DOI: 10.5061/dryad.bnzs7h4f1
title: A common resequencing‐based genetic marker dataset for global maize diversity

description: 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.
description: This maize genomic variants data set contains subset (~46 mln) of
 ~366 million single nucleotide polymorphisms (SNPs) and small insertion
 and deletion (InDels). The dataset was created by aligning whole genome
 resequencing data from 1,515 maize individuals to maize B73 reference
 genome v5 (Hufford et al., 2022; 10.1126/science.abg5289). Variants were
 identified with GATK v4 software and saved to Variant Call Format (VCF)
 files (v4.2). Only variants that pass Hard-Filtering criteria (followed by
 GATK recommendation) were saved. The full variant set can be downloaded
 from MaizeGDB (www.maizegdb.org). The data set in this
 repository contains only high confidence genetic variant set. The filtered
 and imputed variant set was generated by first removing variants where:
 &gt;2 alleles were observed in the population, variants with &gt;
 50% missing data, variants with extremely low &lt; 1,515 or extremely
 high &gt; 33,550 sequencing depth, and variants with inbreeding
 coefficients &gt; 0 resulting in ~46 million variants. Imputation were
 done with Beagle 5.0 (Browning et al., 2021; 10.1016/j.ajhg.2021.08.005)
 with default setting. Each VCF file contains data for a
 single chromosome.  In addition, VCF files for 752
 lines from Wisconsin Association Panel (Mazaheri et al., 2019; https://doi.org/10.1186/s12870-019-1653-x) were also provided. These variants were filtered from high confident genetic variant set with MAF &gt; 5%. All VCF files were gzipped (with bgzip) and split into chromosome level. For each file, a tabix index was provided.
description: Files can be processed with software such as bcftools, bedtools,
 Plink, TASSEL, or basic Linux programs like awk or sed.
 <strong>*_imputed.vcf.gz</strong>
 Files with variants for 1,515 maize individuals, each for one
 chromosome.
 <strong>WiDiv.vcf.gz</strong> File
 with variants for 752 maize inbred lines from Wisconsin Diversity
 Panel.