In order to trace the clonal lineages, seven D. galeata clones from Římov, and four D. galeata, five F1 hybrid and two D. longispina clones from Vír were selected as a set of "common" clones (with frequency exceeding 10% in at least one sample), whereas the other clones were pooled together into the "rare" group.
Tag SNPs were chosen from the designable set of common SNPs (minor allele frequency (MAF)>5%) genotyped in the Caucasian (CEU) population sample of the HapMap Project (Data Release 20/Phase II, NCBI Build 35 assembly, dbSNPb125) using the software Tagzilla (http://tagzilla.nci.nih.gov/), which implements a tagging algorithm based on the pairwise binning method of Carlson et al. [46].
The association of a set of common polymorphic variants, whose frequency differs among ancestral populations with more common forms of non-diabetic ESKD, points to the existence of one or more causative variants with which these associated SNPs are in LD.
Characteristics of the 977 individuals included in these analyses are presented in Table 1, including the frequencies of a set of common chronic conditions (asthma, CKD, diabetes and morbid obesity).
Relative to a control set of genes, the 5′ regulatory sequences of the crystallin network genes show an increased frequency of a set of common transcription factor-binding sites, the most common being those of the Maf family.
As isogenic plant seedlings may display significant phenotypic variability even in highly controlled environments, we established a standardized set of common morphological abnormalities (Fig. 3) whose frequency was assessed across a population randomized by genotype by a scientist lacking knowledge of plant identity.
In GWAS, a large set of common variants, i.e. SNPs with high minor allele frequencies, is genotyped and tested for genetic association with the phenotype of interest.
From the dataset of 2,031 prokaryotic genomes, our method successfully obtained a rich set of common and unique n-grams (n = 12) and weighted them based upon their natural frequency of occurrence across the genomes.
The model-building step involves indexing the entire set of common and unique n-grams and assigning appropriate weight to each n-gram based on its frequency profile across the reference genome set.
These two models can be linked with one common set of base frequencies (3 free parameters) in which case we simply denote them as GTR112 and GTR123.
