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Aesthetic chills, broadly defined as a somatic marker of peak emotional-hedonic responses, are experienced by individuals across a variety of human cultures. Yet individuals vary widely in the propensity of feeling them. These individual differences have been studied in relation to demographics, personality, and neurobiological and physiological factors, but no study to date has explored the genetic etiological sources of variation. To partition genetic and environmental sources of variation in the propensity of feeling aesthetic chills, we fitted a biometrical genetic model to data from 14,127 twins (from 8995 pairs), collected by the Netherlands Twin Register. Both genetic and unique environmental factors accounted for variance in aesthetic chills, with heritability estimated at 0.36 ([0.33, 0.39] 95% CI). We found females more prone than males to report feeling aesthetic chills. However, a test for genotype x sex interaction did not show evidence that heritability differs between sexes. We thus show that the propensity of feeling aesthetic chills is not shaped by nurture alone, but it also reflects underlying genetic propensities.

Estimates from Mendelian randomization studies of unrelated individuals can be biased due to uncontrolled confounding from familial effects. Here we describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family-based analyses can reduce such biases. We illustrate empirically how familial effects can affect estimates using data from 61,008 siblings from the Nord-Trøndelag Health Study and UK Biobank and replicated our findings using 222,368 siblings from 23andMe. Both Mendelian randomization estimates using unrelated individuals and within family methods reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while Mendelian randomization estimates from samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects were strongly attenuated in within-family Mendelian randomization analyses. Our findings indicate the necessity of controlling for population structure and familial effects in Mendelian randomization studies. Family-based study designs have been applied to resolve confounding by population stratification, dynastic effects and assortative mating in genetic association analyses. Here, Brumpton et al. describe theory and simulations for overcoming such biases in Mendelian randomization through within-family studies.

This large-scale genome-wide association analysis of subjects with asthma, hay fever or eczema provides insights into the shared genetic basis of these allergic diseases. The findings suggest that these diseases partly co-occur because they share many genetic risk variants that dysregulate the expression of immune-related genes. Asthma, hay fever (or allergic rhinitis) and eczema (or atopic dermatitis) often coexist in the same individuals 1 , partly because of a shared genetic origin 2 , 3 , 4 . To identify shared risk variants, we performed a genome-wide association study (GWAS; n = 360,838) of a broad allergic disease phenotype that considers the presence of any one of these three diseases. We identified 136 independent risk variants ( P < 3 × 10 −8 ), including 73 not previously reported, which implicate 132 nearby genes in allergic disease pathophysiology. Disease-specific effects were detected for only six variants, confirming that most represent shared risk factors. Tissue-specific heritability and biological process enrichment analyses suggest that shared risk variants influence lymphocyte-mediated immunity. Six target genes provide an opportunity for drug repositioning, while for 36 genes CpG methylation was found to influence transcription independently of genetic effects. Asthma, hay fever and eczema partly coexist because they share many genetic risk variants that dysregulate the expression of immune-related genes.

Acne vulgaris is a highly heritable skin disorder that primarily impacts facial skin. Severely inflamed lesions may leave permanent scars that have been associated with long-term psychosocial consequences. Here, we perform a GWAS meta-analysis comprising 20,165 individuals with acne from nine independent European ancestry cohorts. We identify 29 novel genome-wide significant loci and replicate 14 of the 17 previously identified risk loci, bringing the total number of reported acne risk loci to 46. Using fine-mapping and eQTL colocalisation approaches, we identify putative causal genes at several acne susceptibility loci that have previously been implicated in Mendelian hair and skin disorders, including pustular psoriasis. We identify shared genetic aetiology between acne, hormone levels, hormone-sensitive cancers and psychiatric traits. Finally, we show that a polygenic risk score calculated from our results explains up to 5.6% of the variance in acne liability in an independent cohort. Better understanding of the genetic basis of acne can pave the way to more effective treatments. Here, the authors perform a genome-wide association study meta-analysis of >20,000 cases and identify 29 new acne susceptibility loci, uncovering genetic links to Mendelian hair and skin disorders and other complex traits.

