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Detecting selection using time-series data of allele frequencies with multiple independent reference Loci.

Nishino J - G3 (Bethesda) (2013)

Bottom Line: Recently, in 2013 Feder et al. proposed the frequency increment test (FIT), which evaluates natural selection at a single diallelic locus by the use of time-series data of allele frequencies.Here, we expand upon the FIT by introducing a test that explicitly allows for changes in population size by using information from independent reference loci.Various demographic models suggest that our proposed test is unbiased irrespective of fluctuations in population size when sampling noise can be ignored and that it has greater power to detect selection than the FIT if sufficient reference loci are used.

View Article: PubMed Central - PubMed

Affiliation: Center for Information Biology and DNA Data Bank of Japan, National Institute of Genetics, Research Organization of Information and Systems, Mishima, Shizuoka 411-8540, Japan.

ABSTRACT
Recently, in 2013 Feder et al. proposed the frequency increment test (FIT), which evaluates natural selection at a single diallelic locus by the use of time-series data of allele frequencies. This test is unbiased under conditions of constant population size and no sampling noise. Here, we expand upon the FIT by introducing a test that explicitly allows for changes in population size by using information from independent reference loci. Various demographic models suggest that our proposed test is unbiased irrespective of fluctuations in population size when sampling noise can be ignored and that it has greater power to detect selection than the FIT if sufficient reference loci are used.

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Related in: MedlinePlus

The effects of sampling error on the power of the FITR. The powers of the FITR under various sampling regimes are shown as functions of the selection coefficients for demographic models 3 and 5. All, or 5000, 1000, 500, or 100 individuals in a population were assumed to be sampled. Sampling was assumed to be conducted by binomial sampling at each (R + 1) locus and at each (L + 1) time point. Each point corresponds to the power obtained by 100,000 simulations at the 5% significance level. The number of reference loci, the duration of sampling time, and the number of sampled points were R = 10, T = 1000, and (L + 1) = 3 (upper graphs) or 11 (lower graphs), respectively. The intervals between any two adjacent sampled points were the same at Δt = ti–ti-1 = 500 (top graphs) or 100 (bottom graphs) (i = 1,2,…,L). The initial frequency for all  loci, , were assumed to be 0.5.
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fig5: The effects of sampling error on the power of the FITR. The powers of the FITR under various sampling regimes are shown as functions of the selection coefficients for demographic models 3 and 5. All, or 5000, 1000, 500, or 100 individuals in a population were assumed to be sampled. Sampling was assumed to be conducted by binomial sampling at each (R + 1) locus and at each (L + 1) time point. Each point corresponds to the power obtained by 100,000 simulations at the 5% significance level. The number of reference loci, the duration of sampling time, and the number of sampled points were R = 10, T = 1000, and (L + 1) = 3 (upper graphs) or 11 (lower graphs), respectively. The intervals between any two adjacent sampled points were the same at Δt = ti–ti-1 = 500 (top graphs) or 100 (bottom graphs) (i = 1,2,…,L). The initial frequency for all loci, , were assumed to be 0.5.

Mentions: The effects of sampling error on the type I error rate and power of the FITR are shown in Figure 5. In general, the effects of sampling error on the type I error rate were conservative. As expected, the power decreased as the number of sampled individuals increased. The degree of power reduction differed for different demographic models or values of L. This finding reflects that the power is influenced by the relative magnitudes of changes in allele frequencies at R + 1 loci and sampling errors. As L increased, the relative changes in allele frequencies to the sampling errors decreased. Thus, power was more reduced for larger L. Regarding the demographic models, the population size of Model 5 was larger than that of Model 1. Therefore, the relative changes in allele frequencies to the sampling errors were larger in Model 5, and the degree of power is large in Model 5.


Detecting selection using time-series data of allele frequencies with multiple independent reference Loci.

Nishino J - G3 (Bethesda) (2013)

The effects of sampling error on the power of the FITR. The powers of the FITR under various sampling regimes are shown as functions of the selection coefficients for demographic models 3 and 5. All, or 5000, 1000, 500, or 100 individuals in a population were assumed to be sampled. Sampling was assumed to be conducted by binomial sampling at each (R + 1) locus and at each (L + 1) time point. Each point corresponds to the power obtained by 100,000 simulations at the 5% significance level. The number of reference loci, the duration of sampling time, and the number of sampled points were R = 10, T = 1000, and (L + 1) = 3 (upper graphs) or 11 (lower graphs), respectively. The intervals between any two adjacent sampled points were the same at Δt = ti–ti-1 = 500 (top graphs) or 100 (bottom graphs) (i = 1,2,…,L). The initial frequency for all  loci, , were assumed to be 0.5.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3852378&req=5

fig5: The effects of sampling error on the power of the FITR. The powers of the FITR under various sampling regimes are shown as functions of the selection coefficients for demographic models 3 and 5. All, or 5000, 1000, 500, or 100 individuals in a population were assumed to be sampled. Sampling was assumed to be conducted by binomial sampling at each (R + 1) locus and at each (L + 1) time point. Each point corresponds to the power obtained by 100,000 simulations at the 5% significance level. The number of reference loci, the duration of sampling time, and the number of sampled points were R = 10, T = 1000, and (L + 1) = 3 (upper graphs) or 11 (lower graphs), respectively. The intervals between any two adjacent sampled points were the same at Δt = ti–ti-1 = 500 (top graphs) or 100 (bottom graphs) (i = 1,2,…,L). The initial frequency for all loci, , were assumed to be 0.5.
Mentions: The effects of sampling error on the type I error rate and power of the FITR are shown in Figure 5. In general, the effects of sampling error on the type I error rate were conservative. As expected, the power decreased as the number of sampled individuals increased. The degree of power reduction differed for different demographic models or values of L. This finding reflects that the power is influenced by the relative magnitudes of changes in allele frequencies at R + 1 loci and sampling errors. As L increased, the relative changes in allele frequencies to the sampling errors decreased. Thus, power was more reduced for larger L. Regarding the demographic models, the population size of Model 5 was larger than that of Model 1. Therefore, the relative changes in allele frequencies to the sampling errors were larger in Model 5, and the degree of power is large in Model 5.

Bottom Line: Recently, in 2013 Feder et al. proposed the frequency increment test (FIT), which evaluates natural selection at a single diallelic locus by the use of time-series data of allele frequencies.Here, we expand upon the FIT by introducing a test that explicitly allows for changes in population size by using information from independent reference loci.Various demographic models suggest that our proposed test is unbiased irrespective of fluctuations in population size when sampling noise can be ignored and that it has greater power to detect selection than the FIT if sufficient reference loci are used.

View Article: PubMed Central - PubMed

Affiliation: Center for Information Biology and DNA Data Bank of Japan, National Institute of Genetics, Research Organization of Information and Systems, Mishima, Shizuoka 411-8540, Japan.

ABSTRACT
Recently, in 2013 Feder et al. proposed the frequency increment test (FIT), which evaluates natural selection at a single diallelic locus by the use of time-series data of allele frequencies. This test is unbiased under conditions of constant population size and no sampling noise. Here, we expand upon the FIT by introducing a test that explicitly allows for changes in population size by using information from independent reference loci. Various demographic models suggest that our proposed test is unbiased irrespective of fluctuations in population size when sampling noise can be ignored and that it has greater power to detect selection than the FIT if sufficient reference loci are used.

Show MeSH
Related in: MedlinePlus