Search

Background: Early detection of breast cancer is crucial for optimal patient outcomes but cannot always be accomplished based on symptoms or screening mammography. Biomarker-based screening could aid early detection of breast cancer by improving sensitivity and specificity. Exai Bio has developed a novel liquid biopsy technology that detects and analyzes small non-coding RNAs that are cancer specific, termed orphan non-coding RNAs (oncRNAs). Previous work in patients with diagnosed breast cancer demonstrated that changes in oncRNAs in serum reflected treatment response and event-free survival. In this study, we developed an assay that measures oncRNAs in serum to detect breast cancer across the range of tumor stages and sizes. Methods: Previously, a library of ~260,000 oncRNAs from 32 different cancers was compiled based on smRNA sequences found in tumor tissues and largely absent in tumor-adjacent normal tissues from The Cancer Genome Atlas (TCGA). To refine this library for applications in serum, we sequenced smRNA in 31 control serum samples. These smRNA sequences were filtered from the larger library, reducing its size to 250,332 oncRNAs. The diagnostic performance of these oncRNAs was then assessed in an independent cohort of archived serum samples from 96 female patients with clinically diagnosed, untreated breast cancer and 95 age- and sex-matched individuals with no known history of cancer. We sequenced smRNAs at an average depth of 17.7 million 50-bp single-end reads per sample. Of the 250,332 oncRNAs in our library, 171,981 (68.7%) were detected in our independent study cohort. An ensemble of logistic regression models was trained with 5-fold cross-validation, using only those oncRNAs yielding an odds ratio >1 and observed in >6% of samples within each training set. Results: The cohort of 96 breast cancer patients and 95 matched controls had mean ages of 59.4 and 56.3 years, respectively. Area under the receiver operating characteristic curve (AUC) for detecting breast cancer was 0.94 (95% CI, 0.85–0.96). Sensitivities for detecting breast cancer at 95% specificity ranged from 0.75 to 0.87 among the four breast cancer stages, including a sensitivity of 0.81 for tumor stage I (Table 1); and from 0.67 to 0.87 among the four main TNM T categories (Table 2). Sensitivities at 95% specificity were relatively high for small tumors, at 0.75 (95% CI, 0.40–0.97) for T1b (>5mm to ≤10mm; n = 9) and 0.80 (0.68–0.94) for T1c (>10mm to ≤20mm; n = 37). Conclusions We have demonstrated the potential value of an oncRNA-based liquid biopsy assay by showing that oncRNAs can be used to detect breast cancer in serum samples with high sensitivity, and that detection requires fewer reads than are needed with other platforms. Moreover, we found that this oncRNA-based assay performed well in detecting early-stage breast cancer and small tumors. This suggests that an oncRNA-based liquid biopsy assay may be beneficial for early detection of breast cancer. Table 1. Model sensitivity by tumor stage. For the indicated numbers of cases (N), sensitivity and Pearson-Clopper 95% confidence intervals are reported for tumor detection by the oncRNA-based model at 95% specificity by tumor stage, as defined by the AJCC 7th Edition breast cancer staging system. Table 2. Model sensitivity by tumor size. For the indicated numbers of cases (N), sensitivity and Pearson-Clopper 95% confidence intervals are reported for tumor detection by the oncRNA-based model at 95% specificity by TNM T category, as defined by the AJCC 7th Edition breast cancer staging system. Citation Format: Taylor B. Cavazos, Jeffrey Wang, Oluwadamilare I. Afolabi, Alice Huang, Dung Ngoc Lam, Seda Kilinc, Jieyang Wang, Lisa Fish, Xuan Zhao, Andy Pohl, Helen Li, Kimberly H. Chau, Patrick A. Arensdorf, Fereydoun Hormozdiari, Hani Goodarzi, Babak Alipanahi. Orphan non-coding RNAs for early detection of breast cancer with liquid biopsy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-18.

Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is highly heritable. While COPD is clinically defined by applying thresholds to summary measures of lung function, a quantitative liability score has more power to identify new genetic signals. Here we train a deep convolutional neural network on noisy self-reported and ICD-based labels to predict COPD case/control status from high-dimensional raw spirograms and use the model predictions as a liability score. The machine-learning-based (ML-based) liability score accurately discriminates COPD cases and controls (AUROC = 0.82 ± 0.01) and COPD-related hospitalization (AUROC = 0.89 ± 0.01) without any domain-specific knowledge. Moreover, the ML-based liability score is associated with overall survival (Hazard ratio = 1.22 ± 0.01; P ≤ 2 × 10−16) and exacerbation events (R2 = 0.10 ± 0.01; P ≤ 4 × 10−101). A genome-wide association study on the ML-based liability score replicates existing COPD and lung function loci, but also identifies 67 new loci. Thirty-eight of these have supportive evidence in independent datasets, including a locus near LTBR. We demonstrate the biological plausibility of the novel variants through enrichment analyses, phenome-wide association studies, and generalizability of COPD prediction in multiple datasets. These results provide an example of the potential to improve genetic discovery of disease-relevant variants by training deep neural networks to predict noisy labels from high-dimensional raw data.

