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Although many studies have examined the genetic basis that controls the accumulation of single elements in a RIL population, only a small number [19] [21] have examined multiple elements in multiple populations to investigate the genetic architecture of the ionome.
This was done in conjunction with allele frequency plots across multiple populations to ascertain geographic trends.
The pre-selected biomarkers, chosen for practical reasons, did not include FGF-23, which has been shown in multiple populations to predict CV outcomes and death.
Our study is unique in that we sampled multiple TE families and multiple populations to compare population dynamics between a selfer (A. thaliana) and an outcrosser (A. lyrata).
Our goal is to be able to leverage information across multiple populations, to find causal markers in a multi-population association study.
However they demonstrate the phenomenon of breed-specificity and thus highlight the need for the study of multiple populations to verify genotype - phenotype correlations.
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While traditionally a genetic map has been generated from a single population, recent efforts to create maps from multiple populations, referred to as consensus maps, have gained much interest in the scientific and breeding community.
While traditionally a genetic linkage map has been generated from a single population, recent efforts to create maps from multiple populations, referred to as consensus maps, have gained much interest in the scientific and breeding community.
Because of the low marker density in the existing population-specific linkage maps and the difficulty of understanding the genomic structure of Arachis, two significant integrated consensus maps were recently constructed based on the segregation genotypes of multiple populations, anchored to 20 consensus LGs corresponding to the A and B genomes A01-A100, B01-B10) [ 9, 26].
One approach to the integration of information for multiple populations is to pool the genotypic data and minimize the sum of recombination frequencies (or related metrics), as in the maximum likelihood method [ 46] applied to single populations, e.g. [ 47].
One of our goals for including diverse individuals from multiple populations was to identify sequence variants (SNPs and INDELS) that would be useful in future studies.
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