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International audience; Genome-wide association studies have identified 240 independent loci associated with type 2 diabetes (T2D) risk, but this knowledge has not advanced precision medicine. In contrast, the genetic diagnosis of monogenic forms of diabetes (including maturity-onset diabetes of the young (MODY)) are textbook cases of genomic medicine. Recent studies trying to bridge the gap between monogenic diabetes and T2D have been inconclusive. Here, we show a significant burden of pathogenic variants in genes linked with monogenic diabetes among people with common T2D, particularly in actionable MODY genes, thus implying that there should be a substantial change in care for carriers with T2D. We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8. The carriers with T2D are leaner, which evidences a functional metabolic effect of these mutations. Pathogenic variants in actionable MODY genes are more frequent than was previously expected in common T2D. These results open avenues for future interventions assessing the clinical interest of these pathogenic mutations in precision medicine.

Significance To understand the value and clinical impact of surveying genome-wide disease-causing genes and variants, we used a prospective cohort study design that enrolled volunteers who agreed to have their whole genome sequenced and to participate in deep phenotyping using clinical laboratory tests, metabolomics technologies, and advanced noninvasive imaging. The genomic results are integrated with the phenotype results. Approximately 1 in 6 adult individuals (17.3%) had genetic findings and, when integrated with deep phenotyping data, including family/medical histories with genetic findings, 1 in 9 (11.5%) had genotype and phenotype associations. Genomics and metabolomics association analysis revealed 5.1% of heterozygotes with phenotype manifestations affecting serum metabolite levels. We report observations from our study in which health outcomes and benefits were not measured., Genome sequencing has established clinical utility for rare disease diagnosis. While increasing numbers of individuals have undergone elective genome sequencing, a comprehensive study surveying genome-wide disease-associated genes in adults with deep phenotyping has not been reported. Here we report the results of a 3-y precision medicine study with a goal to integrate whole-genome sequencing with deep phenotyping. A cohort of 1,190 adult participants (402 female [33.8%]; mean age, 54 y [range 20 to 89+]; 70.6% European) had whole-genome sequencing, and were deeply phenotyped using metabolomics, advanced imaging, and clinical laboratory tests in addition to family/medical history. Of 1,190 adults, 206 (17.3%) had at least 1 genetic variant with pathogenic (P) or likely pathogenic (LP) assessment that suggests a predisposition of genetic risk. A multidisciplinary clinical team reviewed all reportable findings for the assessment of genotype and phenotype associations, and 137 (11.5%) had genotype and phenotype associations. A high percentage of genotype and phenotype associations (>75%) was observed for dyslipidemia (n = 24), cardiomyopathy, arrhythmia, and other cardiac diseases (n = 42), and diabetes and endocrine diseases (n = 17). A lack of genotype and phenotype associations, a potential burden for patient care, was observed in 69 (5.8%) individuals with P/LP variants. Genomics and metabolomics associations identified 61 (5.1%) heterozygotes with phenotype manifestations affecting serum metabolite levels in amino acid, lipid and cofactor, and vitamin pathways. Our descriptive analysis provides results on the integration of whole-genome sequencing and deep phenotyping for clinical assessments in adults.

Understanding the impact of rare variants is essential to understanding human health. We analyze rare (MAF, Population-based association analyses of rare genetic variants with complex traits are limited by the availability of data from sufficiently large cohorts. Here, Cirulli et al. report gene-based collapsing analysis of exomes from 49,960 participants of the UK Biobank and 21,866 participants of the Healthy Nevada Project over a total of 4377 traits.

Large-scale sequencing efforts in amyotrophic lateral sclerosis (ALS) have implicated novel genes using gene-based collapsing methods. However, pathogenic mutations may be concentrated in specific genic regions. To address this, we developed two collapsing strategies: One focuses rare variation collapsing on homology-based protein domains as the unit for collapsing, and the other is a gene-level approach that, unlike standard methods, leverages existing evidence of purifying selection against missense variation on said domains. The application of these two collapsing methods to 3093 ALS cases and 8186 controls of European ancestry, and also 3239 cases and 11,808 controls of diversified populations, pinpoints risk regions of ALS genes, including SOD1, NEK1, TARDBP, and FUS. While not clearly implicating novel ALS genes, the new analyses not only pinpoint risk regions in known genes but also highlight candidate genes as well.

The highly transmissible B.1.1.7 variant of SARS-CoV-2, first identified in the United Kingdom, has gained a foothold across the world. Using S gene target failure (SGTF) and SARS-CoV-2 genomic sequencing, we investigated the prevalence and dynamics of this variant in the United States (U.S.), tracking it back to its early emergence. We found that while the fraction of B.1.1.7 varied by state, the variant increased at a logistic rate with a roughly weekly doubling rate and an increased transmission of 40-50%. We revealed several independent introductions of B.1.1.7 into the U.S. as early as late November 2020, with community transmission spreading it to most states within months. We show that the U.S. is on a similar trajectory as other countries where B.1.1.7 became dominant, requiring immediate and decisive action to minimize COVID-19 morbidity and mortality., Genomic epidemiology analyses explain the introduction and transmission of the B.1.1.7 variant of SARS-CoV-2 into the United States, with projections for it to soon be the dominant strain in the country.

Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID-19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, large-effect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7; rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak.

Recently, multiple novel strains of SARS-CoV-2 have been found to share the same deletion of amino acids H69 and V70 in the virus S gene. This includes strain B.1.1.7 / SARS-CoV-2 VUI 202012/01, which has been found to be more infectious than other strains of SARS-CoV-2, and its increasing presence has resulted in new lockdowns in and travel restrictions leaving the UK. Here, we analyze 2 million RT-PCR SARS-CoV-2 tests performed at Helix to identify the rate of S gene dropout, which has been recently shown to occur in tests from individuals infected with strains of SARS-CoV-2 that carry the H69del/V70del mutation. We observe a rise in S gene dropout in the US starting in early October, with 0.25% of our daily SARS-CoV-2-positive tests exhibiting this pattern during the first week. The rate of positive samples with S gene dropout has grown slowly over time, with last week exhibiting the highest level yet, at 0.5%. Focusing on the 14 states for which we have sufficient sample size to assess the frequency of this rare event (n>1000 SARS-CoV-2-positive samples), we see a recent expansion in the Eastern part of the US, concentrated in MA, OH, and FL. However, we cannot say from these data whether the S gene dropout samples we observe here represent the B.1.1.7. strain. Only with an expansion of genomic surveillance sequencing in the US will we know for certain the prevalence of the B.1.1.7 strain in the US.

Background Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. Integrated analysis of data from different modalities has the potential of uncovering novel biomarkers and disease signatures. Methods We collected 1385 data features from diverse modalities, including metabolome, microbiome, genetics, and advanced imaging, from 1253 individuals and from a longitudinal validation cohort of 1083 individuals. We utilized a combination of unsupervised machine learning methods to identify multimodal biomarker signatures of health and disease risk. Results Our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers. Stratification of individuals based on the signatures of these biomarkers identified distinct subsets of individuals with similar health statuses. Subset membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistance, and body mass index. The novel biomarkers in the diabetes signature included 1-stearoyl-2-dihomo-linolenoyl-GPC and 1-(1-enyl-palmitoyl)-2-oleoyl-GPC. Another metabolite, cinnamoylglycine, was identified as a potential biomarker for both gut microbiome health and lean mass percentage. We identified potential early signatures for hypertension and a poor metabolic health outcome. Additionally, we found novel associations between a uremic toxin, p-cresol sulfate, and the abundance of the microbiome genera Intestinimonas and an unclassified genus in the Erysipelotrichaceae family. Conclusions Our methodology and results demonstrate the potential of multimodal data integration, from the identification of novel biomarker signatures to a data-driven stratification of individuals into disease subtypes and stages—an essential step towards personalized, preventative health risk assessment.

Intelligence is highly heritable 1 and a major determinant of human health and well-being 2 . Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence 3-7 , but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

Pompe disease is a metabolic myopathy with a wide spectrum of clinical presentation. The gold-standard diagnostic test is acid alpha-glucosidase assay on skin fibroblasts, muscle or blood. Identification of two GAA pathogenic variants in-trans is confirmatory. Optimal effectiveness of enzyme replacement therapy hinges on early diagnosis, which is challenging in late-onset form of the disease due to non-specific presentation. Next-generation sequencing-based panels effectively facilitate diagnosis, but the sensitivity of whole-exome sequencing (WES) in detecting pathogenic GAA variants remains unknown. We analyzed WES data from 93 patients with confirmed Pompe disease and GAA genotypes based on PCR/Sanger sequencing. After ensuring that the common intronic variant c.-32-13T>G is not filtered out, whole-exome sequencing identified both GAA pathogenic variants in 77/93 (83%) patients. However, one variant was missed in 14/93 (15%), and both variants were missed in 2/93 (2%). One complex indel leading to a severe phenotype was incorrectly called a nonsynonymous substitution c.-32-13T>C due to misalignment. These results demonstrate that WES may fail to diagnose Pompe disease. Clinicians need to be aware of limitations of WES, and consider tests specific to Pompe disease when WES does not provide a diagnosis in patients with proximal myopathy, progressive respiratory failure or other subtle symptoms.

SummaryHigh quality population allele frequencies of DNA variants can be used to discover new biology, and study rare disorders. Here, we created a public catalog of mitochondrial DNA variants based on a population of 195,983 individuals. We focused on 3 criteria: (i) the population is not enriched for mitochondrial disorders, or other clinical phenotypes, (ii) all genomes are sequenced and analyzed in the same clinical laboratory, and (iii) both homoplasmic and heteroplasmic variants are reported. We found that 47% of the mitochondrial genome was invariant in this population, including large stretches in the 2 rRNA genes. This information could be used to annotate the mitochondrial genome in future studies. We also showed how to use this resource for the interpretation of pathogenic variants for rare mitochondrial disorders. For example, 42% of variants previously reported to be pathogenic for Leber Hereditary Optic Neuropathy (LHON) should be reclassified.

