Supplemental Digital Content is available in the text. Mendelian randomization investigations are becoming more powerful and simpler to perform, due to the increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations with risk factors and disease outcomes. However, when using multiple genetic variants from different gene regions in a Mendelian randomization analysis, it is highly implausible that all the genetic variants satisfy the instrumental variable assumptions. This means that a simple instrumental variable analysis alone should not be relied on to give a causal conclusion. In this article, we discuss a range of sensitivity analyses that will either support or question the validity of causal inference from a Mendelian randomization analysis with multiple genetic variants. We focus on sensitivity analyses of greatest practical relevance for ensuring robust causal inferences, and those that can be undertaken using summarized data. Aside from cases in which the justification of the instrumental variable assumptions is supported by strong biological understanding, a Mendelian randomization analysis in which no assessment of the robustness of the findings to violations of the instrumental variable assumptions has been made should be viewed as speculative and incomplete. In particular, Mendelian randomization investigations with large numbers of genetic variants without such sensitivity analyses should be treated with skepticism. Although these assumptions are strict, the causal estimate from an instrumental variable analysis is a valid test statistic for the causal null hypothesis without requiring the assumptions of linearity, homogeneity, or monotonicity.10 In any case, the causal effect of intervention on a risk factor is likely to depend on several aspects of the intervention , and therefore will not precisely correspond to the estimate from a Mendelian randomization analysis.11 Hence, we would urge practitioners to view the assessment of causality as the primary result of a Mendelian randomization, and not to interpret any causal estimate too literally.12We further assume that all valid instrumental variables identify the same causal parameter; we return to this assumption in the discussion. For this interpretation to hold, it is necessary for certain parametric assumptions to hold. In this article, we assume that the effects of (i) the instrumental variables on the risk factor, (ii) the instrumental variables on the outcome, (iii) the risk factor on the outcome are linear without effect modification; and (iv) the association of the genetic variant with the risk factor is homogeneous in the population.13We also assume that the genetic variants are mutually independent in their distributions, although extensions are available for most of the analysis methods in the case of correlated variants, provided that the correlation structure is known.14 However, there are many well-documented ways in which the instrumental variable assumptions may be violated for any particular genetic variant, such as pleiotropy, linkage disequilibrium, and population stratification.15Genetic variants are particularly suitable candidate instrumental variables, as they are fixed at conception, and hence cannot be affected by environmental factors that could otherwise lead to confounding or reverse causation.CRP gene region for C-reactive protein16), or a regulator or inhibitor of the protein . Using one or more variants from such a gene region as instrumental variables would be ideal for a Mendelian randomization analysis, as these genetic variants would be the most likely to satisfy the instrumental variable assumptions, and the most informative proxies for intervention on the risk factor.18 However, such genetic variants do not exist for many risk factors.For risk factors that are soluble protein biomarkers, there is often a gene region that encodes the protein  on genetic associations with the risk factor can be combined with summarized data on genetic associations with the outcome (that are often publicly available for download) to provide causal effect estimates, under the assumption that the genetic variants are all instrumental variables.23 Using multiple genetic variants increases the power of a Mendelian randomization investigation compared with an analysis based on a single variant.24 However, even if only one of the genetic variants is not a valid instrumental variable, the causal estimate based on all the variants from a conventional Mendelian randomization analysis will be biased and type 1  error rates will be inflated.26The approach of using multiple genetic variants in different gene regions is particularly suitable for complex risk factors that are multifactorial and polygenic, such as body mass index,In this article, we describe a range of sensitivity analyses that either support or question the validity of causal inference from a Mendelian randomization analysis with multiple genetic variants. These sensitivity analyses will be useful for judging whether a causal conclusion from such an analysis is plausible or not. We focus on those sensitivity analyses that can be implemented using summarized data only. We consider approaches under two broad categories: methods for assessing the instrumental variable assumptions, and robust analysis methods that rely on a less stringent set of assumptions than a conventional Mendelian randomization analysis.16 and using 17 genetic variants  that have been shown to be associated with CRP at a genome-wide level of significance in a large meta-analysis—see eFigure in Ref. 28 beta-coefficients represent per allele log odds ratios for CAD risk. Ethical approval for the analyses using four genetic variants in the CRP gene region was granted by the Cambridgeshire ethics review committee; for the analyses using 17 genetic variants associated with CRP concentrations and with CAD risk, ethical approval was granted to the constituent studies by local institutional review boards.We illustrate the approaches using the example of estimating the causal effect of C-reactive protein (CRP) on coronary artery disease (CAD) risk using four genetic variants in the CRP gene region,CRP gene region is null , whereas the “causal” estimate using an inverse-variance weighted method based on the genome-wide significant variants (a less reliable approach)22 is negative . Software code for performing the proposed sensitivity analyses is provided in eAppendix A.1 and A.2 (http://links.lww.com/EDE/B114).For reference, the causal estimate based on the genetic variants in the The first set of approaches we consider are those to assess whether the instrumental variable assumptions are likely to be satisfied or not for a set of genetic variants. We consider in turn the assessment of the association with measured confounders, the exploitation of a natural experiment in the form of a gene–environment interaction, examination of a scatter plot combined with a heterogeneity test, and of a funnel plot combined with a test for directional pleiotropy.16 shows the associations of the four variants in the CRP gene region with a range of potential confounders. Associations are no stronger than would be expected by chance alone.The assumption that an instrumental variable is not associated with confounders of the risk factor–outcome association is not fully testable, as not all confounders will be known or measured. However, the associations of genetic variants with measured covariates can be assessed. Lack of association of the instrumental variable with measured covariates does not imply lack of association with all confounders; however, an association with a measured covariate should be investigated carefully for a potential pleiotropic effect of the genetic variant. Figure 29If there are covariates that by biological considerations should be downstream consequences of the risk factor, then the associations of genetic variants with these covariates can be assessed as positive controls to give confidence that the function of the genetic variants matches the known consequences of the risk factor. For instance, inhibition of interleukin-1 by the drug anakinra has been observed to lead to decreased levels of C-reactive protein and interleukin-6 in clinical trials. If genetic variants associated with interleukin-1 are also associated with both these covariates, this makes it more plausible that the variants are good proxies of intervention on interleukin-1 levels.23A benefit of the use of multiple genetic variants is the possibility to differentiate between pleiotropy and mediation, two mechanisms by which a genetic variant may be associated with a measured covariate Figure . If a geFor instance, if increasing body mass index leads to increased blood pressure, then genetic variants that are instrumental variables for body mass index should also be associated with blood pressure. If multiple genetic variants that are candidate instrumental variables for body mass index are all concordantly associated with blood pressure, then it is plausible that the associations are due to mediation, not pleiotropy. In contrast, if only one or two variants are associated with blood pressure, then this is likely to be a manifestation of pleiotropy. Pleiotropy and mediation are not mutually exclusive (both could occur for the same covariate); however, this approach may give an insight into whether the association relates to a single genetic variant or to variants associated with the risk factor more widely.30In some cases, valid causal inference may still be possible even if a genetic variant has a pleiotropic association with a measured covariate; for instance, by adjusting for the covariate in the analysis model. However, if the Mendelian randomization investigation is performed using summarized data, then the investigator is unlikely to be able to adjust for covariates. An alternative approach with summarized data is a multivariable Mendelian randomization analysis, in which genetic associations with the outcome are regressed on the genetic associations with the risk factor and covariates in a multivariable weighted regression model.P < 0.01), and a liberal threshold to maximize power (such as a Bonferroni-corrected threshold taking into account the number of comparisons made).23 A similar approach was previously taken to assess the causal role of lipid fractions on CAD risk.31 If no causal effect is detected even in a liberal analysis, then the plausibility of a null causal finding increases.A practical difficulty of determining which variants to include in a Mendelian randomization analysis using measured covariates, aside from that of