PiRNAs biogenesis and its functions.

piRNAs (piwi-interacting RNA) are a novel class of non-coding small single-stranded RNAs with the length of 26-33 nt. The piRNAs play important biological role through the specific interaction with the piwi proteins of the Argonaute family. piRNA function in embryonic development, maintenance of germline DNA integrity, silencing of transposon transcription, suppressionof translation, formation of heterochromatin, and epigenetic regulation of sex determination. This review summarizes recent research and progress on biogenesis and function of piRNA in eukaryotic species.

[Theory and application of marker-assisted introgression].

Marker-assisted introgression (MAI) is one of the major applications of molecular information in animal breeding aiming at introgression of one or more favorable genes from a line (donor) to another (recipient), while keeping the genetic background of the recipient as much as possible. It consists of three phases. Firstly, cross donor and recipient lines to produce F1; secondly, repeated backcross the F1 individuals and the subsequent progenies with the recipient to recover its genetic background; and thirdly, intercross the backcrossed progenies to fix the introgressed genes in the population. Many factors affect the efficiency of MAI, among which the methods of foreground and background selection are very critical.

Comparison of different foreground and background selection methods in marker-assisted introgression.

Three different methods for foreground selection and four different methods for background selection were compared in terms of the efficiency of marker-assisted introgression of a QTL allele from a donor line into a recipient line and also in terms of the recovery of the recipient genetic background. The results showed that for the introgression of a donor QTL allele, a direct selection on the QTL itself (when the QTL genotype can be directly identified) would ensure that the allele is successfully introgressed and rapidly fixed. However, when a direct selection on the QTL is not feasible, an indirect selection using two closely linked flanking markers can be used, which also shows similar results. For the recovery of the recipient genetic background, if the goal is to recover the whole genetic background of the recipient, genomic similarity selection or marker index selection would be the best choice: Only three generations of backcrosses were required to recover over 98% of the recipient genome. Whereas if the goal is to recover certain background traits of the recipient, MBLUP selection would give the best results, which achieved not only over 99% recovery of the recipient QTL alleles for the background traits after three generations of backcrosses, but also showed the best genetic improvement of these traits.

[Influence of maternal genetic effect on genetic parameter estimates of production traits of cashmere goat].

The derivative-free restricted maximum likelihood (DFREML) method was used to compare the differences of genetic parameter estimates of Inner Mongolian Cashmere Goats under two models, which differ in whether maternal genetic effect is taken into account. The differences between the two models were, tested by likelihood ratio test. The results show that maternal genetic effect highly affects live body weight and cashmere thickness while has no significant effect on raw cashmere weight, staple length, fibre diameter and fibre length.

[Comparison of different models for estimating genetic parameters of early growth traits in cashmere goat].

Using data on early growth traits (including birth weight, weaning weight, daily gain, and yearling weight) of cashmere goat from the Aerbasi White Cashmere Goat Breeding Farm in Inner Mongolia, four different animal models for estimating genetic parameters were compared. The four models differ in the way of handling maternal genetic effect and maternal environmental effect: in model I both maternal genetic and environmental effect were excluded, in model II only maternal genetic effect was included, in model III only maternal environmental effect was included, and in model IV both maternal genetic and environmental effect were included. The variance components under different models were estimated with derivative-free restricted maximum likelihood (DFREML) method using the MTDFREML program. The differences between different models were tested by likelihood ratio test. The results show that both maternal genetic and environmental effect have highly significant effect on birth weight. For weaning weight and daily gain the maternal genetic effect is not significant while the maternal environmental effect is highly significant; for yearling weight the maternal genetic effect is significant while the maternal environmental effect is not.