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.
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.
Strömberg
Ulf
U
Department of Occupational and Environmental Medicine, Lund University, Lund, Sweden. ulf.stromberg@med.lu.se
Björk
Jonas
J
Broberg
Karin
K
Mertens
Fredrik
F
Vineis
Paolo
P
D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D015987:Effect Modifier, Epidemiologic; D005188:False Negative Reactions; D005819:Genetic Markers; D016678:Genome; D006801:Humans; D017720:Molecular Epidemiology / Q000379:methods*; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods*; D012509:Sarcoma / Q000453:epidemiology / Q000235:genetics / Q000473:pathology
D005819:Genetic Markers