An integrative view of diversity and singularity in the living world requires a better understanding of the intricate link between genotypes and phenotypes. Here we re-emphasize the old standpoint that the genotype–phenotype (GP) relationship is best viewed as a connection between two differences, one at the genetic level and one at the phenotypic level. As of today, predominant thinking in biology research is that multiple genes interact with multiple environmental variables  to produce the phenotype. Often, the problem of linking genotypes and phenotypes is framed in terms of genotype and phenotype maps, and such graphical representations implicitly bring us away from the differential view of GP relationships. Here we show that the differential view of GP relationships is a useful explanatory framework in the context of pervasive pleiotropy, epistasis, and environmental effects. In such cases, it is relevant to view GP relationships as differences embedded into differences. Thinking in terms of differences clarifies the comparison between environmental and genetic effects on phenotypes and helps to further understand the connection between genotypes and phenotypes. We sometimes seem to have forgotten that the original question in genetics was not what makes a protein but rather ‘what makes a dog a dog, a man a man.’phenotype, which corresponds to the observable attributes of an individual, was coined in opposition to the genotype, the inherited material transmitted by gametes. Since the early proposal that genotypes and phenotypes form two fundamentally different levels of biological abstraction  compiling the genetic determinants of disease-related phenotypes totals more than 4,300 entries and a total of 2,493 published Genome-Wide Association Studies (GWAS) have been uncovering a wealth of sites in the genome that are statistically associated to complex traits . It is common to find headlines expressing these simplifications, trumpeting to wide audiences the discovery of the “longevity” or “well-being” gene, that sacrifice scientific accuracy to psychological impact. Along these lines, should a gene whose mutation is lethal be called a “life gene”? What these over-simplified formulations truly mean is that variation at a given gene causes variation in a given phenotype . For example, brown hair pigmentation in one human being is not just a product of the genes coding for pigment synthesizing enzymes but also of the presence of cells producing pigments of relevant substrate molecules (such as tyrosine for melanin), and of the amount of received sun light . A difference in hair color between two individuals could be due in some cases to their genetic difference. We note, however, that not all phenotypic changes can be attributed to genetic changes. A difference in hair color could also be caused by non-genetic factors such as age, intensity of solar radiation or hair dyeing, or by a combination of both genetic and non-genetic differences.Mutations isolated from laboratory strains have been instrumental to the understanding of the GP map. Under the classical scheme, a mutation is compared to a wild-type reference, and its phenotypic effects are used to infer gene function. This framework often leads to a semantic shortcut: from a genetic change causing a henotype . In factun light . Thus, tformation of phenotypic traits, while their experimental approach were in fact uncovering genes whose absence or alteration  leads to phenotypic differences . In fact, the sentence “your hair is brown” can be interpreted either as an absolute observation  or with implicit reference to other possibilities (it is brown and not of another color). Misconceptions arise because phenotypes are usually defined relative to possibilities that are not formulated explicitly. Our minds and our language often tend to confuse the objects whose variation is under consideration with the variation itself , its associated phenotypic change (two distinct phenotypic states), and their relationships . The three of us name the assemblage of these elements a “gephe,” but here we simply call it a “genotype–phenotype relationship” (GP relationship). We will show that the GP relationship is much more than a simple and loosely defined interaction between two levels of organization: it is a cause-and-effect connection that facilitates our understanding of phenotypic diversity.Thinking in terms of differences makes apparent an abstract entity that encapsulates both genetic and phenotypic levels. This entity is composed of a variation at a genetic gene is usually defined as a stretch of nucleic acids that is transcribed and codes for an RNA or a polypeptide with a known or presumed function . As previously noted by others .The phenotypic counterpart of the GP relationship refers to a kind of variation  rather than to a state  a difference between two reproductively isolated taxa (living or extinct), (#2) a difference segregating within a population, and (#3) the difference that first appeared during evolution, between an organism harboring the ancestral allele/trait and its direct descendant which evolved the new allele/trait. Of note, the variation in phenotype does not always immediately follow the emergence of the new causing mutation, but can appear later from the singular assortment of alleles that are segregating in the population. For example, a new phenotype of reduced armor plates appeared in a freshwater stickleback population when a recessive EDA allele already present at cryptic levels ended up in a homozygous state in one individual . In the next paragraphs we will show that, compared to the usage of intuitive concepts of things, this detour through increased abstraction may prove more efficient to better understand phenotypic diversity.In summary, the GP relationship is best viewed as a relationship between two variations, one at the genotypic level, and one at the phenotypic level. The human mind can elaborate concepts of increasing abstraction: concepts of things , concepts of change , and concepts of relations , the differential view is considered as too simplistic and researchers often prefer to focus back on phenotypes of single individuals, without explicitly relating them to a phenotypic reference.We argued above that the differential view should always be kept in mind when thinking about the connection between genotypes and phenotypes. GWAS, which represent the most popular method to detect genomic loci that are associated with complex traits in populations, are based on the analysis of differences . NeverthFigure 2A). He indicated the average genotype of a population as a point in the space of all possible genotypes (G space) and the average phenotype of the same population as a corresponding point in the space of all possible phenotypes (P space). The evolutionary process was thus decomposed into four steps: (1) the average phenotype is derived from the development of the distinct genotypes in various environments; (2) migration, mating, and natural selection acts in P space to change the average phenotype of the initial population into the average phenotype of the individuals which will have progeny; (3) the identity of successful parents determines which genotypes are preserved; and (4) genetic processes such as mutation and recombination modify position in G space.In most current articles, the problem of connecting the genotype to the phenotype is framed in terms of genotype and phenotype maps. The first GP map was introduced by Richard Lewontin in his book “The genetic basis of evolutionary change” , a point in the G space and its corresponding point in the P space correspond to the genotype and the phenotype of a single individual , individual genes are connected to individual traits.In another common graphical representation . As an analogy, one can imagine two ways to produce a well-worn leather shoe of a particular shape. One can either assemble the different atoms into the same organization, or one can buy a shoe in a store and then subject it to a series of mechanical forces. We are naturally inclined to compare organisms to machines, and to think in terms of pieces that must be assembled to make a functional whole. However, the rampant metaphor of the designer or maker is inadequate for understanding the origin of present-day organisms , it might be appropriate to keep the traditional decomposition into anatomical structures . Richardrganisms . To undeSLC45A2 gene  are observed to affect several organs at once while others alter only one at a time . For ple5A2 gene . ReasoniDrosophila dunni group. Taken as a single variable, the levels of pigmentation show a complex genetic architecture, but decomposing adult patterns into anatomical sub-units unravels discrete genetic control for each sub-trait . Each Agouti genetic locus appears to be dedicated to the specification of pigmentation in a given body part. Together, they form a group of tightly linked loci that are associated with changes in coat pigmentation.Under the differential concept of GP relationships, one crucial point is to decompose observable traits into a series of semi-independent phenotypic variations, that is to identify the elementary changes that have occurred during evolution. Experimental approaches are available to decompose a given phenotypic difference into appropriate finer sub-variations. For example, crossing plants with different leaf shapes yields a progeny that exhibits a composite range of intermediate leaf shapes. Principal component analysis uncovered elementary leaf shape changes that can together account for the difference in shape between parental lines and that appear to be caused by distinct genomic regions . This suub-trait . A betteLIN28B have been associated to adult height at different ages, other genes have only reached statistical significance in stage-specific studies focusing on fetal growth and height velocity at puberty  interaction occurs when the phenotypic effect of a given genetic change depends on environmental parameters. Similarly, epistasis, or GxG interaction, occurs when the phenotypic effect of a given genetic change depends on the allelic state of at least one other locus . There iFigure 3) is not only due to mutations in Agouti but also to a coding mutation in the MC1R gene that decreases pigmentation . The effect of the MC1R mutation is visible only in presence of the light-colored-associated derived Agouti haplotype. Here the Mc1R locus is considered to interact epistatically with the Agouti locus. In this case, we propose that the GP relationship does not comprise a single phenotypic difference