Biology subjects

Turner, T. N.

Publications and source records attributed to Turner, T. N..

14 recordsLinked to original sources

bioRxiv2026

Multi-omics Differential Inference for Functional Interpretation (MoDIFI): A Statistical Framework to Prioritize Cell Lines for Neurodevelopmental Variants

Noncoding variants contribute to neurodevelopmental disorders (NDDs), but their regulatory effects are often cell-type specific, making it difficult to choose an in vitro model for high-throughput assays such as massively parallel reporter assays. We asked: given a set of noncoding variants, which cell line and regulatory regions are most likely to reveal measurable allele-specific effects? We generated matched multiomics profiles across commonly used NDD in vitro models: human neuronal lines (i.e., IMR-32, SH-SY5Y, SK-N-SH), mouse neuronal lines (i.e., HT-22, Neuro-2a), and a non-neuronal line (i.e., HEK-293), using RNA-seq, ATAC-seq, and Hi-C under consistent conditions. To integrate these orthogonal data types, we developed MoDIFI (Multi-omics Differential Inference for Functional Interpretation), a Bayesian framework that quantifies cell-line-specific regulatory activity by computing posterior inclusion probabilities (PIPs) for differential gene-loop interactions. MoDIFI identifies regulatory regions supported by coordinated 3D contacts, accessibility, and transcriptional output, producing cell-line-resolved regulatory maps that highlight both shared synaptic programs and context-dependent mechanisms. These results provide a practical strategy for prioritizing the most informative cell lines and candidate regulatory elements for targeted functional testing of NDD-relevant noncoding variation.

genomics↗

bioRxiv2025

Generation and Characterization of a Knockout Mouse of an Enhancer of EBF3

Genomic studies of autism and other neurodevelopmental disorders have identified several relevant protein-coding and noncoding variants. One gene with an excess of protein-coding de novo variants is EBF3 that also is the gene underlying the Hypotonia, Ataxia, and Delayed Development Syndrome (HADDS). In previous work, we have identified noncoding de novo variants in an enhancer of EBF3 called hs737 and further showed that there was an enrichment of deletions of this enhancer in individuals with neurodevelopmental disorders. In this present study, we generated a novel mouse line that deletes the highly conserved, orthologous mouse region of hs737 within the Rr169617 regulatory region, and characterized the molecular and phenotypic aspects of this mouse model. This line contains a 1,160 bp deletion within Rr169617 and through heterozygous crosses we found a deviation from Mendelian expectation (p = 0.02) with a significant depletion of the deletion allele (p = 5.8 x 10-4). Rr169617+/- mice had a reduction of Ebf3 expression by 10% and Rr169617-/- mice had a reduction of Ebf3 expression by 20%. Differential expression analyses in E12.5 forebrain, midbrain, and hindbrain in Rr169617+/+versus Rr169617-/- mice identified dysregulated genes including histone genes (i.e., Hist1h1e, Hist1h2bk, Hist1h3i, Hist1h2ao) and other brain development related genes (e.g., Chd5, Ntng1). A priori phenotyping analysis (open field, hole board and light/dark transition) identified sex-specific differences in behavioral traits when comparing Rr169617-/- males versus females; whereby, males were observed to be less mobile, move slower, and spend more time in the dark. Furthermore, both sexes when homozygous for the enhancer deletion displayed body composition differences when compared to wild-type mice. Overall, we show that deletion within Rr169617 reduces the expression of Ebf3 and results in phenotypic outcomes consistent with potential sex specific behavioral differences. This enhancer deletion line provides a valuable resource for others interested in noncoding regions in neurodevelopmental disorders and/or those interested in the gene regulatory network downstream of Ebf3.

