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1. Your best day: An interactive app to translate how time reallocations within a 24-hour day are associated with health measures

Author

Harezlak, J, Dumuid, D, Olds, T, Wake, M, Rasmussen, CL, Pedisic, Z, Hughes, JH, Foster, DJR, Walmsley, R, Atkin, AJ, Straker, L, Fraysse, F, Smith, RT, Neumann, F, Kenett, RS, Mork, PJ, Bennett, D, Doherty, A, Stanford, T, Harezlak, J, Dumuid, D, Olds, T, Wake, M, Rasmussen, CL, Pedisic, Z, Hughes, JH, Foster, DJR, Walmsley, R, Atkin, AJ, Straker, L, Fraysse, F, Smith, RT, Neumann, F, Kenett, RS, Mork, PJ, Bennett, D, Doherty, A, and Stanford, T

Abstract

Reallocations of time between daily activities such as sleep, sedentary behavior and physical activity are differentially associated with markers of physical, mental and social health. An individual's most desirable allocation of time may differ depending on which outcomes they value most, with these outcomes potentially competing with each other for reallocations. We aimed to develop an interactive app that translates how self-selected time reallocations are associated with multiple health measures. We used data from the Australian Child Health CheckPoint study (n = 1685, 48% female, 11-12 y), with time spent in daily activities derived from a validated 24-h recall instrument, %body fat from bioelectric impedance, psychosocial health from the Pediatric Quality of Life Inventory and academic performance (writing) from national standardized tests. We created a user-interface to the compositional isotemporal substitution model with interactive sliders that can be manipulated to self-select time reallocations between activities. The time-use composition was significantly associated with body fat percentage (F = 2.66, P < .001), psychosocial health (F = 4.02, P < .001), and academic performance (F = 2.76, P < .001). Dragging the sliders on the app shows how self-selected time reallocations are associated with the health measures. For example, reallocating 60 minutes from screen time to physical activity was associated with -0.8 [95% CI -1.0 to -0.5] %body fat, +1.9 [1.4 to 2.5] psychosocial score and +4.5 [1.8 to 7.2] academic performance. Our app allows the health associations of time reallocations to be compared against each other. Interactive interfaces provide flexibility in selecting which time reallocations to investigate, and may transform how research findings are disseminated.

Published
2022

3. Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD.

Author

Bhuiyan, A, Wong, TY, Ting, DSW, Govindaiah, A, Souied, EH, Smith, RT, Bhuiyan, A, Wong, TY, Ting, DSW, Govindaiah, A, Souied, EH, and Smith, RT

Abstract

PURPOSE: To build and validate artificial intelligence (AI)-based models for AMD screening and for predicting late dry and wet AMD progression within 1 and 2 years. METHODS: The dataset of the Age-related Eye Disease Study (AREDS) was used to train and validate our prediction model. External validation was performed on the Nutritional AMD Treatment-2 (NAT-2) study. FIRST STEP: An ensemble of deep learning screening methods was trained and validated on 116,875 color fundus photos from 4139 participants in the AREDS study to classify them as no, early, intermediate, or advanced AMD and further stratified them along the AREDS 12 level severity scale. Second step: the resulting AMD scores were combined with sociodemographic clinical data and other automatically extracted imaging data by a logistic model tree machine learning technique to predict risk for progression to late AMD within 1 or 2 years, with training and validation performed on 923 AREDS participants who progressed within 2 years, 901 who progressed within 1 year, and 2840 who did not progress within 2 years. For those found at risk of progression to late AMD, we further predicted the type (dry or wet) of the progression of late AMD. RESULTS: For identification of early/none vs. intermediate/late (i.e., referral level) AMD, we achieved 99.2% accuracy. The prediction model for a 2-year incident late AMD (any) achieved 86.36% accuracy, with 66.88% for late dry and 67.15% for late wet AMD. For the NAT-2 dataset, the 2-year late AMD prediction accuracy was 84%. CONCLUSIONS: Validated color fundus photo-based models for AMD screening and risk prediction for late AMD are now ready for clinical testing and potential telemedical deployment. TRANSLATIONAL RELEVANCE: Noninvasive, highly accurate, and fast AI methods to screen for referral level AMD and to predict late AMD progression offer significant potential improvements in our care of this prevalent blinding disease.

