An Integrated, Scalable, Electronic Video Consent Process to Power Precision Health Research: Large, Population-Based, Cohort Implementation and Scalability Study.

BACKGROUND: Obtaining explicit consent from patients to use their remnant biological samples and deidentified clinical data for research is essential for advancing precision medicine. OBJECTIVE: We aimed to describe the operational implementation and scalability of an electronic universal consent process that was used to power an institutional precision health biobank across a large academic health system. METHODS: The University of California, Los Angeles, implemented the use of innovative electronic consent videos as the primary recruitment tool for precision health research. The consent videos targeted patients aged ≥18 years across ambulatory clinical laboratories, perioperative settings, and hospital settings. Each of these major areas had slightly different workflows and patient populations. Sociodemographic information, comorbidity data, health utilization data (ambulatory visits, emergency room visits, and hospital admissions), and consent decision data were collected. RESULTS: The consenting approach proved scalable across 22 clinical sites (hospital and ambulatory settings). Over 40,000 participants completed the consent process at a rate of 800 to 1000 patients per week over a 2-year time period. Participants were representative of the adult University of California, Los Angeles, Health population. The opt-in rates in the perioperative (16,500/22,519, 73.3%) and ambulatory clinics (2308/3390, 68.1%) were higher than those in clinical laboratories (7506/14,235, 52.7%; P<.001). Patients with higher medical acuity were more likely to opt in. The multivariate analyses showed that African American (odds ratio [OR] 0.53, 95% CI 0.49-0.58; P<.001), Asian (OR 0.72, 95% CI 0.68-0.77; P<.001), and multiple-race populations (OR 0.73, 95% CI 0.69-0.77; P<.001) were less likely to participate than White individuals. CONCLUSIONS: This is one of the few large-scale, electronic video-based consent implementation programs that reports a 65.5% (26,314/40,144) average overall opt-in rate across a large academic health system. This rate is higher than those previously reported for email (3.6%) and electronic biobank (50%) informed consent rates. This study demonstrates a scalable recruitment approach for population health research.

Detection of clinically relevant genetic variants in autism spectrum disorder by whole-genome sequencing.

Autism Spectrum Disorder (ASD) demonstrates high heritability and familial clustering, yet the genetic causes remain only partially understood as a result of extensive clinical and genomic heterogeneity. Whole-genome sequencing (WGS) shows promise as a tool for identifying ASD risk genes as well as unreported mutations in known loci, but an assessment of its full utility in an ASD group has not been performed. We used WGS to examine 32 families with ASD to detect de novo or rare inherited genetic variants predicted to be deleterious (loss-of-function and damaging missense mutations). Among ASD probands, we identified deleterious de novo mutations in six of 32 (19%) families and X-linked or autosomal inherited alterations in ten of 32 (31%) families (some had combinations of mutations). The proportion of families identified with such putative mutations was larger than has been previously reported; this yield was in part due to the comprehensive and uniform coverage afforded by WGS. Deleterious variants were found in four unrecognized, nine known, and eight candidate ASD risk genes. Examples include CAPRIN1 and AFF2 (both linked to FMR1, which is involved in fragile X syndrome), VIP (involved in social-cognitive deficits), and other genes such as SCN2A and KCNQ2 (linked to epilepsy), NRXN1, and CHD7, which causes ASD-associated CHARGE syndrome. Taken together, these results suggest that WGS and thorough bioinformatic analyses for de novo and rare inherited mutations will improve the detection of genetic variants likely to be associated with ASD or its accompanying clinical symptoms.

Mapping autism risk loci using genetic linkage and chromosomal rearrangements.

Autism spectrum disorders (ASDs) are common, heritable neurodevelopmental conditions. The genetic architecture of ASDs is complex, requiring large samples to overcome heterogeneity. Here we broaden coverage and sample size relative to other studies of ASDs by using Affymetrix 10K SNP arrays and 1,181 [corrected] families with at least two affected individuals, performing the largest linkage scan to date while also analyzing copy number variation in these families. Linkage and copy number variation analyses implicate chromosome 11p12-p13 and neurexins, respectively, among other candidate loci. Neurexins team with previously implicated neuroligins for glutamatergic synaptogenesis, highlighting glutamate-related genes as promising candidates for contributing to ASDs.

