer the genetic kinship coefficient (θ) between a pair of individuals, i.e., the probability that a pair of randomly chosen alleles from two individuals each are identical-by-descent (IBD). For this they first estimate k0, k1, k2 (Cotterman coefficients), which are the probabilities of sharing 0, 1 and 2 alleles IBD between a pair of individuals, such that k0+ k1+ k2 = 1. The kinship coefficient θ is then calculated as θ = k1 / 4 + k2 / 2. READ uses an alternative approach, described below. All 3-software produced consistent results.We first inferred relatedness coefficients for pairs of Aşıklı and Çatalhöyük individuals and previously published Anatolian Neolithic individuals. For this we used three different software, NgsRelate calculates the kinship coefficient (θ) between Aşıklı 2 and Barcın M10_352, sharing 3,044 SNPs, as 0.02, suggesting a 4th-5th degree relationship, despite the pair being separated by c.1,000 years, i.e., about 40 generations lcMLkin.NgsRelate using BAM files as input with default parameters. We used genotype likelihoods calculated by the ANGSD programWe ran lcMLkinNgsRelate, lcMLkin assumes no inbred individuals. We ran lcMLkin with population allele frequencies calculated from n = 60 Anatolian early Holocene individuals (Tables Z2 and Z3) including the newly generated data (n = 22) and using default parameters.READREAD calculates and normalizes the mismatch rate  in non-overlapping windows of 1 Mbps across the whole genome using pseudo-haploid data. READ uses this value to infer the degree of relationship , but cannot detect more distant relatives than estimating relatedness down to 3rd degree. READ compares the P0 distribution of each pair to those average unrelated pairs.As a third approach, we used the software READ on each population separately, i.e., with all individuals from each Anatolian Neolithic site as background, with default parameters. For each pair, we used normalized 1-P0 values as a proxy for the kinship coefficient (θ), shown in P0 across all pairs, which represents the mismatch level of an average unrelated pair.We implemented NgsRelate and lcMLkin analyses we used all 60 Anatolian Neolithic individuals (Tables Z2 and Z3) to calculate population allele frequencies. Here, the relatively high genetic homogeneity of Aşıklı Höyük and Boncuklu Höyük relative to the 3 Ceramic Neolithic sites  among putative first-degree pairs . For this, we used ,We tested mtDNA homogeneity for estimating pedigree relationships among Boncuklu Höyük individuals using pairwise mismatch rate of mtDNA sequences, since the Boncuklu Höyük population is genetically homogeneous. For this, we called consensus mtDNA sequences using the method described earlier and calculated the pairwise mismatch rate between mtDNA consensus sequences of each individual pair following.We performed goodness-of-fit tests on contingency tables for BAM file using the samtools mpileup functionHIrisPlex (http://hirisplex.erasmusmc.nl/). Probabilities of 0.968, 0.019 and 0.009 for having intermediate, very pale and pale skin color were obtained respectively, including a high probability of having light hair color (> 0.99). Regarding the eye color prediction, probabilities of 0.547, 0.338 and 0.115 were obtained for brown, blue and intermediate color, respectively.We analyzed a number of functional SNPs  in the high coverage genome of Aşıklı-128. We restricted the analysis to this one individual with the highest coverage (5× ) to avoid excessive amounts of missing data. SNPs associated with phenotypes of interest were called from the MCM6 gene associated with lactose tolerance in Europeans (rs4988235),,,,We further analyzed derived allele variants in the samtools mpileup,PLINK (v. 1.9)“–homozyg,–homozyg-window-snp 50,–homozyg-window-het 1,–homolog-windowsthreshold 0.05,–homozyg-snp 50,–homozyg-kb 500,–homozyg-density 50,–homozyg-gap 100”.We analyzed runs of homozygosity (ROH) in Aşıklı-128, the ancient genome with relatively high genome coverage produced in this study, and also six other relatively high coverage (> 5× ) ancient individuals with published data: ZHB (Bon002), Bar8 (M10-106), Loschbour, Stuttgart, NE1 and WC1 (Table Z3), following Kılınç et al.FROH to estimate the level of inbreeding. FROH measures the individual homozygosity which is the proportion of the genome covered by ROH.,FROH by dividing the summed length of ROH  given noisy data, we performed a set of simulation experiments that we describe here. This is especially important when inferring genetic kinship with ancient genomes due to large and highly variable quantities of missing information. Thus, to investigate the uncertainty in the inferred kinship and Cotterman coefficients based on limited data, we simulated multiple pedigrees (In order to estimate the expected ranges for the kinship coefficient (θ) and Cotterman coefficients (edigrees C using bVCF files (phase 3).