genetics↗

bioRxiv2024

Gene expansions contributing to human brain evolution

Duplicated genes expanded in the human lineage likely contributed to brain evolution, yet challenges exist in their discovery due to sequence-assembly errors. We used a complete telomere-to-telomere genome sequence to identify 213 human-specific gene families. From these, 362 paralogs were found in all modern human genomes tested and brain transcriptomes, making them top candidates contributing to human-universal brain features. Choosing a subset of paralogs, long-read DNA sequencing of hundreds of modern humans revealed previously hidden signatures of selection, including for T-cell marker CD8B. To understand roles in brain development, we generated zebrafish CRISPR "knockout" models of nine orthologs and introduced mRNA-encoding paralogs, effectively "humanizing" larvae. Our findings implicate two genes in possibly contributing to hallmark features of the human brain: GPR89B in dosage-mediated brain expansion and FRMPD2B in altered synapse signaling. Our holistic approach provides insights and a comprehensive resource for studying gene expansion drivers of human brain evolution.

genomics↗

bioRxiv2023

High Coverage Highly Accurate Long-Read Sequencing of a Mouse Neuronal Cell Line Using the PacBio Revio Sequencer

Recently, Pacific Biosciences released a new highly accurate long-read sequencer called the Revio System that is projected to generate 30x HiFi whole-genome sequencing for the human genome within one sequencing SMRT Cell. Mouse and human genomes are similar in size. In this study, we sought to test this new sequencer by characterizing the genome and epigenome of the mouse neuronal cell line Neuro-2a. We generated long-read HiFi whole-genome sequencing on three Revio SMRT Cells, achieving a total coverage of 98x, with 30x, 32x, and 36x coverage respectively for each of the three Revio SMRT Cells. We performed several tests on these data including single-nucleotide variant and small insertion detection using GPU-accelerated DeepVariant, structural variant detection with pbsv, methylation detection with pb-CpG-tools, and generating de novo assemblies with the HiCanu and hifiasm assemblers. Overall, we find consistency across SMRT Cells in coverage, detection of variation, methylation, and de novo assemblies for each of the three SMRT Cells.

genomics↗

bioRxiv2023

acorn: an R package for de novo variant analysis

BackgroundThe study of de novo variation is important for assessing biological characteristics of new variation and for studies related to human phenotypes. Software programs exist to call de novo variants and programs also exist to test the burden of these variants in genomic regions; however, I am unaware of a program that fits in between these two aspects of de novo variant assessment. This intermediate space is important for assessing the quality of de novo variants and to understand the characteristics of the callsets. For this reason, I developed the R package acorn. Resultsacorn is an R package that examines various features of de novo variants including subsetting the data by individual(s), variant type, or genomic region; calculating features including variant change counts, variant lengths, and presence/absence at CpG sites; and characteristics of parental age in relation to de novo variant counts. Conclusionsacorn is an R package that fills a critical gap in assessing de novo variants and will be of benefit to many investigators studying de novo variation.

bioinformatics↗

bioRxiv2023

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

Motivationde 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). ResultsHAT 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 wholegenome sequencing, and highly accurate long-read sequencing data. Availabilityhttps://github.com/TNTurnerLab/HAT Contacttychele@wustl.edu Supplementary informationSupplementary data are available at bioRxiv.

bioinformatics↗

bioRxiv2021

Improved Characterization of Balancing Selection Genome Wide and a Detailed Look at HLA Genes

Balancing selection occurs when multiple alleles are kept at elevated frequencies in equilibrium due to opposing evolutionary pressures. A new statistical method was developed to test for selection using efficient Bayesian techniques. Selection signals in three different data sets, generated with variable sequencing technologies, were compared: clinical trios, HLA NGS typed samples, and whole-genome long-read samples. Genome-wide, selection was observed across multiple gene families whose biological functions favor diversification, revealing established targets as well as 45 novel genes under selection. Using high-resolution HLA typing and long-read sequencing data, for the characterization of the MHC, revealed strong selection in expected peptide-binding domains as well as previously understudied intronic and intergenic regions of the MHC. Surprisingly, SIRPA, demonstrated dramatic selection signal, second only to the MHC in most settings. In conclusion, employing novel statistical approaches and improved sequencing technologies is critical to properly analyze complex genomic regions.

