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The annals of applied statistics2015; 8(4); 2203-2222; doi: 10.1214/14-aoas764

A Novel Spectral Method for Inferring General Diploid Selection from Time Series Genetic Data.

Abstract: The increased availability of time series genetic variation data from experimental evolution studies and ancient DNA samples has created new opportunities to identify genomic regions under selective pressure and to estimate their associated fitness parameters. However, it is a challenging problem to compute the likelihood of non-neutral models for the population allele frequency dynamics, given the observed temporal DNA data. Here, we develop a novel spectral algorithm to analytically and efficiently integrate over all possible frequency trajectories between consecutive time points. This advance circumvents the limitations of existing methods which require fine-tuning the discretization of the population allele frequency space when numerically approximating requisite integrals. Furthermore, our method is flexible enough to handle general diploid models of selection where the heterozygote and homozygote fitness parameters can take any values, while previous methods focused on only a few restricted models of selection. We demonstrate the utility of our method on simulated data and also apply it to analyze ancient DNA data from genetic loci associated with coat coloration in horses. In contrast to previous studies, our exploration of the full fitness parameter space reveals that a heterozygote-advantage form of balancing selection may have been acting on these loci.
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Publication Date: 2015-01-20 PubMed ID: 25598858PubMed Central: PMC4295721DOI: 10.1214/14-aoas764Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This study introduces a novel spectral algorithm which is capable of analytically integrating over all frequency trajectories from consecutive time points in the context of time series genetic variation data. The study highlights the flexibility and efficiency of this method in relation to past methods, demonstrating how it can be used to determine fitness parameters associated with selection in diploid models.

Objective of the Research

  • The study is aimed at narrating how a new spectral algorithm dissects time series genetic variation data to identify genomic regions under selective pressure and estimate their associated fitness parameters.
  • The researchers’ objective is to overcome computational challenges that arise with non-neutral models when attempting to compute the population allele frequency dynamics likelihood given temporary DNA data.

Spectral Algorithm Development

  • The study develops a spectral algorithm that integrates across all possible frequency trajectories between time sequence points. This advancement addresses limitations with existing methods that often called for adjustments in the discretization of population allele frequency space when numerically approximating integrals required.
  • The key benefit of this alternative method is its flexibility to comfortably handle general diploid models of selection. With these models, the fitness parameters for heterozygotes and homozygotes are able to take any value.

Utility of the New Method

  • The utility of this new method is demonstrated using simulated data, highlighting its effectiveness compared to previous methods which were limited to evaluating certain models of selection.
  • The authors applied this method to ancient DNA data from horse genetic loci associated with coat coloration. This analysis revealed that a form of balancing selection favoring heterozygotes may likely have influenced these loci, a finding that contrasts with previous studies.

Conclusions

  • The findings of the research underscore the potential of the spectral algorithm in identifying genomic regions under selective pressure and estimating related fitness parameters.
  • The usage of this method facilitates a more specific analysis of genetic selection models, offering unique insights into the dynamics of genetic variation.

Cite This Article

APA
Steinrücken M, Bhaskar A, Song YS. (2015). A Novel Spectral Method for Inferring General Diploid Selection from Time Series Genetic Data. Ann Appl Stat, 8(4), 2203-2222. https://doi.org/10.1214/14-aoas764

Publication

The annals of applied statistics
ISSN: 1932-6157
NlmUniqueID: 101479511
Country: United States
Language: English
Volume: 8
Issue: 4
Pages: 2203-2222

Researcher Affiliations

Steinrücken, Matthias
  • University of California, Berkeley.
Bhaskar, Anand
  • University of California, Berkeley.
Song, Yun S
  • University of California, Berkeley.

Grant Funding

  • R01 GM094402 / NIGMS NIH HHS

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