Literature DB >> 24288072

Composite interval mapping and mixed models reveal QTL associated with performance and carcass traits on chicken chromosomes 1, 3, and 4.

M F Rosario1, R Gazaffi, A S A M T Moura, M C Ledur, L L Coutinho, A A F Garcia.   

Abstract

Interval mapping (IM) implemented in QTL Express or GridQTL is widely used, but presents some limitations, such as restriction to a fixed model, risk of mapping two QTL when there may be only one and no discrimination of two or more QTL using both cofactors located on the same and other chromosomes. These limitations were overcome with composite interval mapping (CIM). We reported QTL associated with performance and carcass traits on chicken chromosomes 1, 3, and 4 through implementation of CIM and analysis of phenotypic data using mixed models. Thirty-four microsatellite markers were used to genotype 360 F2 chickens from crosses between males from a layer line and females from a broiler line. Sixteen QTL were mapped using CIM and 14 QTL with IM. Furthermore, of those 30 QTL, six were mapped only when CIM was used: for body weight at 35 days (first and third peaks on GGA4), body weight at 41 days (GGA1B and second peak on GGA4), and weights of back and legs (both on GGA4). Three new regions had evidence for QTL presence: one on GGA1B associated with feed intake 35-41 d at 404 cM (LEI0107-ADL0183) and two on GGA4 associated with weight of back at 163 cM (LEI0076-MCW0240) and weight gain 35-41 d, feed efficiency 35-41 d and weight of legs at 241 cM (LEI0085-MCW0174). We dissected one more linked QTL on GGA4, where three QTL for BW35 and two QTL for BW41 were mapped. Therefore, these new regions mapped here need further investigations using high-density SNP to confirm these QTL and identify candidate genes associated with those traits.

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Year:  2013        PMID: 24288072     DOI: 10.1007/s13353-013-0185-6

Source DB:  PubMed          Journal:  J Appl Genet        ISSN: 1234-1983            Impact factor:   3.240


  22 in total

1.  Relaxed significance criteria for linkage analysis.

Authors:  Lin Chen; John D Storey
Journal:  Genetics       Date:  2006-06-18       Impact factor: 4.562

2.  Mapping QTLs on chicken chromosome 1 for performance and carcass traits in a broiler x layer cross.

Authors:  K Nones; M C Ledur; D C Ruy; E E Baron; C M R Melo; A S A M T Moura; E L Zanella; D W Burt; L L Coutinho
Journal:  Anim Genet       Date:  2006-04       Impact factor: 3.169

3.  A simple regression method for mapping quantitative trait loci in line crosses using flanking markers.

Authors:  C S Haley; S A Knott
Journal:  Heredity (Edinb)       Date:  1992-10       Impact factor: 3.821

4.  Quantitative trait loci associated with fatness in a broiler-layer cross.

Authors:  R L R Campos; K Nones; M C Ledur; A S A M T Moura; L F B Pinto; M Ambo; C Boschiero; D C Ruy; E E Baron; K Ninov; C A B Altenhofen; R A M S Silva; M F Rosário; D W Burt; L L Coutinho
Journal:  Anim Genet       Date:  2009-05-16       Impact factor: 3.169

5.  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

6.  Detection of quantitative trait loci influencing dairy traits using a model for longitudinal data.

Authors:  S L Rodriguez-Zas; B R Southey; D W Heyen; H A Lewin
Journal:  J Dairy Sci       Date:  2002-10       Impact factor: 4.034

7.  Quantitative trait loci for performance traits in a broiler x layer cross.

Authors:  M Ambo; A S A M T Moura; M C Ledur; L F B Pinto; E E Baron; D C Ruy; K Nones; R L R Campos; C Boschiero; D W Burt; L L Coutinho
Journal:  Anim Genet       Date:  2008-01-20       Impact factor: 3.169

8.  Multiple marker mapping of quantitative trait loci in a cross between outbred wild boar and large white pigs.

Authors:  S A Knott; L Marklund; C S Haley; K Andersson; W Davies; H Ellegren; M Fredholm; I Hansson; B Hoyheim; K Lundström; M Moller; L Andersson
Journal:  Genetics       Date:  1998-06       Impact factor: 4.562

Review 9.  Second report on chicken genes and chromosomes 2005.

Authors:  M Schmid; I Nanda; H Hoehn; M Schartl; T Haaf; J-M Buerstedde; H Arakawa; R B Caldwell; S Weigend; D W Burt; J Smith; D K Griffin; J S Masabanda; M A M Groenen; R P M A Crooijmans; A Vignal; V Fillon; M Morisson; F Pitel; M Vignoles; A Garrigues; J Gellin; A V Rodionov; S A Galkina; N A Lukina; G Ben-Ari; S Blum; J Hillel; T Twito; U Lavi; L David; M W Feldman; M E Delany; C A Conley; V M Fowler; S B Hedges; R Godbout; S Katyal; C Smith; Q Hudson; A Sinclair; S Mizuno
Journal:  Cytogenet Genome Res       Date:  2005       Impact factor: 1.636

10.  Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution.

Authors: 
Journal:  Nature       Date:  2004-12-09       Impact factor: 49.962

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