Abstract
Single nucleotide polymorphisms (SNPs) are highly abundant, amendable to high-throughput genotyping, and useful for a number of breeding and genetics applications in crops. SNP frequencies vary depending on the species and populations under study, and therefore target SNPs need to be carefully selected to be informative for each application. While multiple SNP genotyping systems are available for rice (Oryza sativa L. and its relatives), they vary in their informativeness, cost, marker density, speed, flexibility, and data quality. In this study, we report the development and performance of the Cornell-IR LD Rice Array (C7AIR), a second-generation SNP array containing 7,098 markers that improves upon the previously released C6AIR. The C7AIR is designed to detect genome-wide polymorphisms within and between subpopulations of O. sativa, as well as O. glaberrima, O. rufipogon and O. nivara. The C7AIR combines top-performing SNPs from several previous rice arrays, including 4,007 SNPs from the C6AIR, 2,056 SNPs from the High Density Rice Array (HDRA), 910 SNPs from the 384-SNP GoldenGate sets, 189 SNPs from the 44K array selected to add information content for elite U.S. tropical japonica rice varieties, and 8 trait-specific SNPs. To demonstrate its utility, we carried out a genome-wide association analysis for plant height, employing the C7AIR across a diversity panel of 189 rice accessions and identified 20 QTLs contributing to plant height. The C7AIR SNP chip has so far been used for genotyping >10,000 rice samples. It successfully differentiates the five subpopulations of Oryza sativa, identifies introgressions from wild and exotic relatives, and is useful for quantitative trait loci (QTL) and association mapping in diverse materials. Moreover, data from the C7AIR provides valuable information that can be used to select informative and reliable SNP markers for conversion to lower-cost genotyping platforms for genomic selection and other downstream applications in breeding.
Highlights
Single nucleotide polymorphisms (SNPs) occur at specific positions in the genome and are the most common form of genetic variation in eukaryotic organisms
The 378 samples contributed by Texas A&M were obtained from the USDA-ARS National Small Grains Collection (Aberdeen, Idaho), the Genetic Stocks-Oryza (GSOR) collection located at the USDA-ARS Dale Bumpers National Rice Research Center (USDA-ARS DBNRRC; Stuttgart, AR), as well as inbred rice breeding materials contributed by the Texas A&M AgriLife Research Center in Beaumont, Texas
Subsequent analyses were performed on C7AIR genotype data obtained on a diverse set of 551 rice samples
Summary
Single nucleotide polymorphisms (SNPs) occur at specific positions in the genome and are the most common form of genetic variation in eukaryotic organisms. High-throughput sequencing and SNP genotyping assays over the last 20 years have generated large amounts of SNP data that can be used to calculate the frequency of SNP alleles in different populations, to track the inheritance of those alleles across generations, and to associate SNP variation with phenotypic variation [2]. This information provides the basis for developing high-value genotyping assays that target particular subsets of SNPs for a variety of downstream applications. Identifying the subsets of SNPs that are most informative, most reliable, and easiest to call is essential for designing optimal, costeffective genotyping approaches for specific applications in genetics and breeding
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