Methods Inf Med - Evaluating strategies for marker ranking in genome-wide association studies of complex traits.

Tópicos

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Resumo

CKGROUND: Genome-wide association studies (GWAS) were highly successful in identifying new susceptibility loci of complex traits. Such studies usually start with genotyping fixed arrays of genetic markers in an initial sample. Out of these markers, some are selected which will be further genotyped in independent samples. Due to the very low a priori probability of a true positive association, the vast majority of all marker signals will turn out to be false positive. Thus, several methods to sort marker data have been proposed which will be evaluated here.OBJECTIVES: We compared statistical properties of ranking by p-values, q-values, the False Positive Report Probability (FPRP) and the Bayesian False-Discovery Probability (BFDP).METHODS: We performed simulation studies for a genomic region derived from GWAS data sets and calculated descriptive statistics as well as mean square errors with regard to the true marker ranking. Additionally, we applied all measures to a GWAS for early onset extreme obesity superimposing a priori information on candidate genes.RESULTS: Despite the known, more extreme probability results for traditional p-values, we observed that both p-values and the BFDP were more precise in reconstructing the "true" order of the markers in a region. In addition, the BFDP was useful to attenuate unexpected effects at a genome-wide scale.CONCLUSIONS: For the purpose of selecting markers from an initial GWAS and within the limits of this study, we recommend either ranking by p-values or the application of a full Bayesian approach for which the BFDP is a first approximation.

Resumo Limpo

ckground genomewid associ studi gwas high success identifi new suscept loci complex trait studi usual start genotyp fix array genet marker initi sampl marker select will genotyp independ sampl due low priori probabl true posit associ vast major marker signal will turn fals posit thus sever method sort marker data propos will evalu hereobject compar statist properti rank pvalu qvalu fals posit report probabl fprp bayesian falsediscoveri probabl bfdpmethod perform simul studi genom region deriv gwas data set calcul descript statist well mean squar error regard true marker rank addit appli measur gwas earli onset extrem obes superimpos priori inform candid genesresult despit known extrem probabl result tradit pvalu observ pvalu bfdp precis reconstruct true order marker region addit bfdp use attenu unexpect effect genomewid scaleconclus purpos select marker initi gwas within limit studi recommend either rank pvalu applic full bayesian approach bfdp first approxim

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