Proteome-Wide Assessment of Clustering of Missense Variants in Neurodevelopmental Disorders Versus Cancer.

Missense de novo variants (DNVs) and missense somatic variants contribute to neurodevelopmental disorders (NDDs) and cancer, respectively. Proteins with statistical enrichment based on analyses of these variants exhibit convergence in the differing NDD and cancer phenotypes. Herein, the question of why some of the same proteins are identified in both phenotypes is examined through investigation of clustering of missense variation at the protein level. Our hypothesis is that missense variation is present in different protein locations in the two phenotypes leading to the distinct phenotypic outcomes. We tested this hypothesis in 1D protein space using our software CLUMP. Furthermore, we newly developed 3D-CLUMP that uses 3D protein structures to spatially test clustering of missense variation for proteome-wide significance. We examined missense DNVs in 39,883 parent-child sequenced trios with NDDs and missense somatic variants from 10,543 sequenced tumors covering five TCGA cancer types and two COSMIC pan-cancer aggregates of tissue types. There were 57 proteins with proteome-wide significant missense variation clustering in NDDs when compared to cancers and 79 proteins with proteome-wide significant missense clustering in cancers compared to NDDs. While our main objective was to identify differences in patterns of missense variation, we also identified a novel NDD protein BLTP2. Overall, our study is innovative, provides new insights into differential missense variation in NDDs and cancer at the protein-level, and contributes necessary information toward building a framework for thinking about prognostic and therapeutic aspects of these proteins.

HAT: de novo variant calling for highly accurate short-read and long-read sequencing data.

MOTIVATION: de novo variants (DNVs) are variants that are present in offspring but not in their parents. DNVs are both important for examining mutation rates as well as in the identification of disease-related variation. While efforts have been made to call DNVs, calling of DNVs is still challenging from parent-child sequenced trio data. We developed Hare And Tortoise (HAT) as an automated DNV detection workflow for highly accurate short-read and long-read sequencing data. Reliable detection of DNVs is important for human genomics and HAT addresses this need. RESULTS: HAT is a computational workflow that begins with aligned read data (i.e. CRAM or BAM) from a parent-child sequenced trio and outputs DNVs. HAT detects high-quality DNVs from Illumina short-read whole-exome sequencing, Illumina short-read whole-genome sequencing, and highly accurate PacBio HiFi long-read whole-genome sequencing data. The quality of these DNVs is high based on a series of quality metrics including number of DNVs per individual, percent of DNVs at CpG sites, and percent of DNVs phased to the paternal chromosome of origin. AVAILABILITY AND IMPLEMENTATION: https://github.com/TNTurnerLab/HAT.

HAT: de novo variant calling for highly accurate short-read and long-read sequencing data.

Motivation: de novo variant (DNV) calling is challenging from parent-child sequenced trio data. We developed Hare And Tortoise (HAT) to work as an automated workflow to detect DNVs in highly accurate short-read and long-read sequencing data. Reliable detection of DNVs is important for human genetics studies (e.g., autism, epilepsy). Results: HAT is a workflow to detect DNVs from short-read and long read sequencing data. This workflow begins with aligned read data (i.e., CRAM or BAM) from a parent-child sequenced trio and outputs DNVs. HAT detects high-quality DNVs from short-read whole-exome sequencing, short-read whole-genome sequencing, and highly accurate long-read sequencing data.

de novo variant calling identifies cancer mutation signatures in the 1000 Genomes Project.

Detection of de novo variants (DNVs) is critical for studies of disease-related variation and mutation rates. To accelerate DNV calling, we developed a graphics processing units-based workflow. We applied our workflow to whole-genome sequencing data from three parent-child sequenced cohorts including the Simons Simplex Collection (SSC), Simons Foundation Powering Autism Research (SPARK), and the 1000 Genomes Project (1000G) that were sequenced using DNA from blood, saliva, and lymphoblastoid cell lines (LCLs), respectively. The SSC and SPARK DNV callsets were within expectations for number of DNVs, percent at CpG sites, phasing to the paternal chromosome of origin, and average allele balance. However, the 1000G DNV callset was not within expectations and contained excessive DNVs that are likely cell line artifacts. Mutation signature analysis revealed 30% of 1000G DNV signatures matched B-cell lymphoma. Furthermore, we found variants in DNA repair genes and at Clinvar pathogenic or likely-pathogenic sites and significant excess of protein-coding DNVs in IGLL5; a gene known to be involved in B-cell lymphomas. Our study provides a new rapid DNV caller for the field and elucidates important implications of using sequencing data from LCLs for reference building and disease-related projects.

