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silking. Furthermore, it was observed that while most QTLs were shared between families, each family appears to have functionally distinct alleles for most QTLs. These observations led the authors to propose a model of "Common genes with uncommon variants" to explain flowering time diversity in maize. They tested their model by documenting an allelic series in the previously studied maize flowering time QTL Vgt1 (vegetation-to-transition1) by controlling for genetic background and estimating the effects of vgt1 in each family. They then went on to identify specific sequence variants that corresponded to the allelic series, including one allele containing a miniature
158:). As of 2009, however, the sequencing of the original parental lines was not yet completed to the degree necessary to perform these analyses. The NAM population has, however, been successfully used for linkage analysis. In the linkage study that has been released, the unique structure of the NAM population, described in the previous section, allowed for joint
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Mitchell; Gael
Pressoir; Jason A. Peiffer; Marco Oropeza Rosas; Torbert R. Rocheford; M. Cinta Romay; Susan Romero; Stella Salvo; Hector Sanchez Villeda; H. Sofia da Silva; Qi Sun; Feng Tian; Narasimham Upadyayula; Doreen Ware; Heather Yates; Jianming Yu; Zhiwu Zhang; Stephen Kresovich; Michael D. McMullen (2009).
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Michael D. McMullen; Stephen
Kresovich; Hector Sanchez Villeda; Peter Bradbury; Huihui Li; Qi Sun; Sherry Flint-Garcia; Jeffry Thornsberry; Charlotte Acharya; Christopher Bottoms; Patrick Brown; Chris Browne; Magen Eller; Kate Guill; Carlos Harjes; Dallas Kroon; Nick Lepak; Sharon E. Mitchell; Brooke
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Ninety-eight percent of the flowering time QTLs identified in this paper were found to affect flowering time by less than one day (as compared to the B73 reference). These relatively small QTL effects, however, were also shown to sum for each family to equal large differences and changes in days to
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with a trait of interest. Association mapping has advantages over linkage analysis in that it can map with high resolution and has high allelic richness, however, it also requires extensive knowledge of SNPs within the genome and is thus only now becoming possible in diverse species such as maize.
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or high-density genotyped, and the results of that sequencing/genotyping overlaid on the recombination blocks identified for each RIL. The result was 5000 RILs that were either fully sequenced or high density genotyped that, due to genotyping with the common 1106 markers, could all be compared to
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NAM takes advantage of both historic and recent recombination events in order to have the advantages of low marker density requirements, high allele richness, high mapping resolution, and high statistical power, with none of the disadvantages of either linkage analysis or association mapping. In
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Edward S. Buckler; James B. Holland; Peter J. Bradbury; Charlotte B. Acharya; Patrick J. Brown; Chris Browne; Elhan Ersoz; Sherry Flint-Garcia; Arturo Garcia; Jeffrey C. Glaubitz; Major M. Goodman; Carlos Harjes; Kate Guill; Dallas E. Kroon; Sara
Larsson; Nicholas K. Lepak; Huihui Li; Sharon E.
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Twenty-five diverse corn lines were chosen as the parental lines for the NAM population in order to encompass the remarkable diversity of maize and preserve historic linkage disequilibrium. Each parental line was crossed to the B73 maize inbred (chosen as a reference line due to its use in the
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in non-maize species. Furthermore, the NAM lines become a powerful public resource for the maize community, and an opportunity for the sharing of maize germplasm as well as the results of maize studies via common databases (see external links), further facilitating future research into maize
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The second aspect of the NAM population characterization is the sequencing of the parental lines. This captures information on the natural variation that went into the population and a record of the extensive recombination captured in the history of maize variation. The first phase of this
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Nested association mapping has tremendous potential for the investigation of agronomic traits in maize and other species. As the initial flowering time study demonstrates, NAM has the power to identify QTLs for agriculturally relevant traits and to relate those QTLs to homologs and
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sequencing was by reduced representation sequencing using next generation sequencing technology, as report in Gore, Chia et al. in 2009. This initial sequencing discovered 1.6 million variable regions in maize, which is now facilitating analysis of a wide range of traits.
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population. The F1 plants were then self-fertilized for six generations in order to create a total of 200 homozygous recombinant inbred lines (RILs) per family, for a total of 5000 RILs within the NAM population. The lines are publicly available through the
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with the same 1106 molecular markers (for this to be possible, the researchers selected markers for which B73 had a rare allele), in order to identify recombination blocks. After genotyping with the 1106 markers, each of the parental lines was either
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Peterson; Gael
Pressoir; Susan Romero; Marco Oropeza Rosas; Stella Salvo; Heather Yates; Mark Hanson; Elizabeth Jones; Stephen Smith; Jeffrey C. Glaubitz; Major Goodman; Doreen Ware; James B. Holland; Edward S. Buckler (2009).