Published
2020

5. Pathway Analysis Integrating Genome-Wide and Functional Data Identifies PLCG2 as a Candidate Gene for Age-Related Macular Degeneration

Author

Waksmunski, AR, Grunin, M, Kinzy, TG, Igo, RP, Haines, JL, Bailey, JNC, Fritsche, LG, Igl, W, Grassmann, F, Sengupta, S, Bragg-Gresham, JL, Burdon, KP, Hebbring, SJ, Wen, C, Gorski, M, Kim, IK, Cho, D, Zack, D, Souied, E, Scholl, HPN, Bala, E, Lee, KE, Hunter, DJ, Sardell, RJ, Mitchell, P, Merriam, JE, Cipriani, V, Hoffman, JD, Schick, T, Lechanteur, YTE, Guymer, RH, Johnson, MP, Jiang, Y, Stanton, CM, Buitendijk, GHS, Zhan, X, Kwong, AM, Boleda, A, Brooks, M, Gieser, L, Ratnapriya, R, Branham, KE, Foerster, JR, Heckenlively, JR, Othman, M, Vote, BJ, Liang, HH, Souzeau, E, McAllister, IL, Isaacs, T, Hall, J, Lake, S, Mackey, DA, Constable, IJ, Craig, JE, Kitchner, TE, Yang, Z, Su, Z, Luo, H, Chen, D, Ouyang, H, Flagg, K, Lin, D, Mao, G, Ferreyra, H, Stark, K, von Strachwitz, CN, Wolf, A, Brandl, C, Rudolph, G, Olden, M, Morrison, MA, Morgan, DJ, Schu, M, Ahn, J, Silvestri, G, Tsironi, EE, Park, KH, Farrer, LA, Orlin, A, Brucker, A, Li, M, Curcio, CA, Mohand-Said, S, Sahel, J-A, Audo, I, Benchaboune, M, Cree, AJ, Rennie, CA, Goverdhan, S, Hagbi-Levi, S, Campochiaro, P, Katsanis, N, Holz, FG, Blond, F, Blanche, H, Deleuze, J-F, Truitt, B, Peachey, NS, Meuer, SM, Myers, CE, Moore, EL, Klein, R, Hauser, MA, Postel, EA, Courtenay, MD, Schwartz, SG, Kovach, JL, Scott, WK, Liew, G, Tan, AG, Gopinath, B, Merriam, JC, Smith, RT, Khan, JC, Shahid, H, Moore, AT, McGrath, JA, Laux, R, Brantley, MA, Agarwal, A, Ersoy, L, Caramoy, A, Langmann, T, Saksens, NTM, de Jong, EK, Hoyng, CB, Cain, MS, Richardson, AJ, Martin, TM, Blangero, J, Weeks, DE, Dhillon, B, van Duijn, CM, Doheny, KF, Romm, J, Klaver, CCW, Hayward, C, Gorin, MB, Klein, ML, Baird, PN, den Hollander, A, Fauser, S, Yates, JRW, Allikmets, R, Wang, JJ, Schaumberg, DA, Klein, BEK, Hagstrom, SA, Chowers, I, Lotery, AJ, Leveillard, T, Zhang, K, Brilliant, MH, Hewitt, AW, Swaroop, A, Chew, EY, Pericak-Vance, MA, DeAngelis, M, Stambolian, D, Iyengar, SK, Weber, BHF, Abecasis, GR, Heid, IM, Waksmunski, AR, Grunin, M, Kinzy, TG, Igo, RP, Haines, JL, Bailey, JNC, Fritsche, LG, Igl, W, Grassmann, F, Sengupta, S, Bragg-Gresham, JL, Burdon, KP, Hebbring, SJ, Wen, C, Gorski, M, Kim, IK, Cho, D, Zack, D, Souied, E, Scholl, HPN, Bala, E, Lee, KE, Hunter, DJ, Sardell, RJ, Mitchell, P, Merriam, JE, Cipriani, V, Hoffman, JD, Schick, T, Lechanteur, YTE, Guymer, RH, Johnson, MP, Jiang, Y, Stanton, CM, Buitendijk, GHS, Zhan, X, Kwong, AM, Boleda, A, Brooks, M, Gieser, L, Ratnapriya, R, Branham, KE, Foerster, JR, Heckenlively, JR, Othman, M, Vote, BJ, Liang, HH, Souzeau, E, McAllister, IL, Isaacs, T, Hall, J, Lake, S, Mackey, DA, Constable, IJ, Craig, JE, Kitchner, TE, Yang, Z, Su, Z, Luo, H, Chen, D, Ouyang, H, Flagg, K, Lin, D, Mao, G, Ferreyra, H, Stark, K, von Strachwitz, CN, Wolf, A, Brandl, C, Rudolph, G, Olden, M, Morrison, MA, Morgan, DJ, Schu, M, Ahn, J, Silvestri, G, Tsironi, EE, Park, KH, Farrer, LA, Orlin, A, Brucker, A, Li, M, Curcio, CA, Mohand-Said, S, Sahel, J-A, Audo, I, Benchaboune, M, Cree, AJ, Rennie, CA, Goverdhan, S, Hagbi-Levi, S, Campochiaro, P, Katsanis, N, Holz, FG, Blond, F, Blanche, H, Deleuze, J-F, Truitt, B, Peachey, NS, Meuer, SM, Myers, CE, Moore, EL, Klein, R, Hauser, MA, Postel, EA, Courtenay, MD, Schwartz, SG, Kovach, JL, Scott, WK, Liew, G, Tan, AG, Gopinath, B, Merriam, JC, Smith, RT, Khan, JC, Shahid, H, Moore, AT, McGrath, JA, Laux, R, Brantley, MA, Agarwal, A, Ersoy, L, Caramoy, A, Langmann, T, Saksens, NTM, de Jong, EK, Hoyng, CB, Cain, MS, Richardson, AJ, Martin, TM, Blangero, J, Weeks, DE, Dhillon, B, van Duijn, CM, Doheny, KF, Romm, J, Klaver, CCW, Hayward, C, Gorin, MB, Klein, ML, Baird, PN, den Hollander, A, Fauser, S, Yates, JRW, Allikmets, R, Wang, JJ, Schaumberg, DA, Klein, BEK, Hagstrom, SA, Chowers, I, Lotery, AJ, Leveillard, T, Zhang, K, Brilliant, MH, Hewitt, AW, Swaroop, A, Chew, EY, Pericak-Vance, MA, DeAngelis, M, Stambolian, D, Iyengar, SK, Weber, BHF, Abecasis, GR, and Heid, IM

