The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.

/pdf/detecting-the-number-of-clusters-of-individuals-using-the-16mlx2qlv8.pdf

Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study.

So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

Dominant markers such as amplified fragment length polymorphisms (AFLPs) provide an economical way of surveying variation at many loci. However, the uncertainty about the underlying genotypes presents a problem for statistical analysis. Similarly, the presence of null alleles and the limitations of genotype calling in polyploids mean that many conventional analysis methods are invalid for many organisms. Here we present a simple approach for accounting for genotypic ambiguity in studies of population structure and apply it to AFLP data from whitefish. The approach is implemented in the program structure version 2.2, which is available from http://pritch.bsd.uchicago.edu/structure.html.

/pdf/inference-of-population-structure-using-multilocus-genotype-19wwjas2e3.pdf

Inference of population structure using multilocus genotype data: dominant markers and null alleles

Massively parallel short-read sequencing technologies, coupled with powerful software platforms, are enabling investigators to analyse tens of thousands of genetic markers. This wealth of data is rapidly expanding and allowing biological questions to be addressed with unprecedented scope and precision. The sizes of the data sets are now posing significant data processing and analysis challenges. Here we describe an extension of the Stacks software package to efficiently use genotype-by-sequencing data for studies of populations of organisms. Stacks now produces core population genomic summary statistics and SNP-by-SNP statistical tests. These statistics can be analysed across a reference genome using a smoothed sliding window. Stacks also now provides several output formats for several commonly used downstream analysis packages. The expanded population genomics functions in Stacks will make it a useful tool to harness the newest generation of massively parallel genotyping data for ecological and evolutionary genetics.

/pdf/stacks-an-analysis-tool-set-for-population-genomics-3dmvyh33iq.pdf

Stacks: an analysis tool set for population genomics

The authors describe the best practices for a growing number of methods that use next-generation sequencing to rapidly discover and assess genetic markers across any genome, with applications from population genomics and quantitative trait locus mapping to marker-assisted selection.

Genome-wide genetic marker discovery and genotyping using next-generation sequencing.

Population genomics has the potential to improve studies of evolutionary genetics, molecular ecology and conservation biology, by facilitating the identification of adaptive molecular variation and by improving the estimation of important parameters such as population size, migration rates and phylogenetic relationships. There has been much excitement in the recent literature about the identification of adaptive molecular variation using the population-genomic approach. However, the most useful contribution of the genomics model to population genetics will be improving inferences about population demography and evolutionary history.

/pdf/the-power-and-promise-of-population-genomics-from-genotyping-12w0e4jatd.pdf

The power and promise of population genomics: from genotyping to genome typing

A series of important new theoretical, experimental and observational studies demonstrate that just a few immigrants can have positive immediate impacts on the evolutionary trajectory of local populations. In many cases, a low level of immigration into small populations has produced fitness benefits that are greater than those predicted by theoretical models, resulting in what has been termed 'genetic rescue'. However, the opposite result (reduced fitness) can also be associated with immigration of genetically divergent individuals. Central to our understanding of genetic rescue are complex interactions among fundamental concepts in evolutionary and population biology, including both genetic and non-genetic (environmental, behavioral and demographic) factors. Developing testable models to predict when genetic rescue is likely to occur is a daunting challenge that will require carefully controlled, multi-generation experiments as well as creative use of information from natural 'experiments'.

/pdf/the-alluring-simplicity-and-complex-reality-of-genetic-1pnvz55bkz.pdf

The alluring simplicity and complex reality of genetic rescue

Genetic rescue can increase the fitness of small, imperiled populations via immigration. A suite of studies from the past decade highlights the value of genetic rescue in increasing population fitness. Nonetheless, genetic rescue has not been widely applied to conserve many of the threatened populations that it could benefit. In this review, we highlight recent studies of genetic rescue and place it in the larger context of theoretical and empirical developments in evolutionary and conservation biology. We also propose directions to help shape future research on genetic rescue. Genetic rescue is a tool that can stem biodiversity loss more than has been appreciated, provides population resilience, and will become increasingly useful if integrated with molecular advances in population genomics.

Genetic rescue to the rescue

Population census size (NC) and effective population sizes (Ne) are two crucial parameters that influence population viability, wildlife management deci- sions, and conservation planning. Genetic estimators of both NC and Ne are increasingly widely used because molecular markers are increasingly available, statistical methods are improving rapidly, and genetic estimators complement or improve upon traditional demographic estimators. We review the kinds and applications of estimators of both NC and Ne, and the often undervalued and misunderstood ratio of effective-to-census size (Ne/NC). We focus on recently improved and well evalu- ated methods that are most likely to facilitate conservation. Finally, we outline areas of future research to improve Ne and NC estimation in wild populations.

https://www.fs.fed.us/rm/pubs_other/rmrs_2010_luikart_g001.pdf

Estimation of census and effective population sizes: the increasing usefulness of DNA-based approaches

Advances in molecular biology offer promise to the study of demographic characteristics of rare or hard-to-capture species, because individuals can now be identified through noninvasive sampling such as fecal collection or hair snags. However, individual genotyping using such methods currently leads to a novel problem that we call a ''shadow effect,'' because some animals not captured previously are believed to be recaptures due to their DNA profile being an indistinguishable shadow of previously captured animals. We evaluate the impact of the shadow effect on the two methods most commonly used in applied population ecology to estimate the size of closed populations: Lincoln-Petersen and multiple-recapture estimators in program CAPTURE. We find that the shadow effect can cause a negative bias in the estimates of both the number of different animals and the number of different genotypes. Furthermore, with Lincoln-Petersen estimators, the shadow effect can cause estimated confidence intervals to decrease even as bias increases. Because the bias arises from heterogeneity in apparent ''capture'' probabilities for animals with genetic shadows vs. those without, a model in program CAPTURE that is robust to capture heterogeneity (M h-jackknife) does not underestimate the number of genotypes in the population and only slightly underestimates the total number of individuals. As the shadow effect increases, CAPTURE is better able to correctly identify heterogeneity in capture probability and to pick Mh-jackknife, so that the higher levels of shadow effect have less bias than medium levels. The shadow effect will occur in all estimates of demographic rates (including sur- vival) that use DNA sampling to determine individual identity, but it can be minimized by increasing the number of individual loci sampled.

/pdf/estimating-animal-abundance-using-noninvasive-dna-sampling-4o21av4t92.pdf

David A. Tallmon

Papers

The power and promise of population genomics: from genotyping to genome typing

The alluring simplicity and complex reality of genetic rescue

Genetic rescue to the rescue

Estimation of census and effective population sizes: the increasing usefulness of DNA-based approaches

Estimating animal abundance using noninvasive dna sampling: promise and pitfalls