distinguishing between pleiotropy and mediation, is that of multiple testing. If there are large numbers of genetic variants and several measured covariates, then it is difficult to set a statistical significance threshold for rejecting a genetic variant as pleiotropic to balance between the desire to exclude invalid instrumental variables and the need to acknowledge the multiple tests. A sensible compromise is to consider multiple thresholds, for example, a conservative threshold to maximize robustness . To our knowledge, no systematic investigation has been conducted as to the degree that collider bias may lead to inappropriate causal inferences in a Mendelian randomization setting, although sensitivity analyses to assess the potential bias in the context of instrumental variable analysis with a single instrument are available.36One potential complication of such an analysis is the possibility of collider bias;37 Although it is possible for the instrumental variable assumptions to be violated for all of the genetic variants, it is unlikely that pleiotropic effects for many different genetic variants would all result in the same direction of association with the outcome in the absence of an underlying causal effect of the risk factor.38 This is particularly true if there is a dose–response relationship in the per allele associations with the risk factor and with the outcome. An example of this is the relationship between low-density lipoprotein cholesterol (LDL-c) and CAD risk. See Figure 3 in Ref. Even if the instrumental variable assumptions are in doubt for some or all of the variants, if several independent genetic variants in different gene regions are concordantly associated with the outcome, then a causal conclusion would seem reasonable.40 In economics, this test is known as an over identification test, as the same causal effect is identified by each of the instrumental variables.41 Heterogeneity can be assessed visually by a scatter plot of the genetic associations with the outcome ( for genetic variant ) against the genetic associations with the risk factor , together with their confidence intervals. Each point on these graphs represents a genetic variant, and the points should be compatible with a straight line through the origin under the null. Any point that substantially deviates from this line should be investigated for potential pleiotropy.In a Mendelian randomization setting, a heterogeneity test is a statistical assessment of the compatibility of instrumental variable estimates based on individual genetic variants.CRP gene region (left) demonstrates homogeneity of estimates, whereas the plot using genome-wide significant variants (right) demonstrates heterogeneity, with several clear outliers .Scatter plots for the example of CRP and CAD risk are given in Figure Q test on the causal estimates from each genetic variant , using the approximate standard errors . This can be performed in standard statistical software packages for inverse-variance weighted meta-analysis. The statistic is calculated asA statistical test for heterogeneity can be performed using Cochran’s  is the (fixed-effect) inverse-variance weighted estimate based on all the genetic variants, and  are the inverse-variance weights. This statistic can be calculated using only summarized data. It should have a chi-squared distribution with  degrees of freedom under the null hypothesis of homogeneity. The amount of heterogeneity can also be expressed using the I2 statistic.42 Other heterogeneity tests include the Sargan test,41 which can be performed using individual-level data, or a likelihood ratio test using summarized data.23 An initial visual inspection for heterogeneity is important, as a formal statistical test may have low power particularly when there are few genetic variants.43 In the example of CRP and CAD risk, the Q statistic using genome-wide significant variants from across the genome is 71.9 , indicating substantial heterogeneity.where The investigation of heterogeneity of causal estimates as an assessment of the instrumental variable assumptions relies on the assumption that all valid instrumental variables identify the same causal parameter. If not, then the heterogeneity test may over-reject the null.44) of the instrumental variable precisions   against the instrumental variable estimates  should be a symmetric funnel, in which more precise estimates are less variable. Any asymmetry in the funnel plot is a sign of directional pleiotropy (pleiotropic effects of genetic variants do not average to zero), meaning that causal estimates from the individual variants are biased on average. Although heterogeneity in causal estimates is concerning, provided that the pleiotropic effects of genetic variants are equally likely to be positive or negative, the overall causal estimate based on all the genetic variants may be unbiased. Directional pleiotropy is more serious, as it suggests that pleiotropic effects are not balanced, and thus that the overall causal estimate is biased. The funnel plot in the example of CRP on CAD risk for the genome-wide significant variants is shown in Figure A funnel plot , then under the InSIDE assumption (see later), the intercept is the average pleiotropic effect of a genetic variant; if the intercept differs from zero, then there is evidence of directional pleiotropy.46 In the example of CRP on CAD risk for the genome-wide significant variants, the P value for the test of directional pleiotropy is 0.61, indicating no evidence of directional pleiotropy.Egger regression is a method for detecting small study bias (often interpreted as publication bias) in a meta-analysis of separate studies.The second category of sensitivity analyses is that of robust analysis methods. Robust analysis methods allow different  assumptions than standard instrumental variable methods. In turn, we consider penalization methods, median-based methods, and Egger regression.We first consider methods in which the contribution of some genetic variants  to the analysis is downweighted . If the causal conclusion from a Mendelian randomization investigation depends only on a single genetic variant (particularly if the estimate from this variant is heterogeneous with those from other variants), then the result may be driven by a pleiotropic effect of that particular variant and not by the causal effect of the risk factor.The simplest way of performing a penalization method is to omit some of the variants from the analysis. This could be done systematically. For example, with a small number of genetic variants, the causal estimates omitting one variant at a time could be considered. Alternatively, it could be done stochastically. For example, we could consider estimates omitting (say) 30% of the genetic variants at a time by selecting the 30% of variants at random a large number of times, and calculating the causal estimate in each case. This sensitivity analysis has been undertaken for the effect of LDL-c on aortic stenosis. See eFigure in Ref. Q statistic for each genetic variant, and omitting any variant whose contribution to the statistic is greater than the upper 95th percentile of a chi-squared distribution on one degree of freedom (3.84). This approach has been applied for investigating the causal effect of lipid fractions on CAD risk.50 More formal penalization methods have been proposed using L1-penalization to downweight the contribution of outlying variants to the analysis in a continuous way.52 These methods have desirable theoretical properties, giving consistent estimates of the causal effect even if up to half of the genetic variants are not valid instrumental variables. However, they require individual-level data and a one-sample setting .A more focused approach to omitting genetic variants is to omit genetic variants from the analysis with heterogeneous instrumental variable estimates. This could be done by calculating the contribution to Cochran’s 51An alternative family of methods that gives consistent estimates when up to half the genetic variants are not valid instrumental variables, but that can be performed using summarized data rather than individual-level data, are median-based methods. If 50% or more of the genetic variants are valid instrumental variables, then the instrumental variable estimates for these variants will all be consistent estimates of the causal effect. In particular, this implies that the median of all the instrumental variable estimates based on the individual genetic variants will be a consistent estimate.53 Then, more precise instrumental variable estimates receive more weight in the weighted median function. The weighted median estimate is consistent under the assumption that genetic variants representing over 50% of the weight in the analysis are valid instruments. This is a subtly different assumption to the assumption that over 50% of the genetic variants are valid instruments, although it is not clear that one or other of the assumptions is more plausible generally. Confidence intervals for the median and weighted estimates can be estimated using bootstrapping.However, the median estimate is likely to be inefficient, as the individual instrumental variable estimates from each genetic variant receive equal weight in the analysis. An alternative is to construct a weighted median estimate, defined as the median of an empirical distribution in which each instrumental variable estimate appears with probability proportional to the inverse of its variance.46 This is the assumption that pleiotropic effects of genetic variants  are independent of instrument strength (known as the InSIDE assumption—Instrument Strength Independent of Direct Effect). This same assumption was considered by Kolesár et al.54 with individual-level data. The motivation for the Egger regression method is that, under the InSIDE assumption, stronger genetic variants should have more reliable estimates of the causal effect than weaker variants. Once the average pleiotropic effect of variants is accounted for through the intercept term in Egger regression, any residual dose–response relationship in the genetic associations provides evidence of a causal effect. The Egger regression estimate is consistent under the InSIDE assumption as the sample size tends to infinity if the correlation between the direct effects and instrument strength is exactly zero; otherwise it is consistent as the sample size and the number of genetic variants both tend to infinity. As previously stated, Egger regression assumes linearity and homogeneity in the associations between the genetic variants, risk factor, and outcome.The Egger regression