but two possible phenotypic differences . The choice between these two phenotypic differences is determined by the genetic background (here at the Agouti locus). The differential view thus remains relatively straightforward for two-loci interactions: the context-dependence of the phenotype is translated into a choice between two possible phenotypic differences. We propose that a GP relationship involving a mutation subjected to multiple epistatic interactions should comprise all possible phenotypic differences that can result from the mutation in all genetic backgrounds. Among all possible phenotypic variations, the phenotypic difference that will be observed is determined by other genetic loci. In general, GxG interactions involve multiple sites that are dispersed across the genome  is the naturally occurring loss-of-function allele of brx in Arabidopsis plants, which is associated with accelerated growth and increased fitness in acidic soils, and with severely reduced root growth compared to wild-type in normal soils  and at least six transitions from environmental to genetic sex determination  occurred across the turtle phylogeny , its associated phenotypic change (distinct phenotypic states), and their relationships. For example, in many turtle species, a change in temperature during egg development is associated with the male/female sex difference , compare the response of a fixed genotype to two different environments (phenotypic plasticity), or compare the sensitivity of two different genotypes to two different environments . In the theoretical case of a population comprising only short-winged heterozygous animal that have been raised in starving conditions and long-winged ones, both genes and environment are responsible for the wing difference between individuals and it is impossible to estimate the proportion of environmental and genetic effects because genes and environment act on distinct levels along the complex causal link between genotypes and phenotypes.Various quantitative methods have been developed to disentangle genetic from environmental effects and to quantify GxE interactions . Yet in t-winged . The lonLactobacillus strain of bacteria show reduced anxiety-related behaviors compared to control mice fed with broth without bacteria , or the shift between two alternatives that are both present (for example two hair colors). Similarly, on the genotype side, a mutation can correspond to the presence/absence of a relevant DNA sequence, or to a nucleotide polymorphism. The differential perspective makes it evident that a loss of phenotype is not necessarily associated with a loss of genetic material, and vice versa. For example, the evolutionary gain of dark pigments covering the entire coat of animals has often been associated with a loss of the c1R gene . Furtherc1R gene , gain orpression .Similarly, the differential perspective on environmental effects highlights the fallacy of the distinction between permissive and instructive signals. A permissive signal is associated with the presence/absence of a phenotype and an instructive signal with the shift between two alternatives that are both present. As argued above, these distinctions at the phenotypic level are not clear-cut.In conclusion, we suggest that the gain/loss and instructive/permissive terminology should be used with caution.A mutation is expected to produce a somewhat reproducible phenotypic variation within a population. Such reproducibility in phenotypic outcome is required to allow genetic evolution and adaptation by natural selection . Indeed,Table 1), sometimes across large phylogenetic distances. For instance, the difference in pigmentation between white and orange Bengal tigers has recently been mapped to a single mutation in the transporter protein gene SLC45A2 . A more dramatic example is the recent evolution of a toxin resistance in three species that diverged more than 500 million years ago – a clam, a snake and a pufferfish – via the same amino acid substitution in a conserved gene , the same genetic code (with few exceptions), and the same basic cellular machinery. It is thus far from paradoxical that individual differences are built upon similarities, and the finding that certain GP relationships persists over long evolutionary times completes the picture.Plasmodium parasites  have been shown to alter the intensity of food search behaviors in both organisms (Another important consequence of the GP relationship perspective is that apparently distinct phenotypic changes caused by similar genetic loci in various organisms can be examined further to uncover what might be a common basic phenotypic change . For exarganisms . It is trganisms , will reIn this paper, we bring back the differential concept of gene  into ourThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Twin and family studies have established the contribution of genetic factors to variation in metabolic, hematologic and immunological parameters. The majority of these studies analyzed single or combined traits into pre-defined syndromes. In the present study, we explore an alternative multivariate approach in which a broad range of metabolic, hematologic, and immunological traits are analyzed simultaneously to determine the resemblance of monozygotic (MZ) twin pairs, twin-spouse pairs and unrelated, non-cohabiting individuals. A total of 517 participants from the Netherlands Twin Register, including 210 MZ twin pairs and 64 twin-spouse pairs, took part in the study. Data were collected on body composition, blood pressure, heart rate, and multiple biomarkers assessed in fasting blood samples, including lipid levels, glucose, insulin, liver enzymes, hematological measurements and cytokine levels. For all 51 measured traits, pair-wise Pearson correlations, correcting for family relatedness, were calculated across all the individuals in the cohort. Hierarchical clustering techniques were applied to group the measured traits into sub-clusters based on similarity. Sub-clusters were observed among metabolic traits and among inflammatory markers. We defined a phenotypic profile as the collection of all the traits measured for a given individual. Average within-pair similarity of phenotypic profiles was determined for the groups of MZ twin pairs, spouse pairs and pairs of unrelated individuals. The average similarity across the full phenotypic profile was higher for MZ twin pairs than for spouse pairs, and lowest for pairs of unrelated individuals. Cohabiting MZ twins were more similar in their phenotypic profile compared to MZ twins who no longer lived together. The correspondence in the phenotypic profile is therefore determined to a large degree by familial, mostly genetic, factors, while household factors contribute to a lesser degree to profile similarity. Twin and family studies have been historically used to examine the effects of genetics and environment on a wide range of complex human phenotypic traits –4. Thereall quantitatively measured traits for that individual and apply correlation metrics to capture the pairwise similarity across a large number of quantitative traits. By comparing the pairwise similarity of the phenotypic profiles within specific groups, which share genes and environment to a different extent, the underlying cause of variation in the phenotypic profile can be explored. To illustrate this, we applied our approach . Family members may resemble one-another due to shared genes and shared environments. In the classical twin design the similarity in MZ and DZ twins is compared to disentangle the effect of these genetic and the shared environmental influences. Here, we use an alternative method by contrasting MZ twin similarity to the similarity in spouse pairs, who share a household but can be considered not to share a genetic background . A higheThe participants in this study were recruited from the Adult Netherlands Twin Register (ANTR), a large twin-family based register for the study of individual differences in health, lifestyle and personality across the lifespan ,20. AdulParticipants were visited between 7 and 10 AM at home or, in a few cases, at work. They were instructed to be fasting and avoid exercise as of the night before and to refrain from smoking and, when possible, medication prior to the visit. Fertile women were bled on a fixed day (day 3) of their menstrual cycle, or during the placebo week of their birth control hormone regimen, if feasible. During the visit, participants were asked to provide information on fasting status, general health, medication use and lifestyle .Height, weight, waist and hip circumference were determined, which were used to calculate body mass index (BMI) and waist-to-hip ration (WHR).Using an OMRON HEM907 blood pressure device, systolic blood pressure (SBP), diastolic blood pressure (DBP) and heart rate (HR) were measured twice, before and after the blood sampling. In the present study we used the average SBP, DBP and HR across the two measurements.Using the Vacutainer system, 8 blood tubes were collected: 2 x 9 ml EDTA, 1 x 9 ml Li heparine, 1 x 9 ml Na heparine, 1 x 4.5 ml CTAD, 1 x 2.5 ml PAX, 1 x 5 ml serum and 1 x 2 ml EDTA. To prevent clotting, all tubes were inverted gently 8–10 times immediately after collection .Total cholesterol, HDL-cholesterol and triglycerides were determined in plasma from a lithium heparin tube, which was stored in melting ice during transport and processed at the laboratory within 6 hours of transport. After centrifugation of the tube for 15 minutes at 1000x g at 4° C, heparin plasma was obtained and divided into 8 subsamples of 0.5 ml, snap-frozen and stored at—30° C. One these subsamples provided lipid levels, using Vitros 250 total cholesterol, Vitros 250 direct HDL cholesterol and Vitros 250 Triglycerides assays  respectively. LDL-cholesterol was calculated using the Friedewald Equation: LDL = Total cholesterol—HDL—(Triglycerides/5) .Insulin was measured using the Immulite 1000 Insulin Method  and glucose was measured using the Vitros 250 Glucose assay .Liver enzymes. GGT, ALT and AST levels in units per liter (U/L) were determined for one of the lithium heparin plasma aliquots, using Vitros assays . Please see The 2 ml EDTA tube was stored at room temperature during transport and used to obtain a hematological profile via the Coulter system . Parameters provided were white blood cell count (WBC), numbers of neutrophils, lymphocytes, monocytes, eosinophils, basophils as well as red blood cell count, hemoglobulin, hematocrit, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), red cell distribution width (RDW), platelet count (PCT) and