Background: Orphan non-coding RNAs (oncRNAs) are a novel category of small non-coding RNAs that are present in the tumor tissue and blood of people with cancer and largely absent in people without cancer. To examine the potential of using oncRNAs for early cancer detection via liquid biopsy, we assessed the oncRNA content of serum from people with and without non-small cell lung cancer (NSCLC) and developed a prediction model for NSCLC. Methods: A total of 540 serum samples were obtained from Indivumed (Hamburg, Germany) and MT Group (Los Angeles, CA) and divided into cohort A for training (150 NSCLC cases, 219 controls; female: 30.7%/36.1%; mean age: 67.9 ± 8.9/62.4 ± 9.2; ever-smoker: 95.3%/26.9%, respectively) and cohort B for internal validation (88 NSCLC cases, 83 controls; female: 40.9%/54.2%; mean age: 62.7 ± 9.2/54.1 ± 12.4; ever-smoker: 89.8%/6.0%, respectively). We used RNA isolated from 0.5 mL of serum to generate and sequence libraries at an average depth of 18.5 ± 6.5 million 50-bp single-end reads using next-generation sequencing. Previously, we created a large catalog of NSCLC oncRNAs found in 999 NSCLC tumor tissues and largely absent in 679 normal samples from The Cancer Genome Atlas (TCGA) smRNA-seq database. This catalog was distilled by removing smRNA species found in the serum of an independent cohort of 31 non-cancer donors to yield a final NSCLC oncRNA catalog of 81,004 distinct oncRNA species. This distilled catalog was the reference for identifying NSCLC oncRNAs in the present study. Using oncRNA data we generated by sequencing samples from cohort A, we trained a logistic regression model for predicting NSCLC presence. The model was then validated in cohort B. Results: From the 540 samples sequenced, we detected 64,379 oncRNAs from the distilled TCGA oncRNA catalog in at least one sample across both cohorts (A: 55,650, B: 47,539).Using 5-fold cross-validation, the AUC of the logistic regression model was 0.95 (95% CI: 0.93-0.97) for the training cohort, and was 0.98 (0.97-0.99) for the validation cohort. Sensitivities for detecting NSCLC at 95% specificity were 0.78 (0.69-0.86) for early stage (I/II) cancer and 0.75 (0.60-0.87) for late stage (III/IV) cancer in the training cohort, and 0.92 (0.83-0.98) and 1.0 (0.85-1.0), respectively, in the validation cohort. Conclusion: These results demonstrate the potential for accurate, sensitive, and early detection of NSCLC through sequencing the oncRNA content of a routine blood draw. The performance of the model trained on one cohort and internally validated in a separate cohort supports the generalizability of this approach in detecting NSCLC. Citation Format: Mehran Karimzadeh, Jeffrey Wang, Aiden Sababi, Oluwadamilare I. Afolabi, Dung Ngoc Lam, Alice Huang, Diana R. Corti, Kristle C. Garcia, Seda Kilinc, Xuan Zhao, Jieyang Wang, Taylor B. Cavazos, Patrick Arensdorf, Kimberly H. Chau, Helen Li, Hani Goodarzi, Lisa Fish, Fereydoun Hormozdiari, Babak Alipanahi. Blood-based early detection of non-small cell lung cancer using orphan noncoding RNAs. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5711.

Objective The aim of this study was to search for genes/variants that modify the effect of LRRK2 mutations in terms of penetrance and age-at-onset of Parkinson's disease. Methods We performed the first genome-wide association study of penetrance and age-at-onset of Parkinson's disease in LRRK2 mutation carriers (776 cases and 1,103 non-cases at their last evaluation). Cox proportional hazard models and linear mixed models were used to identify modifiers of penetrance and age-at-onset of LRRK2 mutations, respectively. We also investigated whether a polygenic risk score derived from a published genome-wide association study of Parkinson's disease was able to explain variability in penetrance and age-at-onset in LRRK2 mutation carriers. Results A variant located in the intronic region of CORO1C on chromosome 12 (rs77395454; P-value = 2.5E-08, beta = 1.27, SE = 0.23, risk allele: C) met genome-wide significance for the penetrance model. Co-immunoprecipitation analyses of LRRK2 and CORO1C supported an interaction between these two proteins. A region on chromosome 3, within a previously reported linkage peak for Parkinson's disease susceptibility, showed suggestive associations in both models (penetrance top variant: P-value = 1.1E-07; age-at-onset top variant: P-value = 9.3E-07). A polygenic risk score derived from publicly available Parkinson's disease summary statistics was a significant predictor of penetrance, but not of age-at-onset. Interpretation This study suggests that variants within or near CORO1C may modify the penetrance of LRRK2 mutations. In addition, common Parkinson's disease associated variants collectively increase the penetrance of LRRK2 mutations. This article is protected by copyright. All rights reserved.

Genome-wide association studies (GWAS) are among the workhorses of statistical genetics, having detected thousands of variants associated with complex traits and diseases. A typical GWAS examines the association between genotypes and the phenotype of interest while adjusting for a set of covariates. While covariates potentially have non-linear effects on the phenotype in many real world settings, due to the challenge of specifying the model, GWAS seldom include non-linear terms. Here we introduce DeepNull, a method that models non-linear covariate effects on phenotypes using a deep neural network (DNN) and then includes the model prediction as a single extra term in the GWAS association. First, using simulated data, we show that DeepNull increases statistical power by up to 20% while maintaining tight control of the type I error in the presence of interactions or non-linear covariate effects. Second, DeepNull maintains similar results to a standard GWAS when covariates have only linear effects on the phenotype. Third, DeepNull detects larger numbers of significant hits and loci (7% additional loci averaged over 10 traits) than standard GWAS in ten phenotypes from the UK Biobank (n=370K). Many of the hits found only by DeepNull are biologically plausible or have previously been reported in the GWAS catalog. Finally, DeepNull improves phenotype prediction by 23% averaged over the same ten phenotypes, the highest improvement was observed in the case of Glaucoma referral probability where DeepNull improves the phenotype prediction by 83%.

Genome-wide association studies (GWASs) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model, GWAS often neglect such terms. Here we introduce DeepNull, a method that identifies and adjusts for non-linear and interactive covariate effects using a deep neural network. In analyses of simulated and real data, we demonstrate that DeepNull maintains tight control of the type I error while increasing statistical power by up to 20% in the presence of non-linear and interactive effects. Moreover, in the absence of such effects, DeepNull incurs no loss of power. When applied to 10 phenotypes from the UK Biobank (n = 370K), DeepNull discovered more hits (+6%) and loci (+7%), on average, than conventional association analyses, many of which are biologically plausible or have previously been reported. Finally, DeepNull improves upon linear modeling for phenotypic prediction (+23% on average)., GWAS often assume a linear phenotype-covariate relationship which may not hold in practice. Here the authors present DeepNull, in which they apply deep learning to identify and adjust for complex non-linear relationships, improving phenotypic prediction and GWAS power.