Abstract

PURPOSE: Age-related macular degeneration (AMD) is the worldwide leading cause of blindness among the elderly. Although genome-wide association studies (GWAS) have identified AMD risk variants, their roles in disease etiology are not well-characterized, and they only explain a portion of AMD heritability. METHODS: We performed pathway analyses using summary statistics from the International AMD Genomics Consortium's 2016 GWAS and multiple pathway databases to identify biological pathways wherein genetic association signals for AMD may be aggregating. We determined which genes contributed most to significant pathway signals across the databases. We characterized these genes by constructing protein-protein interaction networks and performing motif analysis. RESULTS: We determined that eight genes (C2, C3, LIPC, MICA, NOTCH4, PLCG2, PPARA, and RAD51B) "drive" the statistical signals observed across pathways curated in the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and Gene Ontology (GO) databases. We further refined our definition of statistical driver gene to identify PLCG2 as a candidate gene for AMD due to its significant gene-level signals (P < 0.0001) across KEGG, Reactome, GO, and NetPath pathways. CONCLUSIONS: We performed pathway analyses on the largest available collection of advanced AMD cases and controls in the world. Eight genes strongly contributed to significant pathways from the three larger databases, and one gene (PLCG2) was central to significant pathways from all four databases. This is, to our knowledge, the first study to identify PLCG2 as a candidate gene for AMD based solely on genetic burden. Our findings reinforce the utility of integrating in silico genetic and biological pathway data to investigate the genetic architecture of AMD.

Published
2019

6. Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm

Author

Vavvas, DG, Hussain, MA, Bhuiyan, A, Luu, CD, Smith, RT, Guymer, RH, Ishikawa, H, Schuman, JS, Ramamohanarao, K, Vavvas, DG, Hussain, MA, Bhuiyan, A, Luu, CD, Smith, RT, Guymer, RH, Ishikawa, H, Schuman, JS, and Ramamohanarao, K

Abstract

In this paper, we propose a novel classification model for automatically identifying individuals with age-related macular degeneration (AMD) or Diabetic Macular Edema (DME) using retinal features from Spectral Domain Optical Coherence Tomography (SD-OCT) images. Our classification method uses retinal features such as the thickness of the retina and the thickness of the individual retinal layers, and the volume of the pathologies such as drusen and hyper-reflective intra-retinal spots. We extract automatically, ten clinically important retinal features by segmenting individual SD-OCT images for classification purposes. The effectiveness of the extracted features is evaluated using several classification methods such as Random Forrest on 251 (59 normal, 177 AMD and 15 DME) subjects. We have performed 15-fold cross-validation tests for three phenotypes; DME, AMD and normal cases using these data sets and achieved accuracy of more than 95% on each data set with the classification method using Random Forrest. When we trained the system as a two-class problem of normal and eye with pathology, using the Random Forrest classifier, we obtained an accuracy of more than 96%. The area under the receiver operating characteristic curve (AUC) finds a value of 0.99 for each dataset. We have also shown the performance of four state-of-the-methods for classification the eye participants and found that our proposed method showed the best accuracy.

Published
2018

7. Using visuo-kinetic virtual reality to induce illusory spinal movement: The MoOVi illusion

Author

Harvie, DS, Smith, RT, Hunter, EV, Davis, MG, Sterling, M, Lorimer Moseley, G, Harvie, DS, Smith, RT, Hunter, EV, Davis, MG, Sterling, M, and Lorimer Moseley, G

