FST Calculator for Population Differentiation

Determine genetic differentiation between populations and understand the roles of DNA and RNA data.

FST Calculation Tool

What is the Fixation Index (FST) and How is it Calculated?

The Fixation Index, commonly known as FST, is a fundamental metric in population genetics that measures the degree of genetic differentiation between two or more populations. It quantifies what proportion of the total genetic variance within a species is due to differences in allele frequencies between its subpopulations. The core question many researchers face is: do you use DNA or RNA to calculate FST?

The answer is: you can use data derived from either DNA or RNA, but they tell you different things.

• DNA-based FST: This is the most common approach. Genetic variations (like SNPs – Single Nucleotide Polymorphisms) are identified directly from the organisms’ genomic DNA. This provides a comprehensive view of the entire genetic landscape and reflects the cumulative effects of evolutionary forces like mutation, genetic drift, and gene flow over long periods.
• RNA-based FST: This is calculated using data from RNA sequencing (RNA-Seq). Since RNA represents the genes that are actively being expressed (the transcriptome), an FST calculated this way reflects differentiation in gene *expression* levels. This can be influenced by both underlying genetic differences and short-term environmental pressures. It answers a slightly different question: not just “are the populations genetically different?” but “are the populations *functioning* differently at the genetic level?”

This calculator focuses on the mathematical principle, which is the same regardless of the data source. You provide the heterozygosity values, which you would have first calculated from your DNA or RNA dataset. For more information on this, see our article on SNP calling from NGS data.

The FST Formula and Explanation

The most common formula for FST is based on heterozygosity. Heterozygosity is a measure of genetic variation within a population, essentially the probability that two randomly chosen alleles at a given locus are different.

The formula is:

F_ST = (H_T – H_S) / H_T

This formula reveals the reduction in heterozygosity in subpopulations compared to the total population, which is a direct result of population structure. If there is no population structure (i.e., it’s one large, randomly mating population), H_T and H_S will be equal, and FST will be 0.

Variables in the FST Calculation

Variable	Meaning	Unit	Typical Range
HT	Total Heterozygosity	Unitless (Probability)	0 to 1
HS	Average Subpopulation Heterozygosity	Unitless (Probability)	0 to 1
FST	Fixation Index	Unitless (Proportion)	0 (no differentiation) to 1 (complete differentiation)

Interpreting FST Values

Sewall Wright, who developed the F-statistics, provided general guidelines for interpretation. While context is crucial, these values provide a useful starting point for understanding the level of genetic differentiation.

Wright’s FST Interpretation Guidelines

FST Value	Degree of Genetic Differentiation
0.00 – 0.05	Little or no differentiation
0.05 – 0.15	Moderate differentiation
0.15 – 0.25	Great differentiation
Above 0.25	Very great differentiation

For more detail, a GWAS power calculator can help determine the statistical power needed to detect associations in structured populations.

Practical Examples

Example 1: Moderate Differentiation in Plant Populations

A botanist is studying two populations of a wildflower, one on a mountainside and one in a valley. After sequencing DNA from 50 individuals in each population, they calculate the heterozygosity values.

• Inputs:
  • Total Heterozygosity (H_T): 0.45
  • Average Subpopulation Heterozygosity (H_S): 0.38
• Calculation:
  • FST = (0.45 – 0.38) / 0.45 = 0.07 / 0.45 ≈ 0.156
• Result: An FST of 0.156 indicates great genetic differentiation between the mountain and valley populations, likely due to limited gene flow.

Example 2: Low Differentiation in Human Populations

An anthropologist analyzes genetic data from two neighboring human villages to see if a river between them is a significant barrier to gene flow.

• Inputs:
  • Total Heterozygosity (H_T): 0.330
  • Average Subpopulation Heterozygosity (H_S): 0.325
• Calculation:
  • FST = (0.330 – 0.325) / 0.330 = 0.005 / 0.330 ≈ 0.015
• Result: An FST of 0.015 suggests very little genetic differentiation, meaning the river is not a major barrier and there is significant gene flow between the villages. This low value is typical for many human population comparisons.

How to Use This FST Calculator

This calculator simplifies the final step of determining the Fixation Index. Follow these steps:

1. Calculate Heterozygosities: First, you must process your genetic data (from DNA or RNA) using bioinformatics software (like VCFtools, PLINK, or Arlequin) to determine H_T and H_S for the populations and locus of interest.
2. Enter H_T: Input the calculated Total Heterozygosity into the first field. This value represents the expected heterozygosity if all your subpopulations were one single, large, freely-mixing group.
3. Enter H_S: Input the Average Subpopulation Heterozygosity. This is the mean of the expected heterozygosity calculated for each of your separate subpopulations.
4. Interpret the Results: The calculator instantly provides the FST value. Use the chart and the interpretation table above to understand the degree of differentiation it represents. An FST of 0 means the populations are genetically identical at that locus, while 1 means they share no alleles.

