Search

Recent work has revealed an important role for rare, incompletely penetrant inherited coding variants in neurodevelopmental disorders (NDDs). Additionally, we have previously shown that common variants contribute to risk for rare NDDs. Here, we investigate whether common variants exert their effects by modifying gene expression, using multi-cis-expression quantitative trait loci (cis-eQTL) prediction models. We first performed a transcriptome-wide association study for NDDs using 6,987 probands from the Deciphering Developmental Disorders (DDD) study and 9,720 controls, and found one gene,RAB2A, that passed multiple testing correction (p = 6.7×10−7). We then investigated whethercis-eQTLs modify the penetrance of putatively damaging, rare coding variants inherited by NDD probands from their unaffected parents in a set of 1,700 trios. We found no evidence that unaffected parents transmitting putatively damaging coding variants had higher genetically-predicted expression of the variant-harboring gene than their child. In probands carrying putatively damaging variants in constrained genes, the genetically-predicted expression of these genes in blood was lower than in controls (p = 2.7×10−3). However, results for proband-control comparisons were inconsistent across different sets of genes, variant filters and tissues. We find limited evidence that commoncis-eQTLs modify penetrance of rare coding variants in a large cohort of NDD probands.

BackgroundPediatric disorders include a range of highly genetically heterogeneous conditions that are amenable to genome-wide diagnostic approaches. Finding a molecular diagnosis is challenging but can have profound lifelong benefits.MethodsThe Deciphering Developmental Disorders (DDD) study recruited >33,500 individuals from families with severe, likely monogenic developmental disorders from 24 regional genetics services around the UK and Ireland. We collected detailed standardised phenotype data and performed whole-exome sequencing and microarray analysis to investigate novel genetic causes. We developed an augmented variant analysis and re-analysis pipeline to maximise sensitivity and specificity, and communicated candidate variants to clinical teams for validation and diagnostic interpretation. We performed multiple regression analyses to evaluate factors affecting the probability of being diagnosed.ResultsWe reported approximately one candidate variant per parent-offspring trio and 2.5 variants per singleton proband, including both sequence and structural variants. Using clinical and computational approaches to variant classification, we have achieved a diagnosis in at least 34% (4507 probands), of whom 67% have a pathogenicde novomutation. Being recruited as a parent-offspring trio had the largest impact on the chance of being diagnosed (OR=4.70). Probands who were extremely premature (OR=0.39), hadin uteroexposure to antiepileptic medications (OR=0.44), or whose mothers had diabetes (OR=0.52) were less likely to be diagnosed, as were those of African ancestry (OR=0.51).ConclusionsOptimising diagnosis and discovery in highly penetrant genomic disease depends upon ongoing and novel scientific analyses, ethical recruitment and feedback policies, and collaborative clinical-research partnerships.

Protein-coding de novo mutations (DNMs) are significant risk factors in many neurodevelopmental disorders, whereas schizophrenia (SCZ) risk associated with DNMs has thus far been shown to be modest. We analyzed DNMs from 1,695 SCZ-affected trios and 1,077 published SCZ-affected trios to better understand the contribution to SCZ risk. Among 2,772 SCZ probands, exome-wide DNM burden remained modest. Gene set analyses revealed that SCZ DNMs were significantly concentrated in genes that were highly expressed in the brain, that were under strong evolutionary constraint and/or overlapped with genes identified in other neurodevelopmental disorders. No single gene surpassed exome-wide significance; however, 16 genes were recurrently hit by protein-truncating DNMs, corresponding to a 3.15-fold higher rate than the mutation model expectation (permuted 95% confidence interval: 1-10 genes; permuted P = 3 × 10-5). Overall, DNMs explain a small fraction of SCZ risk, and larger samples are needed to identify individual risk genes, as coding variation across many genes confers risk for SCZ in the population.

The impact of genetic variation on overall disease burden has not been comprehensively evaluated. We introduce an approach to estimate the effect of genetic risk factors on disability-adjusted life years (DALYs; 'lost healthy life years'). We use genetic information from 735,748 individuals and consider 80 diseases. Rare variants had the highest effect on DALYs at the individual level. Among common variants, rs3798220 (LPA) had the strongest individual-level effect, with 1.18 DALYs from carrying 1 versus 0 copies. Being in the top 10% versus the bottom 90% of a polygenic score for multisite chronic pain had an effect of 3.63 DALYs. Some common variants had a population-level effect comparable to modifiable risk factors such as high sodium intake and low physical activity. Attributable DALYs vary between males and females for some genetic exposures. Genetic risk factors can explain a sizable number of healthy life years lost both at the individual and population level.

Genome-wide sequencing of human populations has revealed substantial variation among genes in the intensity of purifying selection acting on damaging genetic variants1. Although genes under the strongest selective constraint are highly enriched for associations with Mendelian disorders, most of these genes are not associated with disease and therefore the nature of the selection acting on them is not known2. Here we show that genetic variants that damage these genes are associated with markedly reduced reproductive success, primarily owing to increased childlessness, with a stronger effect in males than in females. We present evidence that increased childlessness is probably mediated by genetically associated cognitive and behavioural traits, which may mean that male carriers are less likely to find reproductive partners. This reduction in reproductive success may account for 20% of purifying selection against heterozygous variants that ablate protein-coding genes. Although this genetic association may only account for a very minor fraction of the overall likelihood of being childless (less than 1%), especially when compared to more influential sociodemographic factors, it may influence how genes evolve over time.

