Biology subjects

Birney, E.

Publications and source records attributed to Birney, E..

32 records · Page 2Linked to original sources

bioRxiv2021

The Medaka Inbred Kiyosu-Karlsruhe (MIKK) Panel

Unraveling the relationship between genetic variation and phenotypic traits remains a fundamental challenge in biology. Mapping variants underlying complex traits while controlling for confounding environmental factors is often problematic. To address this, we have established a vertebrate genetic resource specifically to allow for robust genotype-to-phenotype investigations. The teleost medaka (Oryzias latipes) is an established genetic model system with a long history of genetic research and a high tolerance to inbreeding from the wild. Here we present the Medaka Inbred Kiyosu-Karlsruhe (MIKK) panel: the first near-isogenic panel of 80 inbred lines in a vertebrate model derived from a wild founder population. Inbred lines provide fixed genomes that are a prerequisite for the replication of studies, studies which vary both the genetics and environment in a controlled manner and functional testing. The MIKK panel will therefore enable phenotype-to-genotype association studies of complex genetic traits while allowing for careful control of interacting factors, with numerous applications in genetic research, human health, and drug development and fundamental biology. Here we present a detailed characterisation of the genetic variation across the MIKK panel, which provides a rich and unique genetic resource to the community by enabling large-scale experiments for mapping complex traits.

genomics↗

bioRxiv2021

Genomic variations and epigenomic landscape of the Medaka Inbred Kiyosu-Karlsruhe (MIKK) panel

The teleost medaka (Oryzias latipes) is a well-established vertebrate model system, with a long history of genetic research, and multiple high-quality reference genomes available for several inbred strains (HdrR, HNI and HSOK). Medaka has a high tolerance to inbreeding from the wild, thus allowing one to establish inbred lines from wild founder individuals. We have exploited this feature to create an inbred panel resource: the Medaka Inbred Kiyosu-Karlsruhe (MIKK) panel. This panel of 80 near-isogenic inbred lines contains a large amount of genetic variation inherited from the original wild population. We used Oxford Nanopore Technologies (ONT) long read data to further investigate the genomic and epigenomic landscapes of a subset of the MIKK panel. Nanopore sequencing allowed us to identify a much greater variety of high-quality structural variants compared with Illumina sequencing. We also present results and methods using a pan-genome graph representation of 12 individual medaka lines from the MIKK panel. This graph-based reference MIKK panel genome revealed novel differences between the MIKK panel lines compared to standard linear reference genomes. We found additional MIKK panel-specific genomic content that would be missing from linear reference alignment approaches. We were also able to identify and quantify the presence of repeat elements in each of the lines. Finally, we investigated line-specific CpG methylation and performed differential DNA methylation analysis across the 12 lines. We thus present a detailed analysis of the MIKK panel genomes using long and short read sequence technologies, creating a MIKK panel specific pan genome reference dataset allowing for the investigation of novel variation types that would be elusive using standard approaches.

genomics↗

bioRxiv2021

Visualization of loop extrusion by DNA nanoscale tracing in single cells

The spatial organization of the genome is essential for its functions, including gene expression, DNA replication and repair, as well as chromosome segregation. Biomolecular condensates and loop extrusion have been proposed as the principal driving forces that underlie the formation of chromatin compartments and topologically associating domains, respectively. However, whether the actual 3D-fold of DNA in single cells is consistent with these mechanisms has been difficult to address in situ. Here, we present LoopTrace, a workflow for nanoscale 3D imaging of the genome sequence in structurally well-preserved nuclei in single human cells. Tracing the in situ structure of DNA in thousands of individual cells reveals that genomic DNA folds as a flexible random coil in the absence of loop extruding enzymes such as Cohesin. In the presence of Cohesin and its boundary factor CTCF, reproducibly positioned loop structures dominate the folds, while Cohesin alone leads to randomly positioned loops. The 3D structure and size variability of DNA loops we observe in a large number of single cells allow us to formulate a data-constrained computational model of genomic DNA folding that explains how sparse and dynamic loops in single cells lead to the emergence of compact topological domains in averages of cell populations.

cell biology↗

bioRxiv2020

BOSS-RUNS: a flexible and practical dynamic read sampling framework for nanopore sequencing

