Transcriptomics insights into interpreting AMD-GWAS discoveries for biological and clinical applications

Rinki Ratnapriya

Journal of Translational Genetics and Genomics ›› 2022, Vol. 6 ›› Issue (2) : 240 -256.

PDF
Journal of Translational Genetics and Genomics ›› 2022, Vol. 6 ›› Issue (2) :240 -256. DOI: 10.20517/jtgg.2021.54
review-article

Transcriptomics insights into interpreting AMD-GWAS discoveries for biological and clinical applications

Author information +
History +
PDF

Abstract

Genome-wide association studies (GWAS) have been successful in identifying genetic risk factors for a large number of complex diseases, including age-related macular degeneration (AMD), which is a highly heritable complex disease affecting millions of elderly individuals. However, the progress of elucidating the functional relevance of genetic findings in AMD has been slow, as most risk factors are non-coding, and we have little insight into the causal genes and disease mechanisms. In the last few years, gene expression regulation is emerging as a dominant mechanism through which GWAS risk variants lead to the disease. The purpose of this review is to provide an overview of how transcriptome studies can help in identifying the genes, pathways and therapeutic targets underlying GWAS discoveries in AMD. These approaches help pave the road for mechanistic understanding of GWAS findings and drive translational advances that will lead to improved AMD management and treatment.

Keywords

Age-related macular degeneration / GWAS / transcriptome / gene expression regulation / eQTL / gene regulatory networks / biomarkers

Cite this article

Download citation ▾
Rinki Ratnapriya. Transcriptomics insights into interpreting AMD-GWAS discoveries for biological and clinical applications. Journal of Translational Genetics and Genomics, 2022, 6(2): 240-256 DOI:10.20517/jtgg.2021.54

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

BunielloA,CerezoM.The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019.Nucleic Acids Res2019;47:D1005-12 PMCID:PMC6323933

[2]

LichouF.Functional studies of GWAS variants are gaining momentum.Nat Commun2020;11:6283 PMCID:PMC7722852

[3]

FritscheLG,StambolianD,CurcioCA.Age-related macular degeneration: genetics and biology coming together.Annu Rev Genomics Hum Genet2014;15:151-71 PMCID:PMC4217162

[4]

HellstrandK.An immunopharmacological analysis of adrenaline-induced suppression of human natural killer cell cytotoxicity.Int Arch Allergy Appl Immunol1989;89:334-41

[5]

RatnapriyaR.Genetic architecture of retinal and macular degenerative diseases: the promise and challenges of next-generation sequencing.Genome Med2013;5:84 PMCID:PMC4066589

[6]

FriedmanDS,MuñozB.Eye Diseases Prevalence Research GroupPrevalence of age-related macular degeneration in the United States.Arch Ophthalmol2004;122:564-72

[7]

KleinR,BirdA.The epidemiology of age-related macular degeneration.Am J Ophthalmol2004;137:486-95

[8]

FerrisFL,ClemonsTE.Age-Related Eye Disease Study (AREDS) Research GroupA simplified severity scale for age-related macular degeneration: AREDS Report No. 18.Arch Ophthalmol2005;123:1570-4 PMCID:PMC1473206

[9]

AmbatiJ.Mechanisms of age-related macular degeneration.Neuron2012;75:26-39 PMCID:PMC3404137

[10]

Age-Related Eye Disease Study Research GroupA randomized, placebo-controlled, clinical trial of high-dose supplementation with vitamins C and E, beta carotene, and zinc for age-related macular degeneration and vision loss: AREDS report no. 8.Arch Ophthalmol2001;119:1417-36

[11]

SchultzDW,HumpertAJ.Analysis of the ARMD1 locus: evidence that a mutation in HEMICENTIN-1 is associated with age-related macular degeneration in a large family.Hum Mol Genet2003;12:3315-23

[12]

