Difficulties in appropriate social interaction are characteristic of both children with autism spectrum disorders and children with callous-unemotional traits (who are at risk of developing psychopathy). Extant experimental studies suggest that the nature of atypical social cognition that characterises these two profiles is not identical. However, ‘empathizing’ difficulties have been hypothesised for both groups, raising questions about the degree of aetiological separation between social impairments that characterize each disorder. This study explored the relative contribution of independent vs. shared aetiological influences to social and communication impairments associated with autistic traits and callous-unemotional traits, indexed by parent-report in a population-based cohort of twins.Participants were over 5,000 twin pairs from a UK cohort , assessed for callous-unemotional traits at 7 years and autistic social and communication impairments at 8 years. Multivariate model-fitting was used to explore the relative contribution of independent vs. overlapping genetic/environmental influences on these traits.Both social and communication impairments and callous-unemotional traits were highly heritable, although the genetic and environmental influences accounting for individual differences on each domain were predominantly independent.Extant evidence from experimental and neuro-imaging studies has suggested that, despite some superficially overlapping behaviours, the social difficulties seen in children with autism spectrum disorders and callous-unemotional traits are largely distinct. The current study is the first to demonstrate considerable aetiological independence of the social interaction difficulties seen in children with autism spectrum disorders and those with callous-unemotional traits. Individuals who have either high levels of autism spectrum disorder (ASD) or callous-unemotional traits often display atypical patterns of social interaction. Both groups can, on the surface, behave in ways that lack sensitivity or empathy for other people and their needs. Social and communication difficulties that form part of the diagnostic criteria for ASD reflect disturbances in the ability to relate to others and understand their thoughts and intentions, with consequent problems with friendships, play, and initiating and responding to social overtures . CallousA number of past studies have used questionnaire measures and found that both individuals with ASD or callous-unemotional traits display difficulties in empathic and pro-social behaviour , 6. DataAlthough there has been some progress in comparing and contrasting the socio-affective profile of individuals with ASD and callous-unemotional traits , 12, 13,Participants were drawn from the UK Twins Early Development Study (TEDS), a population-based cohort of twins born in England and Wales between 1994 and 1996 . The TwiThe sample for parent-rated callous-unemotional traits consisted of 7278 pairs (2591 monozygotic (MZ), 2386 dizygotic same-sex (DZ-SS) and 2301 dizygotic opposite-sex twin pairs (DZ-OS)). For social interaction and social communication subscales (derived from the Childhood Autism Spectrum Test) [Callous-unemotional traits were rated by parents. The callous-unemotional traits measure consisted of the four questions incorporated in the DSM-5 specifier  [2) statistic. A significant χ2 of a sub-model indicated that it fitted the data worse than the full model.The current study employed the classic twin design to partition the variance and covariance within traits into unique and common genetic and environmental influences, by comparing the phenotypic resemblance separately for monozygotic and dizygotic twins within as well as across traits. The genetic relatedness of monozygotic twins was set to a coefficient of 1.0 and for dizygotic twins 0.5 . The twin model assumes that twin similarity is due to additive genetic and shared environmental factors. MZ dissimilarity can only be due to non-shared environmental influences, which also includes measurement error , 34. Allect.org) . The goocommon latent additive genetic, shared environmental and non-shared environmental factors. The estimates of monozygotic and dizygotic cross-twin within-trait correlations estimate the univariate genetic and environmental influences on the three traits separately. This model also allows the inspection of possible correlation ratio differences for monozygotic and dizygotic twins of different sexes, thus indicating whether it would be of interest to examine sex-differences formally.A Constrained Correlation Model was fitted for the 5 sex-by-zygosity groups, providing summary statistics reflecting the importance of genetic factors on variation across each trait and the covariance between the traits. The ratio between monozygotic and dizygotic cross-twin cross-trait correlations for the three traits provides an indication of the relative contributions of Our primary question of interest was the magnitude of independent vs. overlapping genetic and environmental variance acting on the three traits. An independent pathway model was used to estimate these . In thisThere was a modest but significant phenotypic overlap between parent ratings for the three traits of interest . The constrained-correlation model indicated that within-trait and cross-trait correlation values for monozygotic and dizygotic twins were similar across males and females; meaning that the magnitude of common and unique genetic and environmental influences is similar across genders. Comparison of correlations between same-sex and opposite-sex dizygotic twin pairs indicated similar degrees of resemblance, suggesting that the same genetic factors influence variation in both males and females. As such, dizygotic opposite-sex pairs were retained in the multivariate analysis.Results of the multivariate model-fitting analyses are shown in For callous-unemotional, social interaction and social communication difficulties, most of the overall genetic influences on population variance were due to variable-specific genetic factors, explaining 74%, 67% and 69% of the heritability, respectively. The common genetic factor acting on all three traits explained a smaller proportion of the heritability: 26%, 33% and 31%, respectively. Specific shared environments were significant only for social communication, albeit small in magnitude, with no common shared environmental influences acting on the covariance between the three variables. Trait-specific non-shared environmental influences were significant for all variables, whereas social interaction appeared to be influenced by non-shared environmental influences that stemmed from the common non-shared environments (E) path, although this is effectively operating as trait-specific non-shared environmental effect as it did not contribute to variance on callous-unemotional traits or social interaction.A) and environmental (rC and rE) correlations between the variables, irrespective of the magnitude of these factors acting on the variable alone (results presented in A = .31) and between callous-unemotional traits and social communication (rA = .23). Low non-shared environmental correlations were also observed between callous-unemotional traits and social interaction (rE = .10) and between callous-unemotional traits and social communication (rE = .07). The genetic and environmental overlap between social interaction and social communication difficulties was also modest  as reported previously [Given the possibility that pleiotropic effects of genes may act on a pair of variables, but not on all three (as assessed by the independent pathway model), we also derived a correlated factors solution of the Cholesky model . This moeviously .The supplemental analysis using teacher-rated callous-unemotional traits and parent rated autistic traits see  to fit aA large body of clinical research demonstrates that difficulties in appropriate empathic and pro-social interaction characterize both children with ASD and children with callous-unemotional traits , 6. TherThis study was the first to examine systematically the degree of overlap between behavioural measures of socio-affective difficulties in individuals with high levels of autistic or callous-unemotional traits in a general population sample of twins aged 7–8 years. The findings show a modest degree of phenotypic correlation between the traits, suggesting that, in the general population, these traits are predominantly distinct. Because we studied twins, we were also able to examine the aetiological overlap between social interaction and social communication difficulties associated with ASD, and callous-unemotional traits . Twin model-fitting indicated that social interaction, social communication, and callous-unemotional traits are all strongly heritable, with negligible impact of shared environmental effects, as reported previously. Less than perfect correlations between identical twins also indicate that child-specific environmental factors are important in explaining variation on these three traits , 29, 40.The independent pathway model indicated that aetiological influences on the three traits are mostly unique to each construct. Independent genetic influences explained 74% of total genetic variance for callous-unemotional; 67% of total genetic variance for social interaction; and 69% for social communication. The remainder of the genetic variance was explained by genetic factors shared between the three traits.These findings are consistent with previous reports of predominantly distinct genetic influences on autistic and psychopathic traits  . Our finOverlapping genetic