Abstract

© 2017 Harvie et al. Background. Illusions that alter perception of the body provide novel opportunities to target brain-based contributions to problems such as persistent pain. One example of this, mirror therapy, uses vision to augment perceived movement of a painful limb to treat pain. Since mirrors can't be used to induce augmented neck or other spinal movement, we aimed to test whether such an illusion could be achieved using virtual reality, in advance of testing its potential therapeutic benefit. We hypothesised that perceived head rotation would depend on visually suggested movement. Method. In a within-subjects repeated measures experiment, 24 healthy volunteers performed neck movements to 50° of rotation, while a virtual reality system delivered corresponding visual feedback that was offset by a factor of 50%-200%-the Motor Offset Visual Illusion (MoOVi)-thus simulating more or less movement than that actually occurring. At 50° of real-world head rotation, participants pointed in the direction that they perceived they were facing. The discrepancy between actual and perceived direction was measured and compared between conditions. The impact of including multisensory (auditory and visual) feedback, the presence of a virtual body reference, and the use of 360° immersive virtual reality with and without three- dimensional properties, was also investigated. Results. Perception of head movement was dependent on visual-kinaesthetic feedback (p = 0:001, partial eta squared = 0.17). That is, altered visual feedback caused a kinaesthetic drift in the direction of the visually suggested movement. The magnitude of the drift was not moderated by secondary variables such as the addition of illusory auditory feedback, the presence of a virtual body reference, or three-dimensionality of the scene. Discussion. Virtual reality can be used to augment perceived movement and body position, such that one can perform a small movement, yet perceive a large one. The MoOVi techniq

Published
2017

8. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants

Author

Fritsche, LG, Igl, W, Bailey, JNC, Grassmann, F, Sengupta, S, Bragg-Gresham, JL, Burdon, KP, Hebbring, SJ, Wen, C, Gorski, M, Kim, IK, Cho, D, Zack, D, Souied, E, Scholl, HPN, Bala, E, Lee, KE, Hunter, DJ, Sardell, RJ, Mitchell, P, Merriam, JE, Cipriani, V, Hoffman, JD, Schick, T, Lechanteur, YTE, Guymer, RH, Johnson, MP, Jiang, Y, Stanton, CM, Buitendijk, GHS, Zhan, X, Kwong, AM, Boleda, A, Brooks, M, Gieser, L, Ratnapriya, R, Branham, KE, Foerster, JR, Heckenlively, JR, Othman, MI, Vote, BJ, Liang, HH, Souzeau, E, McAllister, IL, Isaacs, T, Hall, J, Lake, S, Mackey, DA, Constable, IJ, Craig, JE, Kitchner, TE, Yang, Z, Su, Z, Luo, H, Chen, D, Hong, O, Flagg, K, Lin, D, Mao, G, Ferreyra, H, Starke, K, von