One particularly promising feature of nanopore sequencing is the ability to reject reads, enabling real-time selection of molecules without complex sample preparation. This is based on the idea of deciding whether a molecule warrants full sequencing depending on reading a small initial part. Previously, such decisions have been based on a priori determination of which regions of the genome were considered of interest. Instead, here we consider more general and complex strategies that incorporate already-observed data in order to optimize the rejection strategy and maximise information gain from the sequencing process. For example, in the presence of coverage bias redistributing data from areas of high to areas of low coverage would be desirable. We present BOSS-RUNS, a mathematical and algorithmic framework to calculate the expected benefit of new reads and generate dynamically updated decision strategies for nanopore sequencing. During sequencing, in real time, we quantify the current uncertainty at each site of one or multiple reference genomes, and for each novel DNA fragment being sequenced we decide whether the potential decrease in uncertainty at the sites it will most likely cover warrants reading it in its entirety. This dynamic, adaptive sampling allows real-time focus of sequencing efforts onto areas of highest benefit. We demonstrate the effectiveness of BOSS-RUNS by mitigating coverage bias across and within the species of a microbial community. Additionally, we show that our approach leads to improved variant calling due to its ability to sample more data at the most relevant genomic positions.

genomics↗

bioRxiv2019

RNA modifications detection by comparative Nanopore direct RNA sequencing

RNA molecules undergo a vast array of chemical post-transcriptional modifications (PTMs) that can affect their structure and interaction properties. To date, over 150 naturally occurring PTMs have been identified, however the overwhelming majority of their functions remain elusive. In recent years, a small number of PTMs have been successfully mapped to the transcriptome using experimental approaches relying on high-throughput sequencing. Oxford Nanopore direct-RNA sequencing (DRS) technology has been shown to be sensitive to RNA modifications. We developed and validated Nanocompore, a robust analytical framework to evaluate the presence of modifications in DRS data. To do so, we compare an RNA sample of interest against a non-modified control sample. Our strategy does not require a training set and allows the use of replicates to model biological variability. Here, we demonstrate the ability of Nanocompore to detect RNA modifications at single-molecule resolution in human polyA+ RNAs, as well as in targeted non-coding RNAs. Our results correlate well with orthogonal methods, confirm previous observations on the distribution of N6-methyladenosine sites and provide novel insights into the distribution of RNA modifications in the coding and non-coding transcriptomes. The latest version of Nanocompore can be obtained at https://github.com/tleonardi/nanocompore.

genomics↗

bioRxiv2019

Genomic analysis reveals a functional role for myocardial trabeculae in adults

Since being first described by Leonardo da Vinci in 1513 it has remained an enigma why the endocardial surfaces of the adult heart retain a complex network of muscular trabeculae - with their persistence thought to be a vestige of embryonic development. For causative physiological inference we harness population genomics, image-based intermediate phenotyping and in silico modelling to determine the effect of this complex cardiovascular trait on function. Using deep learning-based image analysis we identified genetic associations with trabecular complexity in 18,097 UK Biobank participants which were replicated in an independently measured cohort of 1,129 healthy adults. Genes in these associated regions are enriched for expression in the fetal heart or vasculature and implicate loci associated with haemodynamic phenotypes and developmental pathways. A causal relationship between increasing trabecular complexity and both ventricular performance and electrical activity are supported by complementary biomechanical simulations and Mendelian randomisation studies. These findings show that myocardial trabeculae are a previously-unrecognised determinant of cardiovascular physiology in adult humans.

genomics↗

bioRxiv2018

Identifying the genetic basis of variation in cell behaviour in human iPS cell lines from healthy donors

Large cohorts of human iPSCs from healthy donors are potentially a powerful tool for investigating the relationship between genetic variants and cellular phenotypes. Here we integrate high content imaging, gene expression and DNA sequence datasets for over 100 human iPSC lines to identify the genetic basis of inter-individual variability in cell behaviour. By applying a dimensionality reduction approach, Probabilistic Estimation of Expression Residuals (PEER), we identified genes that correlated in expression with intrinsic (genetic) and extrinsic (ECM) factors. However, variation in mRNA levels could not account for outlier cell behaviour. Instead, we identified rare, deleterious SNVs in the coding sequence of genes involved in ECM adhesion that occurred in cell lines that were outliers for one or more phenotypes such as cell spreading. These also correlated with altered germ layer differentiation on micropatterned surfaces. Our study thus establishes a strategy for integrating genetic and cell biological measurements for high-throughput analysis.

