Genetic risk scores in complex eye disorders

C H A P T E R

15 Genetic risk scores in complex eye disorders Robert P. Igo, Jr., Jessica N. Cooke Bailey Case Western Reserve University, Cleveland, OH, United States

Introduction In the age of precision medicine, translational researchers are tasked with identifying measurable risk factors for disease, while physicians are tasked with contextualizing those risk factors for use in clinical settings. One such risk factor, recently come into prominence, is genetic variation. Advances in genotyping technology have made available vast quantities of available genetic data for genomic studies of disease, but the practical applications of such studies, in the form of genetic screening and prediction tests, have been slow. Common ocular diseases, which have a prominent genetic component [1], are often mentioned as candidates for genetic testing to determine personal risk and treatment options. However, except for rare, familial forms, these conditions have complex genetic architecture, with potentially hundreds of contributing genetic loci, and in most cases, individual risk factors have only modest effects. This chapter aims to review advances and limitations in predicting outcomes for ocular diseases using genomic data.

Risk scores and their applications A genetic risk score (GRS) estimates disease susceptibility or severity based on the information from one or more associated genetic variants. It may predict overall genetic risk (i.e., the probability of becoming affected on account of genetic factors), predict progression from one stage of disease to another, or may classify individuals into high- and low-risk categories for which different treatments are recommended [2, 3]. At the population level, a GRS can evaluate the overall contribution of genetic factors to an outcome of interest.

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A GRS is most commonly calculated as a weighted sum of the number of “risk alleles” at each marker—the allele associated with greater susceptibility or, in the case of a quantitative trait, greater phenotype value—multiplied by the effect size (or regression coefficient) of the risk allele [4]. Alternatively, a simple unweighted count of risk alleles across all associated markers, sometimes called an “allele score,” may be computed [3]. The GRS may also incorporate environmental predictors such as age and smoking status, or interaction effects between genes and the environment. The predictive power of a GRS for a binary (affected/unaffected) trait may be evaluated by measuring sensitivity or specificity, or positive and negative predictive value, depending on the application. However, these measures of predictive accuracy require a threshold for the GRS above which the test predicts that an individual is or will become affected. Sensitivity and specificity—the power to detect truly affected and unaffected individuals, respectively—may be summarized across all possible thresholds by means of the receiveroperator characteristic (ROC) curve [5, 6]. An area under the ROC curve (AUC) of 0.5 is expected for a purely uninformative test, whereas AUC ¼ 1 indicates a test with perfect predictive power. Tests with clinical utility to identify high-risk individuals generally have an AUC of at least 0.75–0.8 [5]. The AUC of a gene-based test depends on both the risk or variability captured by the measured genetic variation and on overall trait heritability, as discussed below. The concordance index (C-index) is a generalization of the AUC applicable to time-to-event (survival) data. Other measures of predictive accuracy, such as the population attributable risk (PAR), are also sometimes used [4]. For quantitative phenotypes, a natural evaluative measure of GRS is the multiple R2 measure from linear regression, indicating the proportion of outcome variability explained by the predictors (for example, Ref. [7]).

Ocular traits with well-established risk loci and risk scores Age-related macular degeneration Genetics of AMD Age-related macular degeneration (AMD) is the progressive degeneration of the central retina (macula), causing central vision loss (typically) in individuals over 55 years of age. AMD is the leading cause of blindness in the developed world. The genetic component of AMD is better defined than that of most complex diseases. Over half of the disease heritability is accounted for by two major loci: CFH and ARMS2/HTRA1 [8]. Numerous other loci have been identified that contribute to AMD, including ADAMTS9-AS2, COL8A1, CFI, C9, C2-CFB-SKIV2L, VEGFA, TNFRSF10A, TGFBR1, B3GALTL, RAD51B, LIPC, CETP, C3, APOE, SYN3-TIMP3, and SLC16A8 [9]. Additional loci recently reported include COL4A3, PRLR-SPEF2, PILRB-PILRA, KMT2E-SRPK2, TRPM3, MIR6130-RORB, ABCA1, ARHGAP21, RDH5-CD63, ACAD10, CTRB2-CTRB1, TMEM97-VTN, NPLOC4-TSPAN10, CNN2, MMP9, and C20orf85 [10]. Risk scores in AMD Several GRS have been published attempting to predict risk for developing AMD or for progressing from early to later stages based upon varying levels of clinical, lifestyle, and

