Literature DB >> 8056319

Use of multiple genetic markers in prediction of breeding values.

J A Van Arendonk1, B Tier, B P Kinghorn.   

Abstract

Genotypes at a marker locus give information on transmission of genes from parents to offspring and that information can be used in predicting the individuals' additive genetic value at a linked quantitative trait locus (MQTL). In this paper a recursive method is presented to build the gametic relationship matrix for an autosomal MQTL which requires knowledge on recombination rate between the marker locus and the MQTL linked to it. A method is also presented to obtain the inverse of the gametic relationship matrix. This information can be used in a mixed linear model for simultaneous evaluation of fixed effects, gametic effects at the MQTL and additive genetic effects due to quantitative trait loci unlinked to the marker locus (polygenes). An equivalent model can be written at the animal level using the numerator relationship matrix for the MQTL and a method for obtaining the inverse of this matrix is presented. Information on several unlinked marker loci, each of them linked to a different locus affecting the trait of interest, can be used by including an effect for each MQTL. The number of equations per animal in this case is 2m + 1 where m is the number of MQTL. A method is presented to reduce the number of equations per animal to one by combining information on all MQTL and polygenes into one numerator relationship matrix. It is illustrated how the method can accommodate individuals with partial or no marker information. Numerical examples are given to illustrate the methods presented. Opportunities to use the presented model in constructing genetic maps are discussed.

Mesh:

Substances:

Year:  1994        PMID: 8056319      PMCID: PMC1205948     

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


  4 in total

1.  Potential improvements in rate of genetic gain from marker-assisted selection in dairy cattle breeding schemes.

Authors:  T H Meuwissen; J A Van Arendonk
Journal:  J Dairy Sci       Date:  1992-06       Impact factor: 4.034

2.  Multipoint analysis of human quantitative genetic variation.

Authors:  D E Goldgar
Journal:  Am J Hum Genet       Date:  1990-12       Impact factor: 11.025

3.  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

4.  Primer-directed enzymatic amplification of DNA with a thermostable DNA polymerase.

Authors:  R K Saiki; D H Gelfand; S Stoffel; S J Scharf; R Higuchi; G T Horn; K B Mullis; H A Erlich
Journal:  Science       Date:  1988-01-29       Impact factor: 47.728

  4 in total
  18 in total

1.  Mixed model analysis of quantitative trait loci.

Authors:  S Xu; N Yi
Journal:  Proc Natl Acad Sci U S A       Date:  2000-12-19       Impact factor: 11.205

2.  Mapping quantitative trait loci in complex pedigrees: a two-step variance component approach.

Authors:  A W George; P M Visscher; C S Haley
Journal:  Genetics       Date:  2000-12       Impact factor: 4.562

3.  Approximating identity-by-descent matrices using multiple haplotype configurations on pedigrees.

Authors:  Guimin Gao; Ina Hoeschele
Journal:  Genetics       Date:  2005-06-18       Impact factor: 4.562

4.  Detection of quantitative trait loci in outbred populations with incomplete marker data.

Authors:  M C Bink; J A Van Arendonk
Journal:  Genetics       Date:  1999-01       Impact factor: 4.562

5.  Genetic evaluation by best linear unbiased prediction using marker and trait information in a multibreed population.

Authors:  T Wang; R L Fernando; M Grossman
Journal:  Genetics       Date:  1998-01       Impact factor: 4.562

6.  Advances in statistical methods to map quantitative trait loci in outbred populations.

Authors:  I Hoeschele; P Uimari; F E Grignola; Q Zhang; K M Gage
Journal:  Genetics       Date:  1997-11       Impact factor: 4.562

7.  Genetic response from marker assisted selection in an outbred population for differing marker bracket sizes and with two identified quantitative trait loci.

Authors:  R Spelman; H Bovenhuis
Journal:  Genetics       Date:  1998-03       Impact factor: 4.562

8.  Computation of the full likelihood function for estimating variance at a quantitative trait locus.

Authors:  S Xu
Journal:  Genetics       Date:  1996-12       Impact factor: 4.562

9.  Effect of IGF1, GH, and PIT1 markers on the genetic parameters of growth and reproduction traits in Canchim cattle.

Authors:  Daniela do Amaral Grossi; Marcos Eli Buzanskas; Natalia Vinhal Grupioni; Claudia Cristina Paro de Paz; Luciana Correia de Almeida Regitano; Maurício Mello de Alencar; Flávio Schramm Schenkel; Danísio Prado Munari
Journal:  Mol Biol Rep       Date:  2014-09-26       Impact factor: 2.316

10.  Impact of reduced marker set estimation of genomic relationship matrices on genomic selection for feed efficiency in Angus cattle.

Authors:  Megan M Rolf; Jeremy F Taylor; Robert D Schnabel; Stephanie D McKay; Matthew C McClure; Sally L Northcutt; Monty S Kerley; Robert L Weaber
Journal:  BMC Genet       Date:  2010-04-19       Impact factor: 2.797

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.