method was introduced above as a test for directional pleiotropy; this test does not make any assumption about the genetic variants. However, under an assumption that is weaker than standard instrumental variable assumptions, the slope coefficient from the Egger regression method provides an estimate of the causal effect that is consistent asymptotically even if all the genetic variants have pleiotropic effects on the outcome.55 The Egger regression estimate may have much wider confidence intervals than those from other methods in practice, as it relies on variants having different strengths of association with the risk factor. A situation with many independent genetic variants having identical magnitudes of association with the risk factor and with the outcome would intuitively provide strong evidence of a causal effect; however, the Egger estimate in this case would not be identified.The InSIDE assumption may not be satisfied in practice, particularly if the pleiotropic effects of genetic variants on the outcome act via a single confounding variable. There is some evidence for the general plausibility of the InSIDE assumption, as associations of genetic variants with different phenotypic variables have been shown to be largely uncorrelated in an empirical study.53 The median-based and Egger regression methods have also been shown to have lower type 1  error rates than the inverse-variance weighted method in simulation studies with some invalid instrumental variables for finite sample sizes,53 although they were above the nominal level in the case of directional pleiotropy (for the median method), and when the InSIDE assumption was violated (for the Egger regression method).The Egger regression method gives consistent estimates if all the genetic variants are invalid instruments provided that the InSIDE assumption is satisfied, whereas the penalization and median-based methods rely on over half of the genetic variants being valid instrumental variables for consistent estimation. However, the penalization and median-based methods allow more general departures from the instrumental variable assumptions for the invalid instruments. In practice, it would seem prudent to compare estimates from a range of methods. If all methods provide similar estimates, then a causal effect is more plausible. For example, using genetic variants chosen solely on the basis of their association with the risk factor, a broad range of methods affirmed that LDL-c was a causal risk factor for CAD risk. However, the causal effect of HDL-c on CAD risk suggested by a liberal Mendelian randomization analysis using the inverse-variance weighted method see also  was not http://links.lww.com/EDE/B114). The inverse-variance weighted method was originally proposed as a fixed-effect meta-analysis of the causal estimates from each of the genetic variants.48 However, if there is heterogeneity between the causal estimates of different variants (as is the case here), a random-effects model would be more appropriate. In Egger regression, heterogeneity is expected as genetic variants that are not valid instrumental variables but satisfy the InSIDE assumption will give heterogeneous causal estimates. We consider fixed-effect and multiplicative random-effects models for both the inverse-variance weighted and Egger regression methods.56 Also, we consider simple  median and weighted median estimates.The robust methods described in this article were applied to the example of CRP and CAD risk using genome-wide significant variants; the code for performing these analyses is given in eAppendix A.3 , and the adjustment of gene–outcome associations for the risk factor. Substantial attenuation of the association on adjustment for the risk factor is expected if the genetic variant is a valid instrumental variable; however, such attenuation may not occur in practice, for example, due to measurement error in the exposure58—conversely, some attenuation may occur for an invalid instrumental variable. VanderWeele et al.10 suggest using Mendelian randomization as a test for a causal effect without providing an effect estimate, and provide a sensitivity analysis for a pleiotropic effect on an unmeasured confounder. However, this sensitivity analysis is only designed for use with a single genetic variant, so it cannot be applied in the majority of cases.From its initial popularization, proponents of Mendelian randomization have been candid about the stringent and untestable assumptions required in Mendelian randomization.10 over the precise definition of the causal parameter estimated in Mendelian randomization is warranted, although a response would be to have a less literal interpretation of effect estimates in Mendelian randomization and to view the primary finding from a Mendelian randomization investigation as the assessment of causation rather than the estimation of a causal effect. Violations of the assumptions of homogeneity and/or linearity of the causal effect would also lead to difficulties in interpreting the causal estimate, although they are unlikely to lead to inappropriate causal inferences or inflated type 1 error rates under the null.59 A causal estimate is useful to combine and compare evidence from multiple genetic variants, but it can be primarily interpreted as a test of the null hypothesis of no causal effect and only secondarily as a guide to the expected result of intervening on the risk factor in practice. As such, we regard violations of the instrumental variable assumptions necessary for valid causal inferences as first-order concerns, but violations of the assumptions necessary for the estimation of a causal effect as second-order concerns.Much of the criticism of VanderWeele et al.60 It is important in this case that the two samples are similar, particularly with regard to ethnic origin, as it is necessary for the instrumental variable assumptions to hold in both samples, as well as for estimates from each sample to be relevant to the other sample. This is not to discourage the use of summarized data or two-sample Mendelian randomization analyses, but to acknowledge that the bar for evidential quality is even higher in this case.Although the opportunities to assess the validity of genetic variants as instrumental variables are inherently less than if individual-level data were available, all of the sensitivity analyses discussed in this article can equally be performed using summarized data . A further concern with summarized data is the use of two-sample analyses, in which data on the gene–risk factor and gene–outcome associations are taken from nonoverlapping datasets.61 Alternatively, different causal effects may arise under failure of the assumptions of homogeneity of the genetic association with the risk factor or linearity of the effect of the risk factor on the outcome. In this case, the causal estimates presented in this article still provide a valid test of the causal null hypothesis, but do not have an interpretation as estimates of a causal parameter.12In this article, we have assumed that there is a single causal effect of the risk factor on the outcome, and interpreted deviation from this  as evidence that the instrumental variable assumptions are violated for some of the genetic variants. In reality, if genetic variants have different functional effects on the risk factor, then different magnitudes of causal effect may be expected. For instance, genetic variants associated with body mass index may have different biological mechanisms giving rise to the association, and may affect the outcome to different extents. Heterogeneity between causal estimates based on sets of genetic variants grouped according to their biological function may help reveal which mechanisms are causal.62 A notable exception is population stratification, which can be best addressed by choice of study population (a population of uniform ethnicity should be used whenever possible). Population stratification is commonly addressed by the adjustment in the genetic association analyses for genome-wide principal components.63 While this adjustment has proved successful in some cases, it is not guaranteed to eliminate population stratification. Another potential source of bias that does not correspond to pleiotropy is selection bias, including sample ascertainment and informative censoring.64In this article, we have discussed violations of the instrumental variable assumptions primarily using the language of pleiotropy. Some other ways in which the instrumental variable assumptions may be violated  can also be expressed in terms of pleiotropy, and so these situations can be dealt with similarly. In particular, violations of the exclusion restriction assumption  can be expressed as pleiotropic effects.32 Classical  measurement error in the risk factor does not lead to bias in instrumental variable estimates.65 As the misspecification of weights in an allele score does not lead to inappropriate causal inferences,25 it is likely that any plausibly realistic pattern of measurement error would not lead to inflation of type 1 error rates under the null. If there are multiple versions of the risk factor, then this would lead to difficulties in interpreting the causal findings. For example, if body mass index is treated as the risk factor in the analysis, but in fact the true causal risk factor is abdominal obesity (or some other more specific measure of obesity), then the sensitivity analyses of this article would be appropriate for assessing the validity of a causal finding, assuming that the surrogate risk factor  and the true causal risk factor  are correlated. However, they will not help to identify the specific causal risk factor; only biological knowledge can help here.Further potential problems for Mendelian randomization that have been identified include measurement error in the risk factor and multiple versions of the risk factor.We expect the sensitivity analyses discussed in the article to be able to detect violations of the instrumental variable assumptions regardless of how these violations arise, although it is unlikely that the some consistency properties of the robust analysis methods (in particular the Egger regression method) will hold.The increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations are making the application of Mendelian randomization simpler. However, consideration must be given as to the robustness of findings to violations of the instrumental variable assumptions. Although no method can provide an infallible test of causation, the methods for sensitivity analysis described in this article will help to judge whether a causal conclusion from a Mendelian randomization analysis is reasonable or not. Aside from cases in which the selection of the genetic variants and their justification as instrumental variables is motivated by strong biological understanding, a Mendelian randomization analysis in which no assessment of the robustness of the findings has been made should be viewed as speculative.Mendelian randomization investigations are becoming more powerful and simpler to perform, due to the increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations with risk factors and disease outcomes.However, when using multiple genetic variants from different gene regions in a Mendelian randomization analysis, it is highly implausible that all the genetic variants satisfy the instrumental variable assumptions.This means that a simple instrumental variable analysis alone should not be relied on to give a causal conclusion.In this article, we discuss a range of sensitivity analyses that will either support or question the validity of causal inference from a Mendelian randomization analysis with multiple genetic variants.Aside from cases in which the justification of the instrumental variable assumptions is supported by strong biological understanding, a Mendelian randomization analysis in which no assessment of the robustness of the findings to violations of the instrumental variable assumptions has been made should be viewed as speculative and incomplete.In particular, Mendelian randomization investigations with large numbers of genetic variants without such sensitivity analyses should be treated with skepticism.


