Possibilities for increased rates of genetic change in dairy cattle through embryo transfer and embryo splitting are examin'ed, using the multiple ovulation and embryo transfer systems previously proposed. These involve embryo transfer from 1-year-old females (juvenile scheme, generation interval 1-8 years) and from females after 1 lactation (adult scheme, generation interval 3-7 years), with use of males at similar ages. Though selection is less accurate than in conventional progeny testing, the annual rate of genetic improvement can be increased, and even doubled. If the number of transfers is restricted and the inbreeding rate is limiting, the adult scheme for both sexes is preferred. A scheme with 1 024 transfers per year and 512 females milk-recorded per year will sustain a rate of genetic improvement some 30% above that possible by a conventional national progeny-testing programme. Because of the relatively small number of animals involved, it is argued that greater control over recording, breeding and selection should be possible, leading to a larger proportion of the possible genetic gains being realized in practice. Other advantages, and disadvantages of these systems, and their integration in dairy cattle improvement are discussed.

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Behavioral tasks (e.g., Stroop task) that produce replicable group-level effects (e.g., Stroop effect) often fail to reliably capture individual differences between participants (e.g., low test-retest reliability). This “reliability paradox” has led many researchers to conclude that most behavioral tasks cannot be used to develop and advance theories of individual differences. However, these conclusions are derived from statistical models that provide only superficial summary descriptions of behavioral data, thereby ignoring theoretically-relevant data-generating mechanisms that underly individual-level behavior. More generally, such descriptive methods lack the flexibility to test and develop increasingly complex theories of individual differences. To resolve this theory-description gap, we present generative modeling approaches, which involve using background knowledge to specify how behavior is generated at the individual level, and in turn how the distributions of individual-level mechanisms are characterized at the group level—all in a single joint model. Generative modeling shifts our focus away from estimating descriptive statistical “effects” toward estimating psychologically meaningful parameters, while simultaneously accounting for measurement error that would otherwise attenuate individual difference correlations. Using simulations and empirical data from the Implicit Association Test and Stroop, Flanker, Posner Cueing, and Delay Discounting tasks, we demonstrate how generative models yield (1) higher test-retest reliability estimates, and (2) more theoretically informative parameter estimates relative to traditional statistical approaches. Our results reclaim optimism regarding the utility of behavioral paradigms for testing and advancing theories of individual differences, and emphasize the importance of formally specifying and checking model assumptions to reduce theory-description gaps and facilitate principled theory development.

The value of marker-assisted selection (MAS) using linkage disequilibrium between genetic markers and quantitative trait loci (QTL) was examined. To simulate the disequilibrium, four base populations were created, F2, F5, F10 and F20, by random mating from a cross between two inbred lines. Selections were on breeding values estimated from: (1) marker QTL (MQTL) associations (MAS); (2) conventional best linear unbiased prediction (BLUP) methods; and (3) a combination of 1 and 2 (COMB). Alternative cases were studied by varying the parameters (heritability, initial linkage disequilibrium, and distribution of QTL effects). A genome with 100 QTL and 100 markers randomly (but equally) spread over 20 chromosomes, each 100 centiMorgans (cM) in length, was generated. Linkage disequilibrium (over 30 replicates) of QTLs with their nearest marker averaged 0.153, 0.104, 0.068, and 0.047 for the four base populations, and fell to 0.035, 0.025, 0.021, and 0.018, respectively, after ten generations of MAS selection (heritability 0.25). The initial linkage disequilibrium had the greatest effect on the genetic gain by MAS with the responses for the base populations F2>F5>F10>F20. Genetic gains by conventional BLUP selection were usually greater than by MAS. However, MAS contributed to the combined selection (COMB) to give appreciably higher genetic responses. Hybridization of selected lines after several generations of selection contributed little to generating further linkage disequilibrium. Detection of markers closer to the QTL will increase the linkage disequilibrium available for selection. Eventually with very close linkage each QTL allele can be uniquely identified in selection, and selection will then be equivalent to selection on the QTLs themselves.

This paper describes the estimation of genetic changes in farm livestock using field data. The method proposed depends on a difference in rate of change in performance of the population and of the successive progenies of individual sires. The change in the population with time is taken as t+g, where t represents environmental change and g represents genetic change, while the within-sire change is taken as t+½g. Their difference measures half the genetic change.This principle can be applied, with certain precautions, tofielddata where there is some spread of sires over time. The method has two advantages over other methods in that past as well as current changes can be measured and no additional facilities are required. However, selection among sires may bias the estimates of genetic change. Approximate sampling errors of the estimates of change can be obtained and hence the rate of genetic change which a certain body of data will demonstrate to be significant can be estimated.The method was applied to a set of pig records collected over nine years by a private pig breeder. There was considerable change with time in all six traits examined and in at least three of them there appeared to have been a substantial genetic change accounting for the whole of the observed change.

The population of transplant patients at high risk for fracture can be identified using age/gender, pretransplant fracture history, diabetes, obesity, and years of pretransplant kidney failure.

Known genetic loci that affect metric traits may be useful in livestock improvement. Their value depends on the proportion (R) of the total additive genetic variation due to the known loci relative to the heritability of the trait concerned and on the form of selection practised. When normal selection is effective, further information on known loci can add only a little to the rate of improvement. But if normal selection is not very effective, as for characters of low heritability, or if indirect selection on relatives must be used (as for sex-limited or carcass traits) then known loci may add significantly to the rate of improvement possible.Sampling errors in the estimated effects and in the proportion (R) may cause selection effort to be misdirected and may even lead to losses rather than gains in improvement. Such errors are most likely to occur when the heritability of the character is low.Reports on several loci with large effects in the various farm species have been summarised, but the evidence is often inconsistent and contradictory. At present, there appear to be no loci that could be used with confidence in the improvement of economic traits in farm animals.

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