Liability to schizophrenia is inversely correlated with general cognitive ability at both the phenotypic and genetic level. Paradoxically, a modest but consistent positive genetic correlation has been reported between schizophrenia and educational attainment, despite the strong positive genetic correlation between cognitive ability and educational attainment. Here we leverage published GWAS in cognitive ability, education, and schizophrenia to parse biological mechanisms underlying these results. Association analysis based on subsets (ASSET), a pleiotropic meta-analytic technique, allowed jointly associated loci to be identified and characterized. Specifically, we identified subsets of variants associated in the expected (“Concordant”) direction across all three phenotypes (i.e., greater risk for schizophrenia, lower cognitive ability, and lower educational attainment); these were contrasted with variants demonstrating the counterintuitive (“Discordant”) relationship between education and schizophrenia (i.e., greater risk for schizophrenia and higher educational attainment). ASSET analysis revealed 235 independent loci associated with cognitive ability, education and/or schizophrenia at p−8. Pleiotropic analysis successfully identified more than 100 loci that were not significant in the input GWASs, and many of these have been validated by larger, more recent single-phenotype GWAS. Leveraging the joint genetic correlations of cognitive ability, education, and schizophrenia, we were able to dissociate two distinct biological mechanisms: early neurodevelopmental pathways that characterize concordant allelic variation, and adulthood synaptic pruning pathways that were linked to the paradoxical positive genetic association between education and schizophrenia. Further, genetic correlation analyses revealed that these mechanisms contribute not only to the etiopathogenesis of schizophrenia, but also to the broader biological dimensions that are implicated in both general health outcomes and psychiatric illness.

Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84–88) presented a critique of our recently published paper in Cell Reports entitled ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ (Lam et al., Cell Reports, Vol. 21, 2017, 2597–2613). Specifically, Hill offered several interrelated comments suggesting potential problems with our use of a new analytic method called Multi-Trait Analysis of GWAS (MTAG) (Turley et al., Nature Genetics, Vol. 50, 2018, 229–237). In this brief article, we respond to each of these concerns. Using empirical data, we conclude that our MTAG results do not suffer from ‘inflation in the FDR [false discovery rate]’, as suggested by Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84–88), and are not ‘more relevant to the genetic contributions to education than they are to the genetic contributions to intelligence’.

© 2018 Elsevier Inc., To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.

BackgroundRare mutations in genes associated with Mendelian forms of disease are a potential mechanism for sporadic disease. The need to assess the clinical significance of such variants is increasing as personalized medicine and genome sequencing increases.ObjectiveTo evaluate the rate of rare, functional variants in dystonia genes in the general population to improve interpretation of the clinical relevance of potentially pathogenic variants in dystonia cases.MethodsWe performed an “aggregated” collapsing analysis of exome sequence that considered rare coding variants in genes previously associated with dystonia, a rare neurological movement disorder, on 2,372 population controls of European ethnicity. We then performed a pilot study in sporadic dystonia to assess whether there was a substantially greater incidence of individuals with rare variation in dystonia genes.ResultsNearly half of population controls had a rare coding variant when 148 genes associated with a dystonia phenotype were considered. When the subset of genes causing isolated dystonia (14 genes) was evaluated, 3-4% of controls harbored rare qualifying variants. Our pilot study of case exomes was powered to identify a five-fold higher or greater rate of qualifying variants in isolated dystonia genes in sporadic dystonia cases compared to population controls; we did not find such an enrichment.ConclusionsWe provide the first systematic analysis of rare variation in dystonia genes considered collectively. Our findings emphasize the need to consider the overall frequency of variants in rare disease-related genes in the general population when considering their potential role in clinical presentations.

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence3–7, but much about its genetic underpinnings remains to be discovered. Here, we present the largest genetic association study of intelligence to date (N=279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure. We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimer’s dementia and ADHD, and bidirectional causation with strong pleiotropy for schizophrenia. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.

General cognitive function is a prominent human trait associated with many important life outcomes1,2, including longevity3. The substantial heritability of general cognitive function is known to be polygenic, but it has had little explication in terms of the contributing genetic variants4,5,6. Here, we combined cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N=280,360; age range = 16 to 102). We found 9,714 genome-wide significant SNPs (P−8) in 99 independent loci. Most showed clear evidence of functional importance. Among many novel genes associated with general cognitive function wereSGCZ,ATXN1,MAPT,AUTS2, andP2RY6. Within the novel genetic loci were variants associated with neurodegenerative disorders, neurodevelopmental disorders, physical and psychiatric illnesses, brain structure, and BMI. Gene-based analyses found 536 genes significantly associated with general cognitive function; many were highly expressed in the brain, and associated with neurogenesis and dendrite gene sets. Genetic association results predicted up to 4% of general cognitive function variance in independent samples. There was significant genetic overlap between general cognitive function and information processing speed, as well as many health variables including longevity.