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genetic data (for reviews, see Refs. [11, 12]). In a high-performing, nongenetic predictive model of advanced AMD, Chiu et al. [13] reported internal and external C-indices of 0.88 and 0.91, respectively, based upon eight baseline predictors including age, sex, education level, race, smoking status, the presence of pigment abnormality, soft drusen, and maximum drusen size. In genetics-only models, the predictive ability of eight single-nucleotide polymorphisms (SNPs) for AMD [10, 14] and the choroidal neovascularization (CNV) AMD subtype [10, 15], as measured by AUC, ranged from 0.80 to 0.82. Models combining clinical, lifestyle, and genetic data had AUCs of 0.907–0.915 for prediction of 10-year progression to advanced AMD [16, 17]. In the most recent International Age-Related Macular Degeneration Genomics Consortium publication reporting 16 novel loci, Fritsche et al. [10] examined 16,144 patients and 17,832 controls and calculated a weighted 52-SNP GRS combining novel and established loci. In this model, individuals in the highest decile of genetic risk had a 44-fold increased risk of developing advanced AMD compared with those in the lowest decile. GRS has proved useful in AMD gene discovery [12]. Sardell et al. [18] calculated a weighted GRS based on 19 risk variants [9] and sequenced cases and controls with the lowest and highest scores, respectively, in an effort to detect novel risk and protective variants. Focusing clinical trials recruitment on individuals at the highest genetic risk for developing advanced AMD could improve power and reduce sample size requirements [12]. Despite the well-established genetic profile of more than half of the genetic component of AMD [10], a lack of replication in populations not of Western European descent complicates the potential future application of GRS. Even in AMD, where two loci each account for a significant portion of disease heritability in Europeans, index variants for these two loci, CFH and ARMS2/HTRA1, failed to replicate in African Americans, Mexican Americans, and Singaporeans [19]. The A69S variant (rs10490924) in ARMS2 that is common among European-descent individuals and associated with increased AMD risk in non-Hispanic whites and Mexican Americans was, in contrast, protective in non-Hispanic black individuals [20]. Similarly, the CFH Y402H risk variant (rs1061170) common among Caucasians is present in only 5% of Chinese and Japanese individuals [21]. Even among populations of European descent, known loci do not necessarily replicate: in Amish, 19 known AMD risk loci [9] accounted for a lower proportion of AMD risk than in non-Amish Caucasians [22]. The implications of this complexity support the incorporation of ethnic variation in future risk prediction models [12], which would be more feasible with larger studies in more diverse population samples.

Glaucoma Genetics of glaucoma Glaucoma describes a collection of disorders resulting in optic nerve degeneration that are among the leading causes of irreversible blindness worldwide. Age of onset varies by subtype and ranges from birth (congenital) to after age 40 (adult-onset). Glaucoma has a substantial heritable component [23] and several genetic loci contributing to disease have been identified through the application of various statistical genetic techniques [23]. Common forms of glaucoma include primary open-angle glaucoma (POAG), exfoliation glaucoma (XFG), and angleclosure glaucoma (ACG).

