Biology subjects

Gymrek, M.

Publications and source records attributed to Gymrek, M..

33 records · Page 2Linked to original sources

bioRxiv2020

Mechanisms underlying divergent responses of genetically distinct macrophages to IL-4

Mechanisms by which non-coding genetic variation influences gene expression remain only partially understood but are considered to be major determinants of phenotypic diversity and disease risk. Here, we evaluated effects of >50 million SNPs and InDels provided by five inbred strains of mice on the responses of macrophages to interleukin 4 (IL-4), a cytokine that plays pleiotropic roles in immunity and tissue homeostasis. Remarkably, of >600 genes induced >2-fold by IL-4 across the five strains, only 26 genes reached this threshold in all strains. By applying deep learning and motif mutation analyses to epigenetic data for macrophages from each strain, we identified the dominant combinations of lineage determining and signal-dependent transcription factors driving late enhancer activation. These studies further revealed mechanisms by which non-coding genetic variation influences absolute levels of enhancer activity and their dynamic responses to IL-4, thereby contributing to strain-differential patterns of gene expression and phenotypic diversity.

genomics↗

bioRxiv2020

Variable Number Tandem Repeats mediate the expression of proximal genes

Variable Number Tandem Repeats (VNTRs) account for a significant amount of human genetic variation. VNTRs have been implicated in both Mendelian and Complex disorders, but are largely ignored by whole genome analysis pipelines due to the complexity of genotyping and the computational expense. We describe adVNTR-NN, a method that uses shallow neural networks for fast read recruitment. On 55X whole genome data, adVNTR-NN genotyped each VNTR in less than 18 cpu-seconds, while maintaining 100% accuracy on 76% of VNTRs. We used adVNTR-NN to genotype 10,264 VNTRs in 652 individuals from the GTEx project and associated VNTR length with gene expression in 46 tissues. We identified 163 eVNTR loci that were significantly associated with gene expression. Of the 22 eVNTRs in blood where independent data was available, 21 (95%) were replicated in terms of significance and direction of association. 49% of the eVNTR loci showed a strong and likely causal impact on the expression of genes and 80% had maximum effect size at least 0.3. The impacted genes have important role in complex phenotypes including Alzheimers, obesity and familial cancers. Our results point to the importance of studying VNTRs for understanding the genetic basis of complex diseases.

genomics↗

bioRxiv2020

Importance of polymorphic SNPs, short tandem repeats and structural variants for differential gene expression among inbred C57BL/6 and C57BL/10 substrains

Mouse substrains are an invaluable model for understanding disease. We compared C57BL/6J, which is the most commonly used inbred mouse strain, with 8 C57BL/6 and 5 C57BL/10 closely related inbred substrains. Whole genome sequencing and RNA-sequencing analysis yielded 352,631 SNPs, 109,096 INDELs, 150,344 short tandem repeats (STRs), 3,425 structural variants (SVs) and 2,826 differentially expressed genes (DEGenes) among these 14 strains. 312,981 SNPs (89%) distinguished the B6 and B10 lineages. These SNPs were clustered into 28 short segments that are likely due to introgressed haplotypes rather than new mutations. Outside of these introgressed regions, we identified 53 SVs, protein-truncating SNPs and frameshifting INDELs that were associated with DEGenes. Our results can be used for both forward and reverse genetic approaches, and illustrate how introgression and mutational processes give rise to differences among these widely used inbred substrains.

genomics↗

bioRxiv2020

TRTools: a toolkit for genome-wide analysis of tandem repeats

SummaryA rich set of tools have recently been developed for performing genome-wide genotyping of tandem repeats (TRs). However, standardized tools for downstream analysis of these results are lacking. To facilitate TR analysis applications, we present TRTools, a Python library and a suite of command-line tools for filtering, merging, and quality control of TR genotype files. TRTools utilizes an internal harmonization module making it compatible with outputs from a wide range of TR genotypers. AvailabilityTRTools is freely available at https://github.com/gymreklab/TRTools. Contactmgymrek@eng.ucsd.edu Supplementary informationSupplementary data are available at bioRxiv.

