GSBS Student Publications


Recent Progress in Polymorphism-Based Population Genetic Inference

Student Author(s)

Jessica Crisci; Angela Bean; Alfred Simkin

GSBS Program

Bioinformatics & Computational Biology

UMMS Affiliation

Program in Bioinformatics and Integrative Biology



Document Type


Medical Subject Headings

Genetics, Population; Polymorphism, Genetic; Data Interpretation, Statistical


Computational Biology | Evolution | Other Genetics and Genomics | Population Biology


The recent availability of whole-genome sequencing data affords tremendous power for statistical inference. With this, there has been great interest in the development of polymorphism-based approaches for the estimation of population genetic parameters. These approaches seek to estimate, for example, recently fixed or sweeping beneficial mutations, the rate of recurrent positive selection, the distribution of selection coefficients, and the demographic history of the population. Yet despite estimating similar parameters using similar data sets, results between methodologies are far from consistent. We here summarize the current state of the field, compare existing approaches, and attempt to reconcile emerging discrepancies. We also discuss the biases in selection estimators introduced by ignoring the demographic history of the population, discuss the biases in demographic estimators introduced by assuming neutrality, and highlight the important challenge to the field of achieving a true joint estimation procedure to circumvent these confounding effects.


Citation: Crisci JL, Poh YP, Bean A, Simkin A, Jensen JD. Recent Progress in Polymorphism-Based Population Genetic Inference. J Hered. (March-April 2012) 103(2):287-296. doi: 10.1093/jhered/esr128

Related Resources

Link to article in PubMed


Bayesian statistics, demography, likelihood estimation, positive selection