SwaroopA,RickmanCB.Unraveling a multifactorial late-onset disease: from genetic susceptibility to disease mechanisms for age-related macular degeneration.Annu Rev Genomics Hum Genet2009;10:19-43 PMCID:PMC3469316

[13]

WeeksDE,TsaiHJ.Age-related maculopathy: a genomewide scan with continued evidence of susceptibility loci within the 1q31, 10q26, and 17q25 regions.Am J Hum Genet2004;75:174-89 PMCID:PMC1216053

[14]

KleinRJ,ChewEY.Complement factor H polymorphism in age-related macular degeneration.Science2005;308:385-9 PMCID:PMC1512523

[15]

EdwardsAO,AbelKJ,PanhuysenC.Complement factor H polymorphism and age-related macular degeneration.Science2005;308:421-4

[16]

HainesJL,SchmidtS.Complement factor H variant increases the risk of age-related macular degeneration.Science2005;308:419-21

[17]

PriyaRR,SwaroopA.Genetic studies of age-related macular degeneration: lessons, challenges, and opportunities for disease management.Ophthalmology2012;119:2526-36 PMCID:PMC3514599

[18]

FritscheLG,SchuM.AMD Gene ConsortiumSeven new loci associated with age-related macular degeneration.Nat Genet2013;45:433-9, 439e1-2 PMCID:PMC3739472

[19]

FritscheLG,BaileyJN.A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants.Nat Genet2016;48:134-43 PMCID:PMC4745342

[20]

SinghN,RatnapriyaR.Making biological sense of genetic studies of age-related macular degeneration.Adv Exp Med Biol2021;1256:201-19

[21]

WinklerTW,BrandlC.Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease.BMC Med Genomics2020;13:120 PMCID:PMC7449002

[22]

ZwickME,ChakravartiA.Patterns of genetic variation in Mendelian and complex traits.Annu Rev Genomics Hum Genet2000;1:387-407

[23]

YangHJ,CogliatiT,SwaroopA.Vision from next generation sequencing: multi-dimensional genome-wide analysis for producing gene regulatory networks underlying retinal development, aging and disease.Prog Retin Eye Res2015;46:1-30 PMCID:PMC4402139

[24]

RaychaudhuriS,ChinK.A rare penetrant mutation in CFH confers high risk of age-related macular degeneration.Nat Genet2011;43:1232-6 PMCID:PMC3225644

[25]

HelgasonH,DuvvariMR.A rare nonsynonymous sequence variant in C3 is associated with high risk of age-related macular degeneration.Nat Genet2013;45:1371-4

[26]

SeddonJM,MillerEC.Rare variants in CFI, C3 and C9 are associated with high risk of advanced age-related macular degeneration.Nat Genet2013;45:1366-70 PMCID:PMC3902040

[27]

ZhanX,WangC.Identification of a rare coding variant in complement 3 associated with age-related macular degeneration.Nat Genet2013;45:1375-9 PMCID:PMC3812337

[28]

van de VenJP,TanPL.A functional variant in the CFI gene confers a high risk of age-related macular degeneration.Nat Genet2013;45:813-7

[29]

MomozawaY,KamataniY.Low-frequency coding variants in CETP and CFB are associated with susceptibility of exudative age-related macular degeneration in the Japanese population.Hum Mol Genet2016;25:5027-34

[30]

CorominasJ,GeerlingsMJ.Whole-exome sequencing in age-related macular degeneration identifies rare variants in COL8A1, a component of Bruch’s membrane.Ophthalmology2018;125:1433-43 PMCID:PMC6104593

[31]

AndersonDH,GalloNB.The pivotal role of the complement system in aging and age-related macular degeneration: hypothesis re-visited.Prog Retin Eye Res2010;29:95-112 PMCID:PMC3641842

[32]

StantonCM,denHollander AI.Complement factor D in age-related macular degeneration.Invest Ophthalmol Vis Sci2011;52:8828-34 PMCID:PMC3230905

[33]