Objective: The aim of this study was to search for genes/variants that modify the effect of LRRK2 mutations in terms of penetrance and age-at-onset of Parkinson's disease. Methods: We performed the first genome-wide association study of penetrance and age-at-onset of Parkinson's disease in LRRK2 mutation carriers (776 cases and 1,103 non-cases at their last evaluation). Cox proportional hazard models and linear mixed models were used to identify modifiers of penetrance and age-at-onset of LRRK2 mutations, respectively. We also investigated whether a polygenic risk score derived from a published genome-wide association study of Parkinson's disease was able to explain variability in penetrance and age-at-onset in LRRK2 mutation carriers. Results: A variant located in the intronic region of CORO1C on chromosome 12 (rs77395454; P-value=2.5E-08, beta=1.27, SE=0.23, risk allele: C) met genome-wide significance for the penetrance model. A region on chromosome 3, within a previously reported linkage peak for Parkinson's disease susceptibility, showed suggestive associations in both models (penetrance top variant: P-value=1.1E-07; age-at-onset top variant: P-value=9.3E-07). A polygenic risk score derived from publicly available Parkinson's disease summary statistics was a significant predictor of penetrance, but not of age-at-onset. Interpretation: This study suggests that variants within or near CORO1C may modify the penetrance of LRRK2 mutations. In addition, common Parkinson's disease associated variants collectively increase the penetrance of LRRK2 mutations.

Summary Genome-wide association studies (GWASs) require accurate cohort phenotyping, but expert labeling can be costly, time intensive, and variable. Here, we develop a machine learning (ML) model to predict glaucomatous optic nerve head features from color fundus photographs. We used the model to predict vertical cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; p ≤ 5 × 10−8) hits in 156 loci. The ML-based GWAS replicated 62 of 65 GWS loci from a recent VCDR GWAS in the UKB for which two ophthalmologists manually labeled images for 67,040 Europeans. The ML-based GWAS also identified 93 novel loci, significantly expanding our understanding of the genetic etiologies of glaucoma and VCDR. Pathway analyses support the biological significance of the novel hits to VCDR: select loci near genes involved in neuronal and synaptic biology or harboring variants are known to cause severe Mendelian ophthalmic disease. Finally, the ML-based GWAS results significantly improve polygenic prediction of VCDR and primary open-angle glaucoma in the independent EPIC-Norfolk cohort.

We trained and validated risk prediction models for the three major types of skin cancer— basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma—on a cross-sectional and longitudinal dataset of 210,000 consented research participants who responded to an online survey covering personal and family history of skin cancer, skin susceptibility, and UV exposure. We developed a primary disease risk score (DRS) that combined all 32 identified genetic and non-genetic risk factors. Top percentile DRS was associated with an up to 13-fold increase (odds ratio per standard deviation increase >2.5) in the risk of developing skin cancer relative to the middle DRS percentile. To derive lifetime risk trajectories for the three skin cancers, we developed a second and age independent disease score, called DRSA. Using incident cases, we demonstrated that DRSA could be used in early detection programs for identifying high risk asymptotic individuals, and predicting when they are likely to develop skin cancer. High DRSA scores were not only associated with earlier disease diagnosis (by up to 14 years), but also with more severe and recurrent forms of skin cancer., Predicting who will develop skin cancer is difficult. Here, the authors from 23andMe developed a polygenic risk score for skin cancer based on a questionnaire and genetic data from more than 210,000 individuals and suggest that the score could be used in early screening programmes.

Analysis of large genomic datasets, including gnomAD, reveals that partial LRRK2 loss of function is not strongly associated with diseases, serving as an example of how human genetics can be leveraged for target validation in drug discovery. Human genetic variants predicted to cause loss-of-function of protein-coding genes (pLoF variants) provide natural in vivo models of human gene inactivation and can be valuable indicators of gene function and the potential toxicity of therapeutic inhibitors targeting these genes(1,2). Gain-of-kinase-function variants in LRRK2 are known to significantly increase the risk of Parkinson's disease(3,4), suggesting that inhibition of LRRK2 kinase activity is a promising therapeutic strategy. While preclinical studies in model organisms have raised some on-target toxicity concerns(5-8), the biological consequences of LRRK2 inhibition have not been well characterized in humans. Here, we systematically analyze pLoF variants in LRRK2 observed across 141,456 individuals sequenced in the Genome Aggregation Database (gnomAD)(9), 49,960 exome-sequenced individuals from the UK Biobank and over 4 million participants in the 23andMe genotyped dataset. After stringent variant curation, we identify 1,455 individuals with high-confidence pLoF variants in LRRK2. Experimental validation of three variants, combined with previous work(10), confirmed reduced protein levels in 82.5% of our cohort. We show that heterozygous pLoF variants in LRRK2 reduce LRRK2 protein levels but that these are not strongly associated with any specific phenotype or disease state. Our results demonstrate the value of large-scale genomic databases and phenotyping of human loss-of-function carriers for target validation in drug discovery.

Small non-coding RNAs (sncRNAs) have established roles as post-transcriptional regulators of cancer pathogenesis. We recently reported a novel and previously unannotated class of cancer-specific sncRNAs in breast cancer and demonstrated that breast cancer cells exploit a specific sncRNA to promote cancer metastasis. However, the extent to which these sncRNAs, which we have collectively termed orphan non-coding RNAs (oncRNAs), are present in other cancer types is unknown. To address this question and define a high-confidence set of oncRNAs, we used smRNA-seq data from 6 cancer sites (breast, colorectal, kidney, liver, lung, and stomach) and their corresponding normal tissues from The Cancer Genome Atlas (TCGA; 4,445 cancer, 431 normal) and identified a total of 144,695 oncRNAs that are significantly present in cancer and largely absent in normal tissue (Fisher’s Exact Test and Benjamini-Hochberg correction, FDR < 0.1). To evaluate if this set of TCGA-derived oncRNAs could be validated in independent datasets, we examined smRNA-seq data from two large independent cohorts comprising these same cancer and normal tissue types (Indivumed, Hamburg, Germany). Cohort A consists of 4,024 samples (2,245 cancer, 1,779 normal) and cohort B consists of 2,874 samples (2,063 cancer; 811 normal). oncRNAs in these cohorts were annotated following the same procedure used for TCGA data. TCGA-derived oncRNAs were considered validated in the independent cohorts if they were present in a significantly higher number of cancer samples compared to adjacent normal tissue samples. In cohort A, 140,191 (96.9%) of TCGA-derived oncRNAs were detected in at least one sample, of which 74,634 (51.6%) were validated as oncRNAs. In cohort B, 140,147 (96.9%) oncRNAs were observed and 68,366 (47.2%) were validated. The degree of overlap between the validated oncRNAs in each cohort was significant, with 54,294 (37.5%) overlapping oncRNAs (hypergeometric test, P=0). We also found that oncRNAs are informative of cancer tissue of origin, demonstrating the existence of consistent cancer-specific oncRNA expression profiles in independent studies. Using the TCGA-derived oncRNAs as features, we trained an eXtreme Gradient Boosting (XGB) model on TCGA data to classify cancer samples by the 6 tissues of origin. The TCGA-trained model showed high performance when evaluated on both cohorts A and B, achieving accuracies of 91.5% (95% CI: 90.3%-92.7%) and 96% (94.7%-97%), respectively. For comparison, this model achieved an accuracy of 96% (94.5%-97.2%) on held-out TCGA data (80/20 train/test split). Our results show a robust validation of TCGA-derived oncRNAs in external, independently sourced and processed cancer tissue cohorts across a heterogeneous set of cancer sites. Our machine learning model also demonstrates that oncRNA profiles can be used to predict cancer tissue of origin with high generalizability and accuracy. Citation Format: Jeffrey Wang, Helen Li, Lisa Fish, Kimberly H. Chau, Patrick Arensdorf, Hani Goodarzi, Babak Alipanahi. Discovery and validation of orphan noncoding RNA profiles across multiple cancers in TCGA and two independent cohorts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3353.

