Explore the vast range of titles available.

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.

Current genome-wide association studies (GWAS) use commercial genotyping microarrays that can assay over a million single nucleotide polymorphisms (SNPs). The number of SNPs is further boosted by advanced statistical genotype-imputation algorithms and large SNP databases for reference human populations. The testing of a huge number of SNPs needs to be taken into account in the interpretation of statistical significance in such genome-wide studies, but this is complicated by the non-independence of SNPs because of linkage disequilibrium (LD). Several previous groups have proposed the use of the effective number of independent markers ( M e ) for the adjustment of multiple testing, but current methods of calculation for M e are limited in accuracy or computational speed. Here, we report a more robust and fast method to calculate M e . Applying this efficient method [implemented in a free software tool named Genetic type 1 error calculator (GEC)], we systematically examined the M e , and the corresponding p -value thresholds required to control the genome-wide type 1 error rate at 0.05, for 13 Illumina or Affymetrix genotyping arrays, as well as for HapMap Project and 1000 Genomes Project datasets which are widely used in genotype imputation as reference panels. Our results suggested the use of a p -value threshold of ~10 −7 as the criterion for genome-wide significance for early commercial genotyping arrays, but slightly more stringent p -value thresholds ~5 × 10 −8 for current or merged commercial genotyping arrays, ~10 −8 for all common SNPs in the 1000 Genomes Project dataset and ~5 × 10 −8 for the common SNPs only within genes.

The spatiotemporal proteome of the intervertebral disc (IVD) underpins its integrity and function. We present DIPPER, a deep and comprehensive IVD proteomic resource comprising 94 genome-wide profiles from 17 individuals. To begin with, protein modules defining key directional trends spanning the lateral and anteroposterior axes were derived from high-resolution spatial proteomes of intact young cadaveric lumbar IVDs. They revealed novel region-specific profiles of regulatory activities and displayed potential paths of deconstruction in the level- and location-matched aged cadaveric discs. Machine learning methods predicted a ‘hydration matrisome’ that connects extracellular matrix with MRI intensity. Importantly, the static proteome used as point-references can be integrated with dynamic proteome (SILAC/degradome) and transcriptome data from multiple clinical samples, enhancing robustness and clinical relevance. The data, findings, and methodology, available on a web interface ( http://www.sbms.hku.hk/dclab/DIPPER/ ), will be valuable references in the field of IVD biology and proteomic analytics. The backbone of vertebrate animals consists of a series of bones called vertebrae that are joined together by disc-like structures that allow the back to move and distribute forces to protect it during daily activities. It is common for these intervertebral discs to degenerate with age, resulting in back pain and severely reducing quality of life. The mechanical features of intervertebral discs are the result of their proteins. These include extracellular matrix proteins, which form the external scaffolding that binds cells together in a tissue, and signaling proteins, which allow cells to communicate. However, how the levels of different proteins in each region of the disc vary with time has not been fully examined. To establish how protein composition changes with age, Tam, Chen et al. quantified the protein levels and gene activity (which leads to protein production) of intervertebral discs from young and old deceased individuals. They found that the position of different mixtures of proteins in the intervertebral disc changes with age, and that young people have high levels of extracellular matrix proteins and signaling proteins. Levels of these proteins decreased as people got older, as did the amount of proteins produced. To determine which region of the intervertebral disc different proteins were in, Tam, Chen et al. also performed magnetic resonance imaging (MRI) of the samples to correlate image intensity (which represents water content) with the corresponding protein signature. The data obtained provides a high-quality map of how the location of different proteins changes with age, and is available online under the name DIPPER. This database is an informative resource for research into skeletal biology, and it will likely advance the understanding of intervertebral disc degeneration in humans and animals, potentially leading to the development of new treatment strategies for this condition.

