Large multigenerational cohort studies offer powerful ways to study the hereditary effects on various health outcomes. However, accounting for complex kinship relations in big data structures can be methodologically challenging. The traditional kinship model is computationally infeasible when considering thousands of individuals. In this article, we propose a computationally efficient alternative that employs fractional relatedness of family members through a series of founding members. The primary goal of this study is to investigate whether the effect of determinants on health outcome variables differs with and without accounting for family structure. We compare a fixed-effects model without familial effects with several variance components models that account for heritability and shared environment structure. Our secondary goal is to apply the fractional relatedness model in a realistic setting. Lifelines is a three-generation cohort study investigating the biological, behavioral, and environmental determinants of healthy aging. We analyzed a sample of 89,353 participants from 32,452 reconstructed families. Our primary conclusion is that the effect of determinants on health outcome variables does not differ with and without accounting for family structure. However, accounting for family structure through fractional relatedness allows for estimating heritability in a computationally efficient way, showing some interesting differences between physical and mental quality of life heritability. We have shown through simulations that the proposed fractional relatedness model performs better than the standard kinship model, not only in terms of computational time and convenience of fitting using standard functions in R, but also in terms of bias of heritability estimates and coverage.

Structural equation modeling (SEM) is an important research tool, both for path-based model specification (common in the social sciences) and also for matrix-based models (in heavy use in behavior genetics). We developed umx to give more immediate access, relatively concise syntax and helpful defaults for users in these two broad disciplines. umx supports development, modification and comparison of models, as well as both graphical and tabular outputs. The second major focus of umx, behavior genetic models, is supported via functions implementing standard multigroup twin models. These functions support raw and covariance data, including joint ordinal data, and give solutions for ACE models, including support for covariates, common- and independent-pathway models, and gene × environment interaction models. A tutorial site and question forum are also available.

There is a growing body of literature linking religious attendance to prosocial behavior (PB). The main purposes of the present study were to estimate genetic and environmental influences on the frequency of religious attendance (FRA) and to explore whether and how FRA moderates genetic and/or environmental influences on PB. As part of the Nigerian Twin and Sibling Study, 2860 (280 monozygotic male, 417 monozygotic female, 544 dizygotic male, 699 dizygotic female, and 920 opposite-sex dizygotic) twins (mean age = 14.2 years; SD = 1.7 years; age range = 12–18 years) completed a questionnaire regarding FRA and a PB scale. Similar to the findings from western twin samples, FRA showed substantial shared environmental influences of 74% (95% CI = 69%, 78%), with absence of genetic effects. The phenotypic correlation between FRA and PB was modest but positive and significant (r = .12; p < .01), suggesting that PB is higher among more frequent attenders than among less frequent attenders. The results of gene–environment (G × E) interaction model-fitting analysis revealed that FRA changed individual environmental experiences rather than genetic effects on PB such that while genetic variance was stable, non-shared environmental variance declined, leading the total phenotypic variance of PB to decrease with increasing levels of religious attendance.

We recently reported an association of offspring educational attainment with polygenic risk scores (PRS) computed on parent’s non-transmitted alleles for educational attainment using the second GWAS meta-analysis article on educational attainment published by the Social Science Genetic Association Consortium. Here we test the replication of these findings using a more powerful PRS from the third GWAS meta-analysis article by the Consortium. Each of the key findings of our previous paper is replicated using this improved PRS (N = 2335 adolescent twins and their genotyped parents). The association of children’s attainment with their own PRS increased substantially with the standardized effect size, moving from β = 0.134, 95% CI = 0.079, 0.188 for EA2, to β = 0.223, 95% CI = 0.169, 0.278, p < .001, for EA3. Parent’s PRS again predicted the socioeconomic status (SES) they provided to their offspring and increased from β = 0.201, 95% CI = 0.147, 0.256 to β = 0.286, 95% CI = 0.239, 0.333. Importantly, the PRS for alleles not transmitted to their offspring — therefore acting via the parenting environment — was increased in effect size from β = 0.058, 95% CI = 0.003, 0.114 to β = 0.067, 95% CI = 0.012, 0.122, p = .016. As previously found, this non-transmitted genetic effect was fully accounted for by parental SES. The findings reinforce the conclusion that genetic effects of parenting are substantial, explain approximately one-third the magnitude of an individual’s own genetic inheritance and are mediated by parental socioeconomic competence.

