SOX2 (rs1345417: P = 6.51×10−10) and at 5q13.2 near FOXD1 (rs12651896: P = 1.73×10−8). We further replicated our findings in the Uyghurs, a population from China characterized by East Asian-European admixture (N = 721), the CANDELA cohort from five Latin American countries (N = 2301), and the Rotterdam Study cohort of Dutch Europeans (N = 4411). A meta-analysis combining the full GWAS results from the three cohorts of full or partial Asian descent  highlighted a third signal of genome-wide significance at 2q12.3 (rs1866188: P = 5.81×10−11) near EDAR. We performed fine-mapping and prioritized four variants for further experimental verification. CRISPR/Cas9-mediated gene editing provided evidence that rs1345417 and rs12651896 affect the transcriptional activity of the nearby SOX2 and FOXD1 genes, which are both involved in hair development. Finally, suitable statistical analyses revealed that none of the associated variants showed clear signals of selection in any of the populations tested. Contrary to popular speculation, we found no evidence that eyebrow thickness is subject to strong selective pressure.Hair plays an important role in primates and is clearly subject to adaptive selection. While humans have lost most facial hair, eyebrows are a notable exception. Eyebrow thickness is heritable and widely believed to be subject to sexual selection. Nevertheless, few genomic studies have explored its genetic basis. Here, we performed a genome-wide scan for eyebrow thickness in 2961 Han Chinese. We identified two new loci of genome-wide significance, at 3q26.33 near  SOX2, FOXD1 and EDAR genes could affect eyebrow thickness. After fine mapping, we prioritized four variants for experimental verification. CRISPR/Cas9-mediated gene editing provided evidence that the variants rs1345417 and rs12651896 affect the transcriptional activity of the nearby SOX2 and FOXD1 genes. This represents a successful example of a combination of GWAS and CRISPR/Cas9 technology to demonstrate how non-coding variants with regulatory functions may play an important role in common diseases and traits. Finally, suitable statistical analyses suggest that, contrary to popular speculation, eyebrow thickness should not be subject to strong selection pressure, including sexual selection.Hair plays an important role in primates and is clearly subject to adaptive selection. While humans have lost most facial hair, eyebrows are a notable exception. Eyebrow thickness is heritable and widely believed to be subject to sexual selection. Nevertheless, few genomic studies have explored its genetic basis. Here we performed genome-wide association studies for eyebrow thickness in multiple ethnic groups, including Han Chinese, Uyghurs, Latin Americans, and Caucasians. We found solid evidence that novel genetic variants near the  FOXL2 gene are associated with eyebrow thickness. However, these variants have very low minor allele frequencies in East Asians and Europeans, suggesting that in these two populations, eyebrow thickness may well be affected by different genes.Hair has a range of important functions in primates and has been speculated to be subject to intense natural and sexual selection . AlthougTo enhance our understanding of the genetic basis underlying the variation of human eyebrow thickness, we conducted a genome-wide association study in East Asians and Eurasians, followed by a trans-ethnic meta-analysis, to identify genetic variants that affect eyebrow thickness in humans. Moreover, in order to validate the findings from our association analyses, we performed fine mapping and conducted functional genetic experiments. Finally, we applied various statistical genetics methods to search for signals of positive selection in and around the identified candidate genes.PTZL = 7.23×10−70, PUYG = 7.31×10−33, PRS = 4.10×10−106; the CANDELA sample included only male subjects). Age was negatively correlated with eyebrow thickness in TZL, CANDELA, and RS . The GWAS in Han Chinese was based on 7,119,456 SNPs; it identified association signals of genome-wide significance (P<5×10−8) for two genomic regions, located at 3q26.33 (rs1345417: P = 6.51×10−10) and 5q13.2 . The heritability of eyebrow thickness in TZL was 37.6%. The SNPs rs1345417 and rs12651896 explain 3.11% and 2.55% of this heritability, respectively. Although the GWAS in the Uyghurs, which was based on 7,224,952 SNPs, did not detect any genome-wide significant signals , and allelic effects were consistent with those in TZL . The meta-analysis of all three GWAS  identified four loci reaching genome-wide significance , 5q13.2 (rs12651896: P = 2.52×10−13) and 3q22.3 (rs112458845: P = 2.24×10−9) there was one additional locus of genome-wide significance, at 2q12.3 (rs1866188: P = 5.81×10−11)  . The resP = 4.95×10−11 at rs112458845 and an effect allele frequency (EAF) of 0.127, did not reach nominal significance in our GWAS of TZL  and UYG . Furthermore, our meta-analysis revealed strong allelic heterogeneity for rs112458845 , the allelic effects acted in the same direction in all populations . There wP = 0.0141; rs112458845: P = 0.366; rs1345417: P = 4.03×10−3; rs12651896: P = 2.93×10−4; We performed a further replication analysis for all SNPs described above in the Rotterdam Study sample, which comprises 4411 Dutch Europeans. With the exception of rs112458845 at 3q22.3, which was specific to Latin Americans, this analysis replicated all signals at a nominal significance level  play a role in the expression of nearby genes, we conducted CRISPR/Cas9-mediated gene editing for each of them. We targeted the genomic regions surrounding each variant using two different sgRNAs . Our qRT9-sgRNAs . Infectis . Consistent with our expectations, SOX2 expression levels were significantly lower in an edited, G/C heterozygous A375 clone than in the original G/G A375 cells , European (CEU) and African (YRI) populations  is in high LD with rs3827760, a strongly selected functional SNP causing a number of ectodermal related phenotypic changes, as demonstrated previously [EDAR, we found no significant differences in selection for any other SNP in the associated regions , an important transcription factor that is highly expressed in the dermal condensate (DC) and dermal papilla (DP) of growing follicles [SOX2 expression appears to play an important role in the regulation of hair thickness. In murine skin development, from E18.5 onwards, Sox2 expression becomes confined to DPs in thick guard/awl/auchene hairs, but not in thin zigzag hairs [SOX2 expression. The top SNP within the second signal at 5q13.2, rs12651896, is located 242 kb downstream of FOXD1 (Forkhead Box D1). This gene belongs to the forkhead family of transcription factors, whose members are characterized by a distinct forkhead domain. FOXD1 is especially enriched in DC cells, a precursor of DP/dermal sheath niche cells within the mature follicle [EDAR (Ectodysplasin A receptor), a gene which plays an important role in the development of ectodermal tissues such as hair, teeth, and sweat glands [EDAR to be implicated in beard density [The top associated SNP at 3q26.33, rs1345417, is located about 80 kb downstream of the ollicles ,31. Inteag hairs . Moreoveag hairs . These ffollicle . Finally density ,32,33.SOX2 and FOXD1, respectively. In particular, the CRISPR/Cas9-mediated knock-in experiment provided direct evidence that a single substitution at rs1345417 is sufficient to affect the endogenous gene expression of SOX2. Our study represents a successful example of how GWAS and CRISPR/Cas9 technology can be combined to demonstrate the involvement of non-coding variants with regulatory functions in common diseases and normal phenotypic variation.It is notable that all of the signals identified by our GWAS fall into regulatory regions. To date, hundreds of GWAS have been conducted, resulting in the identification of a large number of genetic variants associated with common diseases and phenotypic variation. The majority (~93%) of variants associated with common traits lie within non-coding sequences, complicating their functional evaluation . Recent FOXL2 variant rs112458845 has previously been found to be associated with eyebrow thickness in Latin Americans. Here, we found no significant evidence of association of this variant with this phenotype in East Asians or Europeans. This may partly be due to the frequency distribution of the minor allele, which is rare in East Asians (4%) and absent in Europeans (0%), but relatively common in Native Americans (26%). Additionally, and perhaps more importantly, allelic effect sizes may be population specific due to a unique yet unidentified environmental or genetic background and may thus vary between Latin Americans on the one hand, and East Asians and Europeans on the other. A similar impact of population heterogeneity on allelic effect sizes has been reported for other traits, such as follicular lymphoma [The lymphoma , body bolymphoma , and Typlymphoma .EDAR region around rs3827760, a SNP known to be under strong selection, we found no significant signatures of positive selection for variants associated with eyebrow thickness. These results suggest that eyebrow thickness may not be subject to strong positive selection, at least not via the genes identified here. In this context, it is worth noting that most studies postulating sexual selection of the eyebrow were based on reconstructed attractiveness [It has long been debated whether eyebrow thickness is a selected trait or a ‘neutral feature’ with no apparent link to individual fitness ,6,7. We tiveness ,7, and iSOX2, FOXD1 and EDAR that influence eyebrow thickness. We demonstrated that rs1345417 and rs12651896 affect the transcriptional activity of the nearby SOX2 and FOXD1 genes. Furthermore, we found evidence for population heterogeneity in the genetics of eyebrow thickness. Finally, our results suggest that eyebrow thickness may