influences accounted for a modest amount of the total genetic influences acting on the three traits , also reflected by the modest genetic correlation values. The modelling results indicate that the modest degree of phenotypic overlap observed between the three traits is largely driven by shared genetic, rather than environmental factors. This suggests that there may be some shared processes  increasing risk for high ratings on the other phenotype ; for example, children with poor communication skills may show atypical expressions of empathy. As such, the apparent aetiological overlap between the phenotypes may be a consequence of the observer-questionnaire based approach to measuring the constructs. Future studies using experimental measures of cognitive and affective empathy in children with these profiles would be helpful in determining the nature of the apparent overlap reported here.A strength of this study is that our sample is a general population cohort, allowing us to explore aetiological overlap between the traits across the entire distribution of severity, rather than just within a clinical sample. Limitations of the present study include the relatively low alpha for the parent-rated callous-unemotional traits measure, which could have limited the ability to detect genetic influences, resulting in inflated estimates of error. However, our scale properties are consistent with previous reports of low alphas in the assessment of callous-unemotional traits using items from the anti-social process screening device . NotablyOverall, the current study yielded several important findings. Firstly, the correlation between social, communication and callous-unemotional traits was modest across this population sample, indicating that these traits can be disentangled phenotypically in a 7–8 year-old cohort of twins. Predominantly trait-specific genetic and environmental influences contribute to social interaction difficulties, social communication difficulties and callous-unemotional traits. This finding is consistent with behavioural and neuro-imaging studies that report separate cognitive and neural correlates of these profiles in affected groups and in population samples with high levels of these traits , 12, 13.The implications of these findings are that although both children with high levels of ASD traits and children with high levels of callous-unemotional traits exhibit difficulties in appropriate social behaviour, the underlying drivers of these impairments appear to differ between the profiles. Rating scales for ASD and CU traits appear to differentiate appropriately distinct social/ behavioural constructs that are largely unrelated aetiologically. The small degree of aetiological overlap observed here be a consequence of the questionnaire based approach to measuring the constructs used here, or could reflect a shared third trait that impacts both profiles, such as alexithymia. Further research is needed to examine and compare the neuro-cognitive basis of difficult or un-empathic behaviour seen in children with ASD traits or callous-unemotional traits.S1 Analysis(DOCX)Click here for additional data file.S1 Fig1-3 = Additive genetic factors modelled for each trait separately; C1&3 = Shared environmental factors modelled for each trait separately; E1-3 = Non-shared environmental factors modelled for each trait separately. Correlations for A, C and E factors between pairs of traits are shown. Paths from the A, C and E factors on to the traits reflect the estimated role of each of these factors in contributing to variance on the trait. Path C2 was removed as common environmental factors did not contribute significantly to variance on social interaction difficulties.Abbreviations: A(PDF)Click here for additional data file.S2 Figc = common genetic factors; Ec = common non shared environmental factors; As = specific genetic factors; Cs = specific shared environmental factors; Es = specific non shared environmental factors. h2 = proportion of variance on the trait explained by additive genetic influences; c2 = proportion of variance explained by shared environmental influences; e2 = proportion of variance explained by non-shared environmental influences.Paths estimates are expressed as the proportion of variance accounted for by common or specific aetiological factors. For callous-unemotional, 15% of overall genetic influences are due to shared factors and 85% due to trait specific factors. For social interaction, 26% of overall genetic influences are due to shared factors and 74% due to trait specific factors. For social communication, 39% of overall genetic influences are due to shared factors and 61% due to trait specific factors. Dotted arrows indicate a non-significant path estimate. A(PDF)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.


Most genetic studies assume that the function of a genetic variant is independent of the parent from which it is inherited, but this is not always true. The best known example of parent-of-origin effects arises with respect to alleles at imprinted loci. In classical imprinting, characteristically, either the maternal or paternal copy is expressed, but not both. Only alleles present in one of the parental copies of the gene, the expressed copy, is likely to contribute to disease. It has been postulated that imprinting is important in central nervous system development, and that consequently, imprinted loci may be involved in schizophrenia. If this is true, allowing for parent-of-origin effects might be important in genetic studies of schizophrenia. Here, we use genome-wide association data from one of the world’s largest samples (N = 695) of parent schizophrenia-offspring trios to test for parent-of-origin effects. To maximise power, we restricted our analyses to test two main hypotheses. If imprinting plays a disproportionate role in schizophrenia susceptibility, we postulated a) that alleles showing robust evidence for association to schizophrenia from previous genome-wide association studies should be enriched for parent-of-origin effects and b) that genes at loci imprinted in humans or mice should be enriched both for genome-wide significant associations, and in our sample, for parent-of-origin effects. Neither prediction was supported in the present study. We have shown, that it is unlikely that parent-of-origin effects or imprinting play particularly important roles in schizophrenia, although our findings do not exclude such effects at specific loci nor do they exclude such effects among rare alleles. Schizophrenia is highly heritable disorder , 2 for wImprinting describes the phenomenon whereby genes are subject to regulation by an epigenetic mechanism such that gene expression depends on whether the gene is inherited from the mother or the father. Genes that are known to be imprinted are enriched for expression in the placenta and the brain . It has UBE3A [de novo mutational rates.There is already one example of a known imprinted schizophrenia risk locus, a duplication CNV at 15q11-q13 , 12. At UBE3A , 12, at Studies of schizophrenia and other complex traits are generally based on case-control samples. In these samples, it is not possible to determine the parental origin of autosomal alleles, except where this can be inferred by assaying the epigenetic status of the imprinted cluster , 12. Aim® 6.0 Array as previously described [−6 in parents. Markers with Mendelian errors >1% were excluded. We did not use imputed data since array genotypes provide good coverage of the main loci of interest and moreover, the optimal analytic methods for parent-of-origin require genotypes rather than genotype probabilities.For the primary parent-of-origin analysis we used a dataset comprising 695 parent-offspring trios from Bulgaria typed with the Affymetrix GeneChipescribed , 15. Famhttp://www.broadinstitute.org/mpg/ricopili/). Although most of the present sample was included in that study, parent-of-origin effects were not evaluated. Our present analysis for those effects is therefore independent of any previously reported data.We defined schizophrenia associated variants as those from independent loci reported to be genome-wide significant in the recent publication from the Schizophrenia Working Group of the Psychiatric Genomics Consortium  (PGC). T2≥0.8) for it or c) another GWAS significant marker at the locus. To test whether the loci are enriched for imprinted genes in mice and humans, we used the genes (N = 350) that are present in the physical regions from Extended Data of primary GWAS manuscript of the Schizophrenia Working Group of the PGC [To test whether GWAS significant associated markers are enriched for parent-of-origin effects, we used a) one of the index markers for each locus or a perfect proxy for it or where none was available, either b) the best proxy (r the PGC . The MHCwww.geneimprint.com) a total of 197 genes reported as imprinted in humans (N = 177) and/or mice . We identified 150 genes reported as robustly imprinted in mice from the MouseBook database (www.mousebook.org). Mouse genes were converted to their human orthologues using the conversion tool from the MGI Biomart database (biomart.informatics.jax.org). In total, we identified 247 unique autosomal genes of which 225 contained at least one marker in the PGC schizophrenia dataset  reported by the PGC  we used To test for enrichment of significant associations in the genes known to be imprinted in humans and/or mice in the PGC dataset, we performed a bootstrapping process in which we randomly selected the same number of genomic regions from that dataset as there are putatively imprinted genes, ensuring each locus contained the same number