Literature DB >> 21373796

Gene and QTL detection in a three-way barley cross under selection by a mixed model with kinship information using SNPs.

Marcos Malosetti1, Fred A van Eeuwijk, Martin P Boer, Ana M Casas, Mónica Elía, Marian Moralejo, Prasanna R Bhat, Luke Ramsay, José-Luis Molina-Cano.   

Abstract

Quantitative trait locus (QTL) detection is commonly performed by analysis of designed segregating populations derived from two inbred parental lines, where absence of selection, mutation and genetic drift is assumed. Even for designed populations, selection cannot always be avoided, with as consequence varying correlation between genotypes instead of uniform correlation. Akin to linkage disequilibrium mapping, ignoring this type of genetic relatedness will increase the rate of false-positives. In this paper, we advocate using mixed models including genetic relatedness, or 'kinship' information for QTL detection in populations where selection forces operated. We demonstrate our case with a three-way barley cross, designed to segregate for dwarfing, vernalization and spike morphology genes, in which selection occurred. The population of 161 inbred lines was screened with 1,536 single nucleotide polymorphisms (SNPs), and used for gene and QTL detection. The coefficient of coancestry matrix was estimated based on the SNPs and imposed to structure the distribution of random genotypic effects. The model incorporating kinship, coancestry, information was consistently superior to the one without kinship (according to the Akaike information criterion). We show, for three traits, that ignoring the coancestry information results in an unrealistically high number of marker-trait associations, without providing clear conclusions about QTL locations. We used a number of widely recognized dwarfing and vernalization genes known to segregate in the studied population as landmarks or references to assess the agreement of the mapping results with a priori candidate gene expectations. Additional QTLs to the major genes were detected for all traits as well.

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Year:  2011        PMID: 21373796      PMCID: PMC3082036          DOI: 10.1007/s00122-011-1558-z

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  32 in total

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Journal:  Genetics       Date:  2004-11       Impact factor: 4.562

2.  Power and precision of alternate methods for linkage disequilibrium mapping of quantitative trait loci.

Authors:  H H Zhao; R L Fernando; J C M Dekkers
Journal:  Genetics       Date:  2007-02-04       Impact factor: 4.562

Review 3.  Estimation of quantitative genetic parameters.

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Journal:  Proc Biol Sci       Date:  2008-03-22       Impact factor: 5.349

4.  Multi-environment QTL mixed models for drought stress adaptation in wheat.

Authors:  Ky L Mathews; Marcos Malosetti; Scott Chapman; Lynne McIntyre; Matthew Reynolds; Ray Shorter; Fred van Eeuwijk
Journal:  Theor Appl Genet       Date:  2008-08-12       Impact factor: 5.699

5.  GA-20 oxidase as a candidate for the semidwarf gene sdw1/denso in barley.

Authors:  Qiaojun Jia; Jingjuan Zhang; Sharon Westcott; Xiao-Qi Zhang; Mathew Bellgard; Reg Lance; Chengdao Li
Journal:  Funct Integr Genomics       Date:  2009-03-12       Impact factor: 3.410

6.  Mapping mendelian factors underlying quantitative traits using RFLP linkage maps.

Authors:  E S Lander; D Botstein
Journal:  Genetics       Date:  1989-01       Impact factor: 4.562

7.  Mapping quantitative trait Loci using distorted markers.

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8.  RFLP mapping of five major genes and eight quantitative trait loci controlling flowering time in a winter x spring barley (Hordeum vulgare L.) cross.

Authors:  D A Laurie; N Pratchett; J W Snape; J H Bezant
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9.  A mixed-model quantitative trait loci (QTL) analysis for multiple-environment trial data using environmental covariables for QTL-by-environment interactions, with an example in maize.

Authors:  Martin P Boer; Deanne Wright; Lizhi Feng; Dean W Podlich; Lang Luo; Mark Cooper; Fred A van Eeuwijk
Journal:  Genetics       Date:  2007-10-18       Impact factor: 4.562

10.  Gene expression quantitative trait locus analysis of 16 000 barley genes reveals a complex pattern of genome-wide transcriptional regulation.

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  25 in total

Review 1.  Mapping QTL for agronomic traits in breeding populations.

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Journal:  Theor Appl Genet       Date:  2012-05-22       Impact factor: 5.699

Review 2.  MAGIC populations in crops: current status and future prospects.

Authors:  B Emma Huang; Klara L Verbyla; Arunas P Verbyla; Chitra Raghavan; Vikas K Singh; Pooran Gaur; Hei Leung; Rajeev K Varshney; Colin R Cavanagh
Journal:  Theor Appl Genet       Date:  2015-04-09       Impact factor: 5.699

3.  Whole-genome QTL analysis for MAGIC.

Authors:  Arūnas P Verbyla; Andrew W George; Colin R Cavanagh; Klara L Verbyla
Journal:  Theor Appl Genet       Date:  2014-06-14       Impact factor: 5.699

4.  Genotyping-by-sequencing and multilocation evaluation of two interspecific backcross populations identify QTLs for yield-related traits in pigeonpea.

Authors:  Rachit K Saxena; Sandip Kale; Reyazul Rouf Mir; Nalini Mallikarjuna; Pooja Yadav; Roma Rani Das; Johiruddin Molla; Muniswamy Sonnappa; Anuradha Ghanta; Yamini Narasimhan; Abhishek Rathore; C V Sameer Kumar; Rajeev K Varshney
Journal:  Theor Appl Genet       Date:  2019-12-16       Impact factor: 5.699

5.  Resistance to Rhynchosporium commune in a collection of European spring barley germplasm.

Authors:  Mark E Looseley; Lucie L Griffe; Bianca Büttner; Kathryn M Wright; Jill Middlefell-Williams; Hazel Bull; Paul D Shaw; Malcolm Macaulay; Allan Booth; Günther Schweizer; Joanne R Russell; Robbie Waugh; William T B Thomas; Anna Avrova
Journal:  Theor Appl Genet       Date:  2018-08-27       Impact factor: 5.699

6.  Efficient QTL detection of flowering date in a soybean RIL population using the novel restricted two-stage multi-locus GWAS procedure.

Authors:  Liyuan Pan; Jianbo He; Tuanjie Zhao; Guangnan Xing; Yufeng Wang; Deyue Yu; Shouyi Chen; Junyi Gai
Journal:  Theor Appl Genet       Date:  2018-08-30       Impact factor: 5.699

Review 7.  Major flowering time genes of barley: allelic diversity, effects, and comparison with wheat.

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Journal:  Theor Appl Genet       Date:  2021-05-09       Impact factor: 5.574

8.  Genome dynamics explain the evolution of flowering time CCT domain gene families in the Poaceae.

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9.  Perennial Rye: Genetics of Perenniality and Limited Fertility.

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Journal:  Plants (Basel)       Date:  2021-06-14

Review 10.  Effects of the semi-dwarfing sdw1/denso gene in barley.

Authors:  Anetta Kuczyńska; Maria Surma; Tadeusz Adamski; Krzysztof Mikołajczak; Karolina Krystkowiak; Piotr Ogrodowicz
Journal:  J Appl Genet       Date:  2013-08-22       Impact factor: 3.240

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