not be subject to strong positive selection.In conclusion, we identified three novel genetic variants near The Taizhou Longitudinal Study was carried out following protocols approved and oversight by the Institutional Research Board at Fudan University . The Xinjiang Uyghur Study was conducted with the official approval of the Ethics Committee of the Shanghai Institutes for Biological Sciences (ER-SIBS-261410). The CANDELA ethics approval was obtained from: Universidad Nacional Autonoma de Mexico (Mexico), Universidad de Antioquia (Colombia), Universidad Peruana Cayetano Heredia (Peru), Universidad de Tarapaca (Chile), Universidade Federal do Rio Grande do Sul (Brasil) and University College London . The Rotterdam Study has been approved by the Medical Ethics Committee of the Erasmus MC (registration number MEC 02.1015) and by the Dutch Ministry of Health, Welfare and Sport . All participants provided written informed consent in these four studies.www.trialregister.nl) and into the WHO International Clinical Trials Registry Platform  under shared catalogue number NTR6831. All participants provided written informed consent to participate in the study and to have their information obtained from treating physicians.This study is based on data from four populations : Han ChiEyebrow thickness  was rated by eye on a three-point scale , following an established standard and based on photographic imagery . For theP<1×10−5). The chip genotype data were phased using SHAPEIT [For TZL and UYG, blood samples were collected, and DNA was extracted. All samples were genotyped using the Illumina HumanOmniZhongHua-8 chip, which interrogates 894,517 SNPs. To control for genotype quality, we used PLINK 1.9  to exclu SHAPEIT , and IMP SHAPEIT  was then SHAPEIT ,48. In R SHAPEIT  based on2<0.2) were selected for analysis. Principal component (PC) analysis did not find any outliers in TZL and UYG  using PAINTOR ,15. For To further facilitate the prioritization of variants for functional analysis, we used CADD   and A375 (human melanoma) were purchased from the cell bank of the Chinese Academy of Sciences. HACAT  and SCC13 (skin squamous-cell carcinoma cell line) were purchased from ATCC. Cell lines were routinely tested for mycoplasma infection. Cells were cultured using DMEM+10%FBS+1%Penicillin-Streptomycin.SOX2 promoter. The G>C mutation was introduced by site-directed mutagenesis (Takara). The inserts in each construct were verified by sequencing. The detail information of primer sequences can be found in A 1,495 bp genomic fragment comprising the rs1345417 enhancer was amplified from human genomic DNA and cloned into a pGL3-promoter vector, in which the SV40 promoter was replaced by a For each candidate variant, two sgRNAs were designed, cloned into the lentiCRISPR v2 vector, and packaged into lentivirus as previously described . sgRNAs For target site deletion via lentiCRISPR-sgRNA virus infection, stably infected cells were selected on puromycin. For clone selection, stably infected cells were diluted to allow colonial growth. Single colonies were individually picked for DNA sequencing of the target site. Because protospacer adjacent motives were located as far as 100 bp upstream and 22 bp downstream, the deletion at rs1866188 was considerably larger than for the other sites.For G>C substitution at rs1345417 in A375 cells, we used a CRISPR-Cas9 mediated knock-in strategy. Specifically, in order to substitute the G/G of rs1345417 in A375 cell, a ~1000bp DNA fragment encompassing the rs1345417 site from a Chinese individual with a C/C homozygote genotype was PCR amplified and TA cloned into the pMD18T Vector (Takara) to construct a C-donor plasmid. We then co-transfected the C-donor plasmid and the lenticrispr-rs1345417-sgRNA2 plasmid into A375 cells using the Effectene reagent (Qiagen). 48 h after transfection, the cells were selected on puromycin for 48 h to eliminate untransfected cells. Successfully transfected cells were then diluted for colonial growth. Single colonies were individually picked for DNA sequencing to screen for substitution at the target site. In total, 44 clones were screened.Sequencing of the rs1345417 site: Genomic DNA was extracted from each cell clone using ZR Genomic DNA-tissue MiniPrep (Zymo) following the manufacturer’s protocol. The rs1345417 genomic region was amplified from genomic DNA by nested PCR and TA-cloned into pMD18 T vector for sequencing. At least four individual TA-clones were sequenced for each cell clone. RNA extraction was conducted using Direct-zol RNA MiniPrep Plus (Zymo) following the manufacturer’s protocol. Reverse transcription was conducted using the iscript cDNA synthesis kit (Biorad) following the manufacturer’s protocol. Real-time PCR experiments were conducted on a VIIA7 Fast Real-Time PCR system (Applied Biosystem) using the iTaq universal SYBR Green supermix (Biorad). All statistical analyses were conducted using Microsoft Excel and the GraphPad Prism 6 software.Genomic characteristics resulting from strong recent positive selection include low haplotype diversity and high linkage disequilibrium. We calculated extended haplotype homozygosity (EHH) ,67 for am, we calculated the polygenic eyebrow thickness score (genetic score) asβl is the effect size of the eyebrow thickness increasing allele l, and pml is the frequency of allele l in population m. We first used the four loci identified here in conjunction with allele frequency data from the 1000 Genome Project dataset to estimate the genetic score for eyebrow thickness in each population, with the effect sizes estimated in the meta-analysis. To test whether there was a signature of polygenic adaptation, we then adopted a framework developed by Berg and Coop [To investigate whether the loci associated with eyebrow thickness are more differentiated among populations than expected under neutral genetic drift, for each population and Coop , which bS1 FigLevel 1 (scarce): low eyebrow density, the brow does not cover the skin completely. Level 2 : the eyebrow covers the skin, there is no hair between the two brows. Level 3 (dense): high eyebrow density, the color of the eyebrow is darker than for level 2.(PNG)Click here for additional data file.S2 FigP<5×10−8).Manhattan plot illustrating the results of the genome-wide scan for eyebrow thickness in 721 Uyghurs after adjusting for the top four PCs, gender and age. The red line indicates the threshold for genome-wide statistical significance ((PNG)Click here for additional data file.S3 Figa) TZL, b) UYG, c) CANDELA and d) meta-analysis.(PNG)Click here for additional data file.S4 FigDNA sequences of the regions around a) rs1345417, b) rs12651896, c) rs10061469 and d) rs1866188 in original A375 cells and their CRISPR-Cas9 edited clones. For rs1345417, 417sg1m1-2: clone 1 and 2 edited by sgRNA1. 417sg2m1-3: clone 1–3 edited by sgRNA2. 417-G/C: a single nucleotide substitution event. For rs12651896, 896sg1m1-2: clone 1 and 2 edited by sgRNA1. 896sg2m1-2: clone 1–2 edited by sgRNA2. For rs1866188, clone 1 and 2 edited by both sgRNA1 and sgRNA2.(PNG)Click here for additional data file.S5 FigA) Schematic diagram of the constructed luciferase reporters. Triangles and green rectangles represent the promoter and luciferase gene, respectively. The putative regulatory element  is indicated by yellow rectangles, with the SNP rs1345417 represented as a red line (G>C). pGL3-Basic, empty vector (SV40 promoter); SOX2-Pro, vector containing the SOX2 promoter; SOX2-G, G allele at rs1345417; SOX2-C, C allele at rs1345417. B-D) Three repeats of luciferase reporters in human A375 cells. Bars represent the mean intensity of luciferase gene activity relative to the empty vector (pGL3-Basic) as a control, measured 48h after transfection for a typical experiment. Error bars represent standard errors. *p<0.05, **p<0.01, ***p<0.001, t-test comparing to vector control. N = 3 for each experiment.(PNG)Click here for additional data file.S6 FigAllele frequency data from 53 world-wide populations were taken from the Human Genome Diversity Project. For a) rs1345417, b) rs12651896 and c) rs1866188, ancestral alleles are represented in blue, derived alleles in orange.(PNG)Click here for additional data file.S7 FigEDAR, b) FOXL2, c) SOX2 and d) FOXD1 regions in CEU, CHB+JPT and YRI populations. The yellow line marks the location of the index SNP in each region  h region . The bla(PNG)Click here for additional data file.S8 FigEDAR, b) FOXL2, c) SOX2 and d) FOXD1 regions in CEU, CHB+JPT and YRI populations. The yellow line marks the location of the index SNP in each region  h region . The bla(PNG)Click here for additional data file.S9 FigThe forest plot illustrates the differences in selection coefficients of variants in four independent genomic signals for eyebrow thickness. Differences are shown between three 1000 Genomes populations . For each variant, points (and error bars) indicate the estimated differences in selection coefficients . Each population pair is denoted by a different color. The underlying data were obtained from phase 3 of the 1000 Genomes Project.(PNG)Click here for additional data file.S10 FigThe genetic score was calculated on the basis of the four associated variants, with effect sizes estimated in the meta-analysis and allele frequencies obtained from phase 3 of the 1000 Genomes Project.(PNG)Click here for additional data file.S11 FigPrincipal component analysis of 102,284 SNPs (r2<0.2) in Han Chinese (n = 2961), Uyghurs (n = 721), and three 1000 Genome population samples  placed Han Chinese and Uyghurs into distinct clusters. There were no significant population outliers in our samples.(PNG)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