Strachwitz, CN, Wolf, A, Brandl, C, Rudolph, G, Olden, M, Morrison, MA, Morgan, DJ, Schu, M, Ahn, J, Silvestri, G, Tsironi, EE, Park, KH, Farrer, LA, Orlin, A, Brucker, A, Li, M, Curcio, CA, Mohand-Said, S, Sahel, J-M, Audo, I, Benchaboune, M, Cree, AJ, Rennie, CA, Goverdhan, SV, Grunin, M, Hagbi-Levi, S, Campochiaro, P, Katsanis, N, Holz, FG, Blond, F, Blanche, H, Deleuze, J-F, Igo, RP, Truitt, B, Peachey, NS, Meuer, SM, Myers, CE, Moore, EL, Klein, R, Hauser, MA, Postel, EA, Courtenay, MD, Schwartz, SG, Kovach, JL, Scott, WK, Liew, G, Tan, AG, Gopinath, B, Merriam, JC, Smith, RT, Khan, JC, Shahid, H, Moore, AT, McGrath, JA, Laux, R, Brantley, MA, Agarwal, A, Ersoy, L, Caramoy, A, Langmann, T, Saksens, NTM, de Jong, EK, Hoyng, CB, Cain, MS, Richardson, AJ, Martin, TM, Blangero, J, Weeks, DE, Dhillon, B, van Duijn, CM, Doheny, KF, Romm, J, Klaver, CCW, Hayward, C, Gorin, MB, Klein, ML, Baird, PN, den Hollander, AI, Fauser, S, Yates, JRW, Allikmets, R, Wang, JJ, Schaumberg, DA, Klein, BEK, Hagstrom, SA, Chowers, I, Lotery, AJ, Leveillard, T, Zhang, K, Brilliant, MH, Hewitt, AW, Swaroop, A, Chew, EY, Pericak-Vance, MA, DeAngelis, M, Stambolian, D, Haines, JL, Iyengar, SK, Weber, BHF, Abecasis, GR, Heid, IM, Fritsche, LG, Igl, W, Bailey, JNC, Grassmann, F, Sengupta, S, Bragg-Gresham, JL, Burdon, KP, Hebbring, SJ, Wen, C, Gorski, M, Kim, IK, Cho, D, Zack, D, Souied, E, Scholl, HPN, Bala, E, Lee, KE, Hunter, DJ, Sardell, RJ, Mitchell, P, Merriam, JE, Cipriani, V, Hoffman, JD, Schick, T, Lechanteur, YTE, Guymer, RH, Johnson, MP, Jiang, Y, Stanton, CM, Buitendijk, GHS, Zhan, X, Kwong, AM, Boleda, A, Brooks, M, Gieser, L, Ratnapriya, R, Branham, KE, Foerster, JR, Heckenlively, JR, Othman, MI, Vote, BJ, Liang, HH, Souzeau, E, McAllister, IL, Isaacs, T, Hall, J, Lake, S, Mackey, DA, Constable, IJ, Craig, JE, Kitchner, TE, Yang, Z, Su, Z, Luo, H, Chen, D, Hong, O, Flagg, K, Lin, D, Mao, G, Ferreyra, H, Starke, K, von Strachwitz, CN, Wolf, A, Brandl, C, Rudolph, G, Olden, M, Morrison, MA, Morgan, DJ, Schu, M, Ahn, J, Silvestri, G, Tsironi, EE, Park, KH, Farrer, LA, Orlin, A, Brucker, A, Li, M, Curcio, CA, Mohand-Said, S, Sahel, J-M, Audo, I, Benchaboune, M, Cree, AJ, Rennie, CA, Goverdhan, SV, Grunin, M, Hagbi-Levi, S, Campochiaro, P, Katsanis, N, Holz, FG, Blond, F, Blanche, H, Deleuze, J-F, Igo, RP, Truitt, B, Peachey, NS, Meuer, SM, Myers, CE, Moore, EL, Klein, R, Hauser, MA, Postel, EA, Courtenay, MD, Schwartz, SG, Kovach, JL, Scott, WK, Liew, G, Tan, AG, Gopinath, B, Merriam, JC, Smith, RT, Khan, JC, Shahid, H, Moore, AT, McGrath, JA, Laux, R, Brantley, MA, Agarwal, A, Ersoy, L, Caramoy, A, Langmann, T, Saksens, NTM, de Jong, EK, Hoyng, CB, Cain, MS, Richardson, AJ, Martin, TM, Blangero, J, Weeks, DE, Dhillon, B, van Duijn, CM, Doheny, KF, Romm, J, Klaver, CCW, Hayward, C, Gorin, MB, Klein, ML, Baird, PN, den Hollander, AI, Fauser, S, Yates, JRW, Allikmets, R, Wang, JJ, Schaumberg, DA, Klein, BEK, Hagstrom, SA, Chowers, I, Lotery, AJ, Leveillard, T, Zhang, K, Brilliant, MH, Hewitt, AW, Swaroop, A, Chew, EY, Pericak-Vance, MA, DeAngelis, M, Stambolian, D, Haines, JL, Iyengar, SK, Weber, BHF, Abecasis, GR, and Heid, IM

