Joint Likelihood-free Inference of Number of Selected SNPs and Selection Coefficient in an Evolving Population
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
Austrian and Slovenian Statistical Days 2022
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
The developments of software tools and methodologies in likelihood- free inference have brought the great potential to the inferential study in the field of population genetics. Approximate Bayesian Computation (ABC), is one of the most popular likelihood-free methods used to infer the parameters when the likelihood function is intractable. A common interest in the field of population genetics is the selection coefficients inference. Some approaches look at the data of the current state, but temporal data will capture more information about the evolutionary forces. To our best knowledge, there has been no detailed investigation of methodology that infers the number of selected SNPs from temporal data. In this work, we present an application of Simulated Annealing ABC (SABC) in inferring the number of selected SNPs and the corresponding selection coefficients from temporal allele frequencies data with the summary statistics providing information for the parameters we are interested in. Also, the discrepancy is measured by adaptive $\ell_1$ penalized logistics classification. We show that our method can accurately estimate the value of the selection coefficient and the number of selected targets across different population parameters and also provides uncertainty quantification. Comparison with numbers of existing methods like WFABC \citep{foll2015wfabc} and CLEAR\citep{iranmehr2017clear} is presented.
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