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

Abstract

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.

A genome scan for milk production traits in dairy goats reveals two new mutations in Dgat1 reducing milk fat content

“…Under the conservative assumption that underlying causal genetic variants are the same (or are at least located on the same genes), the present study could help refine the confidence intervals of these bovine QTLs. We also found an orthologous ovine QTL for CHI 5 45 , for CHI 19 but with a large interval (up to 30 Mb) 46,47 and for CHI 21 48 .…”

Genome-Wide Association Study Reveals Candidate Genes for Litter Size Traits in Pelibuey Sheep

“…An SNP was considered to be from a particular gene if it mapped within it. The QTLs were located in the database Jbrowse [35], using online page (https://www.animalgenome.org/) which contains QTLs previously reported in sheep [36][37][38][39].…”

“…as follows: GO:0001701: in uterus embryonic development (ANKRD11); GO:0008585: female gonad development (ARID5B); GO:0070373: negative regulation of ERK1 and ERK2 cascade (DLG1); GO:0032870: cellular response to hormone stimulus (ROBO2); GO:0061364: apoptotic process involved in luteolysis (ROBO2); GO:0032275: luteinizing hormone secretion (CGA); GO:0032870: cellular response to hormone stimulus (CGA); GO:0046621: negative regulation of organ growth (CGA); GO:0046884: follicle-stimulating hormone secretion (CGA). The SNPs were used to identify QTLs previously localized in other studies performed in other sheep populations [36][37][38][39], and all QTLs are shown in Table S2. The analysis of the metabolic pathways in which the significant genes participate, showed that 12 genes are associated with some metabolic pathway ( Table 1).…”

“…The SNPs were used to identify QTLs previously localized in other studies performed in other sheep populations [36][37][38][39], and all QTLs are shown in Table S2. The analysis of the metabolic pathways in which the significant genes participate, showed that 12 genes are associated with some metabolic pathway ( Table 1).…”

Selection signature analysis reveals genes underlying sheep milking performance

“…In order to find genes associated with ovine milk traits, researchers have conducted a selection signature analysis between five non-milk sheep breeds and five milk sheep breeds, and they have identified some milk-related genes such as ABCG2 and SPP1 (Gutierrez-Gil et al, 2014). However, this previous study still had some limitations in experimental design.…”