DuvvariMR,BuitendijkGH.Analysis of rare variants in the C3 gene in patients with age-related macular degeneration.PLoS One2014;9:e94165 PMCID:PMC3988049

[34]

YuY,WongEK.Whole-exome sequencing identifies rare, functional CFH variants in families with macular degeneration.Hum Mol Genet2014;23:5283-93 PMCID:PMC4159152

[35]

SaksensNT,BakkerB.Rare genetic variants associated with development of age-related macular degeneration.JAMA Ophthalmol2016;134:287-93

[36]

PietraszkiewiczA,KwongA.Association of rare predicted loss-of-function variants in cellular pathways with sub-phenotypes in age-related macular degeneration.Ophthalmology2018;125:398-406 PMCID:PMC5820204

[37]

HuangLZ,XieXF.Whole-exome sequencing implicates UBE3D in age-related macular degeneration in East Asian populations.Nat Commun2015;6:6687

[38]

GeerlingsMJ,BakkerB.The functional effect of rare variants in complement genes on C3b degradation in patients with age-related macular degeneration.JAMA Ophthalmol2017;135:39-46

[39]

WagnerEK,VillalongaMB.Mapping rare, deleterious mutations in Factor H: association with early onset, drusen burden, and lower antigenic levels in familial AMD.Sci Rep2016;6:31531 PMCID:PMC5004131

[40]

DuvvariMR,GeerlingsMJ.Whole exome sequencing in patients with the cuticular drusen subtype of age-related macular degeneration.PLoS One2016;11:e0152047 PMCID:PMC4805164

[41]

PrasE,ShoshanyN.Rare genetic variants in Tunisian Jewish patients suffering from age-related macular degeneration.J Med Genet2015;52:484-92

[42]

HoffmanJD,D’AoustL.Rare complement factor H variant associated with age-related macular degeneration in the Amish.Invest Ophthalmol Vis Sci2014;55:4455-60 PMCID:PMC4107619

[43]

RatnapriyaR,GeerlingsMJ.Family-based exome sequencing identifies rare coding variants in age-related macular degeneration.Hum Mol Genet2020;29:2022-34 PMCID:PMC7390936

[44]

HolzFG,FleckensteinM.Recent developments in the treatment of age-related macular degeneration.J Clin Invest2014;124:1430-8 PMCID:PMC3973093

[45]

NelsonMR,PainterJL.The support of human genetic evidence for approved drug indications.Nat Genet2015;47:856-60

[46]

PennesiME,CourtneyRJ.Animal models of age related macular degeneration.Mol Aspects Med2012;33:487-509 PMCID:PMC3770531

[47]

ProvisJM,CornishEE,MadiganMC.Anatomy and development of the macula: specialisation and the vulnerability to macular degeneration.Clin Exp Optom2005;88:269-81

[48]

AdijantoJ.Cultured primary human fetal retinal pigment epithelium (hfRPE) as a model for evaluating RPE metabolism.Exp Eye Res2014;126:77-84 PMCID:PMC4345411

[49]

MauranoMT,RynesE.Systematic localization of common disease-associated variation in regulatory DNA.Science2012;337:1190-5 PMCID:PMC3771521

[50]

DeplanckeB,GardeuxV.The genetics of transcription factor DNA binding variation.Cell2016;166:538-54

[51]

Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues.Science2020;369:1318-30

[52]

StrunnikovaNV,BarbJJ.Transcriptome analysis and molecular signature of human retinal pigment epithelium.Hum Mol Genet2010;19:2468-86 PMCID:PMC2876890

[53]

PinelliM,CutilloL.An atlas of gene expression and gene co-regulation in the human retina.Nucleic Acids Res2016;44:5773-84 PMCID:PMC4937338

[54]

HoshinoA,BrooksMJ.Molecular anatomy of the developing human retina.Dev Cell2017;43:763-79.e4 PMCID:PMC5776731

[55]

