A genome-wide association study identifies protein quantitative trait loci (pQTLs)
There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts - cis effects, and elsewhere in the genome - trans effects. The r...
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| Published in | PLoS genetics Vol. 4; no. 5; p. e1000072 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
01.05.2008
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts - cis effects, and elsewhere in the genome - trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8x10(-57)), CCL4L1 (p = 3.9x10(-21)), IL18 (p = 6.8x10(-13)), LPA (p = 4.4x10(-10)), GGT1 (p = 1.5x10(-7)), SHBG (p = 3.1x10(-7)), CRP (p = 6.4x10(-6)) and IL1RN (p = 7.3x10(-6)) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor (IL6R), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1). We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8x10(-40)), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways. |
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| AbstractList | There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts - cis effects, and elsewhere in the genome - trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8x10(-57)), CCL4L1 (p = 3.9x10(-21)), IL18 (p = 6.8x10(-13)), LPA (p = 4.4x10(-10)), GGT1 (p = 1.5x10(-7)), SHBG (p = 3.1x10(-7)), CRP (p = 6.4x10(-6)) and IL1RN (p = 7.3x10(-6)) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor (IL6R), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1). We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8x10(-40)), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways. There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts-cis effects, and elsewhere in the genome -trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8 x [10.sup.-57]), CCL4L1 (p = 3.9 x [10.sup.-21]), IL18 (p = 6.8 x [10.sup.-13]), LPA (p = 4.4 x [10.sup.-10]), GGT1 (p = 1.5 x [10.sup.-7]), SHBG (p = 3.1 x [10.sup.-7]), CRP (p = 6.4 x [10.sup.-6]) and IL1RN (p = 7.3 x [10.sup.-6]) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor (IL6R), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1). We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8 x [10.sup.-40]), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways. doi:10.1371/journal.pgen.1000072 There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts-cis effects, and elsewhere in the genome -trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8 x [10.sup.-57]), CCL4L1 (p = 3.9 x [10.sup.-21]), IL18 (p = 6.8 x [10.sup.-13]), LPA (p = 4.4 x [10.sup.-10]), GGT1 (p = 1.5 x [10.sup.-7]), SHBG (p = 3.1 x [10.sup.-7]), CRP (p = 6.4 x [10.sup.-6]) and IL1RN (p = 7.3 x [10.sup.-6]) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor (IL6R), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1). We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8 x [10.sup.-40]), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways. There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts – cis effects, and elsewhere in the genome – trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8×10 −57 ), CCL4L1 (p = 3.9×10 −21 ), IL18 (p = 6.8×10 −13 ), LPA (p = 4.4×10 −10 ), GGT1 (p = 1.5×10 −7 ), SHBG (p = 3.1×10 −7 ), CRP (p = 6.4×10 −6 ) and IL1RN (p = 7.3×10 −6 ) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor ( IL6R ), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1) . We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8×10 −40 ), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways. One of the central dogmas of molecular genetics is that DNA is transcribed to RNA which is translated to protein and alterations to proteins can influence human diseases. Genome-wide association studies have recently revealed many new DNA variants that influence human diseases. To complement these efforts, several genome-wide studies have established that DNA variation influences mRNA expression levels. Loci influencing mRNA levels have been termed “eQTLs”. In this study we have performed the first genome-wide association study of the third piece in this jigsaw – the role of DNA variation in relation to protein levels, or “pQTLs”. We analysed 42 proteins measured in blood fractions from the InCHIANTI study. We identified eight cis effects including common variants in or near the IL6R , CCL4 , IL18 , LPA , GGT1 , SHBG , CRP and IL1RN genes, all associated with blood levels of their respective protein products. Mechanisms implicated included altered transcription (GGT1) but also rates of cleavage of bound to unbound soluble receptor ( IL6R ), altered secretion rates of different sized proteins (LPA) and variation in gene copy number (CCL4). Blood levels of many of these proteins are correlated with human diseases and the identification of “pQTLs” may in turn help our understanding of disease. There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts - cis effects, and elsewhere in the genome - trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8×10-57), CCL4L1 (p = 3.9×10-21), IL18 (p = 6.8×10-13), LPA (p = 4.4×10-10), GGT1 (p = 1.5×10-7), SHBG (p = 3.1×10-7), CRP (p = 6.4×10-6) and IL1RN (p = 7.3×10-6) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor (IL6R), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1). We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8×10-40), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways. |
| Audience | Academic |