Abstract

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.

Published
2016

15. Seven new loci associated with age-related macular degeneration

Author

Fritsche, LG, Chen, W, Schu, M, Yaspan, BL, Yu, Y, Thorleifsson, G, Zack, DJ, Arakawa, S, Cipriani, V, Ripke, S, Igo, RP, Buitendijk, GHS, Sim, X, Weeks, DE, Guymer, RH, Merriam, JE, Francis, PJ, Hannum, G, Agarwal, A, Armbrecht, AM, Audo, I, Aung, T, Barile, GR, Benchaboune, M, Bird, AC, Bishop, PN, Branham, KE, Brooks, M, Brucker, AJ, Cade, WH, Cain, MS, Campochiaroll, PA, Chan, C-C, Cheng, C-Y, Chew, EY, Chin, KA, Chowers, I, Clayton, DG, Cojocaru, R, Conley, YP, Cornes, BK, Daly, MJ, Dhillon, B, Edwards, A, Evangelou, E, Fagemess, J, Ferreyra, HA, Friedman, JS, Geirsdottir, A, George, RJ, Gieger, C, Gupta, N, Hagstrom, SA, Harding, SP, Haritoglou, C, Heckenlively, JR, Hoz, FG, Hughes, G, Ioannidis, JPA, Ishibashi, T, Joseph, P, Jun, G, Kamatani, Y, Katsanis, N, Keilhauer, CN, Khan, JC, Kim, IK, Kiyohara, Y, Klein, BEK, Klein, R, Kovach, JL, Kozak, I, Lee, CJ, Lee, KE, Lichtner, P, Lotery, AJ, Meitinger, T, Mitchell, P, Mohand-Saied, S, Moore, AT, Morgan, DJ, Morrison, MA, Myers, CE, Naj, AC, Nakamura, Y, Okada, Y, Orlin, A, Ortube, MC, Othman, MI, Pappas, C, Park, KH, Pauer, GJT, Peachey, NS, Poch, O, Priya, RR, Reynolds, R, Richardson, AJ, Ripp, R, Rudolph, G, Ryu, E, Sahel, J-A, Schaumberg, DA, Scholl, HPN, Schwartz, SG, Scott, WK, Shahid, H, Sigurdsson, H, Silvestri, G, Sivakumaran, TA, Smith, RT, Sobrin, L, Souied, EH, Stambolian, DE, Stefansson, H, Sturgill-Short, GM, Takahashi, A, Tosakulwong, N, Truitt, BJ, Tsironi, EE, Uitterlinden, AG, van Duijn, CM, Vijaya, L, Vingerling, JR, Vithana, EN, Webster, AR, Wichmann, H-E, Winkler, TW, Wong, TY, Wright, AF, Zelenika, D, Zhang, M, Zhao, L, Zhang, K, Klein, ML, Hageman, GS, Lathrop, GM, Stefansson, K, Allikmets, R, Baird, PN, Gorin, MB, Wang, JJ, Klaver, CCW, Seddon, JM, Pericak-Vance, MA, Iyengar, SK, Yates, JRW, Swaroop, A, Weber, BHF, Kubo, M, DeAngelis, MM, Leveillard, T, Thorsteinsdottir, U, Haines, JL, Farrer, LA, Heid, IM, Abecasis, GR, Fritsche, LG, Chen, W, Schu, M, Yaspan, BL, Yu, Y, Thorleifsson, G, Zack, DJ, Arakawa, S, Cipriani, V, Ripke, S, Igo, RP, Buitendijk, GHS, Sim, X, Weeks, DE, Guymer, RH, Merriam, JE, Francis, PJ, Hannum, G, Agarwal, A, Armbrecht, AM, Audo, I, Aung, T, Barile, GR, Benchaboune, M, Bird, AC, Bishop, PN, Branham, KE, Brooks, M, Brucker, AJ, Cade, WH, Cain, MS, Campochiaroll, PA, Chan, C-C, Cheng, C-Y, Chew, EY, Chin, KA, Chowers, I, Clayton, DG, Cojocaru, R, Conley, YP, Cornes, BK, Daly, MJ, Dhillon, B, Edwards, A, Evangelou, E, Fagemess, J, Ferreyra, HA, Friedman, JS, Geirsdottir, A, George, RJ, Gieger, C, Gupta, N, Hagstrom, SA, Harding, SP, Haritoglou, C, Heckenlively, JR, Hoz, FG, Hughes, G, Ioannidis, JPA, Ishibashi, T, Joseph, P, Jun, G, Kamatani, Y, Katsanis, N, Keilhauer, CN, Khan, JC, Kim, IK, Kiyohara, Y, Klein, BEK, Klein, R, Kovach, JL, Kozak, I, Lee, CJ, Lee, KE, Lichtner, P, Lotery, AJ, Meitinger, T, Mitchell, P, Mohand-Saied, S, Moore, AT, Morgan, DJ, Morrison, MA, Myers, CE, Naj, AC, Nakamura, Y, Okada, Y, Orlin, A, Ortube, MC, Othman, MI, Pappas, C, Park, KH, Pauer, GJT, Peachey, NS, Poch, O, Priya, RR, Reynolds, R, Richardson, AJ, Ripp, R, Rudolph, G, Ryu, E, Sahel, J-A, Schaumberg, DA, Scholl, HPN, Schwartz, SG, Scott, WK, Shahid, H, Sigurdsson, H, Silvestri, G, Sivakumaran, TA, Smith, RT, Sobrin, L, Souied, EH, Stambolian, DE, Stefansson, H, Sturgill-Short, GM, Takahashi, A, Tosakulwong, N, Truitt, BJ, Tsironi, EE, Uitterlinden, AG, van Duijn, CM, Vijaya, L, Vingerling, JR, Vithana, EN, Webster, AR, Wichmann, H-E, Winkler, TW, Wong, TY, Wright, AF, Zelenika, D, Zhang, M, Zhao, L, Zhang, K, Klein, ML, Hageman, GS, Lathrop, GM, Stefansson, K, Allikmets, R, Baird, PN, Gorin, MB, Wang, JJ, Klaver, CCW, Seddon, JM, Pericak-Vance, MA, Iyengar, SK, Yates, JRW, Swaroop, A, Weber, BHF, Kubo, M, DeAngelis, MM, Leveillard, T, Thorsteinsdottir, U, Haines, JL, Farrer, LA, Heid, IM, and Abecasis, GR

Abstract

Age-related macular degeneration (AMD) is a common cause of blindness in older individuals. To accelerate the understanding of AMD biology and help design new therapies, we executed a collaborative genome-wide association study, including >17,100 advanced AMD cases and >60,000 controls of European and Asian ancestry. We identified 19 loci associated at P < 5 × 10(-8). These loci show enrichment for genes involved in the regulation of complement activity, lipid metabolism, extracellular matrix remodeling and angiogenesis. Our results include seven loci with associations reaching P < 5 × 10(-8) for the first time, near the genes COL8A1-FILIP1L, IER3-DDR1, SLC16A8, TGFBR1, RAD51B, ADAMTS9 and B3GALTL. A genetic risk score combining SNP genotypes from all loci showed similar ability to distinguish cases and controls in all samples examined. Our findings provide new directions for biological, genetic and therapeutic studies of AMD.

