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. 2019 Mar 7;104(3):503-519.
doi: 10.1016/j.ajhg.2019.01.017. Epub 2019 Feb 28.

GRIK5 Genetically Regulated Expression Associated with Eye and Vascular Phenomes: Discovery through Iteration among Biobanks, Electronic Health Records, and Zebrafish

Gokhan Unlu  1 , Eric R Gamazon  2 , Xinzi Qi  3 , Daniel S Levic  1 , Lisa Bastarache  4 , Joshua C Denny  4 , Dan M Roden  5 , Ilya Mayzus  6 , Max Breyer  3 , Xue Zhong  3 , Anuar I Konkashbaev  3 , Andrey Rzhetsky  6 , Ela W Knapik  1 , Nancy J Cox  7
Affiliations

GRIK5 Genetically Regulated Expression Associated with Eye and Vascular Phenomes: Discovery through Iteration among Biobanks, Electronic Health Records, and Zebrafish

Gokhan Unlu et al. Am J Hum Genet. .

Abstract

Although the use of model systems for studying the mechanism of mutations that have a large effect is common, we highlight here the ways that zebrafish-model-system studies of a gene, GRIK5, that contributes to the polygenic liability to develop eye diseases have helped to illuminate a mechanism that implicates vascular biology in eye disease. A gene-expression prediction derived from a reference transcriptome panel applied to BioVU, a large electronic health record (EHR)-linked biobank at Vanderbilt University Medical Center, implicated reduced GRIK5 expression in diverse eye diseases. We tested the function of GRIK5 by depletion of its ortholog in zebrafish, and we observed reduced blood vessel numbers and integrity in the eye and increased vascular permeability. Analyses of EHRs in >2.6 million Vanderbilt subjects revealed significant comorbidity of eye and vascular diseases (relative risks 2-15); this comorbidity was confirmed in 150 million individuals from a large insurance claims dataset. Subsequent studies in >60,000 genotyped BioVU participants confirmed the association of reduced genetically predicted expression of GRIK5 with comorbid vascular and eye diseases. Our studies pioneer an approach that allows a rapid iteration of the discovery of gene-phenotype relationships to the primary genetic mechanism contributing to the pathophysiology of human disease. Our findings also add dimension to the understanding of the biology driven by glutamate receptors such as GRIK5 (also referred to as GLUK5 in protein form) and to mechanisms contributing to human eye diseases.

Keywords: EHR; disease mechanisms; eye disease; genetics; transcriptome; vascular biology; zebrafish.

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Figures

Figure 1
Figure 1
Experimental Pipeline for the Construction of the Medical Phenome Catalog (A) We utilized transcriptome data in 44 tissues and whole-genome genotype data derived from the same donors from the GTEx Consortium as a reference resource to build gene-expression imputation models. (B) We used the PrediXcan method and these prediction models to impute tissue-level gene expression in the BioVU subjects. This imputed expression level is referred to as the genetically regulated expression (GReX) to distinguish it from measured expression levels. For downstream analysis, we are interested primarily in GReX rather than the trait-altered component of gene expression or other factors (such as environmental regulators). (C) Biobank with genotype data linked to electronic health records (EHRs). GReX is tested for association with a clinical trait that is represented by a phecode. (D) This analytic workflow allows us to generate a comprehensive gene-by-medical-phenome catalog, consisting of all associations, including direction of effect, between the GReX for each tested gene and each clinical trait in the EHR.
Figure 2
Figure 2
GRIK5 Genetically Determined Expression and Association with Disease Phenome (A) On the basis of 100,000 permuted datasets that preserve the pairwise SNP-SNP correlations (i.e., linkage disequilibrium [LD]) and the pairwise trait-trait correlations, the number of associations (p < 0.05) that the genetically determined expression of GRIK5 has with disorders of the eye is significantly greater than expected by chance (empirical p < 0.001). The orange arrow shows the observed number in the actual data, and the green bars show the null distribution from the permuted datasets. (B) A histogram displaying distribution of genetically determined GRIK5 expression in 60,000 BioVU subjects. The dotted line indicates the cutoff for the bottom 2.5% (with extreme, reduced genetically determined GRIK5 expression) of the population. (C) A comorbidity analysis between peripheral vascular disease and eye phenotypes (traits) in the bottom 2.5% of the population from (B). The dotted line shows a p = 0.05 cutoff. Yellow = non-significant, orange = nominally significant (p < 0.05), and red = significant (Bonferroni-adjusted p < 0.05) traits. Only traits present in >20 affected individuals were included.
Figure 3
Figure 3
Loss-of-Function of grik5 in Zebrafish Leads to a Bleeding Phenotype (A) A model of GRIK5 protein structure shows the predicted glutamate-binding pocket targeted by the guide RNA (gRNA) g03. (B) The position of the morpholino (MO) and gRNA CRISPR-Cas9 target sites that were used to generate grik5-depletion models. (C) Experimental design and lateral views of head regions in live embryos. The box depicts brain bleeding and is zoomed in below; the arrow points to the 4th ventricle. (D) A summary of the percentage of embryos with hemorrhage in MO and hGRIK5 mRNA rescue and control groups. Human GRIK5 mRNA that is non-targetable by zebrafish grik5-MO was used in rescue experiments. N = total number of tested animals. The results of independent experiments are indicated by shapes as data points. Mean and standard deviation bars are indicated on the graph. (E) Live images of wild-type (WT) and CRISPR-Cas9-edited grik5g03/g03 mutants. Zoomed-in images of the boxed brain regions are shown below. Arrows mark blood accumulation. (F) Sequences of grik5 alleles. Highlighted regions mark the detected mutations in grik5g03; dashes (-) = deletions; red color = substitutions; and lowercase = insertions. Scale bars represent 0.1 mm in panels C and E, which show examples of pigment blocker (PTU)-treated (C) and untreated (E) embryos.
Figure 4
Figure 4
Comorbidity Analysis of Eye and Vascular Disorders (A) A heatmap shows the relative risk for eye diseases in individuals with a set of vascular disorders. The comorbidity estimates were calculated with Vanderbilt’s Synthetic Derivative (SD), the de-identified image of the electronic health records (EHRs) of >2.6 million participants. There is a 2- to 15-fold increase in risk for eye disease across the set of vascular phenotypes. The “burn” is included as a control phenotype. Notably, 72% of the eye-vascular disease pairings have a relative risk (RR) > 4.0. (B and C) RR analysis of eye and vascular diseases in the MarketScan dataset. (B) Retinal detachment (eye) and defect versus degenerative and vascular disorders of the ear (vascular). (C) Cataract (eye) versus aseptic necrosis of bone (vascular). (B′ and C′) Total sample sizes represented in each gender and age group. (B″ and C″) Sample sizes include only diseased subjects (excluding subjects with neither eye nor vascular diseases analyzed). (B″′ and C″′) The odds ratios of eye versus vascular disorders displayed in gender and age groups.
Figure 5
Figure 5
Shared Genetic Architecture Underlying Eye Disease Phenome (A–D) Q-Q plots show the distribution of association p values for vascular traits and the top age-related macular degeneration (AMD) variants. Using a publicly available genome-wide association study (GWAS) of AMD, we find that top AMD variants (p < 0.05) are significantly enriched for a variety of vascular disorders, including aseptic necrosis of bone (A), degenerative and vascular disorders of the ear (B), and congenital anomalies of the peripheral vascular system (C), although they are not enriched for peripheral vascular disease (D). Enrichment for association with vascular traits persisted in comparison with null sets (n = 1000) of SNPs with a matching minor allele frequency (MAF) (generated from bins of length 5% using the 1000 Genomes EUR samples), distance to the nearest gene (on the basis of GENCODE gene annotation data), and number of linkage disequilibrium (LD) partners (r2 > 0.50).
Figure 6
Figure 6
Blood Plasma Extravasation Revealed by Nanobeads in grik5-Depleted Embryos (A–C) The experimental design (A) and live, spinning disk confocal images (B and C) of the lateral head in Tg(flk1:eGFP) show defects in the central arteries (CtA) and posterior mesencephalic central arteries (PMCtA) of the brain in a control (B) and a grik5-KD zebrafish (3D reconstruction of confocal stacks) (C). (B′ and C′) Confocal images of the eye vessels. There is thinning of the inner optic circle (IOC) in the grik5-KD zebrafish as compared to the control (see arrowheads). A digitized (by ImageJ) depiction of comparable IOC vessel-thinning areas (arrowheads) is demarcated in B″ and C″. The abbreviation NCA = nasal ciliary artery. (D) The experimental design for fluorescent nanobead injection (0.02 μm) and microangiography. (E) A maximum-intensity projection of the grik5-KD zebrafish’s head (dorsal view) shows plasma leakage around the right anterior cerebral vein (ACeV) (arrowhead) and no leakage on the contralateral site (open arrowhead). Leakage is also detected in the right eye and ear (arrowheads) and absent in the contralateral, control site (open arrowheads). 2× zoomed views of boxed regions 1 and 2 are below; the “beads (plasma)” channel shows sites of leakage. The abbreviation a = anterior, and p = posterior. (F and G) Maximum-intensity projection images of trunk intersegmental vessels (ISVs) in Tg(flk1:eGFP) embryos (green) injected with fluorescent nanobeads (magenta) in morphants (F) and genetic mutants (G). Microangiography reveals the leakage of blood plasma (nanobeads) from ISVs into the interstitial space (arrow). The open arrowhead marks no extravasation in wild-type (WT) (+/+) siblings. (H) A graph shows the percentage of embryos with nanobead extravasation. (I) The genotype of CRISPR-Cas9-edited grik5g03 alleles analyzed with microangiography. (J) A schematic summarizing the effect of reduced grik5 expression on vascular integrity. Insets in panels (B), (C), and € indicate the number of embryos (out of the total analyzed) exhibiting the represented phenotype.
Figure 7
Figure 7
A Strategy for the Identification of Disease Mechanisms via EHR- and Biobank-Based Discovery Platforms and an Animal Model as a Validation Tool. We used BioVU as an EHR- and biobank-based discovery platform. Through the transcriptome-wide association method PrediXcan, trait-associated genes were identified from BioVU. GRIK5 displayed significant association with numerous, diverse eye disease traits and was thus selected for functional validation in a zebrafish animal model. Gain- and loss-of-function approaches, such as CRISPR-Cas9-mediated genome editing (KO) and morpholino oligonucleotide-mediated knockdown (KD), led to the discovery of phenotypes in zebrafish. These phenotypes further informed statistical analyses performed with independent phenome datasets (acquired from further biobank genotyping and publicly available genome-wide association studies [GWASs]) in order to replicate eye- and vascular-disease phenotypes implicated in zebrafish. System-level assays in zebrafish provided evidence for disease mechanisms, i.e., vascular traits, associated with GRIK5 expression in participants from BioVU. Subsequent comorbidity studies in all Vanderbilt electronic health record (EHR) data and in more than 150 million subjects extend the zebrafish findings that eye and vascular diseases are comorbid and have shared genetic architecture. The abbreviation OE = overexpression.
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