Abebe T. Hassen

Linkage Disequilibrium in Related Breeding Lines of Chickens

High-density genotyping of single-nucleotide polymorphisms (SNPs) enables detection of quantitative trait loci (QTL) by linkage disequilibrium (LD) mapping using LD between markers and QTL and the subsequent use of this information for marker-assisted selection (MAS). The success of LD mapping and MAS depends on the extent of LD in the populations of interest and the use of associations across populations requires LD between loci to be consistent across populations. To assess the extent and consistency of LD in commercial broiler breeding populations, we used genotype data for 959 and 398 SNPs on chromosomes 1 and 4 on 179–244 individuals from each of nine commercial broiler chicken breeding lines. Results show that LD measured by r2 extends over shorter distances than reported previously in other livestock breeding populations. The LD at short distance (within 1 cM) tended to be consistent across related populations; correlations of LD measured by r for pairs of lines ranged from 0.17 to 0.94 and closely matched the line relationships based on marker allele frequencies. In conclusion, LD-based correlations are good estimates of line relationships and the relationship between a pair of lines a good predictor of LD consistency between the lines.

Extent and Consistency of Linkage Disequilibrium and Identification of DNA Markers for Production and Egg Quality Traits in Commercial Layer Chicken Populations

BackgroundThe genome sequence and a high-density SNP map are now available for the chicken and can be used to identify genetic markers for use in marker-assisted selection (MAS). Effective MAS requires high linkage disequilibrium (LD) between markers and quantitative trait loci (QTL), and sustained marker-QTL LD over generations. This study used data from a 3,000 SNP panel to assess the level and consistency of LD between single nucleotide polymorphisms (SNPs) over consecutive years in two egg-layer chicken lines, and analyzed one line by two methods (SNP-wise association and genome-wise Bayesian analysis) to identify markers associated with egg-quality and egg-production phenotypes.ResultsThe LD between markers pairs was high at short distances (r2 > 0.2 at < 2 Mb) and remained high after one generation (correlations of 0.80 to 0.92 at < 5 Mb) in both lines. Single- and 3-SNP regression analyses using a mixed model with SNP as fixed effect resulted in 159 and 76 significant tests (P < 0.01), respectively, across 12 traits. A Bayesian analysis called BayesB, that fits all SNPs simultaneously as random effects and uses model averaging procedures, identified 33 SNPs that were included in the model >20% of the time (φ > 0.2) and an additional ten 3-SNP windows that had a sum of φ greater than 0.35. Generally, SNPs included in the Bayesian model also had a small P-value in the 1-SNP analyses.ConclusionHigh LD correlations between markers at short distances across two generations indicate that such markers will retain high LD with linked QTL and be effective for MAS. The different association analysis methods used provided consistent results. Multiple single SNPs and 3-SNP windows were significantly associated with egg-related traits, providing genomic positions of QTL that can be useful for both MAS and to identify causal mutations.

High resolution, advanced intercross mapping of host resistance to Salmonella colonization.

Availability of a dense single-nucleotide polymorphism (SNP) map in chickens has allowed for whole-genome QTL mapping for disease resistance. In this study, two F8 advanced intercross lines of broiler by Fayoumi and broiler by Leghorn chickens, and a dense, genome-wide SNP panel were used to map genomic regions associated with host resistance to bacterial colonisation. One week after inoculation of day-old chicks with Salmonella enteritidis (SE), caecum and spleen tissues were collected to quantitate the bacterial load. Of 2733 genotyped SNPs, 875 were homozygous and the remaining SNPs with a minor allele frequency of > 0.2 were individually tested for association with SE burden by utilising a Chi-square log-likelihood test between models with and without SNP genotype. Using a Q-value of 25%, calculated utilising 1207 SNPs with Chi-square P-Value < 1.0 to control for false discovery, 21 SNPs identifying 19 genes were significantly associated with SE bacterial levels. Ten genes were in pathways associated with immune response to Salmonella (toll-like receptor signaling, apoptosis, and MAPK signaling), further supporting their involvement in host resistance pathways. In addition to identifying new candidate genes for bacterial resistance, the trait-associated SNPs may be useful in marker assisted selection programmes for disease resistance.

The Effect of Heritability Estimates on High-Density Single Nucleotide Polymorphism Analyses with Related Animals

Analysis of high-density SNP data in outbred populations to identify SNP that are associated with a quantitative trait requires efficient ways to handle large volumes of data and analyses. When using mixed animal models to account for polygenic effects and relationships, genetic parameters are not known with certainty, but must be chosen to ensure proper evaluation of SNP across chromosomes and lines or breeds. The objectives of this study were to evaluate the influence of heritability on the estimates and significance of SNP effects, to develop efficient computational strategies for analysis of high-density SNP data with uncertain heritability estimates, and to develop strategies to combine SNP test results across lines or breeds. Data included sire SNP genotypes and mean progeny performance from 2 commercial broiler breeding lines. Association analyses were done by fitting each SNP separately as a fixed effect in an animal model, using a range of heritabilities. The heritability used had a limited impact on SNP effect estimates, but affected the SE of estimates and levels of significance. The shape of the frequency distribution of P-values for the test of SNP effects changed from a highly skewed L-shaped curve at low heritability to a right-skewed distribution at high heritability. The P-values for alternative heritabilities could, however, be derived without reanalysis based on a strong linear relationship (R(2) = 0.99) between differences in log-likelihood values of models with and without the SNP at different levels of heritabilities. With uncertain estimates of heritability, line-specific heritabilities that ensure proper evaluation of SNP effects across lines were determined by analysis of simulated sire genotypes and by permutation tests. Resulting heritability estimates were between those obtained from the entire breeding populations and those obtained from the data included in the sample data set. In conclusion, the uncertainty of heritability estimates has a limited impact on SNP effect estimates in association analyses, but a large impact on significance tests. The impact of heritability on tests can, however, be dealt with in a computationally efficient manner by using the strong linear relationship between model statistics under alternate levels of heritability. These approaches allow efficient analysis of large numbers of SNP for multiple traits and populations and pooling of results across populations.