Random regression models (RRM) are widely used to analyze longitudinal data in genetic evaluation systems because they can better account for time-course changes in environmental effects and additive genetic values of animals by fitting the test-day (TD) specific effects. Our objective was to implement a random regression model for the evaluation of dairy production traits in French goats.The data consisted of milk TD records from 30,186 and 32,256 first lactations of Saanen and Alpine goats. Milk yield, fat yield, protein yield, fat content and protein content were considered. Splines were used to model the environmental factors. The genetic and permanent environmental effects were modeled by the same Legendre polynomials. The goodness-of-fit and the genetic parameters derived from functions of the polynomials of orders 0 to 4 were tested. Results were also compared to those from a lactation model with total milk yield calculated over 250 days and to those of a multiple-trait model that considers performance in six periods throughout lactation as different traits. Genetic parameters were consistent between models. Models with fourth-order Legendre polynomials led to the best fit of the data. In order to reduce complexity, computing time, and interpretation, a rank reduction of the variance covariance matrix was performed using eigenvalue decomposition. With a reduction to rank 2, the first two principal components correctly summarized the genetic variability of milk yield level and persistency, with a correlation close to 0 between them.A random regression model was implemented in France to evaluate and select goats for yield traits and persistency, which are independent i.e. no genetic correlation between them, in first lactation.The online version of this article (10.1186/s12711-019-0485-3) contains supplementary material, which is available to authorized users. Random regression test-day (TD) models (RRM) are widely used in genetic evaluations of TD milk production in dairy cows but also in other species such as dairy goats –6. RRM iBesides describing the variability of genetic parameters along the lactation curve, RRM serve to predict estimated breeding values (EBV) for lactation persistency, based on the variation of EBV throughout lactation. A persistent animal is defined as producing on average less milk at the beginning but more at the end of the lactation period than animals with a similar overall production . LactatiVarious functions have been used to model fixed, genetic and permanent environment effects on TD records, including the Wilmink function , LegendrWith eigen decomposition, it becomes possible to reduce the rank of the resulting variance–covariance matrix by ignoring the contribution of the smallest eigenvalues and eigenvectors , 17. ThiTo date, the implementation of RRM in dairy goats has not been studied in France. In this paper, we estimate genetic parameters for milk yield, fat yield, protein yield, fat content, and protein content using RRM with Legendre polynomial functions of different orders, with or without rank reductions, to obtain TD EBV for French dairy goats in first lactation.The data consisted of 193,226 milk TD records from 30,186 first-lactation Saanen goats (234 herds) and 205,841 milk TD records from 32,256 first-lactation Alpine goats (198 herds) from northwestern France, collected between 1995 and 2015. The pedigree consisted of 66,716 and 67,159 Saanen and Alpine goats, respectively. Each lactation included at least four TD between the 7th and 270th day in milk (DIM). Lactation had to last between 180 and 350 days. Goats were milked twice a day and their records were summed to obtain their daily production. More than four animals per herd × test date combination were required. The sires of these goats were artificial-insemination bucks with at least 20 progeny in the dataset, with 379 and 324 sires from the Alpine and Saanen goat breeds, respectively. The dams of the goats had to be known. The traits analyzed were milk, fat and protein yields and fat and protein contents.Five RRM were tested separately for each breed and for all five traits and all had the form:Legendre polynomials of order 0 to 4 (UM_legq) . Given tt DIM 40 .The next step was to test various rank reductions of the genetic variance–covariance matrices based on an eigen decomposition of these matrices. To keep a consistent definition of persistency, the second eigenvector was set, by multiplying by − 1, to be systematically negative at the beginning of lactation, and then positive. For a reduction to rank t et al.  to obtaiThe corresponding EBV for whole lactation performance of goat The evolution of the estimated heritabilities throughout the DIM period and the genetic correlations between DIM that were obtained with RRM were compared to those obtained with a MT model, in which lactation was subdivided into six periods . TD yields and contents for each period We implemented a lactation (LACT) model that was close to that routinely used for official genetic evaluations in France. The total milk, fat and protein yields throughout lactation were calculated as in routine genetic evaluations using the Fleischmann method . Fat andAll the genetic parameters were estimated using the WOMBAT software .For each trait, the heritability of the chaeffer , by diviFor each trait, the genetic variance–covariance matrix between all DIM was obtained following Druet et al.  as \docuHeritabilities for each test day were obtained by dividing the diagonal elements of The genetic correlations between DIM Genetic variances for each trait throughout the whole lactation  and the Pearson correlation coefficients , which indicates that, for all traits and both breeds, the fit to the data improved as the order of the Legendre polynomial used increased. As rank decreased, BIC decreased when the first three PC were kept instead of just the first two, without significant differences between Pearson correlation coefficients (ρ) between observed data and predicted values were calculated to compare adjustment to data between traits and breeds and between RRM and MT models; Additional file The evolution of the residual variance with DIM is another criterion for characterizing the effect of this variation on the quality of the adjustments throughout lactation. In the French dairy goat breeding program, the most important trait for cheese production is protein yield, which can also be expressed as protein content with respect to the level of milk yield. The residual variances for milk yield and protein content for different lactation periods are shown in Fig. mentclass2pt{minimModel Heritabilities for each trait on the whole lactation .The fixed effects, i.e. age at kidding, month of kidding, dry-period length and gestation stage, were modeled with splines in the RRM model. In a previous study , we showWe applied a principal component analysis of the variance–covariance matrix of the most complex model, which showed that it was possible to associate biological significance with each PC. As in our study, van der Werf et al. , Olori eImplementing a RRM for genetic evaluation requires cumbersome testing of the best tradeoffs between model fit and complexity. Pool et al.  comparedmentclass2pt{minimFor all traits, heritability estimates from the p et al.  for the p et al.  found a p et al.  found a mentclass2pt{minimp et al.  for goatmentclasspt{minimap et al.  and Andop et al. . The herp et al.  in cattlp et al.  reportedAs in our study, Mucha et al.  found thWe showed that correlations between EBV from RRM and RRM* were close to 1 for both average and persistency of yield as reported in Leclerc et al.  who compion Fig.  is interThe correlations of the production level for milk yield with the production level for the other traits were very close to those reported by Bélichon et al.  on totalFinally, the development of a test-day model for milk production opens up new perspectives. For example, the study of the genetic relationship between persistency and other traits such as longevity or fertility could help to explain the negative correlation between high production and fitness. The estimated fixed effects of such test-day models, especially the herd-test-day effect, also offer producers important clues on the impact of herd management on these traits. For example, herd test-day estimates can be compared between farms under similar systems in terms of mean and variability throughout the year .In this paper, we show that the genetic parameters obtained with a test-day model using a fourth-order Legendre polynomial  of genetic variance explained by the first three principal components (PC) using leg4.Additional file 2: Figure S1. Coordinates of the first three eigenfunctions of the test-day genetic covariance matrix computed with leg4 in Saanen goats.Additional file 3: Table S2. Pearson correlation coefficients (ρ) between observed and predicted values with the complete and reduced models. MT: multitrait model; legx: Legendre polynomial of order x; legxRz: Legendre polynomial of order x reduced to order z.Additional file 4: Figure S2. Evolution of residual variances with DIM for protein content in Alpine goats .Additional file 5: Table S3. Whole-lactation heritabilities from the LACT model and heritabilities (h_wl) calculated as in [ed as in  in SaaneAdditional file 6: Figure S3. Daily estimated heritabilities for protein content in Saanen goats .Additional file 7: Figure S4. Evolution of heritabilities with DIM in Alpine goats with the leg4 model.Additional file 8: Figure S5. Genetic correlations of protein contents between DIM 111 and other DIM in Saanen goats.Additional file 9: Figure S6. Genetic correlations between DIM 40 and other DIM in Alpine goats from the leg4 model.Additional file 10: Figure S7. Genetic correlations between DIM 40 and other DIM in Saanen goats from the leg4 model.Additional file 11: Figure S8. Contribution to daily protein content of one genetic standard deviation for PC1 and PC2.Additional file 12: Figure S9. EBV of Alpine goats added to the mean production of the population for protein content .


