Fr-A2 with genomic prediction for winter hardiness and frost tolerance, and to assess the potential of combining both measures with a genomic selection index using a high density marker map or a reduced set of pre-selected markers. Cross-validation within two training populations phenotyped for frost tolerance and winter hardiness underpinned the importance of Fr-A2 for frost tolerance especially when upweighting its effect in genomic prediction models, while a combined genomic selection index increased the prediction accuracy for an independent validation population in comparison to training with winter hardiness data alone. The prediction accuracy could moreover be maintained with pre-selected marker sets, which is highly relevant when employing cost reducing fingerprinting techniques such as targeted genotyping-by-sequencing. Genomic selection showed thus large potential to improve or maintain the performance of winter wheat for these difficult, costly, and laborious to phenotype traits.Winter hardiness is a major constraint for autumn sown crops in temperate regions, and thus an important breeding goal in the development of new winter wheat varieties. Winter hardiness is though influenced by many environmental factors rendering phenotypic selection under field conditions a difficult task due to irregular occurrence or absence of winter damage in field trials. Controlled frost tolerance tests in growth chamber experiments are, on the other hand, even with few genotypes, often costly and laborious, which makes a genomic breeding strategy for early generation selection an attractive alternative. The aims of this study were thus to compare the merit of marker-assisted selection using the major frost tolerance QTL  Winter hardiness in wheat is a complex trait that is strongly influenced by a multitude of environmental factors such as the presence of a snow cover, soil fertility, soil heaving or ice encasement, as well as biotic factors like disease pressure or insect damage . Toleran50)  , recover50) , or expo50) . The tes50)  for indu50) . Such frVrn-A1, Vrn-B1, and Vrn-D1 located on the chromosome 5 group both for vernalization response and frost tolerance in wheat  from a commercial breeding program that were phenotyped for frost tolerance in 2017 and for their winter hardiness in 2012 and 2018. The lines were derived from 311 different crosses and came from three distinct breeding cycles of the breeding program. A subpopulation of 181 of these lines was scored for winter hardiness in two trial locations in Austria 2012 on a 1–9 scale with 1 designating a very good winter survival, i.e., a dense plant stand, while 9 referred to a complete winter kill of the plants in a given field plot in early spring. The same scoring system was used for the winter hardiness assessment of another subpopulation of 110 lines in one Eastern Canadian location 2018. The breeding lines were tested in yield plots at all individual trial locations. The F5:7 and doubled haploid lines were replicated twice and randomized together with the unreplicated F4:6 lines in a partially replicated row-column design to allow a correction for spatial trends in the experimental fields. The growing season in all trial locations with winter hardiness data was characterized by periods of very low temperatures with and without snow cover (−1), protein content (%), flowering date (days), and plant height (cm) that were available both from low temperature stressed conditions in Eastern Canada as well as under non-stressed conditions from multiple trial locations in Central Europe. The plant material in this study comprised a total of 504 FA third subpopulation of 213 lines was finally assessed for their frost tolerance in a climate chamber experiment at the Agricultural Research Institute of the Hungarian Academy of Sciences in Martonvásár (Hungary) following the protocol outlined by The phenotypic data for frost tolerance was analyzed with a linear mixed model of the form:ij are the phenotypic observations of the frost damage in percent, µ is the grand mean, and bj is the random effect of the jth block, while the residual effect rij followed a normal distribution with i of the ith line was firstly modelled as random to estimate the genetic variance where yhe BLUEs . 4:6 breeding lines and replicated F5:7 or double haploid breeding lines was assumed in order to estimate heritabilities for each individual trial. Given that a trait was assessed in more than one trial, an across-trial analysis was carried out in the second stage with a linear mixed model of the form:The phenotypic data for winter hardiness and other agronomic traits from the individual trials 2012 and 2018 were firstly analyzed with various models correcting for row and/or column effects as well as autoregressive variance–covariance structure of the residuals , and theij are the BLUEs for the respective trait from the first stage, µ is the grand mean, and gi is the effect of the ith line. The effect of the jth trial tj was fixed, while the effect eij that incorporated both the trial-by-line interaction variance and the residual effect was assumed random and followed a normal distribution with ASReml for the R programming environment  marker analysis was carried out for the markers S2269949, S1077313, S1862541, and S1298957 that were previously reported to be associated with the copy number variation at the frost tolerance locus Fr-A2 on chromosome 5A  (CNV Fr-A2(W) (Leaf samples from the 504 lines were collected from a minimum of 10 plants and used for DNA extraction with the protocol outlined by osome 5A . The misFr-A2(S) , while SFr-A2(W) . Given tThe merit of genomic prediction for winter hardiness and frost tolerance was firstly assessed in a 100 times replicated resampling scheme with the field data from 2012 as well as separately with the measured frost tolerance in the climate chamber experiment 2017. For this purpose, a total of 130 lines were randomly sampled into a training population and 30 lines in a validation population within each of these datasets. The kinship between these lines was estimated by a genomic relationship matrix, which was computed according to the method described by W is a centered N×M marker matrix of the i lines with Wik = Zik + 1 – 2pk and pk being the allele frequency at the kth locus. Genomic estimated breeding values (GEBV) were afterwards derived by genomic best linear unbiased prediction (GBLUP) models that included the obtained genomic relationship matrix:where y is an N×1 vector of BLUEs obtained in the phenotypic analysis, GZ is a random effect design matrix for additive genetic effects, and Gu is an N×1 vector of additive effects with r followed a normal distribution N1 being an N×1 vector where all elements equal 1. Given the prior knowledge about the large influence of the Fr-A2 locus both on winter hardiness and frost tolerance, upweighting the effects of the CNV Fr-A2(S) and CNV Fr-A2(W) haploblocks was tested by modeling them as fixed effects in separate weighted genomic best linear unbiased prediction (WBLUP) models (where ) models :y is again an N×1 vector of BLUEs obtained in the phenotypic analysis and Gu an N×1 vector of additive effects, while Fr2M contained the coding of the haploblock alleles and βFr2 designated the effect of either CNV Fr-A2(S) or CNV Fr-A2(W).where Fr-A2 locus, it was of further interest to investigate the possibility to derive a reduced set of markers that would largely maintain the prediction accuracy of the previous models for designing custom SNP chips or targeted GBS. For this purpose, genome-wide association mapping was conducted within each of the training populations of 130 lines based on a linear mixed model following  model:where α is the fixed marker effect with the corresponding incidence matrix MZ contained the marker codings of the 1–21 markers, and the random marker effects in the vector uM were assumed to follow a normal distribution sommer  could be achieved for all traits that were assessed in the field trials  as by a genomic prediction that featured more than a thousand markers (rGS = 0.527). A similar observation could be made for the prediction of frost tolerance, where marker-assisted prediction with preselected markers performed merely slightly worse (rMAS = 0.563) than genomic prediction (rGS = 0.585). The latter could be additionally improved by modelling the haploblock CNV Fr-A2(S), associated with the copy number variation at the Fr-A2 locus, as a fixed effect (rwGS = 0.626). Modelling a haploblock suggested by CNV Fr-A2(S) explained a larger proportion of the genetic variance (ρG = 20.1%) than its component markers S2269949 (ρG = 16.3%) and S1077313 (ρG = 13.0%). The haploblock CNV Fr-A2(S) suggested by G = 12.6%) than its component marker S1862541 (ρG = 13.9%) in the study at hand, most likely due to ambiguous allele calls for the other involved marker S1298957 within the investigated breeding germplasm.The merit of genomic prediction was firstly assessed by using a resampling scheme within the subpopulations tested for winter hardiness 2012 and frost tolerance 2017. The prediction accuracy for genomic selection was substantially higher than the one for marker-assisted selection with 21 randomly chosen markers in both datasets Figure 2.CNV Fr-A2(S) could furthermore be validated in an independent validation using the winter hardiness data collected in Eastern Canada 2018 (CNV Fr-A2(S) in the prediction models increased the prediction accuracy merely marginally (rwGS = 0.592) in comparison to the basic GBLUP model (rGS = 0.588) when training models with frost tolerance data. The accuracy of predictions based on winter hardiness was generally lower (rGS = 0.398) but