Indirect parental genetic effects may be defined as the influence of parental genotypes on offspring phenotypes over and above that which results from the transmission of genes from parents to their children. However, given the relative paucity of large-scale family-based cohorts around the world, it is difficult to demonstrate parental genetic effects on human traits, particularly at individual loci. In this manuscript, we illustrate how parental genetic effects on offspring phenotypes, including late onset conditions, can be estimated at individual loci in principle using large-scale genome-wide association study (GWAS) data, even in the absence of parental genotypes. Our strategy involves creating “virtual” mothers and fathers by estimating the genotypic dosages of parental genotypes using physically genotyped data from relative pairs. We then utilize the expected dosages of the parents, and the actual genotypes of the offspring relative pairs, to perform conditional genetic association analyses to obtain asymptotically unbiased estimates of maternal, paternal and offspring genetic effects. We apply our approach to 19066 sibling pairs from the UK Biobank and show that a polygenic score consisting of imputed parental educational attainment SNP dosages is strongly related to offspring educational attainment even after correcting for offspring genotype at the same loci. We develop a freely available web application that quantifies the power of our approach using closed form asymptotic solutions. We implement our methods in a user-friendly software package IMPISH ( IM puting P arental genotypes I n S iblings and H alf Siblings) which allows users to quickly and efficiently impute parental genotypes across the genome in large genome-wide datasets, and then use these estimated dosages in downstream linear mixed model association analyses. We conclude that imputing parental genotypes from relative pairs may provide a useful adjunct to existing large-scale genetic studies of parents and their offspring.

Exome sequencing is becoming a standard tool for mapping Mendelian disease-causing (or pathogenic) non-synonymous single nucleotide variants (nsSNVs). Minor allele frequency (MAF) filtering approach and functional prediction methods are commonly used to identify candidate pathogenic mutations in these studies. Combining multiple functional prediction methods may increase accuracy in prediction. Here, we propose to use a logit model to combine multiple prediction methods and compute an unbiased probability of a rare variant being pathogenic. Also, for the first time we assess the predictive power of seven prediction methods (including SIFT, PolyPhen2, CONDEL, and logit) in predicting pathogenic nsSNVs from other rare variants, which reflects the situation after MAF filtering is done in exome-sequencing studies. We found that a logit model combining all or some original prediction methods outperforms other methods examined, but is unable to discriminate between autosomal dominant and autosomal recessive disease mutations. Finally, based on the predictions of the logit model, we estimate that an individual has around 5% of rare nsSNVs that are pathogenic and carries ~22 pathogenic derived alleles at least, which if made homozygous by consanguineous marriages may lead to recessive diseases.

Anecdotal and biographical reports suggest that bipolar disorder may be associated with high IQ or creativity, but evidence for any such connection is weak. To investigate possible associations between scholastic achievement and later bipolar disorder, using prospective data, in a whole-population cohort study. Using individual school grades from all individuals finishing compulsory schooling in Sweden between 1988 and 1997, we tested associations between scholastic achievement at age 15-16 and hospital admission for psychosis between ages 17 and 31, adjusting for potential confounders. Individuals with excellent school performance had a nearly fourfold increased risk of later bipolar disorder compared with those with average grades (hazard ratio HR = 3.79, 95% CI 2.11-6.82). This association appeared to be confined to males. Students with the poorest grades were also at moderately increased risk of bipolar disorder (HR = 1.86, 95% CI 1.06-3.28). These findings provide support for the hypothesis that exceptional intellectual ability is associated with bipolar disorder.

A history of childhood adversity is associated with psychotic disorder, with an increase in risk according to number or severity of exposures. However, it is not known why only some exposed individuals go on to develop psychosis. One possibility is pre-existing genetic vulnerability. Research on gene-environment interaction in psychosis has primarily focused on candidate genes, although the genetic effects are now known to be polygenic. This pilot study investigated whether the effect of childhood adversity on psychosis is moderated by the polygenic risk score for schizophrenia (PRS). Data were utilised from the Genes and Psychosis (GAP) study set in South London, UK. The GAP sample comprises 285 first-presentation psychosis cases and 256 unaffected controls with information on childhood adversity. We studied only white subjects (80 cases and 110 controls) with PRS data, as the PRS has limited predictive ability in patients of African ancestry. The occurrence of childhood adversity was assessed with the Childhood Experience of Care and Abuse Questionnaire (CECA.Q) and the PRS was based on genome-wide meta-analysis results for schizophrenia from the Psychiatric Genomics Consortium. Higher schizophrenia PRS and childhood adversities each predicted psychosis status. Nevertheless, no evidence was found for interaction as departure from additivity, indicating that the effect of polygenic risk scores on psychosis was not increased in the presence of a history of childhood adversity. These findings are compatible with a multifactorial threshold model in which both genetic liability and exposure to environmental risk contribute independently to the etiology of psychosis.