Human genetic variants predicted to cause loss-of-function of protein-coding genes (pLoF variants) provide natural in vivo models of human gene inactivation and can be valuable indicators of gene function and the potential toxicity of therapeutic inhibitors targeting these genes

BackgroundClassical randomisation of clinical trial patients creates a source of genetic variance that may be contributing to the high failure rate seen in neurodegenerative disease trials. Our objective was to quantify genetic difference between randomised trial arms and determine how imbalance can affect trial outcomes.Methods5851 patients with Parkinson’s disease of European ancestry data and two simulated virtual cohorts based on public data were used. Data were resampled at different sizes for 1000 iterations and randomly assigned to the two arms of a simulated trial. False-negative and false-positive rates were estimated using simulated clinical trials, and per cent difference in genetic risk score (GRS) and allele frequency was calculated to quantify variance between arms.Results5851 patients with Parkinson’s disease (mean (SD) age, 61.02 (12.61) years; 2095 women (35.81%)) as well as simulated patients from virtually created cohorts were used in the study. Approximately 90% of the iterations had at least one statistically significant difference in individual risk SNPs between each trial arm. Approximately 5%–6% of iterations had a statistically significant difference between trial arms in mean GRS. For significant iterations, the average per cent difference for mean GRS between trial arms was 130.87%, 95% CI 120.89 to 140.85 (n=200). Glucocerebrocidase (GBA) gene-only simulations see an average 18.86%, 95% CI 18.01 to 19.71 difference in GRS scores between trial arms (n=50). When adding a drug effect of −0.5 points in MDS-UPDRS per year at n=50, 33.9% of trials resulted in false negatives.ConclusionsOur data support the hypothesis that within genetically unmatched clinical trials, genetic heterogeneity could confound true therapeutic effects as expected. Clinical trials should undergo pretrial genetic adjustment or, at the minimum, post-trial adjustment and analysis for failed trials.

