Selection of influential genetic markers among a large number of candidates based on effect estimation rather than hypothesis testing: an approach for genome-wide association studies.

2.50
Hdl Handle:
http://hdl.handle.net/10146/38102
Title:
Selection of influential genetic markers among a large number of candidates based on effect estimation rather than hypothesis testing: an approach for genome-wide association studies.
Authors:
Stromberg, Ulf; Bjork, Jonas; Broberg, Karin; Mertens, Fredrik; Vineis, Paolo
Abstract:
In epidemiologic studies on direct genetic associations, hypothesis testing is primarily considered for evaluating the effects of each candidate genetic marker, eg, single nucleotide polymorphisms. To help investigators protect themselves from over-interpreting statistically significant findings that are not likely to signify a true effect-a problem connected to multiple comparisons-consideration of the false-positive report probability has been proposed. There have also been arguments advocating estimation of effect size rather than hypothesis testing (P value). Here, we propose an estimation-based approach that offers an attractive alternative to the test-based false-positive report probability, when the task is to select promising genetic markers for further analyses. We discuss the potential of this estimation-based approach for genome-wide association studies.
Citation:
Epidemiology 2008, 19 (2):302-308
Journal:
Epidemiology (Cambridge, Mass.)
Issue Date:
Mar-2008
URI:
http://hdl.handle.net/10146/38102
DOI:
10.1097/EDE.0b013e3181632c3d
PubMed ID:
18300718
Additional Links:
http://www.epidem.com/pt/re/epidemiology/abstract.00001648-200803000-00024.htm;jsessionid=LhbhQp9Vv0tmWKm3Ly1yc4YQBJ7x8nnykxDP889CFJRQQQ1WQYhx!1177656273!181195629!8091!-1; http://ovidsp.tx.ovid.com/spb/ovidweb.cgi?QS2=434f4e1a73d37e8cf5d3d7f4c06fe8b47d71255d8c7113802efe2f2418bc1fc47dd0c373ac72ab01023c32fa8c6ea55b9ecb07cc105406d5bdfb4373ab3b362d3c64daa26188d653e6e6cf96fe78e113af4aa63cd37c48f98ef56bd111a07744a1eb27ba2ffaf395c083349f422e1cfdafc024959fd4f578ebd73cd45054e27550c5dbff38a4ad199a5616f340bbfcfb47ab3cf764abf9fab64d0e24da8da218ad5707552b68e96927e6db286d72d5eb2e6c0f8271f2ea002fa45fc4b329887a26acd8d10bfec72030f0e2cfec2b2865095cdec67cbf972340dda3062835fc71
Type:
Article
Language:
en
ISSN:
1044-3983
Sponsors:
This work was partly conducted within the EU Network of Excellence ECNIS (www.ECNIS.org ).
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorStromberg, Ulf-
dc.contributor.authorBjork, Jonas-
dc.contributor.authorBroberg, Karin-
dc.contributor.authorMertens, Fredrik-
dc.contributor.authorVineis, Paolo-
dc.date.accessioned2008-09-24T10:54:25Z-
dc.date.available2008-09-24T10:54:25Z-
dc.date.issued2008-03-
dc.identifier.citationEpidemiology 2008, 19 (2):302-308en
dc.identifier.issn1044-3983-
dc.identifier.pmid18300718-
dc.identifier.doi10.1097/EDE.0b013e3181632c3d-
dc.identifier.urihttp://hdl.handle.net/10146/38102-
dc.description.abstractIn epidemiologic studies on direct genetic associations, hypothesis testing is primarily considered for evaluating the effects of each candidate genetic marker, eg, single nucleotide polymorphisms. To help investigators protect themselves from over-interpreting statistically significant findings that are not likely to signify a true effect-a problem connected to multiple comparisons-consideration of the false-positive report probability has been proposed. There have also been arguments advocating estimation of effect size rather than hypothesis testing (P value). Here, we propose an estimation-based approach that offers an attractive alternative to the test-based false-positive report probability, when the task is to select promising genetic markers for further analyses. We discuss the potential of this estimation-based approach for genome-wide association studies.en
dc.description.sponsorshipThis work was partly conducted within the EU Network of Excellence ECNIS (www.ECNIS.org ).en
dc.language.isoenen
dc.relation.urlhttp://www.epidem.com/pt/re/epidemiology/abstract.00001648-200803000-00024.htm;jsessionid=LhbhQp9Vv0tmWKm3Ly1yc4YQBJ7x8nnykxDP889CFJRQQQ1WQYhx!1177656273!181195629!8091!-1en
dc.relation.urlhttp://ovidsp.tx.ovid.com/spb/ovidweb.cgi?QS2=434f4e1a73d37e8cf5d3d7f4c06fe8b47d71255d8c7113802efe2f2418bc1fc47dd0c373ac72ab01023c32fa8c6ea55b9ecb07cc105406d5bdfb4373ab3b362d3c64daa26188d653e6e6cf96fe78e113af4aa63cd37c48f98ef56bd111a07744a1eb27ba2ffaf395c083349f422e1cfdafc024959fd4f578ebd73cd45054e27550c5dbff38a4ad199a5616f340bbfcfb47ab3cf764abf9fab64d0e24da8da218ad5707552b68e96927e6db286d72d5eb2e6c0f8271f2ea002fa45fc4b329887a26acd8d10bfec72030f0e2cfec2b2865095cdec67cbf972340dda3062835fc71en
dc.subject.meshBayes Theorem-
dc.subject.meshBiometry-
dc.subject.meshEffect Modifiers (Epidemiology)-
dc.subject.meshEpidemiology, Molecular-
dc.subject.meshFalse Negative Reactions-
dc.subject.meshGenetic Markers-
dc.subject.meshGenome-
dc.subject.meshHumans-
dc.subject.meshProportional Hazards Models-
dc.subject.meshRisk Assessment-
dc.subject.meshSarcoma-
dc.titleSelection of influential genetic markers among a large number of candidates based on effect estimation rather than hypothesis testing: an approach for genome-wide association studies.en
dc.typeArticleen
dc.identifier.journalEpidemiology (Cambridge, Mass.)en

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