Inherited and De Novo Genetic Risk for Autism Impacts Shared Networks.

We performed a comprehensive assessment of rare inherited variation in autism spectrum disorder (ASD) by analyzing whole-genome sequences of 2,308 individuals from families with multiple affected children. We implicate 69 genes in ASD risk, including 24 passing genome-wide Bonferroni correction and 16 new ASD risk genes, most supported by rare inherited variants, a substantial extension of previous findings. Biological pathways enriched for genes harboring inherited variants represent cytoskeletal organization and ion transport, which are distinct from pathways implicated in previous studies. Nevertheless, the de novo and inherited genes contribute to a common protein-protein interaction network. We also identified structural variants (SVs) affecting non-coding regions, implicating recurrent deletions in the promoters of DLG2 and NR3C2. Loss of nr3c2 function in zebrafish disrupts sleep and social function, overlapping with human ASD-related phenotypes. These data support the utility of studying multiplex families in ASD and are available through the Hartwell Autism Research and Technology portal.

Localizing unmapped sequences with families to validate the Telomere-to-Telomere assembly and identify new hotspots for genetic diversity.

Although it is ubiquitous in genomics, the current human reference genome (GRCh38) is incomplete: It is missing large sections of heterochromatic sequence, and as a singular, linear reference genome, it does not represent the full spectrum of human genetic diversity. To characterize gaps in GRCh38 and human genetic diversity, we developed an algorithm for sequence location approximation using nuclear families (ASLAN) to identify the region of origin of reads that do not align to GRCh38. Using unmapped reads and variant calls from whole-genome sequences (WGSs), ASLAN uses a maximum likelihood model to identify the most likely region of the genome that a subsequence belongs to given the distribution of the subsequence in the unmapped reads and phasings of families. Validating ASLAN on synthetic data and on reads from the alternative haplotypes in the decoy genome, ASLAN localizes >90% of 100-bp sequences with >92% accuracy and ∼1 Mb of resolution. We then ran ASLAN on 100-mers from unmapped reads from WGS from more than 700 families, and compared ASLAN localizations to alignment of the 100-mers to the recently released T2T-CHM13 assembly. We found that many unmapped reads in GRCh38 originate from telomeres and centromeres that are gaps in GRCh38. ASLAN localizations are in high concordance with T2T-CHM13 alignments, except in the centromeres of the acrocentric chromosomes. Comparing ASLAN localizations and T2T-CHM13 alignments, we identified sequences missing from T2T-CHM13 or sequences with high divergence from their aligned region in T2T-CHM13, highlighting new hotspots for genetic diversity.

Identifying crossovers and shared genetic material in whole genome sequencing data from families.

Large, whole-genome sequencing (WGS) data sets containing families provide an important opportunity to identify crossovers and shared genetic material in siblings. However, the high variant calling error rates of WGS in some areas of the genome can result in spurious crossover calls, and the special inheritance status of the X Chromosome presents challenges. We have developed a hidden Markov model that addresses these issues by modeling the inheritance of variants in families in the presence of error-prone regions and inherited deletions. We call our method PhasingFamilies. We validate PhasingFamilies using the platinum genome family NA1281 (precision: 0.81; recall: 0.97), as well as simulated genomes with known crossover positions (precision: 0.93; recall: 0.92). Using 1925 quads from the Simons Simplex Collection, we found that PhasingFamilies resolves crossovers to a median resolution of 3527.5 bp. These crossovers recapitulate existing recombination rate maps, including for the X Chromosome; produce sibling pair IBD that matches expected distributions; and are validated by the haplotype estimation tool SHAPEIT. We provide an efficient, open-source implementation of PhasingFamilies that can be used to identify crossovers from family sequencing data.

Transmission dynamics of human herpesvirus 6A, 6B and 7 from whole genome sequences of families.

