IMI - Myopia Genetics Report

The knowledge on the genetic background of refractive error and myopia has expanded dramatically in the past few years. This white paper aims to provide a concise summary of current genetic findings and defines the direction where development is needed. We performed an extensive literature search and conducted informal discussions with key stakeholders. Specific topics reviewed included common refractive error, any and high myopia, and myopia related to syndromes. To date, almost 200 genetic loci have been identified for refractive error and myopia, and risk variants mostly carry low risk but are highly prevalent in the general population. Several genes for secondary syndromic myopia overlap with those for common myopia. Polygenic risk scores show overrepresentation of high myopia in the higher deciles of risk. Annotated genes have a wide variety of functions, and all retinal layers appear to be sites of expression. The current genetic findings offer a world of new molecules involved in myopiagenesis. As the missing heritability is still large, further genetic advances are needed. This Committee recommends expanding large-scale, in-depth genetic studies using complementary big data analytics, consideration of gene-environment effects by thorough measurement of environmental exposures, and focus on subgroups with extreme phenotypes and high familial occurrence. Functional characterization of associated variants is simultaneously needed to bridge the knowledge gap between sequence variance and consequence for eye growth

Childhood gene-environment interactions and age-dependent effects of genetic variants associated with refractive error and myopia : The CREAM Consortium

Myopia, currently at epidemic levels in East Asia, is a leading cause of untreatable visual impairment. Genome-wide association studies (GWAS) in adults have identified 39 loci associated with refractive error and myopia. Here, the age-of-onset of association between genetic variants at these 39 loci and refractive error was investigated in 5200 children assessed longitudinally across ages 7-15 years, along with gene-environment interactions involving the major environmental risk-factors, nearwork and time outdoors. Specific variants could be categorized as showing evidence of: (a) early-onset effects remaining stable through childhood, (b) early-onset effects that progressed further with increasing age, or (c) onset later in childhood (N = 10, 5 and 11 variants, respectively). A genetic risk score (GRS) for all 39 variants explained 0.6% (P = 6.6E-08) and 2.3% (P = 6.9E-21) of the variance in refractive error at ages 7 and 15, respectively, supporting increased effects from these genetic variants at older ages. Replication in multi-ancestry samples (combined N = 5599) yielded evidence of childhood onset for 6 of 12 variants present in both Asians and Europeans. There was no indication that variant or GRS effects altered depending on time outdoors, however 5 variants showed nominal evidence of interactions with nearwork (top variant, rs7829127 in ZMAT4; P = 6.3E-04).Peer reviewe

A new polygenic score for refractive error improves detection of children at risk of high myopia but not the prediction of those at risk of myopic macular degeneration

Background High myopia (HM), defined as a spherical equivalent refractive error (SER) ≤ −6.00 diopters (D), is a leading cause of sight impairment, through myopic macular degeneration (MMD). We aimed to derive an improved polygenic score (PGS) for predicting children at risk of HM and to test if a PGS is predictive of MMD after accounting for SER. Methods The PGS was derived from genome-wide association studies in participants of UK Biobank, CREAM Consortium, and Genetic Epidemiology Research on Adult Health and Aging. MMD severity was quantified by a deep learning algorithm. Prediction of HM was quantified as the area under the receiver operating curve (AUROC). Prediction of severe MMD was assessed by logistic regression. Findings In independent samples of European, African, South Asian and East Asian ancestry, the PGS explained 19% (95% confidence interval 17–21%), 2% (1–3%), 8% (7–10%) and 6% (3–9%) of the variation in SER, respectively. The AUROC for HM in these samples was 0.78 (0.75–0.81), 0.58 (0.53–0.64), 0.71 (0.69–0.74) and 0.67 (0.62–0.72), respectively. The PGS was not associated with the risk of MMD after accounting for SER: OR = 1.07 (0.92–1.24). Interpretation Performance of the PGS approached the level required for clinical utility in Europeans but not in other ancestries. A PGS for refractive error was not predictive of MMD risk once SER was accounted fo

Genome-wide association meta-analysis of corneal curvature identifies novel loci and shared genetic influences across axial length and refractive error

Abstract: Corneal curvature, a highly heritable trait, is a key clinical endophenotype for myopia - a major cause of visual impairment and blindness in the world. Here we present a trans-ethnic meta-analysis of corneal curvature GWAS in 44,042 individuals of Caucasian and Asian with replication in 88,218 UK Biobank data. We identified 47 loci (of which 26 are novel), with population-specific signals as well as shared signals across ethnicities. Some identified variants showed precise scaling in corneal curvature and eye elongation (i.e. axial length) to maintain eyes in emmetropia (i.e. HDAC11/FBLN2 rs2630445, RBP3 rs11204213); others exhibited association with myopia with little pleiotropic effects on eye elongation. Implicated genes are involved in extracellular matrix organization, developmental process for body and eye, connective tissue cartilage and glycosylation protein activities. Our study provides insights into population-specific novel genes for corneal curvature, and their pleiotropic effect in regulating eye size or conferring susceptibility to myopia