mean platelet volume (MPV). Please see Nine cytokines were determined in blood from one of the EDTA tubes, which were stored in melting ice during transport. Upon arrival at the laboratory, the EDTA tubes were centrifuged for 20 minutes at 2000x g at 4° C. EDTA plasma was harvested and aliquoted (0.5 ml), snapfrozen in dry ice, and stored at—30° C. Using the Meso Scale Discovery platform and one of the EDTA plasma aliquots, the levels of IL6, TNF-α, GMCSF, IFN-γ, IL10, IL12p70, IL1b, IL2, and IL8 were determined. Please see CRP was obtained from one of the lithium heparin aliquots using the Immulite 1000 CRP assay .Fibrinogen. Fibrinogen levels were obtained from CTAD plasma. The 4.5 ml CTAD tube was stored during transport in melting ice and upon arrival at the laboratory, centrifuged for 20 minutes at 2000x g at 4° C, after which citrated plasma was harvested from the buffy coat and red blood cells, aliquoted (0.5 ml), snap frozen in dry ice, and stored at—30° C. One of these aliquots was used to obtain fibrinogen levels on a STA Compact Analyzer , using STA Fibrinogen .Data were inspected and extreme outliers (defined below) were removed. For individuals who were taking antihypertensive medication N = 24), systolic blood pressure was increased by 14 mmHg and diastolic blood pressure by 10 mmHg . Triglyc, systoliBased on inspection of the distribution of the hematology data we set data to missing when white blood cell count was above 12 (N = 7), neutrophil count above 8 (N = 5), lymphocyte count above 5 (N = 2), monocyte count above 1.1 (N = 6) and basophil count above 10 (N = 1). We further set to missing MCHC when above 24 (N = 1), HCT above. 6 (N = 1), HGB above 14 (N = 1) and red blood cell count above 7 (N = 1). Values were set to missing when individuals were using anti-inflammatory or endocrine medication or had values above 5 for CRP (N = 21), IL6 (N = 9), TNFalpha (N = 22), IFNgamma (N = 17), or above 6 for fibrinogen (N = 7), above 110 for IL10 (N = 24), above 500 for IL12p70 (N = 16), and above 5 for IL1b (N = 11), IL2 (N = 10), and IL8 (N = 14).First, we tested the correlation between all traits in the full sample of 517 individuals by computing partial pairwise Pearson correlations across all the individuals in the cohort (ppcor Package in R), in which we controlled for the relatedness between individuals by taking into account individuals from the same family and their relationship to each other (twin or spouse). In total, 1275 pairwise trait correlations were generated. Based on Bonferroni correction for multiple hypothesis testing, the threshold for significance was < 3.92e-05. We applied a hierarchical clustering technique using the complete linkage method  to grouptwin_correlation_cohab = correlation where twin1 and twin 2 are cohabiting, twin_correlation_not_cohab = correlation, where twin1 and twin2 are not cohabiting, twin_spouse_correlation = correlation and unrelated_individuals = correlation where indiv1 and indiv2 are chosen at random.To establish the relative contribution of genetic and household factors to the variance in all individual traits, we computed the pair-wise correlation between MZ twin pairs (living together and living apart), twin-spouse pairs as well as randomly sampled unrelated individuals as a measure of heritability for each trait. The unrelated individual pairs were constructed by randomly sampling from the cohort for each person excluding their twin and spouse from the cohort. For each of the 51 measured traits we computed: The phenotypic profile p of an individual was defined as a vector of all the phenotypic measurements (n = 51) for that individual. Pearson correlation, capturing the resemblance between pairs of vectors, is used a measure of similarity of two phenotypic profiles. Pearson correlation coefficient is a measure of the linear correlation between two variables X and Y, giving a value between +1 and −1 inclusive, where 1 is total positive correlation, 0 is no correlation, and −1 is total negative correlation. It is furthermore defined as the covariance of the two variables divided by the product of their standard deviations. Pairwise profile similarity between all pairs of individuals was determined for four subgroups: MZ twin pairs sharing (57) or not-sharing a household (153), twin-spouse pairs (64) and pairs of unrelated individuals . Here for each within pair comparison in our four groups we compute a similarity score s = correlation.All statistical analyses were performed in R.We applied hierarchical clustering to elucidate and visualize relationships between the measured traits . Many ofInterestingly, we observed a weak, but significant positive correlation between cotinin, an alkaloid found in tobacco and a metabolite of nicotine, and various inflammatory markers . We also observed a weak positive correlation between heart rate and glucose as well as insulin levels . Other studies have demonstrated that an increased heart rate at rest is associated with glucose levels ,30.A strong correlation was seen between fibrinogen, a coagulation marker, and CRP, a general measure of inflammation . The liver synthesizes both CRP and fibrinogen and in this study they were both positively correlated with body composition . Fibrinogen was also positively correlated with other inflammatory markers .Weak positive correlations were also seen for white blood cell count, an inflammatory marker, with insulin and triglyceride levels  and negatively correlated with HDL .In order to establish the relative importance of genetic and household factors for each trait, we examined the pairwise similarity for each of the separate traits in four groups—unrelated individuals, spouse pairs, non-cohabiting MZ twins and cohabiting MZ twins respectively. MZ pair resemblance for certain traits is more striking than for others , Table 2Evidence for a moderate influence of shared genetic background is seen for other inflammatory markers, platelet measurements, HDL and LDL cholesterol, as well as total cholesterol and creatinine levels. We observed that blood pressure was more correlated in the MZ twins  than in spouses  and unrelated individuals , indicating genetic influences. These findings are consistent with previous work by Xu et al who studied the genetics and environmental effects on BP variability . MZ corrInterestingly, several variables were more correlated in twins living together than those living apart, signifying the role that the environment plays for those traits. The strongest such finding is for cotinine, a biomarker indicating tobacco exposure, for which the correlation coefficient is 0.9  in twins living together and 0.34  in twins living apart. Several other traits including triglyceride levels, waist circumference, heart rate and insulin levels showed a higher concordance in twins living together than those living apart and might have a larger environmental impact.When examining how correlated these individual traits are in twin-spouse pairs and unrelated individuals , only ILWe observe a significantly higher similarity in the profiles of twins than in twin-spouse pairs , which suggests a strong genetic influence on the observed phenotypic profiles. In order to validate this finding, we examined how closely twins would cluster together based on their phenotypic profiles and compared results on the actual data to those computed on a randomly permuted dataset .. For neIn this study we use correlation approaches to explore the similarity of genetically related individuals or individuals sharing a household across a large set of metabolic, hematological and immunological traits. In addition we identify known and novel relationships between the measured phenotypes. The unique design of the study, including twins as well as their spouses, allows us to study the genetic and household effects of the measured phenotypic traits. We define the concept of a phenotypic profile of an individual allowing us to look at multivariate relationships simultaneously to determine the resemblance of monozygotic twins in comparison to their spouses and unrelated individuals.Looking at the overall relationships among all traits several interesting clusters could be identified. Many of the inflammatory markers were highly correlated across this healthy population based cohort. The overall white blood cell count as well as levels of various types of white blood cells  correlated with levels of IL6, a cytokine secreted by T-cells and macrophages to stimulate immune response. As expected, many of the metabolic phenotypes including weight, BMI, and waist circumference, were also correlated with each other and other traits such as LDL, total cholesterol, glucose and insulin levels.Some traits such as heart rate, liver enzyme levels, fibrinogen and CRP were correlated with both metabolic  and inflammatory markers , which have also been seen by Il'yasova et al in 2008 . The bioThe unique design of this study allows us to determine the relative contribution of genetic and household factors to familial resemblance in the metabolic, hematological and immunological traits. In general, when comparing the intra-pair resemblances of the traits of cohabiting MZ twins, resemblance can be a function of both their genes and shared environment, whereas the resemblance of non-cohabiting MZ twins provides an upper limit of the heritability of the trait. In twin-spouse pairs, who share environment only, the resemblance is caused by household factors. Finally, we compare twin-twin and twin-spouse correlations to those obtained in unrelated individuals who share neither genes nor environment.Results of the comparison of similarity across MZ twin pairs, spouse pairs and pairs of unrelated individuals clearly demonstrated the importance of genetic factors on phenotypic profiles as well as many of the individual phenotypic traits in healthy individuals. The profiles among MZ twins, either cohabiting or non-cohabiting, were significantly higher correlated than those of twin-spouse pairs or unrelated individuals, pointing to the importance of genetic factors although a lingering role for childhood environmental factors in non-cohabiting twins cannot be excluded. Some of the most concordant traits included height, telomere length, IL6 receptor levels, and platelet measures. One of the few traits that showed high correspondence in spouse pairs was cotinine level. This may not be surprising, since cotinine levels are reflective of smoking behavior. Concordance rates for smoking are generally high in spouses, both before and after marriage  and smokThe results demonstrate that substantial differences may