A Restricted Partition Method to Detect Single Nucleotide Polymorphisms for a Carcass Trait in Hanwoo
- A Restricted Partition Method to Detect Single Nucleotide Polymorphisms for a Carcass Trait in Hanwoo
- 이제영; 이지홍[이지홍]; 김동철[김동철]; 김종주
- WHOLE-GENOME ASSOCIATION; CATTLE; CHROMOSOME-6; POPULATION; GENES; MDR
- Issue Date
- ASIAN-AUSTRALASIAN ASSOC ANIMAL PRODUCTION SOC
- ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES, v.24, no.11, pp.1525 - 1528
- The purpose of this study was to detect SNPs that were responsible for a carcass trait in Hanwoo populations. A non-parametric model applying a restricted partition method (RPM) was used, which exploited a partitioning algorithm considering statistical criteria for multiple comparison testing. Phenotypic and genotypic data were obtained from the Hanwoo Improvement Center, National Agricultural Cooperation Federation, Korea, in which the pedigree structure comprised 229 steers from 16 paternal half-sib proven sires that were born in Namwon or Daegwanryong livestock testing station between spring of 2002 and fall of 2003. A carcass trait, longissimus dorsi muscle area for each steer was measured after slaughter at approximately 722 days. Three SNPs (19_1, 18_4 and 28_2) near the microsatellite marker ILSTS035 on BTA6, around which the quantitative trait loci (QTL) for meat quality were previously detected, were used in this study. The RPM analyses resulted in two significant interaction effects between SNPs (19_1 and 18_4) and (19_1 and 28_2) at alpha = 0.05 level. However, under a general linear (parametric) model no interaction effect between any pair of the three SNPs was detected, while only one main effect for SNP 19_1 was found for the trait. Also, under another non-parametric model using a multifactor dimensionality reduction (MDR) method, only one interaction effect of the two SNPs (19_1 and 28_2) explained the trait significantly better than the parametric model with the main effect of SNP 19_1. Our results suggest that RPM is a good alternative to model choices that can find associations of the interaction effects of multiple SNPs for quantitative traits in livestock species.
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