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Fatigue and depression are highly comorbid phenotypes with partially overlapping symptoms. The main aims of the present study are to: (i) identify the risk of current fatigue and depression; (ii) determine if the depression symptoms experienced by individuals who are fatigued (N=766) and non-fatigued (N=1849) are different; and (iii) identify if the fatigue symptoms experienced by depressed (N=275) and non-depressed (N=2340) individuals are different, in a community-based sample of Australian twins aged over 50years. Fatigue and depression symptom profiles and classifications were generated using the Schedule of Fatigue and Anergia (SOFA); the General Health Questionnaire; and the Delusions-Symptoms-States Inventory, States of Anxiety and Depression questionnaires. The association between co-occurring fatigue and depression was assessed using prevalence ratios. Differences in the preponderance of fatigue and depression symptoms were assessed using logistic regression modeling. Individuals with either fatigue or depression have an approximately two-fold increased risk for comorbid presentation of both traits, compared to the general population. Logistic regression analysis indicated that fatigued individuals were significantly more likely to report all of the Diagnostic and Statistical Manual of Mental Disorders (DSM) depression symptoms assessed in the study. Similarly, depressed individuals were significantly more likely to report all SOFA fatigue symptoms. Fatigue and depression are highly correlated traits within the community. Depression symptomatology and prevalence are significantly increased in fatigued individuals. Fatigue and especially the symptoms of insomnia and poor concentration are strong predictors of depression. Notably, the association between fatigue and depression is independent of their overlapping symptomatology. Therefore, presentation with fatigue, and in particular the symptoms of insomnia and poor concentration, should be considered as warning signs of depression in older adults.

The identification of genes and regulatory elements underlying the associations discovered by GWAS is essential to understanding the aetiology of complex traits (including diseases). Here, we demonstrate an analytical paradigm of prioritizing genes and regulatory elements at GWAS loci for follow-up functional studies. We perform an integrative analysis that uses summary-level SNP data from multi-omics studies to detect DNA methylation (DNAm) sites associated with gene expression and phenotype through shared genetic effects (i.e., pleiotropy). We identify pleiotropic associations between 7858 DNAm sites and 2733 genes. These DNAm sites are enriched in enhancers and promoters, and >40% of them are mapped to distal genes. Further pleiotropic association analyses, which link both the methylome and transcriptome to 12 complex traits, identify 149 DNAm sites and 66 genes, indicating a plausible mechanism whereby the effect of a genetic variant on phenotype is mediated by genetic regulation of transcription through DNAm. The identification of the causal gene at a GWAS locus remains to be a challenging task. Here, using the SMR & HEIDI method to integrate GWAS, eQTL and mQTL data, Wu et al. map DNA methylation sites to the transcriptome and thereby prioritize functionally relevant genes for 12 human complex traits.

There are thousands of rare human disorders that are caused by single deleterious, protein-coding genetic variants 1 . However, patients with the same genetic defect can have different clinical presentations 2 – 4 , and some individuals who carry known disease-causing variants can appear unaffected 5 . Here, to understand what explains these differences, we study a cohort of 6,987 children assessed by clinical geneticists to have severe neurodevelopmental disorders such as global developmental delay and autism, often in combination with abnormalities of other organ systems. Although the genetic causes of these neurodevelopmental disorders are expected to be almost entirely monogenic, we show that 7.7% of variance in risk is attributable to inherited common genetic variation. We replicated this genome-wide common variant burden by showing, in an independent sample of 728 trios (comprising a child plus both parents) from the same cohort, that this burden is over-transmitted from parents to children with neurodevelopmental disorders. Our common-variant signal is significantly positively correlated with genetic predisposition to lower educational attainment, decreased intelligence and risk of schizophrenia. We found that common-variant risk was not significantly different between individuals with and without a known protein-coding diagnostic variant, which suggests that common-variant risk affects patients both with and without a monogenic diagnosis. In addition, previously published common-variant scores for autism, height, birth weight and intracranial volume were all correlated with these traits within our cohort, which suggests that phenotypic expression in individuals with monogenic disorders is affected by the same variants as in the general population. Our results demonstrate that common genetic variation affects both overall risk and clinical presentation in neurodevelopmental disorders that are typically considered to be monogenic. A genome-wide association study of approximately 7,000 patients with neurodevelopmental disorders demonstrates that overall risk and clinical presentation in putative monogenic disorders is also influenced by common genetic variants present in the general population.