of SNPs as did its matched imprinted gene. We then compared the number of significant SNPs in the imprinted genes with the number of significant SNPs in the 1000 times randomly bootstrapped regions.2 = 1) of it  with an index marker, was genome-wide significant in the PGC data, of which 1 had a nominally significant parent-of-origin effect (P = 0.028). Thus, in total, of 88 markers representing 105 genome-wide significant loci, 5 had nominally significant parent-of-origin effects which is not more than expected by chance .Genome-wide, there was no evidence for inflation in parent-of-origin test statistics . For 63 1) of it . Three o1) of it . The enrACD ) was also on the list of 225 genes which are reported as possibly imprinted in humans or mice (2 = 0.74) for the index SNP that is associated with schizophrenia in the PGC shows no significant parent-of-origin effect in the trios  was not significant after correction for multiple testing . We also did not find evidence for significant enrichment of parent-of-origin effects among the markers mapping to imprinted genes at any of the significance thresholds we examined  was taken to be the experiment-wide significance level. A total of 35 markers spanning six distinct genes surpassed this threshold; markers at QPCT , BTNL2 , LIN28B , MAGI2 , GLIS3  and NTM .In the PGC schizophrenia data, the 225 genes imprinted in humans and/or mice were spanned by 67,883 markers. To derive an approximation for experiment-wide significance level in this set of markers, we used bootstrapping. At each of 1000 simulations we sorted 67,883 p-values in ascending order and recorded the 3395th smallest p-value (corresponding to a null P of 0.05). The average of all such recorded p-values across the 1000 simulations (p≤1.97x102>0.5) with the most strongly associated PGC marker in each gene, we retained 6 independent SNPs, one per gene (2≥0.8). On chromosome 6, the SNP rs1150753 was genome-wide significant in the PGC schizophrenia data . This marker also had a significant parent-of-origin effect in the parent-proband trios data (p = 0.0019) with the schizophrenia-risk allele OR for paternally transmission being 3.16 relative to maternally transmitted alleles . While significant in the context of 6 independent SNPs (corrected p = 0.012), this finding is very modest in the context of the multiple tests outlined above. No other marker had a nominally significant parent-of-origin effect  that the risk associated with any individual variant was dependent on the transmitting parent-of-origin. Moreover, we did not find evidence that the set as a whole showed a larger number of nominally significant associations than expected by chance.ACD chr16:67,691–67,695KB), mapped to such a locus (excluding genes mapping to the extended MHC region). It should be noted that ACD maps to an associated locus that contains at least 26 genes and therefore is by no means considered to be strongly implicated in the disorder. Moreover, we see no support for a parent-of-origin effect at this locus, suggesting the schizophrenia association is not driven by an imprinted gene.We additionally found no evidence to support the second prediction that known or predicted imprinted genes are enriched among schizophrenia associated loci. Indeed only 1 putatively imprinted gene  contained a variant that was actually genome-wide significant. Moreover, our best proxy for that genome-wide significant variant also showed a significant parent of origin effect in the trios. Given a previous report (in this set of trios) of a de novo nonsense mutation in this gene on a proband with schizophrenia [BTNL2 might be considered an attractive candidate imprinted gene for schizophrenia. Additional factors, however, strongly mitigate against this conclusion. First, BTNL2 lies within the extended MHC region of chromosome 6, a schizophrenia-associated locus that is gene dense and spans several million bases. Accordingly, the extent to which any one gene can derive candidacy from GWAS is weak. Second, the candidacy of BTNL2 as an imprinted gene is based only on computational [BTNL2 is predicted to be maternally expressed  do not occur in the disorder, although our data from this analysis are compatible with the hypothesis that they do not.S1 FigThe p-values were calculated with the maximum likelihood ratio test comparing two models with and without stratification on parental mating type as implemented in UNPHASED .(DOCX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


N = 874), handgrip strength  and flexibility  in children and young adults. For balance this was 35 % . Finally, multivariate modeling showed that the phenotypic correlations between the phenotypes in current study (0.07 < r < 0.27) were mostly driven by genetic factors. It is concluded that genetic factors contribute significantly to the variance in muscle strength, flexibility and balance; factors that may play a key role in the individual differences in adolescent exercise ability and sports performance. Physical fitness can be defined as a set of components that determine exercise ability and influence performance in sports. This study investigates the genetic and environmental influences on individual differences in explosive leg strength , handgrip strength, balance, and flexibility (sit-and-reach) in 227 healthy monozygotic and dizygotic twin pairs and 38 of their singleton siblings (mean age 17.2 ± 1.2). Heritability estimates were 49 % (95 % CI 35–60 %) for vertical jump, 59 % (95 % CI 46–69 %) for handgrip strength, 38 % (95 % CI 22–52 %) for balance, and 77 % (95 % CI 69–83 %) for flexibility. In addition, a meta-analysis was performed on all twin studies in children, adolescents and young adults reporting heritability estimates for these phenotypes. Fifteen studies, including results from our own study, were meta-analyzed by computing the weighted average heritability. This showed that genetic factors explained most of the variance in vertical jump (62 %; 95 % CI 47–77 %,  Physical fitness can be defined as a set of components that influences exercise ability and performance in sports  or while contracting (dynamic). The handgrip test, an easy and reliable measure, is by far the most commonly used measure for assessing isometric strength in epidemiological studies .The two main factors that can influence individual differences in physical fitness are innate biological differences and environmental factors. The latter can be subdivided in influences shared with other members of the family  and person-specific or unique environmental influences, which includes error measurement but also comprises person specific exercise participation, training, and coaching. A design that is often used for partitioning total variance in genetic, shared environmental, and unique environmental components is the classical twin design. In a twin study, intrapair resemblance between two types of twin relationships is compared; genetically identical  and non-identical  twins. If the MZ resemblance for physical fitness is comparable to the DZ resemblance and non-zero, this constitutes evidence for shared environmental influences on the phenotype under study. If the MZ resemblance for physical fitness is higher than the DZ resemblance this constitutes evidence for genetic influences on the phenotype. Previous twin studies showed that genetic factors account for a substantial part of the variation in the aforementioned components of physical fitness.Table A further theme that has not been extensively addressed is the extent of overlap between the genetic factors influencing these varied fitness phenotypes. Only a few studies provided some information on the (genetic) co-variation between components of physical fitness. Two studies reported moderate phenotypic correlations for muscle strength measures such as isometric strength measured by handgrip and knee extension. Multivariate modeling of these traits showed that part of the genes affecting muscle strength may be common to isometric strength measured by handgrip and knee extension , balance, and flexibility but heritability estimates vary across samples. Multivariate genetic analyses on all four parameters have not been reported. To replicate and expand the literature on the genetic architecture of physical fitness components, we estimated the heritability of muscle strength measures , balance and flexibility in a large sample of adolescent twins and their siblings and these estimates were incorporated in a meta-analysis on the heritability of muscle strength, flexibility and balance. Finally, in a multivariate design, the source of covariance among these fitness components was examined.548 healthy adolescent twin pairs aged between 16 and 18, enrolled in longitudinal survey studies of the Netherlands Twin Register , 36 dizygotic male pairs (DZM), 57 monozygotic female pairs (MZF), 42 dizygotic female pairs (DZF), 33 dizygotic opposite sex pairs (DOS) and 38 of their singleton siblings. Two additional sibling pairs participated (without a twin), resulting in a sample size of 498 participants. Mean age at time of the laboratory assessment was 17.2 ± 1.2.All participants above 18 provided written consent and if the participants were under 18 consent was given by both of their parents/guardians and assent by the participant. All study procedures were reviewed and approved by the Medical Ethics Review Committee of the VU University Medical Center Amsterdam (NL35634.029.10).On arrival at the laboratory, height and weight  were measured. Subsequently, 4 fitness characteristics were examined: vertical jump, handgrip strength, balance, and flexibility.Explosive strength was measured