Genetic studies in psychiatry have primarily focused on the effects of common genetic variants, but few have investigated the role of rare genetic variants, particularly for major depression. In order to explore the role of rare variants in the gap between estimates of single nucleotide polymorphism (SNP) heritability and twin study heritability, we examined the contribution of common and rare genetic variants to latent traits underlying psychiatric disorders using high-quality imputed genotype data from the UK Biobank. Using a pre-registered analysis, we used items from the UK Biobank Mental Health Questionnaire relevant to three psychiatric disorders: major depression (N = 134,463), bipolar disorder (N = 117,376) and schizophrenia (N = 130,013) and identified a general hierarchical factor for each that described participants’ responses. We calculated participants’ scores on these latent traits and conducted single-variant genetic association testing (MAF > 0.05%), gene-based burden testing and pathway association testing associations with these latent traits. We tested for enrichment of rare variants (MAF 0.05–1%) in genes that had been previously identified by common variant genome-wide association studies, and genes previously associated with Mendelian disorders having relevant symptoms. We found moderate genetic correlations between the latent traits in our study and case–control phenotypes in previous genome-wide association studies, and identified one common genetic variant (rs72657988, minor allele frequency = 8.23%, p = 1.01 × 10−9) associated with the general factor of schizophrenia, but no other single variants, genes or pathways passed significance thresholds in this analysis, and we did not find enrichment in previously identified genes.

Recently, the American College of Medical Genetics (ACMG) recommended the return of actionable secondary findings detected from clinical sequencing. The reported frequency of secondary findings in Asian populations were highly variable and it is unclear whether the uniformity in coverage offered by whole-genome sequencing (WGS) may impact the estimate. In this analysis, we aimed to refine the rate of secondary findings on East Asians through a large-scale WGS study. We classified 1256 protein-altering or splicing variants of the 59 actionable genes detected from WGS of 954 East Asians in strict accordance with the ACMG and the Association for Molecular Pathology guidelines. A total of 21 pathogenic or likely pathogenic variants were detected in 24 of the 954 East Asian genomes with an estimate of 2.5% of East Asians carrying actionable variants. Although the overall estimate of secondary findings was consistent with those reported for non-East Asian ethnicities, genetic and allelic heterogeneity was observed. WGS offers a wider breadth of coverage over WES, which highlights the need to further investigate the variable sensitivity of WES and WGS in the detection of secondary findings. Identifying secondary findings in populations underrepresented in previous genetic literature might improve variant interpretation and has a profound impact on local decision-making with regard to the cost-effectiveness of returning the secondary findings from clinical sequencing.

Objective: Our study examined the stress level and psychological distress of severe acute respiratory syndrome (SARS) survivors 1 year after the outbreak. Method: During the SARS outbreak in 2003, we used the 10-item Perceived Stress Scale (PSS-10) to assess SARS survivors treated in 2 major hospitals (non–health care workers, n = 49; health care workers, n = 30). We invited SARS survivors from the same hospitals (non–health care workers, n = 63; health care workers, n = 33) to complete the PSS-10 again in 2004. At that time, they were also asked to complete the General Health Questionnaire (GHQ-12) and measures of depression, anxiety, and posttraumatic symptoms. PSS-10 scores were also obtained from matched community control subjects during the outbreak (n = 145) and again in 2004 (n = 112). Results: SARS survivors had higher stress levels during the outbreak, compared with control subjects (PSS-10 scores =19.8 and 17.9, respectively; P < 0.01), and this persisted 1 year later (PSS-10 scores =19.9 and 17.3, respectively; P < 0.01) without signs of decrease. In 2004, SARS survivors also showed worrying levels of depression, anxiety, and posttraumatic symptoms. An alarming proportion (64%) scored above the GHQ-12 cut-off that suggests psychiatric morbidity. During the outbreak, health care worker SARS survivors had stress levels similar to those of non–health care workers, but health care workers showed significantly higher stress levels in 2004 (PSS-10 score = 22.8, compared with PSS-10 score = 18.4; P < 0.05) and had higher depression, anxiety, posttraumatic symptoms, and GHQ-12 scores. Conclusions: One year after the outbreak, SARS survivors still had elevated stress levels and worrying levels of psychological distress. The situation of health care worker SARS survivors is particularly worrying. The long-term psychological implications of infectious diseases should not be ignored. Mental health services could play an important role in rehabilitation.