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Sharafeldin N, Zhang J, Singh P, Bosworth A, Chen Y, Patel SK, Wang X, Francisco L, Forman SJ, Wong FL, Ojesina AI, Bhatia S. Genome-wide variants and polygenic risk scores for cognitive impairment following blood or marrow transplantation. Bone Marrow Transplant 2022; 57:925-933. [PMID: 35379913 DOI: 10.1038/s41409-022-01642-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 03/09/2022] [Accepted: 03/11/2022] [Indexed: 11/10/2022]
Abstract
Cognitive impairment is prevalent in blood or marrow transplantation (BMT) recipients, albeit with inter-individual variability. We conducted a genome-wide association study of objective cognitive function assessed longitudinally in 239 adult BMT recipients for discovery and replicated in an independent cohort of 540 BMT survivors. Weighted genome-wide polygenic risk scores (PRS) were constructed using linkage disequilibrium pruned significant SNPs. Forty-four genome-wide significant SNPs were identified using additive (n = 3); codominant (n = 20) and genotype models (n = 21). Each additional copy of a risk allele was associated with a 0.28-point (p = 1.07 × 10-8) to a 1.82-point (p = 6.7 × 10-12) increase in a global deficit score. We replicated two SNPs (rs11634183 and rs12486041) with links to neural integrity. Patients in the top PRS quintile were at increased risk of cognitive impairment in discovery (RR = 1.95, 95%CI: 1.28-2.96, p = 0.002) and replication cohorts (OR = 1.84, 95%CI, 1.02-3.32, p = 0.043). Associations were stronger among individuals with lowest clinical risk for cognitive impairment. These findings support potential utility of PRS-based risk classification in the development of targeted interventions aimed at improving cognitive outcomes in BMT survivors.
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Affiliation(s)
- Noha Sharafeldin
- Institute for Cancer Outcomes and Survivorship, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
| | - Jianqing Zhang
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Purnima Singh
- Institute for Cancer Outcomes and Survivorship, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Yanjun Chen
- Institute for Cancer Outcomes and Survivorship, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Xuexia Wang
- Department of Mathematics, University of North Texas, Denton, TX, USA
| | - Liton Francisco
- Institute for Cancer Outcomes and Survivorship, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Stephen J Forman
- Hematology and Hematopoietic Cell Transplantation, City of Hope, Duarte, CA, USA
| | | | - Akinyemi I Ojesina
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Smita Bhatia
- Institute for Cancer Outcomes and Survivorship, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
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Protocol for Epistasis Detection with Machine Learning Using GenEpi Package. Methods Mol Biol 2021. [PMID: 33733363 DOI: 10.1007/978-1-0716-0947-7_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
To develop medical treatments and prevention, the association between disease and genetic variants needs to be identified. The main goal of genome-wide association study (GWAS) is to discover the underlying reason for vulnerability to disease and utilize this knowledge for the development of prevention and treatment against these diseases. Given the methods available to address the scientific problems involved in the search for epistasis, there is not any standard for detecting epistasis, and this remains a problem due to limited statistical power. The GenEpi package is a Python package that uses a two-level workflow machine learning model to detect within-gene and cross-gene epistasis. This protocol chapter shows the usage of GenEpi with example data. The package uses a three-step procedure to reduce dimensionality, select the within-gene epistasis, and select the cross-gene epistasis. The package also provides a medium to build prediction models with the combination of genetic features and environmental influences.
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