The complex nature of human cognition has resulted in cognitive genomics lagging behind many other fields in terms of gene discovery using genome-wide association study (GWAS) methods. In an attempt to overcome these barriers, the current study utilized GWAS meta-analysis to examine the association of common genetic variation (~8M single-nucleotide polymorphisms (SNP) with minor allele frequency ⩾1%) to general cognitive function in a sample of 35 298 healthy individuals of European ancestry across 24 cohorts in the Cognitive Genomics Consortium (COGENT). In addition, we utilized individual SNP lookups and polygenic score analyses to identify genetic overlap with other relevant neurobehavioral phenotypes. Our primary GWAS meta-analysis identified two novel SNP loci (top SNPs: rs76114856 in the CENPO gene on chromosome 2 and rs6669072 near LOC105378853 on chromosome 1) associated with cognitive performance at the genome-wide significance level (P−8). Gene-based analysis identified an additional three Bonferroni-corrected significant loci at chromosomes 17q21.31, 17p13.1 and 1p13.3. Altogether, common variation across the genome resulted in a conservatively estimated SNP heritability of 21.5% (s.e.=0.01%) for general cognitive function. Integration with prior GWAS of cognitive performance and educational attainment yielded several additional significant loci. Finally, we found robust polygenic correlations between cognitive performance and educational attainment, several psychiatric disorders, birth length/weight and smoking behavior, as well as a novel genetic association to the personality trait of openness. These data provide new insight into the genetics of neurocognitive function with relevance to understanding the pathophysiology of neuropsychiatric illness.

In 2015, for the first time in three decades, novel lipid-lowering drugs for the treatment of hypercholesterolemia were approved by the FDA in the US and EMA in Europe (US Food and Drug Administration 2015a, US Food and Drug Administration 2015b, European Medicines Agency 2015a, European Medicines Agency 2015b). Proprotein convertase subtilisin/kexin type 9 (PCSK9), an enzyme encoded by the PCSK9 gene in humans, is the target of these novel therapeutics. In the early 2000s, PCSK9 was found to play a role in cholesterol homeostasis by promoting the degradation of the low-density lipoprotein (LDL) receptor in the liver, reducing its ability to remove LDL from circulation (Maxwell & Breslow, 2004, Park et al., 2004, Benjannet et al., 2004, Maxwell et al., 2005). At the same time, Dr. Helen H. Hobbs and Dr. Jonathan Cohen at UT-Southwestern had been studying people with very high and very low cholesterol. Based on its role in cholesterol metabolism, they sequenced the PCSK9 gene in people with very low cholesterol and found nonsense mutations that caused the PCSK9 enzyme to lose its function (Cohen et al., 2005). “Resilience” to hypercholesterolemia was conferred on individuals harboring loss-of-function mutations in the PCSK9 gene, opening the door for the development of drugs based on this novel mechanism of action. The pursuit of resilience through genome-wide analyses is now underway and will undoubtedly further our understanding of the biology of disease, provide promising therapeutic targets, and potentially improve human health and aging. This article is protected by copyright. All rights reserved.

Despite the large amount of variation found in the night (scotopic) vision capabilities of healthy volunteers, little effort has been made to characterize this variation and factors, genetic and non-genetic, that influence it. In the largest population of healthy observers measured for scotopic visual acuity (VA) and contrast sensitivity (CS) to date, we quantified the effect of a range of variables on visual performance. We found that young volunteers with excellent photopic vision exhibit great variation in their scotopic VA and CS, and this variation is reliable from one testing session to the next. We additionally identified that factors such as Circadian preference, iris color, astigmatism, depression, sex and education have no significant impact on scotopic visual function. We confirmed previous work showing that the amount of time spent on the vision test influences performance and that laser eye surgery results in worse scotopic vision. We also showed a significant effect of intelligence and photopic visual performance on scotopic VA and CS, but all of these variables collectively explain

Schizophrenia is a severe psychiatric disorder with strong heritability and marked heterogeneity in symptoms, course, and treatment response. There is strong interest in identifying genetic risk factors that can help to elucidate the pathophysiology and that might result in the development of improved treatments. Linkage and genome-wide association studies (GWASs) suggest that the genetic basis of schizophrenia is heterogeneous. However, it remains unclear whether the underlying genetic variants are mostly moderately rare and can be identified by the genotyping of variants observed in sequenced cases in large follow-up cohorts or whether they will typically be much rarer and therefore more effectively identified by gene-based methods that seek to combine candidate variants. Here, we consider 166 persons who have schizophrenia or schizoaffective disorder and who have had either their genomes or their exomes sequenced to high coverage. From these data, we selected 5,155 variants that were further evaluated in an independent cohort of 2,617 cases and 1,800 controls. No single variant showed a study-wide significant association in the initial or follow-up cohorts. However, we identified a number of case-specific variants, some of which might be real risk factors for schizophrenia, and these can be readily interrogated in other data sets. Our results indicate that schizophrenia risk is unlikely to be predominantly influenced by variants just outside the range detectable by GWASs. Rather, multiple rarer genetic variants must contribute substantially to the predisposition to schizophrenia, suggesting that both very large sample sizes and gene-based association tests will be required for securely identifying genetic risk factors. © 2012 The American Society of Human Genetics.

Although genome-wide association (GWA) studies for common variants have thus far succeeded in explaining only a modest fraction of the genetic components of human common diseases, recent advances in next-generation sequencing technologies could rapidly facilitate substantial progress. This outcome is expected if much of the missing genetic control is due to gene variants that are too rare to be picked up by GWA studies and have relatively large effects on risk. Here, we evaluate the evidence for an important role of rare gene variants of major effect in common diseases and outline discovery strategies for their identification.

