Multiple Quantitative Trait Loci Mapping With Cofactors and Application of Alternative Variants of the False Discovery Rate in an Enlarged Granddaughter Design

Abstract

The experimental power of a granddaughter design to detect quantitative trait loci (QTL) in dairy cattle is often limited by the availability of progeny-tested sires, by the ignoring of already identified QTL in the statistical analysis, and by the application of stringent experimentwise significance levels. This study describes an experiment that addressed these points. A large granddaughter design was set up that included sires from two countries (Germany and France), resulting in almost 2000 sires. The animals were genotyped for markers on nine different chromosomes. The QTL analysis was done for six traits separately using a multimarker regression that included putative QTL on other chromosomes as cofactors in the model. Different variants of the false discovery rate (FDR) were applied. Two of them accounted for the proportion of truly null hypotheses, which were estimated to be 0.28 and 0.3, respectively, and were therefore tailored to the experiment. A total of 25 QTL could be mapped when cofactors were included in the model-7 more than without cofactors. Controlling the FDR at 0.05 revealed 31 QTL for the two FDR methods that accounted for the proportion of truly null hypotheses. The relatively high power of this study can be attributed to the size of the experiment, to the QTL analysis with cofactors, and to the application of an appropriate FDR. M UCH effort has been undertaken to identify quanexperiments used the existing male half-sib structure by setting up the so-called granddaughter design (Weller titative trait loci (QTL) associated with genetic et al. 1990). In this design the pedigree consists of sets variation for traits of economic or scientific interest in of genotyped and progeny-tested male half-sib families. livestock species with the aid of genetic markers. HayesThe power to detect QTL in a granddaughter design is and Goddard (2001) estimated the number of QTL in largely influenced by the size of the half-sib families, dairy cattle for a trait that undergoes selection at about which has its limit in the availability of progeny-tested 50 to 100 depending on the size of the effective populasons. Interval mapping is one of the most applied statistition. With regard to this, the majority of QTL remained cal methods in the analysis of granddaughter designs. undetected by the experiments undertaken so far. The This method uses information from consecutive informain reason for this is that the distribution of the QTL mative markers simultaneously to trace the inheritance effects follow likely a gamma distribution with many of a putative QTL (Knott et al. 1996). A drawback of QTL of small effects and only a few of large effects this approach is that it does not take into account QTL (Hayes and Goddard 2001) and that QTL experiments outside the respective marker interval or even QTL on conducted to date are not powerful enough to detect other chromosomes. This might result in a bias of the the QTL of smaller effects.QTL parameter estimates and in a reduced power of In QTL mapping in pigs ...

Application of the a posteriori granddaughter design to the Holstein genome

“…The QTL on BTA14 is in the region that includes DGAT1, which has an effect on net merit because of the economic value associated with milk components (Cole et al, 2010). The SCS findings are consistent with a QTL on BTA6 as previously reported by Bennewitz et al (2004) and Lund et al (2008). Chromosomes with nominal APDG P < 10 − 8 and numbers of grandsire families with t-values >3 for within-family contrasts are shown in Table 3 by chromosome.…”

The efficiency of mapping of quantitative trait loci using cofactor analysis in half-sib design

“…The "high heritability" in the present study, simulates the situation for most yield traits that are routinely measured in progeny testing of AI bulls in dairy cattle and "low heritability" resembles the heritability observed for disease traits. However, we did not observe an increase in power with cofactor analysis as reported earlier [1,3,16,18,20]. De Koning et al [3] reported, using cofactor analysis, that the initial number of five suggestive QTL had increased to eight significant QTL in a study with data with an average of 41 sons per sire and ranging from 21 to 82.…”

“…Using cofactors, they observed eight QTL exceeding the 5% genome-wise significance threshold. Bennewitz et al [1] also observed a 39% increase (from 18 to 25) in the number of QTL detected when cofactors were included in the model in comparison to single chromosome analyses. Schulman et al [16] reported 67% increase (from 6 to 10) in the number of QTL detected with cofactor analysis in comparison with single chromosome analysis.…”

Effect of DGAT1, BTN1A1, OLR1, and STAT1 genes on milk production and reproduction traits in the Czech Fleckvieh breed

“…The bovine BTN1A1 gene has been mapped to chromosome 23 (Taylor et al, 1996). In the same genome region, several QTL for health and production traits have been identified (Ashwell et al, 1997;Bennewitz et al, 2004). Butyrophilin might serve as a possible candidate influencing the variation in the detected QTL.…”