Explore the vast range of titles available.

Macroautophagy and microautophagy are highly conserved eukaryotic cellular processes that degrade cytoplasmic material in lysosomes. Both pathways involve characteristic membrane dynamics regulated by autophagy-related proteins and other molecules, some of which are shared between the two pathways. Over the past few years, the application of new technologies, such as cryo-electron microscopy, coevolution-based structural prediction and in vitro reconstitution, has revealed the functions of individual autophagy gene products, especially in autophagy induction, membrane reorganization and cargo recognition. Concomitantly, mutations in autophagy genes have been linked to human disorders, particularly neurodegenerative diseases, emphasizing the potential pathogenic implications of autophagy defects. Accumulating genome data have also illuminated the evolution of autophagy genes within eukaryotes as well as their transition from possible ancestral elements in prokaryotes.Macroautophagy and microautophagy involve characteristic membrane dynamics regulated by autophagy-related proteins to degrade cytoplasmic material in lysosomes. In this Review, the authors summarize recent progress in elucidating these highly conserved processes, the pathological relevance of autophagy-related genes in Mendelian and complex diseases, and the evolution of the autophagy pathway.

Primary cilia, antenna-like sensory organelles protruding from the surface of most vertebrate cell types, are essential for regulating signalling pathways during development and adult homeostasis. Mutations in genes affecting cilia cause an overlapping spectrum of >30 human diseases and syndromes, the ciliopathies. Given the immense structural and functional diversity of the mammalian cilia repertoire, there is a growing disconnect between patient genotype and associated phenotypes, with variable severity and expressivity characteristic of the ciliopathies as a group. Recent technological developments are rapidly advancing our understanding of the complex mechanisms that control biogenesis and function of primary cilia across a range of cell types and are starting to tackle this diversity. Here, we examine the structural and functional diversity of primary cilia, their dynamic regulation in different cellular and developmental contexts and their disruption in disease.Mutations that affect primary cilia cause ciliopathies with variable severity and expressivity. The diversity of cilia across cell types, tissues and developmental stages enables their function as versatile signalling hubs but may underlie the disconnect between genotype and phenotype. This Review examines the structural and functional diversity of primary cilia, their dynamic regulation in different cellular and developmental contexts and their disruption in disease.

The prevalence of obesity has tripled over the past four decades, imposing an enormous burden on people’s health. Polygenic (or common) obesity and rare, severe, early-onset monogenic obesity are often polarized as distinct diseases. However, gene discovery studies for both forms of obesity show that they have shared genetic and biological underpinnings, pointing to a key role for the brain in the control of body weight. Genome-wide association studies (GWAS) with increasing sample sizes and advances in sequencing technology are the main drivers behind a recent flurry of new discoveries. However, it is the post-GWAS, cross-disciplinary collaborations, which combine new omics technologies and analytical approaches, that have started to facilitate translation of genetic loci into meaningful biology and new avenues for treatment.In this Review, Loos and Yeo summarize our current understanding of the genetic underpinnings of monogenic and polygenic obesity. They highlight the commonalities revealed by recent studies and discuss the implications for treatment and prediction of obesity risk.

Recent advances in sequencing technologies and collaborative efforts have led to substantial progress in identifying the genetic causes of amyotrophic lateral sclerosis (ALS). This momentum has, in turn, fostered the development of putative molecular therapies. In this Review, we outline the current genetic knowledge, emphasizing recent discoveries and emerging concepts such as the implication of distinct types of mutation, variability in mutated genes in diverse genetic ancestries and gene–environment interactions. We also propose a high-level model to synthesize the interdependent effects of genetics, environmental and lifestyle factors, and ageing into a unified theory of ALS. Furthermore, we summarize the current status of therapies developed on the basis of genetic knowledge established for ALS over the past 30 years, and we discuss how developing treatments for ALS will advance our understanding of targeting other neurological diseases.In this Review, the authors discuss our growing knowledge of the underlying genetics of amyotrophic lateral sclerosis (ALS; also known as motor neuron disease). They discuss how this information provides insight into causal disease mechanisms and translational opportunities for developing clinical therapeutics.

Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences 1 – 3 . In principle, computational methods could support the large-scale interpretation of genetic variants. However, state-of-the-art methods 4 – 10 have relied on training machine learning models on known disease labels. As these labels are sparse, biased and of variable quality, the resulting models have been considered insufficiently reliable 11 . Here we propose an approach that leverages deep generative models to predict variant pathogenicity without relying on labels. By modelling the distribution of sequence variation across organisms, we implicitly capture constraints on the protein sequences that maintain fitness. Our model EVE (evolutionary model of variant effect) not only outperforms computational approaches that rely on labelled data but also performs on par with, if not better than, predictions from high-throughput experiments, which are increasingly used as evidence for variant classification 12 – 16 . We predict the pathogenicity of more than 36 million variants across 3,219 disease genes and provide evidence for the classification of more than 256,000 variants of unknown significance. Our work suggests that models of evolutionary information can provide valuable independent evidence for variant interpretation that will be widely useful in research and clinical settings. A new computational method, EVE, classifies human genetic variants in disease genes using deep generative models trained solely on evolutionary sequences.

Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.This Review summarizes the genetic and non-genetic factors that impact the transferability of polygenic risk scores (PRSs) across populations, highlighting the technical challenges of existing PRS construction methods for diverse ancestries and the emerging resources for more widespread use of PRSs.

Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype–phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.Despite the success of human genome-wide association studies (GWAS) in associating genetic variants and complex diseases or traits, criticisms of the usefulness of this study design remain. This Review assesses the pros and cons of GWAS, with a focus on the cardiometabolic field.

A substantial proportion of disease risk for common complex disorders is attributable to environmental exposures and pollutants. An appreciation of how environmental pollutants act on our cells to produce deleterious health effects has led to advances in our understanding of the molecular mechanisms underlying the pathogenesis of chronic diseases, including cancer and cardiovascular, neurodegenerative and respiratory diseases. Here, we discuss emerging research on the interplay of environmental pollutants with the human genome and epigenome. We review evidence showing the environmental impact on gene expression through epigenetic modifications, including DNA methylation, histone modification and non-coding RNAs. We also highlight recent studies that evaluate recently discovered molecular processes through which the environment can exert its effects, including extracellular vesicles, the epitranscriptome and the mitochondrial genome. Finally, we discuss current challenges when studying the exposome — the cumulative measure of environmental influences over the lifespan — and its integration into future environmental health research.Environmental pollutants have been shown to disrupt molecular mechanisms underlying common complex diseases. The authors review the interplay of environmental stressors with the human genome and epigenome as well as other molecular processes, such as production of extracellular vesicles, epitranscriptomic changes and mitochondrial changes, through which the environment can exert its effects.

Long runs of homozygosity (ROH) arise when identical haplotypes are inherited from each parent and thus a long tract of genotypes is homozygous. Cousin marriage or inbreeding gives rise to such autozygosity; however, genome-wide data reveal that ROH are universally common in human genomes even among outbred individuals. The number and length of ROH reflect individual demographic history, while the homozygosity burden can be used to investigate the genetic architecture of complex disease. We discuss how to identify ROH in genome-wide microarray and sequence data, their distribution in human populations and their application to the understanding of inbreeding depression and disease risk.

Most patients with rare diseases do not receive a molecular diagnosis and the aetiological variants and causative genes for more than half such disorders remain to be discovered 1 . Here we used whole-genome sequencing (WGS) in a national health system to streamline diagnosis and to discover unknown aetiological variants in the coding and non-coding regions of the genome. We generated WGS data for 13,037 participants, of whom 9,802 had a rare disease, and provided a genetic diagnosis to 1,138 of the 7,065 extensively phenotyped participants. We identified 95 Mendelian associations between genes and rare diseases, of which 11 have been discovered since 2015 and at least 79 are confirmed to be aetiological. By generating WGS data of UK Biobank participants 2 , we found that rare alleles can explain the presence of some individuals in the tails of a quantitative trait for red blood cells. Finally, we identified four novel non-coding variants that cause disease through the disruption of transcription of ARPC1B , GATA1 , LRBA and MPL . Our study demonstrates a synergy by using WGS for diagnosis and aetiological discovery in routine healthcare. Whole-genome sequencing and phenotype data sharing are introduced in a national health system to streamline diagnosis and to discover coding and non-coding variants that cause rare diseases.