Comparative genomics provides a powerful tool for the identification of genes that encode traits shared between crop plants and model organisms. Pathogen resistance conferred by plant R genes of the nucleotide-binding–leucine-rich-repeat (NB–LRR) class is one such trait with great agricultural importance that occupies a critical position in understanding fundamental processes of pathogen detection and coevolution. The proposed rapid rearrangement of R genes in genome evolution would make comparative approaches tenuous. Here, we test the hypothesis that orthology is predictive of R-gene genomic location in the Solanaceae using the pepper R gene Bs2. Homologs of Bs2 were compared in terms of sequence and gene and protein architecture. Comparative mapping demonstrated that Bs2 shared macrosynteny with R genes that best fit criteria determined to be its orthologs. Analysis of the genomic sequence encompassing solanaceous R genes revealed the magnitude of transposon insertions and local duplications that resulted in the expansion of the Bs2 intron to 27 kb and the frequently detected duplications of the 5′-end of R genes. However, these duplications did not impact protein expression or function in transient assays. Taken together, our results support a conservation of synteny for NB–LRR genes and further show that their distribution in the genome has been consistent with global rearrangements.

In recent years, genome and exome sequencing studies have implicated a plethora of new disease genes with rare causal variants. Here, I review 150 exome sequencing studies that claim to have discovered that a disease can be caused by different rare variants in the same gene, and I determine whether their methods followed the current best-practice guidelines in the interpretation of their data. Specifically, I assess whether studies appropriately assess controls for rare variants throughout the entire gene or implicated region as opposed to only investigating the specific rare variants identified in the cases, and I assess whether studies present sufficient co-segregation data for statistically significant linkage. I find that the proportion of studies performing gene-based analyses has increased with time, but that even in 2015 fewer than 40% of the reviewed studies used this method, and only 10% presented statistically significant co-segregation data. Furthermore, I find that the genes reported in these papers are explaining a decreasing proportion of cases as the field moves past most of the low-hanging fruit, with 50% of the genes from studies in 2014 and 2015 having variants in fewer than 5% of cases. As more studies focus on genes explaining relatively few cases, the importance of performing appropriate gene-based analyses is increasing. It is becoming increasingly important for journal editors and reviewers to require stringent gene-based evidence to avoid an avalanche of misleading disease gene discovery papers.

Objective: To identify the genetic variant that causes autosomal dominantly inherited motor neuron disease in a 4-generation Israeli-Arab family using genetic linkage and whole exome sequencing. Methods: Genetic linkage analysis was performed in this family using Illumina single nucleotide polymorphism chips. Whole exome sequencing was the nu ndertaken on DNA samples from 2a ffected family members using an Illumina 2000 HiSeq platform in pursuit of potentially pathogenic genetic variants that comigrate with the disease in this pedigree. Variants meeting these criteria were then screened in all affected individuals. Results: A novel mutation (p.R191G) in the valosin-containing protein (VCP) gene was identified in the index family. Direct sequencing of the VCP gene in a panel of DNA from 274 unrelated individuals with familial amyotrophic lateral sclerosis (FALS) revealed 5 additional mutations. Among them, 2 were previously identified in pedigrees with a constellation of inclusion body myopathy with Paget disease of the bone and frontotemporal dementia (IBMPFD) and in FALS, and 2 other mutations (p.R159C and p.R155C) in IBMPFD alone. We did not detect VCP gene mutations in DNA from 178 cases of sporadic amyotrophic lateral sclerosis. Conclusions: We report a novel VCP mutation identified in an amyotrophic lateral sclerosis family (p.R191G) with atypical clinical features. In our experience, VCP mutations arise in approximately 1.5% of FALS cases. Our study supports the view that motor neuron disease is part of the clinical spectrum of VCP-associated disease. Neurology � 2012;79:2201–2208

Although there are many methods available for inferring copy-number variants (CNVs) from next-generation sequence data, there remains a need for a system that is computationally efficient but that retains good sensitivity and specificity across all types of CNVs. Here, we introduce a new method, estimation by read depth with single-nucleotide variants (ERDS), and use various approaches to compare its performance to other methods. We found that for common CNVs and high-coverage genomes, ERDS performs as well as the best method currently available (Genome STRiP), whereas for rare CNVs and high-coverage genomes, ERDS performs better than any available method. Importantly, ERDS accommodates both unique and highly amplified regions of the genome and does so without requiring separate alignments for calling CNVs and other variants. These comparisons show that for genomes sequenced at high coverage, ERDS provides a computationally convenient method that calls CNVs as well as or better than any currently available method.

Idiopathic generalized epilepsy (IGE) is a complex disease with high heritability, but little is known about its genetic architecture. Rare copy-number variants have been found to explain nearly 3% of individuals with IGE; however, it remains unclear whether variants with moderate effect size and frequencies below what are reliably detected with genome-wide association studies contribute significantly to disease risk. In this study, we compare the exome sequences of 118 individuals with IGE and 242 controls of European ancestry by using next-generation sequencing. The exome-sequenced epilepsy cases include study subjects with two forms of IGE, including juvenile myoclonic epilepsy (n = 93) and absence epilepsy (n = 25). However, our discovery strategy did not assume common genetic control between the subtypes of IGE considered. In the sequence data, as expected, no variants were significantly associated with the IGE phenotype or more specific IGE diagnoses. We then selected 3,897 candidate epilepsy-susceptibility variants from the sequence data and genotyped them in a larger set of 878 individuals with IGE and 1,830 controls. Again, no variant achieved statistical significance. However, 1,935 variants were observed exclusively in cases either as heterozygous or homozygous genotypes. It is likely that this set of variants includes real risk factors. The lack of significant association evidence of single variants with disease in this two-stage approach emphasizes the high genetic heterogeneity of epilepsy disorders, suggests that the impact of any individual single-nucleotide variant in this disease is small, and indicates that gene-based approaches might be more successful for future sequencing studies of epilepsy predisposition., Journal Article, Research Support, N.I.H. Extramural, Research Support, Non-U.S. Gov't, SCOPUS: ar.j, info:eu-repo/semantics/published