exist in the relative contribution of genetic factors to the various components of a single biological system. The interaction between circulating IL6 and its soluble receptor is a case in point. IL6 is a potent pleiotropic cytokine secreted by T-cells and macrophages that regulates cell growth and differentiation and plays an important role in immune response. Interleukin 6 receptor (IL6R) forms a ligand–receptor complex with IL-6 that is capable of stimulating a variety of cellular responses including proliferation, differentiation and activation of inflammatory processes ,43. RaggA few extensions and limitations of our approach should be mentioned. The current paper provides a proof of principle in the proposed methodology combined with the unique study design, but includes only a snapshot of the variables available. However, the number of phenotypic traits that are examined here can be extended in number as well as towards categorical variables. The development of integrated phenotypic databases and increased use of electronic medical record systems will allow for richer and more comprehensive analyses to be carried out. In addition, longitudinal data can be incorporated. Following phenotypic markers in twins from birth, through adolescence, adulthood and old age and studying the change in their phenotypic landscape though time will be a very valuable extension of this work. Furthermore, validating some of the relationships and correlations presented here in a larger independent cohort would strengthen the findings. The current study compared MZ twin pairs, who share their genes and childhood environment, with spouse pairs, who share their adult environment. The impact of the shared environment in spouse pairs may differ depending on the time the spouses have been together and the extent to which they take part in similar activities. It would be interesting to investigate the similarity of the phenotypic profile as function of shared interests and cohabitation duration in a larger sample. Similarly we assume that sharing a household in adulthood implies that shared environment can affect phenotype. The effect of the shared as well as non-shared environment of the individuals at a young age or in utero is not captured by our study and is a limitation of the approach. Because there is such limited evidence from studies using the classical twin design (comparing MZ to DZ resemblance) that early common environment has an influence on the traits we study, we explored if the current shared environment has an influence. Both approaches seem to lead to similar conclusions: a main effect of shared environment is very hard to detect.In conclusion, we present here a comprehensive analysis of the phenotypic profile and relationships between anthropometric, hematological, metabolic, and inflammatory traits of interest in twin pairs and spouse pairs. We show complex relationships between many of the phenotypic variables, forming metabolic and inflammatory clusters. The increased twin similarity demonstrates the importance of genetic factors while the larger concordance in spouse pairs compared to unrelated pairs and the comparison of cohabiting and non-cohabiting twins indicates that specific household factors may also play a role, albeit small, in the metabolic and immunological profile.S1 Fig(PDF)Click here for additional data file.S2 FigFor each twin pair the rank of how similar the other twin’s profile is in comparison to all the other phenotypic profiles in the cohort is displayed. Panel A shows the results on the actual data, panel B shows the results on a representative permutation of the data. For the majority of the twins, their twin pair falls within the top five most similar individuals based on their phenotypic profile.(PDF)Click here for additional data file.S1 TableThis table contains all the significant partial pairwise correlation values between the measured phenotypic traits. The non-significant values were set to 0.(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


Family-based sequencing studies have unique advantages in enriching rare variants, controlling population stratification, and improving genotype calling. Standard genotype calling algorithms are less likely to call rare variants correctly, often mistakenly calling heterozygotes as reference homozygotes. The consequences of such non-random errors on association tests for rare variants are unclear, particularly in transmission-based tests. In this study, we investigated the impact of genotyping errors on rare variant association tests of family-based sequence data. We performed a comprehensive analysis to study how genotype calling errors affect type I error and statistical power of transmission-based association tests using a variety of realistic parameters in family-based sequencing studies. In simulation studies, we found that biased genotype calling errors yielded not only an inflation of type I error but also a power loss of association tests. We further confirmed our observation using exome sequence data from an autism project. We concluded that non-symmetric genotype calling errors need careful consideration in the analysis of family-based sequence data and we provided practical guidance on ameliorating the test bias. Next-generation sequencing is a powerful tool to dissect the genetic basis of complex diseases. Family-based sequencing studies have been conducted for various disorders such as autism368910121315We investigated the impact of genotype calling errors on transmission-based tests as a function of several parameters: sequence coverage, gene length, calling algorithms, and different models of transmission-based tests2 = 0; r1 = 1%, 5% or 10% in offspring) and 3 , where r1 is the error rate of mistakenly calling heterozygote 0/1 as homozygote 0/0 and r2 is the error rate of calling homozygote 0/0 as heterozygote 0/1. For gene-level analysis, (p − q)2/(p + q), where p and q are the counts of transmitted and non-transmitted alleles from heterozygous parents. In scenario 2  and 3  in terms of the change of the ratio between transmitted and non-transmitted alleles. Although it is not meaningful to interpret power when type I error rate is inflated, we still show the gene-based power results  on TDT statistics. For sequencing studies, the vast majority of variants are rare, and genotype calling is particularly challenging for rare variants. In addition, the standard analysis for rare variants is gene- or group-based strategies, which further complicates the transmission bias given potentially differential error patterns across variants in a gene or a group.Genotyping error has been recognized as one of major influences on genetics association studies and investigated in various situations. This study can be viewed as a continuation of the work of Mitchell Based on both simulated and real data sets, we have assembled a comprehensive picture of how genotype calling errors impact family-based sequencing studies. Heterozygote to reference homozygote errors is by far the most common error type in rare variant calls in sequencing studies, and such errors in offspring in practice could both inflate the type I error and reduce power for transmission-based association tests. The transmission bias will be more severe for regions of low to modest coverage (30X or lower) and will be accumulated when variants are collapsed in longer genes or pathways. Standard genotype calling pipelines  do not take familial structure into account, and further refinement can be accomplished by using algorithms that do consider familial structure  to alleviate the bias.de novo mutation with base errors, de novo mutation is assumed to be extremely rare in the context of complex diseases and should not affect our conclusion. In analysis of real data, we recommend checking the direction of transmission in the top  genes to ensure that they are consistent with theoretical expectation, i.e. the fraction of genes with over-transmission are expected to be approximately 0.5 when no genes are associated with the diseases or >0.5 when genes harbor risk alleles. In situations where top ranked genes show an overall pattern of under-transmission, it may be a warning of genotype calling bias. Based on our study, and given limited resources, it may be desirable to sequence offspring at a higher coverage than parents in the up-front design of sequencing studies to mitigate such transmission bias.Genotype-calling bias will not only inflate type I error but also reduce the power of subsequent association tests. Reducing power may have more detrimental effects given the inherent low power of identifying associated rare variants for complex diseases; such bias makes the rare variant association studies even more challenging. We have tried to use different methods to correct such bias, and results show that the bias can be reduced but not completely eliminated. We illustrate our findings in the design of parent-offspring trio, which is the simplest form of family structure. It will be interesting to explore this direction in future when the software for family-based rare variant association tests becomes more available. Since general pedigrees can be analyzed by treating sub-pedigrees as trios, the bias in trios can be cumulated in general pedigrees, making it a more severe problem. Although we cannot differentiate 1) is much larger than the error rate of calling homozygote 0/0 as heterozygote 0/1 (this error rate is denoted as r2) for rare variants, we considered four scenarios to mimic this error pattern: 1. r2 = 0; r1 = 1%, 5% or 10% in parents; 2. r2 = 0; r1 = 1%, 5% or 10% in offspring; 3. r1 = 0; r2 = 0.1%, 0.5% or 1% in parents; 4. r1 = 0; r2 = 0.1%, 0.5% or 1% in offspring. Although we assumed the error rate of 0.1%, 0.5% and 1% for the scenarios 3 and 4 in the simulations, the occurrence of these two types of error is extremely low in reality due to the nature of genotype calling strategy252627http://genome.sph.umich.edu/wiki/GTDT) that can be viewed as an extension of TDT for a gene-based analysisWe simulated a set of sequence data and only retained rare variants (defined here as MAF  < 0.05) by using chromosome 22 from the 1000 Genomes Project data . We randomly assigned the effect size β = log(4) to 30% of the variants. The power simulation details are described elsewhereWe obtained exome sequence data from a trio study of autism spectrum disorder (ASD). Details of the data are described previously18How to cite this article: Yan, Q. et al. The impact of genotype calling errors on family-based studies. Sci. Rep.6, 28323; doi: 10.1038/srep28323 (2016).