Genome-wide association studies have identified numerous loci linked with complex diseases, for which the molecular mechanisms remain largely unclear. Comprehensive molecular profiling of circulating metabolites captures highly heritable traits, which can help to uncover metabolic pathophysiology underlying established disease variants. We conduct an extended genome-wide association study of genetic influences on 123 circulating metabolic traits quantified by nuclear magnetic resonance metabolomics from up to 24,925 individuals and identify eight novel loci for amino acids, pyruvate and fatty acids. The LPA locus link with cardiovascular risk exemplifies how detailed metabolic profiling may inform underlying aetiology via extensive associations with very-low-density lipoprotein and triglyceride metabolism. Genetic fine mapping and Mendelian randomization uncover wide-spread causal effects of lipoprotein(a) on overall lipoprotein metabolism and we assess potential pleiotropic consequences of genetically elevated lipoprotein(a) on diverse morbidities via electronic health-care records. Our findings strengthen the argument for safe LPA -targeted intervention to reduce cardiovascular risk. Circulating metabolites reflect human health and disease. Here, Kettunen et al . perform a genome-wide association study on 123 circulating metabolic traits and identify novel genetic loci influencing systemic metabolism. They also link new molecular pathways with a known cardiovascular risk factor Lp(a).

Little is known about the genetic architecture of traits affecting educational attainment other than cognitive ability. We used genomic structural equation modeling and prior genome-wide association studies (GWASs) of educational attainment ( n  = 1,131,881) and cognitive test performance ( n  = 257,841) to estimate SNP associations with educational attainment variation that is independent of cognitive ability. We identified 157 genome-wide-significant loci and a polygenic architecture accounting for 57% of genetic variance in educational attainment. Noncognitive genetics were enriched in the same brain tissues and cell types as cognitive performance, but showed different associations with gray-matter brain volumes. Noncognitive genetics were further distinguished by associations with personality traits, less risky behavior and increased risk for certain psychiatric disorders. For socioeconomic success and longevity, noncognitive and cognitive-performance genetics demonstrated associations of similar magnitude. By conducting a GWAS of a phenotype that was not directly measured, we offer a view of genetic architecture of noncognitive skills influencing educational success. Genomic structural equation modeling of genome-wide association data for educational attainment and cognitive test performance is used to estimate the genetic component of variation in educational attainment that is independent of cognitive ability. The study finds that noncognitive skills account for 57% of genetic variation in educational attainment.

Understanding how parents’ cognitive and non-cognitive skills influence offspring education is essential for educational, family and economic policy. We use genetics (GWAS-by-subtraction) to assess a latent, broad non-cognitive skills dimension. To index parental effects controlling for genetic transmission, we estimate indirect parental genetic effects of polygenic scores on childhood and adulthood educational outcomes, using siblings ( N  = 47,459), adoptees ( N  = 6407), and parent-offspring trios ( N  = 2534) in three UK and Dutch cohorts. We find that parental cognitive and non-cognitive skills affect offspring education through their environment: on average across cohorts and designs, indirect genetic effects explain 36–40% of population polygenic score associations. However, indirect genetic effects are lower for achievement in the Dutch cohort, and for the adoption design. We identify potential causes of higher sibling- and trio-based estimates: prenatal indirect genetic effects, population stratification, and assortative mating. Our phenotype-agnostic, genetically sensitive approach has established overall environmental effects of parents’ skills, facilitating future mechanistic work. Understanding how parents’ cognitive and non-cognitive skills influence their children’s educational trajectories is important for educational, family and economic policy. Here, the authors investigate parental influence on children’s education using genetic approaches.

