Dataset Information

A systematic genome-wide analysis of zebrafish protein-coding gene function.

ABSTRACT: Since the publication of the human reference genome, the identities of specific genes associated with human diseases are being discovered at a rapid rate. A central problem is that the biological activity of these genes is often unclear. Detailed investigations in model vertebrate organisms, typically mice, have been essential for understanding the activities of many orthologues of these disease-associated genes. Although gene-targeting approaches and phenotype analysis have led to a detailed understanding of nearly 6,000 protein-coding genes, this number falls considerably short of the more than 22,000 mouse protein-coding genes. Similarly, in zebrafish genetics, one-by-one gene studies using positional cloning, insertional mutagenesis, antisense morpholino oligonucleotides, targeted re-sequencing, and zinc finger and TAL endonucleases have made substantial contributions to our understanding of the biological activity of vertebrate genes, but again the number of genes studied falls well short of the more than 26,000 zebrafish protein-coding genes. Importantly, for both mice and zebrafish, none of these strategies are particularly suited to the rapid generation of knockouts in thousands of genes and the assessment of their biological activity. Here we describe an active project that aims to identify and phenotype the disruptive mutations in every zebrafish protein-coding gene, using a well-annotated zebrafish reference genome sequence, high-throughput sequencing and efficient chemical mutagenesis. So far we have identified potentially disruptive mutations in more than 38% of all known zebrafish protein-coding genes. We have developed a multi-allelic phenotyping scheme to efficiently assess the effects of each allele during embryogenesis and have analysed the phenotypic consequences of over 1,000 alleles. All mutant alleles and data are available to the community and our phenotyping scheme is adaptable to phenotypic analysis beyond embryogenesis.

SUBMITTER: Kettleborough RN

PROVIDER: S-EPMC3743023 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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A systematic genome-wide analysis of zebrafish protein-coding gene function.

Kettleborough Ross N W RN Busch-Nentwich Elisabeth M EM Harvey Steven A SA Dooley Christopher M CM de Bruijn Ewart E van Eeden Freek F Sealy Ian I White Richard J RJ Herd Colin C Nijman Isaac J IJ Fényes Fruzsina F Mehroke Selina S Scahill Catherine C Gibbons Richard R Wali Neha N Carruthers Samantha S Hall Amanda A Yen Jennifer J Cuppen Edwin E Stemple Derek L DL

Nature 20130417 7446

Since the publication of the human reference genome, the identities of specific genes associated with human diseases are being discovered at a rapid rate. A central problem is that the biological activity of these genes is often unclear. Detailed investigations in model vertebrate organisms, typically mice, have been essential for understanding the activities of many orthologues of these disease-associated genes. Although gene-targeting approaches and phenotype analysis have led to a detailed un ...[more]

PMID: 23594742

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Project description:The function of the majority of genes in the human and mouse genomes is unknown. Investigating and illuminating this dark genome is a major challenge for the biomedical sciences. The International Mouse Phenotyping Consortium (IMPC) is addressing this through the generation and broad-based phenotyping of a knockout (KO) mouse line for every protein-coding gene, producing a multidimensional data set that underlies a genome-wide annotation map from genes to phenotypes. Here, we develop a multivariate (MV) statistical approach and apply it to IMPC data comprising 148 phenotypes measured across 4,548 KO lines. There are 4,256 (1.4% of 302,997 observed data measurements) hits called by the univariate (UV) model analysing each phenotype separately, compared to 31,843 (10.5%) hits in the observed data results of the MV model, corresponding to an estimated 7.5-fold increase in power of the MV model relative to the UV model. One key property of the data set is its 55.0% rate of missingness, resulting from quality control filters and incomplete measurement of some KO lines. This raises the question of whether it is possible to infer perturbations at phenotype-gene pairs at which data are not available, i.e., to infer some in vivo effects using statistical analysis rather than experimentation. We demonstrate that, even at missing phenotypes, the MV model can detect perturbations with power comparable to the single-phenotype analysis, thereby filling in the complete gene-phenotype map with good sensitivity. A factor analysis of the MV model's fitted covariance structure identifies 20 clusters of phenotypes, with each cluster tending to be perturbed collectively. These factors cumulatively explain 75% of the KO-induced variation in the data and facilitate biological interpretation of perturbations. We also demonstrate that the MV approach strengthens the correspondence between IMPC phenotypes and existing gene annotation databases. Analysis of a subset of KO lines measured in replicate across multiple laboratories confirms that the MV model increases power with high replicability.

Project description:BackgroundImpaired lung function is predictive of mortality and is a key component of chronic obstructive pulmonary disease. Lung function has a strong genetic component but is also affected by environmental factors such as increased exposure to air pollution, but the effect of their interactions is not well understood.ObjectivesTo identify interactions between genetic variants and air pollution measures which affect COPD risk and lung function. Additionally, to determine whether previously identified lung function genetic association signals showed evidence of interaction with air pollution, considering both individual effects and combined effects using a genetic risk score (GRS).MethodsWe conducted a genome-wide gene-air pollution interaction analysis of spirometry measures with three measures of air pollution at home address: particulate matter (PM2.5 & PM10) and nitrogen dioxide (NO2), in approximately 300,000 unrelated European individuals from UK Biobank. We explored air pollution interactions with previously identified lung function signals and determined their combined interaction effect using a GRS.ResultsWe identified seven new genome-wide interaction signals (P<5×10-8), and a further ten suggestive interaction signals (P<5×10-7). Additionally, we found statistical evidence of interaction for FEV1/FVC between PM2.5 and previously identified lung function signal, rs10841302, near AEBP2, suggesting increased susceptibility as copies of the G allele increased (but size of the impact was small - interaction beta: -0.363 percentage points, 95% CI: -0.523, -0.203 per 5 µg/m3). There was no observed interaction between air pollutants and the weighted GRS.DiscussionWe carried out the largest genome-wide gene-air pollution interaction study of lung function and identified potential effects of clinically relevant size and significance. We observed up to 440 ml lower lung function for certain genotypes when exposed to mean levels of outdoor air pollution, which is approximately equivalent to nine years of average normal loss of lung function in adults.

Dataset Information

A systematic genome-wide analysis of zebrafish protein-coding gene function.

Publications

A systematic genome-wide analysis of zebrafish protein-coding gene function.

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