SUMMARYRare deletions and duplications of genomic segments, collectively known as rare copy number variants (rCNVs), contribute to a broad spectrum of human diseases. To date, most disease-association studies of rCNVs have focused on recognized genomic disorders or on the impact of haploinsufficiency caused by deletions. By comparison, our understanding of duplications in disease remains rudimentary as very few individual genes are known to be triplosensitive (i.e., duplication intolerant). In this study, we meta-analyzed rCNVs from 753,994 individuals across 30 primarily neurological disease phenotypes to create a genome-wide catalog of rCNV association statistics across disorders. We discovered 114 rCNV-disease associations at 52 distinct loci surpassing genome-wide significance (P=3.72×10−6), 42% of which involve duplications. Using Bayesian fine-mapping methods, we further prioritized 38 novel triplosensitive disease genes (e.g., GMEB2 in brain abnormalities), including three known haploinsufficient genes that we now reveal as bidirectionally dosage sensitive (e.g., ANKRD11 in growth abnormalities). By integrating our results with prior literature, we found that disease-associated rCNV segments were enriched for genes constrained against damaging coding variation and identified likely dominant driver genes for about one-third (32%) of rCNV segments based on de novo mutations from exome sequencing studies of developmental disorders. However, while the presence of constrained driver genes was a common feature of many pathogenic large rCNVs across disorders, most of the rCNVs showing genome-wide significant association were incompletely penetrant (mean odds ratio=11.6) and we also identified two examples of noncoding disease-associated rCNVs (e.g., intronic CADM2 deletions in behavioral disorders). Finally, we developed a statistical model to predict dosage sensitivity for all genes, which defined 3,006 haploinsufficient and 295 triplosensitive genes where the effect sizes of rCNVs were comparable to deletions of genes constrained against truncating mutations. These dosage sensitivity scores classified disease genes across molecular mechanisms, prioritized pathogenic de novo rCNVs in children with autism, and revealed features that distinguished haploinsufficient and triplosensitive genes, such as insulation from other genes and local cis-regulatory complexity. Collectively, the cross-disorder rCNV maps and metrics derived in this study provide the most comprehensive assessment of dosage sensitive genomic segments and genes in disease to date and set the foundation for future studies of dosage sensitivity throughout the human genome.

Over 130 X-linked genes have been robustly associated with developmental disorders, and X-linked causes have been hypothesised to underlie the higher developmental disorder rates in males. Here, we evaluate the burden of X-linked coding variation in 11,044 developmental disorder patients, and find a similar rate of X-linked causes in males and females (6.0% and 6.9%, respectively), indicating that such variants do not account for the 1.4-fold male bias. We develop an improved strategy to detect X-linked developmental disorders and identify 23 significant genes, all of which were previously known, consistent with our inference that the vast majority of the X-linked burden is in known developmental disorder-associated genes. Importantly, we estimate that, in male probands, only 13% of inherited rare missense variants in known developmental disorder-associated genes are likely to be pathogenic. Our results demonstrate that statistical analysis of large datasets can refine our understanding of modes of inheritance for individual X-linked disorders., Developmental disorders (DDs) are more prevalent in males, thought to be due to X-linked genetic variation. Here, the authors investigate the burden of X-linked coding variants in 11,044 DD patients, showing that this contributes to ~6% of both male and female cases and therefore does not solely explain male bias in DDs.

Rare copy-number variants (rCNVs) include deletions and duplications that occur infrequently in the global human population and can confer substantial risk for disease. In this study, we aimed to quantify the properties of haploinsufficiency (i.e., deletion intolerance) and triplosensitivity (i.e., duplication intolerance) throughout the human genome. We harmonized and meta-analyzed rCNVs from nearly one million individuals to construct a genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163 dosage sensitive segments associated with at least one disorder. These segments were typically gene dense and often harbored dominant dosage sensitive driver genes, which we were able to prioritize using statistical fine-mapping. Finally, we designed an ensemble machine-learning model to predict probabilities of dosage sensitivity (pHaplopTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559 triplosensitive genes, including 648 that were uniquely triplosensitive. This dosage sensitivity resource will provide broad utility for human disease research and clinical genetics.

SummaryGenome-wide sequencing of human populations has revealed substantial variation among genes in the intensity of purifying selection acting on damaging genetic variants1. While genes under the strongest selective constraint are highly enriched for associations with Mendelian disorders, most of these genes are not associated with disease and therefore the nature of the selection acting on them is not known2. Here we show that genetic variants that damage these genes are associated with markedly reduced reproductive success, primarily due to increased childlessness, with a stronger effect in males than in females. We present evidence that increased childlessness is likely mediated by genetically associated cognitive and behavioural traits, which may mean male carriers are less likely to find reproductive partners. This reduction in reproductive success may account for 20% of purifying selection against heterozygous variants that ablate protein-coding genes. While this genetic association could only account for a very minor fraction of the overall likelihood of being childless (less than 1%), especially when compared to more influential sociodemographic factors, it may influence how genes evolve over time.

