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On the occurrence of false positives in tests of migration under an isolation-with-migration model.

ABSTRACT: The population genetic study of divergence is often carried out using a Bayesian genealogy sampler, like those implemented in ima2 and related programs, and these analyses frequently include a likelihood ratio test of the null hypothesis of no migration between populations. Cruickshank and Hahn (2014, Molecular Ecology, 23, 3133-3157) recently reported a high rate of false-positive test results with ima2 for data simulated with small numbers of loci under models with no migration and recent splitting times. We confirm these findings and discover that they are caused by a failure of the assumptions underlying likelihood ratio tests that arises when using marginal likelihoods for a subset of model parameters. We also show that for small data sets, with little divergence between samples from two populations, an excellent fit can often be found by a model with a low migration rate and recent splitting time and a model with a high migration rate and a deep splitting time.

SUBMITTER: Hey J

PROVIDER: S-EPMC4615538 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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On the occurrence of false positives in tests of migration under an isolation-with-migration model.

Hey Jody J Chung Yujin Y Sethuraman Arun A

Molecular ecology 20151012 20

The population genetic study of divergence is often carried out using a Bayesian genealogy sampler, like those implemented in ima2 and related programs, and these analyses frequently include a likelihood ratio test of the null hypothesis of no migration between populations. Cruickshank and Hahn (2014, Molecular Ecology, 23, 3133-3157) recently reported a high rate of false-positive test results with ima2 for data simulated with small numbers of loci under models with no migration and recent spli ...[more]

PMID: 26456794

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Project description:Background Non-invasive prenatal testing (NIPT) has had an incomparable triumph in prenatal diagnostics in the last decade. Over 1400 research articles have been published, predominantly praising the advantages of this test. Methods The present study identified among the 1400 papers 24 original and one review paper, which were suited to re-evaluate the efficacy of > 750,000 published NIPT-results. Special attention was given to false-positive and false-negative result-rates. Those were discussed under different aspects—mainly from a patient-perspective. Results A 27: 1 rate of false-positive compared to false-negative NIPT results was found. Besides, according to all reported, real-positive, chromosomally aberrant NIPT cases, 90% of those would have been aborted spontaneously before birth. These findings are here discussed under aspects like (i) How efficient is NIPT compared to first trimester screening? (ii) What are the differences in expectations towards NIPT from specialists and the public? and (iii) There should also be children born suffering from not by NIPT tested chromosomal aberrations; why are those never reported in all available NIPT studies? Conclusions Even though much research has been published on NIPT, unbiased figures concerning NIPT and first trimester screening efficacy are yet not available. While false positive rates of different NIPT tests maybe halfway accurate, reported false-negative rates are most likely too low. The latter is as NIPT-cases with negative results for tested conditions are yet not in detail followed up for cases with other genetic or teratogenic caused disorders. This promotes an image in public, that NIPT is suited to replace all invasive tests, and also to solve the problem of inborn errors in humans, if not now then in near future. Overall, it is worth discussing the usefulness of NIPT in practical clinical application. Particularly, asking for unbiased figures concerning the efficacy of first trimester-screening compared to NIPT, and for really comprehensive data on false-positive and false-negative NIPT results. Supplementary Information The online version contains supplementary material available at 10.1186/s13039-022-00612-2.

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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On the occurrence of false positives in tests of migration under an isolation-with-migration model.

Publications

On the occurrence of false positives in tests of migration under an isolation-with-migration model.

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