Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of ∼50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size (Ne = 100), the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.

-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

In Australia, dairy farming is carried out in environments that vary in many ways, including level of feeding and climate variables such as temperature and humidity. The aim of this study was to assess the magnitude of genotype x environment interactions (GxE) on milk production traits (milk yield, protein yield, and fat yield) for a range of environmental descriptors. The environment on individual test days was described by herd size (HS), average herd protein yield (AHTDP), herd test-day coefficient of variation for protein yield (HTDCV), and temperature humidity index (THI). A sire random regression model was used to model the response of a sire's daughters to variation in the environment and to calculate the genetic correlation between the same traits measured in two widely different environments. Using test-day records, rather than average lactation yields, allowed exploitation of within-cow variation as well as between-cow variation at different levels of AHTDP, and led to more accurate estimates of sire breeding values for "response to environment." The greatest GxE observed was due to variation in AHTDP, with a genetic correlation of 0.78 between protein yield when AHTDP = 0.54 kg and protein yield when AHTDP = 1.1 kg (the 5th and 95th percentile of the distribution of AHTDP). The GxE was also observed for THI, with a genetic correlation of 0.90 between protein yield at the 5th and 95th percentile of THI. The use of response to environment estimated breeding values to improve the accuracy of international sire evaluations is discussed.

This chapter provides an overview of statistical methods for genome-wide association studies (GWAS) in animals, plants, and humans. The simplest form of GWAS, a marker-by-marker analysis, is illustrated with a simple example. The problem of selecting a significance threshold that accounts for the large amount of multiple testing that occurs in GWAS is discussed. Population structure causes false positive associations in GWAS if not accounted for, and methods to deal with this are presented. Methodology for more complex models for GWAS, including haplotype-based approaches, accounting for identical by descent versus identical by state, and fitting all markers simultaneously are described and illustrated with examples.

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