YanW,vanZyl T.Cell atlas of the human fovea and peripheral retina.Sci Rep2020;10:9802 PMCID:PMC7299956

[56]

SridharA,FinkbeinerCR.Single-cell transcriptomic comparison of human fetal retina, hPSC-derived retinal organoids, and long-term retinal cultures.Cell Rep2020;30:1644-59.e4 PMCID:PMC7901645

[57]

CowanCS,DeGennaro M.Cell types of the human retina and its organoids at single-cell resolution.Cell2020;182:1623-40.e34 PMCID:PMC7505495

[58]

VoigtAP,MullinNK.Single-cell transcriptomics of the human retinal pigment epithelium and choroid in health and macular degeneration.Proc Natl Acad Sci U S A2019;116:24100-7 PMCID:PMC6883845

[59]

LiangQ,OwenL.Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling.Nat Commun2019;10:5743 PMCID:PMC6917696

[60]

GamazonER,vande Bunt M.GTEx ConsortiumUsing an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation.Nat Genet2018;50:956-67 PMCID:PMC6248311

[61]

OngenH,DelaneauO,NicaAC.GTEx ConsortiumEstimating the causal tissues for complex traits and diseases.Nat Genet2017;49:1676-83

[62]

Kim-HellmuthS,OlivaM.GTEx ConsortiumCell type-specific genetic regulation of gene expression across human tissues.Science2020;369:eaaz8528 PMCID:PMC8051643

[63]

RajT,MostafaviS.Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes.Science2014;344:519-23 PMCID:PMC4910825

[64]

WatanabeK,deLeeuw CA,PosthumaD.Genetic mapping of cell type specificity for complex traits.Nat Commun2019;10:3222 PMCID:PMC6642112

[65]

WangX,SusztakK,LiM.Bulk tissue cell type deconvolution with multi-subject single-cell expression reference.Nat Commun2019;10:380 PMCID:PMC6342984

[66]

AranD,ButteAJ.xCell: digitally portraying the tissue cellular heterogeneity landscape.Genome Biol2017;18:220 PMCID:PMC5688663

[67]

NewmanAM,GreenMR.Robust enumeration of cell subsets from tissue expression profiles.Nat Methods2015;12:453-7 PMCID:PMC4739640

[68]

FuzikJ,MátéZ.Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes.Nat Biotechnol2016;34:175-83 PMCID:PMC4745137

[69]

GrünD,KesterL.Single-cell messenger RNA sequencing reveals rare intestinal cell types.Nature2015;525:251-5

[70]

JaitinDA,Keren-ShaulH.Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types.Science2014;343:776-9 PMCID:PMC4412462

[71]

RegevA,LanderES.Human Cell Atlas Meeting ParticipantsThe human cell atlas.Elife2017;6:e27041 PMCID:PMC5762154

[72]

MathysH,PengZ.Single-cell transcriptomic analysis of Alzheimer’s disease.Nature2019;570:332-7 PMCID:PMC6865822

[73]

AgarwalD,VolpatoV.A single-cell atlas of the human substantia nigra reveals cell-specific pathways associated with neurological disorders.Nat Commun2020;11:4183 PMCID:PMC7442652

[74]

MortazaviA,McCueK,WoldB.Mapping and quantifying mammalian transcriptomes by RNA-Seq.Nat Methods2008;5:621-8

[75]

StarkR,HadfieldJ.RNA sequencing: the teenage years.Nat Rev Genet2019;20:631-56

[76]

WangY.Advances and applications of single-cell sequencing technologies.Mol Cell2015;58:598-609 PMCID:PMC4441954

[77]

SchaidDJ,LarsonNB.From genome-wide associations to candidate causal variants by statistical fine-mapping.Nat Rev Genet2018;19:491-504 PMCID:PMC6050137

[78]

NicolaeDL,ZhangW,DolanME.Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS.PLoS Genet2010;6:e1000888 PMCID:PMC2848547