N = 51,564) of a non-social trait related to autism, systemising, defined as the drive to analyse and build systems. We demonstrate that systemising is heritable and genetically correlated with autism. In contrast, we do not identify significant genetic correlations between social autistic traits and systemising. Supporting this, polygenic scores for systemising are significantly and positively associated with restricted and repetitive behaviour but not with social difficulties in autistic individuals. These findings strongly suggest that the two core domains of autism are genetically dissociable, and point at how to fractionate the genetics of autism.The core diagnostic criteria for autism comprise two symptom domains – social and communication difficulties, and unusually repetitive and restricted behaviour, interests and activities. There is some evidence to suggest that these two domains are dissociable, though this hypothesis has not yet been tested using molecular genetics. We test this using a genome-wide association study ( Varun Warrier et al. report a genome-wide association study of systemising, a non-social trait associated with autism. They find 3 loci associated with systemising and show that this trait has no significant genetic correlations to social phenotypic measures, demonstrating that the social and non-social aspects of autism are genetically distinct. Multiple lines of evidence suggest that these two domains are dissociable3. First, factor and principal component analysis of autism and autistic traits have predominantly identified two factors—a social and a non-social factor9. Second, investigations of autistic traits in large cohorts have demonstrated a positive phenotypic correlation between different social traits and different non-social traits separately, but only a limited correlation between social and non-social traits12. Third, twin genetic correlations between social and non-social symptom domains in autism are low, although both social and non-social trait domains are highly heritable in neurotypical14 or autistic twins15. Fourth, difficulties in social and non-social domains can occur independently of each other17, which has been used to subgroup individuals on the spectrum based on the two domains18. This suggests that the genetic and phenotypic architecture of autism consists of at least two broadly dissociable domains. This has implications for genetic, biological, and clinical studies of autism, since most studies have investigated autism as if it is a unitary condition3. The idea that social and non-social symptom domains are dissociable is unsurprising given their very different nature, and very different underlying neurology and cognitive processes: one related to interpreting animate motion and mental states (theory of mind) and the other related to recognising inanimate objects, events or patterns (systemising)3. Nevertheless, a diagnosis of autism is only given when the social and non-social symptom domains cluster together.The core diagnostic criteria of autism comprises two symptom domains: difficulties in social interactions and communication , and unusually repetitive and restricted behaviour and stereotyped interests N < 5K)25. This has precluded a detailed molecular genetic investigation of the social and non-social domains associated with autism. Given currently available sample sizes with phenotypic information, investigating the genetics of the social and non-social domains in autistic individuals is difficult. However, several studies have demonstrated that the underlying liability for autism is normally distributed in the general population29. Factor analyses have failed to identify discontinuities between clinical autism and autistic traits in the general population30. Autistic traits are heritable33, are elevated in family members of autistic individuals compared to the general population35, and are transmitted intergenerationally37. Factor analysis of autistic traits measures have also identified two different factors in both the general population and autistic individuals—one linked to the social domain, and another linked to the non-social domain, mirroring the factor structure of clinical autism domains38. Studies have further demonstrated moderate to high shared genetics between the extremes of the liability distribution and the rest of the distribution41. One twin study investigated the bivariate genetic correlation between research and clinical autism diagnosis and autistic traits and identified high genetic correlations (0.7 < rg < 0.89)42. Validating this, studies have identified modest shared genetics between autism and autistic traits45. Taken together, there is considerable evidence to suggest that autism represents the extreme end of the autistic traits continuum.However, to date, there has been limited molecular genetic evidence in support of this dissociability hypothesis, partly due to the limited large-scale research on the genetics of social and non-social domains. Most genetic research into the social and non-social domains has been primarily through linkage and genome-wide association studies (GWAS) in relatively small samples of autistic individuals and the general population  (see ‘Methods’ section). Systemising involves identifying input-operation–output (or if-and-then) relationships in order to analyse and build systems, and to understand the laws that govern specific systems49. The hyper-systemising theory of autism proposes that autistic individuals, on average, have superior attention to detail, and a stronger drive to systemise compared to individuals in the general population49. This has been validated in several studies51 including a recent study in more 650,000 individuals, including 36,000 autistic individuals12. Several lines of evidence suggest that autistic individuals have at least intact if not superior systemising. The idea was noted in the earliest papers describing autism by both Asperger52 and Kanner53, although these early papers do not use the term ‘systemising’ but instead comment on strong interests in pattern recognition, the need for order and predictability, excellent memory for facts, and a strong focus on objects and understanding how things work. Further, autistic adults, on average, score higher on the SQ-R compared to individuals in the general population51, a profile also observed in autistic children54. Several items in the SQ-R specifically measure circumscribed interests and insistence on sameness, two of the items mentioned in the DSM-5, and several of these items map onto items on the Autism Spectrum Quotient (AQ), a well validated measure of autistic traits27 ; and second, to investigate whether social and non-social traits related to autism, measured in the general population, are genetically dissociable.N = 51,564) measured using the SQ-R. Following this, and using data from GWAS of social traits genetically correlated with autism 46, and GWAS of social relationship satisfaction55 measured using friendship  and family relationship  satisfaction scales), we investigated whether the social and non-social traits related to autism are genetically dissociable in the general population. A flow chart of the study design is provided in Fig. We first conducted a GWAS of systemising N = 5,564 measN = 51,564) using scores from the SQ-R10. Scores on the SQ-R were normally distributed, with a mean of 71 ± 21 out of 150. As hypothesised based on previous research51, males (76.5 ± 20), on average, scored higher than females (65.4 ± 20.6)  and rs1559586 on chromosome 18 (P = 4.78 × 10−8). The third significant locus was in the males-only GWAS . rs8005092 and rs1559586 lie in regions of high genetic recombination. Linkage-disequilibrium score regression (LDSR) intercept suggested that there was minimal inflation due to population stratification . Similarly, four out of five SNPs tested had concordant effect direction in the GWAS for autism . For these three phenotypes, we additionally assessed effect direction concordance using binomial sign test at less stringent P-value thresholds in the SQ-R GWAS, after LD-based clumping . Binomial sign test was statistically significant at three of the four P-value thresholds  for all three phenotypes but not statistically significant at P = 1E−4, presumably due to the low statistical power (Supplementary Data P < 1 × 10−6) in a GWAS (N = 1981) of ‘insistence on sameness’, a phenotype similar to systemising (see ‘Methods’ section). Four out of five SNPs had a concordant effect direction including the two SNPs with P < 5 × 10−8 in the non-stratified SQ-R GWAS .Systemising was measured in the 23andMe sample N = 5,564 usinP = 1.2 × 10−20). Despite small but significant sex differences in the SQ-R scores, there was no significant difference in P = 0.34) , suggesting a similar polygenic architecture between sexes. The per-SNP effect for the most significant SNPs was small, suggesting a highly polygenic architecture , suggesting a prominent role for regulatory and conserved genomic regions in systemising , demonstrating the relative specificity of the correlation of the SQ-R to autism. Notably, the absolute magnitude of the genetic correlation between autism and the SQ-R is similar to the genetic correlation between autism and self-reported empathy (measured using the Empathy Quotient (EQ60): rg = −0.27 ± 0.07) and scores on the Social and Communication Disorders Checklist (SCDC61): rg = 0.27 ± 0.13). Controlling for the genetic effects of educational attainment on the SQ-R GWAS using genome-wide inferred statistics (GWIS) (see ‘Methods’ section) attenuated the genetic correlation with autism only modestly, suggesting that the SQ-R scores are genetically correlated with autism independently of the genetic effects of education  . Clustering highlighted three broad clusters: a social cluster, a psychiatric cluster, and an intelligence cluster Fig.  and 12. 