Genome-wide sequencing of human populations has revealed substantial variation among genes in the intensity of purifying selection acting on damaging genetic variants

The dispensability of individual genes for viability has interested generations of geneticists. For some genes it is essential to maintain two functional chromosomal copies, while others may tolerate the loss of one or both copies. Exome sequence data from 60,706 individuals provide sufficient observations of rare protein truncating variants (PTVs) to make genome-wide estimates of selection against heterozygous loss of gene function. The cumulative frequency of rare deleterious PTVs is primarily determined by the balance between incoming mutations and purifying selection rather than genetic drift. This enables the estimation of the genome-wide distribution of selection coefficients for heterozygous PTVs and corresponding Bayesian estimates for individual genes. The strength of selection can discriminate the severity, age of onset, and mode of inheritance in Mendelian exome sequencing cases. We find that genes under the strongest selection are enriched in embryonic lethal mouse knockouts, putatively cell-essential genes, Mendelian disease genes, and regulators of transcription. Screening by essentiality, we find a large set of genes under strong selection that likely have critical function but have not yet been extensively annotated in published literature.

SummaryDe novo mutations (DNMs) in protein-coding genes are a well-established cause of developmental disorders (DD). However, known DD-associated genes only account for a minority of the observed excess of such DNMs. To identify novel DD-associated genes, we integrated healthcare and research exome sequences on 31,058 DD parent-offspring trios, and developed a simulation-based statistical test to identify gene-specific enrichments of DNMs. We identified 285 significantly DD-associated genes, including 28 not previously robustly associated with DDs. Despite detecting more DD-associated genes than in any previous study, much of the excess of DNMs of protein-coding genes remains unaccounted for. Modelling suggests that over 1,000 novel DD-associated genes await discovery, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of dominant DDs.

Mobile genetic Elements (MEs) are segments of DNA which can copy themselves and other transcribed sequences through the process of retrotransposition (RT). In humans several disorders have been attributed to RT, but the role of RT in severe developmental disorders (DD) has not yet been explored. Here we identify RT-derived events in 9738 exome sequenced trios with DD-affected probands. We ascertain 9 de novo MEs, 4 of which are likely causative of the patient’s symptoms (0.04%), as well as 2 de novo gene retroduplications. Beyond identifying likely diagnostic RT events, we estimate genome-wide germline ME mutation rate and selective constraint and demonstrate that coding RT events have signatures of purifying selection equivalent to those of truncating mutations. Overall, our analysis represents a comprehensive interrogation of the impact of retrotransposition on protein coding genes and a framework for future evolutionary and disease studies., Retrotransposition events have been linked to some human disorders. Here, Gardner et al. systematically search for mobile genetic elements (ME) in trio whole exome-sequencing datasets and ascertain 9 de novo MEs and further estimate genome-wide germline ME burden and constraint.

SummaryGenetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes critical for an organism’s function will be depleted for such variants in natural populations, while non-essential genes will tolerate their accumulation. However, predicted loss-of-function (pLoF) variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here, we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence pLoF variants in this cohort after filtering for sequencing and annotation artifacts. Using an improved human mutation rate model, we classify human protein-coding genes along a spectrum representing tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve gene discovery power for both common and rare diseases.

We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n=35,584 total samples, 11,986 with ASD). Using an enhanced Bayesian framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate ≤ 0.1. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained for severe neurodevelopmental delay, while 53 show higher frequencies in individuals ascertained for ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most of the risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In human cortex single-cell gene expression data, expression of risk genes is enriched in both excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory/inhibitory imbalance underlying ASD.

Copy number variation (CNV) affecting protein-coding genes contributes substantially to human diversity and disease. Here we characterized the rates and properties of rare genic CNVs (

Purpose: Cancer survivors express anxiety that chemotherapy exposure may lead to transmissible genetic damage in posttreatment children. Preclinical models suggest that chemotherapy exposure may result in considerable genomic alterations in postexposure progeny. Epidemiologic studies have not demonstrated a significant increase in congenital abnormalities in posttreatment children of cancer survivors, but the inherited genome-wide effect of chemotherapy exposure in humans is unknown. Experimental Design: Two testicular cancer survivors cured with chemotherapy who had children pre- and postexposure without sperm banking were identified. Familial germline whole genome sequencing (WGS) was performed for these families, and analytic methods were utilized to identify de novo alterations, including mutations, recombinations, and structural rearrangements in the pre- and postexposure offspring. Results: No increase in de novo germline mutations in postexposure children compared with their preexposure siblings was found. Furthermore, there were no increased short insertion/deletions, recombination frequency, or structural rearrangements in these postexposure children. Conclusions: In two families of male cancer survivors, there was no transmissible genomic impact of significant mutagenic exposure in postexposure children. This study may provide possible reassuring evidence for patients undergoing chemotherapy who are unable to have pretreatment sperm cryopreservation. Expanded cohorts that utilize WGS to identify environmental exposure effects on the inherited genome may inform the generalizability of these results. Clin Cancer Res; 22(9); 2183–9. ©2015 AACR.