Altres ajuts: VR has served on the speakers for Eli Lilly, Rubio, and Shire in the last 5 years. She has received travel awards from Eli Lilly and Co. and Shire for participating in psychiatric meetings. The ADHD Program has received unrestricted educational and research support from Eli Lilly and Co., Janssen-Cilag, Shire, Rovi, Psious, and Laboratorios Rubió in the past 2 years. MC received travel awards for taking part in psychiatric meetings from Shire. CF received travel awards for taking part in psychiatric meetings from Shire and Lundbeck. GEM received travel awards for taking part in psychiatric meetings from Shire. EJSS-B received speaker fees, consultancy, research funding, and conference support from Shire Pharma. Consultancy from Neurotech Solutions, Copenhagen University and Berhanderling, Skolerne & KU Leuven. Book royalties from OUP and Jessica Kingsley. Financial support-Arhus University and Ghent University for visiting Professorship. Editor-in-Chief JCPP-supported by a buy-out of time to University of Southampton and personal Honorarium. King's College London received payments for work conducted by PA: consultancy for Shire, Eli Lilly, Novartis, and Lundbeck; educational and/or research awards from Shire, Eli Lilly, Novartis, Vifor Pharma, GW Pharma, and QbTech; speaker at events sponsored by Shire, Eli Lilly, Janssen-Cilag, and Novartis. JKB has been in the past 3 years a consultant to/member of advisory board of and/or speaker for Shire, Roche, Medice, and Servier. He is not an employee of any of these companies, and not a stock shareholder of any of these companies. He has no other financial or material support, including expert testimony, patents, royalties. In the past year, SVF received income, potential income, travel expenses continuing education support and/or research support from Tris, Otsuka, Arbor, Ironshore, Shire, Akili Interactive Labs, Enzymotec, Sunovion, Supernus, and Genomind. With his institution, he has US patent US20130217707 A1 for the use of sodium-hydrogen exchange inhibitors in the treatment of ADHD. He also receives royalties from books published by Guilford Press: Straight Talk about Your Child's Mental Health, Oxford University Press: Schizophrenia: The Facts and Elsevier: ADHD: Non-Pharmacologic Interventions. He is principal investigator of www.adhdinadults.com. JK has given talks at educational events sponsored by Medice; all funds are received by King's College London and used for studies of ADHD. HL has served as a speaker for Evolan Pharma and Shire and has received research grants from Shire; all outside the submitted work. K-PL served as a speaker for Eli Lilly and received research support from Medice, and travel support from Shire, all outside the submitted work. LAR reported receiving honoraria, serving on the speakers' bureau/advisory board, and/or acting as a consultant for Eli Lilly, Janssen-Cilag, Novartis, and Shire in the last 3 years; receiving authorship royalties from Oxford Press and ArtMed; and receiving travel awards from Shire for his participation in the 2015 WFADHD meetings and from Novartis to take part of the 2016 AACAP meeting. The ADHD and juvenile bipolar disorder outpatient programs chaired by him received unrestricted educational and research support from the following pharmaceutical companies in the last 3 years: Janssen-Cilag, Novartis, and Shire. MC has received travel grants and research support from Eli Lilly and Co., Janssen-Cilag, Shire, and Lundbeck and served as consultant for Eli Lilly and Co., Janssen-Cilag, Shire, and Lundbeck. BF has received educational speaking fees from Medice and Shire. JAR-Q was on the speakers' bureau and/or acted as consultant for Eli Lilly, Janssen-Cilag, Novartis, Shire, Lundbeck, Almirall, Braingaze, Sincrolab, Medice, and Rubió in the last 5 years. He also received travel awards (air tickets + hotel) for taking part in psychiatric meetings from Janssen-Cilag, Rubió, Shire, Medice, and Eli- Lilly. The Department of Psychiatry chaired by him received unrestricted educational and research support from the following companies in the last 5 years: Eli Lilly, Lundbeck, Janssen- Cilag, Actelion, Shire, Ferrer, Oryzon, Roche, Psious, and Rubió. The other authors have nothing to disclose. All members of the 23andMe Research Team are current or former employees of 23andMe, Inc. and hold stock or stock options in 23andMe. Authors of the ADHD Working group of the PGC that participated in this study: Catharina A. Hartman, Ziarih Hawi, Jennifer Crosbie, Sandra Loo, Josephine Elia, Russell Schachar, Christie Burton, Ted Reichborn-Kjennerud, Aribert Rothenberger, Søren Dalsgaard, Irwin Waldman, Mark Bellgrove, Hakon Hakonarson, Johannes Hebebrand, Anke Hinney, and Robert Oades have nothing to disclose. Joseph Biederman 2019-2020: he received research support from Genentech, Headspace Inc., Lundbeck AS, Neurocentria Inc, Pfizer Pharmaceuticals, Roche TCRC Inc., Shire Pharmaceuticals Inc., Sunovion Pharmaceuticals Inc., and Tris. He was a consultant for Akili, Avekshan, Jazz Pharma, and Shire/Takeda. Through MGH CTNI, he participated in a scientific advisory board for Supernus. Dr Biederman's program has received departmental royalties from a copyrighted rating scale used for ADHD diagnoses, paid by Bracket Global, Ingenix, Prophase, Shire, Sunovion, and Theravance; these royalties were paid to the Department of Psychiatry at MGH. Tobias Banaschewski served in an advisory or consultancy role for Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, Shire, and Infectopharm. He received conference support or speaker's fee by Lilly, Medice, and Shire. He received royalties from Hogrefe, Kohlhammer, CIP Medien, and Oxford University Press. James McGough is an expert testimony for Eli Lilly; DSMB for Sunovion. Benjamin Neale is a member of the scientific advisory board at Deep Genomics and consultant for Camp4 Therapeutics, Takeda Pharmaceutical, and Biogen. Ole Andreas Andreassen received speaker's honorarium from Lundbeck, and is a consultant for HealthLytix. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 602805 (Aggressotype) as well as from the European Union H2020 Programme (H2020/2014-2020). The work was also supported by the ECNP Network "ADHD across the Lifespan" (https://www.ecnp.eu/research-innovation/ECNP-networks/List-ECNP-Networks/ADHD.aspx). Over the course of this investigation, PR is a recipient of a pre-doctoral fellowship from the Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR), Generalitat de Catalunya, Spain. The iPSYCH project is funded by the Lundbeck Foundation (grant nos. R102-A9118 and R155-2014-1724) and the universities, and university hospitals of Aarhus and Copenhagen. ADB and NRM's work is also supported by the EU's Horizon 2020 programme. Data handling and analysis was supported by NIMH (1U01MH109514-01 to Michael O'Donovan and ADB). CSM is a recipient of a Sara Borrell contract from the Instituto de Salud Carlos III, Ministerio de Economía, Industria y Competitividad, Spain. K-PL and his team are supported by the Deutsche Forschungsgemeinschaft (DFG: CRU 125, CRC TRR 58 A1/A5, no. 44541416), ERA-Net NEURON/RESPOND, no. 01EW1602B, ERA-Net NEURON/DECODE, no. 01EW1902, and 5-100 Russian Academic Excellence Project. JH thanks Stiftelsen K.G. Jebsen, University of Bergen, the Western Norwegian Health Authorities (Helse Vest). HL thanks the Swedish research council. BC received financial support from the Spanish "Ministerio de Economía y Competitividad" and AGAUR. MSA is a recipient of a contract from the Biomedical Network Research Center on Mental Health (CIBERSAM), Madrid, Spain. MR is a recipient of a Miguel de Servet contract from the Instituto de Salud Carlos III, Spain. The research leading to these results has received funding from the Instituto de Salud Carlos III , and cofinanced by the European Regional Development Fund (ERDF), from the Pla estratègic de recerca i innovació en salut (PERIS); Generalitat de Catalunya, from "la Marató de TV3" (092330/31) and from the Agència de Gestió d'Ajuts Universitaris i de Recerca-AGAUR, Generalitat de Catalunya. ES's work is supported by a personal Hypatia grant from the Radboud University Medical Center. MH received a Veni grant from of the Netherlands Organization for Scientific Research (NWO, grant number 91619115). The NeuroIMAGE study was supported by NIH Grant R01MH62873 (to SVF), NWO Large Investment Grant 1750102007010 (to JKB), ZonMW grant 60-60600-97-193, NWO grants 056-13-015 and 433-09-242, and matching grants from Radboud University Nijmegen Medical Center, University Medical Center Groningen and Accare, and Vrije Universiteit Amsterdam. Organization for Scientific Research (NWO; grant 016-130-669). BF and MK's work is supported by the Dutch National Science Agenda (NWA) for the NeurolabNL project (grant 400-17-602). This paper represents independent research part funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. BF received additional funding from a personal Vici grant of the Dutch. Attention deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by age-inappropriate symptoms of inattention, impulsivity, and hyperactivity that persist into adulthood in the majority of the diagnosed children. Despite several risk factors during childhood predicting the persistence of ADHD symptoms into adulthood, the genetic architecture underlying the trajectory of ADHD over time is still unclear. We set out to study the contribution of common genetic variants to the risk for ADHD across the lifespan by conducting meta-analyses of genome-wide association studies on persistent ADHD in adults and ADHD in childhood separately and jointly, and by comparing the genetic background between them in a total sample of 17,149 cases and 32,411 controls. Our results show nine new independent loci and support a shared contribution of common genetic variants to ADHD in children and adults. No subgroup heterogeneity was observed among children, while this group consists of future remitting and persistent individuals. We report similar patterns of genetic correlation of ADHD with other ADHD-related datasets and different traits and disorders among adults, children, and when combining both groups. These findings confirm that persistent ADHD in adults is a neurodevelopmental disorder and extend the existing hypothesis of a shared genetic architecture underlying ADHD and different traits to a lifespan perspective.