1, we hypothesised that the combination of significant negative genetic correlation with social traits (friendship satisfaction and empathy) and significant positive genetic correlation with SQ-R would be uniquely associated with autism (see ‘Methods’ section). Indeed, across the nine psychiatric conditions for which we had summary GWAS statistics, this combination was uniquely observed for autism , but not on the social and communication subscale of ADOS-G  after adjusting for multiple test . We validated this in 426 additional individuals of which 401 had a diagnosis of autism with RBS-R scores from the EU-AIMS LEAP, AGRE, and Paris cohorts. Here, we identified a concordant effect direction for polygenic scores of the SQ-R , although the results were not significant potentially due to the small sample size. Inverse-variance meta-analysis of the discovery and the validation cohorts marginally improved the significance of the association , and the results remained statistically significant . In a separate sample of 475 autistic individuals from the AGRE cohort, polygenic scores for the SQ-R were not associated with the social and communication subscale of ADOS-G . Meta-analysis of the two cohorts did not produce a statistically significant result  . We note that the lack of association between the polygenic scores for the SQ-R and the ADOS-G social and communication subscale is not indicative of absence of shared genetics, but rather indicative of lower shared genetics between the SQ-R and the ADOS-G social and communication subscale than that between the RBS-R and the SQ-R.Given that our current analysis focussed on the general population, we sought to investigate whether polygenic scores from the SQ-R were associated with social and non-social autism domains in 2221 autistic individuals from the Simons simplex collection (see ‘Methods’ section). We hypothesised that SQ-R may be significantly associated with the non-social domain in autism, but not associated with the social domain in autism. Polygenic scores for SQ-R were significantly associated with scores on the Repetitive Behaviour Scale-Revised (RBS-R) , which was not statistically different from 0. Given the small sample size, the genetic correlation is unlikely to be statistically significant. However, the effect was small and statistically less than 1 .Finally, to further validate the results in autistic individuals, we conducted bivariate genetic correlations on scores on the RBS-R and the ADOS-G social and communication subscale in 2989 individuals from the SSC, AGRE, EU-AIMS LEAP and Paris cohorts . Both the RBS-R Z-score of the 62. We identify high sign concordance of the top SNPs in genetically correlated traits, enrichment for active chromatin marks in fetal and adult brain, and significant polygenic score association with the RBS-R. Polygenic score analysis suggests that the shared genetics between systemising and the non-social domain of autism is considerably higher than the shared genetics between systemising and the social domain of autism. In addition, using a smaller sample of autistic individuals, we provide preliminary evidence that the social and non-social domains in autistic individuals have low shared genetics. Our results highlight the need to collect deeper clinical and cognitive information in autistic individuals to better understand the phenotypic heterogeneity in autism.Here we present, to our knowledge, the largest GWAS of a non-social trait related to autism in the general population—systemising, measured using the SQ-R. We demonstrate that systemising is heritable and genetically correlated with autism. Associated loci are enriched in genomic regions containing brain chromatin signatures and we identify three genome-wide significant loci, but these must be replicated in an independent cohort. Despite the modest sample size, our GWAS is well-powered to investigate genetic correlations between various phenotypes including social traits related to autism, as the 65, the largest GWAS of autism to date has identified fewer loci. One possible explanation is statistical signal-attenuation because of the underlying heterogeneity. However, this does not necessarily suggest that systemising, or the other individual trait domains are less complex. For instance, we observe similar 46, and the largest and most recent GWAS of autism57.Most studies model autism and autistic traits as a single phenotype. This has likely arisen because of the difficulties in recruiting and phenotyping sufficient numbers of autistic people. Our study suggests that both in the general population and in autistic individuals, social and non-social autistic traits and symptom domains are genetically dissociable. This may to some extent explain why, compared to GWAS of other psychiatric conditions of roughly similar sample sizes66. We hypothesise that the dissociation of the two domains will extend to other research modalities in studies of autism and autistic traits. It is important to note that, while our results demonstrate two broadly dissociable autistic trait domains in the general population and in autistic individuals, more research is needed to identify other potentially dissociable domains and to investigate whether this dissociability is driven by different designs of phenotypic instruments (e.g. self-report vs informant report). For example, our research does not make a distinction between communication and social interaction abilities, or between sensory difficulties and repetitive behaviours, and future molecular genetic studies may identify varying levels of overlap between these domains. The same principle applies to other research modalities  investigating the biology of autism and autistic traits. These different symptom domains of autism may contribute to different co-morbidities. Our results identify shared genetics between the social traits related to autism and psychiatric conditions such as schizophrenia and depression, but limited shared genetics between the SQ-R and these conditions.It is important to investigate whether these domains are dissociable in a larger cohort of autistic individuals and identify potential convergence of the two domains in gene expression networks in the developing brain. Our results confirm the need to rethink our understanding of autism as existing along a single dimensionThe current study included participants from 23andMe (primary GWAS - SQ-R), from ALSPAC ) and autistic individuals from the Simons Simplex Collection (SSC), the Autism Genetic Resource Exchange (AGRE), and the EU-AIMS LEAP and PARIS cohorts.68. All participants provided informed consent and answered surveys online according to a human subjects’ research protocol, which was reviewed and approved by Ethical & Independent Review Services, an external AAHRPP-accredited private institutional review board (http://www.eandireview.com). All participants completed the online version of the SQ-R on the 23andMe participant portal. Only participants who were primarily of European ancestry (97% European Ancestry) were selected for the analysis using existing methods69. Unrelated individuals were selected using a segmental identity-by-descent algorithm70. A total of 51,564 participants completed the SQ-R .Research participants in the GWAS of the SQ-R were from 23andMe and are described in detail elsewhere71. This study received ethical approval from the ALSPAC Law-and-Ethics Committee, and the Cambridge Human Biology Research Ethics Committee. Written informed consent was obtained from parent or a responsible legal guardian for the child to participate. Assent was obtained from the child participants where possible. We conducted a GWAS of scores on the SCDC in 5,421 individuals from ALSPAC.ALSPAC is a longitudinal cohort which recruited pregnant mothers in the Avon region of the UK. The ALSPAC cohort comprises 14,541 initial pregnancies from women in Avon resulting in a total of 13,988 children who were alive at 1 year of age. Children were enrolled in additional phases, described in greater detail elsewheren = 2221 unrelated autistic individuals) consists of simplex autistic families, and are described elsewhere72. The AGRE cohort  consists of multiplex autism families, details of which are provided elsewhere73. In addition, we included 401 individuals  from the EU-AIMS LEAP74 and Paris75 cohorts. Across all cohorts, we included only unrelated individuals, who were predominantly of European Ancestry as defined by genetic principal components (5 SD deviations above or below the mean of PC1 and PC2 from the HapMap CEU population).We included data from four cohorts to conduct polygenic score and bivariate genetic correlation analyses. The SSC  from the Nijmegen Biomedical Study (NBS) to provide support for the independent SNPs with 10. The SQ-R taps a variety of domains of systemising, such as interest in mechanical (e.g. car engines), abstract (e.g. mathematics), natural (e.g. the weather), motor (e.g. knitting), and collectible (e.g. stamp collecting) systems. There are 75 items on the SQ-R, with a maximum score of 150 and a minimum score of 0. Scores on the test are normally distributed10. The SQ-R has good cross-cultural stability and good psychometric properties with Cronbach’s alpha ranging from 0.79 to 0.94 in different studies76. Test–retest reliability available in a Dutch sample indicated high reliability of 0.79 (Pearson correlation)76. This was supported by another study in 4058 individuals which identified high internal cohesion77. Exploratory followed by confirmatory factor analysis using Rasch modelling suggests that the SQ-R is unidimensional77. A sex difference has been observed in multiple studies with males, on average, scoring significantly higher than females51. Criterion validity shows that the SQ-R has a modest but significant correlation with the mental rotation test , as well as its subscales78. Autistic individuals, on average, score higher on the SQ-R in multiple different studies79. Further, the SQ-R also predicts autistic traits, with a combination of the SQ-R and the Empathy Quotient predicting as much as 75% of the variance on the autism spectrum quotient, a measure of autistic traits10. The SQ-R has been validated using a short form in a very large population of 600,000 controls and 36,000 autistic individuals12.The primary phenotype for this study is the SQ-R, which was used to conduct a GWAS in participants from 23andMe. The SQ-R is self-report measure of systemising drive, or interest in rule-based patternsIn addition, we used the following secondary phenotypes: SCDC in ALSPAC, ADOS-G social and communication