scholarly journals Advances and limits of using population genetics to understand local adaptation

Author(s):  
Peter Tiffin ◽  
Jeffrey Ross-Ibarra

Local adaptation is an important process shaping within species diversity. In recent years, population genetic analyses, which complement organismal approaches in advancing our understanding of local adaptation have become widespread. Here we focus on using population genetics to address some key questions in local adaptation: What traits are involved? What environmental variables are most important? Does local adaptation target the same genes in related species? Do loci responsible for local adaptation exhibit tradeoffs across environments? After discussing these questions we highlight important limitations to population genetic analyses including challenges with obtaining high quality data, deciding which loci are targets of selection, and limits to identifying the genetic basis of local adaptation.

Author(s):  
Peter Tiffin ◽  
Jeffrey Ross-Ibarra

Local adaptation is an important process shaping within species diversity. In recent years, population genetic analyses, which complement organismal approaches in advancing our understanding of local adaptation have become widespread. Here we focus on using population genetics to address some key questions in local adaptation: What traits are involved? What environmental variables are most important? Does local adaptation target the same genes in related species? Do loci responsible for local adaptation exhibit tradeoffs across environments? After discussing these questions we highlight important limitations to population genetic analyses including challenges with obtaining high quality data, deciding which loci are targets of selection, and limits to identifying the genetic basis of local adaptation.


Author(s):  
Asher D. Cutter

Collections of DNA from nature for many individuals and loci give us the raw material for studying evolution at the molecular level. Chapter 9, “Case studies in molecular population genetics: genotype to phenotype to selection,” dives into several case studies of exciting real-world organisms that demonstrate the application from A to Z of the concepts developed throughout the book. It includes summaries of the natural context for each organism, ranging from armoring in fish (Eda, Pitx1) and color crypsis in mice (Mc1r) to butterfly flight ability (Pgi) and toxin metabolism in Drosophila fruit flies (Cyp6g1, Adh), then walks through the molecular data, their visualization, and their analysis. Complications and caveats to real-world analysis are discussed for how to identify demographic and selective effects in empirical datasets. The approaches include both candidate gene studies and genome scans, and show how different molecular population genetic analyses work in concert with one another. These population genetic analyses also can dovetail with functional molecular genetic experiments and with genetic mapping using crosses or genome-wide association study analysis. Chapter 9 ends by introducing a summary of several advanced topics in molecular population genetics, including concepts and tests for selection on standing variation, the genomic scale of data computation and evolutionary modelling, and connections to human evolution.


Author(s):  
Katharine L Korunes ◽  
Kieran Samuk

AbstractPopulation genetic analyses often use summary statistics to describe patterns of genetic variation and provide insight into evolutionary processes. Among the most fundamental of these summary statistics are π and dXY, which are used to describe genetic diversity within and between populations, respectively. Here, we address a widespread issue in π and dXY calculation: systematic bias generated by missing data of various types. Many popular methods for calculating π and dXY operate on data encoded in the Variant Call Format (VCF), which condenses genetic data by omitting invariant sites. When calculating π and dXY using a VCF, it is often implicitly assumed that missing genotypes (including those at sites not represented in the VCF) are homozygous for the reference allele. Here, we show how this assumption can result in substantial downward bias in estimates of π and dXY that is directly proportional to the amount of missing data. We discuss the pervasive nature and importance of this problem in population genetics, and introduce a user-friendly UNIX command line utility, pixy, that solves this problem via an algorithm that generates unbiased estimates of π and dXY in the face of missing data. We compare pixy to existing methods using both simulated and empirical data, and show that pixy alone produces unbiased estimates of π and dXY regardless of the form or amount of missing data. In sum, our software solves a long-standing problem in applied population genetics and highlights the importance of properly accounting for missing data in population genetic analyses.


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