Gordon Luikart

Bio: Gordon Luikart is an academic researcher from University of Montana. The author has contributed to research in topics: Population & Effective population size. The author has an hindex of 72, co-authored 193 publications receiving 37564 citations. Previous affiliations of Gordon Luikart include Joseph Fourier University & Centre national de la recherche scientifique.

Description and Power Analysis of Two Tests for Detecting Recent Population Bottlenecks from Allele Frequency Data

Abstract: When a population experiences a reduction of its effective size, it generally develops a heterozygosity excess at selectively neutral loci, i.e., the heterozygosity computed from a sample of genes is larger than the heterozygosity expected from the number of alleles found in the sample if the population were at mutation drift equilibrium. The heterozygosity excess persists only a certain number of generations until a new equilibrium is established. Two statistical tests for detecting a heterozygosity excess are described. They require measurements of the number of alleles and heterozygosity at each of several loci from a population sample. The first test determines if the proportion of loci with heterozygosity excess is significantly larger than expected at equilibrium. The second test establishes if the average of standardized differences between observed and expected heterozygosities is significantly different from zero. Type I and II errors have been evaluated by computer simulations, varying sample size, number of loci, bottleneck size, time elapsed since the beginning of the bottleneck and level of variability of loci. These analyses show that the most useful markers for bottleneck detection are those evolving under the infinite allele model (IAM) and they provide guidelines for selecting sample sizes of individuals and loci. The usefulness of these tests for conservation biology is discussed.

Landscape genetics: combining landscape ecology and population genetics

Abstract: Understanding the processes and patterns of gene flow and local adaptation requires a detailed knowledge of how landscape characteristics structure populations. This understanding is crucial, not only for improving ecological knowledge, but also for managing properly the genetic diversity of threatened and endangered populations. For nearly 80 years, population geneticists have investigated how physiognomy and other landscape features have influenced genetic variation within and between populations. They have relied on sampling populations that have been identified beforehand because most population genetics methods have required discrete populations. However, a new approach has emerged for analyzing spatial genetic data without requiring that discrete populations be identified in advance. This approach, landscape genetics, promises to facilitate our understanding of how geographical and environmental features structure genetic variation at both the population and individual levels, and has implications for ecology, evolution and conservation biology. It differs from other genetic approaches, such as phylogeography, in that it tends to focus on processes at finer spatial and temporal scales. Here, we discuss, from a population genetic perspective, the current tools available for conducting studies of landscape genetics.

