Literature DB >> 10893870

A general mixture model approach for mapping quantitative trait loci from diverse cross designs involving multiple inbred lines.

Y Liu1, Z B Zeng.   

Abstract

Most current statistical methods developed for mapping quantitative trait loci (QTL) based on inbred line designs apply to crosses from two inbred lines. Analysis of QTL in these crosses is restricted by the parental genetic differences between lines. Crosses from multiple inbred lines or multiple families are common in plant and animal breeding programmes, and can be used to increase the efficiency of a QTL mapping study. A general statistical method using mixture model procedures and the EM algorithm is developed for mapping QTL from various cross designs of multiple inbred lines. The general procedure features three cross design matrices, W, that define the contribution of parental lines to a particular cross and a genetic design matrix, D, that specifies the genetic model used in multiple line crosses. By appropriately specifying W matrices, the statistical method can be applied to various cross designs, such as diallel, factorial, cyclic, parallel or arbitrary-pattern cross designs with two or multiple parental lines. Also, with appropriate specification for the D matrix, the method can be used to analyse different kinds of cross populations, such as F2 backcross, four-way cross and mixed crosses (e.g. combining backcross and F2). Simulation studies were conducted to explore the properties of the method, and confirmed its applicability to diverse experimental designs.

Mesh:

Year:  2000        PMID: 10893870     DOI: 10.1017/s0016672300004493

Source DB:  PubMed          Journal:  Genet Res        ISSN: 0016-6723            Impact factor:   1.588


  19 in total

1.  Statistical methods for QTL mapping in cereals.

Authors:  Christine A Hackett
Journal:  Plant Mol Biol       Date:  2002 Mar-Apr       Impact factor: 4.076

2.  Rank-based statistical methodologies for quantitative trait locus mapping.

Authors:  Fei Zou; Brian S Yandell; Jason P Fine
Journal:  Genetics       Date:  2003-11       Impact factor: 4.562

3.  Optimal sampling of a population to determine QTL location, variance, and allelic number.

Authors:  Xiao-Lin Wu; Jean-Luc Jannink
Journal:  Theor Appl Genet       Date:  2004-01-23       Impact factor: 5.699

4.  Quantitative trait Loci association mapping by imputation of strain origins in multifounder crosses.

Authors:  Jin J Zhou; Anatole Ghazalpour; Eric M Sobel; Janet S Sinsheimer; Kenneth Lange
Journal:  Genetics       Date:  2011-12-05       Impact factor: 4.562

5.  Mapping of epistatic quantitative trait loci in four-way crosses.

Authors:  Xiao-Hong He; Hongde Qin; Zhongli Hu; Tianzhen Zhang; Yuan-Ming Zhang
Journal:  Theor Appl Genet       Date:  2010-09-09       Impact factor: 5.699

6.  Choice of models for QTL mapping with multiple families and design of the training set for prediction of Fusarium resistance traits in maize.

Authors:  Sen Han; H Friedrich Utz; Wenxin Liu; Tobias A Schrag; Michael Stange; Tobias Würschum; Thomas Miedaner; Eva Bauer; Chris-Carolin Schön; Albrecht E Melchinger
Journal:  Theor Appl Genet       Date:  2015-12-10       Impact factor: 5.699

7.  Combining data from multiple inbred line crosses improves the power and resolution of quantitative trait loci mapping.

Authors:  Renhua Li; Malcolm A Lyons; Henning Wittenburg; Beverly Paigen; Gary A Churchill
Journal:  Genetics       Date:  2005-01-16       Impact factor: 4.562

8.  Quantitative trait locus analysis using recombinant inbred intercrosses: theoretical and empirical considerations.

Authors:  Fei Zou; Jonathan A L Gelfond; David C Airey; Lu Lu; Kenneth F Manly; Robert W Williams; David W Threadgill
Journal:  Genetics       Date:  2005-05-06       Impact factor: 4.562

Review 9.  A review of statistical methods for expression quantitative trait loci mapping.

Authors:  Christina Kendziorski; Ping Wang
Journal:  Mamm Genome       Date:  2006-06-12       Impact factor: 2.957

10.  Genetic analysis of resistance to yellow rust in hexaploid wheat using a mixture model for multiple crosses.

Authors:  M J Christiansen; B Feenstra; I M Skovgaard; S B Andersen
Journal:  Theor Appl Genet       Date:  2006-01-05       Impact factor: 5.699

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