could be improved in a WBLUP model (rwGS = 0.410). Combining GEBVs for both traits by a genomic selection index of the respective WBLUP models resulted furthermore beneficial in the highest prediction accuracy among the investigated models (rGS = 0.596). Additionally, the coefficient of variation for the repeated estimates of the prediction accuracy was reduced in the last case, indicating that employing a genomic selection index might lead to more stable and robust performance predictions for winter hardiness. The same pattern could be observed for the marker-assisted prediction with a subset of 21 preselected markers, with which the genomic selection index was nearly as accurate as with the entire set of 1,413 markers available in this study (rMAS = 0.527). It has to be noticed though that the marker subsets mostly differed between the training populations phenotyped either for winter hardiness and frost tolerance so that the actual number of employed markers varied in this scenario between 39 and 42. Nevertheless, the result gave a strong indication that it is feasible to drastically reduce the marker number and at the same time maintain a high prediction accuracy.The effect of the haploblock Fr-A2 has been established as a major column in the genetic architecture of winter hardiness and frost tolerance in wheat  was likewise larger than by their component markers in the study at hand, especially for the subpopulation assessed in the climate chamber experiment 2017 (ρG = 20.3%). The merit of the haploblock CNV Fr-A2(S) was though much smaller in explaining the genetic variance for winter hardiness in the field trials from 2012 (ρG = 3.0%), but again larger in the independent validation population tested in Eastern Canada 2018 (ρG = 9.2%), which suggests a complex interaction of multiple factors determining winter survival in these trials aside from frost tolerance. Genomic prediction with genome-wide distributed markers resulted thus in higher prediction accuracies than marker-assisted prediction with few significant markers as reported beforehand  as fixed effects into the prediction models was rather small in the independent validation, while the prediction accuracy was generally lower for this scenario (Nevertheless, the advantage of integrating the haploblock scenario Figure 3scenario . A lowerscenario  that migscenario . Updatinscenario , combiniThe benefit of this method was particularly apparent in the marker-assisted prediction with preselected markers Figure 3Considering multiple traits simultaneously can thus be advantageous for developing an optimal marker set, but it is certainly necessary for identifying breeding lines with a desired combination of traits, which is often challenging, e.g., in the presence of negative trade-offs between major agronomic traits like grain yield and protein content . RegardiFr-A2 locus and combining predictions in a genomic selection index. The prediction accuracy could moreover be maintained with reduced sets of preselected markers, which is of high relevance when employing cost-reducing fingerprinting techniques such as targeted GBS. A genomic selection of either the best performing lines or a negative selection against the least winter hardy can thus be routinely conducted every year, whereas the absence or irregular occurrence of winter damage in field trials impedes an efficient phenotypic selection for winter hardiness. The large advantage of implementing genomic selection is therefore the availability of information about line performance when conducting selection decision in early generations, which can be used either to maintain or improve this agronomic trait depending on the importance of winter hardiness in a given breeding program.The focus of this study laid on genomic breeding for winter hardiness and frost tolerance in bread wheat. Genomic prediction showed large potential for the selection of these difficult, costly, and laborious to phenotype traits especially when upweighting the effect of the copy number variation at the Genomic predictions for winter hardiness and frost tolerance allow a yearly selection in 14 early generations for these difficult, laborious and costly to phenotype traits.The datasets for this manuscript are not publicly available due to commercial interests of the wheat breeding program of Saatzucht Donau GesmbH. & CoKG in which they were generated.The authors declare that the experiments comply with the current laws of Austria.SM wrote the manuscript and analyzed the data. JH and CA supported in the statistical analysis. JH and VS conducted the Fr-A2 marker analysis. FL, BP, and ES designed the field trials and collected the phenotypic data in the field. FL and HB initiated and guided through the study. All authors read and approved the final manuscript.This research was funded by the “Beyond Europe” FFG project Canadian Hard Red Winter Wheat breeding by genomic tools, lab and field experiments.FL, CA, and BP were employed by the company Saatzucht Donau GesmbH. & CoKG. ES was employed by C&M Seeds.The remaining 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.


Triticumaestivum L.) based on 14,646 SNPs by six multi-locus models . Our results showed that 328 significant quantitative trait nucleotides (QTNs) were identified in total . Among them, 66 were repeatedly detected by more than two models, and 155 QTNs appeared only in one model, indicating the reliability and complementarity of these models. We also found that the number of significant QTNs for different FAAs varied from 8 to 41, which revealed the complexity of the genetic regulation of metabolism, and further demonstrated the necessity of the multi-locus GWAS. Around these significant QTNs, 15 candidate genes were found to be involved in FAA biosynthesis, and one candidate gene  was functionally identified to influence the content of tryptamine in vitro. Our study demonstrated the power and efficiency of multi-locus GWAS models in crop metabolome research and provided new insights into understanding FAA biosynthesis in wheat.Genome-wide association studies (GWAS) have been widely used to dissect the complex biosynthetic processes of plant metabolome. Most studies have used single-locus GWAS approaches, such as mixed linear model (MLM), and little is known about more efficient algorithms to implement multi-locus GWAS. Here, we report a comprehensive GWAS of 20 free amino acid (FAA) levels in kernels of bread wheat ( Arabidopsis  and the stringent criterion of the significance test (Genome-wide association studies (GWAS) have largely been applied to the genetic dissection of complex traits in plants. With the landmark GWAS study of 107 phenotypes in bidopsis , numeroubidopsis , salinitbidopsis , male inbidopsis , floret bidopsis , and thebidopsis . Of thesbidopsis . Howevernce test . To addrnce test , ISISEM-nce test , pLARmEBnce test , and pKWnce test . Becausence test .Plants produce a vast array of metabolites that provide nutrition and medicine for humans . UnravelTriticumaestivum L.) is one of the most important crops worldwide and provides approximately 20% of the energy, protein and dietary fiber consumed for human , was described as before  < 0.05 and missing data >20% were excluded from further analysis  5,500 V; source temperature 500°C; ion source gas I (GSI), gas II (GSII), curtain gas (CUR) were set at 55, 60, and 25.0 psi, respectively, the collision gas (CAD) was high. A specific set of MRM (multiple reaction monitoring) transitions were monitored for each FAA (Supplementary Table S2), each MRM transition was obtained with a 5 ms pause time and 5 ms Dwell time, data were processed by Analyst 1.5.1 software, peak areas were integrated using a IntelliQuan algorithm. Endogenous concentrations of FAAs were quantified by calculating the peak area in comparison to standard curves obtained from authenticated standards (purchased from Sigma-Aldrich). Calibration curves were drawn by plotting at least four different concentrations of each FAA standard according to the peak area  for MLM.Free amino acid levels were simultaneously studied with a single-locus GWAS model (MLM) and six multi-locus GWAS models. The single-locus model was implemented by FaST-LMM program , while ms and ȳ are the standard deviation (SD) and the mean of each FAA in the population, respectively. Spearman’s rank correlation coefficient was used to calculate the correlation between each pair of FAAs, and statistical significance was obtained by using Student’s t-test.We used s/ȳ × 100 to calculate the values of coefficient variation  for each FAA, where TraesCS1D01G052500 was amplified with the primer using cDNA from Huaimai20 as a template. Clones were digested with BamH I/EcoR I and directionally ligated to the pre-digested pGEX-6p-1 vector. Error-free recombinant proteins were expressed in BL-21 (DE3) competent cells after induced by adding 0.1 mM isopropyl β-D-1-thiogalactopyranoside (IPTG) and growing continually for 12 h at 16°C. Cells were harvested and suspended in the lysis buffer  and lysed by high pressure. The crude extract was collected and clarified by centrifugation at 14,000 g for 1 h at 4°C, and the supernatant was stored at −80°C for future experiments.Full-length cDNA of in vitro enzyme assay for the role of TraesCS1D01G052500 (tryptophan as substrate) was performed in a total volume of 20 μl containing 100 ppm PLP and 50 μM substrate in 50 mM Tris-HCl buffer (pH 8.0). After incubating at 37°C for 30 min, the reaction was stopped by adding 60 μl of methanol. The reaction mixture was then filtered through a 0.2 μm filter (Millipore) before being used for LC-MS analysis.The standard We use the CLUSTALW (version 1.83) program to align the amino acid