Almost all genetic risk factors for autism spectrum disorders (ASDs) can be found in the general population, but the effects of this risk are unclear in people not ascertained for neuropsychiatric symptoms. Using several large ASD consortium and population-based resources (total n > 38,000), we find genome-wide genetic links between ASDs and typical variation in social behavior and adaptive functioning. This finding is evidenced through both LD score correlation and de novo variant analysis, indicating that multiple types of genetic risk for ASDs influence a continuum of behavioral and developmental traits, the severe tail of which can result in diagnosis with an ASD or other neuropsychiatric disorder. A continuum model should inform the design and interpretation of studies of neuropsychiatric disease biology.

Approximately 2% of de novo single nucleotide variants (SNVs) appear as part of clustered mutations that create multinucleotide variants (MNVs). MNVs are an important source of genomic variability as they are more likely to alter an encoded protein than a SNV, which has important implications in disease as well as evolution. Previous studies of MNVs have focused on their mutational origins and have not systematically evaluated their functional impact and contribution to disease. We identified 69,940 MNVs and 106 de novo MNVs in 6,688 exome sequenced parent-offspring trios from the Deciphering Developmental Disorders Study comprising families with severe developmental disorders. We replicated the previously described MNV mutational signatures associated with DNA polymerase zeta, an error-prone translesion polymerase, and the APOBEC family of DNA deaminases. We found that most MNVs within a single codon create a missense change that could not have been created by a SNV. MNV-induced missense changes were, on average, more physico-chemically divergent, more depleted in highly constrained genes (pLI>=0.9) and were under stronger purifying selection compared to SNV-induced missense changes. We found that de novo MNVs were significantly enriched in genes previously associated with developmental disorders in affected children. This demonstrates that MNVs can be more damaging than SNVs even when both induce missense changes and are an important variant type to consider in relation to human disease.

Background Studies of de novo variation, which is identified by exome sequencing parent-offspring families, have successfully identified over 100 genes associated with neurodevelopmental disorders, most notably intellectual disability / developmental delay (ID/DD) or autism spectrum disorders (ASDs). Mutations in the genes first discovered in de novo studies of ASD were specifically enriched in those ASD individuals with comorbid ID/DD, and these genes were commonly assumed to affect both traits. As such, some criticized these studies for failing to separate ID/DD genes from those genes that confer risk to ASD without ID/DD. Winner's curse suggests the effect size for these genes would be greatest in ASD (i.e., the trait it was first discovered in). Through a meta-analysis of de novo variants from 3982 probands ascertained for ASD (892 with comorbid ID/DD) and 5264 probands ascertained for ID/DD (503 with comorbid ASD) we show that, in fact, the reverse is true: previously associated ASD genes are more often mutated in individuals with ID/DD who do not have ASD. Methods We meta-analyzed of 14,048 previously published de novo variants from 9,246 families (father, mother, affected proband). We leveraged detailed phenotyping on 85.9% of the probands to increase power to both identify associated genes and calculate the degree of risk each gene confers for different disorders. Results To identify disease associated genes, we compared the number of observed de novo missense and protein-truncating variants (PTVs) to the number expected from a null mutational model across three sets: ASD+ID/DD, ASD only, and ID/DD only. This yielded a total of 98 significant genes, 8 of which are novel. The contribution of evidence towards each gene's association is overwhelmingly provided by ID/DD compared to ASD (4.9-fold excess; P Discussion Studying de novo variation has been extremely successful for identifying disease-associated genes, with ASD being arguably the flagship trait for this study design. However, the presence of comorbid ID/DD in ASD probands with associated de novo variants begged the question as to how much risk each gene confers to the different disorders. Leveraging detailed phenotyping in a meta-analysis of 3982 ASD and 5264 ID/DD individuals, we identified 98 disease-associated genes, of which only 3 are more strongly associated with ASD than ID/DD. Furthermore, genes previously associated with ASD are more often observed in individuals with ID/DD who themselves do not have ASD. Therefore, researchers should be cautious with using published lists of ASD genes for follow-up functional studies - especially if the goal is to learn about the genetic etiology of core ASD features in the absence of cognitive impairment - namely social and behavioral impairments.

Congenital heart disease (CHD) patients have an increased prevalence of extracardiac congenital anomalies (CAs) and risk of neurodevelopmental disabilities (NDDs). Exome sequencing of 1213 CHD parent-offspring trios identified an excess of protein-damaging de novo mutations, especially in genes highly expressed in the developing heart and brain. These mutations accounted for 20% of patients with CHD, NDD, and CA but only 2% of patients with isolated CHD. Mutations altered genes involved in morphogenesis, chromatin modification, and transcriptional regulation, including multiple mutations in RBFOX2, a regulator of mRNA splicing. Genes mutated in other cohorts examined for NDD were enriched in CHD cases, particularly those with coexisting NDD. These findings reveal shared genetic contributions to CHD, NDD, and CA and provide opportunities for improved prognostic assessment and early therapeutic intervention in CHD patients.