Loneliness typically refers to the feelings of distress and dysphoria resulting from a discrepancy between a person's desired and achieved levels of social relations, and there is now considerable evidence that loneliness is a risk factor for poor psychological and physical health. Loneliness has traditionally been conceptualized as a uniquely human phenomenon. However, over millions of years of evolution, efficient and manifold neural, hormonal, and molecular mechanisms have evolved for promoting companionship and mutual protection/assistance and for organizing adaptive responses when there is a significant discrepancy between the preferred and realized levels of social connection. We review evidence suggesting that loneliness is not a uniquely human phenomenon, but, instead, as a scientific construct, it represents a generally adaptive predisposition that can be found across phylogeny. Central to this argument is the premise that the brain is the key organ of social connections and processes. Comparative studies and animal models, particularly when integrated with human studies, have much to contribute to the understanding of loneliness and its underlying principles, mechanisms, consequences, and potential treatments.

It remains a challenge to determine whether children resemble their parents due to nature, nurture, or a mixture of both. Here we used a design that exploits the distinction between transmitted and non-transmitted alleles in genetic transmission from parent to offspring. Two separate polygenic scores (PGS) were calculated on the basis of the transmitted and non-transmitted alleles. The effect of the non-transmitted PGS is necessarily mediated by parental phenotypes, insofar as they contribute to the rearing environment of the offspring (genetic nurturing). We calculated transmitted and non-transmitted PGSs associated with adult educational attainment (EA) and PGSs associated with childhood ADHD in a general population sample of trios, i.e. child or adult offspring and their parents (N = 1120–2518). We tested if the EA and ADHD (non-)transmitted PGSs were associated with childhood academic achievement and ADHD in offspring. Based on the earlier findings for shared environment, we hypothesized to find genetic nurturing for academic achievement, but not for ADHD. In adults, both transmitted (R2 = 7.6%) and non-transmitted (R2 = 1.7%) EA PGSs were associated with offspring EA, evidencing genetic nurturing. In children around age 12, academic achievement was associated with the transmitted EA PGSs (R2 = 5.7%), but we found no support for genetic nurturing (R2 ~ 0.1%). The ADHD PGSs were not significantly associated with academic achievement (R2 ~ 0.6%). ADHD symptoms in children were only associated with transmitted EA PGSs and ADHD PGSs (R2 = 1–2%). Based on these results, we conclude that the associations between parent characteristics and offspring outcomes in childhood are mainly to be attributable to the effects of genes that are shared by parents and children.

•Low reliability of the inherently noisy rs-fMRI limits the discovery of associated traits.•A measurement model can reveal the “true” associations in the absence of random error.•We show that reliability modelling can benefit behavioral and genetic studies.•The split-session approach can be applied to new and existing rs-fMRI datasets. Resting-state functional magnetic resonance imaging (rs-fMRI) has an inherently low signal-to-noise ratio largely due to thermal and physiological noise that attenuates the functional connectivity (FC) estimates. Such attenuation limits the reliability of FC and may bias its association with other traits. Low reliability also limits heritability estimates. Classical test theory can be used to obtain a true correlation estimate free of random measurement error from parallel tests, such as split-half sessions of a rs-fMRI scan. We applied a measurement model to split-half FC estimates from the resting-state fMRI data of 1003 participants from the Human Connectome Project (HCP) to examine the benefit of reliability modelling of FC in association with traits from various domains. We evaluated the efficiency of the measurement model on extracting a stable and reliable component of FC and its association with several traits for various sample sizes and scan durations. In addition, we aimed to replicate our previous findings of increased heritability estimates when using a measurement model in a longitudinal adolescent twin cohort. The split-half measurement model improved test-retest reliability of FC on average with +0.33 points (from +0.49 to +0.82), improved strength of associations between FC and various traits on average 1.2-fold (range 1.09–1.35), and increased heritability estimates on average with +20% points (from 39% to 59%) for the full HCP dataset. On average, about half of the variance in split-session FC estimates was attributed to the stable and reliable component of FC. Shorter scan durations showed greater benefit of reliability modelling (up to 1.6-fold improvement), with an additional gain for smaller sample sizes (up to 1.8-fold improvement). Reliability modelling of FC based on a split-half using a measurement model can benefit genetic and behavioral studies by extracting a stable and reliable component of FC that is free from random measurement error and improves genetic and behavioral associations.