The Barker hypothesis states that low birth weight (BW) is associated with higher risk of adult onset diseases, including mental disorders like schizophrenia, major depressive disorder (MDD), and attention deficit hyperactivity disorder (ADHD). The main criticism of this hypothesis is that evidence for it comes from observational studies. Specifically, observational evidence does not suffice for inferring causality, because the associations might reflect the effects of confounders. Mendelian randomization (MR) — a novel method that tests causality on the basis of genetic data — creates the unprecedented opportunity to probe the causality in the association between BW and mental disorders in observation studies. We used MR and summary statistics from recent large genome-wide association studies to test whether the association between BW and MDD, schizophrenia and ADHD is causal. We employed the inverse variance weighted (IVW) method in conjunction with several other approaches that are robust to possible assumption violations. MR-Egger was used to rule out horizontal pleiotropy. IVW showed that the association between BW and MDD, schizophrenia and ADHD is not causal (all p > .05). The results of all the other MR methods were similar and highly consistent. MR-Egger provided no evidence for pleiotropic effects biasing the estimates of the effects of BW on MDD (intercept = -0.004, SE = 0.005, p = .372), schizophrenia (intercept = 0.003, SE = 0.01, p = .769), or ADHD (intercept = 0.009, SE = 0.01, p = .357). Based on the current evidence, we refute the Barker hypothesis concerning the fetal origins of adult mental disorders. The discrepancy between our results and the results from observational studies may be explained by the effects of confounders in the observational studies, or by the existence of a small causal effect not detected in our study due to weak instruments. Our power analyses suggested that the upper bound for a potential causal effect of BW on mental disorders would likely not exceed an odds ratio of 1.2.

The expansion of genetic and genomic testing in clinical practice and research and the growing market for at home personal genome testing has led to increased awareness about the impact of this form of testing on insurance. Genetic or genomic information can be requested by providers of mutually rated insurance products, who may then use it when setting premiums or determining eligibility for cover under a particular product. Australian insurers are subject to relevant legislation and an industry standard that was updated in late 2016. In 2018, the Human Genetics Society of Australasia updated its position statement on genetic testing and life insurance to account for these changes and to increase the scope of the statement to include a wider scope of insurance products that are not rated according to community risk, such as life, critical care, and income protection products. Recommendations include that providers of professional education involving genetics should include ethical, legal, and social aspects of insurance discrimination in their curricula; that the Australian government take a more active role in regulating use of genetic information in personal insurance, including enacting a moratorium on use of genetic test results; that information obtained in the course of a research project be excluded; and that there is improved engagement between the insurance industry, regulators, and the genetics profession.

The aim of this study was to examine the effects of genetic and environment influences and sex on injury involvement using two sets of Finnish twin data. The younger participants were 955 twins born between 1983 and 1987, aged 20 to 24 years. The older participants were 12,428 twins born between 1930 and 1957, aged 33 to 60 years. Within-twin correlations in monozygotic and dizygotic twins suggested that genetic effects play no role in injury involvement among young twins, but do have some effect at older ages. The results indicated that environmental factors have greater importance in injury involvement than genetic factors in the younger twin data set (FT12), whereas in a middle-aged (33–60 years) twin data set, genetic effects explained about quarter of the variance in injury involvement. Sex was a strong contributing factor, with males being generally more prone to injuries than females.