Analysis of de novo CNVs (dnCNVs) from the full Simons Simplex Collection (SSC) (N = 2,591 families) replicates prior findings of strong association with autism spectrum disorders (ASDs) and confirms six risk loci (1q21.1, 3q29, 7q11.23, 16p11.2, 15q11.2-13, and 22q11.2). The addition of published CNV data from the Autism Genome Project (AGP) and exome sequencing data from the SSC and the Autism Sequencing Consortium (ASC) shows that genes within small de novo deletions, but not within large dnCNVs, significantly overlap the high-effect risk genes identified by sequencing. Alternatively, large dnCNVs are found likely to contain multiple modest-effect risk genes. Overall, we find strong evidence that de novo mutations are associated with ASD apart from the risk for intellectual disability. Extending the transmission and de novo association test (TADA) to include small de novo deletions reveals 71 ASD risk loci, including 6 CNV regions (noted above) and 65 risk genes (FDR ≤ 0.1). Through analysis of de novo mutations in autism spectrum disorder (ASD), Sanders et al. find that small deletions, but not large deletions/duplications, contain one critical gene. Combining CNV and sequencing data, they identify 6 loci and 65 genes associated with ASD.

Approximately one third of all mammalian genes are essential for life. Phenotypes resulting from mouse knockouts of these genes have provided tremendous insight into gene function and congenital disorders. As part of the International Mouse Phenotyping Consortium effort to generate and phenotypically characterize 5000 knockout mouse lines, we have identified 410 lethal genes during the production of the first 1751 unique gene knockouts. Using a standardised phenotyping platform that incorporates high-resolution 3D imaging, we identified novel phenotypes at multiple time points for previously uncharacterized genes and additional phenotypes for genes with previously reported mutant phenotypes. Unexpectedly, our analysis reveals that incomplete penetrance and variable expressivity are common even on a defined genetic background. In addition, we show that human disease genes are enriched for essential genes identified in our screen, thus providing a novel dataset that facilitates prioritization and validation of mutations identified in clinical sequencing efforts.

A major goal of biomedicine is to understand the function of every gene in the human genome.1 Loss-of-function (LoF) mutations can disrupt both copies of a given gene in humans and phenotypic analysis of such ‘human knockouts’ can provide insight into gene function. Consanguineous unions are more likely to result in offspring who carry LoF mutations in a homozygous state. In Pakistan, consanguinity rates are notably high.2 Here, we sequenced the protein-coding regions of 10,503 adult participants in the Pakistan Risk of Myocardial Infarction Study (PROMIS) designed to understand the determinants of cardiometabolic diseases in South Asians.3 We identified individuals carrying predicted LoF (pLoF) mutations in the homozygous state, and performed phenotypic analysis involving >200 biochemical and disease traits. We enumerated 49,138 rare (

Arhinia, or absence of the nose, is a rare malformation of unknown etiology that is often accompanied by ocular and reproductive defects. Sequencing of 40 people with arhinia revealed that 84% of probands harbor a missense mutation localized to a constrained region of SMCHD1 encompassing the ATPase domain. SMCHD1 mutations cause facioscapulohumeral muscular dystrophy type 2 (FSHD2) via a trans-acting loss-of-function epigenetic mechanism. We discovered shared mutations and comparable DNA hypomethylation patterning between these distinct disorders. CRISPR/Cas9-mediated alteration of smchd1 in zebrafish yielded arhinia-relevant phenotypes. Transcriptome and protein analyses in arhinia probands and controls showed no differences in SMCHD1 mRNA or protein abundance but revealed regulatory changes in genes and pathways associated with craniofacial patterning. Mutations in SMCHD1 thus contribute to distinct phenotypic spectra, from craniofacial malformation and reproductive disorders to muscular dystrophy, which we speculate to be consistent with oligogenic mechanisms resulting in pleiotropic outcomes.

Autism spectrum disorders (ASDs) are a highly heterogeneous group of conditions--phenotypically and genetically--although the link between phenotypic variation and differences in genetic architecture is unclear. This study aimed to determine whether differences in cognitive impairment and symptom severity reflect variation in the degree to which ASD cases reflect de novo or familial influences. Using data from more than 2,000 simplex cases of ASD, we examined the relationship between intelligence quotient (IQ), behavior and language assessments, and rate of de novo loss of function (LOF) mutations and family history of broadly defined psychiatric disease (depressive disorders, bipolar disorder, and schizophrenia; history of psychiatric hospitalization). Proband IQ was negatively associated with de novo LOF rate (P = 0.03) and positively associated with family history of psychiatric disease (P = 0.003). Female cases had a higher frequency of sporadic genetic events across the severity distribution (P = 0.01). High rates of LOF mutation and low frequencies of family history of psychiatric illness were seen in individuals who were unable to complete a traditional IQ test, a group with the greatest degree of language and behavioral impairment. These analyses provide strong evidence that familial risk for neuropsychiatric disease becomes more relevant to ASD etiology as cases become higher functioning. The findings of this study reinforce that there are many routes to the diagnostic category of autism and could lead to genetic studies with more specific insights into individual cases.

Spontaneously arising (de novo) mutations have an important role in medical genetics. For diseases with extensive locus heterogeneity, such as autism spectrum disorders (ASDs), the signal from de novo mutations is distributed across many genes, making it difficult to distinguish disease-relevant mutations from background variation. Here we provide a statistical framework for the analysis of excesses in de novo mutation per gene and gene set by calibrating a model of de novo mutation. We applied this framework to de novo mutations collected from 1,078 ASD family trios, and, whereas we affirmed a significant role for loss-of-function mutations, we found no excess of de novo loss-of-function mutations in cases with IQ above 100, suggesting that the role of de novo mutations in ASDs might reside in fundamental neurodevelopmental processes. We also used our model to identify ∼1,000 genes that are significantly lacking in functional coding variation in non-ASD samples and are enriched for de novo loss-of-function mutations identified in ASD cases.