[79]

NicaAC,DimasAS.Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations.PLoS Genet2010;6:e1000895 PMCID:PMC2848550

[80]

RatnapriyaR,StarostikMR.Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration.Nat Genet2019;51:606-10 PMCID:PMC6441365

[81]

AlbertFW.The role of regulatory variation in complex traits and disease.Nat Rev Genet2015;16:197-212

[82]

CooksonW,AbecasisG,LathropM.Mapping complex disease traits with global gene expression.Nat Rev Genet2009;10:184-94 PMCID:PMC4550035

[83]

GiladY,PritchardJK.Revealing the architecture of gene regulation: the promise of eQTL studies.Trends Genet2008;24:408-15 PMCID:PMC2583071

[84]

HukkuA,LucaF,ImHK.Probabilistic colocalization of genetic variants from complex and molecular traits: promise and limitations.Am J Hum Genet2021;108:25-35 PMCID:PMC7820626

[85]

StrunzT,GrassmannF.A mega-analysis of expression quantitative trait loci in retinal tissue.PLoS Genet2020;16:e1008934 PMCID:PMC7462281

[86]

OrozcoLD,CoxC.Integration of eQTL and a single-cell atlas in the human eye identifies causal genes for age-related macular degeneration.Cell Rep2020;30:1246-59.e6

[87]

LiuB,AbellNS.Genetic analyses of human fetal retinal pigment epithelium gene expression suggest ocular disease mechanisms.Commun Biol2019;2:186 PMCID:PMC6527609

[88]

WhiteMJ,VeatchOJ,Risse-AdamsOS.Strategies for pathway analysis using GWAS and WGS data.Curr Protoc Hum Genet2019;100:e79 PMCID:PMC6391732

[89]

WaksmunskiAR,KinzyTG,HainesJL.International Age-Related Macular Degeneration Genomics ConsortiumPathway analysis integrating genome-wide and functional data identifies PLCG2 as a candidate gene for age-related macular degeneration.Invest Ophthalmol Vis Sci2019;60:4041-51 PMCID:PMC6779289

[90]

SekarS,CuyuganL.Alzheimer’s disease is associated with altered expression of genes involved in immune response and mitochondrial processes in astrocytes.Neurobiol Aging2015;36:583-91 PMCID:PMC4315763

[91]

FromerM,SiebertsSK.Gene expression elucidates functional impact of polygenic risk for schizophrenia.Nat Neurosci2016;19:1442-53 PMCID:PMC5083142

[92]

TianL,BowmanAS,CurcioCA.Transcriptome of the human retina, retinal pigmented epithelium and choroid.Genomics2015;105:253-64 PMCID:PMC4404213

[93]

NewmanAM,HancoxLS.Systems-level analysis of age-related macular degeneration reveals global biomarkers and phenotype-specific functional networks.Genome Med2012;4:16 PMCID:PMC3372225

[94]

PaulyD,DanaN.Cell-type-specific complement expression in the healthy and diseased retina.Cell Rep2019;29:2835-48.e4 PMCID:PMC6911814

[95]

ZhangB.A general framework for weighted gene co-expression network analysis.Stat Appl Genet Mol Biol2005;4:Article17

[96]

CalabreseGM,StainsJP.Integrating GWAS and co-expression network data identifies bone mineral density genes SPTBN1 and MARK3 and an osteoblast functional module.Cell Syst2017;4:46-59.e4 PMCID:PMC5269473

[97]

GustafssonM,AlfredssonL.A validated gene regulatory network and GWAS identifies early regulators of T cell-associated diseases.Sci Transl Med2015;7:313ra178

[98]

MäkinenVP,MengQ.Coronary ARtery DIsease Genome-Wide Replication And Meta-Analysis (CARDIoGRAM) ConsortiumIntegrative genomics reveals novel molecular pathways and gene networks for coronary artery disease.PLoS Genet2014;10:e1004502 PMCID:PMC4102418

