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How to Interpret a Genome-wide Association Study

How to Interpret a Genome-wide Association Study SPECIAL COMMUNICATION How to Interpret a Genome-wide Association Study Thomas A. Pearson, MD, MPH, PhD Genome-wide association (GWA) studies use high-throughput genotyping tech- Teri A. Manolio, MD, PhD nologies to assay hundreds of thousands of single-nucleotide polymor- N THE PAST 2 YEARS, THERE HAS BEEN phisms (SNPs) and relate them to clinical conditions and measurable traits. a dramatic increase in genomic dis- Since 2005, nearly 100 loci for as many as 40 common diseases and traits have coveries involvng complex, non- been identified and replicated in GWA studies, many in genes not previously IMendelian diseases, with nearly suspected of having a role in the disease under study, and some in genomic 100 loci for as many as 40 common dis- regions containing no known genes. GWA studies are an important advance eases robustly identified and repli- in discovering genetic variants influencing disease but also have important cated in genome-wide association limitations, including their potential for false-positive and false-negative re- (GWA) studies (T.A.M.; unpublished data, 2008). These studies use high- sults and for biases related to selection of study participants and genotyping throughput genotyping technologies to errors. Although these studies are clearly many steps removed from actual clini- assay hundreds http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JAMA American Medical Association

How to Interpret a Genome-wide Association Study

JAMA , Volume 299 (11) – Mar 19, 2008

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References (91)

Publisher
American Medical Association
Copyright
Copyright 2008 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.
ISSN
0098-7484
eISSN
1538-3598
DOI
10.1001/jama.299.11.1335
pmid
18349094
Publisher site
See Article on Publisher Site

Abstract

SPECIAL COMMUNICATION How to Interpret a Genome-wide Association Study Thomas A. Pearson, MD, MPH, PhD Genome-wide association (GWA) studies use high-throughput genotyping tech- Teri A. Manolio, MD, PhD nologies to assay hundreds of thousands of single-nucleotide polymor- N THE PAST 2 YEARS, THERE HAS BEEN phisms (SNPs) and relate them to clinical conditions and measurable traits. a dramatic increase in genomic dis- Since 2005, nearly 100 loci for as many as 40 common diseases and traits have coveries involvng complex, non- been identified and replicated in GWA studies, many in genes not previously IMendelian diseases, with nearly suspected of having a role in the disease under study, and some in genomic 100 loci for as many as 40 common dis- regions containing no known genes. GWA studies are an important advance eases robustly identified and repli- in discovering genetic variants influencing disease but also have important cated in genome-wide association limitations, including their potential for false-positive and false-negative re- (GWA) studies (T.A.M.; unpublished data, 2008). These studies use high- sults and for biases related to selection of study participants and genotyping throughput genotyping technologies to errors. Although these studies are clearly many steps removed from actual clini- assay hundreds

Journal

JAMAAmerican Medical Association

Published: Mar 19, 2008

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