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Genetic loci associated with adult-onset POAG that have consistently replicated across multiple studies include 8q22 [24], ABCA1 [25], AFAP1 [25], ATXN2 [26], CAV1 [27], CDKN2B-AS1 [28], FNDC3B [29], FOXC1 [26], GAS7 [26, 29], GMDS [25], MYOC [30], OPTN [31], PMM2 [32], SIX1/SIX6 [24], TGFBR3 [33], TMCO1 [28], TXNRD2 [26], and WDR46 [34]. An additional 14 loci have been recently reported [35, 36] and await confirmation in independent studies. Despite large and well-powered genome-wide association study (GWAS) (e.g., Ref. [26]), known POAG loci account for only a small fraction of the full genetic component of this disease [23, 29]. Further complicating the search for genetic susceptibility factors for POAG is the use of “endophenotypes,” measures related to the disease but not identical to it: optic cup area, intraocular pressure (IOP), and vertical cup-to-disc ratio (VCDR) (reviewed in Ref. [37]). Loci associated with these endophenotypes often compose GRS used to predict POAG case-control status [38–43]. Exfoliation syndrome (XFS) is a major risk factor for XFG, a secondary open-angle glaucoma. Consistently replicating genes associated with XFS and, therefore, XFG includes LOXL1 [44] and CACNA1A [45, 46]. SNPs in LOXL1 are reported to have odds ratios (ORs) of 20 (reviewed in Ref. [47]); however, the risk and protective alleles for two highly associated coding variants are switched in certain populations (reviewed in Refs. [47, 48]). A recent multiethnic GWAS identified seven new loci in or near AGPAT1, POMP, RBMS3, SEMA6A, and TMEM136 [45], but GRS has not been developed for XFG. Primary angle-closure glaucoma (PACG) also has a known genetic component. Nine genes have been implicated in PACG, which are distinct from POAG risk loci [49–51]. To date, there are no known common, consistently replicating genetic loci for pigmentary glaucoma, a secondary form of open-angle glaucoma, despite evidence suggesting that there is a genetic component to this and the closely related pigment dispersion syndrome [52]. Risk scores in glaucoma Prior to the first published GWAS, an early POAG “family score” to predict glaucoma risk was based upon disease status, number of affected relatives, age, sex, and degree of relatedness [53]; one unit increase in this family score correlated with a 1.59-fold increase in odds for POAG. Interestingly, adding IOP to this model did not improve the OR, supporting the role of IOP-independent genetic components in glaucoma. Ramdas et al. [42] developed GRS for OAG based on SNPs associated with VCDR and IOP in a GWAS of 5304 samples. Even in their small subset of 171 OAG cases, scores associated with VCDR were also associated significantly with OAG. In a multiethnic meta-analysis reported in 2014, the International Glaucoma Genetics Consortium (IGGC) reported results from evaluating 21,094 individuals of European ancestry and 6784 individuals of Asian ancestry [43]. This study identified 10 new loci associated with variation in VCDR; in a separate analysis of five case-control studies, these 10 SNPs together with eight previously known VCDR-associated SNPs were used to generate a weighted GRS to predict POAG affectation status; scores were divided into quintiles, and Caucasians in the highest quintile had a 2.5-fold increased risk of POAG compared with those in the lowest quintile. Despite lack of replication of known loci, Hoffman et al. [54] detected significant association between a weighted GRS based on established loci and OAG in African Americans in a

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sample composed of 658 prevalent cases and 6067 controls. In an analysis to evaluate the potential differing genetic architecture of POAG subtypes, Mabuchi et al. reported a weighted GRS based on nine IOP and VCDR-related genetic variants evaluated in a Japanese sample composed of 255 with high-tension glaucoma (HTG), 261 with normal tension glaucoma (NTG), and 246 controls [39]. HTG GRS was significantly higher vs controls and cases with GRS 12 had 2.54 times higher risk of HTG vs all cases. Nannini et al. [41] performed weighted and unweighted GRS analyses based on SNPs associated with VCDR in Latinos; in a model including age, sex, and SNPs significant at the nominal P < 0.005 level in their GWAS, they obtained an AUC of 0.809 for POAG in a sample of 4018 including 229 with POAG [41]. In a southern European Mediterranean population of 391 POAG cases and 383 controls, Zanon-Moreno et al. evaluated an allele score based on four SNPs in known POAG-associated loci (TMCO1, CAV1/CAV2, CDKN2B-AS1, and CDKN2A), in addition to including age and sex [55]. Subjects in the top third of GRS distribution were at 2.92-fold increased risk for POAG compared to those in the bottom third (P < 0.001). The most recently reported GRS relevant to glaucoma are reported based on the UK Biobank [38, 40]. In the UK Biobank and the US-based NEIGHBORHOOD study, Khawaja et al. [38] reported a meta-analysis of over 139,000 individuals of European descent, identifying 112 genomic loci associated with IOP 68 of which were novel. In a regression-based glaucoma-prediction model developed based on these SNPs, the AUC was 0.764 in NEIGHBORHOOD HTG subset and 0.708 in the smaller NTG subset; the AUC was 0.74 in independent glaucoma cases from the UK Biobank. Also relying on the availability of and power afforded by the UK Biobank data, MacGregor et al. [40] recently reported an analysis identifying 101 IOP-associated SNPs, 85 of which were novel, based on the UK Biobank and previously published IGGC for a total sample of over 134,000 including 11,000 glaucoma cases. In an allele score weighted based on the effect in VCDR and IOP analyses, individuals in the top decile had an OR of 5.6 relative to those in the bottom decile.