sequences and construct the neighbor-joining tree by MEGA5. Bootstrap values from 1,000 times are indicated at each node. Bar = 0.1 amino acid substitutions per site.E. coli, with 10–1,250 μM different tryptophan (Sigma) as substrates and a fixed concentration of 50 ppm PLP (Sigma) as co-factor. The kinetic parameters were calculated using Michaelis–Menten model . All reactions were run in duplicate and repeated twice.To determine the kinetic difference between TraesCS1D01G052500 and its homologs in rice (OsTDC1 and OsTDC3), their activities were measured using 50 ng of purified protein expressed from Figure 1). Aspartic acid, glutamic acid, alanine and serine were the most highly abundant FAAs, with average concentrations of 0.37, 0.31, 0.30, 0.30 nmol/mg, respectively, while tyramine, threonine, and tryptamine were the less abundant, with average concentrations of 0.005, 0.02, 0.03 nmol/mg, respectively (Supplementary Table S2). The content of each FAA varied widely within the association panel, with variation ranging from a 2.30-fold difference in tyrosine to a 30.36-fold difference in proline and with the genetic coefficient variation  ranging from 15.9 to 103.2, respectively . The relationships between 20 FAA values were evaluated by Spearman’s rank correlation, and strong positive correlations were identified between most of these FAAs, with the exceptions of tryptamine and tryptophan (Supplementary Table S3).To assess the phenotypic variation for FAAs in dry, mature wheat kernels, the absolute levels of 20 FAAs  were quantified using LC-MS/MS as previously described . VisualiLOD > 3.0 (Supplementary Table S4), and the numbers of QTNs for the above six models were 38, 8, 92, 45, 117, and 28 (Table 1), respectively. Of these QTNs, 66 were detected by at least two different models; some QTNs, such as the association between lysine and SNP BS00003585_51 on chromosome 2B , were simultaneously mapped by five different models (Supplementary Table S4). Only four significant SNP-trait associations were identified by the single-locus model (MLM) (Table 1), and could be also detected by some multi-locus models. Although 18 FAAs were found by FASTmrEMMA to be significantly associated with QTNs, the total number of QTNs is only 38, with an average of 2.1 QTNs per FAA. Comparatively, for the pKWmEB and ISISEM-BLASSO models, the average QTNs per trait reached 6.2 and 4.6, respectively (Table 1). The phenotypic variation explained by different loci varied from 0.1% (tyramine in pKWmEB) to 21.4% (aspartic acid in mrMLM), with an average of 5.6%. We also found that the same QTN shows different effects to explain the phenotypic variation in different models; for instance, the association between arginine and SNP BS00022811_51 on chromosome 7A  with the r2 ranged from 0.1% in FASTmrEMMA to 19.7% in pKWmEB (Supplementary Table S4).To dissect the genetic basis of natural variation for FAA levels in mature wheat kernels, GWAS was performed using seven different models simultaneously. In total, 328 significant QTNs were identified by six multi-locus models  at a critical threshold of Figure 2), indicating the complex genetic regulation of FAAs. The chromosomal distribution of all identified QTNs revealed that A genome had the greatest number of significant associations, while only few QTNs were detected in the D genome . Since QTNs were not distributed evenly on the chromosomes . The candidate genes underlying these QTN hotspots could include transcriptional factors, transporters or some other rate-limiting enzymes of the amino acid metabolic pathway.The number of significant QTNs also varied widely among different FAAs, ranging from 8 for tryptophan to 41 for tyramine , based on the wheat reference genome information (see footnote 2).Notably, the 328 significantly QTNs facilitated the assignment of candidate genes. To identify them, the flanking sequences corresponding to the SNP markers significantly associated with FAA levels were used in BLASTx search against NCBI databaseTraesCS1A01G390300 (encoding a putative glutamate receptor). The high homology (58% identity at amino acid level) between TraesCS1A01G390300 and the glutamate receptor gene AtGLR3.5 (TraesCS1A01G390300 is likely the candidate gene underlying this locus. The SNP RAC875_c1022_3059 was significantly associated with 7 FAAs (Supplementary Table S4), which is comprised a hotspot on chromosome 4A as mentioned above. The high sequence identity (61% at the amino acid level) between adjacently located gene TraesCS4A01G294100 (0.4 Mb to SNP RAC875_c1022_3059) and AtAPM1 , was assigned as the candidate gene underlying the content of asparagine. The associations were further supported by phylogenetic analysis .A significant QTN between the levels of glutamic acid and the SNP Excalibur_c35310_375 was identified on chromosome 1A; this SNP is located 0.5 Mb away from AtGLR3.5  suggestsd AtAPM1 , an aminTraesCS1D01G052500, which encodes a protein annotated as tryptophan decarboxylase, suggesting that TraesCS1D01G052500 catalyzes the key step of tryptamine biosynthesis. Similarly, TraesCS3B01G340000 (encoding a putative tyrosine decarboxylase) was assigned as the candidate gene underlying the levels of tyramine. The high sequence identities between TraesCS1D01G052500 and OsTDC1 .Levels of tryptamine were significantly associated (LOD = 3.8) with the SNP BS00012936_51 on chromosome 1D that is 1.0 Mb away from Table 2). We investigated the phylogenetic relationships among the AATs (or AAPs) by constructing the phylogenetic tree with a neighbor-joining algorithm based on the amino acid sequences of these candidate genes and a collection of nine reported genes . Closer examination of the phylogeny in clade III reveled that TraesCS5D01G031800 lies next to AtCAAT2, AtCAAT3, and AtCAAT4, three cationic amino acid transporters from Arabidopsis . Our analysis also placed TraesCS2B01G493000 and TraesCS7B01G093200 close to AtAAP1, AtAAP6, and AtAAP8 . Moreover, the high sequence identities between TraesCS3B01G253600 and AtGAT1 .Six candidate genes putatively annotated as amino acid transporters (AATs) or amino acid permeases (AAPs) were identified by mGWAS . To characterize the enzymatic properties of TraesCS1D01G052500, recombinant protein was expressed with an N-terminal glutathione S-transferase (GST) tag in E. coli BL-21 and the reaction product was confirmed by commercial standard with LC-MS . An obvious TDC activity showed for tryptophan, and its activity was not inhibited by tyrosine, indicating a high level of substrate specificity toward tryptophan (Supplementary Table S5). We further investigated the enzyme kinetics of TraesCS1D01G052500 and its rice homologs (OsTDC1 and OsTDC3), all of them displayed similar Kcat values for tryptophan (Supplementary Table S5), suggesting that the three proteins have similar TDC activities. Based on these results, we functionally identified TraesCS1D01G052500 as a decarboxylase that catalyzes the biosynthesis of tryptamine from tryptophan in wheat , which further confirmed the correctness of our GWAS results and the candidate gene assignment.As mention above, the association between Supplementary Table S2), indicating the complexity of the biosynthetic processes of FAAs . The levels of lysine  have huge phenotypic variation, with a CV (%) of 77.2, implying the existence of a large number of alleles with high genetic diversity in the wheat germplasms . Obviously, the limitations of mGWAS in wheat relate in part to the large size of the genome and in part to the limited availability of sets of genetic markers . These results indicated the power of these multi-locus methods. Furthermore, the common QTNs appeared in different models confirming the credibility of these multi-locus GWAS approaches.As usual, variation of primary metabolites tends to be controlled by many small-effect loci. To increase the detection power of mGWAS, six multi-locus models were applied in this study. Totally, 328 significant QTNs were identified, however, only 4 SNP-trait associations were found with the single-locus model (MLM) at Table 2). The existing knowledge of plant FAA pathways, the high sequence identities between them and known functions in rice and Arabidopsis further confirmed these candidate genes. Notably, the validation of TraesCS1D01G052500 was detected only by the pKWmEB model , further demonstrating the reliability and effectiveness of these multi-locus methods.Based on these QTNs identified by the six multi-locus methodologies, candidates that have not been identified previously can be explored by searching for a protein or protein cluster that is biochemically related to the associated FAAs encoded at these loci. As a result, our mGWAS has allowed the assignment of 15 candidate genes underlying FAA levels (in vitro. This study proved the power and reliability of multi-locus GWAS models in plant metabolome research and provided new insights into understanding FAA biosynthesis in wheat, which may facilitate metabolomics-based breeding for quality improvement.In this study, a comprehensive GWAS of 20 FAA levels based on 14,646 SNPs in bread wheat was performed by six multi-locus models. Among 328 significant QTNs, 66 were detected by at least two models, and 155 QTNs appeared only in one model. Fifteen candidate genes were assigned to FAA biosynthesis, and one candidate gene was functionally identified WC, YH, and ZH conceived the project and supervised this study. YP, HL, and JC performed most of the experiments. TS, DS, and CZ participated in preparation of the materials. WC and YP analyzed the data. WC wrote the paper. All the authors discussed the results and commented on the 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.