Background: Food fussiness (FF) is common in early childhood and is often associated with the rejection of nutrient-dense foods such as vegetables and fruit. FF and liking for vegetables and fruit are likely all heritable phenotypes; the genetic influence underlying FF may explain the observed genetic influence on liking for vegetables and fruit. Twin analyses make it possible to get a broad-based estimate of the extent of the shared genetic influence that underlies these traits.Objective: We quantified the extent of the shared genetic influence that underlies FF and liking for vegetables and fruit in early childhood with the use of a twin design.Design: Data were from the Gemini cohort, which is a population-based sample of twins born in England and Wales in 2007. Parents of 3-y-old twins (n = 1330 pairs) completed questionnaire measures of their children’s food preferences (liking for vegetables and fruit) and the FF scale from the Children’s Eating Behavior Questionnaire. Multivariate quantitative genetic modeling was used to estimate common genetic influences that underlie FF and liking for vegetables and fruit.Results: Genetic correlations were significant and moderate to large in size between FF and liking for both vegetables (−0.65) and fruit (−0.43), which indicated that a substantial proportion of the genes that influence FF also influence liking. Common genes that underlie FF and liking for vegetables and fruit largely explained the observed phenotypic correlations between them (68–70%).Conclusions: FF and liking for fruit and vegetables in young children share a large proportion of common genetic factors. The genetic influence on FF may determine why fussy children typically reject fruit and vegetables. A sIn the current study, we used pediatric twin data from the Gemini cohort to test this hypothesis. We used multivariate quantitative genetic analyses to quantify the extent to which genetic influences that underlie FF are the same as those that underlie vegetable and fruit liking and the extent to which common genetic influences explain the observed phenotypic associations between FF and vegetable and fruit liking.n = 6754) were contacted by the Office for National Statistics and were invited to participate in the study. Of these, 2402 families returned a baseline questionnaire (36% response rate) when the twins were, on average (±SD), 8.2 ± 2.2 mo old . This study used data from families who completed the measures of food liking and the CEBQ (n = 2686). Ethical approval was granted by the Joint University College London/University College London Hospitals Committee on the Ethics of Human Research.Data were from children participating in the Gemini twin study. Gemini is a birth cohort of twins born between March and December 2007 in England and Wales . All famthe CEBQ  when theZygosity was determined in same-sex pairs with the use of a validated questionnaire . In addiThe measure of food liking used in this study has been described previously . BrieflyParents reported on their children’s fussiness with the use of the CEBQ FF scale, which was designed to assess neophobic, fussy, and picky eating behaviors in children . The 6 iC) (Heritability was estimated with the use of intraclass correlations (ICCs) and maximum-likelihood structural equation modeling (MLSEM). Analyses were conducted on food liking and FF scores that had been residualized for age and sex effects with the use of a regression procedure. This method took into account the exact correlation for age (and sex within same-sex twin pairs) that could inflate the estimate of shared environmental effects (C) .Twin studies make it possible to estimate the extent of genetic influence on a characteristic by comparing the degree of resemblance between monozygotic twin pairs (who share 100% of their genes) and dizygotic twin pairs . ICCs provide an indication of the size of the genetic effect on a single characteristic, whereby the greater the resemblance between monozygotic and dizygotic twins, the larger the genetic influence on that trait. Cross-twin, cross-trait (CT/CT) correlations form the basis of multivariate heritability. They show how, within a twin pair, the score of twin 1 for trait A  varies in relation to the score of twin 2 for trait B . Similar to simple ICCs, higher CT/CT correlations between monozygotic and dizygotic pairs indicate that shared genetic influences are driving the phenotypic association between the traits.A), C, or unique environmental effects (E) by producing a large number of possible variable values and comparing them to the variance-covariance structures observed in the actual data in an iterative process. The estimates selected are those that produce variance-covariance structures that most closely resemble the actual data  FF in twin 1 was correlated with vegetable liking in twin 2, and 2) vegetable liking in twin 1 was correlated with FF in twin 2. These correlations were compared with the phenotypic correlations calculated with the use of Pearson’s product-moment correlation coefficients to identify evidence of the underlying common genetic influence for both traits. Twin correlations were conducted with the use of SPSS version 21 for Windows software (SPSS Inc).CT/CT ICCs were calculated for monozygotic and dizygotic pairs for the residualized vegetable- and fruit-liking scores paired with residualized FF scores. For each combination of food liking and FF, there were 2 CT/CT correlations as follows: 1) pairwise etiologic correlations and 2) bivariate heritability estimates. Etiologic correlations quantify the extent to which common genetic factors or common environments influence 2 phenotypes . Etiologic correlations can be interpreted in the same way as Pearson correlation coefficients such that a positive etiologic correlation indicates that the influences that cause an individual to score higher on one particular phenotype also tend to make them score higher on the other phenotype; in the same way, a negative correlation would indicate that the same influences that cause an individual to score higher on one phenotype tend to make them score lower on the other phenotype. Bivariate estimates quantify the extent to which common factors  explain the observed phenotypic association . We also tested submodels by systematically dropping components of variance  and covariance, but all submodels led to a worsening of fit according to the likelihood ratio test and Aikaike’s information criterion. MLSEM was conducted with the use of Mx Maximum-Likelihood Structural Equation Modeling Software .The MLSEM used a correlated factors model , 27. ThiP < 0.01) and fruit , such that fussier children tended to dislike vegetables and fruit. Sizes of the associations were moderate to large.Sample characteristics are provided in The pairwise CT/CT correlations between each of the food-liking scales and FF are also shown in A, C, and E (single-headed straight arrows) derived for the 3 traits from the multivariate model as well as the etiologic correlations between them (double-headed curved arrows). The univariate estimates for FF established that it was highly heritable  with the majority of the remaining variance being explained by unique environment effects . The shared environment had a very small effect on FF . Univariate estimates were virtually the same for vegetable liking and fruit liking. Heritability was moderate for both vegetable liking  and fruit liking  in keeping with estimates reported previously  and fruit liking .eviously . EstimatThe phenotypic correlations derived from the structural equation modeling, the pairwise etiologic correlations, and the bivariate estimates  are shown in A) indicated significant genetic contributions to the phenotypic associations between liking for vegetables and FF and between liking for fruit and FF. The bivariate heritability estimates were similar for vegetable liking and FF (70%) and for fruit liking and FF (66%), which suggested that the majority of the phenotypic correlation between each of these pairs of traits could be ascribed to common genetic factors, thereby indicating that the majority of the observed phenotypic associations between FF and liking for both vegetables and fruit were genetically mediated.The bivariate heritability estimates  with only a moderate influence of the environment on this characteristic in young children. These results are comparable to findings from twin studies that investigated genetic and environmental influences on food neophobia, which have previously been estimated as 72% heritable in 4–7-y-olds and 78% heritable in 8–11-y-olds , 18.The strong phenotypic associations observed between higher FF and lower liking for vegetables and fruit also support previous findings –5, 9. Thn-propylthiouracil (PROP) and the associated taste 2 receptor member 38 (TAS2R38) gene. PROP sensitivity has been used as a marker for general taste acuity, and the most frequently applied test of human taste function has involved asking people to rate the intensity of this compound   are alsocompound . The usecompound , 29, andcompound –32.Traits such as food preferences and FF are likely to be highly polygenic with many genes each contributing a small amount to the genetic variation in these phenotypes. This contribution makes it difficult to identify the specific genes responsible although there has been progress in the detection of genetic influences on other complex polygenic traits such as obesity  and aspeCommon shared environmental factors were also shown to contribute to the association between FF and vegetable liking (21%) and FF and fruit liking (24%). Although the shared environment only explained a small proportion of the variance in FF (5%), almost all of the shared environmental influences that contributed to FF also contributed to vegetable preferences , whereas almost three-quarters of these influences were shared between FF and fruit preferences . Common shared environmental influences likely include the early family feeding environment, with siblings raised in the same household sharing similar diets, early feeding experiences, and food exposures. Other shared environmental factors such as food availability, common illnesses, and parental modeling may also contribute to the commonalities in children’s food preferences and fussiness.A considerable proportion of the variance in liking for fruit and vegetables and in FF was also independent insofar as the phenotypic associations were not complete. This unique variance may have reflected distinct mechanisms that are exclusive to each trait, which may also include behavioral or psychological traits such as other appetitive or personality phenotypes. Previous research has shown that individuals who are more sensation seeking tend to be much-less food neophobic , 36, posTo our knowledge, this is the first study to explore the extent to which FF and liking for specific food groups share common genetic influences. The large sample size and multivariate design provided robust estimates for the heritability of FF and also for the shared pathways that influence FF and liking for vegetables and fruit. However, there were several limitations that should be acknowledged. The large sample size prohibited behavioral observations, and parent-reported measures of food liking and FF were necessary because of the young age of the children . The chiIn conclusion, this novel investigation into the shared influences that underlie FF and liking for vegetables and fruit in early childhood provides strong evidence that common genetic influences are driving the observed phenotypic associations between these traits. These findings may help to explain why, of all the food groups, vegetables and fruit are the foods that are rejected most often by fussier children.