According to the Sasang theory, humans can be categorized into one of the four Sasang constitution (SC) types. The four SC types are Tae-Yang (TY), Tae-Eum (TE), So-Yang (SY), and So-Eum (SE), which are determined mainly on the basis of anthropometric characteristics, personality, and the balance of the physiological functions of the major organ systems. There is a growing recognition in the complementary and alternative medicine area that SC types have the potential to be a useful scientific tool for the prevention, diagnosis, and treatment of diseases (Cooper, Evidence Based Complementary and Alternative Medicine, Vol. 6 (Suppl. 1), 2009, pp. 1–3). The main purposes of the present study are to estimate genetic and environmental influences on SC types, and to explore genetic and environmental correlations that affect phenotypic associations among the SC types. In total, 1,742 (365 monozygotic male, 173 dizygotic male, 675 monozygotic female, 271 dizygotic female, and 258 opposite-sex dizygotic) twins (mean age = 19.1 ± 3.1 year) completed a Sasang questionnaire. Univariate and multivariate model-fitting analyses were performed. Total (additive and non-additive) genetic influences were 71% for males and 81% for females in TE, 70% for males and 71% for females in SE, and 47% for both sexes in SY. Non-additive genetic effects were substantial, and shared environmental influences were negligible in most SC types. Multivariate model-fitting analysis revealed that non-additive genetic and individual-specific environmental correlations between TE and SE were -0.92 (95% CI [-0.89, -0.93]) and -0.62 (95% CI [-0.57, -0.68]), respectively. The corresponding estimates were -0.55 (95% CI [-0.48, -0.61]) and -0.44 (95% CI [-0.37, -0.51]) between TE and SY and 0.19 (95% CI [0.09, 0.29]) and -0.40 (95% CI [-0.32, -0.47]) between SE and SY. These results suggest that the phenotypic associations among SC types may be mediated by pleiotropic mechanism of genes.

Lam et al. (2018) respond to a commentary of their paper entitled ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ Lam et al. (2017). While Lam et al. (2018) have now provided the recommended quality control metrics for their paper, problems remain. Specifically, Lam et al. (2018) do not dispute that the results of their multi-trait analysis of genome-wide association study (MTAG) analysis has produced a phenotype with a genetic correlation of one with three measures of education, but do claim the associations found are specific to the trait of cognitive ability. In this brief paper, it is empirically demonstrated that the phenotype derived by Lam et al. (2017) is more genetically similar to education than cognitive ability. In addition, it is shown that of the genome-wide significant loci identified by Lam et al. (2017) are loci that are associated with education rather than with cognitive ability.

The present study aimed to estimate heritability of Hwabyung (HB) symptoms in adolescent and young adult twins in South Korea. The sample included 1,601 twins consisting of 143 pairs of monozygotic male (MZM), 67 pairs of dizygotic male (DZM), 295 pairs of monozygotic female (MZF), 114 pairs of dizygotic female (DZF), and 117 pairs of opposite-sex dizygotic (OSDZ) twins and 129 twins with non-participating co-twins (mean age = 19.1 ± 3.1 years; range: 12–29 years). An HB symptom questionnaire was given to twins via a telephone interview. Consistent with the literature of HB, the mean level of HB was significantly higher in females than in males. Maximum likelihood twin correlations for HB were 0.31 (95% CI [0.16, 0.45]) for MZM, 0.19 (95% CI [-0.05, 0.41]) for DZM, 0.50 (95% CI [0.41, 0.58]) for MZF, 0.28 (95% CI [0.11, 0.44]) for DZF, and 0.23 (95% CI [0.05, 0.40]) for OSDZ twins. These patterns of twin correlations suggested the presence of additive genetic influences on HB. Model-fitting analysis showed that additive genetic and individual-specific environmental influences on HB were 44% (95% CI [37, 51]) and 56% (95% CI [49, 63]), respectively. Shared environmental influences were not significant. These parameter estimates were not significantly different between two sexes, and did not change significantly with age in the present sample, suggesting that genetic and environmental influences on HB in both sexes are stable across adolescence and young adulthood.