Facial shape was described using a recently developed extension of the dense-surface correspondence approach, in which the 3D facial surface was partitioned into a set of 63 hierarchically organized modules. Each variant was tested against each of the facial surface modules in a multivariate genetic association-testing framework and meta-analyzed. Additionally, the association between these candidate SNPs and five facial ratios was investigated in the Pittsburgh 3DFN cohort. Two significant associations involving intronic variants of SHBG were found: both rs12150660 (p = 1.07E-07) and rs1799941 (p = 6.15E-06) showed an effect on mandible shape. Rs8023580 (an intronic variant of NR2F2-AS1) showed an association with the total and upper facial width to height ratios . These results indicate that testosterone-related genetic variants affect normal-range facial morphology, and in particular, facial features known to exhibit strong sexual dimorphism in humans.Many factors influence human facial morphology, including genetics, age, nutrition, biomechanical forces, and endocrine factors. Moreover, facial features clearly differ between males and females, and these differences are driven primarily by the influence of sex hormones during growth and development. Specific genetic variants are known to influence circulating sex hormone levels in humans, which we hypothesize, in turn, affect facial features. In this study, we investigated the effects of testosterone-related genetic variants on facial morphology. We tested 32 genetic variants across 22 candidate genes related to levels of testosterone, sex hormone-binding globulin (SHGB) and dehydroepiandrosterone sulfate (DHEAS) in three cohorts of healthy individuals for which 3D facial surface images were available (Pittsburgh 3DFN, Penn State and ALSPAC cohorts; total  Differences in facial morphology between males and females are well documented, with major shape differences apparent in the jaw, lips, eyes, nose and cheek regions . AlthougSeveral studies showed a relationship between testosterone levels (either measured directly or through proxy) and facial morphology, using several different study set-ups and approaches. For example, the second to fourth digit ratio (2D:4D), which is a marker of prenatal androgen exposure, corresponds to measures of facial masculinity and sexually dimorphic facial features . SimilarThe amount of circulating testosterone present in the body is the result of many factors, including genetics. Genome-wide association studies (GWAS) have identified multiple variants that influence testosterone regulation in healthy adults , includi1. Genome-wide data was available for 8,952 subjects of the B2261 study which is titled “Exploring distinctive facial features and their association with known candidate genes.”The study sample consists of 7418 individuals belonging to three separate cohorts: the Pittsburgh 3D Facial Norms (3DFN) cohort , the Pen3D facial surface images were acquired using multiple stereophotogrammetry systems: the VECTRA H1 camera  and the 3dMDface system  were used for the Pittsburgh 3DFN and Penn State cohorts, and the Konica Minolta Vivid 900 laser scanner  was used for the ALSPAC cohort. The process of registration, quality control and segmentation is described in detail in 2, sex, weight, height, facial size and the first four principal components (PCs) of genetic ancestry using partial least-squares regression. The images were adjusted in each dataset separately. After the adjustment, segmentation was performed on all three datasets combined to develop multivariate facial phenotypes representing specific facial modules. Briefly, facial modules were defined by grouping vertices that are strongly correlated and connected using hierarchical spectral clustering. The strength of co-variation between the modules was defined using Escoufier’s RV coefficient  and the Laboratory Corporation of America , supported by 23andMe. Haplotypes were estimated using SHAPEIT2  and impu2 set at 0.2 in a pairwise manner, with a moving window size of 50 variants shifting five variants each step. Finally, related individuals were identified and removed when the proportion of identity by descent (IBD) was higher than 0.125. To control for population structure, self-identified non-European individuals were removed, and then genetic outliers (n = 15) were removed based on z-scores calculated in the first 10 PCs. Z-scores higher than six indicated outliers, who were subsequently removed after which PCA was computed again. These calculations were done using PLINK 1.9  less than 5% or more than 5% genotype data missing, were filtered out. Second, SNPs were pruned for linkage disequilibrium (LD) with rLINK 1.9 . After tSNPs reported to influence testosterone levels in the human body were identified from the literature. Since free circulating testosterone levels are variable and not easy to measure, proxy-measurements have been used to estimate the levels of free testosterone in the human body. Dehydroepiandrosterone (DHEAS) is the most prevalent circulating steroid in humans  and is aF-test approximation , after removing the effects of age, age2, sex, weight, height, facial size and the first four dimensions of ancestry using PLSR. Both the independent (SNP) as the dependent  variables were corrected for these covariates.Canonical correlation analysis (CCA) was used to test the effect of 32 SNPs on facial shape using the multivariate phenotypes derived as described above. CCA is a multivariate testing framework which extracts the linear combination of PCs from a facial module that has maximal correlation with the SNP being tested. The correlation was tested for significance by a Roa’s p-value was obtained with the Student’s T cumulative distribution function . The facial segments are overlapping and hierarchically constructed (thus not completely independent). Therefore we determined 37 independent segments by the Li and Ji method ).Since the direction of the effect through the face shape space determined by the CCA can and will differ across the three cohorts, we use a two-step procedure where CCA is performed in one cohort, after which the two other cohorts were projected onto the direction of the effect found in CCA. Thus, three meta-analyses were performed in a round-robin fashion with each of the three cohorts serving as the discovery set for the CCA and the other two serving as replication sets. Association tests were performed in the replication sets by projecting the PCs onto the loadings calculated in the discovery CCA, which returned a specific genetic effect score. As described in function . The p-vi method . Thirty-n = 4055) and females (n = 2892) separately, and in a strictly post-pubertal cohort .Analyses were repeated for males (n = 2297), including standard facial width-to-height ratios  . We therp-value cut-off was p < 0.00119 . For completeness, associations in subgroups of the total study population were also investigated: males only (n = 856), females only (n = 1323), and post-pubertal (>14 years) participants (n = 1750).A total of 42 of the 43 literature-identified SNPs associated to testosterone levels were available for testing in the Pittsburgh 3DFN cohort . All five ratios were adjusted by sex, age and body size before genetic analysis, then linear models were used to test genetic association between each phenotype and each SNP adjusting for the first four PCs of ancestry using PLINK. The Bonferroni-corrected significance p = 1.07E-07) and rs1799941 (p = 6.15E-06) , it is possible that only one of them is actually functional, or both of them may be in strong LD with yet another untested causal variant  and upper facial width-to-height . rs8023580 is an intronic SNP in NR2F2-AS1, a non-coding RNA showing high expression in the ovary. The complete results are shown in Supplementary Table NR2F2 gene has been implicated in testicular Leydig function, essential for male testosterone production , in particular, has been shown to correlate strongly with traits related to prenatal testosterone exposure, such as second-to-fourth digit ratio. In prior research, individuals with lower digit ratios  showed broader, more robust, and more protrusive mandibles  and facial width to height ratios (rs8023580), indicating that testosterone-related genetic variants affect normal-range facial morphology, and in particular, facial features known to exhibit strong sexual dimorphism in humans.http://www.ncbi.nlm.nih.gov/gap) at accession phs000949.v1.p1. The 3D facial images are available through the FaceBase Consortium (https://www.facebase.org/data/record/#1/isa:dataset/RID=14283). The participants making up the Penn State University dataset were not collected with broad data sharing consent. Given the highly identifiable nature of both facial and genomic information and unresolved issues regarding risk to participants, we opted for a more conservative approach to participant recruitment. Broad data sharing of these collections would thus be in legal and ethical violation of the informed consent obtained from the participants. This restriction is not because of any personal or commercial interests. Additional details can be requested from MS. The ALSPAC data will be made available to bona fide researchers on application to the ASLPAC Executive Committee.The datasets analyzed for this study are available through various sources. For the Pittsburgh dataset, the genotypic markers are available through the dbGaP controlled-access repository  approval was obtained at each recruitment site, and all participants gave their written informed consent before participation; for children, written consent was obtained from a parent or legal guardian. For the Pittsburgh sample, the following local ethics approvals were obtained: University of Pittsburgh IRB PRO09060553 and RB0405013; UT Health Committee for the Protection of Human Subjects HSC-DB-09-0508; Seattle Children’s IRB 12107; University of Iowa Human Subjects Office/IRB 200912764 and 200710721. For the Penn State sample, the following local ethics approvals were obtained: State College, PA (IRB 44929 and 4320); New York, NY (IRB 45727); Urbana-Champaign, IL (IRB 13103); Dublin, Ireland; Rome, Italy; Warsaw, Poland; and Porto, Portugal (IRB 32341); Twinsburg, OH (IRB 2503). For the ALSPAC sample, ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees.KI, HH, and PC performed the modular analyses. ML performed the ratio analysis. JR wrote the first draft of the manuscript under supervision of SW, MM, EF, and JS. PC, JR, EF, JS, SW, and MM conceptualized the design of the study, with valuable feedback of CH-S. JR, EF, JS, SW, and MM organized the PITT cohort, with sample collection supported by JH, GW, and LM. JW and MS organized the PSU cohort and imputed the PSU genetic data. SR coordinated the collection of the ALSPAC images. JR, HH, MS, and JW provided input throughout the analyses and the writing process. All authors contributed to manuscript revision, read and approved the submitted version.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.


FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44–66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding.In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients  is associated with disadvantageous outcomes in 32 out of 100 traits tested. However, precisely because they are rare, most of these variants have yet to be identified and their recessive impact on the global burden of disease is poorly understood. This is of particular importance for the nearly one billion people living in populations where consanguineous marriages are common2, and the burden of genetic disease is thought to be disproportionately due to increased homozygosity of rare, recessive variants5. Although individual recessive variants are difficult to identify, the net directional effect of all recessive variants on phenotypes can be quantified by studying the effect of inbreeding6, which gives rise to autozygosity .Given the pervasive impact of purifying selection on all populations, it is expected that genetic variants with large deleterious effects on evolutionary fitness will be both rare and recessive8 and consequently very large samples are required to study the phenotypic impact of inbreeding9. Here, we meta-analyse results from 119 independent cohorts to quantify the effect of inbreeding on 45 commonly measured complex traits of biomedical or evolutionary importance, and supplement these with analysis of 55 more rarely measured traits included in UK Biobank10.Levels of autozygosity are low in most of the cohorts with genome-wide dataFROH) correlates well with pedigree-based estimates of inbreeding11.We estimate FROH using standard methods and software12 for a total of 1,401,776 individuals in 234 uniform sub-cohorts. The traits measured in each cohort vary according to original study purpose, but together cover a comprehensive range of human phenotypes , arise when identical-by-descent haplotypes are inherited down both sides of a family. The fraction of each autosomal genome in ROH > 1.5 Mb , as well as reduced risk-taking behaviour  and increased disease risk . We show that the observed effects are predominantly associated with rare (not common) variants and, for a subset of traits, differ between men and women. Finally, we introduce a within-siblings method, which confirms that social confounding of FROH is modest for most traits. We therefore conclude that inbreeding depression influences a broad range of human phenotypes through the action of rare, recessive variants.We find that FROH. The within-cohort standard deviation of FROH is strongly correlated with the mean . Cohorts with high rates of consanguinity lie near the FROH = FIS line, since most excess SNP homozygosity is caused by ROH. In contrast, cohorts with small effective population sizes, such as the Amish and Hutterite isolates of North America, have high average FROH, often despite avoidance of mating with known relatives, since identical-by-descent haplotypes are carried by many couples, due to a restricted number of possible ancestors.As expected, cohorts with different demographic histories varied widely in mean FROH within each cohort, taking account of covariates including age, sex, principal components of ancestry and, in family studies, a genomic relationship matrix (GRM)  is associated with having 0.10 fewer children . This effect is due to increased FROH being associated with reduced odds of having any children  as opposed to fewer children among parents . Since autozygosity also decreases the likelihood of having children in the subset of individuals who are, or have been, married,  it appears that the cause is a reduced ability or desire to have children, rather than reduced opportunity. Consistent with this interpretation, we observe no significant effect on the likelihood of marriage   Fig. . All effFROH on a second group of traits, which capture risky or addictive behaviour. Increased FROH is associated with later age at first sex  and fewer sexual partners  but also reduced alcohol consumption , decreased likelihood of smoking , and a lower probability of being a self-declared risk-taker  or exceeding the speed limit on a motorway (p = 4.0 × 10−8). Conservative beliefs are likely to affect these traits, and are known to be confounded with FROH in some populations14, however, fitting religious participation as a covariate in UKB reduces, but does not eliminate the reported effects , a correlate of general cognitive ability  and an increase in the waist to hip ratio .A fourth group relates to body size. We replicate previously reported decreases in height and forced expiratory volumeFROH individuals report significantly lower overall health and slower walking pace, have reduced grip strength , accelerated self-reported facial ageing, and poorer eyesight and hearing. Increased FROH is also associated with faster heart rate , lower haemoglobin , lymphocyte percentage, and total cholesterol .The remaining effects are loosely related to health and frailty; higher FROH differs between men and women , the effect of men Fig. . For exa19. Earlier frequency-based estimators such as FSNP and FGRM20, made use of excess marker homozygosity23 and did not require physical maps. We performed both univariate and multivariate regressions to evaluate the effectiveness of FROH against these measures. The correlations between them range from 0.13 to 0.99 and are strongest in cohorts with high average inbreeding , equivalent to the offspring of first-degree relatives, are found to be, on average, 3.4 [95% CI 2.5–4.3] cm shorter and 3.1 [95% CI 2.5–3.7] times more likely to be childless than an FROH = 0 individual.For example, the effect of p < 0.004). In addition, a meta-analysis of the ratio p = 2 × 10−12).Next, we estimated FROH was estimated in up to 118,773 individuals in sibships , indicating a substantial fraction of these effects is genetic in origin. However, for both adoptees and siblings, the point estimates are less than one, suggesting that non-genetic factors probably contribute a small, but significant, fraction of the observed effects.Finally, the effect of 26, fewer have examined reproductive success as a function of individual inbreeding. Those that did are orders of magnitude smaller in size than the present study, suffer the attendant drawbacks of pedigree analysis, and have found mixed results29. Our genomic approach also reveals that in addition to socio-demographic factors and individual choice, recessive genetic effects have a significant influence on whether individuals reproduce. The discordant effects on fertility and education demonstrate that this is not just a result of genetic correlations between the two domains30.Our results reveal inbreeding depression to be broad in scope, influencing both complex traits related to evolutionary fitness and others where the pattern of selection is less clear. While studies of couples show optimal fertility for those with distant kinshiprG = 0.93; p < 10−100). Genetic correlations with ever-smoking and self-reported risk-taking are lower, but also significant: 0.23–0.27, p < 10−10. Age at first sex is strongly genetically correlated both with the fertility traits, (rG = 0.53–0.57), and number of sexual partners, ever-smoking and risk-taking30 (rG = 0.42–0.60).The effects we see on fertility might be partially mediated through a hitherto unknown effect of autozygosity on decreasing the prevalence of risk-taking behaviours. Significant effects of autozygosity are observed for self-reported risk taking, speeding on motorways, alcohol and smoking behaviour, age at first sexual intercourse and number of sexual partners. Independent evidence for a shared genetic architecture between risk-taking and fertility traits comes from analysis of genetic correlations using LD-score regression in UKB  can infer causality because, in the absence of population structure, genetic variants (SNPs) are randomly distributed between, and within, different social groups. However, this assumption does not hold in studies of inbreeding depression, where, even within a genetically homogeneous population, social groups may have differing attitudes towards consanguinity, and therefore different average FROH and, potentially, different average trait values. We therefore present a number of analyses that discount social confounding as a major factor in our results. Firstly, we show that the effects are consistent across diverse populations, including those where ROH burden is driven by founder effects rather than cultural practices regarding marriage. Effects are also consistent across a 20-fold range of FROH: from low levels, likely unknown to the subject, to extremely high levels only seen in the offspring of first-degree relatives. Secondly, we show that the effects of ROH are consistent in direction and magnitude among adopted individuals, and also for short ROH which are not informative about parental relatedness. Finally, we introduce a within-siblings method, independent of all confounders, that confirms a genetic explanation for most of the observed effects. Variation in FROH between siblings is caused entirely by random Mendelian segregation; we show that higher FROH siblings experience poorer overall health and lower reproductive success, as well as other changes consistent with population-wide estimates. Nevertheless, average effect sizes from both adoptees and siblings are 20% smaller than population-wide estimates, confirming the importance of accounting for social confounding in future studies of human inbreeding depression.Previous studies have shown that associations observed between Our results reveal five large groups of phenotypes sensitive to inbreeding depression, including some known to be closely linked to evolutionary fitness, but also others where the connection is, with current knowledge, more surprising. The effects are mediated by ROH rather than homozygosity of common SNPs, causally implicating rare recessive variants rather than overdominance as the most important underlying mechanism. Identification of these recessive variants will be challenging, but analysis of regional ROH and in particular using whole-genome sequences in large cohorts with sufficient variance in autozygosity will be the first step. Founder populations or those which prefer consanguineous marriage will provide the most power to understand this fundamental phenomenon.see Supplementary Data.FROH on 45 quantitative traits and to assess whether any of these effects differed significantly from zero. Previous work11 has shown that inbreeding coefficients are low in most human populations, and that very large samples are required to reliably estimate the genetic effects of inbreeding13. To maximise sample size, a collaborative consortium (ROHgen6) was established, and research groups administering cohorts with SNP chip genotyping were invited to participate. To ensure that all participants performed uniform and repeatable analyses, a semi-automated software pipeline was developed and executed locally by each research group. This software pipeline required cohorts to provide only quality-controlled genotypes (in plink binary format) and standardised phenotypes (in plain-text) and used standard software  to perform the analyses described below. Results from each cohort were returned to the central ROHgen analysts for meta-analysis.Our initial aim was to estimate the effect of 10. It was therefore decided to add a further 34 quantitative phenotypes and 21 binary traits to the ROHgen analysis. Many of these additional traits were unique to UKB, although 7 were also available in a subset of ROHgen cohorts willing to run additional analyses. In total, the effect of FROH was tested on 100 traits and therefore experiment-wise significance was defined as 5 × 10−4 (=0.05/100).During the initial meta-analysis, genotypes were released for >500,000 samples from the richly phenotyped UK Biobank (UKB)FROH for the individual arrays and FROH the intersection dataset was found to be very strong . Dividing studies using these criteria yielded 234 sub-cohorts. Details of phenotypes contributed by each cohort are available in Supplementary Data In total, 119 independent, genetic epidemiological study cohorts were contributed to ROHgen. Of these, 118 were studies of adults and contributed multiple phenotypes, while 1 was a study of children and contributed only birth weight. To minimise any potential confounding or bias caused by within-study heterogeneity, studies were split into single-ethnicity sub-cohorts wherever applicable. Each sub-cohort was required to use only one genotyping array and be of uniform ancestry and case-status. For example, if a study contained multiple distinct ethnicities, sub-cohorts of each ancestry were created and analysed separately. At minimum, ancestry was defined on a sub-continental scale  but more precise separation was used when deemed necessary, for example, in