Common genetic variants on 5p14.1 associate with autism spectrum disorders.

Autism spectrum disorders (ASDs) represent a group of childhood neurodevelopmental and neuropsychiatric disorders characterized by deficits in verbal communication, impairment of social interaction, and restricted and repetitive patterns of interests and behaviour. To identify common genetic risk factors underlying ASDs, here we present the results of genome-wide association studies on a cohort of 780 families (3,101 subjects) with affected children, and a second cohort of 1,204 affected subjects and 6,491 control subjects, all of whom were of European ancestry. Six single nucleotide polymorphisms between cadherin 10 (CDH10) and cadherin 9 (CDH9)-two genes encoding neuronal cell-adhesion molecules-revealed strong association signals, with the most significant SNP being rs4307059 (P = 3.4 x 10(-8), odds ratio = 1.19). These signals were replicated in two independent cohorts, with combined P values ranging from 7.4 x 10(-8) to 2.1 x 10(-10). Our results implicate neuronal cell-adhesion molecules in the pathogenesis of ASDs, and represent, to our knowledge, the first demonstration of genome-wide significant association of common variants with susceptibility to ASDs.

Individual common variants exert weak effects on the risk for autism spectrum disorders.

While it is apparent that rare variation can play an important role in the genetic architecture of autism spectrum disorders (ASDs), the contribution of common variation to the risk of developing ASD is less clear. To produce a more comprehensive picture, we report Stage 2 of the Autism Genome Project genome-wide association study, adding 1301 ASD families and bringing the total to 2705 families analysed (Stages 1 and 2). In addition to evaluating the association of individual single nucleotide polymorphisms (SNPs), we also sought evidence that common variants, en masse, might affect the risk. Despite genotyping over a million SNPs covering the genome, no single SNP shows significant association with ASD or selected phenotypes at a genome-wide level. The SNP that achieves the smallest P-value from secondary analyses is rs1718101. It falls in CNTNAP2, a gene previously implicated in susceptibility for ASD. This SNP also shows modest association with age of word/phrase acquisition in ASD subjects, of interest because features of language development are also associated with other variation in CNTNAP2. In contrast, allele scores derived from the transmission of common alleles to Stage 1 cases significantly predict case status in the independent Stage 2 sample. Despite being significant, the variance explained by these allele scores was small (Vm< 1%). Based on results from individual SNPs and their en masse effect on risk, as inferred from the allele score results, it is reasonable to conclude that common variants affect the risk for ASD but their individual effects are modest.

Multi-class Modeling Identifies Shared Genetic Risk for Late-onset Epilepsy and Alzheimer's Disease.

Background: Previous studies have established a strong link between late-onset epilepsy (LOE) and Alzheimer's disease (AD). However, their shared genetic risk beyond the APOE gene remains unclear. Our study sought to examine the shared genetic factors of AD and LOE, interpret the biological pathways involved, and evaluate how AD onset may be mediated by LOE and shared genetic risks. Methods: We defined phenotypes using phecodes mapped from diagnosis codes, with patients' records aged 60-90. A two-step Least Absolute Shrinkage and Selection Operator (LASSO) workflow was used to identify shared genetic variants based on prior AD GWAS integrated with functional genomic data. We calculated an AD-LOE shared risk score and used it as a proxy in a causal mediation analysis. We used electronic health records from an academic health center (UCLA Health) for discovery analyses and validated our findings in a multi-institutional EHR database (All of Us). Results: The two-step LASSO method identified 34 shared genetic loci between AD and LOE, including the APOE region. These loci were mapped to 65 genes, which showed enrichment in molecular functions and pathways such as tau protein binding and lipoprotein metabolism. Individuals with high predicted shared risk scores have a higher risk of developing AD, LOE, or both in their later life compared to those with low-risk scores. LOE partially mediates the effect of AD-LOE shared genetic risk on AD (15% proportion mediated on average). Validation results from All of Us were consistent with findings from the UCLA sample. Conclusions: We employed a machine learning approach to identify shared genetic risks of AD and LOE. In addition to providing substantial evidence for the significant contribution of the APOE-TOMM40-APOC1 gene cluster to shared risk, we uncovered novel genes that may contribute. Our study is one of the first to utilize All of Us genetic data to investigate AD, and provides valuable insights into the potential common and disease-specific mechanisms underlying AD and LOE, which could have profound implications for the future of disease prevention and the development of targeted treatment strategies to combat the co-occurrence of these two diseases.