Published
2013

16. Common variants near FRK/COL10A1 and VEGFA are associated with advanced age-related macular degeneration

Author

Yu, Y, Bhangale, TR, Fagerness, J, Ripke, S, Thorleifsson, G, Tan, PL, Souied, EH, Richardson, AJ, Merriam, JE, Buitendijk, GHS, Reynolds, R, Raychaudhuri, S, Chin, KA, Sobrin, L, Evangelou, E, Lee, PH, Lee, AY, Leveziel, N, Zack, DJ, Campochiaro, B, Campochiaro, P, Smith, RT, Barile, GR, Guymer, RH, Hogg, R, Chakravarthy, U, Robman, LD, Gustafsson, O, Sigurdsson, H, Ortmann, W, Behrens, TW, Stefansson, K, Uitterlinden, AG, van Duijn, CM, Vingerling, JR, Klaver, CCW, Allikmets, R, Brantley, MA, Baird, PN, Katsanis, N, Thorsteinsdottir, U, Ioannidis, JPA, Daly, MJ, Graham, RR, Seddon, JM, Yu, Y, Bhangale, TR, Fagerness, J, Ripke, S, Thorleifsson, G, Tan, PL, Souied, EH, Richardson, AJ, Merriam, JE, Buitendijk, GHS, Reynolds, R, Raychaudhuri, S, Chin, KA, Sobrin, L, Evangelou, E, Lee, PH, Lee, AY, Leveziel, N, Zack, DJ, Campochiaro, B, Campochiaro, P, Smith, RT, Barile, GR, Guymer, RH, Hogg, R, Chakravarthy, U, Robman, LD, Gustafsson, O, Sigurdsson, H, Ortmann, W, Behrens, TW, Stefansson, K, Uitterlinden, AG, van Duijn, CM, Vingerling, JR, Klaver, CCW, Allikmets, R, Brantley, MA, Baird, PN, Katsanis, N, Thorsteinsdottir, U, Ioannidis, JPA, Daly, MJ, Graham, RR, and Seddon, JM

Abstract

Despite significant progress in the identification of genetic loci for age-related macular degeneration (AMD), not all of the heritability has been explained. To identify variants which contribute to the remaining genetic susceptibility, we performed the largest meta-analysis of genome-wide association studies to date for advanced AMD. We imputed 6 036 699 single-nucleotide polymorphisms with the 1000 Genomes Project reference genotypes on 2594 cases and 4134 controls with follow-up replication of top signals in 5640 cases and 52 174 controls. We identified two new common susceptibility alleles, rs1999930 on 6q21-q22.3 near FRK/COL10A1 [odds ratio (OR) 0.87; P = 1.1 × 10(-8)] and rs4711751 on 6p12 near VEGFA (OR 1.15; P = 8.7 × 10(-9)). In addition to the two novel loci, 10 previously reported loci in ARMS2/HTRA1 (rs10490924), CFH (rs1061170, and rs1410996), CFB (rs641153), C3 (rs2230199), C2 (rs9332739), CFI (rs10033900), LIPC (rs10468017), TIMP3 (rs9621532) and CETP (rs3764261) were confirmed with genome-wide significant signals in this large study. Loci in the recently reported genes ABCA1 and COL8A1 were also detected with suggestive evidence of association with advanced AMD. The novel variants identified in this study suggest that angiogenesis (VEGFA) and extracellular collagen matrix (FRK/COL10A1) pathways contribute to the development of advanced AMD.

Published
2011

17. Common variants near FRK/COL10A1 and VEGFA are associated with advanced age-related macular degeneration

Author

Yu, Y (Yi), Bhangale, TR, Fagerness, J, Ripke, S, Thorleifsson, G, Tan, PL, Souied, EH, Richardson, AJ, Merriam, JE, Buitendijk, Gabriëlle, Reynolds, R, Raychaudhuri, S, Chin, KA, Sobrin, L, Evangelou, E, Lee, PH, Lee, A Y, Leveziel, N, Zack, DJ, Campochiaro, B, Campochiaro, P, Smith, RT, Barile, GR, Guymer, RH, Hogg, R, Chakravarthy, U, Robman, LD, Gustafsson, O, Sigurdsson, H, Ortmann, W, Behrens, TW, Stefansson, K, Uitterlinden, André, Duijn, Cornelia, Vingerling, Hans, Klaver, Caroline, Allikmets, R, Brantley, MA, Baird, PN, Katsanis, N, Thorsteinsdottir, U, Ioannidis, JPA, Daly, MJ, Graham, RR, Seddon, JM, Yu, Y (Yi), Bhangale, TR, Fagerness, J, Ripke, S, Thorleifsson, G, Tan, PL, Souied, EH, Richardson, AJ, Merriam, JE, Buitendijk, Gabriëlle, Reynolds, R, Raychaudhuri, S, Chin, KA, Sobrin, L, Evangelou, E, Lee, PH, Lee, A Y, Leveziel, N, Zack, DJ, Campochiaro, B, Campochiaro, P, Smith, RT, Barile, GR, Guymer, RH, Hogg, R, Chakravarthy, U, Robman, LD, Gustafsson, O, Sigurdsson, H, Ortmann, W, Behrens, TW, Stefansson, K, Uitterlinden, André, Duijn, Cornelia, Vingerling, Hans, Klaver, Caroline, Allikmets, R, Brantley, MA, Baird, PN, Katsanis, N, Thorsteinsdottir, U, Ioannidis, JPA, Daly, MJ, Graham, RR, and Seddon, JM