While hundreds of thousands of human whole genome sequences (WGS) have been collected in the effort to better understand genetic determinants of disease, these whole genome sequences have less frequently been used to study another major determinant of human health: the human virome. Using the unmapped reads from WGS of over 1000 families, we present insights into the human blood DNA virome, focusing particularly on human herpesvirus (HHV) 6A, 6B, and 7. In addition to extensively cataloguing the viruses detected in WGS of human whole blood and lymphoblastoid cell lines, we use the family structure of our dataset to show that household drives transmission of several viruses, and identify the Mendelian inheritance patterns characteristic of inherited chromsomally integrated human herpesvirus 6 (iciHHV-6). Consistent with prior studies, we find that 0.6% of our dataset's population has iciHHV, and we locate candidate integration sequences for these cases. We document genetic diversity within exogenous and integrated HHV species and within integration sites of HHV-6. Finally, in the first observation of its kind, we present evidence that suggests widespread de novo HHV-6B integration and HHV-7 integration and reactivation in lymphoblastoid cell lines. These findings show that the unmapped read space of WGS is a promising source of data for virology research.

Identification of Social Engagement Indicators Associated With Autism Spectrum Disorder Using a Game-Based Mobile App: Comparative Study of Gaze Fixation and Visual Scanning Methods.

BACKGROUND: Autism spectrum disorder (ASD) is a widespread neurodevelopmental condition with a range of potential causes and symptoms. Standard diagnostic mechanisms for ASD, which involve lengthy parent questionnaires and clinical observation, often result in long waiting times for results. Recent advances in computer vision and mobile technology hold potential for speeding up the diagnostic process by enabling computational analysis of behavioral and social impairments from home videos. Such techniques can improve objectivity and contribute quantitatively to the diagnostic process. OBJECTIVE: In this work, we evaluate whether home videos collected from a game-based mobile app can be used to provide diagnostic insights into ASD. To the best of our knowledge, this is the first study attempting to identify potential social indicators of ASD from mobile phone videos without the use of eye-tracking hardware, manual annotations, and structured scenarios or clinical environments. METHODS: Here, we used a mobile health app to collect over 11 hours of video footage depicting 95 children engaged in gameplay in a natural home environment. We used automated data set annotations to analyze two social indicators that have previously been shown to differ between children with ASD and their neurotypical (NT) peers: (1) gaze fixation patterns, which represent regions of an individual's visual focus and (2) visual scanning methods, which refer to the ways in which individuals scan their surrounding environment. We compared the gaze fixation and visual scanning methods used by children during a 90-second gameplay video to identify statistically significant differences between the 2 cohorts; we then trained a long short-term memory (LSTM) neural network to determine if gaze indicators could be predictive of ASD. RESULTS: Our results show that gaze fixation patterns differ between the 2 cohorts; specifically, we could identify 1 statistically significant region of fixation (P<.001). In addition, we also demonstrate that there are unique visual scanning patterns that exist for individuals with ASD when compared to NT children (P<.001). A deep learning model trained on coarse gaze fixation annotations demonstrates mild predictive power in identifying ASD. CONCLUSIONS: Ultimately, our study demonstrates that heterogeneous video data sets collected from mobile devices hold potential for quantifying visual patterns and providing insights into ASD. We show the importance of automated labeling techniques in generating large-scale data sets while simultaneously preserving the privacy of participants, and we demonstrate that specific social engagement indicators associated with ASD can be identified and characterized using such data.

Improved detection of disease-associated gut microbes using 16S sequence-based biomarkers.