Myopia and refractive error Genetics of myopia and refractive error Myopia, although usually not disabling, is the most prevalent ocular disease, affecting a quarter of the population in the developed countries [56], often developing in childhood, and its prevalence is increasing [57]. Myopia has a prominent genetic component. Estimates of heritability vary widely, from 0.25 to 0.94, depending on the measure used [58–60], but cluster between 0.7 and 0.9 [61]. It also has well-known and common environmental risk factors, especially educational attainment and time spent reading and performing other near work [62, 63]. Genetic studies of myopia have generally either focused on case/control status for high myopia (typically, 6 diopters of correction) or on quantitative measures of corrective error, including spherical equivalent refraction and axial length [64]. Early genetic studies focused on the binary high myopia phenotype, and did not produce replicable risk loci (reviewed in Refs. [61, 64]). In 2013, two large studies of European-ancestry cohorts, from the Consortium for Refractive Error and Myopia (CREAM) [65] and from the 23andMe

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company database [66], described several dozen genetic loci affecting refractive error. The CREAM Consortium [65] conducted a meta-analysis of 27 European and five Asian-ancestry samples and identified 24 novel loci associated with spherical equivalent refractive error, as well as two loci, at GJD2 and RASGRF1, reported in two previous studies of refractive error in Europeans [67, 68]. Using 23andMe database and a survival analysis model, Kiefer et al. [66] discovered 22 loci associated with self-reported myopia age of onset at genomewide significance, including the GJD2 and RASGRF1 genes. Remarkably, 86 markers in 11 loci were genomewide significant in both studies [69], including markers in or near PRSS56/CHRNG, BMP3, LAMA2, SFRP1/ZMAT4, TOX/CA8, BICC1, RDH5, PCCA/ZIC2, GOLGA8B/GJD2, RASGRF1, and MYO1D/TMEM98 [65, 66]. These findings, from predominantly European cohorts, were partially replicated in a Japanese sample, with individual markers at eight loci, GJD2, RASGRF1, BICC1, KCNQ5, CD55, CYP26A1, LRRC4C, and B4GALNT2, achieving studywide significance [70]. Several, more recent GWAS have built on these results with progressively expanding samples. A genomewide meta-analysis of nine European-descent populations for myopia and hyperopia as binary traits confirmed loci at TOX for myopia, and at TOX and GJD2 for hyperopia, at genomewide significance, and an additional 10 loci at significance qualifying for replication [71]. A GWAS of myopia-related traits in a Japanese cohort, with replication in Chinese and Caucasian samples, confirmed the GJD2 locus for myopia, and also identified WNT7B as a risk locus for axial length and corneal curvature [72]. The CREAM, in 2016 [73], conducted a meta-analysis, including a gene  educational attainment interaction effect, of 25 European and 9 Asian cohorts, including many from the previous CREAM study [65]. This study uncovered nine novel risk loci associated with refractive error: FAM150B/ACP1, LINC00340, FBN1, DIS3L-MAP2K1, ARID2-SNAT1, SLC14A2, AREG, GABRR1, and PDE10A, in addition to replicating 17 known loci [73]. A recent meta-analysis, the largest genetic study to date on myopia, incorporated over 250,000 individuals in a GWAS and replication for refractive error [74]. This analysis, which included most previous samples, identified 161 independent association signals, including the 37 reported in the CREAM and 23andMe 2013 papers. The heritability explained by all common genetic variation was 0.172–0.214 in the CREAM and 23andMe subsets of the European sample, but merely 0.053 in the Asian subset. Risk scores in myopia and refractive error As with other complex traits, genetic testing for myopia is in its infancy [59]. GRS for myopia may best be served to recommend interventions such as increased time outdoors, although this intervention has not had a significant effect on refraction in the general population [59]. In the 2013 CREAM study, Verhoeven et al. [65] constructed a GRS from 26 significantly associated variants, which yielded an AUC of 0.67 for predicting myopia vs hyperopia in an independent European cohort (the Rotterdam Study [75]). The odds of myopia vs hyperopia differed by >20-fold between the lowest and highest GRS risk categories. However, the GRS accounted for only 3.4% of the variation in refractive error in the Rotterdam study. The CREAM applied the 26-marker GRS, both weighted and unweighted, to examine the relationship between education and genetic predisposition to myopia [76] in the Rotterdam Study cohort. Education and genetic risk synergistically increased the overall risk of myopia vs emmetropia; the odds of myopia were about 50-fold greater for high-risk individuals with higher education than for low-risk individuals with primary education only [76]. In the V. Genetic testing and genetic risk prediction