cell biology↗

bioRxiv2018

The human leukemia virus HTLV-1 alters the structure and transcription of host chromatin in cis

Chromatin looping controls gene expression by regulating promoter-enhancer contacts, the spread of epigenetic modifications, and the segregation of the genome into transcriptionally active and inactive compartments. We studied the impact on the structure and expression of host chromatin by the human retrovirus HTLV-1. We show that HTLV-1 disrupts host chromatin structure by forming loops between the provirus and the host genome; certain loops depend on the critical chromatin architectural protein CTCF, which we recently showed binds to the HTLV-1 provirus. Finally, we show that the provirus causes two distinct patterns of abnormal transcription of the host genome in cis: bidirectional transcription in the host genome immediately flanking the provirus, and clone-specific transcription in cis at non-contiguous loci up to >300 kb from the integration site. We conclude that HTLV-1 causes insertional mutagenesis up to the megabase range in the host genome in >104 persistently-maintained HTLV-1+ T-cell clones in vivo.

microbiology↗

bioRxiv2018

LiMMBo: a simple, scalable approach for linear mixed models in high-dimensional genetic association studies

Genome-wide association studies have helped to shed light on the genetic architecture of complex traits and diseases. Deep phenotyping of population cohorts is increasingly applied, where multi-to high-dimensional phenotypes are recorded in the individuals. Whilst these rich datasets provide important opportunities to analyse complex trait structures and pleiotropic effects at a genome-wide scale, existing statistical methods for joint genetic analyses are hampered by computational limitations posed by high-dimensional phenotypes. Consequently, such multivariate analyses are currently limited to a moderate number of traits. Here, we introduce a method that combines linear mixed models with bootstrapping (LiMMBo) to enable computationally efficient joint genetic analysis of high-dimensional phenotypes. Our method builds on linear mixed models, thereby providing robust control for population structure and other confounding factors, and the model scales to larger datasets with up to hundreds of phenotypes. We first validate LiMMBo using simulations, demonstrating consistent covariance estimates at greatly reduced computational cost compared to existing methods. We also find LiMMBo yields consistent power advantages compared to univariate modelling strategies, where the advantages of multivariate mapping increases substantially with the phenotype dimensionality. Finally, we applied LiMMBo to 41 yeast growth traits to map their genetic determinants, finding previously known and novel pleiotropic relationships in this high-dimensional phenotype space. LiMMBo is accessible as open source software (https://github.com/HannahVMeyer/limmbo).\n\nAuthor summaryIn multi-trait genetic association studies one is interested in detecting genetic variants that are associated with one or multiple traits. Genetic variants that influence two or more traits are referred to as pleiotropic. Multivariate linear mixed models have been successfully applied to detect pleiotropic effects, by jointly modelling association signals across traits. However, these models are currently limited to a moderate number of phenotypes as the number of model parameters grows steeply with the number of phenotypes, raising a computational burden. We developed LiMMBo, a new approach for the joint analysis of high-dimensional phenotypes. Our method reduces the number of effective model parameters by introducing an intermediate subsampling step. We validate this strategy using simulations, where we apply LiMMBo for the genetic analysis of hundreds of phenotypes, detecting pleiotropic effects for a wide range of simulated genetic architectures. Finally, to illustrate LiMMBo in practice, we apply the model to a study of growth traits in yeast, where we identify pleiotropic effects for traits with formerly known genetic effects as well as revealing previously unconnected traits.

genomics↗

bioRxiv2017

Genomics in healthcare: GA4GH looks to 2022

The Global Alliance for Genomics and Health (GA4GH), the standards-setting body in genomics for healthcare, aims to accelerate biomedical advancement globally. We describe the differences between healthcare- and research-driven genomics, discuss the implications of global, population-scale collections of human data for research, and outline mission-critical considerations in ethics, regulation, technology, data protection, and society. We present a crude model for estimating the rate of healthcare-funded genomes worldwide that accounts for the preparedness of each country for genomics, and infers a progression of cancer-related sequencing over time. We estimate that over 60 million patients will have their genome sequenced in a healthcare context by 2025. This represents a large technical challenge for healthcare systems, and a huge opportunity for research. We identify eight major practical, principled arguments to support the position that virtual cohorts of 100 million people or more would have tangible research benefits.