with a vertical jump test that requires the participants to jump as high as possible, starting from a position of knee bending at a fixed knee angle immediately prior to the jump. Participants were instructed to jump straight up as much as possible and not go sideways. It was allowed to use the arms to help drive the body upwards. A successful jump was defined as one where at take-off the participants had the appropriate knee angle and landed their feet within a 10 cm radius of the start position. Jumping height was defined as the vertical displacement between of the trunk at the beginning and at the end of the jump measured by the displacement of a tapeline attached to the participants’ hip and a clipped to the floor. Best out of 3 jumps was documented (jumping height in centimeters).Participants were instructed to hold a dynamometer  in the dominant hand with arm at the side of the body and elbow at a 90° angle. When ready, the subject was encouraged to squeeze the dynamometer once with maximum effort (in kg), which should be maintained for about 5 s.The Balance Error Scoring System (BESS) . Participants were instructed to sit on the floor with the legs fully extended and the soles of the feet flat against the box. One hand was placed on top of the other palms down. Then the subject reached forward along the measuring scale on the box as far as possible, without bending the knees. Best out of 3 reaches (in centimeters) was used for subsequent analyses.p < 0.05 was considered for statistical significance. First, a so-called saturated model that estimated all parameters freely (a) was fitted to the data. Given the relative small sample size, with no power to test for sex-differences, and since (non-twin) siblings share, like DZ twins, on average 50 % of their genes, parameter estimates were constrained to be equal for males and females and for DZ twins and siblings. Main effects of sex and age and body mass index (BMI) on mean levels of components of physical fitness were considered in the model since these factors are associated with strength , dominant genetic variance/covariance (D) or shared environmental variance/covariance (C) and unique environmental variance/covariance (E). Since C and D effects cannot be estimated simultaneously in the classical twin model, the ratio of the MZ correlations to the DZ correlations was used to determine which model (ACE or ADE) is most appropriate. Significance of variance–covariance components was tested by comparing the model including the specific component (e.g. ADE) to a model in which the component is constraint to be equal to zero (e.g. AE). The pattern of the factor loadings on the latent genetic and environmental factors in a Cholesky decomposition reveals a first insight into the etiology of covariances between the physical fitness components.handgrip/muscle strength/vertical jump/explosive strength/flexibility/sit-and-reach/balance and genes/heritability/twin(s) as key words. In addition, the reference lists of these articles were inspected. Articles  published in English and reporting twin correlations and/or heritability estimates of the vertical jump test, handgrip strength, balance and flexibility (sit-and-reach test) in a sample of children, adolescents and/or young adults up to the age of 30 were included, provided that these phenotypes were roughly comparable (i.e. protocol) to the phenotypes measured in the current study. These papers are shown in Table I2 statistic was used to assess heterogeneity and was calculated as (Q − df)/Q, where Q is Cochran’s heterogeneity statistic and df the degrees of freedom  of this sample was (mean ± SD) 20.6 ± 2.5 for males and 21.8 ± 3.3 for females, comparable to the average 17 year olds in The Netherlands  and handgrip (p < 0.001), whereas females performed better for balance (p < 0.001) and flexibility (p < 0.001). As expected, significant age effects were found on vertical jumping (p = 0.011) and handgrip (p < 0.001). Additionally, significant effects of BMI on mean levels of vertical jump (p = 0.026), handgrip (p = 0.041) and balance (p = 0.011) were detected. Because of significant sex, age, and BMI effects on the mean these factors were taken into account in further model fitting.Means and standard deviations for the fitness components of males and females are shown in Table Table p > 0.05) and were dropped from the model. Heritability estimates were 49 % (95 % CI 35–60 %) for vertical jump, 59 % (95 % CI 46–69 %) for handgrip and 77 % (95 % CI 69–83 %) for flexibility. For balance, modeling started with an ACE model, as twin correlations for balance suggested the presence of shared environmental influences. However, C could be dropped from the model (p < 0.05). Genetic factors explained 38 % (95 % CI 22–52 %) of the variance in balance in our sample. The remaining variance in the four phenotypes was accounted for by unique environmental factors.Model fitting for vertical jump, handgrip and flexibility started with an ADE model. Dominant genetic influences were not significant (N = 874) can be explained by genetic factors. Handgrip measured in a combined sample of 9–25 year olds (N = 4516) showed a weighted average heritability estimate of 63 % (95 % CI 47–73 %). This estimate falls within the CIs of all studies included in this meta-analysis. For balance, a weighted average heritability estimate of 35 %  was found, which falls perfectly in the CIs of the included studies. Four studies reported a heritability estimate for flexibility in children and young adults, ranging from 18 to 77 % (current study). The meta-analytic weighted average heritability of 50 %  falls outside the CIs of these two heritability estimates. The meta-analyses for handgrip and balance showed low heterogeneity (I2 = 18 and 0 %). However, high I2 values were detected for vertical jump and flexibility (46 and 94 %), suggesting that differences in studies are not caused by sampling error only.The results of the meta-analyses are presented in Table p = 0.659). Standardized components from this final model for additive genetic and unique environmental influences on the four components of physical fitness and their covariances are presented in Table rG = 0.46, 95 % CI 0.27–0.65), handgrip and balance  and balance and flexibility . In addition, a significant environmental correlation was found between vertical jump and flexibility .Based on the overall correlational structure, multivariate model fitting was started with an ADE model. Dominant genetic influences were not significant p = 0.69. StandaTo examine the heritability of and genetic co-variation between various components of physical fitness, genetic models were fit to data from 498 late-adolescent twins and their siblings. Univariate modeling showed that a moderate to large part of the individual differences in components of physical fitness is accounted for by genetic differences between individuals. The remaining variance was accounted for by unique environmental effects. Muscle strength, flexibility, and balance all contribute to exercise ability and performance in sports  and showed a rather homogenous picture, whereas the meta-analysis of flexibility showed heterogeneity as two out of five studies report an estimate significantly lower  do not seem to play a major role in explaining individual differences in physical fitness components in our late-adolescent sample. As the correlations for DZ twins/siblings were low and non-significant, shared environment factors were not further considered as a source of variance for these fitness parameters. This does not rule out a small contribution of shared environmental influences, as this is hard to detect in samples of this size, even if the power to detect shared environmental influences was increased by adding siblings to the design. Of interest, twin studies on voluntary exercise behavior show that the influence of these shared environmental factors is significant at young ages, but decreases or has completely disappeared when reaching adolescence (Huppertz et al. In the current study, some of the physical fitness components were moderately, but significantly, associated to each other although they reflect different dimensions of physical fitness (Simons et al. ACTN3 gene. This gene seems to influence the performance of fast skeletal muscle fibers and ACTN3 XX homozygotes may have modestly lower skeletal muscle strength in comparison with R-allele carriers (Yang et al. A major future challenge is to identify the specific genes underlying the heritability of these four components of physical fitness. Candidate genes studied have focused on insulin-like growth factor- and myostatin-related genes and genes involved in inflammatory factors. Linkage analyses revealed several additional regions of interest in the genome, although individual genes could not be identified as yet (see Thomis and Aerssens Some limitations must be considered while interpreting our results. An important assumption underlying twin studies is that twins are fully representative compared to the general population. Silventoinen et al. reported that singletons showed extra variation in weight and strength measured compared to twins, which could lead to inflated heritability estimates (Silventoinen et al. To summarize, the analyses performed in this study confirm a significant contribution of genetic factors to the four physical fitness components and to their association. Understanding the genetic basis of fitness parameters may help us to understand the individual differences in regular voluntary exercise behavior, which show substantial heritability, particularly at the end of adolescence (Huppertz et al.


