4.7 Article

Diversity and selection in sorghum: simultaneous analyses using simple sequence repeats

Journal

THEORETICAL AND APPLIED GENETICS
Volume 111, Issue 1, Pages 23-30

Publisher

SPRINGER
DOI: 10.1007/s00122-005-1952-5

Keywords

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Funding

  1. NIGMS NIH HHS [R01 GM036431] Funding Source: Medline

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Although molecular markers and DNA sequence data are now available for many crop species, our ability to identify genetic variation associated with functional or adaptive diversity is still limited. In this study, our aim was to quantify and characterize diversity in a panel of cultivated and wild sorghums (Sorghum bicolor), establish genetic relationships, and, simultaneously, identify selection signals that might be associated with sorghum domestication. We assayed 98 simple sequence repeat (SSR) loci distributed throughout the genome in a panel of 104 accessions comprising 73 landraces (i.e., cultivated lines) and 31 wild sorghums. Evaluation of SSR polymorphisms indicated that landraces retained 86% of the diversity observed in the wild sorghums. The landraces and wilds were moderately differentiated (F st=0.13), but there was little evidence of population differentiation among racial groups of cultivated sorghums (F st=0.06). Neighbor-joining analysis showed that wild sorghums generally formed a distinct group, and about half the landraces tended to cluster by race. Overall, bootstrap support was low, indicating a history of gene flow among the various cultivated types or recent common ancestry. Statistical methods (Ewens-Watterson test for allele excess, lnRH, and F st) for identifying genomic regions with patterns of variation consistent with selection gave significant results for 11 loci (approx. 15% of the SSRs used in the final analysis). Interestingly, seven of these loci mapped in or near genomic regions associated with domestication-related QTLs (i.e., shattering, seed weight, and rhizomatousness). We anticipate that such population genetics-based statistical approaches will be useful for re-evaluating extant SSR data for mining interesting genomic regions from germplasm collections.

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