Explore the vast range of titles available.

The human gut microbiome plays a key role in human health 1 , but 16S characterization lacks quantitative functional annotation 2 . The fecal metabolome provides a functional readout of microbial activity and can be used as an intermediate phenotype mediating host–microbiome interactions 3 . In this comprehensive description of the fecal metabolome, examining 1,116 metabolites from 786 individuals from a population-based twin study (TwinsUK), the fecal metabolome was found to be only modestly influenced by host genetics (heritability ( H 2 ) = 17.9%). One replicated locus at the NAT2 gene was associated with fecal metabolic traits. The fecal metabolome largely reflects gut microbial composition, explaining on average 67.7% (±18.8%) of its variance. It is strongly associated with visceral-fat mass, thereby illustrating potential mechanisms underlying the well-established microbial influence on abdominal obesity. Fecal metabolic profiling thus is a novel tool to explore links among microbiome composition, host phenotypes, and heritable complex traits. Comprehensive fecal metabolic profiling in 786 individuals from TwinsUK provides insights into the influence of host genetics and gut microbial composition on metabolites that may mediate microbiome-associated phenotypes.

X-chromosome inactivation (XCI) silences one X in female cells to balance sex-differences in X-dosage. A subset of X-linked genes escape XCI, but the extent to which this phenomenon occurs and how it varies across tissues and in a population is as yet unclear. To characterize incidence and variability of escape across individuals and tissues, we conducted a transcriptomic study of escape in adipose, skin, lymphoblastoid cell lines and immune cells in 248 healthy individuals exhibiting skewed XCI. We quantify XCI escape from a linear model of genes’ allelic fold-change and XIST -based degree of XCI skewing. We identify 62 genes, including 19 lncRNAs, with previously unknown patterns of escape. We find a range of tissue-specificity, with 11% of genes escaping XCI constitutively across tissues and 23% demonstrating tissue-restricted escape, including cell type-specific escape across immune cells of the same individual. We also detect substantial inter-individual variability in escape. Monozygotic twins share more similar escape than dizygotic twins, indicating that genetic factors may underlie inter-individual differences in escape. However, discordant escape also occurs within monozygotic co-twins, suggesting environmental factors also influence escape. Altogether, these data indicate that XCI escape is an under-appreciated source of transcriptional differences, and an intricate phenotype impacting variable trait expressivity in females.

Age-related changes in DNA methylation have been implicated in cellular senescence and longevity, yet the causes and functional consequences of these variants remain unclear. To elucidate the role of age-related epigenetic changes in healthy ageing and potential longevity, we tested for association between whole-blood DNA methylation patterns in 172 female twins aged 32 to 80 with age and age-related phenotypes. Twin-based DNA methylation levels at 26,690 CpG-sites showed evidence for mean genome-wide heritability of 18%, which was supported by the identification of 1,537 CpG-sites with methylation QTLs in cis at FDR 5%. We performed genome-wide analyses to discover differentially methylated regions (DMRs) for sixteen age-related phenotypes (ap-DMRs) and chronological age (a-DMRs). Epigenome-wide association scans (EWAS) identified age-related phenotype DMRs (ap-DMRs) associated with LDL (STAT5A), lung function (WT1), and maternal longevity (ARL4A, TBX20). In contrast, EWAS for chronological age identified hundreds of predominantly hyper-methylated age DMRs (490 a-DMRs at FDR 5%), of which only one (TBX20) was also associated with an age-related phenotype. Therefore, the majority of age-related changes in DNA methylation are not associated with phenotypic measures of healthy ageing in later life. We replicated a large proportion of a-DMRs in a sample of 44 younger adult MZ twins aged 20 to 61, suggesting that a-DMRs may initiate at an earlier age. We next explored potential genetic and environmental mechanisms underlying a-DMRs and ap-DMRs. Genome-wide overlap across cis-meQTLs, genotype-phenotype associations, and EWAS ap-DMRs identified CpG-sites that had cis-meQTLs with evidence for genotype-phenotype association, where the CpG-site was also an ap-DMR for the same phenotype. Monozygotic twin methylation difference analyses identified one potential environmentally-mediated ap-DMR associated with total cholesterol and LDL (CSMD1). Our results suggest that in a small set of genes DNA methylation may be a candidate mechanism of mediating not only environmental, but also genetic effects on age-related phenotypes.

Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10–60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn’s disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research. Genetic susceptibility to complex diseases The interaction of genetic predispositions with environmental factors is key to the pathogenesis of complex diseases. A promising approach to understanding this relationship combines a genome-wide association study (GWAS) with the analysis of blood metabolites as functional intermediate phenotypes. The potential of this method is demonstrated by a large-scale cooperation combining data from the German KORA F4 and the British TwinsUK population studies. GWAS data, together with non-targeted metabolomics covering 60 biochemical pathways in 2,820 individuals, have identified 37 genetic loci associated with blood metabolite concentrations, 25 of them with unusually high effect sizes for a GWAS. These associations provide new functional insights for many previously reported associations, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease.

Mitochondria play important roles in cellular processes and disease, yet little is known about how the transcriptional regime of the mitochondrial genome varies across individuals and tissues. By analyzing >11,000 RNA-sequencing libraries across 36 tissue/cell types, we find considerable variation in mitochondrial-encoded gene expression along the mitochondrial transcriptome, across tissues and between individuals, highlighting the importance of cell-type specific and post-transcriptional processes in shaping mitochondrial-encoded RNA levels. Using whole-genome genetic data we identify 64 nuclear loci associated with expression levels of 14 genes encoded in the mitochondrial genome, including missense variants within genes involved in mitochondrial function (TBRG4, MTPAP and LONP1), implicating genetic mechanisms that act in trans across the two genomes. We replicate ~21% of associations with independent tissue-matched datasets and find genetic variants linked to these nuclear loci that are associated with cardio-metabolic phenotypes and Vitiligo, supporting a potential role for variable mitochondrial-encoded gene expression in complex disease. Mitochondria are like the batteries of our cells; they perform the essential task of turning nutrients into chemical energy. A cell relies on its mitochondria for its survival, but they are not completely under the cell’s control. Mitochondria have their own DNA, separate from the cell’s DNA which is stored in the nucleus. It contains a handful of genes, which carry the code for some of the important proteins needed for energy production. These proteins are made in the mitochondria themselves, and their levels are tweaked to meet the cell's current energy needs. To do this, mitochondria make copies of their genes and feed these copies into their own protein-production machinery. By controlling the number of gene copies they make, mitochondria can control the amount of protein they produce. But the process has several steps. The copies come in the form of a DNA-like molecule called RNA and, at first, they contain several genes connected one after the other. To access each gene, the mitochondria need to cut them up. They then process the fragments, fine-tuning the number of copies of each gene. This process – called gene expression – happens in the mitochondria, but they cannot do it on their own; they need proteins that are coded within the DNA in the cell nucleus. Genes in the cell nucleus can affect gene expression in the mitochondria, changing the cell's energy supply. Scientists do not yet know all of the genes involved, or how this might differ between different tissues or among different individuals. To find out, Ali et al. examined more than 11,000 records of RNA sequences from 36 different human cells and tissues, including blood, fat and skin. This revealed a large amount of variation in the expression of mitochondrial genes. The way the mitochondria processed their genes changed in different cells and in different people. To find out which genes in the nucleus were responsible for the differences in the mitochondria, the next step was to compare RNA levels from the mitochondria to the DNA sequences in the nucleus. This is because changes in the DNA sequence between different people – called genetic variants – can also affect how genes work, and how genes are expressed. This comparison revealed 64 genetic variants from DNA in the cell nucleus that are associated with the expression of genes in the mitochondria. Some of these had a known link to genetic variants involved in diseases like the skin condition vitiligo or high blood pressure. So, although mitochondria contain their own DNA, they rely on genes from the cell nucleus to function. Changes to the genes in the nucleus can alter the way that the mitochondria process their own genetic code. Understanding how these two sets of genes interact could reveal how and why mitochondria go wrong. This could aid in future research into illnesses like heart disease and cancer.