“–indep-pairwise 100 25 0.4” and selection those with a minor-allele frequency (MAF) > 0.05 with “–maf 0.05” using the PLINK tool.“–maf 0.05” filtering and removed pseudoautosomal regions from X chromosomes as described in the human reference genome (hs37d5),VCF files for each pedigree using these autosomal and X chromosomal datasets, using either the ped-sim softwarePython 3.8https://github.com/CompEvoMetu/kinshipsim), respectively. Next, we analyzed simulated data using NgsRelate software (version 2)NgsRelate on the simulated pedigrees to produce the distributions of the kinship statistics. We chose NgsRelate for these simulation experiments because its results were generally consistent with the lcMLkin and READ, and it has the further advantage of being able to co-estimate kinship and inbreeding.For simulations, we used the modern-day Tuscany individuals (TSI) from the 1000 Genomes phase 3 datasetped-sim algorithm (with parameters “–intf interfere/nu_p_campbell.tsv–miss_rate 0–keep_phase”) using the genetic mapping data as described above. Because the available version of ped-sim was designed for autosomal simulations, we simulated the X chromosome using in-house code. The molecule inherited from the mother was generated through the following recombination process: the creation of a gamete, a haploid molecule to be inherited by the offspring, began with the random selection of three points  on the mother X chromosome -thus dividing it into four random-sized segments. Each segment of the gamete was obtained by randomly selecting that region from one of the two molecules from the parent , creating a haploid genotype. While sons only possess the single molecule inherited from the mother, daughters also receive a single-copy X chromosome from their fathers , 3,200 (second degree), 2,000 (third degree), 15,600 (unrelated) as well as 700 (siblings), 2,800 (parent-offspring), 800  and 800 . Before studying properties of their distributions, sample sizes were equalized by random selection. Estimates of θ, (a) The full set of 157,172 SNPs,(b) 5,000 randomly chosen SNPs to inform on the lower limit,(c) Randomly chosen SNPs  that match the numbers of overlapping SNPs for ancient individual pairs identified as close relatives in this study: 125,110 for the pair Tepecik-Çiftlik 37-21; 86,247 for the pair Barcın M10_275-M10_271; 80,115 for the pair Barcın L11_216-L11_215; 22,076 for the pair Boncuklu ZHF-ZHJ; 20,810 for the pair Aşıklı 131-136; 18,136 for the pair Boncuklu ZHBJ-ZHAF; 9,151 for the pair Çatalhöyük 2728-2842. Since, the pair Aşıklı 128-133 share 741,193 SNPs, we used the full set of 157,172 SNPs for this Aşıklı pair B.VCF files is significantly more straightforward than using BAM files, we performed NgsRelate with VCF files, which differs from the real situation where we had used BAM files. To account for genotype uncertainty while using VCF data we performed simulations using both diploid and pseudo-haploidized datasets.Since simulating pedigrees starting from k0, k1 and k2 estimates from NgsRelate from the simulated pedigrees. First, we used the 0.025 and 0.975 quantiles of these distributions (two tails of the distributions) to describe the range of expected values for each type of relationship given a certain SNP number, for first-, second-, and third- degrees as well as for parent-offspring, siblings, half-siblings and avuncular relationships.We then analyzed the distributions of θ, Differently, for the unrelated case we considered 0.95 as the upper limit . The mean value, standard deviation and estimated range for diploid and pseudo-haploidized data are summarized in Table Z16  and Table Z17 .k0, k1 and k2 estimates of the X chromosomes computed from two hundred pedigrees. The analysis was performed for the following X chromosomal SNP sets:The detailed first-degree relationships  were studied through the θ, (a) The full set of 179,716 SNPs,(b) 800 randomly chosen SNPs to inform on the lower limit,(c) Randomly chosen SNPs that match the numbers of overlapping SNPs for ancient individual pairs identified as close