Success of genetic association and the prediction of phenotypic traits from DNA are known to depend on the accuracy of phenotype characterization, amongst other parameters. To overcome limitations in the characterization of human iris pigmentation, we introduce a fully automated approach that specifies the areal proportions proposed to represent differing pigmentation types, such as pheomelanin, eumelanin, and non-pigmented areas within the iris. We demonstrate the utility of this approach using high-resolution digital eye imagery and genotype data from 12 selected SNPs from over 3000 European samples of seven populations that are part of the EUREYE study. In comparison to previous quantification approaches, (1) we achieved an overall improvement in eye colour phenotyping, which provides a better separation of manually defined eye colour categories. (2) Single nucleotide polymorphisms (SNPs) known to be involved in human eye colour variation showed stronger associations with our approach. (3) We found new and confirmed previously noted SNP-SNP interactions. (4) We increased SNP-based prediction accuracy of quantitative eye colour. Our findings exemplify that precise quantification using the perceived biological basis of pigmentation leads to enhanced genetic association and prediction of eye colour. We expect our approach to deliver new pigmentation genes when applied to genome-wide association testing. Human eye colour is determined by the type, amount, and distribution of two forms of pigment produced in the melanocytes of the iris, eumelanin and pheomelanin. Eumelanin is a highly compact pigment, packed in ovoid eumelanosomes1Several gene-mapping studies on eye colour were previously conducted by using manually defined phenotype categories6789101112135678910111216H) and saturation (S) measurements introduced to eye colour quantification by Liu et al.H defines the colour itself, which can be related to the type of melanin having more red or yellow components. The S value describes the richness of a certain colour (defined in H) that is supposed to correlate with the amount of eu-or pheo-melanin. The V value is usually discarded, as it is supposed to rather represent the brightness due to different lighting conditions24et al.B-G values from the RGB-space  and proposed a T-index quantity to describe the amount of melanin per iris. Recently, the CIE-L * a * b* values have been used in place of H, S valuesL value describes the lightness, the a* the red/green, and b* the yellow/blue component, of the colour respectively. However, taking a quantity that is averaged over the iris as previous methods212324et al.The first quantitative approach to measure eye colour was proposed by Frudakis22Here, we introduce an improvement of quantitative pigmentation phenotyping based on an automated segmentation of the iris followed by a measurement of the digital equivalents of eumelanin, pheomelanin, and total absence of any pigment in the iris. This clustering is based on manually predefined (assumed) image segments that depict eumelanin, pheomelanin and nonpigmented areas. We exemplified the advantage of this approach by an empirical analysis of high-resolution eye images from over 3000 individuals sampled from seven European countries (EUREYE study). By using genotypes of 12 SNPs previously involved in human eye colour variation that we generated in the same individuals, we demonstrate the impact of this novel pigmentation phenotyping approach on genetic association, epistasis, and prediction.Prior to colour assessment, the iris needed to be segmented from the pupil and sclera. Several approaches have been proposed previously for the segmentation of the iris in digital eye imagery28293031The developed method assesses each pixel according to its digital classification of pheomelanin, eumelanin, and non-pigmentation, using a machine learning approach . The colWe finally considered the proportion of the clustered pixels relative to the segmented iris as our quantitative eye colour phenotype, reflecting the equivalent amounts of non-pigmented, pheomelanin, and eumelanin areas per iris see , the sumH, S values from the HSV spaceOne important motivation behind quantitative measurements is to capture the information about complex phenotypes with a small number of variables. We use a correlation analysis to reveal how well different eye colour quantifications maintain the information about our estimated abundance of different types of melanin. We calculated quantitative iris colour phenotypes using five previously proposed methods: (i) the mean b* , followed by S . The more melanin is distributed on the iris (fewer non-pigmentation) the lower will be the b* indicating the yellow components contained in the melanin and the higher is the a* value for the red components of the melanin. Therefore we find the amount of pheomelanin was most strongly correlated with a* , followed by b* . The amount of eumelanin estimated with our approach was most strongly positively correlated with L21, , followed by S . The colour score C, which summarizes the components a* and b*r = −0.52, P = 1.1e–286) and much weaker positive correlation with pheomelanin , which indicates a reduced utility in predicting the respective phenotypes from colour components, which is discussed in the next paragraph.r = −0.06, P = 0.001), confirming our expectation that these two measurements represent different biological phenotypes.Finally, the amount of pheomelanin estimated with our approach showed only weak correlation with the estimated amount of eumelanin  in the different components of the described eye colour quantities see . To demoa* and b* components most likely obscures important information about the different types of melanin. The one-dimensional PIE score and T-index measurements showed the weakest ability for the separation of intermediate eye colours from either brown  or blue .Considering all pairs of categories, the colour score-luminance space performed least accurately in comparison to all other quantitative methods used. The summary of the R2 (R2 in our study) is beside the allele frequency - an important factor controlling for the power of discovering an unknown causal variantHERC2 rs12913832 and HERC2 rs1129038 being in very high linkage disequilibrium (LD) , we only describe the results for HERC2 rs12913832  followed by S , and eumelanin measured with our approach , and H . The explained variance was considerably less of a*  and pheomelanin as measured with our approach . HERC2 rs12913832  with any of the quantitative eye colour phenotypes tested including H and S used by Liu et al.In contrast to Liu HERC2 rs1129038 and HERC2 rs12913832 due to their strong LD  as well as LYST rs3768056 and DSCR9 rs2835630 solely evident in eumelanin (P = 2.6e–2). Moreover, we confirmed several interaction pairs reported in previous studies using quantitative or categorical eye colour phenotypes, namely: HERC2 rs12913832 and SLC24A4 rs12896399 previously observed for SP = 2.3e–16), b* (P = 3.3e–4), S (P = 4.2e–3), non-pigmentation (P = 5.4e–13), and pheomelanin (P = 8e–9); HERC2 rs12913832 and SCL45A2 rs16891982 previously described in blue vs. non-blueS (P = 1.3e–12), pheomelanin (P = 1.2e–16), non-pigmentation (P = 1.8e–20), S (P = 1.3e–12), Colour score (P = 3.5e–7), PIEscore (P = 3.6e–21), and b* (P = 3.8e–18); HERC2 rs12913832 and TYPR1 rs1325127 previously described in hazel vs. non-hazelP = 7.8e–4); HERC2 rs12913832 and IRF4 rs12203592 previously observed in H and SH (P = 1.9e–3), a* (P = 2.6e–4), and pheomelanin (P = 3.8e–3).Next, we studied the impact of the detailed eye colour phenotyping as achieved with our new method, as well as previously used methods, on epistatic effects between pairs of SNPs that deviate from additivity while correcting for sex and age . The effect of SLC45A2 rs16891982 on the amount of non-pigmentation is almost neutralized with HERC2 rs12913832 heterozygote CT or homozygote TT (R2 = 0.003 P = 1.5e–02). Thus it appears that the allelic states of SLC45A2 rs16891982 will lighten/darken only those irises that are already stated to be “blue” by HERC2 rs12913832 = CC. A similar masking effect can be observed for the interaction pair HERC2 rs12913832 and SLC24A4 rs12896399 . In contrast, the state of TYRP1 rs1325137 tends to increase the amount of pheomelanin if HERC2 rs12913832 has at least one T allele . Condensing all SNP-SNP interactions for certain quantitative eye colour estimates, the following SNP pairs were found to be most relevant: IRF4 rs12203592 × HERC2 rs12913832, SLC24A4 rs12896399 × SLC45A2 rs16891982, HERC2 rs1291383) × SLC24A4 rs12896399, HERC2 rs12913832 × TYRP1 rs1325127, HERC2 rs12913832 × SLC45A2 rs16891982. Consequently, we included them in the prediction analyses considered as a separate model.We exemplified the interaction between  SNP2 in  the domi12896399 . We simis1325137  and obseN = 2,087) and a model-validation set . As prediction accuracy measure we used the percentage of phenotypic variance predicted by the respective model (mean coefficient