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Key Points Significance testing, with appropriate multiple testing correction, is currently the most convenient method for summarizing the evidence for association between a disease and a genetic variant. Inadequate statistical power increases not only the probability of missing genuine associations but also the probability that significant associations represent false-positive findings. Statistical power declines rapidly with decreasing allele frequency and effect size, but it can be enhanced by increasing sample size and by selecting appropriate subjects (for example, family history positive cases and 'super normal' controls). Exome sequencing studies can often identify the mutation responsible for a Mendelian disease by filtering out common variants, synonymous variants or variants that do not co-segregate with disease, and then assigning priority to the remaining variants using bioinformatic tools. Adequate statistical power for rare-variant association analyses in complex diseases requires the aggregation of the effects of multiple rare variants within a defined portion of the genome (for example, a set of related genes). Various computational tools are available for calculating the statistical power of genetic studies. This Review discusses the principles and applications of significance testing and power calculation, including recently proposed gene-based tests for rare variants. Significance testing was developed as an objective method for summarizing statistical evidence for a hypothesis. It has been widely adopted in genetic studies, including genome-wide association studies and, more recently, exome sequencing studies. However, significance testing in both genome-wide and exome-wide studies must adopt stringent significance thresholds to allow multiple testing, and it is useful only when studies have adequate statistical power, which depends on the characteristics of the phenotype and the putative genetic variant, as well as the study design. Here, we review the principles and applications of significance testing and power calculation, including recently proposed gene-based tests for rare variants.

Technological advances make it possible to use high-throughput sequencing as a primary discovery tool of medical genetics, specifically for assaying rare variation. Still this approach faces the analytic challenge that the influence of very rare variants can only be evaluated effectively as a group. A further complication is that any given rare variant could have no effect, could increase risk, or could be protective. We propose here the C-alpha test statistic as a novel approach for testing for the presence of this mixture of effects across a set of rare variants. Unlike existing burden tests, C-alpha, by testing the variance rather than the mean, maintains consistent power when the target set contains both risk and protective variants. Through simulations and analysis of case/control data, we demonstrate good power relative to existing methods that assess the burden of rare variants in individuals.

Sleep is an important indicator of a person’s health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as smartwatches, are a promising alternative to PSG because of their small form factor, continuous monitoring capability, and popularity. Unlike PSG, however, wearables-derived data are noisier and far less information-rich because of the fewer number of modalities and less accurate measurements due to their small form factor. Given these challenges, most consumer devices perform two-stage (i.e., sleep-wake) classification, which is inadequate for deep insights into a person’s sleep health. The challenging multi-class (three, four, or five-class) staging of sleep using data from wrist-worn wearables remains unresolved. The difference in the data quality between consumer-grade wearables and lab-grade clinical equipment is the motivation behind this study. In this paper, we present an artificial intelligence (AI) technique termed sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS), which can perform three-class (wake, NREM, REM) and four-class (wake, light, deep, REM) sleep classification from activity (i.e., wrist-accelerometry-derived locomotion) and two coarse heart rate measures—both of which can be reliably obtained from a consumer-grade wrist-wearable device. Our method relies on raw time-series datasets and obviates the need for manual feature selection. We validated our model using actigraphy and coarse heart rate data from two independent study populations: the Multi-Ethnic Study of Atherosclerosis (MESA; N = 808) cohort and the Osteoporotic Fractures in Men (MrOS; N = 817) cohort. SLAMSS achieves an overall accuracy of 79%, weighted F1 score of 0.80, 77% sensitivity, and 89% specificity for three-class sleep staging and an overall accuracy of 70-72%, weighted F1 score of 0.72-0.73, 64-66% sensitivity, and 89-90% specificity for four-class sleep staging in the MESA cohort. It yielded an overall accuracy of 77%, weighted F1 score of 0.77, 74% sensitivity, and 88% specificity for three-class sleep staging and an overall accuracy of 68-69%, weighted F1 score of 0.68-0.69, 60-63% sensitivity, and 88-89% specificity for four-class sleep staging in the MrOS cohort. These results were achieved with feature-poor inputs with a low temporal resolution. In addition, we extended our three-class staging model to an unrelated Apple Watch dataset. Importantly, SLAMSS predicts the duration of each sleep stage with high accuracy. This is especially significant for four-class sleep staging, where deep sleep is severely underrepresented. We show that, by appropriately choosing the loss function to address the inherent class imbalance, our method can accurately estimate deep sleep time (SLAMSS/MESA: 0.61±0.69 hours, PSG/MESA ground truth: 0.60±0.60 hours; SLAMSS/MrOS: 0.53±0.66 hours, PSG/MrOS ground truth: 0.55±0.57 hours;). Deep sleep quality and quantity are vital metrics and early indicators for a number of diseases. Our method, which enables accurate deep sleep estimation from wearables-derived data, is therefore promising for a variety of clinical applications requiring long-term deep sleep monitoring.

