The distribution of the effects of genes affecting quantitative traits in livestock

Abstract

-Meta-analysis of information from quantitative trait loci (QTL) mapping experiments was used to derive distributions of the effects of genes affecting quantitative traits. The two limitations of such information, that QTL effects as reported include experimental error, and that mapping experiments can only detect QTL above a certain size, were accounted for. Data from pig and dairy mapping experiments were used. Gamma distributions of QTL effects were fitted with maximum likelihood. The derived distributions were moderately leptokurtic, consistent with many genes of small effect and few of large effect. Seventeen percent and 35% of the leading QTL explained 90% of the genetic variance for the dairy and pig distributions respectively. The number of segregating genes affecting a quantitative trait in dairy populations was predicted assuming genes affecting a quantitative trait were neutral with respect to fitness. Between 50 and 100 genes were predicted, depending on the effective population size assumed. As data for the analysis included no QTL of small effect, the ability to estimate the number of QTL of small effect must inevitably be weak. It may be that there are more QTL of small effect than predicted by our gamma distributions. Nevertheless, the distributions have important implications for QTL mapping experiments and Marker Assisted Selection (MAS). Powerful mapping experiments, able to detect QTL of 0.1σ p , will be required to detect enough QTL to explain 90% the genetic variance for a quantitative trait. distribution of gene effects / quantitative trait loci / genetic variance / marker assisted selection

Quantitative trait loci mapping in dairy cattle: review and meta-analysis

“…The meta-analysis of the effect sizes indicated that some chromosomal regions explain a relatively significant proportion of variation in milk production traits. Based on results from QTL mapping experiments in dairy cattle, Hayes and Goddard [29] fitted gamma distributions of QTL effects. These were moderately leptokurtic, consistent with a few genes of large effect and many of small effect.…”

“…Meta-analysis makes the literature review process more transparent, compared with traditional narrative reviews where it is often not clear how the conclusions follow from the data examined [17]. The application of meta-analysis to QTL detection is recent [26,29]. The combining of the results across studies can provide a more precise and consensus estimate of the location of a QTL and its effect as compared with any single study.…”

“…For a paper to be included in this part of the meta-analysis, it needed to include effect information and its standard error (or the ability to derive this). The basis for the QTL effects model is similar to that outlined in Hayes and Goddard [29]. However, in the current application, the focus is on a meta-analysis of effects at the one locus -or at least chromosomal region -rather than across all loci.…”

“…To model the imprecision ofâ i estimating a i , we assume thatâ i |a i ∼ N(a i , ς 2 i ), and consequently, the unconditional distribution of estimated effects will bê a i ∼ N(0, ς 2 i + σ 2 A ). As also considered by Hayes and Goddard [29], there are two other features that need to be modeled in the meta-analysis. Since it is to a certain extent arbitrary which sire allele is labeled as having a positive effect, we will ignore the sign and condition on a i > 0 andâ i > 0.…”

Use of meta-analysis to combine candidate gene association studies: application to study the relationship between the ESR PvuII polymorphism and sow litter size

“…Although in livestock it is not a usual analytical tool it is in medical research, perhaps because of the widely recognised importance of being systematic when reviewing the evidence available on the benefits and risks of medical decisions [54]. Only recently, meta-analysis has been introduced to study genes of interest in livestock [15,19,23] although not in the context of candidate gene studies.…”