Genomewide association studies (GWAS) enable detailed dissections of the genetic basis for organisms' ability to adapt to a changing environment. In long‐term studies of natural populations, individuals are often marked at one point in their life and then repeatedly recaptured. It is therefore essential that a method for GWAS includes the process of repeated sampling. In a GWAS, the effects of thousands of single‐nucleotide polymorphisms (SNPs) need to be fitted and any model development is constrained by the computational requirements. A method is therefore required that can fit a highly hierarchical model and at the same time is computationally fast enough to be useful.Our method fits fixed SNP effects in a linear mixed model that can include both random polygenic effects and permanent environmental effects. In this way, the model can correct for population structure and model repeated measures. The covariance structure of the linear mixed model is first estimated and subsequently used in a generalized least squares setting to fit the SNP effects. The method was evaluated in a simulation study based on observed genotypes from a long‐term study of collared flycatchers in Sweden.The method we present here was successful in estimating permanent environmental effects from simulated repeated measures data. Additionally, we found that especially for variable phenotypes having large variation between years, the repeated measurements model has a substantial increase in power compared to a model using average phenotypes as a response.r package RepeatABEL. It increases the power in GWAS having repeated measures, especially for long‐term studies of natural populations, and the R implementation is expected to facilitate modelling of longitudinal data for studies of both animal and human populations.The method is available in the  The fast development of molecular genetic techniques offers novel integration possibilities by making it feasible to investigate how processes at the molecular genetic level relate to processes at the different phenotypic levels, that is ranging from developmental pathways up to morphological and behavioural traits. Genomewide association studies (GWAS) that link molecular genetic information with phenotypic information have successfully been applied to detect causal mutations and to understand the genetic architecture of complex traits in both plants and animals, including humans , which are the subject of a number of long‐term studies , they are also used as a model for speciation research  and is part of the GenABEL suite of packages at http://www.genabel.org.Here, we develop a user‐friendly statistical method that can be used for GWAS where there are related individuals that may have repeated observations. The method also gives flexible modelling of random effects  for GWAS in general. The method assumes Gaussian phenotypes, and we further investigated the adequacy of the method for use with binary data. In a recent paper, Husby et al.  applied et al. . The Repet al. et al. r (http://www.r-project.org), it is also fairly user‐friendly. Yu et al. , but to simplify the notation only the intercept and SNP effects are included in the following equations.The standard model for testing the significance of a SNP effect can be formulated as a linear modelg, and permanent environmental effects, p, isZ is an incidence matrix relating the individuals to their observed values. The random effects are multivariate GaussianG is the genomic relationship matrix  can be computed from the ratio of the estimate of βsnp and its standard error, that is a standard Wald test statistic, which is equivalent to using the test statisticThe z1 is asymptotically standard normal, where V and μ are computed at the estimated values of the variance components.Here V0 and μ0 are computed from a model where the SNP effect xsnpβsnp has been excluded. A computational advantage of using a score test is that the P‐values can be computed without having to estimate βsnp, but on the other hand the estimates of βsnp needs to be computed separately if required. The score test is implemented in the mmscore function in GenABEL for instance . This can be performed in a computationally efficient way:V0=ΓΛΓ′Eigen decompose ΛΓ−0.5Rotate the linear model to obtain a new, but equivalent, model having independent residuals by pre‐multiplying the left‐ and right‐hand sides with μ and β using ordinary least squares and subsequently compute the sum of squared residuals to get the P‐value using a Wald test for the null hypothesis H0:βsnp=0.Estimate For each SNP fit, the generalized least squares The statistic V0 and can be applied to arbitrary covariance structures, and by setting V0 equal to the identity matrix the model reduces to ordinary linear regression. The hglm package in r  on chromosomes 1–24. 1118 collared flycatchers from the Swedish island of Öland (56∘44′N 16∘40′E) were genotyped on an Illumina iSelect BeadChip . For a Gaussian trait, yu, was simulated as a Gaussian trait described above. The observed phenotype, y, was subsequently given a value y = 1 for yu>τ and y = 0 otherwise, where τ is a given threshold.The binary trait values were simulated using a threshold model where the underlying phenotype, σg2=1, σp2=1, and σe2=1, together with a fixed additive SNP effect of 0·5 (i.e. the difference between homozygotes is 1·0).If otherwise not stated, the data were simulated using the flycatcher data and with random effects having variance components of Two main data structures were simulated. In the balanced scenario, each individual had two observations, and in the unbalanced scenario, the number of observations per individual was on average 2·0 with a variance of 2·0.−log10P‐values  of the genetic variance when no permanent environmental effect was simulated. For populations having a large proportion of closely related individuals, the need to model the two variance components separately increases, whilst in a population of less related individuals it might be sufficient to only include σg2 in the model. In a population of less related individuals, σ^g2 is expected to absorb most of the permanent environmental effects and thereby produce correct P‐values for the SNP effects. However, in our proposed method it is easy to include both variance components and is suggested as the default model.For the flycatcher data, where several individuals are highly related, the two variance components ed Table , althougFor the simulated binary phenotype data Table , the proλ was close to 1·00 for all proportions. The model therefore seems to be suitable for binary data, but the power to detect causal SNPs decreases when the proportion of successes for the analysed trait decreases.The expected and estimated SNP effects for various binary proportions Table  coincider package implementation, RepeatABEL, gives a user‐friendly interface for fitting data having repeated observations, and the proposed algorithm is fast, which makes variance component modelling in GWAS feasible. Our simulations showed that in the case of unbalanced data and data where there is between year variation, inclusion of repeated measures in the analysis increases the power of a GWAS to detect causal variants . The over‐estimation of polygenic effects does not seem to affect the detection of significant SNP effects in the GWAS. However, if large SNP effects are detected and the focus is on estimating the polygenic variance then one should fit a linear mixed model including both the random polygenic effects and the detected SNPs (having a significant effect) as explanatory variables, which is feasible since this model only needs to be fitted once after performing the GWAS.Our proposed method is a two‐stage method where the distribution of the residuals and random effects are estimated in a preliminary model that does not include the SNP effects. In the second stage, a model including the SNP effect is fitted. Consequently, part of the SNP effects will be captured by the random polygenic effect fitted in the first preliminary model. This is seen in the results of Table et al. In most previous analyses, repeated measurements in natural populations have been treated as nuisance information and the GWAS performed on average individual values. Hence, repeated measures are considered as unnecessary extra information complicating the analysis. By contrast, the RepeatABEL package takes advantage of repeated measurements to increase power and add information and the analysis is rather easy to perform for users acquainted with the R environment and GenABEL (Aulchenko One of the earliest methods proposed for GWAS having related individuals was GRAMMAR (Aulchenko, De Koning & Haley et al. (et al. (et al. et al. et al. A couple of methods have been developed recently for GWAS having repeated observations from unrelated individuals. The GEE method proposed by Sitlani et al.  does not (et al.  proposed. et al.  unless uet al. In our simulation study, we found that the crude method of using average individual phenotypic values as response in a GWAS works quite well as long as there are no substantial yearly phenotypic variation and random polygenic effects are included in the model. A possible explanation to this rather surprising result is that the individuals in our simulations are moderately related, and the genomic relationship matrix therefore resembles an identity matrix to some extent. Consequently, the random polygenic effects pick up most of the permanent environmental effects too, when a model using average phenotypes as response is used and random polygenic effects are included in the model. However, long‐term natural studies regularly report significant year effects, ranging from explaining only 2% of the variation to more than 30% (e.g. Qvarnström, Brommer & Gustafsson The repeated measurements model implemented in the RepeatABEL package has a moderate but substantial effect on the power to detect QTL in GWAS of natural populations. The benefit of the method mainly depends on the variance of temporal effects, such as year and age effects, and allows for the estimate of these effects in a GWAS framework. We believe that this development will lead to new findings in GWAS and an increased understanding of evolutionary ecology. Furthermore, understanding evolution driven by natural selection requires studies of wild populations, and with the ever‐cheapening of sequencing, the precision of these studies is increasing. Posing and answering relevant questions in evolutionary biology, including those related to populations' ability to adapt and persist in changing environments, as well as basic research questions about life‐history processes such as reproduction and senescence, require that we ensure that the methods we use suit the data we have.Appendix S1. Transforming underlying effects to observed effects for binary traits.Click here for additional data file.Appendix S2. RepeatABEL vs. GRAMMAR.Click here for additional data file.