In this podcast, we talk to Professor Michael O’Donovan about the latest genetic advances in schizophrenia based on research data from the Schizophrenia Working Group of the Psychiatric Genomics Consortium. Functional and prediction studies from the identified genetic loci are described together with future directions in psychiatric genetics and its interplay with the environment.http://media.biomedcentral.com/content/movies/supplementary/s12916-015-0417-1-s1.mp3The podcast for this interview is available at The online version of this article (doi:10.1186/s12916-015-0417-1) contains supplementary material, which is available to authorized users. Professor Michael O’Donovan is a psychiatrist who underwent doctoral scientific training courtesy of the Medical Research Council (MRC) Training and Travelling Fellowship schemes. He works in the Division of Psychological Medicine and Clinical Neurosciences where he is Professor of Psychiatric Genetics and Deputy Director of the MRC Centre for Neuropsychiatric Genetics and Genomics. His clinical work is at the Cardiff and Vale University Health Board. He has a broad interest in the molecular genetics and neurobiology of mental disorders, and, specifically with reference to the contents of the podcast, is the Lead for the Schizophrenia Group of the Psychiatric Genomics Consortium, a group of over 300 researchers from more than 35 countries Fig. .Fig. 1Mihttp://media.biomedcentral.com/content/movies/supplementary/s12916-015-0417-1-s1.mp3  study. It has really been, once again, a process of evolution; people have recognised that the sample size was a problem, and they have banded together in increasingly large numbers to contribute their samples and their genetic data.For this particular study, the vast majority of the investigators have made what we call the ’individual genotypes’ available to the consortium. Each group mostly performed their own genome-wide association studies (GWASs) and they contributed the individual level genotypes into a central data repository in the Netherlands. This included groups from 34 different countries across the globe. As well as Europe and North America, it also included a number of groups from Australia and from Asia, and in particular Japan, Singapore, and some groups with Chinese samples. It was truly an international collaboration - and it’s getting bigger.We undertook a meta-analysis of all of these raw individual genotype data, and then at the end we sought replication from a separate consortium that for legal reasons could not contribute all of its data. That consortium was led by Decode Genetics in Iceland, and they contributed summary statistics in a format that allows us to combine their results with our primary GWAS results.I expect that people may be aware that in these surveys of essentially the whole of the genome for common genetic variations, we require very stringent statistical thresholds to recognise a finding as being very likely a true positive rather than a statistical chance aberration. We found over 120 such associations that surpassed this threshold, which means that there is a very, very strong chance that these things are true. When we distilled that down and accounted for multiple associations to the same genomic region, we found that we had 108 independent findings. So, there were 108 physically distinct chromosome regions associated with schizophrenia. Each of those regions, in turn, contains one or often more than one gene, so the next task is to try and discover how association points to change in gene function.To explain the actual findings, first of all I would like to make an important caveat for those people who are not geneticists: an association does not pinpoint a gene. An association points to a region of a chromosome. As I said a moment ago, some associated regions contain multiple genes that could be driving this association signal. Some contain apparently only one gene that could be driving the signal. However, there are as yet unrecognised functional elements in the genome. Thus, it is actually possible, for example, that if we find an association to a region that contains only one gene, it might be a functional element that drives changes in a gene some distance away that we are not yet aware of. However, in general it seems to be a valid assumption that the relevant genes that are driving associations are those that are very close to that genetic variant that shows association.With that important caveat in mind, what we actually found was that, as is generally the case in studies like this in other diseases, the associations were to regions containing a whole host of genes that one would never particularly suspect to be involved in schizophrenia. For many of these genes, we do not even have the faintest clue about their function.2 receptor (DRD2), which is the target of all antipsychotic drugs. We found an association that would probably implicate that gene as being involved in the basic aetiology of schizophrenia. We additionally found associations in and around a rather large number of genes that affect glutamate function, which is also one of the more important longstanding neurochemical hypotheses of schizophrenia.We also found a number of associations that appear to point to historical genetic favourites. Schizophrenia, being a somewhat enigmatic disorder in terms of its underlying biology, has been linked to many hypotheses about how the disorder is caused. Some of the leading hypotheses concern alteration in dopamine function, and particularly a receptor called dopamine DThus, we found a range of findings that pointed to some old hypotheses being at least in part relevant to the pathophysiology of schizophrenia. However, most of the findings point to the possibility of novel genes. Really the work is just beginning to try and figure out how those associations may relate to the pathophysiology of the disorder.One way of trying to pursue how genetic associations might relate to broad areas of biology is to do what are called gene set analyses, pathway analyses, or network analyses, where one takes all the findings and tries to link them to groupings of genes that are perhaps expressed in certain tissues or are involved in certain biological processes.One of the approaches that we took was to use publicly available Encyclopaedia of DNA Elements (ENCODE) data. ENCODE is studying the regulatory elements of the human genome. What we did was to extract from that data DNA regulatory elements that appear to be most active in particular tissues and cells, and test whether the genetic associations are enriched in and around those regulatory elements. For example, is there an excess of associations around regulatory elements that appear to be active in neurones or in lymphocytes or in the gut or in the pancreas?In general, we found two main things using that approach. We found that gene regulatory elements that appear to be active in brain tissue and neuronal cell lines were particularly enriched for schizophrenia associations, suggesting, as would have widely been expected, that schizophrenia is in a substantial way a disorder of the brain rather than a disorder of the gut or gut absorption or many of the other hypotheses that been voiced in the history of schizophrenia but still persist.One of the more intriguing novel findings was the enrichment of association in regulatory elements that are particularly active in a number of immune tissues. Again, all this type of work always comes with an important caveat. When we see enrichment of association signals in the vicinity of regulatory elements that appear to be active in immune tissues, what we are not yet sure of is whether those regulatory elements are specific for immune tissues. Could it be that those regulatory elements are also particularly active in the sorts of tissues that you would expect like the brain?We did try to statistically allow for that, and it would appear that there was an independent enrichment of the association signal. Regulatory elements that we did not find to be particularly active in the brain but were active in these immune tissues were also enriched for signals. However, there are a number of unanswered questions at the moment including which genes are exactly affected in the immune tissues. Do these genes only have immune functions or do they have functions in the brain? What change in function is indicated by an association? This is why, in our manuscript, we posed some caution about the interpretation of the immune finding. In some ways this was one of the more interesting results because, if it is true, it supports very longstanding hypotheses that schizophrenia involves sub-optimal immune responses, for example, excessive responses to infection or perhaps some forms of autoimmunity. However, one does not want to jump too far in coming to that conclusion based purely upon genetic data.This analysis was perhaps quite an unexpected finding in many quarters. The background to this is that, in parallel to these common genetic variant studies that have been taking place in schizophrenia, we and others have also been studying rare genetic variation in the disorder in the form of copy number variation, but more recently through DNA sequencing looking for point mutations.de novo copy number variants in schizophrenia and de novo point variations in schizophrenia. It is now clear that these new mutations - ones that occur in a person but not in either of his or her parents - contribute to the disorder in some people with schizophrenia. It is not a high proportion, but these may be important mutations because in theory their effect sizes might be much bigger than these common genetic variants that we have been discussing which individually have very small effect sizes.We have studied both de novo mutations for other neurodevelopmental disorders also tend to cluster in the schizophrenia associated regions. This supports the broad hypothesis that many of the neurodevelopmental disorders are, to a certain extent, aetiologically linked. Here I am referring specifically to intellectual disability and to autism spectrum disorder, though the evidence is wider and includes for example ADHD as well. This is further evidence that there is some kind of commonality between these disorders. I think of them as sharing part of their aetiology, not all, and for the bit in common, they differ to a certain extent by the severity of the mutation type.Having identified genes with these very rare new mutations in our earlier work, we now looked to see if these cluster more than one would expect in the same regions that are showing genome-wide association. We found evidence that they do. Moreover, we also showed that genes that show similar types of The other important implication of that finding of overlap is that one of the criticisms that people levy at researchers who are studying rare genetic variations is: “This is some kind of weird subtype of the disorder that does not really have any relevance to the disorder as a whole in the general population.” The modest degree of convergence between rare genetic variation and common genetic variation suggests that the findings that one might make from these kinds of rarer, bigger effect mutations will have biological relevance for the general population of people with schizophrenia. The reason why this is important is that, in many respects, when one is doing follow-up functional studies  it is easier to do modelling with the rare genetic variation that tends to have larger effects on the disorder, compared with those with rather weak genetic effects.Although we have detected quite a large number of associations, they are the tip of the iceberg. Say we don’t just take these very stringently associated genetic variants but rather go down the list to more weakly associated genetic variants, those that we call ’sub-threshold’ associated. These met some level of significance, but as we go further down the list of significance, we become less confident that any given individual allele is associated with the disease. Nevertheless, the theory is that those that show some evidence for association should be relatively enriched for true associations, even though as we go down the list, for any true association, we drag in many more false ones.Nature by the International Schizophrenia Consortium. However, in the early days the predictive power enabled statistical differentiation between cases and controls, but the sensitivity and specificity were extremely poor. However, as the genome-wide association studies became larger, the power to achieve this discrimination has improved.In this way, we consider all alleles that are more significant than some nominal threshold as ’potential risk’ alleles. We derive them from one dataset; let’s say there are 10,000 of these alleles that surpass some kind of statistical threshold. Subsequently, in an independent sample, and simplifying this procedure to some extent, one simply counts up how many of these risk alleles each person carries. The prediction is that cases from a different study will have a higher number of these risk alleles than the controls do. What