Cross-Sectional Analysis of Exome Sequencing Diagnosis in Patients With Neurologic Phenotypes Facing Barriers to Clinical Testing.

Background and Objectives: Exome sequencing (ES) demonstrates a 20-50 percent diagnostic yield for patients with a suspected monogenic neurologic disease. Despite the proven efficacy in achieving a diagnosis for such patients, multiple barriers for obtaining exome sequencing remain. This study set out to assess the efficacy of ES in patients with primary neurologic phenotypes who were appropriate candidates for testing but had been unable to pursue clinical testing. Methods: A total of 297 patients were identified from the UCLA Clinical Neurogenomics Research Center Biobank, and ES was performed, including bioinformatic assessment of copy number variation and repeat expansions. Information regarding demographics, clinical indication for ES, and reason for not pursuing ES clinically were recorded. To assess diagnostic efficacy, variants were interpreted by a multidisciplinary team of clinicians, bioinformaticians, and genetic counselors in accordance with the American College of Medical Genetics and Genomics variant classification guidelines. We next examined the specific barriers to testing for these patients, including how frequently insurance-related barriers such as coverage denials and inadequate coverage of cost were obstacles to pursuing exome sequencing. Results: The cohort primarily consisted of patients with sporadic conditions (n = 126, 42.4%) of adult-onset (n = 239, 80.5%). Cerebellar ataxia (n = 225, 75.8%) was the most common presenting neurologic phenotype. Our study found that in this population of mostly adult patients with primary neurologic phenotypes that were unable to pursue exome sequencing clinically, 47 (15.8%) had diagnostic results while an additional 24 patients (8.1%) had uncertain results. Of the 297 patients, 206 were initially recommended for clinical exome but 88 (42.7%) could not pursue ES because of insurance barriers, of whom 14 (15.9%) had diagnostic findings, representing 29.8% of all patients with diagnostic findings. In addition, the incorporation of bioinformatic repeat expansion testing was valuable, identifying a total of 8 pathogenic repeat expansions (17.0% of all diagnostic findings) including 3 of the common spinocerebellar ataxias and 2 patients with Huntington disease. Discussion: These findings underscore the importance and value of clinical ES as a diagnostic tool for neurogenetic disease and highlight key barriers that prevent patients from receiving important clinical information with potential treatment and psychosocial implications for patients and family members.

The UCLA ATLAS Community Health Initiative: Promoting precision health research in a diverse biobank.

The UCLA ATLAS Community Health Initiative (ATLAS) has an initial target to recruit 150,000 participants from across the UCLA Health system with the goal of creating a genomic database to accelerate precision medicine efforts in California. This initiative includes a biobank embedded within the UCLA Health system that comprises de-identified genomic data linked to electronic health records (EHRs). The first freeze of data from September 2020 contains 27,987 genotyped samples imputed to 7.9 million SNPs across the genome and is linked with de-identified versions of the EHRs from UCLA Health. Here, we describe a centralized repository of the genotype data and provide tools and pipelines to perform genome- and phenome-wide association studies across a wide range of EHR-derived phenotypes and genetic ancestry groups. We demonstrate the utility of this resource through the analysis of 7 well-studied traits and recapitulate many previous genetic and phenotypic associations.