Although markers identified by genome-wide association studies have individuallystrong statistical significance, their performance in prediction remains limited. Ourgoal was to use animal breeding genomic prediction models to predict additive geneticcontributions for systolic blood pressure (SBP) using whole genome sequencing datawith different validation designs.π. Estimates of predictive accuracy were comparedusing common single-nucleotide polymorphisms (SNPs) versus common and collapsed SNPs,and for prediction within and across families.The additive genetic contributions of SBP were estimated via linear mixed model. Rarevariants (MAF<0.05) were collapsed through the k-means method to create a"collapsed single-nucleotide polymorphisms." Prediction of the additive genomiccontributions of SBP was conducted using genomic Best Linear Unbiased Predictor(GBLUP) and BayesC2 h= 0.18). BayesCπ had slightly betterprediction accuracies than GBLUP. In both models, within-family predictions hadhigher accuracies both in the training and testing set than didacross-family design.Collapsing rare variants via the k-means method and adding to the common SNPs did notimprove prediction accuracies. The prediction model, including both pedigree andgenomic information, achieved a slightly higher accuracy than using either source ofinformation alone.The additive genetic variance of SBP contributed to 18% of the phenotypic variance(Prediction of genetic contributions to complex traits is feasible using whole genomesequencing and statistical methods borrowed from animal breeding. The relatedness ofindividuals between the training and testing set strongly affected the performance ofprediction models. Methods for inclusion of rare variants in these models need moredevelopment. As reIn this study, we applied animal breeding whole genome prediction methods to dataprovided by the Genetic Analysis Workshop 18 (GAW18) to predict the additive geneticcontributions of a complex trait, systolic blood pressure (SBP), in humans. As part ofthis study, we explored 2 methods for validation and the k-means method to collapse andinclude rare variants into the prediction model.β and p = 5).Variance ispartitioned into the additive genetic effect (u) and the repeatedenvironmental effect (c) of each individual. The additive genetic effect(u) was estimated based on degree of additive relatedness determined frompedigree structure. The repeated environmental effect was the environmental effect onan individual's phenotype that is constant across (or common to) repeated measures onthat individual, and independent between different individuals, which was defined byfitting individual identity as an additional random effect [n) of916 individuals (q) without missing phenotype, and included information fromevery examination,We used the real data provided by GAW18, including information from up to 4measurements of SBP per individual. Observed variation in SBP is a function ofgenetic and environmental factors . A lineam effect .The liney is an n  × 1 vector of SBP measurements; X is ann × p matrix containing fixed effects variables including year ofexamination, age, sex, medications usage, and tobacco smoking; β is ap  × 1 vector of fixed effects parameters;Z is an n × q matrix containing dummy variables and relating each of the additivegenetic effect to an individual's phenotype; q  × 1 vector of additive geneticeffects for all individuals where A is a q  × qpedigree-based kinship matrix; T is an n  × q matrixcontaining dummy variables and relating each of the repeated environmental effect toan individual's phenotype; q  × 1 vector of random repeatedenvironmental effects where I is a q  × q identitymatrix assuming independent repeated environmental effects among differentindividuals; and error term where The narrow-sense heritability   using the rounded dosages given by GAW18.Based on 139 unrelated founders from all 20 families, the whole genome sequencemarkers provided in GAW18 were pruned using PLINK 1.07 [Even though many approaches have been developed for collapsing rare variants to testin association studies , approacTo be consistent with the 3 level genotypes of common SNPs, the k-means method wasapplied to each 100-rare-SNP vector generated to partition genotypes into 3 clusters,in which each of 835 individuals belonged to the cluster with the nearest mean ofindividuals in that cluster to minimize the within-cluster sum of square error. Afterclustering, individuals in the same cluster are expected to be genetically closer toeach other compared to individuals from different clusters. Individuals in thecluster with the largest, medium, and smallest cluster size were assigned a collapsedSNP genotype to be 0, 1, and 2, respectively, and the MAF was calculated. A total of26,845 collapsed SNPs were formed from 2,683,921 rare SNPs.By testing different window sizes across all chromosomes, we found that the number ofcollapsed SNPs with a MAF<0.05 decreased as the window size increased Figure. A largeπ, were trained using 2validation designs described in thenext section. The general statistical model is below, and the predicted genomic valueof each individual was defined as the fitted value The whole genome prediction models, genomic Best Linear Unbiased Predictor (GBLUP)and BayesCμ is the overall mean, ais a vector of random additive effects of all loci (of SNP set 1 or set 2) withgenotype matrix M, and error term where K is the whole genome marker-based relationship matrix. The estimates ofmarker effect In GBLUP, it is assumed that sen et al.π, besides sharing the common variance among all loci, a priordistribution was assigned to the additive effect of each locus depending on thevariance π that the given SNPhas zero effect (formula (4)). The algorithm was implemented as in Habier et al[In BayesCier et al.π were implemented via Gibbs sampling. The ratioπ. The initial value for π in BayesCπwas set as 0.9. The total number of iterations in both models was 12,000 with aburn-in of 2000 and a mining rate of 10. Longer chains  did not improve the correlation betweenpredicted value and true value. The correlations were also consistent among multipleshort chains with the same length of 12,000 iterations.Both GBLUP and BayesCTwo different validation designs were used to evaluate the predictive ability ofdifferent models and using different SNP sets. The first was a within-familyprediction with the first 3generations from all 20 families  in thetraining set (TRN), and their descendants from the fourthand fifthgenerations  in the testing set (TST). The second was an across-family predictionusing 5-fold cross-validation. To balance sizes of training and TSTs in eachreplicate of cross-validation, 20 families were ranked by their family sizes, andthen every five families were randomly assigned to five different folds (fourfamilies in each fold) to get about 668 individuals in TRN and about 167 in TST.The accuracies of genomic prediction were measured by the correlation of theestimated additive genetic contributions The prediction accuracies were compared between genome and pedigree based additivegenetic contributions 2 values (coefficient of determination of the linear regression) fromthe model fitting were reported to be the predictive accuracies using genomic only,parent average only, and both genomic and parent average information.where The additive genetic contributions of SBP ranged from −18.9 to 15.8 with mean 0.2and SD 3.5. The estimated variance components, additive genetic variance to 0.37 . When thπ. In general, the BayesCπ outperformed GBLUP basedon both correlation and MSE, althoughthe differences were small (mostly <5%). Themarkers in GBLUP are assumed to share the same normal distribution, whereasBayesCπ fits only a small fraction of the available markers with anassumption that most loci are expected to have zero contribution to the independentvariable, and the remaining nonzero marker effects are normally distributed. It ispossible that the number of causal loci for SBP is relatively small, which is closer tothe assumption of BayesCπ. Similar improvement of BayesCπover GBLUP was found by previous studies [Table  studies ,18.π and GBLUP, the within-family prediction had a strong advantageover across-family prediction, achieving a higher correlation between predicted valueand true value, as well as a decreased MSE. For within-family prediction, TST was formedby the descendants of people in TRN, that is, closely related to each other. For theacross-family prediction, individuals in TST and TRN were from different families, thatis, unrelated to each other. Thus, the relatedness of individuals between TRN and TSTstrongly affected the performance of prediction models. This result was consistent witha genomic prediction study of human height [Validation designs of prediction greatly affected the prediction accuracy. In bothBayesCn height  and seven height ,20.π in within-family prediction indicated that 14% oftotal variants , on average, in TRN contributed when using SNPset 1,and 32% of the additive variance was explained by these SNPs. Thus the whole genomesequence variants detected a large proportion of the heritability (32%). The rest of theheritability might result from rare variants. We attempted to explore an approach in ourmodels to include rare variants, but the addition of the collapsed SNPs did not improvethe prediction accuracies (performance of SNPset 2 vs. set 1 in Table The results from BayesC2) using pedigree based information only is 0.455, higher thanthe 0.353 using whole genome markers only. Combining information from both sources, theprediction accuracy of 0.458 slightly outperformed either of the single sourceprediction. Including the parent average breeding value in genomic evaluations inanimals is a common practice [In within-family prediction, there were 289 individuals from TST with at least 1parentin TRN. Based on the results from the linear regression model (6), the predictionaccuracy  and is not a good candidate forgenomic prediction. With the limitations of the Genetic Analysis Workshop (GAW) data set(blood pressure was the only outcome) and GAW timeline, we did not have an opportunityto explore the impact of our model choice. Strategies that may improve the accuracy ofgenomic prediction might be (a) increasing the reference population size, (b) using atrait with a higher heritability, and (c) including information of relatives in thereference population.π, we showed that prediction of additive genetic contributionsfor a complex trait using whole genome sequencing data in humans is feasible. Theprediction accuracy is strongly affected by the relatedness of individuals between TRNand TST. A large proportion of the additive variance can be explained through inclusionsof whole genome sequence information in the model. The k-means method as implemented inour study for inclusion of rare variants did not improve the prediction. Differentimplementations of k-means or other methods for including rare variants in genomicprediction should be tested. Including both genomic and parent average information inthe prediction model gave a slightly better accuracy than using either one of themalone.By using prediction models borrowed from animal breeding, GBLUP, andBayesCThe authors declare that they have no competing interests.CY designed the study and wrote the manuscript. NL conducted the k-means method andcomputations. CY and NL were responsible for statistical analysis. KAW provided guidancein statistical methodologies. CDE coordinated data acquisition through GAW and providedfeedback on conceptual development. KJM and KEL provided oversight and feedback fromconception of design through manuscript submission. All authors read, revised, andapproved the final manuscript.