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concurrent 23andMe report, Kiefer et al. [66] summed risk alleles across 22 top variants to create a “propensity score” that explained 2.9% of the total variability in age of onset. Individuals in the top 10% of scores were twice as likely to develop myopia by age 10 or by age 25 as an individual in the bottom 10% [66]. Still, the variability explained by these GRS is far below the estimates of total heritability. Two studies employed a GRS constructed from 39 index variants derived from the 2013 CREAM and 23andMe studies to examine variations in genetic risk for myopia-related traits with age. Tideman et al. [77] found that the strength of association between an unweighted 39-SNP GRS and two measures of myopia, axial length and corneal radius, increased threefold between children aged <10 years and adults. Moreover, the set of associated individual markers within the GRS panel and myopia traits changed with age. Fan et al. [78] also revealed an increase in effect of the GRS with age: the GRS, also calculated as a sum of risk alleles, accounted for 0.6% of variability in refractive error at age 7, but 2.3% at age 15. Recently, Mountjoy et al. [79] applied a 44-marker, weighted GRS derived from 23andMe results [80] to the UK Biobank cohort in a Mendelian randomization study to detect causal effects of education on refractive error, measured as spherical equivalence. The GRS explained 4.3% of the variance in refractive error. However, the GRS, when used as the instrumental variable, failed to predict educational attainment, ruling out a causal effect of myopia on education. Tedja et al. [74], within the recent CREAM report, constructed a polygenic risk score (PRS; discussed below) from 7387 independent variants with nominal association P < 0.005. This score accounted for 7.8% of the variation in refractive error in the Rotterdam Study subset, and individuals in the highest 10% of genetic risk had 40-fold greater odds of myopia than the lowest 10% [74]. The PRS predicted myopia vs hyperopia with an AUC of 0.77, a clear improvement over the 2013 CREAM GRS [65].

Other complex ocular traits Age-related cataract and diabetic retinopathy Whereas AMD and glaucoma have relatively well-established genetic risk loci, other common ocular traits have lagged behind, primarily due to lack of identification of risk loci. Although several GWAS have been conducted for age-related cataract [81, 82] and diabetic retinopathy (reviewed in Ref. [83]), no reliably replicated risk locus for either has been described. A GRS constructed from 18 markers in four DNA repair genes was only borderline significantly associated with cataract case/control status, although the OR between highest and lowest risk classes was 2.67 [84].

Fuchs endothelial corneal dystrophy (FECD) FECD, characterized by progressive blurring of vision, is the most common heritable indication for corneal transplants [85]. The heritable component of common, late-onset FECD is dominated by a single locus, TCF4 [86]. In the GWAS first implicating TCF4 in FECD [87], the index marker, rs613872, was associated with a per-allele OR of 5.5. The PAR for rs613874 was 61% over the study sample. A GRS comprising four independent SNPs within the TCF4 V. Genetic testing and genetic risk prediction

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signal predicted FECD case/control status with >75% accuracy, where accuracy was assessed by correct assignment of case/control pairs based on the case having a greater probability of being affected. Association of rs613872 with large effect at TCF4 has been confirmed repeatedly in European-ancestry samples [88–92] (for a meta-analysis, see Ref. [93]). Allele frequencies of this SNP vary widely across populations; the risk allele, G, is rare in African, East Asian, and Native American Human Genome Diversity samples, concordant with reduced prevalence of FECD in those populations [90]. The TCF4 locus was strongly associated with FECD in a Chinese sample, even though the rs613872 risk allele was absent [94]. A GRS derived from a linear regression model incorporating genotype of two TCF4 markers, rs1348047 and rs17089887, and including sex, yielded an AUC of 0.71 for FECD case/control status in the Chinese cohort [94]. A likely candidate for the causal factor at TCF4 is expansion of a trinucleotide repeat within intron 3 of the gene [95]. The presence of >50 copies of the CTG trinucleotide in a Caucasian sample predicted FECD affectation status with sensitivity of 0.79 and specificity of 0.96, a more accurate predictor of FECD than rs613872 [95]. The expanded repeat predicted FECD less well in an Indian cohort, with sensitivity of 0.34 and specificity of 0.96, whereas the rs613872 variant was not significantly associated with FECD in this sample [96]. In the largest FECD GWAS to date, Afshari et al. [97] discovered three additional loci associated with FECD at KANK4, LAMC1, and LINC009970/ATP1B1. A GRS constructed from the top variants at these loci and at TCF4 had strong predictive value (AUC ¼ 0.782), mostly due to the TCF4 marker rs784257, which by itself explained 21.9% of the FECD risk in the discovery sample (AUC ¼ 0.750). The remaining three index variants together accounted for 2.6% of the risk [97]. Estimates of AUC and explained risk may be inflated in this study because they were assessed on the discovery sample rather than an independent one.

