Abstract
BackgroundA genome-wide association study (GWAS) is the foremost strategy used for finding genes that control human diseases and agriculturally important traits, but it often reports false positives. In contrast, its complementary method, linkage analysis, provides direct genetic confirmation, but with limited resolution. A joint approach, using multiple linkage populations, dramatically improves resolution and statistical power. For example, this approach has been used to confirm that many complex traits, such as flowering time controlling adaptation in maize, are controlled by multiple genes with small effects. In addition, genotyping by sequencing (GBS) at low coverage not only produces genotyping errors, but also results in large datasets, making the use of high-throughput sequencing technologies computationally inefficient or unfeasible.ResultsIn this study, we converted raw SNPs into effective recombination bins. The reduced bins not only retain the original information, but also correct sequencing errors from low-coverage genomic sequencing. To further increase the statistical power and resolution, we merged a new temperate maize nested association mapping (NAM) population derived in China (CN-NAM) with the existing maize NAM population developed in the US (US-NAM). Together, the two populations contain 36 families and 7,000 recombinant inbred lines (RILs). One million SNPs were generated for all the RILs with GBS at low coverage. We developed high-quality recombination maps for each NAM population to correct genotyping errors and improve the computational efficiency of the joint linkage analysis. The original one million SNPs were reduced to 4,932 and 5,296 recombination bins with average interval distances of 0.34 cM and 0.28 cM for CN-NAM and US-NAM, respectively. The quantitative trait locus (QTL) mapping for flowering time (days to tasseling) indicated that the high-density, recombination bin map improved resolution of QTL mapping by 50 % compared with that using a medium-density map. We also demonstrated that combining the CN-NAM and US-NAM populations improves the power to detect QTL by 50 % compared to single NAM population mapping. Among the QTLs mapped by joint usage of the US-NAM and CN-NAM maps, 25 % of the QTLs overlapped with known flowering-time genes in maize.ConclusionThis study provides directions and resources for the research community, especially maize researchers, for future studies using the recombination bin strategy for joint linkage analysis. Available resources include efficient usage of low-coverage genomic sequencing, detailed positions for genes controlling maize flowering, and recombination bin maps and flowering- time data for both CN and US NAMs. Maize researchers even have the opportunity to grow both CN and US NAM populations to study the traits of their interest, as the seeds of both NAM populations are available from the seed repository in China and the US.Electronic supplementary materialThe online version of this article (doi:10.1186/s12915-015-0187-4) contains supplementary material, which is available to authorized users.
Highlights
A genome-wide association study (GWAS) is the foremost strategy used for finding genes that control human diseases and agriculturally important traits, but it often reports false positives
Maize researchers even have the opportunity to grow both CN and US nested association mapping (NAM) populations to study the traits of their interest, as the seeds of both NAM populations are available from the seed repository in China and the US
Identification of high-quality lines All recombinant inbred lines (RILs) of the 36 families that comprised the two NAM populations were sequenced with genotyping by sequencing (GBS), resulting in a total of one (0.95) million SNPs
Summary
A genome-wide association study (GWAS) is the foremost strategy used for finding genes that control human diseases and agriculturally important traits, but it often reports false positives. A joint approach, using multiple linkage populations, dramatically improves resolution and statistical power. This approach has been used to confirm that many complex traits, such as flowering time controlling adaptation in maize, are controlled by multiple genes with small effects. Maize exhibits extremely high levels of genetic diversity and phenotypic variation [1, 2]. Studies undertaken to understand the genetic architecture of complex traits and their variations in maize have generally been performed by linkage analysis and/ or association mapping [6]. A genome-wide association study (GWAS) often identifies spurious associations due to population structure and genetic relatedness [7]. Joint linkage analysis, using multiple segregating populations, could overcome some of the inherent limitations associated with single population linkage analysis and genomewide association mapping [8]
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