Fine-mapping quantitative trait loci with a medium density marker panel: efficiency of population structures and comparison of linkage disequilibrium linkage analysis models

Mapping genomic regions affecting milk traits in Sarda sheep by using the OvineSNP50 Beadchip and principal components to perform combined linkage and linkage disequilibrium analysis

Background

The detection of regions that affect quantitative traits (QTL), to implement selection assisted by molecular information, remains of particular interest in dairy sheep for which genetic gain is constrained by the high costs of large-scale phenotype and pedigree recording. QTL detection based on the combination of linkage disequilibrium and linkage analysis (LDLA) is the most suitable approach in family-structured populations. The main issue in performing LDLA mapping is the handling of the identity-by-descent (IBD) probability matrix. Here, we propose the use of principal component analysis (PCA) to perform LDLA mapping for milk traits in Sarda dairy sheep.

Methods

A resource population of 3731 ewes belonging to 161 sire families and genotyped with the OvineSNP50 Beadchip was used to map genomic regions that affect five milk traits. The paternally and maternally inherited gametes of genotyped individuals were reconstructed and IBD probabilities between them were defined both at each SNP position and at the genome level. A QTL detection model fitting fixed effects of principal components that summarize IBD probabilities was tested at each SNP position. Genome-wide (GW) significance thresholds were determined by within-trait permutations.

Results

PCA resulted in substantial dimensionality reduction, in fact 137 and 32 (on average) principal components were able to capture 99% of the IBD variation at the locus and genome levels, respectively. Overall, 2563 positions exceeded the 0.05 GW significance threshold for at least one trait, which clustered into 75 QTL regions most of which affected more than one trait. The strongest signal was obtained for protein content on Ovis aries (OAR) chromosome 6 and overlapped with the region that harbours the casein gene cluster. Additional interesting positions were identified on OAR4 for fat content and on OAR11 for the three yield traits.

Conclusions

PCA is a good strategy to summarize IBD probabilities. A large number of regions associated to milk traits were identified. The outputs provided by the proposed method are useful for the selection of candidate genes, which need to be further investigated to identify causative mutations or markers in strong LD with them for application in selection programs assisted by molecular information.

Using haplotypes for the prediction of allelic identity to fine-map QTL: characterization and properties

BACKGROUND

Numerous methods have been developed over the last decade to predict allelic identity at unobserved loci between pairs of chromosome segments along the genome. These loci are often unobserved positions tested for the presence of quantitative trait loci (QTL). The main objective of this study was to understand from a theoretical standpoint the relation between linkage disequilibrium (LD) and allelic identity prediction when using haplotypes for fine mapping of QTL. In addition, six allelic identity predictors (AIP) were also compared in this study to determine which one performed best in theory and application.

RESULTS

A criterion based on a simple measure of matrix distance was used to study the relation between LD and allelic identity prediction when using haplotypes. The consistency of this criterion with the accuracy of QTL localization, another criterion commonly used to compare AIP, was evaluated on a set of real chromosomes. For this set of chromosomes, the criterion was consistent with the mapping accuracy of a simulated QTL with either low or high effect. As measured by the matrix distance, the best AIP for QTL mapping were those that best captured LD between a tested position and a QTL. Moreover the matrix distance between a tested position and a QTL was shown to decrease for some AIP when LD increased. However, the matrix distance for AIP with continuous predictions in the [0,1] interval was algebraically proven to decrease less rapidly up to a lower bound with increasing LD in the simplest situations, than the discrete predictor based on identity by state between haplotypes (IBS hap), for which there was no lower bound. The expected LD between haplotypes at a tested position and alleles at a QTL is a quantity that increases naturally when the tested position gets closer to the QTL. This behavior was demonstrated with pig and unrelated human chromosomes.

CONCLUSIONS

When the density of markers is high, and therefore LD between adjacent loci can be assumed to be high, the discrete predictor IBS hap is recommended since it predicts allele identity correctly when taking LD into account.

Identification of quantitative trait loci underlying milk traits in Spanish dairy sheep using linkage plus combined linkage disequilibrium and linkage analysis approaches

In this study, 2 procedures were used to analyze a data set from a whole-genome scan, one based on linkage analysis information and the other combing linkage disequilibrium and linkage analysis (LDLA), to determine the quantitative trait loci (QTL) influencing milk production traits in sheep. A total of 1,696 animals from 16 half-sib families were genotyped using the OvineSNP50 BeadChip (Illumina Inc., San Diego, CA) and analysis was performed using a daughter design. Moreover, the same data set has been previously investigated through a genome-wide association (GWA) analysis and a comparison of results from the 3 methods has been possible. The linkage analysis and LDLA methodologies yielded different results, although some significantly associated regions were common to both procedures. The linkage analysis detected 3 overlapping genome-wise significant QTL on sheep chromosome (OAR) 2 influencing milk yield, protein yield, and fat yield, whereas 34 genome-wise significant QTL regions were detected using the LDLA approach. The most significant QTL for protein and fat percentages was detected on OAR3, which was reported in a previous GWA analysis. Both the linkage analysis and LDLA identified many other chromosome-wise significant associations across different sheep autosomes. Additional analyses were performed on OAR2 and OAR3 to determine the possible causality of the most significant polymorphisms identified for these genetic effects by the previously reported GWA analysis. For OAR3, the analyses demonstrated additional genetic proof of the causality previously suggested by our group for a single nucleotide polymorphism located in the α-lactalbumin gene (LALBA). In summary, although the results shown here suggest that in commercial dairy populations, the LDLA method exhibits a higher efficiency to map QTL than the simple linkage analysis or linkage disequilibrium methods, we believe that comparing the 3 analysis methods is the best approach to obtain a global picture of all identifiable QTL segregating in the population at both family-based and population-based levels.