The dispensability of individual genes for viability has interested generations of geneticists. For some genes it is essential to maintain two functional chromosomal copies, while other genes may tolerate the loss of one or both copies. Exome sequence data from 60,706 individuals provide sufficient observations of rare protein truncating variants (PTVs) to make genome-wide estimates of selection against heterozygous loss of gene function. The cumulative frequency of rare deleterious PTVs is primarily determined by the balance between incoming mutations and purifying selection rather than genetic drift. This enables the estimation of the genome-wide distribution of selection coefficients for heterozygous PTVs and corresponding Bayesian estimates for individual genes. The strength of selection can help discriminate the severity, age of onset, and mode of inheritance in Mendelian exome sequencing cases. We find that genes under the strongest selection are enriched in embryonic lethal mouse knockouts, putatively cell-essential genes inferred from human tumor cells, Mendelian disease genes, and regulators of transcription. Using an essentiality screen, we find a large set of genes under strong selection that are likely to have critical function but that have not yet been studied extensively.

Worldwide, hundreds of thousands of humans have had their genomes or exomes sequenced, and access to the resulting data sets can provide valuable information for variant interpretation and understanding gene function. Here, we present a lightweight, flexible browser framework to display large population datasets of genetic variation. We demonstrate its use for exome sequence data from 60,706 individuals in the Exome Aggregation Consortium (ExAC). The ExAC browser provides gene- and transcript-centric displays of variation, a critical view for clinical applications. Additionally, we provide a variant display, which includes population frequency and functional annotation data as well as short read support for the called variant. This browser is open-source, freely available, and has already been used extensively by clinical laboratories worldwide.

Recent research has uncovered an important role for de novo variation in neurodevelopmental disorders. Using aggregated data from 9,246 families with autism spectrum disorder, intellectual disability, or developmental delay, we found that ∼1/3 of de novo variants are independently present as standing variation in the Exome Aggregation Consortium's cohort of 60,706 adults, and these de novo variants do not contribute to neurodevelopmental risk. We further used a loss-of-function (LoF)-intolerance metric, pLI, to identify a subset of LoF-intolerant genes containing the observed signal of associated de novo protein-truncating variants (PTVs) in neurodevelopmental disorders. LoF-intolerant genes also carry a modest excess of inherited PTVs, although the strongest de novo-affected genes contribute little to this excess, thus suggesting that the excess of inherited risk resides in lower-penetrant genes. These findings illustrate the importance of population-based reference cohorts for the interpretation of candidate pathogenic variants, even for analyses of complex diseases and de novo variation.

Reactive oxygen species (ROS) such as hydrogen peroxide (H2O2) govern cellular homeostasis by inducing signaling. H2O2 modulates the activity of phosphatases and many other signaling molecules through oxidation of critical cysteine residues, which led to the notion that initiation of ROS signaling is broad and nonspecific, and thus fundamentally distinct from other signaling pathways. Here, we report that H2O2 signaling bears hallmarks of a regular signal transduction cascade. It is controlled by hierarchical signaling events resulting in a focused response as the results place the mitochondrial respiratory chain upstream of tyrosine-protein kinase Lyn, Lyn upstream of tyrosine-protein kinase SYK (Syk), and Syk upstream of numerous targets involved in signaling, transcription, translation, metabolism, and cell cycle regulation. The active mediators of H2O2 signaling colocalize as H2O2 induces mitochondria-associated Lyn and Syk phosphorylation, and a pool of Lyn and Syk reside in the mitochondrial intermembrane space. Finally, the same intermediaries control the signaling response in tissues and species responsive to H2O2 as the respiratory chain, Lyn, and Syk were similarly required for H2O2 signaling in mouse B cells, fibroblasts, and chicken DT40 B cells. Consistent with a broad role, the Syk pathway is coexpressed across tissues, is of early metazoan origin, and displays evidence of evolutionary constraint in the human. These results suggest that H2O2 signaling is under control of a signal transduction pathway that links the respiratory chain to the mitochondrial intermembrane space-localized, ubiquitous, and ancient Syk pathway in hematopoietic and nonhematopoietic cells.

International audience; Autism spectrum disorders (ASD) are believed to have genetic and environmental origins, yet in only a modest fraction of individuals can specific causes be identified. To identify further genetic risk factors, here we assess the role of de novo mutations in ASD by sequencing the exomes of ASD cases and their parents (n = 175 trios). Fewer than half of the cases (46.3%) carry a missense or nonsense de novo variant, and the overall rate of mutation is only modestly higher than the expected rate. In contrast, the proteins encoded by genes that harboured de novo missense or nonsense mutations showed a higher degree of connectivity among themselves and to previous ASD genes as indexed by protein-protein interaction screens. The small increase in the rate of de novo events, when taken together with the protein interaction results, are consistent with an important but limited role for de novo point mutations in ASD, similar to that documented for de novo copy number variants. Genetic models incorporating these data indicate that most of the observed de novo events are unconnected to ASD; those that do confer risk are distributed across many genes and are incompletely penetrant (that is, not necessarily sufficient for disease). Our results support polygenic models in which spontaneous coding mutations in any of a large number of genes increases risk by 5- to 20-fold. Despite the challenge posed by such models, results from de novo events and a large parallel case-control study provide strong evidence in favour of CHD8 and KATNAL2 as genuine autism risk factors.