Abstract Background PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. Findings To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, (n) -time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. We have also developed an extension to the data format which adds low-overhead support for genotype likelihoods, phase, multiallelic variants, and reference vs. alternate alleles, which is the basis of our planned second release (PLINK 2.0). Conclusions The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

Key Points Genome-wide association studies have identified many novel loci for hundreds of traits. Interestingly, numerous genetic loci have been associated with multiple seemingly distinct traits. These cross-phenotype (CP) associations highlight the relevance of pleiotropy in human disease. There is substantial evidence for CP associations in contemporary gene-mapping studies. Different types of pleiotropy (biological, mediated and spurious pleiotropy) can underlie a CP association. Various analytical approaches have been devised for detecting CP associations, especially methods that are based on summary statistics as opposed to individual-level data. Different methods have relative advantages and disadvantages and are distinguished by their underlying algorithms and by the types of phenotype data that they handle. Study design considerations are crucial for minimizing the identification of spurious CP associations. CP associations can highlight shared biological pathways and, when associated with different diseases, have clinical implications for diagnosis, counselling and treatment. Modern genomic studies are revealing widespread associations between single genetic variants and multiple distinct traits, including diseases. This Review discusses the biological underpinnings of such pleiotropy and the available bioinformatic tools for the detection and characterization of these effects, as well as the implications for understanding human disease. Genome-wide association studies have identified many variants that each affects multiple traits, particularly across autoimmune diseases, cancers and neuropsychiatric disorders, suggesting that pleiotropic effects on human complex traits may be widespread. However, systematic detection of such effects is challenging and requires new methodologies and frameworks for interpreting cross-phenotype results. In this Review, we discuss the evidence for pleiotropy in contemporary genetic mapping studies, new and established analytical approaches to identifying pleiotropic effects, sources of spurious cross-phenotype effects and study design considerations. We also outline the molecular and clinical implications of such findings and discuss future directions of research.

In this study, clonal hematopoiesis with somatic mutations was found in 10% of otherwise healthy people older than 65. The risk of hematologic cancer was substantially increased among these persons; in two cases, the subsequent cancer was related to the clone that predated the cancer. The development of disease often involves dynamic processes that begin years or decades before the clinical onset. In many cases, however, the process of pathogenesis goes undetected until after the patient has symptoms and presents with clinically apparent disease. Cancer arises owing to the combined effects of multiple somatic mutations, which are likely to be acquired at different times. 1 Early mutations may be present many years before disease develops. In some models of cancer development, early mutations lead to clonal expansions by stem cells or other progenitor cells. 2 Such clonal expansions greatly increase the likelihood that later, cooperating mutations would . . .

Inherited alleles account for most of the genetic risk for schizophrenia. However, new ( de novo ) mutations, in the form of large chromosomal copy number changes, occur in a small fraction of cases and disproportionally disrupt genes encoding postsynaptic proteins. Here we show that small de novo mutations, affecting one or a few nucleotides, are overrepresented among glutamatergic postsynaptic proteins comprising activity-regulated cytoskeleton-associated protein (ARC) and N -methyl- d -aspartate receptor (NMDAR) complexes. Mutations are additionally enriched in proteins that interact with these complexes to modulate synaptic strength, namely proteins regulating actin filament dynamics and those whose messenger RNAs are targets of fragile X mental retardation protein (FMRP). Genes affected by mutations in schizophrenia overlap those mutated in autism and intellectual disability, as do mutation-enriched synaptic pathways. Aligning our findings with a parallel case–control study, we demonstrate reproducible insights into aetiological mechanisms for schizophrenia and reveal pathophysiology shared with other neurodevelopmental disorders. The authors report the largest family-trio exome sequencing study of schizophrenia to date; mutations are overrepresented in genes for glutamatergic synaptic proteins and also genes mutated in autism and intellectual disability, providing insights into aetiological mechanisms and pathopshyisology shared with other neurodevelopmental disorders. Pathogenic mechanisms in schizophrenia Two major sequencing studies of the exome — the protein-coding portion of the genome — in schizophrenia sufferers and their relatives are published in this issue of Nature . Together they provide strong pointers to specific pathogenic mechanisms that disrupt the glutamatergic synapses in schizophrenia. In particular, mutations that influence the action of the scaffold protein ARC (activity-regulated cytoskeleton-associated protein) are prominently involved, as are mutations in targets of the fragile X mental retardation protein (FMRP). Defects in FMRP have previously been shown to be associated with autism spectrum disorders.

Abbreviations: GRR, genotype relative risk; GWAS, genome-wide association study; ISC, International Schizophrenia Consortium; LD, linkage disequilibrium; MAF, minor allele frequency; OR, odds ratio; RAF, risk allele frequency; SNP, single nucleotide polymorphism Introduction Complex traits and diseases, such as body-mass index, height, diabetes, heart disease, and psychiatric disorders are undoubtedly caused by multiple genetic and environmental factors, although it has been a major challenge to identify specific genes. Within a 100 kb region up to 9 causal SNPs, each with frequency between 0.005 and 0.02 were allocated to influence disease (causal SNPs). [...]at a locus with 9 such variants, ~20% of the general population would be expected to carry at least one disease risk allele.

Using whole-exome sequencing, the authors identified 244,246 coding-sequence and splice-site ultra-rare variants (URVs) and found that gene-disruptive and putatively protein-damaging URVs were significantly more abundant in schizophrenia cases than in controls. The excess of protein-compromising URVs was concentrated in brain-specific genes, particularly in neuronally expressed genes whose proteins are located at the synapse. By analyzing the exomes of 12,332 unrelated Swedish individuals, including 4,877 individuals affected with schizophrenia, in ways informed by exome sequences from 45,376 other individuals, we identified 244,246 coding-sequence and splice-site ultra-rare variants (URVs) that were unique to individual Swedes. We found that gene-disruptive and putatively protein-damaging URVs (but not synonymous URVs) were more abundant among individuals with schizophrenia than among controls ( P = 1.3 × 10 −10 ). This elevation of protein-compromising URVs was several times larger than an analogously elevated rate for de novo mutations, suggesting that most rare-variant effects on schizophrenia risk are inherited. Among individuals with schizophrenia, the elevated frequency of protein-compromising URVs was concentrated in brain-expressed genes, particularly in neuronally expressed genes; most of this elevation arose from large sets of genes whose RNAs have been found to interact with synaptically localized proteins. Our results suggest that synaptic dysfunction may mediate a large fraction of strong, individually rare genetic influences on schizophrenia risk.