Vitamin D deficiency is a candidate risk factor for a range of adverse health outcomes. In a genome-wide association study of 25 hydroxyvitamin D (25OHD) concentration in 417,580 Europeans we identify 143 independent loci in 112 1-Mb regions, providing insights into the physiology of vitamin D and implicating genes involved in lipid and lipoprotein metabolism, dermal tissue properties, and the sulphonation and glucuronidation of 25OHD. Mendelian randomization models find no robust evidence that 25OHD concentration has causal effects on candidate phenotypes (e.g. BMI, psychiatric disorders), but many phenotypes have (direct or indirect) causal effects on 25OHD concentration, clarifying the epidemiological relationship between 25OHD status and the health outcomes examined in this study. Vitamin D is a precursor of the steroid hormone 1,25-dihydroxyvitamin D3, and its deficiency is associated with many adverse health outcomes. Here, Revez et al. perform a genome-wide association study for circulating 25-hydroxyvitamin D in 417,580 individuals and test for potential causal relationships with other traits using Mendelian randomization.

Although snoring is common in the general population, its aetiology has been largely understudied. Here we report a genetic study on snoring ( n  ~ 408,000; snorers ~ 152,000) using data from the UK Biobank. We identify 42 genome-wide significant loci, with an SNP-based heritability estimate of ~10% on the liability scale. Genetic correlations with body mass index, alcohol intake, smoking, schizophrenia, anorexia nervosa and neuroticism are observed. Gene-based associations identify 173 genes, including DLEU7 , MSRB3 and POC5 , highlighting genes expressed in the brain, cerebellum, lungs, blood and oesophagus. We use polygenic scores (PGS) to predict recent snoring and probable obstructive sleep apnoea (OSA) in an independent Australian sample ( n  ~ 8000). Mendelian randomization analyses suggest a potential causal relationship between high BMI and snoring. Altogether, our results uncover insights into the aetiology of snoring as a complex sleep-related trait and its role in health and disease beyond it being a cardinal symptom of OSA. Snoring is common in the population and tends to be more prevalent in older and/or male individuals. Here, the authors perform GWAS for habitual snoring, identify 41 genomic loci and explore potential causal relationships with anthropometric and cardiometabolic disease traits.

Peter Visscher and colleagues report a new method for approximate conditional and joint association analysis that makes use of summary statistics from meta-analysis of GWAS. They apply this to meta-analysis summary data for height, body mass index and type 2 diabetes. We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.

DNA methylation plays an important role in the regulation of transcription. Genetic control of DNA methylation is a potential candidate for explaining the many identified SNP associations with disease that are not found in coding regions. We replicated 52,916 cis and 2,025 trans DNA methylation quantitative trait loci (mQTL) using methylation from whole blood measured on Illumina HumanMethylation450 arrays in the Brisbane Systems Genetics Study (n = 614 from 177 families) and the Lothian Birth Cohorts of 1921 and 1936 (combined n = 1366). The trans mQTL SNPs were found to be over-represented in 1 Mbp subtelomeric regions, and on chromosomes 16 and 19. There was a significant increase in trans mQTL DNA methylation sites in upstream and 5′ UTR regions. The genetic heritability of a number of complex traits and diseases was partitioned into components due to mQTL and the remainder of the genome. Significant enrichment was observed for height (p = 2.1 × 10 −10 ), ulcerative colitis (p = 2 × 10 −5 ), Crohn’s disease (p = 6 × 10 −8 ) and coronary artery disease (p = 5.5 × 10 −6 ) when compared to a random sample of SNPs with matched minor allele frequency, although this enrichment is explained by the genomic location of the mQTL SNPs.