From karyotypes to precision genomics in 9p deletion and duplication syndromes.

While 9p deletion and duplication syndromes have been studied for several years, small sample sizes and minimal high-resolution data have limited a comprehensive delineation of genotypic and phenotypic characteristics. In this study, we examined genetic data from 719 individuals in the worldwide 9p Network Cohort: a cohort seven to nine times larger than any previous study of 9p. Most breakpoints occur in bands 9p22 and 9p24, accounting for 35% and 38% of all breakpoints, respectively. Bands 9p11 and 9p12 have the fewest breakpoints, with each accounting for 0.6% of all breakpoints. The most common phenotype in 9p deletion and duplication syndromes is developmental delay, and we identified eight known neurodevelopmental disorder genes in 9p22 and 9p24. Since it has been previously reported that some individuals have a secondary structural variant related to the 9p variant, we examined our cohort for these variants and found 97 events. The top secondary variant involved 9q in 14 individuals (1.9%), including ring chromosomes and inversions. We identified a gender bias with significant enrichment for females (p = 0.0006) that may arise from a sex reversal in some individuals with 9p deletions. Genes on 9p were characterized regarding function, constraint metrics, and protein-protein interactions, resulting in a prioritized set of genes for further study. Finally, we achieved precision genomics in one child with a complex 9p structural variation using modern genomic technologies, demonstrating that long-read sequencing will be integral for some cases. Our study is the largest ever on 9p-related syndromes and provides key insights into genetic factors involved in these syndromes.

ACES: Analysis of Conservation with an Extensive list of Species.

MOTIVATION: An abundance of new reference genomes is becoming available through large-scale sequencing efforts. While the reference FASTA for each genome is available, there is currently no automated mechanism to query a specific sequence across all new reference genomes. RESULTS: We developed ACES (Analysis of Conservation with an Extensive list of Species) as a computational workflow to query specific sequences of interest (e.g. enhancers, promoters, exons) against reference genomes with an available reference FASTA. This automated workflow generates BLAST hits against each of the reference genomes, a multiple sequence alignment file, a graphical fragment assembly file and a phylogenetic tree file. These data files can then be used by the researcher in several ways to provide key insights into conservation of the query sequence. AVAILABILITY AND IMPLEMENTATION: ACES is available at https://github.com/TNTurnerLab/ACES. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Coding and noncoding variants in EBF3 are involved in HADDS and simplex autism.

BACKGROUND: Previous research in autism and other neurodevelopmental disorders (NDDs) has indicated an important contribution of protein-coding (coding) de novo variants (DNVs) within specific genes. The role of de novo noncoding variation has been observable as a general increase in genetic burden but has yet to be resolved to individual functional elements. In this study, we assessed whole-genome sequencing data in 2671 families with autism (discovery cohort of 516 families, replication cohort of 2155 families). We focused on DNVs in enhancers with characterized in vivo activity in the brain and identified an excess of DNVs in an enhancer named hs737. RESULTS: We adapted the fitDNM statistical model to work in noncoding regions and tested enhancers for excess of DNVs in families with autism. We found only one enhancer (hs737) with nominal significance in the discovery (p = 0.0172), replication (p = 2.5 × 10-3), and combined dataset (p = 1.1 × 10-4). Each individual with a DNV in hs737 had shared phenotypes including being male, intact cognitive function, and hypotonia or motor delay. Our in vitro assessment of the DNVs showed they all reduce enhancer activity in a neuronal cell line. By epigenomic analyses, we found that hs737 is brain-specific and targets the transcription factor gene EBF3 in human fetal brain. EBF3 is genome-wide significant for coding DNVs in NDDs (missense p = 8.12 × 10-35, loss-of-function p = 2.26 × 10-13) and is widely expressed in the body. Through characterization of promoters bound by EBF3 in neuronal cells, we saw enrichment for binding to NDD genes (p = 7.43 × 10-6, OR = 1.87) involved in gene regulation. Individuals with coding DNVs have greater phenotypic severity (hypotonia, ataxia, and delayed development syndrome [HADDS]) in comparison to individuals with noncoding DNVs that have autism and hypotonia. CONCLUSIONS: In this study, we identify DNVs in the hs737 enhancer in individuals with autism. Through multiple approaches, we find hs737 targets the gene EBF3 that is genome-wide significant in NDDs. By assessment of noncoding variation and the genes they affect, we are beginning to understand their impact on gene regulatory networks in NDDs.