genetics↗

bioRxiv2021

Mutationathon: towards standardization in estimates of pedigree-based germline mutation rates

In the past decade, several studies have estimated the human per-generation germline mutation rate using large pedigrees. More recently, estimates for various non-human species have been published. However, methodological differences among studies in detecting germline mutations and estimating mutation rates make direct comparisons difficult. Here, we describe the many different steps involved in estimating pedigree-based mutation rates, including sampling, sequencing, mapping, variant calling, filtering, and how to appropriately account for false-positive and false-negative rates. For each step, we review the different methods and parameter choices that have been used in the recent literature. Additionally, we present the results from a "Mutationathon", a competition organized among five research labs to compare germline mutation rate estimates for a single pedigree of rhesus macaques. We report almost a two-fold variation in the final estimated rate among groups using different post-alignment processing, calling, and filtering criteria and provide details into the sources of variation across studies. Though the difference among estimates is not statistically significant, this discrepancy emphasizes the need for standardized methods in mutation rate estimations and the difficulty in comparing rates from different studies. Finally, this work aims to provide guidelines for computational and statistical benchmarks for future studies interested in identifying germline mutations from pedigrees.

evolutionary biology↗

bioRxiv2021

ACES: Analysis of Conservation with Expansive Species

MotivationAn abundance of new reference genomes are 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. ResultsWe developed ACES (Analysis of Conservation with Expansive 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. AvailabilityACES is available at https://github.com/TNTurnerLab/ACES Contacttychele@wustl.edu Supplementary informationSupplementary Figure 1 is available on bioRxiv.

bioinformatics↗

bioRxiv2021

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

Detection of de novo variants (DNVs) is critical for studies of disease-related variation and mutation rates. We developed a GPU-based workflow to rapidly call DNVs (HAT) and demonstrated its effectiveness by applying it to 4,216 Simons Simplex Collection (SSC) whole-genome sequenced parent-child trios from DNA derived from blood. In our SSC DNV data, we identified 78 {+/-} 15 DNVs per individual, 18% {+/-} 5% at CpG sites, 75% {+/-} 9% phased to the paternal chromosome of origin, and an average allele balance of 0.49. These calculations are all in line with DNV expectations. We sought to build a control DNV dataset by running HAT on 602 whole-genome sequenced parent-child trios from DNA derived from lymphoblastoid cell lines (LCLs) from the publicly available 1000 Genomes Project (1000G). In our 1000G DNV data, we identified 740 {+/-} 967 DNVs per individual, 14% {+/-} 4% at CpG sites, 61% {+/-} 11% phased to the paternal chromosome of origin, and an average allele balance of 0.41. Of the 602 trios, 80% had > 100 DNVs and we hypothesized the excess DNVs were cell line artifacts. Several lines of evidence in our data suggest that this is true and that 1000G does not appear to be a static reference. By mutation profile analysis, we tested whether these cell line artifacts were random and found that 40% of individuals in 1000G did not have random DNV profiles; rather they had DNV profiles matching B-cell lymphoma. Furthermore, we saw significant excess of protein-coding DNVs in 1000G in the gene IGLL5 that has already been implicated in this cancer. As a result of cell line artifacts, 1000G has variants present in DNA repair genes and at Clinvar pathogenic or likely-pathogenic sites. Our study elucidates important implications of the use of sequencing data from LCLs for both reference building projects as well as disease-related projects whereby these data are used in variant filtering steps.

genomics↗

bioRxiv2020

De Novo Mutation in an Enhancer of EBF3 in simplex autism

Previous research in autism and other neurodevelopmental disorders (NDDs) has indicated an important contribution of de novo protein-coding variants 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 2,671 families with autism, with a specific focus on de novo variation in enhancers with previously characterized in vivo activity. We identified three independent de novo mutations limited to individuals with autism in the enhancer hs737. These mutations result in similar phenotypic characteristics, affect enhancer activity in vitro, and preferentially occur in AAT motifs in the enhancer with predicted disruptions of transcription factor binding. We also find that hs737 is enriched for copy number variation in individuals with NDDs, is dosage sensitive in the human population, is brain-specific, and targets the NDD gene EBF3 that is genome-wide significant for protein coding de novo variants, demonstrating the importance of understanding all forms of variation in the genome. One Sentence SummaryWhole-genome sequencing in thousands of families reveals variants relevant to simplex autism in a brain enhancer of the well-established neurodevelopmental disorder gene EBF3.