Conservation and the genetics of populations

Abstract: Authors of Guest Boxes. Preface. List of Symbols. PART I: INTRODUCTION. 1 Introduction. 1.1 Genetics and conservation. 1.2 What should we conserve?. 1.3 How should we conserve biodiversity?. 1.4 Applications of genetics to conservation. Guest Box 1 by L. S. Mills and M. E. Soule: The role of genetics in conservation. 2 Phenotypic Variation in Natural Populations. 2.1 Color pattern. 2.2 Morphology. 2.3 Behavior. 2.4 Differences among populations. Guest Box 2 by C. J. Foote: Looks can be deceiving: countergradient variation in secondary sexual color in sympatric morphs of sockeye salmon. 3 Genetic Variation in Natural Populations: Chromosomes and Proteins. 3.1 Chromosomes. 3.2 Protein electrophoresis. 3.3 Genetic variation within populations. 3.4 Genetic divergence among populations. 3.5 Strengths and limitations of protein electrophoresis. Guest Box 3 by A. Young and B. G. Murray: Management implications of polploidy in a cytologically complex self-incompatible herb. 4 Genetic Variation in Natural Populations: DNA. 4.1 Mitochondrial and chloroplast DNA. 4.2 Single copy nuclear loci. 4.3 Multilocus techniques. 4.4 Sex-linked markers. 4.5 DNA sequences. 4.6 Additional techniques and the future. 4.7 Genetic variation in natural populations. Guest Box 4 by N. N. FitzSimmons: Multiple markers uncover marine turtle behavior. PART II: MECHANISMS OF EVOLUTIONARY CHANGE. 5 Random Mating Populations: Hardy-Weinberg Principle. 5.1 The Hardy-Weinberg principle. 5.2 Hardy-Weinberg proportions. 5.3 Testing for Hardy-Weinberg proportions. 5.4 Estimation of allele frequencies. 5.5 Sex-linked loci. 5.6 Estimation of genetic variation. Guest Box 5 by V. Castric and L. Bernatchez: Testing alternative explanations for deficiencies of heterozygotes in populations of brook trout in small lakes. 6 Small Populations and Genetic Drift. 6.1 Genetic drift. 6.2 Changes in allele frequency. 6.3 Loss of genetic variation: the inbreeding effect of small populations. 6.4 Loss of allelic diversity. 6.5 Founder effect. 6.6 Genotypic proportions in small populations. 6.7 Fitness effects of genetic drift. Guest Box 6 by P. L. Leberg and D. L. Rogowski: The inbreeding effect of small population size reduces population growth rate in mosquitofish. 7 Effective Population Size. 7.1 Concept of effective population size. 7.2 Unequal sex ratio. 7.3 Nonrandom number of progeny. 7.4 Fluctuating population size. 7.5 Overlapping generations. 7.6 Variance effective population size. 7.7 Cytoplasmic genes. 7.8 Gene genealogies and lineage sorting. 7.9 Limitations of effective population size. 7.10 Effective population size in natural populations. Guest Box 7 by C. R. Miller and L. P. Waits: Estimation of effective population size in Yellowstone grizzly bears. 8 Natural Selection. 8.1 Fitness. 8.2 Single locus with two alleles. 8.3 Multiple alleles. 8.4 Frequency-dependent selection. 8.5 Natural selection in small populations. 8.6 Natural selection and conservation. Guest Box 8 by C. A. Stockwell and M. L. Collyer: Rapid adaptation and conservation. 9 Population Subdivision. 9.1 F-statistics. 9.2 Complete isolation. 9.3 Gene flow. 9.4 Gene flow and genetic drift. 9.5 Cytoplasmic genes and sex-linked markers. 9.6 Gene flow and natural selection. 9.7 Limitations of FST and other measures of subdivision. 9.8 Estimation of gene flow. 9.9 Population subdivision and conservation. Guest Box 9 by C. S. Baker and F. B. Pichler: Hector's dolphin population structure and conservation. 10 Multiple Loci. 10.1 Gametic disequilibrium. 10.2 Small population size. 10.3 Natural selection. 10.4 Population subdivision. 10.5 Hybridization. 10.6 Estimation of gametic disequilibrium. Guest Box 10 by S. H. Forbes: Dating hybrid populations using gametic disequilibrium. 11 Quantitative Genetics. 11.1 Heritability. 11.2 Selection on quantitative traits. 11.3 Quantitative trait loci (QTLs). 11.4 Genetic drift and bottlenecks. 11.5 Divergence among populations (QST). 11.6 Quantitative genetics and conservation. Guest Box 11 by D. W. Coltman: Response to trophy hunting in bighorn sheep. 12 Mutation. 12.1 Process of mutation. 12.2 Selectively neutral mutations. 12.3 Harmful mutations. 12.4 Advantageous mutations. 12.5. Recovery from a bottleneck. Guest Box 12 by M. W. Nachman: Color evolution via different mutations in pocket mice. PART III: GENETICS AND CONSERVATION. 13 Inbreeding Depression. 13.1 Pedigree analysis. 13.2 Gene drop analysis. 13.3 Estimation of F and relatedness with molecular markers. 13.4 Causes of inbreeding depression. 13.5 Measurement of inbreeding depression. 13.6 Genetic load and purging. Guest Box 13 by R. C. Lacy: Understanding inbreeding depression: 20 years of experiments with Peromyscus mice. 14 Demography and Extinction. 14.1 Estimation of population size. 14.2 Inbreeding depression and extinction. 14.3 Population viability analysis. 14.4 Loss of phenotypic variation. 14.5 Loss of evolutionary potential. 14.6 Mitochondrial DNA. 14.7 Mutational meltdown. 14.8 Long-term persistence. 14.9 The 50/500 rule. Guest Box 14 by A. C. Taylor: Noninvasive population size estimation in wombats. 15 Metapopulations and Fragmentation. 15.1 The metapopulation concept. 15.2 Genetic variation in metapopulations. 15.3 Effective population size. 15.4 Population divergence and fragmentation. 15.5 Genetic rescue. 15.6 Long-term population viability. Guest Box 15 by R. C. Vrijenhoek: Fitness loss and genetic rescue in stream-dwelling topminnows. 16 Units of Conservation. 16.1 What should we try to protect?. 16.2 Systematics and taxonomy. 16.3 Phylogeny reconstruction. 16.4 Description of genetic relationships within species. 16.5 Units of conservation. 16.6 Integrating genetic, phenotypic, and environmental information. Guest Box 16 by R. S. Waples: Identifying conservation units in Pacific salmon. 17 Hybridization. 17.1 Natural hybridization. 17.2 Anthropogenic hybridization. 17.3 Fitness consequences of hybridization. 17.4 Detecting and describing hybridization. 17.5 Hybridization and conservation. Guest Box 17 by L. H. Rieseberg: Hybridization and the conservation of plants. 18 Conservation Breeding and Restoration. 18.1 The role of conservation breeding. 18.2 Reproductive technologies and genome banking. 18.3 Founding populations for conservation breeding programs. 18.4 Genetic drift in captive populations. 18.5 Natural selection and adaptation to captivity. 18.6 Genetic management of conservation breeding programs. 18.7 Supportive breeding. 18.8 Reintroductions and translocations. Guest Box 18 by J. V. Briskie: Effects of population bottlenecks on introduced species of birds. 19 Invasive Species. 19.1 Why are invasive species so successful?. 19.2 Genetic analysis of introduced species. 19.3 Establishment and spread of invasive species. 19.4 Hybridization as a stimulus for invasiveness. 19.5 Eradication, management, and control. Guest Box 19 by J. L. Maron: Rapid adaptation of invasive populations of St John's Wort. 20 Forensic and Management Applications of Genetic Identification. 20.1 Species identification. 20.2 Individual identification and probability of identity. 20.3 Parentage testing. 20.4 Sex identification. 20.5 Population assignment. 20.6 Population composition analysis. Guest Box 20 by L. P. Waits: Microsatellite DNA genotyping identifies problem bear and cubs. Glossary. Appendix: Probability and Statistics. Guest Box A by J. F. Crow: Is mathematics necessary?. References. Index