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strategies, the general goal in Nested
Association Mapping is to correlate a phenotype of interest with specific genotypes. One of the creators' stated goals for the NAM population was to be able to perform
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agricultural traits. Given that maize is one of the most important agricultural crops worldwide, such research has powerful implications for the genetic improvement of crops, and subsequently, worldwide
186:(ICIM) to identify 39 QTLs explaining 89% of the variance in days to silking and days to anthesis and 29 QTLs explaining 64% of the variance in the silking-anthesis interval.
49:) is a specific technique that cannot be performed outside of a specifically designed population such as the Maize NAM population, the details of which are described below.
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to ensure genome wide coverage and high statistical power per allele. Linkage analysis, however, has the disadvantages of low mapping resolution and low allele richness.
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in maize by looking for associations between SNPs within the NAM population and quantitative traits of interest (e.g. flowering time, plant height,
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between two different plant lines (as the result of a genetic cross) to identify general regions of interest, with the advantage of requiring few
182:, days to anthesis, and the silking-anthesis interval for nearly one million plants, then performed single and joint stepwise regression and
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these regards, the NAM approach is similar in principle to the MAGIC lines and AMPRILs in
Arabidopsis and the Collaborative Cross in mouse.
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NAM was created as a means of combining the advantages and eliminating the disadvantages of two traditional methods for identifying
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of maize flowering time, and published in the summer of 2009. In this groundbreaking study, the authors scored days to
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Huang, Xuehui; Han, Bin (2014-04-29). "Natural
Variations and Genome-Wide Association Studies in Crop Plants".
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strongly associated with early flowering, and other alleles containing SNPs associated with later flowering.
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549:"Conserved noncoding genomic sequences associated with a flowering-time quantitative trait locus in maize"
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Gore MA, Chia JM, Elshire RJ, et al. (November 2009). "A first-generation haplotype map of maize".
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The first publication in which NAM was used to identify QTLs was authored by the
Buckler lab on the
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Gramene – assembled genomes for many plant genetics systems, including maize, rice, and sorghum
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and wide deployment as one of the most successful commercial inbred lines) to create the
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309:"Genetic design and statistical power of nested association mapping in maize"
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376:"Genetic Properties of the Maize Nested Association Mapping Population"
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MaizeGDB – community database for biological information about maize
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Maize NAMs have helped to map otherwise difficult traits conveying
45:). It is important to note that nested association mapping (unlike
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of the combined NAM families to identify QTLs for flowering time.
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Panzea.org – the official Nested
Association Mapping database
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Yu, J., Holland, J.B., McMullen, M.D., Buckler, E.S. (2008).
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for identifying and dissecting the genetic architecture of
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Salvi S, Sponza G, Morgante M, et al. (July 2007).
494:"The Genetic Architecture of Maize Flowering Time"
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234:Similar designs are also being created for
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132:Figure 1. Creation of the NAM population.
23:) is a technique designed by the labs of
63:linkage analysis and association mapping
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209:resistance, and Poland et al 2011, for
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626:10.1146/annurev-arplant-050213-035715
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184:inclusive composite interval mapping
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205:including Kump et al 2011, for
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101:public maize sequencing project
609:Annual Review of Plant Biology
162:and joint inclusive composite
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671:Maize genome sequence browser
553:Proc. Natl. Acad. Sci. U.S.A
325:10.1534/genetics.107.074245
110:USDA-ARS Maize Stock Center
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17:Nested association mapping
272:Marker assisted selection
267:Family based QTL mapping
574:10.1073/pnas.0704145104
518:10.1126/science.1174276
451:10.1126/science.1177837
400:10.1126/science.1174320
59:quantitative trait loci
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83:linkage disequilibrium
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67:genetic recombination
691:Statistical genetics
249:Arabidopsis thaliana
211:northern leaf blight
207:southern leaf blight
176:genetic architecture
145:As with traditional
565:2007PNAS..10411376S
510:2009Sci...325..714B
443:2009Sci...326.1115G
392:2009Sci...325..737M
262:Association mapping
160:stepwise regression
75:Association mapping
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504:(5941): 714–718.
53:Theory behind NAM
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217:Implications
213:resistance.
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620:: 531–551.
282:QTL mapping
170:Current use
147:QTL mapping
288:References
199:resistance
192:transposon
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467:206521881
122:sequenced
117:genotyped
39:in corn (
685:Category
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593:17595297
526:19661422
459:19965431
416:14667346
408:19661427
343:18202393
313:Genetics
256:See also
42:Zea mays
584:2040906
561:Bibcode
534:8297435
506:Bibcode
498:Science
439:Bibcode
431:Science
388:Bibcode
380:Science
334:2206100
244:sorghum
180:silking
141:Process
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246:, and
240:barley
31:, and
616:(1).
530:S2CID
463:S2CID
412:S2CID
236:wheat
203:fungi
638:PMID
630:ISSN
589:PMID
522:PMID
455:PMID
404:PMID
357:link
339:PMID
79:SNPs
622:doi
579:PMC
569:doi
557:104
514:doi
502:325
447:doi
435:326
396:doi
384:325
329:PMC
321:doi
317:178
201:to
81:in
21:NAM
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