scores and the RBS-R in the other cohorts. We also used a single question which is a measure of ‘insistence on sameness’ in the NBS cohort.61. The questionnaire consists of 12 questions, with scores ranging from 0 to 24, and with higher scores reflecting difficulties in social interaction and communication. The SCDC has good internal consistency (0.93) and good test–retest reliability (0.81)61. The SCDC has reasonable specificity and sensitivity in distinguishing autistic from control individuals80. Previous research has demonstrated that the SCDC is genetically correlated with autism57. We conducted a GWAS of SCDC to investigate whether it is genetically correlated with SQ-R in this study. We used mother-reported SCDC scores on children aged 8. Although SCDC has been measured at different ages in the ALSPAC cohort, we chose SCDC scores measured at age 8 as this has the largest sample size and has high 19 (h2 = 0.24 ± 0.07).The SCDC is a questionnaire that measures difficulties in verbal and nonverbal communication, and social interaction including reciprocal social interaction81: 1 (used for children with little or no phrase speech), 2 (for children with non-fluent speech), 3 , and 4 . For this study, we used the raw totals of the scores from the social domain and the communication domain, combined. Scores for all four modules range from 0 to 24. The ADOS-G has high overall internal consistency, and high test–retest reliability for the social and communication subscales81. The choice for combining the social and communication domain scores were informed by factor analysis which suggested that the two domains contribute to one underlying factor82.We chose two measures of social and non-social traits in autistic individuals. For the social trait, we used the social and communication domain scores from the ADOS-G, a widely used instrument for diagnosing and assessing autism in four cohorts . Participants completed one of the following ADOS-G modulesr < 0.6) and a smaller range of scores (0–8) as it captures fewer repetitive and restrictive behaviour81. Hence, for this study, we used sores on the RBS-R83. The RBS-R is a measure developed to specifically measure restricted and repetitive behaviours in autistic individuals and captures stereotyped, self-injurious, sameness, compulsive, ritualistic, and restricted behaviour84, and has high inter-rater reliability and internal consistency84. The RBS-R comprises 43 questions with scores ranging from 0 to 3 for each item based on a Likert scale.In contrast to the social and communication domain, the restricted and repetitive behaviour domain of the ADOS-G has poor test–retest reliability . A total of 1,030,430 SNPs (including InDels) were genotyped or imputed. SNPs were excluded if: they failed the Hardy–Weinberg equilibrium test at P < 10−20; had a genotype rate of <90%; they failed the parent-offspring transmission test using trio data in the larger 23andMe research participant database; or if allele frequencies were significantly different from the European 1000 Genomes reference data . Phasing was conducted using Beagle (version 3.3.1)85 in batches of 8000–9000 individuals. This was followed by imputation against all-ethnicity 1000 Genomes haplotypes (excluding monomorphic and singleton sites) using Minimac286. Genetic association analyses were restricted to SNPs with a minor-allele frequency > 1%. After quality control, 9,955,952 SNPs (imputed and genotyped) were included in the GWAS.Details of genotyping, imputation and quality control in the 23andMe cohort are provided elsewhereP < 5 × 10−8. Leading SNPs were identified after LD-pruning using Plink (r2 > 0.8). Winner’s curse correction was conducted using an FDR-based shrinking87.Our primary analysis was an additive model of genetic effects and was conducted using a linear regression with age, sex, and the first five ancestry principal components included as covariates. In addition, given the modest sex difference, we also conducted sex-stratified analyses. SNPs were considered significant at a genome-wide threshold of R2 is the proportion of variance explained for SNPj. j, and We calculated variance explained by first standardising the regression estimates and then squaring the estimates. This is equivalent to:61 scores were calculated from children of the 90 s (ALSPAC cohort)71, in children aged 8. In total, SCDC scores were available on N = 7,825 children. From this, we removed individuals for whom complete SCDC scores were not available. After excluding related individuals and individuals with no genetic data, data was available on a total of N = 5,421 unrelated individuals.The SCDCP < 1 × 10−6), and had a minor-allele frequency < 1%, resulting in a total of 526,688 genotyped SNPs. Haplotypes were estimated using data from mothers and children using ShapeIT (v2.r644)88. Imputation was performed using Impute2 V2.2.289 against the 1000 genomes reference panel . Imputed SNPs were excluded from all further analyses if they had a minor-allele frequency < 1% and info < 0.8. After quality control, there were 8,282,911 genotyped and imputed SNPs that were included in subsequent analyses.Participants were genotyped using the Illumina® HumanHap550 quad chip by Sample Logistics and Genotyping Facilities at the Wellcome Sanger Institute and LabCorp (Laboratory Corportation of America) using support from 23andMe. Individuals were excluded based on gender mismatches, high missingness (>3%), and disproportionate heterozygosity. We restricted subsequent analyses to individuals of European descent (CEU), which were identified by multi-dimensional scaling analysis and compared with Hapmap II (release 22). Individuals were also removed if cryptic relatedness, assessed using identity by descent, was >0.1. Genotyped SNPs were filtered out if they had >5% missingness, violated Hardy–Weinberg equilibrium (P < 1 × 10−6), with minor-allele frequency < 0.01 and missing call rates > 2%. We further excluded individuals with genotype missing rates > 5%. The SCDC score was not normally distributed so we log-transformed the scores and ran regression analyses using the first two ancestry principal components and sex as the covariates using Plink 2.0 (ref. 90).Dosage data from BGEN files were converted using hard-calls, with calls with uncertainty > 0.1 treated as missing data. Post-imputation, we excluded SNPs that deviated from Hardy–Weinberg equilibrium (λGC was 1.013. We replicated the previously identified genetic correlation with autism57 (constrained intercept) using our SCDC GWAS . In addition, we also identified a negative genetic correlation between educational attainment58 and SCDC .The log-transformed SCDC scores  had a modest but significant 92 was used to calculate for inflation in test statistics due to unaccounted population stratification. Heritability was calculated using LDSR using the north-west European LD scores. Difference in heritability between males and females was quantified using:Z is the Z-score for the difference in heritability for a trait, P-values were calculated and reported as significant if P < 0.05.LDSR93. We restricted our analyses to the non-stratified analyses due to the high genetic correlation between the sexes and the low statistical power of the sex-stratified GWAS. We conducted gene-based association analyses using MAGMA94 within FUMA and report significant genes after using a stringent Bonferroni corrected P < 0.05. In addition, we conducted enrichment for tissue specific expression and pathway analyses within FUMA. For the significant SNPs, we investigated enrichment for eQTLs using brain tissues in the BRAINEAC and GTEx95 database within FUMA. We further conducted partitioned heritability for tissue-specific active chromatin marks and baseline functional categories using extended methods in LDSR96.For the primary GWAS , we conducted functional annotation using FUMAP < 0.01 that are located in the LD region (r2 > 0.6) with an index SNP. LD structure within a locus was constructed by calculating correlations between SNPs within a locus (1KG v20130502). CAVIAR97 was then applied to the summary association statistics and LD structure for each index SNP to generate potentially causal (credible) SNPs with a posterior probability of 0.95. In total, we identified 14 credible SNPs from the three GWS loci.We fine mapped three genome-wide significant loci  to obtain credible SNPs. First, we selected SNPs with 98. One locus (index SNP rs4146336) was mapped to two genes, LSAMP and PTMAP8, indicating that two credible SNPs (rs13066948 and rs11713893) located in this locus physically interact with these genes.For each locus, candidate genes were identified by mapping credible SNPs based on physical interactions in foetal brain as previously describedP-value < 0.05. For the primary genetic correlation analysis with SQ-R, we included psychiatric conditions102, personality traits105, measures of intelligence107, and social traits related to autism55 including scores on the SCDC, as previous research has investigated the phenotypic correlation between these domains and systemising112.For all phenotypes, we performed genetic correlation without constraining the intercept using LDSR. We identified significant genetic correlations using a Bonferroni adjusted 59, educational attainment58, tiredness113, neuroticism103, subjective wellbeing103, schizophrenia114, major depression102, depressive symptoms103, ADHD63, and chronotype115), have been previously reported to be significantly genetically correlated with autism out of 234 phenotypes tested using LDHub62 (P < 2.1 × 10−4). We excluded college degree from this list, as previous work has identified near perfect genetic correlation between educational attainment and college degree58. In addition, we included data from friendship satisfaction55, family relationship satisfaction55, systemising, and self-reported empathy46, all of which are also significantly genetically correlated with autism with P < 2.1 × 10−4. These four additional phenotypes were not included in the previous paper which investigated genetic correlations with autism. Details of sample sizes with PMIDs/DOIs are provided in Supplementary Data To understand the correlation between systemising and various phenotypes that have been genetically correlated with autism, we used GWAS data from 15 phenotypes including autism. 