Effect of allele frequencies, effect sizes and number of markers on prediction of quantitative traits in chickens

R. Abdollahi-Arpanahi, A. Nejati-Javaremi, A. Pakdel, M. Moradi-Shahrbabak, G. Morota, B. D. Valente, A. Kranis, G. J.M. Rosa, D. Gianola

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


The objective was to assess goodness of fit and predictive ability of subsets of single nucleotide polymorphism (SNP) markers constructed based on minor allele frequency (MAF), effect sizes and varying marker density. Target traits were body weight (BW), ultrasound measurement of breast muscle (BM) and hen house egg production (HHP) in broiler chickens. We used a 600 K Affymetrix platform with 1352 birds genotyped. The prediction method was genomic best linear unbiased prediction (GBLUP) with 354 564 single nucleotide polymorphisms (SNPs) used to derive a genomic relationship matrix (G). Predictive ability was assessed as the correlation between predicted genomic values and corrected phenotypes from a threefold cross-validation. Predictive ability was 0.27 ± 0.002 for BW, 0.33 ± 0.001 for BM and 0.20 ± 0.002 for HHP. For the three traits studied, predictive ability decreased when SNPs with a higher MAF were used to construct G. Selection of the 20% SNPs with the largest absolute effect sizes induced a predictive ability equal to that from fitting all markers together. When density of markers increased from 5 K to 20 K, predictive ability enhanced slightly. These results provide evidence that designing a low-density chip using low-frequency markers with large effect sizes may be useful for commercial usage.

Original languageEnglish (US)
Pages (from-to)123-133
Number of pages11
JournalJournal of Animal Breeding and Genetics
Issue number2
StatePublished - Apr 2014
Externally publishedYes


  • Broiler chicken
  • Genome-enabled prediction
  • Genomic best linear unbiased prediction
  • Marker density
  • Marker effect sizes
  • Minor allele frequency
  • Predictive ability

ASJC Scopus subject areas

  • Food Animals
  • Animal Science and Zoology


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