Genome-wide analyses of exonic copy number variants in a family-based study point to novel autism susceptibility genes.

The genetics underlying the autism spectrum disorders (ASDs) is complex and remains poorly understood. Previous work has demonstrated an important role for structural variation in a subset of cases, but has lacked the resolution necessary to move beyond detection of large regions of potential interest to identification of individual genes. To pinpoint genes likely to contribute to ASD etiology, we performed high density genotyping in 912 multiplex families from the Autism Genetics Resource Exchange (AGRE) collection and contrasted results to those obtained for 1,488 healthy controls. Through prioritization of exonic deletions (eDels), exonic duplications (eDups), and whole gene duplication events (gDups), we identified more than 150 loci harboring rare variants in multiple unrelated probands, but no controls. Importantly, 27 of these were confirmed on examination of an independent replication cohort comprised of 859 cases and an additional 1,051 controls. Rare variants at known loci, including exonic deletions at NRXN1 and whole gene duplications encompassing UBE3A and several other genes in the 15q11-q13 region, were observed in the course of these analyses. Strong support was likewise observed for previously unreported genes such as BZRAP1, an adaptor molecule known to regulate synaptic transmission, with eDels or eDups observed in twelve unrelated cases but no controls (p = 2.3x10(-5)). Less is known about MDGA2, likewise observed to be case-specific (p = 1.3x10(-4)). But, it is notable that the encoded protein shows an unexpectedly high similarity to Contactin 4 (BLAST E-value = 3x10(-39)), which has also been linked to disease. That hundreds of distinct rare variants were each seen only once further highlights complexity in the ASDs and points to the continued need for larger cohorts.

Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative.

BACKGROUND: Large medical centers in urban areas, like Los Angeles, care for a diverse patient population and offer the potential to study the interplay between genetic ancestry and social determinants of health. Here, we explore the implications of genetic ancestry within the University of California, Los Angeles (UCLA) ATLAS Community Health Initiative-an ancestrally diverse biobank of genomic data linked with de-identified electronic health records (EHRs) of UCLA Health patients (N=36,736). METHODS: We quantify the extensive continental and subcontinental genetic diversity within the ATLAS data through principal component analysis, identity-by-descent, and genetic admixture. We assess the relationship between genetically inferred ancestry (GIA) and >1500 EHR-derived phenotypes (phecodes). Finally, we demonstrate the utility of genetic data linked with EHR to perform ancestry-specific and multi-ancestry genome and phenome-wide scans across a broad set of disease phenotypes. RESULTS: We identify 5 continental-scale GIA clusters including European American (EA), African American (AA), Hispanic Latino American (HL), South Asian American (SAA) and East Asian American (EAA) individuals and 7 subcontinental GIA clusters within the EAA GIA corresponding to Chinese American, Vietnamese American, and Japanese American individuals. Although we broadly find that self-identified race/ethnicity (SIRE) is highly correlated with GIA, we still observe marked differences between the two, emphasizing that the populations defined by these two criteria are not analogous. We find a total of 259 significant associations between continental GIA and phecodes even after accounting for individuals' SIRE, demonstrating that for some phenotypes, GIA provides information not already captured by SIRE. GWAS identifies significant associations for liver disease in the 22q13.31 locus across the HL and EAA GIA groups (HL p-value=2.32×10-16, EAA p-value=6.73×10-11). A subsequent PheWAS at the top SNP reveals significant associations with neurologic and neoplastic phenotypes specifically within the HL GIA group. CONCLUSIONS: Overall, our results explore the interplay between SIRE and GIA within a disease context and underscore the utility of studying the genomes of diverse individuals through biobank-scale genotyping linked with EHR-based phenotyping.

Cost-effective methylome sequencing of cell-free DNA for accurately detecting and locating cancer.