of determination R2). Model 1 (without considering interactions) explained 52% of the phenotypic variance of non-pigmented area, 16% of pheomelanin, and 33% of eumelanin  provided increased prediction of 55%  and 20%  for non-pigmentation and pheomelanin, respectively, with eumelanin remaining at 33%, while for S the prediction increases to 44%  and remained the same for H at 24%. Hence, by including the interaction terms in the prediction model, we observed an overall increase in the predictability of our newly derived quantitative eye colour measures. The strongest increase was seen for pheomelanin  that notably is involved in intermediate eye colours. The following interactions were mostly relevant for the prediction of pheomelanin: HERC2 rs12913832 and SLC45A2 rs16891982 , HERC2 rs12913832 and SLC24A4 rs12896399 , as well as IRF4 rs12203592 and HERC2 rs12913832 , , see .Notably, when constraining the quantitative eye colour predictions on the 6 SNPs included in the IrisPlex system for categorical eye colour predictionet al.To demonstrate the potential of the 12 SNPs for predicting quantitative eye colour, in H and S values are recommended for usage.In summary, we have developed an automated computational approach that separates the iris from digital eye images and quantifies iris pigmentation by estimating digital equivalents of eumelanin, pheomelanin, and non-pigmentation. By applying this new approach to high-resolution eye imagery of thousands of Europeans, we demonstrated that it mostly outperformed previous eye colour quantification methods. When only low-resolution imagery is available that does not allow for a distinct clustering of pixels into melanin types, average The power to detect a causal variant in a genome wide association study is known to depend mainly on its effect and frequency in the populationThe study was approved by national ethical committees and met the criteria of the Helsinki declaration. The ethics committees of each of the institutional review boards of the following collaborating eye study centres gave approval: Department of Epidemiology and Biostatistics, National Institute for Health Development, Tallinn, Estonia; Department of Ophthalmology, University of Bergen, School of Medicine, Bergen, Norway; Clinique Ophthalmologique, Universitaire De Creteil, Paris, France; Clinica Oculistica, Universita degli studi di Verona, Italy; Department of Ophthalmology, Aristotle University of Thessaloniki, School of Medicine, Thessaloniki, Greece; Dpto. Salud Publica Universidad Miguel Hernandez, Alicante, El Centro de Investigacion Biomedica en Red de Epidemiologıa y Salud Publica (CIBERESP), Elche, Spain; Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom. Study participants gave informed written consent. Participants were advised that all information would be kept confidential and no identifying information would be kept.http://www.topconmedical.com/categories/imaging-retinalcameras.htm) under normalized conditions. We manually excluded samples where the image quality was limited due to partially closed eyelids or largely dilated pupils that prevented a full view of the iris.Eye images and DNA samples were collected as part of the EUREYE study. EUREYE is a population-based study of age related macular degeneration (AMD) in seven centers located across Europe. Participants were recruited from random sampling of the population aged over 65 years in Bergen (Norway), Tallinn (Estonia), Belfast (UK), Paris Creteil (France), Verona , Thessaloniki (Greece), and Alicante (Spain). Participants were interviewed by fieldworkers, underwent an eye examination including digital capture of the iris, and provided a blood sample for DNA analysis. A detailed description of the EUREYE study including image collection can be found elsewhere3845HERC2 rs12913832, OCA2 rs1800407, SLC24A4 rs12896399, SLC45A2 (MATP) rs16891982, TYR rs1393350, and IRF4 rs12203592. The 6 additional SNPs used were identified to be eye colour predictive in previous studies24UGT1A6), rs9894429 (NPLOC4), rs1129038 (HERC2), rs3768056 (LYST), rs2835630 (DSCR9), and rs1325127 (TYRP1) (see DNA samples were genotyped using the SNaPShot technology (Life Technologies) via a single multiplex assay targeting 12-eye colour SNPs. We used 6 SNPs that were previously identified to predict categorical eye colour in genome wide studies122415183738HERC2 rs1129038 (PHWE = 8.25e–38), HERC2 rs12913832 (PHWE = 2.38e–37), and SLC45A2 rs16891982 (PHWE = 2e–16). In the absence of evidence for genotyping errors, we expect this being caused by the samples coming were from seven European populations as these three SNPs displayed elevated informativeness of ancestry (In) values . For association and prediction analysis, linear models were fitted for both, individual SNPs and a combined model including all SNPs in Matlab (R2007a) while controlling for age, sex and population id. Presence of interactions between pairs of SNPs was tested by comparing nested linear models with and without the interaction term by means of an F-test. DNA-based eye colour prediction was achieved using general linear models Y~b0 + b1X1 + b2X2 + b3X3 + b4age + b5sex + b6pop, where X1 and X2 denote the genotypes of SNP1 and SNP2 respectively and X3 denotes the interaction term on a multiplicative scale. Genotypes were coded additively as counts of minor alleles. P-value for the interaction term (b3) were used and corrected for multiple testing using Bonferroni correction for 12 interaction pairs (396 tests applied). Linear models were fitted to predict the quantitative amounts of melanin from genotypic variation. To study the importance of genetic interaction, we added the most important interaction identified to the predictor using only main effects of SNPs. Quality of prediction was assessed by means of adjusted coefficient of determination (R2) of the models. We used each colour quantification values separately as response variable. To account for over-fitting, we provide the mean R2 from cross validation as estimated from 100 randomized train/holdout experiments where 2/3 of the samples were used as a training set (N = 2087) and 1/3 as a validation set (N = 1000).Deviations from Hardy Weinberg equilibrium (HWE) were calculated using methods described elsewhereThe computational method to perform statistical analyses and extract quantitative eye colour from digital images is available on request.How to cite this article: Wollstein, A. et al. Novel quantitative pigmentation phenotyping enhances genetic association, epistasis, and prediction of human eye colour. Sci. Rep.7, 43359; doi: 10.1038/srep43359 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Hair cortisol concentration (HCC) is a promising measure of long-term hypothalamus-pituitary-adrenal (HPA) axis activity. Previous research has suggested an association between HCC and psychological variables, and initial studies of inter-individual variance in HCC have implicated genetic factors. However, whether HCC and psychological variables share genetic risk factors remains unclear. The aims of the present twin study were to: (i) assess the heritability of HCC; (ii) estimate the phenotypic and genetic correlation between HPA axis activity and the psychological variables perceived stress, depressive symptoms, and neuroticism; using formal genetic twin models and molecular genetic methods, i.e. polygenic risk scores (PRS). HCC was measured in 671 adolescents and young adults. These included 115 monozygotic and 183 dizygotic twin-pairs. For 432 subjects PRS scores for plasma cortisol, major depression, and neuroticism were calculated using data from large genome wide association studies. The twin model revealed a heritability for HCC of 72%. No significant phenotypic or genetic correlation was found between HCC and the three psychological variables of interest. PRS did not explain variance in HCC. The present data suggest that HCC is highly heritable. However, the data do not support a strong biological link between HCC and any of the investigated psychological variables. Understanding the mechanisms through which stress impacts mental health is therefore an important aim of epidemiological research. A core element of the biological stress response is the hypothalamus-pituitary-adrenal (HPA) axis. HPA axis activity is typically measured according to its end product, the steroid hormone cortisol. While prompt HPA axis activation in response to acute stressors is adaptive, long-term dysregulation of basal HPA axis activity and reactivity can have deleterious effects on physiology3. Alterations in HPA axis regulation are observed in subjects suffering from psychiatric disorders, and have been suggested as not only a consequence but also as a premorbid vulnerability factor5. Research has shown that variation in HPA axis regulation is influenced by both environmental and genetic factors6. Furthermore, authors have suggested that the effects of genetic and environmental risk factors for psychiatric disorders might be mediated in part via dysregulation of HPA axis activity7. Investigating if HPA axis regulation shares underlying genetic factors with psychological or psychiatric phenotypes can inform about a true biological link between these, potentially indicating a causal involvement of the HPA axis in the vulnerability for psychiatric disorders.Research has generated robust evidence that chronic stress is a risk factor for mental disorders8. However, single cortisol measures are strongly influenced by factors such as circadian rhythm, physical activity, and nutrition. Thus the assessment of long-term alterations in HPA axis regulation requires the meticulous assessment of cortisol at multiple time points. In recent years, the assessment of hair cortisol concentration (HCC) has been established as a marker of long-term cumulative HPA axis activity10. HCC is usually measured in a 3 centimeter (cm) hair segment cut as close as possible to the scalp. Since hair grows, on average, one cm per month, HCC in a 3 cm sample is considered to reflect cortisol secretion during the preceding 3 month period11. Hair cortisol is assumed to reflect free cortisol, which is the biologically active share of cortisol not bound to corticosteroid binding globulin (CBG)13. Studies investigating the relationship of HCC with cortisol levels in other tissues show the highest correlations (up to r = 0.61) with cumulative or average cortisol measures acquired over several days or weeks17. While these studies which assessed multiple measurement-points have been mainly carried out using saliva and urine, studies which used serum and plasma blood samples, have been