Distortion of allele frequency distributions provides a test for recent population bottlenecks

Abstract: We use population genetics theory and computer simulations to demonstrate that population bottlenecks cause a characteristic mode-shift distortion in the distribution of allele frequencies at selectively neutral loci. Bottlenecks cause alleles at low frequency ( .80) to detect an allele frequency distortion after a bottleneck of < or = 20 breeding individuals when 8 to 10 polymorphic microsatellite loci are analyzed.

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

Abstract: 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.

Arlequin (version 3.0): An integrated software package for population genetics data analysis

Abstract: Arlequin ver 3.0 is a software package integrating several basic and advanced methods for population genetics data analysis, like the computation of standard genetic diversity indices, the estimation of allele and haplotype frequencies, tests of departure from linkage equilibrium, departure from selective neutrality and demographic equilibrium, estimation or parameters from past population expansions, and thorough analyses of population subdivision under the AMOVA framework. Arlequin 3 introduces a completely new graphical interface written in C++, a more robust semantic analysis of input files, and two new methods: a Bayesian estimation of gametic phase from multi-locus genotypes, and an estimation of the parameters of an instantaneous spatial expansion from DNA sequence polymorphism. Arlequin can handle several data types like DNA sequences, microsatellite data, or standard multi-locus genotypes. A Windows version of the software is freely available on http://cmpg.unibe.ch/software/arlequin3.

Human biochemical genetics

Abstract: 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

Primer3—new capabilities and interfaces

Abstract: Polymerase chain reaction (PCR) is a basic molecular biology technique with a multiplicity of uses, including deoxyribonucleic acid cloning and sequencing, functional analysis of genes, diagnosis of diseases, genotyping and discovery of genetic variants. Reliable primer design is crucial for successful PCR, and for over a decade, the open-source Primer3 software has been widely used for primer design, often in high-throughput genomics applications. It has also been incorporated into numerous publicly available software packages and web services. During this period, we have greatly expanded Primer3’s functionality. In this article, we describe Primer3’s current capabilities, emphasizing recent improvements. The most notable enhancements incorporate more accurate thermodynamic models in the primer design process, both to improve melting temperature prediction and to reduce the likelihood that primers will form hairpins or dimers. Additional enhancements include more precise control of primer placement—a change motivated partly by opportunities to use whole-genome sequences to improve primer specificity. We also added features to increase ease of use, including the ability to save and re-use parameter settings and the ability to require that individual primers not be used in more than one primer pair. We have made the core code more modular and provided cleaner programming interfaces to further ease integration with other software. These improvements position Primer3 for continued use with genome-scale data in the decade ahead.