Screening for highly heterozygous chickens in outbred commercial broiler lines to increase detection power for mapping quantitative trait loci

Genome-wide patterns of genetic variation in two domestic chickens

Domestic chickens are excellent models for investigating the genetic basis of phenotypic diversity, as numerous phenotypic changes in physiology, morphology, and behavior in chickens have been artificially selected. Genomic study is required to study genome-wide patterns of DNA variation for dissecting the genetic basis of phenotypic traits. We sequenced the genomes of the Silkie and the Taiwanese native chicken L2 at ∼23- and 25-fold average coverage depth, respectively, using Illumina sequencing. The reads were mapped onto the chicken reference genome (including 5.1% Ns) to 92.32% genome coverage for the two breeds. Using a stringent filter, we identified ∼7.6 million single-nucleotide polymorphisms (SNPs) and 8,839 copy number variations (CNVs) in the mapped regions; 42% of the SNPs have not found in other chickens before. Among the 68,906 SNPs annotated in the chicken sequence assembly, 27,852 were nonsynonymous SNPs located in 13,537 genes. We also identified hundreds of shared and divergent structural and copy number variants in intronic and intergenic regions and in coding regions in the two breeds. Functional enrichments of identified genetic variants were discussed. Radical nsSNP-containing immunity genes were enriched in the QTL regions associated with some economic traits for both breeds. Moreover, genetic changes involved in selective sweeps were detected. From the selective sweeps identified in our two breeds, several genes associated with growth, appetite, and metabolic regulation were identified. Our study provides a framework for genetic and genomic research of domestic chickens and facilitates the domestic chicken as an avian model for genomic, biomedical, and evolutionary studies.

Comparison of information content for microsatellites and SNPs in poultry and cattle

Data were available for 12 poultry microsatellites and 29 poultry single nucleotide polymorphisms (SNPs), and for 34 cattle microsatellites and 36 cattle SNPs. Stochastic permutation was used to determine the number of SNPs needed to obtain the same average information content as a given number of microsatellites. For poultry, the information content averaged 0.71 for the 12 microsatellites compared to 0.72 for the 29 SNPs. For cattle, the information content averaged 0.92 for the 34 microsatellites compared with 0.79 for the 36 SNPs. This study shows that, for each microsatellite, three SNPs are needed to obtain the same average information content.

Use of a novel outbred by inbred F1 cross to detect genetic markers for growth

A unique outbred by inbred F1 resource population was established. The population structure facilitated the unique opportunity of examining gene by genetic background interaction through crossing two modern broiler sires with dams from two unrelated inbred lines, with no selection for growth rate, to produce about 600 F1 chicks. Pools of DNA were generated from the phenotypic extremes (20% high and low) for 8-week body weight for each of the four combinations of sire and dam line. For one sire family, pools were also separately generated for each sex. The pools were genoyped with 25 informative (segregating) microsatellites. This unique F1 cross between outbred and inbred populations allowed use of the inbred alleles as an 'internal control' for polymerase chain reaction amplification quality in DNA pools. Ten microsatellites showed marked differences (P < 0.05) in allele frequencies between high and low pools, suggesting an association between marker and quantitative trait loci (QTL). These differences were verified using selective genotyping. For many markers, differences in allele frequencies between the high and the low pools, or marker effect, varied between the two dam lines and the two sexes, suggesting an interaction between some genes and the genetic background as represented by different dam lines or sexes. The suggestive marker-QTL associations identified in this F1 population demonstrate the efficiency of this population design while different QTL effects in different genetic line crosses and sexes highlight the importance of gene by genetic background interaction in QTL detection.