relatives in this study: 64,969 for the pair Aşıklı 128-133; 10,654 for the pair Barcın L11_216-L11_215; 9,940 for the pair Tepecik-Çiftlik 37-21; 6,558 for the pair Barcın M10_275-M10_271; 2,653 for the pair Aşıklı 131-136; 1,673 for the pair Boncuklu ZHBJ-ZHAF; 1,538 for the pair Boncuklu ZHF-ZHJ; 814 for the pair Çatalhöyük 2728-2842.From the resulting distributions, the 95% confidence intervals (ranges) of the coefficients were determined by considering the 0.025 and 0.975 quantiles. The mean value, standard deviation and estimated ranges resulting from the X chromosome simulation are summarized in Table Z18 .In the attempt to better characterize mother-daughter and sisters relationships, we also computed the mean and variance of the ratio between autosomal  and X chromosomal (estimated using 800 SNPs) θs:k0, k1 and k2, respectively.We note that in Table Z16, the variance in theta estimates shows little dependence on SNP numbers (sample size). We hypothesized that this may be caused by the major contribution of variance in these estimates being the variance in background relatedness among pairs across different simulated pedigrees, rather than sampling error due to differences in randomly chosen SNP sets. In order to test this idea, we measured the effect of SNP sample size on the variance of our estimates, within the same pedigrees only. Specifically, we generated 10 pedigrees and, for each pedigree, the subsampling process was repeated 10 times for a total of 100 simulations. The average values of the coefficients, together with the standard deviations, were computed for each subsampling and for all couples and relations of interest. Results are summarized in Tables Z20-Z23 for θ, distantly related pairs of individuals in this dataset, related beyond the 3rd degree, might tend to be buried in the same buildings or at closer proximity, compared to fully unrelated individuals. For this we applied two types of analysis described below. In both analyses we used the outgroup-f3 statistic between two individuals, calculated as f3, to measure genome-wide genetic similarity, and 1-f3 to measure genetic distance.We studied the question of whether any We selected specific subsets of individuals from Aşıklı Höyük, Boncuklu Höyük, Çatalhöyük and Barcın Höyük. f3) as measure of distance, where the outgroup f3-statistic was calculated as f3, with YRI representing the outgroup, and INDV1 and INDV2 the genotypes of the individuals studied.In each subset, for the chosen individual pairs, we created matrices of spatial distance between each pair, calculated as the Euclidean distance between each pair of burials’ x and y positions in the site plan. For the same pairs of individuals, we also created genetic distance matrices, using .We then calculated the Pearson correlation coefficient between each such pair of spatial and genetic distance matrices, and further calculated a one-sided Mantel test We tested whether individuals buried in or associated with the same building tend to show higher genetic similarity to each other, relative to individuals buried in or associated with other buildings.This test was performed on the Çatalhöyük and Barcın Höyük data, because these were the only sites with > 1 co-burial clusters. Again, we included only individuals who were found not to be closely related, by removing one of each pair of close relatives identified in In Çatalhöyük the data included n = 9 such co-buried individuals associated with Buildings 17, 50, and 114. In Barcın Höyük the data included n = 8 such co-buried individuals associated with Buildings 4, 5, and 14/15.f3, calculated as described above. We first calculated the within-building genetic similarity of all pairs of individuals from each of the 3 buildings, and calculated the mean of these distances. We then created a null distribution of mean co-burial genetic similarities, randomly assigning the 9 or 8 individuals to each building and calculating the mean of within-building similarities, 10,000 times (performed in R using the “sample” function). A one-sided P-value for the alternative hypothesis that within-building genetic similarity would be higher than random was calculated by comparing the observed value with the null.As a measure of genetic similarity, we used the outgroup P-values were calculated as 0.52 and 0.8, respectively.In both Çatalhöyük and Barcın Höyük, the mean similarity within buildings observed was within the randomly expected distribution. The permutation test