Objective: The human gut microbiota has been demonstrated to be associated with a number of host phenotypes, including obesity and a number of obesity-associated phenotypes. This study is aimed at further understanding and describing the relationship between the gut microbiota and obesity-associated measurements obtained from human participants. Subjects/Methods: Here, we utilize genetically informative study designs, including a four-corners design (extremes of genetic risk for BMI and of observed BMI; N = 50) and the BMI monozygotic (MZ) discordant twin pair design (N = 30), in order to help delineate the role of host genetics and the gut microbiota in the development of obesity. Results: Our results highlight a negative association between BMI and alpha diversity of the gut microbiota. The low genetic risk/high BMI group of individuals had a lower gut microbiota alpha diversity when compared to the other three groups. Although the difference in alpha diversity between the lean and heavy groups of the BMI-discordant MZ twin design did not achieve significance, this difference was observed to be in the expected direction, with the heavier participants having a lower average alpha diversity. We have also identified nine OTUs observed to be associated with either a leaner or heavier phenotype, with enrichment for OTUs classified to the Ruminococcaceae and Oxalobacteraceae taxonomic families. Conclusion: Our study presents evidence of a relationship between BMI and alpha diversity of the gut microbiota. In addition to these findings, a number of OTUs were found to be significantly associated with host BMI. These findings may highlight separate subtypes of obesity, one driven by genetic factors, the other more heavily influenced by environmental factors.

Twin registers are wonderful research resources for research applications in medical and behavioral genetics, epidemiology, psychology, molecular genetics, and other areas of research. New registers continue to be launched all over the world as researchers from different disciplines recognize the potential to boost and widen their research agenda. In this article, we discuss multiple aspects that need to be taken into account when initiating a register, from its preliminary sketch to its actual development. This encompasses aspects related to the strategic planning and key elements of research designs, promotion and management of a twin register, including recruitment and retaining of twins and family members of twins, phenotyping, database organization, and collaborations between registers. We also present information on questions unique to twin registers and twin-biobanks, such as the assessment of zygosity by SNP arrays, the design of (biomarker) studies involving related participants, and the analyses of clustered data. Altogether, we provide a number of basic guidelines and recommendations for reflection when planning a twin register.

The phenotypic relationships between body mass index (BMI) and cold-heat patterns have been frequently reported, but the etiology of these relationships remains unknown. We previously demonstrated that the cold pattern (CP) and the heat pattern (HP) were heritable traits. In the present study, we explored underlying genetic and environmental structures of the relationships among BMI and the CP and the HP. Twins (N = 1,752) drawn from the South Korean twin registry completed a cold-heat pattern questionnaire via a telephone interview. The phenotypic correlations among the three phenotypes were moderate but significant. Cross-twin, cross-trait correlations among BMI and the CP and the HP were consistently greater in monozygotic than in dizygotic twins, suggesting the presence of genetic effects on the relationships between BMI and the two patterns. A trivariate Cholesky model was applied to the raw data. The results indicated that the phenotypic relationship between the HP and BMI was completely determined by common genetic influences, while the relationship between the CP and BMI was explained by both common genetic and common individual-specific environmental influences. The genetic correlation between the HP and the CP was not significant, suggesting that the two patterns may be genetically independent from each other. Genetic correlations were 0.31 between the HP and BMI, and -0.22 between the CP and BMI. The individual-specific environmental correlation was -0.22 between HP and CP, and between CP and BMI.