Understanding concepts like the Hardy-Weinberg Equilibrium is essential context for these calculations.

Key Factors That Affect FST

The FST value is not static; it is shaped by several evolutionary forces acting on populations. Understanding these factors is key to correctly interpreting what your FST value means.

• Genetic Drift: This is the random fluctuation of allele frequencies from one generation to the next, and it is a primary driver of differentiation. Smaller populations are more susceptible to drift, which tends to increase FST.
• Gene Flow (Migration): When individuals move between populations and interbreed, they homogenize allele frequencies. High rates of gene flow lead to lower FST values.
• Mutation: Mutation introduces new alleles into a population. While it is the ultimate source of all variation, its rate is generally slow and has a less immediate impact on FST compared to drift and gene flow.
• Natural Selection: If a certain allele is advantageous in one environment but not another (divergent selection), it will drive populations apart, increasing FST at that specific gene locus. Conversely, if an allele is universally advantageous (purifying or balancing selection), it can constrain differentiation.
• Population Size: Small, isolated populations tend to have higher FST values because genetic drift has a much stronger effect, leading to rapid divergence or fixation of alleles.
• Mating System: Inbreeding or assortative mating within populations can reduce observed heterozygosity, which can affect F-statistics and the overall population structure.

Frequently Asked Questions (FAQ)

1. Can I calculate FST from a single individual?

No. FST is a measure of differentiation between *populations*. You need to sample multiple individuals from at least two different populations to perform the calculation.

2. Why are my FST values different when using DNA vs. RNA data?

Because they measure different things. DNA reflects the entire genetic potential, while RNA reflects the genes actively being used. A high FST from RNA data might indicate a rapid adaptive response to different environmental conditions, even if the underlying DNA-based FST is low. For a deeper dive, consider an RNA-seq analysis.

3. What does a negative FST value mean?

Mathematically, a negative FST can occur if H_S is greater than H_T. This can happen due to sampling error or in cases where there is an excess of heterozygotes within subpopulations, possibly due to outbreeding or specific mating systems. For practical purposes, negative FST values are typically treated as zero.

4. Is FST calculated for a single gene or the whole genome?

Both. You can calculate a per-locus FST to see if a specific gene is under divergent selection. More commonly, geneticists calculate an average FST across thousands or millions of loci (e.g., SNPs) to get a genome-wide estimate of overall population divergence.

5. Can I use this calculator for allele frequency data?

This specific calculator uses heterozygosity values. However, heterozygosity is derived from allele frequencies. If you have allele frequencies, you first need to calculate H_S and H_T before using this tool. Other formulas exist to calculate FST directly from allele frequencies.

6. How does FST relate to genetic distance?

FST is a direct measure of genetic distance or differentiation. A higher FST value corresponds to a larger genetic distance between populations, indicating they are more diverged and have less recent gene flow.

7. Does the number of populations I sample affect FST?

Yes. FST is calculated relative to the total set of populations you include. Adding a highly divergent population to your analysis will increase the overall H_T and change the resulting FST values for all pairwise comparisons.

8. What is a “good” FST value?

There is no “good” or “bad” FST. The value is an observation. An FST of 0.02 might be considered low for isolated animal species but high for two adjacent city neighborhoods. The interpretation depends entirely on the biological context, the species’ mobility, and the geographical scale you are studying.

Related Tools and Internal Resources

Explore these resources for a deeper understanding of population genetics and genomic analysis.

• Hardy-Weinberg Equilibrium Calculator

  Check if a population’s genotype frequencies are in equilibrium, a key assumption in many population genetics models.

• Understanding Linkage Disequilibrium

  Learn how non-random association of alleles at different loci can provide insights into a population’s history.

• SNP Calling from NGS Data

  A guide on the bioinformatic process of identifying single nucleotide polymorphisms from raw sequencing data, the first step for an FST analysis.

• Choosing Molecular Markers

  An article discussing the pros and cons of different genetic markers (like SNPs, microsatellites) for population studies.

• GWAS Power Calculator

  Estimate the statistical power of a Genome-Wide Association Study, accounting for factors like population structure.

• RNA-Seq Differential Expression Analysis

  A deep dive into analyzing transcriptome data, relevant for calculating FST from RNA-based information.