bioinformatics↗

bioRxiv2020

The contribution of de novo tandem repeat mutations to autism spectrum disorders

Autism Spectrum Disorder (ASD) is an early onset developmental disorder characterized by deficits in communication and social interaction and restrictive or repetitive behaviors1,2. Family studies demonstrate that ASD has a significant genetic basis3 with contributions both from inherited and de novo variants. While the majority of variance in liability to ASD is estimated to arise from common genetic variation4, it has been estimated that de novo mutations may contribute to 30% of all simplex cases, in which only a single child is affected per family5. Tandem repeats (TRs), consisting of approximately 1-20bp motifs repeated in tandem, comprise one of the largest sources of de novo mutations in humans6. Yet, largely due to technical challenges they present, de novo TR mutations have not yet been characterized on a genome-wide scale, and their contribution to ASD remains unexplored. Here, we develop novel bioinformatics tools for identifying and prioritizing de novo TR mutations from whole genome sequencing (WGS) data and use these to perform a genome-wide characterization of de novo TR mutations in ASD-affected probands and unaffected siblings. Compared to recent work on TRs in ASD7, we explicitly infer mutation events and their precise changes in repeat copy number, and primarily focus on more prevalent stepwise copy number changes rather than large or complex expansions. Our results demonstrate a significant genome-wide excess of TR mutations in ASD probands. TR mutations in probands tend to be larger, enriched in fetal brain regulatory regions, and predicted to be more evolutionarily deleterious compared to mutations observed in unaffected siblings. Overall, our results highlight the importance of considering repeat variants in future studies of de novo mutations.

genomics↗

bioRxiv2020

Deep neural networks identify context-specific determinants of transcription factor binding affinity

Transcription factors (TFs) bind DNA by recognizing highly specific DNA sequence motifs, typically of length 6-12bp. A TF motif can occur tens of thousands of times in the human genome, but only a small fraction of those sites are actually bound. Despite the availability of genome-wide TF binding maps for hundreds of TFs, predicting whether a given motif occurrence is bound and identifying the influential context features remain challenging. Here we present a machine learning framework leveraging existing convolutional neural network architectures and state of the art model interpretation techniques to identify, visualize, and interpret context features most important for determining binding activity for a particular TF. We apply our framework to predict binding at motifs for 38 TFs in a lymphoblastoid cell line and achieve superior classification performance compared to existing frameworks. We compute importance scores for context regions at single base pair resolution and uncover known and novel determinants of TF binding. Finally, we demonstrate that important context bases are under increased purifying selection compared to nearby bases and are enriched in disease-associated variants identified by genome-wide association studies.

genomics↗

bioRxiv2019

A flexible simulation toolkit for designing and evaluating ChIP-sequencing experiments

A major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. We present Tulip, a toolkit for rapidly simulating ChIP-seq data using statistical models of the experimental steps. Tulip may be used for a range of applications, including power analysis for experimental design, benchmarking of analysis tools, and modeling effects of processes such as replication on ChIP-seq signals.

genomics↗

bioRxiv2018

Cooperation of dominant oncogenes with regulatory germline variants shapes clinical outcomes in childhood cancer

INTRODUCTORY PARAGRAPHDeciphering principles of inter-individual tumor heterogeneity is essential for refinement of personalized anti-cancer therapy. Unlike cancers of adulthood, pediatric malignancies including Ewing sarcoma (EwS) feature a striking paucity of somatic alterations except for pathognomonic driver-mutations that cannot explain overt variations in clinical outcome.\n\nHere we demonstrate in the EwS model how cooperation of a dominant oncogene and regulatory variants determine tumor growth, patient survival and drug response.\n\nWe show that binding of the oncogenic EWSR1-FLI1 fusion transcription factor to a polymorphic enhancer-like DNA element controls expression of the transcription factor MYBL2, whose high expression promotes poor patient outcome via activation of pro-proliferative signatures. Analysis of paired germline and tumor whole-genome sequencing data revealed that regulatory variability at this locus is inherited via the germline. CRISPR-mediated interference with this regulatory element almost abolished MYBL2 transcription, and MYBL2 knockdown decreased cell proliferation, cell survival and tumorigenicity of EwS cells. Combined RNA- and ChIP-seq analyses as well as functional experiments and clinical data identified CCNF, BIRC5 and AURKB as direct MYBL2 targets and critical mediators of its phenotype. In drug-response experiments, high MYBL2 levels sensitized EwS cells for inhibition of its activating cyclin dependent kinase CDK2 in vitro and in vivo, suggesting MYBL2 as a predictive biomarker for targeted anti-CDK2-therapy.\n\nCollectively, our findings establish cooperation of somatic mutations and regulatory germline variants as a major determinant of tumor progression and indicate the importance of integrating the regulatory genome in the process of developing new diagnostic and/or therapeutic strategies to fully harness the potential of precision medicine.

