AI Article Synopsis

  • Near-isogenic lines (NILs) are important genetic tools for crop research, specifically in soybeans.
  • The study compares various mapping techniques for genetic introgressions, including single-feature polymorphism analysis and RNA-Seq, highlighting their complementary strengths.
  • The RNA-Seq method provides high mapping resolution and marker utility, and can be optimized through better sampling and corrections, leading to improved SNP discovery rates.

Article Abstract

Near-isogenic lines (NILs) are valuable genetic resources for many crop species, including soybean (Glycine max). The development of new molecular platforms promises to accelerate the mapping of genetic introgressions in these materials. Here, we compare some existing and emerging methodologies for genetic introgression mapping: single-feature polymorphism analysis, Illumina GoldenGate single nucleotide polymorphism (SNP) genotyping, and de novo SNP discovery via RNA-Seq analysis of next-generation sequence data. We used these methods to map the introgressed regions in an iron-inefficient soybean NIL and found that the three mapping approaches are complementary when utilized in combination. The comparative RNA-Seq approach offers several additional advantages, including the greatest mapping resolution, marker depth, and de novo marker utility for downstream fine-mapping analysis. We applied the comparative RNA-Seq method to map genetic introgressions in an additional pair of NILs exhibiting differential seed protein content. Furthermore, we attempted to optimize the comparative RNA-Seq approach by assessing the impact of sequence depth, SNP identification methodology, and post hoc analyses on SNP discovery rates. We conclude that the comparative RNA-Seq approach can be optimized with sufficient sampling and by utilizing a post hoc correction accounting for gene density variation that controls for false discoveries.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2938162PMC
http://dx.doi.org/10.1104/pp.110.158949DOI Listing

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