Literature DB >> 17947400

Linkage disequilibrium in related breeding lines of chickens.

Cristina Andreescu1, Santiago Avendano, Stewart R Brown, Abebe Hassen, Susan J Lamont, Jack C M Dekkers.   

Abstract

High-density genotyping of single-nucleotide polymorphisms (SNPs) enables detection of quantitative trait loci (QTL) by linkage disequilibrium (LD) mapping using LD between markers and QTL and the subsequent use of this information for marker-assisted selection (MAS). The success of LD mapping and MAS depends on the extent of LD in the populations of interest and the use of associations across populations requires LD between loci to be consistent across populations. To assess the extent and consistency of LD in commercial broiler breeding populations, we used genotype data for 959 and 398 SNPs on chromosomes 1 and 4 on 179-244 individuals from each of nine commercial broiler chicken breeding lines. Results show that LD measured by r(2) extends over shorter distances than reported previously in other livestock breeding populations. The LD at short distance (within 1 cM) tended to be consistent across related populations; correlations of LD measured by r for pairs of lines ranged from 0.17 to 0.94 and closely matched the line relationships based on marker allele frequencies. In conclusion, LD-based correlations are good estimates of line relationships and the relationship between a pair of lines a good predictor of LD consistency between the lines.

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Year:  2007        PMID: 17947400      PMCID: PMC2219483          DOI: 10.1534/genetics.107.082206

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  24 in total

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Journal:  Cold Spring Harb Symp Quant Biol       Date:  2003

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4.  Evaluation of linkage disequilibrium measures between multi-allelic markers as predictors of linkage disequilibrium between markers and QTL.

Authors:  H Zhao; D Nettleton; M Soller; J C M Dekkers
Journal:  Genet Res       Date:  2005-08       Impact factor: 1.588

5.  Evaluation of linkage disequilibrium measures between multi-allelic markers as predictors of linkage disequilibrium between single nucleotide polymorphisms.

Authors:  H Zhao; D Nettleton; J C M Dekkers
Journal:  Genet Res       Date:  2007-02       Impact factor: 1.588

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Review 7.  Linkage disequilibrium in humans: models and data.

Authors:  J K Pritchard; M Przeworski
Journal:  Am J Hum Genet       Date:  2001-06-14       Impact factor: 11.025

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Authors: 
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Review 10.  Characterizing linkage disequilibrium in pig populations.

Authors:  Feng-Xing Du; Archie C Clutter; Michael M Lohuis
Journal:  Int J Biol Sci       Date:  2007-02-10       Impact factor: 6.580

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  37 in total

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3.  Genomic selection using low-density marker panels.

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4.  Divergence between human populations estimated from linkage disequilibrium.

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5.  Association mapping and gene-gene interaction for stem rust resistance in CIMMYT spring wheat germplasm.

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6.  The recombination landscape of the zebra finch Taeniopygia guttata genome.

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Journal:  Genome Res       Date:  2010-03-31       Impact factor: 9.043

7.  Pronounced inter- and intrachromosomal variation in linkage disequilibrium across the zebra finch genome.

Authors:  Jessica Stapley; Tim R Birkhead; Terry Burke; Jon Slate
Journal:  Genome Res       Date:  2010-03-31       Impact factor: 9.043

8.  Reliability of genomic predictions across multiple populations.

Authors:  A P W de Roos; B J Hayes; M E Goddard
Journal:  Genetics       Date:  2009-10-12       Impact factor: 4.562

9.  Prediction of haplotypes for ungenotyped animals and its effect on marker-assisted breeding value estimation.

Authors:  Han A Mulder; Mario P L Calus; Roel F Veerkamp
Journal:  Genet Sel Evol       Date:  2010-03-22       Impact factor: 4.297

10.  Comparison of linkage disequilibrium and haplotype diversity on macro- and microchromosomes in chicken.

Authors:  Hendrik-Jan Megens; Richard P M A Crooijmans; John W M Bastiaansen; Hindrik H D Kerstens; Albart Coster; Ruud Jalving; Addie Vereijken; Pradeepa Silva; William M Muir; Hans H Cheng; Olivier Hanotte; Martien A M Groenen
Journal:  BMC Genet       Date:  2009-12-20       Impact factor: 2.797

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