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Maize (Zea mays ssp. mays) populations exhibit vast amounts of genetic and
phenotypic diversity. As sequencing costs have declined, an increasing
number of projects have sought to measure genetic differences between and
within maize populations using whole genome resequencing strategies,
identifying millions of segregating single-nucleotide polymorphisms (SNPs)
and insertions/deletions (InDels). Unlike older genotyping strategies like
microarrays and genotyping by sequencing, resequencing should, in
principle, frequently identify and score common genetic variants. However,
in practice, different projects frequently employ different analytical
pipelines, often employ different reference genome assemblies, and
consistently filter for minor allele frequency within the study
population. This constrains the potential to reuse and remix data on
genetic diversity generated from different projects to address new
biological questions in new ways. Here we employ resequencing data from
1,276 previously published maize samples and 239 newly resequenced maize
samples to generate a single unified marker set of ~366 million
segregating variants and ~46 million high confidence variants scored
across crop wild relatives, landraces as well as tropical and temperate
lines from different breeding eras. We demonstrate that the new variant
set provides increased power to identify known causal flowering time genes
using previously published trait datasets, as well as the potential to
track changes in the frequency of functionally distinct alleles across the
global distribution of modern maize.
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