Landscape Genetics & Connectivity

Genomic Basis of Fitness & Adaptation

Disease Ecology & Genetics

Conservation Genetics

DNA Markers & Technologies

Computational Statistics & Software

 

Development of a Population Genomics Analysis Pipeline (PGAP) to Monitor Genetic Change at Neutral and Adaptive Genes

Summary:

Novel, well-evaluated computation methods can greatly advanced conservation and population genetics. Unfortunately, the statistical performance of many methods is unknown when dealing with vast amounts of multi-locus data and with data from natural population. Much of our research has focused on 'bridging the gap' between theory and application of statistical methods and estimators. This is becoming more difficult as genetic data sets are increasing in size and complexity faster than methods of data analysis can increase in speed, efficiency, and reliability.

We use simulations and empirical evaluations the power, accuracy, and precision of a new and existing computational methods and software designed to detect genetic change and molecular adaptation in natural populations. This will allow us to understand interactions among mechanisms underlying microevolutionary change (genetic drift, gene flow, and selection) and provide the scientific community with the means to conduct population genomics research as efficiently and effectively as possible.

With collaborators, we develop and evaluate Approximate Bayesian methods to estimate and monitor effective population size and to identify adaptive molecular variation. The methods will be applied to existing and new SNP data on domestic and wild fish and wildlife. The methods and software will allow researchers to evaluate the power and reliability of genetic monitoring methods to detect change in genetic parameters using simulated or real genetic marker data sets, and to address new questions not previously feasible.

    Related projects:
  • Software for simulating complex demography and population genetic scenarios
  • Software for simulating spatially explicit landscape genetics scenarios
  • Effective population size and loss of genetic variation in large mammals: Effects of age structure, population fluctuations, and mating system.
Flow chart of steps in NewAge population simulation program to assess effects of demography, life history, and management on genetic variation and performance of new Ne estimators.

There is increasing need to estimate the effective population size (Ne) and rates of loss of genetic variation in natural populations, especially of large mammals and fish that are becoming increasingly isolated around the world. We calculated Ne and rates of loss of heterozygosity and allelic diversity in bison (Bos bison) from Yellowstone National Park using computer simulations of age structured populations with detailed demographic data on age- and sex-specific vital rates. We used a new computer program (NewAge; right) to simulate stable or fluctuating populations (lambda from 1.02 to 1.12), under a range of culling strategies (random, young, or adults only), a wide range of variance in male reproductive success, and for loci with allelic richness ranging from 2 to 20 alleles per locus.

 

Graph showing loss of allelic diversity (AD) (proportion remaining) for a stable population of bison of size Nc = 2,000 for three initial allelic richness values (2 alleles per locus shown by solid lines; 5 alleles, dashed lines; and 20 alleles shown by dotted lines). Horizontal grey dotted line represents the 95% threshold. This is among the most realistic individual-based simulation to date, because it includes age and sex specific vital rates, population size fluctuations, and realistic mating system with male dominance and high variance in male reproductive success (VMRS). The realistic model gives a far more rapid rate of loss of alleles than a traditional Wright-Fisher (ideal) population model: 20 alleles is represented by a grey solid line. From Pérez-Figueroa et al. (in review).

 

Collaborators:

T. Antao, E. Landguth, T. Cozart, A. Pérez-Figueroa, A. M. Beaumont, A. Beja-Pereira, P. England, K. McKelvey, D. Tallmon, M. Schwartz, F. Allendorf, R. Waples, and others...

 

Publications: