Genome-wide analysis of disease progression in age-related macular degeneration

Qiu, Yan; Ding, Ying; Y, Liu; Sun, Tao; Fritsche, Lars G.; Clemons, Traci E; Ratnapriya, Rinki; Ml, Klein; Cook, Richard J.; Fan, Rong; Wei, Lian-Qiang; Gr, Abecasis; Swaroop, Anand; Chew, Ey; Weeks, Daniel E.; W, Chen

doi:10.1093/hmg/ddy002

Cited by 74 publications

(88 citation statements)

References 25 publications

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“…Multiple prospective studies have confirmed that the minor allele of this variant is a risk allele for the incidence of early AMD (HR 1.2–2.3), as well as for the progression to nAMD (HR 1.48–2.5) and GA (HR 1.38–3.65) . Another common variant in the CFH gene is the rs10922109 variant, of which the minor allele is confirmed to be a protective allele for the progression to late stage AMD (HR 0.43) . However, both variants in the CFH gene have not yet been confirmed to be also associated with a more rapid GA enlargement in two prospective studies …”

Section: Genetic Risk Factorsmentioning

confidence: 99%

Risk factors for progression of age‐related macular degeneration

Heesterbeek

Lorés‐Motta

Hoyng

et al. 2020

Ophthalmic Physiologic Optic

213

224

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Purpose Age‐related macular degeneration (AMD) is a degenerative disease of the macula, often leading to progressive vision loss. The rate of disease progression can vary among individuals and has been associated with multiple risk factors. In this review, we provide an overview of the current literature investigating phenotypic, demographic, environmental, genetic, and molecular risk factors, and propose the most consistently identified risk factors for disease progression in AMD based on these studies. Finally, we describe the potential use of these risk factors for personalised healthcare. Recent findings While phenotypic risk factors such as drusen and pigment abnormalities become more important to predict disease progression during the course of the disease, demographic, environmental, genetic and molecular risk factors are more valuable at earlier disease stages. Demographic and environmental risk factors such as age and smoking are consistently reported to be related to disease progression, while other factors such as sex, body mass index (BMI) and education are less often associated. Of all known AMD variants, variants that are most consistently reported with disease progression are rs10922109 and rs570618 in CFH, rs116503776 in C2/CFB/SKIV2L, rs3750846 in ARMS2/HTRA1 and rs2230199 in C3. However, it seems likely that other AMD variants also contribute to disease progression but to a lesser extent. Rare variants have probably a large effect on disease progression in highly affected families. Furthermore, current prediction models do not include molecular risk factors, while these factors can be measured accurately in the blood. Possible promising molecular risk factors are High‐Density Lipoprotein Cholesterol (HDL‐C), Docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), zeaxanthin and lutein. Summary Phenotypic, demographic, environmental, genetic and molecular risk factors can be combined in prediction models to predict disease progression, but the selection of the proper risk factors for personalised risk prediction will differ among individuals and is dependent on their current disease stage. Future prediction models should include a wider set of genetic variants to determine the genetic risk more accurately, and rare variants should be taken into account in highly affected families. In addition, adding molecular factors in prediction models may lead to preventive strategies and personalised advice.

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Section: Genetic Risk Factorsmentioning

confidence: 99%

Risk factors for progression of age‐related macular degeneration

Heesterbeek

Lorés‐Motta

Hoyng

et al. 2020

Ophthalmic Physiologic Optic

213

224

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“…These four regions were also reported to be top gene regions containing variants associated with AMD progression in Yan et al . (). Therefore, we first examined these four candidate regions specifically.…”

Section: Application On Age‐related Eye Disease Study Datamentioning

confidence: 97%

“…More recently, Yan et al . () performed the first GWAS on AMD progression using a similar robust Cox PH model. Multiple risk loci have been identified to be associated with AMD progression.…”

Section: Introductionmentioning

confidence: 99%

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Gene‐based association analysis for bivariate time‐to‐event data through functional regression with copula models

et al. 2019

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Several gene‐based association tests for time‐to‐event traits have been proposed recently to detect whether a gene region (containing multiple variants), as a set, is associated with the survival outcome. However, for bivariate survival outcomes, to the best of our knowledge, there is no statistical method that can be directly applied for gene‐based association analysis. Motivated by a genetic study to discover the gene regions associated with the progression of a bilateral eye disease, age‐related macular degeneration (AMD), we implement a novel functional regression (FR) method under the copula framework. Specifically, the effects of variants within a gene region are modeled through a functional linear model, which then contributes to the marginal survival functions within the copula. Generalized score test statistics are derived to test for the association between bivariate survival traits and the genetic region. Extensive simulation studies are conducted to evaluate the type I error control and power performance of the proposed approach, with comparisons to several existing methods for a single survival trait, as well as the marginal Cox FR model using the robust sandwich estimator for bivariate survival traits. Finally, we apply our method to a large AMD study, the Age‐related Eye Disease Study, and to identify the gene regions that are associated with AMD progression.

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“…Both phenotype and genotype data of AREDS are available from the online repository dbGap (accession: phs000001.v3.p1, and phs001039.v1.p1, respectively). By far, all the studies that analyzed the AREDS data for AMD progression treated the outcome as right-censored (e.g., Ding and others (2017), Yan and others (2018), and Sun and others (2019)), and some only used data from the worst eye in each subject (e.g., Seddon and others (2014)).…”

Section: Real Data Analysismentioning

confidence: 99%

Copula-based semiparametric regression method for bivariate data under general interval censoring

Sun

Ding

2019

Biostatistics

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This research is motivated by discovering and underpinning genetic causes for the progression of a bilateral eye disease, Age-related Macular Degeneration (AMD), of which the primary outcomes, progression times to late-AMD, are bivariate and interval-censored due to intermittent assessment times. We propose a novel class of copula-based semiparametric transformation models for bivariate data under general interval censoring, which includes the case 1 interval censoring (current status data) and case 2 interval censoring. Specifically, the joint likelihood is modeled through a two-parameter Archimedean copula, which can flexibly characterize the dependence between the two margins in both tails. The marginal distributions are modeled through semiparametric transformation models using sieves, with the proportional hazards or odds model being a special case. We develop a computationally efficient sieve maximum likelihood estimation procedure for the unknown parameters, together with a generalized score test for the regression parameter(s).For the proposed sieve estimators of finite-dimensional parameters, we establish their asymptotic normality and efficiency. Extensive simulations are conducted to evaluate the performance of the 2 T. SUN and Y. DING proposed method in finite samples. Finally, we apply our method to a genome-wide analysis of AMD progression using the Age-Related Eye Disease Study (AREDS) data, to successfully identify novel risk variants associated with the disease progression. We also produce predicted joint and conditional progression-free probabilities, for patients with different genetic characteristics.

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Genome-wide analysis of disease progression in age-related macular degeneration

Cited by 74 publications

References 25 publications

Risk factors for progression of age‐related macular degeneration

Risk factors for progression of age‐related macular degeneration

Gene‐based association analysis for bivariate time‐to‐event data through functional regression with copula models

Copula-based semiparametric regression method for bivariate data under general interval censoring

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