Key Points Genome-wide association (GWA) studies in Africa could provide important insights into infectious and chronic non-communicable diseases. GWA studies in Africa encounter methodological challenges that are not commonly observed in European or Asian studies. Low levels of linkage disequilibrium (LD) mean that commercial SNP-genotyping platforms have low power to detect genome-wide associations with common diseases in Africa. High levels of population structure in Africa make it difficult to replicate the findings from a GWA study across different locations. Conversely, the haplotypic diversity and low levels of LD in Africa will make it easier to localize the causal variants responsible for GWA signals of association, which is one of the major roadblocks for GWA studies of European populations. Many of the difficulties of GWA studies in Africa exist because current methods are based on the principle of LD mapping by SNP genotyping. New sequencing technologies will eventually make it possible to conduct GWA studies by genome sequencing of all cases and controls, and this will overcome many of the current difficulties of GWA analysis in Africa. An interim approach, before the advent of GWA by sequencing, is to establish population-specific databases of genome variation that will enable accurate imputation of all common variants in a GWA study, and thereby allow meta-analysis of data from different African populations. GWA studies in Africa will be greatly helped by the 1000 Genomes Project, which provides an important starting point for population-specific databases of genome variation. Developing local resources and local leadership in genetics, genomics and data analysis is of fundamental importance to the success of GWA studies in Africa. Genome-wide association studies are not widespread in Africa, partly because of the challenges of dealing with population structure and high genomic diversity. New approaches in statistical imputation and whole-genome sequencing are now set to exploit these features for fine mapping causal variants. Medical research in Africa has yet to benefit from the advent of genome-wide association (GWA) analysis, partly because the genotyping tools and statistical methods that have been developed for European and Asian populations struggle to deal with the high levels of genome diversity and population structure in Africa. However, the haplotypic diversity of African populations might help to overcome one of the major roadblocks in GWA research, the fine mapping of causal variants. We review the methodological challenges and consider how GWA studies in Africa will be transformed by new approaches in statistical imputation and large-scale genome sequencing.

Individual risk of type 2 diabetes (T2D) is modified by perturbations to the mass, distribution and function of adipose tissue. To investigate the mechanisms underlying these associations, we explored the molecular, cellular and whole-body effects of T2D-associated alleles near KLF14 . We show that KLF14 diabetes-risk alleles act in adipose tissue to reduce KLF14 expression and modulate, in trans, the expression of 385 genes. We demonstrate, in human cellular studies, that reduced KLF14 expression increases pre-adipocyte proliferation but disrupts lipogenesis, and in mice, that adipose tissue–specific deletion of Klf14 partially recapitulates the human phenotype of insulin resistance, dyslipidemia and T2D. We show that carriers of the KLF14 T2D risk allele shift body fat from gynoid stores to abdominal stores and display a marked increase in adipocyte cell size, and that these effects on fat distribution, and the T2D association, are female specific. The metabolic risk associated with variation at this imprinted locus depends on the sex both of the subject and of the parent from whom the risk allele derives. Analysis of the imprinted KLF14 locus shows that the type 2 diabetes risk alleles in this region act in adipocytes to reduce KLF14 expression and modulate the expression of almost 400 genes in trans, leading to a shift in body-fat distribution and insulin resistance specifically in females.

