An Integrative Approach to Genomic Introgression Mapping

dc.contributor.author Severin, Andrew
dc.contributor.author Peiffer, Gregory
dc.contributor.author Severin, Andrew
dc.contributor.author Xu, Wayne
dc.contributor.author Hyten, David
dc.contributor.author Bucciarelli, Bruna
dc.contributor.author O'Rourke, Jamie
dc.contributor.author Bolon, Yung-Tsi
dc.contributor.author Grant, David
dc.contributor.author Farmer, Andrew
dc.contributor.author May, Gregory
dc.contributor.author Vance, Carol
dc.contributor.author Shoemaker, Randy
dc.contributor.author Stupar, Robert
dc.contributor.department Agronomy
dc.date 2018-02-17T15:10:32.000
dc.date.accessioned 2020-06-29T23:07:09Z
dc.date.available 2020-06-29T23:07:09Z
dc.date.issued 2010-09-01
dc.description.abstract <p>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.</p>
dc.description.comments <p>This article is from <em>Plant Physiology</em> 154 (2010): 3–12, doi:<a href="http://dx.doi.org/10.1104/pp.110.158949" target="_blank">10.1104/pp.110.158949</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/agron_pubs/93/
dc.identifier.articleid 1094
dc.identifier.contextkey 8381265
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath agron_pubs/93
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/5067
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/agron_pubs/93/2010_Severin_IntegrativeApproach.pdf|||Sat Jan 15 02:31:08 UTC 2022
dc.source.uri 10.1104/pp.110.158949
dc.subject.disciplines Agriculture
dc.subject.disciplines Agronomy and Crop Sciences
dc.subject.disciplines Genomics
dc.subject.disciplines Plant Breeding and Genetics
dc.title An Integrative Approach to Genomic Introgression Mapping
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 87268d62-4b33-4949-a936-2d6d1ff1cfe2
relation.isOrgUnitOfPublication fdd5c06c-bdbe-469c-a38e-51e664fece7a
File
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
2010_Severin_IntegrativeApproach.pdf
Size:
415.09 KB
Format:
Adobe Portable Document Format
Description:
Collections