Pay more attention to ethnic differences in polycystic ovary syndrome phenotypic expression

Manifestations of diseases are determined by the .combined effect of genetic and environmentalfactors. Different physical, behavioral and social variations affect the prevalence and presentation of common diseases. Polycystic ovary syndrome （PCOS）, with a heterogeneous presentation, is the most common endocrine disorder that affects about 5%-10% women of reproductive age. It is characterized by clinical and/or biochemical hyperandrogenism, ovulatory dysfunction and polycystic ovaries.

A gene expression estimator of intramuscular fat percentage for use in both cattle and sheep

Background：The expression of genes encoding proteins involved in triacyglyceride and fatty acid synthesis and storage in cattle muscle are correlated with intramuscular fat（IMF）%.Are the same genes also correlated with IMF%in sheep muscle,and can the same set of genes be used to estimate IMF%in both species？Results：The correlation between gene expression（microarray） and IMF%in the longissimus muscle（LM） of twenty sheep was calculated.An integrated analysis of this dataset with an equivalent cattle correlation dataset and a cattle differential expression dataset was undertaken.A total of 30 genes were identified to be strongly correlated with IMF%in both cattle and sheep.The overlap of genes was highly significant,8 of the 13 genes in the TAG gene set and 8 of the 13 genes in the FA gene set were in the top 100 and 500 genes respertively most correlated with IMF%in sheep,P-value = 0.Of the 30 genes,CIDEA,THRSP,ACSM1,DGAT2 and FABP4 had the highest average rank in both species.Using the data from two small groups of Brahman cattle（control and Hormone growth promotant-treated[known to decrease IMF%in muscle]） and 22 animals in total,the utility of a direct measure and different estimators of IMF%（ultrasound and gene expression） to differentiate between the two groups were examined.Directly measured IMF%and IMF%estimated from ultrasound scanning could not discriminate between the two groups.However,using gene expression to estimate IMF%discriminated between the two groups.Increasing the number of genes used to estimate IMF%from one to five significantly increased the discrimination power;but increasing the number of genes to 15 resulted in little further improvement.Conclusion：We have demonstrated the utility of a comparative approach to identify robust estimators of IMF%in the LM in cattle and sheep.We have also demonstrated a number of approaches（potentially applicable to much smaller groups of animals than conventional methods） to using gene expression to rank animals for IMF%within a single farm/treatment,or to estimate differences in IMF%between two farms/treatments.

Analysis of Allele Specific Expression——A Survey

Allele specific expression is essential for cellular programming and development and the diversity of cellular phenotypes. Traditional analysis methods utilize RNA and depend on single nucleotide polymorphisms,thus to suffer from limited amount of materials for analysis. The rapid development of next-generation sequencing technologies provides more comprehensive and powerful approaches to analyze the genomic, epigenetic, and transcriptomic data, and further to detect and measure allele specific expressions. It will potentially enhance the understanding of the allele specific expressions, their complexities, and the effect on biological processes. In this paper, we extensively review the state-of-art enabling technologies and tools to analyze, detect, and measure allele specific expressions, compare their features, and point out the future trend of the methods.