Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values

被引:48
作者
Calus, Mario P. L. [1 ]
Meuwissen, Theo H. E. [2 ]
Windig, Jack J. [1 ]
Knol, Egbert F. [3 ]
Schrooten, Chris [4 ]
Vereijken, Addie L. J. [5 ]
Veerkamp, Roel F. [1 ]
机构
[1] Wageningen Univ & Res Ctr, Anim Breeding & Genom Ctr, Anim Sci Grp, NL-8200 AB Lelystad, Netherlands
[2] Univ Life Sci, Dept Anim & Aquacultural Sci, As, Norway
[3] IPG, Beuningen, Netherlands
[4] CRV, Arnhem, Netherlands
[5] Hendrix Genet BV, Boxmeer, Netherlands
关键词
QUANTITATIVE TRAIT LOCI; LINKAGE DISEQUILIBRIUM; ASSISTED SELECTION; IDENTITY; DESCENT; INFORMATION; PARAMETERS;
D O I
10.1186/1297-9686-41-11
中图分类号
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号
0905 ;
摘要
The aim of this paper was to compare the effect of haplotype definition on the precision of QTL-mapping and on the accuracy of predicted genomic breeding values. In a multiple QTL model using identity-by-descent (IBD) probabilities between haplotypes, various haplotype definitions were tested i.e. including 2, 6, 12 or 20 marker alleles and clustering base haplotypes related with an IBD probability of > 0.55, 0.75 or 0.95. Simulated data contained 1100 animals with known genotypes and phenotypes and 1000 animals with known genotypes and unknown phenotypes. Genomes comprising 3 Morgan were simulated and contained 74 polymorphic QTL and 383 polymorphic SNP markers with an average r(2) value of 0.14 between adjacent markers. The total number of haplotypes decreased up to 50% when the window size was increased from two to 20 markers and decreased by at least 50% when haplotypes related with an IBD probability of > 0.55 instead of > 0.95 were clustered. An intermediate window size led to more precise QTL mapping. Window size and clustering had a limited effect on the accuracy of predicted total breeding values, ranging from 0.79 to 0.81. Our conclusion is that different optimal window sizes should be used in QTL-mapping versus genome-wide breeding value prediction.
引用
收藏
页数:10
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