A simple method for estimating genetic diversity in large populations from finite sample sizes

BMC Genomic Data

Table 1 Allelic richness estimated by regression, coalescent and rarefaction

Species	ID	Source data set			Estimated allelic richness
		No. of loci	N	A	Subsampling (n = 120)	ρ (n = 120)	θ _Ewens (n = 120)	θ _coalescent (n = 120)	Rarefaction (n = 120)
Microsatellites
Picea rubens	PR1	6	180	13.00	11.06	11.04	11.98	9.23	10.68
	PR2	6	180	13.33	11.18	11.17	12.29	8.94	10.71
	PR3	6	180	15.33	12.48	12.44	14.13	11.92	12.19
	PR4	6	180	14.83	12.48	12.44	13.67	12.13	11.92
Picea glauca	PG1	6	105	22.83	21.13	21.30	23.49	35.74	20.96
	PG2	6	105	22.83	20.55	20.62	23.49	51.84	20.44
Pinus strobus	PS1	13	102	9.77	9.03	9.13	10.11	17.57	9.03
	PS2	13	102	9.23	8.67	8.73	9.55	15.91	8.68
Thuja occidentalis	TO1	6	100	7.83	7.18	7.17	8.14	12.26	7.17
	TO2	6	100	9.67	8.95	9.00	10.05	16.28	9.09
	TO3	6	100	8.83	7.86	7.95	9.18	14.06	7.95
Allozymes
Pinus strobus	PS1	15	95	3.20	2.97	2.98	3.38	3.34	2.93
	PS2	15	95	3.27	3.09	3.10	3.59	4.15	3.04

Subsampling - allelic richness estimated by repeated random subsampling in pseudosimulated population data sets based on the empirical data. ρ - allelic richness predicted by the regression model (5). θ_Ewens - Allelic richness predicted by the Ewens sampling formula (3), where θ was directly calculated from the empirical data set. θ_coalescent - Allelic richness predicted by the Ewens sampling formula (3), where θ was estimated by coalescent approach from the empirical data set. Rarefaction - Allelic richness predicted by rarefaction of the source empirical data set to the sample size of n = 120.

ISSN: 2730-6844