In traditional East Asian medicine, cold–heat patterns have been widely used in the diagnosis and treatment of patients suffering from various diseases. The present study aimed to estimate the heritability of cold–heat patterns. Trained interviewers administered a cold–heat pattern questionnaire to 1,753 twins (mean age = 19.1 ± 3.1 years) recruited throughout South Korea. Correlations for the cold pattern (CP) were 0.42 (95% CI [0.28, 0.54]) for monozygotic (MZ) males, 0.16 (95% CI [-0.08, 0.39]) for dizygotic (DZ) males, 0.40 (95% CI [0.30, 0.49]) for MZ females, 0.30 (95% CI [0.12, 0.45]) for DZ females, and 0.07 (95% CI [-0.11, 0.25]) for opposite-sex DZ twins. The corresponding twin correlations for the heat pattern (HP) were 0.38 (95% CI [0.24, 0.51]), -0.22 (95% CI [-0.43, 0.02]), 0.34 (95% CI [0.24, 0.43]), 0.21 (95% CI [0.03, 0.37]), and 0.08 (95% CI [-0.10, 0.26]), respectively. These patterns of twin correlations suggested significant genetic effects on the HP and the CP. Model-fitting analysis revealed that heritability estimates in both sexes were 40% (95% CI [38, 42]) for the CP and 33% (95% CI [25, 42]) for the HP, with the remaining variances attributable to unique environmental variances. These estimates did not vary significantly with age during adolescence and young adulthood.

Migraine and tension-type headache (TTH) are often viewed as distinct entities and defined as such in the International Classification of Headache Disorders, 2nd edition (ICHD-II) criteria, although there is also empirical evidence to suggest they may be etiologically similar. This study aims to investigate whether migraine and TTH are etiologically related conditions. First, we explored whether migraine and TTH were associated with the same environmental and lifestyle risk factors at the population level. Second, we examined comorbidity of migraine and TTH in a twin design. By comparing the associations in monozygotic (MZ) and dizygotic (DZ) twin pairs, we investigated whether the comorbidity can be explained by genetic factors that influence both conditions. Results indicated that migraine and TTH were largely associated with the same environmental and lifestyle factors, including younger age, female sex, higher body mass index, more depression, stress at home, and less participation in regular exercise, with consistently stronger effects for migraine than for TTH. Migraine in one twin was significantly associated with TTH in the other twin. A stronger cross-trait, cross-twin association in MZ than DZ twins suggested that this comorbidity may also be partly due to shared genetic factors, although the difference in associations was not significant. In conclusion, our findings are consistent with the hypothesis that migraine and TTH have partly shared etiologies. For both treatment and research, it may be advisable not to make a rigid distinction, but to treat migraine and TTH as related conditions.

Intelligence and educational attainment are strongly genetically correlated. This relationship can be exploited by Multi-Trait Analysis of GWAS (MTAG) to add power to Genome-wide Association Studies (GWAS) of intelligence. MTAG allows the user to meta-analyze GWASs of different phenotypes, based on their genetic correlations, to identify association's specific to the trait of choice. An MTAG analysis using GWAS data sets on intelligence and education was conducted by Lam et al. (2017). Lam et al. (2017) reported 70 loci that they described as ‘trait specific’ to intelligence. This article examines whether the analysis conducted by Lam et al. (2017) has resulted in genetic information about a phenotype that is more similar to education than intelligence.

Research on environmental and genetic pathways to complex traits such as educational attainment (EA) is confounded by uncertainty over whether correlations reflect effects of transmitted parental genes, causal family environments, or some, possibly interactive, mixture of both. Thus, an aggregate of thousands of alleles associated with EA (a polygenic risk score; PRS) may tap parental behaviors and home environments promoting EA in the offspring. New methods for unpicking and determining these causal pathways are required. Here, we utilize the fact that parents pass, at random, 50% of their genome to a given offspring to create independent scores for the transmitted alleles (conventional EA PRS) and a parental score based on alleles not transmitted to the offspring (EA VP_PRS). The formal effect of non-transmitted alleles on offspring attainment was tested in 2,333 genotyped twins for whom high-quality measures of EA, assessed at age 17 years, were available, and whose parents were also genotyped. Four key findings were observed. First, the EA PRS and EA VP_PRS were empirically independent, validating the virtual-parent design. Second, in this family-based design, children's own EA PRS significantly predicted their EA (β = 0.15), ruling out stratification confounds as a cause of the association of attainment with the EA PRS. Third, parental EA PRS predicted the SES environment parents provided to offspring (β = 0.20), and parental SES and offspring EA were significantly associated (β = 0.33). This would suggest that the EA PRS is at least as strongly linked to social competence as it is to EA, leading to higher attained SES in parents and, therefore, a higher experienced SES for children. In a full structural equation model taking account of family genetic relatedness across multiple siblings the non-transmitted allele effects were estimated at similar values; but, in this more complex model, confidence intervals included zero. A test using the forthcoming EA3 PRS may clarify this outcome. The virtual-parent method may be applied to clarify causality in other phenotypes where observational evidence suggests parenting may moderate expression of other outcomes, for instance in psychiatry.

