Genetic Strategies to Understand Human Diabetic Nephropathy: In Silico Strategies for Molecular Data-Association Studies

Marisa Canadas-Garre; Laura J Smyth; Kerry Anderson; Katie Kerr; Amy Jayne McKnight

doi:10.1007/978-1-4939-9841-8_16

Genetic Strategies to Understand Human Diabetic Nephropathy: In Silico Strategies for Molecular Data-Association Studies

Methods Mol Biol. 2020:2067:241-275. doi: 10.1007/978-1-4939-9841-8_16.

Authors

Marisa Canadas-Garre¹, Laura J Smyth¹, Kerry Anderson¹, Katie Kerr¹, Amy Jayne McKnight²

Affiliations

¹ Centre for Public Health, Queen's University Belfast, Northern Ireland, UK.
² Centre for Public Health, Queen's University Belfast, Northern Ireland, UK. a.j.mcknight@qub.ac.uk.

PMID: 31701456
DOI: 10.1007/978-1-4939-9841-8_16

Abstract

Multiple genetic strategies are available to help improve understanding of diabetic nephropathy. This chapter provides detailed methodology for a single-nucleotide polymorphism association study and meta-analysis, using a protocol suitable for targeted SNP or genome-wide association studies, to identify genetic risk factors for diabetic nephropathy.

Keywords: Association; Diabetic nephropathy; Genome-wide; Imputation; Meta-analysis; Plink; RAREMETAL; Rvtests; SNP; Trend.

MeSH terms

Computational Biology / instrumentation
Computational Biology / methods*
Computers
Datasets as Topic
Diabetic Nephropathies / genetics*
Genetic Predisposition to Disease
Genome-Wide Association Study / methods
Humans
Meta-Analysis as Topic*
Polymorphism, Single Nucleotide
Risk Factors
Software