Search

Your search keyword '"Khan JC"' showing total 8 results
Start Over Author "Khan JC" Publication Year Range Last 10 years
8 results on '"Khan JC"'

1. 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

2. 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

3. Evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy.

Author

Sampson DM, Alonso-Caneiro D, Chew AL, La J, Roshandel D, Wang Y, Khan JC, Chelva E, Stevenson PG, and Chen FK

Abstract

Adaptive optics flood illumination ophthalmoscopy (AO-FIO) is an established imaging tool in the investigation of retinal diseases. However, the clinical interpretation of AO-FIO images can be challenging due to varied image quality. Therefore, image quality assessment is essential before interpretation. An image assessment tool will also assist further work on improving the image quality, either during acquisition or post processing. In this paper, we describe, validate and compare two automated image quality assessment methods; the energy of Laplacian focus operator (LAPE; not commonly used but easily implemented) and convolutional neural network (CNN; effective but more complex approach). We also evaluate the effects of subject age, axial length, refractive error, fixation stability, disease status and retinal location on AO-FIO image quality. Based on analysis of 10,250 images of 50 × 50 μm size, at 41 retinal locations, from 50 subjects we demonstrate that CNN slightly outperforms LAPE in image quality assessment. CNN achieves accuracy of 89%, whereas LAPE metric achieves 73% and 80% (for a linear regression and random forest multiclass classifier methods, respectively) compared to ground truth. Furthermore, the retinal location, age and disease are factors that can influence the likelihood of poor image quality., (© 2021. The Author(s).)

Published
2021
Full Text
View/download PDF

4. Retinal Differential Light Sensitivity Variation Across the Macula in Healthy Subjects: Importance of Cone Separation and Loci Eccentricity.

Author

Sampson DM, Roshandel D, Chew AL, Wang Y, Stevenson PG, Cooper MN, Ong E, Wong L, La J, Alonso-Caneiro D, Chelva E, Khan JC, Sampson DD, and Chen FK

Subjects
Cell Count, Healthy Volunteers, Humans, Visual Acuity, Photophobia, Retinal Cone Photoreceptor Cells
Abstract

Purpose: Microperimetry measures differential light sensitivity (DLS) at specific retinal locations. The aim of this study is to examine the variation in DLS across the macula and the contribution to this variation of cone distribution metrics and retinal eccentricity., Methods: Forty healthy eyes of 40 subjects were examined by microperimetry (MAIA) and adaptive optics imaging (rtx1). Retinal DLS was measured using the grid patterns: foveal (2°-3°), macular (3°-7°), and meridional (2°-8° on horizontal and vertical meridians). Cone density (CD), distribution regularity, and intercone distance (ICD) were calculated at the respective test loci coordinates. Linear mixed-effects regression was used to examine the association between cone distribution metrics and loci eccentricity, and retinal DLS., Results: An eccentricity-dependent reduction in DLS was observed on all MAIA grids, which was greatest at the foveal-parafoveal junction (2°-3°) (-0.58 dB per degree, 95% confidence interval [CI]; -0.91 to -0.24 dB, P < 0.01). Retinal DLS across the meridional grid changed significantly with each 1000 cells/deg2 change in CD (0.85 dB, 95% CI; 0.10 to 1.61 dB, P = 0.03), but not with each arcmin change in ICD (1.36 dB, 95% CI; -2.93 to 0.20 dB, P = 0.09)., Conclusions: We demonstrate significant variation in DLS across the macula. Topographical change in cone separation is an important determinant of the variation in DLS at the foveal-parafoveal junction. We caution the extrapolation of changes in DLS measurements to cone distribution because the relationship between these variables is complex., Translational Relevance: Cone density is an independent determinant of DLS in the foveal-parafoveal junction in healthy eyes.

Published
2021
Full Text
View/download PDF

5. Perifoveal interdigitation zone loss in hydroxychloroquine toxicity leads to subclinical bull's eye lesion appearance on near-infrared reflectance imaging.

