Running Powermarker

This program is appropriate for evaluating how informative your markers are in the context of your data set. PowerMarker handles haplotypic or diplotypic data
Before you install PowerMarker you need to install Microsoft.NET framework redistributable.
Download it from the link and install it onto your machine.

LINK: Download POWERMARKER here.


Powermarker Introduction

PowerMarker is a comprehensive set of statistical methods for genetic marker data analysis, designed especially for SSR/SNP data analysis. PowerMarker builds a powerful user interface around both new and traditional statistical methods for population genetic analysis.

Summary statistics

  • Compute sample size
  • Compute number of observation
  • Compute allele number
  • Compute availability (1 - missing proportion)
  • Compute gene diversity using biased or unbiased version
  • Compute polymorphism information content
  • Compute heterozygosity
  • Compute stepwise mutation index which was defined as the maximal proportion of alleles which follow stepwise mutation pattern
  • Compute moment estimator or maximum likelihood estimator of within-population inbreeding coefficient
  • Summarize result at any level
  • Bootstrap across loci to estimate confidence intervals
  • Estimate allele frequency and its variance
  • Bootstrap across individual to estimate confidence interval
  • Estimate genotype frequency and allele covariance
  • Bootstrap across individual to estimate confidence interval
  • Estimate haplotype frequency using EM algorithm
  • Estimate haplotype frequency using BisectionEM algorithm
  • Estimate haplotype frequency using TrioEM algorithm
  • Assign haplotype probabilities for each individual
  • Test Hardy-Weinberg equilibrium by ChiSquare test
  • Test Hardy-Weinberg equilibrium by likelihood ratio test
  • Test Hardy-Weinberg equilibrium by Exact test
  • Compute Hardy-Weinberg disequilibrium statistics
  • Bootstrap across individual to estimate confidence interval for Hardy-Weinberg disequilibrium statistics
  • Estimate linkage disequilibrium D
  • Estimate D'
  • Estimate RSquare
  • Estimate population attributable risk
  • Estimate proportional difference
  • Estimate Yule's Q
  • Estimate two-loci haplotype frequency for computing LD statistics
  • Test two-loci linkage equilibrium by ChiSquare test
  • Test two-loci linkage equilibrium by Exact test
  • Test multi-loci linkage equilibrium by Exact test
  • Prepare 2D matrix for 2D plot

Population structure

  • Estimate population structure with admixture
  • Estimate population structure without admixture
  • Estimate classic coancestry matrix
  • Estimate population specific coancestry matrix
  • Estimate classic two-level F-statistics assuming Hardy-Weinberg equilibrium
  • Estimate classic two-level F-statistics considering inbreeding
  • Estimate classic three-level F-statistics assuming Hardy-Weinberg equilibrium
  • Estimate classic three-level F-statistics considering inbreeding
  • Estimate population specific two-level F-statistics assuming Hardy-Weinberg equilibrium
  • Estimate population specific two-level F-statistics considering inbreeding
  • Bootstrap across loci to estimate confidence interval

Phylogenetic analysis

  • Estimate frequency from DataSet
  • Estimate distance based Frequency data using 19 different methods
  • Construct UPGMA tree
  • Construct NJ tree
  • Bootstrap across loci to construct multiple trees for tree consensus

Association study

  • Allele test
  • Genotype test
  • Trend test
  • Distance test
  • Exact test
  • Genotype based F-test
  • Haplotype trend regression for binary and quantitative traits

Design

  • Choose core set of lines by allele number, allelic diversity, allelic entropy. Selection can be done with simulated annealing, random search or exhaustive search under general constrains
  • Choose haplotype tagging markers from haplotype data
  • Choose haplotype tagging markers from genotype data
  • Choose haplotype tagging markers from trio data

Tools

  • Mantel test
  • Contigency table analysis
  • SNP identification from sequences
  • Parse Structure's result
  • SNP simulation under coalescence model
  • SNP simulation under coalescence model with recombination hotspots