Abstract
BackgroundGenetically, SNP that are in complete linkage disequilibrium with the causative SNP cannot be distinguished from the causative SNP. The Complete Linkage Disequilibrium (CLD) test presented here tests whether a SNP is in complete LD with the causative mutation or not. The performance of the CLD test is evaluated in 1000 simulated datasets.MethodsThe CLD test consists of two steps i.e. analysis I and analysis II. Analysis I consists of an association analysis of the investigated region. The log-likelihood values from analysis I are next ranked in descending order and in analysis II the CLD test evaluates differences in log-likelihood ratios between the best and second best markers. Under the null-hypothesis distribution, the best SNP is in greater LD with the QTL than the second best, while under the alternative-CLD-hypothesis, the best SNP is alike-in-state with the QTL. To find a significance threshold, the test was also performed on data excluding the causative SNP. The 5th, 10th and 50th highest TCLD value from 1000 replicated analyses were used to control the type-I-error rate of the test at p = 0.005, p = 0.01 and p = 0.05, respectively.ResultsIn a situation where the QTL explained 48% of the phenotypic variance analysis I detected a QTL in 994 replicates (p = 0.001), where 972 were positioned in the correct QTL position. When the causative SNP was excluded from the analysis, 714 replicates detected evidence of a QTL (p = 0.001). In analysis II, the CLD test confirmed 280 causative SNP from 1000 simulations (p = 0.05), i.e. power was 28%. When the effect of the QTL was reduced by doubling the error variance, the power of the test reduced relatively little to 23%. When sequence data were used, the power of the test reduced to 16%. All SNP that were confirmed by the CLD test were positioned in the correct QTL position.ConclusionsThe CLD test can provide evidence for a causative SNP, but its power may be low in situations with closely linked markers. In such situations, also functional evidence will be needed to definitely conclude whether the SNP is causative or not.
Highlights
SNP that are in complete linkage disequilibrium with the causative SNP cannot be distinguished from the causative SNP
QTL mapping efforts often result in the detection of genomic regions that explain quantitative trait variation, but seldom in the detection of the causative mutation underlying the trait variation
We propose a test to identify SNP that are in complete LD with the QTL, in order to maximise the genetic evidence for the SNP that is the causative mutation
Summary
Methods developed to genotype high numbers of SNP have permitted to reduce the size of the genomic regions detected. We investigated the effect of precision and power obtained by including the causative mutation among the markers in a QTL mapping experiment [2]. Both power and precision were increased and the results indicated that it would be possible to identify causativeor CLD-SNP. We propose a test to identify SNP that are in complete LD with the QTL, in order to maximise the genetic evidence for the SNP that is the causative mutation. We evaluate the performance of this test using simulated data where the causative SNP is unequivocally known
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