Iris Heid, Gonçalo Abecasis, Sudha Iyengar and colleagues report the results of a large genome-wide association meta-analysis of macular degeneration based on over 43,000 subjects. They identify 16 new risk loci, including some very rare coding variants. Advanced age-related macular degeneration (AMD) is the leading cause of blindness in the elderly, with limited therapeutic options. Here we report on a study of >12 million variants, including 163,714 directly genotyped, mostly rare, protein-altering variants. Analyzing 16,144 patients and 17,832 controls, we identify 52 independently associated common and rare variants (P < 5 × 10−8) distributed across 34 loci. Although wet and dry AMD subtypes exhibit predominantly shared genetics, we identify the first genetic association signal specific to wet AMD, near MMP9 (difference P value = 4.1 × 10−10). Very rare coding variants (frequency <0.1%) in CFH, CFI and TIMP3 suggest causal roles for these genes, as does a splice variant in SLC16A8. Our results support the hypothesis that rare coding variants can pinpoint causal genes within known genetic loci and illustrate that applying the approach systematically to detect new loci requires extremely large sample sizes.

Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these require in-depth and time-consuming analysis of fundus images. Herein, we present an automated computer-based classification algorithm. Algorithm development for AMD classification based on a large collection of color fundus images. Validation is performed on a cross-sectional, population-based study. We included 120 656 manually graded color fundus images from 3654 Age-Related Eye Disease Study (AREDS) participants. AREDS participants were >55 years of age, and non-AMD sight-threatening diseases were excluded at recruitment. In addition, performance of our algorithm was evaluated in 5555 fundus images from the population-based Kooperative Gesundheitsforschung in der Region Augsburg (KORA; Cooperative Health Research in the Region of Augsburg) study. We defined 13 classes (9 AREDS steps, 3 late AMD stages, and 1 for ungradable images) and trained several convolution deep learning architectures. An ensemble of network architectures improved prediction accuracy. An independent dataset was used to evaluate the performance of our algorithm in a population-based study. κ Statistics and accuracy to evaluate the concordance between predicted and expert human grader classification. A network ensemble of 6 different neural net architectures predicted the 13 classes in the AREDS test set with a quadratic weighted κ of 92% (95% confidence interval, 89%-92%) and an overall accuracy of 63.3%. In the independent KORA dataset, images wrongly classified as AMD were mainly the result of a macular reflex observed in young individuals. By restricting the KORA analysis to individuals >55 years of age and prior exclusion of other retinopathies, the weighted and unweighted κ increased to 50% and 63%, respectively. Importantly, the algorithm detected 84.2% of all fundus images with definite signs of early or late AMD. Overall, 94.3% of healthy fundus images were classified correctly. Our deep learning algoritm revealed a weighted κ outperforming human graders in the AREDS study and is suitable to classify AMD fundus images in other datasets using individuals >55 years of age.

Geographic atrophy (GA), a late stage of age-related macular degeneration (AMD), is a major cause of blindness. Even while central visual acuity remains relatively well preserved, GA often causes considerable compromise of visual function and quality of life. No treatment currently exists. We evaluated the safety and efficacy of pegcetacoplan, a complement C3 inhibitor, for treatment of GA. Prospective, multicenter, randomized, sham-controlled phase 2 study. Two hundred forty-six patients with GA. Patients with GA were assigned randomly in a 2:2:1:1 ratio to receive intravitreal injections of 15 mg pegcetacoplan monthly or every other month (EOM) or sham intravitreal injections monthly or EOM for 12 months with follow-up at months 15 and 18. Area and growth of GA were measured using fundus autofluorescence imaging. The primary efficacy end point was mean change in square root GA lesion area from baseline to month 12. Secondary outcome measures included mean change from baseline in GA lesion area without the square root transformation, distance of GA lesion from the fovea, best-corrected visual acuity (BCVA), low-luminance BCVA, and low-luminance visual acuity deficit. The primary safety end point was the number and severity of treatment-emergent adverse events. In patients receiving pegcetacoplan monthly or EOM, the GA growth rate was reduced by 29% (95% confidence interval [CI], 9-49; P = 0.008) and 20% (95% CI, 0-40; P = 0.067) compared with the sham treatment group. Post hoc analysis showed that the effect was greater in the second 6 months of treatment, with observed reductions of 45% (P = 0.0004) and 33% (P = 0.009) for pegcetacoplan monthly and EOM, respectively. Two cases of culture-positive endophthalmitis and 1 case of culture-negative endophthalmitis occurred in the pegcetacoplan monthly group. New-onset investigator-determined exudative AMD was reported more frequently in pegcetacoplan-treated eyes (18/86 eyes [20.9%] and 7/79 eyes [8.9%] in monthly and EOM groups, respectively) than in sham-treated eyes (1/81 eyes [1.2%]). Local C3 inhibition with pegcetacoplan resulted in statistically significant reductions in the growth of GA compared with sham treatment. Phase 3 studies will define the efficacy and safety profile further.

