High resolution of human evolutionary trees with polymorphic microsatellites

Abstract

Genetic variation at hypervariable loci is being used extensively for linkage analysis and individual identification, and may be useful for inter-population studies. Here we show that polymorphic microsatellites (primarily CA repeats) allow trees of human individuals to be constructed that reflect their geographic origin with remarkable accuracy. This is achieved by the analysis of a large number of loci for each individual, in spite of the small variations in allele frequencies existing between populations. Reliable evolutionary relationships could also be established in comparisons among human populations but not among great ape species, probably because of constraints on allele length variation. Among human populations, diversity of microsatellites is highest in Africa, which is in contrast to other nuclear markers and supports the hypothesis of an African origin for humans.

The Indian origin of paternal haplogroup R1a1* substantiates the autochthonous origin of Brahmins and the caste system

“…However, the diversity within the Indian populations, represented by the long branches and links connecting many haplotypes, is also an indicator of their ancestry, geographical differentiation and severe bottlenecks within India, suggesting loss of many of the intermediate haplotypes, thus reducing the reticulation and increasing the branches' length. The observed genetic distances FST 38 and 1ÀPSA 44 within the R1a1* haplogroup, between Central Asians (CA), Europeans (EU), as well as pooled populations of the Indian subcontinent (IS) showed overlapping trends of distribution. FST is based on the total variance in allele frequencies among populations and 1ÀPSA considers shared allele frequencies.…”

“…44 The spatial frequency/diversity maps were generated by the Kriging procedure 45 using SURFER version 8.0 (Golden Software Inc., CO, USA). Spearman's rank correlation coefficient, which has values +1 and À1 for perfect positive and negative correlations, respectively, 46 was calculated for the latitude and longitude with haplogroup frequency and diversity by applying the formula in Microsoft Excel.…”

Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds

“…Many clustering algorithms have been developed employing population genetic data to assign individuals to clusters (Jakobsson and Rosenberg, 2007). Several statistical methods were used to determine which genetic markers contain the most information to discriminate among populations (Rosenberg, 2005;Wilkinson et al, 2011), such as the combined approach of principal component analysis (PCA) and random forest (RF) (Bertolini et al, 2015), multivariate canonical discriminant analysis (Dimauro et al, 2013), the statistic delta (Shriver et al, 1997), and Wright's F st (Bowcock et al, 1994). While all these methodologies yielded reduced marker panels useful for breed identification, the power of assignment varied among analysis methods.…”

Beyond the snapshot: Landscape genetic analysis of time series data reveal responses of American black bears to landscape change

“…We quantified microsatellite genetic diversity using mean number of alleles ( A ), observed heterozygosity ( H O ), and expected heterozygosity ( H E ) over all loci, using program GenAlEx (v. 6.0, Peakall & Smouse, 2006). We estimated inter‐individual GD, by calculating the ratio proportion of shared alleles (Dps, GD = (1 − Dps); Bowcock et al., 1994) for each pairwise combination of individuals using GenAlEx v. 6.0 (Peakall & Smouse, 2006). Dps is a commonly used individual‐based genetic distance measure in landscape genetic studies and has been shown to accurately reflect SGS and connectivity at small spatial scales (Murphy, Dezzani, Pilliod, & Storfer, 2010).…”