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The difficulty of avoiding false positives in genome scans for natural selection.

ABSTRACT: Several studies have found evidence for more positive selection on the chimpanzee lineage compared with the human lineage since the two species split. A potential concern, however, is that these findings may simply reflect artifacts of the data: inaccuracies in the underlying chimpanzee genome sequence, which is of lower quality than human. To test this hypothesis, we generated de novo genome assemblies of chimpanzee and macaque and aligned them with human. We also implemented a novel bioinformatic procedure for producing alignments of closely related species that uses synteny information to remove misassembled and misaligned regions, and sequence quality scores to remove nucleotides that are less reliable. We applied this procedure to re-examine 59 genes recently identified as candidates for positive selection in chimpanzees. The great majority of these signals disappear after application of our new bioinformatic procedure. We also carried out laboratory-based resequencing of 10 of the regions in multiple chimpanzees and humans, and found that our alignments were correct wherever there was a conflict with the published results. These findings throw into question previous findings that there has been more positive selection in chimpanzees than in humans since the two species diverged. Our study also highlights the challenges of searching the extreme tails of distributions for signals of natural selection. Inaccuracies in the genome sequence at even a tiny fraction of genes can produce false-positive signals, which make it difficult to identify loci that have genuinely been targets of selection.

SUBMITTER: Mallick S

PROVIDER: S-EPMC2675981 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature

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The difficulty of avoiding false positives in genome scans for natural selection.

Mallick Swapan S Gnerre Sante S Muller Paul P Reich David D

Genome research 20090501 5

Several studies have found evidence for more positive selection on the chimpanzee lineage compared with the human lineage since the two species split. A potential concern, however, is that these findings may simply reflect artifacts of the data: inaccuracies in the underlying chimpanzee genome sequence, which is of lower quality than human. To test this hypothesis, we generated de novo genome assemblies of chimpanzee and macaque and aligned them with human. We also implemented a novel bioinforma ...[more]

PMID: 19411606

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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.

Dataset Information

The difficulty of avoiding false positives in genome scans for natural selection.

Publications

The difficulty of avoiding false positives in genome scans for natural selection.

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