To examine the genetic and environmental influences on variances in weight, height, and BMI, from birth through 19 years of age, in boys and girls from three continents. Cross-sectional twin study. Data obtained from a total of 23 twin birth-cohorts from four countries: Canada, Sweden, Denmark, and Australia. Participants were Monozygotic (MZ) and dizygotic (DZ) (same- and opposite-sex) twin pairs with data available for both height and weight at a given age, from birth through 19 years of age. Approximately 24,036 children were included in the analyses. Heritability for body weight, height, and BMI was low at birth (between 6.4 and 8.7% for boys, and between 4.8 and 7.9% for girls) but increased over time, accounting for close to half or more of the variance in body weight and BMI after 5 months of age in both sexes. Common environmental influences on all body measures were high at birth (between 74.1-85.9% in all measures for boys, and between 74.2 and 87.3% in all measures for girls) and markedly reduced over time. For body height, the effect of the common environment remained significant for a longer period during early childhood (up through 12 years of age). Sex-limitation of genetic and shared environmental effects was observed. Genetics appear to play an increasingly important role in explaining the variation in weight, height, and BMI from early childhood to late adolescence, particularly in boys. Common environmental factors exert their strongest and most independent influence specifically in pre-adolescent years and more significantly in girls. These findings emphasize the need to target family and social environmental interventions in early childhood years, especially for females. As gene-environment correlation and interaction is likely, it is also necessary to identify the genetic variants that may predispose individuals to obesity.

Peter Visscher and colleagues report an analysis of the heritability explained by common variants identified through genome-wide association studies. They find that 45% of the variance for height can be explained by using a linear model to simultaneously consider the combined effect of common SNPs. SNPs discovered by genome-wide association studies (GWASs) account for only a small fraction of the genetic variation of complex traits in human populations. Where is the remaining heritability? We estimated the proportion of variance for human height explained by 294,831 SNPs genotyped on 3,925 unrelated individuals using a linear model analysis, and validated the estimation method with simulations based on the observed genotype data. We show that 45% of variance can be explained by considering all SNPs simultaneously. Thus, most of the heritability is not missing but has not previously been detected because the individual effects are too small to pass stringent significance tests. We provide evidence that the remaining heritability is due to incomplete linkage disequilibrium between causal variants and genotyped SNPs, exacerbated by causal variants having lower minor allele frequency than the SNPs explored to date.

Key Points Twins are valuable subjects for studies in which control over genetic background and early environmental influences is desired. Monozygotic twins are derived from a single zygote and are therefore matched for genetic background. Dizygotic twins are derived from two zygotes and share the same amount of genetic material as normal siblings. Both types of twins share prenatal and early environmental influences. Twin registries worldwide have established vast collections of longitudinal phenotypic data as well as biological material in twins, offering a valuable resource for studying the molecular biology of complex traits. The classical twin design compares the phenotypic similarity of monozygotic and dizygotic twins to estimate the importance of heritable and environmental influences on complex trait variation. Classical twin studies have provided estimates of heritability for numerous traits in the biomedical, psychiatric and behavioural domain. Multivariate twin studies address the causes of association among phenotypes. Associations can be among different phenotypes or across age and are explained by common genetic or environmental influences. We describe studies that applied the classical twin design to unravel the importance of genetic and environmental influences on variation in DNA methylation, gene expression, metabolomic and proteomic profiles in various tissues and on the composition of gut microbial communities. The comparison of molecular profiles of phenotypically discordant monozygotic twin pairs is a powerful method to identify molecular characteristics associated with complex traits, including point mutations and genomic structural variation, differentially expressed and differentially methylated genes and metabolic profiles. Examples of this approach are given for a range of disorders and traits. Twin studies have long been used for dissecting the relative contributions of genetics and other factors to various phenotypes. This Review discusses how these traditional studies are now being integrated with modern omics technologies to provide a wide range of biological insights. The classical twin study has been a powerful heuristic in biomedical, psychiatric and behavioural research for decades. Twin registries worldwide have collected biological material and longitudinal phenotypic data on tens of thousands of twins, providing a valuable resource for studying complex phenotypes and their underlying biology. In this Review, we consider the continuing value of twin studies in the current era of molecular genetic studies. We conclude that classical twin methods combined with novel technologies represent a powerful approach towards identifying and understanding the molecular pathways that underlie complex traits.