In order to systematically describe the Parkinson’s disease phenome, we performed a series of 832 cross-sectional case-control analyses in a large database. Responses to 832 online survey-based phenotypes including diseases, medications, and environmental exposures were analyzed in 23andMe research participants. For each phenotype, survey respondents were used to construct a cohort of Parkinson’s disease cases and age-matched and sex-matched controls, and an association test was performed using logistic regression. Cohorts included a median of 3899 Parkinson’s disease cases and 49,808 controls, all of European ancestry. Highly correlated phenotypes were removed and the novelty of each significant association was systematically assessed (assigned to one of four categories: known, likely, unclear, or novel). Parkinson’s disease diagnosis was associated with 122 phenotypes. We replicated 27 known associations and found 23 associations with a strong a priori link to a known association. We discovered 42 associations that have not previously been reported. Migraine, obsessive-compulsive disorder, and seasonal allergies were associated with Parkinson’s disease and tend to occur decades before the typical age of diagnosis for Parkinson’s disease. The phenotypes that currently comprise the Parkinson’s disease phenome have mostly been explored in relatively small purpose-built studies. Using a single large dataset, we have successfully reproduced many of these established associations and have extended the Parkinson’s disease phenome by discovering novel associations. Our work paves the way for studies of these associated phenotypes that explore shared molecular mechanisms with Parkinson’s disease, infer causal relationships, and improve our ability to identify individuals at high-risk of Parkinson’s disease.

Chromosome 22q11.2 microdeletions impart a high but incomplete risk for schizophrenia. Possible mechanisms include genome-wide effects of DGCR8 haploinsufficiency. In a proof-of-principle study to assess the power of this model, we used high-quality, whole-genome sequencing of nine individuals with 22q11.2 deletions and extreme phenotypes (schizophrenia, or no psychotic disorder at age >50 years). The schizophrenia group had a greater burden of rare, damaging variants impacting protein-coding neurofunctional genes, including genes involved in neuron projection (nominal P = 0.02, joint burden of three variant types). Variants in the intact 22q11.2 region were not major contributors. Restricting to genes affected by a DGCR8 mechanism tended to amplify between-group differences. Damaging variants in highly conserved long intergenic noncoding RNA genes also were enriched in the schizophrenia group (nominal P = 0.04). The findings support the 22q11.2 deletion model as a threshold-lowering first hit for schizophrenia risk. If applied to a larger and thus better-powered cohort, this appears to be a promising approach to identify genome-wide rare variants in coding and noncoding sequence that perturb gene networks relevant to idiopathic schizophrenia. Similarly designed studies exploiting genetic models may prove useful to help delineate the genetic architecture of other complex phenotypes.

Knowing the sequence specificities of DNA- and RNA-binding proteins is essential for developing models of the regulatory processes in biological systems and for identifying causal disease variants. Here we show that sequence specificities can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified computational approach for pattern discovery. Using a diverse array of experimental data and evaluation metrics, we find that deep learning outperforms other state-of-the-art methods, even when training on in vitro data and testing on in vivo data. We call this approach DeepBind and have built a stand-alone software tool that is fully automatic and handles millions of sequences per experiment. Specificities determined by DeepBind are readily visualized as a weighted ensemble of position weight matrices or as a 'mutation map' that indicates how variations affect binding within a specific sequence.

BackgroundObservational studies have begun to characterize the wide spectrum of phenotypes associated with Parkinson’s disease (PD), but recruiting large numbers of PD cases and assaying a diversity of phenotypes has often been difficult. Here, we set out to systematically describe the PD phenome using a cross-sectional case-control design in a large database.MethodsWe analyzed the association between PD and 840 phenotypes derived from online surveys. For each phenotype, we ran a logistic regression using an average of 5,141 PD cases and 65,459 age- and sex-matched controls. We selected uncorrelated phenotypes, determined statistical significance after correcting for multiple testing, and systematically assessed the novelty of each significant association. We tested whether significant phenotypes were also associated with disease duration in PD cases.FindingsPD diagnosis was associated with 149 independent phenotypes. We replicated 32 known associations and discovered 49 associations that have not previously been reported. We found that migraine, obsessive-compulsive disorder, seasonal allergies, and anemia were associated with PD, but were not significantly associated with PD duration, and tend to occur decades before the average age of diagnosis for PD. Further work is needed to determine whether these phenotypes are PD risk factors or whether they share common disease mechanisms.InterpretationWe used a systematic approach in a single large dataset to assess the spectrum of traits that were associated with PD. Some of these traits may be risk factors for PD, features of the pre-diagnostic phase of disease, or manifestations of PD pathology. The model outputs from all 840 logistic regressions are available to the research community and may be used to generate hypotheses regarding PD etiology.FundingThe Michael J. Fox Foundation, Parkinson’s UK, Barts Charity, National Institute on Aging, and 23andMe, Inc.Research in ContextEvidence before this studyWe used PubMed to perform a MEDLINE database search for review articles published up to January 21st, 2018 that contained the keywords “Parkinson” and “epidemiology” in the title or abstract. We performed additional MEDLINE searches for each phenotype that was significantly associated with PD. Although dozens of phenotypes have been tested for an association with PD, only a few associations have been consistently repeatable (e.g. pesticide exposure, coffee consumption).Added value of this studyWe systematically tested for an association between PD and 840 phenotypes using up to 13,546 cases and 1·3 million controls, making this one of the largest PD epidemiology studies ever conducted. We discovered 49 novel associations that will need to be replicated or validated. We found 44 associations for phenotypes that have previously been studied in relation to PD, but for which an association has not been consistently demonstrated.Implications of all the available evidenceTaken together with results from previous studies, this series of case-control analyses adds evidence for associations between PD and many phenotypes that are not currently thought to be part of the canonical PD phenome. This work paves the way for future studies to assess whether any of these phenotypes represent PD risk factors and whether any of these risk factors are modifiable.