we call the risk profile score is a weighted average of the number of these risk alleles carried. That this approach could statistically differentiate between cases and controls was shown by a study that was published in P-values concerning group differences between cases and controls - when it comes to looking at an individual person, although cases have on average a higher score than the controls, many of the controls have scores higher than many of the cases. In other words, the score distribution from cases overlaps substantially with the score distribution from controls, and as an upshot, we cannot confidently predict who is a case and who is a control based only on their polygenic score.So the principle works certainly, but it is important to note in practice that we still cannot use this for a clinical diagnosis. Although we have high statistical significance - we can obtain very, very small What we can do in a research setting is screen large population samples which have GWAS data and find people who are at relatively higher risk for schizophrenia, even though the vast majority of them will develop schizophrenia, and at the other end of the score distribution, people who are at relatively low genetic risk for the disorder, even though a small proportion of them will in fact develop the disorder. We can then start doing interesting studies about how those two populations differ in terms of biology to try to find out what it is about brain function that relates to high and low risk. The profile scores are useful in a research setting, but they are of no current value in a clinical setting.In principle, the simplest thing that we and others are trying to do is increase the sample sizes, because although we have made many discoveries so far, there is much more to be done. The aim of the Psychiatrics Genomics Consortium is, over the next four years perhaps, to try and get GWAS data on 100,000 cases instead of the current number in the mid-30,000s. Thus, we are looking for more power and more findings.It is also important to try and achieve much better functional interpretation of the common genetic findings. That will require better functional annotation of the genome, in terms of which genetic variants regulate genes in which cell types and in which areas of the brain at what developmental times including foetal life. A lot of background biology is required to try and provide resources that allow us to interpret common genetic findings.We and others are also interested in trying to use the common genetic findings and rare genetic results to sub-classify the heterogeneous disorder into more homogeneous groups. So far, attempts to carve it up by symptom pattern or age at onset, or in various other ways that one might conceptualise, have not really produced convincing results. The reason why we are trying to get more homogeneous populations of people with the disorder is that such membership of distinct groups might be clinically useful, for example, in trying to figure out why some treatments work in some people but not in others, and in overall improving the treatment prospects.de novo mutations, but over the next few years we expect there to be larger samples that will be analysed by whole genome sequencing. Here the objective is that rare genetic variation contributes to some genetic risk, although most of us do not believe that rare genetic variation contributes to the majority of the genetic risk. As I mentioned earlier on in this podcast, it is easier to model the mutations in cells and animals and thereby probe what kind of biological consequences genetic variants have on cellular function and whole organism function, respectively.There is also a lot of work going on in rare genetic variation. Increasingly, people are adopting sequencing approaches - initially exome sequencing as we have done looking for Researchers are also interested in taking the genetic findings and figuring out what kind of physiological changes occur in people without the disorder. Perhaps this can be indexed by observing changes in brain function using functional imaging techniques or varying other methods for assaying brain function. Additionally cognitive function can be determined in terms of measuring brain performance. We in Cardiff, along with colleagues in London led by Shitij Kapur, are trying to use various genetic profiling techniques to try and predict who will and who will not respond to particular treatment types. We regard the genetic findings really as an infrastructure for a whole range of future investigations. It is impossible for me to predict or detail every single way that this is likely to be exploited; the options are almost boundless.There is certainly an environmental contribution to schizophrenia. We have some clues that perhaps perinatal infections or other forms of adverse challenges during pregnancy and surrounding childbirth contribute. There is evidence that very traumatic experiences in childhood may contribute, and that in certain populations that migration, possibly as a result of stress or discrimination, contributes to the disorder. Thus, there are a range of clues about the environment. What is more controversial is whether or not environmental exposures in general increase your risk or only increase your risk with a given genetic background. The latter is often referred to as ’gene-environment interaction’.Environmental studies that include genetic data, and genetic studies that include environmental data, are sub-optimal at the moment. The power to detect so-called gene-environment interactions is significantly less than it is to identify the genetic effects in the first place, although studies are proceeding to try and see how particular genes or whole genetic risk relates to drug abuse in terms of dictating schizophrenia risk. In fact, I and my colleagues, led by Jim van Os at the University of Maastricht, have such a study funded by the European Union. I expect that the main deliverables from that will take several more years, because I suspect the sample sizes are currently inadequate.See the reference list –10.


Patients were identified using the National Patient Register using validated algorithms based on International Classification of Diseases (ICD) 8–10. Univariate quantitative genetic models revealed that all phenotypes  were highly heritable (h2=53–71%). Multivariate models further revealed that schizophrenia was a stronger predictor of violence  than bipolar disorder , and large proportions (51–67%) of these phenotypic correlations were explained by genetic factors shared between each disorder, substance misuse, and violence. Importantly, we found that genetic influences that were unrelated to substance misuse explained approximately a fifth (21% 20–22%) of the correlation with violent criminality in bipolar disorder but none of the same correlation in schizophrenia . These findings highlight the problems of not disentangling common and unique sources of covariance across genetically similar phenotypes as the latter sources may include aetiologically important clues. Clinically, these findings underline the importance of assessing risk of different phenotypes together and integrating interventions for psychiatric disorders, substance misuse, and violence.Patients diagnosed with psychotic disorders  have elevated risks of committing violent acts, particularly if they are comorbid with substance misuse. Despite recent insights from quantitative and molecular genetic studies demonstrating considerable pleiotropy in the genetic architecture of these phenotypes, there is currently a lack of large-scale studies that have specifically examined the aetiological links between psychotic disorders and violence. Using a sample of all Swedish individuals born between 1958 and 1989 ( A more detailed analysis is therefore needed to reveal the extent to which the specific sources of covariance between psychotic disorders and violent crime are aetiologically distinct from substance misuse. To the best of our knowledge, there are currently no quantitative genetic studies that have specifically examined the aetiological overlap between psychotic disorders and violent crime.Twin and family studies have consistently demonstrated that psychotic disorders, substance misuse and violent crime aggregate in families, in large part due to genetic influences.In the present study, we have used nationwide Swedish data for around 1.8 million individuals born between 1958 and 1989 to address this uncertainty. We aimed to provide estimates of the relative contributions of genes and environments to the overlap between psychotic disorders and violent crime. Given the high rates of comorbid substance misuse in these patient groups, we additionally sought to stratify the aetiological determinants to those that were either shared with substance misuse and those that were unique to each disorder. The clarification of these aetiological links is important to inform psychiatric genetic studies and may assist in developing improved risk assessment and management in these disorders.16 To ensure confidentiality, we were granted access to de-identified data after approval from the Regional Research Ethics Committee at Karolinska Institutet (2009/939–31/5). Informed consent was therefore not required.Statistics Sweden maintains all nationwide Swedish longitudinal registries with routinely gathered governmental data. The linkage of individual-level data between different nationwide registers is possible via a unique 10-digit civic registration number assigned to all Swedish residents at birth or upon immigration.17 and the Multi-Generation Register to identify all monozygotic (MZ) twins, dizygotic (DZ) and non-twin full-siblings. The Cause of Death Register provided mortality dates between 1961 and 2009. Individuals who were born before 1961 were therefore only included if they were registered to be alive at the end of 1960 in the national census register. The Migration Register supplied emigration dates throughout the studied period. We identified individuals with hospital discharges using the National Patient Register that encompasses data on psychiatric inpatient care since 1973  8−10) and specialist outpatient care since 2001 (ICD 10). A complete list of the ICD-codes used to define our measures is available in We used the Swedish Total Population Register to identify all individuals born in Sweden between 1958 and 1989 and linked their data with the Swedish Twin Registryn=3 232 010). We identified all MZ twins (n=12 588), DZ twins (n=27 148) and non-twin full-siblings (n=2 369 775) in this sample. In families with multiple offspring, we selected the oldest two siblings born within 5 years from one another to reduce bias because of violation of the equal environment assumption for siblings in our analyses. The final sample included 923 259 twin-sibling pairs .The sample included all individuals born in Sweden between 1958 and 1989, who had not died or migrated before the age of 15 years . We used an omnibus measure of substance misuse that included alcohol and drug-related convictions (crimes against the Narcotic Drugs Act (SFS 1968:64) and driving under the influence of alcohol and/or illicit substances) as well as hospital admissions for either alcohol or drug-related disorders.In line with other work,25 We therefore decided to measure income as a binary variable (lowest decile vs top nine deciles) in the analyses. Approximately 0.2% (n=5694) individuals lacked data on this measure and were consequently excluded in the descriptive analyses. Single status was defined as never having been married either at first diagnosis or at age 30 years (for control subjects). Immigrant background was defined as having at least one biological parent who was born outside of Sweden.Family disposable income, defined as the standardized net sum of earnings and benefits, averaged across both biological parents, at the end of the year that the offspring had reached 15 years of age, was used as a proxy measure for the material living standard during adolescence. If this information was unavailable, we used data from the previous year or until it became available. Previous studies have shown that psychiatric disorders, substance misuse and violent criminality are all non-linearly distributed across socioeconomic strata in Sweden, with the heaviest concentrations being observed in the most deprived groups.n=2 425 703) by excluding individuals who had ever been hospitalised for any diagnosis of a psychiatric disorder.First, descriptive statistics were generated to examine the differences in socio-demographic characteristics and comorbidities across the three psychiatric disorders. From the population sample, we identified healthy control subjects . This approach allowed us to decompose the phenotypic variation of each phenotype into three distinct sources of influence: (a) additive genetic influences, (b) shared environmental influences  and (c) unique environmental influences .n=54 341); and those who had either died or migrated after their 15th birthday (n=220 072).We subsequently tested for the stability of our findings against