genomics↗

bioRxiv2020

Copy numbers of 45S and 5S ribosomal DNA arrays lack meaningful correlation in humans

The ribosomal DNA genes are tandemly arrayed in most eukaryotes and exhibit vast copy number variation. There is growing interest in integrating this variation into genotype-phenotype associations. Here, we explored a possible association of rDNA copy number variation with autism spectrum disorder and found no difference between probands and unaffected siblings. However, rDNA copy number estimates from whole genome sequencing are error-prone, so we sought to use pulsed-field gel electrophoresis, a classic gold-standard method, to validate rDNA copy number genotypes. The electrophoresis approach is not readily applicable to the human 45S arrays due to their size and location on five separate chromosomes; however, it should accurately resolve copy numbers for the shorter 5S arrays that reside on a single chromosome. Previous studies reported tightly correlated, concerted copy number variation between the 45S and 5S arrays, which should enable the validation of 45S copy number estimates with CHEF-gel-verified 5S copy numbers. Here, we show that the previously reported strong concerted copy number variation is likely an artifact of variable data quality in the earlier published 1000 Genomes Project sequences. We failed to detect a meaningful correlation between 45S and 5S copy numbers in the large, high-coverage Simons Simplex Collection dataset as well as in the recent high-coverage 1000 Genomes Project sequences. Our findings illustrate the challenge of genotyping repetitive DNA regions accurately and call into question the accuracy of recently published studies of rDNA copy number variation in cancers and aging that relied on diverse publicly available resources for sequence data.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genomics↗

bioRxiv2020

Ancestry adjustment improves genome-wide estimates of regional intolerance

Genomic regions subject to purifying selection are more likely to carry disease causing mutations. Cross species conservation is often used to identify such regions but has limited resolution to detect selection on short evolutionary timescales such as that occurring in only one species. In contrast, intolerance looks for depletion of variation relative to expectation within a species, allowing species specific features to be identified. When estimating the intolerance of noncoding sequence methods strongly leverage variant frequency distributions. As the expected distributions depend on demography, if not properly controlled for, ancestral population source may obfuscate signals of selection. We demonstrate that properly incorporating demography in intolerance estimation greatly improved variant classification (13% increase in AUC relative to comparison constraint test, CDTS; and 9% relative to conservation). We provide a genome-wide intolerance map that is conditional on demographic history that is likely to be particularly valuable for variant prioritization.

genetics↗

bioRxiv2020

Recent ultra-rare inherited mutations identify novel autism candidate risk genes

Autism is a highly heritable, complex disorder where de novo mutation (DNM) variation contributes significantly to disease risk. Using whole-genome sequencing data from 3,474 families, we investigate another source of large-effect risk variation, ultra-rare mutations. We report and replicate a transmission disequilibrium of private likely-gene disruptive (LGD) mutations in probands but find that 95% of this burden resides outside of known DNM-enriched genes. This variant class more strongly affects multiplex family probands and supports a multi-hit model for autism. Candidate genes with private LGD variants preferentially transmitted to probands converge on the E3 ubiquitin-protein ligase complex, intracellular transport, and Erb signaling protein networks. We estimate these mutations are ~2.5 generations old and significantly younger than other mutations of similar type and frequency in siblings. Overall, private LGD variants are under strong purifying selection and act on a distinct set of genes not yet associated with autism. One sentence summaryUltra-rare autism variants preferentially transmitted to probands are younger and identify distinct gene candidates and functional networks.

genetics↗