AUTHOR=Crawford Jacob E., Lazzaro Brian P.

TITLE=Assessing the Accuracy and Power of Population Genetic Inference from Low-Pass Next-Generation Sequencing Data

JOURNAL=Frontiers in Genetics

VOLUME=Volume 3 - 2012

YEAR=2012

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2012.00066

DOI=10.3389/fgene.2012.00066

ISSN=1664-8021

ABSTRACT=Next-generation sequencing technologies have made it possible to address principle population genetic questions in almost any system, but high error rates associated with this data can introduce significant biases into downstream analyses, so careful consideration of experimental design and interpretation in essential in studies based on short-read sequencing.  Exploration of population genetic analyses based on next-generation sequencing, has revealed some of the potential biases, but previous work has emphasized human population genetics, and further examination of parameters relevant to other systems is necessary, including when sample sizes are small and genetic variation is high.  To assess experimental power to address several principal objectives of population genetic studies under these conditions, we simulated population samples under selective sweep, population growth, and population subdivision models and tested the power to recover the correct model from sequence polymorphism data inferred from 4x, 8x, and 15x short-read data.  We found that estimates of population genetic differentiation and population growth parameters were systematically biased when inference was based on 4x sequencing, but biases were markedly reduced at even 8x read depth.  We also found that the power to identify footprints of positive selection depends on an interaction between read depth and the strength of selection, with strong selection being recovered consistently at all read depths, but weak selection requiring deeper read depths for reliable detection.  Although we have only explored a small subset of the many possible experimental designs, population genetic models and SNP calling approaches, our results reveal some general patterns and provide some assessment of what biases could be expected under similar experimental structures.