Model organisms offer many advantages for the genetic analysis of complex traits. However, identification of specific genes is often hampered by a lack of recombination between the genomes of inbred progenitors. Recently, genome-wide association studies (GWAS) in humans have offered gene-level mapping resolution that is possible because of the large number of accumulated recombinations among unrelated human subjects. To obtain analogous improvements in mapping resolution in mice, we used a 34th generation advanced intercross line (AIL) derived from two inbred strains (SM/J and LG/J). We used simulations to show that familial relationships among subjects must be accounted for when analyzing these data; we then used a mixed model that included polygenic effects to address this problem in our own analysis. Using a combination of F2 and AIL mice derived from the same inbred progenitors, we identified genome-wide significant, subcentimorgan loci that were associated with methamphetamine sensitivity, (e.g., chromosome 18; LOD = 10.5) and non-drug-induced locomotor activity (e.g., chromosome 8; LOD = 18.9). The 2-LOD support interval for the former locus contains no known genes while the latter contains only one gene (Csmd1). This approach is broadly applicable in terms of phenotypes and model organisms and allows GWAS to be performed in multigenerational crosses between and among inbred strains where familial relatedness is often unavoidable.

As new proposals aim to sequence ever larger collection of humans, it is critical to have a quantitative framework to evaluate the statistical power of these projects. We developed a new algorithm, UnseenEst, and applied it to the exomes of 60,706 individuals to estimate the frequency distribution of all protein-coding variants, including rare variants that have not been observed yet in the current cohorts. Our results quantified the number of new variants that we expect to identify as sequencing cohorts reach hundreds of thousands of individuals. With 500K individuals, we find that we expect to capture 7.5% of all possible loss-of-function variants and 12% of all possible missense variants. We also estimate that 2,900 genes have loss-of-function frequency of, United States. National Institutes of Health (U54DK105566), United States. National Institutes of Health (R01GM104371)

Approximately one-third of all mammalian genes are essential for life. Phenotypes resulting from knockouts of these genes in mice have provided tremendous insight into gene function and congenital disorders. As part of the International Mouse Phenotyping Consortium effort to generate and phenotypically characterize 5,000 knockout mouse lines, here we identify 410 lethal genes during the production of the first 1,751 unique gene knockouts. Using a standardized phenotyping platform that incorporates high-resolution 3D imaging, we identify phenotypes at multiple time points for previously uncharacterized genes and additional phenotypes for genes with previously reported mutant phenotypes. Unexpectedly, our analysis reveals that incomplete penetrance and variable expressivity are common even on a defined genetic background. In addition, we show that human disease genes are enriched for essential genes, thus providing a dataset that facilitates the prioritization and validation of mutations identified in clinical sequencing efforts.

A major goal of biomedicine is to understand the function of every gene in the human genome. Null mutations can disrupt both copies of a given gene in humans and phenotypic analysis of such 'human knockouts' can provide insight into gene function. To date, comprehensive analysis of genes knocked out in humans has been limited by the fact that null mutations are infrequent in the general population and so, observing an individual homozygous null for a given gene is exceedingly rare. However, consanguineous unions are more likely to result in offspring who carry homozygous null mutations. In Pakistan, consanguinity rates are notably high. Here, we sequenced the protein-coding regions of 7,078 adult participants living in Pakistan and performed phenotypic analysis to identify homozygous null individuals and to understand consequences of complete gene disruption in humans. We enumerated 36,850 rare (

As new proposals aim to sequence ever larger collection of humans, it is critical to have a quantitative framework to evaluate the statistical power of these projects. We developed a new algorithm, UnseenEst, and applied it to the exomes of 60,706 individuals to estimate the frequency distribution of all protein-coding variants, including rare variants that have not been observed yet in the current cohorts. Our results quantified the number of new variants that we expect to identify as sequencing cohorts reach hundreds of thousands of individuals. With 500K individuals, we find that we expect to capture 7.5% of all possible loss-of-function variants and 12% of all possible missense variants. We also estimate that 2,900 genes have loss-of-function frequency of less than 0.00001 in healthy humans, consistent with very strong intolerance to gene inactivation.

Reactive oxygen species (ROS) such as hydrogen peroxide (H2O2) govern cellular homeostasis by inducing signaling. H2O2 modulates the activity of phosphatases and many other signaling molecules through oxidation of critical cysteine residues, which led to the notion that initiation of ROS signaling is broad and nonspecific, and thus fundamentally distinct from other signaling pathways. Here, we report that H2O2 signaling bears hallmarks of a regular signal transduction cascade. It is controlled by hierarchical signaling events resulting in a focused response as the results place the mitochondrial respiratory chain upstream of tyrosine-protein kinase Lyn, Lyn upstream of tyrosine-protein kinase SYK (Syk), and Syk upstream of numerous targets involved in signaling, transcription, translation, metabolism, and cell cycle regulation. The active mediators of H2O2 signaling colocalize as H2O2 induces mitochondria-associated Lyn and Syk phosphorylation, and a pool of Lyn and Syk reside in the mitochondrial intermembrane space. Finally, the same intermediaries control the signaling response in tissues and species responsive to H2O2 as the respiratory chain, Lyn, and Syk were similarly required for H2O2 signaling in mouse B cells, fibroblasts, and chicken DT40 B cells. Consistent with a broad role, the Syk pathway is coexpressed across tissues, is of early metazoan origin, and displays evidence of evolutionary constraint in the human. These results suggest that H2O2 signaling is under control of a signal transduction pathway that links the respiratory chain to the mitochondrial intermembrane space-localized, ubiquitous, and ancient Syk pathway in hematopoietic and nonhematopoietic cells.

