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Recent reviews in animal breeding (2019)

Since I was asked for recent reviews on the topic, I figure I might as well post them here too. Here’s a few published since 2016. I regularly read these review papers as a sort of whitepill against the idiotic anti-genetics perspectives of the mainstream, and the anti-eugenics views of the leftist academics (despite the history of eugenics as a progressive leftist movement).

The aquaculture sector is significantly behind plant and farm animal production in applying selective breeding, in spite of the fact that it has been suggested that the world aquaculture production could be doubled in 13 years if breeding programmes were supplying stocks for the farmed species. It is estimated that as late as in 2010, only 8.2% of the world’s total aquaculture production was based on material developed in selective breeding programmes. Reported estimates of genetic gain per generation for a key trait like growth rate average 13%, implying that the animal’s potential for growth can be doubled in a time span of only six generations of selection, as demonstrated for major farmed species like Atlantic salmon and Nile tilapia. Likewise are reported genetic gains for improved disease resistance generally very high. This study offers an updated review of published estimates on genetic gains for a range of traits in aquaculture species. Results are highly encouraging and demonstrate a substantial potential for genetic improvement in aquatic productions, in particular for traits such as growth rate and resistance to diseases.

 

Adaption is a process that makes an individual or population more suited to their environment. Long-term adaptation is predicated on ample usable genetic variation. Evolutionary forces influencing the extent and dynamics of genetic variation in a population include random drift, mutation, recombination, selection, and migration; the relative importance of each differs by population (i.e., drift is likely to be more influential in smaller populations) and number of generations exposed to selection (i.e., mutation is expected to contribute substantially to genetic variability following many generations of selection). The infinitesimal model, which underpins most genetic and genomic evaluations, assumes that each quantitative trait is controlled by an infinitely large number of unlinked and non-epistatic loci, each with an infinitely small effect. Under the infinitesimal model, selection is not expected to noticeably alter the allele frequencies, despite a potential substantial change in the population mean; the exception is in the first few generations of selection when genetic variance is expected to decline, after which it stabilizes. Despite the common use of the heritability statistic in quantitative genetics as a descriptor of adaption or response to selection, it is arguably the coefficient of genetic variation that is more informative to gauge adaptation potential and should, therefore, always be cited in such studies; for example, the heritability of fertility traits in dairy cows is generally low, yet the coefficient of genetic variation for most traits is comparable to many other performance traits, thus supporting the observed rapid genetic gain in fertility performance in dairy populations. Empirical evidence from long-term selection studies, across a range of animal and plant species, fails to support the premise that selection will deplete genetic variability. Even after 100 yr (synonymous with 100 generations) of selection in corn for high protein or oil content, there appears to be no obvious plateauing in the response to selection. Although populations in several selection experiments did reach a selection limit after multiple generations of directional selection, this does not equate to an exhaustion of genetic variance; such a declaration is supported by the observed rapid responses to reverse selection once implemented in long-term selection studies. New technologies such as genome-wide enabled selection and genome editing, as well as having the potential to accelerate genetic gain, could also increase the genetic variation, or at least reduce the erosion of genetic variance over time. In conclusion, there is no evidence, either theoretical or empirical, to indicate that dairy cow breeding programs will be unable to adapt to evolving challenges and opportunities, at least not because of an absence of ample genetic variability.

 

Selective breeding has been practiced since domestication, but early breeders commonly selected on appearance (e.g., coat color) rather than performance traits (e.g., milk yield). A breeding index converts information about several traits into a single number used for selection and to predict an animal’s own performance. Calculation of selection indices is straightforward when phenotype and pedigree data are available. Prediction of economic values 3 to 10 yr in the future, when the offspring of matings planned using the index will be lactating, is more challenging. The first USDA selection index included only milk and fat yield, whereas the latest version of the lifetime net merit index includes 13 traits and composites (weighted averages of other additional traits). Selection indices are revised to reflect improved knowledge of biology, new sources of data, and changing economic conditions. Single-trait selection often suffers from antagonistic correlations with traits not in the selection objective. Multiple-trait selection avoids those problems at the cost of less-than-maximal progress for individual traits. How many and which traits to include is not simple to determine because traits are not independent. Many countries use indices that reflect the needs of different producers in different environments. Although the emphasis placed on trait groups differs, most indices include yield, fertility, health, and type traits. Addition of milk composition, feed intake, and other traits is possible, but they are more costly to collect and many are not yet directly rewarded in the marketplace, such as with incentives from milk processing plants. As the number of traits grows, custom selection indices can more closely match genotypes to the environments in which they will perform. Traditional selection required recording lots of cows across many farms, but genomic selection favors collecting more detailed information from cooperating farms. A similar strategy may be useful in less developed countries. Recording important new traits on a fraction of cows can quickly benefit the whole population through genomics.

The bold part is a direct discussion of the usual worry by various human commentators: what if we breed for trait X, and we suddenly get unwanted changes in traits Y and Z? Well, this doesn’t happen because we map the genetic correlations, so we know what we will get. A trivial idea, but apparently, something one can publish a lot of worry-papers about in human genetics.

 

  • Bidanel, J. P., Silalahi, P., Tribout, T., Canario, L., Ducos, A., Garreau, H., … & Schwob, S. (2018). Fifty years of pig breeding in France: outcomes and perspectives. Journées de la Recherche Porcine en France, 50, 61-74.

This synthesis reviews the main changes that have occurred in the pig breeding sector in France since the 1966 Breeding Act. It briefly discusses the first 20 years, which were the subject of a review in 1986. It describes subsequent changes in more detail, in particular the March 1994 decree on pig selection and its organisational consequences. Breeding goals, initially limited to production traits, have then integrated meat quality traits, sow prolificacy and maternal abilities. Regarding tools, implementation of genetic evaluation based on the BLUP animal model in the mid-1990s and development of artificial insemination profoundly changed breeders’ work. A new major change, genomic selection, is currently being implemented. Large genetic gains have been obtained since 1970 for the main components of the breeding goal: they have exceeded 200 g/d for on-test average daily gain, -0.5 points for feed conversion ratio and 12 percentage points for carcass lean content, and approached six additional piglets born alive per litter. These gains have reduced environmental impacts of pig production but also had some detrimental effects: an increase in piglet pre-weaning mortality and greater heterogeneity of performances. Issues for future breeding goals (e.g. inclusion of traits related to welfare, robustness and adaptation), methods and tools (e.g. genomic selection, fine phenotyping, genome editing) are then discussed.

BLUP is ridge regression, so the animal/plant breeders have been using proper statistical models for 20 years, it’s just the human geneticists who are too stupid to use anything but ‘one at a time’-regression (what they call GWAS). To be a little more fair, genetic prediction is easier in breeding because these lines are inbred, and thus have high LDs between variants, meaning that everything is highly correlated and it’s easy to find stuff that predicts something. In humans, the effective population sizes are much larger and thus LD is lower, and making predictive models is harder. The only upside of lower LD is that finding causal variants is easier (again, because less collinearity). This also has the implication that more inbred human races, like Ashkenazim and Finns, are better targets for faster eugenic selection, and because they already have high trait values of interest. Africans, who have very low LD, are better for fine-mapping of causal variants. Finding causal variants is probably more important in the long run because it’s needed for direct editing, whereas good predictive models only helps you with selection (embryo/egg/sperm selection).

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