| Author | Weedon, Michael N Rafiq, Sajjad Hernandez, Dena Dayan, Colin Bennett, Amanda van de Leemput, Joyce Murray, Anna Guralnik, Jack McCarthy, Mark I Melzer, David Rafferty, Ian Rice, Neil Gibbs, J Raphael Corsi, Anna-Maria Henley, William Ferrucci, Luigi Scholz, Sonja Paolisso, Giuseppe Ruokonen, Aimo Washecka, Nicole Li, Rongling Tranah, Greg Bandinelli, Stefania Arepalli, Sampath Hurst, Alison Lauretani, Fulvio Lango, Hana Singleton, Andrew Newman, Anne B Stevens, Kara Simon-Sanchez, Javier Frayling, Timothy M Harris, Tamara Jarvelin, Marjo-Riitta Perry, John R B Britton, Angela Panicker, Vijay |
| AuthorAffiliation | 13 The Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, United Kingdom 3 Laboratory of Neurogenetics, National Institute of Aging, Porter Neuroscience Research Center, Bethesda, Maryland, United States of America 4 Tuscany Regional Health Agency, I.O.T. and Department of Medical and Surgical Critical Care, University of Florence, Florence, Italy 18 Longitudinal Studies Section, Clinical Research Branch, Gerontology Research Center, National Institute on Aging, Baltimore, Maryland, United States of America 17 Geriatric Unit, Azienda Sanitaria di Firenze, Florence, Italy University of Pennsylvania, United States of America 1 Department of Epidemiology and Public Health, Institute of Biomedical and Clinical Sciences, Peninsula College of Medicine and Dentistry, University of Exeter, Devon, United Kingdom 2 Genetics of Complex Traits, Institute of Biomedical and Clinical Sciences, Peninsula College of Medicine and Dentistry, University of Exeter, Devon, United Kingdom 16 Departme |
| AuthorAffiliation_xml | – name: 3 Laboratory of Neurogenetics, National Institute of Aging, Porter Neuroscience Research Center, Bethesda, Maryland, United States of America – name: 4 Tuscany Regional Health Agency, I.O.T. and Department of Medical and Surgical Critical Care, University of Florence, Florence, Italy – name: 10 Laboratory of Epidemiology, Demography and Biometry, National Institute on Aging, Bethesda, Maryland, United States of America – name: 11 Henry Wellcome Laboratories for Integrative Neurosciences and Endocrinology, University of Bristol, Bristol, United Kingdom – name: 6 School of Mathematics and Statistics, University of Plymouth, Plymouth, United Kingdom – name: 5 Department of Geriatric Medicine and Metabolic Diseases, Second University of Naples, Naples, Italy – name: 15 Department of Public Health, Science, and General Practice, University of Oulu, Oulu, Finland – name: University of Pennsylvania, United States of America – name: 14 Department of Clinical Chemistry, University of Oulu, Oulu, Finland – name: 16 Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom – name: 13 The Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, United Kingdom – name: 17 Geriatric Unit, Azienda Sanitaria di Firenze, Florence, Italy – name: 1 Department of Epidemiology and Public Health, Institute of Biomedical and Clinical Sciences, Peninsula College of Medicine and Dentistry, University of Exeter, Devon, United Kingdom – name: 7 Department of Preventive Medicine and Center for Genomics and Bioinformatics, College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America – name: 12 Oxford Centre for Diabetes, Endocrinology and Metabolism, Headington, Oxford, United Kingdom – name: 18 Longitudinal Studies Section, Clinical Research Branch, Gerontology Research Center, National Institute on Aging, Baltimore, Maryland, United States of America – name: 8 University of Pittsburgh, Graduate School of Public Health, Departments of Epidemiology and Medicine, Pittsburgh, Pennsylvania, United States of America – name: 9 San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California, United States of America – name: 2 Genetics of Complex Traits, Institute of Biomedical and Clinical Sciences, Peninsula College of Medicine and Dentistry, University of Exeter, Devon, United Kingdom |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/18464913$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | COPYRIGHT 2008 Public Library of Science Melzer et al. 2008 2008 Melzer et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Melzer D, Perry JRB, Hernandez D, Corsi A-M, Stevens K, et al. (2008) A Genome-Wide Association Study Identifies Protein Quantitative Trait Loci (pQTLs). PLoS Genet 4(5): e1000072. doi:10.1371/journal.pgen.1000072 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Conceived and designed the experiments: D Melzer, J Perry, D Hernandez, T Frayling, A Singleton, L Ferrucci. Performed the experiments: D Hernandez, I Rafferty, J Gibbs, J Simon-Sanchez, S Scholz, S Arepalli, N Washecka, A Britton, J van de Leemput, A Singleton. Analyzed the data: J Perry, K Stevens, A Murray, S Rafiq, H Lango, M Weedon, N Rice, A Hurst, W Hurst, A Singleton. Contributed reagents/materials/analysis tools: A Corsi, F Lauretani, G Paolisso, R Layan, A Newman, G Tranah, T Harris, V Panicker, C D A Bennett, M McCarthy, A Ruokonen, M Järvelin, J Guralnik, S Bandinelli, A Singleton, L Ferrucci. Wrote the paper: D Melzer, J Perry, D Hernandez, T. Frayling, L Ferrucci. These authors also contributed equally to this work. |
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| Title | A genome-wide association study identifies protein quantitative trait loci (pQTLs) |
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