BACKGROUND: Sequencing partial 16S rRNA genes is a cost effective method for quantifying the microbial composition of an environment, such as the human gut. However, downstream analysis relies on binning reads into microbial groups by either considering each unique sequence as a different microbe, querying a database to get taxonomic labels from sequences, or clustering similar sequences together. However, these approaches do not fully capture evolutionary relationships between microbes, limiting the ability to identify differentially abundant groups of microbes between a diseased and control cohort. We present sequence-based biomarkers (SBBs), an aggregation method that groups and aggregates microbes using single variants and combinations of variants within their 16S sequences. We compare SBBs against other existing aggregation methods (OTU clustering and Microphenoor DiTaxa features) in several benchmarking tasks: biomarker discovery via permutation test, biomarker discovery via linear discriminant analysis, and phenotype prediction power. We demonstrate the SBBs perform on-par or better than the state-of-the-art methods in biomarker discovery and phenotype prediction. RESULTS: On two independent datasets, SBBs identify differentially abundant groups of microbes with similar or higher statistical significance than existing methods in both a permutation-test-based analysis and using linear discriminant analysis effect size. . By grouping microbes by SBB, we can identify several differentially abundant microbial groups (FDR <.1) between children with autism and neurotypical controls in a set of 115 discordant siblings. Porphyromonadaceae, Ruminococcaceae, and an unnamed species of Blastocystis were significantly enriched in autism, while Veillonellaceae was significantly depleted. Likewise, aggregating microbes by SBB on a dataset of obese and lean twins, we find several significantly differentially abundant microbial groups (FDR<.1). We observed Megasphaera andSutterellaceae highly enriched in obesity, and Phocaeicola significantly depleted. SBBs also perform on bar with or better than existing aggregation methods as features in a phenotype prediction model, predicting the autism phenotype with an ROC-AUC score of .64 and the obesity phenotype with an ROC-AUC score of .84. CONCLUSIONS: SBBs provide a powerful method for aggregating microbes to perform differential abundance analysis as well as phenotype prediction. Our source code can be freely downloaded from http://github.com/briannachrisman/16s_biomarkers .

Game theoretic centrality: a novel approach to prioritize disease candidate genes by combining biological networks with the Shapley value.

BACKGROUND: Complex human health conditions with etiological heterogeneity like Autism Spectrum Disorder (ASD) often pose a challenge for traditional genome-wide association study approaches in defining a clear genotype to phenotype model. Coalitional game theory (CGT) is an exciting method that can consider the combinatorial effect of groups of variants working in concert to produce a phenotype. CGT has been applied to associate likely-gene-disrupting variants encoded from whole genome sequence data to ASD; however, this previous approach cannot take into account for prior biological knowledge. Here we extend CGT to incorporate a priori knowledge from biological networks through a game theoretic centrality measure based on Shapley value to rank genes by their relevance-the individual gene's synergistic influence in a gene-to-gene interaction network. Game theoretic centrality extends the notion of Shapley value to the evaluation of a gene's contribution to the overall connectivity of its corresponding node in a biological network. RESULTS: We implemented and applied game theoretic centrality to rank genes on whole genomes from 756 multiplex autism families. Top ranking genes with the highest game theoretic centrality in both the weighted and unweighted approaches were enriched for pathways previously associated with autism, including pathways of the immune system. Four of the selected genes HLA-A, HLA-B, HLA-G, and HLA-DRB1-have also been implicated in ASD and further support the link between ASD and the human leukocyte antigen complex. CONCLUSIONS: Game theoretic centrality can prioritize influential, disease-associated genes within biological networks, and assist in the decoding of polygenic associations to complex disorders like autism.

Validity of Online Screening for Autism: Crowdsourcing Study Comparing Paid and Unpaid Diagnostic Tasks.