Mendelian analysis of disorders of immune regulation can provide insight into molecular pathways associated with host defense and immune tolerance.We identified three families with a dominantly inherited complex of cold-induced urticaria, antibody deficiency, and susceptibility to infection and autoimmunity. Immunophenotyping methods included flow cytometry, analysis of serum immunoglobulins and autoantibodies, lymphocyte stimulation, and enzymatic assays. Genetic studies included linkage analysis, targeted Sanger sequencing, and next-generation whole-genome sequencing.Cold urticaria occurred in all affected subjects. Other, variable manifestations included atopy, granulomatous rash, autoimmune thyroiditis, the presence of antinuclear antibodies, sinopulmonary infections, and common variable immunodeficiency. Levels of serum IgM and IgA and circulating natural killer cells and class-switched memory B cells were reduced. Linkage analysis showed a 7-Mb candidate interval on chromosome 16q in one family, overlapping by 3.5 Mb a disease-associated haplotype in a smaller family. This interval includes PLCG2, encoding phospholipase Cγ(2) (PLCγ(2)), a signaling molecule expressed in B cells, natural killer cells, and mast cells. Sequencing of complementary DNA revealed heterozygous transcripts lacking exon 19 in two families and lacking exons 20 through 22 in a third family. Genomic sequencing identified three distinct in-frame deletions that cosegregated with disease. These deletions, located within a region encoding an autoinhibitory domain, result in protein products with constitutive phospholipase activity. PLCG2-expressing cells had diminished cellular signaling at 37°C but enhanced signaling at subphysiologic temperatures.Genomic deletions in PLCG2 cause gain of PLCγ(2) function, leading to signaling abnormalities in multiple leukocyte subsets and a phenotype encompassing both excessive and deficient immune function. (Funded by the National Institutes of Health Intramural Research Programs and others.).

Amyotrophic lateral sclerosis (ALS) is a late-onset neurodegenerative disorder resulting from motor neuron death. Approximately 10% of cases are familial (FALS), typically with a dominant inheritance mode. Despite numerous advances in recent years1-9, nearly 50% of FALS cases have unknown genetic etiology. Here we show that mutations within the profilin 1 (PFN1) gene can cause FALS. PFN1 is critical for monomeric (G)-actin conversion to filamentous (F)-actin. Exome sequencing of two large ALS families revealed different mutations within the PFN1 gene. Additional sequence analysis identified 4 mutations in 7 out of 274 FALS cases. Cells expressing PFN1 mutants contain ubiquitinated, insoluble aggregates that in many cases contain the ALS-associated protein TDP-43. PFN1 mutants also display decreased bound actin levels and can inhibit axon outgrowth. Furthermore, primary motor neurons expressing mutant PFN1 display smaller growth cones with a reduced F-/G-actin ratio. These observations further document that cytoskeletal pathway alterations contribute to ALS pathogenesis.

We sequenced the genomes of ten unrelated individuals and identified heterozygous stop codon-gain variants in protein-coding genes: we then sequenced their transcriptomes and assessed the expression levels of the stop codon-gain alleles. An ANOVA showed statistically significant differences between their expression levels (p = 4 × 10 − 16 ). This difference was almost entirely accounted for by whether the stop codon-gain variant had a second, non-protein-truncating function in or near an alternate transcript: stop codon-gains without alternate functions were generally not found in the cDNA (p = 3 × 10 − 5 ). Additionally, stop codon-gain variants in two intronless genes were not expressed, an unexpected outcome given previous studies. In this study, stop codon-gain variants were either well expressed in all individuals or were never expressed. Our finding that stop codon-gain variants were generally expressed only when they had an alternate function suggests that most naturally occurring stop codon-gain variants in protein-coding genes are either not transcribed or have their transcripts destroyed.