Several genes in the associated regions are known to play roles in craniofacial development or in syndromes affecting the face: MAFB, PAX9, MIPOL1, ALX3, HDAC8, and PAX1. We also tested genotype-phenotype associations reported in two previous genome-wide studies and found evidence of replication for nasal ala length and SNPs in CACNA2D3 and PRDM16. These results provide further evidence that common variants in regions harboring genes of known craniofacial function contribute to normal variation in human facial features. Improved understanding of the genes associated with facial morphology in healthy individuals can provide insights into the pathways and mechanisms controlling normal and abnormal facial morphogenesis.Numerous lines of evidence point to a genetic basis for facial morphology in humans, yet little is known about how specific genetic variants relate to the phenotypic expression of many common facial features. We conducted genome-wide association meta-analyses of 20 quantitative facial measurements derived from the 3D surface images of 3118 healthy individuals of European ancestry belonging to two US cohorts. Analyses were performed on just under one million genotyped SNPs (Illumina OmniExpress+Exome v1.2 array) imputed to the 1000 Genomes reference panel (Phase 3). We observed genome-wide significant associations (p < 5 x 10 There is a great deal of evidence that genes influence facial appearance. This is perhaps most apparent when we look at our own families, since we are more likely to share facial features in common with our close relatives than with unrelated individuals. Nevertheless, little is known about how variation in specific regions of the genome relates to the kinds of distinguishing facial characteristics that give us our unique identities, e.g., the size and shape of our nose or how far apart our eyes are spaced. In this paper, we investigate this question by examining the association between genetic variants across the whole genome and a set of measurements designed to capture key aspects of facial form. We found evidence of genetic associations involving measures of eye, nose, and facial breadth. In several cases, implicated regions contained genes known to play roles in embryonic face formation or in syndromes in which the face is affected. Our ability to connect specific genetic variants to ubiquitous facial traits can inform our understanding of normal and abnormal craniofacial development, provide potential predictive models of evolutionary changes in human facial features, and improve our ability to create forensic facial reconstructions from DNA. Numerous lines of converging evidence indicate that variation in facial morphology has a strong genetic basis. These include the results of human heritability studies using twin and parent-offspring designs –5, MendePAX3 variants and anatomical changes in interorbital region, an intriguing finding given that mutations in PAX3 cause Waardenburg Syndrome type 1 which is characterized by hypertelorism among other morphological abnormalities. Both studies also reported significant associations with measures of nasal projection in their discovery cohorts, although different genomic regions were implicated. In addition, several more focused candidate gene studies of loci implicated in craniofacial syndromes or in developmental pathways involved in craniofacial development have connected one or more craniofacial dimensions or aspects of shape with a small number of common genetic variants [FGFR1 and normal variation in craniofacial morphology, but in each case a different constellation of traits was involved. It is notable that none of the genes from these studies, including FGFR1, were identified in the two previous GWA studies of facial morphology.To date, only a few studies have explicitly tested for associations between aspects of normal human facial morphology and common genetic variants. Among these, two genome-wide association (GWA) studies have been carried out on healthy individuals of European ancestry using 3D facial imaging and a combination of traditional and more advanced morphometric methods to derive phenotypes ,18. Betwvariants –28. At lvariants ,25,28 haFig 1) derived from 3D surface images in two well-characterized samples of unrelated White individuals of European ancestry from the USA: a sample comprised of 2447 individuals collected through the University of Pittsburgh  and an independent sample of 671 individuals collected under the direction of the University of Colorado . All participants were genotyped using the same SNP array (Illumina OmniExpress+Exome v1.2), which included just under one million SNPs, and were imputed to the 1000 Genomes reference panel (Phase 3). We conducted association tests in each sample separately and combined the results using a meta-analytic approach.Thus, while prior studies have detected a handful of biologically plausible genes associated with variation in craniofacial features, it is clear that these efforts are just scratching the surface and the potential for additional discovery is great. In the current study, we performed GWA analyses on a set of 20 craniofacial measurements commonly used in clinical assessment . One of the associations also exceeded our study-wide significance threshold of p < 5 x 10−9, calculated based on 10 independent traits  and the other at 20q12 . Notably, the chromosome 20 association exceeded our strict threshold for study-wide statistical significance. For intercanthal width, we observed two significant associations: one at 1p13.3  and the other at Xq13.2 . There were also significant associations with nasal width at 20p11.22  and nasal ala length at 14q11.2 . We observed a second independent peak on chromosome 20 for nasal width located 371kb upstream of the main peak. The second peak remained  after conditional association analysis adjusting for the effects of rs2424399 on nasal width. Finally we observed a significant association with upper facial depth at 11q22.1 . For all of the above associations, the results were driven primarily by the larger Pittsburgh dataset. The cranial base width (14q21.1), intercanthal width (1p13.3) and upper facial depth associations were at least nominally significant (p < 0.05) in both datasets. Sample-specific association results for all SNPs with p-values less than 5 x 10−7 are listed in S2 Table for the Pittsburgh sample and S3 Table for the Denver sample.We observed two significant associations for cranial base width: one at 14q21.1 . In our data, nasal ala length showed a nominally significant association (p = 0.018) with rs1982862, an intronic variant in CACNA2D3. Conversely, we found no evidence of association between this measure and rs11738462, an intronic variant in C5orf64. The previously observed association between PAX3 and the position of nasion relative to the orbits could not be tested directly. However, we found no evidence of association between the implicated SNP rs7559271 and intercanthal width, which captures aspects of interorbital septum morphology. As a further exploratory analysis we also looked at the association between rs7559271 and several vertical or projective measurements involving nasion, but no significant associations were found for any of these traits. For Liu et al. [Table 2). We observed a strong association (p = 1.70 x 10−5) between nasal ala length and rs4648379, an intronic variant in PRDM16. We also observed an association between rs6555969, a SNP near C5orf50 and upper facial depth (p = 0.005), which is a reasonable approximation of the zygion-nasion distance reported by Liu et al. [TP63, we used measures of intercanthal and outercanthal width; however, we did not observe an association with either measure. Finally, Liu et al. [PAX3, C5orf50 and COL17A1 and the position of nasion relative to the orbits. We tested these three SNPs in our dataset against intercanthal width, a trait involving roughly similar anatomical components. Notably, we found associations between rs974448  and rs6555969  and intercanthal width.In an attempt to replicate the main findings from the prior two GWA studies in Europeans, we tested previously implicated SNPs against traits from our Pittsburgh dataset that capture similar aspects of morphology. This was not possible for every prior genotype-phenotype association given differences in the measurements available. With that limitation in mind, the Pittsburgh dataset was chosen for comparison because it was the larger of our two datasets and the phenotyping protocol was most similar to prior GWA studies. For Paternoster et al. , we atten p = 0.0 with rs1n p = 0.0 with rs1n p = 0.0 with rs1Fig 1A), intercanthal width , nasal width , nasal ala length , and upper facial depth . The most significant of these, meeting the strict study-wide threshold for significance , was the association of cranial base width at 20q12 410kb downstream of MAFB, a transcription factor previously implicated in orofacial clefts [MAFB SNP associated with clefting was 250kb away and not in LD with the SNP observed here. In addition to orofacial clefting, mutations in MAFB cause multicentric carpotarsal osteolysis syndrome, which includes mild facial anomalies. These phenotypes are consistent with the developmental role of MAFB in regulating the migration of cranial neural crest cells during the patterning of skeletomuscular features of the head [MAFB in normal facial variation.Based on meta-analysis, we observed seven associated loci for five facial traits: cranial base width  signals included SNPs implicated in either of the previous two European-focused GWA studies [PRDM16) [CACNA2D3 [PRDM16) showing evidence at p = 1.70 x 10−5. Both prior GWA studies reported an association between SNPs at 2q35 (PAX3) and morphology of the interorbital septum. We tested these SNPs and found an association between rs974448 and intercanthal width (p = 0.002), lending some additional support to the claim that common variants in PAX3 might influence aspects of normal