Invited review: Genomic selection for small ruminants in developed countries: how applicable for the rest of the world?

Improved management and use of estimated breeding values in breeding programmes, have resulted in rapid genetic progress for small ruminants (SR) in Europe and other developed countries. The development of single nucleotide polymorphisms chips opened opportunities for genomic selection (GS) in SR in these countries. Initially focused on production traits (growth and milk), GS has been extended to functional traits (reproductive performance, disease resistance and meat quality). The GS systems have been characterized by smaller reference populations compared with those of dairy cattle and consisting mostly of cross- or multi-breed populations. Molecular information has resulted in gains in accuracy of between 0.05 and 0.27 and proved useful in parentage verification and the identification of QTLs for economically important traits. Except for a few established breeds with some degree of infrastructure, the basic building blocks to support conventional breeding programmes in small holder systems are lacking in most developing countries. In these systems, molecular data could offer quick wins in undertaking parentage verification and genetic evaluations using G matrix, and determination of breed composition. The development of next-generation molecular tools has prompted investigations on genome-wide signatures of selection for mainly adaptive and reproduction traits in SR in developing countries. Here, the relevance of the developments and application of GS and other molecular tools in developed countries to developing countries context is examined. Worth noting is that in the latter, the application of GS in SR will not be a 'one-size fits all' scenario. For breeds with some degree of conventional genetic improvement, classical GS may be feasible. In small holder systems, where production is key, community-based breeding programmes can provide the framework to implement GS. However, in fragile growth systems, for example those found in marginal environments, innovative GS to maximize adaptive diversity will be required. A cost-benefit analysis should accompany any strategy of implementing GS in these systems.

Malaria in the State of Amazonas: a typical Brazilian tropical disease influenced by waves of economic development

In Brazil, more than 99% of malaria cases are reported in the Amazon, and the State of Amazonas accounts for 40% of this total. However, the accumulated experience and challenges in controlling malaria in this region in recent decades have not been reported. Throughout the first economic cycle during the rubber boom (1879 to 1912), malaria was recorded in the entire state, with the highest incidence in the villages near the Madeira River in the Southern part of the State of Amazonas. In the 1970s, during the second economic development cycle, the economy turned to the industrial sector and demanded a large labor force, resulting in a large migratory influx to the capital Manaus. Over time, a gradual increase in malaria transmission was observed in peri-urban areas. In the 1990s, the stimulation of agroforestry, particularly fish farming, led to the formation of permanent Anopheline breeding sites and increased malaria in settlements. The estimation of environmental impacts and the planning of measures to mitigate them, as seen in the construction of the Coari-Manaus gas pipeline, proved effective. Considering the changes occurred since the Amsterdam Conference in 1992, disease control has been based on early diagnosis and treatment, but the development of parasites that are resistant to major antimalarial drugs in Brazilian Amazon has posed a new challenge. Despite the decreased lethality and the gradual decrease in the number of malaria cases, disease elimination, which should be associated with government programs for economic development in the region, continues to be a challenge.

Evaluating the interaction of faecal pellet deposition rates and DNA degradation rates to optimize sampling design for DNA-based mark-recapture analysis of Sonoran pronghorn.

Knowledge of population demographics is important for species management but can be challenging in low-density, wide-ranging species. Population monitoring of the endangered Sonoran pronghorn (Antilocapra americana sonoriensis) is critical for assessing the success of recovery efforts, and noninvasive DNA sampling (NDS) could be more cost-effective and less intrusive than traditional methods. We evaluated faecal pellet deposition rates and faecal DNA degradation rates to maximize sampling efficiency for DNA-based mark-recapture analyses. Deposition data were collected at five watering holes using sampling intervals of 1-7 days and averaged one pellet pile per pronghorn per day. To evaluate nuclear DNA (nDNA) degradation, 20 faecal samples were exposed to local environmental conditions and sampled at eight time points from one to 124 days. Average amplification success rates for six nDNA microsatellite loci were 81% for samples on day one, 63% by day seven, 2% by day 14 and 0% by day 60. We evaluated the efficiency of different sampling intervals (1-10 days) by estimating the number of successful samples, success rate of individual identification and laboratory costs per successful sample. Cost per successful sample increased and success and efficiency declined as the sampling interval increased. Results indicate NDS of faecal pellets is a feasible method for individual identification, population estimation and demographic monitoring of Sonoran pronghorn. We recommend collecting samples <7 days old and estimate that a sampling interval of four to seven days in summer conditions (i.e., extreme heat and exposure to UV light) will achieve desired sample sizes for mark-recapture analysis while also maximizing efficiency [Corrected].

Bayesian dating of shallow phylogenies with a relaxed clock.

Bayesian methods are increasingly being used to estimate divergence times without the restrictive assumption of a global clock. Little is known about their reliability for shallow phylogenies where DNA sequence divergence is low. We analyzed both simulated and real sequences to evaluate dating methods in phylogenies with mid-late Miocene roots. A large number of data sets (5000) with 10 taxa each were simulated under a rate-drift model for trees with 2 topologies (balanced or unbalanced) and with different sets of divergence times (characterized by long or short external branches). Data were analyzed using Bayesian Markov chain Monte Carlo methods in which the prior on divergence times was specified from a birth-death process with species sampling (BDS) or a Dirichlet distribution using the programs MCMCTREE and MULTIDIVTIME. The programs generally performed well on shallow phylogenies, but posterior mean node ages were biased and 95% posterior intervals included true ages in fewer than 95% of trees in some analyses. This typically occurred when the 95% prior interval did not include the true age and/or sequence lengths were or= 1 kbp of quite rapidly evolving sequence may be required to obtain accurate posterior means and usefully narrow posterior intervals.

Bayesian inference of population expansions in domestic bovines.

The past population dynamics of four domestic and one wild species of bovine were estimated using Bayesian skyline plots, a coalescent Markov chain Monte Carlo method that does not require an assumed parametric model of demographic history. Four domestic species share a recent rapid population expansion not visible in the wild African buffalo (Syncerus caffer). The estimated timings of the expansions are consistent with the archaeological records of domestication.

The selection index--then, now, and for the future.

When improvement is desired for several traits that may differ in variability, heritability, economic importance, and in the correlation among their phenotypes and genotypes, simultaneous multiple-trait index selection was more effective than independent culling levels or sequential selection. Such comparisons required definition of aggregate breeding value determined jointly by breeding values and economic importance of the component traits. The economic weight should approximate the partial regression of cost per unit of enterprise output value on breeding value for each trait. These can vary with production and marketing system, with performance of traits, and with breed role (i.e., paternal, maternal, or general) in crossbreeding systems. Genetic gains desired to maintain competitive ranking also may define the relative importance of traits. Because information available to estimate breeding values varies among the ages and categories of individuals under selection and because means are unknown, regressed (BLUP) predictions of trait breeding values are useful. They allow appropriate economic weights to be applied as the last step for predicting aggregate breeding values for individuals of different age classes, and they simplify choosing the proportions of selected breeders from each age class that maximize rate of change in aggregate breeding values. Inappropriate economic weights or errors in the parameters used to predict trait breeding values overestimate realized response in true aggregate breeding value.