cohorts with large representation of Ashkenazi Jews. In case-control studies of disease, separate sub-cohorts were created for cases and controls and phenotypes associated with disease status were not analysed in the case cohort: for example, fasting plasma glucose was not analysed in Type 2 diabetes case cohorts. Occasionally, cohorts had been genotyped on different SNP genotyping microarrays and these were also separated into sub-cohorts. There was one exception (deCODE) to the single microarray rule, where the intersection between all arrays used exceeded 150,000 SNPs. In this cohort the genotype data from all arrays was merged since the correspondence between Data from 119 independent genetic epidemiology studies were included. All subjects gave written informed consent for broad-ranging health and genetic research and all studies were approved by the relevant research ethics committees or boards. PubMed references are given for each study in Supplementary Data All samples were genotyped on high-density , genome-wide SNP microarrays supplied by Illumina or Affymetrix. Genotyping arrays with highly variable genomic coverage  were judged unsuitable for the ROH calling algorithm and were not permitted. Imputed genotypes were also not permitted; only called genotypes in PLINK binary format were accepted. Each study applied their own GWAS quality controls before additional checks were made in the common analysis pipeline: SNPs with >3% missingness or MAF <5% were removed, as were individuals with >3% missing data. Only autosomal genotypes were used for the analyses reported here. Additional, cohort-specific, genotyping information is available in Supplementary Data In total, results are reported for 79 quantitative traits and 21 binary traits. These traits were chosen to represent different domains of health and reproductive success, with consideration given to presumed data availability. Many of these traits have been the subject of existing genome-wide association meta-analyses (GWAMA), and phenotype modelling, such as inclusion of relevant covariates, was copied from the relevant consortia . Further information about individual phenotype modelling is available in Supplementary Note 11. In summary, SNPs with minor allele frequencies below 5% were removed, before continuous ROH SNPs were identified using PLINK with the following parameters: homozyg-window-snp 50; homozyg-snp 50; homozyg-kb 1500; homozyg-gap 1000; homozyg-density 50; homozyg-window-missing 5; homozyg-window-het 1. No linkage disequilibrium pruning was performed. These parameters have been previously shown to call ROH that correspond to autozygous segments in which all SNPs (including those not present on the chip) are homozygous-by-descent, not chance arrangements of independent homozygous SNPs, and inbreeding coefficient estimates calculated by this method (FROH) correlate well with pedigree-based estimates (FPED)11. Moreover, they have also been shown to be robust to array choice6.Runs of homozygosity (ROH) of >1.5 Mb in length were identified using published methodsF) were calculated, FROH and FSNP. We also calculated three additional measures of homozygosity: FROH<5Mb, FROH>5Mb and FSNP_outsideROH.For each sample, two estimates of the inbreeding coefficient , i ϵ {1..n}, then FROH was then calculated asFROH<5Mb and FROH>5Mb are the genomic fractions in ROH of length >5 Mb, and in ROH of length <5 Mb (but >1.5 Mb), respectively, and the length of the autosomal genome is estimated at 3 gigabases (Gb). It follows from this definition thatFROH, a method of moments estimate of inbreeding coefficient was calculated34, referred to here as FSNP, and implemented in PLINK by the command–het.O(HOM) is the observed number of homozygous SNPs, E(HOM) is the expected number of homozygous SNPs, i.e. N is the total number of non-missing genotyped SNPs.Single-point inbreeding coefficients can also be estimated from individual SNP homozygosity without any reference to a genetic map. For comparison with FROH and FSNP are strongly correlated, especially in cohorts with significant inbreeding, since both are estimates of F. To clarify the conditional effects of FROH and FSNP, an additional measure of homozygosity,FSNPoutsideROH, was calculated to describe the SNP homozygosity observed outside ROH.NSNP_ROH is the number of homozygous SNPs found in ROH. Note that:20 as N is the number of SNPs, pi is the reference allele frequency of the ith SNP in the sample population and xi is the number of copies of the reference allele.A further single point estimator of the inbreeding coefficient, described by Yang et al.n samples, for each of the quantitative traits measured in that cohort, trait values were modelled byy is a vector (n × 1) of measured trait values, FROH on the trait, FROH is a known vector (n × 1) of individual FROH, b is a vector (m × 1) of unknown fixed covariate effects , X in a known design matrix (n × m) for the fixed effects, and ε is an unknown vector (n × 1) of residuals.In each cohort of m fixed covariates included in each model were chosen with reference to the leading GWAMA consortium for that trait and are detailed in Supplementary Data The y) was regressed on all fixed covariates to obtain the maximum likelihood solution of the model:For reasons of computational efficiency, it was decided to solve Eq.  in two sε′, which may be considered as the trait values corrected for all known covariates.All subsequent analyses were performed using the vector of trait residuals u is an unknown vector (n × 1) of polygenic effects with multivariate normal distribution of mean 0 and covariance matrix σg2A, where A is the genomic relationship matrix (GRM). In these related cohorts, a GRM was calculated using PLINK v1.9 and Grammar+ residuals of Eq.  were used in subsequent analyses.In cohorts with a high degree of relatedness, mixed-modelling was used to correct for family structure, although, because ROH are not narrow-sense heritable, this was considered less essential than in Genome-Wide Association Studies. Equation  becomes1FROH to obtain the maximum likelihood (ML) solution of the modelTo estimate The sex-specific estimates of mentclasspt{minimaFor all traits, a corresponding estimates of ity Fig.  were obtg is the link function (logit), and where FROH and all applicable covariates  as additional covariates. The modified effect estimates  was measured in many cohorts, this was chosen as the most suitable proxy for socio-economic status. However, since Which of the following do you attend once a week or more often? (You can select more than one), 15.6% of UKB participants selected Religious Group from one of the seven options offered. In the models described, religiosity was coded as 1 for those who selected Religious Group and 0 for those who did not. Although this is likely to be an imperfect measure of actual religious belief it is currently the best available in a large dataset.The analysis of religiosity was only carried out in UKB, where a rough proxy was available. Although no direct questions about religious beliefs were included, participants were asked about their leisure activities. In response to the question 37, and so we sought to investigate if this could influence our results, for example, by the trait extremes being more genetically similar and thus the offspring more homozygous. We see no evidence for an effect of assortative mating on autozygosity, however. Firstly, a polygenic risk score for height  and mean FROH were calculated, along with their associated standard errors. Within each cohort the expectation of ε′ is zero at the mean FROH, however as mean FROH varies between cohorts  were expressed relative to the FROH = 0 intercept, i.e. by subtracting μ from Eq. (13).In each cohort individuals were allocated to one of ten groups of similar rts Fig.  it was nWe compared FROH within full-sibling families. In each of these cohorts, all second-degree, or closer, relatives were identified using KING (parameters:–related–degree 2). Full-siblings were then selected as relative pairs with genomic kinship >0.175 and IBS0 >0.001. This definition includes monozygotic twins, who were intentionally considered as part of full-sibling families. Although monozygotic twins are expected to have identical FROH, they may not have identical trait values, and including additional trait measurements decreases the sampling error of the within-family variance estimate, hence increasing statistical power. Dizygotic twins were also included.In a subset of cohorts, with substantial numbers of related individuals, further analyses were performed to investigate the effect of j) with identified siblings, the values of FROH and trait residual (ε′) were calculated relative to their family mean , i.e. for individual j with n full-siblings Sk where k ϵ {1..n}FROH within-full-siblings , genetic effects were estimated within single-ethnicity sub-cohorts, and meta-analysis of the within-cohort effect sizes was used to combine resultsFROH bins. The between-cohort mean of each of these 16 estimates was then determined by fixed-effect, inverse-variance meta-analysis using the R package metafor39. Results shown in Figs. Each cohort returned estimates and standard errors of: The meta-analysis was also run for various subsets of cohorts, stratified by ancestry as defined in Supplementary Data 40, the assumption of normality is violated when In the analyses of adoptees Fig. , sibling41, implemented in LDSC v1.0.0 (https://github.com/bulik/ldsc). Summary statistics were parsed using default parameters in the LDSC ‘munge_sumstats.py’ script, extracting only variants present in the HapMap 3 reference panel.Genetic correlations were calculated using LD-Score Regression42; however, this could only be the case if FROH underestimates excess homozygosity at the causal loci by at least 162%. We do not believe this to be the case since the maximum FROH measured in many cohorts is around 0.25 (the expectation in the offspring off first-degree relatives), and the effect size estimates from these samples are consistent with the overall estimates (Fig. 6 but not, to our knowledge, for the higher SNP density and error rate of imputed data (see also Supplementary Note FROH is caused by easily-identified long ROH.45A recent paper suggested that ROH may overestimate inbreeding effects by as much as 162%tes Fig. . We noteFurther information on research design is available in the Supplementary InformationData 1Data 2Data 3Data 4Data 5Data 6Data 7Data 8Data 9Data 10Data 11Data 12Data 13Data 14Data 15Data 16Data 17Data 18Data 19Data 20Data 21Data 22Description of Additional Supplementary FilesReporting SummaryPeer Review File