cancer biology↗

bioRxiv2018

Multi-tissue analysis reveals short tandem repeats as ubiquitous regulators of gene expression and complex traits

Short tandem repeats (STRs) have been implicated in a variety of complex traits in humans. However, genome-wide studies of the effects of STRs on gene expression thus far have had limited power to detect associations and provide insights into putative mechanisms. Here, we leverage whole genome sequencing and expression data for 17 tissues from the Genotype-Tissue Expression Project (GTEx) to identify STRs for which repeat number is associated with expression of nearby genes (eSTRs). Our analysis reveals more than 28,000 eSTRs. We employ fine-mapping to quantify the probability that each eSTR is causal and characterize a group of the top 1,400 fine-mapped eSTRs. We identify hundreds of eSTRs linked with published GWAS signals and implicate specific eSTRs in complex traits including height and schizophrenia, inflammatory bowel disease, and intelligence. Overall, our results support the hypothesis that eSTRs contribute to a range of human phenotypes and will serve as a valuable resource for future studies of complex traits.

genomics↗

bioRxiv2018

Profiling the genome-wide landscape of tandem repeat expansions

Tandem Repeat (TR) expansions have been implicated in dozens of genetic diseases, including Huntingtons Disease, Fragile X Syndrome, and hereditary ataxias. Furthermore, TRs have recently been implicated in a range of complex traits, including gene expression and cancer risk. While the human genome harbors hundreds of thousands of TRs, analysis of TR expansions has been mainly limited to known pathogenic loci. A major challenge is that expanded repeats are beyond the read length of most next-generation sequencing (NGS) datasets. We present GangSTR, a novel algorithm for genome-wide profiling of both normal and expanded TRs. GangSTR extracts information from paired-end reads into a unified model to estimate maximum likelihood TR lengths. We validated GangSTR on real and simulated TR expansions and show that GangSTR outperforms alternative methods. We applied GangSTR to more than 150 individuals to profile the landscape of TR expansions in a healthy population and validated novel expansions using orthogonal technologies. Our analysis revealed that each individual harbors dozens of TR alleles longer than standard read lengths and identified hundreds of potentially mis-annotated TRs in the reference genome. GangSTR is packaged as a standalone tool that will likely enable discovery of novel pathogenic variants not currently accessible from NGS.

genomics↗

bioRxiv2018

A reference haplotype panel for genome-wide imputation of short tandem repeats

Short tandem repeats (STRs) are involved in dozens of Mendelian disorders and have been implicated in a variety of complex traits. However, existing technologies focusing on single nucleotide polymorphisms (SNPs) have not allowed for systematic STR association studies. Here, we leverage next-generation sequencing data from 479 families to create a SNP+STR reference haplotype panel for genome-wide imputation of STRs into SNP data. Imputation achieved an average of 97% concordance between genotyped and imputed STR genotypes in an external dataset compared to 63% expected under a random model. Performance varied widely across STRs, with near perfect concordance at bi-allelic STRs vs. 70% at highly polymorphic forensics markers. We demonstrate that imputation increases power over individual SNPs to detect STR associations using simulated phenotypes and gene expression data. This resource will enable the first large-scale STR association studies using existing SNP datasets, and will likely yield new insights into complex traits.

