Functional genomics complements quantitative genetics in identifying disease-gene associations.

An ultimate goal of genetic research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, includ...

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Autores principales:	Yuanfang Guan, Cheryl L Ackert-Bicknell, Braden Kell, Olga G Troyanskaya, Matthew A Hibbs
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6ec8458571e3455bb3f46437d628d67a
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spelling	oai:doaj.org-article:6ec8458571e3455bb3f46437d628d67a2021-11-18T05:51:56ZFunctional genomics complements quantitative genetics in identifying disease-gene associations.1553-734X1553-735810.1371/journal.pcbi.1000991https://doaj.org/article/6ec8458571e3455bb3f46437d628d67a2010-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21085640/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358An ultimate goal of genetic research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, including quantitative trait loci (QTL) linkage mapping and genome-wide association studies (GWAS). However, each of these approaches have technical and biological shortcomings. For example, the amount of heritable variation explained by GWAS is often surprisingly small and the resolution of many QTL linkage mapping studies is poor. The predictive power and interpretation of QTL and GWAS results are consequently limited. In this study, we propose a complementary approach to quantitative genetics by interrogating the vast amount of high-throughput genomic data in model organisms to functionally associate genes with phenotypes and diseases. Our algorithm combines the genome-wide functional relationship network for the laboratory mouse and a state-of-the-art machine learning method. We demonstrate the superior accuracy of this algorithm through predicting genes associated with each of 1157 diverse phenotype ontology terms. Comparison between our prediction results and a meta-analysis of quantitative genetic studies reveals both overlapping candidates and distinct, accurate predictions uniquely identified by our approach. Focusing on bone mineral density (BMD), a phenotype related to osteoporotic fracture, we experimentally validated two of our novel predictions (not observed in any previous GWAS/QTL studies) and found significant bone density defects for both Timp2 and Abcg8 deficient mice. Our results suggest that the integration of functional genomics data into networks, which itself is informative of protein function and interactions, can successfully be utilized as a complementary approach to quantitative genetics to predict disease risks. All supplementary material is available at http://cbfg.jax.org/phenotype.Yuanfang GuanCheryl L Ackert-BicknellBraden KellOlga G TroyanskayaMatthew A HibbsPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 11, p e1000991 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Yuanfang Guan Cheryl L Ackert-Bicknell Braden Kell Olga G Troyanskaya Matthew A Hibbs Functional genomics complements quantitative genetics in identifying disease-gene associations.
description	An ultimate goal of genetic research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, including quantitative trait loci (QTL) linkage mapping and genome-wide association studies (GWAS). However, each of these approaches have technical and biological shortcomings. For example, the amount of heritable variation explained by GWAS is often surprisingly small and the resolution of many QTL linkage mapping studies is poor. The predictive power and interpretation of QTL and GWAS results are consequently limited. In this study, we propose a complementary approach to quantitative genetics by interrogating the vast amount of high-throughput genomic data in model organisms to functionally associate genes with phenotypes and diseases. Our algorithm combines the genome-wide functional relationship network for the laboratory mouse and a state-of-the-art machine learning method. We demonstrate the superior accuracy of this algorithm through predicting genes associated with each of 1157 diverse phenotype ontology terms. Comparison between our prediction results and a meta-analysis of quantitative genetic studies reveals both overlapping candidates and distinct, accurate predictions uniquely identified by our approach. Focusing on bone mineral density (BMD), a phenotype related to osteoporotic fracture, we experimentally validated two of our novel predictions (not observed in any previous GWAS/QTL studies) and found significant bone density defects for both Timp2 and Abcg8 deficient mice. Our results suggest that the integration of functional genomics data into networks, which itself is informative of protein function and interactions, can successfully be utilized as a complementary approach to quantitative genetics to predict disease risks. All supplementary material is available at http://cbfg.jax.org/phenotype.
format	article
author	Yuanfang Guan Cheryl L Ackert-Bicknell Braden Kell Olga G Troyanskaya Matthew A Hibbs
author_facet	Yuanfang Guan Cheryl L Ackert-Bicknell Braden Kell Olga G Troyanskaya Matthew A Hibbs
author_sort	Yuanfang Guan
title	Functional genomics complements quantitative genetics in identifying disease-gene associations.
title_short	Functional genomics complements quantitative genetics in identifying disease-gene associations.
title_full	Functional genomics complements quantitative genetics in identifying disease-gene associations.
title_fullStr	Functional genomics complements quantitative genetics in identifying disease-gene associations.
title_full_unstemmed	Functional genomics complements quantitative genetics in identifying disease-gene associations.
title_sort	functional genomics complements quantitative genetics in identifying disease-gene associations.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/6ec8458571e3455bb3f46437d628d67a
work_keys_str_mv	AT yuanfangguan functionalgenomicscomplementsquantitativegeneticsinidentifyingdiseasegeneassociations AT cheryllackertbicknell functionalgenomicscomplementsquantitativegeneticsinidentifyingdiseasegeneassociations AT bradenkell functionalgenomicscomplementsquantitativegeneticsinidentifyingdiseasegeneassociations AT olgagtroyanskaya functionalgenomicscomplementsquantitativegeneticsinidentifyingdiseasegeneassociations AT matthewahibbs functionalgenomicscomplementsquantitativegeneticsinidentifyingdiseasegeneassociations
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Functional genomics complements quantitative genetics in identifying disease-gene associations.

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