facial morphology.While none of our genome-wide or suggestive  have not yielded statistically significant associations ,18. One Despite these limitations, we have found evidence of genetic association between chromosomal regions containing genes with important roles in facial development and quantitative traits that characterize key features of the normal human craniofacial complex. In addition to improving our general knowledge of the factors that underlie the diversity of facial forms we see in humans, these genotype-phenotype associations may help us better understand the wide range of phenotypic expression and severity seen in some rare genetic syndromes. Common variants in a number of different genes or regulatory elements may contribute to the expression of dysmorphic phenotypes present in these conditions. Moreover, such associations in healthy individuals may aid the search for clues to the etiology of much more common craniofacial anomalies. For example, three of the traits reported here  have all been previously implicated as potential phenotypic risk factors for orofacial clefting, the most common craniofacial birth defect in humans . WeinberInstitutional ethics (IRB) approval was obtained at each recruitment site and all subjects gave their written informed consent prior to participation .S4 Table. In both samples, subjects were excluded if they had a personal history of facial trauma, a personal history of facial reconstructive or plastic surgery, a personal history of orthognathic/jaw surgery or jaw advancement, a personal history of any facial prosthetics or implants, a personal history of any palsy, stroke or neurologic condition affecting the face, a personal or family history of any facial anomaly or birth defect, and/or a personal or family history of any syndrome or congenital condition known to affect the head or face.Our study included two independent samples, each comprised of unrelated self-described White individuals of European ancestry from the United States. The Pittsburgh sample included 2447 unrelated individuals ranging in age from three to 49 years. The majority of these participants (n = 2272) were recruited at research centers in Pittsburgh, Seattle, Houston and Iowa City as part of the FaceBase Consortium’s 3D Facial Norms Dataset, described in detail by Weinberg et al. . The remS22 Fig). Landmarks were collected manually in the Pittsburgh sample as described in Weinberg et al. [Fig 1 and listed in S5 Table. For bilateral measurements, values were summed across the left and right sides in order to minimize the number of traits tested. Trait values were inspected for outliers by computing age- and sex-specific z-scores.3D facial surfaces were captured using digital stereophotogrammetry, a standard imaging method resulting in high-density, geometrically accurate point clouds representing the surface contours of the human body . Facial g et al. . An autog et al. . These mS6 Table shows the number of SNPs omitted and retained for each quality filter.For each study sample, genotyping was performed by the Center for Inherited Disease Research (CIDR). Saliva samples were used to genotype 3,186 participants for 964,193 SNPs on the Illumina  OmniExpress+Exome v1.2 array plus 4,322 custom SNPs chosen in regions of interest based on previous studies of the genetics of facial variation. In addition, 70 duplicates and 72 HapMap control samples were genotyped for quality assurance purposes. Data cleaning was performed by the University of Washington Genetics Coordinating Center (UWGCC) using standard analysis pipelines implemented in the R Environment for Statistical Computing, as previously described . These aImputation was performed to capture information on unobserved SNPs as well as sporadically missing genotypes among genotyped SNPs, using haplotypes from the 1000 Genomes Project  Phase 3 2 < 0.1 in a sliding window of 10 Mb), was used to assess population structure. Supplemental S23 Fig depicts the observed genetic structure of the population across the first two principal components of ancestry . Linear regression was used to test the association between each PC, as the dependent variable, and each SNP in the genome. These analyses confirmed that none of the first 20 principal components were due to local variation in specific genomic regions.Principal component analysis using 96,700 autosomal SNPs pruned from the total panel based on call rate (> 95%), MAF (> 0.05), and LD  using linear regression in order to generate 20 adjusted phenotypes . The inclusion of age and age2 as covariates was done in an effort to adjust for both linear and non-linear aspects of age on the phenotypes. After model fitting different sets of covariates, including more complicated spline functions, we settled on a combination of age and age2 as the most reasonable approach based on akaike information criterion values calculated across age-adjustment models. Linear models were then used to test genetic association between each phenotype and each SNP, under the additive genetic model, while simultaneously adjusting for the first four principal components of ancestry. For SNPs on the X chromosome we coded hemizygous males as 0/2 so they are on the same scale as 0/1/2 females. Analyses were performed separately for the Pittsburgh and Denver cohorts, and combined via inverse variance-weighted meta-analysis. To appropriately model SNP effects, we required that the minor allele be present in at least 30 participants, corresponding to a MAF threshold of 0.6% in the Pittsburgh cohort and 2% in the Denver cohort. SNPs meeting the minor allele count criterion in both Pittsburgh and Denver cohorts were included in the meta-analysis. The final number of genotyped SNPs available for analysis after minor allele filtering was 659,955 for the Pittsburgh sample, 638,772 for the Denver sample, and 637,391 for the meta-analysis. The number of imputed and total (genotyped plus imputed) SNPs is available in S7 Table.Prior to genetic analysis, each of the 20 linear distance measures was adjusted for the effects of sex, age, age−8  for genome-wide statistical significance. Because we expect many of our traits to be correlated, we used the eigenvalue method described by Li and Ji [−9 (i.e. p < 5 x 10−8 divided by 10). Because these thresholds are very conservative, we also reported “suggestive” evidence of association of p < 5 x 10−7 in . Phenotypes were generated using the R Environment for Statistical Computing, and genetic association was performed using PLINK [Given the large number of tests, we used the conventional threshold of p < 5 x 10i and Ji  to deterng PLINK .http://www.ncbi.nlm.nih.gov/gap) at accession number: phs000949.v1.p1. The raw source data for the phenotypes–the 3D facial surface models–are available for the 3D Facial Norms dataset through the FaceBase Consortium (www.facebase.org). Finally, searchable results datasets of the p-values from the studies reported here are available through the FaceBase Human Genomics Analysis Interface (facebase.sdmgenetics.pitt.edu).All of the phenotypic measures and genotypic markers used here are available to the research community through the dbGaP controlled access repository (S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S1 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S2 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S3 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S4 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S5 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S6 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S7 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S8 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S9 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S10 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S11 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S12 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S13 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S14 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S15 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S16 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S17 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S18 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S19 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S20 Fig−8 for genome-wide significance and 5 x 10−7 for suggestive significance.(A) meta-analysis results, (B) Pittsburgh sample results, and (C) Denver sample results. Lines for p-value thresholds set at 5 x 10(PDF)Click here for additional data file.S21 FigLocusZoom plots comparing the (A) discovery analysis, and (B) conditional analysis for the observed genetic association of nasal width near PAX1 at 20p11.22. Genetic association  is shown for genotyped SNPs depicted as stars and imputed SNPs depicted as circles. Shading of the points represent the linkage disequilibrium (r2) between each SNP and the rs2424399 , indicated by purple shading. The blue overlay shows the recombination rate (right y-axis). Positions of genes are shown below the plot. In the discovery analysis, a possible second peak in low-LD with the rs2424399 was observed around chromosomal position 22.0 Mb. After conditioning on rs2424399, variants at position 22.0 Mb showed some independent evidence of association, although not meeting genome-wide or suggestive thresholds for significance.(PDF)Click here for additional data file.S22 FigLandmarks shown in frontal view (A) are n = nasion; prn = pronasale; sn = subnasale; ls = labiale superius; sto = stomion; li = labiale inferius; sl = sublabiale; gn = gnathion; en = endocanthion; ex = exocanthion; al = alare; sbal = subalare; cph = crista philtra; ch = chelion . Landmarks shown in the lateral view (B) are ac = alar curvature point and t = tragion .(PDF)Click here for additional data file.S23 FigThe proportion of total genetic variation explained by each principal component of ancestry is indicated on the axis.(PDF)Click here for additional data file.