Early cancer detection by cell-free DNA faces multiple challenges: low fraction of tumor cell-free DNA, molecular heterogeneity of cancer, and sample sizes that are not sufficient to reflect diverse patient populations. Here, we develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of the cell-free DNA methylome (with > 12-fold enrichment over whole genome bisulfite sequencing in CpG islands), and a computational method to extract methylation information and diagnose patients. Applying our approach to 408 colon, liver, lung, and stomach cancer patients and controls, at 97.9% specificity we achieve 80.7% and 74.5% sensitivity in detecting all-stage and early-stage cancer, and 89.1% and 85.0% accuracy for locating tissue-of-origin of all-stage and early-stage cancer, respectively. Our approach cost-effectively retains methylome profiles of cancer abnormalities, allowing us to learn new features and expand to other cancer types as training cohorts grow.

Correction: Electronic Video Consent to Power Precision Health Research: A Pilot Cohort Study.

[This corrects the article DOI: 10.2196/29123.].

Electronic Video Consent to Power Precision Health Research: A Pilot Cohort Study.

BACKGROUND: Developing innovative, efficient, and institutionally scalable biospecimen consent for remnant tissue that meets the National Institutes of Health consent guidelines for genomic and molecular analysis is essential for precision medicine efforts in cancer. OBJECTIVE: This study aims to pilot-test an electronic video consent that individuals could complete largely on their own. METHODS: The University of California, Los Angeles developed a video consenting approach designed to be comprehensive yet fast (around 5 minutes) for providing universal consent for remnant biospecimen collection for research. The approach was piloted in 175 patients who were coming in for routine services in laboratory medicine, radiology, oncology, and hospital admissions. The pilot yielded 164 completed postconsent surveys. The pilot assessed the usefulness, ease, and trustworthiness of the video consent. In addition, we explored drivers for opting in or opting out. RESULTS: The pilot demonstrated that the electronic video consent was well received by patients, with high scores for usefulness, ease, and trustworthiness even among patients that opted out of participation. The revised more animated video pilot test in phase 2 was better received in terms of ease of use (P=.005) and the ability to understand the information (P<.001). There were significant differences between those who opted in and opted out in their beliefs concerning the usefulness of tissue, trusting researchers, the importance of contributing to science, and privacy risk (P<.001). The results showed that "I trust researchers to use leftover biological specimens to promote the public's health" and "Sharing a biological sample for research is safe because of the privacy protections in place" discriminated opt-in statuses were the strongest predictors (both areas under the curve were 0.88). Privacy concerns seemed universal in individuals who opted out. CONCLUSIONS: Efforts to better educate the community may be needed to help overcome some of the barriers in engaging individuals to participate in precision health initiatives.

ASD symptoms in toddlers and preschoolers: An examination of sex differences.

Although considerable work has documented higher prevalence rates of autism spectrum disorder (ASD) in boys, fewer studies have focused on sex differences within samples of young children at-risk for ASD. This study examined sex differences in ASD symptom domains and ASD screening outcomes among toddlers (18-35 months) and preschoolers (36-72 months) with ASD-related concerns. Participants included 480 children between 18 and 72 months evaluated by university-based ASD specialty clinics. Results revealed significant sex differences in severity of social communication (SC) deficits across age groups. Within the toddler group, girls diagnosed with ASD displayed greater SC deficits according to standardized observation and clinician severity ratings. Within the preschool group, girls diagnosed with ASD were rated by parents as having more severe SC deficits, but these differences were not corroborated by standardized observations or clinician ratings. No sex differences emerged for severity of restricted repetitive behaviors (RRBs) for either age group. Across the entire referred sample, boys and girls did not differ in terms of scores on commonly used screening instruments. Importantly, results suggest that two of the most commonly used ASD screeners (i.e., Modified-Checklist for Autism in Toddlers-Revised with Follow-up and Social Communication Questionnaire ) may underidentify RRBs in toddler and preschool-aged girls as screening scores were only influenced by severity of SC deficits. Greater SC deficits in young girls with ASD along with its impact on screening status suggests greater attention be placed on the under-identification of ASD in girls as well as current screening measures' ability to tap into the topography of ASD symptoms across genders. Autism Res 2020, 13: 157-166. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: In this study, we found that young girls diagnosed with autism spectrum disorder tend to have greater social communication deficits than young boys and that these differences vary by age. Specifically, toddler-aged girls receive higher clinician ratings of social communication deficits when compared to boys, while preschool-aged girls receive higher parent ratings of social communication deficits. For girls, current screening tools seem to be more highly influenced by severity of social communication deficits than by restricted repetitive behaviors.