[99]

LangfelderP.WGCNA: an R package for weighted correlation network analysis.BMC Bioinformatics2008;9:559 PMCID:PMC2631488

[100]

GusevA,ShiH.Integrative approaches for large-scale transcriptome-wide association studies.Nat Genet2016;48:245-52 PMCID:PMC4767558

[101]

LawlorDA,SterneJAC,DaveySmith G.Mendelian randomization: using genes as instruments for making causal inferences in epidemiology.Statist Med2008;27:1133-63

[102]

PengYR,YanW.Molecular classification and comparative taxonomics of foveal and peripheral cells in primate retina.Cell2019;176:1222-37.e22 PMCID:PMC6424338

[103]

MenonM,Davila-VelderrainJ.Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration.Nat Commun2019;10:4902 PMCID:PMC6814749

[104]

van der WijstM,GrootHE.The single-cell eQTLGen consortium.Elife2020;9:e52155 PMCID:PMC7077978

[105]

Sisternes L, Simon N, Tibshirani R, Leng T, Rubin DL. Quantitative SD-OCT imaging biomarkers as indicators of age-related macular degeneration progression.Invest Ophthalmol Vis Sci2014;55:7093

[106]

NiuS,ChenQ,LengT.Fully automated prediction of geographic atrophy growth using quantitative spectral-domain optical coherence tomography biomarkers.Ophthalmology2016;123:1737-50

[107]

LaiTT,YangCM,YangCH.Biomarkers of optical coherence tomography in evaluating the treatment outcomes of neovascular age-related macular degeneration: a real-world study.Sci Rep2019;9:529 PMCID:PMC6345958

[108]

LambertNG,SinghMK.Risk factors and biomarkers of age-related macular degeneration.Prog Retin Eye Res2016;54:64-102 PMCID:PMC4992630

[109]

LauwenS,LefeberDJ.Omics biomarkers in ophthalmology.Invest Ophthalmol Vis Sci2017;58:BIO88-98

[110]

KerstenE,SchellevisRL.Systemic and ocular fluid compounds as potential biomarkers in age-related macular degeneration.Surv Ophthalmol2018;63:9-39

[111]

Bailey JN, Hoffman JD, Sardell RJ, Scott WK, Pericak-Vance MA, Haines JL. The application of genetic risk scores in age-related macular degeneration: a review.J Clin Med2016;5:31 PMCID:PMC4810102

[112]

HeesterbeekTJ,HoyngCB,denHollander AI.Risk factors for progression of age-related macular degeneration.Ophthalmic Physiol Opt2020;40:140-70 PMCID:PMC7155063

[113]

LewisCM.Polygenic risk scores: from research tools to clinical instruments.Genome Med2020;12:44 PMCID:PMC7236300

[114]

DingY,YanQ.AREDS2 Research GroupBivariate analysis of age-related macular degeneration progression using genetic risk scores.Genetics2017;206:119-33 PMCID:PMC5419464

[115]

PengY,ChenQ.Predicting risk of late age-related macular degeneration using deep learning.NPJ Digit Med2020;3:111 PMCID:PMC7453007

[116]

YanQ,XinH.Deep-learning-based prediction of late age-related macular degeneration progression.Nat Mach Intell2020;2:141-50 PMCID:PMC7153739

[117]

GeerlingsMJ,denHollander AI.The complement system in age-related macular degeneration: a review of rare genetic variants and implications for personalized treatment.Mol Immunol2017;84:65-76 PMCID:PMC5380947

[118]

RatnapriyaR.Applications of genomic technologies in retinal degenerative diseases.Adv Exp Med Biol2019;1185:281-5

[119]

HandaJT,DickAD.A systems biology approach towards understanding and treating non-neovascular age-related macular degeneration.Nat Commun2019;10:3347 PMCID:PMC6659646

AI Summary AI Mindmap
PDF

24

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/