Power of QTL detection by either fixed or random models in half-sib designs

The aim of this study was to compare the variance component approach for QTL linkage mapping in half-sib designs to the simple regression method. Empirical power was determined by Monte Carlo simulation in granddaughter designs. The factors studied (base values in parentheses) included the number of sires (5) and sons per sire (80), ratio of QTL variance to total genetic variance (λ = 0.1), marker spacing (10 cM), and QTL allele frequency (0.5). A single bi-allelic QTL and six equally spaced markers with six alleles each were simulated. Empirical power using the regression method was 0.80, 0.92 and 0.98 for 5, 10, and 20 sires, respectively, versus 0.88, 0.98 and 0.99 using the variance component method. Power was 0.74, 0.80, 0.93, and 0.95 using regression versus 0.77, 0.88, 0.94, and 0.97 using the variance component method for QTL variance ratios (λ) of 0.05, 0.1, 0.2, and 0.3, respectively. Power was 0.79, 0.85, 0.80 and 0.87 using regression versus 0.80, 0.86, 0.88, and 0.85 using the variance component method for QTL allele frequencies of 0.1, 0.3, 0.5, and 0.8, respectively. The log10 of type I error profiles were quite flat at close marker spacing (1 cM), confirming the inability to fine-map QTL by linkage analysis in half-sib designs. The variance component method showed slightly more potential than the regression method in QTL mapping

High density linkage disequilibrium maps of chromosome 14 in Holstein and Angus cattle

<p>Abstract</p> <p>Background</p> <p>Linkage disequilibrium (LD) maps can provide a wealth of information on specific marker-phenotype relationships, especially in areas of the genome where positional candidate genes with similar functions are located. A recently published high resolution radiation hybrid map of bovine chromosome 14 (BTA14) together with the bovine physical map have enabled the creation of more accurate LD maps for BTA14 in both dairy and beef cattle.</p> <p>Results</p> <p>Over 500 Single Nucleotide Polymorphism (SNP) markers from both Angus and Holstein animals had their phased haplotypes estimated using GENOPROB and their pairwise r²values compared. For both breeds, results showed that average LD extends at moderate levels up to 100 kilo base pairs (kbp) and falls to background levels after 500 kbp. Haplotype block structure analysis using HAPLOVIEW under the four gamete rule identified 122 haplotype blocks for both Angus and Holstein. In addition, SNP tagging analysis identified 410 SNPs and 420 SNPs in Holstein and Angus, respectively, for future whole genome association studies on BTA14. Correlation analysis for marker pairs common to these two breeds confirmed that there are no substantial correlations between r-values at distances over 10 kbp. Comparison of extended haplotype homozygosity (EHH), which calculates the LD decay away from a core haplotype, shows that in Holstein there is long range LD decay away from the <it>DGAT1 </it>region consistent with the selection for milk fat % in this population. Comparison of EHH values for Angus in the same region shows very little long range LD.</p> <p>Conclusion</p> <p>Overall, the results presented here can be applied in future single or haplotype association analysis for both populations, aiding in confirming or excluding potential polymorphisms as causative mutations, especially around Quantitative Trait Loci regions. In addition, knowledge of specific LD information among markers will aid the research community in selecting appropriate markers for whole genome association studies.</p

Power of QTL detection by either fixed or random models in half-sib designs

The aim of this study was to compare the variance component approach for QTL linkage mapping in half-sib designs to the simple regression method. Empirical power was determined by Monte Carlo simulation in granddaughter designs. The factors studied (base values in parentheses) included the number of sires (5) and sons per sire (80), ratio of QTL variance to total genetic variance ($\lambda = 0.1$), marker spacing (10 cM), and QTL allele frequency (0.5). A single bi-allelic QTL and six equally spaced markers with six alleles each were simulated. Empirical power using the regression method was 0.80, 0.92 and 0.98 for 5, 10, and 20 sires, respectively, versus 0.88, 0.98 and 0.99 using the variance component method. Power was 0.74, 0.80, 0.93, and 0.95 using regression versus 0.77, 0.88, 0.94, and 0.97 using the variance component method for QTL variance ratios ($\lambda$) of 0.05, 0.1, 0.2, and 0.3, respectively. Power was 0.79, 0.85, 0.80 and 0.87 using regression versus 0.80, 0.86, 0.88, and 0.85 using the variance component method for QTL allele frequencies of 0.1, 0.3, 0.5, and 0.8, respectively. The log$_{10}$ of type ${\rm I}$ error profiles were quite flat at close marker spacing (1 cM), confirming the inability to fine-map QTL by linkage analysis in half-sib designs. The variance component method showed slightly more potential than the regression method in QTL mapping