Project ID CM-HD2024_49


Co Supervisor 1A Faculty of Life Sciences & Medicine, School of Life Course & Population Sciences, Section of OphthalmologyWebsite

Co Supervisor 1B Faculty of Life Sciences & Medicine, School of Life Course & Population Sciences, Section of OphthalmologyWebsite

Additional Supervisor Dr Samuel Burden, Dr Haotian Gu

Machine and statistical learning approaches to biological data integration for the study of common eye diseases

(i) Age-related macular degeneration (AMD) is currently the most common blinding disease in the UK but myopia is predicted to become the most important cause of vision loss and disability. A lot has been learned about these diseases through analyses of biological “omics” information, such as that extracted from genomic DNA or gene expression. Building overall disease models, integrating differently sourced biological information is challenging for the retina, but could improve our understanding of these and other diseases and our ability to treat them in the future.
(ii) Integrating omics information of different natures is possible, but requires a large volume data, that is difficult to obtain from the retina, which is quite inaccessible. This project will repurpose existing large but non-specific bulk RNA sequencing data to generate cell line-specific information which in conjunction with genotype data will be used to model retinal conditions including age-related macular degeneration disease (AMD) and common myopia.
(iii) The student will learn about statistical deconvolutional techniques in transcriptomic data. Subsequently, the student will be trained on statistical learning methods that can extend high resolution transcriptomic information in small samples to much larger pool of subjects with low-resolution transcriptomic data available. Additionally, the student will learn the up-to-date methodologies matching genomic and transcriptomic data to model the overall disease risk and identify molecular pathways that are essential to retinal health and disease.
(iv) In the first year the student will analyse and single cell/nucleotide RNA sequencing to output cell-specific clusters. In the second, the student will use deconvolutional and machine learning methods to infer cell line-specific information in large RNA sequencing samples to compare with their disease status. In the third, the student will identify novel biomarkers associated with risk of AMD, myopia and other retinal diseases and clusters of gene transcripts expressed in specific cells that operate in tandem to cause disease.

Representative Publications

1) Meta-analysis of 542,934 subjects of European ancestry identifies new genes and mechanisms predisposing to refractive error and myopia. Hysi PG, Choquet H, Khawaja AP, Wojciechowski R, Tedja MS, Yin J, Simcoe MJ, Patasova K, Mahroo OA, Thai KK, Cumberland PM, Melles RB, Verhoeven VJM, Vitart V, Segre A, Stone RA, Wareham N, Hewitt AW, Mackey DA, Klaver CCW, MacGregor S; Consortium for Refractive Error and Myopia; Khaw PT, Foster PJ; UK Eye and Vision Consortium; Guggenheim JA; 23andMe Inc.; Rahi JS, Jorgenson E, Hammond CJ. Nat Genet. 2020 Apr;52(4):401-407. doi: 10.1038/s41588-020-0599-0. 2) The Potential of Current Polygenic Risk Scores to Predict High Myopia and Myopic Macular Degeneration in Multiethnic Singapore Adults. Kassam I, Foo LL, Lanca C, Xu L, Hoang QV, Cheng CY, Hysi P, Saw SM. Ophthalmology. 2022 Aug;129(8):890-902. doi: 10.1016/j.ophtha.2022.03.022. 3) Machine learning identifying peripheral circulating metabolites associated with intraocular pressure alterations. Qian C, Nusinovici S, Thakur S, Soh ZD, Majithia S, Chee ML, Zhong H, Tham YC, Sabanayagam C, Hysi PG, Cheng CY. Br J Ophthalmol. 2022 May 25:bjophthalmol-2021-320584. doi: 10.1136/bjophthalmol-2021-320584
1) A genome-wide analysis of 340 318 participants identifies four novel loci associated with the age of first spectacle wear. Patasova K, Khawaja AP, Wojciechowski R, Mahroo OA, Falchi M, Rahi JS, Hammond CJ, Hysi PG; UK Biobank Eye & Vision Consortium. Hum Mol Genet. 2022 Aug 25;31(17):3012-3019. doi: 10.1093/hmg/ddac048. 2) Comparison of Associations with Different Macular Inner Retinal Thickness Parameters in a Large Cohort: The UK Biobank. Khawaja AP, Chua S, Hysi PG, Georgoulas S, Currant H, Fitzgerald TW, Birney E, Ko F, Yang Q, Reisman C, Garway-Heath DF, Hammond CJ, Khaw PT, Foster PJ, Patel PJ, Strouthidis N; UK Biobank Eye and Vision Consortium. Ophthalmology. 2020 Jan;127(1):62-71. doi: 10.1016/j.ophtha.2019.08.015. Epub 2019 Aug 21. 3)Genome-wide association meta-analysis highlights light-induced signaling as a driver for refractive error. Tedja MS, Wojciechowski R, …Wong TY, Hewitt AW, Mackey DA, Simpson CL, Pfeiffer N, Pärssinen O, Baird PN, Vitart V, Amin N, van Duijn CM, Bailey-Wilson JE, Young TL, Saw SM, Stambolian D, MacGregor S, Guggenheim JA, Tung JY, Hammond CJ, Klaver CCW. Nat Genet. 2018 Jun;50(6):834-848. doi: 10.1038/s41588-018-0127-7.