genomics↗

bioRxiv2017

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body, by undertaking a Human Cell Atlas Project as an international collaborative effort. The aim would be to define all human cell types in terms of distinctive molecular profiles (e.g., gene expression) and connect this information with classical cellular descriptions (e.g., location and morphology). A comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, as well as provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas.

cell biology↗

bioRxiv2017

ChromoTrace: Reconstruction of 3D Chromosome Configurations by Super-Resolution Microscopy

MotivationThe three-dimensional structure of chromatin plays a key role in genome function, including gene expression, DNA replication, chromosome segregation, and DNA repair. Furthermore the location of genomic loci within the nucleus, especially relative to each other and nuclear structures such as the nuclear envelope and nuclear bodies strongly correlates with aspects of function such as gene expression. Therefore, determining the 3D position of the 6 billion DNA base pairs in each of the 23 chromosomes inside the nucleus of a human cell is a central challenge of biology. Recent advances of super-resolution microscopy in principle enable the mapping of specific molecular features with nanometer precision inside cells. Combined with highly specific, sensitive and multiplexed fluorescence labeling of DNA sequences this opens up the possibility of mapping the 3D path of the genome sequence in situ.\n\nResultsHere we develop computational methodologies to reconstruct the sequence configuration of all human chromosomes in the nucleus from a super-resolution image of a set of fluorescent in situ probes hybridized to the genome in a cell. To test our approach we develop a method for the simulation of chromatin packing in an idealized human nucleus. Our reconstruction method, ChromoTrace, uses suffix trees to assign a known linear ordering of in situ probes on the genome to an unknown set of 3D in situ probe positions in the nucleus from super-resolved images using the known genomic probe spacing as a set of physical distance constraints between probes. We find that ChromoTrace can assign the 3D positions of the majority of loci with high accuracy and reasonable sensitivity to specific genome sequences. By simulating spatial resolution, label multiplexing and noise scenarios we assess algorithm performance under realistic experimental constraints. Our study shows that it is feasible to achieve chromosome-wide reconstruction of the 3D DNA path in chromatin based on super-resolution microscopy images.

bioinformatics↗

bioRxiv2017

Interactions between genetic variation and cellular environment in skeletal muscle gene expression

From whole organisms to individual cells, responses to environmental conditions are influenced by genetic makeup, where the effect of genetic variation on a trait depends on the environmental context. RNA-sequencing quantifies gene expression as a molecular trait, and is capable of capturing both genetic and environmental effects. In this study, we explore opportunities of using allele-specific expression (ASE) to discover cis acting genotype-environment interactions (GxE) - genetic effects on gene expression that depend on an environmental condition. Treating 17 common, clinical traits as approximations of the cellular environment of 267 skeletal muscle biopsies, we identify 10 candidate interaction quantitative trait loci (iQTLs) across 6 traits (12 unique gene-environment trait pairs; 10% FDR per trait) including sex, systolic blood pressure, and low-density lipoprotein cholesterol. Although using ASE is in principle a promising approach to detect GxE effects, replication of such signals can be challenging as validation requires harmonization of environmental traits across cohorts and a sufficient sampling of heterozygotes for a transcribed SNP. Comprehensive discovery and replication will require large human transcriptome datasets, or the integration of multiple transcribed SNPs, coupled with standardized clinical phenotyping.

genetics↗

bioRxiv2016

GARFIELD - GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction

Loci discovered by genome-wide association studies (GWAS) predominantly map outside protein-coding genes. The interpretation of functional consequences of non-coding variants can be greatly enhanced by catalogs of regulatory genomic regions in cell lines and primary tissues. However, robust and readily applicable methods are still lacking to systematically evaluate the contribution of these regions to genetic variation implicated in diseases or quantitative traits. Here we propose a novel approach that leverages GWAS findings with regulatory or functional annotations to classify features relevant to a phenotype of interest. Within our framework, we account for major sources of confounding that current methods do not offer. We further assess enrichment statistics for 27 GWAS traits within regulatory regions from the ENCODE and Roadmap projects. We characterise unique enrichment patterns for traits and annotations, driving novel biological insights. The method is implemented in standalone software and R package to facilitate its application by the research community.

genomics↗