mainly based on single assessments, and the observed correlations were lower or non-significant (e.g. refs20). Studies in pregnant women21 and in subjects with Cushing’s syndrome22 -conditions characterized by pronounced alterations in circulating cortisol- have revealed altered HCC levels, and therefore indicate that HCC is a marker of long-term changes in circulating cortisol. HCC therefore represents an efficient method for the retrospective assessment of long-term cortisol secretion, and thus long-term HPA axis activity.Cortisol is usually measured in saliva, urine, or blood23). A recent meta-analysis based on aggregated data from a total of 124 samples, and comprising 10,289 subjects, showed no consistent associations with mood disorders or self-reported perceived stress and depressiveness, but found that stress-exposed groups as a whole exhibit 22% increased HCC24. While stressful environmental factors play a major role in HPA axis activation, twin studies have indicated that genetic factors have a substantial impact on the secretion of cortisol, especially morning cortisol. Predominantly moderate heritability estimates have been reported in adults (as reviewed in refs27) and adolescents28. The observed heritability of HPA axis regulation suggests a contribution of genetically determined biological mechanisms. To date, the only large genome wide association study (GWAS) systematically investigating the genes underlying HPA axis activity used total plasma cortisol levels in the morning: Bolton et al. (2014) identified associated genetic variants in a region which contains the genes encoding CBG and α1-antitrypsin29. If a strong heritability for HCC could been confirmed, HCC would represent a promising target for genetic studies into long-term HPA axis activity and its relationship to mental health.While HCC would thus appear to be an ideal biomarker for stress-related phenotypes, to date, studies of the association between HCC and measures of stress and mental health have generated inconsistent results  demonstrated heritability of HCC for the first time by investigating an ethnically and socioeconomically diverse sample of 1070 children and adolescents including 533 twin pairs31. The authors showed that 65% of the total variability of HCC was explained by additive genetic effects, and that genetic influences on HCC decreased with age. In subjects with low socioeconomic status, a non-significant trend was observed towards increased genetic influences and reduced shared environmental influences on HCC. The authors hypothesized that genetic influences may be stronger under high stress conditions.The first study on heritability of HCC was conducted in a colony of female vervet monkeys, and heritability estimates of ~30% in high and low stress environments were reported32. Furthermore, increased (p = 0.06) mean cortisol levels were observed in those twins with a history of major depression (MDD). Notably, intermediate cortisol levels were observed in twins without history of MDD from pairs discordant for history of MDD, however these observations were not significant. A study conducted in 125 female twin pairs demonstrated a heritability of 55% for neuroticism and a heritability of up to 69% for morning cortisol secretion in saliva33. However, the authors found no phenotypic or genotypic association between cortisol levels and neuroticism.As mentioned above, a genetic overlap between HPA axis activity and psychological or psychiatric phenotypes would suggest an involvement of the HPA axis in the vulnerability to psychiatric disorder. However, previous studies investigating a possible genetic overlap between cortisol secretion and psychological variables have been limited in both number and size: A study in 29 monozygotic female twin pairs suggested around 40–45% of the total variance in morning and evening saliva cortisol levels is shared by monozygotic twins34. In line with this, recent large genome-wide studies have demonstrated that genetic variants make a substantial contribution to the development of neuroticism36, depressive symptoms36, and MDD37. These studies have also revealed that these risk variants show partial overlap. In a subsequent pilot study, the present authors measured HCC and the three psychological variables in a sample of 109 children and young adults, which included eight monozygotic- and 21 dizygotic twin pairs38. Due to the small sample size, no reliable heritability estimates for HCC could be generated. However, the findings suggested that HCC and the assessed psychological variables may share a common genetic basis.In a previous twin study, the present authors demonstrated that continuous measures of perceived stress, depressive symptoms, and neuroticism were heritable and showed strong phenotypic and genetic correlations in healthy adolescent and young adult twins29; MDD37; and neuroticism35. Since the GWAS of HPA axis activity was based on plasma cortisol levels, the association between HCC and this PRS was explored i.e. the association between HCC and the genetic variants influencing concentration of cortisol in another tissue.The present larger study aimed (i) to estimate the heritability of HCC and (ii) to investigate the question of whether HPA axis activity shows a genetic overlap with the continuous psychological measures perceived stress, depressive symptoms, and neuroticism, using formal genetic (twin models), and molecular genetic methods i.e. polygenic risk score (PRS) analyses. PRS provide a quantitative measure of genetic risk or vulnerability for a given trait. PRS estimation uses GWAS results to predict genetic risk for each individual in an independent genotyped sample. Investigations can then be performed to determine whether this risk is associated with potentially related phenotypes. However, while significant associations can be found using this approach, the explained variance is limited. For the purposes of the present study, PRS were calculated based on results from GWAS of HPA axis activity‘hair sampling and HCC analysis’), the final cohort comprised 671 subjects . The cohort included 116 monozygotic (MZ) and 187 dizygotic (DZ) twin pairs (3 families had two sets of DZ twins), and 14 sets of trizygotic triplets. For the purposes of the present analyses, each set of triplets was considered to be a DZ twin pair with one additional singleton. The cohort included a total of 65 singletons, who were derived from the divided triplets (n = 14) and the siblings of the MZ and DZ twin pairs (n = 51) , is conducted in several phases and investigates somatic- and mental health and related phenotypes  and neuroticism was measured using the respective 20 items of the ‘Junior Eysenck Personality Questionnaire’ (JEPQ42) as the DLSS and JEPQ are validated for children and adolescents. For participants aged 16 years or older, perceived stress was measured using the 10-item ‘Perceived Stress Scale’ (PSS44). Additionally, 168 subjects between 16 and 19 years completed both the PSS and the DLSS, and this overlap was used to harmonize the two scales using item response theory . For participants aged 16 years or older, neuroticism was measured using the respective 12 items of the ‘NEO-Five Factor Inventory revised version’ (NEO-FFI-R44). At all ages, depressive symptoms were assessed using the 34-item ‘Somatic and Psychological Health Report’ . For all subjects, measures of stress (PSS or DLSS), neuroticism, and depressive symptoms were obtained at the time of hair sampling. For some subjects, data were unavailable for perceived stress (n = 1); neuroticism (n = 51); and depressive symptoms (n = 55), since subjects did not fill out the respective questionnaires. In the subgroup that underwent assessment at two time-points, one subject had missing data for perceived stress and depressive symptoms at the second time point.For participants less than 16 years old, perceived stress was measured using the 30-item ‘Daily Life and Stressors Scale’ . Prior to HCC measurement, hair samples were randomly assigned to batches (irrespective of time-point or twin-pair) in order to minimize the effect of batch differences on twin correlations. To assess technical error, 106 hair samples were assessed in duplicate and 27 in triplicate. The hair samples were assayed between April and July 2016 in a total of 35 batches (comprising 27 samples respectively). Samples with extreme high or low values  were re-assayed in order to confirm the extreme values.Using fine scissors, a 3 cm hair swatch of approximately 3 millimeters in diameter was cut as close as possible to the skin from the posterior vertex of the scalp. Hair cortisol was analyzed by TMB and MRB at the Institute of Forensic Medicine, Centre for Forensic Hair Analysis, University of Zurich. Cortisol concentration was measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS), as described by Binz 48. To harmonize data from the two stress rating scales DLSS and PSS, Item Response Theory (IRT) analysis was performed  were winsorized to 3SD on the lg10 scale. Analyses were also conducted to test the effect of experimental variables reported in previous studies of HCC. The analyses included: (i) batch number (n = 35); (ii) study phase (n = 6); (iii) storage time, defined as time between date of collection and date of assay (range = 0.50–4.02 years) and divided into n = 5 groups according to increasing storage time; and (iv) sun exposure, operationalized according to month of assessment (n = 12) and self- and maternal ratings of sun exposure. Since all aforementioned variables changed the fit of the model significantly, they were regressed out from the HCC measurement using a linear model, including the dummy coded variables as fixed effects. Subsequent analyses were carried out with the residuals of this model. The HCC value from the first time point was used in the following analyses; the second time point was only used for assessing stability of HCC over a two-year period in the subset of longitudinally assessed subjects (n = 146).To account for skewness, raw HCC values  were log transformed. Here, the lowest measured value (0.1) was added to each value prior to log49. This makes use of all data points - including those of unpaired twins and singletons - in order to improve estimation of sample means and variances. This approach allows partitioning of the variation into: additive genetic influences (A); shared environmental influences (C); and unique environmental influences (E). Using likelihood ratio chi-square tests, sub-models with only two factors (AE and CE models) were compared with the three-factor models (ACE) in order to estimate the sources