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Systems genetics approaches for understanding complex traits with relevance for human disease

Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and me...

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Autores principales: Allayee, Hooman, Farber, Charles R, Seldin, Marcus M, Williams, Evan Graehl, James, David E, Lusis, Aldons J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10645424/
https://www.ncbi.nlm.nih.gov/pubmed/37962168
http://dx.doi.org/10.7554/eLife.91004
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author Allayee, Hooman
Farber, Charles R
Seldin, Marcus M
Williams, Evan Graehl
James, David E
Lusis, Aldons J
author_facet Allayee, Hooman
Farber, Charles R
Seldin, Marcus M
Williams, Evan Graehl
James, David E
Lusis, Aldons J
author_sort Allayee, Hooman
collection PubMed
description Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.
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spelling pubmed-106454242023-11-14 Systems genetics approaches for understanding complex traits with relevance for human disease Allayee, Hooman Farber, Charles R Seldin, Marcus M Williams, Evan Graehl James, David E Lusis, Aldons J eLife Computational and Systems Biology Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data. eLife Sciences Publications, Ltd 2023-11-14 /pmc/articles/PMC10645424/ /pubmed/37962168 http://dx.doi.org/10.7554/eLife.91004 Text en © 2023, Allayee, Farber et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Computational and Systems Biology
Allayee, Hooman
Farber, Charles R
Seldin, Marcus M
Williams, Evan Graehl
James, David E
Lusis, Aldons J
Systems genetics approaches for understanding complex traits with relevance for human disease
title Systems genetics approaches for understanding complex traits with relevance for human disease
title_full Systems genetics approaches for understanding complex traits with relevance for human disease
title_fullStr Systems genetics approaches for understanding complex traits with relevance for human disease
title_full_unstemmed Systems genetics approaches for understanding complex traits with relevance for human disease
title_short Systems genetics approaches for understanding complex traits with relevance for human disease
title_sort systems genetics approaches for understanding complex traits with relevance for human disease
topic Computational and Systems Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10645424/
https://www.ncbi.nlm.nih.gov/pubmed/37962168
http://dx.doi.org/10.7554/eLife.91004
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