10 of these phenotypes  and SQ-R, self-reported empathy measured using the EQ46 and friendship satisfaction55. We chose friendship satisfaction and self-reported empathy as representative of social traits as these are the most relevant to the social domain of autism for which we had access to GWAS summary statistics. The EQ is a short, 40-item self-report measure of empathy, which has been widely used and has good psychometric properties120. In addition, differences in aspects of empathy compared to the neurotypical population have been widely reported in autism121, and empathy is one of the items in measures such as ADOS-G. Friendship satisfaction was selected as difficulties in making friends is listed as a criteria for an autism diagnosis in the DSM-51.To investigate whether the combination of negative genetic correlation social traits and positive genetic correlation for non-social traits is specific to autism, we conducted a genetic correlation between all psychiatric conditions for which we had access to summary GWAS statistics . Individuals were genotyped on three different platforms: Illumina Omni2.5, Illumina 1Mv3, or Illumina 1Mv1. Informed consent or assent was obtained from all participants. In addition, the research team obtained ethical approval from the Cambridge Human Biology Research Ethics Committee to access and analyse the de-identified data from the SSC. We conducted stringent quality control and imputation separately for each platform. The full pipeline is available here: https://github.com/autism-research-centre/SSC_liftover_imputation. Briefly, individuals were excluded if they had: a genotyping rate < 95%, excessive or low heterozygosity (less or more than 3 SD from the mean), mismatched reported and genetic sex, and families with Mendelian errors > 5%. We further removed SNPs that significantly deviated from Hardy–Weinberg equilibrium (P < 1 × 10−6), had Mendelian errors in >10% of the families, and SNPs that were not genotyped in >10% of the families. We then conducted multi-dimensional scaling using the HapMap3 phase 3 population using the unrelated individuals CEU and TSI populations as representatives of the European population. This was conducted only in the parents to retain unrelated individuals for multi-dimensional scaling. Genetic principal components were calculated using only SNPs with minor-allele frequency > 5%, and pruning the SNPs in Plink using an r2 of 0.2. We excluded families from further downstream analyses if either one the parents were greater or less than 5 standard deviations from the means of the first two genetic principal components calculated using only the unrelated individuals in HapMap3 CEU and TSI populations. Quality control was done using Plink v 1.9 and R. Phasing and imputation were conducted using the Michigan Imputation Server (https://imputationserver.sph.umich.edu/start.html) using the 1000 genomes Phase 3 v5 as the reference panel.We generated polygenic scores for SQ-R  in 2221 probands from the SSC (Discovery dataset). We downloaded genotype data from the SSC from SFARI base (https://choishingwan.github.io/PRSice/) for the SQ-R using the non-stratified GWAS data. We calculated the mean polygenic score for each of the 2221 probands in the SSC, after clumping SNPs using an R2 threshold of 0.1. Prior to generating polygenic scores, we confirmed that the probands were not related to each other using identity by descent PI-HAT > 0.15 as a relatedness cut-off. We used a P-value threshold of 1 as previous research on educational attainment, subjective wellbeing and social relationship satisfaction, all suggest that the maximum variance explained is at a threshold of 1 (refs. 103). This is expected for highly polygenic traits where many SNPs incrementally contribute to the variance explained124. Polygenic scoring was done using standardised scores on two different phenotypes as the dependent variable . We included sex, platform, the first 15 genetic principal components and standardised full-scale IQ as covariates. In addition, for the analysis of ADOS-G, we included the ADOS-G module as a covariate. Linear regression was conducted in R. A total of 135,233 SNPs were included in the polygenic score analyses after clumping and thresholding.Polygenic scores were generated using PRSice2 . The dependent variables were standardised scores on the RBS-R (N = 426) and the ADOS-G social and communication subscale (N = 475). IQ information was unavailable for most individuals, and hence we did not include IQ as a covariate. We combined the results of the EU-AIMS LEAP and Paris cohorts, and the AGRE dataset using inverse-variance weighted fixed-effect meta-analysis using the formula below:βi is the standardised regression coefficient of the polygenic scores, SEi is the associated standard error, and wi is the weight.To validate the polygenic scores, we conducted additional polygenic score analysis using data combined from the AGRE, EU-AIMS LEAP and Paris cohorts. We followed similar quality control and imputation procedures to the SSC cohort. Given that this dataset was a mix of related and unrelated individuals, we chose unrelated individuals using a genomic relationship matrix (GRM) as provided in GCTA (grm-cutoff 0.05)We conducted bivariate genetic correlation using GCTA GREML to test the genetic correlation between the ADOS social and communication domains and the RBS-R scores. We created a GRM after including autistic individuals from the SSC, AGRE, EU-AIMS LEAP, and Paris cohorts. We excluded SNPs and individuals using the same quality control pipeline as applied to the SSC dataset outlined in the section above. We further restricted our analysis only to SNPs with a minor-allele frequency > 1%. We excluded related individuals (–grm-cutoff 0.05) resulting in a total of 2989 individuals. Of this, 2652 individuals had scores for the ADOS social and communication domain and 2550 individuals had scores on the RBS-R. We included sex and the first ten genetic principal components as covariates.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary ItemsSupplementary Data 1-13Reporting Summary


Vaccinium macrocarpon) and other minor crops. Using biparental populations with different degrees of relatedness, we evaluated multiple GS methods for total yield (TY) and mean fruit weight (MFW). Specifically, we compared predictive ability (PA) differences between univariate and multivariate genomic best linear unbiased predictors . We found that MGBLUP provided higher predictive ability (PA) than GBLUP, in scenarios with medium genetic correlation (8–17% increase with corg~0.6) and high genetic correlations (25–156% with corg~0.9), but found no increase when genetic correlation was low. In addition, we found that only a few hundred single nucleotide polymorphism (SNP) markers are needed to reach a plateau in PA for both traits in the biparental populations studied (in full linkage disequilibrium). We observed that higher resemblance among individuals in the training (TP) and validation (VP) populations provided greater PA. Although multivariate GS methods are available, genetic correlations and other factors need to be carefully considered when applying these methods for genetic improvement.The development of high-throughput genotyping has made genome-wide association (GWAS) and genomic selection (GS) applications possible for both model and non-model species. The exploitation of genome-assisted approaches could greatly benefit breeding efforts in American cranberry ( A central goal of genetics is the identification of genotype-phenotype associations. Traditional quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS) are the primary tools for achieving such a goal. Thousands of genetic variants associated with traits of agronomic importance in economically important crops have been identified in the last century , oat (Avena sativa), maize (Zea mays), rice (Oryza sativa), rye , and barley (Hordeum vulgare)  and kiwifruit (Actinidia deliciosa) have made great progress on the implementation of these technologies  , introduced by Meuwissen et al. , has becVarious factors including training population (TP) size, marker density, heritability, magnitude of the LD, trait architecture, resemblance between TP and the validation population (VP), and the interaction of these factors, appear to be the principal forces driving the prediction accuracies of GS  in order to exploit the existent correlations in the data Mrode, . The firLike other woody perennial species, cranberry genetic improvement has been limited by the long interval needed to produce a cultivar  and mean fruit weight (MFW) in cranberry. We used both univariate and multivariate genomic best linear unbiased predictor  approaches together with traditional biparental populations commonly used in cranberry breeding. This research will allow us to understand the benefits of using genomic prediction using related individuals  with the aim of reducing the population-sizes of families to be planted for field evaluation (which is the most expensive part of a cranberry breeding program) while also increasing the number of families evaluated in the field trials. Also, we investigated two scenarios: low or null genetic correlation scenario (in our data the correlation between TY and MFW) and high genetic correlation scenario (in our data the correlation among multiple years). These two scenarios will allow us to investigate the usefulness of MGBLUP to improve the PA in our current GS efforts.N = 148), CNJ04  and GRYG . The parents of the three crosses are highly heterozygous genotypes frequently used in cranberry breeding programs. MQ and CQ are hybrids obtained after three generations of selection from wild materials, BGBLNL and GH1x35 are second-generation hybrids and Stevens is a first-generation hybrid from two wild selected parents. The CNJ02 and CNJ04 populations are planted and maintained at the Rutgers University P.E. Marucci Center, Chatsworth, NJ. The GRYG population is planted and maintained at Valley Corporation, Tomah, Wisconsin. CNJ02 and CNJ04 are half-sibs, and are not closely related with GRYG. Each genotype was clonally propagated and planted in the