BACKGROUND: Obtaining a diagnosis of neuropsychiatric disorders such as autism requires long waiting times that can exceed a year and can be prohibitively expensive. Crowdsourcing approaches may provide a scalable alternative that can accelerate general access to care and permit underserved populations to obtain an accurate diagnosis. OBJECTIVE: We aimed to perform a series of studies to explore whether paid crowd workers on Amazon Mechanical Turk (AMT) and citizen crowd workers on a public website shared on social media can provide accurate online detection of autism, conducted via crowdsourced ratings of short home video clips. METHODS: Three online studies were performed: (1) a paid crowdsourcing task on AMT (N=54) where crowd workers were asked to classify 10 short video clips of children as "Autism" or "Not autism," (2) a more complex paid crowdsourcing task (N=27) with only those raters who correctly rated ≥8 of the 10 videos during the first study, and (3) a public unpaid study (N=115) identical to the first study. RESULTS: For Study 1, the mean score of the participants who completed all questions was 7.50/10 (SD 1.46). When only analyzing the workers who scored ≥8/10 (n=27/54), there was a weak negative correlation between the time spent rating the videos and the sensitivity (ρ=-0.44, P=.02). For Study 2, the mean score of the participants rating new videos was 6.76/10 (SD 0.59). The average deviation between the crowdsourced answers and gold standard ratings provided by two expert clinical research coordinators was 0.56, with an SD of 0.51 (maximum possible SD is 3). All paid crowd workers who scored 8/10 in Study 1 either expressed enjoyment in performing the task in Study 2 or provided no negative comments. For Study 3, the mean score of the participants who completed all questions was 6.67/10 (SD 1.61). There were weak correlations between age and score (r=0.22, P=.014), age and sensitivity (r=-0.19, P=.04), number of family members with autism and sensitivity (r=-0.195, P=.04), and number of family members with autism and precision (r=-0.203, P=.03). A two-tailed t test between the scores of the paid workers in Study 1 and the unpaid workers in Study 3 showed a significant difference (P<.001). CONCLUSIONS: Many paid crowd workers on AMT enjoyed answering screening questions from videos, suggesting higher intrinsic motivation to make quality assessments. Paid crowdsourcing provides promising screening assessments of pediatric autism with an average deviation <20% from professional gold standard raters, which is potentially a clinically informative estimate for parents. Parents of children with autism likely overfit their intuition to their own affected child. This work provides preliminary demographic data on raters who may have higher ability to recognize and measure features of autism across its wide range of phenotypic manifestations.

Addendum to the Acknowledgements: Validity of Online Screening for Autism: Crowdsourcing Study Comparing Paid and Unpaid Diagnostic Tasks.

[This corrects the article DOI: 10.2196/13668.].

The Saccharomyces Genome Database Variant Viewer.

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is the authoritative community resource for the Saccharomyces cerevisiae reference genome sequence and its annotation. In recent years, we have moved toward increased representation of sequence variation and allelic differences within S. cerevisiae. The publication of numerous additional genomes has motivated the creation of new tools for their annotation and analysis. Here we present the Variant Viewer: a dynamic open-source web application for the visualization of genomic and proteomic differences. Multiple sequence alignments have been constructed across high quality genome sequences from 11 different S. cerevisiae strains and stored in the SGD. The alignments and summaries are encoded in JSON and used to create a two-tiered dynamic view of the budding yeast pan-genome, available at http://www.yeastgenome.org/variant-viewer.

Saccharomyces genome database provides new regulation data.

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is the community resource for genomic, gene and protein information about the budding yeast Saccharomyces cerevisiae, containing a variety of functional information about each yeast gene and gene product. We have recently added regulatory information to SGD and present it on a new tabbed section of the Locus Summary entitled 'Regulation'. We are compiling transcriptional regulator-target gene relationships, which are curated from the literature at SGD or imported, with permission, from the YEASTRACT database. For nearly every S. cerevisiae gene, the Regulation page displays a table of annotations showing the regulators of that gene, and a graphical visualization of its regulatory network. For genes whose products act as transcription factors, the Regulation page also shows a table of their target genes, accompanied by a Gene Ontology enrichment analysis of the biological processes in which those genes participate. We additionally synthesize information from the literature for each transcription factor in a free-text Regulation Summary, and provide other information relevant to its regulatory function, such as DNA binding site motifs and protein domains. All of the regulation data are available for querying, analysis and download via YeastMine, the InterMine-based data warehouse system in use at SGD.

Racial and Ethnic Disparities in Geographic Access to Autism Resources Across the US.

