Abstract
To evaluate the influence AMD risk genomic variants have on macular thickness in the normal population. UK Biobank participants with no significant ocular history were included using the UK Biobank Resource (project 2112). Spectral-domain optical coherence tomography (SD-OCT) images were taken and segmented to define retinal layers. The influence of AMD risk single-nucleotide polymorphisms (SNP) on retinal layer thickness was analysed. AMD risk associated SNPs were strongly associated with outer-retinal layer thickness. The inner-segment outer segment (ISOS)-retinal pigment epithelium (RPE) thickness measurement, representing photoreceptor outer segments was most significantly associated with the cumulative polygenic risk score, composed of 33 AMD-associated variants, resulting in a decreased thickness (p = 1.37 × 10–67). Gene–gene interactions involving the NPLOC4-TSPAN10 SNP rs6565597 were associated with significant changes in outer retinal thickness. Thickness of outer retinal layers is highly associated with the presence of risk AMD SNPs. Specifically, the ISOS-RPE measurement. Changes to ISOS-RPE thickness are seen in clinically normal individuals with AMD risk SNPs suggesting structural changes occur at the macula prior to the onset of disease symptoms or overt clinical signs.
Introduction
Age-related macular degeneration (AMD) is the leading cause of vision loss in high-income countries1, affecting more than 180 million people globally2. It is estimated that by the age of 75, approximately 30% of all Americans are affected by the disease3. AMD is a complex, progressive, chorioretinal degenerative disease that affects the macula, the central region of the retina. Three major factors contribute to AMD: advanced age, environmental and genetic risk factors4,5,6,7. Genetic studies have provided valuable insights into the mechanisms underlying AMD. Successful genome-wide association studies (GWAS) in AMD have led to the discovery of several key single nucleotide polymorphisms (SNPs) in genes conferring an increased disease risk6,8. The most recent comprehensive GWAS for AMD identified a total of 34 genomic loci that account for 46% of the genetic variance6. Due to high population frequency and effect sizes, SNPs in the cluster of genes CFH-CFHR1-5 on chromosome 1, near the age-related maculopathy susceptibility 2 (ARMS2) and high-temperature requirement factor A1 (HTRA1) genes on chromosome 10 contribute nearly 80% of AMD’s genetic risk6,9,10,11. The presence of at least one CFH risk allele alone is estimated to account for a population attributable risk fraction for early and late AMD of 10% and 53%, respectively12.
Although many genetic loci appear to confer risk for AMD development, the molecular pathophysiology behind such associations has not been fully elucidated. Furthermore, it is unknown if individuals carrying common risk polymorphisms display retinal phenotypes prior to the development of AMD clinical signs. A recent study examined the association of AMD susceptibility altering variants at CFH-CFHR5 and ARMS2/HTRA1 with macular retinal thickness in both normal individuals and those with AMD13. Their results showed thicker retinas in the perifovea for normal individuals with a protective CFHR1/3 deletion, while eyes of ARMS2/HTRA1 risk allele carriers with early or intermediate AMD had thinner retinas compared to those with CFH-CFHR5 risk alleles. Whilst the focus of many genetic studies in AMD have been on the effects of chromosome 1 and 10 polymorphisms, including those surrounding retinal thickness13,14, the additional genetic loci identified in the aforementioned GWAS have not been further investigated, especially in normal individuals6.
Optical coherence tomography (OCT) imaging has revolutionised our understanding of retinal diseases, including AMD. Spectral-domain OCT (SD-OCT) imaging produces cross-sectional images of retinal layers using optical reflectivity differences between different layers of retinal cells from the retinal nerve fibre layer through to the retinal pigment epithelium. Segmentation software algorithms allow measurement of retinal layer thicknesses using differences in optical reflectivity to detect boundaries between retinal layers in vivo15. The UK Biobank is one of the largest prospective cohorts worldwide16, with a wealth of medical, lifestyle and detailed genetic sequencing data, including extensive data on ophthalmic diseases. This cohort provides the opportunity to investigate the impact of high-risk AMD genetic loci on changes in outer retinal layer thickness in clinically healthy participants from the UK Biobank population. This may provide mechanistic insight into how these genetic loci contribute to the development of AMD and identify novel biomarkers for clinical use.
Methods
UK Biobank is a large-scale multisite cohort study that includes 502,682 participants, all residents of the United Kingdom, who were recruited via the National Health Service. The study was approved by the North West Research Ethics Committee (06/MRE08/65). Informed written consent was obtained from the participants. It was conducted according to the tenets of the Declaration of Helsinki.
The UK Biobank data resource was set up to allow detailed investigation of genetic and environmental determinants of major diseases of later life16. A detailed description of the study methodology has been published elsewhere17. Extensive baseline questionnaires, physical measurements, and biological samples were collected from participants at 22 assessment centres between 2006 and 201017. Participants completed a touchscreen self-administered questionnaire on lifestyle and environmental exposures. The electronic questionnaire contained several inquiries about tobacco smoking habits, including past and current smoking status (UK Biobank Data Field number: 20116). After the initial baseline assessment, 23% (N = 117,279) of UK Biobank members also participated in an ophthalmic examination, a more comprehensive description of which can be found elsewhere18,19. A subset of this group (N = 67,321) also underwent spectral-domain optical coherence tomography (SD-OCT) scans.
Genotypes were available for most participants and their acquisition, imputation and quality control is described elsewhere20.
SD-OCT imaging was performed using the Topcon 3D OCT 1000 Mk2 (Topcon Corp., Tokyo, Japan) after visual acuity, autorefraction and IOP measurements were collected. OCT images were obtained under mesopic conditions, without pupillary dilation, using the 3D macular volume scan (512 A-scans per B-scan; 128 horizontal B-scans in a 6 × 6-mm raster pattern)21,22.
Four SD-OCT measurements of outer retinal layer thickness were selected for our analyses of outer-retinal layer related boundaries as represented in Fig. 1: inner nuclear layer -retinal pigment epithelium (INL-RPE), retinal pigment epithelium-Bruch’s membrane (RPE-BM), and the specific sublayers of the photoreceptor: inner nuclear layer-external limiting membrane (INL-ELM); external limiting membrane-inner segment outer segment (ELM-ISOS); and inner segment outer segment-retinal pigment epithelium (ISOS-RPE)23,24. The accuracy of the segmentation is described here25. Additional details on how we used the algorithm to segment UKBB images are described here22,23. Briefly, the segmentation method includes an automated measure of signal strength, image centration and segmentation failure. In line with our previous work we defined poor image quality as an image with a signal strength of < 45 measured using Version 1.6.1.1 of the Topcon Advanced Boundary Segmentation (TABS) algorithm25. This algorithm is available upon request from Topcon Medical Limited. All segmentation measurements were calculated up to, but not including, the boundary layer. The TABS segmentation algorithm was used to segment the outer retinal layers22,25. The INL-ELM is a proxy measure of the synaptic terminal of the photoreceptor. The ELM-ISOS is representative of the photoreceptor inner segment. The ISOS-RPE measurement is representative of the photoreceptor outer segment. The RPE-BM measurement represents the RPE and BM complex. The anatomy of the outer retinal layers corresponds with the OCT boundaries observed in the retina (Fig. 1), hence the layers have been defined using the above specific definitions…..