Although more than 2,400 genes have been shown to contain variants that cause Mendelian disease, there are still several thousand such diseases yet to be molecularly defined. The ability of new whole-genome sequencing technologies to rapidly indentify most of the genetic variants in any given genome opens an exciting opportunity to identify these disease genes. Here we sequenced the whole genome of a single patient with the dominant Mendelian disease, metachondromatosis (OMIM 156250), and used partial linkage data from her small family to focus our search for the responsible variant. In the proband, we identified an 11 bp deletion in exon four of PTPN11, which alters frame, results in premature translation termination, and co-segregates with the phenotype. In a second metachondromatosis family, we confirmed our result by identifying a nonsense mutation in exon 4 of PTPN11 that also co-segregates with the phenotype. Sequencing PTPN11 exon 4 in 469 controls showed no such protein truncating variants, supporting the pathogenicity of these two mutations. This combination of a new technology and a classical genetic approach provides a powerful strategy to discover the genes responsible for unexplained Mendelian disorders., Author Summary Metachondromatosis (MC) is an autosomal dominant condition characterized by exostoses (osteochondromas), commonly of the hands and feet, and enchondromas of long bone metaphyses and iliac crests. MC exostoses may regress or even resolve over time, and short stature is not characteristic of MC. Here, we sequenced the whole genome of a single patient with MC and used partial linkage data from her small family to focus our search for the responsible variant. In the proband, we identified an 11 bp deletion in exon four of PTPN11, which results in premature translation termination and co-segregates with the phenotype. In a second metachondromatosis family, we identified a nonsense mutation in exon 4 of PTPN11 that also co-segregates with the phenotype. Germline gain-of-function missense mutations in PTPN11 cause an overlapping but distinct group of dominant disorders with involvement of the face, heart, skeleton, skin, and brain, including Noonan syndrome (OMIM 163950), Noonan-like disorder with multiple giant cell lesion syndrome (OMIM 163955), and LEOPARD syndrome (OMIM 151100). Nonsense mutations in PTPN11 have not been described in humans and the loss-of-function PTPN11 mutations we report here are the first to be described in human disease.

Background There is considerable interest in the development of methods to efficiently identify all coding variants present in large sample sets of humans. There are three approaches possible: whole-genome sequencing, whole-exome sequencing using exon capture methods, and RNA-Seq. While whole-genome sequencing is the most complete, it remains sufficiently expensive that cost effective alternatives are important. Results Here we provide a systematic exploration of how well RNA-Seq can identify human coding variants by comparing variants identified through high coverage whole-genome sequencing to those identified by high coverage RNA-Seq in the same individual. This comparison allowed us to directly evaluate the sensitivity and specificity of RNA-Seq in identifying coding variants, and to evaluate how key parameters such as the degree of coverage and the expression levels of genes interact to influence performance. We find that although only 40% of exonic variants identified by whole genome sequencing were captured using RNA-Seq; this number rose to 81% when concentrating on genes known to be well-expressed in the source tissue. We also find that a high false positive rate can be problematic when working with RNA-Seq data, especially at higher levels of coverage. Conclusions We conclude that as long as a tissue relevant to the trait under study is available and suitable quality control screens are implemented, RNA-Seq is a fast and inexpensive alternative approach for finding coding variants in genes with sufficiently high expression levels.

One surprising feature of the recently completed waves of genome-wide association studies is the limited impact of common genetic variation in individually detectable polymorphisms on many human traits. This has been particularly pronounced for studies on psychiatric conditions, which have failed to produce clear, replicable associations for common variants. One popular explanation for these negative findings is that many of these traits may be genetically heterogeneous, leading to the idea that relevant endophenotypes may be more genetically tractable. Aspects of cognition may be the most important endophenotypes for psychiatric conditions such as schizophrenia, leading many researchers to pursue large-scale studies on the genetic contributors of cognitive performance in the normal population as a surrogate for aspects of liability to disease. Here, we perform a genome-wide association study with two tests of executive function, Digit Symbol and Stroop Color-Word, in 1086 healthy volunteers and with an expanded cognitive battery in 514 of these volunteers. We show that, consistent with published studies of the psychiatric conditions themselves, no single common variant has a large effect (explaining >4-8% of the population variation) on the performance of healthy individuals on standardized cognitive tests. Given that these are important endophenotypes, our work is consistent with the idea that identifying rare genetic causes of psychiatric conditions may be more important for future research than identifying genetically homogenous endophenotypes.

To extend the understanding of host genetic determinants of HIV-1 control, we performed a genome-wide association study in a cohort of 2,554 infected Caucasian subjects. The study was powered to detect common genetic variants explaining down to 1.3% of the variability in viral load at set point. We provide overwhelming confirmation of three associations previously reported in a genome-wide study and show further independent effects of both common and rare variants in the Major Histocompatibility Complex region (MHC). We also examined the polymorphisms reported in previous candidate gene studies and fail to support a role for any variant outside of the MHC or the chemokine receptor cluster on chromosome 3. In addition, we evaluated functional variants, copy-number polymorphisms, epistatic interactions, and biological pathways. This study thus represents a comprehensive assessment of common human genetic variation in HIV-1 control in Caucasians., Author Summary The ability to spontaneously control HIV-1 upon infection is highly variable between individuals. To evaluate the contribution of variation in human genes to differences in plasma viral load and in disease progression rates, we performed a genome-wide association study in >2,500 HIV–infected individuals. This study achieved two goals: it completed the analysis of common variation influencing viral control, and it re-assessed the majority of previously reported genetic associations. We show that genetic variants located near the HLA-B and HLA-C genes are the strongest determinants of viral control, and that other independent associations exist in the same region of chromosome 6, the Major Histocompatibility Complex, known to contain a large number of genes involved in immune defense. We could not replicate most of the previously published associations with HIV candidate genes in this large, well-characterized cohort. Overall, common human genetic variation, together with demographic variables, explains up to 22% of the variability in viral load in the Caucasian population.