When Are We Sure? Predictors of Clinician Certainty in the Diagnosis of Autism Spectrum Disorder.

Differential diagnosis of autism spectrum disorder (ASD) is challenging, and uncertainty regarding a child's diagnosis may result in under-identification or prolonged diagnostic pathways. The current study examined diagnostic certainty, or how sure clinicians were that their diagnosis was accurate, among 478 toddler and preschool-aged children referred for possible ASD to academic medical specialty clinics. Overall, 60 percent of diagnoses were made with complete certainty. Clinicians were more certain when positively identifying ASD than ruling it out. Children presenting with a moderate (vs high or low) level of observable ASD symptoms were less likely to have a certain diagnosis. Further, clinicians rated less diagnostic certainty for older children, those with public insurance, and those with higher IQ and adaptive behavior abilities.

Offering to share: how to put heads together in autism neuroimaging.

Data sharing in autism neuroimaging presents scientific, technical, and social obstacles. We outline the desiderata for a data-sharing scheme that combines imaging with other measures of phenotype and with genetics, defines requirements for comparability of derived data and recommendations for raw data, outlines a core protocol including multispectral structural and diffusion-tensor imaging and optional extensions, provides for the collection of prospective, confound-free normative data, and extends sharing and collaborative development not only to data but to the analytical tools and methods applied to these data. A theme in these requirements is the need to preserve creative approaches and risk-taking within individual laboratories at the same time as common standards are provided for these laboratories to build on.

Autistic social impairment in the siblings of children with pervasive developmental disorders.

OBJECTIVE: Sibling recurrence risk in autism has been estimated to be approximately 10%. This study investigated subsyndromal autistic impairments among siblings of probands with pervasive developmental disorders. METHOD: The authors used the Social Responsiveness Scale to obtain quantitative assessments of autistic social impairment in three groups of proband-sibling pairs: 1) autistic children from multiple-incidence families and their closest in age nonautistic brothers (N=49 pairs); 2) children with any pervasive developmental disorder, including autism, and their closest-in-age brothers (N=100 pairs), and 3) children with psychopathology unrelated to autism and their closest-in-age brothers (N=45 pairs). RESULTS: Sibling Social Responsiveness Scale scores were continuously distributed and substantially elevated for both the autistic and pervasive developmental disorder groups. Highest scores (i.e., greatest impairment) were seen among siblings of autistic probands from multiple-incidence families, followed by siblings of probands with any pervasive developmental disorder, then siblings of probands with psychopathology unrelated to autism. CONCLUSIONS: Taken together with previous findings, these results support the notion that genetic susceptibility factors responsible for common, subsyndromal social impairments may be related to the causes of categorically defined pervasive developmental disorders.

Attentional modulation of the gap effect.

The gap effect refers to a reduction in the latency of saccades to peripherally appearing targets when the fixation point disappears a short time before target appearance. The effect has been attributed to a number of potential mechanisms that function to assist in the maintenance of fixation. One such mechanism, attention, has been the focus of some disagreement in the literature regarding the gap effect. In the present study, we had subjects attend to a portion of a complex fixation stimulus. On some trials the attended portion was removed prior to onset of a saccade target whereas on other trials an unattended portion was removed. Subjects were faster to initiate saccades when the attended portion was removed, thus establishing a role of attention in the gap effect. The results have important implications for our understanding of eye movements and the gap effect.