Haplotype analysis incorporating ancestral origins identified novel genetic loci associated with chicken body weight using an advanced intercross line.

BACKGROUND

The genome-wide association study (GWAS) is a powerful method for mapping quantitative trait loci (QTL). However, standard GWAS can detect only QTL that segregate in the mapping population. Crossing populations with different characteristics increases genetic variability but F2 or back-crosses lack mapping resolution due to the limited number of recombination events. This drawback can be overcome with advanced intercross line (AIL) populations, which increase the number recombination events and provide a more accurate mapping resolution. Recent studies in humans have revealed ancestry-dependent genetic architecture and shown the effectiveness of admixture mapping in admixed populations.

RESULTS

Through the incorporation of line-of-origin effects and GWAS on an F AIL population, we identified genes that affect body weight at eight weeks of age (BW8) in chickens. The proposed ancestral-haplotype-based GWAS (testing only the origin regardless of the alleles) revealed three new QTLs on GGA12, GGA15, and GGA20. By using the concepts of ancestral homozygotes (individuals that carry two haplotypes of the same origin) and ancestral heterozygotes (carrying one haplotype of each origin), we identified 632 loci that exhibited high-parent (the heterozygote is better than both parents) and mid-parent (the heterozygote is better than the median of the parents) dominance across 12 chromosomes. Out of the 199 genes associated with BW8, EYA1, PDE1C, and MYC were identified as the best candidate genes for further validation.

CONCLUSIONS

In addition to the candidate genes reported in this study, our research demonstrates the effectiveness of incorporating ancestral information in population genetic analyses, which can be broadly applicable for genetic mapping in populations generated by ancestors with distinct phenotypes and genetic backgrounds. Our methods can benefit both geneticists and biologists interested in the genetic determinism of complex traits.