Genetic strategies to understand human diabetic nephropathy: in silico strategies for molecular data - association studies

Marisa Cañadas-Garre; Laura Smyth; Kerry Anderson; Katie Kerr; Amy 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

Marisa Cañadas-Garre
, Laura Smyth
, Kerry Anderson
, Katie Kerr
, Amy McKnight

School of Medicine, Dentistry and Biomedical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

Abstract

Multiple genetic strategies are available to help improve understanding of diabetic nephropathy. This methods 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.

Original language	English
Title of host publication	Methods in Molecular Biology
Subtitle of host publication	Molecular Methods in Diabetic Nephropathy
Publisher	Springer Nature US
Pages	241-275
Volume	2067
DOIs	https://doi.org/10.1007/978-1-4939-9841-8_16
Publication status	Published - 01 Jan 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1007/978-1-4939-9841-8_16Licence: Unspecified

R3789CPH: Discovery of an integrated risk profile for chronic kidney disease and development of a clinical biomarker panel for personalising medicine (Stratified medicine for chronic kidney disease)
McKnight, A. J. (PI) & Maxwell, P. (CoI)
15/06/2016 → 31/12/2023
Project: Research

Cite this

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title = "Genetic strategies to understand human diabetic nephropathy: in silico strategies for molecular data - association studies",

abstract = "Multiple genetic strategies are available to help improve understanding of diabetic nephropathy. This methods 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.",

author = "Marisa Ca{\~n}adas-Garre and Laura Smyth and Kerry Anderson and Katie Kerr and Amy McKnight",

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Genetic strategies to understand human diabetic nephropathy: in silico strategies for molecular data - association studies. / Cañadas-Garre, Marisa; Smyth, Laura; Anderson, Kerry et al.
Methods in Molecular Biology: Molecular Methods in Diabetic Nephropathy. Vol. 2067 Springer Nature US, 2020. p. 241-275.

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

TY - CHAP

T1 - Genetic strategies to understand human diabetic nephropathy: in silico strategies for molecular data - association studies

AU - Cañadas-Garre, Marisa

AU - Smyth, Laura

AU - Anderson, Kerry

AU - Kerr, Katie

AU - McKnight, Amy

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Multiple genetic strategies are available to help improve understanding of diabetic nephropathy. This methods 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.

AB - Multiple genetic strategies are available to help improve understanding of diabetic nephropathy. This methods 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.

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DO - 10.1007/978-1-4939-9841-8_16

M3 - Chapter (peer-reviewed)

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SP - 241

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BT - Methods in Molecular Biology

PB - Springer Nature US

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Genetic strategies to understand human diabetic nephropathy: in silico strategies for molecular data - association studies

Abstract

UN SDGs

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Projects

R3789CPH: Discovery of an integrated risk profile for chronic kidney disease and development of a clinical biomarker panel for personalising medicine (Stratified medicine for chronic kidney disease)

Cite this