Importance: While research has identified racial and ethnic disparities in access to autism services, the size, extent, and specific locations of these access gaps have not yet been characterized on a national scale. Mapping comprehensive national listings of autism health care services together with the prevalence of autistic children of various races and ethnicities and evaluating geographic regions defined by localized commuting patterns may help to identify areas within the US where families who belong to minoritized racial and ethnic groups have disproportionally lower access to services. Objective: To evaluate differences in access to autism health care services among autistic children of various races and ethnicities within precisely defined geographic regions encompassing all serviceable areas within the US. Design, Setting, and Participants: This population-based cross-sectional study was conducted from October 5, 2021, to June 3, 2022, and involved 530 965 autistic children in kindergarten through grade 12. Core-based statistical areas (CBSAs; defined as areas containing a city and its surrounding commuter region), the Civil Rights Data Collection (CRDC) data set, and 51 071 autism resources (collected from October 1, 2015, to December 18, 2022) geographically distributed into 912 CBSAs were combined and analyzed to understand variation in access to autism health care services among autistic children of different races and ethnicities. Six racial and ethnic categories (American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or other Pacific Islander, and White) assigned by the US Department of Education were included in the analysis. Main Outcomes and Measures: A regularized least-squares regression analysis was used to measure differences in nationwide resource allocation between racial and ethnic groups. The number of autism resources allocated per autistic child was estimated based on the child's racial and ethnic group. To evaluate how the CBSA population size may have altered the results, the least-squares regression analysis was run on CBSAs divided into metropolitan (>50 000 inhabitants) and micropolitan (10 000-50 000 inhabitants) groups. A Mann-Whitney U test was used to compare the model estimated ratio of autism resources to autistic children among specific racial and ethnic groups comprising the proportions of autistic children in each CBSA. Results: Among 530 965 autistic children aged 5 to 18 years, 83.9% were male and 16.1% were female; 0.7% of children were American Indian or Alaska Native, 5.9% were Asian, 14.3% were Black or African American, 22.9% were Hispanic or Latino, 0.2% were Native Hawaiian or other Pacific Islander, 51.7% were White, and 4.2% were of 2 or more races and/or ethnicities. At a national scale, American Indian or Alaska Native autistic children (ß = 0; 95% CI, 0-0; P = .01) and Hispanic autistic children (ß = 0.02; 95% CI, 0-0.06; P = .02) had significant disparities in access to autism resources in comparison with White autistic children. When evaluating the proportion of autistic children in each racial and ethnic group, areas in which Black autistic children (>50% of the population: ß = 0.05; <50% of the population: ß = 0.07; P = .002) or Hispanic autistic children (>50% of the population: ß = 0.04; <50% of the population: ß = 0.07; P < .001) comprised greater than 50% of the total population of autistic children had significantly fewer resources than areas in which Black or Hispanic autistic children comprised less than 50% of the total population. Comparing metropolitan vs micropolitan CBSAs revealed that in micropolitan CBSAs, Black autistic children (ß = 0; 95% CI, 0-0; P < .001) and Hispanic autistic children (ß = 0; 95% CI, 0-0.02; P < .001) had the greatest disparities in access to autism resources compared with White autistic children. In metropolitan CBSAs, American Indian or Alaska Native autistic children (ß = 0; 95% CI, 0-0; P = .005) and Hispanic autistic children (ß = 0.01; 95% CI, 0-0.06; P = .02) had the greatest disparities compared with White autistic children. Conclusions and Relevance: In this study, autistic children from several minoritized racial and ethnic groups, including Black and Hispanic autistic children, had access to significantly fewer autism resources than White autistic children in the US. This study pinpointed the specific geographic regions with the greatest disparities, where increases in the number and types of treatment options are warranted. These findings suggest that a prioritized response strategy to address these racial and ethnic disparities is needed.

Causal Modeling to Mitigate Selection Bias and Unmeasured Confounding in Internet-Based Epidemiology of COVID-19: Model Development and Validation.

