Genetic Polymorphism of 16 X-STR Loci in Xinjiang Uygur Population.

OBJECTIVES: To study the genetic polymorphism and population genetic parameters of 16 X-STR loci in Xinjiang Uygur population. METHODS: The Goldeneye® DNA identification system 17X was used to amplify 16 X-STR loci in 502 unrelated individuals (251 females and 251 males). The amplified products were detected by 3130xl genetic analyzer. Allele frequencies and population genetic parameters were analyzed statistically. The genetic distances between Uygur and other 8 populations were calculated. Multidimensional scaling and phylogenetic tree were constructed based on genetic distance. RESULTS: In the 16 X-STR loci, a total of 67 alleles were detected in 502 Xinjiang Uygur unrelated individuals. The allele frequencies ranged from 0.001 3 to 0.572 4. PIC ranged from 0.568 8 to 0.855 3. The cumulative discrimination power in females and males were 0.999 999 999 999 999 and 0.999 999 999 743 071, respectively. The cumulative mean paternity exclusion chance in trios and in duos were 0.999 999 997 791 859 and 0.999 998 989 000 730, respectively. The genetic distance between Uygur population and Kazakh population was closer, and the genetic distance between Uygur and Han population was farther. CONCLUSIONS: The 16 X-STR loci are highly polymorphic and suitable for identification in Uygur population, which can provide a powerful supplement for the study of individual identification, paternity identification and population genetics.

Application of SifaInDel 45plex System in the Han and Mongolian Populations.

OBJECTIVES: To investigate the genetic polymorphism of InDel loci in SifalnDel 45plex system in the Han population in Jiangsu Province and the Mongolian population in Inner Mongolia, and to evaluate the effectiveness of the system in forensic medicine. METHODS: SifaInDel 45plex system was used for genotyping in blood samples of 398 unrelated individuals from the above two populations, and allele frequencies and population genetic parameters of the two populations were calculated respectively. Eight intercontinental populations in the gnomAD database were used as reference populations. The genetic distances between the two studied populations and eight reference populations were calculated based on the allele frequencies of 27 autosomal-InDels (A-InDels). The phylogenetic trees and multidimensional scaling (MDS) analysis diagrams were constructed accordingly. RESULTS: Among two studied populations, the 27 A-InDels and 16 X-InDels showed no linkage disequilibrium between each other and the allele frequency distributions were in Hardy-Weinberg equilibrium. The CDP of the 27 A-InDels in two studied populations were all higher than 0.999 999 999 9, and the CPEtrio were all less than 0.999 9. The CDP of the 16 X-InDels in Han in Jiangsu and Mongolian in Inner Mongolia female and male samples were 0.999 997 962, 0.999 998 389, and 0.999 818 940, 0.999 856 063, respectively. The CMECtrio were all less than 0.999 9. The results of population genetics showed that the Jiangsu Han nationality, Inner Mongolia Mongolian nationality and East Asian population clustered into one branch, showing closer genetic relationship. The other 7 intercontinental populations clustered into another group. And the above 3 populations displayed distant genetic relationships with the other 7 intercontinental populations. CONCLUSIONS: The InDels in the SifaInDel 45plex system have good genetic polymorphism in the two studied populations, which can be used for forensic individual identification or as an effective complement for paternity identification, and to distinguish different intercontinental populations.

A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression.

BACKGROUND: The identification of gene differential co-expression patterns between cancer stages is a newly developing method to reveal the underlying molecular mechanisms of carcinogenesis. Most researches of this subject lack an algorithm useful for performing a statistical significance assessment involving cancer progression. Lacking this specific algorithm is apparently absent in identifying precise gene pairs correlating to cancer progression. RESULTS: In this investigation we studied gene pair co-expression change by using a stochastic process model for approximating the underlying dynamic procedure of the co-expression change during cancer progression. Also, we presented a novel analytical method named 'Stochastic process model for Identifying differentially co-expressed Gene pair' (SIG method). This method has been applied to two well known prostate cancer data sets: hormone sensitive versus hormone resistant, and healthy versus cancerous. From these data sets, 428,582 gene pairs and 303,992 gene pairs were identified respectively. Afterwards, we used two different current statistical methods to the same data sets, which were developed to identify gene pair differential co-expression and did not consider cancer progression in algorithm. We then compared these results from three different perspectives: progression analysis, gene pair identification effectiveness analysis, and pathway enrichment analysis. Statistical methods were used to quantify the quality and performance of these different perspectives. They included: Re-identification Scale (RS) and Progression Score (PS) in progression analysis, True Positive Rate (TPR) in gene pair analysis, and Pathway Enrichment Score (PES) in pathway analysis. Our results show small values of RS and large values of PS, TPR, and PES; thus, suggesting that gene pairs identified by the SIG method are highly correlated with cancer progression, and highly enriched in disease-specific pathways. From this research, several gene interaction networks inferred could provide clues for the mechanism of prostate cancer progression. CONCLUSION: The SIG method reliably identifies cancer progression correlated gene pairs, and performs well both in gene pair ontology analysis and in pathway enrichment analysis. This method provides an effective means of understanding the molecular mechanism of carcinogenesis by appropriately tracking down the process of cancer progression.