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[…] FSTAT is free, open-source software, available for any operating system (Windows, Linux, Mac OSX). is not! I had a look at your example and FSTAT gives you the following result, which differs from your answer. > fstat(“tests/1_sample.txt”, “sex”, “ab”, “pop”, “n”) $pop $sex FST 0.559513 0.559516 This may be due to the small sample size, but I cannot see why you get that result. Do you have an example of FSTAT giving that result? On the first look, I think this is probably a bug, but would like to ask for some more information in order to decide this. If there are no other files in your test directory, would this be the only test file in the directory? Please update the question if you have any other files in that directory. Best regards, Frank The “Underworld Awakening” is coming in June, and it looks to be a game changer for the franchise. Here is everything we know so far, plus a few important details. The official Twitter account for Underworld: Blood Wars has teased the upcoming release by announcing a theme song. The song, titled “Underworld Awakening,” will be composed by series veterans from the first three games. Featured actors from the first three games, including Kate Beckinsale and Morgana King (Clara), are involved in the project as well. Meanwhile, the series creative director, Andy Mikol, has noted that the new setting of the game will allow the team to develop characters who could play a key role in the story. “The game will be set during the Civil War between the vampires and the Lycans,” Mikol says. “The Underworld Awakening in the third movie of the series gave birth to the vampires, and in this one, it will do the same to the Lycans. Both groups will be trying to dominate the Earth.” In addition to the game’s setting, Mikol’s note also confirms the inclusion of a “familiar” character from the first game of the franchise. “We’re trying to bring back some familiar characters,” he says. The Underworld: Blood Wars video game is scheduled for release on PlayStation 4

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FSTAT is a handy, small, easy to use tool specially designed to help you estimate and test gene diversities and differentiation statistics from codominant genetic markers. It computes both Nei and Weir and Cockerham families of estimators of gene diversities and F-statistics, and tests them using randomisation methods. The following information can be found here: Documentation: Instructions and example input files are provided on the document root. Also, please read the associated README.txt file. Requirements: FSTAT requires only ANSI C and GNU make. FSTAT is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. FSTAT is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Abundance estimates of dominant and co-dominant markers. Includes calculations of the following: Effective number of codominant alleles Effective number of dominant alleles F-statistic Major allele frequency Minor allele frequency Allelic richness (within populations) Allelic richness (among populations) Abundance estimates of dominant and co-dominant markers. Includes calculations of the following: Effective number of codominant alleles Effective number of dominant alleles F-statistic Major allele frequency Minor allele frequency Allelic richness (within populations) Allelic richness (among populations) This package contains functions for calculating “effective numbers” of each of the known types of markers: dominant, codominant, and monomorphic. It includes calculations for Nei’s theta and G-st based on HICKORY/MBEAST, and for Hudson & Jensen’s D and U (for both loci and population samples) based on FSTAT. It is available as part of the CODEVI package.

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In some circumstances, empirical estimators of population genetic parameters are preferable to theoretical estimators. The most popular empirical estimator is the “Nei’s estimator” and its variants. It is known that Nei’s estimator has many shortcomings, and new estimators were suggested to overcome them. One of such estimators is “Weir and Cockerham’s estimator” (W&C) and its variants. Its use is limited due to its fact that it is not commonly used. Another empirical estimator is “Cockerham’s estimator” (C) that is equivalent to “Dobzhansky and Wilson’s estimator” (D&W) in unlinked loci, but differs in linked loci. For example, let us consider estimation of gene diversity H of a population X. The fundamental formula for estimating H using Cockerham’s estimator is given as follows. where is the number of alleles at a locus. (Two alleles at a locus are regarded as different alleles.) is the number of sampled individuals. is the number of different alleles among sampled individuals. is the average frequency of the allele in the population. is the proportion of heterozygous individuals in the population. is the total number of alleles at the locus. is the number of effective alleles. is the mean homozygosity of the population. is the sample allele frequency at the locus. is the population allele frequency. is the sample heterozygosity at the locus. The critical formula for estimating H using W&C’s estimator is as follows. where is the number of alleles at a locus. (Two alleles at a locus are regarded as different alleles.) is the number of sampled individuals. is the number of different alleles among sampled individuals. is the average frequency of the allele in the population. is the proportion of heterozygous individuals in the population. is the total number of alleles at the locus. is the number of effective alleles. is the sample allele frequency at the locus. is the population allele frequency. is the sample heterozygosity at the locus. is the sample heterozygosity expected from Hardy-Weinberg equilibrium. is the population homozygosity. Note that W&C’s estimator is identical to that of D&W, except for the population homozygosity. W&C’s estimator has an advantage over D&W’s estimator in the presence of negative values in the numerator (expected heterozygosity). The utility of FSTAT is that it also computes four alternative estimators of gene diversities and F-statistics

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