BACKGROUND: Selection bias and unmeasured confounding are fundamental problems in epidemiology that threaten study internal and external validity. These phenomena are particularly dangerous in internet-based public health surveillance, where traditional mitigation and adjustment methods are inapplicable, unavailable, or out of date. Recent theoretical advances in causal modeling can mitigate these threats, but these innovations have not been widely deployed in the epidemiological community. OBJECTIVE: The purpose of our paper is to demonstrate the practical utility of causal modeling to both detect unmeasured confounding and selection bias and guide model selection to minimize bias. We implemented this approach in an applied epidemiological study of the COVID-19 cumulative infection rate in the New York City (NYC) spring 2020 epidemic. METHODS: We collected primary data from Qualtrics surveys of Amazon Mechanical Turk (MTurk) crowd workers residing in New Jersey and New York State across 2 sampling periods: April 11-14 and May 8-11, 2020. The surveys queried the subjects on household health status and demographic characteristics. We constructed a set of possible causal models of household infection and survey selection mechanisms and ranked them by compatibility with the collected survey data. The most compatible causal model was then used to estimate the cumulative infection rate in each survey period. RESULTS: There were 527 and 513 responses collected for the 2 periods, respectively. Response demographics were highly skewed toward a younger age in both survey periods. Despite the extremely strong relationship between age and COVID-19 symptoms, we recovered minimally biased estimates of the cumulative infection rate using only primary data and the most compatible causal model, with a relative bias of +3.8% and -1.9% from the reported cumulative infection rate for the first and second survey periods, respectively. CONCLUSIONS: We successfully recovered accurate estimates of the cumulative infection rate from an internet-based crowdsourced sample despite considerable selection bias and unmeasured confounding in the primary data. This implementation demonstrates how simple applications of structural causal modeling can be effectively used to determine falsifiable model conditions, detect selection bias and confounding factors, and minimize estimate bias through model selection in a novel epidemiological context. As the disease and social dynamics of COVID-19 continue to evolve, public health surveillance protocols must continue to adapt; the emergence of Omicron variants and shift to at-home testing as recent challenges. Rigorous and transparent methods to develop, deploy, and diagnosis adapted surveillance protocols will be critical to their success.

The human "contaminome": bacterial, viral, and computational contamination in whole genome sequences from 1000 families.

The unmapped readspace of whole genome sequencing data tends to be large but is often ignored. We posit that it contains valuable signals of both human infection and contamination. Using unmapped and poorly aligned reads from whole genome sequences (WGS) of over 1000 families and nearly 5000 individuals, we present insights into common viral, bacterial, and computational contamination that plague whole genome sequencing studies. We present several notable results: (1) In addition to known contaminants such as Epstein-Barr virus and phiX, sequences from whole blood and lymphocyte cell lines contain many other contaminants, likely originating from storage, prep, and sequencing pipelines. (2) Sequencing plate and biological sample source of a sample strongly influence contamination profile. And, (3) Y-chromosome fragments not on the human reference genome commonly mismap to bacterial reference genomes. Both experiment-derived and computational contamination is prominent in next-generation sequencing data. Such contamination can compromise results from WGS as well as metagenomics studies, and standard protocols for identifying and removing contamination should be developed to ensure the fidelity of sequencing-based studies.

Crowd annotations can approximate clinical autism impressions from short home videos with privacy protections.