Author

Chew AL, Sampson DM, Chelva E, Khan JC, and Chen FK

Subjects
Electroretinography, Fluorescein Angiography, Fovea Centralis diagnostic imaging, Humans, Male, Middle Aged, Multimodal Imaging, Retina physiopathology, Retinal Cone Photoreceptor Cells pathology, Retinal Diseases diagnostic imaging, Retinal Diseases physiopathology, Scotoma diagnostic imaging, Scotoma physiopathology, Tomography, Optical Coherence methods, Visual Field Tests, Antirheumatic Agents toxicity, Fovea Centralis drug effects, Hydroxychloroquine toxicity, Retinal Diseases chemically induced, Scotoma chemically induced
Abstract

Purpose: To characterize the ultrastructural and functional correlates of hydroxychloroquine (HCQ)-induced subclinical bull's eye lesion seen on near-infrared reflectance (NIR) imaging., Methods: An asymptomatic 54-year-old male taking HCQ presented with paracentral ring-like scotoma, abnormal multifocal electroretinography (mfERG) and preserved ellipsoid zone on optical coherence tomography (OCT). Dense raster OCT was performed to create en face reflectivity maps of the interdigitation zone. Macular Integrity Assessment (MAIA) microperimetry and mfERG findings were compared with NIR imaging, en face OCT, retinal thickness profiles and wave-guiding cone density maps derived from flood-illumination adaptive optics (AO) retinal photography., Results: The bull's eye lesion is an oval annular zone of increased reflectivity on NIR with an outer diameter of 1450 µm. This region corresponds exactly to an area of preserved interdigitation zone reflectivity in en face OCT images and of normal cone density on AO imaging. Immediately surrounding the bull's eye lesion is an annular zone (3°-12° eccentricity) of depressed retinal sensitivity on MAIA and reduced amplitude density on mfERG. Wave-guiding cone density at 2° temporal was 25,400 per mm 2 . This declined rapidly to 12,900 and 1200 per mm 2 at 3° and 4°., Conclusion: Multimodal imaging illustrated pathology in the area surrounding the NIR bull's eye, characterized by reduced reflectance, wave-guiding cone density and retinal function. Further studies are required to investigate whether the bull's eye on NIR imaging and en face OCT is prominent or consistent enough for diagnostic use.

Published
2018
Full Text
View/download PDF

6. Impact of Raised Serum Cobalt Levels From Recalled Articular Surface Replacement Hip Prostheses on the Visual Pathway.

Author

Unsworth-Smith T, Khan JC, Khan RJK, Chelva E, Lim CA, Haebich S, and Trevenen ML

Subjects
Aged, Arthroplasty, Replacement, Hip adverse effects, Arthroplasty, Replacement, Hip instrumentation, Cobalt blood, Cohort Studies, Female, Humans, Male, Middle Aged, Prosthesis Design, Cobalt adverse effects, Hip Prosthesis adverse effects, Vision Disorders chemically induced, Visual Pathways drug effects
Abstract

Background: The articular surface replacement (ASR) was recalled in 2010 because of higher than expected revision rates. Patients reported symptoms of neurologic dysfunction including poor vision. This cohort study, using objective measurements, aimed to establish whether a higher incidence of visual function defects exists in ASR patients., Methods: Thirty-three ASR patients and 33 non-ASR controls (control 1) were recruited. Data were compared with normative population data from the visual electrophysiology database (control 2). Patients underwent investigations for serum cobalt levels, psychophysical visual tests, and extensive electrophysiological visual testing., Results: After excluding 2 subjects with pre-existing eye disease, data from 33 ASR patients were compared with the 2 control cohorts. The median serum cobalt level in the ASR group (median, 52 nmol/L [interquartile range, 14-151 nmol/L]) was significantly higher than that in the control 1 cohort (median, 7 nmol/L [interquartile range, 5-14 nmol/L]; P < .0001). The photoreceptor function of patients with an ASR of the hip showed significantly larger electroretinography mixed rod-cone b-wave amplitudes than both control 1 and control 2 cohorts (P = .0294 and .0410, respectively). Abnormalities in macular function as reflected by multifocal and scotopic electroretinography were more prevalent in control 1 (P = .0445 and .0275, respectively). Optic nerve pathway measurements using visual-evoked potential latency was significantly longer in the ASR group compared with those in the control 2 cohort (P = .0201). There were no statistical differences in visual acuity., Conclusion: A statistically significant disturbance in visual electrophysiology was found in the ASR group when compared with the control groups. These differences did not translate to identifiable clinical visual deficits. Orthopedic surgeons need to be aware of the possibility of visual dysfunction in patients with ASR and other metal-on-metal hip arthroplasties; however, routine visual testing is not recommended., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Published
2017
Full Text
View/download PDF