of variance and select the most parsimonious variance structure of the traits.To estimate twin correlations and the heritability of HCC and the three psychological variables of interest, the twin and sibling data were used to generate structural equation models. Model parameters were estimated using the full maximum likelihood method implemented in Mx50). The source of variation, C, captures cultural environment shared by twins within a family regardless of zygosity and could include factors such as sharing the same school, neighborhood, and exposure to infectious diseases. C is distinguished from unique environment, E, which is specific to individuals and may include factors such as accidents; however, it is important note that E also includes measurement error, which is often the major contributor to this source of variance. An ACE Cholesky decomposition model was compared with an AE and a CE Cholesky decomposition. Perceived stress was used as the first, depressive symptoms as the second, neuroticism as the third, and HCC as the last latent factor in order to estimate (i) the genetic variance of HCC that is shared with genes affecting the psychological variables and (ii) the independent genetic variance for HCC after removing the effects of the genes with the primary influence on the psychological variables (for further details see Supp. Text).To investigate the influence of genetic and environmental factors on HCC and the psychological variables of interest, as well as genetic and environmental correlations between these variables, multivariate analysis was performed. This involved use of a simultaneous Cholesky decomposition. A Cholesky decomposition is a good initial multivariate method to use in the absence of a clear model of the factor structure relating a set of correlated variables. Furthermore, taking advantage of having both MZ and DZ twins one can fit a 3-part Cholesky model including decompositions of (co)variance due to A (additive genetics), C (shared environment) and E (unique environment), since one cannot assume that factor loadings between these three sources of variation will be proportional for each of the examined variables. In fact, there are known cases of opposite-signed loadings of factors A and C , socio-economic index , and hair dyeing  showed no significant associations with HCC in the present sample and were thus not included as covariates. Since previous results suggest that heritability of HCC is age-dependent31, a separate analysis was performed in the younger and the older half of the sample, as defined by a median split to test the heritability of HCC in the different age groups. Further details of the twin design and analytical methods, including assumption testing and multivariate modeling, are provided elsewhere52.The fit of each model was assessed according to the differences in log likelihood between the sub and the full models. The most parsimonious model was chosen for the purposes of data interpretation. Sex and puberty which - generally starts earlier in girls- influence the cortisol secretion53 on the Michigan Imputation Server (https://imputationserver.sph.umich.edu/i). Imputation was carried out in two separate waves for the Illumina Human610-Quad and the Core+Exome SNP chips. To account for population stratification  in the PRS analysis, genetic principal components (PC) reflecting the respective ancestry were calculated using EigenSoft 6.0.1 . Further details on genotyping, quality control, and imputation are provided in the Supp. Text.Genotyping was performed using the Illumina Human610-Quad and Core+Exome SNP chips. Quality control included inspection of pedigree, sex, Mendelian errors, and ancestry, as well as filtering for genotyping quality ; SNP and individual call rates (<0.95); Hardy-Weinberg equilibrium failure (P <10–6); and minor allele frequency (<0.01). Subjects were imputed to the Haplotype Reference Consortium (HRC.1.1)https://www.cog-genomics.org/plink2/) was used to compute PRS in accordance with the procedure described by Wray et al.53. For PRS estimation, the results of large GWAS (discovery sample) are used to calculate an aggregated genetic risk score for each individual in an independent genotyped sample (target sample). The PRS represents the sum of the risk alleles, as weighted by their respective estimated effect sizes. PRS provide a quantitative measure of the genetic risk or vulnerability for a given trait: The higher the score, the higher the predisposed genetic risk of the individual for the trait in question. In the present study, PRS were calculated using summary statistics from recent GWAS or GWAS meta-analyses of: (i) plasma cortisol (CORtisol NETwork (CORNET) Consortium29, comprising 12,597 subjects); (ii) MDD . The SNP-sets used to compute the PRS were selected using eight different p-value thresholds  in the respective discovery sample.PLINK 1.90 ; and the imputation wave. One-sided p-values are reported, according to the hypothesis of a positive association of the PRS with HCC and each of the psychological variables. Further details of the PRS analysis are provided in the Supp. Text.To take family structure into account, associations of PRS with HCC and the three psychological variables of interest were tested using linear mixed regression models in GCTA The datasets generated during and/or analyzed during the current study are not publicly available due to privacy regulations but are available from the authors on reasonable request.HCC was measured in 671 subjects. HCC, neuroticism and depressive symptom scores were higher in females than in males. Furthermore, perceived stress, depressive symptoms and neuroticism showed an association with age, and perceived stress and neuroticism showed an interaction of sex and age. Details can be found in Supp. Table st and r = 0.98 with the 2nd assay).Quality control of HCC assessed in duplicate and triplicate revealed a high correlation of the log transformed HCC values for samples re-assayed once  and samples assayed three times . All of these experimental covariates were highly significant. Notably, the analysis demonstrated: (i) a decrease in HCC with increasing storage time; and (ii) maximum values in March  and minimum values in September . Dropping any one of the covariates from the full model  increased variance by 5.9% for batch effects, 1.8% for storage effects, 3.8% for month effects, and 6,4% for study effects . The AE model revealed low and non-significant genetic correlations (rA) between HCC and the three psychological variables  ion Fig. . The C m2 = 0.74) and older (h2 = 0.69) halves of the cohort. Heritabilities for the psychological variables were also broadly consistent between the younger and older halves of the sample  . This is not surprising, given that HCC is an integrated, rather than a point measure of HPA axis activity. It can now be taken as a fact that in adolescents from the general population, a substantial proportion of HCC variance is attributable to genetic factors. At first glance, this may appear surprising for a parameter that is considered a potential biomarker for stress. However, this finding does not preclude environmentally induced changes. Furthermore, the stability of HCC, as measured in a subgroup over a two-year period, is relatively low (r = 0.32). These stability measures are comparable to those found in students aged 17–21 years assessed at three time points over one year (r = 0.25–0.39)56 and to those found in children aged 1, 3, 5, and 8 years, in whom HCC was assessed over periods of two and three years (r = 0.30–0.44)57. In samples with a higher mean age (>30 years), in which intervals of assessment ranged between one month and one year, higher stability measures (r = 0.68−0.86) have been reported58. These findings are in line with the hypothesis of greater changes of HPA axis functioning during childhood and adolescence which are periods marked by dramatic physical, cognitive, social and emotional changes56. Unfortunately, no conclusions can be drawn from the present analyses concerning the heritability of the intra-individual change in HCC over time, since the respective subsample was small and lacked sufficient statistical power for this analysis. However, it is of interest to note, that the heritability estimate of HCC did not differ between the younger and older half of the sample.The analyses generated a heritability estimate for HCC of ~70%, with no significant contribution being found for shared environment. These estimates are nearly identical to those reported by Tucker-Drob and colleagues61, as well as their genetic overlap63, are well established, and the present data are consistent with those of previous reports.Regarding the phenotypic and genetic correlations of HCC with psychological variables, the present analyses demonstrated that shared genetic factors underlie the association between the psychological variables perceived stress, depressive symptoms, and neuroticism. The heritability of neuroticism and depression38, no significant phenotypic or genetic overlap was found between HCC and any of the three psychological variables. Although no study to date has investigated the association between HCC and neuroticism, the present results are consistent with a previous investigation of 125 twin pairs, which showed heritability for neuroticism and morning cortisol secretion in saliva but no phenotypic or genetic overlap between these two variables33.However, contrary to the results of our previous pilot study66, previous reports on the association between HCC and psychological variables in healthy subjects are inconsistent (e.g. refs72). Recently, this was reflected in a meta-analytic study, which found no evidence for a positive association of HCC with subjective stress and depressivity24. However, the meta-analysis confirmed that HCC is increased in conditions of chronic ongoing stress, and therefore represents a marker for the assessment of long-term alterations in cortisol levels in response to environmental influences24.The present findings must be viewed with caution, as they are derived from a cohort of relatively healthy adolescents from the general population. It is possible that the association between HCC and psychological variables may only become apparent for more extreme psychological phenotypes. While studies of subjects with a history of chronic or traumatic psychological disturbance, i.e., conditions which are known to alter HPA axis functioning , have repeatedly reported HCC alterationsIn the present sample, we observed values within the normal range of the applied psychological scales activity, such as