Abstract

Despite significant progress in the identification of genetic loci for age-related macular degeneration (AMD), not all of the heritability has been explained. To identify variants which contribute to the remaining genetic susceptibility, we performed the largest meta-analysis of genome-wide association studies to date for advanced AMD. We imputed 6 036 699 single-nucleotide polymorphisms with the 1000 Genomes Project reference genotypes on 2594 cases and 4134 controls with follow-up replication of top signals in 5640 cases and 52 174 controls. We identified two new common susceptibility alleles, rs1999930 on 6q21-q22.3 near FRK/COL10A1 [odds ratio (OR) 0.87; P = 1.1 x 10(-8)] and rs4711751 on 6p12 near VEGFA (OR 1.15; P = 8.7 x 10(-9)). In addition to the two novel loci, 10 previously reported loci in ARMS2/HTRA1 (rs10490924), CFH (rs1061170, and rs1410996), CFB (rs641153), C3 (rs2230199), C2 (rs9332739), CFI (rs10033900), LIPC (rs10468017), TIMP3 (rs9621532) and CETP (rs3764261) were confirmed with genome-wide significant signals in this large study. Loci in the recently reported genes ABCA1 and COL8A1 were also detected with suggestive evidence of association with advanced AMD. The novel variants identified in this study suggest that angiogenesis (VEGFA) and extracellular collagen matrix (FRK/COL10A1) pathways contribute to the development of advanced AMD.

Published
2011

18. The ERCC6 Gene and Age-Related Macular Degeneration

Author

Baas, DC, Despriet, Dominiek, Gorgels, TGMF (Theo), Bergeron-Sawitzke, J, Uitterlinden, André, Hofman, Bert, Duijn, Cornelia, Merriam, JE, Smith, RT, Barile, GR, Brink, JB, Vingerling, Hans, Klaver, Caroline, Allikmets, R, Dean, M, Bergen, Arthur, Baas, DC, Despriet, Dominiek, Gorgels, TGMF (Theo), Bergeron-Sawitzke, J, Uitterlinden, André, Hofman, Bert, Duijn, Cornelia, Merriam, JE, Smith, RT, Barile, GR, Brink, JB, Vingerling, Hans, Klaver, Caroline, Allikmets, R, Dean, M, and Bergen, Arthur

Abstract

Background: Age-related macular degeneration (AMD) is the leading cause of irreversible visual loss in the developed countries and is caused by both environmental and genetic factors. A recent study (Tuo et al., PNAS) reported an association between AMD and a single nucleotide polymorphism (SNP) (rs3793784) in the ERCC6 (NM_000124) gene. The risk allele also increased ERCC6 expression. ERCC6 is involved in DNA repair and mutations in ERCC6 cause Cockayne syndrome (CS). Amongst others, photosensitivity and pigmentary retinopathy are hallmarks of CS. Methodology/Principal Findings: Separate and combined data from three large AMD case-control studies and a prospective population-based study (The Rotterdam Study) were used to analyse the genetic association between ERCC6 and AMD (2682 AMD cases and 3152 controls). We also measured ERCC6 mRNA levels in retinal pigment epithelium (RPE) cells of healthy and early AMD affected human donor eyes. Rs3793784 conferred a small increase in risk for late AMD in the Dutch population (The Rotterdam and AMRO-NL study), but this was not replicated in two non-European studies (AREDS, Columbia University). In addition, the AMRO-NL study revealed no significant association for 9 other variants spanning ERCC6. Finally, we determined that ERCC6 expression in the human RPE did not depend on rs3793784 genotype, but, interestingly, on AMD status: Early AMD-affected donor eyes had a 50% lower ERCC6 expression than healthy donor eyes (P = 0.018). Conclusions/Significance: Our meta-analysis of four Caucasian cohorts does not replicate the reported association between SNPs in ERCC6 and AMD. Nevertheless, our findings on ERCC6 expression in the RPE suggest that ERCC6 may be functionally involved in AMD. Combining our data with those of the literature, we hypothesize that the AMD-related reduced transcriptional activity of ERCC6 may be caused by diverse, small and heterogeneous genetic and/or environmental determinants.

Published
2010

19. Phreatoplinian eruptions

Author

Sigurdsson, Haraldur, Houghton, BF, Wilson, CJN, Smith, RT, Gilbert, JS, Sigurdsson, Haraldur, Houghton, BF, Wilson, CJN, Smith, RT, and Gilbert, JS

Published
2000

20. Phreatoplinian eruptions

Author

Sigurdsson, Haraldur, Houghton, BF, Wilson, CJN, Smith, RT, Gilbert, JS, Sigurdsson, Haraldur, Houghton, BF, Wilson, CJN, Smith, RT, and Gilbert, JS

Published
2000

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