In genetic and biological studies, the F2 population is one of the most popular and commonly used experimental populations mainly because it can be readily produced and its genome structure possesses several niceties that allow for productive investigation. These niceties include the equivalence between the proportion of recombinants and recombination rates, the capability of providing a complete set of three genotypes for every locus and an analytically attractive first-order Markovian property. Recently, there has been growing interest in using the progeny populations from F2 (advanced populations) because their genomes can be managed to meet specific purposes or can be used to enhance investigative studies. These advanced populations include recombinant inbred populations, advanced intercrossed populations, intermated recombinant inbred populations and immortalized F2 populations. Due to an increased number of meiosis cycles, the genomes of these advanced populations no longer possess the Markovian property and are relatively more complicated and different from the F2 genomes. Although issues related to quantitative trait locus (QTL) mapping using advanced populations have been well documented, still these advanced populations are often investigated in a manner similar to the way F2 populations are studied using a first-order Markovian assumption. Therefore, more efforts are needed to address the complexities of these advanced populations in more details. In this article, we attempt to tackle these issues by first modifying current methods developed under this Markovian assumption to propose an ad hoc method (the Markovian method) and explore its possible problems. We then consider the specific genome structures present in the advanced populations without invoking this assumption to propose a more adequate method (the non-Markovian method) for QTL mapping. Further, some QTL mapping properties related to the confounding problems that result from ignoring epistasis and to mapping closely linked QTL are derived and investigated across the different populations. Simulations show that the non-Markovian method outperforms the Markovian method, especially in the advanced populations subject to selfing. The results presented here may give some clues to the use of advanced populations for more powerful and precise QTL mapping.

Gap junction protein connexin43 (Cx43), encoded by the GJA1 gene, is the most abundant connexin in the cardiovascular system and was reported as a crucial factor maintaining cardiac electrical conduction, as well as having a very important function in facilitating the recycling of potassium ions from hair cells in the cochlea back into the cochlear endolymph during auditory transduction processes. In mammals, bats are the only taxon possessing powered flight, placing exceptional demand on many organismal processes. To meet the demands of flying, the hearts of bats show many specialties. Moreover, ultrasonic echolocation allows bat species to orientate and often detect and locate food in darkness. In this study, we cloned the full-length coding region of GJA1 gene from 12 different species of bats and obtained orthologous sequences from other mammals. We used the maximum likelihood method to analyse the evolution of GJA1 gene in mammals and the lineage of bats. Our results showed this gene is much conserved in mammals, as well as in bats' lineage. Compared with other mammals, we found one private amino acid substitution shared by bats, which is located on the inner loop domain, as well as some species-specific amino acid substitutions. The evolution rate analyses showed the signature of purifying selection on not only different classification level lineages but also the different domains and amino acid residue sites of this gene. Also, we suggested that GJA1 gene could be used as a good molecular marker to do the phylogenetic reconstruction.

An innovative approach to tree breeding called ‘breeding without breeding’ (BWB) is presented. The method, as applied on the material in hand, allows the capture of 75–85% of the genetic response to selection attained through conventional programmes without the need to do any controlled pollination and simplified or possibly no experimental field testing: both considered to be the most resource-demanding activities in breeding programmes. BWB combines the use of genotypic or phenotypic pre-selection of superior individuals, informative DNA markers for fingerprinting and pedigree reconstruction of offspring to assemble naturally created full- and half-sib families resulting from mating among selected parents, and quantitative genetics analyses to identify elite genotypes for further genetic improvement or the establishment of production populations. BWB utility is demonstrated using a retrospective study of Douglas-fir (Pseudotsuga menziesii) progeny tests consisting of offspring produced from 150 controlled crosses among 60 parents and established over three sites. The empirical results are supported by theoretical expectations demonstrating anticipated minimum genetic response compared with conventional approaches. The method's simplicity offers an exceptional opportunity for the development of comparable breeding efforts in developing countries, advanced and new breeding programmes, and economically important and ‘minor’ species.

General and genetic statistical methods are commonly used to deal with microsatellite data (highly variable neutral genetic markers). In this paper, the self-organizing map (SOM) that belongs to the unsupervised artificial neural networks (ANNs) was applied to analyse the structure of 58 European and two Chinese pig populations (Sus scrofa) including commercial lines, local breeds and cosmopolitan breeds. Results were compared with other unsupervised classification or ordination methods such as factorial correspondence analysis, hierarchical clustering from an allele sharing distance and the Bayesian genetic model and with principal components analysis and neighbour joining from allelic frequencies and genetic distances between populations. Like other methods, SOMs were able to classify individuals according to their breed origin and to visualize similarities between breeds. They provided additional information on the within- and between-population diversity, allowed differences between similar populations to be highlighted and helped differentiate different groups of populations.