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The effect of minor allele frequency on the likelihood of obtaining false positives.

ABSTRACT: Determining the most promising single-nucleotide polymorphisms (SNPs) presents a challenge in genome-wide association studies, when hundreds of thousands of association tests are conducted. The power to detect genetic effects is dependent on minor allele frequency (MAF), and genome-wide association studies SNP arrays include SNPs with a wide distribution of MAFs. Therefore, it is critical to understand MAF's effect on the false positive rate.Data from the Framingham Heart Study simulated data (Problem 3, with answers) was used to examine the effects of varying MAFs on the likelihood of false positives. Replication set 1 was used to generate 1 million permutations of case/control status in unrelated individuals. Logistic regression was used to test for the association between each SNP and myocardial infarction using an additive model. We report the number of "significant" tests by MAF at alpha = 10-4, 10-5, and 10-6.Common SNPs exhibited fewer false positives than expected. At alpha = 10-4, SNPs with MAF 25% and 50% resulted in 69.2 [95%CI: 62.8-75.6] and 70.8 [95%CI: 61.3-80.4] false positives, respectively, compared to 100 expected. Rare SNPs exhibited more variability but did not show more false-positive results than expected by chance. However, at alpha = 10-4, MAF = 5% exhibited significantly more false positives (105.5 [95%CI: 81-130.1]) than MAF = 25% and 50%. Similar results were seen at the other alpha values.These results suggest that removal of low MAF SNPs from analysis due to concerns about inflated false-positive results may not be appropriate.

SUBMITTER: Tabangin ME

PROVIDER: S-EPMC2795940 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

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The effect of minor allele frequency on the likelihood of obtaining false positives.

Tabangin Meredith E ME Woo Jessica G JG Martin Lisa J LJ

BMC proceedings 20091215

Determining the most promising single-nucleotide polymorphisms (SNPs) presents a challenge in genome-wide association studies, when hundreds of thousands of association tests are conducted. The power to detect genetic effects is dependent on minor allele frequency (MAF), and genome-wide association studies SNP arrays include SNPs with a wide distribution of MAFs. Therefore, it is critical to understand MAF's effect on the false positive rate.Data from the Framingham Heart Study simulated data (P ...[more]

PMID: 20018033

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Project description:BackgroundA large number of probabilistic models used in sequence analysis assign non-zero probability values to most input sequences. To decide when a given probability is sufficient the most common way is bayesian binary classification, where the probability of the model characterizing the sequence family of interest is compared to that of an alternative probability model. We can use as alternative model a null model. This is the scoring technique used by sequence analysis tools such as HMMER, SAM and INFERNAL. The most prevalent null models are position-independent residue distributions that include: the uniform distribution, genomic distribution, family-specific distribution and the target sequence distribution. This paper presents a study to evaluate the impact of the choice of a null model in the final result of classifications. In particular, we are interested in minimizing the number of false predictions in a classification. This is a crucial issue to reduce costs of biological validation.ResultsFor all the tests, the target null model presented the lowest number of false positives, when using random sequences as a test. The study was performed in DNA sequences using GC content as the measure of content bias, but the results should be valid also for protein sequences. To broaden the application of the results, the study was performed using randomly generated sequences. Previous studies were performed on aminoacid sequences, using only one probabilistic model (HMM) and on a specific benchmark, and lack more general conclusions about the performance of null models. Finally, a benchmark test with P. falciparum confirmed these results.ConclusionsOf the evaluated models the best suited for classification are the uniform model and the target model. However, the use of the uniform model presents a GC bias that can cause more false positives for candidate sequences with extreme compositional bias, a characteristic not described in previous studies. In these cases the target model is more dependable for biological validation due to its higher specificity.

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The effect of minor allele frequency on the likelihood of obtaining false positives.

Publications

The effect of minor allele frequency on the likelihood of obtaining false positives.

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