field using multiple cuttings in a defined 0.46 m2 (5 ft2) square plot to mimic commercial conditions.We used three cranberry biparental populations denominated CNJ02  in at least one of the three cranberry populations. Markers were positioned using the consensus genetic map (anchoring 6074 markers) obtained and described by Schlautman et al. . Only bi2 (1 ft2) metallic square set in each cranberry plot [0.46 m2 (5 ft2)] representing each genotype. Twenty five fruit for each genotype were randomly selected and weighted to calculate MFW as described in Georgi et al.  and mean fruit weight (MFW) were taken over a three-year period for 148 genotypes from the CNJ02 population 2011–2013) and 67 genotypes for CNJ04 (2012-2014); the GRYG population comprised 351 genotypes for which data was collected over a two-year period 2014-2015). TY was determined by harvesting and weighting all the fruit within a 0.09 m and 67 g. TY was Xβ + Zu + ε, where y was the response variable (TY or MFW), X and Z were incidence matrices for fixed and random effects respectively, β was the vector of fixed effects associated to the environment (year-location combination), u was the vector of random effects associated to rows ) to mimic a natural high genetic correlation scenario, whereas the across-environment predictions for both traits were used to build the multivariate response that in our data mimics a low genetic correlation scenario given the low genetic correlation found among these traits .From these models we obtained two types of predictions for the genotype effect, one across environments and another for each environment. The idea was to use the by-environment genotype prediction to fit a multivariate model using each environment genotype predictions as a response from the same trait , X and Z are incidence matrices for fixed and random effects respectively, β is the vector of fixed effects associated to the intercept only, u is the vector of random effects associated to genotypes [g ~ ] . The random effects (u1 … ui and ei) are assumed to be normally distributed with mean zero. X and Z are incidence matrices for fixed and random effects respectively. The distribution of the multivariate response and the phenotypic variance covariance (V) are:where yK is the relationship or covariance matrix for the kth random effect , and R = I is an identity matrix for the residual term. The terms, ukijand σeij the genetic (or any of the kth random terms) and residual covariance between traits “i” and “j” . For more details about the multivariate algorithm used in sommer please look at Covarrubias-Pazaran  genomic best linear unbiased predictor (GBLUP), which used the information from all markers coded in the additive relationship matrix, (2) GBLUP-AD, which included the additive and dominance relationships, (3) GBLUP-ADE, which included the additive, dominance, and epistatic relationships, and (4) Multivariate GBLUP, which exploits the covariance information among traits (or environments) at the level of genotypes and residuals. These models were fitted using the The first comparison among all models was made environment by environment and trait by trait  for each population using the by-environment genotype predictions as response variable. The MGBLUP for this first comparison used as the multivariate response the same trait-environment response than the univariate models plus data of an additional environment (high genetic correlation in our data). A second comparison among models was made using across-environment genotype predictions for each trait. The MGBLUP for this second comparison used as the multivariate response the across-environment genotype predictions for both traits (low genetic correlation scenario in our data).Ag for prediction in a kinship-based model [Ag = MM'/2 Σ pi(1-pi)] ]   for the D matrix computation. The model used has the typical mixed model form; y = Xβ + Zu + ε, where y is the response variable, X and Z are incidence matrices for fixed and random effects, respectively, β is the vector of fixed effects (intercept only), u is the vector of random effects associated to the genotypic effects with the corresponding relationship matrices. For the multivariate GBLUP model only the additive relationship matrix was used, and the model and distributions follow Covarrubias-Pazaran  with different number of markers equally spaced and covering the entire genome across the 12 LGs in cranberry .Given that repeated measures of TY and MFW were taken for the three biparental populations across different years (environments) in the 2011–2015 interval, genetic correlations between years within traits, and genetic correlation between traits were obtained using multivariate mixed models . Similarly, in the same population in 2012 we found an increase of PA from rTY−GBLUP = 0.15 to rTY−MGBLUP = 0.30 between GBLUP in MGBLUP. In the GRYG population in 2014 we found an increase from rTY−GBLUP = 0.26 to rTY−MGBLUP = 0.31 of GBLUP versus MGBLUP, and rTY−GBLUP = 0.31 to rTY−MGBLUP = 0.33 in 2015 (which had a genetic correlation of 0.62 ± 0.19) , and an increase of PA from rTY−GBLUP = 0.28 to rTY−MGBLUP = 0.55 in the year 2012. In the GRYG population in 2014 we found an increase from rTY−GBLUP = 0.36 to rTY−MGBLUP = 0.45 of GBLUP versus MGBLUP, and rTY−GBLUP = 0.34 to rTY−MGBLUP = 0.43 in 2015 (which had shown genetic correlation of 0.93 ± 0.08) . For TY, after 100 iterations of CV (complete sets of 5-fold), we found MGBLUP to be superior compared to GBLUP-A, GBLUP-AD, GBLUP-ADE in all environments and populations, except for CNJ04 where only 1 year of data was available and MGBLUP was not possible to evaluate Figure . For exaTo look at the effect in PA using multivariate models under a low genetic correlation, we used the across-environment genotype predictions as a response for each trait and population (where MGBLUP uses as multivariate response the predictions for both traits which hold a low genetic correlation in our data). We found no differences between GBLUP-A and MGBLUP across all populations, except for CNJ02 where MGBLUP was notoriously much less accurate to predict the genetic BLUP. For example, in CNJ02 the mean PA for GBLUP was 0.34 whereas for MGBLUP was 0.09 for MFW. For TY we obtained a PA of 0.21 for GBLUP and 0.01 for MGBLUP. In the other populations MGBLUP did as well as GBLUP. For example in GRYG population (biggest population), both GBLUP and MGBLUP had a PA of 0.44 for MFW, and 0.3 for TY  with different number of markers equally spaced and covering the entire genome .Multivariate BLUP models were originally proposed in animal breeding to model the genetic correlation among traits, longitudinal data and to model genotype by environment interactions in order to exploit the existent correlations in the data Mrode, . Since tN = 148) and GRYG (N = 351) biparental populations, the genetic correlation between these two traits was equal to zero (considering the standard error), which shows the potential effect in the PA of multivariate GS under a low genetic correlation scenario, where the use of a non-correlated trait does not help and even adds noise to the predictions. For CNJ04 the genetic correlation was different than zero and positive, but with a high SE (Table N = 67) could be an impediment for the correct estimation of the genetic correlation and conclusions based on this small population should be considered carefully.Additionally, we also calculated the genetic correlation between MFW and TY using across-environment genotype predictions in the three biparental populations Table . The anaSE Table . The facThe fact that the genetic correlation between TY and MFW was practically zero -if we consider the estimate and its standard error- is encouraging for breeding purposes given that this means that selection of large berries does not have a positive or negative effect in the final yield and vice versa. Therefore, our data indicates that high-yielding, large-berry varieties could be successfully developed, which is the case in some modern cranberry cultivars recently developed. Even though, our study does not represent the entire germplasm variability available in breeding programs, our study provides an understanding, based on three-biparental populations, of the genetic correlations among TY and MFW and the genetic correlation among years for such traits in cranberry.Genomic selection (GS), first introduced by Meuwissen et al. , has qui2 as expected given that the PA and the h2 are intrinsically connected. We found the same relationship in the PA for TY and MFW  on average among the three biparental populations, which confirming that few markers are required to have enough LD with the causal variant to capture marker effects in the GS model in biparental populations , half-sib individuals from CNJ04, and full-sib individuals from the CNJ02, we found greater PAs as the TP was more related to the VP  due to the full LD typically present in biparental populations. This implies that in structured populations , such as those used in the cranberry breeding programs, a medium marker density is enough to reach maximum PA. The conformation of the TP and its resemblance with the VP were shown to be decisive factors in achieving maximum PA. In addition, we were particularly interested in testing the advantages of using multivariate compared to univariate GBLUP, and the former was shown to provide a positive impact in the PA when the genetic correlation among the responses was high , and to have a negative effect in the PA when the correlation was close to zero. We conclude that the use of multivariate methods to select plants simultaneously for different traits and to predict traits of low heritability should be considered in cranberry breeding, as well as in other fruit crops and understudied species.GC-P, BS, and LD-G performed the analysis and write the manuscript. EG, JP, and JJ-C grew the experimental units and maintained the populations and review the manuscript. NV, MI, JZ, and GC-P planned the research and write the manuscript. All coauthors reviewed and approved the final version of this manuscript.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. The reviewer PRM and handling editor declared their shared affiliation at the time of the review.