The genetic basis of earlobe attachment has been a matter of debate since the early 20th century, such that geneticists argue both for and against polygenic inheritance. Recent genetic studies have identified a few loci associated with the trait, but large-scale analyses are still lacking. Here, we performed a genome-wide association study of lobe attachment in a multiethnic sample of 74,660 individuals from four cohorts (three with the trait scored by an expert rater and one with the trait self-reported). Meta-analysis of the three expert-rater-scored cohorts revealed six associated loci harboring numerous candidate genes, including EDAR, SP5, MRPS22, ADGRG6 (GPR126), KIAA1217, and PAX9. The large self-reported 23andMe cohort recapitulated each of these six loci. Moreover, meta-analysis across all four cohorts revealed a total of 49 significant (p < 5 × 10−8) loci. Annotation and enrichment analyses of these 49 loci showed strong evidence of genes involved in ear development and syndromes with auricular phenotypes. RNA sequencing data from both human fetal ear and mouse second branchial arch tissue confirmed that genes located among associated loci showed evidence of expression. These results provide strong evidence for the polygenic nature of earlobe attachment and offer insights into the biological basis of normal and abnormal ear development. Fil: Shaffer, John R.. University of Pittsburgh; Estados Unidos Fil: Li, Jinxi. University of Chinese Academy of Sciences; China Fil: Lee, Myoung Keun. University of Pittsburgh; Estados Unidos Fil: Roosenboom, Jasmien. University of Pittsburgh; Estados Unidos Fil: Orlova, Ekaterina. University of Pittsburgh; Estados Unidos Fil: Adhikari, Kaustabh. Colegio Universitario de Londres; Reino Unido Fil: Gallo, Carla. Universidad Peruana Cayetano Heredia; Perú Fil: Poletti, Giovanni. Universidad Peruana Cayetano Heredia; Perú Fil: Schuler Faccini, Lavinia. Universidade Federal do Rio Grande do Sul; Brasil Fil: Bortolini, Maria Catira. Universidade Federal do Rio Grande do Sul; Brasil Fil: Canizales Quinteros, Samuel. Universidad Nacional Autónoma de México; México Fil: Rothhammer, Francisco. Universidad de Tarapacá; Chile Fil: Bedoya, Gabriel. Universidad de Antioquia; Colombia Fil: González José, Rolando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; Argentina Fil: Pfeffer, Paige E.. Saint Louis University; Estados Unidos Fil: Wollenschlaeger, Christopher A.. University of Pittsburgh; Estados Unidos Fil: Hecht, Jacqueline T.. University of Texas; Estados Unidos Fil: Wehby, George. University of Iowa; Estados Unidos Fil: Moreno, Lina M.. University of Iowa; Estados Unidos Fil: Ding, Anan. University of Chinese Academy of Sciences; China Fil: Jin, Li. University of Chinese Academy of Sciences; China. Fudan University; China Fil: Yang, Yajun. Fudan University; China Fil: Carlson, Jenna C.. University of Pittsburgh; Estados Unidos Fil: Leslie, Elizabeth J.. University of Pittsburgh; Estados Unidos Fil: Feingold, Eleanor. University of Pittsburgh; Estados Unidos Fil: Marazita, Mary L.. University of Pittsburgh; Estados Unidos Fil: Hinds, David A.. 899 West Evelyn Avenue; Estados Unidos Fil: Cox, Timothy C.. Seattle Children’s Research Institute; Estados Unidos. University of Washington; Estados Unidos. Monash University; Australia Fil: Wang, Sijia. University of Chinese Academy of Sciences; China. Fudan University; China Fil: Ruiz Linares, Andrés. Colegio Universitario de Londres; Reino Unido. Fudan University; China. Aix-Marseille University; Francia Fil: Weinberg, Seth M.. University of Pittsburgh; Estados Unidos

Epidemiologic studies have reported inconsistent results regarding an association between Parkinson disease (PD) and cutaneous melanoma (melanoma). Identifying shared genetic architecture between these diseases can support epidemiologic findings and identify common risk genes and biological pathways. Here we apply polygenic, linkage disequilibrium-informed methods to the largest available case-control, genome-wide association study summary statistic data for melanoma and PD. We identify positive and significant genetic correlation (correlation: 0.17, 95% CI 0.10 to 0.24; P = 4.09 × 10(−06)) between melanoma and PD. We further demonstrate melanoma and PD-inferred gene expression to overlap across tissues (correlation: 0.14, 95% CI 0.06 to 0.22; P = 7.87 × 10(−04)), and highlight seven genes including PIEZO1, TRAPPC2L, and SOX6 as potential mediators of the genetic correlation between melanoma and PD. These findings demonstrate specific, shared genetic architecture between PD and melanoma that manifests at the level of gene expression.

Progress identifying the genetic determinants of personality has historically been slow, with candidate gene studies and small-scale genome-wide association studies yielding few reproducible results. In the UK Biobank study, genetic variants in CADM2 and MSRA were recently shown to influence risk taking behavior and irritability respectively, representing some of the first genomic loci to be associated with aspects of personality. We extend this observation by performing a personality “phenome-scan” across 16 traits in up to 140,487 participants from 23andMe for these two genes. Heritability estimates for these traits ranged from 5-19%, with both CADM2 and MSRA demonstrating significant effects on multiple personality types. These associations covered all aspects of the big five personality domains, including specific facet traits such as compliance, altruism, anxiety and activity / energy. This study both confirms and extends the original observations, highlighting the role of genetics in aspects of mental health and behavior.

Motivation: Picking peaks from experimental NMR spectra is a key unsolved problem for automated NMR protein structure determination. Such a process is a prerequisite for resonance assignment, nuclear overhauser enhancement (NOE) distance restraint assignment, and structure calculation tasks. Manual or semi-automatic peak picking, which is currently the prominent way used in NMR labs, is tedious, time consuming and costly. Results: We introduce new ideas, including noise-level estimation, component forming and sub-division, singular value decomposition (SVD)-based peak picking and peak pruning and refinement. PICKY is developed as an automated peak picking method. Different from the previous research on peak picking, we provide a systematic study of the proposed method. PICKY is tested on 32 real 2D and 3D spectra of eight target proteins, and achieves an average of 88% recall and 74% precision. PICKY is efficient. It takes PICKY on average 15.7 s to process an NMR spectrum. More important than these numbers, PICKY actually works in practice. We feed peak lists generated by PICKY to IPASS for resonance assignment, feed IPASS assignment to SPARTA for fragments generation, and feed SPARTA fragments to FALCON for structure calculation. This results in high-resolution structures of several proteins, for example, TM1112, at 1.25 Å. Availability: PICKY is available upon request. The peak lists of PICKY can be easily loaded by SPARKY to enable a better interactive strategy for rapid peak picking. Contact: mli@uwaterloo.ca