Transcriptional Changes following Long-Term Sensitization Training and <em>In Vivo</em> Serotonin Exposure in <em>Aplysia californica</em>

<div><p>We used <em>Aplysia californica</em> to compare the transcriptional changes evoked by long-term sensitization training and by a treatment meant to mimic this training, <em>in vivo</em> exposure to serotonin. We focused on 5 candidate plasticity genes which are rapidly up-regulated in the <em>Aplysia</em> genus by <em>in vivo</em> serotonin treatment, but which have not yet been tested for regulation during sensitization: CREB1, matrilin, antistasin, eIF3e, and BAT1 homolog. CREB1 was rapidly up-regulated by both treatments, but the regulation following training was transient, falling back to control levels 24 hours after training. This suggests some caution in interpreting the proposed role of CREB1 in consolidating long-term sensitization memory. Both matrilin and eIF3e were up-regulated by <em>in vivo</em> serotonin but not by long-term sensitization training. This suggests that <em>in vivo</em> serotonin may produce generalized transcriptional effects that are not specific to long-term sensitization learning. Finally, neither treatment produced regulation of antistasin or BAT1 homolog, transcripts regulated by <em>in vivo</em> serotonin in the closely related <em>Aplysia kurodai</em>. This suggests either that these transcripts are not regulated by experience, or that transcriptional mechanisms of memory may vary within the <em>Aplysia</em> genus.</p> </div

Transcriptional changes immediately following <i>in vivo</i> serotonin (5-HT) exposure.

<p>A. Protocol for <i>in vivo</i> 5-HT exposure. Experimental animals were immersed in artificial sea water (ASW) with 250 µM 5-HT for 2 hours; pleural ganglia were harvested for qPCR immediately afterwards. Each treated animal was matched with a control animal processed at the same time but immersed in ASW without 5-HT. To ensure this protocol produces long-term sensitization, a parallel behavioral experiment was conducted in which T-SWR durations were measured before (pre-test) and 24 hours after (post-test) treatment with either <i>in vivo</i> 5-HT or ASW. B. Mean T-SWR durations (±1 <i>SEM</i>) before and 24 hours after control ASW (n = 6) or <i>in vivo</i> 5-HT exposure (n = 8). T-SWRs were evoked via weak electrical shock to implanted electrodes in the tail and measured from the time of siphon contraction to the first sign of siphon relaxation. For each animal, pre-test and post-test responding was measured as the mean of 6 T-SWRs alternating between the left and right sides at a 5 minute ISI. The <i>p</i> value shown is for a paired t-test comparing pre-test and post-test responses within the treated group. The same comparison within the control ASW group was not significant. C. Mean transcriptional changes (± 1<i>SEM</i>) following <i>in vivo</i> 5-HT exposure (n = 10 pairs). Fold changes are calculated as the ratio of transcript in each treated animal versus its matched control. Data are shown on a log scale, and the dotted line at 1 indicates no change (equal levels of transcript in the treated and control animal). * Indicates the mean fold-change is significantly different than 1 by a one-sample t-test (p<0.05).</p

Transcriptional changes following long-term sensitization training.

<p>A. Long-term sensitization training protocol. Training consisted of 4 rounds of shock (30 minute interval). In each round, a 10 s shock (90mA AC, 0.5 s on, 0.5 s off) was applied to one side of the body. Pleural ganglia from the trained and untrained side were harvested separately 1 or 24 hours after training ended for qPCR analysis. In the 24-hour group, T-SWR duration was characterized before (pre-test) and 24 hours after (post-test) training. B. Mean T-SWR durations (±1 <i>SEM</i>) before and 24 hours after long-term sensitization in the 24 hour group (n = 14). T-SWRs were evoked via weak electrical shock to implanted electrodes in the tail and measured from the time of siphon contraction to the first sign of siphon relaxation. For each animal, pre-test and post-test responding was measured on the trained and untrained side separately as the mean of 3 T-SWRs. The <i>p</i> value shown is for a paired t-test comparing pre-test and post-test responses on the trained side. The comparison on the untrained side was not significant. C. Mean transcriptional changes (± 1<i>SEM</i>) 1 and 24 hours after long-term sensitization training (<i>n</i>s = 10, 13 respectively except for 1-hour C/EBP where <i>n</i> = 11). Fold changes are calculated as the ratio of transcript from the trained side to the untrained side. Data are shown on a log scale, and the dotted line at 1 indicates no change (equal levels of transcript in the treated and control animal). * Indicates the mean-fold change is significantly different than 1 by a one-sample t-test (p<0.05).</p