Ursus arctos). We combined a pedigree covering ~1,500 individual bears over seven generations with location data from 413 bears, as well as data on bear density, habitat characteristics, and climatic conditions. We found a narrow‐sense heritability of 0.24 (95% CrI: 0.06–0.38) for brown bear head size, showing that the trait can respond to selection at a moderate speed. The environment contributed substantially to phenotypic variation, and we partitioned this into birth year (5.9%), nonadditive among‐individual genetic (15.0%), and residual (50.4%) environmental effects. Brown bear head circumference showed an evolvability of 0.2%, which can generate large changes in the trait mean over some hundreds of generations. Our study is among the first to quantify heritability of a trait in a hunted large carnivore population. Such knowledge about the degree to which species experiencing hunting can respond to selection is crucial for conservation and to make informed management decisions. We show that including important environmental variables when analyzing heritability is key to understanding the dynamics of the evolutionary potential of phenotypic traits.Wild animal populations experience selection pressures from both natural and anthropogenic sources. The availability of extensive pedigrees is increasing along with our ability to quantify the heritability and evolvability of phenotypic traits and thus the speed and potential for evolutionary change in wild populations. The environment may also affect gene expressions in individuals, which may in turn affect the potential of phenotypic traits to respond to selection. Knowledge about the relationship between the genetic and environmental components of phenotypic variation is particularly relevant, given ongoing anthropogenically driven global change. Using a quantitative genetic mixed model, we disentangled the genetic and environmental components of phenotypic variance in a large carnivore, the brown bear ( We estimate the heritability and evolvability of head size (defined as head circumference) and attempt to disentangle the genetic and environmental components of phenotypic variance underlying brown bear head circumference. Head circumference is highly correlated with body mass in brown bears , Norway spruce (Picea abies), and birch (Betula spp.), with the gently rolling landscape consisting mainly of managed forests ranging from clear‐cuts to 100‐year‐old tree stands  from 1985 to 2014. Lone male and female brown bears and females with yearling cubs were immobilized from a helicopter from mid‐April to mid‐May, shortly after den emergence, and fitted with very‐high‐frequency (VHF) collars  with a transition to global positioning system (GPS) collars in later years . Individuals with VHF collars were tracked and locations recorded with standard triangulation every second week, whereas GPS positioning schedules varied across time and individuals, but fixes were obtained at least once every hour. The position data were compiled in the Wireless Remote Animal Monitoring  and dead bears examined by the Swedish State Veterinary Institute , and cause of death are recorded when known. Successful hunters are required to provide this information to official inspectors  captured multiple times and/or also recovered dead, we calculated an error rate from the sum of mismatches between paired loci divided by the total number of loci genotyped.Tissue samples for genetic analysis were stored in 95% EtOH prior to DNA extraction. The amplification and analysis of 16 microsatellites  polygon or as the median of all individual relocations, based on a visual inspection of the individual relocations overlaid with the aforementioned centers and corresponding radius. More details on home range estimation can be found in the Supporting Information Appendix We extracted information on mean bear density, climate, habitat variables, and plant phenology within the individual annual brown bear home ranges from geographic information system (GIS) maps, using R . For subadults, whose home range centers are less stable  had remained in the same area.We extracted environmental covariates from the year prior to capture, because body condition after den emergence reflects the previous year's environmental conditions  was extracted from annual raster grids developed using information from bear genetics from scat collection efforts and the Swedish Large Carnivore Observation Index (LCOI), both of which were collected during the fall hunting season , minimum temperature in May (°C), and mean daily precipitation in the growing season  was extracted from the Swedish National Road Database (http://www.nvdb.se). We extracted the mean TRI and road density within the individual annual home ranges.A terrain ruggedness index (TRI) was derived from a digital elevation model (DEM) covering the study area. The DEM was rasterized with a resolution of 50 × 50 m, and the TRI was calculated based on the “terrain” function in the “raster” package  to estimate the quantitative genetic parameters, with variance partitioned into genetic and environmental components , the random effects included mother id to estimate maternal effects (VM), birth year for natal cohort variance (VBY), and individual id for repeated measures . The sum of these, including the residual  variance (VR), constitutes the total phenotypic variance . Model diagnostics showed that the chains of the model mixed well with no signs of trends, that a sufficient number of iterations were used, and that there was low autocorrelation among iterations (<0.05).Fixed effects included in the model were the environmental covariates bear density, terrain ruggedness, road density, minimum temperature in May, winter severity, mean precipitation, and length of spring. We also added sex to correct for sexual dimorphism and age to correct for head circumference being measured at different ages , as well as the interaction between age and sex. For the fixed effects, we used the default normal priors . Of the marked individuals, 334 (80.9%) and 333 (80.6%) were assigned mothers and fathers, respectively, at a 95% population‐wide confidence level, creating a maternal‐to‐paternal link factor of 1.003. We succeeded in assigning both a mother and a father to 316 individuals (76.5%). Mothers had a mean of 4.6 ± 4.3 offspring assigned in the pedigree (pedigree lifetime reproductive success over the monitoring period ranged from 1 to 22), and fathers were assigned 2.9 ± 2.4 offspring (pedigree lifetime reproductive success over the monitoring period ranged from 1 to 11). Number of offspring ranged from 1 to 4 per litter for mothers  and 1 to 6 per year for fathers (mean 1.69 ± 0.98). Females annually produced young with on average 1.3 ± 0.5 males (range 1–3), and males with on average 1.09 ± 0.29 females (range 1–2).The pedigree contained 1,524 individuals, of which 413 were VHF/GPS‐collared individuals used in our analyses. The genotyping error rate was 0.1%, which was calculated from a subset of individuals that had been genotyped at least twice . Moreover, the resulting maternal effects estimate may also be due to the model having difficulties separating the birth year variance from the maternal effects variance. Twenty offspring (4.8%) were sole offspring assigned to their respective mothers, and 32 mothers (42.1%) were assigned offspring in 1 year alone. As the percentage of the latter is high, this may affect how well the model can separate maternal effects from birth year effects, and therefore cause one of the variances to converge on zero. Residual, or environmental, variance accounted for 50.4% : 0.06–0.38; Table thers 42.% were as2 (Table 2) within the home range and showed a decreasing trend with increasing winter severity . This estimate falls within the range commonly observed for morphological traits in wild animal populations  effects on body size are well documented in mammals, including brown bears  when estimating heritability, the heritability estimate became considerably lower . Thus, consistent and strong selection pressures on brown bear body size are likely necessary for adaptation to occur rapidly. Such selection pressures are common under heavy trophy hunting, and have led to an evolutionary response in species such as bighorn sheep (Ovis canadensis), where trophy size declined under intense trophy hunting (Pigeon et al., We used a pedigree covering ~1,500 individuals from a population of brown bears facing potential selection pressures from anthropogenic sources, such as hunting and climate change. This allowed us to identify moderate heritability of head circumference. Body size is a trait often targeted by trophy hunting (Wielgus et al., None declared.IMR, AZ, JES, and AM planned the study. JES, AZ, SCF, RB, AGH, and SMJGS collected field data. IMR, SCF, RB, AGH, SMJGS, and AZ extracted and formatted the covariates. SCF, SBH, and HGE developed the pedigree. IMR developed the model and wrote the first draft of the manuscript. All authors contributed to subsequent versions. Click here for additional data file.


A copy-number variant (CNV) of 16p11.2 encompassing 30 genes is associated with developmental and psychiatric disorders, head size, and body mass. The genetic mechanisms that underlie these associations are not understood. To determine the influence of 16p11.2 genes on development, we investigated the effects of CNV on craniofacial structure in humans and model organisms. We show that deletion and duplication of 16p11.2 have “mirror” effects on specific craniofacial features that are conserved between human and rodent models of the CNV. By testing dosage effects of individual genes on the shape of the mandible in zebrafish, we identify seven genes with significant effects individually and find evidence for others when genes were tested in combination. The craniofacial phenotypes of 16p11.2 CNVs represent a model for studying the effects of genes on development, and our results suggest that the associated facial gestalts are attributable to the combined effects of multiple genes.  Using 3D morphometric imaging, Qiu et al. demonstrate that large copy-number variants (CNVs) of 16p11.2 have significant effects on craniofacial structure that are conserved in humans and model organisms, and they demonstrate that these craniofacial phenotypes are attributable to the dosage effects of multiple genes within the CNV region. Recent technological advances in genomics have facilitated the discovery of scores of new genetic disorders that have a complex and variable clinical presentation . Unlike Elucidating the genetic mechanisms through which CNVs influence development requires rigorous analysis of quantitative phenotype data in humans and the establishment of model systems in which the genetic mechanisms are conserved. Craniofacial development, in particular, is controlled by genetic mechanisms that are conserved across species . The effThe application of 3D imaging provides detailed quantitative analysis of surface features, enabling more precise measurements of the shape of the head and face. Application of this approach has facilitated the finer characterization of genetic syndromes with characteristic craniofacial features . Applica3D morphometric facial imaging was performed on subjects with 16p11.2 duplications or deletions and controls recruited to the Simons VIP study  includedEighteen features differed significantly between groups at a family-wise error rate of 5% , and 45 Genetic effects were clustered in regions corresponding to major processes of craniofacial development . Deletion of 16p11.2 was associated with significantly larger frontal  and maxillary  dimensions and a shorter (18-19) and narrower nose . By contrast, the duplication was associated with opposite effects, including smaller frontal dimensions (4-2) and significantly wider nose and longer nasal bridge (18-19). Duplications were associated with a narrower labiomental angle (LMA) consistent with a more protrusive chin. A wider LMA was observed in deletion carriers, but the effect did not reach statistical significance in this comparison. Least absolute shrinkage and selection operator (LASSO) logistic regression was performed to select a parsimonious subset of 14 features that could best discriminate each genotype .Facial gestalts associated with the 16p11.2 deletion and duplication were visualized using computer-generated faces in which the features of a model face were adjusted according to the 14 differences described above, including the frontonasal and maxillary distances and the LMA . DimensiTo further visualize the facial gestalts of controls, deletion carriers and duplication carriers respectively, a 3D model of each was generated by averaging of the surface topography of faces from multiple subjects (3dMDvultus version 2.5.0.1). The sample was limited to subjects ages 14–49 years to avoid variability in facial features across development at young ages, and the sample was restricted to males for which there were sufficient numbers of age-matched subjects (>3) for all three genotypes . A faciaBased on linear discriminant analysis (LDA) of craniofacial features, genotypes could be separated into clusters, with better separation for younger subjects . The LDAThese results demonstrate that deletion and duplication carriers