Spontaneously arising (de novo) genetic variants are important in human disease, yet every individual carries many such variants, with a median of 1 de novo variant affecting the protein-coding portion of the genome. A recently described mutational model (Samocha et al., 2014) provides a powerful framework for the robust statistical evaluation of such coding variants, enabling the interpretation of de novo variation in human disease. Here we describe a new open-source software package, denovolyzeR, that implements this model and provides tools for the analysis of de novo coding sequence variants.

Nature 537, 508–514 (2016); doi:10.1038/nature19356 In this Article, the author Wolfgang Wurst was erroneously omitted from the author list. They are associated with the affiliations: HelmholtzZentrum Munich, Institute of Developmental Genetics, 85764 Munich-Neuherberg, Germany; Technical Universityof Munich, Chair of Developmental Genetics, 85764 Munich-Neuherberg, Germany; German Center for Neurodegenerative Diseases (DZNE) Site Munich, 81377 Munich, Germany; and Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany.

Prioritizing missense variants for further experimental investigation is a key challenge in current sequencing studies for exploring complex and Mendelian diseases. A large number of in silico tools have been employed for the task of pathogenicity prediction, including PolyPhen‐2, SIFT, FatHMM, MutationTaster‐2, MutationAssessor, Combined Annotation Dependent Depletion, LRT, phyloP, and GERP++, as well as optimized methods of combining tool scores, such as Condel and Logit. Due to the wealth of these methods, an important practical question to answer is which of these tools generalize best, that is, correctly predict the pathogenic character of new variants. We here demonstrate in a study of 10 tools on five datasets that such a comparative evaluation of these tools is hindered by two types of circularity: they arise due to (1) the same variants or (2) different variants from the same protein occurring both in the datasets used for training and for evaluation of these tools, which may lead to overly optimistic results. We show that comparative evaluations of predictors that do not address these types of circularity may erroneously conclude that circularity confounded tools are most accurate among all tools, and may even outperform optimized combinations of tools.

Genetic studies have revealed thousands of loci predisposing to hundreds of human diseases and traits, revealing important biological pathways and defining novel therapeutic hypotheses. However, the genes discovered to date typically explain less than half of the apparent heritability. Because efforts have largely focused on common genetic variants, one hypothesis is that much of the missing heritability is due to rare genetic variants. Studies of common variants are typically referred to as genomewide association studies, whereas studies of rare variants are often simply called sequencing studies. Because they are actually closely related, we use the terms common variant association study (CVAS) and rare variant association study (RVAS). In this paper, we outline the similarities and differences between RVAS and CVAS and describe a conceptual framework for the design of RVAS. We apply the framework to address key questions about the sample sizes needed to detect association, the relative merits of testing disruptive alleles vs. missense alleles, frequency thresholds for filtering alleles, the value of predictors of the functional impact of missense alleles, the potential utility of isolated populations, the value of gene-set analysis, and the utility of de novo mutations. The optimal design depends critically on the selection coefficient against deleterious alleles and thus varies across genes. The analysis shows that common variant and rare variant studies require similarly large sample collections. In particular, a well-powered RVAS should involve discovery sets with at least 25,000 cases, together with a substantial replication set.

Quantitative trait locus (QTL) mapping techniques are frequently used to identify genomic regions associated with variation in phenotypes of interest. However, the F(2) intercross and congenic strain populations usually employed have limited genetic resolution resulting in relatively large confidence intervals that greatly inhibit functional confirmation of statistical results. Here we use the increased resolution of the combined F(9) and F(10) generations (n = 1455) of the LG,SM advanced intercross to fine-map previously identified QTL associated with the lengths of the humerus, ulna, femur, and tibia. We detected 81 QTL affecting long-bone lengths. Of these, 49 were previously identified in the combined F(2)-F(3) population of this intercross, while 32 represent novel contributors to trait variance. Pleiotropy analysis suggests that most QTL affect three to four long bones or serially homologous limb segments. We also identified 72 epistatic interactions involving 38 QTL and 88 novel regions. This analysis shows that using later generations of an advanced intercross greatly facilitates fine-mapping of confidence intervals, resolving three F(2)-F(3) QTL into multiple linked loci and narrowing confidence intervals of other loci, as well as allowing identification of additional QTL. Further characterization of the biological bases of these QTL will help provide a better understanding of the genetics of small variations in long-bone length.

1543 Background: Cancer survivors who receive chemotherapy express concern that these exposures may induce germ cell mutations that lead to transmissible genetic damage in post-treatment children. ...