circadian rhythm, and reactivity to psychological, physical and pharmacological challenges, which are commonly assessed using multiple saliva or blood samples within a defined sampling scheme. Those measures have been shown to be altered in psychiatric disorders, partially independent of or even contrary to alterations in basal cortisol levels 13. The main signal of the GWAS was observed in the region coding for CBG, the main regulator of the ratio of free and total cortisol29 and might thus differentially affect measures of free and total cortisol. Additionally, HCC represents a measure of accumulated long-term cortisol secretion, while the GWAS was based on a one-time measure of morning plasma cortisol. As described, the correlations between HCC and one time measures of cortisol in saliva and blood are inconsistent. However, in view of the limited power, the results of the PRS analyses must be interpreted with caution, and large future studies of easier-to-recruit, unrelated subjects might generate insights into the associations. Additionally, even though each of the GWAS considered in the present analyses involved cohorts in excess of 10,000 subjects and identified genome-wide genetic variants, larger studies are required. Research has demonstrated that for complex phenotypes, GWAS involving several 100,000 subjects are needed to identify the majority of the common polygenic variation75. The power of PRS calculated using future GWAS will be increased due to a superior signal to noise ratio in these larger datasets.The molecular genetic approach i.e. using polygenic risk scores to investigate the association between HCC and psychological measures generated several interesting results. First, the observation of nominally significant positive associations of the PRS for MDD and the PRS for neuroticism with the psychological variables is consistent with reported formal genetic correlations. This supports the validity of the approach, even in a sample as small as that used in the present analyses. However, the relatively low degree of explained variance, and the fact that the associations only achieved nominal significance at some of the selected thresholds, highlights that our approach lacks sufficient power. Second, the observation that neither the PRS for MDD nor the PRS for neuroticism predicted HCC, and that the PRS for plasma cortisol did not predict perceived stress, depressive symptoms, or neuroticism, parallels the genetic results from twin models, which suggests a lack of genetic overlap between HCC and the psychological variables. Interestingly, no overlap was found between the PRS for plasma cortisol and HCC. For the interpretation of this result it is important to note, that hair cortisol is assumed to reflect free (unbound) cortisol, while the GWAS investigated total (bound and unbound) plasma cortisol concentrations78). Future studies of HCC in adolescents should consider including those measures. Fourth, the self-rating questionnaires for perceived stress and depression do address shorter time frames (last week to last few weeks) than the time frame reflected in the 3 cm segments of hair analyzed for hair cortisol (~3 months). However, we observed a high heritability and stability (over two years) of the psychological measures in our sample. This indicates that these measures largely assess more stable components of the underlying psychological constructs and not merely short term fluctuations. Here, however, we must acknowledge the difficulty for researchers in this area of finding state effects convincingly independent of trait disposition. Fifth, the PRS scores for neuroticism, MDD, and plasma cortisol were derived from adult cohorts, and statistical power was small due to the limited size of the learning and present cohorts. Replication studies in much larger cohorts are required before further conclusions can be drawn, in particular as regards the genetic overlap between plasma cortisol and HCC. Sixth, although the present analyses demonstrate that HCC can be assessed with a high degree of reliability, the quality control analysis demonstrated that various factors influenced HCC, including batch number, season, storage time, and study phase, and had to be statistically controlled for. Fifth, these corrections may have contributed to an overcorrection, and thus to the overlooking of true findings. Seventh, we did not systematically obtain data on medical history or treatment, including oral contraceptives. However, as the major part of our sample is less than 16 years of age, it is unlikely that oral contraceptives and other medications represent a major confounder. Furthermore, unlike cortisol assessed in other tissues6, there is no strong evidence that oral contraceptives have a substantial effect on HCC (e.g. refs79). Eighth, hair dyeing did not affect HCC in the present sample. However, further variables such as frequency of hair washing were not assessed.The present findings should be interpreted with caution, since the study had several limitations. First, the sample size was relatively small in terms of the establishment of twin models, particularly in the case of the exploratory analyses investigating subsamples divided by age, and the detection of small correlations between HCC measures and psychological phenotypes. Second, generalizability of the results to the general population is limited, since a young and relatively healthy cohort was investigated using self-rating questionnaires. Phenotypic and genetic correlations with psychological variables may only become evident in cohorts with more pronounced or specified environmental impacts (e.g. chronic severe stress) and more extreme phenotypes . Under the challenge of more adverse environments, stronger variance might occur in those phenotypes, partially driven by distinct genetic factors. Third, we did control for age and sex and interactions in our analyses. However, there is evidence that the influence of puberty processes on the associations between HPA axis activity with stress and depressive symptoms is best accounted for by assessing pubertal status and timing (e.g. refsOur study demonstrates a high heritability of HCC, but no evidence for a genetic overlap with depressive symptoms, perceived stress and neuroticism. HCC is a reliable measure of long-term HPA axis activity and genetic effects play a major role in inter-individual variability. This knowledge will inform future research, and is of particular relevance in terms of the interpretation of data from cross-sectional studies. If a genetic or phenotypic correlation does exist between HCC and perceived stress, depressive symptoms, or neuroticism, the present analyses have demonstrated that this is difficult to identify in a relatively small sample of young adults from the general population. Further studies are warranted to investigate whether this is also the case in samples with more extreme psychological phenotypes. Particularly in studies for which blood or saliva sampling over several days and multiple time-points is difficult to implement, HCC represents a promising alternative measure for the assessment of long-term HPA axis activation. The high heritability, easy accessibility and cost-effectiveness of HCC render it a promising target for future large scale GWAS of the biological pathways that underlie long-term cortisol secretion and its links to stress-related phenotypes.Supplementary Material


Human health and diseases are influenced by genes and environments . Recent “Social genetic effects”  arise wBaud et al. conducted experiments with cage-dwelling mice to examine the effects of genetic composition of animals’ social environments on psychosocial and physiological phenotypes. Two separate designs evaluated these SGEs. The first design paired inbred C57BL/6J (B6) and DBA/2J (D2) mice as cage-mates under varying social genetic conditions: genetically homogenous (B6/B6 or D2/D2) and genetically heterogeneous (B6/D2). Analysis compared psychosocial and physiological phenotypes between strains and across social genetic conditions. Alongside phenotypic differences between strains, phenotypes also varied depending on social genetic conditions. Differences were primarily found in psychosocial phenotypes—measures of stress and anxiety—but also in wound healing. Pathway analysis of blood gene expression corroborated phenotypic evidence; SGEs on phenotype were reflected in SGEs on patterns of gene expression.n> 2,000) in which mice were housed 3–6 to a cage. Using a combination of directly measured genetic data and pedigree information, analysis decomposed variance into direct genetic effects (the effects of a mouse’s own genes) and SGEs. This analysis identified SGEs for more than one third of 117 psychological and physiologic phenotypes at the p < 0.05 threshold. SGEs accounted for as much as 29% of phenotypic variance but in general were more modest (5% on average among those traits with p < 0.05 SGE).In the second design, the authors examined a large outbred mouse population  are pervasive, affecting many phenotypes; (ii) can be pronounced, contributing as much to phenotypic variance in some cases as direct genetic effects; and (iii) are nonignorable, as they may lead to bias in other estimates if not taken into account.These experiments with mice highlight opportunities and challenges for social genetic research in humans. One opportunity is to investigate social genotypes as environmental measures. There is already human research investigating social phenotypic effects, e.g., the social “contagion” of obesity (“Are your friends making you fat?”) . But ascBaud et al.’s findings further suggest that social genetic factors influencing variation in a given phenotype may be diverse. Thus, genetics previously linked with a particular phenotype may not be the only genetics of interest when considering social genetic effects on that phenotype. Analysis of pleiotropy, e.g., ,15, may In addition to direct effects of the social environment/genome, synergies between social and personal genetics are possible. Specifically, social genotyping could be used to study interactions between a person’s genes and the genes of socially proximate individuals. As Baud et al. note, specific genotypes may have different consequences for an organism’s phenotype depending on the prevalence of that genotype in the social environment , arrow bThese opportunities exist alongside challenges. A primary issue is the extent to which social genotypes are independently determined. Individuals who share traits may be more likely to sort into social units together, a phenomenon called homophily ,17. Sort