Blood eosinophil count is associated with a variety of common complex outcomes in epidemiological observation. The aim of this study was to explore the causal association between determined blood eosinophil count and 20 common complex outcomes (10 metabolic, 6 cardiac, and 4 pulmonary). Through Mendelian randomization, we investigated genetic evidence for the genetically determined eosinophil in association with each outcomes using individual-level LifeLines cohort data (n = 13,301), where a weighted eosinophil genetic risk score comprising five eosinophil associated variants was created. We further examined the associations of the genetically determined eosinophil with those outcomes using summary statistics obtained from genome-wide association study consortia (6 consortia and 14 outcomes). Blood eosinophil count, by a 1-SD genetically increased, was not statistically associated with common complex outcomes in the LifeLines. Using the summary statistics, we showed that a higher genetically determined eosinophil count had a significant association with lower odds of obesity (odds ratio (OR) 0.81, 95% confidence interval (CI) [0.74, 0.89]) but not with the other traits and diseases. To conclude, an elevated eosinophil count is unlikely to be causally associated to higher risk of metabolic, cardiac, and pulmonary outcomes. Further studies with a stronger genetic risk score for eosinophil count may support these results.

Congenital heart disease (CHD) is one of the most common birth defects. More than 200 susceptibility loci have been identified for CHDs, yet a large part of the genetic risk factors remain unexplained. Monozygotic (MZ) twins are thought to be completely genetically identical; however, discordant phenotypes have been found in MZ twins. Recent studies have demonstrated genetic differences between MZ twins. We aimed to test whether copy number variants (CNVs) and/or genetic mutation differences play a role in the etiology of CHDs by using single nucleotide polymorphism (SNP) genotyping arrays and whole exome sequencing of twin pairs discordant for CHDs. Our goal was to identify mutations present only in the affected twins, which could identify novel candidates for CHD susceptibility loci. We present a comprehensive analysis for the CNVs and genetic mutation results of the selected individuals but detected no consistent differences within the twin pairs. Our study confirms that chromosomal structure or genetic mutation differences do not seem to play a role in the MZ twins discordant for CHD.

Genotype by environment interaction in behavioral traits may be assessed by estimating the proportion of variance that is explained by genetic and environmental influences conditional on a measured moderating variable, such as a known environmental exposure. Behavioral traits of interest are often measured by questionnaires and analyzed as sum scores on the items. However, statistical results on genotype by environment interaction based on sum scores can be biased due to the properties of a scale. This article presents a method that makes it possible to analyze the actually observed (phenotypic) item data rather than a sum score by simultaneously estimating the genetic model and an item response theory (IRT) model. In the proposed model, the estimation of genotype by environment interaction is based on an alternative parametrization that is uniquely identified and therefore to be preferred over standard parametrizations. A simulation study shows good performance of our method compared to analyzing sum scores in terms of bias. Next, we analyzed data of 2,110 12-year-old Dutch twin pairs on mathematical ability. Genetic models were evaluated and genetic and environmental variance components estimated as a function of a family's socio-economic status (SES). Results suggested that common environmental influences are less important in creating individual differences in mathematical ability in families with a high SES than in creating individual differences in mathematical ability in twin pairs with a low or average SES.