confounding and model misspecification. We re-fitted the models after removing individuals with an immigrant background  and 12 627 (0.4%) individuals who met criteria for schizophrenia and bipolar disorder, respectively, as well as 2 425 703 healthy control subjects . The meaThe quantitative genetic models were fitted on a sample consisting of 1 846 518 individuals, of which 12 588 were MZ twins, 27 148 DZ twins and 1 806 782 non-twin full-siblings . There wh2) was substantial for schizophrenia , bipolar disorder (h2=62% 57–67%) and substance misuse (h2=53% 51–54%). The remaining variance was entirely explained by unique environmental influences. In contrast, we found that the familial aggregation of violent crime was explained by both additive genetic (h2=54% 47–61%) and shared environmental influences (14% 7–21%). These estimates were stable across sub-samples excluding individuals with immigrant background as well as individuals who had either died or migrated during follow-up  than patients diagnosed with bipolar disorder . Large proportions (51–67%) of the bivariate correlations between the psychotic disorders and violent crime were explained by additive genetic influences shared with substance misuse. In the case of schizophrenia, we found no evidence for additive genetic influences that were unrelated to substance misuse . In contrast, however, such influences accounted for 21% (20–22%) of the correlation between bipolar disorder and violent crime. Unique environmental influences shared between the psychotic disorders and substance misuse were inversely associated with violence risk, which implies that once genetic factors were accounted for, unique environmental factors that increased the risk of both psychotic disorders and substance misuse tended to decrease the risk of violent crime. Although statistically significant, these influenced merely accounted for 3–7% of the correlations. In contrast, disorder-specific unique environmental influences were positively associated with the risk of violent crime convictions and explained between 35 and 36% of the correlations. Complete correlation matrices are presented in Supplementary Tables 3 and 4.The multivariate quantitative genetic models  confirme13 we found substantial heritability estimates for schizophrenia, bipolar disorder, substance misuse and violent crime (h2=53–71%). Schizophrenia was a stronger predictor of violent crime (r=0.32) than bipolar disorder (r=0.23). Consistent with previous quantitative and molecular genetic studies demonstrating important pleiotropic effects between the examined phenotypes,15 we found that 67% of the correlation between schizophrenia and violent crime, and 51% of the correlation between bipolar disorder and violent crime was attributed to additive genetic influences that were shared between the psychotic disorders, substance misuse, and violent crime. This suggests that the increased risk for violence in these patient groups could largely be attributed to the same genetic factors that simultaneously increased their liabilities to substance misuse and to be diagnosed with the psychotic disorders in the first place. However, we additionally observed a novel finding in that the additive genetic influences that were unrelated to substance misuse explained approximately a fifth (21%) of the correlation with violent criminality in bipolar disorder but none of the same correlation in schizophrenia. In other words, we found support for the existence of disorder-specific genetic effects linking bipolar disorder (but not schizophrenia) to increased violence risk. This implies that aetiological models involving psychotic disorders should not only focus on genetic and environmental factors that are shared between the disorders but also on those factors that are unique to each disorder.We report a population-based quantitative genetic study examining the aetiological links between psychotic disorders and violent crime using a sample of ~1.8 million Swedish individuals born between 1958 and 1989. Consistent with previous population-based research,30 and the rates of aggression and violence tend to be more pronounced in patients diagnosed with bipolar disorder who exhibit higher rates of impulsivity.31 Second, it has also been established that the risk of aggression in bipolar disorder increases substantially during acute manic episodes,32 which are characterised by elevated levels of irritability, impatience and impaired insight.33 Genetically informative longitudinal clinical studies are therefore warranted to further explore the ways in which symptoms of mania interplay with substance misuse to generate increased levels of aggression and violence.Explanations for this bipolar-specific genetic effect could include the following points: Clinical studies have demonstrated that impulsivity and risk-taking behaviours are stronger in bipolar disorder than in schizophrenia35 Similarly, only a handful of single-nucleotide polymorphisms have been found to be individually associated with different substance misuse phenotypes but a recent study adopting the genome-wide complex trait analysis approach found that the combined influence of all single-nucleotide polymorphisms explained between 25% to 36% of the variance in different substance misuse phenotypes.36 Data on five common psychiatric disorders from the PGC were recently re-examined using multivariate genome-wide complex trait analysis models and the authors concluded that the genetic risk predictions had improved substantially with the addition of multiple genetically similar phenotypes.37 The presented findings indicate that the additional inclusion of substance misuse and aggression/violence phenotypes into multivariate molecular genetic studies will likely contribute to greater improvements of statistical power and accuracy. However, the findings also highlight problems with not disentangling common and unique sources of covariance across genetically similar phenotypes as the latter sources may include aetiologically important clues.We were unable to tell, based on our methodological approach, what specific genes and environmental factors that influenced the risk for violent crime. The available genome-wide association studies on adulthood antisocial behaviours have thus far been too small and heterogeneous to yield any robust findings and comprehensive meta-analyses have failed to identify a single significant polymorphism associated with aggression.38 we found moderately sized unique environmental influences, which are analogous to within-family estimates.5 This implies that the increased risk of violence in individuals diagnosed with the psychotic disorders was, to a moderate extent (35–36%), attributed to their individual differences, or ‘unique environmental influences', that could not be explained by their familial characteristics. Unique environmental risks that were shared between the psychiatric disorders and substance misuse, on the other hand, tended to decrease the risk of violence but the effect sizes were marginal (3–7%). This replication is an important confirmation of the conclusions drawn by the previous studies as our research design is not as sensitive to misclassification bias and residual confounding due to non-shared genetic risks between non-twin siblings.39In line with previous Swedish nationwide studies that studied these associations using differentially exposed full-siblings,25 whereas the association between peer deviance and drug abuse,40 and associations between sustaining a traumatic brain injury and subsequent violent crime41 and suicide42 appear to be consistent with a causal inference. These findings highlight the importance of accounting for unobserved familial confounders in future studies of putative modifiable environmental risk factors on violence, especially in psychiatric patient groups, as the pleiotropic genetic influences are substantial across all of these traits.In terms of specific unique environmental influences, it has been demonstrated in a number of genetically informative general Swedish population studies that socioeconomic deprivation and urban residence are merely correlates and not causes of either violent crime, substance misuse or psychiatric morbidity,43 these findings also highlight the importance of considering psycho-education provided to the patients and their families as a routine and repeated intervention about the risks of substance misuse. Moreover, the prevention of violent behaviour needs to be considered in all patients, particularly in those with first episodes as contact with the criminal justice system may worsen prognosis. Such preventative strategies could include avoidance of antisocial peers40 and other interventions to reduce the risks of being victimized and other risk factors.One clinical implication of the findings is that, because of the reported strong overlap between these phenotypes, the treatment of the mental illness and comorbid substance misuse should be integrated more fully in mental health services, which in turn may have benefits for patient safety and public health by reducing violence risk. Consistent with the recent UK NICE guideline on treatments for patients diagnosed with psychotic disorders and comorbid substance misuse,13 Second, although the presented estimates  are valid for the entire population, we cannot exclude the possibility that they are moderated by gender. We were nevertheless unable to explore such moderation effects because the stratification into gender-specific twin sub-samples resulted in some bivariate tables containing cells with zero observations, which prevented the reliable estimation of the tetrachoric correlations. Pooling data from different nations with national registers could be explored to address important question. Further work could also examine different crime outcomes, and in particular, sexual crimes as there is now some preliminary evidence that heritability estimates may differ for certain subgroups .44A number of limitations need to be noted. First, the findings related explicitly to the twins and siblings included in the sample. However, the prevalence rates of the phenotypes in the latter sub-sample did not differ materially from equivalent rates in the population sample  and 2 anThird, we could not reliably ascertain their temporal ordering of the phenotypes because they represent severe end points to complex developmental processes. Future aetiological studies should therefore benefit from combining nationwide registry data with detailed longitudinal clinical and subclinical measures of psychotic experiences, substance misuse, aggression and antisocial behaviours. Fourth, we relied on validated definitions of schizophrenia and bipolar disorder that were formulated to ensure a high degree of specificity. Complementary sensitivity analyses using wider definitions of the disorders  were not materially different with the presented findings .45 but a violation of this assumption will only contribute to a downward bias of the additive genetic influences.27 Simulation models comparing models with and without dominance genetic influences rarely observe that the inclusion of such effects changes the overall share of the variance attributed to genetic influences.46 We decided against modelling for such influences to keep the complex models more parsimonious. The equal environments assumption is violated if the shared environmental correlation in MZ twins exceeds that of DZ twins and non-twin full-siblings, which will cause an artificial increase of the heritability estimate.26 However, the impact of this assumption is minimal at best as a recent simulation study demonstrated that the presence of such bias increases heritability estimates by a maximum of 5%.27Last, the specification of the quantitative genetic models presented here assumes the absence of both non-random mating and dominance genetic influences. Furthermore, they assume that the shared environments and prevalence rates of the phenotypes are approximately equal across the groups of twins and non-twin full-siblings. Non-random mating has been established for many of the phenotypesWe found that patients who were diagnosed with psychotic disorders had elevated risks of committing violent offences largely due to genetic influences that simultaneously increased their liabilities to develop their psychiatric morbidity, to engage in substance misuse and also commit violent crimes. Genetic influences that were unique to bipolar disorder explained approximately a fifth of the increased risk of violent crime while equivalent genetic influences in schizophrenia did not contribute to such risk increases. Future research efforts should be directed towards integrating violence-related phenotypes into psychiatric genetic studies and to identify potentially causal unique environmental predictors of violence in these patient groups. Clinically, these findings illustrate the importance of risk assessments that consider substance misuse comorbidity, integrated treatments for multiple adverse risks and strong collaborations between criminal justice, substance misuse and mental health services.