Dataset formats
The input datasets are in gd_snp, gd_genotype, and gd_indivs formats. The output dataset is in gd_snp or gd_genotype format. (Dataset missing?)
What it does
The user specifies a SNP table and two "populations" of individuals, both previously defined using the Galaxy tool to specify individuals from a SNP table. No individual can be in both populations. Other choices are as follows.
Frequency metric. The allele frequencies of a SNP in the two populations can be estimated either by the total number of reads of each allele (if the table is in gd_snp format, but not with gd_genotype), or by adding the frequencies inferred from genotypes of individuals in the populations.
After specifying the frequency metric, the user sets lower bounds on amount of data required at a SNP. For estimating the Fst using read counts, the bound is the minimum count of reads of the two alleles in a population. For estimations based on genotype, the bound is the minimum reported genotype quality per individual.
The user specifies whether the SNPs that violate the lower bound should be ignored or the Fst set to -1.
The user specifies whether SNPs where both populations appear to be fixed for the same allele should be retained or discarded.
Finally, the user chooses which definition of Fst to use: Wright's original definition, the Weir-Cockerham unbiased estimator, or the Reich-Patterson estimator.
A column is appended to the SNP table giving the Fst for each retained SNP.
References:
Sewall Wright (1951) The genetical structure of populations. Ann Eugen 15:323-354.
Weir, B.S. and Cockerham, C. Clark (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358-1370.
Weir, B.S. 1996. Population substructure. Genetic data analysis II, pp. 161-173. Sinauer Associates, Sundand, MA.
David Reich, Kumarasamy Thangaraj, Nick Patterson, Alkes L. Price, and Lalji Singh (2009) Reconstructing Indian population history. Nature 461:489-494, especially Supplement 2.
Their effectiveness for computing FSTs when there are many SNPs but few individuals is discussed in the following paper.
Eva-Maria Willing, Christine Dreyer, Cock van Oosterhout (2012) Estimates of genetic differentiation measured by FST do not necessarily require large sample sizes when using many SNP markers. PLoS One 7:e42649.
Example
input, SNP table:
#{"column_names":["scaf","pos","A","B","qual","ref","rpos","rnuc","1A","1B","1G","1Q","2A","2B","2G","2Q","3A","3B","3G","3Q","4A","4B","4G","4Q",
#"5A","5B","5G","5Q","6A","6B","6G","6Q","pair","dist","prim","rflp"],"dbkey":"canFam2",
#"individuals":[["PB1",9],["PB2",13],["PB3",17],["PB4",21],["PB6",25],["PB8",29]],
#"pos":2,"rPos":7,"ref":6,"scaffold":1,"species":"bear"}
Contig161_chr1_4641264_4641879 115 C T 73.5 chr1 4641382 C 6 0 2 45 8 0 2 51 15 0 2 72 5 0 2 42 6 0 2 45 10 0 2 57 Y 54 0.323 0
Contig113_chr5_11052263_11052603 28 C T 38.2 chr5 11052280 C 1 2 1 12 3 2 1 10 5 0 2 42 2 1 2 13 3 0 2 36 8 0 2 51 Y 161 +99. 0
Contig215_chr5_70946445_70947428 363 T G 28.2 chr5 70946809 C 4 0 2 39 0 5 0 12 9 0 2 54 6 0 2 45 3 3 2 1 9 0 2 54 N 43 0.153 0
etc.
input, Population 1 individuals:
9 PB1 13 PB2
input, Population 2 individuals:
17 PB3 21 PB4
output (minimum read count of 3, discard fixed):
Contig113_chr5_11052263_11052603 28 C T 38.2 chr5 11052280 C 1 2 1 12 3 2 1 10 5 0 2 42 2 1 2 13 3 0 2 36 8 0 2 51 Y 161 +99. 0 0.1636 Contig215_chr5_70946445_70947428 363 T G 28.2 chr5 70946809 C 4 0 2 39 0 5 0 12 9 0 2 54 6 0 2 45 3 3 2 1 9 0 2 54 N 43 0.153 0 0.3846 etc.