Purpose Using exome sequencing, the underlying variants in many persons with autosomal recessive diseases remain undetected. We explored autosomal recessive Stargardt disease (STGD1) as a model to identify the missing heritability. Methods Sequencing of ABCA4 was performed in 8 STGD1 cases with one variant and p.Asn1868Ile in trans, 25 cases with one variant, and 3 cases with no ABCA4 variant. The effect of intronic variants was analyzed using in vitro splice assays in HEK293T cells and patient-derived fibroblasts. Antisense oligonucleotides were used to correct splice defects. Results In 24 of the probands (67%), one known and five novel deep-intronic variants were found. The five novel variants resulted in messenger RNA pseudoexon inclusions, due to strengthening of cryptic splice sites or by disrupting a splicing silencer motif. Variant c.769-784C>T showed partial insertion of a pseudoexon and was found in cis with c.5603A>T (p.Asn1868Ile), so its causal role could not be fully established. Variant c.4253+43G>A resulted in partial skipping of exon 28. Remarkably, antisense oligonucleotides targeting the aberrant splice processes resulted in (partial) correction of all splicing defects. Conclusion Our data demonstrate the importance of assessing noncoding variants in genetic diseases, and show the great potential of splice modulation therapy for deep-intronic variants.

Goncalo Abecasis and colleagues report identification of a rare coding variant in the complement 3 gene that is associated with age-related macular degeneration. Macular degeneration is a common cause of blindness in the elderly. To identify rare coding variants associated with a large increase in risk of age-related macular degeneration (AMD), we sequenced 2,335 cases and 789 controls in 10 candidate loci (57 genes). To increase power, we augmented our control set with ancestry-matched exome-sequenced controls. An analysis of coding variation in 2,268 AMD cases and 2,268 ancestry-matched controls identified 2 large-effect rare variants: previously described p.Arg1210Cys encoded in the CFH gene (case frequency (fcase) = 0.51%; control frequency (fcontrol) = 0.02%; odds ratio (OR) = 23.11) and newly identified p.Lys155Gln encoded in the C3 gene (fcase = 1.06%; fcontrol = 0.39%; OR = 2.68). The variants suggest decreased inhibition of C3 by complement factor H, resulting in increased activation of the alternative complement pathway, as a key component of disease biology.

Background: Advanced age-related macular degeneration (AMD) is a leading cause of blindness. While around half of the genetic contribution to advanced AMD has been uncovered, little is known about the genetic architecture of early AMD. Methods: To identify genetic factors for early AMD, we conducted a genome-wide association study (GWAS) meta-analysis (14,034 cases, 91,214 controls, 11 sources of data including the International AMD Genomics Consortium, IAMDGC, and UK Biobank, UKBB). We ascertained early AMD via color fundus photographs by manual grading for 10 sources and via an automated machine learning approach for > 170,000 photographs from UKBB. We searched for early AMD loci via GWAS and via a candidate approach based on 14 previously suggested early AMD variants. Results: Altogether, we identified 10 independent loci with statistical significance for early AMD: (i) 8 from our GWAS with genome-wide significance (P < 5 × 10^− 8), (ii) one previously suggested locus with experiment-wise significance (P < 0.05/14) in our non-overlapping data and with genome-wide significance when combining the reported and our non-overlapping data (together 17,539 cases, 105,395 controls), and (iii) one further previously suggested locus with experiment-wise significance in our non-overlapping data. Of these 10 identified loci, 8 were novel and 2 known for early AMD. Most of the 10 loci overlapped with known advanced AMD loci (near ARMS2/HTRA1, CFH, C2, C3, CETP, TNFRSF10A, VEGFA, APOE), except two that have not yet been identified with statistical significance for any AMD. Among the 17 genes within these two loci, in-silico functional annotation suggested CD46 and TYR as the most likely responsible genes. Presence or absence of an early AMD effect distinguished the known pathways of advanced AMD genetics (complement/lipid pathways versus extracellular matrix metabolism). Conclusions: Our GWAS on early AMD identified novel loci, highlighted shared and distinct genetics between early and advanced AMD and provides insights into AMD etiology. Our data provide a resource comparable in size to the existing IAMDGC data on advanced AMD genetics enabling a joint view. The biological relevance of this joint view is underscored by the ability of early AMD effects to differentiate the major pathways for advanced AMD.