Evidence map›Paper›PMID 24767139›Full record

ArticleBMC genetics2014

Effect of advanced intercrossing on genome structure and on the power to detect linked quantitative trait loci in a multi-parent population: a simulation study in rice.

Eiji Yamamoto, Hiroyoshi Iwata, Takanari Tanabata, Ritsuko Mizobuchi, Jun-ichi Yonemaru, Toshio Yamamoto, Masahiro Yano

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Article in BMC genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Manipulation of crossover frequency and distribution for plant breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. MAGIC populations in crops: current status and future prospects.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2015
    Review
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Eiji Yamamoto
Hiroyoshi Iwata
Takanari Tanabata
Ritsuko Mizobuchi
Jun-ichi Yonemaru
Toshio YamamotoNational Institute of Agrobiological Sciences, 2-1-2 Kannondai, Tsukuba, Ibaraki 305-8602, Japan. yamamo101040@affrc.go.jp.
Masahiro Yano

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn genetic analysis of agronomic traits, quantitative trait loci (QTLs) that control the same phenotype are often closely linked. Furthermore, many QTLs are localized in specific genomic regions (QTL clusters) that include naturally occurring allelic variations in different genes. Therefore, linkage among QTLs may complicate the detection of each individual QTL. This problem can be resolved by using populations that include many potential recombination sites. Recently, multi-parent populations have been developed and used for QTL analysis. However, their efficiency for detection of linked QTLs has not received attention. By using information on rice, we simulated the construction of a multi-parent population followed by cycles of recurrent crossing and inbreeding, and we investigated the resulting genome structure and its usefulness for detecting linked QTLs as a function of the number of cycles of recurrent crossing.

resultsThe number of non-recombinant genome segments increased linearly with an increasing number of cycles. The mean and median lengths of the non-recombinant genome segments decreased dramatically during the first five to six cycles, then decreased more slowly during subsequent cycles. Without recurrent crossing, we found that there is a risk of missing QTLs that are linked in a repulsion phase, and a risk of identifying linked QTLs in a coupling phase as a single QTL, even when the population was derived from eight parental lines. In our simulation results, using fewer than two cycles of recurrent crossing produced results that differed little from the results with zero cycles, whereas using more than six cycles dramatically improved the power under most of the conditions that we simulated.

conclusionOur results indicated that even with a population derived from eight parental lines, fewer than two cycles of crossing does not improve the power to detect linked QTLs. However, using six cycles dramatically improved the power, suggesting that advanced intercrossing can help to resolve the problems that result from linkage among QTLs.

Indexed as

Crosses, GeneticModels, GeneticQuantitative Trait LociComputer SimulationGenetic DriftGenome, PlantInbreedingOryza

Identifiers

PMID24767139
PMCPMC4101851

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.