Artificial Intelligence (A.I.) solutions are increasingly considered for telemedicine. For these methods to serve children and their families in home settings, it is crucial to ensure the privacy of the child and parent or caregiver. To address this challenge, we explore the potential for global image transformations to provide privacy while preserving the quality of behavioral annotations. Crowd workers have previously been shown to reliably annotate behavioral features in unstructured home videos, allowing machine learning classifiers to detect autism using the annotations as input. We evaluate this method with videos altered via pixelation, dense optical flow, and Gaussian blurring. On a balanced test set of 30 videos of children with autism and 30 neurotypical controls, we find that the visual privacy alterations do not drastically alter any individual behavioral annotation at the item level. The AUROC on the evaluation set was 90.0% ±7.5% for unaltered videos, 85.0% ±9.0% for pixelation, 85.0% ±9.0% for optical flow, and 83.3% ±9.3% for blurring, demonstrating that an aggregation of small changes across behavioral questions can collectively result in increased misdiagnosis rates. We also compare crowd answers against clinicians who provided the same annotations for the same videos as crowd workers, and we find that clinicians have higher sensitivity in their recognition of autism-related symptoms. We also find that there is a linear correlation (r = 0.75, p < 0.0001) between the mean Clinical Global Impression (CGI) score provided by professional clinicians and the corresponding score emitted by a previously validated autism classifier with crowd inputs, indicating that the classifier's output probability is a reliable estimate of the clinical impression of autism. A significant correlation is maintained with privacy alterations, indicating that crowd annotations can approximate clinician-provided autism impression from home videos in a privacy-preserved manner.

An Informatics Analysis to Identify Sex Disparities and Healthcare Needs for Autism across the United States.

Autism is among the most common neurodevelopmental conditions. Timely diagnosis and access to therapeutic resources are essential for positive prognoses, yet long queues and unevenly dispersed resources leave many untreated. Without granular estimates of autism prevalence by geographic area, it is difficult to identify unmet needs and mechanisms to address them. Mining a dataset of 53M children using meaningful geographic regions, we computed autism prevalence across the country. We then performed comparative analysis against 50,000 resources to identify the type and extent of gaps in access to autism services. We find a steady increase in autism diagnoses from K-5, supporting delayed diagnosis of autism, and consistent under-diagnosis of females. We find a significant inverse relationship between prevalence and availability of resources (p < 0.001). While more work is needed to characterize additional trends including racial and ethnicity-based disparities, the identification of resource gaps can direct and prioritize new innovations.

A Method for Localizing Non-Reference Sequences to the Human Genome.

As the last decade of human genomics research begins to bear the fruit of advancements in precision medicine, it is important to ensure that genomics' improvements in human health are distributed globally and equitably. An important step to ensuring health equity is to improve the human reference genome to capture global diversity by including a wide variety of alternative haplotypes, sequences that are not currently captured on the reference genome.We present a method that localizes 100 basepair (bp) long sequences extracted from short-read sequencing that can ultimately be used to identify what regions of the human genome non-reference sequences belong to.We extract reads that don't align to the reference genome, and compute the population's distribution of 100-mers found within the unmapped reads. We use genetic data from families to identify shared genetic material between siblings and match the distribution of unmapped k-mers to these inheritance patterns to determine the the most likely genomic region of a k-mer. We perform this localization with two highly interpretable methods of artificial intelligence: a computationally tractable Hidden Markov Model coupled to a Maximum Likelihood Estimator. Using a set of alternative haplotypes with known locations on the genome, we show that our algorithm is able to localize 96% of k-mers with over 90% accuracy and less than 1Mb median resolution. As the collection of sequenced human genomes grows larger and more diverse, we hope that this method can be used to improve the human reference genome, a critical step in addressing precision medicine's diversity crisis.

Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study.

BACKGROUND: Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult emotion and therefore underperform when applied to child faces. OBJECTIVE: We designed a strategy to gamify the collection and labeling of child emotion-enriched images to boost the performance of automatic child emotion recognition models to a level closer to what will be needed for digital health care approaches. METHODS: We leveraged our prototype therapeutic smartphone game, GuessWhat, which was designed in large part for children with developmental and behavioral conditions, to gamify the secure collection of video data of children expressing a variety of emotions prompted by the game. Independently, we created a secure web interface to gamify the human labeling effort, called HollywoodSquares, tailored for use by any qualified labeler. We gathered and labeled 2155 videos, 39,968 emotion frames, and 106,001 labels on all images. With this drastically expanded pediatric emotion-centric database (>30 times larger than existing public pediatric emotion data sets), we trained a convolutional neural network (CNN) computer vision classifier of happy, sad, surprised, fearful, angry, disgust, and neutral expressions evoked by children. RESULTS: The classifier achieved a 66.9% balanced accuracy and 67.4% F1-score on the entirety of the Child Affective Facial Expression (CAFE) as well as a 79.1% balanced accuracy and 78% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels. This performance is at least 10% higher than all previously developed classifiers evaluated against CAFE, the best of which reached a 56% balanced accuracy even when combining "anger" and "disgust" into a single class. CONCLUSIONS: This work validates that mobile games designed for pediatric therapies can generate high volumes of domain-relevant data sets to train state-of-the-art classifiers to perform tasks helpful to precision health efforts.