Predicting defects in RNA splicing Most eukaryotic messenger RNAs (mRNAs) are spliced to remove introns. Splicing generates uninterrupted open reading frames that can be translated into proteins. Splicing is often highly regulated, generating alternative spliced forms that code for variant proteins in different tissues. RNA-binding proteins that bind specific sequences in the mRNA regulate splicing. Xiong et al. develop a computational model that predicts splicing regulation for any mRNA sequence (see the Perspective by Guigó and Valcárcel). They use this to analyze more than half a million mRNA splicing sequence variants in the human genome. They are able to identify thousands of known disease-causing mutations, as well as many new disease candidates, including 17 new autism-linked genes. Science , this issue 10.1126/science.1254806 ; see also p. 124

Alternative splicing (AS) of precursor RNAs is responsible for greatly expanding the regulatory and functional capacity of eukaryotic genomes. Of the different classes of AS, intron retention (IR) is the least well understood. In plants and unicellular eukaryotes, IR is the most common form of AS, whereas in animals, it is thought to represent the least prevalent form. Using high-coverage poly(A)+ RNA-seq data, we observe that IR is surprisingly frequent in mammals, affecting transcripts from as many as three-quarters of multiexonic genes. A highly correlated set of cis features comprising an “IR code” reliably discriminates retained from constitutively spliced introns. We show that IR acts widely to reduce the levels of transcripts that are less or not required for the physiology of the cell or tissue type in which they are detected. This “transcriptome tuning” function of IR acts through both nonsense-mediated mRNA decay and nuclear sequestration and turnover of IR transcripts. We further show that IR is linked to a cross-talk mechanism involving localized stalling of RNA polymerase II (Pol II) and reduced availability of spliceosomal components. Collectively, the results implicate a global checkpoint-type mechanism whereby reduced recruitment of splicing components coupled to Pol II pausing underlies widespread IR-mediated suppression of inappropriately expressed transcripts.

A universal challenge in genetic studies of autism spectrum disorders (ASDs) is determining whether a given DNA sequence alteration will manifest as disease. Among different population controls, we observed, for specific exons, an inverse correlation between exon expression level in brain and burden of rare missense mutations. For genes that harbor de novo mutations predicted to be deleterious, we found that specific critical exons were significantly enriched in individuals with ASD relative to their siblings without ASD (P < 1.13 × 10(-38); odds ratio (OR) = 2.40). Furthermore, our analysis of genes with high exonic expression in brain and low burden of rare mutations demonstrated enrichment for known ASD-associated genes (P < 3.40 × 10(-11); OR = 6.08) and ASD-relevant fragile-X protein targets (P < 2.91 × 10(-157); OR = 9.52). Our results suggest that brain-expressed exons under purifying selection should be prioritized in genotype-phenotype studies for ASD and related neurodevelopmental conditions.

This study identifies MBNL proteins as negative regulators of alternative splicing events that are differentially regulated between ES cells and other cell types; several lines of evidence show that these proteins repress an ES cell alternative splicing program and the reprogramming of somatic cells to induced pluripotent stem cells. Ben Blencowe and colleagues identify the muscleblind-like RNA binding proteins MBNL1 and MBNL2 as negative regulators of alternative splicing events that are differentially regulated between embryonic stem cells and other cell types. Several lines of evidence show that they are involved in the regulation of embryonic-stem-cell-like alternative splicing patterns. The authors also identify a regulatory role during the reprogramming of fibroblasts to induced pluripotent stem (iPS) cells. Previous investigations of the core gene regulatory circuitry that controls the pluripotency of embryonic stem (ES) cells have largely focused on the roles of transcription, chromatin and non-coding RNA regulators1,2,3. Alternative splicing represents a widely acting mode of gene regulation4,5,6,7,8, yet its role in regulating ES-cell pluripotency and differentiation is poorly understood. Here we identify the muscleblind-like RNA binding proteins, MBNL1 and MBNL2, as conserved and direct negative regulators of a large program of cassette exon alternative splicing events that are differentially regulated between ES cells and other cell types. Knockdown of MBNL proteins in differentiated cells causes switching to an ES-cell-like alternative splicing pattern for approximately half of these events, whereas overexpression of MBNL proteins in ES cells promotes differentiated-cell-like alternative splicing patterns. Among the MBNL-regulated events is an ES-cell-specific alternative splicing switch in the forkhead family transcription factor FOXP1 that controls pluripotency9. Consistent with a central and negative regulatory role for MBNL proteins in pluripotency, their knockdown significantly enhances the expression of key pluripotency genes and the formation of induced pluripotent stem cells during somatic cell reprogramming.

De novo mutations (DNMs) are important in autism spectrum disorder (ASD), but so far analyses have mainly been on the ~1.5% of the genome encoding genes. Here, we performed whole-genome sequencing (WGS) of 200 ASD parent–child trios and characterised germline and somatic DNMs. We confirmed that the majority of germline DNMs (75.6%) originated from the father, and these increased significantly with paternal age only (P=4.2×10−10). However, when clustered DNMs (those within 20 kb) were found in ASD, not only did they mostly originate from the mother (P=7.7×10−13), but they could also be found adjacent to de novo copy number variations where the mutation rate was significantly elevated (P=2.4×10−24). By comparing with DNMs detected in controls, we found a significant enrichment of predicted damaging DNMs in ASD cases (P=8.0×10−9; odds ratio=1.84), of which 15.6% (P=4.3×10−3) and 22.5% (P=7.0×10−5) were non-coding or genic non-coding, respectively. The non-coding elements most enriched for DNM were untranslated regions of genes, regulatory sequences involved in exon-skipping and DNase I hypersensitive regions. Using microarrays and a novel outlier detection test, we also found aberrant methylation profiles in 2/185 (1.1%) of ASD cases. These same individuals carried independently identified DNMs in the ASD-risk and epigenetic genes DNMT3A and ADNP. Our data begins to characterize different genome-wide DNMs, and highlight the contribution of non-coding variants, to the aetiology of ASD.