Systemic lupus erythematosus (SLE) is an autoimmune disease, characterised by increased expression of type I interferon (IFN)-regulated genes and a striking sex imbalance towards females. Through combined genetic, in silico, in vitro, and ex vivo approaches, we define CXorf21 , a gene of hitherto unknown function, which escapes X-chromosome inactivation, as a candidate underlying the Xp21.2 SLE association. We demonstrate that CXorf21 is an IFN-response gene and that the sexual dimorphism in expression is magnified by immunological challenge. Fine-mapping reveals a single haplotype as a potential causal cis-eQTL for CXorf21 . We propose that expression is amplified through modification of promoter and 3′-UTR chromatin interactions. Finally, we show that the CXORF21 protein colocalises with TLR7, a pathway implicated in SLE pathogenesis. Our study reveals modulation in gene expression affected by the combination of two hallmarks of SLE: CXorf21 expression increases in a both an IFN-inducible and sex-specific manner. Systemic lupus erythematosus (SLE) shows a striking bias towards higher prevalence in females. Here, the authors perform fine-mapping of an SLE-associated locus at Xp21.2 and characterise a candidate gene, CXorf21 , as IFN-responsive in immune cells that shows sexually dimorphic expression.

Female somatic X-chromosome inactivation (XCI) balances the X-linked transcriptional dosages between the sexes. Skewed XCI toward one parental X has been observed in several complex human traits, but the extent to which genetics and environment influence skewed XCI is largely unexplored. To address this, we quantify XCI-skew in multiple tissues and immune cell types in a twin cohort. Within an individual, XCI-skew differs between blood, fat and skin tissue, but is shared across immune cell types. XCI skew increases with age in blood, but not other tissues, and is associated with smoking. XCI-skew is increased in twins with Rheumatoid Arthritis compared to unaffected identical co-twins. XCI-skew is heritable in blood of females >55 years old (h 2  = 0.34), but not in younger individuals or other tissues. This results in a Gene x Age interaction that shifts the functional dosage of all X-linked heterozygous loci in a tissue-restricted manner. Skewing of X chromosome inactivation (XCI) occurs when the silencing of one parental X chromosome is non-random. Here, Zito et al. report XCI patterns in lymphoblastoid cell lines, blood, subcutaneous adipose tissue samples and skin samples of monozygotic and dizygotic twins and find XCI skew to associate with tissue and age.

Cardiovascular disease progression is characterised by the dysregulation of lipid metabolism and pro-atherogenic effects of adipose tissue signalling. Recent findings from the analysis of transcriptomic data in bulk tissue has enabled these insights and revealed important changes in gene expression. However, few studies have explored these molecular mechanisms before the onset of cardiovascular disease. We explore associations between future lipid-regulating drug use and cardiometabolic traits ( n  = 103), including DXA scans of body composition at baseline and follow-up 5–10 years later, in a cohort of British twins ( n up to 6963). Utilising transcriptomic profiles from a subset of twins ( n  = 766), we explore the associations between baseline adipose tissue gene expression, clinical traits, and future lipid-regulating drug usage. We then test the joint predictive capacity of clinical traits plus gene expression compared to traditional risk scores using an automated machine learning approach. We find 44 traits are associated with lipid-regulating drug usage including measurements of abdominal fat tissue, cardiovascular health, and lipid metabolism (FDR 5%). Then, we present that adipose tissue gene expression levels at baseline are associated cross-sectionally with 19 of these 44 traits (FDR 5%). By comparing adipose gene expression levels between individuals prescribed lipid-regulating drugs in the future and controls, we discover that genes associated with 16 of these 19 traits produced greater log 2 -fold changes, suggesting shared mechanisms. We reveal 15 differentially expressed genes comparing future lipid-regulating drug users and controls at baseline (FDR 10%), including some implicated in angiogenesis: ESM1 , RCAN2 , and SOCS3 . Functional enrichment with 1212 significantly differentially expressed genes ( p  < 0.05) included molecular mechanisms related to abnormal cardiovascular system electrophysiology ( p  = 1.89 × 10 −3 ), arrhythmia ( p  = 4.02 × 10 −3 ), and mitochondrial pathways ( p  = 1.12 × 10 −3 ). Finally, we confirm inclusion of gene expression levels as features in machine learning models achieves a better AUC (0.919) compared to traditional risk predictors. These findings highlight the potential of bulk transcriptomic data to improve risk stratification for lipid-regulating drug use, offering new insights into the RNA biology of adipose tissue and advancing approaches for cardiovascular disease prevention.