Literature DB >> 22444206

A simple method to approximate gene content in large pedigree populations: application to the myostatin gene in dual-purpose Belgian Blue cattle.

N Gengler1, P Mayeres, M Szydlowski.   

Abstract

Gene content is the number of copies of a particular allele in a genotype of an animal. Gene content can be used to study additive gene action of candidate gene. Usually genotype data are available only for a part of population and for the rest gene contents have to be calculated based on typed relatives. Methods to calculate expected gene content for animals on large complex pedigrees are relatively complex. In this paper we proposed a practical method to calculate gene content using a linear regression. The method does not estimate genotype probabilities but these can be approximated from gene content assuming Hardy-Weinberg proportions. The approach was compared with other methods on multiple simulated data sets for real bovine pedigrees of 1 082 and 907 903 animals. Different allelic frequencies (0.4 and 0.2) and proportions of the missing genotypes (90, 70, and 50%) were considered in simulation. The simulation showed that the proposed method has similar capability to predict gene content as the iterative peeling method, however it requires less time and can be more practical for large pedigrees. The method was also applied to real data on the bovine myostatin locus on a large dual-purpose Belgian Blue pedigree of 235 133 animals. It was demonstrated that the proposed method can be easily adapted for particular pedigrees.

Entities:  

Year:  2007        PMID: 22444206     DOI: 10.1017/S1751731107392628

Source DB:  PubMed          Journal:  Animal        ISSN: 1751-7311            Impact factor:   3.240


  32 in total

1.  Quality control of genotypes using heritability estimates of gene content at the marker.

Authors:  Natalia S Forneris; Andres Legarra; Zulma G Vitezica; Shogo Tsuruta; Ignacio Aguilar; Ignacy Misztal; Rodolfo J C Cantet
Journal:  Genetics       Date:  2015-01-06       Impact factor: 4.562

2.  GenoMatrix: A Software Package for Pedigree-Based and Genomic Prediction Analyses on Complex Traits.

Authors:  Alireza Nazarian; Salvador Alejandro Gezan
Journal:  J Hered       Date:  2016-03-29       Impact factor: 2.645

3.  A Simple Test Identifies Selection on Complex Traits.

Authors:  Tim Beissinger; Jochen Kruppa; David Cavero; Ngoc-Thuy Ha; Malena Erbe; Henner Simianer
Journal:  Genetics       Date:  2018-03-15       Impact factor: 4.562

4.  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

5.  A note on the rationale for estimating genealogical coancestry from molecular markers.

Authors:  Miguel Angel Toro; Luis Alberto García-Cortés; Andrés Legarra
Journal:  Genet Sel Evol       Date:  2011-07-12       Impact factor: 4.297

6.  Genome partitioning of genetic variation for milk production and composition traits in holstein cattle.

Authors:  Eduardo da Cruz Gouveia Pimentel; Malena Erbe; Sven König; Henner Simianer
Journal:  Front Genet       Date:  2011-05-02       Impact factor: 4.599

7.  A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation.

Authors:  John M Hickey; Brian P Kinghorn; Bruce Tier; Julius H J van der Werf; Matthew A Cleveland
Journal:  Genet Sel Evol       Date:  2012-06-19       Impact factor: 4.297

8.  Genomic prediction when some animals are not genotyped.

Authors:  Ole F Christensen; Mogens S Lund
Journal:  Genet Sel Evol       Date:  2010-01-27       Impact factor: 4.297

9.  Genomic estimated breeding values using genomic relationship matrices in a cloned population of loblolly pine.

Authors:  Jaime Zapata-Valenzuela; Ross W Whetten; David Neale; Steve McKeand; Fikret Isik
Journal:  G3 (Bethesda)       Date:  2013-05-20       Impact factor: 3.154

10.  Compatibility of pedigree-based and marker-based relationship matrices for single-step genetic evaluation.

Authors:  Ole F Christensen
Journal:  Genet Sel Evol       Date:  2012-12-03       Impact factor: 4.297

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