7. Analysis of copy number variation at DMBT1 and age-related macular degeneration.

Author

Polley S, Cipriani V, Khan JC, Shahid H, Moore AT, Yates JR, and Hollox EJ

Subjects
Alleles, Calcium-Binding Proteins, Case-Control Studies, Chromosomes, Human, Pair 10, DNA Copy Number Variations, DNA-Binding Proteins, Gene Frequency, Genotype, High-Temperature Requirement A Serine Peptidase 1, Humans, Linkage Disequilibrium, Macular Degeneration pathology, Odds Ratio, Polymorphism, Single Nucleotide, Proteins genetics, Serine Endopeptidases genetics, Tumor Suppressor Proteins, Macular Degeneration genetics, Receptors, Cell Surface genetics
Abstract

Background: DMBT1 is a gene that shows extensive copy number variation (CNV) that alters the number of bacteria-binding domains in the protein and has been shown to activate the complement pathway. It lies next to the ARMS2/HTRA1 genes in a region of chromosome 10q26, where single nucleotide variants have been strongly associated with age-related macular degeneration (AMD), the commonest cause of blindness in Western populations. Complement activation is thought to be a key factor in the pathogenesis of this condition. We sought to investigate whether DMBT1 CNV plays any role in the susceptibility to AMD., Methods: We analysed long-range linkage disequilibrium of DMBT1 CNV1 and CNV2 with flanking single nucleotide polymorphisms (SNPs) using our previously published CNV and HapMap Phase 3 SNP data in the CEPH Europeans from Utah (CEU). We then typed a large cohort of 860 AMD patients and 419 examined age-matched controls for copy number at DMBT1 CNV1 and CNV2 and combined these data with copy numbers from a further 480 unexamined controls., Results: We found weak linkage disequilibrium between DMBT1 CNV1 and CNV2 with the SNPs rs1474526 and rs714816 in the HTRA1/ARMS2 region. By directly analysing copy number variation, we found no evidence of association of CNV1 or CNV2 with AMD., Conclusions: We have shown that copy number variation at DMBT1 does not affect risk of developing age-related macular degeneration and can therefore be ruled out from future studies investigating the association of structural variation at 10q26 with AMD.

Published
2016
Full Text
View/download PDF

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 JN, Grassmann F, Sengupta S, Bragg-Gresham JL, Burdon KP, Hebbring SJ, Wen C, Gorski M, Kim IK, Cho D, Zack D, Souied E, Scholl HP, Bala E, Lee KE, Hunter DJ, Sardell RJ, Mitchell P, Merriam JE, Cipriani V, Hoffman JD, Schick T, Lechanteur YT, Guymer RH, Johnson MP, Jiang Y, Stanton CM, Buitendijk GH, 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, 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-Saïd S, Sahel JA, Audo I, Benchaboune M, Cree AJ, Rennie CA, Goverdhan SV, Grunin M, Hagbi-Levi S, Campochiaro P, Katsanis N, Holz FG, Blond F, Blanché H, Deleuze JF, Igo RP Jr, 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 Jr, Agarwal A, Ersoy L, Caramoy A, Langmann T, Saksens NT, 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 CC, Hayward C, Gorin MB, Klein ML, Baird PN, den Hollander AI, Fauser S, Yates JR, Allikmets R, Wang JJ, Schaumberg DA, Klein BE, Hagstrom SA, Chowers I, Lotery AJ, Léveillard T, Zhang K, Brilliant MH, Hewitt AW, Swaroop A, Chew EY, Pericak-Vance MA, DeAngelis M, Stambolian D, Haines JL, Iyengar SK, Weber BH, Abecasis GR, and Heid IM

Subjects
Genetic Predisposition to Disease, Humans, Mutation, Genome-Wide Association Study, Macular Degeneration genetics
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
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results