Estimating sequencing error rates using families.

BACKGROUND: As next-generation sequencing technologies make their way into the clinic, knowledge of their error rates is essential if they are to be used to guide patient care. However, sequencing platforms and variant-calling pipelines are continuously evolving, making it difficult to accurately quantify error rates for the particular combination of assay and software parameters used on each sample. Family data provide a unique opportunity for estimating sequencing error rates since it allows us to observe a fraction of sequencing errors as Mendelian errors in the family, which we can then use to produce genome-wide error estimates for each sample. RESULTS: We introduce a method that uses Mendelian errors in sequencing data to make highly granular per-sample estimates of precision and recall for any set of variant calls, regardless of sequencing platform or calling methodology. We validate the accuracy of our estimates using monozygotic twins, and we use a set of monozygotic quadruplets to show that our predictions closely match the consensus method. We demonstrate our method's versatility by estimating sequencing error rates for whole genome sequencing, whole exome sequencing, and microarray datasets, and we highlight its sensitivity by quantifying performance increases between different versions of the GATK variant-calling pipeline. We then use our method to demonstrate that: 1) Sequencing error rates between samples in the same dataset can vary by over an order of magnitude. 2) Variant calling performance decreases substantially in low-complexity regions of the genome. 3) Variant calling performance in whole exome sequencing data decreases with distance from the nearest target region. 4) Variant calls from lymphoblastoid cell lines can be as accurate as those from whole blood. 5) Whole-genome sequencing can attain microarray-level precision and recall at disease-associated SNV sites. CONCLUSION: Genotype datasets from families are powerful resources that can be used to make fine-grained estimates of sequencing error for any sequencing platform and variant-calling methodology.

Indels in SARS-CoV-2 occur at template-switching hotspots.

The evolutionary dynamics of SARS-CoV-2 have been carefully monitored since the COVID-19 pandemic began in December 2019. However, analysis has focused primarily on single nucleotide polymorphisms and largely ignored the role of insertions and deletions (indels) as well as recombination in SARS-CoV-2 evolution. Using sequences from the GISAID database, we catalogue over 100 insertions and deletions in the SARS-CoV-2 consensus sequences. We hypothesize that these indels are artifacts of recombination events between SARS-CoV-2 replicates whereby RNA-dependent RNA polymerase (RdRp) re-associates with a homologous template at a different loci ("imperfect homologous recombination"). We provide several independent pieces of evidence that suggest this. (1) The indels from the GISAID consensus sequences are clustered at specific regions of the genome. (2) These regions are also enriched for 5' and 3' breakpoints in the transcription regulatory site (TRS) independent transcriptome, presumably sites of RNA-dependent RNA polymerase (RdRp) template-switching. (3) Within raw reads, these indel hotspots have cases of both high intra-host heterogeneity and intra-host homogeneity, suggesting that these indels are both consequences of de novo recombination events within a host and artifacts of previous recombination. We briefly analyze the indels in the context of RNA secondary structure, noting that indels preferentially occur in "arms" and loop structures of the predicted folded RNA, suggesting that secondary structure may be a mechanism for TRS-independent template-switching in SARS-CoV-2 or other coronaviruses. These insights into the relationship between structural variation and recombination in SARS-CoV-2 can improve our reconstructions of the SARS-CoV-2 evolutionary history as well as our understanding of the process of RdRp template-switching in RNA viruses.