have combinations of facial features that are distinctive for each group. However, the substantial overlap between the faces of CNV carriers and controls is consistent with many subjects having a non-syndromic appearance that is not characterized by gross anomalies. Examination of group differences on each of the individual distances confirms that deletion and duplication groups do not represent outliers on any single measure .Rodent models of 16p11.2 deletion and duplication exhibit a variety of behavioral traits . HoweverRat deletion and duplication models were generated by CRISPR-Cas9 genome editing of the syntenic region, and computed tomography (CT) scans were obtained from a cohort of 75 rats. In addition, CT scans of mouse lines from CNV had a significant effect on craniofacial structure in rat with strong mirror effects across all features between the deletion and duplication models , where a smaller angle corresponds to a more protrusive jaw and a wider angle corresponds to a receding jaw  and TAOK2 . None of the genes with a positive effect size (increased CHA) were statistically significant.We first tested the effect of overexpression of each of the 30 genes in the 16p11.2 region individually, focusing on the lower jaw phenotype, which is more directly analogous to the phenotypes in human and rodent. Consistent with the protruding jaw associated with the duplication in human and rodents, expression of the individual mRNAs resulted in a lower mean CHA relative to controls for a majority of genes tested  can be further modeled in zebrafish. While previous studies have reported mirror effects of CNV on anthropometric traits as well as regional brain volumes in human  and mousKCTD13 has a major effect on head size in zebrafish models  signaling   , and MVPK1)  (MVP  and reguK1) , bear some similarity to features of monogenic disorders that are caused by mutations in components of RAS-MAPK signaling, such as Noonan  and cardAn oligogenic mechanism is unlikely to be unique to the 16p11.2 locus. Rather an oligogenic model may apply in general to the effect of large CNVs on complex traits. For example, the polygenic contribution to height appears to be distributed across a large proportion of the genome . The samDrosophila .Mouse lines are available through the INFRAFRONTIER repository European Mouse Mutant Archive (EM:06133 and EM:06134). Rat lines will also be deposited into the INFRAFRONTIER repository and in the interim can be obtained by request from Yann Herault. Further information and requests for resources and reagents should be directed to the Lead Contact, Jonathan Sebat (Subjects were recruited in conjunction with the Simons VIP study (Simons VIP Consortium). 3D morphometric facial imaging was performed on a subset (N = 359) of subjects with 16p11.2 duplications or deletions from the Simons VIP cohort at 3 sites  using the 3DMD 3-pod camera system. Data analysis was restricted to subjects of European ancestry older than 3 years of age. Additional subjects were excluded due to facial hair, image quality or landmark visibility . The final dataset (N = 228) included 45 deletions, 44 duplications and 139 familial non-carrier controls .The 0.5 Mb region of human 16p11.2 that is commonly deleted or duplicated in these genetic disorders is highly syntenic (same genes in the same order) to chr1:198,100,000-198,583,000 of the rat genome (RatRnor_6.0) and to the orthologous0.5 Mb region of mouse chr7F4 .Del/+) or duplication (Dup/+) of the Sult1a1-Spn genetic interval  Lines were maintained on a pure C57BL/6N C3B genetic background. Rat deletion and duplication models were generated by CRISPR/Cas9 genome editing of Sprague Dawley line . Briefly a deletion of 483,122 bp located at positions chr1:198,100,544-198,583,667 (RatRnor_6.0) and a duplication of the interval from chr1:198,100,545-198,583,458 (RatRnor_6.0), corresponding to the 16p11.2 homologous region of the rat genome, were obtained. For the genotyping, primer pairs were designed for the Del, Dup and an internal control alleles  to amplify specific PCR fragments  using standard conditions .Del/+ , 26 Dup/+  and 26 +/+ siblings . The mouse models of 16p11.2 used in this study consisted of deletion (Del/+) or duplication (Dup/+) of the Sult1a1-Spn genetic interval  scans of rodents including frontal, nasal, maxillary and mandibular regions. For this purpose, regression-based analysis of a defined set of linear or angular features is preferable to dense surface modeling-based approaches  that arehttp://www.3dmd.com). A total of 24 landmarks were placed blind to genotype, including 20 that were landmarked according to Farkas standards . For rats, an image was acquired for the complete skull. For mice, images of the dorsal skull and lower jaw of each animal were acquired separately as part of a previous study . Nineteen landmarks were placed representing the frontal, nasal and maxillary regions, and all pairwise distances between landmarks were normalized to the geometric mean. In addition the mandibular length (ML) and width (MW) were determined by first determining the centroid of multiple landmarks at the lower incisors and the left and right ramus , and theKCTD13, MAPK3, and MVP mRNAs were tested in combinations of two or three. Two way and three way gene interaction models were fitted to test the synergy effect from double-hit or triple-hit groups. Packages “multcomp” from R (version 3.4.1) was used.To model the 16p11.2 BP4-BP5 duplication, we overexpressed individually each gene of the region see . We linekctd13, mapk3, mvp, sez6l2, taok2a, and taok2b. Primers sequences are provided in in vitro using the GeneArt precision gRNA synthesis kit (ThermoFisher) according to the manufacturer’s instructions; 1 nL of injection cocktail containing 50 pg/nl gRNA and 200 pg/nl Cas9 protein (PNA Bio) was injected into the cell of embryos at the 1-cell stage. To determine targeting efficiency in founder (F0) mutants, we extracted genomic DNA from 2 day post-fertilization (dpf) embryos and PCR amplified the region flanking the gRNA target site. PCR products were denatured, reannealed slowly and separated on a 20% TBE 1.0-mm precast polyacrylamide gel (ThermoFisher), which was then incubated in ethidium bromide and imaged on a ChemiDoc system (Bio-Rad) to visualize hetero- and homoduplexes. To estimate the percentage of mosaicism of F0 mutants (n = 5/condition), PCR products were gel purified (QIAGEN), and cloned into a pCR8/GW/TOPO-TA vector (Thermo Fisher). Plasmid was prepped from individual colonies (n = 9-12 colonies/embryo) and Sanger sequenced according to standard procedures.CRISPR/Cas9 genome editing was performed as a model of the reciprocal deletion. We used CHOPCHOP to identLarvae were maintained under standard conditions at 28.5°C until 3 dpf and were positioned and imaged live as described . AutomatSPN, C16orf54, SEZ6L2, ASPHD1, TAOK2, INO80E and FAM57B) on shape of the mandible could be attributable to the differential regulation of these genes. A published dataset was obtained consisting of Affymetrix (Mouse Gene ST 1.0 array) gene expression analysis of the major craniofacial processes of the developing mouse embryo (E10.5-E12.5) (Facebase.org). Samples included mesenchymal and ectodermal cells of the frontonasal and mandibular processes of embryos at E10.5, E11.5 and E12.5 in triplicate, and samples of the maxillary process at E11.5 and E12.5. Summary gene level expression data was obtained (https://www.facebase.org/hatrac/facebase/data/ fb2/FB00000803/rma.summary.names.txt) and the basal expression levels of the seven 16p11.2 genes identified in this study (see We examined whether the significant effects of seven genes (5-E12.5)  (accessitudy see  was comptudy see .http://www.3dmd.com). Symmetric distances were averaged, yielding 156 facial distance measurements. Each distance was normalized to the overall size of the individual’s face, by dividing by the geometric mean of the 156 distances for that individual. Angular measurements of the nose and chin, the nasomental (NMA) and labiomental (LMA) angles respectively, were calculated by triangulating the relevant landmarks. A series of linear mixed-effects models, using package lme4 in R (version 3.4.1), was used to separately test for the effect of deletion and of duplication on each angle or normalized facial distance. Each model controlled for fixed effects of age, head circumference, body mass index (BMI), sex, and ancestry principal components, with a random intercept allowed to account for within-family correlation. Interaction between genotype and sex was included if significant at 5% level. Significant differences according to genotype were determined by a likelihood-ratio test at a family-wise error rate of 5% using Holm’s correction . Missing data on 38 subjects was imputed by using PCs from a sibling nearest in age or a randomly selected parent. After imputation, 15 subjects from two different families were still missing data, 9 of which were familial controls and 6 were duplications. Analyses were performed with and without including the ancestry principal components as a sensitivity analysis, and the results were very similar. Significant correlation was observed between one of the first two principal components and 7 of the 160 craniofacial distances.All subjects were of European ancestry, however regional genetic differences could still explain variation in facial traits. We controlled for ancestry using principal components derived from genetic data subjects. Ancestry principal components were obtained on 213 subjects from Illumina SNP genotype data (Illumina HumanOmniExpress v.1 and v.2) available from the Simons VIP study (www.3dmd.com). To maximize the number of unrelated subjects that were closely matched in age within each group, selection criteria for averaging of faces differed slightly from that of the overall dataset. Only unrelated individuals were included, additional subjects were removed due to image quality (gaps in the surface topography), and the requirement for landmark visibility was relaxed, allowing for frontotemporal landmarks  was performed on linear (mixed) model residuals after adjusting for age, head circumference, body mass index (BMI), sex, and ancestry principal components, for both total subjects and subjects aged < 20 years (A random intercept to account for within-family correlation was included when significant). The 45 distances with FDR q value less than 0.05 were used. We used the function “lda” from the “MASS” package in R (version 3.4.1). Specificity and sensitivity were calculated based on LDA prediction with default values.Generalized linear model (logistic regression) with lasso was performed for both human subjects and mice. Distances with FDR q value less than 0.05 were used for human subjects, while distances with a statistically significant likelihood ratio test at p < 0.05 were used for mouse skulls. We used 10-fold cross validation for lasso with minimum deviance for human subjects, and lasso with minimum Akaike Information Criteria (AIC) for mouse skulls . SpecifiDifferences in facial features between deletion and control lines and differences between duplication and control lines were tested with univariate linear models. We tested all 91 distances on the dorsal skull and two distances on the mandible. Effects that were significant at an FDR of 5% were identified. In addition, as we did previously in human, we identified a set of features that distinguish CNV models from controls by performing least absolute shrinkage and selection operator (LASSO) based on all univariate significant distances by generalized linear model, with AIC as the criteria rule.https://base.sfari.org/). Quantitative data extracted from 3D surface images and CT scans of human, rat, and mouse are available by request from Jonathan Sebat (jsebat@ucsd.edu).Human 3D images and were obtained from the Simons VIP Consortium . ClinicaSupplemental InformationTable S1Table S2Table S3Table S4Table S5Table S6Table S7