Looking forward: capabilities and limitations of risk scores AMD has been considered as one of the success stories of the GWAS paradigm: more than half of the heritable risk for AMD in European populations has been explained by genetic variants associated with the trait at a genomewide level of significance [10], and as described above, GRS for AMD has achieved AUCs of >0.8. However, difficulty of identifying additional AMD risk variants has increased dramatically as most risk loci of substantial effect have already been discovered, and almost half the heritable variation remains to be explained. Moreover, a test with AUC ¼ 0.8 falls short of a truly diagnostic test, which should have an AUC of around 0.99 [5]. Consequently, it is of keen interest to improve predictive power for GRS.

Polygenic risk scores The genetic component of a complex trait like AMD or POAG is determined by many loci across the genome, most with small enough effect that no study with a reasonable sample size reveals them at genomewide significance. The PRS extends the GRS to thousands of markers, attempting to account for all the heritable variation regardless of statistical significance.

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Because the costs of GWAS panel genotyping and even whole-genome sequencing have fallen to the point that these technologies are becoming available for clinical diagnostics, the PRS has become popular [3, 98–100]. Generally, a PRS includes variants with a nominal association p value below a specified, relatively low threshold [98, 99] that balances total information with dilution of the genetic signal with large numbers of weakly associated variants [101]. Normally, markers included in a risk score are assumed to be independent (i.e., not in linkage disequilibrium), and many methods for computing PRS screen the available genetic variants to obtain an effectively independent subset [98–100], although a method that accounts for correlations among associated markers has been developed [102]. The technique of PRS was pioneered in Schizophrenia research [99], and though it has been employed to predict risk and to select treatment options for several common traits (reviewed in Ref. [3]), no true PRS has been published thus far for any of the ocular diseases discussed here, save the very recent reports on refractive error [74] and VCDR [41]. The theoretically achievable predictive power from any test of genetic risk, including a PRS, depends on the heritability explained by measured variants, the risk ratio for siblings of affected individuals, and the overall trait prevalence [6]. Taking 11.8 for the prevalence and 2.2 for sibling relative risk ratio, an AUC of 0.92 is theoretically obtainable for a PRS for AMD. However, even with modern GWAS panels, a PRS will fail to capture all of the heritable variation for complex ocular diseases—a problem termed the “missing heritability” [103]. The technique of genotype imputation may capture some of the variation not accounted for by variants in the marker panel, but is limited to markers common enough to occur in the study sample. Sources of heritability not captured in GWAS include effects of rare genetic variants not captured in the PRS, and effects not usually tested in single-marker association analysis: gene-by-gene interactions (epistasis; Ref. [104]), genomic structural variation, geneby-environment interactions [105] and heritable epigenetic effects such as methylation of cytosine residues of DNA [106]. A further complication is heterogeneity between populations and study samples: a PRS determined from a given study may not be perfectly relevant to another sample even from the same population, if differently ascertained for the trait of interest. We have seen that across ethnic groups, major risk loci for AMD and POAG are not consistent [19]. Moreover, GRS determined for a given ocular trait is often applied to similar measures or conditions, as in the case of POAG and IOP, and refractive error and myopia case/control status.

Clinical utility of risk scores While PRSs are not yet ready for widespread use, they have been shown to be clinically relevant on a subset of patients at the extremes of the population risk distributions. Recent studies showed that they may provide clinically useful information on this subset of patients [2, 3, 107]. A recent study of predictive power of PRS for five common diseases identified individuals at very high risk (OR > 3), who may benefit from preventative measures [2]. Similar success is in principle achievable for ocular diseases, whose heritabilities generally compare well with those of other common traits [1]. In fact, when the results of the IAMDGC study were extrapolated to a general population, individuals within the top decile for a 52-marker GRS had a 22.7% risk for AMD, and the top percentile had a risk of almost 50%, more than

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ninefold greater than the assumed population prevalence [10]. Moreover, identifying affected individuals at low risk for disease [18], and unaffected individuals at high risk, may facilitate discovery of novel genetic variants conferring risk and protection, respectively. In summary, the greatest promise for clinical utility of GRS in common ocular traits, in the near term, is likely to continue to be restricted to identifying individuals at the extremes of genetic risk, but within those extremes may provide valuable information toward diagnosis and treatment. As other types of gene-related data become widely available, such as gene expression and epigenetic markers, GRS may eventually be incorporated into more informative measures of overall physiological risk [3].

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V. Genetic testing and genetic risk prediction