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Chunk #40 — Methods — Sliding window algorithm

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An empirical evaluation of imputation accuracy for association statistics reveals increased type-I error rates in genome-wide associations.
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The complete set of minus log transformed P-values of imputed and directly genotyped markers under a log-addictive model of inheritance was collected and ordered based on chromosomal position. Imputed markers that were considered associated using a pre-defined threshold (10 -5) were determined and classified into concordant and discordant markers in terms of their agreement within the association statistics. A locally developed Perl algorithm constructed sliding windows of different sizes (in this report 1,2 and 3) centered in the imputed markers (concordant or not) and collected the set of minus-log transformed association statistics of the flanking markers. A set of summary statistics, such as mean, median and variance of each sliding window was collected and referenced to the central marker. The complete set of raw results, summary statistics and markers comprised in each window can be obtained upon request.