genomics↗

bioRxiv2017

Targeted Genotyping of Variable Number Tandem Repeats with adVNTR

Whole Genome Sequencing is increasingly used to identify Mendelian variants in clinical pipelines. These pipelines focus on single nucleotide variants (SNVs) and also structural variants, while ignoring more complex repeat sequence variants. We consider the problem of genotyping Variable Number Tandem Repeats (VNTRs), composed of inexact tandem duplications of short (6-100bp) repeating units. VNTRs span 3% of the human genome, are frequently present in coding regions, and have been implicated in multiple Mendelian disorders. While existing tools recognize VNTR carrying sequence, genotyping VNTRs (determining repeat unit count and sequence variation) from whole genome sequenced reads remains challenging. We describe a method, adVNTR, that uses Hidden Markov Models to model each VNTR, count repeat units, and detect sequence variation. adVNTR models can be developed for short-read (Illumina) and single molecule (PacBio) whole genome and exome sequencing, and show good results on multiple simulated and real data sets. adVNTR is available at https://github.com/mehrdadbakhtiari/adVNTR

bioinformatics↗

bioRxiv2017

Quantification of autism recurrence risk by direct assessment of paternal sperm mosaicism

De novo genetic mutations represent a major contributor to pediatric disease, including autism spectrum disorders (ASD), congenital heart disease, and muscular dystrophies1,2, but there are currently no methods to prevent or predict them. These mutations are classically thought to occur either at low levels in progenitor cells or at the time of fertilization1,3 and are often assigned a low risk of recurrence in siblings4,5. Here, we directly assess the presence of de novo mutations in paternal sperm and discover abundant, germline-restricted mosaicism. From a cohort of ASD cases, employing single molecule genotyping, we found that four out of 14 fathers were germline mosaic for a putatively causative mutation transmitted to the affected child. Three of these were enriched or exclusively present in sperm at high allelic fractions (AF; 7-15%); and one was recurrently transmitted to two additional affected children, representing clinically actionable information. Germline mosaicism was further assessed by deep (>90x) whole genome sequencing of four paternal sperm samples, which detected 12/355 transmitted de novo single nucleotide variants that were mosaic above 2% AF, and more than two dozen additional, non-transmitted mosaic variants in paternal sperm. Our results demonstrate that germline mosaicism is an underestimated phenomenon, which has important implications for clinical practice and in understanding the basis of human disease. Genetic analysis of sperm can assess individualized recurrence risk following the birth of a child with a de novo disease, as well as the risk in any male planning to have children.

genetics↗

bioRxiv2017

Quantitative analysis of population-scale family trees using millions of relatives

Family trees have vast applications in multiple fields from genetics to anthropology and economics. However, the collection of extended family trees is tedious and usually relies on resources with limited geographical scope and complex data usage restrictions. Here, we collected 86 million profiles from publicly-available online data from genealogy enthusiasts. After extensive cleaning and validation, we obtained population-scale family trees, including a single pedigree of 13 million individuals. We leveraged the data to partition the genetic architecture of longevity by inspecting millions of relative pairs and to provide insights to population genetics theories on the dispersion of families. We also report a simple digital procedure to overlay other datasets with our resource in order to empower studies with population-scale genealogical data.\n\nOne Sentence SummaryUsing massive crowd-sourced genealogy data, we created a population-scale family tree resource for scientific studies.

genomics↗

bioRxiv2016

A framework to interpret short tandem repeat variation in humans

Identifying regions of the genome that are depleted of mutations can reveal potentially deleterious variants. Short tandem repeats (STRs), also known as microsatellites, are among the largest contributors of de novo mutations in humans and are implicated in a variety of human disorders. However, because of the challenges STRs pose to bioinformatics tools, per-locus studies of STR mutations have been limited to highly ascertained panels of several dozen loci. Here, we harnessed bioinformatics tools and a novel analytical framework to estimate mutation parameters for each STR in the human genome by correlating STR genotypes with local sequence heterozygosity. We applied our method to obtain robust estimates of the impact of local sequence features on mutation parameters and used this to create a framework for measuring constraint at STRs by comparing observed vs. expected mutation rates. Constraint scores identified known pathogenic variants with early onset effects. Our constraint metrics will provide a valuable tool for prioritizing pathogenic STRs in medical genetics studies.

genetics↗