Background HIV-1 specific T cell responses induced by the MRKAd5 HIV-1 gag/pol/nef vaccine are highly variable, and human genetic variation is likely to play a role in the inter-individual differences. Contemporary genomic analyses make it possible to comprehensively assess associations of common genetic variants with phenotypes. We here combine whole-genome genotyping and HLA Class I typing results to provide a global survey of the host mechanisms associated with variability in T cell responses to the vaccine.

A typical paradigm in the investigation of complex human disease is to assess the effects of cis-regulatory polymorphisms implicated in association studies on transcription in cellular expression systems. Evidence from in vitro transfection studies is often assumed to be sufficient evidence for the in vivo functional importance of a polymorphism in the context of human disease, even though many confounding effects (e.g. temporal regulation, tissue specificity, genetic background) are not considered. In this study, we evaluate this assumption directly by examining the translation of in vitro results on allele-specific expression to an in vivo system using four genes that have been well documented through reporter assays to have promoter polymorphisms affecting transcription level: monoamine oxidase A (MAOA), neuropeptide Y (NPY), endothelial nitric oxide synthase (NOS3), and prodynorphin (PDYN). In our study, MAOA was found to have large allelic imbalances, which indicates that there is in vivo variation in the expression of this gene. However, the imbalances observed were not correlated with genotype at the putatively functional polymorphism. PDYN, NOS3 and NPY did not have large allelic imbalances. Overall, there was no statistically significant effect of these polymorphisms on expression level as measured by imbalance ratios in any of these genes. These results suggest that the functional effects of a polymorphism on gene expression may be more complicated and context dependent than is often assumed and also imply that the use of cell-based expression studies to support the role of such polymorphisms in disease etiology should be treated with caution.

Broad-scale differences in crossover rate across the genome have been characterized in most genomes studied. Fine-scale differences, however, have only been examined in a few taxa, such as Arabidopsis, yeast, humans, and mice. No prior studies have directly looked for fine-scale recombination rate heterogeneity in Drosophila. We produced 370 Drosophila pseudoobscura containing a crossover event within the 2-megabase (MB) region between the genes yellow and white. We then examined 19 intervals within this region and determined where the crossovers occurred. We found that recombination events occur nonrandomly on a small scale and that mild “hotspots“ of a few kilobases exist in Drosophila. Among the regions studied, recombination rates varied from 1.4 to 52 cM/MB. We also observed a trend toward high codon bias in regions of high recombination. Finally, we identified a significantly positive correlation between recombination rate and simple repeats, as well as the motif CACAC. These sequence features may contribute to broad-scale variation in crossover rate and, thus, shed light on features associated with crossover rate heterogeneity at a genome-wide scale.

The contagious aspect of yawning is a well-known phenomenon that exhibits variation in the human population. Despite the observed variation, few studies have addressed its intra-individual reliability or the factors modulating differences in the susceptibility of healthy volunteers. Due to its obvious biological basis and impairment in diseases like autism and schizophrenia, a better understanding of this trait could lead to novel insights into these conditions and the general biological functioning of humans. We administered 328 participants a 3-minute yawning video stimulus, a cognitive battery, and a comprehensive questionnaire that included measures of empathy, emotional contagion, circadian energy rhythms, and sleepiness. Individual contagious yawning measurements were found to be highly stable across testing sessions, both in a lab setting and if administered remotely online, confirming that certain healthy individuals are less susceptible to contagious yawns than are others. Additionally, most individuals who failed to contagiously yawn in our study were not simply suppressing their reaction, as they reported not even feeling like yawning in response to the stimulus. In contrast to previous studies indicating that empathy, time of day, or intelligence may influence contagious yawning susceptibility, we found no influence of these variables once accounting for the age of the participant. Participants were less likely to show contagious yawning as their age increased, even when restricting to ages of less than 40 years. However, age was only able to explain 8% of the variability in the contagious yawn response. The vast majority of the variability in this extremely stable trait remained unexplained, suggesting that studies of its inheritance are warranted.

Correction to: European Journal of Human Genetics advance online publication, 3 February 2010; doi:10.1038/ejhg.2010.2

We present the analysis of twenty human genomes to evaluate the prospects for identifying rare functional variants that contribute to a phenotype of interest. We sequenced at high coverage ten “case” genomes from individuals with severe hemophilia A and ten “control” genomes. We summarize the number of genetic variants emerging from a study of this magnitude, and provide a proof of concept for the identification of rare and highly-penetrant functional variants by confirming that the cause of hemophilia A is easily recognizable in this data set. We also show that the number of novel single nucleotide variants (SNVs) discovered per genome seems to stabilize at about 144,000 new variants per genome, after the first 15 individuals have been sequenced. Finally, we find that, on average, each genome carries 165 homozygous protein-truncating or stop loss variants in genes representing a diverse set of pathways., Author Summary We report here the nearly complete genomic sequence of 20 different individuals, determined using “next-generation” sequencing technologies. We use these data to characterize the type of genetic variation carried by humans in a sample of this size, which is to our knowledge the largest set of unrelated genomic sequences that have been reported. We summarize different categories of variation in each genome, and in total across all 20 of the genomes, finding a surprising number of variants predicted to reduce or remove the proteins encoded by many different genes. This work provides important fundamental information about the scope of human genetic variation, and suggests ways to further explore the relationship between these genetic variants and human disease.